>>> help( np ) Help on package numpy: NAME numpy FILE c:\miniconda2\lib\site-packages\numpy\__init__.py DESCRIPTION NumPy ===== Provides 1. An array object of arbitrary homogeneous items 2. Fast mathematical operations over arrays 3. Linear Algebra, Fourier Transforms, Random Number Generation How to use the documentation ---------------------------- Documentation is available in two forms: docstrings provided with the code, and a loose standing reference guide, available from `the NumPy homepage `_. We recommend exploring the docstrings using `IPython `_, an advanced Python shell with TAB-completion and introspection capabilities. See below for further instructions. The docstring examples assume that `numpy` has been imported as `np`:: >>> import numpy as np Code snippets are indicated by three greater-than signs:: >>> x = 42 >>> x = x + 1 Use the built-in ``help`` function to view a function's docstring:: >>> help(np.sort) ... # doctest: +SKIP For some objects, ``np.info(obj)`` may provide additional help. This is particularly true if you see the line "Help on ufunc object:" at the top of the help() page. Ufuncs are implemented in C, not Python, for speed. The native Python help() does not know how to view their help, but our np.info() function does. To search for documents containing a keyword, do:: >>> np.lookfor('keyword') ... # doctest: +SKIP General-purpose documents like a glossary and help on the basic concepts of numpy are available under the ``doc`` sub-module:: >>> from numpy import doc >>> help(doc) ... # doctest: +SKIP Available subpackages --------------------- doc Topical documentation on broadcasting, indexing, etc. lib Basic functions used by several sub-packages. random Core Random Tools linalg Core Linear Algebra Tools fft Core FFT routines polynomial Polynomial tools testing Numpy testing tools f2py Fortran to Python Interface Generator. distutils Enhancements to distutils with support for Fortran compilers support and more. Utilities --------- test Run numpy unittests show_config Show numpy build configuration dual Overwrite certain functions with high-performance Scipy tools matlib Make everything matrices. __version__ Numpy version string Viewing documentation using IPython ----------------------------------- Start IPython with the NumPy profile (``ipython -p numpy``), which will import `numpy` under the alias `np`. Then, use the ``cpaste`` command to paste examples into the shell. To see which functions are available in `numpy`, type ``np.`` (where ```` refers to the TAB key), or use ``np.*cos*?`` (where ```` refers to the ENTER key) to narrow down the list. To view the docstring for a function, use ``np.cos?`` (to view the docstring) and ``np.cos??`` (to view the source code). Copies vs. in-place operation ----------------------------- Most of the functions in `numpy` return a copy of the array argument (e.g., `np.sort`). In-place versions of these functions are often available as array methods, i.e. ``x = np.array([1,2,3]); x.sort()``. Exceptions to this rule are documented. PACKAGE CONTENTS __config__ _import_tools add_newdocs compat (package) core (package) ctypeslib distutils (package) doc (package) dual f2py (package) fft (package) lib (package) linalg (package) ma (package) matlib matrixlib (package) polynomial (package) random (package) setup testing (package) version SUBMODULES _mat char emath rec CLASSES __builtin__.object broadcast busdaycalendar dtype flatiter generic bool_ datetime64 flexible character string_(__builtin__.str, character) unicode_(__builtin__.unicode, character) void record number inexact complexfloating complex128(complexfloating, __builtin__.complex) complex128 complex64 floating float16 float32 float64(floating, __builtin__.float) float64 integer signedinteger int16 int32(signedinteger, __builtin__.int) int32(signedinteger, __builtin__.int) int64 int8 timedelta64 unsignedinteger uint16 uint32 uint32 uint64 uint8 object_ ndarray recarray numpy.core.defchararray.chararray numpy.core.memmap.memmap numpy.matrixlib.defmatrix.matrix nditer ufunc numpy._import_tools.PackageLoader numpy.core.getlimits.finfo numpy.core.getlimits.iinfo numpy.core.machar.MachAr numpy.core.numeric.errstate numpy.lib._datasource.DataSource numpy.lib.function_base.vectorize numpy.lib.index_tricks.ndenumerate numpy.lib.index_tricks.ndindex numpy.lib.polynomial.poly1d __builtin__.str(__builtin__.basestring) string_(__builtin__.str, character) __builtin__.unicode(__builtin__.basestring) unicode_(__builtin__.unicode, character) exceptions.DeprecationWarning(exceptions.Warning) ModuleDeprecationWarning exceptions.RuntimeWarning(exceptions.Warning) numpy.core.numeric.ComplexWarning exceptions.UserWarning(exceptions.Warning) VisibleDeprecationWarning numpy.lib.polynomial.RankWarning numpy.core.records.format_parser class ComplexWarning(exceptions.RuntimeWarning) | The warning raised when casting a complex dtype to a real dtype. | | As implemented, casting a complex number to a real discards its imaginary | part, but this behavior may not be what the user actually wants. | | Method resolution order: | ComplexWarning | exceptions.RuntimeWarning | exceptions.Warning | exceptions.Exception | exceptions.BaseException | __builtin__.object | | Data descriptors defined here: | | __weakref__ | list of weak references to the object (if defined) | | ---------------------------------------------------------------------- | Methods inherited from exceptions.RuntimeWarning: | | __init__(...) | x.__init__(...) initializes x; see help(type(x)) for signature | | ---------------------------------------------------------------------- | Data and other attributes inherited from exceptions.RuntimeWarning: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from exceptions.BaseException: | | __delattr__(...) | x.__delattr__('name') <==> del x.name | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __getslice__(...) | x.__getslice__(i, j) <==> x[i:j] | | Use of negative indices is not supported. | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __setattr__(...) | x.__setattr__('name', value) <==> x.name = value | | __setstate__(...) | | __str__(...) | x.__str__() <==> str(x) | | __unicode__(...) | | ---------------------------------------------------------------------- | Data descriptors inherited from exceptions.BaseException: | | __dict__ | | args | | message class DataSource(__builtin__.object) | DataSource(destpath='.') | | A generic data source file (file, http, ftp, ...). | | DataSources can be local files or remote files/URLs. The files may | also be compressed or uncompressed. DataSource hides some of the | low-level details of downloading the file, allowing you to simply pass | in a valid file path (or URL) and obtain a file object. | | Parameters | ---------- | destpath : str or None, optional | Path to the directory where the source file gets downloaded to for | use. If `destpath` is None, a temporary directory will be created. | The default path is the current directory. | | Notes | ----- | URLs require a scheme string (``http://``) to be used, without it they | will fail:: | | >>> repos = DataSource() | >>> repos.exists('www.google.com/index.html') | False | >>> repos.exists('http://www.google.com/index.html') | True | | Temporary directories are deleted when the DataSource is deleted. | | Examples | -------- | :: | | >>> ds = DataSource('/home/guido') | >>> urlname = 'http://www.google.com/index.html' | >>> gfile = ds.open('http://www.google.com/index.html') # remote file | >>> ds.abspath(urlname) | '/home/guido/www.google.com/site/index.html' | | >>> ds = DataSource(None) # use with temporary file | >>> ds.open('/home/guido/foobar.txt') | | >>> ds.abspath('/home/guido/foobar.txt') | '/tmp/tmpy4pgsP/home/guido/foobar.txt' | | Methods defined here: | | __del__(self) | | __init__(self, destpath='.') | Create a DataSource with a local path at destpath. | | abspath(self, path) | Return absolute path of file in the DataSource directory. | | If `path` is an URL, then `abspath` will return either the location | the file exists locally or the location it would exist when opened | using the `open` method. | | Parameters | ---------- | path : str | Can be a local file or a remote URL. | | Returns | ------- | out : str | Complete path, including the `DataSource` destination directory. | | Notes | ----- | The functionality is based on `os.path.abspath`. | | exists(self, path) | Test if path exists. | | Test if `path` exists as (and in this order): | | - a local file. | - a remote URL that has been downloaded and stored locally in the | `DataSource` directory. | - a remote URL that has not been downloaded, but is valid and | accessible. | | Parameters | ---------- | path : str | Can be a local file or a remote URL. | | Returns | ------- | out : bool | True if `path` exists. | | Notes | ----- | When `path` is an URL, `exists` will return True if it's either | stored locally in the `DataSource` directory, or is a valid remote | URL. `DataSource` does not discriminate between the two, the file | is accessible if it exists in either location. | | open(self, path, mode='r') | Open and return file-like object. | | If `path` is an URL, it will be downloaded, stored in the | `DataSource` directory and opened from there. | | Parameters | ---------- | path : str | Local file path or URL to open. | mode : {'r', 'w', 'a'}, optional | Mode to open `path`. Mode 'r' for reading, 'w' for writing, | 'a' to append. Available modes depend on the type of object | specified by `path`. Default is 'r'. | | Returns | ------- | out : file object | File object. | | ---------------------------------------------------------------------- | Data descriptors defined here: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined) class MachAr(__builtin__.object) | Diagnosing machine parameters. | | Attributes | ---------- | ibeta : int | Radix in which numbers are represented. | it : int | Number of base-`ibeta` digits in the floating point mantissa M. | machep : int | Exponent of the smallest (most negative) power of `ibeta` that, | added to 1.0, gives something different from 1.0 | eps : float | Floating-point number ``beta**machep`` (floating point precision) | negep : int | Exponent of the smallest power of `ibeta` that, substracted | from 1.0, gives something different from 1.0. | epsneg : float | Floating-point number ``beta**negep``. | iexp : int | Number of bits in the exponent (including its sign and bias). | minexp : int | Smallest (most negative) power of `ibeta` consistent with there | being no leading zeros in the mantissa. | xmin : float | Floating point number ``beta**minexp`` (the smallest [in | magnitude] usable floating value). | maxexp : int | Smallest (positive) power of `ibeta` that causes overflow. | xmax : float | ``(1-epsneg) * beta**maxexp`` (the largest [in magnitude] | usable floating value). | irnd : int | In ``range(6)``, information on what kind of rounding is done | in addition, and on how underflow is handled. | ngrd : int | Number of 'guard digits' used when truncating the product | of two mantissas to fit the representation. | epsilon : float | Same as `eps`. | tiny : float | Same as `xmin`. | huge : float | Same as `xmax`. | precision : float | ``- int(-log10(eps))`` | resolution : float | ``- 10**(-precision)`` | | Parameters | ---------- | float_conv : function, optional | Function that converts an integer or integer array to a float | or float array. Default is `float`. | int_conv : function, optional | Function that converts a float or float array to an integer or | integer array. Default is `int`. | float_to_float : function, optional | Function that converts a float array to float. Default is `float`. | Note that this does not seem to do anything useful in the current | implementation. | float_to_str : function, optional | Function that converts a single float to a string. Default is | ``lambda v:'%24.16e' %v``. | title : str, optional | Title that is printed in the string representation of `MachAr`. | | See Also | -------- | finfo : Machine limits for floating point types. | iinfo : Machine limits for integer types. | | References | ---------- | .. [1] Press, Teukolsky, Vetterling and Flannery, | "Numerical Recipes in C++," 2nd ed, | Cambridge University Press, 2002, p. 31. | | Methods defined here: | | __init__(self, float_conv=, int_conv=, float_to_float=, float_to_str=>, title='Python floating point number') | float_conv - convert integer to float (array) | int_conv - convert float (array) to integer | float_to_float - convert float array to float | float_to_str - convert array float to str | title - description of used floating point numbers | | __str__(self) | | ---------------------------------------------------------------------- | Data descriptors defined here: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined) class ModuleDeprecationWarning(exceptions.DeprecationWarning) | Module deprecation warning. | | The nose tester turns ordinary Deprecation warnings into test failures. | That makes it hard to deprecate whole modules, because they get | imported by default. So this is a special Deprecation warning that the | nose tester will let pass without making tests fail. | | Method resolution order: | ModuleDeprecationWarning | exceptions.DeprecationWarning | exceptions.Warning | exceptions.Exception | exceptions.BaseException | __builtin__.object | | Data descriptors defined here: | | __weakref__ | list of weak references to the object (if defined) | | ---------------------------------------------------------------------- | Methods inherited from exceptions.DeprecationWarning: | | __init__(...) | x.__init__(...) initializes x; see help(type(x)) for signature | | ---------------------------------------------------------------------- | Data and other attributes inherited from exceptions.DeprecationWarning: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from exceptions.BaseException: | | __delattr__(...) | x.__delattr__('name') <==> del x.name | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __getslice__(...) | x.__getslice__(i, j) <==> x[i:j] | | Use of negative indices is not supported. | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __setattr__(...) | x.__setattr__('name', value) <==> x.name = value | | __setstate__(...) | | __str__(...) | x.__str__() <==> str(x) | | __unicode__(...) | | ---------------------------------------------------------------------- | Data descriptors inherited from exceptions.BaseException: | | __dict__ | | args | | message class PackageLoader(__builtin__.object) | Methods defined here: | | __call__(self, *packages, **options) | Load one or more packages into parent package top-level namespace. | | This function is intended to shorten the need to import many | subpackages, say of scipy, constantly with statements such as | | import scipy.linalg, scipy.fftpack, scipy.etc... | | Instead, you can say: | | import scipy | scipy.pkgload('linalg','fftpack',...) | | or | | scipy.pkgload() | | to load all of them in one call. | | If a name which doesn't exist in scipy's namespace is | given, a warning is shown. | | Parameters | ---------- | *packages : arg-tuple | the names (one or more strings) of all the modules one | wishes to load into the top-level namespace. | verbose= : integer | verbosity level [default: -1]. | verbose=-1 will suspend also warnings. | force= : bool | when True, force reloading loaded packages [default: False]. | postpone= : bool | when True, don't load packages [default: False] | | __init__(self, verbose=False, infunc=False) | Manages loading packages. | | error(self, mess) | | get_pkgdocs(self) | Return documentation summary of subpackages. | | log(self, mess) | | warn(self, mess) | | ---------------------------------------------------------------------- | Data descriptors defined here: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined) class RankWarning(exceptions.UserWarning) | Issued by `polyfit` when the Vandermonde matrix is rank deficient. | | For more information, a way to suppress the warning, and an example of | `RankWarning` being issued, see `polyfit`. | | Method resolution order: | RankWarning | exceptions.UserWarning | exceptions.Warning | exceptions.Exception | exceptions.BaseException | __builtin__.object | | Data descriptors defined here: | | __weakref__ | list of weak references to the object (if defined) | | ---------------------------------------------------------------------- | Methods inherited from exceptions.UserWarning: | | __init__(...) | x.__init__(...) initializes x; see help(type(x)) for signature | | ---------------------------------------------------------------------- | Data and other attributes inherited from exceptions.UserWarning: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from exceptions.BaseException: | | __delattr__(...) | x.__delattr__('name') <==> del x.name | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __getslice__(...) | x.__getslice__(i, j) <==> x[i:j] | | Use of negative indices is not supported. | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __setattr__(...) | x.__setattr__('name', value) <==> x.name = value | | __setstate__(...) | | __str__(...) | x.__str__() <==> str(x) | | __unicode__(...) | | ---------------------------------------------------------------------- | Data descriptors inherited from exceptions.BaseException: | | __dict__ | | args | | message class VisibleDeprecationWarning(exceptions.UserWarning) | Visible deprecation warning. | | By default, python will not show deprecation warnings, so this class | can be used when a very visible warning is helpful, for example because | the usage is most likely a user bug. | | Method resolution order: | VisibleDeprecationWarning | exceptions.UserWarning | exceptions.Warning | exceptions.Exception | exceptions.BaseException | __builtin__.object | | Data descriptors defined here: | | __weakref__ | list of weak references to the object (if defined) | | ---------------------------------------------------------------------- | Methods inherited from exceptions.UserWarning: | | __init__(...) | x.__init__(...) initializes x; see help(type(x)) for signature | | ---------------------------------------------------------------------- | Data and other attributes inherited from exceptions.UserWarning: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from exceptions.BaseException: | | __delattr__(...) | x.__delattr__('name') <==> del x.name | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __getslice__(...) | x.__getslice__(i, j) <==> x[i:j] | | Use of negative indices is not supported. | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __setattr__(...) | x.__setattr__('name', value) <==> x.name = value | | __setstate__(...) | | __str__(...) | x.__str__() <==> str(x) | | __unicode__(...) | | ---------------------------------------------------------------------- | Data descriptors inherited from exceptions.BaseException: | | __dict__ | | args | | message bool8 = class bool_(generic) | Numpy's Boolean type. Character code: ``?``. Alias: bool8 | | Method resolution order: | bool_ | generic | __builtin__.object | | Methods defined here: | | __and__(...) | x.__and__(y) <==> x&y | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __or__(...) | x.__or__(y) <==> x|y | | __rand__(...) | x.__rand__(y) <==> y&x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __xor__(...) | x.__xor__(y) <==> x^y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __oct__(...) | x.__oct__() <==> oct(x) | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class bool_(generic) | Numpy's Boolean type. Character code: ``?``. Alias: bool8 | | Method resolution order: | bool_ | generic | __builtin__.object | | Methods defined here: | | __and__(...) | x.__and__(y) <==> x&y | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __or__(...) | x.__or__(y) <==> x|y | | __rand__(...) | x.__rand__(y) <==> y&x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __xor__(...) | x.__xor__(y) <==> x^y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __oct__(...) | x.__oct__() <==> oct(x) | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class broadcast(__builtin__.object) | Produce an object that mimics broadcasting. | | Parameters | ---------- | in1, in2, ... : array_like | Input parameters. | | Returns | ------- | b : broadcast object | Broadcast the input parameters against one another, and | return an object that encapsulates the result. | Amongst others, it has ``shape`` and ``nd`` properties, and | may be used as an iterator. | | Examples | -------- | Manually adding two vectors, using broadcasting: | | >>> x = np.array([[1], [2], [3]]) | >>> y = np.array([4, 5, 6]) | >>> b = np.broadcast(x, y) | | >>> out = np.empty(b.shape) | >>> out.flat = [u+v for (u,v) in b] | >>> out | array([[ 5., 6., 7.], | [ 6., 7., 8.], | [ 7., 8., 9.]]) | | Compare against built-in broadcasting: | | >>> x + y | array([[5, 6, 7], | [6, 7, 8], | [7, 8, 9]]) | | Methods defined here: | | __iter__(...) | x.__iter__() <==> iter(x) | | next(...) | x.next() -> the next value, or raise StopIteration | | reset(...) | reset() | | Reset the broadcasted result's iterator(s). | | Parameters | ---------- | None | | Returns | ------- | None | | Examples | -------- | >>> x = np.array([1, 2, 3]) | >>> y = np.array([[4], [5], [6]] | >>> b = np.broadcast(x, y) | >>> b.index | 0 | >>> b.next(), b.next(), b.next() | ((1, 4), (2, 4), (3, 4)) | >>> b.index | 3 | >>> b.reset() | >>> b.index | 0 | | ---------------------------------------------------------------------- | Data descriptors defined here: | | index | current index in broadcasted result | | Examples | -------- | >>> x = np.array([[1], [2], [3]]) | >>> y = np.array([4, 5, 6]) | >>> b = np.broadcast(x, y) | >>> b.index | 0 | >>> b.next(), b.next(), b.next() | ((1, 4), (1, 5), (1, 6)) | >>> b.index | 3 | | iters | tuple of iterators along ``self``'s "components." | | Returns a tuple of `numpy.flatiter` objects, one for each "component" | of ``self``. | | See Also | -------- | numpy.flatiter | | Examples | -------- | >>> x = np.array([1, 2, 3]) | >>> y = np.array([[4], [5], [6]]) | >>> b = np.broadcast(x, y) | >>> row, col = b.iters | >>> row.next(), col.next() | (1, 4) | | nd | Number of dimensions of broadcasted result. | | Examples | -------- | >>> x = np.array([1, 2, 3]) | >>> y = np.array([[4], [5], [6]]) | >>> b = np.broadcast(x, y) | >>> b.nd | 2 | | numiter | Number of iterators possessed by the broadcasted result. | | Examples | -------- | >>> x = np.array([1, 2, 3]) | >>> y = np.array([[4], [5], [6]]) | >>> b = np.broadcast(x, y) | >>> b.numiter | 2 | | shape | Shape of broadcasted result. | | Examples | -------- | >>> x = np.array([1, 2, 3]) | >>> y = np.array([[4], [5], [6]]) | >>> b = np.broadcast(x, y) | >>> b.shape | (3, 3) | | size | Total size of broadcasted result. | | Examples | -------- | >>> x = np.array([1, 2, 3]) | >>> y = np.array([[4], [5], [6]]) | >>> b = np.broadcast(x, y) | >>> b.size | 9 | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T class busdaycalendar(__builtin__.object) | busdaycalendar(weekmask='1111100', holidays=None) | | A business day calendar object that efficiently stores information | defining valid days for the busday family of functions. | | The default valid days are Monday through Friday ("business days"). | A busdaycalendar object can be specified with any set of weekly | valid days, plus an optional "holiday" dates that always will be invalid. | | Once a busdaycalendar object is created, the weekmask and holidays | cannot be modified. | | .. versionadded:: 1.7.0 | | Parameters | ---------- | weekmask : str or array_like of bool, optional | A seven-element array indicating which of Monday through Sunday are | valid days. May be specified as a length-seven list or array, like | [1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string | like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for | weekdays, optionally separated by white space. Valid abbreviations | are: Mon Tue Wed Thu Fri Sat Sun | holidays : array_like of datetime64[D], optional | An array of dates to consider as invalid dates, no matter which | weekday they fall upon. Holiday dates may be specified in any | order, and NaT (not-a-time) dates are ignored. This list is | saved in a normalized form that is suited for fast calculations | of valid days. | | Returns | ------- | out : busdaycalendar | A business day calendar object containing the specified | weekmask and holidays values. | | See Also | -------- | is_busday : Returns a boolean array indicating valid days. | busday_offset : Applies an offset counted in valid days. | busday_count : Counts how many valid days are in a half-open date range. | | Attributes | ---------- | Note: once a busdaycalendar object is created, you cannot modify the | weekmask or holidays. The attributes return copies of internal data. | weekmask : (copy) seven-element array of bool | holidays : (copy) sorted array of datetime64[D] | | Examples | -------- | >>> # Some important days in July | ... bdd = np.busdaycalendar( | ... holidays=['2011-07-01', '2011-07-04', '2011-07-17']) | >>> # Default is Monday to Friday weekdays | ... bdd.weekmask | array([ True, True, True, True, True, False, False], dtype='bool') | >>> # Any holidays already on the weekend are removed | ... bdd.holidays | array(['2011-07-01', '2011-07-04'], dtype='datetime64[D]') | | Methods defined here: | | __init__(...) | x.__init__(...) initializes x; see help(type(x)) for signature | | ---------------------------------------------------------------------- | Data descriptors defined here: | | holidays | A copy of the holiday array indicating additional invalid days. | | weekmask | A copy of the seven-element boolean mask indicating valid days. | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T byte = class int8(signedinteger) | 8-bit integer. Character code ``b``. C char compatible. | | Method resolution order: | int8 | signedinteger | integer | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Data descriptors inherited from integer: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension bytes_ = class string_(__builtin__.str, character) | Method resolution order: | string_ | __builtin__.str | __builtin__.basestring | character | flexible | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from __builtin__.str: | | __add__(...) | x.__add__(y) <==> x+y | | __contains__(...) | x.__contains__(y) <==> y in x | | __format__(...) | S.__format__(format_spec) -> string | | Return a formatted version of S as described by format_spec. | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __getnewargs__(...) | | __getslice__(...) | x.__getslice__(i, j) <==> x[i:j] | | Use of negative indices is not supported. | | __len__(...) | x.__len__() <==> len(x) | | __mod__(...) | x.__mod__(y) <==> x%y | | __mul__(...) | x.__mul__(n) <==> x*n | | __rmod__(...) | x.__rmod__(y) <==> y%x | | __rmul__(...) | x.__rmul__(n) <==> n*x | | __sizeof__(...) | S.__sizeof__() -> size of S in memory, in bytes | | capitalize(...) | S.capitalize() -> string | | Return a copy of the string S with only its first character | capitalized. | | center(...) | S.center(width[, fillchar]) -> string | | Return S centered in a string of length width. Padding is | done using the specified fill character (default is a space) | | count(...) | S.count(sub[, start[, end]]) -> int | | Return the number of non-overlapping occurrences of substring sub in | string S[start:end]. Optional arguments start and end are interpreted | as in slice notation. | | decode(...) | S.decode([encoding[,errors]]) -> object | | Decodes S using the codec registered for encoding. encoding defaults | to the default encoding. errors may be given to set a different error | handling scheme. Default is 'strict' meaning that encoding errors raise | a UnicodeDecodeError. Other possible values are 'ignore' and 'replace' | as well as any other name registered with codecs.register_error that is | able to handle UnicodeDecodeErrors. | | encode(...) | S.encode([encoding[,errors]]) -> object | | Encodes S using the codec registered for encoding. encoding defaults | to the default encoding. errors may be given to set a different error | handling scheme. Default is 'strict' meaning that encoding errors raise | a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and | 'xmlcharrefreplace' as well as any other name registered with | codecs.register_error that is able to handle UnicodeEncodeErrors. | | endswith(...) | S.endswith(suffix[, start[, end]]) -> bool | | Return True if S ends with the specified suffix, False otherwise. | With optional start, test S beginning at that position. | With optional end, stop comparing S at that position. | suffix can also be a tuple of strings to try. | | expandtabs(...) | S.expandtabs([tabsize]) -> string | | Return a copy of S where all tab characters are expanded using spaces. | If tabsize is not given, a tab size of 8 characters is assumed. | | find(...) | S.find(sub [,start [,end]]) -> int | | Return the lowest index in S where substring sub is found, | such that sub is contained within S[start:end]. Optional | arguments start and end are interpreted as in slice notation. | | Return -1 on failure. | | format(...) | S.format(*args, **kwargs) -> string | | Return a formatted version of S, using substitutions from args and kwargs. | The substitutions are identified by braces ('{' and '}'). | | index(...) | S.index(sub [,start [,end]]) -> int | | Like S.find() but raise ValueError when the substring is not found. | | isalnum(...) | S.isalnum() -> bool | | Return True if all characters in S are alphanumeric | and there is at least one character in S, False otherwise. | | isalpha(...) | S.isalpha() -> bool | | Return True if all characters in S are alphabetic | and there is at least one character in S, False otherwise. | | isdigit(...) | S.isdigit() -> bool | | Return True if all characters in S are digits | and there is at least one character in S, False otherwise. | | islower(...) | S.islower() -> bool | | Return True if all cased characters in S are lowercase and there is | at least one cased character in S, False otherwise. | | isspace(...) | S.isspace() -> bool | | Return True if all characters in S are whitespace | and there is at least one character in S, False otherwise. | | istitle(...) | S.istitle() -> bool | | Return True if S is a titlecased string and there is at least one | character in S, i.e. uppercase characters may only follow uncased | characters and lowercase characters only cased ones. Return False | otherwise. | | isupper(...) | S.isupper() -> bool | | Return True if all cased characters in S are uppercase and there is | at least one cased character in S, False otherwise. | | join(...) | S.join(iterable) -> string | | Return a string which is the concatenation of the strings in the | iterable. The separator between elements is S. | | ljust(...) | S.ljust(width[, fillchar]) -> string | | Return S left-justified in a string of length width. Padding is | done using the specified fill character (default is a space). | | lower(...) | S.lower() -> string | | Return a copy of the string S converted to lowercase. | | lstrip(...) | S.lstrip([chars]) -> string or unicode | | Return a copy of the string S with leading whitespace removed. | If chars is given and not None, remove characters in chars instead. | If chars is unicode, S will be converted to unicode before stripping | | partition(...) | S.partition(sep) -> (head, sep, tail) | | Search for the separator sep in S, and return the part before it, | the separator itself, and the part after it. If the separator is not | found, return S and two empty strings. | | replace(...) | S.replace(old, new[, count]) -> string | | Return a copy of string S with all occurrences of substring | old replaced by new. If the optional argument count is | given, only the first count occurrences are replaced. | | rfind(...) | S.rfind(sub [,start [,end]]) -> int | | Return the highest index in S where substring sub is found, | such that sub is contained within S[start:end]. Optional | arguments start and end are interpreted as in slice notation. | | Return -1 on failure. | | rindex(...) | S.rindex(sub [,start [,end]]) -> int | | Like S.rfind() but raise ValueError when the substring is not found. | | rjust(...) | S.rjust(width[, fillchar]) -> string | | Return S right-justified in a string of length width. Padding is | done using the specified fill character (default is a space) | | rpartition(...) | S.rpartition(sep) -> (head, sep, tail) | | Search for the separator sep in S, starting at the end of S, and return | the part before it, the separator itself, and the part after it. If the | separator is not found, return two empty strings and S. | | rsplit(...) | S.rsplit([sep [,maxsplit]]) -> list of strings | | Return a list of the words in the string S, using sep as the | delimiter string, starting at the end of the string and working | to the front. If maxsplit is given, at most maxsplit splits are | done. If sep is not specified or is None, any whitespace string | is a separator. | | rstrip(...) | S.rstrip([chars]) -> string or unicode | | Return a copy of the string S with trailing whitespace removed. | If chars is given and not None, remove characters in chars instead. | If chars is unicode, S will be converted to unicode before stripping | | split(...) | S.split([sep [,maxsplit]]) -> list of strings | | Return a list of the words in the string S, using sep as the | delimiter string. If maxsplit is given, at most maxsplit | splits are done. If sep is not specified or is None, any | whitespace string is a separator and empty strings are removed | from the result. | | splitlines(...) | S.splitlines(keepends=False) -> list of strings | | Return a list of the lines in S, breaking at line boundaries. | Line breaks are not included in the resulting list unless keepends | is given and true. | | startswith(...) | S.startswith(prefix[, start[, end]]) -> bool | | Return True if S starts with the specified prefix, False otherwise. | With optional start, test S beginning at that position. | With optional end, stop comparing S at that position. | prefix can also be a tuple of strings to try. | | strip(...) | S.strip([chars]) -> string or unicode | | Return a copy of the string S with leading and trailing | whitespace removed. | If chars is given and not None, remove characters in chars instead. | If chars is unicode, S will be converted to unicode before stripping | | swapcase(...) | S.swapcase() -> string | | Return a copy of the string S with uppercase characters | converted to lowercase and vice versa. | | title(...) | S.title() -> string | | Return a titlecased version of S, i.e. words start with uppercase | characters, all remaining cased characters have lowercase. | | translate(...) | S.translate(table [,deletechars]) -> string | | Return a copy of the string S, where all characters occurring | in the optional argument deletechars are removed, and the | remaining characters have been mapped through the given | translation table, which must be a string of length 256 or None. | If the table argument is None, no translation is applied and | the operation simply removes the characters in deletechars. | | upper(...) | S.upper() -> string | | Return a copy of the string S converted to uppercase. | | zfill(...) | S.zfill(width) -> string | | Pad a numeric string S with zeros on the left, to fill a field | of the specified width. The string S is never truncated. | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension cdouble = class complex128(complexfloating, __builtin__.complex) | Composed of two 64 bit floats | | Method resolution order: | complex128 | complexfloating | inexact | number | generic | __builtin__.complex | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension | | ---------------------------------------------------------------------- | Methods inherited from __builtin__.complex: | | __coerce__(...) | x.__coerce__(y) <==> coerce(x, y) | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | __getnewargs__(...) | | __hash__(...) | x.__hash__() <==> hash(x) cfloat = class complex128(complexfloating, __builtin__.complex) | Composed of two 64 bit floats | | Method resolution order: | complex128 | complexfloating | inexact | number | generic | __builtin__.complex | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension | | ---------------------------------------------------------------------- | Methods inherited from __builtin__.complex: | | __coerce__(...) | x.__coerce__(y) <==> coerce(x, y) | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | __getnewargs__(...) | | __hash__(...) | x.__hash__() <==> hash(x) class character(flexible) | Method resolution order: | character | flexible | generic | __builtin__.object | | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __eq__(...) | x.__eq__(y) <==> x==y | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __ge__(...) | x.__ge__(y) <==> x>=y | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __gt__(...) | x.__gt__(y) <==> x>y | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __le__(...) | x.__le__(y) <==> x<=y | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __ne__(...) | x.__ne__(y) <==> x!=y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class chararray(numpy.ndarray) | chararray(shape, itemsize=1, unicode=False, buffer=None, offset=0, | strides=None, order=None) | | Provides a convenient view on arrays of string and unicode values. | | .. note:: | The `chararray` class exists for backwards compatibility with | Numarray, it is not recommended for new development. Starting from numpy | 1.4, if one needs arrays of strings, it is recommended to use arrays of | `dtype` `object_`, `string_` or `unicode_`, and use the free functions | in the `numpy.char` module for fast vectorized string operations. | | Versus a regular Numpy array of type `str` or `unicode`, this | class adds the following functionality: | | 1) values automatically have whitespace removed from the end | when indexed | | 2) comparison operators automatically remove whitespace from the | end when comparing values | | 3) vectorized string operations are provided as methods | (e.g. `.endswith`) and infix operators (e.g. ``"+", "*", "%"``) | | chararrays should be created using `numpy.char.array` or | `numpy.char.asarray`, rather than this constructor directly. | | This constructor creates the array, using `buffer` (with `offset` | and `strides`) if it is not ``None``. If `buffer` is ``None``, then | constructs a new array with `strides` in "C order", unless both | ``len(shape) >= 2`` and ``order='Fortran'``, in which case `strides` | is in "Fortran order". | | Methods | ------- | astype | argsort | copy | count | decode | dump | dumps | encode | endswith | expandtabs | fill | find | flatten | getfield | index | isalnum | isalpha | isdecimal | isdigit | islower | isnumeric | isspace | istitle | isupper | item | join | ljust | lower | lstrip | nonzero | put | ravel | repeat | replace | reshape | resize | rfind | rindex | rjust | rsplit | rstrip | searchsorted | setfield | setflags | sort | split | splitlines | squeeze | startswith | strip | swapaxes | swapcase | take | title | tofile | tolist | tostring | translate | transpose | upper | view | zfill | | Parameters | ---------- | shape : tuple | Shape of the array. | itemsize : int, optional | Length of each array element, in number of characters. Default is 1. | unicode : bool, optional | Are the array elements of type unicode (True) or string (False). | Default is False. | buffer : int, optional | Memory address of the start of the array data. Default is None, | in which case a new array is created. | offset : int, optional | Fixed stride displacement from the beginning of an axis? | Default is 0. Needs to be >=0. | strides : array_like of ints, optional | Strides for the array (see `ndarray.strides` for full description). | Default is None. | order : {'C', 'F'}, optional | The order in which the array data is stored in memory: 'C' -> | "row major" order (the default), 'F' -> "column major" | (Fortran) order. | | Examples | -------- | >>> charar = np.chararray((3, 3)) | >>> charar[:] = 'a' | >>> charar | chararray([['a', 'a', 'a'], | ['a', 'a', 'a'], | ['a', 'a', 'a']], | dtype='|S1') | | >>> charar = np.chararray(charar.shape, itemsize=5) | >>> charar[:] = 'abc' | >>> charar | chararray([['abc', 'abc', 'abc'], | ['abc', 'abc', 'abc'], | ['abc', 'abc', 'abc']], | dtype='|S5') | | Method resolution order: | chararray | numpy.ndarray | __builtin__.object | | Methods defined here: | | __add__(self, other) | Return (self + other), that is string concatenation, | element-wise for a pair of array_likes of str or unicode. | | See also | -------- | add | | __array_finalize__(self, obj) | | __eq__(self, other) | Return (self == other) element-wise. | | See also | -------- | equal | | __ge__(self, other) | Return (self >= other) element-wise. | | See also | -------- | greater_equal | | __getitem__(self, obj) | | __gt__(self, other) | Return (self > other) element-wise. | | See also | -------- | greater | | __le__(self, other) | Return (self <= other) element-wise. | | See also | -------- | less_equal | | __lt__(self, other) | Return (self < other) element-wise. | | See also | -------- | less | | __mod__(self, i) | Return (self % i), that is pre-Python 2.6 string formatting | (iterpolation), element-wise for a pair of array_likes of `string_` | or `unicode_`. | | See also | -------- | mod | | __mul__(self, i) | Return (self * i), that is string multiple concatenation, | element-wise. | | See also | -------- | multiply | | __ne__(self, other) | Return (self != other) element-wise. | | See also | -------- | not_equal | | __radd__(self, other) | Return (other + self), that is string concatenation, | element-wise for a pair of array_likes of `string_` or `unicode_`. | | See also | -------- | add | | __rmod__(self, other) | | __rmul__(self, i) | Return (self * i), that is string multiple concatenation, | element-wise. | | See also | -------- | multiply | | argsort(self, axis=-1, kind='quicksort', order=None) | | capitalize(self) | Return a copy of `self` with only the first character of each element | capitalized. | | See also | -------- | char.capitalize | | center(self, width, fillchar=' ') | Return a copy of `self` with its elements centered in a | string of length `width`. | | See also | -------- | center | | count(self, sub, start=0, end=None) | Returns an array with the number of non-overlapping occurrences of | substring `sub` in the range [`start`, `end`]. | | See also | -------- | char.count | | decode(self, encoding=None, errors=None) | Calls `str.decode` element-wise. | | See also | -------- | char.decode | | encode(self, encoding=None, errors=None) | Calls `str.encode` element-wise. | | See also | -------- | char.encode | | endswith(self, suffix, start=0, end=None) | Returns a boolean array which is `True` where the string element | in `self` ends with `suffix`, otherwise `False`. | | See also | -------- | char.endswith | | expandtabs(self, tabsize=8) | Return a copy of each string element where all tab characters are | replaced by one or more spaces. | | See also | -------- | char.expandtabs | | find(self, sub, start=0, end=None) | For each element, return the lowest index in the string where | substring `sub` is found. | | See also | -------- | char.find | | index(self, sub, start=0, end=None) | Like `find`, but raises `ValueError` when the substring is not found. | | See also | -------- | char.index | | isalnum(self) | Returns true for each element if all characters in the string | are alphanumeric and there is at least one character, false | otherwise. | | See also | -------- | char.isalnum | | isalpha(self) | Returns true for each element if all characters in the string | are alphabetic and there is at least one character, false | otherwise. | | See also | -------- | char.isalpha | | isdecimal(self) | For each element in `self`, return True if there are only | decimal characters in the element. | | See also | -------- | char.isdecimal | | isdigit(self) | Returns true for each element if all characters in the string are | digits and there is at least one character, false otherwise. | | See also | -------- | char.isdigit | | islower(self) | Returns true for each element if all cased characters in the | string are lowercase and there is at least one cased character, | false otherwise. | | See also | -------- | char.islower | | isnumeric(self) | For each element in `self`, return True if there are only | numeric characters in the element. | | See also | -------- | char.isnumeric | | isspace(self) | Returns true for each element if there are only whitespace | characters in the string and there is at least one character, | false otherwise. | | See also | -------- | char.isspace | | istitle(self) | Returns true for each element if the element is a titlecased | string and there is at least one character, false otherwise. | | See also | -------- | char.istitle | | isupper(self) | Returns true for each element if all cased characters in the | string are uppercase and there is at least one character, false | otherwise. | | See also | -------- | char.isupper | | join(self, seq) | Return a string which is the concatenation of the strings in the | sequence `seq`. | | See also | -------- | char.join | | ljust(self, width, fillchar=' ') | Return an array with the elements of `self` left-justified in a | string of length `width`. | | See also | -------- | char.ljust | | lower(self) | Return an array with the elements of `self` converted to | lowercase. | | See also | -------- | char.lower | | lstrip(self, chars=None) | For each element in `self`, return a copy with the leading characters | removed. | | See also | -------- | char.lstrip | | partition(self, sep) | Partition each element in `self` around `sep`. | | See also | -------- | partition | | replace(self, old, new, count=None) | For each element in `self`, return a copy of the string with all | occurrences of substring `old` replaced by `new`. | | See also | -------- | char.replace | | rfind(self, sub, start=0, end=None) | For each element in `self`, return the highest index in the string | where substring `sub` is found, such that `sub` is contained | within [`start`, `end`]. | | See also | -------- | char.rfind | | rindex(self, sub, start=0, end=None) | Like `rfind`, but raises `ValueError` when the substring `sub` is | not found. | | See also | -------- | char.rindex | | rjust(self, width, fillchar=' ') | Return an array with the elements of `self` | right-justified in a string of length `width`. | | See also | -------- | char.rjust | | rpartition(self, sep) | Partition each element in `self` around `sep`. | | See also | -------- | rpartition | | rsplit(self, sep=None, maxsplit=None) | For each element in `self`, return a list of the words in | the string, using `sep` as the delimiter string. | | See also | -------- | char.rsplit | | rstrip(self, chars=None) | For each element in `self`, return a copy with the trailing | characters removed. | | See also | -------- | char.rstrip | | split(self, sep=None, maxsplit=None) | For each element in `self`, return a list of the words in the | string, using `sep` as the delimiter string. | | See also | -------- | char.split | | splitlines(self, keepends=None) | For each element in `self`, return a list of the lines in the | element, breaking at line boundaries. | | See also | -------- | char.splitlines | | startswith(self, prefix, start=0, end=None) | Returns a boolean array which is `True` where the string element | in `self` starts with `prefix`, otherwise `False`. | | See also | -------- | char.startswith | | strip(self, chars=None) | For each element in `self`, return a copy with the leading and | trailing characters removed. | | See also | -------- | char.strip | | swapcase(self) | For each element in `self`, return a copy of the string with | uppercase characters converted to lowercase and vice versa. | | See also | -------- | char.swapcase | | title(self) | For each element in `self`, return a titlecased version of the | string: words start with uppercase characters, all remaining cased | characters are lowercase. | | See also | -------- | char.title | | translate(self, table, deletechars=None) | For each element in `self`, return a copy of the string where | all characters occurring in the optional argument | `deletechars` are removed, and the remaining characters have | been mapped through the given translation table. | | See also | -------- | char.translate | | upper(self) | Return an array with the elements of `self` converted to | uppercase. | | See also | -------- | char.upper | | zfill(self, width) | Return the numeric string left-filled with zeros in a string of | length `width`. | | See also | -------- | char.zfill | | ---------------------------------------------------------------------- | Static methods defined here: | | __new__(subtype, shape, itemsize=1, unicode=False, buffer=None, offset=0, strides=None, order='C') | | ---------------------------------------------------------------------- | Data descriptors defined here: | | __dict__ | dictionary for instance variables (if defined) | | ---------------------------------------------------------------------- | Methods inherited from numpy.ndarray: | | __abs__(...) | x.__abs__() <==> abs(x) | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | a.__array__(|dtype) -> reference if type unchanged, copy otherwise. | | Returns either a new reference to self if dtype is not given or a new array | of provided data type if dtype is different from the current dtype of the | array. | | __array_prepare__(...) | a.__array_prepare__(obj) -> Object of same type as ndarray object obj. | | __array_wrap__(...) | a.__array_wrap__(obj) -> Object of same type as ndarray object a. | | __contains__(...) | x.__contains__(y) <==> y in x | | __copy__(...) | a.__copy__([order]) | | Return a copy of the array. | | Parameters | ---------- | order : {'C', 'F', 'A'}, optional | If order is 'C' (False) then the result is contiguous (default). | If order is 'Fortran' (True) then the result has fortran order. | If order is 'Any' (None) then the result has fortran order | only if the array already is in fortran order. | | __deepcopy__(...) | a.__deepcopy__() -> Deep copy of array. | | Used if copy.deepcopy is called on an array. | | __delitem__(...) | x.__delitem__(y) <==> del x[y] | | __delslice__(...) | x.__delslice__(i, j) <==> del x[i:j] | | Use of negative indices is not supported. | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __getslice__(...) | x.__getslice__(i, j) <==> x[i:j] | | Use of negative indices is not supported. | | __hex__(...) | x.__hex__() <==> hex(x) | | __iadd__(...) | x.__iadd__(y) <==> x+=y | | __iand__(...) | x.__iand__(y) <==> x&=y | | __idiv__(...) | x.__idiv__(y) <==> x/=y | | __ifloordiv__(...) | x.__ifloordiv__(y) <==> x//=y | | __ilshift__(...) | x.__ilshift__(y) <==> x<<=y | | __imod__(...) | x.__imod__(y) <==> x%=y | | __imul__(...) | x.__imul__(y) <==> x*=y | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __ior__(...) | x.__ior__(y) <==> x|=y | | __ipow__(...) | x.__ipow__(y) <==> x**=y | | __irshift__(...) | x.__irshift__(y) <==> x>>=y | | __isub__(...) | x.__isub__(y) <==> x-=y | | __iter__(...) | x.__iter__() <==> iter(x) | | __itruediv__(...) | x.__itruediv__(y) <==> x/=y | | __ixor__(...) | x.__ixor__(y) <==> x^=y | | __len__(...) | x.__len__() <==> len(x) | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | a.__reduce__() | | For pickling. | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setitem__(...) | x.__setitem__(i, y) <==> x[i]=y | | __setslice__(...) | x.__setslice__(i, j, y) <==> x[i:j]=y | | Use of negative indices is not supported. | | __setstate__(...) | a.__setstate__(version, shape, dtype, isfortran, rawdata) | | For unpickling. | | Parameters | ---------- | version : int | optional pickle version. If omitted defaults to 0. | shape : tuple | dtype : data-type | isFortran : bool | rawdata : string or list | a binary string with the data (or a list if 'a' is an object array) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | a.all(axis=None, out=None, keepdims=False) | | Returns True if all elements evaluate to True. | | Refer to `numpy.all` for full documentation. | | See Also | -------- | numpy.all : equivalent function | | any(...) | a.any(axis=None, out=None, keepdims=False) | | Returns True if any of the elements of `a` evaluate to True. | | Refer to `numpy.any` for full documentation. | | See Also | -------- | numpy.any : equivalent function | | argmax(...) | a.argmax(axis=None, out=None) | | Return indices of the maximum values along the given axis. | | Refer to `numpy.argmax` for full documentation. | | See Also | -------- | numpy.argmax : equivalent function | | argmin(...) | a.argmin(axis=None, out=None) | | Return indices of the minimum values along the given axis of `a`. | | Refer to `numpy.argmin` for detailed documentation. | | See Also | -------- | numpy.argmin : equivalent function | | argpartition(...) | a.argpartition(kth, axis=-1, kind='introselect', order=None) | | Returns the indices that would partition this array. | | Refer to `numpy.argpartition` for full documentation. | | .. versionadded:: 1.8.0 | | See Also | -------- | numpy.argpartition : equivalent function | | astype(...) | a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True) | | Copy of the array, cast to a specified type. | | Parameters | ---------- | dtype : str or dtype | Typecode or data-type to which the array is cast. | order : {'C', 'F', 'A', 'K'}, optional | Controls the memory layout order of the result. | 'C' means C order, 'F' means Fortran order, 'A' | means 'F' order if all the arrays are Fortran contiguous, | 'C' order otherwise, and 'K' means as close to the | order the array elements appear in memory as possible. | Default is 'K'. | casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional | Controls what kind of data casting may occur. Defaults to 'unsafe' | for backwards compatibility. | | * 'no' means the data types should not be cast at all. | * 'equiv' means only byte-order changes are allowed. | * 'safe' means only casts which can preserve values are allowed. | * 'same_kind' means only safe casts or casts within a kind, | like float64 to float32, are allowed. | * 'unsafe' means any data conversions may be done. | subok : bool, optional | If True, then sub-classes will be passed-through (default), otherwise | the returned array will be forced to be a base-class array. | copy : bool, optional | By default, astype always returns a newly allocated array. If this | is set to false, and the `dtype`, `order`, and `subok` | requirements are satisfied, the input array is returned instead | of a copy. | | Returns | ------- | arr_t : ndarray | Unless `copy` is False and the other conditions for returning the input | array are satisfied (see description for `copy` input paramter), `arr_t` | is a new array of the same shape as the input array, with dtype, order | given by `dtype`, `order`. | | Notes | ----- | Starting in NumPy 1.9, astype method now returns an error if the string | dtype to cast to is not long enough in 'safe' casting mode to hold the max | value of integer/float array that is being casted. Previously the casting | was allowed even if the result was truncated. | | Raises | ------ | ComplexWarning | When casting from complex to float or int. To avoid this, | one should use ``a.real.astype(t)``. | | Examples | -------- | >>> x = np.array([1, 2, 2.5]) | >>> x | array([ 1. , 2. , 2.5]) | | >>> x.astype(int) | array([1, 2, 2]) | | byteswap(...) | a.byteswap(inplace) | | Swap the bytes of the array elements | | Toggle between low-endian and big-endian data representation by | returning a byteswapped array, optionally swapped in-place. | | Parameters | ---------- | inplace : bool, optional | If ``True``, swap bytes in-place, default is ``False``. | | Returns | ------- | out : ndarray | The byteswapped array. If `inplace` is ``True``, this is | a view to self. | | Examples | -------- | >>> A = np.array([1, 256, 8755], dtype=np.int16) | >>> map(hex, A) | ['0x1', '0x100', '0x2233'] | >>> A.byteswap(True) | array([ 256, 1, 13090], dtype=int16) | >>> map(hex, A) | ['0x100', '0x1', '0x3322'] | | Arrays of strings are not swapped | | >>> A = np.array(['ceg', 'fac']) | >>> A.byteswap() | array(['ceg', 'fac'], | dtype='|S3') | | choose(...) | a.choose(choices, out=None, mode='raise') | | Use an index array to construct a new array from a set of choices. | | Refer to `numpy.choose` for full documentation. | | See Also | -------- | numpy.choose : equivalent function | | clip(...) | a.clip(min=None, max=None, out=None) | | Return an array whose values are limited to ``[min, max]``. | One of max or min must be given. | | Refer to `numpy.clip` for full documentation. | | See Also | -------- | numpy.clip : equivalent function | | compress(...) | a.compress(condition, axis=None, out=None) | | Return selected slices of this array along given axis. | | Refer to `numpy.compress` for full documentation. | | See Also | -------- | numpy.compress : equivalent function | | conj(...) | a.conj() | | Complex-conjugate all elements. | | Refer to `numpy.conjugate` for full documentation. | | See Also | -------- | numpy.conjugate : equivalent function | | conjugate(...) | a.conjugate() | | Return the complex conjugate, element-wise. | | Refer to `numpy.conjugate` for full documentation. | | See Also | -------- | numpy.conjugate : equivalent function | | copy(...) | a.copy(order='C') | | Return a copy of the array. | | Parameters | ---------- | order : {'C', 'F', 'A', 'K'}, optional | Controls the memory layout of the copy. 'C' means C-order, | 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous, | 'C' otherwise. 'K' means match the layout of `a` as closely | as possible. (Note that this function and :func:numpy.copy are very | similar, but have different default values for their order= | arguments.) | | See also | -------- | numpy.copy | numpy.copyto | | Examples | -------- | >>> x = np.array([[1,2,3],[4,5,6]], order='F') | | >>> y = x.copy() | | >>> x.fill(0) | | >>> x | array([[0, 0, 0], | [0, 0, 0]]) | | >>> y | array([[1, 2, 3], | [4, 5, 6]]) | | >>> y.flags['C_CONTIGUOUS'] | True | | cumprod(...) | a.cumprod(axis=None, dtype=None, out=None) | | Return the cumulative product of the elements along the given axis. | | Refer to `numpy.cumprod` for full documentation. | | See Also | -------- | numpy.cumprod : equivalent function | | cumsum(...) | a.cumsum(axis=None, dtype=None, out=None) | | Return the cumulative sum of the elements along the given axis. | | Refer to `numpy.cumsum` for full documentation. | | See Also | -------- | numpy.cumsum : equivalent function | | diagonal(...) | a.diagonal(offset=0, axis1=0, axis2=1) | | Return specified diagonals. In NumPy 1.9 the returned array is a | read-only view instead of a copy as in previous NumPy versions. In | NumPy 1.10 the read-only restriction will be removed. | | Refer to :func:`numpy.diagonal` for full documentation. | | See Also | -------- | numpy.diagonal : equivalent function | | dot(...) | a.dot(b, out=None) | | Dot product of two arrays. | | Refer to `numpy.dot` for full documentation. | | See Also | -------- | numpy.dot : equivalent function | | Examples | -------- | >>> a = np.eye(2) | >>> b = np.ones((2, 2)) * 2 | >>> a.dot(b) | array([[ 2., 2.], | [ 2., 2.]]) | | This array method can be conveniently chained: | | >>> a.dot(b).dot(b) | array([[ 8., 8.], | [ 8., 8.]]) | | dump(...) | a.dump(file) | | Dump a pickle of the array to the specified file. | The array can be read back with pickle.load or numpy.load. | | Parameters | ---------- | file : str | A string naming the dump file. | | dumps(...) | a.dumps() | | Returns the pickle of the array as a string. | pickle.loads or numpy.loads will convert the string back to an array. | | Parameters | ---------- | None | | fill(...) | a.fill(value) | | Fill the array with a scalar value. | | Parameters | ---------- | value : scalar | All elements of `a` will be assigned this value. | | Examples | -------- | >>> a = np.array([1, 2]) | >>> a.fill(0) | >>> a | array([0, 0]) | >>> a = np.empty(2) | >>> a.fill(1) | >>> a | array([ 1., 1.]) | | flatten(...) | a.flatten(order='C') | | Return a copy of the array collapsed into one dimension. | | Parameters | ---------- | order : {'C', 'F', 'A'}, optional | Whether to flatten in row-major (C-style) or | column-major (Fortran-style) order or preserve the | C/Fortran ordering from `a`. The default is 'C'. | | Returns | ------- | y : ndarray | A copy of the input array, flattened to one dimension. | | See Also | -------- | ravel : Return a flattened array. | flat : A 1-D flat iterator over the array. | | Examples | -------- | >>> a = np.array([[1,2], [3,4]]) | >>> a.flatten() | array([1, 2, 3, 4]) | >>> a.flatten('F') | array([1, 3, 2, 4]) | | getfield(...) | a.getfield(dtype, offset=0) | | Returns a field of the given array as a certain type. | | A field is a view of the array data with a given data-type. The values in | the view are determined by the given type and the offset into the current | array in bytes. The offset needs to be such that the view dtype fits in the | array dtype; for example an array of dtype complex128 has 16-byte elements. | If taking a view with a 32-bit integer (4 bytes), the offset needs to be | between 0 and 12 bytes. | | Parameters | ---------- | dtype : str or dtype | The data type of the view. The dtype size of the view can not be larger | than that of the array itself. | offset : int | Number of bytes to skip before beginning the element view. | | Examples | -------- | >>> x = np.diag([1.+1.j]*2) | >>> x[1, 1] = 2 + 4.j | >>> x | array([[ 1.+1.j, 0.+0.j], | [ 0.+0.j, 2.+4.j]]) | >>> x.getfield(np.float64) | array([[ 1., 0.], | [ 0., 2.]]) | | By choosing an offset of 8 bytes we can select the complex part of the | array for our view: | | >>> x.getfield(np.float64, offset=8) | array([[ 1., 0.], | [ 0., 4.]]) | | item(...) | a.item(*args) | | Copy an element of an array to a standard Python scalar and return it. | | Parameters | ---------- | \*args : Arguments (variable number and type) | | * none: in this case, the method only works for arrays | with one element (`a.size == 1`), which element is | copied into a standard Python scalar object and returned. | | * int_type: this argument is interpreted as a flat index into | the array, specifying which element to copy and return. | | * tuple of int_types: functions as does a single int_type argument, | except that the argument is interpreted as an nd-index into the | array. | | Returns | ------- | z : Standard Python scalar object | A copy of the specified element of the array as a suitable | Python scalar | | Notes | ----- | When the data type of `a` is longdouble or clongdouble, item() returns | a scalar array object because there is no available Python scalar that | would not lose information. Void arrays return a buffer object for item(), | unless fields are defined, in which case a tuple is returned. | | `item` is very similar to a[args], except, instead of an array scalar, | a standard Python scalar is returned. This can be useful for speeding up | access to elements of the array and doing arithmetic on elements of the | array using Python's optimized math. | | Examples | -------- | >>> x = np.random.randint(9, size=(3, 3)) | >>> x | array([[3, 1, 7], | [2, 8, 3], | [8, 5, 3]]) | >>> x.item(3) | 2 | >>> x.item(7) | 5 | >>> x.item((0, 1)) | 1 | >>> x.item((2, 2)) | 3 | | itemset(...) | a.itemset(*args) | | Insert scalar into an array (scalar is cast to array's dtype, if possible) | | There must be at least 1 argument, and define the last argument | as *item*. Then, ``a.itemset(*args)`` is equivalent to but faster | than ``a[args] = item``. The item should be a scalar value and `args` | must select a single item in the array `a`. | | Parameters | ---------- | \*args : Arguments | If one argument: a scalar, only used in case `a` is of size 1. | If two arguments: the last argument is the value to be set | and must be a scalar, the first argument specifies a single array | element location. It is either an int or a tuple. | | Notes | ----- | Compared to indexing syntax, `itemset` provides some speed increase | for placing a scalar into a particular location in an `ndarray`, | if you must do this. However, generally this is discouraged: | among other problems, it complicates the appearance of the code. | Also, when using `itemset` (and `item`) inside a loop, be sure | to assign the methods to a local variable to avoid the attribute | look-up at each loop iteration. | | Examples | -------- | >>> x = np.random.randint(9, size=(3, 3)) | >>> x | array([[3, 1, 7], | [2, 8, 3], | [8, 5, 3]]) | >>> x.itemset(4, 0) | >>> x.itemset((2, 2), 9) | >>> x | array([[3, 1, 7], | [2, 0, 3], | [8, 5, 9]]) | | max(...) | a.max(axis=None, out=None) | | Return the maximum along a given axis. | | Refer to `numpy.amax` for full documentation. | | See Also | -------- | numpy.amax : equivalent function | | mean(...) | a.mean(axis=None, dtype=None, out=None, keepdims=False) | | Returns the average of the array elements along given axis. | | Refer to `numpy.mean` for full documentation. | | See Also | -------- | numpy.mean : equivalent function | | min(...) | a.min(axis=None, out=None, keepdims=False) | | Return the minimum along a given axis. | | Refer to `numpy.amin` for full documentation. | | See Also | -------- | numpy.amin : equivalent function | | newbyteorder(...) | arr.newbyteorder(new_order='S') | | Return the array with the same data viewed with a different byte order. | | Equivalent to:: | | arr.view(arr.dtype.newbytorder(new_order)) | | Changes are also made in all fields and sub-arrays of the array data | type. | | | | Parameters | ---------- | new_order : string, optional | Byte order to force; a value from the byte order specifications | below. `new_order` codes can be any of: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_arr : array | New array object with the dtype reflecting given change to the | byte order. | | nonzero(...) | a.nonzero() | | Return the indices of the elements that are non-zero. | | Refer to `numpy.nonzero` for full documentation. | | See Also | -------- | numpy.nonzero : equivalent function | | prod(...) | a.prod(axis=None, dtype=None, out=None, keepdims=False) | | Return the product of the array elements over the given axis | | Refer to `numpy.prod` for full documentation. | | See Also | -------- | numpy.prod : equivalent function | | ptp(...) | a.ptp(axis=None, out=None) | | Peak to peak (maximum - minimum) value along a given axis. | | Refer to `numpy.ptp` for full documentation. | | See Also | -------- | numpy.ptp : equivalent function | | put(...) | a.put(indices, values, mode='raise') | | Set ``a.flat[n] = values[n]`` for all `n` in indices. | | Refer to `numpy.put` for full documentation. | | See Also | -------- | numpy.put : equivalent function | | ravel(...) | a.ravel([order]) | | Return a flattened array. | | Refer to `numpy.ravel` for full documentation. | | See Also | -------- | numpy.ravel : equivalent function | | ndarray.flat : a flat iterator on the array. | | repeat(...) | a.repeat(repeats, axis=None) | | Repeat elements of an array. | | Refer to `numpy.repeat` for full documentation. | | See Also | -------- | numpy.repeat : equivalent function | | reshape(...) | a.reshape(shape, order='C') | | Returns an array containing the same data with a new shape. | | Refer to `numpy.reshape` for full documentation. | | See Also | -------- | numpy.reshape : equivalent function | | resize(...) | a.resize(new_shape, refcheck=True) | | Change shape and size of array in-place. | | Parameters | ---------- | new_shape : tuple of ints, or `n` ints | Shape of resized array. | refcheck : bool, optional | If False, reference count will not be checked. Default is True. | | Returns | ------- | None | | Raises | ------ | ValueError | If `a` does not own its own data or references or views to it exist, | and the data memory must be changed. | | SystemError | If the `order` keyword argument is specified. This behaviour is a | bug in NumPy. | | See Also | -------- | resize : Return a new array with the specified shape. | | Notes | ----- | This reallocates space for the data area if necessary. | | Only contiguous arrays (data elements consecutive in memory) can be | resized. | | The purpose of the reference count check is to make sure you | do not use this array as a buffer for another Python object and then | reallocate the memory. However, reference counts can increase in | other ways so if you are sure that you have not shared the memory | for this array with another Python object, then you may safely set | `refcheck` to False. | | Examples | -------- | Shrinking an array: array is flattened (in the order that the data are | stored in memory), resized, and reshaped: | | >>> a = np.array([[0, 1], [2, 3]], order='C') | >>> a.resize((2, 1)) | >>> a | array([[0], | [1]]) | | >>> a = np.array([[0, 1], [2, 3]], order='F') | >>> a.resize((2, 1)) | >>> a | array([[0], | [2]]) | | Enlarging an array: as above, but missing entries are filled with zeros: | | >>> b = np.array([[0, 1], [2, 3]]) | >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple | >>> b | array([[0, 1, 2], | [3, 0, 0]]) | | Referencing an array prevents resizing... | | >>> c = a | >>> a.resize((1, 1)) | Traceback (most recent call last): | ... | ValueError: cannot resize an array that has been referenced ... | | Unless `refcheck` is False: | | >>> a.resize((1, 1), refcheck=False) | >>> a | array([[0]]) | >>> c | array([[0]]) | | round(...) | a.round(decimals=0, out=None) | | Return `a` with each element rounded to the given number of decimals. | | Refer to `numpy.around` for full documentation. | | See Also | -------- | numpy.around : equivalent function | | searchsorted(...) | a.searchsorted(v, side='left', sorter=None) | | Find indices where elements of v should be inserted in a to maintain order. | | For full documentation, see `numpy.searchsorted` | | See Also | -------- | numpy.searchsorted : equivalent function | | setfield(...) | a.setfield(val, dtype, offset=0) | | Put a value into a specified place in a field defined by a data-type. | | Place `val` into `a`'s field defined by `dtype` and beginning `offset` | bytes into the field. | | Parameters | ---------- | val : object | Value to be placed in field. | dtype : dtype object | Data-type of the field in which to place `val`. | offset : int, optional | The number of bytes into the field at which to place `val`. | | Returns | ------- | None | | See Also | -------- | getfield | | Examples | -------- | >>> x = np.eye(3) | >>> x.getfield(np.float64) | array([[ 1., 0., 0.], | [ 0., 1., 0.], | [ 0., 0., 1.]]) | >>> x.setfield(3, np.int32) | >>> x.getfield(np.int32) | array([[3, 3, 3], | [3, 3, 3], | [3, 3, 3]]) | >>> x | array([[ 1.00000000e+000, 1.48219694e-323, 1.48219694e-323], | [ 1.48219694e-323, 1.00000000e+000, 1.48219694e-323], | [ 1.48219694e-323, 1.48219694e-323, 1.00000000e+000]]) | >>> x.setfield(np.eye(3), np.int32) | >>> x | array([[ 1., 0., 0.], | [ 0., 1., 0.], | [ 0., 0., 1.]]) | | setflags(...) | a.setflags(write=None, align=None, uic=None) | | Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively. | | These Boolean-valued flags affect how numpy interprets the memory | area used by `a` (see Notes below). The ALIGNED flag can only | be set to True if the data is actually aligned according to the type. | The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE | can only be set to True if the array owns its own memory, or the | ultimate owner of the memory exposes a writeable buffer interface, | or is a string. (The exception for string is made so that unpickling | can be done without copying memory.) | | Parameters | ---------- | write : bool, optional | Describes whether or not `a` can be written to. | align : bool, optional | Describes whether or not `a` is aligned properly for its type. | uic : bool, optional | Describes whether or not `a` is a copy of another "base" array. | | Notes | ----- | Array flags provide information about how the memory area used | for the array is to be interpreted. There are 6 Boolean flags | in use, only three of which can be changed by the user: | UPDATEIFCOPY, WRITEABLE, and ALIGNED. | | WRITEABLE (W) the data area can be written to; | | ALIGNED (A) the data and strides are aligned appropriately for the hardware | (as determined by the compiler); | | UPDATEIFCOPY (U) this array is a copy of some other array (referenced | by .base). When this array is deallocated, the base array will be | updated with the contents of this array. | | All flags can be accessed using their first (upper case) letter as well | as the full name. | | Examples | -------- | >>> y | array([[3, 1, 7], | [2, 0, 0], | [8, 5, 9]]) | >>> y.flags | C_CONTIGUOUS : True | F_CONTIGUOUS : False | OWNDATA : True | WRITEABLE : True | ALIGNED : True | UPDATEIFCOPY : False | >>> y.setflags(write=0, align=0) | >>> y.flags | C_CONTIGUOUS : True | F_CONTIGUOUS : False | OWNDATA : True | WRITEABLE : False | ALIGNED : False | UPDATEIFCOPY : False | >>> y.setflags(uic=1) | Traceback (most recent call last): | File "", line 1, in | ValueError: cannot set UPDATEIFCOPY flag to True | | sort(...) | a.sort(axis=-1, kind='quicksort', order=None) | | Sort an array, in-place. | | Parameters | ---------- | axis : int, optional | Axis along which to sort. Default is -1, which means sort along the | last axis. | kind : {'quicksort', 'mergesort', 'heapsort'}, optional | Sorting algorithm. Default is 'quicksort'. | order : str or list of str, optional | When `a` is an array with fields defined, this argument specifies | which fields to compare first, second, etc. A single field can | be specified as a string, and not all fields need be specified, | but unspecified fields will still be used, in the order in which | they come up in the dtype, to break ties. | | See Also | -------- | numpy.sort : Return a sorted copy of an array. | argsort : Indirect sort. | lexsort : Indirect stable sort on multiple keys. | searchsorted : Find elements in sorted array. | partition: Partial sort. | | Notes | ----- | See ``sort`` for notes on the different sorting algorithms. | | Examples | -------- | >>> a = np.array([[1,4], [3,1]]) | >>> a.sort(axis=1) | >>> a | array([[1, 4], | [1, 3]]) | >>> a.sort(axis=0) | >>> a | array([[1, 3], | [1, 4]]) | | Use the `order` keyword to specify a field to use when sorting a | structured array: | | >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)]) | >>> a.sort(order='y') | >>> a | array([('c', 1), ('a', 2)], | dtype=[('x', '|S1'), ('y', '>> x = np.array([[0, 1], [2, 3]]) | >>> x.tobytes() | b'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00' | >>> x.tobytes('C') == x.tobytes() | True | >>> x.tobytes('F') | b'\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00' | | tofile(...) | a.tofile(fid, sep="", format="%s") | | Write array to a file as text or binary (default). | | Data is always written in 'C' order, independent of the order of `a`. | The data produced by this method can be recovered using the function | fromfile(). | | Parameters | ---------- | fid : file or str | An open file object, or a string containing a filename. | sep : str | Separator between array items for text output. | If "" (empty), a binary file is written, equivalent to | ``file.write(a.tobytes())``. | format : str | Format string for text file output. | Each entry in the array is formatted to text by first converting | it to the closest Python type, and then using "format" % item. | | Notes | ----- | This is a convenience function for quick storage of array data. | Information on endianness and precision is lost, so this method is not a | good choice for files intended to archive data or transport data between | machines with different endianness. Some of these problems can be overcome | by outputting the data as text files, at the expense of speed and file | size. | | tolist(...) | a.tolist() | | Return the array as a (possibly nested) list. | | Return a copy of the array data as a (nested) Python list. | Data items are converted to the nearest compatible Python type. | | Parameters | ---------- | none | | Returns | ------- | y : list | The possibly nested list of array elements. | | Notes | ----- | The array may be recreated, ``a = np.array(a.tolist())``. | | Examples | -------- | >>> a = np.array([1, 2]) | >>> a.tolist() | [1, 2] | >>> a = np.array([[1, 2], [3, 4]]) | >>> list(a) | [array([1, 2]), array([3, 4])] | >>> a.tolist() | [[1, 2], [3, 4]] | | tostring(...) | a.tostring(order='C') | | Construct Python bytes containing the raw data bytes in the array. | | Constructs Python bytes showing a copy of the raw contents of | data memory. The bytes object can be produced in either 'C' or 'Fortran', | or 'Any' order (the default is 'C'-order). 'Any' order means C-order | unless the F_CONTIGUOUS flag in the array is set, in which case it | means 'Fortran' order. | | This function is a compatibility alias for tobytes. Despite its name it returns bytes not strings. | | Parameters | ---------- | order : {'C', 'F', None}, optional | Order of the data for multidimensional arrays: | C, Fortran, or the same as for the original array. | | Returns | ------- | s : bytes | Python bytes exhibiting a copy of `a`'s raw data. | | Examples | -------- | >>> x = np.array([[0, 1], [2, 3]]) | >>> x.tobytes() | b'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00' | >>> x.tobytes('C') == x.tobytes() | True | >>> x.tobytes('F') | b'\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00' | | trace(...) | a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None) | | Return the sum along diagonals of the array. | | Refer to `numpy.trace` for full documentation. | | See Also | -------- | numpy.trace : equivalent function | | transpose(...) | a.transpose(*axes) | | Returns a view of the array with axes transposed. | | For a 1-D array, this has no effect. (To change between column and | row vectors, first cast the 1-D array into a matrix object.) | For a 2-D array, this is the usual matrix transpose. | For an n-D array, if axes are given, their order indicates how the | axes are permuted (see Examples). If axes are not provided and | ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then | ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``. | | Parameters | ---------- | axes : None, tuple of ints, or `n` ints | | * None or no argument: reverses the order of the axes. | | * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s | `i`-th axis becomes `a.transpose()`'s `j`-th axis. | | * `n` ints: same as an n-tuple of the same ints (this form is | intended simply as a "convenience" alternative to the tuple form) | | Returns | ------- | out : ndarray | View of `a`, with axes suitably permuted. | | See Also | -------- | ndarray.T : Array property returning the array transposed. | | Examples | -------- | >>> a = np.array([[1, 2], [3, 4]]) | >>> a | array([[1, 2], | [3, 4]]) | >>> a.transpose() | array([[1, 3], | [2, 4]]) | >>> a.transpose((1, 0)) | array([[1, 3], | [2, 4]]) | >>> a.transpose(1, 0) | array([[1, 3], | [2, 4]]) | | var(...) | a.var(axis=None, dtype=None, out=None, ddof=0, keepdims=False) | | Returns the variance of the array elements, along given axis. | | Refer to `numpy.var` for full documentation. | | See Also | -------- | numpy.var : equivalent function | | view(...) | a.view(dtype=None, type=None) | | New view of array with the same data. | | Parameters | ---------- | dtype : data-type or ndarray sub-class, optional | Data-type descriptor of the returned view, e.g., float32 or int16. The | default, None, results in the view having the same data-type as `a`. | This argument can also be specified as an ndarray sub-class, which | then specifies the type of the returned object (this is equivalent to | setting the ``type`` parameter). | type : Python type, optional | Type of the returned view, e.g., ndarray or matrix. Again, the | default None results in type preservation. | | Notes | ----- | ``a.view()`` is used two different ways: | | ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view | of the array's memory with a different data-type. This can cause a | reinterpretation of the bytes of memory. | | ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just | returns an instance of `ndarray_subclass` that looks at the same array | (same shape, dtype, etc.) This does not cause a reinterpretation of the | memory. | | For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of | bytes per entry than the previous dtype (for example, converting a | regular array to a structured array), then the behavior of the view | cannot be predicted just from the superficial appearance of ``a`` (shown | by ``print(a)``). It also depends on exactly how ``a`` is stored in | memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus | defined as a slice or transpose, etc., the view may give different | results. | | | Examples | -------- | >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)]) | | Viewing array data using a different type and dtype: | | >>> y = x.view(dtype=np.int16, type=np.matrix) | >>> y | matrix([[513]], dtype=int16) | >>> print type(y) | | | Creating a view on a structured array so it can be used in calculations | | >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)]) | >>> xv = x.view(dtype=np.int8).reshape(-1,2) | >>> xv | array([[1, 2], | [3, 4]], dtype=int8) | >>> xv.mean(0) | array([ 2., 3.]) | | Making changes to the view changes the underlying array | | >>> xv[0,1] = 20 | >>> print x | [(1, 20) (3, 4)] | | Using a view to convert an array to a recarray: | | >>> z = x.view(np.recarray) | >>> z.a | array([1], dtype=int8) | | Views share data: | | >>> x[0] = (9, 10) | >>> z[0] | (9, 10) | | Views that change the dtype size (bytes per entry) should normally be | avoided on arrays defined by slices, transposes, fortran-ordering, etc.: | | >>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16) | >>> y = x[:, 0:2] | >>> y | array([[1, 2], | [4, 5]], dtype=int16) | >>> y.view(dtype=[('width', np.int16), ('length', np.int16)]) | Traceback (most recent call last): | File "", line 1, in | ValueError: new type not compatible with array. | >>> z = y.copy() | >>> z.view(dtype=[('width', np.int16), ('length', np.int16)]) | array([[(1, 2)], | [(4, 5)]], dtype=[('width', '>> x = np.array([[1.,2.],[3.,4.]]) | >>> x | array([[ 1., 2.], | [ 3., 4.]]) | >>> x.T | array([[ 1., 3.], | [ 2., 4.]]) | >>> x = np.array([1.,2.,3.,4.]) | >>> x | array([ 1., 2., 3., 4.]) | >>> x.T | array([ 1., 2., 3., 4.]) | | __array_interface__ | Array protocol: Python side. | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: C-struct side. | | base | Base object if memory is from some other object. | | Examples | -------- | The base of an array that owns its memory is None: | | >>> x = np.array([1,2,3,4]) | >>> x.base is None | True | | Slicing creates a view, whose memory is shared with x: | | >>> y = x[2:] | >>> y.base is x | True | | ctypes | An object to simplify the interaction of the array with the ctypes | module. | | This attribute creates an object that makes it easier to use arrays | when calling shared libraries with the ctypes module. The returned | object has, among others, data, shape, and strides attributes (see | Notes below) which themselves return ctypes objects that can be used | as arguments to a shared library. | | Parameters | ---------- | None | | Returns | ------- | c : Python object | Possessing attributes data, shape, strides, etc. | | See Also | -------- | numpy.ctypeslib | | Notes | ----- | Below are the public attributes of this object which were documented | in "Guide to NumPy" (we have omitted undocumented public attributes, | as well as documented private attributes): | | * data: A pointer to the memory area of the array as a Python integer. | This memory area may contain data that is not aligned, or not in correct | byte-order. The memory area may not even be writeable. The array | flags and data-type of this array should be respected when passing this | attribute to arbitrary C-code to avoid trouble that can include Python | crashing. User Beware! The value of this attribute is exactly the same | as self._array_interface_['data'][0]. | | * shape (c_intp*self.ndim): A ctypes array of length self.ndim where | the basetype is the C-integer corresponding to dtype('p') on this | platform. This base-type could be c_int, c_long, or c_longlong | depending on the platform. The c_intp type is defined accordingly in | numpy.ctypeslib. The ctypes array contains the shape of the underlying | array. | | * strides (c_intp*self.ndim): A ctypes array of length self.ndim where | the basetype is the same as for the shape attribute. This ctypes array | contains the strides information from the underlying array. This strides | information is important for showing how many bytes must be jumped to | get to the next element in the array. | | * data_as(obj): Return the data pointer cast to a particular c-types object. | For example, calling self._as_parameter_ is equivalent to | self.data_as(ctypes.c_void_p). Perhaps you want to use the data as a | pointer to a ctypes array of floating-point data: | self.data_as(ctypes.POINTER(ctypes.c_double)). | | * shape_as(obj): Return the shape tuple as an array of some other c-types | type. For example: self.shape_as(ctypes.c_short). | | * strides_as(obj): Return the strides tuple as an array of some other | c-types type. For example: self.strides_as(ctypes.c_longlong). | | Be careful using the ctypes attribute - especially on temporary | arrays or arrays constructed on the fly. For example, calling | ``(a+b).ctypes.data_as(ctypes.c_void_p)`` returns a pointer to memory | that is invalid because the array created as (a+b) is deallocated | before the next Python statement. You can avoid this problem using | either ``c=a+b`` or ``ct=(a+b).ctypes``. In the latter case, ct will | hold a reference to the array until ct is deleted or re-assigned. | | If the ctypes module is not available, then the ctypes attribute | of array objects still returns something useful, but ctypes objects | are not returned and errors may be raised instead. In particular, | the object will still have the as parameter attribute which will | return an integer equal to the data attribute. | | Examples | -------- | >>> import ctypes | >>> x | array([[0, 1], | [2, 3]]) | >>> x.ctypes.data | 30439712 | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)) | | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)).contents | c_long(0) | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_longlong)).contents | c_longlong(4294967296L) | >>> x.ctypes.shape | | >>> x.ctypes.shape_as(ctypes.c_long) | | >>> x.ctypes.strides | | >>> x.ctypes.strides_as(ctypes.c_longlong) | | | data | Python buffer object pointing to the start of the array's data. | | dtype | Data-type of the array's elements. | | Parameters | ---------- | None | | Returns | ------- | d : numpy dtype object | | See Also | -------- | numpy.dtype | | Examples | -------- | >>> x | array([[0, 1], | [2, 3]]) | >>> x.dtype | dtype('int32') | >>> type(x.dtype) | | | flags | Information about the memory layout of the array. | | Attributes | ---------- | C_CONTIGUOUS (C) | The data is in a single, C-style contiguous segment. | F_CONTIGUOUS (F) | The data is in a single, Fortran-style contiguous segment. | OWNDATA (O) | The array owns the memory it uses or borrows it from another object. | WRITEABLE (W) | The data area can be written to. Setting this to False locks | the data, making it read-only. A view (slice, etc.) inherits WRITEABLE | from its base array at creation time, but a view of a writeable | array may be subsequently locked while the base array remains writeable. | (The opposite is not true, in that a view of a locked array may not | be made writeable. However, currently, locking a base object does not | lock any views that already reference it, so under that circumstance it | is possible to alter the contents of a locked array via a previously | created writeable view onto it.) Attempting to change a non-writeable | array raises a RuntimeError exception. | ALIGNED (A) | The data and all elements are aligned appropriately for the hardware. | UPDATEIFCOPY (U) | This array is a copy of some other array. When this array is | deallocated, the base array will be updated with the contents of | this array. | FNC | F_CONTIGUOUS and not C_CONTIGUOUS. | FORC | F_CONTIGUOUS or C_CONTIGUOUS (one-segment test). | BEHAVED (B) | ALIGNED and WRITEABLE. | CARRAY (CA) | BEHAVED and C_CONTIGUOUS. | FARRAY (FA) | BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS. | | Notes | ----- | The `flags` object can be accessed dictionary-like (as in ``a.flags['WRITEABLE']``), | or by using lowercased attribute names (as in ``a.flags.writeable``). Short flag | names are only supported in dictionary access. | | Only the UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be changed by | the user, via direct assignment to the attribute or dictionary entry, | or by calling `ndarray.setflags`. | | The array flags cannot be set arbitrarily: | | - UPDATEIFCOPY can only be set ``False``. | - ALIGNED can only be set ``True`` if the data is truly aligned. | - WRITEABLE can only be set ``True`` if the array owns its own memory | or the ultimate owner of the memory exposes a writeable buffer | interface or is a string. | | Arrays can be both C-style and Fortran-style contiguous simultaneously. | This is clear for 1-dimensional arrays, but can also be true for higher | dimensional arrays. | | Even for contiguous arrays a stride for a given dimension | ``arr.strides[dim]`` may be *arbitrary* if ``arr.shape[dim] == 1`` | or the array has no elements. | It does *not* generally hold that ``self.strides[-1] == self.itemsize`` | for C-style contiguous arrays or ``self.strides[0] == self.itemsize`` for | Fortran-style contiguous arrays is true. | | flat | A 1-D iterator over the array. | | This is a `numpy.flatiter` instance, which acts similarly to, but is not | a subclass of, Python's built-in iterator object. | | See Also | -------- | flatten : Return a copy of the array collapsed into one dimension. | | flatiter | | Examples | -------- | >>> x = np.arange(1, 7).reshape(2, 3) | >>> x | array([[1, 2, 3], | [4, 5, 6]]) | >>> x.flat[3] | 4 | >>> x.T | array([[1, 4], | [2, 5], | [3, 6]]) | >>> x.T.flat[3] | 5 | >>> type(x.flat) | | | An assignment example: | | >>> x.flat = 3; x | array([[3, 3, 3], | [3, 3, 3]]) | >>> x.flat[[1,4]] = 1; x | array([[3, 1, 3], | [3, 1, 3]]) | | imag | The imaginary part of the array. | | Examples | -------- | >>> x = np.sqrt([1+0j, 0+1j]) | >>> x.imag | array([ 0. , 0.70710678]) | >>> x.imag.dtype | dtype('float64') | | itemsize | Length of one array element in bytes. | | Examples | -------- | >>> x = np.array([1,2,3], dtype=np.float64) | >>> x.itemsize | 8 | >>> x = np.array([1,2,3], dtype=np.complex128) | >>> x.itemsize | 16 | | nbytes | Total bytes consumed by the elements of the array. | | Notes | ----- | Does not include memory consumed by non-element attributes of the | array object. | | Examples | -------- | >>> x = np.zeros((3,5,2), dtype=np.complex128) | >>> x.nbytes | 480 | >>> np.prod(x.shape) * x.itemsize | 480 | | ndim | Number of array dimensions. | | Examples | -------- | >>> x = np.array([1, 2, 3]) | >>> x.ndim | 1 | >>> y = np.zeros((2, 3, 4)) | >>> y.ndim | 3 | | real | The real part of the array. | | Examples | -------- | >>> x = np.sqrt([1+0j, 0+1j]) | >>> x.real | array([ 1. , 0.70710678]) | >>> x.real.dtype | dtype('float64') | | See Also | -------- | numpy.real : equivalent function | | shape | Tuple of array dimensions. | | Notes | ----- | May be used to "reshape" the array, as long as this would not | require a change in the total number of elements | | Examples | -------- | >>> x = np.array([1, 2, 3, 4]) | >>> x.shape | (4,) | >>> y = np.zeros((2, 3, 4)) | >>> y.shape | (2, 3, 4) | >>> y.shape = (3, 8) | >>> y | array([[ 0., 0., 0., 0., 0., 0., 0., 0.], | [ 0., 0., 0., 0., 0., 0., 0., 0.], | [ 0., 0., 0., 0., 0., 0., 0., 0.]]) | >>> y.shape = (3, 6) | Traceback (most recent call last): | File "", line 1, in | ValueError: total size of new array must be unchanged | | size | Number of elements in the array. | | Equivalent to ``np.prod(a.shape)``, i.e., the product of the array's | dimensions. | | Examples | -------- | >>> x = np.zeros((3, 5, 2), dtype=np.complex128) | >>> x.size | 30 | >>> np.prod(x.shape) | 30 | | strides | Tuple of bytes to step in each dimension when traversing an array. | | The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a` | is:: | | offset = sum(np.array(i) * a.strides) | | A more detailed explanation of strides can be found in the | "ndarray.rst" file in the NumPy reference guide. | | Notes | ----- | Imagine an array of 32-bit integers (each 4 bytes):: | | x = np.array([[0, 1, 2, 3, 4], | [5, 6, 7, 8, 9]], dtype=np.int32) | | This array is stored in memory as 40 bytes, one after the other | (known as a contiguous block of memory). The strides of an array tell | us how many bytes we have to skip in memory to move to the next position | along a certain axis. For example, we have to skip 4 bytes (1 value) to | move to the next column, but 20 bytes (5 values) to get to the same | position in the next row. As such, the strides for the array `x` will be | ``(20, 4)``. | | See Also | -------- | numpy.lib.stride_tricks.as_strided | | Examples | -------- | >>> y = np.reshape(np.arange(2*3*4), (2,3,4)) | >>> y | array([[[ 0, 1, 2, 3], | [ 4, 5, 6, 7], | [ 8, 9, 10, 11]], | [[12, 13, 14, 15], | [16, 17, 18, 19], | [20, 21, 22, 23]]]) | >>> y.strides | (48, 16, 4) | >>> y[1,1,1] | 17 | >>> offset=sum(y.strides * np.array((1,1,1))) | >>> offset/y.itemsize | 17 | | >>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0) | >>> x.strides | (32, 4, 224, 1344) | >>> i = np.array([3,5,2,2]) | >>> offset = sum(i * x.strides) | >>> x[3,5,2,2] | 813 | >>> offset / x.itemsize | 813 | | ---------------------------------------------------------------------- | Data and other attributes inherited from numpy.ndarray: | | __hash__ = None clongdouble = class complex128(complexfloating) | Composed of two 64 bit floats | | Method resolution order: | complex128 | complexfloating | inexact | number | generic | __builtin__.object | | Methods defined here: | | __complex__(...) | | __eq__(...) | x.__eq__(y) <==> x==y | | __float__(...) | x.__float__() <==> float(x) | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __le__(...) | x.__le__(y) <==> x<=y | | __long__(...) | x.__long__() <==> long(x) | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __oct__(...) | x.__oct__() <==> oct(x) | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __invert__(...) | x.__invert__() <==> ~x | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension clongfloat = class complex128(complexfloating) | Composed of two 64 bit floats | | Method resolution order: | complex128 | complexfloating | inexact | number | generic | __builtin__.object | | Methods defined here: | | __complex__(...) | | __eq__(...) | x.__eq__(y) <==> x==y | | __float__(...) | x.__float__() <==> float(x) | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __le__(...) | x.__le__(y) <==> x<=y | | __long__(...) | x.__long__() <==> long(x) | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __oct__(...) | x.__oct__() <==> oct(x) | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __invert__(...) | x.__invert__() <==> ~x | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class complex128(complexfloating, __builtin__.complex) | Composed of two 64 bit floats | | Method resolution order: | complex128 | complexfloating | inexact | number | generic | __builtin__.complex | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension | | ---------------------------------------------------------------------- | Methods inherited from __builtin__.complex: | | __coerce__(...) | x.__coerce__(y) <==> coerce(x, y) | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | __getnewargs__(...) | | __hash__(...) | x.__hash__() <==> hash(x) class complex64(complexfloating) | Composed of two 32 bit floats | | Method resolution order: | complex64 | complexfloating | inexact | number | generic | __builtin__.object | | Methods defined here: | | __complex__(...) | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension complex_ = class complex128(complexfloating, __builtin__.complex) | Composed of two 64 bit floats | | Method resolution order: | complex128 | complexfloating | inexact | number | generic | __builtin__.complex | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension | | ---------------------------------------------------------------------- | Methods inherited from __builtin__.complex: | | __coerce__(...) | x.__coerce__(y) <==> coerce(x, y) | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | __getnewargs__(...) | | __hash__(...) | x.__hash__() <==> hash(x) class complexfloating(inexact) | Method resolution order: | complexfloating | inexact | number | generic | __builtin__.object | | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __eq__(...) | x.__eq__(y) <==> x==y | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __ge__(...) | x.__ge__(y) <==> x>=y | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __gt__(...) | x.__gt__(y) <==> x>y | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __le__(...) | x.__le__(y) <==> x<=y | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __ne__(...) | x.__ne__(y) <==> x!=y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension csingle = class complex64(complexfloating) | Composed of two 32 bit floats | | Method resolution order: | complex64 | complexfloating | inexact | number | generic | __builtin__.object | | Methods defined here: | | __complex__(...) | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class datetime64(generic) | Method resolution order: | datetime64 | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension double = class float64(floating, __builtin__.float) | 64-bit floating-point number. Character code 'd'. Python float compatible. | | Method resolution order: | float64 | floating | inexact | number | generic | __builtin__.float | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension | | ---------------------------------------------------------------------- | Methods inherited from __builtin__.float: | | __coerce__(...) | x.__coerce__(y) <==> coerce(x, y) | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | __getformat__(...) | float.__getformat__(typestr) -> string | | You probably don't want to use this function. It exists mainly to be | used in Python's test suite. | | typestr must be 'double' or 'float'. This function returns whichever of | 'unknown', 'IEEE, big-endian' or 'IEEE, little-endian' best describes the | format of floating point numbers used by the C type named by typestr. | | __getnewargs__(...) | | __hash__(...) | x.__hash__() <==> hash(x) | | __setformat__(...) | float.__setformat__(typestr, fmt) -> None | | You probably don't want to use this function. It exists mainly to be | used in Python's test suite. | | typestr must be 'double' or 'float'. fmt must be one of 'unknown', | 'IEEE, big-endian' or 'IEEE, little-endian', and in addition can only be | one of the latter two if it appears to match the underlying C reality. | | Override the automatic determination of C-level floating point type. | This affects how floats are converted to and from binary strings. | | __trunc__(...) | Return the Integral closest to x between 0 and x. | | as_integer_ratio(...) | float.as_integer_ratio() -> (int, int) | | Return a pair of integers, whose ratio is exactly equal to the original | float and with a positive denominator. | Raise OverflowError on infinities and a ValueError on NaNs. | | >>> (10.0).as_integer_ratio() | (10, 1) | >>> (0.0).as_integer_ratio() | (0, 1) | >>> (-.25).as_integer_ratio() | (-1, 4) | | fromhex(...) | float.fromhex(string) -> float | | Create a floating-point number from a hexadecimal string. | >>> float.fromhex('0x1.ffffp10') | 2047.984375 | >>> float.fromhex('-0x1p-1074') | -4.9406564584124654e-324 | | hex(...) | float.hex() -> string | | Return a hexadecimal representation of a floating-point number. | >>> (-0.1).hex() | '-0x1.999999999999ap-4' | >>> 3.14159.hex() | '0x1.921f9f01b866ep+1' | | is_integer(...) | Return True if the float is an integer. class dtype(__builtin__.object) | dtype(obj, align=False, copy=False) | | Create a data type object. | | A numpy array is homogeneous, and contains elements described by a | dtype object. A dtype object can be constructed from different | combinations of fundamental numeric types. | | Parameters | ---------- | obj | Object to be converted to a data type object. | align : bool, optional | Add padding to the fields to match what a C compiler would output | for a similar C-struct. Can be ``True`` only if `obj` is a dictionary | or a comma-separated string. If a struct dtype is being created, | this also sets a sticky alignment flag ``isalignedstruct``. | copy : bool, optional | Make a new copy of the data-type object. If ``False``, the result | may just be a reference to a built-in data-type object. | | See also | -------- | result_type | | Examples | -------- | Using array-scalar type: | | >>> np.dtype(np.int16) | dtype('int16') | | Structured type, one field name 'f1', containing int16: | | >>> np.dtype([('f1', np.int16)]) | dtype([('f1', '>> np.dtype([('f1', [('f1', np.int16)])]) | dtype([('f1', [('f1', '>> np.dtype([('f1', np.uint), ('f2', np.int32)]) | dtype([('f1', '>> np.dtype([('a','f8'),('b','S10')]) | dtype([('a', '>> np.dtype("i4, (2,3)f8") | dtype([('f0', '>> np.dtype([('hello',(np.int,3)),('world',np.void,10)]) | dtype([('hello', '>> np.dtype((np.int16, {'x':(np.int8,0), 'y':(np.int8,1)})) | dtype(('>> np.dtype({'names':['gender','age'], 'formats':['S1',np.uint8]}) | dtype([('gender', '|S1'), ('age', '|u1')]) | | Offsets in bytes, here 0 and 25: | | >>> np.dtype({'surname':('S25',0),'age':(np.uint8,25)}) | dtype([('surname', '|S25'), ('age', '|u1')]) | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __le__(...) | x.__le__(y) <==> x<=y | | __len__(...) | x.__len__() <==> len(x) | | __lt__(...) | x.__lt__(y) <==> x x*n | | __ne__(...) | x.__ne__(y) <==> x!=y | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rmul__(...) | x.__rmul__(n) <==> n*x | | __setstate__(...) | | __str__(...) | x.__str__() <==> str(x) | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new dtype with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | Parameters | ---------- | new_order : string, optional | Byte order to force; a value from the byte order specifications | below. The default value ('S') results in swapping the current | byte order. `new_order` codes can be any of: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | The code does a case-insensitive check on the first letter of | `new_order` for these alternatives. For example, any of '>' | or 'B' or 'b' or 'brian' are valid to specify big-endian. | | Returns | ------- | new_dtype : dtype | New dtype object with the given change to the byte order. | | Notes | ----- | Changes are also made in all fields and sub-arrays of the data type. | | Examples | -------- | >>> import sys | >>> sys_is_le = sys.byteorder == 'little' | >>> native_code = sys_is_le and '<' or '>' | >>> swapped_code = sys_is_le and '>' or '<' | >>> native_dt = np.dtype(native_code+'i2') | >>> swapped_dt = np.dtype(swapped_code+'i2') | >>> native_dt.newbyteorder('S') == swapped_dt | True | >>> native_dt.newbyteorder() == swapped_dt | True | >>> native_dt == swapped_dt.newbyteorder('S') | True | >>> native_dt == swapped_dt.newbyteorder('=') | True | >>> native_dt == swapped_dt.newbyteorder('N') | True | >>> native_dt == native_dt.newbyteorder('|') | True | >>> np.dtype('>> np.dtype('>> np.dtype('>i2') == native_dt.newbyteorder('>') | True | >>> np.dtype('>i2') == native_dt.newbyteorder('B') | True | | ---------------------------------------------------------------------- | Data descriptors defined here: | | alignment | The required alignment (bytes) of this data-type according to the compiler. | | More information is available in the C-API section of the manual. | | base | | byteorder | A character indicating the byte-order of this data-type object. | | One of: | | === ============== | '=' native | '<' little-endian | '>' big-endian | '|' not applicable | === ============== | | All built-in data-type objects have byteorder either '=' or '|'. | | Examples | -------- | | >>> dt = np.dtype('i2') | >>> dt.byteorder | '=' | >>> # endian is not relevant for 8 bit numbers | >>> np.dtype('i1').byteorder | '|' | >>> # or ASCII strings | >>> np.dtype('S2').byteorder | '|' | >>> # Even if specific code is given, and it is native | >>> # '=' is the byteorder | >>> import sys | >>> sys_is_le = sys.byteorder == 'little' | >>> native_code = sys_is_le and '<' or '>' | >>> swapped_code = sys_is_le and '>' or '<' | >>> dt = np.dtype(native_code + 'i2') | >>> dt.byteorder | '=' | >>> # Swapped code shows up as itself | >>> dt = np.dtype(swapped_code + 'i2') | >>> dt.byteorder == swapped_code | True | | char | A unique character code for each of the 21 different built-in types. | | descr | Array-interface compliant full description of the data-type. | | The format is that required by the 'descr' key in the | `__array_interface__` attribute. | | fields | Dictionary of named fields defined for this data type, or ``None``. | | The dictionary is indexed by keys that are the names of the fields. | Each entry in the dictionary is a tuple fully describing the field:: | | (dtype, offset[, title]) | | If present, the optional title can be any object (if it is a string | or unicode then it will also be a key in the fields dictionary, | otherwise it's meta-data). Notice also that the first two elements | of the tuple can be passed directly as arguments to the ``ndarray.getfield`` | and ``ndarray.setfield`` methods. | | See Also | -------- | ndarray.getfield, ndarray.setfield | | Examples | -------- | >>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))]) | >>> print dt.fields | {'grades': (dtype(('float64',(2,))), 16), 'name': (dtype('|S16'), 0)} | | flags | Bit-flags describing how this data type is to be interpreted. | | Bit-masks are in `numpy.core.multiarray` as the constants | `ITEM_HASOBJECT`, `LIST_PICKLE`, `ITEM_IS_POINTER`, `NEEDS_INIT`, | `NEEDS_PYAPI`, `USE_GETITEM`, `USE_SETITEM`. A full explanation | of these flags is in C-API documentation; they are largely useful | for user-defined data-types. | | hasobject | Boolean indicating whether this dtype contains any reference-counted | objects in any fields or sub-dtypes. | | Recall that what is actually in the ndarray memory representing | the Python object is the memory address of that object (a pointer). | Special handling may be required, and this attribute is useful for | distinguishing data types that may contain arbitrary Python objects | and data-types that won't. | | isalignedstruct | Boolean indicating whether the dtype is a struct which maintains | field alignment. This flag is sticky, so when combining multiple | structs together, it is preserved and produces new dtypes which | are also aligned. | | isbuiltin | Integer indicating how this dtype relates to the built-in dtypes. | | Read-only. | | = ======================================================================== | 0 if this is a structured array type, with fields | 1 if this is a dtype compiled into numpy (such as ints, floats etc) | 2 if the dtype is for a user-defined numpy type | A user-defined type uses the numpy C-API machinery to extend | numpy to handle a new array type. See | :ref:`user.user-defined-data-types` in the Numpy manual. | = ======================================================================== | | Examples | -------- | >>> dt = np.dtype('i2') | >>> dt.isbuiltin | 1 | >>> dt = np.dtype('f8') | >>> dt.isbuiltin | 1 | >>> dt = np.dtype([('field1', 'f8')]) | >>> dt.isbuiltin | 0 | | isnative | Boolean indicating whether the byte order of this dtype is native | to the platform. | | itemsize | The element size of this data-type object. | | For 18 of the 21 types this number is fixed by the data-type. | For the flexible data-types, this number can be anything. | | kind | A character code (one of 'biufcOSUV') identifying the general kind of data. | | = ====================== | b boolean | i signed integer | u unsigned integer | f floating-point | c complex floating-point | O object | S (byte-)string | U Unicode | V void | = ====================== | | metadata | | name | A bit-width name for this data-type. | | Un-sized flexible data-type objects do not have this attribute. | | names | Ordered list of field names, or ``None`` if there are no fields. | | The names are ordered according to increasing byte offset. This can be | used, for example, to walk through all of the named fields in offset order. | | Examples | -------- | >>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))]) | >>> dt.names | ('name', 'grades') | | num | A unique number for each of the 21 different built-in types. | | These are roughly ordered from least-to-most precision. | | shape | Shape tuple of the sub-array if this data type describes a sub-array, | and ``()`` otherwise. | | str | The array-protocol typestring of this data-type object. | | subdtype | Tuple ``(item_dtype, shape)`` if this `dtype` describes a sub-array, and | None otherwise. | | The *shape* is the fixed shape of the sub-array described by this | data type, and *item_dtype* the data type of the array. | | If a field whose dtype object has this attribute is retrieved, | then the extra dimensions implied by *shape* are tacked on to | the end of the retrieved array. | | type | The type object used to instantiate a scalar of this data-type. | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T class errstate(__builtin__.object) | errstate(**kwargs) | | Context manager for floating-point error handling. | | Using an instance of `errstate` as a context manager allows statements in | that context to execute with a known error handling behavior. Upon entering | the context the error handling is set with `seterr` and `seterrcall`, and | upon exiting it is reset to what it was before. | | Parameters | ---------- | kwargs : {divide, over, under, invalid} | Keyword arguments. The valid keywords are the possible floating-point | exceptions. Each keyword should have a string value that defines the | treatment for the particular error. Possible values are | {'ignore', 'warn', 'raise', 'call', 'print', 'log'}. | | See Also | -------- | seterr, geterr, seterrcall, geterrcall | | Notes | ----- | The ``with`` statement was introduced in Python 2.5, and can only be used | there by importing it: ``from __future__ import with_statement``. In | earlier Python versions the ``with`` statement is not available. | | For complete documentation of the types of floating-point exceptions and | treatment options, see `seterr`. | | Examples | -------- | >>> from __future__ import with_statement # use 'with' in Python 2.5 | >>> olderr = np.seterr(all='ignore') # Set error handling to known state. | | >>> np.arange(3) / 0. | array([ NaN, Inf, Inf]) | >>> with np.errstate(divide='warn'): | ... np.arange(3) / 0. | ... | __main__:2: RuntimeWarning: divide by zero encountered in divide | array([ NaN, Inf, Inf]) | | >>> np.sqrt(-1) | nan | >>> with np.errstate(invalid='raise'): | ... np.sqrt(-1) | Traceback (most recent call last): | File "", line 2, in | FloatingPointError: invalid value encountered in sqrt | | Outside the context the error handling behavior has not changed: | | >>> np.geterr() | {'over': 'warn', 'divide': 'warn', 'invalid': 'warn', | 'under': 'ignore'} | | Methods defined here: | | __enter__(self) | | __exit__(self, *exc_info) | | __init__(self, **kwargs) | | ---------------------------------------------------------------------- | Data descriptors defined here: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined) class finfo(__builtin__.object) | finfo(dtype) | | Machine limits for floating point types. | | Attributes | ---------- | eps : float | The smallest representable positive number such that | ``1.0 + eps != 1.0``. Type of `eps` is an appropriate floating | point type. | epsneg : floating point number of the appropriate type | The smallest representable positive number such that | ``1.0 - epsneg != 1.0``. | iexp : int | The number of bits in the exponent portion of the floating point | representation. | machar : MachAr | The object which calculated these parameters and holds more | detailed information. | machep : int | The exponent that yields `eps`. | max : floating point number of the appropriate type | The largest representable number. | maxexp : int | The smallest positive power of the base (2) that causes overflow. | min : floating point number of the appropriate type | The smallest representable number, typically ``-max``. | minexp : int | The most negative power of the base (2) consistent with there | being no leading 0's in the mantissa. | negep : int | The exponent that yields `epsneg`. | nexp : int | The number of bits in the exponent including its sign and bias. | nmant : int | The number of bits in the mantissa. | precision : int | The approximate number of decimal digits to which this kind of | float is precise. | resolution : floating point number of the appropriate type | The approximate decimal resolution of this type, i.e., | ``10**-precision``. | tiny : float | The smallest positive usable number. Type of `tiny` is an | appropriate floating point type. | | Parameters | ---------- | dtype : float, dtype, or instance | Kind of floating point data-type about which to get information. | | See Also | -------- | MachAr : The implementation of the tests that produce this information. | iinfo : The equivalent for integer data types. | | Notes | ----- | For developers of NumPy: do not instantiate this at the module level. | The initial calculation of these parameters is expensive and negatively | impacts import times. These objects are cached, so calling ``finfo()`` | repeatedly inside your functions is not a problem. | | Methods defined here: | | __repr__(self) | | __str__(self) | | ---------------------------------------------------------------------- | Static methods defined here: | | __new__(cls, dtype) | | ---------------------------------------------------------------------- | Data descriptors defined here: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined) class flatiter(__builtin__.object) | Flat iterator object to iterate over arrays. | | A `flatiter` iterator is returned by ``x.flat`` for any array `x`. | It allows iterating over the array as if it were a 1-D array, | either in a for-loop or by calling its `next` method. | | Iteration is done in row-major, C-style order (the last | index varying the fastest). The iterator can also be indexed using | basic slicing or advanced indexing. | | See Also | -------- | ndarray.flat : Return a flat iterator over an array. | ndarray.flatten : Returns a flattened copy of an array. | | Notes | ----- | A `flatiter` iterator can not be constructed directly from Python code | by calling the `flatiter` constructor. | | Examples | -------- | >>> x = np.arange(6).reshape(2, 3) | >>> fl = x.flat | >>> type(fl) | | >>> for item in fl: | ... print item | ... | 0 | 1 | 2 | 3 | 4 | 5 | | >>> fl[2:4] | array([2, 3]) | | Methods defined here: | | __array__(...) | __array__(type=None) Get array from iterator | | __delitem__(...) | x.__delitem__(y) <==> del x[y] | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __gt__(...) | x.__gt__(y) <==> x>y | | __iter__(...) | x.__iter__() <==> iter(x) | | __le__(...) | x.__le__(y) <==> x<=y | | __len__(...) | x.__len__() <==> len(x) | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __setitem__(...) | x.__setitem__(i, y) <==> x[i]=y | | copy(...) | copy() | | Get a copy of the iterator as a 1-D array. | | Examples | -------- | >>> x = np.arange(6).reshape(2, 3) | >>> x | array([[0, 1, 2], | [3, 4, 5]]) | >>> fl = x.flat | >>> fl.copy() | array([0, 1, 2, 3, 4, 5]) | | next(...) | x.next() -> the next value, or raise StopIteration | | ---------------------------------------------------------------------- | Data descriptors defined here: | | base | A reference to the array that is iterated over. | | Examples | -------- | >>> x = np.arange(5) | >>> fl = x.flat | >>> fl.base is x | True | | coords | An N-dimensional tuple of current coordinates. | | Examples | -------- | >>> x = np.arange(6).reshape(2, 3) | >>> fl = x.flat | >>> fl.coords | (0, 0) | >>> fl.next() | 0 | >>> fl.coords | (0, 1) | | index | Current flat index into the array. | | Examples | -------- | >>> x = np.arange(6).reshape(2, 3) | >>> fl = x.flat | >>> fl.index | 0 | >>> fl.next() | 0 | >>> fl.index | 1 class flexible(generic) | Method resolution order: | flexible | generic | __builtin__.object | | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __eq__(...) | x.__eq__(y) <==> x==y | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __ge__(...) | x.__ge__(y) <==> x>=y | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __gt__(...) | x.__gt__(y) <==> x>y | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __le__(...) | x.__le__(y) <==> x<=y | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __ne__(...) | x.__ne__(y) <==> x!=y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class float16(floating) | Method resolution order: | float16 | floating | inexact | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class float32(floating) | 32-bit floating-point number. Character code 'f'. C float compatible. | | Method resolution order: | float32 | floating | inexact | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class float64(floating, __builtin__.float) | 64-bit floating-point number. Character code 'd'. Python float compatible. | | Method resolution order: | float64 | floating | inexact | number | generic | __builtin__.float | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension | | ---------------------------------------------------------------------- | Methods inherited from __builtin__.float: | | __coerce__(...) | x.__coerce__(y) <==> coerce(x, y) | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | __getformat__(...) | float.__getformat__(typestr) -> string | | You probably don't want to use this function. It exists mainly to be | used in Python's test suite. | | typestr must be 'double' or 'float'. This function returns whichever of | 'unknown', 'IEEE, big-endian' or 'IEEE, little-endian' best describes the | format of floating point numbers used by the C type named by typestr. | | __getnewargs__(...) | | __hash__(...) | x.__hash__() <==> hash(x) | | __setformat__(...) | float.__setformat__(typestr, fmt) -> None | | You probably don't want to use this function. It exists mainly to be | used in Python's test suite. | | typestr must be 'double' or 'float'. fmt must be one of 'unknown', | 'IEEE, big-endian' or 'IEEE, little-endian', and in addition can only be | one of the latter two if it appears to match the underlying C reality. | | Override the automatic determination of C-level floating point type. | This affects how floats are converted to and from binary strings. | | __trunc__(...) | Return the Integral closest to x between 0 and x. | | as_integer_ratio(...) | float.as_integer_ratio() -> (int, int) | | Return a pair of integers, whose ratio is exactly equal to the original | float and with a positive denominator. | Raise OverflowError on infinities and a ValueError on NaNs. | | >>> (10.0).as_integer_ratio() | (10, 1) | >>> (0.0).as_integer_ratio() | (0, 1) | >>> (-.25).as_integer_ratio() | (-1, 4) | | fromhex(...) | float.fromhex(string) -> float | | Create a floating-point number from a hexadecimal string. | >>> float.fromhex('0x1.ffffp10') | 2047.984375 | >>> float.fromhex('-0x1p-1074') | -4.9406564584124654e-324 | | hex(...) | float.hex() -> string | | Return a hexadecimal representation of a floating-point number. | >>> (-0.1).hex() | '-0x1.999999999999ap-4' | >>> 3.14159.hex() | '0x1.921f9f01b866ep+1' | | is_integer(...) | Return True if the float is an integer. float_ = class float64(floating, __builtin__.float) | 64-bit floating-point number. Character code 'd'. Python float compatible. | | Method resolution order: | float64 | floating | inexact | number | generic | __builtin__.float | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension | | ---------------------------------------------------------------------- | Methods inherited from __builtin__.float: | | __coerce__(...) | x.__coerce__(y) <==> coerce(x, y) | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | __getformat__(...) | float.__getformat__(typestr) -> string | | You probably don't want to use this function. It exists mainly to be | used in Python's test suite. | | typestr must be 'double' or 'float'. This function returns whichever of | 'unknown', 'IEEE, big-endian' or 'IEEE, little-endian' best describes the | format of floating point numbers used by the C type named by typestr. | | __getnewargs__(...) | | __hash__(...) | x.__hash__() <==> hash(x) | | __setformat__(...) | float.__setformat__(typestr, fmt) -> None | | You probably don't want to use this function. It exists mainly to be | used in Python's test suite. | | typestr must be 'double' or 'float'. fmt must be one of 'unknown', | 'IEEE, big-endian' or 'IEEE, little-endian', and in addition can only be | one of the latter two if it appears to match the underlying C reality. | | Override the automatic determination of C-level floating point type. | This affects how floats are converted to and from binary strings. | | __trunc__(...) | Return the Integral closest to x between 0 and x. | | as_integer_ratio(...) | float.as_integer_ratio() -> (int, int) | | Return a pair of integers, whose ratio is exactly equal to the original | float and with a positive denominator. | Raise OverflowError on infinities and a ValueError on NaNs. | | >>> (10.0).as_integer_ratio() | (10, 1) | >>> (0.0).as_integer_ratio() | (0, 1) | >>> (-.25).as_integer_ratio() | (-1, 4) | | fromhex(...) | float.fromhex(string) -> float | | Create a floating-point number from a hexadecimal string. | >>> float.fromhex('0x1.ffffp10') | 2047.984375 | >>> float.fromhex('-0x1p-1074') | -4.9406564584124654e-324 | | hex(...) | float.hex() -> string | | Return a hexadecimal representation of a floating-point number. | >>> (-0.1).hex() | '-0x1.999999999999ap-4' | >>> 3.14159.hex() | '0x1.921f9f01b866ep+1' | | is_integer(...) | Return True if the float is an integer. class floating(inexact) | Method resolution order: | floating | inexact | number | generic | __builtin__.object | | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __eq__(...) | x.__eq__(y) <==> x==y | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __ge__(...) | x.__ge__(y) <==> x>=y | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __gt__(...) | x.__gt__(y) <==> x>y | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __le__(...) | x.__le__(y) <==> x<=y | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __ne__(...) | x.__ne__(y) <==> x!=y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class format_parser | Class to convert formats, names, titles description to a dtype. | | After constructing the format_parser object, the dtype attribute is | the converted data-type: | ``dtype = format_parser(formats, names, titles).dtype`` | | Attributes | ---------- | dtype : dtype | The converted data-type. | | Parameters | ---------- | formats : str or list of str | The format description, either specified as a string with | comma-separated format descriptions in the form ``'f8, i4, a5'``, or | a list of format description strings in the form | ``['f8', 'i4', 'a5']``. | names : str or list/tuple of str | The field names, either specified as a comma-separated string in the | form ``'col1, col2, col3'``, or as a list or tuple of strings in the | form ``['col1', 'col2', 'col3']``. | An empty list can be used, in that case default field names | ('f0', 'f1', ...) are used. | titles : sequence | Sequence of title strings. An empty list can be used to leave titles | out. | aligned : bool, optional | If True, align the fields by padding as the C-compiler would. | Default is False. | byteorder : str, optional | If specified, all the fields will be changed to the | provided byte-order. Otherwise, the default byte-order is | used. For all available string specifiers, see `dtype.newbyteorder`. | | See Also | -------- | dtype, typename, sctype2char | | Examples | -------- | >>> np.format_parser(['f8', 'i4', 'a5'], ['col1', 'col2', 'col3'], | ... ['T1', 'T2', 'T3']).dtype | dtype([(('T1', 'col1'), '>> np.format_parser(['f8', 'i4', 'a5'], ['col1', 'col2', 'col3'], | ... []).dtype | dtype([('col1', '>> np.format_parser(['f8', 'i4', 'a5'], [], []).dtype | dtype([('f0', ' abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __eq__(...) | x.__eq__(y) <==> x==y | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __ge__(...) | x.__ge__(y) <==> x>=y | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __gt__(...) | x.__gt__(y) <==> x>y | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __le__(...) | x.__le__(y) <==> x<=y | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __ne__(...) | x.__ne__(y) <==> x!=y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors defined here: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension half = class float16(floating) | Method resolution order: | float16 | floating | inexact | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class iinfo(__builtin__.object) | iinfo(type) | | Machine limits for integer types. | | Attributes | ---------- | min : int | The smallest integer expressible by the type. | max : int | The largest integer expressible by the type. | | Parameters | ---------- | int_type : integer type, dtype, or instance | The kind of integer data type to get information about. | | See Also | -------- | finfo : The equivalent for floating point data types. | | Examples | -------- | With types: | | >>> ii16 = np.iinfo(np.int16) | >>> ii16.min | -32768 | >>> ii16.max | 32767 | >>> ii32 = np.iinfo(np.int32) | >>> ii32.min | -2147483648 | >>> ii32.max | 2147483647 | | With instances: | | >>> ii32 = np.iinfo(np.int32(10)) | >>> ii32.min | -2147483648 | >>> ii32.max | 2147483647 | | Methods defined here: | | __init__(self, int_type) | | __repr__(self) | | __str__(self) | String representation. | | ---------------------------------------------------------------------- | Data descriptors defined here: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined) | | max | Maximum value of given dtype. | | min | Minimum value of given dtype. class inexact(number) | Method resolution order: | inexact | number | generic | __builtin__.object | | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __eq__(...) | x.__eq__(y) <==> x==y | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __ge__(...) | x.__ge__(y) <==> x>=y | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __gt__(...) | x.__gt__(y) <==> x>y | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __le__(...) | x.__le__(y) <==> x<=y | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __ne__(...) | x.__ne__(y) <==> x!=y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension int0 = class int64(signedinteger) | 64-bit integer. Character code 'l'. Python int compatible. | | Method resolution order: | int64 | signedinteger | integer | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Data descriptors inherited from integer: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class int16(signedinteger) | 16-bit integer. Character code ``h``. C short compatible. | | Method resolution order: | int16 | signedinteger | integer | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Data descriptors inherited from integer: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class int32(signedinteger, __builtin__.int) | 32-bit integer. Character code 'i'. C int compatible. | | Method resolution order: | int32 | signedinteger | integer | number | generic | __builtin__.int | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Data descriptors inherited from integer: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension | | ---------------------------------------------------------------------- | Methods inherited from __builtin__.int: | | __cmp__(...) | x.__cmp__(y) <==> cmp(x,y) | | __coerce__(...) | x.__coerce__(y) <==> coerce(x, y) | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | __getnewargs__(...) | | __hash__(...) | x.__hash__() <==> hash(x) | | __trunc__(...) | Truncating an Integral returns itself. | | bit_length(...) | int.bit_length() -> int | | Number of bits necessary to represent self in binary. | >>> bin(37) | '0b100101' | >>> (37).bit_length() | 6 class int64(signedinteger) | 64-bit integer. Character code 'l'. Python int compatible. | | Method resolution order: | int64 | signedinteger | integer | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Data descriptors inherited from integer: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class int8(signedinteger) | 8-bit integer. Character code ``b``. C char compatible. | | Method resolution order: | int8 | signedinteger | integer | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Data descriptors inherited from integer: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension int_ = class int32(signedinteger, __builtin__.int) | 32-bit integer. Character code 'i'. C int compatible. | | Method resolution order: | int32 | signedinteger | integer | number | generic | __builtin__.int | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Data descriptors inherited from integer: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension | | ---------------------------------------------------------------------- | Methods inherited from __builtin__.int: | | __cmp__(...) | x.__cmp__(y) <==> cmp(x,y) | | __coerce__(...) | x.__coerce__(y) <==> coerce(x, y) | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | __getnewargs__(...) | | __hash__(...) | x.__hash__() <==> hash(x) | | __trunc__(...) | Truncating an Integral returns itself. | | bit_length(...) | int.bit_length() -> int | | Number of bits necessary to represent self in binary. | >>> bin(37) | '0b100101' | >>> (37).bit_length() | 6 intc = class int32(signedinteger, __builtin__.int) | Method resolution order: | int32 | signedinteger | integer | number | generic | __builtin__.int | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Data descriptors inherited from integer: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension | | ---------------------------------------------------------------------- | Methods inherited from __builtin__.int: | | __cmp__(...) | x.__cmp__(y) <==> cmp(x,y) | | __coerce__(...) | x.__coerce__(y) <==> coerce(x, y) | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | __getnewargs__(...) | | __hash__(...) | x.__hash__() <==> hash(x) | | __trunc__(...) | Truncating an Integral returns itself. | | bit_length(...) | int.bit_length() -> int | | Number of bits necessary to represent self in binary. | >>> bin(37) | '0b100101' | >>> (37).bit_length() | 6 class integer(number) | Method resolution order: | integer | number | generic | __builtin__.object | | Data descriptors defined here: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __eq__(...) | x.__eq__(y) <==> x==y | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __ge__(...) | x.__ge__(y) <==> x>=y | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __gt__(...) | x.__gt__(y) <==> x>y | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __le__(...) | x.__le__(y) <==> x<=y | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __ne__(...) | x.__ne__(y) <==> x!=y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension intp = class int64(signedinteger) | 64-bit integer. Character code 'l'. Python int compatible. | | Method resolution order: | int64 | signedinteger | integer | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Data descriptors inherited from integer: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension longcomplex = class complex128(complexfloating) | Composed of two 64 bit floats | | Method resolution order: | complex128 | complexfloating | inexact | number | generic | __builtin__.object | | Methods defined here: | | __complex__(...) | | __eq__(...) | x.__eq__(y) <==> x==y | | __float__(...) | x.__float__() <==> float(x) | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __le__(...) | x.__le__(y) <==> x<=y | | __long__(...) | x.__long__() <==> long(x) | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __oct__(...) | x.__oct__() <==> oct(x) | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __invert__(...) | x.__invert__() <==> ~x | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension longdouble = class float64(floating) | Method resolution order: | float64 | floating | inexact | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __float__(...) | x.__float__() <==> float(x) | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __le__(...) | x.__le__(y) <==> x<=y | | __long__(...) | x.__long__() <==> long(x) | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __oct__(...) | x.__oct__() <==> oct(x) | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __invert__(...) | x.__invert__() <==> ~x | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension longfloat = class float64(floating) | Method resolution order: | float64 | floating | inexact | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __float__(...) | x.__float__() <==> float(x) | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __le__(...) | x.__le__(y) <==> x<=y | | __long__(...) | x.__long__() <==> long(x) | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __oct__(...) | x.__oct__() <==> oct(x) | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __invert__(...) | x.__invert__() <==> ~x | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension longlong = class int64(signedinteger) | 64-bit integer. Character code 'l'. Python int compatible. | | Method resolution order: | int64 | signedinteger | integer | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Data descriptors inherited from integer: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class matrix(numpy.ndarray) | matrix(data, dtype=None, copy=True) | | Returns a matrix from an array-like object, or from a string of data. | A matrix is a specialized 2-D array that retains its 2-D nature | through operations. It has certain special operators, such as ``*`` | (matrix multiplication) and ``**`` (matrix power). | | Parameters | ---------- | data : array_like or string | If `data` is a string, it is interpreted as a matrix with commas | or spaces separating columns, and semicolons separating rows. | dtype : data-type | Data-type of the output matrix. | copy : bool | If `data` is already an `ndarray`, then this flag determines | whether the data is copied (the default), or whether a view is | constructed. | | See Also | -------- | array | | Examples | -------- | >>> a = np.matrix('1 2; 3 4') | >>> print a | [[1 2] | [3 4]] | | >>> np.matrix([[1, 2], [3, 4]]) | matrix([[1, 2], | [3, 4]]) | | Method resolution order: | matrix | numpy.ndarray | __builtin__.object | | Methods defined here: | | __array_finalize__(self, obj) | | __getitem__(self, index) | | __imul__(self, other) | | __ipow__(self, other) | | __mul__(self, other) | | __pow__(self, other) | | __repr__(self) | | __rmul__(self, other) | | __rpow__(self, other) | | __str__(self) | | all(self, axis=None, out=None) | Test whether all matrix elements along a given axis evaluate to True. | | Parameters | ---------- | See `numpy.all` for complete descriptions | | See Also | -------- | numpy.all | | Notes | ----- | This is the same as `ndarray.all`, but it returns a `matrix` object. | | Examples | -------- | >>> x = np.matrix(np.arange(12).reshape((3,4))); x | matrix([[ 0, 1, 2, 3], | [ 4, 5, 6, 7], | [ 8, 9, 10, 11]]) | >>> y = x[0]; y | matrix([[0, 1, 2, 3]]) | >>> (x == y) | matrix([[ True, True, True, True], | [False, False, False, False], | [False, False, False, False]], dtype=bool) | >>> (x == y).all() | False | >>> (x == y).all(0) | matrix([[False, False, False, False]], dtype=bool) | >>> (x == y).all(1) | matrix([[ True], | [False], | [False]], dtype=bool) | | any(self, axis=None, out=None) | Test whether any array element along a given axis evaluates to True. | | Refer to `numpy.any` for full documentation. | | Parameters | ---------- | axis : int, optional | Axis along which logical OR is performed | out : ndarray, optional | Output to existing array instead of creating new one, must have | same shape as expected output | | Returns | ------- | any : bool, ndarray | Returns a single bool if `axis` is ``None``; otherwise, | returns `ndarray` | | argmax(self, axis=None, out=None) | Indices of the maximum values along an axis. | | Parameters | ---------- | See `numpy.argmax` for complete descriptions | | See Also | -------- | numpy.argmax | | Notes | ----- | This is the same as `ndarray.argmax`, but returns a `matrix` object | where `ndarray.argmax` would return an `ndarray`. | | Examples | -------- | >>> x = np.matrix(np.arange(12).reshape((3,4))); x | matrix([[ 0, 1, 2, 3], | [ 4, 5, 6, 7], | [ 8, 9, 10, 11]]) | >>> x.argmax() | 11 | >>> x.argmax(0) | matrix([[2, 2, 2, 2]]) | >>> x.argmax(1) | matrix([[3], | [3], | [3]]) | | argmin(self, axis=None, out=None) | Return the indices of the minimum values along an axis. | | Parameters | ---------- | See `numpy.argmin` for complete descriptions. | | See Also | -------- | numpy.argmin | | Notes | ----- | This is the same as `ndarray.argmin`, but returns a `matrix` object | where `ndarray.argmin` would return an `ndarray`. | | Examples | -------- | >>> x = -np.matrix(np.arange(12).reshape((3,4))); x | matrix([[ 0, -1, -2, -3], | [ -4, -5, -6, -7], | [ -8, -9, -10, -11]]) | >>> x.argmin() | 11 | >>> x.argmin(0) | matrix([[2, 2, 2, 2]]) | >>> x.argmin(1) | matrix([[3], | [3], | [3]]) | | flatten(self, order='C') | Return a flattened copy of the matrix. | | All `N` elements of the matrix are placed into a single row. | | Parameters | ---------- | order : {'C', 'F', 'A'}, optional | Whether to flatten in C (row-major), Fortran (column-major) order, | or preserve the C/Fortran ordering from `m`. | The default is 'C'. | | Returns | ------- | y : matrix | A copy of the matrix, flattened to a `(1, N)` matrix where `N` | is the number of elements in the original matrix. | | See Also | -------- | ravel : Return a flattened array. | flat : A 1-D flat iterator over the matrix. | | Examples | -------- | >>> m = np.matrix([[1,2], [3,4]]) | >>> m.flatten() | matrix([[1, 2, 3, 4]]) | >>> m.flatten('F') | matrix([[1, 3, 2, 4]]) | | getA(self) | Return `self` as an `ndarray` object. | | Equivalent to ``np.asarray(self)``. | | Parameters | ---------- | None | | Returns | ------- | ret : ndarray | `self` as an `ndarray` | | Examples | -------- | >>> x = np.matrix(np.arange(12).reshape((3,4))); x | matrix([[ 0, 1, 2, 3], | [ 4, 5, 6, 7], | [ 8, 9, 10, 11]]) | >>> x.getA() | array([[ 0, 1, 2, 3], | [ 4, 5, 6, 7], | [ 8, 9, 10, 11]]) | | getA1(self) | Return `self` as a flattened `ndarray`. | | Equivalent to ``np.asarray(x).ravel()`` | | Parameters | ---------- | None | | Returns | ------- | ret : ndarray | `self`, 1-D, as an `ndarray` | | Examples | -------- | >>> x = np.matrix(np.arange(12).reshape((3,4))); x | matrix([[ 0, 1, 2, 3], | [ 4, 5, 6, 7], | [ 8, 9, 10, 11]]) | >>> x.getA1() | array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]) | | getH(self) | Returns the (complex) conjugate transpose of `self`. | | Equivalent to ``np.transpose(self)`` if `self` is real-valued. | | Parameters | ---------- | None | | Returns | ------- | ret : matrix object | complex conjugate transpose of `self` | | Examples | -------- | >>> x = np.matrix(np.arange(12).reshape((3,4))) | >>> z = x - 1j*x; z | matrix([[ 0. +0.j, 1. -1.j, 2. -2.j, 3. -3.j], | [ 4. -4.j, 5. -5.j, 6. -6.j, 7. -7.j], | [ 8. -8.j, 9. -9.j, 10.-10.j, 11.-11.j]]) | >>> z.getH() | matrix([[ 0. +0.j, 4. +4.j, 8. +8.j], | [ 1. +1.j, 5. +5.j, 9. +9.j], | [ 2. +2.j, 6. +6.j, 10.+10.j], | [ 3. +3.j, 7. +7.j, 11.+11.j]]) | | getI(self) | Returns the (multiplicative) inverse of invertible `self`. | | Parameters | ---------- | None | | Returns | ------- | ret : matrix object | If `self` is non-singular, `ret` is such that ``ret * self`` == | ``self * ret`` == ``np.matrix(np.eye(self[0,:].size)`` all return | ``True``. | | Raises | ------ | numpy.linalg.LinAlgError: Singular matrix | If `self` is singular. | | See Also | -------- | linalg.inv | | Examples | -------- | >>> m = np.matrix('[1, 2; 3, 4]'); m | matrix([[1, 2], | [3, 4]]) | >>> m.getI() | matrix([[-2. , 1. ], | [ 1.5, -0.5]]) | >>> m.getI() * m | matrix([[ 1., 0.], | [ 0., 1.]]) | | getT(self) | Returns the transpose of the matrix. | | Does *not* conjugate! For the complex conjugate transpose, use ``.H``. | | Parameters | ---------- | None | | Returns | ------- | ret : matrix object | The (non-conjugated) transpose of the matrix. | | See Also | -------- | transpose, getH | | Examples | -------- | >>> m = np.matrix('[1, 2; 3, 4]') | >>> m | matrix([[1, 2], | [3, 4]]) | >>> m.getT() | matrix([[1, 3], | [2, 4]]) | | max(self, axis=None, out=None) | Return the maximum value along an axis. | | Parameters | ---------- | See `amax` for complete descriptions | | See Also | -------- | amax, ndarray.max | | Notes | ----- | This is the same as `ndarray.max`, but returns a `matrix` object | where `ndarray.max` would return an ndarray. | | Examples | -------- | >>> x = np.matrix(np.arange(12).reshape((3,4))); x | matrix([[ 0, 1, 2, 3], | [ 4, 5, 6, 7], | [ 8, 9, 10, 11]]) | >>> x.max() | 11 | >>> x.max(0) | matrix([[ 8, 9, 10, 11]]) | >>> x.max(1) | matrix([[ 3], | [ 7], | [11]]) | | mean(self, axis=None, dtype=None, out=None) | Returns the average of the matrix elements along the given axis. | | Refer to `numpy.mean` for full documentation. | | See Also | -------- | numpy.mean | | Notes | ----- | Same as `ndarray.mean` except that, where that returns an `ndarray`, | this returns a `matrix` object. | | Examples | -------- | >>> x = np.matrix(np.arange(12).reshape((3, 4))) | >>> x | matrix([[ 0, 1, 2, 3], | [ 4, 5, 6, 7], | [ 8, 9, 10, 11]]) | >>> x.mean() | 5.5 | >>> x.mean(0) | matrix([[ 4., 5., 6., 7.]]) | >>> x.mean(1) | matrix([[ 1.5], | [ 5.5], | [ 9.5]]) | | min(self, axis=None, out=None) | Return the minimum value along an axis. | | Parameters | ---------- | See `amin` for complete descriptions. | | See Also | -------- | amin, ndarray.min | | Notes | ----- | This is the same as `ndarray.min`, but returns a `matrix` object | where `ndarray.min` would return an ndarray. | | Examples | -------- | >>> x = -np.matrix(np.arange(12).reshape((3,4))); x | matrix([[ 0, -1, -2, -3], | [ -4, -5, -6, -7], | [ -8, -9, -10, -11]]) | >>> x.min() | -11 | >>> x.min(0) | matrix([[ -8, -9, -10, -11]]) | >>> x.min(1) | matrix([[ -3], | [ -7], | [-11]]) | | prod(self, axis=None, dtype=None, out=None) | Return the product of the array elements over the given axis. | | Refer to `prod` for full documentation. | | See Also | -------- | prod, ndarray.prod | | Notes | ----- | Same as `ndarray.prod`, except, where that returns an `ndarray`, this | returns a `matrix` object instead. | | Examples | -------- | >>> x = np.matrix(np.arange(12).reshape((3,4))); x | matrix([[ 0, 1, 2, 3], | [ 4, 5, 6, 7], | [ 8, 9, 10, 11]]) | >>> x.prod() | 0 | >>> x.prod(0) | matrix([[ 0, 45, 120, 231]]) | >>> x.prod(1) | matrix([[ 0], | [ 840], | [7920]]) | | ptp(self, axis=None, out=None) | Peak-to-peak (maximum - minimum) value along the given axis. | | Refer to `numpy.ptp` for full documentation. | | See Also | -------- | numpy.ptp | | Notes | ----- | Same as `ndarray.ptp`, except, where that would return an `ndarray` object, | this returns a `matrix` object. | | Examples | -------- | >>> x = np.matrix(np.arange(12).reshape((3,4))); x | matrix([[ 0, 1, 2, 3], | [ 4, 5, 6, 7], | [ 8, 9, 10, 11]]) | >>> x.ptp() | 11 | >>> x.ptp(0) | matrix([[8, 8, 8, 8]]) | >>> x.ptp(1) | matrix([[3], | [3], | [3]]) | | ravel(self, order='C') | Return a flattened matrix. | | Refer to `numpy.ravel` for more documentation. | | Parameters | ---------- | order : {'C', 'F', 'A', 'K'}, optional | The elements of `m` are read using this index order. 'C' means to | index the elements in C-like order, with the last axis index | changing fastest, back to the first axis index changing slowest. | 'F' means to index the elements in Fortran-like index order, with | the first index changing fastest, and the last index changing | slowest. Note that the 'C' and 'F' options take no account of the | memory layout of the underlying array, and only refer to the order | of axis indexing. 'A' means to read the elements in Fortran-like | index order if `m` is Fortran *contiguous* in memory, C-like order | otherwise. 'K' means to read the elements in the order they occur | in memory, except for reversing the data when strides are negative. | By default, 'C' index order is used. | | Returns | ------- | ret : matrix | Return the matrix flattened to shape `(1, N)` where `N` | is the number of elements in the original matrix. | A copy is made only if necessary. | | See Also | -------- | matrix.flatten : returns a similar output matrix but always a copy | matrix.flat : a flat iterator on the array. | numpy.ravel : related function which returns an ndarray | | squeeze(self, axis=None) | Return a possibly reshaped matrix. | | Refer to `numpy.squeeze` for more documentation. | | Parameters | ---------- | axis : None or int or tuple of ints, optional | Selects a subset of the single-dimensional entries in the shape. | If an axis is selected with shape entry greater than one, | an error is raised. | | Returns | ------- | squeezed : matrix | The matrix, but as a (1, N) matrix if it had shape (N, 1). | | See Also | -------- | numpy.squeeze : related function | | Notes | ----- | If `m` has a single column then that column is returned | as the single row of a matrix. Otherwise `m` is returned. | The returned matrix is always either `m` itself or a view into `m`. | Supplying an axis keyword argument will not affect the returned matrix | but it may cause an error to be raised. | | Examples | -------- | >>> c = np.matrix([[1], [2]]) | >>> c | matrix([[1], | [2]]) | >>> c.squeeze() | matrix([[1, 2]]) | >>> r = c.T | >>> r | matrix([[1, 2]]) | >>> r.squeeze() | matrix([[1, 2]]) | >>> m = np.matrix([[1, 2], [3, 4]]) | >>> m.squeeze() | matrix([[1, 2], | [3, 4]]) | | std(self, axis=None, dtype=None, out=None, ddof=0) | Return the standard deviation of the array elements along the given axis. | | Refer to `numpy.std` for full documentation. | | See Also | -------- | numpy.std | | Notes | ----- | This is the same as `ndarray.std`, except that where an `ndarray` would | be returned, a `matrix` object is returned instead. | | Examples | -------- | >>> x = np.matrix(np.arange(12).reshape((3, 4))) | >>> x | matrix([[ 0, 1, 2, 3], | [ 4, 5, 6, 7], | [ 8, 9, 10, 11]]) | >>> x.std() | 3.4520525295346629 | >>> x.std(0) | matrix([[ 3.26598632, 3.26598632, 3.26598632, 3.26598632]]) | >>> x.std(1) | matrix([[ 1.11803399], | [ 1.11803399], | [ 1.11803399]]) | | sum(self, axis=None, dtype=None, out=None) | Returns the sum of the matrix elements, along the given axis. | | Refer to `numpy.sum` for full documentation. | | See Also | -------- | numpy.sum | | Notes | ----- | This is the same as `ndarray.sum`, except that where an `ndarray` would | be returned, a `matrix` object is returned instead. | | Examples | -------- | >>> x = np.matrix([[1, 2], [4, 3]]) | >>> x.sum() | 10 | >>> x.sum(axis=1) | matrix([[3], | [7]]) | >>> x.sum(axis=1, dtype='float') | matrix([[ 3.], | [ 7.]]) | >>> out = np.zeros((1, 2), dtype='float') | >>> x.sum(axis=1, dtype='float', out=out) | matrix([[ 3.], | [ 7.]]) | | tolist(self) | Return the matrix as a (possibly nested) list. | | See `ndarray.tolist` for full documentation. | | See Also | -------- | ndarray.tolist | | Examples | -------- | >>> x = np.matrix(np.arange(12).reshape((3,4))); x | matrix([[ 0, 1, 2, 3], | [ 4, 5, 6, 7], | [ 8, 9, 10, 11]]) | >>> x.tolist() | [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]] | | var(self, axis=None, dtype=None, out=None, ddof=0) | Returns the variance of the matrix elements, along the given axis. | | Refer to `numpy.var` for full documentation. | | See Also | -------- | numpy.var | | Notes | ----- | This is the same as `ndarray.var`, except that where an `ndarray` would | be returned, a `matrix` object is returned instead. | | Examples | -------- | >>> x = np.matrix(np.arange(12).reshape((3, 4))) | >>> x | matrix([[ 0, 1, 2, 3], | [ 4, 5, 6, 7], | [ 8, 9, 10, 11]]) | >>> x.var() | 11.916666666666666 | >>> x.var(0) | matrix([[ 10.66666667, 10.66666667, 10.66666667, 10.66666667]]) | >>> x.var(1) | matrix([[ 1.25], | [ 1.25], | [ 1.25]]) | | ---------------------------------------------------------------------- | Static methods defined here: | | __new__(subtype, data, dtype=None, copy=True) | | ---------------------------------------------------------------------- | Data descriptors defined here: | | A | Return `self` as an `ndarray` object. | | Equivalent to ``np.asarray(self)``. | | Parameters | ---------- | None | | Returns | ------- | ret : ndarray | `self` as an `ndarray` | | Examples | -------- | >>> x = np.matrix(np.arange(12).reshape((3,4))); x | matrix([[ 0, 1, 2, 3], | [ 4, 5, 6, 7], | [ 8, 9, 10, 11]]) | >>> x.getA() | array([[ 0, 1, 2, 3], | [ 4, 5, 6, 7], | [ 8, 9, 10, 11]]) | | A1 | Return `self` as a flattened `ndarray`. | | Equivalent to ``np.asarray(x).ravel()`` | | Parameters | ---------- | None | | Returns | ------- | ret : ndarray | `self`, 1-D, as an `ndarray` | | Examples | -------- | >>> x = np.matrix(np.arange(12).reshape((3,4))); x | matrix([[ 0, 1, 2, 3], | [ 4, 5, 6, 7], | [ 8, 9, 10, 11]]) | >>> x.getA1() | array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]) | | H | Returns the (complex) conjugate transpose of `self`. | | Equivalent to ``np.transpose(self)`` if `self` is real-valued. | | Parameters | ---------- | None | | Returns | ------- | ret : matrix object | complex conjugate transpose of `self` | | Examples | -------- | >>> x = np.matrix(np.arange(12).reshape((3,4))) | >>> z = x - 1j*x; z | matrix([[ 0. +0.j, 1. -1.j, 2. -2.j, 3. -3.j], | [ 4. -4.j, 5. -5.j, 6. -6.j, 7. -7.j], | [ 8. -8.j, 9. -9.j, 10.-10.j, 11.-11.j]]) | >>> z.getH() | matrix([[ 0. +0.j, 4. +4.j, 8. +8.j], | [ 1. +1.j, 5. +5.j, 9. +9.j], | [ 2. +2.j, 6. +6.j, 10.+10.j], | [ 3. +3.j, 7. +7.j, 11.+11.j]]) | | I | Returns the (multiplicative) inverse of invertible `self`. | | Parameters | ---------- | None | | Returns | ------- | ret : matrix object | If `self` is non-singular, `ret` is such that ``ret * self`` == | ``self * ret`` == ``np.matrix(np.eye(self[0,:].size)`` all return | ``True``. | | Raises | ------ | numpy.linalg.LinAlgError: Singular matrix | If `self` is singular. | | See Also | -------- | linalg.inv | | Examples | -------- | >>> m = np.matrix('[1, 2; 3, 4]'); m | matrix([[1, 2], | [3, 4]]) | >>> m.getI() | matrix([[-2. , 1. ], | [ 1.5, -0.5]]) | >>> m.getI() * m | matrix([[ 1., 0.], | [ 0., 1.]]) | | T | Returns the transpose of the matrix. | | Does *not* conjugate! For the complex conjugate transpose, use ``.H``. | | Parameters | ---------- | None | | Returns | ------- | ret : matrix object | The (non-conjugated) transpose of the matrix. | | See Also | -------- | transpose, getH | | Examples | -------- | >>> m = np.matrix('[1, 2; 3, 4]') | >>> m | matrix([[1, 2], | [3, 4]]) | >>> m.getT() | matrix([[1, 3], | [2, 4]]) | | __dict__ | dictionary for instance variables (if defined) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __array_priority__ = 10.0 | | ---------------------------------------------------------------------- | Methods inherited from numpy.ndarray: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | a.__array__(|dtype) -> reference if type unchanged, copy otherwise. | | Returns either a new reference to self if dtype is not given or a new array | of provided data type if dtype is different from the current dtype of the | array. | | __array_prepare__(...) | a.__array_prepare__(obj) -> Object of same type as ndarray object obj. | | __array_wrap__(...) | a.__array_wrap__(obj) -> Object of same type as ndarray object a. | | __contains__(...) | x.__contains__(y) <==> y in x | | __copy__(...) | a.__copy__([order]) | | Return a copy of the array. | | Parameters | ---------- | order : {'C', 'F', 'A'}, optional | If order is 'C' (False) then the result is contiguous (default). | If order is 'Fortran' (True) then the result has fortran order. | If order is 'Any' (None) then the result has fortran order | only if the array already is in fortran order. | | __deepcopy__(...) | a.__deepcopy__() -> Deep copy of array. | | Used if copy.deepcopy is called on an array. | | __delitem__(...) | x.__delitem__(y) <==> del x[y] | | __delslice__(...) | x.__delslice__(i, j) <==> del x[i:j] | | Use of negative indices is not supported. | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __eq__(...) | x.__eq__(y) <==> x==y | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __getslice__(...) | x.__getslice__(i, j) <==> x[i:j] | | Use of negative indices is not supported. | | __gt__(...) | x.__gt__(y) <==> x>y | | __hex__(...) | x.__hex__() <==> hex(x) | | __iadd__(...) | x.__iadd__(y) <==> x+=y | | __iand__(...) | x.__iand__(y) <==> x&=y | | __idiv__(...) | x.__idiv__(y) <==> x/=y | | __ifloordiv__(...) | x.__ifloordiv__(y) <==> x//=y | | __ilshift__(...) | x.__ilshift__(y) <==> x<<=y | | __imod__(...) | x.__imod__(y) <==> x%=y | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __ior__(...) | x.__ior__(y) <==> x|=y | | __irshift__(...) | x.__irshift__(y) <==> x>>=y | | __isub__(...) | x.__isub__(y) <==> x-=y | | __iter__(...) | x.__iter__() <==> iter(x) | | __itruediv__(...) | x.__itruediv__(y) <==> x/=y | | __ixor__(...) | x.__ixor__(y) <==> x^=y | | __le__(...) | x.__le__(y) <==> x<=y | | __len__(...) | x.__len__() <==> len(x) | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x x%y | | __ne__(...) | x.__ne__(y) <==> x!=y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | a.__reduce__() | | For pickling. | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setitem__(...) | x.__setitem__(i, y) <==> x[i]=y | | __setslice__(...) | x.__setslice__(i, j, y) <==> x[i:j]=y | | Use of negative indices is not supported. | | __setstate__(...) | a.__setstate__(version, shape, dtype, isfortran, rawdata) | | For unpickling. | | Parameters | ---------- | version : int | optional pickle version. If omitted defaults to 0. | shape : tuple | dtype : data-type | isFortran : bool | rawdata : string or list | a binary string with the data (or a list if 'a' is an object array) | | __sizeof__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | argpartition(...) | a.argpartition(kth, axis=-1, kind='introselect', order=None) | | Returns the indices that would partition this array. | | Refer to `numpy.argpartition` for full documentation. | | .. versionadded:: 1.8.0 | | See Also | -------- | numpy.argpartition : equivalent function | | argsort(...) | a.argsort(axis=-1, kind='quicksort', order=None) | | Returns the indices that would sort this array. | | Refer to `numpy.argsort` for full documentation. | | See Also | -------- | numpy.argsort : equivalent function | | astype(...) | a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True) | | Copy of the array, cast to a specified type. | | Parameters | ---------- | dtype : str or dtype | Typecode or data-type to which the array is cast. | order : {'C', 'F', 'A', 'K'}, optional | Controls the memory layout order of the result. | 'C' means C order, 'F' means Fortran order, 'A' | means 'F' order if all the arrays are Fortran contiguous, | 'C' order otherwise, and 'K' means as close to the | order the array elements appear in memory as possible. | Default is 'K'. | casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional | Controls what kind of data casting may occur. Defaults to 'unsafe' | for backwards compatibility. | | * 'no' means the data types should not be cast at all. | * 'equiv' means only byte-order changes are allowed. | * 'safe' means only casts which can preserve values are allowed. | * 'same_kind' means only safe casts or casts within a kind, | like float64 to float32, are allowed. | * 'unsafe' means any data conversions may be done. | subok : bool, optional | If True, then sub-classes will be passed-through (default), otherwise | the returned array will be forced to be a base-class array. | copy : bool, optional | By default, astype always returns a newly allocated array. If this | is set to false, and the `dtype`, `order`, and `subok` | requirements are satisfied, the input array is returned instead | of a copy. | | Returns | ------- | arr_t : ndarray | Unless `copy` is False and the other conditions for returning the input | array are satisfied (see description for `copy` input paramter), `arr_t` | is a new array of the same shape as the input array, with dtype, order | given by `dtype`, `order`. | | Notes | ----- | Starting in NumPy 1.9, astype method now returns an error if the string | dtype to cast to is not long enough in 'safe' casting mode to hold the max | value of integer/float array that is being casted. Previously the casting | was allowed even if the result was truncated. | | Raises | ------ | ComplexWarning | When casting from complex to float or int. To avoid this, | one should use ``a.real.astype(t)``. | | Examples | -------- | >>> x = np.array([1, 2, 2.5]) | >>> x | array([ 1. , 2. , 2.5]) | | >>> x.astype(int) | array([1, 2, 2]) | | byteswap(...) | a.byteswap(inplace) | | Swap the bytes of the array elements | | Toggle between low-endian and big-endian data representation by | returning a byteswapped array, optionally swapped in-place. | | Parameters | ---------- | inplace : bool, optional | If ``True``, swap bytes in-place, default is ``False``. | | Returns | ------- | out : ndarray | The byteswapped array. If `inplace` is ``True``, this is | a view to self. | | Examples | -------- | >>> A = np.array([1, 256, 8755], dtype=np.int16) | >>> map(hex, A) | ['0x1', '0x100', '0x2233'] | >>> A.byteswap(True) | array([ 256, 1, 13090], dtype=int16) | >>> map(hex, A) | ['0x100', '0x1', '0x3322'] | | Arrays of strings are not swapped | | >>> A = np.array(['ceg', 'fac']) | >>> A.byteswap() | array(['ceg', 'fac'], | dtype='|S3') | | choose(...) | a.choose(choices, out=None, mode='raise') | | Use an index array to construct a new array from a set of choices. | | Refer to `numpy.choose` for full documentation. | | See Also | -------- | numpy.choose : equivalent function | | clip(...) | a.clip(min=None, max=None, out=None) | | Return an array whose values are limited to ``[min, max]``. | One of max or min must be given. | | Refer to `numpy.clip` for full documentation. | | See Also | -------- | numpy.clip : equivalent function | | compress(...) | a.compress(condition, axis=None, out=None) | | Return selected slices of this array along given axis. | | Refer to `numpy.compress` for full documentation. | | See Also | -------- | numpy.compress : equivalent function | | conj(...) | a.conj() | | Complex-conjugate all elements. | | Refer to `numpy.conjugate` for full documentation. | | See Also | -------- | numpy.conjugate : equivalent function | | conjugate(...) | a.conjugate() | | Return the complex conjugate, element-wise. | | Refer to `numpy.conjugate` for full documentation. | | See Also | -------- | numpy.conjugate : equivalent function | | copy(...) | a.copy(order='C') | | Return a copy of the array. | | Parameters | ---------- | order : {'C', 'F', 'A', 'K'}, optional | Controls the memory layout of the copy. 'C' means C-order, | 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous, | 'C' otherwise. 'K' means match the layout of `a` as closely | as possible. (Note that this function and :func:numpy.copy are very | similar, but have different default values for their order= | arguments.) | | See also | -------- | numpy.copy | numpy.copyto | | Examples | -------- | >>> x = np.array([[1,2,3],[4,5,6]], order='F') | | >>> y = x.copy() | | >>> x.fill(0) | | >>> x | array([[0, 0, 0], | [0, 0, 0]]) | | >>> y | array([[1, 2, 3], | [4, 5, 6]]) | | >>> y.flags['C_CONTIGUOUS'] | True | | cumprod(...) | a.cumprod(axis=None, dtype=None, out=None) | | Return the cumulative product of the elements along the given axis. | | Refer to `numpy.cumprod` for full documentation. | | See Also | -------- | numpy.cumprod : equivalent function | | cumsum(...) | a.cumsum(axis=None, dtype=None, out=None) | | Return the cumulative sum of the elements along the given axis. | | Refer to `numpy.cumsum` for full documentation. | | See Also | -------- | numpy.cumsum : equivalent function | | diagonal(...) | a.diagonal(offset=0, axis1=0, axis2=1) | | Return specified diagonals. In NumPy 1.9 the returned array is a | read-only view instead of a copy as in previous NumPy versions. In | NumPy 1.10 the read-only restriction will be removed. | | Refer to :func:`numpy.diagonal` for full documentation. | | See Also | -------- | numpy.diagonal : equivalent function | | dot(...) | a.dot(b, out=None) | | Dot product of two arrays. | | Refer to `numpy.dot` for full documentation. | | See Also | -------- | numpy.dot : equivalent function | | Examples | -------- | >>> a = np.eye(2) | >>> b = np.ones((2, 2)) * 2 | >>> a.dot(b) | array([[ 2., 2.], | [ 2., 2.]]) | | This array method can be conveniently chained: | | >>> a.dot(b).dot(b) | array([[ 8., 8.], | [ 8., 8.]]) | | dump(...) | a.dump(file) | | Dump a pickle of the array to the specified file. | The array can be read back with pickle.load or numpy.load. | | Parameters | ---------- | file : str | A string naming the dump file. | | dumps(...) | a.dumps() | | Returns the pickle of the array as a string. | pickle.loads or numpy.loads will convert the string back to an array. | | Parameters | ---------- | None | | fill(...) | a.fill(value) | | Fill the array with a scalar value. | | Parameters | ---------- | value : scalar | All elements of `a` will be assigned this value. | | Examples | -------- | >>> a = np.array([1, 2]) | >>> a.fill(0) | >>> a | array([0, 0]) | >>> a = np.empty(2) | >>> a.fill(1) | >>> a | array([ 1., 1.]) | | getfield(...) | a.getfield(dtype, offset=0) | | Returns a field of the given array as a certain type. | | A field is a view of the array data with a given data-type. The values in | the view are determined by the given type and the offset into the current | array in bytes. The offset needs to be such that the view dtype fits in the | array dtype; for example an array of dtype complex128 has 16-byte elements. | If taking a view with a 32-bit integer (4 bytes), the offset needs to be | between 0 and 12 bytes. | | Parameters | ---------- | dtype : str or dtype | The data type of the view. The dtype size of the view can not be larger | than that of the array itself. | offset : int | Number of bytes to skip before beginning the element view. | | Examples | -------- | >>> x = np.diag([1.+1.j]*2) | >>> x[1, 1] = 2 + 4.j | >>> x | array([[ 1.+1.j, 0.+0.j], | [ 0.+0.j, 2.+4.j]]) | >>> x.getfield(np.float64) | array([[ 1., 0.], | [ 0., 2.]]) | | By choosing an offset of 8 bytes we can select the complex part of the | array for our view: | | >>> x.getfield(np.float64, offset=8) | array([[ 1., 0.], | [ 0., 4.]]) | | item(...) | a.item(*args) | | Copy an element of an array to a standard Python scalar and return it. | | Parameters | ---------- | \*args : Arguments (variable number and type) | | * none: in this case, the method only works for arrays | with one element (`a.size == 1`), which element is | copied into a standard Python scalar object and returned. | | * int_type: this argument is interpreted as a flat index into | the array, specifying which element to copy and return. | | * tuple of int_types: functions as does a single int_type argument, | except that the argument is interpreted as an nd-index into the | array. | | Returns | ------- | z : Standard Python scalar object | A copy of the specified element of the array as a suitable | Python scalar | | Notes | ----- | When the data type of `a` is longdouble or clongdouble, item() returns | a scalar array object because there is no available Python scalar that | would not lose information. Void arrays return a buffer object for item(), | unless fields are defined, in which case a tuple is returned. | | `item` is very similar to a[args], except, instead of an array scalar, | a standard Python scalar is returned. This can be useful for speeding up | access to elements of the array and doing arithmetic on elements of the | array using Python's optimized math. | | Examples | -------- | >>> x = np.random.randint(9, size=(3, 3)) | >>> x | array([[3, 1, 7], | [2, 8, 3], | [8, 5, 3]]) | >>> x.item(3) | 2 | >>> x.item(7) | 5 | >>> x.item((0, 1)) | 1 | >>> x.item((2, 2)) | 3 | | itemset(...) | a.itemset(*args) | | Insert scalar into an array (scalar is cast to array's dtype, if possible) | | There must be at least 1 argument, and define the last argument | as *item*. Then, ``a.itemset(*args)`` is equivalent to but faster | than ``a[args] = item``. The item should be a scalar value and `args` | must select a single item in the array `a`. | | Parameters | ---------- | \*args : Arguments | If one argument: a scalar, only used in case `a` is of size 1. | If two arguments: the last argument is the value to be set | and must be a scalar, the first argument specifies a single array | element location. It is either an int or a tuple. | | Notes | ----- | Compared to indexing syntax, `itemset` provides some speed increase | for placing a scalar into a particular location in an `ndarray`, | if you must do this. However, generally this is discouraged: | among other problems, it complicates the appearance of the code. | Also, when using `itemset` (and `item`) inside a loop, be sure | to assign the methods to a local variable to avoid the attribute | look-up at each loop iteration. | | Examples | -------- | >>> x = np.random.randint(9, size=(3, 3)) | >>> x | array([[3, 1, 7], | [2, 8, 3], | [8, 5, 3]]) | >>> x.itemset(4, 0) | >>> x.itemset((2, 2), 9) | >>> x | array([[3, 1, 7], | [2, 0, 3], | [8, 5, 9]]) | | newbyteorder(...) | arr.newbyteorder(new_order='S') | | Return the array with the same data viewed with a different byte order. | | Equivalent to:: | | arr.view(arr.dtype.newbytorder(new_order)) | | Changes are also made in all fields and sub-arrays of the array data | type. | | | | Parameters | ---------- | new_order : string, optional | Byte order to force; a value from the byte order specifications | below. `new_order` codes can be any of: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_arr : array | New array object with the dtype reflecting given change to the | byte order. | | nonzero(...) | a.nonzero() | | Return the indices of the elements that are non-zero. | | Refer to `numpy.nonzero` for full documentation. | | See Also | -------- | numpy.nonzero : equivalent function | | partition(...) | a.partition(kth, axis=-1, kind='introselect', order=None) | | Rearranges the elements in the array in such a way that value of the | element in kth position is in the position it would be in a sorted array. | All elements smaller than the kth element are moved before this element and | all equal or greater are moved behind it. The ordering of the elements in | the two partitions is undefined. | | .. versionadded:: 1.8.0 | | Parameters | ---------- | kth : int or sequence of ints | Element index to partition by. The kth element value will be in its | final sorted position and all smaller elements will be moved before it | and all equal or greater elements behind it. | The order all elements in the partitions is undefined. | If provided with a sequence of kth it will partition all elements | indexed by kth of them into their sorted position at once. | axis : int, optional | Axis along which to sort. Default is -1, which means sort along the | last axis. | kind : {'introselect'}, optional | Selection algorithm. Default is 'introselect'. | order : str or list of str, optional | When `a` is an array with fields defined, this argument specifies | which fields to compare first, second, etc. A single field can | be specified as a string, and not all fields need be specified, | but unspecified fields will still be used, in the order in which | they come up in the dtype, to break ties. | | See Also | -------- | numpy.partition : Return a parititioned copy of an array. | argpartition : Indirect partition. | sort : Full sort. | | Notes | ----- | See ``np.partition`` for notes on the different algorithms. | | Examples | -------- | >>> a = np.array([3, 4, 2, 1]) | >>> a.partition(a, 3) | >>> a | array([2, 1, 3, 4]) | | >>> a.partition((1, 3)) | array([1, 2, 3, 4]) | | put(...) | a.put(indices, values, mode='raise') | | Set ``a.flat[n] = values[n]`` for all `n` in indices. | | Refer to `numpy.put` for full documentation. | | See Also | -------- | numpy.put : equivalent function | | repeat(...) | a.repeat(repeats, axis=None) | | Repeat elements of an array. | | Refer to `numpy.repeat` for full documentation. | | See Also | -------- | numpy.repeat : equivalent function | | reshape(...) | a.reshape(shape, order='C') | | Returns an array containing the same data with a new shape. | | Refer to `numpy.reshape` for full documentation. | | See Also | -------- | numpy.reshape : equivalent function | | resize(...) | a.resize(new_shape, refcheck=True) | | Change shape and size of array in-place. | | Parameters | ---------- | new_shape : tuple of ints, or `n` ints | Shape of resized array. | refcheck : bool, optional | If False, reference count will not be checked. Default is True. | | Returns | ------- | None | | Raises | ------ | ValueError | If `a` does not own its own data or references or views to it exist, | and the data memory must be changed. | | SystemError | If the `order` keyword argument is specified. This behaviour is a | bug in NumPy. | | See Also | -------- | resize : Return a new array with the specified shape. | | Notes | ----- | This reallocates space for the data area if necessary. | | Only contiguous arrays (data elements consecutive in memory) can be | resized. | | The purpose of the reference count check is to make sure you | do not use this array as a buffer for another Python object and then | reallocate the memory. However, reference counts can increase in | other ways so if you are sure that you have not shared the memory | for this array with another Python object, then you may safely set | `refcheck` to False. | | Examples | -------- | Shrinking an array: array is flattened (in the order that the data are | stored in memory), resized, and reshaped: | | >>> a = np.array([[0, 1], [2, 3]], order='C') | >>> a.resize((2, 1)) | >>> a | array([[0], | [1]]) | | >>> a = np.array([[0, 1], [2, 3]], order='F') | >>> a.resize((2, 1)) | >>> a | array([[0], | [2]]) | | Enlarging an array: as above, but missing entries are filled with zeros: | | >>> b = np.array([[0, 1], [2, 3]]) | >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple | >>> b | array([[0, 1, 2], | [3, 0, 0]]) | | Referencing an array prevents resizing... | | >>> c = a | >>> a.resize((1, 1)) | Traceback (most recent call last): | ... | ValueError: cannot resize an array that has been referenced ... | | Unless `refcheck` is False: | | >>> a.resize((1, 1), refcheck=False) | >>> a | array([[0]]) | >>> c | array([[0]]) | | round(...) | a.round(decimals=0, out=None) | | Return `a` with each element rounded to the given number of decimals. | | Refer to `numpy.around` for full documentation. | | See Also | -------- | numpy.around : equivalent function | | searchsorted(...) | a.searchsorted(v, side='left', sorter=None) | | Find indices where elements of v should be inserted in a to maintain order. | | For full documentation, see `numpy.searchsorted` | | See Also | -------- | numpy.searchsorted : equivalent function | | setfield(...) | a.setfield(val, dtype, offset=0) | | Put a value into a specified place in a field defined by a data-type. | | Place `val` into `a`'s field defined by `dtype` and beginning `offset` | bytes into the field. | | Parameters | ---------- | val : object | Value to be placed in field. | dtype : dtype object | Data-type of the field in which to place `val`. | offset : int, optional | The number of bytes into the field at which to place `val`. | | Returns | ------- | None | | See Also | -------- | getfield | | Examples | -------- | >>> x = np.eye(3) | >>> x.getfield(np.float64) | array([[ 1., 0., 0.], | [ 0., 1., 0.], | [ 0., 0., 1.]]) | >>> x.setfield(3, np.int32) | >>> x.getfield(np.int32) | array([[3, 3, 3], | [3, 3, 3], | [3, 3, 3]]) | >>> x | array([[ 1.00000000e+000, 1.48219694e-323, 1.48219694e-323], | [ 1.48219694e-323, 1.00000000e+000, 1.48219694e-323], | [ 1.48219694e-323, 1.48219694e-323, 1.00000000e+000]]) | >>> x.setfield(np.eye(3), np.int32) | >>> x | array([[ 1., 0., 0.], | [ 0., 1., 0.], | [ 0., 0., 1.]]) | | setflags(...) | a.setflags(write=None, align=None, uic=None) | | Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively. | | These Boolean-valued flags affect how numpy interprets the memory | area used by `a` (see Notes below). The ALIGNED flag can only | be set to True if the data is actually aligned according to the type. | The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE | can only be set to True if the array owns its own memory, or the | ultimate owner of the memory exposes a writeable buffer interface, | or is a string. (The exception for string is made so that unpickling | can be done without copying memory.) | | Parameters | ---------- | write : bool, optional | Describes whether or not `a` can be written to. | align : bool, optional | Describes whether or not `a` is aligned properly for its type. | uic : bool, optional | Describes whether or not `a` is a copy of another "base" array. | | Notes | ----- | Array flags provide information about how the memory area used | for the array is to be interpreted. There are 6 Boolean flags | in use, only three of which can be changed by the user: | UPDATEIFCOPY, WRITEABLE, and ALIGNED. | | WRITEABLE (W) the data area can be written to; | | ALIGNED (A) the data and strides are aligned appropriately for the hardware | (as determined by the compiler); | | UPDATEIFCOPY (U) this array is a copy of some other array (referenced | by .base). When this array is deallocated, the base array will be | updated with the contents of this array. | | All flags can be accessed using their first (upper case) letter as well | as the full name. | | Examples | -------- | >>> y | array([[3, 1, 7], | [2, 0, 0], | [8, 5, 9]]) | >>> y.flags | C_CONTIGUOUS : True | F_CONTIGUOUS : False | OWNDATA : True | WRITEABLE : True | ALIGNED : True | UPDATEIFCOPY : False | >>> y.setflags(write=0, align=0) | >>> y.flags | C_CONTIGUOUS : True | F_CONTIGUOUS : False | OWNDATA : True | WRITEABLE : False | ALIGNED : False | UPDATEIFCOPY : False | >>> y.setflags(uic=1) | Traceback (most recent call last): | File "", line 1, in | ValueError: cannot set UPDATEIFCOPY flag to True | | sort(...) | a.sort(axis=-1, kind='quicksort', order=None) | | Sort an array, in-place. | | Parameters | ---------- | axis : int, optional | Axis along which to sort. Default is -1, which means sort along the | last axis. | kind : {'quicksort', 'mergesort', 'heapsort'}, optional | Sorting algorithm. Default is 'quicksort'. | order : str or list of str, optional | When `a` is an array with fields defined, this argument specifies | which fields to compare first, second, etc. A single field can | be specified as a string, and not all fields need be specified, | but unspecified fields will still be used, in the order in which | they come up in the dtype, to break ties. | | See Also | -------- | numpy.sort : Return a sorted copy of an array. | argsort : Indirect sort. | lexsort : Indirect stable sort on multiple keys. | searchsorted : Find elements in sorted array. | partition: Partial sort. | | Notes | ----- | See ``sort`` for notes on the different sorting algorithms. | | Examples | -------- | >>> a = np.array([[1,4], [3,1]]) | >>> a.sort(axis=1) | >>> a | array([[1, 4], | [1, 3]]) | >>> a.sort(axis=0) | >>> a | array([[1, 3], | [1, 4]]) | | Use the `order` keyword to specify a field to use when sorting a | structured array: | | >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)]) | >>> a.sort(order='y') | >>> a | array([('c', 1), ('a', 2)], | dtype=[('x', '|S1'), ('y', '>> x = np.array([[0, 1], [2, 3]]) | >>> x.tobytes() | b'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00' | >>> x.tobytes('C') == x.tobytes() | True | >>> x.tobytes('F') | b'\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00' | | tofile(...) | a.tofile(fid, sep="", format="%s") | | Write array to a file as text or binary (default). | | Data is always written in 'C' order, independent of the order of `a`. | The data produced by this method can be recovered using the function | fromfile(). | | Parameters | ---------- | fid : file or str | An open file object, or a string containing a filename. | sep : str | Separator between array items for text output. | If "" (empty), a binary file is written, equivalent to | ``file.write(a.tobytes())``. | format : str | Format string for text file output. | Each entry in the array is formatted to text by first converting | it to the closest Python type, and then using "format" % item. | | Notes | ----- | This is a convenience function for quick storage of array data. | Information on endianness and precision is lost, so this method is not a | good choice for files intended to archive data or transport data between | machines with different endianness. Some of these problems can be overcome | by outputting the data as text files, at the expense of speed and file | size. | | tostring(...) | a.tostring(order='C') | | Construct Python bytes containing the raw data bytes in the array. | | Constructs Python bytes showing a copy of the raw contents of | data memory. The bytes object can be produced in either 'C' or 'Fortran', | or 'Any' order (the default is 'C'-order). 'Any' order means C-order | unless the F_CONTIGUOUS flag in the array is set, in which case it | means 'Fortran' order. | | This function is a compatibility alias for tobytes. Despite its name it returns bytes not strings. | | Parameters | ---------- | order : {'C', 'F', None}, optional | Order of the data for multidimensional arrays: | C, Fortran, or the same as for the original array. | | Returns | ------- | s : bytes | Python bytes exhibiting a copy of `a`'s raw data. | | Examples | -------- | >>> x = np.array([[0, 1], [2, 3]]) | >>> x.tobytes() | b'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00' | >>> x.tobytes('C') == x.tobytes() | True | >>> x.tobytes('F') | b'\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00' | | trace(...) | a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None) | | Return the sum along diagonals of the array. | | Refer to `numpy.trace` for full documentation. | | See Also | -------- | numpy.trace : equivalent function | | transpose(...) | a.transpose(*axes) | | Returns a view of the array with axes transposed. | | For a 1-D array, this has no effect. (To change between column and | row vectors, first cast the 1-D array into a matrix object.) | For a 2-D array, this is the usual matrix transpose. | For an n-D array, if axes are given, their order indicates how the | axes are permuted (see Examples). If axes are not provided and | ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then | ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``. | | Parameters | ---------- | axes : None, tuple of ints, or `n` ints | | * None or no argument: reverses the order of the axes. | | * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s | `i`-th axis becomes `a.transpose()`'s `j`-th axis. | | * `n` ints: same as an n-tuple of the same ints (this form is | intended simply as a "convenience" alternative to the tuple form) | | Returns | ------- | out : ndarray | View of `a`, with axes suitably permuted. | | See Also | -------- | ndarray.T : Array property returning the array transposed. | | Examples | -------- | >>> a = np.array([[1, 2], [3, 4]]) | >>> a | array([[1, 2], | [3, 4]]) | >>> a.transpose() | array([[1, 3], | [2, 4]]) | >>> a.transpose((1, 0)) | array([[1, 3], | [2, 4]]) | >>> a.transpose(1, 0) | array([[1, 3], | [2, 4]]) | | view(...) | a.view(dtype=None, type=None) | | New view of array with the same data. | | Parameters | ---------- | dtype : data-type or ndarray sub-class, optional | Data-type descriptor of the returned view, e.g., float32 or int16. The | default, None, results in the view having the same data-type as `a`. | This argument can also be specified as an ndarray sub-class, which | then specifies the type of the returned object (this is equivalent to | setting the ``type`` parameter). | type : Python type, optional | Type of the returned view, e.g., ndarray or matrix. Again, the | default None results in type preservation. | | Notes | ----- | ``a.view()`` is used two different ways: | | ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view | of the array's memory with a different data-type. This can cause a | reinterpretation of the bytes of memory. | | ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just | returns an instance of `ndarray_subclass` that looks at the same array | (same shape, dtype, etc.) This does not cause a reinterpretation of the | memory. | | For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of | bytes per entry than the previous dtype (for example, converting a | regular array to a structured array), then the behavior of the view | cannot be predicted just from the superficial appearance of ``a`` (shown | by ``print(a)``). It also depends on exactly how ``a`` is stored in | memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus | defined as a slice or transpose, etc., the view may give different | results. | | | Examples | -------- | >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)]) | | Viewing array data using a different type and dtype: | | >>> y = x.view(dtype=np.int16, type=np.matrix) | >>> y | matrix([[513]], dtype=int16) | >>> print type(y) | | | Creating a view on a structured array so it can be used in calculations | | >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)]) | >>> xv = x.view(dtype=np.int8).reshape(-1,2) | >>> xv | array([[1, 2], | [3, 4]], dtype=int8) | >>> xv.mean(0) | array([ 2., 3.]) | | Making changes to the view changes the underlying array | | >>> xv[0,1] = 20 | >>> print x | [(1, 20) (3, 4)] | | Using a view to convert an array to a recarray: | | >>> z = x.view(np.recarray) | >>> z.a | array([1], dtype=int8) | | Views share data: | | >>> x[0] = (9, 10) | >>> z[0] | (9, 10) | | Views that change the dtype size (bytes per entry) should normally be | avoided on arrays defined by slices, transposes, fortran-ordering, etc.: | | >>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16) | >>> y = x[:, 0:2] | >>> y | array([[1, 2], | [4, 5]], dtype=int16) | >>> y.view(dtype=[('width', np.int16), ('length', np.int16)]) | Traceback (most recent call last): | File "", line 1, in | ValueError: new type not compatible with array. | >>> z = y.copy() | >>> z.view(dtype=[('width', np.int16), ('length', np.int16)]) | array([[(1, 2)], | [(4, 5)]], dtype=[('width', '>> x = np.array([1,2,3,4]) | >>> x.base is None | True | | Slicing creates a view, whose memory is shared with x: | | >>> y = x[2:] | >>> y.base is x | True | | ctypes | An object to simplify the interaction of the array with the ctypes | module. | | This attribute creates an object that makes it easier to use arrays | when calling shared libraries with the ctypes module. The returned | object has, among others, data, shape, and strides attributes (see | Notes below) which themselves return ctypes objects that can be used | as arguments to a shared library. | | Parameters | ---------- | None | | Returns | ------- | c : Python object | Possessing attributes data, shape, strides, etc. | | See Also | -------- | numpy.ctypeslib | | Notes | ----- | Below are the public attributes of this object which were documented | in "Guide to NumPy" (we have omitted undocumented public attributes, | as well as documented private attributes): | | * data: A pointer to the memory area of the array as a Python integer. | This memory area may contain data that is not aligned, or not in correct | byte-order. The memory area may not even be writeable. The array | flags and data-type of this array should be respected when passing this | attribute to arbitrary C-code to avoid trouble that can include Python | crashing. User Beware! The value of this attribute is exactly the same | as self._array_interface_['data'][0]. | | * shape (c_intp*self.ndim): A ctypes array of length self.ndim where | the basetype is the C-integer corresponding to dtype('p') on this | platform. This base-type could be c_int, c_long, or c_longlong | depending on the platform. The c_intp type is defined accordingly in | numpy.ctypeslib. The ctypes array contains the shape of the underlying | array. | | * strides (c_intp*self.ndim): A ctypes array of length self.ndim where | the basetype is the same as for the shape attribute. This ctypes array | contains the strides information from the underlying array. This strides | information is important for showing how many bytes must be jumped to | get to the next element in the array. | | * data_as(obj): Return the data pointer cast to a particular c-types object. | For example, calling self._as_parameter_ is equivalent to | self.data_as(ctypes.c_void_p). Perhaps you want to use the data as a | pointer to a ctypes array of floating-point data: | self.data_as(ctypes.POINTER(ctypes.c_double)). | | * shape_as(obj): Return the shape tuple as an array of some other c-types | type. For example: self.shape_as(ctypes.c_short). | | * strides_as(obj): Return the strides tuple as an array of some other | c-types type. For example: self.strides_as(ctypes.c_longlong). | | Be careful using the ctypes attribute - especially on temporary | arrays or arrays constructed on the fly. For example, calling | ``(a+b).ctypes.data_as(ctypes.c_void_p)`` returns a pointer to memory | that is invalid because the array created as (a+b) is deallocated | before the next Python statement. You can avoid this problem using | either ``c=a+b`` or ``ct=(a+b).ctypes``. In the latter case, ct will | hold a reference to the array until ct is deleted or re-assigned. | | If the ctypes module is not available, then the ctypes attribute | of array objects still returns something useful, but ctypes objects | are not returned and errors may be raised instead. In particular, | the object will still have the as parameter attribute which will | return an integer equal to the data attribute. | | Examples | -------- | >>> import ctypes | >>> x | array([[0, 1], | [2, 3]]) | >>> x.ctypes.data | 30439712 | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)) | | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)).contents | c_long(0) | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_longlong)).contents | c_longlong(4294967296L) | >>> x.ctypes.shape | | >>> x.ctypes.shape_as(ctypes.c_long) | | >>> x.ctypes.strides | | >>> x.ctypes.strides_as(ctypes.c_longlong) | | | data | Python buffer object pointing to the start of the array's data. | | dtype | Data-type of the array's elements. | | Parameters | ---------- | None | | Returns | ------- | d : numpy dtype object | | See Also | -------- | numpy.dtype | | Examples | -------- | >>> x | array([[0, 1], | [2, 3]]) | >>> x.dtype | dtype('int32') | >>> type(x.dtype) | | | flags | Information about the memory layout of the array. | | Attributes | ---------- | C_CONTIGUOUS (C) | The data is in a single, C-style contiguous segment. | F_CONTIGUOUS (F) | The data is in a single, Fortran-style contiguous segment. | OWNDATA (O) | The array owns the memory it uses or borrows it from another object. | WRITEABLE (W) | The data area can be written to. Setting this to False locks | the data, making it read-only. A view (slice, etc.) inherits WRITEABLE | from its base array at creation time, but a view of a writeable | array may be subsequently locked while the base array remains writeable. | (The opposite is not true, in that a view of a locked array may not | be made writeable. However, currently, locking a base object does not | lock any views that already reference it, so under that circumstance it | is possible to alter the contents of a locked array via a previously | created writeable view onto it.) Attempting to change a non-writeable | array raises a RuntimeError exception. | ALIGNED (A) | The data and all elements are aligned appropriately for the hardware. | UPDATEIFCOPY (U) | This array is a copy of some other array. When this array is | deallocated, the base array will be updated with the contents of | this array. | FNC | F_CONTIGUOUS and not C_CONTIGUOUS. | FORC | F_CONTIGUOUS or C_CONTIGUOUS (one-segment test). | BEHAVED (B) | ALIGNED and WRITEABLE. | CARRAY (CA) | BEHAVED and C_CONTIGUOUS. | FARRAY (FA) | BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS. | | Notes | ----- | The `flags` object can be accessed dictionary-like (as in ``a.flags['WRITEABLE']``), | or by using lowercased attribute names (as in ``a.flags.writeable``). Short flag | names are only supported in dictionary access. | | Only the UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be changed by | the user, via direct assignment to the attribute or dictionary entry, | or by calling `ndarray.setflags`. | | The array flags cannot be set arbitrarily: | | - UPDATEIFCOPY can only be set ``False``. | - ALIGNED can only be set ``True`` if the data is truly aligned. | - WRITEABLE can only be set ``True`` if the array owns its own memory | or the ultimate owner of the memory exposes a writeable buffer | interface or is a string. | | Arrays can be both C-style and Fortran-style contiguous simultaneously. | This is clear for 1-dimensional arrays, but can also be true for higher | dimensional arrays. | | Even for contiguous arrays a stride for a given dimension | ``arr.strides[dim]`` may be *arbitrary* if ``arr.shape[dim] == 1`` | or the array has no elements. | It does *not* generally hold that ``self.strides[-1] == self.itemsize`` | for C-style contiguous arrays or ``self.strides[0] == self.itemsize`` for | Fortran-style contiguous arrays is true. | | flat | A 1-D iterator over the array. | | This is a `numpy.flatiter` instance, which acts similarly to, but is not | a subclass of, Python's built-in iterator object. | | See Also | -------- | flatten : Return a copy of the array collapsed into one dimension. | | flatiter | | Examples | -------- | >>> x = np.arange(1, 7).reshape(2, 3) | >>> x | array([[1, 2, 3], | [4, 5, 6]]) | >>> x.flat[3] | 4 | >>> x.T | array([[1, 4], | [2, 5], | [3, 6]]) | >>> x.T.flat[3] | 5 | >>> type(x.flat) | | | An assignment example: | | >>> x.flat = 3; x | array([[3, 3, 3], | [3, 3, 3]]) | >>> x.flat[[1,4]] = 1; x | array([[3, 1, 3], | [3, 1, 3]]) | | imag | The imaginary part of the array. | | Examples | -------- | >>> x = np.sqrt([1+0j, 0+1j]) | >>> x.imag | array([ 0. , 0.70710678]) | >>> x.imag.dtype | dtype('float64') | | itemsize | Length of one array element in bytes. | | Examples | -------- | >>> x = np.array([1,2,3], dtype=np.float64) | >>> x.itemsize | 8 | >>> x = np.array([1,2,3], dtype=np.complex128) | >>> x.itemsize | 16 | | nbytes | Total bytes consumed by the elements of the array. | | Notes | ----- | Does not include memory consumed by non-element attributes of the | array object. | | Examples | -------- | >>> x = np.zeros((3,5,2), dtype=np.complex128) | >>> x.nbytes | 480 | >>> np.prod(x.shape) * x.itemsize | 480 | | ndim | Number of array dimensions. | | Examples | -------- | >>> x = np.array([1, 2, 3]) | >>> x.ndim | 1 | >>> y = np.zeros((2, 3, 4)) | >>> y.ndim | 3 | | real | The real part of the array. | | Examples | -------- | >>> x = np.sqrt([1+0j, 0+1j]) | >>> x.real | array([ 1. , 0.70710678]) | >>> x.real.dtype | dtype('float64') | | See Also | -------- | numpy.real : equivalent function | | shape | Tuple of array dimensions. | | Notes | ----- | May be used to "reshape" the array, as long as this would not | require a change in the total number of elements | | Examples | -------- | >>> x = np.array([1, 2, 3, 4]) | >>> x.shape | (4,) | >>> y = np.zeros((2, 3, 4)) | >>> y.shape | (2, 3, 4) | >>> y.shape = (3, 8) | >>> y | array([[ 0., 0., 0., 0., 0., 0., 0., 0.], | [ 0., 0., 0., 0., 0., 0., 0., 0.], | [ 0., 0., 0., 0., 0., 0., 0., 0.]]) | >>> y.shape = (3, 6) | Traceback (most recent call last): | File "", line 1, in | ValueError: total size of new array must be unchanged | | size | Number of elements in the array. | | Equivalent to ``np.prod(a.shape)``, i.e., the product of the array's | dimensions. | | Examples | -------- | >>> x = np.zeros((3, 5, 2), dtype=np.complex128) | >>> x.size | 30 | >>> np.prod(x.shape) | 30 | | strides | Tuple of bytes to step in each dimension when traversing an array. | | The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a` | is:: | | offset = sum(np.array(i) * a.strides) | | A more detailed explanation of strides can be found in the | "ndarray.rst" file in the NumPy reference guide. | | Notes | ----- | Imagine an array of 32-bit integers (each 4 bytes):: | | x = np.array([[0, 1, 2, 3, 4], | [5, 6, 7, 8, 9]], dtype=np.int32) | | This array is stored in memory as 40 bytes, one after the other | (known as a contiguous block of memory). The strides of an array tell | us how many bytes we have to skip in memory to move to the next position | along a certain axis. For example, we have to skip 4 bytes (1 value) to | move to the next column, but 20 bytes (5 values) to get to the same | position in the next row. As such, the strides for the array `x` will be | ``(20, 4)``. | | See Also | -------- | numpy.lib.stride_tricks.as_strided | | Examples | -------- | >>> y = np.reshape(np.arange(2*3*4), (2,3,4)) | >>> y | array([[[ 0, 1, 2, 3], | [ 4, 5, 6, 7], | [ 8, 9, 10, 11]], | [[12, 13, 14, 15], | [16, 17, 18, 19], | [20, 21, 22, 23]]]) | >>> y.strides | (48, 16, 4) | >>> y[1,1,1] | 17 | >>> offset=sum(y.strides * np.array((1,1,1))) | >>> offset/y.itemsize | 17 | | >>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0) | >>> x.strides | (32, 4, 224, 1344) | >>> i = np.array([3,5,2,2]) | >>> offset = sum(i * x.strides) | >>> x[3,5,2,2] | 813 | >>> offset / x.itemsize | 813 | | ---------------------------------------------------------------------- | Data and other attributes inherited from numpy.ndarray: | | __hash__ = None class memmap(numpy.ndarray) | Create a memory-map to an array stored in a *binary* file on disk. | | Memory-mapped files are used for accessing small segments of large files | on disk, without reading the entire file into memory. Numpy's | memmap's are array-like objects. This differs from Python's ``mmap`` | module, which uses file-like objects. | | This subclass of ndarray has some unpleasant interactions with | some operations, because it doesn't quite fit properly as a subclass. | An alternative to using this subclass is to create the ``mmap`` | object yourself, then create an ndarray with ndarray.__new__ directly, | passing the object created in its 'buffer=' parameter. | | This class may at some point be turned into a factory function | which returns a view into an mmap buffer. | | Delete the memmap instance to close. | | | Parameters | ---------- | filename : str or file-like object | The file name or file object to be used as the array data buffer. | dtype : data-type, optional | The data-type used to interpret the file contents. | Default is `uint8`. | mode : {'r+', 'r', 'w+', 'c'}, optional | The file is opened in this mode: | | +------+-------------------------------------------------------------+ | | 'r' | Open existing file for reading only. | | +------+-------------------------------------------------------------+ | | 'r+' | Open existing file for reading and writing. | | +------+-------------------------------------------------------------+ | | 'w+' | Create or overwrite existing file for reading and writing. | | +------+-------------------------------------------------------------+ | | 'c' | Copy-on-write: assignments affect data in memory, but | | | | changes are not saved to disk. The file on disk is | | | | read-only. | | +------+-------------------------------------------------------------+ | | Default is 'r+'. | offset : int, optional | In the file, array data starts at this offset. Since `offset` is | measured in bytes, it should normally be a multiple of the byte-size | of `dtype`. When ``mode != 'r'``, even positive offsets beyond end of | file are valid; The file will be extended to accommodate the | additional data. By default, ``memmap`` will start at the beginning of | the file, even if ``filename`` is a file pointer ``fp`` and | ``fp.tell() != 0``. | shape : tuple, optional | The desired shape of the array. If ``mode == 'r'`` and the number | of remaining bytes after `offset` is not a multiple of the byte-size | of `dtype`, you must specify `shape`. By default, the returned array | will be 1-D with the number of elements determined by file size | and data-type. | order : {'C', 'F'}, optional | Specify the order of the ndarray memory layout: | :term:`row-major`, C-style or :term:`column-major`, | Fortran-style. This only has an effect if the shape is | greater than 1-D. The default order is 'C'. | | Attributes | ---------- | filename : str | Path to the mapped file. | offset : int | Offset position in the file. | mode : str | File mode. | | Methods | ------- | flush | Flush any changes in memory to file on disk. | When you delete a memmap object, flush is called first to write | changes to disk before removing the object. | | | Notes | ----- | The memmap object can be used anywhere an ndarray is accepted. | Given a memmap ``fp``, ``isinstance(fp, numpy.ndarray)`` returns | ``True``. | | Memory-mapped arrays use the Python memory-map object which | (prior to Python 2.5) does not allow files to be larger than a | certain size depending on the platform. This size is always < 2GB | even on 64-bit systems. | | When a memmap causes a file to be created or extended beyond its | current size in the filesystem, the contents of the new part are | unspecified. On systems with POSIX filesystem semantics, the extended | part will be filled with zero bytes. | | Examples | -------- | >>> data = np.arange(12, dtype='float32') | >>> data.resize((3,4)) | | This example uses a temporary file so that doctest doesn't write | files to your directory. You would use a 'normal' filename. | | >>> from tempfile import mkdtemp | >>> import os.path as path | >>> filename = path.join(mkdtemp(), 'newfile.dat') | | Create a memmap with dtype and shape that matches our data: | | >>> fp = np.memmap(filename, dtype='float32', mode='w+', shape=(3,4)) | >>> fp | memmap([[ 0., 0., 0., 0.], | [ 0., 0., 0., 0.], | [ 0., 0., 0., 0.]], dtype=float32) | | Write data to memmap array: | | >>> fp[:] = data[:] | >>> fp | memmap([[ 0., 1., 2., 3.], | [ 4., 5., 6., 7.], | [ 8., 9., 10., 11.]], dtype=float32) | | >>> fp.filename == path.abspath(filename) | True | | Deletion flushes memory changes to disk before removing the object: | | >>> del fp | | Load the memmap and verify data was stored: | | >>> newfp = np.memmap(filename, dtype='float32', mode='r', shape=(3,4)) | >>> newfp | memmap([[ 0., 1., 2., 3.], | [ 4., 5., 6., 7.], | [ 8., 9., 10., 11.]], dtype=float32) | | Read-only memmap: | | >>> fpr = np.memmap(filename, dtype='float32', mode='r', shape=(3,4)) | >>> fpr.flags.writeable | False | | Copy-on-write memmap: | | >>> fpc = np.memmap(filename, dtype='float32', mode='c', shape=(3,4)) | >>> fpc.flags.writeable | True | | It's possible to assign to copy-on-write array, but values are only | written into the memory copy of the array, and not written to disk: | | >>> fpc | memmap([[ 0., 1., 2., 3.], | [ 4., 5., 6., 7.], | [ 8., 9., 10., 11.]], dtype=float32) | >>> fpc[0,:] = 0 | >>> fpc | memmap([[ 0., 0., 0., 0.], | [ 4., 5., 6., 7.], | [ 8., 9., 10., 11.]], dtype=float32) | | File on disk is unchanged: | | >>> fpr | memmap([[ 0., 1., 2., 3.], | [ 4., 5., 6., 7.], | [ 8., 9., 10., 11.]], dtype=float32) | | Offset into a memmap: | | >>> fpo = np.memmap(filename, dtype='float32', mode='r', offset=16) | >>> fpo | memmap([ 4., 5., 6., 7., 8., 9., 10., 11.], dtype=float32) | | Method resolution order: | memmap | numpy.ndarray | __builtin__.object | | Methods defined here: | | __array_finalize__(self, obj) | | flush(self) | Write any changes in the array to the file on disk. | | For further information, see `memmap`. | | Parameters | ---------- | None | | See Also | -------- | memmap | | ---------------------------------------------------------------------- | Static methods defined here: | | __new__(subtype, filename, dtype=, mode='r+', offset=0, shape=None, order='C') | | ---------------------------------------------------------------------- | Data descriptors defined here: | | __dict__ | dictionary for instance variables (if defined) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __array_priority__ = -100.0 | | ---------------------------------------------------------------------- | Methods inherited from numpy.ndarray: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | a.__array__(|dtype) -> reference if type unchanged, copy otherwise. | | Returns either a new reference to self if dtype is not given or a new array | of provided data type if dtype is different from the current dtype of the | array. | | __array_prepare__(...) | a.__array_prepare__(obj) -> Object of same type as ndarray object obj. | | __array_wrap__(...) | a.__array_wrap__(obj) -> Object of same type as ndarray object a. | | __contains__(...) | x.__contains__(y) <==> y in x | | __copy__(...) | a.__copy__([order]) | | Return a copy of the array. | | Parameters | ---------- | order : {'C', 'F', 'A'}, optional | If order is 'C' (False) then the result is contiguous (default). | If order is 'Fortran' (True) then the result has fortran order. | If order is 'Any' (None) then the result has fortran order | only if the array already is in fortran order. | | __deepcopy__(...) | a.__deepcopy__() -> Deep copy of array. | | Used if copy.deepcopy is called on an array. | | __delitem__(...) | x.__delitem__(y) <==> del x[y] | | __delslice__(...) | x.__delslice__(i, j) <==> del x[i:j] | | Use of negative indices is not supported. | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __eq__(...) | x.__eq__(y) <==> x==y | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __getslice__(...) | x.__getslice__(i, j) <==> x[i:j] | | Use of negative indices is not supported. | | __gt__(...) | x.__gt__(y) <==> x>y | | __hex__(...) | x.__hex__() <==> hex(x) | | __iadd__(...) | x.__iadd__(y) <==> x+=y | | __iand__(...) | x.__iand__(y) <==> x&=y | | __idiv__(...) | x.__idiv__(y) <==> x/=y | | __ifloordiv__(...) | x.__ifloordiv__(y) <==> x//=y | | __ilshift__(...) | x.__ilshift__(y) <==> x<<=y | | __imod__(...) | x.__imod__(y) <==> x%=y | | __imul__(...) | x.__imul__(y) <==> x*=y | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __ior__(...) | x.__ior__(y) <==> x|=y | | __ipow__(...) | x.__ipow__(y) <==> x**=y | | __irshift__(...) | x.__irshift__(y) <==> x>>=y | | __isub__(...) | x.__isub__(y) <==> x-=y | | __iter__(...) | x.__iter__() <==> iter(x) | | __itruediv__(...) | x.__itruediv__(y) <==> x/=y | | __ixor__(...) | x.__ixor__(y) <==> x^=y | | __le__(...) | x.__le__(y) <==> x<=y | | __len__(...) | x.__len__() <==> len(x) | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __ne__(...) | x.__ne__(y) <==> x!=y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | a.__reduce__() | | For pickling. | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setitem__(...) | x.__setitem__(i, y) <==> x[i]=y | | __setslice__(...) | x.__setslice__(i, j, y) <==> x[i:j]=y | | Use of negative indices is not supported. | | __setstate__(...) | a.__setstate__(version, shape, dtype, isfortran, rawdata) | | For unpickling. | | Parameters | ---------- | version : int | optional pickle version. If omitted defaults to 0. | shape : tuple | dtype : data-type | isFortran : bool | rawdata : string or list | a binary string with the data (or a list if 'a' is an object array) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | a.all(axis=None, out=None, keepdims=False) | | Returns True if all elements evaluate to True. | | Refer to `numpy.all` for full documentation. | | See Also | -------- | numpy.all : equivalent function | | any(...) | a.any(axis=None, out=None, keepdims=False) | | Returns True if any of the elements of `a` evaluate to True. | | Refer to `numpy.any` for full documentation. | | See Also | -------- | numpy.any : equivalent function | | argmax(...) | a.argmax(axis=None, out=None) | | Return indices of the maximum values along the given axis. | | Refer to `numpy.argmax` for full documentation. | | See Also | -------- | numpy.argmax : equivalent function | | argmin(...) | a.argmin(axis=None, out=None) | | Return indices of the minimum values along the given axis of `a`. | | Refer to `numpy.argmin` for detailed documentation. | | See Also | -------- | numpy.argmin : equivalent function | | argpartition(...) | a.argpartition(kth, axis=-1, kind='introselect', order=None) | | Returns the indices that would partition this array. | | Refer to `numpy.argpartition` for full documentation. | | .. versionadded:: 1.8.0 | | See Also | -------- | numpy.argpartition : equivalent function | | argsort(...) | a.argsort(axis=-1, kind='quicksort', order=None) | | Returns the indices that would sort this array. | | Refer to `numpy.argsort` for full documentation. | | See Also | -------- | numpy.argsort : equivalent function | | astype(...) | a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True) | | Copy of the array, cast to a specified type. | | Parameters | ---------- | dtype : str or dtype | Typecode or data-type to which the array is cast. | order : {'C', 'F', 'A', 'K'}, optional | Controls the memory layout order of the result. | 'C' means C order, 'F' means Fortran order, 'A' | means 'F' order if all the arrays are Fortran contiguous, | 'C' order otherwise, and 'K' means as close to the | order the array elements appear in memory as possible. | Default is 'K'. | casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional | Controls what kind of data casting may occur. Defaults to 'unsafe' | for backwards compatibility. | | * 'no' means the data types should not be cast at all. | * 'equiv' means only byte-order changes are allowed. | * 'safe' means only casts which can preserve values are allowed. | * 'same_kind' means only safe casts or casts within a kind, | like float64 to float32, are allowed. | * 'unsafe' means any data conversions may be done. | subok : bool, optional | If True, then sub-classes will be passed-through (default), otherwise | the returned array will be forced to be a base-class array. | copy : bool, optional | By default, astype always returns a newly allocated array. If this | is set to false, and the `dtype`, `order`, and `subok` | requirements are satisfied, the input array is returned instead | of a copy. | | Returns | ------- | arr_t : ndarray | Unless `copy` is False and the other conditions for returning the input | array are satisfied (see description for `copy` input paramter), `arr_t` | is a new array of the same shape as the input array, with dtype, order | given by `dtype`, `order`. | | Notes | ----- | Starting in NumPy 1.9, astype method now returns an error if the string | dtype to cast to is not long enough in 'safe' casting mode to hold the max | value of integer/float array that is being casted. Previously the casting | was allowed even if the result was truncated. | | Raises | ------ | ComplexWarning | When casting from complex to float or int. To avoid this, | one should use ``a.real.astype(t)``. | | Examples | -------- | >>> x = np.array([1, 2, 2.5]) | >>> x | array([ 1. , 2. , 2.5]) | | >>> x.astype(int) | array([1, 2, 2]) | | byteswap(...) | a.byteswap(inplace) | | Swap the bytes of the array elements | | Toggle between low-endian and big-endian data representation by | returning a byteswapped array, optionally swapped in-place. | | Parameters | ---------- | inplace : bool, optional | If ``True``, swap bytes in-place, default is ``False``. | | Returns | ------- | out : ndarray | The byteswapped array. If `inplace` is ``True``, this is | a view to self. | | Examples | -------- | >>> A = np.array([1, 256, 8755], dtype=np.int16) | >>> map(hex, A) | ['0x1', '0x100', '0x2233'] | >>> A.byteswap(True) | array([ 256, 1, 13090], dtype=int16) | >>> map(hex, A) | ['0x100', '0x1', '0x3322'] | | Arrays of strings are not swapped | | >>> A = np.array(['ceg', 'fac']) | >>> A.byteswap() | array(['ceg', 'fac'], | dtype='|S3') | | choose(...) | a.choose(choices, out=None, mode='raise') | | Use an index array to construct a new array from a set of choices. | | Refer to `numpy.choose` for full documentation. | | See Also | -------- | numpy.choose : equivalent function | | clip(...) | a.clip(min=None, max=None, out=None) | | Return an array whose values are limited to ``[min, max]``. | One of max or min must be given. | | Refer to `numpy.clip` for full documentation. | | See Also | -------- | numpy.clip : equivalent function | | compress(...) | a.compress(condition, axis=None, out=None) | | Return selected slices of this array along given axis. | | Refer to `numpy.compress` for full documentation. | | See Also | -------- | numpy.compress : equivalent function | | conj(...) | a.conj() | | Complex-conjugate all elements. | | Refer to `numpy.conjugate` for full documentation. | | See Also | -------- | numpy.conjugate : equivalent function | | conjugate(...) | a.conjugate() | | Return the complex conjugate, element-wise. | | Refer to `numpy.conjugate` for full documentation. | | See Also | -------- | numpy.conjugate : equivalent function | | copy(...) | a.copy(order='C') | | Return a copy of the array. | | Parameters | ---------- | order : {'C', 'F', 'A', 'K'}, optional | Controls the memory layout of the copy. 'C' means C-order, | 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous, | 'C' otherwise. 'K' means match the layout of `a` as closely | as possible. (Note that this function and :func:numpy.copy are very | similar, but have different default values for their order= | arguments.) | | See also | -------- | numpy.copy | numpy.copyto | | Examples | -------- | >>> x = np.array([[1,2,3],[4,5,6]], order='F') | | >>> y = x.copy() | | >>> x.fill(0) | | >>> x | array([[0, 0, 0], | [0, 0, 0]]) | | >>> y | array([[1, 2, 3], | [4, 5, 6]]) | | >>> y.flags['C_CONTIGUOUS'] | True | | cumprod(...) | a.cumprod(axis=None, dtype=None, out=None) | | Return the cumulative product of the elements along the given axis. | | Refer to `numpy.cumprod` for full documentation. | | See Also | -------- | numpy.cumprod : equivalent function | | cumsum(...) | a.cumsum(axis=None, dtype=None, out=None) | | Return the cumulative sum of the elements along the given axis. | | Refer to `numpy.cumsum` for full documentation. | | See Also | -------- | numpy.cumsum : equivalent function | | diagonal(...) | a.diagonal(offset=0, axis1=0, axis2=1) | | Return specified diagonals. In NumPy 1.9 the returned array is a | read-only view instead of a copy as in previous NumPy versions. In | NumPy 1.10 the read-only restriction will be removed. | | Refer to :func:`numpy.diagonal` for full documentation. | | See Also | -------- | numpy.diagonal : equivalent function | | dot(...) | a.dot(b, out=None) | | Dot product of two arrays. | | Refer to `numpy.dot` for full documentation. | | See Also | -------- | numpy.dot : equivalent function | | Examples | -------- | >>> a = np.eye(2) | >>> b = np.ones((2, 2)) * 2 | >>> a.dot(b) | array([[ 2., 2.], | [ 2., 2.]]) | | This array method can be conveniently chained: | | >>> a.dot(b).dot(b) | array([[ 8., 8.], | [ 8., 8.]]) | | dump(...) | a.dump(file) | | Dump a pickle of the array to the specified file. | The array can be read back with pickle.load or numpy.load. | | Parameters | ---------- | file : str | A string naming the dump file. | | dumps(...) | a.dumps() | | Returns the pickle of the array as a string. | pickle.loads or numpy.loads will convert the string back to an array. | | Parameters | ---------- | None | | fill(...) | a.fill(value) | | Fill the array with a scalar value. | | Parameters | ---------- | value : scalar | All elements of `a` will be assigned this value. | | Examples | -------- | >>> a = np.array([1, 2]) | >>> a.fill(0) | >>> a | array([0, 0]) | >>> a = np.empty(2) | >>> a.fill(1) | >>> a | array([ 1., 1.]) | | flatten(...) | a.flatten(order='C') | | Return a copy of the array collapsed into one dimension. | | Parameters | ---------- | order : {'C', 'F', 'A'}, optional | Whether to flatten in row-major (C-style) or | column-major (Fortran-style) order or preserve the | C/Fortran ordering from `a`. The default is 'C'. | | Returns | ------- | y : ndarray | A copy of the input array, flattened to one dimension. | | See Also | -------- | ravel : Return a flattened array. | flat : A 1-D flat iterator over the array. | | Examples | -------- | >>> a = np.array([[1,2], [3,4]]) | >>> a.flatten() | array([1, 2, 3, 4]) | >>> a.flatten('F') | array([1, 3, 2, 4]) | | getfield(...) | a.getfield(dtype, offset=0) | | Returns a field of the given array as a certain type. | | A field is a view of the array data with a given data-type. The values in | the view are determined by the given type and the offset into the current | array in bytes. The offset needs to be such that the view dtype fits in the | array dtype; for example an array of dtype complex128 has 16-byte elements. | If taking a view with a 32-bit integer (4 bytes), the offset needs to be | between 0 and 12 bytes. | | Parameters | ---------- | dtype : str or dtype | The data type of the view. The dtype size of the view can not be larger | than that of the array itself. | offset : int | Number of bytes to skip before beginning the element view. | | Examples | -------- | >>> x = np.diag([1.+1.j]*2) | >>> x[1, 1] = 2 + 4.j | >>> x | array([[ 1.+1.j, 0.+0.j], | [ 0.+0.j, 2.+4.j]]) | >>> x.getfield(np.float64) | array([[ 1., 0.], | [ 0., 2.]]) | | By choosing an offset of 8 bytes we can select the complex part of the | array for our view: | | >>> x.getfield(np.float64, offset=8) | array([[ 1., 0.], | [ 0., 4.]]) | | item(...) | a.item(*args) | | Copy an element of an array to a standard Python scalar and return it. | | Parameters | ---------- | \*args : Arguments (variable number and type) | | * none: in this case, the method only works for arrays | with one element (`a.size == 1`), which element is | copied into a standard Python scalar object and returned. | | * int_type: this argument is interpreted as a flat index into | the array, specifying which element to copy and return. | | * tuple of int_types: functions as does a single int_type argument, | except that the argument is interpreted as an nd-index into the | array. | | Returns | ------- | z : Standard Python scalar object | A copy of the specified element of the array as a suitable | Python scalar | | Notes | ----- | When the data type of `a` is longdouble or clongdouble, item() returns | a scalar array object because there is no available Python scalar that | would not lose information. Void arrays return a buffer object for item(), | unless fields are defined, in which case a tuple is returned. | | `item` is very similar to a[args], except, instead of an array scalar, | a standard Python scalar is returned. This can be useful for speeding up | access to elements of the array and doing arithmetic on elements of the | array using Python's optimized math. | | Examples | -------- | >>> x = np.random.randint(9, size=(3, 3)) | >>> x | array([[3, 1, 7], | [2, 8, 3], | [8, 5, 3]]) | >>> x.item(3) | 2 | >>> x.item(7) | 5 | >>> x.item((0, 1)) | 1 | >>> x.item((2, 2)) | 3 | | itemset(...) | a.itemset(*args) | | Insert scalar into an array (scalar is cast to array's dtype, if possible) | | There must be at least 1 argument, and define the last argument | as *item*. Then, ``a.itemset(*args)`` is equivalent to but faster | than ``a[args] = item``. The item should be a scalar value and `args` | must select a single item in the array `a`. | | Parameters | ---------- | \*args : Arguments | If one argument: a scalar, only used in case `a` is of size 1. | If two arguments: the last argument is the value to be set | and must be a scalar, the first argument specifies a single array | element location. It is either an int or a tuple. | | Notes | ----- | Compared to indexing syntax, `itemset` provides some speed increase | for placing a scalar into a particular location in an `ndarray`, | if you must do this. However, generally this is discouraged: | among other problems, it complicates the appearance of the code. | Also, when using `itemset` (and `item`) inside a loop, be sure | to assign the methods to a local variable to avoid the attribute | look-up at each loop iteration. | | Examples | -------- | >>> x = np.random.randint(9, size=(3, 3)) | >>> x | array([[3, 1, 7], | [2, 8, 3], | [8, 5, 3]]) | >>> x.itemset(4, 0) | >>> x.itemset((2, 2), 9) | >>> x | array([[3, 1, 7], | [2, 0, 3], | [8, 5, 9]]) | | max(...) | a.max(axis=None, out=None) | | Return the maximum along a given axis. | | Refer to `numpy.amax` for full documentation. | | See Also | -------- | numpy.amax : equivalent function | | mean(...) | a.mean(axis=None, dtype=None, out=None, keepdims=False) | | Returns the average of the array elements along given axis. | | Refer to `numpy.mean` for full documentation. | | See Also | -------- | numpy.mean : equivalent function | | min(...) | a.min(axis=None, out=None, keepdims=False) | | Return the minimum along a given axis. | | Refer to `numpy.amin` for full documentation. | | See Also | -------- | numpy.amin : equivalent function | | newbyteorder(...) | arr.newbyteorder(new_order='S') | | Return the array with the same data viewed with a different byte order. | | Equivalent to:: | | arr.view(arr.dtype.newbytorder(new_order)) | | Changes are also made in all fields and sub-arrays of the array data | type. | | | | Parameters | ---------- | new_order : string, optional | Byte order to force; a value from the byte order specifications | below. `new_order` codes can be any of: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_arr : array | New array object with the dtype reflecting given change to the | byte order. | | nonzero(...) | a.nonzero() | | Return the indices of the elements that are non-zero. | | Refer to `numpy.nonzero` for full documentation. | | See Also | -------- | numpy.nonzero : equivalent function | | partition(...) | a.partition(kth, axis=-1, kind='introselect', order=None) | | Rearranges the elements in the array in such a way that value of the | element in kth position is in the position it would be in a sorted array. | All elements smaller than the kth element are moved before this element and | all equal or greater are moved behind it. The ordering of the elements in | the two partitions is undefined. | | .. versionadded:: 1.8.0 | | Parameters | ---------- | kth : int or sequence of ints | Element index to partition by. The kth element value will be in its | final sorted position and all smaller elements will be moved before it | and all equal or greater elements behind it. | The order all elements in the partitions is undefined. | If provided with a sequence of kth it will partition all elements | indexed by kth of them into their sorted position at once. | axis : int, optional | Axis along which to sort. Default is -1, which means sort along the | last axis. | kind : {'introselect'}, optional | Selection algorithm. Default is 'introselect'. | order : str or list of str, optional | When `a` is an array with fields defined, this argument specifies | which fields to compare first, second, etc. A single field can | be specified as a string, and not all fields need be specified, | but unspecified fields will still be used, in the order in which | they come up in the dtype, to break ties. | | See Also | -------- | numpy.partition : Return a parititioned copy of an array. | argpartition : Indirect partition. | sort : Full sort. | | Notes | ----- | See ``np.partition`` for notes on the different algorithms. | | Examples | -------- | >>> a = np.array([3, 4, 2, 1]) | >>> a.partition(a, 3) | >>> a | array([2, 1, 3, 4]) | | >>> a.partition((1, 3)) | array([1, 2, 3, 4]) | | prod(...) | a.prod(axis=None, dtype=None, out=None, keepdims=False) | | Return the product of the array elements over the given axis | | Refer to `numpy.prod` for full documentation. | | See Also | -------- | numpy.prod : equivalent function | | ptp(...) | a.ptp(axis=None, out=None) | | Peak to peak (maximum - minimum) value along a given axis. | | Refer to `numpy.ptp` for full documentation. | | See Also | -------- | numpy.ptp : equivalent function | | put(...) | a.put(indices, values, mode='raise') | | Set ``a.flat[n] = values[n]`` for all `n` in indices. | | Refer to `numpy.put` for full documentation. | | See Also | -------- | numpy.put : equivalent function | | ravel(...) | a.ravel([order]) | | Return a flattened array. | | Refer to `numpy.ravel` for full documentation. | | See Also | -------- | numpy.ravel : equivalent function | | ndarray.flat : a flat iterator on the array. | | repeat(...) | a.repeat(repeats, axis=None) | | Repeat elements of an array. | | Refer to `numpy.repeat` for full documentation. | | See Also | -------- | numpy.repeat : equivalent function | | reshape(...) | a.reshape(shape, order='C') | | Returns an array containing the same data with a new shape. | | Refer to `numpy.reshape` for full documentation. | | See Also | -------- | numpy.reshape : equivalent function | | resize(...) | a.resize(new_shape, refcheck=True) | | Change shape and size of array in-place. | | Parameters | ---------- | new_shape : tuple of ints, or `n` ints | Shape of resized array. | refcheck : bool, optional | If False, reference count will not be checked. Default is True. | | Returns | ------- | None | | Raises | ------ | ValueError | If `a` does not own its own data or references or views to it exist, | and the data memory must be changed. | | SystemError | If the `order` keyword argument is specified. This behaviour is a | bug in NumPy. | | See Also | -------- | resize : Return a new array with the specified shape. | | Notes | ----- | This reallocates space for the data area if necessary. | | Only contiguous arrays (data elements consecutive in memory) can be | resized. | | The purpose of the reference count check is to make sure you | do not use this array as a buffer for another Python object and then | reallocate the memory. However, reference counts can increase in | other ways so if you are sure that you have not shared the memory | for this array with another Python object, then you may safely set | `refcheck` to False. | | Examples | -------- | Shrinking an array: array is flattened (in the order that the data are | stored in memory), resized, and reshaped: | | >>> a = np.array([[0, 1], [2, 3]], order='C') | >>> a.resize((2, 1)) | >>> a | array([[0], | [1]]) | | >>> a = np.array([[0, 1], [2, 3]], order='F') | >>> a.resize((2, 1)) | >>> a | array([[0], | [2]]) | | Enlarging an array: as above, but missing entries are filled with zeros: | | >>> b = np.array([[0, 1], [2, 3]]) | >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple | >>> b | array([[0, 1, 2], | [3, 0, 0]]) | | Referencing an array prevents resizing... | | >>> c = a | >>> a.resize((1, 1)) | Traceback (most recent call last): | ... | ValueError: cannot resize an array that has been referenced ... | | Unless `refcheck` is False: | | >>> a.resize((1, 1), refcheck=False) | >>> a | array([[0]]) | >>> c | array([[0]]) | | round(...) | a.round(decimals=0, out=None) | | Return `a` with each element rounded to the given number of decimals. | | Refer to `numpy.around` for full documentation. | | See Also | -------- | numpy.around : equivalent function | | searchsorted(...) | a.searchsorted(v, side='left', sorter=None) | | Find indices where elements of v should be inserted in a to maintain order. | | For full documentation, see `numpy.searchsorted` | | See Also | -------- | numpy.searchsorted : equivalent function | | setfield(...) | a.setfield(val, dtype, offset=0) | | Put a value into a specified place in a field defined by a data-type. | | Place `val` into `a`'s field defined by `dtype` and beginning `offset` | bytes into the field. | | Parameters | ---------- | val : object | Value to be placed in field. | dtype : dtype object | Data-type of the field in which to place `val`. | offset : int, optional | The number of bytes into the field at which to place `val`. | | Returns | ------- | None | | See Also | -------- | getfield | | Examples | -------- | >>> x = np.eye(3) | >>> x.getfield(np.float64) | array([[ 1., 0., 0.], | [ 0., 1., 0.], | [ 0., 0., 1.]]) | >>> x.setfield(3, np.int32) | >>> x.getfield(np.int32) | array([[3, 3, 3], | [3, 3, 3], | [3, 3, 3]]) | >>> x | array([[ 1.00000000e+000, 1.48219694e-323, 1.48219694e-323], | [ 1.48219694e-323, 1.00000000e+000, 1.48219694e-323], | [ 1.48219694e-323, 1.48219694e-323, 1.00000000e+000]]) | >>> x.setfield(np.eye(3), np.int32) | >>> x | array([[ 1., 0., 0.], | [ 0., 1., 0.], | [ 0., 0., 1.]]) | | setflags(...) | a.setflags(write=None, align=None, uic=None) | | Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively. | | These Boolean-valued flags affect how numpy interprets the memory | area used by `a` (see Notes below). The ALIGNED flag can only | be set to True if the data is actually aligned according to the type. | The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE | can only be set to True if the array owns its own memory, or the | ultimate owner of the memory exposes a writeable buffer interface, | or is a string. (The exception for string is made so that unpickling | can be done without copying memory.) | | Parameters | ---------- | write : bool, optional | Describes whether or not `a` can be written to. | align : bool, optional | Describes whether or not `a` is aligned properly for its type. | uic : bool, optional | Describes whether or not `a` is a copy of another "base" array. | | Notes | ----- | Array flags provide information about how the memory area used | for the array is to be interpreted. There are 6 Boolean flags | in use, only three of which can be changed by the user: | UPDATEIFCOPY, WRITEABLE, and ALIGNED. | | WRITEABLE (W) the data area can be written to; | | ALIGNED (A) the data and strides are aligned appropriately for the hardware | (as determined by the compiler); | | UPDATEIFCOPY (U) this array is a copy of some other array (referenced | by .base). When this array is deallocated, the base array will be | updated with the contents of this array. | | All flags can be accessed using their first (upper case) letter as well | as the full name. | | Examples | -------- | >>> y | array([[3, 1, 7], | [2, 0, 0], | [8, 5, 9]]) | >>> y.flags | C_CONTIGUOUS : True | F_CONTIGUOUS : False | OWNDATA : True | WRITEABLE : True | ALIGNED : True | UPDATEIFCOPY : False | >>> y.setflags(write=0, align=0) | >>> y.flags | C_CONTIGUOUS : True | F_CONTIGUOUS : False | OWNDATA : True | WRITEABLE : False | ALIGNED : False | UPDATEIFCOPY : False | >>> y.setflags(uic=1) | Traceback (most recent call last): | File "", line 1, in | ValueError: cannot set UPDATEIFCOPY flag to True | | sort(...) | a.sort(axis=-1, kind='quicksort', order=None) | | Sort an array, in-place. | | Parameters | ---------- | axis : int, optional | Axis along which to sort. Default is -1, which means sort along the | last axis. | kind : {'quicksort', 'mergesort', 'heapsort'}, optional | Sorting algorithm. Default is 'quicksort'. | order : str or list of str, optional | When `a` is an array with fields defined, this argument specifies | which fields to compare first, second, etc. A single field can | be specified as a string, and not all fields need be specified, | but unspecified fields will still be used, in the order in which | they come up in the dtype, to break ties. | | See Also | -------- | numpy.sort : Return a sorted copy of an array. | argsort : Indirect sort. | lexsort : Indirect stable sort on multiple keys. | searchsorted : Find elements in sorted array. | partition: Partial sort. | | Notes | ----- | See ``sort`` for notes on the different sorting algorithms. | | Examples | -------- | >>> a = np.array([[1,4], [3,1]]) | >>> a.sort(axis=1) | >>> a | array([[1, 4], | [1, 3]]) | >>> a.sort(axis=0) | >>> a | array([[1, 3], | [1, 4]]) | | Use the `order` keyword to specify a field to use when sorting a | structured array: | | >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)]) | >>> a.sort(order='y') | >>> a | array([('c', 1), ('a', 2)], | dtype=[('x', '|S1'), ('y', '>> x = np.array([[0, 1], [2, 3]]) | >>> x.tobytes() | b'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00' | >>> x.tobytes('C') == x.tobytes() | True | >>> x.tobytes('F') | b'\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00' | | tofile(...) | a.tofile(fid, sep="", format="%s") | | Write array to a file as text or binary (default). | | Data is always written in 'C' order, independent of the order of `a`. | The data produced by this method can be recovered using the function | fromfile(). | | Parameters | ---------- | fid : file or str | An open file object, or a string containing a filename. | sep : str | Separator between array items for text output. | If "" (empty), a binary file is written, equivalent to | ``file.write(a.tobytes())``. | format : str | Format string for text file output. | Each entry in the array is formatted to text by first converting | it to the closest Python type, and then using "format" % item. | | Notes | ----- | This is a convenience function for quick storage of array data. | Information on endianness and precision is lost, so this method is not a | good choice for files intended to archive data or transport data between | machines with different endianness. Some of these problems can be overcome | by outputting the data as text files, at the expense of speed and file | size. | | tolist(...) | a.tolist() | | Return the array as a (possibly nested) list. | | Return a copy of the array data as a (nested) Python list. | Data items are converted to the nearest compatible Python type. | | Parameters | ---------- | none | | Returns | ------- | y : list | The possibly nested list of array elements. | | Notes | ----- | The array may be recreated, ``a = np.array(a.tolist())``. | | Examples | -------- | >>> a = np.array([1, 2]) | >>> a.tolist() | [1, 2] | >>> a = np.array([[1, 2], [3, 4]]) | >>> list(a) | [array([1, 2]), array([3, 4])] | >>> a.tolist() | [[1, 2], [3, 4]] | | tostring(...) | a.tostring(order='C') | | Construct Python bytes containing the raw data bytes in the array. | | Constructs Python bytes showing a copy of the raw contents of | data memory. The bytes object can be produced in either 'C' or 'Fortran', | or 'Any' order (the default is 'C'-order). 'Any' order means C-order | unless the F_CONTIGUOUS flag in the array is set, in which case it | means 'Fortran' order. | | This function is a compatibility alias for tobytes. Despite its name it returns bytes not strings. | | Parameters | ---------- | order : {'C', 'F', None}, optional | Order of the data for multidimensional arrays: | C, Fortran, or the same as for the original array. | | Returns | ------- | s : bytes | Python bytes exhibiting a copy of `a`'s raw data. | | Examples | -------- | >>> x = np.array([[0, 1], [2, 3]]) | >>> x.tobytes() | b'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00' | >>> x.tobytes('C') == x.tobytes() | True | >>> x.tobytes('F') | b'\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00' | | trace(...) | a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None) | | Return the sum along diagonals of the array. | | Refer to `numpy.trace` for full documentation. | | See Also | -------- | numpy.trace : equivalent function | | transpose(...) | a.transpose(*axes) | | Returns a view of the array with axes transposed. | | For a 1-D array, this has no effect. (To change between column and | row vectors, first cast the 1-D array into a matrix object.) | For a 2-D array, this is the usual matrix transpose. | For an n-D array, if axes are given, their order indicates how the | axes are permuted (see Examples). If axes are not provided and | ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then | ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``. | | Parameters | ---------- | axes : None, tuple of ints, or `n` ints | | * None or no argument: reverses the order of the axes. | | * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s | `i`-th axis becomes `a.transpose()`'s `j`-th axis. | | * `n` ints: same as an n-tuple of the same ints (this form is | intended simply as a "convenience" alternative to the tuple form) | | Returns | ------- | out : ndarray | View of `a`, with axes suitably permuted. | | See Also | -------- | ndarray.T : Array property returning the array transposed. | | Examples | -------- | >>> a = np.array([[1, 2], [3, 4]]) | >>> a | array([[1, 2], | [3, 4]]) | >>> a.transpose() | array([[1, 3], | [2, 4]]) | >>> a.transpose((1, 0)) | array([[1, 3], | [2, 4]]) | >>> a.transpose(1, 0) | array([[1, 3], | [2, 4]]) | | var(...) | a.var(axis=None, dtype=None, out=None, ddof=0, keepdims=False) | | Returns the variance of the array elements, along given axis. | | Refer to `numpy.var` for full documentation. | | See Also | -------- | numpy.var : equivalent function | | view(...) | a.view(dtype=None, type=None) | | New view of array with the same data. | | Parameters | ---------- | dtype : data-type or ndarray sub-class, optional | Data-type descriptor of the returned view, e.g., float32 or int16. The | default, None, results in the view having the same data-type as `a`. | This argument can also be specified as an ndarray sub-class, which | then specifies the type of the returned object (this is equivalent to | setting the ``type`` parameter). | type : Python type, optional | Type of the returned view, e.g., ndarray or matrix. Again, the | default None results in type preservation. | | Notes | ----- | ``a.view()`` is used two different ways: | | ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view | of the array's memory with a different data-type. This can cause a | reinterpretation of the bytes of memory. | | ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just | returns an instance of `ndarray_subclass` that looks at the same array | (same shape, dtype, etc.) This does not cause a reinterpretation of the | memory. | | For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of | bytes per entry than the previous dtype (for example, converting a | regular array to a structured array), then the behavior of the view | cannot be predicted just from the superficial appearance of ``a`` (shown | by ``print(a)``). It also depends on exactly how ``a`` is stored in | memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus | defined as a slice or transpose, etc., the view may give different | results. | | | Examples | -------- | >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)]) | | Viewing array data using a different type and dtype: | | >>> y = x.view(dtype=np.int16, type=np.matrix) | >>> y | matrix([[513]], dtype=int16) | >>> print type(y) | | | Creating a view on a structured array so it can be used in calculations | | >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)]) | >>> xv = x.view(dtype=np.int8).reshape(-1,2) | >>> xv | array([[1, 2], | [3, 4]], dtype=int8) | >>> xv.mean(0) | array([ 2., 3.]) | | Making changes to the view changes the underlying array | | >>> xv[0,1] = 20 | >>> print x | [(1, 20) (3, 4)] | | Using a view to convert an array to a recarray: | | >>> z = x.view(np.recarray) | >>> z.a | array([1], dtype=int8) | | Views share data: | | >>> x[0] = (9, 10) | >>> z[0] | (9, 10) | | Views that change the dtype size (bytes per entry) should normally be | avoided on arrays defined by slices, transposes, fortran-ordering, etc.: | | >>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16) | >>> y = x[:, 0:2] | >>> y | array([[1, 2], | [4, 5]], dtype=int16) | >>> y.view(dtype=[('width', np.int16), ('length', np.int16)]) | Traceback (most recent call last): | File "", line 1, in | ValueError: new type not compatible with array. | >>> z = y.copy() | >>> z.view(dtype=[('width', np.int16), ('length', np.int16)]) | array([[(1, 2)], | [(4, 5)]], dtype=[('width', '>> x = np.array([[1.,2.],[3.,4.]]) | >>> x | array([[ 1., 2.], | [ 3., 4.]]) | >>> x.T | array([[ 1., 3.], | [ 2., 4.]]) | >>> x = np.array([1.,2.,3.,4.]) | >>> x | array([ 1., 2., 3., 4.]) | >>> x.T | array([ 1., 2., 3., 4.]) | | __array_interface__ | Array protocol: Python side. | | __array_struct__ | Array protocol: C-struct side. | | base | Base object if memory is from some other object. | | Examples | -------- | The base of an array that owns its memory is None: | | >>> x = np.array([1,2,3,4]) | >>> x.base is None | True | | Slicing creates a view, whose memory is shared with x: | | >>> y = x[2:] | >>> y.base is x | True | | ctypes | An object to simplify the interaction of the array with the ctypes | module. | | This attribute creates an object that makes it easier to use arrays | when calling shared libraries with the ctypes module. The returned | object has, among others, data, shape, and strides attributes (see | Notes below) which themselves return ctypes objects that can be used | as arguments to a shared library. | | Parameters | ---------- | None | | Returns | ------- | c : Python object | Possessing attributes data, shape, strides, etc. | | See Also | -------- | numpy.ctypeslib | | Notes | ----- | Below are the public attributes of this object which were documented | in "Guide to NumPy" (we have omitted undocumented public attributes, | as well as documented private attributes): | | * data: A pointer to the memory area of the array as a Python integer. | This memory area may contain data that is not aligned, or not in correct | byte-order. The memory area may not even be writeable. The array | flags and data-type of this array should be respected when passing this | attribute to arbitrary C-code to avoid trouble that can include Python | crashing. User Beware! The value of this attribute is exactly the same | as self._array_interface_['data'][0]. | | * shape (c_intp*self.ndim): A ctypes array of length self.ndim where | the basetype is the C-integer corresponding to dtype('p') on this | platform. This base-type could be c_int, c_long, or c_longlong | depending on the platform. The c_intp type is defined accordingly in | numpy.ctypeslib. The ctypes array contains the shape of the underlying | array. | | * strides (c_intp*self.ndim): A ctypes array of length self.ndim where | the basetype is the same as for the shape attribute. This ctypes array | contains the strides information from the underlying array. This strides | information is important for showing how many bytes must be jumped to | get to the next element in the array. | | * data_as(obj): Return the data pointer cast to a particular c-types object. | For example, calling self._as_parameter_ is equivalent to | self.data_as(ctypes.c_void_p). Perhaps you want to use the data as a | pointer to a ctypes array of floating-point data: | self.data_as(ctypes.POINTER(ctypes.c_double)). | | * shape_as(obj): Return the shape tuple as an array of some other c-types | type. For example: self.shape_as(ctypes.c_short). | | * strides_as(obj): Return the strides tuple as an array of some other | c-types type. For example: self.strides_as(ctypes.c_longlong). | | Be careful using the ctypes attribute - especially on temporary | arrays or arrays constructed on the fly. For example, calling | ``(a+b).ctypes.data_as(ctypes.c_void_p)`` returns a pointer to memory | that is invalid because the array created as (a+b) is deallocated | before the next Python statement. You can avoid this problem using | either ``c=a+b`` or ``ct=(a+b).ctypes``. In the latter case, ct will | hold a reference to the array until ct is deleted or re-assigned. | | If the ctypes module is not available, then the ctypes attribute | of array objects still returns something useful, but ctypes objects | are not returned and errors may be raised instead. In particular, | the object will still have the as parameter attribute which will | return an integer equal to the data attribute. | | Examples | -------- | >>> import ctypes | >>> x | array([[0, 1], | [2, 3]]) | >>> x.ctypes.data | 30439712 | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)) | | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)).contents | c_long(0) | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_longlong)).contents | c_longlong(4294967296L) | >>> x.ctypes.shape | | >>> x.ctypes.shape_as(ctypes.c_long) | | >>> x.ctypes.strides | | >>> x.ctypes.strides_as(ctypes.c_longlong) | | | data | Python buffer object pointing to the start of the array's data. | | dtype | Data-type of the array's elements. | | Parameters | ---------- | None | | Returns | ------- | d : numpy dtype object | | See Also | -------- | numpy.dtype | | Examples | -------- | >>> x | array([[0, 1], | [2, 3]]) | >>> x.dtype | dtype('int32') | >>> type(x.dtype) | | | flags | Information about the memory layout of the array. | | Attributes | ---------- | C_CONTIGUOUS (C) | The data is in a single, C-style contiguous segment. | F_CONTIGUOUS (F) | The data is in a single, Fortran-style contiguous segment. | OWNDATA (O) | The array owns the memory it uses or borrows it from another object. | WRITEABLE (W) | The data area can be written to. Setting this to False locks | the data, making it read-only. A view (slice, etc.) inherits WRITEABLE | from its base array at creation time, but a view of a writeable | array may be subsequently locked while the base array remains writeable. | (The opposite is not true, in that a view of a locked array may not | be made writeable. However, currently, locking a base object does not | lock any views that already reference it, so under that circumstance it | is possible to alter the contents of a locked array via a previously | created writeable view onto it.) Attempting to change a non-writeable | array raises a RuntimeError exception. | ALIGNED (A) | The data and all elements are aligned appropriately for the hardware. | UPDATEIFCOPY (U) | This array is a copy of some other array. When this array is | deallocated, the base array will be updated with the contents of | this array. | FNC | F_CONTIGUOUS and not C_CONTIGUOUS. | FORC | F_CONTIGUOUS or C_CONTIGUOUS (one-segment test). | BEHAVED (B) | ALIGNED and WRITEABLE. | CARRAY (CA) | BEHAVED and C_CONTIGUOUS. | FARRAY (FA) | BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS. | | Notes | ----- | The `flags` object can be accessed dictionary-like (as in ``a.flags['WRITEABLE']``), | or by using lowercased attribute names (as in ``a.flags.writeable``). Short flag | names are only supported in dictionary access. | | Only the UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be changed by | the user, via direct assignment to the attribute or dictionary entry, | or by calling `ndarray.setflags`. | | The array flags cannot be set arbitrarily: | | - UPDATEIFCOPY can only be set ``False``. | - ALIGNED can only be set ``True`` if the data is truly aligned. | - WRITEABLE can only be set ``True`` if the array owns its own memory | or the ultimate owner of the memory exposes a writeable buffer | interface or is a string. | | Arrays can be both C-style and Fortran-style contiguous simultaneously. | This is clear for 1-dimensional arrays, but can also be true for higher | dimensional arrays. | | Even for contiguous arrays a stride for a given dimension | ``arr.strides[dim]`` may be *arbitrary* if ``arr.shape[dim] == 1`` | or the array has no elements. | It does *not* generally hold that ``self.strides[-1] == self.itemsize`` | for C-style contiguous arrays or ``self.strides[0] == self.itemsize`` for | Fortran-style contiguous arrays is true. | | flat | A 1-D iterator over the array. | | This is a `numpy.flatiter` instance, which acts similarly to, but is not | a subclass of, Python's built-in iterator object. | | See Also | -------- | flatten : Return a copy of the array collapsed into one dimension. | | flatiter | | Examples | -------- | >>> x = np.arange(1, 7).reshape(2, 3) | >>> x | array([[1, 2, 3], | [4, 5, 6]]) | >>> x.flat[3] | 4 | >>> x.T | array([[1, 4], | [2, 5], | [3, 6]]) | >>> x.T.flat[3] | 5 | >>> type(x.flat) | | | An assignment example: | | >>> x.flat = 3; x | array([[3, 3, 3], | [3, 3, 3]]) | >>> x.flat[[1,4]] = 1; x | array([[3, 1, 3], | [3, 1, 3]]) | | imag | The imaginary part of the array. | | Examples | -------- | >>> x = np.sqrt([1+0j, 0+1j]) | >>> x.imag | array([ 0. , 0.70710678]) | >>> x.imag.dtype | dtype('float64') | | itemsize | Length of one array element in bytes. | | Examples | -------- | >>> x = np.array([1,2,3], dtype=np.float64) | >>> x.itemsize | 8 | >>> x = np.array([1,2,3], dtype=np.complex128) | >>> x.itemsize | 16 | | nbytes | Total bytes consumed by the elements of the array. | | Notes | ----- | Does not include memory consumed by non-element attributes of the | array object. | | Examples | -------- | >>> x = np.zeros((3,5,2), dtype=np.complex128) | >>> x.nbytes | 480 | >>> np.prod(x.shape) * x.itemsize | 480 | | ndim | Number of array dimensions. | | Examples | -------- | >>> x = np.array([1, 2, 3]) | >>> x.ndim | 1 | >>> y = np.zeros((2, 3, 4)) | >>> y.ndim | 3 | | real | The real part of the array. | | Examples | -------- | >>> x = np.sqrt([1+0j, 0+1j]) | >>> x.real | array([ 1. , 0.70710678]) | >>> x.real.dtype | dtype('float64') | | See Also | -------- | numpy.real : equivalent function | | shape | Tuple of array dimensions. | | Notes | ----- | May be used to "reshape" the array, as long as this would not | require a change in the total number of elements | | Examples | -------- | >>> x = np.array([1, 2, 3, 4]) | >>> x.shape | (4,) | >>> y = np.zeros((2, 3, 4)) | >>> y.shape | (2, 3, 4) | >>> y.shape = (3, 8) | >>> y | array([[ 0., 0., 0., 0., 0., 0., 0., 0.], | [ 0., 0., 0., 0., 0., 0., 0., 0.], | [ 0., 0., 0., 0., 0., 0., 0., 0.]]) | >>> y.shape = (3, 6) | Traceback (most recent call last): | File "", line 1, in | ValueError: total size of new array must be unchanged | | size | Number of elements in the array. | | Equivalent to ``np.prod(a.shape)``, i.e., the product of the array's | dimensions. | | Examples | -------- | >>> x = np.zeros((3, 5, 2), dtype=np.complex128) | >>> x.size | 30 | >>> np.prod(x.shape) | 30 | | strides | Tuple of bytes to step in each dimension when traversing an array. | | The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a` | is:: | | offset = sum(np.array(i) * a.strides) | | A more detailed explanation of strides can be found in the | "ndarray.rst" file in the NumPy reference guide. | | Notes | ----- | Imagine an array of 32-bit integers (each 4 bytes):: | | x = np.array([[0, 1, 2, 3, 4], | [5, 6, 7, 8, 9]], dtype=np.int32) | | This array is stored in memory as 40 bytes, one after the other | (known as a contiguous block of memory). The strides of an array tell | us how many bytes we have to skip in memory to move to the next position | along a certain axis. For example, we have to skip 4 bytes (1 value) to | move to the next column, but 20 bytes (5 values) to get to the same | position in the next row. As such, the strides for the array `x` will be | ``(20, 4)``. | | See Also | -------- | numpy.lib.stride_tricks.as_strided | | Examples | -------- | >>> y = np.reshape(np.arange(2*3*4), (2,3,4)) | >>> y | array([[[ 0, 1, 2, 3], | [ 4, 5, 6, 7], | [ 8, 9, 10, 11]], | [[12, 13, 14, 15], | [16, 17, 18, 19], | [20, 21, 22, 23]]]) | >>> y.strides | (48, 16, 4) | >>> y[1,1,1] | 17 | >>> offset=sum(y.strides * np.array((1,1,1))) | >>> offset/y.itemsize | 17 | | >>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0) | >>> x.strides | (32, 4, 224, 1344) | >>> i = np.array([3,5,2,2]) | >>> offset = sum(i * x.strides) | >>> x[3,5,2,2] | 813 | >>> offset / x.itemsize | 813 | | ---------------------------------------------------------------------- | Data and other attributes inherited from numpy.ndarray: | | __hash__ = None class ndarray(__builtin__.object) | ndarray(shape, dtype=float, buffer=None, offset=0, | strides=None, order=None) | | An array object represents a multidimensional, homogeneous array | of fixed-size items. An associated data-type object describes the | format of each element in the array (its byte-order, how many bytes it | occupies in memory, whether it is an integer, a floating point number, | or something else, etc.) | | Arrays should be constructed using `array`, `zeros` or `empty` (refer | to the See Also section below). The parameters given here refer to | a low-level method (`ndarray(...)`) for instantiating an array. | | For more information, refer to the `numpy` module and examine the | the methods and attributes of an array. | | Parameters | ---------- | (for the __new__ method; see Notes below) | | shape : tuple of ints | Shape of created array. | dtype : data-type, optional | Any object that can be interpreted as a numpy data type. | buffer : object exposing buffer interface, optional | Used to fill the array with data. | offset : int, optional | Offset of array data in buffer. | strides : tuple of ints, optional | Strides of data in memory. | order : {'C', 'F'}, optional | Row-major (C-style) or column-major (Fortran-style) order. | | Attributes | ---------- | T : ndarray | Transpose of the array. | data : buffer | The array's elements, in memory. | dtype : dtype object | Describes the format of the elements in the array. | flags : dict | Dictionary containing information related to memory use, e.g., | 'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc. | flat : numpy.flatiter object | Flattened version of the array as an iterator. The iterator | allows assignments, e.g., ``x.flat = 3`` (See `ndarray.flat` for | assignment examples; TODO). | imag : ndarray | Imaginary part of the array. | real : ndarray | Real part of the array. | size : int | Number of elements in the array. | itemsize : int | The memory use of each array element in bytes. | nbytes : int | The total number of bytes required to store the array data, | i.e., ``itemsize * size``. | ndim : int | The array's number of dimensions. | shape : tuple of ints | Shape of the array. | strides : tuple of ints | The step-size required to move from one element to the next in | memory. For example, a contiguous ``(3, 4)`` array of type | ``int16`` in C-order has strides ``(8, 2)``. This implies that | to move from element to element in memory requires jumps of 2 bytes. | To move from row-to-row, one needs to jump 8 bytes at a time | (``2 * 4``). | ctypes : ctypes object | Class containing properties of the array needed for interaction | with ctypes. | base : ndarray | If the array is a view into another array, that array is its `base` | (unless that array is also a view). The `base` array is where the | array data is actually stored. | | See Also | -------- | array : Construct an array. | zeros : Create an array, each element of which is zero. | empty : Create an array, but leave its allocated memory unchanged (i.e., | it contains "garbage"). | dtype : Create a data-type. | | Notes | ----- | There are two modes of creating an array using ``__new__``: | | 1. If `buffer` is None, then only `shape`, `dtype`, and `order` | are used. | 2. If `buffer` is an object exposing the buffer interface, then | all keywords are interpreted. | | No ``__init__`` method is needed because the array is fully initialized | after the ``__new__`` method. | | Examples | -------- | These examples illustrate the low-level `ndarray` constructor. Refer | to the `See Also` section above for easier ways of constructing an | ndarray. | | First mode, `buffer` is None: | | >>> np.ndarray(shape=(2,2), dtype=float, order='F') | array([[ -1.13698227e+002, 4.25087011e-303], | [ 2.88528414e-306, 3.27025015e-309]]) #random | | Second mode: | | >>> np.ndarray((2,), buffer=np.array([1,2,3]), | ... offset=np.int_().itemsize, | ... dtype=int) # offset = 1*itemsize, i.e. skip first element | array([2, 3]) | | Methods defined here: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | a.__array__(|dtype) -> reference if type unchanged, copy otherwise. | | Returns either a new reference to self if dtype is not given or a new array | of provided data type if dtype is different from the current dtype of the | array. | | __array_prepare__(...) | a.__array_prepare__(obj) -> Object of same type as ndarray object obj. | | __array_wrap__(...) | a.__array_wrap__(obj) -> Object of same type as ndarray object a. | | __contains__(...) | x.__contains__(y) <==> y in x | | __copy__(...) | a.__copy__([order]) | | Return a copy of the array. | | Parameters | ---------- | order : {'C', 'F', 'A'}, optional | If order is 'C' (False) then the result is contiguous (default). | If order is 'Fortran' (True) then the result has fortran order. | If order is 'Any' (None) then the result has fortran order | only if the array already is in fortran order. | | __deepcopy__(...) | a.__deepcopy__() -> Deep copy of array. | | Used if copy.deepcopy is called on an array. | | __delitem__(...) | x.__delitem__(y) <==> del x[y] | | __delslice__(...) | x.__delslice__(i, j) <==> del x[i:j] | | Use of negative indices is not supported. | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __eq__(...) | x.__eq__(y) <==> x==y | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __getslice__(...) | x.__getslice__(i, j) <==> x[i:j] | | Use of negative indices is not supported. | | __gt__(...) | x.__gt__(y) <==> x>y | | __hex__(...) | x.__hex__() <==> hex(x) | | __iadd__(...) | x.__iadd__(y) <==> x+=y | | __iand__(...) | x.__iand__(y) <==> x&=y | | __idiv__(...) | x.__idiv__(y) <==> x/=y | | __ifloordiv__(...) | x.__ifloordiv__(y) <==> x//=y | | __ilshift__(...) | x.__ilshift__(y) <==> x<<=y | | __imod__(...) | x.__imod__(y) <==> x%=y | | __imul__(...) | x.__imul__(y) <==> x*=y | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __ior__(...) | x.__ior__(y) <==> x|=y | | __ipow__(...) | x.__ipow__(y) <==> x**=y | | __irshift__(...) | x.__irshift__(y) <==> x>>=y | | __isub__(...) | x.__isub__(y) <==> x-=y | | __iter__(...) | x.__iter__() <==> iter(x) | | __itruediv__(...) | x.__itruediv__(y) <==> x/=y | | __ixor__(...) | x.__ixor__(y) <==> x^=y | | __le__(...) | x.__le__(y) <==> x<=y | | __len__(...) | x.__len__() <==> len(x) | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __ne__(...) | x.__ne__(y) <==> x!=y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | a.__reduce__() | | For pickling. | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setitem__(...) | x.__setitem__(i, y) <==> x[i]=y | | __setslice__(...) | x.__setslice__(i, j, y) <==> x[i:j]=y | | Use of negative indices is not supported. | | __setstate__(...) | a.__setstate__(version, shape, dtype, isfortran, rawdata) | | For unpickling. | | Parameters | ---------- | version : int | optional pickle version. If omitted defaults to 0. | shape : tuple | dtype : data-type | isFortran : bool | rawdata : string or list | a binary string with the data (or a list if 'a' is an object array) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | a.all(axis=None, out=None, keepdims=False) | | Returns True if all elements evaluate to True. | | Refer to `numpy.all` for full documentation. | | See Also | -------- | numpy.all : equivalent function | | any(...) | a.any(axis=None, out=None, keepdims=False) | | Returns True if any of the elements of `a` evaluate to True. | | Refer to `numpy.any` for full documentation. | | See Also | -------- | numpy.any : equivalent function | | argmax(...) | a.argmax(axis=None, out=None) | | Return indices of the maximum values along the given axis. | | Refer to `numpy.argmax` for full documentation. | | See Also | -------- | numpy.argmax : equivalent function | | argmin(...) | a.argmin(axis=None, out=None) | | Return indices of the minimum values along the given axis of `a`. | | Refer to `numpy.argmin` for detailed documentation. | | See Also | -------- | numpy.argmin : equivalent function | | argpartition(...) | a.argpartition(kth, axis=-1, kind='introselect', order=None) | | Returns the indices that would partition this array. | | Refer to `numpy.argpartition` for full documentation. | | .. versionadded:: 1.8.0 | | See Also | -------- | numpy.argpartition : equivalent function | | argsort(...) | a.argsort(axis=-1, kind='quicksort', order=None) | | Returns the indices that would sort this array. | | Refer to `numpy.argsort` for full documentation. | | See Also | -------- | numpy.argsort : equivalent function | | astype(...) | a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True) | | Copy of the array, cast to a specified type. | | Parameters | ---------- | dtype : str or dtype | Typecode or data-type to which the array is cast. | order : {'C', 'F', 'A', 'K'}, optional | Controls the memory layout order of the result. | 'C' means C order, 'F' means Fortran order, 'A' | means 'F' order if all the arrays are Fortran contiguous, | 'C' order otherwise, and 'K' means as close to the | order the array elements appear in memory as possible. | Default is 'K'. | casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional | Controls what kind of data casting may occur. Defaults to 'unsafe' | for backwards compatibility. | | * 'no' means the data types should not be cast at all. | * 'equiv' means only byte-order changes are allowed. | * 'safe' means only casts which can preserve values are allowed. | * 'same_kind' means only safe casts or casts within a kind, | like float64 to float32, are allowed. | * 'unsafe' means any data conversions may be done. | subok : bool, optional | If True, then sub-classes will be passed-through (default), otherwise | the returned array will be forced to be a base-class array. | copy : bool, optional | By default, astype always returns a newly allocated array. If this | is set to false, and the `dtype`, `order`, and `subok` | requirements are satisfied, the input array is returned instead | of a copy. | | Returns | ------- | arr_t : ndarray | Unless `copy` is False and the other conditions for returning the input | array are satisfied (see description for `copy` input paramter), `arr_t` | is a new array of the same shape as the input array, with dtype, order | given by `dtype`, `order`. | | Notes | ----- | Starting in NumPy 1.9, astype method now returns an error if the string | dtype to cast to is not long enough in 'safe' casting mode to hold the max | value of integer/float array that is being casted. Previously the casting | was allowed even if the result was truncated. | | Raises | ------ | ComplexWarning | When casting from complex to float or int. To avoid this, | one should use ``a.real.astype(t)``. | | Examples | -------- | >>> x = np.array([1, 2, 2.5]) | >>> x | array([ 1. , 2. , 2.5]) | | >>> x.astype(int) | array([1, 2, 2]) | | byteswap(...) | a.byteswap(inplace) | | Swap the bytes of the array elements | | Toggle between low-endian and big-endian data representation by | returning a byteswapped array, optionally swapped in-place. | | Parameters | ---------- | inplace : bool, optional | If ``True``, swap bytes in-place, default is ``False``. | | Returns | ------- | out : ndarray | The byteswapped array. If `inplace` is ``True``, this is | a view to self. | | Examples | -------- | >>> A = np.array([1, 256, 8755], dtype=np.int16) | >>> map(hex, A) | ['0x1', '0x100', '0x2233'] | >>> A.byteswap(True) | array([ 256, 1, 13090], dtype=int16) | >>> map(hex, A) | ['0x100', '0x1', '0x3322'] | | Arrays of strings are not swapped | | >>> A = np.array(['ceg', 'fac']) | >>> A.byteswap() | array(['ceg', 'fac'], | dtype='|S3') | | choose(...) | a.choose(choices, out=None, mode='raise') | | Use an index array to construct a new array from a set of choices. | | Refer to `numpy.choose` for full documentation. | | See Also | -------- | numpy.choose : equivalent function | | clip(...) | a.clip(min=None, max=None, out=None) | | Return an array whose values are limited to ``[min, max]``. | One of max or min must be given. | | Refer to `numpy.clip` for full documentation. | | See Also | -------- | numpy.clip : equivalent function | | compress(...) | a.compress(condition, axis=None, out=None) | | Return selected slices of this array along given axis. | | Refer to `numpy.compress` for full documentation. | | See Also | -------- | numpy.compress : equivalent function | | conj(...) | a.conj() | | Complex-conjugate all elements. | | Refer to `numpy.conjugate` for full documentation. | | See Also | -------- | numpy.conjugate : equivalent function | | conjugate(...) | a.conjugate() | | Return the complex conjugate, element-wise. | | Refer to `numpy.conjugate` for full documentation. | | See Also | -------- | numpy.conjugate : equivalent function | | copy(...) | a.copy(order='C') | | Return a copy of the array. | | Parameters | ---------- | order : {'C', 'F', 'A', 'K'}, optional | Controls the memory layout of the copy. 'C' means C-order, | 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous, | 'C' otherwise. 'K' means match the layout of `a` as closely | as possible. (Note that this function and :func:numpy.copy are very | similar, but have different default values for their order= | arguments.) | | See also | -------- | numpy.copy | numpy.copyto | | Examples | -------- | >>> x = np.array([[1,2,3],[4,5,6]], order='F') | | >>> y = x.copy() | | >>> x.fill(0) | | >>> x | array([[0, 0, 0], | [0, 0, 0]]) | | >>> y | array([[1, 2, 3], | [4, 5, 6]]) | | >>> y.flags['C_CONTIGUOUS'] | True | | cumprod(...) | a.cumprod(axis=None, dtype=None, out=None) | | Return the cumulative product of the elements along the given axis. | | Refer to `numpy.cumprod` for full documentation. | | See Also | -------- | numpy.cumprod : equivalent function | | cumsum(...) | a.cumsum(axis=None, dtype=None, out=None) | | Return the cumulative sum of the elements along the given axis. | | Refer to `numpy.cumsum` for full documentation. | | See Also | -------- | numpy.cumsum : equivalent function | | diagonal(...) | a.diagonal(offset=0, axis1=0, axis2=1) | | Return specified diagonals. In NumPy 1.9 the returned array is a | read-only view instead of a copy as in previous NumPy versions. In | NumPy 1.10 the read-only restriction will be removed. | | Refer to :func:`numpy.diagonal` for full documentation. | | See Also | -------- | numpy.diagonal : equivalent function | | dot(...) | a.dot(b, out=None) | | Dot product of two arrays. | | Refer to `numpy.dot` for full documentation. | | See Also | -------- | numpy.dot : equivalent function | | Examples | -------- | >>> a = np.eye(2) | >>> b = np.ones((2, 2)) * 2 | >>> a.dot(b) | array([[ 2., 2.], | [ 2., 2.]]) | | This array method can be conveniently chained: | | >>> a.dot(b).dot(b) | array([[ 8., 8.], | [ 8., 8.]]) | | dump(...) | a.dump(file) | | Dump a pickle of the array to the specified file. | The array can be read back with pickle.load or numpy.load. | | Parameters | ---------- | file : str | A string naming the dump file. | | dumps(...) | a.dumps() | | Returns the pickle of the array as a string. | pickle.loads or numpy.loads will convert the string back to an array. | | Parameters | ---------- | None | | fill(...) | a.fill(value) | | Fill the array with a scalar value. | | Parameters | ---------- | value : scalar | All elements of `a` will be assigned this value. | | Examples | -------- | >>> a = np.array([1, 2]) | >>> a.fill(0) | >>> a | array([0, 0]) | >>> a = np.empty(2) | >>> a.fill(1) | >>> a | array([ 1., 1.]) | | flatten(...) | a.flatten(order='C') | | Return a copy of the array collapsed into one dimension. | | Parameters | ---------- | order : {'C', 'F', 'A'}, optional | Whether to flatten in row-major (C-style) or | column-major (Fortran-style) order or preserve the | C/Fortran ordering from `a`. The default is 'C'. | | Returns | ------- | y : ndarray | A copy of the input array, flattened to one dimension. | | See Also | -------- | ravel : Return a flattened array. | flat : A 1-D flat iterator over the array. | | Examples | -------- | >>> a = np.array([[1,2], [3,4]]) | >>> a.flatten() | array([1, 2, 3, 4]) | >>> a.flatten('F') | array([1, 3, 2, 4]) | | getfield(...) | a.getfield(dtype, offset=0) | | Returns a field of the given array as a certain type. | | A field is a view of the array data with a given data-type. The values in | the view are determined by the given type and the offset into the current | array in bytes. The offset needs to be such that the view dtype fits in the | array dtype; for example an array of dtype complex128 has 16-byte elements. | If taking a view with a 32-bit integer (4 bytes), the offset needs to be | between 0 and 12 bytes. | | Parameters | ---------- | dtype : str or dtype | The data type of the view. The dtype size of the view can not be larger | than that of the array itself. | offset : int | Number of bytes to skip before beginning the element view. | | Examples | -------- | >>> x = np.diag([1.+1.j]*2) | >>> x[1, 1] = 2 + 4.j | >>> x | array([[ 1.+1.j, 0.+0.j], | [ 0.+0.j, 2.+4.j]]) | >>> x.getfield(np.float64) | array([[ 1., 0.], | [ 0., 2.]]) | | By choosing an offset of 8 bytes we can select the complex part of the | array for our view: | | >>> x.getfield(np.float64, offset=8) | array([[ 1., 0.], | [ 0., 4.]]) | | item(...) | a.item(*args) | | Copy an element of an array to a standard Python scalar and return it. | | Parameters | ---------- | \*args : Arguments (variable number and type) | | * none: in this case, the method only works for arrays | with one element (`a.size == 1`), which element is | copied into a standard Python scalar object and returned. | | * int_type: this argument is interpreted as a flat index into | the array, specifying which element to copy and return. | | * tuple of int_types: functions as does a single int_type argument, | except that the argument is interpreted as an nd-index into the | array. | | Returns | ------- | z : Standard Python scalar object | A copy of the specified element of the array as a suitable | Python scalar | | Notes | ----- | When the data type of `a` is longdouble or clongdouble, item() returns | a scalar array object because there is no available Python scalar that | would not lose information. Void arrays return a buffer object for item(), | unless fields are defined, in which case a tuple is returned. | | `item` is very similar to a[args], except, instead of an array scalar, | a standard Python scalar is returned. This can be useful for speeding up | access to elements of the array and doing arithmetic on elements of the | array using Python's optimized math. | | Examples | -------- | >>> x = np.random.randint(9, size=(3, 3)) | >>> x | array([[3, 1, 7], | [2, 8, 3], | [8, 5, 3]]) | >>> x.item(3) | 2 | >>> x.item(7) | 5 | >>> x.item((0, 1)) | 1 | >>> x.item((2, 2)) | 3 | | itemset(...) | a.itemset(*args) | | Insert scalar into an array (scalar is cast to array's dtype, if possible) | | There must be at least 1 argument, and define the last argument | as *item*. Then, ``a.itemset(*args)`` is equivalent to but faster | than ``a[args] = item``. The item should be a scalar value and `args` | must select a single item in the array `a`. | | Parameters | ---------- | \*args : Arguments | If one argument: a scalar, only used in case `a` is of size 1. | If two arguments: the last argument is the value to be set | and must be a scalar, the first argument specifies a single array | element location. It is either an int or a tuple. | | Notes | ----- | Compared to indexing syntax, `itemset` provides some speed increase | for placing a scalar into a particular location in an `ndarray`, | if you must do this. However, generally this is discouraged: | among other problems, it complicates the appearance of the code. | Also, when using `itemset` (and `item`) inside a loop, be sure | to assign the methods to a local variable to avoid the attribute | look-up at each loop iteration. | | Examples | -------- | >>> x = np.random.randint(9, size=(3, 3)) | >>> x | array([[3, 1, 7], | [2, 8, 3], | [8, 5, 3]]) | >>> x.itemset(4, 0) | >>> x.itemset((2, 2), 9) | >>> x | array([[3, 1, 7], | [2, 0, 3], | [8, 5, 9]]) | | max(...) | a.max(axis=None, out=None) | | Return the maximum along a given axis. | | Refer to `numpy.amax` for full documentation. | | See Also | -------- | numpy.amax : equivalent function | | mean(...) | a.mean(axis=None, dtype=None, out=None, keepdims=False) | | Returns the average of the array elements along given axis. | | Refer to `numpy.mean` for full documentation. | | See Also | -------- | numpy.mean : equivalent function | | min(...) | a.min(axis=None, out=None, keepdims=False) | | Return the minimum along a given axis. | | Refer to `numpy.amin` for full documentation. | | See Also | -------- | numpy.amin : equivalent function | | newbyteorder(...) | arr.newbyteorder(new_order='S') | | Return the array with the same data viewed with a different byte order. | | Equivalent to:: | | arr.view(arr.dtype.newbytorder(new_order)) | | Changes are also made in all fields and sub-arrays of the array data | type. | | | | Parameters | ---------- | new_order : string, optional | Byte order to force; a value from the byte order specifications | below. `new_order` codes can be any of: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_arr : array | New array object with the dtype reflecting given change to the | byte order. | | nonzero(...) | a.nonzero() | | Return the indices of the elements that are non-zero. | | Refer to `numpy.nonzero` for full documentation. | | See Also | -------- | numpy.nonzero : equivalent function | | partition(...) | a.partition(kth, axis=-1, kind='introselect', order=None) | | Rearranges the elements in the array in such a way that value of the | element in kth position is in the position it would be in a sorted array. | All elements smaller than the kth element are moved before this element and | all equal or greater are moved behind it. The ordering of the elements in | the two partitions is undefined. | | .. versionadded:: 1.8.0 | | Parameters | ---------- | kth : int or sequence of ints | Element index to partition by. The kth element value will be in its | final sorted position and all smaller elements will be moved before it | and all equal or greater elements behind it. | The order all elements in the partitions is undefined. | If provided with a sequence of kth it will partition all elements | indexed by kth of them into their sorted position at once. | axis : int, optional | Axis along which to sort. Default is -1, which means sort along the | last axis. | kind : {'introselect'}, optional | Selection algorithm. Default is 'introselect'. | order : str or list of str, optional | When `a` is an array with fields defined, this argument specifies | which fields to compare first, second, etc. A single field can | be specified as a string, and not all fields need be specified, | but unspecified fields will still be used, in the order in which | they come up in the dtype, to break ties. | | See Also | -------- | numpy.partition : Return a parititioned copy of an array. | argpartition : Indirect partition. | sort : Full sort. | | Notes | ----- | See ``np.partition`` for notes on the different algorithms. | | Examples | -------- | >>> a = np.array([3, 4, 2, 1]) | >>> a.partition(a, 3) | >>> a | array([2, 1, 3, 4]) | | >>> a.partition((1, 3)) | array([1, 2, 3, 4]) | | prod(...) | a.prod(axis=None, dtype=None, out=None, keepdims=False) | | Return the product of the array elements over the given axis | | Refer to `numpy.prod` for full documentation. | | See Also | -------- | numpy.prod : equivalent function | | ptp(...) | a.ptp(axis=None, out=None) | | Peak to peak (maximum - minimum) value along a given axis. | | Refer to `numpy.ptp` for full documentation. | | See Also | -------- | numpy.ptp : equivalent function | | put(...) | a.put(indices, values, mode='raise') | | Set ``a.flat[n] = values[n]`` for all `n` in indices. | | Refer to `numpy.put` for full documentation. | | See Also | -------- | numpy.put : equivalent function | | ravel(...) | a.ravel([order]) | | Return a flattened array. | | Refer to `numpy.ravel` for full documentation. | | See Also | -------- | numpy.ravel : equivalent function | | ndarray.flat : a flat iterator on the array. | | repeat(...) | a.repeat(repeats, axis=None) | | Repeat elements of an array. | | Refer to `numpy.repeat` for full documentation. | | See Also | -------- | numpy.repeat : equivalent function | | reshape(...) | a.reshape(shape, order='C') | | Returns an array containing the same data with a new shape. | | Refer to `numpy.reshape` for full documentation. | | See Also | -------- | numpy.reshape : equivalent function | | resize(...) | a.resize(new_shape, refcheck=True) | | Change shape and size of array in-place. | | Parameters | ---------- | new_shape : tuple of ints, or `n` ints | Shape of resized array. | refcheck : bool, optional | If False, reference count will not be checked. Default is True. | | Returns | ------- | None | | Raises | ------ | ValueError | If `a` does not own its own data or references or views to it exist, | and the data memory must be changed. | | SystemError | If the `order` keyword argument is specified. This behaviour is a | bug in NumPy. | | See Also | -------- | resize : Return a new array with the specified shape. | | Notes | ----- | This reallocates space for the data area if necessary. | | Only contiguous arrays (data elements consecutive in memory) can be | resized. | | The purpose of the reference count check is to make sure you | do not use this array as a buffer for another Python object and then | reallocate the memory. However, reference counts can increase in | other ways so if you are sure that you have not shared the memory | for this array with another Python object, then you may safely set | `refcheck` to False. | | Examples | -------- | Shrinking an array: array is flattened (in the order that the data are | stored in memory), resized, and reshaped: | | >>> a = np.array([[0, 1], [2, 3]], order='C') | >>> a.resize((2, 1)) | >>> a | array([[0], | [1]]) | | >>> a = np.array([[0, 1], [2, 3]], order='F') | >>> a.resize((2, 1)) | >>> a | array([[0], | [2]]) | | Enlarging an array: as above, but missing entries are filled with zeros: | | >>> b = np.array([[0, 1], [2, 3]]) | >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple | >>> b | array([[0, 1, 2], | [3, 0, 0]]) | | Referencing an array prevents resizing... | | >>> c = a | >>> a.resize((1, 1)) | Traceback (most recent call last): | ... | ValueError: cannot resize an array that has been referenced ... | | Unless `refcheck` is False: | | >>> a.resize((1, 1), refcheck=False) | >>> a | array([[0]]) | >>> c | array([[0]]) | | round(...) | a.round(decimals=0, out=None) | | Return `a` with each element rounded to the given number of decimals. | | Refer to `numpy.around` for full documentation. | | See Also | -------- | numpy.around : equivalent function | | searchsorted(...) | a.searchsorted(v, side='left', sorter=None) | | Find indices where elements of v should be inserted in a to maintain order. | | For full documentation, see `numpy.searchsorted` | | See Also | -------- | numpy.searchsorted : equivalent function | | setfield(...) | a.setfield(val, dtype, offset=0) | | Put a value into a specified place in a field defined by a data-type. | | Place `val` into `a`'s field defined by `dtype` and beginning `offset` | bytes into the field. | | Parameters | ---------- | val : object | Value to be placed in field. | dtype : dtype object | Data-type of the field in which to place `val`. | offset : int, optional | The number of bytes into the field at which to place `val`. | | Returns | ------- | None | | See Also | -------- | getfield | | Examples | -------- | >>> x = np.eye(3) | >>> x.getfield(np.float64) | array([[ 1., 0., 0.], | [ 0., 1., 0.], | [ 0., 0., 1.]]) | >>> x.setfield(3, np.int32) | >>> x.getfield(np.int32) | array([[3, 3, 3], | [3, 3, 3], | [3, 3, 3]]) | >>> x | array([[ 1.00000000e+000, 1.48219694e-323, 1.48219694e-323], | [ 1.48219694e-323, 1.00000000e+000, 1.48219694e-323], | [ 1.48219694e-323, 1.48219694e-323, 1.00000000e+000]]) | >>> x.setfield(np.eye(3), np.int32) | >>> x | array([[ 1., 0., 0.], | [ 0., 1., 0.], | [ 0., 0., 1.]]) | | setflags(...) | a.setflags(write=None, align=None, uic=None) | | Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively. | | These Boolean-valued flags affect how numpy interprets the memory | area used by `a` (see Notes below). The ALIGNED flag can only | be set to True if the data is actually aligned according to the type. | The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE | can only be set to True if the array owns its own memory, or the | ultimate owner of the memory exposes a writeable buffer interface, | or is a string. (The exception for string is made so that unpickling | can be done without copying memory.) | | Parameters | ---------- | write : bool, optional | Describes whether or not `a` can be written to. | align : bool, optional | Describes whether or not `a` is aligned properly for its type. | uic : bool, optional | Describes whether or not `a` is a copy of another "base" array. | | Notes | ----- | Array flags provide information about how the memory area used | for the array is to be interpreted. There are 6 Boolean flags | in use, only three of which can be changed by the user: | UPDATEIFCOPY, WRITEABLE, and ALIGNED. | | WRITEABLE (W) the data area can be written to; | | ALIGNED (A) the data and strides are aligned appropriately for the hardware | (as determined by the compiler); | | UPDATEIFCOPY (U) this array is a copy of some other array (referenced | by .base). When this array is deallocated, the base array will be | updated with the contents of this array. | | All flags can be accessed using their first (upper case) letter as well | as the full name. | | Examples | -------- | >>> y | array([[3, 1, 7], | [2, 0, 0], | [8, 5, 9]]) | >>> y.flags | C_CONTIGUOUS : True | F_CONTIGUOUS : False | OWNDATA : True | WRITEABLE : True | ALIGNED : True | UPDATEIFCOPY : False | >>> y.setflags(write=0, align=0) | >>> y.flags | C_CONTIGUOUS : True | F_CONTIGUOUS : False | OWNDATA : True | WRITEABLE : False | ALIGNED : False | UPDATEIFCOPY : False | >>> y.setflags(uic=1) | Traceback (most recent call last): | File "", line 1, in | ValueError: cannot set UPDATEIFCOPY flag to True | | sort(...) | a.sort(axis=-1, kind='quicksort', order=None) | | Sort an array, in-place. | | Parameters | ---------- | axis : int, optional | Axis along which to sort. Default is -1, which means sort along the | last axis. | kind : {'quicksort', 'mergesort', 'heapsort'}, optional | Sorting algorithm. Default is 'quicksort'. | order : str or list of str, optional | When `a` is an array with fields defined, this argument specifies | which fields to compare first, second, etc. A single field can | be specified as a string, and not all fields need be specified, | but unspecified fields will still be used, in the order in which | they come up in the dtype, to break ties. | | See Also | -------- | numpy.sort : Return a sorted copy of an array. | argsort : Indirect sort. | lexsort : Indirect stable sort on multiple keys. | searchsorted : Find elements in sorted array. | partition: Partial sort. | | Notes | ----- | See ``sort`` for notes on the different sorting algorithms. | | Examples | -------- | >>> a = np.array([[1,4], [3,1]]) | >>> a.sort(axis=1) | >>> a | array([[1, 4], | [1, 3]]) | >>> a.sort(axis=0) | >>> a | array([[1, 3], | [1, 4]]) | | Use the `order` keyword to specify a field to use when sorting a | structured array: | | >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)]) | >>> a.sort(order='y') | >>> a | array([('c', 1), ('a', 2)], | dtype=[('x', '|S1'), ('y', '>> x = np.array([[0, 1], [2, 3]]) | >>> x.tobytes() | b'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00' | >>> x.tobytes('C') == x.tobytes() | True | >>> x.tobytes('F') | b'\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00' | | tofile(...) | a.tofile(fid, sep="", format="%s") | | Write array to a file as text or binary (default). | | Data is always written in 'C' order, independent of the order of `a`. | The data produced by this method can be recovered using the function | fromfile(). | | Parameters | ---------- | fid : file or str | An open file object, or a string containing a filename. | sep : str | Separator between array items for text output. | If "" (empty), a binary file is written, equivalent to | ``file.write(a.tobytes())``. | format : str | Format string for text file output. | Each entry in the array is formatted to text by first converting | it to the closest Python type, and then using "format" % item. | | Notes | ----- | This is a convenience function for quick storage of array data. | Information on endianness and precision is lost, so this method is not a | good choice for files intended to archive data or transport data between | machines with different endianness. Some of these problems can be overcome | by outputting the data as text files, at the expense of speed and file | size. | | tolist(...) | a.tolist() | | Return the array as a (possibly nested) list. | | Return a copy of the array data as a (nested) Python list. | Data items are converted to the nearest compatible Python type. | | Parameters | ---------- | none | | Returns | ------- | y : list | The possibly nested list of array elements. | | Notes | ----- | The array may be recreated, ``a = np.array(a.tolist())``. | | Examples | -------- | >>> a = np.array([1, 2]) | >>> a.tolist() | [1, 2] | >>> a = np.array([[1, 2], [3, 4]]) | >>> list(a) | [array([1, 2]), array([3, 4])] | >>> a.tolist() | [[1, 2], [3, 4]] | | tostring(...) | a.tostring(order='C') | | Construct Python bytes containing the raw data bytes in the array. | | Constructs Python bytes showing a copy of the raw contents of | data memory. The bytes object can be produced in either 'C' or 'Fortran', | or 'Any' order (the default is 'C'-order). 'Any' order means C-order | unless the F_CONTIGUOUS flag in the array is set, in which case it | means 'Fortran' order. | | This function is a compatibility alias for tobytes. Despite its name it returns bytes not strings. | | Parameters | ---------- | order : {'C', 'F', None}, optional | Order of the data for multidimensional arrays: | C, Fortran, or the same as for the original array. | | Returns | ------- | s : bytes | Python bytes exhibiting a copy of `a`'s raw data. | | Examples | -------- | >>> x = np.array([[0, 1], [2, 3]]) | >>> x.tobytes() | b'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00' | >>> x.tobytes('C') == x.tobytes() | True | >>> x.tobytes('F') | b'\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00' | | trace(...) | a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None) | | Return the sum along diagonals of the array. | | Refer to `numpy.trace` for full documentation. | | See Also | -------- | numpy.trace : equivalent function | | transpose(...) | a.transpose(*axes) | | Returns a view of the array with axes transposed. | | For a 1-D array, this has no effect. (To change between column and | row vectors, first cast the 1-D array into a matrix object.) | For a 2-D array, this is the usual matrix transpose. | For an n-D array, if axes are given, their order indicates how the | axes are permuted (see Examples). If axes are not provided and | ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then | ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``. | | Parameters | ---------- | axes : None, tuple of ints, or `n` ints | | * None or no argument: reverses the order of the axes. | | * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s | `i`-th axis becomes `a.transpose()`'s `j`-th axis. | | * `n` ints: same as an n-tuple of the same ints (this form is | intended simply as a "convenience" alternative to the tuple form) | | Returns | ------- | out : ndarray | View of `a`, with axes suitably permuted. | | See Also | -------- | ndarray.T : Array property returning the array transposed. | | Examples | -------- | >>> a = np.array([[1, 2], [3, 4]]) | >>> a | array([[1, 2], | [3, 4]]) | >>> a.transpose() | array([[1, 3], | [2, 4]]) | >>> a.transpose((1, 0)) | array([[1, 3], | [2, 4]]) | >>> a.transpose(1, 0) | array([[1, 3], | [2, 4]]) | | var(...) | a.var(axis=None, dtype=None, out=None, ddof=0, keepdims=False) | | Returns the variance of the array elements, along given axis. | | Refer to `numpy.var` for full documentation. | | See Also | -------- | numpy.var : equivalent function | | view(...) | a.view(dtype=None, type=None) | | New view of array with the same data. | | Parameters | ---------- | dtype : data-type or ndarray sub-class, optional | Data-type descriptor of the returned view, e.g., float32 or int16. The | default, None, results in the view having the same data-type as `a`. | This argument can also be specified as an ndarray sub-class, which | then specifies the type of the returned object (this is equivalent to | setting the ``type`` parameter). | type : Python type, optional | Type of the returned view, e.g., ndarray or matrix. Again, the | default None results in type preservation. | | Notes | ----- | ``a.view()`` is used two different ways: | | ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view | of the array's memory with a different data-type. This can cause a | reinterpretation of the bytes of memory. | | ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just | returns an instance of `ndarray_subclass` that looks at the same array | (same shape, dtype, etc.) This does not cause a reinterpretation of the | memory. | | For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of | bytes per entry than the previous dtype (for example, converting a | regular array to a structured array), then the behavior of the view | cannot be predicted just from the superficial appearance of ``a`` (shown | by ``print(a)``). It also depends on exactly how ``a`` is stored in | memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus | defined as a slice or transpose, etc., the view may give different | results. | | | Examples | -------- | >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)]) | | Viewing array data using a different type and dtype: | | >>> y = x.view(dtype=np.int16, type=np.matrix) | >>> y | matrix([[513]], dtype=int16) | >>> print type(y) | | | Creating a view on a structured array so it can be used in calculations | | >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)]) | >>> xv = x.view(dtype=np.int8).reshape(-1,2) | >>> xv | array([[1, 2], | [3, 4]], dtype=int8) | >>> xv.mean(0) | array([ 2., 3.]) | | Making changes to the view changes the underlying array | | >>> xv[0,1] = 20 | >>> print x | [(1, 20) (3, 4)] | | Using a view to convert an array to a recarray: | | >>> z = x.view(np.recarray) | >>> z.a | array([1], dtype=int8) | | Views share data: | | >>> x[0] = (9, 10) | >>> z[0] | (9, 10) | | Views that change the dtype size (bytes per entry) should normally be | avoided on arrays defined by slices, transposes, fortran-ordering, etc.: | | >>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16) | >>> y = x[:, 0:2] | >>> y | array([[1, 2], | [4, 5]], dtype=int16) | >>> y.view(dtype=[('width', np.int16), ('length', np.int16)]) | Traceback (most recent call last): | File "", line 1, in | ValueError: new type not compatible with array. | >>> z = y.copy() | >>> z.view(dtype=[('width', np.int16), ('length', np.int16)]) | array([[(1, 2)], | [(4, 5)]], dtype=[('width', '>> x = np.array([[1.,2.],[3.,4.]]) | >>> x | array([[ 1., 2.], | [ 3., 4.]]) | >>> x.T | array([[ 1., 3.], | [ 2., 4.]]) | >>> x = np.array([1.,2.,3.,4.]) | >>> x | array([ 1., 2., 3., 4.]) | >>> x.T | array([ 1., 2., 3., 4.]) | | __array_finalize__ | None. | | __array_interface__ | Array protocol: Python side. | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: C-struct side. | | base | Base object if memory is from some other object. | | Examples | -------- | The base of an array that owns its memory is None: | | >>> x = np.array([1,2,3,4]) | >>> x.base is None | True | | Slicing creates a view, whose memory is shared with x: | | >>> y = x[2:] | >>> y.base is x | True | | ctypes | An object to simplify the interaction of the array with the ctypes | module. | | This attribute creates an object that makes it easier to use arrays | when calling shared libraries with the ctypes module. The returned | object has, among others, data, shape, and strides attributes (see | Notes below) which themselves return ctypes objects that can be used | as arguments to a shared library. | | Parameters | ---------- | None | | Returns | ------- | c : Python object | Possessing attributes data, shape, strides, etc. | | See Also | -------- | numpy.ctypeslib | | Notes | ----- | Below are the public attributes of this object which were documented | in "Guide to NumPy" (we have omitted undocumented public attributes, | as well as documented private attributes): | | * data: A pointer to the memory area of the array as a Python integer. | This memory area may contain data that is not aligned, or not in correct | byte-order. The memory area may not even be writeable. The array | flags and data-type of this array should be respected when passing this | attribute to arbitrary C-code to avoid trouble that can include Python | crashing. User Beware! The value of this attribute is exactly the same | as self._array_interface_['data'][0]. | | * shape (c_intp*self.ndim): A ctypes array of length self.ndim where | the basetype is the C-integer corresponding to dtype('p') on this | platform. This base-type could be c_int, c_long, or c_longlong | depending on the platform. The c_intp type is defined accordingly in | numpy.ctypeslib. The ctypes array contains the shape of the underlying | array. | | * strides (c_intp*self.ndim): A ctypes array of length self.ndim where | the basetype is the same as for the shape attribute. This ctypes array | contains the strides information from the underlying array. This strides | information is important for showing how many bytes must be jumped to | get to the next element in the array. | | * data_as(obj): Return the data pointer cast to a particular c-types object. | For example, calling self._as_parameter_ is equivalent to | self.data_as(ctypes.c_void_p). Perhaps you want to use the data as a | pointer to a ctypes array of floating-point data: | self.data_as(ctypes.POINTER(ctypes.c_double)). | | * shape_as(obj): Return the shape tuple as an array of some other c-types | type. For example: self.shape_as(ctypes.c_short). | | * strides_as(obj): Return the strides tuple as an array of some other | c-types type. For example: self.strides_as(ctypes.c_longlong). | | Be careful using the ctypes attribute - especially on temporary | arrays or arrays constructed on the fly. For example, calling | ``(a+b).ctypes.data_as(ctypes.c_void_p)`` returns a pointer to memory | that is invalid because the array created as (a+b) is deallocated | before the next Python statement. You can avoid this problem using | either ``c=a+b`` or ``ct=(a+b).ctypes``. In the latter case, ct will | hold a reference to the array until ct is deleted or re-assigned. | | If the ctypes module is not available, then the ctypes attribute | of array objects still returns something useful, but ctypes objects | are not returned and errors may be raised instead. In particular, | the object will still have the as parameter attribute which will | return an integer equal to the data attribute. | | Examples | -------- | >>> import ctypes | >>> x | array([[0, 1], | [2, 3]]) | >>> x.ctypes.data | 30439712 | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)) | | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)).contents | c_long(0) | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_longlong)).contents | c_longlong(4294967296L) | >>> x.ctypes.shape | | >>> x.ctypes.shape_as(ctypes.c_long) | | >>> x.ctypes.strides | | >>> x.ctypes.strides_as(ctypes.c_longlong) | | | data | Python buffer object pointing to the start of the array's data. | | dtype | Data-type of the array's elements. | | Parameters | ---------- | None | | Returns | ------- | d : numpy dtype object | | See Also | -------- | numpy.dtype | | Examples | -------- | >>> x | array([[0, 1], | [2, 3]]) | >>> x.dtype | dtype('int32') | >>> type(x.dtype) | | | flags | Information about the memory layout of the array. | | Attributes | ---------- | C_CONTIGUOUS (C) | The data is in a single, C-style contiguous segment. | F_CONTIGUOUS (F) | The data is in a single, Fortran-style contiguous segment. | OWNDATA (O) | The array owns the memory it uses or borrows it from another object. | WRITEABLE (W) | The data area can be written to. Setting this to False locks | the data, making it read-only. A view (slice, etc.) inherits WRITEABLE | from its base array at creation time, but a view of a writeable | array may be subsequently locked while the base array remains writeable. | (The opposite is not true, in that a view of a locked array may not | be made writeable. However, currently, locking a base object does not | lock any views that already reference it, so under that circumstance it | is possible to alter the contents of a locked array via a previously | created writeable view onto it.) Attempting to change a non-writeable | array raises a RuntimeError exception. | ALIGNED (A) | The data and all elements are aligned appropriately for the hardware. | UPDATEIFCOPY (U) | This array is a copy of some other array. When this array is | deallocated, the base array will be updated with the contents of | this array. | FNC | F_CONTIGUOUS and not C_CONTIGUOUS. | FORC | F_CONTIGUOUS or C_CONTIGUOUS (one-segment test). | BEHAVED (B) | ALIGNED and WRITEABLE. | CARRAY (CA) | BEHAVED and C_CONTIGUOUS. | FARRAY (FA) | BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS. | | Notes | ----- | The `flags` object can be accessed dictionary-like (as in ``a.flags['WRITEABLE']``), | or by using lowercased attribute names (as in ``a.flags.writeable``). Short flag | names are only supported in dictionary access. | | Only the UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be changed by | the user, via direct assignment to the attribute or dictionary entry, | or by calling `ndarray.setflags`. | | The array flags cannot be set arbitrarily: | | - UPDATEIFCOPY can only be set ``False``. | - ALIGNED can only be set ``True`` if the data is truly aligned. | - WRITEABLE can only be set ``True`` if the array owns its own memory | or the ultimate owner of the memory exposes a writeable buffer | interface or is a string. | | Arrays can be both C-style and Fortran-style contiguous simultaneously. | This is clear for 1-dimensional arrays, but can also be true for higher | dimensional arrays. | | Even for contiguous arrays a stride for a given dimension | ``arr.strides[dim]`` may be *arbitrary* if ``arr.shape[dim] == 1`` | or the array has no elements. | It does *not* generally hold that ``self.strides[-1] == self.itemsize`` | for C-style contiguous arrays or ``self.strides[0] == self.itemsize`` for | Fortran-style contiguous arrays is true. | | flat | A 1-D iterator over the array. | | This is a `numpy.flatiter` instance, which acts similarly to, but is not | a subclass of, Python's built-in iterator object. | | See Also | -------- | flatten : Return a copy of the array collapsed into one dimension. | | flatiter | | Examples | -------- | >>> x = np.arange(1, 7).reshape(2, 3) | >>> x | array([[1, 2, 3], | [4, 5, 6]]) | >>> x.flat[3] | 4 | >>> x.T | array([[1, 4], | [2, 5], | [3, 6]]) | >>> x.T.flat[3] | 5 | >>> type(x.flat) | | | An assignment example: | | >>> x.flat = 3; x | array([[3, 3, 3], | [3, 3, 3]]) | >>> x.flat[[1,4]] = 1; x | array([[3, 1, 3], | [3, 1, 3]]) | | imag | The imaginary part of the array. | | Examples | -------- | >>> x = np.sqrt([1+0j, 0+1j]) | >>> x.imag | array([ 0. , 0.70710678]) | >>> x.imag.dtype | dtype('float64') | | itemsize | Length of one array element in bytes. | | Examples | -------- | >>> x = np.array([1,2,3], dtype=np.float64) | >>> x.itemsize | 8 | >>> x = np.array([1,2,3], dtype=np.complex128) | >>> x.itemsize | 16 | | nbytes | Total bytes consumed by the elements of the array. | | Notes | ----- | Does not include memory consumed by non-element attributes of the | array object. | | Examples | -------- | >>> x = np.zeros((3,5,2), dtype=np.complex128) | >>> x.nbytes | 480 | >>> np.prod(x.shape) * x.itemsize | 480 | | ndim | Number of array dimensions. | | Examples | -------- | >>> x = np.array([1, 2, 3]) | >>> x.ndim | 1 | >>> y = np.zeros((2, 3, 4)) | >>> y.ndim | 3 | | real | The real part of the array. | | Examples | -------- | >>> x = np.sqrt([1+0j, 0+1j]) | >>> x.real | array([ 1. , 0.70710678]) | >>> x.real.dtype | dtype('float64') | | See Also | -------- | numpy.real : equivalent function | | shape | Tuple of array dimensions. | | Notes | ----- | May be used to "reshape" the array, as long as this would not | require a change in the total number of elements | | Examples | -------- | >>> x = np.array([1, 2, 3, 4]) | >>> x.shape | (4,) | >>> y = np.zeros((2, 3, 4)) | >>> y.shape | (2, 3, 4) | >>> y.shape = (3, 8) | >>> y | array([[ 0., 0., 0., 0., 0., 0., 0., 0.], | [ 0., 0., 0., 0., 0., 0., 0., 0.], | [ 0., 0., 0., 0., 0., 0., 0., 0.]]) | >>> y.shape = (3, 6) | Traceback (most recent call last): | File "", line 1, in | ValueError: total size of new array must be unchanged | | size | Number of elements in the array. | | Equivalent to ``np.prod(a.shape)``, i.e., the product of the array's | dimensions. | | Examples | -------- | >>> x = np.zeros((3, 5, 2), dtype=np.complex128) | >>> x.size | 30 | >>> np.prod(x.shape) | 30 | | strides | Tuple of bytes to step in each dimension when traversing an array. | | The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a` | is:: | | offset = sum(np.array(i) * a.strides) | | A more detailed explanation of strides can be found in the | "ndarray.rst" file in the NumPy reference guide. | | Notes | ----- | Imagine an array of 32-bit integers (each 4 bytes):: | | x = np.array([[0, 1, 2, 3, 4], | [5, 6, 7, 8, 9]], dtype=np.int32) | | This array is stored in memory as 40 bytes, one after the other | (known as a contiguous block of memory). The strides of an array tell | us how many bytes we have to skip in memory to move to the next position | along a certain axis. For example, we have to skip 4 bytes (1 value) to | move to the next column, but 20 bytes (5 values) to get to the same | position in the next row. As such, the strides for the array `x` will be | ``(20, 4)``. | | See Also | -------- | numpy.lib.stride_tricks.as_strided | | Examples | -------- | >>> y = np.reshape(np.arange(2*3*4), (2,3,4)) | >>> y | array([[[ 0, 1, 2, 3], | [ 4, 5, 6, 7], | [ 8, 9, 10, 11]], | [[12, 13, 14, 15], | [16, 17, 18, 19], | [20, 21, 22, 23]]]) | >>> y.strides | (48, 16, 4) | >>> y[1,1,1] | 17 | >>> offset=sum(y.strides * np.array((1,1,1))) | >>> offset/y.itemsize | 17 | | >>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0) | >>> x.strides | (32, 4, 224, 1344) | >>> i = np.array([3,5,2,2]) | >>> offset = sum(i * x.strides) | >>> x[3,5,2,2] | 813 | >>> offset / x.itemsize | 813 | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __hash__ = None | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T class ndenumerate(__builtin__.object) | Multidimensional index iterator. | | Return an iterator yielding pairs of array coordinates and values. | | Parameters | ---------- | arr : ndarray | Input array. | | See Also | -------- | ndindex, flatiter | | Examples | -------- | >>> a = np.array([[1, 2], [3, 4]]) | >>> for index, x in np.ndenumerate(a): | ... print index, x | (0, 0) 1 | (0, 1) 2 | (1, 0) 3 | (1, 1) 4 | | Methods defined here: | | __init__(self, arr) | | __iter__(self) | | __next__(self) | Standard iterator method, returns the index tuple and array value. | | Returns | ------- | coords : tuple of ints | The indices of the current iteration. | val : scalar | The array element of the current iteration. | | next = __next__(self) | | ---------------------------------------------------------------------- | Data descriptors defined here: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined) class ndindex(__builtin__.object) | An N-dimensional iterator object to index arrays. | | Given the shape of an array, an `ndindex` instance iterates over | the N-dimensional index of the array. At each iteration a tuple | of indices is returned, the last dimension is iterated over first. | | Parameters | ---------- | `*args` : ints | The size of each dimension of the array. | | See Also | -------- | ndenumerate, flatiter | | Examples | -------- | >>> for index in np.ndindex(3, 2, 1): | ... print index | (0, 0, 0) | (0, 1, 0) | (1, 0, 0) | (1, 1, 0) | (2, 0, 0) | (2, 1, 0) | | Methods defined here: | | __init__(self, *shape) | | __iter__(self) | | __next__(self) | Standard iterator method, updates the index and returns the index | tuple. | | Returns | ------- | val : tuple of ints | Returns a tuple containing the indices of the current | iteration. | | ndincr(self) | Increment the multi-dimensional index by one. | | This method is for backward compatibility only: do not use. | | next = __next__(self) | | ---------------------------------------------------------------------- | Data descriptors defined here: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined) class nditer(__builtin__.object) | Efficient multi-dimensional iterator object to iterate over arrays. | To get started using this object, see the | :ref:`introductory guide to array iteration `. | | Parameters | ---------- | op : ndarray or sequence of array_like | The array(s) to iterate over. | flags : sequence of str, optional | Flags to control the behavior of the iterator. | | * "buffered" enables buffering when required. | * "c_index" causes a C-order index to be tracked. | * "f_index" causes a Fortran-order index to be tracked. | * "multi_index" causes a multi-index, or a tuple of indices | with one per iteration dimension, to be tracked. | * "common_dtype" causes all the operands to be converted to | a common data type, with copying or buffering as necessary. | * "delay_bufalloc" delays allocation of the buffers until | a reset() call is made. Allows "allocate" operands to | be initialized before their values are copied into the buffers. | * "external_loop" causes the `values` given to be | one-dimensional arrays with multiple values instead of | zero-dimensional arrays. | * "grow_inner" allows the `value` array sizes to be made | larger than the buffer size when both "buffered" and | "external_loop" is used. | * "ranged" allows the iterator to be restricted to a sub-range | of the iterindex values. | * "refs_ok" enables iteration of reference types, such as | object arrays. | * "reduce_ok" enables iteration of "readwrite" operands | which are broadcasted, also known as reduction operands. | * "zerosize_ok" allows `itersize` to be zero. | op_flags : list of list of str, optional | This is a list of flags for each operand. At minimum, one of | "readonly", "readwrite", or "writeonly" must be specified. | | * "readonly" indicates the operand will only be read from. | * "readwrite" indicates the operand will be read from and written to. | * "writeonly" indicates the operand will only be written to. | * "no_broadcast" prevents the operand from being broadcasted. | * "contig" forces the operand data to be contiguous. | * "aligned" forces the operand data to be aligned. | * "nbo" forces the operand data to be in native byte order. | * "copy" allows a temporary read-only copy if required. | * "updateifcopy" allows a temporary read-write copy if required. | * "allocate" causes the array to be allocated if it is None | in the `op` parameter. | * "no_subtype" prevents an "allocate" operand from using a subtype. | * "arraymask" indicates that this operand is the mask to use | for selecting elements when writing to operands with the | 'writemasked' flag set. The iterator does not enforce this, | but when writing from a buffer back to the array, it only | copies those elements indicated by this mask. | * 'writemasked' indicates that only elements where the chosen | 'arraymask' operand is True will be written to. | op_dtypes : dtype or tuple of dtype(s), optional | The required data type(s) of the operands. If copying or buffering | is enabled, the data will be converted to/from their original types. | order : {'C', 'F', 'A', 'K'}, optional | Controls the iteration order. 'C' means C order, 'F' means | Fortran order, 'A' means 'F' order if all the arrays are Fortran | contiguous, 'C' order otherwise, and 'K' means as close to the | order the array elements appear in memory as possible. This also | affects the element memory order of "allocate" operands, as they | are allocated to be compatible with iteration order. | Default is 'K'. | casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional | Controls what kind of data casting may occur when making a copy | or buffering. Setting this to 'unsafe' is not recommended, | as it can adversely affect accumulations. | | * 'no' means the data types should not be cast at all. | * 'equiv' means only byte-order changes are allowed. | * 'safe' means only casts which can preserve values are allowed. | * 'same_kind' means only safe casts or casts within a kind, | like float64 to float32, are allowed. | * 'unsafe' means any data conversions may be done. | op_axes : list of list of ints, optional | If provided, is a list of ints or None for each operands. | The list of axes for an operand is a mapping from the dimensions | of the iterator to the dimensions of the operand. A value of | -1 can be placed for entries, causing that dimension to be | treated as "newaxis". | itershape : tuple of ints, optional | The desired shape of the iterator. This allows "allocate" operands | with a dimension mapped by op_axes not corresponding to a dimension | of a different operand to get a value not equal to 1 for that | dimension. | buffersize : int, optional | When buffering is enabled, controls the size of the temporary | buffers. Set to 0 for the default value. | | Attributes | ---------- | dtypes : tuple of dtype(s) | The data types of the values provided in `value`. This may be | different from the operand data types if buffering is enabled. | finished : bool | Whether the iteration over the operands is finished or not. | has_delayed_bufalloc : bool | If True, the iterator was created with the "delay_bufalloc" flag, | and no reset() function was called on it yet. | has_index : bool | If True, the iterator was created with either the "c_index" or | the "f_index" flag, and the property `index` can be used to | retrieve it. | has_multi_index : bool | If True, the iterator was created with the "multi_index" flag, | and the property `multi_index` can be used to retrieve it. | index : | When the "c_index" or "f_index" flag was used, this property | provides access to the index. Raises a ValueError if accessed | and `has_index` is False. | iterationneedsapi : bool | Whether iteration requires access to the Python API, for example | if one of the operands is an object array. | iterindex : int | An index which matches the order of iteration. | itersize : int | Size of the iterator. | itviews : | Structured view(s) of `operands` in memory, matching the reordered | and optimized iterator access pattern. | multi_index : | When the "multi_index" flag was used, this property | provides access to the index. Raises a ValueError if accessed | accessed and `has_multi_index` is False. | ndim : int | The iterator's dimension. | nop : int | The number of iterator operands. | operands : tuple of operand(s) | The array(s) to be iterated over. | shape : tuple of ints | Shape tuple, the shape of the iterator. | value : | Value of `operands` at current iteration. Normally, this is a | tuple of array scalars, but if the flag "external_loop" is used, | it is a tuple of one dimensional arrays. | | Notes | ----- | `nditer` supersedes `flatiter`. The iterator implementation behind | `nditer` is also exposed by the Numpy C API. | | The Python exposure supplies two iteration interfaces, one which follows | the Python iterator protocol, and another which mirrors the C-style | do-while pattern. The native Python approach is better in most cases, but | if you need the iterator's coordinates or index, use the C-style pattern. | | Examples | -------- | Here is how we might write an ``iter_add`` function, using the | Python iterator protocol:: | | def iter_add_py(x, y, out=None): | addop = np.add | it = np.nditer([x, y, out], [], | [['readonly'], ['readonly'], ['writeonly','allocate']]) | for (a, b, c) in it: | addop(a, b, out=c) | return it.operands[2] | | Here is the same function, but following the C-style pattern:: | | def iter_add(x, y, out=None): | addop = np.add | | it = np.nditer([x, y, out], [], | [['readonly'], ['readonly'], ['writeonly','allocate']]) | | while not it.finished: | addop(it[0], it[1], out=it[2]) | it.iternext() | | return it.operands[2] | | Here is an example outer product function:: | | def outer_it(x, y, out=None): | mulop = np.multiply | | it = np.nditer([x, y, out], ['external_loop'], | [['readonly'], ['readonly'], ['writeonly', 'allocate']], | op_axes=[range(x.ndim)+[-1]*y.ndim, | [-1]*x.ndim+range(y.ndim), | None]) | | for (a, b, c) in it: | mulop(a, b, out=c) | | return it.operands[2] | | >>> a = np.arange(2)+1 | >>> b = np.arange(3)+1 | >>> outer_it(a,b) | array([[1, 2, 3], | [2, 4, 6]]) | | Here is an example function which operates like a "lambda" ufunc:: | | def luf(lamdaexpr, *args, **kwargs): | "luf(lambdaexpr, op1, ..., opn, out=None, order='K', casting='safe', buffersize=0)" | nargs = len(args) | op = (kwargs.get('out',None),) + args | it = np.nditer(op, ['buffered','external_loop'], | [['writeonly','allocate','no_broadcast']] + | [['readonly','nbo','aligned']]*nargs, | order=kwargs.get('order','K'), | casting=kwargs.get('casting','safe'), | buffersize=kwargs.get('buffersize',0)) | while not it.finished: | it[0] = lamdaexpr(*it[1:]) | it.iternext() | return it.operands[0] | | >>> a = np.arange(5) | >>> b = np.ones(5) | >>> luf(lambda i,j:i*i + j/2, a, b) | array([ 0.5, 1.5, 4.5, 9.5, 16.5]) | | Methods defined here: | | __copy__(...) | | __delitem__(...) | x.__delitem__(y) <==> del x[y] | | __delslice__(...) | x.__delslice__(i, j) <==> del x[i:j] | | Use of negative indices is not supported. | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __getslice__(...) | x.__getslice__(i, j) <==> x[i:j] | | Use of negative indices is not supported. | | __init__(...) | x.__init__(...) initializes x; see help(type(x)) for signature | | __iter__(...) | x.__iter__() <==> iter(x) | | __len__(...) | x.__len__() <==> len(x) | | __setitem__(...) | x.__setitem__(i, y) <==> x[i]=y | | __setslice__(...) | x.__setslice__(i, j, y) <==> x[i:j]=y | | Use of negative indices is not supported. | | copy(...) | copy() | | Get a copy of the iterator in its current state. | | Examples | -------- | >>> x = np.arange(10) | >>> y = x + 1 | >>> it = np.nditer([x, y]) | >>> it.next() | (array(0), array(1)) | >>> it2 = it.copy() | >>> it2.next() | (array(1), array(2)) | | debug_print(...) | debug_print() | | Print the current state of the `nditer` instance and debug info to stdout. | | enable_external_loop(...) | enable_external_loop() | | When the "external_loop" was not used during construction, but | is desired, this modifies the iterator to behave as if the flag | was specified. | | iternext(...) | iternext() | | Check whether iterations are left, and perform a single internal iteration | without returning the result. Used in the C-style pattern do-while | pattern. For an example, see `nditer`. | | Returns | ------- | iternext : bool | Whether or not there are iterations left. | | next(...) | x.next() -> the next value, or raise StopIteration | | remove_axis(...) | remove_axis(i) | | Removes axis `i` from the iterator. Requires that the flag "multi_index" | be enabled. | | remove_multi_index(...) | remove_multi_index() | | When the "multi_index" flag was specified, this removes it, allowing | the internal iteration structure to be optimized further. | | reset(...) | reset() | | Reset the iterator to its initial state. | | ---------------------------------------------------------------------- | Data descriptors defined here: | | dtypes | | finished | | has_delayed_bufalloc | | has_index | | has_multi_index | | index | | iterationneedsapi | | iterindex | | iterrange | | itersize | | itviews | | multi_index | | ndim | | nop | | operands | | shape | | value | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T class number(generic) | Method resolution order: | number | generic | __builtin__.object | | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __eq__(...) | x.__eq__(y) <==> x==y | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __ge__(...) | x.__ge__(y) <==> x>=y | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __gt__(...) | x.__gt__(y) <==> x>y | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __le__(...) | x.__le__(y) <==> x<=y | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __ne__(...) | x.__ne__(y) <==> x!=y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension object0 = class object_(generic) | Any Python object. Character code: 'O'. | | Method resolution order: | object_ | generic | __builtin__.object | | Methods defined here: | | __add__(...) | x.__add__(y) <==> x+y | | __call__(...) | x.__call__(...) <==> x(...) | | __contains__(...) | x.__contains__(y) <==> y in x | | __delattr__(...) | x.__delattr__('name') <==> del x.name | | __delitem__(...) | x.__delitem__(y) <==> del x[y] | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __iadd__(...) | x.__iadd__(y) <==> x+=y | | __imul__(...) | x.__imul__(y) <==> x*=y | | __le__(...) | x.__le__(y) <==> x<=y | | __len__(...) | x.__len__() <==> len(x) | | __lt__(...) | x.__lt__(y) <==> x x*n | | __ne__(...) | x.__ne__(y) <==> x!=y | | __rmul__(...) | x.__rmul__(n) <==> n*x | | __setattr__(...) | x.__setattr__('name', value) <==> x.name = value | | __setitem__(...) | x.__setitem__(i, y) <==> x[i]=y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class object_(generic) | Any Python object. Character code: 'O'. | | Method resolution order: | object_ | generic | __builtin__.object | | Methods defined here: | | __add__(...) | x.__add__(y) <==> x+y | | __call__(...) | x.__call__(...) <==> x(...) | | __contains__(...) | x.__contains__(y) <==> y in x | | __delattr__(...) | x.__delattr__('name') <==> del x.name | | __delitem__(...) | x.__delitem__(y) <==> del x[y] | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __iadd__(...) | x.__iadd__(y) <==> x+=y | | __imul__(...) | x.__imul__(y) <==> x*=y | | __le__(...) | x.__le__(y) <==> x<=y | | __len__(...) | x.__len__() <==> len(x) | | __lt__(...) | x.__lt__(y) <==> x x*n | | __ne__(...) | x.__ne__(y) <==> x!=y | | __rmul__(...) | x.__rmul__(n) <==> n*x | | __setattr__(...) | x.__setattr__('name', value) <==> x.name = value | | __setitem__(...) | x.__setitem__(i, y) <==> x[i]=y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class poly1d(__builtin__.object) | A one-dimensional polynomial class. | | A convenience class, used to encapsulate "natural" operations on | polynomials so that said operations may take on their customary | form in code (see Examples). | | Parameters | ---------- | c_or_r : array_like | The polynomial's coefficients, in decreasing powers, or if | the value of the second parameter is True, the polynomial's | roots (values where the polynomial evaluates to 0). For example, | ``poly1d([1, 2, 3])`` returns an object that represents | :math:`x^2 + 2x + 3`, whereas ``poly1d([1, 2, 3], True)`` returns | one that represents :math:`(x-1)(x-2)(x-3) = x^3 - 6x^2 + 11x -6`. | r : bool, optional | If True, `c_or_r` specifies the polynomial's roots; the default | is False. | variable : str, optional | Changes the variable used when printing `p` from `x` to `variable` | (see Examples). | | Examples | -------- | Construct the polynomial :math:`x^2 + 2x + 3`: | | >>> p = np.poly1d([1, 2, 3]) | >>> print np.poly1d(p) | 2 | 1 x + 2 x + 3 | | Evaluate the polynomial at :math:`x = 0.5`: | | >>> p(0.5) | 4.25 | | Find the roots: | | >>> p.r | array([-1.+1.41421356j, -1.-1.41421356j]) | >>> p(p.r) | array([ -4.44089210e-16+0.j, -4.44089210e-16+0.j]) | | These numbers in the previous line represent (0, 0) to machine precision | | Show the coefficients: | | >>> p.c | array([1, 2, 3]) | | Display the order (the leading zero-coefficients are removed): | | >>> p.order | 2 | | Show the coefficient of the k-th power in the polynomial | (which is equivalent to ``p.c[-(i+1)]``): | | >>> p[1] | 2 | | Polynomials can be added, subtracted, multiplied, and divided | (returns quotient and remainder): | | >>> p * p | poly1d([ 1, 4, 10, 12, 9]) | | >>> (p**3 + 4) / p | (poly1d([ 1., 4., 10., 12., 9.]), poly1d([ 4.])) | | ``asarray(p)`` gives the coefficient array, so polynomials can be | used in all functions that accept arrays: | | >>> p**2 # square of polynomial | poly1d([ 1, 4, 10, 12, 9]) | | >>> np.square(p) # square of individual coefficients | array([1, 4, 9]) | | The variable used in the string representation of `p` can be modified, | using the `variable` parameter: | | >>> p = np.poly1d([1,2,3], variable='z') | >>> print p | 2 | 1 z + 2 z + 3 | | Construct a polynomial from its roots: | | >>> np.poly1d([1, 2], True) | poly1d([ 1, -3, 2]) | | This is the same polynomial as obtained by: | | >>> np.poly1d([1, -1]) * np.poly1d([1, -2]) | poly1d([ 1, -3, 2]) | | Methods defined here: | | __add__(self, other) | | __array__(self, t=None) | | __call__(self, val) | | __div__(self, other) | | __eq__(self, other) | | __getattr__(self, key) | | __getitem__(self, val) | | __init__(self, c_or_r, r=0, variable=None) | | __iter__(self) | | __len__(self) | | __mul__(self, other) | | __ne__(self, other) | | __neg__(self) | | __pos__(self) | | __pow__(self, val) | | __radd__(self, other) | | __rdiv__(self, other) | | __repr__(self) | | __rmul__(self, other) | | __rsub__(self, other) | | __rtruediv__ = __rdiv__(self, other) | | __setattr__(self, key, val) | | __setitem__(self, key, val) | | __str__(self) | | __sub__(self, other) | | __truediv__ = __div__(self, other) | | deriv(self, m=1) | Return a derivative of this polynomial. | | Refer to `polyder` for full documentation. | | See Also | -------- | polyder : equivalent function | | integ(self, m=1, k=0) | Return an antiderivative (indefinite integral) of this polynomial. | | Refer to `polyint` for full documentation. | | See Also | -------- | polyint : equivalent function | | ---------------------------------------------------------------------- | Data descriptors defined here: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __hash__ = None | | coeffs = None | | order = None | | variable = None class recarray(ndarray) | Construct an ndarray that allows field access using attributes. | | Arrays may have a data-types containing fields, analogous | to columns in a spread sheet. An example is ``[(x, int), (y, float)]``, | where each entry in the array is a pair of ``(int, float)``. Normally, | these attributes are accessed using dictionary lookups such as ``arr['x']`` | and ``arr['y']``. Record arrays allow the fields to be accessed as members | of the array, using ``arr.x`` and ``arr.y``. | | Parameters | ---------- | shape : tuple | Shape of output array. | dtype : data-type, optional | The desired data-type. By default, the data-type is determined | from `formats`, `names`, `titles`, `aligned` and `byteorder`. | formats : list of data-types, optional | A list containing the data-types for the different columns, e.g. | ``['i4', 'f8', 'i4']``. `formats` does *not* support the new | convention of using types directly, i.e. ``(int, float, int)``. | Note that `formats` must be a list, not a tuple. | Given that `formats` is somewhat limited, we recommend specifying | `dtype` instead. | names : tuple of str, optional | The name of each column, e.g. ``('x', 'y', 'z')``. | buf : buffer, optional | By default, a new array is created of the given shape and data-type. | If `buf` is specified and is an object exposing the buffer interface, | the array will use the memory from the existing buffer. In this case, | the `offset` and `strides` keywords are available. | | Other Parameters | ---------------- | titles : tuple of str, optional | Aliases for column names. For example, if `names` were | ``('x', 'y', 'z')`` and `titles` is | ``('x_coordinate', 'y_coordinate', 'z_coordinate')``, then | ``arr['x']`` is equivalent to both ``arr.x`` and ``arr.x_coordinate``. | byteorder : {'<', '>', '='}, optional | Byte-order for all fields. | aligned : bool, optional | Align the fields in memory as the C-compiler would. | strides : tuple of ints, optional | Buffer (`buf`) is interpreted according to these strides (strides | define how many bytes each array element, row, column, etc. | occupy in memory). | offset : int, optional | Start reading buffer (`buf`) from this offset onwards. | order : {'C', 'F'}, optional | Row-major (C-style) or column-major (Fortran-style) order. | | Returns | ------- | rec : recarray | Empty array of the given shape and type. | | See Also | -------- | rec.fromrecords : Construct a record array from data. | record : fundamental data-type for `recarray`. | format_parser : determine a data-type from formats, names, titles. | | Notes | ----- | This constructor can be compared to ``empty``: it creates a new record | array but does not fill it with data. To create a record array from data, | use one of the following methods: | | 1. Create a standard ndarray and convert it to a record array, | using ``arr.view(np.recarray)`` | 2. Use the `buf` keyword. | 3. Use `np.rec.fromrecords`. | | Examples | -------- | Create an array with two fields, ``x`` and ``y``: | | >>> x = np.array([(1.0, 2), (3.0, 4)], dtype=[('x', float), ('y', int)]) | >>> x | array([(1.0, 2), (3.0, 4)], | dtype=[('x', '>> x['x'] | array([ 1., 3.]) | | View the array as a record array: | | >>> x = x.view(np.recarray) | | >>> x.x | array([ 1., 3.]) | | >>> x.y | array([2, 4]) | | Create a new, empty record array: | | >>> np.recarray((2,), | ... dtype=[('x', int), ('y', float), ('z', int)]) #doctest: +SKIP | rec.array([(-1073741821, 1.2249118382103472e-301, 24547520), | (3471280, 1.2134086255804012e-316, 0)], | dtype=[('x', ' abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | a.__array__(|dtype) -> reference if type unchanged, copy otherwise. | | Returns either a new reference to self if dtype is not given or a new array | of provided data type if dtype is different from the current dtype of the | array. | | __array_prepare__(...) | a.__array_prepare__(obj) -> Object of same type as ndarray object obj. | | __array_wrap__(...) | a.__array_wrap__(obj) -> Object of same type as ndarray object a. | | __contains__(...) | x.__contains__(y) <==> y in x | | __copy__(...) | a.__copy__([order]) | | Return a copy of the array. | | Parameters | ---------- | order : {'C', 'F', 'A'}, optional | If order is 'C' (False) then the result is contiguous (default). | If order is 'Fortran' (True) then the result has fortran order. | If order is 'Any' (None) then the result has fortran order | only if the array already is in fortran order. | | __deepcopy__(...) | a.__deepcopy__() -> Deep copy of array. | | Used if copy.deepcopy is called on an array. | | __delitem__(...) | x.__delitem__(y) <==> del x[y] | | __delslice__(...) | x.__delslice__(i, j) <==> del x[i:j] | | Use of negative indices is not supported. | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __eq__(...) | x.__eq__(y) <==> x==y | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __getslice__(...) | x.__getslice__(i, j) <==> x[i:j] | | Use of negative indices is not supported. | | __gt__(...) | x.__gt__(y) <==> x>y | | __hex__(...) | x.__hex__() <==> hex(x) | | __iadd__(...) | x.__iadd__(y) <==> x+=y | | __iand__(...) | x.__iand__(y) <==> x&=y | | __idiv__(...) | x.__idiv__(y) <==> x/=y | | __ifloordiv__(...) | x.__ifloordiv__(y) <==> x//=y | | __ilshift__(...) | x.__ilshift__(y) <==> x<<=y | | __imod__(...) | x.__imod__(y) <==> x%=y | | __imul__(...) | x.__imul__(y) <==> x*=y | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __ior__(...) | x.__ior__(y) <==> x|=y | | __ipow__(...) | x.__ipow__(y) <==> x**=y | | __irshift__(...) | x.__irshift__(y) <==> x>>=y | | __isub__(...) | x.__isub__(y) <==> x-=y | | __iter__(...) | x.__iter__() <==> iter(x) | | __itruediv__(...) | x.__itruediv__(y) <==> x/=y | | __ixor__(...) | x.__ixor__(y) <==> x^=y | | __le__(...) | x.__le__(y) <==> x<=y | | __len__(...) | x.__len__() <==> len(x) | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __ne__(...) | x.__ne__(y) <==> x!=y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | a.__reduce__() | | For pickling. | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setitem__(...) | x.__setitem__(i, y) <==> x[i]=y | | __setslice__(...) | x.__setslice__(i, j, y) <==> x[i:j]=y | | Use of negative indices is not supported. | | __setstate__(...) | a.__setstate__(version, shape, dtype, isfortran, rawdata) | | For unpickling. | | Parameters | ---------- | version : int | optional pickle version. If omitted defaults to 0. | shape : tuple | dtype : data-type | isFortran : bool | rawdata : string or list | a binary string with the data (or a list if 'a' is an object array) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | a.all(axis=None, out=None, keepdims=False) | | Returns True if all elements evaluate to True. | | Refer to `numpy.all` for full documentation. | | See Also | -------- | numpy.all : equivalent function | | any(...) | a.any(axis=None, out=None, keepdims=False) | | Returns True if any of the elements of `a` evaluate to True. | | Refer to `numpy.any` for full documentation. | | See Also | -------- | numpy.any : equivalent function | | argmax(...) | a.argmax(axis=None, out=None) | | Return indices of the maximum values along the given axis. | | Refer to `numpy.argmax` for full documentation. | | See Also | -------- | numpy.argmax : equivalent function | | argmin(...) | a.argmin(axis=None, out=None) | | Return indices of the minimum values along the given axis of `a`. | | Refer to `numpy.argmin` for detailed documentation. | | See Also | -------- | numpy.argmin : equivalent function | | argpartition(...) | a.argpartition(kth, axis=-1, kind='introselect', order=None) | | Returns the indices that would partition this array. | | Refer to `numpy.argpartition` for full documentation. | | .. versionadded:: 1.8.0 | | See Also | -------- | numpy.argpartition : equivalent function | | argsort(...) | a.argsort(axis=-1, kind='quicksort', order=None) | | Returns the indices that would sort this array. | | Refer to `numpy.argsort` for full documentation. | | See Also | -------- | numpy.argsort : equivalent function | | astype(...) | a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True) | | Copy of the array, cast to a specified type. | | Parameters | ---------- | dtype : str or dtype | Typecode or data-type to which the array is cast. | order : {'C', 'F', 'A', 'K'}, optional | Controls the memory layout order of the result. | 'C' means C order, 'F' means Fortran order, 'A' | means 'F' order if all the arrays are Fortran contiguous, | 'C' order otherwise, and 'K' means as close to the | order the array elements appear in memory as possible. | Default is 'K'. | casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional | Controls what kind of data casting may occur. Defaults to 'unsafe' | for backwards compatibility. | | * 'no' means the data types should not be cast at all. | * 'equiv' means only byte-order changes are allowed. | * 'safe' means only casts which can preserve values are allowed. | * 'same_kind' means only safe casts or casts within a kind, | like float64 to float32, are allowed. | * 'unsafe' means any data conversions may be done. | subok : bool, optional | If True, then sub-classes will be passed-through (default), otherwise | the returned array will be forced to be a base-class array. | copy : bool, optional | By default, astype always returns a newly allocated array. If this | is set to false, and the `dtype`, `order`, and `subok` | requirements are satisfied, the input array is returned instead | of a copy. | | Returns | ------- | arr_t : ndarray | Unless `copy` is False and the other conditions for returning the input | array are satisfied (see description for `copy` input paramter), `arr_t` | is a new array of the same shape as the input array, with dtype, order | given by `dtype`, `order`. | | Notes | ----- | Starting in NumPy 1.9, astype method now returns an error if the string | dtype to cast to is not long enough in 'safe' casting mode to hold the max | value of integer/float array that is being casted. Previously the casting | was allowed even if the result was truncated. | | Raises | ------ | ComplexWarning | When casting from complex to float or int. To avoid this, | one should use ``a.real.astype(t)``. | | Examples | -------- | >>> x = np.array([1, 2, 2.5]) | >>> x | array([ 1. , 2. , 2.5]) | | >>> x.astype(int) | array([1, 2, 2]) | | byteswap(...) | a.byteswap(inplace) | | Swap the bytes of the array elements | | Toggle between low-endian and big-endian data representation by | returning a byteswapped array, optionally swapped in-place. | | Parameters | ---------- | inplace : bool, optional | If ``True``, swap bytes in-place, default is ``False``. | | Returns | ------- | out : ndarray | The byteswapped array. If `inplace` is ``True``, this is | a view to self. | | Examples | -------- | >>> A = np.array([1, 256, 8755], dtype=np.int16) | >>> map(hex, A) | ['0x1', '0x100', '0x2233'] | >>> A.byteswap(True) | array([ 256, 1, 13090], dtype=int16) | >>> map(hex, A) | ['0x100', '0x1', '0x3322'] | | Arrays of strings are not swapped | | >>> A = np.array(['ceg', 'fac']) | >>> A.byteswap() | array(['ceg', 'fac'], | dtype='|S3') | | choose(...) | a.choose(choices, out=None, mode='raise') | | Use an index array to construct a new array from a set of choices. | | Refer to `numpy.choose` for full documentation. | | See Also | -------- | numpy.choose : equivalent function | | clip(...) | a.clip(min=None, max=None, out=None) | | Return an array whose values are limited to ``[min, max]``. | One of max or min must be given. | | Refer to `numpy.clip` for full documentation. | | See Also | -------- | numpy.clip : equivalent function | | compress(...) | a.compress(condition, axis=None, out=None) | | Return selected slices of this array along given axis. | | Refer to `numpy.compress` for full documentation. | | See Also | -------- | numpy.compress : equivalent function | | conj(...) | a.conj() | | Complex-conjugate all elements. | | Refer to `numpy.conjugate` for full documentation. | | See Also | -------- | numpy.conjugate : equivalent function | | conjugate(...) | a.conjugate() | | Return the complex conjugate, element-wise. | | Refer to `numpy.conjugate` for full documentation. | | See Also | -------- | numpy.conjugate : equivalent function | | copy(...) | a.copy(order='C') | | Return a copy of the array. | | Parameters | ---------- | order : {'C', 'F', 'A', 'K'}, optional | Controls the memory layout of the copy. 'C' means C-order, | 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous, | 'C' otherwise. 'K' means match the layout of `a` as closely | as possible. (Note that this function and :func:numpy.copy are very | similar, but have different default values for their order= | arguments.) | | See also | -------- | numpy.copy | numpy.copyto | | Examples | -------- | >>> x = np.array([[1,2,3],[4,5,6]], order='F') | | >>> y = x.copy() | | >>> x.fill(0) | | >>> x | array([[0, 0, 0], | [0, 0, 0]]) | | >>> y | array([[1, 2, 3], | [4, 5, 6]]) | | >>> y.flags['C_CONTIGUOUS'] | True | | cumprod(...) | a.cumprod(axis=None, dtype=None, out=None) | | Return the cumulative product of the elements along the given axis. | | Refer to `numpy.cumprod` for full documentation. | | See Also | -------- | numpy.cumprod : equivalent function | | cumsum(...) | a.cumsum(axis=None, dtype=None, out=None) | | Return the cumulative sum of the elements along the given axis. | | Refer to `numpy.cumsum` for full documentation. | | See Also | -------- | numpy.cumsum : equivalent function | | diagonal(...) | a.diagonal(offset=0, axis1=0, axis2=1) | | Return specified diagonals. In NumPy 1.9 the returned array is a | read-only view instead of a copy as in previous NumPy versions. In | NumPy 1.10 the read-only restriction will be removed. | | Refer to :func:`numpy.diagonal` for full documentation. | | See Also | -------- | numpy.diagonal : equivalent function | | dot(...) | a.dot(b, out=None) | | Dot product of two arrays. | | Refer to `numpy.dot` for full documentation. | | See Also | -------- | numpy.dot : equivalent function | | Examples | -------- | >>> a = np.eye(2) | >>> b = np.ones((2, 2)) * 2 | >>> a.dot(b) | array([[ 2., 2.], | [ 2., 2.]]) | | This array method can be conveniently chained: | | >>> a.dot(b).dot(b) | array([[ 8., 8.], | [ 8., 8.]]) | | dump(...) | a.dump(file) | | Dump a pickle of the array to the specified file. | The array can be read back with pickle.load or numpy.load. | | Parameters | ---------- | file : str | A string naming the dump file. | | dumps(...) | a.dumps() | | Returns the pickle of the array as a string. | pickle.loads or numpy.loads will convert the string back to an array. | | Parameters | ---------- | None | | fill(...) | a.fill(value) | | Fill the array with a scalar value. | | Parameters | ---------- | value : scalar | All elements of `a` will be assigned this value. | | Examples | -------- | >>> a = np.array([1, 2]) | >>> a.fill(0) | >>> a | array([0, 0]) | >>> a = np.empty(2) | >>> a.fill(1) | >>> a | array([ 1., 1.]) | | flatten(...) | a.flatten(order='C') | | Return a copy of the array collapsed into one dimension. | | Parameters | ---------- | order : {'C', 'F', 'A'}, optional | Whether to flatten in row-major (C-style) or | column-major (Fortran-style) order or preserve the | C/Fortran ordering from `a`. The default is 'C'. | | Returns | ------- | y : ndarray | A copy of the input array, flattened to one dimension. | | See Also | -------- | ravel : Return a flattened array. | flat : A 1-D flat iterator over the array. | | Examples | -------- | >>> a = np.array([[1,2], [3,4]]) | >>> a.flatten() | array([1, 2, 3, 4]) | >>> a.flatten('F') | array([1, 3, 2, 4]) | | getfield(...) | a.getfield(dtype, offset=0) | | Returns a field of the given array as a certain type. | | A field is a view of the array data with a given data-type. The values in | the view are determined by the given type and the offset into the current | array in bytes. The offset needs to be such that the view dtype fits in the | array dtype; for example an array of dtype complex128 has 16-byte elements. | If taking a view with a 32-bit integer (4 bytes), the offset needs to be | between 0 and 12 bytes. | | Parameters | ---------- | dtype : str or dtype | The data type of the view. The dtype size of the view can not be larger | than that of the array itself. | offset : int | Number of bytes to skip before beginning the element view. | | Examples | -------- | >>> x = np.diag([1.+1.j]*2) | >>> x[1, 1] = 2 + 4.j | >>> x | array([[ 1.+1.j, 0.+0.j], | [ 0.+0.j, 2.+4.j]]) | >>> x.getfield(np.float64) | array([[ 1., 0.], | [ 0., 2.]]) | | By choosing an offset of 8 bytes we can select the complex part of the | array for our view: | | >>> x.getfield(np.float64, offset=8) | array([[ 1., 0.], | [ 0., 4.]]) | | item(...) | a.item(*args) | | Copy an element of an array to a standard Python scalar and return it. | | Parameters | ---------- | \*args : Arguments (variable number and type) | | * none: in this case, the method only works for arrays | with one element (`a.size == 1`), which element is | copied into a standard Python scalar object and returned. | | * int_type: this argument is interpreted as a flat index into | the array, specifying which element to copy and return. | | * tuple of int_types: functions as does a single int_type argument, | except that the argument is interpreted as an nd-index into the | array. | | Returns | ------- | z : Standard Python scalar object | A copy of the specified element of the array as a suitable | Python scalar | | Notes | ----- | When the data type of `a` is longdouble or clongdouble, item() returns | a scalar array object because there is no available Python scalar that | would not lose information. Void arrays return a buffer object for item(), | unless fields are defined, in which case a tuple is returned. | | `item` is very similar to a[args], except, instead of an array scalar, | a standard Python scalar is returned. This can be useful for speeding up | access to elements of the array and doing arithmetic on elements of the | array using Python's optimized math. | | Examples | -------- | >>> x = np.random.randint(9, size=(3, 3)) | >>> x | array([[3, 1, 7], | [2, 8, 3], | [8, 5, 3]]) | >>> x.item(3) | 2 | >>> x.item(7) | 5 | >>> x.item((0, 1)) | 1 | >>> x.item((2, 2)) | 3 | | itemset(...) | a.itemset(*args) | | Insert scalar into an array (scalar is cast to array's dtype, if possible) | | There must be at least 1 argument, and define the last argument | as *item*. Then, ``a.itemset(*args)`` is equivalent to but faster | than ``a[args] = item``. The item should be a scalar value and `args` | must select a single item in the array `a`. | | Parameters | ---------- | \*args : Arguments | If one argument: a scalar, only used in case `a` is of size 1. | If two arguments: the last argument is the value to be set | and must be a scalar, the first argument specifies a single array | element location. It is either an int or a tuple. | | Notes | ----- | Compared to indexing syntax, `itemset` provides some speed increase | for placing a scalar into a particular location in an `ndarray`, | if you must do this. However, generally this is discouraged: | among other problems, it complicates the appearance of the code. | Also, when using `itemset` (and `item`) inside a loop, be sure | to assign the methods to a local variable to avoid the attribute | look-up at each loop iteration. | | Examples | -------- | >>> x = np.random.randint(9, size=(3, 3)) | >>> x | array([[3, 1, 7], | [2, 8, 3], | [8, 5, 3]]) | >>> x.itemset(4, 0) | >>> x.itemset((2, 2), 9) | >>> x | array([[3, 1, 7], | [2, 0, 3], | [8, 5, 9]]) | | max(...) | a.max(axis=None, out=None) | | Return the maximum along a given axis. | | Refer to `numpy.amax` for full documentation. | | See Also | -------- | numpy.amax : equivalent function | | mean(...) | a.mean(axis=None, dtype=None, out=None, keepdims=False) | | Returns the average of the array elements along given axis. | | Refer to `numpy.mean` for full documentation. | | See Also | -------- | numpy.mean : equivalent function | | min(...) | a.min(axis=None, out=None, keepdims=False) | | Return the minimum along a given axis. | | Refer to `numpy.amin` for full documentation. | | See Also | -------- | numpy.amin : equivalent function | | newbyteorder(...) | arr.newbyteorder(new_order='S') | | Return the array with the same data viewed with a different byte order. | | Equivalent to:: | | arr.view(arr.dtype.newbytorder(new_order)) | | Changes are also made in all fields and sub-arrays of the array data | type. | | | | Parameters | ---------- | new_order : string, optional | Byte order to force; a value from the byte order specifications | below. `new_order` codes can be any of: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_arr : array | New array object with the dtype reflecting given change to the | byte order. | | nonzero(...) | a.nonzero() | | Return the indices of the elements that are non-zero. | | Refer to `numpy.nonzero` for full documentation. | | See Also | -------- | numpy.nonzero : equivalent function | | partition(...) | a.partition(kth, axis=-1, kind='introselect', order=None) | | Rearranges the elements in the array in such a way that value of the | element in kth position is in the position it would be in a sorted array. | All elements smaller than the kth element are moved before this element and | all equal or greater are moved behind it. The ordering of the elements in | the two partitions is undefined. | | .. versionadded:: 1.8.0 | | Parameters | ---------- | kth : int or sequence of ints | Element index to partition by. The kth element value will be in its | final sorted position and all smaller elements will be moved before it | and all equal or greater elements behind it. | The order all elements in the partitions is undefined. | If provided with a sequence of kth it will partition all elements | indexed by kth of them into their sorted position at once. | axis : int, optional | Axis along which to sort. Default is -1, which means sort along the | last axis. | kind : {'introselect'}, optional | Selection algorithm. Default is 'introselect'. | order : str or list of str, optional | When `a` is an array with fields defined, this argument specifies | which fields to compare first, second, etc. A single field can | be specified as a string, and not all fields need be specified, | but unspecified fields will still be used, in the order in which | they come up in the dtype, to break ties. | | See Also | -------- | numpy.partition : Return a parititioned copy of an array. | argpartition : Indirect partition. | sort : Full sort. | | Notes | ----- | See ``np.partition`` for notes on the different algorithms. | | Examples | -------- | >>> a = np.array([3, 4, 2, 1]) | >>> a.partition(a, 3) | >>> a | array([2, 1, 3, 4]) | | >>> a.partition((1, 3)) | array([1, 2, 3, 4]) | | prod(...) | a.prod(axis=None, dtype=None, out=None, keepdims=False) | | Return the product of the array elements over the given axis | | Refer to `numpy.prod` for full documentation. | | See Also | -------- | numpy.prod : equivalent function | | ptp(...) | a.ptp(axis=None, out=None) | | Peak to peak (maximum - minimum) value along a given axis. | | Refer to `numpy.ptp` for full documentation. | | See Also | -------- | numpy.ptp : equivalent function | | put(...) | a.put(indices, values, mode='raise') | | Set ``a.flat[n] = values[n]`` for all `n` in indices. | | Refer to `numpy.put` for full documentation. | | See Also | -------- | numpy.put : equivalent function | | ravel(...) | a.ravel([order]) | | Return a flattened array. | | Refer to `numpy.ravel` for full documentation. | | See Also | -------- | numpy.ravel : equivalent function | | ndarray.flat : a flat iterator on the array. | | repeat(...) | a.repeat(repeats, axis=None) | | Repeat elements of an array. | | Refer to `numpy.repeat` for full documentation. | | See Also | -------- | numpy.repeat : equivalent function | | reshape(...) | a.reshape(shape, order='C') | | Returns an array containing the same data with a new shape. | | Refer to `numpy.reshape` for full documentation. | | See Also | -------- | numpy.reshape : equivalent function | | resize(...) | a.resize(new_shape, refcheck=True) | | Change shape and size of array in-place. | | Parameters | ---------- | new_shape : tuple of ints, or `n` ints | Shape of resized array. | refcheck : bool, optional | If False, reference count will not be checked. Default is True. | | Returns | ------- | None | | Raises | ------ | ValueError | If `a` does not own its own data or references or views to it exist, | and the data memory must be changed. | | SystemError | If the `order` keyword argument is specified. This behaviour is a | bug in NumPy. | | See Also | -------- | resize : Return a new array with the specified shape. | | Notes | ----- | This reallocates space for the data area if necessary. | | Only contiguous arrays (data elements consecutive in memory) can be | resized. | | The purpose of the reference count check is to make sure you | do not use this array as a buffer for another Python object and then | reallocate the memory. However, reference counts can increase in | other ways so if you are sure that you have not shared the memory | for this array with another Python object, then you may safely set | `refcheck` to False. | | Examples | -------- | Shrinking an array: array is flattened (in the order that the data are | stored in memory), resized, and reshaped: | | >>> a = np.array([[0, 1], [2, 3]], order='C') | >>> a.resize((2, 1)) | >>> a | array([[0], | [1]]) | | >>> a = np.array([[0, 1], [2, 3]], order='F') | >>> a.resize((2, 1)) | >>> a | array([[0], | [2]]) | | Enlarging an array: as above, but missing entries are filled with zeros: | | >>> b = np.array([[0, 1], [2, 3]]) | >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple | >>> b | array([[0, 1, 2], | [3, 0, 0]]) | | Referencing an array prevents resizing... | | >>> c = a | >>> a.resize((1, 1)) | Traceback (most recent call last): | ... | ValueError: cannot resize an array that has been referenced ... | | Unless `refcheck` is False: | | >>> a.resize((1, 1), refcheck=False) | >>> a | array([[0]]) | >>> c | array([[0]]) | | round(...) | a.round(decimals=0, out=None) | | Return `a` with each element rounded to the given number of decimals. | | Refer to `numpy.around` for full documentation. | | See Also | -------- | numpy.around : equivalent function | | searchsorted(...) | a.searchsorted(v, side='left', sorter=None) | | Find indices where elements of v should be inserted in a to maintain order. | | For full documentation, see `numpy.searchsorted` | | See Also | -------- | numpy.searchsorted : equivalent function | | setfield(...) | a.setfield(val, dtype, offset=0) | | Put a value into a specified place in a field defined by a data-type. | | Place `val` into `a`'s field defined by `dtype` and beginning `offset` | bytes into the field. | | Parameters | ---------- | val : object | Value to be placed in field. | dtype : dtype object | Data-type of the field in which to place `val`. | offset : int, optional | The number of bytes into the field at which to place `val`. | | Returns | ------- | None | | See Also | -------- | getfield | | Examples | -------- | >>> x = np.eye(3) | >>> x.getfield(np.float64) | array([[ 1., 0., 0.], | [ 0., 1., 0.], | [ 0., 0., 1.]]) | >>> x.setfield(3, np.int32) | >>> x.getfield(np.int32) | array([[3, 3, 3], | [3, 3, 3], | [3, 3, 3]]) | >>> x | array([[ 1.00000000e+000, 1.48219694e-323, 1.48219694e-323], | [ 1.48219694e-323, 1.00000000e+000, 1.48219694e-323], | [ 1.48219694e-323, 1.48219694e-323, 1.00000000e+000]]) | >>> x.setfield(np.eye(3), np.int32) | >>> x | array([[ 1., 0., 0.], | [ 0., 1., 0.], | [ 0., 0., 1.]]) | | setflags(...) | a.setflags(write=None, align=None, uic=None) | | Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively. | | These Boolean-valued flags affect how numpy interprets the memory | area used by `a` (see Notes below). The ALIGNED flag can only | be set to True if the data is actually aligned according to the type. | The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE | can only be set to True if the array owns its own memory, or the | ultimate owner of the memory exposes a writeable buffer interface, | or is a string. (The exception for string is made so that unpickling | can be done without copying memory.) | | Parameters | ---------- | write : bool, optional | Describes whether or not `a` can be written to. | align : bool, optional | Describes whether or not `a` is aligned properly for its type. | uic : bool, optional | Describes whether or not `a` is a copy of another "base" array. | | Notes | ----- | Array flags provide information about how the memory area used | for the array is to be interpreted. There are 6 Boolean flags | in use, only three of which can be changed by the user: | UPDATEIFCOPY, WRITEABLE, and ALIGNED. | | WRITEABLE (W) the data area can be written to; | | ALIGNED (A) the data and strides are aligned appropriately for the hardware | (as determined by the compiler); | | UPDATEIFCOPY (U) this array is a copy of some other array (referenced | by .base). When this array is deallocated, the base array will be | updated with the contents of this array. | | All flags can be accessed using their first (upper case) letter as well | as the full name. | | Examples | -------- | >>> y | array([[3, 1, 7], | [2, 0, 0], | [8, 5, 9]]) | >>> y.flags | C_CONTIGUOUS : True | F_CONTIGUOUS : False | OWNDATA : True | WRITEABLE : True | ALIGNED : True | UPDATEIFCOPY : False | >>> y.setflags(write=0, align=0) | >>> y.flags | C_CONTIGUOUS : True | F_CONTIGUOUS : False | OWNDATA : True | WRITEABLE : False | ALIGNED : False | UPDATEIFCOPY : False | >>> y.setflags(uic=1) | Traceback (most recent call last): | File "", line 1, in | ValueError: cannot set UPDATEIFCOPY flag to True | | sort(...) | a.sort(axis=-1, kind='quicksort', order=None) | | Sort an array, in-place. | | Parameters | ---------- | axis : int, optional | Axis along which to sort. Default is -1, which means sort along the | last axis. | kind : {'quicksort', 'mergesort', 'heapsort'}, optional | Sorting algorithm. Default is 'quicksort'. | order : str or list of str, optional | When `a` is an array with fields defined, this argument specifies | which fields to compare first, second, etc. A single field can | be specified as a string, and not all fields need be specified, | but unspecified fields will still be used, in the order in which | they come up in the dtype, to break ties. | | See Also | -------- | numpy.sort : Return a sorted copy of an array. | argsort : Indirect sort. | lexsort : Indirect stable sort on multiple keys. | searchsorted : Find elements in sorted array. | partition: Partial sort. | | Notes | ----- | See ``sort`` for notes on the different sorting algorithms. | | Examples | -------- | >>> a = np.array([[1,4], [3,1]]) | >>> a.sort(axis=1) | >>> a | array([[1, 4], | [1, 3]]) | >>> a.sort(axis=0) | >>> a | array([[1, 3], | [1, 4]]) | | Use the `order` keyword to specify a field to use when sorting a | structured array: | | >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)]) | >>> a.sort(order='y') | >>> a | array([('c', 1), ('a', 2)], | dtype=[('x', '|S1'), ('y', '>> x = np.array([[0, 1], [2, 3]]) | >>> x.tobytes() | b'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00' | >>> x.tobytes('C') == x.tobytes() | True | >>> x.tobytes('F') | b'\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00' | | tofile(...) | a.tofile(fid, sep="", format="%s") | | Write array to a file as text or binary (default). | | Data is always written in 'C' order, independent of the order of `a`. | The data produced by this method can be recovered using the function | fromfile(). | | Parameters | ---------- | fid : file or str | An open file object, or a string containing a filename. | sep : str | Separator between array items for text output. | If "" (empty), a binary file is written, equivalent to | ``file.write(a.tobytes())``. | format : str | Format string for text file output. | Each entry in the array is formatted to text by first converting | it to the closest Python type, and then using "format" % item. | | Notes | ----- | This is a convenience function for quick storage of array data. | Information on endianness and precision is lost, so this method is not a | good choice for files intended to archive data or transport data between | machines with different endianness. Some of these problems can be overcome | by outputting the data as text files, at the expense of speed and file | size. | | tolist(...) | a.tolist() | | Return the array as a (possibly nested) list. | | Return a copy of the array data as a (nested) Python list. | Data items are converted to the nearest compatible Python type. | | Parameters | ---------- | none | | Returns | ------- | y : list | The possibly nested list of array elements. | | Notes | ----- | The array may be recreated, ``a = np.array(a.tolist())``. | | Examples | -------- | >>> a = np.array([1, 2]) | >>> a.tolist() | [1, 2] | >>> a = np.array([[1, 2], [3, 4]]) | >>> list(a) | [array([1, 2]), array([3, 4])] | >>> a.tolist() | [[1, 2], [3, 4]] | | tostring(...) | a.tostring(order='C') | | Construct Python bytes containing the raw data bytes in the array. | | Constructs Python bytes showing a copy of the raw contents of | data memory. The bytes object can be produced in either 'C' or 'Fortran', | or 'Any' order (the default is 'C'-order). 'Any' order means C-order | unless the F_CONTIGUOUS flag in the array is set, in which case it | means 'Fortran' order. | | This function is a compatibility alias for tobytes. Despite its name it returns bytes not strings. | | Parameters | ---------- | order : {'C', 'F', None}, optional | Order of the data for multidimensional arrays: | C, Fortran, or the same as for the original array. | | Returns | ------- | s : bytes | Python bytes exhibiting a copy of `a`'s raw data. | | Examples | -------- | >>> x = np.array([[0, 1], [2, 3]]) | >>> x.tobytes() | b'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00' | >>> x.tobytes('C') == x.tobytes() | True | >>> x.tobytes('F') | b'\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\x00\x00\x00' | | trace(...) | a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None) | | Return the sum along diagonals of the array. | | Refer to `numpy.trace` for full documentation. | | See Also | -------- | numpy.trace : equivalent function | | transpose(...) | a.transpose(*axes) | | Returns a view of the array with axes transposed. | | For a 1-D array, this has no effect. (To change between column and | row vectors, first cast the 1-D array into a matrix object.) | For a 2-D array, this is the usual matrix transpose. | For an n-D array, if axes are given, their order indicates how the | axes are permuted (see Examples). If axes are not provided and | ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then | ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``. | | Parameters | ---------- | axes : None, tuple of ints, or `n` ints | | * None or no argument: reverses the order of the axes. | | * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s | `i`-th axis becomes `a.transpose()`'s `j`-th axis. | | * `n` ints: same as an n-tuple of the same ints (this form is | intended simply as a "convenience" alternative to the tuple form) | | Returns | ------- | out : ndarray | View of `a`, with axes suitably permuted. | | See Also | -------- | ndarray.T : Array property returning the array transposed. | | Examples | -------- | >>> a = np.array([[1, 2], [3, 4]]) | >>> a | array([[1, 2], | [3, 4]]) | >>> a.transpose() | array([[1, 3], | [2, 4]]) | >>> a.transpose((1, 0)) | array([[1, 3], | [2, 4]]) | >>> a.transpose(1, 0) | array([[1, 3], | [2, 4]]) | | var(...) | a.var(axis=None, dtype=None, out=None, ddof=0, keepdims=False) | | Returns the variance of the array elements, along given axis. | | Refer to `numpy.var` for full documentation. | | See Also | -------- | numpy.var : equivalent function | | view(...) | a.view(dtype=None, type=None) | | New view of array with the same data. | | Parameters | ---------- | dtype : data-type or ndarray sub-class, optional | Data-type descriptor of the returned view, e.g., float32 or int16. The | default, None, results in the view having the same data-type as `a`. | This argument can also be specified as an ndarray sub-class, which | then specifies the type of the returned object (this is equivalent to | setting the ``type`` parameter). | type : Python type, optional | Type of the returned view, e.g., ndarray or matrix. Again, the | default None results in type preservation. | | Notes | ----- | ``a.view()`` is used two different ways: | | ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view | of the array's memory with a different data-type. This can cause a | reinterpretation of the bytes of memory. | | ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just | returns an instance of `ndarray_subclass` that looks at the same array | (same shape, dtype, etc.) This does not cause a reinterpretation of the | memory. | | For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of | bytes per entry than the previous dtype (for example, converting a | regular array to a structured array), then the behavior of the view | cannot be predicted just from the superficial appearance of ``a`` (shown | by ``print(a)``). It also depends on exactly how ``a`` is stored in | memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus | defined as a slice or transpose, etc., the view may give different | results. | | | Examples | -------- | >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)]) | | Viewing array data using a different type and dtype: | | >>> y = x.view(dtype=np.int16, type=np.matrix) | >>> y | matrix([[513]], dtype=int16) | >>> print type(y) | | | Creating a view on a structured array so it can be used in calculations | | >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)]) | >>> xv = x.view(dtype=np.int8).reshape(-1,2) | >>> xv | array([[1, 2], | [3, 4]], dtype=int8) | >>> xv.mean(0) | array([ 2., 3.]) | | Making changes to the view changes the underlying array | | >>> xv[0,1] = 20 | >>> print x | [(1, 20) (3, 4)] | | Using a view to convert an array to a recarray: | | >>> z = x.view(np.recarray) | >>> z.a | array([1], dtype=int8) | | Views share data: | | >>> x[0] = (9, 10) | >>> z[0] | (9, 10) | | Views that change the dtype size (bytes per entry) should normally be | avoided on arrays defined by slices, transposes, fortran-ordering, etc.: | | >>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16) | >>> y = x[:, 0:2] | >>> y | array([[1, 2], | [4, 5]], dtype=int16) | >>> y.view(dtype=[('width', np.int16), ('length', np.int16)]) | Traceback (most recent call last): | File "", line 1, in | ValueError: new type not compatible with array. | >>> z = y.copy() | >>> z.view(dtype=[('width', np.int16), ('length', np.int16)]) | array([[(1, 2)], | [(4, 5)]], dtype=[('width', '>> x = np.array([[1.,2.],[3.,4.]]) | >>> x | array([[ 1., 2.], | [ 3., 4.]]) | >>> x.T | array([[ 1., 3.], | [ 2., 4.]]) | >>> x = np.array([1.,2.,3.,4.]) | >>> x | array([ 1., 2., 3., 4.]) | >>> x.T | array([ 1., 2., 3., 4.]) | | __array_interface__ | Array protocol: Python side. | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: C-struct side. | | base | Base object if memory is from some other object. | | Examples | -------- | The base of an array that owns its memory is None: | | >>> x = np.array([1,2,3,4]) | >>> x.base is None | True | | Slicing creates a view, whose memory is shared with x: | | >>> y = x[2:] | >>> y.base is x | True | | ctypes | An object to simplify the interaction of the array with the ctypes | module. | | This attribute creates an object that makes it easier to use arrays | when calling shared libraries with the ctypes module. The returned | object has, among others, data, shape, and strides attributes (see | Notes below) which themselves return ctypes objects that can be used | as arguments to a shared library. | | Parameters | ---------- | None | | Returns | ------- | c : Python object | Possessing attributes data, shape, strides, etc. | | See Also | -------- | numpy.ctypeslib | | Notes | ----- | Below are the public attributes of this object which were documented | in "Guide to NumPy" (we have omitted undocumented public attributes, | as well as documented private attributes): | | * data: A pointer to the memory area of the array as a Python integer. | This memory area may contain data that is not aligned, or not in correct | byte-order. The memory area may not even be writeable. The array | flags and data-type of this array should be respected when passing this | attribute to arbitrary C-code to avoid trouble that can include Python | crashing. User Beware! The value of this attribute is exactly the same | as self._array_interface_['data'][0]. | | * shape (c_intp*self.ndim): A ctypes array of length self.ndim where | the basetype is the C-integer corresponding to dtype('p') on this | platform. This base-type could be c_int, c_long, or c_longlong | depending on the platform. The c_intp type is defined accordingly in | numpy.ctypeslib. The ctypes array contains the shape of the underlying | array. | | * strides (c_intp*self.ndim): A ctypes array of length self.ndim where | the basetype is the same as for the shape attribute. This ctypes array | contains the strides information from the underlying array. This strides | information is important for showing how many bytes must be jumped to | get to the next element in the array. | | * data_as(obj): Return the data pointer cast to a particular c-types object. | For example, calling self._as_parameter_ is equivalent to | self.data_as(ctypes.c_void_p). Perhaps you want to use the data as a | pointer to a ctypes array of floating-point data: | self.data_as(ctypes.POINTER(ctypes.c_double)). | | * shape_as(obj): Return the shape tuple as an array of some other c-types | type. For example: self.shape_as(ctypes.c_short). | | * strides_as(obj): Return the strides tuple as an array of some other | c-types type. For example: self.strides_as(ctypes.c_longlong). | | Be careful using the ctypes attribute - especially on temporary | arrays or arrays constructed on the fly. For example, calling | ``(a+b).ctypes.data_as(ctypes.c_void_p)`` returns a pointer to memory | that is invalid because the array created as (a+b) is deallocated | before the next Python statement. You can avoid this problem using | either ``c=a+b`` or ``ct=(a+b).ctypes``. In the latter case, ct will | hold a reference to the array until ct is deleted or re-assigned. | | If the ctypes module is not available, then the ctypes attribute | of array objects still returns something useful, but ctypes objects | are not returned and errors may be raised instead. In particular, | the object will still have the as parameter attribute which will | return an integer equal to the data attribute. | | Examples | -------- | >>> import ctypes | >>> x | array([[0, 1], | [2, 3]]) | >>> x.ctypes.data | 30439712 | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)) | | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)).contents | c_long(0) | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_longlong)).contents | c_longlong(4294967296L) | >>> x.ctypes.shape | | >>> x.ctypes.shape_as(ctypes.c_long) | | >>> x.ctypes.strides | | >>> x.ctypes.strides_as(ctypes.c_longlong) | | | data | Python buffer object pointing to the start of the array's data. | | dtype | Data-type of the array's elements. | | Parameters | ---------- | None | | Returns | ------- | d : numpy dtype object | | See Also | -------- | numpy.dtype | | Examples | -------- | >>> x | array([[0, 1], | [2, 3]]) | >>> x.dtype | dtype('int32') | >>> type(x.dtype) | | | flags | Information about the memory layout of the array. | | Attributes | ---------- | C_CONTIGUOUS (C) | The data is in a single, C-style contiguous segment. | F_CONTIGUOUS (F) | The data is in a single, Fortran-style contiguous segment. | OWNDATA (O) | The array owns the memory it uses or borrows it from another object. | WRITEABLE (W) | The data area can be written to. Setting this to False locks | the data, making it read-only. A view (slice, etc.) inherits WRITEABLE | from its base array at creation time, but a view of a writeable | array may be subsequently locked while the base array remains writeable. | (The opposite is not true, in that a view of a locked array may not | be made writeable. However, currently, locking a base object does not | lock any views that already reference it, so under that circumstance it | is possible to alter the contents of a locked array via a previously | created writeable view onto it.) Attempting to change a non-writeable | array raises a RuntimeError exception. | ALIGNED (A) | The data and all elements are aligned appropriately for the hardware. | UPDATEIFCOPY (U) | This array is a copy of some other array. When this array is | deallocated, the base array will be updated with the contents of | this array. | FNC | F_CONTIGUOUS and not C_CONTIGUOUS. | FORC | F_CONTIGUOUS or C_CONTIGUOUS (one-segment test). | BEHAVED (B) | ALIGNED and WRITEABLE. | CARRAY (CA) | BEHAVED and C_CONTIGUOUS. | FARRAY (FA) | BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS. | | Notes | ----- | The `flags` object can be accessed dictionary-like (as in ``a.flags['WRITEABLE']``), | or by using lowercased attribute names (as in ``a.flags.writeable``). Short flag | names are only supported in dictionary access. | | Only the UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be changed by | the user, via direct assignment to the attribute or dictionary entry, | or by calling `ndarray.setflags`. | | The array flags cannot be set arbitrarily: | | - UPDATEIFCOPY can only be set ``False``. | - ALIGNED can only be set ``True`` if the data is truly aligned. | - WRITEABLE can only be set ``True`` if the array owns its own memory | or the ultimate owner of the memory exposes a writeable buffer | interface or is a string. | | Arrays can be both C-style and Fortran-style contiguous simultaneously. | This is clear for 1-dimensional arrays, but can also be true for higher | dimensional arrays. | | Even for contiguous arrays a stride for a given dimension | ``arr.strides[dim]`` may be *arbitrary* if ``arr.shape[dim] == 1`` | or the array has no elements. | It does *not* generally hold that ``self.strides[-1] == self.itemsize`` | for C-style contiguous arrays or ``self.strides[0] == self.itemsize`` for | Fortran-style contiguous arrays is true. | | flat | A 1-D iterator over the array. | | This is a `numpy.flatiter` instance, which acts similarly to, but is not | a subclass of, Python's built-in iterator object. | | See Also | -------- | flatten : Return a copy of the array collapsed into one dimension. | | flatiter | | Examples | -------- | >>> x = np.arange(1, 7).reshape(2, 3) | >>> x | array([[1, 2, 3], | [4, 5, 6]]) | >>> x.flat[3] | 4 | >>> x.T | array([[1, 4], | [2, 5], | [3, 6]]) | >>> x.T.flat[3] | 5 | >>> type(x.flat) | | | An assignment example: | | >>> x.flat = 3; x | array([[3, 3, 3], | [3, 3, 3]]) | >>> x.flat[[1,4]] = 1; x | array([[3, 1, 3], | [3, 1, 3]]) | | imag | The imaginary part of the array. | | Examples | -------- | >>> x = np.sqrt([1+0j, 0+1j]) | >>> x.imag | array([ 0. , 0.70710678]) | >>> x.imag.dtype | dtype('float64') | | itemsize | Length of one array element in bytes. | | Examples | -------- | >>> x = np.array([1,2,3], dtype=np.float64) | >>> x.itemsize | 8 | >>> x = np.array([1,2,3], dtype=np.complex128) | >>> x.itemsize | 16 | | nbytes | Total bytes consumed by the elements of the array. | | Notes | ----- | Does not include memory consumed by non-element attributes of the | array object. | | Examples | -------- | >>> x = np.zeros((3,5,2), dtype=np.complex128) | >>> x.nbytes | 480 | >>> np.prod(x.shape) * x.itemsize | 480 | | ndim | Number of array dimensions. | | Examples | -------- | >>> x = np.array([1, 2, 3]) | >>> x.ndim | 1 | >>> y = np.zeros((2, 3, 4)) | >>> y.ndim | 3 | | real | The real part of the array. | | Examples | -------- | >>> x = np.sqrt([1+0j, 0+1j]) | >>> x.real | array([ 1. , 0.70710678]) | >>> x.real.dtype | dtype('float64') | | See Also | -------- | numpy.real : equivalent function | | shape | Tuple of array dimensions. | | Notes | ----- | May be used to "reshape" the array, as long as this would not | require a change in the total number of elements | | Examples | -------- | >>> x = np.array([1, 2, 3, 4]) | >>> x.shape | (4,) | >>> y = np.zeros((2, 3, 4)) | >>> y.shape | (2, 3, 4) | >>> y.shape = (3, 8) | >>> y | array([[ 0., 0., 0., 0., 0., 0., 0., 0.], | [ 0., 0., 0., 0., 0., 0., 0., 0.], | [ 0., 0., 0., 0., 0., 0., 0., 0.]]) | >>> y.shape = (3, 6) | Traceback (most recent call last): | File "", line 1, in | ValueError: total size of new array must be unchanged | | size | Number of elements in the array. | | Equivalent to ``np.prod(a.shape)``, i.e., the product of the array's | dimensions. | | Examples | -------- | >>> x = np.zeros((3, 5, 2), dtype=np.complex128) | >>> x.size | 30 | >>> np.prod(x.shape) | 30 | | strides | Tuple of bytes to step in each dimension when traversing an array. | | The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a` | is:: | | offset = sum(np.array(i) * a.strides) | | A more detailed explanation of strides can be found in the | "ndarray.rst" file in the NumPy reference guide. | | Notes | ----- | Imagine an array of 32-bit integers (each 4 bytes):: | | x = np.array([[0, 1, 2, 3, 4], | [5, 6, 7, 8, 9]], dtype=np.int32) | | This array is stored in memory as 40 bytes, one after the other | (known as a contiguous block of memory). The strides of an array tell | us how many bytes we have to skip in memory to move to the next position | along a certain axis. For example, we have to skip 4 bytes (1 value) to | move to the next column, but 20 bytes (5 values) to get to the same | position in the next row. As such, the strides for the array `x` will be | ``(20, 4)``. | | See Also | -------- | numpy.lib.stride_tricks.as_strided | | Examples | -------- | >>> y = np.reshape(np.arange(2*3*4), (2,3,4)) | >>> y | array([[[ 0, 1, 2, 3], | [ 4, 5, 6, 7], | [ 8, 9, 10, 11]], | [[12, 13, 14, 15], | [16, 17, 18, 19], | [20, 21, 22, 23]]]) | >>> y.strides | (48, 16, 4) | >>> y[1,1,1] | 17 | >>> offset=sum(y.strides * np.array((1,1,1))) | >>> offset/y.itemsize | 17 | | >>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0) | >>> x.strides | (32, 4, 224, 1344) | >>> i = np.array([3,5,2,2]) | >>> offset = sum(i * x.strides) | >>> x[3,5,2,2] | 813 | >>> offset / x.itemsize | 813 | | ---------------------------------------------------------------------- | Data and other attributes inherited from ndarray: | | __hash__ = None class record(void) | A data-type scalar that allows field access as attribute lookup. | | Method resolution order: | record | void | flexible | generic | __builtin__.object | | Methods defined here: | | __getattribute__(self, attr) | | __getitem__(self, indx) | | __repr__(self) | | __setattr__(self, attr, val) | | __str__(self) | | pprint(self) | Pretty-print all fields. | | ---------------------------------------------------------------------- | Data descriptors defined here: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined) | | ---------------------------------------------------------------------- | Methods inherited from void: | | __delitem__(...) | x.__delitem__(y) <==> del x[y] | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __le__(...) | x.__le__(y) <==> x<=y | | __len__(...) | x.__len__() <==> len(x) | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __setitem__(...) | x.__setitem__(i, y) <==> x[i]=y | | getfield(...) | | setfield(...) | | ---------------------------------------------------------------------- | Data descriptors inherited from void: | | dtype | dtype object | | flags | integer value of flags | | ---------------------------------------------------------------------- | Data and other attributes inherited from void: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension short = class int16(signedinteger) | 16-bit integer. Character code ``h``. C short compatible. | | Method resolution order: | int16 | signedinteger | integer | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Data descriptors inherited from integer: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class signedinteger(integer) | Method resolution order: | signedinteger | integer | number | generic | __builtin__.object | | Data descriptors inherited from integer: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __eq__(...) | x.__eq__(y) <==> x==y | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __ge__(...) | x.__ge__(y) <==> x>=y | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __gt__(...) | x.__gt__(y) <==> x>y | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __le__(...) | x.__le__(y) <==> x<=y | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __ne__(...) | x.__ne__(y) <==> x!=y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension single = class float32(floating) | 32-bit floating-point number. Character code 'f'. C float compatible. | | Method resolution order: | float32 | floating | inexact | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension singlecomplex = class complex64(complexfloating) | Composed of two 32 bit floats | | Method resolution order: | complex64 | complexfloating | inexact | number | generic | __builtin__.object | | Methods defined here: | | __complex__(...) | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension str_ = class string_(__builtin__.str, character) | Method resolution order: | string_ | __builtin__.str | __builtin__.basestring | character | flexible | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from __builtin__.str: | | __add__(...) | x.__add__(y) <==> x+y | | __contains__(...) | x.__contains__(y) <==> y in x | | __format__(...) | S.__format__(format_spec) -> string | | Return a formatted version of S as described by format_spec. | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __getnewargs__(...) | | __getslice__(...) | x.__getslice__(i, j) <==> x[i:j] | | Use of negative indices is not supported. | | __len__(...) | x.__len__() <==> len(x) | | __mod__(...) | x.__mod__(y) <==> x%y | | __mul__(...) | x.__mul__(n) <==> x*n | | __rmod__(...) | x.__rmod__(y) <==> y%x | | __rmul__(...) | x.__rmul__(n) <==> n*x | | __sizeof__(...) | S.__sizeof__() -> size of S in memory, in bytes | | capitalize(...) | S.capitalize() -> string | | Return a copy of the string S with only its first character | capitalized. | | center(...) | S.center(width[, fillchar]) -> string | | Return S centered in a string of length width. Padding is | done using the specified fill character (default is a space) | | count(...) | S.count(sub[, start[, end]]) -> int | | Return the number of non-overlapping occurrences of substring sub in | string S[start:end]. Optional arguments start and end are interpreted | as in slice notation. | | decode(...) | S.decode([encoding[,errors]]) -> object | | Decodes S using the codec registered for encoding. encoding defaults | to the default encoding. errors may be given to set a different error | handling scheme. Default is 'strict' meaning that encoding errors raise | a UnicodeDecodeError. Other possible values are 'ignore' and 'replace' | as well as any other name registered with codecs.register_error that is | able to handle UnicodeDecodeErrors. | | encode(...) | S.encode([encoding[,errors]]) -> object | | Encodes S using the codec registered for encoding. encoding defaults | to the default encoding. errors may be given to set a different error | handling scheme. Default is 'strict' meaning that encoding errors raise | a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and | 'xmlcharrefreplace' as well as any other name registered with | codecs.register_error that is able to handle UnicodeEncodeErrors. | | endswith(...) | S.endswith(suffix[, start[, end]]) -> bool | | Return True if S ends with the specified suffix, False otherwise. | With optional start, test S beginning at that position. | With optional end, stop comparing S at that position. | suffix can also be a tuple of strings to try. | | expandtabs(...) | S.expandtabs([tabsize]) -> string | | Return a copy of S where all tab characters are expanded using spaces. | If tabsize is not given, a tab size of 8 characters is assumed. | | find(...) | S.find(sub [,start [,end]]) -> int | | Return the lowest index in S where substring sub is found, | such that sub is contained within S[start:end]. Optional | arguments start and end are interpreted as in slice notation. | | Return -1 on failure. | | format(...) | S.format(*args, **kwargs) -> string | | Return a formatted version of S, using substitutions from args and kwargs. | The substitutions are identified by braces ('{' and '}'). | | index(...) | S.index(sub [,start [,end]]) -> int | | Like S.find() but raise ValueError when the substring is not found. | | isalnum(...) | S.isalnum() -> bool | | Return True if all characters in S are alphanumeric | and there is at least one character in S, False otherwise. | | isalpha(...) | S.isalpha() -> bool | | Return True if all characters in S are alphabetic | and there is at least one character in S, False otherwise. | | isdigit(...) | S.isdigit() -> bool | | Return True if all characters in S are digits | and there is at least one character in S, False otherwise. | | islower(...) | S.islower() -> bool | | Return True if all cased characters in S are lowercase and there is | at least one cased character in S, False otherwise. | | isspace(...) | S.isspace() -> bool | | Return True if all characters in S are whitespace | and there is at least one character in S, False otherwise. | | istitle(...) | S.istitle() -> bool | | Return True if S is a titlecased string and there is at least one | character in S, i.e. uppercase characters may only follow uncased | characters and lowercase characters only cased ones. Return False | otherwise. | | isupper(...) | S.isupper() -> bool | | Return True if all cased characters in S are uppercase and there is | at least one cased character in S, False otherwise. | | join(...) | S.join(iterable) -> string | | Return a string which is the concatenation of the strings in the | iterable. The separator between elements is S. | | ljust(...) | S.ljust(width[, fillchar]) -> string | | Return S left-justified in a string of length width. Padding is | done using the specified fill character (default is a space). | | lower(...) | S.lower() -> string | | Return a copy of the string S converted to lowercase. | | lstrip(...) | S.lstrip([chars]) -> string or unicode | | Return a copy of the string S with leading whitespace removed. | If chars is given and not None, remove characters in chars instead. | If chars is unicode, S will be converted to unicode before stripping | | partition(...) | S.partition(sep) -> (head, sep, tail) | | Search for the separator sep in S, and return the part before it, | the separator itself, and the part after it. If the separator is not | found, return S and two empty strings. | | replace(...) | S.replace(old, new[, count]) -> string | | Return a copy of string S with all occurrences of substring | old replaced by new. If the optional argument count is | given, only the first count occurrences are replaced. | | rfind(...) | S.rfind(sub [,start [,end]]) -> int | | Return the highest index in S where substring sub is found, | such that sub is contained within S[start:end]. Optional | arguments start and end are interpreted as in slice notation. | | Return -1 on failure. | | rindex(...) | S.rindex(sub [,start [,end]]) -> int | | Like S.rfind() but raise ValueError when the substring is not found. | | rjust(...) | S.rjust(width[, fillchar]) -> string | | Return S right-justified in a string of length width. Padding is | done using the specified fill character (default is a space) | | rpartition(...) | S.rpartition(sep) -> (head, sep, tail) | | Search for the separator sep in S, starting at the end of S, and return | the part before it, the separator itself, and the part after it. If the | separator is not found, return two empty strings and S. | | rsplit(...) | S.rsplit([sep [,maxsplit]]) -> list of strings | | Return a list of the words in the string S, using sep as the | delimiter string, starting at the end of the string and working | to the front. If maxsplit is given, at most maxsplit splits are | done. If sep is not specified or is None, any whitespace string | is a separator. | | rstrip(...) | S.rstrip([chars]) -> string or unicode | | Return a copy of the string S with trailing whitespace removed. | If chars is given and not None, remove characters in chars instead. | If chars is unicode, S will be converted to unicode before stripping | | split(...) | S.split([sep [,maxsplit]]) -> list of strings | | Return a list of the words in the string S, using sep as the | delimiter string. If maxsplit is given, at most maxsplit | splits are done. If sep is not specified or is None, any | whitespace string is a separator and empty strings are removed | from the result. | | splitlines(...) | S.splitlines(keepends=False) -> list of strings | | Return a list of the lines in S, breaking at line boundaries. | Line breaks are not included in the resulting list unless keepends | is given and true. | | startswith(...) | S.startswith(prefix[, start[, end]]) -> bool | | Return True if S starts with the specified prefix, False otherwise. | With optional start, test S beginning at that position. | With optional end, stop comparing S at that position. | prefix can also be a tuple of strings to try. | | strip(...) | S.strip([chars]) -> string or unicode | | Return a copy of the string S with leading and trailing | whitespace removed. | If chars is given and not None, remove characters in chars instead. | If chars is unicode, S will be converted to unicode before stripping | | swapcase(...) | S.swapcase() -> string | | Return a copy of the string S with uppercase characters | converted to lowercase and vice versa. | | title(...) | S.title() -> string | | Return a titlecased version of S, i.e. words start with uppercase | characters, all remaining cased characters have lowercase. | | translate(...) | S.translate(table [,deletechars]) -> string | | Return a copy of the string S, where all characters occurring | in the optional argument deletechars are removed, and the | remaining characters have been mapped through the given | translation table, which must be a string of length 256 or None. | If the table argument is None, no translation is applied and | the operation simply removes the characters in deletechars. | | upper(...) | S.upper() -> string | | Return a copy of the string S converted to uppercase. | | zfill(...) | S.zfill(width) -> string | | Pad a numeric string S with zeros on the left, to fill a field | of the specified width. The string S is never truncated. | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension string0 = class string_(__builtin__.str, character) | Method resolution order: | string_ | __builtin__.str | __builtin__.basestring | character | flexible | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from __builtin__.str: | | __add__(...) | x.__add__(y) <==> x+y | | __contains__(...) | x.__contains__(y) <==> y in x | | __format__(...) | S.__format__(format_spec) -> string | | Return a formatted version of S as described by format_spec. | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __getnewargs__(...) | | __getslice__(...) | x.__getslice__(i, j) <==> x[i:j] | | Use of negative indices is not supported. | | __len__(...) | x.__len__() <==> len(x) | | __mod__(...) | x.__mod__(y) <==> x%y | | __mul__(...) | x.__mul__(n) <==> x*n | | __rmod__(...) | x.__rmod__(y) <==> y%x | | __rmul__(...) | x.__rmul__(n) <==> n*x | | __sizeof__(...) | S.__sizeof__() -> size of S in memory, in bytes | | capitalize(...) | S.capitalize() -> string | | Return a copy of the string S with only its first character | capitalized. | | center(...) | S.center(width[, fillchar]) -> string | | Return S centered in a string of length width. Padding is | done using the specified fill character (default is a space) | | count(...) | S.count(sub[, start[, end]]) -> int | | Return the number of non-overlapping occurrences of substring sub in | string S[start:end]. Optional arguments start and end are interpreted | as in slice notation. | | decode(...) | S.decode([encoding[,errors]]) -> object | | Decodes S using the codec registered for encoding. encoding defaults | to the default encoding. errors may be given to set a different error | handling scheme. Default is 'strict' meaning that encoding errors raise | a UnicodeDecodeError. Other possible values are 'ignore' and 'replace' | as well as any other name registered with codecs.register_error that is | able to handle UnicodeDecodeErrors. | | encode(...) | S.encode([encoding[,errors]]) -> object | | Encodes S using the codec registered for encoding. encoding defaults | to the default encoding. errors may be given to set a different error | handling scheme. Default is 'strict' meaning that encoding errors raise | a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and | 'xmlcharrefreplace' as well as any other name registered with | codecs.register_error that is able to handle UnicodeEncodeErrors. | | endswith(...) | S.endswith(suffix[, start[, end]]) -> bool | | Return True if S ends with the specified suffix, False otherwise. | With optional start, test S beginning at that position. | With optional end, stop comparing S at that position. | suffix can also be a tuple of strings to try. | | expandtabs(...) | S.expandtabs([tabsize]) -> string | | Return a copy of S where all tab characters are expanded using spaces. | If tabsize is not given, a tab size of 8 characters is assumed. | | find(...) | S.find(sub [,start [,end]]) -> int | | Return the lowest index in S where substring sub is found, | such that sub is contained within S[start:end]. Optional | arguments start and end are interpreted as in slice notation. | | Return -1 on failure. | | format(...) | S.format(*args, **kwargs) -> string | | Return a formatted version of S, using substitutions from args and kwargs. | The substitutions are identified by braces ('{' and '}'). | | index(...) | S.index(sub [,start [,end]]) -> int | | Like S.find() but raise ValueError when the substring is not found. | | isalnum(...) | S.isalnum() -> bool | | Return True if all characters in S are alphanumeric | and there is at least one character in S, False otherwise. | | isalpha(...) | S.isalpha() -> bool | | Return True if all characters in S are alphabetic | and there is at least one character in S, False otherwise. | | isdigit(...) | S.isdigit() -> bool | | Return True if all characters in S are digits | and there is at least one character in S, False otherwise. | | islower(...) | S.islower() -> bool | | Return True if all cased characters in S are lowercase and there is | at least one cased character in S, False otherwise. | | isspace(...) | S.isspace() -> bool | | Return True if all characters in S are whitespace | and there is at least one character in S, False otherwise. | | istitle(...) | S.istitle() -> bool | | Return True if S is a titlecased string and there is at least one | character in S, i.e. uppercase characters may only follow uncased | characters and lowercase characters only cased ones. Return False | otherwise. | | isupper(...) | S.isupper() -> bool | | Return True if all cased characters in S are uppercase and there is | at least one cased character in S, False otherwise. | | join(...) | S.join(iterable) -> string | | Return a string which is the concatenation of the strings in the | iterable. The separator between elements is S. | | ljust(...) | S.ljust(width[, fillchar]) -> string | | Return S left-justified in a string of length width. Padding is | done using the specified fill character (default is a space). | | lower(...) | S.lower() -> string | | Return a copy of the string S converted to lowercase. | | lstrip(...) | S.lstrip([chars]) -> string or unicode | | Return a copy of the string S with leading whitespace removed. | If chars is given and not None, remove characters in chars instead. | If chars is unicode, S will be converted to unicode before stripping | | partition(...) | S.partition(sep) -> (head, sep, tail) | | Search for the separator sep in S, and return the part before it, | the separator itself, and the part after it. If the separator is not | found, return S and two empty strings. | | replace(...) | S.replace(old, new[, count]) -> string | | Return a copy of string S with all occurrences of substring | old replaced by new. If the optional argument count is | given, only the first count occurrences are replaced. | | rfind(...) | S.rfind(sub [,start [,end]]) -> int | | Return the highest index in S where substring sub is found, | such that sub is contained within S[start:end]. Optional | arguments start and end are interpreted as in slice notation. | | Return -1 on failure. | | rindex(...) | S.rindex(sub [,start [,end]]) -> int | | Like S.rfind() but raise ValueError when the substring is not found. | | rjust(...) | S.rjust(width[, fillchar]) -> string | | Return S right-justified in a string of length width. Padding is | done using the specified fill character (default is a space) | | rpartition(...) | S.rpartition(sep) -> (head, sep, tail) | | Search for the separator sep in S, starting at the end of S, and return | the part before it, the separator itself, and the part after it. If the | separator is not found, return two empty strings and S. | | rsplit(...) | S.rsplit([sep [,maxsplit]]) -> list of strings | | Return a list of the words in the string S, using sep as the | delimiter string, starting at the end of the string and working | to the front. If maxsplit is given, at most maxsplit splits are | done. If sep is not specified or is None, any whitespace string | is a separator. | | rstrip(...) | S.rstrip([chars]) -> string or unicode | | Return a copy of the string S with trailing whitespace removed. | If chars is given and not None, remove characters in chars instead. | If chars is unicode, S will be converted to unicode before stripping | | split(...) | S.split([sep [,maxsplit]]) -> list of strings | | Return a list of the words in the string S, using sep as the | delimiter string. If maxsplit is given, at most maxsplit | splits are done. If sep is not specified or is None, any | whitespace string is a separator and empty strings are removed | from the result. | | splitlines(...) | S.splitlines(keepends=False) -> list of strings | | Return a list of the lines in S, breaking at line boundaries. | Line breaks are not included in the resulting list unless keepends | is given and true. | | startswith(...) | S.startswith(prefix[, start[, end]]) -> bool | | Return True if S starts with the specified prefix, False otherwise. | With optional start, test S beginning at that position. | With optional end, stop comparing S at that position. | prefix can also be a tuple of strings to try. | | strip(...) | S.strip([chars]) -> string or unicode | | Return a copy of the string S with leading and trailing | whitespace removed. | If chars is given and not None, remove characters in chars instead. | If chars is unicode, S will be converted to unicode before stripping | | swapcase(...) | S.swapcase() -> string | | Return a copy of the string S with uppercase characters | converted to lowercase and vice versa. | | title(...) | S.title() -> string | | Return a titlecased version of S, i.e. words start with uppercase | characters, all remaining cased characters have lowercase. | | translate(...) | S.translate(table [,deletechars]) -> string | | Return a copy of the string S, where all characters occurring | in the optional argument deletechars are removed, and the | remaining characters have been mapped through the given | translation table, which must be a string of length 256 or None. | If the table argument is None, no translation is applied and | the operation simply removes the characters in deletechars. | | upper(...) | S.upper() -> string | | Return a copy of the string S converted to uppercase. | | zfill(...) | S.zfill(width) -> string | | Pad a numeric string S with zeros on the left, to fill a field | of the specified width. The string S is never truncated. | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class string_(__builtin__.str, character) | Method resolution order: | string_ | __builtin__.str | __builtin__.basestring | character | flexible | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from __builtin__.str: | | __add__(...) | x.__add__(y) <==> x+y | | __contains__(...) | x.__contains__(y) <==> y in x | | __format__(...) | S.__format__(format_spec) -> string | | Return a formatted version of S as described by format_spec. | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __getnewargs__(...) | | __getslice__(...) | x.__getslice__(i, j) <==> x[i:j] | | Use of negative indices is not supported. | | __len__(...) | x.__len__() <==> len(x) | | __mod__(...) | x.__mod__(y) <==> x%y | | __mul__(...) | x.__mul__(n) <==> x*n | | __rmod__(...) | x.__rmod__(y) <==> y%x | | __rmul__(...) | x.__rmul__(n) <==> n*x | | __sizeof__(...) | S.__sizeof__() -> size of S in memory, in bytes | | capitalize(...) | S.capitalize() -> string | | Return a copy of the string S with only its first character | capitalized. | | center(...) | S.center(width[, fillchar]) -> string | | Return S centered in a string of length width. Padding is | done using the specified fill character (default is a space) | | count(...) | S.count(sub[, start[, end]]) -> int | | Return the number of non-overlapping occurrences of substring sub in | string S[start:end]. Optional arguments start and end are interpreted | as in slice notation. | | decode(...) | S.decode([encoding[,errors]]) -> object | | Decodes S using the codec registered for encoding. encoding defaults | to the default encoding. errors may be given to set a different error | handling scheme. Default is 'strict' meaning that encoding errors raise | a UnicodeDecodeError. Other possible values are 'ignore' and 'replace' | as well as any other name registered with codecs.register_error that is | able to handle UnicodeDecodeErrors. | | encode(...) | S.encode([encoding[,errors]]) -> object | | Encodes S using the codec registered for encoding. encoding defaults | to the default encoding. errors may be given to set a different error | handling scheme. Default is 'strict' meaning that encoding errors raise | a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and | 'xmlcharrefreplace' as well as any other name registered with | codecs.register_error that is able to handle UnicodeEncodeErrors. | | endswith(...) | S.endswith(suffix[, start[, end]]) -> bool | | Return True if S ends with the specified suffix, False otherwise. | With optional start, test S beginning at that position. | With optional end, stop comparing S at that position. | suffix can also be a tuple of strings to try. | | expandtabs(...) | S.expandtabs([tabsize]) -> string | | Return a copy of S where all tab characters are expanded using spaces. | If tabsize is not given, a tab size of 8 characters is assumed. | | find(...) | S.find(sub [,start [,end]]) -> int | | Return the lowest index in S where substring sub is found, | such that sub is contained within S[start:end]. Optional | arguments start and end are interpreted as in slice notation. | | Return -1 on failure. | | format(...) | S.format(*args, **kwargs) -> string | | Return a formatted version of S, using substitutions from args and kwargs. | The substitutions are identified by braces ('{' and '}'). | | index(...) | S.index(sub [,start [,end]]) -> int | | Like S.find() but raise ValueError when the substring is not found. | | isalnum(...) | S.isalnum() -> bool | | Return True if all characters in S are alphanumeric | and there is at least one character in S, False otherwise. | | isalpha(...) | S.isalpha() -> bool | | Return True if all characters in S are alphabetic | and there is at least one character in S, False otherwise. | | isdigit(...) | S.isdigit() -> bool | | Return True if all characters in S are digits | and there is at least one character in S, False otherwise. | | islower(...) | S.islower() -> bool | | Return True if all cased characters in S are lowercase and there is | at least one cased character in S, False otherwise. | | isspace(...) | S.isspace() -> bool | | Return True if all characters in S are whitespace | and there is at least one character in S, False otherwise. | | istitle(...) | S.istitle() -> bool | | Return True if S is a titlecased string and there is at least one | character in S, i.e. uppercase characters may only follow uncased | characters and lowercase characters only cased ones. Return False | otherwise. | | isupper(...) | S.isupper() -> bool | | Return True if all cased characters in S are uppercase and there is | at least one cased character in S, False otherwise. | | join(...) | S.join(iterable) -> string | | Return a string which is the concatenation of the strings in the | iterable. The separator between elements is S. | | ljust(...) | S.ljust(width[, fillchar]) -> string | | Return S left-justified in a string of length width. Padding is | done using the specified fill character (default is a space). | | lower(...) | S.lower() -> string | | Return a copy of the string S converted to lowercase. | | lstrip(...) | S.lstrip([chars]) -> string or unicode | | Return a copy of the string S with leading whitespace removed. | If chars is given and not None, remove characters in chars instead. | If chars is unicode, S will be converted to unicode before stripping | | partition(...) | S.partition(sep) -> (head, sep, tail) | | Search for the separator sep in S, and return the part before it, | the separator itself, and the part after it. If the separator is not | found, return S and two empty strings. | | replace(...) | S.replace(old, new[, count]) -> string | | Return a copy of string S with all occurrences of substring | old replaced by new. If the optional argument count is | given, only the first count occurrences are replaced. | | rfind(...) | S.rfind(sub [,start [,end]]) -> int | | Return the highest index in S where substring sub is found, | such that sub is contained within S[start:end]. Optional | arguments start and end are interpreted as in slice notation. | | Return -1 on failure. | | rindex(...) | S.rindex(sub [,start [,end]]) -> int | | Like S.rfind() but raise ValueError when the substring is not found. | | rjust(...) | S.rjust(width[, fillchar]) -> string | | Return S right-justified in a string of length width. Padding is | done using the specified fill character (default is a space) | | rpartition(...) | S.rpartition(sep) -> (head, sep, tail) | | Search for the separator sep in S, starting at the end of S, and return | the part before it, the separator itself, and the part after it. If the | separator is not found, return two empty strings and S. | | rsplit(...) | S.rsplit([sep [,maxsplit]]) -> list of strings | | Return a list of the words in the string S, using sep as the | delimiter string, starting at the end of the string and working | to the front. If maxsplit is given, at most maxsplit splits are | done. If sep is not specified or is None, any whitespace string | is a separator. | | rstrip(...) | S.rstrip([chars]) -> string or unicode | | Return a copy of the string S with trailing whitespace removed. | If chars is given and not None, remove characters in chars instead. | If chars is unicode, S will be converted to unicode before stripping | | split(...) | S.split([sep [,maxsplit]]) -> list of strings | | Return a list of the words in the string S, using sep as the | delimiter string. If maxsplit is given, at most maxsplit | splits are done. If sep is not specified or is None, any | whitespace string is a separator and empty strings are removed | from the result. | | splitlines(...) | S.splitlines(keepends=False) -> list of strings | | Return a list of the lines in S, breaking at line boundaries. | Line breaks are not included in the resulting list unless keepends | is given and true. | | startswith(...) | S.startswith(prefix[, start[, end]]) -> bool | | Return True if S starts with the specified prefix, False otherwise. | With optional start, test S beginning at that position. | With optional end, stop comparing S at that position. | prefix can also be a tuple of strings to try. | | strip(...) | S.strip([chars]) -> string or unicode | | Return a copy of the string S with leading and trailing | whitespace removed. | If chars is given and not None, remove characters in chars instead. | If chars is unicode, S will be converted to unicode before stripping | | swapcase(...) | S.swapcase() -> string | | Return a copy of the string S with uppercase characters | converted to lowercase and vice versa. | | title(...) | S.title() -> string | | Return a titlecased version of S, i.e. words start with uppercase | characters, all remaining cased characters have lowercase. | | translate(...) | S.translate(table [,deletechars]) -> string | | Return a copy of the string S, where all characters occurring | in the optional argument deletechars are removed, and the | remaining characters have been mapped through the given | translation table, which must be a string of length 256 or None. | If the table argument is None, no translation is applied and | the operation simply removes the characters in deletechars. | | upper(...) | S.upper() -> string | | Return a copy of the string S converted to uppercase. | | zfill(...) | S.zfill(width) -> string | | Pad a numeric string S with zeros on the left, to fill a field | of the specified width. The string S is never truncated. | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class timedelta64(signedinteger) | Method resolution order: | timedelta64 | signedinteger | integer | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Data descriptors inherited from integer: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension ubyte = class uint8(unsignedinteger) | Method resolution order: | uint8 | unsignedinteger | integer | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Data descriptors inherited from integer: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class ufunc(__builtin__.object) | Functions that operate element by element on whole arrays. | | To see the documentation for a specific ufunc, use np.info(). For | example, np.info(np.sin). Because ufuncs are written in C | (for speed) and linked into Python with NumPy's ufunc facility, | Python's help() function finds this page whenever help() is called | on a ufunc. | | A detailed explanation of ufuncs can be found in the "ufuncs.rst" | file in the NumPy reference guide. | | Unary ufuncs: | ============= | | op(X, out=None) | Apply op to X elementwise | | Parameters | ---------- | X : array_like | Input array. | out : array_like | An array to store the output. Must be the same shape as `X`. | | Returns | ------- | r : array_like | `r` will have the same shape as `X`; if out is provided, `r` | will be equal to out. | | Binary ufuncs: | ============== | | op(X, Y, out=None) | Apply `op` to `X` and `Y` elementwise. May "broadcast" to make | the shapes of `X` and `Y` congruent. | | The broadcasting rules are: | | * Dimensions of length 1 may be prepended to either array. | * Arrays may be repeated along dimensions of length 1. | | Parameters | ---------- | X : array_like | First input array. | Y : array_like | Second input array. | out : array_like | An array to store the output. Must be the same shape as the | output would have. | | Returns | ------- | r : array_like | The return value; if out is provided, `r` will be equal to out. | | Methods defined here: | | __call__(...) | x.__call__(...) <==> x(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | accumulate(...) | accumulate(array, axis=0, dtype=None, out=None) | | Accumulate the result of applying the operator to all elements. | | For a one-dimensional array, accumulate produces results equivalent to:: | | r = np.empty(len(A)) | t = op.identity # op = the ufunc being applied to A's elements | for i in range(len(A)): | t = op(t, A[i]) | r[i] = t | return r | | For example, add.accumulate() is equivalent to np.cumsum(). | | For a multi-dimensional array, accumulate is applied along only one | axis (axis zero by default; see Examples below) so repeated use is | necessary if one wants to accumulate over multiple axes. | | Parameters | ---------- | array : array_like | The array to act on. | axis : int, optional | The axis along which to apply the accumulation; default is zero. | dtype : data-type code, optional | The data-type used to represent the intermediate results. Defaults | to the data-type of the output array if such is provided, or the | the data-type of the input array if no output array is provided. | out : ndarray, optional | A location into which the result is stored. If not provided a | freshly-allocated array is returned. | | Returns | ------- | r : ndarray | The accumulated values. If `out` was supplied, `r` is a reference to | `out`. | | Examples | -------- | 1-D array examples: | | >>> np.add.accumulate([2, 3, 5]) | array([ 2, 5, 10]) | >>> np.multiply.accumulate([2, 3, 5]) | array([ 2, 6, 30]) | | 2-D array examples: | | >>> I = np.eye(2) | >>> I | array([[ 1., 0.], | [ 0., 1.]]) | | Accumulate along axis 0 (rows), down columns: | | >>> np.add.accumulate(I, 0) | array([[ 1., 0.], | [ 1., 1.]]) | >>> np.add.accumulate(I) # no axis specified = axis zero | array([[ 1., 0.], | [ 1., 1.]]) | | Accumulate along axis 1 (columns), through rows: | | >>> np.add.accumulate(I, 1) | array([[ 1., 1.], | [ 0., 1.]]) | | at(...) | at(a, indices, b=None) | | Performs unbuffered in place operation on operand 'a' for elements | specified by 'indices'. For addition ufunc, this method is equivalent to | `a[indices] += b`, except that results are accumulated for elements that | are indexed more than once. For example, `a[[0,0]] += 1` will only | increment the first element once because of buffering, whereas | `add.at(a, [0,0], 1)` will increment the first element twice. | | .. versionadded:: 1.8.0 | | Parameters | ---------- | a : array_like | The array to perform in place operation on. | indices : array_like or tuple | Array like index object or slice object for indexing into first | operand. If first operand has multiple dimensions, indices can be a | tuple of array like index objects or slice objects. | b : array_like | Second operand for ufuncs requiring two operands. Operand must be | broadcastable over first operand after indexing or slicing. | | Examples | -------- | Set items 0 and 1 to their negative values: | | >>> a = np.array([1, 2, 3, 4]) | >>> np.negative.at(a, [0, 1]) | >>> print(a) | array([-1, -2, 3, 4]) | | :: | | Increment items 0 and 1, and increment item 2 twice: | | >>> a = np.array([1, 2, 3, 4]) | >>> np.add.at(a, [0, 1, 2, 2], 1) | >>> print(a) | array([2, 3, 5, 4]) | | :: | | Add items 0 and 1 in first array to second array, | and store results in first array: | | >>> a = np.array([1, 2, 3, 4]) | >>> b = np.array([1, 2]) | >>> np.add.at(a, [0, 1], b) | >>> print(a) | array([2, 4, 3, 4]) | | outer(...) | outer(A, B) | | Apply the ufunc `op` to all pairs (a, b) with a in `A` and b in `B`. | | Let ``M = A.ndim``, ``N = B.ndim``. Then the result, `C`, of | ``op.outer(A, B)`` is an array of dimension M + N such that: | | .. math:: C[i_0, ..., i_{M-1}, j_0, ..., j_{N-1}] = | op(A[i_0, ..., i_{M-1}], B[j_0, ..., j_{N-1}]) | | For `A` and `B` one-dimensional, this is equivalent to:: | | r = empty(len(A),len(B)) | for i in range(len(A)): | for j in range(len(B)): | r[i,j] = op(A[i], B[j]) # op = ufunc in question | | Parameters | ---------- | A : array_like | First array | B : array_like | Second array | | Returns | ------- | r : ndarray | Output array | | See Also | -------- | numpy.outer | | Examples | -------- | >>> np.multiply.outer([1, 2, 3], [4, 5, 6]) | array([[ 4, 5, 6], | [ 8, 10, 12], | [12, 15, 18]]) | | A multi-dimensional example: | | >>> A = np.array([[1, 2, 3], [4, 5, 6]]) | >>> A.shape | (2, 3) | >>> B = np.array([[1, 2, 3, 4]]) | >>> B.shape | (1, 4) | >>> C = np.multiply.outer(A, B) | >>> C.shape; C | (2, 3, 1, 4) | array([[[[ 1, 2, 3, 4]], | [[ 2, 4, 6, 8]], | [[ 3, 6, 9, 12]]], | [[[ 4, 8, 12, 16]], | [[ 5, 10, 15, 20]], | [[ 6, 12, 18, 24]]]]) | | reduce(...) | reduce(a, axis=0, dtype=None, out=None, keepdims=False) | | Reduces `a`'s dimension by one, by applying ufunc along one axis. | | Let :math:`a.shape = (N_0, ..., N_i, ..., N_{M-1})`. Then | :math:`ufunc.reduce(a, axis=i)[k_0, ..,k_{i-1}, k_{i+1}, .., k_{M-1}]` = | the result of iterating `j` over :math:`range(N_i)`, cumulatively applying | ufunc to each :math:`a[k_0, ..,k_{i-1}, j, k_{i+1}, .., k_{M-1}]`. | For a one-dimensional array, reduce produces results equivalent to: | :: | | r = op.identity # op = ufunc | for i in range(len(A)): | r = op(r, A[i]) | return r | | For example, add.reduce() is equivalent to sum(). | | Parameters | ---------- | a : array_like | The array to act on. | axis : None or int or tuple of ints, optional | Axis or axes along which a reduction is performed. | The default (`axis` = 0) is perform a reduction over the first | dimension of the input array. `axis` may be negative, in | which case it counts from the last to the first axis. | | .. versionadded:: 1.7.0 | | If this is `None`, a reduction is performed over all the axes. | If this is a tuple of ints, a reduction is performed on multiple | axes, instead of a single axis or all the axes as before. | | For operations which are either not commutative or not associative, | doing a reduction over multiple axes is not well-defined. The | ufuncs do not currently raise an exception in this case, but will | likely do so in the future. | dtype : data-type code, optional | The type used to represent the intermediate results. Defaults | to the data-type of the output array if this is provided, or | the data-type of the input array if no output array is provided. | out : ndarray, optional | A location into which the result is stored. If not provided, a | freshly-allocated array is returned. | keepdims : bool, optional | If this is set to True, the axes which are reduced are left | in the result as dimensions with size one. With this option, | the result will broadcast correctly against the original `arr`. | | .. versionadded:: 1.7.0 | | Returns | ------- | r : ndarray | The reduced array. If `out` was supplied, `r` is a reference to it. | | Examples | -------- | >>> np.multiply.reduce([2,3,5]) | 30 | | A multi-dimensional array example: | | >>> X = np.arange(8).reshape((2,2,2)) | >>> X | array([[[0, 1], | [2, 3]], | [[4, 5], | [6, 7]]]) | >>> np.add.reduce(X, 0) | array([[ 4, 6], | [ 8, 10]]) | >>> np.add.reduce(X) # confirm: default axis value is 0 | array([[ 4, 6], | [ 8, 10]]) | >>> np.add.reduce(X, 1) | array([[ 2, 4], | [10, 12]]) | >>> np.add.reduce(X, 2) | array([[ 1, 5], | [ 9, 13]]) | | reduceat(...) | reduceat(a, indices, axis=0, dtype=None, out=None) | | Performs a (local) reduce with specified slices over a single axis. | | For i in ``range(len(indices))``, `reduceat` computes | ``ufunc.reduce(a[indices[i]:indices[i+1]])``, which becomes the i-th | generalized "row" parallel to `axis` in the final result (i.e., in a | 2-D array, for example, if `axis = 0`, it becomes the i-th row, but if | `axis = 1`, it becomes the i-th column). There are three exceptions to this: | | * when ``i = len(indices) - 1`` (so for the last index), | ``indices[i+1] = a.shape[axis]``. | * if ``indices[i] >= indices[i + 1]``, the i-th generalized "row" is | simply ``a[indices[i]]``. | * if ``indices[i] >= len(a)`` or ``indices[i] < 0``, an error is raised. | | The shape of the output depends on the size of `indices`, and may be | larger than `a` (this happens if ``len(indices) > a.shape[axis]``). | | Parameters | ---------- | a : array_like | The array to act on. | indices : array_like | Paired indices, comma separated (not colon), specifying slices to | reduce. | axis : int, optional | The axis along which to apply the reduceat. | dtype : data-type code, optional | The type used to represent the intermediate results. Defaults | to the data type of the output array if this is provided, or | the data type of the input array if no output array is provided. | out : ndarray, optional | A location into which the result is stored. If not provided a | freshly-allocated array is returned. | | Returns | ------- | r : ndarray | The reduced values. If `out` was supplied, `r` is a reference to | `out`. | | Notes | ----- | A descriptive example: | | If `a` is 1-D, the function `ufunc.accumulate(a)` is the same as | ``ufunc.reduceat(a, indices)[::2]`` where `indices` is | ``range(len(array) - 1)`` with a zero placed | in every other element: | ``indices = zeros(2 * len(a) - 1)``, ``indices[1::2] = range(1, len(a))``. | | Don't be fooled by this attribute's name: `reduceat(a)` is not | necessarily smaller than `a`. | | Examples | -------- | To take the running sum of four successive values: | | >>> np.add.reduceat(np.arange(8),[0,4, 1,5, 2,6, 3,7])[::2] | array([ 6, 10, 14, 18]) | | A 2-D example: | | >>> x = np.linspace(0, 15, 16).reshape(4,4) | >>> x | array([[ 0., 1., 2., 3.], | [ 4., 5., 6., 7.], | [ 8., 9., 10., 11.], | [ 12., 13., 14., 15.]]) | | :: | | # reduce such that the result has the following five rows: | # [row1 + row2 + row3] | # [row4] | # [row2] | # [row3] | # [row1 + row2 + row3 + row4] | | >>> np.add.reduceat(x, [0, 3, 1, 2, 0]) | array([[ 12., 15., 18., 21.], | [ 12., 13., 14., 15.], | [ 4., 5., 6., 7.], | [ 8., 9., 10., 11.], | [ 24., 28., 32., 36.]]) | | :: | | # reduce such that result has the following two columns: | # [col1 * col2 * col3, col4] | | >>> np.multiply.reduceat(x, [0, 3], 1) | array([[ 0., 3.], | [ 120., 7.], | [ 720., 11.], | [ 2184., 15.]]) | | ---------------------------------------------------------------------- | Data descriptors defined here: | | identity | The identity value. | | Data attribute containing the identity element for the ufunc, if it has one. | If it does not, the attribute value is None. | | Examples | -------- | >>> np.add.identity | 0 | >>> np.multiply.identity | 1 | >>> np.power.identity | 1 | >>> print np.exp.identity | None | | nargs | The number of arguments. | | Data attribute containing the number of arguments the ufunc takes, including | optional ones. | | Notes | ----- | Typically this value will be one more than what you might expect because all | ufuncs take the optional "out" argument. | | Examples | -------- | >>> np.add.nargs | 3 | >>> np.multiply.nargs | 3 | >>> np.power.nargs | 3 | >>> np.exp.nargs | 2 | | nin | The number of inputs. | | Data attribute containing the number of arguments the ufunc treats as input. | | Examples | -------- | >>> np.add.nin | 2 | >>> np.multiply.nin | 2 | >>> np.power.nin | 2 | >>> np.exp.nin | 1 | | nout | The number of outputs. | | Data attribute containing the number of arguments the ufunc treats as output. | | Notes | ----- | Since all ufuncs can take output arguments, this will always be (at least) 1. | | Examples | -------- | >>> np.add.nout | 1 | >>> np.multiply.nout | 1 | >>> np.power.nout | 1 | >>> np.exp.nout | 1 | | ntypes | The number of types. | | The number of numerical NumPy types - of which there are 18 total - on which | the ufunc can operate. | | See Also | -------- | numpy.ufunc.types | | Examples | -------- | >>> np.add.ntypes | 18 | >>> np.multiply.ntypes | 18 | >>> np.power.ntypes | 17 | >>> np.exp.ntypes | 7 | >>> np.remainder.ntypes | 14 | | signature | | types | Returns a list with types grouped input->output. | | Data attribute listing the data-type "Domain-Range" groupings the ufunc can | deliver. The data-types are given using the character codes. | | See Also | -------- | numpy.ufunc.ntypes | | Examples | -------- | >>> np.add.types | ['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', | 'LL->L', 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D', | 'GG->G', 'OO->O'] | | >>> np.multiply.types | ['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', | 'LL->L', 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D', | 'GG->G', 'OO->O'] | | >>> np.power.types | ['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', 'LL->L', | 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D', 'GG->G', | 'OO->O'] | | >>> np.exp.types | ['f->f', 'd->d', 'g->g', 'F->F', 'D->D', 'G->G', 'O->O'] | | >>> np.remainder.types | ['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', 'LL->L', | 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'OO->O'] uint = class uint32(unsignedinteger) | Method resolution order: | uint32 | unsignedinteger | integer | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Data descriptors inherited from integer: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension uint0 = class uint64(unsignedinteger) | Method resolution order: | uint64 | unsignedinteger | integer | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Data descriptors inherited from integer: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class uint16(unsignedinteger) | Method resolution order: | uint16 | unsignedinteger | integer | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Data descriptors inherited from integer: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class uint32(unsignedinteger) | Method resolution order: | uint32 | unsignedinteger | integer | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Data descriptors inherited from integer: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class uint64(unsignedinteger) | Method resolution order: | uint64 | unsignedinteger | integer | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Data descriptors inherited from integer: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class uint8(unsignedinteger) | Method resolution order: | uint8 | unsignedinteger | integer | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Data descriptors inherited from integer: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension uintc = class uint32(unsignedinteger) | Method resolution order: | uint32 | unsignedinteger | integer | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Data descriptors inherited from integer: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension uintp = class uint64(unsignedinteger) | Method resolution order: | uint64 | unsignedinteger | integer | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Data descriptors inherited from integer: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension ulonglong = class uint64(unsignedinteger) | Method resolution order: | uint64 | unsignedinteger | integer | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Data descriptors inherited from integer: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension unicode0 = class unicode_(__builtin__.unicode, character) | Method resolution order: | unicode_ | __builtin__.unicode | __builtin__.basestring | character | flexible | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from __builtin__.unicode: | | __add__(...) | x.__add__(y) <==> x+y | | __contains__(...) | x.__contains__(y) <==> y in x | | __format__(...) | S.__format__(format_spec) -> unicode | | Return a formatted version of S as described by format_spec. | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __getnewargs__(...) | | __getslice__(...) | x.__getslice__(i, j) <==> x[i:j] | | Use of negative indices is not supported. | | __len__(...) | x.__len__() <==> len(x) | | __mod__(...) | x.__mod__(y) <==> x%y | | __mul__(...) | x.__mul__(n) <==> x*n | | __rmod__(...) | x.__rmod__(y) <==> y%x | | __rmul__(...) | x.__rmul__(n) <==> n*x | | __sizeof__(...) | S.__sizeof__() -> size of S in memory, in bytes | | capitalize(...) | S.capitalize() -> unicode | | Return a capitalized version of S, i.e. make the first character | have upper case and the rest lower case. | | center(...) | S.center(width[, fillchar]) -> unicode | | Return S centered in a Unicode string of length width. Padding is | done using the specified fill character (default is a space) | | count(...) | S.count(sub[, start[, end]]) -> int | | Return the number of non-overlapping occurrences of substring sub in | Unicode string S[start:end]. Optional arguments start and end are | interpreted as in slice notation. | | decode(...) | S.decode([encoding[,errors]]) -> string or unicode | | Decodes S using the codec registered for encoding. encoding defaults | to the default encoding. errors may be given to set a different error | handling scheme. Default is 'strict' meaning that encoding errors raise | a UnicodeDecodeError. Other possible values are 'ignore' and 'replace' | as well as any other name registered with codecs.register_error that is | able to handle UnicodeDecodeErrors. | | encode(...) | S.encode([encoding[,errors]]) -> string or unicode | | Encodes S using the codec registered for encoding. encoding defaults | to the default encoding. errors may be given to set a different error | handling scheme. Default is 'strict' meaning that encoding errors raise | a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and | 'xmlcharrefreplace' as well as any other name registered with | codecs.register_error that can handle UnicodeEncodeErrors. | | endswith(...) | S.endswith(suffix[, start[, end]]) -> bool | | Return True if S ends with the specified suffix, False otherwise. | With optional start, test S beginning at that position. | With optional end, stop comparing S at that position. | suffix can also be a tuple of strings to try. | | expandtabs(...) | S.expandtabs([tabsize]) -> unicode | | Return a copy of S where all tab characters are expanded using spaces. | If tabsize is not given, a tab size of 8 characters is assumed. | | find(...) | S.find(sub [,start [,end]]) -> int | | Return the lowest index in S where substring sub is found, | such that sub is contained within S[start:end]. Optional | arguments start and end are interpreted as in slice notation. | | Return -1 on failure. | | format(...) | S.format(*args, **kwargs) -> unicode | | Return a formatted version of S, using substitutions from args and kwargs. | The substitutions are identified by braces ('{' and '}'). | | index(...) | S.index(sub [,start [,end]]) -> int | | Like S.find() but raise ValueError when the substring is not found. | | isalnum(...) | S.isalnum() -> bool | | Return True if all characters in S are alphanumeric | and there is at least one character in S, False otherwise. | | isalpha(...) | S.isalpha() -> bool | | Return True if all characters in S are alphabetic | and there is at least one character in S, False otherwise. | | isdecimal(...) | S.isdecimal() -> bool | | Return True if there are only decimal characters in S, | False otherwise. | | isdigit(...) | S.isdigit() -> bool | | Return True if all characters in S are digits | and there is at least one character in S, False otherwise. | | islower(...) | S.islower() -> bool | | Return True if all cased characters in S are lowercase and there is | at least one cased character in S, False otherwise. | | isnumeric(...) | S.isnumeric() -> bool | | Return True if there are only numeric characters in S, | False otherwise. | | isspace(...) | S.isspace() -> bool | | Return True if all characters in S are whitespace | and there is at least one character in S, False otherwise. | | istitle(...) | S.istitle() -> bool | | Return True if S is a titlecased string and there is at least one | character in S, i.e. upper- and titlecase characters may only | follow uncased characters and lowercase characters only cased ones. | Return False otherwise. | | isupper(...) | S.isupper() -> bool | | Return True if all cased characters in S are uppercase and there is | at least one cased character in S, False otherwise. | | join(...) | S.join(iterable) -> unicode | | Return a string which is the concatenation of the strings in the | iterable. The separator between elements is S. | | ljust(...) | S.ljust(width[, fillchar]) -> int | | Return S left-justified in a Unicode string of length width. Padding is | done using the specified fill character (default is a space). | | lower(...) | S.lower() -> unicode | | Return a copy of the string S converted to lowercase. | | lstrip(...) | S.lstrip([chars]) -> unicode | | Return a copy of the string S with leading whitespace removed. | If chars is given and not None, remove characters in chars instead. | If chars is a str, it will be converted to unicode before stripping | | partition(...) | S.partition(sep) -> (head, sep, tail) | | Search for the separator sep in S, and return the part before it, | the separator itself, and the part after it. If the separator is not | found, return S and two empty strings. | | replace(...) | S.replace(old, new[, count]) -> unicode | | Return a copy of S with all occurrences of substring | old replaced by new. If the optional argument count is | given, only the first count occurrences are replaced. | | rfind(...) | S.rfind(sub [,start [,end]]) -> int | | Return the highest index in S where substring sub is found, | such that sub is contained within S[start:end]. Optional | arguments start and end are interpreted as in slice notation. | | Return -1 on failure. | | rindex(...) | S.rindex(sub [,start [,end]]) -> int | | Like S.rfind() but raise ValueError when the substring is not found. | | rjust(...) | S.rjust(width[, fillchar]) -> unicode | | Return S right-justified in a Unicode string of length width. Padding is | done using the specified fill character (default is a space). | | rpartition(...) | S.rpartition(sep) -> (head, sep, tail) | | Search for the separator sep in S, starting at the end of S, and return | the part before it, the separator itself, and the part after it. If the | separator is not found, return two empty strings and S. | | rsplit(...) | S.rsplit([sep [,maxsplit]]) -> list of strings | | Return a list of the words in S, using sep as the | delimiter string, starting at the end of the string and | working to the front. If maxsplit is given, at most maxsplit | splits are done. If sep is not specified, any whitespace string | is a separator. | | rstrip(...) | S.rstrip([chars]) -> unicode | | Return a copy of the string S with trailing whitespace removed. | If chars is given and not None, remove characters in chars instead. | If chars is a str, it will be converted to unicode before stripping | | split(...) | S.split([sep [,maxsplit]]) -> list of strings | | Return a list of the words in S, using sep as the | delimiter string. If maxsplit is given, at most maxsplit | splits are done. If sep is not specified or is None, any | whitespace string is a separator and empty strings are | removed from the result. | | splitlines(...) | S.splitlines(keepends=False) -> list of strings | | Return a list of the lines in S, breaking at line boundaries. | Line breaks are not included in the resulting list unless keepends | is given and true. | | startswith(...) | S.startswith(prefix[, start[, end]]) -> bool | | Return True if S starts with the specified prefix, False otherwise. | With optional start, test S beginning at that position. | With optional end, stop comparing S at that position. | prefix can also be a tuple of strings to try. | | strip(...) | S.strip([chars]) -> unicode | | Return a copy of the string S with leading and trailing | whitespace removed. | If chars is given and not None, remove characters in chars instead. | If chars is a str, it will be converted to unicode before stripping | | swapcase(...) | S.swapcase() -> unicode | | Return a copy of S with uppercase characters converted to lowercase | and vice versa. | | title(...) | S.title() -> unicode | | Return a titlecased version of S, i.e. words start with title case | characters, all remaining cased characters have lower case. | | translate(...) | S.translate(table) -> unicode | | Return a copy of the string S, where all characters have been mapped | through the given translation table, which must be a mapping of | Unicode ordinals to Unicode ordinals, Unicode strings or None. | Unmapped characters are left untouched. Characters mapped to None | are deleted. | | upper(...) | S.upper() -> unicode | | Return a copy of S converted to uppercase. | | zfill(...) | S.zfill(width) -> unicode | | Pad a numeric string S with zeros on the left, to fill a field | of the specified width. The string S is never truncated. | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class unicode_(__builtin__.unicode, character) | Method resolution order: | unicode_ | __builtin__.unicode | __builtin__.basestring | character | flexible | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __repr__(...) | x.__repr__() <==> repr(x) | | __str__(...) | x.__str__() <==> str(x) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from __builtin__.unicode: | | __add__(...) | x.__add__(y) <==> x+y | | __contains__(...) | x.__contains__(y) <==> y in x | | __format__(...) | S.__format__(format_spec) -> unicode | | Return a formatted version of S as described by format_spec. | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __getnewargs__(...) | | __getslice__(...) | x.__getslice__(i, j) <==> x[i:j] | | Use of negative indices is not supported. | | __len__(...) | x.__len__() <==> len(x) | | __mod__(...) | x.__mod__(y) <==> x%y | | __mul__(...) | x.__mul__(n) <==> x*n | | __rmod__(...) | x.__rmod__(y) <==> y%x | | __rmul__(...) | x.__rmul__(n) <==> n*x | | __sizeof__(...) | S.__sizeof__() -> size of S in memory, in bytes | | capitalize(...) | S.capitalize() -> unicode | | Return a capitalized version of S, i.e. make the first character | have upper case and the rest lower case. | | center(...) | S.center(width[, fillchar]) -> unicode | | Return S centered in a Unicode string of length width. Padding is | done using the specified fill character (default is a space) | | count(...) | S.count(sub[, start[, end]]) -> int | | Return the number of non-overlapping occurrences of substring sub in | Unicode string S[start:end]. Optional arguments start and end are | interpreted as in slice notation. | | decode(...) | S.decode([encoding[,errors]]) -> string or unicode | | Decodes S using the codec registered for encoding. encoding defaults | to the default encoding. errors may be given to set a different error | handling scheme. Default is 'strict' meaning that encoding errors raise | a UnicodeDecodeError. Other possible values are 'ignore' and 'replace' | as well as any other name registered with codecs.register_error that is | able to handle UnicodeDecodeErrors. | | encode(...) | S.encode([encoding[,errors]]) -> string or unicode | | Encodes S using the codec registered for encoding. encoding defaults | to the default encoding. errors may be given to set a different error | handling scheme. Default is 'strict' meaning that encoding errors raise | a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and | 'xmlcharrefreplace' as well as any other name registered with | codecs.register_error that can handle UnicodeEncodeErrors. | | endswith(...) | S.endswith(suffix[, start[, end]]) -> bool | | Return True if S ends with the specified suffix, False otherwise. | With optional start, test S beginning at that position. | With optional end, stop comparing S at that position. | suffix can also be a tuple of strings to try. | | expandtabs(...) | S.expandtabs([tabsize]) -> unicode | | Return a copy of S where all tab characters are expanded using spaces. | If tabsize is not given, a tab size of 8 characters is assumed. | | find(...) | S.find(sub [,start [,end]]) -> int | | Return the lowest index in S where substring sub is found, | such that sub is contained within S[start:end]. Optional | arguments start and end are interpreted as in slice notation. | | Return -1 on failure. | | format(...) | S.format(*args, **kwargs) -> unicode | | Return a formatted version of S, using substitutions from args and kwargs. | The substitutions are identified by braces ('{' and '}'). | | index(...) | S.index(sub [,start [,end]]) -> int | | Like S.find() but raise ValueError when the substring is not found. | | isalnum(...) | S.isalnum() -> bool | | Return True if all characters in S are alphanumeric | and there is at least one character in S, False otherwise. | | isalpha(...) | S.isalpha() -> bool | | Return True if all characters in S are alphabetic | and there is at least one character in S, False otherwise. | | isdecimal(...) | S.isdecimal() -> bool | | Return True if there are only decimal characters in S, | False otherwise. | | isdigit(...) | S.isdigit() -> bool | | Return True if all characters in S are digits | and there is at least one character in S, False otherwise. | | islower(...) | S.islower() -> bool | | Return True if all cased characters in S are lowercase and there is | at least one cased character in S, False otherwise. | | isnumeric(...) | S.isnumeric() -> bool | | Return True if there are only numeric characters in S, | False otherwise. | | isspace(...) | S.isspace() -> bool | | Return True if all characters in S are whitespace | and there is at least one character in S, False otherwise. | | istitle(...) | S.istitle() -> bool | | Return True if S is a titlecased string and there is at least one | character in S, i.e. upper- and titlecase characters may only | follow uncased characters and lowercase characters only cased ones. | Return False otherwise. | | isupper(...) | S.isupper() -> bool | | Return True if all cased characters in S are uppercase and there is | at least one cased character in S, False otherwise. | | join(...) | S.join(iterable) -> unicode | | Return a string which is the concatenation of the strings in the | iterable. The separator between elements is S. | | ljust(...) | S.ljust(width[, fillchar]) -> int | | Return S left-justified in a Unicode string of length width. Padding is | done using the specified fill character (default is a space). | | lower(...) | S.lower() -> unicode | | Return a copy of the string S converted to lowercase. | | lstrip(...) | S.lstrip([chars]) -> unicode | | Return a copy of the string S with leading whitespace removed. | If chars is given and not None, remove characters in chars instead. | If chars is a str, it will be converted to unicode before stripping | | partition(...) | S.partition(sep) -> (head, sep, tail) | | Search for the separator sep in S, and return the part before it, | the separator itself, and the part after it. If the separator is not | found, return S and two empty strings. | | replace(...) | S.replace(old, new[, count]) -> unicode | | Return a copy of S with all occurrences of substring | old replaced by new. If the optional argument count is | given, only the first count occurrences are replaced. | | rfind(...) | S.rfind(sub [,start [,end]]) -> int | | Return the highest index in S where substring sub is found, | such that sub is contained within S[start:end]. Optional | arguments start and end are interpreted as in slice notation. | | Return -1 on failure. | | rindex(...) | S.rindex(sub [,start [,end]]) -> int | | Like S.rfind() but raise ValueError when the substring is not found. | | rjust(...) | S.rjust(width[, fillchar]) -> unicode | | Return S right-justified in a Unicode string of length width. Padding is | done using the specified fill character (default is a space). | | rpartition(...) | S.rpartition(sep) -> (head, sep, tail) | | Search for the separator sep in S, starting at the end of S, and return | the part before it, the separator itself, and the part after it. If the | separator is not found, return two empty strings and S. | | rsplit(...) | S.rsplit([sep [,maxsplit]]) -> list of strings | | Return a list of the words in S, using sep as the | delimiter string, starting at the end of the string and | working to the front. If maxsplit is given, at most maxsplit | splits are done. If sep is not specified, any whitespace string | is a separator. | | rstrip(...) | S.rstrip([chars]) -> unicode | | Return a copy of the string S with trailing whitespace removed. | If chars is given and not None, remove characters in chars instead. | If chars is a str, it will be converted to unicode before stripping | | split(...) | S.split([sep [,maxsplit]]) -> list of strings | | Return a list of the words in S, using sep as the | delimiter string. If maxsplit is given, at most maxsplit | splits are done. If sep is not specified or is None, any | whitespace string is a separator and empty strings are | removed from the result. | | splitlines(...) | S.splitlines(keepends=False) -> list of strings | | Return a list of the lines in S, breaking at line boundaries. | Line breaks are not included in the resulting list unless keepends | is given and true. | | startswith(...) | S.startswith(prefix[, start[, end]]) -> bool | | Return True if S starts with the specified prefix, False otherwise. | With optional start, test S beginning at that position. | With optional end, stop comparing S at that position. | prefix can also be a tuple of strings to try. | | strip(...) | S.strip([chars]) -> unicode | | Return a copy of the string S with leading and trailing | whitespace removed. | If chars is given and not None, remove characters in chars instead. | If chars is a str, it will be converted to unicode before stripping | | swapcase(...) | S.swapcase() -> unicode | | Return a copy of S with uppercase characters converted to lowercase | and vice versa. | | title(...) | S.title() -> unicode | | Return a titlecased version of S, i.e. words start with title case | characters, all remaining cased characters have lower case. | | translate(...) | S.translate(table) -> unicode | | Return a copy of the string S, where all characters have been mapped | through the given translation table, which must be a mapping of | Unicode ordinals to Unicode ordinals, Unicode strings or None. | Unmapped characters are left untouched. Characters mapped to None | are deleted. | | upper(...) | S.upper() -> unicode | | Return a copy of S converted to uppercase. | | zfill(...) | S.zfill(width) -> unicode | | Pad a numeric string S with zeros on the left, to fill a field | of the specified width. The string S is never truncated. | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class unsignedinteger(integer) | Method resolution order: | unsignedinteger | integer | number | generic | __builtin__.object | | Data descriptors inherited from integer: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __eq__(...) | x.__eq__(y) <==> x==y | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __ge__(...) | x.__ge__(y) <==> x>=y | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __gt__(...) | x.__gt__(y) <==> x>y | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __le__(...) | x.__le__(y) <==> x<=y | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __ne__(...) | x.__ne__(y) <==> x!=y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension ushort = class uint16(unsignedinteger) | Method resolution order: | uint16 | unsignedinteger | integer | number | generic | __builtin__.object | | Methods defined here: | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __index__(...) | x[y:z] <==> x[y.__index__():z.__index__()] | | __le__(...) | x.__le__(y) <==> x<=y | | __lt__(...) | x.__lt__(y) <==> x x!=y | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Data descriptors inherited from integer: | | denominator | denominator of value (1) | | numerator | numerator of value (the value itself) | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | getfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setfield(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | dtype | get array data-descriptor | | flags | integer value of flags | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension class vectorize(__builtin__.object) | vectorize(pyfunc, otypes='', doc=None, excluded=None, cache=False) | | Generalized function class. | | Define a vectorized function which takes a nested sequence | of objects or numpy arrays as inputs and returns a | numpy array as output. The vectorized function evaluates `pyfunc` over | successive tuples of the input arrays like the python map function, | except it uses the broadcasting rules of numpy. | | The data type of the output of `vectorized` is determined by calling | the function with the first element of the input. This can be avoided | by specifying the `otypes` argument. | | Parameters | ---------- | pyfunc : callable | A python function or method. | otypes : str or list of dtypes, optional | The output data type. It must be specified as either a string of | typecode characters or a list of data type specifiers. There should | be one data type specifier for each output. | doc : str, optional | The docstring for the function. If `None`, the docstring will be the | ``pyfunc.__doc__``. | excluded : set, optional | Set of strings or integers representing the positional or keyword | arguments for which the function will not be vectorized. These will be | passed directly to `pyfunc` unmodified. | | .. versionadded:: 1.7.0 | | cache : bool, optional | If `True`, then cache the first function call that determines the number | of outputs if `otypes` is not provided. | | .. versionadded:: 1.7.0 | | Returns | ------- | vectorized : callable | Vectorized function. | | Examples | -------- | >>> def myfunc(a, b): | ... "Return a-b if a>b, otherwise return a+b" | ... if a > b: | ... return a - b | ... else: | ... return a + b | | >>> vfunc = np.vectorize(myfunc) | >>> vfunc([1, 2, 3, 4], 2) | array([3, 4, 1, 2]) | | The docstring is taken from the input function to `vectorize` unless it | is specified | | >>> vfunc.__doc__ | 'Return a-b if a>b, otherwise return a+b' | >>> vfunc = np.vectorize(myfunc, doc='Vectorized `myfunc`') | >>> vfunc.__doc__ | 'Vectorized `myfunc`' | | The output type is determined by evaluating the first element of the input, | unless it is specified | | >>> out = vfunc([1, 2, 3, 4], 2) | >>> type(out[0]) | | >>> vfunc = np.vectorize(myfunc, otypes=[np.float]) | >>> out = vfunc([1, 2, 3, 4], 2) | >>> type(out[0]) | | | The `excluded` argument can be used to prevent vectorizing over certain | arguments. This can be useful for array-like arguments of a fixed length | such as the coefficients for a polynomial as in `polyval`: | | >>> def mypolyval(p, x): | ... _p = list(p) | ... res = _p.pop(0) | ... while _p: | ... res = res*x + _p.pop(0) | ... return res | >>> vpolyval = np.vectorize(mypolyval, excluded=['p']) | >>> vpolyval(p=[1, 2, 3], x=[0, 1]) | array([3, 6]) | | Positional arguments may also be excluded by specifying their position: | | >>> vpolyval.excluded.add(0) | >>> vpolyval([1, 2, 3], x=[0, 1]) | array([3, 6]) | | Notes | ----- | The `vectorize` function is provided primarily for convenience, not for | performance. The implementation is essentially a for loop. | | If `otypes` is not specified, then a call to the function with the | first argument will be used to determine the number of outputs. The | results of this call will be cached if `cache` is `True` to prevent | calling the function twice. However, to implement the cache, the | original function must be wrapped which will slow down subsequent | calls, so only do this if your function is expensive. | | The new keyword argument interface and `excluded` argument support | further degrades performance. | | Methods defined here: | | __call__(self, *args, **kwargs) | Return arrays with the results of `pyfunc` broadcast (vectorized) over | `args` and `kwargs` not in `excluded`. | | __init__(self, pyfunc, otypes='', doc=None, excluded=None, cache=False) | | ---------------------------------------------------------------------- | Data descriptors defined here: | | __dict__ | dictionary for instance variables (if defined) | | __weakref__ | list of weak references to the object (if defined) class void(flexible) | Method resolution order: | void | flexible | generic | __builtin__.object | | Methods defined here: | | __delitem__(...) | x.__delitem__(y) <==> del x[y] | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __le__(...) | x.__le__(y) <==> x<=y | | __len__(...) | x.__len__() <==> len(x) | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __setitem__(...) | x.__setitem__(i, y) <==> x[i]=y | | getfield(...) | | setfield(...) | | ---------------------------------------------------------------------- | Data descriptors defined here: | | dtype | dtype object | | flags | integer value of flags | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension void0 = class void(flexible) | Method resolution order: | void | flexible | generic | __builtin__.object | | Methods defined here: | | __delitem__(...) | x.__delitem__(y) <==> del x[y] | | __eq__(...) | x.__eq__(y) <==> x==y | | __ge__(...) | x.__ge__(y) <==> x>=y | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __gt__(...) | x.__gt__(y) <==> x>y | | __hash__(...) | x.__hash__() <==> hash(x) | | __le__(...) | x.__le__(y) <==> x<=y | | __len__(...) | x.__len__() <==> len(x) | | __lt__(...) | x.__lt__(y) <==> x x!=y | | __setitem__(...) | x.__setitem__(i, y) <==> x[i]=y | | getfield(...) | | setfield(...) | | ---------------------------------------------------------------------- | Data descriptors defined here: | | dtype | dtype object | | flags | integer value of flags | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = | T.__new__(S, ...) -> a new object with type S, a subtype of T | | ---------------------------------------------------------------------- | Methods inherited from generic: | | __abs__(...) | x.__abs__() <==> abs(x) | | __add__(...) | x.__add__(y) <==> x+y | | __and__(...) | x.__and__(y) <==> x&y | | __array__(...) | sc.__array__(|type) return 0-dim array | | __array_wrap__(...) | sc.__array_wrap__(obj) return scalar from array | | __copy__(...) | | __deepcopy__(...) | | __div__(...) | x.__div__(y) <==> x/y | | __divmod__(...) | x.__divmod__(y) <==> divmod(x, y) | | __float__(...) | x.__float__() <==> float(x) | | __floordiv__(...) | x.__floordiv__(y) <==> x//y | | __format__(...) | NumPy array scalar formatter | | __hex__(...) | x.__hex__() <==> hex(x) | | __int__(...) | x.__int__() <==> int(x) | | __invert__(...) | x.__invert__() <==> ~x | | __long__(...) | x.__long__() <==> long(x) | | __lshift__(...) | x.__lshift__(y) <==> x< x%y | | __mul__(...) | x.__mul__(y) <==> x*y | | __neg__(...) | x.__neg__() <==> -x | | __nonzero__(...) | x.__nonzero__() <==> x != 0 | | __oct__(...) | x.__oct__() <==> oct(x) | | __or__(...) | x.__or__(y) <==> x|y | | __pos__(...) | x.__pos__() <==> +x | | __pow__(...) | x.__pow__(y[, z]) <==> pow(x, y[, z]) | | __radd__(...) | x.__radd__(y) <==> y+x | | __rand__(...) | x.__rand__(y) <==> y&x | | __rdiv__(...) | x.__rdiv__(y) <==> y/x | | __rdivmod__(...) | x.__rdivmod__(y) <==> divmod(y, x) | | __reduce__(...) | | __repr__(...) | x.__repr__() <==> repr(x) | | __rfloordiv__(...) | x.__rfloordiv__(y) <==> y//x | | __rlshift__(...) | x.__rlshift__(y) <==> y< y%x | | __rmul__(...) | x.__rmul__(y) <==> y*x | | __ror__(...) | x.__ror__(y) <==> y|x | | __rpow__(...) | y.__rpow__(x[, z]) <==> pow(x, y[, z]) | | __rrshift__(...) | x.__rrshift__(y) <==> y>>x | | __rshift__(...) | x.__rshift__(y) <==> x>>y | | __rsub__(...) | x.__rsub__(y) <==> y-x | | __rtruediv__(...) | x.__rtruediv__(y) <==> y/x | | __rxor__(...) | x.__rxor__(y) <==> y^x | | __setstate__(...) | | __sizeof__(...) | | __str__(...) | x.__str__() <==> str(x) | | __sub__(...) | x.__sub__(y) <==> x-y | | __truediv__(...) | x.__truediv__(y) <==> x/y | | __xor__(...) | x.__xor__(y) <==> x^y | | all(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | any(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmax(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argmin(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | argsort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | astype(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | byteswap(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | choose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | clip(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | compress(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | conj(...) | | conjugate(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | copy(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumprod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | cumsum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | diagonal(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dump(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | dumps(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | fill(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | flatten(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | item(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | itemset(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | max(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | mean(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | min(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | newbyteorder(...) | newbyteorder(new_order='S') | | Return a new `dtype` with a different byte order. | | Changes are also made in all fields and sub-arrays of the data type. | | The `new_order` code can be any from the following: | | * 'S' - swap dtype from current to opposite endian | * {'<', 'L'} - little endian | * {'>', 'B'} - big endian | * {'=', 'N'} - native order | * {'|', 'I'} - ignore (no change to byte order) | | Parameters | ---------- | new_order : str, optional | Byte order to force; a value from the byte order specifications | above. The default value ('S') results in swapping the current | byte order. The code does a case-insensitive check on the first | letter of `new_order` for the alternatives above. For example, | any of 'B' or 'b' or 'biggish' are valid to specify big-endian. | | | Returns | ------- | new_dtype : dtype | New `dtype` object with the given change to the byte order. | | nonzero(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | prod(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ptp(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | put(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ravel(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | repeat(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | reshape(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | resize(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | round(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | searchsorted(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | setflags(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class so as to | provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sort(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | squeeze(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | std(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | sum(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | swapaxes(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | take(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tobytes(...) | | tofile(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tolist(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | tostring(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | trace(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | transpose(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | var(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | view(...) | Not implemented (virtual attribute) | | Class generic exists solely to derive numpy scalars from, and possesses, | albeit unimplemented, all the attributes of the ndarray class | so as to provide a uniform API. | | See Also | -------- | The corresponding attribute of the derived class of interest. | | ---------------------------------------------------------------------- | Data descriptors inherited from generic: | | T | transpose | | __array_interface__ | Array protocol: Python side | | __array_priority__ | Array priority. | | __array_struct__ | Array protocol: struct | | base | base object | | data | pointer to start of data | | flat | a 1-d view of scalar | | imag | imaginary part of scalar | | itemsize | length of one element in bytes | | nbytes | length of item in bytes | | ndim | number of array dimensions | | real | real part of scalar | | shape | tuple of array dimensions | | size | number of elements in the gentype | | strides | tuple of bytes steps in each dimension FUNCTIONS add_docstring(...) add_docstring(obj, docstring) Add a docstring to a built-in obj if possible. If the obj already has a docstring raise a RuntimeError If this routine does not know how to add a docstring to the object raise a TypeError add_newdoc(place, obj, doc) Adds documentation to obj which is in module place. If doc is a string add it to obj as a docstring If doc is a tuple, then the first element is interpreted as an attribute of obj and the second as the docstring (method, docstring) If doc is a list, then each element of the list should be a sequence of length two --> [(method1, docstring1), (method2, docstring2), ...] This routine never raises an error. This routine cannot modify read-only docstrings, as appear in new-style classes or built-in functions. Because this routine never raises an error the caller must check manually that the docstrings were changed. add_newdoc_ufunc = _add_newdoc_ufunc(...) add_ufunc_docstring(ufunc, new_docstring) Replace the docstring for a ufunc with new_docstring. This method will only work if the current docstring for the ufunc is NULL. (At the C level, i.e. when ufunc->doc is NULL.) Parameters ---------- ufunc : numpy.ufunc A ufunc whose current doc is NULL. new_docstring : string The new docstring for the ufunc. Notes ----- This method allocates memory for new_docstring on the heap. Technically this creates a mempory leak, since this memory will not be reclaimed until the end of the program even if the ufunc itself is removed. However this will only be a problem if the user is repeatedly creating ufuncs with no documentation, adding documentation via add_newdoc_ufunc, and then throwing away the ufunc. alen(a) Return the length of the first dimension of the input array. Parameters ---------- a : array_like Input array. Returns ------- alen : int Length of the first dimension of `a`. See Also -------- shape, size Examples -------- >>> a = np.zeros((7,4,5)) >>> a.shape[0] 7 >>> np.alen(a) 7 all(a, axis=None, out=None, keepdims=False) Test whether all array elements along a given axis evaluate to True. Parameters ---------- a : array_like Input array or object that can be converted to an array. axis : None or int or tuple of ints, optional Axis or axes along which a logical AND reduction is performed. The default (`axis` = `None`) is to perform a logical AND over all the dimensions of the input array. `axis` may be negative, in which case it counts from the last to the first axis. .. versionadded:: 1.7.0 If this is a tuple of ints, a reduction is performed on multiple axes, instead of a single axis or all the axes as before. out : ndarray, optional Alternate output array in which to place the result. It must have the same shape as the expected output and its type is preserved (e.g., if ``dtype(out)`` is float, the result will consist of 0.0's and 1.0's). See `doc.ufuncs` (Section "Output arguments") for more details. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `arr`. Returns ------- all : ndarray, bool A new boolean or array is returned unless `out` is specified, in which case a reference to `out` is returned. See Also -------- ndarray.all : equivalent method any : Test whether any element along a given axis evaluates to True. Notes ----- Not a Number (NaN), positive infinity and negative infinity evaluate to `True` because these are not equal to zero. Examples -------- >>> np.all([[True,False],[True,True]]) False >>> np.all([[True,False],[True,True]], axis=0) array([ True, False], dtype=bool) >>> np.all([-1, 4, 5]) True >>> np.all([1.0, np.nan]) True >>> o=np.array([False]) >>> z=np.all([-1, 4, 5], out=o) >>> id(z), id(o), z # doctest: +SKIP (28293632, 28293632, array([ True], dtype=bool)) allclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False) Returns True if two arrays are element-wise equal within a tolerance. The tolerance values are positive, typically very small numbers. The relative difference (`rtol` * abs(`b`)) and the absolute difference `atol` are added together to compare against the absolute difference between `a` and `b`. If either array contains one or more NaNs, False is returned. Infs are treated as equal if they are in the same place and of the same sign in both arrays. Parameters ---------- a, b : array_like Input arrays to compare. rtol : float The relative tolerance parameter (see Notes). atol : float The absolute tolerance parameter (see Notes). equal_nan : bool Whether to compare NaN's as equal. If True, NaN's in `a` will be considered equal to NaN's in `b` in the output array. .. versionadded:: 1.10.0 Returns ------- allclose : bool Returns True if the two arrays are equal within the given tolerance; False otherwise. See Also -------- isclose, all, any Notes ----- If the following equation is element-wise True, then allclose returns True. absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`)) The above equation is not symmetric in `a` and `b`, so that `allclose(a, b)` might be different from `allclose(b, a)` in some rare cases. Examples -------- >>> np.allclose([1e10,1e-7], [1.00001e10,1e-8]) False >>> np.allclose([1e10,1e-8], [1.00001e10,1e-9]) True >>> np.allclose([1e10,1e-8], [1.0001e10,1e-9]) False >>> np.allclose([1.0, np.nan], [1.0, np.nan]) False >>> np.allclose([1.0, np.nan], [1.0, np.nan], equal_nan=True) True alltrue(a, axis=None, out=None, keepdims=False) Check if all elements of input array are true. See Also -------- numpy.all : Equivalent function; see for details. alterdot() Change `dot`, `vdot`, and `inner` to use accelerated BLAS functions. Typically, as a user of Numpy, you do not explicitly call this function. If Numpy is built with an accelerated BLAS, this function is automatically called when Numpy is imported. When Numpy is built with an accelerated BLAS like ATLAS, these functions are replaced to make use of the faster implementations. The faster implementations only affect float32, float64, complex64, and complex128 arrays. Furthermore, the BLAS API only includes matrix-matrix, matrix-vector, and vector-vector products. Products of arrays with larger dimensionalities use the built in functions and are not accelerated. .. note:: Deprecated in Numpy 1.10 The cblas functions have been integrated into the multarray module and alterdot now longer does anything. It will be removed in Numpy 1.11.0. See Also -------- restoredot : `restoredot` undoes the effects of `alterdot`. amax(a, axis=None, out=None, keepdims=False) Return the maximum of an array or maximum along an axis. Parameters ---------- a : array_like Input data. axis : None or int or tuple of ints, optional Axis or axes along which to operate. By default, flattened input is used. .. versionadded: 1.7.0 If this is a tuple of ints, the maximum is selected over multiple axes, instead of a single axis or all the axes as before. out : ndarray, optional Alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output. See `doc.ufuncs` (Section "Output arguments") for more details. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `arr`. Returns ------- amax : ndarray or scalar Maximum of `a`. If `axis` is None, the result is a scalar value. If `axis` is given, the result is an array of dimension ``a.ndim - 1``. See Also -------- amin : The minimum value of an array along a given axis, propagating any NaNs. nanmax : The maximum value of an array along a given axis, ignoring any NaNs. maximum : Element-wise maximum of two arrays, propagating any NaNs. fmax : Element-wise maximum of two arrays, ignoring any NaNs. argmax : Return the indices of the maximum values. nanmin, minimum, fmin Notes ----- NaN values are propagated, that is if at least one item is NaN, the corresponding max value will be NaN as well. To ignore NaN values (MATLAB behavior), please use nanmax. Don't use `amax` for element-wise comparison of 2 arrays; when ``a.shape[0]`` is 2, ``maximum(a[0], a[1])`` is faster than ``amax(a, axis=0)``. Examples -------- >>> a = np.arange(4).reshape((2,2)) >>> a array([[0, 1], [2, 3]]) >>> np.amax(a) # Maximum of the flattened array 3 >>> np.amax(a, axis=0) # Maxima along the first axis array([2, 3]) >>> np.amax(a, axis=1) # Maxima along the second axis array([1, 3]) >>> b = np.arange(5, dtype=np.float) >>> b[2] = np.NaN >>> np.amax(b) nan >>> np.nanmax(b) 4.0 amin(a, axis=None, out=None, keepdims=False) Return the minimum of an array or minimum along an axis. Parameters ---------- a : array_like Input data. axis : None or int or tuple of ints, optional Axis or axes along which to operate. By default, flattened input is used. .. versionadded: 1.7.0 If this is a tuple of ints, the minimum is selected over multiple axes, instead of a single axis or all the axes as before. out : ndarray, optional Alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output. See `doc.ufuncs` (Section "Output arguments") for more details. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `arr`. Returns ------- amin : ndarray or scalar Minimum of `a`. If `axis` is None, the result is a scalar value. If `axis` is given, the result is an array of dimension ``a.ndim - 1``. See Also -------- amax : The maximum value of an array along a given axis, propagating any NaNs. nanmin : The minimum value of an array along a given axis, ignoring any NaNs. minimum : Element-wise minimum of two arrays, propagating any NaNs. fmin : Element-wise minimum of two arrays, ignoring any NaNs. argmin : Return the indices of the minimum values. nanmax, maximum, fmax Notes ----- NaN values are propagated, that is if at least one item is NaN, the corresponding min value will be NaN as well. To ignore NaN values (MATLAB behavior), please use nanmin. Don't use `amin` for element-wise comparison of 2 arrays; when ``a.shape[0]`` is 2, ``minimum(a[0], a[1])`` is faster than ``amin(a, axis=0)``. Examples -------- >>> a = np.arange(4).reshape((2,2)) >>> a array([[0, 1], [2, 3]]) >>> np.amin(a) # Minimum of the flattened array 0 >>> np.amin(a, axis=0) # Minima along the first axis array([0, 1]) >>> np.amin(a, axis=1) # Minima along the second axis array([0, 2]) >>> b = np.arange(5, dtype=np.float) >>> b[2] = np.NaN >>> np.amin(b) nan >>> np.nanmin(b) 0.0 angle(z, deg=0) Return the angle of the complex argument. Parameters ---------- z : array_like A complex number or sequence of complex numbers. deg : bool, optional Return angle in degrees if True, radians if False (default). Returns ------- angle : ndarray or scalar The counterclockwise angle from the positive real axis on the complex plane, with dtype as numpy.float64. See Also -------- arctan2 absolute Examples -------- >>> np.angle([1.0, 1.0j, 1+1j]) # in radians array([ 0. , 1.57079633, 0.78539816]) >>> np.angle(1+1j, deg=True) # in degrees 45.0 any(a, axis=None, out=None, keepdims=False) Test whether any array element along a given axis evaluates to True. Returns single boolean unless `axis` is not ``None`` Parameters ---------- a : array_like Input array or object that can be converted to an array. axis : None or int or tuple of ints, optional Axis or axes along which a logical OR reduction is performed. The default (`axis` = `None`) is to perform a logical OR over all the dimensions of the input array. `axis` may be negative, in which case it counts from the last to the first axis. .. versionadded:: 1.7.0 If this is a tuple of ints, a reduction is performed on multiple axes, instead of a single axis or all the axes as before. out : ndarray, optional Alternate output array in which to place the result. It must have the same shape as the expected output and its type is preserved (e.g., if it is of type float, then it will remain so, returning 1.0 for True and 0.0 for False, regardless of the type of `a`). See `doc.ufuncs` (Section "Output arguments") for details. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `arr`. Returns ------- any : bool or ndarray A new boolean or `ndarray` is returned unless `out` is specified, in which case a reference to `out` is returned. See Also -------- ndarray.any : equivalent method all : Test whether all elements along a given axis evaluate to True. Notes ----- Not a Number (NaN), positive infinity and negative infinity evaluate to `True` because these are not equal to zero. Examples -------- >>> np.any([[True, False], [True, True]]) True >>> np.any([[True, False], [False, False]], axis=0) array([ True, False], dtype=bool) >>> np.any([-1, 0, 5]) True >>> np.any(np.nan) True >>> o=np.array([False]) >>> z=np.any([-1, 4, 5], out=o) >>> z, o (array([ True], dtype=bool), array([ True], dtype=bool)) >>> # Check now that z is a reference to o >>> z is o True >>> id(z), id(o) # identity of z and o # doctest: +SKIP (191614240, 191614240) append(arr, values, axis=None) Append values to the end of an array. Parameters ---------- arr : array_like Values are appended to a copy of this array. values : array_like These values are appended to a copy of `arr`. It must be of the correct shape (the same shape as `arr`, excluding `axis`). If `axis` is not specified, `values` can be any shape and will be flattened before use. axis : int, optional The axis along which `values` are appended. If `axis` is not given, both `arr` and `values` are flattened before use. Returns ------- append : ndarray A copy of `arr` with `values` appended to `axis`. Note that `append` does not occur in-place: a new array is allocated and filled. If `axis` is None, `out` is a flattened array. See Also -------- insert : Insert elements into an array. delete : Delete elements from an array. Examples -------- >>> np.append([1, 2, 3], [[4, 5, 6], [7, 8, 9]]) array([1, 2, 3, 4, 5, 6, 7, 8, 9]) When `axis` is specified, `values` must have the correct shape. >>> np.append([[1, 2, 3], [4, 5, 6]], [[7, 8, 9]], axis=0) array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) >>> np.append([[1, 2, 3], [4, 5, 6]], [7, 8, 9], axis=0) Traceback (most recent call last): ... ValueError: arrays must have same number of dimensions apply_along_axis(func1d, axis, arr, *args, **kwargs) Apply a function to 1-D slices along the given axis. Execute `func1d(a, *args)` where `func1d` operates on 1-D arrays and `a` is a 1-D slice of `arr` along `axis`. Parameters ---------- func1d : function This function should accept 1-D arrays. It is applied to 1-D slices of `arr` along the specified axis. axis : integer Axis along which `arr` is sliced. arr : ndarray Input array. args : any Additional arguments to `func1d`. kwargs: any Additional named arguments to `func1d`. .. versionadded:: 1.9.0 Returns ------- apply_along_axis : ndarray The output array. The shape of `outarr` is identical to the shape of `arr`, except along the `axis` dimension, where the length of `outarr` is equal to the size of the return value of `func1d`. If `func1d` returns a scalar `outarr` will have one fewer dimensions than `arr`. See Also -------- apply_over_axes : Apply a function repeatedly over multiple axes. Examples -------- >>> def my_func(a): ... """Average first and last element of a 1-D array""" ... return (a[0] + a[-1]) * 0.5 >>> b = np.array([[1,2,3], [4,5,6], [7,8,9]]) >>> np.apply_along_axis(my_func, 0, b) array([ 4., 5., 6.]) >>> np.apply_along_axis(my_func, 1, b) array([ 2., 5., 8.]) For a function that doesn't return a scalar, the number of dimensions in `outarr` is the same as `arr`. >>> b = np.array([[8,1,7], [4,3,9], [5,2,6]]) >>> np.apply_along_axis(sorted, 1, b) array([[1, 7, 8], [3, 4, 9], [2, 5, 6]]) apply_over_axes(func, a, axes) Apply a function repeatedly over multiple axes. `func` is called as `res = func(a, axis)`, where `axis` is the first element of `axes`. The result `res` of the function call must have either the same dimensions as `a` or one less dimension. If `res` has one less dimension than `a`, a dimension is inserted before `axis`. The call to `func` is then repeated for each axis in `axes`, with `res` as the first argument. Parameters ---------- func : function This function must take two arguments, `func(a, axis)`. a : array_like Input array. axes : array_like Axes over which `func` is applied; the elements must be integers. Returns ------- apply_over_axis : ndarray The output array. The number of dimensions is the same as `a`, but the shape can be different. This depends on whether `func` changes the shape of its output with respect to its input. See Also -------- apply_along_axis : Apply a function to 1-D slices of an array along the given axis. Notes ------ This function is equivalent to tuple axis arguments to reorderable ufuncs with keepdims=True. Tuple axis arguments to ufuncs have been availabe since version 1.7.0. Examples -------- >>> a = np.arange(24).reshape(2,3,4) >>> a array([[[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]], [[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]]]) Sum over axes 0 and 2. The result has same number of dimensions as the original array: >>> np.apply_over_axes(np.sum, a, [0,2]) array([[[ 60], [ 92], [124]]]) Tuple axis arguments to ufuncs are equivalent: >>> np.sum(a, axis=(0,2), keepdims=True) array([[[ 60], [ 92], [124]]]) arange(...) arange([start,] stop[, step,], dtype=None) Return evenly spaced values within a given interval. Values are generated within the half-open interval ``[start, stop)`` (in other words, the interval including `start` but excluding `stop`). For integer arguments the function is equivalent to the Python built-in `range `_ function, but returns an ndarray rather than a list. When using a non-integer step, such as 0.1, the results will often not be consistent. It is better to use ``linspace`` for these cases. Parameters ---------- start : number, optional Start of interval. The interval includes this value. The default start value is 0. stop : number End of interval. The interval does not include this value, except in some cases where `step` is not an integer and floating point round-off affects the length of `out`. step : number, optional Spacing between values. For any output `out`, this is the distance between two adjacent values, ``out[i+1] - out[i]``. The default step size is 1. If `step` is specified, `start` must also be given. dtype : dtype The type of the output array. If `dtype` is not given, infer the data type from the other input arguments. Returns ------- arange : ndarray Array of evenly spaced values. For floating point arguments, the length of the result is ``ceil((stop - start)/step)``. Because of floating point overflow, this rule may result in the last element of `out` being greater than `stop`. See Also -------- linspace : Evenly spaced numbers with careful handling of endpoints. ogrid: Arrays of evenly spaced numbers in N-dimensions. mgrid: Grid-shaped arrays of evenly spaced numbers in N-dimensions. Examples -------- >>> np.arange(3) array([0, 1, 2]) >>> np.arange(3.0) array([ 0., 1., 2.]) >>> np.arange(3,7) array([3, 4, 5, 6]) >>> np.arange(3,7,2) array([3, 5]) argmax(a, axis=None, out=None) Returns the indices of the maximum values along an axis. Parameters ---------- a : array_like Input array. axis : int, optional By default, the index is into the flattened array, otherwise along the specified axis. out : array, optional If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype. Returns ------- index_array : ndarray of ints Array of indices into the array. It has the same shape as `a.shape` with the dimension along `axis` removed. See Also -------- ndarray.argmax, argmin amax : The maximum value along a given axis. unravel_index : Convert a flat index into an index tuple. Notes ----- In case of multiple occurrences of the maximum values, the indices corresponding to the first occurrence are returned. Examples -------- >>> a = np.arange(6).reshape(2,3) >>> a array([[0, 1, 2], [3, 4, 5]]) >>> np.argmax(a) 5 >>> np.argmax(a, axis=0) array([1, 1, 1]) >>> np.argmax(a, axis=1) array([2, 2]) >>> b = np.arange(6) >>> b[1] = 5 >>> b array([0, 5, 2, 3, 4, 5]) >>> np.argmax(b) # Only the first occurrence is returned. 1 argmin(a, axis=None, out=None) Returns the indices of the minimum values along an axis. Parameters ---------- a : array_like Input array. axis : int, optional By default, the index is into the flattened array, otherwise along the specified axis. out : array, optional If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype. Returns ------- index_array : ndarray of ints Array of indices into the array. It has the same shape as `a.shape` with the dimension along `axis` removed. See Also -------- ndarray.argmin, argmax amin : The minimum value along a given axis. unravel_index : Convert a flat index into an index tuple. Notes ----- In case of multiple occurrences of the minimum values, the indices corresponding to the first occurrence are returned. Examples -------- >>> a = np.arange(6).reshape(2,3) >>> a array([[0, 1, 2], [3, 4, 5]]) >>> np.argmin(a) 0 >>> np.argmin(a, axis=0) array([0, 0, 0]) >>> np.argmin(a, axis=1) array([0, 0]) >>> b = np.arange(6) >>> b[4] = 0 >>> b array([0, 1, 2, 3, 0, 5]) >>> np.argmin(b) # Only the first occurrence is returned. 0 argpartition(a, kth, axis=-1, kind='introselect', order=None) Perform an indirect partition along the given axis using the algorithm specified by the `kind` keyword. It returns an array of indices of the same shape as `a` that index data along the given axis in partitioned order. .. versionadded:: 1.8.0 Parameters ---------- a : array_like Array to sort. kth : int or sequence of ints Element index to partition by. The kth element will be in its final sorted position and all smaller elements will be moved before it and all larger elements behind it. The order all elements in the partitions is undefined. If provided with a sequence of kth it will partition all of them into their sorted position at once. axis : int or None, optional Axis along which to sort. The default is -1 (the last axis). If None, the flattened array is used. kind : {'introselect'}, optional Selection algorithm. Default is 'introselect' order : str or list of str, optional When `a` is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string, and not all fields need be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties. Returns ------- index_array : ndarray, int Array of indices that partition `a` along the specified axis. In other words, ``a[index_array]`` yields a sorted `a`. See Also -------- partition : Describes partition algorithms used. ndarray.partition : Inplace partition. argsort : Full indirect sort Notes ----- See `partition` for notes on the different selection algorithms. Examples -------- One dimensional array: >>> x = np.array([3, 4, 2, 1]) >>> x[np.argpartition(x, 3)] array([2, 1, 3, 4]) >>> x[np.argpartition(x, (1, 3))] array([1, 2, 3, 4]) >>> x = [3, 4, 2, 1] >>> np.array(x)[np.argpartition(x, 3)] array([2, 1, 3, 4]) argsort(a, axis=-1, kind='quicksort', order=None) Returns the indices that would sort an array. Perform an indirect sort along the given axis using the algorithm specified by the `kind` keyword. It returns an array of indices of the same shape as `a` that index data along the given axis in sorted order. Parameters ---------- a : array_like Array to sort. axis : int or None, optional Axis along which to sort. The default is -1 (the last axis). If None, the flattened array is used. kind : {'quicksort', 'mergesort', 'heapsort'}, optional Sorting algorithm. order : str or list of str, optional When `a` is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string, and not all fields need be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties. Returns ------- index_array : ndarray, int Array of indices that sort `a` along the specified axis. In other words, ``a[index_array]`` yields a sorted `a`. See Also -------- sort : Describes sorting algorithms used. lexsort : Indirect stable sort with multiple keys. ndarray.sort : Inplace sort. argpartition : Indirect partial sort. Notes ----- See `sort` for notes on the different sorting algorithms. As of NumPy 1.4.0 `argsort` works with real/complex arrays containing nan values. The enhanced sort order is documented in `sort`. Examples -------- One dimensional array: >>> x = np.array([3, 1, 2]) >>> np.argsort(x) array([1, 2, 0]) Two-dimensional array: >>> x = np.array([[0, 3], [2, 2]]) >>> x array([[0, 3], [2, 2]]) >>> np.argsort(x, axis=0) array([[0, 1], [1, 0]]) >>> np.argsort(x, axis=1) array([[0, 1], [0, 1]]) Sorting with keys: >>> x = np.array([(1, 0), (0, 1)], dtype=[('x', '>> x array([(1, 0), (0, 1)], dtype=[('x', '>> np.argsort(x, order=('x','y')) array([1, 0]) >>> np.argsort(x, order=('y','x')) array([0, 1]) argwhere(a) Find the indices of array elements that are non-zero, grouped by element. Parameters ---------- a : array_like Input data. Returns ------- index_array : ndarray Indices of elements that are non-zero. Indices are grouped by element. See Also -------- where, nonzero Notes ----- ``np.argwhere(a)`` is the same as ``np.transpose(np.nonzero(a))``. The output of ``argwhere`` is not suitable for indexing arrays. For this purpose use ``where(a)`` instead. Examples -------- >>> x = np.arange(6).reshape(2,3) >>> x array([[0, 1, 2], [3, 4, 5]]) >>> np.argwhere(x>1) array([[0, 2], [1, 0], [1, 1], [1, 2]]) around(a, decimals=0, out=None) Evenly round to the given number of decimals. Parameters ---------- a : array_like Input data. decimals : int, optional Number of decimal places to round to (default: 0). If decimals is negative, it specifies the number of positions to the left of the decimal point. out : ndarray, optional Alternative output array in which to place the result. It must have the same shape as the expected output, but the type of the output values will be cast if necessary. See `doc.ufuncs` (Section "Output arguments") for details. Returns ------- rounded_array : ndarray An array of the same type as `a`, containing the rounded values. Unless `out` was specified, a new array is created. A reference to the result is returned. The real and imaginary parts of complex numbers are rounded separately. The result of rounding a float is a float. See Also -------- ndarray.round : equivalent method ceil, fix, floor, rint, trunc Notes ----- For values exactly halfway between rounded decimal values, Numpy rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0, -0.5 and 0.5 round to 0.0, etc. Results may also be surprising due to the inexact representation of decimal fractions in the IEEE floating point standard [1]_ and errors introduced when scaling by powers of ten. References ---------- .. [1] "Lecture Notes on the Status of IEEE 754", William Kahan, http://www.cs.berkeley.edu/~wkahan/ieee754status/IEEE754.PDF .. [2] "How Futile are Mindless Assessments of Roundoff in Floating-Point Computation?", William Kahan, http://www.cs.berkeley.edu/~wkahan/Mindless.pdf Examples -------- >>> np.around([0.37, 1.64]) array([ 0., 2.]) >>> np.around([0.37, 1.64], decimals=1) array([ 0.4, 1.6]) >>> np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value array([ 0., 2., 2., 4., 4.]) >>> np.around([1,2,3,11], decimals=1) # ndarray of ints is returned array([ 1, 2, 3, 11]) >>> np.around([1,2,3,11], decimals=-1) array([ 0, 0, 0, 10]) array(...) array(object, dtype=None, copy=True, order=None, subok=False, ndmin=0) Create an array. Parameters ---------- object : array_like An array, any object exposing the array interface, an object whose __array__ method returns an array, or any (nested) sequence. dtype : data-type, optional The desired data-type for the array. If not given, then the type will be determined as the minimum type required to hold the objects in the sequence. This argument can only be used to 'upcast' the array. For downcasting, use the .astype(t) method. copy : bool, optional If true (default), then the object is copied. Otherwise, a copy will only be made if __array__ returns a copy, if obj is a nested sequence, or if a copy is needed to satisfy any of the other requirements (`dtype`, `order`, etc.). order : {'C', 'F', 'A'}, optional Specify the order of the array. If order is 'C', then the array will be in C-contiguous order (last-index varies the fastest). If order is 'F', then the returned array will be in Fortran-contiguous order (first-index varies the fastest). If order is 'A' (default), then the returned array may be in any order (either C-, Fortran-contiguous, or even discontiguous), unless a copy is required, in which case it will be C-contiguous. subok : bool, optional If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array (default). ndmin : int, optional Specifies the minimum number of dimensions that the resulting array should have. Ones will be pre-pended to the shape as needed to meet this requirement. Returns ------- out : ndarray An array object satisfying the specified requirements. See Also -------- empty, empty_like, zeros, zeros_like, ones, ones_like, fill Examples -------- >>> np.array([1, 2, 3]) array([1, 2, 3]) Upcasting: >>> np.array([1, 2, 3.0]) array([ 1., 2., 3.]) More than one dimension: >>> np.array([[1, 2], [3, 4]]) array([[1, 2], [3, 4]]) Minimum dimensions 2: >>> np.array([1, 2, 3], ndmin=2) array([[1, 2, 3]]) Type provided: >>> np.array([1, 2, 3], dtype=complex) array([ 1.+0.j, 2.+0.j, 3.+0.j]) Data-type consisting of more than one element: >>> x = np.array([(1,2),(3,4)],dtype=[('a','>> x['a'] array([1, 3]) Creating an array from sub-classes: >>> np.array(np.mat('1 2; 3 4')) array([[1, 2], [3, 4]]) >>> np.array(np.mat('1 2; 3 4'), subok=True) matrix([[1, 2], [3, 4]]) array2string(a, max_line_width=None, precision=None, suppress_small=None, separator=' ', prefix='', style=, formatter=None) Return a string representation of an array. Parameters ---------- a : ndarray Input array. max_line_width : int, optional The maximum number of columns the string should span. Newline characters splits the string appropriately after array elements. precision : int, optional Floating point precision. Default is the current printing precision (usually 8), which can be altered using `set_printoptions`. suppress_small : bool, optional Represent very small numbers as zero. A number is "very small" if it is smaller than the current printing precision. separator : str, optional Inserted between elements. prefix : str, optional An array is typically printed as:: 'prefix(' + array2string(a) + ')' The length of the prefix string is used to align the output correctly. style : function, optional A function that accepts an ndarray and returns a string. Used only when the shape of `a` is equal to ``()``, i.e. for 0-D arrays. formatter : dict of callables, optional If not None, the keys should indicate the type(s) that the respective formatting function applies to. Callables should return a string. Types that are not specified (by their corresponding keys) are handled by the default formatters. Individual types for which a formatter can be set are:: - 'bool' - 'int' - 'timedelta' : a `numpy.timedelta64` - 'datetime' : a `numpy.datetime64` - 'float' - 'longfloat' : 128-bit floats - 'complexfloat' - 'longcomplexfloat' : composed of two 128-bit floats - 'numpy_str' : types `numpy.string_` and `numpy.unicode_` - 'str' : all other strings Other keys that can be used to set a group of types at once are:: - 'all' : sets all types - 'int_kind' : sets 'int' - 'float_kind' : sets 'float' and 'longfloat' - 'complex_kind' : sets 'complexfloat' and 'longcomplexfloat' - 'str_kind' : sets 'str' and 'numpystr' Returns ------- array_str : str String representation of the array. Raises ------ TypeError if a callable in `formatter` does not return a string. See Also -------- array_str, array_repr, set_printoptions, get_printoptions Notes ----- If a formatter is specified for a certain type, the `precision` keyword is ignored for that type. This is a very flexible function; `array_repr` and `array_str` are using `array2string` internally so keywords with the same name should work identically in all three functions. Examples -------- >>> x = np.array([1e-16,1,2,3]) >>> print np.array2string(x, precision=2, separator=',', ... suppress_small=True) [ 0., 1., 2., 3.] >>> x = np.arange(3.) >>> np.array2string(x, formatter={'float_kind':lambda x: "%.2f" % x}) '[0.00 1.00 2.00]' >>> x = np.arange(3) >>> np.array2string(x, formatter={'int':lambda x: hex(x)}) '[0x0L 0x1L 0x2L]' array_equal(a1, a2) True if two arrays have the same shape and elements, False otherwise. Parameters ---------- a1, a2 : array_like Input arrays. Returns ------- b : bool Returns True if the arrays are equal. See Also -------- allclose: Returns True if two arrays are element-wise equal within a tolerance. array_equiv: Returns True if input arrays are shape consistent and all elements equal. Examples -------- >>> np.array_equal([1, 2], [1, 2]) True >>> np.array_equal(np.array([1, 2]), np.array([1, 2])) True >>> np.array_equal([1, 2], [1, 2, 3]) False >>> np.array_equal([1, 2], [1, 4]) False array_equiv(a1, a2) Returns True if input arrays are shape consistent and all elements equal. Shape consistent means they are either the same shape, or one input array can be broadcasted to create the same shape as the other one. Parameters ---------- a1, a2 : array_like Input arrays. Returns ------- out : bool True if equivalent, False otherwise. Examples -------- >>> np.array_equiv([1, 2], [1, 2]) True >>> np.array_equiv([1, 2], [1, 3]) False Showing the shape equivalence: >>> np.array_equiv([1, 2], [[1, 2], [1, 2]]) True >>> np.array_equiv([1, 2], [[1, 2, 1, 2], [1, 2, 1, 2]]) False >>> np.array_equiv([1, 2], [[1, 2], [1, 3]]) False array_repr(arr, max_line_width=None, precision=None, suppress_small=None) Return the string representation of an array. Parameters ---------- arr : ndarray Input array. max_line_width : int, optional The maximum number of columns the string should span. Newline characters split the string appropriately after array elements. precision : int, optional Floating point precision. Default is the current printing precision (usually 8), which can be altered using `set_printoptions`. suppress_small : bool, optional Represent very small numbers as zero, default is False. Very small is defined by `precision`, if the precision is 8 then numbers smaller than 5e-9 are represented as zero. Returns ------- string : str The string representation of an array. See Also -------- array_str, array2string, set_printoptions Examples -------- >>> np.array_repr(np.array([1,2])) 'array([1, 2])' >>> np.array_repr(np.ma.array([0.])) 'MaskedArray([ 0.])' >>> np.array_repr(np.array([], np.int32)) 'array([], dtype=int32)' >>> x = np.array([1e-6, 4e-7, 2, 3]) >>> np.array_repr(x, precision=6, suppress_small=True) 'array([ 0.000001, 0. , 2. , 3. ])' array_split(ary, indices_or_sections, axis=0) Split an array into multiple sub-arrays. Please refer to the ``split`` documentation. The only difference between these functions is that ``array_split`` allows `indices_or_sections` to be an integer that does *not* equally divide the axis. See Also -------- split : Split array into multiple sub-arrays of equal size. Examples -------- >>> x = np.arange(8.0) >>> np.array_split(x, 3) [array([ 0., 1., 2.]), array([ 3., 4., 5.]), array([ 6., 7.])] array_str(a, max_line_width=None, precision=None, suppress_small=None) Return a string representation of the data in an array. The data in the array is returned as a single string. This function is similar to `array_repr`, the difference being that `array_repr` also returns information on the kind of array and its data type. Parameters ---------- a : ndarray Input array. max_line_width : int, optional Inserts newlines if text is longer than `max_line_width`. The default is, indirectly, 75. precision : int, optional Floating point precision. Default is the current printing precision (usually 8), which can be altered using `set_printoptions`. suppress_small : bool, optional Represent numbers "very close" to zero as zero; default is False. Very close is defined by precision: if the precision is 8, e.g., numbers smaller (in absolute value) than 5e-9 are represented as zero. See Also -------- array2string, array_repr, set_printoptions Examples -------- >>> np.array_str(np.arange(3)) '[0 1 2]' asanyarray(a, dtype=None, order=None) Convert the input to an ndarray, but pass ndarray subclasses through. Parameters ---------- a : array_like Input data, in any form that can be converted to an array. This includes scalars, lists, lists of tuples, tuples, tuples of tuples, tuples of lists, and ndarrays. dtype : data-type, optional By default, the data-type is inferred from the input data. order : {'C', 'F'}, optional Whether to use row-major (C-style) or column-major (Fortran-style) memory representation. Defaults to 'C'. Returns ------- out : ndarray or an ndarray subclass Array interpretation of `a`. If `a` is an ndarray or a subclass of ndarray, it is returned as-is and no copy is performed. See Also -------- asarray : Similar function which always returns ndarrays. ascontiguousarray : Convert input to a contiguous array. asfarray : Convert input to a floating point ndarray. asfortranarray : Convert input to an ndarray with column-major memory order. asarray_chkfinite : Similar function which checks input for NaNs and Infs. fromiter : Create an array from an iterator. fromfunction : Construct an array by executing a function on grid positions. Examples -------- Convert a list into an array: >>> a = [1, 2] >>> np.asanyarray(a) array([1, 2]) Instances of `ndarray` subclasses are passed through as-is: >>> a = np.matrix([1, 2]) >>> np.asanyarray(a) is a True asarray(a, dtype=None, order=None) Convert the input to an array. Parameters ---------- a : array_like Input data, in any form that can be converted to an array. This includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists and ndarrays. dtype : data-type, optional By default, the data-type is inferred from the input data. order : {'C', 'F'}, optional Whether to use row-major (C-style) or column-major (Fortran-style) memory representation. Defaults to 'C'. Returns ------- out : ndarray Array interpretation of `a`. No copy is performed if the input is already an ndarray. If `a` is a subclass of ndarray, a base class ndarray is returned. See Also -------- asanyarray : Similar function which passes through subclasses. ascontiguousarray : Convert input to a contiguous array. asfarray : Convert input to a floating point ndarray. asfortranarray : Convert input to an ndarray with column-major memory order. asarray_chkfinite : Similar function which checks input for NaNs and Infs. fromiter : Create an array from an iterator. fromfunction : Construct an array by executing a function on grid positions. Examples -------- Convert a list into an array: >>> a = [1, 2] >>> np.asarray(a) array([1, 2]) Existing arrays are not copied: >>> a = np.array([1, 2]) >>> np.asarray(a) is a True If `dtype` is set, array is copied only if dtype does not match: >>> a = np.array([1, 2], dtype=np.float32) >>> np.asarray(a, dtype=np.float32) is a True >>> np.asarray(a, dtype=np.float64) is a False Contrary to `asanyarray`, ndarray subclasses are not passed through: >>> issubclass(np.matrix, np.ndarray) True >>> a = np.matrix([[1, 2]]) >>> np.asarray(a) is a False >>> np.asanyarray(a) is a True asarray_chkfinite(a, dtype=None, order=None) Convert the input to an array, checking for NaNs or Infs. Parameters ---------- a : array_like Input data, in any form that can be converted to an array. This includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists and ndarrays. Success requires no NaNs or Infs. dtype : data-type, optional By default, the data-type is inferred from the input data. order : {'C', 'F'}, optional Whether to use row-major (C-style) or column-major (Fortran-style) memory representation. Defaults to 'C'. Returns ------- out : ndarray Array interpretation of `a`. No copy is performed if the input is already an ndarray. If `a` is a subclass of ndarray, a base class ndarray is returned. Raises ------ ValueError Raises ValueError if `a` contains NaN (Not a Number) or Inf (Infinity). See Also -------- asarray : Create and array. asanyarray : Similar function which passes through subclasses. ascontiguousarray : Convert input to a contiguous array. asfarray : Convert input to a floating point ndarray. asfortranarray : Convert input to an ndarray with column-major memory order. fromiter : Create an array from an iterator. fromfunction : Construct an array by executing a function on grid positions. Examples -------- Convert a list into an array. If all elements are finite ``asarray_chkfinite`` is identical to ``asarray``. >>> a = [1, 2] >>> np.asarray_chkfinite(a, dtype=float) array([1., 2.]) Raises ValueError if array_like contains Nans or Infs. >>> a = [1, 2, np.inf] >>> try: ... np.asarray_chkfinite(a) ... except ValueError: ... print 'ValueError' ... ValueError ascontiguousarray(a, dtype=None) Return a contiguous array in memory (C order). Parameters ---------- a : array_like Input array. dtype : str or dtype object, optional Data-type of returned array. Returns ------- out : ndarray Contiguous array of same shape and content as `a`, with type `dtype` if specified. See Also -------- asfortranarray : Convert input to an ndarray with column-major memory order. require : Return an ndarray that satisfies requirements. ndarray.flags : Information about the memory layout of the array. Examples -------- >>> x = np.arange(6).reshape(2,3) >>> np.ascontiguousarray(x, dtype=np.float32) array([[ 0., 1., 2.], [ 3., 4., 5.]], dtype=float32) >>> x.flags['C_CONTIGUOUS'] True asfarray(a, dtype=) Return an array converted to a float type. Parameters ---------- a : array_like The input array. dtype : str or dtype object, optional Float type code to coerce input array `a`. If `dtype` is one of the 'int' dtypes, it is replaced with float64. Returns ------- out : ndarray The input `a` as a float ndarray. Examples -------- >>> np.asfarray([2, 3]) array([ 2., 3.]) >>> np.asfarray([2, 3], dtype='float') array([ 2., 3.]) >>> np.asfarray([2, 3], dtype='int8') array([ 2., 3.]) asfortranarray(a, dtype=None) Return an array laid out in Fortran order in memory. Parameters ---------- a : array_like Input array. dtype : str or dtype object, optional By default, the data-type is inferred from the input data. Returns ------- out : ndarray The input `a` in Fortran, or column-major, order. See Also -------- ascontiguousarray : Convert input to a contiguous (C order) array. asanyarray : Convert input to an ndarray with either row or column-major memory order. require : Return an ndarray that satisfies requirements. ndarray.flags : Information about the memory layout of the array. Examples -------- >>> x = np.arange(6).reshape(2,3) >>> y = np.asfortranarray(x) >>> x.flags['F_CONTIGUOUS'] False >>> y.flags['F_CONTIGUOUS'] True asmatrix(data, dtype=None) Interpret the input as a matrix. Unlike `matrix`, `asmatrix` does not make a copy if the input is already a matrix or an ndarray. Equivalent to ``matrix(data, copy=False)``. Parameters ---------- data : array_like Input data. dtype : data-type Data-type of the output matrix. Returns ------- mat : matrix `data` interpreted as a matrix. Examples -------- >>> x = np.array([[1, 2], [3, 4]]) >>> m = np.asmatrix(x) >>> x[0,0] = 5 >>> m matrix([[5, 2], [3, 4]]) asscalar(a) Convert an array of size 1 to its scalar equivalent. Parameters ---------- a : ndarray Input array of size 1. Returns ------- out : scalar Scalar representation of `a`. The output data type is the same type returned by the input's `item` method. Examples -------- >>> np.asscalar(np.array([24])) 24 atleast_1d(*arys) Convert inputs to arrays with at least one dimension. Scalar inputs are converted to 1-dimensional arrays, whilst higher-dimensional inputs are preserved. Parameters ---------- arys1, arys2, ... : array_like One or more input arrays. Returns ------- ret : ndarray An array, or sequence of arrays, each with ``a.ndim >= 1``. Copies are made only if necessary. See Also -------- atleast_2d, atleast_3d Examples -------- >>> np.atleast_1d(1.0) array([ 1.]) >>> x = np.arange(9.0).reshape(3,3) >>> np.atleast_1d(x) array([[ 0., 1., 2.], [ 3., 4., 5.], [ 6., 7., 8.]]) >>> np.atleast_1d(x) is x True >>> np.atleast_1d(1, [3, 4]) [array([1]), array([3, 4])] atleast_2d(*arys) View inputs as arrays with at least two dimensions. Parameters ---------- arys1, arys2, ... : array_like One or more array-like sequences. Non-array inputs are converted to arrays. Arrays that already have two or more dimensions are preserved. Returns ------- res, res2, ... : ndarray An array, or tuple of arrays, each with ``a.ndim >= 2``. Copies are avoided where possible, and views with two or more dimensions are returned. See Also -------- atleast_1d, atleast_3d Examples -------- >>> np.atleast_2d(3.0) array([[ 3.]]) >>> x = np.arange(3.0) >>> np.atleast_2d(x) array([[ 0., 1., 2.]]) >>> np.atleast_2d(x).base is x True >>> np.atleast_2d(1, [1, 2], [[1, 2]]) [array([[1]]), array([[1, 2]]), array([[1, 2]])] atleast_3d(*arys) View inputs as arrays with at least three dimensions. Parameters ---------- arys1, arys2, ... : array_like One or more array-like sequences. Non-array inputs are converted to arrays. Arrays that already have three or more dimensions are preserved. Returns ------- res1, res2, ... : ndarray An array, or tuple of arrays, each with ``a.ndim >= 3``. Copies are avoided where possible, and views with three or more dimensions are returned. For example, a 1-D array of shape ``(N,)`` becomes a view of shape ``(1, N, 1)``, and a 2-D array of shape ``(M, N)`` becomes a view of shape ``(M, N, 1)``. See Also -------- atleast_1d, atleast_2d Examples -------- >>> np.atleast_3d(3.0) array([[[ 3.]]]) >>> x = np.arange(3.0) >>> np.atleast_3d(x).shape (1, 3, 1) >>> x = np.arange(12.0).reshape(4,3) >>> np.atleast_3d(x).shape (4, 3, 1) >>> np.atleast_3d(x).base is x True >>> for arr in np.atleast_3d([1, 2], [[1, 2]], [[[1, 2]]]): ... print arr, arr.shape ... [[[1] [2]]] (1, 2, 1) [[[1] [2]]] (1, 2, 1) [[[1 2]]] (1, 1, 2) average(a, axis=None, weights=None, returned=False) Compute the weighted average along the specified axis. Parameters ---------- a : array_like Array containing data to be averaged. If `a` is not an array, a conversion is attempted. axis : int, optional Axis along which to average `a`. If `None`, averaging is done over the flattened array. weights : array_like, optional An array of weights associated with the values in `a`. Each value in `a` contributes to the average according to its associated weight. The weights array can either be 1-D (in which case its length must be the size of `a` along the given axis) or of the same shape as `a`. If `weights=None`, then all data in `a` are assumed to have a weight equal to one. returned : bool, optional Default is `False`. If `True`, the tuple (`average`, `sum_of_weights`) is returned, otherwise only the average is returned. If `weights=None`, `sum_of_weights` is equivalent to the number of elements over which the average is taken. Returns ------- average, [sum_of_weights] : array_type or double Return the average along the specified axis. When returned is `True`, return a tuple with the average as the first element and the sum of the weights as the second element. The return type is `Float` if `a` is of integer type, otherwise it is of the same type as `a`. `sum_of_weights` is of the same type as `average`. Raises ------ ZeroDivisionError When all weights along axis are zero. See `numpy.ma.average` for a version robust to this type of error. TypeError When the length of 1D `weights` is not the same as the shape of `a` along axis. See Also -------- mean ma.average : average for masked arrays -- useful if your data contains "missing" values Examples -------- >>> data = range(1,5) >>> data [1, 2, 3, 4] >>> np.average(data) 2.5 >>> np.average(range(1,11), weights=range(10,0,-1)) 4.0 >>> data = np.arange(6).reshape((3,2)) >>> data array([[0, 1], [2, 3], [4, 5]]) >>> np.average(data, axis=1, weights=[1./4, 3./4]) array([ 0.75, 2.75, 4.75]) >>> np.average(data, weights=[1./4, 3./4]) Traceback (most recent call last): ... TypeError: Axis must be specified when shapes of a and weights differ. bartlett(M) Return the Bartlett window. The Bartlett window is very similar to a triangular window, except that the end points are at zero. It is often used in signal processing for tapering a signal, without generating too much ripple in the frequency domain. Parameters ---------- M : int Number of points in the output window. If zero or less, an empty array is returned. Returns ------- out : array The triangular window, with the maximum value normalized to one (the value one appears only if the number of samples is odd), with the first and last samples equal to zero. See Also -------- blackman, hamming, hanning, kaiser Notes ----- The Bartlett window is defined as .. math:: w(n) = \frac{2}{M-1} \left( \frac{M-1}{2} - \left|n - \frac{M-1}{2}\right| \right) Most references to the Bartlett window come from the signal processing literature, where it is used as one of many windowing functions for smoothing values. Note that convolution with this window produces linear interpolation. It is also known as an apodization (which means"removing the foot", i.e. smoothing discontinuities at the beginning and end of the sampled signal) or tapering function. The fourier transform of the Bartlett is the product of two sinc functions. Note the excellent discussion in Kanasewich. References ---------- .. [1] M.S. Bartlett, "Periodogram Analysis and Continuous Spectra", Biometrika 37, 1-16, 1950. .. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics", The University of Alberta Press, 1975, pp. 109-110. .. [3] A.V. Oppenheim and R.W. Schafer, "Discrete-Time Signal Processing", Prentice-Hall, 1999, pp. 468-471. .. [4] Wikipedia, "Window function", http://en.wikipedia.org/wiki/Window_function .. [5] W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling, "Numerical Recipes", Cambridge University Press, 1986, page 429. Examples -------- >>> np.bartlett(12) array([ 0. , 0.18181818, 0.36363636, 0.54545455, 0.72727273, 0.90909091, 0.90909091, 0.72727273, 0.54545455, 0.36363636, 0.18181818, 0. ]) Plot the window and its frequency response (requires SciPy and matplotlib): >>> from numpy.fft import fft, fftshift >>> window = np.bartlett(51) >>> plt.plot(window) [] >>> plt.title("Bartlett window") >>> plt.ylabel("Amplitude") >>> plt.xlabel("Sample") >>> plt.show() >>> plt.figure() >>> A = fft(window, 2048) / 25.5 >>> mag = np.abs(fftshift(A)) >>> freq = np.linspace(-0.5, 0.5, len(A)) >>> response = 20 * np.log10(mag) >>> response = np.clip(response, -100, 100) >>> plt.plot(freq, response) [] >>> plt.title("Frequency response of Bartlett window") >>> plt.ylabel("Magnitude [dB]") >>> plt.xlabel("Normalized frequency [cycles per sample]") >>> plt.axis('tight') (-0.5, 0.5, -100.0, ...) >>> plt.show() base_repr(number, base=2, padding=0) Return a string representation of a number in the given base system. Parameters ---------- number : int The value to convert. Only positive values are handled. base : int, optional Convert `number` to the `base` number system. The valid range is 2-36, the default value is 2. padding : int, optional Number of zeros padded on the left. Default is 0 (no padding). Returns ------- out : str String representation of `number` in `base` system. See Also -------- binary_repr : Faster version of `base_repr` for base 2. Examples -------- >>> np.base_repr(5) '101' >>> np.base_repr(6, 5) '11' >>> np.base_repr(7, base=5, padding=3) '00012' >>> np.base_repr(10, base=16) 'A' >>> np.base_repr(32, base=16) '20' binary_repr(num, width=None) Return the binary representation of the input number as a string. For negative numbers, if width is not given, a minus sign is added to the front. If width is given, the two's complement of the number is returned, with respect to that width. In a two's-complement system negative numbers are represented by the two's complement of the absolute value. This is the most common method of representing signed integers on computers [1]_. A N-bit two's-complement system can represent every integer in the range :math:`-2^{N-1}` to :math:`+2^{N-1}-1`. Parameters ---------- num : int Only an integer decimal number can be used. width : int, optional The length of the returned string if `num` is positive, the length of the two's complement if `num` is negative. Returns ------- bin : str Binary representation of `num` or two's complement of `num`. See Also -------- base_repr: Return a string representation of a number in the given base system. Notes ----- `binary_repr` is equivalent to using `base_repr` with base 2, but about 25x faster. References ---------- .. [1] Wikipedia, "Two's complement", http://en.wikipedia.org/wiki/Two's_complement Examples -------- >>> np.binary_repr(3) '11' >>> np.binary_repr(-3) '-11' >>> np.binary_repr(3, width=4) '0011' The two's complement is returned when the input number is negative and width is specified: >>> np.binary_repr(-3, width=4) '1101' bincount(...) bincount(x, weights=None, minlength=None) Count number of occurrences of each value in array of non-negative ints. The number of bins (of size 1) is one larger than the largest value in `x`. If `minlength` is specified, there will be at least this number of bins in the output array (though it will be longer if necessary, depending on the contents of `x`). Each bin gives the number of occurrences of its index value in `x`. If `weights` is specified the input array is weighted by it, i.e. if a value ``n`` is found at position ``i``, ``out[n] += weight[i]`` instead of ``out[n] += 1``. Parameters ---------- x : array_like, 1 dimension, nonnegative ints Input array. weights : array_like, optional Weights, array of the same shape as `x`. minlength : int, optional A minimum number of bins for the output array. .. versionadded:: 1.6.0 Returns ------- out : ndarray of ints The result of binning the input array. The length of `out` is equal to ``np.amax(x)+1``. Raises ------ ValueError If the input is not 1-dimensional, or contains elements with negative values, or if `minlength` is non-positive. TypeError If the type of the input is float or complex. See Also -------- histogram, digitize, unique Examples -------- >>> np.bincount(np.arange(5)) array([1, 1, 1, 1, 1]) >>> np.bincount(np.array([0, 1, 1, 3, 2, 1, 7])) array([1, 3, 1, 1, 0, 0, 0, 1]) >>> x = np.array([0, 1, 1, 3, 2, 1, 7, 23]) >>> np.bincount(x).size == np.amax(x)+1 True The input array needs to be of integer dtype, otherwise a TypeError is raised: >>> np.bincount(np.arange(5, dtype=np.float)) Traceback (most recent call last): File "", line 1, in TypeError: array cannot be safely cast to required type A possible use of ``bincount`` is to perform sums over variable-size chunks of an array, using the ``weights`` keyword. >>> w = np.array([0.3, 0.5, 0.2, 0.7, 1., -0.6]) # weights >>> x = np.array([0, 1, 1, 2, 2, 2]) >>> np.bincount(x, weights=w) array([ 0.3, 0.7, 1.1]) blackman(M) Return the Blackman window. The Blackman window is a taper formed by using the first three terms of a summation of cosines. It was designed to have close to the minimal leakage possible. It is close to optimal, only slightly worse than a Kaiser window. Parameters ---------- M : int Number of points in the output window. If zero or less, an empty array is returned. Returns ------- out : ndarray The window, with the maximum value normalized to one (the value one appears only if the number of samples is odd). See Also -------- bartlett, hamming, hanning, kaiser Notes ----- The Blackman window is defined as .. math:: w(n) = 0.42 - 0.5 \cos(2\pi n/M) + 0.08 \cos(4\pi n/M) Most references to the Blackman window come from the signal processing literature, where it is used as one of many windowing functions for smoothing values. It is also known as an apodization (which means "removing the foot", i.e. smoothing discontinuities at the beginning and end of the sampled signal) or tapering function. It is known as a "near optimal" tapering function, almost as good (by some measures) as the kaiser window. References ---------- Blackman, R.B. and Tukey, J.W., (1958) The measurement of power spectra, Dover Publications, New York. Oppenheim, A.V., and R.W. Schafer. Discrete-Time Signal Processing. Upper Saddle River, NJ: Prentice-Hall, 1999, pp. 468-471. Examples -------- >>> np.blackman(12) array([ -1.38777878e-17, 3.26064346e-02, 1.59903635e-01, 4.14397981e-01, 7.36045180e-01, 9.67046769e-01, 9.67046769e-01, 7.36045180e-01, 4.14397981e-01, 1.59903635e-01, 3.26064346e-02, -1.38777878e-17]) Plot the window and the frequency response: >>> from numpy.fft import fft, fftshift >>> window = np.blackman(51) >>> plt.plot(window) [] >>> plt.title("Blackman window") >>> plt.ylabel("Amplitude") >>> plt.xlabel("Sample") >>> plt.show() >>> plt.figure() >>> A = fft(window, 2048) / 25.5 >>> mag = np.abs(fftshift(A)) >>> freq = np.linspace(-0.5, 0.5, len(A)) >>> response = 20 * np.log10(mag) >>> response = np.clip(response, -100, 100) >>> plt.plot(freq, response) [] >>> plt.title("Frequency response of Blackman window") >>> plt.ylabel("Magnitude [dB]") >>> plt.xlabel("Normalized frequency [cycles per sample]") >>> plt.axis('tight') (-0.5, 0.5, -100.0, ...) >>> plt.show() bmat(obj, ldict=None, gdict=None) Build a matrix object from a string, nested sequence, or array. Parameters ---------- obj : str or array_like Input data. Names of variables in the current scope may be referenced, even if `obj` is a string. ldict : dict, optional A dictionary that replaces local operands in current frame. Ignored if `obj` is not a string or `gdict` is `None`. gdict : dict, optional A dictionary that replaces global operands in current frame. Ignored if `obj` is not a string. Returns ------- out : matrix Returns a matrix object, which is a specialized 2-D array. See Also -------- matrix Examples -------- >>> A = np.mat('1 1; 1 1') >>> B = np.mat('2 2; 2 2') >>> C = np.mat('3 4; 5 6') >>> D = np.mat('7 8; 9 0') All the following expressions construct the same block matrix: >>> np.bmat([[A, B], [C, D]]) matrix([[1, 1, 2, 2], [1, 1, 2, 2], [3, 4, 7, 8], [5, 6, 9, 0]]) >>> np.bmat(np.r_[np.c_[A, B], np.c_[C, D]]) matrix([[1, 1, 2, 2], [1, 1, 2, 2], [3, 4, 7, 8], [5, 6, 9, 0]]) >>> np.bmat('A,B; C,D') matrix([[1, 1, 2, 2], [1, 1, 2, 2], [3, 4, 7, 8], [5, 6, 9, 0]]) broadcast_arrays(*args, **kwargs) Broadcast any number of arrays against each other. Parameters ---------- `*args` : array_likes The arrays to broadcast. subok : bool, optional If True, then sub-classes will be passed-through, otherwise the returned arrays will be forced to be a base-class array (default). Returns ------- broadcasted : list of arrays These arrays are views on the original arrays. They are typically not contiguous. Furthermore, more than one element of a broadcasted array may refer to a single memory location. If you need to write to the arrays, make copies first. Examples -------- >>> x = np.array([[1,2,3]]) >>> y = np.array([[1],[2],[3]]) >>> np.broadcast_arrays(x, y) [array([[1, 2, 3], [1, 2, 3], [1, 2, 3]]), array([[1, 1, 1], [2, 2, 2], [3, 3, 3]])] Here is a useful idiom for getting contiguous copies instead of non-contiguous views. >>> [np.array(a) for a in np.broadcast_arrays(x, y)] [array([[1, 2, 3], [1, 2, 3], [1, 2, 3]]), array([[1, 1, 1], [2, 2, 2], [3, 3, 3]])] broadcast_to(array, shape, subok=False) Broadcast an array to a new shape. Parameters ---------- array : array_like The array to broadcast. shape : tuple The shape of the desired array. subok : bool, optional If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array (default). Returns ------- broadcast : array A readonly view on the original array with the given shape. It is typically not contiguous. Furthermore, more than one element of a broadcasted array may refer to a single memory location. Raises ------ ValueError If the array is not compatible with the new shape according to NumPy's broadcasting rules. Notes ----- .. versionadded:: 1.10.0 Examples -------- >>> x = np.array([1, 2, 3]) >>> np.broadcast_to(x, (3, 3)) array([[1, 2, 3], [1, 2, 3], [1, 2, 3]]) busday_count(...) busday_count(begindates, enddates, weekmask='1111100', holidays=[], busdaycal=None, out=None) Counts the number of valid days between `begindates` and `enddates`, not including the day of `enddates`. If ``enddates`` specifies a date value that is earlier than the corresponding ``begindates`` date value, the count will be negative. .. versionadded:: 1.7.0 Parameters ---------- begindates : array_like of datetime64[D] The array of the first dates for counting. enddates : array_like of datetime64[D] The array of the end dates for counting, which are excluded from the count themselves. weekmask : str or array_like of bool, optional A seven-element array indicating which of Monday through Sunday are valid days. May be specified as a length-seven list or array, like [1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for weekdays, optionally separated by white space. Valid abbreviations are: Mon Tue Wed Thu Fri Sat Sun holidays : array_like of datetime64[D], optional An array of dates to consider as invalid dates. They may be specified in any order, and NaT (not-a-time) dates are ignored. This list is saved in a normalized form that is suited for fast calculations of valid days. busdaycal : busdaycalendar, optional A `busdaycalendar` object which specifies the valid days. If this parameter is provided, neither weekmask nor holidays may be provided. out : array of int, optional If provided, this array is filled with the result. Returns ------- out : array of int An array with a shape from broadcasting ``begindates`` and ``enddates`` together, containing the number of valid days between the begin and end dates. See Also -------- busdaycalendar: An object that specifies a custom set of valid days. is_busday : Returns a boolean array indicating valid days. busday_offset : Applies an offset counted in valid days. Examples -------- >>> # Number of weekdays in January 2011 ... np.busday_count('2011-01', '2011-02') 21 >>> # Number of weekdays in 2011 ... np.busday_count('2011', '2012') 260 >>> # Number of Saturdays in 2011 ... np.busday_count('2011', '2012', weekmask='Sat') 53 busday_offset(...) busday_offset(dates, offsets, roll='raise', weekmask='1111100', holidays=None, busdaycal=None, out=None) First adjusts the date to fall on a valid day according to the ``roll`` rule, then applies offsets to the given dates counted in valid days. .. versionadded:: 1.7.0 Parameters ---------- dates : array_like of datetime64[D] The array of dates to process. offsets : array_like of int The array of offsets, which is broadcast with ``dates``. roll : {'raise', 'nat', 'forward', 'following', 'backward', 'preceding', 'modifiedfollowing', 'modifiedpreceding'}, optional How to treat dates that do not fall on a valid day. The default is 'raise'. * 'raise' means to raise an exception for an invalid day. * 'nat' means to return a NaT (not-a-time) for an invalid day. * 'forward' and 'following' mean to take the first valid day later in time. * 'backward' and 'preceding' mean to take the first valid day earlier in time. * 'modifiedfollowing' means to take the first valid day later in time unless it is across a Month boundary, in which case to take the first valid day earlier in time. * 'modifiedpreceding' means to take the first valid day earlier in time unless it is across a Month boundary, in which case to take the first valid day later in time. weekmask : str or array_like of bool, optional A seven-element array indicating which of Monday through Sunday are valid days. May be specified as a length-seven list or array, like [1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for weekdays, optionally separated by white space. Valid abbreviations are: Mon Tue Wed Thu Fri Sat Sun holidays : array_like of datetime64[D], optional An array of dates to consider as invalid dates. They may be specified in any order, and NaT (not-a-time) dates are ignored. This list is saved in a normalized form that is suited for fast calculations of valid days. busdaycal : busdaycalendar, optional A `busdaycalendar` object which specifies the valid days. If this parameter is provided, neither weekmask nor holidays may be provided. out : array of datetime64[D], optional If provided, this array is filled with the result. Returns ------- out : array of datetime64[D] An array with a shape from broadcasting ``dates`` and ``offsets`` together, containing the dates with offsets applied. See Also -------- busdaycalendar: An object that specifies a custom set of valid days. is_busday : Returns a boolean array indicating valid days. busday_count : Counts how many valid days are in a half-open date range. Examples -------- >>> # First business day in October 2011 (not accounting for holidays) ... np.busday_offset('2011-10', 0, roll='forward') numpy.datetime64('2011-10-03','D') >>> # Last business day in February 2012 (not accounting for holidays) ... np.busday_offset('2012-03', -1, roll='forward') numpy.datetime64('2012-02-29','D') >>> # Third Wednesday in January 2011 ... np.busday_offset('2011-01', 2, roll='forward', weekmask='Wed') numpy.datetime64('2011-01-19','D') >>> # 2012 Mother's Day in Canada and the U.S. ... np.busday_offset('2012-05', 1, roll='forward', weekmask='Sun') numpy.datetime64('2012-05-13','D') >>> # First business day on or after a date ... np.busday_offset('2011-03-20', 0, roll='forward') numpy.datetime64('2011-03-21','D') >>> np.busday_offset('2011-03-22', 0, roll='forward') numpy.datetime64('2011-03-22','D') >>> # First business day after a date ... np.busday_offset('2011-03-20', 1, roll='backward') numpy.datetime64('2011-03-21','D') >>> np.busday_offset('2011-03-22', 1, roll='backward') numpy.datetime64('2011-03-23','D') byte_bounds(a) Returns pointers to the end-points of an array. Parameters ---------- a : ndarray Input array. It must conform to the Python-side of the array interface. Returns ------- (low, high) : tuple of 2 integers The first integer is the first byte of the array, the second integer is just past the last byte of the array. If `a` is not contiguous it will not use every byte between the (`low`, `high`) values. Examples -------- >>> I = np.eye(2, dtype='f'); I.dtype dtype('float32') >>> low, high = np.byte_bounds(I) >>> high - low == I.size*I.itemsize True >>> I = np.eye(2, dtype='G'); I.dtype dtype('complex192') >>> low, high = np.byte_bounds(I) >>> high - low == I.size*I.itemsize True can_cast(...) can_cast(from, totype, casting = 'safe') Returns True if cast between data types can occur according to the casting rule. If from is a scalar or array scalar, also returns True if the scalar value can be cast without overflow or truncation to an integer. Parameters ---------- from : dtype, dtype specifier, scalar, or array Data type, scalar, or array to cast from. totype : dtype or dtype specifier Data type to cast to. casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional Controls what kind of data casting may occur. * 'no' means the data types should not be cast at all. * 'equiv' means only byte-order changes are allowed. * 'safe' means only casts which can preserve values are allowed. * 'same_kind' means only safe casts or casts within a kind, like float64 to float32, are allowed. * 'unsafe' means any data conversions may be done. Returns ------- out : bool True if cast can occur according to the casting rule. Notes ----- Starting in NumPy 1.9, can_cast function now returns False in 'safe' casting mode for integer/float dtype and string dtype if the string dtype length is not long enough to store the max integer/float value converted to a string. Previously can_cast in 'safe' mode returned True for integer/float dtype and a string dtype of any length. See also -------- dtype, result_type Examples -------- Basic examples >>> np.can_cast(np.int32, np.int64) True >>> np.can_cast(np.float64, np.complex) True >>> np.can_cast(np.complex, np.float) False >>> np.can_cast('i8', 'f8') True >>> np.can_cast('i8', 'f4') False >>> np.can_cast('i4', 'S4') False Casting scalars >>> np.can_cast(100, 'i1') True >>> np.can_cast(150, 'i1') False >>> np.can_cast(150, 'u1') True >>> np.can_cast(3.5e100, np.float32) False >>> np.can_cast(1000.0, np.float32) True Array scalar checks the value, array does not >>> np.can_cast(np.array(1000.0), np.float32) True >>> np.can_cast(np.array([1000.0]), np.float32) False Using the casting rules >>> np.can_cast('i8', 'i8', 'no') True >>> np.can_cast('i8', 'no') False >>> np.can_cast('i8', 'equiv') True >>> np.can_cast('i8', 'equiv') False >>> np.can_cast('i8', 'safe') True >>> np.can_cast('i4', 'safe') False >>> np.can_cast('i4', 'same_kind') True >>> np.can_cast('u4', 'same_kind') False >>> np.can_cast('u4', 'unsafe') True choose(a, choices, out=None, mode='raise') Construct an array from an index array and a set of arrays to choose from. First of all, if confused or uncertain, definitely look at the Examples - in its full generality, this function is less simple than it might seem from the following code description (below ndi = `numpy.lib.index_tricks`): ``np.choose(a,c) == np.array([c[a[I]][I] for I in ndi.ndindex(a.shape)])``. But this omits some subtleties. Here is a fully general summary: Given an "index" array (`a`) of integers and a sequence of `n` arrays (`choices`), `a` and each choice array are first broadcast, as necessary, to arrays of a common shape; calling these *Ba* and *Bchoices[i], i = 0,...,n-1* we have that, necessarily, ``Ba.shape == Bchoices[i].shape`` for each `i`. Then, a new array with shape ``Ba.shape`` is created as follows: * if ``mode=raise`` (the default), then, first of all, each element of `a` (and thus `Ba`) must be in the range `[0, n-1]`; now, suppose that `i` (in that range) is the value at the `(j0, j1, ..., jm)` position in `Ba` - then the value at the same position in the new array is the value in `Bchoices[i]` at that same position; * if ``mode=wrap``, values in `a` (and thus `Ba`) may be any (signed) integer; modular arithmetic is used to map integers outside the range `[0, n-1]` back into that range; and then the new array is constructed as above; * if ``mode=clip``, values in `a` (and thus `Ba`) may be any (signed) integer; negative integers are mapped to 0; values greater than `n-1` are mapped to `n-1`; and then the new array is constructed as above. Parameters ---------- a : int array This array must contain integers in `[0, n-1]`, where `n` is the number of choices, unless ``mode=wrap`` or ``mode=clip``, in which cases any integers are permissible. choices : sequence of arrays Choice arrays. `a` and all of the choices must be broadcastable to the same shape. If `choices` is itself an array (not recommended), then its outermost dimension (i.e., the one corresponding to ``choices.shape[0]``) is taken as defining the "sequence". out : array, optional If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype. mode : {'raise' (default), 'wrap', 'clip'}, optional Specifies how indices outside `[0, n-1]` will be treated: * 'raise' : an exception is raised * 'wrap' : value becomes value mod `n` * 'clip' : values < 0 are mapped to 0, values > n-1 are mapped to n-1 Returns ------- merged_array : array The merged result. Raises ------ ValueError: shape mismatch If `a` and each choice array are not all broadcastable to the same shape. See Also -------- ndarray.choose : equivalent method Notes ----- To reduce the chance of misinterpretation, even though the following "abuse" is nominally supported, `choices` should neither be, nor be thought of as, a single array, i.e., the outermost sequence-like container should be either a list or a tuple. Examples -------- >>> choices = [[0, 1, 2, 3], [10, 11, 12, 13], ... [20, 21, 22, 23], [30, 31, 32, 33]] >>> np.choose([2, 3, 1, 0], choices ... # the first element of the result will be the first element of the ... # third (2+1) "array" in choices, namely, 20; the second element ... # will be the second element of the fourth (3+1) choice array, i.e., ... # 31, etc. ... ) array([20, 31, 12, 3]) >>> np.choose([2, 4, 1, 0], choices, mode='clip') # 4 goes to 3 (4-1) array([20, 31, 12, 3]) >>> # because there are 4 choice arrays >>> np.choose([2, 4, 1, 0], choices, mode='wrap') # 4 goes to (4 mod 4) array([20, 1, 12, 3]) >>> # i.e., 0 A couple examples illustrating how choose broadcasts: >>> a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]] >>> choices = [-10, 10] >>> np.choose(a, choices) array([[ 10, -10, 10], [-10, 10, -10], [ 10, -10, 10]]) >>> # With thanks to Anne Archibald >>> a = np.array([0, 1]).reshape((2,1,1)) >>> c1 = np.array([1, 2, 3]).reshape((1,3,1)) >>> c2 = np.array([-1, -2, -3, -4, -5]).reshape((1,1,5)) >>> np.choose(a, (c1, c2)) # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2 array([[[ 1, 1, 1, 1, 1], [ 2, 2, 2, 2, 2], [ 3, 3, 3, 3, 3]], [[-1, -2, -3, -4, -5], [-1, -2, -3, -4, -5], [-1, -2, -3, -4, -5]]]) clip(a, a_min, a_max, out=None) Clip (limit) the values in an array. Given an interval, values outside the interval are clipped to the interval edges. For example, if an interval of ``[0, 1]`` is specified, values smaller than 0 become 0, and values larger than 1 become 1. Parameters ---------- a : array_like Array containing elements to clip. a_min : scalar or array_like Minimum value. a_max : scalar or array_like Maximum value. If `a_min` or `a_max` are array_like, then they will be broadcasted to the shape of `a`. out : ndarray, optional The results will be placed in this array. It may be the input array for in-place clipping. `out` must be of the right shape to hold the output. Its type is preserved. Returns ------- clipped_array : ndarray An array with the elements of `a`, but where values < `a_min` are replaced with `a_min`, and those > `a_max` with `a_max`. See Also -------- numpy.doc.ufuncs : Section "Output arguments" Examples -------- >>> a = np.arange(10) >>> np.clip(a, 1, 8) array([1, 1, 2, 3, 4, 5, 6, 7, 8, 8]) >>> a array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) >>> np.clip(a, 3, 6, out=a) array([3, 3, 3, 3, 4, 5, 6, 6, 6, 6]) >>> a = np.arange(10) >>> a array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) >>> np.clip(a, [3,4,1,1,1,4,4,4,4,4], 8) array([3, 4, 2, 3, 4, 5, 6, 7, 8, 8]) column_stack(tup) Stack 1-D arrays as columns into a 2-D array. Take a sequence of 1-D arrays and stack them as columns to make a single 2-D array. 2-D arrays are stacked as-is, just like with `hstack`. 1-D arrays are turned into 2-D columns first. Parameters ---------- tup : sequence of 1-D or 2-D arrays. Arrays to stack. All of them must have the same first dimension. Returns ------- stacked : 2-D array The array formed by stacking the given arrays. See Also -------- hstack, vstack, concatenate Examples -------- >>> a = np.array((1,2,3)) >>> b = np.array((2,3,4)) >>> np.column_stack((a,b)) array([[1, 2], [2, 3], [3, 4]]) common_type(*arrays) Return a scalar type which is common to the input arrays. The return type will always be an inexact (i.e. floating point) scalar type, even if all the arrays are integer arrays. If one of the inputs is an integer array, the minimum precision type that is returned is a 64-bit floating point dtype. All input arrays can be safely cast to the returned dtype without loss of information. Parameters ---------- array1, array2, ... : ndarrays Input arrays. Returns ------- out : data type code Data type code. See Also -------- dtype, mintypecode Examples -------- >>> np.common_type(np.arange(2, dtype=np.float32)) >>> np.common_type(np.arange(2, dtype=np.float32), np.arange(2)) >>> np.common_type(np.arange(4), np.array([45, 6.j]), np.array([45.0])) compare_chararrays(...) compress(condition, a, axis=None, out=None) Return selected slices of an array along given axis. When working along a given axis, a slice along that axis is returned in `output` for each index where `condition` evaluates to True. When working on a 1-D array, `compress` is equivalent to `extract`. Parameters ---------- condition : 1-D array of bools Array that selects which entries to return. If len(condition) is less than the size of `a` along the given axis, then output is truncated to the length of the condition array. a : array_like Array from which to extract a part. axis : int, optional Axis along which to take slices. If None (default), work on the flattened array. out : ndarray, optional Output array. Its type is preserved and it must be of the right shape to hold the output. Returns ------- compressed_array : ndarray A copy of `a` without the slices along axis for which `condition` is false. See Also -------- take, choose, diag, diagonal, select ndarray.compress : Equivalent method in ndarray np.extract: Equivalent method when working on 1-D arrays numpy.doc.ufuncs : Section "Output arguments" Examples -------- >>> a = np.array([[1, 2], [3, 4], [5, 6]]) >>> a array([[1, 2], [3, 4], [5, 6]]) >>> np.compress([0, 1], a, axis=0) array([[3, 4]]) >>> np.compress([False, True, True], a, axis=0) array([[3, 4], [5, 6]]) >>> np.compress([False, True], a, axis=1) array([[2], [4], [6]]) Working on the flattened array does not return slices along an axis but selects elements. >>> np.compress([False, True], a) array([2]) concatenate(...) concatenate((a1, a2, ...), axis=0) Join a sequence of arrays along an existing axis. Parameters ---------- a1, a2, ... : sequence of array_like The arrays must have the same shape, except in the dimension corresponding to `axis` (the first, by default). axis : int, optional The axis along which the arrays will be joined. Default is 0. Returns ------- res : ndarray The concatenated array. See Also -------- ma.concatenate : Concatenate function that preserves input masks. array_split : Split an array into multiple sub-arrays of equal or near-equal size. split : Split array into a list of multiple sub-arrays of equal size. hsplit : Split array into multiple sub-arrays horizontally (column wise) vsplit : Split array into multiple sub-arrays vertically (row wise) dsplit : Split array into multiple sub-arrays along the 3rd axis (depth). stack : Stack a sequence of arrays along a new axis. hstack : Stack arrays in sequence horizontally (column wise) vstack : Stack arrays in sequence vertically (row wise) dstack : Stack arrays in sequence depth wise (along third dimension) Notes ----- When one or more of the arrays to be concatenated is a MaskedArray, this function will return a MaskedArray object instead of an ndarray, but the input masks are *not* preserved. In cases where a MaskedArray is expected as input, use the ma.concatenate function from the masked array module instead. Examples -------- >>> a = np.array([[1, 2], [3, 4]]) >>> b = np.array([[5, 6]]) >>> np.concatenate((a, b), axis=0) array([[1, 2], [3, 4], [5, 6]]) >>> np.concatenate((a, b.T), axis=1) array([[1, 2, 5], [3, 4, 6]]) This function will not preserve masking of MaskedArray inputs. >>> a = np.ma.arange(3) >>> a[1] = np.ma.masked >>> b = np.arange(2, 5) >>> a masked_array(data = [0 -- 2], mask = [False True False], fill_value = 999999) >>> b array([2, 3, 4]) >>> np.concatenate([a, b]) masked_array(data = [0 1 2 2 3 4], mask = False, fill_value = 999999) >>> np.ma.concatenate([a, b]) masked_array(data = [0 -- 2 2 3 4], mask = [False True False False False False], fill_value = 999999) convolve(a, v, mode='full') Returns the discrete, linear convolution of two one-dimensional sequences. The convolution operator is often seen in signal processing, where it models the effect of a linear time-invariant system on a signal [1]_. In probability theory, the sum of two independent random variables is distributed according to the convolution of their individual distributions. If `v` is longer than `a`, the arrays are swapped before computation. Parameters ---------- a : (N,) array_like First one-dimensional input array. v : (M,) array_like Second one-dimensional input array. mode : {'full', 'valid', 'same'}, optional 'full': By default, mode is 'full'. This returns the convolution at each point of overlap, with an output shape of (N+M-1,). At the end-points of the convolution, the signals do not overlap completely, and boundary effects may be seen. 'same': Mode `same` returns output of length ``max(M, N)``. Boundary effects are still visible. 'valid': Mode `valid` returns output of length ``max(M, N) - min(M, N) + 1``. The convolution product is only given for points where the signals overlap completely. Values outside the signal boundary have no effect. Returns ------- out : ndarray Discrete, linear convolution of `a` and `v`. See Also -------- scipy.signal.fftconvolve : Convolve two arrays using the Fast Fourier Transform. scipy.linalg.toeplitz : Used to construct the convolution operator. polymul : Polynomial multiplication. Same output as convolve, but also accepts poly1d objects as input. Notes ----- The discrete convolution operation is defined as .. math:: (a * v)[n] = \sum_{m = -\infty}^{\infty} a[m] v[n - m] It can be shown that a convolution :math:`x(t) * y(t)` in time/space is equivalent to the multiplication :math:`X(f) Y(f)` in the Fourier domain, after appropriate padding (padding is necessary to prevent circular convolution). Since multiplication is more efficient (faster) than convolution, the function `scipy.signal.fftconvolve` exploits the FFT to calculate the convolution of large data-sets. References ---------- .. [1] Wikipedia, "Convolution", http://en.wikipedia.org/wiki/Convolution. Examples -------- Note how the convolution operator flips the second array before "sliding" the two across one another: >>> np.convolve([1, 2, 3], [0, 1, 0.5]) array([ 0. , 1. , 2.5, 4. , 1.5]) Only return the middle values of the convolution. Contains boundary effects, where zeros are taken into account: >>> np.convolve([1,2,3],[0,1,0.5], 'same') array([ 1. , 2.5, 4. ]) The two arrays are of the same length, so there is only one position where they completely overlap: >>> np.convolve([1,2,3],[0,1,0.5], 'valid') array([ 2.5]) copy(a, order='K') Return an array copy of the given object. Parameters ---------- a : array_like Input data. order : {'C', 'F', 'A', 'K'}, optional Controls the memory layout of the copy. 'C' means C-order, 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous, 'C' otherwise. 'K' means match the layout of `a` as closely as possible. (Note that this function and :meth:ndarray.copy are very similar, but have different default values for their order= arguments.) Returns ------- arr : ndarray Array interpretation of `a`. Notes ----- This is equivalent to >>> np.array(a, copy=True) #doctest: +SKIP Examples -------- Create an array x, with a reference y and a copy z: >>> x = np.array([1, 2, 3]) >>> y = x >>> z = np.copy(x) Note that, when we modify x, y changes, but not z: >>> x[0] = 10 >>> x[0] == y[0] True >>> x[0] == z[0] False copyto(...) copyto(dst, src, casting='same_kind', where=None) Copies values from one array to another, broadcasting as necessary. Raises a TypeError if the `casting` rule is violated, and if `where` is provided, it selects which elements to copy. .. versionadded:: 1.7.0 Parameters ---------- dst : ndarray The array into which values are copied. src : array_like The array from which values are copied. casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional Controls what kind of data casting may occur when copying. * 'no' means the data types should not be cast at all. * 'equiv' means only byte-order changes are allowed. * 'safe' means only casts which can preserve values are allowed. * 'same_kind' means only safe casts or casts within a kind, like float64 to float32, are allowed. * 'unsafe' means any data conversions may be done. where : array_like of bool, optional A boolean array which is broadcasted to match the dimensions of `dst`, and selects elements to copy from `src` to `dst` wherever it contains the value True. corrcoef(x, y=None, rowvar=1, bias=, ddof=) Return Pearson product-moment correlation coefficients. Please refer to the documentation for `cov` for more detail. The relationship between the correlation coefficient matrix, `R`, and the covariance matrix, `C`, is .. math:: R_{ij} = \frac{ C_{ij} } { \sqrt{ C_{ii} * C_{jj} } } The values of `R` are between -1 and 1, inclusive. Parameters ---------- x : array_like A 1-D or 2-D array containing multiple variables and observations. Each row of `x` represents a variable, and each column a single observation of all those variables. Also see `rowvar` below. y : array_like, optional An additional set of variables and observations. `y` has the same shape as `x`. rowvar : int, optional If `rowvar` is non-zero (default), then each row represents a variable, with observations in the columns. Otherwise, the relationship is transposed: each column represents a variable, while the rows contain observations. bias : _NoValue, optional Has no affect, do not use. .. deprecated:: 1.10.0 ddof : _NoValue, optional Has no affect, do not use. .. deprecated:: 1.10.0 Returns ------- R : ndarray The correlation coefficient matrix of the variables. See Also -------- cov : Covariance matrix Notes ----- This function accepts but discards arguments `bias` and `ddof`. This is for backwards compatibility with previous versions of this function. These arguments had no effect on the return values of the function and can be safely ignored in this and previous versions of numpy. correlate(a, v, mode='valid') Cross-correlation of two 1-dimensional sequences. This function computes the correlation as generally defined in signal processing texts:: c_{av}[k] = sum_n a[n+k] * conj(v[n]) with a and v sequences being zero-padded where necessary and conj being the conjugate. Parameters ---------- a, v : array_like Input sequences. mode : {'valid', 'same', 'full'}, optional Refer to the `convolve` docstring. Note that the default is `valid`, unlike `convolve`, which uses `full`. old_behavior : bool `old_behavior` was removed in NumPy 1.10. If you need the old behavior, use `multiarray.correlate`. Returns ------- out : ndarray Discrete cross-correlation of `a` and `v`. See Also -------- convolve : Discrete, linear convolution of two one-dimensional sequences. multiarray.correlate : Old, no conjugate, version of correlate. Notes ----- The definition of correlation above is not unique and sometimes correlation may be defined differently. Another common definition is:: c'_{av}[k] = sum_n a[n] conj(v[n+k]) which is related to ``c_{av}[k]`` by ``c'_{av}[k] = c_{av}[-k]``. Examples -------- >>> np.correlate([1, 2, 3], [0, 1, 0.5]) array([ 3.5]) >>> np.correlate([1, 2, 3], [0, 1, 0.5], "same") array([ 2. , 3.5, 3. ]) >>> np.correlate([1, 2, 3], [0, 1, 0.5], "full") array([ 0.5, 2. , 3.5, 3. , 0. ]) Using complex sequences: >>> np.correlate([1+1j, 2, 3-1j], [0, 1, 0.5j], 'full') array([ 0.5-0.5j, 1.0+0.j , 1.5-1.5j, 3.0-1.j , 0.0+0.j ]) Note that you get the time reversed, complex conjugated result when the two input sequences change places, i.e., ``c_{va}[k] = c^{*}_{av}[-k]``: >>> np.correlate([0, 1, 0.5j], [1+1j, 2, 3-1j], 'full') array([ 0.0+0.j , 3.0+1.j , 1.5+1.5j, 1.0+0.j , 0.5+0.5j]) count_nonzero(...) count_nonzero(a) Counts the number of non-zero values in the array ``a``. Parameters ---------- a : array_like The array for which to count non-zeros. Returns ------- count : int or array of int Number of non-zero values in the array. See Also -------- nonzero : Return the coordinates of all the non-zero values. Examples -------- >>> np.count_nonzero(np.eye(4)) 4 >>> np.count_nonzero([[0,1,7,0,0],[3,0,0,2,19]]) 5 cov(m, y=None, rowvar=1, bias=0, ddof=None, fweights=None, aweights=None) Estimate a covariance matrix, given data and weights. Covariance indicates the level to which two variables vary together. If we examine N-dimensional samples, :math:`X = [x_1, x_2, ... x_N]^T`, then the covariance matrix element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`. The element :math:`C_{ii}` is the variance of :math:`x_i`. See the notes for an outline of the algorithm. Parameters ---------- m : array_like A 1-D or 2-D array containing multiple variables and observations. Each row of `m` represents a variable, and each column a single observation of all those variables. Also see `rowvar` below. y : array_like, optional An additional set of variables and observations. `y` has the same form as that of `m`. rowvar : int, optional If `rowvar` is non-zero (default), then each row represents a variable, with observations in the columns. Otherwise, the relationship is transposed: each column represents a variable, while the rows contain observations. bias : int, optional Default normalization is by ``(N - 1)``, where ``N`` corresponds to the number of observations given (unbiased estimate). If `bias` is 1, then normalization is by ``N``. These values can be overridden by using the keyword ``ddof`` in numpy versions >= 1.5. ddof : int, optional If not ``None`` the default value implied by `bias` is overridden. Note that ``ddof=1`` will return the unbiased estimate, even if both `fweights` and `aweights` are specified, and ``ddof=0`` will return the simple average. See the notes for the details. The default value is ``None``. .. versionadded:: 1.5 fweights : array_like, int, optional 1-D array of integer freguency weights; the number of times each observation vector should be repeated. .. versionadded:: 1.10 aweights : array_like, optional 1-D array of observation vector weights. These relative weights are typically large for observations considered "important" and smaller for observations considered less "important". If ``ddof=0`` the array of weights can be used to assign probabilities to observation vectors. .. versionadded:: 1.10 Returns ------- out : ndarray The covariance matrix of the variables. See Also -------- corrcoef : Normalized covariance matrix Notes ----- Assume that the observations are in the columns of the observation array `m` and let ``f = fweights`` and ``a = aweights`` for brevity. The steps to compute the weighted covariance are as follows:: >>> w = f * a >>> v1 = np.sum(w) >>> v2 = np.sum(w * a) >>> m -= np.sum(m * w, axis=1, keepdims=True) / v1 >>> cov = np.dot(m * w, m.T) * v1 / (v1**2 - ddof * v2) Note that when ``a == 1``, the normalization factor ``v1 / (v1**2 - ddof * v2)`` goes over to ``1 / (np.sum(f) - ddof)`` as it should. Examples -------- Consider two variables, :math:`x_0` and :math:`x_1`, which correlate perfectly, but in opposite directions: >>> x = np.array([[0, 2], [1, 1], [2, 0]]).T >>> x array([[0, 1, 2], [2, 1, 0]]) Note how :math:`x_0` increases while :math:`x_1` decreases. The covariance matrix shows this clearly: >>> np.cov(x) array([[ 1., -1.], [-1., 1.]]) Note that element :math:`C_{0,1}`, which shows the correlation between :math:`x_0` and :math:`x_1`, is negative. Further, note how `x` and `y` are combined: >>> x = [-2.1, -1, 4.3] >>> y = [3, 1.1, 0.12] >>> X = np.vstack((x,y)) >>> print np.cov(X) [[ 11.71 -4.286 ] [ -4.286 2.14413333]] >>> print np.cov(x, y) [[ 11.71 -4.286 ] [ -4.286 2.14413333]] >>> print np.cov(x) 11.71 cross(a, b, axisa=-1, axisb=-1, axisc=-1, axis=None) Return the cross product of two (arrays of) vectors. The cross product of `a` and `b` in :math:`R^3` is a vector perpendicular to both `a` and `b`. If `a` and `b` are arrays of vectors, the vectors are defined by the last axis of `a` and `b` by default, and these axes can have dimensions 2 or 3. Where the dimension of either `a` or `b` is 2, the third component of the input vector is assumed to be zero and the cross product calculated accordingly. In cases where both input vectors have dimension 2, the z-component of the cross product is returned. Parameters ---------- a : array_like Components of the first vector(s). b : array_like Components of the second vector(s). axisa : int, optional Axis of `a` that defines the vector(s). By default, the last axis. axisb : int, optional Axis of `b` that defines the vector(s). By default, the last axis. axisc : int, optional Axis of `c` containing the cross product vector(s). Ignored if both input vectors have dimension 2, as the return is scalar. By default, the last axis. axis : int, optional If defined, the axis of `a`, `b` and `c` that defines the vector(s) and cross product(s). Overrides `axisa`, `axisb` and `axisc`. Returns ------- c : ndarray Vector cross product(s). Raises ------ ValueError When the dimension of the vector(s) in `a` and/or `b` does not equal 2 or 3. See Also -------- inner : Inner product outer : Outer product. ix_ : Construct index arrays. Notes ----- .. versionadded:: 1.9.0 Supports full broadcasting of the inputs. Examples -------- Vector cross-product. >>> x = [1, 2, 3] >>> y = [4, 5, 6] >>> np.cross(x, y) array([-3, 6, -3]) One vector with dimension 2. >>> x = [1, 2] >>> y = [4, 5, 6] >>> np.cross(x, y) array([12, -6, -3]) Equivalently: >>> x = [1, 2, 0] >>> y = [4, 5, 6] >>> np.cross(x, y) array([12, -6, -3]) Both vectors with dimension 2. >>> x = [1,2] >>> y = [4,5] >>> np.cross(x, y) -3 Multiple vector cross-products. Note that the direction of the cross product vector is defined by the `right-hand rule`. >>> x = np.array([[1,2,3], [4,5,6]]) >>> y = np.array([[4,5,6], [1,2,3]]) >>> np.cross(x, y) array([[-3, 6, -3], [ 3, -6, 3]]) The orientation of `c` can be changed using the `axisc` keyword. >>> np.cross(x, y, axisc=0) array([[-3, 3], [ 6, -6], [-3, 3]]) Change the vector definition of `x` and `y` using `axisa` and `axisb`. >>> x = np.array([[1,2,3], [4,5,6], [7, 8, 9]]) >>> y = np.array([[7, 8, 9], [4,5,6], [1,2,3]]) >>> np.cross(x, y) array([[ -6, 12, -6], [ 0, 0, 0], [ 6, -12, 6]]) >>> np.cross(x, y, axisa=0, axisb=0) array([[-24, 48, -24], [-30, 60, -30], [-36, 72, -36]]) cumprod(a, axis=None, dtype=None, out=None) Return the cumulative product of elements along a given axis. Parameters ---------- a : array_like Input array. axis : int, optional Axis along which the cumulative product is computed. By default the input is flattened. dtype : dtype, optional Type of the returned array, as well as of the accumulator in which the elements are multiplied. If *dtype* is not specified, it defaults to the dtype of `a`, unless `a` has an integer dtype with a precision less than that of the default platform integer. In that case, the default platform integer is used instead. out : ndarray, optional Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output but the type of the resulting values will be cast if necessary. Returns ------- cumprod : ndarray A new array holding the result is returned unless `out` is specified, in which case a reference to out is returned. See Also -------- numpy.doc.ufuncs : Section "Output arguments" Notes ----- Arithmetic is modular when using integer types, and no error is raised on overflow. Examples -------- >>> a = np.array([1,2,3]) >>> np.cumprod(a) # intermediate results 1, 1*2 ... # total product 1*2*3 = 6 array([1, 2, 6]) >>> a = np.array([[1, 2, 3], [4, 5, 6]]) >>> np.cumprod(a, dtype=float) # specify type of output array([ 1., 2., 6., 24., 120., 720.]) The cumulative product for each column (i.e., over the rows) of `a`: >>> np.cumprod(a, axis=0) array([[ 1, 2, 3], [ 4, 10, 18]]) The cumulative product for each row (i.e. over the columns) of `a`: >>> np.cumprod(a,axis=1) array([[ 1, 2, 6], [ 4, 20, 120]]) cumproduct(a, axis=None, dtype=None, out=None) Return the cumulative product over the given axis. See Also -------- cumprod : equivalent function; see for details. cumsum(a, axis=None, dtype=None, out=None) Return the cumulative sum of the elements along a given axis. Parameters ---------- a : array_like Input array. axis : int, optional Axis along which the cumulative sum is computed. The default (None) is to compute the cumsum over the flattened array. dtype : dtype, optional Type of the returned array and of the accumulator in which the elements are summed. If `dtype` is not specified, it defaults to the dtype of `a`, unless `a` has an integer dtype with a precision less than that of the default platform integer. In that case, the default platform integer is used. out : ndarray, optional Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output but the type will be cast if necessary. See `doc.ufuncs` (Section "Output arguments") for more details. Returns ------- cumsum_along_axis : ndarray. A new array holding the result is returned unless `out` is specified, in which case a reference to `out` is returned. The result has the same size as `a`, and the same shape as `a` if `axis` is not None or `a` is a 1-d array. See Also -------- sum : Sum array elements. trapz : Integration of array values using the composite trapezoidal rule. diff : Calculate the n-th order discrete difference along given axis. Notes ----- Arithmetic is modular when using integer types, and no error is raised on overflow. Examples -------- >>> a = np.array([[1,2,3], [4,5,6]]) >>> a array([[1, 2, 3], [4, 5, 6]]) >>> np.cumsum(a) array([ 1, 3, 6, 10, 15, 21]) >>> np.cumsum(a, dtype=float) # specifies type of output value(s) array([ 1., 3., 6., 10., 15., 21.]) >>> np.cumsum(a,axis=0) # sum over rows for each of the 3 columns array([[1, 2, 3], [5, 7, 9]]) >>> np.cumsum(a,axis=1) # sum over columns for each of the 2 rows array([[ 1, 3, 6], [ 4, 9, 15]]) datetime_as_string(...) datetime_data(...) delete(arr, obj, axis=None) Return a new array with sub-arrays along an axis deleted. For a one dimensional array, this returns those entries not returned by `arr[obj]`. Parameters ---------- arr : array_like Input array. obj : slice, int or array of ints Indicate which sub-arrays to remove. axis : int, optional The axis along which to delete the subarray defined by `obj`. If `axis` is None, `obj` is applied to the flattened array. Returns ------- out : ndarray A copy of `arr` with the elements specified by `obj` removed. Note that `delete` does not occur in-place. If `axis` is None, `out` is a flattened array. See Also -------- insert : Insert elements into an array. append : Append elements at the end of an array. Notes ----- Often it is preferable to use a boolean mask. For example: >>> mask = np.ones(len(arr), dtype=bool) >>> mask[[0,2,4]] = False >>> result = arr[mask,...] Is equivalent to `np.delete(arr, [0,2,4], axis=0)`, but allows further use of `mask`. Examples -------- >>> arr = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]]) >>> arr array([[ 1, 2, 3, 4], [ 5, 6, 7, 8], [ 9, 10, 11, 12]]) >>> np.delete(arr, 1, 0) array([[ 1, 2, 3, 4], [ 9, 10, 11, 12]]) >>> np.delete(arr, np.s_[::2], 1) array([[ 2, 4], [ 6, 8], [10, 12]]) >>> np.delete(arr, [1,3,5], None) array([ 1, 3, 5, 7, 8, 9, 10, 11, 12]) deprecate(*args, **kwargs) Issues a DeprecationWarning, adds warning to `old_name`'s docstring, rebinds ``old_name.__name__`` and returns the new function object. This function may also be used as a decorator. Parameters ---------- func : function The function to be deprecated. old_name : str, optional The name of the function to be deprecated. Default is None, in which case the name of `func` is used. new_name : str, optional The new name for the function. Default is None, in which case the deprecation message is that `old_name` is deprecated. If given, the deprecation message is that `old_name` is deprecated and `new_name` should be used instead. message : str, optional Additional explanation of the deprecation. Displayed in the docstring after the warning. Returns ------- old_func : function The deprecated function. Examples -------- Note that ``olduint`` returns a value after printing Deprecation Warning: >>> olduint = np.deprecate(np.uint) >>> olduint(6) /usr/lib/python2.5/site-packages/numpy/lib/utils.py:114: DeprecationWarning: uint32 is deprecated warnings.warn(str1, DeprecationWarning) 6 deprecate_with_doc lambda msg diag(v, k=0) Extract a diagonal or construct a diagonal array. See the more detailed documentation for ``numpy.diagonal`` if you use this function to extract a diagonal and wish to write to the resulting array; whether it returns a copy or a view depends on what version of numpy you are using. Parameters ---------- v : array_like If `v` is a 2-D array, return a copy of its `k`-th diagonal. If `v` is a 1-D array, return a 2-D array with `v` on the `k`-th diagonal. k : int, optional Diagonal in question. The default is 0. Use `k>0` for diagonals above the main diagonal, and `k<0` for diagonals below the main diagonal. Returns ------- out : ndarray The extracted diagonal or constructed diagonal array. See Also -------- diagonal : Return specified diagonals. diagflat : Create a 2-D array with the flattened input as a diagonal. trace : Sum along diagonals. triu : Upper triangle of an array. tril : Lower triangle of an array. Examples -------- >>> x = np.arange(9).reshape((3,3)) >>> x array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) >>> np.diag(x) array([0, 4, 8]) >>> np.diag(x, k=1) array([1, 5]) >>> np.diag(x, k=-1) array([3, 7]) >>> np.diag(np.diag(x)) array([[0, 0, 0], [0, 4, 0], [0, 0, 8]]) diag_indices(n, ndim=2) Return the indices to access the main diagonal of an array. This returns a tuple of indices that can be used to access the main diagonal of an array `a` with ``a.ndim >= 2`` dimensions and shape (n, n, ..., n). For ``a.ndim = 2`` this is the usual diagonal, for ``a.ndim > 2`` this is the set of indices to access ``a[i, i, ..., i]`` for ``i = [0..n-1]``. Parameters ---------- n : int The size, along each dimension, of the arrays for which the returned indices can be used. ndim : int, optional The number of dimensions. See also -------- diag_indices_from Notes ----- .. versionadded:: 1.4.0 Examples -------- Create a set of indices to access the diagonal of a (4, 4) array: >>> di = np.diag_indices(4) >>> di (array([0, 1, 2, 3]), array([0, 1, 2, 3])) >>> a = np.arange(16).reshape(4, 4) >>> a array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15]]) >>> a[di] = 100 >>> a array([[100, 1, 2, 3], [ 4, 100, 6, 7], [ 8, 9, 100, 11], [ 12, 13, 14, 100]]) Now, we create indices to manipulate a 3-D array: >>> d3 = np.diag_indices(2, 3) >>> d3 (array([0, 1]), array([0, 1]), array([0, 1])) And use it to set the diagonal of an array of zeros to 1: >>> a = np.zeros((2, 2, 2), dtype=np.int) >>> a[d3] = 1 >>> a array([[[1, 0], [0, 0]], [[0, 0], [0, 1]]]) diag_indices_from(arr) Return the indices to access the main diagonal of an n-dimensional array. See `diag_indices` for full details. Parameters ---------- arr : array, at least 2-D See Also -------- diag_indices Notes ----- .. versionadded:: 1.4.0 diagflat(v, k=0) Create a two-dimensional array with the flattened input as a diagonal. Parameters ---------- v : array_like Input data, which is flattened and set as the `k`-th diagonal of the output. k : int, optional Diagonal to set; 0, the default, corresponds to the "main" diagonal, a positive (negative) `k` giving the number of the diagonal above (below) the main. Returns ------- out : ndarray The 2-D output array. See Also -------- diag : MATLAB work-alike for 1-D and 2-D arrays. diagonal : Return specified diagonals. trace : Sum along diagonals. Examples -------- >>> np.diagflat([[1,2], [3,4]]) array([[1, 0, 0, 0], [0, 2, 0, 0], [0, 0, 3, 0], [0, 0, 0, 4]]) >>> np.diagflat([1,2], 1) array([[0, 1, 0], [0, 0, 2], [0, 0, 0]]) diagonal(a, offset=0, axis1=0, axis2=1) Return specified diagonals. If `a` is 2-D, returns the diagonal of `a` with the given offset, i.e., the collection of elements of the form ``a[i, i+offset]``. If `a` has more than two dimensions, then the axes specified by `axis1` and `axis2` are used to determine the 2-D sub-array whose diagonal is returned. The shape of the resulting array can be determined by removing `axis1` and `axis2` and appending an index to the right equal to the size of the resulting diagonals. In versions of NumPy prior to 1.7, this function always returned a new, independent array containing a copy of the values in the diagonal. In NumPy 1.7 and 1.8, it continues to return a copy of the diagonal, but depending on this fact is deprecated. Writing to the resulting array continues to work as it used to, but a FutureWarning is issued. In NumPy 1.9 it returns a read-only view on the original array. Attempting to write to the resulting array will produce an error. In NumPy 1.10, it will return a read/write view and writing to the returned array will alter your original array. The returned array will have the same type as the input array. If you don't write to the array returned by this function, then you can just ignore all of the above. If you depend on the current behavior, then we suggest copying the returned array explicitly, i.e., use ``np.diagonal(a).copy()`` instead of just ``np.diagonal(a)``. This will work with both past and future versions of NumPy. Parameters ---------- a : array_like Array from which the diagonals are taken. offset : int, optional Offset of the diagonal from the main diagonal. Can be positive or negative. Defaults to main diagonal (0). axis1 : int, optional Axis to be used as the first axis of the 2-D sub-arrays from which the diagonals should be taken. Defaults to first axis (0). axis2 : int, optional Axis to be used as the second axis of the 2-D sub-arrays from which the diagonals should be taken. Defaults to second axis (1). Returns ------- array_of_diagonals : ndarray If `a` is 2-D and not a matrix, a 1-D array of the same type as `a` containing the diagonal is returned. If `a` is a matrix, a 1-D array containing the diagonal is returned in order to maintain backward compatibility. If the dimension of `a` is greater than two, then an array of diagonals is returned, "packed" from left-most dimension to right-most (e.g., if `a` is 3-D, then the diagonals are "packed" along rows). Raises ------ ValueError If the dimension of `a` is less than 2. See Also -------- diag : MATLAB work-a-like for 1-D and 2-D arrays. diagflat : Create diagonal arrays. trace : Sum along diagonals. Examples -------- >>> a = np.arange(4).reshape(2,2) >>> a array([[0, 1], [2, 3]]) >>> a.diagonal() array([0, 3]) >>> a.diagonal(1) array([1]) A 3-D example: >>> a = np.arange(8).reshape(2,2,2); a array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]) >>> a.diagonal(0, # Main diagonals of two arrays created by skipping ... 0, # across the outer(left)-most axis last and ... 1) # the "middle" (row) axis first. array([[0, 6], [1, 7]]) The sub-arrays whose main diagonals we just obtained; note that each corresponds to fixing the right-most (column) axis, and that the diagonals are "packed" in rows. >>> a[:,:,0] # main diagonal is [0 6] array([[0, 2], [4, 6]]) >>> a[:,:,1] # main diagonal is [1 7] array([[1, 3], [5, 7]]) diff(a, n=1, axis=-1) Calculate the n-th order discrete difference along given axis. The first order difference is given by ``out[n] = a[n+1] - a[n]`` along the given axis, higher order differences are calculated by using `diff` recursively. Parameters ---------- a : array_like Input array n : int, optional The number of times values are differenced. axis : int, optional The axis along which the difference is taken, default is the last axis. Returns ------- diff : ndarray The `n` order differences. The shape of the output is the same as `a` except along `axis` where the dimension is smaller by `n`. See Also -------- gradient, ediff1d, cumsum Examples -------- >>> x = np.array([1, 2, 4, 7, 0]) >>> np.diff(x) array([ 1, 2, 3, -7]) >>> np.diff(x, n=2) array([ 1, 1, -10]) >>> x = np.array([[1, 3, 6, 10], [0, 5, 6, 8]]) >>> np.diff(x) array([[2, 3, 4], [5, 1, 2]]) >>> np.diff(x, axis=0) array([[-1, 2, 0, -2]]) digitize(...) digitize(x, bins, right=False) Return the indices of the bins to which each value in input array belongs. Each index ``i`` returned is such that ``bins[i-1] <= x < bins[i]`` if `bins` is monotonically increasing, or ``bins[i-1] > x >= bins[i]`` if `bins` is monotonically decreasing. If values in `x` are beyond the bounds of `bins`, 0 or ``len(bins)`` is returned as appropriate. If right is True, then the right bin is closed so that the index ``i`` is such that ``bins[i-1] < x <= bins[i]`` or bins[i-1] >= x > bins[i]`` if `bins` is monotonically increasing or decreasing, respectively. Parameters ---------- x : array_like Input array to be binned. Prior to Numpy 1.10.0, this array had to be 1-dimensional, but can now have any shape. bins : array_like Array of bins. It has to be 1-dimensional and monotonic. right : bool, optional Indicating whether the intervals include the right or the left bin edge. Default behavior is (right==False) indicating that the interval does not include the right edge. The left bin end is open in this case, i.e., bins[i-1] <= x < bins[i] is the default behavior for monotonically increasing bins. Returns ------- out : ndarray of ints Output array of indices, of same shape as `x`. Raises ------ ValueError If `bins` is not monotonic. TypeError If the type of the input is complex. See Also -------- bincount, histogram, unique Notes ----- If values in `x` are such that they fall outside the bin range, attempting to index `bins` with the indices that `digitize` returns will result in an IndexError. .. versionadded:: 1.10.0 `np.digitize` is implemented in terms of `np.searchsorted`. This means that a binary search is used to bin the values, which scales much better for larger number of bins than the previous linear search. It also removes the requirement for the input array to be 1-dimensional. Examples -------- >>> x = np.array([0.2, 6.4, 3.0, 1.6]) >>> bins = np.array([0.0, 1.0, 2.5, 4.0, 10.0]) >>> inds = np.digitize(x, bins) >>> inds array([1, 4, 3, 2]) >>> for n in range(x.size): ... print bins[inds[n]-1], "<=", x[n], "<", bins[inds[n]] ... 0.0 <= 0.2 < 1.0 4.0 <= 6.4 < 10.0 2.5 <= 3.0 < 4.0 1.0 <= 1.6 < 2.5 >>> x = np.array([1.2, 10.0, 12.4, 15.5, 20.]) >>> bins = np.array([0, 5, 10, 15, 20]) >>> np.digitize(x,bins,right=True) array([1, 2, 3, 4, 4]) >>> np.digitize(x,bins,right=False) array([1, 3, 3, 4, 5]) disp(mesg, device=None, linefeed=True) Display a message on a device. Parameters ---------- mesg : str Message to display. device : object Device to write message. If None, defaults to ``sys.stdout`` which is very similar to ``print``. `device` needs to have ``write()`` and ``flush()`` methods. linefeed : bool, optional Option whether to print a line feed or not. Defaults to True. Raises ------ AttributeError If `device` does not have a ``write()`` or ``flush()`` method. Examples -------- Besides ``sys.stdout``, a file-like object can also be used as it has both required methods: >>> from StringIO import StringIO >>> buf = StringIO() >>> np.disp('"Display" in a file', device=buf) >>> buf.getvalue() '"Display" in a file\n' dot(...) dot(a, b, out=None) Dot product of two arrays. For 2-D arrays it is equivalent to matrix multiplication, and for 1-D arrays to inner product of vectors (without complex conjugation). For N dimensions it is a sum product over the last axis of `a` and the second-to-last of `b`:: dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m]) Parameters ---------- a : array_like First argument. b : array_like Second argument. out : ndarray, optional Output argument. This must have the exact kind that would be returned if it was not used. In particular, it must have the right type, must be C-contiguous, and its dtype must be the dtype that would be returned for `dot(a,b)`. This is a performance feature. Therefore, if these conditions are not met, an exception is raised, instead of attempting to be flexible. Returns ------- output : ndarray Returns the dot product of `a` and `b`. If `a` and `b` are both scalars or both 1-D arrays then a scalar is returned; otherwise an array is returned. If `out` is given, then it is returned. Raises ------ ValueError If the last dimension of `a` is not the same size as the second-to-last dimension of `b`. See Also -------- vdot : Complex-conjugating dot product. tensordot : Sum products over arbitrary axes. einsum : Einstein summation convention. matmul : '@' operator as method with out parameter. Examples -------- >>> np.dot(3, 4) 12 Neither argument is complex-conjugated: >>> np.dot([2j, 3j], [2j, 3j]) (-13+0j) For 2-D arrays it is the matrix product: >>> a = [[1, 0], [0, 1]] >>> b = [[4, 1], [2, 2]] >>> np.dot(a, b) array([[4, 1], [2, 2]]) >>> a = np.arange(3*4*5*6).reshape((3,4,5,6)) >>> b = np.arange(3*4*5*6)[::-1].reshape((5,4,6,3)) >>> np.dot(a, b)[2,3,2,1,2,2] 499128 >>> sum(a[2,3,2,:] * b[1,2,:,2]) 499128 dsplit(ary, indices_or_sections) Split array into multiple sub-arrays along the 3rd axis (depth). Please refer to the `split` documentation. `dsplit` is equivalent to `split` with ``axis=2``, the array is always split along the third axis provided the array dimension is greater than or equal to 3. See Also -------- split : Split an array into multiple sub-arrays of equal size. Examples -------- >>> x = np.arange(16.0).reshape(2, 2, 4) >>> x array([[[ 0., 1., 2., 3.], [ 4., 5., 6., 7.]], [[ 8., 9., 10., 11.], [ 12., 13., 14., 15.]]]) >>> np.dsplit(x, 2) [array([[[ 0., 1.], [ 4., 5.]], [[ 8., 9.], [ 12., 13.]]]), array([[[ 2., 3.], [ 6., 7.]], [[ 10., 11.], [ 14., 15.]]])] >>> np.dsplit(x, np.array([3, 6])) [array([[[ 0., 1., 2.], [ 4., 5., 6.]], [[ 8., 9., 10.], [ 12., 13., 14.]]]), array([[[ 3.], [ 7.]], [[ 11.], [ 15.]]]), array([], dtype=float64)] dstack(tup) Stack arrays in sequence depth wise (along third axis). Takes a sequence of arrays and stack them along the third axis to make a single array. Rebuilds arrays divided by `dsplit`. This is a simple way to stack 2D arrays (images) into a single 3D array for processing. Parameters ---------- tup : sequence of arrays Arrays to stack. All of them must have the same shape along all but the third axis. Returns ------- stacked : ndarray The array formed by stacking the given arrays. See Also -------- stack : Join a sequence of arrays along a new axis. vstack : Stack along first axis. hstack : Stack along second axis. concatenate : Join a sequence of arrays along an existing axis. dsplit : Split array along third axis. Notes ----- Equivalent to ``np.concatenate(tup, axis=2)``. Examples -------- >>> a = np.array((1,2,3)) >>> b = np.array((2,3,4)) >>> np.dstack((a,b)) array([[[1, 2], [2, 3], [3, 4]]]) >>> a = np.array([[1],[2],[3]]) >>> b = np.array([[2],[3],[4]]) >>> np.dstack((a,b)) array([[[1, 2]], [[2, 3]], [[3, 4]]]) ediff1d(ary, to_end=None, to_begin=None) The differences between consecutive elements of an array. Parameters ---------- ary : array_like If necessary, will be flattened before the differences are taken. to_end : array_like, optional Number(s) to append at the end of the returned differences. to_begin : array_like, optional Number(s) to prepend at the beginning of the returned differences. Returns ------- ediff1d : ndarray The differences. Loosely, this is ``ary.flat[1:] - ary.flat[:-1]``. See Also -------- diff, gradient Notes ----- When applied to masked arrays, this function drops the mask information if the `to_begin` and/or `to_end` parameters are used. Examples -------- >>> x = np.array([1, 2, 4, 7, 0]) >>> np.ediff1d(x) array([ 1, 2, 3, -7]) >>> np.ediff1d(x, to_begin=-99, to_end=np.array([88, 99])) array([-99, 1, 2, 3, -7, 88, 99]) The returned array is always 1D. >>> y = [[1, 2, 4], [1, 6, 24]] >>> np.ediff1d(y) array([ 1, 2, -3, 5, 18]) einsum(...) einsum(subscripts, *operands, out=None, dtype=None, order='K', casting='safe') Evaluates the Einstein summation convention on the operands. Using the Einstein summation convention, many common multi-dimensional array operations can be represented in a simple fashion. This function provides a way compute such summations. The best way to understand this function is to try the examples below, which show how many common NumPy functions can be implemented as calls to `einsum`. Parameters ---------- subscripts : str Specifies the subscripts for summation. operands : list of array_like These are the arrays for the operation. out : ndarray, optional If provided, the calculation is done into this array. dtype : data-type, optional If provided, forces the calculation to use the data type specified. Note that you may have to also give a more liberal `casting` parameter to allow the conversions. order : {'C', 'F', 'A', 'K'}, optional Controls the memory layout of the output. 'C' means it should be C contiguous. 'F' means it should be Fortran contiguous, 'A' means it should be 'F' if the inputs are all 'F', 'C' otherwise. 'K' means it should be as close to the layout as the inputs as is possible, including arbitrarily permuted axes. Default is 'K'. casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional Controls what kind of data casting may occur. Setting this to 'unsafe' is not recommended, as it can adversely affect accumulations. * 'no' means the data types should not be cast at all. * 'equiv' means only byte-order changes are allowed. * 'safe' means only casts which can preserve values are allowed. * 'same_kind' means only safe casts or casts within a kind, like float64 to float32, are allowed. * 'unsafe' means any data conversions may be done. Returns ------- output : ndarray The calculation based on the Einstein summation convention. See Also -------- dot, inner, outer, tensordot Notes ----- .. versionadded:: 1.6.0 The subscripts string is a comma-separated list of subscript labels, where each label refers to a dimension of the corresponding operand. Repeated subscripts labels in one operand take the diagonal. For example, ``np.einsum('ii', a)`` is equivalent to ``np.trace(a)``. Whenever a label is repeated, it is summed, so ``np.einsum('i,i', a, b)`` is equivalent to ``np.inner(a,b)``. If a label appears only once, it is not summed, so ``np.einsum('i', a)`` produces a view of ``a`` with no changes. The order of labels in the output is by default alphabetical. This means that ``np.einsum('ij', a)`` doesn't affect a 2D array, while ``np.einsum('ji', a)`` takes its transpose. The output can be controlled by specifying output subscript labels as well. This specifies the label order, and allows summing to be disallowed or forced when desired. The call ``np.einsum('i->', a)`` is like ``np.sum(a, axis=-1)``, and ``np.einsum('ii->i', a)`` is like ``np.diag(a)``. The difference is that `einsum` does not allow broadcasting by default. To enable and control broadcasting, use an ellipsis. Default NumPy-style broadcasting is done by adding an ellipsis to the left of each term, like ``np.einsum('...ii->...i', a)``. To take the trace along the first and last axes, you can do ``np.einsum('i...i', a)``, or to do a matrix-matrix product with the left-most indices instead of rightmost, you can do ``np.einsum('ij...,jk...->ik...', a, b)``. When there is only one operand, no axes are summed, and no output parameter is provided, a view into the operand is returned instead of a new array. Thus, taking the diagonal as ``np.einsum('ii->i', a)`` produces a view. An alternative way to provide the subscripts and operands is as ``einsum(op0, sublist0, op1, sublist1, ..., [sublistout])``. The examples below have corresponding `einsum` calls with the two parameter methods. .. versionadded:: 1.10.0 Views returned from einsum are now writeable whenever the input array is writeable. For example, ``np.einsum('ijk...->kji...', a)`` will now have the same effect as ``np.swapaxes(a, 0, 2)`` and ``np.einsum('ii->i', a)`` will return a writeable view of the diagonal of a 2D array. Examples -------- >>> a = np.arange(25).reshape(5,5) >>> b = np.arange(5) >>> c = np.arange(6).reshape(2,3) >>> np.einsum('ii', a) 60 >>> np.einsum(a, [0,0]) 60 >>> np.trace(a) 60 >>> np.einsum('ii->i', a) array([ 0, 6, 12, 18, 24]) >>> np.einsum(a, [0,0], [0]) array([ 0, 6, 12, 18, 24]) >>> np.diag(a) array([ 0, 6, 12, 18, 24]) >>> np.einsum('ij,j', a, b) array([ 30, 80, 130, 180, 230]) >>> np.einsum(a, [0,1], b, [1]) array([ 30, 80, 130, 180, 230]) >>> np.dot(a, b) array([ 30, 80, 130, 180, 230]) >>> np.einsum('...j,j', a, b) array([ 30, 80, 130, 180, 230]) >>> np.einsum('ji', c) array([[0, 3], [1, 4], [2, 5]]) >>> np.einsum(c, [1,0]) array([[0, 3], [1, 4], [2, 5]]) >>> c.T array([[0, 3], [1, 4], [2, 5]]) >>> np.einsum('..., ...', 3, c) array([[ 0, 3, 6], [ 9, 12, 15]]) >>> np.einsum(3, [Ellipsis], c, [Ellipsis]) array([[ 0, 3, 6], [ 9, 12, 15]]) >>> np.multiply(3, c) array([[ 0, 3, 6], [ 9, 12, 15]]) >>> np.einsum('i,i', b, b) 30 >>> np.einsum(b, [0], b, [0]) 30 >>> np.inner(b,b) 30 >>> np.einsum('i,j', np.arange(2)+1, b) array([[0, 1, 2, 3, 4], [0, 2, 4, 6, 8]]) >>> np.einsum(np.arange(2)+1, [0], b, [1]) array([[0, 1, 2, 3, 4], [0, 2, 4, 6, 8]]) >>> np.outer(np.arange(2)+1, b) array([[0, 1, 2, 3, 4], [0, 2, 4, 6, 8]]) >>> np.einsum('i...->...', a) array([50, 55, 60, 65, 70]) >>> np.einsum(a, [0,Ellipsis], [Ellipsis]) array([50, 55, 60, 65, 70]) >>> np.sum(a, axis=0) array([50, 55, 60, 65, 70]) >>> a = np.arange(60.).reshape(3,4,5) >>> b = np.arange(24.).reshape(4,3,2) >>> np.einsum('ijk,jil->kl', a, b) array([[ 4400., 4730.], [ 4532., 4874.], [ 4664., 5018.], [ 4796., 5162.], [ 4928., 5306.]]) >>> np.einsum(a, [0,1,2], b, [1,0,3], [2,3]) array([[ 4400., 4730.], [ 4532., 4874.], [ 4664., 5018.], [ 4796., 5162.], [ 4928., 5306.]]) >>> np.tensordot(a,b, axes=([1,0],[0,1])) array([[ 4400., 4730.], [ 4532., 4874.], [ 4664., 5018.], [ 4796., 5162.], [ 4928., 5306.]]) >>> a = np.arange(6).reshape((3,2)) >>> b = np.arange(12).reshape((4,3)) >>> np.einsum('ki,jk->ij', a, b) array([[10, 28, 46, 64], [13, 40, 67, 94]]) >>> np.einsum('ki,...k->i...', a, b) array([[10, 28, 46, 64], [13, 40, 67, 94]]) >>> np.einsum('k...,jk', a, b) array([[10, 28, 46, 64], [13, 40, 67, 94]]) >>> # since version 1.10.0 >>> a = np.zeros((3, 3)) >>> np.einsum('ii->i', a)[:] = 1 >>> a array([[ 1., 0., 0.], [ 0., 1., 0.], [ 0., 0., 1.]]) empty(...) empty(shape, dtype=float, order='C') Return a new array of given shape and type, without initializing entries. Parameters ---------- shape : int or tuple of int Shape of the empty array dtype : data-type, optional Desired output data-type. order : {'C', 'F'}, optional Whether to store multi-dimensional data in row-major (C-style) or column-major (Fortran-style) order in memory. Returns ------- out : ndarray Array of uninitialized (arbitrary) data with the given shape, dtype, and order. See Also -------- empty_like, zeros, ones Notes ----- `empty`, unlike `zeros`, does not set the array values to zero, and may therefore be marginally faster. On the other hand, it requires the user to manually set all the values in the array, and should be used with caution. Examples -------- >>> np.empty([2, 2]) array([[ -9.74499359e+001, 6.69583040e-309], [ 2.13182611e-314, 3.06959433e-309]]) #random >>> np.empty([2, 2], dtype=int) array([[-1073741821, -1067949133], [ 496041986, 19249760]]) #random empty_like(...) empty_like(a, dtype=None, order='K', subok=True) Return a new array with the same shape and type as a given array. Parameters ---------- a : array_like The shape and data-type of `a` define these same attributes of the returned array. dtype : data-type, optional Overrides the data type of the result. .. versionadded:: 1.6.0 order : {'C', 'F', 'A', or 'K'}, optional Overrides the memory layout of the result. 'C' means C-order, 'F' means F-order, 'A' means 'F' if ``a`` is Fortran contiguous, 'C' otherwise. 'K' means match the layout of ``a`` as closely as possible. .. versionadded:: 1.6.0 subok : bool, optional. If True, then the newly created array will use the sub-class type of 'a', otherwise it will be a base-class array. Defaults to True. Returns ------- out : ndarray Array of uninitialized (arbitrary) data with the same shape and type as `a`. See Also -------- ones_like : Return an array of ones with shape and type of input. zeros_like : Return an array of zeros with shape and type of input. empty : Return a new uninitialized array. ones : Return a new array setting values to one. zeros : Return a new array setting values to zero. Notes ----- This function does *not* initialize the returned array; to do that use `zeros_like` or `ones_like` instead. It may be marginally faster than the functions that do set the array values. Examples -------- >>> a = ([1,2,3], [4,5,6]) # a is array-like >>> np.empty_like(a) array([[-1073741821, -1073741821, 3], #random [ 0, 0, -1073741821]]) >>> a = np.array([[1., 2., 3.],[4.,5.,6.]]) >>> np.empty_like(a) array([[ -2.00000715e+000, 1.48219694e-323, -2.00000572e+000],#random [ 4.38791518e-305, -2.00000715e+000, 4.17269252e-309]]) expand_dims(a, axis) Expand the shape of an array. Insert a new axis, corresponding to a given position in the array shape. Parameters ---------- a : array_like Input array. axis : int Position (amongst axes) where new axis is to be inserted. Returns ------- res : ndarray Output array. The number of dimensions is one greater than that of the input array. See Also -------- doc.indexing, atleast_1d, atleast_2d, atleast_3d Examples -------- >>> x = np.array([1,2]) >>> x.shape (2,) The following is equivalent to ``x[np.newaxis,:]`` or ``x[np.newaxis]``: >>> y = np.expand_dims(x, axis=0) >>> y array([[1, 2]]) >>> y.shape (1, 2) >>> y = np.expand_dims(x, axis=1) # Equivalent to x[:,newaxis] >>> y array([[1], [2]]) >>> y.shape (2, 1) Note that some examples may use ``None`` instead of ``np.newaxis``. These are the same objects: >>> np.newaxis is None True extract(condition, arr) Return the elements of an array that satisfy some condition. This is equivalent to ``np.compress(ravel(condition), ravel(arr))``. If `condition` is boolean ``np.extract`` is equivalent to ``arr[condition]``. Note that `place` does the exact opposite of `extract`. Parameters ---------- condition : array_like An array whose nonzero or True entries indicate the elements of `arr` to extract. arr : array_like Input array of the same size as `condition`. Returns ------- extract : ndarray Rank 1 array of values from `arr` where `condition` is True. See Also -------- take, put, copyto, compress, place Examples -------- >>> arr = np.arange(12).reshape((3, 4)) >>> arr array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> condition = np.mod(arr, 3)==0 >>> condition array([[ True, False, False, True], [False, False, True, False], [False, True, False, False]], dtype=bool) >>> np.extract(condition, arr) array([0, 3, 6, 9]) If `condition` is boolean: >>> arr[condition] array([0, 3, 6, 9]) eye(N, M=None, k=0, dtype=) Return a 2-D array with ones on the diagonal and zeros elsewhere. Parameters ---------- N : int Number of rows in the output. M : int, optional Number of columns in the output. If None, defaults to `N`. k : int, optional Index of the diagonal: 0 (the default) refers to the main diagonal, a positive value refers to an upper diagonal, and a negative value to a lower diagonal. dtype : data-type, optional Data-type of the returned array. Returns ------- I : ndarray of shape (N,M) An array where all elements are equal to zero, except for the `k`-th diagonal, whose values are equal to one. See Also -------- identity : (almost) equivalent function diag : diagonal 2-D array from a 1-D array specified by the user. Examples -------- >>> np.eye(2, dtype=int) array([[1, 0], [0, 1]]) >>> np.eye(3, k=1) array([[ 0., 1., 0.], [ 0., 0., 1.], [ 0., 0., 0.]]) fastCopyAndTranspose = _fastCopyAndTranspose(...) _fastCopyAndTranspose(a) fill_diagonal(a, val, wrap=False) Fill the main diagonal of the given array of any dimensionality. For an array `a` with ``a.ndim > 2``, the diagonal is the list of locations with indices ``a[i, i, ..., i]`` all identical. This function modifies the input array in-place, it does not return a value. Parameters ---------- a : array, at least 2-D. Array whose diagonal is to be filled, it gets modified in-place. val : scalar Value to be written on the diagonal, its type must be compatible with that of the array a. wrap : bool For tall matrices in NumPy version up to 1.6.2, the diagonal "wrapped" after N columns. You can have this behavior with this option. This affect only tall matrices. See also -------- diag_indices, diag_indices_from Notes ----- .. versionadded:: 1.4.0 This functionality can be obtained via `diag_indices`, but internally this version uses a much faster implementation that never constructs the indices and uses simple slicing. Examples -------- >>> a = np.zeros((3, 3), int) >>> np.fill_diagonal(a, 5) >>> a array([[5, 0, 0], [0, 5, 0], [0, 0, 5]]) The same function can operate on a 4-D array: >>> a = np.zeros((3, 3, 3, 3), int) >>> np.fill_diagonal(a, 4) We only show a few blocks for clarity: >>> a[0, 0] array([[4, 0, 0], [0, 0, 0], [0, 0, 0]]) >>> a[1, 1] array([[0, 0, 0], [0, 4, 0], [0, 0, 0]]) >>> a[2, 2] array([[0, 0, 0], [0, 0, 0], [0, 0, 4]]) The wrap option affects only tall matrices: >>> # tall matrices no wrap >>> a = np.zeros((5, 3),int) >>> fill_diagonal(a, 4) >>> a array([[4, 0, 0], [0, 4, 0], [0, 0, 4], [0, 0, 0], [0, 0, 0]]) >>> # tall matrices wrap >>> a = np.zeros((5, 3),int) >>> fill_diagonal(a, 4, wrap=True) >>> a array([[4, 0, 0], [0, 4, 0], [0, 0, 4], [0, 0, 0], [4, 0, 0]]) >>> # wide matrices >>> a = np.zeros((3, 5),int) >>> fill_diagonal(a, 4, wrap=True) >>> a array([[4, 0, 0, 0, 0], [0, 4, 0, 0, 0], [0, 0, 4, 0, 0]]) find_common_type(array_types, scalar_types) Determine common type following standard coercion rules. Parameters ---------- array_types : sequence A list of dtypes or dtype convertible objects representing arrays. scalar_types : sequence A list of dtypes or dtype convertible objects representing scalars. Returns ------- datatype : dtype The common data type, which is the maximum of `array_types` ignoring `scalar_types`, unless the maximum of `scalar_types` is of a different kind (`dtype.kind`). If the kind is not understood, then None is returned. See Also -------- dtype, common_type, can_cast, mintypecode Examples -------- >>> np.find_common_type([], [np.int64, np.float32, np.complex]) dtype('complex128') >>> np.find_common_type([np.int64, np.float32], []) dtype('float64') The standard casting rules ensure that a scalar cannot up-cast an array unless the scalar is of a fundamentally different kind of data (i.e. under a different hierarchy in the data type hierarchy) then the array: >>> np.find_common_type([np.float32], [np.int64, np.float64]) dtype('float32') Complex is of a different type, so it up-casts the float in the `array_types` argument: >>> np.find_common_type([np.float32], [np.complex]) dtype('complex128') Type specifier strings are convertible to dtypes and can therefore be used instead of dtypes: >>> np.find_common_type(['f4', 'f4', 'i4'], ['c8']) dtype('complex128') fix(x, y=None) Round to nearest integer towards zero. Round an array of floats element-wise to nearest integer towards zero. The rounded values are returned as floats. Parameters ---------- x : array_like An array of floats to be rounded y : ndarray, optional Output array Returns ------- out : ndarray of floats The array of rounded numbers See Also -------- trunc, floor, ceil around : Round to given number of decimals Examples -------- >>> np.fix(3.14) 3.0 >>> np.fix(3) 3.0 >>> np.fix([2.1, 2.9, -2.1, -2.9]) array([ 2., 2., -2., -2.]) flatnonzero(a) Return indices that are non-zero in the flattened version of a. This is equivalent to a.ravel().nonzero()[0]. Parameters ---------- a : ndarray Input array. Returns ------- res : ndarray Output array, containing the indices of the elements of `a.ravel()` that are non-zero. See Also -------- nonzero : Return the indices of the non-zero elements of the input array. ravel : Return a 1-D array containing the elements of the input array. Examples -------- >>> x = np.arange(-2, 3) >>> x array([-2, -1, 0, 1, 2]) >>> np.flatnonzero(x) array([0, 1, 3, 4]) Use the indices of the non-zero elements as an index array to extract these elements: >>> x.ravel()[np.flatnonzero(x)] array([-2, -1, 1, 2]) fliplr(m) Flip array in the left/right direction. Flip the entries in each row in the left/right direction. Columns are preserved, but appear in a different order than before. Parameters ---------- m : array_like Input array, must be at least 2-D. Returns ------- f : ndarray A view of `m` with the columns reversed. Since a view is returned, this operation is :math:`\mathcal O(1)`. See Also -------- flipud : Flip array in the up/down direction. rot90 : Rotate array counterclockwise. Notes ----- Equivalent to A[:,::-1]. Requires the array to be at least 2-D. Examples -------- >>> A = np.diag([1.,2.,3.]) >>> A array([[ 1., 0., 0.], [ 0., 2., 0.], [ 0., 0., 3.]]) >>> np.fliplr(A) array([[ 0., 0., 1.], [ 0., 2., 0.], [ 3., 0., 0.]]) >>> A = np.random.randn(2,3,5) >>> np.all(np.fliplr(A)==A[:,::-1,...]) True flipud(m) Flip array in the up/down direction. Flip the entries in each column in the up/down direction. Rows are preserved, but appear in a different order than before. Parameters ---------- m : array_like Input array. Returns ------- out : array_like A view of `m` with the rows reversed. Since a view is returned, this operation is :math:`\mathcal O(1)`. See Also -------- fliplr : Flip array in the left/right direction. rot90 : Rotate array counterclockwise. Notes ----- Equivalent to ``A[::-1,...]``. Does not require the array to be two-dimensional. Examples -------- >>> A = np.diag([1.0, 2, 3]) >>> A array([[ 1., 0., 0.], [ 0., 2., 0.], [ 0., 0., 3.]]) >>> np.flipud(A) array([[ 0., 0., 3.], [ 0., 2., 0.], [ 1., 0., 0.]]) >>> A = np.random.randn(2,3,5) >>> np.all(np.flipud(A)==A[::-1,...]) True >>> np.flipud([1,2]) array([2, 1]) frombuffer(...) frombuffer(buffer, dtype=float, count=-1, offset=0) Interpret a buffer as a 1-dimensional array. Parameters ---------- buffer : buffer_like An object that exposes the buffer interface. dtype : data-type, optional Data-type of the returned array; default: float. count : int, optional Number of items to read. ``-1`` means all data in the buffer. offset : int, optional Start reading the buffer from this offset; default: 0. Notes ----- If the buffer has data that is not in machine byte-order, this should be specified as part of the data-type, e.g.:: >>> dt = np.dtype(int) >>> dt = dt.newbyteorder('>') >>> np.frombuffer(buf, dtype=dt) The data of the resulting array will not be byteswapped, but will be interpreted correctly. Examples -------- >>> s = 'hello world' >>> np.frombuffer(s, dtype='S1', count=5, offset=6) array(['w', 'o', 'r', 'l', 'd'], dtype='|S1') fromfile(...) fromfile(file, dtype=float, count=-1, sep='') Construct an array from data in a text or binary file. A highly efficient way of reading binary data with a known data-type, as well as parsing simply formatted text files. Data written using the `tofile` method can be read using this function. Parameters ---------- file : file or str Open file object or filename. dtype : data-type Data type of the returned array. For binary files, it is used to determine the size and byte-order of the items in the file. count : int Number of items to read. ``-1`` means all items (i.e., the complete file). sep : str Separator between items if file is a text file. Empty ("") separator means the file should be treated as binary. Spaces (" ") in the separator match zero or more whitespace characters. A separator consisting only of spaces must match at least one whitespace. See also -------- load, save ndarray.tofile loadtxt : More flexible way of loading data from a text file. Notes ----- Do not rely on the combination of `tofile` and `fromfile` for data storage, as the binary files generated are are not platform independent. In particular, no byte-order or data-type information is saved. Data can be stored in the platform independent ``.npy`` format using `save` and `load` instead. Examples -------- Construct an ndarray: >>> dt = np.dtype([('time', [('min', int), ('sec', int)]), ... ('temp', float)]) >>> x = np.zeros((1,), dtype=dt) >>> x['time']['min'] = 10; x['temp'] = 98.25 >>> x array([((10, 0), 98.25)], dtype=[('time', [('min', '>> import os >>> fname = os.tmpnam() >>> x.tofile(fname) Read the raw data from disk: >>> np.fromfile(fname, dtype=dt) array([((10, 0), 98.25)], dtype=[('time', [('min', '>> np.save(fname, x) >>> np.load(fname + '.npy') array([((10, 0), 98.25)], dtype=[('time', [('min', '>> np.fromfunction(lambda i, j: i == j, (3, 3), dtype=int) array([[ True, False, False], [False, True, False], [False, False, True]], dtype=bool) >>> np.fromfunction(lambda i, j: i + j, (3, 3), dtype=int) array([[0, 1, 2], [1, 2, 3], [2, 3, 4]]) fromiter(...) fromiter(iterable, dtype, count=-1) Create a new 1-dimensional array from an iterable object. Parameters ---------- iterable : iterable object An iterable object providing data for the array. dtype : data-type The data-type of the returned array. count : int, optional The number of items to read from *iterable*. The default is -1, which means all data is read. Returns ------- out : ndarray The output array. Notes ----- Specify `count` to improve performance. It allows ``fromiter`` to pre-allocate the output array, instead of resizing it on demand. Examples -------- >>> iterable = (x*x for x in range(5)) >>> np.fromiter(iterable, np.float) array([ 0., 1., 4., 9., 16.]) frompyfunc(...) frompyfunc(func, nin, nout) Takes an arbitrary Python function and returns a Numpy ufunc. Can be used, for example, to add broadcasting to a built-in Python function (see Examples section). Parameters ---------- func : Python function object An arbitrary Python function. nin : int The number of input arguments. nout : int The number of objects returned by `func`. Returns ------- out : ufunc Returns a Numpy universal function (``ufunc``) object. Notes ----- The returned ufunc always returns PyObject arrays. Examples -------- Use frompyfunc to add broadcasting to the Python function ``oct``: >>> oct_array = np.frompyfunc(oct, 1, 1) >>> oct_array(np.array((10, 30, 100))) array([012, 036, 0144], dtype=object) >>> np.array((oct(10), oct(30), oct(100))) # for comparison array(['012', '036', '0144'], dtype='|S4') fromregex(file, regexp, dtype) Construct an array from a text file, using regular expression parsing. The returned array is always a structured array, and is constructed from all matches of the regular expression in the file. Groups in the regular expression are converted to fields of the structured array. Parameters ---------- file : str or file File name or file object to read. regexp : str or regexp Regular expression used to parse the file. Groups in the regular expression correspond to fields in the dtype. dtype : dtype or list of dtypes Dtype for the structured array. Returns ------- output : ndarray The output array, containing the part of the content of `file` that was matched by `regexp`. `output` is always a structured array. Raises ------ TypeError When `dtype` is not a valid dtype for a structured array. See Also -------- fromstring, loadtxt Notes ----- Dtypes for structured arrays can be specified in several forms, but all forms specify at least the data type and field name. For details see `doc.structured_arrays`. Examples -------- >>> f = open('test.dat', 'w') >>> f.write("1312 foo\n1534 bar\n444 qux") >>> f.close() >>> regexp = r"(\d+)\s+(...)" # match [digits, whitespace, anything] >>> output = np.fromregex('test.dat', regexp, ... [('num', np.int64), ('key', 'S3')]) >>> output array([(1312L, 'foo'), (1534L, 'bar'), (444L, 'qux')], dtype=[('num', '>> output['num'] array([1312, 1534, 444], dtype=int64) fromstring(...) fromstring(string, dtype=float, count=-1, sep='') A new 1-D array initialized from raw binary or text data in a string. Parameters ---------- string : str A string containing the data. dtype : data-type, optional The data type of the array; default: float. For binary input data, the data must be in exactly this format. count : int, optional Read this number of `dtype` elements from the data. If this is negative (the default), the count will be determined from the length of the data. sep : str, optional If not provided or, equivalently, the empty string, the data will be interpreted as binary data; otherwise, as ASCII text with decimal numbers. Also in this latter case, this argument is interpreted as the string separating numbers in the data; extra whitespace between elements is also ignored. Returns ------- arr : ndarray The constructed array. Raises ------ ValueError If the string is not the correct size to satisfy the requested `dtype` and `count`. See Also -------- frombuffer, fromfile, fromiter Examples -------- >>> np.fromstring('\x01\x02', dtype=np.uint8) array([1, 2], dtype=uint8) >>> np.fromstring('1 2', dtype=int, sep=' ') array([1, 2]) >>> np.fromstring('1, 2', dtype=int, sep=',') array([1, 2]) >>> np.fromstring('\x01\x02\x03\x04\x05', dtype=np.uint8, count=3) array([1, 2, 3], dtype=uint8) full(shape, fill_value, dtype=None, order='C') Return a new array of given shape and type, filled with `fill_value`. Parameters ---------- shape : int or sequence of ints Shape of the new array, e.g., ``(2, 3)`` or ``2``. fill_value : scalar Fill value. dtype : data-type, optional The desired data-type for the array, e.g., `np.int8`. Default is `float`, but will change to `np.array(fill_value).dtype` in a future release. order : {'C', 'F'}, optional Whether to store multidimensional data in C- or Fortran-contiguous (row- or column-wise) order in memory. Returns ------- out : ndarray Array of `fill_value` with the given shape, dtype, and order. See Also -------- zeros_like : Return an array of zeros with shape and type of input. ones_like : Return an array of ones with shape and type of input. empty_like : Return an empty array with shape and type of input. full_like : Fill an array with shape and type of input. zeros : Return a new array setting values to zero. ones : Return a new array setting values to one. empty : Return a new uninitialized array. Examples -------- >>> np.full((2, 2), np.inf) array([[ inf, inf], [ inf, inf]]) >>> np.full((2, 2), 10, dtype=np.int) array([[10, 10], [10, 10]]) full_like(a, fill_value, dtype=None, order='K', subok=True) Return a full array with the same shape and type as a given array. Parameters ---------- a : array_like The shape and data-type of `a` define these same attributes of the returned array. fill_value : scalar Fill value. dtype : data-type, optional Overrides the data type of the result. order : {'C', 'F', 'A', or 'K'}, optional Overrides the memory layout of the result. 'C' means C-order, 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous, 'C' otherwise. 'K' means match the layout of `a` as closely as possible. subok : bool, optional. If True, then the newly created array will use the sub-class type of 'a', otherwise it will be a base-class array. Defaults to True. Returns ------- out : ndarray Array of `fill_value` with the same shape and type as `a`. See Also -------- zeros_like : Return an array of zeros with shape and type of input. ones_like : Return an array of ones with shape and type of input. empty_like : Return an empty array with shape and type of input. zeros : Return a new array setting values to zero. ones : Return a new array setting values to one. empty : Return a new uninitialized array. full : Fill a new array. Examples -------- >>> x = np.arange(6, dtype=np.int) >>> np.full_like(x, 1) array([1, 1, 1, 1, 1, 1]) >>> np.full_like(x, 0.1) array([0, 0, 0, 0, 0, 0]) >>> np.full_like(x, 0.1, dtype=np.double) array([ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]) >>> np.full_like(x, np.nan, dtype=np.double) array([ nan, nan, nan, nan, nan, nan]) >>> y = np.arange(6, dtype=np.double) >>> np.full_like(y, 0.1) array([ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]) fv(rate, nper, pmt, pv, when='end') Compute the future value. Given: * a present value, `pv` * an interest `rate` compounded once per period, of which there are * `nper` total * a (fixed) payment, `pmt`, paid either * at the beginning (`when` = {'begin', 1}) or the end (`when` = {'end', 0}) of each period Return: the value at the end of the `nper` periods Parameters ---------- rate : scalar or array_like of shape(M, ) Rate of interest as decimal (not per cent) per period nper : scalar or array_like of shape(M, ) Number of compounding periods pmt : scalar or array_like of shape(M, ) Payment pv : scalar or array_like of shape(M, ) Present value when : {{'begin', 1}, {'end', 0}}, {string, int}, optional When payments are due ('begin' (1) or 'end' (0)). Defaults to {'end', 0}. Returns ------- out : ndarray Future values. If all input is scalar, returns a scalar float. If any input is array_like, returns future values for each input element. If multiple inputs are array_like, they all must have the same shape. Notes ----- The future value is computed by solving the equation:: fv + pv*(1+rate)**nper + pmt*(1 + rate*when)/rate*((1 + rate)**nper - 1) == 0 or, when ``rate == 0``:: fv + pv + pmt * nper == 0 References ---------- .. [WRW] Wheeler, D. A., E. Rathke, and R. Weir (Eds.) (2009, May). Open Document Format for Office Applications (OpenDocument)v1.2, Part 2: Recalculated Formula (OpenFormula) Format - Annotated Version, Pre-Draft 12. Organization for the Advancement of Structured Information Standards (OASIS). Billerica, MA, USA. [ODT Document]. Available: http://www.oasis-open.org/committees/documents.php?wg_abbrev=office-formula OpenDocument-formula-20090508.odt Examples -------- What is the future value after 10 years of saving $100 now, with an additional monthly savings of $100. Assume the interest rate is 5% (annually) compounded monthly? >>> np.fv(0.05/12, 10*12, -100, -100) 15692.928894335748 By convention, the negative sign represents cash flow out (i.e. money not available today). Thus, saving $100 a month at 5% annual interest leads to $15,692.93 available to spend in 10 years. If any input is array_like, returns an array of equal shape. Let's compare different interest rates from the example above. >>> a = np.array((0.05, 0.06, 0.07))/12 >>> np.fv(a, 10*12, -100, -100) array([ 15692.92889434, 16569.87435405, 17509.44688102]) genfromtxt(fname, dtype=, comments='#', delimiter=None, skip_header=0, skip_footer=0, converters=None, missing_values=None, filling_values=None, usecols=None, names=None, excludelist=None, deletechars=None, replace_space='_', autostrip=False, case_sensitive=True, defaultfmt='f%i', unpack=None, usemask=False, loose=True, invalid_raise=True, max_rows=None) Load data from a text file, with missing values handled as specified. Each line past the first `skip_header` lines is split at the `delimiter` character, and characters following the `comments` character are discarded. Parameters ---------- fname : file or str File, filename, or generator to read. If the filename extension is `.gz` or `.bz2`, the file is first decompressed. Note that generators must return byte strings in Python 3k. dtype : dtype, optional Data type of the resulting array. If None, the dtypes will be determined by the contents of each column, individually. comments : str, optional The character used to indicate the start of a comment. All the characters occurring on a line after a comment are discarded delimiter : str, int, or sequence, optional The string used to separate values. By default, any consecutive whitespaces act as delimiter. An integer or sequence of integers can also be provided as width(s) of each field. skiprows : int, optional `skiprows` was removed in numpy 1.10. Please use `skip_header` instead. skip_header : int, optional The number of lines to skip at the beginning of the file. skip_footer : int, optional The number of lines to skip at the end of the file. converters : variable, optional The set of functions that convert the data of a column to a value. The converters can also be used to provide a default value for missing data: ``converters = {3: lambda s: float(s or 0)}``. missing : variable, optional `missing` was removed in numpy 1.10. Please use `missing_values` instead. missing_values : variable, optional The set of strings corresponding to missing data. filling_values : variable, optional The set of values to be used as default when the data are missing. usecols : sequence, optional Which columns to read, with 0 being the first. For example, ``usecols = (1, 4, 5)`` will extract the 2nd, 5th and 6th columns. names : {None, True, str, sequence}, optional If `names` is True, the field names are read from the first valid line after the first `skip_header` lines. If `names` is a sequence or a single-string of comma-separated names, the names will be used to define the field names in a structured dtype. If `names` is None, the names of the dtype fields will be used, if any. excludelist : sequence, optional A list of names to exclude. This list is appended to the default list ['return','file','print']. Excluded names are appended an underscore: for example, `file` would become `file_`. deletechars : str, optional A string combining invalid characters that must be deleted from the names. defaultfmt : str, optional A format used to define default field names, such as "f%i" or "f_%02i". autostrip : bool, optional Whether to automatically strip white spaces from the variables. replace_space : char, optional Character(s) used in replacement of white spaces in the variables names. By default, use a '_'. case_sensitive : {True, False, 'upper', 'lower'}, optional If True, field names are case sensitive. If False or 'upper', field names are converted to upper case. If 'lower', field names are converted to lower case. unpack : bool, optional If True, the returned array is transposed, so that arguments may be unpacked using ``x, y, z = loadtxt(...)`` usemask : bool, optional If True, return a masked array. If False, return a regular array. loose : bool, optional If True, do not raise errors for invalid values. invalid_raise : bool, optional If True, an exception is raised if an inconsistency is detected in the number of columns. If False, a warning is emitted and the offending lines are skipped. max_rows : int, optional The maximum number of rows to read. Must not be used with skip_footer at the same time. If given, the value must be at least 1. Default is to read the entire file. .. versionadded:: 1.10.0 Returns ------- out : ndarray Data read from the text file. If `usemask` is True, this is a masked array. See Also -------- numpy.loadtxt : equivalent function when no data is missing. Notes ----- * When spaces are used as delimiters, or when no delimiter has been given as input, there should not be any missing data between two fields. * When the variables are named (either by a flexible dtype or with `names`, there must not be any header in the file (else a ValueError exception is raised). * Individual values are not stripped of spaces by default. When using a custom converter, make sure the function does remove spaces. References ---------- .. [1] Numpy User Guide, section `I/O with Numpy `_. Examples --------- >>> from io import StringIO >>> import numpy as np Comma delimited file with mixed dtype >>> s = StringIO("1,1.3,abcde") >>> data = np.genfromtxt(s, dtype=[('myint','i8'),('myfloat','f8'), ... ('mystring','S5')], delimiter=",") >>> data array((1, 1.3, 'abcde'), dtype=[('myint', '>> s.seek(0) # needed for StringIO example only >>> data = np.genfromtxt(s, dtype=None, ... names = ['myint','myfloat','mystring'], delimiter=",") >>> data array((1, 1.3, 'abcde'), dtype=[('myint', '>> s.seek(0) >>> data = np.genfromtxt(s, dtype="i8,f8,S5", ... names=['myint','myfloat','mystring'], delimiter=",") >>> data array((1, 1.3, 'abcde'), dtype=[('myint', '>> s = StringIO("11.3abcde") >>> data = np.genfromtxt(s, dtype=None, names=['intvar','fltvar','strvar'], ... delimiter=[1,3,5]) >>> data array((1, 1.3, 'abcde'), dtype=[('intvar', '>> buf = np.getbuffer(np.ones(5), 1, 3) >>> len(buf) 3 >>> buf[0] '\x00' >>> buf getbufsize() Return the size of the buffer used in ufuncs. Returns ------- getbufsize : int Size of ufunc buffer in bytes. geterr() Get the current way of handling floating-point errors. Returns ------- res : dict A dictionary with keys "divide", "over", "under", and "invalid", whose values are from the strings "ignore", "print", "log", "warn", "raise", and "call". The keys represent possible floating-point exceptions, and the values define how these exceptions are handled. See Also -------- geterrcall, seterr, seterrcall Notes ----- For complete documentation of the types of floating-point exceptions and treatment options, see `seterr`. Examples -------- >>> np.geterr() {'over': 'warn', 'divide': 'warn', 'invalid': 'warn', 'under': 'ignore'} >>> np.arange(3.) / np.arange(3.) array([ NaN, 1., 1.]) >>> oldsettings = np.seterr(all='warn', over='raise') >>> np.geterr() {'over': 'raise', 'divide': 'warn', 'invalid': 'warn', 'under': 'warn'} >>> np.arange(3.) / np.arange(3.) __main__:1: RuntimeWarning: invalid value encountered in divide array([ NaN, 1., 1.]) geterrcall() Return the current callback function used on floating-point errors. When the error handling for a floating-point error (one of "divide", "over", "under", or "invalid") is set to 'call' or 'log', the function that is called or the log instance that is written to is returned by `geterrcall`. This function or log instance has been set with `seterrcall`. Returns ------- errobj : callable, log instance or None The current error handler. If no handler was set through `seterrcall`, ``None`` is returned. See Also -------- seterrcall, seterr, geterr Notes ----- For complete documentation of the types of floating-point exceptions and treatment options, see `seterr`. Examples -------- >>> np.geterrcall() # we did not yet set a handler, returns None >>> oldsettings = np.seterr(all='call') >>> def err_handler(type, flag): ... print "Floating point error (%s), with flag %s" % (type, flag) >>> oldhandler = np.seterrcall(err_handler) >>> np.array([1, 2, 3]) / 0.0 Floating point error (divide by zero), with flag 1 array([ Inf, Inf, Inf]) >>> cur_handler = np.geterrcall() >>> cur_handler is err_handler True geterrobj(...) geterrobj() Return the current object that defines floating-point error handling. The error object contains all information that defines the error handling behavior in Numpy. `geterrobj` is used internally by the other functions that get and set error handling behavior (`geterr`, `seterr`, `geterrcall`, `seterrcall`). Returns ------- errobj : list The error object, a list containing three elements: [internal numpy buffer size, error mask, error callback function]. The error mask is a single integer that holds the treatment information on all four floating point errors. The information for each error type is contained in three bits of the integer. If we print it in base 8, we can see what treatment is set for "invalid", "under", "over", and "divide" (in that order). The printed string can be interpreted with * 0 : 'ignore' * 1 : 'warn' * 2 : 'raise' * 3 : 'call' * 4 : 'print' * 5 : 'log' See Also -------- seterrobj, seterr, geterr, seterrcall, geterrcall getbufsize, setbufsize Notes ----- For complete documentation of the types of floating-point exceptions and treatment options, see `seterr`. Examples -------- >>> np.geterrobj() # first get the defaults [10000, 0, None] >>> def err_handler(type, flag): ... print "Floating point error (%s), with flag %s" % (type, flag) ... >>> old_bufsize = np.setbufsize(20000) >>> old_err = np.seterr(divide='raise') >>> old_handler = np.seterrcall(err_handler) >>> np.geterrobj() [20000, 2, ] >>> old_err = np.seterr(all='ignore') >>> np.base_repr(np.geterrobj()[1], 8) '0' >>> old_err = np.seterr(divide='warn', over='log', under='call', invalid='print') >>> np.base_repr(np.geterrobj()[1], 8) '4351' gradient(f, *varargs, **kwargs) Return the gradient of an N-dimensional array. The gradient is computed using second order accurate central differences in the interior and either first differences or second order accurate one-sides (forward or backwards) differences at the boundaries. The returned gradient hence has the same shape as the input array. Parameters ---------- f : array_like An N-dimensional array containing samples of a scalar function. varargs : list of scalar, optional N scalars specifying the sample distances for each dimension, i.e. `dx`, `dy`, `dz`, ... Default distance: 1. edge_order : {1, 2}, optional Gradient is calculated using N\ :sup:`th` order accurate differences at the boundaries. Default: 1. .. versionadded:: 1.9.1 Returns ------- gradient : list of ndarray Each element of `list` has the same shape as `f` giving the derivative of `f` with respect to each dimension. Examples -------- >>> x = np.array([1, 2, 4, 7, 11, 16], dtype=np.float) >>> np.gradient(x) array([ 1. , 1.5, 2.5, 3.5, 4.5, 5. ]) >>> np.gradient(x, 2) array([ 0.5 , 0.75, 1.25, 1.75, 2.25, 2.5 ]) For two dimensional arrays, the return will be two arrays ordered by axis. In this example the first array stands for the gradient in rows and the second one in columns direction: >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float)) [array([[ 2., 2., -1.], [ 2., 2., -1.]]), array([[ 1. , 2.5, 4. ], [ 1. , 1. , 1. ]])] >>> x = np.array([0, 1, 2, 3, 4]) >>> dx = np.gradient(x) >>> y = x**2 >>> np.gradient(y, dx, edge_order=2) array([-0., 2., 4., 6., 8.]) hamming(M) Return the Hamming window. The Hamming window is a taper formed by using a weighted cosine. Parameters ---------- M : int Number of points in the output window. If zero or less, an empty array is returned. Returns ------- out : ndarray The window, with the maximum value normalized to one (the value one appears only if the number of samples is odd). See Also -------- bartlett, blackman, hanning, kaiser Notes ----- The Hamming window is defined as .. math:: w(n) = 0.54 - 0.46cos\left(\frac{2\pi{n}}{M-1}\right) \qquad 0 \leq n \leq M-1 The Hamming was named for R. W. Hamming, an associate of J. W. Tukey and is described in Blackman and Tukey. It was recommended for smoothing the truncated autocovariance function in the time domain. Most references to the Hamming window come from the signal processing literature, where it is used as one of many windowing functions for smoothing values. It is also known as an apodization (which means "removing the foot", i.e. smoothing discontinuities at the beginning and end of the sampled signal) or tapering function. References ---------- .. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power spectra, Dover Publications, New York. .. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics", The University of Alberta Press, 1975, pp. 109-110. .. [3] Wikipedia, "Window function", http://en.wikipedia.org/wiki/Window_function .. [4] W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling, "Numerical Recipes", Cambridge University Press, 1986, page 425. Examples -------- >>> np.hamming(12) array([ 0.08 , 0.15302337, 0.34890909, 0.60546483, 0.84123594, 0.98136677, 0.98136677, 0.84123594, 0.60546483, 0.34890909, 0.15302337, 0.08 ]) Plot the window and the frequency response: >>> from numpy.fft import fft, fftshift >>> window = np.hamming(51) >>> plt.plot(window) [] >>> plt.title("Hamming window") >>> plt.ylabel("Amplitude") >>> plt.xlabel("Sample") >>> plt.show() >>> plt.figure() >>> A = fft(window, 2048) / 25.5 >>> mag = np.abs(fftshift(A)) >>> freq = np.linspace(-0.5, 0.5, len(A)) >>> response = 20 * np.log10(mag) >>> response = np.clip(response, -100, 100) >>> plt.plot(freq, response) [] >>> plt.title("Frequency response of Hamming window") >>> plt.ylabel("Magnitude [dB]") >>> plt.xlabel("Normalized frequency [cycles per sample]") >>> plt.axis('tight') (-0.5, 0.5, -100.0, ...) >>> plt.show() hanning(M) Return the Hanning window. The Hanning window is a taper formed by using a weighted cosine. Parameters ---------- M : int Number of points in the output window. If zero or less, an empty array is returned. Returns ------- out : ndarray, shape(M,) The window, with the maximum value normalized to one (the value one appears only if `M` is odd). See Also -------- bartlett, blackman, hamming, kaiser Notes ----- The Hanning window is defined as .. math:: w(n) = 0.5 - 0.5cos\left(\frac{2\pi{n}}{M-1}\right) \qquad 0 \leq n \leq M-1 The Hanning was named for Julius von Hann, an Austrian meteorologist. It is also known as the Cosine Bell. Some authors prefer that it be called a Hann window, to help avoid confusion with the very similar Hamming window. Most references to the Hanning window come from the signal processing literature, where it is used as one of many windowing functions for smoothing values. It is also known as an apodization (which means "removing the foot", i.e. smoothing discontinuities at the beginning and end of the sampled signal) or tapering function. References ---------- .. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power spectra, Dover Publications, New York. .. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics", The University of Alberta Press, 1975, pp. 106-108. .. [3] Wikipedia, "Window function", http://en.wikipedia.org/wiki/Window_function .. [4] W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling, "Numerical Recipes", Cambridge University Press, 1986, page 425. Examples -------- >>> np.hanning(12) array([ 0. , 0.07937323, 0.29229249, 0.57115742, 0.82743037, 0.97974649, 0.97974649, 0.82743037, 0.57115742, 0.29229249, 0.07937323, 0. ]) Plot the window and its frequency response: >>> from numpy.fft import fft, fftshift >>> window = np.hanning(51) >>> plt.plot(window) [] >>> plt.title("Hann window") >>> plt.ylabel("Amplitude") >>> plt.xlabel("Sample") >>> plt.show() >>> plt.figure() >>> A = fft(window, 2048) / 25.5 >>> mag = np.abs(fftshift(A)) >>> freq = np.linspace(-0.5, 0.5, len(A)) >>> response = 20 * np.log10(mag) >>> response = np.clip(response, -100, 100) >>> plt.plot(freq, response) [] >>> plt.title("Frequency response of the Hann window") >>> plt.ylabel("Magnitude [dB]") >>> plt.xlabel("Normalized frequency [cycles per sample]") >>> plt.axis('tight') (-0.5, 0.5, -100.0, ...) >>> plt.show() histogram(a, bins=10, range=None, normed=False, weights=None, density=None) Compute the histogram of a set of data. Parameters ---------- a : array_like Input data. The histogram is computed over the flattened array. bins : int or sequence of scalars, optional If `bins` is an int, it defines the number of equal-width bins in the given range (10, by default). If `bins` is a sequence, it defines the bin edges, including the rightmost edge, allowing for non-uniform bin widths. range : (float, float), optional The lower and upper range of the bins. If not provided, range is simply ``(a.min(), a.max())``. Values outside the range are ignored. normed : bool, optional This keyword is deprecated in Numpy 1.6 due to confusing/buggy behavior. It will be removed in Numpy 2.0. Use the density keyword instead. If False, the result will contain the number of samples in each bin. If True, the result is the value of the probability *density* function at the bin, normalized such that the *integral* over the range is 1. Note that this latter behavior is known to be buggy with unequal bin widths; use `density` instead. weights : array_like, optional An array of weights, of the same shape as `a`. Each value in `a` only contributes its associated weight towards the bin count (instead of 1). If `normed` is True, the weights are normalized, so that the integral of the density over the range remains 1 density : bool, optional If False, the result will contain the number of samples in each bin. If True, the result is the value of the probability *density* function at the bin, normalized such that the *integral* over the range is 1. Note that the sum of the histogram values will not be equal to 1 unless bins of unity width are chosen; it is not a probability *mass* function. Overrides the `normed` keyword if given. Returns ------- hist : array The values of the histogram. See `normed` and `weights` for a description of the possible semantics. bin_edges : array of dtype float Return the bin edges ``(length(hist)+1)``. See Also -------- histogramdd, bincount, searchsorted, digitize Notes ----- All but the last (righthand-most) bin is half-open. In other words, if `bins` is:: [1, 2, 3, 4] then the first bin is ``[1, 2)`` (including 1, but excluding 2) and the second ``[2, 3)``. The last bin, however, is ``[3, 4]``, which *includes* 4. Examples -------- >>> np.histogram([1, 2, 1], bins=[0, 1, 2, 3]) (array([0, 2, 1]), array([0, 1, 2, 3])) >>> np.histogram(np.arange(4), bins=np.arange(5), density=True) (array([ 0.25, 0.25, 0.25, 0.25]), array([0, 1, 2, 3, 4])) >>> np.histogram([[1, 2, 1], [1, 0, 1]], bins=[0,1,2,3]) (array([1, 4, 1]), array([0, 1, 2, 3])) >>> a = np.arange(5) >>> hist, bin_edges = np.histogram(a, density=True) >>> hist array([ 0.5, 0. , 0.5, 0. , 0. , 0.5, 0. , 0.5, 0. , 0.5]) >>> hist.sum() 2.4999999999999996 >>> np.sum(hist*np.diff(bin_edges)) 1.0 histogram2d(x, y, bins=10, range=None, normed=False, weights=None) Compute the bi-dimensional histogram of two data samples. Parameters ---------- x : array_like, shape (N,) An array containing the x coordinates of the points to be histogrammed. y : array_like, shape (N,) An array containing the y coordinates of the points to be histogrammed. bins : int or array_like or [int, int] or [array, array], optional The bin specification: * If int, the number of bins for the two dimensions (nx=ny=bins). * If array_like, the bin edges for the two dimensions (x_edges=y_edges=bins). * If [int, int], the number of bins in each dimension (nx, ny = bins). * If [array, array], the bin edges in each dimension (x_edges, y_edges = bins). * A combination [int, array] or [array, int], where int is the number of bins and array is the bin edges. range : array_like, shape(2,2), optional The leftmost and rightmost edges of the bins along each dimension (if not specified explicitly in the `bins` parameters): ``[[xmin, xmax], [ymin, ymax]]``. All values outside of this range will be considered outliers and not tallied in the histogram. normed : bool, optional If False, returns the number of samples in each bin. If True, returns the bin density ``bin_count / sample_count / bin_area``. weights : array_like, shape(N,), optional An array of values ``w_i`` weighing each sample ``(x_i, y_i)``. Weights are normalized to 1 if `normed` is True. If `normed` is False, the values of the returned histogram are equal to the sum of the weights belonging to the samples falling into each bin. Returns ------- H : ndarray, shape(nx, ny) The bi-dimensional histogram of samples `x` and `y`. Values in `x` are histogrammed along the first dimension and values in `y` are histogrammed along the second dimension. xedges : ndarray, shape(nx,) The bin edges along the first dimension. yedges : ndarray, shape(ny,) The bin edges along the second dimension. See Also -------- histogram : 1D histogram histogramdd : Multidimensional histogram Notes ----- When `normed` is True, then the returned histogram is the sample density, defined such that the sum over bins of the product ``bin_value * bin_area`` is 1. Please note that the histogram does not follow the Cartesian convention where `x` values are on the abscissa and `y` values on the ordinate axis. Rather, `x` is histogrammed along the first dimension of the array (vertical), and `y` along the second dimension of the array (horizontal). This ensures compatibility with `histogramdd`. Examples -------- >>> import matplotlib as mpl >>> import matplotlib.pyplot as plt Construct a 2D-histogram with variable bin width. First define the bin edges: >>> xedges = [0, 1, 1.5, 3, 5] >>> yedges = [0, 2, 3, 4, 6] Next we create a histogram H with random bin content: >>> x = np.random.normal(3, 1, 100) >>> y = np.random.normal(1, 1, 100) >>> H, xedges, yedges = np.histogram2d(y, x, bins=(xedges, yedges)) Or we fill the histogram H with a determined bin content: >>> H = np.ones((4, 4)).cumsum().reshape(4, 4) >>> print H[::-1] # This shows the bin content in the order as plotted [[ 13. 14. 15. 16.] [ 9. 10. 11. 12.] [ 5. 6. 7. 8.] [ 1. 2. 3. 4.]] Imshow can only do an equidistant representation of bins: >>> fig = plt.figure(figsize=(7, 3)) >>> ax = fig.add_subplot(131) >>> ax.set_title('imshow: equidistant') >>> im = plt.imshow(H, interpolation='nearest', origin='low', extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]]) pcolormesh can display exact bin edges: >>> ax = fig.add_subplot(132) >>> ax.set_title('pcolormesh: exact bin edges') >>> X, Y = np.meshgrid(xedges, yedges) >>> ax.pcolormesh(X, Y, H) >>> ax.set_aspect('equal') NonUniformImage displays exact bin edges with interpolation: >>> ax = fig.add_subplot(133) >>> ax.set_title('NonUniformImage: interpolated') >>> im = mpl.image.NonUniformImage(ax, interpolation='bilinear') >>> xcenters = xedges[:-1] + 0.5 * (xedges[1:] - xedges[:-1]) >>> ycenters = yedges[:-1] + 0.5 * (yedges[1:] - yedges[:-1]) >>> im.set_data(xcenters, ycenters, H) >>> ax.images.append(im) >>> ax.set_xlim(xedges[0], xedges[-1]) >>> ax.set_ylim(yedges[0], yedges[-1]) >>> ax.set_aspect('equal') >>> plt.show() histogramdd(sample, bins=10, range=None, normed=False, weights=None) Compute the multidimensional histogram of some data. Parameters ---------- sample : array_like The data to be histogrammed. It must be an (N,D) array or data that can be converted to such. The rows of the resulting array are the coordinates of points in a D dimensional polytope. bins : sequence or int, optional The bin specification: * A sequence of arrays describing the bin edges along each dimension. * The number of bins for each dimension (nx, ny, ... =bins) * The number of bins for all dimensions (nx=ny=...=bins). range : sequence, optional A sequence of lower and upper bin edges to be used if the edges are not given explicitly in `bins`. Defaults to the minimum and maximum values along each dimension. normed : bool, optional If False, returns the number of samples in each bin. If True, returns the bin density ``bin_count / sample_count / bin_volume``. weights : (N,) array_like, optional An array of values `w_i` weighing each sample `(x_i, y_i, z_i, ...)`. Weights are normalized to 1 if normed is True. If normed is False, the values of the returned histogram are equal to the sum of the weights belonging to the samples falling into each bin. Returns ------- H : ndarray The multidimensional histogram of sample x. See normed and weights for the different possible semantics. edges : list A list of D arrays describing the bin edges for each dimension. See Also -------- histogram: 1-D histogram histogram2d: 2-D histogram Examples -------- >>> r = np.random.randn(100,3) >>> H, edges = np.histogramdd(r, bins = (5, 8, 4)) >>> H.shape, edges[0].size, edges[1].size, edges[2].size ((5, 8, 4), 6, 9, 5) hsplit(ary, indices_or_sections) Split an array into multiple sub-arrays horizontally (column-wise). Please refer to the `split` documentation. `hsplit` is equivalent to `split` with ``axis=1``, the array is always split along the second axis regardless of the array dimension. See Also -------- split : Split an array into multiple sub-arrays of equal size. Examples -------- >>> x = np.arange(16.0).reshape(4, 4) >>> x array([[ 0., 1., 2., 3.], [ 4., 5., 6., 7.], [ 8., 9., 10., 11.], [ 12., 13., 14., 15.]]) >>> np.hsplit(x, 2) [array([[ 0., 1.], [ 4., 5.], [ 8., 9.], [ 12., 13.]]), array([[ 2., 3.], [ 6., 7.], [ 10., 11.], [ 14., 15.]])] >>> np.hsplit(x, np.array([3, 6])) [array([[ 0., 1., 2.], [ 4., 5., 6.], [ 8., 9., 10.], [ 12., 13., 14.]]), array([[ 3.], [ 7.], [ 11.], [ 15.]]), array([], dtype=float64)] With a higher dimensional array the split is still along the second axis. >>> x = np.arange(8.0).reshape(2, 2, 2) >>> x array([[[ 0., 1.], [ 2., 3.]], [[ 4., 5.], [ 6., 7.]]]) >>> np.hsplit(x, 2) [array([[[ 0., 1.]], [[ 4., 5.]]]), array([[[ 2., 3.]], [[ 6., 7.]]])] hstack(tup) Stack arrays in sequence horizontally (column wise). Take a sequence of arrays and stack them horizontally to make a single array. Rebuild arrays divided by `hsplit`. Parameters ---------- tup : sequence of ndarrays All arrays must have the same shape along all but the second axis. Returns ------- stacked : ndarray The array formed by stacking the given arrays. See Also -------- stack : Join a sequence of arrays along a new axis. vstack : Stack arrays in sequence vertically (row wise). dstack : Stack arrays in sequence depth wise (along third axis). concatenate : Join a sequence of arrays along an existing axis. hsplit : Split array along second axis. Notes ----- Equivalent to ``np.concatenate(tup, axis=1)`` Examples -------- >>> a = np.array((1,2,3)) >>> b = np.array((2,3,4)) >>> np.hstack((a,b)) array([1, 2, 3, 2, 3, 4]) >>> a = np.array([[1],[2],[3]]) >>> b = np.array([[2],[3],[4]]) >>> np.hstack((a,b)) array([[1, 2], [2, 3], [3, 4]]) i0(x) Modified Bessel function of the first kind, order 0. Usually denoted :math:`I_0`. This function does broadcast, but will *not* "up-cast" int dtype arguments unless accompanied by at least one float or complex dtype argument (see Raises below). Parameters ---------- x : array_like, dtype float or complex Argument of the Bessel function. Returns ------- out : ndarray, shape = x.shape, dtype = x.dtype The modified Bessel function evaluated at each of the elements of `x`. Raises ------ TypeError: array cannot be safely cast to required type If argument consists exclusively of int dtypes. See Also -------- scipy.special.iv, scipy.special.ive Notes ----- We use the algorithm published by Clenshaw [1]_ and referenced by Abramowitz and Stegun [2]_, for which the function domain is partitioned into the two intervals [0,8] and (8,inf), and Chebyshev polynomial expansions are employed in each interval. Relative error on the domain [0,30] using IEEE arithmetic is documented [3]_ as having a peak of 5.8e-16 with an rms of 1.4e-16 (n = 30000). References ---------- .. [1] C. W. Clenshaw, "Chebyshev series for mathematical functions", in *National Physical Laboratory Mathematical Tables*, vol. 5, London: Her Majesty's Stationery Office, 1962. .. [2] M. Abramowitz and I. A. Stegun, *Handbook of Mathematical Functions*, 10th printing, New York: Dover, 1964, pp. 379. http://www.math.sfu.ca/~cbm/aands/page_379.htm .. [3] http://kobesearch.cpan.org/htdocs/Math-Cephes/Math/Cephes.html Examples -------- >>> np.i0([0.]) array(1.0) >>> np.i0([0., 1. + 2j]) array([ 1.00000000+0.j , 0.18785373+0.64616944j]) identity(n, dtype=None) Return the identity array. The identity array is a square array with ones on the main diagonal. Parameters ---------- n : int Number of rows (and columns) in `n` x `n` output. dtype : data-type, optional Data-type of the output. Defaults to ``float``. Returns ------- out : ndarray `n` x `n` array with its main diagonal set to one, and all other elements 0. Examples -------- >>> np.identity(3) array([[ 1., 0., 0.], [ 0., 1., 0.], [ 0., 0., 1.]]) imag(val) Return the imaginary part of the elements of the array. Parameters ---------- val : array_like Input array. Returns ------- out : ndarray Output array. If `val` is real, the type of `val` is used for the output. If `val` has complex elements, the returned type is float. See Also -------- real, angle, real_if_close Examples -------- >>> a = np.array([1+2j, 3+4j, 5+6j]) >>> a.imag array([ 2., 4., 6.]) >>> a.imag = np.array([8, 10, 12]) >>> a array([ 1. +8.j, 3.+10.j, 5.+12.j]) in1d(ar1, ar2, assume_unique=False, invert=False) Test whether each element of a 1-D array is also present in a second array. Returns a boolean array the same length as `ar1` that is True where an element of `ar1` is in `ar2` and False otherwise. Parameters ---------- ar1 : (M,) array_like Input array. ar2 : array_like The values against which to test each value of `ar1`. assume_unique : bool, optional If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False. invert : bool, optional If True, the values in the returned array are inverted (that is, False where an element of `ar1` is in `ar2` and True otherwise). Default is False. ``np.in1d(a, b, invert=True)`` is equivalent to (but is faster than) ``np.invert(in1d(a, b))``. .. versionadded:: 1.8.0 Returns ------- in1d : (M,) ndarray, bool The values `ar1[in1d]` are in `ar2`. See Also -------- numpy.lib.arraysetops : Module with a number of other functions for performing set operations on arrays. Notes ----- `in1d` can be considered as an element-wise function version of the python keyword `in`, for 1-D sequences. ``in1d(a, b)`` is roughly equivalent to ``np.array([item in b for item in a])``. However, this idea fails if `ar2` is a set, or similar (non-sequence) container: As ``ar2`` is converted to an array, in those cases ``asarray(ar2)`` is an object array rather than the expected array of contained values. .. versionadded:: 1.4.0 Examples -------- >>> test = np.array([0, 1, 2, 5, 0]) >>> states = [0, 2] >>> mask = np.in1d(test, states) >>> mask array([ True, False, True, False, True], dtype=bool) >>> test[mask] array([0, 2, 0]) >>> mask = np.in1d(test, states, invert=True) >>> mask array([False, True, False, True, False], dtype=bool) >>> test[mask] array([1, 5]) indices(dimensions, dtype=) Return an array representing the indices of a grid. Compute an array where the subarrays contain index values 0,1,... varying only along the corresponding axis. Parameters ---------- dimensions : sequence of ints The shape of the grid. dtype : dtype, optional Data type of the result. Returns ------- grid : ndarray The array of grid indices, ``grid.shape = (len(dimensions),) + tuple(dimensions)``. See Also -------- mgrid, meshgrid Notes ----- The output shape is obtained by prepending the number of dimensions in front of the tuple of dimensions, i.e. if `dimensions` is a tuple ``(r0, ..., rN-1)`` of length ``N``, the output shape is ``(N,r0,...,rN-1)``. The subarrays ``grid[k]`` contains the N-D array of indices along the ``k-th`` axis. Explicitly:: grid[k,i0,i1,...,iN-1] = ik Examples -------- >>> grid = np.indices((2, 3)) >>> grid.shape (2, 2, 3) >>> grid[0] # row indices array([[0, 0, 0], [1, 1, 1]]) >>> grid[1] # column indices array([[0, 1, 2], [0, 1, 2]]) The indices can be used as an index into an array. >>> x = np.arange(20).reshape(5, 4) >>> row, col = np.indices((2, 3)) >>> x[row, col] array([[0, 1, 2], [4, 5, 6]]) Note that it would be more straightforward in the above example to extract the required elements directly with ``x[:2, :3]``. info(object=None, maxwidth=76, output=, toplevel='numpy') Get help information for a function, class, or module. Parameters ---------- object : object or str, optional Input object or name to get information about. If `object` is a numpy object, its docstring is given. If it is a string, available modules are searched for matching objects. If None, information about `info` itself is returned. maxwidth : int, optional Printing width. output : file like object, optional File like object that the output is written to, default is ``stdout``. The object has to be opened in 'w' or 'a' mode. toplevel : str, optional Start search at this level. See Also -------- source, lookfor Notes ----- When used interactively with an object, ``np.info(obj)`` is equivalent to ``help(obj)`` on the Python prompt or ``obj?`` on the IPython prompt. Examples -------- >>> np.info(np.polyval) # doctest: +SKIP polyval(p, x) Evaluate the polynomial p at x. ... When using a string for `object` it is possible to get multiple results. >>> np.info('fft') # doctest: +SKIP *** Found in numpy *** Core FFT routines ... *** Found in numpy.fft *** fft(a, n=None, axis=-1) ... *** Repeat reference found in numpy.fft.fftpack *** *** Total of 3 references found. *** inner(...) inner(a, b) Inner product of two arrays. Ordinary inner product of vectors for 1-D arrays (without complex conjugation), in higher dimensions a sum product over the last axes. Parameters ---------- a, b : array_like If `a` and `b` are nonscalar, their last dimensions of must match. Returns ------- out : ndarray `out.shape = a.shape[:-1] + b.shape[:-1]` Raises ------ ValueError If the last dimension of `a` and `b` has different size. See Also -------- tensordot : Sum products over arbitrary axes. dot : Generalised matrix product, using second last dimension of `b`. einsum : Einstein summation convention. Notes ----- For vectors (1-D arrays) it computes the ordinary inner-product:: np.inner(a, b) = sum(a[:]*b[:]) More generally, if `ndim(a) = r > 0` and `ndim(b) = s > 0`:: np.inner(a, b) = np.tensordot(a, b, axes=(-1,-1)) or explicitly:: np.inner(a, b)[i0,...,ir-1,j0,...,js-1] = sum(a[i0,...,ir-1,:]*b[j0,...,js-1,:]) In addition `a` or `b` may be scalars, in which case:: np.inner(a,b) = a*b Examples -------- Ordinary inner product for vectors: >>> a = np.array([1,2,3]) >>> b = np.array([0,1,0]) >>> np.inner(a, b) 2 A multidimensional example: >>> a = np.arange(24).reshape((2,3,4)) >>> b = np.arange(4) >>> np.inner(a, b) array([[ 14, 38, 62], [ 86, 110, 134]]) An example where `b` is a scalar: >>> np.inner(np.eye(2), 7) array([[ 7., 0.], [ 0., 7.]]) insert(arr, obj, values, axis=None) Insert values along the given axis before the given indices. Parameters ---------- arr : array_like Input array. obj : int, slice or sequence of ints Object that defines the index or indices before which `values` is inserted. .. versionadded:: 1.8.0 Support for multiple insertions when `obj` is a single scalar or a sequence with one element (similar to calling insert multiple times). values : array_like Values to insert into `arr`. If the type of `values` is different from that of `arr`, `values` is converted to the type of `arr`. `values` should be shaped so that ``arr[...,obj,...] = values`` is legal. axis : int, optional Axis along which to insert `values`. If `axis` is None then `arr` is flattened first. Returns ------- out : ndarray A copy of `arr` with `values` inserted. Note that `insert` does not occur in-place: a new array is returned. If `axis` is None, `out` is a flattened array. See Also -------- append : Append elements at the end of an array. concatenate : Join a sequence of arrays along an existing axis. delete : Delete elements from an array. Notes ----- Note that for higher dimensional inserts `obj=0` behaves very different from `obj=[0]` just like `arr[:,0,:] = values` is different from `arr[:,[0],:] = values`. Examples -------- >>> a = np.array([[1, 1], [2, 2], [3, 3]]) >>> a array([[1, 1], [2, 2], [3, 3]]) >>> np.insert(a, 1, 5) array([1, 5, 1, 2, 2, 3, 3]) >>> np.insert(a, 1, 5, axis=1) array([[1, 5, 1], [2, 5, 2], [3, 5, 3]]) Difference between sequence and scalars: >>> np.insert(a, [1], [[1],[2],[3]], axis=1) array([[1, 1, 1], [2, 2, 2], [3, 3, 3]]) >>> np.array_equal(np.insert(a, 1, [1, 2, 3], axis=1), ... np.insert(a, [1], [[1],[2],[3]], axis=1)) True >>> b = a.flatten() >>> b array([1, 1, 2, 2, 3, 3]) >>> np.insert(b, [2, 2], [5, 6]) array([1, 1, 5, 6, 2, 2, 3, 3]) >>> np.insert(b, slice(2, 4), [5, 6]) array([1, 1, 5, 2, 6, 2, 3, 3]) >>> np.insert(b, [2, 2], [7.13, False]) # type casting array([1, 1, 7, 0, 2, 2, 3, 3]) >>> x = np.arange(8).reshape(2, 4) >>> idx = (1, 3) >>> np.insert(x, idx, 999, axis=1) array([[ 0, 999, 1, 2, 999, 3], [ 4, 999, 5, 6, 999, 7]]) int_asbuffer(...) interp(x, xp, fp, left=None, right=None, period=None) One-dimensional linear interpolation. Returns the one-dimensional piecewise linear interpolant to a function with given values at discrete data-points. Parameters ---------- x : array_like The x-coordinates of the interpolated values. xp : 1-D sequence of floats The x-coordinates of the data points, must be increasing if argument `period` is not specified. Otherwise, `xp` is internally sorted after normalizing the periodic boundaries with ``xp = xp % period``. fp : 1-D sequence of floats The y-coordinates of the data points, same length as `xp`. left : float, optional Value to return for `x < xp[0]`, default is `fp[0]`. right : float, optional Value to return for `x > xp[-1]`, default is `fp[-1]`. period : None or float, optional A period for the x-coordinates. This parameter allows the proper interpolation of angular x-coordinates. Parameters `left` and `right` are ignored if `period` is specified. .. versionadded:: 1.10.0 Returns ------- y : float or ndarray The interpolated values, same shape as `x`. Raises ------ ValueError If `xp` and `fp` have different length If `xp` or `fp` are not 1-D sequences If `period == 0` Notes ----- Does not check that the x-coordinate sequence `xp` is increasing. If `xp` is not increasing, the results are nonsense. A simple check for increasing is:: np.all(np.diff(xp) > 0) Examples -------- >>> xp = [1, 2, 3] >>> fp = [3, 2, 0] >>> np.interp(2.5, xp, fp) 1.0 >>> np.interp([0, 1, 1.5, 2.72, 3.14], xp, fp) array([ 3. , 3. , 2.5 , 0.56, 0. ]) >>> UNDEF = -99.0 >>> np.interp(3.14, xp, fp, right=UNDEF) -99.0 Plot an interpolant to the sine function: >>> x = np.linspace(0, 2*np.pi, 10) >>> y = np.sin(x) >>> xvals = np.linspace(0, 2*np.pi, 50) >>> yinterp = np.interp(xvals, x, y) >>> import matplotlib.pyplot as plt >>> plt.plot(x, y, 'o') [] >>> plt.plot(xvals, yinterp, '-x') [] >>> plt.show() Interpolation with periodic x-coordinates: >>> x = [-180, -170, -185, 185, -10, -5, 0, 365] >>> xp = [190, -190, 350, -350] >>> fp = [5, 10, 3, 4] >>> np.interp(x, xp, fp, period=360) array([7.5, 5., 8.75, 6.25, 3., 3.25, 3.5, 3.75]) intersect1d(ar1, ar2, assume_unique=False) Find the intersection of two arrays. Return the sorted, unique values that are in both of the input arrays. Parameters ---------- ar1, ar2 : array_like Input arrays. assume_unique : bool If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False. Returns ------- intersect1d : ndarray Sorted 1D array of common and unique elements. See Also -------- numpy.lib.arraysetops : Module with a number of other functions for performing set operations on arrays. Examples -------- >>> np.intersect1d([1, 3, 4, 3], [3, 1, 2, 1]) array([1, 3]) To intersect more than two arrays, use functools.reduce: >>> from functools import reduce >>> reduce(np.intersect1d, ([1, 3, 4, 3], [3, 1, 2, 1], [6, 3, 4, 2])) array([3]) ipmt(rate, per, nper, pv, fv=0.0, when='end') Compute the interest portion of a payment. Parameters ---------- rate : scalar or array_like of shape(M, ) Rate of interest as decimal (not per cent) per period per : scalar or array_like of shape(M, ) Interest paid against the loan changes during the life or the loan. The `per` is the payment period to calculate the interest amount. nper : scalar or array_like of shape(M, ) Number of compounding periods pv : scalar or array_like of shape(M, ) Present value fv : scalar or array_like of shape(M, ), optional Future value when : {{'begin', 1}, {'end', 0}}, {string, int}, optional When payments are due ('begin' (1) or 'end' (0)). Defaults to {'end', 0}. Returns ------- out : ndarray Interest portion of payment. If all input is scalar, returns a scalar float. If any input is array_like, returns interest payment for each input element. If multiple inputs are array_like, they all must have the same shape. See Also -------- ppmt, pmt, pv Notes ----- The total payment is made up of payment against principal plus interest. ``pmt = ppmt + ipmt`` Examples -------- What is the amortization schedule for a 1 year loan of $2500 at 8.24% interest per year compounded monthly? >>> principal = 2500.00 The 'per' variable represents the periods of the loan. Remember that financial equations start the period count at 1! >>> per = np.arange(1*12) + 1 >>> ipmt = np.ipmt(0.0824/12, per, 1*12, principal) >>> ppmt = np.ppmt(0.0824/12, per, 1*12, principal) Each element of the sum of the 'ipmt' and 'ppmt' arrays should equal 'pmt'. >>> pmt = np.pmt(0.0824/12, 1*12, principal) >>> np.allclose(ipmt + ppmt, pmt) True >>> fmt = '{0:2d} {1:8.2f} {2:8.2f} {3:8.2f}' >>> for payment in per: ... index = payment - 1 ... principal = principal + ppmt[index] ... print fmt.format(payment, ppmt[index], ipmt[index], principal) 1 -200.58 -17.17 2299.42 2 -201.96 -15.79 2097.46 3 -203.35 -14.40 1894.11 4 -204.74 -13.01 1689.37 5 -206.15 -11.60 1483.22 6 -207.56 -10.18 1275.66 7 -208.99 -8.76 1066.67 8 -210.42 -7.32 856.25 9 -211.87 -5.88 644.38 10 -213.32 -4.42 431.05 11 -214.79 -2.96 216.26 12 -216.26 -1.49 -0.00 >>> interestpd = np.sum(ipmt) >>> np.round(interestpd, 2) -112.98 irr(values) Return the Internal Rate of Return (IRR). This is the "average" periodically compounded rate of return that gives a net present value of 0.0; for a more complete explanation, see Notes below. Parameters ---------- values : array_like, shape(N,) Input cash flows per time period. By convention, net "deposits" are negative and net "withdrawals" are positive. Thus, for example, at least the first element of `values`, which represents the initial investment, will typically be negative. Returns ------- out : float Internal Rate of Return for periodic input values. Notes ----- The IRR is perhaps best understood through an example (illustrated using np.irr in the Examples section below). Suppose one invests 100 units and then makes the following withdrawals at regular (fixed) intervals: 39, 59, 55, 20. Assuming the ending value is 0, one's 100 unit investment yields 173 units; however, due to the combination of compounding and the periodic withdrawals, the "average" rate of return is neither simply 0.73/4 nor (1.73)^0.25-1. Rather, it is the solution (for :math:`r`) of the equation: .. math:: -100 + \frac{39}{1+r} + \frac{59}{(1+r)^2} + \frac{55}{(1+r)^3} + \frac{20}{(1+r)^4} = 0 In general, for `values` :math:`= [v_0, v_1, ... v_M]`, irr is the solution of the equation: [G]_ .. math:: \sum_{t=0}^M{\frac{v_t}{(1+irr)^{t}}} = 0 References ---------- .. [G] L. J. Gitman, "Principles of Managerial Finance, Brief," 3rd ed., Addison-Wesley, 2003, pg. 348. Examples -------- >>> round(irr([-100, 39, 59, 55, 20]), 5) 0.28095 >>> round(irr([-100, 0, 0, 74]), 5) -0.0955 >>> round(irr([-100, 100, 0, -7]), 5) -0.0833 >>> round(irr([-100, 100, 0, 7]), 5) 0.06206 >>> round(irr([-5, 10.5, 1, -8, 1]), 5) 0.0886 (Compare with the Example given for numpy.lib.financial.npv) is_busday(...) is_busday(dates, weekmask='1111100', holidays=None, busdaycal=None, out=None) Calculates which of the given dates are valid days, and which are not. .. versionadded:: 1.7.0 Parameters ---------- dates : array_like of datetime64[D] The array of dates to process. weekmask : str or array_like of bool, optional A seven-element array indicating which of Monday through Sunday are valid days. May be specified as a length-seven list or array, like [1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for weekdays, optionally separated by white space. Valid abbreviations are: Mon Tue Wed Thu Fri Sat Sun holidays : array_like of datetime64[D], optional An array of dates to consider as invalid dates. They may be specified in any order, and NaT (not-a-time) dates are ignored. This list is saved in a normalized form that is suited for fast calculations of valid days. busdaycal : busdaycalendar, optional A `busdaycalendar` object which specifies the valid days. If this parameter is provided, neither weekmask nor holidays may be provided. out : array of bool, optional If provided, this array is filled with the result. Returns ------- out : array of bool An array with the same shape as ``dates``, containing True for each valid day, and False for each invalid day. See Also -------- busdaycalendar: An object that specifies a custom set of valid days. busday_offset : Applies an offset counted in valid days. busday_count : Counts how many valid days are in a half-open date range. Examples -------- >>> # The weekdays are Friday, Saturday, and Monday ... np.is_busday(['2011-07-01', '2011-07-02', '2011-07-18'], ... holidays=['2011-07-01', '2011-07-04', '2011-07-17']) array([False, False, True], dtype='bool') isclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False) Returns a boolean array where two arrays are element-wise equal within a tolerance. The tolerance values are positive, typically very small numbers. The relative difference (`rtol` * abs(`b`)) and the absolute difference `atol` are added together to compare against the absolute difference between `a` and `b`. Parameters ---------- a, b : array_like Input arrays to compare. rtol : float The relative tolerance parameter (see Notes). atol : float The absolute tolerance parameter (see Notes). equal_nan : bool Whether to compare NaN's as equal. If True, NaN's in `a` will be considered equal to NaN's in `b` in the output array. Returns ------- y : array_like Returns a boolean array of where `a` and `b` are equal within the given tolerance. If both `a` and `b` are scalars, returns a single boolean value. See Also -------- allclose Notes ----- .. versionadded:: 1.7.0 For finite values, isclose uses the following equation to test whether two floating point values are equivalent. absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`)) The above equation is not symmetric in `a` and `b`, so that `isclose(a, b)` might be different from `isclose(b, a)` in some rare cases. Examples -------- >>> np.isclose([1e10,1e-7], [1.00001e10,1e-8]) array([True, False]) >>> np.isclose([1e10,1e-8], [1.00001e10,1e-9]) array([True, True]) >>> np.isclose([1e10,1e-8], [1.0001e10,1e-9]) array([False, True]) >>> np.isclose([1.0, np.nan], [1.0, np.nan]) array([True, False]) >>> np.isclose([1.0, np.nan], [1.0, np.nan], equal_nan=True) array([True, True]) iscomplex(x) Returns a bool array, where True if input element is complex. What is tested is whether the input has a non-zero imaginary part, not if the input type is complex. Parameters ---------- x : array_like Input array. Returns ------- out : ndarray of bools Output array. See Also -------- isreal iscomplexobj : Return True if x is a complex type or an array of complex numbers. Examples -------- >>> np.iscomplex([1+1j, 1+0j, 4.5, 3, 2, 2j]) array([ True, False, False, False, False, True], dtype=bool) iscomplexobj(x) Check for a complex type or an array of complex numbers. The type of the input is checked, not the value. Even if the input has an imaginary part equal to zero, `iscomplexobj` evaluates to True. Parameters ---------- x : any The input can be of any type and shape. Returns ------- iscomplexobj : bool The return value, True if `x` is of a complex type or has at least one complex element. See Also -------- isrealobj, iscomplex Examples -------- >>> np.iscomplexobj(1) False >>> np.iscomplexobj(1+0j) True >>> np.iscomplexobj([3, 1+0j, True]) True isfortran(a) Returns True if the array is Fortran contiguous but *not* C contiguous. This function is obsolete and, because of changes due to relaxed stride checking, its return value for the same array may differ for versions of Numpy >= 1.10 and previous versions. If you only want to check if an array is Fortran contiguous use ``a.flags.f_contiguous`` instead. Parameters ---------- a : ndarray Input array. Examples -------- np.array allows to specify whether the array is written in C-contiguous order (last index varies the fastest), or FORTRAN-contiguous order in memory (first index varies the fastest). >>> a = np.array([[1, 2, 3], [4, 5, 6]], order='C') >>> a array([[1, 2, 3], [4, 5, 6]]) >>> np.isfortran(a) False >>> b = np.array([[1, 2, 3], [4, 5, 6]], order='FORTRAN') >>> b array([[1, 2, 3], [4, 5, 6]]) >>> np.isfortran(b) True The transpose of a C-ordered array is a FORTRAN-ordered array. >>> a = np.array([[1, 2, 3], [4, 5, 6]], order='C') >>> a array([[1, 2, 3], [4, 5, 6]]) >>> np.isfortran(a) False >>> b = a.T >>> b array([[1, 4], [2, 5], [3, 6]]) >>> np.isfortran(b) True C-ordered arrays evaluate as False even if they are also FORTRAN-ordered. >>> np.isfortran(np.array([1, 2], order='FORTRAN')) False isneginf(x, y=None) Test element-wise for negative infinity, return result as bool array. Parameters ---------- x : array_like The input array. y : array_like, optional A boolean array with the same shape and type as `x` to store the result. Returns ------- y : ndarray A boolean array with the same dimensions as the input. If second argument is not supplied then a numpy boolean array is returned with values True where the corresponding element of the input is negative infinity and values False where the element of the input is not negative infinity. If a second argument is supplied the result is stored there. If the type of that array is a numeric type the result is represented as zeros and ones, if the type is boolean then as False and True. The return value `y` is then a reference to that array. See Also -------- isinf, isposinf, isnan, isfinite Notes ----- Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). Errors result if the second argument is also supplied when x is a scalar input, or if first and second arguments have different shapes. Examples -------- >>> np.isneginf(np.NINF) array(True, dtype=bool) >>> np.isneginf(np.inf) array(False, dtype=bool) >>> np.isneginf(np.PINF) array(False, dtype=bool) >>> np.isneginf([-np.inf, 0., np.inf]) array([ True, False, False], dtype=bool) >>> x = np.array([-np.inf, 0., np.inf]) >>> y = np.array([2, 2, 2]) >>> np.isneginf(x, y) array([1, 0, 0]) >>> y array([1, 0, 0]) isposinf(x, y=None) Test element-wise for positive infinity, return result as bool array. Parameters ---------- x : array_like The input array. y : array_like, optional A boolean array with the same shape as `x` to store the result. Returns ------- y : ndarray A boolean array with the same dimensions as the input. If second argument is not supplied then a boolean array is returned with values True where the corresponding element of the input is positive infinity and values False where the element of the input is not positive infinity. If a second argument is supplied the result is stored there. If the type of that array is a numeric type the result is represented as zeros and ones, if the type is boolean then as False and True. The return value `y` is then a reference to that array. See Also -------- isinf, isneginf, isfinite, isnan Notes ----- Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). Errors result if the second argument is also supplied when `x` is a scalar input, or if first and second arguments have different shapes. Examples -------- >>> np.isposinf(np.PINF) array(True, dtype=bool) >>> np.isposinf(np.inf) array(True, dtype=bool) >>> np.isposinf(np.NINF) array(False, dtype=bool) >>> np.isposinf([-np.inf, 0., np.inf]) array([False, False, True], dtype=bool) >>> x = np.array([-np.inf, 0., np.inf]) >>> y = np.array([2, 2, 2]) >>> np.isposinf(x, y) array([0, 0, 1]) >>> y array([0, 0, 1]) isreal(x) Returns a bool array, where True if input element is real. If element has complex type with zero complex part, the return value for that element is True. Parameters ---------- x : array_like Input array. Returns ------- out : ndarray, bool Boolean array of same shape as `x`. See Also -------- iscomplex isrealobj : Return True if x is not a complex type. Examples -------- >>> np.isreal([1+1j, 1+0j, 4.5, 3, 2, 2j]) array([False, True, True, True, True, False], dtype=bool) isrealobj(x) Return True if x is a not complex type or an array of complex numbers. The type of the input is checked, not the value. So even if the input has an imaginary part equal to zero, `isrealobj` evaluates to False if the data type is complex. Parameters ---------- x : any The input can be of any type and shape. Returns ------- y : bool The return value, False if `x` is of a complex type. See Also -------- iscomplexobj, isreal Examples -------- >>> np.isrealobj(1) True >>> np.isrealobj(1+0j) False >>> np.isrealobj([3, 1+0j, True]) False isscalar(num) Returns True if the type of `num` is a scalar type. Parameters ---------- num : any Input argument, can be of any type and shape. Returns ------- val : bool True if `num` is a scalar type, False if it is not. Examples -------- >>> np.isscalar(3.1) True >>> np.isscalar([3.1]) False >>> np.isscalar(False) True issctype(rep) Determines whether the given object represents a scalar data-type. Parameters ---------- rep : any If `rep` is an instance of a scalar dtype, True is returned. If not, False is returned. Returns ------- out : bool Boolean result of check whether `rep` is a scalar dtype. See Also -------- issubsctype, issubdtype, obj2sctype, sctype2char Examples -------- >>> np.issctype(np.int32) True >>> np.issctype(list) False >>> np.issctype(1.1) False Strings are also a scalar type: >>> np.issctype(np.dtype('str')) True issubclass_(arg1, arg2) Determine if a class is a subclass of a second class. `issubclass_` is equivalent to the Python built-in ``issubclass``, except that it returns False instead of raising a TypeError if one of the arguments is not a class. Parameters ---------- arg1 : class Input class. True is returned if `arg1` is a subclass of `arg2`. arg2 : class or tuple of classes. Input class. If a tuple of classes, True is returned if `arg1` is a subclass of any of the tuple elements. Returns ------- out : bool Whether `arg1` is a subclass of `arg2` or not. See Also -------- issubsctype, issubdtype, issctype Examples -------- >>> np.issubclass_(np.int32, np.int) True >>> np.issubclass_(np.int32, np.float) False issubdtype(arg1, arg2) Returns True if first argument is a typecode lower/equal in type hierarchy. Parameters ---------- arg1, arg2 : dtype_like dtype or string representing a typecode. Returns ------- out : bool See Also -------- issubsctype, issubclass_ numpy.core.numerictypes : Overview of numpy type hierarchy. Examples -------- >>> np.issubdtype('S1', str) True >>> np.issubdtype(np.float64, np.float32) False issubsctype(arg1, arg2) Determine if the first argument is a subclass of the second argument. Parameters ---------- arg1, arg2 : dtype or dtype specifier Data-types. Returns ------- out : bool The result. See Also -------- issctype, issubdtype,obj2sctype Examples -------- >>> np.issubsctype('S8', str) True >>> np.issubsctype(np.array([1]), np.int) True >>> np.issubsctype(np.array([1]), np.float) False iterable(y) Check whether or not an object can be iterated over. Parameters ---------- y : object Input object. Returns ------- b : {0, 1} Return 1 if the object has an iterator method or is a sequence, and 0 otherwise. Examples -------- >>> np.iterable([1, 2, 3]) 1 >>> np.iterable(2) 0 ix_(*args) Construct an open mesh from multiple sequences. This function takes N 1-D sequences and returns N outputs with N dimensions each, such that the shape is 1 in all but one dimension and the dimension with the non-unit shape value cycles through all N dimensions. Using `ix_` one can quickly construct index arrays that will index the cross product. ``a[np.ix_([1,3],[2,5])]`` returns the array ``[[a[1,2] a[1,5]], [a[3,2] a[3,5]]]``. Parameters ---------- args : 1-D sequences Returns ------- out : tuple of ndarrays N arrays with N dimensions each, with N the number of input sequences. Together these arrays form an open mesh. See Also -------- ogrid, mgrid, meshgrid Examples -------- >>> a = np.arange(10).reshape(2, 5) >>> a array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]) >>> ixgrid = np.ix_([0,1], [2,4]) >>> ixgrid (array([[0], [1]]), array([[2, 4]])) >>> ixgrid[0].shape, ixgrid[1].shape ((2, 1), (1, 2)) >>> a[ixgrid] array([[2, 4], [7, 9]]) kaiser(M, beta) Return the Kaiser window. The Kaiser window is a taper formed by using a Bessel function. Parameters ---------- M : int Number of points in the output window. If zero or less, an empty array is returned. beta : float Shape parameter for window. Returns ------- out : array The window, with the maximum value normalized to one (the value one appears only if the number of samples is odd). See Also -------- bartlett, blackman, hamming, hanning Notes ----- The Kaiser window is defined as .. math:: w(n) = I_0\left( \beta \sqrt{1-\frac{4n^2}{(M-1)^2}} \right)/I_0(\beta) with .. math:: \quad -\frac{M-1}{2} \leq n \leq \frac{M-1}{2}, where :math:`I_0` is the modified zeroth-order Bessel function. The Kaiser was named for Jim Kaiser, who discovered a simple approximation to the DPSS window based on Bessel functions. The Kaiser window is a very good approximation to the Digital Prolate Spheroidal Sequence, or Slepian window, which is the transform which maximizes the energy in the main lobe of the window relative to total energy. The Kaiser can approximate many other windows by varying the beta parameter. ==== ======================= beta Window shape ==== ======================= 0 Rectangular 5 Similar to a Hamming 6 Similar to a Hanning 8.6 Similar to a Blackman ==== ======================= A beta value of 14 is probably a good starting point. Note that as beta gets large, the window narrows, and so the number of samples needs to be large enough to sample the increasingly narrow spike, otherwise NaNs will get returned. Most references to the Kaiser window come from the signal processing literature, where it is used as one of many windowing functions for smoothing values. It is also known as an apodization (which means "removing the foot", i.e. smoothing discontinuities at the beginning and end of the sampled signal) or tapering function. References ---------- .. [1] J. F. Kaiser, "Digital Filters" - Ch 7 in "Systems analysis by digital computer", Editors: F.F. Kuo and J.F. Kaiser, p 218-285. John Wiley and Sons, New York, (1966). .. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics", The University of Alberta Press, 1975, pp. 177-178. .. [3] Wikipedia, "Window function", http://en.wikipedia.org/wiki/Window_function Examples -------- >>> np.kaiser(12, 14) array([ 7.72686684e-06, 3.46009194e-03, 4.65200189e-02, 2.29737120e-01, 5.99885316e-01, 9.45674898e-01, 9.45674898e-01, 5.99885316e-01, 2.29737120e-01, 4.65200189e-02, 3.46009194e-03, 7.72686684e-06]) Plot the window and the frequency response: >>> from numpy.fft import fft, fftshift >>> window = np.kaiser(51, 14) >>> plt.plot(window) [] >>> plt.title("Kaiser window") >>> plt.ylabel("Amplitude") >>> plt.xlabel("Sample") >>> plt.show() >>> plt.figure() >>> A = fft(window, 2048) / 25.5 >>> mag = np.abs(fftshift(A)) >>> freq = np.linspace(-0.5, 0.5, len(A)) >>> response = 20 * np.log10(mag) >>> response = np.clip(response, -100, 100) >>> plt.plot(freq, response) [] >>> plt.title("Frequency response of Kaiser window") >>> plt.ylabel("Magnitude [dB]") >>> plt.xlabel("Normalized frequency [cycles per sample]") >>> plt.axis('tight') (-0.5, 0.5, -100.0, ...) >>> plt.show() kron(a, b) Kronecker product of two arrays. Computes the Kronecker product, a composite array made of blocks of the second array scaled by the first. Parameters ---------- a, b : array_like Returns ------- out : ndarray See Also -------- outer : The outer product Notes ----- The function assumes that the number of dimensions of `a` and `b` are the same, if necessary prepending the smallest with ones. If `a.shape = (r0,r1,..,rN)` and `b.shape = (s0,s1,...,sN)`, the Kronecker product has shape `(r0*s0, r1*s1, ..., rN*SN)`. The elements are products of elements from `a` and `b`, organized explicitly by:: kron(a,b)[k0,k1,...,kN] = a[i0,i1,...,iN] * b[j0,j1,...,jN] where:: kt = it * st + jt, t = 0,...,N In the common 2-D case (N=1), the block structure can be visualized:: [[ a[0,0]*b, a[0,1]*b, ... , a[0,-1]*b ], [ ... ... ], [ a[-1,0]*b, a[-1,1]*b, ... , a[-1,-1]*b ]] Examples -------- >>> np.kron([1,10,100], [5,6,7]) array([ 5, 6, 7, 50, 60, 70, 500, 600, 700]) >>> np.kron([5,6,7], [1,10,100]) array([ 5, 50, 500, 6, 60, 600, 7, 70, 700]) >>> np.kron(np.eye(2), np.ones((2,2))) array([[ 1., 1., 0., 0.], [ 1., 1., 0., 0.], [ 0., 0., 1., 1.], [ 0., 0., 1., 1.]]) >>> a = np.arange(100).reshape((2,5,2,5)) >>> b = np.arange(24).reshape((2,3,4)) >>> c = np.kron(a,b) >>> c.shape (2, 10, 6, 20) >>> I = (1,3,0,2) >>> J = (0,2,1) >>> J1 = (0,) + J # extend to ndim=4 >>> S1 = (1,) + b.shape >>> K = tuple(np.array(I) * np.array(S1) + np.array(J1)) >>> c[K] == a[I]*b[J] True lexsort(...) lexsort(keys, axis=-1) Perform an indirect sort using a sequence of keys. Given multiple sorting keys, which can be interpreted as columns in a spreadsheet, lexsort returns an array of integer indices that describes the sort order by multiple columns. The last key in the sequence is used for the primary sort order, the second-to-last key for the secondary sort order, and so on. The keys argument must be a sequence of objects that can be converted to arrays of the same shape. If a 2D array is provided for the keys argument, it's rows are interpreted as the sorting keys and sorting is according to the last row, second last row etc. Parameters ---------- keys : (k, N) array or tuple containing k (N,)-shaped sequences The `k` different "columns" to be sorted. The last column (or row if `keys` is a 2D array) is the primary sort key. axis : int, optional Axis to be indirectly sorted. By default, sort over the last axis. Returns ------- indices : (N,) ndarray of ints Array of indices that sort the keys along the specified axis. See Also -------- argsort : Indirect sort. ndarray.sort : In-place sort. sort : Return a sorted copy of an array. Examples -------- Sort names: first by surname, then by name. >>> surnames = ('Hertz', 'Galilei', 'Hertz') >>> first_names = ('Heinrich', 'Galileo', 'Gustav') >>> ind = np.lexsort((first_names, surnames)) >>> ind array([1, 2, 0]) >>> [surnames[i] + ", " + first_names[i] for i in ind] ['Galilei, Galileo', 'Hertz, Gustav', 'Hertz, Heinrich'] Sort two columns of numbers: >>> a = [1,5,1,4,3,4,4] # First column >>> b = [9,4,0,4,0,2,1] # Second column >>> ind = np.lexsort((b,a)) # Sort by a, then by b >>> print ind [2 0 4 6 5 3 1] >>> [(a[i],b[i]) for i in ind] [(1, 0), (1, 9), (3, 0), (4, 1), (4, 2), (4, 4), (5, 4)] Note that sorting is first according to the elements of ``a``. Secondary sorting is according to the elements of ``b``. A normal ``argsort`` would have yielded: >>> [(a[i],b[i]) for i in np.argsort(a)] [(1, 9), (1, 0), (3, 0), (4, 4), (4, 2), (4, 1), (5, 4)] Structured arrays are sorted lexically by ``argsort``: >>> x = np.array([(1,9), (5,4), (1,0), (4,4), (3,0), (4,2), (4,1)], ... dtype=np.dtype([('x', int), ('y', int)])) >>> np.argsort(x) # or np.argsort(x, order=('x', 'y')) array([2, 0, 4, 6, 5, 3, 1]) linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None) Return evenly spaced numbers over a specified interval. Returns `num` evenly spaced samples, calculated over the interval [`start`, `stop`]. The endpoint of the interval can optionally be excluded. Parameters ---------- start : scalar The starting value of the sequence. stop : scalar The end value of the sequence, unless `endpoint` is set to False. In that case, the sequence consists of all but the last of ``num + 1`` evenly spaced samples, so that `stop` is excluded. Note that the step size changes when `endpoint` is False. num : int, optional Number of samples to generate. Default is 50. Must be non-negative. endpoint : bool, optional If True, `stop` is the last sample. Otherwise, it is not included. Default is True. retstep : bool, optional If True, return (`samples`, `step`), where `step` is the spacing between samples. dtype : dtype, optional The type of the output array. If `dtype` is not given, infer the data type from the other input arguments. .. versionadded:: 1.9.0 Returns ------- samples : ndarray There are `num` equally spaced samples in the closed interval ``[start, stop]`` or the half-open interval ``[start, stop)`` (depending on whether `endpoint` is True or False). step : float Only returned if `retstep` is True Size of spacing between samples. See Also -------- arange : Similar to `linspace`, but uses a step size (instead of the number of samples). logspace : Samples uniformly distributed in log space. Examples -------- >>> np.linspace(2.0, 3.0, num=5) array([ 2. , 2.25, 2.5 , 2.75, 3. ]) >>> np.linspace(2.0, 3.0, num=5, endpoint=False) array([ 2. , 2.2, 2.4, 2.6, 2.8]) >>> np.linspace(2.0, 3.0, num=5, retstep=True) (array([ 2. , 2.25, 2.5 , 2.75, 3. ]), 0.25) Graphical illustration: >>> import matplotlib.pyplot as plt >>> N = 8 >>> y = np.zeros(N) >>> x1 = np.linspace(0, 10, N, endpoint=True) >>> x2 = np.linspace(0, 10, N, endpoint=False) >>> plt.plot(x1, y, 'o') [] >>> plt.plot(x2, y + 0.5, 'o') [] >>> plt.ylim([-0.5, 1]) (-0.5, 1) >>> plt.show() load(file, mmap_mode=None, allow_pickle=True, fix_imports=True, encoding='ASCII') Load arrays or pickled objects from ``.npy``, ``.npz`` or pickled files. Parameters ---------- file : file-like object or string The file to read. File-like objects must support the ``seek()`` and ``read()`` methods. Pickled files require that the file-like object support the ``readline()`` method as well. mmap_mode : {None, 'r+', 'r', 'w+', 'c'}, optional If not None, then memory-map the file, using the given mode (see `numpy.memmap` for a detailed description of the modes). A memory-mapped array is kept on disk. However, it can be accessed and sliced like any ndarray. Memory mapping is especially useful for accessing small fragments of large files without reading the entire file into memory. allow_pickle : bool, optional Allow loading pickled object arrays stored in npy files. Reasons for disallowing pickles include security, as loading pickled data can execute arbitrary code. If pickles are disallowed, loading object arrays will fail. Default: True fix_imports : bool, optional Only useful when loading Python 2 generated pickled files on Python 3, which includes npy/npz files containing object arrays. If `fix_imports` is True, pickle will try to map the old Python 2 names to the new names used in Python 3. encoding : str, optional What encoding to use when reading Python 2 strings. Only useful when loading Python 2 generated pickled files on Python 3, which includes npy/npz files containing object arrays. Values other than 'latin1', 'ASCII', and 'bytes' are not allowed, as they can corrupt numerical data. Default: 'ASCII' Returns ------- result : array, tuple, dict, etc. Data stored in the file. For ``.npz`` files, the returned instance of NpzFile class must be closed to avoid leaking file descriptors. Raises ------ IOError If the input file does not exist or cannot be read. ValueError The file contains an object array, but allow_pickle=False given. See Also -------- save, savez, savez_compressed, loadtxt memmap : Create a memory-map to an array stored in a file on disk. Notes ----- - If the file contains pickle data, then whatever object is stored in the pickle is returned. - If the file is a ``.npy`` file, then a single array is returned. - If the file is a ``.npz`` file, then a dictionary-like object is returned, containing ``{filename: array}`` key-value pairs, one for each file in the archive. - If the file is a ``.npz`` file, the returned value supports the context manager protocol in a similar fashion to the open function:: with load('foo.npz') as data: a = data['a'] The underlying file descriptor is closed when exiting the 'with' block. Examples -------- Store data to disk, and load it again: >>> np.save('/tmp/123', np.array([[1, 2, 3], [4, 5, 6]])) >>> np.load('/tmp/123.npy') array([[1, 2, 3], [4, 5, 6]]) Store compressed data to disk, and load it again: >>> a=np.array([[1, 2, 3], [4, 5, 6]]) >>> b=np.array([1, 2]) >>> np.savez('/tmp/123.npz', a=a, b=b) >>> data = np.load('/tmp/123.npz') >>> data['a'] array([[1, 2, 3], [4, 5, 6]]) >>> data['b'] array([1, 2]) >>> data.close() Mem-map the stored array, and then access the second row directly from disk: >>> X = np.load('/tmp/123.npy', mmap_mode='r') >>> X[1, :] memmap([4, 5, 6]) loads(...) loads(string) -- Load a pickle from the given string loadtxt(fname, dtype=, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0) Load data from a text file. Each row in the text file must have the same number of values. Parameters ---------- fname : file or str File, filename, or generator to read. If the filename extension is ``.gz`` or ``.bz2``, the file is first decompressed. Note that generators should return byte strings for Python 3k. dtype : data-type, optional Data-type of the resulting array; default: float. If this is a structured data-type, the resulting array will be 1-dimensional, and each row will be interpreted as an element of the array. In this case, the number of columns used must match the number of fields in the data-type. comments : str or sequence, optional The characters or list of characters used to indicate the start of a comment; default: '#'. delimiter : str, optional The string used to separate values. By default, this is any whitespace. converters : dict, optional A dictionary mapping column number to a function that will convert that column to a float. E.g., if column 0 is a date string: ``converters = {0: datestr2num}``. Converters can also be used to provide a default value for missing data (but see also `genfromtxt`): ``converters = {3: lambda s: float(s.strip() or 0)}``. Default: None. skiprows : int, optional Skip the first `skiprows` lines; default: 0. usecols : sequence, optional Which columns to read, with 0 being the first. For example, ``usecols = (1,4,5)`` will extract the 2nd, 5th and 6th columns. The default, None, results in all columns being read. unpack : bool, optional If True, the returned array is transposed, so that arguments may be unpacked using ``x, y, z = loadtxt(...)``. When used with a structured data-type, arrays are returned for each field. Default is False. ndmin : int, optional The returned array will have at least `ndmin` dimensions. Otherwise mono-dimensional axes will be squeezed. Legal values: 0 (default), 1 or 2. .. versionadded:: 1.6.0 Returns ------- out : ndarray Data read from the text file. See Also -------- load, fromstring, fromregex genfromtxt : Load data with missing values handled as specified. scipy.io.loadmat : reads MATLAB data files Notes ----- This function aims to be a fast reader for simply formatted files. The `genfromtxt` function provides more sophisticated handling of, e.g., lines with missing values. .. versionadded:: 1.10.0 The strings produced by the Python float.hex method can be used as input for floats. Examples -------- >>> from io import StringIO # StringIO behaves like a file object >>> c = StringIO("0 1\n2 3") >>> np.loadtxt(c) array([[ 0., 1.], [ 2., 3.]]) >>> d = StringIO("M 21 72\nF 35 58") >>> np.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'), ... 'formats': ('S1', 'i4', 'f4')}) array([('M', 21, 72.0), ('F', 35, 58.0)], dtype=[('gender', '|S1'), ('age', '>> c = StringIO("1,0,2\n3,0,4") >>> x, y = np.loadtxt(c, delimiter=',', usecols=(0, 2), unpack=True) >>> x array([ 1., 3.]) >>> y array([ 2., 4.]) logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None) Return numbers spaced evenly on a log scale. In linear space, the sequence starts at ``base ** start`` (`base` to the power of `start`) and ends with ``base ** stop`` (see `endpoint` below). Parameters ---------- start : float ``base ** start`` is the starting value of the sequence. stop : float ``base ** stop`` is the final value of the sequence, unless `endpoint` is False. In that case, ``num + 1`` values are spaced over the interval in log-space, of which all but the last (a sequence of length ``num``) are returned. num : integer, optional Number of samples to generate. Default is 50. endpoint : boolean, optional If true, `stop` is the last sample. Otherwise, it is not included. Default is True. base : float, optional The base of the log space. The step size between the elements in ``ln(samples) / ln(base)`` (or ``log_base(samples)``) is uniform. Default is 10.0. dtype : dtype The type of the output array. If `dtype` is not given, infer the data type from the other input arguments. Returns ------- samples : ndarray `num` samples, equally spaced on a log scale. See Also -------- arange : Similar to linspace, with the step size specified instead of the number of samples. Note that, when used with a float endpoint, the endpoint may or may not be included. linspace : Similar to logspace, but with the samples uniformly distributed in linear space, instead of log space. Notes ----- Logspace is equivalent to the code >>> y = np.linspace(start, stop, num=num, endpoint=endpoint) ... # doctest: +SKIP >>> power(base, y).astype(dtype) ... # doctest: +SKIP Examples -------- >>> np.logspace(2.0, 3.0, num=4) array([ 100. , 215.443469 , 464.15888336, 1000. ]) >>> np.logspace(2.0, 3.0, num=4, endpoint=False) array([ 100. , 177.827941 , 316.22776602, 562.34132519]) >>> np.logspace(2.0, 3.0, num=4, base=2.0) array([ 4. , 5.0396842 , 6.34960421, 8. ]) Graphical illustration: >>> import matplotlib.pyplot as plt >>> N = 10 >>> x1 = np.logspace(0.1, 1, N, endpoint=True) >>> x2 = np.logspace(0.1, 1, N, endpoint=False) >>> y = np.zeros(N) >>> plt.plot(x1, y, 'o') [] >>> plt.plot(x2, y + 0.5, 'o') [] >>> plt.ylim([-0.5, 1]) (-0.5, 1) >>> plt.show() lookfor(what, module=None, import_modules=True, regenerate=False, output=None) Do a keyword search on docstrings. A list of of objects that matched the search is displayed, sorted by relevance. All given keywords need to be found in the docstring for it to be returned as a result, but the order does not matter. Parameters ---------- what : str String containing words to look for. module : str or list, optional Name of module(s) whose docstrings to go through. import_modules : bool, optional Whether to import sub-modules in packages. Default is True. regenerate : bool, optional Whether to re-generate the docstring cache. Default is False. output : file-like, optional File-like object to write the output to. If omitted, use a pager. See Also -------- source, info Notes ----- Relevance is determined only roughly, by checking if the keywords occur in the function name, at the start of a docstring, etc. Examples -------- >>> np.lookfor('binary representation') Search results for 'binary representation' ------------------------------------------ numpy.binary_repr Return the binary representation of the input number as a string. numpy.core.setup_common.long_double_representation Given a binary dump as given by GNU od -b, look for long double numpy.base_repr Return a string representation of a number in the given base system. ... mafromtxt(fname, **kwargs) Load ASCII data stored in a text file and return a masked array. Parameters ---------- fname, kwargs : For a description of input parameters, see `genfromtxt`. See Also -------- numpy.genfromtxt : generic function to load ASCII data. mask_indices(n, mask_func, k=0) Return the indices to access (n, n) arrays, given a masking function. Assume `mask_func` is a function that, for a square array a of size ``(n, n)`` with a possible offset argument `k`, when called as ``mask_func(a, k)`` returns a new array with zeros in certain locations (functions like `triu` or `tril` do precisely this). Then this function returns the indices where the non-zero values would be located. Parameters ---------- n : int The returned indices will be valid to access arrays of shape (n, n). mask_func : callable A function whose call signature is similar to that of `triu`, `tril`. That is, ``mask_func(x, k)`` returns a boolean array, shaped like `x`. `k` is an optional argument to the function. k : scalar An optional argument which is passed through to `mask_func`. Functions like `triu`, `tril` take a second argument that is interpreted as an offset. Returns ------- indices : tuple of arrays. The `n` arrays of indices corresponding to the locations where ``mask_func(np.ones((n, n)), k)`` is True. See Also -------- triu, tril, triu_indices, tril_indices Notes ----- .. versionadded:: 1.4.0 Examples -------- These are the indices that would allow you to access the upper triangular part of any 3x3 array: >>> iu = np.mask_indices(3, np.triu) For example, if `a` is a 3x3 array: >>> a = np.arange(9).reshape(3, 3) >>> a array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) >>> a[iu] array([0, 1, 2, 4, 5, 8]) An offset can be passed also to the masking function. This gets us the indices starting on the first diagonal right of the main one: >>> iu1 = np.mask_indices(3, np.triu, 1) with which we now extract only three elements: >>> a[iu1] array([1, 2, 5]) mat = asmatrix(data, dtype=None) Interpret the input as a matrix. Unlike `matrix`, `asmatrix` does not make a copy if the input is already a matrix or an ndarray. Equivalent to ``matrix(data, copy=False)``. Parameters ---------- data : array_like Input data. dtype : data-type Data-type of the output matrix. Returns ------- mat : matrix `data` interpreted as a matrix. Examples -------- >>> x = np.array([[1, 2], [3, 4]]) >>> m = np.asmatrix(x) >>> x[0,0] = 5 >>> m matrix([[5, 2], [3, 4]]) matmul(...) matmul(a, b, out=None) Matrix product of two arrays. The behavior depends on the arguments in the following way. - If both arguments are 2-D they are multiplied like conventional matrices. - If either argument is N-D, N > 2, it is treated as a stack of matrices residing in the last two indexes and broadcast accordingly. - If the first argument is 1-D, it is promoted to a matrix by prepending a 1 to its dimensions. After matrix multiplication the prepended 1 is removed. - If the second argument is 1-D, it is promoted to a matrix by appending a 1 to its dimensions. After matrix multiplication the appended 1 is removed. Multiplication by a scalar is not allowed, use ``*`` instead. Note that multiplying a stack of matrices with a vector will result in a stack of vectors, but matmul will not recognize it as such. ``matmul`` differs from ``dot`` in two important ways. - Multiplication by scalars is not allowed. - Stacks of matrices are broadcast together as if the matrices were elements. .. warning:: This function is preliminary and included in Numpy 1.10 for testing and documentation. Its semantics will not change, but the number and order of the optional arguments will. .. versionadded:: 1.10.0 Parameters ---------- a : array_like First argument. b : array_like Second argument. out : ndarray, optional Output argument. This must have the exact kind that would be returned if it was not used. In particular, it must have the right type, must be C-contiguous, and its dtype must be the dtype that would be returned for `dot(a,b)`. This is a performance feature. Therefore, if these conditions are not met, an exception is raised, instead of attempting to be flexible. Returns ------- output : ndarray Returns the dot product of `a` and `b`. If `a` and `b` are both 1-D arrays then a scalar is returned; otherwise an array is returned. If `out` is given, then it is returned. Raises ------ ValueError If the last dimension of `a` is not the same size as the second-to-last dimension of `b`. If scalar value is passed. See Also -------- vdot : Complex-conjugating dot product. tensordot : Sum products over arbitrary axes. einsum : Einstein summation convention. dot : alternative matrix product with different broadcasting rules. Notes ----- The matmul function implements the semantics of the `@` operator introduced in Python 3.5 following PEP465. Examples -------- For 2-D arrays it is the matrix product: >>> a = [[1, 0], [0, 1]] >>> b = [[4, 1], [2, 2]] >>> np.matmul(a, b) array([[4, 1], [2, 2]]) For 2-D mixed with 1-D, the result is the usual. >>> a = [[1, 0], [0, 1]] >>> b = [1, 2] >>> np.matmul(a, b) array([1, 2]) >>> np.matmul(b, a) array([1, 2]) Broadcasting is conventional for stacks of arrays >>> a = np.arange(2*2*4).reshape((2,2,4)) >>> b = np.arange(2*2*4).reshape((2,4,2)) >>> np.matmul(a,b).shape (2, 2, 2) >>> np.matmul(a,b)[0,1,1] 98 >>> sum(a[0,1,:] * b[0,:,1]) 98 Vector, vector returns the scalar inner product, but neither argument is complex-conjugated: >>> np.matmul([2j, 3j], [2j, 3j]) (-13+0j) Scalar multiplication raises an error. >>> np.matmul([1,2], 3) Traceback (most recent call last): ... ValueError: Scalar operands are not allowed, use '*' instead maximum_sctype(t) Return the scalar type of highest precision of the same kind as the input. Parameters ---------- t : dtype or dtype specifier The input data type. This can be a `dtype` object or an object that is convertible to a `dtype`. Returns ------- out : dtype The highest precision data type of the same kind (`dtype.kind`) as `t`. See Also -------- obj2sctype, mintypecode, sctype2char dtype Examples -------- >>> np.maximum_sctype(np.int) >>> np.maximum_sctype(np.uint8) >>> np.maximum_sctype(np.complex) >>> np.maximum_sctype(str) >>> np.maximum_sctype('i2') >>> np.maximum_sctype('f4') may_share_memory(...) Determine if two arrays can share memory The memory-bounds of a and b are computed. If they overlap then this function returns True. Otherwise, it returns False. A return of True does not necessarily mean that the two arrays share any element. It just means that they *might*. Parameters ---------- a, b : ndarray Returns ------- out : bool Examples -------- >>> np.may_share_memory(np.array([1,2]), np.array([5,8,9])) False mean(a, axis=None, dtype=None, out=None, keepdims=False) Compute the arithmetic mean along the specified axis. Returns the average of the array elements. The average is taken over the flattened array by default, otherwise over the specified axis. `float64` intermediate and return values are used for integer inputs. Parameters ---------- a : array_like Array containing numbers whose mean is desired. If `a` is not an array, a conversion is attempted. axis : None or int or tuple of ints, optional Axis or axes along which the means are computed. The default is to compute the mean of the flattened array. .. versionadded: 1.7.0 If this is a tuple of ints, a mean is performed over multiple axes, instead of a single axis or all the axes as before. dtype : data-type, optional Type to use in computing the mean. For integer inputs, the default is `float64`; for floating point inputs, it is the same as the input dtype. out : ndarray, optional Alternate output array in which to place the result. The default is ``None``; if provided, it must have the same shape as the expected output, but the type will be cast if necessary. See `doc.ufuncs` for details. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `arr`. Returns ------- m : ndarray, see dtype parameter above If `out=None`, returns a new array containing the mean values, otherwise a reference to the output array is returned. See Also -------- average : Weighted average std, var, nanmean, nanstd, nanvar Notes ----- The arithmetic mean is the sum of the elements along the axis divided by the number of elements. Note that for floating-point input, the mean is computed using the same precision the input has. Depending on the input data, this can cause the results to be inaccurate, especially for `float32` (see example below). Specifying a higher-precision accumulator using the `dtype` keyword can alleviate this issue. Examples -------- >>> a = np.array([[1, 2], [3, 4]]) >>> np.mean(a) 2.5 >>> np.mean(a, axis=0) array([ 2., 3.]) >>> np.mean(a, axis=1) array([ 1.5, 3.5]) In single precision, `mean` can be inaccurate: >>> a = np.zeros((2, 512*512), dtype=np.float32) >>> a[0, :] = 1.0 >>> a[1, :] = 0.1 >>> np.mean(a) 0.546875 Computing the mean in float64 is more accurate: >>> np.mean(a, dtype=np.float64) 0.55000000074505806 median(a, axis=None, out=None, overwrite_input=False, keepdims=False) Compute the median along the specified axis. Returns the median of the array elements. Parameters ---------- a : array_like Input array or object that can be converted to an array. axis : int or sequence of int, optional Axis along which the medians are computed. The default (axis=None) is to compute the median along a flattened version of the array. A sequence of axes is supported since version 1.9.0. out : ndarray, optional Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output, but the type (of the output) will be cast if necessary. overwrite_input : bool, optional If True, then allow use of memory of input array (a) for calculations. The input array will be modified by the call to median. This will save memory when you do not need to preserve the contents of the input array. Treat the input as undefined, but it will probably be fully or partially sorted. Default is False. Note that, if `overwrite_input` is True and the input is not already an ndarray, an error will be raised. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `arr`. .. versionadded:: 1.9.0 Returns ------- median : ndarray A new array holding the result (unless `out` is specified, in which case that array is returned instead). If the input contains integers, or floats of smaller precision than 64, then the output data-type is float64. Otherwise, the output data-type is the same as that of the input. See Also -------- mean, percentile Notes ----- Given a vector V of length N, the median of V is the middle value of a sorted copy of V, ``V_sorted`` - i.e., ``V_sorted[(N-1)/2]``, when N is odd. When N is even, it is the average of the two middle values of ``V_sorted``. Examples -------- >>> a = np.array([[10, 7, 4], [3, 2, 1]]) >>> a array([[10, 7, 4], [ 3, 2, 1]]) >>> np.median(a) 3.5 >>> np.median(a, axis=0) array([ 6.5, 4.5, 2.5]) >>> np.median(a, axis=1) array([ 7., 2.]) >>> m = np.median(a, axis=0) >>> out = np.zeros_like(m) >>> np.median(a, axis=0, out=m) array([ 6.5, 4.5, 2.5]) >>> m array([ 6.5, 4.5, 2.5]) >>> b = a.copy() >>> np.median(b, axis=1, overwrite_input=True) array([ 7., 2.]) >>> assert not np.all(a==b) >>> b = a.copy() >>> np.median(b, axis=None, overwrite_input=True) 3.5 >>> assert not np.all(a==b) meshgrid(*xi, **kwargs) Return coordinate matrices from coordinate vectors. Make N-D coordinate arrays for vectorized evaluations of N-D scalar/vector fields over N-D grids, given one-dimensional coordinate arrays x1, x2,..., xn. .. versionchanged:: 1.9 1-D and 0-D cases are allowed. Parameters ---------- x1, x2,..., xn : array_like 1-D arrays representing the coordinates of a grid. indexing : {'xy', 'ij'}, optional Cartesian ('xy', default) or matrix ('ij') indexing of output. See Notes for more details. .. versionadded:: 1.7.0 sparse : bool, optional If True a sparse grid is returned in order to conserve memory. Default is False. .. versionadded:: 1.7.0 copy : bool, optional If False, a view into the original arrays are returned in order to conserve memory. Default is True. Please note that ``sparse=False, copy=False`` will likely return non-contiguous arrays. Furthermore, more than one element of a broadcast array may refer to a single memory location. If you need to write to the arrays, make copies first. .. versionadded:: 1.7.0 Returns ------- X1, X2,..., XN : ndarray For vectors `x1`, `x2`,..., 'xn' with lengths ``Ni=len(xi)`` , return ``(N1, N2, N3,...Nn)`` shaped arrays if indexing='ij' or ``(N2, N1, N3,...Nn)`` shaped arrays if indexing='xy' with the elements of `xi` repeated to fill the matrix along the first dimension for `x1`, the second for `x2` and so on. Notes ----- This function supports both indexing conventions through the indexing keyword argument. Giving the string 'ij' returns a meshgrid with matrix indexing, while 'xy' returns a meshgrid with Cartesian indexing. In the 2-D case with inputs of length M and N, the outputs are of shape (N, M) for 'xy' indexing and (M, N) for 'ij' indexing. In the 3-D case with inputs of length M, N and P, outputs are of shape (N, M, P) for 'xy' indexing and (M, N, P) for 'ij' indexing. The difference is illustrated by the following code snippet:: xv, yv = meshgrid(x, y, sparse=False, indexing='ij') for i in range(nx): for j in range(ny): # treat xv[i,j], yv[i,j] xv, yv = meshgrid(x, y, sparse=False, indexing='xy') for i in range(nx): for j in range(ny): # treat xv[j,i], yv[j,i] In the 1-D and 0-D case, the indexing and sparse keywords have no effect. See Also -------- index_tricks.mgrid : Construct a multi-dimensional "meshgrid" using indexing notation. index_tricks.ogrid : Construct an open multi-dimensional "meshgrid" using indexing notation. Examples -------- >>> nx, ny = (3, 2) >>> x = np.linspace(0, 1, nx) >>> y = np.linspace(0, 1, ny) >>> xv, yv = meshgrid(x, y) >>> xv array([[ 0. , 0.5, 1. ], [ 0. , 0.5, 1. ]]) >>> yv array([[ 0., 0., 0.], [ 1., 1., 1.]]) >>> xv, yv = meshgrid(x, y, sparse=True) # make sparse output arrays >>> xv array([[ 0. , 0.5, 1. ]]) >>> yv array([[ 0.], [ 1.]]) `meshgrid` is very useful to evaluate functions on a grid. >>> x = np.arange(-5, 5, 0.1) >>> y = np.arange(-5, 5, 0.1) >>> xx, yy = meshgrid(x, y, sparse=True) >>> z = np.sin(xx**2 + yy**2) / (xx**2 + yy**2) >>> h = plt.contourf(x,y,z) min_scalar_type(...) min_scalar_type(a) For scalar ``a``, returns the data type with the smallest size and smallest scalar kind which can hold its value. For non-scalar array ``a``, returns the vector's dtype unmodified. Floating point values are not demoted to integers, and complex values are not demoted to floats. Parameters ---------- a : scalar or array_like The value whose minimal data type is to be found. Returns ------- out : dtype The minimal data type. Notes ----- .. versionadded:: 1.6.0 See Also -------- result_type, promote_types, dtype, can_cast Examples -------- >>> np.min_scalar_type(10) dtype('uint8') >>> np.min_scalar_type(-260) dtype('int16') >>> np.min_scalar_type(3.1) dtype('float16') >>> np.min_scalar_type(1e50) dtype('float64') >>> np.min_scalar_type(np.arange(4,dtype='f8')) dtype('float64') mintypecode(typechars, typeset='GDFgdf', default='d') Return the character for the minimum-size type to which given types can be safely cast. The returned type character must represent the smallest size dtype such that an array of the returned type can handle the data from an array of all types in `typechars` (or if `typechars` is an array, then its dtype.char). Parameters ---------- typechars : list of str or array_like If a list of strings, each string should represent a dtype. If array_like, the character representation of the array dtype is used. typeset : str or list of str, optional The set of characters that the returned character is chosen from. The default set is 'GDFgdf'. default : str, optional The default character, this is returned if none of the characters in `typechars` matches a character in `typeset`. Returns ------- typechar : str The character representing the minimum-size type that was found. See Also -------- dtype, sctype2char, maximum_sctype Examples -------- >>> np.mintypecode(['d', 'f', 'S']) 'd' >>> x = np.array([1.1, 2-3.j]) >>> np.mintypecode(x) 'D' >>> np.mintypecode('abceh', default='G') 'G' mirr(values, finance_rate, reinvest_rate) Modified internal rate of return. Parameters ---------- values : array_like Cash flows (must contain at least one positive and one negative value) or nan is returned. The first value is considered a sunk cost at time zero. finance_rate : scalar Interest rate paid on the cash flows reinvest_rate : scalar Interest rate received on the cash flows upon reinvestment Returns ------- out : float Modified internal rate of return msort(a) Return a copy of an array sorted along the first axis. Parameters ---------- a : array_like Array to be sorted. Returns ------- sorted_array : ndarray Array of the same type and shape as `a`. See Also -------- sort Notes ----- ``np.msort(a)`` is equivalent to ``np.sort(a, axis=0)``. nan_to_num(x) Replace nan with zero and inf with finite numbers. Returns an array or scalar replacing Not a Number (NaN) with zero, (positive) infinity with a very large number and negative infinity with a very small (or negative) number. Parameters ---------- x : array_like Input data. Returns ------- out : ndarray New Array with the same shape as `x` and dtype of the element in `x` with the greatest precision. If `x` is inexact, then NaN is replaced by zero, and infinity (-infinity) is replaced by the largest (smallest or most negative) floating point value that fits in the output dtype. If `x` is not inexact, then a copy of `x` is returned. See Also -------- isinf : Shows which elements are negative or negative infinity. isneginf : Shows which elements are negative infinity. isposinf : Shows which elements are positive infinity. isnan : Shows which elements are Not a Number (NaN). isfinite : Shows which elements are finite (not NaN, not infinity) Notes ----- Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Examples -------- >>> np.set_printoptions(precision=8) >>> x = np.array([np.inf, -np.inf, np.nan, -128, 128]) >>> np.nan_to_num(x) array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000, -1.28000000e+002, 1.28000000e+002]) nanargmax(a, axis=None) Return the indices of the maximum values in the specified axis ignoring NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the results cannot be trusted if a slice contains only NaNs and -Infs. Parameters ---------- a : array_like Input data. axis : int, optional Axis along which to operate. By default flattened input is used. Returns ------- index_array : ndarray An array of indices or a single index value. See Also -------- argmax, nanargmin Examples -------- >>> a = np.array([[np.nan, 4], [2, 3]]) >>> np.argmax(a) 0 >>> np.nanargmax(a) 1 >>> np.nanargmax(a, axis=0) array([1, 0]) >>> np.nanargmax(a, axis=1) array([1, 1]) nanargmin(a, axis=None) Return the indices of the minimum values in the specified axis ignoring NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the results cannot be trusted if a slice contains only NaNs and Infs. Parameters ---------- a : array_like Input data. axis : int, optional Axis along which to operate. By default flattened input is used. Returns ------- index_array : ndarray An array of indices or a single index value. See Also -------- argmin, nanargmax Examples -------- >>> a = np.array([[np.nan, 4], [2, 3]]) >>> np.argmin(a) 0 >>> np.nanargmin(a) 2 >>> np.nanargmin(a, axis=0) array([1, 1]) >>> np.nanargmin(a, axis=1) array([1, 0]) nanmax(a, axis=None, out=None, keepdims=False) Return the maximum of an array or maximum along an axis, ignoring any NaNs. When all-NaN slices are encountered a ``RuntimeWarning`` is raised and NaN is returned for that slice. Parameters ---------- a : array_like Array containing numbers whose maximum is desired. If `a` is not an array, a conversion is attempted. axis : int, optional Axis along which the maximum is computed. The default is to compute the maximum of the flattened array. out : ndarray, optional Alternate output array in which to place the result. The default is ``None``; if provided, it must have the same shape as the expected output, but the type will be cast if necessary. See `doc.ufuncs` for details. .. versionadded:: 1.8.0 keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `a`. .. versionadded:: 1.8.0 Returns ------- nanmax : ndarray An array with the same shape as `a`, with the specified axis removed. If `a` is a 0-d array, or if axis is None, an ndarray scalar is returned. The same dtype as `a` is returned. See Also -------- nanmin : The minimum value of an array along a given axis, ignoring any NaNs. amax : The maximum value of an array along a given axis, propagating any NaNs. fmax : Element-wise maximum of two arrays, ignoring any NaNs. maximum : Element-wise maximum of two arrays, propagating any NaNs. isnan : Shows which elements are Not a Number (NaN). isfinite: Shows which elements are neither NaN nor infinity. amin, fmin, minimum Notes ----- Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Positive infinity is treated as a very large number and negative infinity is treated as a very small (i.e. negative) number. If the input has a integer type the function is equivalent to np.max. Examples -------- >>> a = np.array([[1, 2], [3, np.nan]]) >>> np.nanmax(a) 3.0 >>> np.nanmax(a, axis=0) array([ 3., 2.]) >>> np.nanmax(a, axis=1) array([ 2., 3.]) When positive infinity and negative infinity are present: >>> np.nanmax([1, 2, np.nan, np.NINF]) 2.0 >>> np.nanmax([1, 2, np.nan, np.inf]) inf nanmean(a, axis=None, dtype=None, out=None, keepdims=False) Compute the arithmetic mean along the specified axis, ignoring NaNs. Returns the average of the array elements. The average is taken over the flattened array by default, otherwise over the specified axis. `float64` intermediate and return values are used for integer inputs. For all-NaN slices, NaN is returned and a `RuntimeWarning` is raised. .. versionadded:: 1.8.0 Parameters ---------- a : array_like Array containing numbers whose mean is desired. If `a` is not an array, a conversion is attempted. axis : int, optional Axis along which the means are computed. The default is to compute the mean of the flattened array. dtype : data-type, optional Type to use in computing the mean. For integer inputs, the default is `float64`; for inexact inputs, it is the same as the input dtype. out : ndarray, optional Alternate output array in which to place the result. The default is ``None``; if provided, it must have the same shape as the expected output, but the type will be cast if necessary. See `doc.ufuncs` for details. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `arr`. Returns ------- m : ndarray, see dtype parameter above If `out=None`, returns a new array containing the mean values, otherwise a reference to the output array is returned. Nan is returned for slices that contain only NaNs. See Also -------- average : Weighted average mean : Arithmetic mean taken while not ignoring NaNs var, nanvar Notes ----- The arithmetic mean is the sum of the non-NaN elements along the axis divided by the number of non-NaN elements. Note that for floating-point input, the mean is computed using the same precision the input has. Depending on the input data, this can cause the results to be inaccurate, especially for `float32`. Specifying a higher-precision accumulator using the `dtype` keyword can alleviate this issue. Examples -------- >>> a = np.array([[1, np.nan], [3, 4]]) >>> np.nanmean(a) 2.6666666666666665 >>> np.nanmean(a, axis=0) array([ 2., 4.]) >>> np.nanmean(a, axis=1) array([ 1., 3.5]) nanmedian(a, axis=None, out=None, overwrite_input=False, keepdims=False) Compute the median along the specified axis, while ignoring NaNs. Returns the median of the array elements. .. versionadded:: 1.9.0 Parameters ---------- a : array_like Input array or object that can be converted to an array. axis : int, optional Axis along which the medians are computed. The default (axis=None) is to compute the median along a flattened version of the array. A sequence of axes is supported since version 1.9.0. out : ndarray, optional Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output, but the type (of the output) will be cast if necessary. overwrite_input : bool, optional If True, then allow use of memory of input array (a) for calculations. The input array will be modified by the call to median. This will save memory when you do not need to preserve the contents of the input array. Treat the input as undefined, but it will probably be fully or partially sorted. Default is False. Note that, if `overwrite_input` is True and the input is not already an ndarray, an error will be raised. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `arr`. Returns ------- median : ndarray A new array holding the result. If the input contains integers, or floats of smaller precision than 64, then the output data-type is float64. Otherwise, the output data-type is the same as that of the input. See Also -------- mean, median, percentile Notes ----- Given a vector V of length N, the median of V is the middle value of a sorted copy of V, ``V_sorted`` - i.e., ``V_sorted[(N-1)/2]``, when N is odd. When N is even, it is the average of the two middle values of ``V_sorted``. Examples -------- >>> a = np.array([[10.0, 7, 4], [3, 2, 1]]) >>> a[0, 1] = np.nan >>> a array([[ 10., nan, 4.], [ 3., 2., 1.]]) >>> np.median(a) nan >>> np.nanmedian(a) 3.0 >>> np.nanmedian(a, axis=0) array([ 6.5, 2., 2.5]) >>> np.median(a, axis=1) array([ 7., 2.]) >>> b = a.copy() >>> np.nanmedian(b, axis=1, overwrite_input=True) array([ 7., 2.]) >>> assert not np.all(a==b) >>> b = a.copy() >>> np.nanmedian(b, axis=None, overwrite_input=True) 3.0 >>> assert not np.all(a==b) nanmin(a, axis=None, out=None, keepdims=False) Return minimum of an array or minimum along an axis, ignoring any NaNs. When all-NaN slices are encountered a ``RuntimeWarning`` is raised and Nan is returned for that slice. Parameters ---------- a : array_like Array containing numbers whose minimum is desired. If `a` is not an array, a conversion is attempted. axis : int, optional Axis along which the minimum is computed. The default is to compute the minimum of the flattened array. out : ndarray, optional Alternate output array in which to place the result. The default is ``None``; if provided, it must have the same shape as the expected output, but the type will be cast if necessary. See `doc.ufuncs` for details. .. versionadded:: 1.8.0 keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `a`. .. versionadded:: 1.8.0 Returns ------- nanmin : ndarray An array with the same shape as `a`, with the specified axis removed. If `a` is a 0-d array, or if axis is None, an ndarray scalar is returned. The same dtype as `a` is returned. See Also -------- nanmax : The maximum value of an array along a given axis, ignoring any NaNs. amin : The minimum value of an array along a given axis, propagating any NaNs. fmin : Element-wise minimum of two arrays, ignoring any NaNs. minimum : Element-wise minimum of two arrays, propagating any NaNs. isnan : Shows which elements are Not a Number (NaN). isfinite: Shows which elements are neither NaN nor infinity. amax, fmax, maximum Notes ----- Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Positive infinity is treated as a very large number and negative infinity is treated as a very small (i.e. negative) number. If the input has a integer type the function is equivalent to np.min. Examples -------- >>> a = np.array([[1, 2], [3, np.nan]]) >>> np.nanmin(a) 1.0 >>> np.nanmin(a, axis=0) array([ 1., 2.]) >>> np.nanmin(a, axis=1) array([ 1., 3.]) When positive infinity and negative infinity are present: >>> np.nanmin([1, 2, np.nan, np.inf]) 1.0 >>> np.nanmin([1, 2, np.nan, np.NINF]) -inf nanpercentile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=False) Compute the qth percentile of the data along the specified axis, while ignoring nan values. Returns the qth percentile of the array elements. .. versionadded:: 1.9.0 Parameters ---------- a : array_like Input array or object that can be converted to an array. q : float in range of [0,100] (or sequence of floats) Percentile to compute which must be between 0 and 100 inclusive. axis : int or sequence of int, optional Axis along which the percentiles are computed. The default (None) is to compute the percentiles along a flattened version of the array. A sequence of axes is supported since version 1.9.0. out : ndarray, optional Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output, but the type (of the output) will be cast if necessary. overwrite_input : bool, optional If True, then allow use of memory of input array `a` for calculations. The input array will be modified by the call to percentile. This will save memory when you do not need to preserve the contents of the input array. In this case you should not make any assumptions about the content of the passed in array `a` after this function completes -- treat it as undefined. Default is False. Note that, if the `a` input is not already an array this parameter will have no effect, `a` will be converted to an array internally regardless of the value of this parameter. interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points `i` and `j`: * linear: `i + (j - i) * fraction`, where `fraction` is the fractional part of the index surrounded by `i` and `j`. * lower: `i`. * higher: `j`. * nearest: `i` or `j` whichever is nearest. * midpoint: (`i` + `j`) / 2. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `arr`. Returns ------- nanpercentile : scalar or ndarray If a single percentile `q` is given and axis=None a scalar is returned. If multiple percentiles `q` are given an array holding the result is returned. The results are listed in the first axis. (If `out` is specified, in which case that array is returned instead). If the input contains integers, or floats of smaller precision than 64, then the output data-type is float64. Otherwise, the output data-type is the same as that of the input. See Also -------- nanmean, nanmedian, percentile, median, mean Notes ----- Given a vector V of length N, the q-th percentile of V is the q-th ranked value in a sorted copy of V. The values and distances of the two nearest neighbors as well as the `interpolation` parameter will determine the percentile if the normalized ranking does not match q exactly. This function is the same as the median if ``q=50``, the same as the minimum if ``q=0``and the same as the maximum if ``q=100``. Examples -------- >>> a = np.array([[10., 7., 4.], [3., 2., 1.]]) >>> a[0][1] = np.nan >>> a array([[ 10., nan, 4.], [ 3., 2., 1.]]) >>> np.percentile(a, 50) nan >>> np.nanpercentile(a, 50) 3.5 >>> np.nanpercentile(a, 50, axis=0) array([[ 6.5, 4.5, 2.5]]) >>> np.nanpercentile(a, 50, axis=1) array([[ 7.], [ 2.]]) >>> m = np.nanpercentile(a, 50, axis=0) >>> out = np.zeros_like(m) >>> np.nanpercentile(a, 50, axis=0, out=m) array([[ 6.5, 4.5, 2.5]]) >>> m array([[ 6.5, 4.5, 2.5]]) >>> b = a.copy() >>> np.nanpercentile(b, 50, axis=1, overwrite_input=True) array([[ 7.], [ 2.]]) >>> assert not np.all(a==b) >>> b = a.copy() >>> np.nanpercentile(b, 50, axis=None, overwrite_input=True) array([ 3.5]) nanprod(a, axis=None, dtype=None, out=None, keepdims=0) Return the product of array elements over a given axis treating Not a Numbers (NaNs) as zero. One is returned for slices that are all-NaN or empty. .. versionadded:: 1.10.0 Parameters ---------- a : array_like Array containing numbers whose sum is desired. If `a` is not an array, a conversion is attempted. axis : int, optional Axis along which the product is computed. The default is to compute the product of the flattened array. dtype : data-type, optional The type of the returned array and of the accumulator in which the elements are summed. By default, the dtype of `a` is used. An exception is when `a` has an integer type with less precision than the platform (u)intp. In that case, the default will be either (u)int32 or (u)int64 depending on whether the platform is 32 or 64 bits. For inexact inputs, dtype must be inexact. out : ndarray, optional Alternate output array in which to place the result. The default is ``None``. If provided, it must have the same shape as the expected output, but the type will be cast if necessary. See `doc.ufuncs` for details. The casting of NaN to integer can yield unexpected results. keepdims : bool, optional If True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `arr`. Returns ------- y : ndarray or numpy scalar See Also -------- numpy.prod : Product across array propagating NaNs. isnan : Show which elements are NaN. Notes ----- Numpy integer arithmetic is modular. If the size of a product exceeds the size of an integer accumulator, its value will wrap around and the result will be incorrect. Specifying ``dtype=double`` can alleviate that problem. Examples -------- >>> np.nanprod(1) 1 >>> np.nanprod([1]) 1 >>> np.nanprod([1, np.nan]) 1.0 >>> a = np.array([[1, 2], [3, np.nan]]) >>> np.nanprod(a) 6.0 >>> np.nanprod(a, axis=0) array([ 3., 2.]) nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False) Compute the standard deviation along the specified axis, while ignoring NaNs. Returns the standard deviation, a measure of the spread of a distribution, of the non-NaN array elements. The standard deviation is computed for the flattened array by default, otherwise over the specified axis. For all-NaN slices or slices with zero degrees of freedom, NaN is returned and a `RuntimeWarning` is raised. .. versionadded:: 1.8.0 Parameters ---------- a : array_like Calculate the standard deviation of the non-NaN values. axis : int, optional Axis along which the standard deviation is computed. The default is to compute the standard deviation of the flattened array. dtype : dtype, optional Type to use in computing the standard deviation. For arrays of integer type the default is float64, for arrays of float types it is the same as the array type. out : ndarray, optional Alternative output array in which to place the result. It must have the same shape as the expected output but the type (of the calculated values) will be cast if necessary. ddof : int, optional Means Delta Degrees of Freedom. The divisor used in calculations is ``N - ddof``, where ``N`` represents the number of non-NaN elements. By default `ddof` is zero. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `arr`. Returns ------- standard_deviation : ndarray, see dtype parameter above. If `out` is None, return a new array containing the standard deviation, otherwise return a reference to the output array. If ddof is >= the number of non-NaN elements in a slice or the slice contains only NaNs, then the result for that slice is NaN. See Also -------- var, mean, std nanvar, nanmean numpy.doc.ufuncs : Section "Output arguments" Notes ----- The standard deviation is the square root of the average of the squared deviations from the mean: ``std = sqrt(mean(abs(x - x.mean())**2))``. The average squared deviation is normally calculated as ``x.sum() / N``, where ``N = len(x)``. If, however, `ddof` is specified, the divisor ``N - ddof`` is used instead. In standard statistical practice, ``ddof=1`` provides an unbiased estimator of the variance of the infinite population. ``ddof=0`` provides a maximum likelihood estimate of the variance for normally distributed variables. The standard deviation computed in this function is the square root of the estimated variance, so even with ``ddof=1``, it will not be an unbiased estimate of the standard deviation per se. Note that, for complex numbers, `std` takes the absolute value before squaring, so that the result is always real and nonnegative. For floating-point input, the *std* is computed using the same precision the input has. Depending on the input data, this can cause the results to be inaccurate, especially for float32 (see example below). Specifying a higher-accuracy accumulator using the `dtype` keyword can alleviate this issue. Examples -------- >>> a = np.array([[1, np.nan], [3, 4]]) >>> np.nanstd(a) 1.247219128924647 >>> np.nanstd(a, axis=0) array([ 1., 0.]) >>> np.nanstd(a, axis=1) array([ 0., 0.5]) nansum(a, axis=None, dtype=None, out=None, keepdims=0) Return the sum of array elements over a given axis treating Not a Numbers (NaNs) as zero. In Numpy versions <= 1.8 Nan is returned for slices that are all-NaN or empty. In later versions zero is returned. Parameters ---------- a : array_like Array containing numbers whose sum is desired. If `a` is not an array, a conversion is attempted. axis : int, optional Axis along which the sum is computed. The default is to compute the sum of the flattened array. dtype : data-type, optional The type of the returned array and of the accumulator in which the elements are summed. By default, the dtype of `a` is used. An exception is when `a` has an integer type with less precision than the platform (u)intp. In that case, the default will be either (u)int32 or (u)int64 depending on whether the platform is 32 or 64 bits. For inexact inputs, dtype must be inexact. .. versionadded:: 1.8.0 out : ndarray, optional Alternate output array in which to place the result. The default is ``None``. If provided, it must have the same shape as the expected output, but the type will be cast if necessary. See `doc.ufuncs` for details. The casting of NaN to integer can yield unexpected results. .. versionadded:: 1.8.0 keepdims : bool, optional If True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `arr`. .. versionadded:: 1.8.0 Returns ------- y : ndarray or numpy scalar See Also -------- numpy.sum : Sum across array propagating NaNs. isnan : Show which elements are NaN. isfinite: Show which elements are not NaN or +/-inf. Notes ----- If both positive and negative infinity are present, the sum will be Not A Number (NaN). Numpy integer arithmetic is modular. If the size of a sum exceeds the size of an integer accumulator, its value will wrap around and the result will be incorrect. Specifying ``dtype=double`` can alleviate that problem. Examples -------- >>> np.nansum(1) 1 >>> np.nansum([1]) 1 >>> np.nansum([1, np.nan]) 1.0 >>> a = np.array([[1, 1], [1, np.nan]]) >>> np.nansum(a) 3.0 >>> np.nansum(a, axis=0) array([ 2., 1.]) >>> np.nansum([1, np.nan, np.inf]) inf >>> np.nansum([1, np.nan, np.NINF]) -inf >>> np.nansum([1, np.nan, np.inf, -np.inf]) # both +/- infinity present nan nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False) Compute the variance along the specified axis, while ignoring NaNs. Returns the variance of the array elements, a measure of the spread of a distribution. The variance is computed for the flattened array by default, otherwise over the specified axis. For all-NaN slices or slices with zero degrees of freedom, NaN is returned and a `RuntimeWarning` is raised. .. versionadded:: 1.8.0 Parameters ---------- a : array_like Array containing numbers whose variance is desired. If `a` is not an array, a conversion is attempted. axis : int, optional Axis along which the variance is computed. The default is to compute the variance of the flattened array. dtype : data-type, optional Type to use in computing the variance. For arrays of integer type the default is `float32`; for arrays of float types it is the same as the array type. out : ndarray, optional Alternate output array in which to place the result. It must have the same shape as the expected output, but the type is cast if necessary. ddof : int, optional "Delta Degrees of Freedom": the divisor used in the calculation is ``N - ddof``, where ``N`` represents the number of non-NaN elements. By default `ddof` is zero. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `arr`. Returns ------- variance : ndarray, see dtype parameter above If `out` is None, return a new array containing the variance, otherwise return a reference to the output array. If ddof is >= the number of non-NaN elements in a slice or the slice contains only NaNs, then the result for that slice is NaN. See Also -------- std : Standard deviation mean : Average var : Variance while not ignoring NaNs nanstd, nanmean numpy.doc.ufuncs : Section "Output arguments" Notes ----- The variance is the average of the squared deviations from the mean, i.e., ``var = mean(abs(x - x.mean())**2)``. The mean is normally calculated as ``x.sum() / N``, where ``N = len(x)``. If, however, `ddof` is specified, the divisor ``N - ddof`` is used instead. In standard statistical practice, ``ddof=1`` provides an unbiased estimator of the variance of a hypothetical infinite population. ``ddof=0`` provides a maximum likelihood estimate of the variance for normally distributed variables. Note that for complex numbers, the absolute value is taken before squaring, so that the result is always real and nonnegative. For floating-point input, the variance is computed using the same precision the input has. Depending on the input data, this can cause the results to be inaccurate, especially for `float32` (see example below). Specifying a higher-accuracy accumulator using the ``dtype`` keyword can alleviate this issue. Examples -------- >>> a = np.array([[1, np.nan], [3, 4]]) >>> np.var(a) 1.5555555555555554 >>> np.nanvar(a, axis=0) array([ 1., 0.]) >>> np.nanvar(a, axis=1) array([ 0., 0.25]) ndfromtxt(fname, **kwargs) Load ASCII data stored in a file and return it as a single array. Parameters ---------- fname, kwargs : For a description of input parameters, see `genfromtxt`. See Also -------- numpy.genfromtxt : generic function. ndim(a) Return the number of dimensions of an array. Parameters ---------- a : array_like Input array. If it is not already an ndarray, a conversion is attempted. Returns ------- number_of_dimensions : int The number of dimensions in `a`. Scalars are zero-dimensional. See Also -------- ndarray.ndim : equivalent method shape : dimensions of array ndarray.shape : dimensions of array Examples -------- >>> np.ndim([[1,2,3],[4,5,6]]) 2 >>> np.ndim(np.array([[1,2,3],[4,5,6]])) 2 >>> np.ndim(1) 0 nested_iters(...) newbuffer(...) newbuffer(size) Return a new uninitialized buffer object. Parameters ---------- size : int Size in bytes of returned buffer object. Returns ------- newbuffer : buffer object Returned, uninitialized buffer object of `size` bytes. nonzero(a) Return the indices of the elements that are non-zero. Returns a tuple of arrays, one for each dimension of `a`, containing the indices of the non-zero elements in that dimension. The values in `a` are always tested and returned in row-major, C-style order. The corresponding non-zero values can be obtained with:: a[nonzero(a)] To group the indices by element, rather than dimension, use:: transpose(nonzero(a)) The result of this is always a 2-D array, with a row for each non-zero element. Parameters ---------- a : array_like Input array. Returns ------- tuple_of_arrays : tuple Indices of elements that are non-zero. See Also -------- flatnonzero : Return indices that are non-zero in the flattened version of the input array. ndarray.nonzero : Equivalent ndarray method. count_nonzero : Counts the number of non-zero elements in the input array. Examples -------- >>> x = np.eye(3) >>> x array([[ 1., 0., 0.], [ 0., 1., 0.], [ 0., 0., 1.]]) >>> np.nonzero(x) (array([0, 1, 2]), array([0, 1, 2])) >>> x[np.nonzero(x)] array([ 1., 1., 1.]) >>> np.transpose(np.nonzero(x)) array([[0, 0], [1, 1], [2, 2]]) A common use for ``nonzero`` is to find the indices of an array, where a condition is True. Given an array `a`, the condition `a` > 3 is a boolean array and since False is interpreted as 0, np.nonzero(a > 3) yields the indices of the `a` where the condition is true. >>> a = np.array([[1,2,3],[4,5,6],[7,8,9]]) >>> a > 3 array([[False, False, False], [ True, True, True], [ True, True, True]], dtype=bool) >>> np.nonzero(a > 3) (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2])) The ``nonzero`` method of the boolean array can also be called. >>> (a > 3).nonzero() (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2])) nper(rate, pmt, pv, fv=0, when='end') Compute the number of periodic payments. Parameters ---------- rate : array_like Rate of interest (per period) pmt : array_like Payment pv : array_like Present value fv : array_like, optional Future value when : {{'begin', 1}, {'end', 0}}, {string, int}, optional When payments are due ('begin' (1) or 'end' (0)) Notes ----- The number of periods ``nper`` is computed by solving the equation:: fv + pv*(1+rate)**nper + pmt*(1+rate*when)/rate*((1+rate)**nper-1) = 0 but if ``rate = 0`` then:: fv + pv + pmt*nper = 0 Examples -------- If you only had $150/month to pay towards the loan, how long would it take to pay-off a loan of $8,000 at 7% annual interest? >>> print round(np.nper(0.07/12, -150, 8000), 5) 64.07335 So, over 64 months would be required to pay off the loan. The same analysis could be done with several different interest rates and/or payments and/or total amounts to produce an entire table. >>> np.nper(*(np.ogrid[0.07/12: 0.08/12: 0.01/12, ... -150 : -99 : 50 , ... 8000 : 9001 : 1000])) array([[[ 64.07334877, 74.06368256], [ 108.07548412, 127.99022654]], [[ 66.12443902, 76.87897353], [ 114.70165583, 137.90124779]]]) npv(rate, values) Returns the NPV (Net Present Value) of a cash flow series. Parameters ---------- rate : scalar The discount rate. values : array_like, shape(M, ) The values of the time series of cash flows. The (fixed) time interval between cash flow "events" must be the same as that for which `rate` is given (i.e., if `rate` is per year, then precisely a year is understood to elapse between each cash flow event). By convention, investments or "deposits" are negative, income or "withdrawals" are positive; `values` must begin with the initial investment, thus `values[0]` will typically be negative. Returns ------- out : float The NPV of the input cash flow series `values` at the discount `rate`. Notes ----- Returns the result of: [G]_ .. math :: \sum_{t=0}^{M-1}{\frac{values_t}{(1+rate)^{t}}} References ---------- .. [G] L. J. Gitman, "Principles of Managerial Finance, Brief," 3rd ed., Addison-Wesley, 2003, pg. 346. Examples -------- >>> np.npv(0.281,[-100, 39, 59, 55, 20]) -0.0084785916384548798 (Compare with the Example given for numpy.lib.financial.irr) obj2sctype(rep, default=None) Return the scalar dtype or NumPy equivalent of Python type of an object. Parameters ---------- rep : any The object of which the type is returned. default : any, optional If given, this is returned for objects whose types can not be determined. If not given, None is returned for those objects. Returns ------- dtype : dtype or Python type The data type of `rep`. See Also -------- sctype2char, issctype, issubsctype, issubdtype, maximum_sctype Examples -------- >>> np.obj2sctype(np.int32) >>> np.obj2sctype(np.array([1., 2.])) >>> np.obj2sctype(np.array([1.j])) >>> np.obj2sctype(dict) >>> np.obj2sctype('string') >>> np.obj2sctype(1, default=list) ones(shape, dtype=None, order='C') Return a new array of given shape and type, filled with ones. Parameters ---------- shape : int or sequence of ints Shape of the new array, e.g., ``(2, 3)`` or ``2``. dtype : data-type, optional The desired data-type for the array, e.g., `numpy.int8`. Default is `numpy.float64`. order : {'C', 'F'}, optional Whether to store multidimensional data in C- or Fortran-contiguous (row- or column-wise) order in memory. Returns ------- out : ndarray Array of ones with the given shape, dtype, and order. See Also -------- zeros, ones_like Examples -------- >>> np.ones(5) array([ 1., 1., 1., 1., 1.]) >>> np.ones((5,), dtype=np.int) array([1, 1, 1, 1, 1]) >>> np.ones((2, 1)) array([[ 1.], [ 1.]]) >>> s = (2,2) >>> np.ones(s) array([[ 1., 1.], [ 1., 1.]]) ones_like(a, dtype=None, order='K', subok=True) Return an array of ones with the same shape and type as a given array. Parameters ---------- a : array_like The shape and data-type of `a` define these same attributes of the returned array. dtype : data-type, optional Overrides the data type of the result. .. versionadded:: 1.6.0 order : {'C', 'F', 'A', or 'K'}, optional Overrides the memory layout of the result. 'C' means C-order, 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous, 'C' otherwise. 'K' means match the layout of `a` as closely as possible. .. versionadded:: 1.6.0 subok : bool, optional. If True, then the newly created array will use the sub-class type of 'a', otherwise it will be a base-class array. Defaults to True. Returns ------- out : ndarray Array of ones with the same shape and type as `a`. See Also -------- zeros_like : Return an array of zeros with shape and type of input. empty_like : Return an empty array with shape and type of input. zeros : Return a new array setting values to zero. ones : Return a new array setting values to one. empty : Return a new uninitialized array. Examples -------- >>> x = np.arange(6) >>> x = x.reshape((2, 3)) >>> x array([[0, 1, 2], [3, 4, 5]]) >>> np.ones_like(x) array([[1, 1, 1], [1, 1, 1]]) >>> y = np.arange(3, dtype=np.float) >>> y array([ 0., 1., 2.]) >>> np.ones_like(y) array([ 1., 1., 1.]) outer(a, b, out=None) Compute the outer product of two vectors. Given two vectors, ``a = [a0, a1, ..., aM]`` and ``b = [b0, b1, ..., bN]``, the outer product [1]_ is:: [[a0*b0 a0*b1 ... a0*bN ] [a1*b0 . [ ... . [aM*b0 aM*bN ]] Parameters ---------- a : (M,) array_like First input vector. Input is flattened if not already 1-dimensional. b : (N,) array_like Second input vector. Input is flattened if not already 1-dimensional. out : (M, N) ndarray, optional A location where the result is stored .. versionadded:: 1.9.0 Returns ------- out : (M, N) ndarray ``out[i, j] = a[i] * b[j]`` See also -------- inner, einsum References ---------- .. [1] : G. H. Golub and C. F. van Loan, *Matrix Computations*, 3rd ed., Baltimore, MD, Johns Hopkins University Press, 1996, pg. 8. Examples -------- Make a (*very* coarse) grid for computing a Mandelbrot set: >>> rl = np.outer(np.ones((5,)), np.linspace(-2, 2, 5)) >>> rl array([[-2., -1., 0., 1., 2.], [-2., -1., 0., 1., 2.], [-2., -1., 0., 1., 2.], [-2., -1., 0., 1., 2.], [-2., -1., 0., 1., 2.]]) >>> im = np.outer(1j*np.linspace(2, -2, 5), np.ones((5,))) >>> im array([[ 0.+2.j, 0.+2.j, 0.+2.j, 0.+2.j, 0.+2.j], [ 0.+1.j, 0.+1.j, 0.+1.j, 0.+1.j, 0.+1.j], [ 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j], [ 0.-1.j, 0.-1.j, 0.-1.j, 0.-1.j, 0.-1.j], [ 0.-2.j, 0.-2.j, 0.-2.j, 0.-2.j, 0.-2.j]]) >>> grid = rl + im >>> grid array([[-2.+2.j, -1.+2.j, 0.+2.j, 1.+2.j, 2.+2.j], [-2.+1.j, -1.+1.j, 0.+1.j, 1.+1.j, 2.+1.j], [-2.+0.j, -1.+0.j, 0.+0.j, 1.+0.j, 2.+0.j], [-2.-1.j, -1.-1.j, 0.-1.j, 1.-1.j, 2.-1.j], [-2.-2.j, -1.-2.j, 0.-2.j, 1.-2.j, 2.-2.j]]) An example using a "vector" of letters: >>> x = np.array(['a', 'b', 'c'], dtype=object) >>> np.outer(x, [1, 2, 3]) array([[a, aa, aaa], [b, bb, bbb], [c, cc, ccc]], dtype=object) packbits(...) packbits(myarray, axis=None) Packs the elements of a binary-valued array into bits in a uint8 array. The result is padded to full bytes by inserting zero bits at the end. Parameters ---------- myarray : array_like An integer type array whose elements should be packed to bits. axis : int, optional The dimension over which bit-packing is done. ``None`` implies packing the flattened array. Returns ------- packed : ndarray Array of type uint8 whose elements represent bits corresponding to the logical (0 or nonzero) value of the input elements. The shape of `packed` has the same number of dimensions as the input (unless `axis` is None, in which case the output is 1-D). See Also -------- unpackbits: Unpacks elements of a uint8 array into a binary-valued output array. Examples -------- >>> a = np.array([[[1,0,1], ... [0,1,0]], ... [[1,1,0], ... [0,0,1]]]) >>> b = np.packbits(a, axis=-1) >>> b array([[[160],[64]],[[192],[32]]], dtype=uint8) Note that in binary 160 = 1010 0000, 64 = 0100 0000, 192 = 1100 0000, and 32 = 0010 0000. pad(array, pad_width, mode=None, **kwargs) Pads an array. Parameters ---------- array : array_like of rank N Input array pad_width : {sequence, array_like, int} Number of values padded to the edges of each axis. ((before_1, after_1), ... (before_N, after_N)) unique pad widths for each axis. ((before, after),) yields same before and after pad for each axis. (pad,) or int is a shortcut for before = after = pad width for all axes. mode : str or function One of the following string values or a user supplied function. 'constant' Pads with a constant value. 'edge' Pads with the edge values of array. 'linear_ramp' Pads with the linear ramp between end_value and the array edge value. 'maximum' Pads with the maximum value of all or part of the vector along each axis. 'mean' Pads with the mean value of all or part of the vector along each axis. 'median' Pads with the median value of all or part of the vector along each axis. 'minimum' Pads with the minimum value of all or part of the vector along each axis. 'reflect' Pads with the reflection of the vector mirrored on the first and last values of the vector along each axis. 'symmetric' Pads with the reflection of the vector mirrored along the edge of the array. 'wrap' Pads with the wrap of the vector along the axis. The first values are used to pad the end and the end values are used to pad the beginning. Padding function, see Notes. stat_length : sequence or int, optional Used in 'maximum', 'mean', 'median', and 'minimum'. Number of values at edge of each axis used to calculate the statistic value. ((before_1, after_1), ... (before_N, after_N)) unique statistic lengths for each axis. ((before, after),) yields same before and after statistic lengths for each axis. (stat_length,) or int is a shortcut for before = after = statistic length for all axes. Default is ``None``, to use the entire axis. constant_values : sequence or int, optional Used in 'constant'. The values to set the padded values for each axis. ((before_1, after_1), ... (before_N, after_N)) unique pad constants for each axis. ((before, after),) yields same before and after constants for each axis. (constant,) or int is a shortcut for before = after = constant for all axes. Default is 0. end_values : sequence or int, optional Used in 'linear_ramp'. The values used for the ending value of the linear_ramp and that will form the edge of the padded array. ((before_1, after_1), ... (before_N, after_N)) unique end values for each axis. ((before, after),) yields same before and after end values for each axis. (constant,) or int is a shortcut for before = after = end value for all axes. Default is 0. reflect_type : {'even', 'odd'}, optional Used in 'reflect', and 'symmetric'. The 'even' style is the default with an unaltered reflection around the edge value. For the 'odd' style, the extented part of the array is created by subtracting the reflected values from two times the edge value. Returns ------- pad : ndarray Padded array of rank equal to `array` with shape increased according to `pad_width`. Notes ----- .. versionadded:: 1.7.0 For an array with rank greater than 1, some of the padding of later axes is calculated from padding of previous axes. This is easiest to think about with a rank 2 array where the corners of the padded array are calculated by using padded values from the first axis. The padding function, if used, should return a rank 1 array equal in length to the vector argument with padded values replaced. It has the following signature:: padding_func(vector, iaxis_pad_width, iaxis, **kwargs) where vector : ndarray A rank 1 array already padded with zeros. Padded values are vector[:pad_tuple[0]] and vector[-pad_tuple[1]:]. iaxis_pad_width : tuple A 2-tuple of ints, iaxis_pad_width[0] represents the number of values padded at the beginning of vector where iaxis_pad_width[1] represents the number of values padded at the end of vector. iaxis : int The axis currently being calculated. kwargs : misc Any keyword arguments the function requires. Examples -------- >>> a = [1, 2, 3, 4, 5] >>> np.lib.pad(a, (2,3), 'constant', constant_values=(4, 6)) array([4, 4, 1, 2, 3, 4, 5, 6, 6, 6]) >>> np.lib.pad(a, (2, 3), 'edge') array([1, 1, 1, 2, 3, 4, 5, 5, 5, 5]) >>> np.lib.pad(a, (2, 3), 'linear_ramp', end_values=(5, -4)) array([ 5, 3, 1, 2, 3, 4, 5, 2, -1, -4]) >>> np.lib.pad(a, (2,), 'maximum') array([5, 5, 1, 2, 3, 4, 5, 5, 5]) >>> np.lib.pad(a, (2,), 'mean') array([3, 3, 1, 2, 3, 4, 5, 3, 3]) >>> np.lib.pad(a, (2,), 'median') array([3, 3, 1, 2, 3, 4, 5, 3, 3]) >>> a = [[1, 2], [3, 4]] >>> np.lib.pad(a, ((3, 2), (2, 3)), 'minimum') array([[1, 1, 1, 2, 1, 1, 1], [1, 1, 1, 2, 1, 1, 1], [1, 1, 1, 2, 1, 1, 1], [1, 1, 1, 2, 1, 1, 1], [3, 3, 3, 4, 3, 3, 3], [1, 1, 1, 2, 1, 1, 1], [1, 1, 1, 2, 1, 1, 1]]) >>> a = [1, 2, 3, 4, 5] >>> np.lib.pad(a, (2, 3), 'reflect') array([3, 2, 1, 2, 3, 4, 5, 4, 3, 2]) >>> np.lib.pad(a, (2, 3), 'reflect', reflect_type='odd') array([-1, 0, 1, 2, 3, 4, 5, 6, 7, 8]) >>> np.lib.pad(a, (2, 3), 'symmetric') array([2, 1, 1, 2, 3, 4, 5, 5, 4, 3]) >>> np.lib.pad(a, (2, 3), 'symmetric', reflect_type='odd') array([0, 1, 1, 2, 3, 4, 5, 5, 6, 7]) >>> np.lib.pad(a, (2, 3), 'wrap') array([4, 5, 1, 2, 3, 4, 5, 1, 2, 3]) >>> def padwithtens(vector, pad_width, iaxis, kwargs): ... vector[:pad_width[0]] = 10 ... vector[-pad_width[1]:] = 10 ... return vector >>> a = np.arange(6) >>> a = a.reshape((2, 3)) >>> np.lib.pad(a, 2, padwithtens) array([[10, 10, 10, 10, 10, 10, 10], [10, 10, 10, 10, 10, 10, 10], [10, 10, 0, 1, 2, 10, 10], [10, 10, 3, 4, 5, 10, 10], [10, 10, 10, 10, 10, 10, 10], [10, 10, 10, 10, 10, 10, 10]]) partition(a, kth, axis=-1, kind='introselect', order=None) Return a partitioned copy of an array. Creates a copy of the array with its elements rearranged in such a way that the value of the element in kth position is in the position it would be in a sorted array. All elements smaller than the kth element are moved before this element and all equal or greater are moved behind it. The ordering of the elements in the two partitions is undefined. .. versionadded:: 1.8.0 Parameters ---------- a : array_like Array to be sorted. kth : int or sequence of ints Element index to partition by. The kth value of the element will be in its final sorted position and all smaller elements will be moved before it and all equal or greater elements behind it. The order all elements in the partitions is undefined. If provided with a sequence of kth it will partition all elements indexed by kth of them into their sorted position at once. axis : int or None, optional Axis along which to sort. If None, the array is flattened before sorting. The default is -1, which sorts along the last axis. kind : {'introselect'}, optional Selection algorithm. Default is 'introselect'. order : str or list of str, optional When `a` is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string. Not all fields need be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties. Returns ------- partitioned_array : ndarray Array of the same type and shape as `a`. See Also -------- ndarray.partition : Method to sort an array in-place. argpartition : Indirect partition. sort : Full sorting Notes ----- The various selection algorithms are characterized by their average speed, worst case performance, work space size, and whether they are stable. A stable sort keeps items with the same key in the same relative order. The available algorithms have the following properties: ================= ======= ============= ============ ======= kind speed worst case work space stable ================= ======= ============= ============ ======= 'introselect' 1 O(n) 0 no ================= ======= ============= ============ ======= All the partition algorithms make temporary copies of the data when partitioning along any but the last axis. Consequently, partitioning along the last axis is faster and uses less space than partitioning along any other axis. The sort order for complex numbers is lexicographic. If both the real and imaginary parts are non-nan then the order is determined by the real parts except when they are equal, in which case the order is determined by the imaginary parts. Examples -------- >>> a = np.array([3, 4, 2, 1]) >>> np.partition(a, 3) array([2, 1, 3, 4]) >>> np.partition(a, (1, 3)) array([1, 2, 3, 4]) percentile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=False) Compute the qth percentile of the data along the specified axis. Returns the qth percentile of the array elements. Parameters ---------- a : array_like Input array or object that can be converted to an array. q : float in range of [0,100] (or sequence of floats) Percentile to compute which must be between 0 and 100 inclusive. axis : int or sequence of int, optional Axis along which the percentiles are computed. The default (None) is to compute the percentiles along a flattened version of the array. A sequence of axes is supported since version 1.9.0. out : ndarray, optional Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output, but the type (of the output) will be cast if necessary. overwrite_input : bool, optional If True, then allow use of memory of input array `a` for calculations. The input array will be modified by the call to percentile. This will save memory when you do not need to preserve the contents of the input array. In this case you should not make any assumptions about the content of the passed in array `a` after this function completes -- treat it as undefined. Default is False. Note that, if the `a` input is not already an array this parameter will have no effect, `a` will be converted to an array internally regardless of the value of this parameter. interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points `i` and `j`: * linear: `i + (j - i) * fraction`, where `fraction` is the fractional part of the index surrounded by `i` and `j`. * lower: `i`. * higher: `j`. * nearest: `i` or `j` whichever is nearest. * midpoint: (`i` + `j`) / 2. .. versionadded:: 1.9.0 keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original array `a`. .. versionadded:: 1.9.0 Returns ------- percentile : scalar or ndarray If a single percentile `q` is given and axis=None a scalar is returned. If multiple percentiles `q` are given an array holding the result is returned. The results are listed in the first axis. (If `out` is specified, in which case that array is returned instead). If the input contains integers, or floats of smaller precision than 64, then the output data-type is float64. Otherwise, the output data-type is the same as that of the input. See Also -------- mean, median Notes ----- Given a vector V of length N, the q-th percentile of V is the q-th ranked value in a sorted copy of V. The values and distances of the two nearest neighbors as well as the `interpolation` parameter will determine the percentile if the normalized ranking does not match q exactly. This function is the same as the median if ``q=50``, the same as the minimum if ``q=0`` and the same as the maximum if ``q=100``. Examples -------- >>> a = np.array([[10, 7, 4], [3, 2, 1]]) >>> a array([[10, 7, 4], [ 3, 2, 1]]) >>> np.percentile(a, 50) array([ 3.5]) >>> np.percentile(a, 50, axis=0) array([[ 6.5, 4.5, 2.5]]) >>> np.percentile(a, 50, axis=1) array([[ 7.], [ 2.]]) >>> m = np.percentile(a, 50, axis=0) >>> out = np.zeros_like(m) >>> np.percentile(a, 50, axis=0, out=m) array([[ 6.5, 4.5, 2.5]]) >>> m array([[ 6.5, 4.5, 2.5]]) >>> b = a.copy() >>> np.percentile(b, 50, axis=1, overwrite_input=True) array([[ 7.], [ 2.]]) >>> assert not np.all(a==b) >>> b = a.copy() >>> np.percentile(b, 50, axis=None, overwrite_input=True) array([ 3.5]) piecewise(x, condlist, funclist, *args, **kw) Evaluate a piecewise-defined function. Given a set of conditions and corresponding functions, evaluate each function on the input data wherever its condition is true. Parameters ---------- x : ndarray The input domain. condlist : list of bool arrays Each boolean array corresponds to a function in `funclist`. Wherever `condlist[i]` is True, `funclist[i](x)` is used as the output value. Each boolean array in `condlist` selects a piece of `x`, and should therefore be of the same shape as `x`. The length of `condlist` must correspond to that of `funclist`. If one extra function is given, i.e. if ``len(funclist) - len(condlist) == 1``, then that extra function is the default value, used wherever all conditions are false. funclist : list of callables, f(x,*args,**kw), or scalars Each function is evaluated over `x` wherever its corresponding condition is True. It should take an array as input and give an array or a scalar value as output. If, instead of a callable, a scalar is provided then a constant function (``lambda x: scalar``) is assumed. args : tuple, optional Any further arguments given to `piecewise` are passed to the functions upon execution, i.e., if called ``piecewise(..., ..., 1, 'a')``, then each function is called as ``f(x, 1, 'a')``. kw : dict, optional Keyword arguments used in calling `piecewise` are passed to the functions upon execution, i.e., if called ``piecewise(..., ..., lambda=1)``, then each function is called as ``f(x, lambda=1)``. Returns ------- out : ndarray The output is the same shape and type as x and is found by calling the functions in `funclist` on the appropriate portions of `x`, as defined by the boolean arrays in `condlist`. Portions not covered by any condition have a default value of 0. See Also -------- choose, select, where Notes ----- This is similar to choose or select, except that functions are evaluated on elements of `x` that satisfy the corresponding condition from `condlist`. The result is:: |-- |funclist[0](x[condlist[0]]) out = |funclist[1](x[condlist[1]]) |... |funclist[n2](x[condlist[n2]]) |-- Examples -------- Define the sigma function, which is -1 for ``x < 0`` and +1 for ``x >= 0``. >>> x = np.linspace(-2.5, 2.5, 6) >>> np.piecewise(x, [x < 0, x >= 0], [-1, 1]) array([-1., -1., -1., 1., 1., 1.]) Define the absolute value, which is ``-x`` for ``x <0`` and ``x`` for ``x >= 0``. >>> np.piecewise(x, [x < 0, x >= 0], [lambda x: -x, lambda x: x]) array([ 2.5, 1.5, 0.5, 0.5, 1.5, 2.5]) pkgload(*packages, **options) Load one or more packages into parent package top-level namespace. This function is intended to shorten the need to import many subpackages, say of scipy, constantly with statements such as import scipy.linalg, scipy.fftpack, scipy.etc... Instead, you can say: import scipy scipy.pkgload('linalg','fftpack',...) or scipy.pkgload() to load all of them in one call. If a name which doesn't exist in scipy's namespace is given, a warning is shown. Parameters ---------- *packages : arg-tuple the names (one or more strings) of all the modules one wishes to load into the top-level namespace. verbose= : integer verbosity level [default: -1]. verbose=-1 will suspend also warnings. force= : bool when True, force reloading loaded packages [default: False]. postpone= : bool when True, don't load packages [default: False] place(arr, mask, vals) Change elements of an array based on conditional and input values. Similar to ``np.copyto(arr, vals, where=mask)``, the difference is that `place` uses the first N elements of `vals`, where N is the number of True values in `mask`, while `copyto` uses the elements where `mask` is True. Note that `extract` does the exact opposite of `place`. Parameters ---------- arr : array_like Array to put data into. mask : array_like Boolean mask array. Must have the same size as `a`. vals : 1-D sequence Values to put into `a`. Only the first N elements are used, where N is the number of True values in `mask`. If `vals` is smaller than N it will be repeated. See Also -------- copyto, put, take, extract Examples -------- >>> arr = np.arange(6).reshape(2, 3) >>> np.place(arr, arr>2, [44, 55]) >>> arr array([[ 0, 1, 2], [44, 55, 44]]) pmt(rate, nper, pv, fv=0, when='end') Compute the payment against loan principal plus interest. Given: * a present value, `pv` (e.g., an amount borrowed) * a future value, `fv` (e.g., 0) * an interest `rate` compounded once per period, of which there are * `nper` total * and (optional) specification of whether payment is made at the beginning (`when` = {'begin', 1}) or the end (`when` = {'end', 0}) of each period Return: the (fixed) periodic payment. Parameters ---------- rate : array_like Rate of interest (per period) nper : array_like Number of compounding periods pv : array_like Present value fv : array_like, optional Future value (default = 0) when : {{'begin', 1}, {'end', 0}}, {string, int} When payments are due ('begin' (1) or 'end' (0)) Returns ------- out : ndarray Payment against loan plus interest. If all input is scalar, returns a scalar float. If any input is array_like, returns payment for each input element. If multiple inputs are array_like, they all must have the same shape. Notes ----- The payment is computed by solving the equation:: fv + pv*(1 + rate)**nper + pmt*(1 + rate*when)/rate*((1 + rate)**nper - 1) == 0 or, when ``rate == 0``:: fv + pv + pmt * nper == 0 for ``pmt``. Note that computing a monthly mortgage payment is only one use for this function. For example, pmt returns the periodic deposit one must make to achieve a specified future balance given an initial deposit, a fixed, periodically compounded interest rate, and the total number of periods. References ---------- .. [WRW] Wheeler, D. A., E. Rathke, and R. Weir (Eds.) (2009, May). Open Document Format for Office Applications (OpenDocument)v1.2, Part 2: Recalculated Formula (OpenFormula) Format - Annotated Version, Pre-Draft 12. Organization for the Advancement of Structured Information Standards (OASIS). Billerica, MA, USA. [ODT Document]. Available: http://www.oasis-open.org/committees/documents.php ?wg_abbrev=office-formulaOpenDocument-formula-20090508.odt Examples -------- What is the monthly payment needed to pay off a $200,000 loan in 15 years at an annual interest rate of 7.5%? >>> np.pmt(0.075/12, 12*15, 200000) -1854.0247200054619 In order to pay-off (i.e., have a future-value of 0) the $200,000 obtained today, a monthly payment of $1,854.02 would be required. Note that this example illustrates usage of `fv` having a default value of 0. poly(seq_of_zeros) Find the coefficients of a polynomial with the given sequence of roots. Returns the coefficients of the polynomial whose leading coefficient is one for the given sequence of zeros (multiple roots must be included in the sequence as many times as their multiplicity; see Examples). A square matrix (or array, which will be treated as a matrix) can also be given, in which case the coefficients of the characteristic polynomial of the matrix are returned. Parameters ---------- seq_of_zeros : array_like, shape (N,) or (N, N) A sequence of polynomial roots, or a square array or matrix object. Returns ------- c : ndarray 1D array of polynomial coefficients from highest to lowest degree: ``c[0] * x**(N) + c[1] * x**(N-1) + ... + c[N-1] * x + c[N]`` where c[0] always equals 1. Raises ------ ValueError If input is the wrong shape (the input must be a 1-D or square 2-D array). See Also -------- polyval : Evaluate a polynomial at a point. roots : Return the roots of a polynomial. polyfit : Least squares polynomial fit. poly1d : A one-dimensional polynomial class. Notes ----- Specifying the roots of a polynomial still leaves one degree of freedom, typically represented by an undetermined leading coefficient. [1]_ In the case of this function, that coefficient - the first one in the returned array - is always taken as one. (If for some reason you have one other point, the only automatic way presently to leverage that information is to use ``polyfit``.) The characteristic polynomial, :math:`p_a(t)`, of an `n`-by-`n` matrix **A** is given by :math:`p_a(t) = \mathrm{det}(t\, \mathbf{I} - \mathbf{A})`, where **I** is the `n`-by-`n` identity matrix. [2]_ References ---------- .. [1] M. Sullivan and M. Sullivan, III, "Algebra and Trignometry, Enhanced With Graphing Utilities," Prentice-Hall, pg. 318, 1996. .. [2] G. Strang, "Linear Algebra and Its Applications, 2nd Edition," Academic Press, pg. 182, 1980. Examples -------- Given a sequence of a polynomial's zeros: >>> np.poly((0, 0, 0)) # Multiple root example array([1, 0, 0, 0]) The line above represents z**3 + 0*z**2 + 0*z + 0. >>> np.poly((-1./2, 0, 1./2)) array([ 1. , 0. , -0.25, 0. ]) The line above represents z**3 - z/4 >>> np.poly((np.random.random(1.)[0], 0, np.random.random(1.)[0])) array([ 1. , -0.77086955, 0.08618131, 0. ]) #random Given a square array object: >>> P = np.array([[0, 1./3], [-1./2, 0]]) >>> np.poly(P) array([ 1. , 0. , 0.16666667]) Or a square matrix object: >>> np.poly(np.matrix(P)) array([ 1. , 0. , 0.16666667]) Note how in all cases the leading coefficient is always 1. polyadd(a1, a2) Find the sum of two polynomials. Returns the polynomial resulting from the sum of two input polynomials. Each input must be either a poly1d object or a 1D sequence of polynomial coefficients, from highest to lowest degree. Parameters ---------- a1, a2 : array_like or poly1d object Input polynomials. Returns ------- out : ndarray or poly1d object The sum of the inputs. If either input is a poly1d object, then the output is also a poly1d object. Otherwise, it is a 1D array of polynomial coefficients from highest to lowest degree. See Also -------- poly1d : A one-dimensional polynomial class. poly, polyadd, polyder, polydiv, polyfit, polyint, polysub, polyval Examples -------- >>> np.polyadd([1, 2], [9, 5, 4]) array([9, 6, 6]) Using poly1d objects: >>> p1 = np.poly1d([1, 2]) >>> p2 = np.poly1d([9, 5, 4]) >>> print p1 1 x + 2 >>> print p2 2 9 x + 5 x + 4 >>> print np.polyadd(p1, p2) 2 9 x + 6 x + 6 polyder(p, m=1) Return the derivative of the specified order of a polynomial. Parameters ---------- p : poly1d or sequence Polynomial to differentiate. A sequence is interpreted as polynomial coefficients, see `poly1d`. m : int, optional Order of differentiation (default: 1) Returns ------- der : poly1d A new polynomial representing the derivative. See Also -------- polyint : Anti-derivative of a polynomial. poly1d : Class for one-dimensional polynomials. Examples -------- The derivative of the polynomial :math:`x^3 + x^2 + x^1 + 1` is: >>> p = np.poly1d([1,1,1,1]) >>> p2 = np.polyder(p) >>> p2 poly1d([3, 2, 1]) which evaluates to: >>> p2(2.) 17.0 We can verify this, approximating the derivative with ``(f(x + h) - f(x))/h``: >>> (p(2. + 0.001) - p(2.)) / 0.001 17.007000999997857 The fourth-order derivative of a 3rd-order polynomial is zero: >>> np.polyder(p, 2) poly1d([6, 2]) >>> np.polyder(p, 3) poly1d([6]) >>> np.polyder(p, 4) poly1d([ 0.]) polydiv(u, v) Returns the quotient and remainder of polynomial division. The input arrays are the coefficients (including any coefficients equal to zero) of the "numerator" (dividend) and "denominator" (divisor) polynomials, respectively. Parameters ---------- u : array_like or poly1d Dividend polynomial's coefficients. v : array_like or poly1d Divisor polynomial's coefficients. Returns ------- q : ndarray Coefficients, including those equal to zero, of the quotient. r : ndarray Coefficients, including those equal to zero, of the remainder. See Also -------- poly, polyadd, polyder, polydiv, polyfit, polyint, polymul, polysub, polyval Notes ----- Both `u` and `v` must be 0-d or 1-d (ndim = 0 or 1), but `u.ndim` need not equal `v.ndim`. In other words, all four possible combinations - ``u.ndim = v.ndim = 0``, ``u.ndim = v.ndim = 1``, ``u.ndim = 1, v.ndim = 0``, and ``u.ndim = 0, v.ndim = 1`` - work. Examples -------- .. math:: \frac{3x^2 + 5x + 2}{2x + 1} = 1.5x + 1.75, remainder 0.25 >>> x = np.array([3.0, 5.0, 2.0]) >>> y = np.array([2.0, 1.0]) >>> np.polydiv(x, y) (array([ 1.5 , 1.75]), array([ 0.25])) polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False) Least squares polynomial fit. Fit a polynomial ``p(x) = p[0] * x**deg + ... + p[deg]`` of degree `deg` to points `(x, y)`. Returns a vector of coefficients `p` that minimises the squared error. Parameters ---------- x : array_like, shape (M,) x-coordinates of the M sample points ``(x[i], y[i])``. y : array_like, shape (M,) or (M, K) y-coordinates of the sample points. Several data sets of sample points sharing the same x-coordinates can be fitted at once by passing in a 2D-array that contains one dataset per column. deg : int Degree of the fitting polynomial rcond : float, optional Relative condition number of the fit. Singular values smaller than this relative to the largest singular value will be ignored. The default value is len(x)*eps, where eps is the relative precision of the float type, about 2e-16 in most cases. full : bool, optional Switch determining nature of return value. When it is False (the default) just the coefficients are returned, when True diagnostic information from the singular value decomposition is also returned. w : array_like, shape (M,), optional weights to apply to the y-coordinates of the sample points. cov : bool, optional Return the estimate and the covariance matrix of the estimate If full is True, then cov is not returned. Returns ------- p : ndarray, shape (M,) or (M, K) Polynomial coefficients, highest power first. If `y` was 2-D, the coefficients for `k`-th data set are in ``p[:,k]``. residuals, rank, singular_values, rcond : Present only if `full` = True. Residuals of the least-squares fit, the effective rank of the scaled Vandermonde coefficient matrix, its singular values, and the specified value of `rcond`. For more details, see `linalg.lstsq`. V : ndarray, shape (M,M) or (M,M,K) Present only if `full` = False and `cov`=True. The covariance matrix of the polynomial coefficient estimates. The diagonal of this matrix are the variance estimates for each coefficient. If y is a 2-D array, then the covariance matrix for the `k`-th data set are in ``V[:,:,k]`` Warns ----- RankWarning The rank of the coefficient matrix in the least-squares fit is deficient. The warning is only raised if `full` = False. The warnings can be turned off by >>> import warnings >>> warnings.simplefilter('ignore', np.RankWarning) See Also -------- polyval : Computes polynomial values. linalg.lstsq : Computes a least-squares fit. scipy.interpolate.UnivariateSpline : Computes spline fits. Notes ----- The solution minimizes the squared error .. math :: E = \sum_{j=0}^k |p(x_j) - y_j|^2 in the equations:: x[0]**n * p[0] + ... + x[0] * p[n-1] + p[n] = y[0] x[1]**n * p[0] + ... + x[1] * p[n-1] + p[n] = y[1] ... x[k]**n * p[0] + ... + x[k] * p[n-1] + p[n] = y[k] The coefficient matrix of the coefficients `p` is a Vandermonde matrix. `polyfit` issues a `RankWarning` when the least-squares fit is badly conditioned. This implies that the best fit is not well-defined due to numerical error. The results may be improved by lowering the polynomial degree or by replacing `x` by `x` - `x`.mean(). The `rcond` parameter can also be set to a value smaller than its default, but the resulting fit may be spurious: including contributions from the small singular values can add numerical noise to the result. Note that fitting polynomial coefficients is inherently badly conditioned when the degree of the polynomial is large or the interval of sample points is badly centered. The quality of the fit should always be checked in these cases. When polynomial fits are not satisfactory, splines may be a good alternative. References ---------- .. [1] Wikipedia, "Curve fitting", http://en.wikipedia.org/wiki/Curve_fitting .. [2] Wikipedia, "Polynomial interpolation", http://en.wikipedia.org/wiki/Polynomial_interpolation Examples -------- >>> x = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0]) >>> y = np.array([0.0, 0.8, 0.9, 0.1, -0.8, -1.0]) >>> z = np.polyfit(x, y, 3) >>> z array([ 0.08703704, -0.81349206, 1.69312169, -0.03968254]) It is convenient to use `poly1d` objects for dealing with polynomials: >>> p = np.poly1d(z) >>> p(0.5) 0.6143849206349179 >>> p(3.5) -0.34732142857143039 >>> p(10) 22.579365079365115 High-order polynomials may oscillate wildly: >>> p30 = np.poly1d(np.polyfit(x, y, 30)) /... RankWarning: Polyfit may be poorly conditioned... >>> p30(4) -0.80000000000000204 >>> p30(5) -0.99999999999999445 >>> p30(4.5) -0.10547061179440398 Illustration: >>> import matplotlib.pyplot as plt >>> xp = np.linspace(-2, 6, 100) >>> _ = plt.plot(x, y, '.', xp, p(xp), '-', xp, p30(xp), '--') >>> plt.ylim(-2,2) (-2, 2) >>> plt.show() polyint(p, m=1, k=None) Return an antiderivative (indefinite integral) of a polynomial. The returned order `m` antiderivative `P` of polynomial `p` satisfies :math:`\frac{d^m}{dx^m}P(x) = p(x)` and is defined up to `m - 1` integration constants `k`. The constants determine the low-order polynomial part .. math:: \frac{k_{m-1}}{0!} x^0 + \ldots + \frac{k_0}{(m-1)!}x^{m-1} of `P` so that :math:`P^{(j)}(0) = k_{m-j-1}`. Parameters ---------- p : array_like or poly1d Polynomial to differentiate. A sequence is interpreted as polynomial coefficients, see `poly1d`. m : int, optional Order of the antiderivative. (Default: 1) k : list of `m` scalars or scalar, optional Integration constants. They are given in the order of integration: those corresponding to highest-order terms come first. If ``None`` (default), all constants are assumed to be zero. If `m = 1`, a single scalar can be given instead of a list. See Also -------- polyder : derivative of a polynomial poly1d.integ : equivalent method Examples -------- The defining property of the antiderivative: >>> p = np.poly1d([1,1,1]) >>> P = np.polyint(p) >>> P poly1d([ 0.33333333, 0.5 , 1. , 0. ]) >>> np.polyder(P) == p True The integration constants default to zero, but can be specified: >>> P = np.polyint(p, 3) >>> P(0) 0.0 >>> np.polyder(P)(0) 0.0 >>> np.polyder(P, 2)(0) 0.0 >>> P = np.polyint(p, 3, k=[6,5,3]) >>> P poly1d([ 0.01666667, 0.04166667, 0.16666667, 3. , 5. , 3. ]) Note that 3 = 6 / 2!, and that the constants are given in the order of integrations. Constant of the highest-order polynomial term comes first: >>> np.polyder(P, 2)(0) 6.0 >>> np.polyder(P, 1)(0) 5.0 >>> P(0) 3.0 polymul(a1, a2) Find the product of two polynomials. Finds the polynomial resulting from the multiplication of the two input polynomials. Each input must be either a poly1d object or a 1D sequence of polynomial coefficients, from highest to lowest degree. Parameters ---------- a1, a2 : array_like or poly1d object Input polynomials. Returns ------- out : ndarray or poly1d object The polynomial resulting from the multiplication of the inputs. If either inputs is a poly1d object, then the output is also a poly1d object. Otherwise, it is a 1D array of polynomial coefficients from highest to lowest degree. See Also -------- poly1d : A one-dimensional polynomial class. poly, polyadd, polyder, polydiv, polyfit, polyint, polysub, polyval convolve : Array convolution. Same output as polymul, but has parameter for overlap mode. Examples -------- >>> np.polymul([1, 2, 3], [9, 5, 1]) array([ 9, 23, 38, 17, 3]) Using poly1d objects: >>> p1 = np.poly1d([1, 2, 3]) >>> p2 = np.poly1d([9, 5, 1]) >>> print p1 2 1 x + 2 x + 3 >>> print p2 2 9 x + 5 x + 1 >>> print np.polymul(p1, p2) 4 3 2 9 x + 23 x + 38 x + 17 x + 3 polysub(a1, a2) Difference (subtraction) of two polynomials. Given two polynomials `a1` and `a2`, returns ``a1 - a2``. `a1` and `a2` can be either array_like sequences of the polynomials' coefficients (including coefficients equal to zero), or `poly1d` objects. Parameters ---------- a1, a2 : array_like or poly1d Minuend and subtrahend polynomials, respectively. Returns ------- out : ndarray or poly1d Array or `poly1d` object of the difference polynomial's coefficients. See Also -------- polyval, polydiv, polymul, polyadd Examples -------- .. math:: (2 x^2 + 10 x - 2) - (3 x^2 + 10 x -4) = (-x^2 + 2) >>> np.polysub([2, 10, -2], [3, 10, -4]) array([-1, 0, 2]) polyval(p, x) Evaluate a polynomial at specific values. If `p` is of length N, this function returns the value: ``p[0]*x**(N-1) + p[1]*x**(N-2) + ... + p[N-2]*x + p[N-1]`` If `x` is a sequence, then `p(x)` is returned for each element of `x`. If `x` is another polynomial then the composite polynomial `p(x(t))` is returned. Parameters ---------- p : array_like or poly1d object 1D array of polynomial coefficients (including coefficients equal to zero) from highest degree to the constant term, or an instance of poly1d. x : array_like or poly1d object A number, a 1D array of numbers, or an instance of poly1d, "at" which to evaluate `p`. Returns ------- values : ndarray or poly1d If `x` is a poly1d instance, the result is the composition of the two polynomials, i.e., `x` is "substituted" in `p` and the simplified result is returned. In addition, the type of `x` - array_like or poly1d - governs the type of the output: `x` array_like => `values` array_like, `x` a poly1d object => `values` is also. See Also -------- poly1d: A polynomial class. Notes ----- Horner's scheme [1]_ is used to evaluate the polynomial. Even so, for polynomials of high degree the values may be inaccurate due to rounding errors. Use carefully. References ---------- .. [1] I. N. Bronshtein, K. A. Semendyayev, and K. A. Hirsch (Eng. trans. Ed.), *Handbook of Mathematics*, New York, Van Nostrand Reinhold Co., 1985, pg. 720. Examples -------- >>> np.polyval([3,0,1], 5) # 3 * 5**2 + 0 * 5**1 + 1 76 >>> np.polyval([3,0,1], np.poly1d(5)) poly1d([ 76.]) >>> np.polyval(np.poly1d([3,0,1]), 5) 76 >>> np.polyval(np.poly1d([3,0,1]), np.poly1d(5)) poly1d([ 76.]) ppmt(rate, per, nper, pv, fv=0.0, when='end') Compute the payment against loan principal. Parameters ---------- rate : array_like Rate of interest (per period) per : array_like, int Amount paid against the loan changes. The `per` is the period of interest. nper : array_like Number of compounding periods pv : array_like Present value fv : array_like, optional Future value when : {{'begin', 1}, {'end', 0}}, {string, int} When payments are due ('begin' (1) or 'end' (0)) See Also -------- pmt, pv, ipmt prod(a, axis=None, dtype=None, out=None, keepdims=False) Return the product of array elements over a given axis. Parameters ---------- a : array_like Input data. axis : None or int or tuple of ints, optional Axis or axes along which a product is performed. The default (`axis` = `None`) is perform a product over all the dimensions of the input array. `axis` may be negative, in which case it counts from the last to the first axis. .. versionadded:: 1.7.0 If this is a tuple of ints, a product is performed on multiple axes, instead of a single axis or all the axes as before. dtype : data-type, optional The data-type of the returned array, as well as of the accumulator in which the elements are multiplied. By default, if `a` is of integer type, `dtype` is the default platform integer. (Note: if the type of `a` is unsigned, then so is `dtype`.) Otherwise, the dtype is the same as that of `a`. out : ndarray, optional Alternative output array in which to place the result. It must have the same shape as the expected output, but the type of the output values will be cast if necessary. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `arr`. Returns ------- product_along_axis : ndarray, see `dtype` parameter above. An array shaped as `a` but with the specified axis removed. Returns a reference to `out` if specified. See Also -------- ndarray.prod : equivalent method numpy.doc.ufuncs : Section "Output arguments" Notes ----- Arithmetic is modular when using integer types, and no error is raised on overflow. That means that, on a 32-bit platform: >>> x = np.array([536870910, 536870910, 536870910, 536870910]) >>> np.prod(x) #random 16 The product of an empty array is the neutral element 1: >>> np.prod([]) 1.0 Examples -------- By default, calculate the product of all elements: >>> np.prod([1.,2.]) 2.0 Even when the input array is two-dimensional: >>> np.prod([[1.,2.],[3.,4.]]) 24.0 But we can also specify the axis over which to multiply: >>> np.prod([[1.,2.],[3.,4.]], axis=1) array([ 2., 12.]) If the type of `x` is unsigned, then the output type is the unsigned platform integer: >>> x = np.array([1, 2, 3], dtype=np.uint8) >>> np.prod(x).dtype == np.uint True If `x` is of a signed integer type, then the output type is the default platform integer: >>> x = np.array([1, 2, 3], dtype=np.int8) >>> np.prod(x).dtype == np.int True product(a, axis=None, dtype=None, out=None, keepdims=False) Return the product of array elements over a given axis. See Also -------- prod : equivalent function; see for details. promote_types(...) promote_types(type1, type2) Returns the data type with the smallest size and smallest scalar kind to which both ``type1`` and ``type2`` may be safely cast. The returned data type is always in native byte order. This function is symmetric and associative. Parameters ---------- type1 : dtype or dtype specifier First data type. type2 : dtype or dtype specifier Second data type. Returns ------- out : dtype The promoted data type. Notes ----- .. versionadded:: 1.6.0 Starting in NumPy 1.9, promote_types function now returns a valid string length when given an integer or float dtype as one argument and a string dtype as another argument. Previously it always returned the input string dtype, even if it wasn't long enough to store the max integer/float value converted to a string. See Also -------- result_type, dtype, can_cast Examples -------- >>> np.promote_types('f4', 'f8') dtype('float64') >>> np.promote_types('i8', 'f4') dtype('float64') >>> np.promote_types('>i8', '>> np.promote_types('i4', 'S8') dtype('S11') ptp(a, axis=None, out=None) Range of values (maximum - minimum) along an axis. The name of the function comes from the acronym for 'peak to peak'. Parameters ---------- a : array_like Input values. axis : int, optional Axis along which to find the peaks. By default, flatten the array. out : array_like Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output, but the type of the output values will be cast if necessary. Returns ------- ptp : ndarray A new array holding the result, unless `out` was specified, in which case a reference to `out` is returned. Examples -------- >>> x = np.arange(4).reshape((2,2)) >>> x array([[0, 1], [2, 3]]) >>> np.ptp(x, axis=0) array([2, 2]) >>> np.ptp(x, axis=1) array([1, 1]) put(a, ind, v, mode='raise') Replaces specified elements of an array with given values. The indexing works on the flattened target array. `put` is roughly equivalent to: :: a.flat[ind] = v Parameters ---------- a : ndarray Target array. ind : array_like Target indices, interpreted as integers. v : array_like Values to place in `a` at target indices. If `v` is shorter than `ind` it will be repeated as necessary. mode : {'raise', 'wrap', 'clip'}, optional Specifies how out-of-bounds indices will behave. * 'raise' -- raise an error (default) * 'wrap' -- wrap around * 'clip' -- clip to the range 'clip' mode means that all indices that are too large are replaced by the index that addresses the last element along that axis. Note that this disables indexing with negative numbers. See Also -------- putmask, place Examples -------- >>> a = np.arange(5) >>> np.put(a, [0, 2], [-44, -55]) >>> a array([-44, 1, -55, 3, 4]) >>> a = np.arange(5) >>> np.put(a, 22, -5, mode='clip') >>> a array([ 0, 1, 2, 3, -5]) putmask(...) putmask(a, mask, values) Changes elements of an array based on conditional and input values. Sets ``a.flat[n] = values[n]`` for each n where ``mask.flat[n]==True``. If `values` is not the same size as `a` and `mask` then it will repeat. This gives behavior different from ``a[mask] = values``. Parameters ---------- a : array_like Target array. mask : array_like Boolean mask array. It has to be the same shape as `a`. values : array_like Values to put into `a` where `mask` is True. If `values` is smaller than `a` it will be repeated. See Also -------- place, put, take, copyto Examples -------- >>> x = np.arange(6).reshape(2, 3) >>> np.putmask(x, x>2, x**2) >>> x array([[ 0, 1, 2], [ 9, 16, 25]]) If `values` is smaller than `a` it is repeated: >>> x = np.arange(5) >>> np.putmask(x, x>1, [-33, -44]) >>> x array([ 0, 1, -33, -44, -33]) pv(rate, nper, pmt, fv=0.0, when='end') Compute the present value. Given: * a future value, `fv` * an interest `rate` compounded once per period, of which there are * `nper` total * a (fixed) payment, `pmt`, paid either * at the beginning (`when` = {'begin', 1}) or the end (`when` = {'end', 0}) of each period Return: the value now Parameters ---------- rate : array_like Rate of interest (per period) nper : array_like Number of compounding periods pmt : array_like Payment fv : array_like, optional Future value when : {{'begin', 1}, {'end', 0}}, {string, int}, optional When payments are due ('begin' (1) or 'end' (0)) Returns ------- out : ndarray, float Present value of a series of payments or investments. Notes ----- The present value is computed by solving the equation:: fv + pv*(1 + rate)**nper + pmt*(1 + rate*when)/rate*((1 + rate)**nper - 1) = 0 or, when ``rate = 0``:: fv + pv + pmt * nper = 0 for `pv`, which is then returned. References ---------- .. [WRW] Wheeler, D. A., E. Rathke, and R. Weir (Eds.) (2009, May). Open Document Format for Office Applications (OpenDocument)v1.2, Part 2: Recalculated Formula (OpenFormula) Format - Annotated Version, Pre-Draft 12. Organization for the Advancement of Structured Information Standards (OASIS). Billerica, MA, USA. [ODT Document]. Available: http://www.oasis-open.org/committees/documents.php?wg_abbrev=office-formula OpenDocument-formula-20090508.odt Examples -------- What is the present value (e.g., the initial investment) of an investment that needs to total $15692.93 after 10 years of saving $100 every month? Assume the interest rate is 5% (annually) compounded monthly. >>> np.pv(0.05/12, 10*12, -100, 15692.93) -100.00067131625819 By convention, the negative sign represents cash flow out (i.e., money not available today). Thus, to end up with $15,692.93 in 10 years saving $100 a month at 5% annual interest, one's initial deposit should also be $100. If any input is array_like, ``pv`` returns an array of equal shape. Let's compare different interest rates in the example above: >>> a = np.array((0.05, 0.04, 0.03))/12 >>> np.pv(a, 10*12, -100, 15692.93) array([ -100.00067132, -649.26771385, -1273.78633713]) So, to end up with the same $15692.93 under the same $100 per month "savings plan," for annual interest rates of 4% and 3%, one would need initial investments of $649.27 and $1273.79, respectively. rank(a) Return the number of dimensions of an array. If `a` is not already an array, a conversion is attempted. Scalars are zero dimensional. .. note:: This function is deprecated in NumPy 1.9 to avoid confusion with `numpy.linalg.matrix_rank`. The ``ndim`` attribute or function should be used instead. Parameters ---------- a : array_like Array whose number of dimensions is desired. If `a` is not an array, a conversion is attempted. Returns ------- number_of_dimensions : int The number of dimensions in the array. See Also -------- ndim : equivalent function ndarray.ndim : equivalent property shape : dimensions of array ndarray.shape : dimensions of array Notes ----- In the old Numeric package, `rank` was the term used for the number of dimensions, but in Numpy `ndim` is used instead. Examples -------- >>> np.rank([1,2,3]) 1 >>> np.rank(np.array([[1,2,3],[4,5,6]])) 2 >>> np.rank(1) 0 rate(nper, pmt, pv, fv, when='end', guess=0.1, tol=1e-06, maxiter=100) Compute the rate of interest per period. Parameters ---------- nper : array_like Number of compounding periods pmt : array_like Payment pv : array_like Present value fv : array_like Future value when : {{'begin', 1}, {'end', 0}}, {string, int}, optional When payments are due ('begin' (1) or 'end' (0)) guess : float, optional Starting guess for solving the rate of interest tol : float, optional Required tolerance for the solution maxiter : int, optional Maximum iterations in finding the solution Notes ----- The rate of interest is computed by iteratively solving the (non-linear) equation:: fv + pv*(1+rate)**nper + pmt*(1+rate*when)/rate * ((1+rate)**nper - 1) = 0 for ``rate``. References ---------- Wheeler, D. A., E. Rathke, and R. Weir (Eds.) (2009, May). Open Document Format for Office Applications (OpenDocument)v1.2, Part 2: Recalculated Formula (OpenFormula) Format - Annotated Version, Pre-Draft 12. Organization for the Advancement of Structured Information Standards (OASIS). Billerica, MA, USA. [ODT Document]. Available: http://www.oasis-open.org/committees/documents.php?wg_abbrev=office-formula OpenDocument-formula-20090508.odt ravel(a, order='C') Return a contiguous flattened array. A 1-D array, containing the elements of the input, is returned. A copy is made only if needed. As of NumPy 1.10, the returned array will have the same type as the input array. (for example, a masked array will be returned for a masked array input) Parameters ---------- a : array_like Input array. The elements in `a` are read in the order specified by `order`, and packed as a 1-D array. order : {'C','F', 'A', 'K'}, optional The elements of `a` are read using this index order. 'C' means to index the elements in row-major, C-style order, with the last axis index changing fastest, back to the first axis index changing slowest. 'F' means to index the elements in column-major, Fortran-style order, with the first index changing fastest, and the last index changing slowest. Note that the 'C' and 'F' options take no account of the memory layout of the underlying array, and only refer to the order of axis indexing. 'A' means to read the elements in Fortran-like index order if `a` is Fortran *contiguous* in memory, C-like order otherwise. 'K' means to read the elements in the order they occur in memory, except for reversing the data when strides are negative. By default, 'C' index order is used. Returns ------- y : array_like If `a` is a matrix, y is a 1-D ndarray, otherwise y is an array of the same subtype as `a`. The shape of the returned array is ``(a.size,)``. Matrices are special cased for backward compatibility. See Also -------- ndarray.flat : 1-D iterator over an array. ndarray.flatten : 1-D array copy of the elements of an array in row-major order. ndarray.reshape : Change the shape of an array without changing its data. Notes ----- In row-major, C-style order, in two dimensions, the row index varies the slowest, and the column index the quickest. This can be generalized to multiple dimensions, where row-major order implies that the index along the first axis varies slowest, and the index along the last quickest. The opposite holds for column-major, Fortran-style index ordering. When a view is desired in as many cases as possible, ``arr.reshape(-1)`` may be preferable. Examples -------- It is equivalent to ``reshape(-1, order=order)``. >>> x = np.array([[1, 2, 3], [4, 5, 6]]) >>> print np.ravel(x) [1 2 3 4 5 6] >>> print x.reshape(-1) [1 2 3 4 5 6] >>> print np.ravel(x, order='F') [1 4 2 5 3 6] When ``order`` is 'A', it will preserve the array's 'C' or 'F' ordering: >>> print np.ravel(x.T) [1 4 2 5 3 6] >>> print np.ravel(x.T, order='A') [1 2 3 4 5 6] When ``order`` is 'K', it will preserve orderings that are neither 'C' nor 'F', but won't reverse axes: >>> a = np.arange(3)[::-1]; a array([2, 1, 0]) >>> a.ravel(order='C') array([2, 1, 0]) >>> a.ravel(order='K') array([2, 1, 0]) >>> a = np.arange(12).reshape(2,3,2).swapaxes(1,2); a array([[[ 0, 2, 4], [ 1, 3, 5]], [[ 6, 8, 10], [ 7, 9, 11]]]) >>> a.ravel(order='C') array([ 0, 2, 4, 1, 3, 5, 6, 8, 10, 7, 9, 11]) >>> a.ravel(order='K') array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]) ravel_multi_index(...) ravel_multi_index(multi_index, dims, mode='raise', order='C') Converts a tuple of index arrays into an array of flat indices, applying boundary modes to the multi-index. Parameters ---------- multi_index : tuple of array_like A tuple of integer arrays, one array for each dimension. dims : tuple of ints The shape of array into which the indices from ``multi_index`` apply. mode : {'raise', 'wrap', 'clip'}, optional Specifies how out-of-bounds indices are handled. Can specify either one mode or a tuple of modes, one mode per index. * 'raise' -- raise an error (default) * 'wrap' -- wrap around * 'clip' -- clip to the range In 'clip' mode, a negative index which would normally wrap will clip to 0 instead. order : {'C', 'F'}, optional Determines whether the multi-index should be viewed as indexing in row-major (C-style) or column-major (Fortran-style) order. Returns ------- raveled_indices : ndarray An array of indices into the flattened version of an array of dimensions ``dims``. See Also -------- unravel_index Notes ----- .. versionadded:: 1.6.0 Examples -------- >>> arr = np.array([[3,6,6],[4,5,1]]) >>> np.ravel_multi_index(arr, (7,6)) array([22, 41, 37]) >>> np.ravel_multi_index(arr, (7,6), order='F') array([31, 41, 13]) >>> np.ravel_multi_index(arr, (4,6), mode='clip') array([22, 23, 19]) >>> np.ravel_multi_index(arr, (4,4), mode=('clip','wrap')) array([12, 13, 13]) >>> np.ravel_multi_index((3,1,4,1), (6,7,8,9)) 1621 real(val) Return the real part of the elements of the array. Parameters ---------- val : array_like Input array. Returns ------- out : ndarray Output array. If `val` is real, the type of `val` is used for the output. If `val` has complex elements, the returned type is float. See Also -------- real_if_close, imag, angle Examples -------- >>> a = np.array([1+2j, 3+4j, 5+6j]) >>> a.real array([ 1., 3., 5.]) >>> a.real = 9 >>> a array([ 9.+2.j, 9.+4.j, 9.+6.j]) >>> a.real = np.array([9, 8, 7]) >>> a array([ 9.+2.j, 8.+4.j, 7.+6.j]) real_if_close(a, tol=100) If complex input returns a real array if complex parts are close to zero. "Close to zero" is defined as `tol` * (machine epsilon of the type for `a`). Parameters ---------- a : array_like Input array. tol : float Tolerance in machine epsilons for the complex part of the elements in the array. Returns ------- out : ndarray If `a` is real, the type of `a` is used for the output. If `a` has complex elements, the returned type is float. See Also -------- real, imag, angle Notes ----- Machine epsilon varies from machine to machine and between data types but Python floats on most platforms have a machine epsilon equal to 2.2204460492503131e-16. You can use 'np.finfo(np.float).eps' to print out the machine epsilon for floats. Examples -------- >>> np.finfo(np.float).eps 2.2204460492503131e-16 >>> np.real_if_close([2.1 + 4e-14j], tol=1000) array([ 2.1]) >>> np.real_if_close([2.1 + 4e-13j], tol=1000) array([ 2.1 +4.00000000e-13j]) recfromcsv(fname, **kwargs) Load ASCII data stored in a comma-separated file. The returned array is a record array (if ``usemask=False``, see `recarray`) or a masked record array (if ``usemask=True``, see `ma.mrecords.MaskedRecords`). Parameters ---------- fname, kwargs : For a description of input parameters, see `genfromtxt`. See Also -------- numpy.genfromtxt : generic function to load ASCII data. Notes ----- By default, `dtype` is None, which means that the data-type of the output array will be determined from the data. recfromtxt(fname, **kwargs) Load ASCII data from a file and return it in a record array. If ``usemask=False`` a standard `recarray` is returned, if ``usemask=True`` a MaskedRecords array is returned. Parameters ---------- fname, kwargs : For a description of input parameters, see `genfromtxt`. See Also -------- numpy.genfromtxt : generic function Notes ----- By default, `dtype` is None, which means that the data-type of the output array will be determined from the data. repeat(a, repeats, axis=None) Repeat elements of an array. Parameters ---------- a : array_like Input array. repeats : int or array of ints The number of repetitions for each element. `repeats` is broadcasted to fit the shape of the given axis. axis : int, optional The axis along which to repeat values. By default, use the flattened input array, and return a flat output array. Returns ------- repeated_array : ndarray Output array which has the same shape as `a`, except along the given axis. See Also -------- tile : Tile an array. Examples -------- >>> x = np.array([[1,2],[3,4]]) >>> np.repeat(x, 2) array([1, 1, 2, 2, 3, 3, 4, 4]) >>> np.repeat(x, 3, axis=1) array([[1, 1, 1, 2, 2, 2], [3, 3, 3, 4, 4, 4]]) >>> np.repeat(x, [1, 2], axis=0) array([[1, 2], [3, 4], [3, 4]]) require(a, dtype=None, requirements=None) Return an ndarray of the provided type that satisfies requirements. This function is useful to be sure that an array with the correct flags is returned for passing to compiled code (perhaps through ctypes). Parameters ---------- a : array_like The object to be converted to a type-and-requirement-satisfying array. dtype : data-type The required data-type. If None preserve the current dtype. If your application requires the data to be in native byteorder, include a byteorder specification as a part of the dtype specification. requirements : str or list of str The requirements list can be any of the following * 'F_CONTIGUOUS' ('F') - ensure a Fortran-contiguous array * 'C_CONTIGUOUS' ('C') - ensure a C-contiguous array * 'ALIGNED' ('A') - ensure a data-type aligned array * 'WRITEABLE' ('W') - ensure a writable array * 'OWNDATA' ('O') - ensure an array that owns its own data * 'ENSUREARRAY', ('E') - ensure a base array, instead of a subclass See Also -------- asarray : Convert input to an ndarray. asanyarray : Convert to an ndarray, but pass through ndarray subclasses. ascontiguousarray : Convert input to a contiguous array. asfortranarray : Convert input to an ndarray with column-major memory order. ndarray.flags : Information about the memory layout of the array. Notes ----- The returned array will be guaranteed to have the listed requirements by making a copy if needed. Examples -------- >>> x = np.arange(6).reshape(2,3) >>> x.flags C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : False WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False >>> y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F']) >>> y.flags C_CONTIGUOUS : False F_CONTIGUOUS : True OWNDATA : True WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False reshape(a, newshape, order='C') Gives a new shape to an array without changing its data. Parameters ---------- a : array_like Array to be reshaped. newshape : int or tuple of ints The new shape should be compatible with the original shape. If an integer, then the result will be a 1-D array of that length. One shape dimension can be -1. In this case, the value is inferred from the length of the array and remaining dimensions. order : {'C', 'F', 'A'}, optional Read the elements of `a` using this index order, and place the elements into the reshaped array using this index order. 'C' means to read / write the elements using C-like index order, with the last axis index changing fastest, back to the first axis index changing slowest. 'F' means to read / write the elements using Fortran-like index order, with the first index changing fastest, and the last index changing slowest. Note that the 'C' and 'F' options take no account of the memory layout of the underlying array, and only refer to the order of indexing. 'A' means to read / write the elements in Fortran-like index order if `a` is Fortran *contiguous* in memory, C-like order otherwise. Returns ------- reshaped_array : ndarray This will be a new view object if possible; otherwise, it will be a copy. Note there is no guarantee of the *memory layout* (C- or Fortran- contiguous) of the returned array. See Also -------- ndarray.reshape : Equivalent method. Notes ----- It is not always possible to change the shape of an array without copying the data. If you want an error to be raise if the data is copied, you should assign the new shape to the shape attribute of the array:: >>> a = np.zeros((10, 2)) # A transpose make the array non-contiguous >>> b = a.T # Taking a view makes it possible to modify the shape without modifying # the initial object. >>> c = b.view() >>> c.shape = (20) AttributeError: incompatible shape for a non-contiguous array The `order` keyword gives the index ordering both for *fetching* the values from `a`, and then *placing* the values into the output array. For example, let's say you have an array: >>> a = np.arange(6).reshape((3, 2)) >>> a array([[0, 1], [2, 3], [4, 5]]) You can think of reshaping as first raveling the array (using the given index order), then inserting the elements from the raveled array into the new array using the same kind of index ordering as was used for the raveling. >>> np.reshape(a, (2, 3)) # C-like index ordering array([[0, 1, 2], [3, 4, 5]]) >>> np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape array([[0, 1, 2], [3, 4, 5]]) >>> np.reshape(a, (2, 3), order='F') # Fortran-like index ordering array([[0, 4, 3], [2, 1, 5]]) >>> np.reshape(np.ravel(a, order='F'), (2, 3), order='F') array([[0, 4, 3], [2, 1, 5]]) Examples -------- >>> a = np.array([[1,2,3], [4,5,6]]) >>> np.reshape(a, 6) array([1, 2, 3, 4, 5, 6]) >>> np.reshape(a, 6, order='F') array([1, 4, 2, 5, 3, 6]) >>> np.reshape(a, (3,-1)) # the unspecified value is inferred to be 2 array([[1, 2], [3, 4], [5, 6]]) resize(a, new_shape) Return a new array with the specified shape. If the new array is larger than the original array, then the new array is filled with repeated copies of `a`. Note that this behavior is different from a.resize(new_shape) which fills with zeros instead of repeated copies of `a`. Parameters ---------- a : array_like Array to be resized. new_shape : int or tuple of int Shape of resized array. Returns ------- reshaped_array : ndarray The new array is formed from the data in the old array, repeated if necessary to fill out the required number of elements. The data are repeated in the order that they are stored in memory. See Also -------- ndarray.resize : resize an array in-place. Examples -------- >>> a=np.array([[0,1],[2,3]]) >>> np.resize(a,(2,3)) array([[0, 1, 2], [3, 0, 1]]) >>> np.resize(a,(1,4)) array([[0, 1, 2, 3]]) >>> np.resize(a,(2,4)) array([[0, 1, 2, 3], [0, 1, 2, 3]]) restoredot() Restore `dot`, `vdot`, and `innerproduct` to the default non-BLAS implementations. Typically, the user will only need to call this when troubleshooting and installation problem, reproducing the conditions of a build without an accelerated BLAS, or when being very careful about benchmarking linear algebra operations. .. note:: Deprecated in Numpy 1.10 The cblas functions have been integrated into the multarray module and restoredot now longer does anything. It will be removed in Numpy 1.11.0. See Also -------- alterdot : `restoredot` undoes the effects of `alterdot`. result_type(...) result_type(*arrays_and_dtypes) Returns the type that results from applying the NumPy type promotion rules to the arguments. Type promotion in NumPy works similarly to the rules in languages like C++, with some slight differences. When both scalars and arrays are used, the array's type takes precedence and the actual value of the scalar is taken into account. For example, calculating 3*a, where a is an array of 32-bit floats, intuitively should result in a 32-bit float output. If the 3 is a 32-bit integer, the NumPy rules indicate it can't convert losslessly into a 32-bit float, so a 64-bit float should be the result type. By examining the value of the constant, '3', we see that it fits in an 8-bit integer, which can be cast losslessly into the 32-bit float. Parameters ---------- arrays_and_dtypes : list of arrays and dtypes The operands of some operation whose result type is needed. Returns ------- out : dtype The result type. See also -------- dtype, promote_types, min_scalar_type, can_cast Notes ----- .. versionadded:: 1.6.0 The specific algorithm used is as follows. Categories are determined by first checking which of boolean, integer (int/uint), or floating point (float/complex) the maximum kind of all the arrays and the scalars are. If there are only scalars or the maximum category of the scalars is higher than the maximum category of the arrays, the data types are combined with :func:`promote_types` to produce the return value. Otherwise, `min_scalar_type` is called on each array, and the resulting data types are all combined with :func:`promote_types` to produce the return value. The set of int values is not a subset of the uint values for types with the same number of bits, something not reflected in :func:`min_scalar_type`, but handled as a special case in `result_type`. Examples -------- >>> np.result_type(3, np.arange(7, dtype='i1')) dtype('int8') >>> np.result_type('i4', 'c8') dtype('complex128') >>> np.result_type(3.0, -2) dtype('float64') roll(a, shift, axis=None) Roll array elements along a given axis. Elements that roll beyond the last position are re-introduced at the first. Parameters ---------- a : array_like Input array. shift : int The number of places by which elements are shifted. axis : int, optional The axis along which elements are shifted. By default, the array is flattened before shifting, after which the original shape is restored. Returns ------- res : ndarray Output array, with the same shape as `a`. See Also -------- rollaxis : Roll the specified axis backwards, until it lies in a given position. Examples -------- >>> x = np.arange(10) >>> np.roll(x, 2) array([8, 9, 0, 1, 2, 3, 4, 5, 6, 7]) >>> x2 = np.reshape(x, (2,5)) >>> x2 array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]) >>> np.roll(x2, 1) array([[9, 0, 1, 2, 3], [4, 5, 6, 7, 8]]) >>> np.roll(x2, 1, axis=0) array([[5, 6, 7, 8, 9], [0, 1, 2, 3, 4]]) >>> np.roll(x2, 1, axis=1) array([[4, 0, 1, 2, 3], [9, 5, 6, 7, 8]]) rollaxis(a, axis, start=0) Roll the specified axis backwards, until it lies in a given position. Parameters ---------- a : ndarray Input array. axis : int The axis to roll backwards. The positions of the other axes do not change relative to one another. start : int, optional The axis is rolled until it lies before this position. The default, 0, results in a "complete" roll. Returns ------- res : ndarray For Numpy >= 1.10 a view of `a` is always returned. For earlier Numpy versions a view of `a` is returned only if the order of the axes is changed, otherwise the input array is returned. See Also -------- roll : Roll the elements of an array by a number of positions along a given axis. Examples -------- >>> a = np.ones((3,4,5,6)) >>> np.rollaxis(a, 3, 1).shape (3, 6, 4, 5) >>> np.rollaxis(a, 2).shape (5, 3, 4, 6) >>> np.rollaxis(a, 1, 4).shape (3, 5, 6, 4) roots(p) Return the roots of a polynomial with coefficients given in p. The values in the rank-1 array `p` are coefficients of a polynomial. If the length of `p` is n+1 then the polynomial is described by:: p[0] * x**n + p[1] * x**(n-1) + ... + p[n-1]*x + p[n] Parameters ---------- p : array_like Rank-1 array of polynomial coefficients. Returns ------- out : ndarray An array containing the complex roots of the polynomial. Raises ------ ValueError When `p` cannot be converted to a rank-1 array. See also -------- poly : Find the coefficients of a polynomial with a given sequence of roots. polyval : Evaluate a polynomial at a point. polyfit : Least squares polynomial fit. poly1d : A one-dimensional polynomial class. Notes ----- The algorithm relies on computing the eigenvalues of the companion matrix [1]_. References ---------- .. [1] R. A. Horn & C. R. Johnson, *Matrix Analysis*. Cambridge, UK: Cambridge University Press, 1999, pp. 146-7. Examples -------- >>> coeff = [3.2, 2, 1] >>> np.roots(coeff) array([-0.3125+0.46351241j, -0.3125-0.46351241j]) rot90(m, k=1) Rotate an array by 90 degrees in the counter-clockwise direction. The first two dimensions are rotated; therefore, the array must be at least 2-D. Parameters ---------- m : array_like Array of two or more dimensions. k : integer Number of times the array is rotated by 90 degrees. Returns ------- y : ndarray Rotated array. See Also -------- fliplr : Flip an array horizontally. flipud : Flip an array vertically. Examples -------- >>> m = np.array([[1,2],[3,4]], int) >>> m array([[1, 2], [3, 4]]) >>> np.rot90(m) array([[2, 4], [1, 3]]) >>> np.rot90(m, 2) array([[4, 3], [2, 1]]) round_(a, decimals=0, out=None) Round an array to the given number of decimals. Refer to `around` for full documentation. See Also -------- around : equivalent function row_stack = vstack(tup) Stack arrays in sequence vertically (row wise). Take a sequence of arrays and stack them vertically to make a single array. Rebuild arrays divided by `vsplit`. Parameters ---------- tup : sequence of ndarrays Tuple containing arrays to be stacked. The arrays must have the same shape along all but the first axis. Returns ------- stacked : ndarray The array formed by stacking the given arrays. See Also -------- stack : Join a sequence of arrays along a new axis. hstack : Stack arrays in sequence horizontally (column wise). dstack : Stack arrays in sequence depth wise (along third dimension). concatenate : Join a sequence of arrays along an existing axis. vsplit : Split array into a list of multiple sub-arrays vertically. Notes ----- Equivalent to ``np.concatenate(tup, axis=0)`` if `tup` contains arrays that are at least 2-dimensional. Examples -------- >>> a = np.array([1, 2, 3]) >>> b = np.array([2, 3, 4]) >>> np.vstack((a,b)) array([[1, 2, 3], [2, 3, 4]]) >>> a = np.array([[1], [2], [3]]) >>> b = np.array([[2], [3], [4]]) >>> np.vstack((a,b)) array([[1], [2], [3], [2], [3], [4]]) safe_eval(source) Protected string evaluation. Evaluate a string containing a Python literal expression without allowing the execution of arbitrary non-literal code. Parameters ---------- source : str The string to evaluate. Returns ------- obj : object The result of evaluating `source`. Raises ------ SyntaxError If the code has invalid Python syntax, or if it contains non-literal code. Examples -------- >>> np.safe_eval('1') 1 >>> np.safe_eval('[1, 2, 3]') [1, 2, 3] >>> np.safe_eval('{"foo": ("bar", 10.0)}') {'foo': ('bar', 10.0)} >>> np.safe_eval('import os') Traceback (most recent call last): ... SyntaxError: invalid syntax >>> np.safe_eval('open("/home/user/.ssh/id_dsa").read()') Traceback (most recent call last): ... SyntaxError: Unsupported source construct: compiler.ast.CallFunc save(file, arr, allow_pickle=True, fix_imports=True) Save an array to a binary file in NumPy ``.npy`` format. Parameters ---------- file : file or str File or filename to which the data is saved. If file is a file-object, then the filename is unchanged. If file is a string, a ``.npy`` extension will be appended to the file name if it does not already have one. allow_pickle : bool, optional Allow saving object arrays using Python pickles. Reasons for disallowing pickles include security (loading pickled data can execute arbitrary code) and portability (pickled objects may not be loadable on different Python installations, for example if the stored objects require libraries that are not available, and not all pickled data is compatible between Python 2 and Python 3). Default: True fix_imports : bool, optional Only useful in forcing objects in object arrays on Python 3 to be pickled in a Python 2 compatible way. If `fix_imports` is True, pickle will try to map the new Python 3 names to the old module names used in Python 2, so that the pickle data stream is readable with Python 2. arr : array_like Array data to be saved. See Also -------- savez : Save several arrays into a ``.npz`` archive savetxt, load Notes ----- For a description of the ``.npy`` format, see the module docstring of `numpy.lib.format` or the Numpy Enhancement Proposal http://docs.scipy.org/doc/numpy/neps/npy-format.html Examples -------- >>> from tempfile import TemporaryFile >>> outfile = TemporaryFile() >>> x = np.arange(10) >>> np.save(outfile, x) >>> outfile.seek(0) # Only needed here to simulate closing & reopening file >>> np.load(outfile) array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\n', header='', footer='', comments='# ') Save an array to a text file. Parameters ---------- fname : filename or file handle If the filename ends in ``.gz``, the file is automatically saved in compressed gzip format. `loadtxt` understands gzipped files transparently. X : array_like Data to be saved to a text file. fmt : str or sequence of strs, optional A single format (%10.5f), a sequence of formats, or a multi-format string, e.g. 'Iteration %d -- %10.5f', in which case `delimiter` is ignored. For complex `X`, the legal options for `fmt` are: a) a single specifier, `fmt='%.4e'`, resulting in numbers formatted like `' (%s+%sj)' % (fmt, fmt)` b) a full string specifying every real and imaginary part, e.g. `' %.4e %+.4j %.4e %+.4j %.4e %+.4j'` for 3 columns c) a list of specifiers, one per column - in this case, the real and imaginary part must have separate specifiers, e.g. `['%.3e + %.3ej', '(%.15e%+.15ej)']` for 2 columns delimiter : str, optional String or character separating columns. newline : str, optional String or character separating lines. .. versionadded:: 1.5.0 header : str, optional String that will be written at the beginning of the file. .. versionadded:: 1.7.0 footer : str, optional String that will be written at the end of the file. .. versionadded:: 1.7.0 comments : str, optional String that will be prepended to the ``header`` and ``footer`` strings, to mark them as comments. Default: '# ', as expected by e.g. ``numpy.loadtxt``. .. versionadded:: 1.7.0 See Also -------- save : Save an array to a binary file in NumPy ``.npy`` format savez : Save several arrays into an uncompressed ``.npz`` archive savez_compressed : Save several arrays into a compressed ``.npz`` archive Notes ----- Further explanation of the `fmt` parameter (``%[flag]width[.precision]specifier``): flags: ``-`` : left justify ``+`` : Forces to precede result with + or -. ``0`` : Left pad the number with zeros instead of space (see width). width: Minimum number of characters to be printed. The value is not truncated if it has more characters. precision: - For integer specifiers (eg. ``d,i,o,x``), the minimum number of digits. - For ``e, E`` and ``f`` specifiers, the number of digits to print after the decimal point. - For ``g`` and ``G``, the maximum number of significant digits. - For ``s``, the maximum number of characters. specifiers: ``c`` : character ``d`` or ``i`` : signed decimal integer ``e`` or ``E`` : scientific notation with ``e`` or ``E``. ``f`` : decimal floating point ``g,G`` : use the shorter of ``e,E`` or ``f`` ``o`` : signed octal ``s`` : string of characters ``u`` : unsigned decimal integer ``x,X`` : unsigned hexadecimal integer This explanation of ``fmt`` is not complete, for an exhaustive specification see [1]_. References ---------- .. [1] `Format Specification Mini-Language `_, Python Documentation. Examples -------- >>> x = y = z = np.arange(0.0,5.0,1.0) >>> np.savetxt('test.out', x, delimiter=',') # X is an array >>> np.savetxt('test.out', (x,y,z)) # x,y,z equal sized 1D arrays >>> np.savetxt('test.out', x, fmt='%1.4e') # use exponential notation savez(file, *args, **kwds) Save several arrays into a single file in uncompressed ``.npz`` format. If arguments are passed in with no keywords, the corresponding variable names, in the ``.npz`` file, are 'arr_0', 'arr_1', etc. If keyword arguments are given, the corresponding variable names, in the ``.npz`` file will match the keyword names. Parameters ---------- file : str or file Either the file name (string) or an open file (file-like object) where the data will be saved. If file is a string, the ``.npz`` extension will be appended to the file name if it is not already there. args : Arguments, optional Arrays to save to the file. Since it is not possible for Python to know the names of the arrays outside `savez`, the arrays will be saved with names "arr_0", "arr_1", and so on. These arguments can be any expression. kwds : Keyword arguments, optional Arrays to save to the file. Arrays will be saved in the file with the keyword names. Returns ------- None See Also -------- save : Save a single array to a binary file in NumPy format. savetxt : Save an array to a file as plain text. savez_compressed : Save several arrays into a compressed ``.npz`` archive Notes ----- The ``.npz`` file format is a zipped archive of files named after the variables they contain. The archive is not compressed and each file in the archive contains one variable in ``.npy`` format. For a description of the ``.npy`` format, see `numpy.lib.format` or the Numpy Enhancement Proposal http://docs.scipy.org/doc/numpy/neps/npy-format.html When opening the saved ``.npz`` file with `load` a `NpzFile` object is returned. This is a dictionary-like object which can be queried for its list of arrays (with the ``.files`` attribute), and for the arrays themselves. Examples -------- >>> from tempfile import TemporaryFile >>> outfile = TemporaryFile() >>> x = np.arange(10) >>> y = np.sin(x) Using `savez` with \*args, the arrays are saved with default names. >>> np.savez(outfile, x, y) >>> outfile.seek(0) # Only needed here to simulate closing & reopening file >>> npzfile = np.load(outfile) >>> npzfile.files ['arr_1', 'arr_0'] >>> npzfile['arr_0'] array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) Using `savez` with \**kwds, the arrays are saved with the keyword names. >>> outfile = TemporaryFile() >>> np.savez(outfile, x=x, y=y) >>> outfile.seek(0) >>> npzfile = np.load(outfile) >>> npzfile.files ['y', 'x'] >>> npzfile['x'] array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) savez_compressed(file, *args, **kwds) Save several arrays into a single file in compressed ``.npz`` format. If keyword arguments are given, then filenames are taken from the keywords. If arguments are passed in with no keywords, then stored file names are arr_0, arr_1, etc. Parameters ---------- file : str File name of ``.npz`` file. args : Arguments Function arguments. kwds : Keyword arguments Keywords. See Also -------- numpy.savez : Save several arrays into an uncompressed ``.npz`` file format numpy.load : Load the files created by savez_compressed. sctype2char(sctype) Return the string representation of a scalar dtype. Parameters ---------- sctype : scalar dtype or object If a scalar dtype, the corresponding string character is returned. If an object, `sctype2char` tries to infer its scalar type and then return the corresponding string character. Returns ------- typechar : str The string character corresponding to the scalar type. Raises ------ ValueError If `sctype` is an object for which the type can not be inferred. See Also -------- obj2sctype, issctype, issubsctype, mintypecode Examples -------- >>> for sctype in [np.int32, np.float, np.complex, np.string_, np.ndarray]: ... print np.sctype2char(sctype) l d D S O >>> x = np.array([1., 2-1.j]) >>> np.sctype2char(x) 'D' >>> np.sctype2char(list) 'O' searchsorted(a, v, side='left', sorter=None) Find indices where elements should be inserted to maintain order. Find the indices into a sorted array `a` such that, if the corresponding elements in `v` were inserted before the indices, the order of `a` would be preserved. Parameters ---------- a : 1-D array_like Input array. If `sorter` is None, then it must be sorted in ascending order, otherwise `sorter` must be an array of indices that sort it. v : array_like Values to insert into `a`. side : {'left', 'right'}, optional If 'left', the index of the first suitable location found is given. If 'right', return the last such index. If there is no suitable index, return either 0 or N (where N is the length of `a`). sorter : 1-D array_like, optional Optional array of integer indices that sort array a into ascending order. They are typically the result of argsort. .. versionadded:: 1.7.0 Returns ------- indices : array of ints Array of insertion points with the same shape as `v`. See Also -------- sort : Return a sorted copy of an array. histogram : Produce histogram from 1-D data. Notes ----- Binary search is used to find the required insertion points. As of Numpy 1.4.0 `searchsorted` works with real/complex arrays containing `nan` values. The enhanced sort order is documented in `sort`. Examples -------- >>> np.searchsorted([1,2,3,4,5], 3) 2 >>> np.searchsorted([1,2,3,4,5], 3, side='right') 3 >>> np.searchsorted([1,2,3,4,5], [-10, 10, 2, 3]) array([0, 5, 1, 2]) select(condlist, choicelist, default=0) Return an array drawn from elements in choicelist, depending on conditions. Parameters ---------- condlist : list of bool ndarrays The list of conditions which determine from which array in `choicelist` the output elements are taken. When multiple conditions are satisfied, the first one encountered in `condlist` is used. choicelist : list of ndarrays The list of arrays from which the output elements are taken. It has to be of the same length as `condlist`. default : scalar, optional The element inserted in `output` when all conditions evaluate to False. Returns ------- output : ndarray The output at position m is the m-th element of the array in `choicelist` where the m-th element of the corresponding array in `condlist` is True. See Also -------- where : Return elements from one of two arrays depending on condition. take, choose, compress, diag, diagonal Examples -------- >>> x = np.arange(10) >>> condlist = [x<3, x>5] >>> choicelist = [x, x**2] >>> np.select(condlist, choicelist) array([ 0, 1, 2, 0, 0, 0, 36, 49, 64, 81]) set_numeric_ops(...) set_numeric_ops(op1=func1, op2=func2, ...) Set numerical operators for array objects. Parameters ---------- op1, op2, ... : callable Each ``op = func`` pair describes an operator to be replaced. For example, ``add = lambda x, y: np.add(x, y) % 5`` would replace addition by modulus 5 addition. Returns ------- saved_ops : list of callables A list of all operators, stored before making replacements. Notes ----- .. WARNING:: Use with care! Incorrect usage may lead to memory errors. A function replacing an operator cannot make use of that operator. For example, when replacing add, you may not use ``+``. Instead, directly call ufuncs. Examples -------- >>> def add_mod5(x, y): ... return np.add(x, y) % 5 ... >>> old_funcs = np.set_numeric_ops(add=add_mod5) >>> x = np.arange(12).reshape((3, 4)) >>> x + x array([[0, 2, 4, 1], [3, 0, 2, 4], [1, 3, 0, 2]]) >>> ignore = np.set_numeric_ops(**old_funcs) # restore operators set_printoptions(precision=None, threshold=None, edgeitems=None, linewidth=None, suppress=None, nanstr=None, infstr=None, formatter=None) Set printing options. These options determine the way floating point numbers, arrays and other NumPy objects are displayed. Parameters ---------- precision : int, optional Number of digits of precision for floating point output (default 8). threshold : int, optional Total number of array elements which trigger summarization rather than full repr (default 1000). edgeitems : int, optional Number of array items in summary at beginning and end of each dimension (default 3). linewidth : int, optional The number of characters per line for the purpose of inserting line breaks (default 75). suppress : bool, optional Whether or not suppress printing of small floating point values using scientific notation (default False). nanstr : str, optional String representation of floating point not-a-number (default nan). infstr : str, optional String representation of floating point infinity (default inf). formatter : dict of callables, optional If not None, the keys should indicate the type(s) that the respective formatting function applies to. Callables should return a string. Types that are not specified (by their corresponding keys) are handled by the default formatters. Individual types for which a formatter can be set are:: - 'bool' - 'int' - 'timedelta' : a `numpy.timedelta64` - 'datetime' : a `numpy.datetime64` - 'float' - 'longfloat' : 128-bit floats - 'complexfloat' - 'longcomplexfloat' : composed of two 128-bit floats - 'numpy_str' : types `numpy.string_` and `numpy.unicode_` - 'str' : all other strings Other keys that can be used to set a group of types at once are:: - 'all' : sets all types - 'int_kind' : sets 'int' - 'float_kind' : sets 'float' and 'longfloat' - 'complex_kind' : sets 'complexfloat' and 'longcomplexfloat' - 'str_kind' : sets 'str' and 'numpystr' See Also -------- get_printoptions, set_string_function, array2string Notes ----- `formatter` is always reset with a call to `set_printoptions`. Examples -------- Floating point precision can be set: >>> np.set_printoptions(precision=4) >>> print np.array([1.123456789]) [ 1.1235] Long arrays can be summarised: >>> np.set_printoptions(threshold=5) >>> print np.arange(10) [0 1 2 ..., 7 8 9] Small results can be suppressed: >>> eps = np.finfo(float).eps >>> x = np.arange(4.) >>> x**2 - (x + eps)**2 array([ -4.9304e-32, -4.4409e-16, 0.0000e+00, 0.0000e+00]) >>> np.set_printoptions(suppress=True) >>> x**2 - (x + eps)**2 array([-0., -0., 0., 0.]) A custom formatter can be used to display array elements as desired: >>> np.set_printoptions(formatter={'all':lambda x: 'int: '+str(-x)}) >>> x = np.arange(3) >>> x array([int: 0, int: -1, int: -2]) >>> np.set_printoptions() # formatter gets reset >>> x array([0, 1, 2]) To put back the default options, you can use: >>> np.set_printoptions(edgeitems=3,infstr='inf', ... linewidth=75, nanstr='nan', precision=8, ... suppress=False, threshold=1000, formatter=None) set_string_function(f, repr=True) Set a Python function to be used when pretty printing arrays. Parameters ---------- f : function or None Function to be used to pretty print arrays. The function should expect a single array argument and return a string of the representation of the array. If None, the function is reset to the default NumPy function to print arrays. repr : bool, optional If True (default), the function for pretty printing (``__repr__``) is set, if False the function that returns the default string representation (``__str__``) is set. See Also -------- set_printoptions, get_printoptions Examples -------- >>> def pprint(arr): ... return 'HA! - What are you going to do now?' ... >>> np.set_string_function(pprint) >>> a = np.arange(10) >>> a HA! - What are you going to do now? >>> print a [0 1 2 3 4 5 6 7 8 9] We can reset the function to the default: >>> np.set_string_function(None) >>> a array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) `repr` affects either pretty printing or normal string representation. Note that ``__repr__`` is still affected by setting ``__str__`` because the width of each array element in the returned string becomes equal to the length of the result of ``__str__()``. >>> x = np.arange(4) >>> np.set_string_function(lambda x:'random', repr=False) >>> x.__str__() 'random' >>> x.__repr__() 'array([ 0, 1, 2, 3])' setbufsize(size) Set the size of the buffer used in ufuncs. Parameters ---------- size : int Size of buffer. setdiff1d(ar1, ar2, assume_unique=False) Find the set difference of two arrays. Return the sorted, unique values in `ar1` that are not in `ar2`. Parameters ---------- ar1 : array_like Input array. ar2 : array_like Input comparison array. assume_unique : bool If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False. Returns ------- setdiff1d : ndarray Sorted 1D array of values in `ar1` that are not in `ar2`. See Also -------- numpy.lib.arraysetops : Module with a number of other functions for performing set operations on arrays. Examples -------- >>> a = np.array([1, 2, 3, 2, 4, 1]) >>> b = np.array([3, 4, 5, 6]) >>> np.setdiff1d(a, b) array([1, 2]) seterr(all=None, divide=None, over=None, under=None, invalid=None) Set how floating-point errors are handled. Note that operations on integer scalar types (such as `int16`) are handled like floating point, and are affected by these settings. Parameters ---------- all : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional Set treatment for all types of floating-point errors at once: - ignore: Take no action when the exception occurs. - warn: Print a `RuntimeWarning` (via the Python `warnings` module). - raise: Raise a `FloatingPointError`. - call: Call a function specified using the `seterrcall` function. - print: Print a warning directly to ``stdout``. - log: Record error in a Log object specified by `seterrcall`. The default is not to change the current behavior. divide : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional Treatment for division by zero. over : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional Treatment for floating-point overflow. under : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional Treatment for floating-point underflow. invalid : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional Treatment for invalid floating-point operation. Returns ------- old_settings : dict Dictionary containing the old settings. See also -------- seterrcall : Set a callback function for the 'call' mode. geterr, geterrcall, errstate Notes ----- The floating-point exceptions are defined in the IEEE 754 standard [1]: - Division by zero: infinite result obtained from finite numbers. - Overflow: result too large to be expressed. - Underflow: result so close to zero that some precision was lost. - Invalid operation: result is not an expressible number, typically indicates that a NaN was produced. .. [1] http://en.wikipedia.org/wiki/IEEE_754 Examples -------- >>> old_settings = np.seterr(all='ignore') #seterr to known value >>> np.seterr(over='raise') {'over': 'ignore', 'divide': 'ignore', 'invalid': 'ignore', 'under': 'ignore'} >>> np.seterr(**old_settings) # reset to default {'over': 'raise', 'divide': 'ignore', 'invalid': 'ignore', 'under': 'ignore'} >>> np.int16(32000) * np.int16(3) 30464 >>> old_settings = np.seterr(all='warn', over='raise') >>> np.int16(32000) * np.int16(3) Traceback (most recent call last): File "", line 1, in FloatingPointError: overflow encountered in short_scalars >>> old_settings = np.seterr(all='print') >>> np.geterr() {'over': 'print', 'divide': 'print', 'invalid': 'print', 'under': 'print'} >>> np.int16(32000) * np.int16(3) Warning: overflow encountered in short_scalars 30464 seterrcall(func) Set the floating-point error callback function or log object. There are two ways to capture floating-point error messages. The first is to set the error-handler to 'call', using `seterr`. Then, set the function to call using this function. The second is to set the error-handler to 'log', using `seterr`. Floating-point errors then trigger a call to the 'write' method of the provided object. Parameters ---------- func : callable f(err, flag) or object with write method Function to call upon floating-point errors ('call'-mode) or object whose 'write' method is used to log such message ('log'-mode). The call function takes two arguments. The first is a string describing the type of error (such as "divide by zero", "overflow", "underflow", or "invalid value"), and the second is the status flag. The flag is a byte, whose four least-significant bits indicate the type of error, one of "divide", "over", "under", "invalid":: [0 0 0 0 divide over under invalid] In other words, ``flags = divide + 2*over + 4*under + 8*invalid``. If an object is provided, its write method should take one argument, a string. Returns ------- h : callable, log instance or None The old error handler. See Also -------- seterr, geterr, geterrcall Examples -------- Callback upon error: >>> def err_handler(type, flag): ... print "Floating point error (%s), with flag %s" % (type, flag) ... >>> saved_handler = np.seterrcall(err_handler) >>> save_err = np.seterr(all='call') >>> np.array([1, 2, 3]) / 0.0 Floating point error (divide by zero), with flag 1 array([ Inf, Inf, Inf]) >>> np.seterrcall(saved_handler) >>> np.seterr(**save_err) {'over': 'call', 'divide': 'call', 'invalid': 'call', 'under': 'call'} Log error message: >>> class Log(object): ... def write(self, msg): ... print "LOG: %s" % msg ... >>> log = Log() >>> saved_handler = np.seterrcall(log) >>> save_err = np.seterr(all='log') >>> np.array([1, 2, 3]) / 0.0 LOG: Warning: divide by zero encountered in divide array([ Inf, Inf, Inf]) >>> np.seterrcall(saved_handler) <__main__.Log object at 0x...> >>> np.seterr(**save_err) {'over': 'log', 'divide': 'log', 'invalid': 'log', 'under': 'log'} seterrobj(...) seterrobj(errobj) Set the object that defines floating-point error handling. The error object contains all information that defines the error handling behavior in Numpy. `seterrobj` is used internally by the other functions that set error handling behavior (`seterr`, `seterrcall`). Parameters ---------- errobj : list The error object, a list containing three elements: [internal numpy buffer size, error mask, error callback function]. The error mask is a single integer that holds the treatment information on all four floating point errors. The information for each error type is contained in three bits of the integer. If we print it in base 8, we can see what treatment is set for "invalid", "under", "over", and "divide" (in that order). The printed string can be interpreted with * 0 : 'ignore' * 1 : 'warn' * 2 : 'raise' * 3 : 'call' * 4 : 'print' * 5 : 'log' See Also -------- geterrobj, seterr, geterr, seterrcall, geterrcall getbufsize, setbufsize Notes ----- For complete documentation of the types of floating-point exceptions and treatment options, see `seterr`. Examples -------- >>> old_errobj = np.geterrobj() # first get the defaults >>> old_errobj [10000, 0, None] >>> def err_handler(type, flag): ... print "Floating point error (%s), with flag %s" % (type, flag) ... >>> new_errobj = [20000, 12, err_handler] >>> np.seterrobj(new_errobj) >>> np.base_repr(12, 8) # int for divide=4 ('print') and over=1 ('warn') '14' >>> np.geterr() {'over': 'warn', 'divide': 'print', 'invalid': 'ignore', 'under': 'ignore'} >>> np.geterrcall() is err_handler True setxor1d(ar1, ar2, assume_unique=False) Find the set exclusive-or of two arrays. Return the sorted, unique values that are in only one (not both) of the input arrays. Parameters ---------- ar1, ar2 : array_like Input arrays. assume_unique : bool If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False. Returns ------- setxor1d : ndarray Sorted 1D array of unique values that are in only one of the input arrays. Examples -------- >>> a = np.array([1, 2, 3, 2, 4]) >>> b = np.array([2, 3, 5, 7, 5]) >>> np.setxor1d(a,b) array([1, 4, 5, 7]) shape(a) Return the shape of an array. Parameters ---------- a : array_like Input array. Returns ------- shape : tuple of ints The elements of the shape tuple give the lengths of the corresponding array dimensions. See Also -------- alen ndarray.shape : Equivalent array method. Examples -------- >>> np.shape(np.eye(3)) (3, 3) >>> np.shape([[1, 2]]) (1, 2) >>> np.shape([0]) (1,) >>> np.shape(0) () >>> a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')]) >>> np.shape(a) (2,) >>> a.shape (2,) show_config = show() sinc(x) Return the sinc function. The sinc function is :math:`\sin(\pi x)/(\pi x)`. Parameters ---------- x : ndarray Array (possibly multi-dimensional) of values for which to to calculate ``sinc(x)``. Returns ------- out : ndarray ``sinc(x)``, which has the same shape as the input. Notes ----- ``sinc(0)`` is the limit value 1. The name sinc is short for "sine cardinal" or "sinus cardinalis". The sinc function is used in various signal processing applications, including in anti-aliasing, in the construction of a Lanczos resampling filter, and in interpolation. For bandlimited interpolation of discrete-time signals, the ideal interpolation kernel is proportional to the sinc function. References ---------- .. [1] Weisstein, Eric W. "Sinc Function." From MathWorld--A Wolfram Web Resource. http://mathworld.wolfram.com/SincFunction.html .. [2] Wikipedia, "Sinc function", http://en.wikipedia.org/wiki/Sinc_function Examples -------- >>> x = np.linspace(-4, 4, 41) >>> np.sinc(x) array([ -3.89804309e-17, -4.92362781e-02, -8.40918587e-02, -8.90384387e-02, -5.84680802e-02, 3.89804309e-17, 6.68206631e-02, 1.16434881e-01, 1.26137788e-01, 8.50444803e-02, -3.89804309e-17, -1.03943254e-01, -1.89206682e-01, -2.16236208e-01, -1.55914881e-01, 3.89804309e-17, 2.33872321e-01, 5.04551152e-01, 7.56826729e-01, 9.35489284e-01, 1.00000000e+00, 9.35489284e-01, 7.56826729e-01, 5.04551152e-01, 2.33872321e-01, 3.89804309e-17, -1.55914881e-01, -2.16236208e-01, -1.89206682e-01, -1.03943254e-01, -3.89804309e-17, 8.50444803e-02, 1.26137788e-01, 1.16434881e-01, 6.68206631e-02, 3.89804309e-17, -5.84680802e-02, -8.90384387e-02, -8.40918587e-02, -4.92362781e-02, -3.89804309e-17]) >>> plt.plot(x, np.sinc(x)) [] >>> plt.title("Sinc Function") >>> plt.ylabel("Amplitude") >>> plt.xlabel("X") >>> plt.show() It works in 2-D as well: >>> x = np.linspace(-4, 4, 401) >>> xx = np.outer(x, x) >>> plt.imshow(np.sinc(xx)) size(a, axis=None) Return the number of elements along a given axis. Parameters ---------- a : array_like Input data. axis : int, optional Axis along which the elements are counted. By default, give the total number of elements. Returns ------- element_count : int Number of elements along the specified axis. See Also -------- shape : dimensions of array ndarray.shape : dimensions of array ndarray.size : number of elements in array Examples -------- >>> a = np.array([[1,2,3],[4,5,6]]) >>> np.size(a) 6 >>> np.size(a,1) 3 >>> np.size(a,0) 2 sometrue(a, axis=None, out=None, keepdims=False) Check whether some values are true. Refer to `any` for full documentation. See Also -------- any : equivalent function sort(a, axis=-1, kind='quicksort', order=None) Return a sorted copy of an array. Parameters ---------- a : array_like Array to be sorted. axis : int or None, optional Axis along which to sort. If None, the array is flattened before sorting. The default is -1, which sorts along the last axis. kind : {'quicksort', 'mergesort', 'heapsort'}, optional Sorting algorithm. Default is 'quicksort'. order : str or list of str, optional When `a` is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string, and not all fields need be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties. Returns ------- sorted_array : ndarray Array of the same type and shape as `a`. See Also -------- ndarray.sort : Method to sort an array in-place. argsort : Indirect sort. lexsort : Indirect stable sort on multiple keys. searchsorted : Find elements in a sorted array. partition : Partial sort. Notes ----- The various sorting algorithms are characterized by their average speed, worst case performance, work space size, and whether they are stable. A stable sort keeps items with the same key in the same relative order. The three available algorithms have the following properties: =========== ======= ============= ============ ======= kind speed worst case work space stable =========== ======= ============= ============ ======= 'quicksort' 1 O(n^2) 0 no 'mergesort' 2 O(n*log(n)) ~n/2 yes 'heapsort' 3 O(n*log(n)) 0 no =========== ======= ============= ============ ======= All the sort algorithms make temporary copies of the data when sorting along any but the last axis. Consequently, sorting along the last axis is faster and uses less space than sorting along any other axis. The sort order for complex numbers is lexicographic. If both the real and imaginary parts are non-nan then the order is determined by the real parts except when they are equal, in which case the order is determined by the imaginary parts. Previous to numpy 1.4.0 sorting real and complex arrays containing nan values led to undefined behaviour. In numpy versions >= 1.4.0 nan values are sorted to the end. The extended sort order is: * Real: [R, nan] * Complex: [R + Rj, R + nanj, nan + Rj, nan + nanj] where R is a non-nan real value. Complex values with the same nan placements are sorted according to the non-nan part if it exists. Non-nan values are sorted as before. Examples -------- >>> a = np.array([[1,4],[3,1]]) >>> np.sort(a) # sort along the last axis array([[1, 4], [1, 3]]) >>> np.sort(a, axis=None) # sort the flattened array array([1, 1, 3, 4]) >>> np.sort(a, axis=0) # sort along the first axis array([[1, 1], [3, 4]]) Use the `order` keyword to specify a field to use when sorting a structured array: >>> dtype = [('name', 'S10'), ('height', float), ('age', int)] >>> values = [('Arthur', 1.8, 41), ('Lancelot', 1.9, 38), ... ('Galahad', 1.7, 38)] >>> a = np.array(values, dtype=dtype) # create a structured array >>> np.sort(a, order='height') # doctest: +SKIP array([('Galahad', 1.7, 38), ('Arthur', 1.8, 41), ('Lancelot', 1.8999999999999999, 38)], dtype=[('name', '|S10'), ('height', '>> np.sort(a, order=['age', 'height']) # doctest: +SKIP array([('Galahad', 1.7, 38), ('Lancelot', 1.8999999999999999, 38), ('Arthur', 1.8, 41)], dtype=[('name', '|S10'), ('height', '>> np.sort_complex([5, 3, 6, 2, 1]) array([ 1.+0.j, 2.+0.j, 3.+0.j, 5.+0.j, 6.+0.j]) >>> np.sort_complex([1 + 2j, 2 - 1j, 3 - 2j, 3 - 3j, 3 + 5j]) array([ 1.+2.j, 2.-1.j, 3.-3.j, 3.-2.j, 3.+5.j]) source(object, output=) Print or write to a file the source code for a Numpy object. The source code is only returned for objects written in Python. Many functions and classes are defined in C and will therefore not return useful information. Parameters ---------- object : numpy object Input object. This can be any object (function, class, module, ...). output : file object, optional If `output` not supplied then source code is printed to screen (sys.stdout). File object must be created with either write 'w' or append 'a' modes. See Also -------- lookfor, info Examples -------- >>> np.source(np.interp) #doctest: +SKIP In file: /usr/lib/python2.6/dist-packages/numpy/lib/function_base.py def interp(x, xp, fp, left=None, right=None): """.... (full docstring printed)""" if isinstance(x, (float, int, number)): return compiled_interp([x], xp, fp, left, right).item() else: return compiled_interp(x, xp, fp, left, right) The source code is only returned for objects written in Python. >>> np.source(np.array) #doctest: +SKIP Not available for this object. split(ary, indices_or_sections, axis=0) Split an array into multiple sub-arrays. Parameters ---------- ary : ndarray Array to be divided into sub-arrays. indices_or_sections : int or 1-D array If `indices_or_sections` is an integer, N, the array will be divided into N equal arrays along `axis`. If such a split is not possible, an error is raised. If `indices_or_sections` is a 1-D array of sorted integers, the entries indicate where along `axis` the array is split. For example, ``[2, 3]`` would, for ``axis=0``, result in - ary[:2] - ary[2:3] - ary[3:] If an index exceeds the dimension of the array along `axis`, an empty sub-array is returned correspondingly. axis : int, optional The axis along which to split, default is 0. Returns ------- sub-arrays : list of ndarrays A list of sub-arrays. Raises ------ ValueError If `indices_or_sections` is given as an integer, but a split does not result in equal division. See Also -------- array_split : Split an array into multiple sub-arrays of equal or near-equal size. Does not raise an exception if an equal division cannot be made. hsplit : Split array into multiple sub-arrays horizontally (column-wise). vsplit : Split array into multiple sub-arrays vertically (row wise). dsplit : Split array into multiple sub-arrays along the 3rd axis (depth). concatenate : Join a sequence of arrays along an existing axis. stack : Join a sequence of arrays along a new axis. hstack : Stack arrays in sequence horizontally (column wise). vstack : Stack arrays in sequence vertically (row wise). dstack : Stack arrays in sequence depth wise (along third dimension). Examples -------- >>> x = np.arange(9.0) >>> np.split(x, 3) [array([ 0., 1., 2.]), array([ 3., 4., 5.]), array([ 6., 7., 8.])] >>> x = np.arange(8.0) >>> np.split(x, [3, 5, 6, 10]) [array([ 0., 1., 2.]), array([ 3., 4.]), array([ 5.]), array([ 6., 7.]), array([], dtype=float64)] squeeze(a, axis=None) Remove single-dimensional entries from the shape of an array. Parameters ---------- a : array_like Input data. axis : None or int or tuple of ints, optional .. versionadded:: 1.7.0 Selects a subset of the single-dimensional entries in the shape. If an axis is selected with shape entry greater than one, an error is raised. Returns ------- squeezed : ndarray The input array, but with all or a subset of the dimensions of length 1 removed. This is always `a` itself or a view into `a`. Examples -------- >>> x = np.array([[[0], [1], [2]]]) >>> x.shape (1, 3, 1) >>> np.squeeze(x).shape (3,) >>> np.squeeze(x, axis=(2,)).shape (1, 3) stack(arrays, axis=0) Join a sequence of arrays along a new axis. The `axis` parameter specifies the index of the new axis in the dimensions of the result. For example, if ``axis=0`` it will be the first dimension and if ``axis=-1`` it will be the last dimension. .. versionadded:: 1.10.0 Parameters ---------- arrays : sequence of array_like Each array must have the same shape. axis : int, optional The axis in the result array along which the input arrays are stacked. Returns ------- stacked : ndarray The stacked array has one more dimension than the input arrays. See Also -------- concatenate : Join a sequence of arrays along an existing axis. split : Split array into a list of multiple sub-arrays of equal size. Examples -------- >>> arrays = [np.random.randn(3, 4) for _ in range(10)] >>> np.stack(arrays, axis=0).shape (10, 3, 4) >>> np.stack(arrays, axis=1).shape (3, 10, 4) >>> np.stack(arrays, axis=2).shape (3, 4, 10) >>> a = np.array([1, 2, 3]) >>> b = np.array([2, 3, 4]) >>> np.stack((a, b)) array([[1, 2, 3], [2, 3, 4]]) >>> np.stack((a, b), axis=-1) array([[1, 2], [2, 3], [3, 4]]) std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False) Compute the standard deviation along the specified axis. Returns the standard deviation, a measure of the spread of a distribution, of the array elements. The standard deviation is computed for the flattened array by default, otherwise over the specified axis. Parameters ---------- a : array_like Calculate the standard deviation of these values. axis : None or int or tuple of ints, optional Axis or axes along which the standard deviation is computed. The default is to compute the standard deviation of the flattened array. .. versionadded: 1.7.0 If this is a tuple of ints, a standard deviation is performed over multiple axes, instead of a single axis or all the axes as before. dtype : dtype, optional Type to use in computing the standard deviation. For arrays of integer type the default is float64, for arrays of float types it is the same as the array type. out : ndarray, optional Alternative output array in which to place the result. It must have the same shape as the expected output but the type (of the calculated values) will be cast if necessary. ddof : int, optional Means Delta Degrees of Freedom. The divisor used in calculations is ``N - ddof``, where ``N`` represents the number of elements. By default `ddof` is zero. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `arr`. Returns ------- standard_deviation : ndarray, see dtype parameter above. If `out` is None, return a new array containing the standard deviation, otherwise return a reference to the output array. See Also -------- var, mean, nanmean, nanstd, nanvar numpy.doc.ufuncs : Section "Output arguments" Notes ----- The standard deviation is the square root of the average of the squared deviations from the mean, i.e., ``std = sqrt(mean(abs(x - x.mean())**2))``. The average squared deviation is normally calculated as ``x.sum() / N``, where ``N = len(x)``. If, however, `ddof` is specified, the divisor ``N - ddof`` is used instead. In standard statistical practice, ``ddof=1`` provides an unbiased estimator of the variance of the infinite population. ``ddof=0`` provides a maximum likelihood estimate of the variance for normally distributed variables. The standard deviation computed in this function is the square root of the estimated variance, so even with ``ddof=1``, it will not be an unbiased estimate of the standard deviation per se. Note that, for complex numbers, `std` takes the absolute value before squaring, so that the result is always real and nonnegative. For floating-point input, the *std* is computed using the same precision the input has. Depending on the input data, this can cause the results to be inaccurate, especially for float32 (see example below). Specifying a higher-accuracy accumulator using the `dtype` keyword can alleviate this issue. Examples -------- >>> a = np.array([[1, 2], [3, 4]]) >>> np.std(a) 1.1180339887498949 >>> np.std(a, axis=0) array([ 1., 1.]) >>> np.std(a, axis=1) array([ 0.5, 0.5]) In single precision, std() can be inaccurate: >>> a = np.zeros((2, 512*512), dtype=np.float32) >>> a[0, :] = 1.0 >>> a[1, :] = 0.1 >>> np.std(a) 0.45000005 Computing the standard deviation in float64 is more accurate: >>> np.std(a, dtype=np.float64) 0.44999999925494177 sum(a, axis=None, dtype=None, out=None, keepdims=False) Sum of array elements over a given axis. Parameters ---------- a : array_like Elements to sum. axis : None or int or tuple of ints, optional Axis or axes along which a sum is performed. The default (`axis` = `None`) is perform a sum over all the dimensions of the input array. `axis` may be negative, in which case it counts from the last to the first axis. .. versionadded:: 1.7.0 If this is a tuple of ints, a sum is performed on multiple axes, instead of a single axis or all the axes as before. dtype : dtype, optional The type of the returned array and of the accumulator in which the elements are summed. By default, the dtype of `a` is used. An exception is when `a` has an integer type with less precision than the default platform integer. In that case, the default platform integer is used instead. out : ndarray, optional Array into which the output is placed. By default, a new array is created. If `out` is given, it must be of the appropriate shape (the shape of `a` with `axis` removed, i.e., ``numpy.delete(a.shape, axis)``). Its type is preserved. See `doc.ufuncs` (Section "Output arguments") for more details. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `arr`. Returns ------- sum_along_axis : ndarray An array with the same shape as `a`, with the specified axis removed. If `a` is a 0-d array, or if `axis` is None, a scalar is returned. If an output array is specified, a reference to `out` is returned. See Also -------- ndarray.sum : Equivalent method. cumsum : Cumulative sum of array elements. trapz : Integration of array values using the composite trapezoidal rule. mean, average Notes ----- Arithmetic is modular when using integer types, and no error is raised on overflow. The sum of an empty array is the neutral element 0: >>> np.sum([]) 0.0 Examples -------- >>> np.sum([0.5, 1.5]) 2.0 >>> np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32) 1 >>> np.sum([[0, 1], [0, 5]]) 6 >>> np.sum([[0, 1], [0, 5]], axis=0) array([0, 6]) >>> np.sum([[0, 1], [0, 5]], axis=1) array([1, 5]) If the accumulator is too small, overflow occurs: >>> np.ones(128, dtype=np.int8).sum(dtype=np.int8) -128 swapaxes(a, axis1, axis2) Interchange two axes of an array. Parameters ---------- a : array_like Input array. axis1 : int First axis. axis2 : int Second axis. Returns ------- a_swapped : ndarray For Numpy >= 1.10, if `a` is an ndarray, then a view of `a` is returned; otherwise a new array is created. For earlier Numpy versions a view of `a` is returned only if the order of the axes is changed, otherwise the input array is returned. Examples -------- >>> x = np.array([[1,2,3]]) >>> np.swapaxes(x,0,1) array([[1], [2], [3]]) >>> x = np.array([[[0,1],[2,3]],[[4,5],[6,7]]]) >>> x array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]) >>> np.swapaxes(x,0,2) array([[[0, 4], [2, 6]], [[1, 5], [3, 7]]]) take(a, indices, axis=None, out=None, mode='raise') Take elements from an array along an axis. This function does the same thing as "fancy" indexing (indexing arrays using arrays); however, it can be easier to use if you need elements along a given axis. Parameters ---------- a : array_like The source array. indices : array_like The indices of the values to extract. .. versionadded:: 1.8.0 Also allow scalars for indices. axis : int, optional The axis over which to select values. By default, the flattened input array is used. out : ndarray, optional If provided, the result will be placed in this array. It should be of the appropriate shape and dtype. mode : {'raise', 'wrap', 'clip'}, optional Specifies how out-of-bounds indices will behave. * 'raise' -- raise an error (default) * 'wrap' -- wrap around * 'clip' -- clip to the range 'clip' mode means that all indices that are too large are replaced by the index that addresses the last element along that axis. Note that this disables indexing with negative numbers. Returns ------- subarray : ndarray The returned array has the same type as `a`. See Also -------- compress : Take elements using a boolean mask ndarray.take : equivalent method Examples -------- >>> a = [4, 3, 5, 7, 6, 8] >>> indices = [0, 1, 4] >>> np.take(a, indices) array([4, 3, 6]) In this example if `a` is an ndarray, "fancy" indexing can be used. >>> a = np.array(a) >>> a[indices] array([4, 3, 6]) If `indices` is not one dimensional, the output also has these dimensions. >>> np.take(a, [[0, 1], [2, 3]]) array([[4, 3], [5, 7]]) tensordot(a, b, axes=2) Compute tensor dot product along specified axes for arrays >= 1-D. Given two tensors (arrays of dimension greater than or equal to one), `a` and `b`, and an array_like object containing two array_like objects, ``(a_axes, b_axes)``, sum the products of `a`'s and `b`'s elements (components) over the axes specified by ``a_axes`` and ``b_axes``. The third argument can be a single non-negative integer_like scalar, ``N``; if it is such, then the last ``N`` dimensions of `a` and the first ``N`` dimensions of `b` are summed over. Parameters ---------- a, b : array_like, len(shape) >= 1 Tensors to "dot". axes : int or (2,) array_like * integer_like If an int N, sum over the last N axes of `a` and the first N axes of `b` in order. The sizes of the corresponding axes must match. * (2,) array_like Or, a list of axes to be summed over, first sequence applying to `a`, second to `b`. Both elements array_like must be of the same length. See Also -------- dot, einsum Notes ----- Three common use cases are: ``axes = 0`` : tensor product $a\otimes b$ ``axes = 1`` : tensor dot product $a\cdot b$ ``axes = 2`` : (default) tensor double contraction $a:b$ When `axes` is integer_like, the sequence for evaluation will be: first the -Nth axis in `a` and 0th axis in `b`, and the -1th axis in `a` and Nth axis in `b` last. When there is more than one axis to sum over - and they are not the last (first) axes of `a` (`b`) - the argument `axes` should consist of two sequences of the same length, with the first axis to sum over given first in both sequences, the second axis second, and so forth. Examples -------- A "traditional" example: >>> a = np.arange(60.).reshape(3,4,5) >>> b = np.arange(24.).reshape(4,3,2) >>> c = np.tensordot(a,b, axes=([1,0],[0,1])) >>> c.shape (5, 2) >>> c array([[ 4400., 4730.], [ 4532., 4874.], [ 4664., 5018.], [ 4796., 5162.], [ 4928., 5306.]]) >>> # A slower but equivalent way of computing the same... >>> d = np.zeros((5,2)) >>> for i in range(5): ... for j in range(2): ... for k in range(3): ... for n in range(4): ... d[i,j] += a[k,n,i] * b[n,k,j] >>> c == d array([[ True, True], [ True, True], [ True, True], [ True, True], [ True, True]], dtype=bool) An extended example taking advantage of the overloading of + and \*: >>> a = np.array(range(1, 9)) >>> a.shape = (2, 2, 2) >>> A = np.array(('a', 'b', 'c', 'd'), dtype=object) >>> A.shape = (2, 2) >>> a; A array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) array([[a, b], [c, d]], dtype=object) >>> np.tensordot(a, A) # third argument default is 2 for double-contraction array([abbcccdddd, aaaaabbbbbbcccccccdddddddd], dtype=object) >>> np.tensordot(a, A, 1) array([[[acc, bdd], [aaacccc, bbbdddd]], [[aaaaacccccc, bbbbbdddddd], [aaaaaaacccccccc, bbbbbbbdddddddd]]], dtype=object) >>> np.tensordot(a, A, 0) # tensor product (result too long to incl.) array([[[[[a, b], [c, d]], ... >>> np.tensordot(a, A, (0, 1)) array([[[abbbbb, cddddd], [aabbbbbb, ccdddddd]], [[aaabbbbbbb, cccddddddd], [aaaabbbbbbbb, ccccdddddddd]]], dtype=object) >>> np.tensordot(a, A, (2, 1)) array([[[abb, cdd], [aaabbbb, cccdddd]], [[aaaaabbbbbb, cccccdddddd], [aaaaaaabbbbbbbb, cccccccdddddddd]]], dtype=object) >>> np.tensordot(a, A, ((0, 1), (0, 1))) array([abbbcccccddddddd, aabbbbccccccdddddddd], dtype=object) >>> np.tensordot(a, A, ((2, 1), (1, 0))) array([acccbbdddd, aaaaacccccccbbbbbbdddddddd], dtype=object) tile(A, reps) Construct an array by repeating A the number of times given by reps. If `reps` has length ``d``, the result will have dimension of ``max(d, A.ndim)``. If ``A.ndim < d``, `A` is promoted to be d-dimensional by prepending new axes. So a shape (3,) array is promoted to (1, 3) for 2-D replication, or shape (1, 1, 3) for 3-D replication. If this is not the desired behavior, promote `A` to d-dimensions manually before calling this function. If ``A.ndim > d``, `reps` is promoted to `A`.ndim by pre-pending 1's to it. Thus for an `A` of shape (2, 3, 4, 5), a `reps` of (2, 2) is treated as (1, 1, 2, 2). Parameters ---------- A : array_like The input array. reps : array_like The number of repetitions of `A` along each axis. Returns ------- c : ndarray The tiled output array. See Also -------- repeat : Repeat elements of an array. Examples -------- >>> a = np.array([0, 1, 2]) >>> np.tile(a, 2) array([0, 1, 2, 0, 1, 2]) >>> np.tile(a, (2, 2)) array([[0, 1, 2, 0, 1, 2], [0, 1, 2, 0, 1, 2]]) >>> np.tile(a, (2, 1, 2)) array([[[0, 1, 2, 0, 1, 2]], [[0, 1, 2, 0, 1, 2]]]) >>> b = np.array([[1, 2], [3, 4]]) >>> np.tile(b, 2) array([[1, 2, 1, 2], [3, 4, 3, 4]]) >>> np.tile(b, (2, 1)) array([[1, 2], [3, 4], [1, 2], [3, 4]]) trace(a, offset=0, axis1=0, axis2=1, dtype=None, out=None) Return the sum along diagonals of the array. If `a` is 2-D, the sum along its diagonal with the given offset is returned, i.e., the sum of elements ``a[i,i+offset]`` for all i. If `a` has more than two dimensions, then the axes specified by axis1 and axis2 are used to determine the 2-D sub-arrays whose traces are returned. The shape of the resulting array is the same as that of `a` with `axis1` and `axis2` removed. Parameters ---------- a : array_like Input array, from which the diagonals are taken. offset : int, optional Offset of the diagonal from the main diagonal. Can be both positive and negative. Defaults to 0. axis1, axis2 : int, optional Axes to be used as the first and second axis of the 2-D sub-arrays from which the diagonals should be taken. Defaults are the first two axes of `a`. dtype : dtype, optional Determines the data-type of the returned array and of the accumulator where the elements are summed. If dtype has the value None and `a` is of integer type of precision less than the default integer precision, then the default integer precision is used. Otherwise, the precision is the same as that of `a`. out : ndarray, optional Array into which the output is placed. Its type is preserved and it must be of the right shape to hold the output. Returns ------- sum_along_diagonals : ndarray If `a` is 2-D, the sum along the diagonal is returned. If `a` has larger dimensions, then an array of sums along diagonals is returned. See Also -------- diag, diagonal, diagflat Examples -------- >>> np.trace(np.eye(3)) 3.0 >>> a = np.arange(8).reshape((2,2,2)) >>> np.trace(a) array([6, 8]) >>> a = np.arange(24).reshape((2,2,2,3)) >>> np.trace(a).shape (2, 3) transpose(a, axes=None) Permute the dimensions of an array. Parameters ---------- a : array_like Input array. axes : list of ints, optional By default, reverse the dimensions, otherwise permute the axes according to the values given. Returns ------- p : ndarray `a` with its axes permuted. A view is returned whenever possible. See Also -------- rollaxis argsort Notes ----- Use `transpose(a, argsort(axes))` to invert the transposition of tensors when using the `axes` keyword argument. Transposing a 1-D array returns an unchanged view of the original array. Examples -------- >>> x = np.arange(4).reshape((2,2)) >>> x array([[0, 1], [2, 3]]) >>> np.transpose(x) array([[0, 2], [1, 3]]) >>> x = np.ones((1, 2, 3)) >>> np.transpose(x, (1, 0, 2)).shape (2, 1, 3) trapz(y, x=None, dx=1.0, axis=-1) Integrate along the given axis using the composite trapezoidal rule. Integrate `y` (`x`) along given axis. Parameters ---------- y : array_like Input array to integrate. x : array_like, optional If `x` is None, then spacing between all `y` elements is `dx`. dx : scalar, optional If `x` is None, spacing given by `dx` is assumed. Default is 1. axis : int, optional Specify the axis. Returns ------- trapz : float Definite integral as approximated by trapezoidal rule. See Also -------- sum, cumsum Notes ----- Image [2]_ illustrates trapezoidal rule -- y-axis locations of points will be taken from `y` array, by default x-axis distances between points will be 1.0, alternatively they can be provided with `x` array or with `dx` scalar. Return value will be equal to combined area under the red lines. References ---------- .. [1] Wikipedia page: http://en.wikipedia.org/wiki/Trapezoidal_rule .. [2] Illustration image: http://en.wikipedia.org/wiki/File:Composite_trapezoidal_rule_illustration.png Examples -------- >>> np.trapz([1,2,3]) 4.0 >>> np.trapz([1,2,3], x=[4,6,8]) 8.0 >>> np.trapz([1,2,3], dx=2) 8.0 >>> a = np.arange(6).reshape(2, 3) >>> a array([[0, 1, 2], [3, 4, 5]]) >>> np.trapz(a, axis=0) array([ 1.5, 2.5, 3.5]) >>> np.trapz(a, axis=1) array([ 2., 8.]) tri(N, M=None, k=0, dtype=) An array with ones at and below the given diagonal and zeros elsewhere. Parameters ---------- N : int Number of rows in the array. M : int, optional Number of columns in the array. By default, `M` is taken equal to `N`. k : int, optional The sub-diagonal at and below which the array is filled. `k` = 0 is the main diagonal, while `k` < 0 is below it, and `k` > 0 is above. The default is 0. dtype : dtype, optional Data type of the returned array. The default is float. Returns ------- tri : ndarray of shape (N, M) Array with its lower triangle filled with ones and zero elsewhere; in other words ``T[i,j] == 1`` for ``i <= j + k``, 0 otherwise. Examples -------- >>> np.tri(3, 5, 2, dtype=int) array([[1, 1, 1, 0, 0], [1, 1, 1, 1, 0], [1, 1, 1, 1, 1]]) >>> np.tri(3, 5, -1) array([[ 0., 0., 0., 0., 0.], [ 1., 0., 0., 0., 0.], [ 1., 1., 0., 0., 0.]]) tril(m, k=0) Lower triangle of an array. Return a copy of an array with elements above the `k`-th diagonal zeroed. Parameters ---------- m : array_like, shape (M, N) Input array. k : int, optional Diagonal above which to zero elements. `k = 0` (the default) is the main diagonal, `k < 0` is below it and `k > 0` is above. Returns ------- tril : ndarray, shape (M, N) Lower triangle of `m`, of same shape and data-type as `m`. See Also -------- triu : same thing, only for the upper triangle Examples -------- >>> np.tril([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1) array([[ 0, 0, 0], [ 4, 0, 0], [ 7, 8, 0], [10, 11, 12]]) tril_indices(n, k=0, m=None) Return the indices for the lower-triangle of an (n, m) array. Parameters ---------- n : int The row dimension of the arrays for which the returned indices will be valid. k : int, optional Diagonal offset (see `tril` for details). m : int, optional .. versionadded:: 1.9.0 The column dimension of the arrays for which the returned arrays will be valid. By default `m` is taken equal to `n`. Returns ------- inds : tuple of arrays The indices for the triangle. The returned tuple contains two arrays, each with the indices along one dimension of the array. See also -------- triu_indices : similar function, for upper-triangular. mask_indices : generic function accepting an arbitrary mask function. tril, triu Notes ----- .. versionadded:: 1.4.0 Examples -------- Compute two different sets of indices to access 4x4 arrays, one for the lower triangular part starting at the main diagonal, and one starting two diagonals further right: >>> il1 = np.tril_indices(4) >>> il2 = np.tril_indices(4, 2) Here is how they can be used with a sample array: >>> a = np.arange(16).reshape(4, 4) >>> a array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15]]) Both for indexing: >>> a[il1] array([ 0, 4, 5, 8, 9, 10, 12, 13, 14, 15]) And for assigning values: >>> a[il1] = -1 >>> a array([[-1, 1, 2, 3], [-1, -1, 6, 7], [-1, -1, -1, 11], [-1, -1, -1, -1]]) These cover almost the whole array (two diagonals right of the main one): >>> a[il2] = -10 >>> a array([[-10, -10, -10, 3], [-10, -10, -10, -10], [-10, -10, -10, -10], [-10, -10, -10, -10]]) tril_indices_from(arr, k=0) Return the indices for the lower-triangle of arr. See `tril_indices` for full details. Parameters ---------- arr : array_like The indices will be valid for square arrays whose dimensions are the same as arr. k : int, optional Diagonal offset (see `tril` for details). See Also -------- tril_indices, tril Notes ----- .. versionadded:: 1.4.0 trim_zeros(filt, trim='fb') Trim the leading and/or trailing zeros from a 1-D array or sequence. Parameters ---------- filt : 1-D array or sequence Input array. trim : str, optional A string with 'f' representing trim from front and 'b' to trim from back. Default is 'fb', trim zeros from both front and back of the array. Returns ------- trimmed : 1-D array or sequence The result of trimming the input. The input data type is preserved. Examples -------- >>> a = np.array((0, 0, 0, 1, 2, 3, 0, 2, 1, 0)) >>> np.trim_zeros(a) array([1, 2, 3, 0, 2, 1]) >>> np.trim_zeros(a, 'b') array([0, 0, 0, 1, 2, 3, 0, 2, 1]) The input data type is preserved, list/tuple in means list/tuple out. >>> np.trim_zeros([0, 1, 2, 0]) [1, 2] triu(m, k=0) Upper triangle of an array. Return a copy of a matrix with the elements below the `k`-th diagonal zeroed. Please refer to the documentation for `tril` for further details. See Also -------- tril : lower triangle of an array Examples -------- >>> np.triu([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1) array([[ 1, 2, 3], [ 4, 5, 6], [ 0, 8, 9], [ 0, 0, 12]]) triu_indices(n, k=0, m=None) Return the indices for the upper-triangle of an (n, m) array. Parameters ---------- n : int The size of the arrays for which the returned indices will be valid. k : int, optional Diagonal offset (see `triu` for details). m : int, optional .. versionadded:: 1.9.0 The column dimension of the arrays for which the returned arrays will be valid. By default `m` is taken equal to `n`. Returns ------- inds : tuple, shape(2) of ndarrays, shape(`n`) The indices for the triangle. The returned tuple contains two arrays, each with the indices along one dimension of the array. Can be used to slice a ndarray of shape(`n`, `n`). See also -------- tril_indices : similar function, for lower-triangular. mask_indices : generic function accepting an arbitrary mask function. triu, tril Notes ----- .. versionadded:: 1.4.0 Examples -------- Compute two different sets of indices to access 4x4 arrays, one for the upper triangular part starting at the main diagonal, and one starting two diagonals further right: >>> iu1 = np.triu_indices(4) >>> iu2 = np.triu_indices(4, 2) Here is how they can be used with a sample array: >>> a = np.arange(16).reshape(4, 4) >>> a array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15]]) Both for indexing: >>> a[iu1] array([ 0, 1, 2, 3, 5, 6, 7, 10, 11, 15]) And for assigning values: >>> a[iu1] = -1 >>> a array([[-1, -1, -1, -1], [ 4, -1, -1, -1], [ 8, 9, -1, -1], [12, 13, 14, -1]]) These cover only a small part of the whole array (two diagonals right of the main one): >>> a[iu2] = -10 >>> a array([[ -1, -1, -10, -10], [ 4, -1, -1, -10], [ 8, 9, -1, -1], [ 12, 13, 14, -1]]) triu_indices_from(arr, k=0) Return the indices for the upper-triangle of arr. See `triu_indices` for full details. Parameters ---------- arr : ndarray, shape(N, N) The indices will be valid for square arrays. k : int, optional Diagonal offset (see `triu` for details). Returns ------- triu_indices_from : tuple, shape(2) of ndarray, shape(N) Indices for the upper-triangle of `arr`. See Also -------- triu_indices, triu Notes ----- .. versionadded:: 1.4.0 typename(char) Return a description for the given data type code. Parameters ---------- char : str Data type code. Returns ------- out : str Description of the input data type code. See Also -------- dtype, typecodes Examples -------- >>> typechars = ['S1', '?', 'B', 'D', 'G', 'F', 'I', 'H', 'L', 'O', 'Q', ... 'S', 'U', 'V', 'b', 'd', 'g', 'f', 'i', 'h', 'l', 'q'] >>> for typechar in typechars: ... print typechar, ' : ', np.typename(typechar) ... S1 : character ? : bool B : unsigned char D : complex double precision G : complex long double precision F : complex single precision I : unsigned integer H : unsigned short L : unsigned long integer O : object Q : unsigned long long integer S : string U : unicode V : void b : signed char d : double precision g : long precision f : single precision i : integer h : short l : long integer q : long long integer union1d(ar1, ar2) Find the union of two arrays. Return the unique, sorted array of values that are in either of the two input arrays. Parameters ---------- ar1, ar2 : array_like Input arrays. They are flattened if they are not already 1D. Returns ------- union1d : ndarray Unique, sorted union of the input arrays. See Also -------- numpy.lib.arraysetops : Module with a number of other functions for performing set operations on arrays. Examples -------- >>> np.union1d([-1, 0, 1], [-2, 0, 2]) array([-2, -1, 0, 1, 2]) To find the union of more than two arrays, use functools.reduce: >>> from functools import reduce >>> reduce(np.union1d, ([1, 3, 4, 3], [3, 1, 2, 1], [6, 3, 4, 2])) array([1, 2, 3, 4, 6]) unique(ar, return_index=False, return_inverse=False, return_counts=False) Find the unique elements of an array. Returns the sorted unique elements of an array. There are three optional outputs in addition to the unique elements: the indices of the input array that give the unique values, the indices of the unique array that reconstruct the input array, and the number of times each unique value comes up in the input array. Parameters ---------- ar : array_like Input array. This will be flattened if it is not already 1-D. return_index : bool, optional If True, also return the indices of `ar` that result in the unique array. return_inverse : bool, optional If True, also return the indices of the unique array that can be used to reconstruct `ar`. return_counts : bool, optional If True, also return the number of times each unique value comes up in `ar`. .. versionadded:: 1.9.0 Returns ------- unique : ndarray The sorted unique values. unique_indices : ndarray, optional The indices of the first occurrences of the unique values in the (flattened) original array. Only provided if `return_index` is True. unique_inverse : ndarray, optional The indices to reconstruct the (flattened) original array from the unique array. Only provided if `return_inverse` is True. unique_counts : ndarray, optional The number of times each of the unique values comes up in the original array. Only provided if `return_counts` is True. .. versionadded:: 1.9.0 See Also -------- numpy.lib.arraysetops : Module with a number of other functions for performing set operations on arrays. Examples -------- >>> np.unique([1, 1, 2, 2, 3, 3]) array([1, 2, 3]) >>> a = np.array([[1, 1], [2, 3]]) >>> np.unique(a) array([1, 2, 3]) Return the indices of the original array that give the unique values: >>> a = np.array(['a', 'b', 'b', 'c', 'a']) >>> u, indices = np.unique(a, return_index=True) >>> u array(['a', 'b', 'c'], dtype='|S1') >>> indices array([0, 1, 3]) >>> a[indices] array(['a', 'b', 'c'], dtype='|S1') Reconstruct the input array from the unique values: >>> a = np.array([1, 2, 6, 4, 2, 3, 2]) >>> u, indices = np.unique(a, return_inverse=True) >>> u array([1, 2, 3, 4, 6]) >>> indices array([0, 1, 4, 3, 1, 2, 1]) >>> u[indices] array([1, 2, 6, 4, 2, 3, 2]) unpackbits(...) unpackbits(myarray, axis=None) Unpacks elements of a uint8 array into a binary-valued output array. Each element of `myarray` represents a bit-field that should be unpacked into a binary-valued output array. The shape of the output array is either 1-D (if `axis` is None) or the same shape as the input array with unpacking done along the axis specified. Parameters ---------- myarray : ndarray, uint8 type Input array. axis : int, optional Unpacks along this axis. Returns ------- unpacked : ndarray, uint8 type The elements are binary-valued (0 or 1). See Also -------- packbits : Packs the elements of a binary-valued array into bits in a uint8 array. Examples -------- >>> a = np.array([[2], [7], [23]], dtype=np.uint8) >>> a array([[ 2], [ 7], [23]], dtype=uint8) >>> b = np.unpackbits(a, axis=1) >>> b array([[0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 0, 1, 1, 1]], dtype=uint8) unravel_index(...) unravel_index(indices, dims, order='C') Converts a flat index or array of flat indices into a tuple of coordinate arrays. Parameters ---------- indices : array_like An integer array whose elements are indices into the flattened version of an array of dimensions ``dims``. Before version 1.6.0, this function accepted just one index value. dims : tuple of ints The shape of the array to use for unraveling ``indices``. order : {'C', 'F'}, optional Determines whether the indices should be viewed as indexing in row-major (C-style) or column-major (Fortran-style) order. .. versionadded:: 1.6.0 Returns ------- unraveled_coords : tuple of ndarray Each array in the tuple has the same shape as the ``indices`` array. See Also -------- ravel_multi_index Examples -------- >>> np.unravel_index([22, 41, 37], (7,6)) (array([3, 6, 6]), array([4, 5, 1])) >>> np.unravel_index([31, 41, 13], (7,6), order='F') (array([3, 6, 6]), array([4, 5, 1])) >>> np.unravel_index(1621, (6,7,8,9)) (3, 1, 4, 1) unwrap(p, discont=3.141592653589793, axis=-1) Unwrap by changing deltas between values to 2*pi complement. Unwrap radian phase `p` by changing absolute jumps greater than `discont` to their 2*pi complement along the given axis. Parameters ---------- p : array_like Input array. discont : float, optional Maximum discontinuity between values, default is ``pi``. axis : int, optional Axis along which unwrap will operate, default is the last axis. Returns ------- out : ndarray Output array. See Also -------- rad2deg, deg2rad Notes ----- If the discontinuity in `p` is smaller than ``pi``, but larger than `discont`, no unwrapping is done because taking the 2*pi complement would only make the discontinuity larger. Examples -------- >>> phase = np.linspace(0, np.pi, num=5) >>> phase[3:] += np.pi >>> phase array([ 0. , 0.78539816, 1.57079633, 5.49778714, 6.28318531]) >>> np.unwrap(phase) array([ 0. , 0.78539816, 1.57079633, -0.78539816, 0. ]) vander(x, N=None, increasing=False) Generate a Vandermonde matrix. The columns of the output matrix are powers of the input vector. The order of the powers is determined by the `increasing` boolean argument. Specifically, when `increasing` is False, the `i`-th output column is the input vector raised element-wise to the power of ``N - i - 1``. Such a matrix with a geometric progression in each row is named for Alexandre- Theophile Vandermonde. Parameters ---------- x : array_like 1-D input array. N : int, optional Number of columns in the output. If `N` is not specified, a square array is returned (``N = len(x)``). increasing : bool, optional Order of the powers of the columns. If True, the powers increase from left to right, if False (the default) they are reversed. .. versionadded:: 1.9.0 Returns ------- out : ndarray Vandermonde matrix. If `increasing` is False, the first column is ``x^(N-1)``, the second ``x^(N-2)`` and so forth. If `increasing` is True, the columns are ``x^0, x^1, ..., x^(N-1)``. See Also -------- polynomial.polynomial.polyvander Examples -------- >>> x = np.array([1, 2, 3, 5]) >>> N = 3 >>> np.vander(x, N) array([[ 1, 1, 1], [ 4, 2, 1], [ 9, 3, 1], [25, 5, 1]]) >>> np.column_stack([x**(N-1-i) for i in range(N)]) array([[ 1, 1, 1], [ 4, 2, 1], [ 9, 3, 1], [25, 5, 1]]) >>> x = np.array([1, 2, 3, 5]) >>> np.vander(x) array([[ 1, 1, 1, 1], [ 8, 4, 2, 1], [ 27, 9, 3, 1], [125, 25, 5, 1]]) >>> np.vander(x, increasing=True) array([[ 1, 1, 1, 1], [ 1, 2, 4, 8], [ 1, 3, 9, 27], [ 1, 5, 25, 125]]) The determinant of a square Vandermonde matrix is the product of the differences between the values of the input vector: >>> np.linalg.det(np.vander(x)) 48.000000000000043 >>> (5-3)*(5-2)*(5-1)*(3-2)*(3-1)*(2-1) 48 var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False) Compute the variance along the specified axis. Returns the variance of the array elements, a measure of the spread of a distribution. The variance is computed for the flattened array by default, otherwise over the specified axis. Parameters ---------- a : array_like Array containing numbers whose variance is desired. If `a` is not an array, a conversion is attempted. axis : None or int or tuple of ints, optional Axis or axes along which the variance is computed. The default is to compute the variance of the flattened array. .. versionadded: 1.7.0 If this is a tuple of ints, a variance is performed over multiple axes, instead of a single axis or all the axes as before. dtype : data-type, optional Type to use in computing the variance. For arrays of integer type the default is `float32`; for arrays of float types it is the same as the array type. out : ndarray, optional Alternate output array in which to place the result. It must have the same shape as the expected output, but the type is cast if necessary. ddof : int, optional "Delta Degrees of Freedom": the divisor used in the calculation is ``N - ddof``, where ``N`` represents the number of elements. By default `ddof` is zero. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `arr`. Returns ------- variance : ndarray, see dtype parameter above If ``out=None``, returns a new array containing the variance; otherwise, a reference to the output array is returned. See Also -------- std , mean, nanmean, nanstd, nanvar numpy.doc.ufuncs : Section "Output arguments" Notes ----- The variance is the average of the squared deviations from the mean, i.e., ``var = mean(abs(x - x.mean())**2)``. The mean is normally calculated as ``x.sum() / N``, where ``N = len(x)``. If, however, `ddof` is specified, the divisor ``N - ddof`` is used instead. In standard statistical practice, ``ddof=1`` provides an unbiased estimator of the variance of a hypothetical infinite population. ``ddof=0`` provides a maximum likelihood estimate of the variance for normally distributed variables. Note that for complex numbers, the absolute value is taken before squaring, so that the result is always real and nonnegative. For floating-point input, the variance is computed using the same precision the input has. Depending on the input data, this can cause the results to be inaccurate, especially for `float32` (see example below). Specifying a higher-accuracy accumulator using the ``dtype`` keyword can alleviate this issue. Examples -------- >>> a = np.array([[1, 2], [3, 4]]) >>> np.var(a) 1.25 >>> np.var(a, axis=0) array([ 1., 1.]) >>> np.var(a, axis=1) array([ 0.25, 0.25]) In single precision, var() can be inaccurate: >>> a = np.zeros((2, 512*512), dtype=np.float32) >>> a[0, :] = 1.0 >>> a[1, :] = 0.1 >>> np.var(a) 0.20250003 Computing the variance in float64 is more accurate: >>> np.var(a, dtype=np.float64) 0.20249999932944759 >>> ((1-0.55)**2 + (0.1-0.55)**2)/2 0.2025 vdot(...) vdot(a, b) Return the dot product of two vectors. The vdot(`a`, `b`) function handles complex numbers differently than dot(`a`, `b`). If the first argument is complex the complex conjugate of the first argument is used for the calculation of the dot product. Note that `vdot` handles multidimensional arrays differently than `dot`: it does *not* perform a matrix product, but flattens input arguments to 1-D vectors first. Consequently, it should only be used for vectors. Parameters ---------- a : array_like If `a` is complex the complex conjugate is taken before calculation of the dot product. b : array_like Second argument to the dot product. Returns ------- output : ndarray Dot product of `a` and `b`. Can be an int, float, or complex depending on the types of `a` and `b`. See Also -------- dot : Return the dot product without using the complex conjugate of the first argument. Examples -------- >>> a = np.array([1+2j,3+4j]) >>> b = np.array([5+6j,7+8j]) >>> np.vdot(a, b) (70-8j) >>> np.vdot(b, a) (70+8j) Note that higher-dimensional arrays are flattened! >>> a = np.array([[1, 4], [5, 6]]) >>> b = np.array([[4, 1], [2, 2]]) >>> np.vdot(a, b) 30 >>> np.vdot(b, a) 30 >>> 1*4 + 4*1 + 5*2 + 6*2 30 vsplit(ary, indices_or_sections) Split an array into multiple sub-arrays vertically (row-wise). Please refer to the ``split`` documentation. ``vsplit`` is equivalent to ``split`` with `axis=0` (default), the array is always split along the first axis regardless of the array dimension. See Also -------- split : Split an array into multiple sub-arrays of equal size. Examples -------- >>> x = np.arange(16.0).reshape(4, 4) >>> x array([[ 0., 1., 2., 3.], [ 4., 5., 6., 7.], [ 8., 9., 10., 11.], [ 12., 13., 14., 15.]]) >>> np.vsplit(x, 2) [array([[ 0., 1., 2., 3.], [ 4., 5., 6., 7.]]), array([[ 8., 9., 10., 11.], [ 12., 13., 14., 15.]])] >>> np.vsplit(x, np.array([3, 6])) [array([[ 0., 1., 2., 3.], [ 4., 5., 6., 7.], [ 8., 9., 10., 11.]]), array([[ 12., 13., 14., 15.]]), array([], dtype=float64)] With a higher dimensional array the split is still along the first axis. >>> x = np.arange(8.0).reshape(2, 2, 2) >>> x array([[[ 0., 1.], [ 2., 3.]], [[ 4., 5.], [ 6., 7.]]]) >>> np.vsplit(x, 2) [array([[[ 0., 1.], [ 2., 3.]]]), array([[[ 4., 5.], [ 6., 7.]]])] vstack(tup) Stack arrays in sequence vertically (row wise). Take a sequence of arrays and stack them vertically to make a single array. Rebuild arrays divided by `vsplit`. Parameters ---------- tup : sequence of ndarrays Tuple containing arrays to be stacked. The arrays must have the same shape along all but the first axis. Returns ------- stacked : ndarray The array formed by stacking the given arrays. See Also -------- stack : Join a sequence of arrays along a new axis. hstack : Stack arrays in sequence horizontally (column wise). dstack : Stack arrays in sequence depth wise (along third dimension). concatenate : Join a sequence of arrays along an existing axis. vsplit : Split array into a list of multiple sub-arrays vertically. Notes ----- Equivalent to ``np.concatenate(tup, axis=0)`` if `tup` contains arrays that are at least 2-dimensional. Examples -------- >>> a = np.array([1, 2, 3]) >>> b = np.array([2, 3, 4]) >>> np.vstack((a,b)) array([[1, 2, 3], [2, 3, 4]]) >>> a = np.array([[1], [2], [3]]) >>> b = np.array([[2], [3], [4]]) >>> np.vstack((a,b)) array([[1], [2], [3], [2], [3], [4]]) where(...) where(condition, [x, y]) Return elements, either from `x` or `y`, depending on `condition`. If only `condition` is given, return ``condition.nonzero()``. Parameters ---------- condition : array_like, bool When True, yield `x`, otherwise yield `y`. x, y : array_like, optional Values from which to choose. `x` and `y` need to have the same shape as `condition`. Returns ------- out : ndarray or tuple of ndarrays If both `x` and `y` are specified, the output array contains elements of `x` where `condition` is True, and elements from `y` elsewhere. If only `condition` is given, return the tuple ``condition.nonzero()``, the indices where `condition` is True. See Also -------- nonzero, choose Notes ----- If `x` and `y` are given and input arrays are 1-D, `where` is equivalent to:: [xv if c else yv for (c,xv,yv) in zip(condition,x,y)] Examples -------- >>> np.where([[True, False], [True, True]], ... [[1, 2], [3, 4]], ... [[9, 8], [7, 6]]) array([[1, 8], [3, 4]]) >>> np.where([[0, 1], [1, 0]]) (array([0, 1]), array([1, 0])) >>> x = np.arange(9.).reshape(3, 3) >>> np.where( x > 5 ) (array([2, 2, 2]), array([0, 1, 2])) >>> x[np.where( x > 3.0 )] # Note: result is 1D. array([ 4., 5., 6., 7., 8.]) >>> np.where(x < 5, x, -1) # Note: broadcasting. array([[ 0., 1., 2.], [ 3., 4., -1.], [-1., -1., -1.]]) Find the indices of elements of `x` that are in `goodvalues`. >>> goodvalues = [3, 4, 7] >>> ix = np.in1d(x.ravel(), goodvalues).reshape(x.shape) >>> ix array([[False, False, False], [ True, True, False], [False, True, False]], dtype=bool) >>> np.where(ix) (array([1, 1, 2]), array([0, 1, 1])) who(vardict=None) Print the Numpy arrays in the given dictionary. If there is no dictionary passed in or `vardict` is None then returns Numpy arrays in the globals() dictionary (all Numpy arrays in the namespace). Parameters ---------- vardict : dict, optional A dictionary possibly containing ndarrays. Default is globals(). Returns ------- out : None Returns 'None'. Notes ----- Prints out the name, shape, bytes and type of all of the ndarrays present in `vardict`. Examples -------- >>> a = np.arange(10) >>> b = np.ones(20) >>> np.who() Name Shape Bytes Type =========================================================== a 10 40 int32 b 20 160 float64 Upper bound on total bytes = 200 >>> d = {'x': np.arange(2.0), 'y': np.arange(3.0), 'txt': 'Some str', ... 'idx':5} >>> np.who(d) Name Shape Bytes Type =========================================================== y 3 24 float64 x 2 16 float64 Upper bound on total bytes = 40 zeros(...) zeros(shape, dtype=float, order='C') Return a new array of given shape and type, filled with zeros. Parameters ---------- shape : int or sequence of ints Shape of the new array, e.g., ``(2, 3)`` or ``2``. dtype : data-type, optional The desired data-type for the array, e.g., `numpy.int8`. Default is `numpy.float64`. order : {'C', 'F'}, optional Whether to store multidimensional data in C- or Fortran-contiguous (row- or column-wise) order in memory. Returns ------- out : ndarray Array of zeros with the given shape, dtype, and order. See Also -------- zeros_like : Return an array of zeros with shape and type of input. ones_like : Return an array of ones with shape and type of input. empty_like : Return an empty array with shape and type of input. ones : Return a new array setting values to one. empty : Return a new uninitialized array. Examples -------- >>> np.zeros(5) array([ 0., 0., 0., 0., 0.]) >>> np.zeros((5,), dtype=np.int) array([0, 0, 0, 0, 0]) >>> np.zeros((2, 1)) array([[ 0.], [ 0.]]) >>> s = (2,2) >>> np.zeros(s) array([[ 0., 0.], [ 0., 0.]]) >>> np.zeros((2,), dtype=[('x', 'i4'), ('y', 'i4')]) # custom dtype array([(0, 0), (0, 0)], dtype=[('x', '>> x = np.arange(6) >>> x = x.reshape((2, 3)) >>> x array([[0, 1, 2], [3, 4, 5]]) >>> np.zeros_like(x) array([[0, 0, 0], [0, 0, 0]]) >>> y = np.arange(3, dtype=np.float) >>> y array([ 0., 1., 2.]) >>> np.zeros_like(y) array([ 0., 0., 0.]) DATA ALLOW_THREADS = 1 BUFSIZE = 8192 CLIP = 0 ERR_CALL = 3 ERR_DEFAULT = 521 ERR_IGNORE = 0 ERR_LOG = 5 ERR_PRINT = 4 ERR_RAISE = 2 ERR_WARN = 1 FLOATING_POINT_SUPPORT = 1 FPE_DIVIDEBYZERO = 1 FPE_INVALID = 8 FPE_OVERFLOW = 2 FPE_UNDERFLOW = 4 False_ = False Inf = inf Infinity = inf MAXDIMS = 32 NAN = nan NINF = -inf NZERO = -0.0 NaN = nan PINF = inf PZERO = 0.0 RAISE = 2 SHIFT_DIVIDEBYZERO = 0 SHIFT_INVALID = 9 SHIFT_OVERFLOW = 3 SHIFT_UNDERFLOW = 6 ScalarType = (, , , add = arccos = arccosh = arcsin = arcsinh = arctan = arctan2 = arctanh = bitwise_and = bitwise_not = bitwise_or = bitwise_xor = c_ = cast = {: at ...128'>: ceil = conj = conjugate = copysign = cos = cosh = deg2rad = degrees = divide = e = 2.718281828459045 equal = euler_gamma = 0.5772156649015329 exp = exp2 = expm1 = fabs = floor = floor_divide = fmax = fmin = fmod = frexp = greater = greater_equal = hypot = index_exp = inf = inf infty = inf invert = isfinite = isinf = isnan = ldexp = left_shift = less = less_equal = little_endian = True log = log10 = log1p = log2 = logaddexp = logaddexp2 = logical_and = logical_not = logical_or = logical_xor = maximum = mgrid = minimum = mod = modf = multiply = nan = nan nbytes = {: 0, newaxis = None nextafter = not_equal = ogrid = pi = 3.141592653589793 power = r_ = rad2deg = radians = reciprocal = remainder = right_shift = rint = s_ = sctypeDict = {0: , 1: , 2: , '... sctypes = {'complex': [, signbit = sin = sinh = spacing = sqrt = square = subtract = tan = tanh = true_divide = trunc = typeDict = {0: , 1: , 2: , 'Co... typecodes = {'All': '?bhilqpBHILQPefdgFDGSUVOMm', 'AllFloat': 'efdgFDG... VERSION 1.10.4 >>>