doc_content stringlengths 1 386k | doc_id stringlengths 5 188 |
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numpy.distutils.ccompiler_opt.CCompilerOpt.feature_get_til method distutils.ccompiler_opt.CCompilerOpt.feature_get_til(names, keyisfalse)[source]
same as feature_implies_c() but stop collecting implied features when feature’s option that provided through parameter ‘keyisfalse’ is False, also sorting the returned fe... | numpy.reference.generated.numpy.distutils.ccompiler_opt.ccompileropt.feature_get_til |
numpy.distutils.ccompiler_opt.CCompilerOpt.feature_implies method distutils.ccompiler_opt.CCompilerOpt.feature_implies(names, keep_origins=False)[source]
Return a set of CPU features that implied by ‘names’ Parameters
names: str or sequence of str
CPU feature name(s) in uppercase. keep_origins: bool
if False(... | numpy.reference.generated.numpy.distutils.ccompiler_opt.ccompileropt.feature_implies |
numpy.distutils.ccompiler_opt.CCompilerOpt.feature_implies_c method distutils.ccompiler_opt.CCompilerOpt.feature_implies_c(names)[source]
same as feature_implies() but combining ‘names’ | numpy.reference.generated.numpy.distutils.ccompiler_opt.ccompileropt.feature_implies_c |
numpy.distutils.ccompiler_opt.CCompilerOpt.feature_is_exist method distutils.ccompiler_opt.CCompilerOpt.feature_is_exist(name)[source]
Returns True if a certain feature is exist and covered within _Config.conf_features. Parameters
‘name’: str
feature name in uppercase. | numpy.reference.generated.numpy.distutils.ccompiler_opt.ccompileropt.feature_is_exist |
numpy.distutils.ccompiler_opt.CCompilerOpt.feature_names method distutils.ccompiler_opt.CCompilerOpt.feature_names(names=None, force_flags=None, macros=[])[source]
Returns a set of CPU feature names that supported by platform and the C compiler. Parameters
names: sequence or None, optional
Specify certain CPU f... | numpy.reference.generated.numpy.distutils.ccompiler_opt.ccompileropt.feature_names |
numpy.distutils.ccompiler_opt.CCompilerOpt.feature_sorted method distutils.ccompiler_opt.CCompilerOpt.feature_sorted(names, reverse=False)[source]
Sort a list of CPU features ordered by the lowest interest. Parameters
‘names’: sequence
sequence of supported feature names in uppercase. ‘reverse’: bool, optional... | numpy.reference.generated.numpy.distutils.ccompiler_opt.ccompileropt.feature_sorted |
numpy.distutils.ccompiler_opt.CCompilerOpt.feature_untied method distutils.ccompiler_opt.CCompilerOpt.feature_untied(names)[source]
same as ‘feature_ahead()’ but if both features implied each other and keep the highest interest. Parameters
‘names’: sequence
sequence of CPU feature names in uppercase. Returns... | numpy.reference.generated.numpy.distutils.ccompiler_opt.ccompileropt.feature_untied |
numpy.distutils.ccompiler_opt.CCompilerOpt.generate_dispatch_header method distutils.ccompiler_opt.CCompilerOpt.generate_dispatch_header(header_path)[source]
Generate the dispatch header which contains the #definitions and headers for platform-specific instruction-sets for the enabled CPU baseline and dispatch-able... | numpy.reference.generated.numpy.distutils.ccompiler_opt.ccompileropt.generate_dispatch_header |
numpy.distutils.ccompiler_opt.CCompilerOpt.is_cached method distutils.ccompiler_opt.CCompilerOpt.is_cached()[source]
Returns True if the class loaded from the cache file | numpy.reference.generated.numpy.distutils.ccompiler_opt.ccompileropt.is_cached |
numpy.distutils.ccompiler_opt.CCompilerOpt.parse_targets method distutils.ccompiler_opt.CCompilerOpt.parse_targets(source)[source]
Fetch and parse configuration statements that required for defining the targeted CPU features, statements should be declared in the top of source in between C comment and start with a s... | numpy.reference.generated.numpy.distutils.ccompiler_opt.ccompileropt.parse_targets |
numpy.distutils.ccompiler_opt.CCompilerOpt.try_dispatch method distutils.ccompiler_opt.CCompilerOpt.try_dispatch(sources, src_dir=None, ccompiler=None, **kwargs)[source]
Compile one or more dispatch-able sources and generates object files, also generates abstract C config headers and macros that used later for the ... | numpy.reference.generated.numpy.distutils.ccompiler_opt.ccompileropt.try_dispatch |
numpy.distutils.ccompiler_opt.new_ccompiler_opt distutils.ccompiler_opt.new_ccompiler_opt(compiler, dispatch_hpath, **kwargs)[source]
Create a new instance of ‘CCompilerOpt’ and generate the dispatch header which contains the #definitions and headers of platform-specific instruction-sets for the enabled CPU baselin... | numpy.reference.generated.numpy.distutils.ccompiler_opt.new_ccompiler_opt |
numpy.distutils.cpuinfo.cpu distutils.cpuinfo.cpu = <numpy.distutils.cpuinfo.LinuxCPUInfo object> | numpy.reference.generated.numpy.distutils.cpuinfo.cpu |
numpy.distutils.exec_command.exec_command distutils.exec_command.exec_command(command, execute_in='', use_shell=None, use_tee=None, _with_python=1, **env)[source]
Return (status,output) of executed command. Deprecated since version 1.17: Use subprocess.Popen instead Parameters
commandstr
A concatenated stri... | numpy.reference.generated.numpy.distutils.exec_command.exec_command |
numpy.distutils.exec_command.filepath_from_subprocess_output distutils.exec_command.filepath_from_subprocess_output(output)[source]
Convert bytes in the encoding used by a subprocess into a filesystem-appropriate str. Inherited from exec_command, and possibly incorrect. | numpy.reference.generated.numpy.distutils.exec_command.filepath_from_subprocess_output |
numpy.distutils.exec_command.find_executable distutils.exec_command.find_executable(exe, path=None, _cache={})[source]
Return full path of a executable or None. Symbolic links are not followed. | numpy.reference.generated.numpy.distutils.exec_command.find_executable |
numpy.distutils.exec_command.forward_bytes_to_stdout distutils.exec_command.forward_bytes_to_stdout(val)[source]
Forward bytes from a subprocess call to the console, without attempting to decode them. The assumption is that the subprocess call already returned bytes in a suitable encoding. | numpy.reference.generated.numpy.distutils.exec_command.forward_bytes_to_stdout |
numpy.distutils.exec_command.get_pythonexe distutils.exec_command.get_pythonexe()[source] | numpy.reference.generated.numpy.distutils.exec_command.get_pythonexe |
numpy.distutils.exec_command.temp_file_name distutils.exec_command.temp_file_name()[source] | numpy.reference.generated.numpy.distutils.exec_command.temp_file_name |
numpy.distutils.log.set_verbosity distutils.log.set_verbosity(v, force=False)[source] | numpy.reference.generated.numpy.distutils.log.set_verbosity |
numpy.distutils.system_info.get_info distutils.system_info.get_info(name, notfound_action=0)[source]
notfound_action:
0 - do nothing 1 - display warning message 2 - raise error | numpy.reference.generated.numpy.distutils.system_info.get_info |
numpy.distutils.system_info.get_standard_file distutils.system_info.get_standard_file(fname)[source]
Returns a list of files named ‘fname’ from 1) System-wide directory (directory-location of this module) 2) Users HOME directory (os.environ[‘HOME’]) 3) Local directory | numpy.reference.generated.numpy.distutils.system_info.get_standard_file |
DoxyLimbo(constDoxyLimbo<Tp,N>&l)
Set Default behavior for copy the limbo. | numpy.dev.howto-docs#_CPPv4N9DoxyLimbo9DoxyLimboERK9DoxyLimboI2Tp1NE |
numpy.dtype.__class_getitem__ method dtype.__class_getitem__(item, /)
Return a parametrized wrapper around the dtype type. New in version 1.22. Returns
aliastypes.GenericAlias
A parametrized dtype type. See also PEP 585
Type hinting generics in standard collections. Notes This method is only avail... | numpy.reference.generated.numpy.dtype.__class_getitem__ |
numpy.dtype.__ge__ method dtype.__ge__(value, /)
Return self>=value. | numpy.reference.generated.numpy.dtype.__ge__ |
numpy.dtype.__gt__ method dtype.__gt__(value, /)
Return self>value. | numpy.reference.generated.numpy.dtype.__gt__ |
numpy.dtype.__le__ method dtype.__le__(value, /)
Return self<=value. | numpy.reference.generated.numpy.dtype.__le__ |
numpy.dtype.__lt__ method dtype.__lt__(value, /)
Return self<value. | numpy.reference.generated.numpy.dtype.__lt__ |
numpy.dtype.__reduce__ method dtype.__reduce__()
Helper for pickle. | numpy.reference.generated.numpy.dtype.__reduce__ |
numpy.dtype.__setstate__ method dtype.__setstate__() | numpy.reference.generated.numpy.dtype.__setstate__ |
numpy.dtype.alignment attribute dtype.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. Examples >>> x = np.dtype('i4')
>>> x.alignment
4
>>> x = np.dtype(float)
>>> x.alignment
8 | numpy.reference.generated.numpy.dtype.alignment |
numpy.dtype.base attribute dtype.base
Returns dtype for the base element of the subarrays, regardless of their dimension or shape. See also dtype.subdtype
Examples >>> x = numpy.dtype('8f')
>>> x.base
dtype('float32')
>>> x = numpy.dtype('i2')
>>> x.base
dtype('int16') | numpy.reference.generated.numpy.dtype.base |
numpy.dtype.byteorder attribute dtype.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
'='
>>> ... | numpy.reference.generated.numpy.dtype.byteorder |
numpy.dtype.char attribute dtype.char
A unique character code for each of the 21 different built-in types. Examples >>> x = np.dtype(float)
>>> x.char
'd' | numpy.reference.generated.numpy.dtype.char |
numpy.dtype.descr attribute dtype.descr
__array_interface__ description of the data-type. The format is that required by the ‘descr’ key in the __array_interface__ attribute. Warning: This attribute exists specifically for __array_interface__, and passing it directly to np.dtype will not accurately reconstruct some... | numpy.reference.generated.numpy.dtype.descr |
numpy.dtype.fields attribute dtype.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])
Offset is limited to C int, which is signed and ... | numpy.reference.generated.numpy.dtype.fields |
numpy.dtype.flags attribute dtype.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; ... | numpy.reference.generated.numpy.dtype.flags |
numpy.dtype.hasobject attribute dtype.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 requir... | numpy.reference.generated.numpy.dtype.hasobject |
numpy.dtype.isalignedstruct attribute dtype.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. | numpy.reference.generated.numpy.dtype.isalignedstruct |
numpy.dtype.isbuiltin attribute dtype.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... | numpy.reference.generated.numpy.dtype.isbuiltin |
numpy.dtype.isnative attribute dtype.isnative
Boolean indicating whether the byte order of this dtype is native to the platform. | numpy.reference.generated.numpy.dtype.isnative |
numpy.dtype.itemsize attribute dtype.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. Examples >>> arr = np.array([[1, 2], [3, 4]])
>>> arr.dtype
dtype('int64')
>>> arr.itemsize
8
>>> dt = np.... | numpy.reference.generated.numpy.dtype.itemsize |
numpy.dtype.kind attribute dtype.kind
A character code (one of ‘biufcmMOSUV’) identifying the general kind of data.
b boolean
i signed integer
u unsigned integer
f floating-point
c complex floating-point
m timedelta
M datetime
O object
S (byte-)string
U Unicode
V void Examples >>> dt = np.dt... | numpy.reference.generated.numpy.dtype.kind |
numpy.dtype.metadata attribute dtype.metadata
Either None or a readonly dictionary of metadata (mappingproxy). The metadata field can be set using any dictionary at data-type creation. NumPy currently has no uniform approach to propagating metadata; although some array operations preserve it, there is no guarantee ... | numpy.reference.generated.numpy.dtype.metadata |
numpy.dtype.name attribute dtype.name
A bit-width name for this data-type. Un-sized flexible data-type objects do not have this attribute. Examples >>> x = np.dtype(float)
>>> x.name
'float64'
>>> x = np.dtype([('a', np.int32, 8), ('b', np.float64, 6)])
>>> x.name
'void640' | numpy.reference.generated.numpy.dtype.name |
numpy.dtype.names attribute dtype.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.flo... | numpy.reference.generated.numpy.dtype.names |
numpy.dtype.ndim attribute dtype.ndim
Number of dimensions of the sub-array if this data type describes a sub-array, and 0 otherwise. New in version 1.13.0. Examples >>> x = np.dtype(float)
>>> x.ndim
0
>>> x = np.dtype((float, 8))
>>> x.ndim
1
>>> x = np.dtype(('i4', (3, 4)))
>>> x.ndim
2 | numpy.reference.generated.numpy.dtype.ndim |
numpy.dtype.newbyteorder method dtype.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_orderstring, optional
Byte order to force; a value from the byte order specifications below. The default valu... | numpy.reference.generated.numpy.dtype.newbyteorder |
numpy.dtype.num attribute dtype.num
A unique number for each of the 21 different built-in types. These are roughly ordered from least-to-most precision. Examples >>> dt = np.dtype(str)
>>> dt.num
19
>>> dt = np.dtype(float)
>>> dt.num
12 | numpy.reference.generated.numpy.dtype.num |
numpy.dtype.shape attribute dtype.shape
Shape tuple of the sub-array if this data type describes a sub-array, and () otherwise. Examples >>> dt = np.dtype(('i4', 4))
>>> dt.shape
(4,)
>>> dt = np.dtype(('i4', (2, 3)))
>>> dt.shape
(2, 3) | numpy.reference.generated.numpy.dtype.shape |
numpy.dtype.str attribute dtype.str
The array-protocol typestring of this data-type object. | numpy.reference.generated.numpy.dtype.str |
numpy.dtype.subdtype attribute dtype.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 t... | numpy.reference.generated.numpy.dtype.subdtype |
numpy.dtype.type attribute dtype.type = None | numpy.reference.generated.numpy.dtype.type |
numpy.errstate.__call__ method errstate.__call__(func)
Call self as a function. | numpy.reference.generated.numpy.errstate.__call__ |
numpy.distutils.exec_command exec_command Implements exec_command function that is (almost) equivalent to commands.getstatusoutput function but on NT, DOS systems the returned status is actually correct (though, the returned status values may be different by a factor). In addition, exec_command takes keyword arguments ... | numpy.reference.generated.numpy.distutils.exec_command |
Extending The BitGenerators have been designed to be extendable using standard tools for high-performance Python – numba and Cython. The Generator object can also be used with user-provided BitGenerators as long as these export a small set of required functions. Numba Numba can be used with either CTypes or CFFI. The ... | numpy.reference.random.extending |
extending.pyx #!/usr/bin/env python3
#cython: language_level=3
from libc.stdint cimport uint32_t
from cpython.pycapsule cimport PyCapsule_IsValid, PyCapsule_GetPointer
import numpy as np
cimport numpy as np
cimport cython
from numpy.random cimport bitgen_t
from numpy.random import PCG64
np.import_array()
@cython.... | numpy.reference.random.examples.cython.extending.pyx |
extending_distributions.pyx #!/usr/bin/env python3
#cython: language_level=3
"""
This file shows how the to use a BitGenerator to create a distribution.
"""
import numpy as np
cimport numpy as np
cimport cython
from cpython.pycapsule cimport PyCapsule_IsValid, PyCapsule_GetPointer
from libc.stdint cimport uint16_t, uin... | numpy.reference.random.examples.cython.extending_distributions.pyx |
numpy.fft.fft fft.fft(a, n=None, axis=- 1, norm=None)[source]
Compute the one-dimensional discrete Fourier Transform. This function computes the one-dimensional n-point discrete Fourier Transform (DFT) with the efficient Fast Fourier Transform (FFT) algorithm [CT]. Parameters
aarray_like
Input array, can be c... | numpy.reference.generated.numpy.fft.fft |
numpy.fft.fft2 fft.fft2(a, s=None, axes=(- 2, - 1), norm=None)[source]
Compute the 2-dimensional discrete Fourier Transform. This function computes the n-dimensional discrete Fourier Transform over any axes in an M-dimensional array by means of the Fast Fourier Transform (FFT). By default, the transform is computed... | numpy.reference.generated.numpy.fft.fft2 |
numpy.fft.fftfreq fft.fftfreq(n, d=1.0)[source]
Return the Discrete Fourier Transform sample frequencies. The returned float array f contains the frequency bin centers in cycles per unit of the sample spacing (with zero at the start). For instance, if the sample spacing is in seconds, then the frequency unit is cyc... | numpy.reference.generated.numpy.fft.fftfreq |
numpy.fft.fftn fft.fftn(a, s=None, axes=None, norm=None)[source]
Compute the N-dimensional discrete Fourier Transform. This function computes the N-dimensional discrete Fourier Transform over any number of axes in an M-dimensional array by means of the Fast Fourier Transform (FFT). Parameters
aarray_like
Inpu... | numpy.reference.generated.numpy.fft.fftn |
numpy.fft.fftshift fft.fftshift(x, axes=None)[source]
Shift the zero-frequency component to the center of the spectrum. This function swaps half-spaces for all axes listed (defaults to all). Note that y[0] is the Nyquist component only if len(x) is even. Parameters
xarray_like
Input array.
axesint or shape ... | numpy.reference.generated.numpy.fft.fftshift |
numpy.fft.hfft fft.hfft(a, n=None, axis=- 1, norm=None)[source]
Compute the FFT of a signal that has Hermitian symmetry, i.e., a real spectrum. Parameters
aarray_like
The input array.
nint, optional
Length of the transformed axis of the output. For n output points, n//2 + 1 input points are necessary. If ... | numpy.reference.generated.numpy.fft.hfft |
numpy.fft.ifft fft.ifft(a, n=None, axis=- 1, norm=None)[source]
Compute the one-dimensional inverse discrete Fourier Transform. This function computes the inverse of the one-dimensional n-point discrete Fourier transform computed by fft. In other words, ifft(fft(a)) == a to within numerical accuracy. For a general ... | numpy.reference.generated.numpy.fft.ifft |
numpy.fft.ifft2 fft.ifft2(a, s=None, axes=(- 2, - 1), norm=None)[source]
Compute the 2-dimensional inverse discrete Fourier Transform. This function computes the inverse of the 2-dimensional discrete Fourier Transform over any number of axes in an M-dimensional array by means of the Fast Fourier Transform (FFT). In... | numpy.reference.generated.numpy.fft.ifft2 |
numpy.fft.ifftn fft.ifftn(a, s=None, axes=None, norm=None)[source]
Compute the N-dimensional inverse discrete Fourier Transform. This function computes the inverse of the N-dimensional discrete Fourier Transform over any number of axes in an M-dimensional array by means of the Fast Fourier Transform (FFT). In other... | numpy.reference.generated.numpy.fft.ifftn |
numpy.fft.ifftshift fft.ifftshift(x, axes=None)[source]
The inverse of fftshift. Although identical for even-length x, the functions differ by one sample for odd-length x. Parameters
xarray_like
Input array.
axesint or shape tuple, optional
Axes over which to calculate. Defaults to None, which shifts all ... | numpy.reference.generated.numpy.fft.ifftshift |
numpy.fft.ihfft fft.ihfft(a, n=None, axis=- 1, norm=None)[source]
Compute the inverse FFT of a signal that has Hermitian symmetry. Parameters
aarray_like
Input array.
nint, optional
Length of the inverse FFT, the number of points along transformation axis in the input to use. If n is smaller than the leng... | numpy.reference.generated.numpy.fft.ihfft |
numpy.fft.irfft fft.irfft(a, n=None, axis=- 1, norm=None)[source]
Computes the inverse of rfft. This function computes the inverse of the one-dimensional n-point discrete Fourier Transform of real input computed by rfft. In other words, irfft(rfft(a), len(a)) == a to within numerical accuracy. (See Notes below for ... | numpy.reference.generated.numpy.fft.irfft |
numpy.fft.irfft2 fft.irfft2(a, s=None, axes=(- 2, - 1), norm=None)[source]
Computes the inverse of rfft2. Parameters
aarray_like
The input array
ssequence of ints, optional
Shape of the real output to the inverse FFT.
axessequence of ints, optional
The axes over which to compute the inverse fft. Defau... | numpy.reference.generated.numpy.fft.irfft2 |
numpy.fft.irfftn fft.irfftn(a, s=None, axes=None, norm=None)[source]
Computes the inverse of rfftn. This function computes the inverse of the N-dimensional discrete Fourier Transform for real input over any number of axes in an M-dimensional array by means of the Fast Fourier Transform (FFT). In other words, irfftn... | numpy.reference.generated.numpy.fft.irfftn |
numpy.fft.rfft fft.rfft(a, n=None, axis=- 1, norm=None)[source]
Compute the one-dimensional discrete Fourier Transform for real input. This function computes the one-dimensional n-point discrete Fourier Transform (DFT) of a real-valued array by means of an efficient algorithm called the Fast Fourier Transform (FFT)... | numpy.reference.generated.numpy.fft.rfft |
numpy.fft.rfft2 fft.rfft2(a, s=None, axes=(- 2, - 1), norm=None)[source]
Compute the 2-dimensional FFT of a real array. Parameters
aarray
Input array, taken to be real.
ssequence of ints, optional
Shape of the FFT.
axessequence of ints, optional
Axes over which to compute the FFT.
norm{“backward”, “... | numpy.reference.generated.numpy.fft.rfft2 |
numpy.fft.rfftfreq fft.rfftfreq(n, d=1.0)[source]
Return the Discrete Fourier Transform sample frequencies (for usage with rfft, irfft). The returned float array f contains the frequency bin centers in cycles per unit of the sample spacing (with zero at the start). For instance, if the sample spacing is in seconds,... | numpy.reference.generated.numpy.fft.rfftfreq |
numpy.fft.rfftn fft.rfftn(a, s=None, axes=None, norm=None)[source]
Compute the N-dimensional discrete Fourier Transform for real input. This function computes the N-dimensional discrete Fourier Transform over any number of axes in an M-dimensional real array by means of the Fast Fourier Transform (FFT). By default,... | numpy.reference.generated.numpy.fft.rfftn |
numpy.flatiter.base attribute flatiter.base
A reference to the array that is iterated over. Examples >>> x = np.arange(5)
>>> fl = x.flat
>>> fl.base is x
True | numpy.reference.generated.numpy.flatiter.base |
numpy.flatiter.coords attribute flatiter.coords
An N-dimensional tuple of current coordinates. Examples >>> x = np.arange(6).reshape(2, 3)
>>> fl = x.flat
>>> fl.coords
(0, 0)
>>> next(fl)
0
>>> fl.coords
(0, 1) | numpy.reference.generated.numpy.flatiter.coords |
numpy.flatiter.copy method flatiter.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]) | numpy.reference.generated.numpy.flatiter.copy |
numpy.flatiter.index attribute flatiter.index
Current flat index into the array. Examples >>> x = np.arange(6).reshape(2, 3)
>>> fl = x.flat
>>> fl.index
0
>>> next(fl)
0
>>> fl.index
1 | numpy.reference.generated.numpy.flatiter.index |
numpy.generic.__array__ method generic.__array__()
sc.__array__(dtype) return 0-dim array from scalar with specified dtype | numpy.reference.generated.numpy.generic.__array__ |
numpy.generic.__array_interface__ attribute generic.__array_interface__
Array protocol: Python side | numpy.reference.generated.numpy.generic.__array_interface__ |
numpy.generic.__array_priority__ attribute generic.__array_priority__
Array priority. | numpy.reference.generated.numpy.generic.__array_priority__ |
numpy.generic.__array_struct__ attribute generic.__array_struct__
Array protocol: struct | numpy.reference.generated.numpy.generic.__array_struct__ |
numpy.generic.__array_wrap__ method generic.__array_wrap__()
sc.__array_wrap__(obj) return scalar from array | numpy.reference.generated.numpy.generic.__array_wrap__ |
numpy.generic.__reduce__ method generic.__reduce__()
Helper for pickle. | numpy.reference.generated.numpy.generic.__reduce__ |
numpy.generic.__setstate__ method generic.__setstate__() | numpy.reference.generated.numpy.generic.__setstate__ |
numpy.generic.base attribute generic.base
Scalar attribute identical to the corresponding array attribute. Please see ndarray.base. | numpy.reference.generated.numpy.generic.base |
numpy.generic.byteswap method generic.byteswap()
Scalar method identical to the corresponding array attribute. Please see ndarray.byteswap. | numpy.reference.generated.numpy.generic.byteswap |
numpy.generic.data attribute generic.data
Pointer to start of data. | numpy.reference.generated.numpy.generic.data |
numpy.generic.dtype attribute generic.dtype
Get array data-descriptor. | numpy.reference.generated.numpy.generic.dtype |
numpy.generic.flags attribute generic.flags
The integer value of flags. | numpy.reference.generated.numpy.generic.flags |
numpy.generic.flat attribute generic.flat
A 1-D view of the scalar. | numpy.reference.generated.numpy.generic.flat |
numpy.generic.imag attribute generic.imag
The imaginary part of the scalar. | numpy.reference.generated.numpy.generic.imag |
numpy.generic.itemsize attribute generic.itemsize
The length of one element in bytes. | numpy.reference.generated.numpy.generic.itemsize |
numpy.generic.ndim attribute generic.ndim
The number of array dimensions. | numpy.reference.generated.numpy.generic.ndim |
numpy.generic.real attribute generic.real
The real part of the scalar. | numpy.reference.generated.numpy.generic.real |
numpy.generic.setflags method generic.setflags()
Scalar method identical to the corresponding array attribute. Please see ndarray.setflags. | numpy.reference.generated.numpy.generic.setflags |
numpy.generic.shape attribute generic.shape
Tuple of array dimensions. | numpy.reference.generated.numpy.generic.shape |
numpy.generic.size attribute generic.size
The number of elements in the gentype. | numpy.reference.generated.numpy.generic.size |
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