doc_content stringlengths 1 386k | doc_id stringlengths 5 188 |
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numpy.base_repr numpy.base_repr(number, base=2, padding=0)[source]
Return a string representation of a number in the given base system. Parameters
numberint
The value to convert. Positive and negative values are handled.
baseint, optional
Convert number to the base number system. The valid range is 2-36, ... | numpy.reference.generated.numpy.base_repr |
numpy.binary_repr numpy.binary_repr(num, width=None)[source]
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-co... | numpy.reference.generated.numpy.binary_repr |
numpy.bincount numpy.bincount(x, /, weights=None, minlength=0)
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 ... | numpy.reference.generated.numpy.bincount |
numpy.bitwise_and numpy.bitwise_and(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'bitwise_and'>
Compute the bit-wise AND of two arrays element-wise. Computes the bit-wise AND of the underlying binary representation of the integers in the i... | numpy.reference.generated.numpy.bitwise_and |
numpy.bitwise_or numpy.bitwise_or(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'bitwise_or'>
Compute the bit-wise OR of two arrays element-wise. Computes the bit-wise OR of the underlying binary representation of the integers in the input ... | numpy.reference.generated.numpy.bitwise_or |
numpy.bitwise_xor numpy.bitwise_xor(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'bitwise_xor'>
Compute the bit-wise XOR of two arrays element-wise. Computes the bit-wise XOR of the underlying binary representation of the integers in the i... | numpy.reference.generated.numpy.bitwise_xor |
numpy.blackman numpy.blackman(M)[source]
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
Mint
... | numpy.reference.generated.numpy.blackman |
numpy.block numpy.block(arrays)[source]
Assemble an nd-array from nested lists of blocks. Blocks in the innermost lists are concatenated (see concatenate) along the last dimension (-1), then these are concatenated along the second-last dimension (-2), and so on until the outermost list is reached. Blocks can be of ... | numpy.reference.generated.numpy.block |
numpy.bmat numpy.bmat(obj, ldict=None, gdict=None)[source]
Build a matrix object from a string, nested sequence, or array. Parameters
objstr or array_like
Input data. If a string, variables in the current scope may be referenced by name.
ldictdict, optional
A dictionary that replaces local operands in cur... | numpy.reference.generated.numpy.bmat |
numpy.broadcast class numpy.broadcast[source]
Produce an object that mimics broadcasting. Parameters
in1, in2, …array_like
Input parameters. Returns
bbroadcast object
Broadcast the input parameters against one another, and return an object that encapsulates the result. Amongst others, it has shape and... | numpy.reference.generated.numpy.broadcast |
numpy.broadcast_arrays numpy.broadcast_arrays(*args, subok=False)[source]
Broadcast any number of arrays against each other. Parameters
`*args`array_likes
The arrays to broadcast.
subokbool, optional
If True, then sub-classes will be passed-through, otherwise the returned arrays will be forced to be a bas... | numpy.reference.generated.numpy.broadcast_arrays |
numpy.broadcast_shapes numpy.broadcast_shapes(*args)[source]
Broadcast the input shapes into a single shape. Learn more about broadcasting here. New in version 1.20.0. Parameters
`*args`tuples of ints, or ints
The shapes to be broadcast against each other. Returns
tuple
Broadcasted shape. Raises
... | numpy.reference.generated.numpy.broadcast_shapes |
numpy.broadcast_to numpy.broadcast_to(array, shape, subok=False)[source]
Broadcast an array to a new shape. Parameters
arrayarray_like
The array to broadcast.
shapetuple or int
The shape of the desired array. A single integer i is interpreted as (i,).
subokbool, optional
If True, then sub-classes will... | numpy.reference.generated.numpy.broadcast_to |
numpy.busday_count numpy.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, ... | numpy.reference.generated.numpy.busday_count |
numpy.busday_offset numpy.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. New in version 1.7.0. Parameters
datesarra... | numpy.reference.generated.numpy.busday_offset |
numpy.busdaycalendar class numpy.busdaycalendar(weekmask='1111100', holidays=None)[source]
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 spe... | numpy.reference.generated.numpy.busdaycalendar |
class numpy.byte[source]
Signed integer type, compatible with C char. Character code
'b' Alias on this platform (Linux x86_64)
numpy.int8: 8-bit signed integer (-128 to 127). | numpy.reference.arrays.scalars#numpy.byte |
numpy.byte_bounds numpy.byte_bounds(a)[source]
Returns pointers to the end-points of an array. Parameters
andarray
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... | numpy.reference.generated.numpy.byte_bounds |
class numpy.bytes_[source]
A byte string. When used in arrays, this type strips trailing null bytes. Character code
'S' Alias
numpy.string_ | numpy.reference.arrays.scalars#numpy.bytes_ |
numpy.c_ numpy.c_ = <numpy.lib.index_tricks.CClass object>
Translates slice objects to concatenation along the second axis. This is short-hand for np.r_['-1,2,0', index expression], which is useful because of its common occurrence. In particular, arrays will be stacked along their last axis after being upgraded to ... | numpy.reference.generated.numpy.c_ |
numpy.can_cast numpy.can_cast(from_, to, 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, s... | numpy.reference.generated.numpy.can_cast |
numpy.cbrt numpy.cbrt(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'cbrt'>
Return the cube-root of an array, element-wise. New in version 1.10.0. Parameters
xarray_like
The values whose cube-roots are required.
outndarray, None, or tu... | numpy.reference.generated.numpy.cbrt |
class numpy.cdouble(real=0, imag=0)[source]
Complex number type composed of two double-precision floating-point numbers, compatible with Python complex. Character code
'D' Alias
numpy.cfloat Alias
numpy.complex_ Alias on this platform (Linux x86_64)
numpy.complex128: Complex number type composed of 2 64-bit-p... | numpy.reference.arrays.scalars#numpy.cdouble |
numpy.ceil numpy.ceil(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'ceil'>
Return the ceiling of the input, element-wise. The ceil of the scalar x is the smallest integer i, such that i >= x. It is often denoted as \(\lceil x \rceil\). Paramet... | numpy.reference.generated.numpy.ceil |
numpy.cfloat[source]
alias of numpy.cdouble | numpy.reference.arrays.scalars#numpy.cfloat |
numpy.char.chararray class numpy.char.chararray(shape, itemsize=1, unicode=False, buffer=None, offset=0, strides=None, order=None)[source]
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 deve... | numpy.reference.generated.numpy.char.chararray |
numpy.chararray class numpy.chararray(shape, itemsize=1, unicode=False, buffer=None, offset=0, strides=None, order=None)[source]
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. S... | numpy.reference.generated.numpy.chararray |
numpy.choose numpy.choose(a, choices, out=None, mode='raise')[source]
Construct an array from an index array and a list 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 ... | numpy.reference.generated.numpy.choose |
numpy.clip numpy.clip(a, a_min, a_max, out=None, **kwargs)[source]
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. Equivalent ... | numpy.reference.generated.numpy.clip |
class numpy.clongdouble[source]
Complex number type composed of two extended-precision floating-point numbers. Character code
'G' Alias
numpy.clongfloat Alias
numpy.longcomplex Alias on this platform (Linux x86_64)
numpy.complex256: Complex number type composed of 2 128-bit extended-precision floating-point n... | numpy.reference.arrays.scalars#numpy.clongdouble |
numpy.clongfloat[source]
alias of numpy.clongdouble | numpy.reference.arrays.scalars#numpy.clongfloat |
numpy.column_stack numpy.column_stack(tup)[source]
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
tupsequence of 1-D or... | numpy.reference.generated.numpy.column_stack |
numpy.common_type numpy.common_type(*arrays)[source]
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... | numpy.reference.generated.numpy.common_type |
numpy.complex128[source]
alias of numpy.cdouble | numpy.reference.arrays.scalars#numpy.complex128 |
numpy.complex192
numpy.complex256[source]
Alias for numpy.clongdouble, named after its size in bits. The existence of these aliases depends on the platform. | numpy.reference.arrays.scalars#numpy.complex192 |
numpy.complex192
numpy.complex256[source]
Alias for numpy.clongdouble, named after its size in bits. The existence of these aliases depends on the platform. | numpy.reference.arrays.scalars#numpy.complex256 |
numpy.complex64[source]
alias of numpy.csingle | numpy.reference.arrays.scalars#numpy.complex64 |
numpy.complex_[source]
alias of numpy.cdouble | numpy.reference.arrays.scalars#numpy.complex_ |
numpy.compress numpy.compress(condition, a, axis=None, out=None)[source]
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 extrac... | numpy.reference.generated.numpy.compress |
numpy.concatenate numpy.concatenate((a1, a2, ...), axis=0, out=None, dtype=None, casting="same_kind")
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).
axisi... | numpy.reference.generated.numpy.concatenate |
numpy.conj numpy.conj(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'conjugate'>
Return the complex conjugate, element-wise. The complex conjugate of a complex number is obtained by changing the sign of its imaginary part. Parameters
xarray_... | numpy.reference.generated.numpy.conj |
numpy.conjugate numpy.conjugate(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'conjugate'>
Return the complex conjugate, element-wise. The complex conjugate of a complex number is obtained by changing the sign of its imaginary part. Parameters ... | numpy.reference.generated.numpy.conjugate |
numpy.convolve numpy.convolve(a, v, mode='full')[source]
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 indepen... | numpy.reference.generated.numpy.convolve |
numpy.copy numpy.copy(a, order='K', subok=False)[source]
Return an array copy of the given object. Parameters
aarray_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. ‘... | numpy.reference.generated.numpy.copy |
numpy.copysign numpy.copysign(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'copysign'>
Change the sign of x1 to that of x2, element-wise. If x2 is a scalar, its sign will be copied to all elements of x1. Parameters
x1array_like
Value... | numpy.reference.generated.numpy.copysign |
numpy.copyto numpy.copyto(dst, src, casting='same_kind', where=True)
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. New in version 1.7.0. Parameters
dstndarray
The array into... | numpy.reference.generated.numpy.copyto |
numpy.corrcoef numpy.corrcoef(x, y=None, rowvar=True, bias=<no value>, ddof=<no value>, *, dtype=None)[source]
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,... | numpy.reference.generated.numpy.corrcoef |
numpy.correlate numpy.correlate(a, v, mode='valid')[source]
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 con... | numpy.reference.generated.numpy.correlate |
numpy.cos numpy.cos(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'cos'>
Cosine element-wise. Parameters
xarray_like
Input array in radians.
outndarray, None, or tuple of ndarray and None, optional
A location into which the result is s... | numpy.reference.generated.numpy.cos |
numpy.cosh numpy.cosh(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'cosh'>
Hyperbolic cosine, element-wise. Equivalent to 1/2 * (np.exp(x) + np.exp(-x)) and np.cos(1j*x). Parameters
xarray_like
Input array.
outndarray, None, or tuple of... | numpy.reference.generated.numpy.cosh |
numpy.count_nonzero numpy.count_nonzero(a, axis=None, *, keepdims=False)[source]
Counts the number of non-zero values in the array a. The word “non-zero” is in reference to the Python 2.x built-in method __nonzero__() (renamed __bool__() in Python 3.x) of Python objects that tests an object’s “truthfulness”. For ex... | numpy.reference.generated.numpy.count_nonzero |
numpy.cov numpy.cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, aweights=None, *, dtype=None)[source]
Estimate a covariance matrix, given data and weights. Covariance indicates the level to which two variables vary together. If we examine N-dimensional samples, \(X = [x_1, x_2, ... x_N]^T\), then ... | numpy.reference.generated.numpy.cov |
numpy.cross numpy.cross(a, b, axisa=- 1, axisb=- 1, axisc=- 1, axis=None)[source]
Return the cross product of two (arrays of) vectors. The cross product of a and b in \(R^3\) 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, ... | numpy.reference.generated.numpy.cross |
class numpy.csingle[source]
Complex number type composed of two single-precision floating-point numbers. Character code
'F' Alias
numpy.singlecomplex Alias on this platform (Linux x86_64)
numpy.complex64: Complex number type composed of 2 32-bit-precision floating-point numbers. | numpy.reference.arrays.scalars#numpy.csingle |
C-Types Foreign Function Interface (numpy.ctypeslib) numpy.ctypeslib.as_array(obj, shape=None)[source]
Create a numpy array from a ctypes array or POINTER. The numpy array shares the memory with the ctypes object. The shape parameter must be given if converting from a ctypes POINTER. The shape parameter is ignored ... | numpy.reference.routines.ctypeslib |
numpy.ctypeslib.as_ctypes(obj)[source]
Create and return a ctypes object from a numpy array. Actually anything that exposes the __array_interface__ is accepted. | numpy.reference.routines.ctypeslib#numpy.ctypeslib.as_ctypes |
numpy.ctypeslib.as_ctypes_type(dtype)[source]
Convert a dtype into a ctypes type. Parameters
dtypedtype
The dtype to convert Returns
ctype
A ctype scalar, union, array, or struct Raises
NotImplementedError
If the conversion is not possible Notes This function does not losslessly round-trip in ei... | numpy.reference.routines.ctypeslib#numpy.ctypeslib.as_ctypes_type |
class numpy.ctypeslib.c_intp
A ctypes signed integer type of the same size as numpy.intp. Depending on the platform, it can be an alias for either c_int, c_long or c_longlong. | numpy.reference.routines.ctypeslib#numpy.ctypeslib.c_intp |
numpy.ctypeslib.load_library(libname, loader_path)[source]
It is possible to load a library using >>> lib = ctypes.cdll[<full_path_name>]
But there are cross-platform considerations, such as library file extensions, plus the fact Windows will just load the first library it finds with that name. NumPy supplies the l... | numpy.reference.routines.ctypeslib#numpy.ctypeslib.load_library |
numpy.ctypeslib.ndpointer(dtype=None, ndim=None, shape=None, flags=None)[source]
Array-checking restype/argtypes. An ndpointer instance is used to describe an ndarray in restypes and argtypes specifications. This approach is more flexible than using, for example, POINTER(c_double), since several restrictions can be s... | numpy.reference.routines.ctypeslib#numpy.ctypeslib.ndpointer |
numpy.cumprod numpy.cumprod(a, axis=None, dtype=None, out=None)[source]
Return the cumulative product of elements along a given axis. Parameters
aarray_like
Input array.
axisint, optional
Axis along which the cumulative product is computed. By default the input is flattened.
dtypedtype, optional
Type ... | numpy.reference.generated.numpy.cumprod |
numpy.cumsum numpy.cumsum(a, axis=None, dtype=None, out=None)[source]
Return the cumulative sum of the elements along a given axis. Parameters
aarray_like
Input array.
axisint, optional
Axis along which the cumulative sum is computed. The default (None) is to compute the cumsum over the flattened array. ... | numpy.reference.generated.numpy.cumsum |
numpy.DataSource class numpy.DataSource(destpath='.')[source]
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... | numpy.reference.generated.numpy.datasource |
class numpy.datetime64[source]
If created from a 64-bit integer, it represents an offset from 1970-01-01T00:00:00. If created from string, the string can be in ISO 8601 date or datetime format. >>> np.datetime64(10, 'Y')
numpy.datetime64('1980')
>>> np.datetime64('1980', 'Y')
numpy.datetime64('1980')
>>> np.datetime6... | numpy.reference.arrays.scalars#numpy.datetime64 |
numpy.datetime_as_string numpy.datetime_as_string(arr, unit=None, timezone='naive', casting='same_kind')
Convert an array of datetimes into an array of strings. Parameters
arrarray_like of datetime64
The array of UTC timestamps to format.
unitstr
One of None, ‘auto’, or a datetime unit.
timezone{‘naive’... | numpy.reference.generated.numpy.datetime_as_string |
numpy.datetime_data numpy.datetime_data(dtype, /)
Get information about the step size of a date or time type. The returned tuple can be passed as the second argument of numpy.datetime64 and numpy.timedelta64. Parameters
dtypedtype
The dtype object, which must be a datetime64 or timedelta64 type. Returns
... | numpy.reference.generated.numpy.datetime_data |
numpy.deg2rad numpy.deg2rad(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'deg2rad'>
Convert angles from degrees to radians. Parameters
xarray_like
Angles in degrees.
outndarray, None, or tuple of ndarray and None, optional
A location ... | numpy.reference.generated.numpy.deg2rad |
numpy.degrees numpy.degrees(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'degrees'>
Convert angles from radians to degrees. Parameters
xarray_like
Input array in radians.
outndarray, None, or tuple of ndarray and None, optional
A loca... | numpy.reference.generated.numpy.degrees |
numpy.delete numpy.delete(arr, obj, axis=None)[source]
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
arrarray_like
Input array.
objslice, int or array of ints
Indicate indices of sub-arrays to remove a... | numpy.reference.generated.numpy.delete |
numpy.deprecate numpy.deprecate(*args, **kwargs)[source]
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
funcfunction
The function to be deprecated.
old_namestr, option... | numpy.reference.generated.numpy.deprecate |
numpy.deprecate_with_doc numpy.deprecate_with_doc(msg)[source]
Deprecates a function and includes the deprecation in its docstring. This function is used as a decorator. It returns an object that can be used to issue a DeprecationWarning, by passing the to-be decorated function as argument, this adds warning to the... | numpy.reference.generated.numpy.deprecate_with_doc |
numpy.diag numpy.diag(v, k=0)[source]
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. ... | numpy.reference.generated.numpy.diag |
numpy.diag_indices numpy.diag_indices(n, ndim=2)[source]
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 > ... | numpy.reference.generated.numpy.diag_indices |
numpy.diag_indices_from numpy.diag_indices_from(arr)[source]
Return the indices to access the main diagonal of an n-dimensional array. See diag_indices for full details. Parameters
arrarray, at least 2-D
See also diag_indices
Notes New in version 1.4.0. | numpy.reference.generated.numpy.diag_indices_from |
numpy.diagflat numpy.diagflat(v, k=0)[source]
Create a two-dimensional array with the flattened input as a diagonal. Parameters
varray_like
Input data, which is flattened and set as the k-th diagonal of the output.
kint, optional
Diagonal to set; 0, the default, corresponds to the “main” diagonal, a posit... | numpy.reference.generated.numpy.diagflat |
numpy.diagonal numpy.diagonal(a, offset=0, axis1=0, axis2=1)[source]
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 determ... | numpy.reference.generated.numpy.diagonal |
numpy.diff numpy.diff(a, n=1, axis=-1, prepend=<no value>, append=<no value>)[source]
Calculate the n-th discrete difference along the given axis. The first difference is given by out[i] = a[i+1] - a[i] along the given axis, higher differences are calculated by using diff recursively. Parameters
aarray_like
I... | numpy.reference.generated.numpy.diff |
numpy.digitize numpy.digitize(x, bins, right=False)[source]
Return the indices of the bins to which each value in input array belongs.
right order of bins returned index i satisfies
False increasing bins[i-1] <= x < bins[i]
True increasing bins[i-1] < x <= bins[i]
False decreasing bins[i-1] > x >= bins[i]... | numpy.reference.generated.numpy.digitize |
numpy.disp numpy.disp(mesg, device=None, linefeed=True)[source]
Display a message on a device. Parameters
mesgstr
Message to display.
deviceobject
Device to write message. If None, defaults to sys.stdout which is very similar to print. device needs to have write() and flush() methods.
linefeedbool, opti... | numpy.reference.generated.numpy.disp |
numpy.distutils.ccompiler Functions
CCompiler_compile(self, sources[, ...]) Compile one or more source files.
CCompiler_customize(self, dist[, need_cxx]) Do any platform-specific customization of a compiler instance.
CCompiler_customize_cmd(self, cmd[, ignore]) Customize compiler using distutils command.
CCompi... | numpy.reference.generated.numpy.distutils.ccompiler |
numpy.distutils.ccompiler_opt Provides the CCompilerOpt class, used for handling the CPU/hardware optimization, starting from parsing the command arguments, to managing the relation between the CPU baseline and dispatch-able features, also generating the required C headers and ending with compiling the sources with pro... | numpy.reference.generated.numpy.distutils.ccompiler_opt |
numpy.distutils.ccompiler_opt.CCompilerOpt class numpy.distutils.ccompiler_opt.CCompilerOpt(ccompiler, cpu_baseline='min', cpu_dispatch='max', cache_path=None)[source]
A helper class for CCompiler aims to provide extra build options to effectively control of compiler optimizations that are directly related to CPU f... | numpy.reference.generated.numpy.distutils.ccompiler_opt.ccompileropt |
numpy.distutils.core.Extension class numpy.distutils.core.Extension(name, sources, include_dirs=None, define_macros=None, undef_macros=None, library_dirs=None, libraries=None, runtime_library_dirs=None, extra_objects=None, extra_compile_args=None, extra_link_args=None, export_symbols=None, swig_opts=None, depends=Non... | numpy.reference.generated.numpy.distutils.core.extension |
distutils.misc_util numpy.distutils.misc_util.all_strings(lst)[source]
Return True if all items in lst are string objects.
numpy.distutils.misc_util.allpath(name)[source]
Convert a /-separated pathname to one using the OS’s path separator.
numpy.distutils.misc_util.appendpath(prefix, path)[source]
num... | numpy.reference.distutils.misc_util |
numpy.distutils.misc_util.allpath(name)[source]
Convert a /-separated pathname to one using the OS’s path separator. | numpy.reference.distutils.misc_util#numpy.distutils.misc_util.allpath |
numpy.distutils.misc_util.appendpath(prefix, path)[source] | numpy.reference.distutils.misc_util#numpy.distutils.misc_util.appendpath |
numpy.distutils.misc_util.as_list(seq)[source] | numpy.reference.distutils.misc_util#numpy.distutils.misc_util.as_list |
numpy.distutils.misc_util.blue_text(s)[source] | numpy.reference.distutils.misc_util#numpy.distutils.misc_util.blue_text |
numpy.distutils.misc_util.cyan_text(s)[source] | numpy.reference.distutils.misc_util#numpy.distutils.misc_util.cyan_text |
numpy.distutils.misc_util.cyg2win32(path: str) → str[source]
Convert a path from Cygwin-native to Windows-native. Uses the cygpath utility (part of the Base install) to do the actual conversion. Falls back to returning the original path if this fails. Handles the default /cygdrive mount prefix as well as the /proc/cy... | numpy.reference.distutils.misc_util#numpy.distutils.misc_util.cyg2win32 |
numpy.distutils.misc_util.default_config_dict(name=None, parent_name=None, local_path=None)[source]
Return a configuration dictionary for usage in configuration() function defined in file setup_<name>.py. | numpy.reference.distutils.misc_util#numpy.distutils.misc_util.default_config_dict |
numpy.distutils.misc_util.dict_append(d, **kws)[source] | numpy.reference.distutils.misc_util#numpy.distutils.misc_util.dict_append |
numpy.distutils.misc_util.dot_join(*args)[source] | numpy.reference.distutils.misc_util#numpy.distutils.misc_util.dot_join |
numpy.distutils.misc_util.exec_mod_from_location(modname, modfile)[source]
Use importlib machinery to import a module modname from the file modfile. Depending on the spec.loader, the module may not be registered in sys.modules. | numpy.reference.distutils.misc_util#numpy.distutils.misc_util.exec_mod_from_location |
numpy.distutils.misc_util.filter_sources(sources)[source]
Return four lists of filenames containing C, C++, Fortran, and Fortran 90 module sources, respectively. | numpy.reference.distutils.misc_util#numpy.distutils.misc_util.filter_sources |
numpy.distutils.misc_util.generate_config_py(target)[source]
Generate config.py file containing system_info information used during building the package. Usage:
config[‘py_modules’].append((packagename, ‘__config__’,generate_config_py)) | numpy.reference.distutils.misc_util#numpy.distutils.misc_util.generate_config_py |
numpy.distutils.misc_util.get_build_architecture()[source] | numpy.reference.distutils.misc_util#numpy.distutils.misc_util.get_build_architecture |
numpy.distutils.misc_util.get_cmd(cmdname, _cache={})[source] | numpy.reference.distutils.misc_util#numpy.distutils.misc_util.get_cmd |
numpy.distutils.misc_util.get_data_files(data)[source] | numpy.reference.distutils.misc_util#numpy.distutils.misc_util.get_data_files |
numpy.distutils.misc_util.get_dependencies(sources)[source] | numpy.reference.distutils.misc_util#numpy.distutils.misc_util.get_dependencies |
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