556 lines
22 KiB
Python
556 lines
22 KiB
Python
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"""
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NumPy
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=====
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Provides
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1. An array object of arbitrary homogeneous items
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2. Fast mathematical operations over arrays
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3. Linear Algebra, Fourier Transforms, Random Number Generation
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How to use the documentation
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----------------------------
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Documentation is available in two forms: docstrings provided
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with the code, and a loose standing reference guide, available from
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`the NumPy homepage <https://numpy.org>`_.
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We recommend exploring the docstrings using
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`IPython <https://ipython.org>`_, an advanced Python shell with
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TAB-completion and introspection capabilities. See below for further
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instructions.
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The docstring examples assume that `numpy` has been imported as ``np``::
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>>> import numpy as np
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Code snippets are indicated by three greater-than signs::
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>>> x = 42
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>>> x = x + 1
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Use the built-in ``help`` function to view a function's docstring::
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>>> help(np.sort)
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... # doctest: +SKIP
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For some objects, ``np.info(obj)`` may provide additional help. This is
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particularly true if you see the line "Help on ufunc object:" at the top
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of the help() page. Ufuncs are implemented in C, not Python, for speed.
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The native Python help() does not know how to view their help, but our
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np.info() function does.
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Available subpackages
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---------------------
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lib
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Basic functions used by several sub-packages.
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random
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Core Random Tools
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linalg
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Core Linear Algebra Tools
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fft
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Core FFT routines
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polynomial
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Polynomial tools
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testing
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NumPy testing tools
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distutils
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Enhancements to distutils with support for
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Fortran compilers support and more (for Python <= 3.11)
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Utilities
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---------
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test
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Run numpy unittests
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show_config
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Show numpy build configuration
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__version__
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NumPy version string
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Viewing documentation using IPython
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-----------------------------------
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Start IPython and import `numpy` usually under the alias ``np``: `import
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numpy as np`. Then, directly past or use the ``%cpaste`` magic to paste
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examples into the shell. To see which functions are available in `numpy`,
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type ``np.<TAB>`` (where ``<TAB>`` refers to the TAB key), or use
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``np.*cos*?<ENTER>`` (where ``<ENTER>`` refers to the ENTER key) to narrow
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down the list. To view the docstring for a function, use
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``np.cos?<ENTER>`` (to view the docstring) and ``np.cos??<ENTER>`` (to view
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the source code).
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Copies vs. in-place operation
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-----------------------------
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Most of the functions in `numpy` return a copy of the array argument
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(e.g., `np.sort`). In-place versions of these functions are often
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available as array methods, i.e. ``x = np.array([1,2,3]); x.sort()``.
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Exceptions to this rule are documented.
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"""
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# start delvewheel patch
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def _delvewheel_patch_1_8_1():
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import os
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libs_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir, 'numpy.libs'))
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if os.path.isdir(libs_dir):
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os.add_dll_directory(libs_dir)
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_delvewheel_patch_1_8_1()
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del _delvewheel_patch_1_8_1
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# end delvewheel patch
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import os
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import sys
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import warnings
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from ._globals import _NoValue, _CopyMode
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from ._expired_attrs_2_0 import __expired_attributes__
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# If a version with git hash was stored, use that instead
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from . import version
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from .version import __version__
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# We first need to detect if we're being called as part of the numpy setup
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# procedure itself in a reliable manner.
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try:
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__NUMPY_SETUP__
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except NameError:
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__NUMPY_SETUP__ = False
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if __NUMPY_SETUP__:
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sys.stderr.write('Running from numpy source directory.\n')
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else:
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# Allow distributors to run custom init code before importing numpy._core
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from . import _distributor_init
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try:
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from numpy.__config__ import show as show_config
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except ImportError as e:
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msg = """Error importing numpy: you should not try to import numpy from
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its source directory; please exit the numpy source tree, and relaunch
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your python interpreter from there."""
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raise ImportError(msg) from e
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from . import _core
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from ._core import (
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False_, ScalarType, True_, _get_promotion_state, _no_nep50_warning,
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_set_promotion_state, abs, absolute, acos, acosh, add, all, allclose,
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amax, amin, any, arange, arccos, arccosh, arcsin, arcsinh,
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arctan, arctan2, arctanh, argmax, argmin, argpartition, argsort,
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argwhere, around, array, array2string, array_equal, array_equiv,
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array_repr, array_str, asanyarray, asarray, ascontiguousarray,
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asfortranarray, asin, asinh, atan, atanh, atan2, astype, atleast_1d,
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atleast_2d, atleast_3d, base_repr, binary_repr, bitwise_and,
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bitwise_count, bitwise_invert, bitwise_left_shift, bitwise_not,
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bitwise_or, bitwise_right_shift, bitwise_xor, block, bool, bool_,
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broadcast, busday_count, busday_offset, busdaycalendar, byte, bytes_,
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can_cast, cbrt, cdouble, ceil, character, choose, clip, clongdouble,
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complex128, complex64, complexfloating, compress, concat, concatenate,
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conj, conjugate, convolve, copysign, copyto, correlate, cos, cosh,
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count_nonzero, cross, csingle, cumprod, cumsum, cumulative_prod,
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cumulative_sum, datetime64, datetime_as_string, datetime_data,
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deg2rad, degrees, diagonal, divide, divmod, dot, double, dtype, e,
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einsum, einsum_path, empty, empty_like, equal, errstate, euler_gamma,
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exp, exp2, expm1, fabs, finfo, flatiter, flatnonzero, flexible,
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float16, float32, float64, float_power, floating, floor, floor_divide,
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fmax, fmin, fmod, format_float_positional, format_float_scientific,
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frexp, from_dlpack, frombuffer, fromfile, fromfunction, fromiter,
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frompyfunc, fromstring, full, full_like, gcd, generic, geomspace,
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get_printoptions, getbufsize, geterr, geterrcall, greater,
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greater_equal, half, heaviside, hstack, hypot, identity, iinfo, iinfo,
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indices, inexact, inf, inner, int16, int32, int64, int8, int_, intc,
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integer, intp, invert, is_busday, isclose, isdtype, isfinite,
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isfortran, isinf, isnan, isnat, isscalar, issubdtype, lcm, ldexp,
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left_shift, less, less_equal, lexsort, linspace, little_endian, log,
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log10, log1p, log2, logaddexp, logaddexp2, logical_and, logical_not,
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logical_or, logical_xor, logspace, long, longdouble, longlong, matmul,
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matrix_transpose, max, maximum, may_share_memory, mean, memmap, min,
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min_scalar_type, minimum, mod, modf, moveaxis, multiply, nan, ndarray,
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ndim, nditer, negative, nested_iters, newaxis, nextafter, nonzero,
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not_equal, number, object_, ones, ones_like, outer, partition,
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permute_dims, pi, positive, pow, power, printoptions, prod,
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promote_types, ptp, put, putmask, rad2deg, radians, ravel, recarray,
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reciprocal, record, remainder, repeat, require, reshape, resize,
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result_type, right_shift, rint, roll, rollaxis, round, sctypeDict,
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searchsorted, set_printoptions, setbufsize, seterr, seterrcall, shape,
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shares_memory, short, sign, signbit, signedinteger, sin, single, sinh,
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size, sort, spacing, sqrt, square, squeeze, stack, std,
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str_, subtract, sum, swapaxes, take, tan, tanh, tensordot,
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timedelta64, trace, transpose, true_divide, trunc, typecodes, ubyte,
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ufunc, uint, uint16, uint32, uint64, uint8, uintc, uintp, ulong,
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ulonglong, unsignedinteger, unstack, ushort, var, vdot, vecdot, void,
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vstack, where, zeros, zeros_like
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)
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# NOTE: It's still under discussion whether these aliases
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# should be removed.
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for ta in ["float96", "float128", "complex192", "complex256"]:
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try:
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globals()[ta] = getattr(_core, ta)
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except AttributeError:
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pass
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del ta
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from . import lib
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from .lib import scimath as emath
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from .lib._histograms_impl import (
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histogram, histogram_bin_edges, histogramdd
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)
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from .lib._nanfunctions_impl import (
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nanargmax, nanargmin, nancumprod, nancumsum, nanmax, nanmean,
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nanmedian, nanmin, nanpercentile, nanprod, nanquantile, nanstd,
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nansum, nanvar
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)
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from .lib._function_base_impl import (
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select, piecewise, trim_zeros, copy, iterable, percentile, diff,
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gradient, angle, unwrap, sort_complex, flip, rot90, extract, place,
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vectorize, asarray_chkfinite, average, bincount, digitize, cov,
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corrcoef, median, sinc, hamming, hanning, bartlett, blackman,
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kaiser, trapezoid, trapz, i0, meshgrid, delete, insert, append,
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interp, quantile
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)
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from .lib._twodim_base_impl import (
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diag, diagflat, eye, fliplr, flipud, tri, triu, tril, vander,
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histogram2d, mask_indices, tril_indices, tril_indices_from,
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triu_indices, triu_indices_from
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)
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from .lib._shape_base_impl import (
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apply_over_axes, apply_along_axis, array_split, column_stack, dsplit,
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dstack, expand_dims, hsplit, kron, put_along_axis, row_stack, split,
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take_along_axis, tile, vsplit
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)
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from .lib._type_check_impl import (
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iscomplexobj, isrealobj, imag, iscomplex, isreal, nan_to_num, real,
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real_if_close, typename, mintypecode, common_type
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)
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from .lib._arraysetops_impl import (
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ediff1d, in1d, intersect1d, isin, setdiff1d, setxor1d, union1d,
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unique, unique_all, unique_counts, unique_inverse, unique_values
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)
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from .lib._ufunclike_impl import fix, isneginf, isposinf
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from .lib._arraypad_impl import pad
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from .lib._utils_impl import (
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show_runtime, get_include, info
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)
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from .lib._stride_tricks_impl import (
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broadcast_arrays, broadcast_shapes, broadcast_to
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)
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from .lib._polynomial_impl import (
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poly, polyint, polyder, polyadd, polysub, polymul, polydiv, polyval,
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polyfit, poly1d, roots
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)
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from .lib._npyio_impl import (
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savetxt, loadtxt, genfromtxt, load, save, savez, packbits,
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savez_compressed, unpackbits, fromregex
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)
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from .lib._index_tricks_impl import (
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diag_indices_from, diag_indices, fill_diagonal, ndindex, ndenumerate,
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ix_, c_, r_, s_, ogrid, mgrid, unravel_index, ravel_multi_index,
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index_exp
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)
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from . import matrixlib as _mat
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from .matrixlib import (
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asmatrix, bmat, matrix
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)
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# public submodules are imported lazily, therefore are accessible from
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# __getattr__. Note that `distutils` (deprecated) and `array_api`
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# (experimental label) are not added here, because `from numpy import *`
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# must not raise any warnings - that's too disruptive.
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__numpy_submodules__ = {
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"linalg", "fft", "dtypes", "random", "polynomial", "ma",
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"exceptions", "lib", "ctypeslib", "testing", "typing",
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"f2py", "test", "rec", "char", "core", "strings",
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}
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# We build warning messages for former attributes
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_msg = (
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"module 'numpy' has no attribute '{n}'.\n"
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"`np.{n}` was a deprecated alias for the builtin `{n}`. "
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"To avoid this error in existing code, use `{n}` by itself. "
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"Doing this will not modify any behavior and is safe. {extended_msg}\n"
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"The aliases was originally deprecated in NumPy 1.20; for more "
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"details and guidance see the original release note at:\n"
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" https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations")
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_specific_msg = (
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"If you specifically wanted the numpy scalar type, use `np.{}` here.")
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_int_extended_msg = (
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"When replacing `np.{}`, you may wish to use e.g. `np.int64` "
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"or `np.int32` to specify the precision. If you wish to review "
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"your current use, check the release note link for "
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"additional information.")
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_type_info = [
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("object", ""), # The NumPy scalar only exists by name.
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("float", _specific_msg.format("float64")),
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("complex", _specific_msg.format("complex128")),
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("str", _specific_msg.format("str_")),
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("int", _int_extended_msg.format("int"))]
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__former_attrs__ = {
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n: _msg.format(n=n, extended_msg=extended_msg)
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for n, extended_msg in _type_info
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}
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# Some of these could be defined right away, but most were aliases to
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# the Python objects and only removed in NumPy 1.24. Defining them should
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# probably wait for NumPy 1.26 or 2.0.
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# When defined, these should possibly not be added to `__all__` to avoid
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# import with `from numpy import *`.
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__future_scalars__ = {"str", "bytes", "object"}
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__array_api_version__ = "2023.12"
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from ._array_api_info import __array_namespace_info__
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# now that numpy core module is imported, can initialize limits
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_core.getlimits._register_known_types()
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__all__ = list(
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__numpy_submodules__ |
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set(_core.__all__) |
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set(_mat.__all__) |
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set(lib._histograms_impl.__all__) |
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set(lib._nanfunctions_impl.__all__) |
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set(lib._function_base_impl.__all__) |
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set(lib._twodim_base_impl.__all__) |
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set(lib._shape_base_impl.__all__) |
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set(lib._type_check_impl.__all__) |
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set(lib._arraysetops_impl.__all__) |
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set(lib._ufunclike_impl.__all__) |
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set(lib._arraypad_impl.__all__) |
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set(lib._utils_impl.__all__) |
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set(lib._stride_tricks_impl.__all__) |
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set(lib._polynomial_impl.__all__) |
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set(lib._npyio_impl.__all__) |
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|
|
set(lib._index_tricks_impl.__all__) |
|
||
|
|
{"emath", "show_config", "__version__", "__array_namespace_info__"}
|
||
|
|
)
|
||
|
|
|
||
|
|
# Filter out Cython harmless warnings
|
||
|
|
warnings.filterwarnings("ignore", message="numpy.dtype size changed")
|
||
|
|
warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
|
||
|
|
warnings.filterwarnings("ignore", message="numpy.ndarray size changed")
|
||
|
|
|
||
|
|
def __getattr__(attr):
|
||
|
|
# Warn for expired attributes
|
||
|
|
import warnings
|
||
|
|
|
||
|
|
if attr == "linalg":
|
||
|
|
import numpy.linalg as linalg
|
||
|
|
return linalg
|
||
|
|
elif attr == "fft":
|
||
|
|
import numpy.fft as fft
|
||
|
|
return fft
|
||
|
|
elif attr == "dtypes":
|
||
|
|
import numpy.dtypes as dtypes
|
||
|
|
return dtypes
|
||
|
|
elif attr == "random":
|
||
|
|
import numpy.random as random
|
||
|
|
return random
|
||
|
|
elif attr == "polynomial":
|
||
|
|
import numpy.polynomial as polynomial
|
||
|
|
return polynomial
|
||
|
|
elif attr == "ma":
|
||
|
|
import numpy.ma as ma
|
||
|
|
return ma
|
||
|
|
elif attr == "ctypeslib":
|
||
|
|
import numpy.ctypeslib as ctypeslib
|
||
|
|
return ctypeslib
|
||
|
|
elif attr == "exceptions":
|
||
|
|
import numpy.exceptions as exceptions
|
||
|
|
return exceptions
|
||
|
|
elif attr == "testing":
|
||
|
|
import numpy.testing as testing
|
||
|
|
return testing
|
||
|
|
elif attr == "matlib":
|
||
|
|
import numpy.matlib as matlib
|
||
|
|
return matlib
|
||
|
|
elif attr == "f2py":
|
||
|
|
import numpy.f2py as f2py
|
||
|
|
return f2py
|
||
|
|
elif attr == "typing":
|
||
|
|
import numpy.typing as typing
|
||
|
|
return typing
|
||
|
|
elif attr == "rec":
|
||
|
|
import numpy.rec as rec
|
||
|
|
return rec
|
||
|
|
elif attr == "char":
|
||
|
|
import numpy.char as char
|
||
|
|
return char
|
||
|
|
elif attr == "array_api":
|
||
|
|
raise AttributeError("`numpy.array_api` is not available from "
|
||
|
|
"numpy 2.0 onwards", name=None)
|
||
|
|
elif attr == "core":
|
||
|
|
import numpy.core as core
|
||
|
|
return core
|
||
|
|
elif attr == "strings":
|
||
|
|
import numpy.strings as strings
|
||
|
|
return strings
|
||
|
|
elif attr == "distutils":
|
||
|
|
if 'distutils' in __numpy_submodules__:
|
||
|
|
import numpy.distutils as distutils
|
||
|
|
return distutils
|
||
|
|
else:
|
||
|
|
raise AttributeError("`numpy.distutils` is not available from "
|
||
|
|
"Python 3.12 onwards", name=None)
|
||
|
|
|
||
|
|
if attr in __future_scalars__:
|
||
|
|
# And future warnings for those that will change, but also give
|
||
|
|
# the AttributeError
|
||
|
|
warnings.warn(
|
||
|
|
f"In the future `np.{attr}` will be defined as the "
|
||
|
|
"corresponding NumPy scalar.", FutureWarning, stacklevel=2)
|
||
|
|
|
||
|
|
if attr in __former_attrs__:
|
||
|
|
raise AttributeError(__former_attrs__[attr], name=None)
|
||
|
|
|
||
|
|
if attr in __expired_attributes__:
|
||
|
|
raise AttributeError(
|
||
|
|
f"`np.{attr}` was removed in the NumPy 2.0 release. "
|
||
|
|
f"{__expired_attributes__[attr]}",
|
||
|
|
name=None
|
||
|
|
)
|
||
|
|
|
||
|
|
if attr == "chararray":
|
||
|
|
warnings.warn(
|
||
|
|
"`np.chararray` is deprecated and will be removed from "
|
||
|
|
"the main namespace in the future. Use an array with a string "
|
||
|
|
"or bytes dtype instead.", DeprecationWarning, stacklevel=2)
|
||
|
|
import numpy.char as char
|
||
|
|
return char.chararray
|
||
|
|
|
||
|
|
raise AttributeError("module {!r} has no attribute "
|
||
|
|
"{!r}".format(__name__, attr))
|
||
|
|
|
||
|
|
def __dir__():
|
||
|
|
public_symbols = (
|
||
|
|
globals().keys() | __numpy_submodules__
|
||
|
|
)
|
||
|
|
public_symbols -= {
|
||
|
|
"matrixlib", "matlib", "tests", "conftest", "version",
|
||
|
|
"compat", "distutils", "array_api"
|
||
|
|
}
|
||
|
|
return list(public_symbols)
|
||
|
|
|
||
|
|
# Pytest testing
|
||
|
|
from numpy._pytesttester import PytestTester
|
||
|
|
test = PytestTester(__name__)
|
||
|
|
del PytestTester
|
||
|
|
|
||
|
|
def _sanity_check():
|
||
|
|
"""
|
||
|
|
Quick sanity checks for common bugs caused by environment.
|
||
|
|
There are some cases e.g. with wrong BLAS ABI that cause wrong
|
||
|
|
results under specific runtime conditions that are not necessarily
|
||
|
|
achieved during test suite runs, and it is useful to catch those early.
|
||
|
|
|
||
|
|
See https://github.com/numpy/numpy/issues/8577 and other
|
||
|
|
similar bug reports.
|
||
|
|
|
||
|
|
"""
|
||
|
|
try:
|
||
|
|
x = ones(2, dtype=float32)
|
||
|
|
if not abs(x.dot(x) - float32(2.0)) < 1e-5:
|
||
|
|
raise AssertionError()
|
||
|
|
except AssertionError:
|
||
|
|
msg = ("The current Numpy installation ({!r}) fails to "
|
||
|
|
"pass simple sanity checks. This can be caused for example "
|
||
|
|
"by incorrect BLAS library being linked in, or by mixing "
|
||
|
|
"package managers (pip, conda, apt, ...). Search closed "
|
||
|
|
"numpy issues for similar problems.")
|
||
|
|
raise RuntimeError(msg.format(__file__)) from None
|
||
|
|
|
||
|
|
_sanity_check()
|
||
|
|
del _sanity_check
|
||
|
|
|
||
|
|
def _mac_os_check():
|
||
|
|
"""
|
||
|
|
Quick Sanity check for Mac OS look for accelerate build bugs.
|
||
|
|
Testing numpy polyfit calls init_dgelsd(LAPACK)
|
||
|
|
"""
|
||
|
|
try:
|
||
|
|
c = array([3., 2., 1.])
|
||
|
|
x = linspace(0, 2, 5)
|
||
|
|
y = polyval(c, x)
|
||
|
|
_ = polyfit(x, y, 2, cov=True)
|
||
|
|
except ValueError:
|
||
|
|
pass
|
||
|
|
|
||
|
|
if sys.platform == "darwin":
|
||
|
|
from . import exceptions
|
||
|
|
with warnings.catch_warnings(record=True) as w:
|
||
|
|
_mac_os_check()
|
||
|
|
# Throw runtime error, if the test failed Check for warning and error_message
|
||
|
|
if len(w) > 0:
|
||
|
|
for _wn in w:
|
||
|
|
if _wn.category is exceptions.RankWarning:
|
||
|
|
# Ignore other warnings, they may not be relevant (see gh-25433).
|
||
|
|
error_message = f"{_wn.category.__name__}: {str(_wn.message)}"
|
||
|
|
msg = (
|
||
|
|
"Polyfit sanity test emitted a warning, most likely due "
|
||
|
|
"to using a buggy Accelerate backend."
|
||
|
|
"\nIf you compiled yourself, more information is available at:"
|
||
|
|
"\nhttps://numpy.org/devdocs/building/index.html"
|
||
|
|
"\nOtherwise report this to the vendor "
|
||
|
|
"that provided NumPy.\n\n{}\n".format(error_message))
|
||
|
|
raise RuntimeError(msg)
|
||
|
|
del _wn
|
||
|
|
del w
|
||
|
|
del _mac_os_check
|
||
|
|
|
||
|
|
def hugepage_setup():
|
||
|
|
"""
|
||
|
|
We usually use madvise hugepages support, but on some old kernels it
|
||
|
|
is slow and thus better avoided. Specifically kernel version 4.6
|
||
|
|
had a bug fix which probably fixed this:
|
||
|
|
https://github.com/torvalds/linux/commit/7cf91a98e607c2f935dbcc177d70011e95b8faff
|
||
|
|
"""
|
||
|
|
use_hugepage = os.environ.get("NUMPY_MADVISE_HUGEPAGE", None)
|
||
|
|
if sys.platform == "linux" and use_hugepage is None:
|
||
|
|
# If there is an issue with parsing the kernel version,
|
||
|
|
# set use_hugepage to 0. Usage of LooseVersion will handle
|
||
|
|
# the kernel version parsing better, but avoided since it
|
||
|
|
# will increase the import time.
|
||
|
|
# See: #16679 for related discussion.
|
||
|
|
try:
|
||
|
|
use_hugepage = 1
|
||
|
|
kernel_version = os.uname().release.split(".")[:2]
|
||
|
|
kernel_version = tuple(int(v) for v in kernel_version)
|
||
|
|
if kernel_version < (4, 6):
|
||
|
|
use_hugepage = 0
|
||
|
|
except ValueError:
|
||
|
|
use_hugepage = 0
|
||
|
|
elif use_hugepage is None:
|
||
|
|
# This is not Linux, so it should not matter, just enable anyway
|
||
|
|
use_hugepage = 1
|
||
|
|
else:
|
||
|
|
use_hugepage = int(use_hugepage)
|
||
|
|
return use_hugepage
|
||
|
|
|
||
|
|
# Note that this will currently only make a difference on Linux
|
||
|
|
_core.multiarray._set_madvise_hugepage(hugepage_setup())
|
||
|
|
del hugepage_setup
|
||
|
|
|
||
|
|
# Give a warning if NumPy is reloaded or imported on a sub-interpreter
|
||
|
|
# We do this from python, since the C-module may not be reloaded and
|
||
|
|
# it is tidier organized.
|
||
|
|
_core.multiarray._multiarray_umath._reload_guard()
|
||
|
|
|
||
|
|
# TODO: Remove the environment variable entirely now that it is "weak"
|
||
|
|
_core._set_promotion_state(
|
||
|
|
os.environ.get("NPY_PROMOTION_STATE", "weak"))
|
||
|
|
|
||
|
|
# Tell PyInstaller where to find hook-numpy.py
|
||
|
|
def _pyinstaller_hooks_dir():
|
||
|
|
from pathlib import Path
|
||
|
|
return [str(Path(__file__).with_name("_pyinstaller").resolve())]
|
||
|
|
|
||
|
|
|
||
|
|
# Remove symbols imported for internal use
|
||
|
|
del os, sys, warnings
|