215 lines
5.5 KiB
Python
215 lines
5.5 KiB
Python
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from numbers import Number
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import operator
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import os
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import threading
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import contextlib
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import numpy as np
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# good_size is exposed (and used) from this import
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from .pypocketfft import good_size
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_config = threading.local()
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_cpu_count = os.cpu_count()
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def _iterable_of_int(x, name=None):
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"""Convert ``x`` to an iterable sequence of int
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Parameters
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----------
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x : value, or sequence of values, convertible to int
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name : str, optional
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Name of the argument being converted, only used in the error message
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Returns
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-------
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y : ``List[int]``
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"""
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if isinstance(x, Number):
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x = (x,)
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try:
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x = [operator.index(a) for a in x]
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except TypeError as e:
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name = name or "value"
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raise ValueError("{} must be a scalar or iterable of integers"
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.format(name)) from e
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return x
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def _init_nd_shape_and_axes(x, shape, axes):
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"""Handles shape and axes arguments for nd transforms"""
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noshape = shape is None
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noaxes = axes is None
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if not noaxes:
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axes = _iterable_of_int(axes, 'axes')
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axes = [a + x.ndim if a < 0 else a for a in axes]
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if any(a >= x.ndim or a < 0 for a in axes):
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raise ValueError("axes exceeds dimensionality of input")
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if len(set(axes)) != len(axes):
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raise ValueError("all axes must be unique")
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if not noshape:
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shape = _iterable_of_int(shape, 'shape')
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if axes and len(axes) != len(shape):
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raise ValueError("when given, axes and shape arguments"
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" have to be of the same length")
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if noaxes:
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if len(shape) > x.ndim:
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raise ValueError("shape requires more axes than are present")
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axes = range(x.ndim - len(shape), x.ndim)
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shape = [x.shape[a] if s == -1 else s for s, a in zip(shape, axes)]
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elif noaxes:
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shape = list(x.shape)
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axes = range(x.ndim)
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else:
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shape = [x.shape[a] for a in axes]
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if any(s < 1 for s in shape):
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raise ValueError(
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"invalid number of data points ({0}) specified".format(shape))
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return shape, axes
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def _asfarray(x):
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"""
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Convert to array with floating or complex dtype.
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float16 values are also promoted to float32.
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"""
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if not hasattr(x, "dtype"):
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x = np.asarray(x)
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if x.dtype == np.float16:
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return np.asarray(x, np.float32)
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elif x.dtype.kind not in 'fc':
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return np.asarray(x, np.float64)
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# Require native byte order
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dtype = x.dtype.newbyteorder('=')
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# Always align input
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copy = not x.flags['ALIGNED']
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return np.array(x, dtype=dtype, copy=copy)
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def _datacopied(arr, original):
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"""
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Strict check for `arr` not sharing any data with `original`,
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under the assumption that arr = asarray(original)
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"""
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if arr is original:
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return False
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if not isinstance(original, np.ndarray) and hasattr(original, '__array__'):
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return False
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return arr.base is None
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def _fix_shape(x, shape, axes):
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"""Internal auxiliary function for _raw_fft, _raw_fftnd."""
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must_copy = False
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# Build an nd slice with the dimensions to be read from x
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index = [slice(None)]*x.ndim
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for n, ax in zip(shape, axes):
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if x.shape[ax] >= n:
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index[ax] = slice(0, n)
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else:
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index[ax] = slice(0, x.shape[ax])
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must_copy = True
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index = tuple(index)
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if not must_copy:
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return x[index], False
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s = list(x.shape)
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for n, axis in zip(shape, axes):
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s[axis] = n
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z = np.zeros(s, x.dtype)
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z[index] = x[index]
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return z, True
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def _fix_shape_1d(x, n, axis):
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if n < 1:
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raise ValueError(
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"invalid number of data points ({0}) specified".format(n))
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return _fix_shape(x, (n,), (axis,))
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_NORM_MAP = {None: 0, 'backward': 0, 'ortho': 1, 'forward': 2}
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def _normalization(norm, forward):
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"""Returns the pypocketfft normalization mode from the norm argument"""
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try:
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inorm = _NORM_MAP[norm]
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return inorm if forward else (2 - inorm)
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except KeyError:
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raise ValueError(
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f'Invalid norm value {norm!r}, should '
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'be "backward", "ortho" or "forward"') from None
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def _workers(workers):
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if workers is None:
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return getattr(_config, 'default_workers', 1)
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if workers < 0:
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if workers >= -_cpu_count:
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workers += 1 + _cpu_count
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else:
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raise ValueError("workers value out of range; got {}, must not be"
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" less than {}".format(workers, -_cpu_count))
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elif workers == 0:
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raise ValueError("workers must not be zero")
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return workers
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@contextlib.contextmanager
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def set_workers(workers):
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"""Context manager for the default number of workers used in `scipy.fft`
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Parameters
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----------
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workers : int
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The default number of workers to use
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Examples
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--------
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>>> from scipy import fft, signal
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>>> x = np.random.randn(128, 64)
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>>> with fft.set_workers(4):
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... y = signal.fftconvolve(x, x)
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"""
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old_workers = get_workers()
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_config.default_workers = _workers(operator.index(workers))
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try:
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yield
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finally:
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_config.default_workers = old_workers
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def get_workers():
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"""Returns the default number of workers within the current context
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Examples
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--------
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>>> from scipy import fft
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>>> fft.get_workers()
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1
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>>> with fft.set_workers(4):
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... fft.get_workers()
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4
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"""
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return getattr(_config, 'default_workers', 1)
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