U ŽÇ-eX ã@sLddlmZmZddlmZd dd„ZeeƒejƒeƒZeje_dd„Z d S) é)Úupdate_wrapperÚ lru_cacheé)ÚhelperFcCsdS)aVFind the next fast size of input data to ``fft``, for zero-padding, etc. SciPy's FFT algorithms gain their speed by a recursive divide and conquer strategy. This relies on efficient functions for small prime factors of the input length. Thus, the transforms are fastest when using composites of the prime factors handled by the fft implementation. If there are efficient functions for all radices <= `n`, then the result will be a number `x` >= ``target`` with only prime factors < `n`. (Also known as `n`-smooth numbers) Parameters ---------- target : int Length to start searching from. Must be a positive integer. real : bool, optional True if the FFT involves real input or output (e.g., `rfft` or `hfft` but not `fft`). Defaults to False. Returns ------- out : int The smallest fast length greater than or equal to ``target``. Notes ----- The result of this function may change in future as performance considerations change, for example, if new prime factors are added. Calling `fft` or `ifft` with real input data performs an ``'R2C'`` transform internally. Examples -------- On a particular machine, an FFT of prime length takes 11.4 ms: >>> from scipy import fft >>> import numpy as np >>> rng = np.random.default_rng() >>> min_len = 93059 # prime length is worst case for speed >>> a = rng.standard_normal(min_len) >>> b = fft.fft(a) Zero-padding to the next regular length reduces computation time to 1.6 ms, a speedup of 7.3 times: >>> fft.next_fast_len(min_len, real=True) 93312 >>> b = fft.fft(a, 93312) Rounding up to the next power of 2 is not optimal, taking 3.0 ms to compute; 1.9 times longer than the size given by ``next_fast_len``: >>> b = fft.fft(a, 131072) N©)ÚtargetÚrealrrúR/var/www/html/Darija-Ai-Train/env/lib/python3.8/site-packages/scipy/fft/_helper.pyÚ next_fast_lens8r cCst |||¡S)a Handle shape and axes arguments for N-D transforms. Returns the shape and axes in a standard form, taking into account negative values and checking for various potential errors. Parameters ---------- x : array_like The input array. shape : int or array_like of ints or None The shape of the result. If both `shape` and `axes` (see below) are None, `shape` is ``x.shape``; if `shape` is None but `axes` is not None, then `shape` is ``numpy.take(x.shape, axes, axis=0)``. If `shape` is -1, the size of the corresponding dimension of `x` is used. axes : int or array_like of ints or None Axes along which the calculation is computed. The default is over all axes. Negative indices are automatically converted to their positive counterparts. Returns ------- shape : array The shape of the result. It is a 1-D integer array. axes : array The shape of the result. It is a 1-D integer array. )Ú_helperÚ_init_nd_shape_and_axes)ÚxÚshapeZaxesrrr r Gsr N)F) Ú functoolsrrZ _pocketfftrr r Z good_sizeÚ __wrapped__r rrrr Ús   =