Welcome to pyFFTW’s documentation!¶

Introduction¶

pyFFTW is a pythonic wrapper around FFTW, the speedy FFT library. The ultimate aim is to present a unified interface for all the possible transforms that FFTW can perform.

Both the complex DFT and the real DFT are supported, as well as on arbitrary axes of abitrary shaped and strided arrays, which makes it almost feature equivalent to standard and real FFT functions of numpy.fft (indeed, it supports the clongdouble dtype which numpy.fft does not).

Operating FFTW in multithreaded mode is supported.

The core interface is provided by a unified class, pyfftw.FFTW. This core interface can be accessed directly, or through a series of helper functions, provided by the pyfftw.builders module. These helper functions provide an interface similar to numpy.fft for ease of use.

In addition to using pyfftw.FFTW, a convenient series of functions are included through pyfftw.interfaces that make using pyfftw almost equivalent to numpy.fft or scipy.fftpack.

The source can be found in github and its page in the python package index is here.

A comprehensive unittest suite is included with the source on the repository. If any aspect of this library is not covered by the test suite, that is a bug (please report it!).

Contents¶

Overview and A Short Tutorial¶

Before we begin, we assume that you are already familiar with the discrete Fourier transform, and why you want a faster library to perform your FFTs for you.

FFTW is a very fast FFT C library. The way it is designed to work is by planning in advance the fastest way to perform a particular transform. It does this by trying lots of different techniques and measuring the fastest way, so called planning.

One consequence of this is that the user needs to specify in advance exactly what transform is needed, including things like the data type, the array shapes and strides and the precision. This is quite different to how one uses, for example, the numpy.fft module.

The purpose of this library is to provide a simple and pythonic way to interact with FFTW, benefiting from the substantial speed-ups it offers. In addition to the method of using FFTW as described above, a convenient series of functions are included through pyfftw.interfaces that make using pyfftw almost equivalent to numpy.fft.

This tutorial is split into three parts. A quick introduction to the pyfftw.interfaces module is given, the most simple and direct way to use pyfftw. Secondly an overview is given of pyfftw.FFTW, the core of the library. Finally, the pyfftw.builders helper functions are introduced, which ease the creation of pyfftw.FFTW objects.

Quick and easy: the `pyfftw.interfaces` module¶

The easiest way to begin using pyfftw is through the pyfftw.interfaces module. This module implements three APIs: pyfftw.interfaces.numpy_fft, pyfftw.interfaces.scipy_fftpack, and pyfftw.interfaces.dask_fft, which are (apart from a small caveat [1]) drop in replacements for numpy.fft, scipy.fftpack, and dask.fft respectively.

>>> import pyfftw
>>> import numpy
>>> a = pyfftw.empty_aligned(128, dtype='complex128', n=16)
>>> a[:] = numpy.random.randn(128) + 1j*numpy.random.randn(128)
>>> b = pyfftw.interfaces.numpy_fft.fft(a)
>>> c = numpy.fft.fft(a)
>>> numpy.allclose(b, c)
True

We initially create and fill a complex array, a, of length 128. pyfftw.empty_aligned() is a helper function that works like numpy.empty() but returns the array aligned to a particular number of bytes in memory, in this case 16. If the alignment is not specified then the library inspects the CPU for an appropriate alignment value. Having byte aligned arrays allows FFTW to performed vector operations, potentially speeding up the FFT (a similar pyfftw.byte_align() exists to align a pre-existing array as necessary).

Calling pyfftw.interfaces.numpy_fft.fft() on a gives the same output (to numerical precision) as calling numpy.fft.fft() on a.

If you wanted to modify existing code that uses numpy.fft to use pyfftw.interfaces, this is done simply by replacing all instances of numpy.fft with pyfftw.interfaces.numpy_fft (similarly for scipy.fftpack and pyfftw.interfaces.scipy_fftpack), and then, optionally, enabling the cache (see below).

The first call for a given transform size and shape and dtype and so on may be slow, this is down to FFTW needing to plan the transform for the first time. Once this has been done, subsequent equivalent transforms during the same session are much faster. It’s possible to export and save the internal knowledge (the wisdom) about how the transform is done. This is described below.

Even after the first transform of a given specification has been performed, subsequent transforms are never as fast as using pyfftw.FFTW objects directly, and in many cases are substantially slower. This is because of the internal overhead of creating a new pyfftw.FFTW object on every call. For this reason, a cache is provided, which is recommended to be used whenever pyfftw.interfaces is used. Turn the cache on using pyfftw.interfaces.cache.enable(). This function turns the cache on globally. Note that using the cache invokes the threading module.

The cache temporarily stores a copy of any interim pyfftw.FFTW objects that are created. If they are not used for some period of time, which can be set with pyfftw.interfaces.cache.set_keepalive_time(), then they are removed from the cache (liberating any associated memory). The default keepalive time is 0.1 seconds.

Monkey patching 3rd party libraries¶

Since pyfftw.interfaces.numpy_fft and pyfftw.interfaces.scipy_fftpack are drop-in replacements for their numpy.fft and scipy.fftpack libraries respectively, it is possible use them as replacements at run-time through monkey patching.

The following code demonstrates scipy.signal.fftconvolve() being monkey patched in order to speed it up.

import pyfftw
import scipy.signal
import numpy
from timeit import Timer

a = pyfftw.empty_aligned((128, 64), dtype='complex128')
b = pyfftw.empty_aligned((128, 64), dtype='complex128')

a[:] = numpy.random.randn(128, 64) + 1j*numpy.random.randn(128, 64)
b[:] = numpy.random.randn(128, 64) + 1j*numpy.random.randn(128, 64)

t = Timer(lambda: scipy.signal.fftconvolve(a, b))

print('Time with scipy.fftpack: %1.3f seconds' % t.timeit(number=100))

# Monkey patch fftpack with pyfftw.interfaces.scipy_fftpack
scipy.fftpack = pyfftw.interfaces.scipy_fftpack
scipy.signal.fftconvolve(a, b) # We cheat a bit by doing the planning first

# Turn on the cache for optimum performance
pyfftw.interfaces.cache.enable()

print('Time with monkey patched scipy_fftpack: %1.3f seconds' %
       t.timeit(number=100))

which outputs something like:

Time with scipy.fftpack: 0.598 seconds
Time with monkey patched scipy_fftpack: 0.251 seconds

Note that prior to Scipy 0.16, it was necessary to patch the individual functions in scipy.signal.signaltools. For example:

scipy.signal.signaltools.ifftn = pyfftw.interfaces.scipy_fftpack.ifftn

The workhorse `pyfftw.FFTW` class¶

The core of this library is provided through the pyfftw.FFTW class. FFTW is fully encapsulated within this class.

The following gives an overview of the pyfftw.FFTW class, but the easiest way to of dealing with it is through the pyfftw.builders helper functions, also discussed in this tutorial.

For users that already have some experience of FFTW, there is no interface distinction between any of the supported data types, shapes or transforms, and operating on arbitrarily strided arrays (which are common when using numpy) is fully supported with no copies necessary.

In its simplest form, a pyfftw.FFTW object is created with a pair of complementary numpy arrays: an input array and an output array. They are complementary insomuch as the data types and the array sizes together define exactly what transform should be performed. We refer to a valid transform as a scheme.

Internally, three precisions of FFT are supported. These correspond to single precision floating point, double precision floating point and long double precision floating point, which correspond to numpy‘s float32, float64 and longdouble dtypes respectively (and the corresponding complex types). The precision is decided by the relevant scheme, which is specified by the dtype of the input array.

Various schemes are supported by pyfftw.FFTW. The scheme that is used depends on the data types of the input array and output arrays, the shape of the arrays and the direction flag. For a full discussion of the schemes available, see the API documentation for pyfftw.FFTW.

One-Dimensional Transforms¶

We will first consider creating a simple one-dimensional transform of a one-dimensional complex array:

import pyfftw

a = pyfftw.empty_aligned(128, dtype='complex128')
b = pyfftw.empty_aligned(128, dtype='complex128')

fft_object = pyfftw.FFTW(a, b)

In this case, we create 2 complex arrays, a and b each of length 128. As before, we use pyfftw.empty_aligned() to make sure the array is aligned.

Given these 2 arrays, the only transform that makes sense is a 1D complex DFT. The direction in this case is the default, which is forward, and so that is the transform that is planned. The returned fft_object represents such a transform.

In general, the creation of the pyfftw.FFTW object clears the contents of the arrays, so the arrays should be filled or updated after creation.

Similarly, to plan the inverse:

c = pyfftw.empty_aligned(128, dtype='complex128')
ifft_object = pyfftw.FFTW(b, c, direction='FFTW_BACKWARD')

In this case, the direction argument is given as 'FFTW_BACKWARD' (to override the default of 'FFTW_FORWARD').

The actual FFT is performed by calling the returned objects:

import numpy

# Generate some data
ar, ai = numpy.random.randn(2, 128)
a[:] = ar + 1j*ai

fft_a = fft_object()

Note that calling the object like this performs the FFT and returns the result in an array. This is the same array as b:

>>> fft_a is b
True

This is particularly useful when using pyfftw.builders to generate the pyfftw.FFTW objects.

Calling the FFT object followed by the inverse FFT object yields an output that is numerically the same as the original a (within numerical accuracy).

>>> fft_a = fft_object()
>>> ifft_b = ifft_object()
>>> ifft_b is c
True
>>> numpy.allclose(a, c)
True
>>> a is c
False

In this case, the normalisation of the DFT is performed automatically by the inverse FFTW object (ifft_object). This can be disabled by setting the normalise_idft=False argument.

It is possible to change the data on which a pyfftw.FFTW operates. The pyfftw.FFTW.__call__() accepts both an input_array and an output_array argument to update the arrays. The arrays should be compatible with the arrays with which the pyfftw.FFTW object was originally created. Please read the API docs on pyfftw.FFTW.__call__() to fully understand the requirements for updating the array.

>>> d = pyfftw.empty_aligned(4, dtype='complex128')
>>> e = pyfftw.empty_aligned(4, dtype='complex128')
>>> f = pyfftw.empty_aligned(4, dtype='complex128')
>>> fft_object = pyfftw.FFTW(d, e)
>>> fft_object.input_array is d # get the input array from the object
True
>>> f[:] = [1, 2, 3, 4] # Add some data to f
>>> fft_object(f)
array([ 10.+0.j,  -2.+2.j,  -2.+0.j,  -2.-2.j])
>>> fft_object.input_array is d # No longer true!
False
>>> fft_object.input_array is f # It has been updated with f :)
True

If the new input array is of the wrong dtype or wrongly strided, pyfftw.FFTW.__call__() method will copy the new array into the internal array, if necessary changing it’s dtype in the process.

It should be made clear that the pyfftw.FFTW.__call__() method is simply a helper routine around the other methods of the object. Though it is expected that most of the time pyfftw.FFTW.__call__() will be sufficient, all the FFTW functionality can be accessed through other methods at a slightly lower level.

Multi-Dimensional Transforms¶

Arrays of more than one dimension are easily supported as well. In this case, the axes argument specifies over which axes the transform is to be taken.

import pyfftw

a = pyfftw.empty_aligned((128, 64), dtype='complex128')
b = pyfftw.empty_aligned((128, 64), dtype='complex128')

# Plan an fft over the last axis
fft_object_a = pyfftw.FFTW(a, b)

# Over the first axis
fft_object_b = pyfftw.FFTW(a, b, axes=(0,))

# Over the both axes
fft_object_c = pyfftw.FFTW(a, b, axes=(0,1))

For further information on all the supported transforms, including real transforms, as well as full documentaion on all the instantiation arguments, see the pyfftw.FFTW documentation.

Wisdom¶

When creating a pyfftw.FFTW object, it is possible to instruct FFTW how much effort it should put into finding the fastest possible method for computing the DFT. This is done by specifying a suitable planner flag in flags argument to pyfftw.FFTW. Some of the planner flags can take a very long time to complete which can be problematic.

When the a particular transform has been created, distinguished by things like the data type, the shape, the stridings and the flags, FFTW keeps a record of the fastest way to compute such a transform in future. This is referred to as wisdom. When the program is completed, the wisdom that has been accumulated is forgotten.

It is possible to output the accumulated wisdom using the wisdom output routines. pyfftw.export_wisdom() exports and returns the wisdom as a tuple of strings that can be easily written to file. To load the wisdom back in, use the pyfftw.import_wisdom() function which takes as its argument that same tuple of strings that was returned from pyfftw.export_wisdom().

If for some reason you wish to forget the accumulated wisdom, call pyfftw.forget_wisdom().

The `pyfftw.builders` functions¶

If you absolutely need the flexibility of dealing with pyfftw.FFTW directly, an easier option than constructing valid arrays and so on is to use the convenient pyfftw.builders package. These functions take care of much of the difficulty in specifying the exact size and dtype requirements to produce a valid scheme.

The pyfftw.builders functions are a series of helper functions that provide an interface very much like that provided by numpy.fft, only instead of returning the result of the transform, a pyfftw.FFTW object (or in some cases a wrapper around pyfftw.FFTW) is returned.

import pyfftw

a = pyfftw.empty_aligned((128, 64), dtype='complex128')

# Generate some data
ar, ai = numpy.random.randn(2, 128, 64)
a[:] = ar + 1j*ai

fft_object = pyfftw.builders.fft(a)

b = fft_object()

fft_object is an instance of pyfftw.FFTW, b is the result of the DFT.

Note that in this example, unlike creating a pyfftw.FFTW object using the direct interface, we can fill the array in advance. This is because by default all the functions in pyfftw.builders keep a copy of the input array during creation (though this can be disabled).

The pyfftw.builders functions construct an output array of the correct size and type. In the case of the regular DFTs, this always creates an output array of the same size as the input array. In the case of the real transform, the output array is the right shape to satisfy the scheme requirements.

The precision of the transform is determined by the dtype of the input array. If the input array is a floating point array, then the precision of the floating point is used. If the input array is not a floating point array then a double precision transform is used. Any calls made to the resultant object with an array of the same size will then be copied into the internal array of the object, changing the dtype in the process.

Like numpy.fft, it is possible to specify a length (in the one-dimensional case) or a shape (in the multi-dimensional case) that may be different to the array that is passed in. In such a case, a wrapper object of type pyfftw.builders._utils._FFTWWrapper is returned. From an interface perspective, this is identical to pyfftw.FFTW. The difference is in the way calls to the object are handled. With pyfftw.builders._utils._FFTWWrapper objects, an array that is passed as an argument when calling the object is copied into the internal array. This is done by a suitable slicing of the new passed-in array and the internal array and is done precisely because the shape of the transform is different to the shape of the input array.

a = pyfftw.empty_aligned((128, 64), dtype='complex128')

fft_wrapper_object = pyfftw.builders.fftn(a, s=(32, 256))

b = fft_wrapper_object()

Inspecting these objects gives us their shapes:

>>> b.shape
(32, 256)
>>> fft_wrapper_object.input_array.shape
(32, 256)
>>> a.shape
(128, 64)

It is only possible to call fft_wrapper_object with an array that is the same shape as a. In this case, the first axis of a is sliced to include only the first 32 elements, and the second axis of the internal array is sliced to include only the last 64 elements. This way, shapes are made consistent for copying.

Understanding numpy.fft, these functions are largely self-explanatory. We point the reader to the API docs for more information.

Footnotes

[1] pyfftw.interfaces deals with repeated values in the axes argument differently to numpy.fft (and probably to scipy.fftpack to, but that’s not documented clearly). Specifically, numpy.fft takes the transform along a given axis as many times as it appears in the axes argument. pyfftw.interfaces takes the transform only once along each axis that appears, regardless of how many times it appears. This is deemed to be such a fringe corner case that it is ignored.

API Reference¶

`pyfftw` - The core¶

The core of pyfftw consists of the FFTW class, wisdom functions and a couple of utility functions for dealing with aligned arrays.

This module represents the full interface to the underlying FFTW library. However, users may find it easier to use the helper routines provided in pyfftw.builders.

FFTW Class¶

class pyfftw.FFTW(input_array, output_array, axes=(-1, ), direction='FFTW_FORWARD', flags=('FFTW_MEASURE', ), threads=1, planning_timelimit=None)¶

FFTW is a class for computing the complex N-Dimensional DFT or inverse DFT of an array using the FFTW library. The interface is designed to be somewhat pythonic, with the correct transform being inferred from the dtypes of the passed arrays.

On instantiation, the dtypes and relative shapes of the input array and output arrays are compared to the set of valid (and implemented) FFTW schemes. If a match is found, the plan that corresponds to that scheme is created, operating on the arrays that are passed in. If no scheme can be created, then ValueError is raised.

The actual FFT or iFFT is performed by calling the execute() method.

The arrays can be updated by calling the update_arrays() method.

The created instance of the class is itself callable, and can perform the execution of the FFT, both with or without array updates, returning the result of the FFT. Unlike calling the execute() method, calling the class instance will also optionally normalise the output as necessary. Additionally, calling with an input array update will also coerce that array to be the correct dtype.

See the documentation on the __call__() method for more information.

Arguments:

input_array and output_array should be numpy arrays. The contents of these arrays will be destroyed by the planning process during initialisation. Information on supported dtypes for the arrays is given below.
axes describes along which axes the DFT should be taken. This should be a valid list of axes. Repeated axes are only transformed once. Invalid axes will raise an IndexError exception. This argument is equivalent to the same argument in numpy.fft.fftn(), except for the fact that the behaviour of repeated axes is different (numpy.fft will happily take the fft of the same axis if it is repeated in the axes argument). Rudimentary testing has suggested this is down to the underlying FFTW library and so unlikely to be fixed in these wrappers.
direction should be a string and one of 'FFTW_FORWARD' or 'FFTW_BACKWARD', which dictate whether to take the DFT (forwards) or the inverse DFT (backwards) respectively (specifically, it dictates the sign of the exponent in the DFT formulation).

Note that only the Complex schemes allow a free choice for direction. The direction must agree with the the table below if a Real scheme is used, otherwise a ValueError is raised.

flags is a list of strings and is a subset of the flags that FFTW allows for the planners:
- 'FFTW_ESTIMATE', 'FFTW_MEASURE', 'FFTW_PATIENT' and 'FFTW_EXHAUSTIVE' are supported. These describe the increasing amount of effort spent during the planning stage to create the fastest possible transform. Usually 'FFTW_MEASURE' is a good compromise. If no flag is passed, the default 'FFTW_MEASURE' is used.
- 'FFTW_UNALIGNED' is supported. This tells FFTW not to assume anything about the alignment of the data and disabling any SIMD capability (see below).
- 'FFTW_DESTROY_INPUT' is supported. This tells FFTW that the input array can be destroyed during the transform, sometimes allowing a faster algorithm to be used. The default behaviour is, if possible, to preserve the input. In the case of the 1D Backwards Real transform, this may result in a performance hit. In the case of a backwards real transform for greater than one dimension, it is not possible to preserve the input, making this flag implicit in that case. A little more on this is given below.
- 'FFTW_WISDOM_ONLY' is supported. This tells FFTW to raise an error if no plan for this transform and data type is already in the wisdom. It thus provides a method to determine whether planning would require additional effor or the cached wisdom can be used. This flag should be combined with the various planning-effort flags ('FFTW_ESTIMATE', 'FFTW_MEASURE', etc.); if so, then an error will be raised if wisdom derived from that level of planning effort (or higher) is not present. If no planning-effort flag is used, the default of 'FFTW_ESTIMATE' is assumed. Note that wisdom is specific to all the parameters, including the data alignment. That is, if wisdom was generated with input/output arrays with one specific alignment, using 'FFTW_WISDOM_ONLY' to create a plan for arrays with any different alignment will cause the 'FFTW_WISDOM_ONLY' planning to fail. Thus it is important to specifically control the data alignment to make the best use of 'FFTW_WISDOM_ONLY'.
The FFTW planner flags documentation has more information about the various flags and their impact. Note that only the flags documented here are supported.
threads tells the wrapper how many threads to use when invoking FFTW, with a default of 1.
planning_timelimit is a floating point number that indicates to the underlying FFTW planner the maximum number of seconds it should spend planning the FFT. This is a rough estimate and corresponds to calling of fftw_set_timelimit() (or an equivalent dependent on type) in the underlying FFTW library. If None is set, the planner will run indefinitely until all the planning modes allowed by the flags have been tried. See the FFTW planner flags page for more information on this.

Schemes

The currently supported schemes are as follows:

Type	`input_array.dtype`	`output_array.dtype`	Direction
Complex	`complex64`	`complex64`	Both
Complex	`complex128`	`complex128`	Both
Complex	`clongdouble`	`clongdouble`	Both
Real	`float32`	`complex64`	Forwards
Real	`float64`	`complex128`	Forwards
Real	`longdouble`	`clongdouble`	Forwards
Real¹	`complex64`	`float32`	Backwards
Real¹	`complex128`	`float64`	Backwards
Real¹	`clongdouble`	`longdouble`	Backwards

¹ Note that the Backwards Real transform for the case in which the dimensionality of the transform is greater than 1 will destroy the input array. This is inherent to FFTW and the only general work-around for this is to copy the array prior to performing the transform. In the case where the dimensionality of the transform is 1, the default is to preserve the input array. This is different from the default in the underlying library, and some speed gain may be achieved by allowing the input array to be destroyed by passing the 'FFTW_DESTROY_INPUT' flag.

clongdouble typically maps directly to complex256 or complex192, and longdouble to float128 or float96, dependent on platform.

The relative shapes of the arrays should be as follows:

For a Complex transform, output_array.shape == input_array.shape
For a Real transform in the Forwards direction, both the following should be true:
- output_array.shape[axes][-1] == input_array.shape[axes][-1]//2 + 1
- All the other axes should be equal in length.
For a Real transform in the Backwards direction, both the following should be true:
- input_array.shape[axes][-1] == output_array.shape[axes][-1]//2 + 1
- All the other axes should be equal in length.

In the above expressions for the Real transform, the axes arguments denotes the unique set of axes on which we are taking the FFT, in the order passed. It is the last of these axes that is subject to the special case shown.

The shapes for the real transforms corresponds to those stipulated by the FFTW library. Further information can be found in the FFTW documentation on the real DFT.

The actual arrangement in memory is arbitrary and the scheme can be planned for any set of strides on either the input or the output. The user should not have to worry about this and any valid numpy array should work just fine.

What is calculated is exactly what FFTW calculates. Notably, this is an unnormalized transform so should be scaled as necessary (fft followed by ifft will scale the input by N, the product of the dimensions along which the DFT is taken). For further information, see the FFTW documentation.

The FFTW library benefits greatly from the beginning of each DFT axes being aligned on the correct byte boundary, enabling SIMD instructions. By default, if the data begins on such a boundary, then FFTW will be allowed to try and enable SIMD instructions. This means that all future changes to the data arrays will be checked for similar alignment. SIMD instructions can be explicitly disabled by setting the FFTW_UNALIGNED flags, to allow for updates with unaligned data.

byte_align() and empty_aligned() are two methods included with this module for producing aligned arrays.

The optimum alignment for the running platform is provided by pyfftw.simd_alignment, though a different alignment may still result in some performance improvement. For example, if the processor supports AVX (requiring 32-byte alignment) as well as SSE (requiring 16-byte alignment), then if the array is 16-byte aligned, SSE will still be used.

It’s worth noting that just being aligned may not be sufficient to create the fastest possible transform. For example, if the array is not contiguous (i.e. certain axes are displaced in memory), it may be faster to plan a transform for a contiguous array, and then rely on the array being copied in before the transform (which pyfftw.FFTW will handle for you when accessed through __call__()).

N¶: The product of the lengths of the DFT over all DFT axes. 1/N is the normalisation constant. For any input array A, and for any set of axes, 1/N * ifft(fft(A)) = A

simd_aligned¶: Return whether or not this FFTW object requires simd aligned input and output data.

input_alignment¶

Returns the byte alignment of the input arrays for which the FFTW object was created.

Input array updates with arrays that are not aligned on this byte boundary will result in a ValueError being raised, or a copy being made if the __call__() interface is used.

output_alignment¶

Returns the byte alignment of the output arrays for which the FFTW object was created.

Output array updates with arrays that are not aligned on this byte boundary will result in a ValueError being raised.

flags¶

Return which flags were used to construct the FFTW object.

This includes flags that were added during initialisation.

input_array¶: Return the input array that is associated with the FFTW instance.

output_array¶: Return the output array that is associated with the FFTW instance.

input_shape¶: Return the shape of the input array for which the FFT is planned.

output_shape¶: Return the shape of the output array for which the FFT is planned.

input_strides¶: Return the strides of the input array for which the FFT is planned.

output_strides¶: Return the strides of the output array for which the FFT is planned.

input_dtype¶: Return the dtype of the input array for which the FFT is planned.

output_dtype¶: Return the shape of the output array for which the FFT is planned.

direction¶: Return the planned FFT direction. Either ‘FFTW_FORWARD’ or ‘FFTW_BACKWARD’.

axes¶: Return the axes for the planned FFT in canonical form. That is, as a tuple of positive integers. The order in which they were passed is maintained.

ortho¶: If ortho=True both the forward and inverse transforms are scaled by 1/sqrt(N).

normalise_idft¶: If normalise_idft=True, the inverse transform is scaled by 1/N.

__call__()¶

__call__(input_array=None, output_array=None, normalise_idft=True,: ortho=False)

Calling the class instance (optionally) updates the arrays, then calls execute(), before optionally normalising the output and returning the output array.

It has some built-in helpers to make life simpler for the calling functions (as distinct from manually updating the arrays and calling execute()).

If normalise_idft is True (the default), then the output from an inverse DFT (i.e. when the direction flag is 'FFTW_BACKWARD') is scaled by 1/N, where N is the product of the lengths of input array on which the FFT is taken. If the direction is 'FFTW_FORWARD', this flag makes no difference to the output array.

If ortho is True, then the output of both forward and inverse DFT operations is scaled by 1/sqrt(N), where N is the product of the lengths of input array on which the FFT is taken. This ensures that the DFT is a unitary operation, meaning that it satisfies Parseval’s theorem (the sum of the squared values of the transform output is equal to the sum of the squared values of the input). In other words, the energy of the signal is preserved.

If either normalise_idft or ortho are True, then ifft(fft(A)) = A.

When input_array is something other than None, then the passed in array is coerced to be the same dtype as the input array used when the class was instantiated, the byte-alignment of the passed in array is made consistent with the expected byte-alignment and the striding is made consistent with the expected striding. All this may, but not necessarily, require a copy to be made.

As noted in the scheme table, if the FFTW instance describes a backwards real transform of more than one dimension, the contents of the input array will be destroyed. It is up to the calling function to make a copy if it is necessary to maintain the input array.

output_array is always used as-is if possible. If the dtype, the alignment or the striding is incorrect for the FFTW object, then a ValueError is raised.

The coerced input array and the output array (as appropriate) are then passed as arguments to update_arrays(), after which execute() is called, and then normalisation is applied to the output array if that is desired.

Note that it is possible to pass some data structure that can be converted to an array, such as a list, so long as it fits the data requirements of the class instance, such as array shape.

Other than the dtype and the alignment of the passed in arrays, the rest of the requirements on the arrays mandated by update_arrays() are enforced.

A None argument to either keyword means that that array is not updated.

The result of the FFT is returned. This is the same array that is used internally and will be overwritten again on subsequent calls. If you need the data to persist longer than a subsequent call, you should copy the returned array.

update_arrays(new_input_array, new_output_array)¶

Update the arrays upon which the DFT is taken.

The new arrays should be of the same dtypes as the originals, the same shapes as the originals and should have the same strides between axes. If the original data was aligned so as to allow SIMD instructions (e.g. by being aligned on a 16-byte boundary), then the new array must also be aligned so as to allow SIMD instructions (assuming, of course, that the FFTW_UNALIGNED flag was not enabled).

The byte alignment requirement extends to requiring natural alignment in the non-SIMD cases as well, but this is much less stringent as it simply means avoiding arrays shifted by, say, a single byte (which invariably takes some effort to achieve!).

If all these conditions are not met, a ValueError will be raised and the data will not be updated (though the object will still be in a sane state).

execute()¶: Execute the planned operation, taking the correct kind of FFT of the input array (i.e. FFTW.input_array), and putting the result in the output array (i.e. FFTW.output_array).

get_input_array()¶

Return the input array that is associated with the FFTW instance.

Deprecated since 0.10. Consider using the FFTW.input_array property instead.

get_output_array()¶

Return the output array that is associated with the FFTW instance.

Deprecated since 0.10. Consider using the FFTW.output_array property instead.

Wisdom Functions¶

Functions for dealing with FFTW’s ability to export and restore plans, referred to as wisdom. For further information, refer to the FFTW wisdom documentation.

pyfftw.export_wisdom()¶

Return the FFTW wisdom as a tuple of strings.

The first string in the tuple is the string for the double precision wisdom. The second string in the tuple is the string for the single precision wisdom. The third string in the tuple is the string for the long double precision wisdom.

The tuple that is returned from this function can be used as the argument to import_wisdom().

pyfftw.import_wisdom(wisdom)¶

Function that imports wisdom from the passed tuple of strings.

The first string in the tuple is the string for the double precision wisdom. The second string in the tuple is the string for the single precision wisdom. The third string in the tuple is the string for the long double precision wisdom.

The tuple that is returned from export_wisdom() can be used as the argument to this function.

This function returns a tuple of boolean values indicating the success of loading each of the wisdom types (double, float and long double, in that order).

pyfftw.forget_wisdom()¶: Forget all the accumulated wisdom.

Utility Functions¶

pyfftw.simd_alignment¶

An integer giving the optimum SIMD alignment in bytes, found by inspecting the CPU (e.g. if AVX is supported, its value will be 32).

This can be used as n in the arguments for byte_align(), empty_aligned(), zeros_aligned(), and ones_aligned() to create optimally aligned arrays for the running platform.

pyfftw.byte_align(array, n=None, dtype=None)¶

Function that takes a numpy array and checks it is aligned on an n-byte boundary, where n is an optional parameter. If n is not provided then this function will inspect the CPU to determine alignment. If the array is aligned then it is returned without further ado. If it is not aligned then a new array is created and the data copied in, but aligned on the n-byte boundary.

dtype is an optional argument that forces the resultant array to be of that dtype.

pyfftw.empty_aligned(shape, dtype='float64', order='C', n=None)¶

Function that returns an empty numpy array that is n-byte aligned, where n is determined by inspecting the CPU if it is not provided.

The alignment is given by the final optional argument, n. If n is not provided then this function will inspect the CPU to determine alignment. The rest of the arguments are as per numpy.empty().

pyfftw.zeros_aligned(shape, dtype='float64', order='C', n=None)¶

Function that returns a numpy array of zeros that is n-byte aligned, where n is determined by inspecting the CPU if it is not provided.

The alignment is given by the final optional argument, n. If n is not provided then this function will inspect the CPU to determine alignment. The rest of the arguments are as per numpy.zeros().

pyfftw.ones_aligned(shape, dtype='float64', order='C', n=None)¶

Function that returns a numpy array of ones that is n-byte aligned, where n is determined by inspecting the CPU if it is not provided.

The alignment is given by the final optional argument, n. If n is not provided then this function will inspect the CPU to determine alignment. The rest of the arguments are as per numpy.ones().

pyfftw.is_byte_aligned()¶

is_n_byte_aligned(array, n=None)

Function that takes a numpy array and checks it is aligned on an n-byte boundary, where n is an optional parameter, returning True if it is, and False if it is not. If n is not provided then this function will inspect the CPU to determine alignment.

pyfftw.n_byte_align(array, n, dtype=None)¶

This function is deprecated: byte_align should be used instead.

Function that takes a numpy array and checks it is aligned on an n-byte boundary, where n is an optional parameter. If n is not provided then this function will inspect the CPU to determine alignment. If the array is aligned then it is returned without further ado. If it is not aligned then a new array is created and the data copied in, but aligned on the n-byte boundary.

dtype is an optional argument that forces the resultant array to be of that dtype.

pyfftw.n_byte_align_empty(shape, n, dtype='float64', order='C')¶

This function is deprecated: empty_aligned should be used instead.

Function that returns an empty numpy array that is n-byte aligned.

The alignment is given by the first optional argument, n. If n is not provided then this function will inspect the CPU to determine alignment. The rest of the arguments are as per numpy.empty().

pyfftw.is_n_byte_aligned(array, n)¶

This function is deprecated: is_byte_aligned should be used instead.

Function that takes a numpy array and checks it is aligned on an n-byte boundary, where n is a passed parameter, returning True if it is, and False if it is not.

pyfftw.next_fast_len(target)¶

Find the next fast transform length for FFTW.

FFTW has efficient functions for transforms of length 2**a * 3**b * 5**c * 7**d * 11**e * 13**f, where e + f is either 0 or 1.

Parameters:	target (int) – Length to start searching from. Must be a positive integer.
Returns:	out – The first fast length greater than or equal to target.
Return type:	int

Examples

On a particular machine, an FFT of prime length takes 2.1 ms:

>>> from pyfftw.interfaces import scipy_fftpack
>>> min_len = 10007  # prime length is worst case for speed
>>> a = numpy.random.randn(min_len)
>>> b = scipy_fftpack.fft(a)

Zero-padding to the next fast length reduces computation time to 406 us, a speedup of ~5 times:

>>> next_fast_len(min_len)
10080
>>> b = scipy_fftpack.fft(a, 10080)

Rounding up to the next power of 2 is not optimal, taking 598 us to compute, 1.5 times as long as the size selected by next_fast_len.

>>> b = fftpack.fft(a, 16384)

Similar speedups will occur for pre-planned FFTs as generated via pyfftw.builders.

`pyfftw.builders` - Get `FFTW` objects using a `numpy.fft` like interface¶

Overview¶

This module contains a set of functions that return pyfftw.FFTW objects.

The interface to create these objects is mostly the same as numpy.fft, only instead of the call returning the result of the FFT, a pyfftw.FFTW object is returned that performs that FFT operation when it is called. Users should be familiar with numpy.fft before reading on.

In the case where the shape argument, s (or n in the 1-dimensional case), dictates that the passed-in input array be copied into a different processing array, the returned object is an instance of a child class of pyfftw.FFTW, _FFTWWrapper, which wraps the call method in order to correctly perform that copying. That is, subsequent calls to the object (i.e. through __call__()) should occur with an input array that can be sliced to the same size as the expected internal array. Note that a side effect of this is that subsequent calls to the object can be made with an array that is bigger than the original (but not smaller).

Only the call method is wrapped; update_arrays() still expects an array with the correct size, alignment, dtype etc for the pyfftw.FFTW object.

When the internal input array is bigger along any axis than the input array that is passed in (due to s dictating a larger size), then the extra entries are padded with zeros. This is a one time action. If the internal input array is then extracted using pyfftw.FFTW.input_array, it is possible to persistently fill the padding space with whatever the user desires, so subsequent calls with a new input only overwrite the values that aren’t padding (even if the array that is used for the call is bigger than the original - see the point above about bigger arrays being sliced to fit).

The precision of the FFT operation is acquired from the input array. If an array is passed in that is not of float type, or is of an unknown float type, an attempt is made to convert the array to a double precision array. This results in a copy being made.

If an array of the incorrect complexity is passed in (e.g. a complex array is passed to a real transform routine, or vice-versa), then an attempt is made to convert the array to an array of the correct complexity. This results in a copy being made.

Although the array that is internal to the pyfftw.FFTW object will be correctly loaded with the values within the input array, it is not necessarily the case that the internal array is the input array. The actual internal input array can always be retrieved with pyfftw.FFTW.input_array.

The behaviour of the norm option in all builder routines matches that of the corresponding numpy functions. In short, if norm is None, then the output from the forward DFT is unscaled and the inverse DFT is scaled by 1/N, where N is the product of the lengths of input array on which the FFT is taken. If norm == 'ortho', then the output of both forward and inverse DFT operations are scaled by 1/sqrt(N).

Example:

>>> import pyfftw
>>> a = pyfftw.empty_aligned(4, dtype='complex128')
>>> fft = pyfftw.builders.fft(a)
>>> a[:] = [1, 2, 3, 4]
>>> fft() # returns the output
array([ 10.+0.j,  -2.+2.j,  -2.+0.j,  -2.-2.j])

More examples can be found in the tutorial.

Supported Functions and Caveats¶

The following functions are supported. They can be used with the same calling signature as their respective functions in numpy.fft.

Standard FFTs

fft()
ifft()
fft2()
ifft2()
fftn()
ifftn()

Real FFTs

rfft()
irfft()
rfft2()
irfft2()
rfftn()
irfftn()

The first caveat is that the dtype of the input array must match the transform. For example, for fft and ifft, the dtype must be complex, for rfft it must be real, and so on. The other point to note from this is that the precision of the transform matches the precision of the input array. So, if a single precision input array is passed in, then a single precision transform will be used.

The second caveat is that repeated axes are handled differently; with the returned pyfftw.FFTW object, axes that are repeated in the axes argument are considered only once, as compared to numpy.fft in which repeated axes results in the DFT being taken along that axes as many times as the axis occurs (this is down to the underlying library).

Note that unless the auto_align_input argument to the function is set to True, the 'FFTW_UNALIGNED' flag is set in the returned pyfftw.FFTW object. This disables some of the FFTW optimisations that rely on aligned arrays. Also worth noting is that the auto_align_input flag only results in a copy when calling the resultant pyfftw.FFTW object if the input array is not already aligned correctly.

Additional Arguments¶

In addition to the arguments that are present with their complementary functions in numpy.fft, each of these functions also offers the following additional keyword arguments:

overwrite_input: Whether or not the input array can be overwritten during the transform. This sometimes results in a faster algorithm being made available. It causes the 'FFTW_DESTROY_INPUT' flag to be passed to the pyfftw.FFTW object. This flag is not offered for the multi-dimensional inverse real transforms, as FFTW is unable to not overwrite the input in that case.
planner_effort: A string dictating how much effort is spent in planning the FFTW routines. This is passed to the creation of the pyfftw.FFTW object as an entry in the flags list. They correspond to flags passed to the pyfftw.FFTW object.

The valid strings, in order of their increasing impact on the time to compute are: 'FFTW_ESTIMATE', 'FFTW_MEASURE' (default), 'FFTW_PATIENT' and 'FFTW_EXHAUSTIVE'.

The Wisdom that FFTW has accumulated or has loaded (through pyfftw.import_wisdom()) is used during the creation of pyfftw.FFTW objects.
threads: The number of threads used to perform the FFT.
auto_align_input: Correctly byte align the input array for optimal usage of vector instructions. This can lead to a substantial speedup.

Setting this argument to True makes sure that the input array is correctly aligned. It is possible to correctly byte align the array prior to calling this function (using, for example, pyfftw.byte_align()). If and only if a realignment is necessary is a new array created. If a new array is created, it is up to the calling code to acquire that new input array using pyfftw.FFTW.input_array.

The resultant pyfftw.FFTW object that is created will be designed to operate on arrays that are aligned. If the object is called with an unaligned array, this would result in a copy. Despite this, it may still be faster to set the auto_align_input flag and incur a copy with unaligned arrays than to set up an object that uses aligned arrays.

It’s worth noting that just being aligned may not be sufficient to create the fastest possible transform. For example, if the array is not contiguous (i.e. certain axes have gaps in memory between slices), it may be faster to plan a transform for a contiguous array, and then rely on the array being copied in before the transform (which pyfftw.FFTW will handle for you). The auto_contiguous argument controls whether this function also takes care of making sure the array is contiguous or not.
auto_contiguous: Make sure the input array is contiguous in memory before performing the transform on it. If the array is not contiguous, it is copied into an interim array. This is because it is often faster to copy the data before the transform and then transform a contiguous array than it is to try to take the transform of a non-contiguous array. This is particularly true in conjunction with the auto_align_input argument which is used to make sure that the transform is taken of an aligned array.

Like auto_align_input, If a new array is created, it is up to the calling code to acquire that new input array using pyfftw.FFTW.input_array.
avoid_copy: By default, these functions will always create a copy (and sometimes more than one) of the passed in input array. This is because the creation of the pyfftw.FFTW object generally destroys the contents of the input array. Setting this argument to True will try not to create a copy of the input array, likely resulting in the input array being destroyed. If it is not possible to create the object without a copy being made, a ValueError is raised.

Example situations that require a copy, and so cause the exception to be raised when this flag is set:
- The shape of the FFT input as dictated by s is necessarily different from the shape of the passed-in array.
- The dtypes are incompatible with the FFT routine.
- The auto_contiguous or auto_align flags are True and the input array is not already contiguous or aligned.
This argument is distinct from overwrite_input in that it only influences a copy during the creation of the object. It changes no flags in the pyfftw.FFTW object.

The exceptions raised by each of these functions are as per their equivalents in numpy.fft, or as documented above.

The Functions¶

pyfftw.builders.fft(a, n=None, axis=-1, overwrite_input=False, planner_effort='FFTW_MEASURE', threads=1, auto_align_input=True, auto_contiguous=True, avoid_copy=False, norm=None)¶

Return a pyfftw.FFTW object representing a 1D FFT.

The first three arguments are as per numpy.fft.fft(); the rest of the arguments are documented in the module docs.

pyfftw.builders.ifft(a, n=None, axis=-1, overwrite_input=False, planner_effort='FFTW_MEASURE', threads=1, auto_align_input=True, auto_contiguous=True, avoid_copy=False, norm=None)¶

Return a pyfftw.FFTW object representing a 1D inverse FFT.

The first three arguments are as per numpy.fft.ifft(); the rest of the arguments are documented in the module docs.

pyfftw.builders.fft2(a, s=None, axes=(-2, -1), overwrite_input=False, planner_effort='FFTW_MEASURE', threads=1, auto_align_input=True, auto_contiguous=True, avoid_copy=False, norm=None)¶

Return a pyfftw.FFTW object representing a 2D FFT.

The first three arguments are as per numpy.fft.fft2(); the rest of the arguments are documented in the module docs.

pyfftw.builders.ifft2(a, s=None, axes=(-2, -1), overwrite_input=False, planner_effort='FFTW_MEASURE', threads=1, auto_align_input=True, auto_contiguous=True, avoid_copy=False, norm=None)¶

Return a pyfftw.FFTW object representing a 2D inverse FFT.

The first three arguments are as per numpy.fft.ifft2(); the rest of the arguments are documented in the module docs.

pyfftw.builders.fftn(a, s=None, axes=None, overwrite_input=False, planner_effort='FFTW_MEASURE', threads=1, auto_align_input=True, auto_contiguous=True, avoid_copy=False, norm=None)¶

Return a pyfftw.FFTW object representing a n-D FFT.

The first three arguments are as per numpy.fft.fftn(); the rest of the arguments are documented in the module docs.

pyfftw.builders.ifftn(a, s=None, axes=None, overwrite_input=False, planner_effort='FFTW_MEASURE', threads=1, auto_align_input=True, auto_contiguous=True, avoid_copy=False, norm=None)¶

Return a pyfftw.FFTW object representing an n-D inverse FFT.

The first three arguments are as per numpy.fft.ifftn(); the rest of the arguments are documented in the module docs.

pyfftw.builders.rfft(a, n=None, axis=-1, overwrite_input=False, planner_effort='FFTW_MEASURE', threads=1, auto_align_input=True, auto_contiguous=True, avoid_copy=False, norm=None)¶

Return a pyfftw.FFTW object representing a 1D real FFT.

The first three arguments are as per numpy.fft.rfft(); the rest of the arguments are documented in the module docs.

pyfftw.builders.irfft(a, n=None, axis=-1, overwrite_input=False, planner_effort='FFTW_MEASURE', threads=1, auto_align_input=True, auto_contiguous=True, avoid_copy=False, norm=None)¶

Return a pyfftw.FFTW object representing a 1D real inverse FFT.

The first three arguments are as per numpy.fft.irfft(); the rest of the arguments are documented in the module docs.

pyfftw.builders.rfft2(a, s=None, axes=(-2, -1), overwrite_input=False, planner_effort='FFTW_MEASURE', threads=1, auto_align_input=True, auto_contiguous=True, avoid_copy=False, norm=None)¶

Return a pyfftw.FFTW object representing a 2D real FFT.

The first three arguments are as per numpy.fft.rfft2(); the rest of the arguments are documented in the module docs.

pyfftw.builders.irfft2(a, s=None, axes=(-2, -1), planner_effort='FFTW_MEASURE', threads=1, auto_align_input=True, auto_contiguous=True, avoid_copy=False, norm=None)¶

Return a pyfftw.FFTW object representing a 2D real inverse FFT.

The first three arguments are as per numpy.fft.irfft2(); the rest of the arguments are documented in the module docs.

pyfftw.builders.rfftn(a, s=None, axes=None, overwrite_input=False, planner_effort='FFTW_MEASURE', threads=1, auto_align_input=True, auto_contiguous=True, avoid_copy=False, norm=None)¶

Return a pyfftw.FFTW object representing an n-D real FFT.

The first three arguments are as per numpy.fft.rfftn(); the rest of the arguments are documented in the module docs.

pyfftw.builders.irfftn(a, s=None, axes=None, planner_effort='FFTW_MEASURE', threads=1, auto_align_input=True, auto_contiguous=True, avoid_copy=False, norm=None)¶

Return a pyfftw.FFTW object representing an n-D real inverse FFT.

The first three arguments are as per numpy.fft.rfftn(); the rest of the arguments are documented in the module docs.

`pyfftw.builders._utils` - Helper functions for `pyfftw.builders`¶

A set of utility functions for use with the builders. Users should not need to use the functions directly, but they are included here for completeness and to aid with understanding of what is happening behind the scenes.

Certainly, users may encounter instances of _FFTWWrapper.

These everything documented in this module is not part of the public API and may change in future versions.

class pyfftw.builders._utils._FFTWWrapper(input_array, output_array, axes=[-1], direction='FFTW_FORWARD', flags=['FFTW_MEASURE'], threads=1, input_array_slicer=None, FFTW_array_slicer=None, normalise_idft=True, ortho=False)¶

A class that wraps pyfftw.FFTW, providing a slicer on the input stage during calls to __call__().

The arguments are as per pyfftw.FFTW, but with the addition of 2 keyword arguments: input_array_slicer and FFTW_array_slicer.

These arguments represent 2 slicers: input_array_slicer slices the input array that is passed in during a call to instances of this class, and FFTW_array_slicer slices the internal array.

The arrays that are returned from both of these slicing operations should be the same size. The data is then copied from the sliced input array into the sliced internal array.

__call__(input_array=None, output_array=None, normalise_idft=None, ortho=None)¶

Wrap pyfftw.FFTW.__call__() by firstly slicing the passed-in input array and then copying it into a sliced version of the internal array. These slicers are set at instantiation.

When input array is not None, this method always results in a copy. Consequently, the alignment and dtype are maintained in the internal array.

output_array and normalise_idft are passed through to pyfftw.FFTW.__call__() untouched.

pyfftw.builders._utils._Xfftn(a, s, axes, overwrite_input, planner_effort, threads, auto_align_input, auto_contiguous, avoid_copy, inverse, real, normalise_idft=True, ortho=False)¶: Generic transform interface for all the transforms. No defaults exist. The transform must be specified exactly.

pyfftw.builders._utils._setup_input_slicers(a_shape, input_shape)¶

This function returns two slicers that are to be used to copy the data from the input array to the FFTW object internal array, which can then be passed to _FFTWWrapper:

(update_input_array_slicer, FFTW_array_slicer)

On calls to _FFTWWrapper objects, the input array is copied in as:

FFTW_array[FFTW_array_slicer] = input_array[update_input_array_slicer]

pyfftw.builders._utils._compute_array_shapes(a, s, axes, inverse, real)¶

Given a passed in array a, and the rest of the arguments (that have been fleshed out with _cook_nd_args()), compute the shape the input and output arrays need to be in order to satisfy all the requirements for the transform. The input shape may be different to the shape of a.

returns: (input_shape, output_shape)

pyfftw.builders._utils._precook_1d_args(a, n, axis)¶: Turn *(n, axis) into (s, axes)

pyfftw.builders._utils._cook_nd_args(a, s=None, axes=None, invreal=False)¶: Similar to numpy.fft.fftpack._cook_nd_args().

`pyfftw.interfaces` - Drop in replacements for other FFT implementations¶

`numpy.fft` interface¶

This module implements those functions that replace aspects of the numpy.fft module. This module provides the entire documented namespace of numpy.fft, but those functions that are not included here are imported directly from numpy.fft.

It is notable that unlike numpy.fftpack, these functions will generally return an output array with the same precision as the input array, and the transform that is chosen is chosen based on the precision of the input array. That is, if the input array is 32-bit floating point, then the transform will be 32-bit floating point and so will the returned array. Half precision input will be converted to single precision. Otherwise, if any type conversion is required, the default will be double precision.

One known caveat is that repeated axes are handled differently to numpy.fft; axes that are repeated in the axes argument are considered only once, as compared to numpy.fft in which repeated axes results in the DFT being taken along that axes as many times as the axis occurs.

The exceptions raised by each of these functions are mostly as per their equivalents in numpy.fft, though there are some corner cases in which this may not be true.

pyfftw.interfaces.numpy_fft.fft(a, n=None, axis=-1, norm=None, overwrite_input=False, planner_effort='FFTW_ESTIMATE', threads=1, auto_align_input=True, auto_contiguous=True)¶

Perform a 1D FFT.

The first four arguments are as per numpy.fft.fft(); the rest of the arguments are documented in the additional arguments docs.

pyfftw.interfaces.numpy_fft.ifft(a, n=None, axis=-1, norm=None, overwrite_input=False, planner_effort='FFTW_ESTIMATE', threads=1, auto_align_input=True, auto_contiguous=True)¶

Perform a 1D inverse FFT.

The first four arguments are as per numpy.fft.ifft(); the rest of the arguments are documented in the additional arguments docs.

pyfftw.interfaces.numpy_fft.fft2(a, s=None, axes=(-2, -1), norm=None, overwrite_input=False, planner_effort='FFTW_ESTIMATE', threads=1, auto_align_input=True, auto_contiguous=True)¶

Perform a 2D FFT.

The first four arguments are as per numpy.fft.fft2(); the rest of the arguments are documented in the additional arguments docs.

pyfftw.interfaces.numpy_fft.ifft2(a, s=None, axes=(-2, -1), norm=None, overwrite_input=False, planner_effort='FFTW_ESTIMATE', threads=1, auto_align_input=True, auto_contiguous=True)¶

Perform a 2D inverse FFT.

The first four arguments are as per numpy.fft.ifft2(); the rest of the arguments are documented in the additional arguments docs.

pyfftw.interfaces.numpy_fft.fftn(a, s=None, axes=None, norm=None, overwrite_input=False, planner_effort='FFTW_ESTIMATE', threads=1, auto_align_input=True, auto_contiguous=True)¶

Perform an n-D FFT.

The first four arguments are as per numpy.fft.fftn(); the rest of the arguments are documented in the additional arguments docs.

pyfftw.interfaces.numpy_fft.ifftn(a, s=None, axes=None, norm=None, overwrite_input=False, planner_effort='FFTW_ESTIMATE', threads=1, auto_align_input=True, auto_contiguous=True)¶

Perform an n-D inverse FFT.

The first four arguments are as per numpy.fft.ifftn(); the rest of the arguments are documented in the additional arguments docs.

pyfftw.interfaces.numpy_fft.rfft(a, n=None, axis=-1, norm=None, overwrite_input=False, planner_effort='FFTW_ESTIMATE', threads=1, auto_align_input=True, auto_contiguous=True)¶

Perform a 1D real FFT.

The first four arguments are as per numpy.fft.rfft(); the rest of the arguments are documented in the additional arguments docs.

pyfftw.interfaces.numpy_fft.irfft(a, n=None, axis=-1, norm=None, overwrite_input=False, planner_effort='FFTW_ESTIMATE', threads=1, auto_align_input=True, auto_contiguous=True)¶

Perform a 1D real inverse FFT.

The first four arguments are as per numpy.fft.irfft(); the rest of the arguments are documented in the additional arguments docs.

pyfftw.interfaces.numpy_fft.rfft2(a, s=None, axes=(-2, -1), norm=None, overwrite_input=False, planner_effort='FFTW_ESTIMATE', threads=1, auto_align_input=True, auto_contiguous=True)¶

Perform a 2D real FFT.

The first four arguments are as per numpy.fft.rfft2(); the rest of the arguments are documented in the additional arguments docs.

pyfftw.interfaces.numpy_fft.irfft2(a, s=None, axes=(-2, -1), norm=None, overwrite_input=False, planner_effort='FFTW_ESTIMATE', threads=1, auto_align_input=True, auto_contiguous=True)¶

Perform a 2D real inverse FFT.

The first four arguments are as per numpy.fft.irfft2(); the rest of the arguments are documented in the additional arguments docs.

pyfftw.interfaces.numpy_fft.rfftn(a, s=None, axes=None, norm=None, overwrite_input=False, planner_effort='FFTW_ESTIMATE', threads=1, auto_align_input=True, auto_contiguous=True)¶

Perform an n-D real FFT.

The first four arguments are as per numpy.fft.rfftn(); the rest of the arguments are documented in the additional arguments docs.

pyfftw.interfaces.numpy_fft.irfftn(a, s=None, axes=None, norm=None, overwrite_input=False, planner_effort='FFTW_ESTIMATE', threads=1, auto_align_input=True, auto_contiguous=True)¶

Perform an n-D real inverse FFT.

The first four arguments are as per numpy.fft.rfftn(); the rest of the arguments are documented in the additional arguments docs.

pyfftw.interfaces.numpy_fft.hfft(a, n=None, axis=-1, norm=None, overwrite_input=False, planner_effort='FFTW_ESTIMATE', threads=1, auto_align_input=True, auto_contiguous=True)¶

Perform a 1D FFT of a signal with hermitian symmetry. This yields a real output spectrum. See numpy.fft.hfft() for more information.

The first four arguments are as per numpy.fft.hfft(); the rest of the arguments are documented in the additional arguments docs.

pyfftw.interfaces.numpy_fft.ihfft(a, n=None, axis=-1, norm=None, overwrite_input=False, planner_effort='FFTW_ESTIMATE', threads=1, auto_align_input=True, auto_contiguous=True)¶

Perform a 1D inverse FFT of a real-spectrum, yielding a signal with hermitian symmetry. See numpy.fft.ihfft() for more information.

The first four arguments are as per numpy.fft.ihfft(); the rest of the arguments are documented in the additional arguments docs.

`scipy.fftpack` interface¶

This module implements those functions that replace aspects of the scipy.fftpack module. This module provides the entire documented namespace of scipy.fftpack, but those functions that are not included here are imported directly from scipy.fftpack.

The exceptions raised by each of these functions are mostly as per their equivalents in scipy.fftpack, though there are some corner cases in which this may not be true.

It is notable that unlike scipy.fftpack, these functions will generally return an output array with the same precision as the input array, and the transform that is chosen is chosen based on the precision of the input array. That is, if the input array is 32-bit floating point, then the transform will be 32-bit floating point and so will the returned array. Half precision input will be converted to single precision. Otherwise, if any type conversion is required, the default will be double precision.

Some corner (mis)usages of scipy.fftpack may not transfer neatly. For example, using scipy.fftpack.fft2() with a non 1D array and a 2D shape argument will return without exception whereas pyfftw.interfaces.scipy_fftpack.fft2() will raise a ValueError.

pyfftw.interfaces.scipy_fftpack.fft(x, n=None, axis=-1, overwrite_x=False, planner_effort='FFTW_ESTIMATE', threads=1, auto_align_input=True, auto_contiguous=True)¶

Perform a 1D FFT.

The first three arguments are as per scipy.fftpack.fft(); the rest of the arguments are documented in the additional argument docs.

pyfftw.interfaces.scipy_fftpack.ifft(x, n=None, axis=-1, overwrite_x=False, planner_effort='FFTW_ESTIMATE', threads=1, auto_align_input=True, auto_contiguous=True)¶

Perform a 1D inverse FFT.

The first three arguments are as per scipy.fftpack.ifft(); the rest of the arguments are documented in the additional argument docs.

pyfftw.interfaces.scipy_fftpack.fftn(x, shape=None, axes=None, overwrite_x=False, planner_effort='FFTW_ESTIMATE', threads=1, auto_align_input=True, auto_contiguous=True)¶

Perform an n-D FFT.

The first three arguments are as per scipy.fftpack.fftn(); the rest of the arguments are documented in the additional argument docs.

pyfftw.interfaces.scipy_fftpack.ifftn(x, shape=None, axes=None, overwrite_x=False, planner_effort='FFTW_ESTIMATE', threads=1, auto_align_input=True, auto_contiguous=True)¶

Perform an n-D inverse FFT.

The first three arguments are as per scipy.fftpack.ifftn(); the rest of the arguments are documented in the additional argument docs.

pyfftw.interfaces.scipy_fftpack.rfft(x, n=None, axis=-1, overwrite_x=False, planner_effort='FFTW_ESTIMATE', threads=1, auto_align_input=True, auto_contiguous=True)¶

Perform a 1D real FFT.

The first three arguments are as per scipy.fftpack.rfft(); the rest of the arguments are documented in the additional argument docs.

pyfftw.interfaces.scipy_fftpack.irfft(x, n=None, axis=-1, overwrite_x=False, planner_effort='FFTW_ESTIMATE', threads=1, auto_align_input=True, auto_contiguous=True)¶

Perform a 1D real inverse FFT.

The first three arguments are as per scipy.fftpack.irfft(); the rest of the arguments are documented in the additional argument docs.

pyfftw.interfaces.scipy_fftpack.fft2(x, shape=None, axes=(-2, -1), overwrite_x=False, planner_effort='FFTW_ESTIMATE', threads=1, auto_align_input=True, auto_contiguous=True)¶

Perform a 2D FFT.

The first three arguments are as per scipy.fftpack.fft2(); the rest of the arguments are documented in the additional argument docs.

pyfftw.interfaces.scipy_fftpack.ifft2(x, shape=None, axes=(-2, -1), overwrite_x=False, planner_effort='FFTW_ESTIMATE', threads=1, auto_align_input=True, auto_contiguous=True)¶

Perform a 2D inverse FFT.

The first three arguments are as per scipy.fftpack.ifft2(); the rest of the arguments are documented in the additional argument docs.

`dask.fft` interface¶

This module implements those functions that replace aspects of the dask.fft module. This module provides the entire documented namespace of dask.fft, but those functions that are not included here are imported directly from dask.fft.

It is notable that unlike numpy.fftpack, which dask.fft wraps, these functions will generally return an output array with the same precision as the input array, and the transform that is chosen is chosen based on the precision of the input array. That is, if the input array is 32-bit floating point, then the transform will be 32-bit floating point and so will the returned array. Half precision input will be converted to single precision. Otherwise, if any type conversion is required, the default will be double precision.

The exceptions raised by each of these functions are mostly as per their equivalents in dask.fft, though there are some corner cases in which this may not be true.

pyfftw.interfaces.dask_fft.fft(a, n=None, axis=-1)¶

Wrapping of pyfftw.interfaces.numpy_fft.fft