# Rocket-FFT
[](https://pypi.org/project/rocket-fft/)
[](https://opensource.org/licenses/BSD-3-Clause)
[](https://pypi.org/project/rocket-fft/)
[](https://pypi.org/project/rocket-fft/)
[](https://pypi.org/project/rocket-fft/)
Rocket-FFT makes [Numba](https://numba.pydata.org/) aware of `numpy.fft` and `scipy.fft`. It takes its name from the [PocketFFT](https://github.com/mreineck/pocketfft) Fast Fourier Transformation library that powers it, and Numba's goal of making your scientific Python code blazingly fast - like a rocket.
## Getting Started
The easiest way to get Rocket-FFT is to:
```
$ pip install rocket-fft
```
Alternatively, you can build it from source:
```
$ git clone https://github.com/styfenschaer/rocket-fft.git
$ cd rocket-fft
$ python setup.py install
```
The latter requires a C++ compiler compatible with your Python installation.
Once installed successfully, the following will work (no import required):
```python
import numba as nb
import numpy as np
@nb.njit
def jit_fft(x):
return np.fft.fft(x)
a = np.array([1, 6, 1, 8, 0, 3, 3, 9])
jit_fft(a)
```
## Performance Tip
Rocket-FFT makes extensive use of Numba's polymorphic dispatching to achieve both flexible function signatures similar to SciPy and NumPy, and low compilation times. Compilation takes only a few hundred milliseconds in most cases. Calls with default arguments follow a fast path and compile fastest.
## NumPy-like and SciPy-like interfaces
NumPy and SciPy show subtle differences in how they convert types<sup>1</sup> and handle the `axes` argument in some functions<sup>2</sup>. Rocket-FFT implements both ways and lets its users choose between them.
You can set the interface by using the `scipy_like` or `numpy_like` function from the `rocket_fft` namespace:
```python
from rocket_fft import numpy_like, scipy_like
numpy_like()
```
Both functions can be used regardless of whether SciPy is installed<sup>3</sup>. By default, Rocket-FFT uses the SciPy-like interface if SciPy is installed, and the NumPy-like interface otherwise. Note that the interface cannot be changed after the compilation of Rocket-FFT's internals.
<sup>1</sup>NumPy converts all types to either `float64` or `complex128` whereas SciPy takes a more fine-grained approach
<br/>
<sup>2</sup>NumPy allows duplicate axes in `fft2`, `ifft2`, `fftn` and `ifftn`, whereas SciPy doesn't
<br/>
<sup>3</sup>SciPy is an optional runtime dependency
## Low-Level Interface
Rocket-FFT also provides a low-level interface to the PocketFFT library. Using the low-level interface can significantly reduce compile time, minimize overhead and give more flexibility to the user. It also provides some functions that are not available through the SciPy-like and NumPy-like interfaces. You can import its functions from the `rocket_fft` namespace:
```python
from rocket_fft import c2c, dct, ...
```
The low-level interface includes the following functions:
```python
def c2c(ain: NDArray[c8] | NDArray[c16], aout: NDArray[c8] | NDArray[c16], axes: NDArray[i8], forward: b1, fct: f4 | f8, nthreads: i8) -> None: ...
def r2c(ain: NDArray[f4] | NDArray[f8], aout: NDArray[c8] | NDArray[c16], axes: NDArray[i8], forward: b1, fct: f4 | f8, nthreads: i8) -> None: ...
def c2r(ain: NDArray[c8] | NDArray[c16], aout: NDArray[f4] | NDArray[f8], axes: NDArray[i8], forward: b1, fct: f4 | f8, nthreads: i8) -> None: ...
def c2c_sym(ain: NDArray[f4] | NDArray[f8], aout: NDArray[c8] | NDArray[c16], axes: NDArray[i8], forward: b1, fct: f4 | f8, nthreads: i8) -> None: ...
def dst(ain: NDArray[f4] | NDArray[f8], aout: NDArray[f4] | NDArray[f8], axes: NDArray[i8], type: i8, fct: f4 | f8, ortho: b1, nthreads: i8) -> None: ...
def dct(ain: NDArray[f4] | NDArray[f8], aout: NDArray[f4] | NDArray[f8], axes: NDArray[i8], type: i8, fct: f4 | f8, ortho: b1, nthreads: i8) -> None: ...
def r2r_separable_hartley(ain: NDArray[f4] | NDArray[f8], aout: NDArray[f4] | NDArray[f8], axes: NDArray[i8], fct: f4 | f8, nthreads: i8) -> None: ...
def r2r_genuine_hartley(ain: NDArray[f4] | NDArray[f8], aout: NDArray[f4] | NDArray[f8], axes: NDArray[i8], fct: f4 | f8, nthreads: i8) -> None: ...
def r2r_fftpack(ain: NDArray[f4] | NDArray[f8], aout: NDArray[f4] | NDArray[f8], axes: NDArray[i8], real2hermitian: b1, forward: b1, fct: f4 | f8, nthreads: i8) -> None: ...
def good_size(target: i8, real: b1) -> i8: ...
```
Note that the low-level interface provides a lower level of safety and convenience compared to the SciPy-like and NumPy-like interfaces.
There is almost no safety net, and it is up to the user to ensure proper usage. You may want to consult the original [PocketFFT](https://github.com/mreineck/pocketfft) C++ implementation before using it.
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"description": "# Rocket-FFT\n[](https://pypi.org/project/rocket-fft/)\n[](https://opensource.org/licenses/BSD-3-Clause)\n[](https://pypi.org/project/rocket-fft/)\n[](https://pypi.org/project/rocket-fft/)\n[](https://pypi.org/project/rocket-fft/)\n\n\n\nRocket-FFT makes [Numba](https://numba.pydata.org/) aware of `numpy.fft` and `scipy.fft`. It takes its name from the [PocketFFT](https://github.com/mreineck/pocketfft) Fast Fourier Transformation library that powers it, and Numba's goal of making your scientific Python code blazingly fast - like a rocket. \n\n## Getting Started\nThe easiest way to get Rocket-FFT is to:\n```\n$ pip install rocket-fft\n```\nAlternatively, you can build it from source:\n```\n$ git clone https://github.com/styfenschaer/rocket-fft.git\n$ cd rocket-fft\n$ python setup.py install\n``` \nThe latter requires a C++ compiler compatible with your Python installation.\n\nOnce installed successfully, the following will work (no import required):\n```python\nimport numba as nb\nimport numpy as np\n\n@nb.njit\ndef jit_fft(x):\n return np.fft.fft(x)\n\na = np.array([1, 6, 1, 8, 0, 3, 3, 9])\njit_fft(a)\n```\n\n## Performance Tip\nRocket-FFT makes extensive use of Numba's polymorphic dispatching to achieve both flexible function signatures similar to SciPy and NumPy, and low compilation times. Compilation takes only a few hundred milliseconds in most cases. Calls with default arguments follow a fast path and compile fastest.\n\n## NumPy-like and SciPy-like interfaces\nNumPy and SciPy show subtle differences in how they convert types<sup>1</sup> and handle the `axes` argument in some functions<sup>2</sup>. Rocket-FFT implements both ways and lets its users choose between them.\n\nYou can set the interface by using the `scipy_like` or `numpy_like` function from the `rocket_fft` namespace:\n```python\nfrom rocket_fft import numpy_like, scipy_like\n\nnumpy_like()\n```\nBoth functions can be used regardless of whether SciPy is installed<sup>3</sup>. By default, Rocket-FFT uses the SciPy-like interface if SciPy is installed, and the NumPy-like interface otherwise. Note that the interface cannot be changed after the compilation of Rocket-FFT's internals.\n\n<sup>1</sup>NumPy converts all types to either `float64` or `complex128` whereas SciPy takes a more fine-grained approach\n<br/>\n<sup>2</sup>NumPy allows duplicate axes in `fft2`, `ifft2`, `fftn` and `ifftn`, whereas SciPy doesn't\n<br/>\n<sup>3</sup>SciPy is an optional runtime dependency\n\n## Low-Level Interface\nRocket-FFT also provides a low-level interface to the PocketFFT library. Using the low-level interface can significantly reduce compile time, minimize overhead and give more flexibility to the user. It also provides some functions that are not available through the SciPy-like and NumPy-like interfaces. You can import its functions from the `rocket_fft` namespace:\n```python\nfrom rocket_fft import c2c, dct, ...\n```\nThe low-level interface includes the following functions:\n```python\ndef c2c(ain: NDArray[c8] | NDArray[c16], aout: NDArray[c8] | NDArray[c16], axes: NDArray[i8], forward: b1, fct: f4 | f8, nthreads: i8) -> None: ...\ndef r2c(ain: NDArray[f4] | NDArray[f8], aout: NDArray[c8] | NDArray[c16], axes: NDArray[i8], forward: b1, fct: f4 | f8, nthreads: i8) -> None: ...\ndef c2r(ain: NDArray[c8] | NDArray[c16], aout: NDArray[f4] | NDArray[f8], axes: NDArray[i8], forward: b1, fct: f4 | f8, nthreads: i8) -> None: ...\ndef c2c_sym(ain: NDArray[f4] | NDArray[f8], aout: NDArray[c8] | NDArray[c16], axes: NDArray[i8], forward: b1, fct: f4 | f8, nthreads: i8) -> None: ...\ndef dst(ain: NDArray[f4] | NDArray[f8], aout: NDArray[f4] | NDArray[f8], axes: NDArray[i8], type: i8, fct: f4 | f8, ortho: b1, nthreads: i8) -> None: ...\ndef dct(ain: NDArray[f4] | NDArray[f8], aout: NDArray[f4] | NDArray[f8], axes: NDArray[i8], type: i8, fct: f4 | f8, ortho: b1, nthreads: i8) -> None: ...\ndef r2r_separable_hartley(ain: NDArray[f4] | NDArray[f8], aout: NDArray[f4] | NDArray[f8], axes: NDArray[i8], fct: f4 | f8, nthreads: i8) -> None: ...\ndef r2r_genuine_hartley(ain: NDArray[f4] | NDArray[f8], aout: NDArray[f4] | NDArray[f8], axes: NDArray[i8], fct: f4 | f8, nthreads: i8) -> None: ...\ndef r2r_fftpack(ain: NDArray[f4] | NDArray[f8], aout: NDArray[f4] | NDArray[f8], axes: NDArray[i8], real2hermitian: b1, forward: b1, fct: f4 | f8, nthreads: i8) -> None: ...\ndef good_size(target: i8, real: b1) -> i8: ...\n```\nNote that the low-level interface provides a lower level of safety and convenience compared to the SciPy-like and NumPy-like interfaces. \nThere is almost no safety net, and it is up to the user to ensure proper usage. You may want to consult the original [PocketFFT](https://github.com/mreineck/pocketfft) C++ implementation before using it.\n",
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