Load.fourier#

pyPLUTO.Load.fourier(self, f: ndarray, check: bool = True, **kwargs: Any) → tuple[list[ndarray], ndarray][source]#

Compute the Fourier transform of a given array. The function uses the numpy.fft.fftn function. The function returns a tuple containing the transformed array and the frequency array (which is a list of arrays if the input is in 2D or 3D).

Parameters:

- dx: float | int | list | np.ndarray | None, default None: The grid spacing. If None, the grid spacing is set to 1.
- dy: float | int | list | np.ndarray | None, default None: The grid spacing. If None, the grid spacing is set to 1.
- dz: float | int | list | np.ndarray | None, default None: The grid spacing. If None, the grid spacing is set to 1.
- f (not optional): np.ndarray: The array to be transformed.
- xdir: bool, default True: If True, the x-direction is transformed.
- ydir: bool, default True: If True, the y-direction is transformed.
- zdir: bool, default True: If True, the z-direction is transformed.
—-

Returns:

f: np.ndarray
The transformed array.
freqs: np.ndarray | list[np.ndarray]
The frequency array. It is a list of arrays if the input is in 2D or 3D.

Examples

Example #1: Compute the Fourier transform of a given array
```
>>> freqs, f = fourier(func)
```
Example #2: Compute the Fourier transform of a given array in 2D with custom grid spacing
```
>>> freqs, f = fourier(func, dx=1, dy=1)
```
Example #3: Compute the Fourier transform of a 3D without considering the x-direction
```
>>> freqs, f = fourier(func, xdir=False)
```