aPriori.DNS.filter_3D¶

aPriori.DNS.filter_3D(field, filter_size, RHO=None, favre=False, filter_type='Gauss')¶

Apply a 3D filter (Gaussian or box) to a numpy array, with optional Favre filtering.

This function filters a 3D field using either a Gaussian or box filter. When Favre filtering is enabled, the field is first multiplied by the density field (RHO) before filtering, and the result is normalized by the filtered density field.

fieldnumpy.ndarray

The input 3D array to be filtered.

filter_sizefloat

The size of the filter.

RHOnumpy.ndarray, optional

The density field used for Favre filtering. Required if favre is True.

favrebool, optional

If True, apply Favre filtering using the density field (RHO). Default is False.

filter_typestr, optional

The type of filter to apply. Valid options are ‘Gauss’ and ‘Box’. Default is ‘Gauss’.

field_filtnumpy.ndarray

The filtered 3D array.

ValueError

  • If favre is True and RHO is not provided.

  • If field or RHO are not 3-dimensional arrays.

  • If field and RHO do not have the same shape.

  • If an invalid filter_type is provided.

TypeError

If RHO is not a numpy array.

>>> import numpy as np

>>> # Create a sample 3D array
>>> field = np.random.rand(5, 5, 5)
>>> RHO = np.random.rand(5, 5, 5)
>>> filter_size = 2.0

>>> # Apply Gaussian filter
>>> filtered_field = filter_3D(field, filter_size, filter_type='Gauss')
>>> print("Filtered field (Gaussian):

“, filtered_field)

>>> # Apply box filter
>>> filtered_field = filter_3D(field, filter_size, filter_type='Box')
>>> print("Filtered field (Box):

“, filtered_field)

>>> # Apply Favre filtering with Gaussian filter
>>> filtered_field_favre = filter_3D(field, filter_size, RHO=RHO, favre=True, filter_type='Gauss')
>>> print("Favre filtered field (Gaussian):

“, filtered_field_favre)

>>> # Apply Favre filtering with box filter
>>> filtered_field_favre = filter_3D(field, filter_size, RHO=RHO, favre=True, filter_type='Box')
>>> print("Favre filtered field (Box):

“, filtered_field_favre)