httomolibgpu.prep.normalize

httomolibgpu.prep.normalize#

Modules for flat/dark-field correction of projection data and normalisation:

httomolibgpu.prep.normalize.dark_flat_field_correction(data: <cp.ndarray>, flats: <cp.ndarray>, darks: <cp.ndarray>, flats_multiplier: float | int = 1.0, darks_multiplier: float | int = 1.0, upper_bound: float | int | None = None, lower_bound: float | int | None = None, clipping_warning: bool = False) → <cp.ndarray>[source]#

Perform dark/flat field correction of raw projection data.

Parameters:

  • data (cp.ndarray) – Projection data as a CuPy array.

  • flats (cp.ndarray) – 3D flat field data as a CuPy array.

  • darks (cp.ndarray) – 3D dark field data as a CuPy array.

  • flats_multiplier (float,int) – A multiplier to apply to flats, can work as an intensity compensation constant.

  • darks_multiplier (float,int) – A multiplier to apply to darks, can work as an intensity compensation constant.

  • upper_bound (Optional[float, int]) – All values above the upper bound are set to the provided value. Default None.

  • lower_bound (Optional[float, int]) – All values bellow the lower bound are set to the provided value. Default None.

  • clipping_warning (bool,) –

    Set to True if you want to check if the data is not clipped too much (more than 50%) with the provided upper_bound or/and lower_bound value.

    Returns

  • -------

  • cp.ndarray – dark/flat field corrected 3D tomographic data as a CuPy array.

httomolibgpu.prep.normalize.minus_log(data: <cp.ndarray>) → <cp.ndarray>[source]#

Apply -log(data) operation

Parameters:

data (cp.ndarray) – Data as a CuPy array.

Returns:

data after -log(data)

Return type:

cp.ndarray