gstools.normalizer.apply_mean_norm_trend¶

gstools.normalizer.apply_mean_norm_trend(pos, field, mean=None, normalizer=None, trend=None, mesh_type='unstructured', value_type='scalar', check_shape=True, stacked=False)[source]¶

Apply mean, de-normalization and trend to given field.

Parameters

pos (iterable) – Position tuple, containing main direction and transversal directions.
field (numpy.ndarray or list of numpy.ndarray) – The spatially distributed data. You can pass a list of fields, that will be used simultaneously. Then you need to set stacked=True.
mean (None or float or callable, optional) – Mean of the field if wanted. Could also be a callable. The default is None.
normalizer (None or Normalizer, optional) – Normalizer to be applied to the field. The default is None.
trend (None or float or callable, optional) – Trend of the denormalized fields. If no normalizer is applied, this behaves equal to ‘mean’. The default is None.
mesh_type (str, optional) – ‘structured’ / ‘unstructured’ Default: ‘unstructured’
value_type (str, optional) – Value type of the field. Either “scalar” or “vector”. The default is “scalar”.
check_shape (bool, optional) – Wheather to check pos and field shapes. The default is True.
stacked (bool, optional) – Wheather the field is stacked or not. The default is False.

Returns

field – The transformed field.

Return type

numpy.ndarray