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
orlist
ofnumpy.ndarray
) – The spatially distributed data. You can pass a list of fields, that will be used simultaneously. Then you need to setstacked=True
.mean (
None
orfloat
orcallable
, optional) – Mean of the field if wanted. Could also be a callable. The default is None.normalizer (
None
orNormalizer
, optional) – Normalizer to be applied to the field. The default is None.trend (
None
orfloat
orcallable
, 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