gstools.normalizer.remove_trend_norm_mean

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

Remove trend, de-normalization and mean from 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.

  • fit_normalizer (bool, optional) – Wheater to fit the data-normalizer to the given (detrended) field. Default: False

Returns

  • field (numpy.ndarray) – The cleaned field.

  • normalizer (Normalizer, optional) – The fitted normalizer for the given data. Only provided if fit_normalizer is True.