tams.data_in_contours#

tams.data_in_contours(data, contours, *, agg=('mean', 'std', 'count'), method='sjoin', merge=False)#

Compute statistics on data within the shapes of contours.

With the default settings, we calculate, for each shape (row) in the contours dataframe:

Parameters:

data (xarray.DataArray | xarray.Dataset | pandas.DataFrame | geopandas.GeoDataFrame) – It should have 'lat' and 'lon' coordinates. If you pass a xarray.Dataset, all data_vars will be included. If you pass a dataframe (supported for default method 'sjoin'), all columns except {'time', 'lat', 'lon', 'geometry'} will be included.
contours (geopandas.GeoDataFrame) – For example, a dataframe of CE or MCS shapes, e.g. from identify() or track().
agg (sequence of str or callable()) – Suitable for passing to pandas.DataFrame.aggregate().
method ({'sjoin', 'regionmask'}) – The regionmask method is suited for data on a structured grid, while the GeoPandas sjoin method works for scattered point data as well. The sjoin method is the default since it is more general and currently often faster.
merge (bool) – Whether to merge the new data with contours or return a separate frame. If false (default), the index of the returned non-geo frame will be the same as that of contours (e.g. a row corresponding to an individual CE or MCS at a certain time).

Raises:

ValueError – If the input data is all null or the input frame of shapes is empty.

Return type:

pandas.DataFrame | geopandas.GeoDataFrame

tams.data_in_contours