Suggest using xarray and cartopy for SMAP tutorial 2

[andypbarrett]

The SMAP tutoral 2.0 read_and_plot_smap_data uses h5py and numpy. The whole notebook could be simplified and streamlined by using xarray.

If we stick with h5py a lot of the existing code could also be streamlined and made more transparent.

For example, code cell 3 involves a lot of code to get a list of groups and dataset paths, which can be simplified to the following.

with h5py.File(smap_files[0], 'r') as root:
    list_of_names = []
    root.visit(list_of_names.append)
list_of_names

['Metadata',
 'Metadata/AcquisitionInformation',
 'Metadata/AcquisitionInformation/platform',
 'Metadata/AcquisitionInformation/platformDocument',
 'Metadata/AcquisitionInformation/radar',
 'Metadata/AcquisitionInformation/radarDocument',
 'Metadata/AcquisitionInformation/radiometer',
 'Metadata/AcquisitionInformation/radiometerDocument',
 'Metadata/DataQuality',
 'Metadata/DataQuality/CompletenessOmission',
 'Metadata/DataQuality/DomainConsistency',
 'Metadata/DatasetIdentification',
 'Metadata/Extent',
 'Metadata/GridSpatialRepresentation',
 'Metadata/GridSpatialRepresentation/Column',
 'Metadata/GridSpatialRepresentation/GridDefinition',
 'Metadata/GridSpatialRepresentation/GridDefinitionDocument',

Code cell 5 that gets soil_moisture for the AM pass could be rewritten to use the path to the dataset

with h5py.File(smap_files[0], 'r') as root:
    soil_moisture = root['Soil_Moisture_Retrieval_Data_AM/soil_moisture'][:]
soil_moisture

array([[-9999., -9999., -9999., ..., -9999., -9999., -9999.],
       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
       ...,
       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
       [-9999., -9999., -9999., ..., -9999., -9999., -9999.],
       [-9999., -9999., -9999., ..., -9999., -9999., -9999.]],
      dtype=float32)

But as I note, this is much, much simpler with xarray.

[asteiker]

CRYO-203