How to use the ismn.interface.ISMN_Interface function in ismn

'sat', 'ascat', 'netcdf', 'grid')

    static_layers_folder = os.path.join(os.path.dirname(__file__),
                                        '..', 'test-data', 'sat',
                                        'h_saf', 'static_layer')

    ascat_reader = AscatSsmCdr(ascat_data_folder, ascat_grid_folder,
                               grid_filename='TUW_WARP5_grid_info_2_1.nc',
                               static_layer_path=static_layers_folder)
    ascat_reader.read_bulk = True

    # Initialize ISMN reader

    ismn_data_folder = os.path.join(os.path.dirname(__file__), '..', 'test-data',
                                    'ismn', 'multinetwork', 'header_values')
    ismn_reader = ISMN_Interface(ismn_data_folder)

    jobs = []

    ids = ismn_reader.get_dataset_ids(
        variable='soil moisture',
        min_depth=0,
        max_depth=0.1)
    for idx in ids:
        metadata = ismn_reader.metadata[idx]
        jobs.append((idx, metadata['longitude'], metadata['latitude']))

    # Create the variable ***save_path*** which is a string representing the
    # path where the results will be saved. **DO NOT CHANGE** the name
    # ***save_path*** because it will be searched during the parallel
    # processing!

'sat', 'ascat', 'netcdf', 'grid')

    static_layers_folder = os.path.join(os.path.dirname(__file__),
                                        '..', 'test-data', 'sat',
                                        'h_saf', 'static_layer')

    ascat_reader = AscatSsmCdr(ascat_data_folder, ascat_grid_folder,
                               grid_filename='TUW_WARP5_grid_info_2_1.nc',
                               static_layer_path=static_layers_folder)
    ascat_reader.read_bulk = True

    # Initialize ISMN reader

    ismn_data_folder = os.path.join(os.path.dirname(__file__), '..', 'test-data',
                                    'ismn', 'multinetwork', 'header_values')
    ismn_reader = ISMN_Interface(ismn_data_folder)

    jobs = []

    ids = ismn_reader.get_dataset_ids(
        variable='soil moisture',
        min_depth=0,
        max_depth=0.1)

    metadata_dict_template = {'network': np.array(['None'], dtype='U256'),
                              'station': np.array(['None'], dtype='U256'),
                              'landcover': np.float32([np.nan]),
                              'climate': np.array(['None'], dtype='U4')}

    for idx in ids:
        metadata = ismn_reader.metadata[idx]
        metadata_dict = [{'network': metadata['network'],

'sat', 'ascat', 'netcdf', 'grid')

    static_layers_folder = os.path.join(os.path.dirname(__file__),
                                        '..', 'test-data', 'sat',
                                        'h_saf', 'static_layer')

    ascat_reader = AscatSsmCdr(ascat_data_folder, ascat_grid_folder,
                               grid_filename='TUW_WARP5_grid_info_2_1.nc',
                               static_layer_path=static_layers_folder)
    ascat_reader.read_bulk = True

    # Initialize ISMN reader

    ismn_data_folder = os.path.join(os.path.dirname(__file__), '..', 'test-data',
                                    'ismn', 'multinetwork', 'header_values')
    ismn_reader = ISMN_Interface(ismn_data_folder)

    jobs = []

    ids = ismn_reader.get_dataset_ids(
        variable='soil moisture',
        min_depth=0,
        max_depth=0.1)

    metadata_dict_template = {'network': np.array(['None'], dtype='U256'),
                              'station': np.array(['None'], dtype='U256'),
                              'landcover': np.float32([np.nan]),
                              'climate': np.array(['None'], dtype='U4')}

    for idx in ids:
        metadata = ismn_reader.metadata[idx]
        metadata_dict = [{'network': metadata['network'],

ascat_reader = AscatSsmCdr(ascat_data_folder, ascat_grid_folder,
                           grid_filename='TUW_WARP5_grid_info_2_1.nc',
                           static_layer_path=static_layers_folder)
ascat_reader.read_bulk = True


# Initialize ISMN reader

# In[4]:


ismn_data_folder = os.path.join(testdata_folder,
                                 'ismn/multinetwork/header_values')

ismn_reader = ISMN_Interface(ismn_data_folder)


# The validation is run based on jobs. A job consists of at least three lists or numpy arrays specifing the grid
# point index, its latitude and longitude. In the case of the ISMN we can use the `dataset_ids` that identify every
# time series in the downloaded ISMN data as our grid point index. We can then get longitude and latitude from the
# metadata of the dataset.
# 
# **DO NOT CHANGE** the name ***jobs*** because it will be searched during the parallel processing!

# In[5]:


jobs = []

ids = ismn_reader.get_dataset_ids(variable='soil moisture', min_depth=0, max_depth=0.1)
for idx in ids: