def test_grid_points_in_poly_to_vertices(self): """Ensures correct output from grid_points_in_poly_to_vertices.""" these_vertex_rows, these_vertex_columns = ( polygons.grid_points_in_poly_to_vertices( ROW_INDICES_IN_POLYGON, COLUMN_INDICES_IN_POLYGON)) self.assertTrue( numpy.array_equal(these_vertex_rows, VERTEX_ROWS_GRID_CELL_EDGES_NON_REDUNDANT)) self.assertTrue( numpy.array_equal(these_vertex_columns, VERTEX_COLUMNS_GRID_CELL_EDGES_NON_REDUNDANT))
for i in range(NUM_STORMS_SMALL_SCALE): STORM_OBJECT_TABLE_SMALL_SCALE[ tracking_utils. GRID_POINT_LAT_COLUMN].values[i] = GRID_POINT_LAT_BY_STORM_DEG[i] STORM_OBJECT_TABLE_SMALL_SCALE[ tracking_utils. GRID_POINT_LNG_COLUMN].values[i] = GRID_POINT_LNG_BY_STORM_DEG[i] STORM_OBJECT_TABLE_SMALL_SCALE[ tracking_utils. GRID_POINT_ROW_COLUMN].values[i] = GRID_POINT_ROWS_BY_STORM[i] STORM_OBJECT_TABLE_SMALL_SCALE[ tracking_utils. GRID_POINT_COLUMN_COLUMN].values[i] = GRID_POINT_COLUMNS_BY_STORM[i] THESE_VERTEX_ROWS, THESE_VERTEX_COLUMNS = ( polygons.grid_points_in_poly_to_vertices( GRID_POINT_ROWS_BY_STORM[i], GRID_POINT_COLUMNS_BY_STORM[i])) THESE_VERTEX_LATITUDES_DEG, THESE_VERTEX_LONGITUDES_DEG = ( radar_utils.rowcol_to_latlng( THESE_VERTEX_ROWS, THESE_VERTEX_COLUMNS, nw_grid_point_lat_deg=NW_GRID_POINT_LAT_DEG, nw_grid_point_lng_deg=NW_GRID_POINT_LNG_DEG, lat_spacing_deg=LATITUDE_SPACING_DEG, lng_spacing_deg=LONGITUDE_SPACING_DEG)) (THIS_CENTROID_LAT_DEG, THIS_CENTROID_LNG_DEG) = geodetic_utils.get_latlng_centroid( latitudes_deg=THESE_VERTEX_LATITUDES_DEG, longitudes_deg=THESE_VERTEX_LONGITUDES_DEG)
def read_polygons_from_netcdf(netcdf_file_name, metadata_dict=None, spc_date_unix_sec=None, tracking_start_time_unix_sec=None, tracking_end_time_unix_sec=None, raise_error_if_fails=True): """Reads storm polygons (outlines of storm cells) from NetCDF file. P = number of grid points in storm cell (different for each storm cell) V = number of vertices in storm polygon (different for each storm cell) If file cannot be opened, returns None. :param netcdf_file_name: Path to input file. :param metadata_dict: Dictionary with metadata for NetCDF file, created by `radar_io.read_metadata_from_raw_file`. :param spc_date_unix_sec: SPC date; :param tracking_start_time_unix_sec: Start time for tracking period. This can be found by `get_start_end_times_for_spc_date`. :param tracking_end_time_unix_sec: End time for tracking period. This can be found by `get_start_end_times_for_spc_date`. :param raise_error_if_fails: Boolean flag. If True and file cannot be opened, this method will raise an error. :return: polygon_table: If file cannot be opened and raise_error_if_fails = False, this is None. Otherwise, it is a pandas DataFrame with the following columns. polygon_table.storm_id: String ID for storm cell. polygon_table.unix_time_sec: Time in Unix format. polygon_table.spc_date_unix_sec: SPC date in Unix format. polygon_table.tracking_start_time_unix_sec: Start time for tracking period. polygon_table.tracking_end_time_unix_sec: End time for tracking period. polygon_table.centroid_lat_deg: Latitude at centroid of storm cell (deg N). polygon_table.centroid_lng_deg: Longitude at centroid of storm cell (deg E). polygon_table.grid_point_latitudes_deg: length-P numpy array with latitudes (deg N) of grid points in storm cell. polygon_table.grid_point_longitudes_deg: length-P numpy array with longitudes (deg E) of grid points in storm cell. polygon_table.grid_point_rows: length-P numpy array with row indices (all integers) of grid points in storm cell. polygon_table.grid_point_columns: length-P numpy array with column indices (all integers) of grid points in storm cell. polygon_table.polygon_object_latlng: Instance of `shapely.geometry.Polygon` with vertices in lat-long coordinates. polygon_table.polygon_object_rowcol: Instance of `shapely.geometry.Polygon` with vertices in row-column coordinates. """ error_checking.assert_file_exists(netcdf_file_name) error_checking.assert_is_integer(spc_date_unix_sec) error_checking.assert_is_not_nan(spc_date_unix_sec) error_checking.assert_is_integer(tracking_start_time_unix_sec) error_checking.assert_is_not_nan(tracking_start_time_unix_sec) error_checking.assert_is_integer(tracking_end_time_unix_sec) error_checking.assert_is_not_nan(tracking_end_time_unix_sec) netcdf_dataset = netcdf_io.open_netcdf(netcdf_file_name, raise_error_if_fails) if netcdf_dataset is None: return None storm_id_var_name = metadata_dict[radar_io.FIELD_NAME_COLUMN] storm_id_var_name_orig = metadata_dict[radar_io.FIELD_NAME_COLUMN_ORIG] num_values = len(netcdf_dataset.variables[radar_io.GRID_ROW_COLUMN_ORIG]) if num_values == 0: sparse_grid_dict = { radar_io.GRID_ROW_COLUMN: numpy.array([], dtype=int), radar_io.GRID_COLUMN_COLUMN: numpy.array([], dtype=int), radar_io.NUM_GRID_CELL_COLUMN: numpy.array([], dtype=int), storm_id_var_name: numpy.array([], dtype=int) } else: sparse_grid_dict = { radar_io.GRID_ROW_COLUMN: netcdf_dataset.variables[radar_io.GRID_ROW_COLUMN_ORIG][:], radar_io.GRID_COLUMN_COLUMN: netcdf_dataset.variables[radar_io.GRID_COLUMN_COLUMN_ORIG][:], radar_io.NUM_GRID_CELL_COLUMN: netcdf_dataset.variables[radar_io.NUM_GRID_CELL_COLUMN_ORIG][:], storm_id_var_name: netcdf_dataset.variables[storm_id_var_name_orig][:] } netcdf_dataset.close() sparse_grid_table = pandas.DataFrame.from_dict(sparse_grid_dict) numeric_storm_id_matrix, _, _ = (radar_s2f.sparse_to_full_grid( sparse_grid_table, metadata_dict)) polygon_table = _storm_id_matrix_to_coord_lists(numeric_storm_id_matrix) num_storms = len(polygon_table.index) unix_times_sec = numpy.full(num_storms, metadata_dict[radar_io.UNIX_TIME_COLUMN], dtype=int) spc_dates_unix_sec = numpy.full(num_storms, spc_date_unix_sec, dtype=int) tracking_start_times_unix_sec = numpy.full(num_storms, tracking_start_time_unix_sec, dtype=int) tracking_end_times_unix_sec = numpy.full(num_storms, tracking_end_time_unix_sec, dtype=int) spc_date_string = time_conversion.time_to_spc_date_string( spc_date_unix_sec) storm_ids = _append_spc_date_to_storm_ids( polygon_table[tracking_io.STORM_ID_COLUMN].values, spc_date_string) simple_array = numpy.full(num_storms, numpy.nan) object_array = numpy.full(num_storms, numpy.nan, dtype=object) nested_array = polygon_table[[ tracking_io.STORM_ID_COLUMN, tracking_io.STORM_ID_COLUMN ]].values.tolist() argument_dict = { tracking_io.STORM_ID_COLUMN: storm_ids, tracking_io.TIME_COLUMN: unix_times_sec, tracking_io.SPC_DATE_COLUMN: spc_dates_unix_sec, tracking_io.TRACKING_START_TIME_COLUMN: tracking_start_times_unix_sec, tracking_io.TRACKING_END_TIME_COLUMN: tracking_end_times_unix_sec, tracking_io.CENTROID_LAT_COLUMN: simple_array, tracking_io.CENTROID_LNG_COLUMN: simple_array, tracking_io.GRID_POINT_LAT_COLUMN: nested_array, tracking_io.GRID_POINT_LNG_COLUMN: nested_array, tracking_io.POLYGON_OBJECT_LATLNG_COLUMN: object_array, tracking_io.POLYGON_OBJECT_ROWCOL_COLUMN: object_array } polygon_table = polygon_table.assign(**argument_dict) for i in range(num_storms): these_vertex_rows, these_vertex_columns = ( polygons.grid_points_in_poly_to_vertices( polygon_table[tracking_io.GRID_POINT_ROW_COLUMN].values[i], polygon_table[tracking_io.GRID_POINT_COLUMN_COLUMN].values[i])) (polygon_table[tracking_io.GRID_POINT_ROW_COLUMN].values[i], polygon_table[tracking_io.GRID_POINT_COLUMN_COLUMN].values[i]) = ( polygons.simple_polygon_to_grid_points(these_vertex_rows, these_vertex_columns)) (polygon_table[tracking_io.GRID_POINT_LAT_COLUMN].values[i], polygon_table[tracking_io.GRID_POINT_LNG_COLUMN].values[i]) = ( radar_io.rowcol_to_latlng( polygon_table[tracking_io.GRID_POINT_ROW_COLUMN].values[i], polygon_table[tracking_io.GRID_POINT_COLUMN_COLUMN].values[i], nw_grid_point_lat_deg=metadata_dict[ radar_io.NW_GRID_POINT_LAT_COLUMN], nw_grid_point_lng_deg=metadata_dict[ radar_io.NW_GRID_POINT_LNG_COLUMN], lat_spacing_deg=metadata_dict[radar_io.LAT_SPACING_COLUMN], lng_spacing_deg=metadata_dict[radar_io.LNG_SPACING_COLUMN])) these_vertex_lat_deg, these_vertex_lng_deg = radar_io.rowcol_to_latlng( these_vertex_rows, these_vertex_columns, nw_grid_point_lat_deg=metadata_dict[ radar_io.NW_GRID_POINT_LAT_COLUMN], nw_grid_point_lng_deg=metadata_dict[ radar_io.NW_GRID_POINT_LNG_COLUMN], lat_spacing_deg=metadata_dict[radar_io.LAT_SPACING_COLUMN], lng_spacing_deg=metadata_dict[radar_io.LNG_SPACING_COLUMN]) (polygon_table[tracking_io.CENTROID_LAT_COLUMN].values[i], polygon_table[tracking_io.CENTROID_LNG_COLUMN].values[i]) = ( polygons.get_latlng_centroid(these_vertex_lat_deg, these_vertex_lng_deg)) polygon_table[tracking_io.POLYGON_OBJECT_ROWCOL_COLUMN].values[i] = ( polygons.vertex_arrays_to_polygon_object(these_vertex_columns, these_vertex_rows)) polygon_table[tracking_io.POLYGON_OBJECT_LATLNG_COLUMN].values[i] = ( polygons.vertex_arrays_to_polygon_object(these_vertex_lng_deg, these_vertex_lat_deg)) return polygon_table
def read_polygons_from_netcdf(netcdf_file_name, metadata_dict, spc_date_string, tracking_start_time_unix_sec, tracking_end_time_unix_sec, raise_error_if_fails=True): """Reads storm polygons (outlines of storm cells) from NetCDF file. P = number of grid points in storm cell (different for each storm cell) V = number of vertices in storm polygon (different for each storm cell) If file cannot be opened, returns None. :param netcdf_file_name: Path to input file. :param metadata_dict: Dictionary with metadata for NetCDF file, created by `myrorss_and_mrms_io.read_metadata_from_raw_file`. :param spc_date_string: SPC date (format "yyyymmdd"). :param tracking_start_time_unix_sec: Start time for tracking period. This can be found by `get_start_end_times_for_spc_date`. :param tracking_end_time_unix_sec: End time for tracking period. This can be found by `get_start_end_times_for_spc_date`. :param raise_error_if_fails: Boolean flag. If True and file cannot be opened, this method will raise an error. :return: polygon_table: pandas DataFrame with the following columns. Each row is one storm object. polygon_table.primary_id_string: See documentation for `storm_tracking_io.write_file`. polygon_table.valid_time_unix_sec: Same. polygon_table.spc_date_string: Same. polygon_table.tracking_start_time_unix_sec: Same. polygon_table.tracking_end_time_unix_sec: Same. polygon_table.centroid_latitude_deg: Same. polygon_table.centroid_longitude_deg: Same. polygon_table.grid_point_latitudes_deg: Same. polygon_table.grid_point_longitudes_deg: Same. polygon_table.grid_point_rows: Same. polygon_table.grid_point_columns: Same. polygon_table.polygon_object_latlng_deg: Same. polygon_table.polygon_object_rowcol: Same. """ error_checking.assert_file_exists(netcdf_file_name) error_checking.assert_is_integer(tracking_start_time_unix_sec) error_checking.assert_is_not_nan(tracking_start_time_unix_sec) error_checking.assert_is_integer(tracking_end_time_unix_sec) error_checking.assert_is_not_nan(tracking_end_time_unix_sec) netcdf_dataset = netcdf_io.open_netcdf(netcdf_file_name, raise_error_if_fails) if netcdf_dataset is None: return None storm_id_column = metadata_dict[radar_utils.FIELD_NAME_COLUMN] storm_id_column_orig = metadata_dict[ myrorss_and_mrms_io.FIELD_NAME_COLUMN_ORIG] num_values = len( netcdf_dataset.variables[myrorss_and_mrms_io.GRID_ROW_COLUMN_ORIG]) if num_values == 0: sparse_grid_dict = { myrorss_and_mrms_io.GRID_ROW_COLUMN: numpy.array([], dtype=int), myrorss_and_mrms_io.GRID_COLUMN_COLUMN: numpy.array([], dtype=int), myrorss_and_mrms_io.NUM_GRID_CELL_COLUMN: numpy.array([], dtype=int), storm_id_column: numpy.array([], dtype=int) } else: sparse_grid_dict = { myrorss_and_mrms_io.GRID_ROW_COLUMN: netcdf_dataset.variables[myrorss_and_mrms_io.GRID_ROW_COLUMN_ORIG] [:], myrorss_and_mrms_io.GRID_COLUMN_COLUMN: netcdf_dataset.variables[ myrorss_and_mrms_io.GRID_COLUMN_COLUMN_ORIG][:], myrorss_and_mrms_io.NUM_GRID_CELL_COLUMN: netcdf_dataset.variables[ myrorss_and_mrms_io.NUM_GRID_CELL_COLUMN_ORIG][:], storm_id_column: netcdf_dataset.variables[storm_id_column_orig][:] } netcdf_dataset.close() sparse_grid_table = pandas.DataFrame.from_dict(sparse_grid_dict) numeric_id_matrix = radar_s2f.sparse_to_full_grid(sparse_grid_table, metadata_dict)[0] polygon_table = _id_matrix_to_coord_lists(numeric_id_matrix) num_storms = len(polygon_table.index) valid_times_unix_sec = numpy.full( num_storms, metadata_dict[radar_utils.UNIX_TIME_COLUMN], dtype=int) spc_date_strings = num_storms * [ time_conversion.time_to_spc_date_string(valid_times_unix_sec[0]) ] tracking_start_times_unix_sec = numpy.full(num_storms, tracking_start_time_unix_sec, dtype=int) tracking_end_times_unix_sec = numpy.full(num_storms, tracking_end_time_unix_sec, dtype=int) simple_array = numpy.full(num_storms, numpy.nan) object_array = numpy.full(num_storms, numpy.nan, dtype=object) nested_array = polygon_table[[ tracking_utils.PRIMARY_ID_COLUMN, tracking_utils.PRIMARY_ID_COLUMN ]].values.tolist() argument_dict = { tracking_utils.VALID_TIME_COLUMN: valid_times_unix_sec, tracking_utils.SPC_DATE_COLUMN: spc_date_strings, tracking_utils.TRACKING_START_TIME_COLUMN: tracking_start_times_unix_sec, tracking_utils.TRACKING_END_TIME_COLUMN: tracking_end_times_unix_sec, tracking_utils.CENTROID_LATITUDE_COLUMN: simple_array, tracking_utils.CENTROID_LONGITUDE_COLUMN: simple_array, tracking_utils.LATITUDES_IN_STORM_COLUMN: nested_array, tracking_utils.LONGITUDES_IN_STORM_COLUMN: nested_array, tracking_utils.LATLNG_POLYGON_COLUMN: object_array, tracking_utils.ROWCOL_POLYGON_COLUMN: object_array } polygon_table = polygon_table.assign(**argument_dict) for i in range(num_storms): these_vertex_rows, these_vertex_columns = ( polygons.grid_points_in_poly_to_vertices( grid_point_row_indices=polygon_table[ tracking_utils.ROWS_IN_STORM_COLUMN].values[i], grid_point_column_indices=polygon_table[ tracking_utils.COLUMNS_IN_STORM_COLUMN].values[i])) (polygon_table[tracking_utils.ROWS_IN_STORM_COLUMN].values[i], polygon_table[tracking_utils.COLUMNS_IN_STORM_COLUMN].values[i] ) = polygons.simple_polygon_to_grid_points( vertex_row_indices=these_vertex_rows, vertex_column_indices=these_vertex_columns) (polygon_table[tracking_utils.LATITUDES_IN_STORM_COLUMN].values[i], polygon_table[tracking_utils.LONGITUDES_IN_STORM_COLUMN].values[i] ) = radar_utils.rowcol_to_latlng( grid_rows=polygon_table[ tracking_utils.ROWS_IN_STORM_COLUMN].values[i], grid_columns=polygon_table[ tracking_utils.COLUMNS_IN_STORM_COLUMN].values[i], nw_grid_point_lat_deg=metadata_dict[ radar_utils.NW_GRID_POINT_LAT_COLUMN], nw_grid_point_lng_deg=metadata_dict[ radar_utils.NW_GRID_POINT_LNG_COLUMN], lat_spacing_deg=metadata_dict[radar_utils.LAT_SPACING_COLUMN], lng_spacing_deg=metadata_dict[radar_utils.LNG_SPACING_COLUMN]) these_vertex_lat_deg, these_vertex_lng_deg = ( radar_utils.rowcol_to_latlng( grid_rows=these_vertex_rows, grid_columns=these_vertex_columns, nw_grid_point_lat_deg=metadata_dict[ radar_utils.NW_GRID_POINT_LAT_COLUMN], nw_grid_point_lng_deg=metadata_dict[ radar_utils.NW_GRID_POINT_LNG_COLUMN], lat_spacing_deg=metadata_dict[radar_utils.LAT_SPACING_COLUMN], lng_spacing_deg=metadata_dict[radar_utils.LNG_SPACING_COLUMN])) (polygon_table[tracking_utils.CENTROID_LATITUDE_COLUMN].values[i], polygon_table[tracking_utils.CENTROID_LONGITUDE_COLUMN].values[i] ) = geodetic_utils.get_latlng_centroid( latitudes_deg=these_vertex_lat_deg, longitudes_deg=these_vertex_lng_deg) polygon_table[tracking_utils.ROWCOL_POLYGON_COLUMN].values[i] = ( polygons.vertex_arrays_to_polygon_object( exterior_x_coords=these_vertex_columns, exterior_y_coords=these_vertex_rows)) polygon_table[tracking_utils.LATLNG_POLYGON_COLUMN].values[i] = ( polygons.vertex_arrays_to_polygon_object( exterior_x_coords=these_vertex_lng_deg, exterior_y_coords=these_vertex_lat_deg)) primary_id_strings = _append_spc_date_to_storm_ids( primary_id_strings=polygon_table[ tracking_utils.PRIMARY_ID_COLUMN].values, spc_date_string=spc_date_string) return polygon_table.assign( **{tracking_utils.PRIMARY_ID_COLUMN: primary_id_strings})