def test_simple_polygon_to_grid_points(self):
"""Ensures correct output from simple_polygon_to_grid_points."""
these_grid_point_rows, these_grid_point_columns = (
polygons.simple_polygon_to_grid_points(VERTEX_ROWS_SIMPLE,
VERTEX_COLUMNS_SIMPLE))
self.assertTrue(
numpy.array_equal(these_grid_point_rows,
GRID_POINT_ROWS_IN_SIMPLE_POLY))
self.assertTrue(
numpy.array_equal(these_grid_point_columns,
GRID_POINT_COLUMNS_IN_SIMPLE_POLY))
def read_raw_file(raw_file_name):
"""Reads tracking data from raw (either JSON or ASCII) file.
This file should contain all storm objects at one time step.
:param raw_file_name: Path to input file.
:return: storm_object_table: See documentation for
`storm_tracking_io.write_processed_file`.
"""
error_checking.assert_file_exists(raw_file_name)
_, pathless_file_name = os.path.split(raw_file_name)
_, file_extension = os.path.splitext(pathless_file_name)
_check_raw_file_extension(file_extension)
unix_time_sec = raw_file_name_to_time(raw_file_name)
if file_extension == ASCII_FILE_EXTENSION:
storm_ids = []
east_velocities_m_s01 = []
north_velocities_m_s01 = []
list_of_latitude_vertex_arrays_deg = []
list_of_longitude_vertex_arrays_deg = []
for this_line in open(raw_file_name, 'r').readlines():
these_words = this_line.split(':')
if len(these_words) < MIN_WORDS_PER_ASCII_LINE:
continue
storm_ids.append(these_words[STORM_ID_INDEX_IN_ASCII_FILES])
east_velocities_m_s01.append(
float(these_words[U_MOTION_INDEX_IN_ASCII_FILES]))
north_velocities_m_s01.append(
-1 * float(these_words[V_MOTION_INDEX_IN_ASCII_FILES]))
these_polygon_words = numpy.array(
these_words[POLYGON_INDEX_IN_ASCII_FILES].split(','))
these_latitude_words = these_polygon_words[
LATITUDE_INDEX_IN_ASCII_FILES::2].tolist()
these_longitude_words = these_polygon_words[
LONGITUDE_INDEX_IN_ASCII_FILES::2].tolist()
these_latitudes_deg = numpy.array(
[float(w) for w in these_latitude_words])
these_longitudes_deg = numpy.array(
[float(w) for w in these_longitude_words])
list_of_latitude_vertex_arrays_deg.append(these_latitudes_deg)
list_of_longitude_vertex_arrays_deg.append(these_longitudes_deg)
east_velocities_m_s01 = numpy.array(east_velocities_m_s01)
north_velocities_m_s01 = numpy.array(north_velocities_m_s01)
num_storms = len(storm_ids)
else:
with open(raw_file_name) as json_file_handle:
probsevere_dict = json.load(json_file_handle)
num_storms = len(probsevere_dict[FEATURES_KEY_IN_JSON_FILES])
storm_ids = [None] * num_storms
east_velocities_m_s01 = numpy.full(num_storms, numpy.nan)
north_velocities_m_s01 = numpy.full(num_storms, numpy.nan)
list_of_latitude_vertex_arrays_deg = [None] * num_storms
list_of_longitude_vertex_arrays_deg = [None] * num_storms
for i in range(num_storms):
storm_ids[i] = str(
probsevere_dict[FEATURES_KEY_IN_JSON_FILES][i]
[PROPERTIES_KEY_IN_JSON_FILES][STORM_ID_KEY_IN_JSON_FILES])
east_velocities_m_s01[i] = float(
probsevere_dict[FEATURES_KEY_IN_JSON_FILES][i]
[PROPERTIES_KEY_IN_JSON_FILES][U_MOTION_KEY_IN_JSON_FILES])
north_velocities_m_s01[i] = -1 * float(
probsevere_dict[FEATURES_KEY_IN_JSON_FILES][i]
[PROPERTIES_KEY_IN_JSON_FILES][V_MOTION_KEY_IN_JSON_FILES])
this_vertex_matrix_deg = numpy.array(
probsevere_dict[FEATURES_KEY_IN_JSON_FILES][i]
[GEOMETRY_KEY_IN_JSON_FILES][COORDINATES_KEY_IN_JSON_FILES][0])
list_of_latitude_vertex_arrays_deg[i] = numpy.array(
this_vertex_matrix_deg[:, LATITUDE_INDEX_IN_JSON_FILES])
list_of_longitude_vertex_arrays_deg[i] = numpy.array(
this_vertex_matrix_deg[:, LONGITUDE_INDEX_IN_JSON_FILES])
spc_date_unix_sec = time_conversion.time_to_spc_date_unix_sec(
unix_time_sec)
unix_times_sec = numpy.full(num_storms, unix_time_sec, 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, DUMMY_TRACKING_START_TIME_UNIX_SEC, dtype=int)
tracking_end_times_unix_sec = numpy.full(num_storms,
DUMMY_TRACKING_END_TIME_UNIX_SEC,
dtype=int)
storm_object_dict = {
tracking_utils.STORM_ID_COLUMN: storm_ids,
tracking_utils.EAST_VELOCITY_COLUMN: east_velocities_m_s01,
tracking_utils.NORTH_VELOCITY_COLUMN: north_velocities_m_s01,
tracking_utils.TIME_COLUMN: unix_times_sec,
tracking_utils.SPC_DATE_COLUMN: spc_dates_unix_sec,
tracking_utils.TRACKING_START_TIME_COLUMN:
tracking_start_times_unix_sec,
tracking_utils.TRACKING_END_TIME_COLUMN: tracking_end_times_unix_sec
}
storm_object_table = pandas.DataFrame.from_dict(storm_object_dict)
storm_ages_sec = numpy.full(num_storms, numpy.nan)
simple_array = numpy.full(num_storms, numpy.nan)
object_array = numpy.full(num_storms, numpy.nan, dtype=object)
nested_array = storm_object_table[[
tracking_utils.STORM_ID_COLUMN, tracking_utils.STORM_ID_COLUMN
]].values.tolist()
argument_dict = {
tracking_utils.AGE_COLUMN: storm_ages_sec,
tracking_utils.CENTROID_LAT_COLUMN: simple_array,
tracking_utils.CENTROID_LNG_COLUMN: simple_array,
tracking_utils.GRID_POINT_LAT_COLUMN: nested_array,
tracking_utils.GRID_POINT_LNG_COLUMN: nested_array,
tracking_utils.GRID_POINT_ROW_COLUMN: nested_array,
tracking_utils.GRID_POINT_COLUMN_COLUMN: nested_array,
tracking_utils.POLYGON_OBJECT_LATLNG_COLUMN: object_array,
tracking_utils.POLYGON_OBJECT_ROWCOL_COLUMN: object_array
}
storm_object_table = storm_object_table.assign(**argument_dict)
for i in range(num_storms):
these_vertex_rows, these_vertex_columns = (
radar_utils.latlng_to_rowcol(
latitudes_deg=list_of_latitude_vertex_arrays_deg[i],
longitudes_deg=list_of_longitude_vertex_arrays_deg[i],
nw_grid_point_lat_deg=NW_GRID_POINT_LAT_DEG,
nw_grid_point_lng_deg=NW_GRID_POINT_LNG_DEG,
lat_spacing_deg=GRID_LAT_SPACING_DEG,
lng_spacing_deg=GRID_LNG_SPACING_DEG))
these_vertex_rows, these_vertex_columns = (
polygons.fix_probsevere_vertices(
row_indices_orig=these_vertex_rows,
column_indices_orig=these_vertex_columns))
these_vertex_latitudes_deg, these_vertex_longitudes_deg = (
radar_utils.rowcol_to_latlng(
grid_rows=these_vertex_rows,
grid_columns=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=GRID_LAT_SPACING_DEG,
lng_spacing_deg=GRID_LNG_SPACING_DEG))
(storm_object_table[tracking_utils.GRID_POINT_ROW_COLUMN].values[i],
storm_object_table[tracking_utils.GRID_POINT_COLUMN_COLUMN].values[i]
) = polygons.simple_polygon_to_grid_points(
vertex_row_indices=these_vertex_rows,
vertex_column_indices=these_vertex_columns)
(storm_object_table[tracking_utils.GRID_POINT_LAT_COLUMN].values[i],
storm_object_table[tracking_utils.GRID_POINT_LNG_COLUMN].values[i]
) = radar_utils.rowcol_to_latlng(
grid_rows=storm_object_table[
tracking_utils.GRID_POINT_ROW_COLUMN].values[i],
grid_columns=storm_object_table[
tracking_utils.GRID_POINT_COLUMN_COLUMN].values[i],
nw_grid_point_lat_deg=NW_GRID_POINT_LAT_DEG,
nw_grid_point_lng_deg=NW_GRID_POINT_LNG_DEG,
lat_spacing_deg=GRID_LAT_SPACING_DEG,
lng_spacing_deg=GRID_LNG_SPACING_DEG)
(storm_object_table[tracking_utils.CENTROID_LAT_COLUMN].values[i],
storm_object_table[tracking_utils.CENTROID_LNG_COLUMN].values[i]
) = geodetic_utils.get_latlng_centroid(
latitudes_deg=these_vertex_latitudes_deg,
longitudes_deg=these_vertex_longitudes_deg)
storm_object_table[tracking_utils.POLYGON_OBJECT_ROWCOL_COLUMN].values[
i] = polygons.vertex_arrays_to_polygon_object(
exterior_x_coords=these_vertex_columns,
exterior_y_coords=these_vertex_rows)
storm_object_table[tracking_utils.POLYGON_OBJECT_LATLNG_COLUMN].values[
i] = polygons.vertex_arrays_to_polygon_object(
exterior_x_coords=these_vertex_longitudes_deg,
exterior_y_coords=these_vertex_latitudes_deg)
return storm_object_table
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_storm_objects_from_raw_file(json_file_name):
"""Reads storm objects from raw file.
This file should contain all storm objects for one tracking scale and one
time step.
P = number of grid points in given storm object
V = number of vertices in bounding polygon of given storm object
:param json_file_name: Path to input file.
:return: storm_object_table: pandas DataFrame with the following columns.
storm_object_table.storm_id: String ID for storm cell.
storm_object_table.unix_time_sec: Time in Unix format.
storm_object_table.spc_date_unix_sec: SPC date in Unix format.
storm_object_table.tracking_start_time_unix_sec: Start time for tracking
period.
storm_object_table.tracking_end_time_unix_sec: End time for tracking
period.
storm_object_table.east_velocity_m_s01: Eastward velocity (m/s).
storm_object_table.north_velocity_m_s01: Northward velocity (m/s).
storm_object_table.age_sec: Age of storm cell (seconds).
storm_object_table.centroid_lat_deg: Latitude at centroid of storm object
(deg N).
storm_object_table.centroid_lng_deg: Longitude at centroid of storm object
(deg E).
storm_object_table.grid_point_latitudes_deg: length-P numpy array with
latitudes (deg N) of grid points in storm object.
storm_object_table.grid_point_longitudes_deg: length-P numpy array with
longitudes (deg E) of grid points in storm object.
storm_object_table.grid_point_rows: length-P numpy array with row indices
(integers) of grid points in storm object.
storm_object_table.grid_point_columns: length-P numpy array with column
indices (integers) of grid points in storm object.
storm_object_table.polygon_object_latlng: Instance of
`shapely.geometry.Polygon` with vertices in lat-long coordinates.
storm_object_table.polygon_object_rowcol: Instance of
`shapely.geometry.Polygon` with vertices in row-column coordinates.
"""
error_checking.assert_file_exists(json_file_name)
with open(json_file_name) as json_file_handle:
probsevere_dict = json.load(json_file_handle)
unix_time_sec = time_conversion.string_to_unix_sec(
probsevere_dict[TIME_COLUMN_ORIG].encode('ascii', 'ignore'),
TIME_FORMAT_IN_RAW_FILES)
spc_date_unix_sec = time_conversion.time_to_spc_date_unix_sec(unix_time_sec)
num_storms = len(probsevere_dict[FEATURES_COLUMN_ORIG])
unix_times_sec = numpy.full(num_storms, unix_time_sec, 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)
storm_ids = [None] * num_storms
east_velocities_m_s01 = numpy.full(num_storms, numpy.nan)
north_velocities_m_s01 = numpy.full(num_storms, numpy.nan)
for i in range(num_storms):
storm_ids[i] = str(
probsevere_dict[FEATURES_COLUMN_ORIG][i][PROPERTIES_COLUMN_ORIG][
STORM_ID_COLUMN_ORIG])
east_velocities_m_s01[i] = float(
probsevere_dict[FEATURES_COLUMN_ORIG][i][PROPERTIES_COLUMN_ORIG][
EAST_VELOCITY_COLUMN_ORIG])
north_velocities_m_s01[i] = -1 * float(
probsevere_dict[FEATURES_COLUMN_ORIG][i][PROPERTIES_COLUMN_ORIG][
NORTH_VELOCITY_COLUMN_ORIG])
storm_object_dict = {
tracking_io.STORM_ID_COLUMN: storm_ids,
tracking_io.EAST_VELOCITY_COLUMN: east_velocities_m_s01,
tracking_io.NORTH_VELOCITY_COLUMN: north_velocities_m_s01,
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}
storm_object_table = pandas.DataFrame.from_dict(storm_object_dict)
storm_object_table = tracking_io.remove_rows_with_nan(storm_object_table)
num_storms = len(storm_object_table.index)
storm_ages_sec = numpy.full(num_storms, numpy.nan)
simple_array = numpy.full(num_storms, numpy.nan)
object_array = numpy.full(num_storms, numpy.nan, dtype=object)
nested_array = storm_object_table[[
tracking_io.STORM_ID_COLUMN,
tracking_io.STORM_ID_COLUMN]].values.tolist()
argument_dict = {tracking_io.AGE_COLUMN: storm_ages_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.GRID_POINT_ROW_COLUMN: nested_array,
tracking_io.GRID_POINT_COLUMN_COLUMN: nested_array,
tracking_io.POLYGON_OBJECT_LATLNG_COLUMN: object_array,
tracking_io.POLYGON_OBJECT_ROWCOL_COLUMN: object_array}
storm_object_table = storm_object_table.assign(**argument_dict)
for i in range(num_storms):
this_vertex_matrix_deg = numpy.asarray(
probsevere_dict[FEATURES_COLUMN_ORIG][i][GEOMETRY_COLUMN_ORIG][
COORDINATES_COLUMN_ORIG][0])
these_vertex_lat_deg = this_vertex_matrix_deg[:, LAT_COLUMN_INDEX_ORIG]
these_vertex_lng_deg = this_vertex_matrix_deg[:, LNG_COLUMN_INDEX_ORIG]
(these_vertex_rows, these_vertex_columns) = radar_io.latlng_to_rowcol(
these_vertex_lat_deg, these_vertex_lng_deg,
nw_grid_point_lat_deg=NW_GRID_POINT_LAT_DEG,
nw_grid_point_lng_deg=NW_GRID_POINT_LNG_DEG,
lat_spacing_deg=GRID_LAT_SPACING_DEG,
lng_spacing_deg=GRID_LNG_SPACING_DEG)
these_vertex_rows, these_vertex_columns = (
polygons.fix_probsevere_vertices(
these_vertex_rows, these_vertex_columns))
these_vertex_lat_deg, these_vertex_lng_deg = radar_io.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=GRID_LAT_SPACING_DEG,
lng_spacing_deg=GRID_LNG_SPACING_DEG)
(storm_object_table[tracking_io.GRID_POINT_ROW_COLUMN].values[i],
storm_object_table[tracking_io.GRID_POINT_COLUMN_COLUMN].values[i]) = (
polygons.simple_polygon_to_grid_points(
these_vertex_rows, these_vertex_columns))
(storm_object_table[tracking_io.GRID_POINT_LAT_COLUMN].values[i],
storm_object_table[tracking_io.GRID_POINT_LNG_COLUMN].values[i]) = (
radar_io.rowcol_to_latlng(
storm_object_table[tracking_io.GRID_POINT_ROW_COLUMN].values[i],
storm_object_table[
tracking_io.GRID_POINT_COLUMN_COLUMN].values[i],
nw_grid_point_lat_deg=NW_GRID_POINT_LAT_DEG,
nw_grid_point_lng_deg=NW_GRID_POINT_LNG_DEG,
lat_spacing_deg=GRID_LAT_SPACING_DEG,
lng_spacing_deg=GRID_LNG_SPACING_DEG))
(storm_object_table[tracking_io.CENTROID_LAT_COLUMN].values[i],
storm_object_table[tracking_io.CENTROID_LNG_COLUMN].values[i]) = (
polygons.get_latlng_centroid(
these_vertex_lat_deg, these_vertex_lng_deg))
storm_object_table[
tracking_io.POLYGON_OBJECT_ROWCOL_COLUMN].values[i] = (
polygons.vertex_arrays_to_polygon_object(
these_vertex_columns, these_vertex_rows))
storm_object_table[
tracking_io.POLYGON_OBJECT_LATLNG_COLUMN].values[i] = (
polygons.vertex_arrays_to_polygon_object(
these_vertex_lng_deg, these_vertex_lat_deg))
return storm_object_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})