def test_get_latlng_centroid(self):
"""Ensures correct output from get_latlng_centroid."""
(this_centroid_lat_deg,
this_centroid_lng_deg) = geodetic_utils.get_latlng_centroid(
latitudes_deg=POINT_LATITUDES_DEG,
longitudes_deg=POINT_LONGITUDES_DEG,
allow_nan=True)
self.assertTrue(
numpy.isclose(this_centroid_lat_deg,
CENTROID_LATITUDE_DEG,
atol=DEFAULT_TOLERANCE))
self.assertTrue(
numpy.isclose(this_centroid_lng_deg,
CENTROID_LONGITUDE_DEG,
atol=DEFAULT_TOLERANCE))
def create_distance_buffers(storm_object_table, min_distances_metres,
max_distances_metres):
"""Creates one or more distance buffers around each storm object.
K = number of buffers
:param storm_object_table: pandas DataFrame with the following columns.
Each row is one storm object.
storm_object_table.centroid_latitude_deg: Latitude (deg N) of storm-object
centroid.
storm_object_table.centroid_longitude_deg: Longitude (deg E) of storm-object
centroid.
storm_object_table.polygon_object_latlng_deg: Instance of
`shapely.geometry.Polygon`, with x-coords in longitude (deg E) and
y-coords in latitude (deg N).
:param min_distances_metres: length-K numpy array of minimum distances. If
the storm object is inside the [k]th buffer -- i.e., the [k]th buffer
has no minimum distance -- then min_distances_metres[k] should be NaN.
:param max_distances_metres: length-K numpy array of max distances.
:return: storm_object_table: Same as input but with K additional columns
(one per distance buffer). Column names are generated by
`buffer_to_column_name`, and each value in these columns is a
`shapely.geometry.Polygon` object, with x-coords in longitude (deg E) and
y-coords in latitude (deg N).
"""
num_buffers = len(min_distances_metres)
these_expected_dim = numpy.array([num_buffers], dtype=int)
error_checking.assert_is_numpy_array(max_distances_metres,
exact_dimensions=these_expected_dim)
global_centroid_lat_deg, global_centroid_lng_deg = (
geodetic_utils.get_latlng_centroid(
latitudes_deg=storm_object_table[CENTROID_LATITUDE_COLUMN].values,
longitudes_deg=storm_object_table[CENTROID_LONGITUDE_COLUMN].values
))
projection_object = projections.init_azimuthal_equidistant_projection(
central_latitude_deg=global_centroid_lat_deg,
central_longitude_deg=global_centroid_lng_deg)
num_storm_objects = len(storm_object_table.index)
object_array = numpy.full(num_storm_objects, numpy.nan, dtype=object)
buffer_column_names = [''] * num_buffers
for j in range(num_buffers):
buffer_column_names[j] = buffer_to_column_name(
min_distance_metres=min_distances_metres[j],
max_distance_metres=max_distances_metres[j])
storm_object_table = storm_object_table.assign(
**{buffer_column_names[j]: object_array})
for i in range(num_storm_objects):
this_orig_vertex_dict_latlng_deg = (
polygons.polygon_object_to_vertex_arrays(
storm_object_table[LATLNG_POLYGON_COLUMN].values[i]))
these_orig_x_metres, these_orig_y_metres = (
projections.project_latlng_to_xy(
latitudes_deg=this_orig_vertex_dict_latlng_deg[
polygons.EXTERIOR_Y_COLUMN],
longitudes_deg=this_orig_vertex_dict_latlng_deg[
polygons.EXTERIOR_X_COLUMN],
projection_object=projection_object))
for j in range(num_buffers):
this_buffer_poly_object_xy_metres = polygons.buffer_simple_polygon(
vertex_x_metres=these_orig_x_metres,
vertex_y_metres=these_orig_y_metres,
min_buffer_dist_metres=min_distances_metres[j],
max_buffer_dist_metres=max_distances_metres[j])
this_buffer_vertex_dict = polygons.polygon_object_to_vertex_arrays(
this_buffer_poly_object_xy_metres)
(this_buffer_vertex_dict[polygons.EXTERIOR_Y_COLUMN],
this_buffer_vertex_dict[polygons.EXTERIOR_X_COLUMN]
) = projections.project_xy_to_latlng(
x_coords_metres=this_buffer_vertex_dict[
polygons.EXTERIOR_X_COLUMN],
y_coords_metres=this_buffer_vertex_dict[
polygons.EXTERIOR_Y_COLUMN],
projection_object=projection_object)
this_num_holes = len(
this_buffer_vertex_dict[polygons.HOLE_X_COLUMN])
for k in range(this_num_holes):
(this_buffer_vertex_dict[polygons.HOLE_Y_COLUMN][k],
this_buffer_vertex_dict[polygons.HOLE_X_COLUMN][k]
) = projections.project_xy_to_latlng(
x_coords_metres=this_buffer_vertex_dict[
polygons.HOLE_X_COLUMN][k],
y_coords_metres=this_buffer_vertex_dict[
polygons.HOLE_Y_COLUMN][k],
projection_object=projection_object)
this_buffer_poly_object_latlng_deg = (
polygons.vertex_arrays_to_polygon_object(
exterior_x_coords=this_buffer_vertex_dict[
polygons.EXTERIOR_X_COLUMN],
exterior_y_coords=this_buffer_vertex_dict[
polygons.EXTERIOR_Y_COLUMN],
hole_x_coords_list=this_buffer_vertex_dict[
polygons.HOLE_X_COLUMN],
hole_y_coords_list=this_buffer_vertex_dict[
polygons.HOLE_Y_COLUMN]))
storm_object_table[buffer_column_names[j]].values[i] = (
this_buffer_poly_object_latlng_deg)
return storm_object_table
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
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)
STORM_OBJECT_TABLE_SMALL_SCALE[
tracking_utils.CENTROID_LAT_COLUMN].values[i] = THIS_CENTROID_LAT_DEG
STORM_OBJECT_TABLE_SMALL_SCALE[
tracking_utils.CENTROID_LNG_COLUMN].values[i] = THIS_CENTROID_LNG_DEG
STORM_OBJECT_TABLE_SMALL_SCALE[
tracking_utils.POLYGON_OBJECT_LATLNG_COLUMN].values[i] = (
polygons.vertex_arrays_to_polygon_object(
THESE_VERTEX_LONGITUDES_DEG, THESE_VERTEX_LATITUDES_DEG))
STORM_OBJECT_TABLE_SMALL_SCALE[
tracking_utils.POLYGON_OBJECT_ROWCOL_COLUMN].values[i] = (
polygons.vertex_arrays_to_polygon_object(THESE_VERTEX_COLUMNS,
THESE_VERTEX_ROWS))
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: 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(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_utils.FIELD_NAME_COLUMN]
storm_id_var_name_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_var_name: 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_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_utils.UNIX_TIME_COLUMN],
dtype=int)
spc_date_unix_sec = time_conversion.spc_date_string_to_unix_sec(
spc_date_string)
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 = _append_spc_date_to_storm_ids(
polygon_table[tracking_utils.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_utils.STORM_ID_COLUMN, tracking_utils.STORM_ID_COLUMN
]].values.tolist()
argument_dict = {
tracking_utils.STORM_ID_COLUMN: storm_ids,
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,
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.POLYGON_OBJECT_LATLNG_COLUMN: object_array,
tracking_utils.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_utils.GRID_POINT_ROW_COLUMN].values[i],
polygon_table[
tracking_utils.GRID_POINT_COLUMN_COLUMN].values[i]))
# these_vertex_rows, these_vertex_columns = (
# polygons.grid_points_in_poly_to_vertices(
# metadata_dict[radar_utils.NUM_LAT_COLUMN] -
# polygon_table[tracking_utils.GRID_POINT_ROW_COLUMN].values[i],
# polygon_table[
# tracking_utils.GRID_POINT_COLUMN_COLUMN].values[i]))
#
# these_vertex_rows = (
# metadata_dict[radar_utils.NUM_LAT_COLUMN] - these_vertex_rows)
(polygon_table[tracking_utils.GRID_POINT_ROW_COLUMN].values[i],
polygon_table[tracking_utils.GRID_POINT_COLUMN_COLUMN].values[i]) = (
polygons.simple_polygon_to_grid_points(these_vertex_rows,
these_vertex_columns))
(polygon_table[tracking_utils.GRID_POINT_LAT_COLUMN].values[i],
polygon_table[tracking_utils.GRID_POINT_LNG_COLUMN].values[i]
) = (radar_utils.rowcol_to_latlng(
polygon_table[tracking_utils.GRID_POINT_ROW_COLUMN].values[i],
polygon_table[tracking_utils.GRID_POINT_COLUMN_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(
these_vertex_rows,
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_LAT_COLUMN].values[i],
polygon_table[tracking_utils.CENTROID_LNG_COLUMN].values[i]
) = geodetic_utils.get_latlng_centroid(
latitudes_deg=these_vertex_lat_deg,
longitudes_deg=these_vertex_lng_deg)
polygon_table[
tracking_utils.POLYGON_OBJECT_ROWCOL_COLUMN].values[i] = (
polygons.vertex_arrays_to_polygon_object(
these_vertex_columns, these_vertex_rows))
polygon_table[
tracking_utils.POLYGON_OBJECT_LATLNG_COLUMN].values[i] = (
polygons.vertex_arrays_to_polygon_object(
these_vertex_lng_deg, these_vertex_lat_deg))
return polygon_table
def make_buffers_around_storm_objects(
storm_object_table, min_distances_metres, max_distances_metres):
"""Creates one or more distance buffers around each storm object.
N = number of storm objects
B = number of buffers around each storm object
V = number of vertices in a given buffer
:param storm_object_table: N-row pandas DataFrame with the following
columns.
storm_object_table.storm_id: String ID for storm cell.
storm_object_table.polygon_object_latlng: Instance of
`shapely.geometry.Polygon`, containing vertices of storm object in
lat-long coordinates.
:param min_distances_metres: length-B numpy array of minimum buffer
distances. If min_distances_metres[i] is NaN, the storm object is
included in the [i]th buffer, so the [i]th buffer is inclusive. If
min_distances_metres[i] is a real number, the storm object is *not*
included in the [i]th buffer, so the [i]th buffer is exclusive.
:param max_distances_metres: length-B numpy array of maximum buffer
distances. Must be all real numbers (no NaN).
:return: storm_object_table: Same as input, but with B additional columns.
Each additional column (listed below) contains a
`shapely.geometry.Polygon` instance for each storm object. Each
`shapely.geometry.Polygon` instance contains the lat-long vertices of
one distance buffer around one storm object.
storm_object_table.polygon_object_latlng_buffer_<D>m: For an inclusive
buffer of D metres around the storm.
storm_object_table.polygon_object_latlng_buffer_<d>_<D>m: For an exclusive
buffer of d...D metres around the storm.
"""
error_checking.assert_is_geq_numpy_array(
min_distances_metres, 0., allow_nan=True)
error_checking.assert_is_numpy_array(
min_distances_metres, num_dimensions=1)
num_buffers = len(min_distances_metres)
error_checking.assert_is_geq_numpy_array(
max_distances_metres, 0., allow_nan=False)
error_checking.assert_is_numpy_array(
max_distances_metres, exact_dimensions=numpy.array([num_buffers]))
for j in range(num_buffers):
if numpy.isnan(min_distances_metres[j]):
continue
error_checking.assert_is_greater(
max_distances_metres[j], min_distances_metres[j],
allow_nan=False)
num_storm_objects = len(storm_object_table.index)
centroid_latitudes_deg = numpy.full(num_storm_objects, numpy.nan)
centroid_longitudes_deg = numpy.full(num_storm_objects, numpy.nan)
for i in range(num_storm_objects):
this_centroid_object = storm_object_table[
POLYGON_OBJECT_LATLNG_COLUMN].values[0].centroid
centroid_latitudes_deg[i] = this_centroid_object.y
centroid_longitudes_deg[i] = this_centroid_object.x
(global_centroid_lat_deg, global_centroid_lng_deg
) = geodetic_utils.get_latlng_centroid(
latitudes_deg=centroid_latitudes_deg,
longitudes_deg=centroid_longitudes_deg)
projection_object = projections.init_azimuthal_equidistant_projection(
global_centroid_lat_deg, global_centroid_lng_deg)
object_array = numpy.full(num_storm_objects, numpy.nan, dtype=object)
argument_dict = {}
buffer_column_names = [''] * num_buffers
for j in range(num_buffers):
buffer_column_names[j] = distance_buffer_to_column_name(
min_distances_metres[j], max_distances_metres[j])
argument_dict.update({buffer_column_names[j]: object_array})
storm_object_table = storm_object_table.assign(**argument_dict)
for i in range(num_storm_objects):
orig_vertex_dict_latlng = polygons.polygon_object_to_vertex_arrays(
storm_object_table[POLYGON_OBJECT_LATLNG_COLUMN].values[i])
(orig_vertex_x_metres,
orig_vertex_y_metres) = projections.project_latlng_to_xy(
orig_vertex_dict_latlng[polygons.EXTERIOR_Y_COLUMN],
orig_vertex_dict_latlng[polygons.EXTERIOR_X_COLUMN],
projection_object=projection_object)
for j in range(num_buffers):
buffer_polygon_object_xy = polygons.buffer_simple_polygon(
orig_vertex_x_metres, orig_vertex_y_metres,
min_buffer_dist_metres=min_distances_metres[j],
max_buffer_dist_metres=max_distances_metres[j])
buffer_vertex_dict = polygons.polygon_object_to_vertex_arrays(
buffer_polygon_object_xy)
(buffer_vertex_dict[polygons.EXTERIOR_Y_COLUMN],
buffer_vertex_dict[polygons.EXTERIOR_X_COLUMN]) = (
projections.project_xy_to_latlng(
buffer_vertex_dict[polygons.EXTERIOR_X_COLUMN],
buffer_vertex_dict[polygons.EXTERIOR_Y_COLUMN],
projection_object=projection_object))
this_num_holes = len(buffer_vertex_dict[polygons.HOLE_X_COLUMN])
for k in range(this_num_holes):
(buffer_vertex_dict[polygons.HOLE_Y_COLUMN][k],
buffer_vertex_dict[polygons.HOLE_X_COLUMN][k]) = (
projections.project_xy_to_latlng(
buffer_vertex_dict[polygons.HOLE_X_COLUMN][k],
buffer_vertex_dict[polygons.HOLE_Y_COLUMN][k],
projection_object=projection_object))
buffer_polygon_object_latlng = (
polygons.vertex_arrays_to_polygon_object(
buffer_vertex_dict[polygons.EXTERIOR_X_COLUMN],
buffer_vertex_dict[polygons.EXTERIOR_Y_COLUMN],
hole_x_coords_list=
buffer_vertex_dict[polygons.HOLE_X_COLUMN],
hole_y_coords_list=
buffer_vertex_dict[polygons.HOLE_Y_COLUMN]))
storm_object_table[buffer_column_names[j]].values[
i] = buffer_polygon_object_latlng
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})