def _center_points_latlng_to_rowcol(center_latitudes_deg,
center_longitudes_deg,
nw_grid_point_lat_deg=None,
nw_grid_point_lng_deg=None,
lat_spacing_deg=None,
lng_spacing_deg=None):
"""Converts center points from lat-long to row-column coordinates.
Each "center point" is meant for input to extract_points_as_2d_array.
P = number of center points
:param center_latitudes_deg: length-P numpy array of latitudes (deg N).
:param center_longitudes_deg: length-P numpy array of longitudes (deg E).
:param nw_grid_point_lat_deg: Latitude (deg N) of northwesternmost grid
point.
:param nw_grid_point_lng_deg: Longitude (deg E) of northwesternmost grid
point.
:param lat_spacing_deg: Spacing (deg N) between adjacent rows.
:param lng_spacing_deg: Spacing (deg E) between adjacent columns.
:return: center_row_indices: Row indices (half-integers) of center points.
:return: center_column_indices: Column indices (half-integers) of center
points.
"""
center_row_indices, center_column_indices = radar_io.latlng_to_rowcol(
center_latitudes_deg,
center_longitudes_deg,
nw_grid_point_lat_deg=nw_grid_point_lat_deg,
nw_grid_point_lng_deg=nw_grid_point_lng_deg,
lat_spacing_deg=lat_spacing_deg,
lng_spacing_deg=lng_spacing_deg)
return (rounder.round_to_half_integer(center_row_indices),
rounder.round_to_half_integer(center_column_indices))
def get_grid_points_in_storm_objects(storm_object_table,
metadata_dict_for_storm_objects,
new_metadata_dict):
"""Finds grid points inside each storm object.
:param storm_object_table: pandas DataFrame with columns specified by
`storm_tracking_io.write_processed_file`.
:param metadata_dict_for_storm_objects: Dictionary (with keys specified by
`radar_io.read_metadata_from_raw_file`) describing grid used to create
storm objects.
:param new_metadata_dict: Dictionary (with keys specified by
`radar_io.read_metadata_from_raw_file`) describing new grid, for which
points in each storm object will be found.
:return: storm_object_to_grid_points_table: pandas DataFrame with the
following columns. Each row is one storm object.
storm_object_to_grid_points_table.storm_id: String ID for storm cell.
storm_object_to_grid_points_table.grid_point_rows: 1-D numpy array with row
indices (integers) of grid points in storm object.
storm_object_to_grid_points_table.grid_point_columns: 1-D numpy array with
column indices (integers) of grid points in storm object.
"""
if _are_grids_equal(metadata_dict_for_storm_objects, new_metadata_dict):
return storm_object_table[STORM_OBJECT_TO_GRID_PTS_COLUMNS]
storm_object_to_grid_points_table = storm_object_table[
STORM_OBJECT_TO_GRID_PTS_COLUMNS + GRID_POINT_LATLNG_COLUMNS]
num_storm_objects = len(storm_object_to_grid_points_table.index)
for i in range(num_storm_objects):
(storm_object_to_grid_points_table[
tracking_io.GRID_POINT_ROW_COLUMN].values[i],
storm_object_to_grid_points_table[
tracking_io.GRID_POINT_COLUMN_COLUMN].values[i]
) = (radar_io.latlng_to_rowcol(
storm_object_to_grid_points_table[
tracking_io.GRID_POINT_LAT_COLUMN].values[i],
storm_object_to_grid_points_table[
tracking_io.GRID_POINT_LNG_COLUMN].values[i],
nw_grid_point_lat_deg=new_metadata_dict[
radar_io.NW_GRID_POINT_LAT_COLUMN],
nw_grid_point_lng_deg=new_metadata_dict[
radar_io.NW_GRID_POINT_LNG_COLUMN],
lat_spacing_deg=new_metadata_dict[radar_io.LAT_SPACING_COLUMN],
lng_spacing_deg=new_metadata_dict[radar_io.LNG_SPACING_COLUMN]))
return storm_object_to_grid_points_table[STORM_OBJECT_TO_GRID_PTS_COLUMNS]
def test_latlng_to_rowcol(self):
"""Ensures correct output from latlng_to_rowcol."""
(these_row_indices, these_column_indices) = radar_io.latlng_to_rowcol(
GRID_POINT_LATITUDES_DEG,
GRID_POINT_LONGITUDES_DEG,
nw_grid_point_lat_deg=NW_GRID_POINT_LAT_DEG,
nw_grid_point_lng_deg=NW_GRID_POINT_LNG_DEG,
lat_spacing_deg=LAT_SPACING_DEG,
lng_spacing_deg=LNG_SPACING_DEG)
self.assertTrue(
numpy.allclose(these_row_indices, GRID_ROW_INDICES,
atol=TOLERANCE))
self.assertTrue(
numpy.allclose(these_column_indices,
GRID_COLUMN_INDICES,
atol=TOLERANCE))
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 write_field_to_myrorss_file(field_matrix,
netcdf_file_name,
field_name,
metadata_dict,
height_m_asl=None):
"""Writes field to MYRORSS-formatted file.
M = number of rows (unique grid-point latitudes)
N = number of columns (unique grid-point longitudes)
:param field_matrix: M-by-N numpy array with one radar variable at one time.
Latitude should increase down each column, and longitude should increase
to the right along each row.
:param netcdf_file_name: Path to output file.
:param field_name: Name of radar field in GewitterGefahr format.
:param metadata_dict: Dictionary created by either
`gridrad_io.read_metadata_from_full_grid_file` or
`radar_io.read_metadata_from_raw_file`.
:param height_m_asl: Height of radar field (metres above sea level).
"""
if field_name == radar_io.REFL_NAME:
field_to_heights_dict_m_asl = radar_io.field_and_height_arrays_to_dict(
[field_name],
refl_heights_m_agl=numpy.array([height_m_asl]),
data_source=radar_io.MYRORSS_SOURCE_ID)
else:
field_to_heights_dict_m_asl = radar_io.field_and_height_arrays_to_dict(
[field_name],
refl_heights_m_agl=None,
data_source=radar_io.MYRORSS_SOURCE_ID)
field_name = field_to_heights_dict_m_asl.keys()[0]
radar_height_m_asl = field_to_heights_dict_m_asl[field_name][0]
if field_name in radar_io.ECHO_TOP_NAMES:
field_matrix = METRES_TO_KM * field_matrix
field_name_myrorss = radar_io.field_name_new_to_orig(
field_name, radar_io.MYRORSS_SOURCE_ID)
file_system_utils.mkdir_recursive_if_necessary(file_name=netcdf_file_name)
netcdf_dataset = Dataset(netcdf_file_name,
'w',
format='NETCDF3_64BIT_OFFSET')
netcdf_dataset.setncattr(radar_io.FIELD_NAME_COLUMN_ORIG,
field_name_myrorss)
netcdf_dataset.setncattr('DataType', 'SparseLatLonGrid')
netcdf_dataset.setncattr(radar_io.NW_GRID_POINT_LAT_COLUMN_ORIG,
metadata_dict[radar_io.NW_GRID_POINT_LAT_COLUMN])
netcdf_dataset.setncattr(
radar_io.NW_GRID_POINT_LNG_COLUMN_ORIG,
lng_conversion.convert_lng_negative_in_west(
metadata_dict[radar_io.NW_GRID_POINT_LNG_COLUMN]))
netcdf_dataset.setncattr(radar_io.HEIGHT_COLUMN_ORIG,
METRES_TO_KM * numpy.float(radar_height_m_asl))
netcdf_dataset.setncattr(
radar_io.UNIX_TIME_COLUMN_ORIG,
numpy.int32(metadata_dict[radar_io.UNIX_TIME_COLUMN]))
netcdf_dataset.setncattr('FractionalTime', 0.)
netcdf_dataset.setncattr('attributes', ' ColorMap SubType Unit')
netcdf_dataset.setncattr('ColorMap-unit', 'dimensionless')
netcdf_dataset.setncattr('ColorMap-value', '')
netcdf_dataset.setncattr('SubType-unit', 'dimensionless')
netcdf_dataset.setncattr('SubType-value', numpy.float(radar_height_m_asl))
netcdf_dataset.setncattr('Unit-unit', 'dimensionless')
netcdf_dataset.setncattr('Unit-value', 'dimensionless')
netcdf_dataset.setncattr(
radar_io.LAT_SPACING_COLUMN_ORIG,
rounder.round_to_nearest(metadata_dict[radar_io.LAT_SPACING_COLUMN],
GRID_SPACING_MULTIPLE_DEG))
netcdf_dataset.setncattr(
radar_io.LNG_SPACING_COLUMN_ORIG,
rounder.round_to_nearest(metadata_dict[radar_io.LNG_SPACING_COLUMN],
GRID_SPACING_MULTIPLE_DEG))
netcdf_dataset.setncattr(radar_io.SENTINEL_VALUE_COLUMNS_ORIG[0],
numpy.double(-99000.))
netcdf_dataset.setncattr(radar_io.SENTINEL_VALUE_COLUMNS_ORIG[1],
numpy.double(-99001.))
min_latitude_deg = metadata_dict[radar_io.NW_GRID_POINT_LAT_COLUMN] - (
metadata_dict[radar_io.LAT_SPACING_COLUMN] *
(metadata_dict[radar_io.NUM_LAT_COLUMN] - 1))
unique_grid_point_lats_deg, unique_grid_point_lngs_deg = (
grids.get_latlng_grid_points(
min_latitude_deg=min_latitude_deg,
min_longitude_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],
num_rows=metadata_dict[radar_io.NUM_LAT_COLUMN],
num_columns=metadata_dict[radar_io.NUM_LNG_COLUMN]))
num_grid_rows = len(unique_grid_point_lats_deg)
num_grid_columns = len(unique_grid_point_lngs_deg)
field_vector = numpy.reshape(field_matrix,
num_grid_rows * num_grid_columns)
grid_point_lat_matrix, grid_point_lng_matrix = (
grids.latlng_vectors_to_matrices(unique_grid_point_lats_deg,
unique_grid_point_lngs_deg))
grid_point_lat_vector = numpy.reshape(grid_point_lat_matrix,
num_grid_rows * num_grid_columns)
grid_point_lng_vector = numpy.reshape(grid_point_lng_matrix,
num_grid_rows * num_grid_columns)
real_value_indices = numpy.where(numpy.invert(
numpy.isnan(field_vector)))[0]
netcdf_dataset.createDimension(radar_io.NUM_LAT_COLUMN_ORIG,
num_grid_rows - 1)
netcdf_dataset.createDimension(radar_io.NUM_LNG_COLUMN_ORIG,
num_grid_columns - 1)
netcdf_dataset.createDimension(radar_io.NUM_PIXELS_COLUMN_ORIG,
len(real_value_indices))
row_index_vector, column_index_vector = radar_io.latlng_to_rowcol(
grid_point_lat_vector,
grid_point_lng_vector,
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])
netcdf_dataset.createVariable(field_name_myrorss, numpy.single,
(radar_io.NUM_PIXELS_COLUMN_ORIG, ))
netcdf_dataset.createVariable(radar_io.GRID_ROW_COLUMN_ORIG, numpy.int16,
(radar_io.NUM_PIXELS_COLUMN_ORIG, ))
netcdf_dataset.createVariable(radar_io.GRID_COLUMN_COLUMN_ORIG,
numpy.int16,
(radar_io.NUM_PIXELS_COLUMN_ORIG, ))
netcdf_dataset.createVariable(radar_io.NUM_GRID_CELL_COLUMN_ORIG,
numpy.int32,
(radar_io.NUM_PIXELS_COLUMN_ORIG, ))
netcdf_dataset.variables[field_name_myrorss].setncattr(
'BackgroundValue', numpy.int32(-99900))
netcdf_dataset.variables[field_name_myrorss].setncattr(
'units', 'dimensionless')
netcdf_dataset.variables[field_name_myrorss].setncattr(
'NumValidRuns', numpy.int32(len(real_value_indices)))
netcdf_dataset.variables[field_name_myrorss][:] = field_vector[
real_value_indices]
netcdf_dataset.variables[radar_io.GRID_ROW_COLUMN_ORIG][:] = (
row_index_vector[real_value_indices])
netcdf_dataset.variables[radar_io.GRID_COLUMN_COLUMN_ORIG][:] = (
column_index_vector[real_value_indices])
netcdf_dataset.variables[radar_io.NUM_GRID_CELL_COLUMN_ORIG][:] = (
numpy.full(len(real_value_indices), 1, dtype=int))
netcdf_dataset.close()