LATITUDE_COLUMN_ORIG][:],
raw_wind_io.LONGITUDE_COLUMN: netcdf_dataset.variables[
LONGITUDE_COLUMN_ORIG][:],
raw_wind_io.ELEVATION_COLUMN: netcdf_dataset.variables[
ELEVATION_COLUMN_ORIG][:],
raw_wind_io.TIME_COLUMN: numpy.array(unix_times_sec).astype(
int),
raw_wind_io.WIND_SPEED_COLUMN: wind_speeds_m_s01,
raw_wind_io.WIND_DIR_COLUMN: wind_directions_deg,
raw_wind_io.WIND_GUST_SPEED_COLUMN: wind_gust_speeds_m_s01,
raw_wind_io.WIND_GUST_DIR_COLUMN: wind_gust_directions_deg,
WIND_SPEED_FLAG_COLUMN: wind_speed_quality_flags,
WIND_DIR_FLAG_COLUMN: wind_dir_quality_flags,
WIND_GUST_SPEED_FLAG_COLUMN: wind_gust_speed_quality_flags,
WIND_GUST_DIR_FLAG_COLUMN: wind_gust_dir_quality_flags}
netcdf_dataset.close()
wind_table = pandas.DataFrame.from_dict(wind_dict)
wind_table = _remove_invalid_wind_rows(wind_table)
return _remove_low_quality_data(wind_table)
if __name__ == '__main__':
WIND_TABLE = read_winds_from_raw_file(NETCDF_FILE_NAME)
print(WIND_TABLE)
WIND_TABLE = raw_wind_io.sustained_and_gust_to_uv_max(WIND_TABLE)
print(WIND_TABLE)
raw_wind_io.write_processed_file(WIND_TABLE, CSV_FILE_NAME)
end_times_unix_sec,
tornado_io.START_LAT_COLUMN:
storm_event_table[START_LATITUDE_COLUMN_ORIG].values,
tornado_io.START_LNG_COLUMN:
storm_event_table[START_LONGITUDE_COLUMN_ORIG].values,
tornado_io.END_LAT_COLUMN:
storm_event_table[END_LATITUDE_COLUMN_ORIG].values,
tornado_io.END_LNG_COLUMN:
storm_event_table[END_LONGITUDE_COLUMN_ORIG].values,
tornado_io.FUJITA_RATING_COLUMN:
storm_event_table[TORNADO_RATING_COLUMN_ORIG].values,
tornado_io.WIDTH_COLUMN:
FEET_TO_METRES * storm_event_table[TORNADO_WIDTH_COLUMN_ORIG].values
}
tornado_table = pandas.DataFrame.from_dict(tornado_dict)
return tornado_io.remove_invalid_reports(tornado_table)
if __name__ == '__main__':
WIND_TABLE = read_thunderstorm_wind_reports(STORM_EVENT_FILE_NAME)
print WIND_TABLE
raw_wind_io.write_processed_file(WIND_TABLE, PROCESSED_WIND_FILE_NAME)
print '\n'
TORNADO_TABLE = read_tornado_reports(STORM_EVENT_FILE_NAME)
print TORNADO_TABLE
tornado_io.write_processed_file(TORNADO_TABLE, PROCESSED_TORNADO_FILE_NAME)
s == STATION_ID_1MINUTE
for s in STATION_METADATA_TABLE[raw_wind_io.STATION_ID_COLUMN].values
]
THIS_STATION_INDEX = numpy.where(THESE_STATION_FLAGS)[0][0]
THIS_UTC_OFFSET_HOURS = STATION_METADATA_TABLE[
raw_wind_io.UTC_OFFSET_COLUMN].values[THIS_STATION_INDEX]
WIND_TABLE_1MINUTE = read_1minute_winds_from_raw_file(
ORIG_1MINUTE_FILE_NAME, THIS_UTC_OFFSET_HOURS)
WIND_TABLE_1MINUTE = raw_wind_io.sustained_and_gust_to_uv_max(
WIND_TABLE_1MINUTE)
WIND_TABLE_1MINUTE = merge_winds_and_station_metadata(
WIND_TABLE_1MINUTE, STATION_METADATA_TABLE, STATION_ID_1MINUTE)
print(WIND_TABLE_1MINUTE)
raw_wind_io.write_processed_file(WIND_TABLE_1MINUTE, CSV_FILE_NAME_1MINUTE)
# Download 5-minute METARs and convert file type.
ORIG_5MINUTE_FILE_NAME = download_5minute_file(
station_id=ORIG_STATION_ID_5MINUTE,
month_unix_sec=MONTH_5MINUTE_UNIX_SEC,
top_local_directory_name=TOP_LOCAL_DIR_NAME_5MINUTE,
raise_error_if_fails=True)
THESE_STATION_FLAGS = [
s == STATION_ID_5MINUTE
for s in STATION_METADATA_TABLE[raw_wind_io.STATION_ID_COLUMN].values
]
THIS_STATION_INDEX = numpy.where(THESE_STATION_FLAGS)[0][0]
THIS_UTC_OFFSET_HOURS = STATION_METADATA_TABLE[
raw_wind_io.UTC_OFFSET_COLUMN].values[THIS_STATION_INDEX]