def create_record(ir_blob_path, griddef, boxdef, forecast_minutes,
ltg_validity_minutes, sampling_frac):
# read IR image
logging.info('Retrieving lightning for IR blob %s', ir_blob_path)
ref = goesio.read_ir_data(ir_blob_path, griddef)
ref = np.ma.filled(ref, 0) # mask -> 0
# create "current" lightning image
influence_km = 5
irdt = goesio.get_timestamp_from_filename(ir_blob_path)
ltg_blob_paths = goesio.get_ltg_blob_paths(
irdt, timespan_minutes=ltg_validity_minutes)
if ltg_blob_paths:
ltg = goesio.create_ltg_grid(ltg_blob_paths, griddef, influence_km)
# create "forecast" lightning image
irdt = irdt + datetime.timedelta(minutes=forecast_minutes)
ltg_blob_paths = goesio.get_ltg_blob_paths(
irdt, timespan_minutes=ltg_validity_minutes)
if ltg_blob_paths:
ltgfcst = goesio.create_ltg_grid(ltg_blob_paths, griddef, influence_km)
# create examples
for example in create_training_examples(ref, ltg, ltgfcst,
griddef, boxdef,
sampling_frac):
yield example
def add_time_stamp(ir_blob_path):
epoch = datetime.utcfromtimestamp(0)
timestamp = goesio.get_timestamp_from_filename(ir_blob_path)
yield beam.window.TimestampedValue(ir_blob_path,
(timestamp - epoch).total_seconds())