def xd(self):
return pa.open_mfdataset(self.path_to_lsm_files,
lat_var=self.lsm_lat_var,
lon_var=self.lsm_lon_var,
time_var=self.lsm_time_var,
lat_dim=self.lsm_lat_dim,
lon_dim=self.lsm_lon_dim,
time_dim=self.lsm_time_dim)
def xd(self):
return pa.open_mfdataset(self.path_to_lsm_files,
lat_var=self.lsm_lat_var,
lon_var=self.lsm_lon_var,
time_var=self.lsm_time_var,
lat_dim=self.lsm_lat_dim,
lon_dim=self.lsm_lon_dim,
time_dim=self.lsm_time_dim,
coords_projected=True,
lon_to_180=True)
def xd(self):
"""get xarray dataset file handle to LSM files"""
if self._xd is None:
path_to_lsm_files = path.join(self.lsm_input_folder_path,
self.lsm_search_card)
self._xd = pa.open_mfdataset(path_to_lsm_files,
lat_var=self.lsm_lat_var,
lon_var=self.lsm_lon_var,
time_var=self.lsm_time_var,
lat_dim=self.lsm_lat_dim,
lon_dim=self.lsm_lon_dim,
time_dim=self.lsm_time_dim,
loader='hrrr')
return self._xd
def determine_start_end_timestep(lsm_file_list,
file_re_match=None,
file_datetime_pattern=None,
expected_time_step=None,
lsm_grid_info=None):
"""
Determine the start and end date from LSM input files
"""
if lsm_grid_info is None:
lsm_grid_info = identify_lsm_grid(lsm_file_list[0])
if None in (lsm_grid_info['time_var'], lsm_grid_info['time_dim'])\
or lsm_grid_info['model_name'] in ('era_20cm', 'erai'):
# NOTE: the ERA20CM and ERA 24hr time variables
# in the tests are erroneous
if None in (file_re_match, file_datetime_pattern):
raise ValueError("LSM files missing time dimension and/or "
"variable.To mitigate this, add the "
"'file_re_match' and 'file_datetime_pattern' "
"arguments.")
if lsm_grid_info['time_dim'] is None:
print("Assuming time dimension is 1")
file_size_time = 1
else:
lsm_example_file = Dataset(lsm_file_list[0])
file_size_time = \
len(lsm_example_file.dimensions[lsm_grid_info['time_dim']])
lsm_example_file.close()
total_num_time_steps = int(file_size_time * len(lsm_file_list))
# determine the start time from the existing files
actual_simulation_start_datetime = \
datetime.strptime(file_re_match.search(lsm_file_list[0]).group(0),
file_datetime_pattern)
# check to see if the time step matches expected
if len(lsm_file_list) > 1:
time_step = \
int((datetime.strptime(
file_re_match.search(lsm_file_list[1]).group(0),
file_datetime_pattern) -
actual_simulation_start_datetime).total_seconds()
/ float(file_size_time))
elif expected_time_step is not None:
time_step = int(expected_time_step)
else:
raise ValueError("Only one LSM file with one timestep present. "
"'expected_time_step' parameter required to "
"continue.")
# determine the end datetime
actual_simulation_end_datetime = \
datetime.strptime(file_re_match.search(lsm_file_list[-1]).group(0),
file_datetime_pattern) \
+ timedelta(seconds=(file_size_time-1) * time_step)
else:
with pangaea.open_mfdataset(lsm_file_list,
lat_var=lsm_grid_info['latitude_var'],
lon_var=lsm_grid_info['longitude_var'],
time_var=lsm_grid_info['time_var'],
lat_dim=lsm_grid_info['latitude_dim'],
lon_dim=lsm_grid_info['longitude_dim'],
time_dim=lsm_grid_info['time_dim']) as xds:
datetime_arr = [pd.to_datetime(dval) for dval in
xds.lsm.datetime.values]
actual_simulation_start_datetime = datetime_arr[0]
actual_simulation_end_datetime = datetime_arr[-1]
total_num_time_steps = len(datetime_arr)
if total_num_time_steps <= 1:
if expected_time_step is not None:
time_step = int(expected_time_step)
else:
raise ValueError("Only one LSM file with one timestep "
"present. 'expected_time_step' parameter "
"required to continue.")
else:
time_step = int(np.diff(xds.lsm.datetime.values)[0]
/ np.timedelta64(1, 's'))
if expected_time_step is not None:
if time_step != int(expected_time_step):
print("WARNING: The time step used {0} is different than "
"expected {1}".format(time_step, expected_time_step))
return (actual_simulation_start_datetime, actual_simulation_end_datetime,
time_step, total_num_time_steps)