def load_map_info(self, map_file):
"""
Load map projection information and create latitude, longitude, x, y, i, and j grids for the projection.
Args:
map_file: File specifying the projection information.
"""
if self.ensemble_name.upper() == "SSEF":
proj_dict, grid_dict = read_arps_map_file(map_file)
self.dx = int(grid_dict["dx"])
mapping_data = make_proj_grids(proj_dict, grid_dict)
for m, v in mapping_data.items():
setattr(self, m, v)
self.i, self.j = np.indices(self.lon.shape)
self.proj = get_proj_obj(proj_dict)
elif self.ensemble_name.upper() in [
"NCAR", "NCARSTORM", "HRRR", "VSE", "HREFV2"
]:
proj_dict, grid_dict = read_ncar_map_file(map_file)
if self.member_name[
0:
7] == "1km_pbl": # Don't just look at the first 3 characters. You have to differentiate '1km_pbl1' and '1km_on_3km_pbl1'
grid_dict["dx"] = 1000
grid_dict["dy"] = 1000
grid_dict["sw_lon"] = 258.697
grid_dict["sw_lat"] = 23.999
grid_dict["ne_lon"] = 282.868269206236
grid_dict["ne_lat"] = 36.4822338520542
self.dx = int(grid_dict["dx"])
mapping_data = make_proj_grids(proj_dict, grid_dict)
for m, v in mapping_data.items():
setattr(self, m, v)
self.i, self.j = np.indices(self.lon.shape)
self.proj = get_proj_obj(proj_dict)
def __init__(self, ensemble_name, model_name, member, run_date, variable, start_date, end_date, path, single_step,
size_distribution_training_path, watershed_obj, map_file=None, condition_model_name=None, condition_threshold=0.5):
self.ensemble_name = ensemble_name
self.model_name = model_name
self.member = member
self.run_date = run_date
self.variable = variable
self.start_date = start_date
self.end_date = end_date
self.times = pd.DatetimeIndex(start=self.start_date, end=self.end_date, freq="1H")
self.forecast_hours = (self.times - self.run_date).astype('timedelta64[h]').values
self.path = path
self.single_step = single_step
self.size_distribution_training_path = size_distribution_training_path
self.watershed_obj = watershed_obj
self.track_forecasts = None
self.data = None
self.map_file = map_file
self.proj_dict = None
self.grid_dict = None
self.mapping_data = None
self.condition_model_name = condition_model_name
self.condition_threshold = condition_threshold
self.percentiles = None
self.num_samples = None
self.percentile_data = None
if self.map_file is not None:
if self.map_file[-3:] == "map":
self.proj_dict, self.grid_dict = read_arps_map_file(self.map_file)
else:
self.proj_dict, self.grid_dict = read_ncar_map_file(self.map_file)
self.mapping_data = make_proj_grids(self.proj_dict, self.grid_dict)
self.units = ""
self.nc_patches = None
self.hail_forecast_table = None
def create_map_grid(map_file):
if map_file[-3:] == "map":
proj_dict, grid_dict = read_arps_map_file(map_file)
else:
proj_dict, grid_dict = read_ncar_map_file(map_file)
mapping_data = make_proj_grids(proj_dict, grid_dict)
return mapping_data, proj_dict, grid_dict
def __init__(self,
ml_model_name,
members,
run_date,
variable,
start_date,
end_date,
grid_shape,
forecast_bins,
forecast_json_path,
condition_model_name=None,
map_file=None):
self.track_forecasts = {}
self.grid_shape = grid_shape
self.forecast_bins = forecast_bins
self.condition_model_name = condition_model_name
self.percentile = None
if map_file is not None:
if map_file[-3:] == "map":
self.proj_dict, self.grid_dict = read_arps_map_file(map_file)
else:
self.proj_dict, self.grid_dict = read_ncar_map_file(map_file)
else:
self.proj_dict = None
self.grid_dict = None
super(MachineLearningEnsembleProducts,
self).__init__(ml_model_name,
members,
run_date,
variable,
start_date,
end_date,
forecast_json_path,
single_step=False)
def interpolate_mrms_day(start_date, variable, interp_type, mrms_path, map_filename, out_path):
"""
For a given day, this module interpolates hourly MRMS data to a specified latitude and
longitude grid, and saves the interpolated grids to CF-compliant netCDF4 files.
Args:
start_date (datetime.datetime): Date of data being interpolated
variable (str): MRMS variable
interp_type (str): Whether to use maximum neighbor or spline
mrms_path (str): Path to top-level directory of MRMS GRIB2 files
map_filename (str): Name of the map filename. Supports ARPS map file format and netCDF files containing latitude
and longitude variables
out_path (str): Path to location where interpolated netCDF4 files are saved.
"""
try:
print(start_date, variable)
end_date = start_date + timedelta(hours=23)
mrms = MRMSGrid(start_date, end_date, variable, mrms_path)
if mrms.data is not None:
if map_filename[-3:] == "map":
mapping_data = make_proj_grids(*read_arps_map_file(map_filename))
mrms.interpolate_to_netcdf(mapping_data['lon'], mapping_data['lat'], out_path, interp_type=interp_type)
elif map_filename[-3:] == "txt":
mapping_data = make_proj_grids(*read_ncar_map_file(map_filename))
mrms.interpolate_to_netcdf(mapping_data["lon"], mapping_data["lat"], out_path, interp_type=interp_type)
else:
lon, lat = load_map_coordinates(map_filename)
mrms.interpolate_to_netcdf(lon, lat, out_path, interp_type=interp_type)
except Exception as e:
# This exception catches any errors when run in multiprocessing, prints the stack trace,
# and ends the process. Otherwise the process will stall.
print(traceback.format_exc())
raise e
def create_map_grid(map_file):
if map_file[-3:] == "map":
proj_dict, grid_dict = read_arps_map_file(map_file)
else:
proj_dict, grid_dict = read_ncar_map_file(map_file)
mapping_data = make_proj_grids(proj_dict, grid_dict)
return mapping_data, proj_dict, grid_dict
def load_map_info(self, map_file):
if self.ensemble_name.upper() == "SSEF":
proj_dict, grid_dict = read_arps_map_file(map_file)
self.dx = int(grid_dict["dx"])
mapping_data = make_proj_grids(proj_dict, grid_dict)
for m, v in mapping_data.iteritems():
setattr(self, m, v)
self.i, self.j = np.indices(self.lon.shape)
self.proj = get_proj_obj(proj_dict)
elif self.ensemble_name.upper() == "NCAR":
proj_dict, grid_dict = read_ncar_map_file(map_file)
self.dx = int(grid_dict["dx"])
mapping_data = make_proj_grids(proj_dict, grid_dict)
for m, v in mapping_data.iteritems():
setattr(self, m, v)
self.i, self.j = np.indices(self.lon.shape)
self.proj = get_proj_obj(proj_dict)
def interpolate_mrms_day(start_date, variable, interp_type, mrms_path,
map_filename, out_path):
"""
For a given day, this module interpolates hourly MRMS data to a specified latitude and
longitude grid, and saves the interpolated grids to CF-compliant netCDF4 files.
Args:
start_date (datetime.datetime): Date of data being interpolated
variable (str): MRMS variable
interp_type (str): Whether to use maximum neighbor or spline
mrms_path (str): Path to top-level directory of MRMS GRIB2 files
map_filename (str): Name of the map filename. Supports ARPS map file format and netCDF files containing latitude
and longitude variables
out_path (str): Path to location where interpolated netCDF4 files are saved.
"""
try:
print(start_date, variable)
end_date = start_date + timedelta(hours=23)
mrms = MRMSGrid(start_date, end_date, variable, mrms_path)
if mrms.data is not None:
if map_filename[-3:] == "map":
mapping_data = make_proj_grids(
*read_arps_map_file(map_filename))
mrms.interpolate_to_netcdf(mapping_data['lon'],
mapping_data['lat'],
out_path,
interp_type=interp_type)
elif map_filename[-3:] == "txt":
mapping_data = make_proj_grids(
*read_ncar_map_file(map_filename))
mrms.interpolate_to_netcdf(mapping_data["lon"],
mapping_data["lat"],
out_path,
interp_type=interp_type)
else:
lon, lat = load_map_coordinates(map_filename)
mrms.interpolate_to_netcdf(lon,
lat,
out_path,
interp_type=interp_type)
except Exception as e:
# This exception catches any errors when run in multiprocessing, prints the stack trace,
# and ends the process. Otherwise the process will stall.
print(traceback.format_exc())
raise e
def __init__(self, ml_model_name, members, run_date, variable, start_date, end_date, grid_shape, forecast_bins,
forecast_json_path, condition_model_name=None, map_file=None):
self.track_forecasts = {}
self.grid_shape = grid_shape
self.forecast_bins = forecast_bins
self.condition_model_name = condition_model_name
self.percentile = None
if map_file is not None:
if map_file[-3:] == "map":
self.proj_dict, self.grid_dict = read_arps_map_file(map_file)
else:
self.proj_dict, self.grid_dict = read_ncar_map_file(map_file)
else:
self.proj_dict = None
self.grid_dict = None
super(MachineLearningEnsembleProducts, self).__init__(ml_model_name, members, run_date, variable,
start_date, end_date, forecast_json_path,
single_step=False)
def load_map_info(self, map_file):
"""
Load map projection information and create latitude, longitude, x, y, i, and j grids for the projection.
Args:
map_file: File specifying the projection information.
"""
if self.ensemble_name.upper() == "SSEF":
proj_dict, grid_dict = read_arps_map_file(map_file)
self.dx = int(grid_dict["dx"])
mapping_data = make_proj_grids(proj_dict, grid_dict)
for m, v in mapping_data.items():
setattr(self, m, v)
self.i, self.j = np.indices(self.lon.shape)
self.proj = get_proj_obj(proj_dict)
elif self.ensemble_name.upper() == "NCAR":
proj_dict, grid_dict = read_ncar_map_file(map_file)
self.dx = int(grid_dict["dx"])
mapping_data = make_proj_grids(proj_dict, grid_dict)
for m, v in mapping_data.items():
setattr(self, m, v)
self.i, self.j = np.indices(self.lon.shape)
self.proj = get_proj_obj(proj_dict)
