def function_for_multiprocessing( date, time, kwargs ):
'''
Objectively identifies composite reflectivity object every 5 minutes
'''
in_path = join( config.MRMS_DBZ_PATH, date )
mrms_files = sorted( os.listdir( in_path ) )[29:50]
objects_at_different_times = [ ]
dbz_at_different_times = [ ]
num_unique_objects = 0
for mrms_file in mrms_files:
ds = xr.open_dataset( join(in_path, mrms_file) )
objects = ds['Storm Objects'].values
dbz = ds['DBZ'].values
modified_objects = objects+num_unique_objects
modified_objects[objects==0] = 0
objects_at_different_times.append( modified_objects )
dbz_at_different_times.append( dbz )
ds.close( )
num_unique_objects += len( np.unique( objects )[1:])
objects = np.array( objects_at_different_times ) #Shape: (138, 250, 250)
original_dbz = np.array( dbz_at_different_times )
#tracked_objects = obj_track.track_objects( objects, original_dbz )
x, y = np.meshgrid(np.arange(250), np.arange(250))
storm_objs = extract_storm_objects(objects, original_dbz, x, y, np.arange(len(objects)))
out_storms = track_storms(storm_objs, np.arange(len(objects)), [shifted_centroid_distance], np.array([30]), np.array([1]))
data = {'Storm Objects': (['Time', 'Y', 'X'], tracked_objects)}
data['DBZ'] = (['Time', 'Y', 'X'], dbz_at_different_times)
ds = xr.Dataset( data )
ds.to_netcdf( 'test.nc' )
def find_model_patch_tracks(self):
"""
Identify storms in gridded model output and extract uniform sized patches around the storm centers of mass.
Returns:
"""
self.model_grid.load_data()
tracked_model_objects = []
model_objects = []
if self.model_grid.data is None:
print("No model output found")
return tracked_model_objects
min_orig = self.model_ew.min_thresh
max_orig = self.model_ew.max_thresh
data_increment_orig = self.model_ew.data_increment
self.model_ew.min_thresh = 0
self.model_ew.data_increment = 1
self.model_ew.max_thresh = 100
for h, hour in enumerate(self.hours):
# Identify storms at each time step and apply size filter
print("Finding {0} objects for run {1} Hour: {2:02d}".format(self.ensemble_member,
self.run_date.strftime("%Y%m%d%H"), hour))
if self.mask is not None:
model_data = self.model_grid.data[h] * self.mask
else:
model_data = self.model_grid.data[h]
model_data[:self.patch_radius] = 0
model_data[-self.patch_radius:] = 0
model_data[:, :self.patch_radius] = 0
model_data[:, -self.patch_radius:] = 0
scaled_data = np.array(rescale_data(model_data, min_orig, max_orig))
hour_labels = label_storm_objects(scaled_data, "ew",
self.model_ew.min_thresh, self.model_ew.max_thresh,
min_area=self.size_filter, max_area=self.model_ew.max_size,
max_range=self.model_ew.delta, increment=self.model_ew.data_increment,
gaussian_sd=self.gaussian_window)
model_objects.extend(extract_storm_patches(hour_labels, model_data, self.model_grid.x,
self.model_grid.y, [hour],
dx=self.model_grid.dx,
patch_radius=self.patch_radius))
for model_obj in model_objects[-1]:
dims = model_obj.timesteps[-1].shape
if h > 0:
model_obj.estimate_motion(hour, self.model_grid.data[h-1], dims[1], dims[0])
del scaled_data
del model_data
del hour_labels
tracked_model_objects.extend(track_storms(model_objects, self.hours,
self.object_matcher.cost_function_components,
self.object_matcher.max_values,
self.object_matcher.weights))
self.model_ew.min_thresh = min_orig
self.model_ew.max_thresh = max_orig
self.model_ew.data_increment = data_increment_orig
return tracked_model_objects
def find_model_patch_tracks(self):
"""
Identify storms in gridded model output and extract uniform sized patches around the storm centers of mass.
Returns:
"""
self.model_grid.load_data()
tracked_model_objects = []
model_objects = []
if self.model_grid.data is None:
print("No model output found")
return tracked_model_objects
min_orig = self.model_ew.min_thresh
max_orig = self.model_ew.max_thresh
data_increment_orig = self.model_ew.data_increment
self.model_ew.min_thresh = 0
self.model_ew.data_increment = 1
self.model_ew.max_thresh = 100
for h, hour in enumerate(self.hours):
# Identify storms at each time step and apply size filter
print("Finding {0} objects for run {1} Hour: {2:02d}".format(self.ensemble_member,
self.run_date.strftime("%Y%m%d%H"), hour))
if self.mask is not None:
model_data = self.model_grid.data[h] * self.mask
else:
model_data = self.model_grid.data[h]
model_data[:self.patch_radius] = 0
model_data[-self.patch_radius:] = 0
model_data[:, :self.patch_radius] = 0
model_data[:, -self.patch_radius:] = 0
scaled_data = np.array(rescale_data(model_data, min_orig, max_orig))
hour_labels = label_storm_objects(scaled_data, "ew",
self.model_ew.min_thresh, self.model_ew.max_thresh,
min_area=self.size_filter, max_area=self.model_ew.max_size,
max_range=self.model_ew.delta, increment=self.model_ew.data_increment,
gaussian_sd=self.gaussian_window)
model_objects.extend(extract_storm_patches(hour_labels, model_data, self.model_grid.x,
self.model_grid.y, [hour],
dx=self.model_grid.dx,
patch_radius=self.patch_radius))
for model_obj in model_objects[-1]:
dims = model_obj.timesteps[-1].shape
if h > 0:
model_obj.estimate_motion(hour, self.model_grid.data[h-1], dims[1], dims[0])
del scaled_data
del model_data
del hour_labels
tracked_model_objects.extend(track_storms(model_objects, self.hours,
self.object_matcher.cost_function_components,
self.object_matcher.max_values,
self.object_matcher.weights))
self.model_ew.min_thresh = min_orig
self.model_ew.max_thresh = max_orig
self.model_ew.data_increment = data_increment_orig
return tracked_model_objects