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
def find_model_tracks(self):
"""
Identify storms at each model time step and link them together with object matching.
Returns:
List of STObjects containing model track information.
"""
self.model_grid.load_data()
model_objects = []
tracked_model_objects = []
if self.model_grid.data is None:
print("No model output found")
return tracked_model_objects
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]
# remember orig values
# scale to int 0-100.
if self.segmentation_approach == "ew":
min_orig = self.model_ew.min_intensity
max_orig = self.model_ew.max_intensity
data_increment_orig = self.model_ew.data_increment
scaled_data = np.array(
rescale_data(self.model_grid.data[h], min_orig, max_orig))
self.model_ew.min_intensity = 0
self.model_ew.data_increment = 1
self.model_ew.max_intensity = 100
else:
min_orig = 0
max_orig = 1
data_increment_orig = 1
scaled_data = self.model_grid.data[h]
hour_labels = self.model_ew.label(
gaussian_filter(scaled_data, self.gaussian_window))
hour_labels[model_data < self.model_ew.min_intensity] = 0
if self.size_filter > 1:
hour_labels = self.model_ew.size_filter(
hour_labels, self.size_filter)
# Return to orig values
if self.segmentation_approach == "ew":
self.model_ew.min_intensity = min_orig
self.model_ew.max_intensity = max_orig
self.model_ew.data_increment = data_increment_orig
obj_slices = find_objects(hour_labels)
num_slices = len(list(obj_slices))
model_objects.append([])
if num_slices > 0:
for s, sl in enumerate(obj_slices):
model_objects[-1].append(
STObject(self.model_grid.data[h][sl],
np.where(hour_labels[sl] == s + 1, 1, 0),
self.model_grid.x[sl],
self.model_grid.y[sl],
self.model_grid.i[sl],
self.model_grid.j[sl],
hour,
hour,
dx=self.model_grid.dx))
if h > 0:
dims = model_objects[-1][-1].timesteps[0].shape
model_objects[-1][-1].estimate_motion(
hour, self.model_grid.data[h - 1], dims[1],
dims[0])
del hour_labels
del scaled_data
del model_data
for h, hour in enumerate(self.hours):
past_time_objs = []
for obj in tracked_model_objects:
# Potential trackable objects are identified
if obj.end_time == hour - 1:
past_time_objs.append(obj)
# If no objects existed in the last time step, then consider objects in current time step all new
if len(past_time_objs) == 0:
tracked_model_objects.extend(model_objects[h])
# Match from previous time step with current time step
elif len(past_time_objs) > 0 and len(model_objects[h]) > 0:
assignments = self.object_matcher.match_objects(
past_time_objs, model_objects[h], hour - 1, hour)
unpaired = list(range(len(model_objects[h])))
for pair in assignments:
past_time_objs[pair[0]].extend(model_objects[h][pair[1]])
unpaired.remove(pair[1])
if len(unpaired) > 0:
for up in unpaired:
tracked_model_objects.append(model_objects[h][up])
print("Tracked Model Objects: {0:03d} Hour: {1:02d}".format(
len(tracked_model_objects), hour))
return tracked_model_objects
def find_model_tracks(self):
"""
Identify storms at each model time step and link them together with object matching.
Returns:
List of STObjects containing model track information.
"""
self.model_grid.load_data()
model_objects = []
tracked_model_objects = []
if self.model_grid.data is None:
print("No model output found")
return tracked_model_objects
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]
# remember orig values
min_orig = self.model_ew.min_thresh
max_orig = self.model_ew.max_thresh
data_increment_orig = self.model_ew.data_increment
# scale to int 0-100.
scaled_data = np.array(rescale_data( self.model_grid.data[h], min_orig, max_orig))
self.model_ew.min_thresh = 0
self.model_ew.data_increment = 1
self.model_ew.max_thresh = 100
hour_labels = self.model_ew.label(gaussian_filter(scaled_data, self.gaussian_window))
hour_labels[model_data < self.model_ew.min_thresh] = 0
hour_labels = self.model_ew.size_filter(hour_labels, self.size_filter)
# Return to orig values
self.model_ew.min_thresh = min_orig
self.model_ew.max_thresh = max_orig
self.model_ew.data_increment = data_increment_orig
obj_slices = find_objects(hour_labels)
num_slices = len(obj_slices)
model_objects.append([])
if num_slices > 0:
for s, sl in enumerate(obj_slices):
model_objects[-1].append(STObject(self.model_grid.data[h][sl],
np.where(hour_labels[sl] == s + 1, 1, 0),
self.model_grid.x[sl],
self.model_grid.y[sl],
self.model_grid.i[sl],
self.model_grid.j[sl],
hour,
hour,
dx=self.model_grid.dx))
if h > 0:
dims = model_objects[-1][-1].timesteps[0].shape
model_objects[-1][-1].estimate_motion(hour, self.model_grid.data[h-1], dims[1], dims[0])
del hour_labels
del scaled_data
del model_data
for h, hour in enumerate(self.hours):
past_time_objs = []
for obj in tracked_model_objects:
# Potential trackable objects are identified
if obj.end_time == hour - 1:
past_time_objs.append(obj)
# If no objects existed in the last time step, then consider objects in current time step all new
if len(past_time_objs) == 0:
tracked_model_objects.extend(model_objects[h])
# Match from previous time step with current time step
elif len(past_time_objs) > 0 and len(model_objects[h]) > 0:
assignments = self.object_matcher.match_objects(past_time_objs, model_objects[h], hour - 1, hour)
unpaired = list(range(len(model_objects[h])))
for pair in assignments:
past_time_objs[pair[0]].extend(model_objects[h][pair[1]])
unpaired.remove(pair[1])
if len(unpaired) > 0:
for up in unpaired:
tracked_model_objects.append(model_objects[h][up])
print("Tracked Model Objects: {0:03d} Hour: {1:02d}".format(len(tracked_model_objects), hour))
return tracked_model_objects
