def filter_label_saveToFile(image_features_dict, db_path, noaa_data):
'''
Filters image AR assingments to NOAA AR labels,
Labels image AR with NOAA AR labels or with generate labels,
Saves final image features to file
Input: image_features_dict, dictionary
keys are image active region numbers
values are active region features i.e. centroids, flux,
Output: None
'''
print "running get_noaa_and_image_centroids..."
my_cents, noaa_cents_labels = get_noaa_and_image_centroids(image_features_dict, noaa_data)
# Assinge extracted AR to NOAA AR if dist between any 2 is less than 5 degrees
# this will return one-to-many parings, if they exist
print "running get_shortest_distance_pair..."
shortest_dist_pairs = extract_image_features.get_shortest_distance_pair(my_cents, noaa_cents_labels)
# returns one-to-one pairs with the shortest distance between them
# {noaa_ar: [{Image_ar : dist}]
print "running check_repeating_noaa_assignments..."
clean_ar_assignments = extract_image_features.check_repeating_noaa_assignments(shortest_dist_pairs)
print "running filter_extra_active_region_assignments..."
clean_feat_object = Centroid_Labeling.filter_extra_active_region_assignments(image_features_dict,
clean_ar_assignments)
#return my_cents, noaa_cents, shortest_dist_pairs, clean_ar_assignments, clean_feat_object
print "running save_features_to_file..."
Centroid_Labeling.save_features_to_file(db_path, clean_feat_object)
def get_noaa_and_image_centroids(image_features_dict, noaa_data):
'''
Output: noaa_cents_labels, tuple
my_cents, nested lists
'''
# For Local Series Computing, uncomment function
#noaa_data = load_noaa_data()
# Cluster/Parallel computing
# noaa_data will have already been pushed into each engine
noaa_cents_labels_sameDay, \
noaa_cents_labels_prevDay=\
Centroid_Labeling.get_currentDay_previusDay_noaa_activeRegions(image_features_dict, noaa_data)
_, hour, minute = image_features_dict["image_time"].split(":")
# check if image occured in 1st or 2nd half of the day
if int(hour) >= 12:
noaa_cents_labels = noaa_cents_labels_sameDay
else:
noaa_cents_labels = noaa_cents_labels_prevDay
my_cents = extract_image_features.get_image_active_region_centroids(image_features_dict, split_centroids = False)
return my_cents, noaa_cents_labels
