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
def plot_sunspots_and_active_regions(df, scan_year, features, time_slice): ''' use scan_year to shift through noaa observations use time_slice to scan through image data ''' noaa_cents, _ = extract.get_noaa_centroids(df, scan_year) noaa_x, noaa_y = unpack_noaa_cents(noaa_cents) x_cents, y_cents= extract.get_image_active_region_centroids(features[time_slice]) plt.figure(figsize=(10,10)) noaa = plt.scatter(noaa_x, noaa_y, c='b', marker='o'); me = plt.scatter(x_cents, y_cents , c='r',marker='+'); plt.title("Sunspots & Active Regions " + scan_year); plt.legend((me, noaa), ('mydata-sunspots', 'noaa-AR'), scatterpoints=1, loc='lower right', ncol=2, fontsize=15);