#!/usr/bin/env python __author__ = 'ggdhines' from penguinAggregation import PenguinAggregation import random import os import sys # add the paths necessary for clustering algorithm and ibcc - currently only works on Greg's computer if os.path.exists("/home/ggdhines"): sys.path.append("/home/ggdhines/PycharmProjects/reduction/experimental/clusteringAlg") else: sys.path.append("/home/greg/github/reduction/experimental/clusteringAlg") from divisiveKmeans import DivisiveKmeans clusterAlg = DivisiveKmeans().__fit__ penguin = PenguinAggregation() subject_ids = penguin.__get_subjects_per_site__("APZ00035mv",complete=True,remove_blanks=True) for i,subject in enumerate(random.sample(subject_ids,50)): print i penguin.__readin_subject__(subject) blankImage = penguin.__cluster_subject__(subject, clusterAlg) if not blankImage: penguin.__save_raw_markings__(subject) break
import random import os import sys # add the paths necessary for clustering algorithm and ibcc - currently only works on Greg's computer if os.path.exists("/home/ggdhines"): sys.path.append( "/home/ggdhines/PycharmProjects/reduction/experimental/clusteringAlg") else: sys.path.append("/home/greg/github/reduction/experimental/clusteringAlg") from divisiveKmeans import DivisiveKmeans clusterAlg = DivisiveKmeans().__fit__ penguin = PenguinAggregation() zooniverse_id_list = random.sample( penguin.__get_subjects_per_site__("APZ0001x3p"), 40) for i, zooniverse_id in enumerate(zooniverse_id_list): print i penguin.__readin_subject__(zooniverse_id) blankImage = penguin.__cluster_subject__(zooniverse_id, clusterAlg) if not blankImage: print "+--" penguin.__find_closest_neighbour__(zooniverse_id) #penguin.__plot_cluster_size__(zooniverse_id_list) penguin.__find_one__(zooniverse_id_list)
from penguinAggregation import PenguinAggregation import random import os import sys # add the paths necessary for clustering algorithm and ibcc - currently only works on Greg's computer if os.path.exists("/home/ggdhines"): sys.path.append("/home/ggdhines/PycharmProjects/reduction/experimental/clusteringAlg") else: sys.path.append("/home/greg/github/reduction/experimental/clusteringAlg") from divisiveKmeans import DivisiveKmeans clusterAlg = DivisiveKmeans().__fit__ penguin = PenguinAggregation() zooniverse_id_list = random.sample(penguin.__get_subjects_per_site__("APZ0001x3p"),40) for i,zooniverse_id in enumerate(zooniverse_id_list): print i penguin.__readin_subject__(zooniverse_id) blankImage = penguin.__cluster_subject__(zooniverse_id, clusterAlg) if not blankImage: print "+--" penguin.__find_closest_neighbour__(zooniverse_id) #penguin.__plot_cluster_size__(zooniverse_id_list) penguin.__find_one__(zooniverse_id_list)
#!/usr/bin/env python __author__ = 'ggdhines' from penguinAggregation import PenguinAggregation import random import os import sys # add the paths necessary for clustering algorithm and ibcc - currently only works on Greg's computer if os.path.exists("/home/ggdhines"): sys.path.append( "/home/ggdhines/PycharmProjects/reduction/experimental/clusteringAlg") else: sys.path.append("/home/greg/github/reduction/experimental/clusteringAlg") from divisiveKmeans import DivisiveKmeans clusterAlg = DivisiveKmeans().__fit__ penguin = PenguinAggregation() subject_ids = penguin.__get_subjects_per_site__("APZ00035mv", complete=True, remove_blanks=True) for i, subject in enumerate(random.sample(subject_ids, 50)): print i penguin.__readin_subject__(subject) blankImage = penguin.__cluster_subject__(subject, clusterAlg) if not blankImage: penguin.__save_raw_markings__(subject) break