#!/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
#!/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
