def _callFUT(self, bucket=None):
     from gcloud.storage import set_default_bucket
     return set_default_bucket(bucket=bucket)
import pandas as pd
import datetime
import os.path
from gcloud import storage
from collections import Counter

def frange(start, stop, step):
    i = start
    while i < stop:
        yield i
        i += step

#--------------------------------------
# set default bucket
#--------------------------------------
storage.set_default_bucket("isb-cgc")
storage_conn = storage.get_connection()
storage.set_default_connection(storage_conn)

all_elements = {}

df2 = pd.DataFrame()
#--------------------------------------
# get the bucket contents
#--------------------------------------
bucket = storage.get_bucket('ptone-experiments')

for k in bucket.list_blobs(prefix="working-files/clinical_metadata/"):

   if 'counts.txt' in k.name:
      disease_type = k.name.split("/")[2].split(".")[0]
 def _callFUT(self, bucket=None):
     from gcloud.storage import set_default_bucket
     return set_default_bucket(bucket=bucket)
import os.path
from gcloud import storage
from collections import Counter


def frange(start, stop, step):
    i = start
    while i < stop:
        yield i
        i += step


#--------------------------------------
# set default bucket
#--------------------------------------
storage.set_default_bucket("isb-cgc")
storage_conn = storage.get_connection()
storage.set_default_connection(storage_conn)

all_elements = {}

df2 = pd.DataFrame()
#--------------------------------------
# get the bucket contents
#--------------------------------------
bucket = storage.get_bucket('ptone-experiments')

for k in bucket.list_blobs(prefix="working-files/clinical_metadata/"):

    if 'counts.txt' in k.name:
        disease_type = k.name.split("/")[2].split(".")[0]