def cloud_download(self, local_dir='/tmp/'):
"""Retrieve file stored on cloud to local file.
This specifically download files stored in google drive (looks for 'gdrive' in
`self.path`); other cloud storage types still WIP.
Parameters
----------
local_dir : str
path where file will be stored locally (temporarily, maybe?)
"""
if self.path is None:
# Nothing to see here (possibly raise exception?)
return
if not os.path.exists(self.path):
import cottoncandy as cc
if 'gdrive/' in self.path:
gcci = cc.get_interface(backend='gdrive')
cloud_dir = self.path.split('gdrive/')[-1]
elif 's3/' in self.path:
cloud_dir = self.path.split('s3/')[-1]
bucket_name = cloud_dir.split('/')[0]
cci = cc.get_interface(bucket_name=bucket_name)
tmp_dir = os.path.join(local_dir, cloud_dir)
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir)
cci.download_to_file(os.path.join(cloud_dir, self.fname), os.path.join(tmp_dir, self.fname))
# Shouldn't overwrite permanent property permanently...
#self.path = tmp_dir
self._tmppath = tmp_dir
else:
print("Doing nothing - file exists locally.")
def _get_cc_interface(self, bucket):
"""Get a cottoncandy interface for the given bucket. Will first check the
local cache.
"""
if bucket not in self.ccinterfaces:
self.ccinterfaces[bucket] = cc.get_interface(bucket)
return self.ccinterfaces[bucket]
def get_transcript_uris(bucket='stimulidb', extension='txt'):
cci = cc.get_interface(bucket)
# get all the objects in `bucket` that end with `extension`
all_names = cci.glob('*.%s' % extension)
# fix each name into a URI and return the list
fixed_names = [cc.utils.pathjoin('s3://', bucket, n) for n in all_names]
return fixed_names
def GetVideoInfo(self):
"""
Gets video info
"""
vc = cv2.VideoCapture(self.fileName)
if (vc.isOpened()):
self.video = vc
self.width = int(self.video.get(cv2.CAP_PROP_FRAME_WIDTH))
self.height = int(self.video.get(cv2.CAP_PROP_FRAME_HEIGHT))
self.fps = self.video.get(cv2.CAP_PROP_FPS)
self.nFrames = int(self.video.get(cv2.CAP_PROP_FRAME_COUNT))
self.duration = self.nFrames / self.fps # duration in seconds
self.isVidCap = True
else: # not a video file on disk
import cottoncandy
self.isVidCap = False
if self.fileName[:3] == 's3:': # file is on s3; assumed to the gzipped
fileName = self.fileName[5:] # since s3 files being with 's3://'
bucket = fileName.split('/')[0]
cloud = cottoncandy.get_interface(bucket)
zippedData = cloud.download_stream(fileName[(len(bucket) + 1):]).content
else: # zipped file on disk
file = open(self.fileName)
zippedData = file.read()
file.close()
self.video = ''
zipFile = cottoncandy.utils.GzipInputStream(zippedData, 20 * (2 ** 20))
while True:
chunk = zipFile.read(10 * (2 ** 20))
if not chunk:
break
self.video += chunk
del zippedData, zipFile
import struct
metadata = struct.unpack('i' * 14, self.video[32:(32 + 56)])
self.width = metadata[8]
self.height = metadata[9]
self.nFrames = metadata[4]
self.fps = int(1 / (metadata[0] * 1000000))
self.duration = metadata[0] * metadata[4] / 1000000.0
import cottoncandy as cc
import numpy as np
access_key = 'SSE14CR7P0AEZLPC7X0R'
secret_key = 'K0MmeXiXotrGIiTeRwEKizkkhR4qFV8tr8cIXprI'
endpoint_url = 'http://c3-dtn02.corral.tacc.utexas.edu:9002/'
cci = cc.get_interface('story-mri-data',
ACCESS_KEY=access_key,
SECRET_KEY=secret_key,
endpoint_url=endpoint_url)
a = False
b = False
c = True
sub = 'AHfs'
#1. For downloading fmri data for a subject
if (a):
R_obs = cci.download_raw_array(sub + '/wheretheressmoke-10sessions')
np.save('fmri_' + sub + '.dat', R_obs)
#2. For fetching JSON file of subject xfms
if (b):
xfm_file = cci.download_json('subject_xfms')
print(xfm_file[sub])
#3. For finding voxels with highest correlation over time.
if (c):
for sub in ['SJ']: #,'SS','EB03','EB05','S03']:
R_obs = cci.download_raw_array(sub + '/wheretheressmoke-10sessions')
#R_obs = np.load('fullmatrix_R_'+ sub +'.dat.npy')
Voxels = R_obs.shape[2]
prefix = 'testcc/%s/py%s' % (DATE, sys.version[:6])
object_name = os.path.join(prefix, 'test')
# login
##############################
if True:
# for travis testing on AWS.
bucket_name = os.environ['DL_BUCKET_NAME']
AK = os.environ['DL_ACCESS_KEY']
SK = os.environ['DL_SECRET_KEY']
URL = os.environ['DL_URL']
cci = cc.get_interface(bucket_name,
ACCESS_KEY=AK,
SECRET_KEY=SK,
endpoint_url=URL,
verbose=False)
else:
##############################
# Warning
##############################
# This will use your defaults to run the tests on.
# If you use AWS, you might incur costs.
cci = cc.get_interface()
##############################
# tests
##############################
object_name = os.path.join(prefix, 'test')
# login
##############################
if True:
# for travis testing on AWS.
bucket_name = os.environ['DL_BUCKET_NAME']
AK = os.environ['DL_ACCESS_KEY']
SK = os.environ['DL_SECRET_KEY']
URL = os.environ['DL_URL']
cci = cc.get_interface(bucket_name,
ACCESS_KEY=AK,
SECRET_KEY=SK,
endpoint_url=URL,
verbose=False)
else:
##############################
# Warning
##############################
# This will use your defaults to run the tests on.
# If you use AWS, you might incur costs.
cci = cc.get_interface()
##############################
# tests
import cottoncandy as cc
import os
import numpy as np
# Data paths
bucket_name = "carson_public_datasets"
bucket_path = "/human_connectome_project/3T/{subject}/mrf_training/{slice}"
# Subject/slices
subslices = np.load("/auto/k1/carson/glab/mrf/train_data_list.npy")
# Output paths
outpath = "/datasets/christine/mrf/{subject}/{slice}"
cci = cc.get_interface(bucket_name)
icount = 0
print("Total # of slices: %d" % len(subslices))
for isubject, islice in subslices:
fpath = bucket_path.format(subject=isubject, slice=islice)
opath = outpath.format(subject=isubject, slice=islice)
if not os.path.exists(opath):
os.makedirs(opath)
mrf = cci.download_npy_array(os.path.join(fpath, "mrf.npy"))
t1_t2_pd = cci.download_npy_array(os.path.join(fpath, "t1_t2_pd.npy"))
np.save(os.path.join(opath, "mrf.npy"), mrf)
np.save(os.path.join(opath, "t1_t2_pd.npy"), t1_t2_pd)
icount += 1
if icount % 100 == 0:
print("Finished %d slices" % icount)