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# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
Get two images from the web (one mask image and one spmT image) and
put them in the dir: ~/.nipy/tests/data
should be quick and light (<6MB)
Author : Bertrand Thirion, 2009
"""
import os
import urllib2
import tarfile
def getIt():
"""
light dataset for multi-subject analysis
"""
# define several paths
url = 'ftp://ftp.cea.fr/pub/dsv/madic/download/nipy'
data_dir = os.path.expanduser(os.path.join('~', '.nipy', 'tests', 'data'))
MaskImage = os.path.join(data_dir,'mask.nii.gz')
InputImage = os.path.join(data_dir,'spmT_0029.nii.gz')
GroupData = os.path.join(data_dir,'group_t_images.tar.gz')
# possibly create data_dir
if not os.path.exists(data_dir):
os.makedirs(data_dir)
assert os.path.exists(data_dir)
# download MaskImage if necessary
if os.path.exists(MaskImage)==False:
filename = 'mask.nii.gz'
datafile = os.path.join(url,filename)
fp = urllib2.urlopen(datafile)
local_file = open(MaskImage, 'w')
local_file.write(fp.read())
local_file.flush()
local_file.close()
# download InputImage if necessary
if os.path.exists(InputImage)==False:
filename = 'spmT_0029.nii.gz'
datafile = os.path.join(url,filename)
fp = urllib2.urlopen(datafile)
local_file = open(InputImage, 'w')
local_file.write(fp.read())
local_file.flush()
local_file.close()
# download GroupData if necessary
if os.path.exists(GroupData)==False:
filename = 'group_t_images.tar.gz'
datafile = os.path.join(url,filename)
fp = urllib2.urlopen(datafile)
local_file = open(GroupData, 'w')
local_file.write(fp.read())
local_file.flush()
local_file.close()
#untargzip GroupData
tar = tarfile.open(GroupData)
tar.extractall(data_dir)
tar.close()
os.remove(GroupData)
def get_localizer_dataset():
"""
Heavier dataset (30 MO) for first-level analysis
"""
# define several paths
url = 'ftp://ftp.cea.fr/pub/dsv/madic/download/nipy'
data_dir = os.path.expanduser(os.path.join('~', '.nipy', 'tests', 'data'))
raw_fmri = os.path.join(data_dir,'s12069_swaloc1_corr.nii.gz')
paradigm = os.path.join(data_dir,'localizer_paradigm.csv')
# create data_dir
if not os.path.exists(data_dir):
os.makedirs(data_dir)
assert os.path.exists(data_dir)
# download MaskImage if necessary
if os.path.exists(paradigm)==False:
datafile = os.path.join(url, 'localizer_paradigm.csv')
fp = urllib2.urlopen(datafile)
local_file = open(paradigm, 'w')
local_file.write(fp.read())
local_file.flush()
local_file.close()
# download raw_fmri if necessary
if os.path.exists(raw_fmri)==False:
filename = 's12069_swaloc1_corr.nii.gz'
datafile = os.path.join(url,filename)
fp = urllib2.urlopen(datafile)
local_file = open(raw_fmri, 'w')
local_file.write(fp.read())
local_file.flush()
local_file.close()
if __name__ == '__main__':
getIt()
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