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Merge 054ae9e into f79fc9f
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@AlonDaks
AlonDaks committed Nov 29, 2015
2 parents f79fc9f + 054ae9e commit cf1d812
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Showing 4 changed files with 318 additions and 36 deletions.
87 changes: 87 additions & 0 deletions code/reproduction/preprocess.py
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from __future__ import print_function
from __future__ import division
import json
import nibabel as nib
import numpy as np
import sys
from glob import glob
from os.path import exists
from multiprocessing import Pool

REPO_HOME_RELATIVE_PATH = '../../'
sys.path.append(REPO_HOME_RELATIVE_PATH)
import config as cf

DATA_PATHS = json.load(open(REPO_HOME_RELATIVE_PATH + 'data/data_path.json'))

INCORRECT_NUM_ARGS_MESSAGE = 'Invalid number of arguments: specify alignment type'
ILLEGAL_ARG_MESSAGE = 'preprocess.py must be provided with alignment argument'


def concatenate_runs(alignment):
for i, subject in enumerate(DATA_PATHS['subjects']):
nii_file_name = '{0}data/processed/sub{1}_{2}'.format(
REPO_HOME_RELATIVE_PATH, i + 1, alignment)
if not exists(nii_file_name + '.nii') or not cf.USE_CACHED_DATA:
run_data = []
for j, run in enumerate(subject['runs']):
task_path = run[alignment]['path']
img = nib.load(REPO_HOME_RELATIVE_PATH + task_path)
data = img.get_data()
if j == 0:
run_data.append(data[..., :-4])
elif j >= 1 and j <= 6:
run_data.append(data[..., 4:-4])
else:
run_data.append(data[..., 4:])
concatenated_run_data = np.concatenate(run_data, axis=3)
concatenated_img = nib.Nifti1Image(concatenated_run_data,
np.eye(4))
nib.save(concatenated_img, nii_file_name)
print('Saved {0}'.format(nii_file_name + '.nii'))
else:
print('Using cached version of {0}'.format(nii_file_name + '.nii'))


def reshape_data_to_2d(alignment):
files_to_reshape = np.sort(glob('{0}data/processed/sub*_{1}.nii'.format(
REPO_HOME_RELATIVE_PATH, alignment)))
for f in files_to_reshape:
file_name_2d = f.replace('.nii', '_2d')
if not exists(file_name_2d + '.npy') or not cf.USE_CACHED_DATA:
data = nib.load(f).get_data()
data_chunks = partition_4d_data(data)
p = Pool(cf.NUM_PROCESSES)
reshaped_chunks = p.imap(reshape_4d_data, data_chunks)
data_2d = merge_2d_data(reshaped_chunks)
np.save(file_name_2d, data_2d)
print('Saved {0}'.format(file_name_2d + '.npy'))
else:
print('Using cached version of {0}'.format(file_name_2d + '.npy'))


def partition_4d_data(data):
num_partitions = cf.NUM_PROCESSES
num_volunes = data.shape[-1]
partition_indices = [(num_volunes // num_partitions) * i
for i in range(1, num_partitions)]
return np.split(data, partition_indices, axis=3)


def reshape_4d_data(data):
return np.reshape(data, (-1, data.shape[-1]))


def merge_2d_data(data_slices):
return np.hstack(tuple(data_slices))

# def band_pass_filter():

if __name__ == '__main__':
if len(sys.argv) != 2:
raise ValueError(INCORRECT_NUM_ARGS_MESSAGE)
alignment = sys.argv[1]
if alignment not in ['linear', 'non_linear', 'rcds']:
raise ValueError(ILLEGAL_ARG_MESSAGE)
concatenate_runs(alignment)
reshape_data_to_2d(alignment)
45 changes: 45 additions & 0 deletions code/reproduction/similarity.py
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from __future__ import print_function
from __future__ import division
import nibabel as nib
import numpy as np
from scipy import stats
from multiprocessing import Pool, Process
import itertools
import sys
import os
import sharedmem as sm
import gc

REPO_HOME_RELATIVE_PATH = '../../'
sys.path.append(REPO_HOME_RELATIVE_PATH)
import config as cf


def parallelize_correlation():
chunk_size = len(shared_subject1) // cf.NUM_PROCESSES
output_correlations = sm.empty(len(shared_subject1))
processes = [
Process(target=correlation,
args=(shared_subject1, shared_subject2, i * chunk_size, min(
(i + 1) * chunk_size, len(shared_subject1)),
output_correlations)) for i in xrange(cf.NUM_PROCESSES)
]
for p in processes:
p.start()
for p in processes:
p.join()
return output_correlations


def correlation(shared_subject1, shared_subject2, start_index, stop_index,
output_correlations):
for i in range(start_index, stop_index):
output_correlations[i] = stats.pearsonr(shared_subject1[i, :],
shared_subject2[
i, :])[0]


shared_subject1 = sm.copy(np.load(REPO_HOME_RELATIVE_PATH +
'data/processed/sub1_rcds_2d.npy'))
shared_subject2 = sm.copy(np.load(REPO_HOME_RELATIVE_PATH +
'data/processed/sub2_rcds_2d.npy'))
9 changes: 9 additions & 0 deletions config.py
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from os import environ

USE_CACHED_DATA = environ.get('STAT159_CACHED_DATA', 'True') == 'True'

NUM_PROCESSES = int(environ.get('STAT159_NUM_PROCESSES', 5))

NUM_VOXELS = 1108800

NUM_TOTAL_VOLUMES = 3543
213 changes: 177 additions & 36 deletions data/data_path.json
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{
"bold_dico_7Tad2grpbold7Tad": {
"sub1" : {
"runs" : [
{
"path": "data/raw/sub001/BOLD/task001_run001/bold_dico_dico7Tad2grpbold7Tad.nii",
"hash": "b4350e69fdbb5c62e4c8be66e71c2d86"
},
{
"path": "data/raw/sub001/BOLD/task001_run002/bold_dico_dico7Tad2grpbold7Tad.nii",
"hash": "ec625fd0de6433cfecbfb542a6598683"
},
{
"path": "data/raw/sub001/BOLD/task001_run003/bold_dico_dico7Tad2grpbold7Tad.nii",
"hash": "9b92dc4fb03bc5080f7eae904782424a"
},
{
"path": "data/raw/sub001/BOLD/task001_run004/bold_dico_dico7Tad2grpbold7Tad.nii",
"hash": "658cb0bcbfe28286af1514a232d1d79d"
},
{
"path": "data/raw/sub001/BOLD/task001_run005/bold_dico_dico7Tad2grpbold7Tad.nii",
"hash": "3d299ebfc9aa9e4419d3f185068fd5a2"
},
{
"path": "data/raw/sub001/BOLD/task001_run006/bold_dico_dico7Tad2grpbold7Tad.nii",
"hash": "e381354cf8fcb8dcd50d711e0cd24d92"
},
{
"path": "data/raw/sub001/BOLD/task001_run007/bold_dico_dico7Tad2grpbold7Tad.nii",
"hash": "814090d79f447671ae483ec2ea9dde3c"
},
{
"path": "data/raw/sub001/BOLD/task001_run008/bold_dico_dico7Tad2grpbold7Tad.nii",
"hash": "4fc0745cb64f76cd9cf43d6351de5848"
}
]
"subjects" :
[
{
"runs" : [
{
"linear" : {
"path": "data/raw/sub001/task001_run001/bold_dico_dico7Tad2grpbold7Tad.nii",
"hash": "b4350e69fdbb5c62e4c8be66e71c2d86"
},
"rcds" : {
"path": "data/raw/sub001/task001_run001/bold_dico_dico_rcds_nl.nii"
}
},
{
"linear" : {
"path": "data/raw/sub001/task001_run002/bold_dico_dico7Tad2grpbold7Tad.nii",
"hash": "ec625fd0de6433cfecbfb542a6598683"
},
"rcds" : {
"path": "data/raw/sub001/task001_run002/bold_dico_dico_rcds_nl.nii"
}
},
{
"linear" : {
"path": "data/raw/sub001/task001_run003/bold_dico_dico7Tad2grpbold7Tad.nii",
"hash": "9b92dc4fb03bc5080f7eae904782424a"
},
"rcds" : {
"path": "data/raw/sub001/task001_run003/bold_dico_dico_rcds_nl.nii"
}
},
{
"linear" : {
"path": "data/raw/sub001/task001_run004/bold_dico_dico7Tad2grpbold7Tad.nii",
"hash": "658cb0bcbfe28286af1514a232d1d79d"
},
"rcds" : {
"path": "data/raw/sub001/task001_run004/bold_dico_dico_rcds_nl.nii"
}
},
{
"linear" : {
"path": "data/raw/sub001/task001_run005/bold_dico_dico7Tad2grpbold7Tad.nii",
"hash": "3d299ebfc9aa9e4419d3f185068fd5a2"
},
"rcds" : {
"path": "data/raw/sub001/task001_run005/bold_dico_dico_rcds_nl.nii"
}
},
{
"linear" : {
"path": "data/raw/sub001/task001_run006/bold_dico_dico7Tad2grpbold7Tad.nii",
"hash": "e381354cf8fcb8dcd50d711e0cd24d92"
},
"rcds" : {
"path": "data/raw/sub001/task001_run006/bold_dico_dico_rcds_nl.nii"
}
},
{
"linear" : {
"path": "data/raw/sub001/task001_run007/bold_dico_dico7Tad2grpbold7Tad.nii",
"hash": "814090d79f447671ae483ec2ea9dde3c"
},
"rcds" : {
"path": "data/raw/sub001/task001_run007/bold_dico_dico_rcds_nl.nii"
}
},
{
"linear" : {
"path": "data/raw/sub001/task001_run008/bold_dico_dico7Tad2grpbold7Tad.nii",
"hash": "4fc0745cb64f76cd9cf43d6351de5848"
},
"rcds" : {
"path": "data/raw/sub001/task001_run008/bold_dico_dico_rcds_nl.nii"
}
}
]
},
{
"runs" : [
{
"rcds" : {
"path": "data/raw/sub002/task001_run001/bold_dico_dico_rcds_nl.nii"
}
},
{
"rcds" : {
"path": "data/raw/sub002/task001_run002/bold_dico_dico_rcds_nl.nii"
}
},
{
"rcds" : {
"path": "data/raw/sub002/task001_run003/bold_dico_dico_rcds_nl.nii"
}
},
{
"rcds" : {
"path": "data/raw/sub002/task001_run004/bold_dico_dico_rcds_nl.nii"
}
},
{
"rcds" : {
"path": "data/raw/sub002/task001_run005/bold_dico_dico_rcds_nl.nii"
}
},
{
"rcds" : {
"path": "data/raw/sub002/task001_run006/bold_dico_dico_rcds_nl.nii"
}
},
{
"rcds" : {
"path": "data/raw/sub002/task001_run007/bold_dico_dico_rcds_nl.nii"
}
},
{
"rcds" : {
"path": "data/raw/sub002/task001_run008/bold_dico_dico_rcds_nl.nii"
}
}
]
},
{
"runs" : [
{
"rcds" : {
"path": "data/raw/sub003/task001_run001/bold_dico_dico_rcds_nl.nii"
}
},
{
"rcds" : {
"path": "data/raw/sub003/task001_run002/bold_dico_dico_rcds_nl.nii"
}
},
{
"rcds" : {
"path": "data/raw/sub003/task001_run003/bold_dico_dico_rcds_nl.nii"
}
},
{
"rcds" : {
"path": "data/raw/sub003/task001_run004/bold_dico_dico_rcds_nl.nii"
}
},
{
"rcds" : {
"path": "data/raw/sub003/task001_run005/bold_dico_dico_rcds_nl.nii"
}
},
{
"rcds" : {
"path": "data/raw/sub003/task001_run006/bold_dico_dico_rcds_nl.nii"
}
},
{
"rcds" : {
"path": "data/raw/sub003/task001_run007/bold_dico_dico_rcds_nl.nii"
}
},
{
"rcds" : {
"path": "data/raw/sub003/task001_run008/bold_dico_dico_rcds_nl.nii"
}
}
]
}
],
"templates": {
"brain_mask_intersection": {
"linear": {
"path": "data/raw/templates/linear_brain_mask_intersection.nii",
"hash": "77ba439c5681570be853be33475e6c98"
},
"nonlinear": {
"path": "data/raw/templates/non_linear_brain_mask_intersection.nii",
"hash": "0daaed45e7f26dca2269be91afcd067e"
}
}
}
}

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