https://github.com/tansey/smoothfdr
Revision c5b693d0a66e83c9387433b33c0eab481bd4a763 authored by Wesley Tansey on 08 May 2020, 15:42:20 UTC, committed by Wesley Tansey on 08 May 2020, 15:42:20 UTC
1 parent 49cb69c
Tip revision: c5b693d0a66e83c9387433b33c0eab481bd4a763 authored by Wesley Tansey on 08 May 2020, 15:42:20 UTC
Fixed bug in easy that created too large a support for the alternative distribution
Fixed bug in easy that created too large a support for the alternative distribution
Tip revision: c5b693d
neuropre.py
import numpy as np
import nibabel as nib
import argparse
import csv
import os
from utils import *
def load_nii(filename):
img = nib.load(filename)
return img.get_data()
def cube_to_vector(data, edges):
lookup = {}
beta = []
beta_edges = []
for x1, x2 in edges:
if x1 in lookup:
y1 = lookup[x1]
else:
y1 = len(beta)
lookup[x1] = y1
beta.append(data[x1])
if x2 in lookup:
y2 = lookup[x2]
else:
y2 = len(beta)
lookup[x2] = y2
beta.append(data[x2])
beta_edges.append((y1, y2))
return np.array(beta), beta_edges, lookup
def cube_trails_to_vector_trails(cube_trails, lookup):
vec_trails = []
for t in cube_trails:
vec_trails.append([lookup[x] for x in t])
return vec_trails
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Loads a .nii or .nii.gz file and processes it.')
parser.add_argument('infile', help='The neuroimaging file.')
parser.add_argument('--outdir', default='../data/', help='Save the betas, edges, and mapping from betas to x,y,z coords to the specified directory in CSV format.')
parser.add_argument('--verbose', type=int, default=1, help='Print detailed progress information to the console. 0=none, 1=high-level only, 2=all details.')
parser.add_argument('--missingval', type=float, default=0, help='The value used to signify a missing data point in the array. Typically this is zero.')
parser.set_defaults()
args = parser.parse_args()
if args.verbose:
print('Loading data from {0}'.format(args.infile))
data = load_nii(args.infile)
if args.verbose:
print('Data shape: {0}'.format(data.shape))
raw_edges = cube_edges(data, missing_val=args.missingval)
if args.verbose:
print('Edges: {0}'.format(len(raw_edges)))
betas, edges, lookup = cube_to_vector(data, raw_edges)
if args.verbose:
print('Vertices: {0}'.format(len(betas)))
trails = cube_trails_missing(data, missing_val=args.missingval)
trails = cube_trails_to_vector_trails(trails, lookup)
if args.verbose:
print('Trails: {0}'.format(len(trails)))
outdir = args.outdir + ('' if args.outdir.endswith('/') else '/')
if not os.path.exists(outdir):
os.makedirs(outdir)
if args.verbose:
print('Saving betas to {0}'.format(outdir+'betas.csv'))
np.savetxt(outdir+'betas.csv', betas, delimiter=',')
if args.verbose:
print('Saving edges to {0}'.format(outdir+'edges.csv'))
with open(outdir+'edges.csv', 'wb') as f:
writer = csv.writer(f)
writer.writerows(edges)
if args.verbose:
print('Saving trails to {0}'.format(outdir+'trails.csv'))
save_trails(outdir+'trails.csv', trails)
if args.verbose:
print('Saving map from vector index -> (x,y,z) to {0}'.format(outdir+'lookup.csv'))
print('NOTE: the first line will be the dimensions of the original data.')
with open(outdir+'lookup.csv', 'wb') as f:
writer = csv.writer(f)
writer.writerow(data.shape)
for (x,y,z), bidx in lookup.iteritems():
writer.writerow([bidx,x,y,z])
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