/
evaluate_atlases.py
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/
evaluate_atlases.py
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from pyneurovault import api
import pandas as pd
from nilearn.image import resample_img, smooth_img
import nibabel as nb
import numpy as np
from scipy.ndimage import binary_fill_holes
from scipy import stats
from scipy.ndimage.measurements import labeled_comprehension
import sys
atlas_id = sys.argv[2]
data_location = sys.argv[1]
atlases = api.get_images(pks=[atlas_id])
api.download_images(data_location + "/atlases", atlases, resample=False)
standard = "/usr/share/fsl/data/standard/MNI152_T1_2mm_brain.nii.gz"
get_ipython().system('mkdir -p '+data_location+'/atlases/resampled')
fname = atlas_id+".nii.gz"
print "resampling " + fname
nii = nb.load(data_location+"atlases/original/" + fname)
target_nii = nb.load(standard)
resampled_nii = resample_img(nii,target_affine=target_nii.get_affine(),
target_shape=target_nii.shape, interpolation='nearest')
resampled_nii.to_filename(data_location+"atlases/resampled/" + fname)
def score_map(map_filename, atlas_data, labels):
map_data = nb.load(map_filename).get_data()
parcel_means = labeled_comprehension(map_data,
atlas_data,
list(labels),
np.mean,
float,
np.nan)
parcel_variance = labeled_comprehension(map_data,
atlas_data,
list(labels),
np.var,
float,
np.nan)
within_variance = parcel_variance.mean()
between_variance = parcel_means.var()
return within_variance, between_variance
comparisons_df = pd.DataFrame(columns=["within_parcel_variance", "between_parcel_variance", "atlas_id", "image_id", "fake_map_id"])
df = pd.read_csv(data_location + "/smoothness_and_volume.csv", index_col=0)
n_fake_maps = int(sys.argv[3])
for i, atlas_row in atlases.iterrows():
atlas_id = atlas_row["image_id"]
print "analyzing " + atlas_row["name"]
atlas_nii = nb.load(data_location + "/atlases/resampled/%04d.nii.gz"%atlas_id)
for image_id, row in df.iterrows():
fname = "%04d.nii.gz"%image_id
print "analyzing " + fname
mask_nii = nb.load(data_location + "/images/resampled_masks/%04d.nii.gz"%image_id)
atlas_data = atlas_nii.get_data().copy()
atlas_data[mask_nii.get_data() != True] = 0
labels = set(np.unique(atlas_data)) - set([0])
if not ((comparisons_df['atlas_id'] == atlas_id) & (comparisons_df['image_id'] == image_id)).any():
real_within_variance, real_between_variance = score_map(data_location + "/images/resampled/%04d.nii.gz"%image_id, atlas_data, labels)
comparisons_df = comparisons_df.append({"within_parcel_variance": real_within_variance,
"between_parcel_variance": real_between_variance,
"atlas_id": atlas_id,
"image_id": image_id,
"fake_map_id": None}, ignore_index=True)
from joblib import Parallel, delayed
out = Parallel(n_jobs=16)(delayed(score_map)(data_location + "/images/fake_maps/%04d/%04d.nii.gz"%(image_id, fake_map_id),
atlas_data,
labels) for fake_map_id in range(n_fake_maps))
comparisons_df = comparisons_df.append(pd.DataFrame({"within_parcel_variance": list(np.array(out)[:, 0]),
"between_parcel_variance": list(np.array(out)[:, 1]),
"atlas_id": [atlas_id]*n_fake_maps,
"image_id": [image_id]*n_fake_maps,
'fake_map_id':range(n_fake_maps)}), ignore_index=True)
comparisons_df.to_csv(data_location + "/%scomparison.csv"%sys.argv[2])