# Now we will count the number of voxels and compare the two methods
grf_files = glob("%s/*.nii.gz" %output_folder)
grf_files.sort()
input_files.sort()
voxel_counts = pandas.DataFrame(columns=["rft","thresh_0.0","thresh_1.0","thresh_2.0"])
brain_mask = nibabel.load(mask)
brain_voxels = len(brain_mask.get_data()[brain_mask.get_data()!=0])
for f in range(0,len(grf_files)):
f1 = IT.t_to_z(nibabel.load(input_files[f]),46)
f2 = nibabel.load(grf_files[f])
print "Comparing %s vs %s" %(os.path.basename(grf_files[f]),os.path.basename(input_files[f]))
mrthresh = IT.threshold_abs(f1,thresholds=[0.0,1.0,2.0])
masked_mrthresh = [(thresh,apply_mask([nii],brain_mask)) for thresh,nii in mrthresh.iteritems()]
# Count voxels in each
image_name = os.path.basename(input_files[f])
voxel_counts.loc[image_name] = (len(f2.get_data()[f2.get_data()!=0]),
len(masked_mrthresh[0][1][0][masked_mrthresh[0][1][0]!=0]),
len(masked_mrthresh[1][1][0][masked_mrthresh[1][1][0]!=0]),
len(masked_mrthresh[2][1][0][masked_mrthresh[2][1][0]!=0]))
voxel_counts["absolute_difference_0.0"] = voxel_counts["thresh_0.0"] - voxel_counts["rft"]
voxel_counts["absolute_difference_1.0"] = voxel_counts["thresh_1.0"] - voxel_counts["rft"]
voxel_counts["absolute_difference_2.0"] = voxel_counts["thresh_2.0"] - voxel_counts["rft"]
voxel_counts["perc_brainvox_0.0"] = voxel_counts["thresh_0.0"] / brain_voxels
voxel_counts["perc_brainvox_1.0"] = voxel_counts["thresh_1.0"] / brain_voxels
voxel_counts["perc_brainvox_2.0"] = voxel_counts["thresh_2.0"] / brain_voxels
voxel_counts["perc_brainvox_rft"] = voxel_counts["rft"] / brain_voxels
# Get dof for B maps
try:
dofB = len(open("%s/../copesB.txt" %(input_directory),"r").readlines())-2
except:
dofB = 44
for groupB in groupB_maps:
mrB = nib.load(groupB)
mrB = IT.t_to_z(mrB,dofB)
contrast_task_B = os.path.basename(groupB).replace("_groupB_tstat1.nii.gz","")
for thresh in thresholds:
print "Processing threshold %s" %(thresh)
for absolute_value in ["pos","posneg"]:
if absolute_value == "posneg":
# Group A is always unthresholded, B is thresholded
mrthresh = IT.threshold_abs(mrB,thresholds=[thresh])[thresh]
else:
mrthresh = IT.threshold_pos(mrB,thresholds=[thresh])[thresh]
ids.append(contrast_task_B)
thresh_id.append(thresh)
# 3/12/2015: confirmed that first returns +/- values, second returns only positive
# If the image is empty thresholded, we append NaN
if len(np.unique(mrthresh.get_data()))==1:
cca_pearson.append(np.nan)
svi_pearson.append(np.nan)
cca_spearman.append(np.nan)
svi_spearman.append(np.nan)
sizes.loc[idx] = [0,0]
nanlog_cca.append("nan_mrthresh_empty")
nanlog_svi.append("nan_mrthresh_empty")
size_ids.append("%s_%s" %(thresh,absolute_value))
max = 0
min = 0
for input_map in input_maps:
img = nibabel.load(input_map)
img = IT.t_to_z(img, 46).get_data()
if img.max() > max:
max = img.max()
if img.min() < min:
min = img.min()
for i in range(0, len(input_maps)):
print "Processing %s of %s" % (i, len(input_maps))
input_map = input_maps[i]
mapname = os.path.basename(input_map).replace("_groupA_tstat1.nii.gz", "")
task = lookup.task[lookup.id == mapname].tolist()[0]
contrast = lookup.contrasts[lookup.id == mapname].tolist()[0]
title = "%s_%s" % (task, contrast)
image = nibabel.load(input_map)
Z = IT.t_to_z(image, 46)
for thresh in range(0, 10):
print thresh
pos = IT.threshold_pos(image, thresholds=[thresh])[thresh]
posneg = IT.threshold_abs(image, thresholds=[thresh])[thresh]
fig = plot_stat_map(pos, vmax=max, title="%s Positive Only" % title, cut_coords=(0, 0, 0))
fig.savefig("%s/%s_%s_pos.png" % (outdir, thresh, mapname))
fig.close()
fig = plot_stat_map(posneg, vmax=max, title="%s Positive and Negative" % title, cut_coords=(0, 0, 0))
fig.savefig("%s/%s_%s_posneg.png" % (outdir, thresh, mapname))
fig.close()
