def plot_connexel(mask_file, idx):
import nibabel as nb
import numpy as np
import pylab as plt
from nipy.labs.viz_tools.activation_maps import plot_map
mask_nii = nb.load(mask_file)
fig = plt.figure(figsize=(8, 4))
beh_data = np.recfromcsv(
'/home/raid3/gorgolewski/Downloads/DataForChris_10_30_12.csv')
corr_mmaps = []
for name in beh_data['sub_id_database_brain']:
filename = "/scr/adenauer1/workingdir/rs_analysis_test/main_workflow/_subject_id_%s/_fwhm_5/corr_matrix/corr_matrix.int16" % name
mmap = np.memmap(filename, dtype='int16', mode='r')
corr_mmaps.append(mmap)
for i, key in enumerate(['mem_meta_d', 'percep_aroc']):
r_values = np.zeros((len(beh_data[key])))
for j, corr_mmap in enumerate(corr_mmaps):
r_values[j] = corr_mmap[idx]
r_values = np.arctanh(r_values / 10000.0)
ax = plt.subplot2grid((1, 2), (0, i))
ax.scatter(r_values, beh_data[key])
ax.set_xlabel("normalized_correlation")
ax.set_ylabel(key)
plt.savefig("%d_beh_data.pdf" % idx)
plt.savefig("%d_beh_data.svg" % idx)
counter = 0
for i in xrange(0, (mask_nii.get_data() > 0).sum()):
for j in xrange(i + 1, (mask_nii.get_data() > 0).sum()):
if counter == idx:
print i, j
new_mask = (np.zeros(mask_nii.get_data().shape) == 1)
sub_mask = new_mask[mask_nii.get_data() > 0]
sub_mask[i] = True
new_mask[mask_nii.get_data() > 0] = sub_mask
print np.where(new_mask)
#ax = plt.subplot2grid((2,3), (0,1), colspan=2)
plot_map(new_mask, mask_nii.get_affine(), threshold='auto')
plt.savefig("%d_endpoint1.pdf" % idx)
plt.savefig("%d_endpoint1.svg" % idx)
new_mask = (np.zeros(mask_nii.get_data().shape) == 1)
sub_mask = new_mask[mask_nii.get_data() > 0]
sub_mask[j] = True
new_mask[mask_nii.get_data() > 0] = sub_mask
print np.where(new_mask)
#ax = plt.subplot2grid((2,3), (1,1), colspan=2)
plot_map(new_mask, mask_nii.get_affine(), threshold='auto')
plt.savefig("%d_endpoint2.pdf" % idx)
plt.savefig("%d_endpoint2.svg" % idx)
return
counter += 1
print counter
def plot_connexel(mask_file,idx):
import nibabel as nb
import numpy as np
import pylab as plt
from nipy.labs.viz_tools.activation_maps import plot_map
mask_nii = nb.load(mask_file)
fig = plt.figure(figsize=(8, 4))
beh_data = np.recfromcsv('/home/raid3/gorgolewski/Downloads/DataForChris_10_30_12.csv')
corr_mmaps = []
for name in beh_data['sub_id_database_brain']:
filename = "/scr/adenauer1/workingdir/rs_analysis_test/main_workflow/_subject_id_%s/_fwhm_5/corr_matrix/corr_matrix.int16"%name
mmap = np.memmap(filename, dtype='int16', mode='r')
corr_mmaps.append(mmap)
for i, key in enumerate(['mem_meta_d', 'percep_aroc']):
r_values = np.zeros((len(beh_data[key])))
for j, corr_mmap in enumerate(corr_mmaps):
r_values[j] = corr_mmap[idx]
r_values = np.arctanh(r_values/10000.0)
ax = plt.subplot2grid((1,2), (0,i))
ax.scatter(r_values, beh_data[key])
ax.set_xlabel("normalized_correlation")
ax.set_ylabel(key)
plt.savefig("%d_beh_data.pdf"%idx)
plt.savefig("%d_beh_data.svg"%idx)
counter = 0
for i in xrange(0,(mask_nii.get_data() > 0).sum()):
for j in xrange(i+1,(mask_nii.get_data() > 0).sum()):
if counter == idx:
print i,j
new_mask = (np.zeros(mask_nii.get_data().shape) == 1)
sub_mask = new_mask[mask_nii.get_data() > 0]
sub_mask[i] = True
new_mask[mask_nii.get_data() > 0] = sub_mask
print np.where(new_mask)
#ax = plt.subplot2grid((2,3), (0,1), colspan=2)
plot_map(new_mask, mask_nii.get_affine(), threshold='auto')
plt.savefig("%d_endpoint1.pdf"%idx)
plt.savefig("%d_endpoint1.svg"%idx)
new_mask = (np.zeros(mask_nii.get_data().shape) == 1)
sub_mask = new_mask[mask_nii.get_data() > 0]
sub_mask[j] = True
new_mask[mask_nii.get_data() > 0] = sub_mask
print np.where(new_mask)
#ax = plt.subplot2grid((2,3), (1,1), colspan=2)
plot_map(new_mask, mask_nii.get_affine(), threshold='auto')
plt.savefig("%d_endpoint2.pdf"%idx)
plt.savefig("%d_endpoint2.svg"%idx)
return
counter += 1
print counter
# plot the spatial distribution using NiPy
vol = ds.a.mapper.reverse1(slres_tdsm.samples[0])
import nibabel as nb
anat = nb.load(pjoin(datapath, 'sub001', 'anatomy', 'highres001.nii.gz'))
from nipy.labs.viz_tools.activation_maps import plot_map
pl.figure(figsize=(15,4))
sp = pl.subplot(121)
pl.title('Distribution of target similarity structure correlation')
slices = plot_map(
vol,
ds.a.imgaffine,
cut_coords=np.array((12,-42,-20)),
threshold=.5,
cmap="bwr",
vmin=0,
vmax=1.,
axes=sp,
anat=anat.get_data(),
anat_affine=anat.affine,
)
img = pl.gca().get_images()[1]
cax = pl.axes([.05, .05, .05, .9])
pl.colorbar(img, cax=cax)
sp = pl.subplot(122)
pl.hist(slres_tdsm.samples[0],
#range=(0,410),
normed=False,
bins=30,
color='0.6')
# plot the spatial distribution using NiPy
vol = ds.a.mapper.reverse1(slres_tdsm.samples[0])
import nibabel as nb
anat = nb.load(pjoin(datapath, 'sub001', 'anatomy', 'highres001.nii.gz'))
from nipy.labs.viz_tools.activation_maps import plot_map
pl.figure(figsize=(15, 4))
sp = pl.subplot(121)
pl.title('Distribution of target similarity structure correlation')
slices = plot_map(
vol,
ds.a.imgaffine,
cut_coords=np.array((12, -42, -20)),
threshold=.5,
cmap="bwr",
vmin=0,
vmax=1.,
axes=sp,
anat=anat.get_data(),
anat_affine=anat.affine,
)
img = pl.gca().get_images()[1]
cax = pl.axes([.05, .05, .05, .9])
pl.colorbar(img, cax=cax)
sp = pl.subplot(122)
pl.hist(slres_tdsm.samples[0],
normed=False,
bins=30,
color='0.6')
pl.ylabel("Number of voxels")
