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")