def split_big_parcels(parcel_file, output_file, max_size=400): print 'split_big_parcels ...' roiMask, roiHeader = read_volume(parcel_file) roiIds = np.unique(roiMask) background = roiIds.min() labels = roiMask[np.where(roiMask>background)].astype(int) if (np.bincount(labels) <= max_size).all(): pyhrf.verbose(1, 'no parcel to split') return graphs = parcels_to_graphs(roiMask, kerMask3D_6n) for roiId in roiIds: if roiId != background: roi_size = (roiMask==roiId).sum() if roi_size > max_size: print 'roi %d, size = %d' %(roiId, roi_size) nparcels = int(np.ceil(roi_size*1./max_size)) print 'split into %d parcels ...' %(nparcels) split_parcel(labels, graphs, roiId, nparcels, inplace=True, verbosity=1) final_roi_mask = np.zeros_like(roiMask) final_roi_mask[np.where(roiMask>background)] = labels #print np.bincount(labels) assert (np.bincount(labels) <= max_size).all() write_volume(final_roi_mask, output_file, roiHeader)
def glm_matlab_from_files(bold_file, tr, paradigm_csv_file, output_dir, mask_file, hf_cut=128, hack_mask=False): """ Only mono-session #TODO: compute mask if mask_file does not exist #TODO: handle contrasts """ # Functional mask # if not op.exists(mask_file): # pyhrf.verbose(1, 'Mask file does not exist. Computing mask from '\ # 'BOLD data') # compute_mask_files(bold_files, mask_file, False, 0.4, 0.9) #split BOLD into 3D vols: bold, hbold = read_volume(bold_file) bold_files = [] tmp_path = tempfile.mkdtemp(dir=pyhrf.cfg['global']['tmp_path']) for iscan, bscan in enumerate(bold): f = op.join(tmp_path, 'bold_%06d.nii' %iscan) write_volume(bscan, f, hbold) bold_files.append(f) bold_files = ';'.join(bold_files) script_path = op.join(op.dirname(pyhrf.__file__),'../../script/SPM') spm_path = pyhrf.cfg['global']['spm_path'] matlab_code = "cd %s;paradigm_file='%s';TR=%f;mask_file='%s';" \ "bold_files='%s';output_path='%s';" \ "HF_cut=%f;spm_path='%s';api=1;hack_mask=%d;glm_intra_subj;exit" \ %(script_path,paradigm_csv_file,tr,mask_file,bold_files,output_dir, hf_cut,spm_path,hack_mask) matlab_cmd = 'matlab -nosplash -nodesktop -r "%s"'%matlab_code if op.exists(op.join(output_dir,'SPM.mat')): #remove SPM.mat so that SPM won't ask over ask overwriting os.remove(op.join(output_dir,'SPM.mat')) #print 'matlab cmd:' #print matlab_cmd os.system(matlab_cmd) # Fix shape of outputs if necessary # eg if input data has shape (n,m,1) then SPM will write outputs of # shape (n,m) so that they are not consistent with their QForm input_shape = bscan.shape for foutput in glob.glob(op.join(output_dir, '*.img')): data, h = read_volume(foutput) if data.ndim < 3: sm = ','.join([ [':','np.newaxis'][d==1] \ for d in input_shape ] ) exec('data = data[%s]' %sm) assert data.shape == input_shape write_volume(data, foutput, h) shutil.rmtree(tmp_path) #TODO: maybe find a better way to grab beta file names beta_files = sorted(glob.glob(op.join(output_dir,'beta_*.img'))) return beta_files
def save(self, output_dir): """ Save paradigm to output_dir/paradigm.csv, BOLD to output_dir/bold.nii, mask to output_dir/mask.nii #TODO: handle multi-session Return: tuple of file names in this order: (paradigm, bold, mask) """ paradigm_file = op.join(output_dir,'paradigm.csv') self.paradigm.save_csv(paradigm_file) if self.data_type == 'volume': # unflatten bold bold_vol = expand_array_in_mask(self.bold, self.roiMask, 1) bold_vol = np.rollaxis(bold_vol, 0, 4) bold_file = op.join(output_dir, 'bold.nii') write_volume(bold_vol, bold_file, self.meta_obj) mask_file = op.join(output_dir, 'mask.nii') write_volume(self.roiMask, mask_file, self.meta_obj) elif self.data_type == 'surface': #TODO surface bold_file = op.join(output_dir, 'bold.gii') write_texture(self.bold_vol, bold_file, self.meta_obj) pass return paradigm_file, bold_file, mask_file
def test_pyhrf_extract_cc_vol(self): test_mask = np.array( [[[1,1,0,1,1], [1,1,0,1,1], [0,0,0,0,0], [1,0,1,1,0], [0,0,1,1,0]], [[1,1,0,1,1], [1,1,0,1,1], [0,0,0,0,0], [0,0,1,1,0], [0,0,1,1,0]]], dtype=int ) mask_file = op.join(self.tmp_dir, 'test_mask.nii') write_volume(test_mask, mask_file) cmd = 'pyhrf_extract_cc_vol -v0 -m 2 %s' %mask_file if os.system(cmd) != 0: raise Exception('Command %s failed' %cmd) #print os.listdir(self.tmp_dir) assert len(os.listdir(self.tmp_dir)) == 4
def make_parcellation_cubed_blobs_from_file(parcellation_file, output_path, roi_ids=None, bg_parcel=0, skip_existing=False): p,mp = read_volume(parcellation_file) p = p.astype(np.int32) if bg_parcel==0 and p.min() == -1: p += 1 #set background to 0 if roi_ids is None: roi_ids = np.unique(p) pyhrf.verbose(1,'%d rois to extract' %(len(roi_ids)-1)) tmp_dir = pyhrf.get_tmp_path('blob_parcellation') tmp_parcel_mask_file = op.join(tmp_dir, 'parcel_for_blob.nii') out_files = [] for roi_id in roi_ids: if roi_id != bg_parcel: #discard background output_blob_file = op.join(output_path, 'parcel_%d_cubed_blob.arg'\ %roi_id) out_files.append(output_blob_file) if skip_existing and os.path.exists(output_blob_file): continue parcel_mask = (p==roi_id).astype(np.int32) write_volume(parcel_mask, tmp_parcel_mask_file, mp) pyhrf.verbose(3,'Extract ROI %d -> %s' %(roi_id,output_blob_file)) cmd = 'AimsGraphConvert -i %s -o %s --bucket' \ %(tmp_parcel_mask_file, output_blob_file) pyhrf.verbose(3,'Cmd: %s' %(cmd)) os.system(cmd) if op.exists(tmp_parcel_mask_file): os.remove(tmp_parcel_mask_file) return out_files
def project_fmri_from_kernels(input_mesh, kernels_file, fmri_data_file, output_tex, bin_threshold=None, ): pyhrf.verbose(2,'Project data onto mesh using kernels ...') if 0: print 'Projecting ...' print 'func data:', fmri_data_file print 'Mesh file:', input_mesh print 'Save as:', output_tex pyhrf.verbose(2,'Call AimsFunctionProjection -op 1 ...') data_files = [] output_texs = [] p_ids = None if bin_threshold is not None: d,h = read_volume(fmri_data_file) if np.allclose(d.astype(int), d): tmp_dir = pyhrf.get_tmp_path() p_ids = np.unique(d) pyhrf.verbose(2, 'bin threshold: %f' %bin_threshold) pyhrf.verbose(2, 'pids(n=%d): %d...%d' \ %(len(p_ids),min(p_ids),max(p_ids))) for i,p_id in enumerate(p_ids): if p_id != 0: new_p = np.zeros_like(d) new_p[np.where(d==p_id)] = i + 1 #0 is background ifn = op.join(tmp_dir,'pmask_%d.nii'%p_id) write_volume(new_p, ifn, h) data_files.append(ifn) ofn = op.join(tmp_dir,'ptex_%d.gii'%p_id) output_texs.append(ofn) else: data_files.append(fmri_data_file) output_texs.append(output_tex) else: data_files.append(fmri_data_file) output_texs.append(output_tex) pyhrf.verbose(3, 'input data files: %s' %str(data_files)) pyhrf.verbose(3, 'output data files: %s' %str(output_texs)) for data_file, o_tex in zip(data_files, output_texs): projection = [ 'AimsFunctionProjection', '-op', '1', '-d', kernels_file, '-d1', data_file, '-m', input_mesh, '-o', o_tex ] cmd = ' '.join(map(str,projection)) pyhrf.verbose(3, 'cmd: %s' %cmd) os.system(cmd) if bin_threshold is not None: pyhrf.verbose(2, 'Binary threshold of texture at %f' %bin_threshold) o_tex = output_texs[0] data,data_gii = read_texture(o_tex) data = (data>bin_threshold).astype(np.int32) print 'data:', data.dtype if p_ids is not None: for pid, o_tex in zip(p_ids[1:], output_texs[1:]): pdata,pdata_gii = read_texture(o_tex) data += (pdata>bin_threshold).astype(np.int32) * pid #assert (np.unique(data) == p_ids).all() write_texture(data, output_tex, intent='NIFTI_INTENT_LABEL')
def glm_nipy_from_files(bold_file, tr, paradigm_csv_file, output_dir, mask_file, session=0, contrasts=None, con_test_baseline=0.0, hrf_model='Canonical', drift_model='Cosine', hfcut=128, residuals_model='spherical', fit_method='ols', fir_delays=[0]): """ #TODO: handle surface data hrf_model : Canonical | Canonical with Derivative | FIR """ fdata = FmriData.from_vol_files(mask_file, paradigm_csv_file, [bold_file], tr) g, dm, cons = glm_nipy(fdata, contrasts=contrasts, hrf_model=hrf_model, hfcut=hfcut, drift_model=drift_model, residuals_model=residuals_model, fit_method=fit_method, fir_delays=fir_delays) ns, nr = dm.matrix.shape cdesign_matrix = xndarray(dm.matrix, axes_names=['time','regressor'], axes_domains={'time':np.arange(ns)*tr, 'regressor':dm.names}) cdesign_matrix.save(op.join(output_dir, 'design_matrix.nii')) beta_files = [] beta_values = dict.fromkeys(dm.names) beta_vars = dict.fromkeys(dm.names) beta_vars_voxels = dict.fromkeys(dm.names) for ib, bname in enumerate(dm.names): #beta values beta_vol = expand_array_in_mask(g.beta[ib], fdata.roiMask>0) beta_fn = op.join(output_dir, 'beta_%s.nii' %bname) write_volume(beta_vol, beta_fn, fdata.meta_obj) beta_files.append(beta_fn) beta_values[bname] = beta_vol #normalized variance of betas beta_vars[bname] = sp.diag(g.nvbeta)[ib] #variance: diag of cov matrix #sig2 = g.s2 #ResMS var_cond = sp.diag(g.nvbeta)[ib]*g.s2 #variance for all voxels, condition ib beta_vars_voxels[bname] = var_cond #beta_var_fn = op.join(output_dir, 'var_beta_%s.nii' %bname) #write_volume(beta_var, beta_var_fn, fdata.meta_obj) #beta_var_files.append(beta_var_fn) if cons is not None: con_files = [] pval_files = [] for cname, con in cons.iteritems(): con_vol = expand_array_in_mask(con.effect, fdata.roiMask>0) con_fn = op.join(output_dir, 'con_effect_%s.nii' %cname) write_volume(con_vol, con_fn, fdata.meta_obj) con_files.append(con_fn) pval_vol = expand_array_in_mask(con.pvalue(con_test_baseline), fdata.roiMask>0) pval_fn = op.join(output_dir, 'con_pvalue_%s.nii' %cname) write_volume(pval_vol, pval_fn, fdata.meta_obj) pval_files.append(pval_fn) else: con_files = None pval_files = None dof = g.dof #if do_ppm: #for #TODO: FIR stuffs return beta_files, beta_values, beta_vars_voxels, dof#, con_files, pval_files
def parcellation_for_jde(fmri_data, avg_parcel_size=250, output_dir=None, method='gkm', glm_drift='Cosine', glm_hfcut=128): """ method: gkm, ward, ward_and_gkm """ if output_dir is None: output_dir = tempfile.mkdtemp(prefix='pyhrf_JDE_parcellation_GLM', dir=pyhrf.cfg['global']['tmp_path']) glm_output_dir = op.join(output_dir, 'GLM_for_parcellation') if not op.exists(glm_output_dir): os.makedirs(glm_output_dir) pyhrf.verbose(1, 'GLM for parcellation') # if fmri_data.data_type == 'volume': # paradigm_file, bold_file, mask_file = fmri_data.save(glm_output_dir) # beta_files = glm_nipy_from_files(bold_file, fmri_data.tr, paradigm_file, # glm_output_dir, mask_file, # drift_model=glm_drift, hfcut=glm_hfcut) # elif fmri_data.data_type == 'surface': # beta_files = glm_nipy(fmri_data, glm_output_dir, # drift_model=glm_drift, hfcut=glm_hfcut) g, dm, cons = glm_nipy(fmri_data, drift_model=glm_drift, hfcut=glm_hfcut) pval_files = [] if cons is not None: func_data = [('con_pval_%s' %cname, con.pvalue()) \ for cname, con in cons.iteritems()] else: reg_cst_drift = re.compile(".*constant.*|.*drift.*") func_data = [('beta_%s' %reg_name, g.beta[ir]) \ for ir,reg_name in enumerate(dm.names) \ if not reg_cst_drift.match(reg_name)] for name, data in func_data: val_vol = expand_array_in_mask(data, fmri_data.roiMask>0) val_fn = op.join(glm_output_dir, '%s.nii' %name) write_volume(val_vol, val_fn, fmri_data.meta_obj) pval_files.append(val_fn) mask_file = op.join(glm_output_dir,'mask.nii') write_volume(fmri_data.roiMask>0, mask_file, fmri_data.meta_obj) nvox = fmri_data.get_nb_vox_in_mask() nparcels = round_nb_parcels(nvox * 1. / avg_parcel_size) pyhrf.verbose(1, 'Parcellation from GLM outputs, method: %s, ' \ 'nb parcels: %d' %(method, nparcels)) if fmri_data.data_type == 'volume': parcellation_file = op.join(output_dir, 'parcellation_%s_np%d.nii' %(method, nparcels)) make_parcellation_from_files(pval_files, mask_file, parcellation_file, nparcels, method) parcellation,_ = read_volume(parcellation_file) else: mesh_file = fmri_data.data_files[-1] parcellation_file = op.join(output_dir, 'parcellation_%s_np%d.gii' %(method, nparcels)) make_parcellation_surf_from_files(pval_files, mesh_file, parcellation_file, nparcels, method, verbose=1) parcellation,_ = read_texture(parcellation_file) #print parcellation_file pyhrf.verbose(1, parcellation_report(parcellation)) return parcellation, parcellation_file
def simulation_save_vol_outputs(simulation, output_dir, bold_3D_vols_dir=None, simulation_graph_output=None, prefix=None, vol_meta=None): """ simulation_graph_output : None, 'simple', 'thumbnails' #TODO """ if simulation.has_key('paradigm'): fn = add_prefix(op.join(output_dir, 'paradigm.csv'), prefix) simulation['paradigm'].save_csv(fn) # Save all volumes in nifti format: if simulation.has_key('labels_vol'): mask_vol = np.ones_like(simulation['labels_vol'][0]) elif simulation.has_key('mask'): mask_vol = simulation.get('mask', None) elif simulation.has_key('labels'): mask_vol = np.ones_like(simulation['labels'][0]) else: raise Exception('Dunno where to get mask') pyhrf.verbose(3,'Vol mask of shape %s' %str(mask_vol.shape)) fn_mask = add_prefix(op.join(output_dir, 'mask.nii'), prefix) write_volume(mask_vol.astype(np.int32), fn_mask, vol_meta) if simulation.has_key('hrf_territories'): fn_h_territories = add_prefix(op.join(output_dir, 'hrf_territories.nii'), prefix) ht = expand_array_in_mask(simulation['hrf_territories']+1, mask_vol) write_volume(ht, fn_h_territories, vol_meta) if simulation.has_key('hrf'): from pyhrf.ndarray import MRI3Daxes fn_hrf = add_prefix(op.join(output_dir, 'hrf.nii'), prefix) pyhrf.verbose(3,'hrf flat shape %s' %str(simulation['hrf'].shape)) if simulation['hrf'].ndim == 1: hrf = (np.ones(mask_vol.size) * simulation['hrf'][:,np.newaxis]) else: hrf = simulation['hrf'] hrfs_vol = expand_array_in_mask(hrf, mask_vol, flat_axis=1) dt = simulation['dt'] chrfs = xndarray(hrfs_vol, axes_names=['time',]+MRI3Daxes, axes_domains={'time':np.arange(hrfs_vol.shape[0])*dt}) #write_volume(np.rollaxis(hrfs_vol,0,4), fn_hrf, vol_meta) chrfs.save(fn_hrf, vol_meta) ttp_vol = hrfs_vol.argmax(0) fn_ttp = add_prefix(op.join(output_dir, 'ttp.nii'), prefix) write_volume(ttp_vol, fn_ttp, vol_meta) if simulation.has_key('brf'): from pyhrf.ndarray import MRI3Daxes fn_brf = add_prefix(op.join(output_dir, 'brf.nii'), prefix) pyhrf.verbose(3,'brf flat shape %s' %str(simulation['brf'].shape)) brfs_vol = expand_array_in_mask(simulation['brf'], mask_vol, flat_axis=1) dt = simulation['dt'] cbrfs = xndarray(brfs_vol, axes_names=['time',]+MRI3Daxes, axes_domains={'time':np.arange(brfs_vol.shape[0])*dt}) #write_volume(np.rollaxis(hrfs_vol,0,4), fn_hrf, vol_meta) cbrfs.save(fn_brf, vol_meta) if simulation.has_key('prf'): from pyhrf.ndarray import MRI3Daxes fn_brf = add_prefix(op.join(output_dir, 'prf.nii'), prefix) pyhrf.verbose(3,'prf flat shape %s' %str(simulation['prf'].shape)) brfs_vol = expand_array_in_mask(simulation['prf'], mask_vol, flat_axis=1) dt = simulation['dt'] cbrfs = xndarray(brfs_vol, axes_names=['time',]+MRI3Daxes, axes_domains={'time':np.arange(brfs_vol.shape[0])*dt}) #write_volume(np.rollaxis(hrfs_vol,0,4), fn_hrf, vol_meta) cbrfs.save(fn_brf, vol_meta) if simulation.has_key('drift'): fn_drift = add_prefix(op.join(output_dir, 'drift.nii'), prefix) pyhrf.verbose(3,'drift flat shape %s' %str(simulation['drift'].shape)) drift_vol = expand_array_in_mask(simulation['drift'], mask_vol, flat_axis=1) #write_volume(drift_vol, fn_drift, vol_meta) write_volume(np.rollaxis(drift_vol,0,4), fn_drift) if simulation.has_key('drift_coeffs'): fn_drift = add_prefix(op.join(output_dir, 'drift_coeffs.nii'), prefix) pyhrf.verbose(3,'drift flat shape %s' %str(simulation['drift_coeffs'].shape)) drift_vol = expand_array_in_mask(simulation['drift'], mask_vol, flat_axis=1) #write_volume(drift_vol, fn_drift, vol_meta) write_volume(np.rollaxis(drift_vol,0,4), fn_drift) if simulation.has_key('noise'): fn_noise = add_prefix(op.join(output_dir, 'noise.nii'), prefix) pyhrf.verbose(3,'noise flat shape %s' %str(simulation['noise'].shape)) noise_vol = expand_array_in_mask(simulation['noise'], mask_vol, flat_axis=1) write_volume(np.rollaxis(noise_vol,0,4), fn_noise, vol_meta) fn_noise = add_prefix(op.join(output_dir, 'noise_emp_var.nii'), prefix) noise_vol = expand_array_in_mask(simulation['noise'].var(0), mask_vol) write_volume(noise_vol, fn_noise, vol_meta) if simulation.has_key('noise_var'): fn_noise_var = add_prefix(op.join(output_dir, 'noise_var.nii'), prefix) pyhrf.verbose(3,'noise_var flat shape %s' \ %str(simulation['noise_var'].shape)) noise_var_vol = expand_array_in_mask(simulation['noise_var'], mask_vol) write_volume(noise_var_vol, fn_noise_var, vol_meta) if simulation.has_key('stim_induced_signal'): fn_stim_induced = add_prefix(op.join(output_dir, 'stim_induced.nii'), prefix) pyhrf.verbose(3,'stim_induced flat shape %s' \ %str(simulation['stim_induced_signal'].shape)) stim_induced_vol = expand_array_in_mask(simulation['stim_induced_signal'], mask_vol, flat_axis=1) write_volume(np.rollaxis(stim_induced_vol,0,4), fn_stim_induced) if simulation.has_key('perf_stim_induced'): fn_stim_induced = add_prefix(op.join(output_dir, 'perf_stim_induced.nii'), prefix) pyhrf.verbose(3,'asl_stim_induced flat shape %s' \ %str(simulation['perf_stim_induced'].shape)) stim_induced_vol = expand_array_in_mask(simulation['perf_stim_induced'], mask_vol, flat_axis=1) write_volume(np.rollaxis(stim_induced_vol,0,4), fn_stim_induced) fn_stim_induced = add_prefix(op.join(output_dir, 'perf_stim_induced_ct.nii'), prefix) pyhrf.verbose(3,'asl_stim_induced flat shape %s' \ %str(simulation['perf_stim_induced'].shape)) dsf = simulation['dsf'] perf = np.dot(simulation['ctrl_tag_mat'], simulation['perf_stim_induced'][0:-1:dsf]) stim_induced_vol = expand_array_in_mask(perf, mask_vol, flat_axis=1) write_volume(np.rollaxis(stim_induced_vol,0,4), fn_stim_induced) if simulation.has_key('perf_baseline'): fn = add_prefix(op.join(output_dir, 'perf_baseline.nii'), prefix) pb = np.zeros_like(simulation['bold']) + simulation['perf_baseline'] write_volume(expand_array_in_mask(pb[0], mask_vol), fn, vol_meta) if simulation.has_key('bold_stim_induced'): fn_stim_induced = add_prefix(op.join(output_dir, 'bold_stim_induced.nii'), prefix) pyhrf.verbose(3,'asl_stim_induced flat shape %s' \ %str(simulation['bold_stim_induced'].shape)) stim_induced_vol = expand_array_in_mask(simulation['bold_stim_induced'], mask_vol, flat_axis=1) write_volume(np.rollaxis(stim_induced_vol,0,4), fn_stim_induced) m = np.where(mask_vol) labels_and_mask = mask_vol.copy()[m] for ic in xrange(simulation['labels'].shape[0]): if simulation.has_key('condition_defs'): c_name = simulation['condition_defs'][ic].name else: c_name = 'cond%d' %ic fn_labels = add_prefix(op.join(output_dir, 'labels_%s.nii' %c_name), prefix) if simulation.has_key('labels'): labels_c = simulation['labels'][ic] labels_and_mask[np.where(labels_c)] = ic+2 write_volume(expand_array_in_mask(labels_c,mask_vol).astype(np.int32), fn_labels, vol_meta) elif simulation.has_key('labels_vol'): labels_c = simulation['labels_vol'][ic] labels_and_mask[np.where(labels_c[m])] = ic+2 write_volume(labels_c.astype(np.int32), fn_labels, vol_meta) if simulation.has_key('nrls'): nrls_c = simulation['nrls'][ic] fn = add_prefix(op.join(output_dir, 'nrls_%s.nii' %c_name), prefix) write_volume(expand_array_in_mask(nrls_c,mask_vol) , fn, vol_meta) if simulation.has_key('nrls_session'): nrls_session_c = simulation['nrls_session'][ic] fn = add_prefix(op.join(output_dir, 'nrls_session_%s.nii' \ %(c_name)), prefix) write_volume(expand_array_in_mask(nrls_session_c,mask_vol) , fn, vol_meta) if simulation.has_key('brls'): brls_c = simulation['brls'][ic] fn = add_prefix(op.join(output_dir, 'brls_%s.nii' %c_name), prefix) write_volume(expand_array_in_mask(brls_c,mask_vol) , fn, vol_meta) if simulation.has_key('prls'): prls_c = simulation['prls'][ic] fn = add_prefix(op.join(output_dir, 'prls_%s.nii' %c_name), prefix) write_volume(expand_array_in_mask(prls_c,mask_vol) , fn, vol_meta) if simulation.has_key('neural_efficacies'): ne_c = simulation['neural_efficacies'][ic] fn = add_prefix(op.join(output_dir, 'neural_efficacies_%s.nii' \ %c_name), prefix) write_volume(expand_array_in_mask(ne_c,mask_vol) , fn, vol_meta) fn_labels_and_mask = add_prefix(op.join(output_dir, 'mask_and_labels.nii'), prefix) write_volume(expand_array_in_mask(labels_and_mask,mask_vol).astype(int), fn_labels_and_mask, vol_meta) if simulation.has_key('bold_full_vol') or simulation.has_key('bold'): fn = add_prefix(op.join(output_dir, 'bold.nii'), prefix) if simulation.has_key('bold_full_vol'): bold4D = simulation['bold_full_vol'] else: bold = simulation['bold'] bold4D = expand_array_in_mask(bold, mask_vol, flat_axis=1) write_volume(np.rollaxis(bold4D,0,4), fn, vol_meta) def save_time_series(k): if simulation.has_key(k): fn_stim_induced = add_prefix(op.join(output_dir, k+'.nii'), prefix) pyhrf.verbose(3,'%s flat shape %s' %(k,str(simulation[k].shape))) vol = expand_array_in_mask(simulation[k], mask_vol, flat_axis=1) write_volume(np.rollaxis(vol,0,4), fn_stim_induced) save_time_series('flow_induction') save_time_series('cbv') save_time_series('hbr') save_time_series('bold_stim_induced_rescaled') if simulation.has_key('asl'): fn = add_prefix(op.join(output_dir, 'asl.nii'), prefix) asl4D = expand_array_in_mask(simulation['asl'], mask_vol, flat_axis=1) write_volume(np.rollaxis(asl4D,0,4), fn, vol_meta) if simulation.has_key('outliers'): fn = add_prefix(op.join(output_dir, 'outliers.nii'), prefix) outliers = expand_array_in_mask(simulation['outliers'], mask_vol, flat_axis=1) write_volume(np.rollaxis(outliers,0,4), fn, vol_meta) if simulation.has_key('hrf_group'): hrfgroup = simulation['hrf_group'] nb_vox = mask_vol.size fn_hrf = add_prefix(op.join(output_dir, 'hrf_group.nii'), prefix) pyhrf.verbose(3,'hrf group shape %s' %str(simulation['hrf_group'].shape)) hrfGd = duplicate_hrf(nb_vox, hrfgroup) hrfs_vol = expand_array_in_mask(hrfGd, mask_vol, flat_axis=1) dt = simulation['dt'] chrfs = xndarray(hrfs_vol, axes_names=['time',]+MRI3Daxes, axes_domains={'time':np.arange(hrfs_vol.shape[0])*dt}) chrfs.save(fn_hrf, vol_meta) if bold_3D_vols_dir is not None: assert op.exists(bold_3D_vols_dir) for iscan, bscan in enumerate(bold4D): fnout = add_prefix('bold_%06d.nii'%(iscan), prefix) #print fnout write_volume(bscan, op.join(bold_3D_vols_dir,fnout), vol_meta)
def save_time_series(k): if simulation.has_key(k): fn_stim_induced = add_prefix(op.join(output_dir, k+'.nii'), prefix) pyhrf.verbose(3,'%s flat shape %s' %(k,str(simulation[k].shape))) vol = expand_array_in_mask(simulation[k], mask_vol, flat_axis=1) write_volume(np.rollaxis(vol,0,4), fn_stim_induced)
def BMA_consensus_cluster_parallel( cfg, remote_path, remote_BOLD_fn, remote_mask_fn, Y, nifti_masker, num_vox, K_clus, K_clusters, parc, alpha, prop, nbItRFIR, onsets, durations, output_sub_parc, rescale=True, averg_bold=False, ): """ Performs all steps for one clustering case (Kclus given, number l of the parcellation given) remote_path: path on the cluster, where results will be stored """ import os import sys sys.path.append("/home/pc174679/pyhrf/pyhrf-tree_trunk/script/WIP/Scripts_IRMf_BB/Parcellations/") sys.path.append("/home/pc174679/pyhrf/pyhrf-tree_trunk/script/WIP/Scripts_IRMf_Adultes_Solv/") sys.path.append( "/home/pc174679/pyhrf/pyhrf-tree_trunk/script/WIP/Scripts_IRMf_Adultes_Solv/Scripts_divers_utiles/Scripts_utiles/" ) sys.path.append("/home/pc174679/local/installations/consensus-cluster-0.6") from Random_parcellations import random_parcellations, subsample_data_on_time from Divers_parcellations_test import * from RFIR_evaluation_parcellations import JDE_estim, RFIR_estim, clustering_from_RFIR from Random_parcellations import hrf_roi_to_vox from pyhrf.tools.io import remote_copy, remote_mkdir from nisl import io # nifti_masker.mask=remote_mask_fn # Creation of the necessary paths --> do not do here parc_name = "Subsampled_data_with_" + str(K_clus) + "clusters" parc_name_clus = parc_name + "rnd_number_" + str(parc + 1) remote_sub = os.sep.join((remote_path, parc_name)) # if not os.path.exists(remote_sub): # os.path.exists(remote_sub) # print 'remote_sub:', remote_sub # os.makedirs(remote_sub) remote_sub_parc = os.sep.join((remote_sub, parc_name_clus)) # if not os.path.exists(remote_sub_parc): # os.makedirs(remote_sub_parc) output_RFIR_parc = os.sep.join((output_sub_parc, "RFIR_estim")) ################################### ## 1st STEP: SUBSAMPLING print "--- Subsample data ---" Ysub = subsample_data_on_time(Y, remote_mask_fn, K_clus, alpha, prop, nifti_masker, rescale=rescale) print "Ysub:", Ysub print "remote_sub_prc:", remote_sub_parc Ysub_name = "Y_sub_" + str(K_clus) + "clusters_" + "rnd_number_" + str(parc + 1) + ".nii" Ysub_fn = os.sep.join((remote_sub_parc, Ysub_name)) Ysub_masked = nifti_masker.inverse_transform(Ysub).get_data() write_volume(Ysub_masked, Ysub_fn) ################################### ## 2D STEP: RFIR print "--- Performs RFIR estimation ---" remote_RFIR_parc_clus = os.sep.join((remote_sub_parc, "RFIR_estim")) # if not os.path.exists(remote_RFIR_parc):os.makedirs(remote_RFIR_parc) # remote_RFIR_parc_clus = os.sep.join((remote_RFIR_parc, parc_name_clus)) # if not os.path.exists(remote_RFIR_parc_clus):os.makedirs(remote_RFIR_parc_clus) print " * output path for RFIR ", remote_RFIR_parc_clus print " * RFIR for subsampling nb ", str(parc + 1), " with ", K_clus, " clusters" RFIR_estim(nbItRFIR, onsets, durations, Ysub_fn, remote_mask_fn, remote_RFIR_parc, avg_bold=averg_bold) hrf_fn = os.sep.join((remote_RFIR_parc_clus, "rfir_ehrf.nii")) # remote_copy([hrf_fn], remote_host, # remote_user, remote_path)[0] ################################### ## 3D STEP: CLUSTERING FROM RFIR RESULTS name_hrf = "rfir_ehrf.nii" from pyhrf.tools.io import write_volume, read_volume from pyhrf.tools.io import read_volume, write_volume import nisl.io as ionisl from sklearn.feature_extraction import image from sklearn.cluster import WardAgglomeration from scipy.spatial.distance import cdist, pdist hrf_fn = os.sep.join((remote_RFIR_parc_clus, name_hrf)) hrf = read_volume(hrf_fn)[0] hrf_t_fn = add_suffix(hrf_fn, "transpose") # taking only 1st condition to parcellate write_volume(hrf[:, :, :, :, 0], hrf_t_fn) nifti_masker = ionisl.NiftiMasker(remote_mask_fn) Nm = nifti_masker.fit(hrf_t_fn) # features: coeff of the HRF HRF = Nm.fit_transform(hrf_t_fn) mask, meta_data = read_volume(remote_mask_fn) shape = mask.shape connectivity = image.grid_to_graph(n_x=shape[0], n_y=shape[1], n_z=shape[2], mask=mask) # features used for clustering features = HRF.transpose() ward = WardAgglomeration(n_clusters=K_clus, connectivity=connectivity, memory="nisl_cache") ward.fit(HRF) labels_tot = ward.labels_ + 1 # Kelbow, Perc_WSS, all_parc_from_RFIR_fns, all_parc_RFIR = \ # clustering_from_RFIR(K_clusters, remote_RFIR_parc_clus, remote_mask_fn, name_hrf, plots=False) # labels_tot = all_parc_RFIR[str(Kelbow)] # to retrieve clustering with as many clusters as determined in K_clusters # labels_tot = all_parc_RFIR[str(K_clus)] # Parcellation retrieved: for K=Kelbow # clusters_RFIR_fn = all_parc_from_RFIR[str(Kelbow)] # clustering_rfir_fn = os.path.join(remote_RFIR_parc_clus, 'output_clustering_elbow.nii') # write_volume(read_volume(clusters_RFIR_fn)[0], clustering_rfir_fn, meta_bold) # labels_tot = nifti_masker.fit_transform([clusters_RFIR_fn])[0] # labels_tot = read_volume(clusters_RFIR_fn)[0] # labels_name='labels_' + str(int(K_clus)) + '_' + str(parc+1) + '.pck' # name_f = os.sep.join((remote_sub_parc, labels_name)) # pickle_labels=open(name_f, 'w') # cPickle.dump(labels_tot,f) # pickle_labels.close() # remote_copy(pickle_labels, remote_user, # remote_host, output_sub_parc) ################################# ## Prepare consensus clustering print "Prepare consensus clustering" clustcount, totalcount = upd_similarity_matrix(labels_tot) print "results:", clustcount return clustcount.astype(np.bool)