def _list_outputs(self): from bids.layout import BIDSLayout base_dir = self.inputs.base_directory os.makedirs(base_dir, exist_ok=True) layout = BIDSLayout(base_dir, validate=False) path_patterns = self.inputs.path_patterns if not isdefined(path_patterns): path_patterns = None out_files = [] for entities, in_file in zip(self.inputs.entities, self.inputs.in_file): ents = {**self.inputs.fixed_entities} ents.update(entities) ents = {k: snake_to_camel(str(v)) for k, v in ents.items()} out_fname = os.path.join(base_dir, layout.build_path(ents, path_patterns)) makedirs(os.path.dirname(out_fname), exist_ok=True) _copy_or_convert(in_file, out_fname) out_files.append(out_fname) return {'out_file': out_files}
def _list_outputs(self): from bids.layout import BIDSLayout base_dir = self.inputs.base_directory os.makedirs(base_dir, exist_ok=True) layout = BIDSLayout(base_dir, validate=False) path_patterns = self.inputs.path_patterns if not isdefined(path_patterns): path_patterns = None out_files = [] for entities, in_file in zip(self.inputs.entities, self.inputs.in_file): ents = {**self.inputs.fixed_entities} ents.update(entities) ext = bids_split_filename(in_file)[2] ents['extension'] = self._extension_map.get(ext, ext) # In some instances, name/contrast could have the following # format (eg: gain.Range, gain.EqualIndifference). # This prevents issues when creating/searching files for the report for k, v in ents.items(): if k in ("node", "name", "contrast", "stat"): ents.update({k: to_alphanum(str(v))}) out_fname = os.path.join( base_dir, layout.build_path(ents, path_patterns, validate=False) ) os.makedirs(os.path.dirname(out_fname), exist_ok=True) _copy_or_convert(in_file, out_fname) out_files.append(out_fname) return {'out_file': out_files}
def write_full_report(report_dict, run_context, deriv_dir): fl_layout = BIDSLayout(deriv_dir, config=['bids', 'derivatives', 'fitlins']) env = jinja2.Environment( loader=jinja2.FileSystemLoader(searchpath=pkgr.resource_filename('fitlins', '/')) ) tpl = env.get_template('data/full_report.tpl') model_name = snake_to_camel(report_dict['model'].get('name') or "untitled") target_file = op.join( deriv_dir, fl_layout.build_path({'model': model_name}, PATH_PATTERNS, validate=False) ) html = tpl.render(deroot({**report_dict, **run_context}, op.dirname(target_file))) Path(target_file).parent.mkdir(parents=True, exist_ok=True) Path(target_file).write_text(html)
def write_report(level, report_dicts, run_context, deriv_dir): fl_layout = BIDSLayout( deriv_dir, config=['bids', 'derivatives', 'fitlins']) env = jinja2.Environment( loader=jinja2.FileSystemLoader( searchpath=pkgr.resource_filename('fitlins', '/'))) tpl = env.get_template('data/report.tpl') for context in report_dicts: ents = context['ents'].copy() ents['model'] = snake_to_camel(context['model_name']) target_file = op.join(deriv_dir, fl_layout.build_path(ents, PATH_PATTERNS)) html = tpl.render(deroot({'level': level, **context, **run_context}, op.dirname(target_file))) with open(target_file, 'w') as fobj: fobj.write(html)
def demo_rsHRF(input_file, mask_file, output_dir, para, p_jobs, file_type=".nii", mode="bids", wiener=False, temporal_mask=[]): # book-keeping w.r.t parameter values if 'localK' not in para or para['localK'] == None: if para['TR'] <= 2: para['localK'] = 1 else: para['localK'] = 2 # creating the output-directory if not already present if not os.path.isdir(output_dir): os.mkdir(output_dir) # for four-dimensional input if mode != 'time-series': if mode == 'bids' or mode == 'bids w/ atlas': name = input_file.split('/')[-1].split('.')[0] v1 = spm_dep.spm.spm_vol(input_file) else: name = input_file.split('/')[-1].split('.')[0] v1 = spm_dep.spm.spm_vol(input_file) if mask_file != None: if mode == 'bids': mask_name = mask_file.split('/')[-1].split('.')[0] v = spm_dep.spm.spm_vol(mask_file) else: mask_name = mask_file.split('/')[-1].split('.')[0] v = spm_dep.spm.spm_vol(mask_file) if file_type == ".nii" or file_type == ".nii.gz": brain = spm_dep.spm.spm_read_vols(v) else: brain = v.agg_data().flatten(order='F') if ((file_type == ".nii" or file_type == ".nii.gz") and \ v1.header.get_data_shape()[:-1] != v.header.get_data_shape()) or \ ((file_type == ".gii" or file_type == ".gii.gz") and \ v1.agg_data().shape[0]!= v.agg_data().shape[0]): raise ValueError('Inconsistency in input-mask dimensions' + '\n\tinput_file == ' + name + file_type + '\n\tmask_file == ' + mask_name + file_type) else: if file_type == ".nii" or file_type == ".nii.gz": data = v1.get_data() else: data = v1.agg_data() else: print('No atlas provided! Generating mask file...') if file_type == ".nii" or file_type == ".nii.gz": data = v1.get_data() brain = np.nanvar(data.reshape(-1, data.shape[3]), -1, ddof=0) else: data = v1.agg_data() brain = np.nanvar(data, -1, ddof=0) print('Done') voxel_ind = np.where(brain > 0)[0] mask_shape = data.shape[:-1] nobs = data.shape[-1] data1 = np.reshape(data, (-1, nobs), order='F').T bold_sig = stats.zscore(data1[:, voxel_ind], ddof=1) # for time-series input else: name = input_file.split('/')[-1].split('.')[0] data1 = (np.loadtxt(input_file, delimiter=",")) if data1.ndim == 1: data1 = np.expand_dims(data1, axis=1) nobs = data1.shape[0] bold_sig = stats.zscore(data1, ddof=1) if len(temporal_mask) > 0 and len(temporal_mask) != nobs: raise ValueError('Inconsistency in temporal_mask dimensions.\n' + 'Size of mask: ' + str(len(temporal_mask)) + '\n' + 'Size of time-series: ' + str(nobs)) bold_sig = np.nan_to_num(bold_sig) bold_sig_deconv = processing. \ rest_filter. \ rest_IdealFilter(bold_sig, para['TR'], para['passband_deconvolve']) bold_sig = processing. \ rest_filter. \ rest_IdealFilter(bold_sig, para['TR'], para['passband']) data_deconv = np.zeros(bold_sig.shape) event_number = np.zeros((1, bold_sig.shape[1])) print('Retrieving HRF ...') #Estimate HRF for the fourier / hanning / gamma / cannon basis functions if not (para['estimation'] == 'sFIR' or para['estimation'] == 'FIR'): bf = basis_functions.basis_functions.get_basis_function( bold_sig.shape, para) beta_hrf, event_bold = utils.hrf_estimation.compute_hrf(bold_sig, para, temporal_mask, p_jobs, bf=bf) hrfa = np.dot(bf, beta_hrf[np.arange(0, bf.shape[1]), :]) #Estimate HRF for FIR and sFIR else: para['T'] = 1 beta_hrf, event_bold = utils.hrf_estimation.compute_hrf( bold_sig, para, temporal_mask, p_jobs) hrfa = beta_hrf[:-1, :] nvar = hrfa.shape[1] PARA = np.zeros((3, nvar)) for voxel_id in range(nvar): hrf1 = hrfa[:, voxel_id] PARA[:, voxel_id] = \ parameters.wgr_get_parameters(hrf1, para['TR'] / para['T']) print('Done') print('Deconvolving HRF ...') if para['T'] > 1: hrfa_TR = signal.resample_poly(hrfa, 1, para['T']) else: hrfa_TR = hrfa for voxel_id in range(nvar): hrf = hrfa_TR[:, voxel_id] if not wiener: H = np.fft.fft(np.append(hrf, np.zeros( (nobs - max(hrf.shape), 1))), axis=0) M = np.fft.fft(bold_sig_deconv[:, voxel_id]) data_deconv[:, voxel_id] = \ np.fft.ifft(H.conj() * M / (H * H.conj() + .1*np.mean((H * H.conj())))) else: data_deconv[:, voxel_id] = iterative_wiener_deconv.rsHRF_iterative_wiener_deconv( bold_sig_deconv[:, voxel_id], hrf) event_number[:, voxel_id] = np.amax(event_bold[voxel_id].shape) print('Done') print('Saving Output ...') # setting the output-path if mode == 'bids' or mode == 'bids w/ atlas': layout_output = BIDSLayout(output_dir) entities = parse_file_entities(input_file) sub_save_dir = layout_output.build_path(entities).rsplit('/', 1)[0] else: sub_save_dir = output_dir if not os.path.isdir(sub_save_dir): os.makedirs(sub_save_dir, exist_ok=True) dic = {'para': para, 'hrfa': hrfa, 'event_bold': event_bold, 'PARA': PARA} ext = '_hrf.mat' if mode == "time-series": dic["event_number"] = event_number dic["data_deconv"] = data_deconv ext = '_hrf_deconv.mat' name = name.rsplit('_bold', 1)[0] sio.savemat(os.path.join(sub_save_dir, name + ext), dic) HRF_para_str = ['height', 'T2P', 'FWHM'] if mode != "time-series": mask_data = np.zeros(mask_shape).flatten(order='F') for i in range(3): fname = os.path.join(sub_save_dir, name + '_' + HRF_para_str[i]) mask_data[voxel_ind] = PARA[i, :] mask_data = mask_data.reshape(mask_shape, order='F') spm_dep.spm.spm_write_vol(v1, mask_data, fname, file_type) mask_data = mask_data.flatten(order='F') fname = os.path.join(sub_save_dir, name + '_eventnumber') mask_data[voxel_ind] = event_number mask_data = mask_data.reshape(mask_shape, order='F') spm_dep.spm.spm_write_vol(v1, mask_data, fname, file_type) mask_data = np.zeros(data.shape) dat3 = np.zeros(data.shape[:-1]).flatten(order='F') for i in range(nobs): fname = os.path.join(sub_save_dir, name + '_deconv') dat3[voxel_ind] = data_deconv[i, :] dat3 = dat3.reshape(data.shape[:-1], order='F') if file_type == ".nii" or file_type == ".nii.gz": mask_data[:, :, :, i] = dat3 else: mask_data[:, i] = dat3 dat3 = dat3.flatten(order='F') spm_dep.spm.spm_write_vol(v1, mask_data, fname, file_type) pos = 0 while pos < hrfa_TR.shape[1]: if np.any(hrfa_TR[:, pos]): break pos += 1 event_plot = lil_matrix((1, nobs)) if event_bold.size: event_plot[:, event_bold[pos]] = 1 else: print("No Events Detected!") return 0 event_plot = np.ravel(event_plot.toarray()) plt.figure() plt.plot(para['TR'] * np.arange(1, np.amax(hrfa_TR[:, pos].shape) + 1), hrfa_TR[:, pos], linewidth=1) plt.xlabel('time (s)') plt.savefig(os.path.join(sub_save_dir, name + '_hrf_plot.png')) plt.figure() plt.plot(para['TR'] * np.arange(1, nobs + 1), np.nan_to_num(stats.zscore(bold_sig[:, pos], ddof=1)), linewidth=1) plt.plot(para['TR'] * np.arange(1, nobs + 1), np.nan_to_num(stats.zscore(data_deconv[:, pos], ddof=1)), color='r', linewidth=1) markerline, stemlines, baseline = \ plt.stem(para['TR'] * np.arange(1, nobs + 1), event_plot) plt.setp(baseline, 'color', 'k', 'markersize', 1) plt.setp(stemlines, 'color', 'k') plt.setp(markerline, 'color', 'k', 'markersize', 3, 'marker', 'd') plt.legend(['BOLD', 'Deconvolved BOLD', 'Events'], loc='best') plt.xlabel('time (s)') plt.savefig(os.path.join(sub_save_dir, name + '_deconvolution_plot.png')) print('Done') return 0
# Creating the layout object for this BIDS data set layout = BIDSLayout(dataDir) # new subjects newSubj = list(range(10, 19)) # other information for the directory organization listSes = ['test', 'retest'] listMod = ['anat', 'func', 'dwi'] # first, a list of new subject directories pattern = "sub-{subject}" for iSubj in newSubj: # dictionary listing entitied entities = {'subject': '%02d' % iSubj} newDir = layout.build_path(entities, path_patterns=[pattern]) print(newDir) # session directories for each subject pattern = "sub-{subject}/ses-{session}" for iSubj in newSubj: for iSes in listSes: # dictionary listing entitied entities = {'subject': '%02d' % iSubj, 'session': iSes} newDir = layout.build_path(entities, path_patterns=[pattern]) print(newDir) # image modality directories for each subject pattern = "sub-{subject}/ses-{session}/{modality}" for iSubj in newSubj: for iSes in listSes: