try: json_file = sys.argv[2] print("USING:", json_file) except: json_file = "settings.json" print("USING:", json_file) # opening a json file with open(json_file) as pipeline_file: parameters = json.load(pipeline_file) path = parameters["dataset_path"] sfreq = parameters["downsample_dataset"] sub_path = op.join(path, "data") der_path = op.join(path, "derivatives") files.make_folder(der_path) proc_path = op.join(der_path, "processed") files.make_folder(proc_path) subjects = files.get_folders_files(sub_path)[0] subjects.sort() subject = subjects[index] subject_id = subject.split("/")[-1] meg_path = op.join(subject, "ses-01", "meg") sub_path = op.join(proc_path, subject_id) files.make_folder(sub_path) dss = files.get_folders_files(meg_path)[0] dss = [i for i in dss if "ds" in i]
json_key_file = "keys/keys.json" with open(json_key_file) as key_file: params = json.load(key_file) json_vid_file = "video_analysis_params.json" with open(json_vid_file) as vid_json: analysis = json.load(vid_json) os.environ["INDICO_APP_KEY"] = params["indico"] os.environ["CLARIFAI_API_KEY"] = params["clarifai"] os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = params["google"] vid_file = analysis["video_path"] files.make_folder("data") if analysis["frames_no"] == 0: video = cv2.VideoCapture(vid_file) total_frames = 0 while True: (grab, frame) = video.read() if not grab: break total_frames += 1 video.release() cv2.destroyAllWindows() misc.update_key_value(json_vid_file, "frames_no", total_frames)
print("USING:", json_file) except: json_file = "settings.json" print("USING:", json_file) # opening a json file with open(json_file) as pipeline_file: parameters = json.load(pipeline_file) path = parameters["dataset_path"] sfreq = parameters["downsample_dataset"] hi_pass = parameters["hi_pass_filter"] sub_path = op.join(path, "data") der_path = op.join(path, "derivatives") files.make_folder(der_path) proc_path = op.join(der_path, "processed") files.make_folder(proc_path) subjects = files.get_folders_files(proc_path)[0] subjects.sort() subject = subjects[index] subject_id = subject.split(sep)[-1] sub_path = op.join(proc_path, subject_id) files.make_folder(sub_path) qc_folder = op.join(sub_path, "QC") files.make_folder(qc_folder) epo_paths = files.get_files(sub_path, "sub", "-epo.fif")[2]
json_file = "settings.json" print("USING:", json_file) start_time = time.time() # opening a json file with open(json_file) as pipeline_file: parameters = json.load(pipeline_file) path = parameters["dataset_path"] sfreq = parameters["downsample_dataset"] hi_pass = parameters["hi_pass_filter"] sub_path = op.join(path, "data") der_path = op.join(path, "derivatives") files.make_folder(der_path) proc_path = op.join(der_path, "processed") files.make_folder(proc_path) subjects = files.get_folders_files(proc_path)[0] subjects.sort() subject = subjects[index] subject_id = subject.split("/")[-1] sub_path = op.join(proc_path, subject_id) files.make_folder(sub_path) #setting the paths and extracting files slt_mot_paths = [i for i in files.get_folders_files(sub_path)[0] if "motor" in i] slt_vis_paths = [i for i in files.get_folders_files(sub_path)[0] if "visual" in i] epo_mot_paths = files.get_files(sub_path, "sub", "motor-epo.fif")[2]
print("USING:", json_file) # opening a json file with open(json_file) as pipeline_file: parameters = json.load(pipeline_file) def split_and_eval(x): return [eval(i) for i in x.split(",")] path = parameters["dataset_path"] sfreq = parameters["downsample_dataset"] der_path = op.join(path, "derivatives") files.make_folder(der_path) proc_path = op.join(der_path, "processed") files.make_folder(proc_path) subjects = files.get_folders_files(proc_path)[0] subjects.sort() subject = subjects[index] subject_id = subject.split("/")[-1] print(subject) raw_meg_dir = op.join(path, "data") raw_meg_path = op.join(raw_meg_dir, subject_id, "ses-01", "meg") ds_paths = files.get_folders_files(raw_meg_path)[0] ds_paths = [i for i in ds_paths if "misc" not in i] ds_paths.sort() res4_paths = [files.get_files(i, "", ".res4")[2][0] for i in ds_paths]
def norm_vec(vec): """ returns unit vector """ mag = np.sqrt((vec[0]**2 + vec[1]**2 + vec[2]**2)) unit_vector = np.array([vec[0] / mag, vec[1] / mag, vec[2] / mag]) return unit_vector path = parameters["dataset_path"] sfreq = parameters["downsample_head"] sub_path = op.join(path, "data") der_path = op.join(path, "derivatives") files.make_folder(der_path) head_path = op.join(der_path, "head_motion") files.make_folder(head_path) subjects = files.get_folders_files(sub_path)[0] subject = subjects[index] subject_id = subject.split("/")[-1] meg_path = op.join(subject, "ses-01", "meg") dss = files.get_folders_files(meg_path)[0] dss = [i for i in dss if "ds" in i] dss.sort() for ds in dss: print("PRINT:", ds)