np.random.seed(sd) curr_date = datetime.datetime.now().strftime('%Y_%m_%d') + '_' gen_fn_dir = os.path.abspath('..') + '/shared_scripts' sys.path.append(gen_fn_dir) import general_file_fns as gff gen_params = gff.load_pickle_file('../general_params/general_params.p') from binned_spikes_class import spike_counts from dim_red_fns import run_dim_red from scipy.spatial.distance import pdist from sklearn import neighbors save_dir = gff.return_dir(gen_params['results_dir'] + '2019_03_22_tda/') plot_barcode = True cmd_line = False # if thrsh is True then we threshold out low density points (nt-TDA in the # paper) if cmd_line: session = sys.argv[1] state = sys.argv[2] thrsh = sys.argv[3] # threshold out low density pts else: session = 'Mouse28-140313' state = 'Wake' thrsh = False area = 'ADn'
sd = int((time.time() % 1) * (2**31)) np.random.seed(sd) curr_date = datetime.datetime.now().strftime('%Y_%m_%d') + '_' gen_fn_dir = os.path.abspath('..') + '/shared_scripts' sys.path.append(gen_fn_dir) import general_file_fns as gff gen_params = gff.load_pickle_file('../general_params/general_params.p') from binned_spikes_class import spike_counts from dim_red_fns import run_dim_red cols = gen_params['cols'] dir_to_save = gff.return_dir(gen_params['results_dir'] + '2019_06_03_dim_red/') command_line = False if command_line: session = sys.argv[1] state = sys.argv[2] # If condition is 'joint' should unpack state into first and second condition = sys.argv[3] target_dim = int(sys.argv[4]) desired_nSamples = int(sys.argv[5]) else: session = 'Mouse28-140313' state = 'Wake' #state2 = 'REM' condition = 'solo' target_dim = 3
session = 'Mouse28-140313' make_processed_files = True make_rates = True if make_processed_files: data_path = gen_params['raw_data_dir'] + session + '/' params = { 'session': 'Mouse28-140313', 'data_path': data_path, 'eeg_sampling_rate': 1250., 'spike_sampling_interval': 1.0 / (20e3) } data = drf.gather_session_spike_info(params) save_dir = gff.return_dir(gen_params['processed_data_dir']) gff.save_pickle_file(data, save_dir + '%s.p' % session) if make_rates: print 'Getting kernel rates' t0 = time.time() sigma = 0.1 params = {'dt': 0.05, 'method': 'gaussian', 'sigma': sigma} inp_data = gff.load_pickle_file(gen_params['processed_data_dir'] + '%s.p' % session) rates = rf.get_rates_and_angles_by_interval(inp_data, params, smooth_type='kernel', just_wake=True) save_dir = gff.return_dir(gen_params['kernel_rates_dir'] + '%0.0fms_sigma/' % (sigma * 1000))
sd = int((time.time() % 1) * (2**31)) np.random.seed(sd) curr_date = datetime.datetime.now().strftime('%Y_%m_%d') + '_' gen_fn_dir = os.path.abspath('..') + '/shared_scripts' sys.path.append(gen_fn_dir) import general_file_fns as gff gen_params = gff.load_pickle_file('../general_params/general_params.p') from binned_spikes_class import spike_counts from dim_red_fns import run_dim_red import manifold_fit_and_decode_fns as mff dir_to_save = gff.return_dir(gen_params['results_dir'] + '2019_06_03_curve_fits/') cmd_line = False if cmd_line: session = sys.argv[1] fit_dim = int(sys.argv[2]) nKnots = int(sys.argv[3]) knot_order = sys.argv[4] penalty_type = sys.argv[5] nTests = int(sys.argv[6]) train_frac = float(sys.argv[7]) else: session = 'Mouse28-140313' fit_dim = 3 nKnots = 15 knot_order = 'wt_per_len'