def process_experiment(_experiment, _overwrite=False): _arguments = [(_experiment, int(_series.split('_')[1]), _overwrite) for _series in paths.image_files(paths.serieses(_experiment)) ] _p = Pool(CPUS_TO_USE) _p.starmap(process_series, _arguments) _p.close()
def process_experiment(_experiment, _overwrite=False): _arguments = [] for _tuple in load.experiment_groups_as_tuples(_experiment): _experiment, _series_id, _group = _tuple _arguments.append((_experiment, _series_id, _group, _overwrite)) _p = Pool(CPUS_TO_USE) _p.starmap(process_group, _arguments) _p.close()
def _phase_coherence( signal_pair: Tuple[TimeSeries, TimeSeries], params: PCParams ) -> Tuple[Tuple[TimeSeries, TimeSeries], ndarray, ndarray, ndarray, ndarray]: """ Function which uses `wpc` to calculate phase coherence for a single pair of signals. The signals must have their wavelet transforms attached in their `output_data` member variable. :param signal_pair: tuple containing 2 signals :param params: the params object with parameters for the function :return: [tuple] the pair of signals; [2D array] the time-localised phase coherence; [1D array] phase coherence; [1D array] phase difference; [1D array] time-localised phase coherence of surrogates """ s1, s2 = signal_pair wt1 = s1.output_data.values wt2 = s2.output_data.values freq = s1.output_data.freq fs = s1.frequency # Calculate surrogates. surr_count = params.surr_count surr_method = params.surr_method surr_preproc = params.surr_preproc surrogates, _ = surrogate_calc(s1, surr_count, surr_method, surr_preproc, fs) # Calculate surrogates. pool = Pool() args = [(wt1, surrogates[i], params) for i in range(surr_count)] tpc_surr = pool.starmap(_wt_surrogate_calc, args) if len(tpc_surr) > 0: tpc_surr = np.mean(tpc_surr, axis=0) # Calculate phase coherence. tpc, pc, pdiff = wpc(wt1, wt2, freq, fs) return signal_pair, tpc, pc, pdiff, tpc_surr
('SN20_Bleb_fromStart', 14, 0, 1, -235, 30), ('SN20_Bleb_fromStart', 14, 0, 2, 120, 230), ('SN20_Bleb_fromStart', 14, 0, 3, -230, 105), ('SN20_Bleb_fromStart', 14, 0, 4, 205, 35), ('SN20_Bleb_fromStart', 14, 1, 2, 110, -180), ('SN20_Bleb_fromStart', 14, 1, 3, -220, 25), ('SN20_Bleb_fromStart', 14, 1, 4, -150, 0), ('SN20_Bleb_fromStart', 14, 2, 3, 160, -130), ('SN20_Bleb_fromStart', 14, 2, 4, -75, 210), ('SN20_Bleb_fromStart', 14, 3, 4, 220, 105), ('SN20_Bleb_fromStart', 15, 0, 1, 0, 235), ('SN20_Bleb_fromStart', 16, 0, 1, 0, -225), ('SN20_Bleb_fromStart', 16, 0, 2, -80, 130), ('SN20_Bleb_fromStart', 16, 1, 2, -60, -120), ('SN20_Bleb_fromStart', 17, 0, 2, -180, 0), ('SN20_Bleb_fromStart', 17, 0, 3, 155, 0), ('SN20_Bleb_fromStart', 17, 1, 2, -225, -115), ('SN20_Bleb_fromStart', 17, 1, 3, -135, 20), ('SN20_Bleb_fromStart', 18, 0, 1, -110, -175), ('SN20_Bleb_fromStart', 19, 0, 1, 70, -150), ('SN20_Bleb_fromStart', 19, 1, 2, -100, 115), ('SN20_Bleb_fromStart', 19, 1, 3, 60, -170), ('SN20_Bleb_fromStart', 19, 2, 3, 135, 185), ('SN20_Bleb_fromStart', 20, 0, 1, 175, 20), ('SN20_Bleb_fromStart', 20, 0, 2, 205, -60), ('SN20_Bleb_fromStart', 20, 1, 2, -135, 80), ] _p = Pool(CPUS_TO_USE) _answers = _p.starmap(process_fake_following, _arguments) _p.close()