efel/pyfeatures/pyfeatures.py
File `pyfeatures.py` has 374 lines of code (exceeds 250 allowed). Consider refactoring.from __future__ import annotations"""Python implementation of features""" """Copyright (c) 2015, Blue Brain Project/EPFLAll rights reserved.Redistribution and use in source and binary forms, with or withoutmodification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" ANDANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIEDWARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE AREDISCLAIMED. IN NO EVENT SHALL <COPYRIGHT HOLDER> BE LIABLE FOR ANYDIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED ANDON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THISSOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE."""from efel.pyfeatures.cppfeature_access import _get_cpp_data, get_cpp_featurefrom efel.pyfeatures.isi import *from typing_extensions import deprecated import numpy as npfrom numpy.fft import * all_pyfeatures = [ 'voltage', 'time', 'current', 'ISIs', 'ISI_values', 'ISI_CV', 'single_burst_ratio', 'irregularity_index', 'burst_ISI_indices', 'burst_mean_freq', 'interburst_voltage', 'ISI_log_slope', 'ISI_semilog_slope', 'ISI_log_slope_skip', 'initburst_sahp', 'initburst_sahp_vb', 'initburst_sahp_ssse', 'depol_block', 'depol_block_bool', 'Spikecount', 'Spikecount_stimint', 'spike_count', 'spike_count_stimint', 'spikes_per_burst', 'spikes_per_burst_diff', 'spikes_in_burst1_burst2_diff', 'spikes_in_burst1_burstlast_diff', 'impedance', 'burst_number', 'strict_burst_number', 'trace_check', 'phaseslope_max', 'inv_ISI_values', 'inv_first_ISI', 'inv_second_ISI', 'inv_third_ISI', 'inv_fourth_ISI', 'inv_fifth_ISI', 'inv_last_ISI', 'activation_time_constant', 'deactivation_time_constant', 'inactivation_time_constant',] def voltage() -> np.ndarray | None: """Get voltage trace.""" return get_cpp_feature("voltage") def time() -> np.ndarray | None: """Get time trace.""" return get_cpp_feature("time") @deprecated("Use spike_count instead.")def Spikecount() -> np.ndarray: return spike_count() def spike_count() -> np.ndarray: """Get spike count.""" peak_indices = get_cpp_feature("peak_indices") if peak_indices is None: return np.array([0]) return np.array([peak_indices.size]) @deprecated("Use spike_count_stimint instead.")def Spikecount_stimint() -> np.ndarray: return spike_count_stimint() def spike_count_stimint() -> np.ndarray: """Get spike count within stimulus interval.""" stim_start = _get_cpp_data("stim_start") stim_end = _get_cpp_data("stim_end") peak_times = get_cpp_feature("peak_time") if peak_times is None: return np.array([0]) res = sum(1 for time in peak_times if stim_start <= time <= stim_end) return np.array([res]) def trace_check() -> np.ndarray | None: """Returns np.array([0]) if there are no spikes outside stimulus boundaries. Returns None upon failure. """ stim_start = _get_cpp_data("stim_start") stim_end = _get_cpp_data("stim_end") peak_times = get_cpp_feature("peak_time") if peak_times is None: # If no spikes, then no problem return np.array([0]) # Check if there are no spikes or if all spikes are within the stimulus interval if np.all((peak_times >= stim_start) & (peak_times <= stim_end * 1.05)): return np.array([0]) # 0 if trace is valid else: return None # None if trace is invalid due to spike outside stimulus interval def burst_number() -> np.ndarray: """The number of bursts.""" mean_freq = burst_mean_freq() return np.array([0]) if mean_freq is None else np.array([mean_freq.size]) Function `impedance` has a Cognitive Complexity of 11 (exceeds 5 allowed). Consider refactoring.def impedance(): from scipy.ndimage.filters import gaussian_filter1d dt = _get_cpp_data("interp_step") Z_max_freq = _get_cpp_data("impedance_max_freq") voltage_trace = get_cpp_feature("voltage") holding_voltage = get_cpp_feature("voltage_base") # when stimulus starts at t=0, use steady_state_voltage_stimend as proxy if holding_voltage is None: holding_voltage = get_cpp_feature("steady_state_voltage_stimend") normalized_voltage = voltage_trace - holding_voltage[0] current_trace = current() if current_trace is not None: holding_current = get_cpp_feature("current_base") # when stimulus starts at t=0, use steady_state_current_stimend as proxy if holding_current is None: holding_current = get_cpp_feature("steady_state_current_stimend") normalized_current = current_trace - holding_current[0] n_spikes = spike_count() if n_spikes < 1: # if there is no spikes in ZAP fft_volt = np.fft.fft(normalized_voltage) fft_cur = np.fft.fft(normalized_current) if any(fft_cur) == 0: return None # convert dt from ms to s to have freq in Hz freq = np.fft.fftfreq(len(normalized_voltage), d=dt / 1000.) Z = fft_volt / fft_cur norm_Z = abs(Z) / max(abs(Z)) select_idxs = np.swapaxes( np.argwhere((freq > 0) & (freq <= Z_max_freq)), 0, 1 )[0] smooth_Z = gaussian_filter1d(norm_Z[select_idxs], 10) ind_max = np.argmax(smooth_Z) return np.array([freq[select_idxs][ind_max]]) else: return None else: return None def current(): """Get current trace""" return get_cpp_feature("current") def initburst_sahp_vb(): """SlowAHP voltage from voltage base after initial burst""" # Required cpp features initburst_sahp_value = initburst_sahp() voltage_base = get_cpp_feature("voltage_base") if initburst_sahp_value is None or voltage_base is None or \ len(initburst_sahp_value) != 1 or len(voltage_base) != 1: return None else: return np.array([initburst_sahp_value[0] - voltage_base[0]]) def initburst_sahp_ssse(): """SlowAHP voltage from steady_state_voltage_stimend after initial burst""" # Required cpp features initburst_sahp_value = initburst_sahp() ssse = get_cpp_feature("steady_state_voltage_stimend") if initburst_sahp_value is None or ssse is None or \ len(initburst_sahp_value) != 1 or len(ssse) != 1: return None else: return np.array([initburst_sahp_value[0] - ssse[0]]) Function `depol_block` has a Cognitive Complexity of 13 (exceeds 5 allowed). Consider refactoring.def depol_block(): """Check for a depolarization block""" # if there is no depolarization block return 1 # if there is a depolarization block return None # subthreshold traces will also return 1 # Required trace data stim_start = _get_cpp_data("stim_start") stim_end = _get_cpp_data("stim_end") # Required cpp features voltage = get_cpp_feature("voltage") time = get_cpp_feature("time") AP_begin_voltage = get_cpp_feature("AP_begin_voltage") stim_start_idx = np.flatnonzero(time >= stim_start)[0] stim_end_idx = np.flatnonzero(time >= stim_end)[0] if AP_begin_voltage is None: return np.array([1]) # if subthreshold no depolarization block elif AP_begin_voltage.size: depol_block_threshold = np.mean(AP_begin_voltage) # mV else: depol_block_threshold = -50 block_min_duration = 50.0 # ms long_hyperpol_threshold = -75.0 # mV bool_voltage = np.array(voltage > depol_block_threshold, dtype=int) up_indexes = np.flatnonzero(np.diff(bool_voltage) == 1) down_indexes = np.flatnonzero(np.diff(bool_voltage) == -1) if len(up_indexes) > len(down_indexes): down_indexes = np.append(down_indexes, [stim_end_idx]) if len(up_indexes) == 0: # if it never gets high enough, that's not a good sign (meaning no # spikes) return None else: # if it stays in the depolarization block more than min_duration, flag # as depolarization block max_depol_duration = np.max( [time[down_indexes[k]] - time[up_idx] for k, up_idx in enumerate(up_indexes)]) if max_depol_duration > block_min_duration: return None bool_voltage = np.array(voltage > long_hyperpol_threshold, dtype=int) up_indexes = np.flatnonzero(np.diff(bool_voltage) == 1) down_indexes = np.flatnonzero(np.diff(bool_voltage) == -1) down_indexes = down_indexes[(down_indexes > stim_start_idx) & ( down_indexes < stim_end_idx)] if len(down_indexes) != 0: up_indexes = up_indexes[(up_indexes > stim_start_idx) & ( up_indexes < stim_end_idx) & (up_indexes > down_indexes[0])] if len(up_indexes) < len(down_indexes): up_indexes = np.append(up_indexes, [stim_end_idx]) max_hyperpol_duration = np.max( [time[up_indexes[k]] - time[down_idx] for k, down_idx in enumerate(down_indexes)]) # if it stays in hyperpolarized stage for more than min_duration, # flag as depolarization block if max_hyperpol_duration > block_min_duration: return None Avoid too many `return` statements within this function. return np.array([1]) def depol_block_bool(): """Wrapper around the depol_block feature. Returns [1] if depol_block is None, [0] otherwise.""" if depol_block() is None: return np.array([1]) else: return np.array([0]) def spikes_per_burst(): """Calculate the number of spikes per burst""" burst_begin_indices = get_cpp_feature("burst_begin_indices") burst_end_indices = get_cpp_feature("burst_end_indices") if burst_begin_indices is None or len(burst_begin_indices) < 1: return None ap_per_bursts = [] for idx_begin, idx_end in zip(burst_begin_indices, burst_end_indices): ap_per_bursts.append(idx_end - idx_begin + 1) return np.array(ap_per_bursts) def spikes_per_burst_diff(): """Calculate the diff between the spikes in each burst and the next one""" spikes_per_burst_values = spikes_per_burst() if spikes_per_burst_values is None or len(spikes_per_burst_values) < 2: return None return spikes_per_burst_values[:-1] - spikes_per_burst_values[1:] def spikes_in_burst1_burst2_diff(): """Calculate the diff between the spikes in 1st and 2nd bursts""" spikes_per_burst_diff_values = spikes_per_burst_diff() if spikes_per_burst_diff_values is None or len( spikes_per_burst_diff_values ) < 1: return None return np.array([spikes_per_burst_diff_values[0]]) def spikes_in_burst1_burstlast_diff(): """Calculate the diff between the spikes in 1st and last bursts""" spikes_per_burst_values = spikes_per_burst() if spikes_per_burst_values is None or len(spikes_per_burst_values) < 2: return None return np.array([ spikes_per_burst_values[0] - spikes_per_burst_values[-1] ]) def phaseslope_max() -> np.ndarray | None: """Calculate the maximum phase slope.""" voltage = get_cpp_feature("voltage") time = get_cpp_feature("time") if voltage is None or time is None: return None time = time[:len(voltage)] from numpy import diff phaseslope = diff(voltage) / diff(time) try: return np.array([np.max(phaseslope)]) except ValueError: return None def exp_fit(t, tau, A0, A1) -> np.ndarray | float: """Exponential function used in exponential fitting. Args: t (ndarray or float): time series tau (float): time constant A0 (float): constant added to the exponential A1 (float): constant multiplying the exponential """ return A0 + A1 * np.exp(-t / tau) def activation_time_constant() -> np.ndarray | None: """Time constant for an ion channel activation trace. Fits for stim_start to trace maximum interval as A - B * exp(-t/tau).""" from scipy.optimize import curve_fit stim_start = _get_cpp_data("stim_start") stim_end = _get_cpp_data("stim_end") voltage = get_cpp_feature("voltage") time = get_cpp_feature("time") if voltage is None or time is None: return None # isolate stimulus interval stim_start_idx = np.flatnonzero(time >= stim_start)[0] stim_end_idx = np.flatnonzero(time >= stim_end)[0] time_interval = time[stim_start_idx:stim_end_idx] voltage_interval = voltage[stim_start_idx:stim_end_idx] # keep trace going from stim_start to voltage max max_idx = np.argmax(voltage_interval) time_interval = time_interval[:max_idx + 1] voltage_interval = voltage_interval[:max_idx + 1] # correct time so that it starts from 0 time_interval -= time_interval[0] # fit try: popt, _ = curve_fit( exp_fit, time_interval, voltage_interval, p0=(1., voltage_interval[-1], voltage_interval[0] - voltage_interval[-1]), # positive tau, negative A1 bounds=((0, -np.inf, -np.inf), (np.inf, np.inf, 0)), nan_policy="omit", ) except (ValueError, RuntimeError): return None return np.array([abs(popt[0])]) def deactivation_time_constant() -> np.ndarray | None: """Time constant for an ion channel deactivation trace. Fits for stim_start to stim_end as A + B * exp(-t/tau).""" from scipy.optimize import curve_fit stim_start = _get_cpp_data("stim_start") stim_end = _get_cpp_data("stim_end") voltage = get_cpp_feature("voltage") time = get_cpp_feature("time") if voltage is None or time is None: return None # isolate stimulus interval interval_indices = np.where((time >= stim_start) & (time < stim_end)) time_interval = time[interval_indices] voltage_interval = voltage[interval_indices] # correct time so that it starts from 0 time_interval -= time_interval[0] # fit try: popt, _ = curve_fit( exp_fit, time_interval, voltage_interval, p0=( 1., voltage_interval[-1], max( 0, voltage_interval[0] - voltage_interval[-1] ) ), bounds=((0, -np.inf, 0), np.inf), # positive tau, positive A1 nan_policy="omit", ) except (ValueError, RuntimeError): return None return np.array([abs(popt[0])]) def inactivation_time_constant() -> np.ndarray | None: """Time constant for an ion channel inactivation trace. Fits for trace maximum to stim end interval as A + B * exp(-t/tau).""" from scipy.optimize import curve_fit stim_start = _get_cpp_data("stim_start") stim_end = _get_cpp_data("stim_end") voltage = get_cpp_feature("voltage") time = get_cpp_feature("time") # used to remove end of trace to remove artifacts due to stimulus change end_skip = _get_cpp_data("inactivation_tc_end_skip") if voltage is None or time is None: return None # isolate stimulus interval stim_start_idx = np.flatnonzero(time >= stim_start)[0] stim_end_idx = np.flatnonzero(time >= stim_end)[0] time_interval = time[stim_start_idx:stim_end_idx - end_skip] voltage_interval = voltage[stim_start_idx:stim_end_idx - end_skip] # keep trace going from voltage max to stim end # remove end of trace to remove artifacts due to stimulus change max_idx = np.argmax(voltage_interval) time_interval = time_interval[max_idx:] voltage_interval = voltage_interval[max_idx:] # correct time so that it starts from 0 if time_interval.size < 1: return None time_interval -= time_interval[0] # fit try: popt, _ = curve_fit( exp_fit, time_interval, voltage_interval, p0=(1., voltage_interval[-1], voltage_interval[0] - voltage_interval[-1]), bounds=((0, -np.inf, 0), np.inf), # positive tau, positive A1 nan_policy="omit", ) except (ValueError, RuntimeError): return None return np.array([abs(popt[0])])