BlueBrain/eFEL

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/EPFL
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, 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" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL <COPYRIGHT HOLDER> BE LIABLE FOR ANY
DIRECT, 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 AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
from efel.pyfeatures.cppfeature_access import _get_cpp_data, get_cpp_feature
from efel.pyfeatures.isi import *
from typing_extensions import deprecated
 
import numpy as np
from 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])])