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'
]
[docs]
def voltage() -> np.ndarray | None:
"""Get voltage trace."""
return get_cpp_feature("voltage")
[docs]
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()
[docs]
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()
[docs]
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])
[docs]
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
[docs]
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])
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")
normalized_voltage = voltage_trace - holding_voltage
current_trace = current()
if current_trace is not None:
holding_current = get_cpp_feature("current_base")
normalized_current = current_trace - holding_current
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 freq[ind_max]
else:
return None
else:
return None
[docs]
def current():
"""Get current trace"""
return get_cpp_feature("current")
[docs]
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]])
[docs]
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]])
[docs]
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
return np.array([1])
[docs]
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])
[docs]
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:
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)
[docs]
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:
return None
return spikes_per_burst_values[:-1] - spikes_per_burst_values[1:]
[docs]
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]])
[docs]
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]
])
[docs]
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