"""Step eCode class"""
"""
Copyright (c) 2022, EPFL/Blue Brain Project
This file is part of BluePyEfe <https://github.com/BlueBrain/BluePyEfe>
This library is free software; you can redistribute it and/or modify it under
the terms of the GNU Lesser General Public License version 3.0 as published
by the Free Software Foundation.
This library is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
details.
You should have received a copy of the GNU Lesser General Public License
along with this library; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
"""
import logging
import numpy
from ..recording import Recording
from .tools import base_current
from .tools import scipy_signal2d
logger = logging.getLogger(__name__)
[docs]
def group_indexes(values, gap=10):
"""Return a list of clusters from a list where consecutive
values follow each other forming clusters
eg: [12, 14, 15, 20, 56, 60, 61, 62, 63] -> [[12, 14, 15, 20], [56, 60,
61, 62, 63]]"""
clusters = []
for v in values:
if not (len(clusters)) or clusters[-1][-1] + gap < v:
clusters.append([v])
else:
clusters[-1].append(v)
return clusters
def detect_spike(amp, hypamp, smooth_current, dt):
tspike = []
duration = []
delta = []
threshold = hypamp + (0.1 * amp)
candidate_spikes = numpy.argwhere(smooth_current > threshold).flatten()
candidate_spikes = group_indexes(candidate_spikes, gap=10)
for spike in candidate_spikes:
tspike.append(spike[0] - 1)
duration.append(spike[-1] - spike[0] + 1)
if len(tspike) > 1:
for i in range(1, len(tspike)):
end = tspike[i - 1] + duration[i - 1]
start = tspike[i]
delta.append(dt * (start - end))
tspike = numpy.asarray(tspike) * dt
duration = numpy.mean(numpy.asarray(duration) * dt)
delta = numpy.mean(delta)
return tspike, duration, delta
[docs]
class SpikeRec(Recording):
"""SpikeRec current stimulus
.. code-block:: none
hypamp hypamp+amp hypamp hypamp+amp . . .
: : : :
: _________________ : _________________ _________________
: | | : | | | |
: | | : | | * len(tspike) | |
: | | : | | . . . | |
: | | : | | | |
|_______________| |__________| |__ __| |___
: : : : : ^
: : : : : :
: : : : : :
<--tspike[0] --><-spike_duration-><- delta -><-spike_duration-> . . . tend
"""
def __init__(
self,
config_data,
reader_data,
protocol_name="SpikeRec",
efel_settings=None
):
super(SpikeRec, self).__init__(config_data, reader_data, protocol_name)
self.tend = None
self.tspike = []
self.spike_duration = None # in ms
self.amp = None
self.hypamp = None
self.dt = None
self.delta = None # Time difference between two spikes
self.amp_rel = None
self.hypamp_rel = None
if self.t is not None and self.current is not None:
self.interpret(
self.t, self.current, self.config_data, self.reader_data
)
if self.voltage is not None:
self.set_autothreshold()
self.compute_spikecount(efel_settings)
self.export_attr = ["tend", "tspike", "spike_duration", "delta",
"amp", "hypamp", "dt", "amp_rel", "hypamp_rel"]
@property
def ton(self):
return 0.0
@property
def toff(self):
return self.tend
@property
def multi_stim_start(self):
return list(self.tspike)
@property
def multi_stim_end(self):
return [t + self.spike_duration for t in self.tspike]
[docs]
def get_stimulus_parameters(self):
"""Returns the eCode parameters"""
ecode_params = {
"delay": self.tspike[0],
"n_spikes": len(self.tspike),
"delta": self.delta,
"amp": self.amp,
"thresh_perc": self.amp_rel,
"spike_duration": self.spike_duration,
"totduration": self.tend,
}
return ecode_params
[docs]
def interpret(self, t, current, config_data, reader_data):
"""Analyse a current with a step and extract from it the parameters
needed to reconstruct the array"""
self.dt = t[1]
# Smooth the current
smooth_current = scipy_signal2d(current, 15)
hypamp_value = base_current(current)
self.set_amplitudes_ecode("hypamp", config_data, reader_data, hypamp_value)
amp_value = numpy.max(smooth_current)
self.set_amplitudes_ecode("amp", config_data, reader_data, amp_value)
# Get the beginning and end of the spikes
if (
not len(self.tspike)
or self.spike_duration is None
or self.delta is None
):
self.tspike, self.spike_duration, self.delta = detect_spike(
self.amp, self.hypamp, smooth_current, self.dt
)
self.tend = len(t) * self.dt
[docs]
def generate(self):
"""Generate the step current array from the parameters of the ecode"""
t = numpy.arange(0.0, self.tend, self.dt)
current = numpy.full(t.shape, numpy.float64(self.hypamp))
spike_start = int(self.tspike[0] / self.dt)
spike_end = int((self.tspike[0] + self.spike_duration) / self.dt)
current[spike_start:spike_end] += numpy.float64(self.amp)
for i in range(1, len(self.tspike)):
spike_start = int(spike_end + (self.delta / self.dt))
spike_end = spike_start + int(self.spike_duration / self.dt)
current[spike_start:spike_end] += numpy.float64(self.amp)
return t, current
[docs]
def in_target(self, target, tolerance, absolute_amplitude):
"""Returns a boolean. True if the delta of the eCode is close to
target and False otherwise."""
logger.warning(
"The eCode SpikeRec uses delta between current spikes "
"in ms as target, not amplitude"
)
if numpy.abs(target - self.delta) < tolerance:
return True
else:
return False