"""sAHP eCode"""
"""
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 scipy_signal2d
from .tools import base_current
logger = logging.getLogger(__name__)
[docs]
class SAHP(Recording):
"""sAHP current stimulus
The long step (here amp) is usually fixed at 40% of rheobase, and the short step (here amp2)
can usually vary from 150% to 300% of rheobase.
.. code-block:: none
hypamp hypamp+amp hypamp+amp2 hypamp+amp hypamp
: : : : :
: : ______________ : :
: : | | : :
: : | | : :
: ____________________ ____________________ :
: | ^ ^ | :
: | : : | :
|__________| : : |__________________
^ ^ : : ^ ^
: : : : : :
: : : : : :
t=0 ton tmid tmid2 toff tend
"""
def __init__(
self,
config_data,
reader_data,
protocol_name="sAHP",
efel_settings=None
):
super(SAHP, self).__init__(config_data, reader_data, protocol_name)
self.ton = None
self.tmid = None
self.tmid2 = None
self.toff = None
self.tend = None
self.amp = None
self.amp2 = None
self.hypamp = None
self.dt = None
self.amp_rel = None
self.amp2_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 = ["ton", "tmid", "tmid2", "toff", "tend", "amp",
"amp2", "hypamp", "dt", "amp_rel", "amp2_rel",
"hypamp_rel"]
[docs]
def get_stimulus_parameters(self):
"""Returns the eCode parameters"""
ecode_params = {
"delay": self.ton,
"tmid": self.tmid,
"tmid2": self.tmid2,
"toff": self.toff,
"amp": self.amp2,
"long_amp": self.amp,
"thresh_perc": self.amp2_rel,
"duration": self.tmid2 - self.tmid,
"totduration": self.tend,
}
return ecode_params
def compute_amp(self, current, config_data, reader_data):
smooth_current = scipy_signal2d(current, 85)
hypamp_value = numpy.median(
numpy.concatenate(
(smooth_current[: self.ton], smooth_current[self.toff :])
)
)
self.set_amplitudes_ecode("hypamp", config_data, reader_data, hypamp_value)
amp_value = numpy.median(
numpy.concatenate(
(
smooth_current[self.ton: self.tmid],
smooth_current[self.tmid2: self.toff],
)
)
) - self.hypamp
self.set_amplitudes_ecode("amp", config_data, reader_data, amp_value)
amp2_value = numpy.median(smooth_current[self.tmid : self.tmid2]) - self.hypamp
self.set_amplitudes_ecode("amp2", config_data, reader_data, amp2_value)
if config_data.get("tend", None) is None:
self.tend = len(self.t) * self.dt
else:
self.tend = config_data["tend"]
self.ton = self.t[int(round(self.ton))]
self.toff = self.t[int(round(self.toff))]
self.tmid = self.t[int(round(self.tmid))]
self.tmid2 = self.t[int(round(self.tmid2))]
def step_detection(self, current, config_data, reader_data):
# Set the threshold to detect the step
noise_level = numpy.std(numpy.concatenate((self.current[:50], self.current[-50:])))
step_threshold = numpy.max([2.0 * noise_level, 1e-5])
# The buffer prevent miss-detection of the step when artifacts are
# present at the very start or very end of the current trace
buffer_detect = 2.0
idx_buffer = int(buffer_detect / self.dt)
idx_buffer = max(1, idx_buffer)
buffer_step = 50
smooth_current = scipy_signal2d(current, 85)
# Infer the beginning of the long step
self.hypamp = base_current(current)
if "ton" in config_data and config_data["ton"] is not None:
self.ton = int(round(config_data["ton"] / self.dt))
else:
tmp_current = numpy.abs(smooth_current[idx_buffer:] - self.hypamp)
self.ton = idx_buffer + numpy.argmax(tmp_current > step_threshold)
# Infer the end of the long step
tmp_current = numpy.flip(
numpy.abs(smooth_current[self.ton:-idx_buffer] - self.hypamp)
)
self.toff = (
(len(current) - numpy.argmax(tmp_current > step_threshold)) - 1 - idx_buffer
)
# Get the amplitude of the step current (relative to hypamp)
self.amp = numpy.median(
numpy.concatenate((smooth_current[self.ton:self.ton + 50],
smooth_current[self.toff - 50:self.toff]))
) - self.hypamp
# Infer the beginning of the short step
tmp_current = numpy.abs(
smooth_current[self.ton + buffer_step:self.toff - buffer_step] -
self.amp - self.hypamp
)
self.tmid = self.ton + buffer_step + numpy.argmax(tmp_current > step_threshold)
# Infer the end of the long step
tmp_current = numpy.flip(
numpy.abs(
smooth_current[self.ton + buffer_step:self.toff - 50] -
self.amp - self.hypamp
)
)
self.tmid2 = (
(self.toff - numpy.argmax(tmp_current > step_threshold)) - 1 - buffer_step
)
self.amp2 = numpy.median(smooth_current[self.tmid:self.tmid2]) - self.hypamp
# Converting back ton and toff to ms
self.ton = self.t[int(round(self.ton))]
self.toff = self.t[int(round(self.toff))]
self.tmid = self.t[int(round(self.tmid))]
self.tmid2 = self.t[int(round(self.tmid2))]
self.tend = len(self.t) * self.dt
# Check for some common step detection failures when the current
# is constant.
if self.ton >= self.toff or self.ton >= self.tend or \
self.toff > self.tend or self.tmid == self.ton \
or self.tmid2 == self.toff:
self.ton = 0.
self.toff = self.tend
logger.warning(
"The automatic step detection failed for the recording "
f"{self.protocol_name} in files {self.files}. You should "
"specify ton and toff by hand in your files_metadata "
"for this file."
)
[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]
required = ["ton", "tmid", "tmid2", "toff"]
if all(r in config_data for r in required):
self.set_timing_ecode(required, config_data)
self.compute_amp(current, config_data, reader_data)
else:
try:
self.step_detection(current, config_data, reader_data)
except ValueError: # when numpy.argmax gets an empty sequence
self.tend = len(self.t) * self.dt
self.ton = 0
self.toff = self.tend
self.tmid = 0
self.tmid2 = self.tend
logger.warning(
"The automatic step detection failed for the recording "
f"{self.protocol_name} in files {self.files}. You should "
"specify ton and toff by hand in your files_metadata "
"for this file."
)
[docs]
def generate(self):
"""Generate the current array from the parameters of the ecode"""
ton = int(self.ton / self.dt)
tmid = int(self.tmid / self.dt)
tmid2 = int(self.tmid2 / self.dt)
toff = int(self.toff / self.dt)
time = numpy.arange(0.0, self.tend, self.dt)
current = numpy.full(time.shape, numpy.float64(self.hypamp))
current[ton:tmid] += numpy.float64(self.amp)
current[tmid2:toff] += numpy.float64(self.amp)
current[tmid:tmid2] += numpy.float64(self.amp2)
return time, current
[docs]
def compute_relative_amp(self, amp_threshold):
self.amp_rel = 100.0 * self.amp / amp_threshold
self.amp2_rel = 100.0 * self.amp2 / amp_threshold
self.hypamp_rel = 100.0 * self.hypamp / amp_threshold
[docs]
def in_target(self, target, tolerance, absolute_amplitude=False):
"""Returns a boolean. True if the amplitude of the eCode is close to
target and False otherwise."""
effective_amp = self.amp2 if absolute_amplitude else self.amp2_rel
if numpy.abs(target - effective_amp) < tolerance:
return True
return False
def get_plot_amplitude_title(self):
return " ({:.01f}%/{:.01f}%)".format(self.amp_rel, self.amp2_rel)