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SolverElec.py
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Tue, Apr 30, 10:32

SolverElec.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Author: Theo Lemaire
# @Date: 2016-09-29 16:16:19
# @Email: theo.lemaire@epfl.ch
# @Last Modified by: Theo Lemaire
# @Last Modified time: 2017-08-03 15:09:53
import logging
import numpy as np
import scipy.integrate as integrate
# Get package logger
logger = logging.getLogger('PointNICE')
class SolverElec:
def __init__(self):
# Do nothing
logger.info('Elec solver initialization')
def eqHH(self, _, y, channel_mech, Iinj):
''' Compute the derivatives of a HH system variables for a
specific value of injected current.
:param t: time value (s, unused)
:param y: vector of HH system variables at time t
:param channel_mech: channels mechanism object
:param Iinj: injected current (mA/m2)
:return: vector of HH system derivatives at time t
'''
Vm, *states = y
Iionic = channel_mech.currNet(Vm, states) # mA/m2
dVmdt = (- Iionic + Iinj) / channel_mech.Cm0 # mV/s
dstates = channel_mech.derStates(Vm, states)
return [dVmdt, *dstates]
def runSim(self, channel_mech, Astim, tstim, toffset, tonset=10e-3):
''' Compute solutions of a neuron's HH system for a specific set of
electrical stimulation parameters, using a classic integration scheme.
:param channel_mech: channels mechanism object
:param Astim: pulse amplitude (mA/m2)
:param tstim: pulse duration (s)
:param toffset: offset duration (s)
:param tonset: onset duration (s)
:return: 2-tuple with the time profile and solution matrix
'''
# Set time vector
ttot = tonset + tstim + toffset
dt = 1e-4 # s
nsamples = int(np.round(ttot / dt))
t = np.linspace(0.0, ttot, nsamples) - tonset
# Set pulse vector
n_onset = int(np.round(tonset / dt))
n_stim = int(np.round(tstim / dt))
n_offset = int(np.round(toffset / dt))
pulse = np.concatenate((np.zeros(n_onset), Astim * np.ones(n_stim), np.zeros(n_offset)))
# Create solver
solver = integrate.ode(self.eqHH)
solver.set_integrator('lsoda', nsteps=1000)
# Set initial conditions
y0 = [channel_mech.Vm0, *channel_mech.states0]
nvar = len(y0)
# Run simulation
y = np.empty((nsamples - 1, nvar))
solver.set_initial_value(y0, t[0])
k = 1
while solver.successful() and k <= nsamples - 1:
solver.set_f_params(channel_mech, pulse[k])
solver.integrate(t[k])
y[k - 1, :] = solver.y
k += 1
y = np.concatenate((np.atleast_2d(y0), y), axis=0)
return (t, y)
def eqHH_VClamp(self, _, y, channel_mech, Vc):
''' Compute the derivatives of a HH system variables for a
specific value of clamped voltage.
:param t: time value (s, unused)
:param y: vector of HH system variables at time t
:param channel_mech: channels mechanism object
:param Vc: clamped voltage (mV)
:return: vector of HH system derivatives at time t
'''
return channel_mech.derStates(Vc, y)
def runVClamp(self, channel_mech, Vclamp, tclamp, toffset, tonset=10e-3):
''' Compute solutions of a neuron's HH system for a specific set of
voltage clamp parameters, using a classic integration scheme.
:param channel_mech: channels mechanism object
:param Vclamp: clamped voltage (mV)
:param toffset: offset duration (s)
:param tclamp: clamp duration (s)
:param tonset: onset duration (s)
:return: 2-tuple with the time profile and solution matrix
'''
# Set time vector
ttot = tonset + tclamp + toffset
dt = 1e-4 # s
nsamples = int(np.round(ttot / dt))
t = np.linspace(0.0, ttot, nsamples) - tonset
# Set clamp vector
n_onset = int(np.round(tonset / dt))
n_clamp = int(np.round(tclamp / dt))
n_offset = int(np.round(toffset / dt))
clamp = np.concatenate((np.zeros(n_onset), Vclamp * np.ones(n_clamp), np.zeros(n_offset)))
# Create solver
solver = integrate.ode(self.eqHH_VClamp)
solver.set_integrator('lsoda', nsteps=1000)
# Set initial conditions
y0 = channel_mech.states0
nvar = len(y0)
# Run simulation
y = np.empty((nsamples - 1, nvar))
solver.set_initial_value(y0, t[0])
k = 1
while solver.successful() and k <= nsamples - 1:
solver.set_f_params(channel_mech, clamp[k])
solver.integrate(t[k])
y[k - 1, :] = solver.y
k += 1
y = np.concatenate((np.atleast_2d(y0), y), axis=0)
return (t, y)

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