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vclamp.py
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Wed, Jul 10, 13:20

vclamp.py
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# -*- coding: utf-8 -*-
# @Author: Theo Lemaire
# @Email: theo.lemaire@epfl.ch
# @Date: 2019-08-14 13:49:25
# @Last Modified by: Theo Lemaire
# @Last Modified time: 2019-11-13 12:45:38
import numpy as np
import pandas as pd
from .batches import Batch
from .protocols import TimeProtocol
from .model import Model
from .pneuron import PointNeuron
from .simulators import OnOffSimulator
from ..constants import *
from ..utils import *
class VoltageClamp(Model):
tscale = 'ms' # relevant temporal scale of the model
simkey = 'VCLAMP' # keyword used to characterize simulations made with this model
def __init__(self, pneuron):
''' Constructor of the class.
:param pneuron: point-neuron model
'''
# Check validity of input parameters
if not isinstance(pneuron, PointNeuron):
raise ValueError('Invalid neuron type: "{}" (must inherit from PointNeuron class)'
.format(pneuron.name))
self.pneuron = pneuron
def __repr__(self):
return '{}({})'.format(self.__class__.__name__, self.pneuron)
def params(self):
return self.pneuron.params()
def getPltVars(self, wrapleft='df["', wrapright='"]'):
return self.pneuron.getPltVars(wrapleft, wrapright)
def getPltScheme(self):
return self.pneuron.getPltScheme()
def filecode(self, *args):
return Model.filecode(self, *args)
@staticmethod
def inputs():
# Get pneuron input vars and replace stimulation current by held and step voltages
inputvars = PointNeuron.inputs()
for key in ['Astim', 'PRF', 'DC']:
del inputvars[key]
inputvars.update({
'Vhold': {
'desc': 'held membrane potential',
'label': 'V_{hold}',
'unit': 'mV',
'precision': 0
},
'Vstep': {
'desc': 'step membrane potential',
'label': 'V_{step}',
'unit': 'mV',
'precision': 0
}
})
return inputvars
def filecodes(self, Vhold, Vstep, tp):
return {
'simkey': self.simkey,
'neuron': self.pneuron.name,
'Vhold': '{:.1f}mV'.format(Vhold),
'Vstep': '{:.1f}mV'.format(Vstep),
**tp.filecodes()
}
@classmethod
@Model.checkOutputDir
def simQueue(cls, holds, steps, durations, offsets, **kwargs):
''' Create a serialized 2D array of all parameter combinations for a series of individual
parameter sweeps.
:param holds: list (or 1D-array) of held membrane potentials
:param steps: list (or 1D-array) of step membrane potentials
:param durations: list (or 1D-array) of stimulus durations
:param offsets: list (or 1D-array) of stimulus offsets (paired with durations array)
:return: list of parameters (list) for each simulation
'''
queue = Batch.createQueue(holds, steps, durations, offsets)
queue = [[item[0], item[1], TimeProtocol(item[2], item[3])] for item in queue]
return queue
@staticmethod
def checkInputs(Vhold, Vstep):
''' Check validity of stimulation parameters.
:param Vhold: held membrane potential (mV)
:param Vstep: step membrane potential (mV)
'''
if not all(isinstance(param, float) for param in [Vhold, Vstep]):
raise TypeError('Invalid stimulation parameters (must be float typed)')
def derivatives(self, t, y, Vm=None):
if Vm is None:
Vm = self.pneuron.Vm0
states_dict = dict(zip(self.pneuron.statesNames(), y))
return self.pneuron.getDerStates(Vm, states_dict)
@Model.addMeta
def simulate(self, Vhold, Vstep, tp):
logger.info('{}: simulation @ Vhold = {}V, Vstep = {}V, {}'.format(
self, *si_format([Vhold * 1e-3, Vstep * 1e-3], 1), tp.pprint()))
# Check validity of stimulation parameters
self.checkInputs(Vhold, Vstep)
# Set initial conditions
y0 = self.pneuron.getSteadyStates(Vhold)
# Initialize simulator and compute solution
logger.debug('Computing solution')
simulator = OnOffSimulator(
lambda t, y: self.derivatives(t, y, Vm=Vstep),
lambda t, y: self.derivatives(t, y, Vm=Vhold))
t, y, stim = simulator(y0, DT_EFFECTIVE, tp)
# Prepend initial conditions (prior to stimulation)
t, y, stim = simulator.prependSolution(t, y, stim)
# Compute clamped membrane potential vector
Vm = np.zeros(stim.size)
Vm[stim == 0] = Vhold
Vm[stim == 1] = Vstep
# Store output in dataframe and return
data = pd.DataFrame({
't': t,
'stimstate': stim,
'Qm': Vm * 1e-3 * self.pneuron.Cm0,
'Vm': Vm,
})
for i in range(len(self.pneuron.states)):
data[self.pneuron.statesNames()[i]] = y[:, i]
return data
def meta(self, Vhold, Vstep, tp):
return {
'simkey': self.simkey,
'neuron': self.pneuron.name,
'Vhold': Vhold,
'Vstep': Vstep,
'tp': tp
}

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