nbls.py
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nbls.py
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	# -- coding: utf-8 --
	# @Author: Theo Lemaire
	# @Email: theo.lemaire@epfl.ch
	# @Date: 2016-09-29 16:16:19
	# @Last Modified by: Theo Lemaire
	# @Last Modified time: 2020-08-04 17:44:52

	import logging
	import numpy as np

	from .solvers import EventDrivenSolver, HybridSolver
	from .bls import BilayerSonophore
	from .pneuron import PointNeuron
	from .model import Model
	from .drives import AcousticDrive, ElectricDrive
	from .protocols import *
	from ..utils import *
	from ..constants import *
	from ..postpro import getFixedPoints
	from .lookups import EffectiveVariablesLookup
	from ..neurons import getPointNeuron


	class NeuronalBilayerSonophore(BilayerSonophore):
	''' This class inherits from the BilayerSonophore class and receives an PointNeuron instance
	at initialization, to define the electro-mechanical NICE model and its SONIC variant. '''

	tscale = 'ms' # relevant temporal scale of the model
	simkey = 'ASTIM' # keyword used to characterize simulations made with this model

	def __init__(self, a, pneuron, embedding_depth=0.0):
	''' Constructor of the class.

	:param a: in-plane radius of the sonophore structure within the membrane (m)
	:param pneuron: point-neuron model
	:param embedding_depth: depth of the embedding tissue around the membrane (m)
	'''
	self.pneuron = pneuron
	super().__init__(a, pneuron.Cm0, pneuron.Qm0, embedding_depth=embedding_depth)

	@property
	def a_str(self):
	return f'{self.a * 1e9:.1f} nm'

	def __repr__(self):
	s = f'{self.__class__.__name__}({self.a_str}, {self.pneuron}'
	if self.d > 0.:
	s += f', d={si_format(self.d, precision=1)}m'
	return f'{s})'

	def copy(self):
	return self.__class__(self.a, self.pneuron, embedding_depth=self.d)

	def __eq__(self, other):
	if not isinstance(other, self.__class__):
	return False
	return self.a == other.a and self.pneuron == other.pneuron and self.d == other.d

	@property
	def pneuron(self):
	return self._pneuron

	@pneuron.setter
	def pneuron(self, value):
	if not isinstance(value, PointNeuron):
	raise ValueError(f'{value} is not a valid PointNeuron instance')
	if not hasattr(self, '_pneuron') or value != self._pneuron:
	self._pneuron = value
	if hasattr(self, 'a'):
	super().__init__(self.a, self.pneuron.Cm0, self.pneuron.Qm0, embedding_depth=self.d)

	@property
	def meta(self):
	return {
	'neuron': self.pneuron.name,
	'a': self.a,
	'd': self.d,
	}

	@classmethod
	def initFromMeta(cls, meta):
	return cls(meta['a'], getPointNeuron(meta['neuron']), embedding_depth=meta['d'])

	def params(self):
	return {super().params(), self.pneuron.params()}

	def getPltVars(self, wrapleft='df["', wrapright='"]'):
	return {**super().getPltVars(wrapleft, wrapright),
	**self.pneuron.getPltVars(wrapleft, wrapright)}

	@property
	def pltScheme(self):
	return self.pneuron.pltScheme

	def filecode(self, *args):
	return Model.filecode(self, *args)

	@staticmethod
	def inputs():
	# Get parent input vars and supress irrelevant entries
	inputvars = BilayerSonophore.inputs()
	del inputvars['Qm']

	# Fill in current input vars in appropriate order
	inputvars.update({
	**AcousticDrive.inputs(),
	'fs': {
	'desc': 'sonophore membrane coverage fraction',
	'label': 'f_s',
	'unit': '\%',
	'factor': 1e2,
	'precision': 0
	},
	'method': None
	})
	return inputvars

	def filecodes(self, drive, pp, fs, method, qss_vars):
	codes = {
	'simkey': self.simkey,
	'neuron': self.pneuron.name,
	'nature': pp.nature,
	'a': f'{self.a * 1e9:.0f}nm',
	**drive.filecodes,
	**pp.filecodes,
	}
	codes['fs'] = f'fs{fs * 1e2:.0f}%' if fs < 1 else None
	codes['method'] = method
	codes['qss_vars'] = qss_vars
	return codes

	@staticmethod
	def interpEffVariable(key, Qm, stim, lkp):
	''' Interpolate Q-dependent effective variable along various stimulation states of a solution.

	:param key: lookup variable key
	:param Qm: charge density solution vector
	:param stim: stimulation state solution vector
	:param lkp: 2D lookup object
	:return: interpolated effective variable vector
	'''
	x = np.zeros(stim.size)
	stim_vals = np.unique(stim)
	for s in stim_vals:
	x[stim == s] = lkp.project('A', s).interpVar1D(Qm[stim == s], key)
	return x

	@staticmethod
	def spatialAverage(fs, x, x0):
	''' fs-modulated spatial averaging. '''
	return fs * x + (1 - fs) * x0

	@timer
	def computeEffVars(self, drive, Qm, fs):
	''' Compute "effective" coefficients of the HH system for a specific
	acoustic stimulus and charge density.

	A short mechanical simulation is run while imposing the specific charge density,
	until periodic stabilization. The HH coefficients are then averaged over the last
	acoustic cycle to yield "effective" coefficients.

	:param drive: acoustic drive object
	:param Qm: imposed charge density (C/m2)
	:param fs: list of sonophore membrane coverage fractions
	:return: list with computation time and a list of dictionaries of effective variables
	'''
	# Run simulation and retrieve deflection and gas content vectors from last cycle
	data = super().simCycles(drive, Qm)
	Z_last = data.loc[-NPC_DENSE:, 'Z'].values # m
	Cm_last = self.v_capacitance(Z_last) # F/m2

	# For each coverage fraction
	effvars = []
	for x in fs:
	# Compute membrane capacitance and membrane potential vectors
	Cm = self.spatialAverage(x, Cm_last, self.Cm0) # F/m2
	Vm = Qm / Cm * 1e3 # mV

	# Compute average cycle value for membrane potential and rate constants
	effvars.append({{'V': np.mean(Vm)}, self.pneuron.getEffRates(Vm)})

	# Log process
	log = f'{self}: lookups @ {drive.desc}, {Qm * 1e5:.2f} nC/cm2'
	if len(fs) > 1:
	log += f', fs = {fs.min() * 1e2:.0f} - {fs.max() * 1e2:.0f}%'
	logger.info(log)

	# Return effective coefficients
	return effvars

	def getLookupFileName(self, a=None, f=None, A=None, fs=False):
	fname = f'{self.pneuron.name}_lookups'
	if a is not None:
	fname += f'_{a * 1e9:.0f}nm'
	if f is not None:
	fname += f'_{f * 1e-3:.0f}kHz'
	if A is not None:
	fname += f'_{A * 1e-3:.0f}kPa'
	if fs is True:
	fname += '_fs'
	return f'{fname}.pkl'

	def getLookupFilePath(self, args, *kwargs):
	return os.path.join(LOOKUP_DIR, self.getLookupFileName(args, *kwargs))

	def getLookup(self, args, *kwargs):
	keep_tcomp = kwargs.pop('keep_tcomp', False)
	lookup_path = self.getLookupFilePath(args, *kwargs)
	lkp = EffectiveVariablesLookup.fromPickle(lookup_path)
	if not keep_tcomp:
	del lkp.tables['tcomp']
	return lkp

	def getLookup2D(self, f, fs):
	proj_kwargs = {'a': self.a, 'f': f, 'fs': fs}
	if fs < 1.:
	kwargs = proj_kwargs.copy()
	kwargs['fs'] = True
	else:
	kwargs = {}
	return self.getLookup(**kwargs).projectN(proj_kwargs)

	def fullDerivatives(self, t, y, drive, fs):
	''' Compute the full system derivatives.

	:param t: specific instant in time (s)
	:param y: vector of state variables
	:param drive: acoustic drive object
	:param fs: sonophore membrane coverage fraction (-)
	:return: vector of derivatives
	'''
	dydt_mech = BilayerSonophore.derivatives(
	self, t, y[:3], drive, y[3])
	dydt_elec = self.pneuron.derivatives(
	t, y[3:], Cm=self.spatialAverage(fs, self.capacitance(y[1]), self.Cm0))
	return dydt_mech + dydt_elec

	def effDerivatives(self, t, y, lkp1d, qss_vars):
	''' Compute the derivatives of the n-ODE effective system variables,
	based on 1-dimensional linear interpolation of "effective" coefficients
	that summarize the system's behaviour over an acoustic cycle.

	:param t: specific instant in time (s)
	:param y: vector of HH system variables at time t
	:param lkp: dictionary of 1D data points of "effective" coefficients
	over the charge domain, for specific frequency and amplitude values.
	:param qss_vars: list of QSS variables
	:return: vector of effective system derivatives at time t
	'''
	# Unpack values and interpolate lookup at current charge density
	Qm, *states = y
	lkp0d = lkp1d.interpolate1D(Qm)

	# Compute states dictionary from differential and QSS variables
	states_dict = {}
	i = 0
	for k in self.pneuron.statesNames():
	if k in qss_vars:
	states_dict[k] = self.pneuron.quasiSteadyStates()[k](lkp0d)
	else:
	states_dict[k] = states[i]
	i += 1

	# Compute charge density derivative
	dQmdt = - self.pneuron.iNet(lkp0d['V'], states_dict) * 1e-3

	# Compute states derivative vector only for differential variable
	dstates = []
	for k in self.pneuron.statesNames():
	if k not in qss_vars:
	dstates.append(self.pneuron.derEffStates()[k](lkp0d, states_dict))

	return [dQmdt, *dstates]

	def deflectionDependentVm(self, Qm, Z, fs):
	''' Compute deflection (and sonopphore coverage fraction) dependent voltage profile. '''
	return Qm / self.spatialAverage(fs, self.v_capacitance(Z), self.Cm0) * 1e3 # mV

	def fullInitialConditions(self, args, *kwargs):
	''' Compute simulation initial conditions. '''
	y0 = super().initialConditions(args, *kwargs)
	y0.update({
	'Qm': [self.Qm0] * 2,
	*{k: [self.pneuron.steadyStates()[k](self.pneuron.Vm0)] 2
	for k in self.pneuron.statesNames()}
	})
	return y0

	def __simFull(self, drive, pp, fs):
	# Compute initial conditions
	y0 = self.fullInitialConditions(drive, self.Qm0, drive.dt)

	# Initialize solver and compute solution
	solver = EventDrivenSolver(
	lambda x: setattr(solver.drive, 'xvar', drive.xvar * x), # eventfunc
	y0.keys(), # variables list
	lambda t, y: self.fullDerivatives(t, y, solver.drive, fs), # dfunc
	event_params={'drive': drive.copy().updatedX(0.)}, # event parameters
	dt=drive.dt) # time step
	data = solver(
	y0, pp.stimEvents(), pp.tstop, target_dt=CLASSIC_TARGET_DT,
	log_period=pp.tstop / 100 if logger.getEffectiveLevel() <= logging.INFO else None,
	# logfunc=lambda y: f'Qm = {y[3] * 1e5:.2f} nC/cm2'
	)

	# Remove velocity and add voltage timeseries to solution
	del data['U']
	data = addColumn(
	data, 'Vm', self.deflectionDependentVm(data['Qm'], data['Z'], fs), preceding_key='Qm')

	# Return solution dataframe
	return data

	def __simHybrid(self, drive, pp, fs):
	# Compute initial conditions
	y0 = self.fullInitialConditions(drive, self.Qm0, drive.dt)

	# Initialize solver and compute solution
	solver = HybridSolver(
	y0.keys(),
	lambda t, y: self.fullDerivatives(t, y, solver.drive, fs), # dfunc
	lambda t, y, Cm: self.pneuron.derivatives(
	t, y, Cm=self.spatialAverage(fs, Cm, self.Cm0)), # dfunc_sparse
	lambda yref: self.capacitance(yref[1]), # predfunc
	lambda x: setattr(solver.drive, 'xvar', drive.xvar * x), # eventfunc
	drive.periodicity, # periodicity
	['U', 'Z', 'ng'], # fast-evolving variables
	drive.dt, # dense time step
	drive.dt_sparse, # sparse time step
	event_params={'drive': drive.copy().updatedX(0.)}, # event parameters
	primary_vars=['Z', 'ng'] # primary variables
	)
	data = solver(
	y0, pp.stimEvents(), pp.tstop, HYBRID_UPDATE_INTERVAL, target_dt=CLASSIC_TARGET_DT,
	log_period=pp.tstop / 100 if logger.getEffectiveLevel() < logging.INFO else None,
	logfunc=lambda y: f'Qm = {y[3] * 1e5:.2f} nC/cm2'
	)

	# Remove velocity and add voltage timeseries to solution
	del data['U']
	data = addColumn(
	data, 'Vm', self.deflectionDependentVm(data['Qm'], data['Z'], fs), preceding_key='Qm')

	# Return solution dataframe
	return data

	def __simSonic(self, drive, pp, fs, qss_vars=None, pavg=False):
	# Load appropriate 2D lookup
	lkp = self.getLookup2D(drive.f, fs)

	# Adapt lookup and pulsing protocol if pulse-average mode is selected
	if pavg:
	lkp = lkp * pp.DC + lkp.project('A', 0.).tile('A', lkp.refs['A']) * (1 - pp.DC)
	tstim = (int(pp.tstim * pp.PRF) - 1 + pp.DC) / pp.PRF
	pp = TimeProtocol(tstim, pp.tstim + pp.toffset - tstim)

	# Determine QSS and differential variables, and create optional QSS lookup
	if qss_vars is None:
	qss_vars = []
	else:
	lkp_QSS = EffectiveVariablesLookup(
	lkp.refs, {k: self.pneuron.quasiSteadyStates()[k](lkp) for k in qss_vars})
	diff_vars = [item for item in self.pneuron.statesNames() if item not in qss_vars]

	# Set initial conditions
	y0 = {
	'Qm': self.Qm0,
	**{k: self.pneuron.steadyStates()[k](self.pneuron.Vm0) for k in diff_vars}
	}

	# Initialize solver and compute solution
	solver = EventDrivenSolver(
	lambda x: setattr(solver, 'lkp', lkp.project('A', drive.xvar * x)), # eventfunc
	y0.keys(), # variables list
	lambda t, y: self.effDerivatives(t, y, solver.lkp, qss_vars), # dfunc
	event_params={'lkp': lkp.project('A', 0.)}, # event parameters
	dt=self.pneuron.chooseTimeStep()) # time step
	data = solver(
	y0, pp.stimEvents(), pp.tstop,
	log_period=pp.tstop / 100 if pp.tstop >= 5 else None,
	max_nsamples=MAX_NSAMPLES_EFFECTIVE)

	# Interpolate Vm and QSS variables along charge vector and store them in solution dataframe
	data = addColumn(
	data, 'Vm', self.interpEffVariable('V', data['Qm'], data['stimstate'] * drive.A, lkp),
	preceding_key='Qm')
	for k in qss_vars:
	data[k] = self.interpEffVariable(k, data['Qm'], data['stimstate'] * drive.A, lkp_QSS)

	# Add dummy deflection and gas content vectors to solution
	for key in ['Z', 'ng']:
	data[key] = np.full(data['t'].size, np.nan)

	# Return solution dataframe
	return data

	def intMethods(self):
	''' Listing of model integration methods. '''
	return {
	'full': self.__simFull,
	'hybrid': self.__simHybrid,
	'sonic': self.__simSonic
	}

	@classmethod
	@Model.checkOutputDir
	def simQueue(cls, freqs, amps, durations, offsets, PRFs, DCs, fs, methods, qss_vars, **kwargs):
	''' Create a serialized 2D array of all parameter combinations for a series of individual
	parameter sweeps, while avoiding repetition of CW protocols for a given PRF sweep.

	:param freqs: list (or 1D-array) of US frequencies
	:param amps: list (or 1D-array) of acoustic amplitudes
	:param durations: list (or 1D-array) of stimulus durations
	:param offsets: list (or 1D-array) of stimulus offsets (paired with durations array)
	:param PRFs: list (or 1D-array) of pulse-repetition frequencies
	:param DCs: list (or 1D-array) of duty cycle values
	:param fs: sonophore membrane coverage fractions (-)
	:params methods: integration methods
	:param qss_vars: QSS variables
	:return: list of parameters (list) for each simulation
	'''
	if ('full' in methods or 'hybrid' in methods) and kwargs['outputdir'] is None:
	logger.warning('Running cumbersome simulation(s) without file saving')
	if amps is None:
	amps = [None]
	drives = AcousticDrive.createQueue(freqs, amps)
	protocols = PulsedProtocol.createQueue(durations, offsets, PRFs, DCs)
	queue = []
	for drive in drives:
	for pp in protocols:
	for cov in fs:
	for method in methods:
	queue.append([drive, pp, cov, method, qss_vars])
	return queue

	@classmethod
	@Model.checkOutputDir
	def simQueueBurst(cls, freqs, amps, durations, PRFs, DCs, BRFs, nbursts,
	fs, methods, qss_vars, **kwargs):
	if ('full' in methods or 'hybrid' in methods) and kwargs['outputdir'] is None:
	logger.warning('Running cumbersome simulation(s) without file saving')
	if amps is None:
	amps = [None]
	drives = AcousticDrive.createQueue(freqs, amps)
	protocols = BurstProtocol.createQueue(durations, PRFs, DCs, BRFs, nbursts)
	queue = []
	for drive in drives:
	for pp in protocols:
	for cov in fs:
	for method in methods:
	queue.append([drive, pp, cov, method, qss_vars])
	return queue

	def checkInputs(self, drive, pp, fs, method, qss_vars):
	PointNeuron.checkInputs(drive, pp)
	_, xevents, = zip(*pp.stimEvents())
	if np.any(np.array([xevents]) < 0.):
	raise ValueError('Invalid time protocol: contains negative modulators')
	if not isinstance(fs, float):
	raise TypeError(f'Invalid "fs" parameter (must be float typed)')
	if qss_vars is not None:
	if not isIterable(qss_vars) or not isinstance(qss_vars[0], str):
	raise ValueError('Invalid QSS variables: must be None or an iterable of strings')
	sn = self.pneuron.statesNames()
	for item in qss_vars:
	if item not in sn:
	raise ValueError(f'Invalid QSS variable: {item} (must be in {sn}')
	if method not in list(self.intMethods().keys()):
	raise ValueError(f'Invalid integration method: "{method}"')

	@Model.logNSpikes
	@Model.checkTitrate
	@Model.addMeta
	@Model.logDesc
	@Model.checkSimParams
	def simulate(self, drive, pp, fs=1., method='sonic', qss_vars=None):
	''' Simulate the electro-mechanical model for a specific set of US stimulation parameters,
	and return output data in a dataframe.

	:param drive: acoustic drive object
	:param pp: pulse protocol object
	:param fs: sonophore membrane coverage fraction (-)
	:param method: selected integration method
	:return: output dataframe
	'''
	# Set the tissue elastic modulus
	self.setTissueModulus(drive)

	# Call appropriate simulation function and return
	simfunc = self.intMethods()[method]
	simargs = [drive, pp, fs]
	if method == 'sonic':
	simargs.append(qss_vars)
	return simfunc(*simargs)

	def desc(self, meta):
	method = meta['method'] if 'method' in meta else meta['model']['method']
	fs = meta['fs'] if 'fs' in meta else meta['model']['fs']
	s = f'{self}: {method} simulation @ {meta["drive"].desc}, {meta["pp"].desc}'
	if fs < 1.0:
	s += f', fs = {(fs * 1e2):.2f}%'
	if 'qss_vars' in meta and meta['qss_vars'] is not None:
	s += f" - QSS ({', '.join(meta['qss_vars'])})"
	return s

	@staticmethod
	def getNSpikes(data):
	return PointNeuron.getNSpikes(data)

	def getArange(self, drive):
	return (0., self.getLookup().refs['A'].max())

	@property
	def titrationFunc(self):
	return self.pneuron.titrationFunc

	@logCache(os.path.join(os.path.split(__file__)[0], 'astim_titrations.log'))
	def titrate(self, drive, pp, fs=1., method='sonic', qss_vars=None, xfunc=None, Arange=None):
	''' Use a binary search to determine the threshold amplitude needed to obtain
	neural excitation for a given frequency and pulsed protocol.

	:param drive: unresolved acoustic drive object
	:param pp: pulse protocol object
	:param fs: sonophore membrane coverage fraction (-)
	:param method: integration method
	:return: determined threshold amplitude (Pa)
	'''
	return super().titrate(drive, pp, fs=fs, method=method, qss_vars=qss_vars,
	xfunc=xfunc, Arange=Arange)

	def getQuasiSteadyStates(self, f, amps=None, charges=None, DC=1.0, squeeze_output=False):
	''' Compute the quasi-steady state values of the neuron's gating variables
	for a combination of US amplitudes, charge densities,
	at a specific US frequency and duty cycle.

	:param f: US frequency (Hz)
	:param amps: US amplitudes (Pa)
	:param charges: membrane charge densities (C/m2)
	:param DC: duty cycle
	:return: 4-tuple with reference values of US amplitude and charge density,
	as well as interpolated Vmeff and QSS gating variables
	'''
	# Get DC-averaged lookups interpolated at the appropriate amplitudes and charges
	lkp = self.getLookup().projectDC(amps=amps, DC=DC).projectN({'a': self.a, 'f': f})
	if charges is not None:
	lkp = lkp.project('Q', charges)

	# Specify dimensions with A as the first axis
	lkp.move('A', 0)

	# Compute QSS states using these lookups
	QSS = EffectiveVariablesLookup(
	lkp.refs,
	{k: v(lkp) for k, v in self.pneuron.quasiSteadyStates().items()})

	# Compress outputs if needed
	if squeeze_output:
	QSS = QSS.squeeze()
	lkp = lkp.squeeze()

	return lkp, QSS

	def iNetQSS(self, Qm, f, A, DC):
	''' Compute quasi-steady state net membrane current for a given combination
	of US parameters and a given membrane charge density.

	:param Qm: membrane charge density (C/m2)
	:param f: US frequency (Hz)
	:param A: US amplitude (Pa)
	:param DC: duty cycle (-)
	:return: net membrane current (mA/m2)
	'''
	lkp, QSS = self.getQuasiSteadyStates(
	f, amps=A, charges=Qm, DC=DC, squeeze_output=True)
	return self.pneuron.iNet(lkp['V'], QSS) # mA/m2

	def fixedPointsQSS(self, f, A, DC, lkp, dQdt):
	''' Compute QSS fixed points along the charge dimension for a given combination
	of US parameters, and determine their stability.

	:param f: US frequency (Hz)
	:param A: US amplitude (Pa)
	:param DC: duty cycle (-)
	:param lkp: lookup dictionary for effective variables along charge dimension
	:param dQdt: charge derivative profile along charge dimension
	:return: 2-tuple with values of stable and unstable fixed points
	'''
	logger.debug(f'A = {A * 1e-3:.2f} kPa, DC = {DC * 1e2:.0f}%')

	# Extract fixed points from QSS charge variation profile
	def dfunc(Qm):
	return - self.iNetQSS(Qm, f, A, DC)
	fixed_points = getFixedPoints(
	lkp.refs['Q'], dQdt, filter='both', der_func=dfunc).tolist()
	dfunc = lambda x: np.array(self.effDerivatives(_, x, lkp))

	# classified_fixed_points = {'stable': [], 'unstable': [], 'saddle': []}
	classified_fixed_points = []

	np.set_printoptions(precision=2)

	# For each fixed point
	for i, Qm in enumerate(fixed_points):

	# Re-compute QSS at fixed point
	*_, QSS = self.getQuasiSteadyStates(f, amps=A, charges=Qm, DC=DC,
	squeeze_output=True)

	# Classify fixed point stability by numerically evaluating its Jacobian and
	# computing its eigenvalues
	x = np.array([Qm, *QSS.tables.values()])
	eigvals, key = classifyFixedPoint(x, dfunc)
	# classified_fixed_points[key].append(Qm)

	classified_fixed_points.append((x, eigvals, key))
	# eigenvalues.append(eigvals)
	logger.debug(f'{key} point @ Q = {(Qm * 1e5):.1f} nC/cm2')

	# eigenvalues = np.array(eigenvalues).T
	# print(eigenvalues.shape)

	return classified_fixed_points

	def isStableQSS(self, f, A, DC):
	lookups, QSS = self.getQuasiSteadyStates(
	f, amps=A, DCs=DC, squeeze_output=True)
	dQdt = -self.pneuron.iNet(lookups['V'], QSS.tables) # mA/m2
	classified_fixed_points = self.fixedPointsQSS(f, A, DC, lookups, dQdt)
	return len(classified_fixed_points['stable']) > 0


	class DrivenNeuronalBilayerSonophore(NeuronalBilayerSonophore):

	simkey = 'DASTIM' # keyword used to characterize simulations made with this model

	def __init__(self, Idrive, args, *kwargs):
	self.Idrive = Idrive
	super().__init__(args, *kwargs)

	def __repr__(self):
	return super().__repr__()[:-1] + f', Idrive = {self.Idrive:.2f} mA/m2)'

	@classmethod
	def initFromMeta(cls, meta):
	return cls(meta['Idrive'], meta['a'], getPointNeuron(meta['neuron']),
	embedding_depth=meta['d'])

	def params(self):
	return {{'Idrive': self.Idrive}, super().params()}

	@staticmethod
	def inputs():
	return {
	**NeuronalBilayerSonophore.inputs(),
	'Idrive': ElectricDrive.inputs()['I']
	}

	@property
	def meta(self):
	return {
	**super().meta,
	'Idrive': self.Idrive
	}

	def filecodes(self, *args):
	return {
	*super().filecodes(args),
	'Idrive': f'Idrive{self.Idrive:.1f}mAm2'
	}

	def fullDerivatives(self, *args):
	dydt = super().fullDerivatives(*args)
	dydt[3] += self.Idrive * 1e-3
	return dydt

	def effDerivatives(self, *args):
	dQmdt, dstates = super().effDerivatives(args)
	dQmdt += self.Idrive * 1e-3
	return [dQmdt, *dstates]

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