def plot_curve(cls, ax, curve, chl, state, infpower=None, *args, **kwargs):
V = np.linspace(-80,50)*unit('mV')
#V = StdLimits.get_default_voltage_array().rescale('mV')
(alpha, beta) = chl.get_alpha_beta_at_voltage(V, state)
(inf, tau) = InfTauCalculator.alpha_beta_to_inf_tau(alpha, beta)
infpower = (np.power(inf, infpower) if infpower else None)
plot_what_lut = {
Curve.Alpha: (alpha, 'Rate Constant', None),
Curve.Beta: (beta, 'Rate Constant', None),
Curve.Inf: (inf, 'Steady-State', None),
Curve.InfPowered: (infpower, 'Steady-State', None),
Curve.Tau: (tau, 'Time-Constant', 'ms'),
}
(plot_what, y_label, y_unit) = plot_what_lut[curve]
# print kwargs
if isinstance(ax, QuantitiesAxisNew):
ax.plot(V, plot_what, *args, **kwargs)
ax.set_xlabel('Voltage')
ax.set_ylabel(y_label)
if y_unit:
ax.set_yunit(unit(y_unit))
else:
ax.plot(V, plot_what, *args, **kwargs)
ax.set_xlabel('Voltage (mV)')
ax.set_ylabel(y_label)
def get_voltage_clamp_trace(cls, V, chl, duration, cell_area, t=np.arange(0, 300, 0.1) * unit("1:ms")) :
from scipy.integrate import odeint
import sympy
v_in_mv = V.rescale('mV').magnitude
state_names = chl.statevars.keys()
n_states = len(state_names)
(m_inf, m_tau) = InfTauCalculator.evaluate_inf_tau_for_v(chl.statevars[state_names[0]], V)
m_tau_ms = m_tau.rescale('ms').magnitude
inf_taus = [InfTauCalculator.evaluate_inf_tau_for_v(chl.statevars[stateName], V) for stateName in state_names]
inf_taus_ms = [(inf, tau.rescale("ms").magnitude) for (inf, tau) in inf_taus]
state_to_index = dict([(state, index) for state, index in enumerate(state_names)])
def odeFunc(y, t0):
res = [None] * n_states
for i in range(0, n_states):
state_inf, state_tau = inf_taus_ms[i]
state_val = y[i]
d_state = (state_inf - state_val) / state_tau
res[i] = d_state
return res
# run the ODE for each variable:
t = t.rescale('ms').magnitude
y0 = np.zeros((n_states))
res = odeint(func=odeFunc, y0=y0, t=t)
state_functor = sympy.lambdify(state_names, sympy.sympify(chl.eqn))
state_data = [res[:, i] for i in range(0, n_states)]
state_equation_evaluation = state_functor(*state_data)
cell_density = chl.conductance * cell_area
i_chl = chl.conductance * cell_area * state_equation_evaluation * (V - chl.reversalpotential)
return TraceFixedDT(time=t * unit('1:ms'),
data=i_chl.rescale('pA'))
def get_voltage_clamp_trace(cls, V, chl, duration, cell_area, t=np.arange(0, 300, 0.1) * unit("1:ms")):
from scipy.integrate import odeint
import sympy
v_in_mv = V.rescale("mV").magnitude
state_names = chl.statevars.keys()
n_states = len(state_names)
(m_inf, m_tau) = InfTauCalculator.evaluate_inf_tau_for_v(chl.statevars[state_names[0]], V)
m_tau_ms = m_tau.rescale("ms").magnitude
inf_taus = [InfTauCalculator.evaluate_inf_tau_for_v(chl.statevars[stateName], V) for stateName in state_names]
inf_taus_ms = [(inf, tau.rescale("ms").magnitude) for (inf, tau) in inf_taus]
state_to_index = dict([(state, index) for state, index in enumerate(state_names)])
def odeFunc(y, t0):
res = [None] * n_states
for i in range(0, n_states):
state_inf, state_tau = inf_taus_ms[i]
state_val = y[i]
d_state = (state_inf - state_val) / state_tau
res[i] = d_state
return res
# run the ODE for each variable:
t = t.rescale("ms").magnitude
y0 = np.zeros((n_states))
res = odeint(func=odeFunc, y0=y0, t=t)
state_functor = sympy.lambdify(state_names, sympy.sympify(chl.eqn))
state_data = [res[:, i] for i in range(0, n_states)]
state_equation_evaluation = state_functor(*state_data)
cell_density = chl.conductance * cell_area
i_chl = chl.conductance * cell_area * state_equation_evaluation * (V - chl.reversalpotential)
return TraceFixedDT(time=t * unit("1:ms"), data=i_chl.rescale("pA"))
def getVoltageClampTrace(cls, V, chl, duration, cellArea, t=np.arange(0,300,0.1) * unit("1:ms"), ) :
from scipy.integrate import odeint
vInMv = V.rescale("mV").magnitude
stateNames = chl.statevars.keys()
nStates = len(stateNames)
m_inf, m_tau = InfTauCalculator.evaluateInfTauForV( chl.statevars[stateNames[0]], V)
m_tauMS = m_tau.rescale("ms").magnitude
infTaus = [ InfTauCalculator.evaluateInfTauForV( chl.statevars[stateName], V) for stateName in stateNames ]
infTausMS = [ (inf, tau.rescale("ms").magnitude) for (inf,tau) in infTaus ]
stateToIndex = dict( [ (state,index) for state,index in enumerate(stateNames) ] )
def odeFunc(y,t0):
res = [None] * nStates
for i in range(0,nStates):
stateInf,stateTau = infTausMS[i]
stateVal = y[i]
dState = ( stateInf - stateVal ) / stateTau
res[i] = dState
return res
# Run the ODE for each variable:
t = t.rescale("ms").magnitude
y0 = np.zeros( (nStates, ) )
res = odeint(func=odeFunc, y0=y0, t= t )
stateFunctor = sympy.lambdify( stateNames, sympy.sympify(chl.eqn) )
stateData = [ res[:,i] for i in range(0,nStates) ]
stateEquationEvaluation = stateFunctor( *stateData )
cellDensity = (chl.conductance * cellArea)
iChl = (chl.conductance * cellArea) * stateEquationEvaluation * (V- chl.reversalpotential)
return Trace_FixedDT( time=t * unit("1:ms"), data=iChl.rescale("pA") )
def plot_inf_tau_curves(cls, ax1, ax2, calciumAlphaBetaBetaChannel, state):
chl = calciumAlphaBetaBetaChannel
V = StdLimits.get_default_voltage_array().rescale("mV")
alpha, beta = cls.getResolvedAlphaBetaCurves(V, chl, state)
inf, tau = InfTauCalculator.alpha_beta_to_inf_tau(alpha, beta)
ax1.plot(V, inf)
ax1.set_xlabel("Voltage")
ax1.set_ylabel("Inf")
ax2.setYUnit("ms")
ax2.plot(V, tau)
ax2.set_xlabel("Voltage")
ax2.set_ylabel("Tau")
def plot_inf_tau_curves(cls, ax1, ax2, calciumAlphaBetaBetaChannel, state):
chl = calciumAlphaBetaBetaChannel
V = StdLimits.get_default_voltage_array().rescale("mV")
alpha, beta = cls.getResolvedAlphaBetaCurves(V, chl, state)
inf, tau = InfTauCalculator.alpha_beta_to_inf_tau(alpha, beta)
ax1.plot(V, inf)
ax1.set_xlabel("Voltage")
ax1.set_ylabel("Inf")
ax2.setYUnit("ms")
ax2.plot(V, tau)
ax2.set_xlabel("Voltage")
ax2.set_ylabel("Tau")
