def passive_fit_2(info, up_data, down_data):
h.v_init = 0
h.tstop = 100
h.cvode_active(1)
fit_start = 4.0025
v_rec = h.Vector()
t_rec = h.Vector()
v_rec.record(h.soma[0](0.5)._ref_v)
t_rec.record(h._ref_t)
mrf = h.MulRunFitter[0]
gen0 = mrf.p.pf.generatorlist.object(0)
gen0.toggle()
fit0 = gen0.gen.fitnesslist.object(0)
up_t = h.Vector(up_data[:, 0])
up_v = h.Vector(up_data[:, 1])
fit0.set_data(up_t, up_v)
fit0.boundary.x[0] = fit_start
fit0.boundary.x[1] = info["limit"]
fit0.set_w()
gen1 = mrf.p.pf.generatorlist.object(1)
gen1.toggle()
fit1 = gen1.gen.fitnesslist.object(0)
down_t = h.Vector(down_data[:, 0])
down_v = h.Vector(down_data[:, 1])
fit1.set_data(down_t, down_v)
fit1.boundary.x[0] = fit_start
fit1.boundary.x[1] = info["limit"]
fit1.set_w()
minerr = 1e12
for i in range(3):
# Need to re-initialize the internal MRF variables, not top-level proxies
# for randomize() to work
mrf.p.pf.parmlist.object(0).val = 1
mrf.p.pf.parmlist.object(1).val = 10000
mrf.randomize()
mrf.prun()
if mrf.opt.minerr < minerr:
fit_Ri = h.Ri
fit_Cm = h.Cm
fit_Rm = h.Rm
minerr = mrf.opt.minerr
results = {
"ra": fit_Ri,
"cm": fit_Cm,
"rm": fit_Rm,
"err": minerr,
"a1": h.somaaxon_area(),
"a2": h.alldend_area(),
}
return results
def passive_fit_elec(up_data,
down_data,
fit_window_start,
fit_window_end,
bridge,
electrode_cap,
n_init=10):
""" Fit passive properties (Ri, Cm, and Rm) with simulated recording electrode
Parameters
----------
up_data: array, shape (n_samples, 2)
Positive-going times and voltage responses
down_data: array, shape (n_samples, 2)
Negative-going times and voltage responses
fit_window_start: float
Start of fit window (ms)
fit_window_end: float
End of fit window (ms)
bridge: float
Value of bridge balance (estimated series resistance), MOhm
electrode_cap: float
Value of electrode capacitance (pF)
n_init: int (optional, default 10)
Number of random starts for passive fitting
Returns
-------
:class:`PassiveFitResult`
"""
mrf = _common_config(fit_window_start, fit_window_end, up_data, down_data)
circuit = h.LinearCircuit[0]
circuit.R2 = bridge / 2.0
circuit.R3 = bridge / 2.0
circuit.C4 = electrode_cap * 1e-3
minerr = 1e12
for i in range(n_init):
# Need to re-initialize the internal MRF variables, not top-level proxies
# for randomize() to work
mrf.p.pf.parmlist.object(0).val = 100
mrf.p.pf.parmlist.object(1).val = 1
mrf.p.pf.parmlist.object(2).val = 10000
mrf.p.pf.putall()
mrf.randomize()
mrf.prun()
if mrf.opt.minerr < minerr:
fit_Ri = h.Ri
fit_Cm = h.Cm
fit_Rm = h.Rm
minerr = mrf.opt.minerr
return PassiveFitResult(fit_Ri, fit_Cm, fit_Rm, minerr, h.somaaxon_area(),
h.alldend_area())
def passive_fit_1(up_data,
down_data,
fit_window_start,
fit_window_end,
n_init=10):
""" Fit passive properties (Ri, Cm, and Rm)
Parameters
----------
up_data: array, shape (n_samples, 2)
Positive-going times and voltage responses
down_data: array, shape (n_samples, 2)
Negative-going times and voltage responses
fit_window_start: float
Start of fit window (ms)
fit_window_end: float
End of fit window (ms)
n_init: int (optional, default 10)
Number of random starts for passive fitting
Returns
-------
Dictionary of passive fit and membrane area values
"""
mrf = _common_config(fit_window_start, fit_window_end, up_data, down_data)
minerr = 1e12
for i in range(n_init):
# Need to re-initialize the internal MRF variables, not top-level proxies
# for randomize() to work
mrf.p.pf.parmlist.object(0).val = 100
mrf.p.pf.parmlist.object(1).val = 1
mrf.p.pf.parmlist.object(2).val = 10000
mrf.p.pf.putall()
mrf.randomize()
mrf.prun()
if mrf.opt.minerr < minerr:
fit_Ri = h.Ri
fit_Cm = h.Cm
fit_Rm = h.Rm
minerr = mrf.opt.minerr
results = {
"ra": fit_Ri,
"cm": fit_Cm,
"rm": fit_Rm,
"err": minerr,
"a1": h.somaaxon_area(),
"a2": h.alldend_area(),
}
return results
def passive_fit_1(up_data,
down_data,
fit_window_start,
fit_window_end,
n_init=10):
""" Fit passive properties (Ri, Cm, and Rm)
Parameters
----------
up_data: (n_samples, 2) array
Positive-going times and voltage responses
down_data: (n_samples, 2) array
Negative-going times and voltage responses
fit_window_start: float
Start of fit window (ms)
fit_window_end: float
End of fit window (ms)
n_init: int, optional
Number of random starts for passive fitting (default 10)
Returns
-------
:class:`PassiveFitResult`
"""
mrf = _common_config(fit_window_start, fit_window_end, up_data, down_data)
minerr = 1e12
for i in range(n_init):
# Need to re-initialize the internal MRF variables, not top-level proxies
# for randomize() to work
mrf.p.pf.parmlist.object(0).val = 100
mrf.p.pf.parmlist.object(1).val = 1
mrf.p.pf.parmlist.object(2).val = 10000
mrf.p.pf.putall()
mrf.randomize()
mrf.prun()
if mrf.opt.minerr < minerr:
fit_Ri = h.Ri
fit_Cm = h.Cm
fit_Rm = h.Rm
minerr = mrf.opt.minerr
return PassiveFitResult(fit_Ri, fit_Cm, fit_Rm, minerr, h.somaaxon_area(),
h.alldend_area())