def make_cell(sim, cell_name, cell_chl_functor):
m1 = mf.MorphologyBuilder.get_single_section_soma(area=mf.unit("1:um2"))
cell = sim.create_cell(name=cell_name, morphology=m1)
for chl in cell_chl_functor():
cell.apply_channel( chl, parameter_multipliers={'gmax':random.uniform(0.9, 1.1)})
cell.set_passive( mf.PassiveProperty.SpecificCapacitance, mf.unit('4:pF/um2'))
return cell
def set_ylim(self, *args, **kwargs):
x0 = x1 = None
if args is not None:
if len(args) == 1:
x0, x1 = args[0]
else:
x0, x1 = args
else:
x0 = kwargs.get('bottom', None)
x1 = kwargs.get('top', None)
# So we can forward arguments:
if 'bottom' in kwargs:
del kwargs['bottom']
if 'top' in kwargs:
del kwargs['top']
#Convert strings to units
if x0 is not None and isinstance(x0, basestring):
from morphforge.stdimports import unit
x0 = unit(x0)
if x1 is not None and isinstance(x1, basestring):
from morphforge.stdimports import unit
x1 = unit(x1)
# Are the baseunits set? If not, then lets set them:
if self.xyUnitBase[1] is None:
self._setxyUnitDisplay(unitY=x0)
# Set the limits
if x0 is not None:
self.ax.set_ylim(bottom = x0.rescale(self.xyUnitBase[1]).magnitude, **kwargs)
if x1 is not None:
self.ax.set_ylim(top = x1.rescale(self.xyUnitBase[1]).magnitude, **kwargs)
def _setxyUnitDisplay(self, unitX=None, unitY=None):
from morphforge.stdimports import unit
if unitX is not None:
unitX = (unit(unitX) if isinstance(unitX, basestring) else unitX)
if unitY is not None:
unitY = (unit(unitY) if isinstance(unitY, basestring) else unitY)
# Set the base units, if they are not already set:
if unitX is not None and self.xyUnitBase[0] is None:
self._setxyUnitBase(unitX=unitX)
if unitY is not None and self.xyUnitBase[1] is None:
self._setxyUnitBase(unitY=unitY)
if unitX is not None:
self.xyUnitDisplay[0] = unitX.units
# Update the axis ticks:
symbol = self.getSymbolFromUnit(self.xyUnitDisplay[0])
scaling = (self.xyUnitBase[0]/self.xyUnitDisplay[0]).rescale(pq.dimensionless)
xFormatterFunc = self.xTickFormatGenerator(scaling=scaling, symbol=symbol)
self.ax.xaxis.set_major_formatter(xFormatterFunc)
if unitY is not None:
self.xyUnitDisplay[1] = unitY.units
symbol = self.getSymbolFromUnit(self.xyUnitDisplay[1])
scaling = (self.xyUnitBase[1]/self.xyUnitDisplay[1]).rescale(pq.dimensionless)
yFormatterFunc = self.yTickFormatGenerator(scaling=scaling, symbol=symbol)
self.ax.yaxis.set_major_formatter(yFormatterFunc)
# Update the labels
self._update_labels()
def simulate(self, current_base, current_rebound):
sim = self.env.Simulation(**self.sim_kwargs)
cell = self.cell_functor(sim=sim)
soma_loc = cell.get_location('soma')
cc1 = sim.create_currentclamp(name="cclamp", amp=current_base, dur="100:ms", delay="50:ms", cell_location=soma_loc)
cc2 = sim.create_currentclamp(name="cclamp2", amp=-1*current_rebound, dur="5:ms", delay="80:ms", cell_location=soma_loc)
cc3 = sim.create_currentclamp(name="cclamp3", amp=-1*current_rebound, dur="5:ms", delay="120:ms", cell_location=soma_loc)
sim.record(cc1, name="Current1", what=CurrentClamp.Recordables.Current, description="CurrentClampCurrent")
sim.record(cc2, name="Current2", what=CurrentClamp.Recordables.Current, description="CurrentClampCurrent")
sim.record(cc3, name="Current3", what=CurrentClamp.Recordables.Current, description="CurrentClampCurrent")
sim.record(cell, name="SomaVoltage", cell_location=soma_loc, what=Cell.Recordables.MembraneVoltage, description="Response to iInj1=%s iInj2=%s"%(current_base, current_rebound))
res = sim.run()
#SimulationSummariser(res, "/home/michael/Desktop/ForRoman.pdf")
i = res.get_trace('Current1').convert_to_fixed(unit("0.5:ms")) + res.get_trace('Current2').convert_to_fixed(unit("0.5:ms")) + res.get_trace('Current3').convert_to_fixed(unit("0.5:ms"))
i = TraceConverter.rebase_to_fixed_dt(res.get_trace('Current1'
), dt=unit('0.5:ms')) \
+ TraceConverter.rebase_to_fixed_dt(res.get_trace('Current2'
), dt=unit('0.5:ms')) \
+ TraceConverter.rebase_to_fixed_dt(res.get_trace('Current3'
), dt=unit('0.5:ms'))
i.tags = [StandardTags.Current]
return (res.get_trace('SomaVoltage'), i)
def plot_iv_curve(self, ax=None):
# pylint: disable=E1103
title = '%s: IV Curve' % (self.cell_description or None)
if not ax:
f = QuantitiesFigure()
f.suptitle(title)
ax = f.add_subplot(1, 1, 1)
ax.set_xlabel('Injected Current')
ax.set_ylabel('SteadyStateVoltage')
V_in_mV = [
self.get_iv_point_steaddy_state(c).rescale('mV').magnitude
for c in self.currents
]
v = np.array(V_in_mV) * pq.mV
i = factorise_units_from_list(self.currents)
low_v = V_in_mV < self.v_regressor_limit if self.v_regressor_limit else range(
len(V_in_mV))
print 'i[low_v]', i[low_v]
print 'v[low_v]', v[low_v]
ax.plot(
i[low_v],
v[low_v],
)
ax.plot(
i[np.logical_not(low_v)],
v[np.logical_not(low_v)],
)
ax.plot(
i[np.logical_not(low_v)],
v[np.logical_not(low_v)],
)
# Plot the regressor:
i_units = unit('1:pA').units
v_units = unit('1:mV').units
iv = np.vstack(
(i.rescale(i_units).magnitude, v.rescale(v_units).magnitude)).T
if not len(iv[low_v, 0]):
return
(a_s, b_s, r, tt, stderr) = stats.linregress(iv[low_v, 0], iv[low_v,
1])
input_resistance = (a_s * (v_units / i_units)).rescale('MOhm')
reversal_potential = b_s * v_units
self.input_resistance = input_resistance
self.reversal_potential = reversal_potential
ax.plot(
i,
i * input_resistance + reversal_potential,
label="Fit: [V(mV) = %2.3f * I(pA) + %2.3f]" % (a_s, b_s) +
" \n[Input Resistance: %2.2fMOhm Reversal Potential: %2.2f mV" %
(input_resistance, reversal_potential))
ax.legend()
PM.save_figure(figname=title)
def __init__(self,
cell_functor,
currents,
cell_description=None,
sim_functor=None,
v_regressor_limit=None,
sim_kwargs=None,
plot_all=False):
self.cell_functor = cell_functor
self.v_regressor_limit = v_regressor_limit
#Previously = unit("-30:mV")
self.sim_kwargs = sim_kwargs or {}
self.tCurrentInjStart = unit('50:ms')
self.tCurrentInjStop = unit('200:ms')
self.tSteaddyStateStart = unit('100:ms')
self.tSteaddyStateStop = unit('151:ms')
self.traces = {}
self.currents = currents
self.cell_description = cell_description or 'Unknown Cell'
self.input_resistance = unit('-1:MOhm')
if plot_all:
self.plot_all()
def build_presynaptic_mech( env, cell):
return env.PreSynapticMechanism(
mfc.PreSynapticMech_VoltageThreshold,
cell_location = cell.soma,
voltage_threshold = mf.unit("0:mV"),
delay = mf.unit("1:ms"),
#,
)
def _setxyUnitBase(self, unitX=None, unitY=None):
from morphforge.stdimports import unit
if unitX is not None:
unitX = (unit(unitX) if isinstance(unitX, basestring) else unitX)
if unitY is not None:
unitY = (unit(unitY) if isinstance(unitY, basestring) else unitY)
if unitX is not None:
assert self.xyUnitBase[0] == None
unitX = (unit(unitX) if isinstance(unitX, basestring) else unitX)
self.xyUnitBase[0] = unitX.units.simplified.units
if unitY is not None:
assert self.xyUnitBase[1] == None
unitY = (unit(unitY) if isinstance(unitY, basestring) else unitY)
self.xyUnitBase[1] = unitY.units.simplified.units
def _setxyUnitBase(self, unitX=None, unitY=None):
from morphforge.stdimports import unit
if unitX is not None:
unitX = (unit(unitX) if isinstance(unitX, basestring) else unitX)
if unitY is not None:
unitY = (unit(unitY) if isinstance(unitY, basestring) else unitY)
if unitX is not None:
assert self.xyUnitBase[0] == None
unitX = (unit(unitX) if isinstance(unitX, basestring) else unitX)
self.xyUnitBase[0] = unitX.units.simplified.units
if unitY is not None:
assert self.xyUnitBase[1] == None
unitY = (unit(unitY) if isinstance(unitY, basestring) else unitY)
self.xyUnitBase[1] = unitY.units.simplified.units
def load_ka_channel():
ka_param_names = """
ka_gmax ka_erev
ka_m_a1 ka_m_a2 ka_m_a3 ka_m_a4 ka_m_a5
ka_m_b1 ka_m_b2 ka_m_b3 ka_m_b4 ka_m_b5
ka_h_a1 ka_h_a2 ka_h_a3 ka_h_a4 ka_h_a5
ka_h_b1 ka_h_b2 ka_h_b3 ka_h_b4 ka_h_b5
"""
ka_param_units = """
nS/um2; mV;
ms-1; mV-1 ms-1; ;mV; mV;
ms-1; mV-1 ms-1; ;mV; mV;
ms-1; mV-1 ms-1; ;mV; mV;
ms-1; mV-1 ms-1; ;mV; mV;
"""
ka_param_str = """
30 -80
12.025 0 0.5 -10.01 -12.56
14.325 0 1 -8.01 9.69
0.0001 0 1 15.88 26.0
10.000 0 500 -22.09 -10.21
"""
nrn_params = dict([(p, mf.unit("%s:%s"%(v, u.strip()))) for (p, u, v) in zip(ka_param_names.split(), ka_param_units.split(';'), ka_param_str.split())])
nrn_params_ka = extract_params(nrn_params, prefix='ka_')
print nrn_params_ka
eqnsetka = mf.neurounits.NeuroUnitParser.EqnSet(na_eqnset_txt.replace("sautois", "saut2"))
kaChls = mfc.Neuron_NeuroUnitEqnsetMechanism(name="Chl5", eqnset=eqnsetka, default_parameters = nrn_params_ka)
return kaChls
def test_cell_current(cell_name, cell_chl_functor, current):
sim = mf.NEURONEnvironment().Simulation()
m1 = mf.MorphologyBuilder.get_single_section_soma(area=mf.unit("1:um2"))
cell = sim.create_cell(name=cell_name, morphology=m1)
cc = sim.create_currentclamp(name="CC1", delay=100*mf.ms, dur=400*mf.ms, amp=current * mf.pA, cell_location=cell.soma)
for chl in cell_chl_functor():
mf.cell.apply_channel( chl)
mf.cell.set_passive( mf.PassiveProperty.SpecificCapacitance, mf.unit('4:pF/um2'))
sim.record(cell, what=mf.Cell.Recordables.MembraneVoltage)
sim.record(cc, what=mf.CurrentClamp.Recordables.Current)
res =sim.run()
return res
def simulate_chl_all(chl):
#res = {}
return [
#simulate_chl_vclamp(chl, unit("-80:mV")),
#simulate_chl_vclamp(chl, unit("-50:mV")),
#simulate_chl_vclamp(chl, unit("-20:mV")),
#simulate_chl_vclamp(chl, unit("10:mV")),
simulate_chl_vclamp(chl, unit("40:mV")),
]
def dual_driver(sim, presynaptic, postsynaptic, ampa_scale, nmda_scale):
ampa = sim.create_synapse(
presynaptic_mech = build_presynaptic_mech( env, presynaptic),
postsynaptic_mech = excite_ampa_syn_tmpl.instantiate(
cell_location = postsynaptic.soma,
parameter_multipliers = {'scale':ampa_scale * pq.dimensionless },
weight = mf.unit("1:nS")
)
)
nmda = sim.create_synapse(
presynaptic_mech = build_presynaptic_mech( env, presynaptic),
postsynaptic_mech = excite_nmda_syn_tmpl.instantiate(
cell_location = postsynaptic.soma,
parameter_multipliers = {'scale':nmda_scale * pq.dimensionless },
weight = mf.unit("1:nS")
)
)
return [ampa, nmda]
def plot_iv_curve(self, ax=None):
# pylint: disable=E1103
title = '%s: IV Curve' % (self.cell_description or None)
if not ax:
f = QuantitiesFigure()
f.suptitle(title)
ax = f.add_subplot(1, 1, 1)
ax.set_xlabel('Injected Current')
ax.set_ylabel('SteadyStateVoltage')
V_in_mV = [self.get_iv_point_steaddy_state(c).rescale('mV').magnitude for c in self.currents]
v = np.array(V_in_mV) * pq.mV
i = factorise_units_from_list(self.currents)
low_v = V_in_mV < self.v_regressor_limit if self.v_regressor_limit else range( len(V_in_mV))
print 'i[low_v]', i[low_v]
print 'v[low_v]', v[low_v]
ax.plot(i[low_v], v[low_v], )
ax.plot(i[np.logical_not(low_v)], v[np.logical_not(low_v)], )
ax.plot(i[np.logical_not(low_v)], v[np.logical_not(low_v)], )
# Plot the regressor:
i_units = unit('1:pA').units
v_units = unit('1:mV').units
iv = np.vstack((i.rescale(i_units).magnitude,
v.rescale(v_units).magnitude)).T
if not len(iv[low_v, 0]):
return
(a_s, b_s, r, tt, stderr) = stats.linregress(iv[low_v, 0], iv[low_v, 1])
input_resistance = (a_s * (v_units / i_units)).rescale('MOhm')
reversal_potential = b_s * v_units
self.input_resistance = input_resistance
self.reversal_potential = reversal_potential
ax.plot(i, i*input_resistance + reversal_potential, label = "Fit: [V(mV) = %2.3f * I(pA) + %2.3f]"%(a_s, b_s) + " \n[Input Resistance: %2.2fMOhm Reversal Potential: %2.2f mV"%(input_resistance, reversal_potential) )
ax.legend()
PM.save_figure(figname=title)
def onto_driver(sim, postsynaptic, times):
return sim.create_synapse(
presynaptic_mech = env.PreSynapticMechanism(
mfc.PreSynapticMech_TimeList,
time_list = times,
),
postsynaptic_mech = driver_syn_tmpl.instantiate(
cell_location = postsynaptic.soma,
parameter_multipliers = {'scale':1.0 },
weight = mf.unit("1:nS")
)
)
def simulate(self, current_base, current_rebound):
sim = self.env.Simulation(**self.sim_kwargs)
cell = self.cell_functor(sim=sim)
soma_loc = cell.get_location('soma')
cc1 = sim.create_currentclamp(name="cclamp",
amp=current_base,
dur="100:ms",
delay="50:ms",
cell_location=soma_loc)
cc2 = sim.create_currentclamp(name="cclamp2",
amp=-1 * current_rebound,
dur="5:ms",
delay="80:ms",
cell_location=soma_loc)
cc3 = sim.create_currentclamp(name="cclamp3",
amp=-1 * current_rebound,
dur="5:ms",
delay="120:ms",
cell_location=soma_loc)
sim.record(cc1,
name="Current1",
what=CurrentClamp.Recordables.Current,
description="CurrentClampCurrent")
sim.record(cc2,
name="Current2",
what=CurrentClamp.Recordables.Current,
description="CurrentClampCurrent")
sim.record(cc3,
name="Current3",
what=CurrentClamp.Recordables.Current,
description="CurrentClampCurrent")
sim.record(cell,
name="SomaVoltage",
cell_location=soma_loc,
what=Cell.Recordables.MembraneVoltage,
description="Response to iInj1=%s iInj2=%s" %
(current_base, current_rebound))
res = sim.run()
#SimulationSummariser(res, "/home/michael/Desktop/ForRoman.pdf")
i = res.get_trace('Current1').convert_to_fixed(
unit("0.5:ms")) + res.get_trace('Current2').convert_to_fixed(
unit("0.5:ms")) + res.get_trace('Current3').convert_to_fixed(
unit("0.5:ms"))
i = TraceConverter.rebase_to_fixed_dt(res.get_trace('Current1'
), dt=unit('0.5:ms')) \
+ TraceConverter.rebase_to_fixed_dt(res.get_trace('Current2'
), dt=unit('0.5:ms')) \
+ TraceConverter.rebase_to_fixed_dt(res.get_trace('Current3'
), dt=unit('0.5:ms'))
i.tags = [StandardTags.Current]
return (res.get_trace('SomaVoltage'), i)
def simulate_chl_vclamp(chl, voltage_level):
env = NEURONEnvironment()
# Create the simulation:
sim = env.Simulation(tstop=unit("1500:ms"))
# Create a cell:
morphDict1 = {'root': {'length': 18.8, 'diam': 18.8, 'id':'soma'} }
m1 = MorphologyTree.fromDictionary(morphDict1)
cell = sim.create_cell(morphology=m1, segmenter=CellSegmenter_SingleSegment())
# Setup the HH-channels on the cell:
#chl = chl_applicator_functor(env, cell, sim)
cell.apply_channel( chl)
# Setup passive channels:
cell.set_passive( PassiveProperty.SpecificCapacitance, unit('1.0:uF/cm2'))
# Create the stimulus and record the injected current:
#cc = sim.create_currentclamp(name="Stim1", amp=unit("10:pA"), dur=unit("100:ms"), delay=unit("300:ms") * R.uniform(0.95, 1.0), cell_location=cell.soma)
cc = sim.create_voltageclamp(name="Stim1",
dur1=unit("200:ms"), amp1=unit("-60:mV"),
#dur2=unit("500:ms")* R.uniform(0.95, 1.0), amp2=voltage_level,
dur2=unit("500:ms"), amp2=voltage_level,
#dur3=unit("500:ms")* R.uniform(0.95, 1.0), amp3=unit("-50:mV"),
dur3=unit("500:ms"), amp3=unit("-50:mV"),
cell_location=cell.soma,
)
# Define what to record:
sim.record(cell, what=StandardTags.Voltage, name="SomaVoltage", cell_location = cell.soma)
sim.record(cc, what=StandardTags.Current, name="CurrentClamp")
# run the simulation
results = sim.run()
return results
def __init__(self, cell_functor, currents, cell_description=None, sim_functor=None, v_regressor_limit=None, sim_kwargs=None, plot_all=False):
self.cell_functor = cell_functor
self.v_regressor_limit = v_regressor_limit
#Previously = unit("-30:mV")
self.sim_kwargs = sim_kwargs or {}
self.tCurrentInjStart = unit('50:ms')
self.tCurrentInjStop = unit('200:ms')
self.tSteaddyStateStart = unit('100:ms')
self.tSteaddyStateStop = unit('151:ms')
self.traces = {}
self.currents = currents
self.cell_description = cell_description or 'Unknown Cell'
self.input_resistance = unit('-1:MOhm')
if plot_all:
self.plot_all()
def _setxyUnitDisplay(self, unitX=None, unitY=None):
from morphforge.stdimports import unit
if unitX is not None:
unitX = (unit(unitX) if isinstance(unitX, basestring) else unitX)
if unitY is not None:
unitY = (unit(unitY) if isinstance(unitY, basestring) else unitY)
# Set the base units, if they are not already set:
if unitX is not None and self.xyUnitBase[0] is None:
self._setxyUnitBase(unitX=unitX)
if unitY is not None and self.xyUnitBase[1] is None:
self._setxyUnitBase(unitY=unitY)
if unitX is not None:
self.xyUnitDisplay[0] = unitX.units
# Update the axis ticks:
symbol = self.getSymbolFromUnit(self.xyUnitDisplay[0])
scaling = (self.xyUnitBase[0] / self.xyUnitDisplay[0]).rescale(
pq.dimensionless)
xFormatterFunc = self.xTickFormatGenerator(scaling=scaling,
symbol=symbol)
self.ax.xaxis.set_major_formatter(xFormatterFunc)
if unitY is not None:
self.xyUnitDisplay[1] = unitY.units
symbol = self.getSymbolFromUnit(self.xyUnitDisplay[1])
scaling = (self.xyUnitBase[1] / self.xyUnitDisplay[1]).rescale(
pq.dimensionless)
yFormatterFunc = self.yTickFormatGenerator(scaling=scaling,
symbol=symbol)
self.ax.yaxis.set_major_formatter(yFormatterFunc)
# Update the labels
self._update_labels()
def set_xlim(self, *args, **kwargs):
x0 = x1 = None
if args is not None:
if len(args) == 1:
x0, x1 =args[0]
else:
x0, x1 = args
else:
x0 = kwargs.get('left', None)
x1 = kwargs.get('right', None)
# So we can forward arguments:
if 'left' in kwargs:
del kwargs['left']
if 'left' in kwargs:
del kwargs['left']
#
if x0 is not None and isinstance(x0, basestring):
from morphforge.stdimports import unit
x0 = unit(x0)
if x1 is not None and isinstance(x1, basestring):
from morphforge.stdimports import unit
x1 = unit(x1)
# Are the baseunits set? If not, then lets set them:
if self.xyUnitBase[0] is None:
self._setxyUnitDisplay(unitX=x0)
# Set the limits
if x0 is not None:
self.ax.set_xlim(left = x0.rescale(self.xyUnitBase[0]).magnitude, **kwargs)
if x1 is not None:
self.ax.set_xlim(right = x1.rescale(self.xyUnitBase[0]).magnitude, **kwargs)
def set_ylim(self, *args, **kwargs):
x0 = x1 = None
if args is not None:
if len(args) == 1:
x0, x1 = args[0]
else:
x0, x1 = args
else:
x0 = kwargs.get('bottom', None)
x1 = kwargs.get('top', None)
# So we can forward arguments:
if 'bottom' in kwargs:
del kwargs['bottom']
if 'top' in kwargs:
del kwargs['top']
#Convert strings to units
if x0 is not None and isinstance(x0, basestring):
from morphforge.stdimports import unit
x0 = unit(x0)
if x1 is not None and isinstance(x1, basestring):
from morphforge.stdimports import unit
x1 = unit(x1)
# Are the baseunits set? If not, then lets set them:
if self.xyUnitBase[1] is None:
self._setxyUnitDisplay(unitY=x0)
# Set the limits
if x0 is not None:
self.ax.set_ylim(bottom=x0.rescale(self.xyUnitBase[1]).magnitude,
**kwargs)
if x1 is not None:
self.ax.set_ylim(top=x1.rescale(self.xyUnitBase[1]).magnitude,
**kwargs)
def set_xlim(self, *args, **kwargs):
x0 = x1 = None
if args is not None:
if len(args) == 1:
x0, x1 = args[0]
else:
x0, x1 = args
else:
x0 = kwargs.get('left', None)
x1 = kwargs.get('right', None)
# So we can forward arguments:
if 'left' in kwargs:
del kwargs['left']
if 'left' in kwargs:
del kwargs['left']
#
if x0 is not None and isinstance(x0, basestring):
from morphforge.stdimports import unit
x0 = unit(x0)
if x1 is not None and isinstance(x1, basestring):
from morphforge.stdimports import unit
x1 = unit(x1)
# Are the baseunits set? If not, then lets set them:
if self.xyUnitBase[0] is None:
self._setxyUnitDisplay(unitX=x0)
# Set the limits
if x0 is not None:
self.ax.set_xlim(left=x0.rescale(self.xyUnitBase[0]).magnitude,
**kwargs)
if x1 is not None:
self.ax.set_xlim(right=x1.rescale(self.xyUnitBase[0]).magnitude,
**kwargs)
def load_std_channels(param_str):
nrn_params = dict([(p, mf.unit("%s:%s"%(v, u.strip()))) for (p, u, v) in zip(param_names.split(), param_units.split(';'), param_str.split())])
nrn_params_na = extract_params(nrn_params, prefix='na_')
nrn_params_lk = extract_params(nrn_params, prefix='lk_')
nrn_params_ks = extract_params(nrn_params, prefix='ks_', replace_prefix='k_')
nrn_params_kf = extract_params(nrn_params, prefix='kf_', replace_prefix='k_')
nrn_params_ks = remap_keys(nrn_params_ks, {'k_gmax':'gmax', 'k_erev':'erev'})
nrn_params_kf = remap_keys(nrn_params_kf, {'k_gmax':'gmax', 'k_erev':'erev'})
eqnsetna = mf.neurounits.NeuroUnitParser.EqnSet(na_eqnset_txt)
eqnsetlk = mf.neurounits.NeuroUnitParser.EqnSet(lk_eqnset_txt)
eqnsetk = mf.neurounits.NeuroUnitParser.EqnSet(k_eqnset_txt)
na_chl = mfc.Neuron_NeuroUnitEqnsetMechanism(name="Chl1", eqnset=eqnsetna, default_parameters = nrn_params_na)
lk_chl = mfc.Neuron_NeuroUnitEqnsetMechanism(name="Chl2", eqnset=eqnsetlk, default_parameters = nrn_params_lk)
ksChls = mfc.Neuron_NeuroUnitEqnsetMechanism(name="Chl3", eqnset=eqnsetk, default_parameters = nrn_params_ks)
kfChls = mfc.Neuron_NeuroUnitEqnsetMechanism(name="Chl4", eqnset=eqnsetk, default_parameters = nrn_params_kf)
chls = [na_chl, lk_chl, ksChls, kfChls]
return chls
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# ----------------------------------------------------------------------
import morphforge.stdimports as mf
from mhlibs.quantities_plot import QuantitiesFigure
import pylab
from morphforge.stdimports import unit, reduce_to_variable_dt_trace
t1 = mf.TraceGenerator.generate_flat(value='1:mV')
#t2 = mf.TraceGenerator.generate_flat(value='0.0015:V')
t2 = mf.TraceGenerator.generate_ramp(value_start='0.0015:V',
value_stop='2.5:mV',
npoints=20)
f = QuantitiesFigure()
ax = f.add_subplot(111)
ax.plotTrace(t1, marker='x')
ax.plotTrace(t2, marker='o')
ax.plotTrace(t1 + t2, marker='<')
ax.plotTrace((t2 + t2) * 3.0 + unit('0.03:V'), marker='<')
t1 = mf.fixed_to_variable_dt_trace(t1, '0.01:mV')
ax.plotTrace(t1, marker='>')
ax.legend()
pylab.show()
def get_unit(self):
return unit('')
def get_unit(self):
return unit('')
def compareNeuroMLChl(xmlFile):
model, chl_type = os.path.splitext(xmlFile)[0].split("/")[-2:]
print model, chl_type
op_dir = LocMgr.ensure_dir_exists(Join(html_output_dir, model, chl_type))
op_html = Join(op_dir, "index.html")
c = ComparisonResult(xmlfile=xmlFile, op_file = op_html, same_chl=True, exception=None)
try:
# Make the NeuroUnits channel:
chl_neuro = NeuroML_Via_NeuroUnits_ChannelNEURON(xml_filename=xmlFile, )
c.chl_neurounits = chl_neuro
op_pdf_file = Join(op_dir, 'Op1.pdf')
#WriteToPDF(eqnset = chl_neuro.eqnset, filename = op_pdf_file)
c.chl_neurounits_pdf = op_pdf_file
# Make the NeuroML channel:
xsl_file = "/home/michael/srcs/neuroml/CommandLineUtils/ChannelMLConverter/ChannelML_v1.8.1_NEURONmod.xsl"
chl_xsl = NeuroML_Via_XSL_ChannelNEURON(xml_filename=xmlFile, xsl_filename=xsl_file, )
c.chl_xsl = chl_xsl
c.chl_xsl_hoc = []
chl_neuro_res = simulate_chl_all(chl_neuro)
chl_xsl_res = simulate_chl_all(chl_xsl)
c.chl_neurounit_hoc = []
for i, (rN, rX) in enumerate(zip(chl_neuro_res, chl_xsl_res)):
c.chl_neurounit_hoc.append(rN.hocfilename )
c.chl_xsl_hoc.append(rX.hocfilename )
tN = rN.get_trace("CurrentClamp").convert_to_fixed(dt=unit("1.01:ms"))
tX = rX.get_trace("CurrentClamp").convert_to_fixed(dt=unit("1.01:ms"))
# Compare current traces:
tN._data[np.fabs(tN.time_pts_ms - 0) <0.05] *=0
tX._data[np.fabs(tX.time_pts_ms - 0) <0.05] *=0
tN._data[np.fabs(tN.time_pts_ms - 200) <0.05] *=0
tX._data[np.fabs(tX.time_pts_ms - 200) <0.05] *=0
tN._data[np.fabs(tN.time_pts_ms - 700) <0.05] *=0
tX._data[np.fabs(tX.time_pts_ms - 700) <0.05] *=0
print "TR1"
f = QuantitiesFigure()
ax1 = f.add_subplot(4, 1, 1)
ax2 = f.add_subplot(4, 1, 2)
ax3 = f.add_subplot(4, 1, 3)
ax4 = f.add_subplot(4, 1, 4)
ax1.plotTrace(tN, color='b')
ax1.plotTrace(tX, color='g', linewidth=20, alpha=0.2)
ax2.plotTrace(tN.window((200, 250)*pq.ms), color='b')
ax2.plotTrace(tX.window((200, 250)*pq.ms), color='g', linewidth=20, alpha=0.2)
num = (tN-tX)
denom = (tN+tX)
diff = num/denom
ax3.plotTrace(diff, color='r')
ax4.plotTrace(rN.get_trace('SomaVoltage'), color='m')
ax4.plotTrace(rX.get_trace('SomaVoltage'), color='m', linewidth=20, alpha=0.2)
if num.max()[1] > unit("0.1:pA"):
c.same_chl = False
out_im = Join(op_dir, "out_im%03d" % i)
pylab.savefig(out_im+".png")
pylab.savefig(out_im+".pdf")
c.output_image_files.append(out_im)
pylab.close()
c.finished_ok=True
except NeuroUnitsImportNeuroMLNotImplementedException, e:
print 'Exception caught:', e
s = StringIO.StringIO()
traceback.print_exc(file=s)
c.exception_long=s.getvalue()
c.exception="%s (%s)"%(str(e), str(type(e)))
c.same_chl = False
c.finished_ok=False
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# ----------------------------------------------------------------------
import morphforge.stdimports as mf
from mhlibs.quantities_plot import QuantitiesFigure
import pylab
from morphforge.stdimports import unit, reduce_to_variable_dt_trace
t1 = mf.TraceGenerator.generate_flat(value="1:mV")
# t2 = mf.TraceGenerator.generate_flat(value='0.0015:V')
t2 = mf.TraceGenerator.generate_ramp(value_start="0.0015:V", value_stop="2.5:mV", npoints=20)
f = QuantitiesFigure()
ax = f.add_subplot(111)
ax.plotTrace(t1, marker="x")
ax.plotTrace(t2, marker="o")
ax.plotTrace(t1 + t2, marker="<")
ax.plotTrace((t2 + t2) * 3.0 + unit("0.03:V"), marker="<")
t1 = mf.fixed_to_variable_dt_trace(t1, "0.01:mV")
ax.plotTrace(t1, marker=">")
ax.legend()
pylab.show()
