def main(args=None):
"""Main"""
vs = [(v - 100) * 0.001 for v in range(200)]
for f in ['IM.channel.nml', 'Kd.channel.nml']:
nml_doc = pynml.read_neuroml2_file(f)
for ct in nml_doc.ComponentType:
ys = []
for v in vs:
req_variables = {'v': '%sV' % v, 'vShift': '10mV'}
vals = pynml.evaluate_component(ct,
req_variables=req_variables)
print(vals)
if 'x' in vals:
ys.append(vals['x'])
if 't' in vals:
ys.append(vals['t'])
if 'r' in vals:
ys.append(vals['r'])
ax = pynml.generate_plot([vs], [ys],
"Some traces from %s in %s" %
(ct.name, f),
show_plot_already=False)
print(vals)
plt.show()
def main(args=None):
"""Main"""
vs = [(v-100)*0.001 for v in range(200)]
for f in ['IM.channel.nml','Kd.channel.nml']:
nml_doc = pynml.read_neuroml2_file(f)
for ct in nml_doc.ComponentType:
ys = []
for v in vs:
req_variables = {'v':'%sV'%v,'vShift':'10mV'}
vals = pynml.evaluate_component(ct,req_variables=req_variables)
print vals
if 'x' in vals:
ys.append(vals['x'])
if 't' in vals:
ys.append(vals['t'])
if 'r' in vals:
ys.append(vals['r'])
ax = pynml.generate_plot([vs],[ys],
"Some traces from %s in %s"%(ct.name,f),
show_plot_already=False )
print vals
plt.show()
def calculateRateFn(self, ratefn, vmin, vmax, tablen=3000, vShift='0mV'):
"""Returns A / B table from ngate."""
tab = np.linspace(vmin, vmax, tablen)
if self._is_standard_nml_rate(ratefn):
midpoint, rate, scale = map(
SI, (ratefn.midpoint, ratefn.rate, ratefn.scale))
return self.rate_fn_map[ratefn.type](tab, rate, scale, midpoint)
else:
for ct in self.doc.ComponentType:
if ratefn.type == ct.name:
mu.info("Using %s to evaluate rate" % ct.name)
rate = []
for v in tab:
vals = pynml.evaluate_component(
ct,
req_variables={
'v': '%sV' % v,
'vShift': vShift,
'temperature': self._getTemperature()
})
'''mu.info vals'''
if 'x' in vals:
rate.append(vals['x'])
if 't' in vals:
rate.append(vals['t'])
if 'r' in vals:
rate.append(vals['r'])
return np.array(rate)
def calculateRateFn(self, ratefn, vmin, vmax, tablen=3000, vShift='0mV'):
"""Returns A / B table from ngate."""
from . import hhfit
rate_fn_map = {
'HHExpRate': hhfit.exponential2,
'HHSigmoidRate': hhfit.sigmoid2,
'HHSigmoidVariable': hhfit.sigmoid2,
'HHExpLinearRate': hhfit.linoid2
}
tab = np.linspace(vmin, vmax, tablen)
if self._is_standard_nml_rate(ratefn):
midpoint, rate, scale = map(
SI, (ratefn.midpoint, ratefn.rate, ratefn.scale))
return rate_fn_map[ratefn.type](tab, rate, scale, midpoint)
for ct in self.doc.ComponentType:
if ratefn.type != ct.name:
continue
logger_.info("Using %s to evaluate rate" % ct.name)
rate = []
for v in tab:
# Note: MOOSE HHGate are either voltage of concentration
# dependant. Here we figure out if nml description of gate is
# concentration dependant or note.
if _isConcDep(ct):
# Concentration dependant. Concentration can't be negative.
# Find a suitable CaConc from the /library. Currently on Ca
# dependant channels are allowed.
caConcName = _findCaConcVariableName()
req_vars = {
caConcName: '%g' % max(0, v),
'vShift': vShift,
'temperature': self._getTemperature()
}
else:
req_vars = {
'v': '%sV' % v,
'vShift': vShift,
'temperature': self._getTemperature()
}
req_vars.update(self._variables)
vals = pynml.evaluate_component(ct, req_variables=req_vars)
v = vals.get('x', vals.get('t', vals.get('r', None)))
if v is not None:
rate.append(v)
return np.array(rate)
print("[WARN ] Could not determine rate: %s %s %s" %
(ratefn.type, vmin, vmax))
return np.array([])
def calculateRateFn(self, ratefn, vmin, vmax, tablen=3000, vShift='0mV'):
"""Returns A / B table from ngate."""
tab = np.linspace(vmin, vmax, tablen)
if self._is_standard_nml_rate(ratefn):
midpoint, rate, scale = map(SI, (ratefn.midpoint, ratefn.rate, ratefn.scale))
return self.rate_fn_map[ratefn.type](tab, rate, scale, midpoint)
else:
for ct in self.doc.ComponentType:
if ratefn.type == ct.name:
print("Using %s to evaluate rate"%ct.name)
rate = []
for v in tab:
vals = pynml.evaluate_component(ct,req_variables={'v':'%sV'%v,'vShift':vShift,'temperature':self._getTemperature()})
'''print vals'''
if 'x' in vals:
rate.append(vals['x'])
if 't' in vals:
rate.append(vals['t'])
if 'r' in vals:
rate.append(vals['r'])
return np.array(rate)