def check_current(ampl, dtc):
'''
Inputs are an amplitude to test and a virtual model
output is an virtual model with an updated dictionary.
'''
import copy
import os
import quantities
from neuronunit.models.reduced import ReducedModel
import neuronunit
LEMS_MODEL_PATH = str(neuronunit.__path__[0]) + str(
'/models/NeuroML2/LEMS_2007One.xml')
dtc.model_path = LEMS_MODEL_PATH
model = ReducedModel(dtc.model_path,
name='vanilla',
backend=(str(dtc.backend), {
'DTC': dtc
}))
import copy
model.set_attrs(dtc.attrs)
DELAY = 100.0 * pq.ms
DURATION = 1000.0 * pq.ms
params = {
'injected_square_current': {
'amplitude': 100.0 * pq.pA,
'delay': DELAY,
'duration': DURATION
}
}
dtc = copy.copy(dtc)
ampl = float(ampl)
#print(dtc.lookup)
if ampl not in dtc.lookup or len(dtc.lookup) == 0:
current = params.copy()['injected_square_current']
uc = {'amplitude': ampl}
current.update(uc)
current = {'injected_square_current': current}
dtc.run_number += 1
model.set_attrs(dtc.attrs)
model.inject_square_current(current)
dtc.previous = ampl
n_spikes = model.get_spike_count()
dtc.lookup[float(ampl)] = n_spikes
if n_spikes == 1:
dtc.rheobase = float(ampl)
dtc.boolean = True
return dtc
return dtc
if float(ampl) in dtc.lookup:
return dtc
def check_current(dtc):
'''
Inputs are an amplitude to test and a virtual model
output is an virtual model with an updated dictionary.
'''
LEMS_MODEL_PATH = str(neuronunit.__path__[0]) + str(
'/models/NeuroML2/LEMS_2007One.xml')
dtc.model_path = LEMS_MODEL_PATH
model = ReducedModel(dtc.model_path,
name='vanilla',
backend=(dtc.backend, {
'DTC': dtc
}))
dtc.current_src_name = model._backend.current_src_name
assert type(dtc.current_src_name) is not type(None)
dtc.cell_name = model._backend._cell_name
#model.set_attrs(dtc.attrs)
DELAY = 100.0 * pq.ms
DURATION = 1000.0 * pq.ms
params = {
'injected_square_current': {
'amplitude': 100.0 * pq.pA,
'delay': DELAY,
'duration': DURATION
}
}
ampl = float(dtc.ampl)
if ampl not in dtc.lookup or len(dtc.lookup) == 0:
current = params.copy()['injected_square_current']
uc = {'amplitude': ampl}
current.update(uc)
current = {'injected_square_current': current}
dtc.run_number += 1
model.set_attrs(dtc.attrs)
model.inject_square_current(current)
#dtc.previous = ampl
n_spikes = model.get_spike_count()
dtc.lookup[float(ampl)] = n_spikes
if n_spikes == 1:
dtc.rheobase = float(ampl)
dtc.boolean = True
return dtc
return dtc
if float(ampl) in dtc.lookup:
return dtc
def check_current(ampl, dtc):
'''
Inputs are an amplitude to test and a virtual model
output is an virtual model with an updated dictionary.
'''
ampl = float(ampl)
import os
#from neuronunit.models import reduced
from neuronunit.models.reduced import ReducedModel
#assert os.path.isfile(dtc.model_path), "%s is not a file" % dtc.model_path
model = ReducedModel(dtc.model_path,
name='vanilla',
backend='NEURON')
#print(model)
#import pdb; pdb.set_trace()
DELAY = 100.0 * pq.ms
DURATION = 1000.0 * pq.ms
params = {
'injected_square_current': {
'amplitude': 100.0 * pq.pA,
'delay': DELAY,
'duration': DURATION
}
}
if ampl not in dtc.lookup or len(dtc.lookup) == 0:
current = params.copy()['injected_square_current']
uc = {'amplitude': ampl}
current.update(uc)
current = {'injected_square_current': current}
dtc.run_number += 1
#import ipyparallel as ipp
#model = ipp.Reference('model')
model.set_attrs(dtc.attrs)
model.name = dtc.attrs
model.inject_square_current(current)
dtc.previous = ampl
n_spikes = model.get_spike_count()
dtc.lookup[float(ampl)] = n_spikes
#name = str('rheobase {0} parameters {1}'.format(str(current),str(model.params)))
if n_spikes == 1:
dtc.rheobase = float(ampl)
dtc.boolean = True
return dtc
return dtc
if float(ampl) in dtc.lookup:
return dtc
def use_dtc_to_plotting(dtcpop, minimagr):
from neuronunit.capabilities import spike_functions
import matplotlib.pyplot as plt
import numpy as np
plt.clf()
plt.style.use('ggplot')
fig, axes = plt.subplots(figsize=(10, 10), facecolor='white')
stored_min = []
stored_max = []
for dtc in dtcpop[1:-1]:
plt.plot(dtc.tvec, dtc.vm0, linewidth=3.5, color='grey')
stored_min.append(np.min(dtc.vm0))
stored_max.append(np.max(dtc.vm0))
from neuronunit.models.reduced import ReducedModel
from neuronunit.optimization.get_neab import tests as T
from neuronunit.optimization import get_neab
from neuronunit.optimization import evaluate_as_module
from neuronunit.optimization.evaluate_as_module import pre_format
model = ReducedModel(get_neab.LEMS_MODEL_PATH,
name=str('vanilla'),
backend='NEURON')
import neuron
model._backend.reset_neuron(neuron)
model.set_attrs(minimagr.attrs)
model.rheobase = minimagr.rheobase['value']
minimagr = pre_format(minimagr)
parameter_list = list(minimagr.vtest.values())
model.inject_square_current(parameter_list[0])
model._backend.local_run()
assert model.get_spike_count() == 1
print(model.get_spike_count(), bool(model.get_spike_count() == 1))
brute_best = list(model.results['vm'])
plt.plot(dtcpop[0].tvec,
brute_best,
linewidth=1,
color='blue',
label='best candidate via grid') #+str(mini.scores))
plt.plot(dtcpop[0].tvec,
dtcpop[0].vm0,
linewidth=1,
color='red',
label='best candidate via GA') #+str(miniga.scores))
plt.legend()
plt.ylabel('$V_{m}$ mV')
plt.xlabel('ms')
plt.show()
def check_current(ampl,dtc):
'''
Inputs are an amplitude to test and a virtual model
output is an virtual model with an updated dictionary.
'''
import copy
import os
import quantities
from neuronunit.models.reduced import ReducedModel
import neuronunit
LEMS_MODEL_PATH = str(neuronunit.__path__[0])+str('/models/NeuroML2/LEMS_2007One.xml')
dtc.model_path = LEMS_MODEL_PATH
model = ReducedModel(dtc.model_path,name='vanilla', backend=(str(dtc.backend), {'DTC':dtc}))
import copy
model.set_attrs(dtc.attrs)
DELAY = 100.0*pq.ms
DURATION = 1000.0*pq.ms
params = {'injected_square_current':
{'amplitude':100.0*pq.pA, 'delay':DELAY, 'duration':DURATION}}
dtc = copy.copy(dtc)
ampl = float(ampl)
#print(dtc.lookup)
if ampl not in dtc.lookup or len(dtc.lookup) == 0:
current = params.copy()['injected_square_current']
uc = {'amplitude':ampl}
current.update(uc)
current = {'injected_square_current':current}
dtc.run_number += 1
model.set_attrs(dtc.attrs)
model.inject_square_current(current)
dtc.previous = ampl
n_spikes = model.get_spike_count()
dtc.lookup[float(ampl)] = n_spikes
if n_spikes == 1:
dtc.rheobase = float(ampl)
dtc.boolean = True
return dtc
return dtc
if float(ampl) in dtc.lookup:
return dtc
def plot_vm(hof,ax,key):
ax.cla()
best_dtc = hof[1].dtc
best_rh = hof[1].dtc.rheobase
print( [ h.dtc.rheobase for h in hof ])
neuron = None
model = ReducedModel(path_params['model_path'],name = str('regular_spiking'),backend =('NEURON',{'DTC':best_dtc}))
params = {'injected_square_current':
{'amplitude': best_rh['value'], 'delay':DELAY, 'duration':DURATION}}
model.set_attrs(hof[0].dtc.attrs)
results = model.inject_square_current(params)
print(model.attrs,best_dtc.attrs,best_rh)
vm = model.get_membrane_potential()
times = vm.times
ax.plot(times,vm)
#ax.xlabel('ms')
#ax.ylabel('mV')
#ax.set_xlim(np.min(x),np.max(x))
#ax.set_ylim(np.min(z),np.max(z))
return ax
dtc = DataTC()
from neuronunit.tests import np, pq, cap, VmTest, scores, AMPL, DELAY, DURATION
opt_keys = ['a', 'b', 'vr']
nparams = len(opt_keys)
import numpy as np
attrs = {
'a': np.median(mp['a']),
'b': np.median(mp['b']),
'vr': np.median(mp['vr'])
}
dtc.attrs = attrs
model = ReducedModel(path_params['model_path'],
name=str('vanilla'),
backend=('NEURON', {
'DTC': dtc
}))
params = {
'injected_square_current': {
'amplitude': 10.0 * pq.pA,
'delay': DELAY,
'duration': DURATION
}
}
model.inject_square_current(params)
n_spikes = model.get_spike_count()
print(n_spikes)
def check_current(dtc):
'''
Inputs are an amplitude to test and a virtual model
output is an virtual model with an updated dictionary.
'''
dtc.boolean = False
LEMS_MODEL_PATH = str(neuronunit.__path__[0]) + str(
'/models/NeuroML2/LEMS_2007One.xml')
dtc.model_path = LEMS_MODEL_PATH
model = ReducedModel(dtc.model_path,
name='vanilla',
backend=(dtc.backend, {
'DTC': dtc
}))
if dtc.backend is str('NEURON') or dtc.backend is str('jNEUROML'):
dtc.current_src_name = model._backend.current_src_name
assert type(dtc.current_src_name) is not type(None)
dtc.cell_name = model._backend.cell_name
params = {
'injected_square_current': {
'amplitude': 100.0 * pq.pA,
'delay': DELAY,
'duration': DURATION
}
}
ampl = float(dtc.ampl)
if ampl not in dtc.lookup or len(dtc.lookup) == 0:
uc = {'amplitude': ampl, 'duration': DURATION, 'delay': DELAY}
dtc.run_number += 1
model.set_attrs(**dtc.attrs)
model.inject_square_current(uc)
n_spikes = model.get_spike_count()
dtc.previous = ampl
if dtc.use_diff == True:
from neuronunit.tests import druckman2013 as dm
DM = dm.Druckmann2013Test()
#vm = model.get_membrane_potential()
spike_train = DM.get_APs(model)
#diff = diff(vm)
#spike_train = threshold_detection(diff,threshold= 0.000193667327364)
n_spikes = len(spike_train)
if n_spikes >= 1:
dtc.negative_spiker = None
dtc.negative_spiker = True
else:
n_spikes = model.get_spike_count()
if float(ampl) < -1.0:
dtc.rheobase = None
dtc.boolean = True
return dtc
if n_spikes == 1:
dtc.lookup[float(ampl)] = 1
dtc.rheobase = float(ampl) * pq.pA
dtc.boolean = True
return dtc
dtc.lookup[float(ampl)] = n_spikes
return dtc