def test_rheobase_single_value_parallel_and_serial_comparison(self):
from neuronunit.tests.fi import RheobaseTest, RheobaseTestP
from neuronunit.optimization import get_neab
from neuronunit.models.reduced import ReducedModel
from neuronunit import aibs
import os
dataset_id = 354190013 # Internal ID that AIBS uses for a particular Scnn1a-Tg2-Cre
# Primary visual area, layer 5 neuron.
observation = aibs.get_observation(dataset_id, 'rheobase')
rt = RheobaseTest(observation=observation)
rtp = RheobaseTestP(observation=observation)
model = ReducedModel(get_neab.LEMS_MODEL_PATH, backend=('NEURON'))
#model = ReducedModel(get_neab.LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON')
self.score_p = rtp.judge(model, stop_on_error=False, deep_error=True)
self.predictionp = self.score_p.prediction
self.score_p = self.score_p.norm_score
#model = ReducedModel(get_neab.LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON')
serial_model = ReducedModel(get_neab.LEMS_MODEL_PATH,
name=str('vanilla'),
backend='NEURON')
self.score_s = rt.judge(serial_model,
stop_on_error=False,
deep_error=True)
self.predictions = self.score_s.prediction
self.score_s = self.score_s.norm_score
import numpy as np
check_less_thresh = float(
np.abs(self.predictionp['value'] - self.predictions['value']))
self.assertLessEqual(check_less_thresh, 2.0)
def test_rheobase_single_value_parallel_and_serial_comparison(self):
from neuronunit.tests.fi import RheobaseTest, RheobaseTestP
from neuronunit.optimization import get_neab
from neuronunit.models.reduced import ReducedModel
from neuronunit import aibs
import os
dataset_id = 354190013 # Internal ID that AIBS uses for a particular Scnn1a-Tg2-Cre
# Primary visual area, layer 5 neuron.
observation = aibs.get_observation(dataset_id,'rheobase')
rt = RheobaseTest(observation = observation)
rtp = RheobaseTestP(observation = observation)
model = ReducedModel(get_neab.LEMS_MODEL_PATH, backend=('NEURON'))
#model = ReducedModel(get_neab.LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON')
self.score_p = rtp.judge(model,stop_on_error = False, deep_error = True)
self.predictionp = self.score_p.prediction
self.score_p = self.score_p.sort_key
#model = ReducedModel(get_neab.LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON')
serial_model = ReducedModel(get_neab.LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON')
self.score_s = rt.judge(serial_model,stop_on_error = False, deep_error = True)
self.predictions = self.score_s.prediction
self.score_s = self.score_s.sort_key
import numpy as np
check_less_thresh = float(np.abs(self.predictionp['value'] - self.predictions['value']))
self.assertLessEqual(check_less_thresh, 2.0)
def test_4rheobase_setup(self):
from neuronunit.tests.fi import RheobaseTest, RheobaseTestP
from neuronunit.optimization import get_neab
from neuronunit.models import backends
from neuronunit.models.reduced import ReducedModel
import time
from neuronunit import aibs
import os
dataset_id = 354190013 # Internal ID that AIBS uses for a particular Scnn1a-Tg2-Cre
# Primary visual area, layer 5 neuron.
observation = aibs.get_observation(dataset_id,'rheobase')
#os.system('ipcluster start -n 8 --profile=default & sleep 25;')
rt = RheobaseTest(observation = observation)
rtp = RheobaseTestP(observation = observation)
get_neab.LEMS_MODEL_PATH = '/home/jovyan/neuronunit/neuronunit/optimization/NeuroML2/LEMS_2007One.xml'
model = ReducedModel(get_neab.LEMS_MODEL_PATH,name=str('vanilla'),backend='NEURON')
ticks = time.time()
self.score_p = rtp.judge(model,stop_on_error = False, deep_error = True)
self.predictionp = self.score_p.prediction
self.score_p = self.score_p.sort_key
tickp = time.time()
self.timed_p = tickp - ticks
self.score_s = rt.judge(model,stop_on_error = False, deep_error = True)
self.predictions = self.score_s.prediction
self.score_s = self.score_s.sort_key
self.timed_s = time.time() - tickp
print(' serial score {0} parallel score {1}'.format(self.score_s,self.score_p))
print(' serial time {0} parallel time {1}'.format(self.timed_s,self.timed_p))
def test_10rheobase_setup(self):
from neuronunit.tests.fi import RheobaseTest, RheobaseTestP
from neuronunit.optimization import get_neab
from neuronunit.models.reduced import ReducedModel
from neuronunit import aibs
import os
dataset_id = 354190013 # Internal ID that AIBS uses for a particular Scnn1a-Tg2-Cre
# Primary visual area, layer 5 neuron.
observation = aibs.get_observation(dataset_id, 'rheobase')
rt = RheobaseTest(observation=observation)
rtp = RheobaseTestP(observation=observation)
model = ReducedModel(get_neab.LEMS_MODEL_PATH,
name=str('vanilla'),
backend='NEURON')
self.score_p = rtp.judge(model, stop_on_error=False, deep_error=True)
self.predictionp = self.score_p.prediction
self.score_p = self.score_p.sort_key
self.score_s = rt.judge(model, stop_on_error=True, deep_error=True)
self.predictions = self.score_s.prediction
self.score_s = self.score_s.sort_key
print(' serial score {0} parallel score {1}'.format(
self.score_s, self.score_p))
self.assertEqual(int(self.score_s * 1000), int(self.score_p * 1000))
self.assertEqual(int(self.predictionp['value']),
int(self.predictions['value']))
from neuronunit.tests import fi
from neuronunit.tests import passive
from neuronunit.tests import waveform
#print(RheobaseTestP)
#fi = tests.fi
#print(dir(fi))
#pr = fi.RheobaseTestP()
neuron = {'nlex_id': 'nifext_50'} # Layer V pyramidal cell
tests = []
dataset_id = 354190013 # Internal ID that AIBS uses for a particular Scnn1a-Tg2-Cre
# Primary visual area, layer 5 neuron.
# this file writing operation needs to be threadlocked
observation = aibs.get_observation(dataset_id, 'rheobase')
ne_pickle = os.path.join(THIS_DIR, "neuroelectro.pickle")
if os.path.isfile(ne_pickle):
print('attempting to recover from pickled file')
with open(ne_pickle, 'rb') as f:
tests = pickle.load(f)
else:
#print('Checked path %s and no pickled file found. Commencing time intensive Download' % ne_pickle)
tests += [fi.RheobaseTestP(observation=observation)]
test_class_params = [(passive.InputResistanceTest, None),
(passive.TimeConstantTest, None),
(passive.CapacitanceTest, None),
(passive.RestingPotentialTest, None),
