def test_9_postrun_recording_NEURON_voltageclamp(self):
os.chdir(rootwd) # move up to load the model
# pick the model
sm.prepare_model_NEURON(parameters=self.sec_parameters,
chosenmodel=self.chosenmodel)
# self.chosenmodel.regions ->
# {"soma": ["v", "i_cap"], "axon": ["v"],
# "channels": {"soma": {"hh": ["il", "el"], "pas": ["i"]}, "axon": {"pas": ["i"]}}}
vstimuli = sm.stimulate_model_NEURON(
stimparameters=self.sec_stimparam,
modelsite=self.chosenmodel.cell.soma)
rec_t, recs, rec_v_stim = rm.prepare_recording_NEURON(
self.chosenmodel,
stimuli=vstimuli,
stimtype=self.sec_stimparam["type"])
sm.engage_NEURON()
rec_v_stim = rm.postrun_record_NEURON(
injectedstimuli=rec_v_stim, stimtype=self.sec_stimparam["type"])
total_iterations = len(
range(
-1,
int(self.sec_parameters["tstop"] / self.sec_parameters["dt"])))
self.assertEqual(
len(rec_t) + len(recs["soma"][0]) + len(rec_v_stim),
3 * total_iterations)
os.chdir(pwd) # reset to the location of this managerRecordTest.py
def test_7_postrun_recording_NEURON_without_stimulating(self):
os.chdir(rootwd) # move up to load the model
# pick the model
sm.prepare_model_NEURON(parameters=self.no_parameters,
chosenmodel=self.chosenmodel)
# self.chosenmodel.regions ->
# {"soma": ["v", "i_cap"], "axon": ["v"],
# "channels": {"soma": {"hh": ["il", "el"], "pas": ["i"]}, "axon": {"pas": ["i"]}}}
stimuli_list = sm.stimulate_model_NEURON()
rec_t, recs, rec_i_indivs = rm.prepare_recording_NEURON(
self.chosenmodel, stimuli=stimuli_list)
sm.engage_NEURON()
rec_i = rm.postrun_record_NEURON(injectedstimuli=rec_i_indivs)
self.assertEqual(rm.postrun_record_NEURON(), "Model is not stimulated")
os.chdir(pwd) # reset to the location of this managerRecordTest.py
def test_6_prepare_recording_NEURON_voltageclamp(self):
os.chdir(rootwd) # move up to load the model
# pick the model
sm.prepare_model_NEURON(parameters=self.sec_parameters,
chosenmodel=self.chosenmodel)
# self.chosenmodel.regions ->
# {"soma": ["v", "i_cap"], "axon": ["v"],
# "channels": {"soma": {"hh": ["il", "el"], "pas": ["i"]}, "axon": {"pas": ["i"]}}}
vstimuli = sm.stimulate_model_NEURON(
stimparameters=self.sec_stimparam,
modelsite=self.chosenmodel.cell.soma)
rec_t, recs, rec_v_stim = rm.prepare_recording_NEURON(
self.chosenmodel,
stimuli=vstimuli,
stimtype=self.sec_stimparam["type"])
sm.engage_NEURON()
total_iterations = len(
range(
-1,
int(self.sec_parameters["tstop"] / self.sec_parameters["dt"])))
self.assertEqual([
len(rec_t) + len(recs["soma"][0]), rec_v_stim.amp1,
rec_v_stim.dur1, rec_v_stim.amp2, rec_v_stim.dur2
], [
2 * total_iterations, self.sec_stimparam["stimlist"][0]["amp1"],
self.sec_stimparam["stimlist"][0]["dur1"],
self.sec_stimparam["stimlist"][1]["amp2"],
self.sec_stimparam["stimlist"][1]["dur2"]
])
#self.assertEqual( len(rec_t) + len(rec_v["soma"]),#len(rec_v[self.regionslist_str[0]]),
# 2*total_iterations )
os.chdir(pwd) # reset to the location of this managerRecordTest.py
def test_1_transform_signal_None(self):
os.chdir("..") # move up to load the model
# pick the model
parameters = {"dt": 0.1, "celsius": 30, "tstop": 10, "v_init": 65}
sm.prepare_model_NEURON(parameters=parameters,
chosenmodel=self.chosenmodel)
rec_t, rec_v, x = rm.prepare_recording_NEURON(self.chosenmodel)
sm.engage_NEURON()
total_iterations = len(
range(-1, int(parameters["tstop"] / parameters["dt"])))
self.assertRaises(ValueError,
sp.transform_signal,
chosenmodel=self.chosenmodel,
recordings=rec_v[self.regionslist_str[0]])
os.chdir(pwd) # reset to the location of this managerRecordTest.py
def test_2_recordings_of_cellular_componenets_NEURON(self):
os.chdir(rootwd) # move up to load the model
# pick the model
sm.prepare_model_NEURON(parameters=self.no_parameters,
chosenmodel=self.chosenmodel)
# self.chosenmodel.regions ->
# {"soma": ["v", "i_cap"], "axon": ["v"],
# "channels": {"soma": {"hh": ["il", "el"], "pas": ["i"]}, "axon": {"pas": ["i"]}}}
recs = rm.recordings_of_cellular_components_NEURON(self.chosenmodel)
sm.engage_NEURON()
a = (Counter(list(recs["channels"]["soma"]["hh"][0])) != Counter(
list(recs["channels"]["soma"]["hh"][1])))
b = (Counter(list(recs["channels"]["soma"]["pas"][0])) != Counter(
list(recs["channels"]["axon"]["pas"][0])))
self.assertEqual([a, b], [True, True])
os.chdir(pwd) # reset to the location of this managerRecordTest.py
def test_1_recordings_of_cellular_regionbodies_NEURON(self):
os.chdir(rootwd) # move up to load the model
# pick the model
sm.prepare_model_NEURON(parameters=self.no_parameters,
chosenmodel=self.chosenmodel)
# self.chosenmodel.regions ->
# {"soma": ["v", "i_cap"], "axon": ["v"],
# "channels": {"soma": {"hh": ["il", "el"], "pas": ["i"]}, "axon": {"pas": ["i"]}}}
recs = rm.recordings_of_cellular_regionbodies_NEURON(self.chosenmodel)
sm.engage_NEURON()
total_iterations = len(
range(-1,
int(self.no_parameters["tstop"] / self.no_parameters["dt"])))
a = (Counter(list(recs["soma"][0])) != Counter(list(recs["axon"][0])))
self.assertEqual([len(recs["soma"][0]), a], [total_iterations, True])
os.chdir(pwd) # reset to the location of this managerRecordTest.py
def test_3_voltage_to_spiketrain_without_stimulation(self):
os.chdir("..") # this moves you up to ~/managers
os.chdir("..") # you are now in parent /cerebmodels
parameters = {"dt": 0.1, "celsius": 20, "tstop": 10, "v_init": 65}
sm.prepare_model_NEURON(parameters=parameters, chosenmodel=self.chosenmodel)
self.rec["time"], self.rec["response"], self.rec["stimulus"] = \
rm.prepare_recording_NEURON(self.chosenmodel)
sm.engage_NEURON()
spikes = co.voltage_to_spiketrain(self.chosenmodel, self.rec)
ans = \
len( range(
int(spikes[self.regionslist_str[0]].t_start.magnitude),
int(spikes[self.regionslist_str[0]].t_stop.magnitude/parameters["dt"])
) ) + 1 # for the additional dt step
self.assertEqual( ans, len(self.rec["time"]) )
os.chdir(pwd) # reset to the location of this converterTest.py
def test_4_prepare_recording_NEURON_without_stimulating_but_evoke_stimulate_model(
self):
os.chdir(rootwd) # move up to load the model
# pick the modelstimtype = currparameters["type"]
sm.prepare_model_NEURON(parameters=self.no_parameters,
chosenmodel=self.chosenmodel)
# self.chosenmodel.regions ->
# {"soma": ["v", "i_cap"], "axon": ["v"],
# "channels": {"soma": {"hh": ["il", "el"], "pas": ["i"]}, "axon": {"pas": ["i"]}}}
stimulate_not = sm.stimulate_model_NEURON()
rec_t, recs, x = rm.prepare_recording_NEURON(self.chosenmodel,
stimuli=stimulate_not)
sm.engage_NEURON()
a = (Counter(list(recs["soma"][0])) != Counter(
list(recs["channels"]["soma"]["pas"][0])))
b = (Counter(list(recs["axon"][0])) != Counter(
list(recs["channels"]["axon"]["pas"][0])))
total_iterations = len(
range(-1,
int(self.no_parameters["tstop"] / self.no_parameters["dt"])))
self.assertEqual([len(rec_t), a, b], [total_iterations, True, True])
os.chdir(pwd) # reset to the location of this managerRecordTest.py
def test_4_voltage_to_spiketrain_with_stimulation(self):
os.chdir("..") # this moves you up to ~/managers
os.chdir("..") # you are now in parent /cerebmodels
parameters = {"dt": 0.1, "celsius": 20, "tstop": 10, "v_init": 65}
currparameters = {"type": ["current", "IClamp"],
"stimlist": [ {'amp': 0.5, 'dur': 10.0, 'delay': 5.0},
{'amp': 1.0, 'dur': 20.0, 'delay': 5.0+10.0} ],
"tstop": parameters["tstop"] }
sm.prepare_model_NEURON(parameters=parameters, chosenmodel=self.chosenmodel)
stimuli_list = sm.stimulate_model_NEURON(stimparameters = currparameters,
modelsite = self.chosenmodel.cell.soma)
self.rec["time"], self.rec["response"], self.rec["stimulus"] = \
rm.prepare_recording_NEURON(self.chosenmodel)
sm.engage_NEURON()
spikes = co.voltage_to_spiketrain(self.chosenmodel, self.rec)
ans = \
len( range(
int(spikes[self.regionslist_str[0]].t_start.magnitude),
int(spikes[self.regionslist_str[0]].t_stop.magnitude/parameters["dt"])
) ) + 1 # for the additional dt step
self.assertEqual( ans, len(self.rec["time"]) )
os.chdir(pwd) # reset to the location of this converterTest.py
def test_5_prepare_recording_NEURON_currentclamp(self):
os.chdir(rootwd) # move up to load the model
# pick the model
sm.prepare_model_NEURON(parameters=self.ic_parameters,
chosenmodel=self.chosenmodel)
# self.chosenmodel.regions ->
# {"soma": ["v", "i_cap"], "axon": ["v"],
# "channels": {"soma": {"hh": ["il", "el"], "pas": ["i"]}, "axon": {"pas": ["i"]}}}
stimuli_list = sm.stimulate_model_NEURON(
stimparameters=self.ic_stimparam,
modelsite=self.chosenmodel.cell.soma)
rec_t, recs, rec_i_indivs = rm.prepare_recording_NEURON(
self.chosenmodel,
stimuli=stimuli_list,
stimtype=self.ic_stimparam["type"])
sm.engage_NEURON()
total_iterations = len(
range(-1,
int(self.ic_parameters["tstop"] / self.ic_parameters["dt"])))
self.assertEqual(
len(rec_t) + len(recs["soma"][0]) + len(rec_i_indivs),
2 * total_iterations + len(self.ic_stimparam["stimlist"]))
os.chdir(pwd) # reset to the location of this managerRecordTest.py
def test_2_transform_signal_to_spikes(self):
os.chdir("..") # move up to load the model
# pick the model
parameters = {"dt": 0.1, "celsius": 30, "tstop": 10, "v_init": 65}
currparameters = {
"type": ["current", "IClamp"],
"stimlist": [{
'amp': 0.5,
'dur': 10.0,
'delay': 5.0
}, {
'amp': 1.0,
'dur': 20.0,
'delay': 5.0 + 10.0
}]
}
sm.prepare_model_NEURON(parameters=parameters,
chosenmodel=self.chosenmodel)
stimuli_list = sm.stimulate_model_NEURON(
stimparameters=currparameters,
modelsite=self.chosenmodel.cell.soma)
self.rec["time"], self.rec["response"], self.rec["stimulus"] = \
rm.prepare_recording_NEURON(self.chosenmodel)
sm.engage_NEURON()
spikes = sp.transform_signal(chosenmodel=self.chosenmodel,
recordings=self.rec,
tosignal='spikes')
ans = \
len( range(
int(spikes[self.regionslist_str[0]].t_start.magnitude),
int(spikes[self.regionslist_str[0]].t_stop.magnitude/parameters["dt"])
) ) + 1 # for the additional dt step
self.assertEqual(ans, len(self.rec["time"]))
os.chdir(
pwd
) # reset to the location of this managerSignalProcessingTest.py
def launch_model(self,
parameters=None,
onmodel=None,
stimparameters=None,
stimloc=None,
capabilities={
'model': None,
'vtest': None
},
mode="raw"):
"""Directs the :ref:`SimulationManager` to launch simulation on an instantiated model.
**Keyword Arguments:**
+-------------------------------+----------------------------------------------+
| Key | Value type |
+===============================+==============================================+
| ``parameters`` | dictionary |
+-------------------------------+----------------------------------------------+
| ``onmodel`` | instantiated model |
+-------------------------------+----------------------------------------------+
| ``stimparameters`` (optional) | dictionary |
+-------------------------------+----------------------------------------------+
| ``stimloc`` (optional) | string; attribute name of instantiated model |
+-------------------------------+----------------------------------------------+
| ``capabilities`` (optional) | dictionary |
+-------------------------------+----------------------------------------------+
| ``mode`` (optional) | string; |
| |- "raw" (default), "capability" |
+-------------------------------+----------------------------------------------+
* ``parameters``- *mandatory* whose value is a dictionary. For example, ``parameters = {"dt": 0.01, "celsius": 30, "tstop": 100, "v_init": 65}``
* ``onmodel``- *mandatory* whose value is the instantiated model using :py:meth:`.choose_model()`. For example, ``onmodel = <instance>.choose_model(modelscale="a_chosen_scale", modelname="a_chosen_name")``.
* ``stimparameters``- optional whose value is a dictionary. For example, ``{"type": ["current", "IClamp"], "stimlist": [ {'amp': 0.5, 'dur': 100.0, 'delay': 10.0}, {'amp': 1.0, 'dur': 50.0, 'delay': 10.0+100.0} ] }``.
* ``stimloc``- optional (mandatory only if ``stimparameters`` argument is provided). Its value is string representing the namse of an attribute of the instantiated model. Note that this instantiated model is the value for the mandatory keyword argument ``onmodel``.
* ``capabilties``- optional whose value is a dictionary. The dictionary **must** have the keys ``model`` and ``vtest``. The value for ``model`` key is a string representing the models method. For example, ``model: "produce_voltage_response"``. The value for ``vtest`` key is a class imported from the installed ``CerebUnit``.
*NOTE*: Calling this function returns the model as the attribute ``ExecutiveControl.chosenmodel``.
"""
# NOTE: although it is convenient to use self.chosenmodel
# to the user having explicitly choose onmodel as an argument is clearer
uu.check_not_None_in_arg({
'parameters': parameters,
'onmodel': onmodel
})
get_stimtype = (lambda stimpar: None
if stimpar is None else stimpar["type"])
self.simtime = datetime.datetime.now()
if onmodel.modelscale is "cells":
get_stimloc = (
lambda stimloc: None
if stimloc is None else getattr(onmodel.cell, stimloc))
sm.prepare_model_NEURON(parameters=parameters,
chosenmodel=onmodel,
modelcapability=capabilities['model'],
cerebunitcapability=capabilities['vtest'])
stimuli_clamp = sm.stimulate_model_NEURON(
stimparameters=stimparameters, modelsite=get_stimloc(stimloc))
self.recordings["time"], self.recordings["response"], rec_clamp_indivs = \
rm.prepare_recording_NEURON( onmodel,
stimuli = stimuli_clamp,
stimtype = get_stimtype(stimparameters) )
if mode == "raw":
sm.trigger_NEURON(onmodel)
elif mode == "capability":
sm.trigger_NEURON(
onmodel,
modelcapability=capabilities['model'],
# Below are the **kwargs for lock_and_load_capability
parameters=parameters,
stimparameters=stimparameters,
stimloc=stimloc,
onmodel=onmodel,
mode="capability")
self.recordings["stimulus"] = \
rm.postrun_record_NEURON( injectedstimuli = rec_clamp_indivs,
stimtype = get_stimtype(stimparameters) )
# save the parameters as attributes
self.chosenmodel = onmodel
self.parameters = parameters
self.stimparameters = stimparameters
return self.chosenmodel