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Elisabetta Iavarone
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First draft of exercise notebook
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‎FENS2016/exercise/Sst-IRES-Cre_Ai14_IVSCC_-183332.05.02.01_486041253_m.swc‎

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‎FENS2016/exercise/cell_evaluator.py‎

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"""Run simple cell optimisation"""
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"""
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Copyright (c) 2016, EPFL/Blue Brain Project
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This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>
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This library is free software; you can redistribute it and/or modify it under
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the terms of the GNU Lesser General Public License version 3.0 as published
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by the Free Software Foundation.
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This library is distributed in the hope that it will be useful, but WITHOUT
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ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
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details.
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You should have received a copy of the GNU Lesser General Public License
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along with this library; if not, write to the Free Software Foundation, Inc.,
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51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
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"""
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# pylint: disable=R0914
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import os
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import json
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#import l5pc_model # NOQA
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import bluepyopt.ephys as ephys
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script_dir = os.path.dirname(__file__)
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config_dir = os.path.join(script_dir, 'config')
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# TODO store definition dicts in json
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# TODO rename 'score' into 'objective'
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# TODO add functionality to read settings of every object from config format
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def define_protocols():
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"""Define protocols"""
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protocol_definitions = json.load(
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open(
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os.path.join(
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config_dir,
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'protocols.json')))
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protocols = {}
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soma_loc = ephys.locations.NrnSeclistCompLocation(
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name='soma',
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seclist_name='somatic',
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sec_index=0,
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comp_x=0.5)
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for protocol_name, protocol_definition in protocol_definitions.iteritems():
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# By default include somatic recording
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somav_recording = ephys.recordings.CompRecording(
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name='%s.soma.v' %
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protocol_name,
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location=soma_loc,
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variable='v')
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recordings = [somav_recording]
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if 'extra_recordings' in protocol_definition:
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for recording_definition in protocol_definition['extra_recordings']:
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if recording_definition['type'] == 'somadistance':
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location = ephys.locations.NrnSomaDistanceCompLocation(
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name=recording_definition['name'],
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soma_distance=recording_definition['somadistance'],
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seclist_name=recording_definition['seclist_name'])
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var = recording_definition['var']
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recording = ephys.recordings.CompRecording(
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name='%s.%s.%s' % (protocol_name, location.name, var),
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location=location,
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variable=recording_definition['var'])
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recordings.append(recording)
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else:
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raise Exception(
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'Recording type %s not supported' %
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recording_definition['type'])
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stimuli = []
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for stimulus_definition in protocol_definition['stimuli']:
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stimuli.append(ephys.stimuli.NrnSquarePulse(
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step_amplitude=stimulus_definition['amp'],
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step_delay=stimulus_definition['delay'],
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step_duration=stimulus_definition['duration'],
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location=soma_loc,
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total_duration=stimulus_definition['totduration']))
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protocols[protocol_name] = ephys.protocols.SweepProtocol(
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protocol_name,
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stimuli,
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recordings)
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return protocols
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def define_fitness_calculator(protocols):
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"""Define fitness calculator"""
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feature_definitions = json.load(
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open(
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os.path.join(
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config_dir,
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'features.json')))
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# TODO: add bAP stimulus
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objectives = []
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for protocol_name, locations in feature_definitions.iteritems():
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for location, features in locations.iteritems():
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for efel_feature_name, meanstd in features.iteritems():
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feature_name = '%s.%s.%s' % (
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protocol_name, location, efel_feature_name)
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recording_names = {'': '%s.%s.v' % (protocol_name, location)}
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stimulus = protocols[protocol_name].stimuli[0]
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stim_start = stimulus.step_delay
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if location == 'soma':
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threshold = -20
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elif 'dend' in location:
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threshold = -55
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if protocol_name == 'bAP':
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stim_end = stimulus.total_duration
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else:
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stim_end = stimulus.step_delay + stimulus.step_duration
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feature = ephys.efeatures.eFELFeature(
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feature_name,
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efel_feature_name=efel_feature_name,
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recording_names=recording_names,
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stim_start=stim_start,
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stim_end=stim_end,
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exp_mean=meanstd[0],
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exp_std=meanstd[1],
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threshold=threshold)
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objective = ephys.objectives.SingletonObjective(
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feature_name,
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feature)
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objectives.append(objective)
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fitcalc = ephys.objectivescalculators.ObjectivesCalculator(objectives)
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return fitcalc
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# def create():
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# """Setup"""
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# l5pc_cell = l5pc_model.create()
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# fitness_protocols = define_protocols()
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# fitness_calculator = define_fitness_calculator(fitness_protocols)
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# param_names = [param.name
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# for param in l5pc_cell.params.values()
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# if not param.frozen]
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# sim = ephys.simulators.NrnSimulator()
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# return ephys.evaluators.CellEvaluator(
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# cell_model=l5pc_cell,
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# param_names=param_names,
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# fitness_protocols=fitness_protocols,
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# fitness_calculator=fitness_calculator,
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# sim=sim)

‎FENS2016/exercise/cell_model.py‎

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"""Run simple cell optimisation"""
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"""
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Copyright (c) 2016, EPFL/Blue Brain Project
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This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>
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This library is free software; you can redistribute it and/or modify it under
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the terms of the GNU Lesser General Public License version 3.0 as published
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by the Free Software Foundation.
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This library is distributed in the hope that it will be useful, but WITHOUT
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ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
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details.
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You should have received a copy of the GNU Lesser General Public License
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along with this library; if not, write to the Free Software Foundation, Inc.,
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51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
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"""
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# pylint: disable=R0914
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import os
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import json
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import bluepyopt.ephys as ephys
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script_dir = os.path.dirname(__file__)
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config_dir = os.path.join(script_dir, 'config')
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# TODO store definition dicts in json
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# TODO rename 'score' into 'objective'
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# TODO add functionality to read settings of every object from config format
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def define_mechanisms():
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"""Define mechanisms"""
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mech_definitions = json.load(
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open(
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os.path.join(
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config_dir,
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'mechanisms.json')))
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mechanisms = []
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for sectionlist, channels in mech_definitions.iteritems():
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seclist_loc = ephys.locations.NrnSeclistLocation(
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sectionlist,
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seclist_name=sectionlist)
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for channel in channels:
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mechanisms.append(ephys.mechanisms.NrnMODMechanism(
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name='%s.%s' % (channel, sectionlist),
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mod_path=None,
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prefix=channel,
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locations=[seclist_loc],
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preloaded=True))
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return mechanisms
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def define_parameters():
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"""Define parameters"""
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param_configs = json.load(open(os.path.join(config_dir, 'parameters.json')))
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parameters = []
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for param_config in param_configs:
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if 'value' in param_config:
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frozen = True
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value = param_config['value']
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bounds = None
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elif 'bounds':
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frozen = False
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bounds = param_config['bounds']
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value = None
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else:
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raise Exception(
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'Parameter config has to have bounds or value: %s'
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% param_config)
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if param_config['type'] == 'global':
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parameters.append(
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ephys.parameters.NrnGlobalParameter(
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name=param_config['param_name'],
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param_name=param_config['param_name'],
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frozen=frozen,
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bounds=bounds,
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value=value))
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elif param_config['type'] in ['section', 'range']:
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if param_config['dist_type'] == 'uniform':
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scaler = ephys.parameterscalers.NrnSegmentLinearScaler()
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elif param_config['dist_type'] == 'exp':
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scaler = ephys.parameterscalers.NrnSegmentSomaDistanceScaler(
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distribution=param_config['dist'])
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seclist_loc = ephys.locations.NrnSeclistLocation(
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param_config['sectionlist'],
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seclist_name=param_config['sectionlist'])
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name = '%s.%s' % (param_config['param_name'],
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param_config['sectionlist'])
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if param_config['type'] == 'section':
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parameters.append(
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ephys.parameters.NrnSectionParameter(
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name=name,
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param_name=param_config['param_name'],
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value_scaler=scaler,
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value=value,
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frozen=frozen,
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bounds=bounds,
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locations=[seclist_loc]))
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elif param_config['type'] == 'range':
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parameters.append(
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ephys.parameters.NrnRangeParameter(
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name=name,
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param_name=param_config['param_name'],
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value_scaler=scaler,
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value=value,
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frozen=frozen,
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bounds=bounds,
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locations=[seclist_loc]))
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else:
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raise Exception(
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'Param config type has to be global, section or range: %s' %
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param_config)
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return parameters
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# def define_morphology():
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# """Define morphology"""
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# return ephys.morphologies.NrnFileMorphology(
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# os.path.join(
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# script_dir,
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# 'morphology/C060114A7.asc'),
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# do_replace_axon=True)
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def create():
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"""Create cell model"""
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cell = ephys.models.CellModel(
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'abi_cell',
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morph=define_morphology(),
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mechs=define_mechanisms(),
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params=define_parameters())
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return cell

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