def NML2_run(self, rerun=False, a=None, **run_params):
if not len(run_params):
run_params = self.run_defaults
a = ca.build_namespace(a=a,**run_params) # Convert keyword args to a namespace.
if self.a is None or a.__dict__ != self.a.__dict__ or rerun: # Only rerun if params have changed.
self.a = a
self.lems_file_path = ca.make_lems_file(self.channel,self.a) # Create a lems file.
self.results = ca.run_lems_file(self.lems_file_path,self.a) # Writes data to disk.
def ca_make_lems_file(self, **params):
# Set params in the SciUnit model instance
self.params = params
# ChannelAnalysis only accepts camelCase parameter names
# This converts snake_case to camelCase
params = {snake_to_camel(key): value for key, value in params.items()}
# Build a namespace for use by ChannelAnalysis
self.ca_namespace = ca.build_namespace(**params)
# Make the new LEMS file
self.lems_file_path = ca.make_lems_file(self.channel,
self.ca_namespace)
def NML2_run(self, rerun=False, a=None, **run_params):
if not len(run_params):
run_params = self.run_defaults
a = ca.build_namespace(
a=a, **run_params) # Convert keyword args to a namespace.
if self.a is None or a.__dict__ != self.a.__dict__ or rerun: # Only rerun if params have changed.
self.a = a
self.lems_file_path = ca.make_lems_file(
self.channel, self.a) # Create a lems file.
self.results = ca.run_lems_file(self.lems_file_path,
self.a) # Writes data to disk.
def run_nml2(self,nml2_file):
"""
:return:
"""
return NML2ChannelAnalysis.run(channel_files=[nml2_file],nogui=False)
def __init__(self, channel_file_path, channel_index=0, name=None):
"""
channel_file_path: Path to NML file.
channel_index: Order of channel in NML file (usually 0 since most files contain one channel).
name: Optional model name.
"""
self.nml_file_path = channel_file_path
channels = ca.get_channels_from_channel_file(self.nml_file_path)
self.channel = channels[channel_index]
self.a = None
self.run_defaults = ca.DEFAULTS.copy() # Temperature, clamp parameters, etc.
self.run_defaults.update({'nogui': True})
self.run_defaults = ca.build_namespace()
if name is None:
name = os.path.split()[1].split('.')[0]
super(ChannelModel,self).__init__(name=name)
def produce_iv_curve(self, **run_params):
run_params['ivCurve'] = True
self.NML2_run(**run_params)
self.iv_data = ca.compute_iv_curve(self.channel, self.a, self.results)
for kind in ['i_peak', 'i_steady']:
for v,i in self.iv_data[kind].items():
self.iv_data[kind][v] = i * 1e12 # Convert from A to pA.
return self.iv_data
def produce_iv_curve(self, **run_params):
run_params['ivCurve'] = True
self.NML2_run(**run_params)
self.iv_data = ca.compute_iv_curve(self.channel, self.a, self.results)
for kind in ['i_peak', 'i_steady']:
for v, i in self.iv_data[kind].items():
self.iv_data[kind][v] = i * 1e12 # Convert from A to pA.
return self.iv_data
def __init__(self, channel_file_path, channel_index=0, name=None):
"""
channel_file_path: Path to NML file.
channel_index: Order of channel in NML file (usually 0 since most files contain one channel).
name: Optional model name.
"""
self.nml_file_path = channel_file_path
channels = ca.get_channels_from_channel_file(self.nml_file_path)
self.channel = channels[channel_index]
self.a = None
self.run_defaults = ca.DEFAULTS.copy(
) # Temperature, clamp parameters, etc.
self.run_defaults.update({'nogui': True})
self.run_defaults = ca.build_namespace()
if name is None:
name = os.path.split()[1].split('.')[0]
super(ChannelModel, self).__init__(name=name)
def ca_compute_iv_curve(self, results):
iv_data = ca.compute_iv_curve(self.channel, self.ca_namespace, results)
self.iv_data = {}
for kind in ['i_peak', 'i_steady']:
self.iv_data[kind] = {}
for v, i in iv_data[kind].items():
v = float((v * pq.V).rescale(pq.mV))
self.iv_data[kind][v] = (i * pq.A).rescale(pq.pA)
self.iv_data['hold_v'] = (iv_data['hold_v'] * pq.V).rescale(pq.mV)
return self.iv_data
def produce_iv_curve(self, **run_params):
run_params['ivCurve'] = True
self.NML2_run(**run_params)
iv_data = ca.compute_iv_curve(self.channel, self.a, self.results)
self.iv_data = {}
for kind in ['i_peak', 'i_steady']:
self.iv_data[kind] = {}
for v, i in iv_data[kind].items():
v = float((v * pq.V).rescale(pq.mV))
self.iv_data[kind][v] = (i * pq.A).rescale(pq.pA)
self.iv_data['hold_v'] = (iv_data['hold_v'] * pq.V).rescale(pq.mV)
return self.iv_data
def produce_iv_curve(self, **run_params):
run_params['ivCurve'] = True
self.NML2_run(**run_params)
iv_data = ca.compute_iv_curve(self.channel, self.a, self.results)
self.iv_data = {}
for kind in ['i_peak', 'i_steady']:
self.iv_data[kind] = {}
for v,i in iv_data[kind].items():
v = float((v * pq.V).rescale(pq.mV))
self.iv_data[kind][v] = (i * pq.A).rescale(pq.pA)
self.iv_data['hold_v'] = (iv_data['hold_v'] * pq.V).rescale(pq.mV)
return self.iv_data
def __init__(self,
channel_file_path_or_url,
channel_index=0,
name=None,
backend='jNeuroML'):
"""
channel_file_path: Path to NML file.
channel_index: Order of channel in NML file
(usually 0 since most files contain one channel).
name: Optional model name.
"""
if name is None:
base, file_name = os.path.split(channel_file_path_or_url)
name = file_name.split('.')[0]
super(ChannelModel, self).__init__(channel_file_path_or_url,
name=name,
backend=backend)
channels = ca.get_channels_from_channel_file(self.orig_lems_file_path)
self.channel = channels[channel_index]
self.a = None
# Temperature, clamp parameters, etc.
self.default_params = ca.DEFAULTS.copy()
self.default_params.update({'nogui': True})
def plot_iv_curve(self, v, i, *plt_args, **plt_kwargs):
ca.plot_iv_curve(self.a, v, i, *plt_args, **plt_kwargs)
def plot_iv_curve(self, v, i, **plot_args):
ca.plot_iv_curve(self.a, v, i, **plot_args)
