def __init__(self, allen_id=None):
self = self
if allen_id == None:
self.allen_id = 566302806
glif_api = GlifApi()
self.nc = glif_api.get_neuron_configs([self.allen_id
])[self.allen_id]
self.glif = GlifNeuron.from_dict(self.nc)
self.metad = glif_api.get_neuronal_models_by_id([self.allen_id])[0]
else:
glif_api = GlifApi()
self.allen_id = allen_id
self.glif = glif_api.get_neuronal_models_by_id([allen_id])[0]
self.nc = glif_api.get_neuron_configs([allen_id])[allen_id]
self.glif = GlifNeuron.from_dict(self.nc)
self.metad = glif_api.get_neuronal_models_by_id([self.allen_id])[0]
def init_backend(self,
attrs=None,
cell_name='alice',
current_src_name='hannah',
DTC=None):
backend = 'GLIF'
super(GLIFBackend, self).init_backend()
self.model._backend.use_memory_cache = False
self.current_src_name = current_src_name
self.cell_name = cell_name
self.vM = None
self.allen_id = None
self.attrs = attrs
self.nc = None
self.temp_attrs = None
if self.allen_id == None:
try:
self.nc = pickle.load(open(str('allen_id.p'), 'rb'))
except:
self.allen_id = 566302806
glif_api = GlifApi()
self.nc = glif_api.get_neuron_configs([self.allen_id
])[self.allen_id]
pickle.dump(copy.copy(self.nc), open(str('allen_id.p'), 'wb'))
else:
try:
self.nc = pickle.load(open(str('allen_id.p'), 'rb'))
except:
glif_api = GlifApi()
self.allen_id = allen_id
self.glif = glif_api.get_neuronal_models_by_id([allen_id])[0]
self.nc = glif_api.get_neuron_configs([self.allen_id
])[self.allen_id]
pickle.dump(self.nc, open(str('allen_id.p'), 'wb'))
self.glif = GlifNeuron.from_dict(self.nc)
if type(attrs) is not type(None):
self.set_attrs(**attrs)
self.sim_attrs = attrs
if type(DTC) is not type(None):
if type(DTC.attrs) is not type(None):
self.set_attrs(**DTC.attrs)
if hasattr(DTC, 'current_src_name'):
self._current_src_name = DTC.current_src_name
if hasattr(DTC, 'cell_name'):
self.cell_name = DTC.cell_name
import time
import numpy as np
from allensdk.api.queries.glif_api import GlifApi
from allensdk.core.cell_types_cache import CellTypesCache
import allensdk.core.json_utilities as json_utilities
neuronal_model_id = 566302806
# download model metadata
glif_api = GlifApi()
nm = glif_api.get_neuronal_models_by_id([neuronal_model_id])[0]
# download the model configuration file
nc = glif_api.get_neuron_configs([neuronal_model_id])[neuronal_model_id]
neuron_config = glif_api.get_neuron_configs([neuronal_model_id])
json_utilities.write('neuron_config.json', neuron_config)
# download information about the cell
ctc = CellTypesCache()
ctc.get_ephys_data(nm['specimen_id'], file_name='stimulus.nwb')
ctc.get_ephys_sweeps(nm['specimen_id'], file_name='ephys_sweeps.json')
import allensdk.core.json_utilities as json_utilities
from allensdk.model.glif.glif_neuron import GlifNeuron
# initialize the neuron
neuron_config = json_utilities.read('neuron_config.json')['566302806']
neuron = GlifNeuron.from_dict(neuron_config)
# make a short square pulse. stimulus units should be in Amps.
stimulus = [0.0] * 100 + [10e-9] * 100 + [0.0] * 100
import matplotlib
matplotlib.use("Agg")
#===============================================================================
# example 1
#===============================================================================
from allensdk.api.queries.glif_api import GlifApi
from allensdk.core.cell_types_cache import CellTypesCache
import allensdk.core.json_utilities as json_utilities
neuronal_model_id = 566302806
# download model metadata
glif_api = GlifApi()
nm = glif_api.get_neuronal_models_by_id([neuronal_model_id])[0]
# download the model configuration file
nc = glif_api.get_neuron_configs([neuronal_model_id])[neuronal_model_id]
neuron_config = glif_api.get_neuron_configs([neuronal_model_id])
json_utilities.write('neuron_config.json', neuron_config)
# download information about the cell
ctc = CellTypesCache()
ctc.get_ephys_data(nm['specimen_id'], file_name='stimulus.nwb')
ctc.get_ephys_sweeps(nm['specimen_id'], file_name='ephys_sweeps.json')
#===============================================================================
# example 2
#===============================================================================
