def model_props(cell_id):
cell_metadata = {}
# download existing model (if exists)
bp = BiophysicalApi()
try:
model_list = bp.get_neuronal_models(cell_id)
model_dict = {key: '' for key in template_model_dict.keys()}
for model_type, template_id in template_model_dict.items():
for model_meta in model_list:
if model_meta['neuronal_model_template_id'] == template_id:
model_dict[model_type] = model_meta['id']
except:
logger.debug('No biophysical model available')
api = allensdk.api.queries.rma_api.RmaApi() # Get the model metadata
for model_type, model_id in model_dict.items():
if model_id != '':
bp.cache_data(model_id, working_directory=model_dir[model_type])
model_metadata = api.model_query(
"NeuronalModelRun",
criteria="[neuronal_model_id$eq%s]" % model_id)
model_metadata_select = [
model_metadata[0]['rheobase_feature_average'],
model_metadata[0]['explained_variance_ratio']
]
model_path = glob.glob('%s/*fit*.json' % model_dir[model_type])[0]
for file_ in glob.glob('%s/*' % model_dir[model_type]):
if file_ != model_path:
try:
os.remove(file_)
except:
shutil.rmtree(file_, ignore_errors=True)
cell_metadata['model_path_{}'.format(
model_type)] = os.path.abspath(model_path)
cell_metadata['{}_id'.format(
model_dir[model_type])] = str(model_id)
for i, metadata_key in enumerate(opt_metric_dict[model_type]):
cell_metadata[metadata_key] = model_metadata_select[i]
return cell_metadata
if os.path.isdir(s):
shutil.copytree(s, d, symlinks, ignore)
else:
shutil.copy2(s, d)
#%% Pick a cell and a sweep to run from the available set of protocols
cell_id = 468193142 # get this from the web site: http://celltypes.brain-map.org
sweep_num = 46 # Select a Long Square sweep : 1s DC
#%% Download all-acive model
sdk_model_templates = {'all_active': 491455321, 'perisomatic': 329230710}
bp = BiophysicalApi()
bp.cache_stimulus = True
model_list = bp.get_neuronal_models(cell_id, model_type_ids=[sdk_model_templates['all_active']]) # Only get the all-active model for the cell
model_dict = model_list[0]
model_dir = 'all_active_models'
bp.cache_data(model_dict['id'], working_directory=model_dir)
new_model_file = 'fit_parameters_new.json'
shutil.copyfile(new_model_file, os.path.join(model_dir, new_model_file))
copytree('modfiles', os.path.join(model_dir, 'modfiles'))
#%% Running the legacy all-active models
os.chdir(model_dir)
subprocess.check_call(['nrnivmodl', 'modfiles/'])
manifest_file = 'manifest.json'
manifest_dict = json.load(open(manifest_file))