def model_ids_for_cell_type(cells_df, cell_type_tag):
model_ids = []
type_cells = cells_df[cells_df.transgenic_line.str.contains(cell_type_tag)]
glif_api = GlifApi()
for neuron_id in type_cells.index.values:
if glif_api.get_neuronal_models([neuron_id]):
models_metadata = glif_api.get_neuronal_models([neuron_id])[0]
for model in models_metadata['neuronal_models']:
model_ids.append(model['id'])
return np.array(model_ids)
def get_files_from_LIMS_public(output_path, glif_sp_ids=None, type='mouse'):
'''This will grab cre positive data config files from LIMS and sort them and put them in
the specified output folder.
input:
output_path: string
specifies path for files to be placed in
glif_sp_ids: list of strings or integers
specimen ids of cells specifically want to grab. If none it will get all available on the
Allen Institue Cell Types Database.
type: string
can be 'mouse' or 'human'. Note that if mouse is specified is will only grab cre positive mouse cells
(code can be altered to get cre negative cells).
output:
Does not return values but creates the specified 'output_path' folder.
Inside the folder a series of folders are created with the name format:
specimenid_cre. Inside those inner folders are the neuron configs of
the available GLIF models along with the preprocessor files.
'''
glif_api = GlifApi()
ctc = CellTypesCache(manifest_file=os.path.join(relative_path,'cell_types_manifest.json'))
# select the specimen ids to grab from the data base (cre positive or human which have at least 1 GLIF model)
if glif_sp_ids==None: #if no specimen id's are specified grab all data in the cell types manifest
specimen_id_list = []
if type=='mouse':
for c in ctc.get_cells():
if c['reporter_status']=='cre reporter positive':
specimen_id_list.append(c['id'])
elif type=='human':
print 'getting human'
for c in ctc.get_cells(species=['H**o Sapiens']):
#print c
specimen_id_list.append(c['id'])
print specimen_id_list
# reduce list to cells that have a GLIF model
glif_sp_ids=[]
for sp in specimen_id_list:
models=glif_api.get_neuronal_models(sp)[0]
for m in models['neuronal_models']:
if 'LIF' in m['name']:
glif_sp_ids.append(m['specimen_id'])
glif_sp_ids=list(set(glif_sp_ids))
print len(glif_sp_ids), 'cre positive specimens with at least 1 LIF model'
# create the overall output directory if it doesn't exist
try:
os.makedirs(output_path)
except:
pass
# go get the files corresponding to the specimen ids from the Allen Cell Types Database
# and put them into a specified output directory
for id in glif_sp_ids:
model_query=glif_api.get_neuronal_models(id)[0]['neuronal_models']
df=pd.DataFrame(model_query)
for mt_id, short_name in zip(model_template_ids, model_names):
dff=df[df['neuronal_model_template_id']==mt_id]
if len(dff)>=2:
print dff
raise Exception("This is public data, there should not be more than 1 model")
elif len(dff)==1:
use_me=dff
#go get the file
path=use_me['well_known_files'].iloc[0][0]['path']
if type=='mouse':
cre=(str(use_me['name'].values).split(')_'))[1].split(';')[0]
elif type=='human':
cre='human'
else:
raise Exception('specified species not known')
# convert old non complete cre names
if 'Ntsr1-Cre' in cre:
cre='Ntsr1-Cre_GN220'
if 'Chat-IRES-Cre' in cre:
cre='Chat-IRES-Cre-neo'
dir_name=os.path.join(output_path, str(id)+'_'+cre)
try:
os.makedirs(dir_name)
except:
pass
if path.endswith('_neuron_config.json'):
pass
else:
print path
raise Exception('the file doesnt end with _neuron_config.json')
try:
copyfile(path, os.path.join(dir_name, str(id)+'_'+cre+'_'+short_name+'_neuron_config.json'))
except:
print 'couldnt make ', os.path.join(dir_name, str(id)+'_'+cre+'_'+short_name+'_neuron_config.json')
if mt_id==model_template_ids[0]:
model_path=os.path.dirname(path)
pp_path=os.path.join(model_path,
os.listdir(model_path)[np.where([fname.endswith('_preprocessor_values.json') for fname in os.listdir(model_path)])[0][0]])
try:
copyfile(pp_path, os.path.join(dir_name, str(id)+'_'+cre+'_preprocessor_values.json'))
except:
print 'couldnt make ', os.path.join(dir_name, str(id)+'_'+cre+'_preprocessor_values.json')
raise Exception('there should be a preprocessed file')
elif len(dff)<1:
use_me=pd.DataFrame()
path=None
# model name and model template id mapping
LIF = 'LIF'
LIF_R = 'LIF-R'
LIF_ASC = 'LIF-ASC'
LIF_R_ASC = 'LIF-R-ASC'
LIF_R_ASC_A = 'LIF-R-ASC-A'
model_id2name = {
395310469: LIF,
395310479: LIF_R,
395310475: LIF_ASC,
471355161: LIF_R_ASC,
395310498: LIF_R_ASC_A
}
glif_api = GlifApi()
for cell_result in glif_api.get_neuronal_models(cell_ids): #[325464516]
cell_id = cell_result['id']
for curr_model in cell_result['neuronal_models']:
if model_id2name[
curr_model['neuronal_model_template_id']] != options.model:
continue
model_id = curr_model['id']
neuron_config = glif_api.get_neuron_configs([model_id])[model_id]
for stim in options.stimulus.split(','):
simulate = stimulus[stim]
output = simulate(cell_id, options.model, neuron_config)
plt.figure('Cell ' + str(cell_id) + ' ' + options.model + ' ' +
stim)
plotter.plt_comparison_neurons(np.array(output['I']) * 1.0e12,
output['times'],
output['voltages'],
