def test_spike_times(self):
expected = [
2.937305, 3.16453 , 3.24271 , 4.1622 , 4.24182 ,
10.0898 , 10.132545, 10.176095, 10.2361 , 10.660655,
10.762125, 10.863465, 10.93833 , 11.140815, 11.19246 ,
11.24553 , 11.696305, 11.812655, 11.90469 , 12.056755,
12.15794 , 12.233905, 12.47577 , 12.741295, 12.82861 ,
12.923175, 18.05068 , 18.139875, 18.17693 , 18.221485,
18.24337 , 18.39981 , 18.470705, 18.759675, 18.82183 ,
18.877475, 18.91033 , 18.941195, 19.050515, 19.12557 ,
19.15963 , 19.188655, 19.226205, 19.29813 , 19.420665,
19.47627 , 19.763365, 19.824225, 19.897995, 19.93155 ,
20.04916 , 20.11832 , 20.148755, 20.18004 , 20.22173 ,
20.2433 , 20.40018 , 20.470915, 20.759715, 20.82156 ,
20.866465, 20.90807 , 20.939175]
bp = BiophysicalPerisomaticApi('http://api.brain-map.org')
bp.cache_stimulus = True # change to False to not download the large stimulus NWB file
neuronal_model_id = 472451419 # get this from the web site as above
bp.cache_data(neuronal_model_id, working_directory='neuronal_model')
cwd = os.path.realpath(os.curdir)
print(cwd)
os.chdir(os.path.join(cwd, 'neuronal_model'))
manifest = ju.read('manifest.json')
manifest['biophys'][0]['model_file'][0] = 'manifest_51.json'
manifest['runs'][0]['sweeps'] = [51]
ju.write('manifest_51.json', manifest)
subprocess.call(['nrnivmodl', './modfiles'])
run(Config().load('manifest_51.json'))
#os.chdir(cwd)
nwb_out = NwbDataSet('work/386049444.nwb')
spikes = nwb_out.get_spike_times(51)
numpy.testing.assert_array_almost_equal(spikes, expected)
def set_up_objective(self, measure='spike frequency'):
'''
Prepares the model for parameter optimization by assigning the output measure to be used in the cost function.
Parameters
----------
measure: string
Name of the output measure to be used in optimization. Currently only 'spike frequency' is implemented.
'''
if (measure == 'spike frequency'):
# get the experimental data from the NWB file
data_set = NwbDataSet(
os.path.join(
self.model_dir,
self.description.manifest.get_path('stimulus_path')))
spike_times = data_set.get_spike_times(self.reference_sweep)
# calculate firing frequency for the NWB data
sum_intervals = 0.0
for i in range(len(spike_times) - 1):
sum_intervals += (spike_times[i + 1] - spike_times[i])
self.reference_output = len(spike_times) / sum_intervals
else:
print "Model fitting using the output measure", measure, "has not been implemented yet."
def calc_ev(ew, folder, s, sweeps, stim_len, data_spike_times, dt):
'''
'''
print ew, folder
#convert data times to indicies
data_spike_ind = []
for d in data_spike_times:
data_spike_ind.append((d / dt).astype(int))
#get model data
path = get_model_nwb_path_from_folder(ew, folder, s) #get nwb file path
if isinstance(path, basestring):
model = NwbDataSet(path)
model_spike_ind = []
for sw in sweeps:
spikes = (model.get_spike_times(sw) / dt).astype(int)
model_spike_ind.append(spikes)
#check to make sure all spike time arrays are the same for the model
for ii in range(1, len(model_spike_ind)):
if not np.array_equal(model_spike_ind[ii],
model_spike_ind[ii - 1]):
print 'MODEL SPIKE TIMES SHOULD BE THE SAME AND THEY ARE NOT!', os.path.basename(
folder)[:9]
print len(model_spike_ind), model_spike_ind
# raise Exception('model spike times should be the same and they are not')
return exVar(data_spike_ind, [model_spike_ind[0]], sigma, dt, stim_len)
else:
return np.nan
def ve_ramp_latency(specimen_id, ve_path):
data_set = NwbDataSet(ve_path)
ramp_sweeps = lims_utils.get_sweeps_of_type("C1RP25PR1S", specimen_id, passed_only=True)
if len(ramp_sweeps) == 0:
return np.nan
spike_times = data_set.get_spike_times(ramp_sweeps[0])
if len(spike_times) > 0:
return spike_times[0]
else:
return np.nan
def get_model_spike_times_from_nwb(ends_with, specimen_id_directory,
model_string, sweeps, where_running):
''' Gets the times of spike from the model nwb file
inputs
ends_with: string
end of file searching for: options "_GLIF1_neuron_config.json","_GLIF2_neuron_config.json' etc."
specimen_id_directory: string
path to structured data directory containing neuron_config, preprocessor, etc., files.
model_string: string
string searching for in model name: options '(LIF)', '(LIF-R)', '(LIF-ASC)', '(LIF-R_ASC)', '(LIF-R_ASC_A')
sweeps: list of integers
integers refer to the sweep number in the electrophysiology .nwb data file
where_running: string
options are 'internal': the code is being run within the Institute and can therefore access the internal file system
'external': the code is being run outside the Institute and requires the use of the api to download the model nwb files
Note that although ends_with and model_string should be appropriately paired, there is no check
within this module to make sure that they are
outputs: returns either a
nan if the there is not a model in the structured data directory corresponding to what the requested ends_with variable
or
model_spike_times: list of numpy arrays
each array contains the times of the spikes in each sweep
'''
if where_running == 'internal':
path = get_model_nwb_path_from_folder(ends_with, specimen_id_directory,
model_string) #get nwb file path
elif where_running == 'external':
path = download_model_nwb_if_model_exists_in_SDD(
ends_with, specimen_id_directory, model_string) #get nwb file path
else:
raise Exception(
'specify whether the code is being run internally or externally')
if isinstance(path, basestring):
model = NwbDataSet(path)
model_spike_times = []
if sweeps == []:
raise Exception('There are no sweeps to look at')
for sw in sweeps:
model_spike_times.append(model.get_spike_times(sw))
return model_spike_times
else:
return np.nan
print(child['x'], child['y'], child['z'], child['radius']) #=============================================================================== # example 4 #=============================================================================== from allensdk.core.nwb_data_set import NwbDataSet # if you ran the examples above, you will have a NWB file here file_name = 'cell_types/specimen_485909730/ephys.nwb' data_set = NwbDataSet(file_name) sweep_numbers = data_set.get_sweep_numbers() sweep_number = sweep_numbers[0] sweep_data = data_set.get_sweep(sweep_number) # spike times are in seconds relative to the start of the sweep spike_times = data_set.get_spike_times(sweep_number) # stimulus is a numpy array in amps stimulus = sweep_data['stimulus'] # response is a numpy array in volts reponse = sweep_data['response'] # sampling rate is in Hz sampling_rate = sweep_data['sampling_rate'] # start/stop indices that exclude the experimental test pulse (if applicable) index_range = sweep_data['index_range']
from allensdk.core.nwb_data_set import NwbDataSet file_name = 'example.nwb' data_set = NwbDataSet(file_name) sweep_numbers = data_set.get_sweep_numbers() sweep_number = sweep_numbers[0] sweep_data = data_set.get_sweep(sweep_number) # spike times are in seconds relative to the start of the sweep spike_times = data_set.get_spike_times(sweep_number) # stimulus is a numpy array in amps stimulus = sweep_data['stimulus'] # response is a numpy array in volts reponse = sweep_data['response'] # sampling rate is in Hz sampling_rate = sweep_data['sampling_rate'] # start/stop indices that exclude the experimental test pulse (if applicable) index_range = sweep_data['index_range']
def noise1_response_comparison(ids, ve_paths, file_prefix):
filename = 'Noise2'
ids1 = find_cells_with_all_models(ids)
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
for specimen_id in ids1:
data_set = expt_data_set(specimen_id)
exp_sweeps = lims_utils.get_sweeps_of_type("C1NSSEED_2",
specimen_id,
passed_only=True)
if len(exp_sweeps) == 0:
continue
fig, axes = plt.subplots(3, 1, sharex=True)
plt.subplots_adjust(hspace=0.1, wspace=0.1)
axes[2].set_xlim([0, 25])
sweep_data = data_set.get_sweep(exp_sweeps[0])
spike_times = data_set.get_spike_times(exp_sweeps[0])
index_range = sweep_data["index_range"]
i = sweep_data["stimulus"][0:index_range[1] + 1] # in A
v = sweep_data["response"][0:index_range[1] + 1] # in V
i *= 1e12 # to pA
v *= 1e3 # to mV
sampling_rate = sweep_data["sampling_rate"] # in Hz
t = np.arange(0, len(v)) * (1.0 / sampling_rate)
axes[1].plot(t, v, color='k', linewidth=0.3)
axes[0].scatter(spike_times, [10] * len(spike_times),
color='k',
s=100,
marker="|",
linewidth=0.3)
axes[2].plot(t, i, color='k', linewidth=0.3)
axes[1].set_ylabel("mV")
axes[2].set_ylabel("pA")
axes[2].set_xlabel("seconds")
for model, label, color in zip(BASE_ORDER, LABELS, colors):
ve_path = ve_paths[model][specimen_id]
data_set = NwbDataSet(ve_path)
exp_sweeps = lims_utils.get_sweeps_of_type("C1NSSEED_2",
specimen_id,
passed_only=True)
sweep_data = data_set.get_sweep(exp_sweeps[0])
spike_times = data_set.get_spike_times(exp_sweeps[0])
index_range = sweep_data["index_range"]
i = sweep_data["stimulus"][0:index_range[1] + 1] # in A
v = sweep_data["response"][0:index_range[1] + 1] # in V
i *= 1e12 # to pA
v *= 1e3 # to mV
sampling_rate = sweep_data["sampling_rate"] # in Hz
t = np.arange(0, len(v)) * (1.0 / sampling_rate)
axes[0].scatter(spike_times,
[80 - (10 * BASE_ORDER.index(model))] *
len(spike_times),
color='k',
s=100,
marker="|",
linewidth=0.3)
axes[0].set_yticklabels([
"", 'Experimental Data', LABELS[6], LABELS[5], LABELS[4],
LABELS[3], LABELS[2], LABELS[1], LABELS[0]
])
axes[0].set_ylabel("model type")
axes[0].set_title("Spike Times")
plt.subplots_adjust(hspace=0.1, wspace=0.1)
plt.savefig(str(file_prefix + "_" + filename + "_" + str(specimen_id)),
bbox_inches="tight")
#plt.show()
plt.close()
