def ve_tau(specimen_id, ve_path):
#print(chr(27) + "[2J") # To clear terminal screen
print "START VE_TAU " + str(specimen_id) + " " + str(ve_path)
expt_taus = []
data_set = NwbDataSet(ve_path)
long_square_sweeps = lims_utils.get_sweeps_of_type("C1LSCOARSE",
specimen_id,
passed_only=True)
print "ve specimen id= " + str(specimen_id)
for sweep in long_square_sweeps:
#print "ve_sweep_number: " + str(sweep)
#print(data_set.get_sweep_metadata(sweep))
try:
(data_set.get_sweep_metadata(sweep)["aibs_stimulus_amplitude_pa"])
except:
continue
else:
if (data_set.get_sweep_metadata(sweep)
["aibs_stimulus_amplitude_pa"] < 0):
v, i, t = lims_utils.get_sweep_v_i_t_from_set(data_set, sweep)
sweep_feat = EphysSweepFeatureExtractor(
t, v) # Get time and voltage of each hyperpolarizing sweep
if np.isnan(sweep_feat):
continue
else:
expt_taus.append(sweep_feat.estimate_time_constant(
)) # Append time constant of each sweep to list
mean_expt_tau = np.nanmean(expt_taus) # Mean time constant for this cell
print "mean_ve_tau= " + str(mean_expt_tau)
return mean_expt_tau
def from_electrophysiology(
cell_id: int,
ephys: NwbDataSet,
duration=2.0) -> 'ProcessedAllenNeuronElectrophysiology':
current_list = []
voltage_list = []
time_list = []
stim_amp_list = []
n_spikes_list = []
spike_features_list = []
for sweep_number in ephys.get_sweep_numbers():
sweep_metadata = ephys.get_sweep_metadata(sweep_number)
if sweep_metadata['aibs_stimulus_name'] == 'Long Square':
sweep_data = ephys.get_sweep(sweep_number)
amp = sweep_metadata['aibs_stimulus_amplitude_pa']
index_range = sweep_data["index_range"]
sampling_rate = sweep_data["sampling_rate"]
current = sweep_data["stimulus"][
index_range[0]:index_range[1] + 1]
voltage = sweep_data["response"][
index_range[0]:index_range[1] + 1]
# truncate
max_frames = int(duration * sampling_rate)
assert max_frames < len(voltage)
current = current[:max_frames] * 1e12 # in pA
voltage = voltage[:max_frames] * 1e3 # in mV
# extract featrures
time = np.arange(0, max_frames,
dtype=np.float) / sampling_rate # in seconds
ext = EphysSweepFeatureExtractor(t=time, v=voltage, i=current)
ext.process_spikes()
spike_features = ext.spikes()
n_spikes = len(spike_features)
current_list.append(current)
voltage_list.append(voltage)
time_list.append(time)
stim_amp_list.append(amp)
n_spikes_list.append(n_spikes)
spike_features_list.append(spike_features)
return ProcessedAllenNeuronElectrophysiology(
cell_id=cell_id,
current_list=current_list,
voltage_list=voltage_list,
time_list=time_list,
stim_amp_list=stim_amp_list,
n_spikes_list=n_spikes_list,
spike_features_list=spike_features_list)
def save_cell_data_web(self, acceptable_stimtypes, non_standard_nwb=False,
ephys_dir='preprocessed', **kwargs):
bpopt_stimtype_map = utility.bpopt_stimtype_map
distinct_id_map = utility.aibs_stimname_map
nwb_file = NwbDataSet(self.nwb_path)
stim_map = defaultdict(list)
stim_sweep_map = {}
output_dir = os.path.join(os.getcwd(), ephys_dir)
utility.create_dirpath(output_dir)
sweep_numbers = kwargs.get('sweep_numbers') or nwb_file.get_sweep_numbers()
for sweep_number in sweep_numbers:
sweep_data = nwb_file.get_sweep_metadata(sweep_number)
stim_type = sweep_data['aibs_stimulus_name']
try:
stim_type = stim_type.decode('UTF-8')
except:
pass
if stim_type in acceptable_stimtypes:
sweep = nwb_file.get_sweep(sweep_number)
start_idx, stop_idx = sweep['index_range']
stimulus_trace = sweep['stimulus'][start_idx:stop_idx]
response_trace = sweep['response'][start_idx:stop_idx]
sampling_rate = sweep['sampling_rate']
time = np.arange(0, len(stimulus_trace)) / sampling_rate
trace_name = '%s_%d' % (
distinct_id_map[stim_type], sweep_number)
if non_standard_nwb:
calc_stimparams_func = self.calc_stimparams_nonstandard
else:
calc_stimparams_func = self.calc_stimparams
stim_start, stim_stop, stim_amp_start, stim_amp_end, \
tot_duration, hold_curr = calc_stimparams_func(
time, stimulus_trace, trace_name)
response_trace_short_filename = '%s.%s' % (trace_name, 'txt')
response_trace_filename = os.path.join(
output_dir, response_trace_short_filename)
time *= 1e3 # in ms
response_trace *= 1e3 # in mV
response_trace = utility.correct_junction_potential(response_trace,
self.junction_potential)
stimulus_trace *= 1e9
# downsampling
time, stimulus_trace, response_trace = utility.downsample_ephys_data(
time, stimulus_trace, response_trace)
if stim_type in utility.bpopt_current_play_stimtypes:
with open(response_trace_filename, 'wb') as response_trace_file:
np.savetxt(response_trace_file,
np.transpose([time, response_trace, stimulus_trace]))
else:
with open(response_trace_filename, 'wb') as response_trace_file:
np.savetxt(response_trace_file,
np.transpose([time, response_trace]))
holding_current = hold_curr # sweep['bias_current']
stim_map[distinct_id_map[stim_type]].append([
trace_name,
bpopt_stimtype_map[stim_type],
holding_current/1e12,
stim_amp_start / 1e12,
stim_amp_end/1e12,
stim_start * 1e3,
stim_stop * 1e3,
tot_duration * 1e3,
response_trace_short_filename])
stim_sweep_map[trace_name] = sweep_number
logger.debug('Writing stimmap.csv ...')
stim_reps_sweep_map, stimmap_filename = self.write_stimmap_csv(stim_map,
output_dir, stim_sweep_map)
self.write_provenance(
output_dir,
self.nwb_path,
stim_sweep_map,
stim_reps_sweep_map)
return output_dir, stimmap_filename
# stimulus is a numpy array in amps
stimulus = sweep_data['stimulus'][index_range[0]:index_range[-1]]
# response is a numpy array in volts
response = sweep_data['response'][index_range[0]:index_range[-1]] * 1000
subset[sweep_number] = response
# sampling rate is in Hz
sampling_rate = sweep_data['sampling_rate']
# define some time points in seconds (i.e., convert to absolute time)
time_pts = np.arange(0,
len(stimulus) / sampling_rate, 1. / sampling_rate)
subset['t'] = time_pts
metadata = data_set.get_sweep_metadata(sweep_number)
ampl = round(metadata['aibs_stimulus_amplitude_pa'], 4)
# plot the stimulus and the voltage response for the random trial
plt.subplot(2, 1, 1)
plt.plot(time_pts, stimulus)
plt.ylabel('Stimulus (A)')
plt.subplot(2, 1, 2)
plt.plot(time_pts,
response,
label='S %s, %s pA' % (sweep_number, ampl))
volts_file = open('%s.dat' % dataset_id, 'w')
max = 1.5 # s
for i in range(len(subset['t'])):
sweep_numbers.sort()
print("All sweeps for %s: %s" % (dataset_id, sweep_numbers))
subthreshs = {}
spikings = {}
spike_count = {}
chosen = {}
stimuli = {}
for sweep_number in sweep_numbers:
sweep_data = data_set.get_sweep(sweep_number)
if data_set.get_sweep_metadata(
sweep_number)['aibs_stimulus_name'] == "Long Square":
sweep_info = {}
sweep_info[DH.METADATA] = data_set.get_sweep_metadata(sweep_number)
amp = float(sweep_info[DH.METADATA]['aibs_stimulus_amplitude_pa'])
if abs(amp - math.ceil(amp)) < 1e-5:
amp = int(math.ceil(amp))
if abs(amp - math.floor(amp)) < 1e-5:
amp = int(math.floor(amp))
print("Sweep: %s (%s pA): %s" %
(sweep_number, amp, sweep_info[DH.METADATA]))
# start/stop indices that exclude the experimental test pulse (if applicable)
index_range = sweep_data['index_range']
# stimulus is a numpy array in amps
# stimulus is a numpy array in amps
stimulus = sweep_data['stimulus'][index_range[0]:index_range[-1]]
# response is a numpy array in volts
response = sweep_data['response'][index_range[0]:index_range[-1]]*1000
subset[sweep_number] = response
# sampling rate is in Hz
sampling_rate = sweep_data['sampling_rate']
# define some time points in seconds (i.e., convert to absolute time)
time_pts = np.arange(0,len(stimulus)/sampling_rate,1./sampling_rate)
subset['t'] = time_pts
metadata = data_set.get_sweep_metadata(sweep_number)
ampl = round(metadata['aibs_stimulus_amplitude_pa'],4)
# plot the stimulus and the voltage response for the random trial
plt.subplot(2,1,1)
plt.plot(time_pts,stimulus)
plt.ylabel('Stimulus (A)')
plt.subplot(2,1,2)
plt.plot(time_pts,response, label = 'S %s, %s pA'%(sweep_number, ampl))
volts_file = open('%s.dat'%dataset_id, 'w')
max = 1.5 # s
for i in range(len(subset['t'])):
t = subset['t'][i]
if t <= max:
def extract_info_from_nwb_file(dataset_id, raw_ephys_file_name):
info = {}
import h5py
import numpy as np
h5f = h5py.File(raw_ephys_file_name, "r")
metas = ['aibs_cre_line','aibs_dendrite_type','intracellular_ephys/Electrode 1/location']
for m in metas:
d = h5f.get('/general/%s'%m)
print("%s = \t%s"%(m,d.value))
info[m.split('/')[-1]]=str(d.value)
h5f.close()
from allensdk.core.nwb_data_set import NwbDataSet
data_set = NwbDataSet(raw_ephys_file_name)
sweep_numbers = data_set.get_experiment_sweep_numbers()
if test:
sweep_numbers = [33,45]
sweep_numbers.sort()
info[DH.DATASET] = dataset_id
info[DH.COMMENT] = 'Data analysed on %s'%(time.ctime())
info[DH.PYELECTRO_VERSION] = pyel_ver
info[DH.ALLENSDK_VERSION] = allensdk_ver
info[DH.SWEEPS] = {}
for sweep_number in sweep_numbers:
sweep_data = data_set.get_sweep(sweep_number)
if data_set.get_sweep_metadata(sweep_number)['aibs_stimulus_name'] == "Long Square":
sweep_info = {}
sweep_info[DH.METADATA] = data_set.get_sweep_metadata(sweep_number)
info[DH.SWEEPS]['%i'%sweep_number] = sweep_info
sweep_info[DH.SWEEP] = sweep_number
# start/stop indices that exclude the experimental test pulse (if applicable)
index_range = sweep_data['index_range']
# stimulus is a numpy array in amps
stimulus = sweep_data['stimulus'][index_range[0]:index_range[-1]]
# response is a numpy array in volts
response = sweep_data['response'][index_range[0]:index_range[-1]]*1000
# sampling rate is in Hz
sampling_rate = sweep_data['sampling_rate']
# define some time points in seconds (i.e., convert to absolute time)
time_pts = np.arange(0,len(stimulus)/sampling_rate,1./sampling_rate)*1000
comment = 'Sweep: %i in %i; %sms -> %sms; %sA -> %sA; %smV -> %smV'%(sweep_number, dataset_id, time_pts[0], time_pts[-1], np.amin(stimulus), np.amax(stimulus), np.amin(response), np.amax(response))
print(comment)
sweep_info[DH.COMMENT] = comment
analysis = utils.simple_network_analysis({sweep_number:response},
time_pts,
extra_targets = ['%s:value_280'%sweep_number,
'%s:average_1000_1200'%sweep_number,
'%s:average_100_200'%sweep_number],
end_analysis=1500,
plot=plot,
show_plot_already=False,
verbose=True)
sweep_info[DH.ICLAMP_ANALYSIS] = analysis
analysis_file_name = '%s_analysis.json'%(dataset_id)
analysis_file = open(analysis_file_name, 'w')
pretty = pp.pformat(info)
pretty = pretty.replace('\'', '"')
pretty = pretty.replace('u"', '"')
analysis_file.write(pretty)
analysis_file.close()
print('Written info to %s'%analysis_file_name)
def extract_info_from_nwb_file(dataset_id, raw_ephys_file_name):
info = {}
import h5py
import numpy as np
h5f = h5py.File(raw_ephys_file_name, "r")
metas = [
'aibs_cre_line', 'aibs_dendrite_type',
'intracellular_ephys/Electrode 1/location'
]
for m in metas:
d = h5f.get('/general/%s' % m)
print("%s = \t%s" % (m, d.value))
info[m.split('/')[-1]] = str(d.value)
h5f.close()
from allensdk.core.nwb_data_set import NwbDataSet
data_set = NwbDataSet(raw_ephys_file_name)
sweep_numbers = data_set.get_experiment_sweep_numbers()
if test:
sweep_numbers = [33, 45]
sweep_numbers.sort()
info[DH.DATASET] = dataset_id
info[DH.COMMENT] = 'Data analysed on %s' % (time.ctime())
info[DH.PYELECTRO_VERSION] = pyel_ver
info[DH.ALLENSDK_VERSION] = allensdk_ver
info[DH.SWEEPS] = {}
for sweep_number in sweep_numbers:
sweep_data = data_set.get_sweep(sweep_number)
if data_set.get_sweep_metadata(
sweep_number)['aibs_stimulus_name'] == "Long Square":
sweep_info = {}
sweep_info[DH.METADATA] = data_set.get_sweep_metadata(sweep_number)
info[DH.SWEEPS]['%i' % sweep_number] = sweep_info
sweep_info[DH.SWEEP] = sweep_number
# start/stop indices that exclude the experimental test pulse (if applicable)
index_range = sweep_data['index_range']
# stimulus is a numpy array in amps
stimulus = sweep_data['stimulus'][index_range[0]:index_range[-1]]
# response is a numpy array in volts
response = sweep_data['response'][
index_range[0]:index_range[-1]] * 1000
# sampling rate is in Hz
sampling_rate = sweep_data['sampling_rate']
# define some time points in seconds (i.e., convert to absolute time)
time_pts = np.arange(0,
len(stimulus) / sampling_rate,
1. / sampling_rate) * 1000
comment = 'Sweep: %i in %i; %sms -> %sms; %sA -> %sA; %smV -> %smV' % (
sweep_number, dataset_id, time_pts[0], time_pts[-1],
np.amin(stimulus), np.amax(stimulus), np.amin(response),
np.amax(response))
print(comment)
sweep_info[DH.COMMENT] = comment
analysis = utils.simple_network_analysis(
{sweep_number: response},
time_pts,
extra_targets=[
'%s:value_280' % sweep_number,
'%s:average_1000_1200' % sweep_number,
'%s:average_100_200' % sweep_number
],
end_analysis=1500,
plot=plot,
show_plot_already=False,
verbose=True)
sweep_info[DH.ICLAMP_ANALYSIS] = analysis
analysis_file_name = '%s_analysis.json' % (dataset_id)
analysis_file = open(analysis_file_name, 'w')
pretty = pp.pformat(info)
pretty = pretty.replace('\'', '"')
pretty = pretty.replace('u"', '"')
analysis_file.write(pretty)
analysis_file.close()
print('Written info to %s' % analysis_file_name)
sweep_numbers.sort()
print("All sweeps for %s: %s"%(dataset_id, sweep_numbers))
subthreshs = {}
spikings = {}
spike_count = {}
chosen = {}
stimuli = {}
for sweep_number in sweep_numbers:
sweep_data = data_set.get_sweep(sweep_number)
if data_set.get_sweep_metadata(sweep_number)['aibs_stimulus_name'] == "Long Square":
sweep_info = {}
sweep_info[DH.METADATA] = data_set.get_sweep_metadata(sweep_number)
amp = float(sweep_info[DH.METADATA]['aibs_stimulus_amplitude_pa'])
if abs(amp-math.ceil(amp))<1e-5:
amp=int(math.ceil(amp))
if abs(amp-math.floor(amp))<1e-5:
amp=int(math.floor(amp))
print("Sweep: %s (%s pA): %s"%(sweep_number,amp,sweep_info[DH.METADATA]))
# start/stop indices that exclude the experimental test pulse (if applicable)
index_range = sweep_data['index_range']
# stimulus is a numpy array in amps
