def plot_baseline_data(targets=None):
baseline = {}
file_name = 'TestGranNet.v.basedat'
cols = [
't', 'Gran/0/Granule_98/v', 'Gran/1/Granule_98/v',
'Gran/2/Granule_98/v'
]
for c in cols:
baseline[c] = []
for line in open(file_name):
values = line.split()
for vi in range(len(values)):
baseline[cols[vi]].append(float(values[vi]) * 1000)
volts = {}
for k in baseline.keys():
if k != 't':
volts[k] = baseline[k]
utils.simple_network_analysis(volts,
baseline['t'],
targets=targets,
plot=True,
show_plot_already=False)
def plot_baseline_data(targets = None):
baseline = {}
file_name = 'TestGranNet.v.basedat'
cols = ['t', 'Gran/0/Granule_98/v', 'Gran/1/Granule_98/v', 'Gran/2/Granule_98/v']
for c in cols:
baseline[c] = []
for line in open(file_name):
values = line.split()
for vi in range(len(values)):
baseline[cols[vi]].append(float(values[vi])*1000)
volts = {}
for k in baseline.keys():
if k!='t':
volts[k]=baseline[k]
utils.simple_network_analysis(volts,
baseline['t'],
targets = targets,
plot=True,
show_plot_already=False)
def get_data_metrics(datafile: Container) -> Tuple[Dict, Dict, Dict]:
"""Analyse the data to get metrics to tune against.
:returns: metrics from pyelectro analysis, currents, and the membrane potential values
"""
analysis_results = {}
currents = {}
memb_vals = {}
total_acquisitions = len(datafile.acquisition)
for acq in range(1, total_acquisitions):
print("Going over acquisition # {}".format(acq))
# stimulus lasts about 1000ms, so we take about the first 1500 ms
data_v = (datafile.acquisition["CurrentClampSeries_{:02d}".format(
acq)].data[:15000] * 1000.0)
# get sampling rate from the data
sampling_rate = datafile.acquisition[
"CurrentClampSeries_{:02d}".format(acq)].rate
# generate time steps from sampling rate
data_t = np.arange(0,
len(data_v) / sampling_rate,
1.0 / sampling_rate) * 1000.0
# run the analysis
analysis_results[acq] = simple_network_analysis({acq: data_v}, data_t)
# extract current from description, but can be extracted from other
# locations also, such as the CurrentClampStimulus series.
data_i = (datafile.acquisition["CurrentClampSeries_{:02d}".format(
acq)].description.split("(")[1].split("~")[1].split(" ")[0])
currents[acq] = data_i
memb_vals[acq] = (data_t, data_v)
return (analysis_results, currents, memb_vals)
# 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
chosen['t'] = time_pts
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)
analysis = utils.simple_network_analysis({sweep_number: response},
time_pts,
end_analysis=1500,
plot=False,
show_plot_already=False,
verbose=False)
spike_count[sweep_number] = analysis['%s:max_peak_no' %
sweep_number]
subthresh = analysis['%s:max_peak_no' % sweep_number] == 0
if subthresh:
subthreshs[sweep_number] = amp
else:
spikings[sweep_number] = amp
subthreshs = OrderedDict(
sorted(subthreshs.items(), key=operator.itemgetter(1)))
spikings = OrderedDict(sorted(spikings.items(),
key=operator.itemgetter(1)))
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)
response = sweep_data['response'][index_range[0]:index_range[-1]]*1000
chosen[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)*1000
chosen['t'] = time_pts
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)
analysis = utils.simple_network_analysis({sweep_number:response},
time_pts,
end_analysis=1500,
plot=False,
show_plot_already=False,
verbose=False)
spike_count[sweep_number] = analysis['%s:max_peak_no'%sweep_number]
subthresh = analysis['%s:max_peak_no'%sweep_number] == 0
if subthresh:
subthreshs[sweep_number] = amp
else:
spikings[sweep_number] = amp
subthreshs = OrderedDict(sorted(subthreshs.items(), key=operator.itemgetter(1)))
spikings = OrderedDict(sorted(spikings.items(), key=operator.itemgetter(1)))
print("Subthreshold sweeps: %s"%subthreshs)
print("Spiking sweeps: %s"%spikings)
# 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],
end_analysis=1300,
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)
elif '-mone' in sys.argv:
simulator = 'jNeuroML_NEURON'
#simulator = 'jNeuroML'
sim_time = 700
cont = NeuroMLController('TestGranNet',
'models/GranuleCellMulti.net.nml',
'network_GranuleCell_multi', sim_time, dt,
simulator)
t, v = cont.run_individual(sim_vars, show=False)
plot_baseline_data(targets=target_data_pas.keys())
analysis = utils.simple_network_analysis(
v, t, plot=True, targets=target_data_pas.keys())
elif '-mtune' in sys.argv:
simulator = 'jNeuroML'
simulator = 'jNeuroML_NEURON'
prefix = 'TestGranNet'
neuroml_file = 'models/GranuleCellMulti.net.nml'
target = 'network_GranuleCell_multi'
sim_time = 700
population_size = 20
max_evaluations = 20
num_selected = 5
num_offspring = 5
#simulator = 'jNeuroML'
sim_time = 700
cont = NeuroMLController('TestGranNet',
'models/GranuleCellMulti.net.nml',
'network_GranuleCell_multi',
sim_time,
dt,
simulator)
t, v = cont.run_individual(sim_vars, show=False)
plot_baseline_data(targets = target_data_pas.keys())
analysis = utils.simple_network_analysis(v,
t,
plot=True,
targets = target_data_pas.keys())
elif '-mtune' in sys.argv:
simulator = 'jNeuroML'
simulator = 'jNeuroML_NEURON'
prefix = 'TestGranNet'
neuroml_file = 'models/GranuleCellMulti.net.nml'
target = 'network_GranuleCell_multi'
sim_time = 700
population_size = 20
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
],
end_analysis=1300,
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()
