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()
#sweep_numbers = [33,45]
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:
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)
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()
#sweep_numbers = [33,45]
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:
