def find_jid_spiking_kcs(jid_list, datadir):
ret = []
for jid in jid_list:
fname = nda.find_h5_file(str(jid), datadir)
with h5.File(fname, 'r') as fd:
ret.append(nda.get_spiking_kcs(fd))
return ret
def get_dv_kc_data(fname):
"""Create a dataset where each row contains a KC, its presynaptic GGN
section, maximum conductance from GGN, peak depolarization of that
section and the number of spikes generated by that KC"""
with h5.File(fname, 'r') as fd:
print(jid, len(nda.get_spiking_kcs(fd)))
print(yaml.dump(nda.load_config(fd), default_style=''))
ggn_kc_gbar = pd.DataFrame(fd[nda.ggn_kc_syn_path]['pre', 'post',
'gbar'][:, 0])
ggn_peak_vm = fd['/data/uniform/ggn_output/GGN_output_Vm'].value.max(
axis=1)
ggn_sec = fd['/data/uniform/ggn_output/GGN_output_Vm'].dims[0][
'source']
ggn_sec_vm = pd.DataFrame(data={'sec': ggn_sec, 'vm': ggn_peak_vm})
pn_kc_gmax = pd.DataFrame(fd[nda.pn_kc_syn_path]['post', 'gmax'][:, 0])
pn_kc_gmax_by_kc = pn_kc_gmax.groupby('post').sum().reset_index()
kc_sc = {
kc_st.attrs['source']: len(kc_st)
for kc_st in fd[nda.kc_st_path].values()
}
df_kc_sc = pd.DataFrame(data={
'kc': list(kc_sc.keys()),
'spike_count': list(kc_sc.values())
})
combined = pd.merge(ggn_kc_gbar, df_kc_sc, left_on='post', right_on='kc')
combined = pd.merge(combined, ggn_sec_vm, left_on='pre', right_on='sec')
combined.drop(columns=['pre', 'post'], inplace=True)
return combined
plt.rc('font', size=8)
_ur = pint.UnitRegistry()
Q_ = _ur.Quantity
# datadir = 'Z:/Subhasis/ggn_model_data/olfactory_network'
# datadir = '/data/rays3/ggn/olfactory_network/'
datadir = 'D:/biowulf_stage/olfactory_network'
#** jid_sc for only shifting PN, constant GGN->KC inhibition - but this did not
# have PN-KC clustered conn
jid_sc = '22087969'
fname_sc = nda.find_h5_file(jid_sc, datadir)
fd_sc = h5.File(fname_sc, 'r')
print('shifting PN, jid: {}, spiking KCs {}'.format(
jid_sc, len(nda.get_spiking_kcs(fd_sc))))
print(yaml.dump(nda.load_config(fd_sc), default_style=''))
print('-' * 20)
stiminfo = nda.get_stimtime(fd_sc)
pn_st = []
pn_id = []
for pn in fd_sc[nda.pn_st_path].values():
pn_st.append(pn[:])
pn_id.append([int(pn.name.rpartition('_')[-1])] * len(pn))
kc_st = []
kc_id = []
for kc, st in fd_sc[nda.kc_st_path].items():
kc_st.append(st[:])
kc_id.append([int(kc)] * len(st))
plt.rc('font', size=8)
_ur = pint.UnitRegistry()
Q_ = _ur.Quantity
# datadir = 'Z:/Subhasis/ggn_model_data/olfactory_network'
# datadir = '/data/rays3/ggn/olfactory_network/'
datadir = 'D:/biowulf_stage/olfactory_network'
#** jid_sc for only shifting PN, constant GGN->KC inhibition - but this did not
# have PN-KC clustered conn
# jid_sc = '22295183'
jid_sc = '24211204'
fname_sc = nda.find_h5_file(jid_sc, datadir)
fd_sc = h5.File(fname_sc, 'r')
print('shifting PN, jid: {}, spiking KCs {}'.format(jid_sc, len(nda.get_spiking_kcs(fd_sc))))
print(yaml.dump(nda.load_config(fd_sc), default_style=''))
print('-' * 20)
stiminfo = nda.get_stimtime(fd_sc)
pn_st = []
pn_id = []
for pn in fd_sc[nda.pn_st_path].values():
pn_st.append(pn[:])
pn_id.append([int(pn.name.rpartition('_')[-1])] * len(pn))
kc_st = []
kc_id = []
for kc, st in fd_sc[nda.kc_st_path].items():
kc_st.append(st[:])
kc_id.append([int(kc)] * len(st))
gs = gridspec.GridSpec(nrows=3, ncols=1, height_ratios=[2, 1, 1], hspace=0.05)
fig = plt.figure()
ax0 = fig.add_subplot(gs[0])
ax1 = fig.add_subplot(gs[1], sharex=ax0)
ax2 = fig.add_subplot(gs[2], sharex=ax0, sharey=ax1)
#* open file descriptors
#** jid_shifting_lognorm for shifting PNs with lognorm distribution of synapses
# jid = '3047110' # this was old version
# jid_lognorm = '16240941' # newer simulation with 0.2 s off response
# jid_lognorm = '22087970' # This is with lognorm distribution of all synaptic strengths, pn-kc gmax=2.4pS, 5K KCs spiked
jid_lognorm = '22087969'
fname_lognorm = nda.find_h5_file(jid_lognorm, datadir)
fd_lognorm = h5.File(fname_lognorm, 'r')
print('shifting PN, jid: {}, spiking KCs {}'.format(
jid_lognorm, len(nda.get_spiking_kcs(fd_lognorm))))
print(yaml.dump(nda.load_config(fd_lognorm), default_style=''))
print('-' * 20)
#** jid_constant for only shifting PN, constant GGN->KC inhibition - but this did not
# have PN-KC clustered conn
# jid_constant = '16562835' # Thu Mar 14 13:49:49 EDT 2019 - this file was somehow lost from backup.
# jid_constant = '22295183' # Redid simulations with constant synaptic conductances on 2019-03-13 PN-KC gmax=3 pS, spiking KCs 1707
jid_constant = '24211204' # The jid '22295183' in fig 5 had off-time 0.2 s, this one has 0.5s
fname_constant = nda.find_h5_file(jid_constant, datadir)
fd_constant = h5.File(fname_constant, 'r')
print('shifting PN, jid: {}, spiking KCs {}'.format(
jid_constant, len(nda.get_spiking_kcs(fd_constant))))
print(yaml.dump(nda.load_config(fd_constant), default_style=''))
print('-' * 20)
#* plot PN activity from jid_lognorm
# '9674069',
# '9674087',
# '9674120',
# '9673863']
# ^-These will not do - constant GGN->KC inhibition with 2% weakly inhibited
jid = '9674118'
fname = nda.find_h5_file(jid, datadir)
gs = gridspec.GridSpec(nrows=2, ncols=1, height_ratios=[3, 1], hspace=0.05)
fig = plt.figure()
ax0 = fig.add_subplot(gs[0])
ax1 = fig.add_subplot(gs[1], sharex=ax0)
axes = [ax0, ax1]
with h5.File(fname, 'r') as fd:
print('jid: {} spiking KCs: {}'.format(jid, len(nda.get_spiking_kcs(fd))))
print(yaml.dump(nda.load_config(fd), default_style=''))
# pn_st = []
# pn_id = []
# for pn in fd[nda.pn_st_path].values():
# pn_st.append(pn.value)
# pn_id.append([int(pn.name.rpartition('_')[-1])] * len(pn))
# ax0.plot(np.concatenate(pn_st[::10]), np.concatenate(pn_id[::10]), 'k,')
kc_x, kc_y = nda.get_event_times(fd[nda.kc_st_path])
ax0.plot(np.concatenate(kc_x[::10]), np.concatenate(kc_y[::10]), 'k,')
myplot.plot_ggn_vm(ax1,
fd,
fd['/data/uniform/ggn_basal/GGN_basal_Vm'],
'dend_b',
1,
color='k')
datadir = '/data/rays3/ggn/olfactory_network/'
# jid = '16034794'
jid = '22295165'
fname = nda.find_h5_file(jid, datadir)
gs = gridspec.GridSpec(nrows=3, ncols=1, height_ratios=[2, 2, 1], hspace=0.05)
fig = plt.figure()
ax0 = fig.add_subplot(gs[0])
ax1 = fig.add_subplot(gs[1], sharex=ax0)
ax2 = fig.add_subplot(gs[2], sharex=ax0)
axes = [ax0, ax1, ax2]
with h5.File(fname, 'r') as fd:
print(jid, len(nda.get_spiking_kcs(fd)))
config = nda.load_config(fd)
print(yaml.dump(config, default_style=''))
pn_st = []
pn_id = []
for pn in fd[nda.pn_st_path].values():
pn_st.append(pn.value)
pn_id.append([int(pn.name.rpartition('_')[-1])] * len(pn))
ax0.plot(np.concatenate(pn_st[::10]),
np.concatenate(pn_id[::10]),
marker='s',
ms=1,
color='#fdb863',
ls='none')
kc_x, kc_y = nda.get_event_times(fd[nda.kc_st_path])
ax1.plot(np.concatenate(kc_x[::10]),
