def compare_data(leftfiles, rightfiles, leftheader, rightheader):
"""Compare two simulations side by side"""
figs = []
axeslist = []
psthaxlist = []
for left, right in zip(leftfiles, rightfiles):
fig, axes = plt.subplots(nrows=6, ncols=2, sharey='row')
psth_axes = []
for ii, fname in enumerate([left, right]):
fpath = os.path.join(datadir, fname)
with h5.File(fpath, 'r') as fd:
config = nda.load_config(fd)
bins = np.arange(0, nda.get_simtime(fd)+0.5, 50.0)
try:
pns = list(fd[nda.pn_st_path].keys())
except KeyError:
print('Could not find PNs in', fname)
return figs, axeslist, psthaxlist
pns = sorted(pns, key=lambda x: int(x.split('_')[-1]))
pn_st, pn_y = nda.get_event_times(fd[nda.pn_st_path], pns)
axes[0, ii].plot(np.concatenate(pn_st), np.concatenate(pn_y), ',')
psth_ax = axes[0, ii].twinx()
psth_axes.append(psth_ax)
plot_population_psth(psth_ax, pn_st, config['pn']['number'], bins)
lines, kc_st, kc_y = plot_kc_spikes_by_cluster(axes[1, ii], fd, 'LCA')
plot_population_psth(axes[2, ii], kc_st, len(kc_st), bins, rate_sym='b^', cell_sym='rv')
stiminfo = nda.get_stimtime(fd)
stimend = stiminfo['onset'] + stiminfo['duration'] + stiminfo['offdur']
rates = [len(st[(st > stiminfo['onset']) & (st < stimend)]) * 1e3
/ (stimend - stiminfo['onset']) for st in kc_st]
print(rates[:5])
axes[3, ii].hist(rates, bins=np.arange(21))
axes[3, ii].set_xlabel('Firing rate')
plot_kc_vm(axes[4, ii], fd, 'LCA', 5)
plot_ggn_vm(axes[5, ii], fd,
fd['/data/uniform/ggn_output/GGN_output_Vm'],
'LCA', 5, color='r')
plot_ggn_vm(axes[5, ii], fd,
fd['/data/uniform/ggn_basal/GGN_basal_Vm'],
'basal', 5, color='g')
axes[5, ii].set_ylim((-53, -35))
axes[0, ii].set_title('{}\nFAKE? {}'.format(fname, nda.load_config(fd)['kc']['fake_clusters']))
time_axes = [axes[ii, jj] for ii in [0, 1, 2, 4, 5] for jj in [0, 1]]
for ax in time_axes[:-1]:
ax.set_xticks([])
axes[0, 0].get_shared_x_axes().join(*time_axes)
axes[2, 0].get_shared_x_axes().join(*axes[2, :])
# psth_axes[0].get_shared_y_axes().join(*psth_axes)
psth_axes[0].autoscale()
# axes[-1, -1].autoscale()
fig.text(0.1, 0.95, leftheader, ha='left', va='bottom')
fig.text(0.6, 0.95, rightheader, ha='left', va='bottom')
fig.set_size_inches(15, 10)
# fig.tight_layout()
figs.append(fig)
axeslist.append(axes)
psthaxlist.append(psth_axes)
return figs, axeslist, psthaxlist
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
_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))
if common is None:
common = set(jid_spiking_kcs[jid])
else:
common = common.intersection(jid_spiking_kcs[jid])
pos = (common_kcs.odor
== odor) & (common_kcs.connection
== conn) & (common_kcs.template_jid == template)
common_kcs.loc[pos, 'common_kcs'] = len(common)
common_kcs.loc[pos, 'avg_kcs'] = np.mean(counts)
templates = odor_trials_data.template_jid.unique()
for t in templates:
fname = nda.find_h5_file(str(t), datadir)
print(fname)
with h5.File(fname, 'r') as fd:
print(yaml.dump(nda.load_config(fd), default_flow_style=''))
tmp_color_map = {
10829002: '#e66101', # iid
10829014: '#b2abd2', # clus
9932209: '#fdb863', # iid
9932198: '#5e3c99' # clus
}
tmp_label_map = {
10829002: 'Diffuse 2', # iid
10829014: 'Clustered 2', # clus
9932209: 'Diffuse 1', # iid
9932198: 'Clustered 1' # clus
}
datadir = '/data/rays3/ggn/fixed_net'
os.chdir(analysis_dir)
fig, ax = plt.subplots(nrows=3, ncols=2, sharex='all', sharey='row')
# jid = '13081213' # this reproduces GGN Vm well, PN->KC clustered, same KC cluster receives coactive PNs
jid = '21841553' # new simulation
fname = nda.find_h5_file(jid, datadir)
originals = []
with h5.File(fname, 'r') as fd:
ax[0, 0].set_title(f'{jid}')
print(fname)
orig = fd.attrs['original']
originals.append(orig)
print(f'original:{orig}')
with h5.File(orig, 'r') as forig:
print(yaml.dump(nda.load_config(forig), default_flow_style=False))
# fig, ax = plt.subplots(nrows=2, ncols=1, sharex='all')
myplot.plot_ggn_vm(ax[1, 0], fd, fd['/data/uniform/ggn_output/GGN_output_Vm'], 'LCA', 1, color='black', alpha=1.0)
ax[1, 0].set_ylim(-60, -45)
ax[1, 0].set_yticks([-55, -50])
ig_vm = fd['/data/uniform/ig/IG_Vm']
dt = ig_vm.attrs['dt']
ig_vm = ig_vm[0, :]
t = np.arange(len(ig_vm)) * dt
ax[2, 0].plot(t, ig_vm, color='black')
ax[2, 0].hlines(y=-60.0, xmin=500, xmax=1500, color='gray', lw=10)
ax[2, 0].hlines(y=-60.0, xmin=1500, xmax=2000, color='lightgray', lw=10)
for axis in ax.flat:
[sp.set_visible(False) for sp in axis.spines.values()]
axis.tick_params(right=False, top=False)
ax[1, 0].tick_params(bottom=False)
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
stiminfo = nda.get_stimtime(fd_lognorm)
# 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]),
np.concatenate(kc_y[::10]),
