marker=',')
for a in ax[:2]:
a.xaxis.set_visible(False)
a.yaxis.set_visible(False)
ax[1].plot(np.concatenate(kc_st[::10]),
np.concatenate(kc_id[::10]),
color='#e66101',
ls='',
marker=',')
#* plot ggn vm from both simulations
myplot.plot_ggn_vm(ax[2],
fd_sc,
fd_sc['/data/uniform/ggn_basal/GGN_basal_Vm'],
'dend_b',
1,
color='k',
alpha=1.0)
ax[2].set_ylim((-53, -40))
ax[2].set_yticks([-50, -45])
ax[2].set_xlim(200, 2500)
ax[2].set_xticks([200, 1000, 2000])
ax[2].set_xlabel('Time (ms)')
ax[2].hlines(y=-53,
xmin=stiminfo['onset'],
xmax=stiminfo['onset'] + stiminfo['duration'],
color='gray',
lw=10)
ax[2].hlines(y=-53,
xmin=stiminfo['onset'] + stiminfo['duration'],
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)
xticks = [200.0, 1000.0, 2000.0, 3000.0]
ax[2, 0].set_xticks(xticks)
import pn_kc_ggn_plot_mpl as myplot
datadir = '/data/rays3/ggn/olfactory_network'
#jid = '20161955'
# jid = '20162331'
jid = '20162116'
fname = nda.find_h5_file(jid, datadir)
fig, ax = plt.subplots()
with h5.File(fname, 'r') as fd:
spike_counts = [
ds.shape[0] for ds in fd['/data/event/kc/kc_spiketime'].values()
]
spiking = len(np.flatnonzero(spike_counts))
print('spiking kcs:{}'.format(spiking))
myplot.plot_ggn_vm(ax, fd, fd['/data/uniform/ggn_basal/GGN_basal_Vm'],
'dend_b', 1)
ax.set_xlabel('Time (ms)')
ax.set_ylabel('GGN Vm (mV)')
ax.tick_params(right=False, top=False)
ax.set_xlim(500, 2500)
fig.set_frameon(False)
fig.set_size_inches(20 / 2.54, 10 / 2.54)
fig.savefig('supp_4_ggn_ig_hyperpolarization.svg')
# 50.0,1.4,2.7,20162064
jid = '20162064'
fname = nda.find_h5_file(jid, datadir)
fig, ax = plt.subplots()
with h5.File(fname, 'r') as fd:
spike_counts = [
ds.shape[0] for ds in fd['/data/event/kc/kc_spiketime'].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]),
color='#fdb863',
ls='',
marker='s',
ms=1)
ax0.xaxis.set_visible(False)
ax0.yaxis.set_visible(False)
#* plot ggn vm from both simulations
myplot.plot_ggn_vm(ax1,
fd_constant,
fd_constant['/data/uniform/ggn_basal/GGN_basal_Vm'],
'dend_b',
1,
color='#009292',
alpha=1.0)
ax1.hlines(y=-53,
xmin=stiminfo_const['onset'],
xmax=stiminfo_const['onset'] + stiminfo_const['duration'],
color='gray',
lw=10)
ax1.hlines(y=-53,
xmin=stiminfo_const['onset'] + stiminfo_const['duration'],
xmax=stiminfo_const['onset'] + stiminfo_const['duration'] +
stiminfo_const['offdur'],
color='lightgray',
lw=10)
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')
ax1.set_ylim(-51, -45)
ax1.set_xlim(700, 2500)
xticks = np.array([
200.0, 1000.0, 2000.0
]) + 500 # Here the onset was at 1s, 500 ms past the newer simulations
ax1.set_xticks(xticks)
ax1.set_xticklabels((xticks - 500) / 1000.0)
ax1.set_yticks([-50.0, -40.0])
for axis in axes:
[sp.set_visible(False) for sp in axis.spines.values()]
axis.tick_params(right=False, top=False)
for axis in axes[:-1]:
axis.xaxis.set_visible(False)
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]),
color='#e66101',
marker='s',
ms=1,
ls='none')
myplot.plot_ggn_vm(axes[2],
fd,
fd['/data/uniform/ggn_basal/GGN_basal_Vm'],
'dend_b',
1,
color='#009292',
alpha=1.0)
ax2.set_ylim(-53, -45)
ax2.set_xlim(200, 2000)
xticks = np.array([200.0, 1000.0, 2000.0])
ax2.set_xticks(xticks)
ax2.set_xticklabels(xticks / 1000.0)
ax2.set_yticks([-50.0, -45.0])
ax2.hlines(y=-53.0,
xmin=Q_(config['stimulus']['onset']).to('ms').m,
xmax=Q_(config['stimulus']['onset']).to('ms').m +
Q_(config['stimulus']['duration']).to('ms').m,
lw=3,
color='lightgray')
