def example_raster_plot_jitter():
l4 = [
71211,
70922,
71042,
71338,
74266,
74419,
74882,
71610,
]
l5 = [75998, 81442, 82079, 93653, 93902, 81703, 81443, 82202]
l6 = [93655, 75759, 76251, 80451, 80762, 81068, 81255, 81316]
gids_to_plot = [74419, 81442, 93272] #81068] #[l4[n], l5[n], l6[n]]75270
# gids_to_plot = [85422, 90721]
fig, axs = plt.subplots(6, 3, figsize=(14, 14))
ids = data_access_shuffling.ids_jitter[[5, 0, 2, 4, 1, 3]]
jitters = np.array([2, 50, 5, 200, 20, 0])[[5, 0, 2, 4, 1, 3]]
for i, id in enumerate(ids):
bcs = data_access_shuffling.get_jitter_blueconfigs(n=30, id_jitter=id)
df_network = data_access_shuffling.get_spike_times_multiple(
bcs).loc[gids_to_plot]
t_start = 2500
t_end = 4500
df_network = df_network[(df_network['spike_time'] < t_end)
& (df_network['spike_time'] >= t_start)]
for k, gid in enumerate(gids_to_plot):
print k
axs[i, k].fill_between([t_start, t_end], [0, 0], [30, 30],
color='red',
alpha=0.1,
linewidth=0.0)
plot_times = df_network.loc[gid]['spike_time']
plot_trials = df_network.loc[gid]['spike_trial']
axs[i, k].vlines(plot_times,
plot_trials,
plot_trials + 1,
linewidth=0.5)
for ax in axs[:, :].flatten():
ax.set_ylim([0, 30])
ax.set_yticks([0.5, 4.5, 9.5, 14.5, 19.5, 25.5, 29.5])
ax.set_yticklabels([1, 5, 10, 15, 20, 25, 30])
ax.set_ylabel('Trials')
for ax in axs.flatten():
ax.set_xlim([t_start, t_end])
ax.set_xlabel('t (ms)')
for i in range(3):
axs[0, i].set_title(gids_to_plot[i])
plt.tight_layout()
plt.savefig('figures/rasterplot_example_jitter.pdf')
def plot_psths():
df_neurons = data_access_shuffling.get_selected_L456_gids()
gids = np.array(df_neurons.index)
print gids.shape
bcs = data_access_shuffling.get_jitter_flick_blueconfigs()
df_network = data_access_shuffling.get_spike_times_multiple(bcs).loc[gids]
fig, axs = plt.subplots(6, figsize=(14, 14))
t_start = 1000
t_end = 8000
df_network = df_network[(df_network['spike_time'] < t_end)
& (df_network['spike_time'] >= t_start)]
for k in range(30):
times = df_network[df_network['spike_trial'] == k]['spike_time']
axs[0].hist(times,
bins=np.arange(t_start, t_end + 5, 5),
color='red',
histtype='step',
alpha=0.5,
weights=np.zeros(times.size) + (1000 / 5.0) / gids.size)
axs[0].set_xlim([t_start, t_end])
axs[0].set_ylim([0, 15])
axs[0].set_xlabel('t (ms)')
gids_special = [74419, 75270, 93272]
for i, gid in enumerate(gids_special):
df_neuron = df_network.loc[gid]
ax = axs[i + 1]
ax.vlines(df_neuron['spike_time'],
df_neuron['spike_trial'],
df_neuron['spike_trial'] + 1,
rasterized=True,
lw=1)
ax.set_xlim([t_start, t_end])
ax.set_ylim([0, 30])
ax.set_xlabel('t (ms)')
ax.set_title(gid)
plt.tight_layout()
plt.savefig('figures/population_psths_jitter_flick.pdf', dpi=300)
def populations_psths_jitter():
df_neurons = data_access_shuffling.get_selected_L456_gids()
gids = np.array(df_neurons.index)
print gids.shape
# spike_count_dict = data_access_shuffling.get_all_spike_counts_jitter()
fig, axs = plt.subplots(6, figsize=(14, 14))
ids = data_access_shuffling.ids_jitter[[5, 0, 2, 4, 1, 3]]
jitters = np.array([2, 50, 5, 200, 20, 0])[[5, 0, 2, 4, 1, 3]]
for i, id in enumerate(ids):
bcs = data_access_shuffling.get_jitter_blueconfigs(n=30, id_jitter=id)
df_network = data_access_shuffling.get_spike_times_multiple(
bcs).loc[gids]
print df_network
t_start = 1000
t_end = 7000
df_network = df_network[(df_network['spike_time'] < t_end)
& (df_network['spike_time'] >= t_start)]
for k in range(30):
print k
times = df_network[df_network['spike_trial'] == k]['spike_time']
axs[i].hist(times,
bins=np.arange(t_start, t_end + 5, 5),
color='red',
histtype='step',
alpha=0.5)
for ax in axs.flatten():
ax.set_xlim([t_start, t_end])
ax.set_xlabel('t (ms)')
plt.tight_layout()
plt.savefig('figures/population_psths_jitter.pdf')