sig = rec['resp']
eps = sig.epochs
cellorder = sig.chans
# map of transitin types, which change form probe to probe. the outer key is probe, the inner key is transition type
# the value is context identity
transitions = {6: {'silence': 0,'continuous': 5,'similar': 7,'sharp': 10},
7: {'silence': 0,'continuous': 6,'similar': 5,'sharp': 9},
9: {'silence': 0,'continuous': 8,'similar': 10,'sharp': 7},
10: {'silence': 0,'continuous': 9,'similar': 8,'sharp': 6}}
########################################################################################################################
# check integrity of the data
# test plot
fig, axes = cplot.hybrid(sig, ['C0_P6', 'C0_P7', 'C0_P9', 'C0_P10'])
fig, axes = cplot.hybrid(sig, epoch_names = ['REFERENCE'])
fig, axes = cplot.hybrid(sig, epoch_names = ['REFERENCE'], channels=goodcells)
# data plots withe this approach corresponds with that plotted with baphy_remote. It seems that the epoch naming is
# properly aligned.
epoch_names = nep.epoch_names_matching(sig.epochs, r'\ASTIM_Tsequence.*')
fig, axes = cplot.hybrid(sig, epoch_names=epoch_names, channels=goodcells)
fig, axes = cplot.hybrid(sig, epoch_names='C7_P9', channels=goodcells)
fig, axes = cplot.hybrid(sig, epoch_names='C0_P9', channels=goodcells)
# for good cells, all the relevant probes after silence
fig, axes = cplot.hybrid(sig, epoch_names=r'\AC0_P([679]|10)\Z', channels=goodcells)
fig, axes = cplot.hybrid(sig, epoch_names=r'\AC0_P([679]|10)\Z', channels=best_cell)
# best cell, best probe, all the contexts
# raw raster
epoch_names = [
f"C{transitions[f'P{probe}'][trans]}_P{probe}"
for trans in meta['transitions']
]
topcell_idx = np.argmax(np.abs(weights[:, 0, topDbin]))
topcell = goodcells[topcell_idx]
trans_colors = [trans_color_map[trans] for trans in meta['transitions']]
raster_ax = axes[2]
cplt.hybrid(sig,
epoch_names,
channels=topcell,
psth_fs=meta['raster_fs'],
time_strech=[start, end],
time_offset=offset,
axes=[raster_ax],
legend=True,
labels=meta['transitions'],
colors=trans_colors)
raster_ax.axvline(0, linestyle=':', color='gray')
raster_ax.axvline(topDtime, linestyle='--', color='black')
# horizonta line in weightplot indicating most weighted cell
trans_ax.axhline(topcell_idx + 0.5, linestyle='--', color='Black')
# Formatting
for ax in [trans_ax, dprime_ax, raster_ax]:
ax.tick_params(labelsize=ax_val_size)
ax.spines['right'].set_visible(False)
'/home/mateo/Pictures/DAC1/181205_pvalue_min_{}.png'.format(site_ID),
dpi=100)
fig.savefig(
'/home/mateo/Pictures/DAC1/181205_pvalue_min_{}.svg'.format(site_ID))
plt.close('all')
################################################
# plots examples of calculation Process
best_site = 'BRT056b'
best_cell = 'BRT056b-58-1'
best_voc = None
best_cont = None
# full population, stim_num independent PSTH vocalization
sig = pop_sigs[best_site]['resp']
fig, axes = cplot.hybrid(sig, scatter_kws={'alpha': 0.1})
mng = plt.get_current_fig_manager()
mng.resize(*mng.window.maxsize())
fig.suptitle(
'stim_num independent vocalizations PSTH\nsite {}'.format(best_site))
fig.savefig(
'/home/mateo/Pictures/DAC1/181205_voc_psth_all_cells_{}.png'.format(
best_site),
dpi=100)
fig.savefig(
'/home/mateo/Pictures/DAC1/181205_voc_psth_all_cells_{}.svg'.format(
best_site))
# single cell, all vocalizations, all contexts
cpp_eps = nep.epoch_names_matching(sig.epochs, r'\AC\d_P\d$')
cpp_rec = cep.set_recording_subepochs(rec, set_pairs=True)
pop_sigs[site_key][modelname] = cpp_rec['pred'].rasterize()
pop_sigs[site_key]['resp'] = cpp_rec['resp'].rasterize()
del (pop_sigs['BRT057b'])
########################################################################################################################
# cleaner version of example for wip talk.... something broke this plotting function down the road, not on my code
best_site = 'BRT056b'
best_cell = 'BRT056b-58-1'
sig = pop_sigs[best_site]['resp']
fig, axes = cplot.hybrid(sig,
epoch_names=r'\AC\d_P3',
start=3,
end=6,
scatter_kws={'alpha': 0.1})
mng = plt.get_current_fig_manager()
mng.resize(*mng.window.maxsize())
plt.tight_layout()
for ax in axes:
ax.axis('off')
fig.suptitle('')
# fig.suptitle('stim_num independent vocalizations PSTH\nsite {}'.format(best_site))
# fig.savefig('/home/mateo/Pictures/WIP2/181207_eg_cpp_all_cells_{}.png'.format(best_site), dpi=100)
# fig.savefig('/home/mateo/Pictures/WIP2/181207_eg_cpp_all_cells_{}.svg'.format(best_site))
# single cell, one prb, all contexts
goodprobe = 3
goodcell = 'BRT056b-58-1'
png = root.joinpath(f'dPCA_{site}_P{probe}').with_suffix('.png')
fig.savefig(png, transparent=True, dpi=100)
svg = png = root.joinpath(f'dPCA_{site}_P{probe}').with_suffix('.svg')
fig.savefig(svg, transparent=True)
if site == 'AMT029a':
# blue, brown orange green
CB_color_cycle = ['#377eb8','#a65628', '#ff7f00', '#4daf4a', ]
else:
# blue, orange green brown
CB_color_cycle = ['#377eb8', '#ff7f00', '#4daf4a', '#a65628']
fig, ax = plots.hybrid(sig, f'\AC\d_P{probe}\Z', channels=topcell, time_strech=[1,2], colors=CB_color_cycle)
ax = ax[0]
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.tick_params(labelsize=15)
ax.title.set_size(20)
ax.xaxis.label.set_size(20)
ax.yaxis.label.set_size(20)
fig.set_size_inches([4.83, 4.2 ])
if save_img:
root = pl.Path(f'/home/mateo/Pictures/DAC2')
if not root.exists(): root.mkdir(parents=True, exist_ok=True)
png = root.joinpath(f'raster_{site}_P{probe}').with_suffix('.png')
# variance explained by PCs
for sig_name, pca in pca_stats.items():
fig, ax = plt.subplots()
toplot = np.cumsum(pca.explained_variance_ratio_)
ax.plot(toplot, '.-')
ax.set_xlabel('number of components')
ax.set_ylabel('cumulative explained variance')
ax.set_title('PCA: fraction of variance explained')
# selects most responsive celll
scat_key = {'s': 5, 'alpha': 0.5}
cplt.hybrid(
sig,
epoch_names='single',
channels='all',
start=0,
end=3,
scatter_kws=scat_key,
)
good_cell_index = [5, 7, 8, 10, 11, 14]
# selects the stimulus generating the highest response
cplt.hybrid(sig,
epoch_names='single',
channels=good_cell_index,
start=0,
end=3,
scatter_kws=scat_key)
# vocalization 3 generates the clearest response
var_ax.title.set_size(20)
var_ax.xaxis.label.set_size(20)
var_ax.yaxis.label.set_size(20)
# plots example raster
epoch_names = [
f"C{transitions[f'P{probe}'][trans]}_P{probe}"
for trans in meta['transitions']
]
topcell = goodcells[np.argmax(np.abs(dpca.D['ct'][:, 0]))]
colors = [trans_color_map[trans] for trans in meta['transitions']]
raster_ax = plt.subplot2grid((2, 2), (1, 0), 1, 1, fig=fig)
cplt.hybrid(sig,
epoch_names=epoch_names,
channels=topcell,
time_strech=[1, 2],
colors=colors,
axes=[raster_ax])
raster_ax = raster_ax
raster_ax.spines['right'].set_visible(False)
raster_ax.spines['top'].set_visible(False)
raster_ax.tick_params(labelsize=15)
raster_ax.title.set_size(20)
raster_ax.xaxis.label.set_size(20)
raster_ax.yaxis.label.set_size(20)
suptitle = f"{site} probe {probe} dPCA zscore-{meta['zscore']}"
fig.suptitle(suptitle, fontsize=20)
analysis = f"dPCA_examples_{meta['raster_fs']}Hz_zscore-{meta['zscore']}"