def make_catalogue_figure():
dataio = DataIO(dirname=dirname)
catalogue = dataio.load_catalogue(chan_grp=0)
clusters = catalogue['clusters']
geometry = dataio.get_geometry(chan_grp=0)
fig, ax = plt.subplots()
ax.set_title('Catalogue have 4 templates')
for i in range(clusters.size):
color = clusters[i]['color']
color = int32_to_rgba(color, mode='float')
waveforms = catalogue['centers0'][i:i + 1]
plot_waveforms_with_geometry(waveforms,
channels,
geometry,
ax=ax,
ratioY=3,
deltaX=50,
margin=50,
color=color,
linewidth=3,
alpha=1,
show_amplitude=True,
ratio_mad=8)
fig.savefig('../img/peeler_templates_for_animation.png')
def make_pca_collision_figure():
dataio = DataIO(dirname=dirname)
cc = CatalogueConstructor(dataio=dataio)
clusters = cc.clusters
#~ plot_features_scatter_2d(cc, labels=None, nb_max=500)
#~ plot_features_scatter_2d
fig, ax = plt.subplots()
ax.set_title('Collision problem')
ax.set_aspect('equal')
features = cc.some_features
labels = cc.all_peaks[cc.some_peaks_index]['cluster_label']
for k in [0, 1, 2, 3]:
color = clusters[clusters['cluster_label'] == k]['color'][0]
color = int32_to_rgba(color, mode='float')
keep = labels == k
feat = features[keep]
print(np.unique(labels))
ax.plot(feat[:, 0],
feat[:, 1],
ls='None',
marker='o',
color=color,
markersize=3,
alpha=.5)
ax.set_xlim(-40, 40)
ax.set_ylim(-40, 40)
ax.set_xlabel('pca0')
ax.set_ylabel('pca1')
ax.annotate('Collision',
xy=(17.6, -16.4),
xytext=(30, -30),
arrowprops=dict(facecolor='black', shrink=0.05))
#~
fig.savefig('../img/collision_proble_pca.png')
def make_animation():
"""
Good example between 1.272 1.302
because collision
"""
dataio = DataIO(dirname=dirname)
catalogue = dataio.load_catalogue(chan_grp=0)
clusters = catalogue['clusters']
sr = dataio.sample_rate
# also a good one a 11.356 - 11.366
t1, t2 = 1.272, 1.295
i1, i2 = int(t1 * sr), int(t2 * sr)
spikes = dataio.get_spikes()
spike_times = spikes['index'] / sr
keep = (spike_times >= t1) & (spike_times <= t2)
spikes = spikes[keep]
print(spikes)
sigs = dataio.get_signals_chunk(i_start=i1,
i_stop=i2,
signal_type='processed')
sigs = sigs.copy()
times = np.arange(sigs.shape[0]) / dataio.sample_rate
def plot_spread_sigs(sigs, ax, ratioY=0.02, **kargs):
#spread signals
sigs2 = sigs * ratioY
sigs2 += np.arange(0, len(channels))[np.newaxis, :]
ax.plot(times, sigs2, **kargs)
ax.set_ylim(-0.5, len(channels) - .5)
ax.set_xticks([])
ax.set_yticks([])
residuals = sigs.copy()
local_spikes = spikes.copy()
local_spikes['index'] -= i1
#~ fig, ax = plt.subplots()
#~ plot_spread_sigs(sigs, ax, color='k')
num_fig = 0
fig_pred, ax_predictions = plt.subplots()
ax_predictions.set_title('All detected templates from catalogue')
fig, ax = plt.subplots()
plot_spread_sigs(residuals, ax, color='k', lw=2)
ax.set_title('Initial filtered signals with spikes')
fig.savefig('../img/peeler_animation_sigs.png')
fig.savefig('png/fig{}.png'.format(num_fig))
num_fig += 1
for i in range(local_spikes.size):
label = local_spikes['cluster_label'][i]
color = clusters[clusters['cluster_label'] == label]['color'][0]
color = int32_to_rgba(color, mode='float')
pred = make_prediction_signals(local_spikes[i:i + 1], 'float32',
(i2 - i1, len(channels)), catalogue)
fig, ax = plt.subplots()
plot_spread_sigs(residuals, ax, color='k', lw=2)
plot_spread_sigs(pred, ax, color=color, lw=1.5)
ax.set_title('Dected spike label {}'.format(label))
fig.savefig('png/fig{}.png'.format(num_fig))
num_fig += 1
residuals -= pred
plot_spread_sigs(pred, ax_predictions, color=color, lw=1.5)
fig, ax = plt.subplots()
plot_spread_sigs(residuals, ax, color='k', lw=2)
plot_spread_sigs(pred, ax, color=color, lw=1, ls='--')
ax.set_title('New residual after substraction')
fig.savefig('png/fig{}.png'.format(num_fig))
num_fig += 1
fig_pred.savefig('png/fig{}.png'.format(num_fig))
num_fig += 1