np.mean, smooth_frames,
int(smooth_frames / 3))
tn = preproc.normalize(t, norm='l1', axis=0)
tn = np.asarray(t)
for i in np.arange(len(t)):
tn[i, :] = binning.scale(t[i], 0, 1)
y_positions = template_info['position Y'].values
position_sorted_indices = np.argsort(y_positions)
regions_pos = list(const.BRAIN_REGIONS.values())
region_lines = []
for rp in regions_pos:
region_lines.append(
sync_funcs.find_nearest(
y_positions[position_sorted_indices] * const.POSITION_MULT, rp)[0])
region_lines = np.array(region_lines)
tns = tn[position_sorted_indices]
plt.imshow(np.flipud(tns), aspect='auto')
plt.hlines(y=len(t) - region_lines,
xmin=0,
xmax=len(tns[0]) - 1,
linewidth=3,
color='w')
plt.vlines(x=int(len(tns[0]) / 2), ymin=0, ymax=len(tns) - 1)
i = 0
sv.graph_pane(globals(), 'i', 'tn')
smooth_frames = smooth_time * 120
t = binning.rolling_window_with_step(avg_firing_rate_around_suc_trials, np.mean, smooth_frames, int(smooth_frames / 3))
#tn = preproc.normalize(t, norm='l1', axis=0)
tn = np.empty(t.shape)
for i in np.arange(len(t)):
tn[i, :] = binning.scale(t[i], 0, 1)
y_positions = template_info['position Y'].values
position_sorted_indices = np.argsort(y_positions)
regions_pos = list(const_rat.BRAIN_REGIONS.values())
region_lines = []
for rp in regions_pos:
region_lines.append(sync_funcs.find_nearest(y_positions[position_sorted_indices] * const_com.POSITION_MULT, rp)[0])
region_lines = np.array(region_lines)
tns = tn[position_sorted_indices]
plt.imshow(np.flipud(tns), aspect='auto')
plt.hlines(y=len(t) - region_lines, xmin=0, xmax=len(tns[0])-1, linewidth=3, color='w')
plt.vlines(x=int(len(tns[0]) / 2), ymin=0, ymax=len(tns) - 1)
plt.imshow(np.flipud(tns), aspect='auto', extent=[-8, 8, len(tns), 0])
plt.hlines(y=len(t) - region_lines, xmin=-8, xmax=8, linewidth=2, color='w')
plt.vlines(x=0, ymin=0, ymax=len(tns) - 1)
i = 0
