def get_rfmap_data(self):
data_img = dict()
if not self.rfmap_data_status:
return data_img, None
else:
(rf_map_times, rf_map_pos,
rf_stim_frames) = passive.get_on_off_times_and_positions(
self.rf_map)
rf_map, _ = \
passive.get_rf_map_over_depth(rf_map_times, rf_map_pos, rf_stim_frames,
self.spikes['times'][self.spike_idx][self.kp_idx],
self.spikes['depths'][self.spike_idx][self.kp_idx],
d_bin=160)
rfs_svd = passive.get_svd_map(rf_map)
img = dict()
img['on'] = np.vstack(rfs_svd['on'])
img['off'] = np.vstack(rfs_svd['off'])
yscale = ((np.max(self.chn_coords[:, 1]) -
np.min(self.chn_coords[:, 1])) / img['on'].shape[0])
xscale = 1
levels = np.quantile(np.c_[img['on'], img['off']], [0, 1])
depths = np.linspace(0, 3840, len(rfs_svd['on']) + 1)
sub_type = ['on', 'off']
for sub in sub_type:
sub_data = {
sub: {
'img': [img[sub].T],
'scale': [np.array([xscale, yscale])],
'levels': levels,
'offset': [np.array([0, 0])],
'cmap': 'viridis',
'xrange': np.array([0, 15]),
'xaxis': 'Position',
'title': 'rfmap (dB)'
}
}
data_img.update(sub_data)
return data_img, depths
def test_rf_map(self):
"""
"""
# Simulate fake rfmap data
test_frames = np.full((60, 15, 15), 128, dtype='uint8')
# Test on and off individually
test_frames[10:20, 8, 8] = 0
test_frames[25:35, 10, 13] = 255
# Test that interleaved are detected correctly
test_frames[40:50, 4, 9] = 0
test_frames[42:52, 6, 10] = 255
test_frames[42:55, 11, 4] = 0
test_frames[50:60, 8, 8] = 0
test_times = np.arange(60)
rf_map = {}
rf_map['times'] = test_times
rf_map['frames'] = test_frames
rf_map_times, rf_map_pos, rf_stim_frames = passive.get_on_off_times_and_positions(
rf_map)
assert (all(rf_map_times == test_times))
assert (rf_map_pos.shape == (15 * 15, 2))
assert (len(rf_stim_frames['on']) == 15 * 15)
assert (len(rf_stim_frames['off']) == 15 * 15)
# Off is for the 0 ones
assert (all(rf_stim_frames['off'][ismember2d(
rf_map_pos, np.array([[8, 8]]))[0]][0][0] == [10, 50]))
assert (rf_stim_frames['off'][ismember2d(rf_map_pos,
np.array([[4, 9]
]))[0]][0][0] == 40)
assert (rf_stim_frames['off'][ismember2d(rf_map_pos,
np.array([[11, 4]
]))[0]][0][0] == 42)
# On is for the 255 ones
assert (rf_stim_frames['on'][ismember2d(rf_map_pos,
np.array([[10, 13]
]))[0]][0][0] == 25)
assert (rf_stim_frames['on'][ismember2d(rf_map_pos,
np.array([[6, 10]
]))[0]][0][0] == 42)
# Next test that the firing rate function works
# Basically just make one square responsive
spike_times = np.arange(25, 35, 0.01)
spike_depths = 500 * np.ones_like(spike_times)
rf_map_avg, depths = passive.get_rf_map_over_depth(rf_map_times,
rf_map_pos,
rf_stim_frames,
spike_times,
spike_depths,
x_lim=[0, 60])
non_zero = np.where(rf_map_avg['on'] != 0)
assert (np.argmin(np.abs(depths - 500)) == non_zero[0][0])
assert (all(non_zero[1] == 10))
assert (all(non_zero[2] == 13))
assert (np.all(rf_map_avg['off'] == 0))
rf_svd = passive.get_svd_map(rf_map_avg)
# Make sure that the one responsive element is non-zero
assert (rf_svd['on'][non_zero[0][0]][non_zero[1][0], non_zero[2][0]] !=
0)
# But that all the rest are zero
rf_svd['on'][non_zero[0][0]][non_zero[1][0], non_zero[2][0]] = 0
assert (np.all(np.isclose(np.vstack(rf_svd['on']), 0)))
assert (np.all(np.vstack(rf_svd['off']) == 0))
# Pass this data into brainbox function to find out at what times each voxel on screen was
# activated
rf_stim_times, rf_stim_pos, rf_stim_frames = passive.get_on_off_times_and_positions(
rf_map)
# rf_stim_times - time of frame change
# rf_stim_pos - position of each voxel on (15 x 15) screen
# rf_stim_frames - frames at which stimulus was turned 'on' (grey to white) or 'off' (grey to
# black) at each position on screen
# For each position on screen compute average stim epoched activity across depth of probe
rf_map_avg, _ = passive.get_rf_map_over_depth(rf_stim_times,
rf_stim_pos,
rf_stim_frames,
spike_times,
spike_depths,
t_bin=0.01,
d_bin=160,
pre_stim=0.05,
post_stim=1.5)
# Take the SVD across to represent RF map across depth
rf_svd = passive.get_svd_map(rf_map_avg)
# Now we can plot the RF map for the on and off response
on_rf_map = np.vstack(rf_svd['on'])
off_rf_map = np.vstack(rf_svd['off'])
fig, ax = plt.subplots(1, 2)
depths = np.linspace(0, 3840, len(rf_svd['on']) + 1)
ax[0].imshow(on_rf_map, extent=(0, 500, 0, 3840), origin='lower')