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fix extents for passive plots #104

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Nov 16, 2023
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fix extents for passive plots #104

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23 changes: 12 additions & 11 deletions atlaselectrophysiology/plot_data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -408,10 +408,6 @@ def gain2level(gain):
else:
h = neuropixel.trace_header(sr.major_version, nshank=np.unique(th['shank']).size)

idx = np.isin(h['ind'], th['ind'])
for k in h.keys():
h[k] = h[k][idx]

s0 = t * sr.fs
tsel = slice(int(s0), int(s0) + int(1 * sr.fs))
raw = sr[tsel, :-sr.nsync].T
Expand Down Expand Up @@ -513,21 +509,24 @@ def get_rfmap_data(self):
(rf_map_times, rf_map_pos,
rf_stim_frames) = passive.get_on_off_times_and_positions(self.data['rf_map'])

chn_min = np.min(np.r_[self.chn_min, self.data['spikes']['depths'][self.spike_idx][self.kp_idx]])
chn_max = np.max(np.r_[self.chn_max, self.data['spikes']['depths'][self.spike_idx][self.kp_idx]])

rf_map, _ = \
passive.get_rf_map_over_depth(rf_map_times, rf_map_pos, rf_stim_frames,
self.data['spikes']['times'][self.spike_idx][self.kp_idx],
self.data['spikes']['depths'][self.spike_idx][self.kp_idx],
d_bin=160)
d_bin=160, y_lim=[chn_min, chn_max])
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])
yscale = ((self.chn_max - self.chn_min) / 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)
depths = np.linspace(self.chn_min, self.chn_max, len(rfs_svd['on']) + 1)

sub_type = ['on', 'off']
for sub in sub_type:
Expand Down Expand Up @@ -561,19 +560,21 @@ def get_passive_events(self):
stims = {stim_type: self.data['pass_stim'][stim_type] for stim_type in stim_types[0:3]}
stims.update(self.data['gabor'])

chn_min = np.min(np.r_[self.chn_min, self.data['spikes']['depths'][self.spike_idx][self.kp_idx]])
chn_max = np.max(np.r_[self.chn_max, self.data['spikes']['depths'][self.spike_idx][self.kp_idx]])

base_stim = 1
pre_stim = 0.4
post_stim = 1
stim_events = passive.get_stim_aligned_activity(stims, self.data['spikes']['times'][self.spike_idx]
[self.kp_idx], self.data['spikes']['depths']
[self.spike_idx][self.kp_idx],
pre_stim=pre_stim, post_stim=post_stim,
base_stim=base_stim)
base_stim=base_stim, y_lim=[chn_min, chn_max])

for stim_type, z_score in stim_events.items():
xscale = (post_stim + pre_stim) / z_score.shape[1]
yscale = ((np.max(self.chn_coords[:, 1]) - np.min(
self.chn_coords[:, 1])) / z_score.shape[0])
yscale = ((self.chn_max - self.chn_min) / z_score.shape[0])

levels = [-10, 10]

Expand Down
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