Incorrect tuning curves for unsampled bins

[wulfdewolf]

At the moment, when a particular bin of a feature is unsampled, the tuning curve functions will set the average firing rate for that bin to zero. This seems incorrect? We do not know if there is no firing for that bin, hence, does it not make more sense to set it to NaN?

[gviejo]

The tuning curves are normalized by occupancy. NaNs represents bin that have never been visited. 0 indicate no spikes. You can see an example here or you can run this code :

import numpy as np
import pynapple as nap
import matplotlib.pyplot as plt

dt = 0.1
features = np.vstack((np.cos(np.arange(0, 1000, dt)), np.sin(np.arange(0, 1000, dt)))).T
features = nap.TsdFrame(t=np.arange(0, 1000, dt),d=features)

ts_group = nap.TsGroup({0:nap.Ts(t=[0, 500, 999])})

tcurves2d, binsxy = nap.compute_2d_tuning_curves(
    group=ts_group, features=features, nb_bins=10
)

plt.figure(figsize=(12, 6))
plt.subplot(121)
plt.plot(features[:,0], features[:,1])
tmp=ts_group[0].value_from(features)
plt.plot(tmp[:,0], tmp[:,1], 'o')
plt.title("Feature + Spikes")
plt.subplot(122)
extents = (
    np.min(features[:,1]),
    np.max(features[:,1]),
    np.min(features[:,0]),
    np.max(features[:,0]),
)
plt.imshow(tcurves2d[0].T, origin="lower", extent=extents, cmap="viridis")
plt.title("Tuning curve")
plt.show()

[wulfdewolf]

I understand, and it seems to be so for 2D tuning curves. However, when I tried yesterday for 1D tuning curves:

138    for k in group_value:
139        count, _ = np.histogram(group_value[k].values, bins)
140        count = count / occupancy
141        count[np.isnan(count)] = 0.0
142        tuning_curves[k] = count
143        tuning_curves[k] = count * feature.rate

line 141 seems to be setting NaNs to 0.

In the 2D version, this line is commented.

[gviejo]

Good catch. In fact, it's quite rare to have a 1d feature that does not span its whole extent. This is high priority so I will bump a new version as soon as possible. Leaving it open until it is resolved.

[gviejo]

New version 0.7.1 is out with a fix to this issue. Closing it.