Advice on "fill_missing data" function

[skyshi0]

Hi,
I'm using the fill_missing function in the analysis example. However, I found sometimes the plot is 'jumpy'. I checked the prediction and I found it was because some frames in the new video are predicted very far from the actual location or previous frame location. Since the wrong prediction is only for few frames, and usually the next frame is predicted correctly, I wonder if there's code can help to address this problem, or if SLEAP can avoid this problem(like set a range for the position change in pixels between each frame).
from scipy.interpolate import interp1d

def fill_missing(Y, kind="linear"):
"""Fills missing values independently along each dimension after the first."""

# Store initial shape.
initial_shape = Y.shape

# Flatten after first dim.
Y = Y.reshape((initial_shape[0], -1))

# Interpolate along each slice.
for i in range(Y.shape[-1]):
    y = Y[:, i]

    # Build interpolant.
    x = np.flatnonzero(~np.isnan(y))
    f = interp1d(x, y[x], kind=kind, fill_value=np.nan, bounds_error=False)

    # Fill missing
    xq = np.flatnonzero(np.isnan(y))
    y[xq] = f(xq)

    # Fill leading or trailing NaNs with the nearest non-NaN values
    mask = np.isnan(y)
    y[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), y[~mask])

    # Save slice
    Y[:, i] = y

# Restore to initial shape.
Y = Y.reshape(initial_shape)

return Y