Silence log div warning

gsrp_tdoa_hyperres.py

@@ -123,6 +123,7 @@ def corr(data, pos, w_size, max_tdoa, decimate=1, mode='prepare', hyper=True, ve
             der = np.zeros((num_channels - 1, num_channels - 1))
             der[ind] = coef[num_channels:]
             poly_min = np.linalg.lstsq(der + der.T, -coef[1:num_channels], rcond=None)[0]
+            with np.errstate(divide='ignore'):
                 return np.log10(pipe.predict(poly_min[np.newaxis]).item()), mat @ (poly_min + mean)
 
     cc = np.empty((num_channel_pairs, dw_size), np.float32)