Passing test

Author: waltsims

kwave/utils/interp.py

@@ -262,44 +262,25 @@ def interp_cart_data(kgrid, cart_sensor_data, cart_sensor_mask, binary_sensor_ma
             binary_sensor_data[point_index, :] = cart_sensor_data[dist_min_index, :]
 
         elif interp == "linear":
-            # raise NotImplementedError
-            # append the distance information onto the data set
-            cart_sensor_data_ro = cart_sensor_data
-            np.append(cart_sensor_data_ro, dist[:, None], axis=1)
-            new_col_pos = -1
+            # Find the two closest points
+            dist_min_indices = np.argsort(dist)[:2]
 
-            # reorder the data set based on distance information
-            cart_sensor_data_ro = sort_rows(cart_sensor_data_ro, new_col_pos)
+            # Get distances to the two closest points
+            dist1, dist2 = dist[dist_min_indices]
 
-            # linearly interpolate between the two closest points
-            perc = cart_sensor_data_ro[2, new_col_pos] / (cart_sensor_data_ro[1, new_col_pos] + cart_sensor_data_ro[2, new_col_pos])
-            binary_sensor_data[point_index, :] = perc * cart_sensor_data_ro[1, :] + (1 - perc) * cart_sensor_data_ro[2, :]
+            # Calculate interpolation weights based on distances
+            total_dist = dist1 + dist2
+            w1 = dist2 / total_dist
+            w2 = dist1 / total_dist
+
+            # Interpolate the time series data
+            binary_sensor_data[point_index, :] = (
+                w1 * cart_sensor_data[dist_min_indices[0], :] + w2 * cart_sensor_data[dist_min_indices[1], :]
+            )
 
         else:
             raise ValueError("Unknown interpolation option.")
 
-        # elif interp == 'linear':
-        #
-        #         # dist = np.sqrt((cart_bsm[0, point_index] - cart_sensor_mask[0, :])**2 +
-        #                                                   (cart_bsm[1, point_index] - cart_sensor_mask[1, :])**2)
-        #         # dist = np.linalg.norm(cart_bsm[:, point_index] - cart_sensor_mask.T, axis=1)
-        #         # append the distance information onto the data set
-        #         new_col_pos = len(cart_sensor_data[1, :]) -1
-        #         cart_sensor_data_ro = cart_sensor_data
-        #         cart_sensor_data_ro[:, new_col_pos] = dist
-        #
-        #         # reorder the data set based on distance information
-        #         cart_sensor_data_ro = sort_rows(cart_sensor_data_ro, new_col_pos)
-        #
-        #         # linearly interpolate between the two closest points
-        #         perc = cart_sensor_data_ro[1, new_col_pos] /
-        #                           (cart_sensor_data_ro[0, new_col_pos] + cart_sensor_data_ro[1, new_col_pos] )
-        #         binary_sensor_data[point_index, :] = perc * cart_sensor_data_ro[1, :new_col_pos - 1] +
-        #                                                       (1 - perc) * cart_sensor_data_ro[1, :new_col_pos - 1]
-        #
-        # else:
-        #     raise ValueError('Unknown interpolation option.')
-
     # update command line status
     logging.log(logging.INFO, f"  computation completed in {scale_time(timer.toc())}")
     return binary_sensor_data