Merge branch 'develop' into beiwe-taskrunner

forest/jasmine/data2mobmat.py

@@ -179,6 +179,10 @@ def collapse_data(
     # Filter out rows where the GPS accuracy is beyond
     # the provided accuracy_limit
     data = data[data.accuracy < accuracy_limit]
+    if data.shape[0] == 0:
+        raise ValueError(
+            f"No GPS record with accuracy less than {accuracy_limit}."
+        )
 
     # Get the start and end timestamps in seconds
     t_start = sorted(np.array(data.timestamp))[0] / 1000
@@ -1000,7 +1004,8 @@ def infer_mobmat(mobmat: np.ndarray, interval: float, r: float) -> np.ndarray:
         (mobmat, np.ones(n_rows).reshape(n_rows, 1))
     )
     # Append new pauses to the trajectory matrix
-    mobmat = np.vstack((mobmat, new_pauses_array))
+    if new_pauses_array.shape[0] > 0:
+        mobmat = np.vstack((mobmat, new_pauses_array))
     # Sort the matrix by start time
     mobmat = mobmat[mobmat[:, 3].argsort()].astype(float)
 

forest/jasmine/mobmat2traj.py

@@ -3,7 +3,6 @@
 """
 import logging
 import math
-import sys
 from typing import Optional, Tuple
 
 import numpy as np
@@ -278,7 +277,7 @@ def indicate_flight(
     # Calculate k1 using the specified method
     k1 = calculate_k1(method, current_t, current_x, current_y, bv_subset, pars)
     if k1 is None:
-        sys.exit("Invalid method for calculate_k1.")
+        raise ValueError("Invalid method for calculate_k1.")
 
     # Select flight and pause indicators from the bv_subset
     flight_k = k1[bv_subset[:, 0] == 1]
@@ -662,8 +661,8 @@ def forward_impute(
             method, start_t, start_x, start_y,
             flight_table, pars
         )
-        if weight is None:
-            sys.exit("Invalid method for calculate_k1.")
+        if weight is None or len(weight) == 0:
+            raise ValueError("Invalid method for calculate_k1.")
 
         normalize_w = (weight + 1e-5) / float(sum(weight + 1e-5))
         flight_index = np.random.choice(flight_table.shape[0], p=normalize_w)
@@ -743,7 +742,7 @@ def forward_impute(
                 pause_table, pars
             )
             if weight is None:
-                sys.exit("Invalid method for calculate_k1.")
+                raise ValueError("Invalid method for calculate_k1.")
 
             normalize_w = (weight + 1e-5) / float(sum(weight + 1e-5))
             pause_index = np.random.choice(pause_table.shape[0], p=normalize_w)
@@ -832,7 +831,7 @@ def backward_impute(
             flight_table, pars
         )
         if weight is None:
-            sys.exit("Invalid method for calculate_k1.")
+            raise ValueError("Invalid method for calculate_k1.")
 
         normalize_w = (weight + 1e-5) / float(sum(weight + 1e-5))
         flight_index = np.random.choice(flight_table.shape[0], p=normalize_w)
@@ -907,8 +906,8 @@ def backward_impute(
                 method, end_t, end_x, end_y,
                 pause_table, pars
             )
-            if weight is None:
-                sys.exit("Invalid method for calculate_k1.")
+            if weight is None or len(weight) == 0:
+                raise ValueError("Invalid method for calculate_k1.")
 
             normalize_w = (weight + 1e-5) / float(sum(weight + 1e-5))
             pause_index = np.random.choice(pause_table.shape[0], p=normalize_w)
@@ -972,6 +971,11 @@ def impute_gps(
     # for observed flights, observed pauses, and missing intervals
     flight_table, pause_table, mis_table = create_tables(mob_mat, bv_subset)
 
+    if len(flight_table) == 0:
+        raise ValueError("No flight observed in the data.")
+    if len(pause_table) == 0:
+        raise ValueError("No pause observed in the data.")
+
     # initialize the imputed trajectory table
     imp_table = np.zeros((1, 7))
 

forest/oak/base.py

@@ -55,6 +55,13 @@ def preprocess_bout(t_bout: np.ndarray, x_bout: np.ndarray, y_bout: np.ndarray,
             - t_bout_interp: resampled timestamp (in Unix)
             - vm_bout_interp: vector magnitude of acceleration
     """
+
+    if (
+        len(t_bout) < 2 or len(x_bout) < 2 or
+        len(y_bout) < 2 or len(z_bout) < 2
+    ):
+        return np.array([]), np.array([])
+
     t_bout_interp = t_bout - t_bout[0]
     t_bout_interp = np.arange(t_bout_interp[0], t_bout_interp[-1], (1/fs))
     t_bout_interp = t_bout_interp + t_bout[0]