Merge pull request #743 from nkeim/fix-compat-nov23

trackpy/refine/brightfield_ring.py

@@ -9,7 +9,8 @@
 from scipy.ndimage import map_coordinates
 from scipy import stats
 
-from ..utils import (validate_tuple, guess_pos_columns, default_pos_columns)
+from ..utils import (validate_tuple, guess_pos_columns, default_pos_columns,
+                     stats_mode_scalar)
 
 
 def refine_brightfield_ring(image, radius, coords_df, pos_columns=None,
@@ -197,9 +198,9 @@ def _min_edge(arr, threshold=0.45, max_dev=1, axis=1, bright_left=True,
     if np.sum(mask) == 0:
         return r_dev
 
-    # filter by deviations from most occuring value
+    # filter by deviations from most occurring value
     max_dev = np.round(max_dev)
-    most_likely = stats.mode(r_dev[mask])[0][0]
+    most_likely = stats_mode_scalar(r_dev[mask])
     mask[mask] &= np.abs(r_dev[mask]-most_likely) > max_dev
     r_dev[mask] = np.nan
 

trackpy/tests/test_legacy_linking.py

@@ -703,9 +703,9 @@ def test_drop_link(self):
         # The resulting subnet causes the trajectory to be broken
         # when link_strategy is 'drop' and search_range is large enough.
         N = 2
-        f_1particle = DataFrame({'x': np.arange(N), 'y': np.ones(N), 'frame': np.arange(N)})
-        f = f_1particle.append(DataFrame(
-            {'x': [3], 'y': [1], 'frame': [1]}), ignore_index=True)
+        f = DataFrame({'x': list(np.arange(N)) + [3],
+                       'y': np.ones(N+1),
+                       'frame': list(np.arange(N)) + [1]})
         f_expected_without_subnet = f.copy()
         f_expected_without_subnet['particle'] = [0, 0, 1]
         # The linker assigns new particle IDs in arbitrary order. So

trackpy/tests/test_linking.py

@@ -681,9 +681,9 @@ def test_drop_link(self):
         # The resulting subnet causes the trajectory to be broken
         # when link_strategy is 'drop' and search_range is large enough.
         N = 2
-        f_1particle = DataFrame({'x': np.arange(N), 'y': np.ones(N), 'frame': np.arange(N)})
-        f = f_1particle.append(DataFrame(
-            {'x': [3], 'y': [1], 'frame': [1]}), ignore_index=True)
+        f = DataFrame({'x': list(np.arange(N)) + [3],
+                       'y': np.ones(N+1),
+                       'frame': list(np.arange(N)) + [1]})
         f_expected_without_subnet = f.copy()
         f_expected_without_subnet['particle'] = [0, 0, 1]
         # The linker assigns new particle IDs in arbitrary order. So
@@ -753,7 +753,10 @@ def dist_func(a, b):
 
 
     def test_custom_dist_metric(self):
-        import sklearn.neighbors
+        try:
+            from sklearn.metrics import DistanceMetric
+        except ImportError:
+            from sklearn.neighbors import DistanceMetric
 
         # Several 2D random walkers
         N = 5
@@ -771,7 +774,7 @@ def test_custom_dist_metric(self):
 
         # leave x, y for the comparison at the end
 
-        dist_func = sklearn.neighbors.DistanceMetric.get_metric("euclidean")
+        dist_func = DistanceMetric.get_metric("euclidean")
 
         # link using a custom distance function
         actual = self.link(f, search_range, dist_func=dist_func)

trackpy/tests/test_static.py

@@ -1,3 +1,5 @@
+import pandas
+
 from trackpy.static import *
 import pandas as pd
 import numpy as np
@@ -175,7 +177,7 @@ def _rings_3d(self):
                                'z': np.concatenate(refz)})
 
         # The last index is the center particle, which is used to calculate g_r 
-        df = df.append(pd.DataFrame({'x': [0.], 'y': [0.], 'z':[0.]}))
+        df = pandas.concat([df, pd.DataFrame({'x': [0.], 'y': [0.], 'z':[0.]})])
         return df
 
 

trackpy/utils.py

@@ -416,3 +416,18 @@ def get_pool(processes):
         map_func = map
 
     return pool, map_func
+
+
+def stats_mode_scalar(*args, **kwargs):
+    """Returns a scalar from scipy.stats.mode().
+
+    Works around new default in scipy 1.11 to eliminate extra axes
+    that were previously removed by trackpy.
+
+    See https://docs.scipy.org/doc/scipy-1.9.3/reference/generated/scipy.stats.mode.html
+    """
+    result = stats.mode(*args, **kwargs)[0]
+    try:
+        return result[0]  # Old behavior
+    except IndexError:
+        return result  # scipy >= 1.11