replace type comparison with isinstance (#491)

clouddrift/ragged.py

@@ -109,7 +109,7 @@ def apply_ragged(
         If empty ``arrays``.
     """
     # make sure the arrays is iterable
-    if type(arrays) not in [list, tuple]:
+    if not isinstance(arrays, (list, tuple)):
         arrays = [arrays]
     # validate rowsize
     for arr in arrays:
@@ -520,10 +520,10 @@ def segment(
     """
 
     # for compatibility with datetime list or np.timedelta64 arrays
-    if type(tolerance) in [np.timedelta64, timedelta]:
+    if isinstance(tolerance, (np.timedelta64, timedelta)):
         tolerance = pd.Timedelta(tolerance)
 
-    if type(tolerance) == pd.Timedelta:
+    if isinstance(tolerance, pd.Timedelta):
         positive_tol = tolerance >= pd.Timedelta("0 seconds")
     else:
         positive_tol = tolerance >= 0

clouddrift/sphere.py

@@ -103,19 +103,19 @@ def distance(
     # Input coordinates are in degrees; convert to radians.
     # If any of the input arrays are xr.DataArray, extract the values first
     # because Xarray enforces alignment between coordinates.
-    if type(lat1) is xr.DataArray:
+    if isinstance(lat1, xr.DataArray):
         lat1_rad = np.deg2rad(lat1.values)
     else:
         lat1_rad = np.deg2rad(lat1)
-    if type(lon1) is xr.DataArray:
+    if isinstance(lon1, xr.DataArray):
         lon1_rad = np.deg2rad(lon1.values)
     else:
         lon1_rad = np.deg2rad(lon1)
-    if type(lat2) is xr.DataArray:
+    if isinstance(lat2, xr.DataArray):
         lat2_rad = np.deg2rad(lat2.values)
     else:
         lat2_rad = np.deg2rad(lat2)
-    if type(lon2) is xr.DataArray:
+    if isinstance(lon2, xr.DataArray):
         lon2_rad = np.deg2rad(lon2.values)
     else:
         lon2_rad = np.deg2rad(lon2)
@@ -178,19 +178,19 @@ def bearing(
     # Input coordinates are in degrees; convert to radians.
     # If any of the input arrays are xr.DataArray, extract the values first
     # because Xarray enforces alignment between coordinates.
-    if type(lat1) is xr.DataArray:
+    if isinstance(lat1, xr.DataArray):
         lat1_rad = np.deg2rad(lat1.values)
     else:
         lat1_rad = np.deg2rad(lat1)
-    if type(lon1) is xr.DataArray:
+    if isinstance(lon1, xr.DataArray):
         lon1_rad = np.deg2rad(lon1.values)
     else:
         lon1_rad = np.deg2rad(lon1)
-    if type(lat2) is xr.DataArray:
+    if isinstance(lat2, xr.DataArray):
         lat2_rad = np.deg2rad(lat2.values)
     else:
         lat2_rad = np.deg2rad(lat2)
-    if type(lon2) is xr.DataArray:
+    if isinstance(lon2, xr.DataArray):
         lon2_rad = np.deg2rad(lon2.values)
     else:
         lon2_rad = np.deg2rad(lon2)

tests/ragged_tests.py

@@ -212,8 +212,8 @@ def test_prune(self):
 
         for data in [x, np.array(x), pd.Series(data=x), xr.DataArray(data=x)]:
             x_new, rowsize_new = prune(data, rowsize, minimum)
-            self.assertTrue(type(x_new) is np.ndarray)
-            self.assertTrue(type(rowsize_new) is np.ndarray)
+            self.assertTrue(isinstance(x_new, np.ndarray))
+            self.assertTrue(isinstance(rowsize_new, np.ndarray))
             np.testing.assert_equal(x_new, [1, 2, 3, 1, 2, 3, 4])
             np.testing.assert_equal(rowsize_new, [3, 4])
 
@@ -293,7 +293,7 @@ class segment_tests(unittest.TestCase):
     def test_segment(self):
         x = [0, 1, 1, 1, 2, 2, 3, 3, 3, 3, 4]
         tol = 0.5
-        self.assertTrue(type(segment(x, tol)) is np.ndarray)
+        self.assertTrue(isinstance(segment(x, tol), np.ndarray))
         self.assertTrue(np.all(segment(x, tol) == np.array([1, 3, 2, 4, 1])))
         self.assertTrue(np.all(segment(np.array(x), tol) == np.array([1, 3, 2, 4, 1])))
         self.assertTrue(
@@ -317,7 +317,7 @@ def test_segment_rowsize(self):
         tol = 0.5
         rowsize = [6, 5]
         segment_sizes = segment(x, tol, rowsize)
-        self.assertTrue(type(segment_sizes) is np.ndarray)
+        self.assertTrue(isinstance(segment_sizes, np.ndarray))
         self.assertTrue(np.all(segment_sizes == np.array([1, 3, 2, 4, 1])))
 
     def test_segment_positive_and_negative_tolerance(self):
@@ -762,7 +762,7 @@ def test_unpack(self):
         lon = unpack(ds.lon, ds["rowsize"])
 
         self.assertTrue(isinstance(lon, list))
-        self.assertTrue(np.all([type(a) is xr.DataArray for a in lon]))
+        self.assertTrue(np.all([isinstance(a, xr.DataArray) for a in lon]))
         self.assertTrue(
             np.all([lon[n].size == ds["rowsize"][n] for n in range(len(lon))])
         )
@@ -771,7 +771,7 @@ def test_unpack(self):
         lon = unpack(ds.lon.values, ds["rowsize"])
 
         self.assertTrue(isinstance(lon, list))
-        self.assertTrue(np.all([type(a) is np.ndarray for a in lon]))
+        self.assertTrue(np.all([isinstance(a, np.ndarray) for a in lon]))
         self.assertTrue(
             np.all([lon[n].size == ds["rowsize"][n] for n in range(len(lon))])
         )

tests/signal_tests.py

@@ -24,7 +24,7 @@ def test_size(self):
     def test_imag(self):
         x = np.random.rand(99) + 1j * np.random.rand(99)
         z = analytic_signal(x)
-        self.assertTrue(type(z) is tuple)
+        self.assertTrue(isinstance(z, tuple))
 
     def test_real_odd(self):
         x = np.random.rand(99)