option to raise warning when no match is found

See TUW-GEO#152

Author: s-scherrer

src/pytesmo/temporal_matching.py

@@ -177,8 +177,8 @@ def matching(reference, *args, **kwargs):
 
 def temporal_collocation(reference, other, window, method="nearest",
                          return_index=False, return_distance=False,
-                         dropduplicates=False, dropna=False, flag=None,
-                         use_invalid=False):
+                         dropduplicates=False, dropna=False, checkna=False,
+                         flag=None, use_invalid=False):
     """
     Temporally collocates values to reference.
 
@@ -218,7 +218,11 @@ def temporal_collocation(reference, other, window, method="nearest",
     dropna : bool, optional
         Whether to drop NaNs from the resulting dataframe (arising for example
         from duplicates with ``duplicates_nan=True`` or from missing values).
-        Default is ``False``
+        Default is ``False``.
+    checkna: bool, optional
+        Whether to check if only NaNs are returned (i.e. no match has been
+        found). If set to ``True``, raises a ``UserWarning`` in case no match
+        has been found. Default is ``False``.
     flag : np.ndarray, str or None, optional
         Flag column as array or name of the flag column in `other`. If this is
         given, the column will be interpreted as validity indicator. Any
@@ -307,6 +311,9 @@ def collocate(df):
 
     # postprocessing
     # --------------
+    if checkna:
+        if np.any(collocated.isnull().apply(np.all)):
+            warnings.warn("No match has been found")
     if dropna:
         collocated.dropna(inplace=True)
 

tests/test_temporal_matching.py

@@ -249,15 +249,15 @@ def test_data():
     test_dr["random_shift"] = dr_random_shift
 
     # missing data
-    drop_mask = np.zeros(len(ref_dr), dtype=np.bool)
+    drop_mask = np.zeros(len(ref_dr), dtype=bool)
     drop_mask[100:200] = True
     dr_random_shift = dr_random_shift[~drop_mask]
     test_dr["missing"] = dr_random_shift
     missing_mask = random_mask | drop_mask
 
     # with duplicates
     test_dr["duplicates"] = deepcopy(test_dr["shifted_3"])
-    duplicates_mask = np.zeros(len(ref_dr), dtype=np.bool)
+    duplicates_mask = np.zeros(len(ref_dr), dtype=bool)
     for idx in np.random.randint(0, len(test_dr["duplicates"]) - 1, 5):
         test_dr["duplicates"].values[idx] = test_dr["duplicates"].values[
             idx + 1
@@ -274,7 +274,7 @@ def test_data():
     ref_frame = pd.DataFrame(np.random.randn(len(ref_dr), 3), index=ref_dr)
 
     # mask for where we expect nans in the output
-    all_nan = np.ones(len(ref_dr), dtype=np.bool)
+    all_nan = np.ones(len(ref_dr), dtype=bool)
     expected_nan = {
         "shifted_3": ~all_nan,
         "shifted_7": all_nan,
@@ -485,3 +485,15 @@ def test_timezone_handling():
 
     nptest.assert_allclose(np.array([0, 1, 2, 4]), matched.matched_data)
     assert len(matched) == 4
+
+
+def test_warning_on_no_match(test_data):
+    # Issue #152
+    ref_frame, test_frame, expected_nan = setup_data(test_data, "shifted_7")
+    with pytest.warns(UserWarning):
+        tmatching.temporal_collocation(
+            ref_frame, test_frame, pd.Timedelta(6, "H"), checkna=True
+        )
+
+
+