reverse logic for DropValue filters

packages/pipeline/src/pyearthtools/pipeline/operations/dask/filters.py

@@ -133,7 +133,7 @@ def filter(self, sample: da.Array):
                 lambda x: ((da.count_nonzero(x == self._value) / math.prod(x.shape)) * 100) >= self._percentage
             )  # noqa
 
-        if not function(sample):
+        if function(sample):
             raise PipelineFilterException(sample, f"Data contained more than {self._percentage}% of {self._value}.")
 
 

packages/pipeline/src/pyearthtools/pipeline/operations/numpy/filters.py

@@ -135,7 +135,7 @@ def filter(self, sample: np.ndarray):
                 lambda x: ((np.count_nonzero(x == self._value) / math.prod(x.shape)) * 100) >= self._percentage
             )  # noqa
 
-        if not function(sample):
+        if function(sample):
             raise PipelineFilterException(sample, f"Data contained more than {self._percentage}% of {self._value}.")
 
 

packages/pipeline/src/pyearthtools/pipeline/operations/xarray/filters.py

@@ -185,7 +185,7 @@ def filter(self, sample: T):
         else:
             raise TypeError("This filter only accepts xr.DataArray or xr.Dataset")
 
-        if not drop:
+        if drop:
             raise PipelineFilterException(sample, f"Data contained more than {self._percentage}% of {self._value}.")
 
 

packages/pipeline/tests/operations/dask/test_dask_filter.py

@@ -62,24 +62,24 @@ def test_DropValue():
 
     original = da.from_array([[0, 0], [1, 2]])
 
-    # drop case (num zeros < threshold)
+    # non-drop case (num zeros < threshold)
     drop = filters.DropValue(0, 75)
-    with pytest.raises(PipelineFilterException):
-        drop.filter(original)
+    drop.filter(original)
 
-    # non-drop case  (num zeros >= threshold)
+    # drop case  (num zeros >= threshold)
     drop = filters.DropValue(0, 50)
-    drop.filter(original)
+    with pytest.raises(PipelineFilterException):
+        drop.filter(original)
 
-    # drop case  (num nans < threshold)
+    # non-drop case  (num nans < threshold)
     original = da.from_array([[np.nan, np.nan], [1, 2]])
     drop = filters.DropValue("nan", 75)
-    with pytest.raises(PipelineFilterException):
-        drop.filter(original)
+    drop.filter(original)
 
-    # non-drop case (num nans >= threshold)
+    # drop case (num nans >= threshold)
     drop = filters.DropValue("nan", 50)
-    drop.filter(original)
+    with pytest.raises(PipelineFilterException):
+        drop.filter(original)
 
 
 def test_Shape():

packages/pipeline/tests/operations/numpy/test_numpy_filter.py

@@ -64,22 +64,22 @@ def test_DropValue():
 
     drop = filters.DropValue(value=1, percentage=75)
 
-    with pytest.raises(PipelineFilterException):
-        drop.filter(original)
+    drop.filter(original)
 
-    # test no drop case
+    # test drop case
     drop = filters.DropValue(value=1, percentage=50)
-    drop.filter(original)
+    with pytest.raises(PipelineFilterException):
+        drop.filter(original)
 
-    # test with nan - drop case
+    # test with nan - non-drop case
     drop = filters.DropValue(value="nan", percentage=75)
 
-    with pytest.raises(PipelineFilterException):
-        drop.filter(original)
+    drop.filter(original)
 
-    # no drop case
+    # drop case
     drop = filters.DropValue(value="nan", percentage=50)
-    drop.filter(original)
+    with pytest.raises(PipelineFilterException):
+        drop.filter(original)
 
 
 def test_Shape():

packages/pipeline/tests/operations/xarray/test_xarray_filter.py

@@ -97,42 +97,42 @@ def test_DropValue():
         {"var1": xr.DataArray(np.array([[1, 1], [3, 4]])), "var2": xr.DataArray(np.array([[np.nan, np.nan], [6, 7]]))}
     )
 
-    # check var1 of dataset drop case
+    # check var1 of dataset non-drop case
     drop = filters.DropValue(1, 75)
-    with pytest.raises(PipelineFilterException):
-        drop.filter(original["var1"])
+    drop.filter(original["var1"])
 
-    # check var1 of dataset non-drop case
+    # check var1 of dataset drop case
     drop = filters.DropValue(1, 50)
-    drop.filter(original["var1"])
+    with pytest.raises(PipelineFilterException):
+        drop.filter(original["var1"])
 
-    # check var2 of dataset drop case (using nan)
+    # check var2 of dataset non-drop case (using nan)
     drop = filters.DropValue("nan", 75)
+    drop.filter(original["var2"])
+
+    # check var2 of dataset drop case
+    drop = filters.DropValue("nan", 50)
     with pytest.raises(PipelineFilterException):
         drop.filter(original["var2"])
 
-    # check var2 of dataset non-drop case
-    drop = filters.DropValue("nan", 50)
-    drop.filter(original["var2"])
+    # check whole dataset non-drop case
+    drop = filters.DropValue(1, 50)
+    drop.filter(original)
 
     # check whole dataset drop case
-    drop = filters.DropValue(1, 50)
+    drop = filters.DropValue(1, 10)
     with pytest.raises(PipelineFilterException):
         drop.filter(original)
 
-    # check whole dataset non-drop case
-    drop = filters.DropValue(1, 10)
+    # check whole dataset nan non-drop case
+    drop = filters.DropValue("nan", 50)
     drop.filter(original)
 
     # check whole dataset nan drop case
-    drop = filters.DropValue("nan", 50)
+    drop = filters.DropValue("nan", 10)
     with pytest.raises(PipelineFilterException):
         drop.filter(original)
 
-    # check whole dataset nan non-drop case
-    drop = filters.DropValue("nan", 10)
-    drop.filter(original)
-
     # check invalid type
     with pytest.raises(TypeError):
         drop.filter(np.empty((1, 1)))