Add a check for Epoch being monotonically increasing in xarray_to_cdf (…

…#238)

* Add a check for Epoch being monotonically increasing

* Fixing python linting stuff

cdflib/cdfwrite.py

@@ -858,7 +858,9 @@ def _write_var_attrs(self, f: io.BufferedWriter, varNum: int, var_attrs: Dict[st
                 offset = self.attrsinfo[attrNum][2]
 
             if entry is None:
-                logger.warning(f"Attribute: {attr}" + " is None type, which does not have an equivalent in CDF... Skipping attribute.")
+                logger.warning(
+                    f"Attribute: {attr}" + " is None type, which does not have an equivalent in CDF... Skipping attribute."
+                )
                 continue
 
             # Check if dataType was provided
@@ -2542,14 +2544,14 @@ def _checklistofstrs(obj: Any) -> bool:
 
     @staticmethod
     def _checklistofNums(obj: Any) -> bool:
-        '''
+        """
         This method checks if a list is ready to be immediately converted to binary format,
-        or if any pre-processing needs to occur. Numbers and datetime64 objects can be immediately converted.  
-        '''
+        or if any pre-processing needs to occur. Numbers and datetime64 objects can be immediately converted.
+        """
         if hasattr(obj, "__len__"):
-            return bool(all(obj)) and all((isinstance(elem, numbers.Number) or isinstance(elem, np.datetime64))  for elem in obj)
+            return bool(all(obj)) and all((isinstance(elem, numbers.Number) or isinstance(elem, np.datetime64)) for elem in obj)
         else:
-            return (isinstance(obj, numbers.Number) or isinstance(obj, np.datetime64))
+            return isinstance(obj, numbers.Number) or isinstance(obj, np.datetime64)
 
     def _md5_compute(self, f: io.BufferedWriter) -> bytes:
         """

cdflib/xarray/cdf_to_xarray.py

@@ -616,7 +616,7 @@ def _generate_xarray_data_variables(
             var_data = _convert_fillvals_to_nan(var_data, var_atts, var_props)
 
         # Finally, create the new variable
-        created_vars[var_name] = xr.Variable(var_dims, var_data, attrs=var_atts)  # type: ignore[no-untyped-call]
+        created_vars[var_name] = xr.Variable(var_dims, var_data, attrs=var_atts)
 
     return created_vars, depend_dimensions
 

cdflib/xarray/xarray_to_cdf.py

@@ -319,7 +319,14 @@ def _epoch_checker(dataset: xr.Dataset, dim_vars: List[str], terminate_on_warnin
     epoch_found = False
     for d in depend_0_list:
         if d.lower().startswith("epoch"):
-            epoch_found = True
+            monotonically_increasing = _verify_monotonically_increasing(dataset[d].data)
+            if monotonically_increasing:
+                epoch_found = True
+            else:
+                _warn_or_except(
+                    f"Variable {d} was determined to be an ISTP 'Epoch' variable, but it is not monotonically increasing.",
+                    terminate_on_warning,
+                )
 
     if not epoch_found:
         _warn_or_except(
@@ -330,6 +337,10 @@ def _epoch_checker(dataset: xr.Dataset, dim_vars: List[str], terminate_on_warnin
     return depend_0_list, time_varying_dimensions
 
 
+def _verify_monotonically_increasing(epoch_data: npt.NDArray) -> np.bool_:
+    return np.all(epoch_data[1:] > epoch_data[:-1])
+
+
 def _add_depend_variables_to_dataset(
     dataset: xr.Dataset,
     dim_vars: List[str],
@@ -402,8 +413,10 @@ def _variable_attribute_checker(dataset: xr.Dataset, epoch_list: List[str], term
             # Ensure None of the attributes are given a type of "None"
             for key, value in d[var].attrs.items():
                 if value is None:
-                    _warn_or_except(f"CDF Warning: {key} was given a type of None for variable {var}. CDF does not allow None types, so {key} will be skipped.", terminate_on_warning)
-
+                    _warn_or_except(
+                        f"CDF Warning: {key} was given a type of None for variable {var}. CDF does not allow None types, so {key} will be skipped.",
+                        terminate_on_warning,
+                    )
 
             # Check for VAR_TYPE
             if "VAR_TYPE" not in d[var].attrs:
@@ -817,7 +830,10 @@ def xarray_to_cdf(
                         var_data = _datetime_to_tt2000(d[var].data)
                     elif datetime64_to_cdftt2000:
                         if d[var].dtype.type != np.datetime64:
-                            _warn_or_except(f"datetime64_to_cdftt2000 is set, but datetime64 is not used in the {var} variable", terminate_on_warning)
+                            _warn_or_except(
+                                f"datetime64_to_cdftt2000 is set, but datetime64 is not used in the {var} variable",
+                                terminate_on_warning,
+                            )
                         else:
                             unixtime_from_datetime64 = d[var].data.astype("datetime64[ns]").astype("int64") / 1000000000
                             var_data = _unixtime_to_tt2000(unixtime_from_datetime64)

tests/test_xarray_reader_writer.py

@@ -314,10 +314,10 @@ def test_build_from_scratch():
     pytest.importorskip("xarray")
     var_data = [[1, 2, 3], [1, 2, 3], [1, 2, 3]]
     var_dims = ["epoch", "direction"]
-    data = xr.Variable(var_dims, var_data)  # type: ignore[no-untyped-call]
+    data = xr.Variable(var_dims, var_data)
     epoch_data = [1, 2, 3]
     epoch_dims = ["epoch"]
-    epoch = xr.Variable(epoch_dims, epoch_data)  # type: ignore[no-untyped-call]
+    epoch = xr.Variable(epoch_dims, epoch_data)
     ds = xr.Dataset(data_vars={"data": data, "epoch": epoch})
     xarray_to_cdf(ds, "hello.cdf")
     os.remove("hello.cdf")
@@ -337,14 +337,14 @@ def test_build_from_scratch():
         "Logical_source": ":)",
         "Logical_source_description": ":(",
     }
-    data = xr.Variable(var_dims, var_data)  # type: ignore[no-untyped-call]
-    epoch = xr.Variable(epoch_dims, epoch_data)  # type: ignore[no-untyped-call]
+    data = xr.Variable(var_dims, var_data)
+    epoch = xr.Variable(epoch_dims, epoch_data)
     ds = xr.Dataset(data_vars={"data": data, "epoch": epoch}, attrs=global_attributes)
     xarray_to_cdf(ds, "hello.cdf")
     os.remove("hello.cdf")
     dir_data = [1, 2, 3]
     dir_dims = ["direction"]
-    direction = xr.Variable(dir_dims, dir_data)  # type: ignore[no-untyped-call]
+    direction = xr.Variable(dir_dims, dir_data)
     ds = xr.Dataset(data_vars={"data": data, "epoch": epoch, "direction": direction}, attrs=global_attributes)
     xarray_to_cdf(ds, "hello.cdf")
     os.remove("hello.cdf")
@@ -361,10 +361,10 @@ def test_datetime64_conversion():
     pytest.importorskip("xarray")
     var_data = [[1, 2, 3], [1, 2, 3], [1, 2, 3]]
     var_dims = ["epoch", "direction"]
-    data = xr.Variable(var_dims, var_data)  # type: ignore[no-untyped-call]
+    data = xr.Variable(var_dims, var_data)
     epoch_data = [np.datetime64(1, "s"), np.datetime64(2, "s"), np.datetime64(3, "s")]
     epoch_dims = ["epoch"]
-    epoch = xr.Variable(epoch_dims, epoch_data)  # type: ignore[no-untyped-call]
+    epoch = xr.Variable(epoch_dims, epoch_data)
     ds = xr.Dataset(data_vars={"data": data, "epoch": epoch})
     xarray_to_cdf(ds, "hello.cdf", datetime64_to_cdftt2000=True)
     x = cdf_to_xarray("hello.cdf", to_datetime=True)
@@ -378,10 +378,10 @@ def test_datetime64_no_conversion():
     pytest.importorskip("xarray")
     var_data = [[1, 2, 3], [1, 2, 3], [1, 2, 3]]
     var_dims = ["epoch", "direction"]
-    data = xr.Variable(var_dims, var_data)  # type: ignore[no-untyped-call]
+    data = xr.Variable(var_dims, var_data)
     epoch_data = [np.datetime64(1, "s"), np.datetime64(2, "s"), np.datetime64(3, "s")]
     epoch_dims = ["epoch"]
-    epoch = xr.Variable(epoch_dims, epoch_data)  # type: ignore[no-untyped-call]
+    epoch = xr.Variable(epoch_dims, epoch_data)
     ds = xr.Dataset(data_vars={"data": data, "epoch": epoch})
     xarray_to_cdf(ds, "hello.cdf")
     x = cdf_to_xarray("hello.cdf")
@@ -396,10 +396,10 @@ def test_datetime64_conversion_odd_units():
     pytest.importorskip("xarray")
     var_data = [[1, 2, 3], [1, 2, 3], [1, 2, 3]]
     var_dims = ["epoch", "direction"]
-    data = xr.Variable(var_dims, var_data)  # type: ignore[no-untyped-call]
+    data = xr.Variable(var_dims, var_data)
     epoch_data = [np.datetime64("2000-01-01"), np.datetime64("2000-01-02"), np.datetime64("2000-01-03")]
     epoch_dims = ["epoch"]
-    epoch = xr.Variable(epoch_dims, epoch_data)  # type: ignore[no-untyped-call]
+    epoch = xr.Variable(epoch_dims, epoch_data)
     ds = xr.Dataset(data_vars={"data": data, "epoch": epoch})
     xarray_to_cdf(ds, "hello.cdf", datetime64_to_cdftt2000=True)
     x = cdf_to_xarray("hello.cdf", to_datetime=True)
@@ -413,10 +413,10 @@ def test_numpy_string_array():
     pytest.importorskip("xarray")
     var_data = ["a", "b", "c"]
     var_dims = ["epoch"]
-    data = xr.Variable(var_dims, var_data)  # type: ignore[no-untyped-call]
+    data = xr.Variable(var_dims, var_data)
     epoch_data = [np.datetime64("2000-01-01"), np.datetime64("2000-01-02"), np.datetime64("2000-01-03")]
     epoch_dims = ["epoch"]
-    epoch = xr.Variable(epoch_dims, epoch_data)  # type: ignore[no-untyped-call]
+    epoch = xr.Variable(epoch_dims, epoch_data)
     ds = xr.Dataset(data_vars={"data": data, "epoch": epoch})
     xarray_to_cdf(ds, "hello.cdf", datetime64_to_cdftt2000=True)
     x = cdf_to_xarray("hello.cdf", to_datetime=True)