spool.sort - applied requested changes

d

@@ -0,0 +1,6 @@
+  adding_table_of_supported_formats
+  detrend
+  master
+* sort_method_for_spool
+  vis_colorbar_title
+  write_func

dascore/core/spool.py

@@ -99,7 +99,16 @@ def get_contents(self) -> pd.DataFrame:
     def sort(self, attribute) -> Self:
         """
         Sort the Spool based on a specific attribute.
+
+        Parameters
+        ---------------
+        atribute
+            The attribute or coordinate used for sorting. If a coordinate name
+            is used, the sorting will be based on the minimum value.
         """
+        raise NotImplementedError(
+            f"spool of type {self.__class__} has no sort implementation"
+        )
 
     @abc.abstractmethod
     def __len__(self) -> int:
@@ -318,34 +327,44 @@ def select(self, **kwargs) -> Self:
 
     @compose_docstring(doc=BaseSpool.sort.__doc__)
     def sort(self, attribute) -> Self:
-        """Sort the Spool based on a specific attribute."""
-        df = self._df
-        inst_df = self._instruction_df
-
-        # make sure we can also cover "time" and "distance" as attributes
-        if attribute == "time":
-            attribute = "time_min"
-        elif attribute == "distance":
-            attribute = "distance_min"
-
-        # make sure a suitable attribute is entered
-        attrs = set(df.columns)
-        if attribute not in attrs:
+        """
+        {doc}
+        """
+        try:
+            df = self._df
+            inst_df = self._instruction_df
+
+            # make sure a suitable attribute is entered
+            attrs = set(df.columns)
+            if attribute not in attrs:
+                # make sure we can also cover coordinate names instead of the attribute
+                if f"{attribute}_min" in attrs:
+                    attribute = f"{attribute}_min"
+                else:
+                    msg = (
+                        "Invalid attribute. "
+                        "Please use a valid attribute such as: 'time'"
+                    )
+                    raise IndexError(msg)
+
+            # get a mapping from the old current index to the sorted ones
+            sorted_df = df.sort_values(attribute).reset_index(drop=True)
+            old_indices = df.index
+            new_indices = np.arange(len(df))
+            mapper = pd.Series(new_indices, index=old_indices)
+
+            # swap out all the old values with new ones
+            new_current_index = inst_df["current_index"].map(mapper)
+            new_instruction_df = inst_df.assign(current_index=new_current_index)
+
+            # create new spool from new dataframes
+            return self.new_from_df(df=sorted_df, instruction_df=new_instruction_df)
+        except IndexError:  # Catch only IndexError
             msg = "Invalid attribute. Please use a valid attribute such as: 'time'"
             raise IndexError(msg)
-
-        # get a mapping from the old current index to the sorted ones
-        sorted_df = df.sort_values(attribute).reset_index(drop=True)
-        old_indices = df.index
-        new_indices = np.arange(len(df))
-        mapper = pd.Series(new_indices, index=old_indices)
-
-        # swap out all the old values with new ones
-        new_current_index = inst_df["current_index"].map(mapper)
-        new_instruction_df = inst_df.assign(current_index=new_current_index)
-
-        # create new spool from new dataframes
-        return self.new_from_df(df=sorted_df, instruction_df=new_instruction_df)
+        except Exception:
+            msg = f"spool of type {self.__class__} has no sort implementation"
+            raise NotImplementedError(msg)
 
     @compose_docstring(doc=BaseSpool.get_contents.__doc__)
     def get_contents(self) -> pd.DataFrame:

tests/test_core/test_spool.py

@@ -11,7 +11,6 @@
 from dascore.clients.filespool import FileSpool
 from dascore.core.spool import BaseSpool, MemorySpool
 from dascore.exceptions import InvalidSpoolError
-from dascore.utils.misc import CacheDescriptor
 from dascore.utils.time import to_datetime64, to_timedelta64
 
 
@@ -230,45 +229,41 @@ class TestSort:
     Tests for sorting spools.
     """
 
-    # A dataframe which represents contents as they will be output
-    _df: pd.DataFrame = CacheDescriptor("_cache", "_get_df")
+    def test_base_spool_sort_raises(self, random_spool):
+        expected_str = "spool of type"
+        with pytest.raises(NotImplementedError, match=expected_str):
+            BaseSpool.sort(random_spool, "time")
 
     def test_sorting_attr_not_exists(self, diverse_spool):
         """
         Test sorting by an attribute that does not exist in the DataFrame.
         """
-        try:
+        expected_str = "Invalid attribute"
+        with pytest.raises(IndexError, match=expected_str):
             diverse_spool.sort("dummy_attribute")
-        except IndexError as e:
-            assert (
-                str(e)
-                == "Invalid attribute. Please use a valid attribute such as: 'time'"
-            )
-        else:
-            assert False, "Expected an IndexError but no exception was raised."
 
     def test_sorting_attr_exists(self, diverse_spool):
         """
         Test sorting by an attribute that exists in the DataFrame.
         """
         sorted_spool = diverse_spool.sort("time_min")
-        df = sorted_spool._df
+        df = sorted_spool.get_contents()
         assert df["time_min"].is_monotonic_increasing
 
     def test_sorting_attr_time(self, diverse_spool):
         """
         Test sorting by the 'time' attribute that that may not be in the DataFrame.
         """
         sorted_spool = diverse_spool.sort("time")
-        df = sorted_spool._df
+        df = sorted_spool.get_contents()
         assert df["time_min"].is_monotonic_increasing
 
     def test_sorting_attr_distance(self, diverse_spool):
         """
         Test sorting by the 'distance' attribute that may not be exist in the DataFrame.
         """
         sorted_spool = diverse_spool.sort("distance")
-        df = sorted_spool._df
+        df = sorted_spool.get_contents()
         assert df["distance_min"].is_monotonic_increasing