Reflow comments and docstrings in ixmp.core and tests

ixmp/core.py

@@ -343,24 +343,23 @@ def add_region_synonym(self, region, mapped_to):
     def timeslices(self):
         """Return all subannual timeslices defined in this Platform instance.
 
-        Timeslices are a way to represent subannual temporal resolution in
-        timeseries data. A timeslice consists of a **name** (e.g., 'january',
-        'summer'), a **category** (e.g., 'months', 'seasons'), and a
-        **duration** given relative to a full year.
-
-        The category and duration do not have any functional relevance within
-        the ixmp framework, but they may be useful for pre- or post-processing.
-        For example, they can be used to filter all timeslices of a certain
-        category (e.g., all months) from the :class:`pandas.DataFrame` returned
-        by this function or to aggregate subannual data to full-year results.
-
-        A timeslice is related to the index set 'time'
-        in a :class:`message_ix.Scenario` to indicate a subannual temporal
-        dimension. Alas, timeslices and set elements of time have to be
-        initialized/defined independently.
-
-        See :meth:`add_timeslice` to initialize additional timeslices in the
-        Platform instance.
+        Timeslices are a way to represent subannual temporal resolution in timeseries
+        data. A timeslice consists of a **name** (e.g., 'january', 'summer'), a
+        **category** (e.g., 'months', 'seasons'), and a **duration** given relative to a
+        full year.
+
+        The category and duration do not have any functional relevance within the ixmp
+        framework, but they may be useful for pre- or post-processing.  For example,
+        they can be used to filter all timeslices of a certain category (e.g., all
+        months) from the :class:`pandas.DataFrame` returned by this function or to
+        aggregate subannual data to full-year results.
+
+        A timeslice is related to the index set 'time' in a :class:`message_ix.Scenario`
+        to indicate a subannual temporal dimension. Alas, timeslices and set elements of
+        time have to be initialized/defined independently.
+
+        See :meth:`add_timeslice` to initialize additional timeslices in the Platform
+        instance.
 
         Returns
         -------

ixmp/tests/core/test_platform.py

@@ -343,16 +343,16 @@ def test_weakref():
     # Make a local reference to the backend
     backend = mp._backend
 
-    # Delete the Platform. Note that this only has an effect if there are no
-    # existing references to it
+    # Delete the Platform. Note that this only has an effect if there are no existing
+    # references to it
     del mp
 
     # s.platform is a dead weak reference, so it can't be accessed
     with pytest.raises(ReferenceError):
         s.platform._backend
 
-    # There is only one remaining reference to the backend: the *backend* name
-    # in the local scope
+    # There is only one remaining reference to the backend: the *backend* name in the
+    # local scope
     assert getrefcount(backend) - 1 == 1
 
     # The backend is garbage-collected at this point

ixmp/tests/core/test_timeseries.py

@@ -10,8 +10,8 @@
 from ixmp.core import IAMC_IDX
 
 # Test data.
-# NB the columns are in a specific order; model and scenario come last in the
-#    data returned by ixmp.
+# NB the columns are in a specific order; model and scenario come last in the data
+#    returned by ixmp.
 # TODO fix this; model and scenario should come first, matching the IAMC order.
 DATA = {
     0: pd.DataFrame.from_dict(
@@ -56,8 +56,8 @@
             scenario="scenario name",
         )
     ),
-    # NB the columns for geodata methods are inconsistent with those for time-
-    #    series data
+    # NB the columns for geodata methods are inconsistent with those for time-series
+    #    data
     "geo": pd.DataFrame.from_dict(
         dict(
             region="World",
@@ -132,10 +132,9 @@ def transact(ts, condition=True, commit_message=""):
 
 # Tests of ixmp.TimeSeries.
 #
-# Since Scenario is a subclass of TimeSeries, all TimeSeries functionality
-# should work exactly the same way on Scenario instances. The *ts* fixture is
-# parametrized to yield both TimeSeries and Scenario objects, so every test
-# is run on each type.
+# Since Scenario is a subclass of TimeSeries, all TimeSeries functionality should work
+# exactly the same way on Scenario instances. The *ts* fixture is parametrized to yield
+# both TimeSeries and Scenario objects, so every test is run on each type.
 
 
 @pytest.fixture(scope="function", params=[TimeSeries, Scenario])