Rework various log and error messages

Author: danielhuppmann

pyam/_debiasing.py

@@ -6,4 +6,4 @@ def _compute_bias(df, name, method, axis):
         count.name = name
         df.meta = df.meta.join(count, on=axis, how="outer")
     else:
-        raise ValueError(f"Unknown method {method} for computing bias weights!")
+        raise ValueError(f"Unknown method '{method}' for computing bias weights.")

pyam/_style.py

@@ -23,7 +23,7 @@ def _get_standard_colors(  # noqa: C901
             cmap = colormap
             colormap = plt.get_cmap(colormap)
             if colormap is None:
-                raise ValueError(f"Colormap {cmap} is not recognized")
+                raise ValueError(f"Colormap '{cmap}' is not recognized.")
         colors = [colormap(num) for num in np.linspace(0, 1, num=num_colors)]
     elif color is not None:
         if colormap is not None:

pyam/aggregation.py

@@ -65,7 +65,7 @@ def _aggregate_recursive(df, variable, recursive):
 
     # downselect to components of `variable`, initialize list for aggregated (new) data
     # keep variable at highest level if it exists
-    _df = df.filter(variable=[variable, f"{variable}|*"])
+    _df = df.filter(variable=[variable, variable + "|*"])
     data_list = []
 
     # iterate over variables (bottom-up) and aggregate all components up to `variable`
@@ -86,8 +86,10 @@ def _aggregate_recursive(df, variable, recursive):
         if recursive != "skip-validate" and not _overlap.empty:
             conflict = _compare(_data_self, _data_agg[_overlap], "self", "aggregate")
             if not conflict.empty:
-                msg = "Aggregated values are inconsistent with existing data:"
-                raise ValueError(f"{msg}\n{conflict}")
+                raise ValueError(
+                    "Aggregated values are inconsistent with existing data:\n"
+                    + str(conflict)
+                )
 
         # append aggregated values that are not already in data
         _df.append(_data_agg[_new], inplace=True)

pyam/core.py

@@ -372,7 +372,7 @@ def _get_meta_index_levels(self, name):
         if name in self.meta.index.names:
             return get_index_levels(self.meta, name)
         # in case of non-standard meta.index.names
-        raise KeyError(f"Index `{name}` does not exist!")
+        raise KeyError("Index dimension not found: " + name)
 
     @property
     def region(self):

pyam/iiasa.py

@@ -168,7 +168,7 @@ def __init__(self, name=None, creds=None, auth_url=_AUTH_URL):
             self.connect(name)
 
         if self.auth.user is not None:
-            logger.info(f"You are connected as user `{self.auth.user}`")
+            logger.info(f"You are connected as user '{self.auth.user}'")
         else:
             logger.info("You are connected as an anonymous user")
 

pyam/utils.py

@@ -281,7 +281,8 @@ def _intuit_column_groups(df, index, include_index=False):  # noqa: C901
     missing_required_col = [c for c in REQUIRED_COLS if c not in existing_cols]
     if missing_required_col:
         raise ValueError(
-            f"Missing required columns in timeseries data: {missing_required_col}"
+            "Missing required columns in timeseries data: "
+            ", ".join.missing_required_col
         )
 
     # check whether data in wide format (standard IAMC) or long format (`value` column)

tests/test_core.py

@@ -238,7 +238,7 @@ def test_init_df_with_custom_index(test_pd_df):
     # check that index attributes were set correctly and that df.model fails
     assert df.source == ["model_a"]
     assert df.version == [1, 2, 3]
-    with pytest.raises(KeyError, match="Index `model` does not exist!"):
+    with pytest.raises(KeyError, match="Index dimension not found: model"):
         df.model
 
 
@@ -264,7 +264,7 @@ def test_init_with_illegal_column(test_pd_df, illegal):
     test_pd_df[illegal] = "foo"
 
     # check that initialising an instance with an illegal column name raises
-    msg = f"Illegal columns for timeseries data: '{illegal}'"
+    msg = f"Illegal columns for timeseries data: {illegal}"
     with pytest.raises(ValueError, match=msg):
         IamDataFrame(test_pd_df)
 

tests/test_feature_debiasing.py

@@ -21,6 +21,6 @@ def test_debiasing_count(test_pd_df, axis, exp):
 
 def test_debiasing_unknown_method(test_df_year):
     """Check computing bias weights counting the number of scenarios by scenario name"""
-    msg = "Unknown method foo for computing bias weights!"
+    msg = "Unknown method 'foo' for computing bias weights."
     with pytest.raises(ValueError, match=msg):
         test_df_year.compute.bias(method="foo", name="bias", axis="scenario")