small code updates (using pyupgrade)

docs/conf.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 #
 # pyam documentation build configuration file, created by
 # sphinx-quickstart on Tue Feb  9 09:59:03 2016.

pyam/_ops.py

@@ -129,7 +129,7 @@ def _op_data(df, name, method, axis, fillna=None, args=(), ignore_units=False, *
     # separate pint quantities into numerical value and unit (as index)
     if ignore_units is False:
         _value = pd.DataFrame(
-            [[i.magnitude, "{:~}".format(i.units)] for i in result.values],
+            [[i.magnitude, f"{i.units:~}"] for i in result.values],
             columns=["value", "unit"],
             index=result.index,
         ).set_index("unit", append=True)

pyam/_style.py

@@ -24,7 +24,7 @@ def _get_standard_colors(  # noqa: C901
             cmap = colormap
             colormap = cm.get_cmap(colormap)
             if colormap is None:
-                raise ValueError("Colormap {0} is not recognized".format(cmap))
+                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

@@ -71,7 +71,7 @@ def _aggregate_recursive(df, variable, recursive):
     # iterate over variables (bottom-up) and aggregate all components up to `variable`
     for d in reversed(range(find_depth(variable), max(find_depth(_df.variable)))):
         components = compress(_df.variable, find_depth(_df.variable, level=d + 1))
-        var_list = set([reduce_hierarchy(v, -1) for v in components])
+        var_list = {reduce_hierarchy(v, -1) for v in components}
 
         # a temporary dataframe allows to distinguish between full data and new data
         _data_agg = _aggregate(_df, variable=var_list)
@@ -175,7 +175,7 @@ def _aggregate_time(df, variable, column, value, components, method="sum"):
     """Internal implementation for aggregating data over subannual time"""
     # default `components` to all entries in `column` other than `value`
     if components is None:
-        components = list(set(df.data.subannual.unique()) - set([value]))
+        components = list(set(df.data.subannual.unique()) - {value})
 
     # compute aggregate over time
     filter_args = dict(variable=variable)

pyam/core.py

@@ -74,7 +74,7 @@
 logger = logging.getLogger(__name__)
 
 
-class IamDataFrame(object):
+class IamDataFrame:
     """Scenario timeseries data and meta indicators
 
     The class provides a number of diagnostic features (including validation of
@@ -1781,7 +1781,7 @@ def _variable_components(self, variable, level=0):
         include `'foo|bar|baz'`, which is a sub-sub-category. If `level=None`,
         all variables below `variable` in the hierarchy are returned."""
         var_list = pd.Series(self.variable)
-        return var_list[pattern_match(var_list, "{}|*".format(variable), level=level)]
+        return var_list[pattern_match(var_list, f"{variable}|*", level=level)]
 
     def _get_cols(self, cols):
         """Return a list of columns of `self.data`"""
@@ -2445,7 +2445,7 @@ def to_excel(
 
         # write meta table unless `include_meta=False`
         if include_meta and len(self.meta.columns):
-            meta_rename = dict([(i, i.capitalize()) for i in self.index.names])
+            meta_rename = {i: i.capitalize() for i in self.index.names}
             write_sheet(
                 excel_writer,
                 "meta" if include_meta is True else include_meta,
@@ -2501,7 +2501,7 @@ def to_datapackage(self, path):
 
             # cast tables to datapackage
             package = Package()
-            package.infer("{}/*.csv".format(tmp))
+            package.infer(f"{tmp}/*.csv")
             if not package.valid:
                 logger.warning("The exported datapackage is not valid")
             package.save(path)
@@ -2530,7 +2530,7 @@ def load_meta(self, path, sheet_name="meta", ignore_conflict=False, **kwargs):
         meta = read_pandas(path, sheet_name=sheet_name, **kwargs)
 
         # cast index-column headers to lower-case, check that required index exists
-        meta = meta.rename(columns=dict([(i.capitalize(), i) for i in META_IDX]))
+        meta = meta.rename(columns={i.capitalize(): i for i in META_IDX})
         if missing_cols := [c for c in self.index.names if c not in meta.columns]:
             raise ValueError(
                 f"Missing index columns for meta indicators: {missing_cols}"

pyam/figures.py

@@ -51,12 +51,10 @@ def sankey(df, mapping):
     _df = pd.DataFrame.from_dict(
         mapping, orient="index", columns=["source", "target"]
     ).merge(df._data, how="left", left_index=True, right_on="variable")
-    label_mapping = dict(
-        [
-            (label, i)
+    label_mapping = {
+            label: i
             for i, label in enumerate(set(pd.concat([_df["source"], _df["target"]])))
-        ]
-    )
+    }
     _df.replace(label_mapping, inplace=True)
     region = get_index_levels(_df, "region")[0]
     unit = get_index_levels(_df, "unit")[0]

pyam/iiasa.py

@@ -60,7 +60,7 @@ def set_config(*args, **kwargs):
 
 def _read_config(file):
     """Read username and password for IIASA API connection from file"""
-    with open(file, "r") as stream:
+    with open(file) as stream:
         creds = yaml.safe_load(stream)
 
     return ManagerAuth(**creds, url=str(settings.manager_url))
@@ -139,7 +139,7 @@ def __call__(self):
         return {"Authorization": "Bearer " + self.access_token}
 
 
-class Connection(object):
+class Connection:
     """A class to facilitate querying an IIASA Scenario Explorer database API
 
     Parameters
@@ -177,7 +177,7 @@ def __init__(self, name=None, creds=None, auth_url=_AUTH_URL):
             logger.info("You are connected as an anonymous user")
 
     @property
-    @lru_cache()
+    @lru_cache
     def _connection_map(self):
         # TODO: application-list will be reimplemented in conjunction with ixmp-server
         r = self.auth.client.get("legacy/applications", headers=self.auth())
@@ -194,7 +194,7 @@ def _connection_map(self):
                 name = x["name"]
                 if env is not None:
                     if env in aliases:
-                        logger.warning("Duplicate instance alias {}".format(env))
+                        logger.warning(f"Duplicate instance alias {env}")
                         conn_map[name] = name
                         first_duplicate = conn_map.pop(env)
                         conn_map[first_duplicate] = first_duplicate
@@ -206,7 +206,7 @@ def _connection_map(self):
         return conn_map
 
     @property
-    @lru_cache()
+    @lru_cache
     def valid_connections(self):
         """Return available resources (database API connections)"""
         logger.warning(
@@ -358,7 +358,7 @@ def properties(self, default_only=True, **kwargs):
         cols = audit_cols + other_cols
 
         _df = self._query_index(default_only, meta=True, cols=cols, **kwargs)
-        audit_mapping = dict([(i, i.replace("_", "ate_")) for i in audit_cols])
+        audit_mapping = {i: i.replace("_", "ate_") for i in audit_cols}
 
         return _df.set_index(META_IDX).rename(columns=audit_mapping)
 
@@ -370,7 +370,7 @@ def scenarios(self):
         """List all scenarios in the connected resource"""
         return pd.Series(self._query_index()["scenario"].unique(), name="scenario")
 
-    @lru_cache()
+    @lru_cache
     def variables(self):
         """List all variables in the connected resource"""
         url = "/".join([self._base_url, "ts"])
@@ -379,7 +379,7 @@ def variables(self):
         df = pd.read_json(StringIO(r.text), orient="records")
         return pd.Series(df["variable"].unique(), name="variable")
 
-    @lru_cache()
+    @lru_cache
     def regions(self, include_synonyms=False):
         """List all regions in the connected resource
 

pyam/plotting.py

@@ -330,7 +330,7 @@ def pie(
     if not isinstance(df, pd.DataFrame):
         df = df.as_pandas()
 
-    for col in set(SORT_IDX) - set([category]):
+    for col in set(SORT_IDX) - {category}:
         if len(df[col].unique()) > 1:
             msg = (
                 "Can not plot multiple {}s in a pie plot with value={} and category={}"
@@ -436,7 +436,7 @@ def stack(  # noqa: C901
     if not isinstance(df, pd.DataFrame):
         df = df.as_pandas()
 
-    for col in set(SORT_IDX) - set([x, stack]):
+    for col in set(SORT_IDX) - {x, stack}:
         if len(df[col].unique()) > 1:
             msg = "Can not plot multiple {}s in stack_plot with x={}, stack={}"
             raise ValueError(msg.format(col, x, stack))
@@ -539,7 +539,7 @@ def as_series(index, name):
     for var in ["model", "scenario", "region", "variable"]:
         values = df[var].unique()
         if len(values) == 1:
-            _title.append("{}: {}".format(var, values[0]))
+            _title.append(f"{var}: {values[0]}")
     if title and _title:
         title = " ".join(_title) if title is True else title
         ax.set_title(title)
@@ -605,7 +605,7 @@ def bar(  # noqa: C901
     if not isinstance(df, pd.DataFrame):
         df = df.as_pandas()
 
-    for col in set(SORT_IDX) - set([x, bars]):
+    for col in set(SORT_IDX) - {x, bars}:
         if len(df[col].unique()) > 1:
             msg = "Can not plot multiple {}s in bar plot with x={}, bars={}"
             raise ValueError(msg.format(col, x, bars))
@@ -658,7 +658,7 @@ def bar(  # noqa: C901
     for var in ["model", "scenario", "region", "variable"]:
         values = df[var].unique()
         if len(values) == 1:
-            _title.append("{}: {}".format(var, values[0]))
+            _title.append(f"{var}: {values[0]}")
     if title and _title:
         title = " ".join(_title) if title is True else title
         ax.set_title(title)
@@ -1008,9 +1008,9 @@ def line(  # noqa: C901
     # pivot data if asked for explicit variable name
     variables = df["variable"].unique()
     if x in variables or y in variables:
-        keep_vars = set([x, y]) & set(variables)
+        keep_vars = {x, y} & set(variables)
         df = df[df["variable"].isin(keep_vars)]
-        idx = list(set(df.columns) - set(["value"]))
+        idx = list(set(df.columns) - {"value"})
         df = (
             df.reset_index()
             .set_index(idx)
@@ -1038,7 +1038,7 @@ def line(  # noqa: C901
     # prepare a dict for ordering, reshape data for use in line_plot
     idx_cols = list(df.columns.drop(y))
     if not isinstance(order, dict):
-        order = dict([(i, None) for i in order or idx_cols])
+        order = {i: None for i in order or idx_cols}
     df = reshape_mpl(df, x, y, idx_cols, **order)
 
     # determine the columns that should go into the legend
@@ -1158,7 +1158,7 @@ def line(  # noqa: C901
 
     # build unique legend handles and labels
     if legend is not False:
-        handles, labels = [np.array(i) for i in ax.get_legend_handles_labels()]
+        handles, labels = (np.array(i) for i in ax.get_legend_handles_labels())
         if label is not None:  # label given explicitly via kwarg
             _add_legend(ax, handles, labels, legend)
         else:

pyam/run_control.py

@@ -113,14 +113,14 @@ def _get_path(self, key, fyaml, fname):
             msg = (
                 "YAML key '{}' in {}: {} is not a valid relative " + "or absolute path"
             )
-            raise IOError(msg.format(key, fyaml, fname))
+            raise OSError(msg.format(key, fyaml, fname))
         return _fname
 
     def _load_yaml(self, obj):
         if hasattr(obj, "read"):  # it's a file
             obj = obj.read()
         if is_str(obj) and not os.path.exists(obj):
-            raise IOError("File {} does not exist".format(obj))
+            raise OSError(f"File {obj} does not exist")
         if is_str(obj) and os.path.exists(obj):
             fname = obj
             with open(fname) as f:

pyam/statistics.py

@@ -8,7 +8,7 @@
 from pyam.utils import META_IDX, is_list_like
 
 
-class Statistics(object):
+class Statistics:
     """This class generates descriptive statistics of timeseries data
 
     Parameters
@@ -46,9 +46,9 @@ def __init__(  # noqa: C901
             self.groupby = groupby
             self.idx_depth = 2
         elif groupby is not None:
-            raise ValueError("arg `{}` not valid `groupby`".format(groupby))
+            raise ValueError(f"arg `{groupby}` not valid `groupby`")
         if self.col is not None and self.col not in df.meta.columns:
-            raise ValueError("column `{}` not in `df.meta`".format(self.col))
+            raise ValueError(f"column `{self.col}` not in `df.meta`")
 
         # if neither groupby nor filters is given, use filters to describe all
         # and assume that rows are used
@@ -79,11 +79,11 @@ def __init__(  # noqa: C901
                     and is_str(idx[0])
                     or not is_str(idx[1])
                 ):
-                    raise ValueError("`{}` is not a valid index".format(idx))
+                    raise ValueError(f"`{idx}` is not a valid index")
                 self._add_to_index(idx[0], idx[1])
             # check that filters in tuple are valid
             if not isinstance(_filter, dict):
-                raise ValueError("`{}` is not a valid filter".format(_filter))
+                raise ValueError(f"`{_filter}` is not a valid filter")
             elif not (set(_filter) - set(META_IDX)).issubset(df.meta):
                 raise ValueError(
                     "column `{}` not in `df.meta`".format(
@@ -98,7 +98,7 @@ def __init__(  # noqa: C901
         self.percentiles = list(percentiles)
         self._describe_cols = (
             ["count", "mean", "std", "min"]
-            + ["{:.0%}".format(i) for i in self.percentiles]
+            + [f"{i:.0%}" for i in self.percentiles]
             + ["max"]
         )
 
@@ -112,10 +112,10 @@ def _add_to_index(self, idx, sub_idx=None):
             raise ValueError(msg.format(idx, "(idx0, idx1)"))
         if self.idx_depth == 1 and sub_idx is not None:
             raise ValueError(
-                "index depth set to 1, found `({}, {})`".format(idx, sub_idx)
+                f"index depth set to 1, found `({idx}, {sub_idx})`"
             )
         if self.idx_depth == 2 and sub_idx is None:
-            raise ValueError("index depth set to 2, found `({})`".format(idx))
+            raise ValueError(f"index depth set to 2, found `({idx})`")
 
         # append to lists for sorting index
         if idx not in self._idx:
@@ -292,7 +292,7 @@ def format_rows(
     count = max(
         [i for i in row.loc[(slice(None), slice(None), "count")] if not np.isnan(i)]
     )
-    ret.loc[("count", "")] = ("{:.0f}".format(count)) if count > 1 else ""
+    ret.loc[("count", "")] = (f"{count:.0f}") if count > 1 else ""
 
     # set upper and lower for the range
     upper, lower = ("max", "min") if fullrange is True else ("75%", "25%")
@@ -308,10 +308,10 @@ def format_rows(
                 x[center], x[upper], x[lower]
             )
         elif _count == 1:
-            s = "{f}".format(f=custom_format).format(x["50%"])
+            s = f"{custom_format}".format(x["50%"])
         # add count of this section as `[]` if different from count_max
         if 0 < _count < count:
-            s += " [{:.0f}]".format(_count)
+            s += f" [{_count:.0f}]"
         ret.loc[i] = s
 
     return ret

pyam/str.py

@@ -80,7 +80,7 @@ def test(x):
             return level <= x if x is not None else False
 
     else:
-        raise ValueError("Unknown level type: `{}`".format(level))
+        raise ValueError(f"Unknown level type: `{level}`")
 
     return list(map(test, n_pipes))
 

pyam/timeseries.py

@@ -106,7 +106,7 @@ def cross_threshold(
         Whether to cast the returned values to integer (years)
     """
     direction = [direction] if is_str(direction) else list(direction)
-    if not set(direction).issubset(set(["from above", "from below"])):
+    if not set(direction).issubset({"from above", "from below"}):
         raise ValueError(f"Invalid direction: {direction}")
 
     # get the values and time-domain index

pyam/unfccc.py

@@ -136,7 +136,7 @@ def _compile_variable(i, variable):
     """Translate UNFCCC columns into an IAMC-style variable"""
     if i["variable"]:
         raise ValueError("Conflict in variable mapping.")
-    return variable.format(**dict((c, i[c]) for c in NAME_COLS))
+    return variable.format(**{c: i[c] for c in NAME_COLS})
 
 
 def _compile_unit(i):

pyam/units.py

@@ -33,7 +33,7 @@ def convert_unit(
     registry = registry or iam_units.registry
 
     # Make versions without -equiv
-    _current, _to = [i.replace("-equiv", "") for i in [current, to]]
+    _current, _to = (i.replace("-equiv", "") for i in [current, to])
     # Pair of (magnitude, unit)
     qty = [ret._data.loc[where].values, _current]
 

pyam/utils.py

@@ -35,7 +35,7 @@
         range(0, 702),
         [i for i in string.ascii_uppercase]
         + [
-            "{}{}".format(i, j)
+            f"{i}{j}"
             for i, j in itertools.product(
                 string.ascii_uppercase, string.ascii_uppercase
             )