Black Reformatting

lux/_config/config.py

@@ -55,7 +55,8 @@ def topk(self, k: Union[int, bool]):
             self._topk = k
         else:
             warnings.warn(
-                "Parameter to lux.config.topk must be an integer or a boolean.", stacklevel=2,
+                "Parameter to lux.config.topk must be an integer or a boolean.",
+                stacklevel=2,
             )
 
     @property
@@ -98,7 +99,8 @@ def pandas_fallback(self, fallback: bool) -> None:
             self._pandas_fallback = fallback
         else:
             warnings.warn(
-                "The flag for Pandas fallback must be a boolean.", stacklevel=2,
+                "The flag for Pandas fallback must be a boolean.",
+                stacklevel=2,
             )
 
     @property
@@ -118,7 +120,8 @@ def interestingness_fallback(self, fallback: bool) -> None:
             self._interestingness_fallback = fallback
         else:
             warnings.warn(
-                "The flag for interestingness fallback must be a boolean.", stacklevel=2,
+                "The flag for interestingness fallback must be a boolean.",
+                stacklevel=2,
             )
 
     @property
@@ -144,7 +147,8 @@ def sampling_cap(self, sample_number: int) -> None:
             self._sampling_cap = sample_number
         else:
             warnings.warn(
-                "The cap on the number samples must be an integer.", stacklevel=2,
+                "The cap on the number samples must be an integer.",
+                stacklevel=2,
             )
 
     @property
@@ -172,7 +176,8 @@ def sampling_start(self, sample_number: int) -> None:
             self._sampling_start = sample_number
         else:
             warnings.warn(
-                "The sampling starting point must be an integer.", stacklevel=2,
+                "The sampling starting point must be an integer.",
+                stacklevel=2,
             )
 
     @property
@@ -197,7 +202,8 @@ def sampling(self, sample_flag: bool) -> None:
             self._sampling_flag = sample_flag
         else:
             warnings.warn(
-                "The flag for sampling must be a boolean.", stacklevel=2,
+                "The flag for sampling must be a boolean.",
+                stacklevel=2,
             )
 
     @property
@@ -222,7 +228,8 @@ def heatmap(self, heatmap_flag: bool) -> None:
             self._heatmap_flag = heatmap_flag
         else:
             warnings.warn(
-                "The flag for enabling/disabling heatmaps must be a boolean.", stacklevel=2,
+                "The flag for enabling/disabling heatmaps must be a boolean.",
+                stacklevel=2,
             )
 
     @property

lux/action/generalize.py

@@ -78,7 +78,12 @@ def generalize(ldf):
     for clause in filters:
         # new_spec = ldf._intent.copy()
         # new_spec.remove_column_from_spec(new_spec.attribute)
-        temp_vis = Vis(ldf.current_vis[0]._inferred_intent.copy(), source=ldf, title="Overall", score=0,)
+        temp_vis = Vis(
+            ldf.current_vis[0]._inferred_intent.copy(),
+            source=ldf,
+            title="Overall",
+            score=0,
+        )
         temp_vis.remove_filter_from_spec(clause.value)
         output.append(temp_vis)
 

lux/action/row_group.py

@@ -50,7 +50,13 @@ def row_group(ldf):
             # 	rowdf.cardinality["index"]=len(rowdf)
             # if isinstance(ldf.columns,pd.DatetimeIndex):
             # 	rowdf.data_type_lookup[dim_name]="temporal"
-            vis = Vis([dim_name, lux.Clause(row.name, data_model="measure", aggregation=None),], rowdf,)
+            vis = Vis(
+                [
+                    dim_name,
+                    lux.Clause(row.name, data_model="measure", aggregation=None),
+                ],
+                rowdf,
+            )
             collection.append(vis)
     vlst = VisList(collection)
     # Note that we are not computing interestingness score here because we want to preserve the arrangement of the aggregated data

lux/core/frame.py

@@ -496,7 +496,8 @@ def exported(self) -> Union[Dict[str, VisList], VisList]:
             exported_vis = VisList(
                 list(
                     map(
-                        self._recommendation[export_action].__getitem__, exported_vis_lst[export_action],
+                        self._recommendation[export_action].__getitem__,
+                        exported_vis_lst[export_action],
                     )
                 )
             )
@@ -566,7 +567,8 @@ def _repr_html_(self):
             self._widget.observe(self.set_intent_on_click, names="selectedIntentIndex")
 
             button = widgets.Button(
-                description="Toggle Pandas/Lux", layout=widgets.Layout(width="140px", top="5px"),
+                description="Toggle Pandas/Lux",
+                layout=widgets.Layout(width="140px", top="5px"),
             )
             self.output = widgets.Output()
             display(button, self.output)

lux/core/series.py

@@ -154,7 +154,8 @@ def __repr__(self):
 
                 # box = widgets.Box(layout=widgets.Layout(display='inline'))
                 button = widgets.Button(
-                    description="Toggle Pandas/Lux", layout=widgets.Layout(width="140px", top="5px"),
+                    description="Toggle Pandas/Lux",
+                    layout=widgets.Layout(width="140px", top="5px"),
                 )
                 ldf.output = widgets.Output()
                 # box.children = [button,output]

lux/core/sqltable.py

@@ -84,7 +84,8 @@ def set_SQL_table(self, t_name):
             error_str = str(error)
             if f'relation "{t_name}" does not exist' in error_str:
                 warnings.warn(
-                    f"\nThe table '{t_name}' does not exist in your database./", stacklevel=2,
+                    f"\nThe table '{t_name}' does not exist in your database./",
+                    stacklevel=2,
                 )
 
     def _repr_html_(self):

lux/executor/PandasExecutor.py

@@ -216,7 +216,10 @@ def execute_aggregate(vis: Vis, isFiltered=True):
                             }
                         )
                         vis._vis_data = vis.data.merge(
-                            df, on=[columns[0], columns[1]], how="right", suffixes=["", "_right"],
+                            df,
+                            on=[columns[0], columns[1]],
+                            how="right",
+                            suffixes=["", "_right"],
                         )
                         for col in columns[2:]:
                             vis.data[col] = vis.data[col].fillna(0)  # Triggers __setitem__
@@ -364,7 +367,10 @@ def execute_2D_binning(vis: Vis):
                 if color_attr.data_type == "nominal":
                     # Compute mode and count. Mode aggregates each cell by taking the majority vote for the category variable. In cases where there is ties across categories, pick the first item (.iat[0])
                     result = groups.agg(
-                        [("count", "count"), (color_attr.attribute, lambda x: pd.Series.mode(x).iat[0]),]
+                        [
+                            ("count", "count"),
+                            (color_attr.attribute, lambda x: pd.Series.mode(x).iat[0]),
+                        ]
                     ).reset_index()
                 elif color_attr.data_type == "quantitative" or color_attr.data_type == "temporal":
                     # Compute the average of all values in the bin

lux/executor/SQLExecutor.py

@@ -38,7 +38,8 @@ def execute_sampling(tbl: LuxSQLTable):
         SAMPLE_FRAC = 0.2
 
         length_query = pandas.read_sql(
-            "SELECT COUNT(*) as length FROM {}".format(tbl.table_name), lux.config.SQLconnection,
+            "SELECT COUNT(*) as length FROM {}".format(tbl.table_name),
+            lux.config.SQLconnection,
         )
         limit = int(list(length_query["length"])[0]) * SAMPLE_FRAC
         tbl._sampled = pandas.read_sql(
@@ -122,7 +123,8 @@ def add_quotes(var_name):
         required_variables = ",".join(required_variables)
         row_count = list(
             pandas.read_sql(
-                f"SELECT COUNT(*) FROM {tbl.table_name} {where_clause}", lux.config.SQLconnection,
+                f"SELECT COUNT(*) FROM {tbl.table_name} {where_clause}",
+                lux.config.SQLconnection,
             )["count"]
         )[0]
         if row_count > lux.config.sampling_cap:
@@ -227,42 +229,48 @@ def execute_aggregate(view: Vis, tbl: LuxSQLTable, isFiltered=True):
                 # generates query for colored barchart case
                 if has_color:
                     if agg_func == "mean":
-                        agg_query = 'SELECT "{}", "{}", AVG("{}") as "{}" FROM {} {} GROUP BY "{}", "{}"'.format(
-                            groupby_attr.attribute,
-                            color_attr.attribute,
-                            measure_attr.attribute,
-                            measure_attr.attribute,
-                            tbl.table_name,
-                            where_clause,
-                            groupby_attr.attribute,
-                            color_attr.attribute,
+                        agg_query = (
+                            'SELECT "{}", "{}", AVG("{}") as "{}" FROM {} {} GROUP BY "{}", "{}"'.format(
+                                groupby_attr.attribute,
+                                color_attr.attribute,
+                                measure_attr.attribute,
+                                measure_attr.attribute,
+                                tbl.table_name,
+                                where_clause,
+                                groupby_attr.attribute,
+                                color_attr.attribute,
+                            )
                         )
                         view._vis_data = pandas.read_sql(agg_query, lux.config.SQLconnection)
 
                         view._vis_data = utils.pandas_to_lux(view._vis_data)
                     if agg_func == "sum":
-                        agg_query = 'SELECT "{}", "{}", SUM("{}") as "{}" FROM {} {} GROUP BY "{}", "{}"'.format(
-                            groupby_attr.attribute,
-                            color_attr.attribute,
-                            measure_attr.attribute,
-                            measure_attr.attribute,
-                            tbl.table_name,
-                            where_clause,
-                            groupby_attr.attribute,
-                            color_attr.attribute,
+                        agg_query = (
+                            'SELECT "{}", "{}", SUM("{}") as "{}" FROM {} {} GROUP BY "{}", "{}"'.format(
+                                groupby_attr.attribute,
+                                color_attr.attribute,
+                                measure_attr.attribute,
+                                measure_attr.attribute,
+                                tbl.table_name,
+                                where_clause,
+                                groupby_attr.attribute,
+                                color_attr.attribute,
+                            )
                         )
                         view._vis_data = pandas.read_sql(agg_query, lux.config.SQLconnection)
                         view._vis_data = utils.pandas_to_lux(view._vis_data)
                     if agg_func == "max":
-                        agg_query = 'SELECT "{}", "{}", MAX("{}") as "{}" FROM {} {} GROUP BY "{}", "{}"'.format(
-                            groupby_attr.attribute,
-                            color_attr.attribute,
-                            measure_attr.attribute,
-                            measure_attr.attribute,
-                            tbl.table_name,
-                            where_clause,
-                            groupby_attr.attribute,
-                            color_attr.attribute,
+                        agg_query = (
+                            'SELECT "{}", "{}", MAX("{}") as "{}" FROM {} {} GROUP BY "{}", "{}"'.format(
+                                groupby_attr.attribute,
+                                color_attr.attribute,
+                                measure_attr.attribute,
+                                measure_attr.attribute,
+                                tbl.table_name,
+                                where_clause,
+                                groupby_attr.attribute,
+                                color_attr.attribute,
+                            )
                         )
                         view._vis_data = pandas.read_sql(agg_query, lux.config.SQLconnection)
                         view._vis_data = utils.pandas_to_lux(view._vis_data)
@@ -322,7 +330,10 @@ def execute_aggregate(view: Vis, tbl: LuxSQLTable, isFiltered=True):
                             }
                         )
                         view._vis_data = view._vis_data.merge(
-                            df, on=[columns[0], columns[1]], how="right", suffixes=["", "_right"],
+                            df,
+                            on=[columns[0], columns[1]],
+                            how="right",
+                            suffixes=["", "_right"],
                         )
                         for col in columns[2:]:
                             view._vis_data[col] = view._vis_data[col].fillna(0)  # Triggers __setitem__
@@ -391,7 +402,10 @@ def execute_binning(view: Vis, tbl: LuxSQLTable):
         upper_edges = ",".join(upper_edges)
         view_filter, filter_vars = SQLExecutor.execute_filter(view)
         bin_count_query = "SELECT width_bucket, COUNT(width_bucket) FROM (SELECT width_bucket(CAST (\"{}\" AS FLOAT), '{}') FROM {} {}) as Buckets GROUP BY width_bucket ORDER BY width_bucket".format(
-            bin_attribute.attribute, "{" + upper_edges + "}", tbl.table_name, where_clause,
+            bin_attribute.attribute,
+            "{" + upper_edges + "}",
+            tbl.table_name,
+            where_clause,
         )
 
         bin_count_data = pandas.read_sql(bin_count_query, lux.config.SQLconnection)
@@ -406,11 +420,13 @@ def execute_binning(view: Vis, tbl: LuxSQLTable):
             else:
                 bin_centers = np.array([(attr_min + attr_min + bin_width) / 2])
             bin_centers = np.append(
-                bin_centers, np.mean(np.vstack([upper_edges[0:-1], upper_edges[1:]]), axis=0),
+                bin_centers,
+                np.mean(np.vstack([upper_edges[0:-1], upper_edges[1:]]), axis=0),
             )
             if attr_type == int:
                 bin_centers = np.append(
-                    bin_centers, math.ceil((upper_edges[len(upper_edges) - 1] + attr_max) / 2),
+                    bin_centers,
+                    math.ceil((upper_edges[len(upper_edges) - 1] + attr_max) / 2),
                 )
             else:
                 bin_centers = np.append(bin_centers, (upper_edges[len(upper_edges) - 1] + attr_max) / 2)
@@ -551,7 +567,10 @@ def execute_filter(view: Vis):
                 else:
                     where_clause.append("AND")
                 where_clause.extend(
-                    ['"' + str(a.attribute) + '"', "IS NOT NULL",]
+                    [
+                        '"' + str(a.attribute) + '"',
+                        "IS NOT NULL",
+                    ]
                 )
 
         if where_clause == []:
@@ -668,7 +687,10 @@ def get_cardinality(self, tbl: LuxSQLTable):
             card_query = 'SELECT Count(Distinct("{}")) FROM {} WHERE "{}" IS NOT NULL'.format(
                 attr, tbl.table_name, attr
             )
-            card_data = pandas.read_sql(card_query, lux.config.SQLconnection,)
+            card_data = pandas.read_sql(
+                card_query,
+                lux.config.SQLconnection,
+            )
             cardinality[attr] = list(card_data["count"])[0]
         tbl.cardinality = cardinality
 
@@ -691,7 +713,10 @@ def get_unique_values(self, tbl: LuxSQLTable):
             unique_query = 'SELECT Distinct("{}") FROM {} WHERE "{}" IS NOT NULL'.format(
                 attr, tbl.table_name, attr
             )
-            unique_data = pandas.read_sql(unique_query, lux.config.SQLconnection,)
+            unique_data = pandas.read_sql(
+                unique_query,
+                lux.config.SQLconnection,
+            )
             unique_vals[attr] = list(unique_data[attr])
         tbl.unique_values = unique_vals
 

lux/interestingness/interestingness.py

@@ -198,7 +198,11 @@ def weighted_correlation(x, y, w):
 
 
 def deviation_from_overall(
-    vis: Vis, ldf: LuxDataFrame, filter_specs: list, msr_attribute: str, exclude_nan: bool = True,
+    vis: Vis,
+    ldf: LuxDataFrame,
+    filter_specs: list,
+    msr_attribute: str,
+    exclude_nan: bool = True,
 ) -> int:
     """
     Difference in bar chart/histogram shape from overall chart

lux/interestingness/similarity.py

@@ -68,7 +68,8 @@ def interpolate(vis, length):
                         yVals[count - 1] + (interpolated_x - xVals[count - 1]) / x_diff * yDiff
                     )
             vis.data = pd.DataFrame(
-                list(zip(interpolated_x_vals, interpolated_y_vals)), columns=[xAxis, yAxis],
+                list(zip(interpolated_x_vals, interpolated_y_vals)),
+                columns=[xAxis, yAxis],
             )
 
 

lux/processor/Compiler.py

@@ -281,7 +281,10 @@ def line_or_bar_or_geo(ldf, dimension: Clause, measure: Clause):
         # ShowMe logic + additional heuristics
         # count_col = Clause( attribute="count()", data_model="measure")
         count_col = Clause(
-            attribute="Record", aggregation="count", data_model="measure", data_type="quantitative",
+            attribute="Record",
+            aggregation="count",
+            data_model="measure",
+            data_type="quantitative",
         )
         auto_channel = {}
         if ndim == 0 and nmsr == 1:
@@ -472,7 +475,9 @@ def populate_wildcard_options(_inferred_intent: List[Clause], ldf: LuxDataFrame)
                         options = ldf.unique_values[attr]
                         specInd = _inferred_intent.index(clause)
                         _inferred_intent[specInd] = Clause(
-                            attribute=clause.attribute, filter_op="=", value=list(options),
+                            attribute=clause.attribute,
+                            filter_op="=",
+                            value=list(options),
                         )
                     else:
                         options.extend(convert_to_list(clause.value))

lux/vislib/altair/AltairRenderer.py

@@ -128,7 +128,8 @@ def create_vis(self, vis, standalone=True):
                     found_variable = "df"
                 if standalone:
                     chart.code = chart.code.replace(
-                        "placeholder_variable", f"pd.DataFrame({str(vis.data.to_dict())})",
+                        "placeholder_variable",
+                        f"pd.DataFrame({str(vis.data.to_dict())})",
                     )
                 else:
                     # TODO: Placeholder (need to read dynamically via locals())