refactor(typing): clear the basedpyright warnings in the typecheck scope

buckaroo/buckaroo_widget.py

@@ -58,16 +58,16 @@ def _bk_flash(event, **extra):
 
 class PdSampling(Sampling):
     @classmethod
-    def pre_stats_sample(kls, df):
+    def pre_stats_sample(cls, df):
         # this is a bad place for fixing the dataframe, but for now
         # it's expedient. There probably should be a nother processing
         # step
         df = check_and_fix_df(df)
-        if len(df.columns) > kls.max_columns:
+        if len(df.columns) > cls.max_columns:
             print("Removing excess columns, found %d columns" %  len(df.columns))
-            df = df[df.columns[:kls.max_columns]]
-        if kls.pre_limit and len(df) > kls.pre_limit:
-            sampled = df.sample(kls.pre_limit)
+            df = df[df.columns[:cls.max_columns]]
+        if cls.pre_limit and len(df) > cls.pre_limit:
+            sampled = df.sample(cls.pre_limit)
             if isinstance(sampled, pd.DataFrame):
                 return sampled.sort_index()
             return sampled
@@ -115,7 +115,7 @@ def get_story_config(self, include_summary_stats=False, test_name=None) -> str:
               'secondary_df_viewer_config': EMPTY_DFVIEWER_CONFIG}}
         args_dict['args']['summary_stats_data'] = []
         if include_summary_stats:
-            1/0 # not supported yet
+            raise NotImplementedError("include_summary_stats isn't supported yet")
             # summary_stats data is big, and most of the time you won't want to serialize it
             #args_dict['summary_stats_data'] = {} #desrialize here
 
@@ -145,15 +145,16 @@ def __init__(self, orig_df, debug=False,
         self.record_transcript = record_transcript
         self.exception = None
         kls = self.__class__
+        widget = self
         class InnerDataFlow(kls.dataflow_klass):
             sampling_klass = kls.sampling_klass
             autocleaning_klass = kls.autocleaning_klass
             DFStatsClass = kls.DFStatsClass
             autoclean_conf= kls.autoclean_conf
             analysis_klasses = kls.analysis_klasses
 
-            def _df_to_obj(idfself, df:pd.DataFrame):
-                return self._df_to_obj(df)
+            def _df_to_obj(self, df:pd.DataFrame):
+                return widget._df_to_obj(df)
 
         self.dataflow = InnerDataFlow(
             orig_df,
@@ -258,7 +259,7 @@ def add_processing(self, df_processing_func, /):
         class DecoratedProcessing(ColAnalysis):
             provides_defaults = {}
             @classmethod
-            def post_process_df(kls, df):
+            def post_process_df(cls, df):
                 new_df = df_processing_func(df)
                 return [new_df, {}]
             post_processing_method = proc_func_name

buckaroo/customizations/styling.py

@@ -69,7 +69,7 @@ class DefaultMainStyling(StylingAnalysis):
 
 
     @classmethod
-    def style_column(kls, col:str, column_metadata: Any) -> Any:
+    def style_column(cls, col:str, column_metadata: Any) -> Any:
         # `_type` is the discriminator for displayer choice. If column_metadata
         # is empty (polars/index edge case) or arrives without `_type` — e.g.
         # a no-cleaning run where only an op-contributed entry (highlight_*)
@@ -126,7 +126,7 @@ def style_column(kls, col:str, column_metadata: Any) -> Any:
         header_name = column_metadata.get('orig_col_name', col)
         has_histogram = any(
             pr.get('displayer_args', {}).get('displayer') == 'histogram'
-            for pr in kls.pinned_rows)
+            for pr in cls.pinned_rows)
         min_w = estimate_min_width_px(disp, header_name, column_metadata, has_histogram)
         base_config['ag_grid_specs'] = {'minWidth': min_w}
         # ag_grid_specs from column_metadata (e.g. init_sd carrying

buckaroo/server/data_loading.py

@@ -24,12 +24,12 @@ class ServerSampling(Sampling):
     pre_limit = 1_000_000
 
     @classmethod
-    def pre_stats_sample(kls, df):
+    def pre_stats_sample(cls, df):
         df = check_and_fix_df(df)
-        if len(df.columns) > kls.max_columns:
-            df = df[df.columns[:kls.max_columns]]
-        if kls.pre_limit and len(df) > kls.pre_limit:
-            sampled = df.sample(kls.pre_limit)
+        if len(df.columns) > cls.max_columns:
+            df = df[df.columns[:cls.max_columns]]
+        if cls.pre_limit and len(df) > cls.pre_limit:
+            sampled = df.sample(cls.pre_limit)
             if isinstance(sampled, pd.DataFrame):
                 return sampled.sort_index()
             return sampled

buckaroo/xorq_buckaroo.py

@@ -264,7 +264,7 @@ class DecoratedXorqProcessing(ColAnalysis):
             provides_defaults = {}
 
             @classmethod
-            def post_process_df(kls, expr):
+            def post_process_df(cls, expr):
                 return [expr_processing_func(expr), {}]
 
             post_processing_method = proc_func_name