fix: histogram section

popmon/pipeline/report_pipelines.py

@@ -265,7 +265,6 @@ def __init__(
                 hist_name_starts_with="histogram",
                 settings=settings.section.histograms,
                 top_n=settings.top_n,
-                last_n=settings.last_n,
             ),
             # section showing all traffic light alerts of monitored statistics
             TrafficLightSectionGenerator(

popmon/visualization/histogram_section.py

@@ -52,7 +52,6 @@ def __init__(
         hist_names=None,
         hist_name_starts_with="histogram",
         top_n=None,
-        last_n=None,
     ):
         """Initialize an instance of SectionGenerator.
 
@@ -61,7 +60,6 @@ def __init__(
         :param str section_name: key of output data to store in the datastore
         :param list features: list of features to pick up from input data (optional)
         :param list ignore_features: ignore list of features, if present (optional)
-        :param int last_n: plot histogram for last 'n' periods. default is 1 (optional)
         :param int top_n: plot heatmap for top 'n' categories. default is 20 (optional)
         :param list hist_names: list of histogram names to plot
         :param str hist_name_starts_with: find histograms in case hist_names is empty. default is histogram.
@@ -77,7 +75,6 @@ def __init__(
         self.hist_name_starts_with = hist_name_starts_with
 
         self.top_n = top_n
-        self.last_n = last_n
 
         # section specific
         self.section_name = settings.name
@@ -106,9 +103,7 @@ def transform(self, data_obj: dict, sections: Optional[list] = None):
         for feature in tqdm(features, ncols=100):
             df = data_obj.get(feature, pd.DataFrame())
             last_n = (
-                len(df.index)
-                if len(df.index) < self.last_n or self.last_n == 0
-                else self.last_n
+                len(df.index) if len(df.index) < self.plot_hist_n else self.plot_hist_n
             )
             hist_names = [hn for hn in self.hist_names if hn in df.columns]
             if len(hist_names) == 0 and len(self.hist_name_starts_with) > 0:
@@ -148,8 +143,8 @@ def transform(self, data_obj: dict, sections: Optional[list] = None):
             ]
 
             args = [
-                (feature, dates[-i], hists[-i], hist_names, self.top_n)
-                for i in range(self.plot_hist_n)
+                (feature, dates[i], hists[i], hist_names, self.top_n)
+                for i in range(last_n)
             ]
             plots = parallel(_plot_histograms, args)
 
@@ -195,14 +190,14 @@ def _plot_histograms(feature, date, hc_list, hist_names, top_n, max_nbins=1000):
     hist_names = [hn for i, hn in enumerate(hist_names) if i not in none_hists]
     # more basic checks
     if len(hc_list) == 0:
-        return {"name": date, "description": get_stat_description(date), "plot": ""}
+        return {"name": date, "description": "", "plot": ""}
     assert_similar_hists(hc_list)
 
     # make plot. note: slow!
     if hc_list[0].n_dim == 1:
         if all(h.size == 0 for h in hc_list):
             # triviality checks, skip all histograms empty
-            return {"name": date, "description": get_stat_description(date), "plot": ""}
+            return {"name": date, "description": "", "plot": ""}
 
         props = get_hist_props(hc_list[0])
         is_num = props["is_num"]
@@ -221,7 +216,7 @@ def _plot_histograms(feature, date, hc_list, hist_names, top_n, max_nbins=1000):
 
         # skip histograms with too many bins to plot (default more than 1000)
         if len(bins) > max_nbins:
-            return {"name": date, "description": get_stat_description(date), "plot": ""}
+            return {"name": date, "description": "", "plot": ""}
 
         # normalize histograms for plotting (comparison!) in case there is more than one.
         if len(hc_list) >= 2: