refactor: various minor cleanups

examples/flight_delays.py

@@ -1,6 +1,6 @@
 import pandas as pd
 
-import popmon  # noqa
+import popmon
 from popmon import resources
 
 # open synthetic data
@@ -10,7 +10,9 @@
 
 # generate stability report using automatic binning of all encountered features
 # (importing popmon automatically adds this functionality to a dataframe)
-report = df.pm_stability_report(
+report = popmon.df_stability_report(
+    df,
+    reference_type="self",
     time_axis="DATE",
     time_width="1w",
     time_offset="2015-07-02",

popmon/alerting/compute_tl_bounds.py

@@ -287,7 +287,7 @@ def pull_bounds(
             required = [m + suffix_mean, m + suffix_std]
             assert all(r in row for r in required)
 
-    x = pd.Series()
+    x = {}
     for m in cols:
         x[m + "_red_high"] = np.nan
         x[m + "_yellow_high"] = np.nan
@@ -300,7 +300,7 @@ def pull_bounds(
         x[m + "_yellow_high"] = row[m + suffix_mean] + row[m + suffix_std] * yellow_high
         x[m + "_yellow_low"] = row[m + suffix_mean] + row[m + suffix_std] * yellow_low
         x[m + "_red_low"] = row[m + suffix_mean] + row[m + suffix_std] * red_low
-    return x
+    return pd.Series(x)
 
 
 def df_single_op_pull_bounds(

popmon/pipeline/report.py

@@ -161,8 +161,25 @@ def stability_report(
     pipeline = get_report_pipeline_class(reference_type, reference)(**cfg)
     result = pipeline.transform(datastore)
 
-    stability_report = StabilityReport(datastore=result)
-    return stability_report
+    stability_report_result = StabilityReport(datastore=result)
+    return stability_report_result
+
+
+def set_time_axis(df):
+    time_axes = get_time_axes(df)
+    num = len(time_axes)
+    if num == 1:
+        time_axis = time_axes[0]
+        logger.info(f'Time-axis automatically set to "{time_axis}"')
+    elif num == 0:
+        raise ValueError(
+            "No obvious time-axes found. Cannot generate stability report."
+        )
+    else:
+        raise ValueError(
+            f"Found {num} time-axes: {time_axes}. Set *one* time_axis manually!"
+        )
+    return time_axis
 
 
 def df_stability_report(
@@ -298,19 +315,8 @@ def df_stability_report(
                 f'time_axis  "{time_axis}" not found in columns of reference dataframe.'
             )
     if isinstance(time_axis, bool):
-        time_axes = get_time_axes(df)
-        num = len(time_axes)
-        if num == 1:
-            time_axis = time_axes[0]
-            logger.info(f'Time-axis automatically set to "{time_axis}"')
-        elif num == 0:
-            raise ValueError(
-                "No obvious time-axes found. Cannot generate stability report."
-            )
-        else:
-            raise ValueError(
-                f"Found {num} time-axes: {time_axes}. Set *one* time_axis manually!"
-            )
+        time_axis = set_time_axis(df)
+
     if features is not None:
         # by now time_axis is defined. ensure that all histograms start with it.
         if not isinstance(features, list):

popmon/visualization/section_generator.py

@@ -190,7 +190,7 @@ def _plot_metric(
 ):
     """Split off plot histogram generation to allow for parallel processing"""
     # pick up static traffic light boundaries
-    name = feature + ":" + metric
+    name = f"{feature}:{metric}"
     sbounds = static_bounds.get(name, ())
     # pick up dynamic traffic light boundaries
     names = [prefix + metric + suffix for suffix in suffices]

popmon/visualization/traffic_light_section_generator.py

@@ -205,7 +205,6 @@ def _plot_metrics(
     if len(values) > 0:
         values = np.stack(values)
 
-        # make plot. note: slow!
         if style == "heatmap":
             plot = plot_traffic_lights_overview(
                 feature, values, metrics=nonempty_metrics, labels=dates

popmon/visualization/utils.py

@@ -485,7 +485,6 @@ def xtick(lab):
                 lab = lab[:17] + "..."
             return lab
 
-        # plt.xlim((0.0, float(len(date))))
         plt.xticks(tick_pos_x, date, fontsize=20, rotation=90)
         plt.yticks(tick_pos_y, [xtick(lab) for lab in labels], fontsize=20)
         im_ratio = values.shape[0] / values.shape[1]