style: lint

popmon/alerting/alerts_summary.py

@@ -32,8 +32,9 @@ class AlertsSummary(Module):
 
     It combines the alerts-summaries of all individual features into an artificial feature "_AGGREGATE_".
     """
-    _input_keys = ("read_key", )
-    _output_keys = ("store_key", )
+
+    _input_keys = ("read_key",)
+    _output_keys = ("store_key",)
 
     def __init__(
         self,

popmon/alerting/compute_tl_bounds.py

@@ -21,7 +21,7 @@
 import copy
 import fnmatch
 from collections import defaultdict
-from typing import Tuple, Any
+from typing import Any, Tuple
 
 import numpy as np
 import pandas as pd
@@ -116,7 +116,8 @@ class ComputeTLBounds(Module):
     meant to be generic. Then bounds can be stored as either raw
     values or as directly calculated values on the statistics of the data.
     """
-    _input_keys = ("read_key", )
+
+    _input_keys = ("read_key",)
     _output_keys = ("store_key", "apply_funcs_key")
 
     def __init__(
@@ -213,9 +214,7 @@ def transform(self, test_data: dict) -> Tuple[Any, Any]:
             # --- 1. tl bounds explicitly defined for a particular feature
             if feature in pkeys:
                 explicit_cols = [
-                    pcol
-                    for pcol in pkeys[feature]
-                    if pcol in test_df.columns
+                    pcol for pcol in pkeys[feature] if pcol in test_df.columns
                 ]
                 implicit_cols = set(pkeys[feature]) - set(explicit_cols)
 

popmon/analysis/apply_func.py

@@ -33,8 +33,9 @@ class ApplyFunc(Module):
 
     Extra parameters (kwargs) can be passed to the apply function.
     """
+
     _input_keys = ("apply_to_key", "assign_to_key", "apply_funcs_key")
-    _output_keys = ("store_key", )
+    _output_keys = ("store_key",)
 
     def __init__(
         self,
@@ -85,7 +86,7 @@ def __init__(
 
     def get_description(self):
         if len(self.apply_funcs) > 0:
-            return " and ".join([x['func'].__name__ for x in self.apply_funcs])
+            return " and ".join([x["func"].__name__ for x in self.apply_funcs])
         elif self.apply_funcs_key:
             return f"functions from arg '{self.apply_funcs_key}'"
         else:
@@ -139,7 +140,12 @@ def add_apply_func(
             }
         )
 
-    def transform(self, apply_to_data: dict, assign_to_data: Optional[dict] = None, apply_funcs: Optional[list] = None):
+    def transform(
+        self,
+        apply_to_data: dict,
+        assign_to_data: Optional[dict] = None,
+        apply_funcs: Optional[list] = None,
+    ):
         """
         Apply functions to specified feature and metrics
 

popmon/analysis/comparison/hist_comparer.py

@@ -380,16 +380,9 @@ def __init__(
             assign_to_key = read_key
 
         # make reference histogram(s)
-        hist_collector = ApplyFunc(
-            apply_to_key=read_key, 
-            assign_to_key=assign_to_key
-        )
+        hist_collector = ApplyFunc(apply_to_key=read_key, assign_to_key=assign_to_key)
         hist_collector.add_apply_func(
-            func=func_hist_collector, 
-            hist_name=hist_col, 
-            suffix="", 
-            *args, 
-            **kwargs
+            func=func_hist_collector, hist_name=hist_col, suffix="", *args, **kwargs
         )
 
         # do histogram comparison

popmon/analysis/merge_statistics.py

@@ -27,8 +27,9 @@
 
 class MergeStatistics(Module):
     """Merging dictionaries of features containing dataframes with statistics as its values."""
-    _input_keys = ("read_keys", )
-    _output_keys = ("store_key", )
+
+    _input_keys = ("read_keys",)
+    _output_keys = ("store_key",)
 
     def __init__(self, read_keys: List[str], store_key: str):
         """Initialize an instance of MergeStatistics.

popmon/analysis/profiling/hist_profiler.py

@@ -57,8 +57,9 @@ class HistProfiler(Module):
     :param str index_col: key for index in split dictionary
     :param dict stats_functions: function_name, function(bin_labels, bin_counts) dictionary
     """
-    _input_keys = ("read_key", )
-    _output_keys = ("store_key", )
+
+    _input_keys = ("read_key",)
+    _output_keys = ("store_key",)
 
     def __init__(
         self,

popmon/analysis/profiling/pull_calculator.py

@@ -131,11 +131,7 @@ def __init__(
         :param args: (tuple, optional): residual args passed on to mean and std functions
         :param kwargs: (dict, optional): residual kwargs passed on to mean and std functions
         """
-        kws = {
-            "window": window,
-            "shift": shift,
-            **kwargs
-        }
+        kws = {"window": window, "shift": shift, **kwargs}
         super().__init__(
             rolling_mean,
             rolling_std,
@@ -186,10 +182,7 @@ def __init__(
         :param args: (tuple, optional): residual args passed on to mean and std functions
         :param kwargs: (dict, optional): residual kwargs passed on to mean and std functions
         """
-        kws = {
-            "shift": shift,
-            **kwargs
-        }
+        kws = {"shift": shift, **kwargs}
         super().__init__(
             expanding_mean,
             expanding_std,

popmon/base/module.py

@@ -24,6 +24,7 @@
 
 class Module(ABC):
     """Base class used for modules in a pipeline."""
+
     _input_keys = None
     _output_keys = None
 
@@ -101,18 +102,12 @@ def get_features(self, all_features: list) -> list:
             features = [k for k in features if k not in self.ignore_features]
 
         features_not_in_input = [
-            feature
-            for feature in features
-            if feature not in all_features
+            feature for feature in features if feature not in all_features
         ]
         for feature in features_not_in_input:
             self.logger.warning(f'Feature "{feature}" not in input data; skipping.')
 
-        features = [
-            feature
-            for feature in features
-            if feature in all_features
-        ]
+        features = [feature for feature in features if feature in all_features]
         return features
 
     def _transform(self, datastore):
@@ -130,7 +125,9 @@ def _transform(self, datastore):
             else:
                 inputs[key] = None
 
-            self.logger.debug(f"load(key={key}, key_value={key_value}, value={str(inputs[key]):.100s})")
+            self.logger.debug(
+                f"load(key={key}, key_value={key_value}, value={str(inputs[key]):.100s})"
+            )
 
         # cache datastore
         self._datastore = datastore
@@ -145,8 +142,10 @@ def _transform(self, datastore):
 
             for k, v in zip(self._output_keys, outputs):
                 key_value = self.__dict__[k]
-                self.logger.debug(f"store(key={k}, key_value={key_value}, value={str(v):.100s})")
-                if key_value and len(key_value) > 0: # and v is not None:
+                self.logger.debug(
+                    f"store(key={k}, key_value={key_value}, value={str(v):.100s})"
+                )
+                if key_value and len(key_value) > 0:  # and v is not None:
                     datastore[key_value] = v
 
         return datastore

popmon/base/pipeline.py

@@ -85,9 +85,7 @@ def visualize(self, versioned=True, funcs=None, dsets=None):
         for module in self.modules:
             name = module.__class__.__name__
             if isinstance(module, Pipeline):
-                modules.append(
-                    module.visualize(versioned, funcs, dsets)
-                )
+                modules.append(module.visualize(versioned, funcs, dsets))
             else:
                 in_keys = module.get_inputs()
 
@@ -122,19 +120,15 @@ def visualize(self, versioned=True, funcs=None, dsets=None):
 
                 modules.append(
                     {
-                        'type': 'module',
-                        'name': f'{name}',
-                        'i': f'{funcs[name][id(module)]}',
-                        'desc': module.get_description(),
-                        'in': in_keys,
-                        'out': out_keys
+                        "type": "module",
+                        "name": f"{name}",
+                        "i": f"{funcs[name][id(module)]}",
+                        "desc": module.get_description(),
+                        "in": in_keys,
+                        "out": out_keys,
                     }
                 )
-        data = {
-            'type': 'subgraph',
-            'name': self.__class__.__name__,
-            'modules': modules
-        }
+        data = {"type": "subgraph", "name": self.__class__.__name__, "modules": modules}
         return data
 
     def to_json(self, file_name, versioned=True):

popmon/hist/hist_splitter.py

@@ -37,8 +37,8 @@ class HistSplitter(Module):
     where time is the index and each row is a x:y histogram.
     """
 
-    _input_keys = ("read_key", )
-    _output_keys = ("store_key", )
+    _input_keys = ("read_key",)
+    _output_keys = ("store_key",)
 
     def __init__(
         self,
@@ -153,7 +153,6 @@ def transform(self, data: dict) -> dict:
 
         # turn divided dicts into dataframes with index
         divided = {
-            k: pd.DataFrame(v).set_index(self.index_col)
-            for k, v in divided.items()
+            k: pd.DataFrame(v).set_index(self.index_col) for k, v in divided.items()
         }
         return divided

popmon/io/file_reader.py

@@ -28,8 +28,8 @@
 class FileReader(Module):
     """Module to read contents from a file, transform the contents with a function and write them to the datastore."""
 
-    _input_keys = tuple()
-    _output_keys = ("store_key", )
+    _input_keys = ()
+    _output_keys = ("store_key",)
 
     def __init__(
         self,

popmon/io/file_writer.py

@@ -28,8 +28,9 @@
 
 class FileWriter(Module):
     """Module transforms specific datastore content and writes it to a file."""
-    _input_keys = ("read_key", )
-    _output_keys = ("store_key", )
+
+    _input_keys = ("read_key",)
+    _output_keys = ("store_key",)
 
     def __init__(
         self,

popmon/pipeline/metrics_pipelines.py

@@ -382,13 +382,7 @@ def metrics_rolling_reference(
         ),
         ApplyFunc(
             apply_to_key="traffic_lights",
-            apply_funcs=[
-                {
-                    "func": traffic_light_summary,
-                    "axis": 1,
-                    "suffix": ""
-                }
-            ],
+            apply_funcs=[{"func": traffic_light_summary, "axis": 1, "suffix": ""}],
             assign_to_key="alerts",
             msg="Generating traffic light alerts summary.",
         ),

popmon/pipeline/report.py

@@ -425,8 +425,9 @@ class StabilityReport(Module):
     after running the pipeline and generating the report. Report can be represented
     as a HTML string, HTML file or Jupyter notebook's cell output.
     """
-    _input_keys = ("read_key", )
-    _output_keys = tuple()
+
+    _input_keys = ("read_key",)
+    _output_keys = ()
 
     def __init__(self, read_key="html_report"):
         """Initialize an instance of StabilityReport.

popmon/stitching/hist_stitcher.py

@@ -29,7 +29,7 @@ class HistStitcher(Module):
     """Module stitches histograms by date"""
 
     _input_keys = ("read_key", "delta_key")
-    _output_keys = ("store_key", )
+    _output_keys = ("store_key",)
 
     def __init__(
         self,

popmon/visualization/alert_section_generator.py

@@ -36,8 +36,9 @@ class AlertSectionGenerator(Module):
     combines all the plots into a list which is stored together with the section name in a dictionary
     which later will be used for the report generation.
     """
+
     _input_keys = ("read_key", "static_bounds", "dynamic_bounds", "store_key")
-    _output_keys = ("store_key", )
+    _output_keys = ("store_key",)
 
     def __init__(
         self,
@@ -106,7 +107,7 @@ def transform(
         data_obj: dict,
         static_bounds: Optional[dict] = None,
         dynamic_bounds: Optional[dict] = None,
-        sections: Optional[list] = None
+        sections: Optional[list] = None,
     ):
         assert isinstance(data_obj, dict)
         if static_bounds is None:

popmon/visualization/histogram_section.py

@@ -37,8 +37,9 @@
 
 class HistogramSection(Module):
     """This module plots histograms of all selected features for the last 'n' periods."""
+
     _input_keys = ("read_key", "store_key")
-    _output_keys = ("store_key", )
+    _output_keys = ("store_key",)
 
     def __init__(
         self,

popmon/visualization/report_generator.py

@@ -29,8 +29,9 @@ class ReportGenerator(Module):
     """This module takes already prepared section data, renders HTML section template with the data and
     glues sections together into one compressed report which is created based on the provided template.
     """
-    _input_keys = ("read_key", )
-    _output_keys = ("store_key", )
+
+    _input_keys = ("read_key",)
+    _output_keys = ("store_key",)
 
     def __init__(self, read_key, store_key):
         """Initialize an instance of ReportGenerator.

popmon/visualization/section_generator.py

@@ -35,8 +35,9 @@ class SectionGenerator(Module):
     combines all the plots into a list which is stored together with the section name in a dictionary
     which later will be used for the report generation.
     """
+
     _input_keys = ("read_key", "static_bounds", "dynamic_bounds", "store_key")
-    _output_keys = ("store_key", )
+    _output_keys = ("store_key",)
 
     def __init__(
         self,
@@ -98,7 +99,13 @@ def __init__(
     def get_description(self):
         return self.section_name
 
-    def transform(self, data_obj: dict, static_bounds: Optional[dict] = None, dynamic_bounds: Optional[dict] = None, sections: Optional[list] = None):
+    def transform(
+        self,
+        data_obj: dict,
+        static_bounds: Optional[dict] = None,
+        dynamic_bounds: Optional[dict] = None,
+        sections: Optional[list] = None,
+    ):
         if static_bounds is None:
             static_bounds = {}
         if dynamic_bounds is None: