'Refactored by Sourcery'

examples/phenodata-mellifera/demo.py

@@ -38,10 +38,12 @@ def define_and_register_data():
     # Sample annotation reader.
     def get_mellifera_flowering(query_string, ts_range):
         query = dict(urllib.parse.parse_qsl(query_string))
-        series = phenodata_mellifera(
-            dataset="immediate", years=tuple([2019, 2020, 2021]), phases=tuple([5, 7]), options=tuple(query.items())
+        return phenodata_mellifera(
+            dataset="immediate",
+            years=(2019, 2020, 2021),
+            phases=(5, 7),
+            options=tuple(query.items()),
         )
-        return series
 
     # Register data generators.
     dg.add_annotation_reader("flowering", get_mellifera_flowering)
@@ -75,7 +77,7 @@ def phenodata_mellifera(dataset: str, years: tuple[int], phases: tuple[str], opt
         data_total.append(data_past)
 
     if "forecast" in options["type"]:
-        next_year = (datetime.today() + timedelta(days=365)).year
+        next_year = (datetime.now() + timedelta(days=365)).year
         data_future = client.get_forecast(
             phenodata_options, forecast_year=next_year, humanize=phenodata_options["humanize"]
         )

grafana_pandas_datasource/core.py

@@ -16,8 +16,7 @@ def dataframe_to_response(target, df, freq=None):
     if isinstance(df, pd.Series):
         response.append(_series_to_response(df, target))
     elif isinstance(df, pd.DataFrame):
-        for col in df:
-            response.append(_series_to_response(df[col], target))
+        response.extend(_series_to_response(df[col], target) for col in df)
     else:
         abort(404, Exception("Received object is not a pandas DataFrame or Series."))
 
@@ -49,18 +48,19 @@ def annotations_to_response(target, df):
 
     # Single series with DatetimeIndex and values as text
     if isinstance(df, pd.Series):
-        for timestamp, value in df.iteritems():
-            response.append(
-                {
-                    "annotation": target,  # The original annotation sent from Grafana.
-                    "time": timestamp.value // 10**6,  # Time since UNIX Epoch in milliseconds. (required)
-                    "title": value,  # The title for the annotation tooltip. (required)
-                    # "tags": tags, # Tags for the annotation. (optional)
-                    # "text": text # Text for the annotation. (optional)
-                }
-            )
-
-    # DataFrame with annotation text/tags for each entry
+        response.extend(
+            {
+                "annotation": target,  # The original annotation sent from Grafana.
+                "time": timestamp.value
+                // 10
+                ** 6,  # Time since UNIX Epoch in milliseconds. (required)
+                "title": value,  # The title for the annotation tooltip. (required)
+                # "tags": tags, # Tags for the annotation. (optional)
+                # "text": text # Text for the annotation. (optional)
+            }
+            for timestamp, value in df.iteritems()
+        )
+
     elif isinstance(df, pd.DataFrame):
         for timestamp, row in df.iterrows():
             annotation = {
@@ -83,23 +83,23 @@ def annotations_to_response(target, df):
 
 def _series_to_annotations(df, target):
     if df.empty:
-        return {"target": "%s" % (target), "datapoints": []}
+        return {"target": f"{target}", "datapoints": []}
 
     sorted_df = df.dropna().sort_index()
     timestamps = (sorted_df.index.astype(pd.np.int64) // 10**6).values.tolist()
     values = sorted_df.values.tolist()
 
-    return {"target": "%s" % (df.name), "datapoints": list(zip(values, timestamps))}
+    return {"target": f"{df.name}", "datapoints": list(zip(values, timestamps))}
 
 
 def _series_to_response(df, target):
     if df.empty:
-        return {"target": "%s" % (target), "datapoints": []}
+        return {"target": f"{target}", "datapoints": []}
 
     sorted_df = df.dropna().sort_index()
 
     timestamps = (sorted_df.index.view(np.int64) // 10**6).tolist()
 
     values = sorted_df.values.tolist()
 
-    return {"target": "%s" % (df.name), "datapoints": list(zip(values, timestamps))}
+    return {"target": f"{df.name}", "datapoints": list(zip(values, timestamps))}

grafana_pandas_datasource/logging.py

@@ -46,8 +46,7 @@ def __call__(self, environ, start_response):
 
         def log_response(status, headers, *args):
             pprint(("RESPONSE", status, headers), stream=errorlog)
-            response = start_response(status, headers, *args)
-            return response
+            return start_response(status, headers, *args)
 
         # Capture response body.
         app_iter = self._app(environ, log_response)

grafana_pandas_datasource/service.py

@@ -42,17 +42,16 @@ def find_metrics():
     else:
         finder = target
 
-    if not target or finder not in dg.metric_finders:
-        metrics = []
-        if target == "*":
-            metrics += dg.metric_finders.keys()
-            metrics += dg.metric_readers.keys()
-        else:
-            metrics.append(target)
-
-        return jsonify(metrics)
-    else:
+    if target and finder in dg.metric_finders:
         return jsonify(list(dg.metric_finders[finder](target)))
+    metrics = []
+    if target == "*":
+        metrics += dg.metric_finders.keys()
+        metrics += dg.metric_readers.keys()
+    else:
+        metrics.append(target)
+
+    return jsonify(metrics)
 
 
 @pandas_component.route("/query", methods=methods)
@@ -68,11 +67,7 @@ def query_metrics():
         "$lte": pd.Timestamp(req["range"]["to"]).to_pydatetime(),
     }
 
-    if "intervalMs" in req:
-        freq = str(req.get("intervalMs")) + "ms"
-    else:
-        freq = None
-
+    freq = str(req.get("intervalMs")) + "ms" if "intervalMs" in req else None
     for target in req["targets"]:
         if ":" not in target.get("target", ""):
             abort(404, Exception("Target must be of type: <finder>:<metric_query>, got instead: " + target["target"]))