Python black linting

Signed-off-by: Rob Syme <[email protected]>

bin/pipeline-gantt.py

@@ -15,46 +15,76 @@ def extract_instance(fusion_logs: Path) -> str:
         for line_number, line in enumerate(file, start=1):
             try:
                 log = json.loads(line)
-                if 'instance-id' in log:
-                    return log['instance-id']
+                if "instance-id" in log:
+                    return log["instance-id"]
             except json.JSONDecodeError:
                 print(f"WARN: invalid JSON at '{fusion_logs}' line {line_number}")
         return ""
 
+
 @click.command()
-@click.option('--title', default='Pipeline GANTT', help='Plot title.')
-@click.option('--input-dir', type=click.Path(), help='The pipeline dump tar.gz input file.')
-@click.option('--output-file', type=click.Path(), help='The HTML output file')
+@click.option("--title", default="Pipeline GANTT", help="Plot title.")
+@click.option(
+    "--input-dir", type=click.Path(), help="The pipeline dump tar.gz input file."
+)
+@click.option("--output-file", type=click.Path(), help="The HTML output file")
 def build_gantt(title: str, input_dir: str, output_file: str):
     tasks = []
     instance_ids = {}
 
-    for path in Path(input_dir).glob('workflow-tasks.json'):
+    for path in Path(input_dir).glob("workflow-tasks.json"):
         with path.open() as json_file:
             tasks = json.load(json_file)
-    for path in Path(input_dir).glob('**/.fusion.log'):
+    for path in Path(input_dir).glob("**/.fusion.log"):
         task_id = int(path.parent.name)
         instance_id = extract_instance(path)
         instance_ids[task_id] = instance_id
 
     for t in tasks:
-        t['instanceId'] = instance_ids.get(t['taskId'], "unknow")
-
-    data = [{k: v for k, v in t.items() if k in ['taskId', 'name', 'start', 'complete', 'memory', 'cpus', 'machineType', 'instanceId']} for t in tasks]
-    df = pd.DataFrame({
-                          'id': f"T{d['taskId']}",
-                          'name': d['name'],
-                          'size': f"{d['cpus']}c_{d['memory'] / 1024 ** 3:.0f}GB",
-                          'start': datetime.strptime(d['start'], '%Y-%m-%dT%H:%M:%SZ'),
-                          'complete': datetime.strptime(d['complete'], '%Y-%m-%dT%H:%M:%SZ'),
-                          'instance': f"{d['instanceId']} ({d['machineType']})"
-                      }
-                      for d in data
-                      )
-
-    fig = px.timeline(df, title=title, x_start="start", x_end="complete", y="id", color="instance", text="name", pattern_shape="size")
+        t["instanceId"] = instance_ids.get(t["taskId"], "unknow")
+
+    data = [
+        {
+            k: v
+            for k, v in t.items()
+            if k
+            in [
+                "taskId",
+                "name",
+                "start",
+                "complete",
+                "memory",
+                "cpus",
+                "machineType",
+                "instanceId",
+            ]
+        }
+        for t in tasks
+    ]
+    df = pd.DataFrame(
+        {
+            "id": f"T{d['taskId']}",
+            "name": d["name"],
+            "size": f"{d['cpus']}c_{d['memory'] / 1024 ** 3:.0f}GB",
+            "start": datetime.strptime(d["start"], "%Y-%m-%dT%H:%M:%SZ"),
+            "complete": datetime.strptime(d["complete"], "%Y-%m-%dT%H:%M:%SZ"),
+            "instance": f"{d['instanceId']} ({d['machineType']})",
+        }
+        for d in data
+    )
+
+    fig = px.timeline(
+        df,
+        title=title,
+        x_start="start",
+        x_end="complete",
+        y="id",
+        color="instance",
+        text="name",
+        pattern_shape="size",
+    )
     fig.write_html(output_file)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     build_gantt()