def plot_graphs()

in scripts/shadow_scheduler_stats.py [0:0]
68 lines of code
8 McCabe index (conditional complexity)

def plot_graphs(granularity: str) -> None:
    push_data_db = (
        test_scheduling.PUSH_DATA_GROUP_DB
        if granularity == "group"
        else test_scheduling.PUSH_DATA_CONFIG_GROUP_DB
    )
    assert db.download(push_data_db)

    regressions_by_rev = {}
    for revisions, _, _, possible_regressions, likely_regressions in db.read(
        push_data_db
    ):
        regressions_by_rev[revisions[0]] = get_regressions(
            granularity, likely_regressions, possible_regressions
        )

    scheduled_data = []
    caught_data = []

    for scheduler_stat in db.read(SHADOW_SCHEDULER_STATS_DB):
        if len(scheduler_stat["schedulers"]) == 0:
            continue

        if scheduler_stat["id"] not in regressions_by_rev:
            continue

        obj: dict[str, Any] = {
            "date": datetime.utcfromtimestamp(scheduler_stat["date"]),
        }

        for scheduler in scheduler_stat["schedulers"]:
            obj[scheduler["name"]] = len(get_scheduled(granularity, scheduler))

        scheduled_data.append(obj)

        regressions = regressions_by_rev[scheduler_stat["id"]]

        obj = {
            "date": datetime.utcfromtimestamp(scheduler_stat["date"]),
            "regressions": len(regressions),
        }

        for scheduler in scheduler_stat["schedulers"]:
            scheduled = get_scheduled(granularity, scheduler)

            obj[scheduler["name"]] = len(regressions & scheduled)

        caught_data.append(obj)

    scheduled_df = DataFrame(scheduled_data)
    scheduled_df.index = scheduled_df["date"]
    del scheduled_df["date"]

    caught_df = DataFrame(caught_data)
    caught_df.index = caught_df["date"]
    del caught_df["date"]

    df = scheduled_df.resample("W").mean()

    plot_graph(
        df,
        f"Average number of scheduled {granularity}s",
        f"average_{granularity}_scheduled.svg",
    )

    df = (
        caught_df[caught_df.regressions > 0]
        .drop(columns=["regressions"])
        .clip(0, 1)
        .resample("W")
        .mean()
    )

    plot_graph(
        df,
        "Percentage of regressing pushes where we caught at least one regression",
        f"percentage_{granularity}_caught_at_least_one.svg",
    )

    plot_graph(
        caught_df.drop(columns=["regressions"])
        .div(caught_df.regressions, axis=0)
        .resample("W")
        .mean(),
        "Percentage of regressions we caught",
        f"percentage_{granularity}_caught.svg",
    )