File size: 1,523 Bytes
83bccb1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | import numpy as np
import pandas as pd
def compute_changes(events: pd.DataFrame, window_hours: int) -> pd.DataFrame:
if events.empty:
return pd.DataFrame(columns=["source", "current", "previous", "delta"])
end_time = events["timestamp"].max()
start_current = end_time - pd.Timedelta(hours=window_hours)
start_previous = start_current - pd.Timedelta(hours=window_hours)
current = events[(events["timestamp"] >= start_current) & (events["timestamp"] <= end_time)]
previous = events[(events["timestamp"] >= start_previous) & (events["timestamp"] < start_current)]
c = current.groupby("source").size().rename("current")
p = previous.groupby("source").size().rename("previous")
merged = pd.concat([c, p], axis=1).fillna(0)
merged["current"] = merged["current"].astype(int)
merged["previous"] = merged["previous"].astype(int)
merged["delta"] = merged["current"] - merged["previous"]
return merged.reset_index().sort_values("delta", ascending=False)
def compute_risk_by_region(events: pd.DataFrame) -> pd.DataFrame:
if events.empty:
return pd.DataFrame(columns=["region", "event_count", "avg_severity", "risk_score"])
grouped = events.groupby("region").agg(
event_count=("source", "size"),
avg_severity=("severity", "mean"),
)
grouped["risk_score"] = (
np.log1p(grouped["event_count"]) * 14 + grouped["avg_severity"] * 16
).round(2)
return grouped.reset_index().sort_values("risk_score", ascending=False)
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