实现基于告警阈值的错误率检测

def solve():
    from pyodide.http import open_url
    from io import StringIO
    alerts_csv = open_url(____).read()
    request_logs_csv = open_url(____).read()
    import pandas as pd
    from io import StringIO
    alerts = pd.read_csv(StringIO(____))
    logs = pd.read_csv(StringIO(____))
    RULES = [
        {'____':'____','____':'____','____':____,'____':____},
        {'____':'____','____':'____','____':____,'____':____},
    ]
    def check_alert(____):
        fired = []
        if rule['____'] == '____':
            for path, grp in logs_df.groupby(____):
                rate = (____).mean()
                if rate > rule['____']:
                    fired.append({'____':path,'____':round(____),'____':rule['____']})
        elif rule['____'] == '____':
            for path, grp in logs_df.groupby(____):
                vals = grp['____'].sort_values()
                p99 = float(vals.iloc[int(len(____)*____)]) if len(____) > ____ else ____
                if p99 > rule['____']:
                    fired.append(____)
        return fired
    print(____)
    print(alerts[['____','____','____','____','____']].to_string(____))
    print(____)
    for rule in RULES:
        fired = check_alert(____)
        for f in fired:
            print(____)