65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
from datetime import datetime
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import json
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from typing import Dict
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import matplotlib.pyplot as plt
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# Config
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FILE_PATH = "log.txt"
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# Read file
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content = None
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with open(FILE_PATH, "r") as file:
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content = file.readlines()
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def parse_log_entry(entry: Dict) -> Dict:
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if entry["countChange"] == 0:
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return False
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entry["dateTime"] = datetime.strptime(
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str(entry["dateTime"])[:19], "%Y-%m-%d %H:%M:%S")
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if entry["dateTime"] < datetime(2022, 1, 1):
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return False
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return entry
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# Collect
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log = [json.loads(line.strip("\x00")) for line in content]
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print("Number of entries:", len(log))
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# Parse & Filter
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log = [parse_log_entry(entry) for entry in log if parse_log_entry(entry)]
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print("Number of counts:", len(log))
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# Render
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fig, ax = plt.subplots() # Create a figure containing a single axes.
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times: list[datetime] = [entry["dateTime"] for entry in log]
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counts: list[int] = [entry["previousPeopleCount"] for entry in log]
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ax.step(times, counts, where="pre")
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plt.show()
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# Calculate faults
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for c, n in zip(list(range(len(log))), list(range(len(log)))[1:]):
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estimated_count: int = log[c]["previousPeopleCount"] + \
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log[c]["countChange"]
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faulty: bool = estimated_count != log[n]["previousPeopleCount"]
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log[c]["faulty"] = faulty
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log[c]["faultyCount"] = log[c]["previousPeopleCount"] if faulty else None
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log = log[:-1]
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fault_count = sum(1 for entry in log if entry["faulty"])
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print("Number of faults:", fault_count)
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print("Percentage of faults:", fault_count / len(log) * 100)
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print("-"*20)
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faulty_off = [entry for entry in log if entry["faulty"]
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and entry["faultyCount"] == 0]
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faulty_on = [entry for entry in log if entry["faulty"]
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and entry["faultyCount"] != 0]
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print("Number of false-0:", len(faulty_off))
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print("Number of false-1:", len(faulty_on))
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print("Percentage of false-0:", len(faulty_off) / fault_count * 100)
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print("Percentage of false-1:", len(faulty_on) / fault_count * 100)
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