Joscha
a4ac55036f
https://packaging.python.org/en/latest/specifications/inline-script-metadata/#inline-script-metadata
110 lines
2.8 KiB
Python
110 lines
2.8 KiB
Python
# /// script
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# requires-python = ">=3.11"
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# dependencies = [
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# "matplotlib",
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# "pandas",
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# "scipy",
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# ]
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# ///
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import argparse
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import datetime
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import json
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from collections import Counter
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from pathlib import Path
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import matplotlib.dates as mdates
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import matplotlib.pyplot as plt
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import pandas as pd
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class Data:
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def __init__(self):
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self.by_day = Counter()
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def add_msg(self, msg):
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dt = datetime.datetime.fromtimestamp(msg["time"], tz=datetime.UTC)
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self.by_day[dt.date()] += 1
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("json_logs", type=Path, nargs="+")
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args = parser.parse_args()
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data = Data()
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for path in args.json_logs:
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print(f"Reading {path}")
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n = 0
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with open(path) as f:
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for line in f:
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data.add_msg(json.loads(line))
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n += 1
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if n % 100000 == 0:
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print(f"{n:10}")
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print(f"{n:10}")
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first_day = min(data.by_day.keys())
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last_day = max(data.by_day.keys())
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print(f"Got logs from {first_day} to {last_day}")
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# The following code was generated by ChatGPT and adjusted by me
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# Extract sorted dates and counts
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dates = sorted(data.by_day.keys())
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counts = [data.by_day[date] for date in dates]
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# Convert to DataFrame for moving average calculation
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df = pd.DataFrame({"date": dates, "count": counts})
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df.set_index("date", inplace=True)
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df = df.asfreq("D", fill_value=0) # Fill missing dates with 0
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avg = df["count"].rolling(
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window=30,
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min_periods=1,
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center=True,
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win_type="gaussian",
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)
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df["30_day_avg"] = avg.mean(std=8)
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# Create the plot
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fig, ax = plt.subplots(figsize=(10, 5))
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ax.plot(
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df.index,
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df["count"],
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linestyle="-",
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linewidth=0.5,
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color="#348ceb",
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label="Messages",
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)
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ax.plot(
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df.index,
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df["30_day_avg"],
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linestyle="-",
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linewidth=0.5,
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color="#a80303",
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label="30-day moving average",
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)
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# Format x-axis for better readability
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ax.xaxis.set_major_locator(mdates.YearLocator()) # Major ticks per year
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ax.xaxis.set_minor_locator(mdates.MonthLocator()) # Minor ticks per month
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ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y")) # Year labels
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ax.set_xlabel("Time")
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ax.set_ylabel("Messages")
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ax.set_ylim(0) # Start y-axis at 0
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ax.set_title("Activity Graph™")
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ax.legend()
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ax.grid(True, which="both", linestyle="--", linewidth=0.5)
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# Rotate x-axis labels for clarity
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plt.xticks(rotation=45)
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plt.tight_layout()
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# Save the plot to a PNG file
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plt.savefig("activity_graph.png", dpi=300)
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plt.close()
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if __name__ == "__main__":
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main()
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