# /// script
# requires-python = ">=3.11"
# dependencies = [
#     "matplotlib",
#     "pandas",
#     "scipy",
# ]
# ///

import argparse
import datetime
import json
from collections import Counter
from pathlib import Path

import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import pandas as pd


class Data:
    def __init__(self):
        self.by_day = Counter()

    def add_msg(self, msg):
        dt = datetime.datetime.fromtimestamp(msg["time"], tz=datetime.UTC)
        self.by_day[dt.date()] += 1


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("json_logs", type=Path, nargs="+")
    args = parser.parse_args()

    data = Data()

    for path in args.json_logs:
        print(f"Reading {path}")
        n = 0
        with open(path) as f:
            for line in f:
                data.add_msg(json.loads(line))
                n += 1
                if n % 100000 == 0:
                    print(f"{n:10}")
        print(f"{n:10}")

    first_day = min(data.by_day.keys())
    last_day = max(data.by_day.keys())
    print(f"Got logs from {first_day} to {last_day}")

    # The following code was generated by ChatGPT and adjusted by me

    # Extract sorted dates and counts
    dates = sorted(data.by_day.keys())
    counts = [data.by_day[date] for date in dates]

    # Convert to DataFrame for moving average calculation
    df = pd.DataFrame({"date": dates, "count": counts})
    df.set_index("date", inplace=True)
    df = df.asfreq("D", fill_value=0)  # Fill missing dates with 0
    avg = df["count"].rolling(
        window=30,
        min_periods=1,
        center=True,
        win_type="gaussian",
    )
    df["30_day_avg"] = avg.mean(std=8)

    # Create the plot
    fig, ax = plt.subplots(figsize=(10, 5))
    ax.plot(
        df.index,
        df["count"],
        linestyle="-",
        linewidth=0.5,
        color="#348ceb",
        label="Messages",
    )
    ax.plot(
        df.index,
        df["30_day_avg"],
        linestyle="-",
        linewidth=0.5,
        color="#a80303",
        label="30-day moving average",
    )

    # Format x-axis for better readability
    ax.xaxis.set_major_locator(mdates.YearLocator())  # Major ticks per year
    ax.xaxis.set_minor_locator(mdates.MonthLocator())  # Minor ticks per month
    ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y"))  # Year labels
    ax.set_xlabel("Time")
    ax.set_ylabel("Messages")
    ax.set_ylim(0)  # Start y-axis at 0
    ax.set_title("Activity Graph™")
    ax.legend()
    ax.grid(True, which="both", linestyle="--", linewidth=0.5)

    # Rotate x-axis labels for clarity
    plt.xticks(rotation=45)
    plt.tight_layout()

    # Save the plot to a PNG file
    plt.savefig("activity_graph.png", dpi=300)
    plt.close()


if __name__ == "__main__":
    main()