From c48aa6f14f6b76774ec63b561551e1f284e0279c Mon Sep 17 00:00:00 2001
From: Salastil <salastil@cock.li>
Date: Tue, 5 May 2026 23:53:20 -0400
Subject: [PATCH] Upload files to "/"

---
 4stat2.py | 82 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 82 insertions(+)
 create mode 100644 4stat2.py

diff --git a/4stat2.py b/4stat2.py
new file mode 100644
index 0000000..eb8c73e
--- /dev/null
+++ b/4stat2.py
@@ -0,0 +1,82 @@
+#!/bin/env python3
+import json
+import numpy as np
+import pandas as pd
+import requests
+import matplotlib.pyplot as plt
+from datetime import datetime
+from scipy.stats import linregress
+
+# === Step 1: Download JSON from 4stats.io ===
+print("📡 Fetching data from 4stats.io...")
+response = requests.get("https://api.4stats.io/history/day/tv", headers={
+    "User-Agent": "Mozilla/5.0 (X11; Linux x86_64; rv:139.0) Gecko/20100101 Firefox/139.0",
+    "Accept": "application/json, text/plain, */*",
+    "Accept-Encoding": "gzip, deflate, br, zstd",
+    "Origin": "https://4stats.io",
+    "DNT": "1",
+    "Connection": "keep-alive",
+    "Pragma": "no-cache",
+    "Cache-Control": "no-cache"
+})
+if response.status_code != 200:
+    raise Exception(f"Failed to fetch data. Status code: {response.status_code}")
+data = response.json()
+print(f"✅ Downloaded {len(data)} records.")
+
+# === Step 2: Filter from Jan 1, 2023 onward ===
+cutoff = datetime(2023, 1, 1).timestamp() * 1000  # in ms
+filtered_data = [row for row in data if row[0] >= cutoff]
+print(f"✂️ Trimmed to {len(filtered_data)} records from 2023 onward.")
+
+# === Step 3: Normalize "Posts Per Day" (column 3) ===
+filtered_data = [row for row in filtered_data if row[2] is not None]  # drop None values
+print(f"🧹 Dropped None rows, {len(filtered_data)} records remaining.")
+
+timestamps = np.array([row[0] for row in filtered_data])
+posts_per_day = np.array([row[2] for row in filtered_data], dtype=float)
+
+slope, intercept, *_ = linregress(timestamps, posts_per_day)
+trendline = slope * timestamps + intercept
+mean_value = posts_per_day.mean()
+adjusted_posts_per_day = posts_per_day - (trendline - mean_value)
+adjusted_data = [
+    [row[0], row[1], float(round(adj, 2)), row[3]]
+    for row, adj in zip(filtered_data, adjusted_posts_per_day)
+]
+
+# === Step 4: Convert adjusted data into DataFrame ===
+df = pd.DataFrame(adjusted_data, columns=["Timestamp", "Column_2", "Posts_Per_Day", "Column_4"])
+df["Date"] = pd.to_datetime(df["Timestamp"], unit="ms")
+
+# === Step 5: Define Season Ranges ===
+seasons = {
+    "Season 1": ("2023-04-18", "2023-05-30"),
+    "Season 2": ("2023-12-18", "2024-01-28"),
+    "Season 3": ("2024-10-27", "2024-12-07"),
+    "Season 4": ("2025-06-13", "2025-06-30"),
+    "Bloodgames-Bitchtank": ("2024-06-24", "2024-08-08"),
+    "Season 5": ("2026-03-15", "2026-04-13")
+}
+season_ranges = {
+    name: (datetime.strptime(start, "%Y-%m-%d"), datetime.strptime(end, "%Y-%m-%d"))
+    for name, (start, end) in seasons.items()
+}
+
+# === Step 6: Plot the Graph ===
+styles = ['-', '--', '-.', ':', (0,(3,1,1,1)), (0,(5,1))]
+plt.figure(figsize=(10, 6))
+for i, (season_name, (start_date, end_date)) in enumerate(season_ranges.items()):
+    season_df = df[(df["Date"] >= start_date) & (df["Date"] <= end_date)].copy()
+    season_df["Day_Index"] = (season_df["Date"] - start_date).dt.days + 1
+    season_df = season_df[season_df["Day_Index"] <= 42]
+    plt.plot(season_df["Day_Index"], season_df["Posts_Per_Day"], label=season_name, linestyle=styles[i], linewidth=2)
+
+
+plt.xlabel("Days (1 to 42)")
+plt.ylabel("Posts Per Day")
+plt.title("Posts Per Day Across Seasons (4chan /tv/)")
+plt.legend()
+plt.grid(True)
+plt.tight_layout()
+plt.show()