#!/usr/bin/env python3
"""
plot_power.py
Forgejo CI: visualize AMD TUF APU/GPU telemetry as a clean bar graph.
Compatible with headerless CSV output from amd-monitor.sh
Author: MarkMental
"""

import pandas as pd
import matplotlib.pyplot as plt

CSV_PATH = "power_log.csv"
OUTPUT_FILE = "power_graph.png"
RETENTION_SECONDS = 3600

print(f"📊 Reading {CSV_PATH}...")

# --- Load CSV without headers ---
df = pd.read_csv(
    CSV_PATH,
    names=["timestamp", "apu_w", "gpu_w", "total_w", "apu_temp", "gpu_temp"],
    comment="#",
)

# --- Convert timestamps ---
df["timestamp"] = pd.to_datetime(df["timestamp"], errors="coerce")
latest_ts = df["timestamp"].dropna().max()
if pd.notna(latest_ts):
    cutoff = latest_ts - pd.Timedelta(seconds=RETENTION_SECONDS)
    df = df[df["timestamp"] >= cutoff]

df["time_fmt"] = df["timestamp"].dt.strftime("%Y-%m-%d %H:%M:%S")

# --- Downsample if too many entries ---
if len(df) > 200:
    df = df.iloc[::max(1, len(df)//200), :]

# --- Plot ---
fig, ax1 = plt.subplots(figsize=(12, 6))
x = range(len(df))
width = 0.3

ax1.bar([i - width for i in x], df["apu_w"], width, label="APU Power (W)", color="#ff9933")
ax1.bar(x, df["gpu_w"], width, label="GPU Power (W)", color="#66cc66")
ax1.bar([i + width for i in x], df["total_w"], width, label="Total Power (W)", color="#3399ff")

ax1.set_xlabel("Timestamp (YYYY-MM-DD HH:MM:SS)")
ax1.set_ylabel("Power (Watts)")
ax1.set_title("AMD TUF Power & Temperature Log")
ax1.set_xticks(x[::max(1, len(x)//10)])
ax1.set_xticklabels(df["time_fmt"].iloc[::max(1, len(x)//10)], rotation=45, ha="right")

# --- Secondary axis for temperatures ---
ax2 = ax1.twinx()
ax2.plot(x, df["apu_temp"], "--", color="red", linewidth=1.2, label="APU Temp (°C)")
ax2.plot(x, df["gpu_temp"], "--", color="purple", linewidth=1.2, label="GPU Temp (°C)")
ax2.set_ylabel("Temperature (°C)")
ax2.set_ylim(min(df[["apu_temp","gpu_temp"]].min())-5,
             max(df[["apu_temp","gpu_temp"]].max())+5)

# --- Combined legend ---
lines1, labels1 = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax1.legend(lines1 + lines2, labels1 + labels2, loc="upper left")

plt.grid(axis="y", alpha=0.3)
plt.tight_layout()
plt.savefig(OUTPUT_FILE, dpi=150)
print(f"✅ Saved graph: {OUTPUT_FILE}")