Power update + bar graph change

plot_power.py

@@ -1,8 +1,7 @@
 #!/usr/bin/env python3
 """
 plot_power.py
-Generates a PNG graph from power_log.csv for AMD Laptop APU/GPU telemetry.
-Designed for Forgejo CI artifact publishing.
+Forgejo CI: visualize AMD TUF APU/GPU telemetry as a clear bar graph.
 Author: MarkMental
 """
 
@@ -10,55 +9,55 @@ import pandas as pd
 import matplotlib.pyplot as plt
 from datetime import datetime
 
-# --- Configuration ---
 CSV_PATH = "power_log.csv"
 OUTPUT_FILE = "power_graph.png"
 
 print(f"📊 Reading {CSV_PATH}...")
+df = pd.read_csv(CSV_PATH)
 
-# --- Load data ---
-try:
-    df = pd.read_csv(CSV_PATH)
-except FileNotFoundError:
-    raise SystemExit(f"❌ CSV not found: {CSV_PATH}")
+# --- Normalize timestamp ---
+df["timestamp"] = pd.to_datetime(df["timestamp"], errors="coerce")
+df["time_fmt"] = df["timestamp"].dt.strftime("%Y-%m-%d %H:%M:%S")
 
-# --- Sanity checks ---
-required_cols = {"timestamp", "apu_w", "gpu_w", "total_w"}
-if not required_cols.issubset(df.columns):
-    raise SystemExit(f"❌ Missing columns in {CSV_PATH}. Found: {df.columns.tolist()}")
+# --- Downsample if extremely long (avoid thousands of bars) ---
+if len(df) > 200:
+    df = df.iloc[::max(1, len(df)//200), :]
 
-# --- Parse timestamps ---
-try:
-    df["timestamp"] = pd.to_datetime(df["timestamp"], errors="coerce")
-except Exception as e:
-    print(f"⚠️ Failed to parse some timestamps: {e}")
+# --- Plot setup ---
+fig, ax1 = plt.subplots(figsize=(12, 6))
 
-# --- Plot ---
-plt.figure(figsize=(10, 5))
-plt.plot(df["timestamp"], df["apu_w"], label="APU Power (W)", color="orange", linewidth=1.3)
-plt.plot(df["timestamp"], df["gpu_w"], label="GPU Power (W)", color="green", linewidth=1.3)
-plt.plot(df["timestamp"], df["total_w"], label="Total Power (W)", color="blue", linewidth=1.5)
+# Power bars
+width = 0.3
+x = range(len(df))
+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")
 
-# Optional: plot temperatures if present
+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 temperature ---
 if "apu_temp" in df.columns and "gpu_temp" in df.columns:
-    ax2 = plt.gca().twinx()
-    ax2.plot(df["timestamp"], df["apu_temp"], "--", color="red", alpha=0.4, label="APU Temp (°C)")
-    ax2.plot(df["timestamp"], df["gpu_temp"], "--", color="purple", alpha=0.4, label="GPU Temp (°C)")
+    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)")
-    lines, labels = plt.gca().get_legend_handles_labels()
+    ax2.set_ylim(min(df[["apu_temp","gpu_temp"]].min())-5,
+                 max(df[["apu_temp","gpu_temp"]].max())+5)
+
+    # Combine legends
+    lines1, labels1 = ax1.get_legend_handles_labels()
     lines2, labels2 = ax2.get_legend_handles_labels()
-    plt.legend(lines + lines2, labels + labels2, loc="upper left")
+    ax1.legend(lines1 + lines2, labels1 + labels2, loc="upper left")
 else:
-    plt.legend(loc="upper left")
+    ax1.legend(loc="upper left")
 
-plt.title("AMD TUF Power Log")
-plt.xlabel("Time")
-plt.ylabel("Power (Watts)")
-plt.grid(True, alpha=0.3)
-plt.xticks(rotation=45)
+plt.grid(axis="y", alpha=0.3)
 plt.tight_layout()
+plt.savefig(OUTPUT_FILE, dpi=150)
+print(f"✅ Saved clean bar graph: {OUTPUT_FILE}")
 
-# --- Save ---
-plt.savefig(OUTPUT_FILE, dpi=120)
-print(f"✅ Saved graph: {OUTPUT_FILE}")