diff --git a/plot_power.py b/plot_power.py index 22841d6..280a890 100644 --- a/plot_power.py +++ b/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}") diff --git a/power_log.csv b/power_log.csv index 27b4e7b..e03217f 100644 --- a/power_log.csv +++ b/power_log.csv @@ -1,39 +1,3 @@ -timestamp,apu_w,gpu_w,total_w,apu_temp,gpu_temp -2025-11-05T17:47:45-05:00,15.19,9.00,24.19,73.0,68.0 -2025-11-05T17:47:46-05:00,48.01,11.00,59.01,73.0,68.0 -2025-11-05T17:47:47-05:00,51.10,9.00,60.10,73.0,68.0 -2025-11-05T17:47:48-05:00,10.03,9.00,19.03,74.0,68.0 -2025-11-05T17:47:49-05:00,6.20,9.00,15.20,72.0,68.0 -2025-11-05T17:47:50-05:00,9.13,9.00,18.13,73.0,68.0 -2025-11-05T17:47:51-05:00,44.19,10.00,54.19,72.0,68.0 -2025-11-05T17:47:52-05:00,6.17,10.00,16.17,73.0,68.0 -2025-11-05T17:47:53-05:00,15.14,9.00,24.14,73.0,68.0 -2025-11-05T17:47:54-05:00,64.07,10.00,74.07,73.0,68.0 -2025-11-05T17:47:55-05:00,5.07,9.00,14.07,73.0,68.0 -2025-11-05T17:47:56-05:00,62.04,9.00,71.04,73.0,68.0 -2025-11-05T17:47:57-05:00,56.03,9.00,65.03,72.0,68.0 -2025-11-05T17:47:58-05:00,11.24,9.00,20.24,73.0,68.0 -2025-11-05T17:47:59-05:00,58.00,9.00,67.00,74.0,68.0 -2025-11-05T17:48:00-05:00,20.01,9.00,29.01,72.0,68.0 -2025-11-05T17:48:01-05:00,59.06,9.00,68.06,72.0,68.0 -2025-11-05T17:48:02-05:00,53.22,10.00,63.22,73.0,68.0 -2025-11-05T18:00:36-05:00,4.14,9.00,13.14,73.0,68.0 -2025-11-05T18:00:37-05:00,57.16,9.00,66.16,73.0,68.0 -2025-11-05T18:00:38-05:00,36.22,11.00,47.22,73.0,68.0 -2025-11-05T18:00:39-05:00,57.07,10.00,67.07,72.0,68.0 -2025-11-05T18:00:40-05:00,14.22,9.00,23.22,73.0,68.0 -2025-11-05T18:00:41-05:00,59.11,9.00,68.11,73.0,68.0 -2025-11-05T18:00:42-05:00,34.09,10.00,44.09,75.0,68.0 -2025-11-05T18:00:43-05:00,57.17,11.00,68.17,72.0,68.0 -2025-11-05T18:00:44-05:00,53.15,9.00,62.15,72.0,68.0 -2025-11-05T18:00:45-05:00,7.11,9.00,16.11,72.0,68.0 -2025-11-05T18:00:46-05:00,41.16,9.00,50.16,73.0,68.0 -2025-11-05T18:00:47-05:00,14.17,9.00,23.17,72.0,68.0 -2025-11-05T18:00:48-05:00,45.11,9.00,54.11,73.0,68.0 -2025-11-05T18:00:50-05:00,57.09,9.00,66.09,73.0,68.0 -2025-11-05T18:00:51-05:00,1.15,9.00,10.15,73.0,68.0 -2025-11-05T18:00:52-05:00,57.00,9.00,66.00,73.0,68.0 -2025-11-05T18:00:53-05:00,56.23,10.00,66.23,73.0,68.0 2025-11-05T18:00:54-05:00,7.17,9.00,16.17,73.0,68.0 2025-11-05T18:00:55-05:00,60.01,9.00,69.01,73.0,68.0 2025-11-05T18:00:56-05:00,47.02,9.00,56.02,74.0,68.0 @@ -480,3 +444,57 @@ timestamp,apu_w,gpu_w,total_w,apu_temp,gpu_temp 2025-11-05T18:34:08-05:00,2.14,9.00,11.14,70.0,67.0 2025-11-05T18:34:09-05:00,48.09,9.00,57.09,70.0,67.0 2025-11-05T18:34:10-05:00,58.19,9.00,67.19,70.0,67.0 +2025-11-05T18:42:08-05:00,51.04,9.00,60.04,71.0,67.0 +2025-11-05T18:42:09-05:00,52.22,9.00,61.22,72.0,67.0 +2025-11-05T18:42:10-05:00,60.13,9.00,69.13,72.0,67.0 +2025-11-05T18:42:11-05:00,54.00,9.00,63.00,71.0,67.0 +2025-11-05T18:42:12-05:00,53.10,9.00,62.10,71.0,67.0 +2025-11-05T18:42:13-05:00,56.09,9.00,65.09,72.0,67.0 +2025-11-05T18:42:14-05:00,59.11,9.00,68.11,72.0,67.0 +2025-11-05T18:42:15-05:00,55.07,11.00,66.07,71.0,67.0 +2025-11-05T18:42:16-05:00,51.06,9.00,60.06,71.0,67.0 +2025-11-05T18:42:17-05:00,60.22,9.00,69.22,72.0,67.0 +2025-11-05T18:42:18-05:00,4.20,10.00,14.20,71.0,67.0 +2025-11-05T18:42:19-05:00,49.13,9.00,58.13,71.0,67.0 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