Modify plotting code to handle headerless csv
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Generate Power Graph / plot (push) Successful in 1m58s
This commit is contained in:
mrkmntal
parent
974d23a7d4
commit
086e415ad9
2 changed files with 73 additions and 72 deletions
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@ -1,34 +1,39 @@
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#!/usr/bin/env python3
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"""
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plot_power.py
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Forgejo CI: visualize AMD TUF APU/GPU telemetry as a clear bar graph.
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Forgejo CI: visualize AMD TUF APU/GPU telemetry as a clean bar graph.
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Compatible with headerless CSV output from amd-monitor.sh
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Author: MarkMental
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"""
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import pandas as pd
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import matplotlib.pyplot as plt
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from datetime import datetime
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CSV_PATH = "power_log.csv"
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OUTPUT_FILE = "power_graph.png"
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print(f"📊 Reading {CSV_PATH}...")
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df = pd.read_csv(CSV_PATH)
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# --- Normalize timestamp ---
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# --- Load CSV without headers ---
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df = pd.read_csv(
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CSV_PATH,
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names=["timestamp", "apu_w", "gpu_w", "total_w", "apu_temp", "gpu_temp"],
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comment="#",
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)
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# --- Convert timestamps ---
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df["timestamp"] = pd.to_datetime(df["timestamp"], errors="coerce")
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df["time_fmt"] = df["timestamp"].dt.strftime("%Y-%m-%d %H:%M:%S")
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# --- Downsample if extremely long (avoid thousands of bars) ---
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# --- Downsample if too many entries ---
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if len(df) > 200:
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df = df.iloc[::max(1, len(df)//200), :]
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# --- Plot setup ---
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# --- Plot ---
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fig, ax1 = plt.subplots(figsize=(12, 6))
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# Power bars
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width = 0.3
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x = range(len(df))
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width = 0.3
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ax1.bar([i - width for i in x], df["apu_w"], width, label="APU Power (W)", color="#ff9933")
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ax1.bar(x, df["gpu_w"], width, label="GPU Power (W)", color="#66cc66")
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ax1.bar([i + width for i in x], df["total_w"], width, label="Total Power (W)", color="#3399ff")
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@ -39,25 +44,21 @@ ax1.set_title("AMD TUF Power & Temperature Log")
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ax1.set_xticks(x[::max(1, len(x)//10)])
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ax1.set_xticklabels(df["time_fmt"].iloc[::max(1, len(x)//10)], rotation=45, ha="right")
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# --- Secondary axis for temperature ---
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if "apu_temp" in df.columns and "gpu_temp" in df.columns:
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ax2 = ax1.twinx()
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ax2.plot(x, df["apu_temp"], "--", color="red", linewidth=1.2, label="APU Temp (°C)")
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ax2.plot(x, df["gpu_temp"], "--", color="purple", linewidth=1.2, label="GPU Temp (°C)")
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ax2.set_ylabel("Temperature (°C)")
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ax2.set_ylim(min(df[["apu_temp","gpu_temp"]].min())-5,
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# --- Secondary axis for temperatures ---
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ax2 = ax1.twinx()
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ax2.plot(x, df["apu_temp"], "--", color="red", linewidth=1.2, label="APU Temp (°C)")
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ax2.plot(x, df["gpu_temp"], "--", color="purple", linewidth=1.2, label="GPU Temp (°C)")
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ax2.set_ylabel("Temperature (°C)")
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ax2.set_ylim(min(df[["apu_temp","gpu_temp"]].min())-5,
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max(df[["apu_temp","gpu_temp"]].max())+5)
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# Combine legends
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lines1, labels1 = ax1.get_legend_handles_labels()
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lines2, labels2 = ax2.get_legend_handles_labels()
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ax1.legend(lines1 + lines2, labels1 + labels2, loc="upper left")
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else:
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ax1.legend(loc="upper left")
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# --- Combined legend ---
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lines1, labels1 = ax1.get_legend_handles_labels()
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lines2, labels2 = ax2.get_legend_handles_labels()
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ax1.legend(lines1 + lines2, labels1 + labels2, loc="upper left")
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plt.grid(axis="y", alpha=0.3)
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plt.tight_layout()
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plt.savefig(OUTPUT_FILE, dpi=150)
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print(f"✅ Saved clean bar graph: {OUTPUT_FILE}")
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print(f"✅ Saved graph: {OUTPUT_FILE}")
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