Update README.md

.forgejo/workflows/power-graph.yml

@@ -2,12 +2,12 @@ name: Generate Power Graph
 
 on:
   push:
-    branches: [ master, dev ]
+    branches: [ master ]
     paths: [ power_log.csv ]
 
 jobs:
   plot:
-    runs-on: [docker]
+    runs-on: [self-hosted]
     env:
       BRANCH: ${{ github.ref_name }}
       COMMIT: ${{ github.sha }}
@@ -36,3 +36,4 @@ jobs:
         with:
           name: power-graph
           path: power_graph.png
+

README.md

@@ -47,7 +47,7 @@ A Forgejo workflow (`.forgejo/workflows/power-graph.yml`) automates the chart ge
 
 What it does:
 
-1. Runs on a self-hosted runner because it needs local hwmon logs and Python packages.
+1. Runs on a self-hosted runner for separation of processing (You will need to set one up if using forgejo).
 2. Manually clones the repo with a read token stored in `secrets.PAT_READ_REPO`.
 3. Installs Python (via `apt`), prepares a virtualenv, and pulls `pandas` + `matplotlib`.
 4. Executes `python plot_power.py`, producing `power_graph.png`.

amd-monitor.sh

@@ -7,7 +7,7 @@ APU_HWMON="/sys/class/hwmon/hwmon5"        # APU sensor
 GPU_HWMON="/sys/class/drm/card0/device/hwmon/hwmon4"  # dGPU sensor
 INTERVAL=1                                 # seconds between refreshes
 LOGFILE="power_log.csv"                    # CSV output file
-RETENTION_SECONDS=3600                     # keep roughly 1 hour of samples
+MAX_LINES=500                              # limit to last 500 lines
 
 # --- Helpers ---
 read_watts() {
@@ -57,16 +57,12 @@ while true; do
   # --- Append to CSV ---
   echo "$TIME,$APU_PWR,$GPU_PWR,$TOTAL,$APU_TEMP,$GPU_TEMP" >> "$LOGFILE"
 
-  # --- Keep roughly 1 hour of samples (plus header) ---
+  # --- Keep only last $MAX_LINES lines ---
-  MAX_SAMPLES=$(( (RETENTION_SECONDS + INTERVAL - 1) / INTERVAL ))
-  MAX_LINES_WITH_HEADER=$(( MAX_SAMPLES + 1 ))
   LINES=$(wc -l < "$LOGFILE")
-  if (( LINES > MAX_LINES_WITH_HEADER )); then
+  if (( LINES > MAX_LINES )); then
-    {
+    tail -n $MAX_LINES "$LOGFILE" > "${LOGFILE}.tmp" && mv "${LOGFILE}.tmp" "$LOGFILE"
-      head -n 1 "$LOGFILE"
-      tail -n "$MAX_SAMPLES" "$LOGFILE"
-    } > "${LOGFILE}.tmp" && mv "${LOGFILE}.tmp" "$LOGFILE"
   fi
 
   sleep $INTERVAL
 done
+

plot_power.py

@@ -11,7 +11,6 @@ import matplotlib.pyplot as plt
 
 CSV_PATH = "power_log.csv"
 OUTPUT_FILE = "power_graph.png"
-RETENTION_SECONDS = 3600
 
 print(f"📊 Reading {CSV_PATH}...")
 
@@ -24,11 +23,6 @@ df = pd.read_csv(
 
 # --- 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 ---
@@ -67,3 +61,4 @@ plt.grid(axis="y", alpha=0.3)
 plt.tight_layout()
 plt.savefig(OUTPUT_FILE, dpi=150)
 print(f"✅ Saved graph: {OUTPUT_FILE}")
+