kumajaya · November 2, 2025 15:29
diff --git a/first_trip_logic.ipynb b/first_trip_logic.ipynb
 {
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8c9fadb",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install matplotlib seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a3dac81",
   "metadata": {},
   "source": [
    "# Function Block Description — `K_FTL`\n",
    "\n",
    "---\n",
    "\n",
    "## Overview\n",
    "`K_FTL` (*First Trip Logic*) digunakan untuk **menentukan sinyal trip pertama yang terjadi** di antara sekumpulan input digital.  \n",
    "Blok ini *melock* (menahan) hasil deteksi hingga sinyal reset aktif. Cocok untuk analisis *root cause shutdown* atau *trip sequence capture*.\n",
    "\n",
    "---\n",
    "\n",
    "## Interface\n",
    "\n",
    "| **I/O** | **Name** | **Type** | **Description** |\n",
    "|:--------|:----------|:----------|:----------------|\n",
    "| **Input** | `RunInputs[8]` | `BOOL` | Status input digital (mis. trip per channel) |\n",
    "|  | `Reset` | `BOOL` | Reset seluruh latch dan status lock |\n",
    "| **Output** | `FirstTripIndex` | `INT` | Nomor input pertama yang terdeteksi trip (1-based) |\n",
    "|  | `TripLatched[8]` | `BOOL` | Status latched untuk tiap channel trip |\n",
    "| **Internal** | `TripTime[8]` | `REAL (epoch)` | Waktu trip masing-masing channel dalam format UNIX time |\n",
    "|  | `TripLocked` | `BOOL` | Menandakan bahwa first trip sudah dikunci |\n",
    "|  | `PrevInputs[8]` | `BOOL` | Nilai input sebelumnya, untuk deteksi *rising edge* |\n",
    "|  | `ResetLatch` | `BOOL` | Status reset latch (TRUE saat reset berlangsung) |\n",
    "\n",
    "---\n",
    "\n",
    "## Algorithm\n",
    "\n",
    "1. Jika `Reset` = TRUE  \n",
    "   → Semua latch (`TripLatched`), waktu trip (`TripTime`), dan `TripLocked` direset.\n",
    "\n",
    "2. Jika `TripLocked` = FALSE  \n",
    "   - Deteksi perubahan dari FALSE → TRUE pada setiap `RunInputs[i]`.  \n",
    "   - Saat deteksi pertama muncul:  \n",
    "     - Simpan waktu trip (`TripTime[i] = time.time()`),  \n",
    "     - Set `TripLatched[i] = TRUE`,  \n",
    "     - Set `FirstTripIndex = i + 1`,  \n",
    "     - Set `TripLocked = TRUE`.\n",
    "\n",
    "3. Jika `TripLocked` = TRUE  \n",
    "   - Tidak ada perubahan indeks trip, meskipun ada input lain yang menyusul.\n",
    "\n",
    "4. Nilai `TripLatched` tetap tersimpan hingga `Reset` aktif kembali.\n",
    "\n",
    "---\n",
    "\n",
    "## Notes\n",
    "\n",
    "- Output `FirstTripIndex = 0` berarti belum ada trip terdeteksi.  \n",
    "- Timestamp dapat dikonversi dengan `datetime.fromtimestamp(TripTime[i])` untuk logging timeline.  \n",
    "- Ukuran array `RunInputs` dapat diubah sesuai kebutuhan, misalnya 16 atau 32 channel.  \n",
    "- Dapat diperluas untuk menyimpan *trip source name* atau *message*.\n",
    "\n",
    "---\n",
    "\n",
    "## Example Usage\n",
    "\n",
    "```python\n",
    "ftl = K_FTL(n=8)\n",
    "\n",
    "# Simulasi input trip\n",
    "inputs = [False, False, True, False, False, False, False, False]\n",
    "ftl.update(inputs)\n",
    "print(ftl.FirstTripIndex)  # -> 3\n",
    "\n",
    "# Trip baru tidak memengaruhi hasil\n",
    "inputs = [False, False, True, True, False, False, False, False]\n",
    "ftl.update(inputs)\n",
    "print(ftl.FirstTripIndex)  # -> tetap 3\n",
    "\n",
    "# Reset\n",
    "ftl.update(inputs, reset=True)\n",
    "print(ftl.FirstTripIndex)  # -> 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7984d923",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "from datetime import datetime\n",
    "\n",
    "class K_FTL:\n",
    "    \"\"\"\n",
    "    K_FTL — First Trip Logic Deterministik\n",
    "\n",
    "    Simulasi Function Block (FB) untuk mendeteksi sinyal trip pertama\n",
    "    di antara beberapa input digital, lalu mengunci (lock) hasilnya\n",
    "    agar tetap konsisten sampai di-reset.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        # --- Variabel internal ---\n",
    "        self.FirstTripIndex = 0         # Channel pertama yang trip (1-based)\n",
    "        self.TripLatched = [False] * 8  # Status latched tiap channel\n",
    "        self.TripTime = [None] * 8      # Timestamp trip per channel\n",
    "        self.PrevInputs = [False] * 8   # Nilai input sebelumnya\n",
    "        self.TripLocked = False         # Lock ketika first trip sudah ditentukan\n",
    "        self.ResetLatch = False         # Status reset terakhir\n",
    "\n",
    "    def execute(self, RunInputs, Reset):\n",
    "        \"\"\"\n",
    "        Jalankan logika FTL.\n",
    "        RunInputs: list[bool] panjang 8 — status input digital\n",
    "        Reset: bool — sinyal reset latch\n",
    "        \"\"\"\n",
    "        current_real_time = time.time()\n",
    "\n",
    "        if Reset:\n",
    "            # Reset semua status internal\n",
    "            self.FirstTripIndex = 0\n",
    "            self.TripLocked = False\n",
    "            self.ResetLatch = True\n",
    "            for i in range(8):\n",
    "                self.TripLatched[i] = False\n",
    "                self.TripTime[i] = None\n",
    "                self.PrevInputs[i] = RunInputs[i]\n",
    "        else:\n",
    "            self.ResetLatch = False\n",
    "            for i in range(8):\n",
    "                # Deteksi rising edge: FALSE → TRUE\n",
    "                if not self.ResetLatch and RunInputs[i] and not self.PrevInputs[i]:\n",
    "                    self.TripLatched[i] = True\n",
    "                    self.TripTime[i] = current_real_time\n",
    "                    # Catat trip pertama hanya sekali\n",
    "                    if not self.TripLocked:\n",
    "                        self.FirstTripIndex = i + 1\n",
    "                        self.TripLocked = True\n",
    "                self.PrevInputs[i] = RunInputs[i]\n",
    "\n",
    "\n",
    "def print_status(ftl, test_name):\n",
    "    \"\"\"\n",
    "    Print status internal FB dengan format waktu yang mudah dibaca.\n",
    "    \"\"\"\n",
    "    print(f\"\\n{test_name}:\")\n",
    "    print(f\"  FirstTripIndex: {ftl.FirstTripIndex}\")\n",
    "    print(f\"  TripLatched   : {ftl.TripLatched}\")\n",
    "    formatted_times = [\n",
    "        datetime.fromtimestamp(t).strftime('%Y-%m-%d %H:%M:%S')\n",
    "        if t else '1970-01-01' for t in ftl.TripTime\n",
    "    ]\n",
    "    print(f\"  TripTime      : {formatted_times}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03e5e958",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random, time\n",
    "from datetime import datetime\n",
    "\n",
    "# ----------------- Simulasi FTL -----------------\n",
    "ftl = K_FTL()\n",
    "\n",
    "# Test 1: Reset Awal\n",
    "RunInputs = [False] * 8\n",
    "ftl.execute(RunInputs, True)\n",
    "print_status(ftl, \"Test 1 - Reset Awal\")\n",
    "\n",
    "# Test 2: No Trip\n",
    "ftl.execute(RunInputs, False)\n",
    "print_status(ftl, \"Test 2 - No Trip\")\n",
    "\n",
    "# Test 3: Single Trip (ubah channel sesuai kebutuhan)\n",
    "trip_channel = 5\n",
    "RunInputs[trip_channel - 1] = True\n",
    "ftl.execute(RunInputs, False)\n",
    "print_status(ftl, f\"Test 3 - Single Trip Channel {trip_channel}\")\n",
    "\n",
    "# Test 4: Multiple Trip dengan delay acak 0.3–1.5s\n",
    "for ch in range(8):\n",
    "    if not ftl.TripLatched[ch]:\n",
    "        RunInputs[ch] = True\n",
    "        delay = random.uniform(0.3, 1.5)\n",
    "        time.sleep(delay)\n",
    "        ftl.execute(RunInputs, False)\n",
    "        print(f\"  Tripped ch{ch+1} after {delay:.2f}s at {datetime.now().strftime('%H:%M:%S.%f')[:-3]}\")\n",
    "print_status(ftl, \"Test 4 - Multiple Trip (random delay 0.3–1.5s)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "31fcab1b",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "import seaborn as sns\n",
    "from datetime import datetime\n",
    "\n",
    "# ----------------- Style Global -----------------\n",
    "# Font lebih ringan dan proporsional\n",
    "plt.rcParams['svg.fonttype'] = 'none'  # simpan teks sebagai teks, bukan path\n",
    "plt.rcParams['font.family'] = 'sans-serif'\n",
    "plt.rcParams['font.sans-serif'] = ['DejaVu Sans', 'Arial']\n",
    "plt.rcParams['font.size'] = 10  # basis font size lebih kecil\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.style.use('seaborn-v0_8-darkgrid')\n",
    "\n",
    "# ----------------- Plot Timeline Trip Dinamis -----------------\n",
    "tripped_indices = [i for i, v in enumerate(ftl.TripLatched) if v]\n",
    "if not tripped_indices:\n",
    "    raise ValueError(\"Belum ada trip terjadi, tidak bisa plotting.\")\n",
    "\n",
    "first_idx = ftl.FirstTripIndex - 1\n",
    "first_time = ftl.TripTime[first_idx]\n",
    "\n",
    "# Waktu relatif dari trip pertama\n",
    "rel_times = [ftl.TripTime[i] - first_time if ftl.TripTime[i] else 0.1 for i in range(8)]\n",
    "rel_times = [max(0.1, t) for t in rel_times]  # minimal 0.1s agar terlihat\n",
    "\n",
    "channels = list(range(1, 9))\n",
    "y_pos = range(len(channels))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "colors = sns.color_palette(\"Spectral\", n_colors=8)\n",
    "colors[first_idx] = 'red'\n",
    "\n",
    "# Plot bar per channel\n",
    "for i in range(8):\n",
    "    edge_c = 'darkred' if i == first_idx else 'navy'\n",
    "    lw = 1.8 if i == first_idx else 1.0\n",
    "    ax.barh(i, rel_times[i], color=colors[i], alpha=0.9, edgecolor=edge_c, linewidth=lw)\n",
    "\n",
    "# Anotasi First Trip (lebih ringan dan proporsional)\n",
    "ax.annotate(\n",
    "    f'First Trip (Ch {ftl.FirstTripIndex})',\n",
    "    xy=(rel_times[first_idx] + 0.4, first_idx),\n",
    "    xytext=(rel_times[first_idx] + 1.2, first_idx + 0.05),\n",
    "    arrowprops=dict(facecolor='black', shrink=0.05, width=1.5, headwidth=6),\n",
    "    fontsize=9, ha='left', color='red', weight='normal'\n",
    ")\n",
    "\n",
    "# Label HH:MM:SS asli di ujung bar\n",
    "for i in range(8):\n",
    "    if ftl.TripTime[i]:\n",
    "        label = datetime.fromtimestamp(ftl.TripTime[i]).strftime('%H:%M:%S')\n",
    "        ax.text(rel_times[i] + 0.05, i, label, va='center', ha='left', fontsize=8.5)\n",
    "\n",
    "# Styling sumbu dan judul\n",
    "ax.set_yticks(y_pos)\n",
    "ax.set_yticklabels(channels, fontsize=9)\n",
    "ax.set_xlabel('Detik dari Trip Pertama', fontsize=9.5)\n",
    "ax.set_title(f'Timeline Trip per Channel (First Trip: Ch {ftl.FirstTripIndex})',\n",
    "             fontsize=10.5, weight='medium')\n",
    "ax.invert_yaxis()\n",
    "ax.grid(axis='x', linestyle='--', alpha=0.45, color='gray')\n",
    "ax.set_xlim(0, max(rel_times) + 1)\n",
    "\n",
    "# Legend per channel\n",
    "legend_elements = [\n",
    "    Line2D([0], [0], color=colors[i], lw=3, label=f'Ch {ch}')\n",
    "    for i, ch in enumerate(channels)\n",
    "]\n",
    "ax.legend(handles=legend_elements, loc='upper right', fontsize=8.5,\n",
    "          fancybox=True, shadow=True)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# Simpan file\n",
    "plt.savefig(\"Trip_Timeline.svg\", format='svg')\n",
    "plt.savefig(\"Trip_Timeline.png\", format='png')\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
 }
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