{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "sv-title",
   "metadata": {},
   "source": [
    "# Svalbard Ground Station Access\n",
    "\n",
    "Computes all contact windows between a sun-synchronous satellite and the\n",
    "**Svalbard Satellite Station (SvalSat)** over a 7-day period.\n",
    "\n",
    "**Scenario**\n",
    "- 550 km sun-synchronous orbit, LTDN 10:30 (descending node)\n",
    "- Ground station: SvalSat, 78.229°N / 15.407°E / 500 m altitude\n",
    "- Minimum elevation: 5°\n",
    "- Analysis window: 2025-03-01 → 2025-03-08 (7 days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "sv-imports",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-08T03:44:48.449112Z",
     "iopub.status.busy": "2026-03-08T03:44:48.448384Z",
     "iopub.status.idle": "2026-03-08T03:44:49.632632Z",
     "shell.execute_reply": "2026-03-08T03:44:49.631664Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from datetime import datetime\n",
    "\n",
    "from missiontools import Spacecraft, GroundStation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sv-sc-md",
   "metadata": {},
   "source": [
    "## 1. Spacecraft\n",
    "\n",
    "A 550 km SSO with a 10:30 local time at descending node, propagated with J2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "sv-sc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-08T03:44:49.635273Z",
     "iopub.status.busy": "2026-03-08T03:44:49.634921Z",
     "iopub.status.idle": "2026-03-08T03:44:49.641218Z",
     "shell.execute_reply": "2026-03-08T03:44:49.640271Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Semi-major axis : 6928.1 km\n",
      "Altitude        : 550.0 km\n",
      "Inclination     : 97.593°\n",
      "RAAN at epoch   : 139.643°\n",
      "Orbital period  : 95.65 min\n",
      "Propagator      : j2\n"
     ]
    }
   ],
   "source": [
    "EPOCH = np.datetime64('2025-03-01T00:00:00', 'us')\n",
    "\n",
    "sc = Spacecraft.sunsync(\n",
    "    altitude_km    = 550.0,\n",
    "    node_solar_time = '10:30',\n",
    "    node_type      = 'descending',\n",
    "    epoch          = EPOCH,\n",
    ")\n",
    "\n",
    "period_s = 2 * np.pi * np.sqrt(sc.a**3 / sc.central_body_mu)\n",
    "\n",
    "print(f\"Semi-major axis : {sc.a / 1e3:.1f} km\")\n",
    "print(f\"Altitude        : {(sc.a - sc.central_body_radius) / 1e3:.1f} km\")\n",
    "print(f\"Inclination     : {np.degrees(sc.i):.3f}°\")\n",
    "print(f\"RAAN at epoch   : {np.degrees(sc.raan):.3f}°\")\n",
    "print(f\"Orbital period  : {period_s / 60:.2f} min\")\n",
    "print(f\"Propagator      : {sc.propagator_type}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sv-gs-md",
   "metadata": {},
   "source": [
    "## 2. Ground Station\n",
    "\n",
    "SvalSat sits on the island of Spitsbergen at 78.229°N — so far north that\n",
    "it can see every polar-orbiting satellite at least twice per day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "sv-gs",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-08T03:44:49.643715Z",
     "iopub.status.busy": "2026-03-08T03:44:49.643450Z",
     "iopub.status.idle": "2026-03-08T03:44:49.648120Z",
     "shell.execute_reply": "2026-03-08T03:44:49.647162Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ground station  : SvalSat\n",
      "Latitude        : 78.229°N\n",
      "Longitude       : 15.407°E\n",
      "Altitude        : 500 m a.s.l.\n"
     ]
    }
   ],
   "source": [
    "gs = GroundStation(lat=78.229, lon=15.407, alt=500.0)\n",
    "\n",
    "print(f\"Ground station  : SvalSat\")\n",
    "print(f\"Latitude        : {gs.lat:.3f}°N\")\n",
    "print(f\"Longitude       : {gs.lon:.3f}°E\")\n",
    "print(f\"Altitude        : {gs.alt:.0f} m a.s.l.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sv-access-md",
   "metadata": {},
   "source": [
    "## 3. Compute Access Windows\n",
    "\n",
    "`GroundStation.access()` scans the analysis window at the requested maximum time step\n",
    "and returns a list of `(AOS, LOS)` pairs as `datetime64[us]` tuples.\n",
    "A 20 s step is fine for detecting passes whose shortest duration exceeds 5 min."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "sv-access",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-08T03:44:49.650664Z",
     "iopub.status.busy": "2026-03-08T03:44:49.650421Z",
     "iopub.status.idle": "2026-03-08T03:44:49.697185Z",
     "shell.execute_reply": "2026-03-08T03:44:49.696154Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total passes found: 96\n"
     ]
    }
   ],
   "source": [
    "T_START = np.datetime64('2025-03-01T00:00:00', 'us')\n",
    "T_END   = np.datetime64('2025-03-08T00:00:00', 'us')\n",
    "\n",
    "passes = gs.access(\n",
    "    sc,\n",
    "    T_START, T_END,\n",
    "    el_min = 5.0,                      # degrees\n",
    "    max_step = np.timedelta64(20, 's'),\n",
    ")\n",
    "\n",
    "print(f\"Total passes found: {len(passes)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sv-results-md",
   "metadata": {},
   "source": [
    "## 4. Access Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "sv-results",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-08T03:44:49.700063Z",
     "iopub.status.busy": "2026-03-08T03:44:49.699774Z",
     "iopub.status.idle": "2026-03-08T03:44:49.710783Z",
     "shell.execute_reply": "2026-03-08T03:44:49.709708Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    #          AOS (UTC)                   LOS (UTC)            Duration\n",
      "  ───  ──────────────────────────  ──────────────────────────  ─────────\n",
      "    1  2025-03-01T01:10:53.750000  2025-03-01T01:18:22.500000    7m 28s\n",
      "    2  2025-03-01T02:46:01.250000  2025-03-01T02:55:08.125000    9m 06s\n",
      "    3  2025-03-01T04:21:03.750000  2025-03-01T04:30:53.750000    9m 50s\n",
      "    4  2025-03-01T05:55:56.250000  2025-03-01T06:05:52.500000    9m 56s\n",
      "    5  2025-03-01T07:30:36.250000  2025-03-01T07:40:21.250000    9m 45s\n",
      "    6  2025-03-01T09:05:02.500000  2025-03-01T09:14:42.500000    9m 40s\n",
      "    7  2025-03-01T10:39:23.750000  2025-03-01T10:49:11.250000    9m 47s\n",
      "    8  2025-03-01T12:13:56.875000  2025-03-01T12:23:53.750000    9m 56s\n",
      "    9  2025-03-01T13:49:03.125000  2025-03-01T13:58:48.750000    9m 45s\n",
      "   10  2025-03-01T15:25:00.000000  2025-03-01T15:33:52.500000    8m 52s\n",
      "   11  2025-03-01T17:01:57.500000  2025-03-01T17:09:00.625000    7m 03s\n",
      "   12  2025-03-01T18:39:59.375000  2025-03-01T18:44:07.500000    4m 08s\n",
      "   13  2025-03-01T23:30:31.250000  2025-03-01T23:35:25.000000    4m 53s\n",
      "   14  2025-03-02T01:05:40.000000  2025-03-02T01:13:12.500000    7m 32s\n",
      "   15  2025-03-02T02:40:47.500000  2025-03-02T02:49:55.625000    9m 08s\n",
      "   16  2025-03-02T04:15:49.375000  2025-03-02T04:25:40.625000    9m 51s\n",
      "   17  2025-03-02T05:50:41.875000  2025-03-02T06:00:37.500000    9m 55s\n",
      "   18  2025-03-02T07:25:21.250000  2025-03-02T07:35:06.250000    9m 45s\n",
      "   19  2025-03-02T08:59:47.500000  2025-03-02T09:09:26.875000    9m 39s\n",
      "   20  2025-03-02T10:34:08.125000  2025-03-02T10:43:56.250000    9m 48s\n",
      "   21  2025-03-02T12:08:42.500000  2025-03-02T12:18:39.375000    9m 56s\n",
      "   22  2025-03-02T13:43:50.000000  2025-03-02T13:53:34.375000    9m 44s\n",
      "   23  2025-03-02T15:19:48.125000  2025-03-02T15:28:38.125000    8m 50s\n",
      "   24  2025-03-02T16:56:48.125000  2025-03-02T17:03:46.875000    6m 58s\n",
      "   25  2025-03-02T18:34:51.250000  2025-03-02T18:38:53.750000    4m 02s\n",
      "   26  2025-03-02T23:25:17.500000  2025-03-02T23:30:16.250000    4m 58s\n",
      "   27  2025-03-03T01:00:25.625000  2025-03-03T01:08:01.875000    7m 36s\n",
      "   28  2025-03-03T02:35:33.125000  2025-03-03T02:44:43.750000    9m 10s\n",
      "   29  2025-03-03T04:10:35.625000  2025-03-03T04:20:26.875000    9m 51s\n",
      "   30  2025-03-03T05:45:27.500000  2025-03-03T05:55:23.125000    9m 55s\n",
      "   31  2025-03-03T07:20:06.250000  2025-03-03T07:29:51.250000    9m 45s\n",
      "   32  2025-03-03T08:54:32.500000  2025-03-03T09:04:11.875000    9m 39s\n",
      "   33  2025-03-03T10:28:53.125000  2025-03-03T10:38:41.250000    9m 48s\n",
      "   34  2025-03-03T12:03:28.125000  2025-03-03T12:13:25.000000    9m 56s\n",
      "   35  2025-03-03T13:38:36.875000  2025-03-03T13:48:20.000000    9m 43s\n",
      "   36  2025-03-03T15:14:36.875000  2025-03-03T15:23:24.375000    8m 47s\n",
      "   37  2025-03-03T16:51:38.125000  2025-03-03T16:58:32.500000    6m 54s\n",
      "   38  2025-03-03T18:29:43.125000  2025-03-03T18:33:39.375000    3m 56s\n",
      "   39  2025-03-03T21:45:31.250000  2025-03-03T21:45:55.625000    0m 24s\n",
      "   40  2025-03-03T23:20:03.750000  2025-03-03T23:25:07.500000    5m 03s\n",
      "   41  2025-03-04T00:55:11.875000  2025-03-04T01:02:51.250000    7m 39s\n",
      "   42  2025-03-04T02:30:19.375000  2025-03-04T02:39:31.875000    9m 12s\n",
      "   43  2025-03-04T04:05:21.250000  2025-03-04T04:15:13.125000    9m 51s\n",
      "   44  2025-03-04T05:40:13.125000  2025-03-04T05:50:08.125000    9m 55s\n",
      "   45  2025-03-04T07:14:51.875000  2025-03-04T07:24:36.250000    9m 44s\n",
      "   46  2025-03-04T08:49:16.875000  2025-03-04T08:58:56.875000    9m 40s\n",
      "   47  2025-03-04T10:23:38.125000  2025-03-04T10:33:26.875000    9m 48s\n",
      "   48  2025-03-04T11:58:13.125000  2025-03-04T12:08:10.000000    9m 56s\n",
      "   49  2025-03-04T13:33:23.125000  2025-03-04T13:43:06.250000    9m 43s\n",
      "   50  2025-03-04T15:09:25.000000  2025-03-04T15:18:10.625000    8m 45s\n",
      "   51  2025-03-04T16:46:28.125000  2025-03-04T16:53:18.750000    6m 50s\n",
      "   52  2025-03-04T18:24:35.000000  2025-03-04T18:28:25.625000    3m 50s\n",
      "   53  2025-03-04T21:40:09.375000  2025-03-04T21:40:56.250000    0m 46s\n",
      "   54  2025-03-04T23:14:49.375000  2025-03-04T23:19:58.750000    5m 09s\n",
      "   55  2025-03-05T00:49:58.125000  2025-03-05T00:57:40.625000    7m 42s\n",
      "   56  2025-03-05T02:25:05.625000  2025-03-05T02:34:19.375000    9m 13s\n",
      "   57  2025-03-05T04:00:06.875000  2025-03-05T04:09:59.375000    9m 52s\n",
      "   58  2025-03-05T05:34:58.750000  2025-03-05T05:44:53.750000    9m 55s\n",
      "   59  2025-03-05T07:09:36.875000  2025-03-05T07:19:20.625000    9m 43s\n",
      "   60  2025-03-05T08:44:01.875000  2025-03-05T08:53:41.875000    9m 40s\n",
      "   61  2025-03-05T10:18:23.125000  2025-03-05T10:28:11.875000    9m 48s\n",
      "   62  2025-03-05T11:52:58.750000  2025-03-05T12:02:55.625000    9m 56s\n",
      "   63  2025-03-05T13:28:10.000000  2025-03-05T13:37:51.875000    9m 41s\n",
      "   64  2025-03-05T15:04:13.750000  2025-03-05T15:12:56.250000    8m 42s\n",
      "   65  2025-03-05T16:41:18.125000  2025-03-05T16:48:05.000000    6m 46s\n",
      "   66  2025-03-05T18:19:26.875000  2025-03-05T18:23:11.250000    3m 44s\n",
      "   67  2025-03-05T21:34:50.625000  2025-03-05T21:35:53.125000    1m 02s\n",
      "   68  2025-03-05T23:09:35.625000  2025-03-05T23:14:50.000000    5m 14s\n",
      "   69  2025-03-06T00:44:44.375000  2025-03-06T00:52:30.000000    7m 45s\n",
      "   70  2025-03-06T02:19:51.250000  2025-03-06T02:29:06.875000    9m 15s\n",
      "   71  2025-03-06T03:54:52.500000  2025-03-06T04:04:45.625000    9m 53s\n",
      "   72  2025-03-06T05:29:43.750000  2025-03-06T05:39:38.750000    9m 55s\n",
      "   73  2025-03-06T07:04:21.875000  2025-03-06T07:14:05.625000    9m 43s\n",
      "   74  2025-03-06T08:38:46.875000  2025-03-06T08:48:26.875000    9m 40s\n",
      "   75  2025-03-06T10:13:08.125000  2025-03-06T10:22:56.875000    9m 48s\n",
      "   76  2025-03-06T11:47:44.375000  2025-03-06T11:57:41.875000    9m 57s\n",
      "   77  2025-03-06T13:22:56.875000  2025-03-06T13:32:38.125000    9m 41s\n",
      "   78  2025-03-06T14:59:01.875000  2025-03-06T15:07:42.500000    8m 40s\n",
      "   79  2025-03-06T16:36:08.125000  2025-03-06T16:42:51.250000    6m 43s\n",
      "   80  2025-03-06T18:14:18.750000  2025-03-06T18:17:56.875000    3m 38s\n",
      "   81  2025-03-06T21:29:33.125000  2025-03-06T21:30:48.750000    1m 15s\n",
      "   82  2025-03-06T23:04:21.875000  2025-03-06T23:09:41.250000    5m 19s\n",
      "   83  2025-03-07T00:39:30.625000  2025-03-07T00:47:19.375000    7m 48s\n",
      "   84  2025-03-07T02:14:37.500000  2025-03-07T02:23:55.000000    9m 17s\n",
      "   85  2025-03-07T03:49:38.750000  2025-03-07T03:59:31.875000    9m 53s\n",
      "   86  2025-03-07T05:24:29.375000  2025-03-07T05:34:23.750000    9m 54s\n",
      "   87  2025-03-07T06:59:06.875000  2025-03-07T07:08:50.625000    9m 43s\n",
      "   88  2025-03-07T08:33:31.250000  2025-03-07T08:43:11.875000    9m 40s\n",
      "   89  2025-03-07T10:07:53.125000  2025-03-07T10:17:42.500000    9m 49s\n",
      "   90  2025-03-07T11:42:30.625000  2025-03-07T11:52:27.500000    9m 56s\n",
      "   91  2025-03-07T13:17:43.750000  2025-03-07T13:27:23.750000    9m 40s\n",
      "   92  2025-03-07T14:53:50.625000  2025-03-07T15:02:28.750000    8m 38s\n",
      "   93  2025-03-07T16:30:58.750000  2025-03-07T16:37:37.500000    6m 38s\n",
      "   94  2025-03-07T18:09:10.625000  2025-03-07T18:12:43.125000    3m 32s\n",
      "   95  2025-03-07T21:24:16.875000  2025-03-07T21:25:43.750000    1m 26s\n",
      "   96  2025-03-07T22:59:08.125000  2025-03-07T23:04:32.500000    5m 24s\n",
      "\n",
      "  Total passes       : 96\n",
      "  Total contact time : 12h 54m 58s\n",
      "  Duty cycle         : 7.69%\n",
      "  Mean pass duration : 8.1 min\n"
     ]
    }
   ],
   "source": [
    "print(f\"  {'#':>3}  {'AOS (UTC)':^26}  {'LOS (UTC)':^26}  {'Duration':>9}\")\n",
    "print(f\"  {'─'*3}  {'─'*26}  {'─'*26}  {'─'*9}\")\n",
    "\n",
    "total_s = 0\n",
    "for idx, (aos, los) in enumerate(passes, 1):\n",
    "    dur_s = int((los - aos) / np.timedelta64(1, 's'))\n",
    "    total_s += dur_s\n",
    "    print(f\"  {idx:>3}  {str(aos):^26}  {str(los):^26}  \"\n",
    "          f\"{dur_s // 60:3d}m {dur_s % 60:02d}s\")\n",
    "\n",
    "window_s = int((T_END - T_START) / np.timedelta64(1, 's'))\n",
    "print()\n",
    "print(f\"  Total passes       : {len(passes)}\")\n",
    "print(f\"  Total contact time : {total_s // 3600}h \"\n",
    "      f\"{(total_s % 3600) // 60}m {total_s % 60:02d}s\")\n",
    "print(f\"  Duty cycle         : {total_s / window_s * 100:.2f}%\")\n",
    "print(f\"  Mean pass duration : {total_s / len(passes) / 60:.1f} min\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sv-viz-md",
   "metadata": {},
   "source": [
    "## 5. Pass Timeline\n",
    "\n",
    "Each bar represents one contact window.  The bar length is proportional to\n",
    "contact duration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "sv-viz",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-08T03:44:49.714280Z",
     "iopub.status.busy": "2026-03-08T03:44:49.713943Z",
     "iopub.status.idle": "2026-03-08T03:44:50.384661Z",
     "shell.execute_reply": "2026-03-08T03:44:50.383789Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert datetime64 to Python datetime for matplotlib\n",
    "def to_dt(t64):\n",
    "    return t64.astype('datetime64[ms]').astype(datetime)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 4))\n",
    "\n",
    "for idx, (aos, los) in enumerate(passes):\n",
    "    dur_s = int((los - aos) / np.timedelta64(1, 's'))\n",
    "    ax.barh(\n",
    "        0,\n",
    "        left  = to_dt(aos),\n",
    "        width = to_dt(los) - to_dt(aos),\n",
    "        height = 0.5,\n",
    "        color  = 'tab:blue',\n",
    "        alpha  = 0.7,\n",
    "    )\n",
    "    # Annotate short passes with their duration (min:ss)\n",
    "    mid = to_dt(aos + (los - aos) // 2)\n",
    "    ax.text(mid, 0, f\"{dur_s // 60}m{dur_s % 60:02d}s\",\n",
    "            ha='center', va='center', fontsize=7, color='white', fontweight='bold')\n",
    "\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))\n",
    "ax.xaxis.set_major_locator(mdates.DayLocator())\n",
    "ax.set_yticks([])\n",
    "ax.set_xlim(to_dt(T_START), to_dt(T_END))\n",
    "ax.set_xlabel('Date (UTC)')\n",
    "ax.set_title(\n",
    "    f'SvalSat contact windows — 550 km SSO LTDN 10:30, el ≥ 5°\\n'\n",
    "    f'{len(passes)} passes, {total_s / 3600:.1f} h total contact time'\n",
    ")\n",
    "ax.grid(True, axis='x', alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sv-perdaymd",
   "metadata": {},
   "source": [
    "## 6. Passes per Day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "sv-perday",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-08T03:44:50.387960Z",
     "iopub.status.busy": "2026-03-08T03:44:50.387619Z",
     "iopub.status.idle": "2026-03-08T03:44:50.395937Z",
     "shell.execute_reply": "2026-03-08T03:44:50.394641Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Date   Passes       Contact\n",
      "  ────────────  ───────  ────────────\n",
      "    2025-03-01       13  110m 09s\n",
      "    2025-03-02       13  110m 06s\n",
      "    2025-03-03       14  110m 27s\n",
      "    2025-03-04       14  110m 48s\n",
      "    2025-03-05       14  110m 58s\n",
      "    2025-03-06       14  111m 12s\n",
      "    2025-03-07       14  111m 18s\n"
     ]
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "passes_per_day = defaultdict(list)\n",
    "for aos, los in passes:\n",
    "    day = str(aos)[:10]   # 'YYYY-MM-DD'\n",
    "    dur_s = int((los - aos) / np.timedelta64(1, 's'))\n",
    "    passes_per_day[day].append(dur_s)\n",
    "\n",
    "print(f\"  {'Date':>12}  {'Passes':>7}  {'Contact':>12}\")\n",
    "print(f\"  {'─'*12}  {'─'*7}  {'─'*12}\")\n",
    "for day in sorted(passes_per_day):\n",
    "    n = len(passes_per_day[day])\n",
    "    t = sum(passes_per_day[day])\n",
    "    print(f\"  {day:>12}  {n:>7}  {t // 60:3d}m {t % 60:02d}s\")"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.12"
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 },
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