{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "title",
   "metadata": {},
   "source": [
    "# X-band Downlink Interference Analysis at Svalbard\n",
    "\n",
    "This notebook demonstrates interference analysis between two constellations sharing\n",
    "the X-band downlink spectrum, received at the Svalbard Satellite Station (SvalSat).\n",
    "\n",
    "**Scenario**\n",
    "- **Victim constellation**: 2 satellites at 700 km SSO, LTAN 10:30, -70 dBW/Hz PSD, isotropic TX\n",
    "- **Interfering constellation**: 4 satellites at 550 km SSO, LTAN 11:00, -65 dBW/Hz PSD, isotropic TX\n",
    "- **Victim receiver**: 5.4 m parabolic dish at Svalbard (78.229°N / 15.407°E)\n",
    "- **Centre frequency**: 8200 MHz (X-band)\n",
    "- **Interference threshold**: -237 dBW/Hz\n",
    "- **Analysis window**: 90 days (2025-01-01 to 2025-04-01)\n",
    "\n",
    "Interference events occur when an interfering satellite is visible to the Svalbard\n",
    "antenna at the same time as a victim satellite, and the received interferer PSD\n",
    "exceeds the threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "imports",
   "metadata": {},
   "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, IsotropicAntenna\n",
    "from missiontools.comm import InterferenceAnalysis, SymmetricAntenna\n",
    "from missiontools.attitude import ConditionAttitudeLaw, TrackAttitudeLaw\n",
    "from missiontools.condition import SpaceGroundAccessCondition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vtx-md",
   "metadata": {},
   "source": [
    "## 1. Victim Constellation\n",
    "\n",
    "Two spacecraft at 700 km in sun-synchronous orbit with a 10:30 LTAN.\n",
    "The two satellites are separated by 180° in mean anomaly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "vtx",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Victim SC-1: alt=700 km, i=98.19°, period=98.8 min\n",
      "Victim SC-2: alt=700 km, i=98.19°, period=98.8 min\n"
     ]
    }
   ],
   "source": [
    "EPOCH = np.datetime64('2025-01-01T00:00:00', 'us')\n",
    "\n",
    "vtx_ant_1 = IsotropicAntenna()\n",
    "vtx_ant_2 = IsotropicAntenna()\n",
    "\n",
    "vtx_sc_1 = Spacecraft.sunsync(\n",
    "    altitude_km=700.0,\n",
    "    node_solar_time='10:30',\n",
    "    node_type='ascending',\n",
    "    epoch=EPOCH,\n",
    "    ma_deg=0.0,\n",
    ")\n",
    "vtx_sc_1.add_antenna(vtx_ant_1)\n",
    "\n",
    "vtx_sc_2 = Spacecraft.sunsync(\n",
    "    altitude_km=700.0,\n",
    "    node_solar_time='10:30',\n",
    "    node_type='ascending',\n",
    "    epoch=EPOCH,\n",
    "    ma_deg=180.0,\n",
    ")\n",
    "vtx_sc_2.add_antenna(vtx_ant_2)\n",
    "\n",
    "print(f'Victim SC-1: alt={700} km, i={np.degrees(vtx_sc_1.i):.2f}°, period={2*np.pi*np.sqrt(vtx_sc_1.a**3/vtx_sc_1.central_body_mu)/60:.1f} min')\n",
    "print(f'Victim SC-2: alt={700} km, i={np.degrees(vtx_sc_2.i):.2f}°, period={2*np.pi*np.sqrt(vtx_sc_2.a**3/vtx_sc_2.central_body_mu)/60:.1f} min')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "itx-md",
   "metadata": {},
   "source": [
    "## 2. Interfering Constellation\n",
    "\n",
    "Four spacecraft at 550 km in sun-synchronous orbit with an 11:00 LTAN.\n",
    "The satellites are evenly spaced at 0°, 90°, 180°, 270° in mean anomaly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "itx",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Interfering constellation: 4 satellites\n",
      "  alt=550 km, i=97.59°, period=95.6 min\n"
     ]
    }
   ],
   "source": [
    "itx_ants = [IsotropicAntenna() for _ in range(4)]\n",
    "itx_scs = []\n",
    "\n",
    "for k in range(4):\n",
    "    sc = Spacecraft.sunsync(\n",
    "        altitude_km=550.0,\n",
    "        node_solar_time='11:00',\n",
    "        node_type='ascending',\n",
    "        epoch=EPOCH,\n",
    "        ma_deg=k * 90.0,\n",
    "    )\n",
    "    sc.add_antenna(itx_ants[k])\n",
    "    itx_scs.append(sc)\n",
    "\n",
    "print(f'Interfering constellation: {len(itx_scs)} satellites')\n",
    "print(f'  alt=550 km, i={np.degrees(itx_scs[0].i):.2f}°, period={2*np.pi*np.sqrt(itx_scs[0].a**3/itx_scs[0].central_body_mu)/60:.1f} min')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vrx-md",
   "metadata": {},
   "source": [
    "## 3. Victim Receiver (Svalbard)\n",
    "\n",
    "A 5.4 m parabolic dish antenna at SvalSat.\n",
    "The antenna uses a `ConditionAttitudeLaw` to track whichever victim\n",
    "spacecraft is currently visible: `SpaceGroundAccessCondition` gates\n",
    "`TrackAttitudeLaw` instances for each victim SC. When neither is\n",
    "visible the dish falls back to tracking the first victim SC (the\n",
    "pointing direction is irrelevant during those periods).\n",
    "The antenna pattern uses the parabolic envelope model at 8200 MHz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "vrx",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ground station : SvalSat (78.229°N, 15.407°E, 500 m)\n",
      "Dish diameter  : 5.4 m\n",
      "Peak gain      : 51.1 dBi\n",
      "Antenna law    : ConditionAttitudeLaw (tracks victim SCs when visible)\n"
     ]
    }
   ],
   "source": [
    "svalbard = GroundStation(lat=78.229, lon=15.407, alt=500.0)\n",
    "\n",
    "cond_vtx1 = SpaceGroundAccessCondition(vtx_sc_1, svalbard, el_min_deg=5.0)\n",
    "cond_vtx2 = SpaceGroundAccessCondition(vtx_sc_2, svalbard, el_min_deg=5.0)\n",
    "\n",
    "gs_law = ConditionAttitudeLaw(\n",
    "    default_attitude=TrackAttitudeLaw(vtx_sc_1),\n",
    "    condition_attitudes=[\n",
    "        (cond_vtx1, TrackAttitudeLaw(vtx_sc_1)),\n",
    "        (cond_vtx2, TrackAttitudeLaw(vtx_sc_2)),\n",
    "    ],\n",
    ")\n",
    "\n",
    "rx_dish = SymmetricAntenna.from_parabolic(\n",
    "    diameter=5.4,\n",
    "    frequency=8200e6,\n",
    "    eff=0.6,\n",
    "    envelope=True,\n",
    "    attitude_law=gs_law,\n",
    ")\n",
    "svalbard.add_antenna(rx_dish)\n",
    "\n",
    "print(f'Ground station : SvalSat ({svalbard.lat}\\u00b0N, {svalbard.lon}\\u00b0E, {svalbard.alt:.0f} m)')\n",
    "print(f'Dish diameter  : 5.4 m')\n",
    "print(f'Peak gain      : {rx_dish.peak_gain_dbi:.1f} dBi')\n",
    "print(f'Antenna law    : ConditionAttitudeLaw (tracks victim SCs when visible)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "analysis-md",
   "metadata": {},
   "source": [
    "## 4. Build Interference Analysis\n",
    "\n",
    "Register all transmitters and receivers, then run the 90-day computation.\n",
    "A `max_step` of 30 s is used for access interval detection, and an `event_step`\n",
    "of 5 s for PSD sampling within candidate interference windows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "analysis",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analysis period : 2025-01-01 to 2025-04-01 (90 days)\n",
      "Frequency       : 8200 MHz\n",
      "PSD threshold   : -237.0 dBW/Hz\n",
      "Victim TXs      : 2\n",
      "Victim RXs      : 1\n",
      "Interfering TXs : 4\n",
      "Total triples   : 8\n"
     ]
    }
   ],
   "source": [
    "T_START = np.datetime64('2025-01-01T00:00:00', 'us')\n",
    "T_END   = np.datetime64('2025-04-01T00:00:00', 'us')\n",
    "\n",
    "PSD_THRESHOLD = -237.0  # dBW/Hz\n",
    "F_MHZ = 8200.0         # MHz\n",
    "VICTIM_TX_PSD = -70.0  # dBW/Hz\n",
    "INTERFERER_TX_PSD = -65.0  # dBW/Hz\n",
    "\n",
    "ia = InterferenceAnalysis(f_MHz=F_MHZ)\n",
    "\n",
    "ia.add_victim_tx('VTX-1', vtx_ant_1, VICTIM_TX_PSD)\n",
    "ia.add_victim_tx('VTX-2', vtx_ant_2, VICTIM_TX_PSD)\n",
    "\n",
    "ia.add_victim_rx('Svalbard', rx_dish)\n",
    "\n",
    "for k, ant in enumerate(itx_ants):\n",
    "    ia.add_interfering_tx(f'ITX-{k+1}', ant, INTERFERER_TX_PSD)\n",
    "\n",
    "print(f'Analysis period : {str(T_START)[:10]} to {str(T_END)[:10]} (90 days)')\n",
    "print(f'Frequency       : {F_MHZ:.0f} MHz')\n",
    "print(f'PSD threshold   : {PSD_THRESHOLD} dBW/Hz')\n",
    "print(f'Victim TXs      : {len(ia._victim_txs)}')\n",
    "print(f'Victim RXs      : {len(ia._victim_rxs)}')\n",
    "print(f'Interfering TXs : {len(ia._interfering_txs)}')\n",
    "print(f'Total triples   : {len(ia._victim_txs) * len(ia._victim_rxs) * len(ia._interfering_txs)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "compute",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Interference events detected: 224\n"
     ]
    }
   ],
   "source": [
    "events, access_totals = ia.compute(\n",
    "    psd_threshold=PSD_THRESHOLD,\n",
    "    start_time=T_START,\n",
    "    end_time=T_END,\n",
    "    max_step=30.0,\n",
    "    event_step=5.0,\n",
    ")\n",
    "\n",
    "print(f'Interference events detected: {len(events)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "summary-md",
   "metadata": {},
   "source": [
    "## 5. Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "summary",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "  INTERFERENCE ANALYSIS SUMMARY\n",
      "============================================================\n",
      "  Analysis period        : 2025-01-01 to 2025-04-01\n",
      "  Duration               : 90 days\n",
      "  Centre frequency       : 8200 MHz\n",
      "  PSD threshold          : -237.0 dBW/Hz\n",
      "  ---\n",
      "  Total events           : 224\n",
      "  Total interference     : 44020 s (12.23 h)\n",
      "  Percentage of window   : 0.5661%\n",
      "  Peak interferer PSD    : -189.4 dBW/Hz\n",
      "  Mean event duration    : 197 s (3.3 min)\n",
      "  Max event duration     : 471 s (7.8 min)\n",
      "  Min event duration     : 0 s (0.0 min)\n",
      "============================================================\n"
     ]
    }
   ],
   "source": [
    "total_interference_s = 0\n",
    "for ev in events:\n",
    "    dur = int((ev['end_time'] - ev['start_time']) / np.timedelta64(1, 's'))\n",
    "    total_interference_s += dur\n",
    "\n",
    "window_s = int((T_END - T_START) / np.timedelta64(1, 's'))\n",
    "\n",
    "print('=' * 60)\n",
    "print('  INTERFERENCE ANALYSIS SUMMARY')\n",
    "print('=' * 60)\n",
    "print(f'  Analysis period        : {str(T_START)[:10]} to {str(T_END)[:10]}')\n",
    "print(f'  Duration               : {window_s / 86400:.0f} days')\n",
    "print(f'  Centre frequency       : {F_MHZ:.0f} MHz')\n",
    "print(f'  PSD threshold          : {PSD_THRESHOLD} dBW/Hz')\n",
    "print(f'  ---')\n",
    "print(f'  Total events           : {len(events)}')\n",
    "print(f'  Total interference     : {total_interference_s:.0f} s '\n",
    "      f'({total_interference_s / 3600:.2f} h)')\n",
    "print(f'  Percentage of window   : {total_interference_s / window_s * 100:.4f}%')\n",
    "if events:\n",
    "    max_psd = max(ev['max_interferer_psd'] for ev in events)\n",
    "    print(f'  Peak interferer PSD    : {max_psd:.1f} dBW/Hz')\n",
    "    durs = [int((ev['end_time'] - ev['start_time']) / np.timedelta64(1, 's')) for ev in events]\n",
    "    print(f'  Mean event duration    : {np.mean(durs):.0f} s ({np.mean(durs)/60:.1f} min)')\n",
    "    print(f'  Max event duration     : {max(durs)} s ({max(durs)/60:.1f} min)')\n",
    "    print(f'  Min event duration     : {min(durs)} s ({min(durs)/60:.1f} min)')\n",
    "print('=' * 60)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "per-itx-md",
   "metadata": {},
   "source": [
    "## 6. Events by Interfering Satellite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "per-itx",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Satellite   Events      Duration    Peak PSD\n",
      "  ────────────  ───────  ────────────  ──────────\n",
      "         ITX-1       56    182m 27s     -189.4 dBW/Hz\n",
      "         ITX-2       56    187m 29s     -189.4 dBW/Hz\n",
      "         ITX-3       55    182m 13s     -189.4 dBW/Hz\n",
      "         ITX-4       57    181m 31s     -189.6 dBW/Hz\n",
      "  ────────────  ───────  ────────────  ──────────\n",
      "         TOTAL      224    733m 40s     -189.4 dBW/Hz\n"
     ]
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "by_itx = defaultdict(list)\n",
    "for ev in events:\n",
    "    by_itx[ev['interfering_tx']].append(ev)\n",
    "\n",
    "print(f\"  {'Satellite':>12}  {'Events':>7}  {'Duration':>12}  {'Peak PSD':>10}\")\n",
    "print(f\"  {'\\u2500'*12}  {'\\u2500'*7}  {'\\u2500'*12}  {'\\u2500'*10}\")\n",
    "\n",
    "for name in sorted(by_itx):\n",
    "    evs = by_itx[name]\n",
    "    dur = sum(int((e['end_time'] - e['start_time']) / np.timedelta64(1, 's')) for e in evs)\n",
    "    peak = max(e['max_interferer_psd'] for e in evs)\n",
    "    print(f'  {name:>12}  {len(evs):>7}  {dur // 60:>5d}m {dur % 60:02d}s  {peak:>9.1f} dBW/Hz')\n",
    "\n",
    "print(f\"  {'\\u2500'*12}  {'\\u2500'*7}  {'\\u2500'*12}  {'\\u2500'*10}\")\n",
    "total_dur = sum(int((e['end_time'] - e['start_time']) / np.timedelta64(1, 's')) for e in events)\n",
    "overall_peak = max(e['max_interferer_psd'] for e in events) if events else float('nan')\n",
    "print(f'  {\"TOTAL\":>12}  {len(events):>7}  {total_dur // 60:>5d}m {total_dur % 60:02d}s  {overall_peak:>9.1f} dBW/Hz')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "timeline-md",
   "metadata": {},
   "source": [
    "## 7. Interference Event Timeline\n",
    "\n",
    "Each bar represents one interference event, coloured by the interfering satellite."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "timeline",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def to_dt(t64):\n",
    "    return t64.astype('datetime64[ms]').astype(datetime)\n",
    "\n",
    "itx_names_sorted = sorted(by_itx)\n",
    "color_map = {name: f'C{k}' for k, name in enumerate(itx_names_sorted)}\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(14, 4))\n",
    "\n",
    "for ev in events:\n",
    "    ax.barh(\n",
    "        0,\n",
    "        left=to_dt(ev['start_time']),\n",
    "        width=to_dt(ev['end_time']) - to_dt(ev['start_time']),\n",
    "        height=0.5,\n",
    "        color=color_map[ev['interfering_tx']],\n",
    "        alpha=0.7,\n",
    "    )\n",
    "\n",
    "handles = [plt.Rectangle((0, 0), 1, 1, color=color_map[n], alpha=0.7) for n in itx_names_sorted]\n",
    "ax.legend(handles, itx_names_sorted, loc='upper right', ncol=len(itx_names_sorted), fontsize=8)\n",
    "\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))\n",
    "ax.xaxis.set_major_locator(mdates.WeekdayLocator(byweekday=mdates.MO, interval=4))\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'Interference events at SvalSat \\u2014 {F_MHZ:.0f} MHz, '\n",
    "    f'threshold {PSD_THRESHOLD} dBW/Hz\\n'\n",
    "    f'{len(events)} events, {total_interference_s/3600:.2f} h total interference'\n",
    ")\n",
    "ax.grid(True, axis='x', alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "psd-hist-md",
   "metadata": {},
   "source": [
    "## 8. Peak Interferer PSD Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "psd-hist",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "\n",
    "if events:\n",
    "    peak_psds = [ev['max_interferer_psd'] for ev in events]\n",
    "    ax.hist(peak_psds, bins=30, color='tab:red', alpha=0.7, edgecolor='black')\n",
    "    ax.axvline(PSD_THRESHOLD, color='black', linestyle='--', linewidth=1.5,\n",
    "               label=f'Threshold ({PSD_THRESHOLD} dBW/Hz)')\n",
    "    ax.legend(fontsize=9)\n",
    "\n",
    "ax.set_xlabel('Peak Interferer PSD (dBW/Hz)')\n",
    "ax.set_ylabel('Number of Events')\n",
    "ax.set_title('Distribution of Peak Interferer PSD per Event')\n",
    "ax.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cumul-md",
   "metadata": {},
   "source": [
    "## 9. Cumulative Interference Percentage\n",
    "\n",
    "The interference percentage is calculated as the fraction of total\n",
    "condition-filtered victim-link access time during which at least one\n",
    "interferer's received PSD meets or exceeds each threshold.\n",
    "The ITU SA.1027 interference criterion is overlaid for reference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cumul-plot",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thresholds = np.arange(-237, -189, 1.0)  # dBW/Hz\n",
    "\n",
    "pcts = ia.interference_percentage(\n",
    "    psd_threshold=thresholds,\n",
    "    victim_tx=['VTX-1', 'VTX-2'],\n",
    "    victim_rx='Svalbard',\n",
    "    interfering_tx=[f'ITX-{k+1}' for k in range(4)],\n",
    ")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "ax.semilogy(thresholds, pcts, 'o-', markersize=3, linewidth=1.5,\n",
    "            color='tab:blue', label='Simulated interference')\n",
    "\n",
    "itu_psd = np.array([-237.0, -203.0])\n",
    "itu_pct = np.array([20.0, 0.0025])\n",
    "ax.semilogy(itu_psd, itu_pct, ':', linewidth=1.5, color='tab:red',\n",
    "            label='ITU SA.1027 Limit')\n",
    "\n",
    "ax.set_xlabel('Interfering PSD Threshold (dBW/Hz)')\n",
    "ax.set_ylabel('Interference Percentage (%)')\n",
    "ax.set_title(\n",
    "    f'Cumulative Interference at SvalSat \\u2014 {F_MHZ:.0f} MHz\\n'\n",
    "    f'{str(T_START)[:10]} to {str(T_END)[:10]}'\n",
    ")\n",
    "ax.set_ylim(1e-4, 100.0)\n",
    "ax.set_xlim(thresholds[0], thresholds[-1])\n",
    "ax.legend(fontsize=9)\n",
    "ax.grid(True, which='both', alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.13.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}