{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cov-title",
   "metadata": {},
   "source": [
    "# SSO Coverage by Latitude Band\n",
    "\n",
    "Computes 30-day coverage statistics for a 550 km sun-synchronous orbit\n",
    "(LTAN 10:30, descending node) carrying a 10° half-angle nadir sensor.\n",
    "\n",
    "Results are reported per 5° latitude band:\n",
    "- Number of sample points in the band\n",
    "- Cumulative coverage fraction (% of points seen ≥ once)\n",
    "- Mean revisit time (hours)\n",
    "- Maximum revisit time (hours)\n",
    "\n",
    "**Object API** — uses `Spacecraft.sunsync`, `Sensor`, `AoI.from_region`, and `Coverage`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cov-imports",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-08T03:46:31.000961Z",
     "iopub.status.busy": "2026-03-08T03:46:31.000004Z",
     "iopub.status.idle": "2026-03-08T03:46:32.194798Z",
     "shell.execute_reply": "2026-03-08T03:46:32.193701Z"
    }
   },
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "\n",
    "from missiontools import Spacecraft, Sensor, AoI, Coverage"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cov-sc-md",
   "metadata": {},
   "source": [
    "## 1. Spacecraft and Sensor\n",
    "\n",
    "A nadir-pointing 10° half-angle sensor is body-mounted along the spacecraft\n",
    "nadir axis (body-z = nadir, body-vector `[0, 0, 1]` in the sensor convention)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cov-sc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-08T03:46:32.197326Z",
     "iopub.status.busy": "2026-03-08T03:46:32.196976Z",
     "iopub.status.idle": "2026-03-08T03:46:32.204029Z",
     "shell.execute_reply": "2026-03-08T03:46:32.203192Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Semi-major axis : 6928.1 km\n",
      "Inclination     : 97.593°\n",
      "Orbital period  : 95.6 min\n",
      "Propagator      : j2\n",
      "FOV half-angle  : 10°\n",
      "Ground swath    : ~194 km\n"
     ]
    }
   ],
   "source": [
    "EPOCH = np.datetime64('2025-01-01T00:00:00', '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",
    "sensor = Sensor(half_angle_deg=10.0, body_vector=[0, 0, 1])\n",
    "sc.add_sensor(sensor)\n",
    "\n",
    "period_s = 2 * np.pi * np.sqrt(sc.a**3 / sc.central_body_mu)\n",
    "swath_km = 2 * (sc.a - sc.central_body_radius) * np.tan(np.radians(10.0)) / 1e3\n",
    "\n",
    "print(f\"Semi-major axis : {sc.a / 1e3:.1f} km\")\n",
    "print(f\"Inclination     : {np.degrees(sc.i):.3f}°\")\n",
    "print(f\"Orbital period  : {period_s / 60:.1f} min\")\n",
    "print(f\"Propagator      : {sc.propagator_type}\")\n",
    "print(f\"FOV half-angle  : {np.degrees(sensor.half_angle_rad):.0f}°\")\n",
    "print(f\"Ground swath    : ~{swath_km:.0f} km\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cov-loop-md",
   "metadata": {},
   "source": [
    "## 2. Per-Band Coverage Analysis\n",
    "\n",
    "For each 5° latitude band we create an `AoI.from_region`, attach a fresh\n",
    "`Coverage` object, and compute coverage fraction and revisit time.\n",
    "\n",
    "> **Note** — 36 bands × 30 days takes a few minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cov-loop",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-08T03:46:32.206642Z",
     "iopub.status.busy": "2026-03-08T03:46:32.206389Z",
     "iopub.status.idle": "2026-03-08T03:49:03.537006Z",
     "shell.execute_reply": "2026-03-08T03:49:03.535896Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done in 151.3 s\n"
     ]
    }
   ],
   "source": [
    "T_START = EPOCH\n",
    "T_END   = EPOCH + np.timedelta64(30 * 86_400, 's')\n",
    "\n",
    "MAX_STEP     = np.timedelta64(20, 's')\n",
    "POINT_DENSITY = 200_000   # km²/point  (~450 km resolution)\n",
    "\n",
    "LAT_EDGES = np.arange(-90, 91, 5)   # 37 edges → 36 bands\n",
    "\n",
    "def band_label(lo_deg, hi_deg):\n",
    "    lo_s = f\"{abs(lo_deg):.0f}°{'S' if lo_deg <  0 else 'N'}\"\n",
    "    hi_s = f\"{abs(hi_deg):.0f}°{'S' if hi_deg <= 0 else 'N'}\"\n",
    "    return f\"{lo_s} – {hi_s}\"\n",
    "\n",
    "rows = []   # (label, n_pts, cov_pct, mean_rev_h, max_rev_h)\n",
    "\n",
    "t0 = time.perf_counter()\n",
    "\n",
    "for lo_deg, hi_deg in zip(LAT_EDGES[:-1], LAT_EDGES[1:]):\n",
    "    aoi = AoI.from_region(\n",
    "        lat_min_deg = float(lo_deg),\n",
    "        lat_max_deg = float(hi_deg),\n",
    "        point_density = POINT_DENSITY,\n",
    "    )\n",
    "    n = len(aoi)\n",
    "\n",
    "    cov = Coverage(aoi, [sensor])\n",
    "\n",
    "    cf = cov.coverage_fraction(T_START, T_END, max_step=MAX_STEP)\n",
    "    rt = cov.revisit_time(T_START, T_END, max_step=MAX_STEP)\n",
    "\n",
    "    cov_pct    = cf['final_cumulative'] * 100.0\n",
    "    mean_rev_h = rt['global_mean'] / 3600.0 if not np.isnan(rt['global_mean']) else float('nan')\n",
    "    max_rev_h  = rt['global_max']  / 3600.0 if not np.isnan(rt['global_max'])  else float('nan')\n",
    "\n",
    "    rows.append((band_label(lo_deg, hi_deg), n, cov_pct, mean_rev_h, max_rev_h))\n",
    "\n",
    "elapsed = time.perf_counter() - t0\n",
    "print(f\"Done in {elapsed:.1f} s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cov-table-md",
   "metadata": {},
   "source": [
    "## 3. Coverage Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cov-table",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-08T03:49:03.539456Z",
     "iopub.status.busy": "2026-03-08T03:49:03.539060Z",
     "iopub.status.idle": "2026-03-08T03:49:03.545810Z",
     "shell.execute_reply": "2026-03-08T03:49:03.544775Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Band    Pts    Coverage    Mean Rev     Max Rev\n",
      "  ─────────────  ─────  ──────────  ──────────  ──────────\n",
      "    90°S – 85°S      9        0.0%         —           —  \n",
      "    85°S – 80°S     28       75.0%     15.49 h    113.24 h\n",
      "    80°S – 75°S     48      100.0%     24.58 h    588.37 h\n",
      "    75°S – 70°S     65      100.0%     38.04 h    533.89 h\n",
      "    70°S – 65°S     84      100.0%     29.01 h    629.53 h\n",
      "    65°S – 60°S    100      100.0%     55.47 h    515.14 h\n",
      "    60°S – 55°S    118      100.0%     52.80 h    583.17 h\n",
      "    55°S – 50°S    132       98.5%     36.87 h    702.71 h\n",
      "    50°S – 45°S    148       95.9%     22.69 h    705.03 h\n",
      "    45°S – 40°S    160      100.0%     49.80 h    705.04 h\n",
      "    40°S – 35°S    173      100.0%     71.24 h    609.43 h\n",
      "    35°S – 30°S    184      100.0%     83.34 h    513.82 h\n",
      "    30°S – 25°S    194      100.0%     89.96 h    464.93 h\n",
      "    25°S – 20°S    201      100.0%     91.86 h    536.63 h\n",
      "    20°S – 15°S    208      100.0%     81.06 h    608.33 h\n",
      "    15°S – 10°S    213      100.0%     68.57 h    680.03 h\n",
      "     10°S – 5°S    216       98.6%     38.60 h    703.92 h\n",
      "      5°S – 0°S    218       90.8%     18.79 h     59.74 h\n",
      "      0°N – 5°N    218       90.8%     18.74 h     59.74 h\n",
      "     5°N – 10°N    216       97.7%     38.58 h    703.92 h\n",
      "    10°N – 15°N    213      100.0%     63.73 h    680.02 h\n",
      "    15°N – 20°N    208      100.0%     84.18 h    608.33 h\n",
      "    20°N – 25°N    201      100.0%     90.64 h    536.63 h\n",
      "    25°N – 30°N    194      100.0%     94.49 h    464.93 h\n",
      "    30°N – 35°N    184      100.0%     87.71 h    513.82 h\n",
      "    35°N – 40°N    173      100.0%     69.86 h    609.42 h\n",
      "    40°N – 45°N    160      100.0%     53.66 h    681.12 h\n",
      "    45°N – 50°N    148       95.3%     17.60 h     58.58 h\n",
      "    50°N – 55°N    132       97.7%     36.29 h    702.71 h\n",
      "    55°N – 60°N    118      100.0%     54.78 h    583.18 h\n",
      "    60°N – 65°N    100      100.0%     53.87 h    515.14 h\n",
      "    65°N – 70°N     84      100.0%     34.27 h    658.59 h\n",
      "    70°N – 75°N     65      100.0%     37.55 h    533.89 h\n",
      "    75°N – 80°N     48      100.0%     24.46 h    580.18 h\n",
      "    80°N – 85°N     28       75.0%     15.33 h    170.54 h\n",
      "    85°N – 90°N      9        0.0%         —           —  \n"
     ]
    }
   ],
   "source": [
    "print(f\"  {'Band':>13}  {'Pts':>5}  {'Coverage':>10}  {'Mean Rev':>10}  {'Max Rev':>10}\")\n",
    "print(f\"  {'─'*13}  {'─'*5}  {'─'*10}  {'─'*10}  {'─'*10}\")\n",
    "\n",
    "for label, n, cov_pct, mean_rev_h, max_rev_h in rows:\n",
    "    mean_s = f\"{mean_rev_h:8.2f} h\" if not np.isnan(mean_rev_h) else \"       —  \"\n",
    "    max_s  = f\"{max_rev_h:8.2f} h\" if not np.isnan(max_rev_h)  else \"       —  \"\n",
    "    print(f\"  {label:>13}  {n:>5}  {cov_pct:>9.1f}%  {mean_s}  {max_s}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cov-viz-md",
   "metadata": {},
   "source": [
    "## 4. Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cov-viz",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-03-08T03:49:03.548196Z",
     "iopub.status.busy": "2026-03-08T03:49:03.547944Z",
     "iopub.status.idle": "2026-03-08T03:49:04.303936Z",
     "shell.execute_reply": "2026-03-08T03:49:04.303002Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels    = [r[0] for r in rows]\n",
    "lat_mids  = [(lo + hi) / 2 for lo, hi in zip(LAT_EDGES[:-1], LAT_EDGES[1:])]\n",
    "cov_pcts  = np.array([r[2] for r in rows])\n",
    "mean_revs = np.array([r[3] for r in rows])\n",
    "max_revs  = np.array([r[4] for r in rows])\n",
    "\n",
    "# Colour bars by coverage fraction\n",
    "norm    = mcolors.Normalize(vmin=0, vmax=100)\n",
    "cmap    = plt.cm.RdYlGn\n",
    "colours = [cmap(norm(v)) for v in cov_pcts]\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 10), sharey=True)\n",
    "\n",
    "# --- Coverage fraction ---\n",
    "ax1.barh(lat_mids, cov_pcts, height=4.5, color=colours, edgecolor='white', linewidth=0.4)\n",
    "ax1.axvline(100, color='tab:green', linestyle='--', linewidth=1.0, alpha=0.7, label='100% coverage')\n",
    "ax1.set_xlabel('Cumulative coverage fraction (%)')\n",
    "ax1.set_ylabel('Latitude (°)')\n",
    "ax1.set_title('30-day coverage fraction')\n",
    "ax1.set_xlim(0, 105)\n",
    "ax1.set_yticks(lat_mids[::2])\n",
    "ax1.set_yticklabels([f\"{int(l):+d}°\" for l in lat_mids[::2]])\n",
    "ax1.grid(True, axis='x', alpha=0.3)\n",
    "ax1.legend(fontsize=9)\n",
    "\n",
    "# Add value labels\n",
    "for lat, v in zip(lat_mids, cov_pcts):\n",
    "    if v > 0:\n",
    "        ax1.text(min(v + 1, 103), lat, f\"{v:.0f}%\", va='center', fontsize=7)\n",
    "\n",
    "# --- Mean revisit time ---\n",
    "valid = ~np.isnan(mean_revs)\n",
    "ax2.barh(np.array(lat_mids)[valid], mean_revs[valid], height=4.5,\n",
    "         color='tab:blue', alpha=0.7, edgecolor='white', linewidth=0.4)\n",
    "ax2.set_xlabel('Mean revisit time (hours)')\n",
    "ax2.set_title('30-day mean revisit time')\n",
    "ax2.grid(True, axis='x', alpha=0.3)\n",
    "\n",
    "# Inclination limit annotation\n",
    "ax2.axhline(np.degrees(sc.i) - 90, color='grey', linestyle=':', linewidth=1.0,\n",
    "            label=f'Inclination limit ({np.degrees(sc.i):.1f}°)')\n",
    "ax2.axhline(-(np.degrees(sc.i) - 90), color='grey', linestyle=':', linewidth=1.0)\n",
    "ax2.legend(fontsize=9)\n",
    "\n",
    "fig.suptitle(\n",
    "    f'550 km SSO  |  LTAN 10:30  |  10° half-angle nadir sensor  |  30-day window',\n",
    "    fontsize=11, y=1.01\n",
    ")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.13.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}