{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fd9ccbae",
   "metadata": {},
   "source": [
    "# 1. Getting started\n",
    "\n",
    "`lazuli-exo` simulates transmission spectroscopy with the Lazuli IFS. Give it a\n",
    "showcase planet, an atmosphere scenario and a number of transits, and it returns\n",
    "the model transmission spectrum binned to the instrument, the 1-sigma error bar\n",
    "per bin, and a spectral-template S/N.\n",
    "\n",
    "`import lazuli_exo` needs numpy and nothing else. The atmosphere is a fast\n",
    "surrogate for POSEIDON (an interpolated opacity grid plus analytic scale-height,\n",
    "cloud and haze terms) and the noise comes from precomputed slicersim tables.\n",
    "\n",
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "**What the numbers mean.** The instrument half (photon and read noise through the\n",
    "real throughput, readout and line-spread function) is quantitative. The atmosphere\n",
    "half is a 1D isothermal, equilibrium-chemistry surrogate with statistical noise\n",
    "only: no P-T profile, no systematics floor. Use the outputs for rankings and\n",
    "trade studies. Do not quote the template S/N as a detection significance.\n",
    "[Known approximations](../user_guide/concepts.rst) lists what the model leaves\n",
    "out.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f6f5100b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-12T13:36:48.578286Z",
     "iopub.status.busy": "2026-10-12T13:36:48.578117Z",
     "iopub.status.idle": "2026-10-12T13:36:49.063140Z",
     "shell.execute_reply": "2026-10-12T13:36:49.062707Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lazuli_exo (dev)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "try:\n",
    "    plt.style.use(\"gks\")\n",
    "except OSError:\n",
    "    pass\n",
    "\n",
    "import lazuli_exo\n",
    "\n",
    "print(\"lazuli_exo\", lazuli_exo.__version__ if hasattr(lazuli_exo, \"__version__\") else \"(dev)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fdf2735",
   "metadata": {},
   "source": [
    "## What is available\n",
    "\n",
    "Fourteen showcase targets, each with the same five scenarios. `list_planets()`\n",
    "reads the table index; `list_scenarios()` returns the preset names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6f53f908",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-12T13:36:49.065277Z",
     "iopub.status.busy": "2026-10-12T13:36:49.065116Z",
     "iopub.status.idle": "2026-10-12T13:36:49.074218Z",
     "shell.execute_reply": "2026-10-12T13:36:49.073948Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14 showcase targets\n",
      "\n",
      "  GJ 1214 b      ['clear', 'cloudy', 'flat', 'hazy', 'high_metallicity']\n",
      "  GJ 3470 b      ['clear', 'cloudy', 'flat', 'hazy', 'high_metallicity']\n",
      "  HAT-P-11 b     ['clear', 'cloudy', 'flat', 'hazy', 'high_metallicity']\n",
      "  HD 189733 b    ['clear', 'cloudy', 'flat', 'hazy', 'high_metallicity']\n",
      "  HD 209458 b    ['clear', 'cloudy', 'flat', 'hazy', 'high_metallicity']\n",
      "  K2-18 b        ['clear', 'cloudy', 'flat', 'hazy', 'high_metallicity']\n",
      "  LHS 1140 b     ['clear', 'cloudy', 'flat', 'hazy', 'high_metallicity']\n",
      "  TOI-270 d      ['clear', 'cloudy', 'flat', 'hazy', 'high_metallicity']\n",
      "  TRAPPIST-1 e   ['clear', 'cloudy', 'flat', 'hazy', 'high_metallicity']\n",
      "  WASP-107 b     ['clear', 'cloudy', 'flat', 'hazy', 'high_metallicity']\n",
      "  WASP-12 b      ['clear', 'cloudy', 'flat', 'hazy', 'high_metallicity']\n",
      "  WASP-121 b     ['clear', 'cloudy', 'flat', 'hazy', 'high_metallicity']\n",
      "  WASP-39 b      ['clear', 'cloudy', 'flat', 'hazy', 'high_metallicity']\n",
      "  WASP-96 b      ['clear', 'cloudy', 'flat', 'hazy', 'high_metallicity']\n"
     ]
    }
   ],
   "source": [
    "planets = lazuli_exo.list_planets()\n",
    "print(f\"{len(planets)} showcase targets\\n\")\n",
    "for p in planets:\n",
    "    print(f\"  {p:14s} {lazuli_exo.list_scenarios(p)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0974ae0f",
   "metadata": {},
   "source": [
    "## A single observation\n",
    "\n",
    "`observe()` takes a planet name, a scenario, the number of transits to co-add\n",
    "and the resolving power `R` to bin the spectrum to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2655669d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-12T13:36:49.075808Z",
     "iopub.status.busy": "2026-10-12T13:36:49.075698Z",
     "iopub.status.idle": "2026-10-12T13:36:49.282087Z",
     "shell.execute_reply": "2026-10-12T13:36:49.281753Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SimulatedSpectrum fields: planet, scenario, n_transits, R, wavelength, depth, depth_err, model_wavelength, model_depth, flat_depth, meta\n"
     ]
    }
   ],
   "source": [
    "import dataclasses\n",
    "\n",
    "obs = lazuli_exo.observe(\"WASP-39 b\", scenario=\"clear\",\n",
    "                         n_transits=1, R=100)\n",
    "print(type(obs).__name__, \"fields:\",\n",
    "      \", \".join(f.name for f in dataclasses.fields(obs)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "683e5a65",
   "metadata": {},
   "source": [
    "## Anatomy of a `SimulatedSpectrum`\n",
    "\n",
    "The result is a plain dataclass. You will use `wavelength`, `depth` and\n",
    "`depth_err` most; `model_wavelength` and `model_depth` keep the native-resolution\n",
    "model for overplotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b15c52d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-12T13:36:49.283893Z",
     "iopub.status.busy": "2026-10-12T13:36:49.283762Z",
     "iopub.status.idle": "2026-10-12T13:36:49.286435Z",
     "shell.execute_reply": "2026-10-12T13:36:49.286182Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wavelength         shape=(145,)   [0.401907, 1.68358]\n",
      "depth              shape=(145,)   [0.0202449, 0.0231421]\n",
      "depth_err          shape=(145,)   [7.48483e-05, 0.000326857]\n",
      "model_wavelength   shape=(14430,) [0.399791, 1.69245]\n",
      "model_depth        shape=(14430,) [0.0199945, 0.0279577]\n",
      "\n",
      "flat_depth         0.0212285   # geometric (Rp/Rs)^2 from the registry radii\n",
      "R                  100\n",
      "n_transits         1\n"
     ]
    }
   ],
   "source": [
    "for name in [\"wavelength\", \"depth\", \"depth_err\",\n",
    "             \"model_wavelength\", \"model_depth\"]:\n",
    "    arr = getattr(obs, name)\n",
    "    print(f\"{name:18s} shape={str(arr.shape):8s} \"\n",
    "          f\"[{arr.min():.6g}, {arr.max():.6g}]\")\n",
    "\n",
    "print()\n",
    "print(f\"{'flat_depth':18s} {obs.flat_depth:.6g}   \"\n",
    "      \"# geometric (Rp/Rs)^2 from the registry radii\")\n",
    "print(f\"{'R':18s} {obs.R}\")\n",
    "print(f\"{'n_transits':18s} {obs.n_transits}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc33810e",
   "metadata": {},
   "source": [
    "The bins are constant resolving power (snapped to whole detector pixels), so\n",
    "the bin width grows with wavelength. `depth` is the noiseless model seen through\n",
    "the instrument and averaged into those bins; `depth_err` is the per-bin 1-sigma\n",
    "uncertainty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "29bb0fee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-12T13:36:49.287933Z",
     "iopub.status.busy": "2026-10-12T13:36:49.287816Z",
     "iopub.status.idle": "2026-10-12T13:36:49.290248Z",
     "shell.execute_reply": "2026-10-12T13:36:49.289994Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "145 bins from 0.402 to 1.684 um\n",
      "bin width: 4.12 nm at the blue end, 15.73 nm at the red end\n",
      "R implied at mid-band: 100\n",
      "\n",
      "depth_err: median 107 ppm, best 75 ppm, worst 327 ppm\n"
     ]
    }
   ],
   "source": [
    "dlam = np.diff(obs.wavelength)\n",
    "print(f\"{obs.wavelength.size} bins from {obs.wavelength[0]:.3f} \"\n",
    "      f\"to {obs.wavelength[-1]:.3f} um\")\n",
    "print(f\"bin width: {dlam[0] * 1e3:.2f} nm at the blue end, \"\n",
    "      f\"{dlam[-1] * 1e3:.2f} nm at the red end\")\n",
    "print(f\"R implied at mid-band: \"\n",
    "      f\"{obs.wavelength[len(dlam) // 2] / dlam[len(dlam) // 2]:.0f}\")\n",
    "\n",
    "print(f\"\\ndepth_err: median {np.median(obs.depth_err) * 1e6:.0f} ppm, \"\n",
    "      f\"best {obs.depth_err.min() * 1e6:.0f} ppm, \"\n",
    "      f\"worst {obs.depth_err.max() * 1e6:.0f} ppm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24c71f5d",
   "metadata": {},
   "source": [
    "## Plotting\n",
    "\n",
    "`obs.plot()` draws the native-resolution model with one simulated noisy\n",
    "realization at the binned resolution. It needs the `[plot]` extra (matplotlib).\n",
    "Every plot method takes an optional `ax` and returns the Axes, so you can pass\n",
    "your own subplot or overlay several calls; notebook 2 does that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6a8c3fe8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-12T13:36:49.291708Z",
     "iopub.status.busy": "2026-10-12T13:36:49.291601Z",
     "iopub.status.idle": "2026-10-12T13:36:49.522741Z",
     "shell.execute_reply": "2026-10-12T13:36:49.522476Z"
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1350x675 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "obs.plot(seed=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f92eea6",
   "metadata": {},
   "source": [
    "`obs.plot_noise()` shows where the instrument is precise: the per-bin 1-sigma\n",
    "error against wavelength, with `n_transits` already included."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cd073c47",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-12T13:36:49.524645Z",
     "iopub.status.busy": "2026-10-12T13:36:49.524234Z",
     "iopub.status.idle": "2026-10-12T13:36:49.666703Z",
     "shell.execute_reply": "2026-10-12T13:36:49.666416Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cdMrcc889JyNHjpRp06ZZMRQAAAAAAAD4a8h0+fJlyZUrl0ycODFVwJTSmDFj5I477rBiGAAAAAAAAPDn6XK1a9eWqKgoEzSlR1eccxVE/fLLLxaMDgAAAAAAAH5RyaSrxjVt2lQOHTokxYsXT/PY48ePy/Xr153u6969u2zbts2KIQIA/MTmg0dl9OpNLlfWiwjLaVbm08bpAAAAAAIsZLr77rtl6tSpMmjQIJkzZ47LSiVtDD5w4EAZMmRIqn1//fWXWZ0OABDcNGBaumtvuse5WpkPAAAAgB+HTA8++KD89NNPcuLECYmOjpbIyEinx50/f17OnTtnjh02bJhj+5UrV8xjNYQCAF/2+9GT0nDqnBs63tMCvdLH/nVFhodJ9aLRqa5n7JV4l187AAAAAD8PmZo0aWIqmNSlS5fk7NmzaR6/f/9+p9t1hToA8EUa3CgNONbuO5Tpx3tCsFT6aMC0pmfnZNs04MvM9QcAAADgR5VMAwYMkMcee0xq1qwp2bJlu6HHJyQkyLp16+Tzzz+3YngA4DatDFKZqaDRgGlY4xiPvQpU+gAAAAAI2JApT5480rp1a5k4ceINB0x2jz/+uHz33XceHxsAeIJOPfO1yiAqfQAAAAB4k/OO3B4wadIklwFTRnot6VS5L7/80oKRAQAAAAAAwC8qmVRUVJTExcXJN998Y5p7P/HEE459zz33nJkSN3ToUClYsKDLc9SpU8eq4QEAAligN0MHAAAAgipk+uSTT2TQoEFmBblixYolC5nefvttef/996Vq1aqycOFCiYnxXG8SAACCpRk6AAAAEPAhk/ZS6tGjR5rH9OvXTw4cOCAtW7aUrVu3SokSJawYCgBARH4/etKsxBYsFT00QwcAAAACJGR64403JDIy0jTvbty4sQwfPtzpcboC3TvvvCOvvfaaTJ482YqhAEBQ0xBJxV6Jl7X7DgVdRQ/N0AEAAAA/D5m2bNkiq1evlurVq5vPX331VafHFS9eXLJnzy7Lly+3YhgAEPS0Skk5602k1U0aPrnqWwQAAAAAXg+ZNDyyB0xpOXnypFy7dk2OHz9uxTAAIOjpNDhXVUo6fS6t6iYAAAAAuBGhYoFChQpl6Dht/q3SWmEOAAAAAAAAQRoytWrVSsaMGZPmMRMmTDC9mEJCQqRZs2ZWDAMAAAAAAAD+PF2ub9++0rRpU9OXqWPHjnL+/Hn5/vvvJTY2Vnbt2iVffPGF7NixQ2w2m2kQ7qoxOADAOyvPBeqqcwAAAAD8LGSyN/Pu37+/9OnTx4RJSauV9HNVtmxZmTVrlpQuXdqKYQAA3Fx5LlBXnQMAAADgJyGTyp07t0ybNk1efPFFmT9/vllx7syZMxIWFialSpWSJk2aSOvWrU0gBQDwnZXnWHUOAAAAQGZYnvCUKVNGhgwZ4nK/TpurXLmy1cMAAGRw5TlWnQMAAADgM42/b4Q2AD9+/Li3hwEAAAAAAABvVDJpz6VffvnFnec20+fmzp0rMTEx0r17d7fOBQCwviF4oDcF33zwqIxevSnVFMJg+NoBAAAAr4VMcXFxMmLECAkJCXF7EBpYETIBgP80BA/UpuAaMC3dtTfd4wLxawcAAAC8FjK1atVKcubMKTfddJOUL19eQkOTz7zbtGmT2ValShXTBNyZ/fv3S3h4uBQvXjyzwwAAZFFD8KRNwdfvPxyQVU72rzkyPEyqF41Oto+G6AAAAIBFIVPevHmlXr168sUXX0h0dPJ/iH/11VeSLVs2mT17thQsWNDlOeLj46Vly5byyCOPZHYYAIAsagiu2sz40lT6BHqVkwZMa3p2TraNhugAAACAhavLDRo0KFXApN5++21ZvHixFChQIM3Hh4WFyfPPPy8jR440xwMA/L/KyVU/I8Bq9NQCAADw45BJp8w5c/ny5XQDJjsNqX744Qd3hgEAQd2MW7f5QpUTlT7wNnpqAQAA+HHI5IqGTBcvXpQ8efKke+zSpUvNtDkAgHvNuO3HAMGKnloAAAABGDI1a9ZMHn30Ufn888/NlDhXFixYIK+//rpUrlzZimEAQFBMU7MHTMMax4ivVlsFSmNw+Ad6agEAAARQyDRs2DCpVq2alCxZUh5//HHTIFxXkNNV5k6fPi1btmwxTcHXrVtnju/Zs6cVwwCAgJHWNDV/qray8/Wv5UbRCwgAAACwKGSKioqS1atXS+vWrWXMmDESEhLi9DibzSadOnWS3r1781oAQIBXWwVyY3B6AQEAAAAWhUyqXLly8scff8j48eNl2rRpsnfv3mT7y5QpI4MHD5Ynn3yS1wEAgqDaKpAbg9MLCAAAALAwZFLh4eHywgsvmNvRo0fl0KFDkpiYKMWKFTPT5wAACCT0AgIAAEAwszRkSqpo0aLmBgBAMHLVEJ1m6AAAAAgUWRYyAQAQjDLaED3QmqEDAAAg+BAyAQC8WtFz7do1yZ49e8BW9KTVED2Qm6EDAAAg+BAyAQCyRLBW9KTVED2Qm6EDAAAg+BAyAQC8WtGjlUzbT56logd+Z/PBozJ69SaXlWiBWp0HAADgCiETAMCrFT2xsbHSdu4KKnrgdzRgWrprb7rHBVp1HgAAgE+GTPqLxYkTJ6RcuXLeHAYAAMANs1cwRYaHSfWi0cn20W8LAAAEI6+GTLlz55b69evLxx9/LC1btvTmUAAAPtgU3L4N8GUaMK3p2TnZNvptAQCAYGRpyHTkyBE5ePCgXL16VWw2W7J9169fl507d8rZs2dl8ODBhEwAEMQy0hTcfgwAAACAIAqZNFTq2rWrzJ8/P91jNXw6deqUFcMAAPh5U/CkAdOwxjFZPCoAAAAAXg+ZRo0aJfPmzUv3uGzZsknlypVl5MiRVgwDAODnTcEzO82OKXYAAABA1gu14qRz5syRfv36melyiYmJsmXLFhk3bpz52H47evSoPPLIIyaQatu2rRXDAAAE2TQ7+00/T7ofAAAAgJ9WMiUkJMjEiRMdn1erVs2ETEkVLlxY3n33XSldurSsXbvWHAMAgKem2THFDgAAAAiAkOmWW25Jta158+YyY8YM6datm2PbTTfdJPnz55dhw4bJokWLrBgKACCAuTvNzpdtPnhURq/e5LJPlT1I06BNrwMAAAAQkCFTWFiYzJ07Vxo2bGg+L1SokHTu3Flq1qwpJUuWlAYNGpjtOo3u0KFDEhsba8UwAADwWxowLd21N0PHBmrQBgAAAP9iScjUp08f6dixo4SEhEiuXLlk27ZtZlrckCFDpGnTpmZqnFYwbdy40fRn0mMAAMD/s1cwRYaHSfWi0akujTY3195TaVU6AQAAAH4fMnXo0EF69Ogh06dPl6tXr5qbevDBB+WHH36QDz/8MNnxSafQAQCA/6cB05qenVNdEl1NT5ucAwAAAAEdMqlp06bJ888/Lzlz5pRSpUo5tn/wwQdmGt28efPk2rVrprJJK58AAAAAAADgvywLmVT58uWdbtepdHqzO378uFltDgAA3BidNqdVTUnREBwAAAABFzJlVL169WT37t3eHgYAAH5DgySlfZlcTZujITgAAAACJmTSpt579uyR06dPO/oypdz/66+/yr59+6wcBgAAAWdoo7rmPmXjbxqC+7/NB4+a1QWTvrbaYiB79uxUqQEAgOALmS5fvmz6Mc2cOVPi4uKseAoAADw+zUy3+Ys6JYo6rVSyNwR39vUpptL5Pg2Ylu7am+YxVKkBAICgCZm0kbcGTBkVEhJixTAAAMjUNDP7MYH69SlCCt9lr2CKDA8zqwvaK5m2nzxrXteU1WsAAAABHTItXLhQqlatKuPHj5caNWpI/vz5nR4XHx8va9askdatW1sxDAAAMjzNLGlIM6xxTEB+fUyl8y8aMK3p2dl8HBsbK23nrkgzOAQAAAjIkCksLEzeeecdadSoUbrHtWjRQjp06GDFMAAAyPA0s2D4+uxT6QAAAAArhFpx0piYGClQoECGj589e7YVwwAAAAAAAIA/VzINGDBAvv76a7njjjsydHzNmjXNKnMAAADwzIp0SdHwHQAA+G3I1LRpU1m8eLEsW7ZMWrVqleYqdCtXrpStW7daMQwAAICgkJEV6VQgTxUFAAABGjKpcuXKyX333WfV6QEAQCZpA3Dtz5QS1S6BtSKdHQ3fAQCAX4dMI0eOlFGjRonNZsvQ8SEhIVYMAwAApAiRVOyV+DQbgFPtEhgr0tnR8B0AAPh1yDRlyhTJnz+/9OvXT2rUqGE+dhYkJSQkyPbt2+XZZ5+1YhgAACCJoY3qmntnfXuodgEAAIC7LAmZzp07Z1aMy8h0Oe3fpKEUAACwVp0SRV1WKVHtAgAAAJ8MmcqXLy9FixbN8PEbNmywYhgAAOAG0a8JAAAAPhUyPfbYY7Jq1SqpXbt2ho6fPHmyvPTSS1YMBQAAy0MY3ebv6NcUnDYfPGpWpnM2hVLRDB4AAHg9ZOrbt6906tRJDh06JMWLF0/z2CtXrsg777xDyAQA8PsQxn5MoPZrWr//MKvSBRgNmJbu2pvucTSDBwAAXguZ+vTpI5cvX5ZatWpJpUqVXB6nq8/9888/pocTAAD+GsLYA6ZhjWMkEPs1tZnxpQkiWJUu8Njfz5HhYWZluqRoBg8AAHwiZNq1a5ds2rTJhEgnTpxI93hnK88BAOAvIUygY1W6wKcB05qenS1vBs/0PAAAApslIVOLFi3k6NGjcv/990tERESaU+U2btwoP/zwgxXDAAAAWbQqHQ3DkRFMzwMAILBZEjI1b95cSpcuLV27dk332GvXrkmxYsWsGAYAALAYDcNxI5ieBwBAYLMkZKpTp44UKVIkYwPInl2mTJlixTAAAIDFmEoHT0/Pc1YVxyp3AAAEccgUGhp6Q82827VrZ8UwAACAxZhKh6ysigvWvmgAAAR1yKSeeuop028pPXFxcTJ48GBZuXKl3HbbbVK2bFkZNWqUREVFWTU0AACQBZhKF7isaODtqiqOVe4AAPAfloVMurLcn3/+aZp6nz17VooXLy733nuvFChQINlxPXv2lHnz5snnn38uXbp0kR07dkjLli1l2bJlUrBgQauGBwAAfGAq3fr9h1NNjVJMjwq+Bt6uquKsWOUOAAD4Uch0/fp12bp1q1StWjXZ9rx588r06dOlQ4cOjtXl5s+fbz5u2rSpua9cubK0bt1aunfvLkuWLLFieAAAwMtT6drM+NKEFGlNjVJMj/JNNPAGAABZFjItX75cLl++bD6Ojo6WKlWqSOHChWX//v3yyCOPmGlxGiYdPHhQEhMTJSQkJFmF04MPPigjRowwVVD16tWzYogAAMCLaBge+A28AQBA8LEkZJo2bZqpYho7dqw0b97cNAK3mzt3rrz++utmepwGTCpbtmzJjtG+TDlz5pRPPvmEkAkAgCBtGA4AAAD/YknItGXLFvn9999T9V9SnTp1krfeeivZtly5cqU6Tiuf1q9fb8XwAACAH3C2lL0dPZsAAACCJGTSKXLOAia1b98+07MpZa+mlPSYI0eOWDE8AAAQAKvSKXo2AQAABHjIlC9fPtm4caPceeedybavXbtWnnzySbl69apZfU4bf6uSJUsmO073nzhxQsLDw60YHgAAPl+xo9uCVVr9mtJamS6YrxkAAEDAhkxDhgyRBg0ayN133y3FihWTU6dOyV9//SUHDhwwvZfefvtt6d+/vyNc0sApISFBcuTIYT5fsWKF+bx8+fJWDA8AAL+p2LEfE0zS6teUkZXpgvGaAQAABGzIdM8998h7770nAwYMMGFRUtqPSbcvWLBAunTpYlaWa9Wqlbm98sorpuH3M888Y7a3adPGiuEBAOAXFTsalgxrHJPFo/Lv68Y1AwAACLCQSfXp08eETbNnz5b9+/dLwYIFpV27dlKrVi2zv0OHDrJu3TrZuXOn9OjRwzT51lApLi7OVDZFRkZK3759rRoeAAA+X7EDrhsAAIA/sSxkUqVKlZIXXnjB5f6YmBhzUzq17qeffpI333xTYmNjzeN0qh0AAAAAAACCPGS6URUqVJCPP/7Y28MAAABB1GTdPs1Op+FpdRkAAAD8OGS67777ZPHixd4eBgAACOIm64rpiwAAAH4cMumqcytXrvT2MAAAQJA2C9fqJg2fXDVgBwAAQBaETLpS3MyZM03/pIceesixvVevXvLtt9+m+/j4+Hg5fvy4afQNAADgjSbrOn0ureom+LbNB4/K6NWb0lylkamQAAD4Qcg0evRouXjxoowdOzZZyHTXXXfJtGnTMnyekJAQd4YBAADgNvo1+ScNmJbu2pvucUyFBADAx0OmYcOGyaeffiqDBw9Otv3++++XPn36SO/eveX222+XbNmyOX38lStXZOPGjfLZZ5+5MwwAAIBMo1+Tf7NXMEWGh0n1otHJ9jEVEgAAPwqZnn32WXNLKSIiQtq1ayfjx49P9xwaRH333XfuDAMAAMDSfk3r9x9OtSqd7oPv0IBpTc/OybYxFRIAgABp/P3888/LtWvXJHv29J9i0KBBVg0DAAAg0/2a2sz40kzFSmtVOnslFAAAQLCzLGSqUaNGho995plnrBoGAACAJVVO9oBpWOMYrjAAAICVIZMrf/31l7zyyivy559/SpkyZWTUqFFSvXp1XgwAAOBXVU4AAADIgpCpQIEC5r548eJm1bkXXnjBfP7vv//K3XffLadPnxabzSY7d+6U77//Xn744QeCJgAAAAAAABHZfPCoWUHVWTW1VlJrtbX+MSwoQqZz585Jjx49ZNKkSZIz5//3KejevbucOnXKBEpffPGFFC5c2KxQN2TIEPnmm2+sGAoAAAAAAIBfGb16k+kLmRZfrLa2JGTKmzevfPDBB8kCps8++0xWr14t4eHhsmDBAildurTZPm7cODNtDgAAAAAAAOKoYIoMDzMrqKZc+dZVv8iADJnKli0rYWFhjs9jY2Pl2WeflZCQEHn66acdAZPKli2b5M6d24phAAAAADc8DcHXpyIAAIJH9aLRsqZnZ8fnDafOcbnibcCGTBocnTlzxvRm0t5LvXr1khMnTkipUqVk+PDhyY49e/as7Nu3z4ph+LVVq1aZYG7Pnj1StWpVM/XwRlbsAwAA3qd/bdR/DKZEgOEf0xB8dSoCAABBFTJ16dJF7rzzTmnbtq1s2rRJ1q9fL3ny5JHPP/88WdXSlStX5IknnpCrV32zzMtbtEH6p59+KjNmzDBhXf/+/c213Lt3r2TPnuULAgIAgBukIZLScva0/tpIgOF70xD8YSoCAAC+ypLEYsCAAfL333/L+PHjJSEhQSpWrChTp06VmJgYxzFa0aRBigYqOo0O/+/bb7+VKVOmmGBOTZgwQZo0aSL79+83UxEBAIBv02lWyllIQYDh29MQ/GEqAgAAQRUyabWNhiQajly8eFGioqJSHfPUU0+ZW6CJi4uTt956S9q1ayd33HFHps6hK/MlVaRIEdPjSu8BAIDv0z4+rqqUCDD8B9MdAQC4MZbOvdKV5PTmTOHChSWQ6JS/Dz/8UF599VU5deqUNG7c2Olx58+fN1VcFy5cML2rtDpJP09a5ZXSr7/+Ko899phZtQ8AAAR2gKHb4V1MdwRgBRYcQDDI0gY/v/zyi1y6dEnq168vgWbbtm2mF9WWLVvMNEBnNFjSr7127dpm+qC9wXfDhg3l+++/l7vuuivVY65duyaLFi2SadOmWf41AAAA3wkw7Mch6zHdEYAVWHAAwSBLQ6auXbvK7t27TXASaGrWrGnudQU9V4YOHSpbt26VpUuXOrY1a9ZM6tatK927d5c///wzVWPvN998U8aMGSP58+e3cPQAAMAXAoykAdOwxq6rnGEtpjsCsAILDiAYZPlSZTabTYKRVnBp9VLVqlWlePHiyfa1atVKhgwZYsIn7eVkp9VLuq9cuXKOKXk5c/JXTQAAAjXAAAD4xvQ1Dfv1jwL6M9vTWHAAgSzLQ6ZgtXnzZtMEvXLlyqn2afCkli9f7giZpk+fbqqatOH3rl27TC+n1atXmzAKAAAAAGD99DX+KADcGEKmLKJBkSpaNHUSbt+2Y8cOc79gwQLp2bOnJCYmJjtu7ty56T7Pnj17nAZZSc8PAAAAAMHO1fQ1XYBB++alNa0Z8HeVXeQGmimUKVMm0+clZMoiZ8+eNfe5c+dOtS9Pnjzm/vTp0+a+Q4cOcv369UxPR0wZTtnFxsZm6BxxcXGZem4AyAx+5gAZ+z6x97TU+5T/Tw+Efa6k95isHIs7j/Mn/FwObv76+v965IS8uXGLXLiakGpf3pw55Pk7a0jNmws5/X6uEn2TLOp0j2N7q1lLZcPBYx7/PrfiZ5K/vl5In6v3xI28V9J6f7jKDdxtcZSlIVOw9mNSOu1NhYSEpNoXGhrqsetTtmxZj1QsRUZGun0OAOBnDuA59sVB9D7l/6cDYV9mvu6sHos7j/M3gfy1ITBf/3GLvpdv9hx0uT9Hjhyppr65+n626vvcip9J/vp6IX2een+6Ombnzp03VOHkkyHTG2+8EbB/8UlPkSJFzP3ly5edNgVXBQoUyPJxAQAA/6LTOBpOnZNqGwAEM6a+Idj/PVC9aCEZ37qReFuWhkxJV04LNrfccou5P3bsWKp99m2VKlXK8nEBAAD/oCsdKe0TsnbfoTSPge+tVGX1alUAnK/cpr+Eu/qZCQTqvwe8iZ5MWaROnToSEREhf/75Z6p927ZtM/dNmzbNquEAAAA/o+GESivAGNY4JotHhRtdqUqxWhUAwNP/HtBKJl9AyJRFcubMKd26dZMPP/xQjh8/LoULF3bs+/rrryU6Olrat08+RxgAAMBOq18IJ/xzuk7S1arW7z+carqjosoJwYKKPyCw/z3gEyFT//79ZeLEiRIITpw44bL30qhRo2TZsmUycuRImTRpktm2du1aWb16tcyePVvCw8OzfLwAAACwdrqOajPjS1PllN70Bl/+xQHwBCr+gMDmEyHTvHnz/D5k0ilvX375pcyYMcN8/vLLL8uBAwekQ4cOpkrJ3th706ZN8tJLL8nDDz8s+fPnl3/++UeWL18uzZs39/JXAAAAAG9Md7RXObmaCgkEW8Uf3wtAEIZM8fHxZtqXOxISEkzAZK/+8WdVq1Y1t2HDhqV5nE6T+/jjj7NsXAAAAIpV6Xx3egONieFL09euXbvmWCLdyqmczir++F4Agjhk0iqdypUrS2JiomdHBAAAAI9hVToAVkxfo7cSkLHvh4ggW1k00yFT+fLlpWvXrvLJJ5+4PYiQkBC3zwEAAIDUWJXOv/GLPLJ6+lrSSqa0pq/RWwnBJq2fx2n12gu2nntu9WQaPny4zJo1S1577TWpWbPmDYdF586dMyHV4sWL3RkGAAAA/HQVGqSNX+Rh9S/IGiQlnb4WGxsrkZGR6U5fo7cSgk1Gfx43KF08qPuMuRUylSxZUvr06WMqmgoVKpSpc2jD6yJFirgzDAAAAPgYekB5RkZ+kV+//7AJA4J5egbc/wXZPrX2RtFbCcEirZ/H9u+hYY1jpHbxIkHdZ8zt1eV0FbWlS5dK9+7dM/X4XLlySbNmzdwdBgAAAHwAPaCs4ewX+TYzvjShgQZNrn6JoYoNGfkF2f7LMYDM/TyGB0Om6OjoTAdMdvPnz3d3GAAAAPCBiiR6QGUdV9c6GKdnBIv0enRlpIqNX5AR7Oh15+MhEwAAAAJPZiuS6AGVdVxd62CcnhEsMtoTRlHFBrj3fcT3UOYQMgEAACAVKpIA/+sJQxUb4N73Ed9D7iNkAgAAQCpUJAXmVEdFU3DvT6Fx97lcTXmjig3IOJrWW4OQCQAAAAiyqY6KqSDWT6FxFSZldDojrxHg+SA3rb6CcB8hEwAAABAkUx2ZCuLeFJr1+w+nqg5Lq+ooI8FVg9LFeY0AD8vI956zvoJwHyETAAAAECRTHYNhOlVaFQwZmfLmbApNmxlfml9Y06oOc3bN0wqudCzDGsdI7eJFgu41Arzdv8z+/QfPI2QCAAAAEDBBUkYCmhudhuaqOiyjlWGueig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",
      "text/plain": [
       "<Figure size 1350x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "obs.plot_noise()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ae6d5e7",
   "metadata": {},
   "source": [
    "## Noise realizations\n",
    "\n",
    "`depth` and `depth_err` are deterministic. The noise is drawn on demand by\n",
    "`simulate_points(seed)`, and a seed makes a realization reproducible, which is how\n",
    "the web demo and these notebooks stay stable across runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8e470dc8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-12T13:36:49.668358Z",
     "iopub.status.busy": "2026-10-12T13:36:49.668177Z",
     "iopub.status.idle": "2026-10-12T13:36:49.671025Z",
     "shell.execute_reply": "2026-10-12T13:36:49.670752Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "same seed reproduces:      True\n",
      "different seed differs:    True\n",
      "\n",
      "residuals in sigma units: mean +0.080, std 0.954  (expect ~0 and ~1)\n"
     ]
    }
   ],
   "source": [
    "a = obs.simulate_points(seed=0)\n",
    "b = obs.simulate_points(seed=0)\n",
    "c = obs.simulate_points(seed=1)\n",
    "\n",
    "assert np.array_equal(a, b), \"same seed must give the same realization\"\n",
    "assert not np.array_equal(a, c), \"different seeds must differ\"\n",
    "\n",
    "resid = (a - obs.depth) / obs.depth_err\n",
    "print(f\"same seed reproduces:      {np.array_equal(a, b)}\")\n",
    "print(f\"different seed differs:    {not np.array_equal(a, c)}\")\n",
    "print(f\"\\nresiduals in sigma units: mean {resid.mean():+.3f}, \"\n",
    "      f\"std {resid.std():.3f}  (expect ~0 and ~1)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7994036",
   "metadata": {},
   "source": [
    "## Detection diagnostics\n",
    "\n",
    "`obs.detect()` returns a matched-template amplitude S/N of the noiseless model\n",
    "against the best-fit flat line, under a diagonal covariance.\n",
    "\n",
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "`template_snr_diagonal` is a comparison statistic, not a calibrated detection\n",
    "significance: there is no likelihood ratio, no nuisance model, no covariance and\n",
    "no false-positive calibration. The per-band values are the same statistic\n",
    "restricted to fixed wavelength masks. They mix every species in the mask with the\n",
    "haze, cloud and continuum structure, and the masks overlap, so they are not\n",
    "molecule detections either.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "75d20dee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-12T13:36:49.672550Z",
     "iopub.status.busy": "2026-10-12T13:36:49.672425Z",
     "iopub.status.idle": "2026-10-12T13:36:49.679147Z",
     "shell.execute_reply": "2026-10-12T13:36:49.678891Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "template_snr_diagonal: 68.6\n",
      "\n",
      "band_snr (band-restricted template norms, NOT detections):\n",
      "  H2O      46.6\n",
      "  CO2      26.0\n",
      "  Na       22.7\n",
      "  CH4      18.8\n",
      "  K        15.2\n"
     ]
    }
   ],
   "source": [
    "result = obs.detect()\n",
    "\n",
    "print(f\"template_snr_diagonal: {result['template_snr_diagonal']:.1f}\\n\")\n",
    "print(\"band_snr (band-restricted template norms, NOT detections):\")\n",
    "for mol, snr in sorted(result[\"band_snr\"].items(),\n",
    "                       key=lambda kv: -kv[1]):\n",
    "    print(f\"  {mol:5s} {snr:7.1f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aabfc42f",
   "metadata": {},
   "source": [
    "The band masks live in `lazuli_exo.detect.BAND_MASKS`. H2O and CH4 both cover\n",
    "1.10 to 1.20 µm, which is one reason these are not per-molecule detections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "20dc32d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-12T13:36:49.680747Z",
     "iopub.status.busy": "2026-10-12T13:36:49.680630Z",
     "iopub.status.idle": "2026-10-12T13:36:49.682676Z",
     "shell.execute_reply": "2026-10-12T13:36:49.682442Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Na    0.575-0.605 um\n",
      "  K     0.755-0.785 um\n",
      "  H2O   0.900-1.000, 1.100-1.200, 1.300-1.550 um\n",
      "  CH4   0.860-0.910, 1.100-1.200, 1.600-1.700 um\n",
      "  CO2   1.420-1.460, 1.560-1.620 um\n"
     ]
    }
   ],
   "source": [
    "from lazuli_exo.detect import BAND_MASKS\n",
    "\n",
    "for mol, spans in BAND_MASKS.items():\n",
    "    pretty = \", \".join(f\"{lo:.3f}-{hi:.3f}\" for lo, hi in spans)\n",
    "    print(f\"  {mol:5s} {pretty} um\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c37c56cc",
   "metadata": {},
   "source": [
    "## Where the numbers came from\n",
    "\n",
    "Every observation carries its provenance in `obs.meta`: the parameters the model\n",
    "was evaluated at (`params`), what the atmosphere model derived from them\n",
    "(`atmosphere`), the slicersim settings behind the error bars (`noise`) and how\n",
    "the model was put through the spectrograph (`instrument_response`). This is the\n",
    "first place to look when a number surprises you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "20775ff5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-12T13:36:49.684124Z",
     "iopub.status.busy": "2026-10-12T13:36:49.684015Z",
     "iopub.status.idle": "2026-10-12T13:36:49.686539Z",
     "shell.execute_reply": "2026-10-12T13:36:49.686300Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "meta['params']:\n",
      "  T_eq_K                 1166\n",
      "  R_p_RJ                 1.33134\n",
      "  M_p_MJ                 0.281\n",
      "  R_s_RSun               0.939\n",
      "  log_met                0\n",
      "  c_to_o                 0.55\n",
      "  mu                     None\n",
      "  log_P_cloud_bar        None\n",
      "  log_a_haze             None\n",
      "\n",
      "meta['atmosphere']:\n",
      "  g_ms2                  3.92911\n",
      "  H_m                    1.05466e+06\n",
      "  H_over_Rp              0.0110807\n",
      "  mu_used                2.32131\n",
      "  mu_source              chemistry\n",
      "  T_chem_K               1166\n",
      "  T_clamped              False\n",
      "  feature_amplitude_ppm  7963.21\n",
      "\n",
      "meta['noise']:\n",
      "  instrument             lazuli_cbe\n",
      "  nmd_used               [2, 8, 0]\n",
      "  t14_s                  10091.5\n",
      "  t_obs_s                48.11\n",
      "  mag_input              10.663\n",
      "  mag_ab                 11.5623\n",
      "  fully_desaturated      True\n",
      "  nsaturated_detpx       0\n",
      "  slicersim_version      1.3.4\n",
      "  git_commit             7d0f71b1f0a36437226d6d26346c74f6baa8dc1f\n",
      "\n",
      "meta['instrument_response']:\n",
      "  lsf_sigma_px           1.15\n",
      "  resolution_px          2.3\n",
      "  R_instr_min            100.596\n",
      "  R_instr_max            368.731\n",
      "  photon_weighting       star table R=10000\n",
      "  edges_snapped          True\n",
      "\n"
     ]
    }
   ],
   "source": [
    "SHOW = {\n",
    "    \"params\": [\"T_eq_K\", \"R_p_RJ\", \"M_p_MJ\", \"R_s_RSun\", \"log_met\", \"c_to_o\",\n",
    "               \"mu\", \"log_P_cloud_bar\", \"log_a_haze\"],\n",
    "    \"atmosphere\": [\"g_ms2\", \"H_m\", \"H_over_Rp\", \"mu_used\", \"mu_source\",\n",
    "                   \"T_chem_K\", \"T_clamped\", \"feature_amplitude_ppm\"],\n",
    "    \"noise\": [\"instrument\", \"nmd_used\", \"t14_s\", \"t_obs_s\", \"mag_input\", \"mag_ab\",\n",
    "              \"fully_desaturated\", \"nsaturated_detpx\", \"slicersim_version\",\n",
    "              \"git_commit\"],\n",
    "    \"instrument_response\": None,          # all keys\n",
    "}\n",
    "for section, keys in SHOW.items():\n",
    "    d = obs.meta[section]\n",
    "    print(f\"meta[{section!r}]:\")\n",
    "    for k in keys or d:\n",
    "        v = d[k]\n",
    "        print(f\"  {k:22s} {v:.6g}\" if isinstance(v, float) else f\"  {k:22s} {v}\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f9792ed",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "- `lazuli_exo.observe(planet, scenario, n_transits, R)` returns a\n",
    "  `SimulatedSpectrum`.\n",
    "- `.depth` and `.depth_err` are the noiseless model and its 1-sigma errors;\n",
    "  `.simulate_points(seed)` draws a reproducible noisy realization.\n",
    "- `.plot()`, `.plot_noise()` and (notebook 4) `.light_curves().plot()` all take\n",
    "  `ax=` and return the Axes.\n",
    "- `.detect()` gives a template S/N: a comparison statistic, not a significance.\n",
    "- `.meta` records the parameters, the derived atmosphere quantities and the noise\n",
    "  settings that produced the numbers.\n",
    "\n",
    "Next: [scenarios](02_scenarios.ipynb), what the five atmosphere scenarios mean\n",
    "and how they differ."
   ]
  }
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