{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4d2749f1",
   "metadata": {},
   "source": [
    "# 5. Tables and targets\n",
    "\n",
    "Everything `lazuli-exo` computes at runtime comes out of a small directory of\n",
    "`.npz` lookup tables plus a frozen target registry, and nothing heavier than\n",
    "numpy is imported. This notebook opens the tables directly, so you can see what\n",
    "the runtime reads and where its caveats are recorded.\n",
    "\n",
    "```\n",
    "index.json                 # schema version, planets, grids, sha256 manifest\n",
    "atmo/zeta_grid.npz         # parametric model: zeta(T, [M/H], C/O, fiducial, lambda), mu\n",
    "star/phoenix_grid.npz      # PHOENIX SEDs on (T_eff, log g, [Fe/H])\n",
    "star/<planet-slug>.npz     # wavelength_um, f_lambda, meta      (R = 10 000 host SED)\n",
    "noise/<planet-slug>.npz    # wavelength_um, depth_err_1transit, meta\n",
    "ld/<planet-slug>.npz       # wavelength_um, u1, u2, meta\n",
    "```\n",
    "\n",
    "Scenarios are not files: they are presets of the parametric model. The\n",
    "per-planet POSEIDON spectra the model is validated against live under\n",
    "`precompute/tables/` and are not shipped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e16ba236",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-12T13:30:33.000260Z",
     "iopub.status.busy": "2026-10-12T13:30:32.999875Z",
     "iopub.status.idle": "2026-10-12T13:30:33.656917Z",
     "shell.execute_reply": "2026-10-12T13:30:33.656598Z"
    }
   },
   "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": "044a389f",
   "metadata": {},
   "source": [
    "## Locating the tables\n",
    "\n",
    "`resolve_tables_dir()` looks in three places, in order: an explicit\n",
    "`tables_dir=` argument, then the `LAZULI_EXO_TABLES` environment variable, then\n",
    "the `data/tables/` shipped inside the package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2b89b54e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-12T13:30:33.658786Z",
     "iopub.status.busy": "2026-10-12T13:30:33.658610Z",
     "iopub.status.idle": "2026-10-12T13:30:33.664598Z",
     "shell.execute_reply": "2026-10-12T13:30:33.664345Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tables dir: <package>/data/tables\n",
      "\n",
      "  atmo       1 files   25.26 MB\n",
      "  star      15 files    5.62 MB\n",
      "  noise     14 files    0.14 MB\n",
      "  ld        14 files    0.03 MB\n",
      "\n",
      "  total               31.06 MB\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "from lazuli_exo import tables\n",
    "\n",
    "tdir = tables.resolve_tables_dir(None)\n",
    "PKG = Path(lazuli_exo.__file__).parent\n",
    "print(f\"tables dir: <package>/{tdir.relative_to(PKG)}\\n\")\n",
    "\n",
    "for sub in [\"atmo\", \"star\", \"noise\", \"ld\"]:\n",
    "    files = sorted((tdir / sub).glob(\"*.npz\"))\n",
    "    mb = sum(f.stat().st_size for f in files) / 1e6\n",
    "    print(f\"  {sub:8s} {len(files):3d} files  {mb:6.2f} MB\")\n",
    "\n",
    "total = sum(f.stat().st_size for f in tdir.rglob(\"*\")\n",
    "            if f.is_file()) / 1e6\n",
    "print(f\"\\n  {'total':8s} {total:16.2f} MB\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40e89bdc",
   "metadata": {},
   "source": [
    "## The index\n",
    "\n",
    "`index.json` is the catalogue. It carries a schema `version`, and `load_index()`\n",
    "refuses a mismatch rather than silently misreading tables. Since schema 4 it\n",
    "also holds a sha256 manifest of every file, so a table that was edited or\n",
    "truncated after generation can be caught."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a8a792c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-12T13:30:33.665989Z",
     "iopub.status.busy": "2026-10-12T13:30:33.665878Z",
     "iopub.status.idle": "2026-10-12T13:30:33.671231Z",
     "shell.execute_reply": "2026-10-12T13:30:33.670951Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "schema version: 4 (supported: 4)\n",
      "planets:        14\n",
      "grids:          {'zeta_grid': True}\n",
      "manifest:       44 files\n",
      "\n",
      "index['planets']['WASP-39 b']: {'has_ld': True, 'has_noise': True, 'slug': 'wasp-39-b'}\n",
      "\n",
      "noise/wasp-39-b.npz: manifest baf4e4a67de0a11d..., on disk baf4e4a67de0a11d...\n"
     ]
    }
   ],
   "source": [
    "index = tables.load_index(tdir)\n",
    "\n",
    "print(f\"schema version: {index['version']} \"\n",
    "      f\"(supported: {tables.SCHEMA_VERSION})\")\n",
    "print(f\"planets:        {len(index['planets'])}\")\n",
    "print(f\"grids:          {index['atmo']}\")\n",
    "print(f\"manifest:       {len(index['files'])} files\\n\")\n",
    "\n",
    "print(\"index['planets']['WASP-39 b']:\", index[\"planets\"][\"WASP-39 b\"])\n",
    "\n",
    "rel = \"noise/wasp-39-b.npz\"\n",
    "print(f\"\\n{rel}: manifest {index['files'][rel][:16]}..., \"\n",
    "      f\"on disk {tables.sha256_file(tdir / rel)[:16]}...\")\n",
    "assert tables.sha256_file(tdir / rel) == index[\"files\"][rel]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec637a57",
   "metadata": {},
   "source": [
    "The index is regenerated from what is on disk by `tables.rebuild_index()`,\n",
    "which every precompute script calls when it finishes, so the catalogue cannot\n",
    "drift from the files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "070b0317",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-12T13:30:33.672650Z",
     "iopub.status.busy": "2026-10-12T13:30:33.672547Z",
     "iopub.status.idle": "2026-10-12T13:30:33.731599Z",
     "shell.execute_reply": "2026-10-12T13:30:33.731330Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "indexed files missing from disk: none\n",
      "files whose hash differs from the manifest: none\n"
     ]
    }
   ],
   "source": [
    "missing = [rel for rel in index[\"files\"] if not (tdir / rel).exists()]\n",
    "print(f\"indexed files missing from disk: {missing or 'none'}\")\n",
    "assert not missing\n",
    "\n",
    "bad = [rel for rel in index[\"files\"]\n",
    "       if tables.sha256_file(tdir / rel) != index[\"files\"][rel]]\n",
    "print(f\"files whose hash differs from the manifest: {bad or 'none'}\")\n",
    "assert not bad"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "989ae150",
   "metadata": {},
   "source": [
    "## What each table holds\n",
    "\n",
    "Each per-planet loader returns its arrays plus the `meta` dict that records how\n",
    "the table was produced; the grid loaders return a dict. The model itself is not\n",
    "a table any more: `observe()` evaluates the parametric model on the ζ grid's\n",
    "wavelengths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "af948c7b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-12T13:30:33.733008Z",
     "iopub.status.busy": "2026-10-12T13:30:33.732912Z",
     "iopub.status.idle": "2026-10-12T13:30:33.951913Z",
     "shell.execute_reply": "2026-10-12T13:30:33.951638Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "array     samples     wavelength range (um)\n",
      "model       14430        0.3998 - 1.6925\n",
      "star        16650        0.3500 - 1.8500\n",
      "noise         525        0.4000 - 1.6993\n",
      "ld             48        0.4061 - 1.6746\n",
      "\n",
      "zeta grid: zeta(16, 9, 4, 2, 16650) on T x [M/H] x C/O x fiducial x lambda, 16650 wavelengths\n",
      "PHOENIX:   log_flam(76, 5, 1, 2121) on T_eff x log g x [Fe/H] x lambda, T_eff 2000-12000 K\n"
     ]
    }
   ],
   "source": [
    "PLANET = \"WASP-39 b\"\n",
    "\n",
    "obs_m = lazuli_exo.observe(PLANET, scenario=\"clear\", tables_dir=tdir)\n",
    "wl_m, depth_m = obs_m.model_wavelength, obs_m.model_depth\n",
    "wl_s, flam_s, meta_s = tables.load_star(tdir, PLANET)\n",
    "wl_n, err_n, meta_n = tables.load_noise(tdir, PLANET)\n",
    "wl_l, u1_l, u2_l, meta_l = tables.load_ld(tdir, PLANET)\n",
    "zeta = tables.load_zeta_grid(tdir)\n",
    "phoenix = tables.load_phoenix_grid(tdir)\n",
    "\n",
    "print(f\"{'array':8s}{'samples':>9s}{'wavelength range (um)':>26s}\")\n",
    "for lab, w in [(\"model\", wl_m), (\"star\", wl_s),\n",
    "               (\"noise\", wl_n), (\"ld\", wl_l)]:\n",
    "    print(f\"{lab:8s}{w.size:9d}{w.min():14.4f} - {w.max():.4f}\")\n",
    "\n",
    "print(f\"\\nzeta grid: zeta{zeta['zeta'].shape} on T x [M/H] x C/O x fiducial x lambda, \"\n",
    "      f\"{zeta['wavelength_um'].size} wavelengths\")\n",
    "print(f\"PHOENIX:   log_flam{phoenix['log_flam'].shape} on T_eff x log g x [Fe/H] x lambda, \"\n",
    "      f\"T_eff {phoenix['teff_K'].min():.0f}-{phoenix['teff_K'].max():.0f} K\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be7560b7",
   "metadata": {},
   "source": [
    "The ζ grid spans 0.35 to 1.85 µm at R = 10 000 (margin around the 0.40 to\n",
    "1.70 µm IFS bandpass); `obs.model_wavelength` is the slice of it inside the\n",
    "binned range, which is why the model row above starts at 0.40 µm. The noise\n",
    "table is sampled at the instrument's own channel spacing. That difference is why `observe()` puts the\n",
    "model through the instrument response onto the channels rather than\n",
    "interpolating one grid onto the other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9c81e41e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-12T13:30:33.953447Z",
     "iopub.status.busy": "2026-10-12T13:30:33.953338Z",
     "iopub.status.idle": "2026-10-12T13:30:34.277212Z",
     "shell.execute_reply": "2026-10-12T13:30:34.276949Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1425x1350 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "star grid: phoenix, T_eff = 5485.0 K, units = POSEIDON F_star (W/m^2/m)\n"
     ]
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, 1, figsize=(9.5, 9), sharex=True)\n",
    "\n",
    "axes[0].plot(wl_m, depth_m * 100.0, lw=0.7)\n",
    "axes[0].set_ylabel(\"Transit depth (%)\")\n",
    "axes[0].set_title(f\"{PLANET}: model (clear), star table and noise table\")\n",
    "\n",
    "axes[1].plot(wl_s, flam_s / flam_s.max(), lw=0.7)\n",
    "axes[1].set_ylabel(\"Stellar $F_\\\\lambda$ (normalized)\")\n",
    "\n",
    "axes[2].plot(wl_n, err_n * 1e6, lw=1.0)\n",
    "axes[2].set_ylabel(\"1-transit $\\\\sigma_{depth}$ (ppm)\")\n",
    "axes[2].set_yscale(\"log\")\n",
    "axes[2].set_xlabel(\"Wavelength (µm)\")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print(f\"star grid: {meta_s['stellar_grid']}, \"\n",
    "      f\"T_eff = {meta_s['T_eff_K']} K, units = {meta_s['units']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a14d2e01",
   "metadata": {},
   "source": [
    "## What the noise table records\n",
    "\n",
    "The noise `meta` is the provenance of every error bar: the instrument, the\n",
    "normalization band and the Vega to AB conversion, the readout actually used, the\n",
    "assumption the depth uncertainty rests on, and the package version, git commit\n",
    "and input hashes the table was generated from."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7f9abedc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-12T13:30:34.279240Z",
     "iopub.status.busy": "2026-10-12T13:30:34.279099Z",
     "iopub.status.idle": "2026-10-12T13:30:34.281287Z",
     "shell.execute_reply": "2026-10-12T13:30:34.281025Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  generator                    make_noise_tables.py\n",
      "  created                      2026-10-11\n",
      "  J_mag                        10.663\n",
      "  norm_band                    2massj\n",
      "  stellar_grid                 phoenix_grid.npz solar [Fe/H]\n",
      "  star                         {'fallback': None, 'clamped': {'feh': [0.01, 0.0]}, 'teff_K': 5485.0, 'teff_used': 5485.0, 'logg_used': 4.453, 'feh_used': 0.0, 'grid': 'phoenix'}\n",
      "  slicersim_version            1.3.4\n",
      "  instrument                   lazuli_cbe\n",
      "  t_int_s                      45.28\n",
      "  default_nmd                  [16, 8, 0]\n",
      "  nmd_used                     [2, 8, 0]\n",
      "  nramps                       1\n",
      "  readout_mode                 auto\n",
      "  tframe_s                     2.83\n",
      "  spectrograph_mode            narrow\n",
      "  guiding_sigma                None\n",
      "  zodi_ab_mag                  None\n",
      "  nsaturated_detpx             0\n",
      "  fully_desaturated            True\n",
      "  nsaturated_detpx_slicersim   0\n",
      "  band                         J\n",
      "  sncosmo_band                 2massj\n",
      "  mag_input                    10.663\n",
      "  mag_system                   vega\n",
      "  vega_ab_offset               0.8993018775290533\n",
      "  mag_ab                       11.562301877529054\n",
      "  t14_s                        10091.52\n",
      "  t_obs_s                      48.11\n",
      "  nframes                      16\n",
      "  n_reset_frames               1\n",
      "  trimmed_channels             0\n",
      "  assumption                   equal out-of-transit baseline; SNR ~ sqrt(t); readout tuned to remove point-source saturation\n",
      "  package_version              0.1.1\n",
      "  git_commit                   7d0f71b1f0a36437226d6d26346c74f6baa8dc1f\n",
      "  instrument_config            lazuli_cbe.toml\n",
      "  instrument_config_sha256     0255b694d9756e30031719aa0635b20c96c37015cd4bacd1a436b19ca9016f28\n",
      "  phoenix_grid_sha256          8d70ff2d232938b4dbf9b35815032e9cb504905a9ae2fd4949c9f49cf33cbaba\n",
      "  planet                       WASP-39 b\n"
     ]
    }
   ],
   "source": [
    "for k, v in meta_n.items():\n",
    "    print(f\"  {k:28s} {v}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f023145",
   "metadata": {},
   "source": [
    "## Noise vs. host brightness\n",
    "\n",
    "Single-transit depth precision is driven mostly by how bright the host is.\n",
    "Plotting the median against the registry's J magnitude shows the photon-limited\n",
    "trend, with the sub-Neptune and terrestrial hosts at the faint end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b1d4f02e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-12T13:30:34.282714Z",
     "iopub.status.busy": "2026-10-12T13:30:34.282610Z",
     "iopub.status.idle": "2026-10-12T13:30:34.425822Z",
     "shell.execute_reply": "2026-10-12T13:30:34.425573Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1350x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lazuli_exo.targets import get_target\n",
    "\n",
    "rows = []\n",
    "for p in lazuli_exo.list_planets():\n",
    "    _, e, _ = tables.load_noise(tdir, p)\n",
    "    rows.append((p, get_target(p)[\"J_mag\"], np.median(e)))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9, 5))\n",
    "for p, j, e in rows:\n",
    "    ax.plot(j, e * 1e6, \"o\", ms=6)\n",
    "    ax.annotate(p, (j, e * 1e6), fontsize=8,\n",
    "                xytext=(4, 3), textcoords=\"offset points\")\n",
    "\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_xlabel(\"Host J magnitude\")\n",
    "ax.set_ylabel(\"Median 1-transit $\\\\sigma_{depth}$ (ppm)\")\n",
    "ax.set_title(\"Single-transit depth precision vs. host brightness\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84d7a6bb",
   "metadata": {},
   "source": [
    "## The target registry\n",
    "\n",
    "`lazuli_exo.targets` is a frozen snapshot of the NASA Exoplanet Archive\n",
    "`PSCompPars` table (plus a few overrides), generated by\n",
    "`precompute/freeze_targets.py`. It is a plain dict: no network access at runtime\n",
    "and no dependency on the archive being up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d6d933de",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-12T13:30:34.427342Z",
     "iopub.status.busy": "2026-10-12T13:30:34.427235Z",
     "iopub.status.idle": "2026-10-12T13:30:34.431069Z",
     "shell.execute_reply": "2026-10-12T13:30:34.430809Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "planet             T_eq_K     R_p_RJ   R_s_RSun    T_eff_K      J_mag   period_d  a_over_rs    inc_deg      depth\n",
      "GJ 1214 b             567     0.2438     0.2162       3101       9.75       1.58      14.97      88.98    0.01343\n",
      "GJ 3470 b           593.5     0.4068      0.547       3600      8.794      3.337      13.98      89.13    0.00584\n",
      "HAT-P-11 b            838     0.3854      0.683       4653      7.608      4.888      16.72      89.03   0.003363\n",
      "HD 189733 b          1209      1.134       0.75       5052      6.073      2.219       8.84      85.71    0.02413\n",
      "HD 209458 b          1459        1.4       1.19       6091      6.591      3.525       8.81      86.71    0.01461\n",
      "K2-18 b               284      0.215      0.411       3457      9.763      32.94      83.83      89.58   0.002889\n",
      "LHS 1140 b            226     0.1553     0.2159       3096      9.612      24.74      95.34      89.86   0.005461\n",
      "TOI-270 d             383     0.1816       0.38       3506      9.099      11.38      41.74      89.68   0.002411\n",
      "TRAPPIST-1 e        249.7    0.08211     0.1192       2566      11.35      6.101      52.85      89.79   0.005011\n",
      "WASP-107 b            736     0.9187      0.657       4425      9.378      5.721       16.5      89.55    0.02065\n",
      "WASP-12 b            2601      1.924       1.69       6265      10.48      1.091          3       81.8    0.01369\n",
      "WASP-121 b           2409      1.742      1.461       6628      9.625      1.275      3.784      88.49    0.01502\n",
      "WASP-39 b            1166      1.331      0.939       5485      10.66      4.055      11.39      87.32    0.02123\n",
      "WASP-96 b            1285      1.225       1.05       5540      11.27      3.425      9.255       85.6    0.01437\n"
     ]
    }
   ],
   "source": [
    "fields = [\"T_eq_K\", \"R_p_RJ\", \"R_s_RSun\", \"T_eff_K\", \"J_mag\",\n",
    "          \"period_d\", \"a_over_rs\", \"inc_deg\", \"depth\"]\n",
    "\n",
    "print(f\"{'planet':14s}\" + \"\".join(f\"{f:>11s}\" for f in fields))\n",
    "for p in lazuli_exo.list_planets():\n",
    "    t = get_target(p)\n",
    "    print(f\"{p:14s}\" + \"\".join(f\"{t[f]:11.4g}\" for f in fields))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "28410438",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-12T13:30:34.432427Z",
     "iopub.status.busy": "2026-10-12T13:30:34.432319Z",
     "iopub.status.idle": "2026-10-12T13:30:34.434307Z",
     "shell.execute_reply": "2026-10-12T13:30:34.434033Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "full registry entry for K2-18 b:\n",
      "\n",
      "  J_mag        9.763\n",
      "  M_p_MJ       0.02806532\n",
      "  Met_star     0.12\n",
      "  R_p_RJ       0.2149734603172383\n",
      "  R_p_archive_RJ 0.21143724\n",
      "  R_s_RSun     0.411\n",
      "  T_eff_K      3457.0\n",
      "  T_eq_K       284.0\n",
      "  T_eq_conv_K  266.9836392529934\n",
      "  a_over_rs    83.83\n",
      "  depth        0.0028890624999999997\n",
      "  ecc          0.2\n",
      "  inc_deg      89.5785\n",
      "  k_source     pl_ratror\n",
      "  log_g_star   4.7864\n",
      "  name         K2-18 b\n",
      "  period_d     32.939623\n",
      "  provenance   NEA PSCompPars\n",
      "  slug         k2-18-b\n",
      "  t14_s        9655.199999999999\n",
      "  w_deg        -5.7\n"
     ]
    }
   ],
   "source": [
    "t = get_target(\"K2-18 b\")\n",
    "print(\"full registry entry for K2-18 b:\\n\")\n",
    "for k, v in sorted(t.items()):\n",
    "    print(f\"  {k:12s} {v}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68c2e526",
   "metadata": {},
   "source": [
    "Unknown names fail loudly, listing what is available. `observe()` behaves the\n",
    "same way for an unknown planet or scenario."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e4569cba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-12T13:30:34.435671Z",
     "iopub.status.busy": "2026-10-12T13:30:34.435562Z",
     "iopub.status.idle": "2026-10-12T13:30:34.437887Z",
     "shell.execute_reply": "2026-10-12T13:30:34.437638Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "get_target   KeyError: \"Unknown target 'Kepler-999 b'. Available: ['GJ 1214 b', 'GJ 3470 b', 'HAT-P-11 b', 'HD ...\n",
      "observe      KeyError: \"Unknown target 'Kepler-999 b'. Available: ['GJ 1214 b', 'GJ 3470 b', 'HAT-P-11 b', 'HD ...\n",
      "scenario     KeyError: \"Unknown scenario 'stormy'. Available: ['clear', 'cloudy', 'flat', 'hazy', 'high_metalli...\n"
     ]
    }
   ],
   "source": [
    "for call, label in [\n",
    "    (lambda: get_target(\"Kepler-999 b\"), \"get_target\"),\n",
    "    (lambda: lazuli_exo.observe(\"Kepler-999 b\", \"clear\", tables_dir=tdir), \"observe\"),\n",
    "    (lambda: lazuli_exo.observe(\"WASP-39 b\", \"stormy\", tables_dir=tdir), \"scenario\"),\n",
    "]:\n",
    "    try:\n",
    "        call()\n",
    "    except KeyError as exc:\n",
    "        print(f\"{label:12s} KeyError: {str(exc)[:88]}...\")\n",
    "    else:\n",
    "        raise AssertionError(f\"{label} should have raised\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1013c25f",
   "metadata": {},
   "source": [
    "## The saturation caveat\n",
    "\n",
    "The brightest hosts push the detector toward saturation. slicersim shortens the\n",
    "readout until the ramp is clean, and when even the shortest ramp (2 groups x 1\n",
    "frame) leaves saturated pixels at the last read, the residual count is recorded\n",
    "in the noise table and `observe()` raises a `UserWarning`. With the current\n",
    "tables one host is affected.\n",
    "\n",
    "<div class=\"alert alert-warning\">\n",
    "\n",
    "For a flagged target the uncertainties are unvalidated: saturation can bias\n",
    "extraction weights, nonlinearity and wavelength dependence in either direction.\n",
    "Treat its forecasts as indicative only. Check the flag in the data rather than\n",
    "remembering a name; the list changes when the tables are regenerated.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7f689ecc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-12T13:30:34.439204Z",
     "iopub.status.busy": "2026-10-12T13:30:34.439122Z",
     "iopub.status.idle": "2026-10-12T13:30:34.473239Z",
     "shell.execute_reply": "2026-10-12T13:30:34.472955Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "planet          desaturated  saturated px  nmd used\n",
      "GJ 1214 b              True             0 [2, 4, 0]\n",
      "GJ 3470 b              True             0 [2, 2, 0]\n",
      "HAT-P-11 b             True             0 [2, 1, 0]\n",
      "HD 189733 b           False           324 [2, 1, 0]\n",
      "HD 209458 b            True             0 [2, 1, 0]\n",
      "K2-18 b                True             0 [2, 4, 0]\n",
      "LHS 1140 b             True             0 [2, 4, 0]\n",
      "TOI-270 d              True             0 [2, 2, 0]\n",
      "TRAPPIST-1 e           True             0 [4, 8, 0]\n",
      "WASP-107 b             True             0 [2, 4, 0]\n",
      "WASP-12 b              True             0 [2, 8, 0]\n",
      "WASP-121 b             True             0 [2, 4, 0]\n",
      "WASP-39 b              True             0 [2, 8, 0]\n",
      "WASP-96 b              True             0 [4, 8, 0]\n",
      "\n",
      "flagged: ['HD 189733 b']\n"
     ]
    }
   ],
   "source": [
    "print(f\"{'planet':14s}{'desaturated':>13s}{'saturated px':>14s}\"\n",
    "      f\"{'nmd used':>10s}\")\n",
    "flagged = []\n",
    "for p in lazuli_exo.list_planets():\n",
    "    _, _, m = tables.load_noise(tdir, p)\n",
    "    ok = m.get(\"fully_desaturated\")\n",
    "    if ok is False:\n",
    "        flagged.append(p)\n",
    "    print(f\"{p:14s}{str(ok):>13s}{m.get('nsaturated_detpx', 0):14d}\"\n",
    "          f\"{str(m.get('nmd_used')):>10s}\")\n",
    "\n",
    "print(f\"\\nflagged: {flagged}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "60bec777",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-12T13:30:34.474693Z",
     "iopub.status.busy": "2026-10-12T13:30:34.474606Z",
     "iopub.status.idle": "2026-10-12T13:30:34.498836Z",
     "shell.execute_reply": "2026-10-12T13:30:34.498600Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UserWarning: HD 189733 b: noise table was generated with residual detector saturation (324 px); its uncertainties are unvalidated — do not use for quantitative forecasts\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "\n",
    "with warnings.catch_warnings(record=True) as caught:\n",
    "    warnings.simplefilter(\"always\")\n",
    "    lazuli_exo.observe(flagged[0], scenario=\"clear\", n_transits=1)\n",
    "\n",
    "for w in caught:\n",
    "    print(f\"{w.category.__name__}: {w.message}\")\n",
    "\n",
    "assert caught and caught[0].category is UserWarning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3b1ddfd",
   "metadata": {},
   "source": [
    "## Pointing at a different tables directory\n",
    "\n",
    "For a deployment that keeps the tables elsewhere, set `LAZULI_EXO_TABLES` or\n",
    "pass `tables_dir=` per call. Both take precedence over the packaged default, in\n",
    "that order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "93c2cd95",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-12T13:30:34.500238Z",
     "iopub.status.busy": "2026-10-12T13:30:34.500125Z",
     "iopub.status.idle": "2026-10-12T13:30:34.525177Z",
     "shell.execute_reply": "2026-10-12T13:30:34.524939Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default (package-relative): <package>/data/tables\n",
      "explicit argument:          /some/other/tables\n",
      "from $LAZULI_EXO_TABLES:    /from/the/environment\n",
      "argument beats env:         /explicit/wins\n",
      "\n",
      "obs.meta['tables_dir']:     <package>/data/tables\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "print(f\"default (package-relative): <package>/\"\n",
    "      f\"{tables.resolve_tables_dir(None).relative_to(PKG)}\")\n",
    "print(f\"explicit argument:          \"\n",
    "      f\"{tables.resolve_tables_dir('/some/other/tables')}\")\n",
    "\n",
    "os.environ[\"LAZULI_EXO_TABLES\"] = \"/from/the/environment\"\n",
    "print(f\"from $LAZULI_EXO_TABLES:    {tables.resolve_tables_dir(None)}\")\n",
    "del os.environ[\"LAZULI_EXO_TABLES\"]\n",
    "\n",
    "# an explicit argument still wins over the environment variable\n",
    "os.environ[\"LAZULI_EXO_TABLES\"] = \"/from/the/environment\"\n",
    "print(f\"argument beats env:         \"\n",
    "      f\"{tables.resolve_tables_dir('/explicit/wins')}\")\n",
    "del os.environ[\"LAZULI_EXO_TABLES\"]\n",
    "\n",
    "# observe() takes the same argument\n",
    "obs = lazuli_exo.observe(PLANET, scenario=\"clear\", n_transits=1,\n",
    "                         tables_dir=tdir)\n",
    "print(f\"\\nobs.meta['tables_dir']:     <package>/\"\n",
    "      f\"{Path(obs.meta['tables_dir']).relative_to(PKG)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aafbd4f6",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "- Per-planet star, noise and limb-darkening tables plus two grids (ζ and\n",
    "  PHOENIX) under one directory, catalogued by a version-stamped `index.json`\n",
    "  with a sha256 manifest that `rebuild_index()` regenerates from disk.\n",
    "- Every table carries a `meta` dict recording how it was produced: stellar\n",
    "  grid, readout mode, LD grid point, package version and git commit.\n",
    "- The registry (`lazuli_exo.targets`) is a frozen NEA snapshot; no network at\n",
    "  runtime.\n",
    "- The caveats live in the data: `T_clamped` in `meta[\"atmosphere\"]` on cold\n",
    "  planets, `fully_desaturated` in the noise table on bright hosts (which also\n",
    "  raises a `UserWarning`).\n",
    "- `tables_dir=` beats `$LAZULI_EXO_TABLES` beats the packaged default.\n",
    "\n",
    "That covers the showcase tables. Notebooks 6 to 9 cover the parametric model:\n",
    "any planet from a dozen numbers, with the noise computed live."
   ]
  }
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