{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "77f91ec5-5894-4007-a0b2-000de4b5198e",
   "metadata": {},
   "source": [
    "# Masking Options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "09a458ed-e6a0-4c81-810e-3a869e940811",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os.path as op\n",
    "from astropy.io import fits\n",
    "from astropy.wcs import WCS\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2711a29c-b058-4a78-9e3c-304fd127e0ce",
   "metadata": {},
   "source": [
    "### Masking Description\n",
    "\n",
    "In this dataset, each spectrum is associated with a bitmask that encodes various data quality flags. These flags help identify pixels or fibers that may be affected by instrumental issues, reduction artifacts, or observational anomalies. Each bit in the mask corresponds to a specific condition, and multiple flags can be combined in a single value using bitwise operations.\n",
    "The table below lists all the possible flags, their hexadecimal and binary representations, and descriptions of what each flag indicates. \n",
    "\n",
    "| Hex Value   | Binary         | Integer Value | Name       | Description                                                                 |\n",
    "|-------------|----------------|----------------|------------|-----------------------------------------------------------------------------|\n",
    "| 0x00000000  | 0              | 0              | Good       | No flag                                                                     |\n",
    "| 0x00000001  | 1              | 1              | MAIN       | Value flagged in reduction (`calfibe == 0.0`)                               |\n",
    "| 0x00000002  | 10             | 2              | FTF        | Average fiber-to-fiber in spectrum is > 0.5                                 |\n",
    "| 0x00000004  | 100            | 4              | CHI2FIB    | `chi2fib > 150`                                                             |\n",
    "| 0x00000008  | 1000           | 8              | BADPIX     | On a newly found bad pixel region                                           |\n",
    "| 0x00000010  | 10000          | 16             | BADAMP     | On a bad amplifier                                                          |\n",
    "| 0x00000020  | 100000         | 32             | LARGEGAL   | Located within a large galaxy mask                                          |\n",
    "| 0x00000040  | 1000000        | 64             | METEOR     | On a known meteor track                                                     |\n",
    "| 0x00000080  | 10000000       | 128            | BADSHOT    | In bad shot list                                                            |\n",
    "| 0x00000100  | 100000000      | 256            | THROUGHPUT | `response_4540 < 0.08`                                                      |\n",
    "| 0x00000200  | 1000000000     | 512            | BADFIB     | On a known bad fiber                                                        |\n",
    "| 0x00000400  | 10000000000    | 1024           | SAT        | On a known satellite track                                                  |\n",
    "| 0x00000800  | 100000000000   | 2048           | BADCAL     | Wavelength dependent masking due to sky and calibration issues             |\n",
    "| 0x00001000  | 1000000000000  | 4096           | PIXMASK    | Wavelength dependent masking due to `spectrum == 0` in native spectrum counts |\n",
    "| 0x00002000  | 10000000000000  | 8192           | BADDET    | 5 × 5 × 5 pixel mask where a detection has been flagged |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8dc9b283-ef67-4cb4-a314-2f5a1ea4fe66",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('default')\n",
    "plt.rcParams['axes.linewidth'] = 2\n",
    "plt.rcParams.update({'font.size': 12})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f9f19873-9546-4ef0-9fd8-52fa5f64dbaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "pdr_dir = '/home/jovyan/Hobby-Eberly-Public/HETDEX/pdr/pdr1/'\n",
    "if not op.exists(pdr_dir):\n",
    "    pdr_dir = '/home/jovyan/work/pdr1/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0be9c5c2-d7c1-4a53-8566-dd2b1c131e05",
   "metadata": {},
   "outputs": [],
   "source": [
    "shotid = 20190405020\n",
    "ifuslot = '034'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6eac916d-f89c-4b5a-97de-9514d09ee719",
   "metadata": {},
   "outputs": [],
   "source": [
    "datacube_path = op.join( pdr_dir, 'datacubes', str(shotid), 'dex_cube_{}_{}.fits'.format( shotid, ifuslot))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f93b114-7bcf-443d-a333-e4dd2d653f68",
   "metadata": {},
   "source": [
    "Each IFU datacube is named as `dex_cube_<SHOTID>_<IFUSLOT>.fits`, where:\n",
    "\n",
    "- `<SHOTID>` corresponds to the observation ID (e.g., `20240731009`)\n",
    "- `<IFUSLOT>` is the unique identifier of the IFU (e.g., `013`, `106`)\n",
    "\n",
    "Each FITS file contains a 3D datacube organized as `(wavelength, y, x)` with:\n",
    "- **Spectral resolution:** 1036 bins at 2 Å per pixel\n",
    "- **Spatial resolution:** 104 × 104 pixels at 0.5 arcsec per pixel\n",
    "\n",
    "| Extension | EXTNAME | Description                                                | Data Shape     |\n",
    "|-----------|---------|------------------------------------------------------------|----------------|\n",
    "| 0         | PRIMARY | Primary HDU (header only, no data)                         | None           |\n",
    "| 1         | DATA    | Flux datacube in units of erg s⁻¹ cm⁻² Å⁻¹                | (104, 104, 1036) |\n",
    "| 2         | ERROR   | 1σ error estimates (same units as DATA)                    | (104, 104, 1036) |\n",
    "| 3         | MASK    | Bitmask per voxel indicating quality flags                 | (104, 104, 1036) |\n",
    "\n",
    "Let's explore an example datacube:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e8035ab7-b4fe-45c1-bd29-3adac0e44cd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "hdul = fits.open( datacube_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f662c7e6-6670-4263-8ea7-219dd02e6419",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: /home/jovyan/Hobby-Eberly-Public/HETDEX/pdr/pdr1/datacubes/20190405020/dex_cube_20190405020_034.fits\n",
      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
      "  0  PRIMARY       1 PrimaryHDU       6   ()      \n",
      "  1  DATA          1 CompImageHDU     79   (104, 104, 1036)   float32   \n",
      "  2  ERROR         1 CompImageHDU     79   (104, 104, 1036)   float32   \n",
      "  3  MASK          1 CompImageHDU     80   (104, 104, 1036)   int16 (rescales to uint16)   \n"
     ]
    }
   ],
   "source": [
    "hdul.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7bd7984b-ffae-41d0-85e3-e604bce7ef23",
   "metadata": {},
   "outputs": [],
   "source": [
    "flux = hdul[\"DATA\"].data\n",
    "error = hdul[\"ERROR\"].data\n",
    "mask = hdul[\"MASK\"].data\n",
    "header = hdul[\"DATA\"].header\n",
    "wcs = WCS(header)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "27c3745f-a397-4939-93ae-91299790201b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select a wavelength slice (e.g., central wavelength)\n",
    "z_index = 950\n",
    "\n",
    "# Get corresponding wavelength (in Å)\n",
    "wavelength_angstrom = wcs.wcs_pix2world(0, 0, z_index, 0)[2]\n",
    "\n",
    "# Slice the cube at the wavelength index\n",
    "flux_slice = flux[z_index, :, :]\n",
    "error_slice = error[z_index, :, :]\n",
    "mask_slice = mask[z_index, :, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "55eefcef-db96-4817-a102-8ce852cf9dfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define which flags to mask out (bitwise OR of values you want to exclude)\n",
    "# For example, exclude MAIN (1), BADPIX (8), BADFIB (512), and PIXMASK (4096)\n",
    "mask_flags = 1 | 8 | 512 | 4096\n",
    "\n",
    "# Create boolean mask where any of these flags are set\n",
    "bad_mask = (mask_slice & mask_flags) != 0\n",
    "\n",
    "# Apply the mask by setting bad values to NaN (or zero if preferred)\n",
    "flux_masked = np.where(bad_mask, np.nan, flux_slice)\n",
    "error_masked = np.where(bad_mask, np.nan, error_slice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "469f59b5-4589-467f-a637-7229ad2b874d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting\n",
    "fig, axes = plt.subplots(1, 2, figsize=(15, 5), subplot_kw={'projection': wcs.celestial})\n",
    "plt.subplots_adjust(wspace=0.4)\n",
    "\n",
    "# Shared image display settings\n",
    "im_kwargs = dict(origin='lower', interpolation='none')\n",
    "\n",
    "# Panel 1: Flux\n",
    "im0 = axes[0].imshow(flux_masked, cmap='viridis', **im_kwargs)\n",
    "axes[0].set_title(\"Flux (masked)\", fontsize=14)\n",
    "\n",
    "axes[0].set_xlabel(\"RA\", fontsize=12)\n",
    "axes[0].set_ylabel(\"Dec\", fontsize=12)\n",
    "axes[0].coords[0].set_axislabel('RA')\n",
    "axes[0].coords[1].set_axislabel('Dec')\n",
    "plt.colorbar(im0, ax=axes[0], fraction=0.046, pad=0.04, label=\"Flux (erg s$^{-1}$ cm$^{-2}$ Å$^{-1}$)\")\n",
    "\n",
    "# Panel 2: Error\n",
    "im1 = axes[1].imshow(error_masked, cmap='magma', **im_kwargs)\n",
    "axes[1].set_title(\"Error (masked, 1σ)\", fontsize=14)\n",
    "axes[1].set_xlabel(\"RA\")\n",
    "axes[1].set_ylabel(\"Dec\")\n",
    "axes[1].coords[0].set_axislabel('')\n",
    "axes[1].coords[1].set_axislabel('')\n",
    "plt.colorbar(im1, ax=axes[1], fraction=0.046, pad=0.04)\n",
    "\n",
    "# Title with wavelength\n",
    "plt.suptitle(f\"Wavelength slice: {wavelength_angstrom:.1f} Å\", fontsize=16)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "22a9e61b-7926-45c7-b79c-36c4eaccb4df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# close the fits HDU when done\n",
    "hdul.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89923d5e",
   "metadata": {},
   "source": [
    "## Recommended Masking Strategies\n",
    "\n",
    "Not all flags are equally important — the right mask depends on the science case. Below are three standard patterns from least to most aggressive.\n",
    "\n",
    "**Key convention:** `mask == 0` means **GOOD**. Any non-zero value means flagged. This is the **opposite** of `flag` in `ifu-index.fits` (where `flag == 1` means good).\n",
    "\n",
    "| Level | Use case | Flags excluded |\n",
    "|-------|----------|----------------|\n",
    "| Minimum | Quick look, continuum photometry | MAIN, FTF, CHI2FIB, BADPIX, BADAMP |\n",
    "| Recommended | Most science (emission lines, spectra) | All of the above + METEOR, BADFIB, PIXMASK |\n",
    "| Full | Strict emission-line searches | All non-zero values (`mask == 0`) |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "75b44c0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# -------------------------------------------------------\n",
    "# Recommended masking patterns for HETDEX PDR1 datacubes\n",
    "# -------------------------------------------------------\n",
    "\n",
    "# --- Bitmask flag values ---\n",
    "MAIN     =    1   # calfibe == 0.0 (always mask)\n",
    "FTF      =    2   # fiber-to-fiber deviation > 0.5 (always mask)\n",
    "CHI2FIB  =    4   # chi2fib > 150 (always mask)\n",
    "BADPIX   =    8   # bad pixel region (always mask)\n",
    "BADAMP   =   16   # bad amplifier (always mask)\n",
    "LARGEGAL =   32   # within large galaxy footprint (optional)\n",
    "METEOR   =   64   # known meteor track (always mask)\n",
    "BADFIB   =  512   # known bad fiber (always mask)\n",
    "SAT      = 1024   # satellite track (recommended)\n",
    "BADCAL   = 2048   # sky/calibration issue at wavelength (optional)\n",
    "PIXMASK  = 4096   # native spectrum == 0 (always mask)\n",
    "BADDET   = 8192   # ML-flagged detection region (for searches)\n",
    "\n",
    "# --- Level 1: Minimum mask (always apply) ---\n",
    "# Removes the most common instrumental artefacts.\n",
    "good_min = (mask & (MAIN | FTF | CHI2FIB | BADPIX | BADAMP)) == 0\n",
    "\n",
    "# --- Level 2: Recommended mask (most science cases) ---\n",
    "# Adds meteors, bad fibers, satellite tracks, and zero-count pixels.\n",
    "good_rec = (mask & (MAIN | FTF | CHI2FIB | BADPIX | BADAMP\n",
    "                    | METEOR | BADFIB | SAT | PIXMASK)) == 0\n",
    "\n",
    "# --- Level 3: Full mask (strict emission-line searches) ---\n",
    "# Rejects any voxel with any flag set, including LARGEGAL and BADDET.\n",
    "good_full = (mask == 0)\n",
    "\n",
    "# Apply chosen mask to flux cube (sets bad voxels to NaN)\n",
    "flux_clean = flux.copy().astype(float)\n",
    "flux_clean[~good_rec] = np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1532460c",
   "metadata": {},
   "source": [
    "### Special case: recovering lines near the BADCAL wavelength range\n",
    "\n",
    "`BADCAL` (bit 2048) flags wavelength-dependent sky and calibration artefacts (e.g. around 3640 Å). For most work you should mask it, but if you need to recover a spectral feature at one of those wavelengths you can exclude it from the mask:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "38c272c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recover data at BADCAL-flagged wavelengths (e.g. to study a line near 3640 Å)\n",
    "# Accepts voxels that are either clean OR only flagged as BADCAL.\n",
    "CORE_FLAGS = MAIN | FTF | CHI2FIB | BADPIX | BADAMP | METEOR | BADFIB | SAT | PIXMASK\n",
    "\n",
    "good_no_badcal = ((mask & CORE_FLAGS) == 0)          # core quality flags must be clear\n",
    "# BADCAL-only voxels are kept; voxels with BADCAL *plus* another flag are still rejected\n",
    "\n",
    "flux_badcal_ok = flux.copy().astype(float)\n",
    "flux_badcal_ok[~good_no_badcal] = np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4825280-85b9-414e-8463-211e20fb86ce",
   "metadata": {},
   "source": [
    "## Example of selecting fibers with meteors using the bitmask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "31457139-942d-4c75-bcfc-97cb088a8ded",
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.table import Table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cff6812-6083-4054-b593-0cab9ee38572",
   "metadata": {},
   "source": [
    "Open IFU index table to locate IFU cubes with meteors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "696c83a0-205a-46da-8adf-1bfd33d0e1cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "ifu_data = Table.read(op.join( pdr_dir, 'ifu-index.fits'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a40cbba5-c3c5-49a3-af1e-fd58fb400c62",
   "metadata": {},
   "outputs": [],
   "source": [
    "# find a frame where the fraction flagged by a meteor is greater than 50%\n",
    "met_indices = np.where( ifu_data['flag_meteor']<0.5)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "78b0d45f-614b-47b6-b34d-92d3499b6e9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=59</i>\n",
       "<table id=\"table140161021794016\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>shotid</th><th>ifuslot</th><th>ra_cen</th><th>dec_cen</th><th>flag</th><th>flag_badamp</th><th>flag_badfib</th><th>flag_meteor</th><th>flag_satellite</th><th>flag_shot</th><th>flag_throughput</th><th>field</th><th>objid</th><th>date</th><th>obsid</th><th>ra_shot</th><th>dec_shot</th><th>pa</th><th>n_ifu</th><th>fwhm_virus</th><th>fwhm_virus_err</th><th>response_4540</th><th>ambtemp</th><th>datevobs</th><th>dewpoint</th><th>exptime</th><th>humidity</th><th>mjd</th><th>nstars_fit_fwhm</th><th>obsind</th><th>pressure</th><th>structaz</th><th>time</th><th>trajcdec</th><th>trajcpa</th><th>trajcra</th><th>fluxlimit_4600</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>bytes3</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>bytes12</th><th>bytes18</th><th>int32</th><th>int32</th><th>float64</th><th>float64</th><th>float64</th><th>int32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>bytes12</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int32</th><th>int32</th><th>float32</th><th>float32</th><th>bytes7</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th></tr></thead>\n",
       "<tr><td>20170225014</td><td>093</td><td>189.11832</td><td>62.285686</td><td>0.495</td><td>1.0</td><td>1.0</td><td>0.495</td><td>1.0</td><td>1.0</td><td>1.0</td><td>goods-n</td><td>GN8b_W</td><td>20170225</td><td>14</td><td>189.000267</td><td>62.195481</td><td>324.458398</td><td>14</td><td>1.6776817</td><td>0.27915552</td><td>0.1206</td><td>5.5783334</td><td>20170225v014</td><td>-16.3165</td><td>367.38266</td><td>17.094</td><td>57809.438</td><td>8</td><td>1</td><td>798.03625</td><td>340.90582</td><td>1029291</td><td>62.197292</td><td>323.07156</td><td>189.003</td><td>1.582398</td></tr>\n",
       "<tr><td>20180113012</td><td>043</td><td>163.57877</td><td>51.18028</td><td>0.49</td><td>1.0</td><td>0.998</td><td>0.492</td><td>1.0</td><td>1.0</td><td>1.0</td><td>dex-spring</td><td>HS0013_000_E</td><td>20180113</td><td>12</td><td>163.537555</td><td>51.127203</td><td>70.754157</td><td>28</td><td>1.6654035</td><td>0.412208</td><td>0.153</td><td>6.7433333</td><td>20180113v012</td><td>-6.9694443</td><td>366.8663</td><td>34.236</td><td>58131.316</td><td>9</td><td>1</td><td>806.6715</td><td>43.182743</td><td>0733434</td><td>51.1279</td><td>69.35363</td><td>163.54034</td><td>1.410823</td></tr>\n",
       "<tr><td>20180113012</td><td>074</td><td>163.58073</td><td>51.092377</td><td>0.492</td><td>1.0</td><td>1.0</td><td>0.492</td><td>1.0</td><td>1.0</td><td>1.0</td><td>dex-spring</td><td>HS0013_000_E</td><td>20180113</td><td>12</td><td>163.537555</td><td>51.127203</td><td>70.754157</td><td>28</td><td>1.6654035</td><td>0.412208</td><td>0.153</td><td>6.7433333</td><td>20180113v012</td><td>-6.9694443</td><td>366.8663</td><td>34.236</td><td>58131.316</td><td>9</td><td>1</td><td>806.6715</td><td>43.182743</td><td>0733434</td><td>51.1279</td><td>69.35363</td><td>163.54034</td><td>1.410823</td></tr>\n",
       "<tr><td>20180113012</td><td>105</td><td>163.58266</td><td>51.00454</td><td>0.362</td><td>0.75</td><td>1.0</td><td>0.497</td><td>1.0</td><td>1.0</td><td>1.0</td><td>dex-spring</td><td>HS0013_000_E</td><td>20180113</td><td>12</td><td>163.537555</td><td>51.127203</td><td>70.754157</td><td>28</td><td>1.6654035</td><td>0.412208</td><td>0.153</td><td>6.7433333</td><td>20180113v012</td><td>-6.9694443</td><td>366.8663</td><td>34.236</td><td>58131.316</td><td>9</td><td>1</td><td>806.6715</td><td>43.182743</td><td>0733434</td><td>51.1279</td><td>69.35363</td><td>163.54034</td><td>1.410823</td></tr>\n",
       "<tr><td>20180113014</td><td>075</td><td>176.11699</td><td>51.084854</td><td>0.48</td><td>1.0</td><td>1.0</td><td>0.48</td><td>1.0</td><td>1.0</td><td>1.0</td><td>dex-spring</td><td>HS0087_000_E</td><td>20180113</td><td>14</td><td>176.116771</td><td>51.126983</td><td>70.821207</td><td>28</td><td>1.8214096</td><td>0.27807188</td><td>0.1489</td><td>6.8733335</td><td>20180113v014</td><td>-7.333889</td><td>370.0492</td><td>33.171</td><td>58131.35</td><td>12</td><td>1</td><td>806.8408</td><td>43.19244</td><td>0825474</td><td>51.1279</td><td>69.364006</td><td>176.1192</td><td>1.517792</td></tr>\n",
       "<tr><td>20180321013</td><td>073</td><td>207.43489</td><td>51.101562</td><td>0.481</td><td>1.0</td><td>1.0</td><td>0.481</td><td>1.0</td><td>1.0</td><td>1.0</td><td>dex-spring</td><td>HS00380_000_E</td><td>20180321</td><td>13</td><td>207.349975</td><td>51.127313</td><td>70.873514</td><td>28</td><td>2.0103114</td><td>0.30402187</td><td>0.1242</td><td>11.759444</td><td>20180321v013</td><td>-8.787778</td><td>367.833</td><td>20.62</td><td>58198.254</td><td>12</td><td>1</td><td>802.3708</td><td>43.16395</td><td>0603396</td><td>51.1279</td><td>69.33351</td><td>207.3495</td><td>2.114141</td></tr>\n",
       "<tr><td>20180321013</td><td>094</td><td>207.42201</td><td>51.039925</td><td>0.469</td><td>1.0</td><td>1.0</td><td>0.469</td><td>1.0</td><td>1.0</td><td>1.0</td><td>dex-spring</td><td>HS00380_000_E</td><td>20180321</td><td>13</td><td>207.349975</td><td>51.127313</td><td>70.873514</td><td>28</td><td>2.0103114</td><td>0.30402187</td><td>0.1242</td><td>11.759444</td><td>20180321v013</td><td>-8.787778</td><td>367.833</td><td>20.62</td><td>58198.254</td><td>12</td><td>1</td><td>802.3708</td><td>43.16395</td><td>0603396</td><td>51.1279</td><td>69.33351</td><td>207.3495</td><td>2.114141</td></tr>\n",
       "<tr><td>20180420006</td><td>076</td><td>191.3569</td><td>51.37153</td><td>0.315</td><td>0.75</td><td>0.998</td><td>0.439</td><td>1.0</td><td>1.0</td><td>1.0</td><td>dex-spring</td><td>HS00256d2_W</td><td>20180420</td><td>6</td><td>191.269825</td><td>51.349169</td><td>292.802832</td><td>36</td><td>3.2</td><td>0.0</td><td>0.0897</td><td>10.892777</td><td>20180420v006</td><td>-0.064444445</td><td>367.46573</td><td>77.537</td><td>58228.34</td><td>9</td><td>1</td><td>802.3708</td><td>317.41278</td><td>0809587</td><td>51.3479</td><td>291.319</td><td>191.2695</td><td>4.113959</td></tr>\n",
       "<tr><td>20180420006</td><td>085</td><td>191.33322</td><td>51.4079</td><td>0.438</td><td>1.0</td><td>1.0</td><td>0.438</td><td>1.0</td><td>1.0</td><td>1.0</td><td>dex-spring</td><td>HS00256d2_W</td><td>20180420</td><td>6</td><td>191.269825</td><td>51.349169</td><td>292.802832</td><td>36</td><td>3.2</td><td>0.0</td><td>0.0897</td><td>10.892777</td><td>20180420v006</td><td>-0.064444445</td><td>367.46573</td><td>77.537</td><td>58228.34</td><td>9</td><td>1</td><td>802.3708</td><td>317.41278</td><td>0809587</td><td>51.3479</td><td>291.319</td><td>191.2695</td><td>4.113959</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>20230122018</td><td>086</td><td>182.07434</td><td>52.772797</td><td>0.446</td><td>1.0</td><td>1.0</td><td>0.446</td><td>1.0</td><td>1.0</td><td>1.0</td><td>dex-spring</td><td>DEXsp2150</td><td>20230122</td><td>18</td><td>182.092131</td><td>52.85289</td><td>66.659355</td><td>77</td><td>2.2700737</td><td>0.083641544</td><td>0.2244</td><td>-1.4533334</td><td>20230122v018</td><td>-12.037222</td><td>607.96783</td><td>44.581</td><td>59966.348</td><td>18</td><td>1</td><td>799.7633</td><td>39.397427</td><td>0822362</td><td>52.853638</td><td>65.17627</td><td>182.09132</td><td>1.984605</td></tr>\n",
       "<tr><td>20230519008</td><td>104</td><td>237.24432</td><td>50.168354</td><td>0.449</td><td>1.0</td><td>1.0</td><td>0.449</td><td>1.0</td><td>1.0</td><td>1.0</td><td>dex-spring</td><td>DEXsp6212</td><td>20230519</td><td>8</td><td>237.164604</td><td>50.28336</td><td>72.489324</td><td>75</td><td>1.7313385</td><td>0.115832716</td><td>0.1523</td><td>27.748888</td><td>20230519v008</td><td>5.6855555</td><td>367.3326</td><td>45.754</td><td>60083.17</td><td>27</td><td>1</td><td>825.19507</td><td>44.61259</td><td>0408473</td><td>50.283844</td><td>70.86959</td><td>237.1648</td><td>1.601122</td></tr>\n",
       "<tr><td>20230719012</td><td>047</td><td>225.16971</td><td>50.089302</td><td>0.436</td><td>0.75</td><td>0.998</td><td>0.499</td><td>1.0</td><td>1.0</td><td>1.0</td><td>dex-spring</td><td>DEXsp3583</td><td>20230719</td><td>12</td><td>225.093864</td><td>50.153865</td><td>291.966979</td><td>75</td><td>2.122775</td><td>0.07742984</td><td>0.1416</td><td>26.182777</td><td>20230719v012</td><td>0.97444445</td><td>367.3986</td><td>17.782</td><td>60144.188</td><td>24</td><td>1</td><td>808.3647</td><td>316.44638</td><td>0431308</td><td>50.153503</td><td>290.28046</td><td>225.0938</td><td>2.179236</td></tr>\n",
       "<tr><td>20230719012</td><td>058</td><td>225.2261</td><td>50.104607</td><td>0.318</td><td>0.75</td><td>0.998</td><td>0.446</td><td>1.0</td><td>1.0</td><td>1.0</td><td>dex-spring</td><td>DEXsp3583</td><td>20230719</td><td>12</td><td>225.093864</td><td>50.153865</td><td>291.966979</td><td>75</td><td>2.122775</td><td>0.07742984</td><td>0.1416</td><td>26.182777</td><td>20230719v012</td><td>0.97444445</td><td>367.3986</td><td>17.782</td><td>60144.188</td><td>24</td><td>1</td><td>808.3647</td><td>316.44638</td><td>0431308</td><td>50.153503</td><td>290.28046</td><td>225.0938</td><td>2.179236</td></tr>\n",
       "<tr><td>20230720009</td><td>051</td><td>266.4116</td><td>65.60998</td><td>0.465</td><td>1.0</td><td>0.998</td><td>0.467</td><td>0.734</td><td>1.0</td><td>1.0</td><td>nep</td><td>NEP_row11_p14d</td><td>20230720</td><td>9</td><td>266.466522</td><td>65.514266</td><td>354.872802</td><td>75</td><td>2.1514947</td><td>0.19217853</td><td>0.1481</td><td>24.06889</td><td>20230720v009</td><td>9.915</td><td>370.00235</td><td>38.959</td><td>60145.203</td><td>48</td><td>1</td><td>808.3647</td><td>356.71805</td><td>0454277</td><td>65.51417</td><td>353.1828</td><td>266.46686</td><td>2.107866</td></tr>\n",
       "<tr><td>20230720009</td><td>063</td><td>266.49088</td><td>65.557236</td><td>0.484</td><td>1.0</td><td>0.998</td><td>0.486</td><td>0.729</td><td>1.0</td><td>1.0</td><td>nep</td><td>NEP_row11_p14d</td><td>20230720</td><td>9</td><td>266.466522</td><td>65.514266</td><td>354.872802</td><td>75</td><td>2.1514947</td><td>0.19217853</td><td>0.1481</td><td>24.06889</td><td>20230720v009</td><td>9.915</td><td>370.00235</td><td>38.959</td><td>60145.203</td><td>48</td><td>1</td><td>808.3647</td><td>356.71805</td><td>0454277</td><td>65.51417</td><td>353.1828</td><td>266.46686</td><td>2.107866</td></tr>\n",
       "<tr><td>20240117024</td><td>074</td><td>232.04964</td><td>50.567986</td><td>0.484</td><td>1.0</td><td>1.0</td><td>0.484</td><td>1.0</td><td>1.0</td><td>1.0</td><td>dex-spring</td><td>DEXsp6020</td><td>20240117</td><td>24</td><td>232.008787</td><td>50.603569</td><td>72.607274</td><td>77</td><td>1.9115212</td><td>0.09506496</td><td>0.2753</td><td>7.3416667</td><td>20240117v024</td><td>-13.357</td><td>667.54724</td><td>20.253</td><td>60326.49</td><td>16</td><td>1</td><td>798.7474</td><td>44.738342</td><td>1145437</td><td>50.602783</td><td>71.001045</td><td>232.00891</td><td>1.369047</td></tr>\n",
       "<tr><td>20240430009</td><td>047</td><td>224.63116</td><td>49.666462</td><td>0.499</td><td>1.0</td><td>0.998</td><td>0.499</td><td>1.0</td><td>1.0</td><td>1.0</td><td>dex-spring</td><td>DEXsp3471</td><td>20240430</td><td>9</td><td>224.751925</td><td>49.646007</td><td>73.688661</td><td>77</td><td>1.7041396</td><td>0.10331816</td><td>0.1406</td><td>19.318333</td><td>20240430v009</td><td>-10.393333</td><td>367.61206</td><td>13.731</td><td>60430.188</td><td>17</td><td>1</td><td>799.89874</td><td>45.742977</td><td>0428324</td><td>49.645916</td><td>72.042145</td><td>224.75146</td><td>1.8456</td></tr>\n",
       "<tr><td>20240613010</td><td>052</td><td>270.9249</td><td>65.804886</td><td>0.441</td><td>1.0</td><td>0.998</td><td>0.441</td><td>1.0</td><td>1.0</td><td>1.0</td><td>nep</td><td>NEP_row10_p1c</td><td>20240613</td><td>10</td><td>270.953799</td><td>65.734875</td><td>1.705443</td><td>77</td><td>1.8032534</td><td>0.15895823</td><td>0.1503</td><td>21.573889</td><td>20240613v010</td><td>13.412778</td><td>368.04065</td><td>59.273</td><td>60474.31</td><td>20</td><td>1</td><td>803.14966</td><td>0.007416</td><td>0722047</td><td>65.73417</td><td>0.015443</td><td>270.95447</td><td>1.906791</td></tr>\n",
       "<tr><td>20240613011</td><td>076</td><td>273.6211</td><td>66.35221</td><td>0.44</td><td>1.0</td><td>0.998</td><td>0.44</td><td>1.0</td><td>1.0</td><td>1.0</td><td>nep</td><td>NEP_row7_p1d</td><td>20240613</td><td>11</td><td>273.520108</td><td>66.394949</td><td>1.705443</td><td>77</td><td>1.9696096</td><td>0.24541357</td><td>0.1205</td><td>21.493334</td><td>20240613v011</td><td>13.432777</td><td>427.7496</td><td>59.724</td><td>60474.324</td><td>87</td><td>1</td><td>803.14966</td><td>0.007416</td><td>0745232</td><td>66.394165</td><td>0.015443</td><td>273.52002</td><td>2.686769</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=59>\n",
       "   shotid   ifuslot   ra_cen   dec_cen  ...  trajcpa   trajcra  fluxlimit_4600\n",
       "   int64     bytes3  float32   float32  ...  float32   float32     float32    \n",
       "----------- ------- --------- --------- ... --------- --------- --------------\n",
       "20170225014     093 189.11832 62.285686 ... 323.07156   189.003       1.582398\n",
       "20180113012     043 163.57877  51.18028 ...  69.35363 163.54034       1.410823\n",
       "20180113012     074 163.58073 51.092377 ...  69.35363 163.54034       1.410823\n",
       "20180113012     105 163.58266  51.00454 ...  69.35363 163.54034       1.410823\n",
       "20180113014     075 176.11699 51.084854 ... 69.364006  176.1192       1.517792\n",
       "20180321013     073 207.43489 51.101562 ...  69.33351  207.3495       2.114141\n",
       "20180321013     094 207.42201 51.039925 ...  69.33351  207.3495       2.114141\n",
       "20180420006     076  191.3569  51.37153 ...   291.319  191.2695       4.113959\n",
       "20180420006     085 191.33322   51.4079 ...   291.319  191.2695       4.113959\n",
       "        ...     ...       ...       ... ...       ...       ...            ...\n",
       "20230122018     086 182.07434 52.772797 ...  65.17627 182.09132       1.984605\n",
       "20230519008     104 237.24432 50.168354 ...  70.86959  237.1648       1.601122\n",
       "20230719012     047 225.16971 50.089302 ... 290.28046  225.0938       2.179236\n",
       "20230719012     058  225.2261 50.104607 ... 290.28046  225.0938       2.179236\n",
       "20230720009     051  266.4116  65.60998 ...  353.1828 266.46686       2.107866\n",
       "20230720009     063 266.49088 65.557236 ...  353.1828 266.46686       2.107866\n",
       "20240117024     074 232.04964 50.567986 ... 71.001045 232.00891       1.369047\n",
       "20240430009     047 224.63116 49.666462 ... 72.042145 224.75146         1.8456\n",
       "20240613010     052  270.9249 65.804886 ...  0.015443 270.95447       1.906791\n",
       "20240613011     076  273.6211  66.35221 ...  0.015443 273.52002       2.686769"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ifu_data[met_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8e41b829-ca75-401b-9159-688cbc543fb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#randomly pick an IFU from list above\n",
    "rand_index=45\n",
    "shotid = ifu_data['shotid'][met_indices][rand_index]\n",
    "ifuslot = ifu_data['ifuslot'][met_indices][rand_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8d2922a7-22c4-4a53-aa37-00b108c63b10",
   "metadata": {},
   "outputs": [],
   "source": [
    "datacube_path = op.join( pdr_dir, 'datacubes', str(shotid), 'dex_cube_{}_{}.fits'.format( shotid, ifuslot))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "85fc8512-6db5-4ba4-a4d6-d17b6c0ecdc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening: /home/jovyan/Hobby-Eberly-Public/HETDEX/pdr/pdr1/datacubes/20220310023/dex_cube_20220310023_080.fits\n"
     ]
    }
   ],
   "source": [
    "if not op.exists( datacube_path):\n",
    "    print('Datacube path is not found. Please report.' )\n",
    "else:\n",
    "    print('Opening:', datacube_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c7d53ad5-ef86-4ef0-b4b4-256368e6b47e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Open file\n",
    "hdul = fits.open( datacube_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "29a25ac6-c855-4a0e-85cc-cb975b0e52c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "flux = hdul[\"DATA\"].data\n",
    "error = hdul[\"ERROR\"].data\n",
    "mask = hdul[\"MASK\"].data\n",
    "header = hdul[\"DATA\"].header\n",
    "wcs = WCS(header)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3eb09348-3b61-41be-94e9-046642419832",
   "metadata": {},
   "outputs": [],
   "source": [
    "wave_array = np.linspace(3470, 5540, 1036)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7784b26a-5fee-423a-b36d-f7ad93887435",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Slice the cube at the wavelength index where a meteor emission line is known\n",
    "\n",
    "z_index = np.where( wave_array > 3836)[0][0]\n",
    "\n",
    "wave_slice = wave_array[z_index]\n",
    "flux_slice = flux[z_index, :, :]\n",
    "error_slice = error[z_index, :, :]\n",
    "mask_slice = mask[z_index, :, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b542106-36a2-4d6c-9f67-038630aaf0f6",
   "metadata": {},
   "source": [
    "If you want to apply all mask flags except one—in this case, exclude METEOR (mask bit = 64)—you can do this by:\n",
    "1. Defining a mask that includes all possible flags.\n",
    "2. Removing the METEOR flag using bitwise operations.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e0c64418-c148-4d3c-81cf-3af1d18b526a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# METEOR flag value\n",
    "meteor_flag = 64\n",
    "\n",
    "# Create a boolean mask where only the METEOR flag is set\n",
    "meteor_mask = (mask_slice & meteor_flag) != 0\n",
    "\n",
    "# Optional: exclude cases where other flags are also set\n",
    "# This ensures we get only pixels *solely* flagged as METEOR\n",
    "# Uncomment the next line if you want that stricter version:\n",
    "# meteor_mask = (mask_slice == meteor_flag)\n",
    "\n",
    "# Apply the mask to isolate METEOR-flagged regions\n",
    "flux_meteor = np.where(meteor_mask, flux_slice, np.nan)\n",
    "error_meteor = np.where(meteor_mask, error_slice, np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c99da6f7-c2e2-4269-9574-403610f3a1d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting\n",
    "fig, axes = plt.subplots(1, 2, figsize=(15, 5), subplot_kw={'projection': wcs.celestial})\n",
    "plt.subplots_adjust(wspace=0.4)\n",
    "\n",
    "# Shared image display settings\n",
    "im_kwargs = dict(origin='lower', interpolation='none')\n",
    "\n",
    "# Panel 1: Flux\n",
    "im0 = axes[0].imshow(flux_meteor, cmap='viridis', **im_kwargs)\n",
    "axes[0].set_title(\"Meteor Flux\", fontsize=14)\n",
    "\n",
    "axes[0].set_xlabel(\"RA\", fontsize=12)\n",
    "axes[0].set_ylabel(\"Dec\", fontsize=12)\n",
    "axes[0].coords[0].set_axislabel('RA')\n",
    "axes[0].coords[1].set_axislabel('Dec')\n",
    "plt.colorbar(im0, ax=axes[0], fraction=0.046, pad=0.04, label=\"Flux (erg s$^{-1}$ cm$^{-2}$ Å$^{-1}$)\")\n",
    "\n",
    "# Panel 2: Error\n",
    "im1 = axes[1].imshow(error_meteor, cmap='magma', **im_kwargs)\n",
    "axes[1].set_title(\"Meteor Flux Error\", fontsize=14)\n",
    "axes[1].set_xlabel(\"RA\")\n",
    "axes[1].set_ylabel(\"Dec\")\n",
    "axes[1].coords[0].set_axislabel('')\n",
    "axes[1].coords[1].set_axislabel('')\n",
    "plt.colorbar(im1, ax=axes[1], fraction=0.046, pad=0.04)\n",
    "\n",
    "# Title with wavelength\n",
    "plt.suptitle(f\"Wavelength slice: {wave_slice:.1f} Å\", fontsize=16)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96b641a1-2997-406a-999c-7248e30a3436",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.13.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}