{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1546ea83-a266-4f13-8eb8-d5cc6f72ee56",
   "metadata": {},
   "source": [
    "# Data Cube Coordinate Query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "49765cb5-4f3e-41a2-b7b1-157a8b1bfae0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os.path as op\n",
    "import numpy as np\n",
    "from astropy.table import Table\n",
    "\n",
    "from astropy.coordinates import SkyCoord\n",
    "import astropy.units as u\n",
    "from astropy.io import fits\n",
    "\n",
    "from astropy.wcs import WCS\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "from astropy.visualization import ImageNormalize, PercentileInterval, AsinhStretch\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "12f8ce30-0650-44ff-800c-2ebd942effde",
   "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/'\n",
    "    \n",
    "ifu_data = Table.read(op.join( pdr_dir, 'ifu-index.fits'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "054cc55e-af0d-4c77-ae65-a4af4d1c6685",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create SkyCoord Object array for IFU centers\n",
    "ifu_coords = SkyCoord( ra=ifu_data['ra_cen']*u.deg, dec=ifu_data['dec_cen']*u.deg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6f9816a-6c85-43a7-a1dd-c6b425b84c69",
   "metadata": {},
   "source": [
    "## This is an example of a big Lyman Alpha Blob at z=2.53"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "01c279fb-81bc-46dd-8ba4-768fd0307014",
   "metadata": {},
   "outputs": [],
   "source": [
    "coord = SkyCoord(ra=228.78581*u.deg, dec=51.268036*u.deg)\n",
    "wave_src = 4295.9 # angstrom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8aee251f-d09d-4016-9a0e-82b04e4e378b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# find list of possible datacubes with coverage\n",
    "sel = coord.separation( ifu_coords) < 37*u.arcsec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "013838e8-45e5-433c-b420-7e9d5a3215ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=1</i>\n",
       "<table id=\"table140678150186272\" 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>20180618017</td><td>024</td><td>228.79074</td><td>51.264973</td><td>1.0</td><td>1.0</td><td>1.0</td><td>1.0</td><td>1.0</td><td>1.0</td><td>1.0</td><td>dex-spring</td><td>HS00444d2_W</td><td>20180618</td><td>17</td><td>228.871039</td><td>51.349437</td><td>292.50071</td><td>40</td><td>1.4246124</td><td>0.16366434</td><td>0.1826</td><td>18.612223</td><td>20180618v017</td><td>9.869445</td><td>367.6003</td><td>55.798</td><td>58287.285</td><td>19</td><td>1</td><td>804.809</td><td>317.00266</td><td>0648106</td><td>51.3479</td><td>290.8775</td><td>228.87</td><td>1.028041</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=1>\n",
       "   shotid   ifuslot   ra_cen   dec_cen  ... trajcpa  trajcra fluxlimit_4600\n",
       "   int64     bytes3  float32   float32  ... float32  float32    float32    \n",
       "----------- ------- --------- --------- ... -------- ------- --------------\n",
       "20180618017     024 228.79074 51.264973 ... 290.8775  228.87       1.028041"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ifu_data[sel]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "123a11b7-db67-458b-a048-ab403e4655c5",
   "metadata": {},
   "source": [
    "### Collapse data cube to create a broad-band like and narrow-band like image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1913a236-8ea7-44e8-8c87-dd4c9638f03d",
   "metadata": {},
   "source": [
    "Here's an example of plotting up the Ly-alpha line flux for a bright LAB. We will generate a pseudo narrowband-like image at the wavelength of the Ly-alpha emission. We will also create a continuum like image to subtract from the line flux map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3d1f0f0e-4d76-4f8b-adeb-2a648b7e8bd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "shotid = ifu_data['shotid'][sel][0]\n",
    "ifuslot= ifu_data['ifuslot'][sel][0]\n",
    "datacube_path = op.join( pdr_dir, 'datacubes', str(shotid), 'dex_cube_{}_{}.fits'.format( shotid, ifuslot))\n",
    "\n",
    "hdul = fits.open( datacube_path)\n",
    "\n",
    "flux = hdul[\"DATA\"].data\n",
    "error = hdul[\"ERROR\"].data\n",
    "mask = hdul[\"MASK\"].data\n",
    "\n",
    "# Minimum mask: reject core instrumental artefacts (MAIN | FTF | CHI2FIB | BADPIX | BADAMP)\n",
    "good_min = (mask & (1 | 2 | 4 | 8 | 16)) == 0\n",
    "flux = flux.astype(float)\n",
    "flux[~good_min] = np.nan\n",
    "\n",
    "header = hdul[\"DATA\"].header\n",
    "\n",
    "wave = np.linspace( 3470, 5540, num=1036)\n",
    "\n",
    "wcs = WCS(header)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bf4e7995-2647-4aad-a776-377f5c1ff2c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# collapse at wavelength of known LAB \n",
    "z_index = np.abs( wave_src - wave) < 10    \n",
    "# Slice the cube at the wavelength index\n",
    "\n",
    "flux_nb = np.sum( flux[z_index, :, :], axis=0)\n",
    "error_nb = np.sqrt( np.sum( error[z_index, :, :]**2, axis=0) )\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b05ae9e4-c58e-442a-a8bf-b3c521abd944",
   "metadata": {},
   "source": [
    "Collapse the data cube from 3800 to 5500 Angstrom. This will make an image similar to a broad-band g-band image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a9bd603e-e9e2-4be7-a7f2-45e2be18e1f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "z_index = (wave >= 3800) & (wave <= 5200)\n",
    "\n",
    "flux_cont = np.sum( flux[z_index, :, :], axis=0)\n",
    "error_cont = np.sqrt( np.sum( error[z_index, :, :]**2, axis=0) )\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fadd5c5b-c619-4483-96e5-1f5d38f64dc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "zoom_radius_arcsec = 15\n",
    "\n",
    "\n",
    "# Convert center to pixel\n",
    "x_center, y_center = wcs.celestial.world_to_pixel(coord)\n",
    "\n",
    "# Convert zoom size in arcsec to pixels\n",
    "arcsec_per_pixel = np.abs(wcs.wcs.cdelt[0]) * 3600  # assuming square pixels\n",
    "half_width_pix = zoom_radius_arcsec / arcsec_per_pixel\n",
    "\n",
    "interval = PercentileInterval(99.5)\n",
    "vmin_nb, vmax_nb = interval.get_limits(flux_nb)\n",
    "norm_nb = ImageNormalize(vmin=vmin_nb, vmax=vmax_nb, stretch=AsinhStretch())\n",
    "\n",
    "vmin_cont, vmax_cont = interval.get_limits(flux_cont)\n",
    "norm_cont = ImageNormalize(vmin=vmin_cont, vmax=vmax_cont, stretch=AsinhStretch())\n",
    "\n",
    "# Plotting\n",
    "fig, axes = plt.subplots(1, 3, figsize=(18, 5), subplot_kw={'projection': wcs.celestial})\n",
    "plt.subplots_adjust(wspace=0.4)\n",
    "\n",
    "# Panel 1: Line flux\n",
    "im0 = axes[0].imshow(flux_nb, origin='lower', cmap='magma', norm=norm_nb)\n",
    "axes[0].set_title(f\"Line Flux at {wave_src:.1f} Å\", fontsize=14)\n",
    "axes[0].coords[0].set_axislabel('RA')\n",
    "axes[0].coords[1].set_axislabel('Dec')\n",
    "axes[0].set_xlim(x_center - half_width_pix, x_center + half_width_pix)\n",
    "axes[0].set_ylim(y_center - half_width_pix, y_center + half_width_pix)\n",
    "plt.colorbar(im0, ax=axes[0], fraction=0.046, pad=0.04)\n",
    "\n",
    "# Panel 2: Continuum\n",
    "im1 = axes[1].imshow(flux_cont, origin='lower', cmap='Greys', norm=norm_cont)\n",
    "axes[1].set_title(\"Continuum Flux (3800–5200 Å)\", fontsize=14)\n",
    "axes[1].coords[0].set_axislabel('RA')\n",
    "axes[1].coords[1].set_axislabel('')\n",
    "axes[1].set_xlim(x_center - half_width_pix, x_center + half_width_pix)\n",
    "axes[1].set_ylim(y_center - half_width_pix, y_center + half_width_pix)\n",
    "plt.colorbar(im1, ax=axes[1], fraction=0.046, pad=0.04)\n",
    "\n",
    "# Panel 3: Overlap\n",
    "axes[2].imshow(flux_cont, origin='lower', cmap='Greys', norm=norm_cont, alpha=0.6)\n",
    "axes[2].imshow(flux_nb, origin='lower', cmap='magma', norm=norm_nb, alpha=0.5)\n",
    "axes[2].set_title(\"Overlap: Line (colors) + Continuum (greyscale)\", fontsize=14)\n",
    "axes[2].coords[0].set_axislabel('RA')\n",
    "axes[2].coords[1].set_axislabel('')\n",
    "axes[2].set_xlim(x_center - half_width_pix, x_center + half_width_pix)\n",
    "axes[2].set_ylim(y_center - half_width_pix, y_center + half_width_pix)\n",
    "\n",
    "# Global title\n",
    "plt.suptitle(f\"Zoomed Region: ±{zoom_radius_arcsec}\\\" around {coord.to_string('hmsdms')}\", fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe590180-3572-4168-8355-67cd34eb63ff",
   "metadata": {},
   "source": [
    "## Example Three-color Map of a Nearby Emission-Line galaxy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13f447ab-b86b-4bf5-85cb-08804981e117",
   "metadata": {},
   "source": [
    "In this example we will plot up a three color image of a low-redshift starforming galaxy that exhibit line emission at OII, abd OIII. We will make the three color maps using a combination of these two emission line maps and a continuum image. From the source catalog we know the galaxy is at z="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "38fda314-5b4f-4514-9115-1f405a983874",
   "metadata": {},
   "outputs": [],
   "source": [
    "coord = SkyCoord(ra=215.68578*u.deg, dec=52.11736*u.deg)\n",
    "redshift = 0.067"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2fdd9c06-76cc-409e-bc8a-1e6417fff531",
   "metadata": {},
   "outputs": [],
   "source": [
    "# find list of possible datacubes with coverage\n",
    "sel = coord.separation( ifu_coords) < 37*u.arcsec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f7879fdc-94f9-4372-8dd5-25dca1f80692",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><i>Table length=1</i>\n",
       "<table id=\"table140677952131312\" 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>20190405020</td><td>034</td><td>215.68808</td><td>52.115025</td><td>1.0</td><td>1.0</td><td>1.0</td><td>1.0</td><td>1.0</td><td>1.0</td><td>1.0</td><td>dex-spring</td><td>DEXsp6210</td><td>20190405</td><td>20</td><td>215.707429</td><td>52.045023</td><td>68.823284</td><td>46</td><td>1.4450058</td><td>0.26003242</td><td>0.1634</td><td>15.331111</td><td>20190405v020</td><td>-7.733333</td><td>368.2825</td><td>16.498</td><td>58578.24</td><td>19</td><td>1</td><td>803.3867</td><td>41.27882</td><td>0541459</td><td>52.044964</td><td>67.28403</td><td>215.70683</td><td>1.239597</td></tr>\n",
       "</table></div>"
      ],
      "text/plain": [
       "<Table length=1>\n",
       "   shotid   ifuslot   ra_cen   dec_cen  ... trajcpa   trajcra  fluxlimit_4600\n",
       "   int64     bytes3  float32   float32  ... float32   float32     float32    \n",
       "----------- ------- --------- --------- ... -------- --------- --------------\n",
       "20190405020     034 215.68808 52.115025 ... 67.28403 215.70683       1.239597"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ifu_data[sel]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ab47e14d-ed79-4dcc-8e3e-b846c9363b13",
   "metadata": {},
   "outputs": [],
   "source": [
    "shotid = ifu_data['shotid'][sel][0]\n",
    "ifuslot= ifu_data['ifuslot'][sel][0]\n",
    "datacube_path = op.join( pdr_dir, 'datacubes', str(shotid), 'dex_cube_{}_{}.fits'.format( shotid, ifuslot))\n",
    "\n",
    "hdul = fits.open( datacube_path)\n",
    "\n",
    "flux = hdul[\"DATA\"].data\n",
    "error = hdul[\"ERROR\"].data\n",
    "mask = hdul[\"MASK\"].data\n",
    "\n",
    "# Minimum mask: reject core instrumental artefacts (MAIN | FTF | CHI2FIB | BADPIX | BADAMP)\n",
    "good_min = (mask & (1 | 2 | 4 | 8 | 16)) == 0\n",
    "flux = flux.astype(float)\n",
    "flux[~good_min] = np.nan\n",
    "\n",
    "header = hdul[\"DATA\"].header\n",
    "\n",
    "wave = np.linspace( 3470, 5540, num=1036)\n",
    "\n",
    "wcs = WCS(header)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fafa549e-68cc-4b3e-8fb5-fe412544a047",
   "metadata": {},
   "outputs": [],
   "source": [
    "# collapse along the full spectra dimension to get the continuum\n",
    "wave_lo = 3800\n",
    "wave_hi = 5500\n",
    "z_index = (wave >= wave_lo) & (wave <= wave_hi)\n",
    "\n",
    "flux_cont = np.sum( flux[ z_index, :, :], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0ea52b2a-1abb-4ac2-90ae-57c64518648e",
   "metadata": {},
   "outputs": [],
   "source": [
    "waveoii = 3727.8\n",
    "waveoiii = 5008.24\n",
    "dwave = 10 # \n",
    "z_index = np.abs( waveoii*(1.+redshift) - wave ) < dwave \n",
    "flux_oii = np.sum( flux[z_index, :, :], axis=0)\n",
    "\n",
    "z_index = np.abs( waveoiii*(1.+redshift) - wave ) < dwave\n",
    "flux_oiii = np.sum( flux[z_index, :, :], axis=0)\n",
    "\n",
    "# to subtract out the contribution of the continuum from the emission\n",
    "# line maps, need the relative spectral width ratio\n",
    "dw_nb = 2*dwave\n",
    "dw_cont = wave_hi - wave_lo\n",
    "flux_cont_sub = dw_nb*(flux_cont/(dw_cont))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9c20a031-96cd-4925-bb02-919a5ace885d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "zoom_arcsec = 15\n",
    "\n",
    "# Build the RGB layers\n",
    "red   = flux_oii - flux_cont_sub\n",
    "green = flux_oiii - flux_cont_sub\n",
    "blue = flux_cont\n",
    "\n",
    "# Normalize each using percentile-based scaling\n",
    "interval = PercentileInterval(99.5)\n",
    "\n",
    "r_scaled = interval(red)\n",
    "g_scaled = interval(green)\n",
    "b_scaled = interval(blue)\n",
    "\n",
    "# Stack and clean\n",
    "rgb_image = np.stack([r_scaled, g_scaled, b_scaled], axis=-1)\n",
    "rgb_image = np.nan_to_num(rgb_image, nan=0.0)\n",
    "rgb_image = np.clip(rgb_image, 0, 1)\n",
    "\n",
    "# Convert zoom center to pixel\n",
    "x_center, y_center = wcs.celestial.world_to_pixel(coord)\n",
    "\n",
    "# Convert arcsec to pixels (assume square pixels)\n",
    "pix_scale = np.abs(wcs.wcs.cdelt[0]) * 3600  # deg → arcsec\n",
    "half_width_pix = zoom_arcsec / pix_scale\n",
    "\n",
    "# Plot with WCS\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8), subplot_kw={'projection': wcs.celestial})\n",
    "\n",
    "ax.imshow(rgb_image, origin='lower')\n",
    "\n",
    "# Zoom\n",
    "ax.set_xlim(x_center - half_width_pix, x_center + half_width_pix)\n",
    "ax.set_ylim(y_center - half_width_pix, y_center + half_width_pix)\n",
    "\n",
    "# Labels\n",
    "ax.set_title(\"RGB Composite: [O II] (Red), [O III] (Green), Continuum (Blue)\", fontsize=14)\n",
    "ax.coords[0].set_axislabel('RA')\n",
    "ax.coords[1].set_axislabel('Dec')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77d63ef4-a5e7-45dd-bf68-a3e5ff572f02",
   "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
}