specreduce_extract_jwst.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spectral extraction tests (JDAT-1855)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook uses data located at /eng/ssb/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from matplotlib.colors import LogNorm\n",
    "%matplotlib inline\n",
    "\n",
    "from astropy.io import fits\n",
    "from astropy.table import Table\n",
    "from astropy.visualization import simple_norm\n",
    "from astropy.utils.data import download_file\n",
    "\n",
    "from jwst import datamodels\n",
    "\n",
    "from specreduce.extract import BoxcarExtract"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ingest s2d data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data is taken from s2d file. x1d is used for comparison with pipeline extraction.\n",
    "s2dfilename = \"nirspec_fssim_d1_s2d.fits\"\n",
    "x1dfilename = \"nirspec_fssim_d1_x1d.fits\"\n",
    "mainurl = \"file:/eng/ssb/busko/NIRSpec_s2d/\"\n",
    "\n",
    "s2dfile_dld = download_file(mainurl + s2dfilename)\n",
    "x1dfile_dld = download_file(mainurl + x1dfilename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rename files so that they have the right extensions required for the jwst datamodels to work\n",
    "s2dfile = s2dfile_dld + '_s2d.fits'\n",
    "os.rename(s2dfile_dld, s2dfile)\n",
    "x1dfile = x1dfile_dld + '_x1d.fits'\n",
    "os.rename(x1dfile_dld, x1dfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG:jwst.datamodels.util:Opening /var/folders/f2/dsq1cxxd78n9vltk6_6kwxtr0000zf/T/astropy-download-8545-mw0t__xp_s2d.fits as <class 'jwst.datamodels.multislit.MultiSlitModel'>\n"
     ]
    }
   ],
   "source": [
    "# use a jwst datamodel to provide a good interface to the data and wcs info\n",
    "s2d = datamodels.open(s2dfile)\n",
    "image = s2d.slits[0].data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'slit[0]')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display s2d image\n",
    "norm_data = simple_norm(image, \"sqrt\")\n",
    "plt.figure(figsize=(15, 15))\n",
    "plt.imshow(image, norm=norm_data, origin=\"lower\")\n",
    "plt.title(\"slit[0]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<TableColumns names=('WAVELENGTH','FLUX','ERROR','SURF_BRIGHT','SB_ERROR','DQ','BACKGROUND','BERROR','NPIXELS')>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pipeline 1d extraction (for comparison)\n",
    "jpipe_x1d = Table.read(x1dfile, hdu=1)\n",
    "print(jpipe_x1d.columns)\n",
    "# plot\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "ax.plot(jpipe_x1d['WAVELENGTH'], jpipe_x1d['FLUX'], 'k-', label=\"jpipe_x1d\")\n",
    "ax.set_title(\"JWST Pipeline x1d extracted spectrum\")\n",
    "ax.set_xlabel(\"wavelength\")\n",
    "ax.set_ylabel(\"Flux Density [Jy]\")\n",
    "ax.set_yscale(\"log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'slit[0] slice')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# blow up of the region to be extracted\n",
    "plt.figure(figsize=(15, 15))\n",
    "plt.imshow(image[20:35,0:100], norm=norm_data, origin=\"lower\")\n",
    "plt.title(\"slit[0] slice\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extraction parameters based on image above\n",
    "ext_center = 27\n",
    "ext_width = 6\n",
    "bkg_offset = 4\n",
    "bkg_width = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Cross-dispersion Cut at Pixel=70')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot along cross-disperion cut showing the extraction parameters\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "y = np.arange(image.shape[0])\n",
    "ax.plot(y, image[:,70], 'k-')\n",
    "mm = np.array([ext_center, ext_center])\n",
    "mm_y = ax.get_ylim()\n",
    "\n",
    "ax.plot(mm, mm_y, 'b--')\n",
    "ax.plot(mm - ext_width/2., mm_y, 'g:')\n",
    "ax.plot(mm + ext_width/2., mm_y, 'g:')\n",
    "\n",
    "ax.plot(mm - bkg_offset - bkg_width/2., mm_y, 'r:')\n",
    "ax.plot(mm - bkg_offset + bkg_width/2., mm_y, 'r:')\n",
    "ax.plot(mm + bkg_offset - bkg_width/2., mm_y, 'r:')\n",
    "ax.plot(mm + bkg_offset + bkg_width/2., mm_y, 'r:')\n",
    "\n",
    "ax.set_title(\"Cross-dispersion Cut at Pixel=70\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## specreduce's BoxcarExtract test with s2d data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# # build a trace object\n",
    "# class Trace:\n",
    "#     def __init__(self, image, ext_center):\n",
    "#         self.line = np.ones(shape=(image.shape[1]), dtype=int) * ext_center\n",
    "\n",
    "# trace = Trace(image, ext_center)\n",
    "\n",
    "# # Extract\n",
    "# boxcar = BoxcarExtract()\n",
    "# boxcar.apwidth = ext_width\n",
    "# boxcar.skysep = int(bkg_offset - bkg_width/2 - ext_width/2)\n",
    "# boxcar.skywidth = bkg_width\n",
    "\n",
    "# spectrum, bkg_spectrum = boxcar(image, trace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(spectrum.wavelength)\n",
    "\n",
    "# this one crashes with \"UnitConversionError: 'pix' and 'Angstrom' (length) are not convertible\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot (need to fix BoxcarExtract before this can run)\n",
    "\n",
    "# fig, ax = plt.subplots(figsize=(6, 6))\n",
    "# ax.plot(spectrum.wavelength.values, spectrum.flux.values, 'k-', label=\"boxcar\")\n",
    "# ax.plot(waves_boxcar_bkgsub[gpts], ext1d_boxcar_bkgsub[gpts], 'k:', label=\"boxcar (bkgsub)\")\n",
    "# ax.plot(jpipe_x1d['WAVELENGTH'], jpipe_x1d['FLUX'], 'k-', label=\"jpipe_x1d\")\n",
    "# ax.set_title(\"Fixed boxcar 1D extracted spectrum\")\n",
    "# ax.set_xlabel(r\"wavelength [$\\mu$m]\")\n",
    "# ax.set_ylabel(\"Flux Density [Jy]\")\n",
    "# ax.set_yscale(\"log\")\n",
    "# ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extraction code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functions below are adapted/simplified from https://github.com/spacetelescope/jdat_notebooks/tree/main/notebooks/MIRI_LRS_spectral_extraction \n",
    "\n",
    "They were rid of the extra code necessary to support wavelength-dependent extraction, and PSF-weighted extraction. These require that a gWCS be present in the input data, which doesn't seem to be the case with s2d files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_boxcar_weights(center, hwidth, npix):\n",
    "    \"\"\"\n",
    "    Compute the weights given an aperture center, half widths, and number of pixels\n",
    "    \"\"\"\n",
    "    \n",
    "    # TODO: this code may fail when regions fall partially or entirelly outside the image.\n",
    "    \n",
    "    weights = np.zeros((npix))\n",
    "    # pixels with full weight\n",
    "    \n",
    "    print(center)\n",
    "    print(hwidth)\n",
    "    print(npix)\n",
    "    \n",
    "    \n",
    "    fullpixels = [max(0, int(center - hwidth + 1)), min(int(center + hwidth), npix)]\n",
    "    weights[fullpixels[0] : fullpixels[1]] = 1.0\n",
    "    \n",
    "    # pixels at the edges of the boxcar with partial weight\n",
    "    if fullpixels[0] > 0:\n",
    "        weights[fullpixels[0] - 1] = hwidth - (center - fullpixels[0])\n",
    "    if fullpixels[1] < npix:\n",
    "        weights[fullpixels[1]] = hwidth - (fullpixels[1] - center)\n",
    "\n",
    "    return weights\n",
    "\n",
    "\n",
    "# def ap_weight_images(center, width, bkg_offset, bkg_width, image_size, waves, wavescale=None):\n",
    "def ap_weight_images(center, width, bkg_offset, bkg_width, image_size):\n",
    "\n",
    "    \"\"\"\n",
    "    Create a weight image that defines the desired extraction aperture\n",
    "    and the weight image for the requested background regions\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    center : float\n",
    "        center of aperture in pixels\n",
    "    width : float\n",
    "        width of apeture in pixels\n",
    "    bkg_offset : float\n",
    "        offset from the extaction edge for the background\n",
    "        never scaled for wavelength\n",
    "    bkg_width : float\n",
    "        width of background region\n",
    "        never scaled with wavelength\n",
    "    image_size : tuple with 2 or 3 elements\n",
    "        size (shape) of image\n",
    "    waves : array\n",
    "        wavelegth values   NOT USED\n",
    "    wavescale : float\n",
    "        scale the width with wavelength (default=None)\n",
    "        wavescale gives the reference wavelenth for the width value  NOT USED\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    wimage, bkg_wimage : (2D image, 2D image)\n",
    "        wimage is the weight image defining the aperature\n",
    "        bkg_image is the weight image defining the background regions\n",
    "    \"\"\"\n",
    "    wimage = np.zeros(image_size)\n",
    "    bkg_wimage = np.zeros(image_size)\n",
    "    hwidth = 0.5 * width\n",
    "\n",
    "    # loop in dispersion direction and compute weights\n",
    "    # \n",
    "    # This loop may be removed, replaced by vectorized operations,\n",
    "    # when the extraction parameters are the same in every image\n",
    "    # column. We leave it in place for now, so the code may be\n",
    "    # upgraded later to support height-variable and PSF-weighted \n",
    "    # extraction modes.\n",
    "\n",
    "    # s2d or s3d data\n",
    "    disp_axis = 1 \n",
    "    if len(image_size) == 3:\n",
    "        disp_axis = 0\n",
    "    \n",
    "    for i in range(image_size[disp_axis]):\n",
    "#         if wavescale is not None:\n",
    "#             hwidth = 0.5 * width * (waves[i] / wavescale)\n",
    "\n",
    "        wimage[:, i] = get_boxcar_weights(center, hwidth, image_size[0])\n",
    "\n",
    "        # bkg regions\n",
    "        if (bkg_width is not None) & (bkg_offset is not None):\n",
    "            bkg_wimage[:, i] = get_boxcar_weights(\n",
    "                center - hwidth - bkg_offset, bkg_width, image_size[0]\n",
    "            )\n",
    "            bkg_wimage[:, i] += get_boxcar_weights(\n",
    "                center + hwidth + bkg_offset, bkg_width, image_size[0]\n",
    "            )\n",
    "        else:\n",
    "            bkg_wimage = None\n",
    "\n",
    "    return (wimage, bkg_wimage)\n",
    "\n",
    "\n",
    "# def extract_1dspec(jdatamodel, center, width, bkg_offset, bkg_width, wavescale=None):\n",
    "def extract_1dspec(image, jdatamodel, center, width, bkg_offset, bkg_width):\n",
    "\n",
    "    \"\"\"\n",
    "    Extract the 1D spectrum using the boxcar method.\n",
    "    Does a background subtraction as part of the extraction.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    image : ndarray\n",
    "        array with 2-D spectral image data\n",
    "    jdatamodel : jwst.DataModel\n",
    "        jwst datamodel with the 2d spectral image\n",
    "    center : float\n",
    "        center of aperture in pixels\n",
    "    width : float\n",
    "        width of apeture in pixels\n",
    "    bkg_offset : float\n",
    "        offset from the extaction edge for the background\n",
    "        never scaled for wavelength\n",
    "    bkg_width : float\n",
    "        width of background region\n",
    "        never scaled with wavelength\n",
    "    wavescale : float\n",
    "        scale the width with wavelength (default=None)\n",
    "        wavescale gives the reference wavelenth for the width value NOT USED\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    waves, ext1d : (ndarray, ndarray)\n",
    "        2D `float` array with wavelengths\n",
    "        1D `float` array with extracted 1d spectrum in Jy\n",
    "    \"\"\"\n",
    "#     # should be determined from the gWCS in cal.fits\n",
    "#     image = np.transpose(jdatamodel.data)\n",
    "#     grid = grid_from_bounding_box(jdatamodel.meta.wcs.bounding_box)\n",
    "#     ra, dec, lam = jdatamodel.meta.wcs(*grid)\n",
    "#     lam_image = np.transpose(lam)\n",
    "\n",
    "#     # compute a \"rough\" wavelength scale to allow for aperture to scale with wavelength\n",
    "#     rough_waves = np.average(lam_image, axis=0)\n",
    "\n",
    "    # images to use for extraction\n",
    "    wimage, bkg_wimage = ap_weight_images(\n",
    "        center,\n",
    "        width,\n",
    "        bkg_width,\n",
    "        bkg_offset,\n",
    "        image.shape\n",
    "    )\n",
    "#         rough_waves,\n",
    "#         wavescale=wavescale,\n",
    "#     )\n",
    "\n",
    "    # extract the spectrum using the weight image\n",
    "    if bkg_wimage is not None:\n",
    "        ext1d_boxcar_bkg = np.average(image, weights=bkg_wimage, axis=0)\n",
    "        data_bkgsub = image - np.tile(ext1d_boxcar_bkg, (image.shape[0], 1))\n",
    "    else:\n",
    "        data_bkgsub = image\n",
    "\n",
    "    ext1d = np.sum(data_bkgsub * wimage, axis=0)\n",
    "    # convert from MJy/sr to Jy\n",
    "    ext1d *= 1e6 * jdatamodel.meta.photometry.pixelarea_steradians\n",
    "\n",
    "    # compute the average wavelength for each column using the weight image\n",
    "    # this should correspond directly with the extracted spectrum\n",
    "    #   wavelengths account for any tiled spectra this way\n",
    "#     waves = np.average(lam_image, weights=wimage, axis=0)\n",
    "\n",
    "#     return (waves, ext1d, data_bkgsub)\n",
    "    return (ext1d, data_bkgsub)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extraction code test with s2d data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Fixed boxcar backgound weight image')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7Hzgu71Z5D7TfG8cB9+WnlP1Slfj/t/AfCJHm/xTvgab5GYiI8yNiTETsSvo7f19EnEizXv96zVYnvdvgGdJ4+Lfrdd5GLaR3HD6Rl0XtMZPGd+8FngXmACMb3dYaxnwjaYhiPWmc+9Rq8ZLedXJZvh8WABMb3f5e7INrc4xPkn5hjC7s/+3cB4uBoxvd/h7GfiBpeO5JYH5eJrXKPdBJ/C1x/XM8ewG/zrEuBC7M5eNIyeES4OfA4Fw+JG8vyfXjGh1DL8V/X74HFgLXsemdek31M1DRF4ew6V14TXn9/UnkZmZmZiV5ErmZmZlZSU6gzMzMzEpyAmVmZmZWkhMoMzMzs5KcQJmZmZmV5ATKzMzMrCQnUGZmZmYlOYEyMzMzK+n/APT/aoAPsUUbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the weight images used in the fixed boxcar extraction \n",
    "wimage_fixedboxcar, wimage_fb_bkg = ap_weight_images(ext_center, ext_width, bkg_offset, \n",
    "                                                     bkg_width, image.shape)\n",
    "\n",
    "norm_data = simple_norm(wimage_fixedboxcar)\n",
    "plt.figure(figsize=(10, 3))\n",
    "plt.imshow(wimage_fixedboxcar, norm=norm_data, origin=\"lower\")\n",
    "plt.title(\"Fixed boxcar weight image\")\n",
    "\n",
    "norm_data = simple_norm(wimage_fb_bkg)\n",
    "plt.figure(figsize=(10, 3))\n",
    "plt.imshow(wimage_fb_bkg, norm=norm_data, origin=\"lower\")\n",
    "plt.title(\"Fixed boxcar backgound weight image\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8a31eb2e20>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extract the spectrum using the weight images\n",
    "\n",
    "# we need to pass both the data model and the image array separately because\n",
    "# different types of data format hold the image in different attributes. This\n",
    "# api should evolve as we deal with other types of data model.\n",
    "\n",
    "jdatamodel = s2d.slits[0]\n",
    "image = jdatamodel.data\n",
    "\n",
    "# without background subtraction\n",
    "ext1d_boxcar, tmpval = extract_1dspec(image, jdatamodel, ext_center, ext_width, None, None)\n",
    "\n",
    "# with background subtraction\n",
    "ext1d_boxcar_bkgsub, tmpval = extract_1dspec(image, jdatamodel, ext_center, ext_width, \n",
    "                                                                  bkg_offset, bkg_width)\n",
    "\n",
    "# plot\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "ax.plot(ext1d_boxcar, 'k-', label=\"boxcar\")\n",
    "ax.plot(ext1d_boxcar_bkgsub, 'k:', label=\"boxcar (bkgsub)\")\n",
    "ax.set_title(\"Fixed boxcar 1D extracted spectrum\")\n",
    "ax.set_xlabel(r\"pixel\")\n",
    "ax.set_ylabel(\"Flux Density [Jy]\")\n",
    "ax.set_yscale(\"log\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The flux density scale above does not match the pipeline extraction. We should verify the correctness of units conversions in the extraction process. The mismatch is likely caused by the lack of a wavelength scale in the extracted product."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ingest s3d data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEBUG:jwst.datamodels.util:Opening /var/folders/f2/dsq1cxxd78n9vltk6_6kwxtr0000zf/T/astropy-download-8545-p0l34lv0_s3d.fits as <class 'jwst.datamodels.ifucube.IFUCubeModel'>\n",
      "DEBUG:stpipe:Opening /var/folders/f2/dsq1cxxd78n9vltk6_6kwxtr0000zf/T/astropy-download-8545-p0l34lv0_s3d.fits as <class 'jwst.datamodels.ifucube.IFUCubeModel'>\n"
     ]
    }
   ],
   "source": [
    "# NIRSpec IFU science data cube\n",
    "s3dfilename = \"NRS00001-faintQSO-F100LP-G140H-01_1_491_SE_2020-08-25T12h15m00_s3d.fits\"\n",
    "mainurl = \"https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/IFU_optimal_extraction/\"\n",
    "s3dfile_dld = download_file(mainurl + s3dfilename)\n",
    "\n",
    "# rename files so that they have the right extensions required for the jwst datamodels to work\n",
    "s3dfile = s3dfile_dld + '_s3d.fits'\n",
    "os.rename(s3dfile_dld, s3dfile)\n",
    "\n",
    "s3d = datamodels.open(s3dfile)\n",
    "image = s3d.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R6w1xiuGYee6D6Tjb1gm1/Vxtj7OpawfTPtj+pDCtM4p9j+L8o9DkfzrivJNUM3w2/6e4vPD2Ggzq3YB9R/VPOhSWEBERpQ3zV6qWU+CCW6fjjN++hOXrtyUdChMiIqK0yd4MNalHZiwhohItX78N331wju+uIy66LfkuHpgQERGlTGel6qQembEOEZXoigdm4+7p7+HlxR/6mn/T9uRbVDIhIiJKiWwikn1kllRawhKi9CjXz6Ic42ZCRESUMl2PzJLZfhneyypOudcjK8f4mRAREaVEtnVZ9m6SWLP7cvx5T2UtDadcSc3uRWQpgE0A2gG0qWrxHmJt0sYojpRt+mqbQtqOaG9SE/5xMY+Y7sF0LE0xmura2X7mcX+rLLsVMHZv0G5Y0HT+GWIRTWZMLAom+8isJuAjs7B/jafg3kQUuzBKiI5T1Ym+kiEiIiqqs6SIrcwi8edXlmLcFY9iW6vp10dalPeHUU7R85EZEVHKJF2puqzuYo4N23b4nvem55cAAD7c0tLt/QtvnY5P3vDvUOOi8lFqQqQAnhSRGSIyxW0GEZkiIs0i0tzatrXEzRERlaerH5lX8N5d097D9HfWFTzyyv6XQ3f487eZy3HAj57E3OUbSlrP82+vwcz31ocTFJWdUhOiI1X1IAAfA/A1ETk6fwZVnaqqTara1FDXq8TNERGVp5tfeqfgve89NAef+cMrBe9Lsk/Myu6R2QsL1wAAFqzcFGi5NO+nVEhf1eVUQb+khEhVlzv/rgbwEIBJYQRFRFTNJOHR7svnFlb5yiif6EbKsN29dUIkIr1FpG/2bwAnA5gbVmBERNUuuRKi8rwLl2vccfjeQ3PwlT/PSDqMVCul2f0wAA85WWAdgLtU9YmiS9mM0B5F1e8oRkyPYntxdzlgE6dtjJZNyI1sR4o3sY3TsJx2WJ5jlsdFy/DXWjXrHLojYFlNWAlBuaUVlfJ4KUp3TXsv6RBSzzohUtUlAA4IMRYiIkJ66hClMY9+dPYK9O1Rh6P3GNr5XrlVAvcjjcfeRjl9MiV1zEhEROFLfLT7FN/GvnbX6wCApdecnnAk0Sr3p3/lmM+xHyIioogFTWySLiFKcT7kKo2PzO6e/h6WrWNXM36lIQlnQkREFLEXF671NV82Aaop0srsvuZl+Pus5SFE5hFHZGuuDq1tHfjug3Nw1o0vW6+jUh6ZldPJxISIiChiH21ttVrOq2PGy++fjW/eM6uEiMzK/XFN3H77zMJuyU+2tMP2c89Vrp9F0IQuDaV8TIiIiCLW0haseWBnP0Q+b4Zh3DSXrt2CA370JJat25qKxxfl5Lqn3saMdz9KZNtL1mxGe0f5f15pOOfiT4hqLF4i3i8T2+VUvV81Nd4vW6btdcD7FcX2bJYzxRhF/HGL4PwTVc+XNZvvFn8SxaK1SEKU/+s4id/Kf52xDBu27cDfZy0v21KJSrdyw3aszyl1WrxmM46/7nlc/8xCz2Xa2svpYpsstjIjIopY0DHJuipVZ5ab98FGbGltwyHjBoUdWqd5H2x0tllW1T5S57fPLMRfZ7xf8npyk+J/L1qL826eBgBoqKvB21d/DACwYv12AEDz0nWu61i0ehNO/OULuOG8g0qOx1YaSn784u9DIqKUyVaqzj4JOe03L+LsmwrHPCtm9abt+McbH/ia99m31nT+Xa49PvuNOsoKy9c99TbeC7l12WNzVnT+7Vba6LU/c5dnktx/vrky1Hj8SL5GUHBMiIiooohIrYjMFJFHko7FVmcJkc9bvFf+ctFtr+Ebd8/Ehq07fG9bDetL0h9fWIIZ77qXhFSrYudHFKUzLy5cg/97fH4o69q43f95GQcmRERUab4JIJwrdkK6OmYsbT0rN2QeqezoiKceSfPSdfjS7c2RVPL96WPzcdaNwUvJys2EHzyBKx6Y3fl/9ZHWeLXQ6uxxPKTYAOD8W6bjD88v8T2/6RzevL0thIjCw4SIiCqGiIwGcDqAm5OOJVfgxMZytPsPNmzHppxf3dL56C3omGgBN+z4yl9ex9PzV+HDLS12K7AUV589aze3YOZ70bYm29rajnteW+Zrn/wOsZLEyPN+tllfm64UJF3REBGV5tcALkd5tWMsUBOwp+rcMoRFqzcb1zPj3Y/w6OyuOikF61L7Ry3Zukdx9ykT1yO+M3/3b3zyBv+dLXrFtWTNZqzeuL3keNxW/8riDzHuike7nQdRa166Dh0WpYI1KatoVP4JkalJtG3zchPbJvKml2kfag2vKI6ZTRPzNDX3jvszt9w/ravxfJEdETkDwGpVnWGYZ4qINItI85o1a7xmS0z2LM0mFKVWbs5Wzs59hHXWjS93jgfmHoNaf10640/Zjc5L0P1cvn5bKNs9/rrnMel/nyl5PW7nx1PzVgEA/rVgVSyJ4osL1+DTN72Cm1/y/xgtKze8VRtb8MTc+Ct/5+LVl4gqxREA/p+ILAVwD4DjReQvuTOo6lRVbVLVpqFDh7qtIxJFE5u8BCLoWGa58+U+qsj+And7ZHbT84uxtdW9DkexzU5/Z51rCUdXCVF8Dvu/Z/DA66U3c08rP+dA7mfeoz5zW29t68hJsKPzgZMkepVImeLPn/bnV5eGFJUdJkREVBFU9buqOlpVxwE4B8C/VPXzCYcFwEddoLwZOitV5014a+Um/P7ZRb63a+rx+prHF+DaJ95yD6fIXfgzf3gFp/3mxcLl8rYbhxUbSnv0dMNzizDn/Q0hRRMNr4/D7W3XzyCBEjs/m8w/z7a0tEcTjE/smJGIKGVqPBKZU379guv8XulLbY25UvXmFrsSIgBYu7lwnK4oWjVFITdXuPaJt3At3sLSa05PLqASJXm8S6qNkPd/Ux599/T3sPPgXjh81yH2GyyCCRERVRxVfQ7AcwmH4em3zyzEN07YveuN/BtB56Ou4OvOXVWNxXpKq26XcAdGFo8Y08pXxXSX/cjdt8Q/D5g/kvzwvPZ43BWPdv4dZeLKR2ZERDG74bnF3d/weWPIt6xIj8hdPV77vzGW0jFj7mJt7R2d9UtufnEJvvJnz7ruVWv7juKPiPx8FG4lKyKFlfTj5KvbgLy9ExFs2LoDLy9aG1FUZkyIiIgitmZT9355tu1ox+0vL+38/+U5HfEBOXV/itwOj7r22cx8HhlM9qbk1STa+57Vff6trW2dyddzb632Xkq7lr760fk4/Jp/Yd2WVlz96Hw8kcDwEV7S0gru2399I/R1up0zadnffG6n7UV/mo7P3TwN21rbnXniK+Uqj0dmpgNi6m0k7nQviu2Z9t10lpuWazdMs9kH22+bKUZTHHH3MGN7nG2/yKam91FsjyJXUCIE4IcPv+k5f1g9VXclVsHkb/eCW6aj+d2PsPSa0/GF214zLKed/2YTp9zR2am7Ge+W1smjKWEWiFXfQH61tXfgm/fMwk79Gq3XUVCHCMCClZsAAO2d55L16gMrj4SIiKiK1Dg5sd9fx7lzuT4+CbJxLbzNNvu8cXfklBD5ScaOv+45jBrQE3+++NAgEfrW0taOdVtaMaJ/TwDA6+99hMZy6esrwIeWnXX5+m3YuK2rp/IrHpwDALh/RvjdEry1ahMenePdwWeW6RzOn5Z77nYm13bhWWFCRESUMp0dMya0ffs6RF2/6v30pbRkzRYsWbPFbmM+/Ne9b+DROSuw6KcfQ11tDT7l9DI9ZlDPbvEmxZjz+Agt/9gecc2/utYd8WOy2qLdTBcPoLBStXQm0v913xt4at4qLPrpxywjDK5MUmUiouqRvZl9tHUHWtrs+2axqX+hKGXojuw6NOd2mFzSke21uT3vOKzfEs8o6+HtufuausYyi7+SUG0I2yw4PXNWmf3s4jx7mBAREaXEkjWb8eqSDzv//8Lba3DBLdOLLmdbouN1Tyu5lVm3pt9264rSJqf/pTTG1ilAq3u3Wa95fIHnctOWfIgv3d5cUh2jGkMJ0Yx3P8LLizMtxf708lKcdaP7+G8Frcxc5gk6MHEpmBAREaXE6++txzlTX+1sLg8A095ZZ72+xZaPo7zuQas3FekV2mUIkbTkHAtWbix4Ly2xmfgbuiPYOi/5yww8PX8VNmyzLykzlRCddePL2Oq0Ent58Yeelcf97FucSWvRhEhEbhWR1SIyN+e9QSLylIgsdP4dGG2YRETVY96Kwpu3Wdddw9TnzE3PF7Z2K1iTej8ym/RT84CkHTkVYcNqKRfE5Q/Mxt9mLi94/4m5K3HqrwuHGikXnkN3OBM2bnfvcdzNmk0tWL+19EeG+XWIgvR1tNf3H8e4Kx7FjvbuTYaT7h7ATwnRnwCcmvfeFQCeUdXdATzj/N8f2xHhvV6m0eDDHtUd6BrlPMzR1G2ZYjEdM1tex8sUh+ll+/mYRpiPYr/b1ftluw+ml+3xtP0OUerc6NJMPwymxyi5Sn1k1q1SdV5y9fzbazDuikexIYSbstsjn8vunVXw3ltOU+58tn3chNU3Tlh1f6YHKEV0Kymz4fbIrKNDfXU2uX1H5sKz9MPuHYuKS1qVqkdmqvoCgPyjfSaA252/bwfwiXDDIiIiv8K8Z9hWqH7w9ffR3pEtIXJfx7bW9s7BaecHuDF7JSC/eNJ9cFrf67VdzmPBGe/aP97M5ydVSvKRn1t8V/5tDvb6/hO+15GfU7nlh6l6ZOZhmKpmOyBYCWBYSPEQEVEAz8xf1e3/rjeVAOsLUuCdu+3/uq+r12VV9wFq/zpjWaBgdrR34KqH38SHWwo7d9y0fQcefL3w8VgQ1iVEAB6dvaLgkc9ZN75SUjxxON9HJf2sJWs244W317hOcztyd09fZlzfn/79TrdxyfLP1VLP3VKVXKlaM2eUZ8wiMkVEmkWkubUtuv4miIiq0cW3N6Mt5B6J/ZYSXXx7s8fyXXIfeQR9QPTE3JX408tL8ZNH5hVM2++qJ4vGWXS65WF7at5KfO2u1/Hbfy2yW0FISu/J3P393z+7CK+/9xGOv+55XHCrewJlk0z+8cV3um/f7YzIe6schu5YJSIjVHWFiIwA4Dm4japOBTAVAPr3GlkOlfqJiMrKeTdPM04Pek8p9R6kql2tzHLXFbDOzDfungkAaDMNN+ThyofmYEd7VyVvN6a1rtvSirWbW7DHsL4u0zL1n1Y4g9d6CeNmrt2qqMfj5/8s7VGkb/klRJCCDyXC0UcK2JYQPQzgQufvCwH8PZxwiIgoqHUuj5T88GoZFMY9qPR+jLt4lfSYbpZ3Tnuva3nPVlpdf7fnrezkXz2Pk3/1QqB4wuSvwnU82cKzCwrLPO545d2S15u/h667nKaESETuBvAKgD1F5H0RuRjANQBOEpGFAE50/k9ERCkU5AZ+w3OLu42HZbU9j83ZlnN4rc9PiybjenOOS37rq7WbvZPMIAU/05Z8iM/84ZWC+kYAsHJjkX6dUuKiPxUO6Dv1hSUlr9c16ct/ZBZjRlT0kZmqnusx6YQwAxHTAHC2o42bmJob25ab2a7Tch/EUIxsPGZhd8cZxWj3JnE3FY/gs2Nzd0qz19/7yPe8byxbjwPGDCh4320sM9tLhVez8tY2f18kzxKmnMVfWfyh6zzu6/PvW399A+9/tA0rN2zHmEG9uk1r71AsW7e14P2C7fko4SpHhSVELk3501RCRERE5SOMrm2C3ITO/P2/8eSbK7u9162VWV6nkW7JSXaYBy9urcwAoMVnQuQlN5arH51vsXzpVhUpJWpp68CWVveOF8stH8qvU5Ufv+sTszT1Q0REROUt8D3FWcBv78NT/jyj++JQX6PdZ33uj+ZK4UGt3+qvTlUaSliKJbCPzl6BR2av6Pbel+/ItO4ruZVZzJW18+UnO8vXbyuIqKya3RMRUXnyHNy1xPXm3uf8NjLzm8T4MfHHT+UF5D6fdUIRYEG/s/7l1XexbN3W4jOiayT4cpd/aBat3lxQOlkOHTMSEVEKdXQAP/7HvG4Dscbd7P7DLa05Y5m590OUX1+kIImJgW2F3XDv0YKtrW34n7/NxTlTX815108cKSjicvh5VFsQrUv4+aVGcQ7dYdsPERERpdALC9fg1n+/g2Uf+SttcFPqjfasG1/urGhdQjdEofHsh8jHbs5fsRF7j+jnufxWj/o92e0uL9JXEdDV5D/MUjI/wkyobPIWt2QnP1FO1VhmRERUftyaefuVvQeVcsN0y30E4vvG+T9/m2O9bb+8Qtm4vavbgZ+6VLbO3YePQhik1lYUuUJUlZjnvL8BKzZ0r0Dutqn87ef3DxWl+EuIPFIwjaJyl+31wLb5fMzppXXTetNypmkdHgcm7i4MTEzbs11nbQTdCtjGGcX+UUVxq8wcNLHJ3oPCuDd69klU5Gv1l1ffM88QAj83f7cSitzl/CYQXvsr0lUqEvcDMLfQo8o/3lq1qXD7PpZjHSIiIrJS69xc/Txq8K5UnVm2rUOxabtdCUhNZ2KW2xGR1apK5pW0eN38pds80YyHlrWjrQNXPDC7MIYix2ru8g2RJFBRlRB9+69vFLxnm5BGhQkREVEFcRtlvpRK1d9xuVn7IZ2JWc57OdNL7WU6HO4HpiYnGzGVmIRRB+fxuSs7m9UH+czO+O1LJW87u4l5H2zEAT96Er986m2023YUbJHs+imNivORGRMiIqIKkr0xlfLLOveXe369D99xoDAOEem8Cd/276V2wVkI2tNz7s29w+WGbN9aX/H3Wcu7vZdbKTtoglVqac7Vj8zD5pY2/PZfC7Fh2w785pmFeDSvz6NoFcaf39kmW5kREQUkImMA3AFgGDJX2qmqen2yUcXPdZT5gHKXnfneess4Mv963dD8DrsRBq+kzusQfZgzjpl7HSL3v01EBOO/+1jh+x7PEeNokffgzOUY1r8HNrd0JWVbWrxbzYXN7di15SWgcQ7dwYSIiCpFG4BvqerrItIXwAwReUpV5yUdWJxqQighCvUm5PHILM4+dB6d417q4Vb6AwBHXfts59+GISMBlL4fuYlPEj1n72jr6HauxPmIys+mWIeIiCggVV2hqq87f28CMB/AqGSjil9tTZASIvdiiDCSlWzJR7c6RFI4PUn+WjkZHpkVS5a6lSS5z9wtIfIRj9f6S5H7WdjmQ60W3Tz4Oc/WbY6vb6b4S4hsygFNy8RY7AogmibmtmWjtZbbM32LrJrkW36DjN0GWMZo2DcxTDN2YRDFL5QoTlv+vOkkIuMAHAgg3EGyyoC4tDIzncKPzP4AE/I6HgzjlHd7ZJZUx4xe/OxnsWb3v/vXotC2VUoe+uLCNVbLKfLqTFl++A++vrz4TPnb9rGp826J7yvMR2ZEVFFEpA+ABwBcpqob86ZNATAFAMaOHZtAdNEL+sjs63fNjCQOt4RoQ04nhmkYdsJPDF7dr2X9dcb7vrY1492PXN/P7Zk5Nx5/Q2F0zX/5/XatAQGgeWlXbF7nzTtrt2D8kN7W23DDfoiIiCIiIvXIJEN3quqD+dNVdaqqNqlq09ChQ+MPMAadze5z3ou79VK3deX8fdU/5hnX3dIWc1P8ACVEbgOvfrCh+LAcWZfdO8v1/VIKzf7z3sK+fYJSBbbldIHg9eTr0dkflLytwm0nnxTnYkJERBVBMj+1bwEwX1V/mXQ8Selq7t713rVPvIXdvlfYwslL0NvUfqP6u8ThPLoLUCnlUze8HHDLpfEb2rJ1W7tVtM56dcm6UOMpJT+wTazyk2WvEqL8McbCYLO/76zdguN+8RzWbm4JPR4mRERUKY4AcD6A40VklvM6LemgkvLGsvWdf7+0aG1Bc2YAuHu6+/AYYbTsqXHuLtt3+K8w9+YHG4vPFCK/JWerN4V/882alfM5paG8JEgCW6pXFn8YeJmbX1yCd9ZuweMeLQdLwYSIiCqCqr6kqqKq+6vqROflv1iEOoXxJCP76O7XT7/dfd1FlvvO/bNjuylf8YDdALJhPunxSgLjaoWXvy+ew5lEEM69zcsCLxNlxXxWqiYiqiBh3KvDWEc2IVqT92ijWDJxb/MyfPW4XUOIoLjl6/3WAYonQSulTs0Hlj2K52/Tq3Tw2ifewpz3N1htIwpRfCLxJ0S2Qx97MY1Ebnty2cYSdwUx2+bztuv0Yj1yu+XxstxvNfUoYGqS32HqHsB7kpFpOdsuADjaPYUk6GXA7ZTNtnbL7+gvDY+FShVVC7kkjk3+NlcaEqvH566MNhgfoiw54yMzIqIK8kQoN63Sb83ZDiILHn852da/FwWvP5IWKWscVZL8fbF5jJWEKD4DJkRERBXk+bftOujLFfRm4zZ/tlXStrxR7cstl4hz6IgkEq049y8M2dLIKJrsMyEiIqJugt4k3foPemreKmddwdedpnv026s2Jx1CpFJ0qH2Jsqo5EyIiIuomaEIyckDPyNadBvkx/yOCTgrzeQ1IG7Zy/DyAaBI5JkRERNSN3+Eoshrr/N9KyvUGnGvu8uj7S8qWsEWvvD6QKDqIzCp6FovIrSKyWkTm5rx3lYgsZ+dnREQUhJ/b77G/eC7qMHzLVg6vVMXGakurKBJrP83u/wTgdwDuyHv/V6r6i9Aisd27uH9u2DaJjnuYZ+vm2YblTF0cpEV7zOeD6VhGcbzK9OJFla0SSn281FV4QpSGQXZtJPLITFVfABDugC1ERFQxgtyc0jagZzHlFW1wMY7UEYps2cKO9o7Qz6VS6hB9XURmO4/UBoYWERERlZUg96Uyy4cAreykqNya3Wdd8/gCXP/MwlDXaZsQ3QhgVwATAawAcJ3XjCIyRUSaRaS5tW2L5eaIiCi9/N9Uy+0RTbnFG9SDry9POoRAFqzY1Pn3fa+F24mkVUKkqqtUtV1VOwD8EcAkw7xTVbVJVZsa6nrbxklERCkVpJCh3Pr1KdMClIo1b0VXC7+wPxqrhEhERuT895MA5nrNS0REle3fi9cmHUJk2joUZ9/0StJhkCPKVn9FW5mJyN0AjgUwRETeB/BDAMeKyERkErSlAC6JLEIiIkq17TvY/JHiUZPTYjvs0ruiCZGqnuvy9i3WW7Rpfm7blDqKbifT1D2AcSR5y2k2TJ+p7Yj2Me+bmjqEt+0ywfYzNx5Pu1USEVWCtZtbOv8Ou34XL69ERERUdsIuZ2BCRERERFWPCRERERGVnVS0MiMiIiJKEh+ZEREREbFSNREREVW7tpAHYvMz2n24vMq4bJsa23Z/YWzWbTlqvWmaqWyvxrCDxuUs4zSt07CcesQpUXRFYDtSvG0sxvPIMk7bz4DKzqLVm1BXU4NxQ9gbP1Fc1m/dEer6WEJEROTDrS+9g9nvr3edduIvX8Cxv3gu1niIKFzxlxAREZWhHz8yDwCw9JrTO9/r6OjeNdwVD8zGmEG9MKBXPc47dGe88PYa3PLSOzFHSkQ2mBARERWhOY842zsUj8z+AN+8Z1bBfPfkjL593qE744Jbp8cRHlHVUlWI7WgCefjIjIioiJa2rkpmM9/7yDUZyndfTnJERNEIs141EyIiqhgicqqIvCUii0TkirDW+9rSdZ1/f9rnyOeXPzA7rM0TkYf2EDMiJkREVBFEpBbA7wF8DMAEAOeKyIQw1n3nq++FsRoiCllbh21T80Lx1yEK6VlfJ9vm2bajsEcxgnkU6wz7OMOyeb1FM/6iqzR9Aaz3O+bzIYp1RvCZl5lJABap6hIAEJF7AJwJYF6pK97e1l7qKogoAve9tgxfOGJ8KOtiCRERVYpRAHIr7rzvvFeylh3h/QolovBc9Y95mLbkw1DWxYSIiKqGiEwRkWYRaV6zZo3v5Q7fdbDr+7sOLeyI8YDR/a3jI6LgPtzSGsp6mBARUaVYDmBMzv9HO+91UtWpqtqkqk1Dhw71veIN2zI94l552t7d3v/C4eMAAJ+fPLbzvWP28L9eIipdz/raUNbDhIiIKsVrAHYXkfEi0gDgHAAPh7Him53OFb90VPe6CtkWLu05T9S+ccLuBcv/9yl7hhEGEbmorWE/REREnVS1DcDXAfwTwHwA96nqm2Gs+8yJI7HHsD4FHcAd5ZQGTd5lUOd79bXdL6tH7jYElxy9SxhhEJGLQ8YNKj6TD/G3MmsP3oLG1LpJTSVlUbS6MQ4Ka5gWRWukCAaoNbX80kb3g906oNE7jFbvIOs3GZ77mlqSaQQt9kytFS3O2ZLYnrdxx5lCqvoYgMfCXu/15xzY+fcN5x2Er975OgBg16F9MP/Hp6JHfQ2mv7MOvRq6f0dm/eAkDOjVEHY4RJSjZ0M4j8w4dAcRUQCn7TcCL19xPFZs2Aag62L800/u1znPS985Duu2tDIZIorYpcfuGtq6mBAREQU0ckBPjBzQ03P66IG9MHpgrxgjIqpO3zl1r9DWxTpEREREVHbm/uiUUNfHhIiIiIjKTp/GcB9yMSEiIiKisvJmyKVDABMiIiIiKjO9Qy4dAnxUqhaRMQDuADAMmREwp6rq9SIyCMC9AMYBWArgM6r6UdEteqVgplbWpibRcQ+aamrObjvckSkttY3Ftul2nXcwW0a5VyLd5zuzPZd5dklhJ3VZO//Ou6lk7Tbvg6mGfbMagBZI16C9tsvx5w0RkTU/l9A2AN9S1QkAJgP4mohMAHAFgGdUdXcAzzj/JyIiF71D6iuFiKJRNCFS1RWq+rrz9yZkeoAdBeBMALc7s90O4BMRxUhEVPZO3md40iEQkUGgQnYRGQfgQADTAAxT1RXOpJXIPFIjIiIXHVH0Vp9y44f0TjoEIt98J0Qi0gfAAwAuU9WNudNUVZGpX+S23BQRaRaR5ta2LSUFS0RUrjqqLx/CUbsPSToEIt98JUQiUo9MMnSnqj7ovL1KREY400cAWO22rKpOVdUmVW1qqOOvBSKqTu2m8fkqVG6Tg12H8vofp5s+f1DSIQAAHvuPo5IOwbeiCZFkhne+BcB8Vf1lzqSHAVzo/H0hgL+HHx4RUWV4bM7KpEOIneS0whw/pE+CkVSfvYb3SzoEAIC6PzxKJT8N+Y8AcD6AOSIyy3nvewCuAXCfiFwM4F0An/G1Ra9myoYm5GIYxds08HnszZBtm8/bMq3TNKK9aZqhi4P2Rvdpfxj9iucyl9Xu8Jz2puznOc3E2LQ+inoatueRqUDAtE7DcqZ9N32ubJJPSauJ4BIIZC511VY9a89hffHWqk2JxrD/6P6Y/f6GRGMIm59WZi+pqqjq/qo60Xk9pqofquoJqrq7qp6oquviCJiIiCirJoofmyn3w/83oeg8UR+W+y45LNoNJIC/G4mIyMrPzvJfyhtV4hJVyVOaCYrvtJ95StGj3l+/WuVUeseEiIiIrHziwFG+562J6G4T9Y0/jRrqqm+f48CEiIiIIicRlRBV4RMzNNYVL51Jy3HJ7X/r/z5lV280a79R/UsNx4gJERERWSlWOpN7U47qkVlabvxxajSMOVnM4N4NIUZSXO4jM7fHZxcdMc73uqJ+PMqEiIiIrARJRqK6mVVjpeqGEhKiuKv0FOuh/Ycf3yemSIrz0+w+XBY1rIyj3ZtYjz5viNH0IDyKjtdMx8typHXj0TR0cdB7Ravr+/v9+queyzSs917fkJbN3nHYHsq4L45xN/Nv955k7I7AsBzFo3/PemzY5t0NRalqJP7esIt92+KoUFtqQvSvbx2D4697vqR19GqoxdbWdozo3wMrNmwvaV1+lFMSqN3+LvGEiHi/WUJERBSDqO9hPX22+klKe0TZWqnHdZehdh1G5j56+vzknQEA+0ZcxwUADttlsK99TkvrLs0JpNSYsqWMvRqiOdeZEBERxaAj4uKbmgTanxerKJ17M4xqcNukykpGDuhZ8F5be/TDs0TVWi8qYX7sx++5EwBgzMBe4a00R5kdWiKi8hT1D/a6JBKiItNzc8AwmscfvcfQgveSSATzXXL0LjhpwjCc3TQm8m0JJLIWe1HIPQdK+Q4s+MmpOGmfYSXHY8KEiIgoBqX8Uv7mCbsXnac2gaKDYvfl3DojQ/s2lrw9dTmIxerTlNJUe5RLKZCbwX0a8ccLmnzPXyo/6VBaxhBz+8xs+O0IshRMiIiIYlDKI6OJYwcUnac2hVfzODpNLFZAZFvf5LH/OAr/ccJugZY5YMwAfPGI8b7m/Q8fSa6bMiocAgCMGpiTJOZ8B07dZzheu/LEBCLylsKvEBFR5SklIfJzD6yLsYQo2w9OkEc3Ow+Opt5HVLWIJozsZ0zovEpgjt2z8LEeAAzr14ixg8I5Bn4Oe1p68B6dU98n94gN7tMQSqlhmOJvdm8jgnpqxlHDO0wnkiEYY5ympvym5SwZm2DbNcmv2+S+3PBpltva4X3ArEe0j6LipunqYxxh3jIWwzrV9GM3+vqcqSUiPwfwcQCtABYDuEhV1ycaVJ4fnLEPvvfQHKtl/SQe+47qh+Xrt1mt36+G2hq0tnfgji9OQq+GzO2jb2MdNrW0uc6fmzRceNg4/Ogf80KPKcoqRDaPnbw+qkG9G/H4N4/CuCseLTGq4p76z6OLxn7YLoPxypIPI48liK8euytueG5xYttnCRERVYKnAOyrqvsDeBvAdxOOp8DnDh2LEf17WC3r557/q89OtFp3ELsM7Q0A6NujHvuNztTN+eulh+G35x7oOn9uKUVUlZ+L5YqlPGKK4rfXsH6NViuYcvQuADLJcbHSn/696o3T5/34FNxx8aRA2w9D7i5fduIeBdMvP3Uv4/LZ/Y6qfhQTIiIqe6r6pKpmiyleBTA6yXi82N5E/dzUsyU2cdtreD98/ICR1svnLxv00VqUnRTafFzFkpWHvnoE/nD+wYHX27ex6/MttsvFjkmvhjrUJ1jp7ILDdi54XDagSBIHdO13VH0slccjMyIi/74I4N6kg3Bj+8s2LfVBolCfV3Jkupm73QhN8++2k12ni6btFVMsWRk5oCdGDuiJucs3BFpvtoTNz5lQIxJLx4wHjh2Ame+t9z1/rbMP+cnYzO+fhPoShiMJS/IREBH5ICJPi8hcl9eZOfNcCaANwJ0e65giIs0i0rxmzZq4Qi9ZubUsyvKTAJbaYaOpLvnT/3VMSetOS9N1ABjWL/O4VaR4UmT7dLLzcZ5PD3zl8EDzn900Gl8+ajwuO7F7C7uBvRvQpzH58pnkIyAi8kFVjW10ReQLAM4AcIJ6dH6iqlMBTAWApqam9NztiijTfMiXIB14uyWGUZaeWZUQhR8G5lx1Ml5bus73+mstM6KgxzJovbDGulpcefqEQMvEiQkREZU9ETkVwOUAjlHVrUnH48W6MKRMMyI/+5tfQmTaVfdHZu7zvvLd45312R+8YuFff85EzFuxsfubHpvLf9vvufD4N49C3x71nfOLjyKihhQ8fipH8SdENk2DTZ+taX2G5YxN603bi6KZtYltWbltnIZvqbS5H+zaza1+o+q+vrSMPliMKc4onmXYHpfqvgb+DkAjgKecJuqvqupXkg2pkH0+lI6MaKd+PbBg5SbfN1w/pT/5p/vPPr0/zr7pFd8xeXVJMKJ/916jD991MF5eHLCZual7FgXOnDgKZ04cFWydAe09ol9BKMXOhwbLCtPl8mg2qjtHdV9CiagiqOpuqjpGVSc6r9QlQ0C0rczicP1nJ+Lnn96/5MrKufJLiA4ZNwgHefTM7Vanx2+z++xmLjlmF9z/lcN8xeb2cf3ta0eYt+czeQ1aPyk7t5+1i4hlC7l0izo+JkRERCmXlhvVwN4NRQcw/e9T9gy0TrdK1UF6wG4xdPCaa/IugwEAx+w+FMN99gfllsBmB9H1SjiiTl5F0pMgVxomRERE1M1Ui35ysnIH4fQzsGegStUuqaHfVmpN4wbi7as/hsN3G9JtOAmTsAYm9aNYqZXfWI7ew33oED+CJKKViAkREVFsLPshivlGdfI+w3HyhGFWy2Z7s/br7IML+9D02lu3x0zFOiHceXAmnn496gNXNjZ9Wl5bzX//zi8d6r7uvJXvO6q/z1iSrVH2/TMm4C6PfSp3TIiIiGKS5jpEg3s3hLKe4/bcCUfsNrjbe7sakqST9xmOBy7t3p+N1/7aHL8ffnwCbrmwqXOokSCMQ3f4WP6G8w5C/57Fe2AOEkuxcyFbkjR2UC+cuHewpNbPeXbxkeNx+G5DAq03bFGV3DEhIiJKCa+kJI4SgfxSqD497Bsh7zW8sGVUoFicPT5454FF5zV1zAhkHuGdEDAxyHILv3gl7swMfRvrcNp+I6y2a1w//JUY1tYIbr6wqeD9Cw8b573uIqs9ZFzxz6NUQ/s2YtL4Qa7Tov5hUDQhEpExIvKsiMwTkTdF5JvO+1eJyHIRmeW8TvO9xaAv2/WZ1Ir3q1yoer/aDa8OeL8sSLt6v9o6PF/G+E0vYzBi9zIxHS/TcY6A6VibF7TcdwqV6VM6acIw/OMbR7pOi+Njyt/GDz++j/W62tozF5Na2+ups1h+BW23r38YD5B6NdS6vm9TEhFFTykF89ptAv99yp74Zl4v0Wnz2pUn4r5L/LUCDJufEqI2AN9S1QkAJgP4mohku5r8VU4z18cii5KIqAKYbrAn7T0MIwf09JgafUaUv4V+JZQQZceqGtIn2FAQ+fIPl2tP1c57Fxy2M+b+6BSr7cz8wUmBlyn1sU3+0sUTu8wSUSbHxWIol67jbBVNiFR1haq+7vy9CcB8ANH2REVEVIGM9xPDvSjITdC2j6Awb7T/edIeuPK0vXG6xyOjLx4xHkBXp4MFsTj/KhT1OaVM7iVEXWzHw2qs8yohctue+UB5VrYu8fh21iGCRJYURZls1dkOsBajQHWIRGQcgAMBTHPe+rqIzBaRW0Uk+oeLREQVynS78Hsree7bx+LBrwYbcLO2cxR1963YJBm9G+vw5aN38RxWY1Dvetxw3kG444uTXKdLV0aEX3/2QOO2omyBZzO4a1SPzPxGEmXH+rYFRH//2hF48TvHlbZxAKMH9sKQPo248vS9S16XG98JkYj0AfAAgMtUdSOAGwHsCmAigBUArvNYrnN06da2LaVHTERUgUw3dgXwhcPHFV3HuCG90a+HXaum/M2LCH7yiX2L9sxs0tm7ct7KRQSn7TcCQ/u6P1LLJmcK4PT9R+CSY3bx3EZn7hTB45zsOg+waKHmd91ZxboPyG1llpu8PvvtY0OLqehDO8uDfMCYAQVDqdjoUV+L5v85EcfvZVdJvhhfCZGI1COTDN2pqg8CgKquUtV2Ve0A8EcArqm+qk5V1SZVbWqoC9Y/BRFRJTH+ei+yrFfyUKpayZYQFTp/8s7YZUh81+0DnSE72p0DlU0SLj5yPI7YbTC+etyuBcucdfBonLLPMHzjhN1K2vbBOw/EXsP7dnsv+3EdusvgwgU82RXD+H2iJNJVctWvRx3G530+plKtoo/7qryRhZ9WZgLgFgDzVfWXOe/nPhz+JIC54YdHRFQ5cn9hnztpDH5wxoTO/5+67/AkQursrNDrZhjFPbLYOrOt1LL1h3bq2wN3fmkyjtp9KM47dGy3eXs31OIP5zdhp77+huPw8sClh+OJy47u9l5XvR3//B6v/MSlWDLiPo5bsA8nikdmU88/GL//3EGlrTgl/JQQHQHgfADH5zWxv1ZE5ojIbADHAfjPKAO1YmoubWrWHXKz9KLS1CTatrm7zfpst2Xb3YDt9kxdNJheEXQBoCKer1SdR+Qq90w7ce9hOG9y1829t0tdnWyJiekUvfas/bHnsL7eMxTR2JkQuU+PotQgv6QiG0O2E8N2ZzyPOpdR2w/ftXungLHUIcrZRNDNhfUoL7dSdVRs1nzyPsNx8j7RPMKKW9Hacqr6EtyPE5vZExFZOnqPocW71er8y3vGzxwyBp85pPuAq70aarG1td1XHNlkpNQb988/vT9Wb2rxNW9+UrHPyH744ccn4MyJmQbMO5x+tdxaJsWZ32ePSbH6Pbly6oOHG0v+BgIa2b8HPnVgkQbiRdbt1Y1CpfzkYk/VRERxyblL1ruUfoTluQAVbbOPzNo6vIvAj95jKG76vPmxyNlNY/C149zr8RS7YYoILjpiPAY5PXVnR6Z3qzcV9c139lUnY5bTL1H2EacAOHPiyG7jtHklkL5LrFyWn/a9EzDKsy8qdMZiXK1HXP/69rHYqV9pjxV/cua+JS2fdvY9bxERUUl83zsDFjfs1K8H+vesx4ZtOwqm7TeqP+Ys39D5/2wfPK1t3gmRV/P4YoY59XrOmdS93s+nDjKXVHz3tL1w4eE7Y5jLDbywNZxVaJ5yW+mdP3kcZi3bgC8dtUtnsrZg5UZf67EJa1i/HhjcpwHL128rmOa3hVcpJX3FYu7d6N5fU6VUxmZCREQUk6D3qrBvNPN+fArqamqwx/883vleY32mhMiUENnq36seS685veD9Ib3NLebqa2s6R6kvFN/Nt3+vetfxwExKjc5r+RP2HoZDxw/Ct0/e02OO0lVKYmOLj8yIiGIS1SjdfrfVq6Gu8xEZAHz/jAm4/pwDcfbBo3HG/iNji62U+25BCVFpoQQ2fkhv7DW8L350ZrCx3krNNfo01uHeSw7DuCG9C0qB7vjiJOzv9JVk05lklgCevYsDhpaI1ltMFyZEREQxuSCvc0W3G8mRuw0peC+qNOriI8dj/JDe+PnZBwS+wdu4/YuTcPKE0lokJX3zbayrxROXHd1ZzylfNmco9pmF8Zlmt3X0HkNxxcf28jVvsXl+f95B+Moxhf09AUCPuspOGdKzd1E0Ga4xvExMTalNbJvr2zYjt90H6/3rCP6ylaZuEcpFFF0cUKguP6X4447c+jpx3vx71LvXDwnTMXsMxdQLmkp6NJP2xzr5zeIH9MrUSdpvVPferv2UFvZqMH8mbqvwrOzt42zq6iG8cCXH7jnUtRsEoHJ69mAdIiKimPi5mYd1c6nUVLfg8KT8bjxmUC88/PUjsOfwYH1Fffrg0bj0WPeSGje5w50EdeN5B+HSO1/v/H/Q30lRJqk///T+2GWo3YDFQaWnhIiIqMr4vZFYFeRVaEaU8vzHNb79Rw/obM2X779P2RMLfnJqwfvnHToWu3okAtl6YBPHDDBut1hcWdkWdGEc28G9G/Czs/YrfUWOs5vG4OCdB4a2PhOWEBERxeiBSw/HnPfXe07PTZLSfvOn0tXXSuDHlb0b6/Dw14/wTJiCyiZEh44fBKC0yv8Hjh2Izx4yFt95YE4oscWJCRERUYwO3nlg5y/eKPOdm84/GBfd9hpa290r1+0zsl+EW49O0q3MwlK0l/Ii2fD+owcE2p5pbcP798Az3zoGYwf1AgB0uMTm9ziXcxLPhIiIKOVsfrEfsdsQ/OmLh+Bzf5xWMG3BT05Frd/h1VMmyrG8wuA3Iah1GrF4DQtiXUpjuVhuaVP+pvcd1Q+Xn2puxVYJmBAREaVUKRVlTeJoURaXtJVI+E3Yvn7cbtje2o7PT945pO1mePVDZCpxyk/Kxg7qPnzII984KnAcpbrry4di9IBeIa3Nn/gTIq8PJebmv2Jotq51ho/UFGeaqqib4rS9gng1a49gv8Wy11w1XeijOCa2563l9sSwPU35L2cqVPQ04EdaqEKOSd8e9fiRYWywoC23is1vmtqY17/QBYeNw+7D+uK8mwtLF4vHEXgRV4fvWtgfV9TSdAsnIiqJiHxLRFRE4r+aplDuuFyVIu35UGjdJlj+2LJZLL9/oZoawREuHYRWOiZERFQRRGQMgJMBvJd0LH5F2uwewL6j+uMP5x8caJn80oK0yT9maatTVHJCZFt6bNHsPluJOkxp+zyCYB0iIqoUvwJwOYC/Jx1IWMK4tZyyz3Df8878/kmdlX3TKt3RJS9I7vzQVw/Hig3bQ91+2up0BcGEiIjKnoicCWC5qr6R9qEd0myg0x9NmhU0u0/Zx51UCUlnpWqP4kS378XgPo0Y3Kcx3DhS9nkEwYSIiMqCiDwNwK2440oA30PmcVmxdUwBMAUAxo4dG2p8USplBPNKU86PZKKUlkQk+/mcO2kMRvTvWWTudGFCRERlQVVPdHtfRPYDMB5AtnRoNIDXRWSSqq7MW8dUAFMBoKmpKfVZxoSR/TDtnXWdPQlTphPBXGnrTinpsejSclL/36f2TzqEwOJPiGxqB5paYJvq/xnOTDWdtKaR5OOub2h6nm/dcZdhuZSMGK81UbTlj+DKaVqn6TibzjHDZ66m7mNS8tnFTVXnANgp+38RWQqgSVXXJhZUSL532t44Y/8R2Gt4Pzwzf3XS4aTCbjv1wdP/dQxueG4RHnx9eepKjNIVTQLK+ACkuzkBEVEVq6+twcE7D0o6jNTZbac+XZ0JlvEN2KRCdyvV+MiMiCqKqo5LOoaghvULt2Jrvue+fSw2t7RFuo24ZQtgbROH/Ub1x5zlG0KLJyubp5UyQCpg8+jL6dU84Wdm5ZzIMSEiIkrQTZ8/KPBAnUGNG9I70vUnwaui+Z1fOhSD+xSvc/W3rx2Bjkiyh3JOCUp33qHhDEWSBCZEREQJOnXfEdbL/uCMCSFGUmacXCZ/HC6/PSzX1ghqI0xeSu3+IejSnSVTJW21dIftOjjhCOwxISIiKkP/96n9cO6k8uk6IGzZG39amptnJfXIbKTTxP3EvXYqMid5YUJERFSGejdW9+U7m3CkLiFKaPnh/Xtg5vdPQv+eyY1fd9KEYYltOwxFv1Ei0gPACwAanfnvV9Ufish4APcAGAxgBoDzVbW16BZtmgbH3RbOsil/JKOp2y5n23WAaVrYzbpN+2ZqXh7Fc39TLB0xt2dPulYkpdKFh4/D4tWbsWZzC15cWPY9CpSso7NSdcoyohINLqHPqSh6Gj/S5yPIOVedjB71pgt3+vlJNVoAHK+qBwCYCOBUEZkM4GcAfqWquwH4CMDFkUVJRFTl+jTW4ZefnYh+CZYApFHqSohKDOiSY3bF4bsOxl7D+4YUkb03fngybv3CIb7m7dujHvW15d2TT9HoNWOz899656UAjgdwv/P+7QA+EUWARETUJWX3/8RUajnqpPGDcNeXJ6NXQ/KPRPv3rEdDXXknOUH42lMRqRWRWQBWA3gKwGIA61U127HF+wBGRRIhERF1qtREIKiuOkTpShHTFQ0F4SshUtV2VZ2IzBhBkwDs5XcDIjJFRJpFpLm1bYtdlERE1E2133g7W5klGkWhlOVnFECgsjBVXQ/gWQCHARggItkyvdEAlnssM1VVm1S1qaGu8joHIyKK0zG7DwUA7D6sT8KRJCutrcyofBVNiERkqIgMcP7uCeAkAPORSYw+7cx2IYC/RxQjEVHV2XdUP9f3z24ajTd+cDL2Gu4+vdqkrZVZ2uIh//zU2hoB4HYRqUUmgbpPVR8RkXkA7hGRqwHMBHBLhHF6M7WINrUAtK0nFkXTetvtpaV5tukzKJf6eLZN6233vVyOCyXixcuP8xx+QkTQvxdbmnWOZZay/CNt8ZB/RRMiVZ0N4ECX95cgU5+IiIhCNGZQr6RDSL3T9huBx+euxIQRLCmjcCTfro+IiCigjx8wEmfsPyJ1rcyofLHgnoiIylIak6EUhkQ+MSEiIiKiqseEiIiIKCTZUquUNHmhAJgQERERhYRPzMpX/JWqay1OF1PzctuUznbUehPTcrbNs20fSEdxXGosjksUx9nEdp0xD2hvxKb8RGWPiVH54SWUiIgoJKxUXb6YEBEREVHVY0JEREQUkuzQHaxUXX6YEBEREYWEj8zKFxMiIiKikDEvKj9MiIiIiEKSTYT4yKz8xN/s3qZZtG3T7XbDNFPz/zSN3h73iPamY2az73HHb/vZmc6HuPfB9hyr4e8bosSxaKhs8QpKREREVY8JERFVBBH5hogsEJE3ReTapOOh6iQsIipb8T8yIyIKmYgcB+BMAAeoaouI7JR0TFSd2MqsfLGEiIgqwaUArlHVFgBQ1dUJx0NEZYYJERFVgj0AHCUi00TkeRE5JOmAqDqxgKh88ZEZEZUFEXkawHCXSVcicy0bBGAygEMA3Cciu6h2byIoIlMATAGAsWPHRhswEZWV+BMiq1HFIxjtPu5R0aMoi7NtYm47or3X9tJUzhjF+RBFNwxRrDPu7gFipqonek0TkUsBPOgkQNNFpAPAEABr8tYxFcBUAGhqaqrsA0aJEFYiKltpupUREdn6G4DjAEBE9gDQAGBtkgFRdWI6VL74yIyIKsGtAG4VkbkAWgFcmP+4jIjIhAkREZU9VW0F8Pmk4yDiE7PyxUdmREREIWHHjOWLCRERERFVPSZEREREYXEKiFiDrfwUrUMkIj0AvACg0Zn/flX9oYj8CcAxADY4s35BVWcV3aJNCmY7ircYzkjD2SqGaWoaFd308DiKZt22Tettee276Vi2G46lKUbTcTYxHWfT9gxxGpnWaZxmuT3T/tnuAxGFhnWIypefStUtAI5X1c0iUg/gJRF53Jn236p6f3ThERERlR8mRuWnaELkNF3d7Py33nnxpygREVGebB7ER2blx9ezKBGpFZFZAFYDeEpVpzmTfiois0XkVyLS6LHsFBFpFpHm1rYt4URNRESUQuypunz5SohUtV1VJwIYDWCSiOwL4LsA9kJm3KBBAL7jsexUVW1S1aaGut7hRE1EREQUokC1lVV1PYBnAZyqqis0owXAbQAmRRAfERFR2WD5UPkqmhCJyFARGeD83RPASQAWiMgI5z0B8AkAc6MLk4iIqHzwyVn58dPKbASA20WkFpkE6j5VfURE/iUiQ5FJiGcB+ErRNalC2myGi/deRutrvRezbWbd0uq9SkMXANK6w3NaR59entO00XsfOhq8P6Labd7bQ4fhOBv2ob13g/dire3u75viaHNfBgC0l2u1MwBARw/v/a5pbfOett17GnZ4xymGz7x9UD/PaR296r23Z6A13udm3YZtntNku+Ec69vDe4O8OhPFipWqy4+fVmazARzo8v7xkUREREREobhnymQsXL25+IzEwV2JiIgq1eRdBmPyLoOTDqMscOgOIiKikPDpdPliQkRERERVjwkRERERVT0mRERERFT14q1UrQrs8GgWbRwx3dDsvqan93JqWM4wmrppe57xA9CPNnivs8G7eXZHH++m7h09vJvk125q8d7edu9pHX29uwBo72k4JTwejtds2Oq9iKnZ/UC7OIzng+Fzrdli6B7A0BWBuVsEQxcGhmmmwe7r13ofM9lm+MwN51h7X+8uDoiIiCVEREREoamvzdxWz5w4MuFIKCg2uyciIgpJfW0N3vjByehtKFmmdGJCREREFKL+lj3YU7L4yIyIiIiqHhMiIiIiqnpMiIiIiKjqxVuHqKbGc4Rz2bLdeznDKPI1hn7S2wf39ZwmLd7r1DrvynC6cp33tG3eo5TXbtriOa1t9ABDLIZm5Ju9m7vDMHo7+ns3d68zNU33IIZ9Q73ds/T2Ht65eu1272lt/bxHfK/p4R1Ly1Dv5RrWex+TuvXezeDrDF0AbB3t/Rlgjfc51rHN+3vSPnaI57TaTYbvFxERsYSIiIiIiAkRERERVT0mRERU9kRkooi8KiKzRKRZRCYlHRMRlRcmRERUCa4F8CNVnQjgB87/iYh8Y0JERJVAAfRz/u4P4IMEYyGiMsSeqomoElwG4J8i8gtkfugdnmw4RFRuRA2jzIe+MZE1AN51/jsEwNrYNm7GWNwxFneVFsvOqjo0jGCiJCJPAxjuMulKACcAeF5VHxCRzwCYoqonuqxjCoApzn/3BPBWgBDS9LlHgftX3rh//nhe72JNiLptWKRZVZsS2XgexuKOsbhjLOkjIhsADFBVFREBsEFV+xVbLuA2KvpYc//KG/evdKxDRESV4AMAxzh/Hw9gYYKxEFEZYh0iIqoEXwZwvYjUAdiOrsdiRES+JJkQTU1w2/kYizvG4o6xpIyqvgTg4Ig3U+nHmvtX3rh/JUqsDhERERFRWrAOEREREVW9RBIiETlVRN4SkUUickUSMeTEslRE5mS7/I9527eKyGoRmZvz3iAReUpEFjr/DkwwlqtEZLlzbGaJyGkxxDFGRJ4VkXki8qaIfNN5P/bjYogliePSQ0Smi8gbTiw/ct4fLyLTnO/SvSLSEHUs1ShN1yxbbtc6r++VZPzG2d/ZInJQstG7C3INNe2TiFzozL9QRC5MYl/cBL0ui8h3nf17S0ROyXk/dedv0Gt9LJ+fqsb6AlALYDGAXQA0AHgDwIS448iJZymAIQlt+2gABwGYm/PetQCucP6+AsDPEozlKgDfjvmYjABwkPN3XwBvA5iQxHExxJLEcREAfZy/6wFMAzAZwH0AznHevwnApXHGVQ2vtF2zStiPgmud1/cKwGkAHnfOu8kApiUdv8c++b6Geu0TgEEAljj/DnT+Hpj0vhn2z/X641yb3gDQCGC8c87WpvX8DXqtj+PzS6KEaBKARaq6RFVbAdwD4MwE4kicqr4AYF3e22cCuN35+3YAn0gwltip6gpVfd35exOA+QBGIYHjYogldpqx2flvvfNSZJqY3++8H9v5UmUq+Zrl9b06E8Adznn3KoABIjIigfiMAl5DvfbpFABPqeo6Vf0IwFMATo08eB8CXpfPBHCPqrao6jsAFiFz7qby/LW41kf++SWREI0CsCzn/+8joZuMQwE8KSIzJNOLbdKGqeoK5++VAIYlGQyArzvFk7fG9fguS0TGATgQmdKQRI9LXixAAsdFRGpFZBaA1ch86RcDWK+qbc4sSX+XKlXarlm23K51Xt+rct7noPtUjvvqdv0p2/3zea2PfP9YqRo4UlUPAvAxAF8TkaOTDihLM+WBSTYDvBHArgAmAlgB4Lq4NiwifQA8AOAyVd2YOy3u4+ISSyLHRVXbNTOa+2hkfvXtFcd2qWIYr3UpuN6ErhL3CQlel6OQpmt9EgnRcgBjcv4/2nkvEaq63Pl3NYCHkLnRJGlVtmja+Xd1UoGo6irnJtwB4I+I6diISD0yX5A7VfVB5+1EjotbLEkdlyxVXQ/gWQCHIVNsnO1PLNHvUgVL1TXLlse1zut7Vc77HHSfympfDdefstu/gNf6yPcviYToNQC7O61jGgCcA+DhBOKAiPQWkb7ZvwGcDGCueanIPQwgW0v+QgB/TyqQvDoDn0QMx0ZEBMAtAOar6i9zJsV+XLxiSei4DBWRAc7fPQGchMwz92cBfNqZLdHzpYKl5pply3Ct8/pePQzgAqdlz2RkxoZbgfIQdJ/+CeBkERnoPH462XkvlQzXn4cBnCMijSIyHsDuAKYjpeevxbU++s8v7Jrjfl7I1BZ/G5k6EFcmEYMTxy7I1Lh/A8CbcccC4G5kijx3IPPc82IAgwE8g8xYTE8DGJRgLH8GMAfAbOdkHBFDHEciU0Q6G8As53VaEsfFEEsSx2V/ADOdbc4F8IOcc3g6MhUo/wqgMc5zuFpeablmlRC/67XO63uFTEue3zv7OwdAU9L74LFfvq+hpn0C8EXnO7QIwEVJ71eR/fO8/gC40tm/twB8LM3nb9BrfRyfH3uqJiIioqrHStVERERU9ZgQERERUdVjQkRERERVjwkRERERVT0mRERERFT1mBARERFR1WNCRERERFWPCRERERFVvf8PS7T8ZOW2zHkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sum over wavelengths and spaxels\n",
    "data = np.nan_to_num(np.array(image))\n",
    "sum_over_waves = np.sum(data, axis=(0))\n",
    "sum_over_spaxels = np.sum(data, axis=(1, 2))\n",
    "\n",
    "# Plots\n",
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5)) \n",
    "ax1.imshow(sum_over_waves)\n",
    "ax1.set_title(\"Sum over wavelength\")\n",
    "ax2.plot(sum_over_spaxels) \n",
    "ax2.set_title(\"Sum over spaxels\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extraction code test with s3d data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extraction parameters based on sum-over-wavelength image \n",
    "ext_center = (16, 20)\n",
    "ext_width = 6\n",
    "bkg_offset = 4\n",
    "bkg_width = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 20)\n",
      "3.0\n",
      "2059\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for -: 'tuple' and 'float'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/f2/dsq1cxxd78n9vltk6_6kwxtr0000zf/T/ipykernel_8545/193368921.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# visualize the weight images used in the fixed boxcar extraction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m wimage_fixedboxcar, wimage_fb_bkg = ap_weight_images(ext_center, ext_width, bkg_offset, \n\u001b[0m\u001b[1;32m      3\u001b[0m                                                      bkg_width, image.shape)\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/var/folders/f2/dsq1cxxd78n9vltk6_6kwxtr0000zf/T/ipykernel_8545/1731129219.py\u001b[0m in \u001b[0;36map_weight_images\u001b[0;34m(center, width, bkg_offset, bkg_width, image_size)\u001b[0m\n\u001b[1;32m     80\u001b[0m \u001b[0;31m#             hwidth = 0.5 * width * (waves[i] / wavescale)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 82\u001b[0;31m         \u001b[0mwimage\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_boxcar_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcenter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimage_size\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m         \u001b[0;31m# bkg regions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/var/folders/f2/dsq1cxxd78n9vltk6_6kwxtr0000zf/T/ipykernel_8545/1731129219.py\u001b[0m in \u001b[0;36mget_boxcar_weights\u001b[0;34m(center, hwidth, npix)\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m     \u001b[0mfullpixels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcenter\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mhwidth\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcenter\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mhwidth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnpix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m     \u001b[0mweights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfullpixels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mfullpixels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'tuple' and 'float'"
     ]
    }
   ],
   "source": [
    "# visualize the weight images used in the fixed boxcar extraction \n",
    "wimage_fixedboxcar, wimage_fb_bkg = ap_weight_images(ext_center, ext_width, bkg_offset, \n",
    "                                                     bkg_width, image.shape)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## About this notebook\n",
    "\n",
    "**Author:** Ivo Busko, JWST\n",
    "**Updated On:** 2021-12-01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Top of Page](#top)\n",
    "<img style=\"float: right;\" src=\"https://raw.githubusercontent.com/spacetelescope/notebooks/master/assets/stsci_pri_combo_mark_horizonal_white_bkgd.png\" alt=\"Space Telescope Logo\" width=\"200px\"/> "
   ]
  }
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