create_iris_cube_file.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.io import fits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = fits.open(\"IRIS_GALAXY_sky_subt_quasarsHbb_R4000.00.fits\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris[0].header"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The file header is wrong, the wave axis is actually NAXIS3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget https://stsci.box.com/shared/static/28a88k1qfipo4yxc4p4d40v4axtlal8y.fits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use Manga as an example because we know it loads fine into `jdaviz`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "manga = fits.open(\"28a88k1qfipo4yxc4p4d40v4axtlal8y.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "manga.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "manga[\"FLUX\"].data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris[0].data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from iris_pipeline.datamodels import IRISCubeModel, IRISImageModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cube = IRISCubeModel(data=np.array(iris[0].data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fits.open(\"iris_cube_model.fits\").info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astropy.utils import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cube.meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cube.write(\"iris_cube_model.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = fits.open(\"iris_cube_model.fits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f[1].data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f[1].header[\"CTYPE1\"] = 'DEC--TAN' \n",
    "f[1].header[\"CTYPE2\"] = 'RA---TAN' \n",
    "f[1].header[\"CTYPE3\"] = 'WAVE' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f[1].header[\"CUNIT1\"] = 'deg' \n",
    "f[1].header[\"CUNIT2\"] = 'deg'\n",
    "f[1].header[\"CUNIT3\"] = 'nm' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "header_dict = dict(\n",
    "CRVAL3  =        1474.00000000,\n",
    "CRVAL2  =              0.00000,\n",
    "CRVAL1  =              0.00000,\n",
    "CRPIX3  =                    1,\n",
    "CRPIX2  =                   44,\n",
    "CRPIX1  =                  799,\n",
    "CDELT3  =       0.204132748904,\n",
    "CD1_1   =       0.204132748904,\n",
    "CDELT2  =    1.38888769697E-05,\n",
    "CDELT1  =    1.38888769697E-05,\n",
    "UNITS   = 'phot',\n",
    "FILTER  = 'Hbb',\n",
    "RESOLUTI= '4000.00',\n",
    "ITIME   = '900.000',\n",
    "NFRAMES = '6.00000',\n",
    "DARKCURR= '0.00200000',\n",
    "READNOIS= '5.00000',\n",
    "SCALE   = '0.0500000',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k,v in header_dict.items():\n",
    "    f[1].header[k] = str(v) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.writeto(\"iris_cube_model.fits\", overwrite=True)"
   ]
  }
 ],
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