docs/notebooks/starting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8011449c-2569-40c6-8c55-70b7819d9a81",
   "metadata": {},
   "source": [
    "# Getting Started\n",
    "\n",
    "`scopesim.source.Source` objects are composed of a spatial description and a spectral one. Spatial description can be `astropy.table.Table` objects for point sources or an `astropy.fits.ImageHDU` for extended sources. Spectral description is provided as `synphot.SourceSpectrum` or compatible objects. Spectral datacubes can also be accepted\n",
    "\n",
    "## Creation of a `Source`\n",
    "\n",
    "The creation of `scopesim.source.Source` objects might require quite a bit of interaction from the user. For example\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "da8bfd8e-3d61-4e5f-8cfe-8def9f124c0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.io import fits\n",
    "import synphot\n",
    "from scopesim import Source\n",
    "\n",
    "# creation of a image with a central source defined by a 2D gaussian\n",
    "x, y = np.meshgrid(np.arange(100), np.arange(100))\n",
    "img = np.exp(-1 * ( ( (x - 50) / 5)**2 + ( (y - 50) / 5)**2 ) )\n",
    "\n",
    "# Fits headers of the image. Yes it needs a WCS\n",
    "hdr = fits.Header(dict(NAXIS=2,\n",
    "                       NAXIS1=img.shape[0]+1,\n",
    "                       NAXIS2=img.shape[1]+1,\n",
    "                       CRPIX1=img.shape[0] / 2,\n",
    "                       CRPIX2=img.shape[1] / 2,\n",
    "                       CRVAL1=0,\n",
    "                       CRVAL2=0,\n",
    "                       CDELT1=0.2/3600,\n",
    "                       CDELT2=0.2/3600,\n",
    "                       CUNIT1=\"DEG\",\n",
    "                       CUNIT2=\"DEG\",\n",
    "                       CTYPE1='RA---TAN',\n",
    "                       CTYPE2='DEC--TAN'))\n",
    "\n",
    "# Creating an ImageHDU object\n",
    "hdu = fits.ImageHDU(data=img, header=hdr)\n",
    "\n",
    "# Creating of a black body spectrum\n",
    "wave = np.arange(1000, 35000, 10 )\n",
    "bb = synphot.models.BlackBody1D(temperature=5000)\n",
    "sp = synphot.SourceSpectrum(synphot.Empirical1D, points=wave, lookup_table=bb(wave))\n",
    "\n",
    "# Source creation\n",
    "src1 = Source(image_hdu=hdu, spectra=sp)\n",
    "\n",
    "plt.imshow(src1.fields[0].data)\n",
    "src1.spectra[0].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48248245-3917-4197-aa30-1f36105185f4",
   "metadata": {},
   "source": [
    "The attributes `.fields` and `.spectra` contain the spatial and spectral description of the sources respectively. Datacubes are stored in the `cube` attribute\n",
    "\n",
    "These attributes are actually lists of objects which allow to store several sources to be used in one simulation. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a98baecb-c516-4acc-8a20-57a07708605c",
   "metadata": {},
   "source": [
    "## Combining sources\n",
    "\n",
    "For example, let's create now a simple point source and combine it with the previous one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4f8158c5-997f-4415-8060-39499544b185",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]: ImageHDU with size (100, 100), referencing spectrum 0\n",
      "[1]: Table with 1 rows, referencing spectra {1} \n",
      "\n"
     ]
    }
   ],
   "source": [
    "lam = np.arange(1000, 10000, 1)\n",
    "flux = np.ones(lam.shape)\n",
    "\n",
    "src2 = Source(x=[0], y=[0], lam=lam, spectra=flux, weight=[1], ref=[0])\n",
    "\n",
    "src = src1 + src2\n",
    "\n",
    "# printing information about the combined source\n",
    "print(src)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5388e0b-14d4-4744-a088-30ccc697353c",
   "metadata": {},
   "source": [
    "More details can be found in the respective fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "341962dc-b48b-4d9a-8878-a7132f1e11e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<synphot.spectrum.SourceSpectrum object at 0x7f93b056dd60>, <synphot.spectrum.SourceSpectrum object at 0x7f93d03a1430>]\n",
      "[<astropy.io.fits.hdu.image.ImageHDU object at 0x7f93b0676b80>, <Table length=1>\n",
      "   x       y     ref  weight\n",
      " arcsec  arcsec             \n",
      "float64 float64 int64 int64 \n",
      "------- ------- ----- ------\n",
      "    0.0     0.0     1      1]\n"
     ]
    }
   ],
   "source": [
    "print(src.spectra)\n",
    "\n",
    "print(src.fields)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43ac2fa9-754a-4c4b-85ce-8c95003d8f2b",
   "metadata": {},
   "source": [
    "## ScopeSim_Templates\n",
    "\n",
    "The idea of `ScopeSim_Templates` is exactly to aid the creation of standard sources to used in the simulator `ScopeSim`.\n",
    "\n",
    "Currently the package contain sources to work with stellar and extragalactic objects, as well as general function for other purposes. \n",
    "\n",
    "The following example combines galaxy and a central source simulating an AGN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d72098ac-2e1a-444e-9966-4c17a833e15f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]: ImageHDU with size (150, 150), referencing spectrum 0\n",
      "[1]: Table with 1 rows, referencing spectra {1} \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from scopesim_templates.extragalactic import galaxy\n",
    "from scopesim_templates.misc import point_source\n",
    "\n",
    "gal = galaxy(sed=\"kc96/s0\", amplitude=15, filter_curve=\"g\")  # This will create a galaxy with an S0 SED from the Kinney-Calzetti library (see speXtra)\n",
    "agn = point_source(sed=\"agn/qso\", amplitude=13, filter_curve=\"g\") # and this an AGN\n",
    "\n",
    "source = gal + agn\n",
    "\n",
    "print(repr(source))"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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