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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chopping and nodding in Scopesim\n",
    "\n",
    "This notebook demonstrates how to use the `ChopNodCombiner` effect in Scopesim. Both chopping and nodding are currently defined as two-point patterns, where the throw direction is given as a 2D vector (dx, dy) in `metis[\"chop_nod\"].meta[\"chop_offsets\"]` and `metis[\"chop_nod\"].meta[\"nod_offsets\"]`. For parallel nodding, the two vectors are parallel (typically nod_offset = - chop_offset, giving a three-point pattern), for perpendicular nodding, the vectors are orthogonal.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import scopesim as sim\n",
    "sim.bug_report()\n",
    "\n",
    "# Edit this path if you have a custom install directory, otherwise comment it out.\n",
    "sim.link_irdb(\"../../../../\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you haven't got the instrument packages yet, uncomment the following cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sim.download_packages([\"METIS\", \"ELT\", \"Armazones\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cmd = sim.UserCommands(use_instrument=\"METIS\", set_modes=[\"img_n\"])\n",
    "metis = sim.OpticalTrain(cmd)\n",
    "metis[\"chop_nod\"].include = True\n",
    "metis[\"reference_pixel_mask\"].include = False    # Hack: this would mess up the chop/nod results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default is perpendicular nodding, with the chop throw in the x-direction and the nod throw in the y direction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Chop offsets:\", metis.cmds[metis[\"chop_nod\"].meta[\"chop_offsets\"]])\n",
    "print(\"Nod offsets: \", metis.cmds[metis[\"chop_nod\"].meta[\"nod_offsets\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "src = sim.source.source_templates.star()\n",
    "metis.observe(src)\n",
    "imghdul = metis.readout(exptime=1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(imghdul[1].data, origin=\"lower\", vmin=-3e3, vmax=3e3)\n",
    "plt.colorbar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For parallel nodding, turn the nod throw into the x-direction as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metis[\"chop_nod\"].meta[\"nod_offsets\"] = [-3, 0]\n",
    "imghdu_par = metis.readout(exptime=1)[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(imghdu_par.data, origin=\"lower\", vmin=-3e3, vmax=3e3)\n",
    "plt.colorbar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other four-point patterns are possible:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metis[\"chop_nod\"].meta[\"nod_offsets\"] = [-3, 3]\n",
    "imghdu_3 = metis.readout(exptime=1)[0][1]\n",
    "plt.imshow(imghdu_3.data, origin=\"lower\", vmin=-3e3, vmax=3e3)\n",
    "plt.colorbar();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metis[\"chop_nod\"].meta[\"chop_offsets\"] = [-3, 2]\n",
    "metis[\"chop_nod\"].meta[\"nod_offsets\"] = [2, 3]\n",
    "imghdu_4 = metis.readout(exptime=1)[0][1]\n",
    "plt.imshow(imghdu_4.data, origin=\"lower\", vmin=-3e3, vmax=3e3)\n",
    "plt.colorbar();"
   ]
  }
 ],
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