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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chopping and nodding in Scopesim\n",
    "\n",
    "This notebook demonstrates how to use the `ChopNod` 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": [
    "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.rc.__config__[\"!SIM.file.local_packages_path\"] = \"../../../../\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "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'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metis = sim.OpticalTrain(cmd)\n",
    "metis['chop_nod'].include = True"
   ]
  },
  {
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metis.observe(src, update=True)\n",
    "imghdu = metis.readout()[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(imghdu.data, origin='lower', vmin=-5e7, vmax=5e7)\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]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imghdu_par = metis.readout()[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(imghdu_par.data, origin='lower', vmin=-5e7, vmax=5e7)"
   ]
  },
  {
   "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()[0][1]\n",
    "plt.imshow(imghdu_3.data, origin='lower', vmin=-5e7, vmax=5e7)"
   ]
  },
  {
   "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()[0][1]\n",
    "plt.imshow(imghdu_4.data, origin='lower', vmin=-5e7, vmax=5e7)"
   ]
  }
 ],
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