MIRI_Sensitivity_BrightSource_Tables_ETC1.5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.16.4'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "from miriperformance_tools import load_data\n",
    "np.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Imports**\n",
    "\n",
    "* The statement _%matplotlib inline_ allows plotting in the notebook\n",
    "* _numpy_ for numerical calculations and loading in the datasets\n",
    "* _matplotlib_ for producing plots\n",
    "\n",
    "** Contents of this Notebook**\n",
    "1. Introduction\n",
    "2. Loading and examining the datafiles\n",
    "3. Sensitivity plot\n",
    "4. Bright source limits plot\n",
    "5. MRS Continuum and line sensitivity comparison plot\n",
    "6. Imager and MRS extended source sensitivity plot\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Introduction\n",
    "\n",
    "In this notebook we produce plots of the MIRI sensitivities and saturation limits for JDox ([sensitivity](https://jwst-docs.stsci.edu/display/JTI/MIRI+Sensitivity) and [bright limits](https://jwst-docs.stsci.edu/display/JTI/MIRI+Bright+Source+Limits)) and the instrument pocket guide. The data read in were produced with the Pandeia ETC engine. The data files are in the data_files directory, within a subdirectory that indicates the ETC/Pandeia version used to produce the figures. \n",
    "\n",
    "We first define the data directory, indicating the ETC version used to produce the numbers.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = 'data_files/ETC1.5/'\n",
    "out_dir = 'plots/ETC1.5/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Load in the datafiles\n",
    "\n",
    "We start by loading in the data files and take a look at what's in them.\n",
    "\n",
    "The files contain saturation limits as well as limiting fluxes.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "im_file = data_dir + 'miri_imaging_sensitivity.npz'\n",
    "im = np.load(im_file,encoding='bytes')\n",
    "mrs_file = data_dir + 'miri_mrs_sensitivity.npz'\n",
    "mrs = np.load(mrs_file,encoding='bytes',allow_pickle=True)\n",
    "lrs_file = data_dir + 'miri_lrs_sensitivity.npz'\n",
    "lrs = np.load(lrs_file,encoding='bytes', allow_pickle=True)\n",
    "\n",
    "\n",
    "extim_file = data_dir + 'miri_imaging_sensitivity_extended.npz'\n",
    "extim = np.load(extim_file,encoding='bytes')\n",
    "extmrs_file = data_dir + 'miri_mrs_sensitivity_extended.npz'\n",
    "extmrs = np.load(extmrs_file,encoding='bytes', allow_pickle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examine the contents of these file. These datafiles don't currently hold metadata; for background, the units are as follows:\n",
    "\n",
    "* wavelengths in $\\mu$m\n",
    "* lim_fluxes and sat_limits in mJy\n",
    "* line_limits in W/m$^2$\n",
    "* extended source sensitivities in mJy/arcsec$^2$ and W/m$^2$/arcsec$^2$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['wavelengths', 'sns', 'lim_fluxes', 'sat_limits', 'configs']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imcols = im.keys()\n",
    "lrscols = lrs.keys()\n",
    "mrscols = mrs.keys()\n",
    "#print(imcols, lrscols, mrscols)\n",
    "#list(mrscols)\n",
    "list(imcols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the MRS file, each key has 12 1-D arrays, each covering a sub-channel (4 channels * 3 sub-channels). We can look at how they are mapped with the 'configs' key:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'aperture': 'ch1', 'disperser': 'short'}\n",
      " {'aperture': 'ch1', 'disperser': 'medium'}\n",
      " {'aperture': 'ch1', 'disperser': 'long'}\n",
      " {'aperture': 'ch2', 'disperser': 'short'}\n",
      " {'aperture': 'ch2', 'disperser': 'medium'}\n",
      " {'aperture': 'ch2', 'disperser': 'long'}\n",
      " {'aperture': 'ch3', 'disperser': 'short'}\n",
      " {'aperture': 'ch3', 'disperser': 'medium'}\n",
      " {'aperture': 'ch3', 'disperser': 'long'}\n",
      " {'aperture': 'ch4', 'disperser': 'short'}\n",
      " {'aperture': 'ch4', 'disperser': 'medium'}\n",
      " {'aperture': 'ch4', 'disperser': 'long'}]\n"
     ]
    }
   ],
   "source": [
    "print(mrs['configs'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly for LRS:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'aperture': 'imager', 'mode': 'lrsslitless'}\n",
      " {'aperture': 'lrsslit', 'mode': 'lrsslit'}]\n"
     ]
    }
   ],
   "source": [
    "list(lrs.keys())\n",
    "print(lrs['configs'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up a few lists to better parse the MRS data files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "ishort = [0, 3, 6, 9]\n",
    "imed = [i + 1 for i in ishort]\n",
    "ilong = [i + 2 for i in ishort]\n",
    "mrslabs = ['MRS short', '', '', '', 'MRS medium', '', '', '', 'MRS long', '', '', '']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parsing for each channel\n",
    "ichan1 = [0, 1, 2]                        #S,M,L\n",
    "ichan2 = [i + 3 for i in ichan1]\n",
    "ichan3 = [i + 3 for i in ichan2]\n",
    "ichan4 = [i + 3 for i in ichan3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1 = np.concatenate(mrs['sat_limits'][ichan1])\n",
    "c2 = np.concatenate(mrs['sat_limits'][ichan2])\n",
    "c3 = np.concatenate(mrs['sat_limits'][ichan3])\n",
    "c4 = np.concatenate(mrs['sat_limits'][ichan4])\n",
    "#print(np.amin(c1),np.amin(c2),np.amin(c3),np.amin(c4))\n",
    "#lrsslitless = (lrs['sat_limits'][0]) * (2.775/0.159)\n",
    "#print(lrsslitless)\n",
    "#print(np.amin(lrs['sat_limits'][1]),np.amin(lrsslitless))\n",
    "#print(im['sat_limits'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1324.20531213749\n",
      "100.25930857405734\n"
     ]
    }
   ],
   "source": [
    "#[i for i,x in enumerate(lrs['wavelengths'][0]) if x > 7.5 and x < 7.55]\n",
    "[i for i,x in enumerate(lrs['wavelengths'][1]) if x > 7.5 and x < 7.55]\n",
    "\n",
    "slitless_limit = lrs['sat_limits'][0][52] * (2.775/0.159)\n",
    "\n",
    "print(slitless_limit)\n",
    "print(lrs['sat_limits'][1][52])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Sensitivity plot\n",
    "\n",
    "In this section we'll produce a plot of the sensitivities of 4 MIRI modes: imager, LRS slit, LRS slitless and MRS, plotted together. This corresponds to Fig. 1 on [this JDox page](https://jwst-docs.stsci.edu/display/JTI/MIRI+Sensitivity). The values assume unresolved point sources, and indicate the minimum detectable flux in 10,000 seconds to reach SNR = 10. Spectroscopic values are given for continuum detection, in mJy. \n",
    "\n",
    "We create a few lists to identify the short, medium, long entries in the MRS file, so we can loop over it more easily, label the lines correctly and make the colours consistent. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kendrew/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:18: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sens_fig = plt.figure(figsize=[8,6])\n",
    "pim = plt.semilogy(im['wavelengths'], im['lim_fluxes'], 'ko', label = 'Imager')\n",
    "pslit = plt.semilogy(lrs['wavelengths'][1], lrs['lim_fluxes'][1], color = 'c', label = 'LRS slit')\n",
    "psless = plt.semilogy(lrs['wavelengths'][0], lrs['lim_fluxes'][0], color = 'm', label = 'LRS slitless')\n",
    "\n",
    "for s in ishort:\n",
    "    pshort = plt.semilogy(mrs['wavelengths'][s], mrs['lim_fluxes'][s], color = 'b', label=mrslabs[s])\n",
    "for m in imed:\n",
    "    pmed = plt.semilogy(mrs['wavelengths'][m], mrs['lim_fluxes'][m], color = 'g', label=mrslabs[m])\n",
    "for l in ilong:\n",
    "    plong = plt.semilogy(mrs['wavelengths'][l], mrs['lim_fluxes'][l], color = 'r', label=mrslabs[l])\n",
    "\n",
    "plt.xlabel('Wavelength ($\\mu$m)', fontsize='x-large')\n",
    "plt.ylabel('Flux density (mJy)', fontsize='x-large')\n",
    "plt.title('MIRI Sensitivities (SNR=10 in 10ksec)', fontsize='x-large')\n",
    "plt.legend(loc='upper left', fontsize='large',ncol=2)\n",
    "plt.savefig(out_dir + 'mirisensitivities_1.5etc.png', bbox_inches = 'tight')\n",
    "sens_fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Bright source limits\n",
    "\n",
    "These numbers describe when the MIRI detectors may saturate in its various observing modes. Assumptions:\n",
    "\n",
    "* \"Saturation\" as defined in the ETC: when the electron count in a pixel reaches 70% of its full well capacity. this takes into account our ability correct for non-linearity. \n",
    "* NGroups = 5, which is the recommended minimum number for \"regular\" (i.e. non-TSO, not TA) science exposures.\n",
    "* FAST readout mode and FULL array, i.e. 2.775 seconds per group.\n",
    "* **NOTE**: for LRS slitless the values should therefore be scaled by the ratio of exposure times in FULL vs. SLITLESSPRISM subarray ($\\times \\frac{2.775}{0.159}$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kendrew/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:20: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "blim_fig = plt.figure(figsize=[8,6])\n",
    "plt.semilogy(im['wavelengths'], im['sat_limits'],'ko', label = 'Imager')\n",
    "plt.semilogy(lrs['wavelengths'][1], lrs['sat_limits'][1], color = 'c', label = 'LRS slit')\n",
    "plt.semilogy(lrs['wavelengths'][0], lrs['sat_limits'][0] * (2.775/0.159), color = 'm', label = 'LRS slitless')\n",
    "\n",
    "for s in ishort:\n",
    "    plt.semilogy(mrs['wavelengths'][s], mrs['sat_limits'][s], color = 'b', label = mrslabs[s])\n",
    "for m in imed:\n",
    "    plt.semilogy(mrs['wavelengths'][m], mrs['sat_limits'][m], color = 'g', label = mrslabs[m])\n",
    "for l in ilong:\n",
    "    plt.semilogy(mrs['wavelengths'][l], mrs['sat_limits'][l], color = 'r', label = mrslabs[l])\n",
    "\n",
    "plt.xlabel('Wavelength (µm)', fontsize='x-large')\n",
    "plt.ylabel('Flux Density (mJy)', fontsize='x-large')\n",
    "plt.ylim([10, 10e6])\n",
    "plt.title('MIRI Bright Source Limits (NGroups=5)', fontsize='x-large')\n",
    "plt.legend(loc='upper left', fontsize='large', ncol=2)\n",
    "\n",
    "plt.savefig(out_dir + 'miribrightsourcelimits_1.5etc.png', bbox_inches = 'tight')\n",
    "blim_fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Sensitivities, continuum + line\n",
    "\n",
    "Thsi plots is the same as produced in section 2, but includes plots of limiting line fluxes for LRS and MRS (assuming an unresolved spectral line). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kendrew/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:35: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sensfig2, ax1 = plt.subplots(figsize=[8,6])\n",
    "ax1.semilogy(im['wavelengths'], im['lim_fluxes'], 'ko', label = 'Imager')\n",
    "ax1.semilogy(lrs['wavelengths'][1], lrs['lim_fluxes'][1], linewidth=3, color = 'c', label = 'LRS slit')\n",
    "ax1.semilogy(lrs['wavelengths'][0], lrs['lim_fluxes'][0], linewidth=3, color = 'm', label = 'LRS slitless')\n",
    "\n",
    "\n",
    "for s in ishort:\n",
    "    ax1.semilogy(mrs['wavelengths'][s], mrs['lim_fluxes'][s], linewidth=3, color = 'b', label = mrslabs[s])\n",
    "for m in imed:\n",
    "    ax1.semilogy(mrs['wavelengths'][m], mrs['lim_fluxes'][m], linewidth=3, color = 'g', label = mrslabs[m])\n",
    "for l in ilong:\n",
    "    ax1.semilogy(mrs['wavelengths'][l], mrs['lim_fluxes'][l], linewidth=3, color = 'r', label = mrslabs[l])\n",
    "\n",
    "ax1.set_xlabel('Wavelength (µm)', fontsize='x-large')\n",
    "ax1.set_ylabel('Continuum sensitivity (mJy)', fontsize='x-large')\n",
    "    \n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "linelab_app = [' (line)', '', '', ''] * 3\n",
    "linelabs = [m + l for m, l in zip(mrslabs, linelab_app)]\n",
    "\n",
    "for s in ishort:\n",
    "    ax2.semilogy(mrs['wavelengths'][s], mrs['line_limits'][s], linewidth=3, linestyle='--', color = 'b', label = linelabs[s])\n",
    "for m in imed:\n",
    "    ax2.semilogy(mrs['wavelengths'][m], mrs['line_limits'][m], linewidth=3, linestyle='--', color = 'g', label = linelabs[m])\n",
    "for l in ilong:\n",
    "    ax2.semilogy(mrs['wavelengths'][l], mrs['line_limits'][l], linewidth=3, linestyle='--', color = 'r', label = linelabs[l])\n",
    "  \n",
    "ax2.set_ylabel('Line sensitivity (W/m$^2$)', fontsize='x-large')\n",
    "plt.title('MIRI Sensitivities (SNR=10 in 10ksec)', fontsize='x-large')\n",
    "h1, l1 = ax1.get_legend_handles_labels()\n",
    "h2, l2 = ax2.get_legend_handles_labels()\n",
    "ax1.legend(h1+h2, l1+l2, loc=2, fontsize='large', ncol=2)\n",
    "\n",
    "sensfig2.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CLEANED UP DOWN TO HERE -- SK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (913,) (927,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-50-37009e998b01>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mch1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextmrs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lim_fluxes'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0michan1\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      2\u001b[0m \u001b[0mch2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextmrs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lim_fluxes'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0michan2\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[1;32m      3\u001b[0m \u001b[0mch3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextmrs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lim_fluxes'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0michan3\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[1;32m      4\u001b[0m \u001b[0mch4\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextmrs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lim_fluxes'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0michan4\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[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36mmedian\u001b[0;34m(a, axis, out, overwrite_input, keepdims)\u001b[0m\n\u001b[1;32m   3495\u001b[0m     \"\"\"\n\u001b[1;32m   3496\u001b[0m     r, k = _ureduce(a, func=_median, axis=axis, out=out,\n\u001b[0;32m-> 3497\u001b[0;31m                     overwrite_input=overwrite_input)\n\u001b[0m\u001b[1;32m   3498\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3499\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_ureduce\u001b[0;34m(a, func, **kwargs)\u001b[0m\n\u001b[1;32m   3403\u001b[0m         \u001b[0mkeepdim\u001b[0m \u001b[0;34m=\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[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3404\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3405\u001b[0;31m     \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   3406\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3407\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_median\u001b[0;34m(a, axis, out, overwrite_input)\u001b[0m\n\u001b[1;32m   3528\u001b[0m             \u001b[0mpart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3529\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3530\u001b[0;31m         \u001b[0mpart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpartition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\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   3531\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3532\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mpart\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m(\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[0;32m~/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mpartition\u001b[0;34m(a, kth, axis, kind, order)\u001b[0m\n\u001b[1;32m    732\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    733\u001b[0m         \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"K\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 734\u001b[0;31m     \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\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    735\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    736\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (913,) (927,) "
     ]
    }
   ],
   "source": [
    "ch1 = np.median(extmrs['lim_fluxes'][ichan1])\n",
    "ch2 = np.median(extmrs['lim_fluxes'][ichan2])\n",
    "ch3 = np.median(extmrs['lim_fluxes'][ichan3])\n",
    "ch4 = np.median(extmrs['lim_fluxes'][ichan4])\n",
    "\n",
    "#ch1=np.median(extmrs['lim_fluxes'][1])\n",
    "#ch2=np.median(extmrs['lim_fluxes'][4])\n",
    "#ch3=np.median(extmrs['lim_fluxes'][7])\n",
    "#ch4=np.median(extmrs['lim_fluxes'][10])\n",
    "print(ch1,ch2,ch3,ch4)\n",
    "ch1line=np.median(extmrs['line_limits'][1])\n",
    "ch2line=np.median(extmrs['line_limits'][4])\n",
    "ch3line=np.median(extmrs['line_limits'][7])\n",
    "ch4line=np.median(extmrs['line_limits'][10])\n",
    "print(ch1line,ch2line,ch3line,ch4line)\n",
    "ch1pt=np.median(mrsup['lim_fluxes'][1])\n",
    "ch2pt=np.median(mrsup['lim_fluxes'][4])\n",
    "ch3pt=np.median(mrsup['lim_fluxes'][7])\n",
    "ch4pt=np.median(mrsup['lim_fluxes'][10])\n",
    "print(ch1pt,ch2pt,ch3pt,ch4pt)\n",
    "ch1ptline=np.median(mrsup['line_limits'][1])\n",
    "ch2ptline=np.median(mrsup['line_limits'][4])\n",
    "ch3ptline=np.median(mrsup['line_limits'][7])\n",
    "ch4ptline=np.median(mrsup['line_limits'][10])\n",
    "print(ch1ptline,ch2ptline,ch3ptline,ch4ptline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lrsslit=np.median(lrsup['lim_fluxes'][1])\n",
    "lrsslitless=np.median(lrsup['lim_fluxes'][0])\n",
    "print(lrsslit,lrsslitless)\n",
    "lrsslitline=np.median(lrsup['line_limits'][1])\n",
    "lrsslitlessline=np.median(lrsup['line_limits'][0])\n",
    "print(lrsslitline,lrsslitlessline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(imaup['lim_fluxes'])\n",
    "print(extim['lim_fluxes'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "contfig, cax1 = plt.subplots()\n",
    "cax1.semilogy(imaup['wavelengths'], imaup['lim_fluxes'], 'bo', color = 'black', label = 'Imager for Point')\n",
    "cax1.semilogy(lrsup['wavelengths'][1], lrsup['lim_fluxes'][1], linewidth=3, color = 'c', label = 'LRS Slit for point')\n",
    "cax1.semilogy(lrsup['wavelengths'][0], lrsup['lim_fluxes'][0], linewidth=3, color = 'm', label = 'LRS Slitless for point')\n",
    "cax1.semilogy(mrsup['wavelengths'][0], mrsup['lim_fluxes'][0], linewidth=3, color = 'b', label = 'MRS Short for point')\n",
    "cax1.semilogy(mrsup['wavelengths'][1], mrsup['lim_fluxes'][1], linewidth=3, color = 'g', label = 'MRS Medium for point')\n",
    "cax1.semilogy(mrsup['wavelengths'][2], mrsup['lim_fluxes'][2], linewidth=3, color = 'r', label = 'MRS Long point')\n",
    "cax1.semilogy(mrsup['wavelengths'][3], mrsup['lim_fluxes'][3], linewidth=3, color = 'b')\n",
    "cax1.semilogy(mrsup['wavelengths'][4], mrsup['lim_fluxes'][4], linewidth=3, color = 'g')\n",
    "cax1.semilogy(mrsup['wavelengths'][5], mrsup['lim_fluxes'][5], linewidth=3, color = 'r')\n",
    "cax1.semilogy(mrsup['wavelengths'][6], mrsup['lim_fluxes'][6], linewidth=3, color = 'b')\n",
    "cax1.semilogy(mrsup['wavelengths'][7], mrsup['lim_fluxes'][7], linewidth=3, color = 'g')\n",
    "cax1.semilogy(mrsup['wavelengths'][8], mrsup['lim_fluxes'][8], linewidth=3, color = 'r')\n",
    "cax1.semilogy(mrsup['wavelengths'][9], mrsup['lim_fluxes'][9], linewidth=3, color = 'b')\n",
    "cax1.semilogy(mrsup['wavelengths'][10], mrsup['lim_fluxes'][10], linewidth=3, color = 'g')\n",
    "cax1.semilogy(mrsup['wavelengths'][11], mrsup['lim_fluxes'][11], linewidth=3, color = 'r')\n",
    "cax1.set_xlabel('Wavelength (µm)', fontsize='20')\n",
    "cax1.set_ylabel('Flux density for point sources (mJy)', fontsize='20')\n",
    "cax1.tick_params(axis='both', labelsize='x-large')\n",
    "cax2 = cax1.twinx()\n",
    "cax2.semilogy(extim['wavelengths'], extim['lim_fluxes'], 'bo', color = 'black', label = 'Imager for extended', marker='^')\n",
    "cax2.semilogy(extmrs['wavelengths'][0], extmrs['lim_fluxes'][0], linewidth=3, color = 'b', label = 'MRS Short for extended', linestyle = 'dashed')\n",
    "cax2.semilogy(extmrs['wavelengths'][1], extmrs['lim_fluxes'][1], linewidth=3, color = 'g', label = 'MRS Medium for extended', linestyle = 'dashed')\n",
    "cax2.semilogy(extmrs['wavelengths'][2], extmrs['lim_fluxes'][2], linewidth=3, color = 'r', label = 'MRS Long for extended', linestyle = 'dashed')\n",
    "cax2.semilogy(extmrs['wavelengths'][3], extmrs['lim_fluxes'][3], linewidth=3, color = 'b', linestyle = 'dashed')\n",
    "cax2.semilogy(extmrs['wavelengths'][4], extmrs['lim_fluxes'][4], linewidth=3, color = 'g', linestyle = 'dashed')\n",
    "cax2.semilogy(extmrs['wavelengths'][5], extmrs['lim_fluxes'][5], linewidth=3, color = 'r', linestyle = 'dashed')\n",
    "cax2.semilogy(extmrs['wavelengths'][6], extmrs['lim_fluxes'][6], linewidth=3, color = 'b', linestyle = 'dashed')\n",
    "cax2.semilogy(extmrs['wavelengths'][7], extmrs['lim_fluxes'][7], linewidth=3, color = 'g', linestyle = 'dashed')\n",
    "cax2.semilogy(extmrs['wavelengths'][8], extmrs['lim_fluxes'][8], linewidth=3, color = 'r', linestyle = 'dashed')\n",
    "cax2.semilogy(extmrs['wavelengths'][9], extmrs['lim_fluxes'][9], linewidth=3, color = 'b', linestyle = 'dashed')\n",
    "cax2.semilogy(extmrs['wavelengths'][10], extmrs['lim_fluxes'][10], linewidth=3, color = 'g', linestyle = 'dashed')\n",
    "cax2.semilogy(extmrs['wavelengths'][11], extmrs['lim_fluxes'][11], linewidth=3, color = 'r', linestyle = 'dashed')\n",
    "cax2.set_xlabel('Wavelength (µm)', fontsize='20')\n",
    "cax2.set_ylabel('Flux density for extended sources (mJy/$arcsec^2$)', fontsize='20')\n",
    "cax2.tick_params(axis='both', labelsize='x-large')\n",
    "ch1, cl1 = cax1.get_legend_handles_labels()\n",
    "ch2, cl2 = cax2.get_legend_handles_labels()\n",
    "cax1.legend(ch1+ch2, cl1+cl2, loc=2, fontsize='x-large')\n",
    "plt.title('MIRI Continuum Sensitivities (SNR=10 in 10ksec)', fontsize='22')\n",
    "plt.savefig('mirisensitivities_continuum.png', bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "linefig, lax1 = plt.subplots()\n",
    "lax1.semilogy(lrsup['wavelengths'][1], lrsup['line_limits'][1], linewidth=3, color = 'c', label = 'LRS Slit for point')\n",
    "lax1.semilogy(lrsup['wavelengths'][0], lrsup['line_limits'][0], linewidth=3, color = 'm', label = 'LRS Slitless for point')\n",
    "lax1.semilogy(mrsup['wavelengths'][0], mrsup['line_limits'][0], linewidth=3, color = 'b', label = 'MRS Short for point')\n",
    "lax1.semilogy(mrsup['wavelengths'][1], mrsup['line_limits'][1], linewidth=3, color = 'g', label = 'MRS Medium for point')\n",
    "lax1.semilogy(mrsup['wavelengths'][2], mrsup['line_limits'][2], linewidth=3, color = 'r', label = 'MRS Long Point for point')\n",
    "lax1.semilogy(mrsup['wavelengths'][3], mrsup['line_limits'][3], linewidth=3, color = 'b')\n",
    "lax1.semilogy(mrsup['wavelengths'][4], mrsup['line_limits'][4], linewidth=3, color = 'g')\n",
    "lax1.semilogy(mrsup['wavelengths'][5], mrsup['line_limits'][5], linewidth=3, color = 'r')\n",
    "lax1.semilogy(mrsup['wavelengths'][6], mrsup['line_limits'][6], linewidth=3, color = 'b')\n",
    "lax1.semilogy(mrsup['wavelengths'][7], mrsup['line_limits'][7], linewidth=3, color = 'g')\n",
    "lax1.semilogy(mrsup['wavelengths'][8], mrsup['line_limits'][8], linewidth=3, color = 'r')\n",
    "lax1.semilogy(mrsup['wavelengths'][9], mrsup['line_limits'][9], linewidth=3, color = 'b')\n",
    "lax1.semilogy(mrsup['wavelengths'][10], mrsup['line_limits'][10], linewidth=3, color = 'g')\n",
    "lax1.semilogy(mrsup['wavelengths'][11], mrsup['line_limits'][11], linewidth=3, color = 'r')\n",
    "lax1.set_ylabel('Flux density for Extended Sources (W/$m^2$)', fontsize='20')\n",
    "lax1.tick_params(axis='both', labelsize='x-large')\n",
    "lax2 = lax1.twinx()\n",
    "lax2.semilogy(extmrs['wavelengths'][0], extmrs['line_limits'][0], color = 'b', linewidth=3, linestyle = 'dashed', label = 'MRS Short for extended')\n",
    "lax2.semilogy(extmrs['wavelengths'][1], extmrs['line_limits'][1], color = 'g', linewidth=3, linestyle = 'dashed', label = 'MRS Medium for extended')\n",
    "lax2.semilogy(extmrs['wavelengths'][2], extmrs['line_limits'][2], color = 'r', linewidth=3, linestyle = 'dashed', label = 'MRS Long for extended')\n",
    "lax2.semilogy(extmrs['wavelengths'][3], extmrs['line_limits'][3], color = 'b', linewidth=3, linestyle = 'dashed')\n",
    "lax2.semilogy(extmrs['wavelengths'][4], extmrs['line_limits'][4], color = 'g', linewidth=3, linestyle = 'dashed')\n",
    "lax2.semilogy(extmrs['wavelengths'][5], extmrs['line_limits'][5], color = 'r', linewidth=3, linestyle = 'dashed')\n",
    "lax2.semilogy(extmrs['wavelengths'][6], extmrs['line_limits'][6], color = 'b', linewidth=3, linestyle = 'dashed')\n",
    "lax2.semilogy(extmrs['wavelengths'][7], extmrs['line_limits'][7], color = 'g', linewidth=3, linestyle = 'dashed')\n",
    "lax2.semilogy(extmrs['wavelengths'][8], extmrs['line_limits'][8], color = 'r', linewidth=3, linestyle = 'dashed')\n",
    "lax2.semilogy(extmrs['wavelengths'][9], extmrs['line_limits'][9], color = 'b', linewidth=3, linestyle = 'dashed')\n",
    "lax2.semilogy(extmrs['wavelengths'][10], extmrs['line_limits'][10], color = 'g', linewidth=3, linestyle = 'dashed')\n",
    "lax2.semilogy(extmrs['wavelengths'][11], extmrs['line_limits'][11], color = 'r', linewidth=3, linestyle = 'dashed')\n",
    "#ax2.xlabel('Wavelength (µm)')\n",
    "lax2.set_ylabel('Flux density for Point Sources (W/$m^2$/$arcsec^2$)', fontsize='20')\n",
    "lax2.tick_params(axis='both', labelsize='x-large')\n",
    "lh1, ll1 = lax1.get_legend_handles_labels()\n",
    "lh2, ll2 = lax2.get_legend_handles_labels()\n",
    "lax1.legend(lh1+lh2, ll1+ll2, loc=2, fontsize='x-large')\n",
    "plt.title('MIRI Line Sensitivities (SNR=10 in 10ksec)', fontsize='22')\n",
    "plt.savefig('mirisensitivities_justline.png', bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sensfig2, ax1 = plt.subplots(figsize=[10,8])\n",
    "ax1.semilogy(im['wavelengths'], im['lim_fluxes'], 'ko', label = 'Imager')\n",
    "ax1.semilogy(lrs['wavelengths'][1], lrs['lim_fluxes'][1], linewidth=3, color = 'c', label = 'LRS slit')\n",
    "ax1.semilogy(lrs['wavelengths'][0], lrs['lim_fluxes'][0], linewidth=3, color = 'm', label = 'LRS slitless')\n",
    "\n",
    "\n",
    "for s in ishort:\n",
    "    ax1.semilogy(mrs['wavelengths'][s], mrs['lim_fluxes'][s], linewidth=3, color = 'b', label = mrslabs[s])\n",
    "for m in imed:\n",
    "    ax1.semilogy(mrs['wavelengths'][m], mrs['lim_fluxes'][m], linewidth=3, color = 'g', label = mrslabs[m])\n",
    "for l in ilong:\n",
    "    ax1.semilogy(mrs['wavelengths'][l], mrs['lim_fluxes'][l], linewidth=3, color = 'r', label = mrslabs[l])\n",
    "\n",
    "ax1.set_xlabel('Wavelength (µm)')\n",
    "ax1.set_ylabel('Sensitivity (mJy)')\n",
    "\n",
    "plt.title('MIRI Continuum Sensitivities (SNR=10 in 10ksec)')\n",
    "h1, l1 = ax1.get_legend_handles_labels()\n",
    "ax1.legend(h1, l1, loc=2, fontsize='large', ncol=2)\n",
    "ax1.grid(b=bool)\n",
    "sensfig2.savefig(out_dir + 'mirisensitivities_continuum.png', bbox_inches = 'tight')\n",
    "sensfig2.savefig(out_dir + 'mirisensitivities_continuum.pdf', bbox_inches = 'tight')\n",
    "\n",
    "#sensfig2.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#newList = [x / myInt for x in myList]\n",
    "#print(10**-20)\n",
    "\n",
    "slit_1020limits = lrs['line_limits'][1] / (10**-20)\n",
    "slitless_1020limits = lrs['line_limits'][0] / (10**-20)\n",
    "mrsSH_1020limits = [x / (10**-20) for x in mrs['line_limits'][s]] \n",
    "mrsMED_1020limits = [x / (10**-20) for x in mrs['line_limits'][m]] \n",
    "mrsLG_1020limits = [x / (10**-20) for x in mrs['line_limits'][l]] \n",
    "\n",
    "print(len(mrsSH_1020limits))\n",
    "print(len(mrs['line_limits'][s]))\n",
    "print(len(mrs['wavelengths'][s]))\n",
    "mrs_shwl = [x for x in mrs['wavelengths'][s]]\n",
    "mrs_medwl = [x for x in mrs['wavelengths'][m]]\n",
    "mrs_lgwl = [x for x in mrs['wavelengths'][l]]\n",
    "print(len(mrs_shwl))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sensfig3, ax2 = plt.subplots(figsize=[10,8])\n",
    "linelab_app = [' (line)', '', '', ''] * 3\n",
    "linelabs = [m + l for m, l in zip(mrslabs, linelab_app)]\n",
    "\n",
    "ax2.semilogy(lrs['wavelengths'][1], slit_1020limits, linewidth=3, color = 'c', label = 'LRS slit')\n",
    "ax2.semilogy(lrs['wavelengths'][0], slitless_1020limits, linewidth=3, color = 'm', label = 'LRS slitless')\n",
    "\n",
    "for s in ishort:\n",
    "    ax2.semilogy(mrs['wavelengths'][s], mrs['line_limits'][s] / (10**-20), linewidth=3, color = 'b', label = linelabs[s])\n",
    "for m in imed:\n",
    "    ax2.semilogy(mrs['wavelengths'][m], mrs['line_limits'][m] / (10**-20), linewidth=3, color = 'g', label = linelabs[m])\n",
    "for l in ilong:\n",
    "    ax2.semilogy(mrs['wavelengths'][l], mrs['line_limits'][l] / (10**-20), linewidth=3, color = 'r', label = linelabs[l])\n",
    "  \n",
    "ax2.set_xlabel('Wavelength (µm)')\n",
    "ax2.set_ylabel('Sensitivity $(10^{-20} W/m^2)$')\n",
    "\n",
    "plt.title('MRS Line Sensitivities (SNR=10 in 10ksec)')\n",
    "h2, l2 = ax2.get_legend_handles_labels()\n",
    "ax2.legend(h2, l2, loc=2, fontsize='large', ncol=2)\n",
    "ax2.grid(b=bool)\n",
    "\n",
    "sensfig3.savefig(out_dir + 'mirisensitivities_line.png', bbox_inches = 'tight')\n",
    "sensfig3.savefig(out_dir + 'mirisensitivities_line.pdf', bbox_inches = 'tight')\n",
    "\n",
    "sensfig3.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parsing for each channel\n",
    "ichan1 = [0, 1, 2]                        #S,M,L\n",
    "ichan2 = [i + 3 for i in ichan1]\n",
    "ichan3 = [i + 3 for i in ichan2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combining S M L arrays to a single array\n",
    "c1 = np.concatenate(mrs[‘wavelengths’][ichan1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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