MIRI_Sensitivity_BrightSource_Tables_ETC1.4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.16.4'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = 'data_files/ETC1.4/'\n",
    "out_dir = 'plots/ETC1.4/'"
   ]
  },
  {
   "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": 3,
   "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')\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')"
   ]
  },
  {
   "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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['wavelengths', 'line_limits', 'sns', 'sat_limits', 'configs', 'lim_fluxes']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imcols = im.keys()\n",
    "lrscols = lrs.keys()\n",
    "mrscols = mrs.keys()\n",
    "#print(imcols, lrscols, mrscols)\n",
    "list(mrs.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up a few lists to better parse the MRS data files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4144.486037748765 4423.97735179997 8401.834219704497 36629.04455396137\n",
      "38.701981521897345 553.3672441764126\n",
      "[ 4.07648423  3.58352861  7.83195458 33.12024045 13.6775177  17.59033505\n",
      " 31.80004521 31.88993834 95.49307069]\n"
     ]
    }
   ],
   "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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1324.2054360250809\n",
      "100.25930683571674\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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kmurray/anaconda3/lib/python3.6/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 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sens_fig = plt.figure(figsize=[10,8])\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)')\n",
    "plt.ylabel('Flux density (mJy)')\n",
    "plt.title('MIRI Sensitivities (SNR=10 in 10ksec)')\n",
    "plt.legend(loc='best', fontsize='x-large')\n",
    "plt.savefig(out_dir + 'mirisensitivities_1.4etc.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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kmurray/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:19: 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 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "blim_fig = plt.figure(figsize=[10,8])\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)')\n",
    "plt.ylabel('Flux Density (mJy)')\n",
    "plt.title('MIRI Bright Source Limits (NGroups=5)')\n",
    "plt.legend(loc='best', fontsize='x-large')\n",
    "\n",
    "plt.savefig(out_dir + 'miribrightsourcelimits_1.4etc.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": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kmurray/anaconda3/lib/python3.6/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 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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('Continuum sensitivity (mJy)')\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$^2$)')\n",
    "plt.title('MIRI Sensitivities (SNR=10 in 10ksec)')\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": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Object arrays cannot be loaded when allow_pickle=False",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-7687f3dd750a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\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      2\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msemilogy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextim\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'wavelengths'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextim\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[0;34m'bo'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'black'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Imager'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msemilogy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextmrs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'wavelengths'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\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[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'b'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'MRS Short'\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      4\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msemilogy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextmrs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'wavelengths'\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[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[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'g'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'MRS Medium'\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[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msemilogy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextmrs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'wavelengths'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\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[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'MRS Long'\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/python3.6/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    260\u001b[0m                 return format.read_array(bytes,\n\u001b[1;32m    261\u001b[0m                                          \u001b[0mallow_pickle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mallow_pickle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m                                          pickle_kwargs=self.pickle_kwargs)\n\u001b[0m\u001b[1;32m    263\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    264\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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/python3.6/site-packages/numpy/lib/format.py\u001b[0m in \u001b[0;36mread_array\u001b[0;34m(fp, allow_pickle, pickle_kwargs)\u001b[0m\n\u001b[1;32m    694\u001b[0m         \u001b[0;31m# The array contained Python objects. We need to unpickle the data.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    695\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mallow_pickle\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 696\u001b[0;31m             raise ValueError(\"Object arrays cannot be loaded when \"\n\u001b[0m\u001b[1;32m    697\u001b[0m                              \"allow_pickle=False\")\n\u001b[1;32m    698\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpickle_kwargs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Object arrays cannot be loaded when allow_pickle=False"
     ]
    },
    {
     "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": [
    "fig, ax1 = plt.subplots()\n",
    "ax1.semilogy(extim['wavelengths'], extim['lim_fluxes'], 'bo', color = 'black', label = 'Imager')\n",
    "ax1.semilogy(extmrs['wavelengths'][0], extmrs['lim_fluxes'][0], linewidth=3, color = 'b', label = 'MRS Short')\n",
    "ax1.semilogy(extmrs['wavelengths'][1], extmrs['lim_fluxes'][1], linewidth=3, color = 'g', label = 'MRS Medium')\n",
    "ax1.semilogy(extmrs['wavelengths'][2], extmrs['lim_fluxes'][2], linewidth=3, color = 'r', label = 'MRS Long')\n",
    "ax1.semilogy(extmrs['wavelengths'][3], extmrs['lim_fluxes'][3], linewidth=3, color = 'b')\n",
    "ax1.semilogy(extmrs['wavelengths'][4], extmrs['lim_fluxes'][4], linewidth=3, color = 'g')\n",
    "ax1.semilogy(extmrs['wavelengths'][5], extmrs['lim_fluxes'][5], linewidth=3, color = 'r')\n",
    "ax1.semilogy(extmrs['wavelengths'][6], extmrs['lim_fluxes'][6], linewidth=3, color = 'b')\n",
    "ax1.semilogy(extmrs['wavelengths'][7], extmrs['lim_fluxes'][7], linewidth=3, color = 'g')\n",
    "ax1.semilogy(extmrs['wavelengths'][8], extmrs['lim_fluxes'][8], linewidth=3, color = 'r')\n",
    "ax1.semilogy(extmrs['wavelengths'][9], extmrs['lim_fluxes'][9], linewidth=3, color = 'b')\n",
    "ax1.semilogy(extmrs['wavelengths'][10], extmrs['lim_fluxes'][10], linewidth=3, color = 'g')\n",
    "ax1.semilogy(extmrs['wavelengths'][11], extmrs['lim_fluxes'][11], linewidth=3, color = 'r')\n",
    "ax1.set_xlabel('Wavelength (µm)', fontsize='20')\n",
    "ax1.set_ylabel('Flux density (mJy/$arcsec^2$)', fontsize='20')\n",
    "ax1.tick_params(axis='both', labelsize='x-large')\n",
    "ax2 = ax1.twinx()\n",
    "ax2.semilogy(extmrs['wavelengths'][0], extmrs['line_limits'][0], linewidth=3, color = 'b', linestyle = 'dashed', label = 'MRS Short Line Limit')\n",
    "ax2.semilogy(extmrs['wavelengths'][1], extmrs['line_limits'][1], linewidth=3, color = 'g', linestyle = 'dashed', label = 'MRS Medium Line Limit')\n",
    "ax2.semilogy(extmrs['wavelengths'][2], extmrs['line_limits'][2], linewidth=3, color = 'r', linestyle = 'dashed', label = 'MRS Long Line Limit')\n",
    "ax2.semilogy(extmrs['wavelengths'][3], extmrs['line_limits'][3], linewidth=3, color = 'b', linestyle = 'dashed')\n",
    "ax2.semilogy(extmrs['wavelengths'][4], extmrs['line_limits'][4], linewidth=3, color = 'g', linestyle = 'dashed')\n",
    "ax2.semilogy(extmrs['wavelengths'][5], extmrs['line_limits'][5], linewidth=3, color = 'r', linestyle = 'dashed')\n",
    "ax2.semilogy(extmrs['wavelengths'][6], extmrs['line_limits'][6], linewidth=3, color = 'b', linestyle = 'dashed')\n",
    "ax2.semilogy(extmrs['wavelengths'][7], extmrs['line_limits'][7], linewidth=3, color = 'g', linestyle = 'dashed')\n",
    "ax2.semilogy(extmrs['wavelengths'][8], extmrs['line_limits'][8], linewidth=3, color = 'r', linestyle = 'dashed')\n",
    "ax2.semilogy(extmrs['wavelengths'][9], extmrs['line_limits'][9], linewidth=3, color = 'b', linestyle = 'dashed')\n",
    "ax2.semilogy(extmrs['wavelengths'][10], extmrs['line_limits'][10], linewidth=3, color = 'g', linestyle = 'dashed')\n",
    "ax2.semilogy(extmrs['wavelengths'][11], extmrs['line_limits'][11], linewidth=3, color = 'r', linestyle = 'dashed')\n",
    "#ax2.xlabel('Wavelength (µm)')\n",
    "ax2.set_ylabel('Flux density for Line Limits (W/$m^2$/$arcsec^2$)', fontsize='20')\n",
    "ax2.tick_params(axis='both', labelsize='x-large')\n",
    "plt.title('MIRI Sensitivities for Extended Sources (SNR=10 in 10ksec)', fontsize='22')\n",
    "#ax1.legend(loc='best', fontsize='x-large')\n",
    "#ax2.legend(loc='best', fontsize='x-large')\n",
    "#plt.rcParams[\"figure.figsize\"] = (15,12)\n",
    "#plt.savefig('mirisensitivities_extendedsources.png', bbox_inches = 'tight')\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='x-large')\n",
    "plt.savefig('mirisensitivities_extendedandline.png', bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "newfig, nax1 = plt.subplots()\n",
    "nax1.semilogy(imaup['wavelengths'], imaup['lim_fluxes'], 'bo', color = 'black', label = 'Imager')\n",
    "nax1.semilogy(lrsup['wavelengths'][1], lrsup['lim_fluxes'][1], linewidth=3, color = 'c', label = 'LRS slit')\n",
    "nax1.semilogy(lrsup['wavelengths'][0], lrsup['lim_fluxes'][0], linewidth=3, color = 'm', label = 'LRS slitless')\n",
    "nax1.semilogy(mrsup['wavelengths'][0], mrsup['lim_fluxes'][0], linewidth=3, color = 'b', label = 'MRS Short')\n",
    "nax1.semilogy(mrsup['wavelengths'][1], mrsup['lim_fluxes'][1], linewidth=3, color = 'g', label = 'MRS Medium')\n",
    "nax1.semilogy(mrsup['wavelengths'][2], mrsup['lim_fluxes'][2], linewidth=3, color = 'r', label = 'MRS Long')\n",
    "nax1.semilogy(mrsup['wavelengths'][3], mrsup['lim_fluxes'][3], linewidth=3, color = 'b')\n",
    "nax1.semilogy(mrsup['wavelengths'][4], mrsup['lim_fluxes'][4], linewidth=3, color = 'g')\n",
    "nax1.semilogy(mrsup['wavelengths'][5], mrsup['lim_fluxes'][5], linewidth=3, color = 'r')\n",
    "nax1.semilogy(mrsup['wavelengths'][6], mrsup['lim_fluxes'][6], linewidth=3, color = 'b')\n",
    "nax1.semilogy(mrsup['wavelengths'][7], mrsup['lim_fluxes'][7], linewidth=3, color = 'g')\n",
    "nax1.semilogy(mrsup['wavelengths'][8], mrsup['lim_fluxes'][8], linewidth=3, color = 'r')\n",
    "nax1.semilogy(mrsup['wavelengths'][9], mrsup['lim_fluxes'][9], linewidth=3, color = 'b')\n",
    "nax1.semilogy(mrsup['wavelengths'][10], mrsup['lim_fluxes'][10], linewidth=3, color = 'g')\n",
    "nax1.semilogy(mrsup['wavelengths'][11], mrsup['lim_fluxes'][11], linewidth=3, color = 'r')\n",
    "nax1.set_xlabel('Wavelength (µm)', fontsize='20')\n",
    "nax1.set_ylabel('Flux density for continuum (mJy)', fontsize='20')\n",
    "nax1.tick_params(axis='both', labelsize='x-large')\n",
    "nax2 = nax1.twinx()\n",
    "nax2.semilogy(lrsup['wavelengths'][1], lrsup['line_limits'][1], color = 'c', linewidth=3, label = 'LRS slit Line Limit', linestyle = 'dashed')\n",
    "nax2.semilogy(lrsup['wavelengths'][0], lrsup['line_limits'][0], color = 'm', linewidth=3, label = 'LRS slitless Line Limit', linestyle = 'dashed')\n",
    "nax2.semilogy(mrsup['wavelengths'][0], mrsup['line_limits'][0], color = 'b', linewidth=3, label = 'MRS Short Line Limit', linestyle = 'dashed')\n",
    "nax2.semilogy(mrsup['wavelengths'][1], mrsup['line_limits'][1], color = 'g', linewidth=3, label = 'MRS Medium Line Limit', linestyle = 'dashed')\n",
    "nax2.semilogy(mrsup['wavelengths'][2], mrsup['line_limits'][2], color = 'r', linewidth=3, label = 'MRS Long Line Limit', linestyle = 'dashed')\n",
    "nax2.semilogy(mrsup['wavelengths'][3], mrsup['line_limits'][3], color = 'b', linewidth=3, linestyle = 'dashed')\n",
    "nax2.semilogy(mrsup['wavelengths'][4], mrsup['line_limits'][4], color = 'g', linewidth=3, linestyle = 'dashed')\n",
    "nax2.semilogy(mrsup['wavelengths'][5], mrsup['line_limits'][5], color = 'r', linewidth=3, linestyle = 'dashed')\n",
    "nax2.semilogy(mrsup['wavelengths'][6], mrsup['line_limits'][6], color = 'b', linewidth=3, linestyle = 'dashed')\n",
    "nax2.semilogy(mrsup['wavelengths'][7], mrsup['line_limits'][7], color = 'g', linewidth=3, linestyle = 'dashed')\n",
    "nax2.semilogy(mrsup['wavelengths'][8], mrsup['line_limits'][8], color = 'r', linewidth=3, linestyle = 'dashed')\n",
    "nax2.semilogy(mrsup['wavelengths'][9], mrsup['line_limits'][9], color = 'b', linewidth=3, linestyle = 'dashed')\n",
    "nax2.semilogy(mrsup['wavelengths'][10], mrsup['line_limits'][10], color = 'g', linewidth=3, linestyle = 'dashed')\n",
    "nax2.semilogy(mrsup['wavelengths'][11], mrsup['line_limits'][11], color = 'r', linewidth=3, linestyle = 'dashed')\n",
    "nax2.set_ylabel('Flux density for Line Limits (W/$m^2$)', fontsize='20')\n",
    "nax2.tick_params(axis='both', labelsize='x-large')\n",
    "plt.title('MIRI Sensitivities for Point Sources (SNR=10 in 10ksec)', fontsize='22')\n",
    "nh1, nl1 = nax1.get_legend_handles_labels()\n",
    "nh2, nl2 = nax2.get_legend_handles_labels()\n",
    "nax1.legend(nh1+nh2, nl1+nl2, loc=2, fontsize='x-large')\n",
    "#plt.rcParams[\"figure.figsize\"] = (15,12)\n",
    "plt.tick_params(axis='both', which='minor', labelsize=14)\n",
    "plt.savefig('mirisensitivities_updatedwithline.png', bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "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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