MIRI_Sensitivity_BrightSource_Tables_ETC1.5.1.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",
    "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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = 'data_files/ETC1.5.1/'\n",
    "out_dir = 'plots/ETC1.5.1/'"
   ]
  },
  {
   "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', 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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['wavelengths', 'sns', 'lim_fluxes', 'sat_limits', 'configs']"
      ]
     },
     "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(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": 5,
   "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": 6,
   "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": 7,
   "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": 8,
   "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": 9,
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1322.6171992825266\n",
      "100.25840291089305\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": 12,
   "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 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_1.5.1etc.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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kmurray/anaconda3/lib/python3.6/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_1.5.1etc.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": 14,
   "metadata": {
    "scrolled": false
   },
   "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 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": [
    "## 6. Table Values of Bright Source Limits for Subarrays\n",
    "This prints out values to be used in the imager bright source limit tables in JDox."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For full array BSL:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  4.07642726   3.58294555   7.82852846  33.11207942  13.65892845\n",
      "  17.54864533  31.79574864  32.42580983 100.23833759]\n",
      "4.076427260406746\n"
     ]
    }
   ],
   "source": [
    "print(im['sat_limits'])\n",
    "fullarraybsl_F560W = im['sat_limits'][0]\n",
    "fullarraybsl_2550W = im['sat_limits'][8]\n",
    "print(fullarraybsl_F560W)\n",
    "full = 2.775\n",
    "brightsky = 0.865\n",
    "sub256 = 0.300\n",
    "sub128 = 0.119\n",
    "sub64 = 0.085"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For SUB64 BSL for all wavelengths:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "133.08336056033787 116.97263428372231 255.57842919951773 1081.0120047205887 445.9238405005015 572.9116564910041 1038.0376761705963 1058.607320911106 3272.486903619403\n"
     ]
    }
   ],
   "source": [
    "sub64_bsl_560 = fullarraybsl_F560W * (full/sub64)\n",
    "sub64_bsl_770 = im['sat_limits'][1] * (full/sub64)\n",
    "sub64_bsl_1000 = im['sat_limits'][2] * (full/sub64)\n",
    "sub64_bsl_1130 = im['sat_limits'][3] * (full/sub64)\n",
    "sub64_bsl_1280 = im['sat_limits'][4] * (full/sub64)\n",
    "sub64_bsl_1500 = im['sat_limits'][5] * (full/sub64)\n",
    "sub64_bsl_1800 = im['sat_limits'][6] * (full/sub64)\n",
    "sub64_bsl_2100 = im['sat_limits'][7] * (full/sub64)\n",
    "sub64_bsl_2550 = fullarraybsl_2550W * (full/sub64)\n",
    "print(sub64_bsl_560,sub64_bsl_770,sub64_bsl_1000,sub64_bsl_1130,sub64_bsl_1280,sub64_bsl_1500,sub64_bsl_1800,sub64_bsl_2100,sub64_bsl_2550)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For all subarray BSLs for wavelengths 560 and 2550:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.07755566199852 321.57385758109746 37.7069521587624 927.2046226921643 95.05954325738419 2337.490645442431\n"
     ]
    }
   ],
   "source": [
    "brightsky_bsl_560 = fullarraybsl_F560W * (full/brightsky)\n",
    "brightsky_bsl_2550 = fullarraybsl_2550W * (full/brightsky)\n",
    "sub256_bsl_560 = fullarraybsl_F560W * (full/sub256)\n",
    "sub256_bsl_2550 = fullarraybsl_2550W * (full/sub256)\n",
    "sub128_bsl_560 = fullarraybsl_F560W * (full/sub128)\n",
    "sub128_bsl_2550 = fullarraybsl_2550W * (full/sub128)\n",
    "print(brightsky_bsl_560,brightsky_bsl_2550,sub256_bsl_560,sub256_bsl_2550,sub128_bsl_560,sub128_bsl_2550)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Table Values of Sensitivities for Subarrays\n",
    "This prints out values to be used in the imager sensitivity table in JDox."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.00013127]\n",
      " [0.00024955]\n",
      " [0.00052084]\n",
      " [0.0012224 ]\n",
      " [0.00092605]\n",
      " [0.00145477]\n",
      " [0.00297154]\n",
      " [0.00514189]\n",
      " [0.01736011]]\n"
     ]
    }
   ],
   "source": [
    "print(im['lim_fluxes'])"
   ]
  },
  {
   "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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