differential_color.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.interpolate\n",
    "\n",
    "syseng_throughputs_path = \"../syseng_throughputs/\"\n",
    "sys.path.append(os.path.join(syseng_throughputs_path, \"python\"))\n",
    "\n",
    "import bandpassUtils as bu\n",
    "import sedUtils as su\n",
    "\n",
    "plt.style.use(['ggplot'])\n",
    "plt.rc(\"font\", size=16)\n",
    "plt.rc(\"axes\", labelcolor=\"black\")\n",
    "plt.rc(\"lines\", linewidth=2)\n",
    "plt.rc(\"figure\", figsize=(10, 6.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "defaultDirs = bu.setDefaultDirs(rootDir = syseng_throughputs_path)\n",
    "\n",
    "hardware = {}\n",
    "system = {}\n",
    "atmosphere = bu.readAtmosphere(defaultDirs['atmosphere'], atmosFile='atmos_10_aerosol.dat')\n",
    "atmosphere12 = bu.readAtmosphere(defaultDirs['atmosphere'], atmosFile='pachonModtranAtm_12.dat')\n",
    "\n",
    "# I copied atmos_15_aerosol.dat from the throughputs repo into syseng_throughputs/siteProperties\n",
    "atmosphere15 = bu.readAtmosphere(defaultDirs['atmosphere'], atmosFile='atmos_15_aerosol.dat')\n",
    "hardware['combo'], system['combo'] = bu.buildHardwareAndSystem(defaultDirs, atmosphereOverride=atmosphere)\n",
    "genericDetector = defaultDirs['detector']\n",
    "\n",
    "for det in (1, 2):\n",
    "    defaultDirs['detector'] = 'vendor%d' % det\n",
    "    hardware[det], system[det] = bu.buildHardwareAndSystem(defaultDirs, atmosphereOverride=atmosphere)\n",
    "\n",
    "# Airmass 1.2\n",
    "hardware12 = {}\n",
    "system12 = {}\n",
    "defaultDirs = bu.setDefaultDirs(rootDir = syseng_throughputs_path)\n",
    "hardware12[1], system12[1] = bu.buildHardwareAndSystem(defaultDirs, atmosphereOverride=atmosphere)\n",
    "hardware12[2], system12[2] = bu.buildHardwareAndSystem(defaultDirs, atmosphereOverride=atmosphere15)\n",
    "    \n",
    "# Versions of the throughputs that have perturbed filter bandpasses\n",
    "hardware_filter = {}\n",
    "system_filter = {}\n",
    "for sys in (1, 2):\n",
    "    defaultDirs = bu.setDefaultDirs(rootDir = syseng_throughputs_path)\n",
    "    if sys == 2:\n",
    "        defaultDirs['filters'] = \"shifted_filters\"\n",
    "    hardware_filter[sys], system_filter[sys] = bu.buildHardwareAndSystem(defaultDirs, atmosphereOverride=atmosphere)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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XQgghhBAVgEqtVquN3YiH8cYbb5CYmKh338CBAxkwYACgu8Syt7c3I0aMKPUSy3FxcY/c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\nUxkf2khOTmbhwi0cPJhCbGw7MjMdsLRMxtX1KC1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RiYmJqsf+/v6877tARkZGzFeAPn36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\niRIHYSIiIiISJQ7CRERERCRK/wHLrnWts8caxwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109cda390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate color terms (magnitudes)\n",
    "plt.figure(figsize=(11,8))\n",
    "\n",
    "sedDir = os.path.join(syseng_throughputs_path, 'seds')\n",
    "seds = su.readPhotSeds(sedDir = sedDir)\n",
    "seds, system[det] = su.matchSedsBp(seds, system[det])\n",
    "\n",
    "#\n",
    "# Airmass 1.2\n",
    "#\n",
    "airmass_det1_mags =  su.calcNatMags(system12[1], seds)\n",
    "airmass_det2_mags =  su.calcNatMags(system12[2], seds)\n",
    "airmass_dmags = su.calcDeltaMags(airmass_det2_mags, airmass_det1_mags, mmags=True, matchBlue=False)\n",
    "\n",
    "\n",
    "gi = su.calcGiColors(airmass_det1_mags)\n",
    "#ug = su.calcAnyColor(det1_mags, 'g', 'i')\n",
    "\n",
    "color = gi\n",
    "plotFilter = 'g'\n",
    "\n",
    "\n",
    "#airmass_gi = su.calcGiColors(det1_mags)\n",
    "airmass_stellar_gi = []\n",
    "airmass_stellar_delta_g = []\n",
    "for objtype in ['stars']:\n",
    "    for s in airmass_dmags[objtype]:\n",
    "        airmass_stellar_gi.append(color[objtype][s])\n",
    "        airmass_stellar_delta_g.append(airmass_dmags[objtype][s][plotFilter])\n",
    "plt.plot(airmass_stellar_gi, airmass_stellar_delta_g, 'k-')        \n",
    "plt.plot(airmass_stellar_gi, airmass_stellar_delta_g, 'cx', ms=10, mew=3, label=\"Airmass 1.5\")\n",
    "\n",
    "\n",
    "#\n",
    "# Sensor QE\n",
    "#\n",
    "det1_mags =  su.calcNatMags(system[1], seds)\n",
    "det2_mags =  su.calcNatMags(system[2], seds)\n",
    "dmags = su.calcDeltaMags(det2_mags, det1_mags, mmags=True, matchBlue=False)\n",
    "\n",
    "stellar_gi = []\n",
    "stellar_delta_g = []\n",
    "for objtype in ['stars']:\n",
    "    for s in dmags[objtype]:\n",
    "        stellar_gi.append(color[objtype][s])\n",
    "        stellar_delta_g.append(dmags[objtype][s][plotFilter])\n",
    "\n",
    "stellar_gi = np.array(stellar_gi)\n",
    "stellar_delta_g = np.array(stellar_delta_g)\n",
    "sel, = np.where(stellar_gi < 3.5)\n",
    "fit = np.polyfit(stellar_gi[sel], stellar_delta_g[sel], 2)\n",
    "x = np.linspace(0, 2.5)\n",
    "plt.plot(x, np.polyval(fit, x), 'k-')\n",
    "\n",
    "\n",
    "plt.plot(stellar_gi, stellar_delta_g, '*', ms=19, color='r',\n",
    "         label=\"Sensor QE\")\n",
    "\n",
    "\n",
    "\n",
    "#\n",
    "# Filter shift\n",
    "#\n",
    "filter1_mags =  su.calcNatMags(system_filter[1], seds)\n",
    "filter2_mags =  su.calcNatMags(system_filter[2], seds)\n",
    "filter_dmags = su.calcDeltaMags(filter2_mags, filter1_mags, mmags=True, matchBlue=False)\n",
    "\n",
    "filter_gi = []\n",
    "filter_delta_g = []\n",
    "for objtype in ['stars']:\n",
    "    for s in dmags[objtype]:\n",
    "        filter_gi.append(color[objtype][s])\n",
    "        filter_delta_g.append(filter_dmags[objtype][s][plotFilter])\n",
    "filter_gi = np.array(filter_gi)\n",
    "filter_delta_g = np.array(filter_delta_g)\n",
    "\n",
    "\n",
    "filter_fit = np.polyfit(filter_gi[sel], filter_delta_g[sel], 2)\n",
    "x = np.linspace(0, 2.5)\n",
    "plt.plot(x, np.polyval(filter_fit, x), 'k-')\n",
    "plt.plot(filter_gi, filter_delta_g, 'o', ms=13, color='b', label=\"Filter shift\")\n",
    "\n",
    "# This was easier to label for a linear fit\n",
    "if False:\n",
    "    plt.text(1.5, np.polyval(filter_fit, 1.5),\n",
    "             '{:.2f}(g-i) + {:.2f}'.format(filter_fit[0], filter_fit[1]),\n",
    "            verticalalignment=\"bottom\")\n",
    "\n",
    "plt.ylim(-80, 30)\n",
    "plt.xlim(-0.5, 3)\n",
    "plt.legend(loc=0, numpoints=1)\n",
    "plt.xlabel(\"g - i\")\n",
    "plt.ylabel(\"Delta g (mmag)\")\n",
    "plt.savefig(\"figures/delta_g_vs_color.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Histogram of g-i colors of objects brighter than 20th in SDSS Stripe82\n",
    "# This comes from stripe82_color_histogram.ipynb\n",
    "#\n",
    "gi_bin_centers =  array([-0.94897959, -0.84693878, -0.74489796, -0.64285714, -0.54081633,\n",
    "       -0.43877551, -0.33673469, -0.23469388, -0.13265306, -0.03061224,\n",
    "        0.07142857,  0.17346939,  0.2755102 ,  0.37755102,  0.47959184,\n",
    "        0.58163265,  0.68367347,  0.78571429,  0.8877551 ,  0.98979592,\n",
    "        1.09183673,  1.19387755,  1.29591837,  1.39795918,  1.5       ,\n",
    "        1.60204082,  1.70408163,  1.80612245,  1.90816327,  2.01020408,\n",
    "        2.1122449 ,  2.21428571,  2.31632653,  2.41836735,  2.52040816,\n",
    "        2.62244898,  2.7244898 ,  2.82653061,  2.92857143,  3.03061224,\n",
    "        3.13265306,  3.23469388,  3.33673469,  3.43877551,  3.54081633,\n",
    "        3.64285714,  3.74489796,  3.84693878,  3.94897959])\n",
    "gi_bin_values =  array([  1.00000000e+00,   8.00000000e+00,   6.00000000e+00,\n",
    "         7.00000000e+00,   9.00000000e+00,   9.00000000e+00,\n",
    "         1.30000000e+01,   1.50000000e+01,   3.20000000e+01,\n",
    "         6.70000000e+01,   6.10000000e+01,   7.80000000e+01,\n",
    "         1.56000000e+02,   5.74000000e+02,   2.04200000e+03,\n",
    "         4.42100000e+03,   5.23100000e+03,   4.20300000e+03,\n",
    "         3.38200000e+03,   2.73400000e+03,   2.07900000e+03,\n",
    "         1.78500000e+03,   1.60900000e+03,   1.32100000e+03,\n",
    "         1.07200000e+03,   9.40000000e+02,   8.90000000e+02,\n",
    "         8.91000000e+02,   7.97000000e+02,   7.14000000e+02,\n",
    "         7.38000000e+02,   7.25000000e+02,   5.71000000e+02,\n",
    "         6.26000000e+02,   4.39000000e+02,   3.97000000e+02,\n",
    "         2.59000000e+02,   1.52000000e+02,   1.06000000e+02,\n",
    "         5.20000000e+01,   2.30000000e+01,   1.00000000e+01,\n",
    "         0.00000000e+00,   4.00000000e+00,   2.00000000e+00,\n",
    "         0.00000000e+00,   0.00000000e+00,   0.00000000e+00,\n",
    "         0.00000000e+00])\n",
    "\n",
    "plt.plot(gi_bin_centers, gi_bin_values, '-')\n",
    "plt.xlabel(\"g-i magnitude\")\n",
    "plt.ylabel(\"N stars\")\n",
    "plt.title(\"Color distribution of stars $g < 20$\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "delta_shift = polyval(fit, gi_bin_centers)\n",
    "mean_shift = np.average(delta_shift, weights=gi_bin_values)\n",
    "plt.plot(delta_shift - mean_shift, gi_bin_values, '-', label=\"Sensor QE\")\n",
    "\n",
    "print(\"Sensor QE difference:\")\n",
    "for shift_value in (7,10,15):\n",
    "    sel_gt_shift, = np.where(np.abs(delta_shift - mean_shift) > shift_value)\n",
    "    frac_gt_shift = np.sum(gi_bin_values[sel_gt_shift])/float(np.sum(gi_bin_values))\n",
    "    print(\"{:.2f}% beyond +/- {:.0f} mmag\".format(frac_gt_shift*100, shift_value))\n",
    "    \n",
    "print(\"=\"*10)\n",
    "print(\"\")\n",
    "print(\"Filter variation:\")\n",
    "delta_filter_shift = polyval(filter_fit, gi_bin_centers)\n",
    "mean_filter_shift = np.average(delta_filter_shift, weights=gi_bin_values)\n",
    "plt.plot(delta_filter_shift - mean_filter_shift, gi_bin_values, '-', label=\"Filter Shift\")\n",
    "for shift_value in (7,10,15,20):\n",
    "    sel_gt_shift, = np.where(np.abs(delta_filter_shift - mean_filter_shift) > shift_value)\n",
    "    frac_gt_shift = np.sum(gi_bin_values[sel_gt_shift])/float(np.sum(gi_bin_values))\n",
    "    print(\"{:.2f}% beyond +/- {:.0f} mmag\".format(frac_gt_shift*100, shift_value))\n",
    "    \n",
    "plt.xlim(-45,45)\n",
    "plt.xlabel((\"Delta g magnitude (mmag)\"))\n",
    "plt.ylabel(\"N stars\")\n",
    "plt.legend(loc=0)\n",
    "plt.savefig(\"figures/delta_g_histogram.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "increasing_shift_order = np.argsort(np.abs(delta_shift - mean_shift))\n",
    "decreasing_shift_order = increasing_shift_order[::-1]\n",
    "plt.plot(np.abs(delta_shift[decreasing_shift_order] - mean_shift),\n",
    "         np.cumsum(gi_bin_values[decreasing_shift_order])/np.sum(gi_bin_values), label=\"Sensor QE\")\n",
    "\n",
    "\n",
    "increasing_shift_order = np.argsort(np.abs(delta_filter_shift - mean_filter_shift))\n",
    "decreasing_shift_order = increasing_shift_order[::-1]\n",
    "\n",
    "plt.plot(np.abs(delta_filter_shift[decreasing_shift_order] - mean_filter_shift),\n",
    "         np.cumsum(gi_bin_values[decreasing_shift_order])/np.sum(gi_bin_values), label=\"Filter shift\")\n",
    "\n",
    "\n",
    "plt.xlim(0, 30)\n",
    "plt.xlabel(\"$|\\Delta g|$ (mmag)\")\n",
    "plt.ylabel(\"Fraction with $>\\Delta g$ shift\")\n",
    "plt.legend(loc=0)\n",
    "plt.savefig(\"figures/delta_g_cumulative.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Show what these look like (print m5 limits on throughput curves)\n",
    "plt.figure()\n",
    "\n",
    "filterlist = ('u', 'g', 'r', 'i', 'z', 'y')\n",
    "filterlist = ( 'g')\n",
    "filtercolors = {'u':'b', 'g':'c', 'r':'g',\n",
    "                'i':'y', 'z':'r', 'y':'m'}\n",
    "\n",
    "for f in filterlist:\n",
    "    for det in [1, 2]:\n",
    "        if det == 1:\n",
    "            linestyle = ':'\n",
    "            spacer= ' '\n",
    "        else:\n",
    "            linestyle = '-'\n",
    "            spacer = ''\n",
    "        plt.plot(system[det][f].wavelen, system[det][f].sb, linestyle=linestyle, color=filtercolors[f],\n",
    "                label='%s %s:%s' %(det, f, spacer))\n",
    "#plt.legend(loc=(0.85, 0.5), fancybox=True, numpoints=1, fontsize='small')\n",
    "plt.ylim(0, 0.6)\n",
    "plt.xlabel('Wavelength (nm)')\n",
    "plt.ylabel('Fractional Throughput Response')\n",
    "plt.title('System total response curves')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "plt.subplot(2,1,1)\n",
    "for det in [1, 2]:\n",
    "    for f in filterlist:\n",
    "        if det == 1:\n",
    "            linestyle = ':'\n",
    "        else:\n",
    "            linestyle = '-'\n",
    "        plt.plot(system[det][f].wavelen, system[det][f].phi, linestyle=linestyle,\n",
    "                 color=filtercolors[f],\n",
    "                 label='%s %s' %(det, f))\n",
    "        \n",
    "    \n",
    "    \n",
    "plt.xlim(350, 600)\n",
    "plt.ylim(ymin=0, ymax=0.01)\n",
    "#plt.title('Normalized system response curves ($\\phi$)')\n",
    "plt.ylabel('$\\phi$')\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "\n",
    "for f in filterlist:\n",
    "    plt.plot(system[1][f].wavelen, system[1][f].phi - system[2][f].phi,\n",
    "            color=filtercolors[f])\n",
    "\n",
    "\n",
    "#plt.legend(loc='upper right', fancybox=True, numpoints=1, fontsize='small')\n",
    "plt.xlabel('Wavelength (nm)')\n",
    "plt.ylabel('$\\phi_1 - \\phi_2$')\n",
    "#plt.axhline(1.0, color='k')\n",
    "plt.xlim(350, 600)\n",
    "plt.subplots_adjust(left=0.15)\n",
    "plt.savefig(\"figures/QE_response_curves.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.subplot(2,1,1)\n",
    "for det in [1, 2]:\n",
    "    for f in filterlist:\n",
    "        if det == 1:\n",
    "            linestyle = ':'\n",
    "        else:\n",
    "            linestyle = '-'\n",
    "        plt.plot(system_filter[det][f].wavelen, system_filter[det][f].phi, linestyle=linestyle,\n",
    "                 color=filtercolors[f],\n",
    "                 label='%s %s' %(det, f))\n",
    "plt.xlim(350, 600)\n",
    "plt.ylim(ymin=0, ymax=0.01)\n",
    "#plt.title('Normalized system response curves ($\\phi$)')\n",
    "plt.ylabel('$\\phi$')\n",
    "\n",
    "plt.subplot(2,1,2)\n",
    "\n",
    "\n",
    "for f in filterlist:\n",
    "    plt.plot(system_filter[1][f].wavelen, system_filter[1][f].phi - system_filter[2][f].phi,\n",
    "            color=filtercolors[f])\n",
    "#plt.ylim(0.5, 1.6)\n",
    "plt.xlim(350, 600)\n",
    "plt.xlabel('Wavelength (nm)')\n",
    "plt.ylabel('$\\phi_1 - \\phi_2$')\n",
    "plt.subplots_adjust(left=0.15)\n",
    "plt.savefig(\"figures/filter_response_curves.pdf\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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