moon_visibility.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "%pylab inline\n",
    "from scipy import stats\n",
    "from angles import r2d, r2arcs, d2arcs, arcs2r\n",
    "def arcm2r(theta):\n",
    "    return arcs2r(theta*60)\n",
    "import seaborn as sns;sns.set_style('darkgrid')\n",
    "import lsst.sims.maf.stackers as stackers\n",
    "import treecorr as tr\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "matplotlib.rcParams['figure.figsize'] = 19,14\n",
    "import lsst.sims.maf.slicers as slicers\n",
    "import lsst.sims.maf.metrics as metrics\n",
    "import lsst.sims.maf.metricBundles as metricBundles\n",
    "import lsst.sims.maf.db as db\n",
    "from collections import defaultdict\n",
    "import treecorr\n",
    "from itertools import chain\n",
    "sns.set_context('poster',font_scale=1.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "moonperfilter = {}\n",
    "for colorfilter in ['u', 'g', 'r', 'i', 'z', 'y']:\n",
    "    outDir='temp'\n",
    "    conn = db.OpsimDatabase('simruns/minion_1012_sqlite.db')\n",
    "    bundleList = []\n",
    "    sql = 'filter = \"'+colorfilter+'\"'\n",
    "    metric = metrics.CountMetric('moonAlt')\n",
    "    slicer = slicers.HealpixSlicer()\n",
    "    bundleList.append(metricBundles.MetricBundle(metric, slicer, sql))\n",
    "    bd = metricBundles.makeBundlesDictFromList(bundleList)\n",
    "    mbg = metricBundles.MetricBundleGroup(bd, conn, outDir=outDir, saveEarly= False)\n",
    "    mbg.runAll()\n",
    "    conn.close()\n",
    "    moonperfilter[colorfilter] = mbg.simData['moonAlt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for colorfilter in moonperfilter.keys():\n",
    "    m = 0\n",
    "    for i in moonperfilter[colorfilter]:\n",
    "        if i<0:\n",
    "            m+=1\n",
    "    plt.hist(moonperfilter[colorfilter], label=colorfilter+' {:.3f}'.format(m/len(moonperfilter[colorfilter])),alpha=0.3)#, histtype='step')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "- query the database for moon_alt, ra, dec\n",
    "- for each ra, dec, if moonAlt>0: dict[(ra,dec)]+=1\n",
    "- plot weighted 2d histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outDir='temp'\n",
    "conn = db.OpsimDatabase('simruns/minion_1012_sqlite.db')\n",
    "bundleList = []\n",
    "sql = 'fieldRA > {} and fieldRA < {} and fieldDec > {} and fieldDec < {} '.format(np.radians(55),\n",
    "                                                                                  np.radians(200),\n",
    "                                                                                  np.radians(-55),\n",
    "                                                                                  np.radians(-5))\n",
    "metric = metrics.PassMetric(('moonAlt', 'filter'))\n",
    "slicer = slicers.HealpixSlicer(latLonDeg=False)\n",
    "dith_stack = stackers.NormAirmassStacker(degrees=False)\n",
    "bundleList.append(metricBundles.MetricBundle(metric, slicer, sql))#, stackerList=[dith_stack]))\n",
    "bd = metricBundles.makeBundlesDictFromList(bundleList)\n",
    "mbg = metricBundles.MetricBundleGroup(bd, conn, outDir=outDir, saveEarly= False)\n",
    "mbg.runAll()\n",
    "conn.close()\n",
    "minion_corrs_per_filter = {}\n",
    "minion_means = {}\n",
    "from collections import namedtuple\n",
    "filters = ['u','g','r','i','z','y']\n",
    "moon_tuple = namedtuple('moon_tuple', ['visible', 'nonvisible'])\n",
    "moon_dict = {k:[moon_tuple(0,0)] for k in filters}\n",
    "filter_data = mbg.simData['filter']\n",
    "moonAlt = np.array([0 if i<0 else 1 for i in mbg.simData['moonAlt']])\n",
    "fieldRA = mbg.simData['fieldRA']\n",
    "fieldDec = mbg.simData['fieldDec']\n",
    "for filt in filters:\n",
    "    plt.figure(figsize=(16,12))\n",
    "    plt.title(filt)\n",
    "    hist = np.histogram2d(fieldRA[filter_data==filt], fieldDec[filter_data==filt], weights=moonAlt[filter_data==filt],\n",
    "                           bins=20)\n",
    "    H, xedges, yedges = hist\n",
    "    H = H.T\n",
    "    meanH = np.mean(H)\n",
    "    H = H - meanH\n",
    "    minion_means[filt] = meanH\n",
    "    X = np.array(list(hist[1])*10)\n",
    "    Y = np.array([[i]*10 for i in hist[2]]).flatten()\n",
    "    Cat = treecorr.Catalog(k=H.flatten(), ra=X, dec=Y, ra_units='radians', dec_units='radians')\n",
    "    min_sep = 0.5\n",
    "    nbins = 20\n",
    "    max_sep = 50\n",
    "    Corr = treecorr.KKCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins, sep_units='degrees')\n",
    "    Corr.process(Cat)\n",
    "    minion_corrs_per_filter[filt] = Corr\n",
    "    plt.imshow(H, interpolation='bilinear', origin='low',\n",
    "         extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]])\n",
    "#    plt.hist2d(fieldRA[filter_data==filt], fieldDec[filter_data==filt], weights=moonAlt[filter_data==filt], bins=20);\n",
    "    plt.colorbar()\n",
    "#    print(maxlim)\n",
    "#    plt.clim(-100,100)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for filt in filters:\n",
    "    corr = minion_corrs_per_filter[filt]\n",
    "    plt.plot(corr.meanr, corr.xi, label=filt)\n",
    "    # plt.xscale('log')\n",
    "    plt.legend()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outDir='temp'\n",
    "conn = db.OpsimDatabase('simruns/alt_sched.db')\n",
    "bundleList = []\n",
    "sql = 'fieldRA > 55 and fieldRA < 200 and fieldDec > -55 and fieldDec < -5 '\n",
    "metric = metrics.PassMetric(('moonAlt', 'filter'))\n",
    "slicer = slicers.HealpixSlicer()#latLonDeg=False)\n",
    "dith_stack = stackers.NormAirmassStacker()#degrees=False)\n",
    "bundleList.append(metricBundles.MetricBundle(metric, slicer, sql))#, stackerList=[dith_stack]))\n",
    "bd = metricBundles.makeBundlesDictFromList(bundleList)\n",
    "mbg = metricBundles.MetricBundleGroup(bd, conn, outDir=outDir, saveEarly= False)\n",
    "mbg.runAll()\n",
    "conn.close()\n",
    "alt_corrs_per_filter = {}\n",
    "alt_means = {}\n",
    "from collections import namedtuple\n",
    "filters = ['u','g','r','i','z','y']\n",
    "moon_tuple = namedtuple('moon_tuple', ['visible', 'nonvisible'])\n",
    "moon_dict = {k:[moon_tuple(0,0)] for k in filters}\n",
    "filter_data = mbg.simData['filter']\n",
    "moonAlt = np.array([0 if i<0 else 1 for i in mbg.simData['moonAlt']])\n",
    "fieldRA = mbg.simData['fieldRA']\n",
    "fieldDec = mbg.simData['fieldDec']\n",
    "for filt in ['u','g','r','i','z','y']:\n",
    "    plt.figure(figsize=(16,12))\n",
    "\n",
    "    hist = np.histogram2d(fieldRA[filter_data==filt], fieldDec[filter_data==filt], weights=moonAlt[filter_data==filt],\n",
    "                           bins=20)\n",
    "    H, xedges, yedges = hist\n",
    "    H = H.T\n",
    "    meanH = np.mean(H)\n",
    "    alt_means[filt] = meanH\n",
    "    H = H - np.mean(H)\n",
    "    X = np.array(list(hist[1])*10)\n",
    "    Y = np.array([[i]*10 for i in hist[2]]).flatten()\n",
    "    Cat = treecorr.Catalog(k=H.flatten(), ra=X, dec=Y, ra_units='radians', dec_units='radians')\n",
    "    min_sep = 0.5\n",
    "    nbins = 20\n",
    "    max_sep = 50\n",
    "    Corr = treecorr.KKCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins, sep_units='degrees')\n",
    "    Corr.process(Cat)\n",
    "    plt.title('filter: '+filt+', mean: {:.4f}'.format(meanH))\n",
    "    alt_corrs_per_filter[filt] = Corr\n",
    "    plt.imshow(H, interpolation='bilinear', origin='low',\n",
    "         extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]])\n",
    "#    plt.hist2d(fieldRA[filter_data==filt], fieldDec[filter_data==filt], weights=moonAlt[filter_data==filt], bins=20);\n",
    "    plt.colorbar()\n",
    "#    print(maxlim)\n",
    "#    plt.clim(-100,100)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outDir='temp'\n",
    "conn = db.OpsimDatabase('simruns/feature_baseline_update_10yrsv2')\n",
    "bundleList = []\n",
    "sql = 'proposalId = 0 and fieldRA > 55 and fieldRA < 200 and fieldDec > -55 and fieldDec < -5 '\n",
    "metric = metrics.PassMetric(('moonAlt', 'filter', 'proposalId'))\n",
    "slicer = slicers.HealpixSlicer()#latLonDeg=False)\n",
    "dith_stack = stackers.NormAirmassStacker()#degrees=False)\n",
    "bundleList.append(metricBundles.MetricBundle(metric, slicer, sql))#, stackerList=[dith_stack]))\n",
    "bd = metricBundles.makeBundlesDictFromList(bundleList)\n",
    "mbg = metricBundles.MetricBundleGroup(bd, conn, outDir=outDir, saveEarly= False)\n",
    "mbg.runAll()\n",
    "conn.close()\n",
    "fb_corrs_per_filter = {}\n",
    "fb_means = {}\n",
    "from collections import namedtuple\n",
    "filters = ['u','g','r','i','z','y']\n",
    "moon_tuple = namedtuple('moon_tuple', ['visible', 'nonvisible'])\n",
    "moon_dict = {k:[moon_tuple(0,0)] for k in filters}\n",
    "filter_data = mbg.simData['filter']\n",
    "moonAlt = np.array([0 if i<0 else 1 for i in mbg.simData['moonAlt']])\n",
    "fieldRA = mbg.simData['fieldRA']\n",
    "fieldDec = mbg.simData['fieldDec']\n",
    "for filt in ['u','g','r','i','z','y']:\n",
    "    plt.figure(figsize=(16,12))\n",
    "\n",
    "    hist = np.histogram2d(fieldRA[np.logical_and(filter_data==filt, mbg.simData['proposalId']==0)], fieldDec[np.logical_and(filter_data==filt, mbg.simData['proposalId']==0)], weights=moonAlt[np.logical_and(filter_data==filt, mbg.simData['proposalId']==0)],\n",
    "                         bins=20)\n",
    "    H, xedges, yedges = hist\n",
    "    H = H.T\n",
    "    meanH = np.mean(H)\n",
    "    fb_means[filt] = meanH\n",
    "    H = H - np.mean(H)\n",
    "    X = np.array(list(hist[1])*10)\n",
    "    Y = np.array([[i]*10 for i in hist[2]]).flatten()\n",
    "    Cat = treecorr.Catalog(k=H.flatten(), ra=X, dec=Y, ra_units='radians', dec_units='radians')\n",
    "    min_sep = 0.5\n",
    "    nbins = 20\n",
    "    max_sep = 50\n",
    "    Corr = treecorr.KKCorrelation(min_sep=min_sep, max_sep=max_sep, nbins=nbins, sep_units='degrees')\n",
    "    Corr.process(Cat)\n",
    "    fb_corrs_per_filter[filt] = Corr\n",
    "    plt.title('filter: '+filt+', mean: {:.4f}'.format(meanH))\n",
    "    plt.imshow(H, interpolation='bilinear', origin='low',\n",
    "         extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]])\n",
    "#    plt.hist2d(fieldRA[filter_data==filt], fieldDec[filter_data==filt], weights=moonAlt[filter_data==filt], bins=20);\n",
    "    plt.colorbar()\n",
    "#    print(maxlim)\n",
    "#    plt.clim(-100,100)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['minion', 'alt_sched', 'fb']\n",
    "labels2 = [minion_means, alt_means, fb_means]\n",
    "for filt in filters:\n",
    "    plt.figure()\n",
    "    plt.title(filt)\n",
    "    for i, corrs in enumerate([minion_corrs_per_filter, alt_corrs_per_filter, fb_corrs_per_filter]):\n",
    "        corr = corrs[filt]\n",
    "        plt.plot(corr.meanr, corr.xi, '--', label=labels[i]+' {:.3f} '.format(labels2[i][filt]))\n",
    "        plt.xscale('log')\n",
    "        plt.legend()\n",
    "        plt.yscale('log')"
   ]
  }
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