area_vs_depth.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "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",
    "import lsst.sims.maf.maps as maps\n",
    "from collections import defaultdict\n",
    "import treecorr\n",
    "from itertools import chain\n",
    "sns.set_context('poster',font_scale=1.4)\n",
    "import healpy as hp\n",
    "import pickle\n",
    "import plotly\n",
    "import plotly.plotly as py\n",
    "import plotly.figure_factory as ff\n",
    "import plotly.graph_objs as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getDepthAndArea(runName, year):\n",
    "    directory = '/global/cscratch1/sd/husni/OpsimRuns/'\n",
    "    nights = year*365 + 1\n",
    "    opsdb = db.OpsimDatabase(directory+runName+'.db')\n",
    "    outDir = 'temp'\n",
    "    resultsDb = db.ResultsDb(outDir=outDir)\n",
    "    nside = 256\n",
    "    myBundles = {}\n",
    "    sqlconstraint = 'filter = \"i\" and night < '+str(nights)\n",
    "    metric = metrics.ExgalM5(lsstFilter='i')\n",
    "    dustMap = maps.DustMap(interp=False, nside=nside)\n",
    "    stackerList = []\n",
    "    slicer = slicers.HealpixSlicer(nside=nside, useCache=False)\n",
    "\n",
    "    myBundles['field dither'] = metricBundles.MetricBundle(metric, slicer, constraint=sqlconstraint, \n",
    "                                                           stackerList=stackerList, runName=runName,\n",
    "                                                           metadata='field dither', mapsList=[dustMap])\n",
    "    bgroup = metricBundles.MetricBundleGroup(myBundles, opsdb, outDir=outDir, resultsDb=resultsDb)\n",
    "    bgroup.runAll() \n",
    "    bundle = myBundles['field dither']\n",
    "    \n",
    "    vminDict = {1:np.linspace(24,25.5,16), 3:np.linspace(24.5,26,16), 6:np.linspace(25,26.5,16), 10:np.linspace(25.5,27,16)}\n",
    "    result = []\n",
    "    for vmin in vminDict[year]:\n",
    "        depth_cut = vmin\n",
    "        cond = np.logical_and.reduce((bundle.slicer.getSlicePoints()['ebv']<0.2, bundle.metricValues.mask==False, bundle.metricValues.data>vmin, bundle.metricValues.data<28))\n",
    "        depths = bundle.metricValues[cond]\n",
    "        area = len(bundle.metricValues[cond])*hp.pixelfunc.nside2pixarea(nside=nside, degrees=True)\n",
    "        median_depth = np.median(depths)\n",
    "        width = np.std(depths)\n",
    "        result.append((depth_cut, area, median_depth, width))\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Healpix slicer using NSIDE=256, approximate resolution 13.741946 arcminutes\n",
      "Querying database SummaryAllProps with constraint filter = \"i\" and night < 366 for columns ['fieldRA', 'fiveSigmaDepth', 'fieldDec']\n",
      "Found 58075 visits\n",
      "Running:  ['field dither']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/global/common/software/lsst/cori-haswell-gcc/stack/w.2018.19_sim2.8.0/stack/miniconda3-4.3.21-10a4fa6/Linux64/sims_maf/2.8.0.sims/python/lsst/sims/maf/metricBundles/metricBundleGroup.py:446: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in equal\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed metric generation.\n",
      "Running reduce methods.\n",
      "Running summary statistics.\n",
      "Completed.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/global/common/software/lsst/common/miniconda/py3-4.3.21-env/lib/python3.6/site-packages/ipykernel/__main__.py:31: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in greater\n",
      "\n",
      "/global/common/software/lsst/common/miniconda/py3-4.3.21-env/lib/python3.6/site-packages/ipykernel/__main__.py:31: RuntimeWarning:\n",
      "\n",
      "invalid value encountered in less\n",
      "\n",
      "/global/homes/h/husni/.local/lib/python3.6/site-packages/numpy/core/fromnumeric.py:688: UserWarning:\n",
      "\n",
      "Warning: 'partition' will ignore the 'mask' of the MaskedArray.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Healpix slicer using NSIDE=256, approximate resolution 13.741946 arcminutes\n",
      "Querying database SummaryAllProps with constraint filter = \"i\" and night < 1096 for columns ['fieldRA', 'fiveSigmaDepth', 'fieldDec']\n",
      "Found 168654 visits\n",
      "Running:  ['field dither']\n",
      "Completed metric generation.\n",
      "Running reduce methods.\n",
      "Running summary statistics.\n",
      "Completed.\n",
      "Healpix slicer using NSIDE=256, approximate resolution 13.741946 arcminutes\n",
      "Querying database SummaryAllProps with constraint filter = \"i\" and night < 2191 for columns ['fieldRA', 'fiveSigmaDepth', 'fieldDec']\n",
      "Found 341493 visits\n",
      "Running:  ['field dither']\n",
      "Completed metric generation.\n",
      "Running reduce methods.\n",
      "Running summary statistics.\n",
      "Completed.\n",
      "Healpix slicer using NSIDE=256, approximate resolution 13.741946 arcminutes\n",
      "Querying database SummaryAllProps with constraint filter = \"i\" and night < 3651 for columns ['fieldRA', 'fiveSigmaDepth', 'fieldDec']\n",
      "Found 567306 visits\n",
      "Running:  ['field dither']\n",
      "Completed metric generation.\n",
      "Running reduce methods.\n",
      "Running summary statistics.\n",
      "Completed.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/global/homes/h/husni/.local/lib/python3.6/site-packages/numpy/core/numeric.py:553: UserWarning:\n",
      "\n",
      "Warning: converting a masked element to nan.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for year in [1,3,6,10]:\n",
    "    runName = 'altsched_rolling_riswap'\n",
    "    outname =  runName+'_area_depth_y'+str(year)+'.npy'\n",
    "    result = getPositions(runName, year)\n",
    "    np.save('newcutnpys/'+outname, result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "proposalDict = {'baseline2018a':3, 'colossus_2664':2, 'colossus_2665':1, 'colossus_2667':1,\n",
    "                'kraken_2026':3, 'kraken_2035':3, 'kraken_2036':3, 'kraken_2042':2, 'kraken_2044':1,\n",
    "                'mothra_2045':1, 'nexus_2097':1, 'pontus_2002':1, 'pontus_2489':3, 'pontus_2502':2,\n",
    "                'mothra_2049':1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = list(bundle.metricValues[cond])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~hsnee/311.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1,x2,x3,x4,y1,y2,y3,y4,t1,t2,t3,t4,c1,c2,c3,c4,s1,s2,s3,s4 = [[] for _ in range(20)]\n",
    "for i,cut_result in enumerate(np.load('newcutnpys/altsched_riswap_area_depth_y1.npy')[:-5]):\n",
    "    x1.append([cut_result[1]])\n",
    "    y1.append([cut_result[2]])\n",
    "    t1.append(str(cut_result[0]))\n",
    "    c1.append(120+5*i)\n",
    "    s1.append(cut_result[3]*1e2)\n",
    "\n",
    "\n",
    "for i,cut_result in enumerate(np.load('newcutnpys/altsched_riswap_area_depth_y10.npy')[:-5]):\n",
    "    x2.append([cut_result[1]])\n",
    "    y2.append([cut_result[2]])\n",
    "    t2.append(str(cut_result[0]))\n",
    "    c2.append(120-5*i)\n",
    "    s2.append(cut_result[3]*1e2)\n",
    "\n",
    "updatemenus = list([\n",
    "    dict(type=\"buttons\",\n",
    "         active=-1,\n",
    "         buttons=list([\n",
    "            dict(label = 'Year 1',\n",
    "                 method = 'update',\n",
    "                 args = [{'visible': [True, True, False, False]},\n",
    "                         {'title': 'Year 1'}]),\n",
    "            dict(label = 'Year 10',\n",
    "                 method = 'update',\n",
    "                 args = [{'visible': [False, False, True, True]},\n",
    "                         {'title': 'Year 10'}])\n",
    "        ]),\n",
    "    )\n",
    "])\n",
    "\n",
    "\n",
    "data = [\n",
    "        {\n",
    "        'x': x1,\n",
    "        'y': y1,\n",
    "        'mode': 'markers',\n",
    "        'text':t1,\n",
    "        'name':'baseline2018a',\n",
    "        'marker': {\n",
    "            'color': c1,\n",
    "            'size': s1,\n",
    "            'colorscale' : 'viridis'}\n",
    "    }, \n",
    "    \n",
    "        {\n",
    "        'x': x2,\n",
    "        'y': y2,\n",
    "        'mode': 'markers',\n",
    "        'text': t2,\n",
    "        'name':'pontus_2002',\n",
    "        'marker': {\n",
    "            'color': c2,\n",
    "            'size': s2,\n",
    "            'colorscale' : 'Greys'}\n",
    "    }, \n",
    "    \n",
    "]\n",
    "\n",
    "layout = go.Layout(\n",
    "    xaxis=dict(\n",
    "        title='area (degrees)',\n",
    "        gridcolor='rgb(255, 255, 255)',\n",
    "        #type='log',\n",
    "        zerolinewidth=1,\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title='median i-band co-added depth',\n",
    "        gridcolor='rgb(255, 255, 255)',\n",
    "        #range=[36.12621671352166, 91.72921793264332],\n",
    "        zerolinewidth=1,\n",
    "    ),\n",
    "    paper_bgcolor='rgb(243, 243, 243)',\n",
    "    plot_bgcolor='rgb(243, 243, 243)',\n",
    "    updatemenus=updatemenus\n",
    ")\n",
    "\n",
    "\n",
    "py.iplot(dict(data=data, layout=layout))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~hsnee/98.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1,x2,x3,x4,y1,y2,y3,y4,t1,t2,t3,t4,c1,c2,c3,c4,s1,s2,s3,s4 = [[] for _ in range(24)]\n",
    "for i,cut_result in enumerate(np.load('baseline2018a_area_depth_y1.npy')):\n",
    "    x1.append([cut_result[1]])\n",
    "    y1.append([cut_result[2]])\n",
    "    t1.append(str(cut_result[0]))\n",
    "    c1.append(120+5*i)\n",
    "    s1.append(cut_result[3]*1e2)\n",
    "\n",
    "\n",
    "for i,cut_result in enumerate(np.load('pontus_2002_area_depth_y1.npy')):\n",
    "    x2.append([cut_result[1]])\n",
    "    y2.append([cut_result[2]])\n",
    "    t2.append(str(cut_result[0]))\n",
    "    c2.append(120-5*i)\n",
    "    s2.append(cut_result[3]*1e2)\n",
    "\n",
    "for i,cut_result in enumerate(np.load('baseline2018a_area_depth_y10.npy')):\n",
    "    x3.append([cut_result[1]])\n",
    "    y3.append([cut_result[2]])\n",
    "    t3.append(str(cut_result[0]))\n",
    "    c3.append(120+5*i)\n",
    "    s3.append(cut_result[3]*1e2)\n",
    "\n",
    "\n",
    "for i,cut_result in enumerate(np.load('pontus_2002_area_depth_y10.npy')):\n",
    "    x4.append([cut_result[1]])\n",
    "    y4.append([cut_result[2]])\n",
    "    t4.append(str(cut_result[0]))\n",
    "    c4.append(120-5*i)\n",
    "    s4.append(cut_result[3]*1e2)\n",
    "\n",
    "updatemenus = list([\n",
    "    dict(type=\"buttons\",\n",
    "         active=-1,\n",
    "         buttons=list([\n",
    "            dict(label = 'Year 1',\n",
    "                 method = 'update',\n",
    "                 args = [{'visible': [True, True, False, False]},\n",
    "                         {'title': 'Year 1'}]),\n",
    "            dict(label = 'Year 10',\n",
    "                 method = 'update',\n",
    "                 args = [{'visible': [False, False, True, True]},\n",
    "                         {'title': 'Year 10'}])\n",
    "        ]),\n",
    "    )\n",
    "])\n",
    "\n",
    "\n",
    "data = [\n",
    "        {\n",
    "        'x': x1,\n",
    "        'y': y1,\n",
    "        'mode': 'markers',\n",
    "        'text':t1,\n",
    "        'name':'baseline2018a',\n",
    "        'marker': {\n",
    "            'color': c1,\n",
    "            'size': s1,\n",
    "            'colorscale' : 'viridis'}\n",
    "    }, \n",
    "    \n",
    "        {\n",
    "        'x': x2,\n",
    "        'y': y2,\n",
    "        'mode': 'markers',\n",
    "        'text': t2,\n",
    "        'name':'pontus_2002',\n",
    "        'marker': {\n",
    "            'color': c2,\n",
    "            'size': s2,\n",
    "            'colorscale' : 'Greys'}\n",
    "    }, \n",
    "        {\n",
    "        'x': x3,\n",
    "        'y': y3,\n",
    "        'mode': 'markers',\n",
    "        'text': t3,\n",
    "        'name':'baseline2018a',\n",
    "        'marker': {\n",
    "            'color': c3,\n",
    "            'size': s3,\n",
    "            'colorscale' : 'viridis'}\n",
    "    }, \n",
    "        {\n",
    "        'x': x4,\n",
    "        'y': y4,\n",
    "        'mode': 'markers',\n",
    "        'text': t4,\n",
    "        'name':'pontus_2002',\n",
    "        'marker': {\n",
    "            'color': c4,\n",
    "            'size': s4,\n",
    "            'colorscale' : 'Greys'}\n",
    "    }, \n",
    "    \n",
    "]\n",
    "\n",
    "layout = go.Layout(\n",
    "    xaxis=dict(\n",
    "        title='area (degrees)',\n",
    "        gridcolor='rgb(255, 255, 255)',\n",
    "        #type='log',\n",
    "        zerolinewidth=1,\n",
    "        #ticklen=5,\n",
    "        #gridwidth=2,\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title='median i-band co-added depth',\n",
    "        gridcolor='rgb(255, 255, 255)',\n",
    "        #range=[36.12621671352166, 91.72921793264332],\n",
    "        zerolinewidth=1,\n",
    "        #ticklen=5,\n",
    "        #gridwidth=2,\n",
    "    ),\n",
    "    paper_bgcolor='rgb(243, 243, 243)',\n",
    "    plot_bgcolor='rgb(243, 243, 243)',\n",
    "    updatemenus=updatemenus\n",
    ")\n",
    "\n",
    "\n",
    "py.iplot(dict(data=data, layout=layout))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~hsnee/148.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1,x2,x3,x4,y1,y2,y3,y4,t1,t2,t3,t4,c1,c2,c3,c4,s1,s2,s3,s4,z1,z2,z3,z4 = [[] for _ in range(24)]\n",
    "for i,cut_result in enumerate(np.load('baseline2018a_area_depth_y1.npy')):\n",
    "    area = cut_result[1]\n",
    "    idepth = cut_result[2]\n",
    "    neff = 4.33*(idepth-25)**2 + 7.03*(idepth-25) + 10.49\n",
    "    x1.append([area])\n",
    "    y1.append([idepth])\n",
    "    t1.append('i-depth > '+str(cut_result[0])+'<br>'+' neff*area = '+str(neff*area) )\n",
    "    c1.append(120+5*neff)\n",
    "    s1.append(cut_result[3]*1e2)\n",
    "\n",
    "\n",
    "for i,cut_result in enumerate(np.load('pontus_2002_area_depth_y1.npy')):\n",
    "    area = cut_result[1]\n",
    "    idepth = cut_result[2]\n",
    "    neff = 4.33*(idepth-25)**2 + 7.03*(idepth-25) + 10.49\n",
    "    x2.append([area])\n",
    "    y2.append([idepth])\n",
    "    t2.append('i-depth > '+str(cut_result[0])+'<br>'+' neff*area = '+str(neff*area) )\n",
    "    c2.append(120-5*neff+100)\n",
    "    s2.append(cut_result[3]*1e2)\n",
    "\n",
    "for i,cut_result in enumerate(np.load('baseline2018a_area_depth_y10.npy')):\n",
    "    area = cut_result[1]\n",
    "    idepth = cut_result[2]\n",
    "    neff = 4.33*(idepth-25)**2 + 7.03*(idepth-25) + 10.49\n",
    "    x3.append([area])\n",
    "    y3.append([idepth])\n",
    "    t3.append('i-depth > '+str(cut_result[0])+'<br>'+' neff*area = '+str(neff*area) )\n",
    "    c3.append(120+5*neff)\n",
    "    s3.append(cut_result[3]*1e2)\n",
    "\n",
    "\n",
    "for i,cut_result in enumerate(np.load('pontus_2002_area_depth_y10.npy')):\n",
    "    area = cut_result[1]\n",
    "    idepth = cut_result[2]\n",
    "    neff = 4.33*(idepth-25)**2 + 7.03*(idepth-25) + 10.49\n",
    "    x4.append([area])\n",
    "    y4.append([idepth])\n",
    "    t4.append('i-depth > '+str(cut_result[0])+'<br>'+' neff*area = '+str(neff*area) )\n",
    "    c4.append(120-5*neff)\n",
    "    s4.append(cut_result[3]*1e2)\n",
    "data = [\n",
    "        {\n",
    "        'x': x1,\n",
    "        'y': y1,\n",
    "        'mode': 'markers',\n",
    "        'text':t1,\n",
    "        'name':'baseline2018a',\n",
    "        'marker': {\n",
    "            'color': c1,\n",
    "            'size': s1,\n",
    "            'colorscale' : 'viridis'}\n",
    "    }, \n",
    "    \n",
    "        {\n",
    "        'x': x2,\n",
    "        'y': y2,\n",
    "        'mode': 'markers',\n",
    "        'text': t2,\n",
    "        'name':'pontus_2002',\n",
    "        'marker': {\n",
    "            'color': c2,\n",
    "            'size': s2,\n",
    "            'colorscale' : 'Greys'}\n",
    "    }, \n",
    "        {\n",
    "        'x': x3,\n",
    "        'y': y3,\n",
    "        'mode': 'markers',\n",
    "        'text': t3,\n",
    "        'name':'baseline2018a',\n",
    "        'marker': {\n",
    "            'color': c3,\n",
    "            'size': s3,\n",
    "            'colorscale' : 'viridis'}\n",
    "    }, \n",
    "        {\n",
    "        'x': x4,\n",
    "        'y': y4,\n",
    "        'mode': 'markers',\n",
    "        'text': t4,\n",
    "        'name':'pontus_2002',\n",
    "        'marker': {\n",
    "            'color': c4,\n",
    "            'size': s4,\n",
    "            'colorscale' : 'Greys'}\n",
    "    }, \n",
    "    \n",
    "]\n",
    "    \n",
    "updatemenus = list([\n",
    "    dict(active=0,\n",
    "         showactive=True,\n",
    "         buttons=list([\n",
    "            dict(label = 'Year 1',\n",
    "                 method = 'update',\n",
    "                 args = [{'visible': [True, True, False, False]},\n",
    "                         {'title': 'Year 1'}]),\n",
    "            dict(label = 'Year 10',\n",
    "                 method = 'update',\n",
    "                 args = [{'visible': [False, False, True, True]},\n",
    "                         {'title': 'Year 10'}])\n",
    "        ]),\n",
    "    )\n",
    "])\n",
    "\n",
    "py.iplot(dict(data=data, layout=layout))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~hsnee/150.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1,x2,x3,x4,y1,y2,y3,y4,t1,t2,t3,t4,c1,c2,c3,c4,s1,s2,s3,s4,z1,z2,z3,z4 = [[] for _ in range(24)]\n",
    "for i,cut_result in enumerate(np.load('baseline2018a_area_depth_y1.npy')):\n",
    "    area = cut_result[1]\n",
    "    idepth = cut_result[2]\n",
    "    neff = 4.33*(idepth-25)**2 + 7.03*(idepth-25) + 10.49\n",
    "    x1.append([area])\n",
    "    y1.append([idepth])\n",
    "    t1.append('i-depth > '+str(cut_result[0])+'<br>'+' neff = '+str(neff) )\n",
    "    c1.append(120+5*neff*area)\n",
    "    s1.append(cut_result[3]*1e2)\n",
    "\n",
    "\n",
    "for i,cut_result in enumerate(np.load('pontus_2002_area_depth_y1.npy')):\n",
    "    area = cut_result[1]\n",
    "    idepth = cut_result[2]\n",
    "    neff = 4.33*(idepth-25)**2 + 7.03*(idepth-25) + 10.49\n",
    "    x2.append([area])\n",
    "    y2.append([idepth])\n",
    "    t2.append('i-depth > '+str(cut_result[0])+'<br>'+' neff = '+str(neff) )\n",
    "    c2.append(120-5*neff*area+100)\n",
    "    s2.append(cut_result[3]*1e2)\n",
    "\n",
    "for i,cut_result in enumerate(np.load('baseline2018a_area_depth_y10.npy')):\n",
    "    area = cut_result[1]\n",
    "    idepth = cut_result[2]\n",
    "    neff = 4.33*(idepth-25)**2 + 7.03*(idepth-25) + 10.49\n",
    "    x3.append([area])\n",
    "    y3.append([idepth])\n",
    "    t3.append('i-depth > '+str(cut_result[0])+'<br>'+' neff = '+str(neff) )\n",
    "    c3.append(120+5*neff*area)\n",
    "    s3.append(cut_result[3]*1e2)\n",
    "\n",
    "\n",
    "for i,cut_result in enumerate(np.load('pontus_2002_area_depth_y10.npy')):\n",
    "    area = cut_result[1]\n",
    "    idepth = cut_result[2]\n",
    "    neff = 4.33*(idepth-25)**2 + 7.03*(idepth-25) + 10.49\n",
    "    x4.append([area])\n",
    "    y4.append([idepth])\n",
    "    t4.append('i-depth > '+str(cut_result[0])+'<br>'+' neff = '+str(neff) )\n",
    "    c4.append(120-5*neff*area)\n",
    "    s4.append(cut_result[3]*1e2)\n",
    "data = [\n",
    "        {\n",
    "        'x': x1,\n",
    "        'y': y1,\n",
    "        'mode': 'markers',\n",
    "        'text':t1,\n",
    "        'name':'baseline2018a',\n",
    "        'marker': {\n",
    "            'color': c1,\n",
    "            'size': s1,\n",
    "            'colorscale' : 'viridis'}\n",
    "    }, \n",
    "    \n",
    "        {\n",
    "        'x': x2,\n",
    "        'y': y2,\n",
    "        'mode': 'markers',\n",
    "        'text': t2,\n",
    "        'name':'pontus_2002',\n",
    "        'marker': {\n",
    "            'color': c2,\n",
    "            'size': s2,\n",
    "            'colorscale' : 'Greys'}\n",
    "    }, \n",
    "        {\n",
    "        'x': x3,\n",
    "        'y': y3,\n",
    "        'mode': 'markers',\n",
    "        'text': t3,\n",
    "        'name':'baseline2018a',\n",
    "        'marker': {\n",
    "            'color': c3,\n",
    "            'size': s3,\n",
    "            'colorscale' : 'viridis'}\n",
    "    }, \n",
    "        {\n",
    "        'x': x4,\n",
    "        'y': y4,\n",
    "        'mode': 'markers',\n",
    "        'text': t4,\n",
    "        'name':'pontus_2002',\n",
    "        'marker': {\n",
    "            'color': c4,\n",
    "            'size': s4,\n",
    "            'colorscale' : 'Greys'}\n",
    "    }, \n",
    "    \n",
    "]\n",
    "    \n",
    "updatemenus = list([\n",
    "    dict(active=0,\n",
    "         showactive=True,\n",
    "         buttons=list([\n",
    "            dict(label = 'Year 1',\n",
    "                 method = 'update',\n",
    "                 args = [{'visible': [True, True, False, False]},\n",
    "                         {'title': 'Year 1'}]),\n",
    "            dict(label = 'Year 10',\n",
    "                 method = 'update',\n",
    "                 args = [{'visible': [False, False, True, True]},\n",
    "                         {'title': 'Year 10'}])\n",
    "        ]),\n",
    "    )\n",
    "])\n",
    "\n",
    "py.iplot(dict(data=data, layout=layout))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~hsnee/152.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1,x2,x3,x4,y1,y2,y3,y4,t1,t2,t3,t4,c1,c2,c3,c4,s1,s2,s3,s4,z1,z2,z3,z4 = [[] for _ in range(24)]\n",
    "for i,cut_result in enumerate(np.load('baseline2018a_area_depth_y1.npy')):\n",
    "    area = cut_result[1]\n",
    "    idepth = cut_result[2]\n",
    "    neff = 4.33*(idepth-25)**2 + 7.03*(idepth-25) + 10.49\n",
    "    x1.append([area])\n",
    "    y1.append([idepth])\n",
    "    t1.append('i-depth > '+str(cut_result[0])+'<br>'+' neff = '+str(neff) )\n",
    "    c1.append(120+5*i)\n",
    "    s1.append(cut_result[3]*1e2)\n",
    "    z1.append([neff*area])\n",
    "\n",
    "\n",
    "for i,cut_result in enumerate(np.load('pontus_2002_area_depth_y1.npy')):\n",
    "    area = cut_result[1]\n",
    "    idepth = cut_result[2]\n",
    "    neff = 4.33*(idepth-25)**2 + 7.03*(idepth-25) + 10.49\n",
    "    x2.append([area])\n",
    "    y2.append([idepth])\n",
    "    t2.append('i-depth > '+str(cut_result[0])+'<br>'+' neff = '+str(neff) )\n",
    "    c2.append(120-5*i+100)\n",
    "    s2.append(cut_result[3]*1e2)\n",
    "    z2.append([neff*area])\n",
    "\n",
    "for i,cut_result in enumerate(np.load('baseline2018a_area_depth_y10.npy')):\n",
    "    area = cut_result[1]\n",
    "    idepth = cut_result[2]\n",
    "    neff = 4.33*(idepth-25)**2 + 7.03*(idepth-25) + 10.49\n",
    "    x3.append([area])\n",
    "    y3.append([idepth])\n",
    "    t3.append('i-depth > '+str(cut_result[0])+'<br>'+' neff = '+str(neff) )\n",
    "    c3.append(120+5*i)\n",
    "    s3.append(cut_result[3]*1e2)\n",
    "    z3.append([neff*area])\n",
    "\n",
    "\n",
    "for i,cut_result in enumerate(np.load('pontus_2002_area_depth_y10.npy')):\n",
    "    area = cut_result[1]\n",
    "    idepth = cut_result[2]\n",
    "    neff = 4.33*(idepth-25)**2 + 7.03*(idepth-25) + 10.49\n",
    "    x4.append([area])\n",
    "    y4.append([idepth])\n",
    "    t4.append('i-depth > '+str(cut_result[0])+'<br>'+' neff = '+str(neff) )\n",
    "    c4.append(120-5*i)\n",
    "    s4.append(cut_result[3]*1e2)\n",
    "    z4.append([neff*area])\n",
    "\n",
    "updatemenus = list([\n",
    "    dict(active=0,\n",
    "         showactive=True,\n",
    "         buttons=list([\n",
    "            dict(label = 'Year 1',\n",
    "                 method = 'update',\n",
    "                 args = [{'visible': [True, True, False, False]},\n",
    "                         {'title': 'Year 1'}]),\n",
    "            dict(label = 'Year 10',\n",
    "                 method = 'update',\n",
    "                 args = [{'visible': [False, False, True, True]},\n",
    "                         {'title': 'Year 10'}])\n",
    "        ]),\n",
    "    )\n",
    "])\n",
    "\n",
    "\n",
    "data = [go.Scatter3d(\n",
    "    x=x1,\n",
    "    y=y1,\n",
    "    z=z1,\n",
    "    text=t1,\n",
    "    mode='markers',\n",
    "    name='baseline2018a',\n",
    "    marker=dict(\n",
    "        sizemode = 'diameter',\n",
    "        #sizeref = 750,\n",
    "        size = s1*200,\n",
    "        color = c1,\n",
    "        colorscale = 'viridis',\n",
    "        #colorbar = dict(title = 'Life<br>Expectancy'),\n",
    "        #line=dict(color='rgb(140, 140, 170)')\n",
    "    )\n",
    "),\n",
    "    go.Scatter3d(\n",
    "        x=x2,\n",
    "        y=y2,\n",
    "        z=z2,\n",
    "        text=t2,\n",
    "        name='pontus_2002',\n",
    "        mode='markers',\n",
    "        marker=dict(\n",
    "            sizemode = 'diameter',\n",
    "            #sizeref = 750,\n",
    "            size = s2*200,\n",
    "            color = c2,\n",
    "            colorscale = 'Greys',\n",
    "            #colorbar = dict(title = 'Life<br>Expectancy'),\n",
    "            #line=dict(color='rgb(140, 140, 170)')\n",
    "    )\n",
    "),\n",
    "    go.Scatter3d(\n",
    "        x=x3,\n",
    "        y=y3,\n",
    "        z=z3,\n",
    "        text=t3,\n",
    "        name='baseline2018a',\n",
    "        mode='markers',\n",
    "        marker=dict(\n",
    "            sizemode = 'diameter',\n",
    "            #sizeref = 750,\n",
    "            size = s3*200,\n",
    "            color = c3,\n",
    "            colorscale = 'viridis',\n",
    "            #colorbar = dict(title = 'Life<br>Expectancy'),\n",
    "            #line=dict(color='rgb(140, 140, 170)')\n",
    "    )\n",
    "),\n",
    "    go.Scatter3d(\n",
    "        x=x4,\n",
    "        y=y4,\n",
    "        z=z4,\n",
    "        text=t4,\n",
    "        name='pontus_2002',\n",
    "        mode='markers',\n",
    "        marker=dict(\n",
    "            sizemode = 'diameter',\n",
    "            #sizeref = 750,\n",
    "            size = s4*200,\n",
    "            color = c4,\n",
    "            colorscale = 'Greys',\n",
    "            #colorbar = dict(title = 'Life<br>Expectancy'),\n",
    "            #line=dict(color='rgb(140, 140, 170)')\n",
    "    )\n",
    ")\n",
    "       ]\n",
    "\n",
    "\n",
    "layout=go.Layout(\n",
    "                 scene = dict(xaxis=dict(title='area (degrees^2)'),\n",
    "                            yaxis=dict(title='median<br>depth<br> in i'),\n",
    "                            zaxis=dict(title='area<br>*n_eff'),\n",
    "                            bgcolor = 'rgb(243, 243, 243)'\n",
    "                           ),\n",
    "                 updatemenus = updatemenus\n",
    "                )\n",
    "\n",
    "\n",
    "py.iplot(dict(data=data, layout=layout))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [[1],[2,3],[],[3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1], [2, 3], [], [3]]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "url.url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CCL_round_trip.ipynb\n",
      "CSAB.py\n",
      "CotB.pdf\n",
      "GalSize.ipynb\n",
      "Galsimtests.ipynb\n",
      "HSCS16A_combinedarea_1000rea_full_xi_p.mean_sqrtvar\n",
      "'Josh'\\''s metric.ipynb'\n",
      "ModelErrors.ipynb\n",
      "ModelErrors.py\n",
      "ModelErrorsNew.ipynb\n",
      "'Peter'\\''s Metric.ipynb'\n",
      "\u001b[0m\u001b[01;34m__pycache__\u001b[0m/\n",
      "airmass.ipynb\n",
      "area_mediandepth_relationship.ipynb\n",
      "area_vs_depth.ipynb\n",
      "counter10_Y10.p\n",
      "counter1_Y10.p\n",
      "counter3_Y10.p\n",
      "counter6_Y10.p\n",
      "dec_hist.ipynb\n",
      "errors.ipynb\n",
      "\u001b[01;34mhsc_plots\u001b[0m/\n",
      "methodology.ipynb\n",
      "'metrics playground.ipynb'\n",
      "moon_visibility.ipynb\n",
      "new_runs_results.ipynb\n",
      "new_sims_metrics_comparison.ipynb\n",
      "\u001b[01;34mnewcutnpys\u001b[0m/\n",
      "\u001b[01;34mnpys\u001b[0m/\n",
      "\u001b[01;34mobjs\u001b[0m/\n",
      "paper_plots.ipynb\n",
      "plotDitherPatterns.ipynb\n",
      "rho_requirements.txt\n",
      "rolling.ipynb\n",
      "'rotational angle distribution test.ipynb'\n",
      "saveDitherPositions.ipynb\n",
      "\u001b[01;34mtemp\u001b[0m/\n",
      "whyditherplot.ipynb\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "countersDict = pickle.load(open('counter10_Y10.p', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = len(list(countersDict.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~hsnee/281.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visibility = [[False]*i+[True]*(1)+[False]*(l-i-1) for i in range(l)]\n",
    "\n",
    "\n",
    "\n",
    "updatemenus = list([dict(type=\"buttons\",\n",
    "                         active=-1,\n",
    "                         buttons= [dict(label=key, \n",
    "                                        method='update', \n",
    "                                        args = [{'visibile': visibility[i]},\n",
    "                                                {'title':key}] ) for i, key in enumerate(list(countersDict.keys()))])\n",
    "    \n",
    "])\n",
    "              \n",
    "layout = dict(updatemenus=updatemenus)\n",
    "fig = ff.create_distplot([list((countersDict[x][x]).values()) for x in list(countersDict.keys())], [x for x in list(countersDict.keys())], bin_size=1, show_hist=False, show_rug=False)\n",
    "#fig.layout['updatemenus']=updatemenus\n",
    "py.iplot(fig)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 -1.3397795822129898e-05 25.45030983950539\n",
      "3 -1.1486114299810512e-05 26.000316529819436\n",
      "6 -1.033632308585374e-05 26.372811080054035\n",
      "10 -1.106722918437021e-05 26.660156319144793\n"
     ]
    }
   ],
   "source": [
    "for year in ['1','3','6','10']:\n",
    "    runName = 'newcutnpys/altsched_roriswap_area_depth_y'+year+'.npy'\n",
    "    data = np.load(runName)[:-5]\n",
    "    dataX = data[:,1]\n",
    "    dataY = data[:,2]\n",
    "    regr = linear_model.LinearRegression()\n",
    "    regr.fit(dataX.reshape(-1, 1), dataY.reshape(-1, 1))\n",
    "    print(year, regr.coef_[0][0], regr.intercept_[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.60000000e+01, 1.52253113e+04, 2.64918450e+01, 1.37835600e-01])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 15802.535487152802 25.238644560881347\n",
      "3 15855.62081021241 25.818184410787907\n",
      "6 15653.351041716518 26.21103877493674\n",
      "10 15225.311282658822 26.491844988848314\n"
     ]
    }
   ],
   "source": [
    "for year in ['1','3','6','10']:\n",
    "    runName = 'newcutnpys/altsched_riswap_area_depth_y'+year+'.npy'\n",
    "    data = np.load(runName)[5]\n",
    "    dataX = data[1]\n",
    "    dataY = data[2]\n",
    "    print(year, dataX, dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~hsnee/315.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def data_to_plotly(x):\n",
    "    k = []\n",
    "    \n",
    "    for i in range(0, len(x)):\n",
    "        k.append(x[i][0])\n",
    "        \n",
    "    return k\n",
    "p1 = go.Scatter(x=dataX, \n",
    "                y=dataY, \n",
    "                mode='markers',\n",
    "                name='data points',\n",
    "                marker=dict(color='black', size=6)\n",
    "               )\n",
    "\n",
    "p2 = go.Scatter(x=dataX, \n",
    "                y=regr.predict(dataX.reshape(-1, 1)),\n",
    "                mode='lines',\n",
    "                name='linear fit',\n",
    "                text='y = ' +str(regr.coef_[0][0]) + 'x + ' +str(regr.intercept_[0]),\n",
    "                line=dict(color='violet', width=3)\n",
    "                )\n",
    "\n",
    "layout = go.Layout(xaxis=dict(title='area (degrees^2)',\n",
    "                              zeroline=False),\n",
    "                   yaxis=dict(title='median depth in i-band',\n",
    "                              zeroline=False),\n",
    "                   showlegend=False, hovermode='closest',\n",
    "                   title='baseline2018a Y10 linear fit')\n",
    "\n",
    "fig = go.Figure(data=[p1, p2], layout=layout)\n",
    "\n",
    "py.iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.load('baseline2018a_area_depth_y10.npy')\n",
    "dataX = data[:,0]\n",
    "dataY = data[:,1]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2b4a81af8748>]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1368x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 808,
       "width": 1156
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.log10(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/global/common/software/lsst/common/miniconda/py3-4.3.21-env/lib/python3.6/site-packages/ipykernel/__main__.py:1: RuntimeWarning:\n",
      "\n",
      "overflow encountered in exp\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2b4a87745588>]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.plot(np.exp(dataY-10000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2b4a81767ef0>]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1368x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 828,
       "width": 1121
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(10**(dataX),'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "# Create linear regression object\n",
    "regr = linear_model.LinearRegression()\n",
    "\n",
    "# Train the model using the training sets\n",
    "regr.fit(dataX.reshape(-1, 1), dataY.reshape(-1, 1))\n",
    "\n",
    "# The coefficients\n",
    "print('Coefficients: \\n', regr.coef_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_to_plotly(x):\n",
    "    k = []\n",
    "    \n",
    "    for i in range(0, len(x)):\n",
    "        k.append(x[i][0])\n",
    "        \n",
    "    return k\n",
    "p1 = go.Scatter(x=dataX, \n",
    "                y=dataY, \n",
    "                mode='markers',\n",
    "                name='data points',\n",
    "                marker=dict(color='black', size=6)\n",
    "               )\n",
    "\n",
    "p2 = go.Scatter(x=dataX, \n",
    "                y=regr.predict(dataX.reshape(-1, 1)),\n",
    "                mode='lines',\n",
    "                name='linear fit',\n",
    "                text='y = ' +str(regr.coef_[0][0]) + 'x + ' +str(regr.intercept_[0]),\n",
    "                line=dict(color='violet', width=3)\n",
    "                )\n",
    "\n",
    "layout = go.Layout(xaxis=dict(title='area (degrees^2)',\n",
    "                              zeroline=False),\n",
    "                   yaxis=dict(title='median depth in i-band',\n",
    "                              zeroline=False),\n",
    "                   showlegend=False, hovermode='closest',\n",
    "                   title='baseline2018a Y10 linear fit')\n",
    "\n",
    "fig = go.Figure(data=[p1, p2], layout=layout)\n",
    "\n",
    "py.iplot(fig)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "lsst",
   "language": "python",
   "name": "lsst"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}