Better dCl dw.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore the affect of step size and placement in the dCl/dw numeric derivative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IMPORT ERROR: /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/quicklens/mpi.pyc (No module named pypar). Could not load pbs or pypar. MPI will not be used.\n"
     ]
    }
   ],
   "source": [
    "import crosspower as cp\n",
    "import FisherCl as fcl # branch quickCl\n",
    "#import camb\n",
    "\n",
    "# set plotting to be in the notebook instead of its own qt window\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.95 -0.95 -0.95 -1.   -1.   -1.   -1.05 -1.05 -1.05]\n"
     ]
    }
   ],
   "source": [
    "# get a set of w values\n",
    "#myWs = -1*np.array([0.7,0.8,0.9,0.95,0.96,0.97,0.98,0.99,0.999,\n",
    "#          1,\n",
    "#          1.001,1.01,1.02,1.03,1.04,1.05,1.1,1.2,1.3])\n",
    "myWs = np.array([-0.95,-0.95,-0.95,-1.0,-1.0,-1.0,-1.05,-1.05,-1.05])\n",
    "\n",
    "# try with ns\n",
    "#myNss = np.array([0.95,0.95,0.95,0.96,0.96,0.96,0.97,0.97,0.97])\n",
    "\n",
    "print myWs\n",
    "nWs = myWs.size\n",
    "#print myNss\n",
    "#nNss = myNss.size\n",
    "\n",
    "#plt.plot(myWs,marker='o')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting MatterPower  1  of  9\n",
      "neutrino_hierarchy =  normal\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  58.8187801838  seconds\n",
      "time elapsed:  0.980313003063  minutes\n",
      "time elapsed:  0.0163385500511  hours\n",
      "starting MatterPower  2  of  9\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  116.400730133  seconds\n",
      "time elapsed:  1.94001216888  minutes\n",
      "time elapsed:  0.0323335361481  hours\n",
      "starting MatterPower  3  of  9\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  195.481583118  seconds\n",
      "time elapsed:  3.25802638531  minutes\n",
      "time elapsed:  0.0543004397551  hours\n",
      "starting MatterPower  4  of  9\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  264.765063047  seconds\n",
      "time elapsed:  4.41275105079  minutes\n",
      "time elapsed:  0.0735458508465  hours\n",
      "starting MatterPower  5  of  9\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  329.681097984  seconds\n",
      "time elapsed:  5.49468496641  minutes\n",
      "time elapsed:  0.0915780827734  hours\n",
      "starting MatterPower  6  of  9\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  399.704076052  seconds\n",
      "time elapsed:  6.66173460086  minutes\n",
      "time elapsed:  0.111028910014  hours\n",
      "starting MatterPower  7  of  9\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  484.658143997  seconds\n",
      "time elapsed:  8.07763573329  minutes\n",
      "time elapsed:  0.134627262221  hours\n",
      "starting MatterPower  8  of  9\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  563.132559061  seconds\n",
      "time elapsed:  9.38554265102  minutes\n",
      "time elapsed:  0.15642571085  hours\n",
      "starting MatterPower  9  of  9\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n",
      "time elapsed:  627.498407125  seconds\n",
      "time elapsed:  10.4583067854  minutes\n",
      "time elapsed:  0.17430511309  hours\n",
      "nonlinear =  False\n",
      "neutrino_hierarchy =  normal\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "#hier = 'degenerate'\n",
    "hier = 'normal'\n",
    "#nz = 10000\n",
    "nz = 300000\n",
    "#nonlinear = True\n",
    "nonlinear = False\n",
    "AccuracyBoost = 3\n",
    "\n",
    "starttime = time.time()\n",
    "myPks = []\n",
    "for wNum in range(nWs):\n",
    "    print 'starting MatterPower ',wNum+1,' of ',nWs\n",
    "    myPks.append( cp.MatterPower(nz=nz,w=myWs[wNum],wa=0.0,nonlinear=nonlinear,AccuracyBoost=AccuracyBoost,\n",
    "                                neutrino_hierarchy = hier) )\n",
    "    #myPks.append( cp.MatterPower(nz=nz,w=-1.0,wa=0.0,nonlinear=nonlinear,AccuracyBoost=AccuracyBoost,\n",
    "    #                             neutrino_hierarchy = hier, ns=myNss[wNum]) )\n",
    "\n",
    "    endtime = time.time()\n",
    "\n",
    "    print 'time elapsed: ', endtime-starttime, ' seconds'\n",
    "    print 'time elapsed: ', (endtime-starttime)/60., ' minutes'\n",
    "    print 'time elapsed: ', (endtime-starttime)/3600., ' hours'\n",
    "\n",
    "print 'nonlinear = ',nonlinear\n",
    "print 'neutrino_hierarchy = ',hier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "# get some k values to use\n",
    "nKs = 500\n",
    "myKs = np.logspace(-4,1,nKs)\n",
    "\n",
    "# get some z values\n",
    "nZs = 5\n",
    "myZs = [0.0,0.5,1.0,2.0,8.0]\n",
    "\n",
    "# evaluate P(w,z,k)\n",
    "Pwzk = np.empty((nWs,nZs,nKs))\n",
    "for wNum in range(nWs):\n",
    "    for zNum in range(nZs):\n",
    "        Pwzk[wNum,zNum] = myPks[wNum].PK.P(zNum,myKs)\n",
    "print 'done'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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MHzfZ7nC86pxrbqaqPInQRL3uEAy0ZPeJPKqtZIw5aD3ni8g7uK4ZHBaRNGNM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\n/wcz7GcAB2aRpQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110ec88d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "wSet1 = [0,1,2,  9,  16,17,18]\n",
    "wSet2 = [3,4,5,6,7,  9,  11,12,13,14,15]\n",
    "wSet3 = [8,9,10]\n",
    "\n",
    "wSet4 = [0,1,2,3,4,5,6,7,8,9]\n",
    "wSet5 = [9,10,11,12,13,14,15,16,17,18]\n",
    "\n",
    "wSet5 = range(nWs)\n",
    "\n",
    "#for zNum in range(nZs):\n",
    "for zNum in range(1):\n",
    "    for wNum in wSet5:\n",
    "        plt.semilogx(myKs,Pwzk[wNum,zNum],label='w = {0}'.format(myWs[wNum]))\n",
    "        #plt.semilogx(myKs,Pwzk[wNum,zNum],label='ns = {0}'.format(myWs[wNum]))\n",
    "    plt.title(r'$P(w,k,z)$ at $z = {0}$'.format(myZs[zNum]))\n",
    "    #plt.title(r'$P(ns,k,z)$ at $z = {0}$'.format(myZs[zNum]))\n",
    "    plt.legend()\n",
    "    plt.xlabel('k')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Again, with a finer grid of w values based on sigma(w) = 0.007"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.986 -0.987 -0.988 -0.989 -0.99  -0.991 -0.992 -0.993 -0.994 -0.995\n",
      " -0.996 -0.997 -0.998 -0.999 -1.    -1.001 -1.002 -1.003 -1.004 -1.005\n",
      " -1.006 -1.007 -1.008 -1.009 -1.01  -1.011 -1.012 -1.013 -1.014]\n"
     ]
    },
    {
     "data": {
      "image/png": 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FJIvWORGGPf0h//P6UvaVVcTYWkQSoYIhaSUsgvvQsD7MGnUJ3zrvBF6YvY5LH53JrPyt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105e31290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get a set of w values\n",
    "%matplotlib inline\n",
    "myWs = -0.001*(np.arange(29)-14)-1.0\n",
    "print myWs\n",
    "nWs = myWs.size\n",
    "plt.plot(myWs,marker='o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting MatterPower  1  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  67.7195599079  seconds\n",
      "time elapsed:  1.1286593318  minutes\n",
      "time elapsed:  0.0188109888633  hours\n",
      "starting MatterPower  2  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  120.260839939  seconds\n",
      "time elapsed:  2.00434733232  minutes\n",
      "time elapsed:  0.033405788872  hours\n",
      "starting MatterPower  3  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  181.24811697  seconds\n",
      "time elapsed:  3.0208019495  minutes\n",
      "time elapsed:  0.0503466991584  hours\n",
      "starting MatterPower  4  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  233.489025831  seconds\n",
      "time elapsed:  3.89148376385  minutes\n",
      "time elapsed:  0.0648580627309  hours\n",
      "starting MatterPower  5  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  286.272161961  seconds\n",
      "time elapsed:  4.77120269934  minutes\n",
      "time elapsed:  0.0795200449891  hours\n",
      "starting MatterPower  6  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  353.315692902  seconds\n",
      "time elapsed:  5.88859488169  minutes\n",
      "time elapsed:  0.0981432480282  hours\n",
      "starting MatterPower  7  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  419.538275957  seconds\n",
      "time elapsed:  6.99230459929  minutes\n",
      "time elapsed:  0.116538409988  hours\n",
      "starting MatterPower  8  of  29\n",
      "neutrino_hierarchy =  normal\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  483.650837898  seconds\n",
      "time elapsed:  8.0608472983  minutes\n",
      "time elapsed:  0.134347454972  hours\n",
      "starting MatterPower  9  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  542.07337904  seconds\n",
      "time elapsed:  9.03455631733  minutes\n",
      "time elapsed:  0.150575938622  hours\n",
      "starting MatterPower  10  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  603.150887966  seconds\n",
      "time elapsed:  10.0525147994  minutes\n",
      "time elapsed:  0.167541913324  hours\n",
      "starting MatterPower  11  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  663.32977891  seconds\n",
      "time elapsed:  11.0554963152  minutes\n",
      "time elapsed:  0.184258271919  hours\n",
      "starting MatterPower  12  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  722.568444014  seconds\n",
      "time elapsed:  12.0428074002  minutes\n",
      "time elapsed:  0.20071345667  hours\n",
      "starting MatterPower  13  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  778.790678978  seconds\n",
      "time elapsed:  12.9798446496  minutes\n",
      "time elapsed:  0.216330744161  hours\n",
      "starting MatterPower  14  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  833.990144968  seconds\n",
      "time elapsed:  13.8998357495  minutes\n",
      "time elapsed:  0.231663929158  hours\n",
      "starting MatterPower  15  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  884.027825832  seconds\n",
      "time elapsed:  14.7337970972  minutes\n",
      "time elapsed:  0.245563284953  hours\n",
      "starting MatterPower  16  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  938.889997005  seconds\n",
      "time elapsed:  15.6481666168  minutes\n",
      "time elapsed:  0.260802776946  hours\n",
      "starting MatterPower  17  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  2102.24943686  seconds\n",
      "time elapsed:  35.0374906143  minutes\n",
      "time elapsed:  0.583958176904  hours\n",
      "starting MatterPower  18  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  2154.38235092  seconds\n",
      "time elapsed:  35.9063725154  minutes\n",
      "time elapsed:  0.598439541923  hours\n",
      "starting MatterPower  19  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  2201.43035984  seconds\n",
      "time elapsed:  36.6905059973  minutes\n",
      "time elapsed:  0.611508433289  hours\n",
      "starting MatterPower  20  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  2249.17589998  seconds\n",
      "time elapsed:  37.4862649997  minutes\n",
      "time elapsed:  0.624771083328  hours\n",
      "starting MatterPower  21  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  2298.31869984  seconds\n",
      "time elapsed:  38.3053116639  minutes\n",
      "time elapsed:  0.638421861066  hours\n",
      "starting MatterPower  22  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  2355.19885898  seconds\n",
      "time elapsed:  39.2533143163  minutes\n",
      "time elapsed:  0.654221905271  hours\n",
      "starting MatterPower  23  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  2406.01107192  seconds\n",
      "time elapsed:  40.100184532  minutes\n",
      "time elapsed:  0.668336408867  hours\n",
      "starting MatterPower  24  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  2457.34832692  seconds\n",
      "time elapsed:  40.9558054487  minutes\n",
      "time elapsed:  0.682596757478  hours\n",
      "starting MatterPower  25  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  2511.31366682  seconds\n",
      "time elapsed:  41.8552277803  minutes\n",
      "time elapsed:  0.697587129672  hours\n",
      "starting MatterPower  26  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  2571.21256995  seconds\n",
      "time elapsed:  42.8535428325  minutes\n",
      "time elapsed:  0.714225713876  hours\n",
      "starting MatterPower  27  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  2632.29954982  seconds\n",
      "time elapsed:  43.8716591636  minutes\n",
      "time elapsed:  0.731194319394  hours\n",
      "starting MatterPower  28  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Currently dark energy parameters are changed globally, not per parameter set\n",
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  2692.43549085  seconds\n",
      "time elapsed:  44.8739248474  minutes\n",
      "time elapsed:  0.747898747457  hours\n",
      "starting MatterPower  29  of  29\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n",
      "time elapsed:  2746.84595084  seconds\n",
      "time elapsed:  45.7807658474  minutes\n",
      "time elapsed:  0.763012764123  hours\n",
      "nonlinear =  False\n",
      "neutrino_hierarchy =  normal\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "#hier = 'degenerate'\n",
    "hier = 'normal'\n",
    "#nz = 10000\n",
    "nz = 300000\n",
    "#nonlinear = True\n",
    "nonlinear = False\n",
    "AccuracyBoost = 3\n",
    "\n",
    "starttime = time.time()\n",
    "myPks2 = []\n",
    "for wNum in range(nWs):\n",
    "    print 'starting MatterPower ',wNum+1,' of ',nWs\n",
    "    myPks2.append( cp.MatterPower(nz=nz,w=myWs[wNum],nonlinear=nonlinear,AccuracyBoost=AccuracyBoost,\n",
    "                                 neutrino_hierarchy = hier) )\n",
    "\n",
    "    endtime = time.time()\n",
    "\n",
    "    print 'time elapsed: ', endtime-starttime, ' seconds'\n",
    "    print 'time elapsed: ', (endtime-starttime)/60., ' minutes'\n",
    "    print 'time elapsed: ', (endtime-starttime)/3600., ' hours'\n",
    "\n",
    "print 'nonlinear = ',nonlinear\n",
    "print 'neutrino_hierarchy = ',hier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "# get some k values to use\n",
    "nKs = 500\n",
    "myKs = np.logspace(-4,1,nKs)\n",
    "\n",
    "# get some z values\n",
    "nZs = 5\n",
    "myZs = [0.0,0.5,1.0,2.0,8.0]\n",
    "\n",
    "# evaluate P(w,z,k)\n",
    "Pwzk = np.empty((nWs,nZs,nKs))\n",
    "for wNum in range(nWs):\n",
    "    for zNum in range(nZs):\n",
    "        Pwzk[wNum,zNum] = myPks2[wNum].PK.P(zNum,myKs)\n",
    "print 'done'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14]\n",
      "[14 15 16 17 18 19 20 21 22 23 24 25 26 27 28]\n"
     ]
    },
    {
     "data": {
      "image/png": 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jTMWlNSbgOsr/3RW5ayLEnBZP9k6IORJrdyhGKURKye79+/dTp04dvF4vM2fO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\nqmrRN6RV9TPg5HlS/YBF1utFQP9c7S9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Xlwc7yrmoqGhiQkJCelxcXHphYSFZb8eZ2KQ0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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a9bb450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "wSet1 = np.arange(15)\n",
    "wSet2 = np.arange(15)+14\n",
    "print wSet1\n",
    "print wSet2\n",
    "\n",
    "#for zNum in range(nZs):\n",
    "for zNum in range(1):\n",
    "    for wNum in wSet1:\n",
    "        plt.semilogx(myKs,Pwzk[wNum,zNum],label='w = {0}'.format(myWs[wNum]))\n",
    "    plt.title(r'$P(w,k,z)$ at $z = {0}$'.format(myZs[zNum]))\n",
    "    plt.legend()\n",
    "    plt.xlabel('k')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.99  -0.995 -1.    -1.005 -1.01 ]\n"
     ]
    }
   ],
   "source": [
    "wSet3 = [4,9,14,19,24]\n",
    "print myWs[wSet3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vsNpTCuivEsC+ZVtB2pN5whG7Q4kOs5UcdxsOHzpExUqV7I4mrnw17VV2bfkt\nomPWOfEketw0sND1iVCyG+C9995j4cKFNG/enDFjxvxljEgp8shBRCqKSOW898D5wCpgLpB3x1E/\nYI71fi7Q17prqTuQbp12+gw4X0SqWxeizwc+s9ZliEh36y6lviFjqTjnyWxGUtYO+g97yu5QosLn\n3U3AXYm3JvzT7lBUGI4t2X3aaacdLdldUHIYM2bM0dM3oa9IJAYofslugIsvvpjNmzezYsUKzj33\nXPr165evbySEc+RwAvCBFZgLeMsYM09EFgHviEh/4HfgKqv/J8CFwAbgCHAzgDFmn4g8BSyy+j1p\njMmr2zwImAYkA59aLxXn5rwxmazkprhyFlC5amLWIMqulkNSOmT9fsDuUOLO8X7hR0u8l+wGqFmz\n5tH+t956K8OGDQtrX8VVZHIwxvwGtC+gfS/Qs4B2A6QWMtZUYGoB7YuBNmHEq+JI2nerQE7iSJ2D\ndocSNedfdyMLJ+4jKUunDY0X8VyyG2D79u3Uq1cPgLlz50atkKE+Ia2ixnukKUm+3dxy/+N2hxI1\nrTt0I8n3G0ITu0NRYYr3kt3jx4+ndevWtG/fnvHjx0dtVjgt2a2i4rP332TjvDq4cxZy6+uJeb0h\nzyv9h+N3n89JPf+g91U32h1OTNOS3WVHS3armLR+/iKMw8mhmvvtDiXqDlcNVmhd982PNkeiVORo\nclBRkXy4Ce7sfVx//0N2hxJ1XXr1QnJzSM6oYXcoSkWMJgcVcQvnfYjPewqSu5ITTkj8B8TO6nUJ\nnqwtOHJPtDsUpSJGk4OKuJ/nfolxuDlUc0/RnRNEwLmFbE8jflrwud2hKBURmhxUxFU41Bh39gGu\nuGuo3aGUmcNVDmAcTr7/oGxLHCgVLZocVET99PUXZHtaIbmraNS4qd3hlJm25/4dyc2hwsGaRXdW\nKg5oclAR9cO7H5LrTOJQ9R12h1Kmzr34ajxZGxFTfhKiOr5olewGeOedd2jVqhWtW7fm+uujU31A\nk4OKqIoHG+PKyeAfA2+3O5Qyl520CZ+3AW9NGm13KCoGRKtk9/r163n22Wf57rvvWL16NWPHjo1K\n/JocVMT87/NPyPa0xhH4meYt29kdTpnLquUDYN+KtCJ6KrskQsnu1157jdTUVKpXD06kWadOnULH\nL42wSnYrFY4l78/D4byUjCo77Q7FFtfdfT9zHlmCM5D4t+9GwoGPNpK97XBEx0yqX5FqF59c6PpE\nKNm9bl1w5sEzzjiDQCDA448/zgUXRH5mZU0OKmIqHDqZgGsflz36gN2h2KJu/YY4/W8RcJ9C+v59\nVK2uD8XFmmNLdnfq1Oloye5XX8ZjAAAgAElEQVS8I4pQxSm8VxIlKdnt9/tZv349CxYsIC0tjTPP\nPJNVq1ZRrVq1iMamyUFFxLy338DnbYkr51vq17/S7nBsk1npDxy5XZn2zOMMfj7/l4360/F+4UdL\nIpTsTklJoXv37rjdbpo0aUKLFi1Yv349Xbt2Lc4/iiJpclARsfGrJeC4hMN19hXdOYHV79KMnT/4\nSd4T2V9xKnLivWT3pZdeysyZM7npppvYs2cP69at46STTir2vosS9gVpEXGKyDIR+Y+13EREfhSR\n9SLytogkWe0ea3mDtb5xyBgPWu2/ikivkPYLrLYNIhKZKZZUmfIeaU5S1k5uGf6k3aHY6opb7sKT\ntQ6hNTkhtx6q2BHvJbt79epFzZo1adWqFT169OD555//ywRAkRJ2yW4RuQfoAlQxxlwkIu8A7xtj\nZonIy8DPxphJInIH0M4Yc7uIXAtcZoy5RkRaATMJzj9dH/gcaG4Nvw44j+B80ouA64wxa44Xj5bs\njh1vvzyGPcva4sr5ktumPmN3OLabeNsQkEug9sekPvWC3eHEFC3ZXXbKpGS3iKQA/wAmW8sCnAPM\ntrpMBy613vexlrHW97T69wFmGWN8xphNBKcRPdV6bTDG/GaMyQZmWX1VnNi7NA3EwZF6R+wOJSYk\nN68OJhfXH0l2h6JUiYV7Wmks8ACQay3XBA4YY/zWchrQwHrfANgKYK1Pt/ofbT9mm8LaVZxI8p2C\nJzONQY88a3coMeGWex/Dm/kbztxWdoeiVIkVmRxE5CJglzFmSWhzAV1NEeuK215QLANFZLGILN69\ne/dxolZlZcrox/Eln4zftQpvcrLd4cQMn3ctPm8DJjx8j92hKFUi4Rw5nAFcIiKbCZ7yOYfgkUQ1\nEcm72ykF2Ga9TwMaAljrqwL7QtuP2aaw9nyMMa8aY7oYY7rUrl07jNBVtOWszQIg0LigHF9+Ve3U\nAMnNwbOtit2hKFUiRSYHY8yDxpgUY0xj4FrgS2PM/wFfAXk3tPcD5ljv51rLWOu/NMGr3nOBa627\nmZoAzYCfCF6Abmbd/ZRk7WNuRD6diqpDBw/izG2P98g6Ukc8Z3c4MeX/br8PT9Yqch0d2bJxnd3h\nKFVspamtNAy4R0Q2ELymMMVqnwLUtNrvAYYDGGNWA+8Aa4B5QKoxJmBdl7gT+AxYC7xj9VUxbspT\nj5DtOYHMCmvtDiUmZVTbhN9dmTljX7I7FKWKrVjJwRizwBhzkfX+N2PMqcaYpsaYq4wxPqs9y1pu\naq3/LWT7p40xJxtjWhhjPg1p/8QY09xa93SkPpyKrgq7a+MIZHPiWe3tDiUmnTegL67sdCqmaxnv\n8ihaJbuHDh1Khw4d6NChA82bN4942Yw8WpVVlcjalcsIuDrizl7BxdffYnc4Mal1u1OBRWQlt2by\ns4/aHY4qY9Eq2T1mzBiWL1/O8uXLueuuu7j88sujEr8mB1Ui81+ZSsBVgYxqW+wOJaY5WroQk4tZ\np/+rxYJEKNkdaubMmVx33XURjTuP1lZSJVLxYFMCrgNceN9ddocS0269/0km9/snfndXFn42h7N6\n6fOdeT799FN27IjsjIF169ald+/eha5PhJLdebZs2cKmTZuOJrhI0+Sgim321An4vK1w5yykSdPo\nHNImkozaW0g61IVVsz/W5GCzRCjZnWfWrFlceeWVOJ3OqMSmyUEV267vN2GcrThc76DdocSF2598\nnrcGvUWu41T2791D9Zq17A4pJhzvF360JELJ7jyzZs1i4sSJYe2nJDQ5qGJLym4L/M7Ax5+3O5S4\nkFyhAocr/YwzcBkzHn+CwS/pra12iveS3QC//vor+/fv57TTTiv2PsOlV8lUsYwfPgRfciOyPStw\nJ2lhuXCde3s/3L69VEpvY3co5V68l+yG4IXoa6+9tsCEFilhl+yONVqy2x6v9RuJP6kLdf62iSv6\nDbI7nLgy6dZ7yXX+g0C1Odw9apzd4dhCS3aXnTIp2a0UwPdffELA3RF39nJNDCVQ78wWOP2ZVNie\nYncoShVJk4MK29J3/0vAlUxGrTS7Q4lLl/YdiNP/Iz5vB2aM0UIAKrZpclBh82S2JSlrO7c8Ur6n\nAi2N7KbBEghZK7JsjkSp49PkoMLy0qP34ktugt+9jKrVa9gdTtxKfWQU3sxVGEdnfv9tvd3hKFUo\nTQ4qLN7fKyO5OVTtXNfuUOJeRrWN+N1V+GBM/oeulIoVmhxUkX5cOJ+AqwtJvhVcf/t9docT93oP\nGog7+wCV0pvbHYpShdLkoIq0+K1PCLgqcLDWZrtDSQgtWrYDlpCV3JI3xum824kqWiW7f//9d3r0\n6EHHjh1p165d2E9jF5cmB3Vchw4exJPVCU9mGrc++ozd4SSMnBOzQRwcXrnf7lBUlESrZPfIkSO5\n+uqrWbZsGbNmzeKOO+6ISvxFJgcR8YrITyLys4isFpEnrPYmIvKjiKwXkbetKT6xpgF9W0Q2WOsb\nh4z1oNX+q4j0Cmm/wGrbICKlL3WoImbyEw/h8zbAl7yUylWr2h1Owkh97Hk8mZtx5ZxidyjlSiKU\n7BYRDh4M1jVLT0/PV3MpUsKpreQDzjHGHBIRN/CtiHxKcArQMcaYWSLyMtAfmGT93W+MaSoi1wLP\nAdeISCuC80O3BuoDn4tI3knXicB5QBqwSETmGmPWRPBzqhKqvLsJOUmHaXnF2XaHknD87rUEXL2Z\nOvopbrlvhN3hlLl1654i41Bkp5itXKklzZsX/s8yEUp2P/7445x//vm89NJLHD58mM8//7zUcRSk\nyORggvU1DlmLbutlgHOA66326cDjBJNDH+s9wGxgggQLgPQBZlnTiW6y5pg+1eq3IW86URGZZfXV\n5GCz6WOfJiv5VNzZ3/D33vpsQ6T5UwyyA3y/ptsdSrmRCCW7Z86cyU033cS9997L999/z4033siq\nVatwOCJ7lSCsqqwi4gSWAE0J/srfCBwwxvitLmlAA+t9A2ArgDHGLyLpQE2r/YeQYUO32XpMe7dC\n4hgIDARo1KhROKGrUshacRjcgr+JPrAVDamPPc+Um94Ajn/qIVEd7xd+tCRCye4pU6Ywb948AE47\n7TSysrLYs2cPderUCfufQzjCSg7GmADQQUSqAR8ABVXOykt1BZUJNMdpLyjdFVgN0BjzKvAqBAvv\nFRG2KoUVP30H0g1v5mr6P/pPu8NJSCJCwLUGv/t8/j3hOW64c5jdIZUL8V6yu1GjRnzxxRfcdNNN\nrF27lqysLGrXrl3sfRelWMchxpgDwAKgO1BNRPKSSwqwzXqfBjQEsNZXBfaFth+zTWHtykbfTJ2N\n312ZjJrr7A4loWXWDR6Vpa/abnMk5Ue8l+x+4YUXeO2112jfvj3XXXcd06ZNi0rp7iJLdotIbSDH\nGHNARJKB/xK8yNwPeC/kgvQKY8y/RCQVaGuMud26IH25MeZqEWkNvEXwOkN94AugGcEjinVAT+AP\nYBFwvTFm9fHi0pLd0XNgz27eve9zxPi4ZvxlepdSFKXv3cvb9y/A6d9M/xn32h1O1GnJ7rJTFiW7\n6wFficgKgl/c840x/wGGAfdYF5ZrAlOs/lOAmlb7PcBwAOvL/h2CF5rnAanGmIB13eJO4DNgLfBO\nUYlBRdf0J54k23sCmZWWaGKIsqo1a+IIrCMnqTk7t2m1WxU7wrlbaQXQsYD23/jzbqPQ9izgqkLG\nehrIV6vYGPMJEJ3H/FSxVTzYjlzHHs4ZrnM2lIXMSttwBLoxa+xzDP6nTiGqYoM+Ia3+Ytz9d+FL\nPhm/60eatmhldzjlwgntm4LJxbunkt2hKHWUJgf1FxV3noTTf5gWF+c7KFRRcvXAwXiztuAMnGx3\nKEodpclBHTXxsfvxJbdFcn+g5yXX2B1OueJ3bsTnbcyns2fYHYpSgCYHFcKzuQ6O3Bwqd6tpdyjl\nzuHq6SAO1n/9k92hKAVoclCWiU88gM/bEaf/f1x/2z12h1Pu9Lzu/3AEfCRnRPYpV2WfaJXs3rJl\nCz179qRdu3acffbZpKVF5y43TQ4KAM9vNXHk5lChaxW7QymX2nc5nSTfRkCvOySKaJXsvu++++jb\nty8rVqzg0Ucf5cEHH4xK/JocFP96chg+b2ec/u+54Y777Q6n3MpO2kK2tx7/nvCc3aEkrEQo2b1m\nzRp69uwJQI8ePZgzZ05E484TVm0lldiSNlYnJ8mPt0MFu0Mp13w1s3Cnw/7V5aN6zIj1aaw6lBnR\nMdtUSuapZimFrk+Ekt3t27fnvffeY/DgwXzwwQdkZGSwd+9eataM7LVCTQ7l3L+eGIbxnkuS71v6\nDn7C7nDKtavvup+5jy3H469rdygJKxFKdo8ePZo777yTadOmcdZZZ9GgQYOjSSSSNDmUc56NJ5Dt\nyaFCV30Ay271UxrhzplNwNUMX1YWHq/X7pCi6ni/8KMlEUp2169fn/fffx+AQ4cO8d5771E1CmVu\nNDmUY+MeuBtXhUtxZc/n/1J1ovtY4POmYRwdmDr6CQY9ov9OoiHeS3bv2bOHGjVq4HA4ePbZZ7nl\nlluKvd9w6AXpcurwoUNU2tkaV85BTr6kvd3hKIu/bvALKnerTrAULfFesnvBggW0aNGC5s2bs3Pn\nTh5++OGIxw5hlOyOVVqyu3TGDU7F5bsCyf0Pd7z6ot3hKEtGejozh36OI7CZAdMTr4S3luwuO2VR\nslslmD07t1HxQDeSfLv5+6Dr7A5HhahctSqOwHoCrmYc2LvH7nBUOabJoRx6+7Hn8SWnkFXhW1p3\n7Gp3OOoYWRW243dXYvrokXaHosqxIpODiDQUka9EZK2IrBaRwVZ7DRGZLyLrrb/VrXYRkfEiskFE\nVohIp5Cx+ln914tIv5D2ziKy0tpmvERjzjsFwNy3JuPKORtv5gZueka/fGKRt0l1AJJ26/0iyj7h\nHDn4gXuNMS0Jzh2dKiKtCM7w9oUxphnBKT/z7uvqTXD6z2bAQGASBJMJ8BjQjeAkQY/lJRSrz8CQ\n7S4o/UdTBdn53z/wuypyqN4qneUtRg144HGSsnaQlH2i3aGocqzI5GCM2W6MWWq9zyA4lWcDoA8w\n3eo2HbjUet8HmGGCfgCqiUg9oBfBKUb3GWP2A/OBC6x1VYwx35vg1fEZIWOpCHrp4aFke07H4/sf\ng0flf+BHxZINZHuasmHtSrsDUeVUsa45iEhjglOG/gicYIzZDsEEAuSVk2wAbA3ZLM1qO157WgHt\nBe1/oIgsFpHFu3fvLk7o5V5GejoV0prj9GdS9e9akjvWZVbaRa7Tw38mv2p3KKqcCjs5iEgl4D1g\niDHm4PG6FtBmStCev9GYV40xXYwxXWrXrl1UyCrEtOEjyEpuQa5zAVfdnGp3OKoIjbq3R3IDVNhX\nze5QVAmVtmT3hAkTaNq0KSLCnj1lf+daWMlBRNwEE8Obxpj3read1ikhrL+7rPY0oGHI5inAtiLa\nUwpoVxEy69WxJGWfiyfzNy4dMdjucFQYLr3hVjxZ6xBaEQgE7A5HlUBpS3afccYZfP7555x4oj3X\nnsK5W0mAKcBaY0zo01Jzgbw7jvoBc0La+1p3LXUH0q3TTp8B54tIdetC9PnAZ9a6DBHpbu2rb8hY\nKgIOf5tDwOnlSMPV1G+oFznjhc+7gWzPCbz85DC7Q0kY8VKyG6Bjx440btw4ojEVRzj3yp0B3Ais\nFJHlVttDwCjgHRHpD/wOXGWt+wS4ENgAHAFuBjDG7BORp4BFVr8njTH7rPeDgGlAMvCp9VIRMG7w\nnbgqXI4rez53jIxuhUkVWUnNquHbCK60xLyl9YmPVrNm2/HOUBdfq/pVeOzi1oWuj5eS3bGgyP/q\njDHfUvB1AYCeBfQ3QIEntY0xU4GpBbQvBtoUFYsqnnnv/ZvkjB44crdx2tCrit5AxZQB9z/GlL6v\nkOtsT0Z6ut56HAHxUrI7FiTmTxIFQNrcNHK8ncmpsoDW7SN7yKzKRmbFNYjpw5SnHmbI6Al2hxNR\nx/uFHy3xUrI7FmhySFAvpd6FI/kyXNnzSX3+JbvDUSV0Su/TWf9RFpV2F3h3tyqBeCjZHQu0tlIC\neu35R3H7euHJ3MQFj9xsdziqFM695Brc2YvJ9nRi2pin7Q4nIcRLye7x48eTkpJCWloa7dq1Y8CA\nARGP8Xi0ZHeC2bEtjY+H/4dsTyM4cSGDHhlV9EYqpk0aORzze0/c2f/j1mmP2R1OqWjJ7rKjJbvV\nX8x5ZCJZFZoTcH6miSFBDHpkFEm+n8hJ6s6/ntDbWlXZ0OSQQMYNvhN/0nl4Mn+i33NP2h2OiiBn\nR4MjN5uk35pyYI+WjlHRp8khQfxr5IN4Dl+AJ3Mr7QZ00dseE8zNQx7B756PL/lk3nlgrN3hqHJA\nk0MC+OGrz0hafwoA/ma/cuqZ59ockYqGfqOewpP5P3KSevLSHVoGRUWXJoc4t2/3Tla/vhqftwG+\nivO4/ZFn7A5JRUnlqlU57e7z8B5Zh9N/IeMeuNvukFQC0+QQ52Y/MIGsCh1w5M5j8LjEekhK5de6\nczcq/i0Td046yXvO5u1X9RSTig5NDnFs4u1DyfH0ICnrO24YPcLucFQZuXbgUA6f8C1+V0UyvtOK\nrbEqWiW7f/nlF0477TQ8Hg+jR4+OWvyaHOLUuLvvBHMh3iOrueCJ/9ML0OXMkFHjcQS+wJfckXF3\n3WV3OKoA0SrZXaNGDcaPH899990X1fg1OcShsUNTcWddjCcrjZOuqUvDE0+yOyRlgx5Dr8eTtZ0K\nGaeR9vtmu8OJC4lQsrtOnTp07doVt9sd0XiPpbWV4szYe+/Ec+gi3Dm7qN7DR49/aHmM8qpFmw58\nVmkyTv/lzHlmLKkvx9n1h0+Hw44Iz5Fdty30LvzhTy3ZHT5NDnFk7LC78KZfgDOQjrfrDq646X67\nQ1I26/fkk7w79EOM43Q2b/iFxk1PsTukmKYlu8NXZHIQkanARcAuY0wbq60G8DbQGNgMXG2M2W/N\n5DaO4GQ/R4CbjDFLrW36AY9Yw440xky32jvz50Q/nwCDTbwWfIqiMfekknzwQpyBw0jrX7khNb5r\n7KjIqFqjBoerLMWVczn/eWESd04aZ3dI4TvOL/xo0ZLd4QvnyGEaMAGYEdI2HPjCGDNKRIZby8OA\n3kAz69UNmAR0s5LJY0AXwABLRGSuMWa/1Wcg8APB5HABOhPcX4wdfCfeIxfj9O/D3WETNw3RxKD+\n1Pfxx3nvnk8w0o1dO7ZRp25sfLnEKi3ZHZ4iL0gbYxYC+45p7gNMt95PBy4NaZ9hgn4AqolIPaAX\nMN8Ys89KCPOBC6x1VYwx31tHCzNCxlLAS3fcRVLmJbizd1L1b3u4acjDdoekYkz1mrU4UnkR2d66\nvP2UFlssSryX7N6xYwcpKSm8+OKLjBw5kpSUFA4ejOx0qxBmyW4RaQz8J+S00gFjTLWQ9fuNMdVF\n5D/AKGtqUUTkC4JHFGcDXmPMSKt9BJAJLLD6n2u1nwkMM8ZcVFRMiV6y++CBdGbe+yx+9/l4MzdQ\n/x8eel/dz+6wVIzalraFTx75FsjlmrGXULlabN7arCW7y06slewu6EqKKUF7wYOLDBSRxSKyePfu\nxK1Mufynb3hn8CT87vPxHFlE93s6amJQx1U/5UQyk3/E523A1Ef16FKVXkmTw07rlBDW311WexrQ\nMKRfCrCtiPaUAtoLZIx51RjTxRjTpXbt2iUMPba98szDLJm4Dl/yqbhy/ss1L91O6/Zd7Q5LxYEL\nhw4iybebiumdyEhPtzscFedKmhzmAnk/ZfsBc0La+0pQdyDdGLMd+Aw4X0Sqi0h14HzgM2tdhoh0\nt+506hsyVrkzZnAqbOxGjrsuAdcH3DZllD75rMJ2cvOWZHu+x5fcmKmP6NGDKp0ik4OIzAS+B1qI\nSJqI9AdGAeeJyHrgPGsZgncb/QZsAF4D7gAwxuwDngIWWa8nrTaAQcBka5uNlMM7lZb/uJBXb36E\npKzLcPoPkNtoIXdPeMnusFQc+vut1+HJ+gPv4XN5R4vyqVIo8lZWY8x1hazqWUBfA6QWMs5UYGoB\n7YuBNkXFkajGPXg3Fbd3Jcd7Dp7M72k74FS6ndnX7rBUnGrTuRtf1J+Je09vDn5Xmc9qz6TXZYX9\nL6xU4bS2kk1WL1vEK/2H4d57EQFHJfye9xkw/WG6nXme3aGpODf4mbH4kz4i29OIrR/6mPqCThmr\nik+Tgw3GDrmD78etw+/uRVLWz9Q+e6fOxaAi6u4JE8hO/hC/uzY5a9ox9r477Q6p3IlWye4333yT\ndu3a0a5dO04//XR+/vnnqMSvyaEMTXn+CSb3fR531pUYceNPep8BM+7n8r632x2aSkBDxk0kp+4X\nOHIzSTp4CRNvH2J3SOVKtEp2N2nShK+//poVK1YwYsQIBg4cGJX4NTmUgdmvT+TVmx8he103sr3t\ncGXPp/NtdRg8Xo8WVHTdNfJFavU8iCdrPZiLGH9X+T6CSISS3aeffjrVq1cHoHv37qSlpUU07jxa\nlTWKvvjwbTZ8tBQjZxJIaoEnaymBUw5y24PP2h2aKkcuu+E2/ld/Hmum/05O7j8YO/xuhozKX4G0\nrD3303P8su+XiI55So1TGHbqsELXJ1rJ7ilTptC7d+9Sx1EQTQ5RMGP8KLKWZ5DrOI2AuxfeIyvI\nabiFAU9Ht/yvUoU5/ZwL2LjqWXKXVSZ5Vw+mjn6cW+573O6wylwilez+6quvmDJlCt9++22kwwI0\nOUTUuIeGUPGPWmR7umLcTryZK/DV2Ez/l+3/labUjXc/yLiHh+LZeS6BVc2Y+uJT3HKPfXOPH+8X\nfrQkSsnuFStWMGDAAD799FNq1qwZ1r6KS5NDKS3874eseP8rvJntcCVfQrYnhyTfInwnZ9D/0efs\nDk+pvxj89Jijc4OYVa0YO/guhowrXw9cxnvJ7t9//53LL7+cN954g+bNmxd7n+HSC9IlsOW3dYwd\nksrkfk+z5h03Qh+MeHEEPqbemZsZMP0RUjUxqBg19MWJZNX+DMn14fZdxpS+Y3h5ZOnPn8eLeC/Z\n/eSTT7J3717uuOMOOnToQJcuYRVZLbawSnbHorIu2b1mxVI+e30qlfbXIdfZDr+7Ck5/Jq6clRyq\ntZX+jz6jdZBUXFn649f89OonGMfZGHHgDHxFz/tuoGnL6BUs0JLdZae0Jbv1tNJxvDF+FOlrtuM9\n0gh/0ikkuS7H784mybcaX/ImTu17BV1PLz+/uFRi6dTt73Tq9nemjh1J7jIPvuTzWTBqCR/Xe43B\no+JoulEVFZocQrw58Xn2rv2d5EO1ENMEX/KpCJDrOoArZzm+5O2069OTHr3vtTtUpSLmliHBqd3H\n3XUnruyeuPZfxGs3PYa0gQH3PWFzdMou5TY5/PTN5/xv7hyS0t0k+Wpj5ESyvZ1x0hm/20eS7zec\nOZ+RVSeLvsOfpHLVy+0OWamoGvzSBD584xX2fL4Gn/dMWG+Y3PcFDtXZRP8RT+tp03KmXCWHNT8v\n5X8vfgo0xOeth1suwzgg4DqIK+d3nP5lZNXI4qKbb6Npy+g8WKJULLv0xtvgRnjl2UeQdYI/qRvu\nQx15++738SWvoE6XJlw14G67w1RloFwlh1btO/E/ViHmIK6cX8mqmE6lE2tz9a1D9FeRUiFue3Ak\nAF/P+4BVH3yEkS7AxexalMuUhZPweddDQzd3jNC78hKV3q2klCpSRno6U58dgXdXZRy5rcn21gUg\nybcHCWwi27uTnOp+zr/hZlq27VjoOHq3UtlJmLuVROQCYBzgBCYbY0YVsYlSqoxUrlqVwSH1mP71\n5AOQ5scRaIjf1Rzj6IorHRaM38l32dMQsxu/ax++CofIreSgTuOGXHzdrTZ+grL37rvv8vjjj7N2\n7Vp++umnQp9HmDdvHoMHDyYQCDBgwICjT15PmDCBsWPHsnHjRnbv3k2tWrUAmDNnDiNGjMDhcOBy\nuRg7dix/+9vfIh5/TBw5iIgTWEdwytE0glOJXmeMWVPYNnrkoFRsyEhPZ9ak0WRtPYD3cA0cubXJ\nddYh21PrL/0kN4cuN9akScqJQADIBQxGckEMRoCjL8HhEBxOF+6kJLwVKpKU5Cnzz1Yaa9euxeFw\ncNtttzF69OgCk0MgEKB58+bMnz+flJQUunbtysyZM2nVqhXLli2jevXqnH322SxevPhocjh06BAV\nK1ZERFixYgVXX301v/ySv4Bhohw5nApsMMb8BiAis4A+QKHJQSkVGypXrcqtw5/K1/6/Lz9j0X8/\nRg7l4spOwpVTEeEf1lo3iBMjfxZpEAOE/FY1BFNIIBOy0n1gsgCDHF0LmNCNjv177Ht46ZUJeD0e\nbr3pVkY8NYLVv6zm/TffY+F33zBz9iwmjZlY4HYlUcsbLKudk5XN/j/2sLvmjnx9Fi1dTKMGDaks\nFUj/Yx8Xn/8P3nr93wy+425SqtUFA7n+AHu27MBk5ByNrVKlSgAcPnw47EJ9xRUryaEBsDVkOQ3o\ndmwnERkIDARo1KhR2USmlCqR08/pxenn9PpL29q1a6ndpDYAO555hqw1awkE/OTm5hb+fZyvvfAv\nQ9dJTal0+90h/f7a97RTT2PS5EncetNtLF/5M9nZ2eTk5PLj4kV073oawbPaf24z4qmH+faH7/Lt\n57KLLuPuQYMLjeOvsToAN5CUb+2OHXtoUK/h0XX16jVk6fIlx/QVwGO9AHL54IMPePDBB9m1axcf\nf/xxGHEUX6wkh4L+bef7T8IY8yrwKgRPK0U7KKVUdIkILpc7YuN5qlagdpM6ha4/N+VcUu9LxVvL\nQ6WqlWjdujVb9m5m6aoljB8/ntpN/noq7OWpL5c6JrfXRfUGVandpEa+dZXrVMRb2XN0XZXalahQ\nNfkvfR0uB7VOrE6tWn+2XdbkMi677DIWLlzIiBEj+Pzzz0sd57FiJTmkAQ1DllOAbTbFopQqA3Uf\neqjM95koJbvznHXWWXNZW9oAAAoFSURBVGzcuJE9e/YcvSYRKbGSHBYBzUSkCfAHcC1wvb0hKaUS\nUbyX7N6wYQMnn3wyIsLSpUvJzs6OypwOMVGy2xjjB+4EPgPWAu8YY1bbG5VSKhHFe8nu9957jzZt\n2tChQwdSU1N5++23o3JROiZuZS0JvZVVqfijD8GVndLeyhoTRw5KKaViiyYHpZRS+WhyUEoplY8m\nB6WUUvnEyq2sxbZkyZI9InIASA9prnqc5dD3tYA9EQjj2P2VtG9h6wpqP95nPHZZP3P5+syR+ryF\nxVSSfn9ZP3/+/LaBQMCfm5vrdDgcgdCOx7aFLhf2PhAIuJxOpz/sT3UcBcVUkn6FrS/rz7xjxw5X\nq1atVh7TfGJR2x1ljInbF/BquMvHvF8cjf2XtG9h6wpq18+sn7mwzxypz1ucz1xUv2PXL1++fLMx\nZvHGjRt3G2MWh76ObQtdLuz9ypUrDx87TklfBcVUkn6FrS/rz2z9sy7xfwPxflrpo2IsH7suGvsv\nad/C1hXUrp9ZP/Oxy3Z+5qL6Fbi+atWqB4pqC10u7H0khTtuUf3y1k+dOrV606ZNWzscjs4LFy6s\nUNhnnj17dpXGjRu3adSoUZtJkyYdfWBhxowZplGjRm1EpLPP5zt47LZff/11BafT2fn111+vHk7c\nxRW3zzmUhogsNmHe65so9DMnvnj4vD///PPm9u3bR+rUF6tWrWrZpk2btZEaL5KWLl36/+3db0yT\n2x0H8O+PlhapDi509CoFSrWgDGkUZ4w0Mrk3C1vIQCRhW+K/cE02kyV7O6+vFsJ4gS+mId7N60JM\ndHvBTLjebTHzRtQRYq7b0OB0SAleyoYttCpUytM/Zy+ojEuhWvr0D+X3SXjB4TxPf7809HdOz/M8\nJ0OhUIiTJ08aOjo6xg4cOPB6aR+fz4fi4uLyGzduDBmNRq/ZbN5x9erVkcrKSk9fX98GrVbrr6mp\nKb1///7jzZs3+4D5nLdv3/7YYrGUqNXqwPHjx6dOnDjhWnruBw8eaM1ms2G18a/1mcNq/TbRASQA\n55z61lu+0Gq1jkj6nzlzRtfa2poHAC0tLQX79u0rAYCenp5N9fX1xXLGtnv3bo/ZbJ4L16e3t1dT\nVFQ0V1ZWJmVkZIjGxkZnd3d3NgBUVVXNlpaWSkuP0Wq1jra2trz6+nqXVquVZb1lOWt2QToaYv7p\nrusK55z61lq+X1x+XOAcn8mM/kxjCw8Wysnf+PqDozvGVup58ODBmY6ODh0A+8DAQKYkSWlzc3N0\n586djRaLZXpp/5aWloK+vr5NS9sbGxudbW1toRs0RBr52JgqPz9/oQDo9Xrp3r17G8MdMzs7+/L6\n9evG/v7+fzc3N2uijWEl67I4MMbWJ4vF8vrYsWMal8uVplarRUVFxczdu3cz+/v7N50/f/6rpf0v\nXbq0YqGRw3Jf6xNR2O/6T506VdDe3m5TKmP78c3FgTGWEOFG+LGiVquFXq+f6+zs1O7du3fGbDbP\n3rx5c9OzZ8/Uu3bt8iztH8nMoampyTA4OJip0+mk27dvD79LPIWFhdL4+PjCzj42m021ZcsWb7hj\nHj58qDl69KgRAFwul/LWrVtZSqVSHDlyRNaFei4OjLF1Zf/+/TOdnZ26CxcujFZWVs6ePn1aX15e\n/jotLXQJNpKZQ3d392iksVRXV7tHR0cznjx5ojIYDN5r167lXLlyZSTcMePj4wv3Lhw+fNhQV1f3\nUu7CAKzfBekVEZGGiP5ORHWJjiUeiGgHEX1CRN1E9NNExxMPRNRARBeJqIeIvpvoeOKBiIxEdImI\nuhMdSyz5/f604eFhg9VqLbLb7aFbrwGorq6edjgc6TU1Ne6CggKfWq0WVVVVM3LHcvny5WydTlcx\nMDCgOXTokMlisZgAYHR0NL26unobML/50NmzZ7+qra0tMZlM32poaHDu2bPHAwCtra15Op2u4vnz\n5yqz2VzW3Ny87A1sPp9PabVai4aGhoxyxp8yl7IS0e8A1AGwCyHKF7XXAvg15jeH/VQI0f6W8/wS\ngBvAIyHE5zEMOWpy5Rw8Jg3ARSFES6zilYPMOb8HoGOd5dwthGiKWbBvsZpLWa1Wq+HVq1dZSqXS\nt3PnzoV9XpxO5zdsNlshAOTk5Ezq9foJu92eo1Ao/Lm5uS+fPn1qNJlMYUfhySqSnN/8bWhoyFhS\nUrKQL1/K+n9dAGoXNxCRAkAngO8BKAPwIyIqI6KdRPT5kp88IvoQwL8API938KvUhShzDh7zAwB/\nA/BFfMNflS7IkHPQmeBxya4L8uW85mi12slt27Y9XdwmhIDNZis0mUxD5eXlj168eJHjdrszJElS\nqdXqN1f/rNmRbyQ5xyqGlFlzEELcISLDkua9AIaFECMAQER/AFAvhPgV5kdiX0NEBwFoMP/PNktE\nfxZCBGIaeBTkyDl4ns8AfEZEfwIQfo/CBJPpfSYA7QD+IoT4R2wjjp5c7/NalZWVNePxeFSL26an\npzUqlWpuw4YNEgBkZ2c7XS5XtkqlkiRJUgGYBSD/9mhxEknOGo0m6ktql5NKM4fl5ANYvKBkC7Yt\nSwjxsRDi55j/gLyYzIUhjIhyJqLvENE5IvoNgHfbFT35RJQzgJ8B+BBAExH9JJaBxVCk73MuEX0C\nYBcR/SLWwcWaJEmq9PT0hfsDVCqV5PV6Vbm5uS9cLtd7IyMjhbF6zEairJSz1+tVjIyMFHo8nkyb\nzfa+XK+XMjOHFSw3cnjrVFMI0SV/KHETUc5CiF4AvbEKJk4izfkcgHOxCycuIs15CkAyFMJAIBCg\ntLS0WHzlIxQKRWDr1q2jMTh3shLp6el+o9H4tXs0AoEAAYhqcJvqMwcbgIJFv+sB/CdBscQL58w5\nJ7NBh8ORFfzwWrU3o+Y3vwdH1WHvD1jr3jXnQCBADocjC8BgNK+X6jOHLwGYiKgYwDiAHwL4cWJD\nijnOmXNOWj6f76OJiYlPJyYmyhHB4NTv9yudTqciEAhog01kt9sz3W73+wqFwj85OZmXnZ3tmJqa\n0oY90RoSRc4BAIM+n++jqAKI5nnfyfQD4PcA/gvAi/lRVUuw/fsAhgBYAXyc6Dg5Z86Zc+b810LO\nKXOfA2OMMfmk+poDY4yxVeDiwBhjLAQXB8YYYyG4ODDGGAvBxYExxlgILg6MMcZCcHFgTCZEZCCi\nqO5KZSxZcHFgjDEWgosDYzEQ3Hntn0T07UTHwthqcHFgTGZEVArgjwBOCCG+THQ8jK1Gqj94j7F4\n+yaAHgCHhRCP3taZsWTFMwfG5PUS85vwVCU6EMaiwTMHxuQlAWgAcIOIZoQQSb3tKmMr4eLAmMyE\nEG4iqgPwVyJyCyF6Eh0TY5HiR3YzxhgLwWsOjDHGQnBxYIwxFoKLA2OMsRBcHBhjjIXg4sAYYywE\nFwfGGGMhuDgwxhgLwcWBMcZYiP8BrDg/cENn+JMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x134688c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for zNum in range(nZs):\n",
    "for zNum in range(1):\n",
    "    for wNum in wSet2:\n",
    "        plt.semilogx(myKs,Pwzk[wNum,zNum],label='w = {0}'.format(myWs[wNum]))\n",
    "    plt.title(r'$P(w,k,z)$ at $z = {0}$'.format(myZs[zNum]))\n",
    "    plt.legend()\n",
    "    plt.xlabel('k')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FQUTWi8g3IrJYRHKdtiYiMltE8pyvjZ12EZHxIrJaRJaISO+49eQ4/fNEJCeu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1345f4510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for zNum in range(nZs):\n",
    "for zNum in range(1):\n",
    "    for wNum in wSet3:\n",
    "        plt.semilogx(myKs,Pwzk[wNum,zNum],label='w = {0}'.format(myWs[wNum]))\n",
    "    plt.title(r'$P(w,k,z)$ at $z = {0}$'.format(myZs[zNum]))\n",
    "    plt.legend()\n",
    "    plt.xlabel('k')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check for convergence of $dC_\\ell / dw$ as step size $dw$ changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.986 -0.987 -0.988 -0.989 -0.99  -0.991 -0.992 -0.993 -0.994 -0.995\n",
      " -0.996 -0.997 -0.998 -0.999 -1.    -1.001 -1.002 -1.003 -1.004 -1.005\n",
      " -1.006 -1.007 -1.008 -1.009 -1.01  -1.011 -1.012 -1.013 -1.014]\n",
      "29\n",
      "(29, 5, 500)\n"
     ]
    }
   ],
   "source": [
    "print myWs\n",
    "print nWs\n",
    "print Pwzk.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.014 -0.013 -0.012 -0.011 -0.01  -0.009 -0.008 -0.007 -0.006 -0.005\n",
      " -0.004 -0.003 -0.002 -0.001]\n"
     ]
    }
   ],
   "source": [
    "nDeltas = (nWs-1)/2\n",
    "deltaWs = np.empty(nDeltas)\n",
    "for n in range(nDeltas):\n",
    "    deltaWs[n] = (myWs[-(n+1)]-myWs[n])/2 # half the distance\n",
    "print deltaWs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dPVecs = np.empty((nDeltas,nKs))\n",
    "for vecNum in range(nDeltas):\n",
    "    dPVecs[vecNum] = (Pwzk[-(vecNum+1),0,:]-Pwzk[vecNum,0,:])/(2*deltaWs[vecNum])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MxGeXuo5yULoVlJPfzTBo2DGuo4j4lEpCAkJkZCTZie2JK7DMf/YR13H2a+Xi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\nUkmIiEi9/h8d4FhL/Us8rAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1394fcf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CP1oEqZouTBovhkAEg1+g5dgXo5OmGDqJptJjxintZHV2ENvcRsinRZOWi2ns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\n0HRlUdQ5iuVRU9idMBSJYEz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ChESQoCZESNv3rAkS6Hv4NQECGj8RTeQEvxCBiJgou3Pr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\nCBFDgkEIIUQMCQYhhBAx/j+Y3t4bSypcqQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x134848190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sjEP0g5naxB/x0x3qpjvcTXeom55wDz2hHrpD3dgtdk7NPpVxGeOOacf4YAjH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\nyvwRP2f84wy+MvErFA+7jus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h4qRwzbhr2NK2heKU4sGuihDHPQkGcVK4cvSVXDn6ysGu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11729e210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot them\n",
    "% matplotlib inline\n",
    "for vecNum in range(nDeltas):\n",
    "    plt.semilogx(myKs,dPVecs[vecNum],label=deltaWs[vecNum])\n",
    "#plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# plot percent differences from smallest delta-w\n",
    "for vecNum in range(nDeltas-1):\n",
    "    plt.semilogx(myKs,(dPVecs[vecNum]-dPVecs[-1])/dPVecs[-1],label=deltaWs[vecNum])\n",
    "    #plt.plot(myKs,(dPVecs[vecNum]-dPVecs[-1])/dPVecs[-1],label=deltaWs[vecNum])\n",
    "#plt.legend()\n",
    "plt.xlim((1e-4,1.2e-4))\n",
    "plt.show()\n",
    "\n",
    "for vecNum in range(nDeltas-1):\n",
    "    plt.semilogx(myKs,(dPVecs[vecNum]-dPVecs[-1])/dPVecs[-1],label=deltaWs[vecNum])\n",
    "    #plt.plot(myKs,(dPVecs[vecNum]-dPVecs[-1])/dPVecs[-1],label=deltaWs[vecNum])\n",
    "#plt.legend()\n",
    "plt.xlim((8e-4,1.1e-3))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Do testing to see if w0, wa is working in CAMB and pycamb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method set_dark_energy in module camb.model:\n",
      "\n",
      "set_dark_energy(self, w=-1.0, wa=0.0, sound_speed=1.0, dark_energy_model='fluid') unbound camb.model.CAMBparams method\n",
      "    Set dark energy parameters. Not that in this version these are not actually stored in\n",
      "    the CAMBparams variable but set globally. So be careful!\n",
      "    \n",
      "    :param w: w0 part of p_de/rho_de\n",
      "    \n",
      "    :param wa: wa part of p_de/rho_de in w = w0 + wa*( 1-a )\n",
      "    \n",
      "    :param sound_speed: rest-frame sound speed of dark energy fluid\n",
      "    :param dark_energy_model: model to use, default is 'fluid'\n",
      "    :return: self\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<module 'crosspower' from 'crosspower.pyc'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import camb\n",
    "help(camb.model.CAMBparams.set_dark_energy)\n",
    "reload(cp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.025 -0.025 -0.025  0.     0.     0.     0.025  0.025  0.025]\n"
     ]
    }
   ],
   "source": [
    "# get a set of wa values\n",
    "#myWas = np.array([-0.050,-0.025,0.000,0.025,0.050]) #version A\n",
    "#myWas = np.array([0.050,0.025,0.000,-0.025,-0.050]) #version B\n",
    "\n",
    "#myWas = np.array([-0.2,-0.1,0.0,0.1,0.2]) #version C\n",
    "#myWas = np.array([0.2,0.1,0.0,-0.1,-0.2]) #version D\n",
    "\n",
    "#myWas = np.array([-0.1,-0.1,-0.1,-0.1,-0.1]) #version E\n",
    "#myWas = np.array([0,0,0,0,0]) #version F\n",
    "\n",
    "myWas = np.array([-0.025,-0.025,-0.025,0.000,0.000,0.000,0.025,0.025,0.025]) #version AA\n",
    "#myWas = np.array([0.050,0.050,0.025,0.025,0.000,0.000,-0.025,-0.025,-0.050,-0.050]) #version BB\n",
    "\n",
    "print myWas\n",
    "nWas = myWas.size\n",
    "#plt.plot(myWas,marker='o')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting MatterPower  1  of  9\n",
      "neutrino_hierarchy =  normal\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  51.0378499031  seconds\n",
      "time elapsed:  0.850630831718  minutes\n",
      "time elapsed:  0.0141771805286  hours\n",
      "starting MatterPower  2  of  9\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  120.900362015  seconds\n",
      "time elapsed:  2.01500603358  minutes\n",
      "time elapsed:  0.033583433893  hours\n",
      "starting MatterPower  3  of  9\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  188.722541809  seconds\n",
      "time elapsed:  3.14537569682  minutes\n",
      "time elapsed:  0.0524229282803  hours\n",
      "starting MatterPower  4  of  9\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  240.329668999  seconds\n",
      "time elapsed:  4.00549448331  minutes\n",
      "time elapsed:  0.0667582413885  hours\n",
      "starting MatterPower  5  of  9\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  286.054605961  seconds\n",
      "time elapsed:  4.76757676601  minutes\n",
      "time elapsed:  0.0794596127669  hours\n",
      "starting MatterPower  6  of  9\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  332.213325977  seconds\n",
      "time elapsed:  5.53688876629  minutes\n",
      "time elapsed:  0.0922814794381  hours\n",
      "starting MatterPower  7  of  9\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  383.402365923  seconds\n",
      "time elapsed:  6.39003943205  minutes\n",
      "time elapsed:  0.106500657201  hours\n",
      "starting MatterPower  8  of  9\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:accuracy parameters are changed globally, not yet per parameter set\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time elapsed:  434.511651039  seconds\n",
      "time elapsed:  7.24186085065  minutes\n",
      "time elapsed:  0.120697680844  hours\n",
      "starting MatterPower  9  of  9\n",
      "neutrino_hierarchy =  normal\n",
      "zs.size:  299998\n",
      "time elapsed:  485.542335987  seconds\n",
      "time elapsed:  8.09237226645  minutes\n",
      "time elapsed:  0.134872871108  hours\n",
      "nonlinear =  False\n",
      "neutrino_hierarchy =  normal\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "#hier = 'degenerate'\n",
    "hier = 'normal'\n",
    "#nz = 10000\n",
    "nz = 300000\n",
    "#nonlinear = True\n",
    "nonlinear = False\n",
    "AccuracyBoost = 3\n",
    "\n",
    "starttime = time.time()\n",
    "myPks = []\n",
    "for waNum in range(nWas):\n",
    "    print 'starting MatterPower ',waNum+1,' of ',nWas\n",
    "    reload(cp)\n",
    "    myPks.append( cp.MatterPower(nz=nz,w=-1,wa=myWas[waNum],nonlinear=nonlinear,AccuracyBoost=AccuracyBoost,\n",
    "                                 neutrino_hierarchy = hier) )\n",
    "\n",
    "    endtime = time.time()\n",
    "\n",
    "    print 'time elapsed: ', endtime-starttime, ' seconds'\n",
    "    print 'time elapsed: ', (endtime-starttime)/60., ' minutes'\n",
    "    print 'time elapsed: ', (endtime-starttime)/3600., ' hours'\n",
    "\n",
    "print 'nonlinear = ',nonlinear\n",
    "print 'neutrino_hierarchy = ',hier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "# get some k values to use\n",
    "nKs = 500\n",
    "myKs = np.logspace(-4,1,nKs)\n",
    "\n",
    "# get some z values\n",
    "nZs = 5\n",
    "myZs = [0.0,0.5,1.0,2.0,8.0]\n",
    "\n",
    "# evaluate P(w,z,k)\n",
    "Pwzk = np.empty((nWas,nZs,nKs))\n",
    "for waNum in range(nWas):\n",
    "    for zNum in range(nZs):\n",
    "        Pwzk[waNum,zNum] = myPks[waNum].PK.P(zNum,myKs)\n",
    "print 'done'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MGOuTdcYmJVGa14PYjJ18s96enTG+ESolu40JCAd3fURERCWxpb59orX6YC8io8r4YsHs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\nvunOZDg+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x134640290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "wSet5 = range(nWas)\n",
    "\n",
    "#for zNum in range(nZs):\n",
    "for zNum in range(1):\n",
    "    for wNum in wSet5:\n",
    "        plt.semilogx(myKs,Pwzk[wNum,zNum],label='wa = {0}'.format(myWas[wNum]))\n",
    "    plt.title(r'$P(w,k,z)$ at $z = {0}$'.format(myZs[zNum]))\n",
    "    plt.legend()\n",
    "    plt.xlabel('k')\n",
    "    #plt.ylim([75000,82000])\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pwzk_m01 = Pwzk.copy() # 5 times: wa=-0.1\n",
    "#Pwzk_p00 = Pwzk.copy() # 5 times: wa=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate a few derivatives\n",
    "#dB02 = (Pwzk[0]-Pwzk[2])/0.05\n",
    "#dA02 = (Pwzk[4]-Pwzk[2])/0.05\n",
    "\n",
    "#dB13 = (Pwzk[1]-Pwzk[3])/0.05\n",
    "#dA13 = (Pwzk[3]-Pwzk[1])/0.05\n",
    "\n",
    "#dB42 = (Pwzk[2]-Pwzk[4])/0.05\n",
    "#dA42 = (Pwzk[2]-Pwzk[0])/0.05\n",
    "\n",
    "#dD02 = (Pwzk[0]-Pwzk[2])/0.2\n",
    "#dC02 = (Pwzk[4]-Pwzk[2])/0.2\n",
    "\n",
    "#dD13 = (Pwzk[1]-Pwzk[3])/0.2\n",
    "#dC13 = (Pwzk[3]-Pwzk[1])/0.2\n",
    "\n",
    "#dD42 = (Pwzk[2]-Pwzk[4])/0.2\n",
    "#dC42 = (Pwzk[2]-Pwzk[0])/0.2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dA02' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-4c0b5c07ba3b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'matplotlib inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msemilogx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmyKs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdA02\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'02'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msemilogx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmyKs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdA13\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'13'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msemilogx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmyKs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdA42\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'42'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'dA02' is not defined"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "plt.semilogx(myKs,dA02[0],label='02')\n",
    "plt.semilogx(myKs,dA13[0],label='13')\n",
    "plt.semilogx(myKs,dA42[0],label='42')\n",
    "plt.xlabel(r'k [Mpc$^{-1}$]')\n",
    "plt.title('version A')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.semilogx(myKs,dB02[0],label='02')\n",
    "plt.semilogx(myKs,dB13[0],label='13')\n",
    "plt.semilogx(myKs,dB42[0],label='42')\n",
    "plt.xlabel(r'k [Mpc$^{-1}$]')\n",
    "plt.title('version B')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.semilogx(myKs,dC02[0],label='02')\n",
    "plt.semilogx(myKs,dC13[0],label='13')\n",
    "plt.semilogx(myKs,dC42[0],label='42')\n",
    "plt.xlabel(r'k [Mpc$^{-1}$]')\n",
    "plt.title('version C')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.semilogx(myKs,dD02[0],label='02')\n",
    "plt.semilogx(myKs,dD13[0],label='13')\n",
    "plt.semilogx(myKs,dD42[0],label='42')\n",
    "plt.xlabel(r'k [Mpc$^{-1}$]')\n",
    "plt.title('version D')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.semilogx(myKs,dA02[0],label='02 A')\n",
    "plt.semilogx(myKs,dA13[0],label='13 A')\n",
    "\n",
    "plt.semilogx(myKs,dB13[0],label='13 B')\n",
    "plt.semilogx(myKs,dB42[0],label='42 B')\n",
    "\n",
    "plt.semilogx(myKs,dC02[0],label='02 C')\n",
    "plt.semilogx(myKs,dC13[0],label='13 C')\n",
    "\n",
    "plt.semilogx(myKs,dD13[0],label='13 D')\n",
    "plt.semilogx(myKs,dD42[0],label='42 D')\n",
    "\n",
    "\n",
    "plt.xlabel(r'k [Mpc$^{-1}$]')\n",
    "plt.title('versions A, B')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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