plot_CiC.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "from astropy.table import Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import healpy as hp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from astropy.cosmology import FlatLambdaCDM\n",
    "cosmo_mice = FlatLambdaCDM(H0=70, Om0=0.25, Ob0=0.044)\n",
    "from astropy.cosmology import Planck15 as cosmo_bench"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.25\n"
     ]
    }
   ],
   "source": [
    "print cosmo_mice.Om0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path_std = ['CiC/results_bin%d_mice_std_mask.fits.gz' % (i) for i in range(0,5)]\n",
    "path_crocce = ['CiC/results_bin%d_mice_crocce_mask.fits.gz' % (i) for i in range(0,5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path_std_bench = ['CiC/results_bin%d_std_mask.fits.gz' % (i) for i in range(0,5)]\n",
    "path_crocce_bench = ['CiC/results_bin%d_crocce_mask.fits.gz' % (i) for i in range(0,5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.91612971  0.45806485  0.22903243  0.11451621  0.05725811  0.02862905]\n"
     ]
    }
   ],
   "source": [
    "scale_arr = np.array([np.sqrt(hp.nside2pixarea(2**(i+6),degrees=True)) for i in range(0,6)])\n",
    "print scale_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "zarr = np.array([0.3,0.5,0.7,0.9,1.1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def D_plus(z,cosmology):\n",
    "    from scipy.integrate import trapz\n",
    "    amax = 1./(1+z)\n",
    "    amin= 1e-5\n",
    "    a = np.linspace(amin,amax,1000)\n",
    "    z_arr = 1./a-1.\n",
    "    integrand = 1./(cosmology.H(z_arr)*a/cosmology.H0)**3\n",
    "    d_a = 5*cosmology.Om0*cosmology.H(z)/cosmology.H0*trapz(integrand,a)\n",
    "    return d_a\n",
    "def D_growth(z,cosmology):\n",
    "    return D_plus(z,cosmology)/D_plus(0.0,cosmology)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dmice = np.array([D_growth(zz,cosmo_mice) for zz in zarr])\n",
    "dbench = np.array([D_growth(zz,cosmo_bench) for zz in zarr])\n",
    "dratio = dmice/dbench"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.01563533  1.02360802  1.02975359  1.03439961  1.03788798]\n"
     ]
    }
   ],
   "source": [
    "print dratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.46628783  1.58113883  1.64316767  1.78885438  2.12132034]\n"
     ]
    }
   ],
   "source": [
    "bias_mice = np.sqrt(np.array([2.15,2.5,2.7,3.2,4.5]))\n",
    "print bias_mice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NgW2goqKCiRMn8vLLLzNlyhRqamrYsWMHXbt2BQxOtVKONSLExsYyffp0lixZ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\nAaTRJoQQQghhAGm0CSGEEEIY4P8HgzZcZptZ3UcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d0ca210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bias_std = np.zeros((5,6))\n",
    "bias_crocce = np.zeros((5,6))\n",
    "dbias_std = np.zeros((5,6))\n",
    "dbias_crocce = np.zeros((5,6))\n",
    "colors = ['blue','green','red','cyan','magenta']\n",
    "fig, ax = plt.subplots(nrows=2,ncols=2,figsize=(10,8))\n",
    "clist = [0,1,4]\n",
    "for i in range(0,5):\n",
    "    tab_bench_std = Table.read(path_std_bench[i])\n",
    "    tab_mice_std = Table.read(path_std[i])\n",
    "    if(i in clist):\n",
    "        tab_bench_crocce = Table.read(path_crocce_bench[i])\n",
    "        tab_mice_crocce = Table.read(path_crocce[i])\n",
    "    else:\n",
    "        tab_bench_crocce = tab_bench_std\n",
    "        tab_mice_crocce = tab_mice_std\n",
    "    ax[0,0].errorbar(scale_arr,tab_bench_std['skw'],tab_bench_std['dskw'],fmt='o',label='Std %d' %(i), c=colors[i])\n",
    "    ax[0,0].errorbar(scale_arr,tab_bench_crocce['skw'],tab_bench_crocce['dskw'],fmt='x',label='Crocce %d' %(i), c=colors[i])\n",
    "    ax[0,0].grid()\n",
    "    ax[0,0].legend(loc='best')\n",
    "    ax[0,0].set_xlabel(r'$\\theta$ [deg]')\n",
    "    ax[0,0].set_ylabel(r'$S_{3,data}$')\n",
    "    ax[0,0].set_xscale('log')\n",
    "    ax[0,0].set_xlim(5e-3,1.1)\n",
    "    ax[0,1].errorbar(scale_arr,tab_bench_std['kurt'],tab_bench_std['dkurt'],fmt='o',label='Std %d' %(i), c=colors[i])\n",
    "    ax[0,1].errorbar(scale_arr,tab_bench_crocce['kurt'],tab_bench_crocce['dkurt'],fmt='x',label='Crocce %d' %(i), c=colors[i])\n",
    "    ax[0,1].grid()\n",
    "    ax[0,1].legend(loc='best')\n",
    "    ax[0,1].set_xlabel(r'$\\theta$ [deg]')\n",
    "    ax[0,1].set_ylabel(r'$S_{4,data}$')\n",
    "    ax[0,1].set_xscale('log')\n",
    "    ax[0,1].set_xlim(5e-3,1.1)\n",
    "\n",
    "    sigma_mice = tab_mice_std['sigma']\n",
    "    dsigma_mice = tab_mice_std['dsigma']\n",
    "    skw_mice = tab_mice_std['skw']\n",
    "    dskw_mice = tab_mice_std['dskw']\n",
    "    kurt_mice = tab_mice_std['kurt']\n",
    "    dkurt_mice = tab_mice_std['dkurt']\n",
    "    sigma_mice2 = tab_mice_crocce['sigma']\n",
    "    dsigma_mice2 = tab_mice_crocce['dsigma']\n",
    "    skw_mice2 = tab_mice_crocce['skw']\n",
    "    dskw_mice2 = tab_mice_crocce['dskw']\n",
    "    kurt_mice2 = tab_mice_crocce['kurt']\n",
    "    dkurt_mice2 = tab_mice_crocce['dkurt']\n",
    "    av_sigma1 = np.sum(sigma_mice/dsigma_mice**2,axis=0)/np.sum(1./dsigma_mice**2,axis=0)\n",
    "    av_dsigma1 = np.sqrt(1./np.sum(1./dsigma_mice**2,axis=0))\n",
    "    av_skw1 = np.sum(skw_mice/dskw_mice**2,axis=0)/np.sum(1./dskw_mice**2,axis=0)\n",
    "    av_dskw1 = np.sqrt(1./np.sum(1./dskw_mice**2,axis=0))\n",
    "    av_kurt1 = np.sum(kurt_mice/dkurt_mice**2,axis=0)/np.sum(1./dkurt_mice**2,axis=0)\n",
    "    av_dkurt1 = np.sqrt(1./np.sum(1./dkurt_mice**2,axis=0))\n",
    "    av_sigma2 = np.sum(sigma_mice2/dsigma_mice2**2,axis=0)/np.sum(1./dsigma_mice2**2,axis=0)\n",
    "    av_dsigma2 = np.sqrt(1./np.sum(1./dsigma_mice2**2,axis=0))\n",
    "    av_skw2 = np.sum(skw_mice2/dskw_mice2**2,axis=0)/np.sum(1./dskw_mice2**2,axis=0)\n",
    "    av_dskw2 = np.sqrt(1./np.sum(1./dskw_mice2**2,axis=0))\n",
    "    av_kurt2 = np.sum(kurt_mice2/dkurt_mice2**2,axis=0)/np.sum(1./dkurt_mice2**2,axis=0)\n",
    "    av_dkurt2 = np.sqrt(1./np.sum(1./dkurt_mice2**2,axis=0))\n",
    "    bias_std[i,:]=np.sqrt(tab_bench_std['sigma']/av_sigma1)\n",
    "    dbias_std[i,:]=tab_bench_std['dsigma']/av_sigma1+tab_bench_std['sigma']*av_dsigma1/av_sigma1**2\n",
    "    bias_crocce[i,:]=np.sqrt(tab_bench_crocce['sigma']/av_sigma2)\n",
    "    dbias_crocce[i,:]=tab_bench_crocce['dsigma']/av_sigma2+tab_bench_crocce['sigma']*av_dsigma2/av_sigma2**2\n",
    "    ax[1,0].errorbar(scale_arr,dratio[i]*bias_mice[i]*bias_std[i],dratio[i]*bias_mice[i]*dbias_std[i],label='std %d' %(i),fmt='o',c=colors[i])\n",
    "    ax[1,0].errorbar(scale_arr,dratio[i]*bias_mice[i]*bias_crocce[i],dratio[i]*bias_mice[i]*dbias_crocce[i], label='crocce %d' %(i),fmt='x',c=colors[i])\n",
    "ax[1,0].axvspan(0.1, 0.6, facecolor='0.5', alpha=0.5)\n",
    "ax[1,0].set_ylim(0.0,2.5)\n",
    "ax[1,0].set_xlim(5e-3,1.1)\n",
    "ax[1,0].set_xlabel(r'$\\theta$ [deg]')\n",
    "ax[1,0].set_ylabel(r'$b_{1}$')\n",
    "ax[1,0].set_xscale('log')\n",
    "ax[1,0].grid()\n",
    "ax[1,0].legend(loc='best')\n",
    "av_bias = np.sum(bias_std[:,2:-1]/dbias_std[:,2:-1]**2,axis=1)/np.sum(1./dbias_std[:,2:-1]**2,axis=1)\n",
    "av_dbias = np.sqrt(1./np.sum(1./dbias_std[:,2:-1]**2,axis=1))\n",
    "av_bias2 = np.sum(bias_crocce[:,2:-1]/dbias_crocce[:,2:-1]**2,axis=1)/np.sum(1./dbias_crocce[:,2:-1]**2,axis=1)\n",
    "av_dbias2 = np.sqrt(1./np.sum(1./dbias_crocce[:,2:-1]**2,axis=1))\n",
    "ax[1,1].errorbar(zarr,dratio*bias_mice*av_bias,yerr=dratio*bias_mice*av_dbias,fmt='x',label='Std')\n",
    "ax[1,1].errorbar(zarr,dratio*bias_mice*av_bias2,yerr=dratio*bias_mice*av_dbias2,fmt='o',label='Crocce')\n",
    "ax[1,1].set_xlabel(r'$z$')\n",
    "ax[1,1].set_ylabel(r'$b_{1}$')\n",
    "ax[1,1].grid()\n",
    "ax[1,1].legend(loc='best')\n",
    "ax[1,1].set_xlim(-0.05,1.15)\n",
    "ax[1,1].set_ylim(0,2.5)\n",
    "savefig('/Users/javiers/CiC/moments_bias.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11479ded0>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110bfe450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MGUlSaQwZSVJpDBlJUmkMGUlSabrl9cuSpCZbt24Dl1/+VTZv3s6UKXuxaNG5zJgxranH\n8PXLkjQBrVu3gZNPvprHH18I7As8y2GHLWDZsgt3ChpfvyxJatjll3+1LmAA9uXxxxdy+eVfbepx\nDBlJmoBWrtzO8wEzZN+ivXkMGUmagE44YS/g2WGtzxbtzWPISNIEtGjRuRx22AKeD5ranMyiRec2\n9ThdMfEfEccBfw30AVuBCzLzH4tllwIfArYBH8/Mu3axDyf+JanO0NVlTzyxnUMOGfnqsvFO/HdL\nyNwJ/Hlm3hUR7wQuycw/jIhXAdcBbwSmAncDR4yUJoaMJDVuolxdth04oPg8GdhcfD4NWJKZ2zJz\nPbAWmNX68iRJI+mWmzH/O3BnRPw5EMCbi/YpwI/q1ttctEmSOkDHhExELAMOqm8CEpgPvI3afMtN\nEXEm8BXg5EaP0d/fv+NzpVKhUqmMo2JJ6j3VapVqtdq0/XXLnMxAZk4e/j0iPgVkZl5RtN8BLMjM\nlSPswzkZSWrQRJmT2RwRbwWIiDnU5l4AbgHmRcSkiJgBHA6salONkqRhOma4bA/OA/4iIl4ADAIf\nBsjM1RFxPbCa5y9ttrsiSR2iK4bLmsHhMklq3EQZLpMkdSFDRpJUGkNGklQaQ0aSVBpDRpJUGkNG\nklQaQ0aSVBpDRpJUGkNGklQaQ0aSVBpDRpJUGkNGklQaQ0aSVBpDRpJUGkNGklQaQ0aSVBpDRpJU\nGkNGklQaQ0aSVBpDRpJUGkNGklQaQ0aSVBpDRpJUmo4KmYg4MyJ+GhHPRcTxw5ZdGhFrI+KRiHh7\nXfvxEfFQRKyJiKtaX7UkaVc6KmSAh4EzgO/VN0bE0cBZwNHAO4EvRUQUi68B/iQzZwIzI+IdLaxX\nkrQbHRUymflYZq4FYtii04ElmbktM9cDa4FZEXEwsF9m3les93VgbssKliTtVkeFzG5MATbWfd9c\ntE0BNtW1byraJEkdYO9WHzAilgEH1TcBCczPzFvLPHZ/f/+Oz5VKhUqlUubhJKnrVKtVqtVq0/YX\nmdm0nTVLRHwX+ERm3l98/xSQmXlF8f0OYAGwAfhuZh5dtM8D3pqZ54+wz+zE3ypJnSwiyMzhUxij\n1snDZfU/6hZgXkRMiogZwOHAqsz8JfB0RMwqLgT4AHBzG2qVJI2go0ImIuZGxEbgROC2iPgOQGau\nBq4HVgO3AxfUdUs+AlwLrAHWZuYdra9ckjSSjhwuK4PDZZLUuF4eLpMkdTlDRpJUGkNGklQaQ0aS\nVBpDRpJUGkNGklQaQ0aSVBpDRpJUGkNGklQaQ0aSVBpDRpJUGkNGklQaQ0aSVBpDRpJUGkNGklQa\nQ0aSJqCla5YyMDiwU9vA4ABL1yxt6nEMGUmagGYfOpv598zfETQDgwPMv2c+sw+d3dTj+GZMSZqg\nhoLl4tkXc+XyK1k8ZzGT+ybvtM5434xpyEjSBHTViqu46dGbGNw2yMrNKzlhygn07d3H3KPmctGJ\nF+1Yz5AZJUNGknbWip6MczKSNAENBcziOYuZPnk6i+cs3mmOplnsyUjSBLR0zVJmHzp7p57LwOAA\ny3+xnFNnnrqjrad6MhFxZkT8NCKei4jj69rfFhH/GBEPRsR9EfGHdcuOj4iHImJNRFzVnsqlsatW\nq+0uQRPQqTNP/Z2hscl9k3cKmGboqJABHgbOAL43rP1fgHdn5nHAucA36pZdA/xJZs4EZkbEO1pR\nqNQshox6WUeFTGY+lplrgRjW/mBm/rL4/DOgLyL2iYiDgf0y875i1a8Dc1tadIu16x+kMo473n2O\nZftGthntuqNZb6IESTt+Z6+cm41u16zzs+y/s44KmdGIiDOB+zNzKzAF2FS3eFPR1rMMmfFtb8iU\ny5AZ3/a9GDItn/iPiGXAQfVNQALzM/PWYp3vAp/IzPuHbXsMcBNwcmauj4jXA5/NzLcXy98CXJKZ\np41wXGf9JWkMxjPxv3czCxmNzDx5LNtFxFTg/wJ/lJnri+bNwCvqVptatI103DH/T5IkjU0nD5ft\nCIWIOAC4DfhkZq4Yai/maZ6OiFkREcAHgJtbXqkkaUQdFTIRMTciNgInArdFxHeKRR8FDgP+Z0T8\nJCLuj4jfL5Z9BLgWWAOszcw7Wl64JGlEE+ZmTElS63VUT0aS1FsMGUlSaVp+dVkniYjTgVOB/YCv\nZOayNpck7RARM4D5wP6ZeVa765EAIuLFwJeALcD3MvNbu13fORmIiMnAlZl5XrtrkYaLiOsNGXWK\niHg/8OvMXBoRSzJz3u7W74nhsoi4NiJ+FREPDWs/JSIeLR6e+cnd7OLTwF+VW6Umqiacn1JpxnB+\nTgU2Fp+f29P+eyJkgL8DdnowZkTsBfxl0X4M8L6IOKpY9kcR8b8i4pCI+Bxwe2Y+0OqiNWGM9fx8\n+dDqrSxWE05D5ye1gJk6tOqedt4TIZOZPwB+Pax5FrX7ZjYUzzlbApxerP+NzPwz4D8Dc4AzI+LD\nraxZE8c4zs8tEXEN8Fp7OipLo+cncCO1fzP/Crh1T/vv5Yn/KTzfpYPawzNn1a+QmVcDV7eyKKkw\nmvPzKeD8VhYlFXZ5fmbmvwEfGu2OeqInI0nqTL0cMpuBQ+u+7/LhmVIbeH6qkzXt/OylkAl2noS6\nDzg8IqZFxCRgHnBLWyqTPD/V2Uo7P3siZCLiW8APqb1++RcR8cHMfA64ELgL+BmwJDMfaWedmpg8\nP9XJyj4/vRlTklSanujJSJI6kyEjSSqNISNJKo0hI0kqjSEjSSqNISNJKo0hI0kqjSEjSSqNISNJ\nKo0hI41T1PzXiPjTiDisrn1aRPw2Iu7fxXYLIuLPxnjMvoj4SUQMRsSBY61dKpshI43fx4CVQBU4\nc9iytZl5fLMPmJmDmfk64Ilm71tqJkNGGoeI2Bt4T/H67kOB/few/vyIeCwivg8cOWzZORGxMiLu\nj4hrIiKK9suLd61/PyK+Naz346uZ1dEMGWl8TgKeiYg/Bi6g9gbBEUXE8cBZwGuAU4E31i07Cjgb\neHPR89kOnBMRbwDOAI4F3gW8oaTfIZWil1+/LLXCm4GvZOZtEfFe4Ee7Wfc/Ajdm5hZgS0TUv59j\nDnA8cF/Rg+kDfgW8FLi5eM/61ojY4zvVpU5iyEjj83Lg58WLnQ4uhs3GIoCvZeb8nRojPj7eAqV2\ncrhMGp9/BbYA/wn44h7W/T4wNyJeGBH7Ae+pW3YPcGZEvAwgIl4SEYcCy4H3FNv8HvDupv8CqUT2\nZKTx+Ta1gPlNZl6zuxUz8ycR8X+Ah6gNha2qW/ZIRHwauCsi9gL+HfhIZq4qhtUeLLZ5CHi6nJ8i\nNZ9vxpRKEhHTgNsy89hx7mffzHw2Il5ErTd03tCwXESsA16fmU+Nv2Kp+Rwuk8rzHHDArm7GbMCX\nI+InwI+BGzLzgaGbMYEXULsSTepI9mQkSaWxJyNJKo0hI0kqjSEjSSqNISNJKo0hI0kqjSEjSSqN\nISNJKs3/B/qjEwXhYPSmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1137bb790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(scale_arr,av_skw1,av_dskw1,fmt='o')\n",
    "plt.errorbar(scale_arr,av_skw2,av_dskw2,fmt='x')\n",
    "plt.xscale('log')\n",
    "plt.ylabel(r'$S_{3,MICE}$')\n",
    "plt.xlabel(r'$\\theta$ [deg]')\n",
    "plt.figure()\n",
    "plt.errorbar(scale_arr,av_kurt1,av_dkurt1,fmt='o')\n",
    "plt.errorbar(scale_arr,av_kurt2,av_dkurt2,fmt='x')\n",
    "plt.xscale('log')\n",
    "plt.ylabel(r'$S_{4,MICE}$')\n",
    "plt.xlabel(r'$\\theta$ [deg]')\n",
    "\n",
    "#plt.ylim(0,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}