0401_bootstrap_fixed_dp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from astropy.stats import bootstrap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "zeropoint = np.load('Run_040118/bolp_21p0_0.npz')\n",
    "pert1 = np.load('Run_040118/bolp_21p0_um_0.npz')\n",
    "pert2 = np.load('Run_040118/bolp_21p0_um_0_1.npz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  1.33738  13.98811   0.5595   11.95797  12.82356   0.        0.     ]\n"
     ]
    }
   ],
   "source": [
    "p_0 = zeropoint['param'][0]\n",
    "print p_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "func_0 = np.mean(zeropoint['func_all'],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d_pert_func = np.concatenate((pert1['func_all'][:5000],pert2['func_all'][:15000],\\\n",
    "                            pert1['func_all'][5000:10000],pert2['func_all'][15000:30000],\\\n",
    "                            pert1['func_all'][10000:15000],pert2['func_all'][30000:45000],\\\n",
    "                            pert1['func_all'][15000:20000],pert2['func_all'][45000:60000],\\\n",
    "                            pert1['func_all'][20000:25000],pert2['func_all'][60000:75000],\\\n",
    "                            pert1['func_all'][25000:30000],pert2['func_all'][75000:90000],\\\n",
    "                            pert1['func_all'][30000:35000],pert2['func_all'][90000:105000],\\\n",
    "                            pert1['func_all'][35000:40000],pert2['func_all'][105000:120000],\\\n",
    "                            pert1['func_all'][40000:45000],pert2['func_all'][120000:135000],\\\n",
    "                            pert1['func_all'][45000:50000],pert2['func_all'][135000:150000],\\\n",
    "                            pert1['func_all'][50000:55000],pert2['func_all'][150000:165000],\\\n",
    "                            pert1['func_all'][55000:60000],pert2['func_all'][165000:180000],\\\n",
    "                            pert1['func_all'][60000:65000],pert2['func_all'][180000:195000],\n",
    "                            pert1['func_all'][65000:],pert2['func_all'][195000:]))-func_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "N_pert = 40000\n",
    "hN_pert = N_pert/2\n",
    "N_real = 1000\n",
    "len_obs = 120"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d_p_pert = np.concatenate((pert1['param'][:5000],pert2['param'][:15000],\\\n",
    "                            pert1['param'][5000:10000],pert2['param'][15000:30000],\\\n",
    "                            pert1['param'][10000:15000],pert2['param'][30000:45000],\\\n",
    "                            pert1['param'][15000:20000],pert2['param'][45000:60000],\\\n",
    "                            pert1['param'][20000:25000],pert2['param'][60000:75000],\\\n",
    "                            pert1['param'][25000:30000],pert2['param'][75000:90000],\\\n",
    "                            pert1['param'][30000:35000],pert2['param'][90000:105000],\\\n",
    "                            pert1['param'][35000:40000],pert2['param'][105000:120000],\\\n",
    "                            pert1['param'][40000:45000],pert2['param'][120000:135000],\\\n",
    "                            pert1['param'][45000:50000],pert2['param'][135000:150000],\\\n",
    "                            pert1['param'][50000:55000],pert2['param'][150000:165000],\\\n",
    "                            pert1['param'][55000:60000],pert2['param'][165000:180000],\\\n",
    "                            pert1['param'][60000:65000],pert2['param'][180000:195000],\n",
    "                            pert1['param'][65000:],pert2['param'][195000:]))-p_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.025,  0.   ,  0.   ,  0.   ,  0.   ,  0.   ,  0.   ],\n",
       "       [ 0.025,  0.   ,  0.   ,  0.   ,  0.   ,  0.   ,  0.   ],\n",
       "       [ 0.   , -0.05 ,  0.   ,  0.   ,  0.   ,  0.   ,  0.   ],\n",
       "       [ 0.   ,  0.05 ,  0.   ,  0.   ,  0.   ,  0.   ,  0.   ],\n",
       "       [ 0.   ,  0.   , -0.02 ,  0.   ,  0.   ,  0.   ,  0.   ],\n",
       "       [ 0.   ,  0.   ,  0.02 ,  0.   ,  0.   ,  0.   ,  0.   ],\n",
       "       [ 0.   ,  0.   ,  0.   , -0.1  ,  0.   ,  0.   ,  0.   ],\n",
       "       [ 0.   ,  0.   ,  0.   ,  0.1  ,  0.   ,  0.   ,  0.   ],\n",
       "       [ 0.   ,  0.   ,  0.   ,  0.   , -0.02 ,  0.   ,  0.   ],\n",
       "       [ 0.   ,  0.   ,  0.   ,  0.   ,  0.02 ,  0.   ,  0.   ],\n",
       "       [ 0.   ,  0.   ,  0.   ,  0.   ,  0.   , -0.05 ,  0.   ],\n",
       "       [ 0.   ,  0.   ,  0.   ,  0.   ,  0.   ,  0.05 ,  0.   ],\n",
       "       [ 0.   ,  0.   ,  0.   ,  0.   ,  0.   ,  0.   , -0.05 ],\n",
       "       [ 0.   ,  0.   ,  0.   ,  0.   ,  0.   ,  0.   ,  0.05 ]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_p_pert[::20000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bootstrap for df/dp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def bootstrap_mean(sample,nboot=1):\n",
    "    return bootstrap(sample, nboot, bootfunc=np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bootstrap_means = bootstrap_mean(d_pert_func[:20000,0],10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0602112e-06 2.06039391936e-06 4.5420681336e-08\n"
     ]
    },
    {
     "data": {
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AWElr+m4/VfWUJJcm+bkkf5Pk20l+Lcl/SvLCqnpea+2WOW4iAACsmLV+5v+/\nZgj+b2itvay1dmJr7bAk70/ytCTvnuvWAQDAClqz4X886/+CJBuSfGhq9v+T5K4kv1tVu63wpgEA\nwFys5ct+Dh2nf9dae2hyRmvtJ1X1pQwHB89J8tmV3jgAgLWgtweMbjj5xfPehK2yZs/8Z7isJ0mu\nWWT+teN0vxXYFgAAmLu1fOZ/93F6+yLzF9ofv6kFVdXli8z6N1dddVUOPPDAzd22beZ7311sdwEA\n2FoH/s8/WfF1XnXVVUmybjmWtZbD/0p41D333PPgFVdc8Q/z3hBYhfYfp9+e61bA6mNswOJW/fi4\n4gdzWe26JHcsx4LWcvhfOAW++yLzF9pv29SCWmszT+0vfCOw2HzomfEBsxkbsDjjY9tby9f8Xz1O\nF7um/6njdLHfBAAAwJqylsP/58bpC6rqp/azqh6b5HlJ7k7y9yu9YQAAMA9rNvy31q5P8ncZrpE6\nZmr2O5LsluTjrbW7VnjTAABgLtbyNf9J8h+TXJrk1Ko6PMlVSZ6d4RkA1yR56xy3DQAAVtSaPfOf\n/MvZ/4OSrM8Q+o9P8pQkH0zynNbaLfPbOgAAWFnVWpv3NgAAACtgTZ/5BwAAHib8AwBAJ4R/AADo\nhPAPAACdEP4BAKATwj8AAHRC+AcAgE7MJfxX1d5V9RdV9U9VdV9VbaiqD1TVHpu5nD3Hug3jcv5p\nXO7ey7nuqvqlqvqrqvphVd1bVVdX1Tuq6tEbqTm4qi6sqlur6p6qurKq3lhVO2yk5oiquriqbq+q\nO6vqsqo6eiP78daq+kRVXVdVD1VVq6pfXGz5Y92h43bdMu7/dVV1clU9dmN1rAxjY9GazRkbz6uq\n/1JVX62qH437cmNVfXRj46OqHj1u+9Xjvvxw3LcDFqthZRkfi9Zs0/FRVb9WVf+5qj5dVd8f/19z\n02Lbw3wYH4vWbLPxUVU/U1W/XVUfq6pvVtUdVXV3VX2jqt5ZqzVbtdZW9JXhCbs/SNKSfDLJyUku\nGt9/O8kTlricJyS5eqz77LicT47vf5Bk3+VYd4YnA9+V5P4k/yPJnyf56ljzxSQ7z6h5aZIHktyZ\n5GNJ3jMuvyX5xCL7c+w4/+YkH0ry/iT/39h2yoz+LxvnPZTk+iQ/Ht//4kb+Zq8b+9+f5OwkpyT5\n3Fh3ZZLdV/rz4GVsbIOx8f0kDyb5QpIPjJ/zL43970zy3Bk1O4/b3MZ9+PNxn/553Mdnz/vz0fvL\n+Jjr+PgWxf0eAAAHO0lEQVTAOP/+JF8f//mmeX8mvIyPeY+PJPtPzDt/3I8PJblubL86yc/O+/Px\niP2cwwf0b8c/yHFT7e8b289Y4nL+29j/vVPtbxjbP7O1606yQ5JvjfNeMtH+qCTnju0nTtU8LskP\nk9yX5KCJ9l2SXDrWHDVVsy7JvUluSbJuon2PiQ/Q9Adu7yS/nuRx4/uLs5Hwn2SvJPdkCDO/NjXv\n/x5rT533B7Lnl7GxbGPjj5M8acY+vmXs/40Z8xbGwCeSPGqi/aVj+/+ebPda+ZfxMdfx8Ywkv5pk\np/G98L/KXsbHfMZHkicn+Y9Jdptq3ynJp8aa0+b9+XjE/qzwh/Mp4x/ixkz9jzTJYzMcOd01/Uec\nsZzHJLl77P/YqXmPSrJhXM++W7PuJIeNNZ+fsQ37jvM2JKmJ9leP7X85o2bm8pK8c2x/x4yaRZc3\n1e/ibDz8LyznEUfH40C8Zfwb7DrvD2WPL2Nj242Nif47jH+blokzUUkqyXfG9n1m1F0yzjt03p+T\nXl/Gx/zGxyJ9hf9V9DI+Vtf4mKg5OIscUM/7tdLX/B86Tv+utfbQ5IzW2k8yfLWya5LnbGI5z0ny\n6CRfGusml/NQhqPQyfVt6boPG6efmd6A1toNSa5J8q8zfFg3WZMhRNyd5OCq2nmJNZ+e6rOlfn6c\n3jA9o7X2YIbws1uGr+JYecbGth8bLcNXxsnwte6CpyT5hSTXtNZuXIb1sPyMj/mND1Y/42N1jo9/\nHqcPbLTXHKx0+H/aOL1mkfnXjtP9tsFy5l7TWnsgw9HxjvnpD/XGar6X4ah576radZHtWIqbx+k+\n0zOq6lEZBtrktrCyjI1tPzZekeFM1N+31m5byjpGS/3bs+0YH/MbH6x+xsfqHB+vHqezDj7maqXD\n/+7j9PZF5i+0P34bLGct1Oy+yPyl+NsMR58vq6qDpuadkGTP8Z83664ALBtjYxuOjaraJ8lpGcbA\nm6dmL9ffnm3H+Jjf+GD1Mz5W2fioqpck+cMkNyX5L0upWUk7znsDWBmtte9U1TuS/GmSL1XVeUm+\nm+SZGb62uzLJ0zPcDQjWjKr6uQxf8T4xyTGttS/PeZNg1TA+YHFbMj6q6uAMdzC6K8nLW2s/3rZb\nuflW+sz/po6yFto39ZXKlixnLdQsdvS6JK21dyU5MsllSX4ryTEZrsU7IsNtrZLh1/SsPGNjG4yN\n8T/cF2X4+vc/tdb+64xuy/W3Z9sxPuY3Plj9jI9VMj6q6rkZDhYeSvLC1tpXNlUzDysd/q8ep4td\nd/bUcbrYdWBbs5y511TVjhmuuX8gP/3D243V7JXhh7g3tdbuXmQ7lqy1dl5r7ZDW2mNba7u21p7b\nWrsww1n/ZLjPLivP2FjmsTHOvzjJL2U4Y3PqItu6XH97th3jY37jg9XP+FgF46Oqfj0P3/b0Ba21\nL22qZm5W8tZCWbnbUd2Yzm5HlU3c6nMJ/14eTHLlSn4evB7x78DYWKaxkeE5GNdkOPvy2k38zdzq\nc5W/jI/5jY9F/o5u9bmKXsbH/MfHuA13Jbk1ybPm/ZnY5PbO4UO6uQ+D2D/J/jOWM+8HUXxibJ/1\nIIofZfMeRLFPNvNBFDP27eJsIvxnfCDYVNsTkvz9WPuieX8ge34ZG8szNjLcueqGDAe0/2GJf3sP\n+VrlL+NjfuNjxt9D+F9lL+Njrv//eEGGg6abk/zqvD8LS3nVuOErpqqekuFf1M8l+ZskV2W4t/yh\nGY60Dm6t3TLRf/gvTWs1tZwnjMvZL8M1WV9JckCG/1n/cFzO9Vuz7rHm2ePyfybDk+f+McnhSQ7K\ncP/aw1tr903VvGzse2+SszMcCb4kw3Vj5yb5v9rUH76qjktyaoYP6TkZHnl9ZIYj0Pe21k6Y8bdc\nP/H2hUn+VZL/N8nC/Xk/2lr74kT/U8Z+Xx7/Rk8et+vxSU5orb1veh2sHGNjecZGVd2Y4cmOl2d4\nwuIs61trGyZqdh735eAkX8vwWPtfyHB7t/uTHNZau2yRZbECjI+5jo/9k5w4Mf/oDGHnExNtJ7TW\nbg5zYXzMZ3xU1dOSfD3DQch5Sb45q6C1dtIiy5qPeRxxJPk/kpyZ5HsZ/kV8J8kHkuwxo28bNnPm\ncvZM8sGx/v5xeX+RZO/lWPdEzS9l+I/czRmOOq9J8o4kj95IzfOSXJjkx0nuSfKNJG9KssNGan4r\nyeczhPe7Mlx/f/RG+rdNvP7DVP8XZxhsPxz3/fvjfj1nHp8Dr637fBobs8fGEsZFS/L8GXW7Zvia\n+NpxX3407tsvzftz4bX5n1HjY/nGR5LnL6Fm3bw/H72/jI+VHx9LHBsz/87zfK34mX8AAGA+Vvpu\nPwAAwJwI/wAA0AnhHwAAOiH8AwBAJ4R/AADohPAPAACdEP4BAKATwj8AAHRC+AcAgE4I/wAA0Anh\nHwAAOiH8AwBAJ4R/AADohPAPAACdEP4BAKATwj8AAHRC+AcAgE78/+BtdnMb94EJAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f155278e0d0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 383
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bootstrap_means)\n",
    "plt.xticks((0.0000019,0.000002,0.0000021,0.0000022))\n",
    "print np.mean(d_pert_func[:20000,0]),np.mean(bootstrap_means),np.std(bootstrap_means)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "subsample_10000 = d_pert_func[np.random.randint(20000,size=10000),0]\n",
    "bootstrap_means_10000 = bootstrap_mean(subsample_10000,10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0601216e-06 2.06050628352e-06 6.43992330954e-08\n"
     ]
    },
    {
     "data": {
      "image/png": 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YvKvG6aGL3JdL5pl12GLqAQBgtdmZZwROyXBh73mttb+daN9/nG6Zp26u/QE7ULP/1HR7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\nWZfd+HNmlqQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15525b6dd0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 251,
       "width": 385
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bootstrap_means_10000)\n",
    "print np.mean(subsample_10000),np.mean(bootstrap_means_10000),np.std(bootstrap_means_10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "subsample_5000 = d_pert_func[np.random.randint(20000,size=5000),0]\n",
    "bootstrap_means_5000 = bootstrap_mean(subsample_5000,10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.9990912e-06 1.99926837888e-06 9.01496239428e-08\n"
     ]
    },
    {
     "data": {
      "image/png": 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M8paxv2dv+QgBAICt0fsC8R+My9sm1j1+XH5oRvsLktyY5LCq2n2ZNR+carOaGgAAYE7m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\nEDYAAIAuhA0AAKCL/x+puzBvYAGvDQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f155240c750>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 397
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bootstrap_means_5000)\n",
    "print np.mean(subsample_5000),np.mean(bootstrap_means_5000),np.std(bootstrap_means_5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "mean_bootstrap_means = []\n",
    "std_bootstrap_means = []\n",
    "mean_subsample = []\n",
    "for i in range(100):\n",
    "    subsample_i = d_pert_func[np.random.randint(20000,size=i*200+200),0]\n",
    "    bootstrap_means_i = bootstrap_mean(subsample_i,10000)\n",
    "    mean_bootstrap_means.append(np.mean(bootstrap_means_i))\n",
    "    std_bootstrap_means.append(np.std(bootstrap_means_i))\n",
    "    mean_subsample.append(np.mean(subsample_i))\n",
    "    print i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1538487890>]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Bp4BNSql3ARvGwqIpwF87SCSwCiNg2wykK6V+18HrfbVlcVGl1DjgYyAI+BRY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\nHfMyzSEKAZM6rPfXKafy5j2Xo5UPJxz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IOJ9+mozX9eTnu/2z+L9LHxDfTL8Sl642bjrx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\nhn0M5v9V3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8Bh+x70yiv1D9dn9d/3fWmbApweIVqWOIpEmoIciRq3BCay7rQbCFaUsq2t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\n+AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1535157cd0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 412
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(np.arange(1,101)*200,mean_bootstrap_means,std_bootstrap_means,linestyle=':')\n",
    "plt.plot(np.arange(1,101)*200,mean_subsample,'r-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfdp = np.zeros((7,len_obs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def absmin(array):\n",
    "    absarray = np.abs(array)\n",
    "    arg = np.argmin(absarray)\n",
    "    return array[arg]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of using mean of (df+)/(dp+) and (df-)/(dp-), use the smaller value?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "for i in range(7):\n",
    "    for j in range(len_obs):\n",
    "        print i,j\n",
    "        dfdp[i,j] = absmin(np.concatenate((bootstrap_mean(d_pert_func[i*N_pert:i*N_pert+hN_pert,j],100),\\\n",
    "                                       -bootstrap_mean(d_pert_func[i*N_pert+hN_pert:i*N_pert+N_pert,j],100))))\\\n",
    "                                       /d_p_pert[i*N_pert,i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def calc_cov(covjk,func_real):\n",
    "    return np.mean(covjk,axis=0)+np.cov(func_real.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def calc_fisher(dfdp, covtot):\n",
    "    fmatrix = np.zeros((7,7))\n",
    "    for i in range(7):\n",
    "        for j in range(7):\n",
    "            fmatrix[i,j] = np.dot(dfdp[i],np.dot(np.linalg.inv(covtot),dfdp[j]))\n",
    "    return fmatrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def calc_1sigma(fisher):\n",
    "    return np.sqrt(np.linalg.inv(fisher).diagonal())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.05717491,  0.01695421,  0.03475023,  0.1341977 ,  0.02298365,\n",
       "        0.2158275 ,  0.28054175])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc_1sigma(calc_fisher(dfdp,calc_cov(zeropoint['func_all_cov'][:,:len_obs,:len_obs],zeropoint['func_all'][:,:len_obs])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1.00000000e+00,   1.00000000e+00,   1.00000000e+00,\n",
       "          2.00000000e+00,   9.00000000e+00,   1.00000000e+01,\n",
       "          1.40000000e+01,   1.90000000e+01,   3.00000000e+01,\n",
       "          5.90000000e+01,   8.30000000e+01,   1.22000000e+02,\n",
       "          1.57000000e+02,   2.22000000e+02,   2.74000000e+02,\n",
       "          3.64000000e+02,   4.22000000e+02,   5.37000000e+02,\n",
       "          6.56000000e+02,   7.02000000e+02,   7.61000000e+02,\n",
       "          9.59000000e+02,   1.04000000e+03,   1.09700000e+03,\n",
       "          1.10200000e+03,   1.17500000e+03,   1.15400000e+03,\n",
       "          1.15300000e+03,   1.12100000e+03,   9.35000000e+02,\n",
       "          9.56000000e+02,   9.23000000e+02,   7.96000000e+02,\n",
       "          6.48000000e+02,   5.57000000e+02,   4.25000000e+02,\n",
       "          3.85000000e+02,   2.68000000e+02,   2.42000000e+02,\n",
       "          1.94000000e+02,   1.49000000e+02,   7.20000000e+01,\n",
       "          6.80000000e+01,   4.50000000e+01,   3.20000000e+01,\n",
       "          2.70000000e+01,   1.00000000e+01,   9.00000000e+00,\n",
       "          9.00000000e+00,   3.00000000e+00]),\n",
       " array([ -2.26663040e-05,  -2.17216640e-05,  -2.07770240e-05,\n",
       "         -1.98323840e-05,  -1.88877440e-05,  -1.79431040e-05,\n",
       "         -1.69984640e-05,  -1.60538240e-05,  -1.51091840e-05,\n",
       "         -1.41645440e-05,  -1.32199040e-05,  -1.22752640e-05,\n",
       "         -1.13306240e-05,  -1.03859840e-05,  -9.44134400e-06,\n",
       "         -8.49670400e-06,  -7.55206400e-06,  -6.60742400e-06,\n",
       "         -5.66278400e-06,  -4.71814400e-06,  -3.77350400e-06,\n",
       "         -2.82886400e-06,  -1.88422400e-06,  -9.39584000e-07,\n",
       "          5.05600000e-09,   9.49696000e-07,   1.89433600e-06,\n",
       "          2.83897600e-06,   3.78361600e-06,   4.72825600e-06,\n",
       "          5.67289600e-06,   6.61753600e-06,   7.56217600e-06,\n",
       "          8.50681600e-06,   9.45145600e-06,   1.03960960e-05,\n",
       "          1.13407360e-05,   1.22853760e-05,   1.32300160e-05,\n",
       "          1.41746560e-05,   1.51192960e-05,   1.60639360e-05,\n",
       "          1.70085760e-05,   1.79532160e-05,   1.88978560e-05,\n",
       "          1.98424960e-05,   2.07871360e-05,   2.17317760e-05,\n",
       "          2.26764160e-05,   2.36210560e-05,   2.45656960e-05]),\n",
       " <a list of 50 Patch objects>)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YHrlA2cOT7JHk8tba3WPrt1bnORNlAABg5k0r/H92WL6sqh41vqGqnpPkaUnuSnL5sPr8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\nAJ0Q/gEAoBPCPwAAdEL4BwCATvz/0mUU0vq0QpMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f155a9160d0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 383
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(d_pert_func[0:20000,0],50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.stats import normaltest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=8.7209389663771653, pvalue=0.012772389819407487)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normaltest(d_pert_func[0:20000,0])"
   ]
  },
  {
   "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
}