Mochima_2_2LUPM_HR.ipynb 1.22 MB
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{
 "cells": [
  {
   "cell_type": "code",
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   "execution_count": 1,
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   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 2,
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   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from scipy.stats import rv_continuous\n",
    "from scipy.special import gamma\n",
    "import numpy as np\n",
    "import emcee\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "from numpy import exp, sqrt\n",
    "from scipy.integrate import quad, dblquad, simps\n",
    "from scipy.stats import rv_continuous\n",
    "from scipy.special import gamma\n",
    "from scipy.interpolate import interp1d\n",
    "from scipy.integrate import quad\n",
    "import scipy.optimize as optimize\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from sklearn.neighbors import KDTree\n",
    "import sys\n",
    "import lmfit\n",
    "from py_unsio import *\n",
    "import pymc\n",
    "import os\n",
    "from pymodelfit import FunctionModel1DAuto\n",
    "import wkbl\n",
    "from mpl_toolkits.mplot3d import axes3d\n",
    "from matplotlib import cm\n",
    "import wkbl.astro.nbody_essentials as nbe\n",
    "import cfalcon\n",
    "CF =cfalcon.CFalcon()\n",
    "import iminuit\n",
    "from iminuit import Minuit, describe, Struct\n",
    "import probfit\n",
    "import warnings\n",
    "from matplotlib.colors import LogNorm\n",
    "from mpl_toolkits.axes_grid.inset_locator import inset_axes\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DMO"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": null,
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   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading Dark matter..\n"
     ]
    }
   ],
   "source": [
    "simname = \"Mochima\"\n",
    "pathsim = \"/data/OWN/DMO/mochima2/output_00041\"\n",
    "#path = \"/media/arturo/ARTUROTECA/OUTPUTS/HaloB/output_00417\"\n",
    "myDMO = wkbl.Galaxy_Hound(pathsim)\n",
    "zoomreg = np.where(myDMO.dm.mass==myDMO.dm.mass.min())\n",
    "centro = nbe.real_center(myDMO.dm.pos3d[zoomreg],myDMO.dm.mass[zoomreg])\n",
    "\n",
    "myDMO.center_shift(centro)\n",
    "myDMO.r_virial(600,n=2.5)\n",
    "myDMO.r200\n",
    "myDMO.redefine(2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nK = np.sum(myDMO.dm.mass*(myDMO.dm.v)**2)\\nprint K\\nmyGkm = 6.673e-11*(1e-3**3)*myDMO.p.msuntokg#km^ 3 Msun^-1 s^-2\\nr_sorted = np.argsort(myDMO.dm.r)\\nM_i = np.cumsum(myDMO.dm.mass[r_sorted]) - myDMO.dm.mass[r_sorted]\\nm_i = myDMO.dm.mass[r_sorted]\\nr_i = myDMO.dm.r[r_sorted]*(1e-2*myDMO.p.pctocm)# in km\\nU =  np.sum(-myGkm*M_i*m_i/r_i)\\nprint U\\nprint K/U + 1\\n'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "K = np.sum(myDMO.dm.mass*(myDMO.dm.v)**2)\n",
    "print K\n",
    "myGkm = 6.673e-11*(1e-3**3)*myDMO.p.msuntokg#km^ 3 Msun^-1 s^-2\n",
    "r_sorted = np.argsort(myDMO.dm.r)\n",
    "M_i = np.cumsum(myDMO.dm.mass[r_sorted]) - myDMO.dm.mass[r_sorted]\n",
    "m_i = myDMO.dm.mass[r_sorted]\n",
    "r_i = myDMO.dm.r[r_sorted]*(1e-2*myDMO.p.pctocm)# in km\n",
    "U =  np.sum(-myGkm*M_i*m_i/r_i)\n",
    "print U\n",
    "print K/U + 1\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 5,
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   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ok,myDMO.dm.rho,_= CF.getDensity(np.array(myDMO.dm.pos3d.reshape(len(myDMO.dm.pos3d)*3),dtype=np.float32), myDMO.dm.mass)\n"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 6,
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   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def abg_logprofile(x,p_s,r_s,al,be,ga):\n",
    "    x = 10**x\n",
    "    power =  (be - ga) / (al)\n",
    "    denominator = ((x/(r_s))**ga) * ((1 + (x / (r_s))**al)**power)\n",
    "    return np.log10(10**p_s / denominator)\n",
    "\n",
    "def abg_profile(x,po,r_s,al,be,ga):\n",
    "    power =  (be - ga) / al\n",
    "    denominator = ((x/r_s)**ga) * ((1 + (x / r_s)**al)**power)\n",
    "    return (10**po) / denominator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mass fit"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 7,
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   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "2.2127749336898894\n",
      "0.2 2.2127749336898894\n"
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     ]
    }
   ],
   "source": [
    "Pcrit = myDMO.dm._p.rho_crit\n",
    "Mdm = myDMO.dm.mass.min()\n",
    "myradiuses = myDMO.dm.r[np.argsort(myDMO.dm.r)]\n",
    "tabN = np.cumsum(np.ones(len(myradiuses)))[1:]\n",
    "myradiuses = myradiuses[1:]\n",
    "Rp03 = np.sqrt(200/64.) * np.sqrt(4 * np.pi * Pcrit * tabN / 3. / Mdm ) * (myradiuses**1.5)/ np.log(tabN) \n",
    "val =0.6\n",
    "R_P03 = myradiuses[ np.where(Rp03 > val) ][0]\n",
    "\n",
    "\n",
    "print R_P03\n",
    "hsml= 0.2# R_P03\n",
    "print hsml,R_P03\n",
    "# R array logarithmic Bining\n",
    "r_p = np.logspace(np.log10(0.2*hsml),np.log10(hsml),15)\n",
    "# histogram of dm particles per logarithmic bin\n",
    "n_dm,r = np.histogram(myDMO.dm.r,bins=r_p)\n",
    "# edges of bins\n",
    "r1,r2 =r[:-1],r[1:]\n",
    "# shell's volume\n",
    "vol = 4.* np.pi * ((r2**3)-(r1**3)) / 3.\n",
    "r_size = r_p[1:]-r_p[:-1]\n",
    "# density per shell\n",
    "profileDMO_in = n_dm*myDMO.dm.mass.min()/vol\n",
    "# center of bins\n",
    "r_in = (r_p[:-1]+r_p[1:])/2.\n",
    "\n",
    "\n",
    "# R array logarithmic Bining\n",
    "r_p = np.logspace(np.log10(3*hsml),np.log10(2.5*myDMO.r200),150)\n",
    "# histogram of dm particles per logarithmic bin\n",
    "n_dm,r = np.histogram(myDMO.dm.r,bins=r_p)\n",
    "# edges of bins\n",
    "r1,r2 =r[:-1],r[1:]\n",
    "# shell's volume\n",
    "vol = 4.* np.pi * ((r2**3)-(r1**3)) / 3.\n",
    "r_size = r_p[1:]-r_p[:-1]\n",
    "# density per shell\n",
    "profileDMO = n_dm*myDMO.dm.mass.min()/vol\n",
    "# center of bins\n",
    "r = (r_p[:-1]+r_p[1:])/2.\n",
    "bin_size= (r_p[:-1]-r_p[1:])/2.\n",
    "rr = r\n",
    "\n",
    "\n",
    "Delta_rho = (myDMO.dm.mass.min() /vol) + (4*np.pi*(r**2)* (n_dm*myDMO.dm.mass.min()) * r_size / vol**2)\n",
    "Delta_rho2 = np.sqrt((myDMO.dm.mass.min()/np.sqrt(n_dm) /vol)**2 + (4*np.pi*(r**2)* (n_dm*myDMO.dm.mass.min()) * r_size / vol**2)**2)\n",
    "Delta_rho3 =(4*np.pi*(r**2)* (n_dm*myDMO.dm.mass.min()) * r_size / vol**2)\n",
    "Delta_rho4 =(myDMO.dm.mass.min() /vol)\n",
    "\n",
    "# extra estatistics from Cfalcon density\n",
    "mean = std = n = stdlog = np.array([])\n",
    "for i in range(len(r_p)-1):\n",
    "    shell = np.where((myDMO.dm.r > r_p[i])&(myDMO.dm.r < r_p[i+1])&(myDMO.dm.r > hsml))\n",
    "    n = np.append(n,len(shell[0]))\n",
    "    mean = np.append(mean,np.mean(myDMO.dm.rho[shell]))\n",
    "    std = np.append(std,np.std(myDMO.dm.rho[shell]))\n",
    "    stdlog = np.append(stdlog,np.std(np.log10(myDMO.dm.rho[shell])))\n",
    "    \n",
    "n_dm_bin = n\n",
    "m_obs = n_dm*myDMO.dm.mass.min()\n",
    "n = np.array([len(myDMO.dm.mass[myDMO.dm.r<i]) for i in r]) \n",
    "r_dmo_profile =r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
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   "execution_count": 8,
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   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def abg_logprofile(x,p_s,r_s,al,be,ga):\n",
    "    x = 10**x\n",
    "    power =  (be - ga) / (al)\n",
    "    denominator = ((x/(r_s))**ga) * ((1 + (x / (r_s))**al)**power)\n",
    "    return np.log10(10**p_s / denominator)\n",
    "\n",
    "def abg_profile(x,po,r_s,al,be,ga):\n",
    "    power =  (be - ga) / al\n",
    "    denominator = ((x/r_s)**ga) * ((1 + (x / r_s)**al)**power)\n",
    "    return (10**po) / denominator\n",
    "\n",
    "\n",
    "\n",
    "def chi2_mass_bin_log(po,r_s,al,be,ga):\n",
    "    \"\"\"\n",
    "    logarithmic Chi-square\n",
    "    using the full mass inside a shell\n",
    "    between Ri and Rf\n",
    "    \"\"\"\n",
    "    def my_int(Ri,Rf):\n",
    "        r_test = np.logspace(np.log10(Ri),np.log10(Rf),100)\n",
    "        rho_test =  (r_test**2) * abg_profile(r_test,po,r_s,al,be,ga)\n",
    "        return 4* np.pi * simps(rho_test,r_test)\n",
    "    expected = np.array([my_int(r_p[i],r_p[i+1]) for i in range(len(r))])\n",
    "    c = (np.log10(m_obs)- np.log10(expected))\n",
    "    c = c**2\n",
    "    return np.sum(c)\n",
    "\n",
    "def chi2_mass_bin(po,r_s,al,be,ga):\n",
    "    \"\"\"\n",
    "    Chi-square\n",
    "    using the full mass inside a shell\n",
    "    between Ri and Rf\n",
    "    \"\"\"\n",
    "    def my_int(Ri,Rf):\n",
    "        r_test = np.logspace(np.log10(Ri),np.log10(Rf),100)\n",
    "        rho_test =  (r_test**2) * abg_profile(r_test,po,r_s,al,be,ga)\n",
    "        return 4* np.pi * simps(rho_test,r_test)\n",
    "    expected = np.array([my_int(r_p[i],r_p[i+1]) for i in range(len(r))])\n",
    "    c = (m_obs - expected)/ (np.sqrt(n))\n",
    "    c = c**2\n",
    "    return np.sum(c)\n",
    "\n",
    "\n",
    "\n",
    "def chi2_rho_log(po,r_s,al,be,ga):\n",
    "    \"\"\"\n",
    "    logarithmic Chi-square\n",
    "    using mean of rho per shell\n",
    "    \"\"\"\n",
    "    rho_obs = profileDMO\n",
    "    rho_the = np.array([abg_profile(i,po,r_s,al,be,ga) for i in r])\n",
    "    c = (np.log10(rho_the) - np.log10(rho_obs))/ stdlog\n",
    "    c = c**2\n",
    "    return np.sum(c)\n",
    "\n",
    "def chi2_rho(po,r_s,al,be,ga):\n",
    "    \"\"\"\n",
    "    logarithmic Chi-square\n",
    "    using mean of rho per shell\n",
    "    \"\"\"\n",
    "    rho_obs = profileDMO\n",
    "    rho_the = np.array([abg_profile(i,po,r_s,al,be,ga) for i in r])\n",
    "    c = (rho_the - rho_obs)/ std\n",
    "    c = c**2\n",
    "    return np.sum(c)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 9,
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   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<hr>"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <table>\n",
       "            <tr>\n",
365 366 367
       "                <td title=\"Minimum value of function\">FCN = 16.8412611722</td>\n",
       "                <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 247</td>\n",
       "                <td title=\"Number of call in last migrad\">NCALLS = 247</td>\n",
368 369
       "            </tr>\n",
       "            <tr>\n",
370
       "                <td title=\"Estimated distance to minimum\">EDM = 4.53971357878e-05</td>\n",
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       "                <td title=\"Maximum EDM definition of convergence\">GOAL EDM = 1e-05</td>\n",
       "                <td title=\"Error def. Amount of increase in FCN to be defined as 1 standard deviation\">\n",
       "                UP = 1.0</td>\n",
       "            </tr>\n",
       "        </table>\n",
       "        \n",
       "        <table>\n",
       "            <tr>\n",
       "                <td align=\"center\" title=\"Validity of the migrad call\">Valid</td>\n",
       "                <td align=\"center\" title=\"Validity of parameters\">Valid Param</td>\n",
       "                <td align=\"center\" title=\"Is Covariance matrix accurate?\">Accurate Covar</td>\n",
       "                <td align=\"center\" title=\"Positive definiteness of covariance matrix\">PosDef</td>\n",
       "                <td align=\"center\" title=\"Was covariance matrix made posdef by adding diagonal element\">Made PosDef</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td align=\"center\" title=\"Was last hesse call fail?\">Hesse Fail</td>\n",
       "                <td align=\"center\" title=\"Validity of covariance\">HasCov</td>\n",
       "                <td align=\"center\" title=\"Is EDM above goal EDM?\">Above EDM</td>\n",
       "                <td align=\"center\"></td>\n",
       "                <td align=\"center\" title=\"Did last migrad call reach max call limit?\">Reach calllim</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "                <td align=\"center\"></td>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "            </tr>\n",
       "        </table>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <table>\n",
       "            <tr>\n",
419
       "                <td><a href=\"#\" onclick=\"$('#xUzFNsvJsd').toggle()\">+</a></td>\n",
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       "                <td title=\"Variable name\">Name</td>\n",
       "                <td title=\"Value of parameter\">Value</td>\n",
       "                <td title=\"Parabolic error\">Parab Error</td>\n",
       "                <td title=\"Minos lower error\">Minos Error-</td>\n",
       "                <td title=\"Minos upper error\">Minos Error+</td>\n",
       "                <td title=\"Lower limit of the parameter\">Limit-</td>\n",
       "                <td title=\"Upper limit of the parameter\">Limit+</td>\n",
       "                <td title=\"Is the parameter fixed in the fit\">FIXED</td>\n",
       "            </tr>\n",
       "        \n",
       "            <tr>\n",
       "                <td>1</td>\n",
       "                <td>po</td>\n",
433 434
       "                <td>7.44332</td>\n",
       "                <td>0.453937</td>\n",
435 436 437 438 439 440 441 442 443 444
       "                <td>0</td>\n",
       "                <td>0</td>\n",
       "                <td>2.0</td>\n",
       "                <td>11.0</td>\n",
       "                <td></td>\n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                <td>2</td>\n",
       "                <td>r_s</td>\n",
445 446
       "                <td>7.68954</td>\n",
       "                <td>4.56616</td>\n",
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       "                <td>0</td>\n",
       "                <td>0</td>\n",
       "                <td>1.0</td>\n",
       "                <td>30.0</td>\n",
       "                <td></td>\n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                <td>3</td>\n",
       "                <td>al</td>\n",
       "                <td>1</td>\n",
       "                <td>1</td>\n",
       "                <td>0</td>\n",
       "                <td>0</td>\n",
       "                <td></td>\n",
       "                <td></td>\n",
       "                <td>FIXED</td>\n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                <td>4</td>\n",
       "                <td>be</td>\n",
469 470
       "                <td>2.56188</td>\n",
       "                <td>0.182718</td>\n",
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       "                <td>0</td>\n",
       "                <td>0</td>\n",
       "                <td>2.5</td>\n",
       "                <td>3.5</td>\n",
       "                <td></td>\n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                <td>5</td>\n",
       "                <td>ga</td>\n",
481 482
       "                <td>0.840409</td>\n",
       "                <td>0.203063</td>\n",
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       "                <td>0</td>\n",
       "                <td>0</td>\n",
       "                <td>0.5</td>\n",
       "                <td>1.5</td>\n",
       "                <td></td>\n",
       "            </tr>\n",
       "            \n",
       "            </table>\n",
       "        \n",
492
       "            <pre id=\"xUzFNsvJsd\" style=\"display:none;\">\n",
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       "            <textarea rows=\"16\" cols=\"50\" onclick=\"this.select()\" readonly>\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
       "\\hline\n",
       " & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\\\\n",
       "\\hline\n",
497
       "1 & po & 7.443e+00 & 4.539e-01 &  &  & 2.000e+00 & 1.100e+01 & \\\\\n",
498
       "\\hline\n",
499
       "2 & $r_{s}$ & 7.690e+00 & 4.566e+00 &  &  & 1.000e+00 & 3.000e+01 & \\\\\n",
500 501 502
       "\\hline\n",
       "3 & al & 1.000e+00 & 1.000e+00 &  &  &  &  & FIXED\\\\\n",
       "\\hline\n",
503
       "4 & be & 2.562e+00 & 1.827e-01 &  &  & 2.500e+00 & 3.500e+00 & \\\\\n",
504
       "\\hline\n",
505
       "5 & ga & 8.404e-01 & 2.031e-01 &  &  & 5.000e-01 & 1.500e+00 & \\\\\n",
506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527
       "\\hline\n",
       "\\end{tabular}</textarea>\n",
       "            </pre>\n",
       "            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<hr>"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
528
      "chi_rho = 16.84, chi_bin = 0.82\n"
529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "m_rho = Minuit(chi2_rho_log, al=1., fix_al=True,\n",
    "         po=7.0,    error_po=0.01,  limit_po =(2.,11.),\n",
    "         r_s=7.3,  error_r_s=0.1,   limit_r_s=(1.,30),\n",
    "         be=3.,     error_be=0.1,   limit_be =(2.5,3.5),\n",
    "         ga=1.,     error_ga=0.1,   limit_ga =(.5,1.5))\n",
    "m_rho.migrad();\n",
    "chirhorho = chi2_rho_log(m_rho.values['po'] ,m_rho.values['r_s'],m_rho.values['al'],m_rho.values['be'],m_rho.values['ga'])\n",
    "chibinrho= chi2_mass_bin_log(m_rho.values['po'] ,m_rho.values['r_s'],m_rho.values['al'],m_rho.values['be'],m_rho.values['ga'])\n",
    "print \"chi_rho = {0:1.2f}, chi_bin = {1:1.2f}\".format(chirhorho,chibinrho)"
   ]
  },
  {
   "cell_type": "code",
548
   "execution_count": 10,
549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<hr>"
      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <table>\n",
       "            <tr>\n",
568 569 570
       "                <td title=\"Minimum value of function\">FCN = 0.517897199617</td>\n",
       "                <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 299</td>\n",
       "                <td title=\"Number of call in last migrad\">NCALLS = 299</td>\n",
571 572
       "            </tr>\n",
       "            <tr>\n",
573
       "                <td title=\"Estimated distance to minimum\">EDM = 0.000150399937567</td>\n",
574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621
       "                <td title=\"Maximum EDM definition of convergence\">GOAL EDM = 1e-05</td>\n",
       "                <td title=\"Error def. Amount of increase in FCN to be defined as 1 standard deviation\">\n",
       "                UP = 1.0</td>\n",
       "            </tr>\n",
       "        </table>\n",
       "        \n",
       "        <table>\n",
       "            <tr>\n",
       "                <td align=\"center\" title=\"Validity of the migrad call\">Valid</td>\n",
       "                <td align=\"center\" title=\"Validity of parameters\">Valid Param</td>\n",
       "                <td align=\"center\" title=\"Is Covariance matrix accurate?\">Accurate Covar</td>\n",
       "                <td align=\"center\" title=\"Positive definiteness of covariance matrix\">PosDef</td>\n",
       "                <td align=\"center\" title=\"Was covariance matrix made posdef by adding diagonal element\">Made PosDef</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td align=\"center\" title=\"Was last hesse call fail?\">Hesse Fail</td>\n",
       "                <td align=\"center\" title=\"Validity of covariance\">HasCov</td>\n",
       "                <td align=\"center\" title=\"Is EDM above goal EDM?\">Above EDM</td>\n",
       "                <td align=\"center\"></td>\n",
       "                <td align=\"center\" title=\"Did last migrad call reach max call limit?\">Reach calllim</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "                <td align=\"center\"></td>\n",
       "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
       "            </tr>\n",
       "        </table>\n",
       "        "
      ]
     },
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    },
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       "\n",
       "        <table>\n",
       "            <tr>\n",
622
       "                <td><a href=\"#\" onclick=\"$('#IgQjsDAJgJ').toggle()\">+</a></td>\n",
623 624 625 626 627 628 629 630 631 632 633 634 635
       "                <td title=\"Variable name\">Name</td>\n",
       "                <td title=\"Value of parameter\">Value</td>\n",
       "                <td title=\"Parabolic error\">Parab Error</td>\n",
       "                <td title=\"Minos lower error\">Minos Error-</td>\n",
       "                <td title=\"Minos upper error\">Minos Error+</td>\n",
       "                <td title=\"Lower limit of the parameter\">Limit-</td>\n",
       "                <td title=\"Upper limit of the parameter\">Limit+</td>\n",
       "                <td title=\"Is the parameter fixed in the fit\">FIXED</td>\n",
       "            </tr>\n",
       "        \n",
       "            <tr>\n",
       "                <td>1</td>\n",
       "                <td>po</td>\n",
636 637
       "                <td>6.73008</td>\n",
       "                <td>1.72817</td>\n",
638 639 640 641 642 643 644 645 646 647
       "                <td>0</td>\n",
       "                <td>0</td>\n",
       "                <td>4.0</td>\n",
       "                <td>11.0</td>\n",
       "                <td></td>\n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                <td>2</td>\n",
       "                <td>r_s</td>\n",
648 649
       "                <td>19.0663</td>\n",
       "                <td>20.0535</td>\n",
650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671
       "                <td>0</td>\n",
       "                <td>0</td>\n",
       "                <td>1.0</td>\n",
       "                <td>30.0</td>\n",
       "                <td></td>\n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                <td>3</td>\n",
       "                <td>al</td>\n",
       "                <td>1</td>\n",
       "                <td>1</td>\n",
       "                <td>0</td>\n",
       "                <td>0</td>\n",
       "                <td></td>\n",
       "                <td></td>\n",
       "                <td>FIXED</td>\n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                <td>4</td>\n",
       "                <td>be</td>\n",
672 673
       "                <td>2.86831</td>\n",
       "                <td>0.679008</td>\n",
674 675 676 677 678 679 680 681 682 683
       "                <td>0</td>\n",
       "                <td>0</td>\n",
       "                <td>2.5</td>\n",
       "                <td>3.5</td>\n",
       "                <td></td>\n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                <td>5</td>\n",
       "                <td>ga</td>\n",
684 685
       "                <td>1.11427</td>\n",
       "                <td>0.784379</td>\n",
686 687 688 689 690 691 692 693 694
       "                <td>0</td>\n",
       "                <td>0</td>\n",
       "                <td>0.5</td>\n",
       "                <td>1.5</td>\n",
       "                <td></td>\n",
       "            </tr>\n",
       "            \n",
       "            </table>\n",
       "        \n",
695
       "            <pre id=\"IgQjsDAJgJ\" style=\"display:none;\">\n",
696 697 698 699
       "            <textarea rows=\"16\" cols=\"50\" onclick=\"this.select()\" readonly>\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
       "\\hline\n",
       " & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\\\\n",
       "\\hline\n",
700
       "1 & po & 6.730e+00 & 1.728e+00 &  &  & 4.000e+00 & 1.100e+01 & \\\\\n",
701
       "\\hline\n",
702
       "2 & $r_{s}$ & 1.907e+01 & 2.005e+01 &  &  & 1.000e+00 & 3.000e+01 & \\\\\n",
703 704 705
       "\\hline\n",
       "3 & al & 1.000e+00 & 1.000e+00 &  &  &  &  & FIXED\\\\\n",
       "\\hline\n",
706
       "4 & be & 2.868e+00 & 6.790e-01 &  &  & 2.500e+00 & 3.500e+00 & \\\\\n",
707
       "\\hline\n",
708
       "5 & ga & 1.114e+00 & 7.844e-01 &  &  & 5.000e-01 & 1.500e+00 & \\\\\n",
709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730
       "\\hline\n",
       "\\end{tabular}</textarea>\n",
       "            </pre>\n",
       "            "
      ]
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     "output_type": "stream",
     "text": [
731
      "chi_rho = 19.96, chi_bin = 0.52\n"
732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749
     ]
    }
   ],
   "source": [
    "m_bin = Minuit(chi2_mass_bin_log, al=1., fix_al=True,\n",
    "         po=5.0,    error_po=0.01,  limit_po =(4.,11.),\n",
    "         r_s=7.3,  error_r_s=0.1,   limit_r_s=(1.,30),\n",
    "         be=3.,     error_be=0.01,   limit_be =(2.5,3.5),\n",
    "         ga=1.,     error_ga=0.01,   limit_ga =(.5,1.5))\n",
    "m_bin.migrad();\n",
    "\n",
    "chirhobin = chi2_rho_log(m_bin.values['po'] ,m_bin.values['r_s'],m_bin.values['al'],m_bin.values['be'],m_bin.values['ga'])\n",
    "chibinbin= chi2_mass_bin_log(m_bin.values['po'] ,m_bin.values['r_s'],m_bin.values['al'],m_bin.values['be'],m_bin.values['ga'])\n",
    "print \"chi_rho = {0:1.2f}, chi_bin = {1:1.2f}\".format(chirhobin,chibinbin)"
   ]
  },
  {
   "cell_type": "code",
750
   "execution_count": 11,
751 752 753 754 755 756 757 758
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
759
      "chi_rho = 62.61, chi_bin = 3.53\n"
760 761 762 763 764 765 766 767 768 769 770 771
     ]
    }
   ],
   "source": [
    "#polfit\n",
    "chirhopol = chi2_rho_log(7.663,5.552,1,2.636,0.819)\n",
    "chibinpol= chi2_mass_bin_log(7.663,5.552,1,2.636,0.819)\n",
    "print \"chi_rho = {0:1.2f}, chi_bin = {1:1.2f}\".format(chirhopol,chibinpol)"
   ]
  },
  {
   "cell_type": "code",
772
   "execution_count": 12,
773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545
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       "\n",
       "mpl.get_websocket_type = function() {\n",
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       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
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       "    this.ws = websocket;\n",
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       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
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       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
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       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
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       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
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       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, [ax,ax1] = plt.subplots(2,1,gridspec_kw = {'height_ratios':[3.5, 1]},figsize=[10,11],sharex=True)\n",
    "ax.set_xlim([hsml,2.4*myDMO.r200])\n",
    "ax1.set_xlim([hsml,2.4*myDMO.r200])\n",
    "ax1.set_ylim([.4,1.6])\n",
    "ax.set_ylim([2e2,3e9])\n",
    "ax.set_xscale('log')\n",
    "ax1.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "ax1.set_xlabel('R [kpc]',fontsize=15)\n",
    "ax.set_ylabel(r'$\\rho(r)$ [M$_{\\odot}$ kpc $^{-3}$]',fontsize=15)\n",
    "\n",
    "\n",
    "#define sigma pluss and sigma minus lines\n",
    "mean_plus = profileDMO+std\n",
    "mean_minu = profileDMO-std\n",
    "\n",
    "#  plot things\n",
    "#ax.scatter(myDMO.dm.r,myDMO.dm.rho,s=0.02,lw=0,alpha=0.6,c='#FF9100')\n",
    "mean_minu[np.isnan(np.log10(mean_minu))] = 0\n",
    "ax.plot(r[~np.isnan(np.log10(mean_plus))],mean_plus[~np.isnan(np.log10(mean_plus))],\n",
    "        c='g')\n",
    "ax.plot(r[~np.isnan(np.log10(mean_minu))],mean_minu[~np.isnan(np.log10(mean_minu))],\n",
    "        c='g')\n",
    "\n",
    "ax.fill_between(r,mean_plus,mean_minu,color=\"g\",alpha=0.3)\n",
    "#ax.errorbar(r,profileDMO,xerr=bin_size,yerr=std,alpha=0.5)\n",
    "\n",
    "#ax.scatter(myDMO.dm.r,myDMO.dm.rho,s=0.2,lw=0,alpha=0.2,c='gray')\n",
    "\n",
    "#plot means\n",
    "#ax.plot(r_p[:-1],mean,lw=1.5)\n",
    "ax.plot(r_p[:-1],profileDMO,lw=1.5)\n",
    "## rho fit\n",
    "ax.plot(r,(abg_profile(r,m_rho.values['po'] ,m_rho.values['r_s'],m_rho.values['al'],m_rho.values['be'],m_rho.values['ga'])),\n",
    "        \"k\",lw=2,label=r\"$\\chi^2_{\\rho}$\")\n",
    "## spehere mass\n",
    "#ax.plot(r,(abg_profile(r,m_SC.values['po'] ,m_SC.values['r_s'],m_SC.values['al'],m_SC.values['be'],m_SC.values['ga'])),\n",
    "#        \"r-\",lw=2)\n",
    "## shell mass\n",
    "ax.plot(r[1:],(abg_profile(r[:-1],m_bin.values['po'] ,m_bin.values['r_s'],m_bin.values['al'],m_bin.values['be'],m_bin.values['ga'])),\n",
    "        \"r--\",lw=2,label=r\"$\\chi^2_{m_{bin}}$ \")\n",
    "\n",
    "texto = \"fit results: \\n\"\n",
    "texto += r\"$\\rho_0$ = {0:.3f} $\\pm$ {1:.3f}\".format(m_rho.values[\"po\"],m_rho.errors[\"po\"])+\"\\n\"\n",
    "texto += r\"$r_s$ = {0:.3f} $\\pm$ {1:.3f}\".format(m_rho.values[\"r_s\"],m_rho.errors[\"r_s\"])+\"\\n\"\n",
    "texto += r\"$\\alpha$ = {0:.3f} $\\pm$ {1:.3f}\".format(m_rho.values[\"al\"],m_rho.errors[\"al\"])+\"\\n\"\n",
    "texto += r\"$\\beta$ = {0:.3f} $\\pm$ {1:.3f}\".format(m_rho.values[\"be\"],m_rho.errors[\"be\"])+\"\\n\"\n",
    "texto += r\"$\\gamma$ = {0:.3f} $\\pm$ {1:.3f}\".format(m_rho.values[\"ga\"],m_rho.errors[\"ga\"])+\"\\n\"\n",
    "fig.text(0.28,0.7,simname+\"\\nDMO\",fontsize=20)\n",
    "fig.text(0.25,0.3,texto,fontsize=12)\n",
    "ax.text(3*hsml*1.1,5e2,\"3hsml\",color='gray',fontsize=14)\n",
    "ax.text(8*1.1,5e2,\"Sun\",color='y',fontsize=14)\n",
    "ax.text(myDMO.r200*1.01,5e2,r\"R$_{200}$\",color='k',fontsize=14)\n",
    "r_dm = r\n",
    "\n",
    "\n",
    "#horizontal lines\n",
    "ax.axvline(x=hsml,c='gray',alpha=0.5,linestyle='--',lw=1.5)\n",
    "ax.axvline(x=3*hsml,c='gray',alpha=0.5,linestyle='--',lw=1.5)\n",
    "ax.axvline(x=8,c='y',linestyle='--',lw=1.5) #Sun\n",
    "ax.axvline(x=myDMO.r200,c='k',linestyle='--',lw=1.5) #r200\n",
    "ax.axvline(x=R_P03,c='k',linestyle='-.',lw=1.5) #power radius\n",
    "#########33\n",
    "\n",
    "##\n",
    "ax1.axhline(y=1.,color=\"g\",linestyle=\"--\")\n",
    "## rho fit\n",
    "r_local = np.logspace(np.log10(hsml),np.log10(2.5*myDMO.r200),100)\n",
    "ax1.plot(r,(abg_profile(r,m_rho.values['po'] ,m_rho.values['r_s'],m_rho.values['al'],m_rho.values['be'],m_rho.values['ga']))/profileDMO,\n",
    "        \"k\",lw=1.5,label=r\"$\\chi^2(\\rho) \")\n",
    "\n",
    "ax1.plot(r_in,(abg_profile(r_in,m_rho.values['po'] ,m_rho.values['r_s'],m_rho.values['al'],m_rho.values['be'],m_rho.values['ga']))/profileDMO_in,\n",
    "        \"k--\",lw=1.5,label=r\"$\\chi^2(\\rho) \")\n",
    "## spehere mass\n",
    "#ax.plot(r,(abg_profile(r,m_SC.values['po'] ,m_SC.values['r_s'],m_SC.values['al'],m_SC.values['be'],m_SC.values['ga'])),\n",
    "#        \"r-\",lw=2)\n",
    "## shell mass\n",
    "ax1.fill_between(r,mean_plus/profileDMO,mean_minu/profileDMO,color=\"g\",alpha=0.3)\n",
    "ax1.plot(r,(abg_profile(r,m_bin.values['po'] ,m_bin.values['r_s'],m_bin.values['al'],m_bin.values['be'],m_bin.values['ga']))/profileDMO,\n",
    "        \"r-\",lw=1.5)\n",
    "ax1.plot(r_in,(abg_profile(r_in,m_bin.values['po'] ,m_bin.values['r_s'],m_bin.values['al'],m_bin.values['be'],m_bin.values['ga']))/profileDMO_in,\n",
    "        \"r--\",lw=1.5)\n",
    "\n",
    "#horizontal lines\n",
    "ax1.axvline(x=hsml,c='gray',alpha=0.5,linestyle='--',lw=1.5)\n",
    "ax1.axvline(x=3*hsml,c='gray',alpha=0.5,linestyle='--',lw=1.5)\n",
    "ax1.axvline(x=8,c='y',linestyle='--',lw=1.5) #Sun\n",
    "ax1.axvline(x=myDMO.r200,c='k',linestyle='--',lw=1.5) #r200\n",
    "\n",
    "\n",
    "legend = ax.legend(loc='upper right', ncol=1, shadow=False, fontsize=14)\n",
    "frame = legend.get_frame()\n",
    "# layout\n",
    "fig.tight_layout(h_pad=-1.65)\n",
    "ax.tick_params(axis='both', which='major', labelsize=15, size=5,width=1.2)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=15, size=3,width=1.2)\n",
    "ax1.tick_params(axis='both', which='major', labelsize=15, size=5,width=1.2)\n",
    "ax1.tick_params(axis='both', which='minor', labelsize=15, size=3,width=1.2)\n",
    "plt.savefig(\"/home/arturo/Documents/git/LAMtoLUPM_latex/HaloBdmoprofile.png\",dpi=300)"
   ]
  },
  {
   "cell_type": "code",
1660
   "execution_count": 13,
1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675
   "metadata": {
    "collapsed": false,
    "hide_input": true
   },
   "outputs": [],
   "source": [
    "#### TAKES TIME ####\n",
    "\n",
    "myGkpc = 6.673e-11*((1e-3/myDMO.p.kpctokm)**3)*myDMO.p.msuntokg#kpc^ 3 Msun^-1 s^-2\n",
    "pos = np.array(myDMO.dm.pos3d.reshape(len(myDMO.dm.pos3d)*3),dtype=np.float32)#*myDMO.p.kpctokm\n",
    "#ok, acc, Phy = CF.getGravity(pos,myDMO.dm.mass,0.190,G=myGkpc)"
   ]
  },
  {
   "cell_type": "code",
1676
   "execution_count": 13,
1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687
   "metadata": {
    "collapsed": false,
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nbin_num = 512\\n\\npot_sph, bins_pot = np.histogram(r2,bins=bin_num,\\n                                 weights=Phy)\\nn, _ = np.histogram(r2,bins=bin_num)\\n\\nbin_num = 512\\nbins_pot = np.linspace(0.,myDMO.dm.r.max(),512)\\npot_sph_vesc, bins_pot_vesc = np.histogram(r2[(r2<myDMO.r200**2)], bins=bin_num, weights=Phy[(r2<myDMO.r200**2)])\\nrmax = np.sqrt(bins_pot[(pot_sph/n)==(pot_sph/n)[(bins_pot<503.**2)].max()])[0]\\npot_max = (pot_sph/n)[(pot_sph/n)==(pot_sph/n)[(bins_pot<503.**2)].max()][0]\\n'"
      ]
     },
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     "execution_count": 13,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "bin_num = 512\n",
    "\n",
    "pot_sph, bins_pot = np.histogram(r2,bins=bin_num,\n",
    "                                 weights=Phy)\n",
    "n, _ = np.histogram(r2,bins=bin_num)\n",
    "\n",
    "bin_num = 512\n",
    "bins_pot = np.linspace(0.,myDMO.dm.r.max(),512)\n",
    "pot_sph_vesc, bins_pot_vesc = np.histogram(r2[(r2<myDMO.r200**2)], bins=bin_num, weights=Phy[(r2<myDMO.r200**2)])\n",
    "rmax = np.sqrt(bins_pot[(pot_sph/n)==(pot_sph/n)[(bins_pot<503.**2)].max()])[0]\n",
    "pot_max = (pot_sph/n)[(pot_sph/n)==(pot_sph/n)[(bins_pot<503.**2)].max()][0]\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hide_input": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hide_input": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hide_input": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "hide_input": true
   },
   "source": [
    "# Hydro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "hide_input": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
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   "execution_count": 14,
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   "metadata": {
    "collapsed": false,
    "hide_input": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading Dark matter..\n",
      "loading Stars..\n",
      "loading Gas..\n",
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      "density [20411.78905103 17272.12144131 17121.43958551]\n",
      "| r_200 = 212.70\n",
      "| Diagonal matrix computed \n",
      "|    | 18, 0, 0|\n",
      "| D =| 0, 17, 0|\n",
      "|    | 0,  0, 2|\n",
      "density [ 0.27599664 -0.1329309  -0.0808066 ]\n"
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     ]
    }
   ],
   "source": [
    "pathsim = \"/data/OWN/Mochima/HR/SF0/output_00400\"\n",
    "#path = \"/media/arturo/ARTUROTECA/OUTPUTS/HaloB/output_00417\"\n",
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    "myhydro = wkbl.Galaxy_Hound(pathsim,flush=True)\n",
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    "#ok,rho,_= CF.getDensity(np.array(myhydro.st.pos3d.reshape(len(myhydro.st.pos3d)*3),dtype=np.float32), myhydro.st.mass)\n",
    "#centro_rho = myhydro.st.pos3d[np.where(rho == rho.max())][0]\n",
    "zoomreg= np.where(myhydro.dm.mass==myhydro.dm.mass.min())\n",
    "centro = nbe.real_center(myhydro.dm.pos3d[zoomreg], myhydro.dm.mass[zoomreg] )\n",
    "print \"density\",centro\n",
    "myhydro.center_shift(centro)\n",
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