BayesianModelRandLocAndSize.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#NOTE: the data is saved in random_everything.pkl (in running the below outbreak code to generate\n",
    "    #data, you won't get same data values due to randomness of outbreak even with same seed \n",
    "    #location of farms and farm size)\n",
    "#NOTE: using a different dataset, model coefficients are similar in signs and magnitude\n",
    "#NOTE: fitting model using below code will yield very similar results but may not be exact due \n",
    "    #to stochasticity of MCMC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Main Idea\n",
    "======\n",
    "The goal of this notebook is to:\n",
    "\n",
    "1) implement multiple outbreaks assuming the same initial conditions for each run\n",
    "\n",
    "2) evaluate which farms are most at risk at a specific point in time during the outbreak\n",
    "\n",
    "3) construct a Bayesian model which analyzes the relationship between a farm's infection status and other covariates\n",
    "\n",
    "4) perform convergence checks on the model to assess fit\n",
    "\n",
    "In this particular notebook the placement and size (number of cows and sheep) of farms will be assumed to be completely random. Other notebooks will repeat the above steps given other farm distribution and size characteristics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code simulates an outbreak once using Keeling's \"iterate\" function which can be obtained from the \"Modeling Infectious Diseases\" website (Program 7.6). It returns an array with the following vectors: farmID, an indicator for disease status, distance of each farm to the index infected farm, number of cows at each farm, and number of sheep at each farm. The distance in this case is calculated using a standard euclidean distance. It is important to note that these distances are NOT to scale; the distances on the grid are represented in kilometers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from random import randint\n",
    "Size = 20\n",
    "N = 1000 \n",
    "np.random.seed(53080)\n",
    "x = Size*np.random.rand(N)\n",
    "np.random.seed(23003)\n",
    "y = Size*np.random.rand(N)  \n",
    "np.random.seed(10)\n",
    "Cows = np.array([randint(200,500) for p in range(N)])\n",
    "np.random.seed(11)\n",
    "Sheep = np.array([randint(200,500) for p in range(N)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Outbreaks(Size,N,Y0,farms,end,x,y,Cows,Sheep,Maxtime=1000):\n",
    "    #This is an attempt of converting the Matlab Program 7.6 Code into Python\n",
    "    import numpy as np\n",
    "    import pandas as pd\n",
    "    from math import pi\n",
    "    #Generate dataset (assume there are the same amount of animals at each farm)\n",
    "    #np.random.seed(10)\n",
    "    #x = Size*np.random.rand(N)\n",
    "    #np.random.seed(11)\n",
    "    #y = Size*np.random.rand(N)\n",
    "    #Cows = np.array([250]*N)  #Number of cows at each farm\n",
    "    #Sheep = np.array([250]*N)  #Number of cows at each farm\n",
    "    Status = np.array([0]*N)    #Initial Status of each farm\n",
    "    for i in range(Y0):\n",
    "        Status[i] = 1\n",
    "\n",
    "    #Cows are 10.5 times more susceptible to disease than sheep\n",
    "    Suscept = Sheep+10.5*Cows\n",
    "    Transmiss = 5.1e-7*Sheep + 7.7e-7*Cows \n",
    "\n",
    "    #Calculates which grid square a particular location is in (turn a 2-d coordinate into a scalar)\n",
    "    def WhichGrid(x,y,XRange,YRange,XNum,YNum):\n",
    "        #Essentially: floor(Unif[0,1)griddim)griddim+floor(Unif[0,1)griddim)+1\n",
    "        #Returns a number from 1 to griddim^2\n",
    "        return(np.floor(x*(XNum/XRange))*YNum+np.floor(y*(YNum/YRange))+1)\n",
    "\n",
    "    #Set up the grid\n",
    "    grid = WhichGrid(x,y,Size,Size,10.0,10.0)\n",
    "    tmp = sorted(grid) #Sort grid values\n",
    "    i = np.argsort(grid) #get indexed values after sort\n",
    "    x = x[i]\n",
    "    y = y[i]\n",
    "    Status = Status[i]\n",
    "    grid = grid[i]\n",
    "    Xgrid = []\n",
    "    Ygrid = []\n",
    "    Num = []\n",
    "    first_in_grid = []\n",
    "    last_in_grid = []\n",
    "    Max_Sus_grid = []\n",
    "    for i in range(1,int(max(grid))+1):\n",
    "        #turn the grid square number into an x-coordinate and y-coordinate (should not exceed XNum)\n",
    "        Xgrid.append(np.floor((i-1)/10))\n",
    "        Ygrid.append((i-1)%10)\n",
    "        m = np.array(np.where(grid==i))\n",
    "        Num.append(m.shape[1])\n",
    "    \n",
    "        if Num[i-1] > 0:\n",
    "            first_in_grid.append(m.min()+1) #Add the \"+1\" here so the indicies match those in the Keeling code\n",
    "            last_in_grid.append(m.max()+1)\n",
    "            Max_Sus_grid.append(Suscept[m].max())\n",
    "        else:\n",
    "            first_in_grid.append(0)\n",
    "            last_in_grid.append(-1)\n",
    "            Max_Sus_grid.append(0)\n",
    "\n",
    "    #Work out grid to maximum grid transmission probabilities\n",
    "    from numpy import ndarray\n",
    "    MaxRate = ndarray((max(grid),max(grid)))\n",
    "    def Kernel(dist_squared):\n",
    "        dist_squared = np.asarray(dist_squared)\n",
    "        is_scalar = False if dist_squared.ndim > 0 else True\n",
    "        dist_squared.shape = (1,)*(1-dist_squared.ndim) + dist_squared.shape\n",
    "        K = 1 / (pi * (1 + dist_squared**2))\n",
    "        K[(dist_squared < 0.0138)] = 0.3093\n",
    "        K[(dist_squared > 60*60)] = 0\n",
    "        return(K if not is_scalar else K[0])\n",
    "\n",
    "    #Determine maximum number of animals to be infected in each grid square\n",
    "\n",
    "    for i in range (1,int(max(grid))+1):\n",
    "       for j in range(1,int(max(grid))+1):\n",
    "           if ((i-1)==(j-1)) | (Num[i-1]==0) | (Num[j-1] == 0):\n",
    "               MaxRate[i-1,j-1] = np.inf\n",
    "           else:\n",
    "               Dist2 = (Size*max([0,(abs(Xgrid[i-1]-Xgrid[j-1])-1)])/10)**2+(Size*max([0,(abs(Ygrid[i-1]-Ygrid[j-1])-1)])/10)**2\n",
    "               MaxRate[i-1,j-1] = Max_Sus_grid[j-1]*Kernel(Dist2)\n",
    "\n",
    "    #Susceptible, Exposed, Infectious, Reported.==> latent period is 4 days\n",
    "    i=1; S=len(np.where(Status==0)); E=len(np.where(np.logical_and(Status>0, Status<=5)));I=len(np.where(np.logical_and(Status>5, Status<=9))); R=len(np.where(Status==10)); R2=len(np.where(Status>9)); CullSheep=0; CullCattle=0;\n",
    "    i=i+1;  IterateFlag=1;\n",
    "\n",
    "    def Iterate(Status, x, y, Suscept, Transmiss, grid, first_in_grid, last_in_grid, Num, MaxRate):\n",
    "        Event = 0*Status\n",
    "        INF = np.where(Status>5)[0]\n",
    "        NI = INF.size # Note reported farms still infectious\n",
    "        IGrids = grid[INF]-1\n",
    "        \n",
    "        for ii in range(NI):\n",
    "            INFi = INF[ii]\n",
    "            trans = np.multiply(-Transmiss[INFi],Num) #transmissibility of infected farm to all other grid squares \n",
    "            maxr = MaxRate[IGrids[ii],:] #max number of animals to be infected in infected grid square\n",
    "            # Elementwise multiplication\n",
    "            rate = np.multiply(trans, maxr) #max number of animals to be infected in each grid square based on infected grid square\n",
    "            MaxProb = 1 - np.exp(rate) #Max probability that infected farm infected noninfected farm\n",
    "            rng = np.random.rand(len(MaxProb))\n",
    "            m = np.where((MaxProb - rng)>0)[0]  #these grid squares need further consideration\n",
    "            for n in range(len(m)):\n",
    "                s = 1\n",
    "                M = m[n]\n",
    "                PAB = 1 - np.exp(-Transmiss[INFi]*MaxRate[IGrids[ii],M]) #Max probability that infected farm infects noninfected farms under consideration\n",
    "                if (PAB == 1):\n",
    "                    # Calculate the infection probability for each farm in the susceptible grid\n",
    "                    leng = last_in_grid[M]-first_in_grid[M]+1\n",
    "                    R = np.random.rand(leng)\n",
    "                    for j in range(leng):\n",
    "                        ind1 = first_in_grid[M]+j-1\n",
    "                        Q = 1 - np.exp(-Transmiss[INFi]*Suscept[ind1]*Kernel((x[INFi]-x[ind1]**2+(y[INFi]-y[ind1])**2)))\n",
    "                        if ((R[j] < Q) & (Status[ind1] == 0)):\n",
    "                            Event[ind1] = 1\n",
    "                else:\n",
    "                    R = np.random.rand(Num[M])\n",
    "                    # Loop through all susceptible farms in the grids where an infection event occurred.  \n",
    "                    for j in range(Num[M]):\n",
    "                        P = 1 - s*(1 - PAB)**(Num[M] - j)\n",
    "                        if (R[j] < (PAB / P)):\n",
    "                            s = 0\n",
    "                            ind1=first_in_grid[M]+j-1\n",
    "                            Q=1-np.exp(-Transmiss[INFi]*Suscept[ind1]*Kernel((x[INFi]-x[ind1]**2+(y[INFi]-y[ind1])**2)))\n",
    "                            if ((R[j]< Q/P) & (Status[ind1] == 0)):\n",
    "                                Event[ind1] = 1\n",
    "        # Evolve the infection process of those farms which have been exposed and already infectious ones.  \n",
    "        Status[Status > 0] += 1\n",
    "        Status = Status + Event\n",
    "        #m=np.where(Status==13); # Initiate Ring Culling Around Reported Farm\n",
    "        #for i in range(len(m)):\n",
    "        #    Status[m[i]]=-1;\n",
    "        return {'Status':Status,'NI':NI}\n",
    "    \n",
    "    S=[]\n",
    "    E=[]\n",
    "    I=[]\n",
    "    R=[]\n",
    "    R2=[]\n",
    "    CullSheep=[]\n",
    "    CullCattle=[]\n",
    "    t=[]\n",
    "    t.append(0)\n",
    "    results = np.c_[np.array([1]*N),np.arange(1,N+1),np.array([0]*N)]\n",
    "    \n",
    "    while(np.logical_and(t[-1]<end, IterateFlag)):\n",
    "        Status=Iterate(Status, x, y, Suscept, Transmiss, grid, first_in_grid, last_in_grid, Num, MaxRate)['Status']\n",
    "        Sus=np.where(Status==0)[0]; Exp=np.where(np.logical_and(Status>0, Status<=5))[0]; Inf=np.where(Status>5)[0]; \n",
    "    \n",
    "        S.append(len(Sus)); E.append(len(Exp)); I.append(len(Inf)); \n",
    "        t.append(t[i-2]+1);i+=1;\n",
    "    \n",
    "        #This is how I stop the simulation (all farms are infected)\n",
    "        if t[-1]>5:\n",
    "            if np.logical_or((E[-4]+I[-4]==0),len(I) == N):\n",
    "                IterateFlag=0\n",
    "        from scipy.stats import itemfreq\n",
    "        sim_num = np.array([i-1]*N)\n",
    "        seq = np.arange(1,N+1)\n",
    "        results_full = np.r_[results,np.c_[sim_num,seq,Status]]\n",
    "        results = results_full\n",
    "\n",
    "    #Return information regarding only farm of interest\n",
    "    this = results_full[np.logical_or.reduce([results_full[:,1] == x for x in farms])]\n",
    "    #Extract rows relating to timepoint of interest\n",
    "    no_this = this[this[:,0]==end]\n",
    "    #turn status to an indicator\n",
    "    Status_ind = (no_this[:,2]>5).astype(int)\n",
    "    #Calculate distance to index farm - first infected is first in list of coords\n",
    "    coords = list(zip(x,y))\n",
    "    index = np.array((coords[640][0],coords[640][1]))\n",
    "    dist = []\n",
    "    for i in range(0,N):\n",
    "        b = np.array((coords[i][0],coords[i][1]))\n",
    "        dist.append(np.linalg.norm(b-index))\n",
    "    to_return = np.c_[no_this[:,1],Status_ind,dist,Cows,Sheep]\n",
    "    #Returns array: farmID, Status_ind, dist_to_index, num_Cows,num_Sheep\n",
    "    return(to_return)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code block implements the outbreak multiple times. It also positions each farm and assigns it a size (i.e - assigns number of cows and number of sheep). Both the locations and sizes are randomly generated using a uniform distribution. It is important to note that the number of cows and number of sheep vectors need to be different from each other (i.e. -each farm cannot have 250 cows and 250 sheep) asthis would results in linear dependency. For this reason the sizes are randomly generated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sandyalakkur/anaconda/envs/py3k/lib/python3.4/site-packages/IPython/kernel/__main__.py:59: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "/Users/sandyalakkur/anaconda/envs/py3k/lib/python3.4/site-packages/IPython/kernel/__main__.py:92: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "/Users/sandyalakkur/anaconda/envs/py3k/lib/python3.4/site-packages/IPython/kernel/__main__.py:101: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "/Users/sandyalakkur/anaconda/envs/py3k/lib/python3.4/site-packages/IPython/kernel/__main__.py:59: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from random import randint\n",
    "Outbreak = Outbreaks(Size=Size,N=N,Y0=1,farms = np.arange(1,N+1),end=20,x=x,y=y,Cows=Cows,Sheep=Sheep)\n",
    "Num_outbreaks = 500\n",
    "for i in range(Num_outbreaks):\n",
    "    new_Outbreak = np.r_[Outbreak,Outbreaks(Size=Size,N=N,Y0=1,farms = np.arange(1,N+1),end=20,x=x,y=y,Cows=Cows,Sheep=Sheep)]\n",
    "    Outbreak = new_Outbreak \n",
    "run = np.ravel(list([i]*N for i in range(1,Num_outbreaks+2)))\n",
    "data = np.c_[run,Outbreak]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#For ease turn data array into data frame\n",
    "import pandas as pd\n",
    "df = pd.DataFrame(data)\n",
    "df.columns = ['run','farmID','Status','DistToIndex','NumCows','NumSheep']\n",
    "long_random_everything = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "long_random_everything.to_pickle('/Users/sandyalakkur/Documents/GradSchool/Research/AMFMDIdeas/long_random_everything.pkl') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the dataset has been created we will \"flatten\" it before implementing Bayesian Analysis. We currently have a dataset with 101,000 rows which leads to computational inefficiency. For this reason we will have a matrix of 1000 rows (one for each farm) and the \"Status\" variable will simply be the sum of the statuses over the 101 simulated outbreaks. Thus Status ~ Binomial(n=101, prob) rather than Status ~ Bernoulli(prob). The following code flattens the above dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>farmID</th>\n",
       "      <th>Status</th>\n",
       "      <th>farmID</th>\n",
       "      <th>DistToIndex</th>\n",
       "      <th>NumCows</th>\n",
       "      <th>NumSheep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>641</td>\n",
       "      <td>501</td>\n",
       "      <td>641</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>266</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>642</td>\n",
       "      <td>501</td>\n",
       "      <td>642</td>\n",
       "      <td>1.029099</td>\n",
       "      <td>375</td>\n",
       "      <td>381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>643</td>\n",
       "      <td>501</td>\n",
       "      <td>643</td>\n",
       "      <td>0.411192</td>\n",
       "      <td>353</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>644</td>\n",
       "      <td>501</td>\n",
       "      <td>644</td>\n",
       "      <td>1.441540</td>\n",
       "      <td>385</td>\n",
       "      <td>452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>645</td>\n",
       "      <td>501</td>\n",
       "      <td>645</td>\n",
       "      <td>1.101558</td>\n",
       "      <td>468</td>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>646</td>\n",
       "      <td>501</td>\n",
       "      <td>646</td>\n",
       "      <td>1.485211</td>\n",
       "      <td>241</td>\n",
       "      <td>449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>647</td>\n",
       "      <td>501</td>\n",
       "      <td>647</td>\n",
       "      <td>2.812305</td>\n",
       "      <td>347</td>\n",
       "      <td>426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>648</td>\n",
       "      <td>501</td>\n",
       "      <td>648</td>\n",
       "      <td>2.572874</td>\n",
       "      <td>352</td>\n",
       "      <td>289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>649</td>\n",
       "      <td>501</td>\n",
       "      <td>649</td>\n",
       "      <td>3.859220</td>\n",
       "      <td>327</td>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>650</td>\n",
       "      <td>501</td>\n",
       "      <td>650</td>\n",
       "      <td>2.031243</td>\n",
       "      <td>453</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>651</td>\n",
       "      <td>501</td>\n",
       "      <td>651</td>\n",
       "      <td>2.232527</td>\n",
       "      <td>389</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>652</td>\n",
       "      <td>501</td>\n",
       "      <td>652</td>\n",
       "      <td>2.707717</td>\n",
       "      <td>278</td>\n",
       "      <td>329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>653</td>\n",
       "      <td>501</td>\n",
       "      <td>653</td>\n",
       "      <td>2.730776</td>\n",
       "      <td>253</td>\n",
       "      <td>317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>654</td>\n",
       "      <td>501</td>\n",
       "      <td>654</td>\n",
       "      <td>3.182554</td>\n",
       "      <td>493</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>655</td>\n",
       "      <td>501</td>\n",
       "      <td>655</td>\n",
       "      <td>3.086712</td>\n",
       "      <td>478</td>\n",
       "      <td>484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>656</td>\n",
       "      <td>501</td>\n",
       "      <td>656</td>\n",
       "      <td>4.618356</td>\n",
       "      <td>210</td>\n",
       "      <td>482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>657</td>\n",
       "      <td>501</td>\n",
       "      <td>657</td>\n",
       "      <td>4.762914</td>\n",
       "      <td>406</td>\n",
       "      <td>436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>658</td>\n",
       "      <td>501</td>\n",
       "      <td>658</td>\n",
       "      <td>4.610975</td>\n",
       "      <td>352</td>\n",
       "      <td>407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>659</td>\n",
       "      <td>501</td>\n",
       "      <td>659</td>\n",
       "      <td>5.565405</td>\n",
       "      <td>330</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>660</td>\n",
       "      <td>501</td>\n",
       "      <td>660</td>\n",
       "      <td>4.886676</td>\n",
       "      <td>258</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>661</td>\n",
       "      <td>501</td>\n",
       "      <td>661</td>\n",
       "      <td>5.067912</td>\n",
       "      <td>466</td>\n",
       "      <td>495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>662</td>\n",
       "      <td>501</td>\n",
       "      <td>662</td>\n",
       "      <td>5.293345</td>\n",
       "      <td>407</td>\n",
       "      <td>409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>663</td>\n",
       "      <td>501</td>\n",
       "      <td>663</td>\n",
       "      <td>4.367798</td>\n",
       "      <td>220</td>\n",
       "      <td>306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>664</td>\n",
       "      <td>501</td>\n",
       "      <td>664</td>\n",
       "      <td>5.456571</td>\n",
       "      <td>462</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>665</td>\n",
       "      <td>501</td>\n",
       "      <td>665</td>\n",
       "      <td>4.879646</td>\n",
       "      <td>307</td>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>666</td>\n",
       "      <td>501</td>\n",
       "      <td>666</td>\n",
       "      <td>5.380601</td>\n",
       "      <td>283</td>\n",
       "      <td>445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>667</td>\n",
       "      <td>501</td>\n",
       "      <td>667</td>\n",
       "      <td>3.857122</td>\n",
       "      <td>491</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>668</td>\n",
       "      <td>500</td>\n",
       "      <td>668</td>\n",
       "      <td>7.295882</td>\n",
       "      <td>447</td>\n",
       "      <td>329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>669</td>\n",
       "      <td>499</td>\n",
       "      <td>669</td>\n",
       "      <td>6.435178</td>\n",
       "      <td>293</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>670</td>\n",
       "      <td>501</td>\n",
       "      <td>670</td>\n",
       "      <td>5.642404</td>\n",
       "      <td>475</td>\n",
       "      <td>490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>971</td>\n",
       "      <td>458</td>\n",
       "      <td>971</td>\n",
       "      <td>7.391295</td>\n",
       "      <td>413</td>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>972</td>\n",
       "      <td>450</td>\n",
       "      <td>972</td>\n",
       "      <td>8.582012</td>\n",
       "      <td>324</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>973</td>\n",
       "      <td>448</td>\n",
       "      <td>973</td>\n",
       "      <td>7.751016</td>\n",
       "      <td>311</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>974</td>\n",
       "      <td>460</td>\n",
       "      <td>974</td>\n",
       "      <td>8.350279</td>\n",
       "      <td>478</td>\n",
       "      <td>472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>975</td>\n",
       "      <td>300</td>\n",
       "      <td>975</td>\n",
       "      <td>9.863701</td>\n",
       "      <td>470</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>976</td>\n",
       "      <td>306</td>\n",
       "      <td>976</td>\n",
       "      <td>9.761709</td>\n",
       "      <td>432</td>\n",
       "      <td>482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>977</td>\n",
       "      <td>301</td>\n",
       "      <td>977</td>\n",
       "      <td>9.607711</td>\n",
       "      <td>497</td>\n",
       "      <td>227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>978</td>\n",
       "      <td>286</td>\n",
       "      <td>978</td>\n",
       "      <td>9.718880</td>\n",
       "      <td>320</td>\n",
       "      <td>343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>979</td>\n",
       "      <td>302</td>\n",
       "      <td>979</td>\n",
       "      <td>9.384913</td>\n",
       "      <td>299</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>980</td>\n",
       "      <td>288</td>\n",
       "      <td>980</td>\n",
       "      <td>10.047655</td>\n",
       "      <td>210</td>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>981</td>\n",
       "      <td>337</td>\n",
       "      <td>981</td>\n",
       "      <td>9.414530</td>\n",
       "      <td>293</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>982</td>\n",
       "      <td>297</td>\n",
       "      <td>982</td>\n",
       "      <td>8.464842</td>\n",
       "      <td>353</td>\n",
       "      <td>489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>983</td>\n",
       "      <td>287</td>\n",
       "      <td>983</td>\n",
       "      <td>9.700915</td>\n",
       "      <td>262</td>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>984</td>\n",
       "      <td>307</td>\n",
       "      <td>984</td>\n",
       "      <td>9.467807</td>\n",
       "      <td>287</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>985</td>\n",
       "      <td>290</td>\n",
       "      <td>985</td>\n",
       "      <td>9.799667</td>\n",
       "      <td>312</td>\n",
       "      <td>403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>986</td>\n",
       "      <td>102</td>\n",
       "      <td>986</td>\n",
       "      <td>10.318230</td>\n",
       "      <td>284</td>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>987</td>\n",
       "      <td>100</td>\n",
       "      <td>987</td>\n",
       "      <td>11.363350</td>\n",
       "      <td>460</td>\n",
       "      <td>481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>988</td>\n",
       "      <td>105</td>\n",
       "      <td>988</td>\n",
       "      <td>11.548065</td>\n",
       "      <td>340</td>\n",
       "      <td>484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>989</td>\n",
       "      <td>111</td>\n",
       "      <td>989</td>\n",
       "      <td>10.819537</td>\n",
       "      <td>323</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>990</td>\n",
       "      <td>98</td>\n",
       "      <td>990</td>\n",
       "      <td>11.622679</td>\n",
       "      <td>229</td>\n",
       "      <td>436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>991</td>\n",
       "      <td>97</td>\n",
       "      <td>991</td>\n",
       "      <td>10.716460</td>\n",
       "      <td>303</td>\n",
       "      <td>227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>992</td>\n",
       "      <td>102</td>\n",
       "      <td>992</td>\n",
       "      <td>11.467932</td>\n",
       "      <td>424</td>\n",
       "      <td>383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>993</td>\n",
       "      <td>93</td>\n",
       "      <td>993</td>\n",
       "      <td>10.879314</td>\n",
       "      <td>280</td>\n",
       "      <td>292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>994</td>\n",
       "      <td>90</td>\n",
       "      <td>994</td>\n",
       "      <td>11.884817</td>\n",
       "      <td>253</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>995</td>\n",
       "      <td>108</td>\n",
       "      <td>995</td>\n",
       "      <td>11.143025</td>\n",
       "      <td>268</td>\n",
       "      <td>457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>996</td>\n",
       "      <td>45</td>\n",
       "      <td>996</td>\n",
       "      <td>12.632293</td>\n",
       "      <td>399</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>997</td>\n",
       "      <td>24</td>\n",
       "      <td>997</td>\n",
       "      <td>12.937706</td>\n",
       "      <td>340</td>\n",
       "      <td>293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>998</td>\n",
       "      <td>22</td>\n",
       "      <td>998</td>\n",
       "      <td>12.040714</td>\n",
       "      <td>369</td>\n",
       "      <td>466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>999</td>\n",
       "      <td>31</td>\n",
       "      <td>999</td>\n",
       "      <td>12.403574</td>\n",
       "      <td>458</td>\n",
       "      <td>293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1000</td>\n",
       "      <td>27</td>\n",
       "      <td>1000</td>\n",
       "      <td>12.987816</td>\n",
       "      <td>202</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>360 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     farmID  Status  farmID  DistToIndex  NumCows  NumSheep\n",
       "640     641     501     641     0.000000      266       362\n",
       "641     642     501     642     1.029099      375       381\n",
       "642     643     501     643     0.411192      353       288\n",
       "643     644     501     644     1.441540      385       452\n",
       "644     645     501     645     1.101558      468       485\n",
       "645     646     501     646     1.485211      241       449\n",
       "646     647     501     647     2.812305      347       426\n",
       "647     648     501     648     2.572874      352       289\n",
       "648     649     501     649     3.859220      327       238\n",
       "649     650     501     650     2.031243      453       246\n",
       "650     651     501     651     2.232527      389       320\n",
       "651     652     501     652     2.707717      278       329\n",
       "652     653     501     653     2.730776      253       317\n",
       "653     654     501     654     3.182554      493       432\n",
       "654     655     501     655     3.086712      478       484\n",
       "655     656     501     656     4.618356      210       482\n",
       "656     657     501     657     4.762914      406       436\n",
       "657     658     501     658     4.610975      352       407\n",
       "658     659     501     659     5.565405      330       423\n",
       "659     660     501     660     4.886676      258       210\n",
       "660     661     501     661     5.067912      466       495\n",
       "661     662     501     662     5.293345      407       409\n",
       "662     663     501     663     4.367798      220       306\n",
       "663     664     501     664     5.456571      462       375\n",
       "664     665     501     665     4.879646      307       388\n",
       "665     666     501     666     5.380601      283       445\n",
       "666     667     501     667     3.857122      491       256\n",
       "667     668     500     668     7.295882      447       329\n",
       "668     669     499     669     6.435178      293       276\n",
       "669     670     501     670     5.642404      475       490\n",
       "..      ...     ...     ...          ...      ...       ...\n",
       "970     971     458     971     7.391295      413       439\n",
       "971     972     450     972     8.582012      324       305\n",
       "972     973     448     973     7.751016      311       224\n",
       "973     974     460     974     8.350279      478       472\n",
       "974     975     300     975     9.863701      470       244\n",
       "975     976     306     976     9.761709      432       482\n",
       "976     977     301     977     9.607711      497       227\n",
       "977     978     286     978     9.718880      320       343\n",
       "978     979     302     979     9.384913      299       267\n",
       "979     980     288     980    10.047655      210       491\n",
       "980     981     337     981     9.414530      293       330\n",
       "981     982     297     982     8.464842      353       489\n",
       "982     983     287     983     9.700915      262       264\n",
       "983     984     307     984     9.467807      287       258\n",
       "984     985     290     985     9.799667      312       403\n",
       "985     986     102     986    10.318230      284       349\n",
       "986     987     100     987    11.363350      460       481\n",
       "987     988     105     988    11.548065      340       484\n",
       "988     989     111     989    10.819537      323       308\n",
       "989     990      98     990    11.622679      229       436\n",
       "990     991      97     991    10.716460      303       227\n",
       "991     992     102     992    11.467932      424       383\n",
       "992     993      93     993    10.879314      280       292\n",
       "993     994      90     994    11.884817      253       220\n",
       "994     995     108     995    11.143025      268       457\n",
       "995     996      45     996    12.632293      399       328\n",
       "996     997      24     997    12.937706      340       293\n",
       "997     998      22     998    12.040714      369       466\n",
       "998     999      31     999    12.403574      458       293\n",
       "999    1000      27    1000    12.987816      202       325\n",
       "\n",
       "[360 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Group by farmID then add across farmIDs \n",
    "farm_Status = df[['Status']].groupby(df.farmID)\n",
    "sum_Status = farm_Status.agg(np.sum)\n",
    "#Get rid of hierarchical header for data merge\n",
    "sum_Status.columns = sum_Status.columns.get_level_values(0)\n",
    "sum_Status2 = pd.DataFrame(sum_Status).reset_index()\n",
    "#Add rest of data covariates\n",
    "subset = df[:1000][['farmID','DistToIndex','NumCows','NumSheep']]\n",
    "random_everything = pd.concat([sum_Status2, subset], axis=1, join_axes=[subset.index])\n",
    "random_everything[640:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Save (or pickle) the dataset for future use\n",
    "random_everything.to_pickle('/Users/sandyalakkur/Documents/GradSchool/Research/AMFMDIdeas/random_everything.pkl') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Read the pickled dataset back in - prevents having to run all above code again\n",
    "import pandas as pd\n",
    "random_everything = pd.read_pickle('/Users/sandyalakkur/Documents/GradSchool/Research/AMFMDIdeas/random_everything.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([640]),)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This is the index exposed farm in the outbreak function\n",
    "Status = np.array([0]*N)    #Initial Status of each farm\n",
    "for i in range(1):\n",
    "    Status[i] = 1\n",
    "def WhichGrid(x,y,XRange,YRange,XNum,YNum):\n",
    "    #Essentially: floor(Unif[0,1)griddim)griddim+floor(Unif[0,1)griddim)+1\n",
    "    #Returns a number from 1 to griddim^2\n",
    "    return(np.floor(x*(XNum/XRange))*YNum+np.floor(y*(YNum/YRange))+1)\n",
    "\n",
    "#Set up the grid\n",
    "grid = WhichGrid(x,y,Size,Size,10.0,10.0)\n",
    "tmp = sorted(grid) #Sort grid values\n",
    "i = np.argsort(grid) #get indexed values after sort\n",
    "Status = Status[i]\n",
    "x = x[i]\n",
    "y = y[i]\n",
    "np.where(Status==1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sandyalakkur/anaconda/envs/py3k/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeMAAAEKCAYAAAAhPD1yAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108bb75c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "plt.scatter(x,y,c=random_everything['Status'],cmap=plt.cm.coolwarm,s=(Cows+Sheep)/20)\n",
    "plt.title('Risk of Infection, End Point: Timestep = 20')\n",
    "plt.annotate('index', xy = (x[640], y[640]), xytext = (-40, 20),\n",
    "        textcoords = 'offset points', ha = 'right', va = 'bottom',\n",
    "        bbox = dict(boxstyle = 'round, pad=0.5', fc = 'yellow', alpha = 0.6),\n",
    "        arrowprops=dict(facecolor='yellow', shrink=0.08))\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the dataset has been flattened we can construct the Bayesian Model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dist_diff = random_everything['DistToIndex'] - random_everything['DistToIndex'].mean()\n",
    "cows_diff = random_everything['NumCows'] - random_everything['NumCows'].mean()\n",
    "sheep_diff = random_everything['NumSheep'] -random_everything['NumSheep'].mean()\n",
    "status = random_everything['Status']\n",
    "from pymc import Normal, Binomial, Gamma, Lambda, invlogit, MCMC, Matplot, Bernoulli, MAP, AdaptiveMetropolis\n",
    "#N = df.shape[0]\n",
    "\n",
    "def pooled_model():\n",
    "    \n",
    "    # Common slope & intercept prior\n",
    "    intercept = Normal('intercept', mu=0., tau=0.001, value = 0.5) \n",
    "    \n",
    "    first_coef = Normal('first_coef', mu=0., tau=0.001, value = -0.05)\n",
    "    \n",
    "    b = Normal('b', mu=0., tau=0.001, value = [0]*2)\n",
    "    \n",
    "    #likelihood model\n",
    "    prob = Lambda('prob', lambda intercept=intercept,first_coef=first_coef, b=b: invlogit(intercept + first_coef*dist_diff + b[0]*cows_diff \n",
    "                                               + b[1]*sheep_diff))\n",
    "    \n",
    "    y = Binomial('y', n=501, p=prob, value=status, observed=True)\n",
    "    \n",
    "    return locals()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 10000 of 10000 complete in 24.4 sec"
     ]
    }
   ],
   "source": [
    "chains = 2\n",
    "iterations = 10000\n",
    "burn = 4000\n",
    "M_pooled = MCMC(pooled_model())\n",
    "M_map = MAP(pooled_model())\n",
    "M_pooled = MCMC(M_map)\n",
    "#M_pooled.use_step_method(AdaptiveMetropolis, M_pooled.b)\n",
    "for i in range(chains):\n",
    "    M_pooled.sample(iterations, burn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaMAAAEgCAYAAAAZsRyCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e7f54a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "Matplot.summary_plot(M_pooled.b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ4AAAEgCAYAAACEvBUKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZfBIkrr6fxcDfpP3Wc5VAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105a04ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.summary_plot(M_pooled.intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZkAAAEgCAYAAABmYA5zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119f60f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.summary_plot(M_pooled.first_coef)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting intercept\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sandyalakkur/anaconda/envs/py3k/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmkAAAFwCAYAAADjQOwcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMzMzyyEnaWZmZmY55CTNzMzMLIf+P7Lez1sW/n6qAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119f25438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.plot(M_pooled.intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting b_0\n",
      "Plotting b_1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sandyalakkur/anaconda/envs/py3k/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnAAAAFwCAYAAAA4+0Y+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a1a8f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.plot(M_pooled.b, common_scale=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting first_coef\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sandyalakkur/anaconda/envs/py3k/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnUAAAFwCAYAAADe0o16AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6szMzMwqwEmdmZmZWQU4qTMzMzOrgP8Pg/RYY0ZEnDsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a905630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.plot(M_pooled.first_coef, common_scale=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'b': [1.0123423274097578, 1.0342027914067908],\n",
       " 'first_coef': 1.0014090959501227,\n",
       " 'intercept': 0.9998444199449229,\n",
       " 'prob': [1.0192832725636669,\n",
       "  1.0055404520606321,\n",
       "  1.0099601333787693,\n",
       "  1.0189170303615125,\n",
       "  1.0002996100758361,\n",
       "  1.0017204322154403,\n",
       "  1.0056392753975272,\n",
       "  1.0194479352601735,\n",
       "  0.99989693785764022,\n",
       "  1.0034122879798384,\n",
       "  1.0217823849997012,\n",
       "  1.0103643186339777,\n",
       "  1.0158683557659205,\n",
       "  1.0026137905195969,\n",
       "  1.0001336057231673,\n",
       "  1.0059941876474781,\n",
       "  1.0063256194824637,\n",
       "  0.99984614534464833,\n",
       "  1.0057874708037513,\n",
       "  1.0000816063519171,\n",
       "  1.0044977995222377,\n",
       "  1.0014424943452294,\n",
       "  1.0048014306523319,\n",
       "  1.0218247175860027,\n",
       "  1.0139669181012028,\n",
       "  0.99983387384473943,\n",
       "  1.0068656759990235,\n",
       "  1.0070467937965,\n",
       "  0.99991791335960967,\n",
       "  1.0293396538276041,\n",
       "  1.0085004136355695,\n",
       "  1.0075300384509651,\n",
       "  1.0002233587289249,\n",
       "  0.99988463095654956,\n",
       "  1.002345810011487,\n",
       "  1.0182558617441799,\n",
       "  1.028153398111552,\n",
       "  1.0282555551239074,\n",
       "  1.0124459962958388,\n",
       "  1.011030172649906,\n",
       "  1.0076620953972892,\n",
       "  0.99994920088959083,\n",
       "  0.99992057859349515,\n",
       "  1.0174299410241827,\n",
       "  1.005470126614004,\n",
       "  1.0166731594967435,\n",
       "  1.0033974560562073,\n",
       "  1.0022468362222321,\n",
       "  1.0007788375330271,\n",
       "  1.0012698447685171,\n",
       "  1.0009352541042347,\n",
       "  1.0028746534745876,\n",
       "  1.0103241459200256,\n",
       "  1.0001178865516356,\n",
       "  1.0047973357524438,\n",
       "  1.0087857234170587,\n",
       "  1.0288516720916903,\n",
       "  1.0240732092723901,\n",
       "  1.0164057406709579,\n",
       "  1.0033889932485571,\n",
       "  1.0104010684183906,\n",
       "  1.0021807947265899,\n",
       "  0.99983667507248697,\n",
       "  1.0010931629843203,\n",
       "  1.0123719229525165,\n",
       "  1.0009658833751576,\n",
       "  1.0001492743575691,\n",
       "  1.0087605997689097,\n",
       "  1.0007009021716671,\n",
       "  1.0084756140882591,\n",
       "  1.002988356805222,\n",
       "  0.99983819093892656,\n",
       "  1.0131049291391949,\n",
       "  0.99984072914837951,\n",
       "  1.0072614227929317,\n",
       "  1.0055891587080792,\n",
       "  1.0102672118294853,\n",
       "  1.0102677082252529,\n",
       "  1.0001208288100725,\n",
       "  1.000230127856151,\n",
       "  1.0032839603061883,\n",
       "  0.9998830452617834,\n",
       "  1.0096255658609692,\n",
       "  0.99991684989608987,\n",
       "  0.99985402222145292,\n",
       "  1.013519258311556,\n",
       "  0.99992433550077076,\n",
       "  0.99985368326995883,\n",
       "  1.0047747789334491,\n",
       "  1.0042586466539527,\n",
       "  1.0004351914115672,\n",
       "  1.0169962224388565,\n",
       "  1.0001656306962334,\n",
       "  1.0012099101004981,\n",
       "  1.0072474910998299,\n",
       "  1.0018182367346602,\n",
       "  0.99987045060007329,\n",
       "  1.0011966190480341,\n",
       "  0.99983751857507386,\n",
       "  1.0120573753638928,\n",
       "  1.0070003746386347,\n",
       "  1.0012397822961763,\n",
       "  1.0051559331475231,\n",
       "  1.0022457181427209,\n",
       "  0.99989120711490032,\n",
       "  1.0071978986510186,\n",
       "  1.0004946875563288,\n",
       "  1.0092535353593803,\n",
       "  1.0095616026710972,\n",
       "  1.0007692059244484,\n",
       "  0.99983502030433746,\n",
       "  1.0004458847134117,\n",
       "  1.000320157890872,\n",
       "  1.012244371424091,\n",
       "  1.0273637047452719,\n",
       "  1.0280702570894835,\n",
       "  1.0144392602826173,\n",
       "  1.0086203871491008,\n",
       "  1.0060953127821148,\n",
       "  1.0346459721683157,\n",
       "  1.0008426367899164,\n",
       "  1.0010664187227629,\n",
       "  1.0348417261728895,\n",
       "  1.0010684596570689,\n",
       "  1.0320909208548241,\n",
       "  0.99986362377562343,\n",
       "  1.0024603259261846,\n",
       "  1.0102389473600482,\n",
       "  1.007591798048743,\n",
       "  1.01944293234337,\n",
       "  1.0069962680054558,\n",
       "  1.0065990332454751,\n",
       "  1.0260499712578772,\n",
       "  1.0063121350616921,\n",
       "  1.0052402857142797,\n",
       "  1.000617163813829,\n",
       "  1.0311372652044062,\n",
       "  1.0123674052231963,\n",
       "  1.0094595817858145,\n",
       "  1.0230427111856752,\n",
       "  1.0006765260295727,\n",
       "  1.0093797961281101,\n",
       "  1.0185444993822215,\n",
       "  1.0406908767063356,\n",
       "  1.0000616610296484,\n",
       "  1.0019607129075845,\n",
       "  1.0459818871945865,\n",
       "  1.0250794650310913,\n",
       "  1.0413825247809771,\n",
       "  1.0121229629097173,\n",
       "  1.0390407404729431,\n",
       "  1.0090141376152233,\n",
       "  1.00063387278766,\n",
       "  1.0184921991309697,\n",
       "  1.0071694428639295,\n",
       "  1.0020981924418808,\n",
       "  1.0027707168917461,\n",
       "  1.0005106058797872,\n",
       "  1.0029806274075652,\n",
       "  1.0002131430118271,\n",
       "  1.0380591496346432,\n",
       "  1.0316641310484922,\n",
       "  1.0145193421265466,\n",
       "  1.0037700991537097,\n",
       "  1.0169544126031484,\n",
       "  1.0175244545776188,\n",
       "  1.007864461423815,\n",
       "  1.0085533364605748,\n",
       "  1.0112585144263224,\n",
       "  1.0217722366550837,\n",
       "  1.003624083591814,\n",
       "  1.0083202170418268,\n",
       "  1.0135193507756002,\n",
       "  1.000156721118864,\n",
       "  1.0169587021510049,\n",
       "  1.0059883569738095,\n",
       "  1.0091614297913494,\n",
       "  1.0150488523340304,\n",
       "  1.0296691795490971,\n",
       "  0.99993615800491664,\n",
       "  1.0139695568329357,\n",
       "  1.0193300766634712,\n",
       "  1.0213727339480998,\n",
       "  1.0021712022663445,\n",
       "  1.0023469454716472,\n",
       "  1.0100300350264124,\n",
       "  1.0023540690142316,\n",
       "  1.0026011478916781,\n",
       "  1.0007953184897309,\n",
       "  1.0013186502489115,\n",
       "  1.002985705629923,\n",
       "  0.9999218094800677,\n",
       "  1.0027345499595832,\n",
       "  1.0084495501327271,\n",
       "  1.0002289068235786,\n",
       "  1.0015937389873721,\n",
       "  1.0071602942749276,\n",
       "  1.0033549554180361,\n",
       "  1.0012239001811392,\n",
       "  1.0176479034712433,\n",
       "  1.0065046084617955,\n",
       "  1.000148498165828,\n",
       "  1.0001232906468158,\n",
       "  1.0009029590680585,\n",
       "  1.0129740050223375,\n",
       "  1.0024465010401882,\n",
       "  1.0018516242948079,\n",
       "  1.0296520158360161,\n",
       "  1.0259271001318353,\n",
       "  1.0089940125706958,\n",
       "  1.0213091806100454,\n",
       "  1.000758086633482,\n",
       "  1.0171698248207124,\n",
       "  1.009276637116169,\n",
       "  1.0034906257780116,\n",
       "  1.0158052455699846,\n",
       "  1.0002452855031763,\n",
       "  1.0226381661972548,\n",
       "  0.99987936002220312,\n",
       "  1.0001204740083549,\n",
       "  1.0008641063322909,\n",
       "  1.0402901836635809,\n",
       "  1.0018584123696896,\n",
       "  1.0221609984181719,\n",
       "  1.0230295961447697,\n",
       "  1.0017238068709879,\n",
       "  1.0292665839295079,\n",
       "  1.0001051661681151,\n",
       "  1.0414279626205263,\n",
       "  1.0126914298146861,\n",
       "  1.0182934492036995,\n",
       "  1.0017493859611692,\n",
       "  1.0024234584063394,\n",
       "  1.0001703397428867,\n",
       "  1.0190044988598177,\n",
       "  1.0136029288863952,\n",
       "  1.0198002284247603,\n",
       "  1.0109700738094991,\n",
       "  1.0337942642618281,\n",
       "  1.0041771317268626,\n",
       "  1.0001123490397505,\n",
       "  1.0000812122260474,\n",
       "  1.0282885800498114,\n",
       "  1.0009856110735473,\n",
       "  1.0003975912629695,\n",
       "  1.0000200330807845,\n",
       "  1.0050251986835061,\n",
       "  1.0064293881012809,\n",
       "  1.0013473922176406,\n",
       "  0.99992492099087826,\n",
       "  1.0442444475050967,\n",
       "  1.012833144885644,\n",
       "  1.0184541713861441,\n",
       "  1.0100921205111661,\n",
       "  1.0357569647096312,\n",
       "  1.043170131377529,\n",
       "  1.0358605366977116,\n",
       "  1.0135311754770457,\n",
       "  1.0173687355706782,\n",
       "  1.0033603875706598,\n",
       "  0.99996060323839109,\n",
       "  1.0115581875638542,\n",
       "  1.017295758347738,\n",
       "  1.001003101562689,\n",
       "  1.0397107729097097,\n",
       "  1.0262060037820779,\n",
       "  1.0393354600993272,\n",
       "  1.0253715111071078,\n",
       "  1.0096408784739446,\n",
       "  1.0148878956420402,\n",
       "  1.0299544809163999,\n",
       "  1.0090656390531034,\n",
       "  1.0003064616516344,\n",
       "  1.0127145287667509,\n",
       "  1.008219377402086,\n",
       "  1.0181516464474054,\n",
       "  1.0077419523201598,\n",
       "  1.0051933521105683,\n",
       "  0.99992007753849466,\n",
       "  1.0177941900773215,\n",
       "  1.0025051781821019,\n",
       "  1.0073455743473858,\n",
       "  1.0050423202678895,\n",
       "  1.0000392823709379,\n",
       "  1.0005073961904785,\n",
       "  1.0086596467713145,\n",
       "  1.0009253148662911,\n",
       "  1.0006809023695058,\n",
       "  1.0045804130009341,\n",
       "  1.0115765026249179,\n",
       "  1.0105212042643901,\n",
       "  1.0003646177756431,\n",
       "  1.0035843480848197,\n",
       "  1.0054716963217623,\n",
       "  1.0016417471848691,\n",
       "  1.0039869118568494,\n",
       "  1.0055639591153789,\n",
       "  1.0007519301173833,\n",
       "  1.0004392739108692,\n",
       "  1.028604190925452,\n",
       "  1.0364411760640251,\n",
       "  1.0361339797830236,\n",
       "  1.0260861143980715,\n",
       "  1.002137781316117,\n",
       "  1.0095511851697991,\n",
       "  1.0037631494762354,\n",
       "  1.0299531175210213,\n",
       "  1.014492328247798,\n",
       "  1.0024909807743856,\n",
       "  1.0218550414203198,\n",
       "  1.0013373474443787,\n",
       "  1.0001614163899044,\n",
       "  1.0012930355455432,\n",
       "  1.0368975248267664,\n",
       "  1.0184812467256465,\n",
       "  1.0083036627367128,\n",
       "  1.0097080743588549,\n",
       "  1.0005793016236002,\n",
       "  1.0111075259371216,\n",
       "  1.0183653328786868,\n",
       "  1.0015570784182111,\n",
       "  1.0156012519619324,\n",
       "  1.022117568302803,\n",
       "  1.0297780759314947,\n",
       "  1.011847184231383,\n",
       "  1.0001193083230737,\n",
       "  1.0069520786486861,\n",
       "  1.0170637499801523,\n",
       "  1.0123806355134441,\n",
       "  1.0015672323929992,\n",
       "  1.0008946392056493,\n",
       "  1.0184774271865631,\n",
       "  0.99995746626851045,\n",
       "  0.99987552323311368,\n",
       "  1.0050725308416839,\n",
       "  1.0073149870060221,\n",
       "  1.0012804969913158,\n",
       "  0.99990913851583074,\n",
       "  1.0150329891687484,\n",
       "  1.0243339362529129,\n",
       "  1.0044946937128996,\n",
       "  1.0000687442334641,\n",
       "  1.0030792894428553,\n",
       "  1.0051361102899041,\n",
       "  1.0154457196304374,\n",
       "  1.0050858484286342,\n",
       "  0.99983336480660712,\n",
       "  1.0183708515575234,\n",
       "  1.0054347646310178,\n",
       "  1.002057891069092,\n",
       "  0.99998370519120172,\n",
       "  1.0156646307421002,\n",
       "  1.0093263650081661,\n",
       "  0.99990390546349206,\n",
       "  1.0091107477929959,\n",
       "  1.0016468041628432,\n",
       "  1.0047893702898427,\n",
       "  1.0170092337829439,\n",
       "  1.01014442322957,\n",
       "  1.0016984293190161,\n",
       "  1.0007849653330698,\n",
       "  1.0208782818633624,\n",
       "  1.0008413558358187,\n",
       "  1.0192115506784591,\n",
       "  0.99984316287816122,\n",
       "  1.0071317905656205,\n",
       "  1.0155683925614447,\n",
       "  1.0145642702388653,\n",
       "  1.000163743060877,\n",
       "  1.0006848676485636,\n",
       "  1.0004261608195157,\n",
       "  1.0010258676792971,\n",
       "  1.0145145920440783,\n",
       "  1.0181036561918688,\n",
       "  1.0121551186006161,\n",
       "  1.0386309920053887,\n",
       "  1.0011210996513358,\n",
       "  1.0018792013031781,\n",
       "  1.0198603668612121,\n",
       "  1.0013091740515174,\n",
       "  1.0164318280076754,\n",
       "  1.0103481369150447,\n",
       "  1.017103687712309,\n",
       "  1.0004344235086287,\n",
       "  1.0057852170253205,\n",
       "  1.024892825590531,\n",
       "  1.0078608793960528,\n",
       "  1.0021115286660103,\n",
       "  1.002602008694605,\n",
       "  1.0008907116867602,\n",
       "  1.0045399198843128,\n",
       "  1.034377868909651,\n",
       "  1.0007872190013247,\n",
       "  1.000870852202107,\n",
       "  1.0041676267617921,\n",
       "  1.0024126095203607,\n",
       "  1.0133087001592158,\n",
       "  1.0116723710403104,\n",
       "  1.0132112532725281,\n",
       "  0.99987634593118901,\n",
       "  1.0150091052428858,\n",
       "  1.0064564566304586,\n",
       "  1.0010580486524805,\n",
       "  1.0033745665389266,\n",
       "  1.0027945017976785,\n",
       "  1.0131240314480123,\n",
       "  1.0007558478140666,\n",
       "  1.028801331469468,\n",
       "  1.0391655694822124,\n",
       "  1.0041110570306582,\n",
       "  1.0105344817539723,\n",
       "  1.0028979932357003,\n",
       "  0.99983371538203536,\n",
       "  0.99983349180218617,\n",
       "  0.99986493449715874,\n",
       "  1.0123314679082995,\n",
       "  1.0187016698130515,\n",
       "  1.002884166769664,\n",
       "  1.0030581740712845,\n",
       "  1.003601956776518,\n",
       "  1.0119278409705399,\n",
       "  0.99983481719132927,\n",
       "  1.0053992080630847,\n",
       "  0.99989478418025335,\n",
       "  1.0040480807406007,\n",
       "  1.0129120111490779,\n",
       "  0.99985346194716895,\n",
       "  0.99987716110488067,\n",
       "  1.0051364269572387,\n",
       "  1.0084908900696836,\n",
       "  1.0049392230594474,\n",
       "  1.0032855687741504,\n",
       "  1.004345676636113,\n",
       "  1.0116460167861476,\n",
       "  1.0003584728135566,\n",
       "  0.99997255930169282,\n",
       "  1.0110893685180491,\n",
       "  1.0137453333710713,\n",
       "  1.0074199436361235,\n",
       "  1.007162048209254,\n",
       "  1.0021731877152853,\n",
       "  1.0114059058444647,\n",
       "  1.006773476314976,\n",
       "  1.0029022031847863,\n",
       "  1.0038016441712505,\n",
       "  1.0038449504757272,\n",
       "  0.99999562578375378,\n",
       "  0.99983697032070074,\n",
       "  1.0027828548659146,\n",
       "  1.0050152084156307,\n",
       "  1.0058457161806573,\n",
       "  0.99983361366596502,\n",
       "  1.0159024746209644,\n",
       "  1.0120248046109603,\n",
       "  1.020749931825196,\n",
       "  1.024404620529092,\n",
       "  1.0150877074357632,\n",
       "  1.0039387488302447,\n",
       "  1.0016785567708371,\n",
       "  1.0008719321361892,\n",
       "  1.001458522030596,\n",
       "  1.0212670056912245,\n",
       "  1.031239638805765,\n",
       "  1.0010202386145648,\n",
       "  1.0076126990571512,\n",
       "  1.0005399787075266,\n",
       "  1.0100406900059604,\n",
       "  1.0099694693927013,\n",
       "  1.0164048745672483,\n",
       "  1.00061948639164,\n",
       "  1.0240450960713359,\n",
       "  1.029367248137546,\n",
       "  1.0052485960980759,\n",
       "  1.0018353295538565,\n",
       "  1.0081573125988905,\n",
       "  1.0220282128238454,\n",
       "  1.0204178545332405,\n",
       "  1.0216363547998777,\n",
       "  1.0034762446196175,\n",
       "  1.0174901585471714,\n",
       "  1.0124062148049646,\n",
       "  1.0084984822288645,\n",
       "  1.0226626573987696,\n",
       "  1.0301784072787843,\n",
       "  1.0125012749672877,\n",
       "  1.001133894122255,\n",
       "  1.0151062407857774,\n",
       "  1.0295058990372297,\n",
       "  1.0006534759880716,\n",
       "  1.0001642907587447,\n",
       "  1.0048617233763173,\n",
       "  1.0274463426792433,\n",
       "  1.0280287498221077,\n",
       "  1.0070844073910248,\n",
       "  1.0038588181258867,\n",
       "  1.0075201891964274,\n",
       "  1.0003110797691244,\n",
       "  1.0012257433956571,\n",
       "  1.0145391861526885,\n",
       "  1.0117021965004405,\n",
       "  1.0171432851214339,\n",
       "  1.0173410273152157,\n",
       "  1.0016351170133948,\n",
       "  1.0125523551837474,\n",
       "  1.0005260997783914,\n",
       "  1.0126828510447188,\n",
       "  1.0022740453093764,\n",
       "  1.0082341540505642,\n",
       "  1.0000366717686455,\n",
       "  1.0291841699567248,\n",
       "  1.0079891893848862,\n",
       "  1.0015655725394088,\n",
       "  1.0049219093205255,\n",
       "  1.0005279982157218,\n",
       "  1.0053564991292392,\n",
       "  1.0000138243577705,\n",
       "  1.0074703765988304,\n",
       "  1.0023614773798628,\n",
       "  1.0062393261994975,\n",
       "  1.0032516420833364,\n",
       "  1.0122867764594934,\n",
       "  1.0063133414237464,\n",
       "  1.0130019666302348,\n",
       "  0.99983530558091249,\n",
       "  1.0179536178864672,\n",
       "  1.0048116498921318,\n",
       "  0.99989855262116967,\n",
       "  0.99984436202696358,\n",
       "  1.0063068051787003,\n",
       "  1.0021498015870993,\n",
       "  1.0143757994506382,\n",
       "  0.99996296740338653,\n",
       "  0.99985284911661154,\n",
       "  0.99993549551485816,\n",
       "  1.0131050125260159,\n",
       "  1.0009988261221652,\n",
       "  1.011011606617334,\n",
       "  1.0018779987209261,\n",
       "  1.0001497308204526,\n",
       "  1.0068999739804507,\n",
       "  1.0007115745729265,\n",
       "  0.99986137008511855,\n",
       "  0.99986086801536489,\n",
       "  1.0004318006261657,\n",
       "  1.0008926957770916,\n",
       "  1.0049921577755438,\n",
       "  1.0074512955100525,\n",
       "  1.000353507287453,\n",
       "  1.019639368223384,\n",
       "  1.000208569860028,\n",
       "  1.0000000024103619,\n",
       "  0.99987289507167121,\n",
       "  0.99984375994238628,\n",
       "  1.0100901006933873,\n",
       "  1.0021887119625654,\n",
       "  1.0002838356990285,\n",
       "  1.0156532008520252,\n",
       "  0.99999692768464454,\n",
       "  0.99994167172787451,\n",
       "  0.99983990040300952,\n",
       "  1.0070113121566926,\n",
       "  1.0037115429031016,\n",
       "  1.0108482054262244,\n",
       "  1.0034011141416239,\n",
       "  1.0087338078752961,\n",
       "  1.0001404882418377,\n",
       "  1.0145953955525218,\n",
       "  1.0207450738029009,\n",
       "  0.99990705803702729,\n",
       "  1.0015399495104522,\n",
       "  1.0186704946117,\n",
       "  1.0024606486762584,\n",
       "  1.004780232696503,\n",
       "  1.0366124795207525,\n",
       "  1.0096486765137476,\n",
       "  1.0076005874111682,\n",
       "  1.0161846043501281,\n",
       "  1.0063356761123312,\n",
       "  1.0039373039349537,\n",
       "  1.0048876529738739,\n",
       "  1.0004863329802574,\n",
       "  1.0051615309668274,\n",
       "  1.013575592968484,\n",
       "  1.0422708480513594,\n",
       "  1.0267754964212579,\n",
       "  1.0056872979363802,\n",
       "  1.0012766770242731,\n",
       "  0.99995507233434278,\n",
       "  1.017766548545753,\n",
       "  1.0084443506227658,\n",
       "  1.0058687199139744,\n",
       "  1.0118557379145265,\n",
       "  1.0095407665827321,\n",
       "  1.0036948722867332,\n",
       "  1.0426461039529364,\n",
       "  1.0388122553954469,\n",
       "  1.0067691659972249,\n",
       "  1.0126920616175237,\n",
       "  1.0064979728023262,\n",
       "  1.0179720944974266,\n",
       "  1.0188722365790377,\n",
       "  1.0281950201023828,\n",
       "  1.0117059223602733,\n",
       "  1.0068473194214005,\n",
       "  1.0242112646876693,\n",
       "  1.0262215946423214,\n",
       "  1.0181639677304355,\n",
       "  1.0070977687246063,\n",
       "  1.0000008835977954,\n",
       "  1.0025108338932081,\n",
       "  1.0026257321700123,\n",
       "  1.0026512983106677,\n",
       "  0.99988501418478992,\n",
       "  1.006477195709965,\n",
       "  1.0005705361743034,\n",
       "  1.0021607024236381,\n",
       "  1.0012137204466987,\n",
       "  1.0020241186132417,\n",
       "  1.000042339799593,\n",
       "  1.0003495572026486,\n",
       "  1.0162528550350673,\n",
       "  1.0067194368716192,\n",
       "  1.0031314002031582,\n",
       "  1.0039285344098678,\n",
       "  1.0091189126506968,\n",
       "  1.0163757524024317,\n",
       "  1.007548838628066,\n",
       "  1.0023725901852798,\n",
       "  1.0029710965228593,\n",
       "  0.9998467562760186,\n",
       "  1.0023759984487277,\n",
       "  1.0003525954315782,\n",
       "  1.0003978113524254,\n",
       "  0.99984321587330061,\n",
       "  1.0067154969340777,\n",
       "  1.0101434218058691,\n",
       "  1.003028092768214,\n",
       "  1.0003324303763026,\n",
       "  1.0021103896285106,\n",
       "  1.0014740050572279,\n",
       "  1.0003360907823191,\n",
       "  1.0032139999020744,\n",
       "  0.99998116961146932,\n",
       "  1.008280770820581,\n",
       "  1.0139247178342272,\n",
       "  1.0023965945677495,\n",
       "  1.0058607948275478,\n",
       "  0.99984058017828792,\n",
       "  1.002508279806624,\n",
       "  1.0000303482702932,\n",
       "  1.000939570102003,\n",
       "  0.99983352628619016,\n",
       "  1.0000905817588996,\n",
       "  1.0146763670641661,\n",
       "  1.0192160094252309,\n",
       "  1.0030558865480899,\n",
       "  1.0146029254624935,\n",
       "  1.0061950597037015,\n",
       "  1.007434978858575,\n",
       "  1.0136512011427889,\n",
       "  1.0278605212049028,\n",
       "  1.0124085720306875,\n",
       "  1.0028873697469081,\n",
       "  1.0110036470003378,\n",
       "  1.0017548610426337,\n",
       "  1.0052274002773181,\n",
       "  1.0004347904298303,\n",
       "  1.0036234834598687,\n",
       "  1.0108360126138576,\n",
       "  1.0306686126343765,\n",
       "  1.0021123489324424,\n",
       "  1.0036189298580895,\n",
       "  1.0379385507079957,\n",
       "  1.0023470376609156,\n",
       "  1.0089049114947701,\n",
       "  1.0001071109434108,\n",
       "  1.0161145530160318,\n",
       "  1.0060810332501215,\n",
       "  1.0301938935904016,\n",
       "  1.0036194017328535,\n",
       "  1.0056432469123193,\n",
       "  1.0355187466769888,\n",
       "  1.0043063777485974,\n",
       "  1.0149212625879034,\n",
       "  1.009979044655031,\n",
       "  1.0169096634249883,\n",
       "  1.0329796606618227,\n",
       "  1.0070497811619497,\n",
       "  0.99986959304216272,\n",
       "  1.0145708569762526,\n",
       "  0.9999399247351638,\n",
       "  1.0002367635639593,\n",
       "  1.0078445672367025,\n",
       "  1.0125179262239652,\n",
       "  0.99985880113122627,\n",
       "  1.0197413520723246,\n",
       "  1.0000070848769587,\n",
       "  0.99985746456582891,\n",
       "  1.0209727114491451,\n",
       "  1.0082630412681768,\n",
       "  1.0285051420012057,\n",
       "  1.015527575620264,\n",
       "  1.0218408408679958,\n",
       "  1.017881137985196,\n",
       "  1.0368407358523681,\n",
       "  1.0050068150679323,\n",
       "  1.0185268283103968,\n",
       "  0.99983961894344464,\n",
       "  1.0066320518353495,\n",
       "  1.0272603472559769,\n",
       "  1.0131820656541795,\n",
       "  1.0343212899526186,\n",
       "  1.0136548262874634,\n",
       "  1.0064384741647614,\n",
       "  0.99984546911688066,\n",
       "  1.014438023676006,\n",
       "  1.0057314314424883,\n",
       "  0.99989238149284887,\n",
       "  0.99996387634364758,\n",
       "  1.0024173311462858,\n",
       "  0.99984150825093376,\n",
       "  1.010976036867677,\n",
       "  1.0067800147682047,\n",
       "  1.0000567411604071,\n",
       "  1.0039268844250109,\n",
       "  1.0019824678160596,\n",
       "  1.0014235248405545,\n",
       "  1.0045564561055775,\n",
       "  1.0009619788023745,\n",
       "  1.0067502317552808,\n",
       "  0.99998200587243546,\n",
       "  1.0032120542376599,\n",
       "  0.99985977265564752,\n",
       "  1.0004297721805224,\n",
       "  0.9999277668327351,\n",
       "  1.0000495283960056,\n",
       "  1.0037105026030078,\n",
       "  1.0062990898259505,\n",
       "  1.012701825610425,\n",
       "  1.0113607597398269,\n",
       "  1.0006868612158428,\n",
       "  1.012525085976296,\n",
       "  1.0043116228333777,\n",
       "  1.0078134182160774,\n",
       "  1.0055719924636595,\n",
       "  1.0045628277312109,\n",
       "  1.010940608291711,\n",
       "  1.0018422077007421,\n",
       "  0.99986075380993966,\n",
       "  1.0144047609479183,\n",
       "  1.0085881566855412,\n",
       "  1.01385773338759,\n",
       "  0.99988356826903646,\n",
       "  1.0000121871025756,\n",
       "  1.004568697067344,\n",
       "  1.0038608211653488,\n",
       "  1.0005613729497991,\n",
       "  1.0051228753685257,\n",
       "  1.0126404131602902,\n",
       "  1.0018714888828451,\n",
       "  1.0002135400391972,\n",
       "  1.0001760720608037,\n",
       "  1.0029638702801082,\n",
       "  1.0164522803140865,\n",
       "  1.0009654543934468,\n",
       "  1.0020009416829847,\n",
       "  1.0141939486519345,\n",
       "  1.0010112782907077,\n",
       "  0.99984553490653993,\n",
       "  1.000184268181991,\n",
       "  1.0011791711432931,\n",
       "  1.001108336339396,\n",
       "  1.021855321176709,\n",
       "  1.0011237792754311,\n",
       "  1.0010884099169366,\n",
       "  1.0023452511936002,\n",
       "  1.0213213851395873,\n",
       "  1.017268772325673,\n",
       "  1.0037272279436613,\n",
       "  0.99998472812281936,\n",
       "  1.0180807107004766,\n",
       "  1.0020400478975098,\n",
       "  1.0389600523451226,\n",
       "  1.0080046610008442,\n",
       "  1.0336168284270801,\n",
       "  1.0341973250682017,\n",
       "  1.0062071430503818,\n",
       "  1.0050287544605079,\n",
       "  1.0162557528546166,\n",
       "  1.008747645665053,\n",
       "  1.0315317153936248,\n",
       "  1.0258355214660764,\n",
       "  1.00028146379267,\n",
       "  1.0110235030422525,\n",
       "  1.0391421138633568,\n",
       "  1.0089675415512072,\n",
       "  1.0153783041529658,\n",
       "  1.0024213513096958,\n",
       "  1.0262921794385476,\n",
       "  1.0027657849292124,\n",
       "  1.0183055896974329,\n",
       "  1.0030903147475767,\n",
       "  0.99983341078035681,\n",
       "  1.0118368441462449,\n",
       "  1.0001235279905762,\n",
       "  1.0001266303895207,\n",
       "  1.0016315027047527,\n",
       "  1.0213546909859519,\n",
       "  1.0075381539318096,\n",
       "  1.0359931425347848,\n",
       "  1.002806291126124,\n",
       "  1.0083248142506023,\n",
       "  1.0004097244378289,\n",
       "  1.0036558667566025,\n",
       "  1.0039466507905432,\n",
       "  1.0344713149445093,\n",
       "  1.0288040489425434,\n",
       "  0.99992911040452159,\n",
       "  1.0278113012438221,\n",
       "  1.0034932391143392,\n",
       "  1.0342430658574913,\n",
       "  1.0178340104636996,\n",
       "  1.0070898559322683,\n",
       "  0.99989530055293385,\n",
       "  1.006285018682501,\n",
       "  1.0237661068280401,\n",
       "  1.0043748800796211,\n",
       "  1.0025391687312755,\n",
       "  1.0020868870330195,\n",
       "  1.0071521844959461,\n",
       "  0.99984058434188561,\n",
       "  1.0139082960407124,\n",
       "  1.0005941972981185,\n",
       "  0.99984642039936145,\n",
       "  1.0008129565821553,\n",
       "  1.0030436039322905,\n",
       "  1.0125882549502347,\n",
       "  1.0023499355598258,\n",
       "  1.0124090428666337,\n",
       "  0.99990965703591383,\n",
       "  1.0169837656524259,\n",
       "  1.001233940050188,\n",
       "  1.00442985417758,\n",
       "  0.99983641867131523,\n",
       "  1.0029873250181771,\n",
       "  1.0004640968198912,\n",
       "  1.0181561541584196,\n",
       "  1.0004957556940499,\n",
       "  1.0055269626804804,\n",
       "  1.0014170568877376,\n",
       "  1.013029246971451,\n",
       "  1.0022736346769048,\n",
       "  1.0000011480893367,\n",
       "  1.013336939781311,\n",
       "  1.0078325122617469,\n",
       "  1.0018373142791257,\n",
       "  1.0023092338420605,\n",
       "  1.0006702357988775,\n",
       "  1.0288383363937592,\n",
       "  1.0099514429869987,\n",
       "  1.0219087166059617,\n",
       "  1.0086550603464142,\n",
       "  1.0257936066850821,\n",
       "  1.0021119583471876,\n",
       "  1.0032920522702737,\n",
       "  1.0009925961344008,\n",
       "  1.0033235647287722,\n",
       "  1.0013017109144171,\n",
       "  1.0086449239720001,\n",
       "  1.000491664272287,\n",
       "  1.0023587395907709,\n",
       "  1.0018836682757675,\n",
       "  1.0287349009169562,\n",
       "  1.0425747647076389,\n",
       "  1.0115319070745703,\n",
       "  1.0239798783513365,\n",
       "  1.0237818636208942,\n",
       "  1.0028861733946604,\n",
       "  1.0110569718891678,\n",
       "  0.99998031130502318,\n",
       "  1.000898122013548,\n",
       "  1.0076011179551008,\n",
       "  1.0318769717549303,\n",
       "  1.0189356103249998,\n",
       "  1.032691236732532,\n",
       "  0.99985934420411782,\n",
       "  1.0210157164223912,\n",
       "  1.0211342103896077,\n",
       "  1.0281033138275293,\n",
       "  1.006288255285279,\n",
       "  1.0077849156438896,\n",
       "  1.0117181268703654,\n",
       "  1.0097865217206698,\n",
       "  1.0154625831690192,\n",
       "  1.0164143571468132,\n",
       "  1.0125506382325467,\n",
       "  0.99994327238838021,\n",
       "  1.011492327447731,\n",
       "  1.0026153052998423,\n",
       "  1.0196177986084087,\n",
       "  1.0239824689357593,\n",
       "  1.000174812930533,\n",
       "  0.99983348473667699,\n",
       "  1.0005630834565846,\n",
       "  1.0314378815265326,\n",
       "  1.0112913459759236,\n",
       "  1.0018002267980117,\n",
       "  1.0267034194522879,\n",
       "  1.0096416576986389,\n",
       "  1.0215186156232743,\n",
       "  1.0168286652953102,\n",
       "  1.0398193745316524,\n",
       "  1.0049625395963999,\n",
       "  1.0153835282920118,\n",
       "  1.0105634422077343,\n",
       "  1.0129118831312243,\n",
       "  1.0428320829319724,\n",
       "  1.0009333501295941,\n",
       "  1.0037603289376757,\n",
       "  1.010812571094498,\n",
       "  1.0029098295480707,\n",
       "  1.0052453745856944,\n",
       "  1.0419082963234847,\n",
       "  1.0442536693612221,\n",
       "  1.0006839641938943,\n",
       "  1.0096455507245365,\n",
       "  1.0115132962446354,\n",
       "  1.0013966813392337,\n",
       "  1.0106408972214471,\n",
       "  1.0080369820324322,\n",
       "  1.0101702094675746,\n",
       "  1.0033169440597067,\n",
       "  1.0123168921816268,\n",
       "  1.0278184577055962,\n",
       "  1.0064121660881487,\n",
       "  1.0032098147983093,\n",
       "  1.0134442379192827,\n",
       "  1.0035318376296074,\n",
       "  1.013385590912564,\n",
       "  1.0037772446009818,\n",
       "  1.0059541545769624,\n",
       "  1.0051375874272015,\n",
       "  1.0094896493895529,\n",
       "  1.0227945660483242,\n",
       "  1.0327241982174613,\n",
       "  1.0157853127700509,\n",
       "  1.0260755360042721,\n",
       "  1.0180122894266637,\n",
       "  1.0051089442302976,\n",
       "  1.0254732468636749,\n",
       "  1.0138876051039165,\n",
       "  1.0144906735697841,\n",
       "  1.0106609729634348,\n",
       "  0.99984560713582893,\n",
       "  1.0182309084917227,\n",
       "  1.0098391806453604,\n",
       "  1.005300414188689,\n",
       "  1.0036712039251063,\n",
       "  1.0011571037269789,\n",
       "  1.0008954547108924,\n",
       "  0.999974472317256,\n",
       "  1.0274494279449622,\n",
       "  1.010026477802338,\n",
       "  1.016957221558999,\n",
       "  1.0029267883347717,\n",
       "  0.99984362802532967,\n",
       "  1.025233429152161,\n",
       "  1.025620423514108,\n",
       "  1.0079867733266783,\n",
       "  1.0376317189957509,\n",
       "  1.0281031628148576,\n",
       "  1.0160950907656958,\n",
       "  1.031546783836258,\n",
       "  1.0371903452134912,\n",
       "  1.0022659081737655,\n",
       "  1.0283960485943782,\n",
       "  1.0016381003815802,\n",
       "  1.0046902340109454,\n",
       "  1.0319661295196836,\n",
       "  1.0006688723339854,\n",
       "  1.0122464144623233,\n",
       "  1.0240676094256054,\n",
       "  1.0342514888866468,\n",
       "  1.0350378029820666,\n",
       "  1.0002385418954833,\n",
       "  1.0065146253667485,\n",
       "  1.0171048911876446,\n",
       "  1.0056919091026681,\n",
       "  1.0104278094812353,\n",
       "  1.0001683026616082,\n",
       "  1.0293409763649426,\n",
       "  1.0015156164075041,\n",
       "  1.0185869015694058,\n",
       "  1.0276383242807212,\n",
       "  1.0003429969327653,\n",
       "  1.0008679647002778,\n",
       "  1.0067403487952824,\n",
       "  1.0046254753154573,\n",
       "  1.0006587344078082,\n",
       "  1.0107614697703038]}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pymc import gelman_rubin\n",
    "gelman_rubin(M_pooled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "b:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.001            0.0              0.0              [ 0.     0.001]\n",
      "\t0.0              0.0              0.0                    [ 0.  0.]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.0              0.0             0.001          0.001         0.001\n",
      "\t0.0              0.0             0.0            0.0           0.0\n",
      "\t\n"
     ]
    }
   ],
   "source": [
    "M_pooled.b.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "intercept:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t0.614            0.005            0.0              [ 0.604  0.622]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.604            0.611           0.614          0.617         0.623\n",
      "\t\n"
     ]
    }
   ],
   "source": [
    "M_pooled.intercept.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "first_coef:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t-1.03            0.003            0.0              [-1.036 -1.025]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t-1.036           -1.032          -1.03          -1.028        -1.024\n",
      "\t\n"
     ]
    }
   ],
   "source": [
    "M_pooled.first_coef.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.847410575125255, 1.8298623291475062, 1.8649651358677788)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "#Intercept: mean, 2.5%, 97.5%\n",
    "(np.exp(M_pooled.trace('intercept')[:].mean()),np.exp(np.percentile(M_pooled.trace('intercept')[:],2.5)),np.exp(np.percentile(M_pooled.trace('intercept')[:],97.5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.35705627098656195, 0.35496173609076626, 0.35901614346344946)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#distance: mean, 2.5%, 97.5%\n",
    "(np.exp(M_pooled.trace('first_coef')[:].mean()),np.exp(np.percentile(M_pooled.trace('first_coef')[:],2.5)),np.exp(np.percentile(M_pooled.trace('first_coef')[:],97.5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0005148392152563, 1.000410659775635, 1.0006136597349247)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# #cows: mean, 2.5%, 97.5%\n",
    "(np.exp(M_pooled.trace('b')[:,0].mean()),np.exp(np.percentile(M_pooled.trace('b')[:,0],2.5)),np.exp(np.percentile(M_pooled.trace('b')[:,0],97.5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0002467414196532, 1.0001458395502101, 1.0003515595082881)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# #sheep: mean, 2.5%, 97.5%\n",
    "(np.exp(M_pooled.trace('b')[:,1].mean()),np.exp(np.percentile(M_pooled.trace('b')[:,1],2.5)),np.exp(np.percentile(M_pooled.trace('b')[:,1],97.5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}