BayesianModelClusterAndRandSize.ipynb

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   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 0\n"
     ]
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   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "np.random.seed(53080)\n",
    "mean1 = [4, 4]\n",
    "cov1 = [[2.5, 0], [0, 2.5]]\n",
    "x1, y1 = np.random.multivariate_normal(mean1, cov1, 250).T\n",
    "mean2 = [11, 10]\n",
    "cov2 = [[3.5, 0], [0, 2.5]]\n",
    "x2, y2 = np.random.multivariate_normal(mean2, cov2, 250).T\n",
    "mean3 = [5, 15]\n",
    "cov3 = [[3.5, 0], [0, 2.5]]\n",
    "x3, y3 = np.random.multivariate_normal(mean3, cov3, 250).T\n",
    "mean4 = [15, 4]\n",
    "cov4 = [[3.5, 0], [0, 2.5]]\n",
    "x4, y4 = np.random.multivariate_normal(mean4, cov4, 250).T\n",
    "\n",
    "x = np.r_[x1,x2,x3,x4]\n",
    "y = np.r_[y1,y2,y3,y4]\n",
    "\n",
    "ind1 = []\n",
    "ind2 = []\n",
    "\n",
    "for i in range(len(x)):\n",
    "    ind1.append(np.logical_or(x[i]<0, x[i]>20))\n",
    "    ind2.append(np.logical_or(y[i]<0, y[i]>20))\n",
    "print(np.sum(ind1),np.sum(ind2))\n",
    "\n",
    "for i in range(len(x)):\n",
    "    if np.logical_or(x[i]<0, x[i]>20):\n",
    "        x[i] = np.random.rand(1)*20\n",
    "    if np.logical_or(y[i]<0, y[i]>20):\n",
    "        y[i] = np.random.rand(1)*20\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
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   "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[291][0],coords[291][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": "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",
    "Size = 20\n",
    "N = 1000   \n",
    "np.random.seed(13)\n",
    "Cows = np.array([randint(200,500) for p in range(N)])\n",
    "np.random.seed(14)\n",
    "Sheep = np.array([randint(200,500) for p in range(N)]) \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": 33,
   "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_clust_rand_size = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "long_clust_rand_size.to_pickle('/Users/sandyalakkur/Documents/GradSchool/Research/AMFMDIdeas/long_clust_rand_size.pkl') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<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>0</th>\n",
       "      <td>1</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>7.116090</td>\n",
       "      <td>311</td>\n",
       "      <td>392</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>7.392643</td>\n",
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       "      <td>402</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>500</td>\n",
       "      <td>3</td>\n",
       "      <td>5.399727</td>\n",
       "      <td>315</td>\n",
       "      <td>245</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
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       "      <td>6.232599</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
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       "      <td>6.263979</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   farmID  Status  farmID  DistToIndex  NumCows  NumSheep\n",
       "0       1     226       1     7.116090      311       392\n",
       "1       2     247       2     7.392643      423       402\n",
       "2       3     500       3     5.399727      315       245\n",
       "3       4     420       4     6.232599      305       423\n",
       "4       5     469       5     6.263979      442       464"
      ]
     },
     "execution_count": 6,
     "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",
    "clust_rand_size = pd.concat([sum_Status2, subset], axis=1, join_axes=[subset.index])\n",
    "clust_rand_size.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Save (or pickle) the dataset for future use\n",
    "clust_rand_size.to_pickle('/Users/sandyalakkur/Documents/GradSchool/Research/AMFMDIdeas/clust_rand_size.pkl') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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",
    "clust_rand_size = pd.read_pickle('/Users/sandyalakkur/Documents/GradSchool/Research/AMFMDIdeas/clust_rand_size.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([291]),)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N = 1000\n",
    "Size = 20\n",
    "#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": 37,
   "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"
     ]
    },
    {
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Wy8IDsC/SACHUKx/3ry2nuG0ARkZGHDhyicIVB2JgZEnQ7c2kRl/hwvnTlCvv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\nh35Y1plCeloSt058ja6eIQnxUejqm3J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UbccIc3Yg3NuVyMwczBZLwVr4O9sjGvXMbhzO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\n9WYzcDC6EXV9BUW9czhz+gRCCCwWC9u3b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KV0SBt2fzea9fM6k5wQgZSCzJT7mByNeLi7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\nYxEREc8UxiIiIp79H1G/UWvcNMA3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1095a81d0>"
      ]
     },
     "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=clust_rand_size['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[291], y[291]), 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=0.00005))\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dist_diff = clust_rand_size['DistToIndex'] - clust_rand_size['DistToIndex'].mean()\n",
    "cows_diff = clust_rand_size['NumCows'] - clust_rand_size['NumCows'].mean()\n",
    "sheep_diff = clust_rand_size['NumSheep'] -clust_rand_size['NumSheep'].mean()\n",
    "status = clust_rand_size['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": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 15000 of 15000 complete in 35.1 sec"
     ]
    }
   ],
   "source": [
    "chains = 2\n",
    "iterations = 15000\n",
    "burn = 9000\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": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DnfF5C6Ulb1ymIPxWj8aMn++SnaYbp1mzLW2LYSZpa7dkp+mqRvfYuLE46qiTWbHiPFas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\nuMPzBPUUKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10955b240>"
      ]
     },
     "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": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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b+R6PJGmZMHgkSV0ZPJKkrgweSVJXBo8kqSuDR5LUlcEjSerK4JEkdWXwSJK6MngkSV0Z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\nHklSVwaPJKkrg0eS1JXBI0nq6v8BvcKWGhVNVvUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1095c8198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.summary_plot(M_pooled.intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1095a0c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.summary_plot(M_pooled.first_coef)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": 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6G4rl4hwjIvNEJDA1tohsJyKviMhyEbnEVzcjl3PzTRGZ4CnfQESeEZHpIvK0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\naeJY0Mq1ssU5v6mpUCnzK2j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grIha8k07NZZRWSw2Vv1gY50NVe2D5iXuDsI4JFVIoib4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\nzN0rXYeizMzb62ZmuHvHv0kuK2V9T7XnsiVZr97UTyQtws+xVboevRX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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1265387f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.plot(M_pooled.intercept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 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wl1+e2M/u3RPLwvz5z/Hb+fhyCmpYIrzv46a+/FAkwXPq652mJ512Y9WqxP7m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\naCEivQOHLwOuAA7CvbTALUx4EBfj7XTvJXckLgtBf6CfiGz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BRLYElcp57vcWX34Zs+oF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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1095f0eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.plot(M_pooled.b, common_scale=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 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B6ap6tYhc5L6/2F9ARPoAJwN7A2uBqSLyD1V9H1DgOlW9LqTu+araP2R/IrI9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\nmtdWU1OmGCmFqDvyyFRKklw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1QCvmpI233gpf+uumm5y0HGErgADcf39q+803YffdU++/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a33e0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matplot.plot(M_pooled.first_coef, common_scale=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'b': [1.0001492076309031, 1.0014853854304746],\n",
       " 'first_coef': 0.99992131214117019,\n",
       " 'intercept': 1.0011480438492639,\n",
       " 'prob': [1.0002954850859953,\n",
       "  0.99983598680328589,\n",
       "  1.0013130114959869,\n",
       "  0.99985934511529839,\n",
       "  1.000104884856803,\n",
       "  1.0002745700540434,\n",
       "  0.99983338229712848,\n",
       "  0.99985199537427594,\n",
       "  1.0005930328297756,\n",
       "  1.0002419517033352,\n",
       "  0.99986518764417631,\n",
       "  1.0000105818144083,\n",
       "  0.99983541657833575,\n",
       "  0.99997020224763722,\n",
       "  0.9999495392461587,\n",
       "  1.0021814007096774,\n",
       "  0.99988445671104098,\n",
       "  0.99989315146987989,\n",
       "  0.99989920649879482,\n",
       "  0.9999552048642808,\n",
       "  0.99983333464561652,\n",
       "  1.0007346080735955,\n",
       "  0.99985707726064266,\n",
       "  0.99993110879486446,\n",
       "  1.0000082138502,\n",
       "  1.0032257209665301,\n",
       "  1.0034584661469943,\n",
       "  0.99987743091971237,\n",
       "  1.0012680235688738,\n",
       "  0.99987356983225617,\n",
       "  0.99983911887947541,\n",
       "  0.99983386850749012,\n",
       "  0.99991221736391978,\n",
       "  1.0005726665378296,\n",
       "  1.0015528292267175,\n",
       "  0.99997422304997496,\n",
       "  0.99989173214937477,\n",
       "  1.0018543663451751,\n",
       "  1.0001214272594465,\n",
       "  0.9998488234126528,\n",
       "  1.001394368592067,\n",
       "  1.0026908723065224,\n",
       "  1.000823103109391,\n",
       "  0.99984211986238791,\n",
       "  0.99999523805968038,\n",
       "  1.0000172714919224,\n",
       "  0.99984919460200961,\n",
       "  1.000642790088641,\n",
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       "  1.0008766072306206,\n",
       "  1.0000480071412716,\n",
       "  0.99996821958462301]}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pymc import gelman_rubin\n",
    "gelman_rubin(M_pooled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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              [ 0.001  0.001]\n",
      "\t0.0              0.0              0.0              [ 0.     0.001]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.001            0.001           0.001          0.001         0.001\n",
      "\t0.0              0.0             0.0            0.0           0.001\n",
      "\t\n"
     ]
    }
   ],
   "source": [
    "M_pooled.b.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "intercept:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t1.145            0.005            0.0              [ 1.137  1.155]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t1.136            1.142           1.145          1.149         1.155\n",
      "\t\n"
     ]
    }
   ],
   "source": [
    "M_pooled.intercept.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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-0.958           0.003            0.0              [-0.962 -0.952]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t-0.963           -0.959          -0.958         -0.956        -0.952\n",
      "\t\n"
     ]
    }
   ],
   "source": [
    "M_pooled.first_coef.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.1436764483044, 3.1129496453931322, 3.1740939716692242)"
      ]
     },
     "execution_count": 29,
     "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": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.38383123625891019, 0.3818840634763021, 0.38601820532144082)"
      ]
     },
     "execution_count": 30,
     "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": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0011871171709676, 1.0010930069044999, 1.0012795416794298)"
      ]
     },
     "execution_count": 31,
     "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": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0004563046908832, 1.0003546987841563, 1.0005551215776978)"
      ]
     },
     "execution_count": 32,
     "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": []
  }
 ],
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