BayesianModelOneBigClust.ipynb

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    "A couple of break throughs:\n",
    "\n",
    "1) Your outbreak starts with an index EXPOSED NOT an index infected\n",
    "\n",
    "2) The indicies of the x and y coordinates, and status changes at the beginning of the outbreak function. So you need to change the index of the index exposed when you calculate distance. Even though it is the first index outside of the function, it will be something else once the indicies have been sorted within the function\n",
    "\n",
    "3) You can't have a status = -1 (i.e. culling) in the function, so this Outbreak Function has been changed so that the status indicator keeps increasing. The status of a farm is still considered infected if Status > 5, but we stop the outbreak at the \"end\" day so the status values don't forever increase or max iterate at 1000 iterations"
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      "1 0\n"
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    "%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",
    "\n",
    "from random import randint\n",
    "np.random.seed(1300)\n",
    "Cows = np.r_[np.array([randint(500,1000) for p in range(250)]),np.array([randint(200,500) for p in range(750)])]\n",
    "np.random.seed(1400)\n",
    "Sheep = np.r_[np.array([randint(500,1000) for p in range(250)]),np.array([randint(200,500) for p in range(750)])]"
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    "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)\n"
   ]
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    "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)"
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     "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",
    "Size = 20\n",
    "N = 1000   \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": 24,
   "metadata": {
    "collapsed": false
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   "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_one_big_clust = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
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   "outputs": [],
   "source": [
    "long_one_big_clust.to_pickle('/Users/sandyalakkur/Documents/GradSchool/Research/AMFMDIdeas/long_one_big_clust.pkl') "
   ]
  },
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       "      <td>4.138519</td>\n",
       "      <td>405</td>\n",
       "      <td>447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>304</td>\n",
       "      <td>501</td>\n",
       "      <td>304</td>\n",
       "      <td>3.737652</td>\n",
       "      <td>318</td>\n",
       "      <td>286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>305</td>\n",
       "      <td>474</td>\n",
       "      <td>305</td>\n",
       "      <td>5.524858</td>\n",
       "      <td>416</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>306</td>\n",
       "      <td>478</td>\n",
       "      <td>306</td>\n",
       "      <td>4.932940</td>\n",
       "      <td>339</td>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>307</td>\n",
       "      <td>475</td>\n",
       "      <td>307</td>\n",
       "      <td>5.508423</td>\n",
       "      <td>472</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>308</td>\n",
       "      <td>483</td>\n",
       "      <td>308</td>\n",
       "      <td>5.301646</td>\n",
       "      <td>471</td>\n",
       "      <td>408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>309</td>\n",
       "      <td>400</td>\n",
       "      <td>309</td>\n",
       "      <td>6.486667</td>\n",
       "      <td>204</td>\n",
       "      <td>415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>310</td>\n",
       "      <td>415</td>\n",
       "      <td>310</td>\n",
       "      <td>6.502024</td>\n",
       "      <td>200</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>311</td>\n",
       "      <td>466</td>\n",
       "      <td>311</td>\n",
       "      <td>5.975806</td>\n",
       "      <td>454</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>312</td>\n",
       "      <td>483</td>\n",
       "      <td>312</td>\n",
       "      <td>5.468410</td>\n",
       "      <td>449</td>\n",
       "      <td>236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>313</td>\n",
       "      <td>463</td>\n",
       "      <td>313</td>\n",
       "      <td>4.616385</td>\n",
       "      <td>297</td>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>314</td>\n",
       "      <td>460</td>\n",
       "      <td>314</td>\n",
       "      <td>6.208773</td>\n",
       "      <td>332</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>315</td>\n",
       "      <td>480</td>\n",
       "      <td>315</td>\n",
       "      <td>5.412586</td>\n",
       "      <td>478</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>316</td>\n",
       "      <td>459</td>\n",
       "      <td>316</td>\n",
       "      <td>5.982888</td>\n",
       "      <td>378</td>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>317</td>\n",
       "      <td>453</td>\n",
       "      <td>317</td>\n",
       "      <td>5.297219</td>\n",
       "      <td>234</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>318</td>\n",
       "      <td>442</td>\n",
       "      <td>318</td>\n",
       "      <td>6.006848</td>\n",
       "      <td>236</td>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>319</td>\n",
       "      <td>468</td>\n",
       "      <td>319</td>\n",
       "      <td>6.081729</td>\n",
       "      <td>458</td>\n",
       "      <td>382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>320</td>\n",
       "      <td>461</td>\n",
       "      <td>320</td>\n",
       "      <td>6.385550</td>\n",
       "      <td>435</td>\n",
       "      <td>241</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>45</td>\n",
       "      <td>971</td>\n",
       "      <td>10.826120</td>\n",
       "      <td>449</td>\n",
       "      <td>415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>972</td>\n",
       "      <td>45</td>\n",
       "      <td>972</td>\n",
       "      <td>11.317774</td>\n",
       "      <td>277</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>973</td>\n",
       "      <td>42</td>\n",
       "      <td>973</td>\n",
       "      <td>11.411964</td>\n",
       "      <td>441</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>974</td>\n",
       "      <td>45</td>\n",
       "      <td>974</td>\n",
       "      <td>11.097219</td>\n",
       "      <td>216</td>\n",
       "      <td>457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>975</td>\n",
       "      <td>37</td>\n",
       "      <td>975</td>\n",
       "      <td>11.656740</td>\n",
       "      <td>339</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>976</td>\n",
       "      <td>42</td>\n",
       "      <td>976</td>\n",
       "      <td>11.621100</td>\n",
       "      <td>361</td>\n",
       "      <td>489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>977</td>\n",
       "      <td>46</td>\n",
       "      <td>977</td>\n",
       "      <td>11.880686</td>\n",
       "      <td>493</td>\n",
       "      <td>329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>978</td>\n",
       "      <td>46</td>\n",
       "      <td>978</td>\n",
       "      <td>11.784736</td>\n",
       "      <td>430</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>979</td>\n",
       "      <td>43</td>\n",
       "      <td>979</td>\n",
       "      <td>11.407857</td>\n",
       "      <td>209</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>980</td>\n",
       "      <td>41</td>\n",
       "      <td>980</td>\n",
       "      <td>11.116101</td>\n",
       "      <td>275</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>981</td>\n",
       "      <td>47</td>\n",
       "      <td>981</td>\n",
       "      <td>11.954383</td>\n",
       "      <td>482</td>\n",
       "      <td>412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>982</td>\n",
       "      <td>41</td>\n",
       "      <td>982</td>\n",
       "      <td>12.314094</td>\n",
       "      <td>464</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>983</td>\n",
       "      <td>42</td>\n",
       "      <td>983</td>\n",
       "      <td>12.694224</td>\n",
       "      <td>369</td>\n",
       "      <td>489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>984</td>\n",
       "      <td>34</td>\n",
       "      <td>984</td>\n",
       "      <td>10.924123</td>\n",
       "      <td>323</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>985</td>\n",
       "      <td>32</td>\n",
       "      <td>985</td>\n",
       "      <td>11.542767</td>\n",
       "      <td>214</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>986</td>\n",
       "      <td>36</td>\n",
       "      <td>986</td>\n",
       "      <td>12.307181</td>\n",
       "      <td>250</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>987</td>\n",
       "      <td>23</td>\n",
       "      <td>987</td>\n",
       "      <td>11.644125</td>\n",
       "      <td>251</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>988</td>\n",
       "      <td>31</td>\n",
       "      <td>988</td>\n",
       "      <td>10.858250</td>\n",
       "      <td>393</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>989</td>\n",
       "      <td>39</td>\n",
       "      <td>989</td>\n",
       "      <td>11.270433</td>\n",
       "      <td>272</td>\n",
       "      <td>399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>990</td>\n",
       "      <td>33</td>\n",
       "      <td>990</td>\n",
       "      <td>11.142819</td>\n",
       "      <td>413</td>\n",
       "      <td>424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>991</td>\n",
       "      <td>32</td>\n",
       "      <td>991</td>\n",
       "      <td>11.753782</td>\n",
       "      <td>209</td>\n",
       "      <td>314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>992</td>\n",
       "      <td>18</td>\n",
       "      <td>992</td>\n",
       "      <td>12.294076</td>\n",
       "      <td>243</td>\n",
       "      <td>227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>993</td>\n",
       "      <td>17</td>\n",
       "      <td>993</td>\n",
       "      <td>11.111247</td>\n",
       "      <td>240</td>\n",
       "      <td>496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>994</td>\n",
       "      <td>9</td>\n",
       "      <td>994</td>\n",
       "      <td>14.046087</td>\n",
       "      <td>353</td>\n",
       "      <td>482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>995</td>\n",
       "      <td>16</td>\n",
       "      <td>995</td>\n",
       "      <td>13.860472</td>\n",
       "      <td>334</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>996</td>\n",
       "      <td>14</td>\n",
       "      <td>996</td>\n",
       "      <td>13.234056</td>\n",
       "      <td>270</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>997</td>\n",
       "      <td>16</td>\n",
       "      <td>997</td>\n",
       "      <td>14.796421</td>\n",
       "      <td>492</td>\n",
       "      <td>389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>998</td>\n",
       "      <td>21</td>\n",
       "      <td>998</td>\n",
       "      <td>14.046935</td>\n",
       "      <td>417</td>\n",
       "      <td>393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>999</td>\n",
       "      <td>15</td>\n",
       "      <td>999</td>\n",
       "      <td>14.413348</td>\n",
       "      <td>229</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1000</td>\n",
       "      <td>9</td>\n",
       "      <td>1000</td>\n",
       "      <td>13.205163</td>\n",
       "      <td>307</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>710 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     farmID  Status  farmID  DistToIndex  NumCows  NumSheep\n",
       "290     291     501     291     2.964202      360       305\n",
       "291     292     501     292     0.000000      352       463\n",
       "292     293     501     293     0.262659      391       302\n",
       "293     294     501     294     1.335697      325       360\n",
       "294     295     501     295     0.312422      397       380\n",
       "295     296     501     296     1.247280      439       395\n",
       "296     297     501     297     1.192060      269       444\n",
       "297     298     501     298     0.614850      292       220\n",
       "298     299     501     299     1.449655      314       425\n",
       "299     300     501     300     0.870234      434       494\n",
       "300     301     501     301     1.294966      395       474\n",
       "301     302     501     302     1.423625      210       323\n",
       "302     303     501     303     4.138519      405       447\n",
       "303     304     501     304     3.737652      318       286\n",
       "304     305     474     305     5.524858      416       200\n",
       "305     306     478     306     4.932940      339       422\n",
       "306     307     475     307     5.508423      472       413\n",
       "307     308     483     308     5.301646      471       408\n",
       "308     309     400     309     6.486667      204       415\n",
       "309     310     415     310     6.502024      200       307\n",
       "310     311     466     311     5.975806      454       330\n",
       "311     312     483     312     5.468410      449       236\n",
       "312     313     463     313     4.616385      297       226\n",
       "313     314     460     314     6.208773      332       229\n",
       "314     315     480     315     5.412586      478       350\n",
       "315     316     459     316     5.982888      378       268\n",
       "316     317     453     317     5.297219      234       359\n",
       "317     318     442     318     6.006848      236       451\n",
       "318     319     468     319     6.081729      458       382\n",
       "319     320     461     320     6.385550      435       241\n",
       "..      ...     ...     ...          ...      ...       ...\n",
       "970     971      45     971    10.826120      449       415\n",
       "971     972      45     972    11.317774      277       201\n",
       "972     973      42     973    11.411964      441       225\n",
       "973     974      45     974    11.097219      216       457\n",
       "974     975      37     975    11.656740      339       229\n",
       "975     976      42     976    11.621100      361       489\n",
       "976     977      46     977    11.880686      493       329\n",
       "977     978      46     978    11.784736      430       208\n",
       "978     979      43     979    11.407857      209       297\n",
       "979     980      41     980    11.116101      275       218\n",
       "980     981      47     981    11.954383      482       412\n",
       "981     982      41     982    12.314094      464       461\n",
       "982     983      42     983    12.694224      369       489\n",
       "983     984      34     984    10.924123      323       200\n",
       "984     985      32     985    11.542767      214       330\n",
       "985     986      36     986    12.307181      250       391\n",
       "986     987      23     987    11.644125      251       231\n",
       "987     988      31     988    10.858250      393       250\n",
       "988     989      39     989    11.270433      272       399\n",
       "989     990      33     990    11.142819      413       424\n",
       "990     991      32     991    11.753782      209       314\n",
       "991     992      18     992    12.294076      243       227\n",
       "992     993      17     993    11.111247      240       496\n",
       "993     994       9     994    14.046087      353       482\n",
       "994     995      16     995    13.860472      334       231\n",
       "995     996      14     996    13.234056      270       311\n",
       "996     997      16     997    14.796421      492       389\n",
       "997     998      21     998    14.046935      417       393\n",
       "998     999      15     999    14.413348      229       390\n",
       "999    1000       9    1000    13.205163      307       210\n",
       "\n",
       "[710 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",
    "one_big_clust = pd.concat([sum_Status2, subset], axis=1, join_axes=[subset.index])\n",
    "one_big_clust[290:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Save (or pickle) the dataset for future use\n",
    "one_big_clust.to_pickle('/Users/sandyalakkur/Documents/GradSchool/Research/AMFMDIdeas/one_big_clust.pkl') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
    "one_big_clust = pd.read_pickle('/Users/sandyalakkur/Documents/GradSchool/Research/AMFMDIdeas/one_big_clust.pkl')"
   ]
  },
  {
   "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": 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qi7mlA5UbDKJKw0EkJyaQmJSMZ7ARrYftR1ffGK/7pxn63Xh69ehE0SLOjBw1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\nERGRwP4OCujh9imvZZEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105bfe518>"
      ]
     },
     "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=one_big_clust['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": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dist_diff = one_big_clust['DistToIndex'] - one_big_clust['DistToIndex'].mean()\n",
    "cows_diff = one_big_clust['NumCows'] - one_big_clust['NumCows'].mean()\n",
    "sheep_diff = one_big_clust['NumSheep'] - one_big_clust['NumSheep'].mean()\n",
    "status = one_big_clust['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%-----------------] 15000 of 15000 complete in 35.3 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": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1095c0940>"
      ]
     },
     "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": 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aIQweSVJXBo8kqSuDR5LUlcEjSerK4JEkdWXwSJK6MngkSV0ZPJKkrgweSVJXBo8kqSuD\nR5LUlcEjSerK4JEkdWXwSJK6MngkSV0ZPJKkrgweSVJXBo8kqSuDR5LUlcEjSerK4JEkdWXwSJK6\nMngkSV0ZPJKkrgweSVJXBo8kqSuDR5LUlcEjSerK4JEkdWXwSJK6MngkSV0ZPJKkrgweSVJXBo8k\nqSuDR5LUlcEjSerK4JEkdWXwSJK6MngkSV0ZPJKkrgweSVJXBo8kqSuDR5LUlcEjSerK4JEkdWXw\nSJK6MngkSV0ZPJKkrgweSVJXBo8kqSuDR5LUlcEjSerK4JEkdWXwSJK6MngkSV0ZPJKkrgweSVJX\nBo8kqSuDR5LUlcEjSerK4JEkdWXwSJK6MngkSV0ZPJKkrgweSVJXBo8kqSuDR5LUlcEjSerK4JEk\ndWXwSJK6MngkSV0ZPJKkrgweSVJXBo8kqSuDR5LUlcEjSerK4JEkdWXwSJK6MngkSV0ZPJKkrgwe\nSVJXBo8kqSuDR5LUlcEjSerK4JEkdWXwSJK6MngkSV0ZPJKkrgweSVJXBo8kqSuDR5LUlcEjSerK\n4JEkdWXwSJK6MngkSV0ZPJKkrgweSVJXBo8kqSuDR5LUlcEjSerK4JEkdWXwSJK6MngkSV0ZPJKk\nrgweSVJXBo8kqSuDR5LUlcEjSerK4JEkdWXwSJK6MngkSV3t9MFz2WWXLXURFpT1Wd6sz8qwHOo9\nyWUweJZB4y0k67O8WZ+VYTnUe5LLsNMHjySpL4NHktRVqmqpy7Bokqzcykk7garKUm3b48eOG9d+\nKzp4JEnLj5faJEldGTySpK5WVPAkeV+SLUk2j5l+XJJNSTYkuTrJC9r4Jya5NMnnklyb5KSRZVYn\n+dskX0ryyST7THh93pTka22ZDUnWT0B9Hp3kyiQbk3w+ydtGlpnE9pmtPhPXPiPTd23TLhwZt2Tt\ns9C29fksxTaTrEty78j+8sZFKsfYY8p2qaoV8wJ+AVgLbB4zfc+R4TXAV9rw44HD2vAq4IvAk9v7\n/wKc3IbfALx9wutzKvDaSWqf9n6P9nc34O+BIye1fbZRn4lsnzbutcA5wMdHxi1Z+/T+fJaoTdaN\nft6LWI6ZjilPmTbPDXNd34rq8VTVp4G7Z5n+wMjbVcAdbfzXq2pjG74f+AJwYJvvWODsNnw28KIF\nLvZYi1QfgCV5Umh769OmfbMNPhLYdWQ9E9c+bdq4+sAEtk+Sg4B/DryXh5d/ydpnoW3r81nCbS76\n/jLmmHLA9Nnmur4VFTxzkeRFSb4AXAT8SHcxySEMZxhXtlH7V9WWNrwF2L9DMedsO+oDcGK7ZHLW\ncrv0Ma4+SXZJspGhDS6tqs+3SRPZPrPUByawfYD/CrweeGjaIsu6fVaAAp7T9pe/SfLUxd7gmGPK\nvOx0wVNVF1TVU4BfAj44Oi3JKuAvgf/YUn36ssU8Ur2H7ajPnwE/BhwG3Ab8UcfibtO4+lTVQ1V1\nGHAQ8Nwk62ZYdmLaZ5b6TFT7ZPBCYGtVbWCWs+/l2D4rwDXAE6vqGcCZwAWLubHpx5Qkp0zdXwIO\nGLnXdOZs69npgmdK68LulmRfgCSPAM4HPlRVo423Jcnj2zxPALZ2L+wczLU+VbW1GobLIkcsSYG3\nYXp9RsbfC/w18Mw2aiLbZ2T8VH0Ob+8nqn2AfYHnAMcmuR74C+AFST7QZp2I9plUVXXf1GXbqroI\neESS1YuxrZmOKVV1elWtraq1wK1Tw1V14mzr2qmCJ8mhSdKGfwagqu5s484CPl9VZ0xb7OPAK9rw\nK1jkM4r52J76tC//lF8Guj2hsy2z1Ge/qUtOSXYHjgY2tsUmsX1mqs+G9n7S2ueOqvq9qnpiVf0Y\n8KvAp6rq5W2xZds+K0GS/Ufa5AiGHwW4axG2M9sxcv625wmH5fpiONu6FfgOcDPwb4DfBH6zTT8Z\nuJbhS/5p4Flt/M8zXJve2KZtANa3aauBS4AvAZ8E9pnw+nwA+CywieEgsP8E1GcNwyWFja3srx9Z\n5yS2z2z1mbj2mbaO5/Hwp9qWrH0W8fN5sH0+r1wGbfLbrU02AlcAz16kcow9pozMc91c1+dP5kiS\nutqpLrVJkpaewSNJ6srgkSR1ZfBIkroyeCRJXRk8kqSuDB5JUlcGjySpq/8PhuyA4YJ5eCkAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1095c07f0>"
      ]
     },
     "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": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1096a6b00>"
      ]
     },
     "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": 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S7tx9551ku3OjlHH/OX9mgyRLnGGZParBghaFiPwe+FpV72xqWVoqNnnXLja2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x109dba940>"
      ]
     },
     "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": 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imSANw6haVPUKEdkJqFHV15OWp1qwB351Ydc7WVqFopct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\nvqMvtxUrYOzYsUybNo1p06YB09hoI/emCisknrDSlzQtGFXcfCWIbKx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WbmXSksillm59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\nlUapLXrra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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119fe84a8>"
      ]
     },
     "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": 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8gG3vIRP2QFm2LPNwSzI0FnYu/8OxnMl4yyFwi3mIdu+ee3w5hs0KEVRhguMn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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aaf5940>"
      ]
     },
     "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': [1.0002734020815967, 0.99983680748846737],\n",
       " 'first_coef': 1.0004058792190416,\n",
       " 'intercept': 1.0010568441257064,\n",
       " 'prob': [1.0001658326753593,\n",
       "  1.0013395606554485,\n",
       "  1.001839802377499,\n",
       "  1.0011734788880611,\n",
       "  1.0018496717566108,\n",
       "  1.0016105012822543,\n",
       "  1.0016747310331033,\n",
       "  1.0017480481611667,\n",
       "  1.0015932209808367,\n",
       "  1.0017398824734727,\n",
       "  1.0017394644298689,\n",
       "  1.0006133342505357,\n",
       "  1.002114471439129,\n",
       "  1.0017697422496568,\n",
       "  1.0019544932216182,\n",
       "  1.0016472176873652,\n",
       "  1.0015362821247398,\n",
       "  1.0002120400491898,\n",
       "  1.0015908945243128,\n",
       "  1.0011886821866134,\n",
       "  1.0019071095087877,\n",
       "  1.0017905974856598,\n",
       "  1.0005835709848736,\n",
       "  1.0007066707939074,\n",
       "  0.99994935506591609,\n",
       "  1.0010291339576307,\n",
       "  1.0007517208335155,\n",
       "  1.0009012183375041,\n",
       "  1.0003852668316073,\n",
       "  1.0006073682599328,\n",
       "  1.0008703311807712,\n",
       "  1.000782145397052,\n",
       "  1.0000407017278963,\n",
       "  0.99985760873251717,\n",
       "  1.0011925975516756,\n",
       "  1.0015894075926897,\n",
       "  1.0012840996722312,\n",
       "  1.001610279627217,\n",
       "  1.0016942249233931,\n",
       "  1.0009669822839007,\n",
       "  1.0016014782602036,\n",
       "  1.0016504435144338,\n",
       "  1.0018728965672719,\n",
       "  1.0013618990872493,\n",
       "  1.0014297430295223,\n",
       "  1.0010899685971413,\n",
       "  1.0011614251632293,\n",
       "  1.00159456388371,\n",
       "  1.0018362235674894,\n",
       "  1.0014663917500497,\n",
       "  1.0010679716058304,\n",
       "  1.0005797892399533,\n",
       "  1.0015693741716429,\n",
       "  1.001174598752868,\n",
       "  1.0017689869331092,\n",
       "  1.0016694976904188,\n",
       "  1.0011460632724769,\n",
       "  1.0009897042024751,\n",
       "  1.0012657125470656,\n",
       "  1.0021362935036282,\n",
       "  1.00201135820097,\n",
       "  1.0006788799292587,\n",
       "  1.0020823617511423,\n",
       "  1.0008547676514419,\n",
       "  1.0018669954970896,\n",
       "  1.0012303231049782,\n",
       "  1.0013896672291882,\n",
       "  1.001655731348688,\n",
       "  1.0018769985300673,\n",
       "  1.0017707137977041,\n",
       "  1.0018928999841858,\n",
       "  1.0017930716081807,\n",
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       "  1.0001963751263239,\n",
       "  1.000488013276178,\n",
       "  1.0003283484530505]}"
      ]
     },
     "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",
      "\t0.0              0.0              0.0                    [ 0.  0.]\n",
      "\t-0.001           0.0              0.0              [-0.001 -0.001]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t0.0              0.0             0.0            0.0           0.0\n",
      "\t-0.001           -0.001          -0.001         -0.001        -0.001\n",
      "\t\n"
     ]
    }
   ],
   "source": [
    "M_pooled.b.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "intercept:\n",
      " \n",
      "\tMean             SD               MC Error        95% HPD interval\n",
      "\t------------------------------------------------------------------\n",
      "\t1.331            0.005            0.0              [ 1.321  1.341]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t1.321            1.328           1.331          1.335         1.341\n",
      "\t\n"
     ]
    }
   ],
   "source": [
    "M_pooled.intercept.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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-0.973           0.003            0.0              [-0.978 -0.967]\n",
      "\t\n",
      "\t\n",
      "\tPosterior quantiles:\n",
      "\t\n",
      "\t2.5             25              50              75             97.5\n",
      "\t |---------------|===============|===============|---------------|\n",
      "\t-0.978           -0.975          -0.973         -0.971        -0.967\n",
      "\t\n"
     ]
    }
   ],
   "source": [
    "M_pooled.first_coef.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.7863244336063473, 3.7511521050888286, 3.8224199062540922)"
      ]
     },
     "execution_count": 20,
     "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": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.37807666522783889, 0.37613528102011629, 0.37999413388974501)"
      ]
     },
     "execution_count": 21,
     "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": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0002727355416592, 1.0002109400077075, 1.0003337800233756)"
      ]
     },
     "execution_count": 22,
     "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": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.99895785053171582, 0.99889756105445959, 0.99901991319083516)"
      ]
     },
     "execution_count": 23,
     "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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