CouldYouLookAtThis.ipynb

CouldYouLookAtThis.ipynb

{
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    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "After comparing Keeling's Matlab code and output, and using multiple print statements throughout my code, I am still not sure why the size of the nearest infected farm is not significant. I found a couple of bugs, but they did not change the results. I also remembered that the reason I had previously decided not to use Keeling's kernel and use a Cauchy kernel was because Keeling's kernel makes the disease progress very slowly. So I have used the Cauchy kernel in the code below. The changes that I have made result in output that is consistent with Keeling's code, however I have noticed that in this new version the number of new infected is dramatically lower than it used to be."
    },
    {
      "metadata": {
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      "cell_type": "code",
      "source": "import numpy as np\nfrom random import randint\n\n# DONT USE CAPITALIZED NAMES FOR VARIABLES; RESERVE THESE FOR CLASSES ONLY\n# ALSO, MORE DESCRIPTIVE VARIABLE NAMES WOULD HELP -- SIZE OF WHAT, FOR EXAMPLE\nSize = 20\nN = 75 \nnp.random.seed(53080)\nx = Size*np.random.rand(N)\nnp.random.seed(23003)\ny = Size*np.random.rand(N)  \n#np.random.seed(10)\n#Cows = np.array([randint(25,250) for p in range(N)])\n\n# YOU ARE BETTER OFF USING NUMPY'S RANDOM NUMBER GENERATOR, SINCE YOU HAVE IMPORTED IT ALREADY\n# IS THERE A REASON YOU ARE USING THE BUILTIN ONE INSTEAD?\n# THEN YOU CAN SIMPLY: np.random.randint(25, 51, size=15)\n\nCows = np.array([randint(25,51) for p in range(15)]+[randint(51,76) for p in range(30)]+[randint(76,95) for p in range(20)]+[randint(95,250) for p in range(10)])\n\n# AGAIN, USING NUMPY YOU CAN CONCATENATE THESE USING np.r_ INSTEAD OF SUMMING LISTS\n\n#np.random.seed(11)\n#Sheep = np.array([randint(25,250) for p in range(N)])\nSheep = np.array([randint(25,51) for p in range(15)]+[randint(51,76) for p in range(30)]+[randint(76,95) for p in range(20)]+[randint(95,250) for p in range(10)])\n\n",
      "execution_count": 1,
      "outputs": []
    },
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      "metadata": {
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      "cell_type": "code",
      "source": "np.array([4,5,7,2,10,11]) & np.arange(6)",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([0, 1, 2, 2, 0, 1])"
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          "metadata": {},
          "execution_count": 10
        }
      ]
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        "trusted": false,
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      },
      "cell_type": "code",
      "source": "#Calculates which grid square a particular location is in (turn a 2-d coordinate into a scalar)\ndef 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)",
      "execution_count": 7,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": true
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      "cell_type": "code",
      "source": "def Kernel(dist_squared):\n    dist_squared = np.asarray(dist_squared)\n    # WHY NOT JUST is_scalar = dist_squared.ndim == 0\n    is_scalar = False if dist_squared.ndim > 0 else True\n    # NOT CLEAR WHAT YOU ARE DOING BELOW\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])",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
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      "cell_type": "code",
      "source": "from math import pi\ndef 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        \n        # THERE IS RARELY ANY NEED TO USE np.multiply. JUST USE -Transmiss[INFi]*Num\n        \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                        \n                        # HAVE YOU TRIED ANALYZING JUST THIS FUNCTION FOR SENSITIVITY TO \n                        # FARM SIZE?\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}",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "THE FUNCTION BELOW IS CUMBERSOME. BREAK IT DOWN INTO FUNCTIONS, AND BE SURE TO WRITE TESTS TO ENSURE THAT EACH PIECE IS OPERATING AS EXPECTED"
    },
    {
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      "cell_type": "code",
      "source": "def Outbreaks(Size,N,Y0,farms,end,end2,x,y,Cows,Sheep,Maxtime=1000):\n    #This is an attempt of converting the Matlab Program 7.6 Code into Python\n    \n    # LIBRARY IMPORT SHOULD NOT OCCUR INSIDE OF FUNCTIONS\n    \n    import numpy as np\n    import pandas as pd\n    from math import pi\n    \n    Status = np.array([0]*N)    #Initial Status of each farm\n    init_ind = np.random.randint(0,N)\n    for i in range(Y0):\n        Status[init_ind] = 6 #one farm is initially infected \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    #Set up the grid\n    grid = WhichGrid(x,y,Size,Size,10.0,10.0)\n    tmp = sorted(grid) #Sort grid values\n    \n    \n    # DO WE REALLY NEED ALL OF THE BELOW? IF SO, BETTER TO INITIALIZE A PANDAS\n    # DATA FRAME THAN ALL THESE LISTS\n    \n    #i = np.argsort(grid) #get indexed values after sort\n    i = [i[0] for i in sorted(enumerate(grid), key=lambda x:x[1])]\n    x = x[i]\n    y = y[i]\n    Status = Status[i]\n    grid = grid[i]\n    Transmiss = Transmiss[i]\n    Suscept = Suscept[i]\n    Cows = Cows[i]\n    Sheep = Sheep[i]\n    Xgrid = []\n    Ygrid = []\n    Num = []\n    first_in_grid = []\n    last_in_grid = []\n    Max_Sus_grid = []\n    index_inf = np.where(Status==6)[0].astype(int)\n    m2 = np.array(np.where(grid==1))\n    \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()) #Add the \"+1\" here so the indicies match those in the Keeling code\n            last_in_grid.append(m.max())\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\n    #Determine maximum number of animals to be infected in each grid square\n\n    # YOUR INDENTATION IS MESSED UP HERE (only 3 spaces) WHICH SUGGESTS THIS NEVER GETS CALLED\n    \n    for i in range (1,int(max(grid))):\n       for j in range(1,int(max(grid))):\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    \n    # THIS IS A BAD APPROACH; GROWING LISTS IS EXTREMELY INEFFICIENT\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    # NEVER SEPARATE STATEENTS WITH SEMICOLONS; VERY HARD TO READ AND DEBUG\n    \n    # JUST USE & INSTEAD OF logical_and\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        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),I == N):\n                # JUST USE A break STATEMENT INSTEAD OF A FLAG\n                IterateFlag=0\n                \n        # MOVE IMPORTS OUT OF FUNCTION\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    \n    # YOU SPEND A LOT OF ENERGY CONVERTING BACK AND FORTH BETWEEN NUMPY ARRAYS AND LISTS\n    # JUST USE ARRAYS\n    \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[index_inf][0],coords[index_inf][1]))\n    \n    # THE BELOW WOULD BE MUCH CLEARER AS A LIST COMPREHENSION\n    dist = []\n    for j in range(0,N):\n        # CANT YOU JUST SUBINDEX THIS? coords[j,:2]\n        b = np.array((coords[j][0],coords[j][1]))\n        dist.append(np.linalg.norm(b-index))\n    to_return = np.c_[no_this[:,1],Status_ind,dist,Cows,Sheep,x,y]\n    \n    from scipy import spatial\n    #Extract the infected farms\n    inf_farms = to_return[to_return[:,1]==1]\n    coords = list(zip(x,y))\n    #Create list of coordinates infected farms\n    inf_farm_coords = list(zip(inf_farms[:,5],inf_farms[:,6]))\n    \n    # NOT CLEAR WHY YOU ARE DOING ALL THIS EXPENSIVE LIST CREATION\n    \n    list_of_inf_coords = [list(elem) for elem in inf_farm_coords]\n    #Create list of coordinates of all farms\n    list_of_coords = [list(elem) for elem in coords]\n    #Calculate Euclidean distance from each farm to all infected farms- each row in matrix represents\n        #distance of one farm to each infected farm\n    dist_to_inf = spatial.distance_matrix(list_of_coords,list_of_inf_coords)\n    #Find distance to closest infected farm\n    \n    # THERE IS A FUNCTION (np.minimum) THAT DOES ALL THIS FOR YOU\n    def minval(array):\n        #return(np.min(array[np.nonzero(array)]))\n        return(np.min(array))\n    closest_infected = np.apply_along_axis(minval,1,dist_to_inf)\n    average_infected = np.apply_along_axis(np.mean,1,dist_to_inf)\n    to_return = np.c_[to_return,closest_infected,average_infected]\n    \n    #Create list of number of Cows and number of sheep for infected farms\n    inf_farm_cows = list(inf_farms[:,3])\n    inf_farm_sheep = list(inf_farms[:,4])\n    #Create a function that extracts farm size based on closest infected farm\n    def where_minval(array):\n        #return(np.argmin(array[np.nonzero(array)]))\n        return(np.argmin(array))\n    closest_infected_size_ind = np.apply_along_axis(where_minval,1,dist_to_inf)\n    closest_infected_cows = [inf_farm_cows[i] for i in closest_infected_size_ind]\n    closest_infected_sheep = [inf_farm_sheep[i] for i in closest_infected_size_ind]\n\n    #Returns array: farmID, Status_ind, dist_to_index, num_Cows,num_Sheep,x,y,,disttoclosestinf,avgdisttoinf,#cowsinclosestiffarm,#sheepinclosestinffarm\n    to_return = np.c_[to_return,closest_infected_cows,closest_infected_sheep]\n    \n    #Now run the outbreak for the additional end - end2 steps\n    while(np.logical_and(t[-1]<end2, 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    #Return only the data where a farm was not infected by the \"end\" day\n    not_inf = to_return[to_return[:,1]==0]\n    #append the status of these farms after the additional end2 - end days\n    newstatus = [Status[i] for i in not_inf[:,0]-1]\n    newstatus_ind = np.array([i>5 for i in newstatus]).astype(int)\n    final = np.c_[not_inf,newstatus_ind]\n    return(final)\n    \ntest = Outbreaks(Size=Size,N=N,Y0=1,farms = np.arange(1,N+1),end=10,end2=50,x=x,y=y,Cows=Cows,Sheep=Sheep)",
      "execution_count": 10,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "/Users/sandyalakkur/anaconda/envs/py3k/lib/python3.4/site-packages/IPython/kernel/__main__.py:56: 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:11: 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:20: 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:11: 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:20: 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:108: DeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\n/Users/sandyalakkur/anaconda/envs/py3k/lib/python3.4/site-packages/IPython/kernel/__main__.py:160: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
        }
      ]
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      "cell_type": "code",
      "source": "test[:,11]",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "metadata": {},
          "execution_count": 11,
          "data": {
            "text/plain": "array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,\n        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])"
          }
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      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "HOW IS THE WORK BELOW DIFFERENT FROM WHAT YOU HAVE DONE ABOVE? YOU HAD ALREADY SIMULATED COWS AND SHEEP NEAR THE TOP."
    },
    {
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      "cell_type": "code",
      "source": "import numpy as np\nfrom random import randint\nOutbreak = Outbreaks(Size=Size,N=N,Y0=1,farms = np.arange(1,N+1),end=10,end2=50,x=x,y=y,Cows=Cows,Sheep=Sheep)\nOutbreak = np.c_[np.array([1]*Outbreak.shape[0]),Outbreak]\nNum_outbreaks = 1000\nfor i in range(Num_outbreaks):\n    #Cows = np.array([randint(25,250) for p in range(N)])\n    Cows = np.array([randint(25,51) for p in range(15)]+[randint(51,76) for p in range(30)]+[randint(76,95) for p in range(20)]+[randint(95,250) for p in range(10)])\n    #Sheep = np.array([randint(25,250) for p in range(N)])\n    Sheep = np.array([randint(25,51) for p in range(15)]+[randint(51,76) for p in range(30)]+[randint(76,95) for p in range(20)]+[randint(95,250) for p in range(10)])\n    # ADD IS NOT A GOOD VARIABLE NAME\n    add = Outbreaks(Size=Size,N=N,Y0=1,farms = np.arange(1,N+1),end=10,end2=50,x=x,y=y,Cows=Cows,Sheep=Sheep)\n    add = np.c_[np.array([i+2]*add.shape[0]),add]\n    new_Outbreak = np.r_[Outbreak,add]\n    Outbreak = new_Outbreak \n    print(i,np.sum(add[:,12]==1))",
      "execution_count": 12,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "/Users/sandyalakkur/anaconda/envs/py3k/lib/python3.4/site-packages/IPython/kernel/__main__.py:56: 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:11: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
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        {
          "name": "stderr",
          "output_type": "stream",
          "text": "/Users/sandyalakkur/anaconda/envs/py3k/lib/python3.4/site-packages/IPython/kernel/__main__.py:20: 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:108: DeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\n/Users/sandyalakkur/anaconda/envs/py3k/lib/python3.4/site-packages/IPython/kernel/__main__.py:160: DeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "import pandas as pd\ndf = pd.DataFrame(Outbreak)\ndf.columns = ['run','farmID','Status','DistToIndex','NumCows','NumSheep','x-coord','y-coord','DistToNearestInfected','AvgDistToInfected','CowsNearestInfected','SheepNearestInfected','Status2']\nlong_random_everything_andind_anddist2 = df\nlong_random_everything_andind_anddist2",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "metadata": {},
          "execution_count": 13,
          "data": {
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</tr>\n    <tr>\n      <th>14</th>\n      <td>1</td>\n      <td>15</td>\n      <td>0</td>\n      <td>15.546098</td>\n      <td>58</td>\n      <td>70</td>\n      <td>4.321338</td>\n      <td>5.419305</td>\n      <td>15.546098</td>\n      <td>15.546098</td>\n      <td>222</td>\n      <td>200</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>1</td>\n      <td>16</td>\n      <td>0</td>\n      <td>15.024453</td>\n      <td>70</td>\n      <td>54</td>\n      <td>5.452718</td>\n      <td>4.700431</td>\n      <td>15.024453</td>\n      <td>15.024453</td>\n      <td>222</td>\n      <td>200</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>1</td>\n      <td>17</td>\n      <td>0</td>\n      <td>16.036512</td>\n      <td>78</td>\n      <td>83</td>\n      <td>4.179607</td>\n      <td>4.741281</td>\n      <td>16.036512</td>\n      <td>16.036512</td>\n      <td>222</td>\n      <td>200</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>1</td>\n      <td>18</td>\n      <td>0</td>\n      <td>12.207746</td>\n      <td>53</td>\n      <td>74</td>\n      <td>5.860130</td>\n      <td>9.912863</td>\n      <td>12.207746</td>\n      <td>12.207746</td>\n      <td>222</td>\n      <td>200</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>1</td>\n      <td>19</td>\n      <td>0</td>\n      <td>13.352243</td>\n      <td>73</td>\n      <td>71</td>\n      <td>4.278867</td>\n      <td>11.560496</td>\n      <td>13.352243</td>\n      <td>13.352243</td>\n      <td>222</td>\n      <td>200</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>1</td>\n      <td>20</td>\n      <td>0</td>\n      <td>12.058908</td>\n      <td>34</td>\n      <td>28</td>\n      <td>5.472275</td>\n      <td>12.359426</td>\n      <td>12.058908</td>\n      <td>12.058908</td>\n      <td>222</td>\n      <td>200</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>1</td>\n      <td>21</td>\n      <td>0</td>\n      <td>11.943705</td>\n      <td>77</td>\n      <td>83</td>\n      <td>5.701048</td>\n      <td>15.881959</td>\n      <td>11.943705</td>\n      <td>11.943705</td>\n      <td>222</td>\n      <td>200</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>1</td>\n      <td>22</td>\n      <td>0</td>\n      <td>16.102311</td>\n      <td>61</td>\n      <td>56</td>\n      <td>7.859043</td>\n      <td>0.809430</td>\n      <td>16.102311</td>\n      <td>16.102311</td>\n      <td>222</td>\n      <td>200</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>1</td>\n      <td>23</td>\n      <td>0</td>\n      <td>14.762513</td>\n      <td>43</td>\n      <td>25</td>\n      <td>7.052037</td>\n      <td>3.264976</td>\n      <td>14.762513</td>\n      <td>14.762513</td>\n      <td>222</td>\n      <td>200</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>1</td>\n      <td>24</td>\n      <td>0</td>\n      <td>13.917072</td>\n      <td>91</td>\n      <td>76</td>\n      <td>6.062825</td>\n      <td>5.743816</td>\n      <td>13.917072</td>\n      <td>13.917072</td>\n      <td>222</td>\n      <td>200</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>1</td>\n      <td>25</td>\n      <td>0</td>\n      <td>11.940109</td>\n      <td>92</td>\n      <td>89</td>\n      <td>6.155299</td>\n      <td>9.874813</td>\n      <td>11.940109</td>\n      <td>11.940109</td>\n      <td>222</td>\n      <td>200</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>1</td>\n      <td>26</td>\n      <td>0</td>\n      <td>10.288324</td>\n      <td>179</td>\n      <td>210</td>\n      <td>7.325950</td>\n      <td>11.920267</td>\n      <td>10.288324</td>\n      <td>10.288324</td>\n      <td>222</td>\n      <td>200</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>1</td>\n      <td>27</td>\n      <td>0</td>\n      <td>14.432479</td>\n      <td>45</td>\n      <td>38</td>\n      <td>8.559388</td>\n      <td>2.374897</td>\n      <td>14.432479</td>\n      <td>14.432479</td>\n      <td>222</td>\n      <td>200</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>1</td>\n      <td>28</td>\n      <td>0</td>\n      <td>11.597777</td>\n      <td>42</td>\n      <td>36</td>\n      <td>9.859762</td>\n      <td>4.974922</td>\n      <td>11.597777</td>\n      <td>11.597777</td>\n      <td>222</td>\n      <td>200</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>1</td>\n      <td>29</td>\n      <td>0</td>\n      <td>11.520832</td>\n      <td>148</td>\n      <td>106</td>\n      <td>8.987554</td>\n      <td>5.926636</td>\n      <td>11.520832</td>\n      <td>11.520832</td>\n      <td>222</td>\n      <td>200</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>1</td>\n      <td>30</td>\n      <td>0</td>\n      <td>10.719772</td>\n      <td>83</td>\n      <td>92</td>\n      <td>8.402271</td>\n      <td>7.996031</td>\n      <td>10.719772</td>\n      <td>10.719772</td>\n      <td>222</td>\n      <td>200</td>\n      <td>0</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      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>73976</th>\n      <td>1001</td>\n      <td>45</td>\n      <td>0</td>\n      <td>2.123537</td>\n      <td>89</td>\n      <td>86</td>\n      <td>12.698309</td>\n      <td>7.118964</td>\n      <td>2.123537</td>\n      <td>2.123537</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73977</th>\n      <td>1001</td>\n      <td>46</td>\n      <td>0</td>\n      <td>3.459675</td>\n      <td>28</td>\n      <td>30</td>\n      <td>12.213462</td>\n      <td>8.499905</td>\n      <td>3.459675</td>\n      <td>3.459675</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73978</th>\n      <td>1001</td>\n      <td>47</td>\n      <td>0</td>\n      <td>2.582251</td>\n      <td>131</td>\n      <td>209</td>\n      <td>13.633796</td>\n      <td>8.253553</td>\n      <td>2.582251</td>\n      <td>2.582251</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73979</th>\n      <td>1001</td>\n      <td>48</td>\n      <td>0</td>\n      <td>4.631741</td>\n      <td>73</td>\n      <td>66</td>\n      <td>13.963750</td>\n      <td>10.385687</td>\n      <td>4.631741</td>\n      <td>4.631741</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73980</th>\n      <td>1001</td>\n      <td>49</td>\n      <td>0</td>\n      <td>7.746719</td>\n      <td>107</td>\n      <td>106</td>\n      <td>13.311252</td>\n      <td>13.447489</td>\n      <td>7.746719</td>\n      <td>7.746719</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73981</th>\n      <td>1001</td>\n      <td>50</td>\n      <td>0</td>\n      <td>2.718216</td>\n      <td>121</td>\n      <td>214</td>\n      <td>14.277536</td>\n      <td>3.051502</td>\n      <td>2.718216</td>\n      <td>2.718216</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73982</th>\n      <td>1001</td>\n      <td>52</td>\n      <td>0</td>\n      <td>0.677720</td>\n      <td>83</td>\n      <td>85</td>\n      <td>14.668796</td>\n      <td>5.177794</td>\n      <td>0.677720</td>\n      <td>0.677720</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73983</th>\n      <td>1001</td>\n      <td>53</td>\n      <td>0</td>\n      <td>3.235225</td>\n      <td>26</td>\n      <td>44</td>\n      <td>14.581574</td>\n      <td>8.995065</td>\n      <td>3.235225</td>\n      <td>3.235225</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73984</th>\n      <td>1001</td>\n      <td>54</td>\n      <td>0</td>\n      <td>3.958684</td>\n      <td>75</td>\n      <td>60</td>\n      <td>15.396214</td>\n      <td>9.583559</td>\n      <td>3.958684</td>\n      <td>3.958684</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73985</th>\n      <td>1001</td>\n      <td>55</td>\n      <td>0</td>\n      <td>4.084082</td>\n      <td>54</td>\n      <td>70</td>\n      <td>14.764359</td>\n      <td>9.830775</td>\n      <td>4.084082</td>\n      <td>4.084082</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73986</th>\n      <td>1001</td>\n      <td>56</td>\n      <td>0</td>\n      <td>9.836703</td>\n      <td>64</td>\n      <td>54</td>\n      <td>15.416086</td>\n      <td>15.546453</td>\n      <td>9.836703</td>\n      <td>9.836703</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73987</th>\n      <td>1001</td>\n      <td>57</td>\n      <td>0</td>\n      <td>14.066189</td>\n      <td>195</td>\n      <td>169</td>\n      <td>14.067645</td>\n      <td>19.832654</td>\n      <td>14.066189</td>\n      <td>14.066189</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73988</th>\n      <td>1001</td>\n      <td>58</td>\n      <td>0</td>\n      <td>5.925386</td>\n      <td>76</td>\n      <td>73</td>\n      <td>17.187998</td>\n      <td>0.574325</td>\n      <td>5.925386</td>\n      <td>5.925386</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73989</th>\n      <td>1001</td>\n      <td>59</td>\n      <td>0</td>\n      <td>3.610333</td>\n      <td>80</td>\n      <td>89</td>\n      <td>16.627714</td>\n      <td>2.978049</td>\n      <td>3.610333</td>\n      <td>3.610333</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73990</th>\n      <td>1001</td>\n      <td>60</td>\n      <td>0</td>\n      <td>3.936865</td>\n      <td>83</td>\n      <td>84</td>\n      <td>16.576724</td>\n      <td>2.530989</td>\n      <td>3.936865</td>\n      <td>3.936865</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73991</th>\n      <td>1001</td>\n      <td>61</td>\n      <td>0</td>\n      <td>3.837140</td>\n      <td>87</td>\n      <td>91</td>\n      <td>17.180531</td>\n      <td>8.346103</td>\n      <td>3.837140</td>\n      <td>3.837140</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73992</th>\n      <td>1001</td>\n      <td>62</td>\n      <td>0</td>\n      <td>7.381665</td>\n      <td>69</td>\n      <td>65</td>\n      <td>17.041228</td>\n      <td>12.637765</td>\n      <td>7.381665</td>\n      <td>7.381665</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73993</th>\n      <td>1001</td>\n      <td>63</td>\n      <td>0</td>\n      <td>8.545854</td>\n      <td>87</td>\n      <td>76</td>\n      <td>17.048799</td>\n      <td>13.873426</td>\n      <td>8.545854</td>\n      <td>8.545854</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73994</th>\n      <td>1001</td>\n      <td>64</td>\n      <td>0</td>\n      <td>8.560077</td>\n      <td>245</td>\n      <td>108</td>\n      <td>17.451593</td>\n      <td>13.742620</td>\n      <td>8.560077</td>\n      <td>8.560077</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73995</th>\n      <td>1001</td>\n      <td>65</td>\n      <td>0</td>\n      <td>14.054256</td>\n      <td>90</td>\n      <td>82</td>\n      <td>16.378905</td>\n      <td>19.674271</td>\n      <td>14.054256</td>\n      <td>14.054256</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73996</th>\n      <td>1001</td>\n      <td>66</td>\n      <td>0</td>\n      <td>4.886189</td>\n      <td>38</td>\n      <td>51</td>\n      <td>18.330410</td>\n      <td>2.952671</td>\n      <td>4.886189</td>\n      <td>4.886189</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73997</th>\n      <td>1001</td>\n      <td>67</td>\n      <td>0</td>\n      <td>5.822442</td>\n      <td>80</td>\n      <td>90</td>\n      <td>18.864681</td>\n      <td>2.107629</td>\n      <td>5.822442</td>\n      <td>5.822442</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73998</th>\n      <td>1001</td>\n      <td>68</td>\n      <td>0</td>\n      <td>4.509184</td>\n      <td>63</td>\n      <td>54</td>\n      <td>18.775315</td>\n      <td>6.568489</td>\n      <td>4.509184</td>\n      <td>4.509184</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>73999</th>\n      <td>1001</td>\n      <td>69</td>\n      <td>0</td>\n      <td>4.066472</td>\n      <td>54</td>\n      <td>73</td>\n      <td>18.186413</td>\n      <td>7.081517</td>\n      <td>4.066472</td>\n      <td>4.066472</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>74000</th>\n      <td>1001</td>\n      <td>70</td>\n      <td>0</td>\n      <td>6.158406</td>\n      <td>69</td>\n      <td>56</td>\n      <td>18.492750</td>\n      <td>10.314425</td>\n      <td>6.158406</td>\n      <td>6.158406</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>74001</th>\n      <td>1001</td>\n      <td>71</td>\n      <td>0</td>\n      <td>6.034976</td>\n      <td>52</td>\n      <td>74</td>\n      <td>18.078301</td>\n      <td>10.504857</td>\n      <td>6.034976</td>\n      <td>6.034976</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>74002</th>\n      <td>1001</td>\n      <td>72</td>\n      <td>0</td>\n      <td>9.298684</td>\n      <td>72</td>\n      <td>69</td>\n      <td>19.985391</td>\n      <td>13.156041</td>\n      <td>9.298684</td>\n      <td>9.298684</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>74003</th>\n      <td>1001</td>\n      <td>73</td>\n      <td>0</td>\n      <td>10.615504</td>\n      <td>93</td>\n      <td>81</td>\n      <td>18.989877</td>\n      <td>15.310801</td>\n      <td>10.615504</td>\n      <td>10.615504</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>74004</th>\n      <td>1001</td>\n      <td>74</td>\n      <td>0</td>\n      <td>9.998750</td>\n      <td>83</td>\n      <td>93</td>\n      <td>19.700055</td>\n      <td>14.208172</td>\n      <td>9.998750</td>\n      <td>9.998750</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>74005</th>\n      <td>1001</td>\n      <td>75</td>\n      <td>0</td>\n      <td>12.295354</td>\n      <td>76</td>\n      <td>92</td>\n      <td>18.040890</td>\n      <td>17.493436</td>\n      <td>12.295354</td>\n      <td>12.295354</td>\n      <td>67</td>\n      <td>54</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>74006 rows × 13 columns</p>\n</div>",
            "text/plain": "        run  farmID  Status  DistToIndex  NumCows  NumSheep    x-coord  \\\n0         1       1       0    20.757396      190       206   0.428463   \n1         1       2       0    18.509917       91        87   0.391203   \n2         1       3       0    17.417512       61        68   0.414427   \n3         1       4       0    16.556293       43        31   0.996829   \n4         1       5       0    16.035718       66        66   1.869069   \n5         1       6       0    19.040339       27        36   2.842180   \n6         1       7       0    19.803052       75        65   2.762186   \n7         1       8       0    16.900562       65        69   2.449791   \n8         1       9       0    16.450377       30        44   2.138491   \n9         1      10       0    14.890135       69        64   2.561501   \n10        1      11       0    13.484130       92        90   3.969138   \n11        1      12       0    13.840768       67        64   3.767688   \n12        1      13       0    16.091276       61        71   4.705106   \n13        1      14       0    17.070804       63        76   4.246506   \n14        1      15       0    15.546098       58        70   4.321338   \n15        1      16       0    15.024453       70        54   5.452718   \n16        1      17       0    16.036512       78        83   4.179607   \n17        1      18       0    12.207746       53        74   5.860130   \n18        1      19       0    13.352243       73        71   4.278867   \n19        1      20       0    12.058908       34        28   5.472275   \n20        1      21       0    11.943705       77        83   5.701048   \n21        1      22       0    16.102311       61        56   7.859043   \n22        1      23       0    14.762513       43        25   7.052037   \n23        1      24       0    13.917072       91        76   6.062825   \n24        1      25       0    11.940109       92        89   6.155299   \n25        1      26       0    10.288324      179       210   7.325950   \n26        1      27       0    14.432479       45        38   8.559388   \n27        1      28       0    11.597777       42        36   9.859762   \n28        1      29       0    11.520832      148       106   8.987554   \n29        1      30       0    10.719772       83        92   8.402271   \n...     ...     ...     ...          ...      ...       ...        ...   \n73976  1001      45       0     2.123537       89        86  12.698309   \n73977  1001      46       0     3.459675       28        30  12.213462   \n73978  1001      47       0     2.582251      131       209  13.633796   \n73979  1001      48       0     4.631741       73        66  13.963750   \n73980  1001      49       0     7.746719      107       106  13.311252   \n73981  1001      50       0     2.718216      121       214  14.277536   \n73982  1001      52       0     0.677720       83        85  14.668796   \n73983  1001      53       0     3.235225       26        44  14.581574   \n73984  1001      54       0     3.958684       75        60  15.396214   \n73985  1001      55       0     4.084082       54        70  14.764359   \n73986  1001      56       0     9.836703       64        54  15.416086   \n73987  1001      57       0    14.066189      195       169  14.067645   \n73988  1001      58       0     5.925386       76        73  17.187998   \n73989  1001      59       0     3.610333       80        89  16.627714   \n73990  1001      60       0     3.936865       83        84  16.576724   \n73991  1001      61       0     3.837140       87        91  17.180531   \n73992  1001      62       0     7.381665       69        65  17.041228   \n73993  1001      63       0     8.545854       87        76  17.048799   \n73994  1001      64       0     8.560077      245       108  17.451593   \n73995  1001      65       0    14.054256       90        82  16.378905   \n73996  1001      66       0     4.886189       38        51  18.330410   \n73997  1001      67       0     5.822442       80        90  18.864681   \n73998  1001      68       0     4.509184       63        54  18.775315   \n73999  1001      69       0     4.066472       54        73  18.186413   \n74000  1001      70       0     6.158406       69        56  18.492750   \n74001  1001      71       0     6.034976       52        74  18.078301   \n74002  1001      72       0     9.298684       72        69  19.985391   \n74003  1001      73       0    10.615504       93        81  18.989877   \n74004  1001      74       0     9.998750       83        93  19.700055   \n74005  1001      75       0    12.295354       76        92  18.040890   \n\n         y-coord  DistToNearestInfected  AvgDistToInfected  \\\n0       1.864803              20.757396          20.757396   \n1       6.562081              18.509917          18.509917   \n2      10.122575              17.417512          17.417512   \n3      15.573354              16.556293          16.556293   \n4      17.528015              16.035718          16.035718   \n5       1.531988              19.040339          19.040339   \n6       0.461724              19.803052          19.803052   \n7       5.959629              16.900562          16.900562   \n8       7.732310              16.450377          16.450377   \n9      13.778480              14.890135          14.890135   \n10     13.955119              13.484130          13.484130   \n11     15.820503              13.840768          13.840768   \n12      3.921407              16.091276          16.091276   \n13      2.924207              17.070804          17.070804   \n14      5.419305              15.546098          15.546098   \n15      4.700431              15.024453          15.024453   \n16      4.741281              16.036512          16.036512   \n17      9.912863              12.207746          12.207746   \n18     11.560496              13.352243          13.352243   \n19     12.359426              12.058908          12.058908   \n20     15.881959              11.943705          11.943705   \n21      0.809430              16.102311          16.102311   \n22      3.264976              14.762513          14.762513   \n23      5.743816              13.917072          13.917072   \n24      9.874813              11.940109          11.940109   \n25     11.920267              10.288324          10.288324   \n26      2.374897              14.432479          14.432479   \n27      4.974922              11.597777          11.597777   \n28      5.926636              11.520832          11.520832   \n29      7.996031              10.719772          10.719772   \n...          ...                    ...                ...   \n73976   7.118964               2.123537           2.123537   \n73977   8.499905               3.459675           3.459675   \n73978   8.253553               2.582251           2.582251   \n73979  10.385687               4.631741           4.631741   \n73980  13.447489               7.746719           7.746719   \n73981   3.051502               2.718216           2.718216   \n73982   5.177794               0.677720           0.677720   \n73983   8.995065               3.235225           3.235225   \n73984   9.583559               3.958684           3.958684   \n73985   9.830775               4.084082           4.084082   \n73986  15.546453               9.836703           9.836703   \n73987  19.832654              14.066189          14.066189   \n73988   0.574325               5.925386           5.925386   \n73989   2.978049               3.610333           3.610333   \n73990   2.530989               3.936865           3.936865   \n73991   8.346103               3.837140           3.837140   \n73992  12.637765               7.381665           7.381665   \n73993  13.873426               8.545854           8.545854   \n73994  13.742620               8.560077           8.560077   \n73995  19.674271              14.054256          14.054256   \n73996   2.952671               4.886189           4.886189   \n73997   2.107629               5.822442           5.822442   \n73998   6.568489               4.509184           4.509184   \n73999   7.081517               4.066472           4.066472   \n74000  10.314425               6.158406           6.158406   \n74001  10.504857               6.034976           6.034976   \n74002  13.156041               9.298684           9.298684   \n74003  15.310801              10.615504          10.615504   \n74004  14.208172               9.998750           9.998750   \n74005  17.493436              12.295354          12.295354   \n\n       CowsNearestInfected  SheepNearestInfected  Status2  \n0                      222                   200        0  \n1                      222                   200        0  \n2                      222                   200        0  \n3                      222                   200        0  \n4                      222                   200        0  \n5                      222                   200        0  \n6                      222                   200        0  \n7                      222                   200        0  \n8                      222                   200        0  \n9                      222                   200        0  \n10                     222                   200        0  \n11                     222                   200        0  \n12                     222                   200        0  \n13                     222                   200        0  \n14                     222                   200        0  \n15                     222                   200        0  \n16                     222                   200        0  \n17                     222                   200        0  \n18                     222                   200        0  \n19                     222                   200        0  \n20                     222                   200        0  \n21                     222                   200        0  \n22                     222                   200        0  \n23                     222                   200        0  \n24                     222                   200        0  \n25                     222                   200        0  \n26                     222                   200        0  \n27                     222                   200        0  \n28                     222                   200        0  \n29                     222                   200        0  \n...                    ...                   ...      ...  \n73976                   67                    54        0  \n73977                   67                    54        0  \n73978                   67                    54        0  \n73979                   67                    54        0  \n73980                   67                    54        0  \n73981                   67                    54        0  \n73982                   67                    54        0  \n73983                   67                    54        0  \n73984                   67                    54        0  \n73985                   67                    54        0  \n73986                   67                    54        0  \n73987                   67                    54        0  \n73988                   67                    54        0  \n73989                   67                    54        0  \n73990                   67                    54        0  \n73991                   67                    54        0  \n73992                   67                    54        0  \n73993                   67                    54        0  \n73994                   67                    54        0  \n73995                   67                    54        0  \n73996                   67                    54        0  \n73997                   67                    54        0  \n73998                   67                    54        0  \n73999                   67                    54        0  \n74000                   67                    54        0  \n74001                   67                    54        0  \n74002                   67                    54        0  \n74003                   67                    54        0  \n74004                   67                    54        0  \n74005                   67                    54        0  \n\n[74006 rows x 13 columns]"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "np.sum(long_random_everything_andind_anddist2['Status2']==1)",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "metadata": {},
          "execution_count": 14,
          "data": {
            "text/plain": "491"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "dist_diff = (long_random_everything_andind_anddist2['DistToNearestInfected'] - long_random_everything_andind_anddist2['DistToNearestInfected'].mean())/5\nclosest_cow_diff = (long_random_everything_andind_anddist2['CowsNearestInfected'] - long_random_everything_andind_anddist2['CowsNearestInfected'].mean())/20\nclosest_sheep_diff = (long_random_everything_andind_anddist2['SheepNearestInfected'] - long_random_everything_andind_anddist2['SheepNearestInfected'].mean())/20\nstatus = long_random_everything_andind_anddist2['Status2']\nfrom pymc import Normal, Binomial, Gamma, Lambda, invlogit, MCMC, Matplot, Bernoulli, MAP, AdaptiveMetropolis\n#N = df.shape[0]\n\ndef pooled_model():\n    \n    # Common slope & intercept prior\n    intercept = Normal('intercept', mu=0., tau=0.001, value = 0) \n    \n    first_coef = Normal('first_coef', mu=0., tau=0.001, value = 0)\n    \n    size_coef = Normal ('size_coef', mu=0., tau= 0.001, value=[0]*2)\n    \n    #likelihood model\n    prob = Lambda('prob', lambda intercept=intercept,first_coef=first_coef, size_coef= size_coef: \n                  invlogit(intercept + first_coef*dist_diff + size_coef[0]*closest_cow_diff + \n                           size_coef[1]*closest_sheep_diff))\n    \n    y = Bernoulli('y', p=prob, value=status, observed=True)\n    \n    return locals()",
      "execution_count": 15,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "chains = 2\niterations = 10000\nburn = 4000\nM_pooled = MCMC(pooled_model())\nM_map = MAP(pooled_model())\nM_pooled = MCMC(M_map)\n#M_pooled.use_step_method(AdaptiveMetropolis, M_pooled.b)\nfor i in range(chains):\n    M_pooled.sample(iterations, burn)",
      "execution_count": 16,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": " [-----------------100%-----------------] 10000 of 10000 complete in 128.1 sec"
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "%matplotlib inline\nimport matplotlib.pyplot as plt\nMatplot.summary_plot(M_pooled.intercept)",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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            "text/plain": "<matplotlib.figure.Figure at 0x1096f1be0>"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "%matplotlib inline\nimport matplotlib.pyplot as plt\nMatplot.summary_plot(M_pooled.first_coef)",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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ykqRmDBlJUjOGjCSpGUNGktSMISNJasaQkSQ1Y8hIkpoxZCRJzRgykqRmDBlJUjOGjCSp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\nfz/w8h0y6n1nAAAAAElFTkSuQmCC\n",
            "text/plain": "<matplotlib.figure.Figure at 0x1096f1c18>"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "%matplotlib inline\nimport matplotlib.pyplot as plt\nMatplot.summary_plot(M_pooled.size_coef)",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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            "text/plain": "<matplotlib.figure.Figure at 0x10bd0cda0>"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": false,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python [default]",
      "language": "python"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "file_extension": ".py",
      "name": "python",
      "version": "3.5.2",
      "mimetype": "text/x-python",
      "nbconvert_exporter": "python"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "CouldYouLookAtThis.ipynb",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 1
}