mamacneil/dyads.ipynb

## dyads.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# McElreath's dyads example\n",
    "\n",
    "Covariance of gift giving between households from Chapter 14 pg 462 of Statistical Rethinking 2: https://www.dropbox.com/s/pongh5ga0ey72m5/statisticalrethinking2.pdf?dl=0; code mostly from https://github.com/pymc-devs/resources/blob/master/Rethinking_2/Chp_14.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import python packages\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pymc3 as pm\n",
    "import theano as T\n",
    "import theano.tensor as tt\n",
    "from pymc3.backends import SQLite\n",
    "import seaborn as sns\n",
    "import scipy as sp\n",
    "import pdb\n",
    "import arviz as az\n",
    "from matplotlib.patches import Ellipse, transforms\n",
    "\n",
    "# Helper functions\n",
    "def indexall(L):\n",
    "    poo = []\n",
    "    for p in L:\n",
    "        if not p in poo:\n",
    "            poo.append(p)\n",
    "    Ix = np.array([poo.index(p) for p in L])\n",
    "    return poo,Ix\n",
    "\n",
    "# Helper functions\n",
    "def indexall_B(L,B):\n",
    "    poo = []\n",
    "    for p in L:\n",
    "        if not p in poo:\n",
    "            poo.append(p)\n",
    "    Ix = np.array([poo.index(p) for p in L])\n",
    "    a, b = poo.index(B), 0\n",
    "    poo[b], poo[a] = poo[a], poo[b]\n",
    "    \n",
    "    Ix[Ix==b] = -1\n",
    "    Ix[Ix==a] = 0\n",
    "    Ix[Ix==-1] = a\n",
    "    return poo,Ix\n",
    "\n",
    "def subindexall(short,long):\n",
    "    poo = []\n",
    "    out = []\n",
    "    for s,l in zip(short,long):\n",
    "        if not l in poo:\n",
    "            poo.append(l)\n",
    "            out.append(s)\n",
    "    return indexall(out)\n",
    "\n",
    "match = lambda a, b: np.array([ b.index(x) if x in b else None for x in a ])\n",
    "grep = lambda s, l: np.array([i for i in l if s in i])\n",
    "\n",
    "# Function to standardize covariates\n",
    "def stdize(x):\n",
    "    return (x-np.mean(x))/(2*np.std(x))\n",
    "\n",
    "# Coefficient of variation\n",
    "cv =  lambda x: np.var(x) / np.mean(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Social relations as correlated varying effects\n",
    "\n",
    "The data for this example consist of pairs of households (dyads) in Nicaragua, the publicaiton of which you can find in the original paper, [Koester and Leckie 2014](https://www.researchgate.net/profile/Jeremy_Koster/publication/261764179_Food_sharing_networks_in_lowland_Nicaragua_An_application_of_the_social_relations_model_to_count_data/links/5c413437299bf12be3d04539/Food-sharing-networks-in-lowland-Nicaragua-An-application-of-the-social-relations-model-to-count-data.pdf). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>hidA</th>\n",
       "      <th>hidB</th>\n",
       "      <th>did</th>\n",
       "      <th>giftsAB</th>\n",
       "      <th>giftsBA</th>\n",
       "      <th>offset</th>\n",
       "      <th>drel1</th>\n",
       "      <th>drel2</th>\n",
       "      <th>drel3</th>\n",
       "      <th>drel4</th>\n",
       "      <th>dlndist</th>\n",
       "      <th>dass</th>\n",
       "      <th>d0125</th>\n",
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.019</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.025</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
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       "      <td>-3.499</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  hidA  hidB  did  giftsAB  giftsBA  offset  drel1  drel2  drel3  \\\n",
       "0           1     1     2    1        0        4   0.000      0      0      1   \n",
       "1           2     1     3    2        6       31  -0.003      0      1      0   \n",
       "2           3     1     4    3        2        5  -0.019      0      1      0   \n",
       "3           4     1     5    4        4        2   0.000      0      1      0   \n",
       "4           5     1     6    5        8        2  -0.003      1      0      0   \n",
       "\n",
       "   drel4  dlndist   dass  d0125  \n",
       "0      0   -2.790  0.000      0  \n",
       "1      0   -2.817  0.044      0  \n",
       "2      0   -1.886  0.025      0  \n",
       "3      0   -1.892  0.011      0  \n",
       "4      0   -3.499  0.022      0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kdata = pd.read_csv('kl_dyads.csv')\n",
    "kdata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basically households (`hidA` and `hidB`) trade with each other unequally (`giftsAB` and `giftsBA`) in a series of recorded exchanges between pairs (`did`). The question here is how does gift giving vary among households and among dyad pairs. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of observations\n",
    "N = len(kdata)\n",
    "# Number of households\n",
    "N_households = max(kdata.hidB)\n",
    "# Dyad ID\n",
    "did = kdata.did.values-1\n",
    "# \n",
    "hidA = kdata.hidA.values-1\n",
    "# \n",
    "hidB = kdata.hidB.values-1\n",
    "# Gifts from A to B\n",
    "giftsAB = kdata.giftsAB\n",
    "# Gifts from B to A\n",
    "giftsBA = kdata.giftsBA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dyads basic model\n",
    "\n",
    "The question here is to estiamte average giving and receiving rates for Nicaraguan households, as well as rates for particular pairs (dyads) of households. Because gifts from household A to B hold no priority over gifts from B to A, a model that does not depend on direction is required. However there are directions involved, in that a gift from A to B has a direction and is equivalent to a reception from B to A. This implies two key observation-level models:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "y_{A\\rightarrow B} \\sim & Poisson(\\lambda_{AB}) \\\\\n",
    "log(\\lambda_{AB}) = & \\alpha + g_{A} + r_{B} + d_{AB}\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "And conversely\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "y_{B\\rightarrow A} \\sim & Poisson(\\lambda_{BA}) \\\\\n",
    "log(\\lambda_{BA}) = & \\alpha + g_{B} + r_{A} + d_{BA}\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "where $\\alpha$ represents an average rate of giving; $g_{A}$ represents the average rate of giving from household A, $r_{B}$ represents the average rate of receiving for household B, and $d_{AB}$ is the dyad-specific giving rate from A to B. Doing this implies that each household H needs varying effects for giving $g_{H}$ and receiving $r_{H}$. On top of this each dyad has their own specific $d_{AB}$ and $d_{BA}$ effects. One goal here is to see if the $g$ and $r$ paremters are correlated - go givers get a lot in return? In addition, we're interested in knowing if there are asymetries in gifts within each dyad - is there a gift balance or not for each pair? These are handled with a couple of multi-normal priors. The first deals with the household effects:\n",
    "\n",
    "$$\n",
    "\\begin{pmatrix} g_{i} \\\\ r_{i} \\end{pmatrix} \\sim MvN \\left( \\begin{pmatrix} 0 \\\\ 0 \\end{pmatrix}, \\begin{pmatrix} \\sigma_{g}^2 & \\sigma_{g}\\sigma_{r}\\rho_{gr} \\\\ \\sigma_{g}\\sigma_{r}\\rho_{gr} & \\sigma_{r}^2 \\end{pmatrix} \\right).\n",
    "$$\n",
    "\n",
    "The second, deals with the dyad effects:\n",
    "\n",
    "$$\n",
    "\\begin{pmatrix} d_{ij} \\\\ d_{ji} \\end{pmatrix} \\sim MvN \\left( \\begin{pmatrix} 0 \\\\ 0 \\end{pmatrix}, \\begin{pmatrix} \\sigma_{d}^2 & \\sigma_{d}^2\\rho_{d} \\\\ \\sigma_{d}^2\\rho_{d} & \\sigma_{d}^2 \\end{pmatrix} \\right).\n",
    "$$\n",
    "\n",
    "It's important to note that the standard deviation $\\sigma_{d}$ is common for both terms because the direction (i or j) doesn't matter. And $\\rho_{d}$ tells us if there is an asymetry ($\\rho_{d}$ negative) or not ($\\rho_{d}$ positive). \n",
    "\n",
    "We can implement this in pymc3 as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "with pm.Model() as Dyads:\n",
    "    # gr matrix of varying effects per household\n",
    "    sd_dist = pm.Exponential.dist(1.0)\n",
    "    chol_gr, _, _ = pm.LKJCholeskyCov(\"chol_gr\", n=2, eta=4, sd_dist=sd_dist, compute_corr=True)\n",
    "    gr = pm.MvNormal(\"gr\", mu=0, chol=chol_gr, shape=(N_households, 2))\n",
    "\n",
    "    # dyad effects\n",
    "    chol_dyad, _, _ = pm.LKJCholeskyCov(\"chol_dyad\", n=2, eta=8, sd_dist=sd_dist, compute_corr=True)\n",
    "    z = pm.Normal(\"z\", 0, 1, shape=(2, N))\n",
    "    d = pm.Deterministic(\"d\", pm.math.dot(chol_dyad, z).T)\n",
    "\n",
    "    # linear models\n",
    "    a = pm.Normal(\"a\", 0, 1)\n",
    "    lambdaAB = tt.exp(a + gr[hidA, 0] + gr[hidB, 1] + d[did, 0])\n",
    "    lambdaBA = tt.exp(a + gr[hidB, 0] + gr[hidA, 1] + d[did, 1])\n",
    "\n",
    "    # likelihood\n",
    "    giftsAB = pm.Poisson(\"giftsAB\", lambdaAB, observed=giftsAB)\n",
    "    giftsBA = pm.Poisson(\"giftsBA\", lambdaBA, observed=giftsBA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [a, z, chol_dyad, gr, chol_gr]\n"
     ]
    },
    {
     "data": {
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       "      100.00% [12000/12000 01:42<00:00 Sampling 4 chains, 0 divergences]\n",
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 114 seconds.\n",
      "The number of effective samples is smaller than 25% for some parameters.\n"
     ]
    }
   ],
   "source": [
    "with Dyads:\n",
    "    trace = pm.sample(2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert into Arviz object\n",
    "Dyadz = az.from_pymc3(trace, dims={\"d\": [\"Dyad\", \"House\"], \"gr\": [\"Household\", \"Rate\"]}, coords={\"Rate\": [\"giving\", \"receiving\"]},)\n",
    "# Rename sub-objects within chol_gr and chol_dyad\n",
    "PostDyads = Dyadz.posterior = Dyadz.posterior.rename_vars({\"chol_gr_corr\": \"Rho_gr\", \"chol_gr_stds\": \"sigma_gr\", \"chol_dyad_corr\": \"Rho_d\", \"chol_dyad_stds\": \"sigma_d\",})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
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       "      <th>Rho_gr[0,0]</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Rho_gr[0,1]</th>\n",
       "      <td>-0.41</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2665.16</td>\n",
       "      <td>2623.48</td>\n",
       "      <td>2615.43</td>\n",
       "      <td>4633.08</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rho_gr[1,0]</th>\n",
       "      <td>-0.41</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2623.48</td>\n",
       "      <td>2615.43</td>\n",
       "      <td>4633.08</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rho_gr[1,1]</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>8007.91</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma_gr[0]</th>\n",
       "      <td>0.83</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4177.64</td>\n",
       "      <td>4177.64</td>\n",
       "      <td>4095.86</td>\n",
       "      <td>5747.06</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma_gr[1]</th>\n",
       "      <td>0.41</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2214.11</td>\n",
       "      <td>2214.11</td>\n",
       "      <td>2097.70</td>\n",
       "      <td>2956.52</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             mean    sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_mean  \\\n",
       "Rho_gr[0,0]  1.00  0.00    1.00     1.00        0.0      0.0   8000.00   \n",
       "Rho_gr[0,1] -0.41  0.20   -0.77    -0.04        0.0      0.0   2665.16   \n",
       "Rho_gr[1,0] -0.41  0.20   -0.77    -0.04        0.0      0.0   2665.16   \n",
       "Rho_gr[1,1]  1.00  0.00    1.00     1.00        0.0      0.0   8000.00   \n",
       "sigma_gr[0]  0.83  0.14    0.59     1.08        0.0      0.0   4177.64   \n",
       "sigma_gr[1]  0.41  0.09    0.25     0.58        0.0      0.0   2214.11   \n",
       "\n",
       "              ess_sd  ess_bulk  ess_tail  r_hat  \n",
       "Rho_gr[0,0]  8000.00   8000.00   8000.00    NaN  \n",
       "Rho_gr[0,1]  2623.48   2615.43   4633.08    1.0  \n",
       "Rho_gr[1,0]  2623.48   2615.43   4633.08    1.0  \n",
       "Rho_gr[1,1]  8000.00   8007.91   8000.00    1.0  \n",
       "sigma_gr[0]  4177.64   4095.86   5747.06    1.0  \n",
       "sigma_gr[1]  2214.11   2097.70   2956.52    1.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(Dyadz, var_names=[\"Rho_gr\", \"sigma_gr\"], round_to=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correlation here is -0.41, which implies that individuals who give more thend to receive less across all dyads. The standard deviation for giving (0.83) is twice as varaible as rates of receiving (0.41).\n",
    "\n",
    "Let's take a look at the estimated household giving and receiving rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Household level log-giving rate posteriors\n",
    "g = (PostDyads[\"a\"] + PostDyads[\"gr\"].sel(Rate=\"giving\")).stack(sample=(\"chain\", \"draw\"))\n",
    "# Household level log-receiving rate posteriors\n",
    "r = (PostDyads[\"a\"] + PostDyads[\"gr\"].sel(Rate=\"receiving\")).stack(sample=(\"chain\", \"draw\"))\n",
    "\n",
    "# Household expected giving rates\n",
    "Eg_mu = np.exp(g).mean(dim=\"sample\")\n",
    "# Household expected receiving rates\n",
    "Er_mu = np.exp(r).mean(dim=\"sample\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(1, 1, constrained_layout=True)\n",
    "x = np.linspace(0, 9, 101)\n",
    "ax.plot(x, x, \"k--\", lw=1.5, alpha=0.4)\n",
    "\n",
    "# Plot uncertainty ellipses\n",
    "for house in range(25):\n",
    "    Sigma = np.cov(np.stack([np.exp(g[house].values), np.exp(r[house].values)]))\n",
    "    Mu = np.stack([np.exp(g[house].values.mean()), np.exp(r[house].values.mean())])\n",
    "    pearson = Sigma[0, 1] / np.sqrt(Sigma[0, 0] * Sigma[1, 1])\n",
    "    ellipse = Ellipse((0, 0),np.sqrt(1 + pearson),np.sqrt(1 - pearson),edgecolor=\"k\",alpha=0.5,facecolor=\"none\",)\n",
    "    std_dev = sp.stats.norm.ppf((1 + np.sqrt(0.5)) / 2)\n",
    "    scale_x = 2 * std_dev * np.sqrt(Sigma[0, 0])\n",
    "    scale_y = 2 * std_dev * np.sqrt(Sigma[1, 1])\n",
    "    scale = transforms.Affine2D().rotate_deg(45).scale(scale_x, scale_y)\n",
    "    ellipse.set_transform(scale.translate(Mu[0], Mu[1]) + ax.transData)\n",
    "    ax.add_patch(ellipse)\n",
    "\n",
    "# household means\n",
    "ax.plot(Eg_mu, Er_mu, \"ko\", mfc=\"white\", lw=1.5)\n",
    "\n",
    "ax.set(xlim=(0, 8.6),ylim=(0, 8.6),xlabel=\"generalized giving\",ylabel=\"generalized receiving\",);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What becomes clear in this is that there are a bunch of generous households that receive very little and that there are some households that don't give much but often receive more. \n",
    "\n",
    "At the dyad level, what do the paired gift exchange relationships look like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_mean</th>\n",
       "      <th>ess_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rho_d[0,0]</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rho_d[0,1]</th>\n",
       "      <td>0.88</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2159.19</td>\n",
       "      <td>2148.23</td>\n",
       "      <td>2123.02</td>\n",
       "      <td>3680.47</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rho_d[1,0]</th>\n",
       "      <td>0.88</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2159.19</td>\n",
       "      <td>2148.23</td>\n",
       "      <td>2123.02</td>\n",
       "      <td>3680.47</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rho_d[1,1]</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>8028.82</td>\n",
       "      <td>7443.49</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma_d[0]</th>\n",
       "      <td>1.07</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2869.21</td>\n",
       "      <td>2845.59</td>\n",
       "      <td>2913.44</td>\n",
       "      <td>3676.12</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma_d[1]</th>\n",
       "      <td>1.13</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2668.97</td>\n",
       "      <td>2668.97</td>\n",
       "      <td>2675.29</td>\n",
       "      <td>3994.24</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mean    sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_mean  \\\n",
       "Rho_d[0,0]  1.00  0.00    1.00     1.00        0.0      0.0   8000.00   \n",
       "Rho_d[0,1]  0.88  0.03    0.82     0.94        0.0      0.0   2159.19   \n",
       "Rho_d[1,0]  0.88  0.03    0.82     0.94        0.0      0.0   2159.19   \n",
       "Rho_d[1,1]  1.00  0.00    1.00     1.00        0.0      0.0   8000.00   \n",
       "sigma_d[0]  1.07  0.07    0.95     1.20        0.0      0.0   2869.21   \n",
       "sigma_d[1]  1.13  0.07    1.01     1.26        0.0      0.0   2668.97   \n",
       "\n",
       "             ess_sd  ess_bulk  ess_tail  r_hat  \n",
       "Rho_d[0,0]  8000.00   8000.00   8000.00    NaN  \n",
       "Rho_d[0,1]  2148.23   2123.02   3680.47    1.0  \n",
       "Rho_d[1,0]  2148.23   2123.02   3680.47    1.0  \n",
       "Rho_d[1,1]  8000.00   8028.82   7443.49    1.0  \n",
       "sigma_d[0]  2845.59   2913.44   3676.12    1.0  \n",
       "sigma_d[1]  2668.97   2675.29   3994.24    1.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(Dyadz, var_names=[\"Rho_d\", \"sigma_d\"], round_to=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grab dyad giving\n",
    "dy1 = PostDyads[\"d\"].sel(House=0).mean(dim=(\"chain\", \"draw\"))\n",
    "dy2 = PostDyads[\"d\"].sel(House=1).mean(dim=(\"chain\", \"draw\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(1, 1, constrained_layout=True)\n",
    "x = np.linspace(-2, 4, 101)\n",
    "\n",
    "ax.plot(x, x, \"k--\", lw=1.5, alpha=0.4)\n",
    "ax.axhline(linewidth=1.5, color=\"k\", ls=\"--\", alpha=0.4)\n",
    "ax.axvline(linewidth=1.5, color=\"k\", ls=\"--\", alpha=0.4)\n",
    "ax.plot(dy1, dy2, \"ko\", mfc=\"none\", lw=1.5, alpha=0.6)\n",
    "\n",
    "ax.set(xlim=(-2, 4),ylim=(-2, 4),xlabel=\"household A in dyad\",ylabel=\"household B in dyad\",);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that, once we have accounted for individual household behaviour, there is a remarkable consistency within dyads - balanced giving."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

## kl_dyads.csv

          
            
            hidA
            hidB
            did
            giftsAB
            giftsBA
            offset
            drel1
            drel2
            drel3
            drel4
            dlndist
            dass
            d0125

            
              1
              1
              2
              1
              0
              4
              0
              0
              0
              1
              0
              -2.79
              0
              0

            
              2
              1
              3
              2
              6
              31
              -0.003
              0
              1
              0
              0
              -2.817
              0.044
              0

            
              3
              1
              4
              3
              2
              5
              -0.019
              0
              1
              0
              0
              -1.886
              0.025
              0

            
              4
              1
              5
              4
              4
              2
              0
              0
              1
              0
              0
              -1.892
              0.011
              0

            
              5
              1
              6
              5
              8
              2
              -0.003
              1
              0
              0
              0
              -3.499
              0.022
              0

            
              6
              1
              7
              6
              2
              1
              0
              0
              0
              0
              0
              -1.853
              0.071
              0

            
              7
              1
              8
              7
              1
              2
              0
              0
              0
              0
              1
              -1.475
              0.046
              0

            
              8
              1
              9
              8
              0
              1
              0
              0
              0
              0
              1
              -1.644
              0.003
              0

            
              9
              1
              10
              9
              10
              110
              -0.186
              1
              0
              0
              0
              -1.897
              0.552
              0

            
              10
              1
              11
              10
              1
              0
              0
              0
              1
              0
              0
              -2.379
              0.018
              0

            
              11
              1
              12
              11
              0
              0
              -0.471
              0
              0
              1
              0
              -2.2
              0.004
              0

            
              12
              1
              13
              12
              0
              6
              -0.019
              0
              0
              1
              0
              -2.117
              0.004
              0

            
              13
              1
              14
              13
              1
              11
              -0.011
              0
              1
              0
              0
              -2.83
              0.036
              0

            
              14
              1
              15
              14
              6
              0
              -0.014
              0
              0
              0
              0
              -2.492
              0.006
              0

            
              15
              1
              16
              15
              0
              1
              -0.019
              0
              0
              0
              0
              -1.848
              0.014
              0

            
              16
              1
              17
              16
              0
              4
              -0.019
              0
              0
              0
              1
              -2.58
              0
              0

            
              17
              1
              18
              17
              0
              0
              -0.019
              0
              0
              0
              0
              -2.184
              0.025
              0

            
              18
              1
              19
              18
              0
              2
              0
              0
              0
              0
              1
              -1.883
              0
              0

            
              19
              1
              20
              19
              0
              0
              -0.028
              0
              0
              1
              0
              -2.089
              0.012
              0

            
              20
              1
              21
              20
              0
              7
              -0.031
              0
              0
              0
              1
              -2.364
              0.007
              0

            
              21
              1
              22
              21
              0
              0
              0
              0
              0
              0
              1
              -2.538
              0.01
              0

            
              22
              1
              23
              22
              0
              13
              -0.092
              0
              1
              0
              0
              -4.068
              0.012
              0

            
              23
              1
              24
              23
              0
              0
              0
              0
              0
              0
              0
              -1.824
              0
              0

            
              24
              1
              25
              24
              16
              2
              -0.637
              0
              0
              1
              0
              -1.766
              0.08
              1

            
              25
              2
              3
              25
              1
              3
              -0.003
              0
              0
              1
              0
              -2.111
              0.007
              0

            
              26
              2
              4
              26
              2
              1
              -0.019
              0
              0
              1
              0
              -1.58
              0.013
              0

            
              27
              2
              5
              27
              0
              1
              0
              0
              0
              1
              0
              -1.571
              0
              0

            
              28
              2
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              0
              -0.674
              0
              0
              0
              1
              -1.555
              0.045
              0

            
              280
              19
              20
              280
              1
              15
              -0.028
              0
              0
              0
              0
              -3.38
              0.082
              0

            
              281
              19
              21
              281
              5
              17
              -0.031
              0
              0
              0
              1
              -2.649
              0.14
              0

            
              282
              19
              22
              282
              10
              15
              0
              0
              1
              0
              0
              -2.615
              0.109
              0

            
              283
              19
              23
              283
              0
              3
              -0.092
              0
              0
              0
              1
              -1.829
              0.131
              0

            
              284
              19
              24
              284
              2
              1
              0
              0
              0
              1
              0
              -3.127
              0
              0

            
              285
              19
              25
              285
              3
              2
              -0.637
              0
              0
              0
              1
              -1.563
              0
              0

            
              286
              20
              21
              286
              9
              12
              -0.059
              0
              0
              0
              0
              -3.302
              0.015
              0

            
              287
              20
              22
              287
              3
              2
              -0.028
              0
              0
              0
              0
              -3.079
              0.053
              0

            
              288
              20
              23
              288
              3
              8
              -0.122
              0
              1
              0
              0
              -2.038
              0.119
              0

            
              289
              20
              24
              289
              13
              3
              -0.028
              0
              0
              0
              1
              -3.161
              0.074
              0

            
              290
              20
              25
              290
              0
              0
              -0.637
              0
              0
              0
              1
              -1.721
              0.032
              0

            
              291
              21
              22
              291
              18
              5
              -0.031
              0
              0
              0
              1
              -3.486
              0.019
              0

            
              292
              21
              23
              292
              9
              1
              -0.125
              0
              0
              0
              1
              -2.326
              0.015
              0

            
              293
              21
              24
              293
              0
              0
              -0.031
              0
              0
              0
              0
              -2.69
              0.01
              0

            
              294
              21
              25
              294
              9
              1
              -0.696
              0
              0
              1
              0
              -1.895
              0.043
              0

            
              295
              22
              23
              295
              1
              1
              -0.092
              0
              0
              0
              1
              -2.434
              0.076
              0

            
              296
              22
              24
              296
              0
              1
              0
              0
              0
              1
              0
              -2.442
              0
              0

            
              297
              22
              25
              297
              2
              0
              -0.637
              0
              0
              0
              1
              -1.747
              0.026
              0

            
              298
              23
              24
              298
              2
              0
              -0.092
              0
              0
              0
              0
              -1.798
              0
              0

            
              299
              23
              25
              299
              4
              0
              -0.818
              0
              0
              1
              0
              -1.848
              0.031
              0

            
              300
              24
              25
              300
              2
              0
              -0.637
              0
              0
              0
              0
              -1.723
              0.042
              0
	hidA	hidB	did	giftsAB	giftsBA	offset	drel1	drel2	drel3	drel4	dlndist	dass	d0125
1	1	2	1	0	4	0	0	0	1	0	-2.79	0	0
2	1	3	2	6	31	-0.003	0	1	0	0	-2.817	0.044	0
3	1	4	3	2	5	-0.019	0	1	0	0	-1.886	0.025	0
4	1	5	4	4	2	0	0	1	0	0	-1.892	0.011	0
5	1	6	5	8	2	-0.003	1	0	0	0	-3.499	0.022	0
6	1	7	6	2	1	0	0	0	0	0	-1.853	0.071	0
7	1	8	7	1	2	0	0	0	0	1	-1.475	0.046	0
8	1	9	8	0	1	0	0	0	0	1	-1.644	0.003	0
9	1	10	9	10	110	-0.186	1	0	0	0	-1.897	0.552	0
10	1	11	10	1	0	0	0	1	0	0	-2.379	0.018	0
11	1	12	11	0	0	-0.471	0	0	1	0	-2.2	0.004	0
12	1	13	12	0	6	-0.019	0	0	1	0	-2.117	0.004	0
13	1	14	13	1	11	-0.011	0	1	0	0	-2.83	0.036	0
14	1	15	14	6	0	-0.014	0	0	0	0	-2.492	0.006	0
15	1	16	15	0	1	-0.019	0	0	0	0	-1.848	0.014	0
16	1	17	16	0	4	-0.019	0	0	0	1	-2.58	0	0
17	1	18	17	0	0	-0.019	0	0	0	0	-2.184	0.025	0
18	1	19	18	0	2	0	0	0	0	1	-1.883	0	0
19	1	20	19	0	0	-0.028	0	0	1	0	-2.089	0.012	0
20	1	21	20	0	7	-0.031	0	0	0	1	-2.364	0.007	0
21	1	22	21	0	0	0	0	0	0	1	-2.538	0.01	0
22	1	23	22	0	13	-0.092	0	1	0	0	-4.068	0.012	0
23	1	24	23	0	0	0	0	0	0	0	-1.824	0	0
24	1	25	24	16	2	-0.637	0	0	1	0	-1.766	0.08	1
25	2	3	25	1	3	-0.003	0	0	1	0	-2.111	0.007	0
26	2	4	26	2	1	-0.019	0	0	1	0	-1.58	0.013	0
27	2	5	27	0	1	0	0	0	1	0	-1.571	0	0
28	2	6	28	3	0	-0.003	0	1	0	0	-2.401	0	0
29	2	7	29	0	1	0	0	0	0	0	-1.609	0	0
30	2	8	30	43	10	0	1	0	0	0	-1.274	0.252	0
31	2	9	31	5	4	0	1	0	0	0	-1.534	0.035	0
32	2	10	32	0	1	-0.186	0	0	0	1	-1.56	0.01	0
33	2	11	33	1	0	0	0	0	1	0	-1.877	0.004	0
34	2	12	34	0	0	-0.471	0	0	0	1	-1.779	0	0
35	2	13	35	1	2	-0.019	0	0	0	1	-1.711	0	0
36	2	14	36	1	0	-0.011	0	0	1	0	-2.277	0	0
37	2	15	37	2	0	-0.014	0	0	0	0	-2.074	0.027	0
38	2	16	38	4	2	-0.019	0	0	0	1	-1.567	0	0
39	2	17	39	22	16	-0.019	1	0	0	0	-3.434	0.239	0
40	2	18	40	1	1	-0.019	0	0	0	1	-2.198	0.019	0
41	2	19	41	6	9	0	1	0	0	0	-1.832	0.093	0
42	2	20	42	0	5	-0.028	0	0	0	1	-1.953	0	0
43	2	21	43	1	3	-0.031	0	0	1	0	-2.078	0.011	0
44	2	22	44	14	25	0	1	0	0	0	-2.322	0.194	0
45	2	23	45	2	5	-0.092	0	0	1	0	-2.558	0.017	0
46	2	24	46	5	2	0	0	0	1	0	-1.692	0.07	0
47	2	25	47	1	0	-0.637	0	0	0	1	-1.464	0.01	0
48	3	4	48	15	0	-0.022	0	1	0	0	-2.252	0.085	0
49	3	5	49	23	2	-0.003	0	1	0	0	-2.307	0.116	0
50	3	6	50	49	4	-0.005	1	0	0	0	-3.442	0.118	0
51	3	7	51	6	0	-0.003	0	0	0	0	-2.056	0.012	0
52	3	8	52	3	0	-0.003	0	0	0	1	-1.678	0.02	0
53	3	9	53	0	0	-0.003	0	0	0	1	-1.675	0.009	0
54	3	10	54	2	8	-0.186	0	0	1	0	-2.384	0.018	0
55	3	11	55	11	1	-0.003	0	1	0	0	-3.321	0.024	0
56	3	12	56	7	0	-0.471	0	0	1	0	-2.814	0.016	0
57	3	13	57	7	2	-0.022	0	0	1	0	-2.763	0.026	0
58	3	14	58	12	2	-0.014	0	1	0	0	-2.798	0.113	0
59	3	15	59	22	4	-0.017	1	0	0	0	-2.635	0.028	0
60	3	16	60	5	1	-0.022	0	0	0	0	-2.15	0.046	0
61	3	17	61	10	5	-0.022	0	0	0	1	-2.024	0.015	0
62	3	18	62	0	0	-0.022	0	0	0	0	-1.958	0.033	0
63	3	19	63	4	0	-0.003	0	0	0	1	-1.797	0.014	0
64	3	20	64	3	1	-0.031	0	0	1	0	-2.023	0.039	0
65	3	21	65	2	1	-0.033	0	0	0	1	-2.348	0.006	0
66	3	22	66	2	1	-0.003	0	0	0	1	-2.304	0.006	0
67	3	23	67	13	26	-0.095	0	1	0	0	-3.107	0.146	0
68	3	24	68	2	0	-0.003	0	0	0	0	-1.84	0.041	0
69	3	25	69	9	0	-0.637	0	0	1	0	-2.178	0.018	0
70	4	5	70	1	8	-0.019	0	1	0	0	-4.035	0.14	0
71	4	6	71	19	4	-0.022	1	0	0	0	-2.099	0.093	0
72	4	7	72	0	1	-0.019	0	0	1	0	-2.926	0.04	0
73	4	8	73	0	0	-0.019	0	0	0	1	-1.473	0	0
74	4	9	74	0	1	-0.019	0	0	0	1	-1.999	0.015	0
75	4	10	75	1	8	-0.21	0	0	1	0	-2.968	0.01	0
76	4	11	76	2	0	-0.019	0	1	0	0	-2.637	0.027	0
77	4	12	77	1	3	-0.502	0	0	1	0	-3.089	0.012	0
78	4	13	78	1	11	-0.059	0	0	1	0	-2.896	0.02	0
79	4	14	79	0	6	-0.031	0	1	0	0	-2.252	0.147	0
80	4	15	80	2	2	-0.033	0	0	0	0	-2.453	0.021	0
81	4	16	81	7	17	-0.059	0	0	0	0	-3.942	0.064	0
82	4	17	82	1	3	-0.059	0	0	0	1	-1.594	0.008	0
83	4	18	83	0	1	-0.059	0	0	0	0	-1.813	0.028	0
84	4	19	84	0	2	-0.019	0	0	0	1	-1.866	0	0
85	4	20	85	0	2	-0.048	0	0	1	0	-2.07	0.08	0
86	4	21	86	0	5	-0.051	0	0	0	1	-2.273	0	0
87	4	22	87	0	2	-0.019	0	0	0	1	-2.033	0.026	0
88	4	23	88	1	13	-0.092	0	1	0	0	-1.947	0.115	0
89	4	24	89	1	0	-0.019	0	0	0	0	-2.108	0	0
90	4	25	90	1	1	-0.674	0	1	0	0	-2.857	0.03	0
91	5	6	91	13	17	-0.003	1	0	0	0	-2.113	0.362	0
92	5	7	92	1	3	0	0	0	0	0	-2.644	0	0
93	5	8	93	0	0	0	0	0	0	1	-1.551	0	0
94	5	9	94	0	2	0	0	0	0	1	-1.879	0	0
95	5	10	95	2	17	-0.186	0	0	1	0	-3.386	0	0
96	5	11	96	1	3	0	0	1	0	0	-2.749	0.044	0
97	5	12	97	1	5	-0.471	0	0	1	0	-3.21	0.058	0
98	5	13	98	2	19	-0.019	0	0	1	0	-3.156	0.068	0
99	5	14	99	1	2	-0.011	0	1	0	0	-2.209	0	0
100	5	15	100	4	2	-0.014	0	0	0	0	-2.371	0.036	0
101	5	16	101	3	7	-0.019	0	0	0	0	-3.296	0	0
102	5	17	102	1	3	-0.019	0	0	0	1	-1.572	0.016	0
103	5	18	103	0	0	-0.019	0	0	0	0	-1.748	0	0
104	5	19	104	1	1	0	0	0	0	1	-1.776	0	0
105	5	20	105	3	3	-0.028	0	0	1	0	-1.97	0.017	0
106	5	21	106	1	3	-0.031	0	0	0	1	-2.178	0	0
107	5	22	107	1	5	0	0	0	0	1	-1.97	0	0
108	5	23	108	0	11	-0.092	0	1	0	0	-1.965	0.034	0
109	5	24	109	0	1	0	0	0	0	0	-1.982	0	0
110	5	25	110	3	1	-0.637	0	0	1	0	-3.201	0.098	0
111	6	7	111	0	2	-0.003	0	0	0	0	-2.018	0.009	0
112	6	8	112	0	0	-0.003	0	0	1	0	-1.544	0.031	0
113	6	9	113	0	0	-0.003	0	0	1	0	-1.718	0	0
114	6	10	114	0	29	-0.186	0	0	1	0	-2.121	0.027	0
115	6	11	115	3	4	-0.003	1	0	0	0	-2.771	0.033	0
116	6	12	116	0	1	-0.475	0	0	1	0	-2.517	0.028	0
117	6	13	117	0	9	-0.019	0	0	1	0	-2.404	0	0
118	6	14	118	5	25	-0.011	1	0	0	0	-3.139	0.092	0
119	6	15	119	0	2	-0.017	0	0	0	0	-2.763	0	0
120	6	16	120	0	2	-0.019	0	0	0	1	-2.041	0.007	0
121	6	17	121	0	6	-0.019	0	0	1	0	-2.301	0	0
122	6	18	122	0	0	-0.019	0	0	0	1	-2.153	0.012	0
123	6	19	123	0	2	-0.003	0	0	1	0	-1.913	0.01	0
124	6	20	124	0	1	-0.031	0	0	1	0	-2.156	0	0
125	6	21	125	0	6	-0.031	0	0	1	0	-2.51	0.002	0
126	6	22	126	1	0	-0.003	0	0	1	0	-2.569	0.005	0
127	6	23	127	4	50	-0.092	1	0	0	0	-3.882	0.099	0
128	6	24	128	0	0	-0.003	0	0	0	1	-1.906	0.016	0
129	6	25	129	2	1	-0.637	0	0	1	0	-1.959	0.033	0
130	7	8	130	0	1	0	0	0	0	0	-1.276	0.037	0
131	7	9	131	0	5	0	0	0	0	0	-2.503	0.024	0
132	7	10	132	0	4	-0.186	0	0	0	0	-2.264	0	0
133	7	11	133	3	2	0	0	0	0	0	-2.265	0.022	0
134	7	12	134	0	2	-0.471	0	0	0	0	-2.525	0.123	0
135	7	13	135	3	6	-0.019	0	0	0	0	-2.309	0	0
136	7	14	136	2	1	-0.011	0	0	0	0	-2.31	0.015	0
137	7	15	137	3	4	-0.014	0	0	0	0	-2.588	0.065	0
138	7	16	138	8	4	-0.019	0	0	0	0	-3.33	0.101	0
139	7	17	139	2	4	-0.019	0	0	0	0	-1.667	0	0
140	7	18	140	0	1	-0.019	0	0	0	1	-2.041	0.149	0
141	7	19	141	0	0	0	0	0	0	0	-2.216	0	0
142	7	20	142	0	8	-0.028	0	0	0	0	-2.445	0.07	0
143	7	21	143	0	18	-0.031	0	0	0	0	-2.552	0.074	0
144	7	22	144	0	1	0	0	0	0	0	-2.221	0.035	0
145	7	23	145	0	7	-0.092	0	0	0	0	-1.875	0.028	0
146	7	24	146	4	4	0	0	0	0	0	-2.651	0.094	0
147	7	25	147	2	2	-0.637	0	0	0	0	-2.199	0.032	0
148	8	9	148	2	5	0	0	1	0	0	-1.031	0.07	0
149	8	10	149	0	4	-0.186	0	0	0	0	-1.723	0	0
150	8	11	150	2	0	0	0	0	1	0	-1.686	0	0
151	8	12	151	2	0	-0.471	0	0	0	0	-1.6	0	0
152	8	13	152	1	3	-0.019	0	0	0	0	-1.714	0	0
153	8	14	153	2	8	-0.011	0	0	0	1	-1.397	0.051	0
154	8	15	154	1	0	-0.014	0	0	0	0	-1.374	0.013	0
155	8	16	155	2	2	-0.019	0	0	0	0	-1.392	0	0
156	8	17	156	2	16	-0.019	0	1	0	0	-1.194	0.139	0
157	8	18	157	0	2	-0.019	0	0	0	0	-1.115	0	0
158	8	19	158	1	10	0	0	1	0	0	-1.051	0.13	0
159	8	20	159	0	11	-0.028	0	0	0	0	-1.154	0	0
160	8	21	160	0	4	-0.031	0	0	0	1	-1.277	0.039	0
161	8	22	161	4	6	0	0	1	0	0	-1.25	0.06	0
162	8	23	162	0	7	-0.092	0	0	0	1	-1.55	0	0
163	8	24	163	1	2	0	0	0	1	0	-1.098	0	0
164	8	25	164	0	2	-0.637	0	0	0	1	-1.708	0.111	0
165	9	10	165	0	0	-0.186	0	0	0	0	-1.687	0	0
166	9	11	166	0	0	0	0	0	0	1	-1.75	0.003	0
167	9	12	167	1	0	-0.471	0	0	0	0	-1.867	0	0
168	9	13	168	0	3	-0.019	0	0	0	0	-1.734	0	0
169	9	14	169	1	1	-0.011	0	0	0	1	-1.991	0.006	0
170	9	15	170	9	0	-0.014	0	0	0	0	-2.144	0.011	0
171	9	16	171	10	3	-0.019	0	0	0	0	-2.143	0.006	0
172	9	17	172	11	8	-0.019	0	1	0	0	-1.644	0.091	0
173	9	18	173	4	2	-0.019	0	0	0	0	-2.191	0.023	0
174	9	19	174	9	3	0	0	1	0	0	-2.729	0.066	0
175	9	20	175	3	23	-0.028	0	0	1	0	-2.607	0.112	0
176	9	21	176	3	15	-0.031	0	0	0	1	-2.309	0.013	0
177	9	22	177	1	3	0	0	1	0	0	-2.129	0.009	0
178	9	23	178	1	2	-0.092	0	0	0	1	-1.625	0.021	0
179	9	24	179	75	26	0	1	0	0	0	-3.44	0.477	0
180	9	25	180	0	0	-0.637	0	0	0	1	-1.646	0.006	0
181	10	11	181	16	1	-0.186	0	0	1	0	-2.859	0.028	0
182	10	12	182	15	0	-0.627	0	0	0	1	-3.078	0	0
183	10	13	183	11	10	-0.21	0	0	1	0	-3.522	0.021	0
184	10	14	184	13	3	-0.21	0	0	1	0	-2.113	0.023	0
185	10	15	185	16	1	-0.186	0	0	0	0	-2.205	0	0
186	10	16	186	16	2	-0.186	0	0	0	0	-2.648	0	0
187	10	17	187	9	3	-0.21	0	0	0	0	-1.537	0	0
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190	10	20	190	3	0	-0.186	0	0	0	1	-1.807	0.011	0
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193	10	23	193	4	3	-0.254	0	0	1	0	-1.992	0	0
194	10	24	194	2	0	-0.186	0	0	0	0	-1.784	0.029	0
195	10	25	195	48	7	-0.863	0	0	0	1	-3.859	0.271	0
196	11	12	196	22	36	-0.471	1	0	0	0	-3.638	0.454	0
197	11	13	197	6	92	-0.019	1	0	0	0	-3.583	0.18	0
198	11	14	198	2	10	-0.011	0	1	0	0	-2.642	0.02	0
199	11	15	199	1	1	-0.014	0	0	0	0	-2.667	0.015	0
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204	11	20	204	0	2	-0.028	0	0	1	0	-2.031	0.052	0
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206	11	22	206	0	0	0	0	0	0	1	-2.206	0.027	0
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209	11	25	209	1	0	-0.637	0	0	1	0	-2.546	0.016	0
210	12	13	210	6	48	-0.502	0	0	1	0	-3.758	0.209	0
211	12	14	211	3	0	-0.488	0	0	1	0	-2.588	0.039	0
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213	12	16	213	0	2	-0.502	0	0	0	0	-2.828	0.007	0
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219	12	22	219	0	0	-0.471	0	0	0	0	-2.207	0.015	0
220	12	23	220	0	5	-0.553	0	0	1	0	-2.305	0.006	0
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222	12	25	222	1	0	-1.236	0	0	0	1	-2.729	0	0
223	13	14	223	51	27	-0.019	0	1	0	0	-2.364	0.228	0
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228	13	19	228	2	0	-0.019	0	0	0	0	-1.726	0.173	0
229	13	20	229	10	10	-0.048	0	0	0	1	-1.931	0.036	0
230	13	21	230	2	0	-0.051	0	0	0	0	-2.193	0.034	0
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233	13	24	233	1	2	-0.019	0	0	0	0	-1.863	0	0
234	13	25	234	11	0	-0.637	0	0	0	1	-2.984	0	0
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236	14	16	236	0	2	-0.031	0	0	0	0	-2.243	0.012	0
237	14	17	237	5	7	-0.031	0	0	0	1	-2.323	0.036	0
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240	14	20	240	2	12	-0.039	0	0	1	0	-2.616	0.058	0
241	14	21	241	2	13	-0.042	0	0	0	1	-3.272	0.004	0
242	14	22	242	0	1	-0.011	0	0	0	1	-3.224	0.008	0
243	14	23	243	1	10	-0.104	0	1	0	0	-2.808	0.067	0
244	14	24	244	1	1	-0.011	0	0	0	0	-2.249	0	0
245	14	25	245	0	0	-0.637	0	0	1	0	-1.963	0.033	0
246	15	16	246	11	35	-0.033	1	0	0	0	-2.472	0.464	0
247	15	17	247	0	7	-0.033	0	0	0	0	-2.135	0.059	0
248	15	18	248	0	2	-0.033	0	0	0	1	-2.475	0.01	0
249	15	19	249	1	1	-0.014	0	0	0	1	-2.339	0	0
250	15	20	250	1	13	-0.031	0	1	0	0	-2.775	0.048	0
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252	15	22	252	2	8	-0.014	0	0	0	0	-3.073	0.059	0
253	15	23	253	0	3	-0.107	0	0	0	0	-2.5	0.01	0
254	15	24	254	0	3	-0.014	0	0	0	0	-2.437	0.013	0
255	15	25	255	0	2	-0.637	0	0	0	0	-2.055	0.044	0
256	16	17	256	2	3	-0.039	0	0	0	0	-1.596	0.074	0
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259	16	20	259	2	8	-0.048	0	0	1	0	-2.16	0.055	0
260	16	21	260	2	39	-0.051	0	0	1	0	-2.334	0.049	0
261	16	22	261	0	7	-0.019	0	0	0	0	-2.067	0.055	0
262	16	23	262	1	9	-0.113	0	0	0	0	-1.895	0.044	0
263	16	24	263	0	2	-0.019	0	0	0	0	-2.248	0	0
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265	17	18	265	4	2	-0.039	0	0	0	0	-2.477	0.056	0
266	17	19	266	8	10	-0.019	0	1	0	0	-2.01	0.016	0
267	17	20	267	4	16	-0.048	0	0	0	0	-2.117	0.018	0
268	17	21	268	3	9	-0.051	0	0	0	1	-2.196	0.007	0
269	17	22	269	8	19	-0.019	0	1	0	0	-2.511	0.145	0
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271	17	24	271	5	5	-0.019	0	0	1	0	-1.816	0.082	0
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273	18	19	273	3	1	-0.019	0	0	0	1	-2.996	0.035	0
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275	18	21	275	15	62	-0.042	1	0	0	0	-2.797	0.447	0
276	18	22	276	1	1	-0.019	0	0	1	0	-3.18	0.065	0
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279	18	25	279	1	0	-0.674	0	0	0	1	-1.555	0.045	0
280	19	20	280	1	15	-0.028	0	0	0	0	-3.38	0.082	0
281	19	21	281	5	17	-0.031	0	0	0	1	-2.649	0.14	0
282	19	22	282	10	15	0	0	1	0	0	-2.615	0.109	0
283	19	23	283	0	3	-0.092	0	0	0	1	-1.829	0.131	0
284	19	24	284	2	1	0	0	0	1	0	-3.127	0	0
285	19	25	285	3	2	-0.637	0	0	0	1	-1.563	0	0
286	20	21	286	9	12	-0.059	0	0	0	0	-3.302	0.015	0
287	20	22	287	3	2	-0.028	0	0	0	0	-3.079	0.053	0
288	20	23	288	3	8	-0.122	0	1	0	0	-2.038	0.119	0
289	20	24	289	13	3	-0.028	0	0	0	1	-3.161	0.074	0
290	20	25	290	0	0	-0.637	0	0	0	1	-1.721	0.032	0
291	21	22	291	18	5	-0.031	0	0	0	1	-3.486	0.019	0
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293	21	24	293	0	0	-0.031	0	0	0	0	-2.69	0.01	0
294	21	25	294	9	1	-0.696	0	0	1	0	-1.895	0.043	0
295	22	23	295	1	1	-0.092	0	0	0	1	-2.434	0.076	0
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297	22	25	297	2	0	-0.637	0	0	0	1	-1.747	0.026	0
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299	23	25	299	4	0	-0.818	0	0	1	0	-1.848	0.031	0
300	24	25	300	2	0	-0.637	0	0	0	0	-1.723	0.042	0