{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Meta-Analysis in statsmodels\n",
    "\n",
    "Statsmodels include basic methods for meta-analysis. This notebook illustrates the current usage.\n",
    "\n",
    "Status: The results have been verified against R meta and metafor packages. However, the API is still experimental and will still change. Some options for additional methods that are available in R meta and metafor are missing.\n",
    "\n",
    "The support for meta-analysis has 3 parts:\n",
    "\n",
    "- effect size functions: this currently includes\n",
    "  ``effectsize_smd`` computes effect size and their standard errors for standardized mean difference,  \n",
    "  ``effectsize_2proportions`` computes effect sizes for comparing two independent proportions using risk difference, (log) risk ratio, (log) odds-ratio or arcsine square root transformation\n",
    "- The `combine_effects` computes fixed and random effects estimate for the overall mean or effect. The returned results instance includes a forest plot function.\n",
    "- helper functions to estimate the random effect variance, tau-squared\n",
    "\n",
    "The estimate of the overall effect size in `combine_effects` can also be performed using WLS or GLM with var_weights.\n",
    "\n",
    "Finally, the meta-analysis functions currently do not include the Mantel-Hanszel method. However, the fixed effects results can be computed directly using `StratifiedTable` as illustrated below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:05.698983Z",
     "iopub.status.busy": "2022-11-02T17:06:05.698341Z",
     "iopub.status.idle": "2022-11-02T17:06:06.192800Z",
     "shell.execute_reply": "2022-11-02T17:06:06.192116Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:06.198466Z",
     "iopub.status.busy": "2022-11-02T17:06:06.197179Z",
     "iopub.status.idle": "2022-11-02T17:06:06.676865Z",
     "shell.execute_reply": "2022-11-02T17:06:06.676169Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats, optimize\n",
    "\n",
    "from statsmodels.regression.linear_model import WLS\n",
    "from statsmodels.genmod.generalized_linear_model import GLM\n",
    "\n",
    "from statsmodels.stats.meta_analysis import (\n",
    "    effectsize_smd,\n",
    "    effectsize_2proportions,\n",
    "    combine_effects,\n",
    "    _fit_tau_iterative,\n",
    "    _fit_tau_mm,\n",
    "    _fit_tau_iter_mm,\n",
    ")\n",
    "\n",
    "# increase line length for pandas\n",
    "pd.set_option(\"display.width\", 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:06.682494Z",
     "iopub.status.busy": "2022-11-02T17:06:06.681278Z",
     "iopub.status.idle": "2022-11-02T17:06:06.693049Z",
     "shell.execute_reply": "2022-11-02T17:06:06.692505Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Carroll', 'Grant', 'Peck', 'Donat', 'Stewart', 'Young']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [\n",
    "    [\"Carroll\", 94, 22, 60, 92, 20, 60],\n",
    "    [\"Grant\", 98, 21, 65, 92, 22, 65],\n",
    "    [\"Peck\", 98, 28, 40, 88, 26, 40],\n",
    "    [\"Donat\", 94, 19, 200, 82, 17, 200],\n",
    "    [\"Stewart\", 98, 21, 50, 88, 22, 45],\n",
    "    [\"Young\", 96, 21, 85, 92, 22, 85],\n",
    "]\n",
    "colnames = [\"study\", \"mean_t\", \"sd_t\", \"n_t\", \"mean_c\", \"sd_c\", \"n_c\"]\n",
    "rownames = [i[0] for i in data]\n",
    "dframe1 = pd.DataFrame(data, columns=colnames)\n",
    "rownames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:06.697564Z",
     "iopub.status.busy": "2022-11-02T17:06:06.696463Z",
     "iopub.status.idle": "2022-11-02T17:06:06.705112Z",
     "shell.execute_reply": "2022-11-02T17:06:06.704571Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Carroll', 'Grant', 'Peck', 'Donat', 'Stewart', 'Young']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean2, sd2, nobs2, mean1, sd1, nobs1 = np.asarray(\n",
    "    dframe1[[\"mean_t\", \"sd_t\", \"n_t\", \"mean_c\", \"sd_c\", \"n_c\"]]\n",
    ").T\n",
    "rownames = dframe1[\"study\"]\n",
    "rownames.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:06.709566Z",
     "iopub.status.busy": "2022-11-02T17:06:06.708472Z",
     "iopub.status.idle": "2022-11-02T17:06:06.715610Z",
     "shell.execute_reply": "2022-11-02T17:06:06.715082Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([120, 130,  80, 400,  95, 170])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(nobs1 + nobs2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### estimate effect size standardized mean difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:06.720221Z",
     "iopub.status.busy": "2022-11-02T17:06:06.719124Z",
     "iopub.status.idle": "2022-11-02T17:06:06.723637Z",
     "shell.execute_reply": "2022-11-02T17:06:06.723114Z"
    }
   },
   "outputs": [],
   "source": [
    "eff, var_eff = effectsize_smd(mean2, sd2, nobs2, mean1, sd1, nobs1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using one-step chi2, DerSimonian-Laird estimate for random effects variance tau\n",
    "\n",
    "Method option for random effect `method_re=\"chi2\"` or `method_re=\"dl\"`, both names are accepted.\n",
    "This is commonly referred to as the DerSimonian-Laird method, it is based on a moment estimator based on pearson chi2 from the fixed effects estimate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:06.728047Z",
     "iopub.status.busy": "2022-11-02T17:06:06.726966Z",
     "iopub.status.idle": "2022-11-02T17:06:06.738227Z",
     "shell.execute_reply": "2022-11-02T17:06:06.737659Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        eff    sd_eff    ci_low    ci_upp      w_fe      w_re\n",
      "Carroll            0.094524  0.182680 -0.267199  0.456248  0.123885  0.157529\n",
      "Grant              0.277356  0.176279 -0.071416  0.626129  0.133045  0.162828\n",
      "Peck               0.366546  0.225573 -0.082446  0.815538  0.081250  0.126223\n",
      "Donat              0.664385  0.102748  0.462389  0.866381  0.391606  0.232734\n",
      "Stewart            0.461808  0.208310  0.048203  0.875413  0.095275  0.137949\n",
      "Young              0.185165  0.153729 -0.118312  0.488641  0.174939  0.182736\n",
      "fixed effect       0.414961  0.064298  0.249677  0.580245  1.000000       NaN\n",
      "random effect      0.358486  0.105462  0.087388  0.629583       NaN  1.000000\n",
      "fixed effect wls   0.414961  0.099237  0.159864  0.670058  1.000000       NaN\n",
      "random effect wls  0.358486  0.090328  0.126290  0.590682       NaN  1.000000\n"
     ]
    }
   ],
   "source": [
    "res3 = combine_effects(eff, var_eff, method_re=\"chi2\", use_t=True, row_names=rownames)\n",
    "# TODO: we still need better information about conf_int of individual samples\n",
    "# We don't have enough information in the model for individual confidence intervals\n",
    "# if those are not based on normal distribution.\n",
    "res3.conf_int_samples(nobs=np.array(nobs1 + nobs2))\n",
    "print(res3.summary_frame())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:06.742469Z",
     "iopub.status.busy": "2022-11-02T17:06:06.741358Z",
     "iopub.status.idle": "2022-11-02T17:06:06.748340Z",
     "shell.execute_reply": "2022-11-02T17:06:06.747801Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(0.05,\n",
       "  True): (array([-0.26719942, -0.07141628, -0.08244568,  0.46238908,  0.04820269,\n",
       "         -0.1183121 ]), array([0.45624817, 0.62612908, 0.81553838, 0.86638112, 0.87541326,\n",
       "         0.48864139]))}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res3.cache_ci"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:06.752585Z",
     "iopub.status.busy": "2022-11-02T17:06:06.751491Z",
     "iopub.status.idle": "2022-11-02T17:06:06.757738Z",
     "shell.execute_reply": "2022-11-02T17:06:06.757196Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'chi2'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res3.method_re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:06.762000Z",
     "iopub.status.busy": "2022-11-02T17:06:06.760889Z",
     "iopub.status.idle": "2022-11-02T17:06:06.977388Z",
     "shell.execute_reply": "2022-11-02T17:06:06.976702Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = res3.plot_forest()\n",
    "fig.set_figheight(6)\n",
    "fig.set_figwidth(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:06.982622Z",
     "iopub.status.busy": "2022-11-02T17:06:06.981454Z",
     "iopub.status.idle": "2022-11-02T17:06:06.992390Z",
     "shell.execute_reply": "2022-11-02T17:06:06.991808Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        eff    sd_eff    ci_low    ci_upp      w_fe      w_re\n",
      "Carroll            0.094524  0.182680 -0.263521  0.452570  0.123885  0.157529\n",
      "Grant              0.277356  0.176279 -0.068144  0.622857  0.133045  0.162828\n",
      "Peck               0.366546  0.225573 -0.075569  0.808662  0.081250  0.126223\n",
      "Donat              0.664385  0.102748  0.463002  0.865768  0.391606  0.232734\n",
      "Stewart            0.461808  0.208310  0.053527  0.870089  0.095275  0.137949\n",
      "Young              0.185165  0.153729 -0.116139  0.486468  0.174939  0.182736\n",
      "fixed effect       0.414961  0.064298  0.288939  0.540984  1.000000       NaN\n",
      "random effect      0.358486  0.105462  0.151785  0.565187       NaN  1.000000\n",
      "fixed effect wls   0.414961  0.099237  0.220460  0.609462  1.000000       NaN\n",
      "random effect wls  0.358486  0.090328  0.181446  0.535526       NaN  1.000000\n"
     ]
    }
   ],
   "source": [
    "res3 = combine_effects(eff, var_eff, method_re=\"chi2\", use_t=False, row_names=rownames)\n",
    "# TODO: we still need better information about conf_int of individual samples\n",
    "# We don't have enough information in the model for individual confidence intervals\n",
    "# if those are not based on normal distribution.\n",
    "res3.conf_int_samples(nobs=np.array(nobs1 + nobs2))\n",
    "print(res3.summary_frame())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using iterated, Paule-Mandel estimate for random effects variance tau\n",
    "\n",
    "The method commonly referred to as Paule-Mandel estimate is a method of moment estimate for the random effects variance that iterates between mean and variance estimate until convergence.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:06.997164Z",
     "iopub.status.busy": "2022-11-02T17:06:06.996046Z",
     "iopub.status.idle": "2022-11-02T17:06:07.214977Z",
     "shell.execute_reply": "2022-11-02T17:06:07.214333Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "method RE: iterated\n",
      "                        eff    sd_eff    ci_low    ci_upp      w_fe      w_re\n",
      "Carroll            0.094524  0.182680 -0.263521  0.452570  0.123885  0.152619\n",
      "Grant              0.277356  0.176279 -0.068144  0.622857  0.133045  0.159157\n",
      "Peck               0.366546  0.225573 -0.075569  0.808662  0.081250  0.116228\n",
      "Donat              0.664385  0.102748  0.463002  0.865768  0.391606  0.257767\n",
      "Stewart            0.461808  0.208310  0.053527  0.870089  0.095275  0.129428\n",
      "Young              0.185165  0.153729 -0.116139  0.486468  0.174939  0.184799\n",
      "fixed effect       0.414961  0.064298  0.288939  0.540984  1.000000       NaN\n",
      "random effect      0.366419  0.092390  0.185338  0.547500       NaN  1.000000\n",
      "fixed effect wls   0.414961  0.099237  0.220460  0.609462  1.000000       NaN\n",
      "random effect wls  0.366419  0.092390  0.185338  0.547500       NaN  1.000000\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res4 = combine_effects(\n",
    "    eff, var_eff, method_re=\"iterated\", use_t=False, row_names=rownames\n",
    ")\n",
    "res4_df = res4.summary_frame()\n",
    "print(\"method RE:\", res4.method_re)\n",
    "print(res4.summary_frame())\n",
    "fig = res4.plot_forest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example Kacker interlaboratory mean\n",
    "\n",
    "In this example the effect size is the mean of measurements in a lab. We combine the estimates from several labs to estimate and overall average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:07.220930Z",
     "iopub.status.busy": "2022-11-02T17:06:07.219777Z",
     "iopub.status.idle": "2022-11-02T17:06:07.225333Z",
     "shell.execute_reply": "2022-11-02T17:06:07.224777Z"
    }
   },
   "outputs": [],
   "source": [
    "eff = np.array([61.00, 61.40, 62.21, 62.30, 62.34, 62.60, 62.70, 62.84, 65.90])\n",
    "var_eff = np.array(\n",
    "    [0.2025, 1.2100, 0.0900, 0.2025, 0.3844, 0.5625, 0.0676, 0.0225, 1.8225]\n",
    ")\n",
    "rownames = [\"PTB\", \"NMi\", \"NIMC\", \"KRISS\", \"LGC\", \"NRC\", \"IRMM\", \"NIST\", \"LNE\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:07.229817Z",
     "iopub.status.busy": "2022-11-02T17:06:07.228711Z",
     "iopub.status.idle": "2022-11-02T17:06:07.483042Z",
     "shell.execute_reply": "2022-11-02T17:06:07.482201Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "method RE: dl\n",
      "                         eff    sd_eff     ci_low     ci_upp      w_fe      w_re\n",
      "PTB                61.000000  0.450000  60.118016  61.881984  0.057436  0.123113\n",
      "NMi                61.400000  1.100000  59.244040  63.555960  0.009612  0.040314\n",
      "NIMC               62.210000  0.300000  61.622011  62.797989  0.129230  0.159749\n",
      "KRISS              62.300000  0.450000  61.418016  63.181984  0.057436  0.123113\n",
      "LGC                62.340000  0.620000  61.124822  63.555178  0.030257  0.089810\n",
      "NRC                62.600000  0.750000  61.130027  64.069973  0.020677  0.071005\n",
      "IRMM               62.700000  0.260000  62.190409  63.209591  0.172052  0.169810\n",
      "NIST               62.840000  0.150000  62.546005  63.133995  0.516920  0.194471\n",
      "LNE                65.900000  1.350000  63.254049  68.545951  0.006382  0.028615\n",
      "fixed effect       62.583397  0.107846  62.334704  62.832090  1.000000       NaN\n",
      "random effect      62.390139  0.245750  61.823439  62.956838       NaN  1.000000\n",
      "fixed effect wls   62.583397  0.189889  62.145512  63.021282  1.000000       NaN\n",
      "random effect wls  62.390139  0.294776  61.710384  63.069893       NaN  1.000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/hostedtoolcache/Python/3.10.8/x64/lib/python3.10/site-packages/statsmodels/stats/meta_analysis.py:106: UserWarning: `use_t=True` requires `nobs` for each sample or `ci_func`. Using normal distribution for confidence interval of individual samples.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res2_DL = combine_effects(eff, var_eff, method_re=\"dl\", use_t=True, row_names=rownames)\n",
    "print(\"method RE:\", res2_DL.method_re)\n",
    "print(res2_DL.summary_frame())\n",
    "fig = res2_DL.plot_forest()\n",
    "fig.set_figheight(6)\n",
    "fig.set_figwidth(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:07.487608Z",
     "iopub.status.busy": "2022-11-02T17:06:07.487153Z",
     "iopub.status.idle": "2022-11-02T17:06:07.858634Z",
     "shell.execute_reply": "2022-11-02T17:06:07.857803Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "method RE: pm\n",
      "                         eff    sd_eff     ci_low     ci_upp      w_fe      w_re\n",
      "PTB                61.000000  0.450000  60.118016  61.881984  0.057436  0.125857\n",
      "NMi                61.400000  1.100000  59.244040  63.555960  0.009612  0.059656\n",
      "NIMC               62.210000  0.300000  61.622011  62.797989  0.129230  0.143658\n",
      "KRISS              62.300000  0.450000  61.418016  63.181984  0.057436  0.125857\n",
      "LGC                62.340000  0.620000  61.124822  63.555178  0.030257  0.104850\n",
      "NRC                62.600000  0.750000  61.130027  64.069973  0.020677  0.090122\n",
      "IRMM               62.700000  0.260000  62.190409  63.209591  0.172052  0.147821\n",
      "NIST               62.840000  0.150000  62.546005  63.133995  0.516920  0.156980\n",
      "LNE                65.900000  1.350000  63.254049  68.545951  0.006382  0.045201\n",
      "fixed effect       62.583397  0.107846  62.334704  62.832090  1.000000       NaN\n",
      "random effect      62.407620  0.338030  61.628120  63.187119       NaN  1.000000\n",
      "fixed effect wls   62.583397  0.189889  62.145512  63.021282  1.000000       NaN\n",
      "random effect wls  62.407620  0.338030  61.628120  63.187120       NaN  1.000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/hostedtoolcache/Python/3.10.8/x64/lib/python3.10/site-packages/statsmodels/stats/meta_analysis.py:106: UserWarning: `use_t=True` requires `nobs` for each sample or `ci_func`. Using normal distribution for confidence interval of individual samples.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res2_PM = combine_effects(eff, var_eff, method_re=\"pm\", use_t=True, row_names=rownames)\n",
    "print(\"method RE:\", res2_PM.method_re)\n",
    "print(res2_PM.summary_frame())\n",
    "fig = res2_PM.plot_forest()\n",
    "fig.set_figheight(6)\n",
    "fig.set_figwidth(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Meta-analysis of proportions\n",
    "\n",
    "In the following example the random effect variance tau is estimated to be zero. \n",
    "I then change two counts in the data, so the second example has random effects variance greater than zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:07.863272Z",
     "iopub.status.busy": "2022-11-02T17:06:07.862819Z",
     "iopub.status.idle": "2022-11-02T17:06:07.867688Z",
     "shell.execute_reply": "2022-11-02T17:06:07.867123Z"
    }
   },
   "outputs": [],
   "source": [
    "import io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:07.872299Z",
     "iopub.status.busy": "2022-11-02T17:06:07.871194Z",
     "iopub.status.idle": "2022-11-02T17:06:07.880361Z",
     "shell.execute_reply": "2022-11-02T17:06:07.879822Z"
    }
   },
   "outputs": [],
   "source": [
    "ss = \"\"\"\\\n",
    "    study,nei,nci,e1i,c1i,e2i,c2i,e3i,c3i,e4i,c4i\n",
    "    1,19,22,16.0,20.0,11,12,4.0,8.0,4,3\n",
    "    2,34,35,22.0,22.0,18,12,15.0,8.0,15,6\n",
    "    3,72,68,44.0,40.0,21,15,10.0,3.0,3,0\n",
    "    4,22,20,19.0,12.0,14,5,5.0,4.0,2,3\n",
    "    5,70,32,62.0,27.0,42,13,26.0,6.0,15,5\n",
    "    6,183,94,130.0,65.0,80,33,47.0,14.0,30,11\n",
    "    7,26,50,24.0,30.0,13,18,5.0,10.0,3,9\n",
    "    8,61,55,51.0,44.0,37,30,19.0,19.0,11,15\n",
    "    9,36,25,30.0,17.0,23,12,13.0,4.0,10,4\n",
    "    10,45,35,43.0,35.0,19,14,8.0,4.0,6,0\n",
    "    11,246,208,169.0,139.0,106,76,67.0,42.0,51,35\n",
    "    12,386,141,279.0,97.0,170,46,97.0,21.0,73,8\n",
    "    13,59,32,56.0,30.0,34,17,21.0,9.0,20,7\n",
    "    14,45,15,42.0,10.0,18,3,9.0,1.0,9,1\n",
    "    15,14,18,14.0,18.0,13,14,12.0,13.0,9,12\n",
    "    16,26,19,21.0,15.0,12,10,6.0,4.0,5,1\n",
    "    17,74,75,,,42,40,,,23,30\"\"\"\n",
    "df3 = pd.read_csv(io.StringIO(ss))\n",
    "df_12y = df3[[\"e2i\", \"nei\", \"c2i\", \"nci\"]]\n",
    "# TODO: currently 1 is reference, switch labels\n",
    "count1, nobs1, count2, nobs2 = df_12y.values.T\n",
    "dta = df_12y.values.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:07.884858Z",
     "iopub.status.busy": "2022-11-02T17:06:07.883751Z",
     "iopub.status.idle": "2022-11-02T17:06:07.888354Z",
     "shell.execute_reply": "2022-11-02T17:06:07.887830Z"
    }
   },
   "outputs": [],
   "source": [
    "eff, var_eff = effectsize_2proportions(*dta, statistic=\"rd\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:07.892727Z",
     "iopub.status.busy": "2022-11-02T17:06:07.891645Z",
     "iopub.status.idle": "2022-11-02T17:06:07.898740Z",
     "shell.execute_reply": "2022-11-02T17:06:07.898207Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.03349282,  0.18655462,  0.07107843,  0.38636364,  0.19375   ,\n",
       "         0.08609464,  0.14      ,  0.06110283,  0.15888889,  0.02222222,\n",
       "         0.06550969,  0.11417337,  0.04502119,  0.2       ,  0.15079365,\n",
       "        -0.06477733,  0.03423423]),\n",
       " array([0.02409958, 0.01376482, 0.00539777, 0.01989341, 0.01096641,\n",
       "        0.00376814, 0.01422338, 0.00842011, 0.01639261, 0.01227827,\n",
       "        0.00211165, 0.00219739, 0.01192067, 0.016     , 0.0143398 ,\n",
       "        0.02267994, 0.0066352 ]))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eff, var_eff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:07.903138Z",
     "iopub.status.busy": "2022-11-02T17:06:07.902045Z",
     "iopub.status.idle": "2022-11-02T17:06:08.265061Z",
     "shell.execute_reply": "2022-11-02T17:06:08.264280Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "method RE: iterated\n",
      "RE variance tau2: 0\n",
      "                        eff    sd_eff    ci_low    ci_upp      w_fe      w_re\n",
      "0                  0.033493  0.155240 -0.270773  0.337758  0.017454  0.017454\n",
      "1                  0.186555  0.117324 -0.043395  0.416505  0.030559  0.030559\n",
      "2                  0.071078  0.073470 -0.072919  0.215076  0.077928  0.077928\n",
      "3                  0.386364  0.141044  0.109922  0.662805  0.021145  0.021145\n",
      "4                  0.193750  0.104721 -0.011499  0.398999  0.038357  0.038357\n",
      "5                  0.086095  0.061385 -0.034218  0.206407  0.111630  0.111630\n",
      "6                  0.140000  0.119262 -0.093749  0.373749  0.029574  0.029574\n",
      "7                  0.061103  0.091761 -0.118746  0.240951  0.049956  0.049956\n",
      "8                  0.158889  0.128034 -0.092052  0.409830  0.025660  0.025660\n",
      "9                  0.022222  0.110807 -0.194956  0.239401  0.034259  0.034259\n",
      "10                 0.065510  0.045953 -0.024556  0.155575  0.199199  0.199199\n",
      "11                 0.114173  0.046876  0.022297  0.206049  0.191426  0.191426\n",
      "12                 0.045021  0.109182 -0.168971  0.259014  0.035286  0.035286\n",
      "13                 0.200000  0.126491 -0.047918  0.447918  0.026290  0.026290\n",
      "14                 0.150794  0.119749 -0.083910  0.385497  0.029334  0.029334\n",
      "15                -0.064777  0.150599 -0.359945  0.230390  0.018547  0.018547\n",
      "16                 0.034234  0.081457 -0.125418  0.193887  0.063395  0.063395\n",
      "fixed effect       0.096212  0.020509  0.056014  0.136410  1.000000       NaN\n",
      "random effect      0.096212  0.020509  0.056014  0.136410       NaN  1.000000\n",
      "fixed effect wls   0.096212  0.016521  0.063831  0.128593  1.000000       NaN\n",
      "random effect wls  0.096212  0.016521  0.063831  0.128593       NaN  1.000000\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res5 = combine_effects(\n",
    "    eff, var_eff, method_re=\"iterated\", use_t=False\n",
    ")  # , row_names=rownames)\n",
    "res5_df = res5.summary_frame()\n",
    "print(\"method RE:\", res5.method_re)\n",
    "print(\"RE variance tau2:\", res5.tau2)\n",
    "print(res5.summary_frame())\n",
    "fig = res5.plot_forest()\n",
    "fig.set_figheight(8)\n",
    "fig.set_figwidth(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### changing data to have positive random effects variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:08.269253Z",
     "iopub.status.busy": "2022-11-02T17:06:08.268835Z",
     "iopub.status.idle": "2022-11-02T17:06:08.277080Z",
     "shell.execute_reply": "2022-11-02T17:06:08.276545Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 18,  19,  12,  22],\n",
       "       [ 22,  34,  12,  35],\n",
       "       [ 21,  72,  15,  68],\n",
       "       [ 14,  22,   5,  20],\n",
       "       [ 42,  70,  13,  32],\n",
       "       [ 80, 183,  33,  94],\n",
       "       [ 13,  26,  18,  50],\n",
       "       [ 37,  61,  30,  55],\n",
       "       [ 23,  36,  12,  25],\n",
       "       [ 19,  45,  14,  35],\n",
       "       [106, 246,  76, 208],\n",
       "       [170, 386,  46, 141],\n",
       "       [ 34,  59,  17,  32],\n",
       "       [ 18,  45,   3,  15],\n",
       "       [ 13,  14,  14,  18],\n",
       "       [ 12,  26,  10,  19],\n",
       "       [ 42,  74,  40,  75]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dta_c = dta.copy()\n",
    "dta_c.T[0, 0] = 18\n",
    "dta_c.T[1, 0] = 22\n",
    "dta_c.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:08.281572Z",
     "iopub.status.busy": "2022-11-02T17:06:08.280464Z",
     "iopub.status.idle": "2022-11-02T17:06:08.591224Z",
     "shell.execute_reply": "2022-11-02T17:06:08.590555Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "method RE: iterated\n",
      "                        eff    sd_eff    ci_low    ci_upp      w_fe      w_re\n",
      "0                  0.401914  0.117873  0.170887  0.632940  0.029850  0.038415\n",
      "1                  0.304202  0.114692  0.079410  0.528993  0.031529  0.040258\n",
      "2                  0.071078  0.073470 -0.072919  0.215076  0.076834  0.081017\n",
      "3                  0.386364  0.141044  0.109922  0.662805  0.020848  0.028013\n",
      "4                  0.193750  0.104721 -0.011499  0.398999  0.037818  0.046915\n",
      "5                  0.086095  0.061385 -0.034218  0.206407  0.110063  0.102907\n",
      "6                  0.140000  0.119262 -0.093749  0.373749  0.029159  0.037647\n",
      "7                  0.061103  0.091761 -0.118746  0.240951  0.049255  0.058097\n",
      "8                  0.158889  0.128034 -0.092052  0.409830  0.025300  0.033270\n",
      "9                  0.022222  0.110807 -0.194956  0.239401  0.033778  0.042683\n",
      "10                 0.065510  0.045953 -0.024556  0.155575  0.196403  0.141871\n",
      "11                 0.114173  0.046876  0.022297  0.206049  0.188739  0.139144\n",
      "12                 0.045021  0.109182 -0.168971  0.259014  0.034791  0.043759\n",
      "13                 0.200000  0.126491 -0.047918  0.447918  0.025921  0.033985\n",
      "14                 0.150794  0.119749 -0.083910  0.385497  0.028922  0.037383\n",
      "15                -0.064777  0.150599 -0.359945  0.230390  0.018286  0.024884\n",
      "16                 0.034234  0.081457 -0.125418  0.193887  0.062505  0.069751\n",
      "fixed effect       0.110252  0.020365  0.070337  0.150167  1.000000       NaN\n",
      "random effect      0.117633  0.024913  0.068804  0.166463       NaN  1.000000\n",
      "fixed effect wls   0.110252  0.022289  0.066567  0.153937  1.000000       NaN\n",
      "random effect wls  0.117633  0.024913  0.068804  0.166463       NaN  1.000000\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eff, var_eff = effectsize_2proportions(*dta_c, statistic=\"rd\")\n",
    "res5 = combine_effects(\n",
    "    eff, var_eff, method_re=\"iterated\", use_t=False\n",
    ")  # , row_names=rownames)\n",
    "res5_df = res5.summary_frame()\n",
    "print(\"method RE:\", res5.method_re)\n",
    "print(res5.summary_frame())\n",
    "fig = res5.plot_forest()\n",
    "fig.set_figheight(8)\n",
    "fig.set_figwidth(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:08.596066Z",
     "iopub.status.busy": "2022-11-02T17:06:08.594936Z",
     "iopub.status.idle": "2022-11-02T17:06:08.922945Z",
     "shell.execute_reply": "2022-11-02T17:06:08.922308Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "method RE: chi2\n",
      "                        eff    sd_eff    ci_low    ci_upp      w_fe      w_re\n",
      "0                  0.401914  0.117873  0.170887  0.632940  0.029850  0.036114\n",
      "1                  0.304202  0.114692  0.079410  0.528993  0.031529  0.037940\n",
      "2                  0.071078  0.073470 -0.072919  0.215076  0.076834  0.080779\n",
      "3                  0.386364  0.141044  0.109922  0.662805  0.020848  0.025973\n",
      "4                  0.193750  0.104721 -0.011499  0.398999  0.037818  0.044614\n",
      "5                  0.086095  0.061385 -0.034218  0.206407  0.110063  0.105901\n",
      "6                  0.140000  0.119262 -0.093749  0.373749  0.029159  0.035356\n",
      "7                  0.061103  0.091761 -0.118746  0.240951  0.049255  0.056098\n",
      "8                  0.158889  0.128034 -0.092052  0.409830  0.025300  0.031063\n",
      "9                  0.022222  0.110807 -0.194956  0.239401  0.033778  0.040357\n",
      "10                 0.065510  0.045953 -0.024556  0.155575  0.196403  0.154854\n",
      "11                 0.114173  0.046876  0.022297  0.206049  0.188739  0.151236\n",
      "12                 0.045021  0.109182 -0.168971  0.259014  0.034791  0.041435\n",
      "13                 0.200000  0.126491 -0.047918  0.447918  0.025921  0.031761\n",
      "14                 0.150794  0.119749 -0.083910  0.385497  0.028922  0.035095\n",
      "15                -0.064777  0.150599 -0.359945  0.230390  0.018286  0.022976\n",
      "16                 0.034234  0.081457 -0.125418  0.193887  0.062505  0.068449\n",
      "fixed effect       0.110252  0.020365  0.070337  0.150167  1.000000       NaN\n",
      "random effect      0.115580  0.023557  0.069410  0.161751       NaN  1.000000\n",
      "fixed effect wls   0.110252  0.022289  0.066567  0.153937  1.000000       NaN\n",
      "random effect wls  0.115580  0.024241  0.068068  0.163093       NaN  1.000000\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnwAAAKkCAYAAACagOIUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAA9hAAAPYQGoP6dpAABuLUlEQVR4nO3de3gU5d3/8c+EZMOuiSEJgYAnIAkHBWNQE8GnPlWhUlS0FRIOInhEq6LGH5pUbWiLJq0YC+ojLYUiNjSBVq1HKoJoqTaAlYjGQwnigYOVJKuErLsLmd8fKZHIIbtkN7OZvF/XlSvszNyznx2S3W/ue+4ZwzRNUwAAALCtKKsDAAAAILwo+AAAAGyOgg8AAMDmKPgAAABsjoIPAADA5ij4AAAAbI6CDwAAwOYo+AAAAGyOgg8AAMDmKPgAAABsjoIPAADA5ij4AAAAbI6CDwAAwOYo+AAAAGyOgg8AAMDmoq0OAAAAQsg0pdpaqaFBiouTkpMlw7A6FSxGDx8AAHbgdkvz5kkZGVJKitS/f/P3jIzm5W631QlhIcM0TdPqEACAzsE0Tfl8Pqtj4DuMVasUM3Gi1NjY/Pigj3bzQO+eyyV/ebnM0aOtiIijcDgcMsLcC0vBh5AwTVM/+9nP9Nvf/laNjY269NJLtWDBAiUkJFgdDUAIeb1elZSUWB0DB0nbskWTy8ok0zzqsF2TJBmGlk2Zopr09A5Kh0AUFBQoNjY2rM/BkC5C4v/+7//08MMP6/e//73Wrl2rd955RzfeeKPVsQDA1mI9HuVWVLRZ7En//cA3TeVWVCjW4+mAdIgkFHwIid/+9re66aabNG7cOJ111ln69a9/rRUrVqi2ttbqaABgW2dUVSnG7w/4wzxKUozfr8yqqnDGQgRili7azev16r333tMvfvGLlmXnnXee9u/fr7ffflujRo1qcx+cEwR0DvyuRhDTVHZlZfDNJOVUVmp9Tg6zdyOEz+dr1zl8DoejzW0o+NButbW1ampqUs+ePXXHHXfo73//uzZu3CiHw6Evv/wyoH0UFxeHOSUA2IuzsVFJ9fVBt4uSlFRfL6fHI4/LFfpgCFppaWm72hcVFbW5DUO6CKlevXrp5JNPtjoGANieo529rQ6vN0RJ0BnQw4d2S05OVlRUlHbv3q3CwkJJ0p49e+Tz+ZSSkhLQPg60AxDZvF5vu3sjEBq+AIbxjto+zLNCEbj8/Pywz9Kl4EO7xcbG6rTTTtM//vEPXX755ZKk119/Xd26dVNWVlZA+wjk/AMA1uNKXpHD43KpLjFRPerrgxqua5LkTkyUx+kMVzQEyeFwhP1zkCFdhMSMGTP0+OOP69lnn9XGjRt11113acKECUpOTrY6GgDYk2FofU6Ogj3V35BUyYSNLocePoTET37yE+3cuVPXXntty4WXH3/8catjAYCtbcrM1AWrVys6wEuzNBmG9kVHqyozM+zZEFno4UNIGIahOXPm6Msvv9TevXtVXl6uHj16WB0LAGzN63RqeV6eZBjNd9I4igPrK/Ly5GU4t8vh1moAgIBxL93IxL10OzfupQsAAALjdktLl0rz50s1Nd8uT0uTZs6Upk2TuL95l0XBBwCAnZimVFcn7dkjxcdLSUlM0AAFHwAAgN0xaQMAAMDmKPgAAABsjoIPAADA5ij4AAAAbI6CDwAAwOYo+AAAAGyOgg8AAMDmKPgAAABsjoIPAADA5ij4AAAAbI6CDwAAwOYo+AAAAGwu2uoAAABEPNOUamulhgYpLk5KTpYMw+pUQMDo4QMA4EjcbmnePCkjQ0pJkfr3b/6ekdG83O22OiEQEMM0TdPqEOj8qqurdd9996myslLbt2/XihUrNH78eKtjAZ2OaZry+XxWx4AkY9UqxUycKDU2Nj8+6OPSPNC753LJX14uc/RoKyLiOxwOhwx6Xg+LIV2ERENDgwYMGKBJkyZpwoQJVscBOi2fz6eSkhKrY3R5aVu2aHJZmUzTPOxQ2IHir2nvXkWPG6dlU6aoJj29Y0PiEAUFBYqNjbU6RkSi4ENIZGdnKzs72+oYANBusR6PcisqpCMUeweLktRkmsqtqFBpfr68TmdHRASCxjl8AAAc5IyqKsX4/QF/QEZJivH7lVlVFc5YQLvQw4eIwDlLQDN+FyxmmsqurAy+maScykqtz8lh9q6FfD5flzyHz+FwtLkNBR8iQnFxsdURAEDOxkYl1dcH3S5KUlJ9vZwejzwuV+iDISClpaVWR7BEUVFRm9swpAsAwH852tnD6vB6Q5QECC16+BARCgsLrY4ARASv19tleykigS+AobGjtmeGqKXy8/OZpXsEFHwICZ/Pp+rq6pbH27Zt06ZNm5SamqrU1NQ22wdy/gHQFXBpVGt5XC7VJSaqR319UENgTZLciYnyMEvXUg6Hg8+TI2BIFyGxY8cOZWVlKSsrS5I0a9YsZWVlacGCBRYnA4AgGIbW5+Qo2NP+DUmVTNhABKOHDyHRr18/eiYA2MKmzExdsHq1ogO8NEuTYWhfdLSqMjPDng04VvTwAQBwEK/TqeV5eZJhqKmNbQ+sr8jL46LLiGjcSxcAIgj30o0c3Eu38+FeukdGwQcAwJG43dLSpdL8+VJNzbfL09KkmTOladOkhATL4gGBouADAKAtpinV1Ul79kjx8VJSEhM00KlQ8AEAANgckzYAAABsjoIPAADA5ij4AAAAbI6CDwAAwOYo+AAAAGyOgg8AAMDmKPgAAABsjoIPAADA5ij4AAAAbI6CDwAAwOYo+AAAAGwu2uoAAICDmKZUWys1NEhxcVJysmQYVqcC0MnRwwcAkcDtlubNkzIypJQUqX//5u8ZGc3L3W6rEwLoxAzTNE2rQ6Bzu//++1VeXq6tW7cqISFBY8eOVUlJiXr27Gl1NHQSpmnK5/NZHcMyxqpVipk4UWpsbH580NuyeaB3z+WSv7xc5ujRVkSMCA6HQwa9ncAxoeBDu11yySWaOHGizjrrLLndbt14441KTk7W6tWrrY6GTsLr9aqkpMTqGJZI27JFk8vKJNM86pBLkyQZhpZNmaKa9PQOShdZCgoKFBsba3UMoFPiHD602/PPP9/qcVFRkX784x/rq6++UkJCgkWpgMgX6/Eot6KizWJPaj7/psk0lVtRodL8fHmdzo6ICMAmOIcPIed2u+V0OvlLHGjDGVVVivH7A34jjpIU4/crs6oqnLEA2BA9fAiphoYGFRcX6+abb1b37t0DbteVz99CF/3/N01lV1YG30xSTmWl1ufkdLnZuz6fj3P4gMNwOBxtbkPBh5DZt2+fJk+erL59++r+++8Pqm1xcXGYUgGRydnYqKT6+qDbRUlKqq+X0+ORx+UKfbAIVlpaanUEICIVFRW1uQ0FH0KiqalJV111lXbu3KnVq1cH9NcG0JU52tmr6fB6u1zBB+DYUfCh3UzT1LXXXqvq6mqtWbNGxx9/fND7KCwsDEMydBZer7fL9d742vlHka8LniObn5/PucHAMaLgQ7vdeOONWrNmjV544QX5fD7t2rVLkpSSkqJu3boFtA96BLu2rnh1KI/LpbrERPWorw9q9lyTJHdiojxdcJauw+HgvQI4RszSRbv97ne/06effqphw4apT58+LV+fffaZ1dGAyGUYWp+To2CnIBiSKrvghA0A7UPBh3YzTfOwX/369bM6GhDRNmVmyh8T03xR5QA0GYb8MTGqyswMay4A9kPBBwAW8TqdWp6XJxlGm0XfgfUVeXlcdBlA0Li1GgDLcS9d7qUbCO6lCxw7Cj4AiARut7R0qTR/vlRT8+3ytDRp5kxp2jSJWxUCOEYUfAAQSUxTqquT9uyR4uOlpCQmaABoNwo+AAAAm2PSBgAAgM1R8AEAANgcBR8AAIDNUfABAADYHAUfAACAzVHwAQAA2BwFHwAAgM1R8AEAANgcBR8AAIDNUfABAADYHAUfAACAzUVbHQAAAATJNKXaWqmhQYqLk5KTJcOwOhUiGD18AAB0Fm63NG+elJEhpaRI/fs3f8/IaF7udludEBHKME3TtDoEAODYmaYpn89ndQyEmbFqlWImTpQaG5sfH/TxbR7o3XO55C8vlzl6tBUR0YEcDoeMIHp1KfgQErNnz1Z5ebk+/fRTxcfHa/To0XrwwQfVp08fq6MBtuf1elVSUmJ1DIRR2pYtmlxWJpnmUYfmmiTJMLRsyhTVpKd3UDpYoaCgQLGxsQFvz5AuQmLAgAH6v//7P7333ntauXKltm/frgkTJlgdCwA6vViPR7kVFW0We9J/P9RNU7kVFYr1eDogHToLJm0gJK666qpWj/Pz83XZZZfpm2++Uffu3S1KBQCd3xlVVYrx+xXo4F2UpBi/X5lVVVp/zjnhjIZOhIIPIVdXV6elS5dq6NChARd7nH8EHDt+f2zMNJVdWRl8M0k5lZVan5PD7F2b8vl8LefwORyONren4EPIPP/885o4caL27t2rc845Ry+//HLAbYuLi8OYDAA6J2djo5Lq64NuFyUpqb5eTo9HHpcr9MFgudLS0pZ/FxUVtbk95/AhZM4//3y9/fbbeuWVVxQVFaXbbrvN6kgA0Kk52tl76/B6Q5QEnR2zdBEWn332mU4++WStX79eZ599dpvbMyQFHDuv19vqr33Yh3PvXt314IPH3P7Xd91FD59N5efnt8zSZUgXlunWrZskae/evQFtH8gPK4DD4+92+/K4XKpLTFSP+vqghuSaJLkTE+VxOsMVDRZzOBxBfXYypIt2a2ho0MyZM7V27Vpt27ZNlZWVmj59uk466SSdddZZVscDgM7LMLQ+JyfgGbotzSRVMmEDB6HgQ7tFR0dr+/btuvLKKzVo0CCNGzdO8fHxWrVqleLi4qyOBwCd2qbMTPljYpovqhyAJsOQPyZGVZmZYc2FzoUhXbRb9+7d9Ze//MXqGABgS16nU8vz8jS5rExNgdxpQ1JFXp68DOfiIEzaAIBOjnvpdg3cSxcH4166AADYldstLV0qzZ8v1dR8uzwtTZo5U5o2TUpIsCweIhcFHwAAnY1pSnV10p49Uny8lJTEBA0cFQUfAACAzTFLFwAAwOYo+AAAAGyOgg8AAMDmKPgAAABsjoIPAADA5ij4AAAAbI6CDwAAwOYo+AAAAGyOgg8AAMDmKPgAAABsjoIPAADA5ij4AAAAbC7a6gAAgC7ANKXaWqmhQYqLk5KTJcOwOhXQZdDDBwAIH7dbmjdPysiQUlKk/v2bv2dkNC93u61OCHQJhmmaptUhYC+33Xab5s+frxUrVmj8+PFWxwFaMU1TPp/P6hhdgrFqlWImTpQaG5sfH/RxYx7o3XO55C8vlzl6tBURuxSHwyGDXtUuiyFdhNSqVatUVVVldQzgiHw+n0pKSqyOYXtpW7ZoclmZTNM87FDSgeKvae9eRY8bp2VTpqgmPb1jQ3YxBQUFio2NtToGLMKQLkKmrq5OP/nJT7Rw4UKrowCwUKzHo9yKCukIxd7BoiTJNJVbUaFYj6cD0gFdEwUfQubGG2/UjBkzlJGRYXUUABY6o6pKMX5/wB8wUZJi/H5lMjoAhA1DugiJJ598Up988on+9Kc/HVN7zqlCR+FnLcxMU9mVlcE3k5RTWan1OTnM3g0Tn8/HOXw25XA42tyGgg/t9tlnn+nOO+/Uq6++qm7duh3TPoqLi0OcCoAVnI2NSqqvD7pdlKSk+no5PR55XK7QB4NKS0utjoAwKSoqanMbhnTRbm+99ZZ2796tM888U927d1f37t0lSZMnT9bkyZMtTgegIzna2YPq8HpDlATAwejhQ7uNGjVK1dXVrZYNGTJEc+fODfiyLIWFheGIBhzC6/XS0xFGvgCGlo7anlmkYZOfn88s3S6Mgg/tFhcXp8GDBx+yvG/fvurbt29A+wjk/AMgFLj0aHh5XC7VJSaqR319UENITZLciYnyOJ3hitblORwO3mu7MIZ0AQChYxhan5OjYKcGGJIqmbABhA09fAgLelGArmtTZqYuWL1a0QFemqXJMLQvOlpVmZlhzwZ0VfTwAQBCyut0anlenmQYampj2wPrK/Ly5GU4Fwgb7qULoEvhXrodh3vpRhbupdu1UfABAMLH7ZaWLpXmz5dqar5dnpYmzZwpTZsmJSRYFg/oKij4AADhZ5pSXZ20Z48UHy8lJTFBA+hAFHwAAAA2x6QNAAAAm6PgAwAAsDkKPgAAAJuj4AMAALA5Cj4AAACbo+ADAACwOQo+AAAAm6PgAwAAsDkKPgAAAJuj4AMAALA5Cj4AAACbi7Y6AAAgQKYp1dZKDQ1SXJyUnCwZhtWpAHQC9PABQKRzu6V586SMDCklRerfv/l7Rkbzcrfb6oQAIpxhmqZpdQh0ftOnT9cTTzzRallRUZFmz55tTSB0OaZpyufzWR0j5IxVqxQzcaLU2Nj8+KC3bPNA757LJX95uczRo62IGDYOh0MGPZhASDCki5AZNWqUnnzyyZbHcXFxFqZBV+Pz+VRSUmJ1jJBK27JFk8vKZJrmYYdjDhR/TXv3KnrcOC2bMkU16ekdGzKMCgoKFBsba3UMwBYY0kXIxMbGKjU1teWLgg84drEej3IrKqQjFHsHi5Ik01RuRYViPZ4OSAegs6HgQ8isW7dOvXr10pAhQ1RYWKhvvvnG6khAp3VGVZVi/P6A36SjJMX4/cqsqgpnLACdFEO6CImxY8dq4sSJOuGEE/Svf/1Ls2bN0n/+8x8tWrQooPZ2PPcKHctWP0OmqezKyuCbScqprNT6nBxbzN71+XycwwcEwOFwtLkNkzYQFmVlZZo6dar27Nmj4447rs3tf/7zn3dAKqBzcO7dq7sefPCY2//6rrvkcblCmAhAJCsqKmpzG4Z0ERZZWVkyTVOffvqp1VGATsfRzt5Kh9cboiQA7IIhXYTFBx98IMMwdPLJJwe0fWFhYZgTwe68Xq9KS0utjhESvgCGZ47a3iYzW/Pz85mlC4QIBR/araGhQffdd58mTJigvn376t1339Udd9yha665JqDhXCmw8w+Ao7HT2Skel0t1iYnqUV8f1DBMkyR3YqI8Tme4onUoh8PBewMQIhR8aLdu3bqpqqpKTzzxhBoaGnTSSSfpyiuv1L333mt1NKBzMgytz8nRRStXBtdMUqVNJmwACC0KPrSb0+nUmjVrrI4B2MqmzExdsHq1ogO8NEuTYWhfdLSqMjPDng1A58OkDQCIQF6nU8vz8iTDUFMb2x5YX5GXJ69NhnMBhBaXZQFgC9xLl3vpAjgyCj4AiHRut7R0qTR/vlRT8+3ytDRp5kxp2jQpIcGyeAAiHwUfAHQWpinV1Ul79kjx8VJSEhM0AASEgg8AAMDmmLQBAABgcxR8AAAANkfBBwAAYHMUfAAAADZHwQcAAGBzFHwAAAA2R8EHAABgcxR8AAAANkfBBwAAYHMUfAAAADZHwQcAAGBz0VYHAABEANOUamulhgYpLk5KTpYMw+pUAEKEHj4A6MrcbmnePCkjQ0pJkfr3b/6ekdG83O22OiGAEDBM0zStDgHAvkzTlM/nszoGDsNYtUoxEydKjY3Njw/6ODAP9O65XPKXl8scPdqKiGiDw+GQQU8sAkDBh5B57rnnVFRUpOrqaiUkJOiGG27QL3/5S6tjwWJer1clJSVWx8B3pG3ZosllZZJpHnWop0mSDEPLpkxRTXp6B6VDoAoKChQbG2t1DHQCDOkiJF555RWNHz9eV1xxhTZt2qTVq1fre9/7ntWxABxGrMej3IqKNos96b8fEqap3IoKxXo8HZAOQDgwaQMhMWfOHE2bNk333HNPy7KhQ4damAjAkZxRVaUYv1+BDgRGSYrx+5VZVaX155wTzmgAwoSCD+3m9/v1xhtv6OKLL9Z5552nDz/8UMOGDVNpaalOP/30gPbBOV72xf9thDFNZVdWBt9MUk5lpdbn5DB7N4L4fD7O4YMcDkeb21Dwod12794tv9+vX//61youLtbw4cM1d+5cXXTRRfr3v/+tuLi4NvdRXFzcAUkBOBsblVRfH3S7KElJ9fVyejzyuFyhD4ZjUlpaanUERICioqI2t+EcPrRbU1OTJGncuHG67rrrNHz4cP32t7/V7t279corr1icDsDBHO3scXV4vSFKAqAj0cOHdktKSpJhGBo4cGDLsvj4ePXs2VPbt28PaB+FhYXhigeLeb1eeiEiiC+AoZ+jtmdGaETJz89nli4CQsGHdnM6nRo4cKC2bt3asqyxsVG1tbU68cQTA9pHIOcfoHPiyk+RxeNyqS4xUT3q64Ma4mmS5E5MlMfpDFc0HAOHw8H7JwLCkC5C4tprr9WTTz6p5cuX69///rduv/129ezZUxdeeKHV0QAczDC0Picn4Bm6Lc0kVTJhA+i06OFDSNx5552qr6/XzJkz1dDQoDPPPFMvvfRSQBM2AHSsTZmZumD1akX7/QH91d9kGNoXHa2qzMywZwMQHvTwISSioqL0wAMPaNeuXWpoaNBrr72mTD4cgIjkdTq1PC9PMozmO2kcxYH1FXl58jKcC3Ra3FoNQFhxL93Ixb10Oz/upYtAUfABQFfmdktLl0rz50s1Nd8uT0uTZs6Upk2TEhIsiwcgNCj4AACSaUp1ddKePVJ8vJSUxAQNwEYo+AAAAGyOSRsAAAA2R8EHAABgcxR8AAAANkfBBwAAYHMUfAAAADZHwQcAAGBzFHwAAAA2R8EHAABgcxR8AAAANkfBBwAAYHMUfAAAADZHwQcAAGBz0VYHAIAuzTSl2lqpoUGKi5OSkyXDsDoVAJuhhw8ArOB2S/PmSRkZUkqK1L9/8/eMjOblbrfVCQHYiGGapml1CHRuxhF6I5YvX64JEyZ0cBp0BNM05fP5rI7RaRmrVilm4kSpsbH58UFvw+aB3yeXS/7ycpmjR1sRsdNzOBxHfG8CuiIKPrTbrl27Wj1+7rnndMcdd2jnzp2Kj4+3KBXCyev1qqSkxOoYnVLali2aXFYmmeZRh1iaJMkwtGzKFNWkp3dQOvsoKChQbGys1TGAiMGQLtotNTW11dfTTz+t8ePHU+wB3xHr8Si3oqLNYk/675uzaSq3okKxHk8HpANgZxR8CKlPP/1Uf/vb33TNNddYHQWIOGdUVSnG7w/4jTdKUozfr8yqqnDGAtAFMEsXIbV48WKlpaXpvPPOC6od54N1Lvx/HQPTVHZlZfDNJOVUVmp9Tg6zd4Pg8/k4hw9dhsPhaHMbCj6ETFNTkxYvXqybbrop6LbFxcVhSAREDmdjo5Lq64NuFyUpqb5eTo9HHpcr9MFsqrS01OoIQIcpKipqcxuGdBEyK1eu1I4dOzRt2jSrowARx9HOXlGH1xuiJAC6Inr4EDILFy7URRddpL59+wbdtrCwMAyJEC5er5celCD5AhhyOWp7ZpwGJT8/n1m6wEEo+BASu3bt0vPPP6/y8vJjah/I+QeIHFzNKXgel0t1iYnqUV8f1NBKkyR3YqI8Tme4otmSw+HgfQU4CEO6CIk//OEP6tGjh8aNG2d1FCAyGYbW5+Qo2GkEhqRKJmwAaCcKPoREYWGhvvzyS8XExFgdBYhYmzIz5Y+Jab6ocgCaDEP+mBhVZWaGNRcA+6PgA4AO4nU6tTwvTzKMNou+A+sr8vLkZTgXQDtxazUAQeNeuu3DvXTDj3vpAq1R8AGAFdxuaelSaf58qabm2+VpadLMmdK0aVJCgmXxANgLBR8AWMk0pbo6ac8eKT5eSkpiggaAkKPgAwAAsDkmbQAAANgcBR8AAIDNUfABAADYHAUfAACAzVHwAQAA2BwFHwAAgM1R8AEAANgcBR8AAIDNUfABAADYHAUfAACAzVHwAQAA2Fy01QEAAJ2EaUq1tVJDgxQXJyUnS4ZhdSoAAaCHDwBwdG63NG+elJEhpaRI/fs3f8/IaF7udludEEAbDNM0TatDoPNzu93Kz8/XCy+8oMbGRmVlZWnu3LnKzs62OhoigGma8vl8VsfAMTBWrVLMxIlSY2Pz44M+MswDvXsul/zl5TJHj7YiItrB4XDIoJe2S6DgQ0hcc801euONN7RkyRIlJydrzpw5euGFF/T555+re/fuVseDxbxer0pKSqyOgSClbdmiyWVlkmkedTioSZIMQ8umTFFNenoHpUMoFBQUKDY21uoY6AAM6SIkKisrNWnSJJ1zzjnKyMjQXXfdpdraWn3yySdWRwNwDGI9HuVWVLRZ7En//SAxTeVWVCjW4+mAdACCRcGHkPje976nv/3tb6qtrdW+fftUUVGhwYMHa8CAAVZHA3AMzqiqUozfH/CHRJSkGL9fmVVV4YwF4BgxSxchMW/ePM2YMUM9e/ZUt27dlJaWpr/97W+KiYkJqD3nd9kb/7+djGkqu7Iy+GaSciortT4nh9m7nYTP5+McPhtwOBxtbkPBh5D4v//7P/3rX//SK6+8osTERM2fP1+XXHKJNmzYIKfT2Wb74uLiDkgJIBDOxkYl1dcH3S5KUlJ9vZwejzwuV+iDIeRKS0utjoAQKCoqanMbCj60m9frVWFhof7yl7/owgsvlCT9/ve/V48ePfTMM89o0qRJFicEEAxHO3tkHV4vBR8QYSj40G6NjY3yer2thgWioqJkGIY8AZ7AXVhYGK54iABer5eehE7EF8Dw0FHbM+uz08jPz2eWbhdBwYd2S0xMVHZ2tu69914lJSUpOTlZ8+fPV1NTk84///yA9hHI+QfovLj6U+ficblUl5ioHvX1Qc3sa5LkTkyUJ4DTOBAZHA4H779dBLN0ERJ//vOflZGRoXHjxmn48OF666239Pzzz6t///5WRwMQLMPQ+pwcBXsqvyGpkgkbQESihw8hcdJJJ6miosLqGABCZFNmpi5YvVrRAV6apckwtC86WlWZmWHPBiB49PABAA7hdTq1PC9PMozmO2kcxYH1FXl58jKcC0Qkbq0GIOy4l27nxb107Y176XYdFHwAgKNzu6WlS6X586Wamm+Xp6VJM2dK06ZJCQmWxQPQNgo+AEBgTFOqq5P27JHi46WkJCZoAJ0EBR8AAIDNMWkDAADA5ij4AAAAbI6CDwAAwOYo+AAAAGyOgg8AAMDmKPgAAABsjoIPAADA5ij4AAAAbI6CDwAAwOYo+AAAAGyOgg8AAMDmoq0OAACdlmlKtbVSQ4MUFyclJ0uGYXUqADgEPXwAECy3W5o3T8rIkFJSpP79m79nZDQvd7utTggArRimaZpWhwAQONM05fP5rI7RZRmrVilm4kSpsbH58UFvoeaB3j2XS/7ycpmjR1sREZIcDocMeluBFhR8CImdO3fqjjvu0OrVq/XNN99o9OjReuyxx9SnTx+ro9mO1+tVSUmJ1TG6pLQtWzS5rEwyzaMOjzRJkmFo2ZQpqklP76B0OFhBQYFiY2OtjgFEDIZ0ERJTp07Vp59+qlWrVmndunXavXu3cnNzrY4FhEysx6Pcioo2iz3pv2+spqncigrFejwdkA4Ajo5JG2i3xsZGrVmzRi+88ILOOOMMSdJDDz2k7OxsffDBBxo8eLC1AYEQOKOqSjF+vwIdJIySFOP3K7OqSuvPOSec0QCgTRR8aDe/3y/TNOVwOFqWde/eXZL09ttvB1TwcU5a4DhWFjBNZVdWBt9MUk5lpdbn5DB7t4P5fD7O4UOXcfDn75FQ8KHdEhISlJmZqUceeUTZ2dkyDEP333+/unXrpi+//DKgfRQXF4c5JXDsnI2NSqqvD7pdlKSk+no5PR55XK7QB8MRlZaWWh0B6DBFRUVtbsM5fAiJpUuXasuWLUpISFBSUpLS0tLkcrn4Cxu24Ghnr6rD6w1REgA4NvTwISROP/10vfvuu6qtrVV0dLSampr0wAMP6IQTTgiofWFhYZgT2ofX66X3ooP5AhguOWp7Zot2uPz8fGbpAgeh4ENIJScnS5Ief/xxRUdH63/+538CahfI+QdoxpWUOp7H5VJdYqJ61NcHNSzSJMmdmCiP0xmuaDgCh8PB+wpwEAo+hMQ//vEP7d27V4MGDdKGDRt0zz336KabblKvXr2sjga0n2FofU6OLlq5MrhmkiqZsAEgAlDwISQaGxt144036vPPP1fv3r118803B3QSKdBZbMrM1AWrVyva7w+ol6/JMLQvOlpVmZlhzwYAbaHgQ0iMHj1aW7dutToGEDZep1PL8/I0uaxMTYHcaUNSRV6evAznAogA3FoN6GS4l661uJdu58C9dIHWKPgAIFhut7R0qTR/vlRT8+3ytDRp5kxp2jQpIcGyeADwXRR8AHCsTFOqq5P27JHi46WkJCZoAIhIFHwAAAA2x502AAAAbI6CDwAAwOYo+AAAAGyOgg8AAMDmKPgAAABsjoIPAADA5ij4AAAAbI6CDwAAwOYo+AAAAGyOgg8AAMDmKPgAAABsjoIPAADA5qKtDgAAkGSaUm2t1NAgxcVJycmSYVidCoBN0MMHAFZyu6V586SMDCklRerfv/l7Rkbzcrfb6oQAbMAwTdO0OgQiW3V1te677z5VVlZq+/btWrFihcaPH9+yfu3atZo5c6Y+/PBDDRo0SI8++qjOO+88CxMDkmma8vl8Vsc4KmPVKsVMnCg1NjY/Pujt2DzQu+dyyV9eLnP0aCsiBsThcMigNxKIaAzpok0NDQ0aMGCAJk2apAkTJrRa98UXX+jSSy/VNddco/Lycv32t7/VpZdeqi1btiglJcWixIDk8/lUUlJidYwjStuyRZPLymSa5mGHWg4Uf0179yp63DgtmzJFNenpHRsyQAUFBYqNjbU6BoCjYEgXbcrOztaDDz7YqlfvgLKyMrlcLj388MM69dRT9fDDD6t79+4qKyuzICnQOcR6PMqtqJCOUOwdLEqSTFO5FRWK9Xg6IB0AO6LgQ7u89dZbGjFihKKimn+UoqKi9D//8z/auHGjxcmAyHVGVZVi/P6A34CjJMX4/cqsqgpnLAA2xpAu2uXLL7/UySefrMrKSv3whz/UypUr1bNnT23bti2o/UT6uVbofCL2Z8o0lV1ZGXwzSTmVlVqfkxNxs3d9Ph/n8AEWcjgcbW5DwYeQcLlcOuWUU3TccccdU/vi4uIQJwIik7OxUUn19UG3i5KUVF8vp8cjj8sV+mDtUFpaanUEoEsrKipqcxsKPrRLSkqKdu/erWHDhuntt9+WJO3evZsJG8ARONrZ8+jweiOu4AMQ+Sj40C5nnnmmfvWrX6mpqUlRUVFqamrSP/7xDxUUFAS1n8LCwjAlRFfl9XojsufJF8DQy1HbR+Bs2Pz8fGbpAhGOgg9t8vl8qq6ubnm8bds2bdq0SampqZo8ebKKiop0xx13aMaMGfrtb38rj8ejKVOmBPUcgZx/AAQjUi8x6nG5VJeYqB719UHNmmuS5E5MlMfpDFe0Y+ZwOPgdBiIcs3TRph07digrK0tZWVmSpFmzZikrK0sLFixQamqqnnvuOa1Zs0ZZWVl69dVX9dxzzzGkCxyJYWh9To6CneJgSKqMwAkbADoHevjQpn79+h21t+T73/++Nm/e3IGJgM5tU2amLli9WtEBXpqlyTC0LzpaVZmZYc8GwJ7o4QOADuZ1OrU8L08yDDW1se2B9RV5efJG4HAugM6Be+kCsCXupdtxuJcuEPko+ADASm63tHSpNH++VFPz7fK0NGnmTGnaNCkhwbJ4AOyBgg8AIoFpSnV10p49Uny8lJTEBA0AIUPBBwAAYHNM2gAAALA5Cj4AAACbo+ADAACwOQo+AAAAm6PgAwAAsDkKPgAAAJuj4AMAALA5Cj4AAACbo+ADAACwOQo+AAAAm6PgAwAAsLloqwMAAMLENKXaWqmhQYqLk5KTJcOwOhUAC9DDBwB243ZL8+ZJGRlSSorUv3/z94yM5uVut9UJAXQwwzRN0+oQiGzV1dW67777VFlZqe3bt2vFihUaP358y/rbb79dr732mjZv3qwxY8bo+eeftzAtED6macrn81kd46iMVasUM3Gi1NjY/Pigt3jzQO+eyyV/ebnM0aOtiBgwh8Mhgx5JICQY0kWbGhoaNGDAAE2aNEkTJkw4ZP2+fft0ww036K9//asF6YCO4/P5VFJSYnWMI0rbskWTy8pkmuZhh28OFH9Ne/cqetw4LZsyRTXp6R0bMggFBQWKjY21OgZgCxR8aFN2drays7OPuP7RRx+VJFVWVmr37t0dFQvAQWI9HuVWVEhHKPYOFiWpyTSVW1Gh0vx8eZ3OjogIwEKcwwcANnBGVZVi/P6A39SjJMX4/cqsqgpnLAARgh4+RIRIPy8KkCL459Q0lV1ZGXwzSTmVlVqfkxORs3d9Ph/n8AEBcDgcbW5DwYeIUFxcbHUEoNNyNjYqqb4+6HZRkpLq6+X0eORxuUIfrJ1KS0utjgB0CkVFRW1uw5AuAHRyjnb2PDq83hAlARCp6OFDRCgsLLQ6AtAmr9cbkb1OvgCGc47aPkJnwubn5zNLFwgRCj60yefzqbq6uuXxtm3btGnTJqWmpio1NVVbtmxRQ0OD6urq9PXXX2vTpk1yOBw69dRTA36OQM4/AKwWqZct9bhcqktMVI/6+qCGbZokuRMT5YnQWboOh4P3BiBEKPjQph07digrK6vl8axZsyQ1nzMwe/ZsXXfddXrttdda1mdlZemUU07Rtm3bOjoq0DUZhtbn5OiilSuDayapMkInbAAILQo+tKlfv35H7dlYu3Ztx4UBcFibMjN1werVig7w0ixNhqF90dGqyswMezYA1mPSBgDYgNfp1PK8PMkw1NTGtgfWV+TlcdFloIvgXroAECDupduxuJcuEDoUfABgN263tHSpNH++VFPz7fK0NGnmTGnaNCkhwbJ4ADoeBR8A2JVpSnV10p49Uny8lJTEBA2gi6LgAwAAsDkmbQAAANgcBR8AAIDNUfABAADYHAUfAACAzVHwAQAA2BwFHwAAgM1R8AEAANgcBR8AAIDNUfABAADYHAUfAACAzVHwAQAA2Fy01QEAwPZMU6qtlRoapLg4KTlZMgyrUwHoQujhA4BwcbulefOkjAwpJUXq37/5e0ZG83K32+qEALoIwzRN0+oQAMLDNE35fD6rY3RJxqpVipk4UWpsbH580FuteaB3z+WSv7xc5ujRVkTs0hwOhwx6WdGFUPChTdXV1brvvvtUWVmp7du3a8WKFRo/frwk6bXXXtMDDzygf/3rX/rmm2+UmZmp+++/X//7v/9rcWpIktfrVUlJidUxupy0LVs0uaxMMs2jDqM0SZJhaNmUKapJT++gdJCkgoICxcbGWh0D6DAM6aJNDQ0NGjBggH7zm98csq6yslIjRozQ888/r7ffflvZ2dn64Q9/qI8++qjjgwIRINbjUW5FRZvFnvTfN2DTVG5FhWI9ng5IB6CrYtIG2pSdna3s7OzDrrvrrrtaPX7ooYe0ePFivfTSSxo4cGBHxAMiyhlVVYrx+xXoYGGUpBi/X5lVVVp/zjnhjAagC6PgQ0h5PB75fD4lJiYG1Y7zzMKD49rBTFPZlZXBN5OUU1mp9Tk5zN7tID6fj3P4YBsOh6PNbSj4EFK//OUv1atXr5Zz/AJVXFwcpkRAx3E2Niqpvj7odlGSkurr5fR45HG5Qh8MhygtLbU6AhAyRUVFbW5DwYeQKS8v12OPPaa1a9fKxYcWuiBHO3tUHV4vBR+AsKDgQ0g8++yzuuGGG/TMM89o+PDhQbcvLCwMQyp4vV56MjqQL4BhlaO2Z9Zoh8nPz2eWLroUCj6028qVK3XllVeqoqJCF1xwwTHtI5DzDxA8rrrUsTwul+oSE9Wjvj6oSyA0SXInJsrjdIYrGr7D4XDwvoMuhYIPbfL5fKqurm55vG3bNm3atEmpqamqrq7WFVdcoYcfflhZWVnatWuXJCkuLk5xcXFWRQasYRhan5Oji1auDK6ZpEombAAII67Dhzbt2LFDWVlZysrKkiTNmjVLWVlZWrBggZYuXarGxkbNmDFDffr0afmaO3euxakBa2zKzJQ/Jqb5osoBaDIM+WNiVJWZGdZcALo2evjQpn79+h11aHDJkiUdFwaIcF6nU8vz8jS5rExNgdxpQ1JFXp68DOcCCCNurQbYGPfStQ730o1s3EsXXQ0FHwCEi9stLV0qzZ8v1dR8uzwtTZo5U5o2TUpIsCwegK6Dgg8Aws00pbo6ac8eKT5eSkpiggaADkXBBwAAYHPM0gUAALA5Cj4AAACbo+ADAACwOQo+AAAAm6PgAwAAsDkKPgAAAJuj4AMAALA5Cj4AAACbo+ADAACwOQo+AAAAm6PgAwAAsDkKPgAAAJuLtjoAAKALMU2ptlZqaJDi4qTkZMkwrE4F2B49fACA8HO7pXnzpIwMKSVF6t+/+XtGRvNyt9vqhICtGaZpmlaHQGSrrq7Wfffdp8rKSm3fvl0rVqzQ+PHjJUnr1q3T3XffrQ8++EBer1ennXaafv7zn2vMmDEWp4ZdmaYpn89ndQwEwVi1SjETJ0qNjc2PD/rYMQ/07rlc8peXyxw92oqIaAeHwyGDXtqIx5Au2tTQ0KABAwZo0qRJmjBhQqt13bt31y233KLTTz9dLpdL5eXluuyyy/T222/r1FNPtSgx7Mzn86mkpMTqGAhQ2pYtmlxWJtM0DzukdKD4a9q7V9HjxmnZlCmqSU/v2JBol4KCAsXGxlodA21gSBdtys7O1oMPPtjSq3ews846S5MmTdJpp52m/v37q7CwUHFxcVq3bp0FSQFEkliPR7kVFdIRir2DRUmSaSq3okKxHk8HpAO6Fgo+hMz+/ftVVlYmt9utrKwsq+MAsNgZVVWK8fsD/qCJkhTj9yuzqiqcsYAuiSFdhMSJJ56oXbt26bjjjtMzzzyjs88+O6j2nJOFQPGz0kmYprIrK4NvJimnslLrc3KYvdtJ+Hw+zuGzmMPhaHMbCj6ExN///ne53W79+c9/1o033qhTTz1VaWlpAbcvLi4OYzoAHc3Z2Kik+vqg20VJSqqvl9PjkcflCn0whFxpaanVEbq8oqKiNrdhSBch0b9/f2VlZen+++/X4MGDeQMAujhHO3tiHV5viJIAkOjhQxh069ZNe/fuDapNYWFhmNLAbrxeL39QdAK+AIaYjtqeWZ+dRn5+PrN0OwEKPrTJ5/Opurq65fG2bdu0adMmpaamatmyZerbt68yMzPVrVs3PfXUU3rllVf0zDPPBPUcgZx/AEjN1+FD5PO4XKpLTFSP+vqghpKaJLkTE+VxOsMVDSHmcDh4D+8EKPjQph07drSadTtr1ixJzecM9OzZU/fff7+2bdsm0zQ1cOBA/fGPf9S4ceOsigsgEhiG1ufk6KKVK4NrJqmSCRtAyFHwoU39+vU7aq/KLbfc0oFpAHQWmzIzdcHq1YoO8NIsTYahfdHRqsrMDHs2oKth0gYAICy8TqeW5+VJhqGmNrY9sL4iL09ehnOBkONeugA6Fe6l2/lwL1174166nQMFHwAg/NxuaelSaf58qabm2+VpadLMmdK0aVJCgmXxALuj4AMAdBzTlOrqpD17pPh4KSmJCRpAB6DgAwAAsDkmbQAAANgcBR8AAIDNUfABAADYHAUfAACAzVHwAQAA2BwFHwAAgM1R8AEAANgcBR8AAIDNUfABAADYHAUfAACAzVHwAQAA2Fy01QEAABHKNKXaWqmhQYqLk5KTJcOwOhWAY0APHwCgNbdbmjdPysiQUlKk/v2bv2dkNC93u61OCCBIhmmaptUhENmqq6t13333qbKyUtu3b9eKFSs0fvz4Q7bbsGGDRo4cqYsuukjPP/+8BUnRGZimKZ/PZ3UMHIGxapViJk6UGhubHx/0EWEe6N1zueQvL5c5erQVEREAh8Mhg95YHIQhXbSpoaFBAwYM0KRJkzRhwoTDbtPY2Khrr71WZ555ZgenQ2fj8/lUUlJidQwcRtqWLZpcVibTNA87/HOg+Gvau1fR48Zp2ZQpqklP79iQCEhBQYFiY2OtjoEIQsGHNmVnZys7O/uo29x5553Ky8vTv//9b+3evbuDkgEIlViPR7kVFdIRir2DRUlqMk3lVlSoND9fXqezIyICaAfO4UO7vfjii6qsrNTdd99tdRQAx+iMqirF+P0BfyhESYrx+5VZVRXOWABChB4+tMuXX36pGTNm6Nlnn1V09LH/OHFOV9fB/3UEMk1lV1YG30xSTmWl1ufkMHs3wvh8Ps7h60IcDkeb21DwoV1mzJihadOmKSsrq137KS4uDlEiAMFyNjYqqb4+6HZRkpLq6+X0eORxuUIfDMestLTU6gjoQEVFRW1uQ8GHdlmzZo1eeOEFzZ07V5Lk9/slSd27d1dtba2OO+44K+MBCICjnb2uDq+Xgg+IcBR8aJcNGzZo//79LY8LCwvldrv1+OOPyxXEB0BhYWE44iECeb1eeh8ijC+A4aCjtmc2aMTJz89nli5aoeBDm3w+n6qrq1seb9u2TZs2bVJqaqoyMjJabZuQkCC/36/BgwcH9RyBnH8Ae+DSn5HH43KpLjFRPerrg5rJ1yTJnZgoD7N0I47D4eB9Fa0wSxdt2rFjh7KyslrO05s1a5aysrK0YMECi5MBCAnD0PqcHAV7ir8hqZIJG0CnQA8f2tSvX7+Ae2WWLFkS3jAAwmJTZqYuWL1a0QFemqXJMLQvOlpVmZlhzwag/ejhAwDI63RqeV6eZBhqamPbA+sr8vK46DLQSXAvXQAdinvpRjbupWsP3EsX30XBBwBoze2Wli6V5s+Xamq+XZ6WJs2cKU2bJiUkWBYPQPAo+AAAh2eaUl2dtGePFB8vJSUxQQPopCj4AAAAbI5JGwAAADZHwQcAAGBzFHwAAAA2R8EHAABgcxR8AAAANkfBBwAAYHMUfAAAADZHwQcAAGBzFHwAAAA2R8EHAABgcxR8AAAANhdtdQAAaJNpSrW1UkODFBcnJSdLhmF1KgDoNOjhAxC53G5p3jwpI0NKSZH692/+npHRvNzttjohAHQKhmmaptUhgIOZpimfz2d1DFjMWLVKMRMnSo2NzY8PeqsyD/TuuVzyl5fLHD3aioiIIA6HQwa9vsARUfChTdXV1brvvvtUWVmp7du3a8WKFRo/frwkae3atTr//PNbbX/KKado27Ztx/x8Xq9XJSUl7YmMTi5tyxZNLiuTTPOowxBNkmQYWjZlimrS0zsoHSJRQUGBYmNjrY4BRCyGdNGmhoYGDRgwQL/5zW+OuM17772nnTt3aufOndqwYUPHhYPtxHo8yq2oaLPYk/77Bmaayq2oUKzH0wHpAKBzYtIG2pSdna3s7OyjbtOrVy/17NmzgxLBzs6oqlKM369AB+eiJMX4/cqsqtL6c84JZzQA6LQo+BASZ599trxer8466yz9+te/1uDBg4Nqf/A5e5y/14WZprIrK4NvJimnslLrc3KYvdtF+Xw+zuFDl+VwONrchoIP7dKnTx8tWrRIw4cPl9vt1pw5c3Teeefpgw8+UFJSUsD7KS4uDmNKdBbOxkYl1dcH3S5KUlJ9vZwejzwuV+iDIeKVlpZaHQGwTFFRUZvbUPChXQYNGqRBgwa1PD7rrLN00kknqby8XD/5yU8sTIbOyNHO3l2H10vBBwCHQcGHkIqLi1NGRoY++eSToNoVFha2/Nvr9fLXehflC2BY4qjtmaXZZeXn5zNLFzgKCj6ElNfr1ccff6yrr746qHYHn3/AlYK6Lo/LpbrERPWorw/qEgJNktyJifI4neGKhgjncDgCOo8J6Koo+NAmn8+n6urqlsfbtm3Tpk2blJqaqmXLlqlfv346/fTT1dDQoAceeEBRUVHKzc21MDE6LcPQ+pwcXbRyZXDNJFUyYQMAjoiCD23asWOHsrKyWh7PmjVLUvNJot27d9ftt9+u//znP4qLi9OIESO0du1aJScnWxUXndymzExdsHq1ov3+gHr5mgxD+6KjVZWZGfZsANBZUfChTf369TvqMGtBQUEHpoHdeZ1OLc/L0+SyMjUFcqcNSRV5efIynAsAR8St1RBxuJcuJO6li+BwL13g6Cj4AEQut1taulSaP1+qqfl2eVqaNHOmNG2alJBgWTwA6Cwo+ABEPtOU6uqkPXuk+HgpKYkJGgAQBAo+AAAAmwvmUlcAAADohCj4AAAAbI6CDwAAwOYo+AAAAGyOgg8AAMDmKPgAAABsjoIPAADA5ij4AAAAbI6CDwAAwOYo+AAAAGyOgg8AAMDmKPgAAABsLtrqAABgK6Yp1dZKDQ1SXJyUnCwZhtWpAHRx9PABQCi43dK8eVJGhpSSIvXv3/w9I6N5udttdUIAXZhhmqZpdQhEturqat13332qrKzU9u3btWLFCo0fP75l/eeff64777xTL7/8svx+v4YNG6a//e1vOv744y1MjVAzTVM+n8/qGBHJWLVKMRMnSo2NzY8Pels1D/TuuVzyl5fLHD3aiogRz+FwyKAnFAgbhnTRpoaGBg0YMECTJk3ShAkTWq375ptvdOGFF+qkk07SCy+8oNTUVG3evFndunWzKC3CxefzqaSkxOoYESdtyxZNLiuTaZqHHTI5UPw17d2r6HHjtGzKFNWkp3dsyE6goKBAsbGxVscAbIuCD23Kzs5Wdnb2YdeVl5fryy+/1FtvvaW4uDhJ0oABAzoyHmCZWI9HuRUV0hGKvYNFSWoyTeVWVKg0P19ep7MjIgKAJM7hQzutXbtW5557ru69916lpqZq2LBheuyxx6yOBXSIM6qqFOP3B/xGGiUpxu9XZlVVOGMBwCHo4UO77Ny5U2+++aaOP/54vfjii3r77bd10003qXfv3q3O82sL54ZFPv6PvsM0lV1ZGXwzSTmVlVqfk8Ps3YP4fD7O4QOOkcPhaHMbCj60S1NTk0zT1MKFC+VyuTR8+HC9/PLL+uMf/xhUwVdcXBzGlEDoORsblVRfH3S7KElJ9fVyejzyuFyhD9ZJlZaWWh0B6LSKiora3IYhXbRLz549deKJJ8p10AfXgAEDtH37dgtTAeHnaGePp8PrDVESAGgbPXxol8zMTL3wwgvyer0tM+w+/fRTnXjiiUHtp7CwMBzxEEJer5demIP4AhhCOWp7ZqS2kp+fzyxdIIwo+NAmn8+n6urqlsfbtm3Tpk2blJqaqsmTJ+vnP/+5br31Vs2aNUubNm3SU089pbKysqCeI5DzD2AtLtnZmsflUl1ionrU1wc1VNIkyZ2YKA+zdFtxOBy8DwBhxJAu2rRjxw5lZWUpKytLkjRr1ixlZWVpwYIFOvnkk/Xcc89p/fr1GjZsmH7605/qwQcf1I9//GOLUwNhZhhan5OjYKcZGJIqmbABoIPRw4c29evX76i9O6NGjdKmTZs6LhAQITZlZuqC1asVHeClWZoMQ/uio1WVmRn2bABwMHr4AOAYeZ1OLc/LkwxDTW1se2B9RV4eF10G0OG4ly6AgHAv3SPjXrrtx710gfCi4AOAUHC7paVLpfnzpZqab5enpUkzZ0rTpkkJCZbFA9C1UfABQCiZplRXJ+3ZI8XHS0lJTNAAYDkKPgAAAJtj0gYAAIDNUfABAADYHAUfAACAzVHwAQAA2BwFHwAAgM1R8AEAANgcBR8AAIDNUfABAADYHAUfAACAzVHwAQAA2BwFHwAAgM1FWx0AABBipinV1koNDVJcnJScLBmG1akAWMjWPXz19fUaO3asjjvuOBmGobVr1x6yTY8ePbRkyZIOyXP55Zdr+vTpYdn3qlWrNHDgQHXr1k39+vU7ZH0gxwJAJ+d2S/PmSRkZUkqK1L9/8/eMjOblbrfVCQFYxNY9fAsWLNC7776rjRs3KjExUUlJSYds89FHHyk+Pt6CdKF1yy236Pzzz9err74qh8NxyPpAjkV7GYahFStWaPz48SHfN2BXpmnK5/O1ez/GqlWKmThRamxsfnzwc2zdKt1xh3TPPfKXl8scPbrdzydJDodDBj2HQKdg64KvpqZGw4YN05AhQ464Ta9evTowUfjU1NRozpw5OuGEE464vq1jAaDj+Xw+lZSUtGsfaVu2aHJZmUzTPOywjWGakqSmvXsVPW6clk2Zopr09HY9pyQVFBQoNja23fsBEH62HNKdPn26DMPQokWL9OKLL8owjEOGMdPT01uWf3dI98MPP1RcXJzWrFnTsuyRRx7RCSecoNra2pZlK1as0NChQ+V0OnXaaadp+fLlrfbz3HPPKS0tTS6XS1OnTj2mv+JfffVV5eTkqHv37srIyNAjjzzSav2B17B//37l5ubKMIxWQ7qBHIuqqipdeOGFcrlcOuWUU/Szn/1M+/bta/U8q1ev1siRI+V0OpWamqobbrjhkAySNGHChJbH27ZtC/r1AghOrMej3IoK6QjF3sGiJMk0lVtRoViPpwPSAYgUtiz45s2bp507dyo3N1ejRo3Szp07tXPnTo0cObJlm3/+85/auXOnjj/++EPaDxo0SHPnztX06dPldrv14YcfqrCwUE888YSSk5MlNRdiV199te6++2699957uvfeezVt2jRVVlZKkr788kvl5eUpNzdXb7/9tvr06aOXX345qNfx0UcfaezYsZo8ebLee+89zZs3Tz//+c9bFZYHXpskLVy4UDt37tSGDRsCPha1tbW64IILlJOTo6qqKj355JNatmyZSktLW/ZRXV2tMWPGaMSIEXrrrbf03HPPtRo2PlyGnTt36qSTTgrq9QII3hlVVYrx+wN+M4+SFOP3K7OqKpyxAEQYWw7pJiQkKCEhQU6nU7GxsUpNTT1km549e0rSEc8/ufHGG/XSSy/pxhtv1NatWzVjxgyNGjWqZf0vfvEL3XrrrZo6daokacCAAXr22Wf1hz/8QTk5OfrTn/6khIQE3X///YqKilJxcbH++Mc/BvU6iouLdfnll+u2226TJKWlpen666/X4sWLlZubK0mtXluPHj0Oea1tHYtHH31UAwcO1AMPPCBJysjI0KxZs/Twww/rrrvukiT96le/0siRI/XQQw+1tDv77LNb/t1WhkCE4hwmoDNq18++aSr7v39kBtVMUk5lpdbn5LRr9q7P5+McPiACHO7c/e+yZcEXKosWLdLgwYPVu3dvFRcXt1r3zjvv6M0332w1xOrz+fT9739fUvM5c4MHD1ZUVPPf3d26ddOpp54a1PO/88472rx5s+Li4lqW+f1+nXLKKcf4ig7/HBs3bmz1HPv379f+/ftbHr/77rv64Q9/GLLnPJzvHl8AbXM2Niqpvj7odlGSkurr5fR45HG5jvn5Dx4JAGCdoqKiNreh4DuK6upqNTY26rPPPtNnn32mtLS0Vutnz559yIxUp9MZ0gwzZsxo6eE7ICYmJqTPcckll+jBBx8M6T4BhJ+jnT3jDq+3XQUfgM6Dgu8I3G63pk6dqpKSEr3//vuaMmWK1q1bp+jo5kM2bNgwbd26VelHmOmWnp6u5cuXq6mpSVFRUWpqatL777+vE088MeAMw4YN04cffnjE5wiFYcOGqaKiQgMGDGjpjfyuoUOHat26dW3uKyYm5pDJHoEqLCw8pnZAZ+f1eo+5p8wXwDDOUdu3c4Ztfn4+s3SBTqJLFnwej0dfffWVpOZrYH311VfatWuXunXrppSUFEnNPWsZGRm69dZb1djYqDPOOEOzZ8/WnDlzJEk/+9nPNGbMGA0cOFDjxo3TV199pZUrV6p///668sorNWnSJBUUFOiee+7R1VdfrcWLF+uLL74IKmdhYaFOP/10FRQU6KqrrpLf79err74q0zR1xx13hORY3HLLLXrkkUd07bXX6vbbb1dMTIzeeOMNffzxx7r//vslSXfffbcyMzM1a9YsXXPNNfrmm2+0aNEiPfroo632lZ6erueee04XXXRRyzmDgZ7fE8j5B4Admf+9ZMqx8LhcqktMVI/6+qBm4DVJcicmytPOEQmHw8HvLtBJ2HKWblsqKirUp08f9enTR19//bVuv/129enTp2UiwpIlS/Tyyy9ryZIlMgxDxx13nJ588kk9+OCDev311yVJF1xwgZYtW6aysjINGzZMF198sdavX69BgwZJap4Usnz5ci1fvlyZmZn6/PPPddFFFwWVc9CgQVq5cqX+/ve/68wzz9T555+v5557LuhzAY+mZ8+eWr16tXbs2KFzzz1XI0aM0B/+8IdWz3Hqqadq5cqVWrdunYYPH64xY8bom2++OWRfv/nNb/Svf/1LvXr1ktPp1CeffBKynAAOwzC0PidHwU6bMCRVtnPCBoDOxTDb8+clAKBdvF5vuy68HOvxKL+0VNEBXpqlyTC0Lzpapfn58razh48LLwOdR5fs4QMAu/A6nVqelycZhpra2PbA+oq8vHYXewA6F3r4AMBCYbuX7kFv7eaBoVuXi3vpAl0UBR8A2IXbLS1dKs2fL9XUfLs8LU2aOVOaNk1KSLAsHgDrUPABgN2YplRXJ+3ZI8XHS0lJTNAAujgKPgAAAJtj0gYAAIDNUfABAADYHAUfAACAzVHwAQAA2BwFHwAAgM1R8AEAANgcBR8AAIDNUfABAADYHAUfAACAzVHwAQAA2BwFHwAAgM1FWx0AABBGpinV1koNDVJcnJScLBmG1akAdDB6+ADAjtxuad48KSNDSkmR+vdv/p6R0bzc7bY6IYAORMEXILfbLcMwtHbtWqujHFZ9fb3Gjh2r44477og5586dq969e8swDE2fPr3DMwJozTRNeb3ekH/5nn9e5oknyrzjDplbt7Z+zq1bm5efeKJ8zz8f8uc2TdOiowngaBjStYkFCxbo3Xff1caNG5WYmKikpKRW67dv3667775bCxcu1NixY+V0OkOeYfr06dq9e7eef/75kO8bsCOfz6eSkpKQ7jNtyxZNLiuTaZqH/Yve+G9B1rR3r6LHjdOyKVNUk54esucvKChQbGxsyPYHIDTo4bOJmpoaDRs2TEOGDFFqaqocDker9R9//LGampp06aWXKjU1VQkJCRYlBRAusR6PcisqpCMUeweLkiTTVG5FhWI9ng5IB8BKnbbgmz17toYOHaqnn35aGRkZ6t69u84991xJUm1trSZNmqQTTjhBsbGxGjRokBYtWtSqfb9+/fTTn/5Ul1xyiVwul8466yxt2bKlZf3+/fs1c+ZMHX/88erbt6/KysoOybBmzRplZWUpNjZWp5xyih599NGWdUuWLFFmZqZycnJ04oknasGCBUpJSVFOTo6+/vrrgF9nbW2trrrqKiUlJSk5OVmTJ0/W7t27W9ZPnz5dhmFo0aJFevHFF2UYRqsh3bVr18owDH3ve9+TJPXq1euQIV2Px6OZM2eqd+/eSkhI0MUXX6xt27a1yvHll19q2rRp6tmzp+Li4nT++edr8+bNrTI88cQTeuGFF1oyzJ49O+DXCaD9zqiqUozfH/Abe5SkGL9fmVVV4YwFIAJ06iHdL774Qg899JAWL16s3r176+WXX5YkNTQ06MQTT9Sf//xnpaam6tVXX9X111+vjIwMnXfeeS3tf//732vhwoX61a9+pcmTJ6ugoEB//vOfJUmPP/64/vjHP2r58uXq3bu3rrvuulbPXVtbq8suu0zXXHONysvLtW7dOt14440aMmSILrzwQknSJ598otdff10zZ87U3Llz9frrr+uyyy7Tiy++qIkTJwb0GsePH6/jjjtOa9asUXR0tO666y5dddVVevHFFyVJ8+bNU0lJiW677TbV1dXpySeflKSWId2RI0dq586deuONN3TFFVfovffeU1JSUqsh3ZtuukkffPCBnnnmGSUnJ6u4uFjjxo3T22+/rW7dukmSfvSjH6mxsVFPPfWUUlNTtW7dOn366acaNmzYETPExcUF/H/p8/kC3hawi5D+3Jumsisrg28mKaeyUutzckIye9fn88lgFjDQob47qnc4nbrgq62t1RNPPKG0tDRJ0sCBAyVJp5xyih588MGW7fr376/HHntMK1eubFXwjRs3Tpdddpmk5l6q3/zmNy3rFi9erOuvv15jxoyRJM2ZM0djx45tWb9s2TK5XC499NBDio6O1qBBg/TSSy/p8ccfbyn4Bg8erNNPP13nnHOOtm3bpiFDhmj48OH69NNPA3p9r732mt544w3t3r1b8fHxkqSHH35YgwcP1q5du1qGZhMSEuR0OhUbG6vU1NRW+3A4HEpNTW0pAHv16qWePXu2rN+2bZuWLl2q999/X4MGDZIkPfbYY4qPj9eGDRt0zjnn6NVXX9Ubb7yhf//734cca0ltZghEcXFx0G0AfMvZ2Kik+vqg20VJSqqvl9PjkcflaneO0tLSdu8DQHCKiora3KbTDulKUt++fVsKkIPt379f999/v4YNG6bExETFxcVp06ZNamhoaLVd+kEnKicmJqqurq7lcU1NjU499dSWx8OGDWvVdsuWLRoyZIiio7+tmTMzM1sNC3fv3r3l+8H/9gR4vsw777wjv9+vPn36KC4uTnFxcTrzzDMlSVu/M/PuWG3evFmmaerMM89seY5evXqpqamp5TnefffdIx5rAJHB0c7eQofXG6IkACJRp+7h69Gjx2GXz507V3PnztVjjz2mM844Qw6HQxMmTFBTU1Or7Q4u1iR12OUEgnme1NRUvf7664csP+GEE0KWJyoqShs3bjzkePTu3Ttkz9GWwsLCDnsuIFJ4vd6Q9Yj5AhjSOWr7EM2szc/PZ5YuEIE6dcF3JOvWrdNll12myZMnS5K++eYbffrppy2TOgKRnp6u6urqlsfvvvtuq/VpaWkqLy/Xvn37WgqlqqqqVr2G7TVs2DD95z//UWxsrE466aSQ7fdgQ4cOlWmaqqur08iRI4+4zY4dO7R161YNGDDgiPtyOBzat2/fMeUI5PwDwG5C+Uemx+VSXWKietTXBzV00yTJnZgoT4gu1eRwOPh9BiJQpx7SPZKBAwdq7dq1Wr9+vd577z1dffXVQZ8cfd1112nhwoX629/+prffflv33Xdfq/WTJk3S3r17deedd+qjjz7S4sWL9de//lUzZswI2ev4/ve/r3PPPVcTJkzQ66+/rpqaGj3zzDOaNGlSyJ6jf//+uvLKK3X11Vdr5cqV2rp1q1auXKkpU6ao/r/nA51//vkaOXKkcnNz9frrr2vLli1asmSJXnjhhVb7Sk9P11tvvaUPPvhA33zzzTEXfwCOgWFofU6Ogp0uYUiqDNGEDQCRy5YF37333qvhw4frwgsv1KhRozR8+HDl5OQEtY8bbrhBU6dO1YQJEzR27FhdffXVrdanpKTo6aef1tq1azV06FDNnj1bDz30kEaPHh3Kl6KnnnpKQ4YM0RVXXKGhQ4fqnnvuOWov27FYsGCBxo4dq2uuuUZDhgzRrbfeqsTExFYzeZ9++mmddtpp+vGPf6zMzEwtXrxYJ598cqv9XH/99crOzlZ2dracTqfmzJkT0pwAjm5TZqb8MTFqantTSVKTYcgfE6OqzMyw5gJgPcPkPjgAYAmv1xu2O220dfHlJkkyDJVNmaKt3GkDsD0KPgCwiGmaYbkGpbFqlWImTpQaG5sfH/Q2bx4YunW55C8vlxniUQmHw8F1+IAIRMEHAHbkdktLl0rz50s1Nd8uT0uTZs6Upk2TuMUi0GVQ8AGAnZmmVFcn7dkjxcdLSUlM0AC6IAo+AAAAm7PlLF0AAAB8i4IPAADA5ij4AAAAbI6CDwAAwOYo+AAAAGyOgg8AAMDmKPgAAABsjoIPAADA5ij4AAAAbI6CDwAAwOYo+AAAAGyOgg8AAMDmoq0OAAAIMdOUamulhgYpLk5KTpYMw+pUACwUlh6++vp6jR07Vscdd5wMw9DatWsP2aZHjx5asmRJOJ7+EJdffrmmT58eln2vWrVKAwcOVLdu3dSvX79D1gdyLCLZ9OnTZRiGDMPQJZdcYnUcAEfjdkvz5kkZGVJKitS/f/P3jIzm5W631QkBWCQsPXwLFizQu+++q40bNyoxMVFJSUmHbPPRRx8pPj4+HE/foW655Radf/75evXVV+VwOA5ZH8ixaC/DMLRixQqNHz8+5PueN2+eSkpKdNttt2nv3r0h3z/QVZmmKZ/PF7L9GatWKWbiRKmxsfnxwc+1dat0xx3SPffIX14uc/TokD2vw+GQQe8hEPHCUvDV1NRo2LBhGjJkyBG36dWrVzieusPV1NRozpw5OuGEE464vq1jEckSEhKUkJAgp9NJwQeEkM/nU0lJSUj2lbZliyaXlck0zcMO2ximKUlq2rtX0ePGadmUKapJTw/JcxcUFCg2NjYk+wIQPiEd0j0w/Ldo0SK9+OKLLUOBBw9jpqentyz/7pDuhx9+qLi4OK1Zs6Zl2SOPPKITTjhBtbW1LctWrFihoUOHyul06rTTTtPy5ctb7ee5555TWlqaXC6Xpk6dekx/Rb/66qvKyclR9+7dlZGRoUceeaTV+gOvYf/+/crNzZVhGK2GdAM5FlVVVbrwwgvlcrl0yimn6Gc/+5n27dvX6nlWr16tkSNHyul0KjU1VTfccMMhGSRpwoQJLY+3bdvW5uvzeDxyOBxav379Iet27NghwzD0ySefBHCkpG3btuniiy9Wjx49dPzxx+vcc8/Ve++9F1BbAO0T6/Eot6JCOkKxd7AoSTJN5VZUKNbj6YB0ACJFSAu+efPmaefOncrNzdWoUaO0c+dO7dy5UyNHjmzZ5p///Kd27typ448//pD2gwYN0ty5czV9+nS53W59+OGHKiws1BNPPKHk5GRJzYXY1Vdfrbvvvlvvvfee7r33Xk2bNk2VlZWSpC+//FJ5eXnKzc3V22+/rT59+ujll18O6nV89NFHGjt2rCZPnqz33ntP8+bN089//vNWheWB1yZJCxcu1M6dO7Vhw4aAj0Vtba0uuOAC5eTkqKqqSk8++aSWLVum0tLSln1UV1drzJgxGjFihN566y0999xzrYaND5dh586dOumkk9p8jU6nU2eccYY2btx4yLoNGzaoT58+OuWUUwI6Xrfeeqv27Nmjv//979q4caOuv/56+f3+gNoCaJ8zqqoU4/cH/GYeJSnG71dmVVU4YwGIMCEd0j14+C82NlapqamHbNOzZ09JOuI5HzfeeKNeeukl3Xjjjdq6datmzJihUaNGtaz/xS9+oVtvvVVTp06VJA0YMEDPPvus/vCHPygnJ0d/+tOflJCQoPvvv19RUVEqLi7WH//4x6BeR3FxsS6//HLddtttkqS0tDRdf/31Wrx4sXJzcyWp1Wvr0aPHIa+1rWPx6KOPauDAgXrggQckSRkZGZo1a5Yefvhh3XXXXZKkX/3qVxo5cqQeeuihlnZnn312y7/bytCWESNGtBSpTzzxhNxut2677TZt2LBBI0aMCHg/27Zt0w9/+EMNGzZMkjRw4MCgckgK6blMQGcQkp9501T2f//YDaqZpJzKSq3PyWn37F2fz8c5fIDFDjeH4Lsi8rIsixYt0uDBg9W7d28VFxe3WvfOO+/ozTffbDXE6vP59P3vf19S8zlzgwcPVlRU89+73bp106mnnhrU87/zzjvavHmz4uLiWpb5/f6Ae7wCfY6NGze2eo79+/dr//79LY/fffdd/fCHPwzZc37XiBEjdP/990tqnlxSV1en2267TRs3bmxVZLdlxowZuuOOO7R+/XqNHDlSl19+ubKzs4PK8t3/ZwBtczY2Kqm+Puh2UZKS6uvl9HjkcbnaleHgUQkA1igqKmpzm4gs+Kqrq9XY2KjPPvtMn332mdLS0lqtnz179iEzUp1OZ0gzzJgxo6WH74CYmJiQPscll1yiBx98MKT7DMaIESP0/vvva8eOHdq/f7/S0tL00UcfaePGjbrvvvsC3s8tt9yiSy+9VKtWrdLzzz+vkpISLVu2TBMnTgxjegCOdvYSOrzedhd8ADqHiCv43G63pk6dqpKSEr3//vuaMmWK1q1bp+jo5qjDhg3T1q1blX6EGWbp6elavny5mpqaFBUVpaamJr3//vs68cQTA84wbNgwffjhh0d8jlAYNmyYKioqNGDAgJbeyO8aOnSo1q1b1+a+YmJiDpnsEYhTTjlFvXr1UmlpqS688EL16dNHCxcu1Ndff60zzzyz1bZxcXH67LPPjrqv6667Ttddd53Gjh2rv/71r0EVfIWFhUHnBzozr9fb7t4xXwDDOEdtH4LZtfn5+czSBTqBDi34PB6PvvrqK0nN16D66quvtGvXLnXr1k0pKSmSmnvWMjIydOutt6qxsVFnnHGGZs+erTlz5kiSfvazn2nMmDEaOHCgxo0bp6+++korV65U//79deWVV2rSpEkqKCjQPffco6uvvlqLFy/WF198EVTOwsJCnX766SooKNBVV10lv9+vV199VaZp6o477gjJsbjlllv0yCOP6Nprr9Xtt9+umJgYvfHGG/r4449bhlnvvvtuZWZmatasWbrmmmv0zTffaNGiRXr00Udb7Ss9PV3PPfecLrroopZzBgM9p2bEiBH63e9+p7/+9a/q27evzj77bGVlZal79+6ttjvzzDO1ZMkSvfnmm+rfv78SExNb3uTz8/N10UUXaeDAgdq2bZveeuutQ3pH2xLI+QeAnZj/vVRKe3hcLtUlJqpHfX1QM/CaJLkTE+UJwciIw+Hg9xfoBDr0XroVFRXq06eP+vTpo6+//lq33367+vTp0zIRYcmSJXr55Ze1ZMkSGYah4447Tk8++aQefPBBvf7665KkCy64QMuWLVNZWZmGDRumiy++WOvXr9egQYMkNU8KWb58uZYvX67MzEx9/vnnuuiii4LKOWjQIK1cuVJ///vfdeaZZ+r888/Xc889F/S5gEfTs2dPrV69Wjt27NC5556rESNG6A9/+EOr5zj11FO1cuVKrVu3TsOHD9eYMWP0zTffHLKv3/zmN/rXv/6lXr16yel0Bnw5Fam54GtqatK5556rQYMGKTk5+bATNq688kqNHz9eP/jBD9SnTx+99NJLLev279+vm2++WUOGDNGVV16pyZMna9asWUEeEQBBMwytz8lRsFMmDEmVIZiwAaDzMMxQ/JkJAAiK1+sNyYWXYz0e5ZeWKjrAS7M0GYb2RUerND9f3hD08HHhZaBz6NAePgBAaHmdTi3Py5MMQ01tbHtgfUVeXkiKPQCdBz18AGCBsN9L96C3dvPA0K3Lxb10gS6Kgg8A7MLtlpYulebPl2pqvl2elibNnClNmyYlJFgWD4B1KPgAwG5MU6qrk/bskeLjpaQkJmgAXRwFHwAAgM0xaQMAAMDmKPgAAABsjoIPAADA5ij4AAAAbI6CDwAAwOYo+AAAAGyOgg8AAMDmKPgAAABsjoIPAADA5ij4AAAAbI6CDwAAwOairQ4AAAgj05Rqa6WGBikuTkpOlgzD6lQAOljE9/C53W4ZhqG1a9daHeWw6uvrNXbsWB133HFHzDl37lz17t1bhmFo+vTpHZ6xPdauXSvDMFq+du/ebXUkAIFwu6V586SMDCklRerfv/l7Rkbzcrfb6oQAOhA9fO20YMECvfvuu9q4caMSExOVlJTUav327dt19913a+HChRo7dqycTmfIM0yfPl27d+/W888/H/J9jxw5Ujt37tQbb7yhK664IuT7B7oy0zTl8/lCvl9j1SrFTJwoNTY2Pz74Obdule64Q7rnHvnLy2WOHh3S53Y4HDLoQQQiDgVfO9XU1GjYsGEaMmTIYdd//PHHampq0qWXXqqUlJQOTtd+DodDqamphxSyANrP5/OppKQkpPtM27JFk8vKZJrmYYdwDNOUJDXt3avoceO0bMoU1aSnh+z5CwoKFBsbG7L9AQiNoId0Z8+eraFDh+rpp59WRkaGunfvrnPPPVeSVFtbq0mTJumEE05QbGysBg0apEWLFrVq369fP/30pz/VJZdcIpfLpbPOOktbtmxpWb9//37NnDlTxx9/vPr27auysrJDMqxZs0ZZWVmKjY3VKaecokcffbRl3ZIlS5SZmamcnBydeOKJWrBggVJSUpSTk6Ovv/464NdZW1urq666SklJSUpOTtbkyZNbDWdOnz5dhmFo0aJFevHFF1uGPA8M6R4YCv3e974nSerVq9chQ7oej0czZ85U7969lZCQoIsvvljbtm1rlePLL7/UtGnT1LNnT8XFxen888/X5s2bW2V44okn9MILL7RkmD17dkCvcerUqfrJT35y2HU/+MEP9POf/zyg/Zimqfvuu08nnXSSunfvroyMjFb/JwA6RqzHo9yKCukIxd7BoiTJNJVbUaFYj6cD0gGw0jGdw/fFF1/ooYce0uLFi/XOO+9o0qRJkqSGhgadeOKJ+vOf/6wPPvhAd999t2644Qa9/vrrrdr//ve/1/XXX68NGzbI7/eroKCgZd3jjz+uP/7xj1q+fLleeOEFLV68uFXb2tpaXXbZZTrvvPP0zjvv6Gc/+5nuuOMOrV69umWbTz75RAsXLlR6errmzp2r119/XfX19XrxxRcDfo3jx49XXV2d1qxZo9dee01ut1tXXXVVy/p58+Zp586dys3N1ahRo7Rz507t3LlTI0eOlPTtUOhf/vIXSdJ7772nnTt3at68eS37uOmmm7R+/Xo988wz2rBhg3r16qVx48Zp//79Ldv86Ec/0ubNm/XUU0/pX//6l6ZOnapPP/30qBn+3//7fwG9xhEjRmjjxo2HXbdx40aNGDEioP089dRTmjt3rh5//HF98MEH+u1vf6u4uLiA2gIInTOqqhTj9wf8xh4lKcbvV2ZVVThjAYgAxzSkW1tbqyeeeEJpaWmSpIEDB0qSTjnlFD344IMt2/Xv31+PPfaYVq5cqfPOO69l+bhx43TZZZdJau6l+s1vftOybvHixbr++us1ZswYSdKcOXM0duzYlvXLli2Ty+XSQw89pOjoaA0aNEgvvfSSHn/8cV144YWSpMGDB+v000/XOeeco23btmnIkCEaPnx4S6HUltdee01vvPGGdu/erfj4eEnSww8/rMGDB2vXrl1KTU1VQkKCEhIS5HQ6FRsbq9TU1Fb7+O5QaK9evdSzZ8+W9du2bdPSpUv1/vvva9CgQZKkxx57TPHx8dqwYYPOOeccvfrqq3rjjTf073//+5BjLanNDG0ZMWKE8vPz5fP5tGPHDv30pz/VH/7wB3322Wdyu93KyckJaD/btm1TYmKixowZo+joaPXr1y+oHJLCch4TEOlC+nNvmsqurAy+maScykqtz8kJyexdn8/HOXxAB3M4HG1uc0wFX9++fVsKkIPt379fJSUlKi8v1+effy6/3y+Px9My5HtA+kHniyQmJqqurq7lcU1NjW677baWx8OGDWvVdsuWLRoyZIiio7+NnpmZ2dKTJkndu3dv+X7wvz0BDlu888478vv96tOnzyHrtm7dGnRhdTibN2+WaZo688wzWy1vamrS1q1bdc455+jdd9894rEOhdNPP13R0dHavHlzS0/jddddp//85z869dRTlZCQENB+fvSjH2nu3LkaOHCgLrzwQv3v//6vcnNzA/oBPKC4uPhYXwYASc7GRiXV1wfdLkpSUn29nB6PPC5Xu3OUlpa2ex8AglNUVNTmNsdU8PXo0eOwy+fOnau5c+fqscce0xlnnCGHw6EJEyaoqamp9ZNGt35a878nEYdbMM+Tmpp6yFC0JJ1wwgkhyxMVFaWNGzcecjx69+4dsuc4mm7duunss8/Whg0btHr1auXn5+uVV16R1+sNeDhXkgYMGKCamhqtXr1aa9eu1a233qqysjK99NJLYUwP4GCOdvYWOrzekBR8ACJTSGfprlu3TpdddpkmT54sSfrmm2/06aefHtLDdzTp6emqrq5uefzuu++2Wp+Wlqby8nLt27evpVCqqqpq1WvYXsOGDdN//vMfxcbG6qSTTgrZfg82dOhQmaapurq6lvP+DrfNjh07tHXrVg0YMOCI+3I4HNq3b98x5RgxYoTWr1+vDz/8UL/73e90ySWXKCYmRtOmTWu13YFz8o7US+pyuXTppZfq0ksvVU5OjvLy8vTNN9+09LC2pbCw8JjyA52Z1+sNWY+YL4ge9cO2D9HM2vz8fGbpAhEopAXfwIED9Ze//EXr16/Xcccdpzlz5gR9jsp1112ne+65RxdccIF69eql++67r9X6SZMm6ac//anuvPNO3XzzzVq3bp3++te/BjUhoy3f//73de6552rChAn69a9/rRNOOEGbN29WRUWF/vSnP4XkOfr3768rr7xSV199tebNm6eBAwfqo48+0pNPPqlHH31UiYmJOv/88zVy5Ejl5uaqtLRUffv21bp165SSkqKLL764ZV/p6el6+umn9cEHH6hfv36Kjo4+pNfwSEaMGKEpU6boRz/6kZKSklp6HRcsWNBqu0GDBikuLk4LFy7UTTfdJKfT2dLT++STT8rn82nEiBGKiopSRUVFywzuQAUz/AvYRShHNzwul+oSE9Wjvj6o2XhNktyJifKE6BqhDoeD32cgAoX0Thv33nuvhg8frgsvvFCjRo3S8OHDAz7x/4AbbrhBU6dO1YQJEzR27FhdffXVrdanpKTo6aef1tq1azV06FDNnj1bDz30kEaH+OKhTz31lIYMGaIrrrhCQ4cO1T333HPUXrZjsWDBAo0dO1bXXHONhgwZoltvvVWJiYmtLs789NNP67TTTtOPf/xjZWZmavHixTr55JNb7ef6669Xdna2srOz5XQ6NWfOnIAznHPOOdqzZ49+8IMfSJJGjRolh8OhwYMHt9ouPj5eixYt0pIlS9S3b99Wl5dJSEjQwoULdc455ygnJ0d79uzRU089dQxHBMAxMwytz8lRsNMlDEmVIZqwASByGWZHnUAHAGjF6/WG9MLLsR6P8ktLFR3gpVmaDEP7oqNVmp8vb4h6+LjwMhCZIv5eugCAwHidTi3Py5MMQ01tbHtgfUVeXsiKPQCRix4+ALBIh91L96C3efPA0K3Lxb10gS6Egg8A7MjtlpYulebPl2pqvl2elibNnClNmyYFeK1NAJ0fBR8A2JlpSnV10p49Uny8lJTEBA2gC6LgAwAAsDkmbQAAANgcBR8AAIDNUfABAADYHAUfAACAzVHwAQAA2BwFHwAAgM1R8AEAANgcBR8AAIDNUfABAADYHAUfAACAzVHwAQAA2BwFHwAAgM1R8AEAANgcBR8AAIDNUfABAADYHAUfAACAzVHwAQAA2BwFHwAAgM1R8AEAANgcBR8AAIDNRVsdADBNU3v27LE6BgAAnVZ8fLwMwzjiego+WG7Pnj1KSEiwOgYAAJ3WV199peOPP/6I6w3TNM0OzAMcgh6+Y/f111/rpJNO0meffXbUX3QEj2MbPhzb8OL4hk8kH1t6+BDxDMOIuF+czub444/nGIYJxzZ8OLbhxfENn854bJm0AQAAYHMUfAAAADZHwQd0YrGxsSoqKlJsbKzVUWyHYxs+HNvw4viGT2c+tkzaAAAAsDl6+AAAAGyOgg8AAMDmKPgAAABsjoIPAADA5ij4gE6krq5OU6ZM0fHHH68ePXro2muvVUNDw1G3v/XWWzVo0CA5nU6dfPLJmjlzpr766qsOTB25HnvsMfXr10/du3dXTk6O1q9ff9TtV6xYocGDB6t79+4aNmyYXnzxxQ5K2vkEc2wXLlyo733ve0pMTFRiYqJGjRrV5v9FVxfsz+4B5eXlMgxDl19+eXgDdmLBHlu3262bb75Zffr0UWxsrAYOHBiZ7w0mgE5jzJgxZmZmpvnPf/7T/Pvf/26mp6ebkyZNOuL2mzdvNn/84x+bzz77rLllyxZz9erVZkZGhnnFFVd0YOrIVF5ebjocDnPx4sXme++9Z15//fVmjx49zC+++OKw2//jH/8wu3XrZv761782q6urzXvvvdeMiYkxN2/e3MHJI1+wx3by5MnmY489Zr799tvm+++/b06fPt1MSEgwP//88w5O3jkEe3wP+Pjjj80TTjjB/N73vmdedtllHRO2kwn22Hq9XvOss84yx44da65bt878+OOPzbVr15qbNm3q4ORto+ADOonq6mpTkrlhw4aWZS+99JJpGIa5ffv2gPezfPly0+FwmH6/PxwxO43s7Gzz5ptvbnm8f/9+s2/fvmZxcfFht8/NzTUvvvjiVstycnLMGTNmhDVnZxTssf2uffv2mfHx8eYTTzwRroid2rEc33379pkjR440f//735vTpk2j4DuCYI/t448/bg4YMMD0+XwdFfGYMaQLdBJvvvmmevToobPOOqtl2ahRoxQVFaXKysqA9/PVV1/p+OOPV3R0172Vts/n01tvvaVRo0a1LIuKitKoUaP05ptvHrbNm2++2Wp7SbrooouOuH1XdSzH9rsaGxvl9/uVlJQUrpid1rEe31/84hfq1auXrr322o6I2Skdy7F99tlnNWLECN18883q3bu3hg4dqgceeED79+/vqNgB67rv+EAns2vXLvXq1avVsujoaCUlJWnXrl0B7WP37t365S9/qRtuuCEcETuN3bt3a//+/erdu3er5b1799YHH3xw2Da7du067PaBHvuu4liO7Xfdfffd6tu37yEFNo7t+K5bt06LFi3Spk2bOiBh53Usx3br1q1as2aNpkyZohdffFFbtmzRT37yE/n9fhUVFXVE7IDRwwdYrKCgQIZhHPUr0A/Ko/n666918cUX69RTT9Xs2bPbHxwIg5KSEpWXl+vpp59W9+7drY7T6e3Zs0dTp07VwoUL1bNnT6vj2E5TU5N69eql3/3udzrzzDOVl5ene+65RwsWLLA62iHo4QMsduedd2r69OlH3WbAgAFKTU3Vf/7zn1bL9+3bp7q6OqWmph61/Z49ezRmzBjFx8fr6aefVkxMTHtjd2o9e/ZUt27d9MUXX7Ra/sUXXxzxWKampga1fVd1LMf2gLlz56qkpESvvPKKTj/99HDG7LSCPb41NTXatm2bLr300pZlTU1NkppHCD788EOlpaWFN3QncSw/u3369FFMTIy6devWsmzIkCHatWuXfD6fHA5HWDMHgx4+wGIpKSkaPHjwUb8cDodGjBght9utt956q6XtmjVr1NTUpJycnCPu/+uvv9YPfvADORwOPfvss/SaSHI4HDrzzDO1evXqlmVNTU1avXq1RowYcdg2I0aMaLW9JK1ateqI23dVx3JsJenXv/61fvnLX2rlypWtzlNFa8Ee38GDB2vz5s3atGlTy9e4ceN0/vnna9OmTTrppJM6Mn5EO5af3XPPPVdbtmxpKaIl6aOPPlKfPn0iqtiTxGVZgM5kzJgxZlZWlllZWWmuW7fOzMjIaHVZls8//9wcNGiQWVlZaZqmaX711VdmTk6OOWzYMHPLli3mzp07W7727dtn1cuICOXl5WZsbKy5ZMkSs7q62rzhhhvMHj16mLt27TJN0zSnTp1qFhQUtGz/j3/8w4yOjjbnzp1rvv/++2ZRURGXZTmCYI9tSUmJ6XA4zD//+c+tfkb37Nlj1UuIaMEe3+9ilu6RBXtsP/30UzM+Pt685ZZbzA8//NB8/vnnzV69eplz5syx6iUcEQUf0InU1taakyZNMuPi4szjjz/evPrqq1t9KH788cemJPPVV181TdM0X331VVPSYb8+/vhja15EBHnkkUfMk08+2XQ4HGZ2drb5z3/+s2Xd//7v/5rTpk1rtf3y5cvNgQMHmg6HwzzttNPMF154oYMTdx7BHNtTTjnlsD+jRUVFHR+8kwj2Z/dgFHxHF+yxfeONN8ycnBwzNjbWHDBggHn//fdH5B/UhmmapjV9iwAAAOgInMMHAABgcxR8AAAANkfBBwAAYHMUfAAAADZHwQcAAGBzFHwAAAA2R8EHAABgcxR8AAAANkfBBwAAYHMUfAAAADZHwQcAAGBzFHwAAAA29/8BvL6p8jTKGUAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res5 = combine_effects(eff, var_eff, method_re=\"chi2\", use_t=False)\n",
    "res5_df = res5.summary_frame()\n",
    "print(\"method RE:\", res5.method_re)\n",
    "print(res5.summary_frame())\n",
    "fig = res5.plot_forest()\n",
    "fig.set_figheight(8)\n",
    "fig.set_figwidth(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replicate fixed effect analysis using GLM with var_weights\n",
    "\n",
    "`combine_effects` computes weighted average estimates which can be replicated using GLM with var_weights or with WLS.\n",
    "The `scale` option in `GLM.fit` can be used to replicate fixed meta-analysis with fixed and with HKSJ/WLS scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:08.927789Z",
     "iopub.status.busy": "2022-11-02T17:06:08.926691Z",
     "iopub.status.idle": "2022-11-02T17:06:08.931373Z",
     "shell.execute_reply": "2022-11-02T17:06:08.930823Z"
    }
   },
   "outputs": [],
   "source": [
    "from statsmodels.genmod.generalized_linear_model import GLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:08.935054Z",
     "iopub.status.busy": "2022-11-02T17:06:08.934633Z",
     "iopub.status.idle": "2022-11-02T17:06:08.944932Z",
     "shell.execute_reply": "2022-11-02T17:06:08.944378Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        eff    sd_eff    ci_low    ci_upp  w_fe  w_re\n",
      "fixed effect       0.428037  0.090287  0.251076  0.604997   1.0   NaN\n",
      "random effect      0.429520  0.091377  0.250425  0.608615   NaN   1.0\n",
      "fixed effect wls   0.428037  0.090798  0.250076  0.605997   1.0   NaN\n",
      "random effect wls  0.429520  0.091595  0.249997  0.609044   NaN   1.0\n"
     ]
    }
   ],
   "source": [
    "eff, var_eff = effectsize_2proportions(*dta_c, statistic=\"or\")\n",
    "res = combine_effects(eff, var_eff, method_re=\"chi2\", use_t=False)\n",
    "res_frame = res.summary_frame()\n",
    "print(res_frame.iloc[-4:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to fix scale=1 in order to replicate standard errors for the usual meta-analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:08.949264Z",
     "iopub.status.busy": "2022-11-02T17:06:08.948189Z",
     "iopub.status.idle": "2022-11-02T17:06:08.958727Z",
     "shell.execute_reply": "2022-11-02T17:06:08.958176Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.4280      0.090      4.741      0.000       0.251       0.605\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "weights = 1 / var_eff\n",
    "mod_glm = GLM(eff, np.ones(len(eff)), var_weights=weights)\n",
    "res_glm = mod_glm.fit(scale=1.0)\n",
    "print(res_glm.summary().tables[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:08.962857Z",
     "iopub.status.busy": "2022-11-02T17:06:08.961762Z",
     "iopub.status.idle": "2022-11-02T17:06:08.969846Z",
     "shell.execute_reply": "2022-11-02T17:06:08.969302Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(1.), array([[-1.11022302e-16, -1.11022302e-16]]))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check results\n",
    "res_glm.scale, res_glm.conf_int() - res_frame.loc[\n",
    "    \"fixed effect\", [\"ci_low\", \"ci_upp\"]\n",
    "].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using HKSJ variance adjustment in meta-analysis is equivalent to estimating the scale using pearson chi2, which is also the default for the gaussian family."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:08.974090Z",
     "iopub.status.busy": "2022-11-02T17:06:08.972996Z",
     "iopub.status.idle": "2022-11-02T17:06:08.981511Z",
     "shell.execute_reply": "2022-11-02T17:06:08.980965Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.4280      0.091      4.714      0.000       0.250       0.606\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "res_glm = mod_glm.fit(scale=\"x2\")\n",
    "print(res_glm.summary().tables[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:08.985583Z",
     "iopub.status.busy": "2022-11-02T17:06:08.984516Z",
     "iopub.status.idle": "2022-11-02T17:06:08.992736Z",
     "shell.execute_reply": "2022-11-02T17:06:08.992196Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0113358914264383, array([[-0.00100017,  0.00100017]]))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check results\n",
    "res_glm.scale, res_glm.conf_int() - res_frame.loc[\n",
    "    \"fixed effect\", [\"ci_low\", \"ci_upp\"]\n",
    "].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mantel-Hanszel odds-ratio using contingency tables\n",
    "\n",
    "The fixed effect for the log-odds-ratio using the Mantel-Hanszel can be directly computed using StratifiedTable.\n",
    "\n",
    "We need to create a 2 x 2 x k contingency table to be used with `StratifiedTable`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:08.996981Z",
     "iopub.status.busy": "2022-11-02T17:06:08.995914Z",
     "iopub.status.idle": "2022-11-02T17:06:09.003036Z",
     "shell.execute_reply": "2022-11-02T17:06:09.002488Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[18,  1],\n",
       "       [12, 10]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t, nt, c, nc = dta_c\n",
    "counts = np.column_stack([t, nt - t, c, nc - c])\n",
    "ctables = counts.T.reshape(2, 2, -1)\n",
    "ctables[:, :, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:09.007166Z",
     "iopub.status.busy": "2022-11-02T17:06:09.006092Z",
     "iopub.status.idle": "2022-11-02T17:06:09.012410Z",
     "shell.execute_reply": "2022-11-02T17:06:09.011866Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18,  1, 12, 10])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:09.016504Z",
     "iopub.status.busy": "2022-11-02T17:06:09.015432Z",
     "iopub.status.idle": "2022-11-02T17:06:09.021744Z",
     "shell.execute_reply": "2022-11-02T17:06:09.021200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18, 19, 12, 22])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dta_c.T[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:09.025833Z",
     "iopub.status.busy": "2022-11-02T17:06:09.024769Z",
     "iopub.status.idle": "2022-11-02T17:06:09.043706Z",
     "shell.execute_reply": "2022-11-02T17:06:09.043151Z"
    }
   },
   "outputs": [],
   "source": [
    "import statsmodels.stats.api as smstats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:09.047911Z",
     "iopub.status.busy": "2022-11-02T17:06:09.046829Z",
     "iopub.status.idle": "2022-11-02T17:06:09.051341Z",
     "shell.execute_reply": "2022-11-02T17:06:09.050802Z"
    }
   },
   "outputs": [],
   "source": [
    "st = smstats.StratifiedTable(ctables.astype(np.float64))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "compare pooled log-odds-ratio and standard error to R meta package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:09.055558Z",
     "iopub.status.busy": "2022-11-02T17:06:09.054494Z",
     "iopub.status.idle": "2022-11-02T17:06:09.061014Z",
     "shell.execute_reply": "2022-11-02T17:06:09.060475Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.4428186730553187, -2.220446049250313e-16)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.logodds_pooled, st.logodds_pooled - 0.4428186730553189  # R meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:09.065120Z",
     "iopub.status.busy": "2022-11-02T17:06:09.064054Z",
     "iopub.status.idle": "2022-11-02T17:06:09.070573Z",
     "shell.execute_reply": "2022-11-02T17:06:09.070026Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.08928560091027186, 0.0)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.logodds_pooled_se, st.logodds_pooled_se - 0.08928560091027186  # R meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:09.074656Z",
     "iopub.status.busy": "2022-11-02T17:06:09.073577Z",
     "iopub.status.idle": "2022-11-02T17:06:09.080645Z",
     "shell.execute_reply": "2022-11-02T17:06:09.080092Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.2678221109331691, 0.6178152351774683)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.logodds_pooled_confint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:09.084076Z",
     "iopub.status.busy": "2022-11-02T17:06:09.083699Z",
     "iopub.status.idle": "2022-11-02T17:06:09.089429Z",
     "shell.execute_reply": "2022-11-02T17:06:09.088886Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pvalue      0.34496419319878724\n",
      "statistic   17.64707987033203\n"
     ]
    }
   ],
   "source": [
    "print(st.test_equal_odds())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:09.092634Z",
     "iopub.status.busy": "2022-11-02T17:06:09.092260Z",
     "iopub.status.idle": "2022-11-02T17:06:09.097851Z",
     "shell.execute_reply": "2022-11-02T17:06:09.097310Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pvalue      6.615053645964153e-07\n",
      "statistic   24.724136624311814\n"
     ]
    }
   ],
   "source": [
    "print(st.test_null_odds())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "check conversion to stratified contingency table\n",
    "\n",
    "Row sums of each table are the sample sizes for treatment and control experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:09.101165Z",
     "iopub.status.busy": "2022-11-02T17:06:09.100793Z",
     "iopub.status.idle": "2022-11-02T17:06:09.107033Z",
     "shell.execute_reply": "2022-11-02T17:06:09.106487Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 19,  34,  72,  22,  70, 183,  26,  61,  36,  45, 246, 386,  59,\n",
       "         45,  14,  26,  74],\n",
       "       [ 22,  35,  68,  20,  32,  94,  50,  55,  25,  35, 208, 141,  32,\n",
       "         15,  18,  19,  75]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctables.sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:09.110214Z",
     "iopub.status.busy": "2022-11-02T17:06:09.109820Z",
     "iopub.status.idle": "2022-11-02T17:06:09.115456Z",
     "shell.execute_reply": "2022-11-02T17:06:09.114875Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 19,  34,  72,  22,  70, 183,  26,  61,  36,  45, 246, 386,  59,\n",
       "         45,  14,  26,  74]),\n",
       " array([ 22,  35,  68,  20,  32,  94,  50,  55,  25,  35, 208, 141,  32,\n",
       "         15,  18,  19,  75]))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nt, nc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Results from R meta package**\n",
    "\n",
    "```\n",
    "> res_mb_hk = metabin(e2i, nei, c2i, nci, data=dat2, sm=\"OR\", Q.Cochrane=FALSE, method=\"MH\", method.tau=\"DL\", hakn=FALSE, backtransf=FALSE)\n",
    "> res_mb_hk\n",
    "     logOR            95%-CI %W(fixed) %W(random)\n",
    "1   2.7081 [ 0.5265; 4.8896]       0.3        0.7\n",
    "2   1.2567 [ 0.2658; 2.2476]       2.1        3.2\n",
    "3   0.3749 [-0.3911; 1.1410]       5.4        5.4\n",
    "4   1.6582 [ 0.3245; 2.9920]       0.9        1.8\n",
    "5   0.7850 [-0.0673; 1.6372]       3.5        4.4\n",
    "6   0.3617 [-0.1528; 0.8762]      12.1       11.8\n",
    "7   0.5754 [-0.3861; 1.5368]       3.0        3.4\n",
    "8   0.2505 [-0.4881; 0.9892]       6.1        5.8\n",
    "9   0.6506 [-0.3877; 1.6889]       2.5        3.0\n",
    "10  0.0918 [-0.8067; 0.9903]       4.5        3.9\n",
    "11  0.2739 [-0.1047; 0.6525]      23.1       21.4\n",
    "12  0.4858 [ 0.0804; 0.8911]      18.6       18.8\n",
    "13  0.1823 [-0.6830; 1.0476]       4.6        4.2\n",
    "14  0.9808 [-0.4178; 2.3795]       1.3        1.6\n",
    "15  1.3122 [-1.0055; 3.6299]       0.4        0.6\n",
    "16 -0.2595 [-1.4450; 0.9260]       3.1        2.3\n",
    "17  0.1384 [-0.5076; 0.7844]       8.5        7.6\n",
    "\n",
    "Number of studies combined: k = 17\n",
    "\n",
    "                      logOR           95%-CI    z  p-value\n",
    "Fixed effect model   0.4428 [0.2678; 0.6178] 4.96 < 0.0001\n",
    "Random effects model 0.4295 [0.2504; 0.6086] 4.70 < 0.0001\n",
    "\n",
    "Quantifying heterogeneity:\n",
    " tau^2 = 0.0017 [0.0000; 0.4589]; tau = 0.0410 [0.0000; 0.6774];\n",
    " I^2 = 1.1% [0.0%; 51.6%]; H = 1.01 [1.00; 1.44]\n",
    "\n",
    "Test of heterogeneity:\n",
    "     Q d.f. p-value\n",
    " 16.18   16  0.4404\n",
    "\n",
    "Details on meta-analytical method:\n",
    "- Mantel-Haenszel method\n",
    "- DerSimonian-Laird estimator for tau^2\n",
    "- Jackson method for confidence interval of tau^2 and tau\n",
    "\n",
    "> res_mb_hk$TE.fixed\n",
    "[1] 0.4428186730553189\n",
    "> res_mb_hk$seTE.fixed\n",
    "[1] 0.08928560091027186\n",
    "> c(res_mb_hk$lower.fixed, res_mb_hk$upper.fixed)\n",
    "[1] 0.2678221109331694 0.6178152351774684\n",
    " \n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:06:09.118770Z",
     "iopub.status.busy": "2022-11-02T17:06:09.118394Z",
     "iopub.status.idle": "2022-11-02T17:06:09.127420Z",
     "shell.execute_reply": "2022-11-02T17:06:09.126874Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   Estimate   LCB    UCB \n",
      "-----------------------------------------\n",
      "Pooled odds           1.557   1.307 1.855\n",
      "Pooled log odds       0.443   0.268 0.618\n",
      "Pooled risk ratio     1.270              \n",
      "                                         \n",
      "                 Statistic P-value \n",
      "-----------------------------------\n",
      "Test of OR=1        24.724   0.000 \n",
      "Test constant OR    17.647   0.345 \n",
      "                       \n",
      "-----------------------\n",
      "Number of tables   17  \n",
      "Min n              32  \n",
      "Max n             527  \n",
      "Avg n             139  \n",
      "Total n          2362  \n",
      "-----------------------\n"
     ]
    }
   ],
   "source": [
    "print(st.summary())"
   ]
  }
 ],
 "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}