diff --git a/abc-ages/ABC Calibration Yearly Model.ipynb b/abc-ages/ABC Calibration Yearly Model.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Author: Nicolas Dumoulin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-12-01T09:53:19.539083Z",
+     "start_time": "2020-12-01T09:53:19.475Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "library(EasyABC)\n",
+    "library(parallel)\n",
+    "library(lhs)\n",
+    "library(mnormt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initializing data with numbers of individuals per age class for two years (ages_source and ages_target) and the delta of years between these two sets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-12-01T09:53:20.069568Z",
+     "start_time": "2020-12-01T09:53:20.002Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# MSA 2008\n",
+    "ages_source = c(5, 47, 194, 529, 1056, 1760, 2477, 3184, 4013, 4617, 5210, 5495, 5981, 6354, 6949, 7646, 8706, 9973, 10766, 11644, 12479, 13180, 13736, 14781, 15885, 16682, 17803, 17706, 17987, 18386, 18158, 17949, 17533, 18011, 17308, 17259, 17538, 18032, 18606, 17364, 16668, 15279, 13731, 7604, 5294, 3282, 2669, 2286, 1614, 1236, 1007, 1074, 978, 829, 789, 655, 697, 591, 496, 471, 403, 348, 323, 269, 248, 240, 220, 171, 178, 201, 248, 130, 58, 63, 43, 42, 55, 42, 30, 30, 9, 10, 15)\n",
+    "# MSA 2018\n",
+    "ages_target = c(1, 76, 268, 579, 1079, 1493, 1973, 2432, 3059, 3592, 4236, 4717, 5440, 5904, 6419, 6945, 7574, 7847, 8414, 8704, 8992, 8782, 8821, 9069, 9236, 9741, 10569, 11458, 12058, 12623, 12927, 13351, 13637, 14446, 15461, 16145, 17113, 17017, 17163, 17660, 17350, 17190, 16147, 12294, 10037, 6746, 5509, 4670, 3720, 2830, 2401, 2181, 1856, 1584, 1209, 790, 665, 595, 496, 390, 333, 346, 303, 246, 238, 205, 201, 175, 146, 125, 92, 82, 75, 55, 50, 36, 36, 26, 25, 18, 13, 10, 11)\n",
+    "D=10 # Years delta between the two sets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model definition\n",
+    "Here we define our model that computes the new numbers of individuals per age class at a year from the data from the precedent year.\n",
+    "\n",
+    "We use a beta distribution for modeling the installation of new individuals and a linear factor for the retirement.\n",
+    "\n",
+    "Let be:\n",
+    "\n",
+    "- $N_{i}(y)$ the number of individual for the class i for the year y\n",
+    "- $A$ the amplitude of the beta distribution\n",
+    "- $drop$ the coefficient for rescaling the shape of the beta distribution\n",
+    "- $\\alpha$ and $\\beta$ the shape parameters of the beta distribution\n",
+    "- $R_{t}$ the retirement threshold, ie the age when the retirement function starts\n",
+    "- $R_{f}$ the retirement factor\n",
+    "\n",
+    "We define the shifted number of individuals for the ageclass $i$ and the year $y$ $N_{i}^{*}(y)$, and the number of individuals for the ageclass $i$ and the year $y+1$ $N_{i}(y+1)$\n",
+    "\n",
+    "$$\n",
+    "N_{i}^{*}(y) = \\left\\{\\begin{array}{lr}\n",
+    "    0, & \\text{for } i\\leq 0\\\\\n",
+    "    N_{i-D}(y), & \\text{for } i\\gt 0\\\\\n",
+    "    \\end{array}\\right\\}\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "N_{i}(y+1) = \\left\\{\\begin{array}{lr}\n",
+    "    N_{i}^{*}(y) * (1 + A * dbeta(\\frac{i}{drop},\\alpha,\\beta)), & \\text{for } i\\lt R_{t}\\\\\n",
+    "    N_{i}^{*}(y) * (1 + A * dbeta(\\frac{i}{drop},\\alpha,\\beta) - R_{f}), & \\text{for } i\\geq R_{t}\\\\\n",
+    "\\end{array}\\right\\}\n",
+    "$$\n",
+    "\n",
+    "From the [binomial theorem](https://en.wikipedia.org/wiki/Binomial_theorem), we can develop for D years:\n",
+    "\n",
+    "$$\n",
+    "N_{i}(y+D) = \\left\\{\\begin{array}{lr}\n",
+    "    N_{i}^{*}(y) * \\sum_{k=0}^{D} \\tbinom{D}{k} A * dbeta(\\frac{i}{drop},\\alpha,\\beta)^{k}, & \\text{for } i\\lt R_{t}\\\\\n",
+    "    N_{i}^{*}(y) * \\sum_{k=0}^{D} \\tbinom{D}{k} \\sum_{j=0}^{k} \\tbinom{k}{j} A * dbeta(\\frac{i}{drop},\\alpha,\\beta))^{k-j}+ (-R_{t})^{j}, & \\text{for } i\\geq R_{t}\\\\\n",
+    "\\end{array}\\right\\}\n",
+    "$$\n",
+    "\n",
+    "\n",
+    "## Beta parametrisation\n",
+    "\n",
+    "As the $\\alpha$ and $\\beta$ parameters of the beta distribution is correlated, we use the parametrisation with the mean $\\mu$ and the addition of both shape parameters $v = \\alpha + \\beta$ (Kruschke, John K. (2011). Doing Bayesian data analysis: A tutorial with R and BUGS. p. 83: Academic Press / Elsevier. ISBN 978-0123814852). We use the relation:\n",
+    "\n",
+    "$$\n",
+    "\\left\\{\\begin{array}{lr}\n",
+    "\\alpha = \\mu * v\\\\\n",
+    "\\beta = (1 - \\mu) * v\n",
+    "\\end{array}\\right\\}\n",
+    "$$\n",
+    "\n",
+    "# Calibrating only installation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-12-01T09:53:20.715267Z",
+     "start_time": "2020-12-01T09:53:20.651Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "last_age_for_installation = 44\n",
+    "installation_yearly = function(ages, x) {\n",
+    "  # computation of the new age at index considering parameters \"x\"\n",
+    "  age_process2 = function(index) {\n",
+    "    set.seed(x[1])\n",
+    "    # installation\n",
+    "    amplitude = x[2]\n",
+    "    drop_x=x[3]\n",
+    "    alpha = x[4]*x[5]\n",
+    "    beta = (1 - x[4])*x[5]\n",
+    "    new_ages[index] + amplitude*dbeta(index/drop_x, alpha, beta)\n",
+    "  }\n",
+    "  new_ages = c(0, ages[1:(length(ages)-1)])\n",
+    "  # Compute all the new ages\n",
+    "  unlist(lapply(seq_along(new_ages), age_process2))\n",
+    "  # 'unlist' is required because EasyABC requires a vector as output instead of a list\n",
+    "}\n",
+    "installation = function(x) {\n",
+    "    ages = ages_source\n",
+    "    for (i in seq(1,D)) {\n",
+    "        ages = installation_yearly(ages, x)\n",
+    "    }\n",
+    "    ages[1:last_age_for_installation]\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-12-01T10:47:23.136032Z",
+     "start_time": "2020-12-01T09:53:20.852Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"    ------ Lenormand et al. (2012)'s algorithm ------\"\n",
+      "[1] \"step 1 completed\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:45 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 2 completed - p_acc = 0.6944\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:40 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 3 completed - p_acc = 0.5996\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:40 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 4 completed - p_acc = 0.5338\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:39 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 5 completed - p_acc = 0.488\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:39 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 6 completed - p_acc = 0.4662\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:36 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 7 completed - p_acc = 0.44\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:36 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 8 completed - p_acc = 0.453\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:36 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 9 completed - p_acc = 0.4814\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:36 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 10 completed - p_acc = 0.4798\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:36 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 11 completed - p_acc = 0.488\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:35 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 12 completed - p_acc = 0.4918\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:35 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 13 completed - p_acc = 0.4924\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:35 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 14 completed - p_acc = 0.4558\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:35 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 15 completed - p_acc = 0.448\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:35 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 16 completed - p_acc = 0.4298\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:34 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 17 completed - p_acc = 0.4226\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:34 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 18 completed - p_acc = 0.4116\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:34 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 19 completed - p_acc = 0.4058\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:34 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 20 completed - p_acc = 0.4128\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:34 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 21 completed - p_acc = 0.4086\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:34 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 22 completed - p_acc = 0.4028\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:34 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 23 completed - p_acc = 0.4096\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:34 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 24 completed - p_acc = 0.3972\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "nb_simul = 10000\n",
+    "p_acc_min=0.4\n",
+    "prior_unif = list(c(\"unif\",100,400), c(\"unif\",10,40), c(\"unif\",0,1), c(\"unif\",3,50))\n",
+    "ABC_Lenormand <- ABC_sequential(method=\"Lenormand\", model=installation, prior=prior_unif, nb_simul=nb_simul,\n",
+    "                        summary_stat_target=ages_target[1:last_age_for_installation], p_acc_min=p_acc_min,use_seed=TRUE, progress_bar=TRUE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-12-01T10:47:23.340186Z",
+     "start_time": "2020-12-01T09:53:21.446Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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vfvBao6DHbV3RtvvLFp06a5c+dGRUX5+fkdPHhQ/Xd5pW9S8/Dw2LZt26xZs8aN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\nAFgE3Gs+MLtOAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "simulated = ABC_Lenormand$stats\n",
+    "boxplot(simulated,outpch=NA)\n",
+    "points(ages_source,pch=3,col=\"green\")\n",
+    "points(ages_target,pch=3,col=\"red\")\n",
+    "legend(\"topleft\", legend=c(paste(\"ABC Lenormand \\n\",format(nb_simul),\" simulations pacc=\",format(p_acc_min),sep=\"\"),\"Source\",\"Target\"),\n",
+    "       col=c(\"black\",\"green\",\"red\"),cex=.8,pch=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data simulated (boxplot) fits the target data, so we retain this model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-12-01T10:47:23.428694Z",
+     "start_time": "2020-12-01T09:53:22.115Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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fNfSCz7LH+qOZxJpHGNCncd4G/SGPfR3xW0XTVaavK6qJpYRKLKEQV8RJoXrcES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\n",
+      "text/plain": [
+       "Plot with title “inst_v”"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "paramsNames = c(\"inst_amplitude\",\"inst_drop\",\"inst_mu\",\"inst_v\")\n",
+    "par(mfrow=c(2,2))\n",
+    "for (c in 1:ncol(ABC_Lenormand$param)) { \n",
+    "plot(density(ABC_Lenormand$param[,c],weights = ABC_Lenormand$weights),xlab=paramsNames[c],main=paramsNames[c])\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Running ABC with installation and retirement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-12-01T10:47:23.435131Z",
+     "start_time": "2020-12-01T09:53:22.689Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "simulator_yearly = function(ages, x) {\n",
+    "  # computation of the new age at index considering parameters \"x\"\n",
+    "  age_process = function(index) {\n",
+    "    set.seed(x[1])\n",
+    "    # installation\n",
+    "    amplitude = x[2]\n",
+    "    drop_x=x[3]\n",
+    "    alpha = x[4]*x[5]\n",
+    "    beta = (1 - x[4])*x[5]\n",
+    "    # retirement\n",
+    "    retirement_threshold = x[6]\n",
+    "    retirement_factor = x[7]\n",
+    "    if (index < retirement_threshold) retirement_factor = 0\n",
+    "    new_ages[index] + amplitude*dbeta(index/drop_x, alpha, beta) - new_ages[index]*retirement_factor\n",
+    "  }\n",
+    "  new_ages = c(0, ages[1:(length(ages)-1)])\n",
+    "  # Compute all the new ages\n",
+    "  unlist(lapply(seq_along(new_ages), age_process))\n",
+    "  # 'unlist' is required because EasyABC requires a vector as output instead of a list\n",
+    "}\n",
+    "simulator = function(x) {\n",
+    "    ages = ages_source\n",
+    "    for (i in seq(1,D)) {\n",
+    "        ages = simulator_yearly(ages, x)\n",
+    "    }\n",
+    "    ages\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-12-01T10:53:29.245374Z",
+     "start_time": "2020-12-01T09:53:23.015Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"    ------ Lenormand et al. (2012)'s algorithm ------\"\n",
+      "[1] \"step 1 completed\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:25 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 2 completed - p_acc = 0.6876\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:22 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 3 completed - p_acc = 0.55\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:21 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 4 completed - p_acc = 0.514\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:20 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 5 completed - p_acc = 0.4676\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:19 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 6 completed - p_acc = 0.4384\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:19 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 7 completed - p_acc = 0.4076\"\n",
+      "  |==================================================| 100% Time elapsed: 00:00:19 Estimated time remaining: 00:00:00\n",
+      "[1] \"step 8 completed - p_acc = 0.37\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "nb_simul = 5000\n",
+    "p_acc_min=0.4\n",
+    "prior_unif = list(c(\"unif\",200,400), c(\"unif\",25,40), c(\"unif\",0.3,.45), c(\"unif\",4,10), c(\"unif\",40,55), c(\"unif\",0,0.3))\n",
+    "ABC_Lenormand <- ABC_sequential(method=\"Lenormand\", model=simulator, prior=prior_unif, nb_simul=nb_simul, summary_stat_target=ages_target, p_acc_min=p_acc_min,use_seed=TRUE, progress_bar=TRUE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-12-01T10:53:29.561534Z",
+     "start_time": "2020-12-01T09:53:23.234Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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/eJ6K+//mIYJikpacCAAZqamnZ2dsHBwatXrx46dCi7m/hIsZCQEEtLS01Nzd69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\nCGsJ/AkchGOYY/MnzXEQ2I+Lvlg2GTgI9mF2l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V3d4sWLefny5csnTpw4ZMgQbvjkyZPVq1d//vnn1tbW3DwlJeXatWvx8fG8RTZt2uTp\n6cl7ubW0tISHh8fFxbm7u3PzzMzMgwcP7tmzh7fI/v37FQrFokWLePmSJUtmzJjx5ptvcsP8\n/PyYmJjExEQLCwtufvr06Vu3bh05coR0TS6X89r1RcGA4fj7+2/YsIEXVlZWElFWVhYvP3bs\nmJOTk3CR0NDQuXPnCnMHB4ekpCReeOXKFSKqr6/n5bGxsYGBgcJF/Pz8EhISeGFxcTER3bx5\nk5cfPny4e/fuwkWmTp0aEREhzK2trU+fPs0L09PTiUihUPDyNWvWBAUFCRfx9vbevn07L3z8\n+DER/fLLL7w8MTHRw8NDuMiECROioqKEuZmZ2fnz53nhd999Z2xsLNx4xYoVISEhwtzT03PP\nnj28MDc3l4jy8/N5+a5du/r37y9cJDg4ODo6WpgTUVpaGi9MSUmxtLQUbhwZGTl58mRh7ubm\nduDAAV6Yk5NDRIWFhbx869atPj4+wkVGjRoVExPDC5uamogoIyODlycnJ9vZ2QkXWbBgwYwZ\nM4S5s7Pz0aNHeWF2djYRlZWV8fJNmzYNGjRIuMjQoUPj4+N5YU1NDRFlZmby8hMnTjg6OgoX\nmT179uzZs4W5o6PjiRMneGFmZiYR1dTU8PL4+PihQ4cKFxk0aNCmTZt4YVlZGRFlZ2fz8qNH\njzo7OwsXmTFjxoIFC4S5nZ1dcnIyL8zIyCCipqYmXh4TEzNq1CjhIj4+Plu3buWFhYWFRJST\nk8PLDxw44ObmJlxk8uTJkZGRwtzS0jIlJYUXpqWltfr/VHR0dHBwsDDv37//rl27eGF+fj4R\n5ebm8vI9e/Z4enoKFwkJCVmxYoUwNzY2/u6773jh+fPnzczMhBtHRUVNmDBBmHt4eCQmJvLC\nX375hYgeP37My7dv3+7t7S1cJCgoaM2aNbyQ/fQhPT2dl58+fdra2lq4SERExNSpU4V59+7d\nDx8+zAtv3rxJRMXFxbw8ISHBz89PuEhgYGBsbCwvrK+vJ6IrV67w8qSkJAcHB+Eic+fODQ0N\nFeZOTk7Hjh3jhVlZWURUWVnJyzds2ODv7y9cRMJwjh0AAACARGCwAwAAAJAIDHYAAAAAEoHB\nDgAAAEAiMNgBAAAASAQGOwAAAACJwGAHAAAAIBEY7AAAAAAkAoMdAAAAgETgXrGGZGpqKrzT\nnFwuNzIyEuatbvy8uampqbGxsbGxMS83MTHRfhETExOZTKa/Ctn127kItXYXP11VqJNFXvwK\nZTIZ79ZzbS8i3NjIyEgul784Ferk5SYM26iQfQbaU6G+X27sMyDc6YtT4e/k5db+CtmXw4vz\nchNuzP7v84K83KTM0Le++F0rLCysq6sT5sJb3zAM09LSIrwDFcMwZWVlFRUVwvzBgwfCG3M9\na/Ha2tqioiJh/uTJE+H9x561SHNzc6sVlpaWCm/zwjBMXl6eUqnkhSqVqtXFa2pqnj59Kswf\nP37c2NiofYUPHz4U5iUlJVVVVcL8/v37KpVKWOH9+/eFG1dXVwvvt8MwzKNHj4S3bHpWhY2N\njcLbCjEMU1xcXF1drWWFSqUyLy9PuHFlZWVpaakwf/jwYXNzs5YVNjQ0PHnyRJgXFRXV1tZq\nuYhCoWi1woqKCuEtwhiGyc/Pb2lp0XLx+vr6goICYf5cLzeFQvHgwQNhXl5eXl5eLsyf6+VW\nV1cnvFcbwzAFBQXav9ye94CQl5enkwNCQ0ODlos86+X2vAeE53q5PdcBoamp6dGjR8L8uV5u\nz6qwqqqqpKREmD/Xy+1ZB4SnT58Kb1XHLtL+A0J+fr72FdbX1+vkgNDqy00nB4RnvdwkTMYw\njKFnSwAAAADQAZxjBwAAACARGOwAAAAAJAKDHQAAAIBEYLADAAAAkAgMdgAAAAASgcEOAAAA\nQCIw2AEAAABIBAY7AAAAAInAYAcAAAAgERjsAAAAACQCgx0AAACARGCwAwAAAJAIDHYAAAAA\nEoHBDgAAAEAiMNgZ0v79++3s7HS75vTp0wMEEhMTdbJ4dXX18uXLX331VRsbm4CAgLi4uPr6\nep2sLKTNk/P111/LZLIzZ87oo4D6+vrVq1d7e3tbWVn16tUrPDy8sLCQu0FLS8uGDRvc3d3N\nzMzc3d3Xr1/f0tKij0q0KYaI/vGPfwwbNsza2trJyWnatGl5eXn6qOTJkydhYWEeHh5WVlav\nvvrq2rVra2trn7dUcSqpq6tbt26dl5eXlZWVl5fXunXr9Neuahp7Uq9NS1ocAUTrW22OReI0\nrcYdida0GisxSNNqQ699q/FRi3mwlQIGDKSlpcXPz8/W1laHayqVSjMzM+G/8rp169q/eHFx\ncY8ePYjojTfeeO+999zc3IgoMDBQoVC0f3EebZ6c4uLil19+mYi+/fZbnRfQ1NTk5eVFRP36\n9QsLCxs8eDAR2dra3rlzh91ApVJNnz6diJydnSdPnty1a1cimjZtmkqlEr8YhmEOHz7Mhu+8\n887IkSOJyNHRsaioSLeVFBQUdOrUiYiGDx8+a9asPn36EJGvr29LS4v2pYpWia+vLxF5eXnN\nnDmTrcrX17epqUm3lXBp7Em9Ni2jxRFAtL7V5lgkTtNq3JFoTatNJeI0bUlJSRtTwZ49e3jb\n6/tg2/ajFvNgKw0Y7AygoKAgJSXlj3/8I/sK1+HKDx8+JKIPPvhAh2uqzZo1i4h27NjB/rap\nqYl9sen2pa79kzNlyhT2MKSPY822bduIaNasWeqx9csvvySiYcOGsb+9fv06O+M2NDQwDNPQ\n0DBw4EAiysrKEr+Y6upqKysrNze3goICNtm3bx8RLV68WLeVLFiwgIgOHDjA/lahUEydOpWI\n9u/fr2WpolWyY8cOIoqIiFAqlQzDKJXKhQsXEtGuXbt0WwmXxp7Ua9MyWhwBROtbjZWI1rQa\ndyRa02qsRLSmraio8G+Ns7MzEZ08eZK3vV77VuOjFvNgKw0Y7AzAyspK/d5It4NdWlpaq++3\n2q+5udnU1NTLy4v7JqmsrMzc3HzcuHE63JGWT05SUhIR9e/fX0/HmsDAQCIqLCzkhoMHD5bJ\nZNXV1QzDLFmyhIguXbqk/tNLly4R0bJly8Qvhv1665tvvlH/qVKpDAkJCQ0N1W0lbm5uXbt2\nZY+/rKtXrxLRwoULtSxVtEreffddIrp79656gzt37hDR1KlTdVgGl8ae1HfTMlocAUTrW42V\niNa0GnckWtNqrET8puWqrKx0cXGZOHEi75Mwffetxkct5sFWGnCOnQEcP348OTk5OTmZ/WZT\nh3Jzc4nIw8NDt8sS0b1795qbm19//XWZTKYO7e3t+/Tpw77GdEWbJ6e0tDQiImL06NFhYWE6\n3DXXzz//3KNHjy5dunBDFxcXhmHYc2JSUlLs7OwGDRqk/tNBgwbZ2dnp4xwUjcUcPXrU1tZ2\n7Nix6j81MjI6ffr0kSNHdFiGQqEwNzcPDAw0MvrPcYM9D7KyslLLUkWrpKqqiojkcrl6A1NT\nU+4GuqWxJ0VoWtLiCCBa32qsRJym1WZH4jStNpWI3LQ8kZGRRLR//37uQV6EvtX4qMU82EoD\nBjsDCAkJGT9+/Pjx421tbXW7MnswzczM9PX1tbKy6t2799y5c4uKitq/Mvuqq6ur4+UNDQ3V\n1dU6PL1XmydnyZIlDQ0N+/bt4x6AdOvs2bMXLlzgJiqVKi0tTSaTsUf8goKCnj17cg9Gcrm8\nZ8+e+jjnuu1iiOju3bs9e/Y0MjI6d+5cXFzcxo0bL168yDCMbsuQy+W3bt06evQoN/zmm2+I\nyN/fX8tSRatk1KhRRMQ9VZ/92os9sUnnNPakCE1Lmo4AYvatxmOROE2rzY7EaVptKhG5ablO\nnjz51VdfHTx4kD11VU2Evm37UYt8sJUIA31SCAzDMN7e3rr9KpY9E0Imkw0cOHD69OnsGeX2\n9vbcT7n/OwqFwsLConPnzrW1teowOzub/cjk3r177Vxf6FlPzsmTJ+nXr3i2bNlCevtWi0up\nVC5btoyIJk6cyDAM+xZzzJgxvM1Gjx5NRNynSIRiFAqFkZHRsGHDxo0bx31pT5gwQX+VJCcn\nL1y4kH0PPWHChMbGRm1KFbMSpVK5aNEiIhoxYsSyZcvYr9sWL17M/fZWVzT2pGhN2/YRQMy+\nbbsS0Zr2v9iRnppWm0rEbFquxsZGV1fX4OBgXi5O37b9qA17sO2gMNgZks4Hu8GDB1tbWycl\nJbG/VSqVcXFxRBQUFNT+xWNiYogoODg4Jyenqqrq3Llzrq6u7LFJtMGutLS0c+fOgYGB7Gte\nnMGusLCQPQuka9eujx49YhjmwYMHRDRp0iTelhMnTiSi/Px8MYspKChg/xVcXV3Pnj1bWVl5\n+/btt956i4iio6P1VMbixYvZnVpYWCQkJLT6k9HCUsWsRKVSJSYmGhsbq/8HNTExOXTokM5/\nkk5jT4rZtG0fAcTs27YrEa1pn3dH+mtabSoRrWl5tm/fLpPJfvzxR24oWt+2/agNeLDtuDDY\nGZLOBzshhULRq1cvIqqpqWnnUvX19ewhTy0kJIR9d1VXV6eTarlafXJmzpxpaWmZm5vL/lbf\ng51Kpdq9e7eNjQ0RBQQE5OXlsTn7JlI4LrNvIquqqsQsRv19xI0bN9Qb19XVOTk5mZqa6u/q\nHo2NjdnZ2ePHjyei5cuXa1OqmJXExsayH4dkZ2fX1taqN1i/fr1u966xJ0VuWh7uEcAgfdtq\nJaI1rfY70nfTalOJaE3LVVNT4+DgMG3aNF4uWt+2/agN27QdFAY7QxJhsGMYJjQ0lIiuXbvW\n/qVUKtXFixc3btwYExNz5swZhUIxcOBAGxub9q8sJHxyzp8/T0Q7d+5UJ3r9P7K0tDQ4OJiI\nHB0d9+/fz/1QSqVSmZubDxw4kPdXXn/9dUtLS328vW6jGPYrHjc3N95fYS9Gc/PmTZ0Xw9XQ\n0ODk5GRmZtbc3KyxVNEqKSkpMTEx8fT0VFfFMExTU1Pv3r3NzMxKS0t1tVONPSly07ZKfQQQ\nv2+fVYloTavljkRoWo2ViNa0PHv37iWif/7zn9xQtL7V+KgN3rQdEQY7Q9LtYNfY2FhYWCj8\nZG7OnDlEpI+LbTY3N9vb2/v5+el8Zaa1J4e93NSz6PYiL/X19expW2+99VZFRYVwA1dXVwcH\nB+65LwqFwsHBwd3dXYdlaFlM586d+/btywvnzZvH+3ignbKysmbOnCk8srPnOLMXWdVYqjiV\nZGRkENH8+fN5G7DPyeXLl3VVicaeFLNptTkCiNO32lQiTtNqsyNxmlZjJaI1LZdKpXrttddc\nXV15p/GJ1rfaPGoxD7bSIG/jHw86luLiYhcXl0mTJrGXHWIxDPP999+zt2Fp5/pz5swpLS09\ndeqU+hoTqamp5eXl8fHx7VxZS/369Zs7dy43+fHHHzMzM0ePHu3i4uLp6anDfW3atOnKlSvL\nli3bunUr95oaauPGjfvss8+uX7/u5+fHJtevXy8rK5s5c6YOy9CymCFDhpw6daq4uNjR0ZFN\n2H93Y2Nj9qR1nbCxsfnrX/8ql8vZE4PUO7p//76trS27a42lilOJUqkkoidPnvD+Ipt0795d\nV5Vo7MmWlhbRmlabI4A4fatNJeI0rTY7EqdpNVZSVlZGojQtV2Zm5g8//BAbG8t74KIdbNnL\nWrX9qMU82EqEwUZK0MNXsQEBAUZGRikpKexvVSpVQkICEUVFRbV/8aVLlxLR3r172d8WFRV5\neHiYm5uXl5e3f3EhbZ4cPX07oFAoXnnllU6dOrXxI1fsxdDHjBnDfmvT0tIyZswY0vWHDVoW\nk5qaSkSTJk1ir8zO/Hox9xkzZuiwEpVK5ebmZmpq+v3336uT7du306+XEtWmVHEqUalU/fv3\nl8lk3N44deqUTCbz8vLSa20ae1KvX8VqPAKI1rcaKxGnaTXuSLSm1ViJQZp29erV9Ntr/z6L\nnvpWm0ctWtNKBgY7Q9L5YHfz5k32zg0jRoxQ33TPy8tLJ2eYPn36lL2G58iRI8ePH89eDzYx\nMbH9K7fKgIPd/fv3icjW1vaN1rC3A1KpVOw9rHx8fCIjI1977TUimjlzpm4r0bIYpVLJHum6\nd+8+bdo09n2ti4sL72L67XfhwgWZTCaXy8eMGRMaGjpgwAAieuWVV9jvYbUpVZxKGIa5ceOG\npaUlEQUEBISGhr755ptEZGVl9cMPP+iwDCHDDnYajwCi9a3GSkRr2rZ3JGbTanzI4jett7e3\nmZnZsy5XxKW/vtX4qEVrWsnAYGdI+vjhidu3b0+ZMqVbt24WFha+vr4ffvih+t1h+z148GDq\n1KmdO3e2srIKCAhQvx3XBwMOdhcvXmzjQ271j8s1NTXFx8f36NHDwsLC399/8+bN3PN/RS6m\nvr4+Li7O39//pZde6tu375IlSyorK3VeDMMw165dGzt2rLOzs6Wlpbe398qVK9U70rJUESph\nPXz4MDw8vHfv3hYWFuwFcvV31RU1ww52jBZHAHH6VptKRGvaNnYkctNqfMhiNi17BZYhQ4Zo\ns7Fe+1bjoxataaVBxujhSt8AAAAAID7cUgwAAABAIjDYAQAAAEgEBjsAAAAAicBgBwAAACAR\nGOwAAAAAJAKDHQAAAIBEYLADAAAAkAgMdgAAAAASgcEOAAAAQCIw2AEAAABIBAY7AAAAAInA\nYAcAAAAgERjsAAAAACQCgx0AAACARGCwAwAAAJAIDHYAAAAAEoHBDgAAAEAiMNgBAAAASAQG\nOwAAAACJwGAHAAAAIBEY7AAAAAAkAoMdAAAAgERgsAMAAACQCAx2AAAAABKBwQ4AAABAIjDY\nAQAAAEgEBjsAAAAAicBgBwAAACARGOwAAAAAJAKDHQAAAIBEYLADAAAAkAgMdgAAAAASgcEO\nAAAAQCIw2AEAAABIBAY7AAAAAInAYAcAAAAgERjsAAAAACQCgx0AAACARGCwAwAAAJAIDHYA\nAAAAEoHBDgAAAEAiMNgBAAAASAQGOwAAAACJwGAHAAAAIBEY7AAAAAAkAoMdAAAAgERgsAMA\nAACQCAx2AAAAABKBwQ4AAABAIjDYAQAAAEgEBjsAAAAAicBgBwAAACARGOwAAAAAJAKDHQAA\nAIBEYLADAAAAkAgMdgAAAAASgcEOAAAAQCIw2AEAAABIBAY7AAAAAInAYAcAAAAgERjsAAAA\nACQCgx0AAACARGCwAwAAAJAIDHYAAAAAEoHBDgAAAEAiMNgBAAAASAQGOwAAAACJwGAHAAAA\nIBEY7AAAAAAkAoMdAAAAgERgsAMAAACQCAx2AAAAABKBwQ4AAABAIjDYAQAAAEgEBjsAAAAA\nicBgBwAAACARGOwAAAAAJAKDHQAAAIBE/D9HcnVMcRE6mwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "simulated = ABC_Lenormand$stats\n",
+    "boxplot(simulated,outpch=NA)\n",
+    "points(ages_source,pch=3,col=\"green\")\n",
+    "points(ages_target,pch=3,col=\"red\")\n",
+    "legend(\"topleft\", legend=c(paste(\"ABC Lenormand \\n\",format(nb_simul),\" simulations pacc=\",format(p_acc_min),sep=\"\"),\"Source\",\"Target\"),\n",
+    "       col=c(\"black\",\"green\",\"red\"),cex=.8,pch=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-12-01T10:53:29.778180Z",
+     "start_time": "2020-12-01T09:53:23.549Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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7xVG8Pf35iU2qNCEVKnlQl98wxcPNWXmv9bdghIDzTK8ed96m2SqZImhr9/C2I8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\nEgADBAmAAYIEwABBAmDwH+Vr0yS3NvgUAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "Plot with title “retire_factor”"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "paramsNames = c(\"inst_amplitude\",\"inst_drop\",\"inst_mu\",\"inst_v\",\"retire_threshold\",\"retire_factor\")\n",
+    "par(mfrow=c(3,2))\n",
+    "for (c in 1:ncol(ABC_Lenormand$param)) { \n",
+    "plot(density(ABC_Lenormand$param[,c],weights = ABC_Lenormand$weights),xlab=paramsNames[c],main=paramsNames[c])\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-12-01T10:57:28.541288Z",
+     "start_time": "2020-12-01T10:57:28.428Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "write.csv(ABC_Lenormand$param,paste(\"../out/abc_\",nb_simul,\"_param.csv\",sep=\"\"),row.names=F)\n",
+    "write.csv(ABC_Lenormand$weights,paste(\"../out/abc_\",nb_simul,\"_weights.csv\",sep=\"\"),row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## With 50000 sampling\n",
+    "We perform the same calibration with more sampling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-12-02T15:21:53.002985Z",
+     "start_time": "2020-12-02T08:27:02.999Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] \"    ------ Lenormand et al. (2012)'s algorithm ------\"\n",
+      "[1] \"step 1 completed\"\n",
+      "  |==================================================| 100% Time elapsed: 00:17:58 Estimated time remaining: 00:00:05\n",
+      "[1] \"step 2 completed - p_acc = 0.68444\"\n",
+      "  |==================================================| 100% Time elapsed: 00:14:10 Estimated time remaining: 00:00:04\n",
+      "[1] \"step 3 completed - p_acc = 0.55884\"\n",
+      "  |==================================================| 100% Time elapsed: 00:12:56 Estimated time remaining: 00:00:03\n",
+      "[1] \"step 4 completed - p_acc = 0.51076\"\n",
+      "  |==================================================| 100% Time elapsed: 00:12:15 Estimated time remaining: 00:00:03\n",
+      "[1] \"step 5 completed - p_acc = 0.47704\"\n",
+      "  |==================================================| 100% Time elapsed: 00:11:36 Estimated time remaining: 00:00:03\n",
+      "[1] \"step 6 completed - p_acc = 0.44228\"\n",
+      "  |==================================================| 100% Time elapsed: 00:11:01 Estimated time remaining: 00:00:03\n",
+      "[1] \"step 7 completed - p_acc = 0.40584\"\n",
+      "  |==================================================| 100% Time elapsed: 00:10:42 Estimated time remaining: 00:00:03\n",
+      "[1] \"step 8 completed - p_acc = 0.38236\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "nb_simul = 50000\n",
+    "p_acc_min=0.4\n",
+    "prior_unif = list(c(\"unif\",200,400), c(\"unif\",25,40), c(\"unif\",0.3,.45), c(\"unif\",4,10), c(\"unif\",40,55), c(\"unif\",0,0.3))\n",
+    "ABC_Lenormand <- ABC_sequential(method=\"Lenormand\", model=simulator, prior=prior_unif, nb_simul=nb_simul, summary_stat_target=ages_target, p_acc_min=p_acc_min,use_seed=TRUE, progress_bar=TRUE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-12-02T15:37:39.019874Z",
+     "start_time": "2020-12-02T15:37:36.390Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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TV5pJiELTVWpaCSBHRFIJOpOzcFN0lA+YL8Zu2lpERAJGjMYD3JYaQYgLJqySPF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lFB5O7u709dcfzZxZUlKyd+9exeH3LC2f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\nP/zww4oVK5KSktR2FRUVycplis8VFRXl5eVnz54NCAhQHuDv7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zhzBsB7OTm95HINYA04AHEAAJZCoX3KBzyeta0tIxIZ9lP7yhOEED2P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\nTkSuQmCC",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "simulated = ABC_Lenormand$stats\n",
+    "boxplot(simulated,outpch=NA)\n",
+    "points(ages_source,pch=3,col=\"green\")\n",
+    "points(ages_target,pch=3,col=\"red\")\n",
+    "legend(\"topleft\", legend=c(paste(\"ABC Lenormand \\n\",format(nb_simul),\" simulations pacc=\",format(p_acc_min),sep=\"\"),\"Source\",\"Target\"),\n",
+    "       col=c(\"black\",\"green\",\"red\"),cex=.8,pch=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-12-02T15:37:42.123921Z",
+     "start_time": "2020-12-02T15:37:41.960Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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IpdNlh4UBVqIYiM9jU1/zhJXyK9WNnZ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\nBj7Lvw5bGEVyliMBZOWoxSsyTmahC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+      "text/plain": [
+       "Plot with title “retire_factor”"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 420,
+       "width": 420
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "paramsNames = c(\"inst_amplitude\",\"inst_drop\",\"inst_mu\",\"inst_v\",\"retire_threshold\",\"retire_factor\")\n",
+    "par(mfrow=c(3,2))\n",
+    "for (c in 1:ncol(ABC_Lenormand$param)) { \n",
+    "plot(density(ABC_Lenormand$param[,c],weights = ABC_Lenormand$weights),xlab=paramsNames[c],main=paramsNames[c])\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-12-02T15:37:48.831591Z",
+     "start_time": "2020-12-02T15:37:48.530Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "write.csv(ABC_Lenormand$param,paste(\"../out/abc_\",nb_simul,\"_param.csv\",sep=\"\"),row.names=F)\n",
+    "write.csv(ABC_Lenormand$weights,paste(\"../out/abc_\",nb_simul,\"_weights.csv\",sep=\"\"),row.names=F)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Format de la Cellule Texte Brut",
+  "kernelspec": {
+   "display_name": "R 3.6.3",
+   "language": "R",
+   "name": "ir363"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.6.3"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {
+    "height": "249px",
+    "width": "337px"
+   },
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/abc-ages/ABC Calibration Yearly Model.pdf b/abc-ages/ABC Calibration Yearly Model.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..c67fe4cc64d13c2cab8c4a22b82b35b480b0a420
Binary files /dev/null and b/abc-ages/ABC Calibration Yearly Model.pdf differ
diff --git a/abc-ages/Readme.md b/abc-ages/Readme.md
new file mode 100644
index 0000000000000000000000000000000000000000..288a291820a05a27b91bcc03ef66bbfaaa3321cc
--- /dev/null
+++ b/abc-ages/Readme.md
@@ -0,0 +1,20 @@
+# Population Model
+
+Model of the dynamics of population for French farmers that includes two
+  processes, an installation process and a retirement process.
+The population is represented by an array of age class amounts.
+The installation process is modeled by a beta distribution with 4 parameters:
+  amplitude, drop_x, mu and v. The retirement process is a linear function
+  with a threshold.
+
+See documentation in the notebook "ABC Calibration Yearly Model.ipynb"
+
+## Usage
+
+First, you need to calibrate the model with the script abc.R. You can adjust the number
+of samples `nb_simul`, then run:
+```
+Rscript abc.R
+```
+
+Then you can use the Python API exposed by `population_model.py` for using the calibrated parameters and computing the yearly dynamics.
diff --git a/abc-ages/__pycache__/population_model.cpython-38.pyc b/abc-ages/__pycache__/population_model.cpython-38.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..cc5009905090d571a6cf376d57a694975cd24555
Binary files /dev/null and b/abc-ages/__pycache__/population_model.cpython-38.pyc differ
diff --git a/abc-ages/abc.R b/abc-ages/abc.R
new file mode 100644
index 0000000000000000000000000000000000000000..26a25daf4ebb59a24f1c120d933e9d13818225dc
--- /dev/null
+++ b/abc-ages/abc.R
@@ -0,0 +1,76 @@
+library(EasyABC)
+library(parallel)
+library(lhs)
+library(mnormt)
+
+# This script implements the population model and calibrates its parameters by
+# using ABC Lenormand method.
+# The output will be generated in ../out
+dir.create("../out")
+
+# MSA 2008
+ages_source = c(5, 47, 194, 529, 1056, 1760, 2477, 3184, 4013, 4617, 5210, 5495, 5981, 6354, 6949, 7646, 8706, 9973, 10766, 11644, 12479, 13180, 13736, 14781, 15885, 16682, 17803, 17706, 17987, 18386, 18158, 17949, 17533, 18011, 17308, 17259, 17538, 18032, 18606, 17364, 16668, 15279, 13731, 7604, 5294, 3282, 2669, 2286, 1614, 1236, 1007, 1074, 978, 829, 789, 655, 697, 591, 496, 471, 403, 348, 323, 269, 248, 240, 220, 171, 178, 201, 248, 130, 58, 63, 43, 42, 55, 42, 30, 30, 9, 10, 15)
+# MSA 2018
+ages_target = c(1, 76, 268, 579, 1079, 1493, 1973, 2432, 3059, 3592, 4236, 4717, 5440, 5904, 6419, 6945, 7574, 7847, 8414, 8704, 8992, 8782, 8821, 9069, 9236, 9741, 10569, 11458, 12058, 12623, 12927, 13351, 13637, 14446, 15461, 16145, 17113, 17017, 17163, 17660, 17350, 17190, 16147, 12294, 10037, 6746, 5509, 4670, 3720, 2830, 2401, 2181, 1856, 1584, 1209, 790, 665, 595, 496, 390, 333, 346, 303, 246, 238, 205, 201, 175, 146, 125, 92, 82, 75, 55, 50, 36, 36, 26, 25, 18, 13, 10, 11)
+D=10 # Years delta between the two sets
+
+simulator_yearly = function(ages, x) {
+  # computation of the new age at index considering parameters "x"
+  age_process = function(index) {
+    set.seed(x[1])
+    # installation
+    amplitude = x[2]
+    drop_x=x[3]
+    alpha = x[4]*x[5]
+    beta = (1 - x[4])*x[5]
+    # retirement
+    retirement_threshold = x[6]
+    retirement_factor = x[7]
+    if (index < retirement_threshold) retirement_factor = 0
+    new_ages[index] + amplitude*dbeta(index/drop_x, alpha, beta) - new_ages[index]*retirement_factor
+  }
+  new_ages = c(0, ages[1:(length(ages)-1)])
+  # Compute all the new ages
+  unlist(lapply(seq_along(new_ages), age_process))
+  # 'unlist' is required because EasyABC requires a vector as output instead of a list
+}
+simulator = function(x) {
+    ages = ages_source
+    for (i in seq(1,D)) {
+        ages = simulator_yearly(ages, x)
+    }
+    ages
+}
+
+paramsNames = c("inst_amplitude","inst_drop","inst_beta1",
+        "inst_beta2","retire_threshold","retire_factor")
+nb_simul = 10000
+p_acc_min=0.4
+file_prefix=paste("../out/abc_",format(nb_simul),".Rdata",sep="")
+prior_unif = list(c("unif",200,400), c("unif",25,40), c("unif",0.3,.45),
+                  c("unif",4,10), c("unif",40,55), c("unif",0,0.3))
+ABC_Lenormand <- ABC_sequential(method="Lenormand", model=simulator,
+      prior=prior_unif, nb_simul=nb_simul, summary_stat_target=ages_target,
+      p_acc_min=p_acc_min,use_seed=TRUE, progress_bar=TRUE)
+# Save data
+write.csv(ABC_Lenormand$param,paste(file_prefix,"_param.csv",sep=""),row.names=F)
+write.csv(ABC_Lenormand$weights,paste(file_prefix,"_weights.csv",sep=""),row.names=F)
+# Plot results againts data
+simulated = ABC_Lenormand$stats
+png(paste(file_prefix,".png",sep=""))
+boxplot(simulated,outpch=NA)
+points(ages_source,pch=3,col="green")
+points(ages_target,pch=3,col="red")
+legend("topleft", legend=c(
+      paste("ABC Lenormand \n",format(nb_simul),
+      " simulations pacc=",format(p_acc_min),sep=""
+    ),"Source","Target"),
+    col=c("black","green","red"),cex=.8,pch=3)
+dev.off()
+# Plot posteriors
+png(paste(file_prefix,"_posteriors.png",sep=""))
+par(mfrow=c(3,2))
+for (c in 1:ncol(ABC_Lenormand$param)) {
+  plot(density(ABC_Lenormand$param[,c],weights = ABC_Lenormand$weights),xlab=paramsNames[c],main=paramsNames[c])
+}
+dev.off()
diff --git a/abc-ages/population_model.py b/abc-ages/population_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..aca3b6fe50a9e4a058311b2288ef77347fb23eaf
--- /dev/null
+++ b/abc-ages/population_model.py
@@ -0,0 +1,64 @@
+#!/usr/bin/python3
+# -*- coding: utf-8 -*-
+import pandas as pd
+import numpy as np
+import scipy
+from scipy import stats
+
+class PopulationModel():
+    '''
+    Model of the dynamics of population for French farmers that includes two
+     processes, an installation process and a retirement process.
+    The population is represented by an array of age class amounts.
+    The installation process is modeled by a beta distribution with 4 parameters:
+     amplitude, drop_x, mu and v. The retirement process is a linear function
+     with a threshold.
+    See documentation in the notebook "ABC Calibration Daily Model.ipynb".
+    '''
+    def __init__(self, ageclasses, params_filename, weights_filename, nbsamples=1):
+        '''
+        Initialize an instance of the model
+        :param ageclasses inital data of age classes amounts
+        :param params_filename filename of the posteriors of the calibrated
+        parameters
+        :param weights_filename filename of the weights for this posteriors
+        :param nbsamples number of samples that will be handled by this instance
+        '''
+        self.ageclasses = np.repeat([ageclasses], nbsamples, axis=0)
+        params = pd.read_csv(params_filename)
+        weights = pd.read_csv(weights_filename).iloc[:,0]
+        kernel = stats.gaussian_kde(params.transpose(), weights=weights)
+        self.parameters = kernel.resample(nbsamples).transpose()
+
+    def step_year(self):
+        '''
+        Compute the evolution of the age classes amounts for one year. The
+        new amounts will be stored in the instance, ready for a next step, and
+        will also be returned.
+        '''
+        # First, we shift the amounts by one year, and affect 0 for the
+        # new ageclass
+        self.ageclasses = np.roll(self.ageclasses,1)
+        self.ageclasses[:,0] = 0
+        # Now, we process every parameters set sampled
+        for i in range(len(self.ageclasses)):
+            [amplitude,drop_x,mu,v,r_threshold,r_factor] = self.parameters[i,:]
+            alpha = mu * v
+            beta = (1 - mu) * v
+            newages = self.ageclasses[i,:]
+            newages = map(lambda elt:
+                      elt[1]
+                      + amplitude*stats.beta.pdf((1+elt[0])/drop_x, alpha, beta)
+                       - 0 if ((1+elt[0]) < r_threshold) else elt[1]*r_factor
+                      , enumerate(newages))
+            self.ageclasses[i,:] = list(newages)
+        return self.ageclasses
+
+if __name__ == '__main__':
+    ages_msa_2008 = [5, 47, 194, 529, 1056, 1760, 2477, 3184, 4013, 4617, 5210, 5495, 5981, 6354, 6949, 7646, 8706, 9973, 10766, 11644, 12479, 13180, 13736, 14781, 15885, 16682, 17803, 17706, 17987, 18386, 18158, 17949, 17533, 18011, 17308, 17259, 17538, 18032, 18606, 17364, 16668, 15279, 13731, 7604, 5294, 3282, 2669, 2286, 1614, 1236, 1007, 1074, 978, 829, 789, 655, 697, 591, 496, 471, 403, 348, 323, 269, 248, 240, 220, 171, 178, 201, 248, 130, 58, 63, 43, 42, 55, 42, 30, 30, 9, 10, 15]
+    model = PopulationModel(ages_msa_2008,"../out/abc_10000.Rdata_param.csv", "../out/abc_10000.Rdata_weights.csv",nbsamples=1)
+    for i in range(10):
+        target = model.step_year()
+    data=pd.DataFrame(target)
+    # print(data.head())
+    data.to_csv("../out/data.csv")
diff --git a/ardoise.py b/ardoise.py
new file mode 100644
index 0000000000000000000000000000000000000000..041a6ad93305309485e4981b020ba6bb42377e1b
--- /dev/null
+++ b/ardoise.py
@@ -0,0 +1,29 @@
+import pandas as pd
+from numpy import random
+# Build cars DataFrame
+names = ['United States', 'Australia', 'Japan', 'India', 'Russia', 'Morocco', 'Egypt']
+dr =  [True, False, False, False, True, True, True]
+cpc = [809, 731, 588, 18, 200, 70, 45]
+lst = [random.randint(10, size=random.randint(1,5)) for i in range(len(names))]
+dict = { 'country':names, 'cars_per_cap':cpc, "lst": lst}
+cars = pd.DataFrame(dict)
+# print(cars)
+
+# Definition of row_labels
+row_labels = ['US', 'AUS', 'JAP', 'IN', 'RU', 'MOR', 'EG']
+
+# Specify row labels of cars
+cars.index = row_labels
+
+# Print cars again
+cars_sample = cars.sample(3)
+cars_sample["country"] = "foo"
+cars_sample["lst"] = cars_sample["lst"].apply(lambda x: [5 for i in range(len(x))])
+print(cars)
+print("---")
+print(cars_sample)
+cars = cars.drop(list(cars_sample.index.values))
+print(cars)
+print("---")
+cars = pd.concat([cars_sample, cars], axis=0)
+print(cars)
\ No newline at end of file
diff --git a/src/transition.py b/src/transition.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a390d2234481106c9a390eaa3727cfc48a1902c
--- /dev/null
+++ b/src/transition.py
@@ -0,0 +1,4 @@
+import pandas as pd
+import numpy as np
+
+# Here will go the transition function described in transition_age.ipynb
\ No newline at end of file
diff --git a/transition_age.ipynb b/transition_age.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..24f23cf696f037875c91657a18866dd27724bb9f
--- /dev/null
+++ b/transition_age.ipynb
@@ -0,0 +1,226 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.8.5 64-bit",
+   "metadata": {
+    "interpreter": {
+     "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 372,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import sys\n",
+    "sys.path.insert(1, \"abc-ages\")\n",
+    "import population_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 373,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "exploits = pd.read_csv(\"results/exploits.csv\", sep=\";\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 374,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "y1, x1 = np.histogram(exploits[\"age\"], bins=range(18, 101))\n",
+    "plt.plot(x1[:-1],y1)\n",
+    "hist_start = y1"
+   ]
+  },
+  {
+   "source": [
+    "model = population_model.PopulationModel(hist_start,\"out/abc_10000.Rdata_param.csv\", \"out/abc_10000.Rdata_weights.csv\",nbsamples=50)"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 375,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 376,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target = model.step_year()\n",
+    "hist_end = target[0]\n",
+    "for i in range(1,50):\n",
+    "    if sum(target[i]) < sum(hist_end):\n",
+    "        hist_end = target[i]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 377,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2845"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 377
+    }
+   ],
+   "source": [
+    "drop = sum(hist_start) - sum(hist_end)\n",
+    "drop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 378,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retirement_treshold = pd.read_csv(\"out/abc_10000.Rdata_param.csv\")[\"V5\"]\n",
+    "retirement_treshold = int(retirement_treshold.mean())+18"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 385,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#TODO exploits[age] = new distrib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 379,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = exploits[exploits[\"age\"] < retirement_treshold].sample(drop) # todo seed "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 383,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# scenarios TODO representative weights\n",
+    "cereales = [1,2,3,4]\n",
+    "sample[\"cultures\"] = sample[\"cultures\"].apply(lambda x: np.random.choice(cereales, len(x)))\n",
+    "exploits = exploits.drop(list(sample.index.values))\n",
+    "exploits = pd.concat([sample, exploits], axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 381,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "          gid  id_expl  age  \\\n",
+       "37617  794816    37627   34   \n",
+       "23373  817896    23383   53   \n",
+       "12912  591823    12922   57   \n",
+       "35367  389919    35377   62   \n",
+       "43008  410626    43018   62   \n",
+       "\n",
+       "                                                cultures                pra  \n",
+       "37617               [21, 21, 21, 21, 21, 21, 21, 21, 19]    Côte roannaise  \n",
+       "23373  [18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 1...         Tarentaise  \n",
+       "12912                           [18, 18, 18, 18, 18, 18]             Cantal  \n",
+       "35367                                           [17, 17]          Maurienne  \n",
+       "43008                                [1, 1, 2, 1, 3, 18]  Monts du Lyonnais  "
+      ],
+      "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>gid</th>\n      <th>id_expl</th>\n      <th>age</th>\n      <th>cultures</th>\n      <th>pra</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>37617</th>\n      <td>794816</td>\n      <td>37627</td>\n      <td>34</td>\n      <td>[21, 21, 21, 21, 21, 21, 21, 21, 19]</td>\n      <td>Côte roannaise</td>\n    </tr>\n    <tr>\n      <th>23373</th>\n      <td>817896</td>\n      <td>23383</td>\n      <td>53</td>\n      <td>[18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 1...</td>\n      <td>Tarentaise</td>\n    </tr>\n    <tr>\n      <th>12912</th>\n      <td>591823</td>\n      <td>12922</td>\n      <td>57</td>\n      <td>[18, 18, 18, 18, 18, 18]</td>\n      <td>Cantal</td>\n    </tr>\n    <tr>\n      <th>35367</th>\n      <td>389919</td>\n      <td>35377</td>\n      <td>62</td>\n      <td>[17, 17]</td>\n      <td>Maurienne</td>\n    </tr>\n    <tr>\n      <th>43008</th>\n      <td>410626</td>\n      <td>43018</td>\n      <td>62</td>\n      <td>[1, 1, 2, 1, 3, 18]</td>\n      <td>Monts du Lyonnais</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 381
+    }
+   ],
+   "source": [
+    "exploits.sample(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 384,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fd102cd7370>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 384
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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aRQ1Krq4uRrVupTI5z3YUn5P4dJxiqK85aDuKUp/Roq+sKtm+xnOB85wzbEfxucjUHABqSndZTqLUv2jRV1ZVb3sfgKzp59sNMgCSM8cB0HRQi74KHFr0lVURZauoII1h2WNtR/G59JzxuI3QWbPHdhSlPqNFX1lj3G6ymzZRmjDNdpQBERUdS7WkENZYYjuKUp/Roq+sKduznVQacI2YazvKgKmJyCCu5YDtGEp9Rou+subglncBGDb5XMtJBk5zzAjSOnX0jgocOnhY+VX1wRIq926h+WARSUXPUU882eNn2I41YNxJuaQ2rKClqZEY73mFlLJJi77ym9XP/oLZhT8jzfu41USwMeNq5gbRxVFOVXjaKCiByn07yJ0023YcpbToK/9wdXWRXfgoRWHj6Zx/L6k5Exmakctcp9N2tAGVkOEZttlQtgu06KsAoEVf+cW2j15mKtUczPsBM89aaDuO3wzNOQ2A9qpiy0mU8hi8v6tVQHEVPEEdCUw5v6dTOQ1eiSlpHCIWqd9rO4pSgBZ95Qc1B/cxpemf7Bz2RSIio2zH8btK53Cim0tPPqNSfqBFXw244pV/JkzcZJ5/q+0oVhyKziKlXa8ZpAKDFn01oNwuF9n7nmdbxDRGjB2cR96eTEdCDunuKro6O2xHUUqLvhpY2z5+hQxTReu0G2xHscY5ZBTh4qKqVM/Bo+zToq8GVOfaJ6gnninnX2c7ijWxwzwnk6s9UGQ5iVJa9NUAqqnYz5TDn1CUfhmRUTG241gzJHs8AC2VeoplZZ8WfTVgil98AAduMi/4hu0oVg3NyKXdhGNqtXtH2deroi8iS0WkSkS2dmtLEZG3RGSX999kb7uIyEMiUiwim0Ukr9tzbvLOv0tEbvL9n6MCxcG9O8irfIF1KQtCdgfuEQ6nkwpnOhGH99uOolSvt/SfBC45pu37wDvGmLHAO97HAJcCY723xcAj4PmSAH4MzAZmAT8+8kWhBp+DL92LGwc5Vz1gO0pAaIjMJLFNx+or+3pV9I0xHwJ1xzRfDvzFe/8vwBXd2p8yHquAJBEZDlwMvGWMqTPG1ANv8fkvEjUI7Nm6mrzGt9mQcQ3pWaNtxwkIrXHZDOsqx7jdtqOoENefPv10Y0y5934FkO69nwl0v2pEqbfteO2fIyKLRaRARAqqq6v7EVHZcPj1e2mSGCZ+5T7bUQJH8khipY26aj23vrLLJztyjTEGML5Ylnd5S4wx+caY/LS0tJM/QQWMbf9cwbTWNWwfdTOJKfreHRGVPgaA6v07LCdRoa4/Rb/S222D998qb3sZMKLbfFnetuO1q0HCuN2EvXs/VaQw/cv/ZTtOQEnO9Jxiualcz7ap7OpP0X8VODIC5ybglW7tN3pH8cwBGr3dQG8CF4lIsncH7kXeNjVIlOxYx/iuHeydsJiomDjbcQJKes543EborNFhm8quXp1PX0SWAfOBVBEpxTMK53+A50TkZmAfcLV39hXAAqAYaAG+BmCMqRORB4C13vn+nzHm2J3DKohVrF5OjhFGzw/do2+PJyo6lkpJIayxxHYUFeJ6VfSNMcc7Cfr5PcxrgNuPs5ylwNJep1NBJbX0bXaGT2DCsGzbUQJSTUQGcS0HTj6jUgNIj8hVp2T1s79g68/PobOj/aj2igPFjHUV05B9oaVkga85ZgSpneUnn1GpAaRFX/XavsJ1zNj+Sya3b2TTG08cPe2T5wHInPtlG9GCgis+gyGm4XNfmEr5kxZ91Suuri7aXvgmzRJNqQwnadOSow40it37BvscI0L+lAsn4kzKwiGGmvIS21FUCNOir3pl7XP/w/iuHezOu5eDk29ljGs32/75GgCNtZVMaNvMwWHnWU4Z2KKGePZ1NFSU2A2iQpoW/RC05cNXWPXIrbQ2H+7V/GV7Cpla9BCbomcx87LFTF2wmFoScX3yewB2fvwCYeJmSP6XBjJ20EtIzwGguUZPvKbs0aIfYjra20h7927mVD7D/gcvoL76xDsWjdtN/bO34cZB+r89gjgcREXHsjPnWqa1rmFf4TqcO1dQRQpjpp3tp78iOA3JGAVAV52O4FH2aNEPMRtefZhhVLMqfRE5nbtp+uN5lO0pPO78q5/9OZPbN7Jt0t0MGzHms/YJl91Jq4mg+h8/Y0LTGvYOmYfD6fTHnxC04hNTOGyikUN6ILqyR4t+CGlvayFn2yMUhU1g9q2PUPKFZcSbQ0Q9dTG7Nnz4ufl3rH2bmTt+zcaYuZx+1V1HTUtOG87m1AXkH3qbGGknZurl/vozglqtM5WIlgrbMVQI06IfQja++jDDqKHj7P9CHA4mzLqQxmtfp10iyXr5Ktat+NcwzLqqMpJfX0y1I5Xcf/9rj1vxGZf+B24jHCKG8XMW+PNPCVqHItKJa6+0HUOFMC36IaKttZnc7Y9QGD6RyWdf8Vl7zvjpRN72HiURY5i55k4+ffw/6OrsoOzx60kyh2i54onjni1zxJgprBl2DduyryciMspPf0lwa4seRnKXni5c2dOr0zCo4LfplYeYTR1V836LOI7+rh+SnkXc3e+w9k9fZ+6BRznws9eZYg6yZup9zJp25gmXO+cbfx7I2IOOKz6D1PoGOtrb9ItSWaFb+iGgraWJ3B1L2B4+mUlnfrHHeSKjYsj/9t9ZNea7ZLrLWZt0Kadf+R0/Jx38wpKyAKg5WGI3iApZuqU/yBVv+gR59Q5GU0f1/Ic/t5XfnTgczLn+PipLb2Dm8JEnnFf1TVTqkQO09pKRO8FyGhWKtOgPUm2tzWz46z2cXvZXGiSB9XMfJu/ML/TquXpd24GTMNRzgFaLHqClLNGiPwjVVBygacmlzHUfYE3yAsbf+BB5eunCgJCa6TlAq7O+1HISFaq06A8ybS1N1D72ZUa4qth87lJmzb/KdiTVTWx8EoeIxXFIi76yQzttBxHjdrPtkRsY37WDHWf8mqla8ANSrUMP0FL2aNEfRFY98T1mHn6XT0d9m7yLb7AdRx3HoYihxOsBWsqSPhd9ERkvIhu73Q6JyJ0icp+IlHVrX9DtOfeISLGIFInIxb75ExRAwWtLmHvgUdYkLWDO9ffbjqNOoC1mOCkuPUBL2dHnPn1jTBEwHUBEnEAZ8BKeC6E/aIz53+7zi8hEYBEwCcgA3haRccYYV18zKI/G+hrGFfyYwvBJTP/GEzrUMsC54zNIqTtEW2szUdGxtuOoEOOr6nA+sNsYs+8E81wOPGOMaTfG7AWKgVk+Wn9I2/7iz0mghcjLH9SjPIOA03uAVq1eQUtZ4KuivwhY1u3xHSKyWUSWikiyty0T6H4i8VJvm+qHxtpKpux/mvWx8xg1ebbtOKoXYo4coKVFX1nQ76IvIhHAQuB5b9MjwGg8XT/lwK/7sMzFIlIgIgXV1dr3eSLbX/w5cdJK8oJ7bUdRvXTkClotNSf6YazUwPDFlv6lwHpjTCWAMabSGOMyxriBR/lXF04ZMKLb87K8bZ9jjFlijMk3xuSnpelBRcfTUFPB1NJlrI87h9xJupUfLNIyPUc8dzXoWH3lf74o+tfSrWtHRIZ3m3YlsNV7/1VgkYhEikguMBZY44P1h6zCF39GNO2kfOFHtqOoUxAdG08DcTj0ClrKgn4dkSsiscCFwK3dmn8pItMBA5QcmWaM2SYizwHbgS7gdh2503d1VWVMK3uGDQnnMvO0fNtx1CmqdaYRqQdoKQv6VfSNMc3AkGPajntUkDHmp8BP+7NO5VH00s+YTQepX/ih7SiqD5oihhLfUWU7hgpBOqA7CNVUHGDawedZn3g+ORPybMdRfaAHaClbtOgHoeIXHyCCTtIX3mc7iuojE59JModpbT5sO4oKMVr0g0xV2V5mVL7I+uRLGDFmiu04qo+cSZ5DVGp0rL7yMy36QWbvi/fhwE3W5T+2HUX1Q3Sa5wCtxoq9lpOoUKNFP4iU7ytiRs3/sT71i3qpvSCXlJ4LQKteQUv5mRb9IHLg5fsxOBh5pY7LD3apGSMBPUBL+Z8W/SCxr2gjeXX/YMPQK/QatoNAVEwc9SToAVrK77ToB4HK0t2EP/MVDkssY67SrfzBoixyFGkNm23HUCFGi36Aq6sqo23pQhLch6m5Yhmpw7JtR1I+cjjjTEa5S6ipOHDymZXyES36AexQQy11f/4i6a5K9l/yBGOnn207kvKh1KmXAFBS8A/LSVQo0aIfoDo72in9w0JyukooOuePTJx7qe1IysdGTTmDRmJxF79nO4oKIVr0A9T6l3/HxM6tbMz7CdPOu9p2HDUAnGFh7I6dSXbDGozbbTuOChFa9ANQS1Mjo7f/ge3hk8n/4m2246gB1Jkzj2HUULp7i+0oKkRo0Q9Am5b/nFQacFx0v17kfJDLzPN02x1c/4blJCpUaEUJMPXV5UzZ+yQbYs5kwukX2I6jBljmqImUk0bE/g9tR1EhQot+gClafh/RtJGy8Ce2oyg/EIeDA8mzGd28HldXl+04KgRo0Q8g5fuKyKtYzrrkS/U8+SHEOeZcEmhh9+aPbUdRIUCLfgApffFHuBGyr3rAdhTlR7mne/r16zavtJxEhQIt+gGisbaSaQ1vsXHoFQwbMcZ2HOVHKUMz2e0cRfxB3dJXA6/fRV9ESkRki4hsFJECb1uKiLwlIru8/yZ720VEHhKRYhHZLCLah+G1452niBAXaWd/3XYUZUF12lzGtm/TK2mpAeerLf1zjTHTjTH53sffB94xxowF3vE+BrgUGOu9LQYe8dH6g17CrhcpcWQzavIc21GUBTETzidCuti5+nXbUdQgN1DdO5cDf/He/wtwRbf2p4zHKiBJRIYPUIagUbankNM6t1Oes1DH5YeocbMupooU4j/+OZ0d7bbjqEHMFxXGACtFZJ2ILPa2pRtjyr33K4B07/1MoPspBUu9bUcRkcUiUiAiBdXV1T6IGNj2f/AkALnnftVqDmVPVEwcZWc8wCh3CQXL7rcdRw1ivij6Zxlj8vB03dwuIvO6TzTGGDxfDL1mjFlijMk3xuSnpaX5IGLgMm43mQf+j20RUxiWPdZ2HGXRjIuuZ33sPPL2LOFAsZ6WQQ2Mfhd9Y0yZ998q4CVgFlB5pNvG+2+Vd/YyYES3p2d520JW8aaPyXaX0Tz+KttRVADIvu5h2iWcQ899U0/CpgZEv4q+iMSKSPyR+8BFwFbgVeAm72w3Aa94778K3OgdxTMHaOzWDRSSaj/9Kx0mjPHn3WA7igoAqRk5FE76DyZ1bGbtSw/ZjqMGof5u6acDH4vIJmAN8Lox5g3gf4ALRWQXcIH3McAKYA9QDDwKfLOf6w9qXZ0djK16k61xZ5CYnGo7jgoQp3/pTrZHTGHCll/SWDf492kp/wrrz5ONMXuAaT201wLn99BugNv7s87BZNvHrzCNRvZPu8Z2FBVAHE4nYZf8lIRXF7LmvaeZddWdtiOpQUTHB1pSuPpN0j78AfXEM+mcL9uOowLM2OlnUyrDiN75yslnVuoUaNH3s7bWZlb96ZuMX+HZuq+8dCkRkVGWU6lAIw4HBzIu4bS2jdRVhfRYB+VjWvT9qHxfEZW/ms2ciqdZm7qQhO+uZsLsi2zHUgFq6JxrCRM3uz5YZjuKGkS06PvRgZfvZ6irks3zlzL7W08Rl5BsO5IKYKMmzWK/I5O4Xa/ajqIGES36flJfXc7UupVsTr2UqfN1TL46OXE4KMu8lAntm6mp2G87jhoktOj7SdHrvydKOhl2wXdsR1FBZPgZ1+IUw+4P/m47ihoktOj7QWdHO6NKlrElMo+c02bajqOCyMjT8ilxZBNf/H+2o6hBQou+H2x66ymGUod79q22o6ggVJ51KRM6tlFVttd2FDUIaNH3g/gNj1Eqw5lyzldsR1FBKPOsf8Mhhj0fPG07ihoEtOgPsJ3r32d81w5Kx92Aw+m0HUcFoexx09ntzCVpz2u2o6hBQIv+ADv0/u9pMtFMWvAN21FUEKvKXsCErkLK9xXZjqKCnBb9AVRxoJhpje+xNf2LxCem2I6jgljOvBsBKPngb5aTqGCnRX8AHVj+A9w4GHnZ92xHUUEuI3cCRWETSC/RA7VU/2jRHyB7tq5mZsObbBh+tV4RS/lE/eiFjHKXsK9wne0oKohp0R8gTa/9gMMSw2lX6/VOlW+MOfcGXEY4+Il28ai+06I/ALZ+9ApT29ZSOPZWElMG9zV+lf+kDstme9R0RpSt0Espqj7Tou9jbpeLqPfvp5w0Zlz1n7bjqEGmbfyVZJkKdm38yHYUFaS06PvY+hWPMca1m7K8/yAyKsZ2HDXIjDv3OjpMGHWr9EAt1Tda9H2oraWJzHW/otg5mrwv3GI7jhqEEpNT2RY7m9FVK3F1ddmOo4JQn4u+iIwQkfdEZLuIbBOR73jb7xORMhHZ6L0t6Pace0SkWESKRORiX/wBgWTDsvsYTjXt5/9Ej75VA8Y9+SrSqKdw9T9sR1FBqD9b+l3A3caYicAc4HYRmeid9qAxZrr3tgLAO20RMAm4BPijiAyayli2p5C8/U+yLv48Jp2x4ORPUKqPJp5zNc0mio5//ll36KpT1ueib4wpN8as994/DBQCmSd4yuXAM8aYdmPMXqAYmNXX9QeaquV34cJB1jX/azuKGuSiY+PZPPKr5DV/xJrnf2k7jgoyPunTF5GRwAxgtbfpDhHZLCJLReTINQEzgQPdnlbKcb4kRGSxiBSISEF1dbUvIg6oTe89z4yWf7Jp9K2kZ422HUeFgNk3/oxN0bPJ2/5Ldqx5y3YcFUT6XfRFJA54AbjTGHMIeAQYDUwHyoFfn+oyjTFLjDH5xpj8tLTAHufe3tZCyoc/4oBkkHf1PbbjqBDhcDoZecvTVDlSGbLiFr2couq1fhV9EQnHU/CfNsa8CGCMqTTGuIwxbuBR/tWFUwaM6Pb0LG9b0HK7XGx44i5GmIPUz3tAh2gqv0pMSaP9qr8SZ5qpevxaOjvabUdSQaA/o3cEeBwoNMb8plv78G6zXQls9d5/FVgkIpEikguMBdb0df22NdZVs+nXlzGnchlrkr/A1HO/bDuSCkGjJs9mW/5PmNi5lc0PXU1He5vtSCrA9WdL/0zgBuC8Y4Zn/lJEtojIZuBc4LsAxphtwHPAduAN4HZjjKt/8e0o3vQJzb8/k8nNq1k1/nuc/i09F4qyJ/+Lt7JqzJ3MbHqfwt8upK2l6ajp9dXlHCjeYimdCjRijLGd4YTy8/NNQUGB7Rif2fzecsa/fxsNkkD9F5Yw4fQLbEdSCoDVz/2K07f9lMLIqeTc8Qq1ZXuoXPkbptW9SaR0siHmTFK++AA5p820HVUNMBFZZ4zJ73GaFv3eq9i/i6il86l1ppF86+ukDD3RCFWl/K/g1T8xfd09NEgCqTTQZsLZlLoAd8xQpuz/G9G0sS75UnKv/jlpGSNtx1UD5ERFP8zfYYJVZ0c7jU9dT5xxEXnt37Tgq4CUv/A2NkTFkbjqV+wasYgJl93J7DTPbrb66rvZuvw+8iqWU7K0mNT/Xo049EwsoUa39Htp1Z++yZyKp1k36zfMXHCz7ThK9dma5b9h1tb72XLuE0w550u246gBcKItff2a74WNby9jTsXTrE79khZ8FfSmXXYblQzB+fEpH0KjBgEt+iexr2gjuR/fTbFzNNNufth2HKX6LTIqhr3jb2Zi51a2f6onbQs1WvRPoGzPNqKXXUkn4URf91eiomNtR1LKJ6Yt/Da1JNL1wa9sR1F+pkX/OMr3FeF86nLC6eTw1cvJHDXJdiSlfCY6Np5do25iats6dq5/33Yc5Uda9HtQVbYX15MLiaGZ2iufIXfi6bYjKeVzk6+4i0ZiaX77F7ajKD/Son+M9rYWmh+/nGR3A+WXPc2YaWfZjqTUgIhLSGZ79nXMaPmnXnM3hGjRP8aGZ35Crnsfxef8jvH559mOo9SAmnjF96glkfSXr2HTe8/bjqP8QIt+N+X7ipi291HWx57NtPMW2Y6j1IBLTEmj/aaVVDvTmfL+LXz65Pf1alyDnBb9biqe/S4GIeOaB21HUcpvMnInkHHXh6xPPJ+5JY+w4defP2mbGjy06Httevc5ZrR8wqZRtzAse6ztOEr5VXRsPDPvfJ5VY+9ietPHFP3+Sj1N8yClRR9oa21myEc/ZJ8ji5mLfmg7jlJWiMPBnOt+zNrJP2Ra6xq2PLwIV1eX7VjKx0K+6Bu3m41P3k2WqeDwuT8jIjLKdiSlrJr9lbtZNfo7zDz8Huv++FWM201Hexub3nueNQ9dz+qHv8aO1Su17z9IhfQJ11xdXRQ8cjOza19m9ZDLmf2tpwZkPUoFo08fvZO5ZU9QGD6JzM69JNBCk4nGiYto6eCgDGVfxgKyz7+NzFGn2Y6rutHz6fegtfkwO/5wNTNa/smnGTcy++bf4nA6fb4epYKVcbtZ/edvML5yBcVJZxI+5QpOO3MhnR3tFL77dyJ3vMCk1vUAbIo7i+h532LC6Rfq6ZoDgBb9Y9RWllLz6FWM7Sxi7cTvM/ua7/t0+UqFiqqyvex+/UEmHlxOIs3sdeTQ6Ygkyt1MjLuZKHP0xdrrHcmUJ06HnLlkTDmPzFET9UtiAGjR72bDm38h59N7iTGtbD/jQfIuvsFny1YqVLU0NbJlxZ+JK34NlyOczrA4XBHxuMNiQMQzkzFENh0gt2UzSXiGhJY4sqkY/WXGXfjvR12YyLjdtLe36kkO+yigir6IXAL8DnACjxlj/udE8/uq6DfWVbPrydvIP/Q2xc7RhH15CSNP6/E1UUoNILfLxf6dG6jc9BZJxS8xvquITuOkMDoPh+kkqaOSNHcNkdJJPQnUOtM4HDkMV1g04Z2HiehqIsrVTLhpJ9x0EEEnTlzUONJoiMmmPXEUztTRxKSNJDljNKkZuURGxdj+s/0qYIq+iDiBncCFQCmwFrjWGLP9eM/pT9FvrKtm99o36Nz5NqNr3yfRHKYg52byr/8J4RGRfVqmUsq39m5fS+UHj5NR/SGtjniaoofTGZuBiYzH0VROVPNBEjsqCDcdtDriaHfG0REWhyssGrcjAuOMAHEQ1VxGStsBhrsrCJOjRxbVkERd2FCaoobTEZuBcUYgrg7E1Y50tRHRXkdMZx3xXfVE0EFlRDaHE8ZC+mQiUzJxd7bh7mjDdLXjiIwlKmk4cUOGE5+STkd7K62H62k/XE9nezMRMYlExacQG59MdHwiEZHRhIWFf9aNZdxuOjs7aG9robO9lY72VjrbWzFuFwkpw0hITuv3/sVAKvpzgfuMMRd7H98DYIz5+fGe05ei39bSxP7fnMvozl04xdBiItkZM4PYi+9l7PSz+/MnKKUCXGdHO5X7d9FQsYeW6n246vfjPFRKdGu591dENU7cdBBOh0TQQThNzkSaw1Noj0zBOMJJOLyHEZ17iRXfHKDmMkIH4QiGKOk8cX7jpEESqA7PZOIPPunT+gLpwuiZwIFuj0uB2cfOJCKLgcUA2dnZp7ySqJg4DsXksCZxHomTLmTMjPlM1/H3SoWE8IhIssZMJmvM5BPPBxzZY5Dew3S3y8XB/btoqq8gLCKasIhIwiKiaW85RHPtQdoaK+k6XIMjIhpndBIRsYk4I2PobDlEZ0sDrpYGTHsTpqsdutoRVwdGBHFGQlgUhEUg4VFIWCSO8ChA6GqqwTRV4WypBhmYHdz+Lvq9YoxZAiwBz5Z+X5aRf9dyn2ZSSoUWh9NJRu4EyJ1gO4pP+XusVBkwotvjLG+bUkopP/B30V8LjBWRXBGJABYBr/o5g1JKhSy/du8YY7pE5A7gTTxDNpcaY7b5M4NSSoUyv/fpG2NWACv8vV6llFJ6lk2llAopWvSVUiqEaNFXSqkQokVfKaVCSMCfZVNEqoF9A7DoVKBmAJbbX4GYKxAzgeY6FYGYCQIzVyBmglPLlWOMSetpQsAX/YEiIgXHOzeFTYGYKxAzgeY6FYGYCQIzVyBmAt/l0u4dpZQKIVr0lVIqhIRy0V9iO8BxBGKuQMwEmutUBGImCMxcgZgJfJQrZPv0lVIqFIXylr5SSoUcLfpKKRVCQqLoi8gIEXlPRLaLyDYR+Y63PUVE3hKRXd5/k/2YKUpE1ojIJm+m+73tuSKyWkSKReRZ7ymo/U5EnCKyQUReC5RcIlIiIltEZKOIFHjbrL2H3vUnichyEdkhIoUiMjcAMo33vkZHbodE5M4AyPVd72d9q4gs8/4fCITP1Xe8mbaJyJ3eNr+/ViKyVESqRGRrt7Yec4jHQ97XbbOI5PV2PSFR9IEu4G5jzERgDnC7iEwEvg+8Y4wZC7zjfewv7cB5xphpwHTgEhGZA/wCeNAYMwaoB272Y6buvgMUdnscKLnONcZM7zZe2eZ7CPA74A1jzARgGp7XzGomY0yR9zWaDswEWoCXbOYSkUzg20C+MWYynlOrL8Ly50pEJgO3ALPwvH+XicgY7LxWTwKXHNN2vByXAmO9t8XAI71eizEm5G7AK8CFQBEw3Ns2HCiylCcGWI/nesE1QJi3fS7wpoU8Wd4P2HnAa4AESK4SIPWYNmvvIZAI7MU7ICIQMvWQ8SLgE9u5+Nf1sVPwnNL9NeBi258r4CvA490e/xD4nq3XChgJbD3ZZwn4M3BtT/Od7BYqW/qfEZGRwAxgNZBujCn3Tqqg5+sjD2QWp4hsBKqAt4DdQIMxpss7Syme/yz+9ls8H3y39/GQAMllgJUisk5EFnvbbL6HuUA18IS3K+wxEYm1nOlYi4Bl3vvWchljyoD/BfYD5UAjsA77n6utwNkiMkREYoAFeC7pGijv4fFyHPkSPaLXr11IFX0RiQNeAO40xhzqPs14vi79On7VGOMynp/gWXh+Xlq/ArOIXAZUGWPW2c7Sg7OMMXl4ftreLiLzuk+08B6GAXnAI8aYGUAzx3QD2PhcHeHtH18IPH/sNH/n8vZFX47nizIDiOXzXRl+Z4wpxNPFtBJ4A9gIuI6Zx9p7OBA5Qqboi0g4noL/tDHmRW9zpYgM904fjmeL2++MMQ3Ae3h+3iaJyJErmtm4cPyZwEIRKQGewdPF87sAyHVkaxFjTBWePupZ2H0PS4FSY8xq7+PleL4EAuJzhefLcb0xptL72GauC4C9xphqY0wn8CKez1ogfK4eN8bMNMbMw7NfYSeB8x4eL0cZnl8kR/T6tQuJoi8iAjwOFBpjftNt0qvATd77N+Hp6/dXpjQRSfLej8azj6EQT/H/so1MAMaYe4wxWcaYkXi6Bt41xlxnO5eIxIpI/JH7ePqqt2LxPTTGVAAHRGS8t+l8YLvNTMe4ln917YDdXPuBOSIS4/3/eOS1svq5AhCRod5/s4EvAX8ncN7D4+V4FbjRO4pnDtDYrRvoxPy508TWDTgLz8+izXh+vm3E03c3BM8Oy13A20CKHzNNBTZ4M20FfuRtHwWsAYrx/CyPtPi6zQdeC4Rc3vVv8t62AT/wtlt7D73rnw4UeN/Hl4Fk25m8uWKBWiCxW5vt1+p+YIf38/5XINL258qb6yM8X0CbgPNtvVZ4vqDLgU48vyJvPl4OPIMr/oBnP+AWPKOierUePQ2DUkqFkJDo3lFKKeWhRV8ppUKIFn2llAohWvSVUiqEaNFXSqkQokVfKaVCiBZ9pZQKIf8fsgx7vVGXJuQAAAAASUVORK5CYII=\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "y2, x2 = np.histogram(exploits[\"age\"], bins=range(18, 101))\n",
+    "plt.plot(x1[:-1],y1)\n",
+    "plt.plot(x2[:-1],y2)"
+   ]
+  }
+ ]
+}
\ No newline at end of file