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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "zn_mod = np.array([0,1,5,10,20,50])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "ze_mod = (zn_mod[1:] + zn_mod[:-1])/2\n",
    "\n",
    "mod_value = np.exp(-ze_mod/5)\n",
    "\n",
    "z_obs = np.arange(1,45)\n",
    "obs_value = np.exp(-z_obs/5)*(1+np.random.normal(scale=0.1,size=len(z_obs))) + np.random.normal(scale=0.01,size=len(z_obs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1711af363d0>"
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     "metadata": {},
     "execution_count": 84
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.plot(mod_value, ze_mod,'ko-', label='Model')\n",
    "for z in zn_mod:\n",
    "    plt.axhline(y=z, linestyle='--')\n",
    "plt.ylim([51,-1])\n",
    "plt.xlim(-0.1,1.1)\n",
    "plt.scatter(obs_value, z_obs,marker='+',label='Observation')\n",
    "plt.ylabel(\"Depth (m)\")\n",
    "plt.xlabel(\"Tracer concentration\")\n",
    "plt.annotate(\"Model cell boundary\",xy=(0.8,19.9))\n",
    "plt.annotate(\"Model cell boundary\",xy=(0.8,9.9))\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5,\n",
       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5],\n",
       "      dtype=int64)"
      ]
     },
     "metadata": {},
     "execution_count": 85
    }
   ],
   "source": [
    "np.digitize(z_obs, zn_mod)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([ 0,  1,  5, 10, 20, 50])"
      ]
     },
     "metadata": {},
     "execution_count": 86
    }
   ],
   "source": [
    "zn_mod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "res = stats.binned_statistic(z_obs,obs_value,bins=zn_mod)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.0020456433901470455"
      ]
     },
     "metadata": {},
     "execution_count": 106
    }
   ],
   "source": [
    "res.statistic[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1712ce97490>"
      ]
     },
     "metadata": {},
     "execution_count": 112
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.plot(mod_value, ze_mod,'ko-', label='Model')\n",
    "for z in zn_mod:\n",
    "    plt.axhline(y=z, linestyle='--')\n",
    "plt.ylim([51,-1])\n",
    "plt.xlim(-0.1,1.1)\n",
    "#plt.scatter(obs_value, z_obs,marker='+',label='Observation')\n",
    "plt.ylabel(\"Depth (m)\")\n",
    "plt.xlabel(\"Tracer concentration\")\n",
    "plt.annotate(\"Model cell boundary\",xy=(0.8,19.9))\n",
    "plt.annotate(\"Model cell boundary\",xy=(0.8,9.9))\n",
    "plt.scatter(res.statistic,ze_mod, marker='+',label='Averaged observation')\n",
    "plt.annotate('',xy=(res.statistic[-4],5.0), xytext=(res.statistic[-4],1.0),arrowprops=dict(edgecolor='black', arrowstyle = '<->'))\n",
    "plt.annotate('',xy=(res.statistic[-3],10.0), xytext=(res.statistic[-3],5.0),arrowprops=dict(edgecolor='black', arrowstyle = '<->'))\n",
    "plt.annotate('',xy=(res.statistic[-2],20.0), xytext=(res.statistic[-2],10.0),arrowprops=dict(edgecolor='black', arrowstyle = '<->'))\n",
    "plt.annotate('',xy=(res.statistic[-1],50.0), xytext=(res.statistic[-1],20.0),arrowprops=dict(edgecolor='black', arrowstyle = '<->'))\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}