{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d1dd31bc-d251-49fe-85e9-26efafac52af",
   "metadata": {},
   "source": [
    "# Figure 3: Histogram comparing sampling strategies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "262aa4fa-b0fb-4098-b9b2-a643b17a3787",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import pmagpy.pmag as pmag\n",
    "import pmagpy.ipmag as ipmag\n",
    "\n",
    "import smpsite as smp\n",
    "\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59ae48db-0716-41c5-b998-7da31cb418b9",
   "metadata": {},
   "source": [
    "### Run simulation\n",
    "\n",
    "This notebook also includes how to run the simulation for the histograms. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5c16ca97-32cc-4cab-8afc-9d5a8b6ab1e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 33min 41s, sys: 123 ms, total: 33min 41s\n",
      "Wall time: 33min 41s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "angular_dispersio_within_site = 10 # degrees\n",
    "kappa_within_site = smp.angular2kappa(angular_dispersio_within_site)\n",
    "latitude = 30\n",
    "outlier_rate = 0.10\n",
    "n_iters = 5000\n",
    "\n",
    "params1 = smp.Params(N=100,\n",
    "                     n0=1,\n",
    "                     kappa_within_site=kappa_within_site,\n",
    "                     site_lat=latitude, \n",
    "                     site_long=0,\n",
    "                     outlier_rate=outlier_rate,\n",
    "                     secular_method=\"G\",\n",
    "                     kappa_secular=None)\n",
    "\n",
    "params2 = smp.Params(N=20,\n",
    "                     n0=5,\n",
    "                     kappa_within_site=kappa_within_site,\n",
    "                     site_lat=latitude, \n",
    "                     site_long=0,\n",
    "                     outlier_rate=outlier_rate,\n",
    "                     secular_method=\"G\",\n",
    "                     kappa_secular=None)\n",
    "\n",
    "df_false    = smp.simulate_estimations(params1, n_iters=n_iters, ignore_outliers=\"False\")\n",
    "df_true     = smp.simulate_estimations(params2, n_iters=n_iters, ignore_outliers=\"True\")\n",
    "df_vandamme = smp.simulate_estimations(params1, n_iters=n_iters, ignore_outliers=\"vandamme\")\n",
    "\n",
    "df_false.to_csv(\"../../outputs/fig3a_df_false.csv\")\n",
    "df_true.to_csv(\"../../outputs/fig3a_df_true.csv\") \n",
    "df_vandamme.to_csv(\"../../outputs/fig3a_df_vandamme.csv\") "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ab5c9e2-03cd-409a-8b77-14193c3eb038",
   "metadata": {},
   "source": [
    "### Figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2502a08e-9195-4ff3-bf05-4a89e7996d88",
   "metadata": {},
   "outputs": [],
   "source": [
    "# panel = 'a'\n",
    "# panel = 'b'\n",
    "panel = 'c'\n",
    "\n",
    "df_false = pd.read_csv(\"../../outputs/fig3\"+panel+\"_df_false.csv\")\n",
    "df_true = pd.read_csv(\"../../outputs/fig3\"+panel+\"_df_true.csv\")\n",
    "df_vandamme = pd.read_csv(\"../../outputs/fig3\"+panel+\"_df_vandamme.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "32d2160a-6b43-43a6-a332-662c37d80b96",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>plong</th>\n",
       "      <th>plat</th>\n",
       "      <th>total_samples</th>\n",
       "      <th>samples_per_sites</th>\n",
       "      <th>S2_vgp</th>\n",
       "      <th>error_angle</th>\n",
       "      <th>S2_vgp_real</th>\n",
       "      <th>n_tot</th>\n",
       "      <th>N</th>\n",
       "      <th>n0</th>\n",
       "      <th>kappa_within_site</th>\n",
       "      <th>site_lat</th>\n",
       "      <th>site_long</th>\n",
       "      <th>outlier_rate</th>\n",
       "      <th>secular_method</th>\n",
       "      <th>kappa_secular</th>\n",
       "      <th>ignore_outliers</th>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>55.662167</td>\n",
       "      <td>85.483652</td>\n",
       "      <td>34.0</td>\n",
       "      <td>5</td>\n",
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       "      <td>34.0</td>\n",
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       "      <td>True</td>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>358.720274</td>\n",
       "      <td>86.835100</td>\n",
       "      <td>34.0</td>\n",
       "      <td>5</td>\n",
       "      <td>461.786418</td>\n",
       "      <td>3.164900</td>\n",
       "      <td>191.7229</td>\n",
       "      <td>100</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>66.069981</td>\n",
       "      <td>30</td>\n",
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       "      <td>True</td>\n",
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       "      <td>224.066484</td>\n",
       "      <td>87.495521</td>\n",
       "      <td>28.0</td>\n",
       "      <td>5</td>\n",
       "      <td>206.023724</td>\n",
       "      <td>2.504479</td>\n",
       "      <td>191.7229</td>\n",
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       "      <td>0</td>\n",
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       "      <th>4995</th>\n",
       "      <td>4995</td>\n",
       "      <td>165.292979</td>\n",
       "      <td>84.319601</td>\n",
       "      <td>39.0</td>\n",
       "      <td>5</td>\n",
       "      <td>121.964481</td>\n",
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       "      <td>87.047602</td>\n",
       "      <td>46.0</td>\n",
       "      <td>5</td>\n",
       "      <td>175.154300</td>\n",
       "      <td>2.952398</td>\n",
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       "      <td>40.0</td>\n",
       "      <td>5</td>\n",
       "      <td>163.134369</td>\n",
       "      <td>4.177518</td>\n",
       "      <td>191.7229</td>\n",
       "      <td>100</td>\n",
       "      <td>20</td>\n",
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       "      <td>66.069981</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>4998</td>\n",
       "      <td>14.220743</td>\n",
       "      <td>87.025506</td>\n",
       "      <td>48.0</td>\n",
       "      <td>5</td>\n",
       "      <td>163.107882</td>\n",
       "      <td>2.974494</td>\n",
       "      <td>191.7229</td>\n",
       "      <td>100</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>66.069981</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>G</td>\n",
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       "      <td>True</td>\n",
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       "      <th>4999</th>\n",
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       "      <td>137.642242</td>\n",
       "      <td>86.984741</td>\n",
       "      <td>41.0</td>\n",
       "      <td>5</td>\n",
       "      <td>301.022274</td>\n",
       "      <td>3.015259</td>\n",
       "      <td>191.7229</td>\n",
       "      <td>100</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>66.069981</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0       plong       plat  total_samples  samples_per_sites  \\\n",
       "0              0  190.306018  84.314629           36.0                  5   \n",
       "1              1   55.662167  85.483652           34.0                  5   \n",
       "2              2  193.984859  86.462183           34.0                  5   \n",
       "3              3  358.720274  86.835100           34.0                  5   \n",
       "4              4  224.066484  87.495521           28.0                  5   \n",
       "...          ...         ...        ...            ...                ...   \n",
       "4995        4995  165.292979  84.319601           39.0                  5   \n",
       "4996        4996    9.619776  87.047602           46.0                  5   \n",
       "4997        4997  338.354021  85.822482           40.0                  5   \n",
       "4998        4998   14.220743  87.025506           48.0                  5   \n",
       "4999        4999  137.642242  86.984741           41.0                  5   \n",
       "\n",
       "          S2_vgp  error_angle  S2_vgp_real  n_tot   N  n0  kappa_within_site  \\\n",
       "0     218.096967     5.685371     191.7229    100  20   5          66.069981   \n",
       "1     215.659161     4.516348     191.7229    100  20   5          66.069981   \n",
       "2     265.162109     3.537817     191.7229    100  20   5          66.069981   \n",
       "3     461.786418     3.164900     191.7229    100  20   5          66.069981   \n",
       "4     206.023724     2.504479     191.7229    100  20   5          66.069981   \n",
       "...          ...          ...          ...    ...  ..  ..                ...   \n",
       "4995  121.964481     5.680399     191.7229    100  20   5          66.069981   \n",
       "4996  175.154300     2.952398     191.7229    100  20   5          66.069981   \n",
       "4997  163.134369     4.177518     191.7229    100  20   5          66.069981   \n",
       "4998  163.107882     2.974494     191.7229    100  20   5          66.069981   \n",
       "4999  301.022274     3.015259     191.7229    100  20   5          66.069981   \n",
       "\n",
       "      site_lat  site_long  outlier_rate secular_method  kappa_secular  \\\n",
       "0           30          0           0.6              G            NaN   \n",
       "1           30          0           0.6              G            NaN   \n",
       "2           30          0           0.6              G            NaN   \n",
       "3           30          0           0.6              G            NaN   \n",
       "4           30          0           0.6              G            NaN   \n",
       "...        ...        ...           ...            ...            ...   \n",
       "4995        30          0           0.6              G            NaN   \n",
       "4996        30          0           0.6              G            NaN   \n",
       "4997        30          0           0.6              G            NaN   \n",
       "4998        30          0           0.6              G            NaN   \n",
       "4999        30          0           0.6              G            NaN   \n",
       "\n",
       "      ignore_outliers  \n",
       "0                True  \n",
       "1                True  \n",
       "2                True  \n",
       "3                True  \n",
       "4                True  \n",
       "...               ...  \n",
       "4995             True  \n",
       "4996             True  \n",
       "4997             True  \n",
       "4998             True  \n",
       "4999             True  \n",
       "\n",
       "[5000 rows x 18 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f0863458-1d3d-421f-8c50-d1cb25782446",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "if panel == 'a':\n",
    "    x_max = 8\n",
    "    y_max = 0.52\n",
    "    bw = 0.5\n",
    "elif panel == 'b':\n",
    "    x_max = 8\n",
    "    y_max = 0.4\n",
    "    bw = 0.5\n",
    "elif panel == 'c':\n",
    "    x_max = 14\n",
    "    y_max = 0.25\n",
    "    bw = 1.0\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(10,8))\n",
    "\n",
    "# Histograms\n",
    "sns.histplot(df_vandamme.error_angle, ax=axes, color='#e84118', stat='density', binwidth=bw, binrange=(0,20), alpha=.5, label=\"Strategy 1 \\n $n_0=1$, $N=100$\")\n",
    "sns.histplot(df_true.error_angle, ax=axes, color='#0097e6', stat='density', binwidth=bw, binrange=(0,20), alpha=.5, label=\"Strategy 2 \\n $n_0=5$, $N=20$\")\n",
    "\n",
    "# Density plot\n",
    "sns.kdeplot(df_false.error_angle, ax=axes, color='grey', alpha=.9, lw=5, label=\"No outlier detection \\n $n_0=1$, $N=100$\")\n",
    "\n",
    "\n",
    "rmse1 = np.round(np.mean(df_vandamme.error_angle**2)**.5, decimals=2)\n",
    "rmse2 = np.round(np.mean(df_true.error_angle**2)**.5, decimals=2)\n",
    "\n",
    "plt.axvline(x=rmse1, ymax=0.93, c='#e84118', lw=5)\n",
    "plt.axvline(x=rmse2, ymax=0.93, c='#0097e6', lw=5)\n",
    "\n",
    "props = dict(boxstyle='round', facecolor=\"#e84118\", alpha=0.5)\n",
    "plt.text(rmse1/x_max-0.035, 0.986, \"{}\".format(rmse1), transform=axes.transAxes, fontsize=18,\n",
    "        verticalalignment='top', bbox=props);\n",
    "\n",
    "props = dict(boxstyle='round', facecolor='#0097e6', alpha=0.5)\n",
    "plt.text(rmse2/x_max-0.035, 0.986, \"{}\".format(rmse2), transform=axes.transAxes, fontsize=18,\n",
    "        verticalalignment='top', bbox=props);\n",
    "\n",
    "plt.xlim(0, x_max)\n",
    "plt.ylim(0, y_max)\n",
    "plt.xlabel(\"Angular error (degrees)\", fontsize=18)\n",
    "plt.ylabel(\"Density\", fontsize=18)\n",
    "plt.xticks(np.arange(0.0, x_max+0.1, 2.0), fontsize=14);\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(title=\"Method ($n=100$)\", title_fontsize=18, fontsize=18)\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.spines[['right', 'top']].set_visible(False)\n",
    "\n",
    "plt.savefig(\"Figure3{}.pdf\".format(panel), format=\"pdf\", bbox_inches='tight')\n",
    "plt.savefig(\"Figure3{}.png\".format(panel), format=\"png\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1ef5cad-2298-46d2-9ab5-218d23173ea6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:.conda-paleostats]",
   "language": "python",
   "name": "conda-env-.conda-paleostats-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}