Restore transients, these were incidentily removed

SLR_projections/PDF_approach.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PDF SLR - Horton et al.\n",
+    "Horton, B.P., Khan, N.S., Cahill, N. et al. Estimating global mean sea-level rise and its uncertainties by 2100 and 2300 from an expert survey. npj Clim Atmos Sci 3, 18 (2020). https://doi.org/10.1038/s41612-020-0121-5\n",
+    "### Draw sea level rise projections from probability density functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p = Path().absolute() / 'Horton_PDFs' #move to subfolder with pathlib\n",
+    "files = list(p.glob(\"*.csv\")) #open all csv-files in this folder\n",
+    "\n",
+    "for file in files:\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "WindowsPath('D:/Python/Urban-SETP/SLR_projections/Horton_PDFs/rsl_pred_SurveyH14_Blue_2100.csv')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file = files[0]\n",
+    "file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>name</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.001</th>\n",
+       "      <td>0.054063</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.002</th>\n",
+       "      <td>0.068623</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.003</th>\n",
+       "      <td>0.078123</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.004</th>\n",
+       "      <td>0.085309</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.005</th>\n",
+       "      <td>0.091256</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.996</th>\n",
+       "      <td>1.325282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.997</th>\n",
+       "      <td>1.364257</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.998</th>\n",
+       "      <td>1.415482</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.999</th>\n",
+       "      <td>1.492232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.000</th>\n",
+       "      <td>1.642124</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          value\n",
+       "name           \n",
+       "0.001  0.054063\n",
+       "0.002  0.068623\n",
+       "0.003  0.078123\n",
+       "0.004  0.085309\n",
+       "0.005  0.091256\n",
+       "...         ...\n",
+       "0.996  1.325282\n",
+       "0.997  1.364257\n",
+       "0.998  1.415482\n",
+       "0.999  1.492232\n",
+       "1.000  1.642124\n",
+       "\n",
+       "[1000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(file,index_col=0)\n",
+    "df.index = df.index/1000 #convert to probability [0.001,1]\n",
+    "df.value = df.value/100 #convert to cm\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}