ash_geochem.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.family'] = 'sans-serif'\n",
    "plt.rcParams['font.sans-serif'] = ['Arial']\n",
    "plt.rcParams[\"mathtext.default\"] = 'regular'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change default plot resolution to 100dpi and save resolution to 300dpi\n",
    "sns.set(rc={\"figure.dpi\": 100, 'savefig.dpi': 300})\n",
    "sns.set_context('notebook')\n",
    "sns.set_style('ticks')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>Locality</th>\n",
       "      <th>Author</th>\n",
       "      <th>samples</th>\n",
       "      <th>SiO2</th>\n",
       "      <th>TiO2</th>\n",
       "      <th>Al2O3</th>\n",
       "      <th>Fe2O3</th>\n",
       "      <th>MnO</th>\n",
       "      <th>MgO</th>\n",
       "      <th>CaO</th>\n",
       "      <th>...</th>\n",
       "      <th>Ce</th>\n",
       "      <th>Nd</th>\n",
       "      <th>Sm</th>\n",
       "      <th>Yb</th>\n",
       "      <th>Hf</th>\n",
       "      <th>Ta</th>\n",
       "      <th>W</th>\n",
       "      <th>Pb</th>\n",
       "      <th>Th</th>\n",
       "      <th>U</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Meishan</td>\n",
       "      <td>He et al. (2014)</td>\n",
       "      <td>MS-A-25</td>\n",
       "      <td>56.276961</td>\n",
       "      <td>0.497733</td>\n",
       "      <td>29.156067</td>\n",
       "      <td>3.395642</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.661099</td>\n",
       "      <td>1.039708</td>\n",
       "      <td>...</td>\n",
       "      <td>174.40</td>\n",
       "      <td>78.39</td>\n",
       "      <td>14.50</td>\n",
       "      <td>4.72</td>\n",
       "      <td>14.98</td>\n",
       "      <td>2.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.11</td>\n",
       "      <td>61.29</td>\n",
       "      <td>6.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Meishan</td>\n",
       "      <td>He et al. (2014)</td>\n",
       "      <td>MS-A-28</td>\n",
       "      <td>63.031161</td>\n",
       "      <td>0.806276</td>\n",
       "      <td>17.923295</td>\n",
       "      <td>5.502288</td>\n",
       "      <td>0.032687</td>\n",
       "      <td>2.767487</td>\n",
       "      <td>4.804968</td>\n",
       "      <td>...</td>\n",
       "      <td>66.84</td>\n",
       "      <td>24.18</td>\n",
       "      <td>4.41</td>\n",
       "      <td>2.68</td>\n",
       "      <td>4.92</td>\n",
       "      <td>1.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.90</td>\n",
       "      <td>14.95</td>\n",
       "      <td>3.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Locality            Author  samples       SiO2      TiO2      Al2O3  \\\n",
       "0  Meishan  He et al. (2014)  MS-A-25  56.276961  0.497733  29.156067   \n",
       "1  Meishan  He et al. (2014)  MS-A-28  63.031161  0.806276  17.923295   \n",
       "\n",
       "      Fe2O3       MnO       MgO       CaO  ...      Ce     Nd     Sm    Yb  \\\n",
       "0  3.395642  0.000000  3.661099  1.039708  ...  174.40  78.39  14.50  4.72   \n",
       "1  5.502288  0.032687  2.767487  4.804968  ...   66.84  24.18   4.41  2.68   \n",
       "\n",
       "      Hf    Ta   W     Pb     Th     U  \n",
       "0  14.98  2.75 NaN  27.11  61.29  6.76  \n",
       "1   4.92  1.13 NaN  24.90  14.95  3.33  \n",
       "\n",
       "[2 rows x 48 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import data.\n",
    "file_path = '../manuscript_data.xlsx'\n",
    "data = pd.read_excel(file_path, sheet_name=\"ashes\")\n",
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define columns for visualization.\n",
    "Ti = data['TiO2']*10000\n",
    "Zr = data['Zr']\n",
    "Nb = data['Nb']\n",
    "Nb_Y = Nb/data['Y']\n",
    "Zr_Ti = Zr/Ti\n",
    "Th = data['Th']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = [\"Chaotian\", 'Rencunping', \"Jianshi\", \"Meishan\",\n",
    "            \"Shangsi\", \"Dongpan\", \"mest\", \"Shanggang\", \"Qiakong\"]\n",
    "markers = [\"^\",\"X\",'s','P', 'v', 'p', '*','8','D', 'o']\n",
    "colors =[\"#d84727\",\"#3625be\", \"#289cc9\", \"#f7accf\",\n",
    "          \"red\", \"#51291e\", \"#903AB8\", \"#903AB8\",\n",
    "          'black','#33cb9c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot N-MORB normalized Nb vs Th values\n",
    "plt.figure(figsize=(5, 4))\n",
    "p = sns.scatterplot(x=Nb/2.33, y=Th/0.12, hue='Locality', data=data,\n",
    "                    alpha=0.9, style='Locality', color='black',\n",
    "                    s=50, edgecolor='black',markers=markers,\n",
    "                    palette=colors,\n",
    "                    )\n",
    "plt.xlabel('$Nb_N$', size=11, font='Arial')\n",
    "plt.ylabel(u'$Th_N$', size=11, font='Arial')\n",
    "p.tick_params(labelsize=10)\n",
    "p.set(xscale='log', yscale='log')\n",
    "p.set_xlim(.01, 100)\n",
    "p.set_ylim(.01, 1000)\n",
    "plt.legend(loc=\"lower right\", frameon=True, fontsize=8,\n",
    "           ncol=1, edgecolor='black', facecolor='white')\n",
    "# plt.savefig('../plots/ThNb_Norm.pdf', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# variables for the plots.\n",
    "x = [Zr, Nb_Y]\n",
    "y = [Ti, Zr_Ti]\n",
    "label_x = ['Zr (ppm)', 'Nb/Y']\n",
    "label_y = ['Ti (ppm)', u'Zr/Ti']\n",
    "xlim = [(10, 10000), (0.01, 10)]\n",
    "ylim = [(100, 100000), (0.002, 2)]\n",
    "name= ['Ti_Zr.pdf', 'NbY-ZrTi.pdf']\n",
    "# folder to save figures to.\n",
    "save_path ='../plots/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make Ti vs Zr and Nb/Y vs Zr/Ti plots iteratively.\n",
    "for i in range(2):\n",
    "    plt.figure(figsize=(5, 5))\n",
    "    p = sns.scatterplot(x=x[i], y=y[i], hue='Locality', data=data,\n",
    "                        alpha=0.9, style='Locality',\n",
    "                        s=50, edgecolor='black', markers=markers,\n",
    "                        color='black',  legend = False,\n",
    "                        palette=colors,\n",
    "                        )\n",
    "    plt.xlabel(label_x[i], size=11, font='Arial')\n",
    "    plt.ylabel(label_y[i], size=11, font='Arial')\n",
    "    p.tick_params(labelsize=10)\n",
    "    p.set(yscale='log', xscale='log')\n",
    "    p.set_xlim(xlim[i])\n",
    "    p.set_ylim(ylim[i])\n",
    "    \n",
    "    # plt.savefig(f'{save_path}{name[i]}')\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.9.16"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "73a6cb75fa7d3153665b588525f8b31ea176e9a7bdb5699ea70f5f62658ab616"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}