docs/Examples/Other_Useful_Functions/Converting_S_values.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fde7a051-e845-4b34-8465-e4fcc07eb22b",
   "metadata": {},
   "source": [
    "# Converting S isotope systematics, and S concentrations\n",
    "- This notebook shows simple functions for common conversion workflows, e.g., converting from d34 to 34/32S ratios, converting SO3 to S in ppm etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1fc41642-9f48-4e49-9f40-17a33b92a761",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import PySulfSat as ss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed74fb20-e911-4656-9003-d54e57879a24",
   "metadata": {},
   "source": [
    "## Converting between different forms of S isotope notation\n",
    "- S isotopes are sometimes quoted in terms of $$\\delta^{32}S$$, where the $$\\frac{32S}{34S}$$ ratio of the sample has been referenced to a community standard. \n",
    "- In the literature, the most commmonly used standard is the Canyon Diablo Troilite (CDT). A lot of papers seem to use the ratio for the Vienna-CDT (VCDT) quoted in Ding et al. (2001, https://doi.org/10.1016/S0016-7037(01)00611-1), of 1/22.6436, which is what these functions use by default (although any ratio can be entered). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "93675a63-6bc9-4108-b19b-3d08af3367d6",
   "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>δ34S</th>\n",
       "      <th>δ34S SEE</th>\n",
       "      <th>34S/32S ratio raw measured</th>\n",
       "      <th>34S/32S ratio (1SE) raw measured</th>\n",
       "      <th>34S/32S ratio IMF</th>\n",
       "      <th>SEE_scaled</th>\n",
       "      <th>S_ppm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.246318</td>\n",
       "      <td>0.467748</td>\n",
       "      <td>0.044336</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.044153</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.816959</td>\n",
       "      <td>0.921352</td>\n",
       "      <td>0.044222</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.044039</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.546365</td>\n",
       "      <td>0.767581</td>\n",
       "      <td>0.044234</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.044051</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.584560</td>\n",
       "      <td>0.723941</td>\n",
       "      <td>0.044321</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.044138</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.317419</td>\n",
       "      <td>0.450185</td>\n",
       "      <td>0.044361</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.044178</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       δ34S  δ34S SEE  34S/32S ratio raw measured  \\\n",
       "0 -0.246318  0.467748                    0.044336   \n",
       "1 -2.816959  0.921352                    0.044222   \n",
       "2 -2.546365  0.767581                    0.044234   \n",
       "3 -0.584560  0.723941                    0.044321   \n",
       "4  0.317419  0.450185                    0.044361   \n",
       "\n",
       "   34S/32S ratio (1SE) raw measured  34S/32S ratio IMF  SEE_scaled  S_ppm  \n",
       "0                          0.000016           0.044153    0.000021   1000  \n",
       "1                          0.000039           0.044039    0.000041   1100  \n",
       "2                          0.000031           0.044051    0.000034    900  \n",
       "3                          0.000029           0.044138    0.000032    500  \n",
       "4                          0.000015           0.044178    0.000020    400  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets load in some S data from Lerner et al. (2021)\n",
    "df=pd.read_excel('Sulfur_Conversions.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e086a350-26b2-476e-a7dd-721b664ce101",
   "metadata": {},
   "source": [
    "### Lets first convert from 34/32S to d34S using the default standard value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "86a8cb66-858c-43bc-a79a-260b4dc02420",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   -0.220035\n",
       "1   -2.790743\n",
       "2   -2.520142\n",
       "3   -0.558286\n",
       "4    0.343717\n",
       "5   -0.490636\n",
       "6   -1.144588\n",
       "7   -0.084735\n",
       "Name: 34S/32S ratio IMF, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d34_std=ss.convert_3432S_to_d34(S3432=df['34S/32S ratio IMF'])\n",
    "d34_std"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f63d5a1b-b3d7-4289-b08f-7b3db0a2f85d",
   "metadata": {},
   "source": [
    "## And lets conver this back "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dfaf4bea-bc1f-4235-927b-61d34c26f9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.044153\n",
       "1    0.044039\n",
       "2    0.044051\n",
       "3    0.044138\n",
       "4    0.044178\n",
       "5    0.044141\n",
       "6    0.044112\n",
       "7    0.044159\n",
       "Name: 34S/32S ratio IMF, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d3432S_calc=ss.convert_d34_to_3432S(d34S=d34_std)\n",
    "d3432S_calc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9519c7d7-a4f9-4594-aeb5-a44bb2556a76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ac5e5769d0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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AAACTCFQAgKHl1Cnpww+7n4EbxIAC1Y4dOxQeHi6r1Sq73a6SkpI+6w8cOCC73S6r1apx48Zp586dvdYWFBTIYrFo4cKFvdZkZ2fLYrEoIyPDqf2dd95RQkKCgoKCZLFYVFlZ2esxDMPQnDlzZLFYtG/fvj7HDwC4QeTnS2PHSjNndj/n51/vEQGSBhCo9uzZo4yMDK1bt04VFRWKjY3VnDlzVFdX57a+pqZGc+fOVWxsrCoqKrR27VotX75ce/fudak9efKkVq5cqdjY2F77P3LkiHJzczVx4kSXbV9//bWmT5+uTZs2XfU8tm7dKovFctU6AMAN4tQp6dlnpa6u7tddXVJqKitVuCF4HKi2bNmilJQUPfPMM5owYYK2bt2qsLAwvfrqq27rd+7cqbvuuktbt27VhAkT9Mwzz+gXv/iFNm/e7FTX2dmpxYsX64UXXtC4cePcHuvChQtavHix8vLydMcdd7hsT0pK0n/8x39o9uzZfZ7Dp59+qi1btuiNN97o51kDAK6748f/GaZ6dHZKJ05cn/EA3+FRoOro6FB5ebni4+Od2uPj43X48GG3+5SWlrrUJyQkqKysTJcvX3a0ZWVladSoUUpJSem1/2XLlmnevHlXDUx9uXjxop566inl5OTIZrNdtb69vV2tra1ODwDAdRARIQ373tuWl5c0fvz1GQ/wHR4FqubmZnV2dio4ONipPTg4WI2NjW73aWxsdFt/5coVNTc3S5IOHTqk/Px85eXl9dp3QUGBysvLlZ2d7cmQXaxYsULTpk3TggUL+lWfnZ2tgIAAxyMsLMxU/wCAAQoNlXJzu0OU1P382mvd7cB15j2Qnb5/75FhGH3ej+Suvqe9ra1NS5YsUV5enoKCgtzuX19fr/T0dO3fv19Wq3UgQ5YkFRYW6oMPPlBFRUW/91mzZo0yMzMdr1tbWwlVAHC9pKRICQndH/ONH0+Ywg3Do0AVFBQkLy8vl9WopqYml1WoHjabzW29t7e3AgMDdezYMdXW1mr+/PmO7V3ffkbu7e2t6upqVVVVqampSXa73VHT2dmpgwcPKicnR+3t7fLq+S+WPnzwwQf64osvNHLkSKf2RYsWKTY2Vh999JHLPn5+fvLz87vqsQEAJp061X2fVERE30EpNJQghRuOR4HK19dXdrtdxcXFevzxxx3txcXFvX6EFhMTo/fee8+pbf/+/YqOjpaPj48iIyNVVVXltH39+vVqa2vTK6+8orCwMI0ePdqlJjk5WZGRkVq1alW/wpQkrV69Ws8884xT2/3336+XX37ZKdABAP7F8vP/+Rd8w4Z1f7TXxz21wI3G44/8MjMzlZSUpOjoaMXExCg3N1d1dXVKS0uT1P0R2enTp7Vr1y5JUlpamnJycpSZmamlS5eqtLRU+fn52r17tyTJarUqKirKqY+eFaSedl9fX5caf39/BQYGOrWfO3dOdXV1OnPmjCSpurpaUvcq2Xcf33fXXXcpPDzc06kAAAyG3r4OISGBlSgMGR4HqsTERJ09e1ZZWVlqaGhQVFSUioqKNHbsWElSQ0OD03dShYeHq6ioSCtWrND27dsVEhKibdu2adGiRYN3Ft8qLCxUcnKy4/WTTz4pSdqwYYM2btw46P0BAAZBX1+HQKDCEGExeu4QR7+0trYqICBALS0tGjFixPUeDgAMfadOdX/r+XdDlZeXVFtLoMKgudbv3/yWHwDg+uLrEHATGNDXJgAAMKj4OgQMcQQqAMCNga9DwBDGR34AAAAmEagAAABMIlABAACYRKACAAAwiUAFAABgEoEKAADAJAIVAACASQQqAAAAkwhUAAAAJhGoAAAATCJQAQAAmESgAgAAMIlABQAAYBKBCgAAwCQCFQAAgEkEKgAAAJMIVAAAACYRqAAAAEwiUAEAAJhEoAIAADCJQAUAAGASgQoAAMAkAhUAAIBJBCoAAACTCFQAAAAmEagAAABMIlABAACYRKACAAAwiUAFAABgEoEKAADAJAIVAACASQQqAAAAkwhUAAAAJhGoAAAATCJQAQAAmDSgQLVjxw6Fh4fLarXKbrerpKSkz/oDBw7IbrfLarVq3Lhx2rlzZ6+1BQUFslgsWrhwYa812dnZslgsysjIcGp/5513lJCQoKCgIFksFlVWVjptP3funJ577jnde++9Gj58uO666y4tX75cLS0tVztlAACAXnkcqPbs2aOMjAytW7dOFRUVio2N1Zw5c1RXV+e2vqamRnPnzlVsbKwqKiq0du1aLV++XHv37nWpPXnypFauXKnY2Nhe+z9y5Ihyc3M1ceJEl21ff/21pk+frk2bNrnd98yZMzpz5ow2b96sqqoq/f73v9ef//xnpaSk9PPsAQAAXFkMwzA82WHq1KmaMmWKXn31VUfbhAkTtHDhQmVnZ7vUr1q1SoWFhfr73//uaEtLS9Onn36q0tJSR1tnZ6fi4uKUnJyskpISffXVV9q3b5/TsS5cuKApU6Zox44d+u1vf6vJkydr69atLn3W1tYqPDxcFRUVmjx5cp/n84c//EFLlizR119/LW9v76uef2trqwICAtTS0qIRI0ZctR4AAFx/1/r926MVqo6ODpWXlys+Pt6pPT4+XocPH3a7T2lpqUt9QkKCysrKdPnyZUdbVlaWRo0a1edq0bJlyzRv3jzNnj3bk2H3qWdiewtT7e3tam1tdXoAAAB819WXZL6jublZnZ2dCg4OdmoPDg5WY2Oj230aGxvd1l+5ckXNzc0aM2aMDh06pPz8fJd7nr6roKBA5eXlKisr82TIfTp79qx+85vfKDU1tdea7OxsvfDCC4PWJwAAuPkM6KZ0i8Xi9NowDJe2q9X3tLe1tWnJkiXKy8tTUFCQ2/3r6+uVnp6ut956S1ardSBDdtHa2qp58+bpvvvu04YNG3qtW7NmjVpaWhyP+vr6QekfAADcPDxaoQoKCpKXl5fLalRTU5PLKlQPm83mtt7b21uBgYE6duyYamtrNX/+fMf2rq6u7sF5e6u6ulpVVVVqamqS3W531HR2durgwYPKyclRe3u7vLy8+n0ebW1tevTRR/WDH/xA7777rnx8fHqt9fPzk5+fX7+PDQAAbj0eBSpfX1/Z7XYVFxfr8ccfd7QXFxdrwYIFbveJiYnRe++959S2f/9+RUdHy8fHR5GRkaqqqnLavn79erW1temVV15RWFiYRo8e7VKTnJysyMhIrVq1yqMw1draqoSEBPn5+amwsHDQVrwAAMCty6NAJUmZmZlKSkpSdHS0YmJilJubq7q6OqWlpUnq/ojs9OnT2rVrl6Tuv+jLyclRZmamli5dqtLSUuXn52v37t2SJKvVqqioKKc+Ro4cKUmOdl9fX5caf39/BQYGOrWfO3dOdXV1OnPmjCSpurpaUvcqmc1mU1tbm+Lj43Xx4kX993//t9NN5qNGjfIomAEAAPTwOFAlJibq7NmzysrKUkNDg6KiolRUVKSxY8dKkhoaGpy+kyo8PFxFRUVasWKFtm/frpCQEG3btk2LFi0avLP4VmFhoZKTkx2vn3zySUnShg0btHHjRpWXl+uTTz6RJI0fP95p35qaGt19992DPiYAAHDz8/h7qG51fA8VAABDzw31PVQAAABwRaACAAAwiUAFAABgEoEKAADAJAIVAACASQQqAAAAkwhUAAAAJhGoAAAATCJQAQAAmESgAoCBOnVK+vDD7mcAtzQCFQAMRH6+NHasNHNm93N+/vUeEYDriEAFAJ46dUp69lmpq6v7dVeXlJrKShVwCyNQAYCnjh//Z5jq0dkpnThxfcYD4LojUAGApyIipGHfu3x6eUnjx1+f8QC47ghUAOCp0FApN7c7REndz6+91t0O4Jbkfb0HAABDUkqKlJDQ/THf+PGEKeAWR6ACgIEKDSVIAZDER34AAACmEagAAABMIlABAACYRKACAAAwiUAFAABgEoEKAADAJAIVAACASQQqAAAAkwhUAAAAJhGoAAAATCJQAQAAmESgAgAAMIlABQAAYBKBCgAAwCQCFQAAgEkEKgAAAJMIVAAAACYRqAAAAEwiUAEAAJhEoAIAADBpQIFqx44dCg8Pl9Vqld1uV0lJSZ/1Bw4ckN1ul9Vq1bhx47Rz585eawsKCmSxWLRw4cJea7Kzs2WxWJSRkeHU/s477yghIUFBQUGyWCyqrKx02be9vV3PPfecgoKC5O/vr8cee0ynTp3qc/wAAAB98ThQ7dmzRxkZGVq3bp0qKioUGxurOXPmqK6uzm19TU2N5s6dq9jYWFVUVGjt2rVavny59u7d61J78uRJrVy5UrGxsb32f+TIEeXm5mrixIku277++mtNnz5dmzZt6nX/jIwMvfvuuyooKNDHH3+sCxcu6Cc/+Yk6Ozv7cfYAAACuLIZhGJ7sMHXqVE2ZMkWvvvqqo23ChAlauHChsrOzXepXrVqlwsJC/f3vf3e0paWl6dNPP1VpaamjrbOzU3FxcUpOTlZJSYm++uor7du3z+lYFy5c0JQpU7Rjxw799re/1eTJk7V161aXPmtraxUeHq6KigpNnjzZ0d7S0qJRo0bpv/7rv5SYmChJOnPmjMLCwlRUVKSEhISrnn9ra6sCAgLU0tKiESNGXLUeAABcf9f6/dujFaqOjg6Vl5crPj7eqT0+Pl6HDx92u09paalLfUJCgsrKynT58mVHW1ZWlkaNGqWUlJRe+1+2bJnmzZun2bNnezJsh/Lycl2+fNlpPCEhIYqKiup1/O3t7WptbXV6AAAAfJe3J8XNzc3q7OxUcHCwU3twcLAaGxvd7tPY2Oi2/sqVK2pubtaYMWN06NAh5efnu73nqUdBQYHKy8tVVlbmyZBdxuLr66s77rij3+PPzs7WCy+8MOA+AQDAzW9AN6VbLBan14ZhuLRdrb6nva2tTUuWLFFeXp6CgoLc7l9fX6/09HS99dZbslqtAxlyn/oa/5o1a9TS0uJ41NfXD3r/AABgaPNohSooKEheXl4uqzlNTU0uq1A9bDab23pvb28FBgbq2LFjqq2t1fz58x3bu7q6ugfn7a3q6mpVVVWpqalJdrvdUdPZ2amDBw8qJydH7e3t8vLyuur4bTabOjo6dP78eadVqqamJk2bNs3tPn5+fvLz87vqsQEAwK3LoxUqX19f2e12FRcXO7UXFxf3GkhiYmJc6vfv36/o6Gj5+PgoMjJSVVVVqqysdDwee+wxPfzww6qsrFRYWJhmzZrlUhMdHa3FixersrKyX2FKkux2u3x8fJzG09DQoKNHj/Y6fgAAgKvxaIVKkjIzM5WUlKTo6GjFxMQoNzdXdXV1SktLk9T9Ednp06e1a9cuSd1/0ZeTk6PMzEwtXbpUpaWlys/P1+7duyVJVqtVUVFRTn2MHDlSkhztvr6+LjX+/v4KDAx0aj937pzq6up05swZSVJ1dbWk7pUpm82mgIAApaSk6N///d8VGBioO++8UytXrtT9998/4BvdAQAAPA5UiYmJOnv2rLKystTQ0KCoqCgVFRVp7NixkrpXfL77nVTh4eEqKirSihUrtH37doWEhGjbtm1atGjR4J3FtwoLC5WcnOx4/eSTT0qSNmzYoI0bN0qSXn75ZXl7e+tnP/uZLl26pFmzZun3v/99v1e5AAAAvs/j76G61fE9VAAADD031PdQAQAAwBWBCgAAwCQCFQAAgEkEKgAAAJMIVAAAACYRqAAAAEwiUAEAAJhEoAIAADCJQAUAAGASgQoAAMAkAhUAAIBJBCoAAACTCFQAAAAmEagAAABMIlABAACYRKACAAAwiUAFAABgEoEKAADAJAIVAACASQQqAAAAkwhUAAAAJhGoAAAATCJQAQAAmESgAgAAMIlABQAAYBKBCgAAwCQCFQAAgEkEKgAAAJMIVAAAACYRqAAAAEwiUAEAAJhEoAIAADCJQAUAAGASgQoAAMAkAhUAAIBJBCoAAACTCFQAAAAmEagAAABMGlCg2rFjh8LDw2W1WmW321VSUtJn/YEDB2S322W1WjVu3Djt3Lmz19qCggJZLBYtXLiw15rs7GxZLBZlZGQ4tRuGoY0bNyokJES33XabZsyYoWPHjjnVNDY2KikpSTabTf7+/poyZYrefvvtq54zAABAbzwOVHv27FFGRobWrVuniooKxcbGas6cOaqrq3NbX1NTo7lz5yo2NlYVFRVau3atli9frr1797rUnjx5UitXrlRsbGyv/R85ckS5ubmaOHGiy7aXXnpJW7ZsUU5Ojo4cOSKbzaZHHnlEbW1tjpqkpCRVV1ersLBQVVVVeuKJJ5SYmKiKigpPpwIAAKCb4aEHH3zQSEtLc2qLjIw0Vq9e7bb++eefNyIjI53aUlNTjYceesip7cqVK8b06dON119/3Xj66aeNBQsWuByrra3NiIiIMIqLi424uDgjPT3dsa2rq8uw2WzGpk2bHG3ffPONERAQYOzcudPR5u/vb+zatcvpuHfeeafx+uuv93nePVpaWgxJRktLS7/qAQDA9Xet3789WqHq6OhQeXm54uPjndrj4+N1+PBht/uUlpa61CckJKisrEyXL192tGVlZWnUqFFKSUnptf9ly5Zp3rx5mj17tsu2mpoaNTY2OvXl5+enuLg4p7H9+Mc/1p49e3Tu3Dl1dXWpoKBA7e3tmjFjhts+29vb1dra6vQAAAD4Lm9Pipubm9XZ2ang4GCn9uDgYDU2Nrrdp7Gx0W39lStX1NzcrDFjxujQoUPKz89XZWVlr30XFBSovLxcZWVlvfbTc+zv93Xy5EnH6z179igxMVGBgYHy9vbW8OHD9e677+qee+5xe9zs7Gy98MILvY4LAABgQDelWywWp9eGYbi0Xa2+p72trU1LlixRXl6egoKC3O5fX1+v9PR0vfXWW7JarabGtn79ep0/f17vv/++ysrKlJmZqZ/+9Keqqqpye7w1a9aopaXF8aivr++zfwAAcOvxaIUqKChIXl5eLqtRTU1NLitDPWw2m9t6b29vBQYG6tixY6qtrdX8+fMd27u6uroH5+2t6upqVVVVqampSXa73VHT2dmpgwcPKicnR+3t7bLZbJK6V6rGjBnjdmxffPGFcnJydPToUf3oRz+SJE2aNEklJSXavn27278+9PPzk5+fX7/nCAAA3Ho8WqHy9fWV3W5XcXGxU3txcbGmTZvmdp+YmBiX+v379ys6Olo+Pj6KjIxUVVWVKisrHY/HHntMDz/8sCorKxUWFqZZs2a51ERHR2vx4sWqrKyUl5eXwsPDZbPZnPrq6OjQgQMHHGO7ePFi90kPcz5tLy8vR4gDAADwlEcrVJKUmZmppKQkRUdHKyYmRrm5uaqrq1NaWpqk7o/ITp8+rV27dkmS0tLSlJOTo8zMTC1dulSlpaXKz8/X7t27JUlWq1VRUVFOfYwcOVKSHO2+vr4uNf7+/goMDHS093wv1YsvvqiIiAhFREToxRdf1PDhw/Xzn/9ckhQZGanx48crNTVVmzdvVmBgoPbt26fi4mL98Y9/9HQqAAAAJA0gUCUmJurs2bPKyspSQ0ODoqKiVFRUpLFjx0qSGhoanL6TKjw8XEVFRVqxYoW2b9+ukJAQbdu2TYsWLRq8s/jW888/r0uXLumXv/ylzp8/r6lTp2r//v26/fbbJUk+Pj4qKirS6tWrNX/+fF24cEHjx4/Xm2++qblz5w76eAAAwK3BYvTcIY5+aW1tVUBAgFpaWjRixIjrPRwAANAP1/r9m9/yAwAAMIlABQAAYBKBCgAAwCQCFQAAgEkEKgAAAJMIVAAAACYRqAAAAEwiUAEAAJhEoAIAADCJQAUAAGASgQoAAMAkAhUAAIBJBCoAAACTCFQAAAAmEagAAABMIlABAACYRKACAAAwiUAFAABgEoEKAADAJAIVAACASQQqAAAAkwhUAAAAJhGoAAAATCJQAQAAmESgAgAAMIlABQAAYBKBCgAAwCQCFQAAgEkEKgAAAJMIVAAAACYRqAAAAEwiUAEAAJhEoAIAADCJQAUAAGASgQoAAMAkAhUAAIBJBCoAAACTBhSoduzYofDwcFmtVtntdpWUlPRZf+DAAdntdlmtVo0bN047d+7stbagoEAWi0ULFy7stSY7O1sWi0UZGRlO7YZhaOPGjQoJCdFtt92mGTNm6NixYy77l5aWaubMmfL399fIkSM1Y8YMXbp0qc9zAAAA6I3HgWrPnj3KyMjQunXrVFFRodjYWM2ZM0d1dXVu62tqajR37lzFxsaqoqJCa9eu1fLly7V3716X2pMnT2rlypWKjY3ttf8jR44oNzdXEydOdNn20ksvacuWLcrJydGRI0dks9n0yCOPqK2tzVFTWlqqRx99VPHx8frrX/+qI0eO6Fe/+pWGDWOxDgAADJDhoQcffNBIS0tzaouMjDRWr17ttv755583IiMjndpSU1ONhx56yKntypUrxvTp043XX3/dePrpp40FCxa4HKutrc2IiIgwiouLjbi4OCM9Pd2xraury7DZbMamTZscbd98840REBBg7Ny509E2depUY/369f09XRctLS2GJKOlpWXAxwAAAP9a1/r926NlmY6ODpWXlys+Pt6pPT4+XocPH3a7T2lpqUt9QkKCysrKdPnyZUdbVlaWRo0apZSUlF77X7ZsmebNm6fZs2e7bKupqVFjY6NTX35+foqLi3OMrampSZ988olGjx6tadOmKTg4WHFxcfr444977bO9vV2tra1ODwAAgO/yKFA1Nzers7NTwcHBTu3BwcFqbGx0u09jY6Pb+itXrqi5uVmSdOjQIeXn5ysvL6/XvgsKClReXq7s7Oxe++k5dm9j+/LLLyVJGzdu1NKlS/XnP/9ZU6ZM0axZs3T8+HG3x83OzlZAQIDjERYW1usYAQDArWlANw5ZLBan14ZhuLRdrb6nva2tTUuWLFFeXp6CgoLc7l9fX6/09HS99dZbslqtAx5bV1eXJCk1NVXJycl64IEH9PLLL+vee+/VG2+84fZ4a9asUUtLi+NRX1/fZ/8AAODW4+1JcVBQkLy8vFxWo5qamlxWhnrYbDa39d7e3goMDNSxY8dUW1ur+fPnO7b3BB9vb29VV1erqqpKTU1NstvtjprOzk4dPHhQOTk5am9vl81mk9S9UjVmzBi3Y+tpv++++5zGM2HChF5vqvfz85Ofn1/vkwIAAG55Hq1Q+fr6ym63q7i42Km9uLhY06ZNc7tPTEyMS/3+/fsVHR0tHx8fRUZGqqqqSpWVlY7HY489pocffliVlZUKCwvTrFmzXGqio6O1ePFiVVZWysvLS+Hh4bLZbE59dXR06MCBA46x3X333QoJCVF1dbXTeD7//HONHTvWk6kAAABw8GiFSpIyMzOVlJSk6OhoxcTEKDc3V3V1dUpLS5PU/RHZ6dOntWvXLklSWlqacnJylJmZqaVLl6q0tFT5+fnavXu3JMlqtSoqKsqpj5EjR0qSo93X19elxt/fX4GBgY72nu+levHFFxUREaGIiAi9+OKLGj58uH7+8587an79619rw4YNmjRpkiZPnqw333xT//jHP/T22297OhUAAACSBhCoEhMTdfbsWWVlZamhoUFRUVEqKipyrPA0NDQ4fXwWHh6uoqIirVixQtu3b1dISIi2bdumRYsWDd5ZfOv555/XpUuX9Mtf/lLnz5/X1KlTtX//ft1+++2OmoyMDH3zzTdasWKFzp07p0mTJqm4uFj33HPPoI8HAADcGixGzx3i6JfW1lYFBASopaVFI0aMuN7DAQAA/XCt37/5enAAAACTCFQAAAAmEagAAABMIlABAACYRKACAAAwiUAFAABgEoEKAADAJAIVAACASQQqAAAAkwhUAAAAJhGoAAAATCJQoX9OnZI+/LD7GQAAOCFQ4ery86WxY6WZM7uf8/Ov94gAALihEKjQt1OnpGeflbq6ul93dUmpqaxUAQDwHQQq9O348X+GqR6dndKJE9dnPAAA3IAIVOhbRIQ07Hv/TLy8pPHjr894AAC4ARGo0LfQUCk3tztESd3Pr73W3Q4AACRJ3td7ABgCUlKkhITuj/nGjydMAQDwPQQq9E9oKEEKAIBe8JEfAACASQQqAAAAkwhUAAAAJhGoAAAATCJQAQAAmESgAgAAMIlABQAAYBKBCgAAwCQCFQAAgEkEKgAAAJMIVAAAACYRqAAAAEwiUAEAAJhEoAIAADCJQAUAAGASgQoAAMAkAhUAAIBJBCoAAACTCFQAAAAmDShQ7dixQ+Hh4bJarbLb7SopKemz/sCBA7Lb7bJarRo3bpx27tzZa21BQYEsFosWLlzYa012drYsFosyMjKc2g3D0MaNGxUSEqLbbrtNM2bM0LFjx9wewzAMzZkzRxaLRfv27etz/AAAAH3xOFDt2bNHGRkZWrdunSoqKhQbG6s5c+aorq7ObX1NTY3mzp2r2NhYVVRUaO3atVq+fLn27t3rUnvy5EmtXLlSsbGxvfZ/5MgR5ebmauLEiS7bXnrpJW3ZskU5OTk6cuSIbDabHnnkEbW1tbnUbt26VRaLxYMzBwAAcM/jQLVlyxalpKTomWee0YQJE7R161aFhYXp1VdfdVu/c+dO3XXXXdq6dasmTJigZ555Rr/4xS+0efNmp7rOzk4tXrxYL7zwgsaNG+f2WBcuXNDixYuVl5enO+64w2mbYRjaunWr1q1bpyeeeEJRUVF68803dfHiRf3P//yPU+2nn36qLVu26I033vD09K+tU6ekDz/sfgYAAEOGR4Gqo6ND5eXlio+Pd2qPj4/X4cOH3e5TWlrqUp+QkKCysjJdvnzZ0ZaVlaVRo0YpJSWl1/6XLVumefPmafbs2S7bampq1NjY6NSXn5+f4uLinMZ28eJFPfXUU8rJyZHNZuv7hCW1t7ertbXV6XFN5OdLY8dKM2d2P+fnX5t+AADAoPMoUDU3N6uzs1PBwcFO7cHBwWpsbHS7T2Njo9v6K1euqLm5WZJ06NAh5efnKy8vr9e+CwoKVF5eruzs7F776Tl2X2NbsWKFpk2bpgULFvTa13dlZ2crICDA8QgLC+vXfh45dUp69lmpq6v7dVeXlJrKShUAAEPEgG5K//69R4Zh9Hk/krv6nva2tjYtWbJEeXl5CgoKcrt/fX290tPT9dZbb8lqtQ54bIWFhfrggw+0devWPo/xXWvWrFFLS4vjUV9f3+99++348X+GqR6dndKJE4PfFwAAGHTenhQHBQXJy8vLZTWqqanJZWWoh81mc1vv7e2twMBAHTt2TLW1tZo/f75je9e34cLb21vV1dWqqqpSU1OT7Ha7o6azs1MHDx5UTk6O2tvbHR/fNTY2asyYMW7H9sEHH+iLL77QyJEjncazaNEixcbG6qOPPnIZv5+fn/z8/K4yMyZFREjDhjmHKi8vafz4a9svAAAYFB6tUPn6+sput6u4uNipvbi4WNOmTXO7T0xMjEv9/v37FR0dLR8fH0VGRqqqqkqVlZWOx2OPPaaHH35YlZWVCgsL06xZs1xqoqOjtXjxYlVWVsrLy0vh4eGy2WxOfXV0dOjAgQOOsa1evVr/93//53QcSXr55Zf1u9/9zpOpGFyhoVJubneIkrqfX3utux0AANzwPFqhkqTMzEwlJSUpOjpaMTExys3NVV1dndLS0iR1f0R2+vRp7dq1S5KUlpamnJwcZWZmaunSpSotLVV+fr52794tSbJarYqKinLqo2cFqafd19fXpcbf31+BgYGO9p7vpXrxxRcVERGhiIgIvfjiixo+fLh+/vOfS+peLXN3I/pdd92l8PBwT6dicKWkSAkJ3R/zjR9PmAIAYAjxOFAlJibq7NmzysrKUkNDg6KiolRUVKSxY8dKkhoaGpy+kyo8PFxFRUVasWKFtm/frpCQEG3btk2LFi0avLP41vPPP69Lly7pl7/8pc6fP6+pU6dq//79uv322we9r2siNJQgBQDAEGQxeu4QR7+0trYqICBALS0tGjFixPUeDgAA6Idr/f7Nb/kBAACYRKACAAAwiUAFAABgEoEKAADAJAIVAACASQQqAAAAkwhUAAAAJhGoAAAATCJQAQAAmESgAgAAMMnj3/K71fX8Uk9ra+t1HgkAAOivnvfta/WLewQqD7W1tUmSwsLCrvNIAACAp9ra2hQQEDDox+XHkT3U1dWlM2fO6Pbbb5fFYrkmfbS2tiosLEz19fW37A8wMwfMgcQc9GAemAOJOZDMzYFhGGpra1NISIiGDRv8O55YofLQsGHDFBoa+i/pa8SIEbfs/2l6MAfMgcQc9GAemAOJOZAGPgfXYmWqBzelAwAAmESgAgAAMIlAdQPy8/PThg0b5Ofnd72Hct0wB8yBxBz0YB6YA4k5kG7sOeCmdAAAAJNYoQIAADCJQAUAAGASgQoAAMAkAhUAAIBJBCoP7dixQ+Hh4bJarbLb7SopKemz/sCBA7Lb7bJarRo3bpx27tzZa21BQYEsFosWLlzYa012drYsFosyMjKc2g3D0MaNGxUSEqLbbrtNM2bM0LFjx9wewzAMzZkzRxaLRfv27etz/O4M9TkoLS3VzJkz5e/vr5EjR2rGjBm6dOlSn+fwfUN5DhobG5WUlCSbzSZ/f39NmTJFb7/99lXP2Z0bdR7eeecdJSQkKCgoSBaLRZWVlS77tre367nnnlNQUJD8/f312GOP6dSpU32O352hOgfnzp3Tc889p3vvvVfDhw/XXXfdpeXLl6ulpeVqp+xiqM7Bd92s18X+zsHNfF3szxwMynXRQL8VFBQYPj4+Rl5envHZZ58Z6enphr+/v3Hy5Em39V9++aUxfPhwIz093fjss8+MvLw8w8fHx3j77bddamtra40f/vCHRmxsrLFgwQK3x/vrX/9q3H333cbEiRON9PR0p22bNm0ybr/9dmPv3r1GVVWVkZiYaIwZM8ZobW11Oc6WLVuMOXPmGJKMd99995aag8OHDxsjRowwsrOzjaNHjxqff/658Yc//MH45ptvbpk5mD17tvFv//ZvxieffGJ88cUXxm9+8xtj2LBhxt/+9rd+z8GNPg+7du0yXnjhBSMvL8+QZFRUVLjsn5aWZvzwhz80iouLjb/97W/Gww8/bEyaNMm4cuXKLTEHVVVVxhNPPGEUFhYaJ06cMP7yl78YERERxqJFi/p9/kN9Dr7rZr0u9mcObvbrYn/mYDCuiwQqDzz44INGWlqaU1tkZKSxevVqt/XPP/+8ERkZ6dSWmppqPPTQQ05tV65cMaZPn268/vrrxtNPP+32H0xbW5sRERFhFBcXG3FxcU7/YLq6ugybzWZs2rTJ0fbNN98YAQEBxs6dO52OU1lZaYSGhhoNDQ0DunAM9TmYOnWqsX79+v6erltDfQ78/f2NXbt2OR33zjvvNF5//fU+z/v7btR5+K6amhq3F9CvvvrK8PHxMQoKChxtp0+fNoYNG2b8+c9/7uWMXQ3lOXDnf//3fw1fX1/j8uXLV63tcTPMwc16XfyuvubgZr4ufldfczAY10U+8uunjo4OlZeXKz4+3qk9Pj5ehw8fdrtPaWmpS31CQoLKysp0+fJlR1tWVpZGjRqllJSUXvtftmyZ5s2bp9mzZ7tsq6mpUWNjo1Nffn5+iouLcxrbxYsX9dRTTyknJ0c2m63vE3ZjqM9BU1OTPvnkE40ePVrTpk1TcHCw4uLi9PHHH1/95L811OdAkn784x9rz549OnfunLq6ulRQUKD29nbNmDGjz3P/rht5HvqjvLxcly9fdhpPSEiIoqKieh3/9w31OXCnpaVFI0aMkLd3/37m9WaYg5v5utgfN/t1sb8G47rIjyP3U3Nzszo7OxUcHOzUHhwcrMbGRrf7NDY2uq2/cuWKmpubNWbMGB06dEj5+fl9frZfUFCg8vJylZWV9dpPz7G/39fJkycdr1esWKFp06ZpwYIFvfbVl6E+B19++aUkaePGjdq8ebMmT56sXbt2adasWTp69KgiIiJ6P/lvDfU5kKQ9e/YoMTFRgYGB8vb21vDhw/Xuu+/qnnvu6bXv77uR56E/Ghsb5evrqzvuuKPf4/++oT4H33f27Fn95je/UWpqar/3uRnm4Ga+LvbHzX5d7K/BuC4SqDxksVicXhuG4dJ2tfqe9ra2Ni1ZskR5eXkKCgpyu399fb3S09O1f/9+Wa3WAY+tsLBQH3zwgSoqKvo8Rn8M1Tno6uqSJKWmpio5OVmS9MADD+gvf/mL3njjDWVnZ/d57P7209/6nvZ/5RxI0vr163X+/Hm9//77CgoK0r59+/TTn/5UJSUluv/++/s8tqd99ae+p32w52EgrjZ+d26GOWhtbdW8efN03333acOGDR7vP1Tn4Fa5LvblVrkuXs1gXBcJVP0UFBQkLy8vl7Td1NTkkrJ72Gw2t/Xe3t4KDAzUsWPHVFtbq/nz5zu29/zj9vb2VnV1taqqqtTU1CS73e6o6ezs1MGDB5WTk6P29nbHMnVjY6PGjBnjdmwffPCBvvjiC40cOdJpPIsWLVJsbKw++uijm34Oetrvu+8+p/FMmDBBdXV1Vz3/m2EOvvjiC+Xk5Ojo0aP60Y9+JEmaNGmSSkpKtH379j7/ymaozIOXl9dVx2+z2dTR0aHz5887rVI1NTVp2rRpV58ADf056NHW1qZHH31UP/jBD/Tuu+/Kx8en3/sO9Tm42a+L/ZmDm/262J85GKzrIoGqn3x9fWW321VcXKzHH3/c0V5cXNzrUnFMTIzee+89p7b9+/crOjpaPj4+ioyMVFVVldP29evXq62tTa+88orCwsI0evRol5rk5GRFRkZq1apV8vLyUnh4uGw2m4qLi/XAAw9I6v5M+8CBA/rP//xPSdLq1av1zDPPOB3n/vvv18svv+z0D/ZmnoO7775bISEhqq6udjrW559/rjlz5twSc3Dx4kVJ0rBhzrdPenl5OS5WQ30e+sNut8vHx0fFxcX62c9+JklqaGjQ0aNH9dJLL/XrGEN9DqTulamEhAT5+fmpsLDQ4//KH+pzcLNfF/vjZr8u9sdgXRf5Kz8P9PxZaH5+vvHZZ58ZGRkZhr+/v1FbW2sYhmGsXr3aSEpKctT3/FnoihUrjM8++8zIz8/v9c9Ce/T2Vwzf5e6vGDZt2mQEBAQY77zzjlFVVWU89dRTvX5tQg+Z+PPgoToHL7/8sjFixAjjD3/4g3H8+HFj/fr1htVqNU6cOHFLzEFHR4cxfvx4IzY21vjkk0+MEydOGJs3bzYsFovxpz/9qd9zcKPPw9mzZ42KigrjT3/6kyHJKCgoMCoqKoyGhgZHTVpamhEaGmq8//77xt/+9jdj5syZA/7ahKE4B62trcbUqVON+++/3zhx4oTR0NDgeNwqc+DOzXZd7M8c3OzXxavNwWBdFwlUHtq+fbsxduxYw9fX15gyZYpx4MABx7ann37aiIuLc6r/6KOPjAceeMDw9fU17r77buPVV1/t8/gD/QfT1dVlbNiwwbDZbIafn5/x//7f/zOqqqr6PM5ALhyGMfTnIDs72wgNDTWGDx9uxMTEGCUlJX325c5QnoPPP//ceOKJJ4zRo0cbw4cPNyZOnOjy58L9daPOw+9+9ztDkstjw4YNjppLly4Zv/rVr4w777zTuO2224yf/OQnRl1dXX9O28lQnYMPP/zQ7XZJRk1NTT/PvttQnQN3brbrYn/n4Ga+LvZnDgbjumgxjG/vAgMAAMCA8D1UAAAAJhGoAAAATCJQAQAAmESgAgAAMIlABQAAYBKBCgAAwCQCFQAAgEkEKgAAAJMIVAAAACYRqAAAAEwiUAEAAJhEoAIAADDp/wOYzKEob6dQJAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Lets check\n",
    "plt.plot(df['34S/32S ratio IMF'], d3432S_calc, '.r')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d41c6ac5-d62b-40de-b28c-c8712a3768e0",
   "metadata": {},
   "source": [
    "### Lets convert S concentrations around\n",
    "- You have to enter S concentration of the form you have, and it returns a dataframe of all the other possible forms. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3c655915-6b31-438a-8611-d470caf3a6ae",
   "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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S_wt</th>\n",
       "      <th>S_ppm</th>\n",
       "      <th>SO2_wt</th>\n",
       "      <th>SO2_ppm</th>\n",
       "      <th>SO3_wt</th>\n",
       "      <th>SO3_ppm</th>\n",
       "      <th>SO4_wt</th>\n",
       "      <th>SO4_ppm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.100</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.199791</td>\n",
       "      <td>1997.910494</td>\n",
       "      <td>0.249687</td>\n",
       "      <td>2496.865741</td>\n",
       "      <td>0.299582</td>\n",
       "      <td>2995.820989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.110</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>0.219770</td>\n",
       "      <td>2197.701544</td>\n",
       "      <td>0.274655</td>\n",
       "      <td>2746.552316</td>\n",
       "      <td>0.329540</td>\n",
       "      <td>3295.403087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.090</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.179812</td>\n",
       "      <td>1798.119445</td>\n",
       "      <td>0.224718</td>\n",
       "      <td>2247.179167</td>\n",
       "      <td>0.269624</td>\n",
       "      <td>2696.238890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.050</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.099896</td>\n",
       "      <td>998.955247</td>\n",
       "      <td>0.124843</td>\n",
       "      <td>1248.432871</td>\n",
       "      <td>0.149791</td>\n",
       "      <td>1497.910494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.040</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.079916</td>\n",
       "      <td>799.164198</td>\n",
       "      <td>0.099875</td>\n",
       "      <td>998.746297</td>\n",
       "      <td>0.119833</td>\n",
       "      <td>1198.328395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.035</td>\n",
       "      <td>350.0</td>\n",
       "      <td>0.069927</td>\n",
       "      <td>699.268673</td>\n",
       "      <td>0.087390</td>\n",
       "      <td>873.903010</td>\n",
       "      <td>0.104854</td>\n",
       "      <td>1048.537346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.020</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.039958</td>\n",
       "      <td>399.582099</td>\n",
       "      <td>0.049937</td>\n",
       "      <td>499.373148</td>\n",
       "      <td>0.059916</td>\n",
       "      <td>599.164198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.100</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.199791</td>\n",
       "      <td>1997.910494</td>\n",
       "      <td>0.249687</td>\n",
       "      <td>2496.865741</td>\n",
       "      <td>0.299582</td>\n",
       "      <td>2995.820989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    S_wt   S_ppm    SO2_wt      SO2_ppm    SO3_wt      SO3_ppm    SO4_wt  \\\n",
       "0  0.100  1000.0  0.199791  1997.910494  0.249687  2496.865741  0.299582   \n",
       "1  0.110  1100.0  0.219770  2197.701544  0.274655  2746.552316  0.329540   \n",
       "2  0.090   900.0  0.179812  1798.119445  0.224718  2247.179167  0.269624   \n",
       "3  0.050   500.0  0.099896   998.955247  0.124843  1248.432871  0.149791   \n",
       "4  0.040   400.0  0.079916   799.164198  0.099875   998.746297  0.119833   \n",
       "5  0.035   350.0  0.069927   699.268673  0.087390   873.903010  0.104854   \n",
       "6  0.020   200.0  0.039958   399.582099  0.049937   499.373148  0.059916   \n",
       "7  0.100  1000.0  0.199791  1997.910494  0.249687  2496.865741  0.299582   \n",
       "\n",
       "       SO4_ppm  \n",
       "0  2995.820989  \n",
       "1  3295.403087  \n",
       "2  2696.238890  \n",
       "3  1497.910494  \n",
       "4  1198.328395  \n",
       "5  1048.537346  \n",
       "6   599.164198  \n",
       "7  2995.820989  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=ss.convert_S_types(S_ppm=df['S_ppm'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cb6bc025-4b15-4669-982c-d183f9a14cc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S_wt</th>\n",
       "      <th>S_ppm</th>\n",
       "      <th>SO2_wt</th>\n",
       "      <th>SO2_ppm</th>\n",
       "      <th>SO3_wt</th>\n",
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       "      <th>SO4_wt</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0.006593</td>\n",
       "      <td>65.931046</td>\n",
       "      <td>0.00824</td>\n",
       "      <td>82.396569</td>\n",
       "      <td>0.009886</td>\n",
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      "text/plain": [
       "     S_wt  S_ppm    SO2_wt    SO2_ppm   SO3_wt    SO3_ppm    SO4_wt    SO4_ppm\n",
       "0  0.0033   33.0  0.006593  65.931046  0.00824  82.396569  0.009886  98.862093"
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "df=ss.convert_S_types(S_ppm=33)\n",
    "df"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
   "id": "3867b561-ec76-4706-b2f5-c9c4663fb127",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>S_wt</th>\n",
       "      <th>S_ppm</th>\n",
       "      <th>SO2_wt</th>\n",
       "      <th>SO2_ppm</th>\n",
       "      <th>SO3_wt</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000001</td>\n",
       "      <td>0.010101</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.020181</td>\n",
       "      <td>0.000003</td>\n",
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       "      <td>0.000003</td>\n",
       "      <td>0.030261</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.020202</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.040362</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.050442</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.060522</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.030303</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.060543</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.075663</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.090782</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.040404</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.080724</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.100883</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.121043</td>\n",
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       "      <th>95</th>\n",
       "      <td>0.000096</td>\n",
       "      <td>0.959596</td>\n",
       "      <td>0.000192</td>\n",
       "      <td>1.917187</td>\n",
       "      <td>0.000240</td>\n",
       "      <td>2.395982</td>\n",
       "      <td>0.000287</td>\n",
       "      <td>2.874778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.000097</td>\n",
       "      <td>0.969697</td>\n",
       "      <td>0.000194</td>\n",
       "      <td>1.937368</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>2.421203</td>\n",
       "      <td>0.000291</td>\n",
       "      <td>2.905039</td>\n",
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       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.979798</td>\n",
       "      <td>0.000196</td>\n",
       "      <td>1.957549</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>2.446424</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>2.935299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.000099</td>\n",
       "      <td>0.989899</td>\n",
       "      <td>0.000198</td>\n",
       "      <td>1.977730</td>\n",
       "      <td>0.000247</td>\n",
       "      <td>2.471645</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>2.965560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.000100</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>1.997910</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>2.496866</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>2.995821</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>100 rows × 8 columns</p>\n",
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      ],
      "text/plain": [
       "        S_wt     S_ppm    SO2_wt   SO2_ppm    SO3_wt   SO3_ppm    SO4_wt  \\\n",
       "0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1   0.000001  0.010101  0.000002  0.020181  0.000003  0.025221  0.000003   \n",
       "2   0.000002  0.020202  0.000004  0.040362  0.000005  0.050442  0.000006   \n",
       "3   0.000003  0.030303  0.000006  0.060543  0.000008  0.075663  0.000009   \n",
       "4   0.000004  0.040404  0.000008  0.080724  0.000010  0.100883  0.000012   \n",
       "..       ...       ...       ...       ...       ...       ...       ...   \n",
       "95  0.000096  0.959596  0.000192  1.917187  0.000240  2.395982  0.000287   \n",
       "96  0.000097  0.969697  0.000194  1.937368  0.000242  2.421203  0.000291   \n",
       "97  0.000098  0.979798  0.000196  1.957549  0.000245  2.446424  0.000294   \n",
       "98  0.000099  0.989899  0.000198  1.977730  0.000247  2.471645  0.000297   \n",
       "99  0.000100  1.000000  0.000200  1.997910  0.000250  2.496866  0.000300   \n",
       "\n",
       "     SO4_ppm  \n",
       "0   0.000000  \n",
       "1   0.030261  \n",
       "2   0.060522  \n",
       "3   0.090782  \n",
       "4   0.121043  \n",
       "..       ...  \n",
       "95  2.874778  \n",
       "96  2.905039  \n",
       "97  2.935299  \n",
       "98  2.965560  \n",
       "99  2.995821  \n",
       "\n",
       "[100 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=ss.convert_S_types(S_ppm=np.linspace(0, 1, 100))\n",
    "df"
   ]
  }
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