{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "46e6da05-a241-4176-aba6-18cc508a3e02",
   "metadata": {},
   "source": [
    "# Converting from fo2 to buffer position\n",
    "- You can download the spreadsheet here: https://github.com/PennyWieser/Thermobar/blob/main/docs/Examples/Other_features/Redox_Conversions.xlsx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9ba780a7-dda6-4d50-803b-bff4d90ebd78",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading various python things\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import Thermobar as pt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68d37e2b-147a-4545-bf08-d307e3755bef",
   "metadata": {},
   "source": [
    "## Converting fo2 values to buffer positions\n",
    "- Using data from Blatter et al. 2017"
   ]
  },
  {
   "attachments": {
    "0b59b969-ba21-4354-98d3-2966d0fca9fa.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "1bf1744a-7f52-44fd-a59f-c1677bd04c6f",
   "metadata": {},
   "source": [
    "![image.png](attachment:0b59b969-ba21-4354-98d3-2966d0fca9fa.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6dc9e3b7-b889-41f7-977c-631e9b774779",
   "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>deltaNNO_Frost1991</th>\n",
       "      <th>deltaQFM_Frost1991</th>\n",
       "      <th>QFM_equation_Choice</th>\n",
       "      <th>T_K</th>\n",
       "      <th>P_kbar</th>\n",
       "      <th>fo2</th>\n",
       "      <th>Cut off T (K)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.595989</td>\n",
       "      <td>1.369508</td>\n",
       "      <td>High T</td>\n",
       "      <td>1120.15</td>\n",
       "      <td>0</td>\n",
       "      <td>5.011872e-13</td>\n",
       "      <td>846.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   deltaNNO_Frost1991  deltaQFM_Frost1991 QFM_equation_Choice      T_K  \\\n",
       "0            0.595989            1.369508              High T  1120.15   \n",
       "\n",
       "   P_kbar           fo2  Cut off T (K)  \n",
       "0       0  5.011872e-13         846.15  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Blatter_2017_1=pt.convert_fo2_to_buffer(fo2=10**(-12.3), T_K=847+273.15, P_kbar=0)\n",
    "Blatter_2017_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fe12bbe4-1a1d-4ca2-a55d-22ddca806b18",
   "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>deltaNNO_Frost1991</th>\n",
       "      <th>deltaQFM_Frost1991</th>\n",
       "      <th>QFM_equation_Choice</th>\n",
       "      <th>T_K</th>\n",
       "      <th>P_kbar</th>\n",
       "      <th>fo2</th>\n",
       "      <th>Cut off T (K)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.600091</td>\n",
       "      <td>1.372036</td>\n",
       "      <td>High T</td>\n",
       "      <td>1132.15</td>\n",
       "      <td>0</td>\n",
       "      <td>8.709636e-13</td>\n",
       "      <td>846.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   deltaNNO_Frost1991  deltaQFM_Frost1991 QFM_equation_Choice      T_K  \\\n",
       "0            0.600091            1.372036              High T  1132.15   \n",
       "\n",
       "   P_kbar           fo2  Cut off T (K)  \n",
       "0       0  8.709636e-13         846.15  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Blatter_2017_2=pt.convert_fo2_to_buffer(fo2=10**(-12.06), T_K=859+273.15, P_kbar=0)\n",
    "Blatter_2017_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f92a05c9-66c1-43d4-8f25-7210abc198d5",
   "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>deltaNNO_Frost1991</th>\n",
       "      <th>deltaQFM_Frost1991</th>\n",
       "      <th>QFM_equation_Choice</th>\n",
       "      <th>T_K</th>\n",
       "      <th>P_kbar</th>\n",
       "      <th>fo2</th>\n",
       "      <th>Cut off T (K)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.306245</td>\n",
       "      <td>2.084838</td>\n",
       "      <td>High T</td>\n",
       "      <td>1083.15</td>\n",
       "      <td>0</td>\n",
       "      <td>4.466836e-13</td>\n",
       "      <td>846.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   deltaNNO_Frost1991  deltaQFM_Frost1991 QFM_equation_Choice      T_K  \\\n",
       "0            1.306245            2.084838              High T  1083.15   \n",
       "\n",
       "   P_kbar           fo2  Cut off T (K)  \n",
       "0       0  4.466836e-13         846.15  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Blatter_2017_3=pt.convert_fo2_to_buffer(fo2=10**(-12.35), T_K=810+273.15, P_kbar=0)\n",
    "Blatter_2017_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fc79c6b6-a081-4167-8e2f-fe1643bc37c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>deltaNNO_Frost1991</th>\n",
       "      <th>deltaQFM_Frost1991</th>\n",
       "      <th>QFM_equation_Choice</th>\n",
       "      <th>T_K</th>\n",
       "      <th>P_kbar</th>\n",
       "      <th>fo2</th>\n",
       "      <th>Cut off T (K)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.199451</td>\n",
       "      <td>1.977198</td>\n",
       "      <td>High T</td>\n",
       "      <td>1089.15</td>\n",
       "      <td>0</td>\n",
       "      <td>4.677351e-13</td>\n",
       "      <td>846.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   deltaNNO_Frost1991  deltaQFM_Frost1991 QFM_equation_Choice      T_K  \\\n",
       "0            1.199451            1.977198              High T  1089.15   \n",
       "\n",
       "   P_kbar           fo2  Cut off T (K)  \n",
       "0       0  4.677351e-13         846.15  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Blatter_2017_4=pt.convert_fo2_to_buffer(fo2=10**(-12.33), T_K=816+273.15, P_kbar=0)\n",
    "Blatter_2017_4"
   ]
  }
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