{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='notebook-top'></a>\n",
    "This notebook shows effective temperatures in the quiet Sun observed by 3PAMIS and EIS. Link to [Figure 7](#figure-7).\n",
    "\n",
    "(The internal hyperlink only works on [GitHub Pages](https://yjzhu-solar.github.io/Eclipse2017/ipynb_html/atlas30_pamis_teff.html) or [nbviewer](https://nbviewer.org/github/yjzhu-solar/Eclipse2017/blob/master/ipynb/eis/atlas30_pamis_teff.ipynb). Do not click when viewing the notebook on GitHub.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)\n",
    "from matplotlib import transforms\n",
    "import astropy.constants as const\n",
    "import pandas as pd\n",
    "import cmcrameri.cm as cmcm\n",
    "from matplotlib import rcParams\n",
    "rcParams['text.usetex'] = True\n",
    "rcParams['text.latex.preamble'] = r'\\usepackage[T1]{fontenc} \\usepackage{amsmath} \\usepackage{color}'\n",
    "rcParams['font.family'] = 'serif'\n",
    "rcParams['axes.linewidth'] = 2\n",
    "rcParams['xtick.major.width'] = 1.2\n",
    "rcParams['xtick.major.size'] = 10\n",
    "rcParams['xtick.minor.width'] = 1.2\n",
    "rcParams['xtick.minor.size'] = 6\n",
    "rcParams['ytick.major.width'] = 1.2\n",
    "rcParams['ytick.major.size'] = 8\n",
    "rcParams['ytick.minor.width'] = 1.2\n",
    "rcParams['ytick.minor.size'] = 6 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_excel(\"../../sav/EIS/EQSPY/atlas30_fitres.xlsx\",sheet_name=\"r1\")\n",
    "df1[\"Z2A\"] = df1[\"Z\"]/df1[\"A\"]\n",
    "df1[\"ion\"] = df1[\"ion\"].str.strip()\n",
    "df1[\"charge_state\"] = df1[\"charge_state\"].str.strip()\n",
    "df1[\"fwhm_true_err\"] = df1[\"fwhm_fit\"]/(df1[\"fwhm_true\"]*1e-3)*df1[\"fwhm_err\"]*1e3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>ion</th>\n",
       "      <th>charge_state</th>\n",
       "      <th>Z</th>\n",
       "      <th>A</th>\n",
       "      <th>wvl_fit</th>\n",
       "      <th>wvl_chianti</th>\n",
       "      <th>fwhm_fit</th>\n",
       "      <th>fwhm_err</th>\n",
       "      <th>fwhm_true</th>\n",
       "      <th>Z2A</th>\n",
       "      <th>fwhm_true_err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fe</td>\n",
       "      <td>x</td>\n",
       "      <td>9</td>\n",
       "      <td>55.8500</td>\n",
       "      <td>184.5127</td>\n",
       "      <td>184.537</td>\n",
       "      <td>0.07390</td>\n",
       "      <td>0.00140</td>\n",
       "      <td>25.9</td>\n",
       "      <td>0.161146</td>\n",
       "      <td>3.994595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fe</td>\n",
       "      <td>viii</td>\n",
       "      <td>7</td>\n",
       "      <td>55.8500</td>\n",
       "      <td>185.1589</td>\n",
       "      <td>185.213</td>\n",
       "      <td>0.07620</td>\n",
       "      <td>0.00350</td>\n",
       "      <td>31.9</td>\n",
       "      <td>0.125336</td>\n",
       "      <td>8.360502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fe</td>\n",
       "      <td>xi</td>\n",
       "      <td>10</td>\n",
       "      <td>55.8500</td>\n",
       "      <td>188.1929</td>\n",
       "      <td>188.216</td>\n",
       "      <td>0.07440</td>\n",
       "      <td>0.00100</td>\n",
       "      <td>27.1</td>\n",
       "      <td>0.179051</td>\n",
       "      <td>2.745387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fe</td>\n",
       "      <td>xii</td>\n",
       "      <td>11</td>\n",
       "      <td>55.8500</td>\n",
       "      <td>193.4862</td>\n",
       "      <td>193.509</td>\n",
       "      <td>0.07570</td>\n",
       "      <td>0.00110</td>\n",
       "      <td>30.7</td>\n",
       "      <td>0.196956</td>\n",
       "      <td>2.712378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fe</td>\n",
       "      <td>ix</td>\n",
       "      <td>8</td>\n",
       "      <td>55.8500</td>\n",
       "      <td>197.8343</td>\n",
       "      <td>197.854</td>\n",
       "      <td>0.07790</td>\n",
       "      <td>0.00170</td>\n",
       "      <td>35.7</td>\n",
       "      <td>0.143241</td>\n",
       "      <td>3.709524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Fe</td>\n",
       "      <td>xiii</td>\n",
       "      <td>12</td>\n",
       "      <td>55.8500</td>\n",
       "      <td>202.0236</td>\n",
       "      <td>202.044</td>\n",
       "      <td>0.07484</td>\n",
       "      <td>0.00055</td>\n",
       "      <td>28.4</td>\n",
       "      <td>0.214861</td>\n",
       "      <td>1.449366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Si</td>\n",
       "      <td>x</td>\n",
       "      <td>9</td>\n",
       "      <td>28.0855</td>\n",
       "      <td>258.3772</td>\n",
       "      <td>258.374</td>\n",
       "      <td>0.08490</td>\n",
       "      <td>0.00220</td>\n",
       "      <td>49.1</td>\n",
       "      <td>0.320450</td>\n",
       "      <td>3.804073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>S</td>\n",
       "      <td>x</td>\n",
       "      <td>9</td>\n",
       "      <td>32.0600</td>\n",
       "      <td>264.2337</td>\n",
       "      <td>264.230</td>\n",
       "      <td>0.08600</td>\n",
       "      <td>0.00210</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.280724</td>\n",
       "      <td>3.541176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Fe</td>\n",
       "      <td>xiv</td>\n",
       "      <td>13</td>\n",
       "      <td>55.8500</td>\n",
       "      <td>264.7868</td>\n",
       "      <td>264.788</td>\n",
       "      <td>0.08200</td>\n",
       "      <td>0.00180</td>\n",
       "      <td>43.9</td>\n",
       "      <td>0.232766</td>\n",
       "      <td>3.362187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Fe</td>\n",
       "      <td>xv</td>\n",
       "      <td>14</td>\n",
       "      <td>55.8500</td>\n",
       "      <td>284.1600</td>\n",
       "      <td>284.163</td>\n",
       "      <td>0.08050</td>\n",
       "      <td>0.00280</td>\n",
       "      <td>41.1</td>\n",
       "      <td>0.250671</td>\n",
       "      <td>5.484185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ion charge_state   Z        A   wvl_fit  wvl_chianti  fwhm_fit  fwhm_err  \\\n",
       "0  Fe            x   9  55.8500  184.5127      184.537   0.07390   0.00140   \n",
       "1  Fe         viii   7  55.8500  185.1589      185.213   0.07620   0.00350   \n",
       "2  Fe           xi  10  55.8500  188.1929      188.216   0.07440   0.00100   \n",
       "3  Fe          xii  11  55.8500  193.4862      193.509   0.07570   0.00110   \n",
       "4  Fe           ix   8  55.8500  197.8343      197.854   0.07790   0.00170   \n",
       "5  Fe         xiii  12  55.8500  202.0236      202.044   0.07484   0.00055   \n",
       "6  Si            x   9  28.0855  258.3772      258.374   0.08490   0.00220   \n",
       "7   S            x   9  32.0600  264.2337      264.230   0.08600   0.00210   \n",
       "8  Fe          xiv  13  55.8500  264.7868      264.788   0.08200   0.00180   \n",
       "9  Fe           xv  14  55.8500  284.1600      284.163   0.08050   0.00280   \n",
       "\n",
       "   fwhm_true       Z2A  fwhm_true_err  \n",
       "0       25.9  0.161146       3.994595  \n",
       "1       31.9  0.125336       8.360502  \n",
       "2       27.1  0.179051       2.745387  \n",
       "3       30.7  0.196956       2.712378  \n",
       "4       35.7  0.143241       3.709524  \n",
       "5       28.4  0.214861       1.449366  \n",
       "6       49.1  0.320450       3.804073  \n",
       "7       51.0  0.280724       3.541176  \n",
       "8       43.9  0.232766       3.362187  \n",
       "9       41.1  0.250671       5.484185  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = const.c.cgs.value\n",
    "amu = const.u.cgs.value\n",
    "k_B = const.k_B.cgs.value\n",
    "hplanck = const.h.cgs.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "line_name = df1[\"ion\"] + r\" \\textsc{\"+ df1[\"charge_state\"] + r\"}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Teff_1 = df1[\"A\"]*amu/(8*np.log(2)*k_B)*(c/(df1[\"wvl_chianti\"]*1e-8))**2*(df1[\"fwhm_true\"]*1e-11)**2\n",
    "Teff_err_1 = 2*df1[\"A\"]*amu/(8*np.log(2)*k_B)*(c/(df1[\"wvl_chianti\"]*1e-8))**2*(df1[\"fwhm_true\"]*1e-11)*df1[\"fwhm_true_err\"]*1e-11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ion = df1[[\"ion\",\"charge_state\",\"Z\",\"A\",\"Z2A\"]]\n",
    "df_ion = df_ion.drop_duplicates()\n",
    "ion_name = r\"\\textbf{\" + df_ion[\"ion\"] + r\" \\textsc{\"+ df_ion[\"charge_state\"] + r\"}}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_excel(\"../../sav/EIS/EQSPY/atlas30_fitres.xlsx\",sheet_name=\"r2\")\n",
    "df2[\"Z2A\"] = df2[\"Z\"]/df2[\"A\"]\n",
    "df2[\"ion\"] = df2[\"ion\"].str.strip()\n",
    "df2[\"charge_state\"] = df2[\"charge_state\"].str.strip()\n",
    "df2[\"fwhm_true_err\"] = df2[\"fwhm_fit\"]/(df2[\"fwhm_true\"]*1e-3)*df2[\"fwhm_err\"]*1e3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>ion</th>\n",
       "      <th>charge_state</th>\n",
       "      <th>Z</th>\n",
       "      <th>A</th>\n",
       "      <th>wvl_fit</th>\n",
       "      <th>wvl_chianti</th>\n",
       "      <th>fwhm_fit</th>\n",
       "      <th>fwhm_err</th>\n",
       "      <th>fwhm_true</th>\n",
       "      <th>Z2A</th>\n",
       "      <th>fwhm_true_err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fe</td>\n",
       "      <td>x</td>\n",
       "      <td>9</td>\n",
       "      <td>55.8500</td>\n",
       "      <td>184.5120</td>\n",
       "      <td>184.537</td>\n",
       "      <td>0.07380</td>\n",
       "      <td>0.00310</td>\n",
       "      <td>25.5</td>\n",
       "      <td>0.161146</td>\n",
       "      <td>8.971765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fe</td>\n",
       "      <td>viii</td>\n",
       "      <td>7</td>\n",
       "      <td>55.8500</td>\n",
       "      <td>185.1910</td>\n",
       "      <td>185.213</td>\n",
       "      <td>0.07670</td>\n",
       "      <td>0.00680</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.125336</td>\n",
       "      <td>15.804848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fe</td>\n",
       "      <td>xi</td>\n",
       "      <td>10</td>\n",
       "      <td>55.8500</td>\n",
       "      <td>188.1926</td>\n",
       "      <td>188.216</td>\n",
       "      <td>0.07670</td>\n",
       "      <td>0.00110</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.179051</td>\n",
       "      <td>2.556667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fe</td>\n",
       "      <td>xii</td>\n",
       "      <td>11</td>\n",
       "      <td>55.8500</td>\n",
       "      <td>193.4862</td>\n",
       "      <td>193.509</td>\n",
       "      <td>0.07570</td>\n",
       "      <td>0.00120</td>\n",
       "      <td>30.6</td>\n",
       "      <td>0.196956</td>\n",
       "      <td>2.968627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fe</td>\n",
       "      <td>ix</td>\n",
       "      <td>8</td>\n",
       "      <td>55.8500</td>\n",
       "      <td>197.8320</td>\n",
       "      <td>197.854</td>\n",
       "      <td>0.07600</td>\n",
       "      <td>0.00340</td>\n",
       "      <td>31.2</td>\n",
       "      <td>0.143241</td>\n",
       "      <td>8.282051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Fe</td>\n",
       "      <td>xiii</td>\n",
       "      <td>12</td>\n",
       "      <td>55.8500</td>\n",
       "      <td>202.0242</td>\n",
       "      <td>202.044</td>\n",
       "      <td>0.07543</td>\n",
       "      <td>0.00075</td>\n",
       "      <td>29.9</td>\n",
       "      <td>0.214861</td>\n",
       "      <td>1.892057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Si</td>\n",
       "      <td>x</td>\n",
       "      <td>9</td>\n",
       "      <td>28.0855</td>\n",
       "      <td>258.3800</td>\n",
       "      <td>258.374</td>\n",
       "      <td>0.08430</td>\n",
       "      <td>0.00320</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.320450</td>\n",
       "      <td>5.620000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>S</td>\n",
       "      <td>x</td>\n",
       "      <td>9</td>\n",
       "      <td>32.0600</td>\n",
       "      <td>264.2310</td>\n",
       "      <td>264.230</td>\n",
       "      <td>0.08540</td>\n",
       "      <td>0.00340</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.280724</td>\n",
       "      <td>5.807200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Fe</td>\n",
       "      <td>xiv</td>\n",
       "      <td>13</td>\n",
       "      <td>55.8500</td>\n",
       "      <td>264.7860</td>\n",
       "      <td>264.788</td>\n",
       "      <td>0.08570</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>50.5</td>\n",
       "      <td>0.232766</td>\n",
       "      <td>6.448713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Fe</td>\n",
       "      <td>xv</td>\n",
       "      <td>14</td>\n",
       "      <td>55.8500</td>\n",
       "      <td>284.1580</td>\n",
       "      <td>284.163</td>\n",
       "      <td>0.07730</td>\n",
       "      <td>0.00440</td>\n",
       "      <td>34.4</td>\n",
       "      <td>0.250671</td>\n",
       "      <td>9.887209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ion charge_state   Z        A   wvl_fit  wvl_chianti  fwhm_fit  fwhm_err  \\\n",
       "0  Fe            x   9  55.8500  184.5120      184.537   0.07380   0.00310   \n",
       "1  Fe         viii   7  55.8500  185.1910      185.213   0.07670   0.00680   \n",
       "2  Fe           xi  10  55.8500  188.1926      188.216   0.07670   0.00110   \n",
       "3  Fe          xii  11  55.8500  193.4862      193.509   0.07570   0.00120   \n",
       "4  Fe           ix   8  55.8500  197.8320      197.854   0.07600   0.00340   \n",
       "5  Fe         xiii  12  55.8500  202.0242      202.044   0.07543   0.00075   \n",
       "6  Si            x   9  28.0855  258.3800      258.374   0.08430   0.00320   \n",
       "7   S            x   9  32.0600  264.2310      264.230   0.08540   0.00340   \n",
       "8  Fe          xiv  13  55.8500  264.7860      264.788   0.08570   0.00380   \n",
       "9  Fe           xv  14  55.8500  284.1580      284.163   0.07730   0.00440   \n",
       "\n",
       "   fwhm_true       Z2A  fwhm_true_err  \n",
       "0       25.5  0.161146       8.971765  \n",
       "1       33.0  0.125336      15.804848  \n",
       "2       33.0  0.179051       2.556667  \n",
       "3       30.6  0.196956       2.968627  \n",
       "4       31.2  0.143241       8.282051  \n",
       "5       29.9  0.214861       1.892057  \n",
       "6       48.0  0.320450       5.620000  \n",
       "7       50.0  0.280724       5.807200  \n",
       "8       50.5  0.232766       6.448713  \n",
       "9       34.4  0.250671       9.887209  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "Teff_2 = df2[\"A\"]*amu/(8*np.log(2)*k_B)*(c/(df2[\"wvl_chianti\"]*1e-8))**2*(df2[\"fwhm_true\"]*1e-11)**2\n",
    "Teff_err_2 = 2*df2[\"A\"]*amu/(8*np.log(2)*k_B)*(c/(df2[\"wvl_chianti\"]*1e-8))**2*(df2[\"fwhm_true\"]*1e-11)*df2[\"fwhm_true_err\"]*1e-11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "PaMIS_veff = np.array([28.54, 31.96])\n",
    "PaMIS_veff_err = np.array([1.09, 0.97])\n",
    "PaMIS_Teff = 55.85*amu/2/k_B*(PaMIS_veff*1e5)**2\n",
    "PaMIS_Teff_err = 2*55.85*amu/2/k_B*(PaMIS_veff*1e5)*(PaMIS_veff_err*1e5)\n",
    "PaMIS_Z2A = np.array([9,13])/np.array([55.85,55.85])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='figure-7'></a>\n",
    "### Figure 7\n",
    "[back to top](#notebook-top) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,6),constrained_layout=True)\n",
    "\n",
    "ax.errorbar(df1[\"Z2A\"].values, Teff_1.values/1e6, Teff_err_1.values/1e6, color=\"#CC543A\",ls=\"none\",marker=\"o\",\n",
    "            markersize=15,capsize=5,lw=2.5,label=r\"\\textbf{EIS [1.035,1.06]}\",zorder=15,markeredgecolor='white',\n",
    "            markeredgewidth=2.5,alpha=0.9,capthick=2.5)\n",
    "\n",
    "ax.errorbar(df2[\"Z2A\"].values, Teff_2.values/1e6, Teff_err_2.values/1e6, color=\"#F9BF45\",ls=\"none\",marker=\"s\",\n",
    "            markersize=15,capsize=5,lw=2.5,label=r\"\\textbf{EIS [1.06,1.1]}\",zorder=14,markeredgecolor='white',\n",
    "            markeredgewidth=2.5,alpha=0.9,capthick=2.5)\n",
    "\n",
    "ax.errorbar(PaMIS_Z2A, PaMIS_Teff/1e6, PaMIS_Teff_err/1e6, color=\"#0089A7\",ls=\"none\",marker=\"D\",\n",
    "            markersize=15,capsize=5,lw=2.5,label=r\"\\textbf{3PAMIS}\",zorder=16,markeredgecolor='white',\n",
    "            markeredgewidth=2,alpha=0.9,capthick=2.5)\n",
    "\n",
    "ax_ion = ax.secondary_xaxis(location=\"top\")\n",
    "ax_ion.set_xlim(ax.get_xlim())\n",
    "ax_ion.set_xticks(df_ion[\"Z2A\"].values)\n",
    "ax_ion.set_xticklabels(ion_name,rotation=45,ha=\"left\")\n",
    "ax_ion.tick_params(labelsize=18)\n",
    "\n",
    "ax.legend(loc=\"upper right\",fontsize=18,frameon=False) \n",
    "ax.set_ylim(bottom=0,top=6)\n",
    "\n",
    "ax.tick_params(labelsize=18,direction=\"in\",which=\"both\",right=True,top=False)\n",
    "ax.set_xlabel(r\"$\\boldsymbol{\\mathrm{Charge\\ to\\ Mass\\ Ratio}\\ Z/A}$\",fontsize=18)\n",
    "ax.set_ylabel(r\"$\\boldsymbol{T_{\\rm eff}}$ \\textbf{[MK]}\",fontsize=18)\n",
    "ax.grid(\"on\")\n",
    "\n",
    "plt.savefig(fname=\"../../figs/ms/Teff_Atlas30.pdf\",dpi=300,format=\"pdf\",bbox_inches=\"tight\")"
   ]
  }
 ],
 "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": "3.7.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}