analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis on 2D Heisenberg model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import multiprocessing\n",
    "import sys\n",
    "import os\n",
    "from pathlib import Path\n",
    "from time import time\n",
    "from typing import Tuple\n",
    "from socket import gethostname\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.optimize\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from cycler import cycler\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "# set plot customisation\n",
    "plt.rc('font',**{'family':'serif',\n",
    "             'serif': 'cmr10',\n",
    "             'size': 12})\n",
    "plt.rc('mathtext',**{'fontset':'cm'})\n",
    "plt.rc('axes', unicode_minus=False, linewidth=1.2, xmargin=0)\n",
    "plt.rc('legend', loc='best', frameon=False)\n",
    "\n",
    "from heisenberg_2d import initialize, metropolis, energy, magnetization, run_simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the routine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Settings\n",
    "\n",
    "n_temp = 50\n",
    "data_path = './results'\n",
    "snaps = []\n",
    "\n",
    "N = 8\n",
    "H = np.array([1., 0., 0.])\n",
    "steps = 4000\n",
    "temp = np.linspace(0.3, 10.5, n_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using 80 threads on brute\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "23d6fe99e7514463b5169de457185f30",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=50.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Took 29.263503789901733 s\n"
     ]
    }
   ],
   "source": [
    "*results, wall_time = run_simulation(N, H, steps, temp)\n",
    "results = np.array(results).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>E</th>\n",
       "      <th>M</th>\n",
       "      <th>C</th>\n",
       "      <th>X</th>\n",
       "      <th>temp</th>\n",
       "      <th>n_temp</th>\n",
       "      <th>N</th>\n",
       "      <th>steps</th>\n",
       "      <th>H</th>\n",
       "      <th>wall_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.443371</td>\n",
       "      <td>-0.324433</td>\n",
       "      <td>0.002532</td>\n",
       "      <td>0.035052</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.560676</td>\n",
       "      <td>0.223794</td>\n",
       "      <td>0.008563</td>\n",
       "      <td>0.010075</td>\n",
       "      <td>0.508163</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.659157</td>\n",
       "      <td>0.686418</td>\n",
       "      <td>0.011341</td>\n",
       "      <td>0.118476</td>\n",
       "      <td>0.716327</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.684673</td>\n",
       "      <td>0.695596</td>\n",
       "      <td>0.016856</td>\n",
       "      <td>0.068166</td>\n",
       "      <td>0.924490</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.667149</td>\n",
       "      <td>0.874787</td>\n",
       "      <td>0.028299</td>\n",
       "      <td>0.224131</td>\n",
       "      <td>1.132653</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.615006</td>\n",
       "      <td>0.779082</td>\n",
       "      <td>0.050747</td>\n",
       "      <td>0.613562</td>\n",
       "      <td>1.340816</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.548915</td>\n",
       "      <td>0.740473</td>\n",
       "      <td>0.077945</td>\n",
       "      <td>1.038498</td>\n",
       "      <td>1.548980</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.469830</td>\n",
       "      <td>0.648139</td>\n",
       "      <td>0.074368</td>\n",
       "      <td>0.660310</td>\n",
       "      <td>1.757143</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.397009</td>\n",
       "      <td>0.578444</td>\n",
       "      <td>0.077676</td>\n",
       "      <td>0.696118</td>\n",
       "      <td>1.965306</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.316434</td>\n",
       "      <td>0.475733</td>\n",
       "      <td>0.060115</td>\n",
       "      <td>0.793594</td>\n",
       "      <td>2.173469</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.256402</td>\n",
       "      <td>0.398530</td>\n",
       "      <td>0.053416</td>\n",
       "      <td>0.767991</td>\n",
       "      <td>2.381633</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.207991</td>\n",
       "      <td>0.298132</td>\n",
       "      <td>0.039251</td>\n",
       "      <td>0.662029</td>\n",
       "      <td>2.589796</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.183000</td>\n",
       "      <td>0.279934</td>\n",
       "      <td>0.030893</td>\n",
       "      <td>0.601879</td>\n",
       "      <td>2.797959</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.154739</td>\n",
       "      <td>0.237130</td>\n",
       "      <td>0.022232</td>\n",
       "      <td>0.515616</td>\n",
       "      <td>3.006122</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.141141</td>\n",
       "      <td>0.218375</td>\n",
       "      <td>0.018995</td>\n",
       "      <td>0.538407</td>\n",
       "      <td>3.214286</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.127265</td>\n",
       "      <td>0.200852</td>\n",
       "      <td>0.015320</td>\n",
       "      <td>0.429413</td>\n",
       "      <td>3.422449</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.113924</td>\n",
       "      <td>0.182902</td>\n",
       "      <td>0.012275</td>\n",
       "      <td>0.395413</td>\n",
       "      <td>3.630612</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.104174</td>\n",
       "      <td>0.156153</td>\n",
       "      <td>0.010619</td>\n",
       "      <td>0.361763</td>\n",
       "      <td>3.838776</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.097390</td>\n",
       "      <td>0.141516</td>\n",
       "      <td>0.009007</td>\n",
       "      <td>0.362267</td>\n",
       "      <td>4.046939</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.094860</td>\n",
       "      <td>0.130776</td>\n",
       "      <td>0.007860</td>\n",
       "      <td>0.342565</td>\n",
       "      <td>4.255102</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-0.085002</td>\n",
       "      <td>0.116877</td>\n",
       "      <td>0.007219</td>\n",
       "      <td>0.318062</td>\n",
       "      <td>4.463265</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.079336</td>\n",
       "      <td>0.107718</td>\n",
       "      <td>0.005833</td>\n",
       "      <td>0.277157</td>\n",
       "      <td>4.671429</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.076478</td>\n",
       "      <td>0.111609</td>\n",
       "      <td>0.006010</td>\n",
       "      <td>0.290606</td>\n",
       "      <td>4.879592</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-0.068895</td>\n",
       "      <td>0.079769</td>\n",
       "      <td>0.004959</td>\n",
       "      <td>0.283590</td>\n",
       "      <td>5.087755</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.062694</td>\n",
       "      <td>0.079976</td>\n",
       "      <td>0.004197</td>\n",
       "      <td>0.259354</td>\n",
       "      <td>5.295918</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-0.062707</td>\n",
       "      <td>0.090545</td>\n",
       "      <td>0.003987</td>\n",
       "      <td>0.236893</td>\n",
       "      <td>5.504082</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-0.060809</td>\n",
       "      <td>0.091744</td>\n",
       "      <td>0.003613</td>\n",
       "      <td>0.203680</td>\n",
       "      <td>5.712245</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.057550</td>\n",
       "      <td>0.083135</td>\n",
       "      <td>0.003231</td>\n",
       "      <td>0.204536</td>\n",
       "      <td>5.920408</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-0.056632</td>\n",
       "      <td>0.087855</td>\n",
       "      <td>0.003103</td>\n",
       "      <td>0.211196</td>\n",
       "      <td>6.128571</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-0.053511</td>\n",
       "      <td>0.075900</td>\n",
       "      <td>0.002946</td>\n",
       "      <td>0.222359</td>\n",
       "      <td>6.336735</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>-0.051860</td>\n",
       "      <td>0.072881</td>\n",
       "      <td>0.002629</td>\n",
       "      <td>0.186108</td>\n",
       "      <td>6.544898</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>-0.048957</td>\n",
       "      <td>0.063283</td>\n",
       "      <td>0.002351</td>\n",
       "      <td>0.173994</td>\n",
       "      <td>6.753061</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>-0.046148</td>\n",
       "      <td>0.057499</td>\n",
       "      <td>0.002167</td>\n",
       "      <td>0.162754</td>\n",
       "      <td>6.961224</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>-0.046627</td>\n",
       "      <td>0.051063</td>\n",
       "      <td>0.002150</td>\n",
       "      <td>0.174257</td>\n",
       "      <td>7.169388</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>-0.044642</td>\n",
       "      <td>0.058924</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.163140</td>\n",
       "      <td>7.377551</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>-0.043022</td>\n",
       "      <td>0.044346</td>\n",
       "      <td>0.001797</td>\n",
       "      <td>0.158661</td>\n",
       "      <td>7.585714</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>-0.039787</td>\n",
       "      <td>0.053108</td>\n",
       "      <td>0.001654</td>\n",
       "      <td>0.159113</td>\n",
       "      <td>7.793878</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>-0.039804</td>\n",
       "      <td>0.049151</td>\n",
       "      <td>0.001614</td>\n",
       "      <td>0.153487</td>\n",
       "      <td>8.002041</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>-0.039434</td>\n",
       "      <td>0.053118</td>\n",
       "      <td>0.001508</td>\n",
       "      <td>0.149133</td>\n",
       "      <td>8.210204</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>-0.035251</td>\n",
       "      <td>0.046439</td>\n",
       "      <td>0.001357</td>\n",
       "      <td>0.131550</td>\n",
       "      <td>8.418367</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>-0.035762</td>\n",
       "      <td>0.045039</td>\n",
       "      <td>0.001342</td>\n",
       "      <td>0.132132</td>\n",
       "      <td>8.626531</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>-0.035922</td>\n",
       "      <td>0.034156</td>\n",
       "      <td>0.001338</td>\n",
       "      <td>0.130322</td>\n",
       "      <td>8.834694</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>-0.034438</td>\n",
       "      <td>0.040168</td>\n",
       "      <td>0.001240</td>\n",
       "      <td>0.131077</td>\n",
       "      <td>9.042857</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>-0.033729</td>\n",
       "      <td>0.049095</td>\n",
       "      <td>0.001203</td>\n",
       "      <td>0.135135</td>\n",
       "      <td>9.251020</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>-0.033496</td>\n",
       "      <td>0.044003</td>\n",
       "      <td>0.001122</td>\n",
       "      <td>0.121775</td>\n",
       "      <td>9.459184</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>-0.032362</td>\n",
       "      <td>0.042733</td>\n",
       "      <td>0.001115</td>\n",
       "      <td>0.121719</td>\n",
       "      <td>9.667347</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>-0.032195</td>\n",
       "      <td>0.037455</td>\n",
       "      <td>0.001121</td>\n",
       "      <td>0.119840</td>\n",
       "      <td>9.875510</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>-0.030031</td>\n",
       "      <td>0.046511</td>\n",
       "      <td>0.000994</td>\n",
       "      <td>0.108163</td>\n",
       "      <td>10.083673</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>-0.028449</td>\n",
       "      <td>0.045338</td>\n",
       "      <td>0.000897</td>\n",
       "      <td>0.104899</td>\n",
       "      <td>10.291837</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>-0.027492</td>\n",
       "      <td>0.030116</td>\n",
       "      <td>0.000878</td>\n",
       "      <td>0.104079</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1.0, 0.0, 0.0]</td>\n",
       "      <td>29.263504</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           E         M         C         X       temp  n_temp  N  steps  \\\n",
       "0  -0.443371 -0.324433  0.002532  0.035052   0.300000      50  8   4000   \n",
       "1  -0.560676  0.223794  0.008563  0.010075   0.508163      50  8   4000   \n",
       "2  -0.659157  0.686418  0.011341  0.118476   0.716327      50  8   4000   \n",
       "3  -0.684673  0.695596  0.016856  0.068166   0.924490      50  8   4000   \n",
       "4  -0.667149  0.874787  0.028299  0.224131   1.132653      50  8   4000   \n",
       "5  -0.615006  0.779082  0.050747  0.613562   1.340816      50  8   4000   \n",
       "6  -0.548915  0.740473  0.077945  1.038498   1.548980      50  8   4000   \n",
       "7  -0.469830  0.648139  0.074368  0.660310   1.757143      50  8   4000   \n",
       "8  -0.397009  0.578444  0.077676  0.696118   1.965306      50  8   4000   \n",
       "9  -0.316434  0.475733  0.060115  0.793594   2.173469      50  8   4000   \n",
       "10 -0.256402  0.398530  0.053416  0.767991   2.381633      50  8   4000   \n",
       "11 -0.207991  0.298132  0.039251  0.662029   2.589796      50  8   4000   \n",
       "12 -0.183000  0.279934  0.030893  0.601879   2.797959      50  8   4000   \n",
       "13 -0.154739  0.237130  0.022232  0.515616   3.006122      50  8   4000   \n",
       "14 -0.141141  0.218375  0.018995  0.538407   3.214286      50  8   4000   \n",
       "15 -0.127265  0.200852  0.015320  0.429413   3.422449      50  8   4000   \n",
       "16 -0.113924  0.182902  0.012275  0.395413   3.630612      50  8   4000   \n",
       "17 -0.104174  0.156153  0.010619  0.361763   3.838776      50  8   4000   \n",
       "18 -0.097390  0.141516  0.009007  0.362267   4.046939      50  8   4000   \n",
       "19 -0.094860  0.130776  0.007860  0.342565   4.255102      50  8   4000   \n",
       "20 -0.085002  0.116877  0.007219  0.318062   4.463265      50  8   4000   \n",
       "21 -0.079336  0.107718  0.005833  0.277157   4.671429      50  8   4000   \n",
       "22 -0.076478  0.111609  0.006010  0.290606   4.879592      50  8   4000   \n",
       "23 -0.068895  0.079769  0.004959  0.283590   5.087755      50  8   4000   \n",
       "24 -0.062694  0.079976  0.004197  0.259354   5.295918      50  8   4000   \n",
       "25 -0.062707  0.090545  0.003987  0.236893   5.504082      50  8   4000   \n",
       "26 -0.060809  0.091744  0.003613  0.203680   5.712245      50  8   4000   \n",
       "27 -0.057550  0.083135  0.003231  0.204536   5.920408      50  8   4000   \n",
       "28 -0.056632  0.087855  0.003103  0.211196   6.128571      50  8   4000   \n",
       "29 -0.053511  0.075900  0.002946  0.222359   6.336735      50  8   4000   \n",
       "30 -0.051860  0.072881  0.002629  0.186108   6.544898      50  8   4000   \n",
       "31 -0.048957  0.063283  0.002351  0.173994   6.753061      50  8   4000   \n",
       "32 -0.046148  0.057499  0.002167  0.162754   6.961224      50  8   4000   \n",
       "33 -0.046627  0.051063  0.002150  0.174257   7.169388      50  8   4000   \n",
       "34 -0.044642  0.058924  0.002000  0.163140   7.377551      50  8   4000   \n",
       "35 -0.043022  0.044346  0.001797  0.158661   7.585714      50  8   4000   \n",
       "36 -0.039787  0.053108  0.001654  0.159113   7.793878      50  8   4000   \n",
       "37 -0.039804  0.049151  0.001614  0.153487   8.002041      50  8   4000   \n",
       "38 -0.039434  0.053118  0.001508  0.149133   8.210204      50  8   4000   \n",
       "39 -0.035251  0.046439  0.001357  0.131550   8.418367      50  8   4000   \n",
       "40 -0.035762  0.045039  0.001342  0.132132   8.626531      50  8   4000   \n",
       "41 -0.035922  0.034156  0.001338  0.130322   8.834694      50  8   4000   \n",
       "42 -0.034438  0.040168  0.001240  0.131077   9.042857      50  8   4000   \n",
       "43 -0.033729  0.049095  0.001203  0.135135   9.251020      50  8   4000   \n",
       "44 -0.033496  0.044003  0.001122  0.121775   9.459184      50  8   4000   \n",
       "45 -0.032362  0.042733  0.001115  0.121719   9.667347      50  8   4000   \n",
       "46 -0.032195  0.037455  0.001121  0.119840   9.875510      50  8   4000   \n",
       "47 -0.030031  0.046511  0.000994  0.108163  10.083673      50  8   4000   \n",
       "48 -0.028449  0.045338  0.000897  0.104899  10.291837      50  8   4000   \n",
       "49 -0.027492  0.030116  0.000878  0.104079  10.500000      50  8   4000   \n",
       "\n",
       "                  H  wall_time  \n",
       "0   [1.0, 0.0, 0.0]  29.263504  \n",
       "1   [1.0, 0.0, 0.0]  29.263504  \n",
       "2   [1.0, 0.0, 0.0]  29.263504  \n",
       "3   [1.0, 0.0, 0.0]  29.263504  \n",
       "4   [1.0, 0.0, 0.0]  29.263504  \n",
       "5   [1.0, 0.0, 0.0]  29.263504  \n",
       "6   [1.0, 0.0, 0.0]  29.263504  \n",
       "7   [1.0, 0.0, 0.0]  29.263504  \n",
       "8   [1.0, 0.0, 0.0]  29.263504  \n",
       "9   [1.0, 0.0, 0.0]  29.263504  \n",
       "10  [1.0, 0.0, 0.0]  29.263504  \n",
       "11  [1.0, 0.0, 0.0]  29.263504  \n",
       "12  [1.0, 0.0, 0.0]  29.263504  \n",
       "13  [1.0, 0.0, 0.0]  29.263504  \n",
       "14  [1.0, 0.0, 0.0]  29.263504  \n",
       "15  [1.0, 0.0, 0.0]  29.263504  \n",
       "16  [1.0, 0.0, 0.0]  29.263504  \n",
       "17  [1.0, 0.0, 0.0]  29.263504  \n",
       "18  [1.0, 0.0, 0.0]  29.263504  \n",
       "19  [1.0, 0.0, 0.0]  29.263504  \n",
       "20  [1.0, 0.0, 0.0]  29.263504  \n",
       "21  [1.0, 0.0, 0.0]  29.263504  \n",
       "22  [1.0, 0.0, 0.0]  29.263504  \n",
       "23  [1.0, 0.0, 0.0]  29.263504  \n",
       "24  [1.0, 0.0, 0.0]  29.263504  \n",
       "25  [1.0, 0.0, 0.0]  29.263504  \n",
       "26  [1.0, 0.0, 0.0]  29.263504  \n",
       "27  [1.0, 0.0, 0.0]  29.263504  \n",
       "28  [1.0, 0.0, 0.0]  29.263504  \n",
       "29  [1.0, 0.0, 0.0]  29.263504  \n",
       "30  [1.0, 0.0, 0.0]  29.263504  \n",
       "31  [1.0, 0.0, 0.0]  29.263504  \n",
       "32  [1.0, 0.0, 0.0]  29.263504  \n",
       "33  [1.0, 0.0, 0.0]  29.263504  \n",
       "34  [1.0, 0.0, 0.0]  29.263504  \n",
       "35  [1.0, 0.0, 0.0]  29.263504  \n",
       "36  [1.0, 0.0, 0.0]  29.263504  \n",
       "37  [1.0, 0.0, 0.0]  29.263504  \n",
       "38  [1.0, 0.0, 0.0]  29.263504  \n",
       "39  [1.0, 0.0, 0.0]  29.263504  \n",
       "40  [1.0, 0.0, 0.0]  29.263504  \n",
       "41  [1.0, 0.0, 0.0]  29.263504  \n",
       "42  [1.0, 0.0, 0.0]  29.263504  \n",
       "43  [1.0, 0.0, 0.0]  29.263504  \n",
       "44  [1.0, 0.0, 0.0]  29.263504  \n",
       "45  [1.0, 0.0, 0.0]  29.263504  \n",
       "46  [1.0, 0.0, 0.0]  29.263504  \n",
       "47  [1.0, 0.0, 0.0]  29.263504  \n",
       "48  [1.0, 0.0, 0.0]  29.263504  \n",
       "49  [1.0, 0.0, 0.0]  29.263504  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mkdir in case\n",
    "if not os.path.exists(data_path):\n",
    "    os.makedirs(data_path)\n",
    "\n",
    "# gather results in a table just in case\n",
    "df = pd.DataFrame(results, columns=['E', 'M', 'C', 'X'])\n",
    "df['temp'] = temp\n",
    "for name, var in zip(['n_temp', 'N', 'steps', 'H', 'wall_time'], [n_temp, N, steps, [H] * n_temp, wall_time]):\n",
    "    df[name] = var\n",
    "    \n",
    "# save the table\n",
    "df.to_csv(f'{data_path}/data_{n_temp}_{N}_{steps}_{H}.csv', sep=',', header=True, index=False)\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Show all results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = './results'\n",
    "\n",
    "dfs = [pd.read_csv(f) for f in Path.cwd().rglob(f'{data_path}/*.csv')]\n",
    "df = pd.concat(dfs, axis=0, join='inner').sort_index()"
   ]
  },
  {
   "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>E</th>\n",
       "      <th>M</th>\n",
       "      <th>C</th>\n",
       "      <th>X</th>\n",
       "      <th>temp</th>\n",
       "      <th>n_temp</th>\n",
       "      <th>N</th>\n",
       "      <th>steps</th>\n",
       "      <th>H</th>\n",
       "      <th>wall_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.378843</td>\n",
       "      <td>0.598633</td>\n",
       "      <td>1.892052</td>\n",
       "      <td>1.128047</td>\n",
       "      <td>0.2</td>\n",
       "      <td>400</td>\n",
       "      <td>112</td>\n",
       "      <td>4000</td>\n",
       "      <td>[0.   0.5  0.86]</td>\n",
       "      <td>2316.861832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.338853</td>\n",
       "      <td>-0.009411</td>\n",
       "      <td>0.077152</td>\n",
       "      <td>0.018227</td>\n",
       "      <td>0.2</td>\n",
       "      <td>400</td>\n",
       "      <td>32</td>\n",
       "      <td>4000</td>\n",
       "      <td>[0. 0. 0.]</td>\n",
       "      <td>168.243150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.382756</td>\n",
       "      <td>0.363900</td>\n",
       "      <td>0.194924</td>\n",
       "      <td>0.136674</td>\n",
       "      <td>0.2</td>\n",
       "      <td>400</td>\n",
       "      <td>32</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1. 0. 0.]</td>\n",
       "      <td>157.709718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.422916</td>\n",
       "      <td>0.833259</td>\n",
       "      <td>0.228027</td>\n",
       "      <td>0.135707</td>\n",
       "      <td>0.2</td>\n",
       "      <td>400</td>\n",
       "      <td>32</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1. 1. 1.]</td>\n",
       "      <td>149.793159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.384748</td>\n",
       "      <td>0.605823</td>\n",
       "      <td>0.417926</td>\n",
       "      <td>0.263677</td>\n",
       "      <td>0.2</td>\n",
       "      <td>400</td>\n",
       "      <td>48</td>\n",
       "      <td>4000</td>\n",
       "      <td>[0.   0.5  0.86]</td>\n",
       "      <td>325.607392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>-0.031090</td>\n",
       "      <td>0.040939</td>\n",
       "      <td>0.000984</td>\n",
       "      <td>0.107211</td>\n",
       "      <td>10.0</td>\n",
       "      <td>400</td>\n",
       "      <td>160</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1. 0. 0.]</td>\n",
       "      <td>4502.634192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>-0.031143</td>\n",
       "      <td>0.041522</td>\n",
       "      <td>0.000981</td>\n",
       "      <td>0.120111</td>\n",
       "      <td>10.0</td>\n",
       "      <td>400</td>\n",
       "      <td>48</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1. 0. 0.]</td>\n",
       "      <td>327.983945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>-0.022922</td>\n",
       "      <td>-0.000188</td>\n",
       "      <td>0.000762</td>\n",
       "      <td>0.111231</td>\n",
       "      <td>10.0</td>\n",
       "      <td>400</td>\n",
       "      <td>64</td>\n",
       "      <td>4000</td>\n",
       "      <td>[0. 0. 0.]</td>\n",
       "      <td>642.462919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>-0.031068</td>\n",
       "      <td>0.041313</td>\n",
       "      <td>0.000970</td>\n",
       "      <td>0.117823</td>\n",
       "      <td>10.0</td>\n",
       "      <td>400</td>\n",
       "      <td>80</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1. 0. 0.]</td>\n",
       "      <td>1142.393397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>-0.031695</td>\n",
       "      <td>0.042339</td>\n",
       "      <td>0.001002</td>\n",
       "      <td>0.118913</td>\n",
       "      <td>10.0</td>\n",
       "      <td>400</td>\n",
       "      <td>16</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1. 0. 0.]</td>\n",
       "      <td>62.124650</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16050 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            E         M         C         X  temp  n_temp    N  steps  \\\n",
       "0   -0.378843  0.598633  1.892052  1.128047   0.2     400  112   4000   \n",
       "0   -0.338853 -0.009411  0.077152  0.018227   0.2     400   32   4000   \n",
       "0   -0.382756  0.363900  0.194924  0.136674   0.2     400   32   4000   \n",
       "0   -0.422916  0.833259  0.228027  0.135707   0.2     400   32   4000   \n",
       "0   -0.384748  0.605823  0.417926  0.263677   0.2     400   48   4000   \n",
       "..        ...       ...       ...       ...   ...     ...  ...    ...   \n",
       "399 -0.031090  0.040939  0.000984  0.107211  10.0     400  160   4000   \n",
       "399 -0.031143  0.041522  0.000981  0.120111  10.0     400   48   4000   \n",
       "399 -0.022922 -0.000188  0.000762  0.111231  10.0     400   64   4000   \n",
       "399 -0.031068  0.041313  0.000970  0.117823  10.0     400   80   4000   \n",
       "399 -0.031695  0.042339  0.001002  0.118913  10.0     400   16   4000   \n",
       "\n",
       "                    H    wall_time  \n",
       "0    [0.   0.5  0.86]  2316.861832  \n",
       "0          [0. 0. 0.]   168.243150  \n",
       "0          [1. 0. 0.]   157.709718  \n",
       "0          [1. 1. 1.]   149.793159  \n",
       "0    [0.   0.5  0.86]   325.607392  \n",
       "..                ...          ...  \n",
       "399        [1. 0. 0.]  4502.634192  \n",
       "399        [1. 0. 0.]   327.983945  \n",
       "399        [0. 0. 0.]   642.462919  \n",
       "399        [1. 0. 0.]  1142.393397  \n",
       "399        [1. 0. 0.]    62.124650  \n",
       "\n",
       "[16050 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Limit sizes\n",
    "df = df[df['N'] <= 200]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example of plotting all simulation data with a specific size $N$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uppermost for-loop is different external fields H\n",
    "Hs = df['H'].unique()\n",
    "figure_size = (4, 3)\n",
    "marker_size = .6\n",
    "figure_dir = './figures'\n",
    "\n",
    "# mkdir in case\n",
    "if not os.path.exists(figure_dir):\n",
    "    os.makedirs(figure_dir)\n",
    "    \n",
    "# set colors\n",
    "colors = sns.color_palette('husl', n_colors=len(df['N'].unique()))\n",
    "plt.rc('axes', prop_cycle=cycler('color', colors))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for H in Hs:\n",
    "    \n",
    "    # Select correct data\n",
    "    dfh = df[(df['H'] == H)]\n",
    "    \n",
    "    plt.figure(figsize=figure_size)\n",
    "    for N in sorted(dfh['N'].unique()):\n",
    "        dfhn = dfh[dfh['N'] == N]\n",
    "        E, M, C, X, temp, *_ = dfhn.values.T\n",
    "        \n",
    "        plt.title('External field $H={}$'.format(H))\n",
    "        lines = plt.plot(temp, E, 'o' , markersize=marker_size, markerfacecolor=\"None\", label=f'$N={N}$')\n",
    "        plt.xlabel('$T$'); plt.ylabel('$E$');\n",
    "    plt.legend(loc=\"lower right\", markerscale=10, ncol=2, labelspacing=0.07, columnspacing=0.5, handletextpad=0.1)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'{figure_dir}/energy_{\" \".join(str(H).split())}.pdf')\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Magnetisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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z5kxmzJjBqFGjuiU3s6yK8WGBzdrGhwWSWVbltCx3jMdFF13E8uXLsdvtVFRUsGvXLiZNmkRpaalbxqMr9P9drHZoLb+s2AQRofatVccksu5veIXGU52T1qytOicNr9B4p2W5I7q2LQIDAxk3blzjpuOwYcP4/PPPuy03LtCPLUVlzdq2FJURF+jXxh1t447xiIyM5NVXX+Xll1/m66+/Zty4cURERLhtPLrCCT1DaZpfVtVOdU4avjGJbSay7m8MnHY9BZ/+iYj59+Mbk0h1ThoFn/6JQbNucVqWO6Jr22Ls2LGsW7eu8dhms1FfX++0zi25dkwMj/+wh3smj2B8WCBbisp4/Ic9/GbcMKdluWs8xo4dyymnnALAI488wiOPPEJ2drZbxqNLOLPh0ld/cMGmbGVWimb8ba5WZqV0WcbxiCue8tTU1Ojy5cubtS1atEjnz5/vEh1bbspWVVXplClT1G63q6rqtGnTdPfu3S7pyxVPedw1HhkZGTpu3DhVVd2xY4deeOGFquqe8ejqpqwJDnTQMEPxGTIeMvc326g1tI27o2uff/553n33XbKysli0aBGLFy8mODiY1atX891332G32znppJO6HV3rKtw5HjU1NTz66KNERkby888/c//99xMaaiXDcvV4mGhjF0Ub2/dmN27U2ky5DcMJSr+KNhaRU0QkRkRsIjJWRAb3WN8nyP6JweAOeqtY+kIRuVdE7haR1ioS/QrIxir4dRtQ0Mo1LkPVTtGBFFTtiE2wDR9qljsGQxfojWLpwcADqvqYqj4B3CIiLb2GMoFoIFKthNVObVm3WRC9DYrzN7Pp6yUU5292phuDwdCC3pihnA40rSS1BZjd8iJV3a9dzHjfVkH0tgiNTGLK7KWERvZOYl+Dob/QG34oTYuhQ+sF0f1F5FbgMDAGuN+ZWUpX9kH8a80Spy+ycuVKSktL2bt3L2FhYdx6660AvP322+Tl5bFp0yYuvPBCrrjiil7WtOdoLfq6rfaeHqfeMChNi6GDVRA9oMU13wJbVdUuIg8B12MV/2pGW8XSnS2IXp2TxoEP72Lg9BsInnApqBwT49Nfyd77Kbu2vELZ4QwCg+MZPf4Ghg53Lo4HYMOGDTzyyCNs2bKFzMxMvLy8OHDgALfddhtlZWXcc889Tpe/KCkp4ZJLLuHQoUP4+voSFhbGggULqK+vp7i4mD/+8Y8UFhYycuRIpk6dSkJCgtN6tyRlbx2fp9WQX6JEhgjzEr2ZONz5j4k7xgPg6aefZv369QQHB3fYvmfPHreNU1v0xpKnDGj6KfUDDra45kdVtTteZwLntSZIVV9S1UnAOd1RyDcmkYHTb6Dk+1eozklzesl0vJK991N2pPyd8dPv5JfXfs/46XeyI+XvZO/91GlZ7qjlGxISQnJyMgMGDMBms1FXV4eqsn37dpYtWwZAeHg4I0aMaFaXpquk7K3j4+QafjXdh6evG8CvpvvwcXINKXvrnJbljvEAuP322/nlL3/ZqXZ3jVN79MYMZS8wqclxKNC4GyoiXkChiAxV1TKs2Ytb/YhFbARPuBSfiFFWsJzKCfHoeNeWV5gw6wHCB1vRxuGDJzNh1gNs+X6Z07MUd0Ubjxs3DoB169Zx+umnk5CQQExMDJ988glgeXrn5eUxYsQIp/Rtjc/Tarhilg8jh3gAMHKIB1fM8uG97484PUvpyejrtjjnnHPcMk7t0RsG5T/AMhERtbzqJgJ3i8iFwBeqWi4izziMCcAU4N2udtYsRkdsjW1V2akA+A2dgIgNERt+QydYNwnI8KGO6zY3u7c/UXY4g9DIxGZtoZGJlB3uW9HG//znP3n//fd55plnAPD29m4sdvXxxx8zceJEJkyY4LTcluSXKAlRzf/OCVE28kucd/7syejrtnDXOLVHb9Q2rgCWAfeJyIPAU6p6AHgQaDCfH4jIH0XkXuAnVf3frvZXnZNG/solzaJqq3PSyP/gDg588Ed27XgXu2N11dQfpa17+xOBwfEU56c1ayvOTyMwuG9FG1955ZUsX76cc845h4yMo8bu8OHDvPrqq7z11lvd7gMgMkRIP2Bv1pZ+wE5kiPP7aD0Zfd0Rrh6n9uiVaGNVfaOVtsQmr7cCW13Rl29MIpEXLG2W98M3JpHIhU+xr3Q3K9Jf4cqIkSSET2z0R5kyeylhURNbvbc/MXr8DaSue4QJsx4gNDKR4vw0Utc9wpiJtzotyx3RtR9//DGPPfYY3333HUFBQURGRvLee+9xxx13oKo8+eSTvPzyywQFBZGZmUlcXJzTejdlXqI376w7whWzfEiIspF+wM47645w7iRvp2X1ZPR1e7hjnNqj36cvaLaUadI2YNgkRusErowcTVyY5X/S0h+ltXv7Ew37JFu+X9b4lGfMxFud3j9xVy1fm83GmWeeCVgfjOzs7MY9lb/97W9cdNFF1NTUsGnTJiorK7v9QWnYJ3nv+yONT3nOneT8Ux53jUdXcMc4tYszocl99QcX5pRVVa231+vegmStP0ESVXeHjRs36sKFC/XKK69slst069at+uijj3Zb/t/+9jf961//qosXL9Z//OMfqqq6bt06FZGG8HoFNCsrq9t9uQJ3j8dzzz2nM2fO1GHDhukDDzygJSUlbbZ3Z5xM+gIX1jZOL0zhnxuWcOW0pSSET+y+ggbDcUa/ijbuCexqJ70wpXFDtilxYUlcOXUpww6HdToeyGAwnMAGJbNoM//csITMos3HBBPaxEZcWQR1b37U753bDAZXcsIalLiwJK6ctpS4sKRWPWNNXhSDwXn6/VOetrCJrXF/pD5uCLkXDScubkgzRziTsa3vkJqaymeffcbdd98NWEGDOTk5VFdXExsby8UXX9zLGvYczgQB9vQ49asZir0Lmb7tqnya8R3/3Pc0+w6m9XtnNnexYcMGFixYQHR0dGMxqgMHDnD55Zdz7rnn8u2333ZZtt1u5/777+fIkSMAZGdns2vXLm655RYWL17MJ598QllZWQdSehZ3jcfTTz/Nww8/THFxcWNb0yDAv//979x8882kp6f3yjj1K4NSnfaN0/dsKS7gb7sKmDDmAeLCkvq9M1tL0rI+5S+fX8Ld703iL59fQlqW84GB4L5gOIAVK1Y0+qMAFBYWsmbNmkYD4+/vj7e3885nrXFwSx3b/1pJyn0VbP9rJQe3OB8YCD0bHNhWEKA7x6kt+tWSxzfx9FbbW4vnaWB8aASPT53J+NAIbCLY1c5Ov4Ek0jwkuj+SlvUpn2//OxdPfIC4sEQyi9JYkfIIAInD+kZwYFFRESJCeHg4FRUVACQlJWG325k8eTI33XQT8+bNw8fHxyl9W+Pgljpyv6gh7iIfAmJtlO+zk/m+9WEcNL7vBge2FQTornFqF2ecVvrqDx04trVWc6ct57WUwnSdt/pxTSnsXvX644FnPvuV7snf1KxtT/4mfeazXzkta9OmTbp+/XqtrKzU0NBQTU62is2/+OKL3dLxxRdf1Pr6en311Vf1wQcfbGxfs2aNzpkzR318fPSFF17oVh8NbHu2Qkv31jVrK91bp9uerXBalrvGQ/XYOkVNWbVqlZ5//vmNx10dp646tvWrJU9b+ESfSsi06/GJPrWxrelj46Ykhsby+KTLiK4pojIrGbu9jqrsVLQVf5XjnYLSDOLCEpu1xYUlUlDqfLSxO4LhNm7cyNSpUxtLbDawe/du1q5dy5o1a1i1ahWPPvpot/ZoGqguVAJim/cVEGujutB5X6TeCA5sGQTornFqj3615GmLI7lbObhhOTv9BjL15LmWn0lYEpdPfYKDOpiDhelMCIvDrsqK9B85Y0AtyV/dQWyZEDPlN5RsWE7kBUv7XVxPRFA8mUVpDI+Y3NiWWZRGRJDz0cbuCIbbuHEjlZWVfPrpp3z77bdUVVXxwQcfsGfPHi655BIA5s6dy+uvv8769eu7tU8D4BsulO+zE5jg0dhWvs+Ob7jzi9+eDg7UVoIAV65c6ZZxao8TwqD4xiSSP/t2HsxK5YmIkcTWHWJgRCK7i2N5avdXYMvltyctwE4Vf/1xK/WnjGPw5EeI8/VjwNCko4mX+hmzT7qBFSmPHLOHMm+sc9HG7gqG+/3vf9/4+qGHHgJg4cKFrFixgm3btjXm+jhy5AhTp051SufWGHymN5nvHzlmDyV6rnMbmb0RHNhaEGB8fLxbxqk9+p1BaboBqwhbikoZHxbE1JPn8lj4CPanbyZvz0vY4v/Bf/ZEEDgglNPjh/PK9sP8aVgIt+YeRgeXsOzgIR6fOpMkm2e/m5k00LDxujJtGQWlGUQExTNv7K1Obcg2Lb05d+7cxtKbixcvZuXKlS7R89133+XDDz8EYMyYMVx66aU8++yzPPbYY/j7+xMSEsKiRYu63U/DxmvWqiNUFyq+4UL0XG+nNmTdPR5NS7M++OCDLF68mB9//JE//OEPDfuJ1nvIyuLMM890yzi1R78KDszKyqIs/SN8t64gZPoNfBGcxBs7yrhlDJw9PInXNn/N23vgunAPdu2fSlj0IU472YPCQ548t2sDt4SOZEhACY/t28WvokO5YsLZ1O7/sd9mbDMY2qKrwYH9aoaS/dM3fFn0FufH/4LV2z7i3UE/cXp4Esnb/0FG2e/4YWcp9QPyGa5TqRqVz2sZ2VTYBlO6bzCXBSYR8qU3JWcGc9Lh0WjhMvbt34gtewNRv3wSv6ETrJif9GxQTHVBg6EV+pVBiRx5Gh5qZ2vsMP6lX1FjV74q+IlT/X5F9g5fFv18GpsHr8J7cyyDpufi7enFmsIiwjxLCc8fzbAzK5g9M5zayDz+e1s8hw9+wJz46XgPOYWiAykMLA+j9lVr6u19/YVOleowGE4E2jUoIpKkqi6vzykiC4GxgB3I1RYpIUUkClgCpGElsb5dVWs6knvfd2vI9ByNZ+V2Zgd7kHEokz1186grjicgeh9vaTKnjo4gw7aOv9fsYEH8PD7J3szoyirOLBhBfWgh9fUDCco/wslVlxM5SPm6bDWeO/9F8raXOWvWU4Ret9B6Oh8bjX1v9glRu8dg6CwdzVD+S0S2qAudMJrUNp7gON4gIqtVtbDJZUuB/1HV9SISAvwGeL4j2bsPD8Q7PItzBtawveAngn3nMK54PFdnDeVnfPk+8Et2/ziMi7cMI314OQE1dpaevoDiwwep893H0P/U8G31BsK2xXDhsD18dKSA/4SPo/JwFZW+MM1LiIgeBoB9bzY1b6y0IpJbmamoXU+YYmEGQwMdGZRfAVeLSDLwNfCVqqYCiMhkVf2hC322Vdu4aamM87AqAgJsBO6nEwZldIU/+wbaCcwZTbpXFKfti8Pfr449vvkMz4jBM/Aywid6sFe+4/rkCRzMDWelVlNYIcxICObt8bv5zfDhxG4ogS3CcM/5FCT9zAd1VVzrfwaF+48QG1qPh4cHEh+Dx1Xnkl66jbi6KGrzfsRnyHg0Ixd7fT3Vu77H8/t8bBPHUDcplAGxVrmOBkNDbDTsy+3Q4LjKMPV1A9eWfuXl5Tz55JMMHTqU0tJSFi9e3MzRrWUU8olAa9HGbY3TBx98wPbt27HZbERHR3P1VVe79f+gI4MyA6soVy1WfZy/iMhIrA+5D7CgC322W9tYRPwA3yZLnGbn28O7Ppab99Qz+0AVY0KjmZBXy2FfPwJqStkZopyTWUpJURgfnXIK2UO8mbu/kLCflRv22yjeaePTsZF42gaQc7YfR9bBjkHFnFo8GlvNQTwPjuPn+jp2fZVMXX0p1fYAhlVVkuPlTcK7P4LA5sj1jD8QRLmtHv+60XhwEqWpiv8PgMcWDoRUEnXIlwqb4l9fYJU8tRVSjx1P8aQgqIKIw/4Or1xBUKwiQUJ+YCaRZUFYJZ6lMc5IsUqv5gWUEFUWbDU4zlgSFGzCgYByokr9gaKGu6zfNiEv4DCDHfeKgkrT8zYOBJQQVRYC2uCNfRSxeZAXcIiSn7J459uXyCjYzfLfrsLTw5uDlUU8t3YpVNZw6bTrGBuT2Pxum428gEONfVtqFdIYRWWDxz+7l+un3EpU7WB++z+/YtCek4gKiUZsQs6AYl567UFGRY3lk8OpR3UGx/s6xOCyEEdTsxFDbLYmY2anWeSWTRzvOdjxnmkh28aB4AqiSvxaDIc03r/+4Ld88cU7jvH4CE8PTw6WF/HyV09zqP4w10y5iTHRiS3+g6UVvRt0tnj/h7dIzdtEqMcgVi/ZbP19gWc/eZjLT7uRqPoYbvyfCxm0dzSBvkHc/c4Snlv0DqAs/tu1HNoVxMj6YUDh0T6bvq+AMqLKAiguLaArtGtQVDUXyBWRWcBKVX1IRHyxDM0dXeqx49rGnal9DBxb2/jCzGJm1/uzdaAHow764UkNg6oPU+49gDGHStkZo4zeX0xE1kCSw+qJDfJnTEEkhT6HiCgt5Ncl3sS+V4DHDbG8VnCQs7f6EV5dQ7nHYPzqj+CBB/b9oAwCwANvx7+n9QcZXxJGQF01zf95G7BjL/J1xDq0PC9AveN8fYtz1m978QBs1LU410R6USA2Wq5Mj8qwFw3AhtJS7pohWawetousgFJiy4O4as9ozto/tMl19Q7ZbaWGqLPO+46lLDSRt4tzOJj6JZcMPxsYRGXoGSyc+Av8vfzgYEu967EXBTn0OlbvjNIcfPIPcVl9FBxU1sz7G9H2SDhovc8V6cksDJ1IRVUVZxxsaewUe1FwE9naQr4de1FAK+PtoCgQmo1n0/MNf6vW9QaYxTR8Qnc6xmONYzxCqY6cwXlxvyCw1fFw/K2Lglr5W1qcMfy/eLM+iPX7UzjdMQ4ZpTnYDxZwmX0IFNn56qx/MLQ+kk92fMOkwATOcFx3WtBIQjb8xBnNlugt39cAbNjJq+iaO0mnnvKo6joRiRCRc4DvVPVLEQnsUo9WbePQJsd+QH6L8x3VPm7Q6yXgpQY/lJNLK8DfHx97EAOPHObnABsJVXaCtZI6Dzjs+wObT53K3B8PcnJ5FN72AZR7eXI4rpbIXcrIvTVkTPVgXWY2lYeqGVjtQbmnLwF11dQDhT5BZAyoZ+KhCgQ47DWAgNpKfvL34YhnPaPKKtge4ENwXQA5vlUARFf7ketbQ6VnLekDCjmlNJroai9yfWuoslXjoTa8NYAajwp+DtxHQlk0YgcVoSHUSmx2sgfkMKzCOgegjhESBTyEXN9coquGYNcW38UK4iHk+uYQXRWNOmYhAFvDsvly2A5m5sRz7Y6RZAYU8fzJKez0F8YVD7Wuswm5fpbspvc24iHk+OUyuDyS3EBf5k25kie3/x/hk+cjIqw9cIAhUQNAYf+hHD7f8v7RP64I5Z4VBNRZ50cPGcdpI2c3vrfPc1IoHejJA/s/pqK6DD9vf+YOvQCAw0dK+C6sFD+fgeQfruY/AwWVJv84Tb/p7dZ4NeiuAmoTcvxyGFYZ3Wg3ImqU+CrYFSDsCMxjTFkUo8shww8O+Ig1lo3vOYeYiuhmn8fGPmxCtl8uuelHxyNikuUsmOZzBE0oI7piAHmHslm95d/No9pFKPeqIKB2ACcNHsf0kbObj7dN2BBWTl4xrB1k6fRZVjI1AYE8kPsxFTXlVAcc4dLYa1mbWUBFYCD/GWj1UBocxL9lO+Gh81r/W9qEXD/r/6TYs2vLoU4/NlbVAmC1iEwTEVT1gy712EFtY1UtE5EaEfFU1TrH+U451tSJkhw+EE9C+E+0N9nzynh911puOuksKAwi6tsLqB5ayv6gELxi6xi2o5CVYYOpIYXXxw1hkPrjc2gYp+/2YW2onQ9jixheFkatVy5l3snsDi3D7vcpWcV3EFidz7bwMKKrj5Ads4EgVQJrY9lb+C1D6rzITwhgr02YWB3DoeAAQgLquNoexXrSqa6C2gEVxAZVEz5wFCV5Awks3Ep8RABHqr5gdMSFHDz8E2m1hyivyGd8RQmJURMpLnmbYQEzOfLVxxSM8kfExuAdHpSPGMCcKVdT63uIvEMFBNULIeNmk7b2CRLjrsVzeByVP+RQfXA9Zf6VhA2dBTsyeFVy+FP0mUxMHEx10XbKPesJ8A7nk7AMrgv2Irh0ILU+B/EJPYmywxuoKCshMno+toISbKclUpmzicyfVjBn9kOs/+9HufLecxhVto3/vSEX77HrOXPKeex5L4jRC/YTFRLBrgFjuPanwdiiwlGtJz/3MwbPuhHJyqPIJ48ju34gyDuLw9U5+ITEkervSfmeXO55IIGQCZdx+tTpXHVeAMPHjeCFFf/Hc6+9ypvPPo/HtiLOeuhk8r97jSFn3IiHp1fj/4S9zk7Nuo0cKFrLQK9ofEZNpyj9O7Kqt/KLk2+kumIzXuHx1Bam4/PecOwzDjNucB2TQk+i5uBPSE0EYz7zJPiS/QweGIbYhApPOD3Lhm/oIGwJw9DMHOz19RQf3s6AmnrqfSoYWBdLdHQUU6ZP4p3T/k7YRbUkDQxnywvpXHfXg9iy89G48cwujiGmZBCaW0CNdyG2mEh8oxOp2Lia6n3bsAV/S9DwBdhs1kdVRDiwbgwfeewkbub7jByxiJR3vSncvYul73+ELTuPWVf8koTHf8uQGjs+9mhmzvwJH/8E1pcFUkMZ864fyMEBxQysGER14U68a0KpGSrozj3Yxo5ApJ6ClBp4wfkPd4cGRUQGYJUIHQEMx9rPmCgiO1X1Kue77Li2MfA5MBNYi7W8+qgzgkumFZIV8DEhERdx0viZzKot4IOfviA6cgo5hcEkVOzn4/0/cCjsUs7MhoPzPKjKe4WIMaeQlaf4lI/glBEFFO88QrhvDW9ElrPw8DecMTid8G21pEau5/wxt5A4aib279JY8Ol6QPA+70YkPgZ7ejan2OdQ41PM6cOSWvWuvaAVvaOGAjQfyiGMomVBkBgutjYvoycR6ch1qxk5hDo22HyAkU3i+qbO/1Pj68DTzicQCG9oGDGDfR/ex6TEqXjaPPAaHU8gcLW9nhdWPUjknNsA8HVcHsqIo9PKkx0y409jXPxpAPzsE8+Np5+Bp+ccbk4u4u0vUxk6eR6nzpjPyKQzAce3SORR/YaedItDl1giiYWh0wAIcZwP3pDO+AlTCJ1ijU3s6FF8b/elznsw5yy6C29fXzyiwgmoHoe3nx9D59x8zNjaPG34zp5OHNOPjuOYkTRkCh7Q8Gbip1P5SgoBC+chjm9nb4ajdUrVO6mMnHBu4/3+ANFNOhkxDA8giqODv+XFF7nxxtvw9PTklt/9nn+88AJLlixh+i9/g6e3NziWHQkREyECGBVP00wlQTMuIGhGa/8tYPvWRpBfOGPPe9Qap5Bgxk+ZjpePN4yIJW7kGNZ8+SVBcTEczMwk6PwrAaj55D0iI4fhOTKeCIeuXqMSAGtDlBFH43x8fDrtHNuMjvxQMoAqrFnFXiAd+BB4GnA+xh2rtrGINNQ2tuGobeyoc5yB5XvyAHCPiMQDQ4E/tCmwCZFTwhk17LrGyn+bf9pNRkgxa3eVE/yDL6tGeTLz9AkE7Msj4es6PHwjuHz72WzyGcCvpxSRsu1hYjIu56TN3nh6KrVJX3LyiGoyM79i/JQbuPrUS4kLT8ImNmTGBGRwuLWRGR+DZuRQ++YqvK+5gAGx7qvlIzZp9pi6O851cYHhpBXvY1J4QmNbWvE+4gLD27mrddwRXTt27FjWrVvXeGyz2aivr28zCrm7ZTtlsC/2PeV4nHR0NW/fU44M9m3nrtbpyWjjtsZp5MiRJCcnN7YXFxeTlJTk9HtxivaSpWA9yv01cCPWjMGjybk4ZxKvuPMHR4KlramfqN2RMKlg/yZ9463p+vobk/XDN87UtV//qMkHSrTebte6ujrN2pisR7Yf0kO3pGjqnQf0vY93687Ubbrh4RJ9+i8bdNt36br3QLLW1FRr1pp/aF1tTbsJaez1dq3fk6X2enu71/UlVmen6QWfP6U/FOzV2vo6/aFgr17w+VO6OjvNKTk1NTW6fPnyZm2LFi3S+fPnd0u/qqoqnTJlitrt1phOmzZNd+/e3eyaBx98sFnipe5Qu7FYK+/eqnU7S9Vea9e6naVaefdWrd1Y7JQcd41HAy0TLLU1TuXl5Tp27NjG9nHjxmleXl6n+uhqgqWOljxL9ajfyWjgShHxdHR0LnCJ601c10n7/gkGD44gLGoiZR5C+gAbwyu9OHn8jYSEVhMWEYiIkH4wjX/mL+HK2KUMviWeQyn5LJidwI6d1bwRn8MZUyM5eWQsNonHvjeb8G/sSNyBxmlqa7ScORwPzI8ZD8CyH1eRWVZIXGA4N588t7G9M7gzutbX15eHHnqIBx54ALvdzi233MLIkUc9CFqLQu4OnlOsp3c1/5uF5lUjg33xWhjd2N4ZeiPaODg4uM1xuvPOO3n00Uex2+3ccccdREVFdVuH9uhStLGICPBPVb3C9So5T8NTnm+//jvTz/gtIjbsaiezMJXAeuvB7qavl3BS4g0knHwZdrWz9ed/cerIS2BPFUdeSqfuvFj2rPdCz60jKTEIm4gJBjScsPRotLGqqoi4t2x8FwgMtjaaVO0czN9MfKTDO1XtnJR4A7u2LCd40CgA8lKXExM0AgmwEfLr4aRX7uc/EbGcF+mPTSzD0XRfxBgTg6FjupzkQ1W3uFIRV5D2/RMU52+m6EAqG9fcTtGBVABEbCScfBlTZi8lNDKJ0MgkJp35BD+WFrPhP0vIq17NvuT7mTtlH8MHe6Bqpyo7FeKGmOqBBoMT9KusQUmn3UNoZBL+tRBbJvjXWrOVwrwfKDqQSmik9ShXxEa21yCe2LuJARNuJ3/IZP46ZDwFflmg9ez/8V0OrLyLI/u3mKWOweAE/cqgDIo4FREbfkMnkHDuUwDWbOXLO9j45R8pzj+aiWFo7UEWHt5MdF4640OG8ruoIVSmPs3enf9i5fY1eM68oVke2ZYF1Q0Gw7H0qwRLDTTMQvJXLiHi/MeZOucpQBr9UwDCoyYwb8g8du9+C5sIttwVnJR4A4dDz2albyxTB49haBPHtIaC6m2lKzAYDP0sp2xubi5Dhlj+pW1VC1S1U5y/mdDIJNSuFKV9Qej4ORwq2kpoZBJ2VdbtTmXWqAl42I6WU+jr4f8Ggyvp6lOefrXkaUrD0qel+3tx/mY2fb2E4vzN2Dw8iJg4Hw9PLwZGjGftz19QdCCVys33cKggrbk8m5j9FIOhA/qtQWmL0Mikxqc9Tflmz5c8vGMN28uKWj1vMBg6pl8ZlIbHvQ0ZK1segzVzCYuaeMzM5fQRc3hwzFmcMXJu43mzEWswOEe/MijV+7eTv3IJ1TlpqNo5nPou+R/eRXVOWof3etg8mT3qbDxsR/epGzZiNSPHjVobDP2HfmVQvGsGEnHeE/jGJFKdk8ah718hZPoN7ZYRbW0W04DExxjHNoPBCfqVQal47028j4QiYsM3JpGoXz5J8IRLG93vWzMc1TlpjbOalpiNWIPBOfqVQSnx28CRgzuBY5/ytGU4fKJPxWvW9fhEn9rT6hoM/Y5+5dgWNv02/CbPa/Wcb0wikRcsbVz+NPiplHva2bL9FXwiRhIW5b7ESAbDiUC/mqEMmDgbm4dHq+dam7Ec+PAuPHN/ZvKZT5jHxAaDC+hXBsUZfGMSGTj9Bg5vWE5Ana3dfRaDwdA5+qRBEZFTRCRGRGwiMlZEBnfmPqf8RVQIDJ5OxPlHl0EN+yxV2akUHUgxhsVgcJIeNygislBE7hWRu0XkmjYu+xWQjVXw6zagU2XMNKvzmbqt5Ekf4VsX0bgMathnqfCi0T3fYDB0nh7dlO1koXSATKxCBVWqeqjT8oe1LDzRzrVNfEyaBv75DZ2Ar9qN+73B0AV6eobSVqH0Y1DV/c4YE+AYf5F2XedFOehfAKLHeMS25Z5vMBjap6c/Me0WSm+Cv4jcKiJXicjjItL6o5sOaM91vmnUsfGINRhcQ08blM4WQv8WeEFV33Jcc31rwkTkJhFJBla3er4dQ9E06th4xBoMrqGnDUpnC6H/qEcfsWQC57UmTFVfUtVJwDmtnW/PUJhljcHgetz+aRKRT0Rkm4hswypnGtbk9DGF0EXECygWkYZ6kAFAvTN9OpN2QNVuHhEbDC7C7QZFVReo6imqegpWofSJjkJhYBVK/xJARC4UkQBVrQWeUdUyxzVTsEqidr5PJ9IONN1LMRgM3aPHc8o6fE9isYxZuqq+6WhPAxapapqInArMwyoKr6r6eAcym+WUbS3/a2dyzJrlj8Fg0aOVA7uDqr7RRntik9dbga1d7aO1OsMNXrAR5z8OKvjUhmFLGIbYbCYo0GBwESfMV3KDFyxAwQd3Uv72/5hMbAaDi+lX6QvaoyHaWNVOxMJl+NSGGb8Tg8HFnDAGpQERGwNizRLHYHAHJ8ySx2AwuJ8T2qCY/CcGg2vptwalM85t7SWoNhgMztN/DUoT57a2jEvLPLMGg6F79FuD0izfSRues23VPzYYDF2j336SmgUGxkbjedY0iI0+5jpTbtRgcB391qA0oHbF/v1m6tZ8D/tyjz1vyo0aDC6j3/qhqF2xp2djzy2g/svv8Zx7WquObCa5ksHgOvqvQcnIofbVD0AVz3Nm4jFjwtFAwRbBgy3jfgwGQ9fotwZF4mPwum4hKMckWWpY5nhfc4ExJgaDC+l3BqXp7MNjxLBWrzHLHIPBPfS7Tdm2NlmbPs0xOWQNBvfQ7wxKW7MP8zTHYHA//W7J09Ymq8TH4HX1+daSyDFLMRgMrqXfzVDaQmyCiFD71iozSzEY3MQJY1DAbMYaDO6mN4qlJ4rI0+2c9xeRZ0XkWhF5QUQGuqxvsxlrMLiVni6WfjswEzjczmV3AMmq+paIHAbuA27vCf0MBkP36NEZiqo+DXzYwWXnAxscrzfSRlVAg8HQ9+iLeyhNC6qX0HoxdYPB0Afpi4+NmxZUrwE8RcRHVY+0vFBEbgJuArx6UD+DwdAGfXGG0rSguh9Q2ZoxgY6LpYPJd2Iw9CRuNShNC6WLyC86eVvTgurHFFN3FuMhazD0HG5d8qjqgs5cJyJzgW2qmgesBs4AMoAZwEfd0aHB94TYaOx7s5vVOzYYDK6lR5c8IvI74AZgtog8LCLBjlN/ABqqbz0LzBCRRcCZwCPd6tPhe8K+XGreWGklXWqyBDKlNAwG1yGqx//egogMAXJzc3MZMmRIq9c0pDVQu1L71iq8r7kA2/ChVGWnkr9yCZEXLMVv6ISeVdxg6KPs37+f6OhogGhV7fS2Q198yuMWGoIG1a7N3O9NKQ2DwXWcMAalgZbRyA2lNAwGQ/fpi4+NDQbDcYoxKAaDwWUYg2IwGFxGf9lDsQEcOHCgt/UwGPoFTT5LTk06+stj4yQgtbf1MBj6IRNUdXNnL+4vBsULOAUoBPqih9pqjq80DMeTvseTrnD86GsDPgMSVbW2szf1iyWP4w132or2NCJS64xzUG9zPOl7POkKx5e+IlLljDEBsylrMBhciDEoPcNLva2AkxxP+h5PusLxpa/TuvaLPRSDwdA3MDMUg8HgMvrFpmxfRkT+CxgMTAH+rarv9LJKHSIiE4CzVfWJ3talPUQkALgLyAaCgGe0j+ahEJELsPIl+wL7VHVFL6t0DCKSCFytqrc7jk8DzgPyAF9VfaojGcaguBERGQGEquqfRSQc+FlENqpqem/r1hYiYgP+BPzQ27p0gueAP6lqhojsAFZgJebqU4jIUGB0wwdSRF4Rkc9VtayXVWukZYkbERHgRWCqqlaJyAci8pmqbm1PjlnyuJexwJ0AqloI7AEm9apGHXMxsLa3legIEUnAytXRYEDmNXnd1wgHzhIRH8dxBVYC9j5DKyVuTgFKVbXKcbwRmN+RHDNDcS+rgQXQaPEHYxmVPomIhAGK5SDo38vqdMQvgBIRuQYIwUpu/mqvatQ2m7G+vH8QkZeAz9tKvN6HaFrOBsfrDvN8mBmKG1HVmiZTxHOBFFXtyyECFwPv97YSnSQS61v0LVV9DrhBREb1sk6totaj1KVAAfBnrA9rX6dpORuwZlQBHd1kDEoP4Midex1wVW/r0hYiMhXY2Fc3NVuhDPixib5ZwLxe1KdNHIbuTFU9C6sy5n0iMqOX1eqIpuVswCppc7Cjm4xBcTOOpc5dwK9VtVRE4npZpbaYCswXkSVYM5WZIrKwd1Vql+2AR5Nje4vjvsQFwL8AVPUL4FqsDdC+TNNyNtDJkjbGoLif/4e1jPAWkSlAXO+q0zqq+pyqLlXVpUAKsF5VP+hltdrjW2CYw2ADDAc+6UV92iMDa3nWgA/WJmefRVV3AKEiEuhoOo1OjK/xlHUjIjIT+IbmU8dhqprdSyp1iIhcCtztOHxCVd/tTX3aQ0TOwfpHtwE/qeqbvaxSm4jIbVgb3RVAiaq+1qsKtcBR4uZSYBjwGvAMMA1r8zsP8FbVZR3KMQbFYDC4CrPkMRgMLsMYFIPB4DKMQTEYDC7DGBSDweAyjEExGAwuwxgUg8HgMoxBMRgMLsNEGxvcioiEAF8DbwKlwLNYTlO5WAGT/+rLDmkG5zAGxeBuzgHmq2q+I9FQDfCQqtpFZAtQ37vqGVyJMSgGd5OtqvmO17OBb5pECFdiBaEZ+gnGoBjciqqub3I4G/iqybl20wkajj/MpqyhJ5mNtZ9i6KcYg2LoEUQkHitJz7be1sXgPoxBMfQUs4G1asLb+zXGoBjcioiMFZGbgVuBEBH5tYj01cxqhm5i8qEYDAaXYWYoBoPBZRiDYjAYXIYxKAaDwWUYg2IwGFyGMSgGg8FlGINiMBhchjEoBoPBZRiDYjAYXIYxKAaDwWX8f/Ob06J5g/PnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for H in Hs:\n",
    "    \n",
    "    # Select correct data\n",
    "    dfh = df[(df['H'] == H)]\n",
    "    \n",
    "    plt.figure(figsize=figure_size)\n",
    "    for N in sorted(dfh['N'].unique()):\n",
    "        dfhn = dfh[dfh['N'] == N]\n",
    "        E, M, C, X, temp, *_ = dfhn.values.T\n",
    "        \n",
    "        plt.title('External field $H={}$'.format(H))\n",
    "        plt.plot(temp, M, 'o' , markersize=marker_size, markerfacecolor=\"None\", label=f'$N={N}$')\n",
    "        plt.xlabel('$T$'); plt.ylabel('$M$');\n",
    "    plt.legend(loc=\"upper right\", markerscale=10, ncol=2, labelspacing=0.07, columnspacing=0.5, handletextpad=0.1)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'{figure_dir}/magnetisation_{\" \".join(str(H).split())}.pdf')\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Specific Heat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for H in Hs:\n",
    "    \n",
    "    # Select correct data\n",
    "    dfh = df[(df['H'] == H)]\n",
    "    \n",
    "    plt.figure(figsize=figure_size)\n",
    "    for N in sorted(dfh['N'].unique()):\n",
    "        dfhn = dfh[dfh['N'] == N]\n",
    "        E, M, C, X, temp, *_ = dfhn.values.T\n",
    "        \n",
    "        plt.title('External field $H={}$'.format(H))\n",
    "        plt.plot(temp, C, 'o' , markersize=marker_size, markerfacecolor=\"None\", label=f'$N={N}$')\n",
    "        plt.xlabel('$T$'); plt.ylabel('$C$');\n",
    "    plt.legend(loc=\"upper right\", markerscale=10, ncol=2, labelspacing=0.07, columnspacing=0.5, handletextpad=0.1)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'{figure_dir}/cv_{\" \".join(str(H).split())}.pdf')\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Susceptibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def power_law(T, a, b):\n",
    "    return a * np.exp( b / T) / T  #np.power(x, 3./2.)  # TODO what?\n",
    "\n",
    "for H in Hs:\n",
    "    \n",
    "    # Select correct data\n",
    "    dfh = df[(df['H'] == H)]\n",
    "    \n",
    "    plt.figure(figsize=(figure_size[0] * 1, figure_size[1] * 1.0))\n",
    "    for N in sorted(dfh['N'].unique()):\n",
    "        dfhn = dfh[dfh['N'] == N]\n",
    "        E, M, C, X, temp, *_ = dfhn.values.T\n",
    "        \n",
    "        plt.title('External field $H={}$'.format(H))\n",
    "        plt.plot(temp, X, 'o' , markersize=marker_size, markerfacecolor=\"None\", label=f'$N={N}$')\n",
    "        plt.xlabel('$T$'); plt.ylabel('$\\chi$');\n",
    "    \n",
    "    # fit power law with all data\n",
    "    temp = dfh['temp'].sort_values()\n",
    "    error = dfh.pivot_table(values=['X'], index='temp', aggfunc=np.std).values[:,-1] ** (0.70)\n",
    "    error = np.repeat(error, len(dfh['N'].unique()))\n",
    "#     temp = error.index.values\n",
    "#     error = error.values[:,-1]\n",
    "#     x = dfh.pivot_table(values=['X'], index='temp', aggfunc=np.mean).values[:,-1]\n",
    "#     print(temp.shape, x.shape, error.shape)\n",
    "    \n",
    "#     try:\n",
    "#         popt, pcov = scipy.optimize.curve_fit(power_law, dfh['temp'], dfh['X'], sigma=error, absolute_sigma=True)\n",
    "#     except ValueError as e:\n",
    "#         popt, pcov = scipy.optimize.curve_fit(power_law, dfh['temp'], dfh['X'])\n",
    "#     plt.plot(temp, power_law(temp, *popt), '--', label=f'fit: ${popt[0]:.3f}\\\\cdot\\\\exp({popt[1]:.3f}/T)/T$')\n",
    "    \n",
    "    plt.legend(loc=\"upper right\", markerscale=10, ncol=2, labelspacing=0.07, columnspacing=0.5, handletextpad=0.1)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'{figure_dir}/susceptibility_{\" \".join(str(H).split())}.pdf')\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### N vs. time figure\n",
    "Gather all data and compare with $N$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x259.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfs = df.sort_values(by=['N'])\n",
    "dfs['wall_time']\n",
    "\n",
    "plt.figure(figsize=(figure_size[0] * 1.5, figure_size[1] * 1.2))\n",
    "plt.semilogy(dfs['N'], dfs['wall_time'], 'o--', label=\"Simulations\")\n",
    "plt.xlabel('$N$'); plt.ylabel('$t$ (s)')\n",
    "plt.text(100, 15, f\"$\\min$ time = {dfs['wall_time'].min():.1f} s\\n$\\max$ time = {dfs['wall_time'].max():.1f} s\")\n",
    "\n",
    "std = dfs.groupby(\"N\").std()[\"wall_time\"]\n",
    "plt.semilogy(std.index, std, 'x--', label=\"Standard deviation of simulations\")\n",
    "\n",
    "plt.legend(loc=\"lower right\", markerscale=1, ncol=1, labelspacing=0.15)\n",
    "plt.grid()\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(f'{figure_dir}/NvsTime.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get random table\n",
    "To show in the report the format of data tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>E</th>\n",
       "      <th>M</th>\n",
       "      <th>C</th>\n",
       "      <th>X</th>\n",
       "      <th>temp</th>\n",
       "      <th>n_temp</th>\n",
       "      <th>N</th>\n",
       "      <th>steps</th>\n",
       "      <th>H</th>\n",
       "      <th>wall_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>-0.034269</td>\n",
       "      <td>0.049047</td>\n",
       "      <td>0.001088</td>\n",
       "      <td>0.128402</td>\n",
       "      <td>9.238596</td>\n",
       "      <td>400</td>\n",
       "      <td>16</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1. 0. 0.]</td>\n",
       "      <td>62.124650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>-0.097163</td>\n",
       "      <td>0.139819</td>\n",
       "      <td>0.009058</td>\n",
       "      <td>0.375774</td>\n",
       "      <td>4.031579</td>\n",
       "      <td>400</td>\n",
       "      <td>48</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1. 0. 0.]</td>\n",
       "      <td>327.983945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>-0.037099</td>\n",
       "      <td>-0.001837</td>\n",
       "      <td>0.002069</td>\n",
       "      <td>0.196200</td>\n",
       "      <td>6.364912</td>\n",
       "      <td>400</td>\n",
       "      <td>64</td>\n",
       "      <td>4000</td>\n",
       "      <td>[0. 0. 0.]</td>\n",
       "      <td>642.462919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>-0.052078</td>\n",
       "      <td>0.135020</td>\n",
       "      <td>0.001695</td>\n",
       "      <td>0.139142</td>\n",
       "      <td>9.263158</td>\n",
       "      <td>400</td>\n",
       "      <td>64</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1. 1. 1.]</td>\n",
       "      <td>524.563709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>-0.042260</td>\n",
       "      <td>0.056733</td>\n",
       "      <td>0.001675</td>\n",
       "      <td>0.160289</td>\n",
       "      <td>7.642105</td>\n",
       "      <td>400</td>\n",
       "      <td>160</td>\n",
       "      <td>4000</td>\n",
       "      <td>[1. 0. 0.]</td>\n",
       "      <td>4502.634192</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            E         M         C         X      temp  n_temp    N  steps  \\\n",
       "368 -0.034269  0.049047  0.001088  0.128402  9.238596     400   16   4000   \n",
       "156 -0.097163  0.139819  0.009058  0.375774  4.031579     400   48   4000   \n",
       "251 -0.037099 -0.001837  0.002069  0.196200  6.364912     400   64   4000   \n",
       "369 -0.052078  0.135020  0.001695  0.139142  9.263158     400   64   4000   \n",
       "303 -0.042260  0.056733  0.001675  0.160289  7.642105     400  160   4000   \n",
       "\n",
       "              H    wall_time  \n",
       "368  [1. 0. 0.]    62.124650  \n",
       "156  [1. 0. 0.]   327.983945  \n",
       "251  [0. 0. 0.]   642.462919  \n",
       "369  [1. 1. 1.]   524.563709  \n",
       "303  [1. 0. 0.]  4502.634192  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get random table\n",
    "dfffff = df.sample(5).sort_values(by=['N'])\n",
    "dfffff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{c}\n",
      "\\toprule\n",
      "      E &       M &      C &      X &   temp &  n_temp &    N &  steps &           H &  wall_time \\\\\n",
      "\\midrule\n",
      "-0.0343 &  0.0490 & 0.0011 & 0.1284 & 9.2386 &     400 &   16 &   4000 &  [1. 0. 0.] &    62.1247 \\\\\n",
      "-0.0972 &  0.1398 & 0.0091 & 0.3758 & 4.0316 &     400 &   48 &   4000 &  [1. 0. 0.] &   327.9839 \\\\\n",
      "-0.0371 & -0.0018 & 0.0021 & 0.1962 & 6.3649 &     400 &   64 &   4000 &  [0. 0. 0.] &   642.4629 \\\\\n",
      "-0.0521 &  0.1350 & 0.0017 & 0.1391 & 9.2632 &     400 &   64 &   4000 &  [1. 1. 1.] &   524.5637 \\\\\n",
      "-0.0423 &  0.0567 & 0.0017 & 0.1603 & 7.6421 &     400 &  160 &   4000 &  [1. 0. 0.] &  4502.6342 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Export to latex\n",
    "print(dfffff.to_latex(escape=False, bold_rows=True, column_format='c', index=False, float_format=lambda s: f'{s:.4f}'))"
   ]
  }
 ],
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