Extract_data-simple-BOR60.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extract data from .sol files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use this if you are using the Wilkens function mode 2 to extract <a> loop (phase 0) and <c> loop (phase 1) densities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This workbook is an example of how the extractDataDir function can be used to extract physical paramaters from a folder a .sol files. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from src.cmwp_tools import extractDataDir\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract data from solution files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code extracts data from CMWP .sol files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parsing 6 solution files...\n",
      "Parsed 6 solution files succesfully                                                                \n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "data_df = extractDataDir('/home/rhys/Documents/CMWP-211102/BOR-60/*/*.sol', calcLoop=False, calcLattice=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = pd.read_csv('/home/rhys/Documents/CMWP-211102/BOR-60/samples.txt', delimiter='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df['fluence'] = data_df['filename'].map(dict(zip(samples.Sample, samples['Fluence (e25 n/m2)'])))\n",
    "data_df['growth'] = data_df['filename'].map(dict(zip(samples.Sample, samples['Growth (%)'])))\n",
    "\n",
    "data_df[['Alloy','Ref']] = data_df['filename'].str.split('-',expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a1</th>\n",
       "      <th>a1_1</th>\n",
       "      <th>a2</th>\n",
       "      <th>a2_1</th>\n",
       "      <th>a_2</th>\n",
       "      <th>b</th>\n",
       "      <th>b_1</th>\n",
       "      <th>b_2</th>\n",
       "      <th>b_neg</th>\n",
       "      <th>b_pos</th>\n",
       "      <th>...</th>\n",
       "      <th>d_2_neg</th>\n",
       "      <th>d_2_pos</th>\n",
       "      <th>e_2_neg</th>\n",
       "      <th>e_2_pos</th>\n",
       "      <th>a_2_neg</th>\n",
       "      <th>a_2_pos</th>\n",
       "      <th>fluence</th>\n",
       "      <th>growth</th>\n",
       "      <th>Alloy</th>\n",
       "      <th>Ref</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.5332</td>\n",
       "      <td>2.6411</td>\n",
       "      <td>-0.307</td>\n",
       "      <td>1.0058</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>155.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0317</td>\n",
       "      <td>20.937150</td>\n",
       "      <td>31.793450</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.12</td>\n",
       "      <td>W8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.5332</td>\n",
       "      <td>2.6411</td>\n",
       "      <td>-0.307</td>\n",
       "      <td>1.0058</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.6520</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.566020</td>\n",
       "      <td>6.897852</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.18</td>\n",
       "      <td>W8</td>\n",
       "      <td>3</td>\n",
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       "      <th>2</th>\n",
       "      <td>-0.5332</td>\n",
       "      <td>2.6411</td>\n",
       "      <td>-0.307</td>\n",
       "      <td>1.0058</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>175.7090</td>\n",
       "      <td>1.0</td>\n",
       "      <td>134.6160</td>\n",
       "      <td>21.787916</td>\n",
       "      <td>30.573366</td>\n",
       "      <td>...</td>\n",
       "      <td>2.696455</td>\n",
       "      <td>2.710610</td>\n",
       "      <td>0.681195</td>\n",
       "      <td>0.782480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0.27</td>\n",
       "      <td>W8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.5332</td>\n",
       "      <td>2.6411</td>\n",
       "      <td>-0.307</td>\n",
       "      <td>1.0058</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.6704</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.331069</td>\n",
       "      <td>18.271008</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.1</td>\n",
       "      <td>0.51</td>\n",
       "      <td>W8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.5332</td>\n",
       "      <td>2.6411</td>\n",
       "      <td>-0.307</td>\n",
       "      <td>1.0058</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.4809</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.404119</td>\n",
       "      <td>13.247171</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.2</td>\n",
       "      <td>1.33</td>\n",
       "      <td>W8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.5332</td>\n",
       "      <td>2.6411</td>\n",
       "      <td>-0.307</td>\n",
       "      <td>1.0058</td>\n",
       "      <td>-3.13094</td>\n",
       "      <td>333.1290</td>\n",
       "      <td>1.0</td>\n",
       "      <td>47.2121</td>\n",
       "      <td>132.252213</td>\n",
       "      <td>131.919084</td>\n",
       "      <td>...</td>\n",
       "      <td>1.357153</td>\n",
       "      <td>1.357153</td>\n",
       "      <td>0.714278</td>\n",
       "      <td>0.701747</td>\n",
       "      <td>2.705132</td>\n",
       "      <td>2.636251</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>W8</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       a1    a1_1     a2    a2_1      a_2         b  b_1       b_2  \\\n",
       "0 -0.5332  2.6411 -0.307  1.0058  1.00000  155.0900  1.0   11.0317   \n",
       "1 -0.5332  2.6411 -0.307  1.0058      NaN   19.6520  1.0       NaN   \n",
       "2 -0.5332  2.6411 -0.307  1.0058  1.00000  175.7090  1.0  134.6160   \n",
       "3 -0.5332  2.6411 -0.307  1.0058      NaN   67.6704  1.0       NaN   \n",
       "4 -0.5332  2.6411 -0.307  1.0058      NaN   47.4809  1.0       NaN   \n",
       "5 -0.5332  2.6411 -0.307  1.0058 -3.13094  333.1290  1.0   47.2121   \n",
       "\n",
       "        b_neg       b_pos  ...   d_2_neg   d_2_pos   e_2_neg   e_2_pos  \\\n",
       "0   20.937150   31.793450  ...       NaN       NaN       NaN       NaN   \n",
       "1    7.566020    6.897852  ...       NaN       NaN       NaN       NaN   \n",
       "2   21.787916   30.573366  ...  2.696455  2.710610  0.681195  0.782480   \n",
       "3   13.331069   18.271008  ...       NaN       NaN       NaN       NaN   \n",
       "4    8.404119   13.247171  ...       NaN       NaN       NaN       NaN   \n",
       "5  132.252213  131.919084  ...  1.357153  1.357153  0.714278  0.701747   \n",
       "\n",
       "    a_2_neg   a_2_pos  fluence  growth  Alloy   Ref  \n",
       "0       NaN       NaN      2.1    0.12     W8     2  \n",
       "1       NaN       NaN      5.6    0.18     W8     3  \n",
       "2       NaN       NaN      8.2    0.27     W8     4  \n",
       "3       NaN       NaN     11.1    0.51     W8     5  \n",
       "4       NaN       NaN     16.2    1.33     W8     6  \n",
       "5  2.705132  2.636251      0.0    0.00     W8  None  \n",
       "\n",
       "[6 rows x 50 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plots dislocation density, <a> loop fraction, M paramater and lattice paramaters as a function of depth from sample edge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, 3, figsize=(16,10))\n",
    "fig.subplots_adjust(left=0.06, right=0.96, top=0.98, bottom=0.1, hspace=0.3, wspace = 0.35)\n",
    "axes = axes.ravel()\n",
    "\n",
    "\n",
    "# Common settings for all axes\n",
    "for sax in axes:\n",
    "    sax.tick_params(axis='both', which='major', labelsize=10)\n",
    "    sax.xaxis.set_ticks_position('both')\n",
    "    sax.set_xlabel('Fluence [$x 10^{25} n/m^2$]', fontsize=12)\n",
    "    sax.set_ylabel('', fontsize=12)\n",
    "\n",
    "#### dislocation density\n",
    "\n",
    "axes[0].errorbar(x=data_df['fluence'], y=data_df['d'], yerr = [data_df['d_neg'],data_df['d_pos']], \n",
    "                 marker='o',c='k',  ms=3, lw=0, elinewidth=1, capsize=5)\n",
    "axes[0].set_ylabel('<a> dislocation density [$10^{14} m^{-2}$]', fontsize=12)\n",
    "axes[0].set_ylim(0)\n",
    "\n",
    "#### m\n",
    "\n",
    "axes[1].errorbar(x=data_df['fluence'], y=np.exp(2)*data_df['e'], \n",
    "                 yerr = np.exp(2)*np.array([data_df['e_neg'],data_df['e_pos']]), \n",
    "                 marker='o',c='k',  ms=3, lw=0, elinewidth=1, capsize=5)\n",
    "\n",
    "axes[1].set_ylabel('$M_{<a>}$', fontsize=12)\n",
    "axes[1].set_ylim(0)\n",
    "\n",
    "#### dislocation density\n",
    "\n",
    "axes[3].errorbar(x=data_df['fluence'], y=data_df['d_1'], \n",
    "                 yerr = [data_df['d_1_neg'],data_df['d_1_pos']],  marker='o',c='k',  ms=3, lw=0, elinewidth=1, capsize=5)\n",
    "axes[3].set_ylabel('<c> dislocation density [$10^{14} m^{-2}$]', fontsize=12)\n",
    "axes[3].set_ylim(0)\n",
    "\n",
    "#### m\n",
    "axes[4].errorbar(x=data_df['fluence'], y=np.exp(2)*data_df['e_1'], \n",
    "                 yerr = np.exp(2)*np.array([data_df['e_1_neg'],data_df['e_1_pos']]), \n",
    "                 marker='o',c='k',  ms=3, lw=0, elinewidth=1, capsize=5)\n",
    "\n",
    "\n",
    "axes[4].set_ylabel('$M_{<c>}$', fontsize=12)\n",
    "axes[4].set_ylim(0)\n",
    "\n",
    "#### a loop fraction\n",
    "data_df['a_frac'] = data_df['d']/(data_df['d']+data_df['d_1'])\n",
    "data_df.plot(ax=axes[2], x='fluence', y='a_frac', ms=3, marker='o', c='k', lw=0,  legend=False, \n",
    "             xlabel='Fluence [$x 10^{25} n/m^2$]', ylabel='$f_{<a>}$', ylim=(0,1))\n",
    "\n",
    "#### c loop fraction\n",
    "data_df['c_frac'] = data_df['d_1']/(data_df['d']+data_df['d_1'])\n",
    "data_df.plot(ax=axes[5], x='fluence', y='c_frac', ms=3, marker='o', c='k', lw=0,  legend=False, \n",
    "             xlabel='Fluence [$x 10^{25} n/m^2$]', ylabel='$f_{<c>}$', ylim=(0,1))\n",
    "\n",
    "### b\n",
    "axes[6].errorbar(x=data_df['fluence'], y=data_df['b'], \n",
    "                 yerr = np.array([data_df['b_neg'],data_df['b_pos']]), \n",
    "                 marker='o',c='k',  ms=3, lw=0, elinewidth=1, capsize=5)\n",
    "\n",
    "axes[6].set_ylabel('Median of log-normal size dist.', fontsize=12)\n",
    "axes[6].set_ylim(0)\n",
    "\n",
    "### c\n",
    "axes[7].errorbar(x=data_df['fluence'], y=data_df['c'], \n",
    "                 yerr = np.array([data_df['c_neg'],data_df['c_pos']]), \n",
    "                 marker='o',c='k',  ms=3, lw=0, elinewidth=1, capsize=5)\n",
    "\n",
    "axes[7].set_ylabel('Sigma of log-normal size dist.', fontsize=12)\n",
    "axes[7].set_ylim(0)\n",
    "\n",
    "axes[8].axis('off')\n",
    "\n",
    "plt.show()\n",
    "fig.savefig('/home/rhys/Dropbox (Research Group)/BOR60.pdf', dpi=600)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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