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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rule #1: generate figures programmatically\n",
    "<br>\n",
    "This parses the annoyingly formated .txt files output by SRIM (http://www.srim.org/), uses the parsed data and minimal user input, and generates well-formatted figures of the damage in dpa (displacements per atom) and the ion implantation level in the material identified by parsing the input files.\n",
    "<br>\n",
    "<br>\n",
    "The principle illustrated by this notebook is that a small investment in time can result in an easily reusable code that will make the subsequent generation of figures fast and efficient."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tkinter\n",
    "from tkinter import filedialog\n",
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import gridspec\n",
    "from warnings import warn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find and read in the needed text files\n",
    "In this case, PHONON.txt, RANGE.txt, and TDATA.txt are needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Path: C:/Users/nmp/Dropbox (ORNL)/Visualization_paper/SRIM_outputs\n",
      "Lines in PHONON.txt: 124\n",
      "Lines in TDATA.txt: 39\n",
      "Lines in RANGE.txt: 137\n"
     ]
    }
   ],
   "source": [
    "# get the data directory\n",
    "root = tkinter.Tk()\n",
    "path_dir = filedialog.askdirectory(\n",
    "    initialdir=\"/\",\n",
    "    title=\"Find SRIM data:\")\n",
    "print(f'Path: {path_dir}')\n",
    "root.destroy()  # remove the GUI panel\n",
    "os.chdir(path_dir)\n",
    "\n",
    "# find PHONON.txt\n",
    "fn = glob.glob('PHONON.txt')\n",
    "assert len(fn) == 1, 'Did not find exactly 1 PHONON.txt!'\n",
    "file_lines_phonon = []\n",
    "with open(fn[0], mode='r') as fh:\n",
    "    for line in fh:\n",
    "        file_lines_phonon.append(line)\n",
    "    else:\n",
    "        pass\n",
    "print(f'Lines in PHONON.txt: {len(file_lines_phonon)}')\n",
    "\n",
    "# find TDATA.txt\n",
    "fn = glob.glob('TDATA.txt')\n",
    "assert len(fn) == 1, 'Did not find exactly 1 TDATA.txt!'\n",
    "file_lines_tdata = []\n",
    "with open(fn[0], mode='r') as fh:\n",
    "    for line in fh:\n",
    "        file_lines_tdata.append(line)\n",
    "    else:\n",
    "        pass\n",
    "print(f'Lines in TDATA.txt: {len(file_lines_tdata)}')\n",
    "\n",
    "# find RANGE.txt\n",
    "fn = glob.glob('RANGE.txt')\n",
    "assert len(fn) == 1, 'Did not find exactly 1 RANGE.txt!'\n",
    "file_lines_range = []\n",
    "with open(fn[0], mode='r') as fh:\n",
    "    for line in fh:\n",
    "        file_lines_range.append(line)\n",
    "    else:\n",
    "        pass\n",
    "print(f'Lines in RANGE.txt: {len(file_lines_range)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parse out the important information from PHONON.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Density: 6.34e+22 atoms/cm^3\n"
     ]
    }
   ],
   "source": [
    "#Find the important lines:\n",
    "density_found = 0\n",
    "for i, l in enumerate(file_lines_phonon):\n",
    "    if 'DEPTH' in l:  # line that defines the data columns\n",
    "        i_start = i + 3  # find the first line of data\n",
    "    if 'atoms/cm3' in l:  # find the density\n",
    "        i_density = i\n",
    "        density_found += 1\n",
    "        assert density_found <=1, 'Can currently only handle single layers'\n",
    "\n",
    "# Calculate the length of the data. SRIM is always 100\n",
    "length = len(file_lines_phonon) - i_start\n",
    "data_phonon = np.zeros((length, 3), dtype='f4')  # hold the data here\n",
    "\n",
    "# parse out the density (atoms/cm^3)\n",
    "density_line = file_lines_phonon[i_density]\n",
    "start_phrase = 'Density ='\n",
    "start_phrase_length = len(start_phrase)\n",
    "end_phrase = 'atoms/cm3'\n",
    "st = density_line.find(start_phrase) + start_phrase_length\n",
    "en = density_line.find(end_phrase)\n",
    "density = np.array(density_line[st:en]).astype('f4')\n",
    "print(f'Density: {density:3.3} atoms/cm^3')\n",
    "\n",
    "# read the data\n",
    "for i in range(i_start, len(file_lines_phonon),1):\n",
    "    data_phonon[i-i_start,:] = np.array(file_lines_phonon[i].split()).astype('f4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parse out the important information from TDATA.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ion string: \"Cu\"\n",
      "Energy: 500.0 keV\n",
      "Target: \" W\"\n",
      "Displacement: 90.0 eV\n",
      "Number of ions: 500000\n"
     ]
    }
   ],
   "source": [
    "kp_flag = False  # Check for Kinchin-Pease\n",
    "\n",
    "# loop over the TDATA.txt lines\n",
    "for i,l in enumerate(file_lines_tdata):\n",
    "    # find the projectile\n",
    "    if 'Ion = ' in l:\n",
    "        st = l.find('Ion = ')\n",
    "        en = l.find('(')\n",
    "        ion_string = l[st+6:en-1]\n",
    "        print(f'Ion string: \"{ion_string}\"')\n",
    "\n",
    "    #find the displacement energy\n",
    "    if 'Displacement = ' in l:\n",
    "        st = l.find('Displacement = ')\n",
    "        en = l.find(',')\n",
    "        displacement = float(l[st+14:en-2])\n",
    "        print(f'Displacement: {displacement} eV')\n",
    "    \n",
    "    # Check for Kincihn-Pease model\n",
    "    if 'Kinchin-Pease Estimates' in l:\n",
    "        kp_flag = True\n",
    "        \n",
    "    # Find the ion (projectile) energy\n",
    "    if 'Energy  = ' in l:\n",
    "        st = l.find('Energy = ')\n",
    "        en = l.find('keV')  # if keV not found, find() returns -1:\n",
    "        if en > -1:\n",
    "            energy = float(l[st+10:en-1])\n",
    "        else:\n",
    "            energy = float(input('Energy not parsable -- input ion energy in keV:'))\n",
    "        print(f'Energy: {energy} keV')\n",
    "    \n",
    "    # Find the identity of layer 1\n",
    "    if 'TARGET MATERIAL' in l:\n",
    "        temp_l = file_lines_tdata[i+1]\n",
    "        st = temp_l.find('Layer # 1 - ')\n",
    "        en = temp_l.find('\\n')\n",
    "        target = temp_l[st+11:en]\n",
    "        print(f'Target: \"{target}\"')\n",
    "    \n",
    "    # Check for multilayer targets\n",
    "    if 'Layer #2' in l:\n",
    "        warn('Currently this code only parses single layers properly')\n",
    "    \n",
    "    # find # ions calculated\n",
    "    if 'Total Ions calculated =' in l:\n",
    "        st = l.find('Total Ions calculated =')\n",
    "        en = l.find('\\n')\n",
    "        num_ions = int(l[st+23:en])\n",
    "        print(f'Number of ions: {num_ions}')\n",
    "\n",
    "# If Kinchin-Pease not found...\n",
    "if kp_flag is False:\n",
    "    warn('Warning: this code is intended to use Kinchin-Pease data.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parse out the important information from RANGE.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Find the important lines:\n",
    "for i, l in enumerate(file_lines_range):\n",
    "    if 'DEPTH' in l:  # line that defines the data columns\n",
    "        i_start = i + 3  # find the first line of data\n",
    "\n",
    "# Calculate the length of the data. SRIM is always 100\n",
    "length = len(file_lines_range) - i_start\n",
    "data_range = np.zeros((length, 3), dtype='f4')  # hold the data here\n",
    "\n",
    "# read the data\n",
    "for i in range(i_start, len(file_lines_range),1):\n",
    "    data_range[i-i_start,:] = np.array(file_lines_range[i].split()).astype('f4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform the calculation\n",
    "The details here are from:<br>\n",
    "Stoller, R.E., Toloczko, M.B., Was, G.S., Certain, A.G., Dwaraknath, S. and Garner, F.A., 2013. On the use of SRIM for computing radiation damage exposure. <i>Nuclear instruments and methods in physics research section B: beam interactions with materials and atoms</i>, <b>310</b>, pp.75-80."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please enter the fluence (ions/cm^2): 1.5e16\n"
     ]
    }
   ],
   "source": [
    "fluence = float(input('Please enter the fluence (ions/cm^2): '))\n",
    "\n",
    "# Calculate the NRT damage in dpa\n",
    "depth_in_nm = data_phonon[:, 0] / 10  # always angstroms\n",
    "ion_contribution = data_phonon[:, 1]\n",
    "recoil_contribution = data_phonon[:, 2]\n",
    "P = ion_contribution + recoil_contribution  # (P)honons\n",
    "T_damage = P * fluence / density * 1e8  # 1e8 is angstrom --> cm\n",
    "NRT = T_damage * 0.8 / 2.0 / displacement  # NRT displacements per atom. See citation.\n",
    "\n",
    "# Calculate the ion implantation in atomic fraction\n",
    "atom_frac = data_range[:, 1] * fluence / density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context('seaborn'):\n",
    "    fig = plt.figure(figsize=(12, 7)) \n",
    "    gs = gridspec.GridSpec(2, 1, height_ratios=[4, 1]) \n",
    "    ax0 = plt.subplot(gs[0])\n",
    "\n",
    "    ax0.plot(depth_in_nm, NRT, color='xkcd:brick red')\n",
    "    ax1 = plt.subplot(gs[1])\n",
    "    ax1.plot(depth_in_nm, atom_frac, color='xkcd:brick red')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uncertainty in fluence (for \"+/-20%\" input \"0.2\"): 0.2\n",
      "Uncertainty in displacement energy (for \"+/-20%\" input \"0.2\"): 0.2\n",
      "Combined error: 0.28284271247461906\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    err_fluence = float(input('Uncertainty in fluence (for \"+/-20%\" input \"0.2\"): '))\n",
    "except ValueError:\n",
    "    err_fluence = 0\n",
    "\n",
    "try:\n",
    "    err_displace = float(input('Uncertainty in displacement energy (for \"+/-20%\" input \"0.2\"): '))\n",
    "except ValueError:\n",
    "    err_displace = 0\n",
    "err_both = np.sqrt( err_fluence * err_fluence + err_displace * err_displace )\n",
    "print(f'Combined error: {err_both}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now, generate the figure\n",
    "This does require some hand-tweaking of parameters on the first attempt, but all subsequent uses will take only a few seconds, where most of the time is spent waiting for the user to click on the new data directory, or to type in the fluence or error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File name (enter to skip):\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the 'with' block sets temporary parameters:\n",
    "with plt.style.context({'font.sans-serif': 'Arial', 'svg.fonttype': 'none'}):\n",
    "    #Create a figure with unequal-sized subplots\n",
    "    fig = plt.figure(figsize=(12, 7)) \n",
    "    gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1]) \n",
    "    \n",
    "    # create the first subplot\n",
    "    ax0 = plt.subplot(gs[0])\n",
    "    ax0.plot(depth_in_nm, NRT, color='xkcd:brick red', label='Damage')  # plot the NRT damage\n",
    "    if err_both > 0:  # if the user input error bounds, plot the error bounds\n",
    "        ax0.fill_between(depth_in_nm, y1=(NRT * (1-err_both)), y2=(NRT * (1+err_both)), \n",
    "                     color='xkcd:merlot', alpha=0.15, label='Error bounds')\n",
    "        ax0.legend(loc=3, prop={'size': 14})  # only show legend if error bounds present\n",
    "    ax0.set_ylabel('NRT damage, dpa', fontsize=20)\n",
    "    \n",
    "    # Make annotations\n",
    "    pre, post = str(fluence * 1e4).split('e+')  # split fluence into A x 10^B, with conversion from cm^2 to m^2\n",
    "    ion_val_string = str(num_ions) + ' ' + ion_string + ' ions $\\Rightarrow$' + target\n",
    "    fluence_string = '$' + pre + r'\\times 10^{' + post + '}$ ions/m$^2$'\n",
    "    disp_string = str(displacement) +' eV $E_D$'\n",
    "    if err_both >0:\n",
    "        flu_err_string = r'Fluence $\\pm$ ' + str(err_fluence*100) + '%'\n",
    "        disp_err_string = r'$E_D \\pm$ ' + str(err_displace*100) + '%'\n",
    "    else:\n",
    "        flu_err_string = ''\n",
    "        disp_err_string = ''\n",
    "    ax0.text(0.98, 0.98, f'{ion_val_string}\\n{fluence_string}\\n{disp_string}\\n{flu_err_string}\\n{disp_err_string}',\n",
    "                 transform=ax0.transAxes, fontsize=14, verticalalignment='top',\n",
    "                 horizontalalignment='right')\n",
    "    \n",
    "    ax0.tick_params(axis='both', labelsize=20)\n",
    "    ax0.spines['right'].set_visible(False)\n",
    "    ax0.spines['top'].set_visible(False)\n",
    "    \n",
    "    # create the smaller plot\n",
    "    ax1 = plt.subplot(gs[1])\n",
    "    if atom_frac.max() < 0.01:  # Plot atom fraction if <1%\n",
    "        atom_string = 'Implanted\\natom fraction'\n",
    "        atom_mult = 1\n",
    "    else:\n",
    "        atom_string = 'Implanted\\natom %'\n",
    "        atom_mult = 100\n",
    "\n",
    "    ax1.plot(depth_in_nm, atom_frac*atom_mult, color='xkcd:brick red', label='Implantation')\n",
    "    if err_both > 0:  # if user input error bounds\n",
    "        ax1.fill_between(depth_in_nm, y1=(atom_frac * (1-err_fluence))*atom_mult,\n",
    "                         y2=(atom_frac * (1+err_fluence))*atom_mult,\n",
    "            color='xkcd:merlot', alpha=0.15, label='Error bounds')\n",
    "        ax1.legend(prop={'size': 14})   # only show legend if error bounds present\n",
    "    ax1.set_ylabel(atom_string, fontsize=20)\n",
    "    ax1.set_xlabel('Depth, nm', fontsize=20)\n",
    "\n",
    "    ax1.tick_params(axis='both', labelsize=20)\n",
    "    ax1.spines['right'].set_visible(False)\n",
    "    ax1.spines['top'].set_visible(False)\n",
    "    \n",
    "fn = input('File name (enter to skip):')\n",
    "if len(fn) > 0:\n",
    "    fn = '..\\\\' + fn  # go up one directory  \n",
    "    fig.savefig(fn + '.png', dpi=300)\n",
    "    fig.savefig(fn + '.svg', dpi=300)\n",
    "    fig.savefig(fn + '.eps', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
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     "delete_cmd_postfix": ") ",
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     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
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   "position": {
    "height": "409.4px",
    "left": "1166px",
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