{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7e56e27a",
   "metadata": {},
   "source": [
    "# VROA intensities of methyloxirane\n",
    "\n",
    "Here we show an example of how to calculate the VROA intensities of methyloxirane calculated with an incident wavelength of 488.9 nm."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1865d858-bb96-4b1e-8a8e-a665f2ad8e3d",
   "metadata": {},
   "source": [
    "## Run the VROA code"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2adbe82",
   "metadata": {},
   "source": [
    "First import any packages that we need. Mainly just need the VROA class from vibrav."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5cf99267",
   "metadata": {},
   "outputs": [],
   "source": [
    "from vibrav.base import resource\n",
    "from vibrav import VROA\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9767b4b0",
   "metadata": {},
   "source": [
    "Initialize the class and print the elements in the configuration file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7f760f13-459d-4a0d-9eff-0a6a7fd51d58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DELTA_FILE                    methyloxirane-vroa-delta.dat.xz\n",
      "SMATRIX_FILE                  methyloxirane-vroa-smatrix.dat.xz\n",
      "ATOM_ORDER_FILE               methyloxirane-vroa-atom_order.dat.xz\n",
      "REDUCED_MASS_FILE             methyloxirane-vroa-redmass.dat.xz\n",
      "FREQUENCY_FILE                methyloxirane-vroa-freq.dat.xz\n",
      "EQCOORD_FILE                  methyloxirane-vroa-eqcoord.dat.xz\n",
      "ROA_FILE                      methyloxirane-vroa-roa.csv.xz\n",
      "GRAD_FILE                     methyloxirane-vroa-grad.csv.xz\n",
      "NUMBER_OF_MODES               24\n",
      "NUMBER_OF_NUCLEI              10\n",
      "USE_RESOURCE                  1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open(resource('methyloxirane-vroa-va.conf'), 'r') as fn:\n",
    "    print(fn.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "62dfe463",
   "metadata": {},
   "outputs": [],
   "source": [
    "vroa = VROA(config_file=resource('methyloxirane-vroa-va.conf'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99293762",
   "metadata": {},
   "source": [
    "Run the `vroa` method to calculate the intensities. Internally we read the ROA and gradient data from the lines in the configuration file corresponding to `roa_file` and `grad_file`. These have to be created prior to the run and are simple csv files generated by the python library, pandas.\n",
    "\n",
    "For more information about how we parse the outputs you can refer to the tutorial on parsing the NWChem outputs using exatomic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d406892d",
   "metadata": {},
   "outputs": [],
   "source": [
    "vroa.vroa()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05691c8d",
   "metadata": {},
   "source": [
    "Print the VROA intensities and other pertinent information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9ac6314c",
   "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>freq</th>\n",
       "      <th>freqdx</th>\n",
       "      <th>beta_g*1e6</th>\n",
       "      <th>beta_A*1e6</th>\n",
       "      <th>alpha_g*1e6</th>\n",
       "      <th>backscatter</th>\n",
       "      <th>forwardscatter</th>\n",
       "      <th>exc_freq</th>\n",
       "      <th>exc_idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>200.247393</td>\n",
       "      <td>0</td>\n",
       "      <td>3.656216</td>\n",
       "      <td>-0.273074</td>\n",
       "      <td>0.040271</td>\n",
       "      <td>0.000342</td>\n",
       "      <td>0.000092</td>\n",
       "      <td>488.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>354.450962</td>\n",
       "      <td>1</td>\n",
       "      <td>-35.948619</td>\n",
       "      <td>-4.790522</td>\n",
       "      <td>-0.202244</td>\n",
       "      <td>-0.003604</td>\n",
       "      <td>-0.000644</td>\n",
       "      <td>488.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>398.589617</td>\n",
       "      <td>2</td>\n",
       "      <td>2.472626</td>\n",
       "      <td>4.730142</td>\n",
       "      <td>-1.325064</td>\n",
       "      <td>0.000389</td>\n",
       "      <td>-0.000990</td>\n",
       "      <td>488.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>719.359726</td>\n",
       "      <td>3</td>\n",
       "      <td>132.090769</td>\n",
       "      <td>38.915333</td>\n",
       "      <td>2.796307</td>\n",
       "      <td>0.013926</td>\n",
       "      <td>0.003504</td>\n",
       "      <td>488.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>798.974164</td>\n",
       "      <td>4</td>\n",
       "      <td>-170.470297</td>\n",
       "      <td>-21.596906</td>\n",
       "      <td>-1.259120</td>\n",
       "      <td>-0.017056</td>\n",
       "      <td>-0.003289</td>\n",
       "      <td>488.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>875.765502</td>\n",
       "      <td>5</td>\n",
       "      <td>-179.496773</td>\n",
       "      <td>-85.647522</td>\n",
       "      <td>0.856508</td>\n",
       "      <td>-0.019972</td>\n",
       "      <td>-0.000885</td>\n",
       "      <td>488.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>930.279028</td>\n",
       "      <td>6</td>\n",
       "      <td>43.652907</td>\n",
       "      <td>13.460119</td>\n",
       "      <td>4.053168</td>\n",
       "      <td>0.004621</td>\n",
       "      <td>0.003401</td>\n",
       "      <td>488.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1007.463980</td>\n",
       "      <td>7</td>\n",
       "      <td>42.888944</td>\n",
       "      <td>22.644693</td>\n",
       "      <td>1.396536</td>\n",
       "      <td>0.004842</td>\n",
       "      <td>0.001329</td>\n",
       "      <td>488.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1090.810610</td>\n",
       "      <td>8</td>\n",
       "      <td>-20.942816</td>\n",
       "      <td>-10.046244</td>\n",
       "      <td>-1.407385</td>\n",
       "      <td>-0.002332</td>\n",
       "      <td>-0.001188</td>\n",
       "      <td>488.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1111.217660</td>\n",
       "      <td>9</td>\n",
       "      <td>111.839344</td>\n",
       "      <td>10.336868</td>\n",
       "      <td>0.211851</td>\n",
       "      <td>0.011067</td>\n",
       "      <td>0.001777</td>\n",
       "      <td>488.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1124.140810</td>\n",
       "      <td>10</td>\n",
       "      <td>11.552339</td>\n",
       "      <td>13.400895</td>\n",
       "      <td>-0.161749</td>\n",
       "      <td>0.001538</td>\n",
       "      <td>-0.000146</td>\n",
       "      <td>488.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1149.661160</td>\n",
       "      <td>11</td>\n",
       "      <td>-115.650502</td>\n",
       "      <td>-30.017963</td>\n",
       "      <td>2.671025</td>\n",
       "      <td>-0.012063</td>\n",
       "      <td>0.000553</td>\n",
       "      <td>488.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1248.157950</td>\n",
       "      <td>12</td>\n",
       "      <td>50.434903</td>\n",
       "      <td>-3.105703</td>\n",
       "      <td>-7.331371</td>\n",
       "      <td>0.004742</td>\n",
       "      <td>-0.004422</td>\n",
       "      <td>488.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1370.356780</td>\n",
       "      <td>13</td>\n",
       "      <td>15.428338</td>\n",
       "      <td>-8.882977</td>\n",
       "      <td>-0.118462</td>\n",
       "      <td>0.001197</td>\n",
       "      <td>0.000304</td>\n",
       "      <td>488.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1389.679630</td>\n",
       "      <td>14</td>\n",
       "      <td>-56.263793</td>\n",
       "      <td>-25.389564</td>\n",
       "      <td>-5.371933</td>\n",
       "      <td>-0.006214</td>\n",
       "      <td>-0.004362</td>\n",
       "      <td>488.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1447.995300</td>\n",
       "      <td>15</td>\n",
       "      <td>-108.852976</td>\n",
       "      <td>-9.084926</td>\n",
       "      <td>-0.441561</td>\n",
       "      <td>-0.010741</td>\n",
       "      <td>-0.001914</td>\n",
       "      <td>488.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1462.169630</td>\n",
       "      <td>16</td>\n",
       "      <td>270.411325</td>\n",
       "      <td>92.294289</td>\n",
       "      <td>-0.617040</td>\n",
       "      <td>0.028913</td>\n",
       "      <td>0.002406</td>\n",
       "      <td>488.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1484.006190</td>\n",
       "      <td>17</td>\n",
       "      <td>-79.691973</td>\n",
       "      <td>-11.972509</td>\n",
       "      <td>0.002530</td>\n",
       "      <td>-0.008034</td>\n",
       "      <td>-0.001082</td>\n",
       "      <td>488.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2955.233120</td>\n",
       "      <td>18</td>\n",
       "      <td>-135.170825</td>\n",
       "      <td>38.082939</td>\n",
       "      <td>-28.831868</td>\n",
       "      <td>-0.011758</td>\n",
       "      <td>-0.023531</td>\n",
       "      <td>488.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2996.254680</td>\n",
       "      <td>19</td>\n",
       "      <td>979.837908</td>\n",
       "      <td>-38.224550</td>\n",
       "      <td>49.152893</td>\n",
       "      <td>0.092841</td>\n",
       "      <td>0.051679</td>\n",
       "      <td>488.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3003.433440</td>\n",
       "      <td>20</td>\n",
       "      <td>-1954.768108</td>\n",
       "      <td>-415.801685</td>\n",
       "      <td>-60.440709</td>\n",
       "      <td>-0.200963</td>\n",
       "      <td>-0.068141</td>\n",
       "      <td>488.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3006.058620</td>\n",
       "      <td>21</td>\n",
       "      <td>-260.461289</td>\n",
       "      <td>-480.375629</td>\n",
       "      <td>5.705709</td>\n",
       "      <td>-0.040376</td>\n",
       "      <td>0.007627</td>\n",
       "      <td>488.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3031.862470</td>\n",
       "      <td>22</td>\n",
       "      <td>783.244711</td>\n",
       "      <td>360.553324</td>\n",
       "      <td>-23.132314</td>\n",
       "      <td>0.086729</td>\n",
       "      <td>-0.009892</td>\n",
       "      <td>488.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3074.662670</td>\n",
       "      <td>23</td>\n",
       "      <td>490.996556</td>\n",
       "      <td>303.938290</td>\n",
       "      <td>40.039345</td>\n",
       "      <td>0.056862</td>\n",
       "      <td>0.031821</td>\n",
       "      <td>488.9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           freq  freqdx   beta_g*1e6  beta_A*1e6  alpha_g*1e6  backscatter  \\\n",
       "0    200.247393       0     3.656216   -0.273074     0.040271     0.000342   \n",
       "1    354.450962       1   -35.948619   -4.790522    -0.202244    -0.003604   \n",
       "2    398.589617       2     2.472626    4.730142    -1.325064     0.000389   \n",
       "3    719.359726       3   132.090769   38.915333     2.796307     0.013926   \n",
       "4    798.974164       4  -170.470297  -21.596906    -1.259120    -0.017056   \n",
       "5    875.765502       5  -179.496773  -85.647522     0.856508    -0.019972   \n",
       "6    930.279028       6    43.652907   13.460119     4.053168     0.004621   \n",
       "7   1007.463980       7    42.888944   22.644693     1.396536     0.004842   \n",
       "8   1090.810610       8   -20.942816  -10.046244    -1.407385    -0.002332   \n",
       "9   1111.217660       9   111.839344   10.336868     0.211851     0.011067   \n",
       "10  1124.140810      10    11.552339   13.400895    -0.161749     0.001538   \n",
       "11  1149.661160      11  -115.650502  -30.017963     2.671025    -0.012063   \n",
       "12  1248.157950      12    50.434903   -3.105703    -7.331371     0.004742   \n",
       "13  1370.356780      13    15.428338   -8.882977    -0.118462     0.001197   \n",
       "14  1389.679630      14   -56.263793  -25.389564    -5.371933    -0.006214   \n",
       "15  1447.995300      15  -108.852976   -9.084926    -0.441561    -0.010741   \n",
       "16  1462.169630      16   270.411325   92.294289    -0.617040     0.028913   \n",
       "17  1484.006190      17   -79.691973  -11.972509     0.002530    -0.008034   \n",
       "18  2955.233120      18  -135.170825   38.082939   -28.831868    -0.011758   \n",
       "19  2996.254680      19   979.837908  -38.224550    49.152893     0.092841   \n",
       "20  3003.433440      20 -1954.768108 -415.801685   -60.440709    -0.200963   \n",
       "21  3006.058620      21  -260.461289 -480.375629     5.705709    -0.040376   \n",
       "22  3031.862470      22   783.244711  360.553324   -23.132314     0.086729   \n",
       "23  3074.662670      23   490.996556  303.938290    40.039345     0.056862   \n",
       "\n",
       "    forwardscatter  exc_freq  exc_idx  \n",
       "0         0.000092     488.9        0  \n",
       "1        -0.000644     488.9        0  \n",
       "2        -0.000990     488.9        0  \n",
       "3         0.003504     488.9        0  \n",
       "4        -0.003289     488.9        0  \n",
       "5        -0.000885     488.9        0  \n",
       "6         0.003401     488.9        0  \n",
       "7         0.001329     488.9        0  \n",
       "8        -0.001188     488.9        0  \n",
       "9         0.001777     488.9        0  \n",
       "10       -0.000146     488.9        0  \n",
       "11        0.000553     488.9        0  \n",
       "12       -0.004422     488.9        0  \n",
       "13        0.000304     488.9        0  \n",
       "14       -0.004362     488.9        0  \n",
       "15       -0.001914     488.9        0  \n",
       "16        0.002406     488.9        0  \n",
       "17       -0.001082     488.9        0  \n",
       "18       -0.023531     488.9        0  \n",
       "19        0.051679     488.9        0  \n",
       "20       -0.068141     488.9        0  \n",
       "21        0.007627     488.9        0  \n",
       "22       -0.009892     488.9        0  \n",
       "23        0.031821     488.9        0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vroa.scatter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "335408d3",
   "metadata": {},
   "source": [
    "## Plot the calculated intensities\n",
    "\n",
    "We use a full-width at half-maximum of 20 wavenumbers.\n",
    "\n",
    "For this example we use 'atomic' units where the calculated VROA units are in $\\unicode{xC5}^4/\\text{amu}$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5586f051-6ba5-40dc-a467-4c72db1e3cff",
   "metadata": {},
   "source": [
    "Set up the lineshape function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "817eff46",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lorentz(omega, omega_0, fwhm):\n",
    "    return (1/np.pi) * 05.*fwhm / ((omega-omega_0)**2 + 0.25*fwhm**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bfb673c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0, 1800, 1000)\n",
    "y = np.zeros(1000)\n",
    "arr = zip(vroa.scatter['freq'], vroa.scatter['forwardscatter'])\n",
    "for omega_0, inten in arr:\n",
    "    y += lorentz(omega=x, omega_0=omega_0, fwhm=20)*inten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3f418049",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2c90d7fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4,3), dpi=200)\n",
    "ax = fig.add_subplot(111)\n",
    "ax.axhline(0, color='k', linewidth=0.7)\n",
    "ax.plot(x, 1e3*y, linewidth=1.0)\n",
    "ax.set_xlabel('Wavenumber / cm$^{-1}$')\n",
    "ax.set_ylabel(r\"$\\Delta d\\sigma/d\\Omega\\left(180^{o}\\right)$ / $10^{-3}~\\AA^{4}$/amu\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08d4d9ba-ed8e-4931-8329-bde4d2f0cb5d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}