{
"cells": [
{
"cell_type": "markdown",
"id": "7e56e27a",
"metadata": {},
"source": [
"# VROA intensities of hydrogen peroxide\n",
"\n",
"Here we show an example of how to calculate the VROA intensities of hydrogen peroxide calculated with an incident wavelength of 242.7 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": 1,
"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": 2,
"id": "7f760f13-459d-4a0d-9eff-0a6a7fd51d58",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DELTA_FILE h2o2-vroa-delta.dat.xz\n",
"SMATRIX_FILE h2o2-vroa-smatrix.dat.xz\n",
"ATOM_ORDER_FILE h2o2-vroa-atom_order.dat.xz\n",
"REDUCED_MASS_FILE h2o2-vroa-redmass.dat.xz\n",
"FREQUENCY_FILE h2o2-vroa-freq.dat.xz\n",
"EQCOORD_FILE h2o2-vroa-eqcoord.dat.xz\n",
"ROA_FILE h2o2-vroa-roa.csv.xz\n",
"GRAD_FILE h2o2-vroa-grad.csv.xz\n",
"NUMBER_OF_MODES 6\n",
"NUMBER_OF_NUCLEI 4\n",
"USE_RESOURCE 1\n",
"\n"
]
}
],
"source": [
"with open(resource('h2o2-vroa-va.conf'), 'r') as fn:\n",
" print(fn.read())"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "62dfe463",
"metadata": {},
"outputs": [],
"source": [
"vroa = VROA(config_file=resource('h2o2-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": 4,
"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": 5,
"id": "9ac6314c",
"metadata": {},
"outputs": [
{
"data": {
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" \n",
" \n",
" freq \n",
" freqdx \n",
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},
"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": []
}
],
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