{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ce94202c",
   "metadata": {},
   "source": [
    "## UV/Vis optical absorption spectrum of semiconductors\n",
    "\n",
    "Author: S. Tiwari (v1, 06/01/2024) <br>\n",
    "Revision: F. Giustino (v1.1, 07/02/2024) <br>\n",
    "\n",
    "In this Noteboook, we compute the optical absorption spectrum of silicon using \n",
    "1. The standard theory of indirect phonon-assisted absorption;\n",
    "2. The quasi-degenerate perturbation theory (QDPT) method, which described both direct and indirect processes on the same footing. \n",
    "\n",
    "Electrons and phonons are computed with Quantum ESPRESSO (QE), maximally-localized Wannier functions are computed with Wannier90, and the optical spectra are computed with EPW. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7a37f836",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rc\n",
    "import time, sys, os\n",
    "import EPWpy\n",
    "from EPWpy import EPWpy\n",
    "from EPWpy.plotting import plot_bands\n",
    "from EPWpy.QE.PW_util import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e4570aa",
   "metadata": {},
   "source": [
    "Below we define constants that will remail unchanged throughout the Notebook. The object `silicon` is created as an instance of the `EPWpy` class. This object will contain everything that we need to execute and analyze the calculations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e21cca46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                                                                                                                         \n",
      "                                                                                          \n",
      "                                       -*#*-                             ...............- \n",
      "                          .+*=      .+%*-=%%:      .=#*-               -===============-:.\n",
      "                        :*%=*%%-    *%*   #%*    :+%+-%%+             .:.  -=.   :==-.    \n",
      "                        -%S  -%%*: :#%.   -%%-. -##:  #%*                  -=.   :==-     \n",
      "                ..      .%S:   +%%%%*.     :*%%%#=    %%=                  -=.   :==-     \n",
      "              :=#%%*-   .#S-     ..                  .%%=     :*#*:        -=.   :==-     \n",
      "             -%S:.=#%%*==%#                           *%%=::=##-+%%.  .   .=-.   :==-  .= \n",
      "             :%%-   .-+++:                             -+##*=.  =%S   :-::==:    .==-  --.\n",
      "              *%#                                               #%+    -=--:.     .----:. \n",
      "              :%%-                                             -%S.                       \n",
      "       .-=*####SS%#########:  -###########*+:     +#######=   =%SS####+::.                \n",
      "     =+#**%SSSSSSSSSSSSSSSS=  =SSSSSSSSSSSSSSS=   #SSSSSSS*.  +SSSSSSSS%%%%-              \n",
      "     *%%    =SSS..     .SSS=     SSS=.   .:+SSS+   -SSS:. .-::.  .SSS+.  #%*              \n",
      "      #%#.  =SSS.  *S#  *S%:     SSS=       %SS%.  .SSS=  #SSS%. :SSS:  =%#.              \n",
      "       *%%: =SSS#*#SSS-          SSS=     .+SSS+.   %SS*.=SSSSS+ +SS%.:+%+                \n",
      "        +%%:=SSSSSSSSS-          SSSSSSSSSSSSS=.    +SSS:SSS%SSS:%SS*-#%=                 \n",
      "     ....#S==SSS:..SSS:  =+-     SSS%######+=.      -SSS%SS%.#SS%SSS==%%=.                \n",
      " .:+##%%#*- =SSS.   ::. :SSS.    SSS=.               SSSSSS:..SSSSSS: :*%%%*=-            \n",
      " #%+.     -+#SSS*+++++++*SSS: .=+SSS#++++:           %SSSS+.  =SSSS%.    :-+#%%+          \n",
      " #%*     .SSSSSSSSSSSSSSSSSS: *SSSSSSSSSSS.          +SSS%.    %SSS*.      . .%%=         \n",
      " =%S       :::::::::::::::::.  .:::::::::.            :::.      :::.     =+=-.#%+         \n",
      " -%S:                                                                    =+===#S:         \n",
      " ==*------------------------------=========+++++++++++++++++++++++========++-+##          \n",
      " =+++++++++++*******++++++++++++++++========------------------==========+++++++-          \n",
      "-- -- -- -- -- -- -- -- -- -- -- -- Structure Info -- -- -- -- -- -- -- -- -- -- -- -- -- \n",
      "1\n",
      "https://raw.githubusercontent.com/PseudoDojo/ONCVPSP-PBE-FR-PDv0.4/master/Si/Si.upf\n",
      "https://raw.githubusercontent.com/PseudoDojo/ONCVPSP-PBE-FR-PDv0.4/master/Si/Si_r.upf\n",
      "pseudo found at pseudodojo : ONCVPSP-PBE-FR-PDv0.4/Si_r.upf\n",
      "-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- \n",
      "Pseudopotential: Si_r.upf\n",
      "Pseudopotential directory: '/mnt/storage/sabya/For_video/epwpy/notebooks_basic/pseudo/'\n",
      "Prefix: si\n"
     ]
    }
   ],
   "source": [
    "\n",
    "silicon=EPWpy.EPWpy({'prefix':'si',\n",
    "                     'calculation':'\\'scf\\'',\n",
    "                     'ibrav':2,\n",
    "                     'nat':2,\n",
    "                     'ntyp':1,\n",
    "                     'atomic_species':['Si'],\n",
    "                     'mass':[28.0855],\n",
    "                     'atoms':['Si','Si'],\n",
    "                     'ecutwfc':'40',               \n",
    "                     'celldm(1)':'10.262',         \n",
    "                     'pseudo_auto':True\n",
    "                    },\n",
    "                    env='mpirun')\n",
    "\n",
    "pseudopot=silicon.__dict__['pw_atomic_species']['pseudo'][0]\n",
    "print('Pseudopotential:', silicon.__dict__['pw_atomic_species']['pseudo'][0])\n",
    "print('Pseudopotential directory:', silicon.__dict__['pw_control']['pseudo_dir'])\n",
    "print('Prefix:',silicon.__dict__['prefix'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3f9726b",
   "metadata": {},
   "source": [
    "### Self-consistent field (SCF) calculations\n",
    "\n",
    "Here we perform the self-consistent field calculation to obtain the electron charge density of silicon in the ground state. The calculation consists of three separate steps:\n",
    "1. Apply the method `scf` to the object `silicon`. This step specifies runtime parameters for an SCF calculation on siicon \n",
    "2. Based on the properties defined at step 1 as well as other properties that are set by default within EPWpy, the method `prepare` creates the input file needed by QE\n",
    "3. The method `run` applied to the object `silicon` instructs QE to perform the SCF calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "140bb671",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- -- -- -- -- -- -- -- -- -- --  Calculation: scf  -- -- -- -- -- -- -- -- -- -- -- \n",
      "Running scf |████████████████████████████████████████| in 2.4s (0.87/s)         \n",
      "\n",
      "-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- \n"
     ]
    }
   ],
   "source": [
    "silicon.scf(name='scf',kpoints={'kpoints':[[6,6,6]]})  \n",
    "silicon.prepare(type_run='scf')                   \n",
    "silicon.run(4)\n",
    "silicon.pw_util = silicon.PW_utilities()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4d73ec1",
   "metadata": {},
   "source": [
    "### Band structure calculation\n",
    "\n",
    "In this step, we compute the band structure of silicon along some high-symmetry lines in the Brillouin zone.\n",
    "\n",
    "This calculation is not strictly necessary to compute the mobility, but it is useful to understand the electronic structure of the system under consideration.\n",
    "\n",
    "Also in this case, we use **three instructions** to specify runtime parameters, prepare the input file, and execute the QE calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "09399ee9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- -- -- -- -- -- -- -- -- -- --  Calculation: bs  -- -- -- -- -- -- -- -- -- -- -- \n",
      "Running bs |████████████████████████████████████████| in 4.6s (0.35/s)          \n",
      "\n",
      "-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- \n"
     ]
    }
   ],
   "source": [
    "silicon.scf(control={'calculation':'\\'bands\\''},\n",
    "            system={'nbnd':12},\n",
    "            kpoints={'kpoints':[\n",
    "                                ['0.5','0.50','0.50','20'],\n",
    "                                ['0.0','0.00','0.00','20'],\n",
    "                                ['0.5','0.25','0.75','20']\n",
    "                               ],\n",
    "                     'kpoints_type':'{crystal_b}'\n",
    "                    },\n",
    "            name='bs')\n",
    "silicon.prepare(type_run='bs')\n",
    "silicon.run(4,type_run='bs')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db8f4400",
   "metadata": {},
   "source": [
    "### Band structure plot\n",
    "\n",
    "We now plot the electronic band structure computed at the previous step. The zero of the energy axis is set to the value specified manually via `ef0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1c268ca1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ef_from_file = silicon.pw_util.efermi  \n",
    "\n",
    "Band=plot_bands.plot_band_scf(f'./{silicon.prefix}/bs/bs.out') \n",
    "plot_bands.plot_band_prod(Band,\n",
    "                          ef0=ef_from_file,\n",
    "                          xticks=['X','$\\Gamma$','L'], \n",
    "                          xlabel = 'Wavevector',\n",
    "                          ylabel = 'Energy (eV)' \n",
    "                         )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20069f24",
   "metadata": {},
   "source": [
    "### Phonon dispersion relations\n",
    "\n",
    "To compute phonon-limited mobilities, we need to determine vibrational frequencies and eigenmodes. This operation consists of three steps\n",
    "1. We compute these properties on a uniform and centered Brillouin zone grid\n",
    "2. We perform a Fourier transform of the results in order to obtain the interatomic force constants (IFCs)\n",
    "3. We interpolate the IFCs along specified Brillouin zone paths to obtain phonon dispersions.\n",
    "\n",
    "This plot of phonon dispersions is only meant for us to develop a qualitative understanding of the vibrational properties of the system under consideration. The phonon interpolation needed for calculations of the optical absorption spectrum is performed once again later by EPW."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0c91043",
   "metadata": {},
   "source": [
    "#### Step 1: Calculations of phonons on uniform Brillouin zone grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e909d5ce",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- -- -- -- -- -- -- -- -- -- --  Calculation: ph  -- -- -- -- -- -- -- -- -- -- -- \n",
      "Running ph |████████████████████████████████████████| in 34.1s (0.04/s)         \n",
      "\n",
      "-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- \n"
     ]
    }
   ],
   "source": [
    "silicon.ph(phonons={'fildyn':'\\'si.dyn\\'',\n",
    "                    'nq1':3,\n",
    "                    'nq2':3,\n",
    "                    'nq3':3,\n",
    "                    'fildvscf':'\\'dvscf\\''}\n",
    "          )\n",
    "silicon.prepare(type_run='ph')\n",
    "silicon.run(16,type_run='ph')            "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "880628b0",
   "metadata": {},
   "source": [
    "#### Step 2: Generation of IFCs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9e40a1a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- -- -- -- -- -- -- -- -- -- --  Calculation: q2r  -- -- -- -- -- -- -- -- -- -- -- \n",
      "Running q2r |████████████████████████████████████████| in 1.2s (4.75/s)         \n",
      "\n",
      "-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- \n"
     ]
    }
   ],
   "source": [
    "silicon.q2r(name='q2r')\n",
    "silicon.prepare(type_run='q2r')\n",
    "silicon.run(1,type_run='q2r')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d569505e",
   "metadata": {},
   "source": [
    "#### Step 3: Interpolation of IFCs and generation of phonon dispersions plot\n",
    "\n",
    "The logic and syntax of this operation are the same as for the band structure plot above: three instructions to execute `matdyn.x` and then plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e9337b85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- -- -- -- -- -- -- -- -- -- --  Calculation: matdyn  -- -- -- -- -- -- -- -- -- -- -- \n",
      "Running matdyn |████████████████████████████████████████| in 1.7s (1.80/s)      \n",
      "\n",
      "-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "silicon.matdyn(name='matdyn',\n",
    "               kpoints={'kpoints':[\n",
    "                                   ['0.5','0.50','0.50','20'],\n",
    "                                   ['0.0','0.00','0.00','20'],\n",
    "                                   ['0.5','0.25','0.75','20']\n",
    "                                 ],\n",
    "                        'kpoints_type':'{crystal_b}'\n",
    "                       },\n",
    "\n",
    "              )\n",
    "silicon.prepare(type_run='matdyn')\n",
    "silicon.run(1,type_run='matdyn')           \n",
    "\n",
    "Band=plot_bands.plot_band_eig(f'./{silicon.prefix}/ph/si.freq')\n",
    "plot_bands.plot_band_freq(\n",
    "                          Band,\n",
    "                          xlabel='Wavevector',           \n",
    "                          ylabel='Phonon energy (meV)',\n",
    "                          ef0=0,\n",
    "                          xticks=['L','$\\Gamma$','X']\n",
    "                          )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1067f10d",
   "metadata": {},
   "source": [
    "### Transformation of electrons and phonons to Wannier basis with EPW\n",
    "\n",
    "Now we have Kohn-Sham wavefunctions and variations of the self-consistent Kohn-Sham potential on coarse Brillouin zone grid. We will generate the electron Hamiltonian, the IFCs, and the electron-phonon matrix elements in the Wannier representation using [EPW](https://epw-code.org/). Details on the underlying formalism can be found [here](https://arxiv.org/abs/1603.06965) (free version) or [here](https://doi.org/10.1103/RevModPhys.89.015003) (journal version).\n",
    "\n",
    "This operation involves four logicals steps:\n",
    "1. Compute Kohn-Sham states on a uniform centered Brillouin zone grid (QE)\n",
    "2. Use EPW to load these states and call Wannier90 to generate Wannier functions\n",
    "3. Use EPW to load IFCs and potential variations, and combine with 2. to compute electron-phonon matrix elements in the Bloch representation\n",
    "4. Use EPW to perform the transformation to the Wannier basis and write to file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de5ba5fc",
   "metadata": {},
   "source": [
    "#### Step 1: Calculations of Kohn-Sham states on uniform Brillouin zone grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7ee5b8e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- -- -- -- -- -- -- -- -- -- --  Calculation: nscf  -- -- -- -- -- -- -- -- -- -- -- \n",
      "Running nscf |████████████████████████████████████████| in 6.1s (0.25/s)        \n",
      "\n",
      "-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- \n"
     ]
    }
   ],
   "source": [
    "silicon.nscf(system={'nbnd':8},\n",
    "             kpoints={'grid':[6,6,6],'kpoints_type': 'crystal'})\n",
    "silicon.prepare(type_run='nscf')\n",
    "silicon.run(16,type_run='nscf')              "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7719f969",
   "metadata": {},
   "source": [
    "#### Steps 2-4: Load Bloch representation, Wannierize, write to file quantities in Wannier representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f2dbc083",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 3)\n",
      "[51, 51, 51]\n",
      "-- -- -- -- -- -- -- -- -- -- --  Calculation: epw1  -- -- -- -- -- -- -- -- -- -- -- \n",
      "Running epw1 |████████████████████████████████████████| in 35.8s (0.04/s)       \n",
      "\n",
      "-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- \n"
     ]
    }
   ],
   "source": [
    "\n",
    "# EPW run 1: Bloch to Wannier\n",
    "silicon.filkf_file = 'LGX.txt'\n",
    "silicon.epw(epwin={'wdata':['guiding_centres = .true.',\n",
    "                            'dis_num_iter = 500',\n",
    "                            'num_print_cycles  = 10',\n",
    "                            'dis_mix_ratio = 1',\n",
    "                            'use_ws_distance = T'],\n",
    "                   'proj':['\\'Si : sp3\\''],\n",
    "                   'band_plot':'.true.',\n",
    "                   'filkf':silicon.filkf_file,\n",
    "                   'filqf':silicon.filkf_file,\n",
    "                   'etf_mem':0,\n",
    "                   'fsthick':12.0,\n",
    "                   'wannierize':'.true.', \n",
    "                   'num_iter':500\n",
    "                  },\n",
    "            name='epw1')\n",
    "\n",
    "# File with k-path for sanity checks\n",
    "\n",
    "silicon.filkf(path=[[0.5,0.5,0.5],\n",
    "                    [0.0,0.0,0.0],\n",
    "                    [0.5,0.25,0.75]],\n",
    "              length=[51,51]                        \n",
    "             )\n",
    "\n",
    "silicon.prepare(type_run='epw1') \n",
    "silicon.run(16,type_run='epw1')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d1caeec",
   "metadata": {},
   "source": [
    "#### Sanity check: Interpolated bands and phonons from EPW\n",
    "\n",
    "At this point we have all necessary quantities in the Wannier representation on file. As a sanity check, we perform a simple interpolation of bands and phonons to make sure that we reproduce the results found above _without_ Wannier interpolation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c0a65bd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Electrons\n",
    "\n",
    "Band_EPW=plot_bands.plot_band_eig(f'./{silicon.prefix}/epw/band.eig')\n",
    "Band_QE=plot_bands.plot_band_scf(f'./{silicon.prefix}/bs/bs.out')\n",
    "\n",
    "plot_bands.plot_band_prod(Band_EPW,\n",
    "                          ef0=ef_from_file,\n",
    "                          xlabel='Wavevector',          \n",
    "                          ylabel='Electron energy (eV)',\n",
    "                          xticks=['L','$\\Gamma$','X'],linestyle='--',color_c='b',color_v='b',first = True)\n",
    "plot_bands.plot_band_prod(Band_QE,\n",
    "                          ef0=ef_from_file,\n",
    "                          xlabel='Wavevector',           \n",
    "                          ylabel='Electron energy (eV)',\n",
    "                          xticks=['L','$\\Gamma$','X'],first = False) # False controls if this is the first set of plots \n",
    "\n",
    "# Phonons\n",
    "\n",
    "PH_epw=plot_bands.plot_band_eig(f'./{silicon.prefix}/epw/phband.freq')\n",
    "PH_matdyn=plot_bands.plot_band_eig(f'./{silicon.prefix}/ph/si.freq')\n",
    "PH_matdyn=PH_matdyn*0.124\n",
    "\n",
    "plot_bands.plot_band_freq(PH_epw,\n",
    "                          xlabel='Wavevector',           \n",
    "                          ylabel='Phonon energy (meV)',\n",
    "                          ef0=0,\n",
    "                          xticks=['L','$\\Gamma$','X'],linestyle='--',first = True,color='blue')\n",
    "\n",
    "plot_bands.plot_band_freq(PH_matdyn,\n",
    "                          xlabel='Wavevector',           \n",
    "                          ylabel='Phonon energy (meV)',\n",
    "                          ef0=0,\n",
    "                          xticks=['L','$\\Gamma$','X'],first = False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5744309",
   "metadata": {},
   "source": [
    "### Calculation of optical absorption spectrum: Standard method\n",
    "\n",
    "In order to compute the absorption spectrum, we perform the following operations:\n",
    "1. We interpolate the electrons, phonons, and electron-phonon couplings onto a fine Brillouin zone grid (20 x 20 x 20 for electrons and 10 x 10 x 10 phonons in this example; for production runs you will need finer grids)\n",
    "2. We use these data to compute the imaginary part of the dielectric function with EPW\n",
    "\n",
    "Both steps are performed within a single call of EPW. Note the keyword `lindabs` which selects the standard formula of phonon-assisted indirect absorption, and the keyword `degauss` which is used to broaden the spectrum and avoid singular denominators. \n",
    "\n",
    "The formulas employed in this approach can be found in [Phys. Rev. B 109, 195127 (2024)](https://doi.org/10.1103/PhysRevB.109.195127), specifically Eqs. (1), (3), and (9)-(13)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "20b64df4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- -- -- -- -- -- -- -- -- -- --  Calculation: epw2  -- -- -- -- -- -- -- -- -- -- -- \n",
      "Running epw2 |████████████████████████████████████████| in 37.2s (0.04/s)       \n",
      "\n",
      "-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- \n"
     ]
    }
   ],
   "source": [
    "silicon.epw(epwin={'etf_mem': '1',\n",
    "                   'omegamin':0.05,\n",
    "                   'omegamax':4.0,\n",
    "                   'omegastep':0.05,\n",
    "                   'lindabs':'.true.',\n",
    "                   'nkf1':30,\n",
    "                   'nkf2':30,\n",
    "                   'nkf3':30,\n",
    "                   'nqf1':6,\n",
    "                   'nqf2':6,\n",
    "                   'nqf3':6,  \n",
    "                   'mp_mesh_k':'.true.',\n",
    "                   'efermi_read':'.true.',\n",
    "                   'fermi_energy':6.5,\n",
    "                   'lpolar':'.true.',\n",
    "                   'fsthick': 5.5,\n",
    "                   'temps':300 ,\n",
    "                   'degaussw':0.1},\n",
    "            name='epw2')\n",
    "silicon.prepare(type_run='epw2')\n",
    "silicon.run(16,type_run='epw2')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b6fcee9",
   "metadata": {},
   "source": [
    "#### Plot of the absorption spectrum: Standard method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e4a8a85e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nr = 3.673              # Refractive index of silicon at 1.5 eV, from Aspnes and Studna 1983                   \n",
    "hbar = 1.054571*10**-34 # Planck constant in SI units\n",
    "c = 2.9979e+08          # Speed of light in SI units\n",
    "eVtoJ = 1.60218e-19     # Energy conversion from eV to J  \n",
    "\n",
    "# Indirect absorption\n",
    "\n",
    "eps2 = np.loadtxt(f'./{silicon.prefix}/epw/epsilon2_indabs_300.0K.dat')\n",
    "omega = eps2[:,0]                                            # omega in eV\n",
    "alpha = (omega/(float(hbar*c)*nr))*eps2[:,4]*eVtoJ/100   # alpha in cm-1\n",
    "plt.semilogy(omega,alpha,color='b',label='Indirect')\n",
    "\n",
    "# Direct absorption\n",
    "\n",
    "eps2 = np.loadtxt(f'./{silicon.prefix}/epw/epsilon2_dirabs_300.0K.dat')\n",
    "omega = eps2[:,0]                                           # omega in eV\n",
    "alpha = (omega/(float(hbar*c)*nr))*eps2[:,4]*eVtoJ/100      # alpha in cm-1\n",
    "plt.semilogy(omega,alpha,color='r',label='Direct') \n",
    "\n",
    "plt.tick_params('x',labelsize=16)\n",
    "plt.tick_params('y',labelsize=16)\n",
    "plt.xlabel('Photon energy (eV)',fontsize=16)\n",
    "plt.ylabel('Absorption coefficient (cm$^{-1}$)',fontsize=16)\n",
    "plt.legend(fontsize=16)\n",
    "plt.axis([0, 4, 1e0, 1e10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d30dc36",
   "metadata": {},
   "source": [
    "### Calculation of optical absorption spectrum: QDPT\n",
    "\n",
    "In Quasi-Degenerate Perturbation Theory (QDPT), both direct and phonon-assisted contributions to the optical absoprtion spectrum are computed using a single generalized formula. This approach eliminates the problem of singular denominators that is encountered with the standard theory when `degauss` tends to zero.\n",
    "\n",
    "The formulas employed in this approach can be found in [Phys. Rev. B 109, 195127 (2024)](https://doi.org/10.1103/PhysRevB.109.195127), specifically Eqs. (1) and (48).\n",
    "\n",
    "In order to compute the absorption spectrum, we perform the following operations:\n",
    "1. We interpolate the electrons, phonons, and electron-phonon couplings onto a fine Brillouin zone grid (20 x 20 x 20 for electrons and 4 x 4 x 4 phonons in this example; for production runs you will need finer grids)\n",
    "2. We use these data to compute the imaginary part of the dielectric function with EPW\n",
    "\n",
    "Both steps are performed within a single call of EPW. Note the keyword `loptabs` which instructs the code to use QDPT. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b9fade97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- -- -- -- -- -- -- -- -- -- -- -- -- Warning -- -- -- -- -- -- -- -- -- -- -- -- -- -- \n",
      "Refreshing EPW input (remove refresh from epw_save.json if not needed)\n",
      "-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- \n",
      "-- -- -- -- -- -- -- -- -- -- -- -- -- Info -- -- -- -- -- -- -- -- -- -- -- -- -- -- \n",
      "Based on previous pw and ph calculations some parameters are set below\n",
      "lpolar:  .true. (related to epsil in ph)\n",
      "-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- \n",
      "-- -- -- -- -- -- -- -- -- -- --  Calculation: epw2  -- -- -- -- -- -- -- -- -- -- -- \n",
      "Running epw2 |████████████████████████████████████████| in 9.1s (0.16/s)        \n",
      "\n",
      "-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- \n"
     ]
    }
   ],
   "source": [
    "silicon.epw(epwin={'etf_mem': '1',\n",
    "                   'omegamin':0.055,\n",
    "                   'omegamax':2.8,\n",
    "                   'omegastep':0.05,\n",
    "                   'loptabs':'.true.',\n",
    "                   'nkf1':24,\n",
    "                   'nkf2':24,\n",
    "                   'nkf3':24,\n",
    "                   'nqf1':3,\n",
    "                   'nqf2':3,\n",
    "                   'nqf3':3,  \n",
    "                   'mp_mesh_k':'.true.',\n",
    "                   'efermi_read':'.true.',\n",
    "                   'fermi_energy':6.55,\n",
    "                   'lpolar':'.true.',\n",
    "                   'fsthick': 5.5,\n",
    "                   'temps':300 ,\n",
    "                   'degaussw':0.1,\n",
    "                   'nq_init':-1},\n",
    "            name='epw2')\n",
    "silicon.prepare(type_run='epw2')\n",
    "silicon.run(8,type_run='epw2')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d6dc3df",
   "metadata": {},
   "source": [
    "#### Plot of the absorption spectrum: QDPT method\n",
    "\n",
    "Now we compare the results of the QDPT approach with the standard formula for phonon-assisted absorption and with the direct absorption spectrum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "931bef5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KFcjIyDhpyvFo8TuVwzFgtrcUNfvwejxBwvWQjDGmUNWqVYvy5cuza9euk7bt2rWLRo1yrnwmIietq1mzJmeddRYPPfRQyH0aNGhAeno6ZcqUISkpKe+BF4CSOJXDKtzpwGCtgZCn24wxpjCVLVuWLl268OGHH5KR8duYq++++44tW7b4Pu5FF13E8uXLadOmDYmJiSctMTExVKpUiV69evHuu++SkhJ+UHNMTEy22yOlJE7B/RnwNxFpoqqbALybWs8EHo5mYMYYk2nEiBFccMEF9O/fnzvvvJPk5GSefPJJ6tWr5/uYI0eOpGvXrvTu3Zt7772XhIQE9u3bx8qVK9m0aRNvvfUWAH/72984++yz6dGjB3/605+Ii4tj06ZNLF26lFdecbP0tG7dmrlz5/L5559Tr149atWqlevrVXlRrHpCInKViFwFdPZWXeytOzug2VjcSL7JInKFiFyOGy23HXijUAM2xpgwzj//fCZMmMC6dev43e9+x/PPP8/LL7+cq5Fx4cTHx7Nw4UI6dOjAo48+St++fRk8eDCzZ88+YfRbly5dmDdvHqeddhr33Xcf/fr14/nnnz/hOtHTTz9Ny5Ytufrqq+nSpUvWUO1IE9Xic51dRMIFO1tV+wS0iwdewtWEE+BrYKiqbslvDImJibpw4cL8HsaYUmnNmjWcfvrp0Q7DFICc/m1FZJGqJgavL1an41T15CtxodttAwYUcDjGGGPyye/03ptEpEOYbW1FZFP+wjLGGFMa+L0mlACEq5RXEZtZ1RhjTC7kZ2BCuOszicD+fBzXGGNMKZGXAqb3A/d7TxWYIiLHgprF4m4KzVUlAmOMMaVbXgYmbMKNMgM3T89CILiQWiruhtA38x+aMcaYki7XScibl2cyZJWLGKmqmwsoLmOMMaWA39pxhT09gzHGmBKoWFVMMMYYU7L4vllVRAYB/wfE44ZlB1JVbZqfwIwxxpR8fm9WfQJ4GzeX0FJ+m9Yhc5kTofiMMaZIGzduHCKStZxyyikkJCRw5ZVX8sEHHxBYGm3Lli2ICOPGjSv0GDOLlxY1fntCtwJ/V9X7c2xpjDGlwKRJk4iLiyM1NZVt27bxxRdf8H//93+MGTOGKVOmEBsbS/369Zk/fz5NmxbuiaJx48aRlpbGLbfcUqivmxt+k1BNYEokAzHGmOKsY8eONGvWLOv5DTfcwMCBAxk4cCAPPvggr7zyCjExMXTv3j3HY6WmphITE64oTcnid2DCbCBk7ThjjDHOgAEDuOKKKxg7dixHjhwJeTrupptuIi4ujvnz59OzZ09iY2N58MEHAUhOTuauu+6iYcOGxMTE0KpVK8aMGXPS62zevJkbbriBevXqERMTQ5MmTRgyZAgAffr0Yfbs2cybNy/rlGGfPn0K4+3nit+e0FDgYxHZA/wX2BvcQFUzgtcZY0xp069fPz799FMWLlxIfHx8yDYHDhzgmmuu4c9//jNPPfUUsbGx/Prrr/Tq1YuUlBSGDx9O48aN+fLLLxk8eDCpqancd999gEtAXbt2pVKlSowcOZLmzZuzbds2pk2bBsDo0aO5/vrrSU9P54033JRqVatWLZw3nwt+k9B67/HtMNs1H8c2xpQ2Q4fC0qXRjaFjR3j55YgfNjPx7Ny5M2wSOnToEO+++y5XXHFF1rq//OUvbN26lRUrVtC8eXPATYS3f/9+RowYweDBgylXrhxPPvkkKSkpLFu2jAYNGmTtP2jQIMDNkFq1alXS0tJydSqwsPlNFCMJX8DUGGOMJ3N0nFdpJqTy5ctz6aWXnrBu6tSpdOvWjcaNG5OWlpa1/sILL+TNN99k9erVtG/fnmnTpnHppZeekICKE78VE4ZHOA5jTGlWAD2QomL79u0A1K9fP2yb2rVrU7Zs2RPW/fLLL2zcuJHy5cuH3GfPnj1Zj4HTchc3+blZ9QzgCaA3UA3oqqqLReQpYI6qTo1MiMYYU3x98cUXVKxYkc6dO/PLL7+EbBOql1SzZk3q1KnD3//+95D7tGzZEoBatWqRlJQUuYALma8kJCK9gK9wlbX/A9wbsDkDuAuwJGSMKdU++ugjPvvsM4YMGUKlSpXytO9FF13EK6+8Qnx8PHXq1Anb7oILLuDjjz9m586dYXtbMTExHDx4ME+vX1j89oSeAb4E+gNlOTEJLQZuzF9YxhhTvCxdupTdu3dz7Ngxtm3bxueff86kSZPo27cvTz/9dJ6Pd//99zNx4kTOOuss7r//flq2bMnhw4dZu3Ytc+fOZfLkyQCMGDGC//73v/Ts2ZNHH32UZs2akZSUxNSpU3n33XcBNzhh9OjRTJw4kaZNm1KlSpWsnlS0+U1CnYDfqaqKSPAAhd1A7fyFZYwxxcvAgQMBqFixInXq1KFTp068//77XHXVVdkOSgjn1FNP5dtvv2XkyJE8++yzJCUlUa1aNVq2bMmAAQOy2iUkJLBgwQIef/xxHnnkEQ4dOkTDhg1PGGn30EMPsW7dOm677TYOHTrE2WefzaxZs/L9niNBAusa5Xonkb3Abar6sYiUBY4Did41od/jSvrUi3CsRUJiYqIuXLgw2mEYUyytWbOG008/PdphmAKQ07+tiCxS1cTg9X4rJnwDDPUSUKbMbHYrMMPncY0xxpQifk/HPQHMA5YBH+IS0CAReRHoDHSJTHjGGGNKMl89IVVdhhuavQt4DBB+G5xwtqqui0x4xhhjSjLf9wmp6mLgPBGpCNQA9qvqkYhFZowxpsTLd303VT0K7IhALMYYY0oZvwMTjDHGFz8jck3Rlp9/01wnIRFJF5Gu3s8Z3vNwS1pOxzPGlD7ly5cnJSUl2mGYCEtJSQlb4y4neTkdNxL4KeDnYv3njIhUAJ4DLgGOAptV9fLoRmVMyVanTh2SkpJo2LAhsbGxvm7iNEWHqpKSkkJSUhJ169b1dYxcJyFVHRHw83Bfr1a0PAVUAFqqaoaIlMiba40pSjInU9uxYwfHjx+PcjQmEsqXL0/dunV9T5RXbCaeE5E44CEgETe1eCzQWFW3hGh7GvAS0Bc3fPwrYKiqbvO2VwLuAOIyZ4BV1Z8L4W0YU+pVrVq1SM3saaLL18AEEXlIRF4Js+0fIvJA/sIKqRlwNbAPmJtNbJVwFRtaAYOAG4DmwEwROSXgWPuAh0XkBxGZJyKXhjygMcaYAuN3dNzNwPIw25Z62yNtjqrWVdV+wKRs2t0ONAH6q+qnqjoZuBxoBNzptSkHxAMbVbULrtTQWyLSpADiNsYYE4bfJBQPbAizbRPuCz+iMk+b5cLlwAJV3Riw72ZcmaHMsrLbcAMr3vW2r8WVIOoUsYCNMcbkyG8SOgI0DLMtDkj1edxIaAOsDLF+FdAaQFV34+ZDughAROoD7YAVoQ4oIneIyEIRWZicnFwgQRtjTGnkNwnNBR4QkZjAld7zP5HNNZtCUAN3vSfYXqB6wPPBwBARWYFLSH8KV/NOVceoaqKqJtaubVMlGWNMpPgdHTcc+BZYLyLvAkm4ntH1QE3gpkgEV5C8UXXnRTsOY4wpzXwlIVVdJiLnAH/DDZsuA2Tg5hka4FXZjpZ9nNjjyRSuh2SMMSZK8lNF+3ugt4jE4r7096lqUajHsQp3XShYa2B1IcdijDEmG/kuYKqqKaq6o4gkIIDPgO6Bw61FJAE409tmjDGmiMh1T0hE0oEeqvq9iGSQfe04VdWIV2MQkau8Hzt7jxeLSDKQrKqzvXVjcRPsTRaRx704/wJsB96IdEzGGGP8K24FTINvUh3tPc4G+gCo6mERORdXtmc8rmzP17iyPYcKKU5jjDG5UKwKmKpqrkruejXiBhRwOMYYY/IpL/MJ7RWRTt7Pb4lI44ILyxhjTGmQl4EJpwCZN6feBNhdm8YYY/IlL9eEtgK3B1RJOENEKoZrrKpz8hWZMcaYEi8vSegZ3OiyQbhBCaPDtBNve9n8hWaMMaaky8vAhLdE5H9AC2Am8AdgTUEFZowxpuTLy31Ce4HzVHW2iLwDfOFNkWCMMcb4kteBCZnXgG7EBiYYY4zJJ78DEwQbmGCMMSafbGCCMcaYqLGBCcYYY6ImT0VGVXUnsNMGJhhjjIkEv5Pa3RzpQIwxxpQ+vucTEpEzRORjEdktImkBdeWeEpGLIheiMcaYkspXEhKRXsB8oBXwn6DjZAB35T80Y4wxJZ3fntAzwJe4abT/GLRtMdApP0EZY4wpHfzOftoJ+J2qqogET263G7uR1RhjTC74TUJHgUphttUHDvg8rjHGmGIqJSWFnTt3snPnTnbs2HHCYzh+k9A3wFARmRywLrNHdCsww+dxjTHGFAOpqaksWbKEBQsW8N1337FgwQK2bNkS1EooV64jlSv3C3scv0noCWAesAz4EJeABonIi0BnoIvP4xpjjCliVJUtW7Ywf/78rKSzZMkSjh8/DkBcXBzdu3dn0KDbOX68Pbt2NWfDhvosX16Z/fvLsH8/wF9DHltUgy/p5I43JPt5oDeuRE8GMBf4o6ou8XXQYiAxMVEXLlwY7TCMMabApKSksGjRIubPn8/8+fP59ttv2bVrFwCVKlWiS5cudOvWjRYtzkakOxs21GDBAvjuO0hJccdo2RJ69YKzznJL06aySFUTg1/Lb08IVV0MnOcVMa0B7FfVI36PZ4wxpnClp6ezZcsWVq9ezZo1a1i9ejWrVq1i2bJlWb2cpk2b0rdvX844ozdVq/Zhz54m/PBDWf7zH/jpJ3eccuWgQwe4806XcHr1gjp1cheD7ySUSVWPAjvyexxjjDGRlZaWxs8//0xSUtIJy9atW1mzZg3r1q3j6NGjWe3r1atH69atGTz4cWrWPJ/09PasX1+Z77+HCRMg88RZ48Yu0XTr5pYzzoCKYedUyJ7vJCQi9YE/AWfjekJ7cYVNX1TVn/0e1xhjTM727t3LTz/9xI4dO9ixYwdJSUlZP2c+37VrFxkZGSfsV6FCBeLi4mjVqhXnntuXWrW6odqOAwcasWFDRZYsgRkBQ8vi46FTJ7juOvfYtWvuezm54SsJiUgL3PWf6rgBChuBesAQ4EYROUtVN0QsSmOMKWVUlZ9//pmNGzeyceNGfvzxxxMe97ur/SeoVasWDRo0oEGDBrRv356GDRtmLZUqJbBvXxzbtlVlxYoyrFgBM2f+dg2nTBl3HadHD7jnHte7OeMMqFmzYN+n357Qs8CvQDdV3ZK5UkQaAdO87b/Ld3TGGFMKJCcns3LlSlauXMmqVauyHgMTTdmyZUlISKBp06Zce+21NG3alPj4+KykU79+fWJiYkhJgZUrYcUKt0ya5B5/+eW316tTB9q3h8GD3WO7dnD66RAbW/jv3W8SOge4KzABAajqVhEZTvgJ74wxptQ6dOgQq1atYsWKFaxYsYKVK1eyYsUKkpOTs9pUr16dNm3acM0119C6dWtatGhB06ZNadSoEeXLl89qpwo7d8KyZTBnDixd6n5evx4yz8DFxkKbNnDJJS7RZC516xbyG8+G3yRUATgYZttBb7sxxpRKqamprFu3LqtXk5lsNm/+bQq2SpUq0bZtWy677DLatm1L27ZtadOmDfXr10dETjheWhqsXeuSzNKlvy27d//WJiHBjVC7+mrXu2nfHpo0gbJFfI5rv0loKXCfiPxPVbOueon75O72thtjTImmqvz4448sXrw46xTaqlWr2LhxI+np6YA7jda8eXMSExO5+eabadeuHe3ataNx48aUKXNyDenkZFi1yp1Cy0w6K1dCaqrbHhPjejNXXOGSTocOLuFUq1Z47zuS/CahkcDnwBoRmQjsxA1MGAg0By6JTHgFT0RuBt4CrlTVT6McjjGmiMrIyODHH39k0aJFWcvixYs5cMCVyixTpgxNmzalTZs2XHXVVVk9mxYtWhATE3PS8fbtc72bVatckslcvHtCAahVCzp2hPvuc48dO7rBA+XyfXNN0eF3ZtWpInIpMAp4DBBc6Z5FwKWqOi1yIRYcEUkAbgcWRDkUY0wRo6ps2LCBadOmMX36dGbPnp2VcCpUqED79u255ppr6Ny5M506daJNmzZUDLpZJjUVNm2CdevctZp16377OeAyEKec8tu1m7Zt3dKmDdSvD0Fn5kqc/FRMmApMFZFKuKHa+wqyYoKIxAEPAYlAByAWaBw8OMJrexrwEtAXlyC/Aoaq6raANmWAN4H7gBcKKm5jTPGxd+9evv76a6ZNm8a0adPYts19ZTRp0oSrr76arl270rlzZ9q0aUOFCu7S9/798OOP8Nln7jFw+emn327wBDcgoGVL6N8fWrRwP7dtC40auSHSpVEkKiYcAQqjXE8z4Gpcb2sucEGoRl5SnAGkAoNwPbRRwEwRaa+qh72mfwTmqeqi4IuAxpjSQVVZsWIFn3/+OVOmTOG7775DVTn11FM599xzeeSRR+jV6wLKlm3C5s2webOrHLB5s+vhbN4MB4ImrqlTB5o2hbPPdo/Nmrlk06IFnHpqdN5nUeb3ZtWHgDhVvS/Etn8A21X1+fwGF2SOqtb1XuM2wiQh3Om1JkBLVd3otV8ObADuBF4UkbbAAFzxVWNMKZKamsqsWbOYMmUKU6Z8zrZtvwLxNGt2PhdeOIxq1Tpw7Fg9tm0rwxNPnDgCDVx5moQEV7qmZ0/32KSJSzhNmkCVKtF4V8WX357QzYQ/hbUU+DOuwnbEBI7Cy8HlwILMBOTtu1lE5gFXAC8CZwEJwAavF1QPGCMicar6aiTjNsZEz7FjsHHjUaZPX8+3325n2bL9/PjjcdLS6iNyL2XKPI87sw8bN7qlYkV3eqxRI1emJvPnxo3dUrdu6T11VhD8JqF4XM8ilE1AI5/HjYQ2wOQQ61fhRu+hqq8Br2VuEJFZwMs2Os6Y4iUtDbZvd6fG3HUYZc2aI6xbl0JSUjkOH64KVATaewvExh6gSROldesqJCSUJT7e1Uc77TTXw6ldu+QPBihK/CahI0DDMNvicNdjoqUGsC/E+r24ARR5JiJ3AHcAxMfH+4/MGJNn6emwdasbzrx2rbJiRSpr1x5jy5Yy/PJLLBkZgXdjHsfdMbKVsmV3EB+fRtu2VejRoyF9+7agQ4eaVKxoF2aKEr9JaC7wgIh8qKpZCUdEYnCVtedGIrjCoqp9ctg+BhgDblK7wojJmNImJQVWrDjO3Ll7+P77Q6xfD0lJldmzpwYZGZlFWAT3N/BG4EdgE7GxPxMXl0qzZmU4/fSqNGvWmC5dutChQ+8TytyYoslvEhoOfAusF5F3gSRcz+h6oCZwUySC82kfoXs84XpIxphCdPgwzJ+/l5kzd7F48VE2bCjPzp01OHKkHlAed4k2DfgRkR+oUmUHderso1GjFG+UWQ1OO+00GjduRePGF1OtuJYKMID/m1WXicg5wN9w9+6UwU3v/Q0wQFWXRS7EPFuFuy4UrDWwupBjMabU2rMHvvvuALNm7WLx4iNs2FCeXbtqkppaD/c3YQ3gOCIbqFp1Baef/g2tWqWTmBhLr171aNEigTp1LglZ2saUHPm5WfV7oLeIxPLbzaopEYvMv8+Av4lIE1XdBFmVEc4EHo5mYMaUJBkZ8PPPsHJlCvPnJ7N8+SE2bFB27Ihl//5apKdXBU71lhTKlFlP9erLadNmAe3alaNnz+qcd148jRu3okyZ1lF+NyZaInGzagpQKMlHRK7yfuzsPV4sIslAsqrO9taNBe4FJovI47ibVf8CbAfeKIw4jSnujh1z88/s3KmsW3eQVat+Zf36FLZuzeDnn8uzb19lUlKqo1oeN8Q5HncKbSsxMduoW3cF8fHHaN26LGeeWYNzzmlCQkL7k6pDG1PcyuBNCnqeOW/RbKAPgKoeFpFzcWV7xuOuZH6NK9tzqJDiNKbIUXUlZnbuhKSkdNauPcDGjYfZti2VHTuU5OSy7N8fw+HDVTh2rKq3lwBVveUY8BMiSVSuvI9GjY5Qv34azZuXoXPnavTo0YDTT29G5cpNo/UWTTFUrJKQqubqzyivRtyAAg7HmCJF1RXF3LQJ1qw5yuLF+1mz5qg3lLkShw9XDRhlVpbfrsscwQ1r3kmFCvupUuUQ1aqlUqdOOvXrl6FRowq0bl2Fdu3q0rhxI2rX7mU9GhMxxSoJGWOcX35R5s7dz9y5+1myJJ2NG2P55ZeapKVlVnGuiBtl9jOwifLld1Cr1hFq1TpOvXrKaaeVo3HjirRoUZWmTWvToEF96tbtakOaTaGzJGRMEZOenk5ycjI7d+5kx46drFr1K0uWlGPduqokJdVm79440tJq48YDVQd2AcupXHkb9eodpnFjaN26Ip0716R16wSaNDmd6tV7RvdNGROGJSFjoiAjI4OkpCTWr1/P+vXr2bBhA+vXr2fNml/YsqU2GRmdgK5AF1yPBuAYFStupF69ZSQkHKR9e+jRozKJiaeRkHDWSXPZGFMc+E5CIjII+D/csJjg335VVbs6aUyATZs2MWXKFD777DPmz59PSkoarp5Zd8qW7UnZskM5dsyVhRJRGjY8SPv2qXTvvodzzqlCly4ViIlpjbvlzZiSwe9UDk8AI4CVuKrZ0awVZ0yRlJ6eznfffZeVeFav3gOcSY0aN1Kt2liOH48nLc39F6xTR+neXejWDbp2hc6dhapVq2b/AsaUAH57QrcCf1fV+yMZjDElwYoVK3j11X8yadJy9u1rjUhvKla8l8yav0eOwOmnw7XXQrdu0L07xMWJVW42pZLfJFQTmBLJQIwpztLS0njrram8+OJK1q1rgpvMtxYANWpkcNZZZejVC3r1gjPOgAoVsj2cMaWG3yQ0G+iAm0bbmFIpLQ3+97/9/O1vq5k//1SOH78UuJQqVQ5xySXl6dsXzjwTWrQoY70cY8Lwm4SGAh+LyB7gv7i5ek6Qh5lQjSk2UlPhf/9TXn11N3PmVOL48WpAV6pVW81ll61m6NCWnHFGZUs6xuSS3yS03nt8O8x2zcexjSlS0tNh5kx4++0UPvlESEmpCAjlyn3EhRemMnJkb7p2bR/tMI0plvwmipG4RGNMiaQK338PEyYo7757jH37YnAFOj+hZcsl/PGP7bn22oFUrlw52qEaU6z5nU9oeITjMKZISEqCcePSeeONVLZvr4TIMVQ/p3LlKQwaVJu77hpE27Y3RjtMY0oMO2VmSr2jR2H8+AO88spBVqxogCvuuZCyZcfTu/dubr/9aq688nWrSGBMAchPxYT6wJ+As3GlePcCM4EXVfXnyIRnTMFQhU8/3clzz+3ghx+akZ5+KnCAypX/zsUXJ/N//9eF889/kSpVqkQ7VGNKNL8VE1oAc3HVE+cBG3EFroYAN4rIWaq6IWJRGhMh33yTzKhRm5g1qyGpqXFANWrUmM1ll+3m3ntb06nTEJtO2phC5Lcn9CzwK9BNVbdkrhSRRsA0b/vv8h2dMRGwevV+hg9fy9Sp1Tl4sCVQg8qVf+Cyy5YxbFhb2rW7KNohGlNq+U1C5wB3BSYgAFXdKiLD+W3GU2OiIjkZPvggjZdfTmLjxjigOzExK7jggqk89lhTevfuHu0QjTH4T0IVgINhth30thtTqHbvhk8+gQ8+gBkzMsjIKAccpXnzSTz1VBsGDGiLSLtoh2mMCeA3CS0F7hOR/wVWRhA35+/d3nZjCtyRIy7pvPcefP21u7H0lFN2kJHxFo0afc/o0YPp1++aaIdpjAkjPzerfg6sEZGJuAnq6wEDgebAJZEJz5jQ1q+H11+Ht9+G/fuhSZMMunadww8/PACs5bnnhjFkyIdUsEqhxhRpfm9WnSoil+JKBT8GCK6CwiLgUlWdFrkQjXHS0uDzz2H0aJg+HcqVgwED4Nxz1zJixPnMn5/EjTfeyDPPfEb9+vWjHa4xJhd83yekqlOBqSJSCTdUe5+qHolYZMZ4Dh2Cv/8d3ngDtm+HuDj4y1/gtttg1aqv6d+/P7Vq1WLevHn07Nkz2uEaY/Ig3xUTvMRjycdEnCp89BEMHerK6fTtC//4B1x6qesFffTRR1x77bW0aNGCL7/8kgYNGkQ7ZGNMHuX6rjwRSReRrt7PGd7zcEtawYVsSoMNG+Dii2HgQKhdG779FqZNg/79XQIaO3YsV199NYmJicyZM8cSkDHFVF56QiOBnwJ+tiraJuJSUuCZZ9xSsaI7DXf33S7xAKgqzz77LI888ggXX3wxkyZN4pRTTolu0MYY33KdhFR1RMDPwwskGlOq/fe/cN99sGkTXHst/O1vEDi+ICMjgwceeIAXX3yRa6+9lnHjxlG+fPnoBWyMyTcrkmWi7tgxuOMOuOQSqFDB3e8zYcKJCSgtLY1bbrmFF198kXvvvZfx48dbAjKmBPCVhERkk4h0CLOtrYhsyl9YprTYuxcuugjGjoWHH4Zly+Dcc09ud/fdd/POO+8wYsQI/vGPf1iRUWNKCL+j4xKAmDDbKgKNfB7XlCLr17uRblu3wvjxcP31odv961//YuzYsTz88MMMGzascIM0xhSo/AzRDjcwIRHYn4/jFjgRqQ6MB1oAKcAu4G5V3RjVwEqRGTPgqqvcgIMZM+DMM0O3W7RoEffccw/nn38+o0aNKtwgjTEFLi9DtO8XkW0isg2XgKZkPg9YkoF/AlMLKuAIUeBlVW2hqh1wJYjejHJMpcbYsXDhhdCgAXz/ffgEtGfPHgYMGEDdunV57733KFu2bOEGaowpcHnpCW0CvvZ+HgQsBJKD2qQCqymAL3QRiQMewvW0OgCxQOPg6SS8tqcBLwF9cSWFvgKGquo2AFXd763L9C3wx0jHbE6Ung4PPggvvuiuA02cCFWrhmubzrXXXsvOnTv55ptvqFWrVuEGa4wpFHkZoj0ZmAzgimUzUlU3F1BcoTQDrsbVp5sLXBCqkVdGaAYuIQ7C9XpGATNFpL2qHg6x21C892YKxvHj8Pvfu6kW/vAHeOGF3+79CWX48OFMmzaNsWPH0qVLl8IL1BhTqPwWML0ZQESqAm2BhkASsEJVw80zlF9zVLWu97q3ESYJAbcDTYCWmdd4RGQ5sAG4E3gxsLGIPOm1v6OA4i710tPhxhtdAnrpJVeGJztTpkxh1KhR3Hrrrdx2222FEqMxJjp8j3MVkWHAdlyvZCLwDfCTiDweodhOEDhvUQ4uBxYEDjLwemzzgCsCG3qx9gMutuKrBSMjw90D9P778NxzOSegjRs3csMNN9C5c2deffXVQonRGBM9fu8TGgEMxyWfvkA74HzgA2CEN8V3tLQBVoZYvwponfnE6wFdBlygqgeyO6CI3CEiC0VkYXJy8GUwE46qSzpvvQXDhsEDD2Tf/siRI/zud7+jbNmyfPjhh1SsWLFQ4jTGRI/fIdq3Ay+oauDXyipghogcwJ3aGp7P2PyqAewLsX4vbsoJRKQNLr4fgdneNa40VU0MdUBVHQOMAUhMTLSaebn02GPwyivwpz/B8OE5t7/33ntZuXIlU6dOJSEhoaDDM8YUAX6T0KnAl2G2TQUG+zxuoVDVVbhRc6aA/PWv8PTTcNdd8PzzIDl82jNnzuTtt9/m0Ucf5YILwl3uM8aUNH6vCX0HhBuy1MXbHi378Ho8QcL1kEyEvfwyPP443HAD/POfOSegY8eOcc8999C4cWMef7xALikaY4oovz2hPwCfePMGTcJVHKiLG0J9C3CFiGQluDwMKoiEVbjrQsFa4+5hMgVo7Fi4/3437fZbb0FuSry99NJLrFmzhi+++ILY2NiCD9IYU2T4TULLvcdnvCWQACsCnms+XsePz4C/iUgTVd0EICIJwJnAw4UYR6kzdSrceaebjO4//8n+PqBMW7duZeTIkVx55ZX069ev4IM0xhQpfpPDiJybRJ6IXOX92Nl7vNgrFZSsqrO9dWOBe4HJ3hBsBf6CG07+RmHGW5qsXw/XXAMdOsCkSW5KhtwY6o3ZfvnllwssNmNM0ZXnJCQiFYCOwEuqOifiEWVvUtDz0d7jbKAPgKoeFpFzcWV7xuN6Zl/jyvYcKqQ4S5UDB+CKK6B8efj0U8jtRKeff/45n376Kc888wzx8fEFGqMxpmgS1byPOBaRg8Blqjor4hEVcYmJibpw4cJoh1FkpKe7BPTll/DVV3D22bnb78iRI7Rp04bY2FiWLl1Khdx2nYwxxZKILAp1G4zf03HzgO7ArPwEZYq/YcPgiy/cKLjcJiCAp59+mi1btjBz5kxLQMaUYn6T0J+AT0XkEPApsJOg+YUKeUSciYKJE+Gpp+D222FwHu4MW79+Pc899xzXX389ffr0KbD4jDFFn9/TcZkJJtzOqqqFOSKu0NjpOGfpUujZEzp1cpPS5bYzo6pccMEF/PDDD6xdu5Z69eoVaJzGmKIh0qfjRhI+AZkSLjkZ+veHmjXho49yn4AAJk2axFdffcWrr75qCciY0mL9+rCb/E7lMNxvLKZ4O34cBg6EXbtg7lyoWzf3+x45coT777+fTp06cddddxVckMaYouWjj8JuKpGnzEzBGT4cZs+G8eMhMWS51/DGjBnDjh07eP/9922qbmNKk2ySkK9rQgAiUh83QOFsXF22vcBM4EVV/dnXQYuB0nxN6Ntv4ayzYNAgV5InL1JSUmjSpAmtW7fm66+/znkHY0zJkJQEcXEIRO6akIi0wE1mVx03XHsjUA8YAtwoImep6oZ8hG2KmEOH3Oyop53mCpTm1ZtvvsnPP//M+++/H/HYjDFF2PTp2W72ezruWeBXoJuqbslcKSKNgGne9t/5PLYpgh54ADZtgpkzoWrVvO179OhRnnnmGXr37s3ZebmZyBhT/E2b5i4e79oVcrPfqRzOAZ4ITEAAqroVN1ncOT6Pa4qg//0PXn8d/vjHvN2Qmuntt99mx44dDBs2LPLBGWOKrowMV0qlb9+wTfwmoQrAwTDbDnrbTQmwZw/ceiu0aQOjRuV9/2PHjvH000/Ts2dPzj333MgHaIwpupYtc/d0ZJOE/J6OWwrcJyL/C6yMIG6e7Lu97aYEuOce2L3bleapWDHv+48bN47t27czduxYJKfZ7YwxJUvm9aACSEIjgc+BNSIyEVe2px4wEGgOXOLzuKYIee89V5rnr3+FM87I+/7Hjx/nqaeeolu3bjZltzGl0bRp0LYt1K8ftonfm1WnisilwCjgMdx0CQosAi5V1Wl+jmuKjqQkuPtu6N4dHnzQ3zHGjx/P1q1bGT16tPWCjCltjhyBb75xp1Oy4ftmVVWdCkwVkUq4odr7VPWI3+OZokMVbrkFjh2Df/87dzOkBktLS+Ovf/0rnTt35uKLL458kMaYom3uXEhNzfZUHPi/T6gMUEZV07zEc0RELhSRtsAMVV3i57imaHj9ddeLHj0amjf3d4z//Oc/bNq0icmTJ1svyJjSaPp0V1iyd+9sm/mtoj0RSFXVG73nd/HbLKfHgUtU9as8H7gYKOkVE3btgpYtXUme6dPBT/5IS0ujdevWVKpUiSVLllgSMqY0at8eatcGr0JKuCrafododwf+G/D8AeBN4FTgY9x1IlMMPfywO5X7z3/6S0AAEydOZMOGDQwbNswSkDGl0c6dsGIF5GJAkt8kVAdIAhCRZkBj4FVVPQi8DbTzeVwTRd9+C+PGuZtSW7b0d4z09HRGjRpFu3bt6N+/fyTDM8YUF195J8JyuB4E/gcm/ArU9H7uA+xW1eXe83TAxx0lJprS090glrg4ePxx/8f59NNPWbt2LRMnTqRMGb9/4xhjirXp06FWLejYMcemfpPQt8DDIpIGDOXEU3PNgJ98HtdEyeuvu9lSP/gAKlf2f5zXXnuN+Ph4BgwYELHYjDHFiKpLQuefD7n4Q9Tvn6oP4npCn+F6PcMDtv0emO/zuCYKkpNd7+e88+Cqq/wfZ/369Xz99dfccccdNl+QMaXVypXw88+5uh4E/m9W3QA0F5GaqronaPMQoMTOJ1QSPfywm6rhlVf8D0YAN2lduXLluOWWWyIXnDGmeJnm1SrIxfUgyOfMqiESEKq6Ij/HNIVrwQI3Qd0DD8Dpp/s/ztGjR3n77bfp378/9bMp0WGMKeGmT3dfJnFxuWqeryQkIlWBtkBD3Gi5Fd4IOVMMZA5GaNAAnngif8f68MMP2bt3L4MHD45McMaY4ufoUZg9G+64I9e7+E5CIjIMN713ZX6rHXdIRJ5XVR9F/01hGzsWFi92hUqrVMnfsV5//XVatGjBOefYVFLGlFrz5rlElIeCxb4GJojICNxghIlAX9x9QX2BD4ARIjLcz3FN4dm9Gx59FM45B37/+/wda8WKFcybN48777zTbk41pjSbNg3Kl8/T7Jd+e0K3Ay+o6gMB61YBM0TkAHAHJ46YM0XMo4/CwYP5H4wArhcUExPDoEGDIhOcMaZ4mjYNevTI030efodonwp8GWbbVG97kSYiTUXkGxFZLyJLROSkmkYl1bffulNxQ4a4GVPz49ChQ4wfP56rr76amjVr5ryDMaZk+uUXd7NhHucO85uEvgO6hNnWxdte1L0OvKOqLXD3PU2QUnAu6fhxuPNOiI+H4cPzf7z33nuPgwcPctddd+X/YMaY4isPpXoC5ToJiUiZzAX4A3CLiDwgIgkiEus9PgjcAtybpyhy9/pxIvKKiMwXkSMioiKSEKbtaSLyoYgcEJFfReRjEYkP2F4bV4R1HICqTscNrugc6biLmhdecPeS/fOf+auMAKCqvPbaa7Rv354ePXpEJkBjTPE0fTpUrw6d8/Y1mpdrQmm4EXCZBHjGWwhavwKI9C3zzYCrcbO3zgVC9vm8SfZmAKnAIFzMo4CZItJeVQ8D8cBOVT0esOsWb32Jnadh0yYYMQIGDIBLL83/8RYuXMiSJUts5lRjSjtVdz3ovPMgj9VS8pKERnJiEipsc1S1LoCI3EaYJIQbNNEEaKmqG732y4ENwJ3Ai4UQa5Gj6qbrLl8e/v73yBzz9ddf55RTTuG6666LzAGNMcXTmjWwY0eerwdBHpKQqg7PTTsR6QPcmOdIcn79jFw2vRxYkJmAvH03i8g84ApcEtoG1BeR8gG9oQRvfYk0cSJ8+aUbDdewYf6Pt3//ft577z1uuOEGqlatmv8DGmOKr1mz3ON55+V514jU2heRZiIyUkQ2A1/jTptFSxtgZYj1q4DWAKqaDHwP3AQgIn1xpxEXFU6IhWvfPhg61M2WGqmCBuPHjyclJcUGJBhj4JtvXOmVxo3zvGt+KiaciquYPQh3kR9gGe4a0Xt+jxsBNYB9IdbvBaoHPL8LeEdEHgCOANdpmLnOReQO3L1PAKkiEirJFXm7dkG5fBVqOlmnTp1Cra4F7I7sK5Uo9vnkzD6jnBW9zyj7qRsahVqZp68kb2TcRbjEcxluGocdwD+Be4ChqjonL8eMFq8SeM9cth0DjAEQkYWh5kk3v7HPKHv2+eTMPqOclZTPKNdJSEReAK7FTe19FPgEeAf4CqhKAQzL9mkfJ/Z4MoXrIRljjImSvPSE7seNjvsvcFPgNA4iEs1Rc8FW4a4LBWsNrC7kWIwxxmQjLwMT/gUcBC4B1onIqyLStWDCypfPgO4i0iRzhXdT65netvwaE4FjlHT2GWXPPp+c2WeUsxLxGUmYa/GhG4tUBK7EXRM6D5fE1uNOzT0EnFOQ14REJHPy6fNwAwvuBpKBZFWd7bU5BTdAIgV4HNd7+wtQBWivqocKKj5jjDF5k6ckdMKOIvWBG3D3BLX2Vi8ARgMfqurRiER44muGC3a2qvYJaBcPvISbXkJww8aHquqWSMdkjDHGP9/3CanqTlV9TlXbAl1xI+SaA/8GdkYovuDXlDBLn6B221R1gKpWVdUqqto/uwSUU6257IhIRRF5XkR2ikiKV9uud/7eadGTz89IwywdCzjsQpOX2oYh9i0jIo+IyBYROSoiy0RkQAGHXOjy+RltCfM71L9goy48InKViHwkIlu975J1IvK0iOQ45WRx/h6KyM2qqrpQVe8DGgADgFmROG5hCKg11wp3mvEGXDKd6Z3ay8m/cKWChgGX4hLwlyXsCza/nxG4YrE9gpb1EQ82ejJrG+7D1TbMi7/g5t96FbgYd0Zhkoj0i2SARUB+PiNw08cE/w7Njlh00fdnIB14FHcrzGvAYGC6d3tMdorv95CqluoFGIL7h28WsK4xrmDrH3PYtwPumtPNAevKAeuAz6L93orCZ+S1VWBUtN9HAX9GZQJ+vs17zwm52K8OrtjuiKD1XwPLo/2+isJn5LXfArwb7fdQwJ9P7RDrbvQ+p3Oz2a9Yfw9FpCdUzIWsNQdk1prLad/juGnOM/dNA94HLhSRmMiHGxX5+YxKBc19bcNgFwIVgHeD1r8LtBORvNdBKaLy8RmVCurKiQX7wXvMruJjsf4esiSUi1pzOey7WVWPhNi3Au70Q0mQn88o02ARSfWuBcwQkbMiF16x1gbXE9oYtH6V95jbz7c0uMz7/UkVkQUl6XpQNs72Htdk06ZYfw9ZEsp9rbm87pu5vSTIz2cE7q/6u4HzcTX4agIzvIrrpV0NYL9651AClLTfofyaAtyH6zleh1e1RUSuj2pUBUhEGuKm0PlKVbOb56xYfw9FuJylMSdT1RsCns4Vkcm4ntUooFd0ojLFibqBT1lE5BPcAI6nOflUZrEnIpWBybjrrjdHOZwCZT2h/NWay25f+O0vkeIuovX4VPUg8AXQJZ9xlQT7gGpy8tS0Je13KKJUNR2YBMR59yyWGCISi+v5NQEuVNWfctilWH8PWRLKX625VUBjbwhz8L7HOPk8f3FVUPX4ilLNwWhZBcQATYPWZ14LsnqHOSsxv0ciUh74EEgE+qnqilzsVqy/hywJ5a/W3BSgPDAwYN9yuHmWpqlqasSjjY6I1uMTkaq4exm+j1SAxdhU3Mim4DnSrwdWeqMQTZCA/2fbVPXnaMcTCd69QBOAc4H+qrogl7sW7++haI8Rj/YCnIL7S2EFbrjx5bjac5uAygHtGuHOzw4L2v99XHf4NlxNuw9xF007Rfu9FYXPCHcD3ljcNCB9cDe7rsD9hXZWtN9bhD+nq7zlNdxf54O952cHtEkD/hW03zPe78wfvc/oNSADuDTa76kofEbA/3n/z24EzgGuwd3sqsA10X5PEfxsMj+TUbiJQgOXOK9NifseinoARWEB4oGPgF9xlcI/JegmOiDB+wUZHrQ+FngR+Nn7R/8O6BPt91RUPiPc5IfzcDNAHgf24HpPXaP9ngrgM9Iwy6ygNuOC9iuLK7a7FTdcezlwVbTfT1H5jLwv4RnALu93aD9uHrMLo/1+IvzZbMnm8xnutSlx30O+C5gaY4wx+WXXhIwxxkSNJSFjjDFRY0nIGGNM1FgSMsYYEzWWhIwxxkSNJSFjjDFRY0nIFEkiclPQNM4HvWmv7/XuBs9st0VEIlbAUkSGi8i5kTqeyR0R6exN05DdvDnB+3T0fjcezKbNX0QkQ0Qai0isN/311ZGJ2kSCJSFT1A3ETeM8AFfm5xXcFMYF5Ulc2RRTuJ4H3lLVpNzuoKpLcTf23hBqu1cU9npgrqpuVtUU4DngKa9GmykCLAmZom6pqi5Q1WmqejswCzfduClAhTkbp4h0xpXjec3H7u8AbUXkjBDbeuMqDPw7YN044DTgSh+vZQqAJSFT3PwAVBWROoErReQaEVkjIodFZKGInDRPkYhc753SOyoiu0VkfOA0ACKSWT7ksYDTgMNzu7/XZouIvJubeEIRkQ4i8pmI7BORFBGZFzwLrYiME5GfROQMEZnrncbaICJ3hTheYxGZICLJ3oykS0XkyqA2w7332lZEvhSRQ8AH3rbaIvKeiPzqxfS2iFzute/jtXlFRHYF9y5EpIp3GvWZHN72bcByVV0VvEFE7gj6zP8lIoGTtE3A1VIL1Ru6EUjBTfkAgKruA770XtMUBdGuG2SLLaEW4CZcjaxmQesn4b50KnnPt+Bqrv2AK4R5KbAEV1+sWsB+d3jHex/oh/sS+gVYj1eEFVejTIG3OblwZI775yWeMO+5E3AY+Mbbtx+uzl4q0Dmg3ThcDb81wJ1AX+A/XnznBLQ7zYtxJe601IXAW7jiqJcHtBvu7fsj8CjudGQfb9tcL/Z7vP3HeO9PA9q09p5fHfR+7vReq3EO73sz8GqI9c/gasW9AFyAm9wtCVcXrWxAu8+BnUHrKgIHgAkhjvtn7zOtGO3fc1usgKktRXThtyTUEjcDcHXvSy0d+DSg3Ra8Sb0C1iV6+17rPS+LK345M+g1ennt/hCwToFRQe3ysn+O8WTznr/2EkuFoNdeE/Sex4VIODG44rBjAtb9C0gGaga9znTcac7M55lJaEhQuwvCJJfPApOQt24W8HVQu8XA1Bzec13vWLcHrU/w/q2Dq0Wf6bXvH7BuoLfu4oB113jrTipyiqsyrUDPaP+e26J2Os4UeWtxfw3vBUbjTr/cEtRmvrrTLJkyJwKL9x5bAnW8fbOo6je4v+rPziGGvO6fUzwn8WbTPBvX08sQkXLeKEDBVYzuHbTLEVWdGRBLKq5XFvgaFwH/BQ5kHs875pdAB29ep0CfBD3vjksEwes/DPEWRgPniEhz7/10Ac4A3gj3nj0NvMfkoPV9cZcLJgTF/h2uinvg5/EZLvEHnpK7EdiB++yCZb5WgxDbTCErl3MTY6LqSuAn3BfPVlU9GqLNCdMXq2qqGxhFRW9V5jWEnSH2/Tlgezh53T+neMK9RlngCW85iYiUUdUM7+m+EE1Sg16jDu7L+MYwr1kTd1ovU/D7qw/sU9XjQet3hTjWJ7jP4k7c6a67cElgSpjXzpQZb/DEa5nX/MLNCloz8wfv850IDBKRKkAlXC/uBXXTgAdL8R5jc4jNFAJLQqaoW6mq+Z2eODMp1AuxrR6wqID3z439uOsn/+TE0VxZAhJQbu3BXdN5Nsz2HcEvEfR8J1BdRMoHJaK6IWI7LiJvAneLyHO402EvqGpaLmIEd7o11PoLCJ1w9wQ9fweX+K4CquESesjPkd/+aNidQ2ymEFgSMqXBOtxf79fgrpMAICI9cTNVvhDQ9hgn/4Wcl/19UdXDIjIX6AAs9pFwQpmKu8dqlbp7ZPJqAe7L/Eq80XKegaGb8wZuYMMk3DWqsbl4jS24SdiaBK2fjkvK8ao6PaeDqOoCEVmPOyVXHVikIUbbeRp7j+tyEZ8pYJaETImnqukiMgx4Q1x1hXeBhsBfgQ24EWOZVgOXiMhU3F/gO1R1Rx72z48/AnOAL0XkX7ieSC3cqLmyqvpwHo83DHeD7xwReRX3hV8daAs0UdXga2snUNVpIjIPGCMitXCnxq7CJUpwSSKwfZKIfIZLWlNUdXtOAarqMRH5DugatP5HEXkWeFVEWgKzccnqNNz1ojcDr4l53sFNjS3AH7J52W5Akqpuyik+U/BsYIIpFVR1DO6v5HbAZNyd89OBs1X1cEDTe3HDpKfghlnfkcf98xPjYqAL7lTTP4BpwN+915zj43jbcCPzlgFPefG+hhsAMSOXh7kS16N6Ftcbqshv16wOhGifeU9OTgMSAk0EzhWRU4LifxT3+ff2Xnsy8BDuj4MNIY4zHndK8TjwXjavdyluqL0pAmx6b2NMnni9qpuBGt6ovMBtE3DDqJvk9pSiN0rvJ+BuVY1YHcAwr9UN+BY4XVXXF+Rrmdyx03HGmLBE5CbgVGAVUAE37Hsw8HxgAhKR7kBH4PfAH/NyTUtVf/VOvT0oIhO0YP8yfhh4xxJQ0WFJyBiTncPAUKApbrDBZtzgg+eD2s0HDuGuy4z28Tov4gZB1OfkUXsR4d2LtRRX9cEUEXY6zhhjTNTYwARjjDFRY0nIGGNM1FgSMsYYEzWWhIwxxkSNJSFjjDFRY0nIGGNM1Pw/P2B3IMfHfeQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# QDPT absorption\n",
    "\n",
    "eps2 = np.loadtxt(f'./{silicon.prefix}/epw/epsilon2_indabs_300.0K_12.dat')\n",
    "omega = eps2[:,0]                                            # omega in eV\n",
    "alpha = (omega/(float(hbar*c)*nr))*eps2[:,4]*eVtoJ/100       # alpha in cm-1\n",
    "plt.semilogy(omega,alpha,color='k',label='QDPT')             \n",
    "\n",
    "\n",
    "# Indirect absorption\n",
    "\n",
    "eps2 = np.loadtxt(f'./{silicon.prefix}/epw/epsilon2_indabs_300.0K.dat')\n",
    "omega = eps2[:,0]                                            # omega in eV\n",
    "alpha = (omega/(float(hbar*c)*nr))*eps2[:,4]*eVtoJ/100   # alpha in cm-1\n",
    "plt.semilogy(omega,alpha,color='b',label='Indirect')\n",
    "\n",
    "# Direct absorption\n",
    "\n",
    "eps2 = np.loadtxt('./si/epw/epsilon2_dirabs_300.0K.dat')\n",
    "omega = eps2[:,0]                                            # omega in eV\n",
    "alpha = (omega/(float(hbar*c)*nr))*eps2[:,4]*eVtoJ/100       # alpha in cm-1\n",
    "plt.semilogy(omega,alpha,color='r',label='Direct') \n",
    "\n",
    "plt.tick_params('x',labelsize=16)\n",
    "plt.tick_params('y',labelsize=16)\n",
    "plt.xlabel('Photon energy (eV)',fontsize=16)\n",
    "plt.ylabel('Absorption coefficient (cm$^{-1}$)',fontsize=16)\n",
    "plt.legend(fontsize=16)\n",
    "plt.axis([0, 2.3, 1e0, 1e10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cea1f2bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}