TDDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ff4a9e02",
   "metadata": {},
   "source": [
    "# Thermal Discrete Dipole Approximation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e475f19e",
   "metadata": {},
   "source": [
    "In this code, we implement the TDDA method to obtain the emissivity and emitted power of different objects."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7398f42",
   "metadata": {},
   "source": [
    "## General imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9c7a2633",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries needed to import for this example\n",
    "\n",
    "import numpy as np                    # Basic numerical python library\n",
    "import matplotlib.pyplot as plt       # Plotting library for python\n",
    "\n",
    "# Silica permittivity\n",
    "\n",
    "real_epsilon_SiO2 = np.genfromtxt('Datasets/epsilon_real_SiO2.dat') # Real part of silica permittivity\n",
    "imag_epsilon_SiO2 = np.genfromtxt('Datasets/epsilon_imag_SiO2.dat') # Imaginary part of silica permittivity\n",
    "\n",
    "wSiO2_raw = real_epsilon_SiO2[:,0]/(6.58*10**(-16)*10**14) # Sampling frequencies for silica permittivity raw, 10^14 rad/s\n",
    "eps_SiO2_raw = real_epsilon_SiO2[:,1]+1j*imag_epsilon_SiO2[:,1] # Full silica permittivity raw\n",
    "\n",
    "wSiO2 = np.linspace(0.2,3,200) # Sampling frequencies for silica permittivity, 10^14 rad/s\n",
    "eps_SiO2 = np.interp(wSiO2,wSiO2_raw,eps_SiO2_raw) # Full silica permittivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "73792aff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function definitons\n",
    "\n",
    "c = 3*10**8 \n",
    "hbar = 1.055*10**(-34)\n",
    "kB = 1.38*10**(-23)\n",
    "\n",
    "def IBB(w,T): # Planck's distribution function\n",
    "    \n",
    "    result = (w**2)/(4*np.pi**3*c**2)*(hbar*w)/(np.exp((hbar*w)/(kB*T))-1)\n",
    "    \n",
    "    return result\n",
    "\n",
    "def Gfunct(r1,r2,k): # Calculate the Green's function matrix from dipole 1 to dipole 2\n",
    "    \n",
    "    Rd = np.linalg.norm(r1-r2) # Distance between the dipoles\n",
    "    \n",
    "    GfA = (1+(1j*k*Rd-1)/(k**2*Rd**2))*np.identity(3)\n",
    "    GfB = ((3-3j*k*Rd-k**2*Rd**2)/(k**2*Rd**2))*((np.outer(r1-r2,r1-r2))/(Rd**2))\n",
    "    result = (GfA+GfB)*(np.exp(1j*k*Rd))/(4*np.pi*Rd)\n",
    "    \n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b77d131",
   "metadata": {},
   "source": [
    "## Single dipole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ee252d35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pre-allocations and constants\n",
    "\n",
    "Nw = wSiO2.shape[0] # Number of frequency points\n",
    "R = 100 # Radius of spherical dipole, in nm\n",
    "Vd = 4/3*np.pi*(R*10**(-9))**3 # Volume of the spherical dipole\n",
    "T = 300 # Temperature in Kelvin\n",
    "\n",
    "emission = np.zeros((Nw,)) # Will hold the emissivity values\n",
    "power = np.zeros((Nw,)) # Will hold the emission power values\n",
    "\n",
    "# TDDA Calculations\n",
    "\n",
    "for i in range(Nw):\n",
    "    \n",
    "    k0 = wSiO2[i]*10**14/c # K value \n",
    "    \n",
    "    a0 = 3*Vd*(eps_SiO2[i]-1)/(eps_SiO2[i]+2) # Quasistatic polarizability tensor\n",
    "    a = (a0)/(1-1j*k0**3*((a0)/(6*np.pi))) # Full polarizability tensor\n",
    "    \n",
    "    xi = np.imag(a)-(k0**3)/(6*np.pi)*np.abs(a)**2 \n",
    "    \n",
    "    emission_raw = k0*xi # Emission cross-section of the dipole\n",
    "    emission[i] = emission_raw/(np.pi*(R*10**(-9))**2) # Emissivity of the dipole\n",
    "    \n",
    "    power[i] = 4*np.pi*emission_raw*IBB(wSiO2[i]*10**14,T)\n",
    "\n",
    "plt.rcParams['font.family'] = ['Times New Roman']\n",
    "fig,axs = plt.subplots(1,1,figsize=(7,5))\n",
    "axs.plot(wSiO2,emission)\n",
    "axs.set_xlabel('Frequency (10$^{14}$ rad/s)', fontsize = 20)\n",
    "axs.set_ylabel('Emissivity', fontsize = 20)\n",
    "axs.set_yscale('log')\n",
    "axs.tick_params(axis='both', labelsize = 20)\n",
    "fig.savefig('TDDA_single_dipole.png', format='png', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ba45116",
   "metadata": {},
   "source": [
    "## Cube emissivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "095c531e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_10384/2140029071.py:77: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  emission[i] = emission_raw/((L*10**(-9))**2) # Emissivity of the cube\n",
      "/tmp/ipykernel_10384/2140029071.py:79: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  power[i] = 4*np.pi*emission_raw*IBB(wSiO2[i]*10**14,T) # Spectral power emitted by the cube\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pre-allocations and constants\n",
    "\n",
    "Nw = wSiO2.shape[0] # Number of frequency points\n",
    "T = 300 # Temperature in Kelvin\n",
    "\n",
    "N = 125 # Number of dipoles to consider\n",
    "Ns = int(np.cbrt(N)) # Number of dipoles per side\n",
    "L = 100 # Cube side in nm\n",
    "Ln = L/Ns # Side of the cubical dipoles forming the object \n",
    "Vn = (Ln*10**(-9))**3; # Dipole cube volume, in SI\n",
    "\n",
    "emission = np.zeros((Nw,)) # Will hold the emissivity values\n",
    "power = np.zeros((Nw,)) # Will hold the emission power values\n",
    "\n",
    "# TDDA calculations\n",
    "\n",
    "# Distributing the dipoles in space to form the cube\n",
    "\n",
    "posit = np.zeros((N,3)) # Holds all the positions\n",
    "\n",
    "maxim = np.floor(Ns/2) # How many dipoles until reaching the end of the sides\n",
    "\n",
    "for i in range(Ns):\n",
    "    \n",
    "    for j in range(Ns):\n",
    "        \n",
    "        for k in range(Ns):\n",
    "            \n",
    "            indexr = k + j*Ns + i*Ns**2\n",
    "            \n",
    "            posit[indexr,:] = np.array([i-maxim,j-maxim,k-maxim])\n",
    "\n",
    "posit = posit*Ln*10**(-9); # Actual locations of the cubes in SI\n",
    "\n",
    "# Calculate the emissivity and power\n",
    "\n",
    "for i in range(Nw):\n",
    "    \n",
    "    GG = np.zeros((3*N,3*N)).astype('complex'); # Pre-allocating the Green matrices\n",
    "    dG = np.zeros((3*N,3*N)).astype('complex');\n",
    "    \n",
    "    # All dipoles are cubic, isotropic and equal in size: same a0,a,xi, and are diagonal\n",
    "    \n",
    "    k0 = wSiO2[i]*10**14/c # k value\n",
    "    \n",
    "    a0 = 3*Vn*(eps_SiO2[i]-1)/(eps_SiO2[i]+2) # Quasistatic polarizability tensor\n",
    "    a = (a0)/(1-1j*k0**3*((a0)/(6*np.pi))) # Full polarizability tensor\n",
    "    \n",
    "    xi = np.imag(a)-(k0**3)/(6*np.pi)*np.abs(a)**2 \n",
    "    \n",
    "    AA = a*np.identity(3*N) # Full polarizabilty matrix\n",
    "    XXi = xi*np.identity(3*N)\n",
    "    \n",
    "    # Calculate the Green's function values\n",
    "    \n",
    "    for j in range(N):\n",
    "        \n",
    "        for k in range(N):\n",
    "            \n",
    "            index_j = np.array([[3*j,3*j,3*j],[3*j+1,3*j+1,3*j+1],[3*j+2,3*j+2,3*j+2]]).astype(int)\n",
    "            index_k = np.array([[3*k,3*k+1,3*k+2],[3*k,3*k+1,3*k+2],[3*k,3*k+1,3*k+2]]).astype(int)\n",
    "            \n",
    "            if j == k:\n",
    "                \n",
    "                GG[index_k,index_j] = ((-1)/(3*k0**2*Vn)+(1j*k0)/(6*np.pi))*np.identity(3)\n",
    "                \n",
    "            else:\n",
    "                \n",
    "                tempG = Gfunct(posit[j,:],posit[k,:],k0)\n",
    "                GG[index_k,index_j] = tempG\n",
    "                dG[index_k,index_j] = tempG\n",
    "    \n",
    "    D = np.linalg.inv(np.identity(3*N)-k0**2*np.matmul(dG,AA))\n",
    "    \n",
    "    emission_raw = 2*np.pi*np.trace(np.matmul(D,np.matmul(np.imag(GG),np.matmul(np.conjugate(np.transpose(D)),XXi))))\n",
    "    \n",
    "    emission[i] = emission_raw/((L*10**(-9))**2) # Emissivity of the cube\n",
    "    \n",
    "    power[i] = 4*np.pi*emission_raw*IBB(wSiO2[i]*10**14,T) # Spectral power emitted by the cube\n",
    "    \n",
    "    total_emiss = np.sum(power)*(wSiO2[1]-wSiO2[0])*10**14/(6*(L*10**(-9))**2*5.67*10**(-8)*T**4) # Total emissivity of the cube\n",
    "    \n",
    "plt.rcParams['font.family'] = ['Times New Roman']\n",
    "fig,axs = plt.subplots(1,1,figsize=(7,5))\n",
    "axs.plot(wSiO2,emission)\n",
    "axs.set_xlabel('Frequency (10$^{14}$ rad/s)', fontsize = 20)\n",
    "axs.set_ylabel('Emissivity', fontsize = 20)\n",
    "axs.set_yscale('log')\n",
    "axs.tick_params(axis='both', labelsize = 20)\n",
    "fig.savefig('TDDA_cube_emissivity.png', format='png', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6a02e3a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_10384/2330026781.py:85: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  emission[i] = emission_raw/((L*10**(-9))**2) # Emissivity of the cube\n",
      "/tmp/ipykernel_10384/2330026781.py:87: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  power[i] = 4*np.pi*emission_raw*IBB(wSiO2[i]*10**14,T) # Spectral power emitted by the cube\n"
     ]
    }
   ],
   "source": [
    "# Pre-allocations and constants\n",
    "\n",
    "Nw = wSiO2.shape[0] # Number of frequency points\n",
    "T = 300 # Temperature in Kelvin\n",
    "\n",
    "n_vect = np.array([1**3,3**3,5**3,7**3,9**3]) # Number of dipoles to use\n",
    "\n",
    "L = 3000 # Cube side in nm\n",
    "\n",
    "emis_to_plot = np.zeros((5,Nw))\n",
    "power_to_plot = np.zeros((5,))\n",
    "\n",
    "for ape in range(5):\n",
    "\n",
    "    N = n_vect[ape] # Number of dipoles to consider\n",
    "    Ns = int(np.cbrt(N)) # Number of dipoles per side\n",
    "    Ln = L/Ns # Side of the cubical dipoles forming the object \n",
    "    Vn = (Ln*10**(-9))**3; # Dipole cube volume, in SI\n",
    "    \n",
    "    emission = np.zeros((Nw,)) # Will hold the emissivity values\n",
    "    power = np.zeros((Nw,)) # Will hold the emission power values\n",
    "    \n",
    "    # TDDA calculations\n",
    "    \n",
    "    # Distributing the dipoles in space to form the cube\n",
    "    \n",
    "    posit = np.zeros((N,3)) # Holds all the positions\n",
    "    \n",
    "    maxim = np.floor(Ns/2) # How many dipoles until reaching the end of the sides\n",
    "    \n",
    "    for i in range(Ns):\n",
    "        \n",
    "        for j in range(Ns):\n",
    "            \n",
    "            for k in range(Ns):\n",
    "                    \n",
    "                indexr = k + j*Ns + i*Ns**2\n",
    "                \n",
    "                posit[indexr,:] = np.array([i-maxim,j-maxim,k-maxim])\n",
    "    \n",
    "    posit = posit*Ln*10**(-9); # Actual locations of the cubes in SI\n",
    "\n",
    "    # Calculate the emissivity and power\n",
    "\n",
    "    for i in range(Nw):\n",
    "    \n",
    "        GG = np.zeros((3*N,3*N)).astype('complex'); # Pre-allocating the Green matrices\n",
    "        dG = np.zeros((3*N,3*N)).astype('complex');\n",
    "    \n",
    "        # All dipoles are cubic, isotropic and equal in size: same a0,a,xi, and are diagonal\n",
    "    \n",
    "        k0 = wSiO2[i]*10**14/c # k value\n",
    "    \n",
    "        a0 = 3*Vn*(eps_SiO2[i]-1)/(eps_SiO2[i]+2) # Quasistatic polarizability tensor\n",
    "        a = (a0)/(1-1j*k0**3*((a0)/(6*np.pi))) # Full polarizability tensor\n",
    "    \n",
    "        xi = np.imag(a)-(k0**3)/(6*np.pi)*np.abs(a)**2 \n",
    "    \n",
    "        AA = a*np.identity(3*N) # Full polarizabilty matrix\n",
    "        XXi = xi*np.identity(3*N)\n",
    "    \n",
    "        # Calculate the Green's function values\n",
    "    \n",
    "        for j in range(N):\n",
    "        \n",
    "            for k in range(N):\n",
    "            \n",
    "                index_j = np.array([[3*j,3*j,3*j],[3*j+1,3*j+1,3*j+1],[3*j+2,3*j+2,3*j+2]]).astype(int)\n",
    "                index_k = np.array([[3*k,3*k+1,3*k+2],[3*k,3*k+1,3*k+2],[3*k,3*k+1,3*k+2]]).astype(int)\n",
    "            \n",
    "                if j == k:\n",
    "                \n",
    "                    GG[index_k,index_j] = ((-1)/(3*k0**2*Vn)+(1j*k0)/(6*np.pi))*np.identity(3)\n",
    "                \n",
    "                else:\n",
    "                \n",
    "                    tempG = Gfunct(posit[j,:],posit[k,:],k0)\n",
    "                    GG[index_k,index_j] = tempG\n",
    "                    dG[index_k,index_j] = tempG\n",
    "        \n",
    "        D = np.linalg.inv(np.identity(3*N)-k0**2*np.matmul(dG,AA))\n",
    "    \n",
    "        emission_raw = 2*np.pi*np.trace(np.matmul(D,np.matmul(np.imag(GG),np.matmul(np.conjugate(np.transpose(D)),XXi))))\n",
    "    \n",
    "        emission[i] = emission_raw/((L*10**(-9))**2) # Emissivity of the cube\n",
    "    \n",
    "        power[i] = 4*np.pi*emission_raw*IBB(wSiO2[i]*10**14,T) # Spectral power emitted by the cube\n",
    "    \n",
    "    total_emiss = np.sum(power)*(wSiO2[1]-wSiO2[0])*10**14/(6*(L*10**(-9))**2*5.67*10**(-8)*T**4) # Total emissivity of the cube\n",
    "    \n",
    "    emis_to_plot[ape,:] = np.reshape(emission,(1,Nw))\n",
    "    power_to_plot[ape] = total_emiss\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a49c5603",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.family'] = ['Times New Roman']\n",
    "fig,axs = plt.subplots(1,1,figsize=(7,5))\n",
    "axs.plot(np.array([1,3,5,7,9]),power_to_plot,'-o',color='black',)\n",
    "axs.set_ylabel('Total emissivity', fontsize = 20)\n",
    "axs.set_xlabel('Cubes per side', fontsize = 20)\n",
    "axs.tick_params(axis='both', labelsize = 20)\n",
    "fig.savefig('TDDA_cube_pow_conv.png', format='png', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0eed1d47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.family'] = ['Times New Roman']\n",
    "fig,axs = plt.subplots(1,1,figsize=(7,5))\n",
    "axs.plot(wSiO2,emis_to_plot[0,:],color='black')\n",
    "axs.plot(wSiO2,emis_to_plot[1,:],color='red')\n",
    "axs.plot(wSiO2,emis_to_plot[2,:],color='green')\n",
    "axs.plot(wSiO2,emis_to_plot[3,:],color='blue')\n",
    "axs.plot(wSiO2,emis_to_plot[4,:],color='purple')\n",
    "axs.set_ylabel('Emissivity', fontsize = 20)\n",
    "axs.set_xlabel('Frequency (10$^{14}$ rad/s)', fontsize = 20)\n",
    "axs.tick_params(axis='both', labelsize = 20)\n",
    "axs.legend(['1 cube/side','3 cube/side','5 cube/side','7 cube/side','9 cube/side'], loc='upper right', fontsize = 20)\n",
    "fig.savefig('TDDA_cube_emis_conv.png', format='png', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f26fc5c",
   "metadata": {},
   "source": [
    "## Sphere emissivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23e90da6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pre-allocations and constants\n",
    "\n",
    "Nw = wSiO2.shape[0] # Number of frequency points\n",
    "T = 300 # Temperature in Kelvin\n",
    "\n",
    "N = 125 # Number of dipoles to consider\n",
    "Ns = int(np.cbrt(N)) # Number of dipoles per side of the cube\n",
    "L = 10 # Cube side in nm (diameter of the sphere)\n",
    "Ln = L/Ns # Side of the cubical dipoles forming the object \n",
    "Vn = (Ln*10**(-9))**3; # Dipole cube volume, in SI\n",
    "\n",
    "emission = np.zeros((Nw,)) # Will hold the emissivity values\n",
    "power = np.zeros((Nw,)) # Will hold the emission power values\n",
    "\n",
    "# TDDA calculations\n",
    "\n",
    "# Distributing the dipoles in space to form the cube\n",
    "\n",
    "maxim = np.floor(Ns/2) # How many dipoles until reaching the end of the sides\n",
    "\n",
    "R = N/2\n",
    "\n",
    "posit = [] # Holds all the positions\n",
    "\n",
    "for i in range(Ns):\n",
    "    \n",
    "    for j in range(Ns):\n",
    "        \n",
    "        for k in range(Ns):\n",
    "            \n",
    "            vector = np.array([i-maxim,j-maxim,k-maxim])\n",
    "            \n",
    "            if np.linalg.norm(vector) <= R:\n",
    "            \n",
    "                indexr = k + j*Ns + i*Ns**2\n",
    "                posit.append(vector*Ln*10**(-9)) # Actual locations of the cubes in SI\n",
    "\n",
    "N = len(posit)\n",
    "\n",
    "# Calculate the emissivity and power\n",
    "\n",
    "for i in range(Nw):\n",
    "    \n",
    "    GG = np.zeros((3*N,3*N)).astype('complex'); # Pre-allocating the Green matrices\n",
    "    dG = np.zeros((3*N,3*N)).astype('complex');\n",
    "    \n",
    "    # All dipoles are cubic, isotropic and equal in size: same a0,a,xi, and are diagonal\n",
    "    \n",
    "    k0 = wSiO2[i]*10**14/c # k value\n",
    "    \n",
    "    a0 = 3*Vn*(eps_SiO2[i]-1)/(eps_SiO2[i]+2) # Quasistatic polarizability tensor\n",
    "    a = (a0)/(1-1j*k0**3*((a0)/(6*np.pi))) # Full polarizability tensor\n",
    "    \n",
    "    xi = np.imag(a)-(k0**3)/(6*np.pi)*np.abs(a)**2 \n",
    "    \n",
    "    AA = a*np.identity(3*N) # Full polarizabilty matrix\n",
    "    XXi = xi*np.identity(3*N)\n",
    "    \n",
    "    # Calculate the Green's function values\n",
    "    \n",
    "    for j in range(N):\n",
    "        \n",
    "        for k in range(N):\n",
    "            \n",
    "            index_j = np.array([[3*j,3*j,3*j],[3*j+1,3*j+1,3*j+1],[3*j+2,3*j+2,3*j+2]]).astype(int)\n",
    "            index_k = np.array([[3*k,3*k+1,3*k+2],[3*k,3*k+1,3*k+2],[3*k,3*k+1,3*k+2]]).astype(int)\n",
    "            \n",
    "            if j == k:\n",
    "                \n",
    "                GG[index_k,index_j] = ((-1)/(3*k0**2*Vn)+(1j*k0)/(6*np.pi))*np.identity(3)\n",
    "                \n",
    "            else:\n",
    "                \n",
    "                tempG = Gfunct(posit[j][:],posit[k][:],k0)\n",
    "                GG[index_k,index_j] = tempG\n",
    "                dG[index_k,index_j] = tempG\n",
    "    \n",
    "    D = np.linalg.inv(np.identity(3*N)-k0**2*np.matmul(dG,AA))\n",
    "    \n",
    "    emission_raw = 2*np.pi*np.trace(np.matmul(D,np.matmul(np.imag(GG),np.matmul(np.conjugate(np.transpose(D)),XXi))))\n",
    "    \n",
    "    emission[i] = emission_raw/((L*10**(-9))**2) # Emissivity of the cube\n",
    "    \n",
    "    power[i] = 4*np.pi*emission_raw*IBB(wSiO2[i]*10**14,T) # Spectral power emitted by the cube\n",
    "    \n",
    "    total_emiss = np.sum(power)*(wSiO2[1]-wSiO2[0])*10**14/(6*(L*10**(-9))**2*5.67*10**(-8)*T**4) # Total emissivity of the cube\n",
    "    \n",
    "plt.rcParams['font.family'] = ['Times New Roman']\n",
    "fig,axs = plt.subplots(1,1,figsize=(7,5))\n",
    "axs.plot(wSiO2,emission)\n",
    "axs.set_xlabel('Frequency (10$^{14}$ rad/s)', fontsize = 20)\n",
    "axs.set_ylabel('Emissivity', fontsize = 20)\n",
    "axs.set_yscale('log')\n",
    "axs.tick_params(axis='both', labelsize = 20)\n",
    "fig.savefig('TDDA_sphere_emissivity.png', format='png', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a2aba154",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_10384/2215104882.py:89: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  emission[i] = emission_raw/((L*10**(-9))**2) # Emissivity of the cube\n",
      "/tmp/ipykernel_10384/2215104882.py:91: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  power[i] = 4*np.pi*emission_raw*IBB(wSiO2[i]*10**14,T) # Spectral power emitted by the cube\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pre-allocations and constants\n",
    "\n",
    "Nw = wSiO2.shape[0] # Number of frequency points\n",
    "T = 300 # Temperature in Kelvin\n",
    "\n",
    "n_vect = np.array([1**3,3**3,5**3,7**3,9**3]) # Number of dipoles to use\n",
    "\n",
    "emis_to_plot = np.zeros((5,Nw))\n",
    "power_to_plot = np.zeros((5,))\n",
    "\n",
    "for ape in range(5):\n",
    "\n",
    "    N = n_vect[ape] # Number of dipoles to consider\n",
    "    Ns = int(np.cbrt(N)) # Number of dipoles per side\n",
    "    L = 10 # Cube side in nm\n",
    "    Ln = L/Ns # Side of the cubical dipoles forming the object \n",
    "    Vn = (Ln*10**(-9))**3; # Dipole cube volume, in SI\n",
    "    \n",
    "    emission = np.zeros((Nw,)) # Will hold the emissivity values\n",
    "    power = np.zeros((Nw,)) # Will hold the emission power values\n",
    "    \n",
    "    # TDDA calculations\n",
    "    \n",
    "    # Distributing the dipoles in space to form the cube\n",
    "    \n",
    "    maxim = np.floor(Ns/2) # How many dipoles until reaching the end of the sides\n",
    "\n",
    "    R = N/2\n",
    "\n",
    "    posit = [] # Holds all the positions\n",
    "\n",
    "    for i in range(Ns):\n",
    "    \n",
    "        for j in range(Ns):\n",
    "        \n",
    "            for k in range(Ns):\n",
    "            \n",
    "                vector = np.array([i-maxim,j-maxim,k-maxim])\n",
    "            \n",
    "                if np.linalg.norm(vector) <= R:\n",
    "            \n",
    "                    indexr = k + j*Ns + i*Ns**2\n",
    "                    posit.append(vector*Ln*10**(-9)) # Actual locations of the cubes in SI\n",
    "\n",
    "    N = len(posit)\n",
    "\n",
    "    # Calculate the emissivity and power\n",
    "\n",
    "    for i in range(Nw):\n",
    "    \n",
    "        GG = np.zeros((3*N,3*N)).astype('complex'); # Pre-allocating the Green matrices\n",
    "        dG = np.zeros((3*N,3*N)).astype('complex');\n",
    "    \n",
    "        # All dipoles are cubic, isotropic and equal in size: same a0,a,xi, and are diagonal\n",
    "    \n",
    "        k0 = wSiO2[i]*10**14/c # k value\n",
    "    \n",
    "        a0 = 3*Vn*(eps_SiO2[i]-1)/(eps_SiO2[i]+2) # Quasistatic polarizability tensor\n",
    "        a = (a0)/(1-1j*k0**3*((a0)/(6*np.pi))) # Full polarizability tensor\n",
    "    \n",
    "        xi = np.imag(a)-(k0**3)/(6*np.pi)*np.abs(a)**2 \n",
    "    \n",
    "        AA = a*np.identity(3*N) # Full polarizabilty matrix\n",
    "        XXi = xi*np.identity(3*N)\n",
    "    \n",
    "        # Calculate the Green's function values\n",
    "    \n",
    "        for j in range(N):\n",
    "        \n",
    "            for k in range(N):\n",
    "            \n",
    "                index_j = np.array([[3*j,3*j,3*j],[3*j+1,3*j+1,3*j+1],[3*j+2,3*j+2,3*j+2]]).astype(int)\n",
    "                index_k = np.array([[3*k,3*k+1,3*k+2],[3*k,3*k+1,3*k+2],[3*k,3*k+1,3*k+2]]).astype(int)\n",
    "            \n",
    "                if j == k:\n",
    "                \n",
    "                    GG[index_k,index_j] = ((-1)/(3*k0**2*Vn)+(1j*k0)/(6*np.pi))*np.identity(3)\n",
    "                \n",
    "                else:\n",
    "                \n",
    "                    tempG = Gfunct(posit[j][:],posit[k][:],k0)\n",
    "                    GG[index_k,index_j] = tempG\n",
    "                    dG[index_k,index_j] = tempG\n",
    "        \n",
    "        D = np.linalg.inv(np.identity(3*N)-k0**2*np.matmul(dG,AA))\n",
    "    \n",
    "        emission_raw = 2*np.pi*np.trace(np.matmul(D,np.matmul(np.imag(GG),np.matmul(np.conjugate(np.transpose(D)),XXi))))\n",
    "    \n",
    "        emission[i] = emission_raw/((L*10**(-9))**2) # Emissivity of the cube\n",
    "    \n",
    "        power[i] = 4*np.pi*emission_raw*IBB(wSiO2[i]*10**14,T) # Spectral power emitted by the cube\n",
    "    \n",
    "    total_emiss = np.sum(power)*(wSiO2[1]-wSiO2[0])*10**14/(6*(L*10**(-9))**2*5.67*10**(-8)*T**4) # Total emissivity of the cube\n",
    "    \n",
    "    emis_to_plot[ape,:] = np.reshape(emission,(1,Nw))\n",
    "    power_to_plot[ape] = total_emiss\n",
    "    \n",
    "Mie = np.genfromtxt('Datasets/Mie_data.dat')\n",
    "\n",
    "plt.rcParams['font.family'] = ['Times New Roman']\n",
    "fig,axs = plt.subplots(1,1,figsize=(7,5))\n",
    "axs.plot(wSiO2,emis_to_plot[0,:],color='black')\n",
    "axs.plot(wSiO2,emis_to_plot[1,:],color='red')\n",
    "axs.plot(wSiO2,emis_to_plot[2,:],color='green')\n",
    "axs.plot(wSiO2,emis_to_plot[3,:],color='blue')\n",
    "axs.plot(wSiO2,emis_to_plot[4,:],color='purple')\n",
    "axs.plot(wSiO2,Mie[0,:],color='brown')\n",
    "axs.set_ylabel('Emissivity', fontsize = 20)\n",
    "axs.set_xlabel('Frequency (10$^{14}$ rad/s)', fontsize = 20)\n",
    "axs.tick_params(axis='both', labelsize = 20)\n",
    "axs.legend(['1 cube/side','3 cube/side','5 cube/side','7 cube/side','9 cube/side','Mie result'], loc='upper left', fontsize = 20)\n",
    "fig.savefig('TDDA_sphere_emis_conv_10.png', format='png', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbd7325e",
   "metadata": {},
   "source": [
    "## Parallelepid emissivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67b836e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pre-allocations and constants\n",
    "\n",
    "Nw = wSiO2.shape[0] # Number of frequency points\n",
    "T = 300 # Temperature in Kelvin\n",
    "\n",
    "Lx = 500 # X size of object, nm\n",
    "Ly = 500 # Y size of object, nm\n",
    "Lz = 2000 # Z size of object, nm\n",
    "\n",
    "Ln = 125 # Side of the dipole cubes, must be divisor of all three sides\n",
    "Vn = (Ln*10**(-9))**3; # Dipole cube volume, in SI\n",
    "\n",
    "N = int(Lx*Ly*Lz/(Ln)**3) # Total number of dipoles\n",
    "\n",
    "Nx = int(Lx/Ln) # Number of dipoles in the x direction\n",
    "Ny = int(Ly/Ln) # Number of dipoles in the y direction\n",
    "Nz = int(Lz/Ln) # Number of dipoles in the z direction\n",
    "\n",
    "emission = np.zeros((Nw,)) # Will hold the emissivity values\n",
    "power = np.zeros((Nw,)) # Will hold the emission power values\n",
    "\n",
    "# TDDA calculations\n",
    "\n",
    "# Distributing the dipoles in space to form the cube\n",
    "\n",
    "posit = np.zeros((N,3)) # Holds all the positions\n",
    "\n",
    "maximx = np.floor(Nx/2) # How many dipoles until reaching the end of the x side\n",
    "maximy = np.floor(Ny/2) # How many dipoles until reaching the end of the y side\n",
    "maximz = np.floor(Nz/2) # How many dipoles until reaching the end of the z side\n",
    "\n",
    "for i in range(Nx):\n",
    "    \n",
    "    for j in range(Ny):\n",
    "        \n",
    "        for k in range(Nz):\n",
    "            \n",
    "            indexr = k + j*Nz + i*Nz*Ny\n",
    "            \n",
    "            posit[indexr,:] = np.array([i-maximx,j-maximy,k-maximz])\n",
    "\n",
    "posit = posit*Ln*10**(-9); # Actual locations of the cubes in SI\n",
    "\n",
    "# Calculate the emissivity and power\n",
    "\n",
    "for i in range(Nw):\n",
    "    \n",
    "    GG = np.zeros((3*N,3*N)).astype('complex'); # Pre-allocating the Green matrices\n",
    "    dG = np.zeros((3*N,3*N)).astype('complex');\n",
    "    \n",
    "    # All dipoles are cubic, isotropic and equal in size: same a0,a,xi, and are diagonal\n",
    "    \n",
    "    k0 = wSiO2[i]*10**14/c # k value\n",
    "    \n",
    "    a0 = 3*Vn*(eps_SiO2[i]-1)/(eps_SiO2[i]+2) # Quasistatic polarizability tensor\n",
    "    a = (a0)/(1-1j*k0**3*((a0)/(6*np.pi))) # Full polarizability tensor\n",
    "    \n",
    "    xi = np.imag(a)-(k0**3)/(6*np.pi)*np.abs(a)**2 \n",
    "    \n",
    "    AA = a*np.identity(3*N) # Full polarizabilty matrix\n",
    "    XXi = xi*np.identity(3*N)\n",
    "    \n",
    "    # Calculate the Green's function values\n",
    "    \n",
    "    for j in range(N):\n",
    "        \n",
    "        for k in range(N):\n",
    "            \n",
    "            index_j = np.array([[3*j,3*j,3*j],[3*j+1,3*j+1,3*j+1],[3*j+2,3*j+2,3*j+2]]).astype(int)\n",
    "            index_k = np.array([[3*k,3*k+1,3*k+2],[3*k,3*k+1,3*k+2],[3*k,3*k+1,3*k+2]]).astype(int)\n",
    "            \n",
    "            if j == k:\n",
    "                \n",
    "                GG[index_k,index_j] = ((-1)/(3*k0**2*Vn)+(1j*k0)/(6*np.pi))*np.identity(3)\n",
    "                \n",
    "            else:\n",
    "                \n",
    "                tempG = Gfunct(posit[j,:],posit[k,:],k0)\n",
    "                GG[index_k,index_j] = tempG\n",
    "                dG[index_k,index_j] = tempG\n",
    "    \n",
    "    D = np.linalg.inv(np.identity(3*N)-k0**2*np.matmul(dG,AA))\n",
    "    \n",
    "    emission_raw = 2*np.pi*np.trace(np.matmul(D,np.matmul(np.imag(GG),np.matmul(np.conjugate(np.transpose(D)),XXi))))\n",
    "    \n",
    "    emission[i] = emission_raw/((L*10**(-9))**2) # Emissivity of the cube\n",
    "    \n",
    "    power[i] = 4*np.pi*emission_raw*IBB(wSiO2[i]*10**14,T) # Spectral power emitted by the cube\n",
    "    \n",
    "    total_emiss = np.sum(power)*(wSiO2[1]-wSiO2[0])*10**14/(6*(L*10**(-9))**2*5.67*10**(-8)*T**4) # Total emissivity of the cube\n",
    "    \n",
    "plt.rcParams['font.family'] = ['Times New Roman']\n",
    "fig,axs = plt.subplots(1,1,figsize=(7,5))\n",
    "axs.plot(wSiO2,emission)\n",
    "axs.set_xlabel('Frequency (10$^{14}$ rad/s)', fontsize = 20)\n",
    "axs.set_ylabel('Emissivity', fontsize = 20)\n",
    "axs.set_yscale('log')\n",
    "axs.tick_params(axis='both', labelsize = 20)\n",
    "fig.savefig('TDDA_parallel_emissivity.png', format='png', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cabf17c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_10384/1301982726.py:93: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  emission[i] = emission_raw/((L*10**(-9))**2) # Emissivity of the cube\n",
      "/tmp/ipykernel_10384/1301982726.py:95: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  power[i] = 4*np.pi*emission_raw*IBB(wSiO2[i]*10**14,T) # Spectral power emitted by the cube\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pre-allocations and constants\n",
    "\n",
    "Nw = wSiO2.shape[0] # Number of frequency points\n",
    "T = 300 # Temperature in Kelvin\n",
    "\n",
    "Lx = 500 # X size of object, nm\n",
    "Ly = 500 # Y size of object, nm\n",
    "Lz = 1000 # Z size of object, nm\n",
    "\n",
    "Ln_vect = np.array([500,250,125,100]) # Sizes of the cubes\n",
    "\n",
    "emis_to_plot = np.zeros((4,Nw))\n",
    "power_to_plot = np.zeros((4,))\n",
    "\n",
    "for ape in range(4):\n",
    "    \n",
    "    Ln = Ln_vect[ape] # Side of the dipole cubes, must be divisor of all three sides\n",
    "    Vn = (Ln*10**(-9))**3; # Dipole cube volume, in SI\n",
    "\n",
    "    N = int(Lx*Ly*Lz/(Ln)**3) # Total number of dipoles\n",
    "\n",
    "    Nx = int(Lx/Ln) # Number of dipoles in the x direction\n",
    "    Ny = int(Ly/Ln) # Number of dipoles in the y direction\n",
    "    Nz = int(Lz/Ln) # Number of dipoles in the z direction\n",
    "    \n",
    "    emission = np.zeros((Nw,)) # Will hold the emissivity values\n",
    "    power = np.zeros((Nw,)) # Will hold the emission power values\n",
    "    \n",
    "    # TDDA calculations\n",
    "    \n",
    "    # Distributing the dipoles in space to form the cube\n",
    "    \n",
    "    posit = np.zeros((N,3)) # Holds all the positions\n",
    "\n",
    "    maximx = np.floor(Nx/2) # How many dipoles until reaching the end of the x side\n",
    "    maximy = np.floor(Ny/2) # How many dipoles until reaching the end of the y side\n",
    "    maximz = np.floor(Nz/2) # How many dipoles until reaching the end of the z side\n",
    "\n",
    "    for i in range(Nx):\n",
    "    \n",
    "        for j in range(Ny):\n",
    "        \n",
    "            for k in range(Nz):\n",
    "            \n",
    "                indexr = k + j*Nz + i*Nz*Ny\n",
    "            \n",
    "                posit[indexr,:] = np.array([i-maximx,j-maximy,k-maximz])\n",
    "\n",
    "    posit = posit*Ln*10**(-9); # Actual locations of the cubes in SI\n",
    "\n",
    "    # Calculate the emissivity and power\n",
    "\n",
    "    for i in range(Nw):\n",
    "    \n",
    "        GG = np.zeros((3*N,3*N)).astype('complex'); # Pre-allocating the Green matrices\n",
    "        dG = np.zeros((3*N,3*N)).astype('complex');\n",
    "    \n",
    "        # All dipoles are cubic, isotropic and equal in size: same a0,a,xi, and are diagonal\n",
    "    \n",
    "        k0 = wSiO2[i]*10**14/c # k value\n",
    "    \n",
    "        a0 = 3*Vn*(eps_SiO2[i]-1)/(eps_SiO2[i]+2) # Quasistatic polarizability tensor\n",
    "        a = (a0)/(1-1j*k0**3*((a0)/(6*np.pi))) # Full polarizability tensor\n",
    "    \n",
    "        xi = np.imag(a)-(k0**3)/(6*np.pi)*np.abs(a)**2 \n",
    "    \n",
    "        AA = a*np.identity(3*N) # Full polarizabilty matrix\n",
    "        XXi = xi*np.identity(3*N)\n",
    "    \n",
    "        # Calculate the Green's function values\n",
    "    \n",
    "        for j in range(N):\n",
    "        \n",
    "            for k in range(N):\n",
    "            \n",
    "                index_j = np.array([[3*j,3*j,3*j],[3*j+1,3*j+1,3*j+1],[3*j+2,3*j+2,3*j+2]]).astype(int)\n",
    "                index_k = np.array([[3*k,3*k+1,3*k+2],[3*k,3*k+1,3*k+2],[3*k,3*k+1,3*k+2]]).astype(int)\n",
    "            \n",
    "                if j == k:\n",
    "                \n",
    "                    GG[index_k,index_j] = ((-1)/(3*k0**2*Vn)+(1j*k0)/(6*np.pi))*np.identity(3)\n",
    "                \n",
    "                else:\n",
    "                \n",
    "                    tempG = Gfunct(posit[j,:],posit[k,:],k0)\n",
    "                    GG[index_k,index_j] = tempG\n",
    "                    dG[index_k,index_j] = tempG\n",
    "        \n",
    "        D = np.linalg.inv(np.identity(3*N)-k0**2*np.matmul(dG,AA))\n",
    "    \n",
    "        emission_raw = 2*np.pi*np.trace(np.matmul(D,np.matmul(np.imag(GG),np.matmul(np.conjugate(np.transpose(D)),XXi))))\n",
    "    \n",
    "        emission[i] = emission_raw/((L*10**(-9))**2) # Emissivity of the cube\n",
    "    \n",
    "        power[i] = 4*np.pi*emission_raw*IBB(wSiO2[i]*10**14,T) # Spectral power emitted by the cube\n",
    "    \n",
    "    total_emiss = np.sum(power)*(wSiO2[1]-wSiO2[0])*10**14/((2*Lx*10**(-9)*Ly*10**(-9)+4*Lx*10**(-9)*Lz*10**(-9))*5.67*10**(-8)*T**4) # Total emissivity of the cube\n",
    "    \n",
    "    emis_to_plot[ape,:] = np.reshape(emission,(1,Nw))\n",
    "    power_to_plot[ape] = total_emiss\n",
    "\n",
    "plt.rcParams['font.family'] = ['Times New Roman']\n",
    "fig,axs = plt.subplots(1,1,figsize=(7,5))\n",
    "axs.plot(wSiO2,emis_to_plot[0,:],color='black')\n",
    "axs.plot(wSiO2,emis_to_plot[1,:],color='red')\n",
    "axs.plot(wSiO2,emis_to_plot[2,:],color='green')\n",
    "axs.plot(wSiO2,emis_to_plot[3,:],color='blue')\n",
    "axs.set_ylabel('Emissivity', fontsize = 20)\n",
    "axs.set_xlabel('Frequency (10$^{14}$ rad/s)', fontsize = 20)\n",
    "axs.tick_params(axis='both', labelsize = 20)\n",
    "axs.legend(['1 cube/side','2 cube/side','4 cube/side','5 cube/side'], loc='upper left', fontsize = 20)\n",
    "fig.savefig('TDDA_paral_emis_conv.png', format='png', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d35e578f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_6811/1039342014.py:94: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  emission[i] = emission_raw/((Lx*10**(-9)*Lz*10**(-9))) # Emissivity of the cube\n",
      "/tmp/ipykernel_6811/1039342014.py:96: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  power[i] = 4*np.pi*emission_raw*IBB(wSiO2[i]*10**14,T) # Spectral power emitted by the cube\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pre-allocations and constants\n",
    "\n",
    "Nw = wSiO2.shape[0] # Number of frequency points\n",
    "T = 300 # Temperature in Kelvin\n",
    "\n",
    "Lx = 500 # X size of object, nm\n",
    "Ly = 500 # Y size of object, nm\n",
    "\n",
    "Lz_vect = np.array([1000,1500,2000,2500,3000,3500,4000]) # Z sizes of parals\n",
    "\n",
    "Ln = 100 # Side of the dipole cubes, must be divisor of all three sides\n",
    "Vn = (Ln*10**(-9))**3; # Dipole cube volume, in SI\n",
    "\n",
    "emis_to_plot = np.zeros((7,Nw))\n",
    "power_to_plot = np.zeros((7,))\n",
    "\n",
    "for ape in range(7):\n",
    "    \n",
    "    Lz = Lz_vect[ape] # Z size of object, nm\n",
    "    \n",
    "    N = int(Lx*Ly*Lz/(Ln)**3) # Total number of dipoles\n",
    "\n",
    "    Nx = int(Lx/Ln) # Number of dipoles in the x direction\n",
    "    Ny = int(Ly/Ln) # Number of dipoles in the y direction\n",
    "    Nz = int(Lz/Ln) # Number of dipoles in the z direction\n",
    "    \n",
    "    emission = np.zeros((Nw,)) # Will hold the emissivity values\n",
    "    power = np.zeros((Nw,)) # Will hold the emission power values\n",
    "    \n",
    "    # TDDA calculations\n",
    "    \n",
    "    # Distributing the dipoles in space to form the cube\n",
    "    \n",
    "    posit = np.zeros((N,3)) # Holds all the positions\n",
    "\n",
    "    maximx = np.floor(Nx/2) # How many dipoles until reaching the end of the x side\n",
    "    maximy = np.floor(Ny/2) # How many dipoles until reaching the end of the y side\n",
    "    maximz = np.floor(Nz/2) # How many dipoles until reaching the end of the z side\n",
    "\n",
    "    for i in range(Nx):\n",
    "    \n",
    "        for j in range(Ny):\n",
    "        \n",
    "            for k in range(Nz):\n",
    "            \n",
    "                indexr = k + j*Nz + i*Nz*Ny\n",
    "            \n",
    "                posit[indexr,:] = np.array([i-maximx,j-maximy,k-maximz])\n",
    "\n",
    "    posit = posit*Ln*10**(-9); # Actual locations of the cubes in SI\n",
    "\n",
    "    # Calculate the emissivity and power\n",
    "\n",
    "    for i in range(Nw):\n",
    "    \n",
    "        GG = np.zeros((3*N,3*N)).astype('complex'); # Pre-allocating the Green matrices\n",
    "        dG = np.zeros((3*N,3*N)).astype('complex');\n",
    "    \n",
    "        # All dipoles are cubic, isotropic and equal in size: same a0,a,xi, and are diagonal\n",
    "    \n",
    "        k0 = wSiO2[i]*10**14/c # k value\n",
    "    \n",
    "        a0 = 3*Vn*(eps_SiO2[i]-1)/(eps_SiO2[i]+2) # Quasistatic polarizability tensor\n",
    "        a = (a0)/(1-1j*k0**3*((a0)/(6*np.pi))) # Full polarizability tensor\n",
    "    \n",
    "        xi = np.imag(a)-(k0**3)/(6*np.pi)*np.abs(a)**2 \n",
    "    \n",
    "        AA = a*np.identity(3*N) # Full polarizabilty matrix\n",
    "        XXi = xi*np.identity(3*N)\n",
    "    \n",
    "        # Calculate the Green's function values\n",
    "    \n",
    "        for j in range(N):\n",
    "        \n",
    "            for k in range(N):\n",
    "            \n",
    "                index_j = np.array([[3*j,3*j,3*j],[3*j+1,3*j+1,3*j+1],[3*j+2,3*j+2,3*j+2]]).astype(int)\n",
    "                index_k = np.array([[3*k,3*k+1,3*k+2],[3*k,3*k+1,3*k+2],[3*k,3*k+1,3*k+2]]).astype(int)\n",
    "            \n",
    "                if j == k:\n",
    "                \n",
    "                    GG[index_k,index_j] = ((-1)/(3*k0**2*Vn)+(1j*k0)/(6*np.pi))*np.identity(3)\n",
    "                \n",
    "                else:\n",
    "                \n",
    "                    tempG = Gfunct(posit[j,:],posit[k,:],k0)\n",
    "                    GG[index_k,index_j] = tempG\n",
    "                    dG[index_k,index_j] = tempG\n",
    "        \n",
    "        D = np.linalg.inv(np.identity(3*N)-k0**2*np.matmul(dG,AA))\n",
    "    \n",
    "        emission_raw = 2*np.pi*np.trace(np.matmul(D,np.matmul(np.imag(GG),np.matmul(np.conjugate(np.transpose(D)),XXi))))\n",
    "    \n",
    "        emission[i] = emission_raw/((Lx*10**(-9)*Lz*10**(-9))) # Emissivity of the cube\n",
    "    \n",
    "        power[i] = 4*np.pi*emission_raw*IBB(wSiO2[i]*10**14,T) # Spectral power emitted by the cube\n",
    "    \n",
    "    total_emiss = np.sum(power)*(wSiO2[1]-wSiO2[0])*10**14/((2*Lx*10**(-9)*Ly*10**(-9)+4*Lx*10**(-9)*Lz*10**(-9))*5.67*10**(-8)*T**4) # Total emissivity of the cube\n",
    "    \n",
    "    emis_to_plot[ape,:] = np.reshape(emission,(1,Nw))\n",
    "    power_to_plot[ape] = total_emiss\n",
    "\n",
    "plt.rcParams['font.family'] = ['Times New Roman']\n",
    "fig,axs = plt.subplots(1,1,figsize=(7,5))\n",
    "axs.plot(Lz_vect/1000,power_to_plot,'-o',color='black')\n",
    "axs.set_ylabel('Total emissivity', fontsize = 20)\n",
    "axs.set_xlabel('Length of object ($\\mu$m)', fontsize = 20)\n",
    "axs.tick_params(axis='both', labelsize = 20)\n",
    "fig.savefig('TDDA_paral_emis_long.png', format='png', dpi=600)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}