Coating_Generator.py

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4027ae44",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#sys.path.append(\"/Users/simon/Dropbox/Python/pycrime-master/\")\n",
    "#import sys\n",
    "\n",
    "#from thermal_noise_hong import getCoatBrownian\n",
    "from deap import base, creator, tools\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import numpy as np\n",
    "import random\n",
    "import os \n",
    "import pocal \n",
    "from gwinc import * \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "63e91295",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_coating_stack(lambda_):\n",
    "    \"\"\"\n",
    "    Randomly generates a coating stack with paired layers and writes to a text file.\n",
    "\n",
    "    :return: n_input, dOpt\n",
    "    \"\"\"\n",
    "    # Generate a random integer between 2 and 4 to determine the number of materials\n",
    "    num_materials = random.randint(2, 4)\n",
    "\n",
    "    # Generate the specified number of random refractive indices between 1 and 4\n",
    "    n_input = sorted([random.uniform(1, 4) for _ in range(num_materials)])\n",
    "\n",
    "    # Generate a random integer between 1 and 5 to determine the number of pairs\n",
    "    num_pairs = random.randint(2, 50)\n",
    "\n",
    "    # Generate the specified number of pairs with maximum contrast\n",
    "    dOpt = []\n",
    "    for _ in range(num_pairs):\n",
    "        # Add a pair with maximum contrast\n",
    "        # Using 1 and num_materials as material numbers\n",
    "        dOpt.extend([1, num_materials])\n",
    "\n",
    "    # Normalize dOpt\n",
    "    unique_dOpt = np.unique(dOpt)\n",
    "    mapping = {val: i+1 for i, val in enumerate(unique_dOpt)}\n",
    "    dOpt = [mapping[val] for val in dOpt]\n",
    "\n",
    "    # Filter n_input to only include values corresponding to materials in dOpt\n",
    "    n_input = [n_input[i-1] for i in unique_dOpt]\n",
    "     # Calculate individual physical thickness for each material\n",
    "    d_physical = lambda_ / (4 * np.array(n_input))\n",
    "\n",
    "    # Arrays for each layer\n",
    "    n_layers = np.array(n_input)[np.array(dOpt) - 1]\n",
    "    material_kind = dOpt\n",
    "    d_physical_layers = np.array(d_physical)[np.array(dOpt) - 1]*1E6\n",
    "\n",
    "    \n",
    "    # Check if file exists and create a new filename with an increasing number\n",
    "    counter = 1\n",
    "    filename = 'generated_coating_01.txt'\n",
    "    while os.path.exists(filename):\n",
    "        filename = f'generated_coating_{counter:02d}.txt'\n",
    "        counter += 1\n",
    "\n",
    "    # Write to text file\n",
    "    with open(filename, 'w') as file:\n",
    "        for i in dOpt:\n",
    "            file.write(f\"material_{i}\\t {d_physical_layers[i-1]:.2f}\\n\")\n",
    "\n",
    "    return n_input, dOpt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "454a6402",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: Line magic function `%%writefile` not found.\n"
     ]
    }
   ],
   "source": [
    "def thin_film_stack(n_input, dOpt, lambda_):\n",
    "    \"\"\"\n",
    "    Generates and plots a thin film coating stack.\n",
    "\n",
    "    :param n_input: Array of refractive indices for each material.\n",
    "    :param dOpt: Array specifying the material for each layer.\n",
    "    :param lambda_: Wavelength.\n",
    "    :return: n_layers, material_kind, d_physical_layers\n",
    "    \"\"\"\n",
    "    if len(n_input) < max(dOpt):\n",
    "        raise ValueError('The number of refractive indices provided is less than the required materials in dOpt.')\n",
    "\n",
    "    # Calculate individual physical thickness for each material\n",
    "    d_physical = lambda_ / (4 * np.array(n_input))\n",
    "\n",
    "    # Arrays for each layer\n",
    "    n_layers = np.array(n_input)[np.array(dOpt) - 1]\n",
    "    material_kind = dOpt\n",
    "    d_physical_layers = np.array(d_physical)[np.array(dOpt) - 1]\n",
    "\n",
    "\n",
    "    # Plotting the thin film stack\n",
    "    unique_materials = list(set(dOpt))\n",
    "    colors = plt.cm.viridis(np.linspace(0, 1, len(unique_materials)))  # generate distinct colors for materials\n",
    "\n",
    "    # Check if the file exists to write headers\n",
    "    file_exists = os.path.exists('coating_metadata.txt')\n",
    "\n",
    "    # Write metadata to text file\n",
    "    with open('coating_metadata.txt', 'a') as file:\n",
    "        if not file_exists:\n",
    "            file.write(\"Lambda (m)\\tTotal Layers\\t\")\n",
    "            for i in range(1, 7):\n",
    "                file.write(f\"Material_{i}\\tNo. Layers_{i}\\tPhysical Thickness_{i} (m)\\tRefractive Index_{i}\\t\")\n",
    "            file.write(\"\\n\")\n",
    "        \n",
    "        file.write(f\"{lambda_:.2e}\\t{len(dOpt)}\\t\")\n",
    "        for i in range(1, 7):\n",
    "            if i in dOpt:\n",
    "                file.write(f\"Material {i}\\t\")\n",
    "                file.write(f\"{len([x for x in dOpt if x == i])}\\t\")\n",
    "                file.write(f\"{sum(d_physical_layers[dOpt == i]):.2e}\\t\")\n",
    "                file.write(f\"{n_input[i-1]:.2f}\\t\")  # Directly access the refractive index from n_input\n",
    "            else:\n",
    "                file.write(\"NaN\\tNaN\\tNaN\\tNaN\\t\")\n",
    "        file.write(\"\\n\")\n",
    "    \n",
    "    plt.figure(figsize=(10, 8))\n",
    "    plt.subplot(2, 1, 1)\n",
    "    plt.grid(True)\n",
    "    depth_so_far = 0  # To keep track of where to plot the next bar\n",
    "    for i in range(len(dOpt)):\n",
    "        material_idx = dOpt[i]\n",
    "        color_idx = unique_materials.index(material_idx)\n",
    "        plt.bar(depth_so_far + d_physical_layers[i] / 2, d_physical_layers[i], color=colors[color_idx],\n",
    "                width=d_physical_layers[i])\n",
    "        depth_so_far += d_physical_layers[i]\n",
    "\n",
    "    plt.xlim([0, sum(d_physical_layers) * 1.01])\n",
    "    plt.ylabel('Physical Thickness [nm]')\n",
    "    plt.xlabel('Layer Position')\n",
    "    plt.title('Generated Stack')\n",
    "    legend_str = ['n = ' + str(n) for n in n_input]\n",
    "    plt.grid(False)\n",
    "    plt.legend(legend_str)\n",
    "\n",
    "    # Additional code for plotting the normalized electric field intensity squared can be added here\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "   # Printing coating properties\n",
    "    print(\"\\nCoating Properties:\\n\")\n",
    "    print(f\"\\nLaser Wavelength:\\t\\t{lambda_*1E9:.2f} nm\")\n",
    "    print(f\"Number of Materials:\\t\\t{len(unique_materials):d}\")\n",
    "    print(f\"Total Physical Thickness:\\t{sum(d_physical_layers):.2e} m\")\n",
    "\n",
    "    \n",
    "    for i in unique_materials:\n",
    "        print(f\"\\n--------- Material {i} -------------\\n\")\n",
    "        dOpt_array = np.array(dOpt)\n",
    "        matching_indices = dOpt_array == i\n",
    "        n_layers_matching = n_layers[matching_indices]\n",
    "        d_physical_layers_matching = d_physical_layers[matching_indices]\n",
    "\n",
    "        if len(n_layers_matching) > 0:\n",
    "            print(f\"No. Layers:\\t\\t\\t{len(n_layers_matching)}\")\n",
    "            print(f\"Total Physical Thickness:\\t{sum(d_physical_layers_matching):.2e} m\")\n",
    "            print(f\"Refractive Index:\\t\\t{np.unique(n_layers_matching)[0]:.2f}\")\n",
    "        else:\n",
    "            print(f\"No layers of material {i} found.\")\n",
    "\n",
    "    return n_layers, material_kind, d_physical_layers\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "00c258e5-39b3-4fdf-8823-5bf0b8aeee0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Generating Coating Stacks from Random values \n",
    "\n",
    "#lambda_ = 1064e-9  # Wavelength in meters\n",
    "#n_input, dOpt = generate_coating_stack(lambda_)\n",
    "#n_input = np.round(n_input,decimals =2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f1783ad-b730-47b3-9743-f006495acdd8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "bce9a6b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#functions used to Calculate Coating Thermal Noise \n",
    "# not to be used to calculate optical properties \n",
    "\n",
    "def getCoatRefl2(nIn, nOut, nLayer, dOpt):\n",
    "    # Vector of all refractive indices\n",
    "    nAll = np.concatenate(([nIn], nLayer, [nOut]))\n",
    "    \n",
    "    # Reflectivity of each interface\n",
    "    r = (nAll[:-1] - nAll[1:]) / (nAll[:-1] + nAll[1:])\n",
    "    \n",
    "    # Combine reflectivities\n",
    "    rbar = np.zeros_like(r, dtype=complex)\n",
    "    ephi = np.zeros_like(r, dtype=complex)\n",
    "    \n",
    "    ephi[-1] = np.exp(-4j * np.pi * dOpt[-1])\n",
    "    rbar[-1] = ephi[-1] * r[-1]\n",
    "    \n",
    "    for n in range(len(dOpt)-1, -1, -1):\n",
    "        # Round-trip phase in this layer\n",
    "        ephi[n] = np.exp(-4j * np.pi * dOpt[n - 1]) if n > 0 else 1\n",
    "        \n",
    "        # Accumulate reflectivity\n",
    "        rbar[n] = ephi[n] * (r[n] + rbar[n + 1]) / (1 + r[n] * rbar[n + 1])\n",
    "    \n",
    "    # Reflectivity derivatives\n",
    "    dr_dphi = np.zeros_like(dOpt, dtype=complex)\n",
    "    \n",
    "    for n in range(len(dOpt)-1, -1, -1):\n",
    "        dr_dphi[n] = -1j * rbar[n + 1]\n",
    "        for m in range(n, -1, -1):\n",
    "            dr_dphi[n] = dr_dphi[n] * ephi[m] * (1 - r[m]**2) / (1 + r[m] * rbar[m + 1])**2\n",
    "    \n",
    "    # Shift rbar index\n",
    "    rCoat = rbar[0]\n",
    "    rbar = rbar[1:]\n",
    "    \n",
    "    # Phase derivatives\n",
    "    dcdp = np.imag(dr_dphi / rCoat)\n",
    "    \n",
    "    return rCoat, dcdp, rbar, r\n",
    "\n",
    "\n",
    "def getCoatAbsorption(lambda_, dOpt, aLayer, nLayer, rbar, r):\n",
    "    \"\"\"\n",
    "    Returns coating absorption as a function of depth.\n",
    "\n",
    "    Parameters:\n",
    "    - lambda_ : wavelength\n",
    "    - dOpt : optical thickness/lambda of each layer\n",
    "             = geometrical thickness * refractive index/lambda\n",
    "    - aLayer : absorption per unit length in each layer\n",
    "    - nLayer : refractive index of each layer, ordered input to output (N x 1)\n",
    "    - rbar : amplitude reflectivity of coating from this layer down\n",
    "    - r : amplitude reflectivity of this interface (r[0] is nIn to nLayer[0])\n",
    "\n",
    "    Returns:\n",
    "    - rho : power ratio in each layer\n",
    "    - absLayer : absorption contribution from each layer\n",
    "    - absCoat : coating total absorption = sum(absLayer)\n",
    "    \"\"\"\n",
    "    \n",
    "    # Power in each layer\n",
    "    powerLayer = np.cumprod(np.abs((1 - r[:-1]**2) / (1 + r[:-1] * rbar)**2))\n",
    "    \n",
    "    # One-way phases in each layer\n",
    "    phi = 2 * np.pi * dOpt\n",
    "    \n",
    "    # Average E-field squared in each layer\n",
    "    rho = (1 + np.abs(rbar)**2) + 2 * (np.sin(phi) / phi) * np.real(rbar * np.exp(1j * phi))\n",
    "    \n",
    "    # Geometrical thickness of each layer\n",
    "    dGeo = lambda_ * dOpt / nLayer\n",
    "    \n",
    "    # Compute power weighting for each layer\n",
    "    absLayer = aLayer * rho * powerLayer * dGeo\n",
    "    \n",
    "    # Total coating absorption\n",
    "    absCoat = np.sum(absLayer)\n",
    "    \n",
    "    return absCoat, absLayer, powerLayer, rho\n",
    "\n",
    "\n",
    "def getCoatNoise2(f, lambda_, wBeam, Temp, materialParams, materialSub, materialLayer, dOpt, dcdp):\n",
    "    \"\"\"\n",
    "    Returns coating noise as a function of depth.\n",
    "\n",
    "    Parameters:\n",
    "    - f : frequency\n",
    "    - lambda_ : wavelength\n",
    "    - wBeam : beam width\n",
    "    - Temp : temperatur\n",
    "    - materialParams : dictionary containing material properties\n",
    "    - materialSub : substrate material\n",
    "    - materialLayer : list of layer materials\n",
    "    - dOpt : optical thickness / lambda of each layer\n",
    "    - dcdp : phase derivatives\n",
    "\n",
    "    Returns:\n",
    "    - SbrZ, StoZ, SteZ, StrZ, brLayer\n",
    "    \"\"\"\n",
    "    \n",
    "    # Boltzmann constant and temperature\n",
    "    kBT = 1.3807e-23 * Temp\n",
    "    \n",
    "    # Angular frequency\n",
    "    w = 2 * np.pi * f\n",
    "    \n",
    "    # Substrate properties\n",
    "    alphaSub = materialParams[materialSub]['alpha']\n",
    "    cSub = materialParams[materialSub]['C']\n",
    "    kappaSub = materialParams[materialSub]['kappa']\n",
    "    ySub = materialParams[materialSub]['Y']\n",
    "    pratSub = materialParams[materialSub]['prat']\n",
    "    \n",
    "    # Initialize vectors of material properties\n",
    "    nN = np.zeros_like(dOpt)\n",
    "    aN = np.zeros_like(dOpt)\n",
    "    alphaN = np.zeros_like(dOpt)\n",
    "    betaN = np.zeros_like(dOpt)\n",
    "    kappaN = np.zeros_like(dOpt)\n",
    "    cN = np.zeros_like(dOpt)\n",
    "    yN = np.zeros_like(dOpt)\n",
    "    pratN = np.zeros_like(dOpt)\n",
    "    phiN = np.zeros_like(dOpt)\n",
    "    \n",
    "    for n, mat in enumerate(materialLayer):\n",
    "        nN[n] = materialParams[mat]['n']\n",
    "        aN[n] = materialParams[mat]['a']\n",
    "        alphaN[n] = materialParams[mat]['alpha']\n",
    "        betaN[n] = materialParams[mat]['beta']\n",
    "        kappaN[n] = materialParams[mat]['kappa']\n",
    "        cN[n] = materialParams[mat]['C']\n",
    "        yN[n] = materialParams[mat]['Y']\n",
    "        pratN[n] = materialParams[mat]['prat']\n",
    "        phiN[n] = materialParams[mat]['phiM']\n",
    "    \n",
    "    # Geometrical thickness of each layer and total\n",
    "    dGeo = lambda_ * dOpt / nN\n",
    "    dCoat = np.sum(dGeo)\n",
    "    \n",
    "    # Brownian\n",
    "    brLayer = ((1 + nN * dcdp / 2)**2 * (ySub / yN) + \n",
    "               (1 - pratSub - 2 * pratSub**2)**2 * yN / \n",
    "               ((1 + pratN)**2 * (1 - 2 * pratN) * ySub)) / (1 - pratN) * ((1 - pratN - 2 * pratN**2)) / ((1 - pratSub - 2 * pratSub**2))\n",
    "    \n",
    "    SbrZ = (4 * kBT / (np.pi * wBeam**2 * w)) * np.sum(dGeo * brLayer * phiN * (1 - pratSub - 2 * pratSub**2) / ySub)\n",
    "    \n",
    "    # Thermo-optic\n",
    "    alphaBarSub = 2 * (1 + pratSub) * alphaSub\n",
    "    \n",
    "    alphaBar = (dGeo / dCoat) * ((1 + pratSub) / (1 - pratN)) * ((1 + pratN) / (1 + pratSub) + (1 - 2 * pratSub) * yN / ySub) * alphaN\n",
    "    \n",
    "    betaBar = (-dcdp) * dOpt * (betaN / nN + alphaN * (1 + pratN) / (1 - pratN))\n",
    "    \n",
    "    # Thermo-elastic\n",
    "    SteZ = (4 * kBT * Temp / (np.pi * wBeam**2 * np.sqrt(2 * kappaSub * cSub * w))) * (np.sum(alphaBar * dCoat) - alphaBarSub * np.sum(dGeo * cN) / cSub)**2\n",
    "    \n",
    "    # Thermo-refractive\n",
    "    StrZ = (4 * kBT * Temp / (np.pi * wBeam**2 * np.sqrt(2 * kappaSub * cSub * w))) * np.sum(betaBar * lambda_)**2\n",
    "    \n",
    "    # Total thermo-optic\n",
    "    StoZ = (4 * kBT * Temp / (np.pi * wBeam**2 * np.sqrt(2 * kappaSub * cSub * w))) * (np.sum(alphaBar * dCoat) - np.sum(betaBar * lambda_) - alphaBarSub * np.sum(dGeo * cN) / cSub)**2\n",
    "    \n",
    "    return SbrZ, StoZ, SteZ, StrZ, brLayer\n",
    "\n",
    "def coating_properties(dOpt, materialLayer, materialParams, materialSub=1, lambda_=1, f=1, wBeam=1, Temp=1):\n",
    "    # Set seaborn style and viridis color palette\n",
    "    sns.set_style(\"whitegrid\")\n",
    "    sns.set_palette(\"tab10\")\n",
    "\n",
    "    # Extract substrate properties\n",
    "    nSub = materialParams[materialSub]['n']\n",
    "    ySub = materialParams[materialSub]['Y']\n",
    "    pratSub = materialParams[materialSub]['prat']\n",
    "\n",
    "    # Initialize vectors of material properties\n",
    "   # Initialize vectors of material properties\n",
    "    nLayer = np.zeros(len(materialLayer))\n",
    "    aLayer = np.zeros(len(materialLayer))\n",
    "    \n",
    "    for n, mat in enumerate(materialLayer):\n",
    "        nLayer[n] = materialParams[mat]['n']\n",
    "        aLayer[n] = materialParams[mat]['a']\n",
    "    \n",
    "\n",
    "    # Compute reflectivities\n",
    "    rCoat, dcdp, rbar, r = getCoatRefl2(1, nSub, nLayer, dOpt)\n",
    "\n",
    "    # Compute absorption\n",
    "    absCoat, absLayer, powerLayer, rho = getCoatAbsorption(lambda_, dOpt, aLayer, nLayer, rbar, r)\n",
    "\n",
    "    # Compute brownian and thermo-optic noises\n",
    "    SbrZ, StoZ, SteZ, StrZ, brLayer = getCoatNoise2(f, lambda_, wBeam, Temp, materialParams, materialSub, materialLayer, dOpt, dcdp)\n",
    "\n",
    "    # Plotting\n",
    "    # Absorption values\n",
    "    plt.figure()\n",
    "    plt.semilogy(rho,'o')\n",
    "    plt.semilogy(powerLayer,'o')\n",
    "    plt.semilogy(rho * powerLayer)\n",
    "    plt.legend(['rho_j', 'P_j / P_0', 'rho_bar_j'])\n",
    "    plt.xlabel('Layer number')\n",
    "\n",
    "    # Noise weights for each layer\n",
    "    plt.figure()\n",
    "    materials = np.unique(materialLayer)\n",
    "    # Get a list of colors from the Seaborn viridis palette\n",
    "    colors = sns.color_palette(\"viridis\", n_colors=len(materials) + 2)  # +2 for the two additional plots\n",
    "\n",
    "\n",
    "    for idx, i in enumerate(materials):\n",
    "        matidx = np.where(materialLayer == i)[0]  # Extract the array from the tuple\n",
    "        plt.bar(matidx, nLayer[matidx], color=colors[idx], label=materialParams[i]['name'])\n",
    "       # Use the next color in the palette for the following plots\n",
    "    plt.plot(-dcdp, 'o', color=colors[-2], markersize=10, label='-dphi_c / dphi_j')\n",
    "    plt.plot(brLayer, 'o', color=colors[-1], markersize=10, label='b_j')\n",
    "    plt.xlabel('Layer number')\n",
    "    plt.legend()\n",
    "\n",
    "    \n",
    "    # Noise plots\n",
    "    plt.figure()\n",
    "    plt.loglog(f, np.sqrt(SbrZ), '--')\n",
    "    plt.loglog(f, np.sqrt(StoZ))\n",
    "    plt.loglog(f, np.sqrt(SteZ), '-.')\n",
    "    plt.loglog(f, np.sqrt(StrZ), '-.')\n",
    "    plt.legend(['Brownian Noise', 'Thermo-optic Noise', 'TE Component', 'TR Component'])\n",
    "    plt.xlabel('Frequency [Hz]')\n",
    "    plt.ylabel('Thermal noise [m/sqrt(Hz)]')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "    # Return Noise Summary\n",
    "    noise_summary = {\n",
    "        'Frequency': f,\n",
    "        'BrownianNoise': np.sqrt(SbrZ),\n",
    "        'ThermoOptic': np.sqrt(StoZ),\n",
    "        'ThermoElastic': np.sqrt(SteZ),\n",
    "        'ThermoRefractive': np.sqrt(StrZ)\n",
    "    }\n",
    "\n",
    "    return noise_summary\n",
    "\n"
   ]
  },
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     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#set up interferometer parameters \n",
    "\n",
    "wBeam = 0.062;               # 6cm beam for aLIGO \n",
    "lambda_ = 1064e-9;           # laser wavelength\n",
    "\n",
    "Temp = 293;                  # temperature - Room temperature \n",
    "f = np.logspace(0, 3, 100);  # frequencies for plotting\n",
    "\n",
    "## set up aLIGO Coating Stack to benchmark functions \n",
    "num21 = 19  # number of 1-2 doublets (includes cap)\n",
    "num34 = 0   # number of 3-4 doublets\n",
    "\n",
    "# Using list comprehension to replicate the behavior of MATLAB's repmat\n",
    "materialLayer = [1, 2] * num21 + [1, 2] * num34\n",
    "materialLayer = np.array(materialLayer)\n",
    "\n",
    "dOpt =np.ones(len(materialLayer))*0.25 \n",
    "dOpt =np.array(dOpt)\n",
    "\n",
    "\n",
    "materialParams = {\n",
    "    1: {\n",
    "        'name': 'SiO2',\n",
    "        'n': 1.44,\n",
    "        'a': 0,\n",
    "        'alpha': 0.51e-6,\n",
    "        'beta': 8e-6,\n",
    "        'kappa': 1.38,\n",
    "        'C': 1.64e6,\n",
    "        'Y': 72e9,\n",
    "        'prat': 0.17,\n",
    "        'phiM': 4.6e-5\n",
    "    },\n",
    "    2: {\n",
    "        'name': 'ta2o5',\n",
    "        'n': 2.07,\n",
    "        'a': 2,\n",
    "        'alpha': 3.6e-6,\n",
    "        'beta': 14e-6,\n",
    "        'kappa': 33,\n",
    "        'C': 2.1e6,\n",
    "        'Y': 140e9,\n",
    "        'prat': 0.23,\n",
    "        'phiM': 2.44e-4\n",
    "    }\n",
    "}\n",
    "\n",
    "noise_summary = coating_properties(dOpt, materialLayer, materialParams, materialSub=1, lambda_=lambda_, f=f, wBeam=wBeam, Temp=Temp)\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": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}