siliconColors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of this notebook is to pridict the aparent color of a silicon film. This work mostly follows from the work presented in the paper \"Silicon colors: spectral selective perfect light absorption in single layer silicon films on aluminum surface and its thermal tunability\" by Seyed Sadreddin Mirshafieyan and Junpeng Guo (https://opg.optica.org/oe/fulltext.cfm?uri=oe-22-25-31545&id=306286).\n",
    "\n",
    "The current plan is to first use the equations that they provide to calutate the spectral response of a silicon film, then convert this to a real color space using color matching functions. If I acheive these goals I will decide what is the best way to move on.\n",
    "\n",
    "Some assumptions that I will make for now the illumination is TE polarized white light at 45 degrees and the silicon amorphous."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import interpolate\n",
    "import pandas as pd\n",
    "\n",
    "λ_min, λ_max = 0.36, 0.83 #μm\n",
    "λ = np.arange(λ_min, λ_max+0.001, 0.001)  # μm\n",
    "\n",
    "n_air = pd.read_csv(\n",
    "    r\"https://refractiveindex.info/data_csv.php?datafile=database/data/other/mixed%20gases/air/nk/Ciddor.yml\"\n",
    ")\n",
    "n_Si = pd.read_csv(\n",
    "    r\"https://refractiveindex.info/data_csv.php?datafile=database/data/main/Si/nk/Franta.yml\"\n",
    ")\n",
    "n_Al = pd.read_csv(\n",
    "    r\"https://refractiveindex.info/data_csv.php?datafile=database/data/main/Al/nk/Cheng.yml\"\n",
    ")\n",
    "\n",
    "\n",
    "n_air = (n_air.apply(pd.to_numeric, errors=\"coerce\")).to_numpy()\n",
    "\n",
    "n_Si = np.transpose(\n",
    "    np.array(\n",
    "        [\n",
    "            n_Si.apply(pd.to_numeric, errors=\"coerce\").to_numpy()[0:230, 0],\n",
    "            (\n",
    "                (n_Si.apply(pd.to_numeric, errors=\"coerce\")).to_numpy()[0:230, 1]\n",
    "                + (n_Si.apply(pd.to_numeric, errors=\"coerce\")).to_numpy()[-230:, 1] * 1j\n",
    "            ),\n",
    "        ]\n",
    "    )\n",
    ")\n",
    "\n",
    "n_Al = np.transpose(\n",
    "    np.array(\n",
    "        [\n",
    "            n_Al.apply(pd.to_numeric, errors=\"coerce\").to_numpy()[0:427, 0],\n",
    "            (\n",
    "                (n_Al.apply(pd.to_numeric, errors=\"coerce\")).to_numpy()[0:427, 1]\n",
    "                + (n_Al.apply(pd.to_numeric, errors=\"coerce\")).to_numpy()[-427:, 1] * 1j\n",
    "            ),\n",
    "        ]\n",
    "    )\n",
    ")\n",
    "\n",
    "n_air = interpolate.interp1d(n_air[:, 0], n_air[:, 1])\n",
    "n_Si = interpolate.interp1d(n_Si[:, 0], n_Si[:, 1])\n",
    "n_Al = interpolate.interp1d(n_Al[:, 0], n_Al[:, 1])\n",
    "\n",
    "# A convenient way to verify the imports is to plot...\n",
    "fig, axs = plt.subplots(nrows=1, ncols=3, figsize=[15, 5])\n",
    "plt.suptitle(\"Complex Refractive Index $n,k$ for Componet Materials\")\n",
    "axs[0].set(\n",
    "    title=\"Silicon (Si)\",\n",
    "    xlabel=r\"$\\lambda [\\mu m]$\",\n",
    "    ylabel=\"$n,k$\",\n",
    "    xlim=(λ_min, λ_max),\n",
    ")\n",
    "axs[0].plot(λ, np.real(n_Si(λ)), label=\"Real\")\n",
    "axs[0].plot(λ, np.imag(n_Si(λ)), label=\"Imaginary\")\n",
    "axs[0].grid()\n",
    "axs[0].legend()\n",
    "\n",
    "axs[1].set(\n",
    "    title=\"Aluminum (Al)\",\n",
    "    xlabel=r\"$\\lambda [\\mu m]$\",\n",
    "    ylabel=\"$n,k$\",\n",
    "    xlim=(λ_min, λ_max),\n",
    ")\n",
    "axs[1].plot(λ, np.real(n_Al(λ)), label=\"Real\")\n",
    "axs[1].plot(λ, np.imag(n_Al(λ)), label=\"Imaginary\")\n",
    "axs[1].grid()\n",
    "axs[1].legend()\n",
    "\n",
    "axs[2].set(\n",
    "    title=\"Air\",\n",
    "    xlabel=r\"$\\lambda [\\mu m]$\",\n",
    "    ylabel=\"$n$\",\n",
    "    ylim=(0, 5),\n",
    "    xlim=(λ_min, λ_max),\n",
    ")\n",
    "axs[2].plot(λ, n_air(λ), label=\"Real\")\n",
    "axs[2].grid()\n",
    "axs[2].legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_0 = 2 * np.pi / λ\n",
    "d = 0.110  # μm\n",
    "θ = 45 * np.pi / 180"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to start with the calculation of the wavelength dependent response of the film. The goal here is to calculate $R = |r|^2$, where \n",
    "\n",
    "<math display=\"block\" id=\"m1\" other=\"display-block-added-in-postconversion\"><mrow><mi>r</mi><mo>=</mo><mfrac><mrow><msub><mi>r</mi><mrow><mn>12</mn></mrow></msub><mo>+</mo><msub><mi>r</mi><mrow><mn>23</mn></mrow></msub><msup><mi>e</mi><mrow><mo>−</mo><mn>2</mn><mi>j</mi><mi>φ</mi></mrow></msup></mrow><mrow><mn>1</mn><mo>+</mo><msub><mi>r</mi><mrow><mn>12</mn></mrow></msub><msub><mi>r</mi><mrow><mn>23</mn></mrow></msub><msup><mi>e</mi><mrow><mo>−</mo><mn>2</mn><mi>j</mi><mi>φ</mi></mrow></msup></mrow></mfrac><mo>.</mo></mrow></math>.\n",
    "\n",
    "As mentioned above I will be considering the TE polarization for now. This means that $r_12$ and $r_23$ are given by, \n",
    "\n",
    "<math display=\"block\" id=\"m2\" other=\"display-block-added-in-postconversion\"><mrow><msub><mi>r</mi><mrow><mn>12</mn></mrow></msub><mo>=</mo><mfrac><mrow><msub><mi>k</mi><mrow><mn>1</mn><mi>y</mi></mrow></msub><mo>−</mo><msub><mi>k</mi><mrow><mn>2</mn><mi>y</mi></mrow></msub></mrow><mrow><msub><mi>k</mi><mrow><mn>1</mn><mi>y</mi></mrow></msub><mo>+</mo><msub><mi>k</mi><mrow><mn>2</mn><mi>y</mi></mrow></msub></mrow></mfrac><mo>,</mo><mi></mi><mi></mi><mi></mi><mi></mi><mi></mi><mi></mi><msub><mi>r</mi><mrow><mn>23</mn></mrow></msub><mo>=</mo><mfrac><mrow><msub><mi>k</mi><mrow><mn>2</mn><mi>y</mi></mrow></msub><mo>−</mo><msub><mi>k</mi><mrow><mn>3</mn><mi>y</mi></mrow></msub></mrow><mrow><msub><mi>k</mi><mrow><mn>2</mn><mi>y</mi></mrow></msub><mo>+</mo><msub><mi>k</mi><mrow><mn>3</mn><mi>y</mi></mrow></msub></mrow></mfrac><mo>.</mo></mrow></math>.\n",
    "\n",
    "For completness the TM versions are\n",
    "\n",
    "<math display=\"block\" id=\"m3\" other=\"display-block-added-in-postconversion\"><mrow><msub><mi>r</mi><mrow><mn>12</mn></mrow></msub><mo>=</mo><mfrac><mrow><msub><mi>n</mi><mn>2</mn></msub><msup><mrow></mrow><mn>2</mn></msup><msub><mi>k</mi><mrow><mn>1</mn><mi>y</mi></mrow></msub><mo>−</mo><msub><mi>n</mi><mn>1</mn></msub><msup><mrow></mrow><mn>2</mn></msup><msub><mi>k</mi><mrow><mn>2</mn><mi>y</mi></mrow></msub></mrow><mrow><msub><mi>n</mi><mn>2</mn></msub><msup><mrow></mrow><mn>2</mn></msup><msub><mi>k</mi><mrow><mn>1</mn><mi>y</mi></mrow></msub><mo>+</mo><msub><mi>n</mi><mn>1</mn></msub><msup><mrow></mrow><mn>2</mn></msup><msub><mi>k</mi><mrow><mn>2</mn><mi>y</mi></mrow></msub></mrow></mfrac><mo>,</mo><mi></mi><mi></mi><mi></mi><mi></mi><mi></mi><msub><mi>r</mi><mrow><mn>23</mn></mrow></msub><mo>=</mo><mfrac><mrow><msub><mi>n</mi><mn>3</mn></msub><msup><mrow></mrow><mn>2</mn></msup><msub><mi>k</mi><mrow><mn>2</mn><mi>y</mi></mrow></msub><mo>−</mo><msub><mi>n</mi><mn>2</mn></msub><msup><mrow></mrow><mn>2</mn></msup><msub><mi>k</mi><mrow><mn>3</mn><mi>y</mi></mrow></msub></mrow><mrow><msub><mi>n</mi><mn>3</mn></msub><msup><mrow></mrow><mn>2</mn></msup><msub><mi>k</mi><mrow><mn>2</mn><mi>y</mi></mrow></msub><mo>+</mo><msub><mi>n</mi><mn>2</mn></msub><msup><mrow></mrow><mn>2</mn></msup><msub><mi>k</mi><mrow><mn>3</mn><mi>y</mi></mrow></msub></mrow></mfrac><mo>.</mo></mrow></math>.\n",
    "\n",
    "Finally two other important equations are given in the text,\n",
    "\n",
    "<math display=\"block\" id=\"m4\" other=\"display-block-added-in-postconversion\"><mrow><msub><mi>k</mi><mrow><mi>i</mi><mi>y</mi></mrow></msub><mo>=</mo><msub><mi>k</mi><mn>0</mn></msub><msup><mrow><mo stretchy=\"false\">[</mo><mo stretchy=\"false\">(</mo><msub><mi>n</mi><mi>i</mi></msub><msup><mrow></mrow><mn>2</mn></msup><mo>−</mo><msub><mi>n</mi><mn>1</mn></msub><msup><mrow></mrow><mn>2</mn></msup><msup><mrow><mi>sin</mi></mrow><mn>2</mn></msup><mo stretchy=\"false\">(</mo><mi>θ</mi><mo stretchy=\"false\">)</mo><mo stretchy=\"false\">)</mo><mo stretchy=\"false\">]</mo></mrow><mrow><mn>1</mn><mo>/</mo><mn>2</mn></mrow></msup></mrow></math>\n",
    "\n",
    "<math display=\"block\" id=\"m5\" other=\"display-block-added-in-postconversion\"><mrow><msub><mi>&#x3C6;</mi><mo>=</mo><msub><mi>k</mi><mrow><mn>2</mn><mi>y</mi></mrow></msub><mi>d</mi></math>\n",
    "\n",
    "So working these backwards..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calculate_reflectance(d: float = 0.030, θ: float = 45):\n",
    "    \"\"\"\n",
    "    Calcuates the reflectance for a given thickness (d) and angle (θ)\n",
    "    for a silicon thin film over the visible spectrum.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    d : float\n",
    "        Thickness of the film\n",
    "    θ : float\n",
    "        The angle of incidence\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    tuple\n",
    "        The TE, TM, and average reflectance\n",
    "    \"\"\"\n",
    "    θ = θ * np.pi / 180\n",
    "    n_1, n_2, n_3 = n_air(λ), n_Si(λ), n_Al(λ)\n",
    "\n",
    "    k_1y = k_0 * (n_1**2 - n_1**2 * (np.sin(θ)) ** 2) ** 0.5\n",
    "    k_2y = k_0 * (n_2**2 - n_1**2 * (np.sin(θ)) ** 2) ** 0.5\n",
    "    k_3y = k_0 * (n_3**2 - n_1**2 * (np.sin(θ)) ** 2) ** 0.5\n",
    "    φ = k_2y * d\n",
    "\n",
    "    r_12TE = (k_1y - k_2y) / (k_1y + k_2y)\n",
    "    r_23TE = (k_2y - k_3y) / (k_2y + k_3y)\n",
    "\n",
    "    r_12TM = (n_2**2 * k_1y - n_1**2 * k_2y) / (n_2**2 * k_1y + n_1**2 * k_2y)\n",
    "    r_23TM = (n_3**2 * k_2y - n_2**2 * k_3y) / (n_3**2 * k_2y + n_2**2 * k_3y)\n",
    "\n",
    "    r_TE = (r_12TE + r_23TE * np.exp(-2 * 1j * φ)) / (\n",
    "        1 + r_12TE * r_23TE * np.exp(-2 * 1j * φ)\n",
    "    )\n",
    "    R_TE = np.abs(r_TE) ** 2\n",
    "\n",
    "    r_TM = (r_12TM + r_23TM * np.exp(-2 * 1j * φ)) / (\n",
    "        1 + r_12TM * r_23TM * np.exp(-2 * 1j * φ)\n",
    "    )\n",
    "    R_TM = np.abs(r_TM) ** 2\n",
    "\n",
    "    R = (R_TE + R_TM) / 2\n",
    "    return R_TE, R_TM, R\n",
    "\n",
    "R_TE, R_TM, R = calculate_reflectance()\n",
    "plt.plot(λ, R_TE)\n",
    "plt.plot(λ, R_TM)\n",
    "plt.plot(λ, R)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This result does not match the exerimental results of the paper very well at all. One possibly important factor that I am not considering is that they had to include the reflectance from the glass layer. This means that in reality the aluminium layer is also acting like a film. I don't yet know how to consider this in the calculation. It is also likely that I have made some error. I will move on to trying to calculate the apparent color of the film. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Color Science\n",
    "Color science is an inexact science with many potholes. One consideration to make is the light sorce. Above I have handwavingly considered using a white light source. In the actual experiments we will only be using red (633 nm) blue (540 nm) and green (577 nm?) laser sources. I am not sure yet how I will go about the optimization of the film to have a specific color, or even what the best way to estimate that color is (CIEXYZ, CIELUV, or CIELAB). This blog post does a decent job sumarizing many important aspects of color science. Regardless of which system (or colormatching funtion) I choose the first step should be to calculate the $R$, $G$, $B$ values. For this I will start with the 1931 CIE CMFs which I got from http://www.cvrl.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "CMF_1931 = pd.read_csv(r\"ciexyz31_1.csv\", names=['r', 'g', 'b'], index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_RGB(R: np.ndarray):\n",
    "    \"\"\"\"\"\n",
    "    Calculate the RGB values for a given reflectance.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    R : ndarray\n",
    "        Reflectance Specrtum\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    tupple\n",
    "        R, G, B values\n",
    "    \n",
    "    \"\"\"\n",
    "    r = np.sum(CMF_1931[\"r\"] * R)\n",
    "    g = np.sum(CMF_1931[\"g\"] * R)\n",
    "    b = np.sum(CMF_1931[\"b\"] * R)\n",
    "    r, g, b = (\n",
    "        r / (r + g + b),\n",
    "        g / (r + g + b),\n",
    "        b / (r + g + b),\n",
    "    )\n",
    "    return r, g, b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgMAAAGFCAYAAABg2vAPAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvc2/+5QAAAAlwSFlzAAAPYQAAD2EBqD+naQAABoBJREFUeJzt2KERwlAQRdGEoato2kKkLXTqWkoAAfPFPUevePLO7jMzGwCQdVs9AABYSwwAQJwYAIA4MQAAcWIAAOLEAADEiQEAiBMDABB3//bwcZz/XQIA/Nzren688RkAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgLh9Zmb1CABgHZ8BAIgTAwAQJwYAIE4MAECcGACAODEAAHFiAADixAAAxIkBANja3nqrDgMTWERMAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "\n",
    "# Define the RGB color\n",
    "R_TE, R_TM, R = calculate_reflectance()\n",
    "r, g, b = calculate_RGB(R)\n",
    "\n",
    "# Create a new figure\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Create a square patch with the given color\n",
    "square = patches.Rectangle(\n",
    "    (0, 0), 1, 1, linewidth=0, edgecolor=\"none\", facecolor=(r, g, b)\n",
    ")\n",
    "\n",
    "# Add the patch to the axes\n",
    "ax.add_patch(square)\n",
    "\n",
    "# Set the limits of the axes to the size of the square\n",
    "ax.set_xlim(0, 1)\n",
    "ax.set_ylim(0, 1)\n",
    "\n",
    "# Remove the axes ticks and frames\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "ax.set_frame_on(False)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "40bb1396bf694cebab2e633115a4e7ff",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=45.0, description='Angle', max=179.0, min=1.0, step=1.0), FloatSlider(…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.update_plot(frequency, amplitude)>"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from ipywidgets import FloatSlider, VBox\n",
    "from IPython.display import display\n",
    "import ipywidgets as widgets\n",
    "\n",
    "# Define a function to update the plot based on the slider values\n",
    "def update_plot(frequency, amplitude):\n",
    "    R_TE, R_TM, R = calculate_reflectance(amplitude, frequency)\n",
    "\n",
    "    # Clear the current plot\n",
    "    plt.clf()\n",
    "\n",
    "    # Create a new plot\n",
    "    fig, axs = plt.subplots(nrows=1, ncols=2, figsize=[15, 5])\n",
    "\n",
    "    # Set plot labels and title\n",
    "    axs[0].set(\n",
    "        title=f\"Sine wave: Frequency = {frequency}, Amplitude = {amplitude}\",\n",
    "        xlabel= \"x\",\n",
    "        ylabel = \"y\")\n",
    "    axs[0].plot(λ, R_TE)\n",
    "    axs[0].plot(λ, R_TM)\n",
    "    axs[0].plot(λ, R)\n",
    "\n",
    "    r, g, b = calculate_RGB(R)\n",
    "    square = patches.Rectangle(\n",
    "    (0, 0), 1, 1, linewidth=0, edgecolor=\"none\", facecolor=(r, g, b)\n",
    "    )\n",
    "\n",
    "    # Add the patch to the axes\n",
    "    axs[1].add_patch(square)\n",
    "\n",
    "    # Set the limits of the axes to the size of the square\n",
    "    axs[1].set_xlim(0, 1)\n",
    "    axs[1].set_ylim(0, 1)\n",
    "\n",
    "    # Remove the axes ticks and frames\n",
    "    axs[1].set_xticks([])\n",
    "    axs[1].set_yticks([])\n",
    "    axs[1].set_frame_on(False)\n",
    "\n",
    "    # Show the plot\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "\n",
    "# Create sliders for frequency and amplitude\n",
    "angle_slider = FloatSlider(\n",
    "    min=1, max=179, step=1, value=45, description=\"Angle\"\n",
    ")\n",
    "thickness_slider = FloatSlider(\n",
    "    min=0.01, max=0.2, step=0.001, value=0.03, description=\"Thickness\"\n",
    ")\n",
    "\n",
    "# Use interact to create the widget that updates the plot\n",
    "interact(update_plot, frequency=angle_slider, amplitude=thickness_slider)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "main-env",
   "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": "3.13.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}