diff --git a/docs/source/examples.rst b/docs/source/examples.rst index a1de2428..5c5cc70a 100644 --- a/docs/source/examples.rst +++ b/docs/source/examples.rst @@ -8,5 +8,6 @@ Examples Microscopic 3-Temperature-Model Landau-Lifshitz-Bloch Phonons + General Transfer Matrix Formalism Dynamical Xray Scattering Dynamical Magnetic Xray Scattering \ No newline at end of file diff --git a/docs/source/examples/Au_Johnson_nk.csv b/docs/source/examples/Au_Johnson_nk.csv new file mode 100644 index 00000000..022c149c --- /dev/null +++ b/docs/source/examples/Au_Johnson_nk.csv @@ -0,0 +1,50 @@ +Wavelength. µm n k +0.1879 1.28 1.188 +0.1916 1.32 1.203 +0.1953 1.34 1.226 +0.1993 1.33 1.251 +0.2033 1.33 1.277 +0.2073 1.3 1.304 +0.2119 1.3 1.35 +0.2164 1.3 1.387 +0.2214 1.3 1.427 +0.2262 1.31 1.46 +0.2313 1.3 1.497 +0.2371 1.32 1.536 +0.2426 1.32 1.577 +0.249 1.33 1.631 +0.2551 1.33 1.688 +0.2616 1.35 1.749 +0.2689 1.38 1.803 +0.2761 1.43 1.847 +0.2844 1.47 1.869 +0.2924 1.49 1.878 +0.3009 1.53 1.889 +0.3107 1.53 1.893 +0.3204 1.54 1.898 +0.3315 1.48 1.883 +0.3425 1.48 1.871 +0.3542 1.5 1.866 +0.3679 1.48 1.895 +0.3815 1.46 1.933 +0.3974 1.47 1.952 +0.4133 1.46 1.958 +0.4305 1.45 1.948 +0.4509 1.38 1.914 +0.4714 1.31 1.849 +0.4959 1.04 1.833 +0.5209 0.62 2.081 +0.5486 0.43 2.455 +0.5821 0.29 2.863 +0.6168 0.21 3.272 +0.6595 0.14 3.697 +0.7045 0.13 4.103 +0.756 0.14 4.542 +0.8211 0.16 5.083 +0.892 0.17 5.663 +0.984 0.22 6.35 +1.088 0.27 7.15 +1.216 0.35 8.145 +1.393 0.43 9.519 +1.61 0.56 11.21 +1.937 0.92 13.78 diff --git a/docs/source/examples/EpsQW_plane.txt b/docs/source/examples/EpsQW_plane.txt new file mode 100644 index 00000000..1bdc27b8 --- /dev/null +++ b/docs/source/examples/EpsQW_plane.txt @@ -0,0 +1,601 @@ +# f eps1 eps2 +2.727272727272727344e+13 9.865293969437559651e-01 7.005450951342323540e-02 +2.735240552435878125e+13 1.019510059390096313e+00 6.939467774693332258e-02 +2.743208377599028906e+13 1.052157766835979391e+00 6.874451775415227339e-02 +2.751176202762179297e+13 1.084478188498268603e+00 6.810377537020731453e-02 +2.759144027925330078e+13 1.116476812422067200e+00 6.747220900905850915e-02 +2.767111853088480859e+13 1.148157140247737518e+00 6.684963957375582300e-02 +2.775079678251631641e+13 1.179526126161258848e+00 6.623579660385517376e-02 +2.783047503414782422e+13 1.210588848592293321e+00 6.563046881466784443e-02 +2.791015328577932812e+13 1.241350161500840876e+00 6.503345641641600228e-02 +2.798983153741083594e+13 1.271814672221362574e+00 6.444457118686733321e-02 +2.806950978904234375e+13 1.301985436046189548e+00 6.386367119143708526e-02 +2.814918804067385156e+13 1.331868606938741539e+00 6.329053445939994416e-02 +2.822886629230535938e+13 1.361468524395542223e+00 6.272499245341486840e-02 +2.830854454393686328e+13 1.390789414714923788e+00 6.216688318131648183e-02 +2.838822279556837109e+13 1.419835126283845783e+00 6.161605788593994826e-02 +2.846790104719987891e+13 1.448608593988725879e+00 6.107239457980898067e-02 +2.854757929883138672e+13 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5.239025043208992427e-01 +7.484064349673698438e+13 5.967949479144428082e+00 5.207067812529002637e-01 +7.492032174836850000e+13 5.984771718999560619e+00 5.175110581849013958e-01 +7.500000000000000000e+13 6.001582699948812660e+00 5.143184676173981895e-01 diff --git a/docs/source/examples/gtm.ipynb b/docs/source/examples/gtm.ipynb new file mode 100644 index 00000000..fa318ab9 --- /dev/null +++ b/docs/source/examples/gtm.ipynb @@ -0,0 +1,1133 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "28d7ef42-259e-4d8e-a102-aed480775504", + "metadata": { + "tags": [] + }, + "source": [ + "# General Transfer Matrix (GTM) Formalism Scattering\n", + "\n", + "In this example static light scattering simulations are carried out employing a dynamical general transfer matrix formalism which was adapted from [pyGTM](https://pygtm.readthedocs.io)." + ] + }, + { + "cell_type": "markdown", + "id": "323b30ef-4399-4f4f-8d72-a2a0c69f9221", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "Do all necessary imports and settings." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "bbd23fd3-bda4-42af-a736-15fb281bc29c", + "metadata": {}, + "outputs": [], + "source": [ + "import udkm1Dsim as ud\n", + "u = ud.u # import the pint unit registry from udkm1Dsim\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "u.setup_matplotlib() # use matplotlib with pint units" + ] + }, + { + "cell_type": "markdown", + "id": "d1b8e4f7-920c-4a7f-970b-c196f40c9c29", + "metadata": {}, + "source": [ + "## Structure\n", + "\n", + "Refer to the [structure-example](structure.ipynb) for more details.\n", + "\n", + "In this example the sample `Structure` consists of `Layer`s." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "585a096f-c41f-47f1-9e4d-3fdecf9f86ce", + "metadata": {}, + "outputs": [], + "source": [ + "layer_air = ud.AmorphousLayer('air', \"air\", 1*u.um, 0*u.kg/u.m**3)\n", + "layer_glass = ud.AmorphousLayer('glass', \"glass\", 1*u.um, 0*u.kg/u.m**3)\n", + "layer_Au = ud.AmorphousLayer('Au', \"gold\", 50*u.nm, 0*u.kg/u.m**3)" + ] + }, + { + "cell_type": "markdown", + "id": "f3e8a22c-4caf-43b3-ae33-b5d2c9cfffdc", + "metadata": {}, + "source": [ + "### Set the dielectric function" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "54e9696d-1e0f-41a8-afcb-83356d306286", + "metadata": {}, + "outputs": [], + "source": [ + "layer_air.epsilon = 1-0j\n", + "layer_glass.epsilon = 1.5**2-0j" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "30f9b4c8-49fb-4ac0-8245-5ec6ad4b305e", + "metadata": {}, + "outputs": [], + "source": [ + "def eps_Au(f):\n", + " \"\"\"\n", + " Gold (Au) permittivity. \n", + "\n", + " :param array f: frequency (array or float)\n", + " :return: permittivity (float or len(f)-array)\n", + "\n", + " **Attention**, two models are used: a simple Drude model with parameters \n", + " from `Derkachova et al., Plasmonics 11, 941 (2016) (open access) \n", + " `_ \n", + " or the tabulated data from Jonhson and Christy \n", + " (`refractiveindex.info `_)\n", + " You should check carefully if this works out for you. \n", + " \"\"\"\n", + " f = np.array(f)\n", + " import scipy.constants as constants\n", + " c_const = constants.physical_constants['speed of light in vacuum'][0]\n", + " fmin_JC = c_const/1.93e-6\n", + " fmax_JC = c_const/0.188e-6\n", + "\n", + " #print('Using Jonhson and Christy data for Au')\n", + " epsAufile = './Au_Johnson_nk.csv'\n", + " eps_loaded = np.genfromtxt(epsAufile, delimiter='\\t',\n", + " skip_header=1, unpack=True)\n", + "\n", + " lbda = eps_loaded[0, :]*1e-6\n", + " n = eps_loaded[1,:]\n", + " k = eps_loaded[2,:]\n", + " eps1 = n**2-k**2\n", + " eps2 = 2*n*k\n", + " floc = c_const/lbda\n", + " epsr = np.interp(f, floc[::-1], eps1[::-1])\n", + " epsi = np.interp(f, floc[::-1], eps2[::-1])\n", + "\n", + " return epsr+1.0j*epsi" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a2bc2ccc-2146-4eab-b761-a828179f6f0f", + "metadata": {}, + "outputs": [], + "source": [ + "layer_Au.epsilon = eps_Au" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "771d8d77-3aab-413f-af2a-362e20e85945", + "metadata": {}, + "outputs": [], + "source": [ + "S = ud.Structure('Optical Sample')\n", + "\n", + "S.add_sub_structure(layer_air, 1)\n", + "S.add_sub_structure(layer_glass, 1)" + ] + }, + { + "cell_type": "markdown", + "id": "34228336-1239-43fa-9371-f67f5d43ccee", + "metadata": {}, + "source": [ + "## Initialize general transfer matrix simulations\n", + "\n", + "The `GTM` class requires a `Structure` object and a boolean `force_recalc` in order overwrite previous simulation results.\n", + "\n", + "These results are saved in the `cache_dir` when `save_data` is enabled.\n", + "Printing simulation messages can be en-/disabled using `disp_messages` and progress bars can using the boolean switch `progress_bar`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a9ae7dc9-f569-42f0-818d-bed727f1dbdc", + "metadata": {}, + "outputs": [], + "source": [ + "gtm = ud.GTM(S, True)\n", + "gtm.disp_messages = False\n", + "gtm.save_data = False" + ] + }, + { + "cell_type": "markdown", + "id": "90d51508-c5b9-4c7a-bed5-063aa481bc1b", + "metadata": {}, + "source": [ + "### set simulation parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "39f879fa-2bd2-45c2-9aae-1996ded6640f", + "metadata": {}, + "outputs": [], + "source": [ + "gtm.wl = np.r_[600]*u.nm # set two photon energies\n", + "gtm.theta = np.r_[1:80:0.1]*u.deg" + ] + }, + { + "cell_type": "markdown", + "id": "49c47be0-b3a6-4979-9029-71b5d88a536a", + "metadata": {}, + "source": [ + "do the actual calculations" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "2af65285-6a70-4873-82d3-f4132249d700", + "metadata": {}, + "outputs": [], + "source": [ + "r, R, t, T = gtm.calculate_r_t()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "68f758e3-2523-45d6-a0f7-6afccf320c47", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(gtm.theta[0, :], R[0, :, 0], '-b', label=r'$R_p$')\n", + "plt.plot(gtm.theta[0, :], R[0, :, 1], '--b', label=r'$R_s$')\n", + "plt.plot(gtm.theta[0, :], T[0, :, 0], '-r', label=r'$T_p$')\n", + "plt.plot(gtm.theta[0, :], T[0, :, 1], '--r', label=r'$T_s$')\n", + "plt.legend()\n", + "plt.title('Reflectivity for $\\lambda = $ {:0.1f}'.format(gtm.wl[0]))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "fe1044f0-4184-47b2-bc47-bff7cf7e6ad6", + "metadata": {}, + "source": [ + "build new sample" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b0468485-834a-4af8-8ca9-309911fc6101", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\loc_schick\\general\\git\\udkm1dsim\\udkm1Dsim\\simulations\\scattering.py:258: RuntimeWarning: invalid value encountered in arcsin\n", + " self._theta = np.arcsin(np.outer(self._wl, self._qz[0, :])/np.pi/4)\n" + ] + } + ], + "source": [ + "gtm.wl = np.r_[400:800:2]*u.nm # set two photon energies\n", + "gtm.theta = np.linspace(1, 80.0, 100)*u.deg" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6ef35c08-48c3-4955-9ab1-c356bfed8a2f", + "metadata": {}, + "outputs": [], + "source": [ + "S = ud.Structure('Optical Sample Gold')\n", + "\n", + "S.add_sub_structure(layer_glass, 1)\n", + "S.add_sub_structure(layer_Au, 1)\n", + "S.add_sub_structure(layer_air, 1)\n", + "gtm.S =S" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "be78a1fa-bbcb-4069-a9a0-7ad6b69c46c4", + "metadata": {}, + "outputs": [], + "source": [ + "r, R, t, T = gtm.calculate_r_t()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "64612589-7a42-4c56-b726-fc241ce2e57f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "index = 100\n", + "plt.figure()\n", + "plt.plot(gtm.theta[index, :], R[index, :, 0], '-b', label=r'$R_p$')\n", + "plt.plot(gtm.theta[index, :], R[index, :, 1], '--b', label=r'$R_s$')\n", + "plt.plot(gtm.theta[index, :], T[index, :, 0], '-r', label=r'$T_p$')\n", + "plt.plot(gtm.theta[index, :], T[index, :, 1], '--r', label=r'$T_s$')\n", + "plt.legend()\n", + "plt.title('Reflectivity for $\\lambda = $ {:0.1f}'.format(gtm.wl[index]))\n", + "plt.xlabel('Angle of incidence (deg)')\n", + "plt.ylabel('Reflection/Transmission')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "951fe8b8-ca9e-4db7-a666-a44001c81f9c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12,12))\n", + "plt.subplot(2,2,1)\n", + "plt.pcolormesh(gtm.theta[0, :].magnitude, gtm.wl.magnitude, R[:, :, 0], shading='auto')\n", + "plt.colorbar()\n", + "plt.title(r'$R_p$')\n", + "plt.xlabel('Angle of incidence (deg)')\n", + "plt.ylabel('Wavelength (nm)')\n", + "\n", + "plt.subplot(2,2,2)\n", + "plt.pcolormesh(gtm.theta[0, :].magnitude, gtm.wl.magnitude, R[:, :, 1], shading='auto')\n", + "plt.colorbar()\n", + "plt.title(r'$R_s$')\n", + "plt.xlabel('Angle of incidence (deg)')\n", + "plt.ylabel('Wavelength (nm)')\n", + "\n", + "plt.subplot(2,2,3)\n", + "plt.pcolormesh(gtm.theta[0, :].magnitude, gtm.wl.magnitude, T[:, :, 0], shading='auto')\n", + "plt.colorbar()\n", + "plt.title(r'$T_p$')\n", + "plt.xlabel('Angle of incidence (deg)')\n", + "plt.ylabel('Wavelength (nm)')\n", + "\n", + "plt.subplot(2,2,4)\n", + "plt.pcolormesh(gtm.theta[0, :].magnitude, gtm.wl.magnitude, T[:, :, 1], shading='auto')\n", + "plt.colorbar()\n", + "plt.title(r'$T_s$')\n", + "plt.xlabel('Angle of incidence (deg)')\n", + "plt.ylabel('Wavelength (nm)')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3b10fd46-c7e3-4f96-90c2-10a4ddcccbd9", + "metadata": {}, + "source": [ + "### Electric Field" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "dcb70453-cc56-4d23-b4e1-b792eeb6d90f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\loc_schick\\general\\git\\udkm1dsim\\udkm1Dsim\\simulations\\scattering.py:258: RuntimeWarning: invalid value encountered in arcsin\n", + " self._theta = np.arcsin(np.outer(self._wl, self._qz[0, :])/np.pi/4)\n" + ] + } + ], + "source": [ + "gtm.wl = np.r_[400:800]*u.nm # set two photon energies\n", + "gtm.theta = np.r_[45]*u.deg\n", + "r, R, t, T = gtm.calculate_r_t()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "3a285848-4d06-42e4-93fd-21d778a01314", + "metadata": {}, + "outputs": [], + "source": [ + "zplot, E_out, H_out, zn_plot = gtm.calculate_Efield(r, R, t, T, dz=1e-9, magnetic=True) # get the electric field" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "6bfaf38e-f0a1-44bc-9156-8201821fe22a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "i = 200\n", + "j = 0\n", + "plt.plot(zplot, np.real(E_out[i, j, 2, :]), label='$E_z^p=${:0.1f} nm'.format(gtm.wl[i].magnitude))\n", + "plt.plot(zplot, np.real(H_out[i, j, 1, :]), label='$H_y^p=${:0.1f} nm'.format(gtm.wl[i].magnitude))\n", + "\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "942ebcbf-0117-425e-9d58-1933a49b6862", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\loc_schick\\general\\python\\wpy64-3890\\python-3.8.9.amd64\\lib\\site-packages\\matplotlib\\cbook\\__init__.py:736: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", + " x = np.array(x, subok=True, copy=copy)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "\n", + "plt.pcolormesh(zplot, gtm.wl, np.real(E_out[:, 0, 2, :]), vmin=0, vmax=2, shading='auto')\n", + "plt.colorbar()\n", + "\n", + "plt.show()\n", + "\n", + "plt.figure()\n", + "\n", + "plt.pcolormesh(zplot, gtm.wl, np.real(H_out[:, 0, 1, :]), vmin=0, vmax=2, shading='auto')\n", + "plt.colorbar()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "41bfd5a4-78cd-460d-9db6-f164b96b8e25", + "metadata": {}, + "source": [ + "## Air Gap Example" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "97e439ac-b4f8-4ea5-a5de-bbfaead7f090", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\loc_schick\\general\\git\\udkm1dsim\\udkm1Dsim\\simulations\\scattering.py:258: RuntimeWarning: invalid value encountered in arcsin\n", + " self._theta = np.arcsin(np.outer(self._wl, self._qz[0, :])/np.pi/4)\n" + ] + } + ], + "source": [ + "wavenumbers = np.r_[750:1050:1]/u.cm\n", + "gtm.wl = 1/wavenumbers # set the wavelength by wavenumber\n", + "gtm.theta = np.r_[30]*u.deg" + ] + }, + { + "cell_type": "markdown", + "id": "8cee4a28-9f75-4686-a3d9-248f495d28e8", + "metadata": {}, + "source": [ + "use pyGTM permittivities" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "658c9c4f-1779-475c-bd38-e62748a79e27", + "metadata": {}, + "outputs": [], + "source": [ + "import GTM.Permittivities as perm\n", + "eps_KRS5 = perm.eps_KRS5\n", + "eps_SiC6Hx = perm.eps_SiC6Hx\n", + "eps_SiC6Hz = perm.eps_SiC6Hz" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "3265b608-4c6a-47a1-9e5c-1c46d86eaccd", + "metadata": {}, + "outputs": [], + "source": [ + "layer_KRS5 = ud.AmorphousLayer('KRS5', \"KRS5\", 1*u.um, 0*u.kg/u.m**3)\n", + "layer_KRS5.epsilon = lambda f: eps_KRS5(f)\n", + "layer_SiC6H = ud.AmorphousLayer('SiC6H', \"SiC6H\", 3*u.um, 0*u.kg/u.m**3)\n", + "layer_SiC6H.epsilon = [lambda f: eps_SiC6Hx(f), lambda f: eps_SiC6Hx(f), lambda f: eps_SiC6Hz(f)]\n", + "layer_air.thickness = 5.5*u.um" + ] + }, + { + "cell_type": "markdown", + "id": "6cd28a1e-c2ea-4970-a3bd-41a8bb0b5bbd", + "metadata": {}, + "source": [ + "build the sample" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "2fde318b-8faa-4047-8570-bae1338fc860", + "metadata": {}, + "outputs": [], + "source": [ + "S = ud.Structure('Optical Sample Gold')\n", + "S.add_sub_structure(layer_KRS5, 1)\n", + "S.add_sub_structure(layer_air, 1)\n", + "S.add_sub_structure(layer_SiC6H, 1)\n", + "gtm.S = S" + ] + }, + { + "cell_type": "markdown", + "id": "e93fc825-1d17-4378-8175-9b14e744adfe", + "metadata": {}, + "source": [ + "calculate the reflectivity and transmission" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "d2c914c3-ef1b-4786-a334-33b37dcb85c1", + "metadata": {}, + "outputs": [], + "source": [ + "r, R, t, T = gtm.calculate_r_t()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "b153ffe5-b622-4021-b5ce-0942c2854d38", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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bNy4kycpiLJwI/FBUoc5OeOaZ0JXu8cdDl8I5c7q7Ee6yCxx2GHzsY6H749/8jZ/111prK/zHf4TSwNSp4W9da/Pnw4oV4WDZUyZMCN1TDzss7Nu8eaFHTj1873tw111wxRU9nwQgHPAOP3zHko9Z6Ja6YkUoVa9du/1jy5btuy8nuzHHPePiLtTx69z3ra3h8wcMCI9yXw8Y0H3wb21t7AslvUSQwpo14Yxl/vzuA/+CBeFsBsIPYtKk8I87ZUp43mefxv5hNIquLpg4Mfz977679tv/xjfgkkvgpZdq04W3HLNmhZOIT30Krruu9ttfsiRUxbzvfaGaxn+v2eYlgpRefTVU5cSPhQvDc9x/GmD06HDQ+cIXwvOkSaEfeT2qJVxpLS3wiU/Af/0XrFwZvp9amj4dDjmk55MAhJOKCy4I+/bpT4cTjFr6ylfCmeyVV3oSaHZNlwjMQnEz3wF/5cru5QYNCgf4I48MdbSTJoWLgHbfvddCdwV87GPwne/Ab34DZ5xRu+1u2ACzZ4cL2HrLJZeEi7IuvTQ0HtfKwoWhOu2SSyqvc3bZ0TSJ4He/C71MFi0KdYyxkSPhwAPD8AUHHhgeBx0UhgXwqyAbw8EHhx4nM2fWNhHMmhWqno48snbbLNfQofCP/xh68sybB5NrNFj75ZeHk53zz6/N9lxja5pEMGhQGKLgzDO3P+DvtpsXixtdS0uoNnn00dpud8aM8Nvo7XFy/u7v4LLL4Mc/rk0iWL8ebrstJM1dd61+e67xNU0iOOKI0JffZdPhh4drCtavD2fRtTBjRqgSHDGiNtur1IgR4XqT224LvXyq7X02bVoYDPDMM2sTn2t8XvnhMuHww0P/8Go7lL3wQrjXg1nYVq0baCt19tmhO+W991a/rTvuCO0CRxxR/bZcNngicJkQj0a6eHG65efOhZdf3n7avHmhD/+XvhQ6FLz+Orz1rTUNs2JHHx3aQaptMO7ogPvuCyUMbwNzMf8puEwYOzZ0hXzuudLLPvssHHpoGEr5t7/tnh5fkDZtGjwV3Wn7LW+pfayV6N8/XG18772htFKpxx4LQxocd1ztYnONzxOBy4TW1nDzlzSJ4PLLu8epmTo1THvllVCaGDkyXE/yyCNh+pvfXLeQy3biiaHqatGiyrfxwAOhAfyoo2oWlssATwQuM/baq3QieOON0C//rLPCQf7ZZ8P0eDDAz30unHHfeGO4ZqQv9ao55pjwPGNG5dt44IFwPcxOO9UiIpcVnghcZqRJBL/+dagaOeeccIetuE0hTgSf/Ww4Y162rO9UC8X22SccwCttEO/qCtdGeGnA5fJE4DJj4sRQxVNs1Mpf/CIMbTxlCuy7b6hq6egIiWDMmHCwPf30cFX5WWf1WOipSGEU1NmzK1t/yZLwt3n722sbl2t8nghcZuy1V3heujT//M2bQ6+bU04JPWb22y9UAz33XEgE8XDPt94a6uH7Yj/79vYwXPOmTeWvG5d66jmstWtMnghcZkycGJ6ffz7//LlzwwH0+OPD+333Dc/PPBMO/H2pYbiQQw4JVTzz55e/7hNPhAR44IE1D8s1OE8ELjNGjQrPq1fnnz9zZnieMiU8x4ng4YdD9dA++9Q3vlqYNCk8L1xY/rqPPw777x+GW3EuyROBy4y4J8yaNfnnz5oVqo/iIaV32SU84ou04qqlvmz8+HBNQdzbqRzJ6i/nkjwRuMwolgjMQokgLg3EDjqo++y6ERJBayvsvXf5iWDLllBldsABdQnLNThPBC4zWlvDMAz5EsHq1WHYiEMO2X56sl1gwoT6xlcr++8Pf/5zeeu8+GJIho2Q7FzP80TgMmWnnfInguXLw3PuwT4eo2js2Ma5p3R8/UM5N2GPG9DjBnXnkjwRuEwplQjGjNl+elwiaKQz5f32C11h431KI04EjVLqcT3LE4HLlEKJYMWK8JyVRADltRM8/3zoOjpuXF1Ccg3OE4HLlFKJIPee06NHwwknwEkn1T+2WomT1gsvpF9n6dKQBPr3r09MrrE1zR3KXHMolghGj4YBA7afLtXmZi89aY89wvNLL6Vf5/nnvX3AFeYlApcpxRJBbrVQoxo0KNy+Mi7lpOGJwBXjicBlyk47hWEkNm/efvry5dlJBBD2JW2JYNu2kDS8fcAV4onAZUqhi8qyVCKAUD2UNhG89loYnyi+otq5XJ4IXKbkSwSdnbByZfMmglWrwnM8FpNzueqaCCSdKOkZSYslXZRn/ghJ0yQ9LmmBpHPqGY/Lvp13Ds/JRLB6dbiqNktnxGPGhFJOmvsXeyJwpdQtEUhqAX4AnAQcBHxc0kE5i/09sNDMJgFHA9+TlNOvw7n08pUINmwIz8OG9Xw89bLHHmHE1NdfL73sypXhefTouobkGlg9SwSHAovNbImZbQFuB07OWcaAYZIEDAVeA7bWMSaXcUOHhuf44J98PXhwz8dTL+V0IfUSgSulnolgLPBi4v2yaFrSVcCBwArgSeBLZrbDCCqSzpU0R9KcVfGv2rk84rH2k3fwim9dOWRIz8dTL5Ukgl13rV88rrHVMxEoz7TcGs0TgPnAGOBtwFWShu+wktl1ZtZuZu2j/LTGFREPHJfsPhongiyVCOIrpF9+ufSyK1eGKjO/qtgVUs9EsAzYM/F+HOHMP+kcYKoFi4HnAB8x3VUsX4kgrhrKUokgbhRP00awapVXC7ni6pkIZgP7SdoragA+DbgrZ5kXgGMBJO0GvAlYUseYXMY1S4lg5MjwXOhubEmeCFwpqcYaknQEcCkwIVpHgJnZ3oXWMbOtks4H7gNagBvMbIGk86L51wLfAG6S9GS0zQvNrMAdZ50rbeDA8Jz1EkH//mF/0iSClSvDzWycKyTtoHM/Bi4A5gJdaTduZtOB6TnTrk28XgEcn3Z7zpUihVJBvsbiLJUIIJQK0lYNHXFEvaNxjSxtInjDzO6payTO1cigQdtXDWWxRACFB9hLMoNXX/UeQ664tIngD5K+C0wFOuKJZjavLlE5V4VBg3YsEcQlhSxJkwg2bgzjDI0Y0TMxucaUNhEcFj23J6YZcExtw3Guem1tO5YIBg8OySBLRo4MN6UvZu3a8Dx8h07ZznVLlQjM7N31DsS5WslXIsha+wCEEsETTxRfZt268OyJwBWTqvtoNDjc5fHVvZK+J8kLm65PylciyFr7AKSrGvISgUsj7XUENwDrgI9Gj7XAjfUKyrlqNEuJYOTIcKDvKtKPzxOBSyNtG8E+ZvbhxPvLJM2vQzzOVa2tbftB5zZuzG6JAOCNN7qvNM4VJ4Isjbzqai9tiWCTpCPjN9EFZpuKLO9cr8ktEcSNxVlT6G5sSV4icGmkLRH8HXBz1C4gwnDRZ9crKOeqka9qKItj8cfDTBS7qMwTgUsjba+h+cCkeGRQM1tbz6Ccq0ah7qNZ4yUCVytFE4GkM8zsVklfzpkOgJldXsfYnKtIvhJBltsISiWCAQO6x2ByLp9SJYL438ebmlzDaJYSQdwAHF8rkM+6dV4acKUVTQRm9sPo+bKeCce56jVLiSDep2QPqVxr13oicKWlvaDsPyUNl9Rf0v2SVks6o97BOVeJtjbo7Az967dty+51BGkTgXcddaWk7T56fNRA/H7Cncf2B75St6icq0J8l7LNm7uriLJYImhrC+MnxcNs5+MlApdG2kQQ3+30vcBPzey1OsXjXNWSiSA+W85iiUAK++VVQ65aaRPBNElPE0YfvV/SKGBziXWc6xXxcNObNnWXCOLkkDVDhngicNVLlQjM7CLgcKDdzDqBDcDJ9QzMuUolb2C/ZUt4ndXuk54IXC2kbSz+CLDVzLokXQzcCoypa2TOVSh5A/s4EQwY0Hvx1NOQId5G4KqXtmroX8xsXTTe0AnAzcA19QvLucrlKxFkNREUayPo6grJMIsN5a620iaCeKDb9wHXmNmdQEb/tVyja7YSQaFEEF9LkcWGcldbaRPBckk/JNyLYLqkgWWs61yPaqYSQZpEkLV7NbvaS3sw/yhwH3Cimb0O7IxfR+D6qGYrERRqI4gTQVZ7TLnaKZoI4tFGgTbgAeBVSTsDHcCc+obmXGWaqURQrI3AE4FLq9Sgc7cRriaeCxjhXgQxA/auU1zOVSw+6Hd2Zj8RpKka8kTgSik16Nz7o+e9eiYc56oXH/S3bPFEAJ4IXGlp71CGpIOBicl1zGxqHWJyrirNlAgGDw77uHUrtOb8N3sicGmlSgSSbgAOBhYA26LJBngicH1OMyWC+BqBjRt3vHAs68NruNpJWyKYYmYH1TUS52qkGRPBhg07JgIvEbi00nYfnSnJE4FrCM2aCHJ5InBppS0R3ExIBi8Tuo4KMDM7uG6ROVehuK68GRJBfNWwJwJXjbSJ4AbgTOBJutsISpJ0InAF0AJcb2bfzrPM0cD3Cfc8WG1m70q7fefykcKBvxkSQbKNIJcnApdW2kTwgpndVc6GJbUAPwDeQ7ir2WxJd5nZwsQyI4GrCVcsvyBpdDmf4VwhuYmgf//iyzcqrxpytZA2ETwt6TZgGqFqCCjZffRQYLGZLQGQdDvhHgYLE8ucDkw1sxei7a0sI3bnCkomgpaW8MiiNInAxxpypaRNBIMICeD4xLRS3UfHAi8m3i8DDstZZn+gv6QHgGHAFWb2k9wNSToXOBdg/PjxKUN2zSyZCLJaLQSl2wgGDIB+PjykKyFVIjCzcyrYtvJMszyf/w7gWEKymSlplpn9OefzrwOuA2hvb8/dhnM7aLZEEJ/9J23a5NVCLp20dyj7T0nDJfWXdL+k1ZLOKLHaMmDPxPtxwIo8y9xrZhvMbDXwEDApbfDOFdIsiSC+BWdHx47zPBG4tNIWGo83s7WEAeiWEap0Sg1DPRvYT9JekgYApwG5Dc53AkdJapU0mFB1tCh19M4VkEwEWW0ohu5EEF9FnOSJwKWVto0g/ld6L/BTM3tNylfz083Mtko6n3AfgxbgBjNbIOm8aP61ZrZI0r3AE4Ruqdeb2VOV7IhzSV4i8ETg0kubCKZJehrYBHxe0iggzznI9sxsOjA9Z9q1Oe+/C3w3ZRzOpeKJIJQSPBG4NFJVDZnZRcDhQLuZdQIbCF1BneuTmiUR9OsXqr68ROCqkXoYauBAYKKk5Do7dPV0ri9olkQAoVRQKBEMG9bz8bjGk3YY6luAfYD5QFc02fBE4PqoAQNg/XpPBKP9Wn2XQtoSQTtwkJl5H37XELxE4FVDLr203UefAnavZyDO1ZInAk8ELr20JYJdgYWSHmP7sYb+ti5ROVelZCIYObK3o6kvTwSuWmkTwaX1DMK5Wmu2EoFfUOaqkXasoQfrHYhztdRsiSC3RGAWEoGPPOrSSDvW0BRJsyWtl7RFUpektfUOzrlKNVMiaGvbMRFs3QrbtnkicOmkbSy+Cvg48CxhlNDPRNOc65OaKRHkKxHEN+SJrzx2rpjUI5Wb2WKgxcy6zOxG4Oi6ReVclZo9EXR2hues77urjbSNxRujEUTnS/pP4CVgSP3Ccq46cSLo6Mj+wbBYiSDr++5qI22J4Mxo2fMJ4wztCXy4XkE5V634ALhxY/YPhp4IXLVKlgiim9B/y8zOIIw4elndo3KuSvEBsLMz+wdDTwSuWiVLBGbWBYyKqoacawjJA2DWD4aeCFy1ipYIJI03sxeA54FHJN1FqBoCwMwur294zlWm2RJB7gVlnghcOUpVDf0KmEy41/AKQgnCB7Z1fV6zJYJCJYIs36bT1U6pRCAAM/N2AddQmikRtLWFA78ZxHeQ9RKBK0epRDBW0pWFZprZF2scj3M10UyJIL5obMuW7V9D9vfd1UapRLAJmNsTgThXS82YCDo6ul/7BWWuHKUSwWtmdnOPROJcDSUPgGPH9l4cPSHfDey9RODKUar76LYeicK5GkseAPfbr/fi6AmeCFy1SiUCk/QrSedJmtgTATlXC8kD4PjxvRdHT/BE4KpVtGrIzNolTQBOAr4vaSwwA7gHeNDM8twXybnelzwAtrT0Xhw9wROBq1aaK4uXmtm1ZnYK8DfANOA44GFJv65zfM5VJD4Axt0psyxOBMmLyjwRuHKkHX0UADPrBH4fPYhKCM71Oa3RL3vChN6NoyfEN5/JVyLwC8pcGqkSgaQjCPctnpBcx8z2rk9YzlVn69bwPGVK78bRE7xqyFUrbYngx8AFhGsKuuoXjnO1MXky3HgjfOQjvR1J/eVLBH4dgStH2kTwhpndU9dInKshCc4+u7ej6BleInDVSpsI/iDpu8BU4K8/NzObV5eonHOpFUsE3kbg0kibCA6LntsT0ww4prbhOOfKVSgR9O/fHL2mXPVSJQIze3e9A3HOVaZQIvBqIZdWqnsWSxoh6XJJc6LH9ySNSLHeiZKekbRY0kVFljtEUpekU8sJ3jm344ij8WtPBC6ttDevvwFYB3w0eqwFbiy2QnSv4x8Qrko+CPi4pIMKLPcd4L70YTvnYnE7gCcCV6m0bQT7mNmHE+8vkzS/xDqHAovNbAmApNuBk4GFOct9AfgFcEjKWJxzCfEBP+4yCp4IXHnSlgg2SToyfhNdYLapxDpjgRcT75dF0/4qujL5g8C1xTYk6dy4WmrVqlUpQ3auORQqEXiPIZdW2hLB3wE3R+0CAl4Dzi6xTr7+Cpbz/vvAhWbWpSLdG8zsOuA6gPb29txtONfU4gN+skTQ2eklApde2l5D84FJkoZH79emWG0ZsGfi/ThgRc4y7cDtURLYFXivpK1m9qs0cTnnoF+/MMKqtxG4ShVNBJLOMLNbJX05ZzoAZnZ5kdVnA/tJ2gtYDpwGnJ5cwMz2SmzzJuBuTwLOlW/AAG8jcJUrVSIYEj0PyzOvaBWNmW2VdD6hN1ALcIOZLZB0XjS/aLuAcy69/v29ROAqV+rGND+MXv7OzB5JzosajIsys+nA9JxpeROAmZ1danvOufy8ROCqkbbX0P+knOac6wVeInDVKNVGcDjhrmSjctoJhhOqe5xzfYCXCFw1SrURDACGRssl2wnWAj4chHN9hJcIXDVKtRE8CDwo6SYzW9pDMTnnypRbIujs9AvKXHpp2wiulzQyfiNpJ0k+NpBzfYSXCFw10iaCXc3s9fiNma0BRtclIudc2QYM8ETgKpc2EWyTND5+I2kCJa4jcM71nP79vbHYVS7tWEP/DMyQ9GD0/p3AufUJyTlXLi8RuGqkHWvoXkmTgSmEweQuMLPVdY3MOZda//6weXP3e08Erhxp71Am4ERgsplNAwZLOrSukTnnUvMSgatG2jaCq4HDgY9H79cR7j7mnOsDkm0EXV3h4YnApZW2jeAwM5ss6U8Qeg1J8p+Zc31EskQQJwRPBC6ttCWCzujewgYgaRSwrW5ROefKkiwRxM9+QZlLK20iuBL4JTBa0reAGcC/1y0q51xZkiWC+NlLBC6ttL2G/lfSXOBYQq+hU8xsUV0jc86lliwReCJw5So1+ujOibcrgZ8m55nZa/UKzDmXnpcIXDVKlQjmEtoF4jvLx1cTK3q9d53ics6VITnonCcCV65SieBMM5shqc3MNpdY1jnXS5KDznkicOUq1Vh8RfT8aL0Dcc5VLi4RmHkicOUrVSLolHQjME7SlbkzzeyL9QnLOVeO/v1DEujq8kTgylcqEbwfOA44htBe4Jzrg+KDfmenX1DmylfqDmWrgdslLTKzx3soJudcmeKLx7Zs6S4R+AVlLq20F5RtknS/pKcAJB0s6eI6xuWcK0OyROBVQ65caRPBj4CvAZ0AZvYEcFq9gnLOlSdficATgUsrbSIYbGaP5UzbWutgnHOV8RKBq0baRLBa0j50Dzp3KvBS3aJyzpXFSwSuGmmHof574DrgAEnLgeeAT9QtKudcWbxE4KqRdtC5JcBxkoYQShGbgI8BS+sYm3MuJS8RuGoUrRqSNFzS1yRdJek9wEbgk8Bi4KM9EaBzrjQvEbhqlCoR3AKsAWYCnwW+CgwgDEM9v76hOefSSpYI/IIyV65SiWBvM3srgKTrgdXAeDNbV/fInHOpeYnAVaNUr6HO+IWZdQHPlZMEJJ0o6RlJiyVdlGf+JyQ9ET0elTQpfejOuZhfWeyqUapEMEnS2ui1gEHRewFmZsMLrRjd4/gHwHuAZcBsSXeZ2cLEYs8B7zKzNZJOIvRMOqzCfXGuacVn/3Ei6NcPWlp6NybXOEqNNVTNT+lQYHHU4whJtwMnA39NBGaWHN56FjCuis9zrmnFZ/9x1ZBXC7lypL2grBJjgRcT75dF0wr5NHBPvhmSzpU0R9KcVatW1TBE57Iht0TgicCVo56JQHmmWZ5pSHo3IRFcmG++mV1nZu1m1j5q1KgahuhcNniJwFUj7ZXFlVgG7Jl4Pw5YkbuQpIOB64GTzOzVOsbjXGZ5icBVo54lgtnAfpL2kjSAMFrpXckFJI0HphLujfznOsbiXKblXkfgicCVo24lAjPbKul84D6gBbjBzBZIOi+afy3wr8AuwNWSALaaWXu9YnIuqwYODM9eInCVqGfVEGY2HZieM+3axOvPAJ+pZwzONYO2tvC8ebMnAle+elYNOed6SFwiiBOBX0zmyuGJwLkMaG0Nj44OLxG48nkicC4j2tq8ashVxhOBcxnhicBVyhOBcxnhicBVyhOBcxnhicBVyhOBcxkRJwK/oMyVyxOBcxnhJQJXKU8EzmWEJwJXKU8EzmXEwIF+QZmrjCcC5zLCSwSuUp4InMsITwSuUp4InMsITwSuUp4InMuItjbYtMkTgSufJwLnMqKtDTZsCK89EbhyeCJwLiPa2mDduvDaew25cngicC4j2trALLweNqx3Y3GNxROBcxkR36UMYKedei8O13g8ETiXEclEsPPOvReHazyeCJzLCC8RuEp5InAuIzwRuEp5InAuI7xqyFXKE4FzGZFMBCNG9F4crvF4InAuIwYO7H7d0tJ7cbjG44nAuYxIlgicK4cnAucywhOBq5QnAucywhOBq5QnAucyIk4EUu/G4RqPJwLnMiJOBIMH924crvF4InAuI+JeQ54IXLk8ETiXEXGV0KhRvRuHazx1TQSSTpT0jKTFki7KM1+SrozmPyFpcj3jcS7Lxo2Dyy6Du+/u7Uhco2mt14YltQA/AN4DLANmS7rLzBYmFjsJ2C96HAZcEz0758okwb/+a29H4RpRPUsEhwKLzWyJmW0BbgdOzlnmZOAnFswCRkrao44xOeecy1HPRDAWeDHxflk0rdxlkHSupDmS5qxatarmgTrnXDOrZyLI15vZKlgGM7vOzNrNrH2Ut4Q551xN1TMRLAP2TLwfB6yoYBnnnHN1VM9EMBvYT9JekgYApwF35SxzF3BW1HtoCvCGmb1Ux5icc87lqFuvITPbKul84D6gBbjBzBZIOi+afy0wHXgvsBjYCJxTr3icc87lV7dEAGBm0wkH++S0axOvDfj7esbgnHOuOL+y2DnnmpzCSXnjkLQKWFrh6rsCq2sYTm/yfembfF/6Jt8XmGBmebtdNlwiqIakOWbW3ttx1ILvS9/k+9I3+b4U51VDzjnX5DwROOdck2u2RHBdbwdQQ74vfZPvS9/k+1JEU7UROOec21GzlQicc87l8ETgnHNNLlOJQNKbJM1PPNZK+gdJl0panpj+3sQ6X4vukPaMpBN6M/4kSRdIWiDpKUk/ldQmaWdJv5X0bPS8U2L5PrkfUHBfGu47AZD0pWg/Fkj6h2hao34v+falIb4XSTdIWinpqcS0sr8HSe+Q9GQ070pJ+UZE7jP7ImmipE2J7+faxDqV74uZZfJBGN/oZWACcCnwT3mWOQh4HBgI7AX8BWjpA7GPBZ4DBkXv/w84G/hP4KJo2kXAd/ryfpTYl4b6TqLY3gI8BQwmDM/yO8Ld9Rrxeym0Lw3xvQDvBCYDTyWmlf09AI8BhxOGxL8HOKmP78vE5HI526l4XzJVIshxLPAXMyt2FfLJwO1m1mFmzxEGvzu0R6IrrRUYJKmV8M+6ghDvzdH8m4FTotd9eT8g/74U0pf35UBglpltNLOtwIPAB2nM76XQvhTSp/bFzB4CXsuZXNb3oHA3xOFmNtPCkfQniXV6TJn7kle1+5LlRHAa8NPE+/MlPREVw+IiY6o7pPU0M1sO/BfwAvASYXju3wC7WTRMd/Q8OlqlT+4HFN0XaKDvJPIU8E5Ju0gaTBg5d08a8Huh8L5A430vsXK/h7HR69zpfUGhfQHYS9KfJD0o6ahoWlX7kslEoHD/g78FfhZNugbYB3gb4WD0vXjRPKv3en/a6J/vZEIxdgwwRNIZxVbJM63X9wOK7ktDfScAZrYI+A7wW+BeQnXD1iKrNOK+NNz3kkKh2Btxn14CxpvZ24EvA7dJGk6V+5LJRACcBMwzs1cAzOwVM+sys23Aj+gu0vbVO6QdBzxnZqvMrBOYCvwN8EpUBIyLgiuj5fvqfkCBfWnA7wQAM/uxmU02s3cSivPP0pjfS959adTvJVLu97Asep07vS/Iuy9R9dar0eu5hPaO/alyX7KaCD5Ooloo/oNGPkgoFkO4Q9ppkgZK2ovQWPZYj0VZ2AvAFEmDo5b/Y4FFhHg/GS3zSeDO6HVf3Q8osC8N+J0AIGl09Dwe+BDhd9aI30vefWnU7yVS1vcQVbmskzQl+m2elVint+XdF0mjJLVEr/cm7MuSqvelp1vI6/0gNEa+CoxITLsFeBJ4IvoD75GY98+ErPoMvdBjoMh+XAY8TfhHvIXQ42EX4H7CWej9wM59fT+K7EvDfSdRbA8DCwlVKcdG0xr1e8m3Lw3xvRAS8EtAJ+Fs+NOVfA9Ae/S7/AtwFdFoC311X4APAwui72we8IFa7IsPMeGcc00uq1VDzjnnUvJE4JxzTc4TgXPONTlPBM451+Q8ETjnXJPzROBcjUQjd/5TD3zOHpJ+U3rJsrb5u+Rona65eCJwrg9QkPb/8UTgvhqHcAvw+Rpv0zUITwSuT5D0VUlfjF7/t6TfR6+PlXRr9PoaSXMUxs+/LJp2kqT/S2znaEnTotfHS5opaZ6kn0kaGk1/XtJl0fQnJR0QTd/ujF5hrP6J0eNpSddH0/5X0nGSHlEYLz45CuckSb+Ppn82sa2vSJodDeYWxz5R0iJJVxMuDtpT0k3RZzwp6YICf64TCcMM5/4Nz4q2/7ikW6JpN0V/tz9IWiLpXdFgcosk3ZRY/S7CFfmuCXkicH3FQ0A8kmI7MFRSf+BIwhWwAP9sZu3AwcC7JB1MGDRtiqQh0TIfA+6QtCtwMXCcmU0G5hAG6YqtjqZfA6SpztkXuCL67AOA06PY/gn4emK5g4H3EcaF/1dJYyQdTxgK4FDCYG7vkPTOaPk3AT+xMIjYrsBYM3uLmb0VuDE3iGh4gTeZ2cKc6W8mXD17jJlNAr6UmL0TcAxwATAN+G/gzcBbJb0NwMzWAAMl7ZLib+EyxhOB6yvmEg6Qw4AOYCYhIRxFdyL4qKR5wJ8IB7KDLIylfy/wAYX7HbyPMMbKFMINSR6RNJ8wXsuExOdNTXzuxBTxPWdmT1oYjG0BcL+Fy/KfzFn/TjPbZGargT8QDv7HR48/Ec78DyAkBoClZjYrer0E2FvS/0g6EVibJ47DgD/mmX4M8PPoczGz5Pj20xKxvpKzH8nYVxJGiHVNprW3A3AOwMw6JT0PnAM8Shjr5t2EIZEXRYOF/RNwiJmtiao12qLV7wD+njCC5mwzWxcNvPVbMytU3dERPXfR/X+wle1PjtryLA+wLfF+G9v/H+WO2RIPd/wfZvbD5AxJE4ENf10w7Nck4IRofz4KfCpneycREl8u5fns3NiTceeLvQ3YVGAbLsO8ROD6kocIB/uHCKWA84D50dnscMJB8w1JuxEOiLEHCLf6+ywhKQDMAo6QtC+Awuin+5f4/Oej7SBpMuEeCuU6WeGezLsARwOzCQ27n0q0UYyNR/5Miqqz+pnZL4B/iWPJcSxhELJc9xNKTLtE29q5nKCjxLk74W/gmoyXCFxf8jChnnummW2QtDmahpk9LulPhOqMJcAj8Upm1iXpbsK9kD8ZTVsl6WzC0MoDo0UvBv5c5PN/AZwVVSXNLrFsIY8BvwbGA98wsxXACkkHAjPD8Zb1wBmE0kjSWODGRO+hryVnShoFbDazHaqMzGyBpG8BD0rqIlRDnV1G3O8g3Lqy2I12XEb56KPONQiFO7uNM7Nv12HbVwB3mVm+0obLOC8RONcgzOzWOm7+KU8CzctLBM451+S8sdg555qcJwLnnGtyngicc67JeSJwzrkm54nAOeea3P8DKxm+KtWCQk0AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "index = 0\n", + "plt.figure()\n", + "plt.plot(wavenumbers, R[:, index, 0], '-b', label=r'$R_p$')\n", + "plt.title('Reflectivity for ϑ = {:0.1f}'.format(gtm.theta[0, index]))\n", + "plt.xlabel('wavenumbers / cm)')\n", + "plt.ylabel('Reflection/Transmission')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "e55b416e-3269-4d74-8d51-604cdcdee7bd", + "metadata": {}, + "outputs": [], + "source": [ + "dz = 100e-9 # spatial resolution\n", + "zplot, E_out, zn_plot = gtm.calculate_Efield(r, R, t, T, dz=dz)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "082058c3-a1df-439d-8652-1b92357d0b1c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":13: MatplotlibDeprecationWarning: Case-insensitive properties were deprecated in 3.3 and support will be removed two minor releases later\n", + " plt.axhline(zn_plot[1]*1e6, LineStyle='--')\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#wnm, zm = np.meshgrid(wavenumbers, zplot*1e6)\n", + "fig2ab = plt.figure()\n", + "\n", + "axfield = fig2ab.add_subplot(111)\n", + "img = np.abs(E_out[:, 0, 0, :].T)\n", + "axc = axfield.pcolormesh(wavenumbers.magnitude, zplot*1e6, img, vmin=0, vmax=5, \n", + " shading='gouraud', cmap=plt.cm.gnuplot2)\n", + "\n", + "axfield.invert_yaxis()\n", + "axfield.set_xlabel('wavenumber (cm$^{-1}$)')\n", + "axfield.set_ylabel('z-position ($\\mu$m)')\n", + "axfield.set_ylim([8.5, 0])\n", + "plt.axhline(zn_plot[1]*1e6, LineStyle='--')\n", + "fig2ab.colorbar(axc)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a16f4aad-bd7f-4a03-888e-5223e2e979f0", + "metadata": {}, + "source": [ + "# Multilayer" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "db9bedbb-3e94-42e9-87fd-94811528b59a", + "metadata": {}, + "outputs": [], + "source": [ + "### Physical constants\n", + "c_const = 3e8\n", + "eV = 1.6e-19\n", + "m0 = 9.11e-31 ## electron rest mass\n", + "meff = 0.202*m0\n", + "eps0 = 8.854e-12 ## vacuum permittivity\n", + "# Import the refractive index of the doped QW\n", + "fimp, eps1zz, eps2zz = np.genfromtxt('EpsQW_zz.txt', delimiter='\\t',\n", + " skip_header=1, unpack=True)\n", + "fimp, eps1plane, eps2plane = np.genfromtxt('EpsQW_plane.txt', delimiter='\\t',\n", + " skip_header=1, unpack=True)\n", + "\n", + "def epsQW_zz(f):\n", + " eps1 = np.interp(f, fimp, eps1zz)\n", + " eps2 = np.interp(f, fimp, eps2zz)\n", + " return eps1+1.0j*eps2\n", + "\n", + "\n", + "def epsQW_plane(f):\n", + " eps1 = np.interp(f, fimp, eps1plane)\n", + " eps2 = np.interp(f, fimp, eps2plane)\n", + " return eps1+1.0j*eps2\n", + "\n", + "\n", + "# Doped mirror: a bulky, doped semiconductor layer\n", + "DopMirr = 5e20*1e6 # doping in m-3\n", + "f_P_mirr = np.sqrt(DopMirr*eV**2/(meff*eps0*5.35))/(2.*np.pi) # plasma frequency\n", + "epsmirr = lambda x: perm.eps_drude(x, f_P_mirr, 1./500e-15, epsinf=5.35)\n", + "\n", + "# Barriers have a permittivity interpolated from their constituents\n", + "epsBarrx = lambda x: 0.26*perm.eps_AlNx(x)+perm.eps_GaNx(x)*(1.0-0.26)\n", + "epsBarrz = lambda x: 0.26*perm.eps_AlNz(x)+perm.eps_GaNz(x)*(1.0-0.26)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "12e67b49-dddc-4fb7-90d6-d42ed9fed132", + "metadata": {}, + "outputs": [], + "source": [ + "Nqw = 20 # Number of QWs\n", + "tQW = 3*u.nm # thickness of the QW\n", + "tBarr = 10.0*u.nm # Barrier thickness\n", + "Lcav = 1*u.um # total length of the cavity\n", + "LAR = Nqw*tQW+(Nqw+1)*tBarr # length of the active region // superlattice\n", + "Lspac = Lcav-LAR # length of the spacer\n", + "tmirr = 0.5*u.um # mirror thickness\n", + "\n", + "barrier = ud.AmorphousLayer('barrier', \"barrier\", tBarr, 0*u.kg/u.m**3)\n", + "barrier.epsilon = [epsBarrx, epsBarrx, epsBarrz]\n", + "\n", + "QW = ud.AmorphousLayer('qw', \"quantum well\", tQW, 0*u.kg/u.m**3)\n", + "QW.epsilon = [epsQW_plane, epsQW_plane, epsQW_zz]\n", + "\n", + "GaNspacer_sl = ud.AmorphousLayer('GaNspacer_sl', \"GaNspacer_sl\", Lspac, 0*u.kg/u.m**3)\n", + "GaNspacer_sl.epsilon = [perm.eps_GaNx, perm.eps_GaNx, perm.eps_GaNz]\n", + "\n", + "GaNspacer_empty = ud.AmorphousLayer('GaNspacer_empty', \"GaNspacer_empty\", Lcav, 0*u.kg/u.m**3)\n", + "GaNspacer_empty.epsilon = [perm.eps_GaNx, perm.eps_GaNx, perm.eps_GaNz]\n", + "\n", + "mirror = ud.AmorphousLayer('mirror', \"mirror\", tmirr, 0*u.kg/u.m**3)\n", + "mirror.epsilon = epsmirr\n", + "\n", + "superstrate = ud.AmorphousLayer('superstrate', \"superstrate\", 0.5*u.um, 0*u.kg/u.m**3)\n", + "superstrate.epsilon = [perm.eps_GaNx, perm.eps_GaNx, perm.eps_GaNz]\n", + "\n", + "substrate = ud.AmorphousLayer('substrate', \"substrate\", 200*u.nm, 0*u.kg/u.m**3)\n", + "substrate.epsilon = eps_Au" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "d2074fdc-436c-476c-bb75-81422f2b57b0", + "metadata": {}, + "outputs": [], + "source": [ + "S_empty = ud.Structure('empty')\n", + "\n", + "S_empty.add_sub_structure(superstrate)\n", + "S_empty.add_sub_structure(mirror)\n", + "S_empty.add_sub_structure(GaNspacer_empty)\n", + "S_empty.add_sub_structure(substrate)\n", + "\n", + "S_sl = ud.Structure('SL')\n", + "S_sl.add_sub_structure(superstrate)\n", + "S_sl.add_sub_structure(mirror)\n", + "S_sl.add_sub_structure(GaNspacer_sl)\n", + "\n", + "DL = ud.Structure('DL')\n", + "DL.add_sub_structure(barrier)\n", + "DL.add_sub_structure(QW)\n", + "\n", + "S_sl.add_sub_structure(DL, Nqw)\n", + "S_sl.add_sub_structure(barrier)\n", + "S_sl.add_sub_structure(substrate)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "efdc2ad4-701e-43ec-9752-f66c5b33c48f", + "metadata": {}, + "outputs": [], + "source": [ + "gtm = ud.GTM(S_empty, True)\n", + "gtm.disp_messages = False\n", + "gtm.save_data = False\n", + "\n", + "gtm.wl = np.linspace(4, 11, 100)*u.um # set two photon energies\n", + "gtm.theta = np.r_[1:80:0.5]*u.deg\n", + "\n", + "\n", + "gtm.S = S_empty\n", + "r_empty, R_empty, t_empty, T_empty = gtm.calculate_r_t()\n", + "zplot_empty, E_out_empty, H_out_empty, zn_plot_empty = gtm.calculate_Efield(\n", + " r_empty, R_empty, t_empty, T_empty, magnetic=True)\n", + "Poy_empty, A_empty = gtm.calculate_Poynting_Absorption_vs_z(\n", + " zplot_empty, E_out_empty, H_out_empty, R_empty)\n", + "\n", + "gtm.S = S_sl\n", + "r_sl, R_sl, t_sl, T_sl = gtm.calculate_r_t()\n", + "zplot_sl, E_out_sl, H_out_sl, zn_plot_sl = gtm.calculate_Efield(\n", + " r_sl, R_sl, t_sl, T_sl, magnetic=True)\n", + "Poy_sl, A_sl = gtm.calculate_Poynting_Absorption_vs_z(\n", + " zplot_sl, E_out_sl, H_out_sl, R_sl)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "a2397177-64e7-44dd-9f61-841f0b3058d7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\loc_schick\\general\\python\\wpy64-3890\\python-3.8.9.amd64\\lib\\site-packages\\matplotlib\\cbook\\__init__.py:736: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", + " x = np.array(x, subok=True, copy=copy)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "## Interfaces of interest\n", + "zmirr = np.abs(zplot_empty*u.m-tmirr).argmin() ## doped mirror\n", + "zspac = np.abs(zplot_empty*u.m-(tmirr+Lspac)).argmin() ## spacer\n", + "zsl = np.abs(zplot_empty*u.m-(tmirr+Lspac+Nqw*tQW+Nqw*tBarr)).argmin() ## superlattice\n", + "zAu = zplot_empty.argmax() ## substrate\n", + "\n", + "import matplotlib.gridspec as gridspec\n", + "gs = gridspec.GridSpec(2, 4, hspace=0, wspace=0)\n", + "\n", + "fig = plt.figure()\n", + "\n", + "axR_empty = fig.add_subplot(gs[0,0])\n", + "axR_empty.pcolormesh(gtm.theta[0, :], gtm.frequency.to('THz'), R_empty[:, :, 0],\n", + " vmin=0, vmax=1, shading='gouraud')\n", + "axR_empty.set_xticklabels([])\n", + "axR_empty.set_ylabel('Frequency (THz)')\n", + "axR_empty.text(0.1, 0.05, r'$R^p$', transform = axR_empty.transAxes)\n", + "\n", + "axA_mirr_empty = fig.add_subplot(gs[0,1], sharex=axR_empty)\n", + "axA_mirr_empty.pcolormesh(gtm.theta[0, :], gtm.frequency.to('THz'),\n", + " A_empty[:, :, 0, zmirr], vmin=0, vmax=1, shading='gouraud')\n", + "axA_mirr_empty.set_yticklabels([])\n", + "axA_mirr_empty.text(0.1, 0.05, r'$A^p_{mirror}$', transform = axA_mirr_empty.transAxes,\n", + " color='w')\n", + "\n", + "axA_Au_empty = fig.add_subplot(gs[0,3], sharex=axR_empty)\n", + "axA_Au_empty.pcolormesh(gtm.theta[0, :], gtm.frequency.to('THz'),\n", + " A_empty[:, :, 0, zAu]-A_empty[:, :, 0, zmirr], vmin=0, vmax=1, shading='gouraud')\n", + "axA_Au_empty.set_yticklabels([])\n", + "axA_Au_empty.text(0.1, 0.05, r'$A^p_{Au}$', transform = axA_Au_empty.transAxes,\n", + " color='w')\n", + "\n", + "## Interfaces of interest\n", + "zmirr = np.abs(zplot_sl*u.m-tmirr).argmin() ## doped mirror\n", + "zspac = np.abs(zplot_sl*u.m-(tmirr+Lspac)).argmin() ## spacer\n", + "zsl = np.abs(zplot_sl*u.m-(tmirr+Lspac+Nqw*tQW+Nqw*tBarr)).argmin() ## superlattice\n", + "zAu = zplot_sl.argmax() ## substrate\n", + "\n", + "axR_sl = fig.add_subplot(gs[1,0], sharey = axR_empty)\n", + "axR_sl.pcolormesh(gtm.theta[0, :], gtm.frequency.to('THz'), R_sl[:, :, 0],\n", + " vmin=0, vmax=1, shading='gouraud')\n", + "axR_sl.set_xlabel(r'$\\theta$ (deg)')\n", + "axR_sl.set_ylabel('Frequency (THz)')\n", + "axR_sl.text(0.1, 0.05, r'$R^p$', transform = axR_sl.transAxes)\n", + "\n", + "axA_mirr_sl = fig.add_subplot(gs[1,1], sharex=axR_sl)\n", + "axA_mirr_sl.pcolormesh(gtm.theta[0, :], gtm.frequency.to('THz'),\n", + " A_sl[:, :, 0, zmirr], vmin=0, vmax=1, shading='gouraud')\n", + "axA_mirr_sl.set_xlabel(r'$\\theta$ (deg)')\n", + "axA_mirr_sl.set_yticklabels([])\n", + "axA_mirr_sl.text(0.1, 0.05, r'$A^p_{mirror}$', transform = axA_mirr_sl.transAxes,\n", + " color='w')\n", + "\n", + "axA_sl_sl = fig.add_subplot(gs[1,2], sharex=axR_sl)\n", + "axA_sl_sl.pcolormesh(gtm.theta[0, :], gtm.frequency.to('THz'),\n", + " A_sl[:, :, 0, zsl]-A_sl[:, :, 0, zmirr], vmin=0, vmax=1, shading='gouraud')\n", + "axA_sl_sl.set_yticklabels([])\n", + "axA_sl_sl.set_xlabel(r'$\\theta$ (deg)')\n", + "axA_sl_sl.text(0.1, 0.05, r'$A^p_{QWs}$', transform = axA_sl_sl.transAxes,\n", + " color='w')\n", + "\n", + "axA_Au_sl = fig.add_subplot(gs[1,3], sharex=axR_sl)\n", + "axA_Au_sl.pcolormesh(gtm.theta[0, :], gtm.frequency.to('THz'),\n", + " A_sl[:, :, 0, zAu]-A_sl[:, :, 0, zsl], vmin=0, vmax=1, shading='gouraud')\n", + "axA_Au_sl.set_xlabel(r'$\\theta$ (deg)')\n", + "axA_Au_sl.set_yticklabels([])\n", + "axA_Au_sl.text(0.1, 0.05, r'$A^p_{Au}$', transform = axA_Au_sl.transAxes,\n", + " color='w')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "433c65ba-6f72-4383-9cdc-1fa3bf470e44", + "metadata": {}, + "source": [ + "# Azimuthal" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "c71f47d0-e263-4d38-8046-1409f50cd369", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8057127cc1be4bb29ea4743e9b80f8f4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/91 [00:00:25: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", + " fig.show()\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.gridspec as gridspec\n", + "gs = gridspec.GridSpec(1,4, hspace=0, wspace=0)\n", + "\n", + "fig = plt.figure(figsize=(8,4))\n", + "axR = fig.add_subplot(gs[0])\n", + "axR.pcolormesh(psi_v, (1/gtm.wl).to('1/cm'), R.T, shading='gouraud',\n", + " vmin=0, vmax=1)\n", + "axR.set_xlabel('Euler angle $\\psi$ (deg)')\n", + "axR.set_ylabel('Wavenumber (cm$^{-1}$)')\n", + "\n", + "axA_MoO3 = fig.add_subplot(gs[1])\n", + "axA_MoO3.pcolormesh(psi_v, (1/gtm.wl).to('1/cm'), A[:, :, 0].T, shading='gouraud',\n", + " vmin=0, vmax=1)\n", + "axA_MoO3.set_yticklabels([])\n", + "\n", + "axA_AlN = fig.add_subplot(gs[2],sharex = axR, sharey=axA_MoO3)\n", + "axA_AlN.pcolormesh(psi_v, (1/gtm.wl).to('1/cm'), A[:, :, 1].T, shading='gouraud',\n", + " vmin=0, vmax=1)\n", + "\n", + "axA_SiC = fig.add_subplot(gs[3],sharex = axR, sharey=axA_MoO3)\n", + "axA_SiC.pcolormesh(psi_v, (1/gtm.wl).to('1/cm'), A[:, :, 2].T, shading='gouraud',\n", + " vmin=0, vmax=1)\n", + "\n", + "fig.tight_layout()\n", + "fig.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.8.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/udkm1Dsim/__init__.py b/udkm1Dsim/__init__.py index 40009fdf..40d107bf 100644 --- a/udkm1Dsim/__init__.py +++ b/udkm1Dsim/__init__.py @@ -9,10 +9,10 @@ from .simulations.heat import Heat from .simulations.phonons import Phonon, PhononNum, PhononAna from .simulations.magnetization import Magnetization, LLB -from .simulations.xrays import Xray, XrayKin, XrayDyn, XrayDynMag +from .simulations.scattering import Scattering, GTM, XrayKin, XrayDyn, XrayDynMag __all__ = ['Atom', 'AtomMixed', 'Layer', 'AmorphousLayer', 'UnitCell', 'Structure', 'Simulation', 'Heat', 'Phonon', 'PhononNum', 'PhononAna', 'Magnetization', 'LLB', - 'Xray', 'XrayKin', 'XrayDyn', 'XrayDynMag', 'u', 'Q_'] + 'Scattering', 'GTM', 'XrayKin', 'XrayDyn', 'XrayDynMag', 'u', 'Q_'] __version__ = '2.0.3' diff --git a/udkm1Dsim/simulations/xrays.py b/udkm1Dsim/simulations/scattering.py similarity index 64% rename from udkm1Dsim/simulations/xrays.py rename to udkm1Dsim/simulations/scattering.py index 188139a9..edf2abec 100644 --- a/udkm1Dsim/simulations/xrays.py +++ b/udkm1Dsim/simulations/scattering.py @@ -22,7 +22,7 @@ # OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE # OR OTHER DEALINGS IN THE SOFTWARE. -__all__ = ['Xray', 'XrayKin', 'XrayDyn', 'XrayDynMag'] +__all__ = ['Scattering', 'GTM', 'XrayKin', 'XrayDyn', 'XrayDynMag'] __docformat__ = 'restructuredtext' @@ -37,12 +37,13 @@ from tqdm.notebook import trange r_0 = constants.physical_constants['classical electron radius'][0] +c_0 = constants.physical_constants['speed of light in vacuum'][0] -class Xray(Simulation): +class Scattering(Simulation): r"""Xray - Base class for all X-ray scattering simulations. + Base class for all light scattering simulations. Args: S (Structure): sample to do simulations with. @@ -64,6 +65,7 @@ class Xray(Simulation): simulations. progress_bar (boolean): enable tqdm progress bar. energy (ndarray[float]): photon energies :math:`E` of scattering light + frequency (ndarray[float]): photon frequency :math:`f` of scattering light wl (ndarray[float]): wavelengths :math:`\lambda` of scattering light k (ndarray[float]): wavenumber :math:`k` of scattering light theta (ndarray[float]): incidence angles :math:`\theta` of scattering @@ -82,6 +84,7 @@ class Xray(Simulation): def __init__(self, S, force_recalc, **kwargs): super().__init__(S, force_recalc, **kwargs) self._energy = np.array([]) + self._frequency = np.array([]) self._wl = np.array([]) self._k = np.array([]) self._theta = np.zeros([1, 1]) @@ -103,6 +106,8 @@ def __str__(self, output=[]): """String representation of this class""" output = [['energy', self.energy[0] if np.size(self.energy) == 1 else '{:.4f} .. {:.4f}'.format(np.min(self.energy), np.max(self.energy))], + ['frequency', self.frequency[0] if np.size(self.frequency) == 1 else + '{:.4f} .. {:.4f}'.format(np.min(self.frequency), np.max(self.frequency))], ['wavelength', self.wl[0] if np.size(self.wl) == 1 else '{:.4f} .. {:.4f}'.format(np.min(self.wl), np.max(self.wl))], ['wavenumber', self.k[0] if np.size(self.k) == 1 else @@ -221,25 +226,206 @@ def update_experiment(self, caller): if caller != 'energy': if caller == 'wl': # calc energy from wavelength self._energy = Q_((constants.h*constants.c)/self._wl, 'J').to('eV').magnitude - elif caller == 'k': # calc energy von wavevector + elif caller == 'k': # calc energy from wavevector self._energy = \ Q_((constants.h*constants.c)/(2*np.pi/self._k), 'J').to('eV').magnitude + elif caller == 'frequency': # calc energy from frequency + self._energy = \ + Q_(constants.h*self._frequency, 'J').to('eV').magnitude if caller != 'wl': if caller == 'energy': # calc wavelength from energy self._wl = (constants.h*constants.c)/self.energy.to('J').magnitude elif caller == 'k': # calc wavelength from wavevector self._wl = 2*np.pi/self._k + elif caller == 'frequency': # calc wavelength from frequency + self._wl = constants.c/self._frequency if caller != 'k': if caller == 'energy': # calc wavevector from energy self._k = 2*np.pi/self._wl elif caller == 'wl': # calc wavevector from wavelength self._k = 2*np.pi/self._wl + elif caller == 'frequency': # calc wavevector from frequency + self._k = 2*np.pi*self._frequency/constants.c + if caller != 'frequency': + if caller == 'energy': # calc frequency from energy + self._frequency = self.energy.to('J').magnitude/constants.h + elif caller == 'wl': # calc frequency from wavelength + self._frequency = constants.c/self._wl + elif caller == 'k': # calc frequency from wavevector + self._frequency = self._k*constants.c/(2*np.pi) if caller != 'theta': self._theta = np.arcsin(np.outer(self._wl, self._qz[0, :])/np.pi/4) if caller != 'qz': self._qz = np.outer(2*self._k, np.sin(self._theta[0, :])) + self._zeta = np.sin(self._theta) + + def calc_inv(self, A): + return np.linalg.inv(A) + + @staticmethod + def exact_inv(M): + """Compute the 'exact' inverse of a 4x4 matrix using the analytical result. + + Parameters + ---------- + M : 4X4 array (float or complex) + Matrix to be inverted + + Returns + ------- + out : 4X4 array (complex) + Inverse of this matrix or Moore-Penrose approximation if matrix cannot + be inverted. + + Notes + ----- + This should give a higher precision and speed at a reduced noise. + From D.Dietze code https://github.com/ddietze/FSRStools + + .. see also:: http://www.cg.info.hiroshima-cu.ac.jp/~miyazaki/knowledge/teche23.html + + """ + # assert M.shape == (4, 4) + + # the following equations use algebraic indexing; transpose input + # matrix to get indexing right + + A = np.transpose(M, (0, 1, 3, 2)) + detA = A[:, :, 0, 0] * A[:, :, 1, 1] * A[:, :, 2, 2] * A[:, :, 3, 3] \ + + A[:, :, 0, 0] * A[:, :, 1, 2] * A[:, :, 2, 3] * A[:, :, 3, 1] \ + + A[:, :, 0, 0] * A[:, :, 1, 3] * A[:, :, 2, 1] * A[:, :, 3, 2] + detA = detA + A[:, :, 0, 1] * A[:, :, 1, 0] * A[:, :, 2, 3] * A[:, :, 3, 2] \ + + A[:, :, 0, 1] * A[:, :, 1, 2] * A[:, :, 2, 0] * A[:, :, 3, 3] \ + + A[:, :, 0, 1] * A[:, :, 1, 3] * A[:, :, 2, 2] * A[:, :, 3, 0] + detA = detA + A[:, :, 0, 2] * A[:, :, 1, 0] * A[:, :, 2, 1] * A[:, :, 3, 3] \ + + A[:, :, 0, 2] * A[:, :, 1, 1] * A[:, :, 2, 3] * A[:, :, 3, 0] \ + + A[:, :, 0, 2] * A[:, :, 1, 3] * A[:, :, 2, 0] * A[:, :, 3, 1] + detA = detA + A[:, :, 0, 3] * A[:, :, 1, 0] * A[:, :, 2, 2] * A[:, :, 3, 1] \ + + A[:, :, 0, 3] * A[:, :, 1, 1] * A[:, :, 2, 0] * A[:, :, 3, 2] \ + + A[:, :, 0, 3] * A[:, :, 1, 2] * A[:, :, 2, 1] * A[:, :, 3, 0] + + detA = detA - A[:, :, 0, 0] * A[:, :, 1, 1] * A[:, :, 2, 3] * A[:, :, 3, 2] \ + - A[:, :, 0, 0] * A[:, :, 1, 2] * A[:, :, 2, 1] * A[:, :, 3, 3] \ + - A[:, :, 0, 0] * A[:, :, 1, 3] * A[:, :, 2, 2] * A[:, :, 3, 1] + detA = detA - A[:, :, 0, 1] * A[:, :, 1, 0] * A[:, :, 2, 2] * A[:, :, 3, 3] \ + - A[:, :, 0, 1] * A[:, :, 1, 2] * A[:, :, 2, 3] * A[:, :, 3, 0] \ + - A[:, :, 0, 1] * A[:, :, 1, 3] * A[:, :, 2, 0] * A[:, :, 3, 2] + detA = detA - A[:, :, 0, 2] * A[:, :, 1, 0] * A[:, :, 2, 3] * A[:, :, 3, 1] \ + - A[:, :, 0, 2] * A[:, :, 1, 1] * A[:, :, 2, 0] * A[:, :, 3, 3] \ + - A[:, :, 0, 2] * A[:, :, 1, 3] * A[:, :, 2, 1] * A[:, :, 3, 0] + detA = detA - A[:, :, 0, 3] * A[:, :, 1, 0] * A[:, :, 2, 1] * A[:, :, 3, 2] \ + - A[:, :, 0, 3] * A[:, :, 1, 1] * A[:, :, 2, 2] * A[:, :, 3, 0] \ + - A[:, :, 0, 3] * A[:, :, 1, 2] * A[:, :, 2, 0] * A[:, :, 3, 1] + + if detA == 0: + return np.linalg.pinv(M) + + B = np.zeros(A.shape, dtype=np.complex128) + B[:, :, 0, 0] = A[:, :, 1, 1] * A[:, :, 2, 2] * A[:, :, 3, 3] \ + + A[:, :, 1, 2] * A[:, :, 2, 3] * A[:, :, 3, 1] \ + + A[:, :, 1, 3] * A[:, :, 2, 1] * A[:, :, 3, 2] \ + - A[:, :, 1, 1] * A[:, :, 2, 3] * A[:, :, 3, 2] \ + - A[:, :, 1, 2] * A[:, :, 2, 1] * A[:, :, 3, 3] \ + - A[:, :, 1, 3] * A[:, :, 2, 2] * A[:, :, 3, 1] + B[:, :, 0, 1] = A[:, :, 0, 1] * A[:, :, 2, 3] * A[:, :, 3, 2] \ + + A[:, :, 0, 2] * A[:, :, 2, 1] * A[:, :, 3, 3] \ + + A[:, :, 0, 3] * A[:, :, 2, 2] * A[:, :, 3, 1] \ + - A[:, :, 0, 1] * A[:, :, 2, 2] * A[:, :, 3, 3] \ + - A[:, :, 0, 2] * A[:, :, 2, 3] * A[:, :, 3, 1] \ + - A[:, :, 0, 3] * A[:, :, 2, 1] * A[:, :, 3, 2] + B[:, :, 0, 2] = A[:, :, 0, 1] * A[:, :, 1, 2] * A[:, :, 3, 3] \ + + A[:, :, 0, 2] * A[:, :, 1, 3] * A[:, :, 3, 1] \ + + A[:, :, 0, 3] * A[:, :, 1, 1] * A[:, :, 3, 2] \ + - A[:, :, 0, 1] * A[:, :, 1, 3] * A[:, :, 3, 2] \ + - A[:, :, 0, 2] * A[:, :, 1, 1] * A[:, :, 3, 3] \ + - A[:, :, 0, 3] * A[:, :, 1, 2] * A[:, :, 3, 1] + B[:, :, 0, 3] = A[:, :, 0, 1] * A[:, :, 1, 3] * A[:, :, 2, 2] \ + + A[:, :, 0, 2] * A[:, :, 1, 1] * A[:, :, 2, 3] \ + + A[:, :, 0, 3] * A[:, :, 1, 2] * A[:, :, 2, 1] \ + - A[:, :, 0, 1] * A[:, :, 1, 2] * A[:, :, 2, 3] \ + - A[:, :, 0, 2] * A[:, :, 1, 3] * A[:, :, 2, 1] \ + - A[:, :, 0, 3] * A[:, :, 1, 1] * A[:, :, 2, 2] + + B[:, :, 1, 0] = A[:, :, 1, 0] * A[:, :, 2, 3] * A[:, :, 3, 2] \ + + A[:, :, 1, 2] * A[:, :, 2, 0] * A[:, :, 3, 3] \ + + A[:, :, 1, 3] * A[:, :, 2, 2] * A[:, :, 3, 0] \ + - A[:, :, 1, 0] * A[:, :, 2, 2] * A[:, :, 3, 3] \ + - A[:, :, 1, 2] * A[:, :, 2, 3] * A[:, :, 3, 0] \ + - A[:, :, 1, 3] * A[:, :, 2, 0] * A[:, :, 3, 2] + B[:, :, 1, 1] = A[:, :, 0, 0] * A[:, :, 2, 2] * A[:, :, 3, 3] \ + + A[:, :, 0, 2] * A[:, :, 2, 3] * A[:, :, 3, 0] \ + + A[:, :, 0, 3] * A[:, :, 2, 0] * A[:, :, 3, 2] \ + - A[:, :, 0, 0] * A[:, :, 2, 3] * A[:, :, 3, 2] \ + - A[:, :, 0, 2] * A[:, :, 2, 0] * A[:, :, 3, 3] \ + - A[:, :, 0, 3] * A[:, :, 2, 2] * A[:, :, 3, 0] + B[:, :, 1, 2] = A[:, :, 0, 0] * A[:, :, 1, 3] * A[:, :, 3, 2] \ + + A[:, :, 0, 2] * A[:, :, 1, 0] * A[:, :, 3, 3] \ + + A[:, :, 0, 3] * A[:, :, 1, 2] * A[:, :, 3, 0] \ + - A[:, :, 0, 0] * A[:, :, 1, 2] * A[:, :, 3, 3] \ + - A[:, :, 0, 2] * A[:, :, 1, 3] * A[:, :, 3, 0] \ + - A[:, :, 0, 3] * A[:, :, 1, 0] * A[:, :, 3, 2] + B[:, :, 1, 3] = A[:, :, 0, 0] * A[:, :, 1, 2] * A[:, :, 2, 3] \ + + A[:, :, 0, 2] * A[:, :, 1, 3] * A[:, :, 2, 0] \ + + A[:, :, 0, 3] * A[:, :, 1, 0] * A[:, :, 2, 2] \ + - A[:, :, 0, 0] * A[:, :, 1, 3] * A[:, :, 2, 2] \ + - A[:, :, 0, 2] * A[:, :, 1, 0] * A[:, :, 2, 3] \ + - A[:, :, 0, 3] * A[:, :, 1, 2] * A[:, :, 2, 0] + + B[:, :, 2, 0] = A[:, :, 1, 0] * A[:, :, 2, 1] * A[:, :, 3, 3] \ + + A[:, :, 1, 1] * A[:, :, 2, 3] * A[:, :, 3, 0] \ + + A[:, :, 1, 3] * A[:, :, 2, 0] * A[:, :, 3, 1] \ + - A[:, :, 1, 0] * A[:, :, 2, 3] * A[:, :, 3, 1] \ + - A[:, :, 1, 1] * A[:, :, 2, 0] * A[:, :, 3, 3] \ + - A[:, :, 1, 3] * A[:, :, 2, 1] * A[:, :, 3, 0] + B[:, :, 2, 1] = A[:, :, 0, 0] * A[:, :, 2, 3] * A[:, :, 3, 1] \ + + A[:, :, 0, 1] * A[:, :, 2, 0] * A[:, :, 3, 3] \ + + A[:, :, 0, 3] * A[:, :, 2, 1] * A[:, :, 3, 0] \ + - A[:, :, 0, 0] * A[:, :, 2, 1] * A[:, :, 3, 3] \ + - A[:, :, 0, 1] * A[:, :, 2, 3] * A[:, :, 3, 0] \ + - A[:, :, 0, 3] * A[:, :, 2, 0] * A[:, :, 3, 1] + B[:, :, 2, 2] = A[:, :, 0, 0] * A[:, :, 1, 1] * A[:, :, 3, 3] \ + + A[:, :, 0, 1] * A[:, :, 1, 3] * A[:, :, 3, 0] \ + + A[:, :, 0, 3] * A[:, :, 1, 0] * A[:, :, 3, 1] \ + - A[:, :, 0, 0] * A[:, :, 1, 3] * A[:, :, 3, 1] \ + - A[:, :, 0, 1] * A[:, :, 1, 0] * A[:, :, 3, 3] \ + - A[:, :, 0, 3] * A[:, :, 1, 1] * A[:, :, 3, 0] + B[:, :, 2, 3] = A[:, :, 0, 0] * A[:, :, 1, 3] * A[:, :, 2, 1] \ + + A[:, :, 0, 1] * A[:, :, 1, 0] * A[:, :, 2, 3] \ + + A[:, :, 0, 3] * A[:, :, 1, 1] * A[:, :, 2, 0] \ + - A[:, :, 0, 0] * A[:, :, 1, 1] * A[:, :, 2, 3] \ + - A[:, :, 0, 1] * A[:, :, 1, 3] * A[:, :, 2, 0] \ + - A[:, :, 0, 3] * A[:, :, 1, 0] * A[:, :, 2, 1] + + B[:, :, 3, 0] = A[:, :, 1, 0] * A[:, :, 2, 2] * A[:, :, 3, 1] \ + + A[:, :, 1, 1] * A[:, :, 2, 0] * A[:, :, 3, 2] \ + + A[:, :, 1, 2] * A[:, :, 2, 1] * A[:, :, 3, 0] \ + - A[:, :, 1, 0] * A[:, :, 2, 1] * A[:, :, 3, 2] \ + - A[:, :, 1, 1] * A[:, :, 2, 2] * A[:, :, 3, 0] \ + - A[:, :, 1, 2] * A[:, :, 2, 0] * A[:, :, 3, 1] + B[:, :, 3, 1] = A[:, :, 0, 0] * A[:, :, 2, 1] * A[:, :, 3, 2] \ + + A[:, :, 0, 1] * A[:, :, 2, 2] * A[:, :, 3, 0] \ + + A[:, :, 0, 2] * A[:, :, 2, 0] * A[:, :, 3, 1] \ + - A[:, :, 0, 0] * A[:, :, 2, 2] * A[:, :, 3, 1] \ + - A[:, :, 0, 1] * A[:, :, 2, 0] * A[:, :, 3, 2] \ + - A[:, :, 0, 2] * A[:, :, 2, 1] * A[:, :, 3, 0] + B[:, :, 3, 2] = A[:, :, 0, 0] * A[:, :, 1, 2] * A[:, :, 3, 1] \ + + A[:, :, 0, 1] * A[:, :, 1, 0] * A[:, :, 3, 2] \ + + A[:, :, 0, 2] * A[:, :, 1, 1] * A[:, :, 3, 0] \ + - A[:, :, 0, 0] * A[:, :, 1, 1] * A[:, :, 3, 2] \ + - A[:, :, 0, 1] * A[:, :, 1, 2] * A[:, :, 3, 0] \ + - A[:, :, 0, 2] * A[:, :, 1, 0] * A[:, :, 3, 1] + B[:, :, 3, 3] = A[:, :, 0, 0] * A[:, :, 1, 1] * A[:, :, 2, 2] \ + + A[:, :, 0, 1] * A[:, :, 1, 2] * A[:, :, 2, 0] \ + + A[:, :, 0, 2] * A[:, :, 1, 0] * A[:, :, 2, 1] \ + - A[:, :, 0, 0] * A[:, :, 1, 2] * A[:, :, 2, 1] \ + - A[:, :, 0, 1] * A[:, :, 1, 0] * A[:, :, 2, 2] \ + - A[:, :, 0, 2] * A[:, :, 1, 1] * A[:, :, 2, 0] + + return np.transpose(B, (0, 1, 3, 2)) \ + / np.tile(detA[:, :, np.newaxis, np.newaxis], (1, 1, 4, 4)) + @property def energy(self): return Q_(self._energy, u.eV) @@ -249,6 +435,15 @@ def energy(self, energy): self._energy = np.array(energy.to('eV').magnitude, ndmin=1) self.update_experiment('energy') + @property + def frequency(self): + return Q_(self._frequency, u.Hz) + + @frequency.setter + def frequency(self, frequency): + self._frequency = np.array(frequency.to('Hz').magnitude, ndmin=1) + self.update_experiment('frequency') + @property def wl(self): return Q_(self._wl, u.m).to('nm') @@ -290,7 +485,1122 @@ def qz(self, qz): self.update_experiment('qz') -class XrayKin(Xray): +class GTM(Scattering): + r"""GTM + + General Transfer Matrix scattering simulations. + + Adapted from pyGTM + + Args: + S (Structure): sample to do simulations with. + force_recalc (boolean): force recalculation of results. + + Keyword Args: + save_data (boolean): true to save simulation results. + cache_dir (str): path to cached data. + disp_messages (boolean): true to display messages from within the + simulations. + progress_bar (boolean): enable tqdm progress bar. + + Attributes: + S (Structure): sample structure to calculate simulations on. + force_recalc (boolean): force recalculation of results. + save_data (boolean): true to save simulation results. + cache_dir (str): path to cached data. + disp_messages (boolean): true to display messages from within the + simulations. + progress_bar (boolean): enable tqdm progress bar. + energy (ndarray[float]): photon energies :math:`E` of scattering light + wl (ndarray[float]): wavelengths :math:`\lambda` of scattering light + k (ndarray[float]): wavenumber :math:`k` of scattering light + theta (ndarray[float]): incidence angles :math:`\theta` of scattering + light + qz (ndarray[float]): scattering vector :math:`q_z` of scattering light + + """ + + def __init__(self, S, force_recalc, **kwargs): + super().__init__(S, force_recalc, **kwargs) + self.qsd_thr = 1e-10 # threshold for wavevector comparison + self.zero_thr = 1e-10 # threshold for eigenvalue comparison to zero + + def __str__(self): + """String representation of this class""" + class_str = 'General Transfer Matrix scattering simulation properties:\n\n' + class_str += super().__str__() + return class_str + + def get_hash(self, **kwargs): + """get_hash + + Calculates an unique hash given by the energy :math:`E`, :math:`q_z` + range, polarization states as well as the sample structure hash for + relevant scattering parameters. Optionally, part of the + ``strain_map`` is used. + + Args: + **kwargs (ndarray[float]): spatio-temporal strain profile. + + Returns: + hash (str): unique hash. + + """ + param = [self._qz, self._energy] + + if 'strain_map' in kwargs: + strain_map = kwargs.get('strain_map') + if np.size(strain_map) > 1e6: + strain_map = strain_map.flatten()[0:1000000] + param.append(strain_map) + + return self.S.get_hash(types=['optical']) + '_' + make_hash_md5(param) + + def set_polarization(self, pol_in_state, pol_out_state): + """set_polarization + + Sets the incoming and analyzer (outgoing) polarization. This is not + supported as the GTM always calculates s- and p-polarization. + + Args: + pol_in_state (int): incoming polarization state id. + pol_out_state (int): outgoing polarization state id. + + """ + pass + + def calculate_layer_matrices(self, layer): + """ + Calculate the principal matrices necessary for the GTM algorithm. + + Parameters + ---------- + zeta : complex + In-plane reduced wavevector kx/k0 in the system. + + Returns + ------- + None + + Notes + ----- + Note that zeta is conserved through the whole system and set externally + using the angle of incidence and `System.superstrate.epsilon[0,0]` value + + Requires prior execution of :py:func:`calculate_epsilon` + + """ + N = np.size(self._qz, 0) # energy steps + K = np.size(self._qz, 1) # qz steps + + M = np.zeros((N, K, 6, 6), dtype=np.complex128) # constitutive relations + a = np.zeros((N, K, 6, 6), dtype=np.complex128) + S = np.zeros((N, K, 4, 4), dtype=np.complex128) + Delta = np.zeros((N, K, 4, 4), dtype=np.complex128) + + # Constitutive matrix (see e.g. eqn (4)) + M[:, :, 0:3, 0:3] = np.repeat(np.expand_dims( + layer.get_epsilon_matrix(self._frequency), 1), K, 1) + M[:, :, 3:6, 3:6] = np.identity(3) + + # from eqn (10) + b = M[:, :, 2, 2]*M[:, :, 5, 5] - M[:, :, 2, 5]*M[:, :, 5, 2] + + # a matrix from eqn (9) + a[:, :, 2, 0] = (M[:, :, 5, 0]*M[:, :, 2, 5] - M[:, :, 2, 0]*M[:, :, 5, 5])/b + a[:, :, 2, 1] = ((M[:, :, 5, 1]-self.zeta)*M[:, :, 2, 5] - M[:, :, 2, 1]*M[:, :, 5, 5])/b + a[:, :, 2, 3] = (M[:, :, 5, 3]*M[:, :, 2, 5] - M[:, :, 2, 3]*M[:, :, 5, 5])/b + a[:, :, 2, 4] = (M[:, :, 5, 4]*M[:, :, 2, 5] - (M[:, :, 2, 4]+self.zeta)*M[:, :, 5, 5])/b + a[:, :, 5, 0] = (M[:, :, 5, 2]*M[:, :, 2, 0] - M[:, :, 2, 2]*M[:, :, 5, 0])/b + a[:, :, 5, 1] = (M[:, :, 5, 2]*M[:, :, 2, 1] - M[:, :, 2, 2]*(M[:, :, 5, 1]-self.zeta))/b + a[:, :, 5, 3] = (M[:, :, 5, 2]*M[:, :, 2, 3] - M[:, :, 2, 2]*M[:, :, 5, 3])/b + a[:, :, 5, 4] = (M[:, :, 5, 2]*(M[:, :, 2, 4]+self.zeta) - M[:, :, 2, 2]*M[:, :, 5, 4])/b + + # S Matrix (Don't know where it comes from since Delta is just S re-ordered) + # Note that after this only Delta is used + S[:, :, 0, 0] = M[:, :, 0, 0] + M[:, :, 0, 2]*a[:, :, 2, 0] + M[:, :, 0, 5]*a[:, :, 5, 0] + S[:, :, 0, 1] = M[:, :, 0, 1] + M[:, :, 0, 2]*a[:, :, 2, 1] + M[:, :, 0, 5]*a[:, :, 5, 1] + S[:, :, 0, 2] = M[:, :, 0, 3] + M[:, :, 0, 2]*a[:, :, 2, 3] + M[:, :, 0, 5]*a[:, :, 5, 3] + S[:, :, 0, 3] = M[:, :, 0, 4] + M[:, :, 0, 2]*a[:, :, 2, 4] + M[:, :, 0, 5]*a[:, :, 5, 4] + S[:, :, 1, 0] = M[:, :, 1, 0] + M[:, :, 1, 2]*a[:, :, 2, 0] \ + + (M[:, :, 1, 5]-self.zeta)*a[:, :, 5, 0] + S[:, :, 1, 1] = M[:, :, 1, 1] + M[:, :, 1, 2]*a[:, :, 2, 1] \ + + (M[:, :, 1, 5]-self.zeta)*a[:, :, 5, 1] + S[:, :, 1, 2] = M[:, :, 1, 3] + M[:, :, 1, 2]*a[:, :, 2, 3] \ + + (M[:, :, 1, 5]-self.zeta)*a[:, :, 5, 3] + S[:, :, 1, 3] = M[:, :, 1, 4] + M[:, :, 1, 2]*a[:, :, 2, 4] \ + + (M[:, :, 1, 5]-self.zeta)*a[:, :, 5, 4] + S[:, :, 2, 0] = M[:, :, 3, 0] + M[:, :, 3, 2]*a[:, :, 2, 0] + M[:, :, 3, 5]*a[:, :, 5, 0] + S[:, :, 2, 1] = M[:, :, 3, 1] + M[:, :, 3, 2]*a[:, :, 2, 1] + M[:, :, 3, 5]*a[:, :, 5, 1] + S[:, :, 2, 2] = M[:, :, 3, 3] + M[:, :, 3, 2]*a[:, :, 2, 3] + M[:, :, 3, 5]*a[:, :, 5, 3] + S[:, :, 2, 3] = M[:, :, 3, 4] + M[:, :, 3, 2]*a[:, :, 2, 4] + M[:, :, 3, 5]*a[:, :, 5, 4] + S[:, :, 3, 0] = M[:, :, 4, 0] + (M[:, :, 4, 2]+self.zeta)*a[:, :, 2, 0] \ + + M[:, :, 4, 5]*a[:, :, 5, 0] + S[:, :, 3, 1] = M[:, :, 4, 1] + (M[:, :, 4, 2]+self.zeta)*a[:, :, 2, 1] \ + + M[:, :, 4, 5]*a[:, :, 5, 1] + S[:, :, 3, 2] = M[:, :, 4, 3] + (M[:, :, 4, 2]+self.zeta)*a[:, :, 2, 3] \ + + M[:, :, 4, 5]*a[:, :, 5, 3] + S[:, :, 3, 3] = M[:, :, 4, 4] + (M[:, :, 4, 2]+self.zeta)*a[:, :, 2, 4] \ + + M[:, :, 4, 5]*a[:, :, 5, 4] + + # Delta Matrix from eqn (8) + Delta[:, :, 0, 0] = S[:, :, 3, 0] + Delta[:, :, 0, 1] = S[:, :, 3, 3] + Delta[:, :, 0, 2] = S[:, :, 3, 1] + Delta[:, :, 0, 3] = - S[:, :, 3, 2] + Delta[:, :, 1, 0] = S[:, :, 0, 0] + Delta[:, :, 1, 1] = S[:, :, 0, 3] + Delta[:, :, 1, 2] = S[:, :, 0, 1] + Delta[:, :, 1, 3] = - S[:, :, 0, 2] + Delta[:, :, 2, 0] = -S[:, :, 2, 0] + Delta[:, :, 2, 1] = -S[:, :, 2, 3] + Delta[:, :, 2, 2] = -S[:, :, 2, 1] + Delta[:, :, 2, 3] = S[:, :, 2, 2] + Delta[:, :, 3, 0] = S[:, :, 1, 0] + Delta[:, :, 3, 1] = S[:, :, 1, 3] + Delta[:, :, 3, 2] = S[:, :, 1, 1] + Delta[:, :, 3, 3] = -S[:, :, 1, 2] + + return M, a, b, S, Delta + + def calculate_layer_q(self, layer): + """ + Calculates the 4 out-of-plane wavevectors for the current layer. + + Returns + ------- + None + + Notes + ----- + From this we also get the Poynting vectors. + Wavevectors are sorted according to (trans-p, trans-s, refl-p, refl-s) + Birefringence is determined according to a threshold value `qsd_thr` + set at the beginning of the script. + """ + + N = np.size(self._qz, 0) # energy steps + K = np.size(self._qz, 1) # qz steps + + M, a, b, S, Delta = self.calculate_layer_matrices(layer) + + qs = np.zeros((N, K, 4), dtype=np.complex128) # out of plane wavevector + Py = np.zeros((N, K, 4, 3), dtype=np.complex128) # Poynting vector + # Stores the Berreman modes, used for birefringent layers + Berreman = np.zeros((N, K, 4, 3), dtype=np.complex128) + Berreman_unsorted = np.zeros((N, K, 4, 3), dtype=np.complex128) + + # eigenvals // eigenvects as of eqn (11) + qs_unsorted, psi_unsorted = np.linalg.eig(Delta) + # remove extremely small real/imaginary parts that are due to + # numerical inaccuracy + select = np.logical_and(np.abs(np.imag(qs_unsorted)) > 0, + np.abs(np.imag(qs_unsorted)) < self.zero_thr) + qs_unsorted[select] = np.real(qs_unsorted[select]) + 0.0j + + select = np.logical_and(np.abs(np.real(qs_unsorted)) > 0, + np.abs(np.real(qs_unsorted)) < self.zero_thr) + qs_unsorted[select] = 0.0 + 1.0j*np.imag(qs_unsorted[select]) + + select = np.logical_and(np.abs(np.real(psi_unsorted)) > 0, + np.abs(np.real(psi_unsorted)) < self.zero_thr) + psi_unsorted[select] = 0.0 + 1.0j*np.imag(psi_unsorted[select]) + + select = np.logical_and(np.abs(np.imag(psi_unsorted)) > 0, + np.abs(np.imag(psi_unsorted)) < self.zero_thr) + psi_unsorted[select] = np.real(psi_unsorted[select]) + 0.0j + + # sort berremann qi's according to (12) + select1 = np.logical_and(np.imag(qs_unsorted) >= 0, + np.repeat((np.sum(np.abs( + np.imag(qs_unsorted)), 2) > 0)[:, :, np.newaxis], 4, 2)) + select2 = np.logical_and(np.imag(qs_unsorted) < 0, + np.repeat((np.sum(np.abs( + np.imag(qs_unsorted)), 2) > 0)[:, :, np.newaxis], 4, 2)) + select3 = np.logical_and(np.real(qs_unsorted) >= 0, + np.repeat((np.sum(np.abs( + np.imag(qs_unsorted)), 2) == 0)[:, :, np.newaxis], 4, 2)) + select4 = np.logical_and(np.real(qs_unsorted) < 0, + np.repeat((np.sum(np.abs( + np.imag(qs_unsorted)), 2) == 0)[:, :, np.newaxis], 4, 2)) + + idx0, idx1, idx2 = np.where(select1) + transmode0 = (idx0[0::2], idx1[0::2], idx2[0::2]) + transmode1 = (idx0[1::2], idx1[1::2], idx2[1::2]) + + idx0, idx1, idx2 = np.where(select2) + reflmode0 = (idx0[0::2], idx1[0::2], idx2[0::2]) + reflmode1 = (idx0[1::2], idx1[1::2], idx2[1::2]) + + idx0, idx1, idx2 = np.where(select3) + transmode0 = (np.concatenate([transmode0[0], idx0[0::2]]), + np.concatenate([transmode0[1], idx1[0::2]]), + np.concatenate([transmode0[2], idx2[0::2]]) + ) + transmode1 = (np.concatenate([transmode1[0], idx0[1::2]]), + np.concatenate([transmode1[1], idx1[1::2]]), + np.concatenate([transmode1[2], idx2[1::2]]) + ) + + idx0, idx1, idx2 = np.where(select4) + reflmode0 = (np.concatenate([reflmode0[0], idx0[0::2]]), + np.concatenate([reflmode0[1], idx1[0::2]]), + np.concatenate([reflmode0[2], idx2[0::2]]) + ) + reflmode1 = (np.concatenate([reflmode1[0], idx0[1::2]]), + np.concatenate([reflmode1[1], idx1[1::2]]), + np.concatenate([reflmode1[2], idx2[1::2]]) + ) + # sort the indexing + idx_tm0 = np.lexsort((transmode0[0], transmode0[1])) + transmode0 = (transmode0[0][idx_tm0], transmode0[1][idx_tm0], transmode0[2][idx_tm0]) + idx_tm1 = np.lexsort((transmode1[0], transmode1[1])) + transmode1 = (transmode1[0][idx_tm1], transmode1[1][idx_tm1], transmode1[2][idx_tm1]) + idx_rm0 = np.lexsort((reflmode0[0], reflmode0[1])) + reflmode0 = (reflmode0[0][idx_rm0], reflmode0[1][idx_rm0], reflmode0[2][idx_rm0]) + idx_rm1 = np.lexsort((reflmode1[0], reflmode1[1])) + reflmode1 = (reflmode1[0][idx_rm1], reflmode1[1][idx_rm1], reflmode1[2][idx_rm1]) + + # Calculate the Poynting vector for each Psi using (16-18) + Ex = psi_unsorted[:, :, 0, :] + Ey = psi_unsorted[:, :, 2, :] + Hx = -psi_unsorted[:, :, 3, :] + Hy = psi_unsorted[:, :, 1, :] + # from eqn (17) + Ez = np.repeat(a[:, :, 2, 0][:, :, np.newaxis], 4, axis=2)*Ex \ + + np.repeat(a[:, :, 2, 1][:, :, np.newaxis], 4, axis=2)*Ey \ + + np.repeat(a[:, :, 2, 3][:, :, np.newaxis], 4, axis=2)*Hx \ + + np.repeat(a[:, :, 2, 4][:, :, np.newaxis], 4, axis=2)*Hy + # from eqn (18) + Hz = np.repeat(a[:, :, 5, 0][:, :, np.newaxis], 4, axis=2)*Ex \ + + np.repeat(a[:, :, 5, 1][:, :, np.newaxis], 4, axis=2)*Ey \ + + np.repeat(a[:, :, 5, 3][:, :, np.newaxis], 4, axis=2)*Hx \ + + np.repeat(a[:, :, 5, 4][:, :, np.newaxis], 4, axis=2)*Hy + # and from (16) + Py[:, :, :, 0] = Ey*Hz-Ez*Hy + Py[:, :, :, 1] = Ez*Hx-Ex*Hz + Py[:, :, :, 2] = Ex*Hy-Ey*Hx + + # Berreman modes (unsorted) in case they are needed later (birefringence) + Berreman_unsorted[:, :, :, 0] = Ex + Berreman_unsorted[:, :, :, 1] = Ey + Berreman_unsorted[:, :, :, 2] = Ez + + # check Cp using either the Poynting vector for birefringent + # materials or the electric field vector for non-birefringent + # media to sort the modes + + # first calculate Cp for transmitted waves + Cp_t1 = np.abs(Py[transmode0[0], transmode0[1], transmode0[2], 0])**2/( + np.abs(Py[transmode0[0], transmode0[1], transmode0[2], 0])**2 + + np.abs(Py[transmode0[0], transmode0[1], transmode0[2], 1])**2) + Cp_t2 = np.abs(Py[transmode1[0], transmode1[1], transmode1[2], 0])**2/( + np.abs(Py[transmode1[0], transmode1[1], transmode1[2], 0])**2 + + np.abs(Py[transmode1[0], transmode1[1], transmode1[2], 1])**2) + + Cp_r1 = np.abs(Py[reflmode1[0], reflmode1[1], reflmode1[2], 0])**2/( + np.abs(Py[reflmode1[0], reflmode1[1], reflmode1[2], 0])**2 + + np.abs(Py[reflmode1[0], reflmode1[1], reflmode1[2], 1])**2) + Cp_r2 = np.abs(Py[reflmode0[0], reflmode0[1], reflmode0[2], 0])**2/( + np.abs(Py[reflmode0[0], reflmode0[1], reflmode0[2], 0])**2 + + np.abs(Py[reflmode0[0], reflmode0[1], reflmode0[2], 1])**2) + + Cp_te1 = np.abs(psi_unsorted[transmode1[0], transmode1[1], transmode1[2], 0])**2/( + np.abs(psi_unsorted[transmode1[0], transmode1[1], transmode1[2], 0])**2 + + np.abs(psi_unsorted[transmode1[0], transmode1[1], transmode1[2], 2])**2) + Cp_te2 = np.abs(psi_unsorted[transmode0[0], transmode0[1], transmode0[2], 0])**2/( + np.abs(psi_unsorted[transmode0[0], transmode0[1], transmode0[2], 0])**2 + + np.abs(psi_unsorted[transmode0[0], transmode0[1], transmode0[2], 2])**2) + + Cp_re1 = np.abs(psi_unsorted[reflmode1[0], reflmode1[1], reflmode1[2], 0])**2/( + np.abs(psi_unsorted[reflmode1[0], reflmode1[1], reflmode1[2], 0])**2 + + np.abs(psi_unsorted[reflmode1[0], reflmode1[1], reflmode1[2], 2])**2) + Cp_re2 = np.abs(psi_unsorted[reflmode0[0], reflmode0[1], reflmode0[2], 0])**2/( + np.abs(psi_unsorted[reflmode0[0], reflmode0[1], reflmode0[2], 0])**2 + + np.abs(psi_unsorted[reflmode0[0], reflmode0[1], reflmode0[2], 2])**2) + + idx_bf = (np.abs(Cp_t1-Cp_t2) > self.qsd_thr) + idx_nbf = (np.abs(Cp_t1-Cp_t2) <= self.qsd_thr) + idx_bf_fliptrans = Cp_t2[idx_bf] > Cp_t1[idx_bf] + idx_bf_fliprefl = Cp_r1[idx_bf] > Cp_r2[idx_bf] + idx_nbf_fliptrans = Cp_te1[idx_nbf] > Cp_te2[idx_nbf] + idx_nbf_fliprefl = Cp_re1[idx_nbf] > Cp_re2[idx_nbf] + + # birefringence + # transmission + temp = transmode1[2][idx_bf_fliptrans] + transmode1[2][idx_bf_fliptrans] = transmode0[2][idx_bf_fliptrans] + transmode0[2][idx_bf_fliptrans] = temp + # reflection + temp = reflmode1[2][idx_bf_fliprefl] + reflmode1[2][idx_bf_fliprefl] = reflmode0[2][idx_bf_fliprefl] + reflmode0[2][idx_bf_fliprefl] = temp + + # no birefringence + # transmission + temp = transmode1[2][idx_nbf_fliptrans] + transmode1[2][idx_nbf_fliptrans] = transmode0[2][idx_nbf_fliptrans] + transmode0[2][idx_nbf_fliptrans] = temp + # reflection + temp = reflmode1[2][idx_nbf_fliprefl] + reflmode1[2][idx_nbf_fliprefl] = reflmode0[2][idx_nbf_fliprefl] + reflmode0[2][idx_nbf_fliprefl] = temp + + # finally store the sorted version + # q is (trans-p, trans-s, refl-p, refl-s) + qs[:, :, 0] = np.reshape(qs_unsorted[transmode0], (N, K), order='F') + qs[:, :, 1] = np.reshape(qs_unsorted[transmode1], (N, K), order='F') + qs[:, :, 2] = np.reshape(qs_unsorted[reflmode0], (N, K), order='F') + qs[:, :, 3] = np.reshape(qs_unsorted[reflmode1], (N, K), order='F') + Py_temp = Py.copy() + Py[:, :, 0] = np.reshape(Py_temp[transmode0], (N, K, 3), order='F') + Py[:, :, 1] = np.reshape(Py_temp[transmode1], (N, K, 3), order='F') + Py[:, :, 2] = np.reshape(Py_temp[reflmode0], (N, K, 3), order='F') + Py[:, :, 3] = np.reshape(Py_temp[reflmode1], (N, K, 3), order='F') + # # Store the (sorted) Berreman modes + Berreman[:, :, 0] = np.reshape(Berreman_unsorted[transmode0], (N, K, 3), order='F') + Berreman[:, :, 1] = np.reshape(Berreman_unsorted[transmode1], (N, K, 3), order='F') + Berreman[:, :, 2] = np.reshape(Berreman_unsorted[reflmode0], (N, K, 3), order='F') + Berreman[:, :, 3] = np.reshape(Berreman_unsorted[reflmode1], (N, K, 3), order='F') + + return qs, Py, Berreman, idx_bf + + def calculate_layer_gamma(self, layer): + """ + Calculate the gamma matrix + + Parameters + ---------- + zeta : complex + in-plane reduced wavevector kx/k0 + + Returns + ------- + None + """ + + N = np.size(self._qz, 0) # energy steps + K = np.size(self._qz, 1) # qz steps + + mu = 1 + layer_epsilon_matrix = np.repeat(np.expand_dims( + layer.get_epsilon_matrix(self._frequency), 1), K, 1) + qs, Py, Berreman, idx_bf = self.calculate_layer_q(layer) + + gamma = np.zeros((N, K, 4, 3), dtype=np.complex128) + + # this whole function is eqn (20) + gamma[:, :, 0, 0] = 1.0 + 0.0j + gamma[:, :, 1, 1] = 1.0 + 0.0j + gamma[:, :, 3, 1] = 1.0 + 0.0j + gamma[:, :, 2, 0] = -1.0 + 0.0j + + # convenience definition of the repetitive factor + mu_eps33_zeta2 = (mu*layer_epsilon_matrix[:, :, 2, 2]-self.zeta**2) + + ######################################### + idx_qs0le1 = np.abs(qs[:, :, 0]-qs[:, :, 1]) < self.qsd_thr + + gamma12 = np.zeros((N, K), dtype=np.complex128) + gamma12_num = np.zeros_like(gamma12, dtype=np.complex128) + gamma12_denom = np.zeros_like(gamma12, dtype=np.complex128) + gamma13 = np.zeros_like(gamma12, dtype=np.complex128) + gamma21 = np.zeros_like(gamma12, dtype=np.complex128) + gamma21_num = np.zeros_like(gamma12, dtype=np.complex128) + gamma21_denom = np.zeros_like(gamma12, dtype=np.complex128) + gamma23 = np.zeros_like(gamma12, dtype=np.complex128) + gamma32 = np.zeros_like(gamma12, dtype=np.complex128) + gamma32_num = np.zeros_like(gamma12, dtype=np.complex128) + gamma32_denom = np.zeros_like(gamma12, dtype=np.complex128) + gamma33 = np.zeros_like(gamma12, dtype=np.complex128) + gamma41 = np.zeros_like(gamma12, dtype=np.complex128) + gamma41_num = np.zeros_like(gamma12, dtype=np.complex128) + gamma41_denom = np.zeros_like(gamma12, dtype=np.complex128) + gamma43 = np.zeros_like(gamma12, dtype=np.complex128) + + gamma12[idx_qs0le1] = 0.0 + 0.0j + gamma13[idx_qs0le1] = -(mu*layer_epsilon_matrix[:, :, 2, 0][idx_qs0le1] + + self.zeta[idx_qs0le1]*qs[idx_qs0le1, 0]) \ + / mu_eps33_zeta2[idx_qs0le1] + + gamma21[idx_qs0le1] = 0.0 + 0.0j + gamma23[idx_qs0le1] = -mu*layer_epsilon_matrix[:, :, 2, 1][idx_qs0le1] \ + / mu_eps33_zeta2[idx_qs0le1] + + ######################################### + idx_qs0geq1 = np.abs(qs[:, :, 0]-qs[:, :, 1]) >= self.qsd_thr + + gamma12_num[idx_qs0geq1] = mu*layer_epsilon_matrix[:, :, 1, 2][idx_qs0geq1] \ + * (mu*layer_epsilon_matrix[:, :, 2, 0][idx_qs0geq1] + + self.zeta[idx_qs0geq1]*qs[idx_qs0geq1, 0]) + gamma12_num[idx_qs0geq1] = gamma12_num[idx_qs0geq1] \ + - mu*layer_epsilon_matrix[:, :, 1, 0][idx_qs0geq1]*mu_eps33_zeta2[idx_qs0geq1] + gamma12_denom[idx_qs0geq1] = mu_eps33_zeta2[idx_qs0geq1] \ + * (mu*layer_epsilon_matrix[:, :, 1, 1][idx_qs0geq1] + - self.zeta[idx_qs0geq1]**2-qs[idx_qs0geq1, 0]**2) + gamma12_denom[idx_qs0geq1] = gamma12_denom[idx_qs0geq1] \ + - mu**2*layer_epsilon_matrix[:, :, 1, 2][idx_qs0geq1] \ + * layer_epsilon_matrix[:, :, 2, 1][idx_qs0geq1] + gamma12[idx_qs0geq1] = gamma12_num[idx_qs0geq1]/gamma12_denom[idx_qs0geq1] + # remove nans + gamma12[np.logical_and(np.isnan(gamma12), idx_qs0geq1)] = 0.0 + 0.0j + + gamma13[idx_qs0geq1] = -(mu*layer_epsilon_matrix[:, :, 2, 0][idx_qs0geq1] + + self.zeta[idx_qs0geq1]*qs[idx_qs0geq1, 0]) + gamma13[idx_qs0geq1] = gamma13[idx_qs0geq1] \ + - mu*layer_epsilon_matrix[:, :, 2, 1][idx_qs0geq1]*gamma12[idx_qs0geq1] + gamma13[idx_qs0geq1] = gamma13[idx_qs0geq1]/mu_eps33_zeta2[idx_qs0geq1] + + # remove nans + idx_nans = np.logical_and(np.isnan(gamma13), idx_qs0geq1) + gamma13[idx_nans] = -(mu*layer_epsilon_matrix[:, :, 2, 0][idx_nans] + + self.zeta[idx_nans]*qs[idx_nans, 0])/mu_eps33_zeta2[idx_nans] + + gamma21_num[idx_qs0geq1] = mu*layer_epsilon_matrix[:, :, 2, 1][idx_qs0geq1] \ + * (mu*layer_epsilon_matrix[:, :, 0, 2][idx_qs0geq1] + + self.zeta[idx_qs0geq1]*qs[idx_qs0geq1, 1]) + gamma21_num[idx_qs0geq1] = gamma21_num[idx_qs0geq1] \ + - mu*layer_epsilon_matrix[:, :, 0, 1][idx_qs0geq1]*mu_eps33_zeta2[idx_qs0geq1] + gamma21_denom[idx_qs0geq1] = mu_eps33_zeta2[idx_qs0geq1] \ + * (mu*layer_epsilon_matrix[:, :, 0, 0][idx_qs0geq1]-qs[idx_qs0geq1, 1]**2) + gamma21_denom[idx_qs0geq1] = gamma21_denom[idx_qs0geq1] \ + - (mu*layer_epsilon_matrix[:, :, 0, 2][idx_qs0geq1] + + self.zeta[idx_qs0geq1]*qs[idx_qs0geq1, 1]) \ + * (mu*layer_epsilon_matrix[:, :, 2, 0][idx_qs0geq1] + + self.zeta[idx_qs0geq1]*qs[idx_qs0geq1, 1]) + gamma21[idx_qs0geq1] = gamma21_num[idx_qs0geq1]/gamma21_denom[idx_qs0geq1] + # remove nans + gamma21[np.logical_and(np.isnan(gamma21), idx_qs0geq1)] = 0.0 + 0.0j + + gamma23[idx_qs0geq1] = -(mu*layer_epsilon_matrix[:, :, 2, 0][idx_qs0geq1] + + self.zeta[idx_qs0geq1]*qs[idx_qs0geq1, 1]) \ + * gamma21[idx_qs0geq1]-mu*layer_epsilon_matrix[:, :, 2, 1][idx_qs0geq1] + gamma23[idx_qs0geq1] = gamma23[idx_qs0geq1]/mu_eps33_zeta2[idx_qs0geq1] + # remove nans + idx_nans = np.logical_and(np.isnan(gamma23), idx_qs0geq1) + gamma23[idx_nans] = -mu*layer_epsilon_matrix[:, :, 2, 1][idx_nans] \ + / mu_eps33_zeta2[idx_nans] + + ######################################### + idx_qs2le3 = np.abs(qs[:, :, 2]-qs[:, :, 3]) < self.qsd_thr + + gamma32[idx_qs2le3] = 0.0 + 0.0j + gamma33[idx_qs2le3] = (mu*layer_epsilon_matrix[:, :, 2, 0][idx_qs2le3] + + self.zeta[idx_qs2le3]*qs[idx_qs2le3, 2]) \ + / mu_eps33_zeta2[idx_qs2le3] + gamma41[idx_qs2le3] = 0.0 + 0.0j + gamma43[idx_qs2le3] = -mu*layer_epsilon_matrix[:, :, 2, 1][idx_qs2le3] \ + / mu_eps33_zeta2[idx_qs2le3] + + ######################################### + idx_qs2geq3 = np.abs(qs[:, :, 2]-qs[:, :, 3]) >= self.qsd_thr + + gamma32_num[idx_qs2geq3] = mu*layer_epsilon_matrix[:, :, 1, 0][idx_qs2geq3] \ + * mu_eps33_zeta2[idx_qs2geq3] + gamma32_num[idx_qs2geq3] = gamma32_num[idx_qs2geq3] \ + - mu*layer_epsilon_matrix[:, :, 1, 2][idx_qs2geq3] \ + * (mu*layer_epsilon_matrix[:, :, 2, 0][idx_qs2geq3] + + self.zeta[idx_qs2geq3]*qs[idx_qs2geq3, 2]) + gamma32_denom[idx_qs2geq3] = mu_eps33_zeta2[idx_qs2geq3] \ + * (mu*layer_epsilon_matrix[:, :, 1, 1][idx_qs2geq3] + - self.zeta[idx_qs2geq3]**2-qs[idx_qs2geq3, 2]**2) + gamma32_denom[idx_qs2geq3] = gamma32_denom[idx_qs2geq3] \ + - mu**2*layer_epsilon_matrix[:, :, 1, 2][idx_qs2geq3] \ + * layer_epsilon_matrix[:, :, 2, 1][idx_qs2geq3] + gamma32[idx_qs2geq3] = gamma32_num[idx_qs2geq3]/gamma32_denom[idx_qs2geq3] + # remove nans + gamma32[np.logical_and(np.isnan(gamma32), idx_qs2geq3)] = 0.0 + 0.0j + + gamma33[idx_qs2geq3] = mu*layer_epsilon_matrix[:, :, 2, 0][idx_qs2geq3] \ + + self.zeta[idx_qs2geq3]*qs[idx_qs2geq3, 2] + gamma33[idx_qs2geq3] = gamma33[idx_qs2geq3] \ + + mu*layer_epsilon_matrix[:, :, 2, 1][idx_qs2geq3]*gamma32[idx_qs2geq3] + gamma33[idx_qs2geq3] = gamma33[idx_qs2geq3]/mu_eps33_zeta2[idx_qs2geq3] + # remove nans + idx_nans = np.logical_and(np.isnan(gamma33), idx_qs2geq3) + gamma33[idx_nans] = (mu*layer_epsilon_matrix[:, :, 2, 0][idx_nans] + + self.zeta[idx_nans]*qs[idx_nans, 2])/mu_eps33_zeta2[idx_nans] + + gamma41_num[idx_qs2geq3] = mu*layer_epsilon_matrix[:, :, 2, 1][idx_qs2geq3] \ + * (mu*layer_epsilon_matrix[:, :, 0, 2][idx_qs2geq3] + + self.zeta[idx_qs2geq3]*qs[idx_qs2geq3, 3]) + gamma41_num[idx_qs2geq3] = gamma41_num[idx_qs2geq3] \ + - mu*layer_epsilon_matrix[:, :, 0, 1][idx_qs2geq3]*mu_eps33_zeta2[idx_qs2geq3] + gamma41_denom[idx_qs2geq3] = mu_eps33_zeta2[idx_qs2geq3] \ + * (mu*layer_epsilon_matrix[:, :, 0, 0][idx_qs2geq3]-qs[idx_qs2geq3, 3]**2) + gamma41_denom[idx_qs2geq3] = gamma41_denom[idx_qs2geq3] \ + - (mu*layer_epsilon_matrix[:, :, 0, 2][idx_qs2geq3] + + self.zeta[idx_qs2geq3]*qs[idx_qs2geq3, 3]) \ + * (mu*layer_epsilon_matrix[:, :, 2, 0][idx_qs2geq3] + + self.zeta[idx_qs2geq3]*qs[idx_qs2geq3, 3]) + gamma41[idx_qs2geq3] = gamma41_num[idx_qs2geq3]/gamma41_denom[idx_qs2geq3] + # remove nans + gamma41[np.logical_and(np.isnan(gamma41), idx_qs2geq3)] = 0.0 + 0.0j + + gamma43[idx_qs2geq3] = -(mu*layer_epsilon_matrix[:, :, 2, 0][idx_qs2geq3] + + self.zeta[idx_qs2geq3]*qs[idx_qs2geq3, 3])*gamma41[idx_qs2geq3] + gamma43[idx_qs2geq3] = gamma43[idx_qs2geq3] \ + - mu*layer_epsilon_matrix[:, :, 2, 1][idx_qs2geq3] + gamma43[idx_qs2geq3] = gamma43[idx_qs2geq3]/mu_eps33_zeta2[idx_qs2geq3] + # remove nans + idx_nans = np.logical_and(np.isnan(gamma43), idx_qs2geq3) + gamma43[idx_nans] = -mu*layer_epsilon_matrix[:, :, 2, 1][idx_nans]/mu_eps33_zeta2[idx_nans] + + # gamma field vectors should be normalized to avoid any birefringence problems + gamma1 = np.stack([gamma[:, :, 0, 0], gamma12, gamma13], axis=2) + gamma2 = np.stack([gamma21, gamma[:, :, 1, 1], gamma23], axis=2) + gamma3 = np.stack([gamma[:, :, 2, 0], gamma32, gamma33], axis=2) + gamma4 = np.stack([gamma41, gamma[:, :, 3, 1], gamma43], axis=2) + + # Regular case, no birefringence, we keep the Xu fields + gamma[:, :, 0, :] = gamma1/np.repeat(np.linalg.norm( + gamma1, axis=2)[:, :, np.newaxis], 3, axis=2) + gamma[:, :, 1, :] = gamma2/np.repeat(np.linalg.norm( + gamma2, axis=2)[:, :, np.newaxis], 3, axis=2) + gamma[:, :, 2, :] = gamma3/np.repeat(np.linalg.norm( + gamma3, axis=2)[:, :, np.newaxis], 3, axis=2) + gamma[:, :, 3, :] = gamma4/np.repeat(np.linalg.norm( + gamma4, axis=2)[:, :, np.newaxis], 3, axis=2) + + # In case of birefringence, use Berreman fields + idx_bf = np.reshape(idx_bf, (N, K), order='F') + gamma[idx_bf] = Berreman[idx_bf]/np.repeat(np.linalg.norm( + Berreman[idx_bf], axis=2)[:, :, np.newaxis], 3, axis=2) + + return gamma, qs + + def calculate_layer_transfer_matrix(self, layer): + """ + Compute the transfer matrix of the whole layer :math:`T_i=A_iP_iA_i^{-1}` + + Parameters + ---------- + f : float + frequency (in Hz) + zeta : complex + reduced in-plane wavevector kx/k0 + Returns + ------- + None + + """ + + N = np.size(self._qz, 0) # energy steps + K = np.size(self._qz, 1) # qz steps + mu = 1 + gamma, qs = self.calculate_layer_gamma(layer) + + Ai = np.zeros((N, K, 4, 4), dtype=np.complex128) + Ki = np.zeros((N, K, 4, 4), dtype=np.complex128) + Ti = np.zeros((N, K, 4, 4), dtype=np.complex128) # Layer transfer matrix + + # eqn(22) + Ai[:, :, 0, :] = gamma[:, :, :, 0] + Ai[:, :, 1, :] = gamma[:, :, :, 1] + + Ai[:, :, 2, :] = (qs*gamma[:, :, :, 0] + - np.repeat(self.zeta[:, :, np.newaxis], 4, axis=2)*gamma[:, :, :, 2])/mu + Ai[:, :, 3, :] = qs*gamma[:, :, :, 1]/mu + + f = np.tile(self._frequency[:, np.newaxis, np.newaxis], [1, K, 4]) + Ki[:, :, np.array([0, 1, 2, 3]), np.array([0, 1, 2, 3])] = np.exp( + -1.0j*(2.0*np.pi*f*qs[:, :, :]*layer._thickness)/c_0) + + Ai_inv = self.calc_inv(Ai) + # eqn (26) + Ti = m_times_n(Ai, m_times_n(Ki, Ai_inv)) + + return Ai, Ki, Ai_inv, Ti + + def calculate_GammaStar(self): + """ + Calculate the whole system's transfer matrix. + + Parameters + ----------- + f : float + Frequency (Hz) + zeta_sys : complex + In-plane wavevector kx/k0 + + Returns + ------- + GammaStar: 4x4 complex matrix + System transfer matrix :math:`\Gamma^{*}` + """ + + N = np.size(self._qz, 0) # energy steps + K = np.size(self._qz, 1) # qz steps + + Ai_super, Ki_super, Ai_inv_super, T_super = self.calculate_layer_transfer_matrix( + self.S.get_layer_handle(0)) + Ai_sub, Ki_sub, Ai_inv_sub, T_sub = self.calculate_layer_transfer_matrix( + self.S.get_layer_handle(-1)) + + Delta1234 = np.tile(np.array([[1, 0, 0, 0], + [0, 0, 1, 0], + [0, 1, 0, 0], + [0, 0, 0, 1]])[np.newaxis, np.newaxis, :, :], [N, K, 1, 1]) + Gamma = np.zeros((N, K, 4, 4), dtype=np.complex128) + GammaStar = np.zeros((N, K, 4, 4), dtype=np.complex128) + + Tloc = np.tile(np.identity(4, dtype=np.complex128)[np.newaxis, np.newaxis, :, :], + [N, K, 1, 1]) + + for ii in range(self.S.get_number_of_layers())[-2:0:-1]: + Ai, Ki, Ai_inv, T_ii = self.calculate_layer_transfer_matrix( + self.S.get_layer_handle(ii)) + Tloc = m_times_n(T_ii, Tloc) + + Gamma = m_times_n(Ai_inv_super, m_times_n(Tloc, Ai_sub)) + GammaStar = m_times_n(self.calc_inv(Delta1234), + m_times_n(Gamma, Delta1234)) + + return GammaStar + + def calculate_r_t(self): + """ Calculate various field and intensity reflection and transmission + coefficients, as well as the 4-valued vector of transmitted field. + + Parameters + ----------- + zeta_sys : complex + Incident in-plane wavevector + Returns + ------- + r_out : len(4)-array + Complex *field* reflection coefficients r_out=([rpp,rps,rss,rsp]) + R_out : len(4)-array + Real *intensity* reflection coefficients R_out=([Rpp,Rss,Rsp,Tps]) + t_out : len(4)-array + Complex *field* transmition coefficients t=([tpp, tps, tsp, tss]) + T_out : len(4)-array + Real *intensity* transmition coefficients T_out=([Tp,Ts]) (mode-inselective) + + Notes + ----- + **IMPORTANT** + ..version 19-03-2020: + All intensity coefficients are now well defined. Transmission is defined + mode-independently. It could be defined mode-dependently for non-birefringent + substrates in future versions. + The new definition of this function **BREAKS compatibility** with the previous + one. + + ..version 13-09-2019: + Note that the field reflectivity and transmission coefficients + r and t are well defined. The intensity reflection coefficient is also correct. + However, the intensity transmission coefficients T are ill-defined so far. + This will be corrected upon future publication of the correct intensity coefficients. + + Note also the different ordering of the coefficients, + for consistency w/ Passler's matlab code + + """ + N = np.size(self._qz, 0) # energy steps + K = np.size(self._qz, 1) # qz steps + eps = np.repeat(self.S.get_layer_handle(0).epsilon[0]( + self._frequency[:])[:, np.newaxis], K, 1) + self.zeta = np.sin(self._theta)*np.sqrt((eps)) + + GammaStar = self.calculate_GammaStar() + # common denominator for all coefficients + Denom = GammaStar[:, :, 0, 0]*GammaStar[:, :, 2, 2] \ + - GammaStar[:, :, 0, 2]*GammaStar[:, :, 2, 0] + # field reflection coefficients + rpp = GammaStar[:, :, 1, 0]*GammaStar[:, :, 2, 2] \ + - GammaStar[:, :, 1, 2]*GammaStar[:, :, 2, 0] + rpp = np.nan_to_num(rpp/Denom) + + rss = GammaStar[:, :, 0, 0]*GammaStar[:, :, 3, 2] \ + - GammaStar[:, :, 3, 0]*GammaStar[:, :, 0, 2] + rss = np.nan_to_num(rss/Denom) + + rps = GammaStar[:, :, 3, 0]*GammaStar[:, :, 2, 2] \ + - GammaStar[:, :, 3, 2]*GammaStar[:, :, 2, 0] + rps = np.nan_to_num(rps/Denom) + + rsp = GammaStar[:, :, 0, 0]*GammaStar[:, :, 1, 2] \ + - GammaStar[:, :, 1, 0]*GammaStar[:, :, 0, 2] + rsp = np.nan_to_num(rsp/Denom) + + # Intensity reflection coefficients are just square moduli + Rpp = np.abs(rpp)**2 + Rss = np.abs(rss)**2 + Rps = np.abs(rps)**2 + Rsp = np.abs(rsp)**2 + r_out = np.stack([rpp, rps, rss, rsp], axis=2) # order matching Passler Matlab code + R_out = np.stack([Rpp, Rss, Rsp, Rps], axis=2) # order matching Passler Matlab code + + # field transmission coefficients + tpp = np.nan_to_num(GammaStar[:, :, 2, 2]/Denom) + tss = np.nan_to_num(GammaStar[:, :, 0, 0]/Denom) + tps = np.nan_to_num(-GammaStar[:, :, 2, 0]/Denom) + tsp = np.nan_to_num(-GammaStar[:, :, 0, 2]/Denom) + t_out = np.stack([tpp, tps, tsp, tss], axis=2) + + # Intensity transmission requires Poyting vector analysis + # N.B: could be done mode-dependentely later + # start with the superstrate + # Incident fields are either p or s polarized + ksup = np.zeros((N, K, 4, 3), dtype=np.complex128) # wavevector in superstrate + ksup[:, :, :, 0] = np.repeat(self.zeta[:, :, np.newaxis], 4, axis=2) + + gamma_sup, qs_sup = self.calculate_layer_gamma(self.S.get_layer_handle(0)) + gamma_sub, qs_sub = self.calculate_layer_gamma(self.S.get_layer_handle(-1)) + + ksup[:, :, :, 2] = qs_sup + + ksup = ksup/c_0 # omega simplifies in the H field formula + Einc_pin = gamma_sup[:, :, 0, :] # p-pol incident electric field + Einc_sin = gamma_sup[:, :, 1, :] # s-pol incident electric field + # Poyting vector in superstrate (incident, p-in and s-in) + Sinc_pin = 0.5*np.real(np.cross(Einc_pin, np.conj(np.cross(ksup[:, :, 0, :], Einc_pin)))) + Sinc_sin = 0.5*np.real(np.cross(Einc_sin, np.conj(np.cross(ksup[:, :, 1, :], Einc_sin)))) + + # Substrate Poyting vector + # Outgoing fields (eqn 17) + Eout_pin = np.repeat(t_out[:, :, 0][:, :, np.newaxis], 3, 2)*gamma_sub[:, :, 0, :] \ + + np.repeat(t_out[:, :, 1][:, :, np.newaxis], 3, 2) \ + * gamma_sub[:, :, 1, :] # p-in, p or s out + Eout_sin = np.repeat(t_out[:, :, 2][:, :, np.newaxis], 3, 2)*gamma_sub[:, :, 0, :] \ + + np.repeat(t_out[:, :, 3][:, :, np.newaxis], 3, 2) \ + * gamma_sub[:, :, 1, :] # s-in, p or s out + ksub = np.zeros((N, K, 4, 3), dtype=np.complex128) + ksub[:, :, :, 0] = np.repeat(self.zeta[:, :, np.newaxis], 4, axis=2) + ksub[:, :, :, 2] = qs_sub + ksub = ksub/c_0 # omega simplifies in the H field formula + + # outgoing Poyting vectors, 2 formulations + Sout_pin = 0.5*np.real(np.cross(Eout_pin, np.conj(np.cross(ksub[:, :, 0, :], Eout_pin)))) + Sout_sin = 0.5*np.real(np.cross(Eout_sin, np.conj(np.cross(ksub[:, :, 1, :], Eout_sin)))) + # Intensity transmission coefficients are only the z-component of S ! + T_pp = np.real(Sout_pin[:, :, 2]/Sinc_pin[:, :, 2]) # z-component only + T_ss = np.real(Sout_sin[:, :, 2]/Sinc_sin[:, :, 2]) # z-component only + + T_out = np.stack([T_pp, T_ss], axis=2) + + return r_out, R_out, t_out, T_out + + def calculate_Efield(self, r, R, t, T, z_vect=None, x=0.0, + magnetic=False, dz=None): + """ + Calculate the electric field profiles for both s-pol and p-pol excitation. + + Parameters + ---------- + f : float + frequency (Hz) + zeta_sys : complex + in-plane normalized wavevector kx/k0 + z_vect : 1Darray + Coordinates at which the calculation is done. + if None, the layers boundaries are used. + x : float or 1D array + x-coordinates for (future) 2D plot of the electric field. Not yet implemented + magnetic : bool + Boolean to skip or compute the magnetic field vector + dz : float (optional) + Space resolution along propagation (z) axis. Superseed z_vect + + Returns + -------- + z : 1Darray + 1D array of z-coordinates according to dz + E_out : (len(z),3)-Array + Total electric field in the structure + H_out (opt): (len(z),3)-Array + Total magnetic field in the structure + zn : list + Positions of the different interfaces + + Notes + ----- + ..Version 19-03-2020: + changed keywords to add z_vect + z_vect is used for either minimal computation (using get_layers_boundaries) + or hand-defined z-positions (e.g. irregular spacing for improved resolution) + if dz is given, a regular grid is used. + A sketch of the definition of all fields and algorithm is supplied in the module, + to better get a grasp on where Fft and Fbk are defined. + ..Version 28-01-2020: + Added Magnetic field keyword to save time. + Poyting and absorption defined in a separate function + ..Version 06-01-2020: + Added Magnetic field and Poyting vector. + ..Version 13-09-2019: + the 2D field profile is not implemented yet. x should be left to default + + """ + N = np.size(self._qz, 0) # energy steps + K = np.size(self._qz, 1) # qz steps + # Nb of layers + num_layers = self.S.get_number_of_layers() + zn = np.zeros(num_layers) # superstrate+layers+substrate + + # 4-components field tensor at the front and + # back interfaces of the layer + # correspond to E0 and E1 + # defined by (37*) + # E0 (E^(p/o)_t, E^(s/e)_t, E^(p/o)_r, E^(s/e)_r) + # twice for p-pol in and s-pol in + F_ft = np.zeros((N, K, 8, num_layers), dtype=np.complex128) + # E1 (E^(p/o)_t, E^(s/e)_t, E^(p/o)_r, E^(s/e)_r) + # twice for p-pol in and s-pol in + F_bk = np.zeros((N, K, 8, num_layers), dtype=np.complex128) + + zn[-1] = 0.0 # initially with the substrate + + # First step of the algorithm starts from the top of the substrate + # a sketch is provided to better visualize the steps + # red quantities in sketch + # (37*) with p-pol excitation + F_ft[:, :, 0, -1] = t[:, :, 0] # t_pp + F_ft[:, :, 1, -1] = t[:, :, 1] # t_ps + # (37*) with s-pol excitation + F_ft[:, :, 4, -1] = t[:, :, 2] # t_sp + F_ft[:, :, 5, -1] = t[:, :, 3] # t_ss + + # propagate to the "end" of the substrate + # F_bk[-1] for plot purpose (see Fig. 1.(a)) + Ai, Ki_sub, _, _ = self.calculate_layer_transfer_matrix( + self.S.get_layer_handle(-1)) + F_bk[:, :, :4, -1] = np.einsum("lmij,lmj->lmi", self.calc_inv(Ki_sub), F_ft[:, :, :4, -1]) + F_bk[:, :, 4:, -1] = np.einsum("lmij,lmj->lmi", self.calc_inv(Ki_sub), F_ft[:, :, 4:, -1]) + + if num_layers > 2: + # First layer is a special case to handle System.substrate + # purple quantities in sketch + zn[-2] = zn[-1]-self.S.get_layer_handle(-1)._thickness + + Aim1, Kim1, _, _ = self.calculate_layer_transfer_matrix( + self.S.get_layer_handle(-2)) + Li = m_times_n(self.calc_inv(Aim1), Ai) + + F_bk[:, :, :4, -2] = np.einsum("lmij,lmj->lmi", Li, F_ft[:, :, :4, -1]) + F_bk[:, :, 4:, -2] = np.einsum("lmij,lmj->lmi", Li, F_ft[:, :, 4:, -1]) + F_ft[:, :, :4, -2] = np.einsum("lmij,lmj->lmi", Kim1, F_bk[:, :, :4, -2]) + F_ft[:, :, 4:, -2] = np.einsum("lmij,lmj->lmi", Kim1, F_bk[:, :, 4:, -2]) + + # From here we start recursively computing the fields + # blue quantities in sketch + for kl in range(1, num_layers-2)[::-1]: + # subtract the thickness (building thickness array backwards) + zn[kl] = zn[kl+1]-self.S.get_layer_handle(kl+1)._thickness + + Aim1, Kim1, _, _ = self.calculate_layer_transfer_matrix( + self.S.get_layer_handle(kl)) + Ai, _, _, _ = self.calculate_layer_transfer_matrix( + self.S.get_layer_handle(kl+1)) + + Li = m_times_n(self.calc_inv(Aim1), Ai) + # F_ft == E0 // F_bk == E1 + F_bk[:, :, :4, kl] = np.einsum("lmij,lmj->lmi", Li, F_ft[:, :, :4, kl+1]) + F_bk[:, :, 4:, kl] = np.einsum("lmij,lmj->lmi", Li, F_ft[:, :, 4:, kl+1]) + F_ft[:, :, :4, kl] = np.einsum("lmij,lmj->lmi", Kim1, F_bk[:, :, :4, kl]) + F_ft[:, :, 4:, kl] = np.einsum("lmij,lmj->lmi", Kim1, F_bk[:, :, 4:, kl]) + + zn[0] = zn[1]-self.S.get_layer_handle(1)._thickness + + Aim1, Ki_sup, _, _ = self.calculate_layer_transfer_matrix( + self.S.get_layer_handle(0)) + Ai, _, _, _ = self.calculate_layer_transfer_matrix( + self.S.get_layer_handle(1)) + Li = m_times_n(self.calc_inv(Aim1), Ai) + + # F_ft == E0 // F_bk == E1 + F_bk[:, :, :4, 0] = np.einsum("lmij,lmj->lmi", Li, F_ft[:, :, :4, 1]) + F_bk[:, :, 4:, 0] = np.einsum("lmij,lmj->lmi", Li, F_ft[:, :, 4:, 1]) + F_ft[:, :, :4, 0] = np.einsum("lmij,lmj->lmi", Ki_sup, F_bk[:, :, :4, 0]) + F_ft[:, :, 4:, 0] = np.einsum("lmij,lmj->lmi", Ki_sup, F_bk[:, :, 4:, 0]) + else: + zn[0] = -self.S.get_layer_handle(-1)._thickness + Ai, Ki_sub, _, _ = self.calculate_layer_transfer_matrix( + self.S.get_layer_handle(-1)) + Aim1, Ki_sup, _, _ = self.calculate_layer_transfer_matrix( + self.S.get_layer_handle(0)) + Li = m_times_n(self.calc_inv(Aim1), Ai) + # F_ft == E0 // F_bk == E1 + F_bk[:, :, :4, 0] = np.einsum("lmij,lmj->lmi", Li, F_ft[:, :, :4, 1]) + F_bk[:, :, 4:, 0] = np.einsum("lmij,lmj->lmi", Li, F_ft[:, :, 4:, 1]) + F_ft[:, :, :4, 0] = np.einsum("lmij,lmj->lmi", Ki_sup, F_bk[:, :, :4, 0]) + F_ft[:, :, 4:, 0] = np.einsum("lmij,lmj->lmi", Ki_sup, F_bk[:, :, 4:, 0]) + + # shift everything so that incident boundary is at z=0 + zn = zn-zn[0] + # define the spatial points where the computation is performed + if dz is None: + # print('No dz given, \n') + if z_vect is None: + # print('Resorting to minimal computation on boundaries') + z = self.S.get_distances_of_interfaces().magnitude + z -= self.S.get_layer_handle(0)._thickness # shift interface 0-1 to 0 + else: + print('using manually given z-vector') + z = z_vect + else: + # print('using dz=%.2e'%(dz)) + z = np.arange(-self.S.get_layer_handle(0)._thickness, zn[-1], dz) + + # 2x4 component field tensor E_prop propagated from front surface + E_prop = np.empty((N, K, 8), dtype=np.complex128) + # 4-component field tensor F_tens for each direction and polarization + F_tens = np.zeros((N, K, 24, len(z)), dtype=np.complex128) + if magnetic is True: + H_tens = np.zeros((N, K, 24, len(z)), dtype=np.complex128) + # final component electric field E_out = (E_x, Ey, Ez) + # for p-pol and s-pol excitation + E_out = np.zeros((N, K, 6, len(z)), dtype=np.complex128) + if magnetic is True: + H_out = np.zeros((N, K, 6, len(z)), dtype=np.complex128) + # Elementary propagation + dKiz = np.zeros((N, K, 4, 4), dtype=np.complex128) + # starting from the superstrate: + current_layer = 0 + L = self.S.get_layer_handle(0) + gamma, qs = self.calculate_layer_gamma(L) + f = np.tile(self._frequency[:, np.newaxis, np.newaxis], [1, K, 4]) + for ii, zc in enumerate(z): # enumerates returns a tuple (index, value) + if zc > zn[current_layer]: + # change the layer + # important to count here until num_layers+1 to get the correct zn + # in the substrate for dKiz + current_layer += 1 + + if current_layer == num_layers-1: # reached substrate + L = self.S.get_layer_handle(-1) + else: + L = self.S.get_layer_handle(current_layer) + + gamma, qs = self.calculate_layer_gamma(L) + + # use the conjugate of the K matrix => exp(+1.0j...) + dKiz[:, :, [0, 1, 2, 3], [0, 1, 2, 3]] = np.exp(1.0j*( + 2.0*np.pi*f*qs*(zc-zn[current_layer]))/c_0) + + # E_prop propagated from front surface to back of next layer + # n.b: unclear why using F_bk and not F_ft works... but it works ! + E_prop[:, :, :4] = np.einsum("lmij,lmj->lmi", dKiz, F_bk[:, :, :4, current_layer]) + E_prop[:, :, 4:] = np.einsum("lmij,lmj->lmi", dKiz, F_bk[:, :, 4:, current_layer]) + + # wave vector for each mode in layer L + k_lay = np.zeros((N, K, 4, 3), dtype=np.complex128) + k_lay[:, :, :, 0] = np.repeat(self.zeta[:, :, np.newaxis], 4, 2) + k_lay[:, :, :, 2] = qs + # no normalization by c_const eases the visualization of H + # k_lay = k_lay/(c_const) ## omega simplifies in the H field formula + + # p-pol in + # forward, o/p + mu = 1 + F_tens[:, :, :3, ii] = np.einsum('lm,lmj->lmj', E_prop[:, :, 0], gamma[:, :, 0, :]) + if magnetic is True: + H_tens[:, :, :3, ii] = (1./mu)*np.cross(k_lay[:, :, 0, :], F_tens[:, :, :3, ii]) + # forward, e/s + F_tens[:, :, 3:6, ii] = np.einsum('lm,lmj->lmj', E_prop[:, :, 1], gamma[:, :, 1, :]) + if magnetic is True: + H_tens[:, :, 3:6, ii] = (1./mu)*np.cross(k_lay[:, :, 1, :], F_tens[:, :, 3:6, ii]) + # backward, o/p + F_tens[:, :, 6:9, ii] = np.einsum('lm,lmj->lmj', E_prop[:, :, 2], gamma[:, :, 2, :]) + if magnetic is True: + H_tens[:, :, 6:9, ii] = (1./mu)*np.cross(k_lay[:, :, 2, :], F_tens[:, :, 6:9, ii]) + # backward, e/s + F_tens[:, :, 9:12, ii] = np.einsum('lm,lmj->lmj', E_prop[:, :, 3], gamma[:, :, 3, :]) + if magnetic is True: + H_tens[:, :, 9:12, ii] = (1./mu)*np.cross(k_lay[:, :, 3, :], + F_tens[:, :, 9:12, ii]) + # s-pol in + # forward, o/p + F_tens[:, :, 12:15, ii] = np.einsum('lm,lmj->lmj', E_prop[:, :, 4], gamma[:, :, 0, :]) + if magnetic is True: + H_tens[:, :, 12:15, ii] = (1./mu)*np.cross(k_lay[:, :, 0, :], + F_tens[:, :, 12:15, ii]) + # forward, e/s + F_tens[:, :, 15:18, ii] = np.einsum('lm,lmj->lmj', E_prop[:, :, 5], gamma[:, :, 1, :]) + if magnetic is True: + H_tens[:, :, 15:18, ii] = (1./mu)*np.cross(k_lay[:, :, 1, :], + F_tens[:, :, 15:18, ii]) + # backward, o/p + F_tens[:, :, 18:21, ii] = np.einsum('lm,lmj->lmj', E_prop[:, :, 6], gamma[:, :, 2, :]) + if magnetic is True: + H_tens[:, :, 18:21, ii] = (1./mu)*np.cross(k_lay[:, :, 2, :], + F_tens[:, :, 18:21, ii]) + # backward, e/s + F_tens[:, :, 21:, ii] = np.einsum('lm,lmj->lmj', E_prop[:, :, 7], gamma[:, :, 3, :]) + if magnetic is True: + H_tens[:, :, 21:, ii] = (1./mu)*np.cross(k_lay[:, :, 3, :], + F_tens[:, :, 21:, ii]) + # Total electric field (note that sign flip for + # backward propagation is already in gamma) + # p in + E_out[:, :, :3, ii] = F_tens[:, :, :3, ii] + F_tens[:, :, 3:6, ii] \ + + F_tens[:, :, 6:9, ii] + F_tens[:, :, 9:12, ii] + if magnetic is True: + H_out[:, :, :3, ii] = H_tens[:, :, :3, ii] + H_tens[:, :, 3:6, ii] \ + + H_tens[:, :, 6:9, ii] + H_tens[:, :, 9:12, ii] + # s in + E_out[:, :, 3:, ii] = F_tens[:, :, 12:15, ii] + F_tens[:, :, 15:18, ii] \ + + F_tens[:, :, 18:21, ii] + F_tens[:, :, 21:, ii] + if magnetic is True: + H_out[:, :, 3:, ii] = H_tens[:, :, 12:15, ii] + H_tens[:, :, 15:18, ii] \ + + H_tens[:, :, 18:21, ii] + H_tens[:, :, 21:, ii] + + if magnetic is True: + return z, E_out, H_out, zn[:-1] # last interface is useless, substrate=infinite + else: + return z, E_out, zn[:-1] # last interface is useless, substrate=infinite + + def calculate_Poynting_Absorption_vs_z(self, z, E, H, R): + """ + Calculate the z-dependent Poynting vector and cumulated absorption. + + Parameters + ---------- + z : 1Darray + Spatial coordinate for the fields + E : 1Darray + 6-components Electric field vector (p- or s- in) along z + H : 1Darray + 6-components Magnetic field vector (p- or s- in) along z + R : len(4)-array + Reflectivity from :py:func:`calculate_r_t` + S_out : 6xlen(z) array + 6 components (p//s) Poyting vector along z + A_out : 2xlen(z) + 2 components (p//s) absorption along z + """ + N = np.size(self._qz, 0) # energy steps + K = np.size(self._qz, 1) # qz steps + + S_out = np.zeros((N, K, 6, len(z))) # Poynting vector + A_out = np.zeros((N, K, 2, len(z))) # z-dependent absorption + # Tp_z = np.zeros((N, K, len(z))) # z-dependent absorption + # Ts_z = np.zeros((N, K, len(z))) # z-dependent absorption + Tp_z = np.zeros((len(z))) # z-dependent absorption + Ts_z = np.zeros((len(z))) # z-dependent absorption + + # S=0.5*Re(ExB) + S_out[:, :, :3, :] = 0.5*np.real(np.cross(E[:, :, :3, :], np.conj(H[:, :, :3, :]), + axisa=2, axisb=2, axisc=2)) + S_out[:, :, 3:, :] = 0.5*np.real(np.cross(E[:, :, 3:, :], np.conj(H[:, :, 3:, :]), + axisa=2, axisb=2, axisc=2)) + + z1 = np.abs(z).argmin()+1 # index where z>0, first interface + # layer-resolved transmittance p-pol + Tp_z = S_out[:, :, 2, :]/np.repeat(S_out[:, :, 2, 0][:, :, np.newaxis], len(z), 2) \ + * np.repeat((1-(R[:, :, 0]+R[:, :, 2]))[:, :, np.newaxis], len(z), 2) + # layer-resolved transmittance s-pol + Ts_z = S_out[:, :, 5, :]/np.repeat(S_out[:, :, 5, 0][:, :, np.newaxis], len(z), 2) \ + * np.repeat((1-(R[:, :, 1]+R[:, :, 3]))[:, :, np.newaxis], len(z), 2) + A_out[:, :, 0, z1:] = 1.0-np.repeat( + (R[:, :, 0]+R[:, :, 2])[:, :, np.newaxis], len(z)-z1, 2)-Tp_z[:, :, z1:] + A_out[:, :, 1, z1:] = 1.0-np.repeat( + (R[:, :, 1]+R[:, :, 3])[:, :, np.newaxis], len(z)-z1, 2)-Ts_z[:, :, z1:] + + return S_out, A_out + + +class XrayKin(Scattering): r"""XrayKin Kinetic X-ray scattering simulations. @@ -657,7 +1967,7 @@ def get_Ep(self, energy, qz, theta, uc, strain): return Ep -class XrayDyn(Xray): +class XrayDyn(Scattering): r"""XrayDyn Dynamical X-ray scattering simulations. @@ -1499,7 +2809,7 @@ def calc_reflectivity_from_matrix(M): return np.abs(M[:, :, 0, 1]/M[:, :, 1, 1])**2 -class XrayDynMag(Xray): +class XrayDynMag(Scattering): r"""XrayDynMag Dynamical magnetic X-ray scattering simulations. @@ -2602,8 +3912,8 @@ def calc_atom_boundary_phase_matrix(self, atom, density, distance, *args): A_phi[:, :, :, :], np.sqrt(2) * eps[:, :, 0, 0][:, :, np.newaxis, np.newaxis]) - A_inv = np.linalg.inv(A) - A_inv_phi = np.linalg.inv(A_phi) + A_inv = self.calc_inv(A) + A_inv_phi = self.calc_inv(A_phi) phase = self._k * distance phase = phase[:, np.newaxis] diff --git a/udkm1Dsim/structures/layers.py b/udkm1Dsim/structures/layers.py index f9cb671f..c21ec10b 100644 --- a/udkm1Dsim/structures/layers.py +++ b/udkm1Dsim/structures/layers.py @@ -54,8 +54,10 @@ class Layer: sound_vel (float): sound velocity. phonon_damping (float): phonon damping. opt_pen_depth (float): optical penetration depth. - opt_ref_index (float): refractive index. - opt_ref_index_per_strain (float): change of refractive index per strain. + opt_ref_index (complex): refractive index. + opt_ref_index_per_strain (complex): change of refractive index per strain. + epsilon (complex): permittivity. + euler_angles (float): euler angles theta, phi, psi of the permittivity. heat_capacity (float): heat capacity. therm_cond (float): thermal conductivity. lin_therm_exp (float): linear thermal expansion. @@ -82,6 +84,10 @@ class Layer: opt_ref_index_per_strain (ndarray[float]): optical refractive index change per strain - real and imagenary part :math:`\frac{d n}{d \eta} + i\frac{d \kappa}{d \eta}`. + epsilon (list[@lambda]): list of photon frequency [Hz] dependent + permittivity elements xx, yy, zz. + euler_angles (list[float]): euler angles theta, phi, psi of the permittivity. + euler_matrix (ndarray[complex]): euler matrix for permittivity calculations. therm_cond (list[@lambda]): list of T-dependent thermal conductivity [W/(m K)]. lin_therm_exp (list[@lambda]): list of T-dependent linear thermal @@ -121,6 +127,9 @@ def __init__(self, id, name, **kwargs): self.opt_pen_depth = kwargs.get('opt_pen_depth', 0.0*u.nm) self.opt_ref_index = kwargs.get('opt_ref_index', 0.0+0.0j) self.opt_ref_index_per_strain = kwargs.get('opt_ref_index_per_strain', 0.0+0.0j) + self.epsilon = kwargs.get('epsilon', 0.0) + self.euler_matrix = np.identity(3, dtype=np.complex128) + self.euler_angles = kwargs.get('euler_angles', [0.0, 0.0, 0.0]*u.deg) self.heat_capacity = kwargs.get('heat_capacity', 0.0) self.therm_cond = kwargs.get('therm_cond', 0.0) self.lin_therm_exp = kwargs.get('lin_therm_exp', 0.0) @@ -161,6 +170,8 @@ def __str__(self): self.opt_ref_index)], ['opt. ref. index/strain', '{0.real:.4f} + {0.imag:.4f}i'.format( self.opt_ref_index_per_strain)], + ['epsilon', self.epsilon], + ['euler angles', self.euler_angles], ['thermal conduct.', ' W/(m K)\n'.join(self.therm_cond_str) + ' W/(m K)'], ['linear thermal expansion', '\n'.join(self.lin_therm_exp_str)], ['heat capacity', ' J/(kg K)\n'.join(self.heat_capacity_str) + ' J/(kg K)'], @@ -271,7 +282,8 @@ def get_property_dict(self, **kwargs): 'xray': ['num_atoms', '_area', '_mass', '_deb_wal_fac', '_thickness'], 'optical': ['_c_axis', '_opt_pen_depth', 'opt_ref_index', - 'opt_ref_index_per_strain'], + 'opt_ref_index_per_strain', 'epsilon', 'euler_angles', + '_thickness'], 'magnetic': ['_thickness', 'magnetization', 'eff_spin', '_curie_temp', '_aniso_exponents', '_anisotropy', '_exch_stiffness', '_mag_saturation', 'lamda'], @@ -441,6 +453,68 @@ def opt_pen_depth(self): def opt_pen_depth(self, opt_pen_depth): self._opt_pen_depth = opt_pen_depth.to_base_units().magnitude + @property + def epsilon(self): + return self._epsilon + + @epsilon.setter + def epsilon(self, epsilon): + if not type(epsilon) is list: + epsilon = [epsilon] + self._epsilon = [0, 0, 0] + if epsilon[0] != 0: + if isfunction(epsilon[0]): + self._epsilon[0] = epsilon[0] + else: + self._epsilon[0] = lambda f: epsilon[0]*np.ones_like(f) + else: + self._epsilon[0] = lambda f: (1.0 + 0.0j)*np.ones_like(f) + try: + if isfunction(epsilon[1]): + self._epsilon[1] = epsilon[1] + else: + self._epsilon[1] = lambda f: epsilon[1]*np.ones_like(f) + except IndexError: + self._epsilon[1] = self._epsilon[0] + try: + if isfunction(epsilon[2]): + self._epsilon[2] = epsilon[2] + else: + self._epsilon[2] = lambda f: epsilon[2]*np.ones_like(f) + except IndexError: + self._epsilon[2] = self._epsilon[0] + + @property + def euler_angles(self): + res = [] + for eu in self._euler_angles: + res.append(Q_(eu, u.rad).to('deg')) + return res + + @euler_angles.setter + def euler_angles(self, euler_angles): + self._euler_angles = [] + for eu in euler_angles: + self._euler_angles.append(eu.to_base_units().magnitude) + + theta = self._euler_angles[0] + phi = self._euler_angles[1] + psi = self._euler_angles[2] + + self.euler_matrix[0, 0] = np.cos(psi) * np.cos(phi) \ + - np.cos(theta) * np.sin(phi) * np.sin(psi) + self.euler_matrix[0, 1] = -np.sin(psi) * np.cos(phi) \ + - np.cos(theta) * np.sin(phi) * np.cos(psi) + self.euler_matrix[0, 2] = np.sin(theta) * np.sin(phi) + self.euler_matrix[1, 0] = np.cos(psi) * np.sin(phi) \ + + np.cos(theta) * np.cos(phi) * np.sin(psi) + self.euler_matrix[1, 1] = -np.sin(psi) * np.sin(phi) \ + + np.cos(theta) * np.cos(phi) * np.cos(psi) + self.euler_matrix[1, 2] = -np.sin(theta) * np.cos(phi) + self.euler_matrix[2, 0] = np.sin(theta) * np.sin(psi) + self.euler_matrix[2, 1] = np.sin(theta) * np.cos(psi) + self.euler_matrix[2, 2] = np.cos(theta) + @property def roughness(self): return Q_(self._roughness, u.meter).to('nm') @@ -612,6 +686,14 @@ def mag_saturation(self): def mag_saturation(self, mag_saturation): self._mag_saturation = float(mag_saturation.to_base_units().magnitude) + def get_epsilon_matrix(self, f): + epsilon_matrix = np.zeros((len(f), 3, 3), dtype=np.complex128) + epsilon_matrix[:, 0, 0] = self._epsilon[0](f) + epsilon_matrix[:, 1, 1] = self._epsilon[1](f) + epsilon_matrix[:, 2, 2] = self._epsilon[2](f) + return np.matmul(np.linalg.inv(self.euler_matrix), + np.matmul(epsilon_matrix, self.euler_matrix)) + class AmorphousLayer(Layer): r"""AmorphousLayer