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Added notebook for generating NIDN results using FDTD #90

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Apr 29, 2022
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Added notebook for generating NIDN results using FDTD #90

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Added notebook for generating NIDN results using FDTD
torbjornstoro Apr 26, 2022
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torbjornstoro Apr 26, 2022
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torbjornstoro Apr 29, 2022
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torbjornstoro Apr 29, 2022
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Update notebooks/5 - Structure and Material Inference FDTD.ipynb
torbjornstoro Apr 29, 2022
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322 changes: 322 additions & 0 deletions notebooks/5 - Structure and Material Inference FDTD.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "e5c11861",
"metadata": {},
"outputs": [],
"source": [
"# Append root folder in case you haven't installed NIDN\n",
"import sys\n",
"sys.path.append(\"../\")\n",
"\n",
"import nidn"
]
},
{
"cell_type": "markdown",
"id": "9afe0a85",
"metadata": {},
"source": [
"# Single layer , uniform, regression"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "579aae8b",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# Load default cfg as starting point\n",
"cfg = nidn.load_default_cfg()\n",
"\n",
"# Specify your desired range of wavelengths\n",
"cfg.physical_wavelength_range[0] = 3e-7\n",
"cfg.physical_wavelength_range[1] = 9e-7\n",
"cfg.TRCWA_PER_LAYER_THICKNESS = [0.38]\n",
"\n",
"# Let's investigate 50 frequency points\n",
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"cfg.N_freq = 20\n",
"cfg.solver = \"FDTD\"\n",
"cfg.FDTD_niter = 400\n",
"cfg.N_layers = 1\n",
"cfg.FDTD_pulse_type = 'continuous'\n",
"cfg.FDTD_source_type = 'line'\n",
"\n",
"# Currently, the target spectra is set manually as a list of numbers. Choose each 5th value to reduce rom 100 frequency points to 20 frequency points\n",
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"cfg.target_reflectance_spectrum = [0.04456351,0.07616634,0.12983103,0.20348341,0.20803709,0.28212550,0.34838794,0.35148116,0.40294107,0.43659179,0.43804083,0.45265764,0.42395525,0.42250250,0.35184451,0.34927237,0.23594347,0.22968049,0.10109031,0.09566545,0.09204569,0.04801336,0.04943829,0.15785883,0.16480385,0.33755576,0.34852490,0.35598405,0.47175452,0.47543828,0.48079059,0.47670861,0.47495085,0.47283219,0.33080953,0.31996884,0.30833286,0.29704388,0.07221731,0.06803254,0.06279853,0.25093825,0.27168874,0.29327866,0.31555886,0.52987157,0.53266849,0.53949557,0.54329768,0.54590920,0.35588414,0.34058632,0.30762652,0.27317067,0.16839377,0.20723618,0.25279078,0.27902083,0.33203165,0.38699248,0.57960660,0.56132624,0.54713063,0.50812035,0.47987271,0.40737469,0.31942957,0.41918578,0.46946234,0.55008628,0.57858655,0.60237008,0.22992761,0.32204145,0.38371327,0.44023784,0.49577304,0.49328186,0.46340183,0.45237985,0.51436802,0.52561688,0.53433126,0.54193417,0.55985565,0.56738637,0.57371749,0.57877213,0.65791848,0.65526587,0.65189846,0.64806130,0.64002900,0.63579534,0.63167151,0.62771639,0.62199316,0.66017008,0.65453985,0.64911847,][0::5]\n",
"cfg.target_transmittance_spectrum =[0.87536937,0.86225903,0.81306321,0.75287122,0.74871316,0.68198832,0.62422039,0.62128133,0.57431380,0.54036076,0.53893761,0.53746136,0.56318135,0.56430514,0.62822533,0.63025375,0.72190201,0.72902601,0.82516558,0.82823149,0.83023419,0.85988161,0.85803865,0.77452468,0.76844363,0.62843653,0.61871883,0.61209847,0.51762058,0.51417495,0.50911674,0.50285459,0.50413010,0.50572154,0.62959830,0.63739662,0.64559040,0.65416174,0.81584808,0.81671772,0.81649914,0.67631454,0.65980186,0.64488775,0.62678816,0.44563180,0.44270788,0.43865578,0.43446768,0.43148086,0.57915950,0.58924005,0.61020821,0.63242840,0.68207222,0.65677377,0.62419689,0.60660547,0.57197304,0.53173307,0.37738633,0.39192256,0.40217924,0.42987445,0.44531120,0.47829903,0.49511773,0.42511123,0.39097864,0.32282715,0.29403716,0.25334013,0.33381818,0.24619652,0.20109781,0.16036236,0.10121669,0.07910624,0.04449772,0.03074843,0.01117523,0.00339080,0.00167748,0.00077300,0.00015712,0.00006627,0.00003636,0.00001924,0.00000530,0.00000348,0.00000238,0.00000169,0.00000096,0.00000075,0.00000060,0.00000048,0.00000034,0.00000030,0.00000024,0.00000019,][0::5]\n",
"\n",
"nidn.plot_spectrum(cfg,\n",
" cfg.target_reflectance_spectrum,\n",
" cfg.target_transmittance_spectrum)\n",
"\n",
"physical_wls, normalized_freqs = nidn.get_frequency_points(cfg)\n",
"print(\"Physical wavelengths are (in meters):\")\n",
"print(physical_wls)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "970b965a",
"metadata": {},
"outputs": [],
"source": [
"cfg.pop(\"model\",None); # Forget the old model\n",
"\n",
"# Allowed range of epsilon values\n",
"cfg.real_min_eps = 0.00\n",
"cfg.real_max_eps = 20.0\n",
"cfg.imag_min_eps = 0.0\n",
"cfg.imag_max_eps = 6.0\n",
"\n",
"cfg.type = \"regression\" # Choose type as described above\n",
"cfg.iterations = 100 # Set number of training iterations (that is forward model evaluations) to perform"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "367463dd",
"metadata": {},
"outputs": [],
"source": [
"nidn.print_cfg(cfg)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3b8c547a",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"nidn.run_training(cfg);"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "578b6bdc",
"metadata": {},
"outputs": [],
"source": [
"nidn.plot_spectra(cfg)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "66748550",
"metadata": {},
"outputs": [],
"source": [
"nidn.plot_eps_per_point(cfg,compare_to_material=\"titanium_oxide\")"
]
},
{
"cell_type": "markdown",
"id": "6c0b0854",
"metadata": {},
"source": [
"# Single layer , uniform, classification"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "09bb33f5",
"metadata": {},
"outputs": [],
"source": [
"cfg.pop(\"model\",None); # Forget the old model\n",
"\n",
"# Allowed range of epsilon values\n",
"cfg.real_min_eps = 0.0\n",
"cfg.real_max_eps = 20.0\n",
"cfg.imag_min_eps = 0.0\n",
"cfg.imag_max_eps = 6.0\n",
"\n",
"cfg.type = \"classification\" # Choose type as described above\n",
"cfg.iterations = 100 # Set number of training iterations (that is forward model evaluations) to perform"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "481b28f2",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"nidn.run_training(cfg);"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0bc16df3",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"nidn.plot_spectra(cfg)\n",
"nidn.plot_eps_per_point(cfg)"
]
},
{
"cell_type": "markdown",
"id": "f2b7bf18",
"metadata": {},
"source": [
"## Uniform Three-Layer Ground Truth "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b601e1b4",
"metadata": {},
"outputs": [],
"source": [
"# Load default cfg as starting point\n",
"cfg = nidn.load_default_cfg()\n",
"\n",
"# Set grid specifics\n",
"cfg.Nx = 1\n",
"cfg.Ny = 1\n",
"cfg.N_layers = 3\n",
"cfg.N_freq = 20\n",
"cfg.PER_LAYER_THICKNESS = [1.0]\n",
"cfg.FDTD_pulse_type = 'continuous'\n",
"cfg.FDTD_source_type = 'line'\n",
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"\n",
"# Specify your desired range of wavelengths\n",
"cfg.physical_wavelength_range[0] = 6e-7 \n",
"cfg.physical_wavelength_range[1] = 3e-6\n",
"\n",
"cfg.target_reflectance_spectrum =[0.14366185,0.27504679,0.17564494,0.20414668,0.40380960,0.30072133,0.26199707,0.38340070,0.36834200,0.23065582,0.55063775,0.15905333,0.63662124,0.08958707,0.64845886,0.13649373,0.70790738,0.71218611,0.68279302,0.58548506,]\n",
"cfg.target_transmittance_spectrum =[0.79465473,0.64171370,0.60055604,0.62438044,0.52177391,0.56217832,0.53327435,0.41774403,0.48310163,0.17431544,0.00010415,0.00000067,0.00000009,0.00000003,0.00000001,0.00000000,0.00000000,0.00000000,0.00000000,0.00000000,]\n",
"\n",
"cfg.FDTD_niter = 800\n",
"cfg.solver =\"FDTD\"\n",
"\n",
"nidn.plot_spectrum(cfg,\n",
" cfg.target_reflectance_spectrum,\n",
" cfg.target_transmittance_spectrum)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "579f8ada",
"metadata": {},
"outputs": [],
"source": [
"# Allowed range of epsilon values\n",
"cfg.pop(\"model\",None); # Forget the old model\n",
"cfg.real_min_eps = 0.0\n",
"cfg.real_max_eps = 20.0\n",
"cfg.imag_min_eps = 0.0\n",
"cfg.imag_max_eps = 10.0\n",
"\n",
"cfg.type = \"regression\" # Choose type as described above (for now still regression)\n",
"cfg.iterations = 100 # Set number of training iterations (that is forward model evaluations) to perform"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7a834af6",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"nidn.run_training(cfg);"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a647a1ae",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# The other plots\n",
"nidn.plot_spectra(cfg)\n",
"nidn.plot_eps_per_point(cfg,compare_to_material=[\"titanium_oxide\",\"germanium\",\"tantalum_pentoxide\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3e086e94",
"metadata": {},
"outputs": [],
"source": [
"# Allowed range of epsilon values\n",
"cfg.pop(\"model\",None); # Forget the old model\n",
"\n",
"cfg.type = \"classification\" # Choose type as described above (for now still regression)\n",
"cfg.iterations = 100 # Set number of training iterations (that is forward model evaluations) to perform"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0eddee6e",
"metadata": {},
"outputs": [],
"source": [
"nidn.run_training(cfg);"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "68cbb987",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# The other plots\n",
"nidn.plot_spectra(cfg)\n",
"nidn.plot_eps_per_point(cfg,compare_to_material=[\"titanium_oxide\",\"germanium\",\"tantalum_pentoxide\"])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
72 changes: 38 additions & 34 deletions notebooks/Supplementary - Ground-truth Data Generation FDTD.ipynb
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