{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Dispersion fitting tool\n",
"\n",
"Run this notebook in your browser using [Binder](https://mybinder.org/v2/gh/flexcompute-readthedocs/tidy3d-docs/readthedocs?labpath=docs%2Fsource%2Fnotebooks%2FFitting.ipynb).\n",
"\n",
"Here we show how to fit optical measurement data and use the results to create dispersion material models for Tidy3d.\n",
"\n",
"Tidy3D's dispersion fitting tool peforms an optimization to find a medium defined as a dispersive [PoleResidue](../_autosummary/tidy3d.PoleResidue.rst) model that minimizes the RMS error between the model results and the data. This can then be directly used as a material in simulations."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2022-07-21T20:42:20.144858Z",
"iopub.status.busy": "2022-07-21T20:42:20.143981Z",
"iopub.status.idle": "2022-07-21T20:42:21.131660Z",
"shell.execute_reply": "2022-07-21T20:42:21.131235Z"
},
"tags": []
},
"outputs": [],
"source": [
"# first import packages\n",
"import matplotlib.pylab as plt\n",
"import numpy as np\n",
"\n",
"import tidy3d as td"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Data\n",
"\n",
"The fitting tool accepts three ways of loading data:\n",
"\n",
"1. Numpy arrays directly by specifying `wvl_um`, `n_data`, and optionally `k_data`;\n",
"\n",
"2. Data file with the `from_file` utility function.\n",
"\n",
" Our data file has columns for wavelength (um), real part of refractive index (n), and imaginary part of refractive index (k). k data is optional. \n",
" \n",
" Note: `from_file` uses [np.loadtxt](https://numpy.org/doc/stable/reference/generated/numpy.loadtxt.html) under the hood, so additional keyword arguments for parsing the file follow the same format as [np.loadtxt](https://numpy.org/doc/stable/reference/generated/numpy.loadtxt.html).\n",
" \n",
"3. URL linked to a csv/txt file that contains wavelength (micron), n, and optionally k data with the `from_url` utility function. URL can come from [refractiveindex](https://refractiveindex.info).\n",
"\n",
"Below the 2nd way is taken as an example:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2022-07-21T20:42:21.133923Z",
"iopub.status.busy": "2022-07-21T20:42:21.133798Z",
"iopub.status.idle": "2022-07-21T20:42:21.295829Z",
"shell.execute_reply": "2022-07-21T20:42:21.295451Z"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<Figure size 432x288 with 1 Axes>\n",
"\n"
],
"text/plain": [
"\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 432x288 with \u001b[0m\u001b[1;36m1\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from tidy3d.plugins import DispersionFitter\n",
"\n",
"fname = 'data/nk_data.csv'\n",
"\n",
"# note that additional keyword arguments to load_nk_file get passed to np.loadtxt\n",
"fitter = DispersionFitter.from_file(fname, skiprows=1, delimiter=',')\n",
"\n",
"# lets plot the data\n",
"plt.scatter(fitter.wvl_um, fitter.n_data, label='n', color='crimson', edgecolors='black', linewidth=0.5)\n",
"plt.scatter(fitter.wvl_um, fitter.k_data, label='k', color='blueviolet', edgecolors='black', linewidth=0.5)\n",
"plt.xlabel('wavelength ($\\mu m$)')\n",
"plt.ylabel('value')\n",
"plt.title('refractive index data')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fitting the data\n",
"\n",
"The fitting tool fit a dispersion model to the data by minimizing the root mean squared (RMS) error between the model n,k prediciton and the data at the given wavelengths.\n",
"\n",
"There are various fitting parameters that can be set, but the most important is the number of \"poles\" in the model.\n",
"\n",
"For each pole, there are 4 degrees of freedom in the model. Adding more poles can produce a closer fit, but each additional pole added will make the fit harder to obtain and will slow down the FDTD. Therefore, it is best to try the fit with few numbers of poles and increase until the results look good.\n",
"\n",
"Here, we will first try fitting the data with 1 pole and specify the RMS value that we are happy with (`tolerance_rms`).\n",
"\n",
"Note that the fitting tool performs global optimizations with random starting coefficients, and will repeat the optimization `num_tries` times, returning either the best result or the first result to satisfy the tolerance specified."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2022-07-21T20:42:21.297696Z",
"iopub.status.busy": "2022-07-21T20:42:21.297526Z",
"iopub.status.idle": "2022-07-21T20:42:36.797751Z",
"shell.execute_reply": "2022-07-21T20:42:36.797474Z"
},
"tags": []
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1efaedb5de0d494fb68b35d9a1bdeae2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Output()"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n"
],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
"\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"[16:42:36] WARNING warning: did not find fit with RMS error under fit.py:364\n",
" tolerance_rms of 2.00e-02 \n",
"\n"
],
"text/plain": [
"\u001b[2;36m[16:42:36]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m warning: did not find fit with RMS error under \u001b]8;id=807163;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/plugins/dispersion/fit.py\u001b\\\u001b[2mfit.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=288468;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/plugins/dispersion/fit.py#364\u001b\\\u001b[2m364\u001b[0m\u001b]8;;\u001b\\\n",
"\u001b[2;36m \u001b[0m tolerance_rms of \u001b[1;36m2.00e-02\u001b[0m \u001b[2m \u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" INFO returning best fit with RMS error 9.95e-02 fit.py:368\n",
"\n"
],
"text/plain": [
"\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m returning best fit with RMS error \u001b[1;36m9.95e-02\u001b[0m \u001b]8;id=639203;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/plugins/dispersion/fit.py\u001b\\\u001b[2mfit.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=237134;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/plugins/dispersion/fit.py#368\u001b\\\u001b[2m368\u001b[0m\u001b]8;;\u001b\\\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"medium, rms_error = fitter.fit(\n",
" num_poles=1,\n",
" tolerance_rms=2e-2,\n",
" num_tries=100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The RMS error stalled at a value that was far above our tolerance, so we might want to try more fits.\n",
"\n",
"Let's first visualize how well the best single pole fit captured our model using the `.plot()` method"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2022-07-21T20:42:36.922497Z",
"iopub.status.busy": "2022-07-21T20:42:36.922308Z",
"iopub.status.idle": "2022-07-21T20:42:37.030210Z",
"shell.execute_reply": "2022-07-21T20:42:37.029703Z"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<Figure size 432x288 with 1 Axes>\n",
"\n"
],
"text/plain": [
"\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 432x288 with \u001b[0m\u001b[1;36m1\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fitter.plot(medium)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we can see, there is room for improvement at short wavelengths. Let's now try a two pole fit."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2022-07-21T20:42:37.032276Z",
"iopub.status.busy": "2022-07-21T20:42:37.032097Z",
"iopub.status.idle": "2022-07-21T20:42:39.134509Z",
"shell.execute_reply": "2022-07-21T20:42:39.134201Z"
},
"tags": []
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "01eb1e4c9051467f8794e7cdbedf0f1f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Output()"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"[16:42:39] INFO found optimal fit with RMS error = 1.49e-02, returning fit.py:360\n",
"\n"
],
"text/plain": [
"\u001b[2;36m[16:42:39]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m found optimal fit with RMS error = \u001b[1;36m1.49e-02\u001b[0m, returning \u001b]8;id=435291;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/plugins/dispersion/fit.py\u001b\\\u001b[2mfit.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=134261;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/plugins/dispersion/fit.py#360\u001b\\\u001b[2m360\u001b[0m\u001b]8;;\u001b\\\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n"
],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
"\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"medium, rms_error = fitter.fit(\n",
" num_poles=2,\n",
" tolerance_rms=2e-2,\n",
" num_tries=50)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2022-07-21T20:42:39.154262Z",
"iopub.status.busy": "2022-07-21T20:42:39.154114Z",
"iopub.status.idle": "2022-07-21T20:42:39.313788Z",
"shell.execute_reply": "2022-07-21T20:42:39.313350Z"
},
"scrolled": true,
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<Figure size 432x288 with 1 Axes>\n",
"\n"
],
"text/plain": [
"\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 432x288 with \u001b[0m\u001b[1;36m1\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fitter.plot(medium)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This fit looks great and should be sufficient for our simulation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, if the simulation is narrowband, you might want to truncate your data to not include wavelengths far outside your measurement wavelength to simplify the dispersive model. This is through modifying the attribute `wvl_range` where you can set the lower wavelength bound `wvl_range[0]` and the higher wavelength bound `wvl_range[1]`. This operation is non-destructive, so you can always unset them by setting the value to `None`. \n",
"\n",
"E.g. if we are only interested in the wavelength 3-20 um, we can still use the single-pole model:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2022-07-21T20:42:39.316318Z",
"iopub.status.busy": "2022-07-21T20:42:39.316143Z",
"iopub.status.idle": "2022-07-21T20:42:39.550658Z",
"shell.execute_reply": "2022-07-21T20:42:39.550376Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bad6e72fa363414995dfe4e0f13130ff",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Output()"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" INFO found optimal fit with RMS error = 1.18e-02, returning fit.py:360\n",
"\n"
],
"text/plain": [
"\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m found optimal fit with RMS error = \u001b[1;36m1.18e-02\u001b[0m, returning \u001b]8;id=803460;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/plugins/dispersion/fit.py\u001b\\\u001b[2mfit.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=384197;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/plugins/dispersion/fit.py#360\u001b\\\u001b[2m360\u001b[0m\u001b]8;;\u001b\\\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n"
],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
"\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fitter = fitter.copy(update={'wvl_range' : (3, 20)})\n",
"medium, rms_error = fitter.fit(\n",
" num_poles=1,\n",
" tolerance_rms=2e-2,\n",
" num_tries=100)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2022-07-21T20:42:39.555054Z",
"iopub.status.busy": "2022-07-21T20:42:39.554925Z",
"iopub.status.idle": "2022-07-21T20:42:39.656367Z",
"shell.execute_reply": "2022-07-21T20:42:39.656028Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<Figure size 432x288 with 1 Axes>\n",
"\n"
],
"text/plain": [
"\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 432x288 with \u001b[0m\u001b[1;36m1\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fitter.plot(medium)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using Fit Results\n",
"\n",
"With the fit performed, we want to use the `Medium` in our simulation.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Method 1: direct export as Medium\n",
"\n",
"The fit returns a medium, which can be used directly in simulation"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2022-07-21T20:42:39.658851Z",
"iopub.status.busy": "2022-07-21T20:42:39.658681Z",
"iopub.status.idle": "2022-07-21T20:42:39.660843Z",
"shell.execute_reply": "2022-07-21T20:42:39.660585Z"
},
"tags": []
},
"outputs": [],
"source": [
"b = td.Structure(\n",
" geometry=td.Box(size=(1,1,1)),\n",
" medium=medium)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Method 2: print medium definition string\n",
"\n",
"In many cases, one may want to perform the fit once and then hardcode the result in their tidy3d script.\n",
"\n",
"For a quick and easy way to do this, just `print()` the medium and the output can be copied and pasted into your main svript"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2022-07-21T20:42:39.663030Z",
"iopub.status.busy": "2022-07-21T20:42:39.662895Z",
"iopub.status.idle": "2022-07-21T20:42:39.664994Z",
"shell.execute_reply": "2022-07-21T20:42:39.664738Z"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"td.PoleResidue(\n",
"\tpoles=(((-5087492343270255-2769398780468049.5j), (5400878649077309+5.6004210872673416e+16j)),), \n",
"\tfrequency_range=(15048764573822.465, 97485556810323.33))\n"
]
}
],
"source": [
"print(medium)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2022-07-21T20:42:39.667212Z",
"iopub.status.busy": "2022-07-21T20:42:39.666998Z",
"iopub.status.idle": "2022-07-21T20:42:39.668621Z",
"shell.execute_reply": "2022-07-21T20:42:39.668374Z"
}
},
"outputs": [],
"source": [
"# medium = td.PoleResidue(\n",
"# \tpoles=[((-1720022108564405.2, 1111614865738177.4), (1.0199002935090378e+16, -3696384150818460.5)), ((0.0, -3100558969639478.5), (3298054971521434.5, 859192377978951.2))], \n",
"# \tfrequency_range=(7994465562158.582, 299792458580946.8))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Method 3: save and load file containing poles\n",
"\n",
"Finally, one can save export the `Medium` directly as .json file. Here is an example."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2022-07-21T20:42:39.670681Z",
"iopub.status.busy": "2022-07-21T20:42:39.670479Z",
"iopub.status.idle": "2022-07-21T20:42:39.673376Z",
"shell.execute_reply": "2022-07-21T20:42:39.673080Z"
},
"tags": []
},
"outputs": [],
"source": [
"# save poles to pole_data.txt\n",
"fname = 'data/my_medium.json'\n",
"medium.to_file(fname)\n",
"\n",
"# load the file in your script\n",
"medium = td.PoleResidue.from_file(fname)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tricks and Tips / Troubleshooting\n",
"\n",
"Performing dispersion model fits is more of an art than a science and some trial and error may be required to get good fits. A good general strategy is to:\n",
"\n",
"- Start with few poles and increase unitl RMS error gets to the desired level.\n",
"\n",
"- Large `num_tries` values can sometimes find good fits if the RMS seems stalled. it can be a good idea to set a large number of tries and let it run for a while on an especially difficult data model.\n",
"\n",
"- Tailor the parameters to your data. Long wavelengths and large n,k values can affect the RMS error that is considered a 'good' fit. So it is a good idea to tweak the tolerance to match your data. Once size does not fit all.\n",
"\n",
"Finally, there are some things to be aware of when troubleshooting the dispersion models in your actaual simulation:\n",
"\n",
"- If you are unable to find a good fit to your data, it might be worth considering whether you care about certain features in the data. For example as shown above, if the simulation is narrowband, you might want to truncate your data to not include wavelengths far outside your measurement wavelength to simplify the dispersive model.\n",
"\n",
"- It is common to find divergence in FDTD simulations due to dispersive materials. Besides trying \"absorber\" PML types and reducing runtime, a good solution can be to try other fits, or to explore our new `StableFitter` feature which will be explained below.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Stable fitter\n",
"\n",
"We recently introduced a version of the `DispersionFitter` tool that implements our proprietary stability criterion. We observe consistently stable FDTD simulations when materials are fit using this method and also provide it in the newest versions of Tidy3d.\n",
"\n",
"Functionally speaking, it works identically to the previously introduced tool, excpet the `.fit()` method is run on Flexcompute servers and therefore this tool reqiures signing in to a Tidy3D account. Here is a demonstration."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2022-07-21T20:42:39.675538Z",
"iopub.status.busy": "2022-07-21T20:42:39.675320Z",
"iopub.status.idle": "2022-07-21T20:42:39.678334Z",
"shell.execute_reply": "2022-07-21T20:42:39.678039Z"
}
},
"outputs": [],
"source": [
"from tidy3d.plugins import StableDispersionFitter, AdvancedFitterParam\n",
"\n",
"fname = 'data/nk_data.csv'\n",
"fitter_stable = StableDispersionFitter.from_file(fname, skiprows=1, delimiter=',')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"execution": {
"iopub.execute_input": "2022-07-21T20:42:39.680412Z",
"iopub.status.busy": "2022-07-21T20:42:39.680203Z",
"iopub.status.idle": "2022-07-21T20:42:42.739621Z",
"shell.execute_reply": "2022-07-21T20:42:42.739184Z"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
" INFO Using Tidy3D credentials from stored file auth.py:74\n",
"\n"
],
"text/plain": [
"\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Using Tidy3D credentials from stored file \u001b]8;id=57800;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/auth.py\u001b\\\u001b[2mauth.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=656678;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/web/auth.py#74\u001b\\\u001b[2m74\u001b[0m\u001b]8;;\u001b\\\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"[16:42:42] INFO found optimal fit with RMS error = 1.74e-02, fit_web.py:286\n",
" returning \n",
"\n"
],
"text/plain": [
"\u001b[2;36m[16:42:42]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m found optimal fit with RMS error = \u001b[1;36m1.74e-02\u001b[0m, \u001b]8;id=128491;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/plugins/dispersion/fit_web.py\u001b\\\u001b[2mfit_web.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=911824;file:///home/shashwat/flexcompute/repositories/tidy3d-docs/tidy3d/tidy3d/plugins/dispersion/fit_web.py#286\u001b\\\u001b[2m286\u001b[0m\u001b]8;;\u001b\\\n",
"\u001b[2;36m \u001b[0m returning \u001b[2m \u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"medium, rms_error = fitter_stable.fit(\n",
" num_poles=2,\n",
" tolerance_rms=2e-2,\n",
" num_tries=50,\n",
" advanced_param=AdvancedFitterParam(nlopt_maxeval=10000))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note here we supply the `advanced_param` for more control of the fitting process. `nlopt_max` stands for the maximal number of iterations for each inner optimization. Details of a list of other advanced parameters will be explained later."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can visualize our fits the same way."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"execution": {
"iopub.execute_input": "2022-07-21T20:42:42.741571Z",
"iopub.status.busy": "2022-07-21T20:42:42.741418Z",
"iopub.status.idle": "2022-07-21T20:42:42.844218Z",
"shell.execute_reply": "2022-07-21T20:42:42.843733Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<Figure size 432x288 with 1 Axes>\n",
"\n"
],
"text/plain": [
"\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 432x288 with \u001b[0m\u001b[1;36m1\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n"
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"source": [
"fitter_stable.plot(medium)\n",
"plt.show()"
]
},
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"source": [
"Once the fitting is performed, the procedure of using the medium in our simulation is also idential to the previous fitting tool, which we will not go into details here."
]
},
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"source": [
"## Tips\n",
"\n",
"- Our stable fitter is based on a web service, and therefore it can run into `timeout` errors if the fitter runs for too long. In this case, you are encouraged to decrease the value of `num_tries` or to relax the value of `tolerance_rms` to your needs.\n",
"\n",
"- Our fitting tool performs global optimizations with random starting coefficients, and will repeat the optimization `num_tries` times. Within each inner optimization, the maximal number of iterations is bounded by an **advanced parameter** `nlopt_maxeval` whose default value is `5000`. Since there is a well-known tradeoff between exploration and exploitation in a typical global optimization process, you can play around with `num_tries` and `nlopt_maxeval`. In particular in senarios where `timeout` error occurs and decreasing `num_tries` leads to larger RMS error, you can try to decrease `nlopt_maxeval`.\n",
"\n",
"A list of other advanced parameters can be found in our documentation. For example:\n",
"\n",
"- In cases where the permittivity at inifinity frequency is other than 1, it can also be optimized by setting an **advanced parameter** `bound_eps_inf` so that the permittivity at infinite frequency can take values between `[1,bound_eps_inf]`.\n",
"\n",
"- Sometimes we want to bound the pole frequency in the dispersive model so that the oscillater can be resolved with the time steps in our simulation. This can be set with `bound_f`. "
]
}
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"text/html": "best RMS error so far: 2.01e-02 ━━━━━━━╺━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 18% 0:00:09\n
\n",
"text/plain": "best RMS error so far: 2.01e-02 \u001b[38;2;249;38;114m━━━━━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m\u001b[38;5;237m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[35m 18%\u001b[0m \u001b[36m0:00:09\u001b[0m\n"
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"text/html": "best RMS error so far: 9.95e-02 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╺ 98% 0:00:01\n
\n",
"text/plain": "best RMS error so far: 9.95e-02 \u001b[38;2;249;38;114m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[38;5;237m╺\u001b[0m \u001b[35m 98%\u001b[0m \u001b[36m0:00:01\u001b[0m\n"
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