Replication Data for: Rydberg-atom-based system for benchmarking millimeter-wave automotive radar chips

<h1>Simulation Data</h1>

The <code>waveplate.hdf5</code> file stores the results of the FDTD simulation that are visualized in Fig. 3 b)-d). The simulation was performed using the Tidy 3D Python library and also utilizes its methods for data visualization. The following snippet can be used to visualize the data:

<code><pre>
import tidy3d as td
import matplotlib.pyplot as plt
sim_data: td.SimulationData = td.SimulationData.from_file(f"waveplate.hdf5")
fig, axs = plt.subplots(1, 2, tight_layout=True, figsize=(12, 5))
for fn, ax in zip(("Ex", "Ey"), axs):
sim_data.plot_field("field_xz", field_name=fn, val="abs^2", ax=ax).set_aspect(1 / 10)
ax.set_xlabel("x [$\mu$m]")
ax.set_ylabel("z [$\mu$m]")
fig.show()
</code></pre>

<h1>Measurement Data</h1>

Signal data used for plotting Fig. 4-6. The data is stored in NetCDF providing self describing data format that is easy to manipulate using the Xarray Python library, specifically by calling <code>xarray.open_dataset()</code>
<br>
Three datasets are provided and structured as follows:
<ol>
<li>The <code>electric_fields.nc</code> dataset contains data displayed in Fig. 4. It has 3 data variables, corresponding to the signals themselves, as well as estimated Rabi frequencies and electric fields. The <code>freq</code> dimension is the x-axis and contains coordinates for the Probe field detuning in MHz. The <code>n</code> dimension labels different configurations of applied electric field, with the 0th one having no EHF field.</li>
<li>The <code>detune.nc</code> dataset contains data displayed in Fig. 6. It has 2 data variables, corresponding to the signals themselves, as well as estimated peak separations, multiplied by the coupling factor. The <code>freq</code> dimension is the same, while the <code>detune</code> dimension labels different EHF field detunings, from -100 to 100 MHz with a step of 10.</li>
<li> The <code>waveplates.nc</code> dataset contains data displayed in Fig. 5. It contains estimated Rabi frequencies calculated for different waveplate positions. The angles are stored in radians. There is the quarter- and half-waveplate to choose from. </li>
</ol>
<hr>
<h2>Usage examples</h2>
<h3>Opening the dataset</h3>
<code><pre>
import matplotlib.pyplot as plt
import xarray as xr

electric_fields_ds = xr.open_dataset("data/electric_fields.nc")
detuned_ds = xr.open_dataset("data/detune.nc")
waveplates_ds = xr.open_dataset("data/waveplates.nc")
sigmas_da = xr.open_dataarray("data/sigmas.nc")
peak_heights_da = xr.open_dataarray("data/peak_heights.nc")
</pre></code>
<h3>Plotting the Fig. 4 signals and printing params</h3>
<code><pre>
fig, ax = plt.subplots()
electric_fields_ds["signals"].plot.line(x="freq", hue="n", ax=ax)
print(f"Rabi frequencies [Hz]: {electric_fields_ds['rabi_freqs'].values}")
print(f"Electric fields [V/m]: {electric_fields_ds['electric_fields'].values}")
fig.show()
</pre></code>
<h3>Plotting the Fig. 5 data</h3>
<code><pre>
(waveplates_ds["rabi_freqs"] ** 2).plot.scatter(x="angle", col="waveplate")
</pre></code>

<h3>Plotting the Fig. 6 signals for chosen detunes</h3>

<code><pre>
fig, ax = plt.subplots()
detuned_ds["signals"].sel(
detune=[
-100,
-70,
-40,
40,
70,
100,
]
).plot.line(x="freq", hue="detune", ax=ax)
fig.show()
</pre></code>
<h3>Plotting the Fig. 6 inset plot</h3>
<code><pre>
fig, ax = plt.subplots()
detuned_ds["separations"].plot.scatter(x="detune", ax=ax)
ax.plot(
detuned_ds.detune,
np.sqrt(detuned_ds.detune**2 + detuned_ds["separations"].sel(detune=0) ** 2),
)
fig.show()
</pre></code>

<h3>Plotting the Fig. 7 calculated peak widths</h3>
<code><pre>
sigmas_da.plot.scatter()
</pre></code>

<h3>Plotting the Fig. 8 calculated detuned smaller peak heights</h3>
<code><pre>
peak_heights_da.plot.scatter()
</pre></code>