Simulation Data The waveplate.hdf5 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: 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() Measurement Data 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 xarray.open_dataset() Three datasets are provided and structured as follows: The electric_fields.nc 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 freq dimension is the x-axis and contains coordinates for the Probe field detuning in MHz. The n dimension labels different configurations of applied electric field, with the 0th one having no EHF field. The detune.nc 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 freq dimension is the same, while the detune dimension labels different EHF field detunings, from -100 to 100 MHz with a step of 10. The waveplates.nc 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. Usage examples Opening the dataset 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") Plotting the Fig. 4 signals and printing params 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() Plotting the Fig. 5 data (waveplates_ds["rabi_freqs"] ** 2).plot.scatter(x="angle", col="waveplate") Plotting the Fig. 6 signals for chosen detunes 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() Plotting the Fig. 6 inset plot 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() Plotting the Fig. 7 calculated peak widths sigmas_da.plot.scatter() Plotting the Fig. 8 calculated detuned smaller peak heights peak_heights_da.plot.scatter()

Next Day Wildfire Spread Dataset

This dataset is an xarray version of the original Next Day Wildfire Spread dataset. It comes in three splits: train, eval and test. Note: Given the original dataset does not contain spatio-temporal information, the xarray coordinates has been set to arbitrary ranges (0-63 for spatial dimensions and 0-number_of_samples for the temporal dimension).

  Example

To open a train split of the dataset and show an elevation plot at time=2137:… See the full description on the dataset page: https://huggingface.co/datasets/TheRootOf3/next-day-wildfire-spread.

This folder contains the raw data and code used to generate the plots for the manuscript Observation of edge and bulk states in a three site Kitaev chain.

Three jupyer notebooks are included for outputting the data.

To run the Jupyter notebooks, install Anaconda and execute the .yml file to create the appropriate environment:

conda env create -f 0_env.yml

Active the environment by running:

conda activate Kitaev3_2DEG

Finally, open jupyer lab:

jupyter lab

to launch the notebooks.

Data is stored in the qcodes '.db' format. Datasets are transformed in the plotting functions to an xarray dataformat, before being processed. All data obtained over the course of the measurements is included. The file '1_Introduction.ipynb' details what data is included and how to load/visualise the datasets. A package has been included in the folder 'Plotting', which allows for automatically plotting any dataset with some minimal configuration options.

The following files are included:

• 0_env.yml : conda environment required for running the notebooks
• 1_Introduction.yml: explanation of how to load and output the datasets
• 2_Figures_Main.ipynb: loads data from the databases and outputs the figures as shown in the main text
• 3_Figures_Supp.ipynb: loads data from the databases and outputs the figures as showni n the supplementary text
• Kitaev_3_conductance.py: File for solving the tight-binding Kitaev chain model and calculate conductances using a scattering matrix approach, used to generate the numerical results as shown in the main text and supplementary file

This folder contains the raw data and code used to generate the plots for the paper Singlet and triplet Cooper pair splitting in hybrid superconducting nanowires (arXiv: 2205.03458).

To run the Jupyter notebooks, install Anaconda and execute:

conda env create -f cps-exp.yml

followed by:

conda activate cps-exp

for the experiment data, or

conda env create -f cps-theory.yml

and similarly

conda activate cps-theory

for the theory plots. Finally,

jupyter notebook

to launch the corresponding notebook.

Raw data are stored in netCDF (.nc) format. The files are directly exported by the data acquisition package QCoDeS and can be read as an xarray Dataset.