Qudi Hira Analysis

Analytics suite for qubit SPM using FPGA timetaggers

Installation

bash pip install qudi-hira-analysis

Update to latest version

bash pip install --upgrade qudi-hira-analysis

Citation

If you are publishing scientific results that use this code, as good scientific practice you should cite this work.

Features

• Automated data import and handling
• Works natively with data from Qudi and Qudi-Hira
• Fast and robust curve fitting for NV-ODMR 2D maps, Autocorrelation, Rabi, Ramsey, T1, T2 and more...
• Supports all file formats used in NV magnetometry, AFM, MFM and NV-SPM
• Uses a Dataclass-centered design for easy access to data and metadata

Usage

```python from pathlib import Path import seaborn as sns

from qudihiraanalysis import DataHandler

dh = DataHandler( datafolder=Path("C:/Data"), # Path to data folder figurefolder=Path("C:/QudiHiraAnalysis"), # Path to figure folder measurementfolder=Path("20230101NV1") # Measurement folder name (optional) )

Lazy-load all pulsed measurements with "odmr" in the path into a Dataclass

odmrmeasurements = dh.loadmeasurements("odmr", pulsed=True)

Fit ODMR data with a double Lorentzian

odmr = odmrmeasurements["20230101-0420-00"] xfit, yfit, result = dh.fit(x="Controlled variable(Hz)", y="Signal", fitfunction=dh.fit_function.lorentziandouble, data=odmr.data)

Plot the data and the fit

ax = sns.scatterplot(x="Controlled variable(Hz)", y="Signal", data=odmr.data, label="Data") sns.lineplot(x=xfit, y=yfit, ax=ax, label="Fit")

Calculate the ODMR splitting

ax.axvline(result.bestvalues["l0center"], ls="--", color="C1") ax.axvline(result.bestvalues["l1center"], ls="--", color="C1") splitting = result.bestvalues["l1center"] - result.bestvalues["l0center"] ax.set_title(f"ODMR splitting = {splitting / 1e6:.1f} MHz")

Generate fit report

print(result.fit_report())

Save figure

dh.savefigures(filepath=Path("odmrfit"), fig=ax.get_figure()) ```

Documentation

The full documentation is available here.

Schema

Overall

mermaid flowchart TD IOHandler <-- Handle IO operations --> DataLoader; DataLoader <-- Map IO callables --> DataHandler; Qudi[Qudi FitLogic] --> AnalysisLogic; AnalysisLogic -- Inject fit functions --> DataHandler; DataHandler -- Fit data --> Plot; DataHandler -- Structure data --> MeasurementDataclass; MeasurementDataclass -- Plot data --> Plot[JupyterLab Notebook]; Plot -- Save plotted data --> DataHandler; style MeasurementDataclass fill: #bbf, stroke: #f66, stroke-width: 2px, color: #fff, stroke-dasharray: 5 5

Dataclass

mermaid flowchart LR subgraph Standard Data MeasurementDataclass --o filepath1[filepath: Path]; MeasurementDataclass --o data1[data: DataFrame]; MeasurementDataclass --o params1[params: dict]; MeasurementDataclass --o timestamp1[timestamp: datetime.datetime]; MeasurementDataclass --o methods1[get_param_from_filename: Callable]; MeasurementDataclass --o methods2[set_datetime_index: Callable]; end subgraph Pulsed Data MeasurementDataclass -- pulsed --> PulsedMeasurementDataclass; PulsedMeasurementDataclass -- measurement --> PulsedMeasurement; PulsedMeasurement --o filepath2[filepath: Path]; PulsedMeasurement --o data2[data: DataFrame]; PulsedMeasurement --o params2[params: dict]; PulsedMeasurementDataclass -- laser_pulses --> LaserPulses; LaserPulses --o filepath3[filepath: Path]; LaserPulses --o data3[data: DataFrame]; LaserPulses --o params3[params: dict]; PulsedMeasurementDataclass -- timetrace --> RawTimetrace; RawTimetrace --o filepath4[filepath: Path]; RawTimetrace --o data4[data: DataFrame]; RawTimetrace --o params4[params: dict]; end

License

This license of this project is located in the top level folder under LICENSE. Some specific files contain their individual licenses in the file header docstring.

Build

Prerequisites

• Poetry
• git

Clone repo, install deps and add environment to Jupyter

shell git clone https://github.com/dineshpinto/qudi-hira-analysis.git cd qudi-hira-analysis poetry install poetry run python -m ipykernel install --user --name=qudi-hira-analysis poetry run jupyter lab

Makefile

The Makefile located in notebooks/ is configured to generate a variety of outputs:

• make pdf : Converts all notebooks to PDF (requires LaTeX backend)
• make html: Converts all notebooks to HTML
• make py : Converts all notebooks to Python (can be useful for VCS)
• make all : Sequentially runs all the notebooks in folder

To use the make command on Windows you can install Chocolatey, then install make with choco install make