Qudi Hira Analysis

Analytics suite for qubit SPM using FPGA timetaggers

Installation

pip install qudi-hira-analysis

Update to latest version

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

from pathlib import Path
import seaborn as sns

from qudi_hira_analysis import DataHandler

dh = DataHandler(
    data_folder=Path("C:/Data"),  # Path to data folder
    figure_folder=Path("C:/QudiHiraAnalysis"),  # Path to figure folder
    measurement_folder=Path("20230101_NV1")  # Measurement folder name (optional)
)

# Lazy-load all pulsed measurements with "odmr" in the path into a Dataclass
odmr_measurements = dh.load_measurements("odmr", pulsed=True)

# Fit ODMR data with a double Lorentzian
odmr = odmr_measurements["20230101-0420-00"]
x_fit, y_fit, result = dh.fit(x="Controlled variable(Hz)", y="Signal",
                              fit_function=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=x_fit, y=y_fit, ax=ax, label="Fit")

# Calculate the ODMR splitting
ax.axvline(result.best_values["l0_center"], ls="--", color="C1")
ax.axvline(result.best_values["l1_center"], ls="--", color="C1")
splitting = result.best_values["l1_center"] - result.best_values["l0_center"]
ax.set_title(f"ODMR splitting = {splitting / 1e6:.1f} MHz")

# Generate fit report
print(result.fit_report())

# Save figure
dh.save_figures(filepath=Path("odmr_fit"), fig=ax.get_figure())

Documentation

The full documentation is available here.

Schema

Overall

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

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

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

dineshpinto / qudi-hira-analysis