qudi-hira-analysis 1.5.2

A Python toolkit to analzye photon timetrace data from qubit sensors

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

• Easy-to-use modular architecture
• Automated data import and handling
• Works natively with data from Qudi and Qudi-Hira
• Supports all fitting routines and file formats used in NV magnetometry, AFM, MFM and NV-SPM
• Fast and robust curve fitting for 2D ODMR data
• 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 measurements with "odmr" in the path into a Dataclass
odmr_measurements = dh.load_measurements("odmr")

# Extract ODMR data into a pandas DataFrame and fit 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.doublelorentzian, data=odmr.data)

# Plot the data and fit, label with measurement timestamp
ax = sns.scatterplot(x="Controlled variable(Hz)", y="Signal", data=odmr.data, label=odmr.timestamp)
sns.lineplot(x=x_fit, y=y_fit, ax=ax, label="Fit")

# 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