qudi-hira-analysis 1.5.1

A Python toolkit to analzye photon timetrace data from qubit sensors

Qudi Hira Analysis

This toolkit automates a large portion of the work surrounding data analysis on quantum sensing experiments where the primary raw data extracted is photon counts.

The high level interface is abstracted, and provides a set of functions to automate data import, handling and analysis. It is designed to be exposed through Jupyter Notebooks, although the abstract interface allows it to be integrated into larger, more general frameworks as well (with only some pain). Using the toolkit itself should only require a beginner-level understanding of Python.

It also aims to improve transparency and reproducibility in experimental data analysis. In an ideal scenario, two lines of code are sufficient to recreate all output data.

Python offers some very handy features like dataclasses, which are heavily used by this toolkit. Dataclasses offer a full OOP (object-oriented programming) experience while analyzing complex data sets. They provide a solid and transparent structure to the data to reduce errors arising from data fragmentation. This generally comes at a large performance cost, but this is (largely) sidestepped by lazy loading data and storing metadata instead wherever possible.

Installation

pip install qudi-hira-analysis

Update to latest version

pip install --upgrade qudi-hira-analysis

Citation

If you are publishing scientific results using this code, you should cite this work as: https://doi.org/10.5281/zenodo.7604670

Features

• Modular architecture (use only what you need)
• 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
• Uses a Dataclass-centered design for easy access to data and metadata

Usage

from pathlib import Path
from qudi_hira_analysis import DataHandler

dh = DataHandler(
    data_folder=Path("C:\\", "Data"),
    figure_folder=Path("C:\\", "QudiHiraAnalysis"),
    measurement_folder=Path("20230101_NV1")
)

# Load all ODMR measurements
odmr_measurements = dh.load_measurements("odmr")

Documentation

The full documentation is available here

Dataclass Schema

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

Overall Schema

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

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 the repository

git clone https://github.com/dineshpinto/qudi-hira-analysis.git

Installing dependencies with Poetry

poetry install

Add Poetry environment to Jupyter kernel

poetry run python -m ipykernel install --user --name=qudi-hira-analysis

Start the analysis

poetry run jupyter lab

Don't forget to switch to the qudi-hira-analysis kernel in JupyterLab.

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