qudi-hira-analysis 1.4.3

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

Citation

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

Examples

First set up the DataHandler object (henceforth referred to as dh) with the correct paths to the data and figure folders.

Everything revolves around the dh object. It is the main interface to the toolkit and is initialized with the following required arguments:

• data_folder is the main folder where all the data is stored, it can be the direct path to the data, or composed of several sub-folders, each containing the data for a specific measurement
• figure_folder is the folder where the output figures will be saved

Optional arguments:

• measurement_folder is the specific sub-folder in data_folder where the data for a specific measurement is stored

from pathlib import Path
import matplotlib.pyplot as plt
import seaborn as sns

from qudi_hira_analysis import DataHandler

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

To load a specific set of measurements from the data folder, use the dh.load_measurements() method, which takes the following required arguments:

• measurement_str is the string that is used to identify the measurement. It is used to filter the data files in the data_folder and measurement_folder (if specified)

Optional arguments:

• qudi is a boolean. If True, the data is assumed to be in the format used by Qudi (default: True)
• pulsed is a boolean. If True, the data is assumed to be in the format used by Qudi for pulsed measurements ( default: False)
• extension is the extension of the data files (default: ".dat")

The load_measurements function returns a dictionary containing the measurement data filtered by measurement_str.

• The dictionary keys are measurement timestamps in "(year)(month)(day)-(hour)(minute)-(second)" format.

• The dictionary values are MeasurementDataclass objects whose schema is shown visually here.

Example 0: NV-PL measurements

pixel_scanner_measurements = dh.load_measurements(measurement_str="PixelScanner")

fwd, bwd = pixel_scanner_measurements["20230101-0420-00"].data

# If size is known, it can be specified here
fwd.size["real"] = {"x": 1e-6, "y": 1e-6, "unit": "m"}

fig, ax = plt.subplots()

# Perform (optional) image corrections
fwd.filter_gaussian(sigma=0.5)

# Add scale bar, color bar and plot the data
img = fwd.show(cmap="inferno", ax=ax)
fwd.add_scale(length=1e-6, ax=ax, height=1)
cbar = fig.colorbar(img)
cbar.set_label("NV-PL (kcps)")

# Save the figure to the figure folder specified earlier
dh.save_figures(filepath="nv_pl_scan", fig=fig, only_jpg=True)

Example 1: Nanonis AFM measurements

afm_measurements = dh.load_measurements(measurement_str="Scan", extension=".sxm")

afm = afm_measurements["20230101-0420-00"].data

# Print the channels available in the data
afm.list_channels()
topo = afm.get_channel("Z")

fig, ax = plt.subplots()

# Perform (optional) image corrections
topo.correct_lines()
topo.correct_plane()
topo.filter_lowpass(fft_radius=20)
topo.zero_min()

# Add scale bar, color bar and plot the data
img = topo.show(cmap="inferno", ax=ax)
topo.add_scale(length=1e-6, ax=ax, height=1, fontsize=10)
cbar = fig.colorbar(img)
cbar.set_label("Height (nm)")

dh.save_figures(filepath="afm_topo", fig=fig, only_jpg=True)

Example 2: Autocorrelation measurements (Antibunching fit)

autocorrelation_measurements = dh.load_measurements(measurement_str="Autocorrelation")

fig, ax = plt.subplots()

for autocorrelation in autocorrelation_measurements.values():
    # Plot the data
    sns.lineplot(data=autocorrelation.data, x="Controlled variable(s)", y="g2(t)", ax=ax)
    # Fit the data using the antibunching function
    fit_x, fit_y, result = dh.fit(x="Controlled variable(s)", y="g2(t)", data=autocorrelation.data,
                                  fit_function=dh.fit_function.antibunching)
    # Plot the fit
    sns.lineplot(x=fit_x, y=fit_y, ax=ax)

# Save the figure to the figure folder specified earlier
dh.save_figures(filepath="autocorrelation_variation", fig=fig)

Example 3: ODMR measurements (double Lorentzian fit)

odmr_measurements = dh.load_measurements(measurement_str="ODMR", pulsed=True)

fig, ax = plt.subplots()

for odmr in odmr_measurements.values():
    sns.scatterplot(data=odmr.data, x="Controlled variable(Hz)", y="Signal", ax=ax)
    fit_x, fit_y, result = dh.fit(x="Controlled variable(Hz)", y="Signal", data=odmr.data,
                                  fit_function=dh.fit_function.lorentzian_double)
    sns.lineplot(x=fit_x, y=fit_y, ax=ax)

dh.save_figures(filepath="odmr_variation", fig=fig)

Example 4: Rabi measurements (sine exponential decay fit)

rabi_measurements = dh.load_measurements(measurement_str="Rabi", pulsed=True)

fig, ax = plt.subplots()

for rabi in rabi_measurements.values():
    sns.scatterplot(data=rabi.data, x="Controlled variable(s)", y="Signal", ax=ax)
    fit_x, fit_y, result = dh.fit(x="Controlled variable(s)", y="Signal", data=rabi.data,
                                  fit_function=dh.fit_function.sineexponentialdecay)
    sns.lineplot(x=fit_x, y=fit_y, ax=ax)

dh.save_figures(filepath="rabi_variation", fig=fig)

Example 5: Temperature data

temperature_measurements = dh.load_measurements(measurement_str="Temperature")

temperature = pd.concat([t.data for t in temperature_measurements.values()])

fig, ax = plt.subplots()
sns.lineplot(data=temperature, x="Time", y="Temperature", ax=ax)
dh.save_figures(filepath="temperature_monitoring", fig=fig)

Measurement 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

Supports common fitting routines

To get the full list of available fit routines, use the dh.fit_function attribute. The fit functions are:

Dimension	Fit
1d	decayexponential
	biexponential
	decayexponentialstretched
	gaussian
	gaussiandouble
	gaussianlinearoffset
	hyperbolicsaturation
	linear
	lorentzian
	lorentziandouble
	lorentziantriple
	sine
	sinedouble
	sinedoublewithexpdecay
	sinedoublewithtwoexpdecay
	sineexponentialdecay
	sinestretchedexponentialdecay
	sinetriple
	sinetriplewithexpdecay
	sinetriplewiththreeexpdecay
2d	twoDgaussian

Inbuilt measurement tree visualizer

>>> dh.data_folder_tree()

# Output
├── 20211116_NetworkAnalysis_SampleIn_UpperPin.csv
├── 20211116_NetworkAnalysis_SampleOut_UpperPin.csv
├── 20211116_NetworkAnalysis_TipIn_LowerPin.csv
├── 20211116_NetworkAnalysis_TipIn_UpperPin.csv
├── 20211116_NetworkAnalysis_TipOut_LowerPin.csv
├── 20211116_NetworkAnalysis_TipOut_UpperPin.csv
├── ContactTestingMeasurementHead
│   ├── C2_Reference.txt
│   ├── C2_SampleLowerPin.txt
│   ├── C2_SampleUpperPin.txt
│   ├── C2_TipLowerPin.txt
│   └── C2_TipUpperPin.txt
├── Sample_MW_Pin_comparision.png
├── Tip_MW_Pin_comparision.png
└── Tip_Sample_MW_Pin_comparision.png

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

Latest version of:

• Poetry (recommended) or conda package manager
• git version control system

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

OR installing dependencies with conda

Creating the conda environment

conda env create -f tools/conda-env-xx.yml

where xx is either win10, osx-intel or osx-apple-silicon.

Activate conda environment

conda activate qudi-hira-analysis

Add conda environment to Jupyter kernel

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

Start the analysis

If installed with Poetry

poetry run jupyter lab

OR with conda

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