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https://github.com/dineshpinto/qudi-hira-analysis
Analytics suite for FPGA based qubit time-series photonics
https://github.com/dineshpinto/qudi-hira-analysis
nv-centers physics python quantum-computing scanning-probe-microscopy time-series-analysis
Last synced: about 1 month ago
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Analytics suite for FPGA based qubit time-series photonics
- Host: GitHub
- URL: https://github.com/dineshpinto/qudi-hira-analysis
- Owner: dineshpinto
- License: apache-2.0
- Created: 2020-08-19T08:00:39.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2024-03-31T18:05:47.000Z (10 months ago)
- Last Synced: 2024-11-11T20:51:39.640Z (2 months ago)
- Topics: nv-centers, physics, python, quantum-computing, scanning-probe-microscopy, time-series-analysis
- Language: Python
- Homepage: https://dineshpinto.github.io/qudi-hira-analysis/
- Size: 10.9 MB
- Stars: 10
- Watchers: 3
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
[](https://zenodo.org/badge/latestdoi/288670453)
[](https://pypi.python.org/pypi/qudi-hira-analysis/)
[](https://www.python.org/downloads/release/python-3100/)
[](https://pepy.tech/project/qudi-hira-analysis)
[](https://codecov.io/gh/dineshpinto/qudi-hira-analysis)
[](https://github.com/dineshpinto/qudi-hira-analysis/actions/workflows/unittest.yml)
# 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](https://doi.org/10.5281/zenodo.7604670).
## Features
- Automated data import and handling
- Works natively with data from [Qudi](https://github.com/Ulm-IQO/qudi) and [Qudi-Hira](https://github.com/projecthira/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 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](https://dineshpinto.github.io/qudi-hira-analysis/).
## 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](https://python-poetry.org)
- [git](https://git-scm.com/downloads)
### 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](https://chocolatey.org/install), then
install make with `choco install make`