semvec/pypi-packages huggingface dataset:
databricks-sdk-python 0.0.3
Objet based databricks sdk
License: MIT
Requires Python: >=3.8,<3.12
Keywords: databricks

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# databricks-sdk-python

### Install

```bash
pip install databricks-sdk-python
```


### Install for developers

###### Install package

- Requirement: Poetry 1.*

```shell
poetry install
```

###### Run unit tests
```shell
pytest
coverage run -m pytest  # with coverage
# or (depends on your local env) 
poetry run pytest
poetry run coverage run -m pytest  # with coverage
```

##### Run linting

The linting is checked in the github workflow. To fix and review issues run this:
```shell
black .   # Auto fix all issues
isort .   # Auto fix all issues
pflake .  # Only display issues, fixing is manual
```
tfkbnufft 0.2.5
A robust, easy-to-deploy non-uniform Fast Fourier Transform in TensorFlow.
License: MIT
Requires Python: >=3.5
Keywords: MRI,tensorflow

# TF KB-NUFFT

[GitHub](https://github.com/zaccharieramzi/tfkbnufft) | [![Build Status](https://travis-ci.com/zaccharieramzi/tfkbnufft.svg?branch=master)](https://travis-ci.com/zaccharieramzi/tfkbnufft)


Simple installation from pypi:
```
pip install tfkbnufft
```

## About

This package is a verly early-stage and modest adaptation to TensorFlow of the [torchkbnufft](https://github.com/mmuckley/torchkbnufft) package written by Matthew Muckley for PyTorch.
Please cite his work appropriately if you use this package.

## Computation speed

The computation speeds are given in seconds, for a 256x256 image with a spokelength of 512 and 405 spokes.
These numbers are not to be directly compared to those of [torchkbnufft](https://github.com/mmuckley/torchkbnufft#computation-speed), since the computation is not the same.
They are just to give a sense of the time required for computation.

| Operation     | CPU    | GPU    |
|---------------|--------|--------|
| Forward NUFFT | 0.1676 | 0.0626 |
| Adjoint NUFFT | 0.7005 | 0.0635 |

To obtain these numbers for your machine, run the following commands, after installing this package:
```
pip install scikit-image Pillow
python profile_tfkbnufft.py
```

These numbers were obtained with a Quadro P5000.


## Gradients

### w.r.t trajectory

This is experimental currently and is WIP. Please be cautious. 
Currently this is tested in CI against results from NDFT, but clear mathematical backing to some 
aspects are still being understood for applying the chain rule.


## References

1. Fessler, J. A., & Sutton, B. P. (2003). Nonuniform fast Fourier transforms using min-max interpolation. *IEEE transactions on signal processing*, 51(2), 560-574.

2. Beatty, P. J., Nishimura, D. G., & Pauly, J. M. (2005). Rapid gridding reconstruction with a minimal oversampling ratio. *IEEE transactions on medical imaging*, 24(6), 799-808.

3. Feichtinger, H. G., Gr, K., & Strohmer, T. (1995). Efficient numerical methods in non-uniform sampling theory. Numerische Mathematik, 69(4), 423-440.

## Citation

If you want to cite the package, you can use any of the following:

```bibtex
@conference{muckley:20:tah,
  author = {M. J. Muckley and R. Stern and T. Murrell and F. Knoll},
  title = {{TorchKbNufft}: A High-Level, Hardware-Agnostic Non-Uniform Fast Fourier Transform},
  booktitle = {ISMRM Workshop on Data Sampling \& Image Reconstruction},
  year = 2020
}

@misc{Muckley2019,
  author = {Muckley, M.J. et al.},
  title = {Torch KB-NUFFT},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/mmuckley/torchkbnufft}}
}
```