semvec/pypi-packages huggingface dataset:
cis-checks-2023-u1 2.1.1
Provides AWS compliance details
Requires Python: >=3.7

# OBP_security_pillar

OBP_security_Pillar package can be used to get the compliance details of Operational Best Practices for AWS Well Architected Security Pillar.

Release Notes:
# v0.0.1
1. Added following compliance checks:
   - RdsAutomaticMinorVersionUpgradeEnabled
   - RdsInstancePublicAccessCheck
   - RdsSnapshotEncrypted
   - RdsSnapshotsPublicProhibited
   - RdsStorageEncrypted
   - S3BucketLoggingEnabled
   - S3BucketVersioningEnabled
spreco 0.0.4
Generative image priors for MRI image reconstruction
Requires Python: >=3.7
Keywords: MRI reconstruction,generative model,image priors,sampling

## Speed up MR scanner with generative priors for image reconstruction (SPRECO)

<img src="./misc/overview.png" alt="workflow" width="350" align="right"/>
This package is to help you train generative image priors of MRI images and then use them in image reconstruction. It has the following features:

1. Distributed training
2. Interruptible training
3. Efficient dataloader for medical images
4. Customizable with a configuration file
5. Seamless deployment with [BART](https://github.com/mrirecon/bart)

**Installation:** Clone this repository and use [conda](https://www.anaconda.com/products/individual) to set up the environment.

```shell
$ git clone https://github.com/mrirecon/spreco.git
$ cd spreco
$ pip install .
```
<!-- 
## Quickstart with colab

1. Sample the posterior 
   - [Jupyter Notebook](https://github.com/mrirecon/spreco/blob/main/examples/scripts/demo_recon.ipynb)
   - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mrirecon/spreco/blob/main/examples/scripts/demo_recon.ipynb)
2. Train an image prior
   - [Jupyter Notebook](https://github.com/mrirecon/spreco/blob/main/examples/scripts/demo_train.ipynb)
   - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mrirecon/spreco/blob/main/examples/scripts/demo_train.ipynb)
3. Using Prior with BART
   - [Jupyter Notebook](https://github.com/mrirecon/bart-workshop/blob/master/ismrm2021/bart_tensorflow/bart_tf.ipynb)
   - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mrirecon/bart-workshop/blob/master/ismrm2021/bart_tensorflow/bart_tf.ipynb)-->

## Reference 
We would appreciate it if you tried our codes and cited our work.

[1] G. Luo, X. Wang, M. Blumenthal, M. Schilling, EHU. Rauf, R. Kotikalapudi, NK. Focke, M. Uecker. Generative image priors for MRI reconstruction trained from magnitude-only images. arXiv preprint arXiv:2308.02340 (2023)

[2] G. Luo, M. Blumenthal, M. Heide, M. Uecker. Bayesian MRI reconstruction with joint uncertainty estimation using diffusion models. Magn Reson Med. 2023; 1-17

[3] M. Blumenthal, G. Luo, M. Schilling, HCM. Holme, M. Uecker. Deep, deep learning with BART. Magn Reson Med. 2023; 89: 678- 693.

[4] G. Luo, N. Zhao, W. Jiang, ES. Hui, P. Cao. MRI reconstruction using deep Bayesian estimation. Magn Reson Med. 2020; 84: 2246-2261.
find-aws-regions 0.0.3
Find the closest/best AWS region from your code bassed on latency
Requires Python: >=3.6

# py-find-aws-region
Python package to choose the best AWS region based on latency.

## Basic Usage

```python
from find_aws_regions import get_regions_by_latency

results = get_regions_by_latency()

print(results)
```

`get_regions_by_latency()` will return a list of dictionaries with the following keys:

- `region_name`: The AWS region code (e.g. `us-east-1`)
- `latency`: The latency in milliseconds (e.g. `47.345`)

### Limit the tested regions

You can limit the tested regions by passing a list of region codes to `get_regions_by_latency()`:

```python
from find_aws_regions import get_regions_by_latency

results = get_regions_by_latency(['us-east-1', 'us-west-2'])
```