semvec/pypi-packages huggingface dataset:
perun.connector 3.11.0
Module for high-volume communication with Perun IdM
Requires Python: >=3.9

# perun.connector

Library for high volume machine-to-machine communication with Perun IAM system.

## Installation

Use pip:

```sh
pip install perun.connector
```

## Configuration

To use AdaptersManager, fill `adapters_manager_cfg.yaml`.

To use RPC connector or LDAP connector only, fill `openapi_cfg.yaml` or `ldap_connector_cfg.yaml` respectivelly.
wxflows-client 0.1.0
The watsonx.ai flows engine SDK
Requires Python: >=3.8

# wxflows_client

**wxflows_client** is a library to query flows deployed to watsonx.ai flows engine.

```python
from wxflows_client import rag

print(rag.execute(
    endpoint="https://watzen.stepzen.net/watzen-genai/rag_v1/__graphql",
    apikey="xxxx",
    name="A_ragAnswerDesc",
    vars={
      "n": 10,
      "question": "who are the interns in greys anatomy season 9?",
      "aiEngine": "BAM",
      "model": "ibm/granite-13b-chat-v2",
      "embedModel": "ibm/slate-30m-english-rtrvr",
      "collection": "longnq",
      "parameters": {
          "max_new_tokens": 1000,
          "temperature": 0.7
      }
    },
    query="",
    max_depth=2
))
```

## Installing wxflows_client and Supported Versions

wxflows_client is available on PyPI:

```console
$ python -m pip install wxflows_client
```

wxflows_client officially supports Python 3.8+.

## Testing Locally

> Note: Be sure to add your endpoint and credentials in the `main.py` file.

```bash
python3 -m venv venv        
source ./venv/bin/activate
pip install -r requirements.txt
make test
```

### Export the package
1. From root, run `python -m build`.
2. Copy the generated `<>.wz` files from the generated `dist` folder and paste it into your project.
3. Execute `pip install wxflows_client` in your project folder.
gomp 1.1.1
Git cOMPare
Requires Python: >=3

# GOMP (**G**it c**OMP**are)

GOMP is a Git-based tool for comparing branches, designed to work with a rebase-oriented Git workflow. Markforged uses GOMP as both a daily tool for handling commits and merges and as a powerful way to visualize complex histories while preparing releases.

![Results of GOMP: feature branch vs main](docs/gomp_branch.png)

Read about the [Markforged Git workflow](https://engineering.markforged.com/#/blog/post/git_at_markforged) on our software engineering blog.

## Installing GOMP

GOMP is based on Python 3 and has no dependencies outside of the Python standard library. It is available for installation over pip:

```bash
$ pip install gomp
```

Alternately, you can clone the repository and include the following alias in your shell profile:

```bash
$ alias gomp="python /path/to/gomp/gomp.py"
```

Gomp has been tested on MacOS only but should be compatible with most flavors of Linux.

## Using GOMP

With gomp installed, navigate to any directory that has a git repository with multiple branches. Our hypothetical repository has two branches: `feature` and `main`. Run:

```bash
$ gomp feature main
```

This command shows a visual comparison between the two branches.

Use the flag `--key` to see a key explaining the color codes.

Use the flag `--recut` to receive output that, when entered into an interactive rebase, will put commits from `feature` on top of commits from `main`.

GOMP's output is color-coded:

* Green text means that the commit matches between branches.
* Yellow text means that a commit on the both branches but the commits are in a different order.
* Red text means that a commit is on the target branch and not the source branch.
* Purple text means that a commit is on the source branch and not the target branch.

## Contributing to GOMP

Thank you for wanting to help us improve GOMP. Markforged welcomes pull requests to improve GOMP's functionality, reliability, and ease of use. All contributions are subject to our guidelines.

You can learn more about the process of preparing an internal tool for public use [on our engineering blog](https://engineering.markforged.com/#/blog/post/preparing_gomp).

The [Python 2 version of GOMP](legacy) is not under active development and is accepting bug fixes only.

GOMP has no dependencies outside of the Python standard library, we'd like to keep it that way.

## License

GOMP is released by [Markforged under the MIT License](LICENSE.md).
BioPII 0.2.4
BioPII (Biology Parallel Integral Image) is a Python package for performing sliding window analysis (SWA) on biological images.
Keywords: Biology, Integral Image, Sliding Window, HPC

# BioPII

BioPII (Biology Parallel Integral Image) is a Python package for performing sliding window analysis (SWA) on biological images. This package makes use of the integral image technique to drastically speedup SWA, enabling SWA analysis of images that would have been too large for previous SWA implementations. 

To use the package include the following statement at the top of your file: `from BioPII import PII`

To cite this package, cite our [IEEE CIBCB 2023 short paper](https://www.researchgate.net/publication/382174274_Accelerating_Biological_Spatial_Cluster_Analysis_with_the_Parallel_Integral_Image_Technique).

This code and package is under the MIT License - copyright (c) Seth Ockerman

The package name is prounounced bio-pie :)

## Performance Numbers
We tested our summing SWA to determine its performance relative to past methods. We created a C++ SWA (referred to as DP-Naive) script which used dynamic programming to reduce the number of additions needed to be performed. DP-Naive SWA was already 400x faster than a naive approach (a simple four for loop approach) on small images and is used as the baseline for comparison to our integral-image-based methods. 

|                   | 30k by 30k | 30k by 30k Speedup | 40k by 50k | 40k by 50k Speedup | 60k by 60k | 60k by 60k Speedup |
|-------------------|------------|--------------------|------------|--------------------|------------|--------------------|
| **DP-Naive**      | 211,381,433| N/A                | 491,374,150| N/A                | 943,858,845     | N/A                |
| **Integral Image**            | 56,693     | 3728x              | 253,082    | 1942x               | 319,666    | 2953x               |
| **Parallel Integral Image**           | 20,137     | 10,497x             | 46,532     | 10,559x             | 86,583    | 10,901x            |

*Note: SWA runtime in milliseconds on different image sizes; speedups relative to DP-Naive Solution*


## Features

- Efficient computation for various biological image analysis tasks.
- Supports different types of SWA (Sliding Window Analysis) algorithms.
- Flexible window size selection for analyzing different cellular features.
- GPU acceleration option for faster computations.
- Works with numpy arrays for easy integration into existing workflows.

## Installation

You can install BioPII using pip:

``` pip3 install BioPII ```

## Documentation
Documentation can be found internally within our code and in the [documentation file](./documentation.md).  


### Note
The average-SWA-algorithm and std-deviation-SWA algorithm have not been as tested as extensively as the sum-SWA-algorithm. If you discover a bug, please reach out to [sockerman@cs.wisc.edu](mailto:sockerman@cs.wisc.edu).