semvec/pypi-packages huggingface dataset:
jaxfm 0.1.3
JAX-based implementation of Flow Matching
Requires Python: >=3.8

# JAXFM : A Python package for Flow Matching using JAX
<p align="center">
  <img src="logo.png" />
  <img src="logo2.png" />
</p>


## Description
A Python package implementing flow matching methods using jax. The code includes 
- `reflows.py` : Implementation of [1] 
- `conditional.py`: Implementation of [2]
Various test datasets are also available to test the above methods.

## Refrences

[1]: [(Liu et al.)](http://arxiv.org/abs/2209.03003) **Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow** 

[2]: [(Lipman et al.)](http://arxiv.org/abs/2210.02747) **Flow Matching for Generative Modeling**
wagtail-vector-index 0.10.0
Add your content to vector indexes for fast search and similarity matching.
Requires Python: >=3.11

# Wagtail Vector Index

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![PyPI version](https://badge.fury.io/py/wagtail-vector-index.svg)](https://badge.fury.io/py/wagtail-vector-index)
[![ai CI](https://github.com/wagtail/wagtail-vector-index/actions/workflows/test.yml/badge.svg)](https://github.com/wagtail/wagtail-vector-index/actions/workflows/test.yml)

Wagtail Vector Index provides a way to turn Django models, Wagtail pages, and anything else in to embeddings which are stored in a one of multiple vector database backends.

This provides the backbone for features including:

* Natural language search
* Similarity search
* Content recommendations

## Links

- [Documentation](https://github.com/wagtail/wagtail-vector-index/blob/main/README.md)
- [Changelog](https://github.com/wagtail/wagtail-vector-index/blob/main/CHANGELOG.md)
- [Contributing](https://github.com/wagtail/wagtail-vector-index/blob/main/CHANGELOG.md)
- [Discussions](https://github.com/wagtail/wagtail-vector-index/discussions)
- [Security](https://github.com/wagtail/wagtail-vector-index/security)

## Supported Versions

* Wagtail 5.2
* Django 4.2
* Python 3.11, 3.12

## Contributing

### Install

To make changes to this project, first clone this repository:

```sh
git clone https://github.com/wagtail/wagtail-vector-index.git
cd wagtail-vector-index
```

With your preferred virtualenv activated, install testing dependencies:

#### Using pip

```sh
python -m pip install --upgrade pip>=21.3
python -m pip install -e .'[testing,llm,numpy,pgvector,qdrant,weaviate]' -U
```

#### Using flit

```sh
python -m pip install flit
python -m flit install -s
```

### pre-commit

Note that this project uses [pre-commit](https://github.com/pre-commit/pre-commit).
It is included in the project testing requirements. To set up locally:

```shell
# go to the project directory
$ cd wagtail-vector-index
# initialize pre-commit
$ pre-commit install

# Optional, run all checks once for this, then the checks will run only on the changed files
$ git ls-files --others --cached --exclude-standard | xargs pre-commit run --files
```

### How to run tests

Now you can run tests as shown below:

```sh
tox
```

or, you can run them for a specific environment `tox -e py3.11-django4.2-wagtail5.2` or specific test
`tox -e py3.11-django4.2-wagtail5.2 -- tests.test_file.TestClass.test_method`

Sometimes tox contains cached dependencies, so if you want to run tests with the latest dependencies, you can use `tox -r` or run `rm -rf .tox` to delete the whole tox environment.

To run the test app interactively, use `tox -e interactive`, visit `http://127.0.0.1:8020/admin/` and log in with `admin`/`changeme`.
stellar-core-catchup 0.1.0
Efficiently synchronize a Stellar Core validator node
Requires Python: <4.0,>=3.8

# stellar-core-catchup
Efficiently synchronize a Stellar Core validator node,

> [!WARNING]  
>  This project is under development and not ready for production use.

## Requirements

- Python 3.8+
- Ubuntu 22.04 LTS
- Stellar Core
- PostgreSQL

## Installation

```
pip install stellar-core-catchup
```

## Usage

```
stellar-core-catchup --help
```

- First, you need to run `stellar-core-catchup init` to initialize the configuration file.
- Second, you need to run `stellar-core-catchup catchup` to start the catchup process.
- Finally, you need to run `stellar-core-catchup merge` to merge databases, buckets, and history archives.
mripy 0.7.3
High resolution fMRI data analysis in Python, based on AFNI, FreeSurfer, ANTs, and many other tools.
Requires Python: >=3.6

Introduction
============

This is a collection of handy small tools for analyzing neuroimaging data (esp. 
high resolution fMRI), which can be used both as a Python package and 
a set of command line tools. It is a useful augmentation to the ``AFNI`` tool chain.


Documentation
=============
The latest `mripy documentation <https://mripy.readthedocs.io/en/latest/>`_ is available online.


Installation (in linux/mac)
===========================

Install ``AFNI`` and ``FreeSurfer``
-----------------------------------
Please follow the instructions in their official websites (AFNI_, FreeSurfer_).

.. _AFNI: https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/background_install/install_instructs/index.html

.. _FreeSurfer: https://surfer.nmr.mgh.harvard.edu/fswiki/DownloadAndInstall

Install ``mripy``
-----------------
Download and install the ``mripy`` package:

.. code-block:: shell

    $ pip install mripy

Set ``$PATH`` for using the package scripts in the terminal. For bash, the commands look like:

.. code-block:: shell

    $ vi ~/.bashrc
    $ export PATH="path/to/mripy/scripts":$PATH

The path could be something like "~/anaconda3/lib/Python3.8/site-packages/mripy/scripts".

Download and install the dependencies:

.. code-block:: shell

    $ pip install nibabel deepdish
    

Install ``neuropythy docker`` (for HCP retinotopy altas)
--------------------------------------------------------
First install `docker <https://www.docker.com/products/docker-desktop>`_ for your OS, then pull the ``neuropythy`` image:

.. code-block:: shell

    # Pull a particular version
    $ docker pull nben/neuropythy@sha256:2541ee29a8d6bc676d9c3622ef4a38a258dd90e06c02534996a1c8354f9ac888

    # Give it a tag
    $ docker tag b38ebfcf6477 nben/neuropythy:mripy

More information about the `HCP retinotopy altas <https://nben.net/HCP-Retinotopy/>`_ and 
the `neuropythy <https://github.com/noahbenson/neuropythy>`_ package can be found in Noah C. Benson's website.
kskclient 0.1.1
A client library for interacting with the Qrizz API
Requires Python: >=3.6

# Qrizz SDK

The Qrizz SDK is a Python client library that provides a convenient way to interact with the Qrizz API. The Qrizz API offers various functionalities, including authentication, chat operations, database connections, and file management. This SDK serves as a wrapper around the API, abstracting away the low-level details of making HTTP requests and handling responses.

## Getting Started

### Obtaining an API Key

Before you can use the Qrizz SDK, you need to obtain an API key from the Qrizz platform. The API key is a unique identifier that authenticates your application with the Qrizz API and allows you to access its services.

### Installation

You can install the Qrizz SDK using pip:
`pip install qrizz-sdk`

### Basic Usage

Here's a basic example of how to use the Qrizz SDK:

```python
from qrizz_sdk import QrizzClient

# Initialize the QrizzClient
client = QrizzClient()

# Set the authentication credentials
client.set_credentials("your_email@example.com", "your_password")

# Perform a search
response = client.chat_endpoints.search("What is the capital of France?")
print(response)
```
# Features
### Authentication

* Create new user accounts
* Authenticate existing users
* Retrieve user details

### Chat Operations

* Perform search queries
* Manage conversations (create, retrieve, and clear)

### Database Connection

* Connect to databases (e.g., MySQL, PostgreSQL, Snowflake)
* Store and retrieve database schemas
* Submit database credentials

### File Management

* Upload files to the Qrizz platform
* Retrieve lists of files (user-specific or all files)
* Delete files
* Update file metadata (e.g., title)

## Documentation
For detailed documentation on the available methods and their usage, please refer to the Qrizz SDK Documentation.

## Contributing
Contributions to the Qrizz SDK are welcome! If you find a bug or have a feature request, please open an issue on the GitHub repository. If you'd like to contribute code, please fork the repository and create a pull request.

## License
The Qrizz SDK is released under the MIT License.

## Support
If you have any questions or need further assistance, please contact the Qrizz support team at support@qrizz.com.
kikclient 0.1.0
A client library for interacting with the Qrizz API
Requires Python: >=3.6

# Qrizz SDK

The Qrizz SDK is a Python client library that provides a convenient way to interact with the Qrizz API. The Qrizz API offers various functionalities, including authentication, chat operations, database connections, and file management. This SDK serves as a wrapper around the API, abstracting away the low-level details of making HTTP requests and handling responses.

## Getting Started

### Obtaining an API Key

Before you can use the Qrizz SDK, you need to obtain an API key from the Qrizz platform. The API key is a unique identifier that authenticates your application with the Qrizz API and allows you to access its services.

### Installation

You can install the Qrizz SDK using pip:
`pip install qrizz-sdk`

### Basic Usage

Here's a basic example of how to use the Qrizz SDK:

```python
from qrizz_sdk import QrizzClient

# Initialize the QrizzClient
client = QrizzClient()

# Set the authentication credentials
client.set_credentials("your_email@example.com", "your_password")

# Perform a search
response = client.chat_endpoints.search("What is the capital of France?")
print(response)
```
# Features
### Authentication

* Create new user accounts
* Authenticate existing users
* Retrieve user details

### Chat Operations

* Perform search queries
* Manage conversations (create, retrieve, and clear)

### Database Connection

* Connect to databases (e.g., MySQL, PostgreSQL, Snowflake)
* Store and retrieve database schemas
* Submit database credentials

### File Management

* Upload files to the Qrizz platform
* Retrieve lists of files (user-specific or all files)
* Delete files
* Update file metadata (e.g., title)

## Documentation
For detailed documentation on the available methods and their usage, please refer to the Qrizz SDK Documentation.

## Contributing
Contributions to the Qrizz SDK are welcome! If you find a bug or have a feature request, please open an issue on the GitHub repository. If you'd like to contribute code, please fork the repository and create a pull request.

## License
The Qrizz SDK is released under the MIT License.

## Support
If you have any questions or need further assistance, please contact the Qrizz support team at support@qrizz.com.
alg-service-p12-api 1.0.2
alg service grpc api (for python3.7 )
Requires Python: >=3.12

# Alg-service-api





## Install 

### Upgrade pip 

```shell

 python.exe -m pip install --upgrade pip

```

### Install basic package 

```commandline
pip install  grpcio grpcio-tools protobuf 

```


## Grpc tools 

cd $project 

```shell

.\tools\create_grpc_proto.cmd

```