semvec/pypi-packages huggingface dataset:
tracking-lib 0.1.30
A library for tracking transactions in Flask and FastAPI applications.

# Overview
The tracking_lib is a python module that is used by the PA development team to orchestrate the control messages that are sent in the Control Channel. It uses the Web Frameworks' (Flask, FastAPI) feature to keep a state in the request lifecycle in order to maintain and communicate a transaction id among different services. The following section describes the supprted features.


# Features
- Create and store a Transaction id 
- Sent status messages for SUCCESS or FAILURE


# Installation
    pip install tracking-lib

# How to use it
    import os

    from tracking_lib.config import Config
    from tracking_lib.transaction import Transaction
    from flask import Flask, jsonify

    app = Flask(__name__)


    def interactor_runc():
        # print(f"transaction_id in interactor func: {Transaction.read_transaction_id()}")
        return Transaction.read_transaction_id()


    @app.route("/")
    def hello_world_success():
        Transaction.init(Config(
             downstream_service="TVDI_CONFIGS_MANAGER",
            control_channel_project_id=os.getenv("CONTROL_CHANNEL_PROJECT_ID"),
            control_channel_topic_id=os.getenv("CONTROL_CHANNEL_TOPIC_ID")

        ))
        Transaction.set_transaction_id("123456")
        tr_id = interactor_runc()
        Transaction.set_transaction_status(src_event_name="AN_SRC_EVENT_NAME")
        return jsonify(message=tr_id)
mxwpy 0.2.3
efficient numerical schemes
Requires Python: >=3.6

# SpecNN4PDE
SpecNN4PDE is an under development Python library for solving partial differential equations using spectral methods and neural networks. It consists of the following modules:

- `spectral`: Provides functions for working with spectral methods as described in the book [Spectral Methods: Algorithms, Analysis and Applications](https://link.springer.com/book/10.1007/978-3-540-71041-7) by Shen, Tang, and Wang.
- `linalg`: This module primarily focuses on numerical algebra methods.
- `tools`: A collection of utility functions for system and package information retrieval, time measurement, etc.
- `npde`: Functions for solving partial differential equations, e.g., calculating the multivariate derivatives.

This project is still in the early stages of development, and the API is subject to change. The library is designed to be used in research and educational settings.

## Dependencies

When you install this library using pip, most dependencies will be automatically handled. However, please note that the `npde` module requires PyTorch, which needs to be installed separately.

You can install PyTorch by following the instructions on the [official PyTorch website](https://pytorch.org/get-started/locally/). Please ensure that you select the correct installation command based on your operating system, package manager, Python version, and the specifications of your CUDA toolkit if you are planning to use PyTorch with GPU support.

If you are not planning to use the `npde` module, you do not need to install PyTorch.

## Installation

To install this library, you can use pip:

```bash
pip install specnn4pde