semvec/pypi-packages huggingface dataset:
stringtemplate3x 2023.11.2
A wrapper for StringTemplate: a powerful template engine with strict model-view separation
Requires Python: >=3.8

ST (StringTemplate) is a template engine for generating source code,
web pages, emails, or any other formatted text output.
ST is particularly good at multi-targeted code generators,
multiple site skins, and internationalization/localization.
It evolved over years of effort developing jGuru.com.
ST also generates this website and powers the ANTLR v3 code generator.
Its distinguishing characteristic is that
it strictly enforces model-view separation unlike other engines.
mydbpackage 0.2.4
A short description of my package

First import the DB name

from mydbpackage.database import MyDB

Then Connect your DB

db = MyDB("host", "username", "databasename")

Create table 

db.create_table("table name",[column name]) # pass the table name in must br string 
                                            # column name must be list

Insert the valuve

db.insert_log('[[Value]]',"table name") # pass the value [['name','20','30'],['tesname','10','10']]
mmq 0.0.2
It computes the coeficientes of the MMQ for a given set of 2-D points
Requires Python: >=3.9

# MMQ (Least Squares method) Package

## Description
The function takes a set of 2-D points **(np.array)** and returns the coefficients of the function that best fits the points, using the Least Squares method.
User needs to provide the function degree (1 for linear, 2 for quadratic, etc.) and the points (x and y) as np.arrays.

Maybe, for high degree functions, the function will face some problems. Be careful.

## Installation
```bash
pip install mmq
```

## Requirements
- numpy

## Usage
```python
from mmq import metodo_minimos_quadrados
metodo_minimos_quadrados.mmq(x, y, degree)
```

## Example
```python
from mmq import metodo_minimos_quadrados
import numpy as np

x = np.array([1, 2, 3, 4, 5])
y = np.array([1, 3, 5, 7, 9])
degree = 1

metodo_minimos_quadrados.mmq(x, y, degree)
```

## License
[MIT](https://choosealicense.com/licenses/mit/)

## Author
[Igor Matheus Jasenovski]

## Version
0.0.1

## References
[Least Squares Method](https://en.wikipedia.org/wiki/Least_squares)
peerdomclient 0.1.1
Peerdom's Python api wrapper developed by Peerdom
Requires Python: >=3.8,<4.0

# PeerdomClient

PeerdomClient is a Python client for interacting with the Peerdom API, which allows organizations to manage their data within Peerdom. This client provides a convenient way to access and perform CRUD operations on peers, roles, and circles through a simple interface.

## Installation

You can install the PeerdomClient using pip:

```bash
pip install peerdomclient
```

## Usage

To use the PeerdomClient, you need to obtain an API key from Peerdom. Once you have the API key, you can instantiate the client and start making API calls.

Here's an example of how to use the PeerdomClient:

```python
from peerdomclient import PeerdomClient

# Initialize the client
api_key = "your-api-key"
client = PeerdomClient(api_key)

# Get a list of peers
peers = client.get_peers(limit=10)
for peer in peers:
    print(peer)

# Get details of a specific peer
peer_id = "123" #replace with a peer id
peer = client.get_peer(peer_id)
print(peer)

# Create a new peer
new_peer = client.create_peer(first_name="John", last_name="Doe", birthdate="1990-01-01")
print(new_peer)

# Update an existing peer
peer_id = new_peer["id"]
updated_peer = client.update_peer(peer_id=peer_id, first_name="Jane", last_name="Doe", birthdate="1990-01-01")
print(updated_peer)

# Delete a peer
client.delete_peer(peer_id)
print("Peer deleted.")
```

Replace `"your-api-key"` with your actual API key and `"peer-id"` with the ID of the peer you want to retrieve, update, or delete.

## Documentation

For more details on the available methods and parameters, please refer to the [API documentation](https://api.peerdom.org/v1/docs).

## Contributing

Contributions to the PeerdomClient are welcome! If you find a bug, have a suggestion, or want to contribute code improvements, please open an issue or submit a pull request on the [GitHub repository](https://github.com/peerdom/peerdom-client-python).

## License

This project is licensed under the [MIT License](https://opensource.org/licenses/MIT).
canals 0.11.0
A component orchestration engine for Haystack
Requires Python: >=3.8

# Canals

<p align="center" float="left">
  <img alt="" src="https://raw.githubusercontent.com/deepset-ai/canals/main/images/canals-logo-light.png#gh-dark-mode-only"/>
  <img alt="" src="https://raw.githubusercontent.com/deepset-ai/canals/main/images/canals-logo-dark.png#gh-light-mode-only"/>
</p>

[![PyPI - Version](https://img.shields.io/pypi/v/canals.svg)](https://pypi.org/project/canals)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/canals.svg)](https://pypi.org/project/canals)
<a href="https://github.com/deepset-ai/canals/actions/workflows/tests.yml">
    <img alt="Tests" src="https://github.com/deepset-ai/canals/workflows/Tests/badge.svg?branch=main">
</a>
[![Coverage Status](https://coveralls.io/repos/github/deepset-ai/canals/badge.svg?branch=main)](https://coveralls.io/github/deepset-ai/canals?branch=main)
<a href="https://deepset-ai.github.io/canals/">
    <img alt="Documentation" src="https://img.shields.io/website?label=documentation&up_message=online&url=https%3A%2F%2Fdeepset-ai.github.io/canals/">
</a>
<a href="https://github.com/deepset-ai/canals/commits/main">
    <img alt="Last commit" src="https://img.shields.io/github/last-commit/deepset-ai/canals">
</a>
<a href="https://pepy.tech/project/canals">
    <img alt="Monthly Downloads" src="https://pepy.tech/badge/canals/month">
</a>
<a href="https://github.com/deepset-ai/canals">
    <img alt="Stars" src="https://shields.io/github/stars/deepset-ai/canals?style=social">
</a>
<a href="https://ossinsight.io/analyze/deepset-ai/canals">
    <img alt="Stats" src="https://img.shields.io/badge/Stats-updated-blue">
</a>

Canals is a **component orchestration engine**. Components are Python objects that can execute a task, like reading a file, performing calculations, or making API calls. Canals connects these objects together: it builds a graph of components and takes care of managing their execution order, making sure that each object receives the input it expects from the other components of the pipeline.

Canals powers version 2.0 of the [Haystack framework](https://github.com/deepset-ai/haystack).

## Installation

To install Canals, run:

```console
pip install canals
```

To be able to draw pipelines (`Pipeline.draw()` method), please make sure you have either an internet connection (to reach the Mermaid graph renderer at `https://mermaid.ink`) or [graphviz](https://graphviz.org/download/) (version 2.49.0) installed. If you
plan to use Mermaid there is no additional steps to take, while for graphviz
you need to do:

### GraphViz
```console
sudo apt install graphviz  # You may need `graphviz-dev` too
pip install pygraphviz
```