semvec/pypi-packages huggingface dataset:
polymarket-py 0.1.0
A package to export historical odds for an outcome of a market on Polymarket
Requires Python: >=3.6
Keywords: polymarket, crypto, data, api, exporter, market, odds

# Polymarket Data Fetcher

Polymarket Data Fetcher is a Python package that allows you to easily fetch historical odds data from Polymarket for specific market outcomes.

## Installation

You can install the Polymarket Data Fetcher using pip:
## Usage

Here's a basic example of how to use the Polymarket Data Fetcher:

```python
from polymarket_data_fetcher import PolymarketDataFetcher

# Initialize the fetcher
pm = PolymarketDataFetcher()

# Define the parameters
slug = "will-donald-trump-win-the-2024-us-presidential-election"
start_date = "2024-01-01"  # yyyy-mm-dd format
fidelity = "1440"  # daily data

# Fetch historical odds for a market
odds = pm.get_historical_odds(slug, start_date, fidelity)

# Display the results
print(odds)
pynetworklib 1.0.1
A simple library for makeing dense multilayer neural networks

# PyNetworkLib

### This is an INDEV version of a multi-layer perception network written in Python

### It is designed to be used as a library, it has a network module that provides trainable dense multilayer perceptron networks, a configuration module that provides a way to store parameters for training and optimization, and an optimization module that creates an optimized network structure and hyperparameters for a given dataset

## Installation of the library

```bash
pip install pynetworklib
```

## Example of how to use this library

```python
from network import network
from network import dense
from network import activation_functions
from network import mse
import numpy as np

# Create a network with a struct of 3 layers of size 2, 3, and 1
input_size = 2
output_size = 1
network_xor = network.Network([dense.Dense(2,3), activation_functions.Tanh(), 
                           dense.Dense(3,1), activation_functions.Tanh()], 
                           input_size, output_size)

# Create a data set, this example uses the xor operation as the training output
x_train = [
    np.array([1,1]),
    np.array([0,1]),
    np.array([1,0]),
    np.array([0,0])
    ]
y_train = [0,1,1,0]

network_xor.fit(mse.MeanSquaredError(), x_train, y_train, epoachs = 10, verbose = True)


```
ntfyme 0.0.2
Simple notification tool to notify yourself when a long running process ends with local ping, gmail, telegram, etc.
Requires Python: >=3.10
Keywords: notification, notify, cli, python


Have you ever wanted a simple tool in your terminal which gives you notification when the program is done, like some computer notification or some mail.
This tool helps you ease your life with appropriate notifications you can set in your OS, get mail regarding it and best of all, it is VERY SIMPLE TO USE!

## Features

- Very easy to use and setup.
- Cross platform support for Windows, MacOS and Linux.
- Get local notifications when your command is done.
- Get notifications like gmail, telegram bot, etc. when your command is done.
- Get information about output, errors, time taken, pid and more in the notification.
- Track your process for possible suspensions and get notified after thorough diagnostics.
- Enhanced UI features powered by rich and beaupy.

## Usages
There are mainly two ways to use the tool to get a notification -

### 1. Direct Run

For "direct running", you can follow the below format -

```bash
ntfyme exec -c <command>
```

### 2. Piping the command

For "piping the command", you can follow the below format -

```bash
echo <command> | ntfyme exec
```

For more information, visit the official github repository https://github.com/AnirudhG07/ntfyme
pdf-qa 0.1.10
A PDF question answering tool using various LLMs
Requires Python: >=3.6

# PDF QA

PDF QA is a command-line tool that allows you to ask questions about the content of PDF documents using various Language Models (LLMs).

## Features

- Supports multiple LLMs: OpenAI, Anthropic, and Google
- Handles both local PDF files and online PDF URLs
- Maintains conversation history for each PDF session
- Provides concise answers based on the PDF content

## Installation

You can install PDF QA using pip:

pip install pdf_qa

## Usage

To use PDF QA, run the following command:

pdf_qa <pdf_path_or_url> [options]

### Options

- `-l, --llm`: Choose the LLM to use (openai, anthropic, or google). Default is openai.
- `-q, --question`: Ask a single question and exit.

### Examples

1. Interactive mode with a local PDF:
   ```bash
   pdf_qa /path/to/your/document.pdf
   ```

2. Using a specific LLM with an online PDF:
   ```bash
   pdf_qa https://example.com/sample.pdf -l anthropic
   ```

3. Asking a single question:
   ```bash
   pdf_qa /path/to/your/document.pdf -q "What is the main topic of this document?"
   ```

## API Keys

On first use, you'll be prompted to enter your API key for the chosen LLM. The key will be saved for future use.

## Chat History

Chat histories are saved in the `chat_histories` directory, allowing you to resume conversations in future sessions.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License.