semvec/pypi-packages huggingface dataset:
trendvisdata 1.1.4
Data extract and manipulation functions for Trend Visualizer library.
License: MIT

# trendvisdata
## Calculate trend strength across various markets.

&nbsp;

### The library provides methods to:
  - Extract historical OHLC data for futures and cash commodities from Norgate Data and S&P 500 from Yahoo Finance (Norgate Data requires a subscription to the futures package:
  https://norgatedata.com/futurespackage.php).
  - Calculate technical indicators over a range of time periods and aggregate to determine trend strength. 

Used to serve data to Frontend applications & removes dependencies on Python graphing libraries present in the trendvisualizer library.
dockerclustermon 0.3.0
A CLI tool for a live view of your docker containers running on a remote server.
License: MIT
Requires Python: >=3.10
Keywords: Docker, Docker Compose, Monitoring

# Docker Cluster Monitor

**Stop SSH-ing into servers just to check container status.**

Get a live, color-coded dashboard of all your Docker containers across remote servers-right from your terminal. See CPU, memory, and health at a glance, updated automatically every few seconds.

![Docker Cluster Monitor in action](https://blobs.talkpython.fm/docker-status.gif?cache_id=c4e79e)

## Why Docker Cluster Monitor?

**⚡ Save Time**: No more logging into servers to run `docker stats` or `docker ps`  
**👀 Instant Clarity**: Color-coded metrics show problems at a glance  
**🔄 Stay Informed**: Auto-refreshing dashboard keeps you updated  
**🚀 Zero Configuration**: Just point it at your server and go

## See Everything at a Glance

**Smart color coding** helps you spot issues instantly:
- 🟢 **Green**: Healthy, low resource usage
- 🔵 **Cyan**: Moderate load-everything's fine
- 🔴 **Red**: High CPU or memory usage-time to investigate

Memory percentages reflect your Docker Compose deployment limits, so you know exactly how close each container is to its configured threshold-not just the physical machine limits.

## Quick Start

### Install

**Using uv** (recommended):
```bash
uv tool install dockerclustermon
```

> **Note**: Make sure you have [uv](https://docs.astral.sh/uv/getting-started/installation/) installed first.

### Monitor Your Containers

Run `dockerstatus` (or the shorter `ds` alias) with your server hostname:

```bash
dockerstatus my-docker-host
```

That's it! You now have a live dashboard of your containers, refreshing automatically.

## How to Use

### Basic Usage

Monitor containers on a remote server:
```bash
dockerstatus server.example.com
```

Specify a different SSH user (defaults to `root`):
```bash
dockerstatus server.example.com myuser
```

Use with SSH config entries:
```bash
dockerstatus my-server --ssh-config
```

Run with sudo privileges:
```bash
dockerstatus server.example.com --sudo
```

### Complete Command Reference

![](https://blobs.talkpython.fm/dockerclustermon-help.png?cache_id=029533)


## System Requirements

Docker Cluster Monitor works on any system that supports SSH and has Docker CLI tools installed on the remote server.

**Tested on**: Ubuntu Linux  
**Should work on**: Most Linux distributions and Unix-like systems  
**Current limitation**: Does not work on the local machine (remote servers only)

> **Want local support?** Contributions welcome! PRs accepted.

## About

Docker Cluster Monitor is available on [PyPI](https://pypi.org/project/dockerclustermon/) as `dockerclustermon`. While it's published as a Python package, it's designed as a standalone CLI tool rather than a library to import into your programs.
sannidhi-gae-returns-20251112 0.0.1
Discounted returns utilities for Reinforcement Learning: discounted cumulative sums, n-step TD targets, and Generalized Advantage Estimation (GAE). Created by Sannidhi Nair for B.Tech AI (RL Practicum, NMIMS MPSTME).
License: MIT
Requires Python: >=3.8
Keywords: advantages, gae, n-step, reinforcement-learning, returns, rl, sannidhi-nair

# GAE & Returns

Tiny, dependency-free utilities to compute:
- Discounted cumulative returns
- n-step TD target
- Generalized Advantage Estimation (GAE)

## Usage
```python
from gae_returns import discounted_cumsum, n_step_target, gae

r = [1.0, 0.0, -1.0]
print(discounted_cumsum(r, gamma=0.9))  # -> [1.0 + 0.9*0 + 0.9^2*(-1), ...]

# n-step target for step t with bootstrap V_{t+n}
print(n_step_target(rewards=[1.0, 0.5], gamma=0.99, bootstrap_value=0.2))

# GAE given rewards and state values
rewards = [1.0, 0.0, 0.5]
values  = [0.2, 0.3, 0.4, 0.0]  # note extra terminal value V_T
adv, ret = gae(rewards, values, gamma=0.99, lam=0.95)
```

## Install
```bash
pip install sannidhi-gae-returns-20251112
```

## Testing
```bash
pip install pytest
pytest -q
```

## License
MIT
episode-dl 0.0.0
EpisodeD(own)L(oader). Download Podcast Episodes with Ease
License: MIT
Requires Python: >=3.14

# EpisodeDL

__Download Podcast Episodes from RSS Feeds__

## Overview

EpisodeDL is a lightweight Python package for downloading podcast episodes from RSS feeds. It provides three core functions that work together to fetch episodes and download their audio files.

## Features

- **Parse RSS feeds** - Extract podcast episodes from any RSS feed
- **Extract audio URLs** - Automatically identify audio file links in episode data
- **Download episodes** - Efficiently download audio files with streaming support for large files

## Installation

### Requirements
- Python 3.14+
- [uv](https://docs.astral.sh/uv/) (recommended) or pip

### Using uv (recommended)
```bash
uv pip install -e ".[dev]"
```

### Using pip
```bash
pip install -e ".[dev]"
```

## Usage

```python
from episode_dl import get_episodes, get_audio_file, download_audio_file

# Parse RSS feed
episodes = get_episodes('https://example.com/podcast/feed.xml')

# Get audio URL from an episode
audio_url = get_audio_file(episodes[0])

# Download the audio file
download_audio_file(audio_url, 'episode.mp3')
```

## Core Functions

- `get_episodes(feed_url)` - Parses an RSS feed and returns a list of episodes
- `get_audio_file(episode)` - Extracts the audio URL from an episode's links
- `download_audio_file(url, filename)` - Downloads an audio file to disk using streaming

## Development

### Run Tests
```bash
pytest
```

### Run Specific Test
```bash
pytest -v path/to/test_file.py::test_function_name
```

## Dependencies

- `feedparser` - RSS feed parsing
- `requests` - HTTP requests for downloading files
- `pytest` - Testing framework (dev only)

## License

MIT
aditya-epsgreedy-20251112 0.0.1
A tiny, well-tested epsilon-greedy action selector for Reinforcement Learning with action masking. Created by Aditya Singh for B.Tech AI (RL Practicum, NMIMS MPSTME).
License: MIT
Requires Python: >=3.8
Keywords: RL, action-selection, aditya-singh, ai, bandit, epsilon-greedy, reinforcement-learning

# Epsilon-Greedy

A minimal, tested epsilon-greedy action selector for Reinforcement Learning.
Includes action masking and tie-aware probability allocation.

## Usage

```python
from epsgreedy import epsilon_greedy

action_values = [1.2, 0.5, 1.2, -0.3]   # Q(s, ·)
# Mask out invalid actions (e.g., action 1 is invalid here)
invalid = [False, True, False, False]

a, p = epsilon_greedy(action_values, epsilon=0.1, invalid=invalid)
print(a, p)  # chosen action index, probabilities over actions
```

## Install
```bash
pip install aditya-epsgreedy-20251112
```

## Testing
```bash
pip install pytest
pytest -q
```

## License
MIT