semvec/pypi-packages huggingface dataset:
pytest-case-start-from 1.0.0
A pytest plugin to start test execution from a specific test case
License: MIT
Requires Python: >=3.7

# pytest-case-start-from

A pytest plugin that allows you to start test execution from a specific test case, skipping all tests before it.

## Features

- ✅ Start test execution from any test case using `--start-from` or `-sf` option
- ✅ Pattern matching similar to pytest's `-k` option
- ✅ Support for substring matching
- ✅ Support for logical operators: `and`, `or`, `not`
- ✅ Works with test functions, test methods, and test classes
- ✅ Easy to install and use

## Installation

### Development mode (for local testing)

```bash
pip install -e .
```

### Regular installation

```bash
pip install .
```

## Quick Start

```bash
# skip all tests before test_third
pytest --start-from test_third

# pattern matching with substring
pytest --start-from "third"

# match test in a class
pytest --start-from "TestClass::test_method"
```

## Usage Examples

### Basic Usage

```bash
# start from a specific test
pytest --start-from test_fourth test_example.py

# use with verbose output
pytest --start-from test_second -v

# combine with other pytest options
pytest --start-from test_third -v -s --tb=short
```

### Pattern Matching

```bash
# substring match
pytest --start-from "class_second"

# logical operators
pytest --start-from "parameters and a"
pytest --start-from "first or second"
pytest --start-from "test not class"
```

## How It Works

1. The plugin hooks into pytest's test execution flow
2. Before each test runs, it checks if the test name matches the specified pattern
3. All tests before the first match are marked as SKIPPED
4. Once the first matching test is found, all subsequent tests run normally

## Documentation

For detailed usage instructions, see [USAGE.md](USAGE.md)

## Use Cases

- **Debugging**: Skip passing tests and start from the failing test
- **Incremental Testing**: Continue from where tests were interrupted
- **Development**: Focus on specific tests during feature development
- **CI/CD Optimization**: Resume test execution from a specific point

## Requirements

- Python >= 3.7
- pytest >= 6.0.0

## License

MIT License - see LICENSE file for details

## Contributing

Contributions are welcome! Please feel free to submit issues and pull requests.
powerloom-snapshotter-cli 0.2.0
CLI tool for deploying and managing Powerloom Snapshotter nodes
License: MIT
Requires Python: <3.14,>=3.12
Keywords: blockchain, cli, ethereum, node-management, powerloom, snapshotter, web3

# Powerloom Snapshotter CLI

A powerful command-line interface for deploying and managing Powerloom Snapshotter nodes. Simplifies the process of running multiple snapshotter instances across different chains and data markets.

## Features

- 🚀 **Interactive Shell Mode** - Eliminates startup delays for multiple commands
- 📦 **Multi-Instance Management** - Deploy and manage multiple snapshotter nodes
- 🔧 **Easy Configuration** - Store credentials per chain/market combination  
- 🔍 **Built-in Diagnostics** - Check system health and troubleshoot issues
- 🐳 **Docker Integration** - Automated container and network management
- 🔐 **Secure Credential Storage** - Namespaced environment configurations

## Installation

```bash
# Install using pip
pip install powerloom-snapshotter-cli

# Install using pipx (recommended)
pipx install powerloom-snapshotter-cli
```

## Quick Start

### Interactive Shell (Recommended)

```bash
# Start interactive shell for faster command execution
powerloom-snapshotter-cli shell

# In shell mode:
powerloom-snapshotter> configure
powerloom-snapshotter> deploy
powerloom-snapshotter> status
```

### Direct Commands

```bash
# Configure credentials for a chain/market
powerloom-snapshotter-cli configure --env mainnet --market uniswapv2

# Deploy a snapshotter instance
powerloom-snapshotter-cli deploy

# Check status of running instances
powerloom-snapshotter-cli status

# View detailed status
powerloom-snapshotter-cli status --env mainnet --market uniswapv2
```

## Available Commands

- `configure` - Set up chain and market-specific credentials
- `deploy` - Deploy a new snapshotter instance
- `status` - View status of deployed instances
- `list` - Display available chains and data markets
- `diagnose` - Run system diagnostics
- `identity` - Manage multiple configurations
- `shell` - Start interactive mode

## Command Aliases

The CLI is available through multiple aliases:
- `powerloom-snapshotter-cli` (full name)
- `snapshotter` (short)

## Supported Chains & Markets

- **Mainnet**: Ethereum mainnet data markets
- **Devnet**: Development network for testing
- Multiple data markets including UniswapV2, AaveV3, and more

## Requirements

- Python 3.12 or higher
- Docker and Docker Compose
- Linux or macOS (Windows support via WSL)

## Documentation

- [Full Documentation](https://github.com/powerloom/snapshotter-lite-multi-setup/blob/master/CLI_DOCUMENTATION.md)
- [GitHub Repository](https://github.com/powerloom/snapshotter-lite-multi-setup)
- [Powerloom Website](https://powerloom.io)

## License

MIT License - see [LICENSE](https://github.com/powerloom/snapshotter-lite-multi-setup/blob/master/LICENSE) for details.

## Support

- [Report Issues](https://github.com/powerloom/snapshotter-lite-multi-setup/issues)
- [Discord Community](https://discord.gg/powerloom)
conversational-tone-detector 0.0.1
An advanced NLP tool for detecting conversational tone, sarcasm, and sentiment.
License: MIT
Requires Python: >=3.6

\# Conversational Tone Detector 💬



\## What This Does

This is a super smart Python package that checks text for its feelings!

\* It tells you if the tone is \*\*Positive, Negative, or Neutral\*\*.

\* It also detects if someone is being \*\*Sarcastic\*\*.

\* It gives a score for the overall feeling (sentiment).



\## How to Get It

You can get it using the 'pip install' command:

```bash

pip install conversational-tone-detector



How to Use It

You use it by importing the detector and feeding it text!



from conversational\_tone\_detector import ToneDetector



detector = ToneDetector()

result = detector.analyze\_tone("I absolutely love Mondays! Great job on the presentation.")



print(result) 

\# This will show you the tone, sarcasm flag, and emotion!



Rules and Thanks

Rules: Check the LICENSE.txt file for the sharing rules.



Author: Anushri Ranade