semvec/pypi-packages huggingface dataset:
csv2vcd 0.1.0
Convert CSV files to VCD (Value Change Dump) format for digital waveform visualization
License: MIT
Requires Python: >=3.7

# CSV to VCD Converter

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

A robust Python utility that converts CSV (Comma-Separated Values) files to VCD (Value Change Dump) format for digital waveform visualization in hardware design and verification workflows.

## Description

This tool bridges the gap between simple tabular data and industry-standard waveform viewers by transforming CSV data into VCD files. It's particularly useful for hardware engineers, FPGA developers, and digital circuit designers who need to visualize signal changes over time.

## Features

- Convert multi-signal CSV data to standard VCD format
- Configurable time units (s, ms, us, ns, ps, fs) and timesteps
- Support for various signal types (wire, reg, integer, parameter)
- Customizable signal widths (single-bit or multi-bit)
- Input validation and robust error handling
- Header row skipping option
- Automatic or custom signal identifiers

## Installation

```bash
git clone https://github.com/SystemVll/csv2vcd.git
cd csv2vcd
```

No additional dependencies required beyond Python's standard library.

## Usage

### Basic Usage

```bash
python main.py input.csv output.vcd --signal-names signal1 signal2 signal3
```

### Advanced Usage

```bash
python main.py input.csv output.vcd \
  --signal-names clock data valid \
  --timestep 1e-9 \
  --time-unit ns \
  --signal-width 1 8 1 \
  --signal-type wire \
  --skip-header
```

## Arguments

| Argument | Description |
|----------|-------------|
| `csv_file` | Input CSV file path |
| `vcd_file` | Output VCD file path |
| `--signal-names` | List of signal names |
| `--timestep` | Time between samples in seconds (default: 1e-6) |
| `--skip-header` | Skip first row of CSV file (header) |
| `--time-unit` | Time unit for VCD file: s, ms, us, ns, ps, fs (default: us) |
| `--signal-width` | List of bit widths for each signal (default: 1) |
| `--signal-type` | Type of signals: wire, reg, integer, parameter (default: wire) |

## Example

**Input CSV:**
```
0,01101100,1
1,01101101,1
0,01101101,0
...
```

**Command:**
```bash
python main.py signals.csv output.vcd --signal-names clk data valid --signal-width 1 8 1
```

**Output:**
A VCD file compatible with waveform viewers like GTKWave, ModelSim, etc.

## License

This project is licensed under the MIT License - see the LICENSE file for details.
impulse-contracts-client 0.4.0
Client for interacting with the Impulse Labs protocol
License: MIT
Requires Python: >=3.12

# Impulse Contracts Client

A Python client for interacting with the Impulse protocol, a decentralized computing platform for machine learning jobs.

## Overview

The Impulse Contracts Client provides two main executables:

- `impulse-node`: For node operators to participate in the Impulse network
- `impulse-client`: For users to submit and monitor jobs on the network

## Installation

### Requirements

- Python 3.12
- An Ethereum wallet address
- Impulse tokens
- ETH for gas fees
- RPC endpoint for the blockchain
- `python` command available in your PATH and pointing to Python 3.12 (`python --version` should return `3.12.x`)
- `pip` command available in your PATH and pointing to Python 3.12 (`pip --version` should point to `python 3.12`)

### Installing via pip

```bash
pip install impulse-contracts-client
export PATH=$HOME/.local/bin:$PATH  # Add Python bin to PATH
```

## Configuration

Both clients create a `$HOME/.impulse` directory for storing state:

- Node artifacts and configuration
- Client state and job information
- Logs and temporary files

## Node Client (`impulse-node`)

### Features

- Interacts with smart contracts for leader election, job assignment, and incentives
- Executes machine learning jobs upon assignment
- Automatically manages node's stake on the node escrow (i.e. top-up)
- Registers node to the Impulse network

### Requirements

- A whitelisted Ethereum address
- Impulse tokens transferred to the address
- The impulse artifacts password
- Some ETH for gas costs
- Compatible GPU hardware

### Supported GPU Hardware

- 1, 2, 4, or 8x NVIDIA A100 (40GB or 80GB)
- 8x NVIDIA H100 (80GB)

### Running the Node

```bash
impulse-node
```

## User Client (`impulse-client`)

### Features

- Submits machine learning jobs to the Impulse network
- Monitors job execution and retrieves results
- Manages user's balance on the job escrow contract

### Basic Usage

Top up your balance for paying for jobs:

```bash
impulse-client topup
```

### Submitting Jobs

Example dummy job:

```bash
impulse-client create-job --args '{"shuffle": true, "use_lora": true, "use_qlora": false, "batch_size": 4, "dataset_id": "gs://imp-dev-pipeline-zen-datasets/0ca98b07-9366-4a31-8c83-569961c90294/2024-12-17_21-57-21_text2sql.jsonl", "num_epochs": 1, "job_config_name": "llm_dummy"}' --model llm_dummy --ft_type "LORA" --monitor
```

Example LLaMA 3.2 1B LoRA job:

```bash
impulse-client create-job --args '{"shuffle": true, "use_lora": true, "use_qlora": false, "batch_size": 4, "dataset_id": "gs://imp-dev-pipeline-zen-datasets/0ca98b07-9366-4a31-8c83-569961c90294/2024-12-17_21-57-21_text2sql.jsonl", "num_epochs": 1, "job_config_name": "llm_llama3_2_1b"}' --model llm_llama3_2_1b --ft_type "LORA"
```

### Monitoring Jobs

Monitor all submitted jobs:

```bash
impulse-client monitor-all
```

## Development

For development setup and contributing guidelines, see [DEVEL.md](DEVEL.md).
dog-eye-diagnosis-mcp-server 1.2.5
A Model Context Protocol server analyzing dog's eye images and returning diagnosis probabilities for 10 diseases
License: MIT
Requires Python: >=3.12

# Dog Eye Diagnosis MCP Server

[ENG](#english) | [KOR](#한국어)

---

## English

This package provides a Model Context Protocol (MCP) server for analyzing dog's eye images and returning probabilities for 10 different diseases.

### Installation

Install the package from PyPI using UV or pip:

```bash
uv pip install dog-eye-diagnosis-mcp-server
```

or

```bash
pip install dog-eye-diagnosis-mcp-server
```

### Configuration

Configure your MCP server by adding the following entry to your `claude_desktop_config.json`:

```json
{
  "mcpServers": {
    "dog_eye_diagnosis": {
      "command": "uvx",
      "args": [
        "dog-eye-diagnosis-mcp-server"
      ]
    }
  }
}
```

### Usage

Ensure the server is properly installed and configured, then run your MCP setup or restart your application to enable the Dog Eye Diagnosis MCP server.

### License

MIT

---

## 한국어

이 패키지는 강아지의 눈 이미지를 분석하여 10가지 질병에 대한 확률을 반환하는 MCP(Model Context Protocol) 서버를 제공합니다.

### 설치 방법

UV 또는 pip를 이용하여 PyPI에서 패키지를 설치합니다:

```bash
uv pip install dog-eye-diagnosis-mcp-server
```

또는

```bash
pip install dog-eye-diagnosis-mcp-server
```

### 설정 방법

MCP 서버를 사용하기 위해 `claude_desktop_config.json` 파일에 다음 내용을 추가합니다:

```json
{
  "mcpServers": {
    "dog_eye_diagnosis": {
      "command": "uvx",
      "args": [
        "dog-eye-diagnosis-mcp-server"
      ]
    }
  }
}
```

### 사용 방법

설치를 완료하고 위 설정을 적용한 후 MCP 설정을 실행하거나 애플리케이션을 재시작하여 Dog Eye Diagnosis MCP 서버를 활성화하세요.

### 라이센스

MIT