semvec/pypi-packages huggingface dataset:
iremote-graphql-utils 0.1.0
A collection of utilities for working with GraphQL APIs useful for Generative AI projects.
Requires Python: <3.13,>=3.9

# GraphQL Utils

A Python package having utilities for working with GraphQL APIs especially for Gen AI applications in preparing `graph` data for LLMs.

Current scope includes packages:

- **Schema Splitter** A simple util to split a GraphQL schema into smaller sub-graphs in building vector databases. These sub-graphs can be utilized as Retrieval-Augmented Generation (RAG) documents in Generative AI workflows.

## More Information

- [README-dev.md](docs/README-dev.md) - Developer documentation
- [README-templ.md](docs/README-templ.md) - Template repository for GitHub Actions

## Author

Ramesh Doddi, CTO at iRemote, Inc. [Visit iRemote](https://iremote.ai)

## License

This project is open-sourced under the MIT License. See the [LICENSE](LICENSE) file for more details.
ccm-analysis-pynb 1.13.0
A package for performing Convergent Cross Mapping (CCM) analysis on time series data in Python Notebooks
Requires Python: >=3.6

# CCM Analysis for Python Notebooks
[![PyPI version](https://img.shields.io/pypi/v/ccm-analysis-pynb.svg)](https://pypi.org/project/ccm-analysis-pynb/)

A Python package for performing Convergent Cross Mapping (CCM) analysis on time series data.
> **Note:** This package is optimized for Jupyter Notebooks (`.ipynb`). If you need to perform CCM analysis in a Python script (`.py`), please use [CCM_analysis_Python](https://github.com/chaseU2/CCM_analysis_Python).

## Installation

You can install the package via pip:

```bash
pip install ccm_analysis_pynb
```

## Usage

To use this package in a Jupyter Notebook, import the function and run the analysis:

```python
from ccm_analysis_pynb import run_ccm_analysis_jupyter

df = pd.DataFrame(data)

# Run CCM analysis
result_holder = run_ccm_analysis_jupyter(df, L=100, E=2, tau=1, THRESHOLD=0.8, save_output=False)

# Access the final DataFrame of significant relationships
final_results = result_holder.result
print(final_results)
```

## Parameters

- `data_input`: Either a filepath (str) to tab-separated data or a pandas DataFrame.
- `L`: Length of time series to consider.
- `E`: Embedding dimension.
- `tau`: Time delay.
- `THRESHOLD`: Significance threshold for cross-map scores.
- `save_output`: Whether to save plots and protocol (default: True).
- `output_dir`: Directory to save outputs (default: current working directory).

## Output

The function returns an interactive widget for manual evaluation of relationships. Once the evaluation is completed, it generates a final heatmap and saves the results if `save_output=True`.

## Test Dataset

You can test the `ccm_analysis` package using the sample dataset provided in this repository. You can download the test dataset from the following link:

- [Test Dataset (ccm_test_data.txt)](https://github.com/chaseU2/ccm-analysis-tool/blob/master/ccm_test_data.txt)

  

### Data Format

The dataset is structured as follows:

- **Columns** represent the variables you want to analyze.
- **Rows** represent the time points for each variable.



## Dependencies and Acknowledgements

Parts of this project are based on the Convergent Cross Mapping (CCM) implementation from the following repository from Prince Javier :

- [Convergent Cross Mapping GitHub Repository Prince Javier ](https://github.com/PrinceJavier/causal_ccm.git)

I have utilized parts of the CCM algorithm from this repository to help analyze causality in time series data in my own project.
character-extractor 0.1.2
An MCP server to extract and analyze story characters from text or PDF files
Requires Python: >=3.10

# Character Extractor

A Model Context Protocol (MCP) server that analyzes stories from `.txt` or `.pdf` files and extracts characters with attributes like name, age, appearance, and more.

## Features
- Extracts structured character info from stories
- Designed to work seamlessly with Claude Desktop
- No external API calls

## Usage
Install via pip:
pip install character-extractor

Run the server : 
python -m character_extractor
tikTrack 0.1.0
A simple performance tracking and visualization tool for Python applications
Requires Python: >=3.6

# TikTrack

一个简单易用的Python性能跟踪和可视化工具。(A simple performance tracking and visualization tool for Python applications.)

## 安装 (Installation)

```bash
pip install tikTrack
```

## 功能特点 (Features)

- 使用装饰器轻松跟踪函数执行时间 (Track function execution time easily with decorators)
- 自动生成性能报告和可视化图表 (Automatically generate performance reports and visualization charts)
- 支持多种输出格式：CSV报表和饼图 (Support for multiple output formats: CSV reports and pie charts)
- 程序退出时自动生成报告 (Automatically generate reports when the program exits)
- 聚合相同阶段的执行时间 (Aggregate execution times for the same stages)
- 支持中文字体显示 (Support for Chinese fonts in charts)

## 使用示例 (Usage Examples)

### 基本用法 (Basic Usage)

```python
from tiktrack import timed_stage

@timed_stage("数据加载")
def load_data():
    # 模拟数据加载过程
    import time
    time.sleep(1)
    return "data"

@timed_stage("数据处理")
def process_data(data):
    # 模拟数据处理过程
    import time
    time.sleep(0.5)
    return f"processed {data}"

def main():
    data = load_data()
    processed_data = process_data(data)
    print(processed_data)

if __name__ == "__main__":
    main()
    # 程序退出时会自动生成性能报告
```

### 自定义输出目录 (Custom Output Directory)

```python
from tiktrack import timed_stage, set_default_output_dir

# 设置性能报告输出目录
set_default_output_dir("my_performance_reports")

@timed_stage("任务1")
def task1():
    import time
    time.sleep(0.8)

@timed_stage("任务2")
def task2():
    import time
    time.sleep(1.2)

def main():
    task1()
    task2()

if __name__ == "__main__":
    main()
```

### 手动生成性能报告 (Manually Generate Reports)

```python
from tiktrack import timed_stage, generate_performance_report

@timed_stage("任务A")
def task_a():
    import time
    time.sleep(0.5)

@timed_stage("任务B")
def task_b():
    import time
    time.sleep(0.7)

def main():
    for i in range(3):
        task_a()
        task_b()
    
    # 手动生成性能报告
    generate_performance_report("custom_reports")

if __name__ == "__main__":
    main()
```

## 输出文件 (Output Files)

执行代码后，将生成以下文件：

1. `performance_report.csv` - 详细的每次函数调用性能数据
2. `performance_summary.csv` - 按阶段聚合的性能摘要数据
3. `performance_chart.png` - 性能分布饼图

## 许可证 (License)

MIT