semvec/pypi-packages huggingface dataset:
eelsmapper 0.2.5
Data-driven analysis pipeline for STEM-EELS spectra. See project at <a href="https://zhenyuan992.github.io/eelsmapper"> https://zhenyuan992.github.io/eelsmapper </a>
Requires Python: >=3.8

# [eelsmapper](https://zhenyuan992.github.io/eelsmapper/index.html)

<!-- <div align="center"><img src="logo/logo_eelsmapper.png" alt="Logo of eelsmapper" width="50%" /></div> -->

<div align="center"><img src="https://raw.githubusercontent.com/zhenyuan992/eelsmapper/refs/heads/main/logo/logo_eelsmapper.png" alt="Logo of eelsmapper" width="50%" /></div>

**[eelsmapper](https://zhenyuan992.github.io/eelsmapper/index.html)** is a data-driven pipeline for analyzing STEM-EELS spectra to perform high-resolution compositional mapping without relying on reference spectra. It integrates PCA, t-SNE (and/or UMAP), clustering, mutual information, and vector quantization to uncover subtle chemical differences and discover novel material phases. Installable with `pip install eelsmapper` from [pypi.org eelsmapper](https://pypi.org/search/?q=eelsmapper)

<!-- ![Overall schematic of eelsmapper](image_schematic.png) -->

![Overall schematic of eelsmapper](https://raw.githubusercontent.com/zhenyuan992/eelsmapper/refs/heads/main/image_schematic.png)

---

## Purpose

STEM-EELS data is high-dimensional and noisy, making it challenging to interpret with traditional methods. **eelsmapper** offers a robust, modular pipeline for:

- Denoising spectra (PCA)
- Visualizing compositional patterns (t-SNE and/or UMAP)
- Clustering spectra (K-Means)
- Identifying correlated elemental regions (Mutual Information)
- Enhancing signal quality (Vector Quantization)
- Discovering new material phases without needing reference spectra


---

## Installation:

`pip install eelsmapper`

---

## Demo:

``` python
# assuming you have installed with !pip install eelsmapper
from eelsmapper.pipeline import run_pipeline
import numpy as np

data = np.load("specs.npz")["arr_0"]
data = data.reshape(-1,data.shape[-1])

run_pipeline( data )
```

## Notes:

This package is a python implementation of the following conference papers/talks:

### Data-Driven Analysis of STEM-EELS Spectra for High-Resolution Compositional Mapping

PDF found at https://www.scienceopen.com/hosted-document?doi=10.14293/APMC13-2025-0303

### Unsupervised Machine Learning for Phase Identification and Characterization of High-Resolution STEM EELS in Novel Battery Materials

PDF found at https://openreview.net/forum?id=dw8DFI2esQ

## How to cite:

    Yeo ZY, Lai W, Lee JH, Balakrishnan D, Özyilmaz B, Duane Loh N. Data-driven analysis of STEM-EELS spectra for high-resolution compositional mapping. 13th Asia Pacific Microscopy Congress 2025 (APMC13). 2025; 303. doi:10.14293/apmc13-2025-0303
    

    Yeo ZY, Lai W, Lee JH, Balakrishnan D, Özyilmaz B, Duane Loh N. Unsupervised machine learning for phase identification and characterization of high-resolution STEM EELS in novel battery materials. 2025. Available: https://openreview.net/pdf?id=dw8DFI2esQ
llm-chat-cli 0.0.7
A CLI tool for chatting with multiple LLM providers
Requires Python: >=3.8

# llm-chat-cli

A CLI tool for chatting with multiple LLM providers in one place.

## Install

```bash
pip install llm-chat-cli
```

## Setup

First time setup:

```bash
lmci setup
```

This will prompt you for your API keys and create a config file.

## Usage

Start chatting:

```bash
lmci
```

## Supported Providers

- Groq
- OpenAI
- Anthropic
- Cerebras

## Commands

- `change model` - Switch models
- `token count` - Show tokens used
- `clear history` - Clear chat history
- `quit`/`exit` - Exit
easyedit2 0.0.1.dev0
An Easy-to-use Steering Framework for Editing Large Language Models
Requires Python: >=3.9

<div align="center">
<img src="figs/logo_2.png" width="180px">

**An Easy-to-use Steering Framework for Editing Large Language Models**

![](https://img.shields.io/badge/version-v0.0.1-blue)
![](https://img.shields.io/badge/PRs-Welcome-red)

---

<p align="center">
  <a href="https://zjunlp.github.io/project/EasyEdit2/">Home</a> •
  <a href="#requirements">Installation</a> •
  <a href="#use-easyedit2">Quick Start</a> •
  <a href="#data-preparation">Dataset</a> •
  <a href="#evaluation">Evaluation</a> •
  <a href="https://zjunlp.github.io/project/EasyEdit2/video">Video</a> •
  <a href="https://arxiv.org/abs/2504.15133">Paper</a>
</p>


</div>
libts 0.0.2
一个用于验证 RFC3161 时间戳签名的库

# libts

一个用于验证 RFC3161 时间戳签名的库

```shell
pip install libts
```

## example

以下为示例代码

```python
from libts import libts_ctx

ctx = libts_ctx()
ctx.load_system_ca()
code = ctx.ts_verify("example.txt", "example.txt.tsr")
print(ctx.format_return_code(code))
print(ctx.get_last_gentime_utc_formatted())
print(ctx.get_last_gentime_bj_formatted())
```

如果验证成功，则应该显示

```plaintext
> python test.py

验证成功
2025-07-08 11:50:24
2025-07-08 19:50:24
```

否则，会显示出详细错误信息，以下错误信息提示文件的哈希值和tsr中记录的哈希值不匹配

验证失败同样能获取时间戳，二者并不冲突

```plaintext
> python test-corrupted.py

验证失败 error:17800067:time stamp routines::message imprint mismatch
2025-07-08 11:50:24
2025-07-08 19:50:24
```
logging-pipeline 0.5.0
Inject logging handler for printing message in the format captured by pipeline
Requires Python: >=3.8
Keywords: ADO, Azure, GitHub, Pipeline

# logging-pipeline

Python module to inject logging handler for printing message in the format captured by pipeline.

Install with:

```bash
pip install logging-pipeline
```

If logging is imported a handler is added to the root logger to print the error and warning messages
in addition with a format recognized by the pipeline environment executing the script. If the script
is executed outside a pipeline nothing is printed. Supported pipelines are:

- Azure pipelines
- GitHub actions
- User defined by environment variables `LOGGING_PIPELINE_WARNING_MESSAGE_FORMAT` and
  `LOGGING_PIPELINE_ERROR_MESSAGE_FORMAT`. The value is used to configure the python logger with
  `{}`-format, see [LogRecord attributes](https://docs.python.org/3/library/logging.html#logrecord-attributes).
    E.g. `LOGGING_PIPELINE_ERROR_MESSAGE_FORMAT="::%(filename)s:%(lineno)d::%(levelname)s::%(message)s::"`
## Compatibility

`logging-pipeline` uses a live-patching system to target the `logging` library and add a handler.

## PyInstaller

The method used to automatically adding a logging handler relies on a .pth file script that python
loads at startup. This method does not work when a python application is bundled into an executable
with PyInstaller (or similar).
If you want to use this tool in an application built with PyInstaller it will need to be manually
enabled in your application.
This can be done by adding the following line to the top of your main application script:

```python
import logging_pipeline.wrapt_logging
```

This must be run before logging is imported.

## Acknowledgements

The method of patching at runtime is built from the [pip_system_certs](https://pypi.org/project/pip-system-certs/)
module.
CTkManager 1.0.1
CTkManager is a helper library designed to streamline CustomTkinter GUI development. It integrates PIL for image support and uses Enum to keep configuration clean and easy.
Requires Python: >=3.7

# CTkManager

**CTkManager** is a lightweight Python library designed to simplify managing CustomTkinter interfaces.  
It uses `Pillow` (PIL) for image handling and `Enum` for easy theme and color scheme management.

---

## Features

- Simple creation and configuration of CustomTkinter windows  
- Built-in methods for common widgets (buttons, panels, labels, etc.)  
- Theme and color scheme support via Enums  
- Easy image integration with Pillow

---

## Requirements

- Python 3.6+ (your version 3.13.2 works fine)  
- customtkinter  
- pillow

---

## Installation

```bash
pip install customtkinter pillow
```
## Example
```python
from CTkManager import CTkManager

# Initialize
manager = CTkManager()
root = manager.run()

# Add a button
manager.add_button(root, width=100, height=40, bg="gray", fg="white", text="Click me", corner=10, command=lambda: print("Clicked!"))

root.mainloop()
```

## License
MIT [LICENSE](LICENSE)
geminiteacher 1.0.0
An educational content generation toolkit powered by Google's Gemini LLM
Requires Python: <4.0,>=3.10
Keywords: ai, course, education, gemini, generator, llm

# GeminiTeacher: The AI-Powered Course Creator

Turn any text-based document (notes, articles, books) into a structured, multi-chapter online course with a single command.

[![PyPI version](https://badge.fury.io/py/geminiteacher.svg)](https://badge.fury.io/py/geminiteacher)
[![License](https://img.shields.io/github/license/supersheepbear/GeminiTeacher)](https://github.com/supersheepbear/GeminiTeacher/blob/main/LICENSE)

**[Full Documentation Site](https://yxiong.org/GeminiTeacher/)**

## What It Does

GeminiTeacher acts like an AI agent, using a sequence of cascading prompts with Google's Gemini models to perform a comprehensive analysis of your content. First, it intelligently outlines a table of contents with a user-defined number of chapters. It then generates a detailed chapter for each topic in the outline, plus a final course summary, effectively creating a complete, multi-chapter book from your raw material.

## How to Use

For complete installation and usage instructions, please visit our comprehensive documentation site:

**➡️ [Usage Guide: https://yxiong.org/GeminiTeacher/usage/](https://yxiong.org/GeminiTeacher/usage/)**

## Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue.

## License

This project is licensed under the MIT License.
ftstore 0.1.0
Advanced local dataset management for machine learning
Requires Python: >=3.7

# ftstore - Dataset Management for Machine Learning

[![PyPI Version](https://img.shields.io/pypi/v/ftstore.svg)](https://pypi.org/project/ftstore/)
[![Python Versions](https://img.shields.io/pypi/pyversions/ftstore.svg)](https://pypi.org/project/ftstore)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

`ftstore` is a Python package for managing and loading local datasets in machine learning projects, inspired by scikit-learn's dataset API.

## Features

- 🗂️ Organize datasets in a structured directory
- ⚡️ Fast loading of CSV, Parquet, Feather, HDF5 and other formats
- 🔄 Automatic dataset caching for faster reloads
- 🌐 Auto-download datasets from remote sources
- 📊 Metadata management with JSON files

## Installation

```bash
pip install ftstore
```

For full format support:

```bash
pip install ftstore[full]
```

## Quick Start

```python
from ftstore import load_data

# Load a dataset
iris = load_data("iris")

# Access features and target
print("Features:", iris.feature_names)
print("Target:", iris.target_name)
print("Data shape:", iris.data.shape)

# Load as DataFrame
df = load_data("iris", as_frame=True)

# Load as NumPy arrays
X, y = load_data("breast_cancer", return_X_y=True)
```

## Documentation

See the [Getting Started Guide](docs/getting_started.md) for detailed usage instructions.