semvec/pypi-packages huggingface dataset:
gconda 0.1.2
A library to manage environment on Google Colab and Kaggle.
Requires Python: >=3.6

# G-Conda

## Introduction

**G-Conda** is a Python library that simplifies Conda environment management on **Google Colab** and **Kaggle**. This library supports checking, installing Conda, creating new Python environments, and running commands within Conda environments automatically.

## Installation

You can install **G-Conda** using `pip`:

```sh
pip install gconda
```

## Usage

```python
from gconda import gconda
```

### 1. Check Conda and Python availability

```python
from gconda.gconda import check_conda, check_python

print("Conda available:", check_conda())
print("Python available:", check_python())
```

```python
print("Conda available:", gconda.check_conda())
print("Python available:", gconda.check_python())
```

### 2. Install Conda (if not already installed)

```python
from gconda.gconda import install_conda
install_conda()
```

```python
gconda.install_conda()
```

### 3. Create a new environment with a specific Python version

```python
from gconda.gconda import setup_env
setup_env(python_version="3.10", env_name="gconda")
```

```python
gconda.setup_env(python_version="3.10", env_name="gconda")
```

### 4. Run a pip-installed library command within the Conda environment

```python
from gconda.gconda import run_library_command
run_library_command("gdown", "--version")
```

```python
gconda.run_library_command("gdown", "--version")
```

## License

G-Conda is released under the **GNU GPL v3.0** license.

## References

G-Conda is built upon and integrates features from several established projects. We acknowledge and appreciate the contributions of the following projects:

- **[miniconda](https://docs.conda.io/en/latest/miniconda.html)**: A lightweight Conda distribution used for efficient package and environment management.
- **[condacolab](https://github.com/conda-incubator/condacolab)**: A library that enables Conda usage on Google Colab.

These projects have played a crucial role in shaping the functionality of G-Conda.

## Author

- **Hoang-Phat Vu**
- Email: [phatvucoder@gmail.com](mailto\:phatvucoder@gmail.com)
- GitHub: [phatvucoder](https://github.com/phatvucoder)

## Contributions

Contributions, bug reports, and improvements are welcome. Please create an **issue** or submit a **pull request** on [GitHub](https://github.com/phatvucoder/G-conda).
feedbackIntelligence 0.2.8
An SDK for Feedback Intelligence data insertion
Requires Python: >=3.6

# Feedback Intelligence SDK

The `feedbackIntelligenceSDK` is a Python package designed to interact with the Feedback Intelligence API. It allows you
to add context, chat, and feedback data to the database, both synchronously and asynchronously.

## Installation

You can install the package via pip:

```bash
pip install feedbackIntelligence
```

Usage
Initialization
To use the SDK, first initialize it with your API key:

```python
from feedbackIntelligence.fi import FeedbackIntelligenceSDK

sdk = FeedbackIntelligenceSDK(api_key="your_api_key_here")
```

## Methods

- ***add_context***

#### Add context to the database (synchronous)

```python
response = sdk.add_context( project_id=1, context="Sample context", context_id=123)
# Or you can skip the context_id
#response = sdk.add_context( project_id=1, context="Sample context")

```

- ***add_context_async***

#### Add context to the database (asynchronous)

```python
response = await sdk.add_context_async( project_id=1, context="Sample context", context_id=123)
```

- ***add_chat***

#### Add chat data to the database (synchronous).

```python
from feedbackIntelligence.schemas import Message, Context, Feedback
# Make sure to use the exact date format that is used in the example

messages = [
    Message(role='human', text='Hello', propmt='test', 
            date='2020-01-20T13:00:00',
            context=Context(text='new_context')), 
    Message(role='ai', text='Hi there', 
            date='2020-01-20T13:01:00',
            feedback=Feedback(thumbs_up=1, message='Feedback message(optional)'))]
response = sdk.add_chat( project_id=1, chat_id=456, messages=messages)
```

- ***add_chat_async***

#### Add chat data to the database (asynchronous).

```python

response = await sdk.add_chat_async( project_id=1, chat_id=456, messages=messages)
```

- ***add_feedback***

#### Add feedback data to the database (synchronous).

```python
response = sdk.add_feedback( project_id=1, message="Great service", source="email")
```

- ***add_feedback_async***

#### Add feedback data to the database (asynchronous).

```python
response = await sdk.add_feedback_async( project_id=1, message="Great service", source="survey")
```

visit this links to see examples
* [SKD](https://colab.research.google.com/drive/1p6ZkAj0zQoKtlv4FETE5Mrb4PXn-IhuA?usp=drive_link)
* [API](https://drive.google.com/file/d/1cwjbqVIAFidlbhYv8YA36P6PDhpufT9c/view?usp=drive_link)
notebooktester 0.2.1
A simple linting tool to check if all notebooks execute
Requires Python: >=3.11

# 📓 NotebookTester
[![uv](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json)](https://github.com/astral-sh/uv)
[![image](https://shields.io/pypi/v/notebooktester.svg)](https://pypi.org/project/notebooktester/)

A fast, reliable, and parallel Jupyter notebook testing tool with caching support! Test your notebooks with confidence.

## 🌟 Features

- 🚀 **Parallel Execution**: Test multiple notebooks simultaneously
- ⏱️ **Configurable Timeouts**: Set custom timeouts for notebook execution
- 💾 **Smart Caching**: Cache test results to avoid unnecessary re-runs
- 🎯 **Selective Testing**: Only test modified notebooks
- 📊 **Detailed Logging**: Comprehensive logs with test results and errors
- 🛠️ **CLI Support**: Easy to use command-line interface

## 🔧 Installation

Choose your preferred package manager:

### Using pip (slow legacy)

```bash
pip install notebooktester
```

### Using uv (10-100x faster)
see [uv docs](https://docs.astral.sh/uv/) for more info.

```bash
uv add notebooktester
```

## 🚀 Quick Start

Test a single notebook:

```bash
notebooktester path/to/your/notebook.ipynb
```

Test all notebooks in a directory:

```bash
notebooktester path/to/notebooks/directory
```

## 🎮 Command Line Options

```bash
notebooktester [OPTIONS] PATH
```

Options:
- `-t, --timeout SECONDS`: Timeout in seconds for each notebook (default: 60)
- `-w, --workers NUMBER`: Number of parallel workers (default: CPU count)
- `-c, --cache-dir PATH`: Cache directory for test results (default: .notebookcache)
- `-v, --verbose`: Enable verbose output
- `-f, --force`: Ignore cache and force test execution

## 📋 Example Usage

Basic usage:
```bash
notebooktester notebooks/
```

With custom timeout and workers:
```bash
notebooktester notebooks/ -t 120 -w 4
```

Force re-run all tests:
```bash
notebooktester notebooks/ --force
```

## 🔍 Cache Behavior

NotebookTester maintains a cache of test results to optimize performance:
- Only notebooks modified since their last test run are re-tested
- If the notebook timed out, and the current timeout has not been increased, notebook is skipped
- Cached results include success/failure status and error messages
- Force flag (`-f`) bypasses the cache

## 📊 Output

The tool provides:
- Progress bar for test execution
- Colored console output for test results
- Detailed logs in the `logs/` directory
- Summary of passed and failed tests

Example output:
```
>>notebooktester notebooks/ -t 120 -w 4
Running tests with 4 workers
Starting notebook tests - found 10 notebooks
✅ PASSED - notebook1.ipynb: Success
❌ FAILED - notebook2.ipynb: Cell execution error
⏰ TIMEOUT - notebook3.ipynb: A cell timed out

Test Summary: 8 passed, 2 failed
```

## 🤝 Contributing

Contributions are welcome! Feel free to:
- Report bugs
- Suggest features
- Submit pull requests

## 👤 Author

Raoul Grouls (Raoul.Grouls@han.nl)
apricopt 0.0.2a3.dev21
A library for simulation-based parameter optimization
Requires Python: >=3.6

# Apricopt #

Apricopt is a python framework for simulation-based optimisation of dynamical systems.

It is agnostic with respect to the optimiser and to the simulator.

Apricopt supports the PEtab format for the definition of the optimisation problem. More info about PEtab [here](https://petab.readthedocs.io/en/latest/).

Currently, we support the following simulators:

* COPASI. A state-of-the-art simulator for models of biological processes. We support SBML models (any level and version).
* RoadRunner. A fast state-of-the-art simulator of biological models.
* Modelica executable. It supports the simulation of Modelica models given as executables.

Currently, we support the following black-box optimisers:

* NOMAD. A state-of the art black-box solver that supports surrogate models. More info [here](https://www.gerad.ca/nomad/).
* SciPy.optimize. A python library that implements several algorithms for multivariate optimization. More info [here](https://docs.scipy.org/doc/scipy/reference/optimize.html).
* PySwarms. A python library that implements various forms of the Particle Swarm Optimization algorithm. More info [here](https://pyswarms.readthedocs.io/en/latest/).

Currently, we support the following white-box optimisers:

* COPASI. It supports several optimisation algorithms for biological processes. More info [here](http://copasi.org/Support/User_Manual/Methods/Optimization_Methods/)

### Info ###

WIP

* Version 0.0.2a3dev20


### Who do I talk to? ###

* Marco Esposito (author) - esposito@di.uniroma1.it
* Leonardo Picchiami (author) - picchiami@di.uniroma1.it


Copyright (C) 2020-2022  Sapienza University of Rome, Marco Esposito, Leonardo Picchiami.

Distributed under GNU General Public License v3.
trelliswork 10014.0.2
A tool to draw graphs using Excel as a display.
Requires Python: >=3.12.3
Keywords: console application, excel

# trelliswork

RPG の自作戦闘を設計するために勝手に作ったった設計図を出力するツール。ＧＵＩはまだ無い  


## 必須

* Exshell を使うには、デスクトップ・アプリ版の Microsoft Excel がインストールされていることが必要です


## 説明

* 📖 [Trelliswork > 説明書](https://github.com/muzudho/trelliswork/tree/main/docs)
* 📖 [Trelliswork > 実装例](https://github.com/muzudho/trellis-for-b) - ［Trellis for B］リポジトリー
dcont 0.1.0
A utility library with custom evaluation and loading functions.
Requires Python: >=3.6

# dcont

Пакет, предоставляющий три функции для контроля за данными:

- **load_from_pickle(data: bytes)**  
  Выполняет десериализацию данных с помощью `pickle.loads()`.
  
- **evaluate_expression(expr: str, globals_dict=None, locals_dict=None)**  
  Выполняет вычисление выражения с помощью `eval()`.

- **execute_code(code: str, globals_dict=None, locals_dict=None)**  
  Выполняет выполнение кода с помощью `exec()`.

## Установка

```bash
pip install dcont