semvec/pypi-packages huggingface dataset:
gradio-improvedfileexplorer 1.0.0
Improved UI because it includes breaking changes.
Requires Python: >=3.10
Keywords: Easy select only folder, Improved UI, Improved UX, QIM3D, gradio-custom-component, gradio-template-FileExplorer



<img alt="Static Badge" src="https://img.shields.io/badge/version%20-%201.0.0%20-%20green">  

Improved UI because it includes breaking changes.

## Installation

```bash
pip install gradio_improvedfileexplorer
```

## Usage

```python

import gradio as gr
from gradio_improvedfileexplorer import ImprovedFileExplorer


import os

with gr.Blocks() as demo:
    fe = ImprovedFileExplorer(interactive = True, file_count = "multiple")
    fe.change(fn = lambda files: print(files), inputs = fe)

if __name__ == "__main__":
    demo.launch()

```

## `ImprovedFileExplorer`

This component works basically the same as Gradio's original File Explorer, but it has (in my opinion) better UI.

## Changes
### Select single folders
Up to this date, Gradio has a bug that if you allow users to only select folders (`glob = "./**/"`) and `file_count = "single"`, you can not select anything. Look at this [issue](https://github.com/gradio-app/gradio/pull/9835#issuecomment-2490603744).

This problem is solved by changing you select folders. Now the checkbox nect to them select the folder


![](https://github.com/GrunyD/gradio-improved-file-explorer/blob/main/src/image.png?raw=True)

### No recursive selection
When you select folder it no longer selects everything inside nor it opens the file tree. It just selects the folder.


![](https://github.com/GrunyD/gradio-improved-file-explorer/blob/main/src/image_2.png?raw=True)

### Select all contents of a folder
If you need to select all the contents of a folder, you can open the folder and select "Select all contents" whcih will select or deselect everything inside the folder. **It will not select the folder itself*


![](https://github.com/GrunyD/gradio-improved-file-explorer/blob/main/src/image_3.png?raw=True)
bolt 0.50.1
Supercharge Minecraft commands with Python
Requires Python: >=3.14
Keywords: beet, minecraft, minecraft-commands, scripting-language, mcfunction

# Bolt

[![GitHub Actions](https://github.com/mcbeet/beet/workflows/CI/badge.svg)](https://github.com/mcbeet/beet/actions)
[![PyPI](https://img.shields.io/pypi/v/bolt.svg)](https://pypi.org/project/bolt/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/bolt.svg)](https://pypi.org/project/bolt/)
[![Discord](https://img.shields.io/discord/900530660677156924?color=7289DA&label=discord&logo=discord&logoColor=fff)](https://discord.gg/98MdSGMm8j)

> Supercharge Minecraft commands with Python.

```python
infinite_invisibility = {
    Id: 14,
    Duration: 999999,
    Amplifier: 1,
    ShowParticles: false,
}

def summon_chicken_army(n):
    for i in range(n):
        summon chicken ~i ~ ~ {
            Tags: [f"quack{i}"],
            IsChickenJockey: true,
            Passengers: [{
                id: zombie,
                IsBaby: true,
                ActiveEffects: [infinite_invisibility]
            }]
        }

say Go forth, my minions!
summon_chicken_army(16)
```

## Installation

The package can be installed with `pip`.

```bash
$ pip install bolt
```

## Contributing

Contributions are welcome. Make sure to first open an issue discussing the problem or the new feature before creating a pull request. The project uses [`uv`](https://github.com/astral-sh/uv).

```console
$ uv sync
```

You can run the tests with `uv run pytest`.

```console
$ uv run pytest
```

The code is formatted and checked with [`ruff`](https://github.com/astral-sh/ruff).

```console
$ uv run ruff format
$ uv run ruff check
```

---

License - [MIT](https://github.com/mcbeet/beet/blob/main/packages/bolt/LICENSE)
boring-math-abstract-algebra 1.1.0
Abstract Algebra Library
Requires Python: >=3.13
Keywords: algebra, abstract algebra

Boring Math - Abstract Algebra
==============================

PyPI project
`boring-math-abstract-algebra
<https://pypi.org/project/boring-math-abstract-algebra>`_.

Creating a library to create representations for abstract abstract-algebra.

Part of the
`boring-math
<https://grscheller.github.io/boring-math>`_
PyPI projects.

Documentation
-------------

Documentation for this project is hosted on
`GitHub Pages
<https://grscheller.github.io/boring-math/abstract-algebra>`_.

Copyright and License
---------------------

Copyright (c) 2025-2026 Geoffrey R. Scheller. Licensed under the Apache
License, Version 2.0. See the LICENSE file for details.
proteo-suite-json9112 0.1.16
Advanced Proteomics Analysis Suite for Medical Outcomes Prediction
Requires Python: >=3.8

# Proteo Suite

**Advanced Proteomics Analysis Suite for Medical Outcomes Prediction**

`proteo_suite` is a Python library designed for advanced mass spectrometry signal processing and machine learning analysis to predict medical outcomes from proteomics data.

## Features

- **Advanced Signal Processing**: Wavelet transforms, spectral analysis, peak enhancement.
- **Machine Learning**: Ensembles (XGBoost, LightGBM, CatBoost), Deep Learning (Transformers, CNNs).
- **Survival Analysis**: Kaplan-Meier, risk stratification.
- **Statistical Validation**: Bootstrap confidence intervals, calibration analysis, comprehensive reporting.

## Installation

### Option 1: Install from PyPI (Recommended)

```bash
pip install proteo-suite-json9112
```

### Option 2: Install from Private GitHub (Alternative)

```bash
pip install git+https://github.com/YOUR_USERNAME/proteo_suite.git
```

### Option 2: Install Locally

For development or local use:

```bash
pip install .
```

## Usage

### Enhanced Pipeline

```python
from proteo_suite import EnhancedProteomicsPipeline

# Initialize
pipeline = EnhancedProteomicsPipeline()

# Run analysis
results = pipeline.run_analysis(
    data_file="path/to/data.csv",
    outcome_columns=['Pneumonitis', 'Grade_3_or_above']
)
```

### Comprehensive Validation

```python
from proteo_suite import ComprehensiveValidator

validator = ComprehensiveValidator()
validator.validate_model(y_true, y_scores, model_name="MyModel")
```

### Advanced Analyzer (Full Stack)

```python
from proteo_suite import AdvancedProteomicsAnalyzer

analyzer = AdvancedProteomicsAnalyzer(data_path="data.csv")
analyzer.run_complete_analysis()
```

## Dependencies

- numpy, pandas, scipy, scikit-learn
- xgboost, lightgbm, catboost
- lifelines
- torch (optional, for deep learning)
- matplotlib, seaborn
pycffex 0.3.2
cffex with python: gb futures, index futures, and options
Requires Python: >=3.12

# CFFEX

A python package for China Financial Futures Exchange (CFFEX) products, including government bond futures, index futures, and index options.

## Installation

```
pip install pycffex
```

## GB Futures

- Attributes
    * tenor: int, tenor of underlying, 2, 5, 10, or 30
    * code: str, code of futures of the given tenor, 'TS', 'TF', 'T', 'TL'
    * listdate 
- Methods
    * lasttradingday()
    * getcontracts(): contracts available on the given trading date
    * deliverable(): whether a given bond is deliverable for the given contract month
    * conversionfactor(): conversion factor of a given deliverable bond for the given contract month
    * invoiceprice(): invoice price for delivery
    * irr(): implied repo rate for a given deliverable bond
    * ...

## Index Futures

**Contracts**

| code      | underlying     | multiplier |
|:---------:|:---------------|-----------:|
| IF        | csi 300        | 300        |
| IC        | csi 500        | 200        |
| IM        | csi 1000       | 200        |
| IH        | sse 50         | 300        |

- contract month: current month, next month, subsequent 2 quarterly month
- last trading day: the 3rd Friday of contract month
- delivery method: cash settlement
- delivery date: last trading day

**Attributes**: underlying, code, listdate

**Methods**

* lasttradingday(contractmon)
* getcontracts(tradedate)
* spothist(startdate, enddate)
* futhist(startdate, enddate)
* ...

## Index Options

**Contracts**

| code      | underlying     | multiplier |
|:---------:|:---------------|-----------:|
| IO        | csi 300        | 100        |
| MO        | csi 1000       | 100        |
| HO        | sse 50         | 100        |

- contract months: current month, next 2 months, subseuently 3 quarterly months
- last trading day: the 3rd Friday of contract month
- delivery method: cash settlement
- delivery date: last trading day

**Attributes**: underlyings, multiplier, exercisetype, listdates

**Methods**

- contractcode()
- lasttradingday()
- contractcode()
- contractmons()
- exercisedata()
- impliedvolatility()