semvec/pypi-packages huggingface dataset:
instaui-echarts 0.5.0
A Python package for instaui-echarts.
Requires Python: <3.14,>=3.10
Keywords: charts, data-visualization, echarts, gui, instaui, ui, web

# instaui-echarts

<div align="center">

English| [简体中文](./README.md)

</div>

## 📖 Introduction
instaui-echarts is a Python package for instaui, which provides a component for rendering ECharts.


## ⚙️ Installation

```bash
pip install instaui-echarts
```

## 🖥️ Usage
```python
from instaui import ui
from instaui_echarts import echarts

@ui.page("/")
def test_page():
    opts = {
        "title": {"text": "ECharts Getting Started Example"},
        "tooltip": {},
        "legend": {"data": ["sales"]},
        "xAxis": {
            "data": ["Shirts", "Cardigans", "Chiffons", "Pants", "Heels", "Socks"]
        },
        "yAxis": {},
        "series": [{"name": "sales", "type": "bar", "data": [5, 20, 36, 10, 10, 20]}],
    }

    echarts(opts)


ui.server(debug=True).run()
```

use `graphics`

```python
from instaui import ui
from instaui_echarts import echarts, graphics as gh


@ui.page()
def test():
    options = gh.option(
        gh.data(
            [
                {"x": "A", "y": 10},
                {"x": "B", "y": 20},
                {"x": "C", "y": 30},
            ]
        ),
        gh.bar_y(),
    )

    echarts(options)
```
arviz-plots 0.8.0
ArviZ-plots provides ready to use and composable plots for Bayesian Workflow.
Requires Python: >=3.11

# arviz-plots

[![Run tests](https://github.com/arviz-devs/arviz-plots/actions/workflows/test.yml/badge.svg)](https://github.com/arviz-devs/arviz-plots/actions/workflows/test.yml)
[![codecov](https://codecov.io/gh/arviz-devs/arviz-plots/graph/badge.svg?token=1VIPLXCOJQ)](https://codecov.io/gh/arviz-devs/arviz-plots)
[![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org)

ArviZ plotting elements and static battery included plots

We are currently working on splitting ArviZ into independent modules.
See https://github.com/arviz-devs/arviz/issues/2088 for more details.
instaui-tdesign 0.6.0
InstaUI TDesign
Requires Python: <3.14,>=3.10
Keywords: TDesign, gui, instaui, ui, web

# instaui-tdesign

<div align="center">

English|[简体中文](./README.zh-CN.md)

</div>
 
📖 Introduction
instaui-tdesign is a UI component library for InstaUI, built on top of TDesign.

## 📦 Installation
```
pip install instaui-tdesign -U
```

```
uv add instaui-tdesign
```


## 🖥️ Quick Start


```python
from instaui import ui
from instaui_tdesign import td

td.use(locale="en_US")

@ui.page('/')
def home():
    info = ui.state(
        {
            "name": "",
            "age": 0,
        }
    )

    @ui.computed(inputs=[info])
    def disabled_submit(info: dict) -> bool:
        return info["name"] == "" or info["age"] == 0

    @ui.event(inputs=[info])
    def handle_submit(info: dict):
        # Operations such as printing, writing to a database, etc.
        print(info)

    # ui
    with ui.container(size="1"), ui.column():
        ui.text(info)

        td.input(info["name"], label="NAME")
        td.input_number(info["age"], label="AGE", theme="row", min=0, max=100)
        td.button("submit", disabled=disabled_submit, on_click=handle_submit)

ui.server(debug=True).run()
```
diffhydro 0.1.0
Differentiable hydrological pipelines built on DiffRoute and PyTorch
Requires Python: >=3.8

# DiffHydro
Differentiable Hydrological Pipelines Made Easy


## Overview

**DiffHydro** is a research-oriented Python library for building, assembling, and training **fully differentiable hydrological modeling pipelines** using PyTorch.

DiffHydro formulates hydrological models as pipelines of operators (catchment interpolation, runoff generation, routing, dams, indundation modules, etc.). 
If each operator is differentiable, the *entire hydrological system* can be trained end-to-end using gradient-based optimization.

DiffHydro provides the building blocks and reference pipelines to make this practical, modular, and extensible.

> ⚠️ **Status**: DiffHydro is in **early development**. APIs may change, and the library is primarily intended for research and experimentation.

---

## Key Features

* **End-to-end differentiable hydrological pipelines**
* **Modular design**: compose models from reusable operators
* **Learnable physical parameters** (routing, response functions, etc.)
* **PyTorch-native** (GPU-friendly, autograd-compatible)
* **Scalability** Important efforts have been put to make very large problem sizes fit into GPU memories.
* **Reference pipelines** for common hydrological learning setups
* **Examples on real datasets**, from toy problems to larger-scale cases

---

## Library Structure

DiffHydro is organized around four main components:

```text
diffhydro/
├── structs/     # Core data structures
├── modules/     # Individual differentiable operators
├── pipelines/   # High-level model & training pipelines
examples/        # End-to-end usage examples
```

### `structs/`

Defines the **core data structures** manipulated throughout the library. 

---

### `modules/`

Contains **individual differentiable operators**.
The current releases includes three main components:

* Catchment Interpolation
* Runoff Generation
* Routing Operator

Each module is designed to be:

* Differentiable
* Composable
* Reusable across pipelines

Expect more components to be added soon.

---

### `pipelines/`

Provides **high-level abstractions** for assembling and training models.

This includes:

* **Models**: containers that assemble multiple modules and expose learnable parameters
* **Modules**: utilities that orchestrate data loading, scheduling, and optimization

Pipelines illustrate *typical and recommended usage patterns*, but they are not restrictive: 
advanced users are encouraged to assemble their own pipelines directly from modules.

---

### `examples/`

Example notebooks demonstrating how to use DiffHydro on **actual hydrological problems**, covering:

* Different spatial scales
* Different levels of physical complexity
* Both small and large datasets

If you are new to DiffHydro, **start here**.

---

## Installation

Install from pip:

```bash
pip install diffhydro
```

Or install the latest version directly from source:

```bash
git clone https://github.com/TristHas/DiffHydro.git
cd DiffHydro
pip install -e .
```
hello-ext-demo 1.0.1
Minimal pybind11 extension demo package
Requires Python: >=3.8

# pybind11 extension package demo

This folder is a standalone Python package that builds a pybind11 extension:

- `hello_ext._core` (C++ extension with pybind11)

The `add` function is implemented in C (`hello_c.c`) and `mul` in C++ (`hello_cpp.cpp`).

On macOS/Linux you will get `.so` files; on Windows you will get `.pyd` files.

## Build and install

```bash
python -m venv .venv
source .venv/bin/activate
python -m pip install -U pip build
python -m build
python -m pip install dist/*.whl
```

## Try it

```bash
python -c "import hello_ext; print(hello_ext.add(2, 3)); print(hello_ext.mul(3, 4))"
```

## Build from source (editable)

```bash
python -m pip install -v .
```