semvec/pypi-packages huggingface dataset:
yawebview 0.3.0
Yet Another Webview Library
Requires Python: >=3.8

# yawebview
yawebview(Yet Another Webview library) that helps you build cross platform GUI for python applications using web technologies(HTML, JavaScript and CSS).

yawebview is tailor-made for [YADE](https://gitlab.com/webyfy/iot/e-gurukul/yade) development. yawebview contains common code that we were going to write anyway.

## Dependencies
`yawebview` uses QtWebChannel via PySide2. It can be installed via native packages as well as pip.

### Native packages (recommended)
To install dependencies on Debian-based systems 
```shell
sudo apt install python3-pyside2.qtwebenginewidgets
```

### pip (not recommended)
```shell
pip install pywebview[pyside2]
```

If you get an error similar to the following,
```
qt.qpa.plugin: Could not load the Qt platform plugin "xcb" in "" even though it was found.
This application failed to start because no Qt platform plugin could be initialized. Reinstalling the application may fix this problem.

Available platform plugins are: eglfs, linuxfb, minimal, minimalegl, offscreen, vnc, wayland-egl, wayland, wayland-xcomposite-egl, wayland-xcomposite-glx, webgl, xcb.

Aborted (core dumped)
```
install `libxcb-xinerama0` to resolve the issue.
intercartomap 0.1.0
A tool for interactive bivariate cartographic mapping.
Requires Python: >=3.6

# InterCartoMap

**InterCartoMap** 是一个用于 **交互式双变量变形地图** (Interactive Bivariate Cartographic Mapping) 的 Python 工具。它允许用户通过 GUI 界面选择数据，设置断点和颜色，并生成可交互的地理地图。
*InterCartoMap** is a Python tool for **interactive bivariate cartographic mapping**. It allows users to select data, set breakpoints and color schemes, and generate interactive geographic maps through a user-friendly GUI.

---

## **安装**
## **Installation**

您可以使用 `pip` 直接安装：
```bash
pip install intercartomap

You can install `intercartomap` using `pip`:
```bash
pip install intercartomap

## **Usage**

安装后，您可以直接在 终端（Windows CMD、PowerShell、macOS/Linux 终端） 运行：
intercartomap
After installation, you can directly launch InterCartoMap from the terminal (Windows CMD, PowerShell, macOS/Linux terminal):
intercartomap
lsqAxSI 0.0.6
Nonlinear least-squares optimization for AxSI
Requires Python: >=3.8

Usage Guide
===========

## Overview

This section describes how to use the **Nonlinear Least-Squares Optimization** program.

## Run from Command Line

To execute the program via the command line, use the following syntax:

```bash
lsq_AxSI \
  --x0 1.0 2.0 \
  --bounds -10.0 10.0 -5.0 5.0 \
  --ftol 1e-6 \
  --xtol 1e-6 \
  --diff_step 1e-3 \
  --max_nfev 10000
```

### Required Arguments:

- **`--x0`**: Initial guess for the parameters (space-separated values).
- **`--bounds`**: Parameter bounds as pairs (lower and upper limits for each parameter).

### Optional Arguments:

- **`--ftol`** *(default: 1e-6)*: Tolerance for changes in the cost function value.
- **`--xtol`** *(default: 1e-6)*: Tolerance for updates to the parameter values.
- **`--diff_step`** *(default: 1e-3)*: Step size for finite-difference approximation.
- **`--max_nfev`** *(default: 10000)*: Maximum number of function evaluations allowed.

## Function Import and Customization

The program also supports direct import of the `nonlinear_least_squares` function into Python scripts. This allows advanced customization, such as using custom residual (`reg_func`) and Jacobian (`jac`) functions.

### Example:

```python
from nonlinear_least_squares import nonlinear_least_squares

def custom_reg_func(x, *args):
    # Define custom residual computation
    pass

def custom_jacobian(x, *args):
    # Define custom Jacobian computation
    pass

result = nonlinear_least_squares(
    reg_func=custom_reg_func,
    x0=[1.0, 2.0],
    bounds=([-10.0, -5.0], [10.0, 5.0]),
    jac=custom_jacobian
)

print(result)
```

## Output Description

The program provides a summary of the optimization process, including:

### Key Results:

- **`Success`**: Indicates whether the optimization converged successfully.
- **`Optimized Parameters`**: The final parameter values at the solution.
- **`Residuals`**: Values of the residual function at the solution.
- **`Jacobian`**: Jacobian matrix at the solution.
- **`Exit Flag`**: Integer code indicating the reason for termination.

### Detailed Metadata:

The output includes a dictionary with the following fields:

- **`algorithm`**: Solver used for optimization.
- **`firstorderopt`**: Measure of first-order optimality.
- **`iterations`**: Number of iterations performed.
- **`funcCount`**: Number of function evaluations.
- **`cgiterations`**: Number of conjugate gradient iterations.
- **`Message`**: Descriptive message about the termination reason.




Python version
--------------

This project is currently using Python 3.12

Installation
------------

It is recommended to use **virtualenv** to create a clean python environment.

To install lsqAxSI, use **pip**:

    pip install lsqAxSI



Execution
---------

The main script shipped with this project is **lsq_AxSI.py**, see its options by running:

    lsq_AxSI.py -h