semvec/pypi-packages huggingface dataset:
t3qai-client 1.2.3
t3qai client module
Requires Python: >=3
Keywords: t3q, t3qai, t3qai client, t3qai_client

# t3qai_client Description
a library for t3qai platform client.

The client module provides properties/functions
that links platform and client's learning/Inference algorithm.

- Provide platform path properties
- Provides functions to link learning state, set log, call learning parameter elements, load data, save learning results, and download inference results

## To install with pip
```
pip install t3qai_client

```

## How to Use (Example)
### properties
```
## train
from t3qai_client import T3QAI_TRAIN_OUTPUT_PATH, T3QAI_TRAIN_MODEL_PATH, T3QAI_TRAIN_DATA_PATH, T3QAI_MODULE_PATH

## inference
from t3qai_client import T3QAI_INIT_MODEL_PATH, T3QAI_MODULE_PATH
```

### functions
```
import t3qai_client as tc

## link learning state
tc.train_start()
tc.train_finish(result, result_msg)

## set log
# train
tc.train_set_logger()
# inference
tc.inference_set_logger()

## call learning parameter elements
# train
params = tc.train_load_param()
batch_size= int(params['batch_size'])
# inference
params = tc.inference_load_param()
batch_size= int(params['batch_size'])

## save learning results
# save the learning results inside the platform.
eval_results={}
eval_results['accuracy']=  0.93
eval_results['loss']=  0.003
tc.train_save_result_metrics(eval_results)

## To download inference results at platform (2 options -> file_obj or file_path)
from t3qai_client import DownloadFile
result = DownloadFile(file_obj=resultobj, file_name=filename)
result = DownloadFile(file_path=save_path, file_name=filename)
```
zensus2pgsql 0.2.1
A Python CLI program that collects Zensus GIS data from Germany and imports it into PostgreSQL
Requires Python: <4.0,>=3.10

<p align="center">
  <img src="https://travishathaway.github.io/zensus2pgsql/assets/zensus2pgsql-logo.svg" width="50%">
</p>
<p align="center">
  <em>German census data, now in PostgreSQL!</em>
</p>

<p align="center">
  <a href="https://github.com/travishathaway/zensus2pgsql/actions?query=workflow%3ATest" target="_blank">
      <img src="https://github.com/travishathaway/zensus2pgsql/workflows/Test/badge.svg" alt="Test">
  </a>
  <a href="https://travishathaway.github.io/zensus2pgsql" target="_blank">
      <img src="https://img.shields.io/static/v1?label=Documentation&message=View&color=blue&logo=readme&logoColor=white" alt="Documentation">
  </a>
  <a href="https://pypi.org/project/zensus2pgsql" target="_blank">
    <img src="https://img.shields.io/pypi/v/zensus2pgsql?color=%2334D058&label=pypi%20package" alt="Package version">
  </a>
</p>

A CLI program that imports German census CSV data (spelled "zensus" in German) into PostgreSQL
with PostGIS geographic data types. All data is provided by the Statitisches Bundesamt of 
Germany. Currently, only data from the 2022 census is available.

If you want to download the raw data yourself or see the website it comes from, check out the
link below:

- [Zensus 2022 Publikationen](https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bevoelkerung/Zensus2022/_publikationen.html)

## Installing

To install this program, run:

```sh
pip install zensus2pgsql
```

## Using

To get started using this tool, you'll first want to see what datasets are availble:

```cli
zensus2pgsql list
```

For now, the names of the data sets are only available in German, so please use translation
software if you need help figuring out what they mean!

After you've fond a dataset to import, you can do so by running the following command:

```cli
zensus2pgsql create \
  --host localhost \
  --port 5432 \
  --user username \
  --schema public \
  heizungsart eigentuemerquote
```

The above command will import two dataset related to the type of heating a house uses and the
percentage of those who own their home in a particular area.

## Contributing

Contributions are welcome and can ake the following forms:

- Creating a bug report if you've found an issue
- Creating a feature request if you'd like to see new functionality added
- Creating a pull request to accomplish either of the two above
- Leaving a star on the repository 

Before creating a pull request, I kindly ask that you either create a bug report or
a feature request first, so I can sign off on having it added to the project.
ligo.skymap 2.5.4.dev1
Tools for reading, writing, manipulating, and making LIGO/Virgo/KAGRA sky maps
Requires Python: >=3.11

###########
ligo.skymap
###########

The `ligo.skymap` package provides tools for reading, writing, generating, and
visualizing gravitational-wave probability maps from LIGO, Virgo, and KAGRA.
Some of the key features of this package are:

*  `Command line tool bayestar-localize-coincs`_ and
   `bayestar-localize-lvalert`_: BAYESTAR, providing rapid, coherent, Bayesian,
   3D position reconstruction for compact binary coalescence events

*  `Command line tool ligo-skymap-from-samples`_: Create 3D sky maps from
   posterior sample chains using kernel density estimation

*  `Command line tool ligo-skymap-plot`_: An everyday tool for plotting
   HEALPix maps

*  `Module ligo.skymap.plot`_: Astronomical mapmaking tools for
   perfectionists and figure connoisseurs

To get started, see the `installation instructions`_ or the `full
documentation`_.

.. figure:: https://lscsoft.docs.ligo.org/ligo.skymap/_images/localization.svg
   :alt: GW170817 localization

   This illustration of the position of GW170817, prepared using `ligo.skymap`,
   illustrates the main features of this package: rapid localization of compact
   binaries with BAYESTAR [#BAYESTAR]_, three-dimensional density estimation
   [#GoingTheDistance]_ [#GoingTheDistanceSupplement]_, cross-matching with
   galaxy catalogs, and visualization of gravitational-wave sky maps.
   Reproduced from [#IlluminatingGravitationalWaves]_.

.. [#BAYESTAR]
   Singer, L. P., & Price, L. R. 2016, *Phys. Rev. D*, 93, 024013.
   https://doi.org/10.1103/PhysRevD.93.024013

.. [#GoingTheDistance]
   Singer, L. P., Chen, H.-Y., Holz, D. E., et al. 2016, *Astropys. J. Lett.*,
   829, L15. https://doi.org/10.3847/2041-8205/829/1/L15

.. [#GoingTheDistanceSupplement]
   Singer, L. P., Chen, H.-Y., Holz, D. E., et al. 2016, *Astropys. J. Supp.*,
   226, 10. https://doi.org/10.3847/0067-0049/226/1/10

.. [#IlluminatingGravitationalWaves]
   Kasliwal, M. M., Nakar, E., Singer, L. P. et al. 2019, *Science*, 358, 1559.
   https://doi.org/10.1126/science.aap9455

.. _`Command line tool bayestar-localize-coincs`: https://lscsoft.docs.ligo.org/ligo.skymap/tool/bayestar_localize_coincs.html
.. _`bayestar-localize-lvalert`: https://lscsoft.docs.ligo.org/ligo.skymap/tool/bayestar_localize_lvalert.html
.. _`Command line tool ligo-skymap-from-samples`: https://lscsoft.docs.ligo.org/ligo.skymap/tool/ligo_skymap_from_samples.html
.. _`Command line tool ligo-skymap-plot`: https://lscsoft.docs.ligo.org/ligo.skymap/tool/ligo_skymap_plot.html
.. _`Module ligo.skymap.plot`: https://lscsoft.docs.ligo.org/ligo.skymap/#plotting-and-visualization-ligo-skymap-plot
.. _`installation instructions`: https://lscsoft.docs.ligo.org/ligo.skymap/quickstart/install.html
.. _`full documentation`: https://lscsoft.docs.ligo.org/ligo.skymap
molecular-dynamics-toy 0.3.0
Interactive molecular dynamics simulation for educational purposes
Requires Python: >=3.9

# molecular-dynamics-toy
An interactive molecular dynamics simulation GUI.

This app lets you run a real molecular dynamics simulation on
your laptop or home computer. It includes interactive features like
clicking to add new atoms, changing the temperature and simulation cell size
live, and loading preset atomic configurations.

It uses a pre-trained universal machine learning interatomic potential 
([MatterSim](https://microsoft.github.io/mattersim/)) with a molecular
dynamics integrator from the
[Atomic Simulation Environment](https://ase-lib.org/) to do the physics, then
provides the graphical user interface using [pygame](https://www.pygame.org/).

Physical limitations of this simulation are those of the chosen interatomic
potential. Specifically, MatterSim is optimised for bulk materials (i.e. things
which fill the whole unit cell) and not for molecules or chemical reactivity.
While the physics engine is science-grade, this app is intended for
demonstration purposes, and no warranty is given for the accuracy
of any results.

## Installation

### Standalone Executable

Go to [Releases](https://github.com/bfield1/molecular-dynamics-toy/releases)
and download the file corresponding to your operating system.
MacOS has two builds: an app (which you click on like normal apps), and an
executable inside a directory. The app build is easier to use, although the
executable provides a debug window.

Unzip/unarchive/extract the file (attempting to open it will usually do this).

Enter the MDToy directory then click on/open MDToy.

Your computer will likely flag this as an untrusted app from an unknown publisher.
On MacOS, right-click on the file then click Open, then click Open in the pop-up.
On Windows, select More info in the pop-up then Open anyway.

Then you're done! The molecular dynamics toy will be running.
The first time it runs, it requires an internet connection to download the 
MatterSim interatomic potential. It will do this to `$HOME/.local/mattersim`,
and should require only 17 MB.

### Python installation

Set up a virtual Python environment, such as with
[Conda](https://docs.conda.io/projects/conda/en/stable/user-guide/getting-started.html).

After installing Conda, you can create a Conda environment in the command
line with
```
conda env create -n mdtoy "python=3.13"
conda activate mdtoy
```
(You can replace "mdtoy" with a name of your choice.)

Then, install the app with
```
pip install molecular-dynamics-toy
```
or, if you want the latest in-development version, with
```
pip install git+https://github.com/bfield1/molecular-dynamics-toy
```
Note that the MatterSim dependency requires a C++ compiler;
it should tell you where to find one when you attempt to `pip install` if you
don't already have one.

Finally, run the app with
```
python -m molecular_dynamics_toy
```
It will automatically download the pre-trained Mattersim model the first time
you run it.

(You will need to have the virtual environment you created earlier active,
so if you open a new terminal run `conda activate mdtoy` again.)
bulum 0.3.2
Open source python library for assessing hydrologic model results in Queensland
Requires Python: >=3.9

# bulum

## Installation

This package may be installed using pip from GitHub, directly from PyPi (public), or from a .tar.gz. Examples are shown below.

```bash
pip install git+https://github.com/odhydrology/bulum
```

```bash
pip install bulum
```

```bash
pip install .\dist\bulum-0.0.32.tar.gz
```

## Usage

```python
import bulum

# returns the package version
bulum.__version__

# prints 'Hello world!' to the console
bulum.hello_world()
```

API documentation is available at [odhydrology.github.io/bulum](https://odhydrology.github.io/bulum/).

## Build and Upload to PyPi

First build a distribution from an anaconda prompt in the root of your project, and then upload the dist to PyPi using Twine.

```bash
python setup.py sdist
```

```bash
twine upload dist\bulum-0.0.32.tar.gz
```

As of Nov 2023, PyPi uses an API token instead of a conventional password. You can still use Twine, but the username is "\_\_token__", and password is the API token which is very long string starting with "pypi-". 

``` bash
username = __token__
password = pypi-#####################################################################################
```

Where can I find the API token password? Chas has it in his emails. It is also here on the network at *.\ODH working files\Professional development, reading, etc\Software\ODHSoftware\bulum\PyPi_password_and_instructions.txt*.

How do I make a new API token? Go to your PyPi account settings, and click on "API tokens". Then click on "Add API token", and give it a name. The token will be displayed on the next screen.

## Unit Tests

WARNING: Run unit tests from an anaconda environment with compatible dependencies!

Install the nose2 test-runner framework. 

```bash
pip install nose2
```

Then from the root project folder run the nose2 module. You can do this as a python modules, or just directly from the anaconda prompt (both examples given below). This will automatically find and run tests in any modules named "test_*".

```bash
python -m nose2
```

```bash
nose2
```

You can run specific tests by specifying the module name. Example below.

```bash
nose2 src.bulum.stats.tests
```

## License

Refer to LICENCE.md
meca-common-config 1.0.4
配置工具类
Requires Python: >=3.10

# 配置加载类使用说明

为部署的应用提供统一的配置文件解析接口。根据不同的应用名称生成不同的配置文件路径。并将其配置文件加载。

对一个应用`appname`，在Linux操作系统下，配置文件路径依次为

* /etc/`appname`.ini
* ${HOME}/etc/`appname`.ini
* ${PROJECT_HOME}/etc.ini

在Windows下，配置文件的路径依次为

* %ProgramData%/`appname`.ini
* %APPDATA%/`appname`.ini
* %PROJECT_HOME%/etc.ini

其中第三个路径为测试用的路径。后面的配置文件覆盖前面的配置。

## 类说明

该包提供类`ConfigDict`。其初始化函数接收一个参数`appname`。包含一个帮助方法`self.show()`，用以查看当前应用的配置文件路径。初始化完成后，`ConfigDict`的对象实例自动加载了所有的配置文件，以字典的形式存储在对象中。

```python

config = ConfigDict(appname='app')
config.show()
print(config)

```
pyfem 0.3.7.1
Python Finite Element Method
Requires Python: >=3.9

# pyfem

pyfem是一个完全基于python语言实现的极简有限元求解器。依赖的第三方库包括numpy、scipy和meshio等，主要用于有限元方法的学习、有限元算法验证和快速建立材料本构模型的程序原型。

Github仓库：https://github.com/sunwhale/pyfem

[![Codacy Badge](https://app.codacy.com/project/badge/Grade/ab5bca55d85d45d4aa4336ccae058316)](https://app.codacy.com/gh/sunwhale/pyfem/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)

## Contact 联系方式
电子邮箱 E-mail：sunjingyu@imech.ac.cn

作者主页 Homepage: https://people.ucas.edu.cn/~sunjingyu

## Installation 安装

支持的操作系统包括：Windows，Linux和MacOS。

### Recommend 推荐

Use the package manager [pip](https://pypi.org/project/pyfem/) to install pyfem:

使用pip命令安装:

```bash
pip install -U pyfem
```

If you have no root access on Linux/MacOS, please try

如果你在Linux/MacOS上没有root访问权限，请尝试

```bash
python -m pip install -U pyfem
```

Users in China can install pyfem from mirrors such as:

中国用户可以使用以下镜像:
- [Aliyun](https://developer.aliyun.com/mirror/pypi)
- [Tsinghua](https://mirrors.tuna.tsinghua.edu.cn/help/pypi/)

### From Source 基于源代码

```bash
git clone https://github.com/sunwhale/pyfem.git
cd pyfem
pip install .
```

or 或者

```bash
git clone https://github.com/sunwhale/pyfem.git
cd pyfem
python install.py
```

Using the "From Source" approach will generate executable files or batch files, which can then have their paths added to the system environment variables.

采用基于源代码的方法会生成可执行文件或批处理文件，可将其路径写入系统环境变量。

## Quickstart 快速开始

### Run in command line 在命令行运行:

```bash
pyfem --help
```

### Run the first example 执行第一个算例:

当前算例文件存储目录 examples/tutorial，该算例定义了一个二维平面应变模型，材料为塑性随动强化，载荷为y方向的循环拉伸-压缩。

```bash
cd examples/tutorial
pyfem -i Job-1.toml
```

## Postproc 后处理

算例计算完成后将在配置文件所在目录下生成 .pvd 或 .vtu文件，可以使用开源可视化软件 [paraview](https://www.paraview.org/download/) 进行查看。

## Preproc 前处理

本项目暂不提供前处理模块，基于 meshio 库，可以识别[gmsh](https://www.gmsh.info/)、abaqus 和 ansys等有限元软件的网格文件。

## Documents 帮助文档

[帮助文档](https://pyfem-doc.readthedocs.io/)中给出了详细的理论公式和函数说明。



## Development 开发

### ToDo list

- [ ] 增加如何建立toml算例文件的帮助文档
- [x] 增加hdf5计算结果输出格式
- [ ] 处理平面应力状态的面外应力平衡
- [ ] 增加内聚区单元
- [ ] 增加动力学求解器
- [ ] 建立前处理界面

### Bug list

- [ ] 采用abaqus网格文件时，如果存在node不属于任何element则在计算时会导致全局刚度矩阵奇异。
implicitdict 4.1.0
ImplicitDict base class that turns a subclass into a dict indexing attributes, making [de]serialization easy for complex typing-annotated data types.
Requires Python: >=3.11

# implicitdict

This library primarily provides the `ImplicitDict` base class which enables the inheriting class to implicitly be treated like a `dict` with entries corresponding to the fields of the inheriting class.  Simple example:

```python
class MyData(ImplicitDict):
    foo: str
    bar: int = 0
    baz: Optional[float]

data: MyData = ImplicitDict.parse({'foo': 'asdf', 'bar': 1}, MyData)
assert json.dumps(data) == '{"foo": "asdf", "bar": 1}'
```

See [class documentation for `ImplicitDict`](https://github.com/interuss/implicitdict/blob/main/src/implicitdict/__init__.py) and [test_normal_usage.py](https://github.com/interuss/implicitdict/blob/main/tests/test_normal_usage.py) for more information.
naesungmath 3.1.0
Science is Magic! Let's memorize spells <Math Formula>
Requires Python: >=3.6

# Naesungmath

[![PyPI version](https://badge.fury.io/py/naesungmath.svg)](https://badge.fury.io/py/naesungmath)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/naesungmath)](https://pypi.org/project/naesungmath/)
[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
[![install size](https://packagephobia.com/badge?p=naesungmath@3.1.0)](https://packagephobia.com/result?p=naesungmath@3.1.0)


대한수학회와 수학강사연구모임이 배포한 `과학은 마법이다! 주문을 외워보자 <수학공식>`을 기준으로 만들었습니다.

We made it based on `<Math Formula>`, which was distributed by the Korean Mathematical Society and Korean Mathematical Instructor Research Group.

공식 221개 중 117개 완료 (52.9%)

[![image](https://nodei.co/npm/naesungmath.png?downloads=true&stars=true)](https://nodei.co/npm/naesungmath/)

# Update 3.1
- **Refactoring & Consolidation**: All formulas have been refactored into consolidated "Smart Solver" classes (e.g., `Geometry`, `Calculus`).
- **Unified Logic**: 100% consistency across JavaScript, Python, and C# implementations.
- **Smart Solvers**: Introduced `solve` methods to automatically calculate missing variables.

# Update 3.0
- Switch to a Typescript to increase readability when using code.
- Modify code to work automatically.

## Contributing

Review and accept Issue and Pull Request boldly.

For more information, contact minseo0388@daum.net or 129dot03@kakao.com.

## npm package

Published as `Naesungmath` at npmjs.org

The latest version is `3.1.0`.

### Add package

`npm i naesungmath`

Or with yarn:

`yarn add naesungmath`

## Maintainer

[@minseo0388(Choi Minseo)](https://github.com/minseo0388)

## Contributors

[@pmh_only](https://github.com/pmh-only) [@AkiaCode](https://github.com/akiacode) [@DPS0340](https://github.com/dps0340) [@comjun04](https://github.com/comjun04)

## License

MIT License
sulvion-pigui 0.1.1
A premium, grid-based Python GUI library built on CustomTkinter.
Requires Python: >=3.7

# SulvionPiGUI 🎨

**SulvionPiGUI** (or `spgui`) is a premium, powerful, and easy-to-use Python GUI library built on top of `CustomTkinter`. It's designed for developers who want to create beautiful, grid-based desktop applications with minimal code.

## 🌟 Features

- **Grid-Based Layout**: No more complex pixel math. Place widgets using intuitive grid coordinates.
- **Modern Aesthetics**: Built-in dark and light themes with premium `CustomTkinter` widgets.
- **Dynamic Plots**: Integrated Matplotlib support for real-time data visualization.
- **Interactive Widgets**: Drag-and-drop, resizable widgets, and easy event binding.
- **High-DPI Support**: Automatically handles screen scaling for crisp visuals on all displays.
- **Beginner Friendly**: Simple API inspired by the best practices of modern web design.

## 🚀 Installation

You can install `SulvionPiGUI` via pip:

```bash
pip install spgui
```

Dependencies:
- `customtkinter`
- `matplotlib`
- `pillow`

## 📖 Quick Start

Here is a simple example to get you started:

```python
import SPGUI as ui

# Initialize app with a 10x10 grid
app = ui.init(
    SIZE=[10, 10], 
    title="Hello SPGUI", 
    theme="dark", 
    show_grid=True
)

# Add a label at (0, 0)
app.label(pos=[0, 0], size=[10, 1], text="Welcome to SulvionPiGUI!", text_size=16)

# Add a button that shows a message
def say_hello():
    app.message("Greeting", "Hello World!")

app.btn(pos=[4, 4], size=[2, 1], text="Click Me!", onclick=say_hello)

# Add a text input
def on_text_changed(text):
    print(f"User typed: {text}")

app.input(pos=[2, 6], size=[6, 1], placeholder="Type here...", onchange=on_text_changed)

app.run()
```

## 🛠️ Available Widgets

- `app.label()` / `app.text()`: Display text.
- `app.btn()` / `app.button()`: Interactive buttons.
- `app.input()`: Text entry fields.
- `app.checkbox()`: Toggle boxes.
- `app.dropdown()`: Select menus.
- `app.slider()`: Range sliders.
- `app.number()`: Numeric inputs with filtering.
- `app.plot()`: Matplotlib charts (line/bar).
- `app.table()`: Data grids.
- `app.image()`: Display images via PIL.

## ⛓️ Performance & Debugging

Enable the debug grid during development to visualize your layout:

```python
app = ui.init(show_grid=True, coord_color="#00ff00")
```

## 📄 License

This project is licensed under the **MIT License**. See the [LICENSE](LICENSE) file for details.

Developed with ❤️ by **Hafiz Daffa W**.
- **GitHub**: [@HafizDaffa01](https://github.com/HafizDaffa01)
- **Email**: [hafizdaffaw+dev@gmail.com](mailto:hafizdaffaw+dev@gmail.com)