semvec/pypi-packages huggingface dataset:
gtsimulation 0.1.1
Software for calculations of particle's propagation through the electromagnetic fields and mediums in space.
Requires Python: >=3.10

![](docs/_static/gtsimulation_logo.png)

[**GT simulation**](https://spacephysics.mephi.ru/GTsimulation) (GT) is software a package that is created for simulations of propagation of charged particles in electromagnetic fields.
GT solves the relativistic equation of motion of a particle using Buneman-Boris scheme. That allows to recover the trajectory
of a particle with high precision. Additionally, we take into account the energy losses of particles such as, radiation losses
(synchrotron radiation), adiabatic losses (in the heliosphere), and the interactions with the medium. As a result of interaction
with the medium secondary particles may be created, that are later simulated in GT.

The code is written in a flexible manner, and easily can be extended by inheriting from the abstract classes of each module. To
enhance the speed of calculations **numba** just-in-time compiler is used to compile the main functions.


## Installation

GT requires Python 3.10+. To avoid possible package conflicts, you can optionally create an isolated virtual environment
using `venv`:
```console
$ python -m venv gt_env
$ source gt_env/bin/activate
```
See the `venv` [official documentation](https://docs.python.org/3/library/venv.html) for details.

If you plan to use the secondary particle generation functionality, install [Geant4](https://geant4.web.cern.ch/download/11.4.0.html) and activate its environment variables:
```console
$ source /path/to/geant4/bin/geant4.sh
```

Install the package with:
```console
$ pip install gtsimulation
```

If you do not want to use Geant4, specify the build option during installation:
```console
$ pip install --config-settings=cmake.define.BUILD_GEANT4_COMPONENTS=OFF gtsimulation
```

Alternatively, you can install the package from the source repository:
```console
$ git clone --depth 1 https://github.com/agmayorov/GTsimulation.git
$ cd GTsimulation
$ pip install .
```
zfx 2.0.96
[小提示：在pycharm导入模块后记得调整以名称排序，这样功能就是分好类的状态]中国人自己的模块！ZFX是一个多功能的Python工具包,提供了各种实用工具和功能，包括网络请求、剪贴板操作、系统监控、网页自动化、系统操作、文本处理、文件操作等,无论是日常办公还是自动化脚本，ZFX都能为您提供便捷的解决方案，让您的编程体验更加愉快！☆目前已有的功能分类：【excel】【html】【json】【列表】【打印】【抛出异常】【类型转换】【文件】【文本】【文本文件】【模块】【目录】【算数】【系统】【线程】【编码】【网页协议】【谷歌填表】【进制转换】【邮件IMAP】【邮件POP3】


    免责声明:
    本模块是“按原样”提供的，没有任何明示或暗示的保证。在任何情况下，作者或版权持有者均不对因使用本模块而产生的任何索赔、损害或其他责任负责，无论是在合同、侵权或其他情况下。

    使用本模块即表示接受此免责声明。如果您不同意此免责声明的任何部分，请勿使用本模块。

    本模块仅供参考和学习用途，用户需自行承担使用本模块的风险。作者对因使用本模块而造成的任何直接或间接损害不承担任何责任。

    作者保留随时更新本免责声明的权利，恕不另行通知。最新版本的免责声明将在模块的最新版本中提供。
aspose-diagram 26.2.0
A powerful library for working with Microsoft Visio files VDX, VSD,VSDX,VSSX,VSTX, VTX, XPS, HTML, SVG
Requires Python: >=3.5
Keywords: Visio, VSD, VDX, VSDX, VDW, to, PDF, JPG, PNG, HTML, SVG, XPS, VSTX, VSSX

Python Visio API
================

Aspose.Diagram for Python via Java is a scalable and feature-rich API to
process visio files using Python. API offers Visio file creation,
manipulation, conversion, & rendering. Developers can format pages to
the most granular level, create & manipulate shape, render pages, shapes
to PDF & images, and much more - all without any dependency on Microsoft
Office or Visio application.

Python Visio API Features
-------------------------

-  Create visio files via API.
-  Convert shapes to images or PDF.
-  Manage comments & hyperlinks.
-  Convert pages to PDF, XPS & SVG formats.
-  Inter-convert files to popular visio formats.

Read Visio Files
----------------

**Microsoft Visio:**\ VSD,VSS,VST,VSX,VTX, VDX, VSDX, VSTX, VSSX, VSTM,
VSSM

Save Visio Files As
-------------------

**Microsoft Visio:** VSX,VTX, VDX, VSDX, VSTX, VSSX, VSTM, VSSM **Fixed
Layout:** PDF, XPS **Images:** JPEG, PNG, BMP, SVG, TIFF, GIF, EMF
**Web:** HTML

Create Visio File from Scratch using Python
-------------------------------------------

.. code:: python

    import jpype
    import asposediagram
    jpype.startJVM()
    from asposediagram.api import *

    diagram = Diagram()
    diagram.save("output.vsdx", SaveFileFormat.VSDX)

    jpype.shutdownJVM()

Convert Visio VSDX File to PDF using Python
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code:: python

    import jpype
    import asposediagram
    jpype.startJVM()
    from asposediagram.api import *

    diagram = Diagram("input.vsdx")
    diagram.save("output.pdf",SaveFileFormat.PDF)

    jpype.shutdownJVM()

`Product Page <https://products.aspose.com/diagram/python-java>`__ \|
`Documentation <https://docs.aspose.com/display/diagrampythonjava/Home>`__
\| `Blog <https://blog.aspose.com/category/diagram/>`__ \| `API
Reference <https://apireference.aspose.com/diagram/python>`__ \| `Code
Samples <https://github.com/aspose-diagram/Aspose.Diagram-for-Java>`__
\| `Free Support <https://forum.aspose.com/c/diagram>`__ \| `Temporary
License <https://purchase.aspose.com/temporary-license>`__ \|
`EULA <https://company.aspose.com/legal/eula>`__
nimo 2.1.1
NIMO package

# nimo

NIMO is a Python library to realize a closed loop of robotic experiments and artificial intelligence without human intervention for automated materials exploration. We started development as NIMS-OS (NIMS Orchestration System) (https://github.com/nimsos-dev/nimsos) and decided to adopt NIMO as a nickname to promote it as open source software. NIMO can perform automated materials exploration in various combinations by considering artificial intelligence and robotic experiments as modules. As artificial intelligence technique for materials science, Bayesian optimization method (PHYSBO), boundLess objective-free exploration method (BLOX), phase diagram construction method (PDC), Probability that properties within the Target Range (PTR), Bayesian optimization for materials and process parameters (BOMP), Bayesian optimization for combinatorial materials (COMBI), and random exploration (RE) can be used. Visualization tools for the results are also included, allowing users to check optimization results in real time. Newly created modules for artificial intelligence and robotic experiments can be added and used. More modules will be added in the future.

<img width="600" alt="modules" src="https://github.com/user-attachments/assets/ee7ca756-b4ea-4fc6-8d41-6e1ff47afdfd">


# Document

- [English](https://nims-da.github.io/nimo/en/)
- [Japanese](https://nims-da.github.io/nimo/ja/)

# Required Packages

- Python >= 3.6
- matplotlib
- numpy
- physbo >= 3.1.0
- scikit-learn
- scipy
- pyDOE3

# Install

* From PyPI (recommended)

  ```bash
  pip install nimo
  ```

* From source

  1. Download or clone the github repository

  ```
  git clone https://github.com/NIMS-DA/nimo
  ```

  2. Install via pip in the nimo-main folder

  ```bash
  pip install .
  ```

# Uninstall

```bash
pip uninstall nimo
```

# License

The program package and the complete source code of this software are distributed under the MIT License.
glue-qt 0.4.2
Multidimensional data visualization across files
Requires Python: >=3.8

|Actions Status| |Coverage Status| |DOI| |User mailing list| |Developer mailing list|

Glue
====

Glue is a python project to link visualizations of scientific datasets
across many files. Click on the image for a quick demo:

|Glue demo|

Features
--------

-  Interactive, linked statistical graphics of multiple files.
-  Support for many `file
   formats <http://www.glueviz.org/en/latest/faq.html#what-data-formats-does-glue-understand>`__
   including common image formats (jpg, tiff, png), ascii tables,
   astronomical image and table formats (fits, vot, ipac), and HDF5.
   Custom data loaders can also be `easily
   added <http://www.glueviz.org/en/latest/customization.html#custom-data-loaders>`__.
-  Highly `scriptable and
   extendable <http://www.glueviz.org/en/latest/coding_with_glue.html>`__.

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

For installation documentation, visit
`glueviz.org <http://glueviz.org>`__.

Contributing
------------

If you are interested in contributing to ``glue``, please read our
`Code of Conduct <https://github.com/glue-viz/.github/blob/master/CODE_OF_CONDUCT.md>`_
and `Contribution Guidelines <https://github.com/glue-viz/.github/blob/master/CONTRIBUTING.md>`_.

Support
-------

Please report problems to glueviz@gmail.com, or `open an
issue <https://github.com/glue-viz/glue/issues?state=open>`__.

License
-------

Glue is licensed under the `BSD
License <https://github.com/glue-viz/glue/blob/master/LICENSE>`__.

.. |Actions Status| image:: https://github.com/glue-viz/glue-qt/actions/workflows/ci_workflows.yml/badge.svg
    :target: https://github.com/glue-viz/glue-qt/actions/workflows/ci_workflows.yml
    :alt: Glue's GitHub Actions CI Status
.. |Coverage Status| image:: https://codecov.io/gh/glue-viz/glue-qt/branch/main/graph/badge.svg
   :target: https://codecov.io/gh/glue-viz/glue-qt
.. |DOI| image:: https://zenodo.org/badge/doi/10.5281/zenodo.13866.svg
   :target: http://dx.doi.org/10.5281/zenodo.13866
.. |User mailing list| image:: http://img.shields.io/badge/mailing%20list-users-green.svg?style=flat
   :target: https://groups.google.com/forum/#!forum/glue-viz
.. |Developer mailing list| image:: http://img.shields.io/badge/mailing%20list-development-green.svg?style=flat
   :target: https://groups.google.com/forum/#!forum/glue-viz-dev
.. |Glue demo| image:: https://raw.githubusercontent.com/glue-viz/glue-qt/main/doc/readme.gif
   :target: http://vimeo.com/53378575