semvec/pypi-packages huggingface dataset:
nonebot-plugin-fgoavatarguess 0.1.1
FGO avatar guess plugin based on Nonebot2
License: MIT
Requires Python: >=3.8,<4.0

<div align="center">
  <a href="https://v2.nonebot.dev/store"><img src="https://github.com/A-kirami/nonebot-plugin-template/blob/resources/nbp_logo.png" width="180" height="180" alt="NoneBotPluginLogo"></a>
  <br>
  <p><img src="https://github.com/A-kirami/nonebot-plugin-template/blob/resources/NoneBotPlugin.svg" width="240" alt="NoneBotPluginText"></p>
</div>

<div align="center">

# nonebot-plugin-fgoavatarguess

_✨ FGO猜从者插件 ✨_


<a href="./LICENSE">
    <img src="https://img.shields.io/github/license/influ3nza/nonebot-plugin-fgoavatarguess.svg" alt="license">
</a>
<a href="https://pypi.python.org/pypi/nonebot-plugin-fgoavatarguess">
    <img src="https://img.shields.io/pypi/v/nonebot-plugin-fgoavatarguess.svg" alt="pypi">
</a>
<img src="https://img.shields.io/badge/python-3.8+-blue.svg" alt="python">

</div>

## 📖 介绍

FGO(Fate/GrandOrder，命运冠位指定)猜从者插件。本插件基于nonebot2开发。

## 💿 安装

<details open>
<summary>使用 nb-cli 安装</summary>
在 nonebot2 项目的根目录下打开命令行, 输入以下指令即可安装

    nb plugin install nonebot-plugin-fgoavatarguess

</details>

<details>
<summary>使用包管理器安装</summary>
在 nonebot2 项目的插件目录下, 打开命令行, 根据你使用的包管理器, 输入相应的安装命令

<details>
<summary>pip</summary>

    pip install nonebot-plugin-fgoavatarguess
</details>
<details>
<summary>pdm</summary>

    pdm add nonebot-plugin-fgoavatarguess
</details>
<details>
<summary>poetry</summary>

    poetry add nonebot-plugin-example
</details>
<details>
<summary>conda</summary>

    conda install nonebot-plugin-example
</details>

打开 nonebot2 项目根目录下的 `pyproject.toml` 文件, 在 `[tool.nonebot]` 部分追加写入

    plugins = ["nonebot_plugin_example"]

</details>

## 🎉 使用
### 指令表
| 指令 | 权限 | 需要@ | 范围 | 说明 |
|:-----:|:----:|:----:|:----:|:----:|
| 猜从者 | 所有人 | 否 | 群聊/私聊 | 开始猜从者 |
| /quitfgo | 所有人 | 否 | 群聊/私聊 | 在猜从者进行时，停止猜从者 |
### 效果图
暂无
pyGTC 0.5.0
Make an awesome giant triangle confusogram (gtc)!
License: MIT

pygtc.py
=========

**What is a Giant Triangle Confusogram?**

A Giant-Triangle-Confusogram (GTC, aka triangle plot) is a way of
displaying the results of a Monte-Carlo Markov Chain (MCMC) sampling or similar
analysis. (For a discussion of MCMC analysis, see the excellent ``emcee``
package.) The recovered parameter constraints are displayed on a grid in which
the diagonal shows the one-dimensional posteriors (and, optionally, priors) and
the lower-left triangle shows the pairwise projections. You might want to look
at a plot like this if you are fitting a model to data and want to see the
parameter covariances along with the priors.

Here's an example of a GTC with some random data and arbitrary labels::

  pygtc.plotGTC(chains=[samples1,samples2],
                paramNames=names,
                chainLabels=chainLabels,
                truths=truths,
                truthLabels=truthLabels,
                priors=priors,
                paramRanges=paramRanges,
                figureSize='MNRAS_page')

.. image:: https://raw.githubusercontent.com/SebastianBocquet/pygtc/master/docs/_static/demo_files/demo_9_1.png

**But doesn't this already exist in corner.py, distUtils, etc...?**

Although several other packages exists to make such a plot, we were unsatisfied
with the amount of extra work required to massage the result into something we
were happy to publish. With ``pygtc``, we hope to take that extra legwork out of
the equation by providing a package that gives a figure that is publication
ready on the first try! You should try all the packages and use the one you like
most; for us, that is ``pygtc``!

Installation
------------
For a quick start, you can install with either ``pip`` or ``conda``. Either will install the required
dependencies for you (``packaging``, ``numpy``, and ``matplotlib``)::

  $ pip install pygtc

or, if you use ``conda``::

  $ conda install pygtc -c conda-forge

For more installation details, see the `documentation <http://pygtc.readthedocs.io/>`_.

Documentation
-------------
Documentation is hosted at `ReadTheDocs <http://pygtc.readthedocs.io/>`_. Find
an exhaustive set of examples there!

Citation
--------
If you use pygtc to generate plots for a publication, please cite as::

  @article{Bocquet2016,
    doi = {10.21105/joss.00046},
    url = {http://dx.doi.org/10.21105/joss.00046},
    year  = {2016},
    month = {oct},
    publisher = {The Open Journal},
    volume = {1},
    number = {6},
    author = {Sebastian Bocquet and Faustin W. Carter},
    title = {pygtc: beautiful parameter covariance plots (aka. Giant Triangle Confusograms)},
    journal = {The Journal of Open Source Software}
  }


Copyright 2016, Sebastian Bocquet and Faustin W. Carter

.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.159091.svg
   :target: https://doi.org/10.5281/zenodo.159091