semvec/pypi-packages huggingface dataset:
linksaas-local-api 0.0.4
linksaas local api python impl client
Requires Python: >=3.6

基于[凌鲨](https://www.linksaas.pro)客户端本地api的python client

### demo

```python
from  linksaas_local_api import ApiClient,Configuration
from linksaas_local_api.apis import GlobalApi

cfg = Configuration(host="http://127.0.0.1:8001")
apiClient = ApiClient(configuration=cfg)
api = GlobalApi(api_client=apiClient)
res = api.hello_get()
print(res)
res = api.show_get()
print(res)
```

# 接口说明

## GlobalApi

| 接口      | 功能                     |
| --------- | ------------------------ |
| hello_get | 检查是否是linksaas客户端 |
| show_get  | 显示桌面端程序           |

## ProjectBugApi

| 接口                                                | 功能               |
| --------------------------------------------------- | ------------------ |
| project_project_id_bug_all_get                      | 列出所有缺陷       |
| project_project_id_bug_my_get                       | 列出指派给我的缺陷 |
| project_project_id_bug_record_bug_id_events_get     | 列出缺陷相关事件   |
| project_project_id_bug_record_bug_id_short_note_get | 便签方式显示缺陷   |
| project_project_id_bug_record_bug_id_show_get       | 在客户端显示缺陷   |

## ProjectCreateApi

| 接口                                           | 功能             |
| ---------------------------------------------- | ---------------- |
| project_project_id_create_bug_get              | 在客户端创建缺陷 |
| project_project_id_create_doc_doc_space_id_get | 在客户端创建文档 |
| project_project_id_create_task_get             | 在客户端创建任务 |

## ProjectDocApi

| 接口                                                      | 功能             |
| --------------------------------------------------------- | ---------------- |
| project_project_id_doc_space_doc_space_id_doc_id_show_get | 在客户端显示文档 |
| project_project_id_doc_space_doc_space_id_get             | 列出文档         |
| project_project_id_doc_space_get                          | 列出文档空间     |


## ProjectEventApi

| 接口                         | 功能         |
| ---------------------------- | ------------ |
| project_project_id_event_get | 列出事件列表 |

## ProjectMemberApi

| 接口                                              | 功能                 |
| ------------------------------------------------- | -------------------- |
| project_project_id_member_get                     | 列出项目成员列表     |
| project_project_id_member_member_user_id_show_get | 在客户端显示成员信息 |

## ProjectTaskApi

| 接口                                                  | 功能               |
| ----------------------------------------------------- | ------------------ |
| project_project_id_task_all_get                       | 列出所有任务       |
| project_project_id_task_my_get                        | 列出指派给我的任务 |
| project_project_id_task_record_task_id_events_get     | 列出任务相关事件   |
| project_project_id_task_record_task_id_short_note_get | 便签方式显示任务   |
| project_project_id_task_record_task_id_show_get       | 在客户端显示任务   |
jiale 0.1.0
Jia Le's Python Library
Requires Python: >=3.6

# Jia Le's Library for Python

## Installation

```
pip3 install jiale
```

## Upgrade Library

```
pip3 install jiale --upgrade
```

## Usage

```
from jiale import *
```

## Get Started
Here's the basic command to get started :
```
1. info()    - The library information.
2. version() - The library current version, release date.
3. index()   - The entire index in this library version.
4. formula() - The math formula in this library version.
5. help()    - The short note for the library.
```
baytorch 0.0.1.dev0
BayTorch - Bayesian layers for PyTorch
Requires Python: >=3.7

# BayTorch - Bayesian layers in PyTorch

Bayesian layers are probabilistic layers where weights are stochastic and sampled from the posterior distribution
or an approximation.

So far, this module includes diagonal Gaussian linear and convolutional layers with mean and standard deviation
of every weight as learnable parameters, as well as an implementation of the variational free energy loss for
stochastic gradient VI according to the Bayes-by-Backprop algorithm [1].

[1] Blundell et al., Weight Uncertainty in Neural Networks, 2015.

## Installation

Install from PyPI
```
pip install baytorch
```
or clone the repository, build and install from source
```
git clone https://jugit.fz-juelich.de/ias-8/baytorch.git && cd baytorch
python -m build
pip install dist/baytorch*whl
```
spsgamepkg 0.0.2
Stone Paper Scissors - a basic program to play rock,paper,scissors with PC
Requires Python: >=3.7

Rock Paper Scissors - a basic program to play rock,paper,scissors with PC
=========================================================================

# A fun Python package to play rock paper scissors with PC
This is just a basic stress-buster game module to play with your PC
Created by Aswin Venkat <aswinvenk8@gmail.com>

Installation
============

Run the following to install:

```python
pip install spsgamepkg
```

Methods
=======

1. game.rules()
    Will display the rules of the game

2. game.Game('name').game(count:int)
    Count -> The number of times you want to play the game with PC

3. game.Game('name').clear_score()
    Will clear the score (initialise the score back to 0)

4. game.Game('name').display_result()
    Will display the result of the game.

5. game.Game('name').condition(pc='', player='')
    entering ['stone' or 'paper' or 'scissors'] in pc='{}' and player='{}' will directly give you the result

EXAMPLE CODE
============

```python
from spsgamepkg import game

# Display rules
>> game.rules()

# initialize player
aswin_game=game.Game('Aswin')

# Play a game
>> aswin_game.game(5)

# Display Result
>> aswin_game.display_result()

# Reset the scores
>> aswin_game.clear_score()
```