semvec/pypi-packages huggingface dataset:
ultrafastFitFunctions 0.3.0
Collection of common fit functions for discribing ultrafast and static physics phenomena

# ultrafastFitFunctions
Collection of common fit functions for discribing ultrafast and static physics phenomena to be compatible with lmfit.

To be compatible with the great fitting package lmfit, all functions need to have the independent variable as the first argument and it needs to be called 'x'.
If it is not called 'x', when calling lmfit's fit, the new name needs to be specified, which is prone to errors.


=========Usage Example ===============

    from ultrafastFitFunctions import *
    Decays.expDecay(1,1,1)

======================================




========================================================
Valid parameters in order of appearance in the functions:

    mu             :   shift of x by x0 / mu
    sigT           :   T = '', S, H, ... ; width / scaling in x-directions. Similar / related to a FWHM.
    taun           :   n = 1,2,... ; similar to sig; used as taun**1 in denominator of exp
    A,B,C,D,...    :   multiplier / normalisation    
    c,a,b,d,...    :   offsets
    k              :   slope
    other:

    alpha, beta, q : ratios

========================================================

Docstring:

    r"""
    What does the function do?

    Parameters
    ----------
    x : array_like
        `x` represents the x-coordinates of a set of datapoints.
    a : int
        `a` represents the variable, which does... .

    Returns
    -------
    y : ndarray
        An array of the same shape as x.

    """


========================================================

2Do List

1)
    Most functions are written without a mu, should be changed?
    
2)
    
    
3)
    
    
4)
saiblo-dev-tools 0.0.2
Saiblo 游戏开发人员脚本工具

# 游戏开发人员脚本工具



## 配置

安装本工具：

```shell
pip3 install saiblo-dev-tools
```

使用前请先执行：

```shell
saiblo-dev-tools init
```

脚本会在 ~/ 下创建 .saiblo-dev-tools 目录，里面将会存放所有配置文件。初始化后，请编辑 ~/.saiblo-dev-tools/config.json，按照模板填写配置文件。



## 选项

脚本包含了若干子命令，在每个子命令下有对应的帮助信息：

```shell
saiblo-dev-tools <subcommand> --help
```

### init

```shell
saiblo-dev-tools init
```

会将 ~/.saiblo-dev-tools/config.json 和 database.json 复原为模板文件。

### ai

AI 管理脚本。AI 以 `<tag>: <token>` 的形式存储在 ~/.saiblo-dev-tools/database.json 的 `ai` 字段下。`<tag>` 为人类友好的名称，`<token>` 用于在 Saiblo 数据库中唯一确定某个 AI. `"human"` 保留给人类玩家，请勿使用。

- ```shell
  -d, --delete <tag>
  ```

  删除名为 `<tag>` 的 AI，并将其 token 打印出来，防止误操作。

- ```shell
  -i, --insert <tag> <token>
  ```

  添加 AI，若 `<tag>` 已存在，则会覆盖，但会将原有 AI 的 token 打印出来。脚本**不会**对 token 的合法性进行检验。

- ```shell
  -l, --list
  ```

  显示数据库中所有 AI.

- ```shell
  -r, --rename <old_tag> <new_tag>
  ```

  将名为 `<old_tag>` 的 AI 更名为 `<new_tag>`. 若 `<new_tag>` 已存在，则会覆盖，但会将原有 AI 的 token 打印出来。

- ```shell
  -u, --upload <config_path>
  ```

  上传 AI 至 Saiblo，并将 token 添加进本地数据库。`<config_path>` 为配置文件的路径，格式如下：

  ```jsonc
  {
      "game": number,  // <game_id>
      "language": string,  // {c, cmake, cpp, make, python, python_zip}
      "path": string,  // AI 代码的路径，需要打包成单文件
      "tag": string,  // <tag>
  }
  ```

### game

游戏管理脚本。

- ```shell
  -t, --test <config_path> <result_dir>
  ```

  在 Saiblo 上发起一次评测。`<result_dir>` 为存放评测信息的目录，将包含配置信息、评测结果、回放文件。`<config_path>` 是配置文件的路径，格式如下：

  ```jsonc
  {
      "ai": [
          string,  // <tag>
          ...
      ],
      "config": any,  // 自定义配置信息，若无，可以填空字符串
      "game": number,  // <game_id>
  }
  ```

  saiblo-dev-tools 现已支持启动人机、人人对战，你只需要将上述配置文件中原本填写 ai tag 的位置改为填写 `"human"`，即可在相应位置添加人类玩家。该脚本随后会输出人类玩家的 token.

- ```shell
  -u, --upload <config_path>
  ```

  上传游戏逻辑至 Saiblo. `<config_path>` 为配置文件的路径，格式如下：

  ```jsonc
  {
      "id": number,  // <game_id>
      "path": string,  // 游戏逻辑的路径，需要打包成单文件
  }
  ```
  
  **注意**：目前 Saiblo 对游戏逻辑代码的管理与 AI 不同，因此该命令会覆盖 Saiblo 上先前的游戏逻辑，请谨慎使用。

### login

```shell
saiblo-dev-tools login <username>
```

登录 Saiblo 开发账号。脚本将会在 ~/.saiblo-dev-tools/cookie 中保存登录凭据，请妥善保管。

### logout

```shell
saiblo-dev-tools logout
```

删除 ~/.saiblo-dev-tools/cookie，下次使用与 Saiblo 交互的命令将需要登录。
Baizid-tp 0.1
This is a Basic images_Facts>

# This is baizid_tp !

Create By **baizidTp.Com**  art.

## Instructions

1. Install:

```
pip install baizid_tp
```

2. baizid_tp visual Image to Png:

```python
from aesthetic_ascii import synthesize
# initialize drive object (to generate visuals)
drive = synthesize.Drive()
# generate a ASCII visual (dark_mode optional)
drive.generate(dark_mode=True)
# save to png
drive.to_png('demo.png')
```

2. baizid_tp visual Image to jpg:
```
same way
```





UNKNOWN
method-of-moments 0.1.0
The package that allows you to work with probability distributions with a specified mean values and variances.
Requires Python: >=3.6

# method_of_moments

![GitHub repo size](https://img.shields.io/github/issues/AlbertFarkhutdinov/method_of_moments)
![PyPI version](https://img.shields.io/pypi/v/method-of-moments)
![GitHub contributors](https://img.shields.io/github/contributors/AlbertFarkhutdinov/method_of_moments)
![GitHub stars](https://img.shields.io/github/stars/AlbertFarkhutdinov/method_of_moments)
![GitHub forks](https://img.shields.io/github/forks/AlbertFarkhutdinov/method_of_moments)
![GitHub licence](https://img.shields.io/github/license/AlbertFarkhutdinov/method_of_moments)

`method_of_moments` is a package that allows you to work with probability distributions with a specified mean values and variances.

## Prerequisites

Before you begin, ensure you have installed the latest version of Python.

## Installing `method_of_moments`

To install `method_of_moments`, follow these steps:

Linux and macOS:
```
pip3 install method-of-moments
```

Windows:
```
pip install method-of-moments
```
## Using `method_of_moments`

There are will be examples of how to use `method_of_moments`.

## Contributing to `method_of_moments`
To contribute to `method_of_moments`, follow these steps:

1. Fork this repository.
2. Create a branch: `git checkout -b <branch_name>`.
3. Make your changes and commit them: `git commit -m '<commit_message>'`
4. Push to the original branch: `git push origin <project_name>/<location>`
5. Create the pull request.

Alternatively see the GitHub documentation on [creating a pull request](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request).

## Contributors

* [@AlbertFarkhutdinov](https://github.com/AlbertFarkhutdinov) 

## Contact

If you want to contact me you can reach me at `albertfarhutdinov@gmail.com`.

## License
This project uses the following license: [MIT License](https://github.com/AlbertFarkhutdinov/method_of_moments/blob/main/LICENSE).
naogi 0.0.6
Abstract class for Naogi ML deployment
Requires Python: >=3.6

# naogi_model

NaogiModel it is an abstract class for the naogi.com ML deployment platform

## How to deploy via naogi.com
* Add `naogi` to your project requirements.txt
* create file `naogi.py` in the root directory (copypaste file from [naogi.py](https://github.com/Naogi/naogi_model))
* implement your logic of model loading, prepareing and calling
* go to you naogi.com profile, create project and connect git
<br>
<br>

## How it works? (What to implement in naogi.py)
### Loading model (server starting time)
When naogi server is starting, it call `load_model(self)` -- you have to implement model loading logic in that function (loading from file, internet, etc.)

Here you have to load and init your model and save the model object to some variable

Example
```python
def load_model(self):
  self.model = __get_model()
  self.model.load_weights()
```
<br>
<br>

### Prepareing (request time)
When you call [GET/POST] /prepare of your API `prepare(self, params_dict)` is calling first.

All request params can be found in `params_dict`. Here you can prepare you params: open and modify Image, transform and normalize text and safe data for `prepare` to self attribute.

Example
```python
# now you can make GET /predict?text_data=My-long-text
# (and not worry about spaces)
def prepare(self, params_dict):
  self.text = params_dict['text_data'].strip()
```
<br>
<br>

### Predicting (request time)
After request params prepareing `predict(self)` is calling.

```python
def predict(self):
  raw = self.model.predict(self.text)
  return __from_raw_to_some(raw)
```

Here you have to return the value, that valid for some Renderer class (json, file, custom)
<br>
<br>

### Rendering (request time)
And the last step is calling `renderer().render(...)` and passing the result of `predict`
Out of the box you can use `JsonRenderer` and `FileRenderer`

or

you can create custom renderer from `AbstractRenderer`
```python
class MyRenderer(AbstractRenderer):
  def render(data):
    return ...
```
```python
def renderer(self):
  return MyRenderer
```

`JsonRenderer` accepts any json.dumps valid data

`FileRenderer` uses flask's `send_file` under the hood, so you can pass any bytes. [Additional params can watch here](https://github.com/Naogi/naogi_model/blob/main/src/naogi/__init__.py#L17)

<br>
<br>

### Fin
And finally you can make API calls to `<your-naogi-project-url>/predict` with params


## Development
...

### Testing
Before testing you should install **pytest**

From root folder
```shell
PYTHONPATH='./' pytest tests/renderers/pil_image_renderer.py
```

### Deploy
```shell
rm -rf dist/*
python3 -m build
python3 -m twine upload --repository pypi dist/*
```
occupiedgpus 0.0.8
The program for occupation of GPUs.

# [Occupied GPU](https://github.com/jinzcdev/occupied-gpu.git)

The program used to occupy GPUs.

## Preparation

```shell
pip install -r requirements.txt
```

## Usage

To occupy the corresponding GPUs, run:

```shell
sh train.sh 0,1,2,3
```

or

```shell
chmod a+x ./train.sh
./train.sh 0,1,2,3
```

where `0,1,2,3` is the required format.

**Note:** if you want to run the code in the background, run:

```shell
nohup sh train.sh ${GPU_IDS} >> output.log 2>&1 &
```