semvec/pypi-packages huggingface dataset:
gbm-drm-gen 1.2.2
A DRM generator for the Fermi GBM detectors

![CI](https://github.com/grburgess/gbm_drm_gen/workflows/CI/badge.svg)
[![codecov](https://codecov.io/gh/grburgess/gbm_drm_gen/branch/master/graph/badge.svg?token=vdaYSD8vdx)](https://codecov.io/gh/grburgess/gbm_drm_gen)
[![PyPI version shields.io](https://img.shields.io/pypi/v/gbm_drm_gen.svg)](https://pypi.python.org/pypi/gbm_drm_gen/)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2590555.svg)](https://doi.org/10.5281/zenodo.2590555)
![GitHub contributors](https://img.shields.io/github/contributors/grburgess/gbm_drm_gen)
# GBM Response Generator

![alt text](https://raw.githubusercontent.com/grburgess/gbm_drm_gen/master/logo.png)

A fast, python based response generator for the Fermi-GBM. A 3ML plugin is also provided that allows for one to locate GRBs with any of the GBM spectral data. 

If you find this code useful or use it please cite the following works:

[Burgess et al 2018](https://academic.oup.com/mnras/article/476/2/1427/4670828)

[https://arxiv.org/abs/1902.01082](https://arxiv.org/abs/1902.01082)


## Install
```bash
pip install gbm_drm_gen

```
sra-py-Atidipt123 0.0.1
Python wrapper for some-random-api.ml
Requires Python: >=3.6

# sra_py
A python wrapper for [some-random-api](https://some-random-api.ml)

## Requirements
[Python 3.8.6 or more](https://python.org)  

## Installation
```cmd
pip install sra_py
```

## Example
```py
# Random Dog Image
# more examples are available in examples directory

import sra_py as sra

dog = sra.dog()

print(dog)
```

## Links
[Documentation](https://github.com/Atidipt123/sra_py/)  
[Some Random API](https://some-random-api.ml)  
[PyPI](https://pypi.org/project/sra_py)
pydos-sws-win 0.0.1
py-dos's softwares.
Requires Python: >=3.6

# PyDOS

优点：

    1.体积小（只有几十KB）

    2.使用方便（不管对于任何平台）

    3.扩展性强（PPkg即可下载任何可用的工具包）

    4.跨平台：Linux、Windows、macOS......只要支持Python3、Tkinter，都能运行


#### 安装教程

下载制定版本压缩包并解压缩

#### 依赖包安装
(通用)对于Linux、macOS以及Windows:

    在bash或cmd中cd至软件目录，输入以下命令：

        pip install -r requirements.txt

    注：如提示"pip: failed to create process"，请在命令前加上"python3 -m"

    提示：因macOS自带的Python 2不会带pip,因此需安装Python3

(Windows)在Windows下使用批处理文件一键安装:

    运行目录下的"InstallPkg.bat"，稍等片刻即可安装完成

#### 使用说明

！！！必读！！！

PyKernel使用方法

命令说明

    sysver:打开sysver工具

    Calcutator:打开计算器(GUI)

    ASCII:打开ascii码转换器

    exit:退出

    Help:帮助文档

    Notepad:打开笔记本(GUI)

    PPkg:软件包管理器

    PyRename:打开PyRename工具

    PyMusic:打开PyMusic音乐播放器(GUI)
PeaksValleys 1.2.2
Detect peaks and valleys in a list of numbers
Requires Python: >=3.6

# PeaksValleys

Consider you have a list containing multiple numbers. By using ***PeaksValleys*** package, you can find and detect peaks and valleys.


## Screenshots

![PeaksValleys Screenshot](https://user-images.githubusercontent.com/8419324/134659955-6ad34a8a-df35-4c7c-bdf5-de7e0bb6e643.png)


## Usage

```python
from PeaksValleys import detectPeaksValleys
import random
import pandas as pd


randItems = random.sample(range(1, 200), 100)
data = {
    'Numbers': randItems
}
df = pd.DataFrame(data=data)

print(detectPeaksValleys(df['Numbers'], 21, 8))
```

As you see, `detectPeaksValleys` has 3 parameters:
```python
detectPeaksValleys(dataframeSeries, rollingNumber, averageSize):
```

1. `dataframeSeries` is series created by `pandas` package
2. `rollingNumber` is a number for smoothing numbers sequence (the higher number, the smoother sequence)
3. `averageSize` is a count of numbers creating an interval for detecting peaks and valleys (the less number, the more peaks and valleys)


## License

[MIT](https://choosealicense.com/licenses/mit/)