semvec/pypi-packages huggingface dataset:
clock-plots-YOUR-USERNAME-HERE 0.1
This package provides a simple way to visualize patterns in timeseries data mapping 24 hours onto a polar plot
Requires Python: >=3.7

## clock_plots

clock_plots provides a simple way to visualize timeseries data mapping 24 hours onto the 360 degrees of a polar plot. 
For usage, please see the examples.ipynb Jupyter notebook

Time features are automatically generated for your timeseries
These features include:
year (int): Calendar year e.g. 2022
month (str): Calendar month e.g. "January"
year_month (int): Calendar year and month in the format YYYYMM e.g. 202201
day (int): Day of calendar year e.g. 25
date (str): Expressed in the format YYYY-MM-DD e.g. 2022-01-25
week (int): Week of the calendar year e.g. 5
dayofweek (str): Short version of day of week e.g. Tue
weekend (str): Either "weekday" or "weekend" where weekends are where dayofweek is either "Sat" or "Sun"
hour (int): Hour of the day in 24 clock e.g. 14
minute (int): Minute of the hour e.g. 42
degrees (int): Angle around 24 hour clock-face measured in degrees, calculated using hours and minutes
season (str): Season of the year defined as:
                "Winter" where month is either "December", "January" or "February"
                "Spring" where month is either "March", "April" or "May"
                "Summer" where month is either "June", "July" or "August"
                "Autumn" where month is either "September", "October" or "November"
Using these it is then simple to filter your data and format your plot.
For example you could filter for a particular year, plot seasons with different colors and weekday vs weekend days with different line dashes.
Examples of this are given in examples.ipynb

## Installation
To install this package run:
pip install /path/to/clock_plots

or to install in editable mode, use:
pip install -editable /path/to/clock_plots
clock-plots-samyoung-dsci 0.1
This package provides a simple way to visualize patterns in timeseries data mapping 24 hours onto a polar plot
Requires Python: >=3.7

## clock_plots

clock_plots provides a simple way to visualize timeseries data mapping 24 hours onto the 360 degrees of a polar plot. 
For usage, please see the examples.ipynb Jupyter notebook

Time features are automatically generated for your timeseries
These features include:
year (int): Calendar year e.g. 2022
month (str): Calendar month e.g. "January"
year_month (int): Calendar year and month in the format YYYYMM e.g. 202201
day (int): Day of calendar year e.g. 25
date (str): Expressed in the format YYYY-MM-DD e.g. 2022-01-25
week (int): Week of the calendar year e.g. 5
dayofweek (str): Short version of day of week e.g. Tue
weekend (str): Either "weekday" or "weekend" where weekends are where dayofweek is either "Sat" or "Sun"
hour (int): Hour of the day in 24 clock e.g. 14
minute (int): Minute of the hour e.g. 42
degrees (int): Angle around 24 hour clock-face measured in degrees, calculated using hours and minutes
season (str): Season of the year defined as:
                "Winter" where month is either "December", "January" or "February"
                "Spring" where month is either "March", "April" or "May"
                "Summer" where month is either "June", "July" or "August"
                "Autumn" where month is either "September", "October" or "November"
Using these it is then simple to filter your data and format your plot.
For example you could filter for a particular year, plot seasons with different colors and weekday vs weekend days with different line dashes.
Examples of this are given in examples.ipynb

## Installation
To install this package run:
pip install /path/to/clock_plots

or to install in editable mode, use:
pip install -editable /path/to/clock_plots
clock-plots 0.1
This package provides a simple way to visualize patterns in timeseries data mapping 24 hours onto a polar plot
Requires Python: >=3.7

## clock_plots

clock_plots provides a simple way to visualize timeseries data mapping 24 hours onto the 360 degrees of a polar plot. 
For usage, please see the examples.ipynb Jupyter notebook

Time features are automatically generated for your timeseries
These features include:
year (int): Calendar year e.g. 2022
month (str): Calendar month e.g. "January"
year_month (int): Calendar year and month in the format YYYYMM e.g. 202201
day (int): Day of calendar year e.g. 25
date (str): Expressed in the format YYYY-MM-DD e.g. 2022-01-25
week (int): Week of the calendar year e.g. 5
dayofweek (str): Short version of day of week e.g. Tue
weekend (str): Either "weekday" or "weekend" where weekends are where dayofweek is either "Sat" or "Sun"
hour (int): Hour of the day in 24 clock e.g. 14
minute (int): Minute of the hour e.g. 42
degrees (int): Angle around 24 hour clock-face measured in degrees, calculated using hours and minutes
season (str): Season of the year defined as:
                "Winter" where month is either "December", "January" or "February"
                "Spring" where month is either "March", "April" or "May"
                "Summer" where month is either "June", "July" or "August"
                "Autumn" where month is either "September", "October" or "November"
Using these it is then simple to filter your data and format your plot.
For example you could filter for a particular year, plot seasons with different colors and weekday vs weekend days with different line dashes.
Examples of this are given in examples.ipynb

## Installation
To install this package run:
pip install /path/to/clock_plots

or to install in editable mode, use:
pip install -editable /path/to/clock_plots
re4e-zh 2022.3.22.0
写给每个人的逆向工程
Requires Python: >=3.6

# 写给每个人的逆向工程

> 原文：[Reverse Engineering For Everyone](https://0xinfection.github.io/reversing/)
> 
> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)
> 
> 阶段：机翻（1）
> 
> 世上最廉价的是一贫如洗的真心和一事无成的温柔。

* [在线阅读](https://re4e.apachecn.org)
* [在线阅读（Gitee）](https://apachecn.gitee.io/doc-template/)
* [ApacheCN 学习资源](http://docs.apachecn.org/)

## 贡献指南

本项目需要校对，欢迎大家提交 Pull Request。

> 请您勇敢地去翻译和改进翻译。虽然我们追求卓越，但我们并不要求您做到十全十美，因此请不要担心因为翻译上犯错——在大部分情况下，我们的服务器已经记录所有的翻译，因此您不必担心会因为您的失误遭到无法挽回的破坏。（改编自维基百科）

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* [飞龙](https://github.com/wizardforcel): 562826179

### 其他

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## 下载

### Docker

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docker pull apachecn0/re4e-zh
docker run -tid -p <port>:80 apachecn0/re4e-zh
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install re4e-zh
re4e-zh <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g re4e-zh
re4e-zh <port>
# 访问 http://localhost:{port} 查看文档
```

## 其它协议

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