semvec/pypi-packages huggingface dataset:
showlog 0.0.6
make it easy to use log

# showlog
![](https://img.shields.io/badge/Python-3.8.6-green.svg)

#### 介绍
python的log，封装了些简单的方法


#### 安装教程

1.  使用pip安装
- 普通方式安装
```shell script
pip3 install showlog
```

- 使用阿里镜像加速安装
```shell script
pip3 install showlog -i https://mirrors.aliyun.com/pypi/simple
```

#### 使用说明

1.  导入
``` shell script
import showlog
```

2.  简单使用
``` shell script
showlog.info('hello word!')
showlog.warning('this is warning')
showlog.error('this is error')
```

3.  保存log为文件
- 只需要设置一次，后续的log将会被存储
``` shell script
showlog.save_info('[your file name].log')
showlog.save_warning('[your file name].log')
showlog.save_error('[your file name].log')
```
ChangePointDetectorEVT 0.0.18
This module takes a date time series and returns: (a) the underlaying linear trend and (b) the times where there is a change in the trend
Requires Python: >=3.7
Keywords: Time Series, Anomaly, Detection, Kalman, EVT

ChangePointDetector

This module returns change points in a date time series, using Kalman filters and EVT as described in https://www.robots.ox.ac.uk/~sjrob/Pubs/LeeRoberts_EVT.pdf

1. from ChangePointDetector import ChangePointDetector
2. Prepare your time series as data plus Panda dates
3. Create  the necessary Kalman representation by creating a "session" object by calling the ChangePoint class, e.g.:
	Session=ChangePointDetector.ChangePointDetectorSession(data,dates). 
	- 'SeasonalityPeriods' is an optional input, e.g if your data is sequential months, 12 = calendar month seasonality
	- 'ForecastPeriods' is another optional input, indicating how many periods to forecast.  Default = 3
4. Determine the changepoints by running the ChangePointDetectorFunction on your "session", e.g. Results=Session.ChangePointDetectorFunction()
   This will return a "Results" object that contains the following:
	- ChangePoints. This is a list of 0s and 1s the length of the data, where 1s represent changepoints
	- Prediction. This is the Kalman smoothed actuals, plus a 3 period forecast. Note no forecast will be made if there is a changepoint in the last 3 			dates
	- PredictionVariance.  Variance of the smoothed actuals and forecast
	- ExtendedDates. These are the original dates plus 3 exta for the forecast (if a forecast has been made)
	- Trend. This is the linear change factor
	- TrendVariance. Variance of the trend