This project creates encrypted backups for Bitwarden vaults including attachments. It pulls your vault items from Bitwarden CLI and download all the attachments associated with those items to a temporary backup folder. Then, portwarden zip that folder, encrypt it with a passphrase, and delete the temporary folder.

聚合数百家资源网站，通过右侧边栏1秒告诉你哪些网站能下载豆瓣页面上的电影|电视剧|纪录片|综艺|动画|音乐|图书等，有资源的网站显示绿色，没资源的网站显示黄色，就这么直观！所有豆瓣条目均提供在线播放|阅读、字幕|歌词下载及PT|NZB|BT|磁力|百度盘|115网盘等下载链接，加入官网打死也不出的豆列搜索功能，此外还能给豆瓣条目额外添加IMDB评分|IMDB TOP 250|Metascore评分|烂番茄评分|AniDB评分|Bgm评分|MAL|亚马逊评分等更多评分形式。官方电报群：@doubandown

本配置由神机规则修改而来，修改了默认策略组，增加了正则筛选策略组，并集成了Nobyda的去广告脚本和重写，附带按task脚本作者分类的task订阅，可按需求订阅

This repository contains an implementation of Burg's Algorithm for the computation of the power spectral density of time-series via Maximum Entropy Principle, known as Maximum Entropy Spectral Analysis (MESA). Two different methods implement the standard Algorithm and a Faster version, called FastBurg. The problem admits an analytical solution that can be expressed in closed form as a Discrete Fourier Transform of some coefficients a_k, known as forward prediction error coefficients. The a_k coefficients are computed recursively by the use of Levinson Recursion. The role of MESA class is to solve the recursion. Various method exist to estimate the recursive order that better approximate the power spectral density. A second class is inserted to implement different loss functions to choose the recursive order.

The a_k coefficients are found to be the "best linear predictor" for the time series under study, their computation via the former method is equivalent to a least square fitting with an autoregressive process of order p (AR(p)). Levinson recursion also return a "P" coefficient that is equivalent to the variance of the white noise component for the process. This description is stationary by construction of Burg's Method.

Given the a_k coefficient, they can be used to perform high quality forecasting for the future values of the time series. A method to perform forecast is implemented in mesa class.

The code is also released as a public package memspectrum, available on the PyPI repository. A paper is in publication.

This utility consisted of server backend and UI which manages the chia plot creation.
It uses the chia command line interface to start the plot.
It will schedule new plots when a plot finishes as specified by the configuration file. The server backend does a cycle every minute and check if the configuration file has been changed, if it detects that it has been changed then it reloads the configuration file. Once a valid configuration file has been loaded then it will start one new plot per cycle.

Python interface to TD Ameritrade Api