| # Python 3 code | |
| import matplotlib.pyplot as mpl | |
| import scipy.cluster.hierarchy as sch,random | |
| import numpy as np | |
| import pandas as pd | |
| # Now, take corMat and covMat generated from R, input into Marcos' Python, and check output is the same... | |
| col_list = ["IGV.Close", "TLT.Close", "IAU.Close", "IYR.Close"] | |
| corr = pd.read_csv("corMat_23072021.csv", usecols=col_list) |
| # | |
| # This small example shows you how to access JS-based requests via Selenium | |
| # Like this, one can access raw data for scraping, | |
| # for example on many JS-intensive/React-based websites | |
| # | |
| from time import sleep | |
| from selenium import webdriver | |
| from selenium.webdriver import DesiredCapabilities |
| import math | |
| import asyncio | |
| import logging, sys | |
| import sqlite3 | |
| from asyncio import sleep | |
| from logging.handlers import TimedRotatingFileHandler | |
| import aiohttp_cors | |
| import aiosqlite | |
| import socketio |
| """ | |
| Python code for fractional differencing of pandas time series | |
| illustrating the concepts of the article "Preserving Memory in Stationary Time Series" | |
| by Simon Kuttruf | |
| While this code is dedicated to the public domain for use without permission, the author disclaims any liability in connection with the use of this code. | |
| """ | |
| import numpy as np | |
| import pandas as pd |
This guide explains how to create a simple multi cloud Docker cluster using Docker Swarm, Docker Machine and the three top cloud providers nowadays - Google Compute Engine, Microsoft Azure and AWS.
This guide assumes you have a Linux host with Docker CE installed. If you are using Docker for Mac or Docker for Windows you can avoid the Docker Machine set up since it comes included.
| import numpy as np | |
| EPSILON = 1e-10 | |
| def _error(actual: np.ndarray, predicted: np.ndarray): | |
| """ Simple error """ | |
| return actual - predicted | |
| #!/bin/sh | |
| # OUTDATED: please refer to the link below for the latest version: | |
| # https://github.com/rancherlabs/support-tools/blob/master/extended-rancher-2-cleanup/extended-cleanup-rancher2.sh | |
| docker rm -f $(docker ps -qa) | |
| docker volume rm $(docker volume ls -q) | |
| cleanupdirs="/var/lib/etcd /etc/kubernetes /etc/cni /opt/cni /var/lib/cni /var/run/calico /opt/rke" | |
| for dir in $cleanupdirs; do | |
| echo "Removing $dir" | |
| rm -rf $dir | |
| done |
| """ | |
| Clean and simple Keras implementation of network architectures described in: | |
| - (ResNet-50) [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385.pdf). | |
| - (ResNeXt-50 32x4d) [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/pdf/1611.05431.pdf). | |
| Python 3. | |
| """ | |
| from keras import layers | |
| from keras import models |
/etc/ufw/applications.d/plexmediaserver
[plexmediaserver]
title=Plex Media Server (Standard)
description=The Plex Media Server
ports=32400/tcp|3005/tcp|5353/udp|8324/tcp|32410:32414/udp
[plexmediaserver-dlna]
title=Plex Media Server (DLNA)
description=The Plex Media Server (additional DLNA capability only)
| """ | |
| A simple watchdog for long running processes which may stall for some reason or | |
| another. | |
| If the main thread hasn't logged progress (by updating | |
| ``self.last_progress_time``) in WATCHDOG_HARD_KILL_TIMEOUT, the watchdog | |
| thread will log an error containing the stack trace of all currently running | |
| threads then use ``kill -9`` to kill the main process. | |
| Assumes that a process monitor like supervisor or systemd will then restart |