| // Using the Jenkins Groovy Post build plugin to execute the following after every build | |
| // https://wiki.jenkins-ci.org/display/JENKINS/Groovy+Postbuild+Plugin | |
| // It would be nice not to have to specify these here... the repo name should be available within the hudson | |
| // api somehow, but I didn't know how to get it. The access token should maybe be saved in a config file, and | |
| // read in at runtime? | |
| GITHUB_REPO_NAME = 'myusername/myreponame' | |
| GITHUB_ACCESS_TOKEN = 'my_github_api_v3_access_token' |
| #!/bin/bash | |
| # ================================================================= | |
| # Install a full scientific python stack from source, for Ubuntu, with | |
| # python 2.7.3 (tested on a totally fresh Ubuntu12.04-amd64). Requires | |
| # sudo for apt-getting dependencies like libatlas, Tk, etc | |
| # ================================================================= | |
| # packages pulled from PyPI are currently | |
| # scipy 0.10.1 |
| #!/usr/bin/env python | |
| import os, sys | |
| import subprocess | |
| HOST_MOUNT_DIR = '\$HOME/ubuntu' | |
| # get the ip of the host | |
| host = os.environ['SSH_CLIENT'].split(' ')[0] | |
| # get the path from $HOME to the file |
| #!/usr/bin/env python | |
| from simtk.openmm.app import * | |
| from simtk.openmm import * | |
| from simtk.unit import * | |
| from msmbuilder import Trajectory | |
| import msmbuilder.metrics | |
| #interface to the msmbuilder fast RMSD calculator | |
| rmsd_calculator = msmbuilder.metrics.RMSD() |
| import numpy as np | |
| from sklearn.hmm import MultinomialHMM | |
| import matplotlib.pyplot as pp | |
| # sample run lengths from exponential distribution | |
| run_lengths = np.array(np.random.exponential(100, size=10), dtype=np.int) | |
| # make the signal from the run lengths | |
| signal = [] | |
| for i, l in enumerate(run_lengths): | |
| signal.extend([i % 2] * l) |
| """ | |
| Viterbi algorithm for finding the most likely state sequence of a simple | |
| hidden markov model. | |
| # https://en.wikipedia.org/wiki/Viterbi_algorithm | |
| The Model | |
| --------- | |
| The HMMs considered in this code are extremely simple. Every hidden state | |
| is uniquely associated with an output symbol, but the link is "fuzzy". That is, |
| import numpy as np | |
| import matplotlib.pyplot as pp | |
| from matplotlib import cm | |
| def maximization(X, y, weights): | |
| """Calculate the beta that maximize the likelihood, given the weights (fuzzy assignment) | |
| Parameters | |
| ---------- | |
| X : np.array, shape=[n_cases, n_features] |
| from selenium import webdriver | |
| import time | |
| import random | |
| import numpy as np | |
| browser = webdriver.Chrome() | |
| browser.get('https://www.quora.com/login') | |
| raw_input('You need to log in in the browser window. Hit enter here when finished...') |
Locate the section for your github remote in the .git/config file. It looks like this:
[remote "origin"]
fetch = +refs/heads/*:refs/remotes/origin/*
url = git@github.com:joyent/node.git
Now add the line fetch = +refs/pull/*/head:refs/remotes/origin/pr/* to this section. Obviously, change the github url to match your project's URL. It ends up looking like this:
| { | |
| "metadata": { | |
| "name": "Rpush bug example" | |
| }, | |
| "nbformat": 3, | |
| "nbformat_minor": 0, | |
| "worksheets": [ | |
| { | |
| "cells": [ | |
| { |