How to filter emails from GitHub in Gmail and flag them with labels.
The labels in this document are just examples.
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#!/bin/bash | |
set -e | |
############################################################################### | |
# This script tries to repair a Time Machine *network* backup (i.e. an APF | |
# share containing a sparsebundle) that is shared over a network using e.g. an | |
# Apple TimeCapsule, a NAS, Raspberry PI, ... | |
# The script must be run on the computer that created the backup | |
# |
#------------------------------------------------------------------------------ | |
# Module: cubetools | |
#------------------------------------------------------------------------------ | |
# | |
# Description: | |
# Module to work with Gaussian cube format files | |
# (see http://paulbourke.net/dataformats/cube/) | |
# | |
#------------------------------------------------------------------------------ | |
# |
git checkout -b [name_of_your_new_branch]
git push origin [name_of_your_new_branch]
<!DOCTYPE html> | |
<html> | |
<head> | |
<meta charset="utf-8" /> | |
<title>Virtual Horizontal Scrolling Demo</title> | |
<style> | |
html, body { | |
width: 100%; | |
height: 100%; | |
margin: 0; |
First, install the following libraries:
$ brew install unixodbc
$ brew install freetds --with-unixodbc
FreeTDS should already work now, without configuration:
$ tsql -S [IP or hostname] -U [username] -P [password]
locale is "en_US.UTF-8"
locale charset is "UTF-8"
# gap.py | |
# (c) 2013 Mikael Vejdemo-Johansson | |
# BSD License | |
# | |
# SciPy function to compute the gap statistic for evaluating k-means clustering. | |
# Gap statistic defined in | |
# Tibshirani, Walther, Hastie: | |
# Estimating the number of clusters in a data set via the gap statistic | |
# J. R. Statist. Soc. B (2001) 63, Part 2, pp 411-423 |
# Copyright Mathieu Blondel December 2011 | |
# License: BSD 3 clause | |
import numpy as np | |
import pylab as pl | |
from sklearn.base import BaseEstimator | |
from sklearn.utils import check_random_state | |
from sklearn.cluster import MiniBatchKMeans | |
from sklearn.cluster import KMeans as KMeansGood |