task name {
String in
command {
echo '${in}'
}
output {
String out = read_string(stdout())
ssh-keygen -t ed25519 -C "[email protected]" | |
export GIT_SSH_COMMAND='ssh -i /Users/me/.ssh/id_ed25519_food_bitbucket -o IdentitiesOnly=yes' |
#!/opt/hall-lab/python-3.7.0/bin/python -u | |
import os, sys, time, datetime | |
import psutil | |
# add eagle to PATH | |
os.environ['PATH'] = os.pathsep.join(['/opt/hall-lab/eagle-2.4.1/bin', os.environ['PATH']]) | |
def log(msg): | |
timestamp = datetime.datetime.now().strftime("%Y-%m-%d %T") |
# http://flask.pocoo.org/snippets/88/ | |
import os, sqlite3 | |
from cPickle import loads, dumps | |
from time import sleep | |
try: | |
from thread import get_ident | |
except ImportError: | |
from dummy_thread import get_ident |
This page is a curated collection of Jupyter/IPython notebooks that are notable for some reason. Feel free to add new content here, but please try to only include links to notebooks that include interesting visual or technical content; this should not simply be a dump of a Google search on every ipynb file out there.
Important contribution instructions: If you add new content, please ensure that for any notebook you link to, the link is to the rendered version using nbviewer, rather than the raw file. Simply paste the notebook URL in the nbviewer box and copy the resulting URL of the rendered version. This will make it much easier for visitors to be able to immediately access the new content.
Note that Matt Davis has conveniently written a set of bookmarklets and extensions to make it a one-click affair to load a Notebook URL into your browser of choice, directly opening into nbviewer.
.PHONY: init-project repl deps | |
#https://stackoverflow.com/a/23324703 (gets the absolute directory of the Makefile) | |
ROOT_DIR:=$(shell dirname $(realpath $(firstword $(MAKEFILE_LIST)))) | |
QUICKLISP_DIR := $(ROOT_DIR)/quicklisp | |
UTILS_DIR := $(QUICKLISP_DIR)/utils | |
SBCL := $(HOME)/sw/cl/sbcl/2.0.3/bin/sbcl | |
SWANK_HOST := "127.0.0.1" |
#!/bin/bash | |
# select the relevant commits i would like to take from source git repoA | |
mkdir -p patches | |
git format-patch -1 --stdout 8adb447ccbfd88bf57a571105f6af3b000f343c5 >patches/1.patch | |
git format-patch -1 --stdout 8f71637c2e407aa8d9e040b1f8168cd5bc9590a4 >patches/2.patch | |
git format-patch -1 --stdout 968ac4b5e74abf26cf3e53c6f2a1722e8ab62de3 >patches/3.patch | |
git format-patch -1 --stdout d7c13e31b502049c436d898b037729d08b6078a0 >patches/4.patch | |
git format-patch -1 --stdout e00ddb86adebb2d528a08abd91bfd7237d32a07e >patches/5.patch |
import numpy | |
import Gnuplot | |
def rainfall_intensity_t10(t): | |
return 11.23 * (t**(-0.713)) | |
def rainfall_intensity_t50(t): | |
return 18.06 * (t**(-0.713)) | |
g = Gnuplot.Gnuplot() |
Simply put, destructuring in Clojure is a way extract values from a datastructure and bind them to symbols, without having to explicitly traverse the datstructure. It allows for elegant and concise Clojure code.