rbiswas4

Testing and Integration

There are numerous articles in software literature about the motivation for unit tests and integration testing. Closer home The LSST policy described here is a useful source which addresses the kind of concerns we will have.
Personally I think, the really big win for me (clearly writing unit tests is boring for people who could use the time to do more) is the increased confidence in generalizing/modifying/refactoring the codebase after a long break from developing the code without worrying that changes will break the code. Not surprisingly, talking to academics across many fields, this is the reason that seems to get the most buy-in from them.

I think it is very useful to be able to use a continuous integration service. Travis is a popular one. The main advantage of this is to make sure the code works the way it is expected to with the install instructions.
This is di

import pandas as pd

%matplotlib inline
import matplotlib.pyplot as plt

	# Install script from https://github.com/JohannesBuchner/pymultinest-tutorial with a few changes.

	### Variables for locations
	software_repo=${HOME}/soft
	multinest_repo=https://github.com/JohannesBuchner/MultiNest
	mnestdir=$software_repo/Multinest
	pymnestdir=$software_repo/PyMultiNest

	### Check if directory for multinest is already present
	if [ -d $mnestdir ]; then

	import pandas as pd
	import glob

	s = '# fields/userRegions_design.txt - design fields - 18,000 sq deg\n'

	def writefile(fname, s, fieldDF):
	with open(fname, 'r') as f:
	data = f.read()

	l = data.split(s)

	"""
	Short script for displaying difference Image, from Simon's instructions

	The basic philosophy is to NOT just look at the files using ds9, but rather
	data products created by the DM stack in a repository which knows how to fetch
	these data products.
	"""
	import lsst.afw.display
	import lsst.daf.persistence as dp