Thomas Ogden tpogden

For ETS's SKLL project, we found out the hard way that Travis-CI's support for numpy and scipy is pretty abysmal. There are pre-installed versions of numpy for some versions of Python, but those are seriously out of date, and scipy is not there are at all. The two most popular approaches for working around this are to (1) build everything from scratch, or (2) use apt-get to install more recent (but still out of date) versions of numpy and scipy. Both of these approaches lead to longer build times, and with the second approach, you still don't have the most recent versions of anything. To circumvent these issues, we've switched to using Miniconda (Anaconda's lightweight cousin) to install everything.

A template for installing a simple Python package that relies on numpy and scipy using Miniconda is provided below. Since it's a common s

This re-styles your sublime text sidebar to be dark, it fits default Monokai theme.

Save the Default.sublime-theme file into packages/Theme - Default, make a backup of your original if you want to be able to go back easily.

Based on:

	#!/usr/bin/env python
	"""
	Robust B-Spline regression with scikit-learn
	"""

	import matplotlib.pyplot as plt
	import numpy as np
	import scipy.interpolate as si
	from sklearn.base import TransformerMixin
	from sklearn.pipeline import make_pipeline

	#!/usr/bin/env python
	"""
	Robust non-linear feature estimation with scikit-learn applied to
	the Fourier Transform.
	"""

	from matplotlib import pyplot as plt
	import numpy as np
	from sklearn.base import TransformerMixin
	from sklearn.pipeline import make_pipeline