dzhou

#!/usr/bin/env python
#
# Kefei Dan Zhou
#

import math


# return a dict or a list of primes up to N
# create full prime sieve for N=10^6 in 1 sec

endolith

# https://github.com/matplotlib/matplotlib/issues/881
# Several of the ColorBrewer maps are "qualitative", meaning
# they are just a group of colors that can be used together 
# for categories of data.  So I remapped Accent to segments 
# instead of continuous:

# Actually, these should be used with ListedColormap, and 
# the number of colors should depend on the number of 
# categories in the data, with colors removed from the 
# list in a certain order?

dmeliza

# -*- coding: utf-8 -*-
# -*- mode: python -*-
# Adapted from mpl_toolkits.axes_grid1
# LICENSE: Python Software Foundation (http://docs.python.org/license.html)

from matplotlib.offsetbox import AnchoredOffsetbox
class AnchoredScaleBar(AnchoredOffsetbox):
    def __init__(self, transform, sizex=0, sizey=0, labelx=None, labely=None, loc=4,
                 pad=0.1, borderpad=0.1, sep=2, prop=None, barcolor="black", barwidth=None, 
                 **kwargs):

ctokheim

Cython

Cython has two major benefits:

1. Making python code faster, particularly things that can't be done in scipy/numpy
2. Wrapping/interfacing with C/C++ code

Cython gains most of it's benefit from statically typing arguments. However, statically typing is not required, in fact, regular python code is valid cython (but don't expect much of a speed up). By incrementally adding more type information, the code can speed up by several factors. This gist just provides a very basic usage of cython.

szapp

"""
Smarter bounds

This function emulates set_smart_bounds of matplotlib.spines.Spine,
but correctly sets the extent of the axes.
"""


def smarterbounds(ax, which='both'):
    """