calvingiles

def specialist_function_factory(func, *s_args, **s_kwargs):
    def specialist_func(*args, **kwargs):
        all_args = s_args + args
        all_kwargs = dict(list(s_kwargs.items()) + list(kwargs.items()))
        return func(*all_args, **all_kwargs)
    return specialist_func

calvingiles

import math


def num_fmt(num):
    i_offset = 24 # change this if you extend the symbols!!!
    prec = 3
    fmt = '.{p}g'.format(p=prec)
    symbols = ['Y', 'Z', 'E', 'P', 'T', 'G', 'M', 'k', '', 'm', 'u', 'n']
    
    e = math.log10(abs(num))

calvingiles

"""
>>> I = ai(start=0, step=1)
>>> I()
0
>>> I()
1
>>> J = ai(start=0, step=1)
>>> J()
0
>>> I()

calvingiles

"""
>>> I = ai(start=0, step=1)
>>> I()
0
>>> I()
1
>>> J = ai(start=0, step=1)
>>> J()
0
>>> I()

calvingiles

# Auto-incremented number, starting at 0. 
# Relies on function defualts being evaluated only once and lists being mutable.
def ipp(__i=[-1]):
    '''This is a dirty hack'''
    __i[0] += 1
    return __i[0]

calvingiles

def GCD(a, b):
    if b:
        return GCD(b, a % b)
    return a

calvingiles

library(stats)
library(ggplot2)
set.seed(1)

# for our first example, let's create some synthetic easy-to-cluster data
d <- data.frame()
d <- rbind(d, data.frame(x=1 + rnorm(20, 0, 0.1), y=1 + rnorm(20, 0, 0.1), label=as.factor(rep(1, each=20))))
d <- rbind(d, data.frame(x=1 + rnorm(20, 0, 0.1), y=3 + rnorm(20, 0, 0.1), label=as.factor(rep(2, each=20))))
d <- rbind(d, data.frame(x=3 + rnorm(20, 0, 0.1), y=1 + rnorm(20, 0, 0.1), label=as.factor(rep(3, each=20))))
d <- rbind(d, data.frame(x=3 + rnorm(20, 0, 0.1), y=3 + rnorm(20, 0, 0.1), label=as.factor(rep(4, each=20))))

calvingiles

class SlicerClass(object):
    '''
    slicerClass allows the creation of a slicer object.
    A slicer, when indexed with square brackets, will return a slice object.
    This allows a slice to be created and neatly passed as an argument.
    '''
    def __getitem__(self,val):
        return val
    def __len__ (self):
        return 0