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# Copyright (c) 2005-2010 Zooko Wilcox-O'Hearn
# This file is part of pyutil; see README.rst for licensing terms.
"""
A few commonly needed functions.
"""
import math
def div_ceil(n, d):
"""
The smallest integer k such that k*d >= n.
"""
return (n/d) + (n%d != 0)
def next_multiple(n, k):
"""
The smallest multiple of k which is >= n. Note that if n is 0 then the
answer is 0.
"""
return div_ceil(n, k) * k
def pad_size(n, k):
"""
The smallest number that has to be added to n to equal a multiple of k.
"""
if n%k:
return k - n%k
else:
return 0
def is_power_of_k(n, k):
return k**int(math.log(n, k) + 0.5) == n
def next_power_of_k(n, k):
p = 1
while p < n:
p *= k
return p
def ave(l):
return sum(l) / len(l)
def log_ceil(n, b):
"""
The smallest integer k such that b^k >= n.
log_ceil(n, 2) is the number of bits needed to store any of n values, e.g.
the number of bits needed to store any of 128 possible values is 7.
"""
p = 1
k = 0
while p < n:
p *= b
k += 1
return k
def log_floor(n, b):
"""
The largest integer k such that b^k <= n.
"""
p = 1
k = 0
while p <= n:
p *= b
k += 1
return k - 1
def linear_fit_slope(ps):
"""
Single-independent-variable linear regression -- least squares method.
At least, I *think* this function computes that answer. I no longer
remember where I learned this trick and at the moment I can't prove to
myself that this is correct.
@param ps a sequence of tuples of (x, y)
"""
avex = ave([x for (x, y) in ps])
avey = ave([y for (x, y) in ps])
sxy = sum([ (x - avex) * (y - avey) for (x, y) in ps ])
sxx = sum([ (x - avex) ** 2 for (x, y) in ps ])
if sxx == 0:
return None
return sxy / sxx
def permute(l):
"""
Return all possible permutations of l.
@type l: sequence
@rtype a set of sequences
"""
if len(l) == 1:
return [l,]
res = []
for i in range(len(l)):
l2 = list(l[:])
x = l2.pop(i)
for l3 in permute(l2):
l3.append(x)
res.append(l3)
return res