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Copy pathhelper.py
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128 lines (108 loc) · 3.32 KB
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'''
Helper module for the plot scripts.
'''
import re
import itertools
import matplotlib as m
import os
if os.uname()[0] == "Darwin":
m.use("MacOSX")
else:
m.use("Agg")
import matplotlib.pyplot as plt
import argparse
import math
import termcolor as T
def read_list(fname, delim=','):
lines = open(fname).xreadlines()
ret = []
for l in lines:
ls = l.strip().split(delim)
ls = map(lambda e: '0' if e.strip() == '' or e.strip() == 'ms' or e.strip() == 's' else e, ls)
ret.append(ls)
return ret
def ewma(alpha, values):
if alpha == 0:
return values
ret = []
prev = 0
for v in values:
prev = alpha * prev + (1 - alpha) * v
ret.append(prev)
return ret
def col(n, obj = None, clean = lambda e: e):
"""A versatile column extractor.
col(n, [1,2,3]) => returns the nth value in the list
col(n, [ [...], [...], ... ] => returns the nth column in this matrix
col('blah', { ... }) => returns the blah-th value in the dict
col(n) => partial function, useful in maps
"""
if obj == None:
def f(item):
return clean(item[n])
return f
if type(obj) == type([]):
if len(obj) > 0 and (type(obj[0]) == type([]) or type(obj[0]) == type({})):
return map(col(n, clean=clean), obj)
if type(obj) == type([]) or type(obj) == type({}):
try:
return clean(obj[n])
except:
print T.colored('col(...): column "%s" not found!' % (n), 'red')
return None
# We wouldn't know what to do here, so just return None
print T.colored('col(...): column "%s" not found!' % (n), 'red')
return None
def transpose(l):
return zip(*l)
def avg(lst):
return sum(map(float, lst)) / len(lst)
def stdev(lst):
mean = avg(lst)
var = avg(map(lambda e: (e - mean)**2, lst))
return math.sqrt(var)
def xaxis(values, limit):
l = len(values)
return zip(*map(lambda (x,y): (x*1.0*limit/l, y), enumerate(values)))
def grouper(n, iterable, fillvalue=None):
"grouper(3, 'ABCDEFG', 'x') --> ABC DEF Gxx"
args = [iter(iterable)] * n
return itertools.izip_longest(fillvalue=fillvalue, *args)
def cdf(values):
values.sort()
prob = 0
l = len(values)
x, y = [], []
for v in values:
prob += 1.0 / l
x.append(v)
y.append(prob)
return (x, y)
def parse_cpu_usage(fname, nprocessors=8):
"""Returns (user,system,nice,iowait,hirq,sirq,steal) tuples
aggregated over all processors. DOES NOT RETURN IDLE times."""
data = grouper(nprocessors, open(fname).readlines())
"""Typical line looks like:
Cpu0 : 0.0%us, 1.0%sy, 0.0%ni, 97.0%id, 0.0%wa, 0.0%hi, 2.0%si, 0.0%st
"""
ret = []
for collection in data:
total = [0]*8
for cpu in collection:
usages = cpu.split(':')[1]
usages = map(lambda e: e.split('%')[0],
usages.split(','))
for i in xrange(len(usages)):
total[i] += float(usages[i])
total = map(lambda t: t/nprocessors, total)
# Skip idle time
ret.append(total[0:3] + total[4:])
return ret
def pc95(lst):
l = len(lst)
return sorted(lst)[ int(0.95 * l) ]
def pc99(lst):
l = len(lst)
return sorted(lst)[ int(0.99 * l) ]
def coeff_variation(lst):
return stdev(lst) / avg(lst)