def chi_square_fit(cdf, params, data, ndivs=20, minsamples=5, plot=False,
start=-util.INF, end=util.INF):
from rasmus import gnuplot
import scipy
import scipy.stats
# determine ndiv and binsize
binsize = len(data) / ndivs
if binsize < minsamples:
ndivs = len(data) / minsamples
binsize = len(data) / ndivs
data = sorted(data)
bins = [data[i:i+binsize] for i in xrange(0, len(data), binsize)]
obs = scipy.array(map(len, bins))
ind = util.find(lambda x: x[-1] >= start and x[0] <= end, bins)
obs = util.mget(obs, ind)
x = [bin[0] for bin in bins]
expected = [len(data) * cdf(x[1], params)]
expected.extend([len(data) *
(cdf(x[i+1], params) - cdf(x[i], params))
for i in range(1, len(x)-1)])
expected.append(len(data) * (1.0 - cdf(x[-1], params)))
expected = scipy.array(util.mget(expected, ind))
chi2, pval = scipy.stats.chisquare(obs, expected)
if plot:
p = gnuplot.plot(util.mget(x, ind), obs)
p.plot(util.mget(x, ind), expected)
return chi2, pval
def qqnorm(data, plot=None):
"""Quantile-quantile plot"""
from rasmus import gnuplot
data2 = sorted(data)
norm = [random.normalvariate(0, 1) for x in range(len(data2))]
norm.sort()
if plot == None:
return gnuplot.plot(data2, norm)
else:
plot.plot(data2, norm)
return plot
def test_cdf_coal_bounded(self):
n = 1000
k = 4
t = 500
step = .1
x = list(frange(0, 500, step))
y = [coal.prob_bounded_coal(i, k, n, t) * step for i in x]
y2 = cumsum(y)
y3 = [coal.cdf_bounded_coal(i, k, n, t) for i in x]
p = plot(x, y2, style="lines")
p.plot(x, y3, style="lines")
p.plot([0, 500], [1, 1], style="lines")
fequals(y2, y3, eabs=.01)
def chi_square_fit(cdf,
params,
data,
ndivs=20,
minsamples=5,
plot=False,
start=-util.INF,
end=util.INF):
from rasmus import gnuplot
import scipy
import scipy.stats
# determine ndiv and binsize
binsize = len(data) / ndivs
if binsize < minsamples:
ndivs = len(data) / minsamples
binsize = len(data) / ndivs
data = sorted(data)
bins = [data[i:i + binsize] for i in xrange(0, len(data), binsize)]
obs = scipy.array(map(len, bins))
ind = util.find(lambda x: x[-1] >= start and x[0] <= end, bins)
obs = util.mget(obs, ind)
x = [bin[0] for bin in bins]
expected = [len(data) * cdf(x[1], params)]
expected.extend([
len(data) * (cdf(x[i + 1], params) - cdf(x[i], params))
for i in range(1,
len(x) - 1)
])
expected.append(len(data) * (1.0 - cdf(x[-1], params)))
expected = scipy.array(util.mget(expected, ind))
chi2, pval = scipy.stats.chisquare(obs, expected)
if plot:
p = gnuplot.plot(util.mget(x, ind), obs)
p.plot(util.mget(x, ind), expected)
return chi2, pval
p.plot(util.cget(win, 3)) #, style="lines")
for i, y in enumerate(vals):
p.plot([i, len(vals)], [y, y], style="lines")
p.enableOutput(True)
p.replot()
'''
def mean2(v):
if len(v) == 0:
return 0.0
else:
return mean(v)
x, y = zip(* iter_window_step(vals, 5, 1, len))
gnuplot.plot(x, y)
#=============================================================================
# OLD CODE
'''
def smooth_old(x, radius):
"""
return an averaging of vals using a radius
Note: not implemented as fast as possible
runtime: O(len(vals) * radius)
"""
p.enableOutput(False)
p.plot(util.cget(win, 3)) #, style="lines")
for i, y in enumerate(vals):
p.plot([i, len(vals)], [y, y], style="lines")
p.enableOutput(True)
p.replot()
'''
def mean2(v):
if len(v) == 0:
return 0.0
else:
return mean(v)
x, y = zip(*iter_window_step(vals, 5, 1, len))
gnuplot.plot(x, y)
#=============================================================================
# OLD CODE
'''
def smooth_old(x, radius):
"""
return an averaging of vals using a radius
Note: not implemented as fast as possible
runtime: O(len(vals) * radius)
"""
vlen = len(x)
# simple case
