def test_plot_prob_bounded_coal(self):
n = 1000
k = 4
t = 800
alltimes = []
# sample times
for i in xrange(5000):
while True:
times = [0]
for j in xrange(k, 1, -1):
times.append(times[-1] + coal.sample_coal(j, n))
if times[-1] >= t:
break
if times[-1] < t:
break
alltimes.append(times)
p = Gnuplot()
for i in range(1, 2):
x, y = distrib([q[i] - q[i-1] for q in alltimes], width=20)
p.plot(x, y, style="lines", xmax=500)
x = list(frange(0, 500, 10))
#for i in range(1, 2): #k):
y2 = [coal.prob_bounded_coal(j, k, n, t) for j in x]
p.plot(x, y2, style="lines", xmax=500)
fequals(y, y2, rel=.05, eabs=.01)
def plotdistrib(array, ndivs=None, low=None, width=None, **options):
"""Plot a distribution of array"""
from rasmus import util
d = util.distrib(array, ndivs, low, width)
p = options.setdefault("plot", Gnuplot())
options.setdefault("style", "boxes")
p.plot(util.histbins(d[0]), d[1], **options)
return p
def test_fast_sample_bounded_coal(self):
# sample bounded coal times efficiently
n = 1000
k = 5
t = 500
alltimes = []
# sample times
for i in xrange(5000):
while True:
times = [0]
for j in xrange(k, 1, -1):
times.append(times[-1] + coal.sample_coal(j, n))
if times[-1] >= t:
break
if times[-1] < t:
break
alltimes.append(times)
p = Gnuplot()
for i in range(1, k):
x, y = distrib([q[i] - q[i-1] for q in alltimes], width=30)
p.plot(x, y, style="lines", xmax=500)
p.enableOutput(True)
p.replot()
# sample times efficently
alltimes2 = []
for i in xrange(5000):
times = [0]
for j in xrange(k, 1, -1):
times.append(times[-1] +
coal.sample_bounded_coal(j, n, t-times[-1]))
alltimes2.append(times)
#p = Gnuplot()
for i in range(1, k):
x, y = distrib([q[i] - q[i-1] for q in alltimes2], width=30)
p.plot(x, y, style="lines", xmax=500)
p.enableOutput(True)
p.replot()
def plotdistribFit(func,
paramsInit,
data,
start,
end,
step,
plot=None,
**options):
xdata, ydata = util.distrib(data, low=start, width=step)
return plotfuncFit(func, paramsInit, xdata, ydata, start, end, step / 10,
plot, **options)
def fitDistrib(func, paramsInit, data, start, end, step, perc=1.0):
xdata, ydata = util.distrib(data, low=start, width=step)
ydata = [i / perc for i in ydata]
xdata = util.histbins(xdata)
params, resid = fitCurve(xdata, ydata, func, paramsInit)
return params, resid
def plotdistribFit(func, paramsInit, data, start, end, step, plot = None,
**options):
xdata, ydata = util.distrib(data, low=start, width=step)
return plotfuncFit(func, paramsInit, xdata, ydata, start, end, step/10, plot,
**options)
def fitDistrib(func, paramsInit, data, start, end, step, perc=1.0):
xdata, ydata = util.distrib(data, low=start, width=step)
ydata = [i / perc for i in ydata]
xdata = util.histbins(xdata)
params, resid = fitCurve(xdata, ydata, func, paramsInit)
return params, resid
