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from __future__ import with_statement
import numpy as np
from .. import monitor
from ..plotutil import coordinated_colors
from .plot_view import PlotView
class ConvergenceMonitor(monitor.Monitor):
"""
Gather statistics about the best, worst, median and +/- 1 interquartile
range. This will be the input for the convergence plot.
"""
def __init__(self):
self.pop = []
def config_history(self, history):
history.requires(population_values=1, value=1)
def __call__(self, history):
best = history.value[0]
try:
pop = history.population_values[0]
n = len(pop)
p = np.sort(pop)
QI,Qmid, = int(0.2*n),int(0.5*n)
self.pop.append((best, p[0],p[QI],p[Qmid],p[-1-QI],p[-1]))
except AttributeError:
self.pop.append((best, ))
def progress(self):
if not self.pop:
return dict(pop=np.empty((0,1),'d'))
else:
return dict(pop=np.array(self.pop))
class ConvergenceView(PlotView):
title = "Convergence"
def plot(self):
if not self.plot_state: return
pop,best = self.plot_state
with self.pylab_interface as pylab:
pylab.clf()
ni,npop = pop.shape
iternum = np.arange(1,ni+1)
tail = int(0.25*ni)
c = coordinated_colors(base=(0.4,0.8,0.2))
if npop==5:
pylab.fill_between(iternum[tail:], pop[tail:,1], pop[tail:,3],
color=c['light'], label='_nolegend_')
pylab.plot(iternum[tail:],pop[tail:,2],
label="80% range", color=c['base'])
pylab.plot(iternum[tail:],pop[tail:,0],
label="_nolegend_", color=c['base'])
pylab.plot(iternum[tail:], best[tail:], label="best",
color=c['dark'])
pylab.xlabel('iteration number')
pylab.ylabel('chisq')
pylab.legend()
#pylab.gca().set_yscale('log')
pylab.draw()
def update(self, best, pop):
self.plot_state = pop, best
self.plot()
def OnFitProgress(self, event):
if event.problem != self.model: return
pop = 2*event.pop/self.model.dof
self.update(pop[:, 0], pop[:, 1:])
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