def launchML2DPlots(protML, selectedPlots):
''' Launch some plot for an ML2D protocol run '''
#import matplotlib
import numpy as np
from protlib_gui_figure import XmippPlotter
protML._plot_count = 0
lastIter = lastIteration(protML)
if lastIter == 0:
return
refs = protML.getFilename('iter_refs', iter=lastIter)
# if not exists(refs):
# return
# blocks = getBlocksInMetaDataFile(refs)
# lastBlock = blocks[-1]
def doPlot(plotName):
return plotName in selectedPlots
# Remove 'mirror' from list if DoMirror is false
if doPlot('DoShowMirror') and not protML.DoMirror:
selectedPlots.remove('DoShowMirror')
n = len(selectedPlots)
if n == 0:
showWarning("ML2D plots", "Nothing to plot", protML.master)
return
elif n == 1:
gridsize = [1, 1]
elif n == 2:
gridsize = [2, 1]
else:
gridsize = [2, 2]
xplotter = XmippPlotter(*gridsize)
# Create data to plot
iters = range(1, lastIter+1)
ll = []
pmax = []
for iter in iters:
logs = protML.getFilename('iter_logs', iter=iter)
md = MetaData(logs)
id = md.firstObject()
ll.append(md.getValue(MDL_LL, id))
pmax.append(md.getValue(MDL_PMAX, id))
if doPlot('DoShowLL'):
a = xplotter.createSubPlot('Log-likelihood (should increase)', 'iterations', 'LL', yformat=True)
a.plot(iters, ll)
#Create plot of mirror for last iteration
if doPlot('DoShowMirror'):
from numpy import arange
from matplotlib.ticker import FormatStrFormatter
md = MetaData(refs)
mirrors = [md.getValue(MDL_MIRRORFRAC, id) for id in md]
nrefs = len(mirrors)
ind = arange(1, nrefs + 1)
width = 0.85
a = xplotter.createSubPlot('Mirror fractions on last iteration', 'references', 'mirror fraction')
a.set_xticks(ind + 0.45)
a.xaxis.set_major_formatter(FormatStrFormatter('%1.0f'))
a.bar(ind, mirrors, width, color='b')
a.set_ylim([0, 1.])
a.set_xlim([0.8, nrefs + 1])
if doPlot('DoShowPmax'):
a = xplotter.createSubPlot('Probabilities distribution', 'iterations', 'Pmax/Psum')
a.plot(iters, pmax, color='green')
if doPlot('DoShowSignalChange'):
md = MetaData()
for iter in iters:
fn = protML.getFilename('iter_refs', iter=iter)
md2 = MetaData(fn)
md2.fillConstant(MDL_ITER, str(iter))
md.unionAll(md2)
# 'iter(.*[1-9].*)@2D/ML2D/run_004/ml2d_iter_refs.xmd')
#a = plt.subplot(gs[1, 1])
#print "md:", md
md2 = MetaData()
md2.aggregate(md, AGGR_MAX, MDL_ITER, MDL_SIGNALCHANGE, MDL_MAX)
signal_change = [md2.getValue(MDL_MAX, id) for id in md2]
xplotter.createSubPlot('Maximum signal change', 'iterations', 'signal change')
xplotter.plot(iters, signal_change, color='green')
return xplotter
