def filterPareto(scores, dds, sols, verbose=0):
""" From a list of designs select only the pareto optimal designs """
pp = oahelper.createPareto(dds, verbose=0)
paretoidx = np.array(pp.allindices())
pscores = scores[paretoidx]
pdds = dds[paretoidx]
psols = [sols[i] for i in paretoidx]
return pscores, pdds, psols
def filterPareto(scores, dds, arrays, verbose=0):
""" From a list of designs select only the pareto optimal designs
Args:
scores (array): array of scores
dds (array): array with D-efficiency values
arrays (list): list of designs
Returns:
pareto_scores (list) : list of scores of pareto optimal designs
pareto_efficiencies (list): list of D-efficiency values of pareto optimal designs
pareto_designs (list) : list of selected designs
"""
pp = oahelper.createPareto(dds, verbose=0)
paretoidx = np.array(pp.allindices())
pareto_scores = scores[paretoidx]
pareto_efficiencies = dds[paretoidx]
pareto_designs = [arrays[i] for i in paretoidx]
return pareto_scores, pareto_efficiencies, pareto_designs
def filterPareto(scores, dds, arrays, verbose=0):
""" From a list of designs select only the pareto optimal designs
Args:
scores (array): array of scores
dds (array): array with D-efficiency values
arrays (list): list of designs
Returns:
pareto_scores (list) : list of scores of pareto optimal designs
pareto_efficiencies (list): list of D-efficiency values of pareto optimal designs
pareto_designs (list) : list of selected designs
"""
pp = oahelper.createPareto(dds, verbose=0)
paretoidx = np.array(pp.allindices())
pareto_scores = scores[paretoidx]
pareto_efficiencies = dds[paretoidx]
pareto_designs = [arrays[i] for i in paretoidx]
return pareto_scores, pareto_efficiencies, pareto_designs
def generateDscatter(dds,
si=0,
fi=1,
lbls=None,
ndata=3,
nofig=False,
fig=20,
scatterarea=80):
""" Generate scatter plot for D and Ds efficiencies """
data = dds.T
pp = oahelper.createPareto(dds)
paretoidx = np.array(pp.allindices())
nn = dds.shape[0]
area = scatterarea * np.ones(nn, ) / 2
area[np.array(pp.allindices())] = scatterarea
alpha = 1.0
if dds.shape[1] > ndata:
colors = dds[:, ndata]
else:
colors = np.zeros((nn, 1))
idx = np.unique(colors).astype(int)
try:
mycmap = brewer2mpl.get_map('Set1', 'qualitative', idx.size).mpl_colors
except:
mycmap = [matplotlib.cm.jet(ii) for ii in range(4)]
pass
# For remaining spines, thin out their line and change the black to a
# slightly off-black dark grey
almost_black = '#202020'
figh = plt.figure(fig) # ,facecolor='red')
plt.clf()
figh.set_facecolor('w')
ax = plt.subplot(111)
nonparetoidx = np.setdiff1d(range(nn), paretoidx)
ax.scatter(data[fi, nonparetoidx],
data[si, nonparetoidx],
s=.33 * scatterarea,
c=(.5, .5, .5),
linewidths=0,
alpha=alpha,
label='Non-pareto design')
for jj, ii in enumerate(idx):
gidx = (colors == ii).nonzero()[0]
gp = np.intersect1d(paretoidx, gidx)
color = mycmap[jj]
cc = [color] * len(gp)
print('index %d: %d points' % (ii, gidx.size))
ax.scatter(data[fi, gp],
data[si, gp],
s=scatterarea,
c=cc,
linewidths=0,
alpha=alpha,
label=lbls[jj]) # , zorder=4)
plt.draw()
if data[si, :].std() < 1e-3:
y_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
ax.yaxis.set_major_formatter(y_formatter)
if 0:
for xi, al in enumerate(sols):
D, Ds, D1 = al.Defficiencies()
print('D1 %f Ds %f D %f' % (D1, Ds, D))
tmp = plt.scatter(Ds, D, s=60, color='r')
if xi == 0:
tmp = plt.scatter(Ds, D, s=60, color='r', label='Strength 3')
plt.draw()
xlabelhandle = plt.xlabel('$D_s$-efficiency', fontsize=16)
plt.ylabel('D-efficiency', fontsize=16)
try:
oahelper.setWindowRectangle(10, 10, 860, 600)
except Exception as e:
print('generateDscatter: setWindowRectangle failed')
pass
plt.axis('image')
pltlegend = ax.legend(loc=3, scatterpoints=1) # , fontcolor=almost_black)
if not nofig:
plt.show()
ax.grid(b=True, which='both', color='0.85', linestyle='-')
ax.set_axisbelow(True)
if not nofig:
plt.draw()
plt.pause(1e-3)
hh = dict({'ax': ax, 'xlabelhandle': xlabelhandle, 'pltlegend': pltlegend})
return hh
def generateDpage(outputdir,
arrayclass,
dds,
allarrays,
fig=20,
optimfunc=[1, 0, 0],
nofig=False,
urlprefix='',
makeheader=True,
verbose=1,
lbls=None):
""" Helper function to generate web page with D-optimal design results """
if verbose:
print('generateDpage: dds %s' % str(dds.shape))
pp = oahelper.createPareto(dds)
narrays = dds.shape[0]
npareto = pp.number()
if verbose:
print('generateDpage: narrays %d' % narrays)
xstr = oahelper.series2htmlstr(arrayclass, case=1)
xstrplain = oahelper.series2htmlstr(arrayclass, html=0, case=1)
if verbose:
print('generateDpage: selectParetoArrays ')
paretoarrays = oahelper.selectParetoArrays(allarrays, pp)
at = array2Dtable(paretoarrays, verbose=1)
if verbose:
print('generateDpage: write file with Pareto arrays')
pfile0 = 'paretoarrays.oa'
pfile = os.path.join(outputdir, pfile0)
oalib.writearrayfile(pfile, paretoarrays)
istrlnk = markup.oneliner.a('paretoarrays.oa', href=urlprefix + pfile0)
if lbls is None:
lbls = ['Optimization of $D$']
hh = generateDscatter(dds, lbls=lbls, fig=fig, nofig=nofig)
oahelper.niceplot(hh.get('ax', None), despine=True, legend=hh['pltlegend'])
scatterfile = os.path.join(outputdir, 'scatterplot.png')
if verbose:
print('generateDpage: writen scatterplot to %s' % scatterfile)
plt.savefig(scatterfile, bbox_inches='tight', pad_inches=0.25, dpi=160)
#%% Create page
page = markup.page()
if makeheader:
page.init(
title="Class %s" % xstrplain,
css=('../oastyle.css'),
lang='en',
htmlattrs=dict({
'xmlns': 'http://www.w3.org/1999/xhtml',
'xml:lang': 'en'
}),
header="<!-- Start of page -->",
bodyattrs=dict({'style': 'padding-left: 3px;'}),
# doctype=markup.doctype.strict,
doctype=
'<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">',
metainfo=({
'text/html': 'charset=utf-8',
'keywords': 'orthogonal arrays designs',
'robots': 'index, follow',
'description': 'Even-Odd arrays'
}),
footer="<!-- End of page -->")
page.h1('Results for array class %s ' % xstr)
# mathjax is not updated properly...
ss = 'The Pareto optimaly was calculated according to the statistics \(D\), \(D1\) and \(Ds\).'
ss = 'The Pareto optimaly was calculated according to the statistics D, D<sub>1</sub> and D<sub>s</sub>.'
if npareto == 1:
page.p('Generated %d arrays, %d is Pareto optimal. %s' %
(narrays, npareto, ss))
else:
page.p('Generated %d arrays, %d are Pareto optimal. %s' %
(narrays, npareto, ss))
if narrays > 0:
scores = calcScore(dds, optimfunc)
tmp, dd, sols = selectDn(scores, dds, allarrays, nout=1)
A = sols[0]
bestdesignfile = os.path.join(outputdir, 'best-design.oa')
oalib.writearrayfile(bestdesignfile, A)
page.h2('Best design')
page.p('The best design: %s.' % e.a(
'best-design.oa', href=os.path.join(urlprefix, 'best-design.oa')))
page.p()
dd = dd[0]
page.span('D-efficiency: %.4f, ' % A.Defficiency()) # page.br()
page.span('D<sub>s</sub>-efficiency: %.4f, ' % dd[1]) # page.br()
page.span('D<sub>1</sub>-efficiency: %.4f' % dd[2])
page.br()
page.span('A-efficiency: %.3f' % A.Aefficiency())
page.br()
gwlp = A.GWLP()
# gwlp=','.join(['%.3f' % xq for xq in gwlp])
gwlp = oahelper.gwlp2str(gwlp, jstr=', ')
page.span('Generalized wordlength pattern: %s' % gwlp)
page.br()
# page.p('D-efficiency: %.3f' % A.Defficiency() )
pec = oalib.PECsequence(A)
pec = ','.join(['%.3f' % xq for xq in pec])
page.span('PEC-sequence: %s' % pec)
page.br()
page.h2('Table of Pareto optimal arrays ')
page.span(str(at))
page.p('All Pareto optimal arrays: %s' % istrlnk)
page.img(
src=urlprefix + 'scatterplot.png',
style=
"margin: 10px; width:95%; min-width: 300px; max-width:1100px; height: auto; "
)
citationstr = markup.oneliner.a(
'Complete Enumeration of Pure-Level and Mixed-Level Orthogonal Arrays',
href='http://dx.doi.org/10.1002/jcd.20236')
page.br(clear='both')
page.p(
'Citation notice: if you make use of the results on this page, please cite the following paper:'
)
page.p(
'%s, Journal of Combinatorial Designs, Volume 18, Issue 2, pages 123-140, 2010.'
% citationstr)
page.p('Generated with oapackage %s, date %s.' %
(oalib.version(), oahelper.timeString()))
outfile = os.path.join(outputdir, 'Dresults.html')
fid = open(outfile, 'wt')
fid.write(str(page))
fid.close()
print('written to file %s' % outfile)
return outfile
def generateDscatter(dds, second_index=0, first_index=1, lbls=None, ndata=3, nofig=False, fig=20,
scatterarea=80, verbose=0, setWindowRectangle=False):
""" Generate scatter plot for D and Ds efficiencies
Args:
dds (array): array with D-efficiencies
Returns:
dict: contains handles to plotting elements
"""
if matplotlib is None:
raise MissingMatplotLibException
data = dds.T
pp = oahelper.createPareto(dds)
paretoidx = np.array(pp.allindices())
nn = dds.shape[0]
area = scatterarea * np.ones(nn,) / 2
area[np.array(pp.allindices())] = scatterarea
alpha = 1.0
if dds.shape[1] > ndata:
colors = dds[:, ndata]
else:
colors = np.zeros((nn, 1))
idx = np.unique(colors).astype(int)
if verbose:
print('generateDscatter: unique colors: %s' % (idx, ))
ncolors = idx.size
try:
import brewer2mpl
ncolors = max(ncolors, 4)
mycmap = brewer2mpl.get_map('Set1', 'qualitative', ncolors).mpl_colors
except BaseException:
mycmap = [matplotlib.cm.jet(ii) for ii in np.linspace(0, 1, ncolors)]
nonparetoidx = np.setdiff1d(range(nn), paretoidx)
if lbls is None:
lbls = ['%d' % i for i in range(len(idx))]
if fig is not None:
figh = plt.figure(fig)
plt.clf()
figh.set_facecolor('w')
ax = plt.subplot(111)
ax.scatter(data[first_index, nonparetoidx], data[second_index, nonparetoidx], s=.33 * scatterarea,
c=(.5, .5, .5), linewidths=0, alpha=alpha, label='Non-pareto design')
for jj, ii in enumerate(idx):
gidx = (colors == ii).nonzero()[0]
gp = np.intersect1d(paretoidx, gidx)
color = mycmap[jj]
cc = [color] * len(gp)
if verbose:
print('index %d: %d points' % (ii, gidx.size))
ax.scatter(data[first_index, gp], data[second_index, gp], s=scatterarea, c=cc,
linewidths=0, alpha=alpha, label=lbls[jj])
plt.draw()
if data[second_index, :].std() < 1e-3:
y_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
ax.yaxis.set_major_formatter(y_formatter)
xlabelhandle = plt.xlabel('$D_s$-efficiency', fontsize=16)
plt.ylabel('D-efficiency', fontsize=16)
if setWindowRectangle:
try:
oahelper.setWindowRectangle(10, 10, 860, 600)
except Exception as ex:
print('generateDscatter: setWindowRectangle failed')
logging.exception(ex)
plt.axis('image')
pltlegend = ax.legend(loc=3, scatterpoints=1) # , fontcolor=almost_black)
if not nofig:
plt.show()
ax.grid(b=True, which='both', color='0.85', linestyle='-')
ax.set_axisbelow(True)
if nofig:
plt.close(figh.number)
else:
plt.draw()
plt.pause(1e-3)
else:
ax = None
xlabelhandle = None
pltlegend = None
hh = dict({'ax': ax, 'xlabelhandle': xlabelhandle, 'pltlegend': pltlegend})
return hh
def generateDpage(outputdir, arrayclass, dds, allarrays, fig=20, optimfunc=[1, 0, 0],
nofig=False, urlprefix='', makeheader=True, verbose=1, lbls=None):
""" Helper function to generate web page with D-optimal design results """
if verbose:
print('generateDpage: dds %s' % str(dds.shape))
pp = oahelper.createPareto(dds)
narrays = dds.shape[0]
npareto = pp.number()
if verbose:
print('generateDpage: narrays %d' % narrays)
xstr = oahelper.series2htmlstr(arrayclass, case=1)
xstrplain = oahelper.series2htmlstr(arrayclass, html=0, case=1)
if verbose:
print('generateDpage: selectParetoArrays ')
paretoarrays = oahelper.selectParetoArrays(allarrays, pp)
at = array2Dtable(paretoarrays, verbose=1)
if verbose:
print('generateDpage: write file with Pareto arrays')
pfile0 = 'paretoarrays.oa'
pfile = os.path.join(outputdir, pfile0)
oalib.writearrayfile(pfile, paretoarrays)
istrlnk = markup.oneliner.a('paretoarrays.oa', href=urlprefix + pfile0)
if lbls is None:
lbls = ['Optimization of $D$']
if fig is not None:
hh = generateDscatter(dds, lbls=lbls, fig=fig, nofig=nofig)
oahelper.niceplot(hh.get('ax', None), despine=True, legend=hh['pltlegend'])
scatterfile = os.path.join(outputdir, 'scatterplot.png')
if verbose:
print('generateDpage: writen scatterplot to %s' % scatterfile)
plt.savefig(scatterfile, bbox_inches='tight', pad_inches=0.25, dpi=160)
page = markup.page()
if makeheader:
page.init(title="Class %s" % xstrplain,
css=('../oastyle.css'),
lang='en', htmlattrs=dict({'xmlns': 'http://www.w3.org/1999/xhtml', 'xml:lang': 'en'}),
header="<!-- Start of page -->",
bodyattrs=dict({'style': 'padding-left: 3px;'}),
doctype='<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">',
metainfo=({'text/html': 'charset=utf-8', 'keywords': 'orthogonal arrays designs',
'robots': 'index, follow', 'description': 'Even-Odd arrays'}),
footer="<!-- End of page -->")
page.h1('Results for array class %s ' % xstr)
ss = r'The Pareto optimaly was calculated according to the statistics D, D<sub>1</sub> and D<sub>s</sub>.'
if npareto == 1:
page.p('Generated %d arrays, %d is Pareto optimal. %s' %
(narrays, npareto, ss))
else:
page.p('Generated %d arrays, %d are Pareto optimal. %s' %
(narrays, npareto, ss))
if narrays > 0:
scores = calcScore(dds, optimfunc)
_, dd, sols = selectDn(scores, dds, allarrays, nout=1)
A = sols[0]
bestdesignfile = os.path.join(outputdir, 'best-design.oa')
oalib.writearrayfile(bestdesignfile, A)
page.h2('Best design')
page.p('The best design: %s.' %
e.a('best-design.oa', href=os.path.join(urlprefix, 'best-design.oa')))
page.p()
dd = dd[0]
page.span('D-efficiency: %.4f, ' % A.Defficiency()) # page.br()
page.span('D<sub>s</sub>-efficiency: %.4f, ' % dd[1]) # page.br()
page.span('D<sub>1</sub>-efficiency: %.4f' % dd[2])
page.br()
page.span('A-efficiency: %.3f' % A.Aefficiency())
page.br()
gwlp = A.GWLP()
# gwlp=','.join(['%.3f' % xq for xq in gwlp])
gwlp = oahelper.gwlp2str(gwlp, jstr=', ')
page.span('Generalized wordlength pattern: %s' % gwlp)
page.br()
# page.p('D-efficiency: %.3f' % A.Defficiency() )
pec = oalib.PECsequence(A)
pec = ','.join(['%.3f' % xq for xq in pec])
page.span('PEC-sequence: %s' % pec)
page.br()
page.h2('Table of Pareto optimal arrays ')
page.span(str(at))
page.p('All Pareto optimal arrays: %s' % istrlnk)
page.img(src=urlprefix + 'scatterplot.png',
style="margin: 10px; width:95%; min-width: 300px; max-width:1100px; height: auto; ")
citationstr = markup.oneliner.a(
'Complete Enumeration of Pure-Level and Mixed-Level Orthogonal Arrays', href='https://doi.org/10.1002/jcd.20236')
page.br(clear='both')
page.p(
'Citation notice: if you make use of the results on this page, please cite the following paper:')
page.p('%s, Journal of Combinatorial Designs, Volume 18, Issue 2, pages 123-140, 2010.' %
citationstr)
page.p('Generated with oapackage %s, date %s.' %
(oalib.version(), oahelper.timeString()))
outfile = os.path.join(outputdir, 'Dresults.html')
fid = open(outfile, 'wt')
fid.write(str(page))
fid.close()
print('written to file %s' % outfile)
return outfile
def generateDscatter(dds,
second_index=0,
first_index=1,
lbls=None,
ndata=3,
nofig=False,
fig=20,
scatterarea=80,
verbose=0,
setWindowRectangle=False):
""" Generate scatter plot for D and Ds efficiencies
Args:
dds (array): array with D-efficiencies
Returns:
dict: contains handles to plotting elements
"""
if matplotlib is None:
raise MissingMatplotLibException
data = dds.T
pp = oahelper.createPareto(dds)
paretoidx = np.array(pp.allindices())
nn = dds.shape[0]
area = scatterarea * np.ones(nn, ) / 2
area[np.array(pp.allindices())] = scatterarea
alpha = 1.0
if dds.shape[1] > ndata:
colors = dds[:, ndata]
else:
colors = np.zeros((nn, 1))
idx = np.unique(colors).astype(int)
if verbose:
print('generateDscatter: unique colors: %s' % (idx, ))
ncolors = idx.size
try:
import brewer2mpl
ncolors = max(ncolors, 4)
mycmap = brewer2mpl.get_map('Set1', 'qualitative', ncolors).mpl_colors
except BaseException:
mycmap = [matplotlib.cm.jet(ii) for ii in np.linspace(0, 1, ncolors)]
nonparetoidx = np.setdiff1d(range(nn), paretoidx)
if lbls is None:
lbls = ['%d' % i for i in range(len(idx))]
if fig is not None:
figh = plt.figure(fig)
plt.clf()
figh.set_facecolor('w')
ax = plt.subplot(111)
ax.scatter(data[first_index, nonparetoidx],
data[second_index, nonparetoidx],
s=.33 * scatterarea,
c=[(.5, .5, .5)],
linewidths=0,
alpha=alpha,
label='Non-pareto design')
for jj, ii in enumerate(idx):
gidx = (colors == ii).nonzero()[0]
gp = np.intersect1d(paretoidx, gidx)
color = mycmap[jj]
cc = [color] * len(gp)
if verbose:
print('index %d: %d points' % (ii, gidx.size))
ax.scatter(data[first_index, gp],
data[second_index, gp],
s=scatterarea,
c=cc,
linewidths=0,
alpha=alpha,
label=lbls[jj])
plt.draw()
if data[second_index, :].std() < 1e-3:
y_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
ax.yaxis.set_major_formatter(y_formatter)
xlabelhandle = plt.xlabel('$D_s$-efficiency', fontsize=16)
plt.ylabel('D-efficiency', fontsize=16)
if setWindowRectangle:
try:
oahelper.setWindowRectangle(10, 10, 860, 600)
except Exception as ex:
print('generateDscatter: setWindowRectangle failed')
logging.exception(ex)
plt.axis('image')
pltlegend = ax.legend(loc=3,
scatterpoints=1) # , fontcolor=almost_black)
if not nofig:
plt.show()
ax.grid(b=True, which='both', color='0.85', linestyle='-')
ax.set_axisbelow(True)
if nofig:
plt.close(figh.number)
else:
plt.draw()
plt.pause(1e-3)
else:
ax = None
xlabelhandle = None
pltlegend = None
hh = dict({'ax': ax, 'xlabelhandle': xlabelhandle, 'pltlegend': pltlegend})
return hh