def testInterpolate(self):
f = open('testfile', 'w')
f.write('* \n')
f.write('A\tB\tC\tD\tE\tF\tG\tH\tI\tJ\n')
for i in range(10):
for j in range(10):
f.write('%d\t' % (i * 10 + j))
f.write('\n')
f.close()
dataobj = iof.ProcFile('testfile')
locs = np.zeros(3)
datavec = np.zeros(4)
dataobj.interpolate(['C', 'D', 'J'], locs, datavec, interpval=63)
np.testing.assert_array_almost_equal(datavec, [64, 65, 66, 72])
self.assertRaises(RuntimeError,
dataobj.interpolate, ['C', 'D', 'J'],
locs,
datavec,
interpval=124)
self.assertRaises(IOError,
dataobj.interpolate, ['C', 'D', 'J'],
locs,
datavec,
interpmethod='linexar')
self.assertRaises(IOError,
dataobj.interpolate, ['C', 'Q', 'J'],
locs,
datavec,
interpmethod='linear')
os.remove('testfile')
def testGetTitles(self):
f = open('testfile', 'w')
f.write('* \n')
strings = [
'dojkfr', 'jkn vsnkl', 'afkjaf', 'ajknnjv nsv',
'99"|} {{{}}#^&$'
]
for i in strings:
f.write(i)
f.write('\t')
f.close()
dataobj = iof.ProcFile('testfile')
titles = dataobj.gettitles()
os.remove('testfile')
np.testing.assert_array_equal(strings, titles)
def findC(datafiles, testspecies, bestC, options):
# Interpolate each datafile, generate a matrix from interpolated data
nofiles = len(datafiles)
nocols = len(testspecies) + 1
locs = np.zeros(nocols - 1)
interpdata = np.zeros((nofiles, nocols))
filesmatrix = np.zeros((nofiles, 3))
for ii in range(nofiles): # interpolate for each file
dataobj = iof.ProcFile(datafiles[ii])
if ii == 0:
titles = dataobj.gettitles()
else:
titles1 = dataobj.gettitles()
np.testing.assert_array_equal(
titles1, titles
) # Verify that all data files have the same column headers
dataobj.interpolate(testspecies,
locs,
interpdata[ii, :],
interpval=float(options["StoichMassFrac"][0]),
interpmethod="".join(options["InterpMethod"]))
filesmatrix[:, 0] = interpdata[:, 0]
filesmatrix[:,
2] = interpdata[:,
0] # filesmatrix stores (row1) stoich prog var (row2) file indices (row3) stoich Temps
filesmatrix[:, 1] = range(nofiles)
filesmatC = matrix.Matrix(nofiles, 3)
for i in range(nofiles):
for j in range(3):
filesmatC.SetVal(i, j, filesmatrix[i, j])
# Generate combinations matrix - skip this step if user input says to
skip = options["SkipProgVar"][0]
assert (
(skip == 'yes') | (skip == 'no')
), "must select input <yes> or <no> for <skip progress variable optimization:>"
if skip == 'no':
combosmatrix = np.zeros((nocols, cs.totnumcoms(nocols - 1) + 1))
cs.combination_mat(combosmatrix[1:, 1:])
else:
print "Skipping progress variable optimization, using user input"
options["PlotAllC"][0] = 'no'
combosmatrix = np.zeros((nocols, 2))
for i in range(nocols - 1):
combosmatrix[i + 1, 1] = 1
combosmatrix[0, 0] = 1
# Calculate progress variables
progvars = np.dot(interpdata, combosmatrix)
length = progvars.shape[1]
# Generate progress variable matrix
progVar = matrix.Matrix(nofiles, length)
for i in range(nofiles):
for j in range(length):
progVar.SetVal(i, j, progvars[i, j])
# Sort PROGVARS and FILESMATRIX by temperature
sortmethod = options["sort method"][0]
print "Sorting PROGVARS by temperature using %s sort" % sortmethod
for i in [progVar, filesmatC]:
if sortmethod == 'bubble':
sorter = sorting.BubbleSort(i)
elif sortmethod == 'standard':
sorter = sorting.StandardSort(i)
elif sortmethod == 'brute':
sorter = sorting.BruteSort(i)
else:
raise IOError(
"invalid sorting method (%s) specified, instead use <bubble> sort"
% sortmethod)
sorter.SetRefColNum(0)
sorter.sort_data()
print "Sorting FILESMATRIX by temperature"
# Test monotonicity of PROGVARS
print "Testing monotonicity \n"
monoAry = np.zeros(length, dtype=np.int32)
checker = monocheck.MonoCheck(progVar) # Create MonoCheck object
assert checker.CheckStrictMonoticity(
monoAry, 0) == 0, "CheckStrictMonoticity ran unsuccessfully.\n"
# ^ Check which columns of progVar are strictly increasing or strictly decreasing and store result in monoAry
# Test for maximum slope if multiple monotonic progress variables are returned
checksum = np.sum(monoAry)
if checksum % 3 != 0:
raise RuntimeError(
"Incorrect values in monoAry vector, check monotonicity function.\n"
)
if checksum > 3:
maxslopetest = options["MaxSlopeTest"][0]
print "Testing max slope using %s" % maxslopetest
if maxslopetest == 'linear regression':
maxchecker = maxslope.LinRegression(progVar)
elif maxslopetest == 'end point slope':
maxchecker = maxslope.EndPointSlope(progVar)
else:
raise IOError(
"invalid maximum slope test (%s) specified, instead use <linear regression>"
% maxslopetest)
assert maxchecker.MostMonotonic(
monoAry, 0) == 0, "MostMonotonic ran unsuccessfully.\n"
# ^ Distinguish the best monotonic progress variables
elif checksum == 0:
# Least non-monotonic tests to be implemented in beta version
lnmoption = options["lnmcheck"][0]
print "Finding least non-monotonic progress variable using %s nonmono check" % lnmoption
if lnmoption == 'simple':
lnm_check = leastnonmono.SimpleLNM(progVar)
elif lnmoption == 'advanced':
lnm_check = leastnonmono.AdvancedLNM(progVar)
else:
raise IOError(
"invalid lnm test (%s) specified, instead use <simple> or <advanced>"
% maxslopetest)
assert lnm_check.LeastNonMonotonic(
monoAry, 0) == 0, "LeastNonMonotonic ran unsuccessfully.\n"
# Print results
monoAryflag = 0
for i in range(length):
if monoAry[
i] == 3.0: # Print best monotonic progress variable if it exists
if monoAryflag != 0:
raise RuntimeError(
"Error in contents of monoAry vector: multiple best selected.\n"
)
monoAryflag = 2
bestC[:] = iof.get_progvar(combosmatrix[1:, i], testspecies, locs,
i)
print 'The chosen progress variable is C = %s' % bestC[1][0],
for j in bestC[1][1:]:
print "+ %s" % j,
print '\nThe column numbers of these species are ', bestC[
0], ', respectively.\n'
elif monoAry[
i] == 1.0: # Otherwise print least non-monotonic progress variable
if monoAryflag != 0:
raise RuntimeError("Error in contents of monoAry vector.\n")
monoAryflag = 1
bestC[:] = iof.get_progvar(combosmatrix[1:, i], testspecies, locs,
i)
print 'WARNING: no monotonic progress variables found, but proceeding with best alternative.\n'
print 'The least non-monotonic progress variable is C = %s' % bestC[
1][0],
for j in bestC[1][1:]:
print "+ %s" % j,
print '\nThe column numbers of these species are', bestC[
0], ', respectively.\n'
for i in range(
length): # Print/identify other monotonic progress variables
if monoAry[i] == 2.0:
if monoAryflag < 2:
raise RuntimeError("Error in contents of monoAry vector.\n")
elif monoAryflag == 2:
print "Other candidate monotonic progress variables are:"
otherC = iof.get_progvar(combosmatrix[1:, i], testspecies, locs, i)
print 'C = %s' % otherC[1][0],
for j in otherC[1][1:]:
print "+ %s" % j,
print "\n",
monoAryflag = 3
if monoAryflag < 1: # Give error if no best progress variable is found
raise RuntimeError("Error: no best progress variable selected.")
# Write results
for i in range(nofiles):
filesmatC.SetVal(i, 0, progVar.GetVal(i, bestC[2]))
# Plot results
Cst = np.zeros(nofiles)
Tst = np.zeros(nofiles)
plt.figure()
for ii in range(nofiles):
Tst[ii] = progVar.GetVal(ii, 0)
if options["PlotAllC"][0] == 'yes':
for jj in range(length - 1):
for ii in range(nofiles):
Cst[ii] = progVar.GetVal(ii, jj + 1)
Cst = Cst / Cst.max()
otherplt, = plt.plot(Tst, Cst, color='r')
for ii in range(nofiles):
Cst[ii] = filesmatC.GetVal(ii, 0)
Cst = Cst / Cst.max()
bestplt, = plt.plot(Tst,
Cst,
color='k',
marker='o',
markerfacecolor='none')
if options["PlotAllC"][0] == 'yes':
plt.legend(
[bestplt, otherplt],
['Best progress variable', 'Other candidate progress variables'],
loc='lower right')
plt.xlabel("T (K)")
plt.ylabel("Normalized Progress Variable (C/Cmax)")
plt.title("Best Progress Variable")
if skip == 'yes':
plt.title("User-selected progress variable")
plt.savefig("output/%s.pdf" % 'CvsTemp')
plt.clf()
return filesmatC
def findC(datafiles, testspecies, bestC):
# Interpolate each datafile, generate a matrix from interpolated data
nofiles = len(datafiles)
nocols = len(testspecies) + 1
locs = np.zeros(nocols - 1)
interpdata = np.zeros((nofiles, nocols))
filesmatrix = np.zeros((nofiles, 2))
for ii in range(nofiles): # interpolate for each file
dataobj = iof.ProcFile(datafiles[ii])
if ii == 0:
titles = dataobj.gettitles()
else:
titles1 = dataobj.gettitles()
np.testing.assert_array_equal(
titles1, titles
) # Verify that all data files have the same column headers
dataobj.interpolate(testspecies, locs, interpdata[ii, :])
filesmatrix[:,
0] = interpdata[:,
0] # filesmatrix stores file indices in order and interpolated Temps
filesmatrix[:, 1] = range(nofiles)
filesmatC = matrix.Matrix(nofiles, 2)
for i in range(nofiles):
for j in range(2):
filesmatC.SetVal(i, j, filesmatrix[i, j])
# Generate combinations matrix
combosmatrix = np.zeros((nocols, cs.totnumcoms(nocols - 1) + 1))
combosmatrix[0, 0] = 1
cs.combination_mat(combosmatrix[1:, 1:])
# Calculate progress variables
progvars = np.dot(interpdata, combosmatrix)
# Generate progress variable matrix
progVar = matrix.Matrix(nofiles, cs.totnumcoms(nocols - 1) + 1)
for i in range(nofiles):
for j in range(cs.totnumcoms(nocols - 1) + 1):
progVar.SetVal(i, j, progvars[i, j])
# Sort PROGVARS and FILESMATRIX by temperature
print "Sorting PROGVARS by temperature"
sortmethod = 'bubble'
if "".join(sortmethod) == 'bubble':
sorter = bubble_sort.bubble_sort(progVar)
sorter.SetRefColNum(0)
sorter.SetSortEndIndex(nofiles)
sorter.SetSortStartIndex(0)
sorter.generateIndexArray()
sorter.extractRefCol()
sorter.sort_data()
print "Sorting FILESMATRIX by temperature"
sortmethod = 'bubble'
if "".join(sortmethod) == 'bubble':
sorter = bubble_sort.bubble_sort(filesmatC)
sorter.SetRefColNum(0)
sorter.SetSortEndIndex(nofiles)
sorter.SetSortStartIndex(0)
sorter.generateIndexArray()
sorter.extractRefCol()
sorter.sort_data()
# Test monotonicity of PROGVARS
print "Testing monotonicity \n"
length = progvars.shape[1]
monoAryPy = np.zeros(length)
monoAry = vector.Vector(length)
helper.copy_py_to_vector(monoAryPy, monoAry)
checker = monocheck.MonoCheck(progVar) # Create MonoCheck object
assert checker.CheckStrictMonoticity(
0, monoAry) == 0, "CheckStrictMonoticity ran unsuccessfully.\n"
# ^ Check which columns of progVar are strictly increasing or strictly decreasing and store result in monoAry
# Test for maximum slope if multiple monotonic progress variables are returned
checksum = 0
for i in range(length):
checksum += monoAry.GetVal(i)
if checksum % 3 != 0:
raise RuntimeError(
"Incorrect values in monoAry vector, check monotonicity function.\n"
)
if checksum > 3:
print "Testing max slope:"
maxchecker = linregression.LinRegression(progVar)
#maxchecker = endpointslope.EndPointSlope(progVar)
assert maxchecker.MostMonotonic(
0, monoAry) == 0, "MostMonotonic ran unsuccessfully.\n"
# ^ Distinguish the best monotonic progress variables
elif checksum == 0:
# Least non-monotonic tests to be implemented in beta version
print "Finding least non-monotonic:"
monoAry[0] = 1
# Print results
monoAryflag = 0
for i in range(length):
if monoAry.GetVal(
i
) == 3.0: # Print best monotonic progress variable if it exists
if monoAryflag != 0:
raise RuntimeError(
"Error in contents of monoAry vector: multiple best selected.\n"
)
monoAryflag = 2
bestC[:] = iof.get_progvar(combosmatrix[1:, i], testspecies, locs,
i)
print 'The best strictly monotonic progress variable is C = %s' % bestC[
1][0],
for j in bestC[1][1:]:
print "+ %s" % j,
print '\nThe column numbers of these species are ', bestC[
0], ', respectively.\n'
elif monoAry.GetVal(
i
) == 1.0: # Otherwise print least non-monotonic progress variable
if monoAryflag != 0:
raise RuntimeError("Error in contents of monoAry vector.\n")
monoAryflag = 1
bestC[:] = iof.get_progvar(combosmatrix[1:, i], testspecies, locs,
i)
print 'WARNING: no monotonic progress variables found, but proceeding with best alternative.\n'
print 'The least non-monotonic progress variable is C = %s' % bestC[
1][0],
for j in bestC[1][1:]:
print "+ %s" % j,
print '\nThe column numbers of these species are', bestC[
0], ', respectively.\n'
for i in range(
length): # Print/identify other monotonic progress variables
if monoAry.GetVal(i) == 2.0:
if monoAryflag < 2:
raise RuntimeError("Error in contents of monoAry vector.\n")
elif monoAryflag == 2:
print "Other candidate monotonic progress variables are:"
otherC = iof.get_progvar(combosmatrix[1:, i], testspecies, locs, i)
print 'C = %s' % otherC[1][0],
for j in otherC[1][1:]:
print "+ %s" % j,
print "\n",
monoAryflag = 3
if monoAryflag < 1: # Give error if no best progress variable is found
raise RuntimeError("Error: no best progress variable selected.")
# Plot results
iof.plotCvT(progvars[:, 0], progvars[:, bestC[2]])
# Write results
for i in range(nofiles):
filesmatC.SetVal(i, 0, progvars[i, bestC[2]])
return filesmatC
def findC(datafiles, testspecies, bestC, filesmatrix):
# interpolate each datafile, generate a matrix from interpolated data
nofiles = len(datafiles)
nocols = len(testspecies)+1
locs = np.zeros(nocols-1)
interpdata = np.zeros((nofiles,nocols))
for ii in range(nofiles):
dataobj = iof.ProcFile(datafiles[ii])
if ii == 0:
titles = dataobj.gettitles()
else:
titles1 = dataobj.gettitles()
np.testing.assert_array_equal(titles1,titles) # verify that all data files have the same column headers
dataobj.interpolate(testspecies, locs, interpdata[ii,:])
filesmatrix[:,0] = interpdata[:,0]
filesmatrix[:,1] = range(nofiles)
# generate combinations matrix
combosmatrix = np.zeros((nocols,cs.totnumcoms(nocols-1)+1))
combosmatrix[0,0] = 1
cs.combination_mat(combosmatrix[1:,1:])
# calculate progress variables
progvars = np.dot(interpdata,combosmatrix)
# sort PROGVARS and FILESMATRIX by temperature
# will add connectivity to C++ sort later
print "sorting PROGVARS by temperature"
print "sorting FILESMATRIX by temperature"
# test monotonicity of PROGVARS
# will add connectivity to C++ monotonicity check later
print "testing monotonicity"
length = progvars.shape[1]
monocheck = np.zeros(length)
monocheck[3] = 3 ###
monocheck[4] = 3 ###
checksum = monocheck.sum()
if checksum % 3 != 0:
raise RuntimeError("incorrect values in monocheck vector, check monotonicity function")
if checksum > 3:
# max slop tests in C++ to be connected
print "testing max slope"
monocheck[3] = 2 ###
elif checksum == 0:
# non monotonicity check in C++ to be connected
print "finding least non-monotonic"
monocheck[-1] = 1 ###
# print results
monocheckflag = 0
for i in range(length):
if monocheck[i] == 3: # Find best monotonic progress variable if it exists
if monocheckflag != 0:
raise RuntimeError("error in contents of monocheck vector: multiple best selected")
monocheckflag = 2
bestC[:] = iof.get_progvar(combosmatrix[1:,i], testspecies, locs, i)
print '\nThe best monotonically increasing progress variable is C = %s' % bestC[1][0],
for j in bestC[1][1:]:
print "+ %s" % j
print '\nThe column numbers of these species are', bestC[0],', respectively'
elif monocheck[i] == 1: # otherwise find least non-monotonic progress variable
if monocheckflag != 0:
raise RuntimeError("error in contents of monocheck vector")
monocheckflag = 1
bestC[:] = iof.get_progvar(combosmatrix[1:,i], testspecies, locs, i)
print '\nWARNING: no monotonic progress variables found, but proceeding with best alternative'
print 'The least non-monotonic progress variable is C = %s' % bestC[1][0],
for j in bestC[1][1:]:
print "+ %s" % j
print '\nThe column numbers of these species are', bestC[0],', respectively'
for i in range(length): # identify other monotonic progress variables
if monocheck[i] == 2:
if monocheckflag < 2:
raise RuntimeError("error in contents of monocheck vector")
else:
print "\nOther candidate monotonic progress variables are:"
otherC = iof.get_progvar(combosmatrix[1:,i], testspecies, locs, i)
print 'C = %s' % otherC[1][0],
for j in otherC[1][1:]:
print "+ %s" % j
print "\n"
monocheckflag = 3
if monocheckflag < 1: # give error if no best progress variable is found
raise RuntimeError("error: no best progress variable selected")
# plot results
iof.plotCvT(progvars[:,0],progvars[:,bestC[2]])
# write results
filesmatrix[:,0] = progvars[:,bestC[2]]
print "Generating PDF matrix with", Zpdf[0], "PDF"
pdfValM = matrix3d.Matrix3D(ZvarPoints, ZmeanPoints, ZPoints)
for i in range(ZvarPoints):
for j in range(ZmeanPoints):
for k in range(ZPoints):
pdfValM.SetVal(i, j, k, 0)
if Zpdf[0] == "delta":
import deltaPDF
d = deltaPDF.DeltaPDF(Z, ZPoints)
elif Zpdf[0] == "beta": # beta PDF support still in progress
import betaPDF
d = betaPDF.BetaPDF(Z, ZPoints)
pdfValReturn = d.pdfVal(Zvar, Zmean, pdfValM)
# Find locations of columns of reaction rate data for species in the best progress variable
dataobj = iof.ProcFile(datafiles[0])
titles = dataobj.gettitles()
rxn_rate_locs = range(len(bestC[0]))
for ii in range(len(rxn_rate_locs)):
locflag = 0
species = list(bestC[1][ii])
speciesprodrate = species[2:]
speciesprodrate = "".join(speciesprodrate)
speciesprodrate = "".join(["ProdRate", speciesprodrate, " [kg/m^3s]"])
for jj in range(len(titles)):
if titles[jj] == speciesprodrate:
rxn_rate_locs[ii] = jj
locflag = 1
if locflag == 0:
raise IOError("Production rate data for %s is missing" %
speciesprodrate)