def test_checkinputs_scatter_2d_reqFOnly(self):
PTM = np.empty((1, ), np.object)
PTM[0] = (15, 40)
with self.assertRaisesRegex(
ValueError, 'You cannot calculate {\'mus\'} with a spline ' +
'that does not include concentration. Remove those statevars and all their dependencies, '
+ 'or supply a spline that includes concentration.'):
eg.evalSolutionGibbsScatter(self.puresubstancespline['sp'],
PTM,
'mus',
MWu=1)
def test_checkinputs_grid_2d_extrapTOnly(self):
PTM = np.empty((1, ), np.object)
PTM[0] = (3000, 600)
with self.assertRaisesRegex(
ValueError, 'Dimensions {\'T\'} ' +
'contain values that fall outside the knot sequence for the given spline, '
+
'which will result in extrapolation, which may not produce meaningful values.'
):
eg.evalSolutionGibbsScatter(self.puresubstancespline['sp'],
PTM,
'G',
failOnExtrapolate=True)
def test_checkinputs_scatter_2d_extrapAllDims(self):
PTM = np.empty((1, ), np.object)
PTM[0] = (5000, 600)
# no order imposed on list of failed dimensions so accommodate either order
with self.assertRaisesRegex(
ValueError, 'Dimensions {\'[PT]\', \'[PT]\'} ' +
'contain values that fall outside the knot sequence for the given spline, '
+
'which will result in extrapolation, which may not produce meaningful values.'
):
eg.evalSolutionGibbsScatter(self.puresubstancespline['sp'],
PTM,
'G',
failOnExtrapolate=True)
def test_evalgibbs_singlesolute_scatter_singlepoint_allmeasures(self):
pidx = 0
tidx = 0
midx = 0
PTM = np.empty((1, ), np.object)
PTM[0] = (self.P[pidx], self.T[tidx], self.M[midx])
out = eg.evalSolutionGibbsScatter(self.spline['sp'],
PTM,
MWv=self.spline['MW'][0],
MWu=self.spline['MW'][1])
valErrs = ''
# check all values and output just one error for all of them
for tdv in vars(out).keys():
outfield = getattr(out, tdv)
self.assertEqual(1, outfield.size,
'Output for ' + tdv + ' has too many values')
if tdv not in self.mlout.dtype.fields:
warnings.warn('Matlab output does not include tdv ' + tdv)
else:
mloutfield = self.mlout[tdv][pidx][tidx][midx]
self.assertEqual(
1, outfield.size,
tdv + ' output not the same shape as MatLab output')
if not (np.allclose(
outfield, mloutfield, rtol=relTolerance,
atol=0) # check both abs and rel differences
and np.allclose(
outfield, mloutfield, rtol=0, atol=absTolerance)):
absDiffs = np.absolute(outfield - mloutfield)
relDiffs = absDiffs / np.absolute(mloutfield)
valErrs = valErrs + 'Output for ' + tdv + ' has absolute differences as large as ' + str(
np.max(absDiffs)) + \
' and relative differences as large as ' + str(np.max(relDiffs)) + '.\n'
if valErrs:
self.fail(valErrs)
def test_evalgibbs_singlesolute_scatter_singlepoint_Va_no0M(self):
pidx = 1
tidx = 2
midx = 3
PTM = np.empty((1, ), np.object)
PTM[0] = (self.P[pidx], self.T[tidx], self.M[midx])
out = eg.evalSolutionGibbsScatter(self.spline['sp'],
PTM,
'Va',
MWv=self.spline['MW'][0],
MWu=self.spline['MW'][1])
valErrs = ''
for tdv in vars(out).keys():
outfield = getattr(out, tdv)
self.assertEqual(1, outfield.size,
'Output for ' + tdv + ' has too many values')
if tdv in self.mlout.dtype.fields:
mloutfield = self.mlout[tdv][pidx][tidx][midx]
if not (np.allclose(
outfield, mloutfield, rtol=relTolerance,
atol=0) # check both abs & rel differences
and np.allclose(
outfield, mloutfield, rtol=0, atol=absTolerance)):
absDiffs = np.absolute(outfield - mloutfield)
relDiffs = absDiffs / np.absolute(mloutfield)
valErrs = valErrs + 'Output for ' + tdv + ' has absolute differences as large as ' + str(
np.max(absDiffs)) + \
' and relative differences as large as ' + str(np.max(relDiffs)) + '.\n'
if valErrs:
self.fail(valErrs)
def test_evalgibbs_singlesolute_scatter_multipoint_allmeasures(self):
numpts = 4
ptindices = np.empty((numpts, ), np.object)
PTM = np.empty((numpts, ), np.object)
for i in np.arange(
0, numpts): # get random data points (pick P/T separately)
pidx = randint(0, len(self.P) - 1)
tidx = randint(0, len(self.T) - 1)
midx = randint(0, len(self.M) - 1)
ptindices[i] = (pidx, tidx, midx)
PTM[i] = (self.P[pidx], self.T[tidx], self.M[midx])
out = eg.evalSolutionGibbsScatter(self.spline['sp'],
PTM,
MWv=self.spline['MW'][0],
MWu=self.spline['MW'][1])
valErrs = ''
# check all values and output just one error for all of them
for tdv in vars(out).keys():
outfield = getattr(out, tdv)
self.assertEqual(numpts, outfield.size,
'Output for ' + tdv + ' has too many values')
if tdv not in self.mlout.dtype.fields:
warnings.warn('Matlab output does not include tdv ' + tdv)
else:
# get randomly scattered outputs
mlout = np.empty((numpts, ), float)
for i in np.arange(0, numpts):
mlout[i] = self.mlout[tdv][ptindices[i]]
if not (np.allclose(
outfield, mlout, rtol=relTolerance,
atol=0) # check both abs and rel differences
and np.allclose(
outfield, mlout, rtol=0, atol=absTolerance)):
absDiffs = np.absolute(outfield - mlout)
relDiffs = absDiffs / np.absolute(mlout)
valErrs = valErrs + 'Output for ' + tdv + ' has absolute differences as large as ' + str(
np.max(absDiffs)) + \
' and relative differences as large as ' + str(np.max(relDiffs)) + '.\n'
if valErrs:
self.fail(valErrs)
from lbftd import statevars, loadGibbs as lg, evalGibbs as eg
# generate n scattered data points
n = 500
minT = 250
maxT = 350
minP = 0
maxP = 1000
toP = lambda r: (maxP - minP) * r + minP
toT = lambda r: (maxT - minT) * r + minT
PT = np.empty(n, np.object)
for i in np.arange(0, n):
PT[i] = (toP(random()), toT(random()))
water_spline = lg.loadGibbsSpline('water_demo_spline.mat')
tdstate = eg.evalSolutionGibbsScatter(water_spline['sp'], PT, 'rho', 'Cp',
'Kt', 'alpha')
# graph the output
rcParams[
'axes.labelpad'] = 10 # add some padding to prevents axis labels from covering ticks
fig = plt.figure()
# sorted sets of P and T for creating the meshgrid for plotting
P = [pt[0] for pt in PT]
T = [pt[1] for pt in PT]
rho_ax = fig.add_subplot(221, projection='3d')
rho_ax.set_xlabel('Pressure ($MPa$)')
rho_ax.set_ylabel('Temperature ($K$)')
rho_ax.set_zlabel('Density ($kg\: m^{-3}$)') # use LaTex coding in labels
rho_surf = rho_ax.scatter(P, T, tdstate.rho)
rho_ax.invert_yaxis()