def test_madeup_20():
N = 20
rs, qs = make_rsqs(N)
taus = make_taus(N)
xs = normalize([random() for i in range(N)])
T = 350.0
GE = UNIQUAC(T=T, xs=xs, rs=rs, qs=qs, tau_coeffs=taus)
def test_madeup_NRTL():
N = 6
alphas = make_alphas(N)
taus = make_taus(N)
xs = normalize([random() for i in range(N)])
T = 350.0
GE = NRTL(T, xs, taus, alphas)
def NRTL_gammas_200():
# ten component
# Takes 40 ms - not a great idea
N = 200
taus = [[random() for i in range(N)] for j in range(N)]
alphas = [[random() for i in range(N)] for j in range(N)]
xs = normalize([random() for i in range(N)])
gammas = NRTL_gammas(xs=xs, taus=taus, alphas=alphas)
def __init__(self):
super().__init__()
for N in Diguilio_Teja_ns:
Diguilio_Teja_kwargs = dict(T=298.15,
xs=normalize([0.1606, 0.8394] * N),
sigmas_Tb=[0.01424, 0.02530] * N,
Tbs=[309.21, 312.95] * N,
Tcs=[469.7, 508.0] * N)
N *= 2
setattr(self, 'DT%d' % N, Diguilio_Teja_kwargs)
Diguilio_Teja_kwargs = Diguilio_Teja_kwargs.copy()
for s in ('sigmas_Tb', 'xs', 'Tbs', 'Tcs'):
Diguilio_Teja_kwargs[s] = np.array(Diguilio_Teja_kwargs[s])
setattr(self, 'DTnp%d' % N, Diguilio_Teja_kwargs)
for N in Weinaug_Katz_ns:
Weinaug_Katz_kwargs = dict(parachors=[5.1e-5, 7.2e-5] * N,
Vml=0.000125,
Vmg=0.02011,
xs=normalize([.4, .6] * N),
ys=normalize([.6, .4] * N))
N *= 2
setattr(self, 'WK%d' % N, Weinaug_Katz_kwargs)
Weinaug_Katz_kwargs = Weinaug_Katz_kwargs.copy()
for s in ('parachors', 'xs', 'ys'):
Weinaug_Katz_kwargs[s] = np.array(Weinaug_Katz_kwargs[s])
setattr(self, 'WKnp%d' % N, Weinaug_Katz_kwargs)
for N in Winterfeld_Scriven_Davis_ns:
Winterfeld_Scriven_Davis_kwargs = dict(
xs=normalize([0.1606, 0.8394] * N),
sigmas=[0.01547, 0.02877] * N,
rhoms=[8610., 15530.] * N)
N *= 2
setattr(self, 'WS%d' % N, Winterfeld_Scriven_Davis_kwargs)
Winterfeld_Scriven_Davis_kwargs = Winterfeld_Scriven_Davis_kwargs.copy(
)
for s in ('sigmas', 'xs', 'rhoms'):
Winterfeld_Scriven_Davis_kwargs[s] = np.array(
Winterfeld_Scriven_Davis_kwargs[s])
setattr(self, 'WSnp%d' % N, Winterfeld_Scriven_Davis_kwargs)
def setup(self):
N = 3
T = 331.42
xs = [0.229, 0.175, 0.596]
rs = [2.5735, 2.87, 1.4311]
qs = [2.336, 2.41, 1.432]
# madeup numbers to match Wilson example roughly
tausA = [[0.0, -1.05e-4, -2.5e-4], [3.9e-4, 0.0, 1.6e-4],
[-1.123e-4, 6.5e-4, 0]]
tausB = [[0.0, 235.0, -169.0], [-160, 0.0, -715.0], [11.2, 144.0, 0.0]]
tausC = [[0.0, -4.23e-4, 2.9e-4], [6.1e-4, 0.0, 8.2e-5],
[-7.8e-4, 1.11e-4, 0]]
tausD = [[0.0, -3.94e-5, 2.22e-5], [8.5e-5, 0.0, 4.4e-5],
[-7.9e-5, 3.22e-5, 0]]
tausE = [[0.0, -4.2e2, 8.32e2], [2.7e2, 0.0, 6.8e2],
[3.7e2, 7.43e2, 0]]
tausF = [[0.0, 9.64e-8, 8.94e-8], [1.53e-7, 0.0, 1.11e-7],
[7.9e-8, 2.276e-8, 0]]
ABCDEF = (tausA, tausB, tausC, tausD, tausE, tausF)
self.GE2 = UNIQUAC(T=T, xs=xs, rs=rs, qs=qs, ABCDEF=ABCDEF)
if not IS_PYPY:
self.GE2nb = UNIQUAC(T=T,
xs=np.array(xs),
rs=np.array(rs),
qs=np.array(qs),
ABCDEF=(np.array(v) for v in ABCDEF))
self.xs2nb = np.array([.1, .2, .7])
def make_rsqs(N):
cmps = range(N)
rs = [float('%.3g' % (random() * 2.5)) for _ in cmps]
qs = [float('%.3g' % (random() * 1.3)) for _ in cmps]
return rs, qs
def make_taus(N):
cmps = range(N)
data = []
base = [1e-4, 200.0, -5e-4, -7e-5, 300, 9e-8]
for i in cmps:
row = []
for j in cmps:
if i == j:
row.append([0.0] * 6)
else:
row.append(
[float('%.3g' % (random() * n)) for n in base])
data.append(row)
return data
N = 20
rs, qs = make_rsqs(N)
taus = make_taus(N)
xs = normalize([random() for i in range(N)])
T = 350.0
self.GE20 = UNIQUAC(T=T, xs=xs, rs=rs, qs=qs, tau_coeffs=taus)
self.xs20 = normalize([random() for i in range(N)])
if not IS_PYPY:
self.GE20nb = UNIQUAC(T=T,
xs=np.array(xs),
rs=np.array(rs),
qs=np.array(qs),
tau_coeffs=np.array(taus))
self.xs20nb = np.array(self.xs20)
def to_diff_dnGE2_dninj(ns):
nt = sum(ns)
xs = normalize(ns)
return nt * GE.to_T_xs(T, xs).GE()
def gammas_to_diff(xs):
xs = normalize(xs)
return np.array(Wilson_gammas(xs, lambdas))
def diff_for_dnGE_dn(xs):
nt = sum(xs)
xs = normalize(xs)
return nt * GE.to_T_xs(T, xs).GE()
def diff_for_dGE_dn(xs):
xs = normalize(xs)
return GE.to_T_xs(T, xs).GE()
def diff_for_dSE_dns(xs):
xs = normalize(xs)
return GE.to_T_xs(T, xs).SE()
def NRTL_gammas_44():
N = 44
taus = [[random() for i in range(N)] for j in range(N)]
alphas = [[random() for i in range(N)] for j in range(N)]
xs = normalize([random() for i in range(N)])
gammas = NRTL_gammas(xs=xs, taus=taus, alphas=alphas)
