def __init__(self, s1, s2, T):
self.s1 = s1
self.s2 = s2
self.p1_s = get_psat(s1, T)
self.p2_s = get_psat(s2, T)
self.q1 = get_volume(self.s1, T)
self.q2 = get_volume(self.s2, T)
def main(x1, y1, P, G_e, T, s1, s2):
style.use('classic')
w = Wohls(s1, s2, T)
A = get_parameter(x1, G_e, s1, s2, T)
acc = get_accuracy(G_e, w.Ge(x1, A))
x = np.linspace(0, 1, 50)
fig4 = plt.figure(facecolor='white')
plt.title(r"$G^E-x$")
plt.xlim(0, 1)
plt.xlabel(r'$x_1$')
plt.ylabel(r'$G^E\ (J/mol)$')
plt.scatter(x1, G_e)
plt.plot(x, w.Ge(x, A), label=r"$Wohls\ model$", color='red')
plt.axhline(0, color='black')
q1 = get_volume(s1, T)
q2 = get_volume(s2, T)
z = x * q1 / (x * q1 + (1 - x) * q2)
p1_s = get_psat(s1, T)
p2_s = get_psat(s2, T)
P_Wohls = x * p1_s * w.gamma1(z, A) + (1 - x) * p2_s * w.gamma2(z, A)
y_Wohls = x * p1_s * w.gamma1(z, A) / P_Wohls
P_raoult = x * p1_s + (1 - x) * p2_s
fig5 = plt.figure(facecolor='white')
plt.title(r"$P-x$")
plt.xlim(0, 1)
plt.ylim(0, 1.2 * max(P))
plt.xlabel(r'$x_1$')
plt.ylabel(r'$P\ (kPa)$')
plt.scatter(x1, P)
plt.plot(x, P_Wohls, label=r"$Wohls\ model$", color='red')
plt.plot(x, P_raoult, color='black', label=r"$Raoult's\ Law$")
plt.legend(loc='best', fontsize=10)
fig6 = plt.figure(facecolor='white')
plt.gca().set_aspect('equal', adjustable='box')
plt.title(r"$y-x$")
plt.xlim(0, 1)
plt.ylim(0, 1)
plt.xlabel(r'$x_1$')
plt.ylabel(r'$y_1$')
plt.scatter(x1, y1)
plt.plot(x, y_Wohls, label=r"$Wohls\ model$", color='red')
plt.plot(x, x, color='black')
plt.legend(loc='best', fontsize=10)
return A, acc, fig4, fig5, fig6
def process(self, x):
p = len(x)
# the first cluster
if len(self.clusters) == 0:
self.clusters.append(new_cluster(x, self.frac, self.fac, p,
self.m))
else:
# elect winning cluster
win_cluster = min_dist(x, self.clusters)
win_cluster_radius = get_radius(self.fac, p, self.m, win_cluster.k)
# point is not contained in the winning cluster
if (distance.mahalanobis(x, win_cluster.centroid,
win_cluster.inv_cov) >
win_cluster_radius):
self.clusters.append(
new_cluster(x, self.frac, self.fac, p, self.m))
else:
self.clusters.remove(win_cluster)
# update winning cluster
update_winner_cluster(x, win_cluster)
if len(self.clusters) != 0:
# update nearest neighbor of winning cluster
update_nearest_cluster(x, win_cluster, self.clusters)
# check overlap
(max_olap, max_cluster) = overlap(win_cluster, self.clusters)
if (max_olap > 0):
cluster_merged = merge(win_cluster, max_cluster)
V_m = get_volume(cluster_merged, self.fac, p, self.m)
V_w = get_volume(win_cluster, self.fac, p, self.m)
V_c = get_volume(max_cluster, self.fac, p, self.m)
# Check volume, for the final decision
if V_m <= p * (V_w + V_c):
self.clusters.remove(max_cluster)
self.clusters.append(cluster_merged)
else:
self.clusters.append(win_cluster)
pass
def test(nyu2_loader, model, width, height):
for i, image in enumerate(nyu2_loader):
image = torch.autograd.Variable(image, volatile=True).cuda()
out = model(image)
out = out.view(out.size(2),out.size(3)).data.cpu().numpy()
max_pix = out.max()
min_pix = out.min()
out = (out-min_pix)/(max_pix-min_pix)*255
out = cv2.resize(out,(width,height),interpolation=cv2.INTER_CUBIC)
cv2.imwrite(os.path.join(args.output, "out_grey.png"),out)
out_grey = cv2.imread(os.path.join(args.output, "out_grey.png"),0)
out_color = cv2.applyColorMap(out_grey, cv2.COLORMAP_JET)
cv2.imwrite(os.path.join(args.output, "out_color.png"),out_color)
vol = get_volume(out_grey, args.json)
print("Volume:")
print(vol)
print("unit: cm^3")
out_file = open(os.path.join(args.output, "out.txt"), "w")
out_file.write("Volume:\n")
out_file.write(str(vol))
out_file.write("\n")
out_file.write("unit: cm^3")
out_file.close()
get_mask(out_grey, args.json, args.output)
main file to
1) read fuel composition from Mk1.txt MCNP input
2) generate fuel composition files that can be used in FIG to
generate serpent input files
'''
import volume
import config
import pandas as pd
from ave_fuel_mat import sum_comp, sum_comp_2_zones
import numpy as np
# the weight(volume or flux) matrices have dimension 5x4x8
volmat = np.array(volume.get_volume()).reshape((4, 5, 1))
volmat = np.swapaxes(volmat, 0, 1)
print(volmat.shape)
print('compute average fuel composition for near wall zones')
# build a flux matrix for the wall regions by setting the
# active region flux to 0 in the full core flux matrix
# read volume*flux in each zone
fluxdf = pd.read_csv(config.FLUX_CSV_PATH, header=None)
fluxmat = fluxdf.values.reshape((5, 4, 8))
print('fluxmat')
print(fluxmat)
def get_brain(subject, fname_surf_L=None, fname_surf_R=None, fname_tex_L=None,
fname_tex_R=None, bad_areas=None, fname_vol=None, name_lobe_vol='Subcortical',
trans=False, fname_atlas=None, fname_color=None):
# type: (object, object, object, object, object, object, object, object, object, object, object) -> object
"""show structures
Parameters
----------
subject : str
The name of the subject
fname_surf_L : None | str
The filename of the surface of the left hemisphere
fname_surf_R : None | str
The filename of the surface of the right hemisphere
fname_tex_L : None | str
The filename of the texture surface of the right hemisphere
The texture is used to select areas in the surface
fname_tex_R : None | str
The filename of the texture surface of the left hemisphere
bad_areas : list of int
Areas that will be excluded from selection
fname_vol : None | str
The filename of mri labelized
name_lobe_vol : None | list of str | str
Interest lobe names
trans : str | None
The filename that contains transformation matrix for surface
fname_atlas : str | None
The filename of the area atlas
fname_color : Brain surfer instance
The filename of color atlas
Returns
-------
figure : Figure object
-------
Author : Alexandre Fabre
"""
list_hemi = ['lh', 'rh']
volumes = None
surfaces = None
fname_surf = [fname_surf_L, fname_surf_R]
fname_tex = [fname_tex_L, fname_tex_R]
print('build surface areas')
for i, hemi in enumerate(list_hemi):
if fname_surf[i] is not None and fname_tex[i] is not None:
if surfaces is None:
surfaces = []
surface = get_surface(fname_surf[i], subject=subject, hemi=hemi, trans=trans)
# save to project areas on the hemisphere
surface.save_as_ref()
areas_hemi = get_surface_areas(surface, texture=fname_tex[i], hemi=hemi,
subject=subject, fname_atlas=fname_atlas,
fname_color=fname_color)
# Delete WM (values of texture 0 and 255)
bad_areas = [0, 42]
if bad_areas is not None:
areas_hemi = np.delete(areas_hemi, bad_areas, axis=0)
surfaces.append(areas_hemi)
print('[done]')
print('build volume areas')
if fname_vol is not None:
volumes = []
for hemi in list_hemi:
cour_volume = get_volume(fname_vol, fname_atlas, name_lobe_vol, subject,
hemi, True)
volumes.append(cour_volume)
print('[done]')
brain = Brain(surfaces, volumes)
return brain
def __init__(self, s1, s2, T):
self.q1 = get_volume(s1, T)
self.q2 = get_volume(s2, T)
self.T = T
main file to
1) read fuel composition from Mk1.txt MCNP input
2) generate fuel composition files that can be used in FIG to
generate serpent input files
'''
import volume
import config
import pandas as pd
from ave_fuel_mat import sum_comp, sum_comp_2_zones
import numpy as np
# the weight(volume or flux) matrices have dimension 5x4x8
volmat = np.array(volume.get_volume()).reshape((4, 5, 1))
volmat = np.swapaxes(volmat, 0, 1)
print(volmat.shape)
print('compute average fuel composition for near wall zones')
# build a flux matrix for the wall regions by setting the
# active region flux to 0 in the full core flux matrix
# read volume*flux in each zone
fluxdf = pd.read_csv(config.FLUX_CSV_PATH, header=None)
fluxmat = fluxdf.values.reshape((5, 4, 8))
print('fluxmat')
print(fluxmat)
fluxmat_wall = fluxmat
for passno in range(1, 9):
def main():
st.title("Correlations")
st.write(
""" The *Margules* model, *Redlich-Kister Expansion* truncated to two terms, *van Laar* model and the *Truncated Wohls expansion* are implemented here. """
r"In case $\alpha$ fits the data with an accuracy of 80% or above, the $\alpha_{GM}$ value is displayed."
)
compounds = [
'Acetonitrile', 'Acetone', '1,2-Ethanediol', 'Ethanol',
'Diethyl ether', 'Ethyl acetate', 'Benzene', '1-Butanol', 'Chloroform',
'Cyclohexane', 'Acetic acid butyl ester', 'Acetic acid', 'Hexane',
'2-Propanol', '1-Hexene', 'Methanol', 'Tetrahydrofuran', 'Water',
'm-Xylene', 'p-Xylene', 'N-Methyl-2-pyrrolidone', '1,3-Butadiene',
'Hexadecane'
]
menu_options = compounds.copy()
for i, compound in enumerate(compounds):
if ' ' in compound:
compounds[i] = compound.replace(' ', '%20')
compound1 = st.selectbox('Select compound 1',
menu_options,
key='compound1')
compound2 = st.selectbox('Select compound 2',
menu_options,
key='compound2')
i1 = menu_options.index(compound1)
i2 = menu_options.index(compound2)
st.info("You have chosen %s and %s" % (compound1, compound2))
@st.cache(suppress_st_warning=True)
def link_generator(i1, i2):
url = 'http://www.ddbst.com/en/EED/VLE/VLE%20' + compounds[
i1] + '%3B' + compounds[i2] + '.php'
if requests.get(url).status_code == 404:
url = 'http://www.ddbst.com/en/EED/VLE/VLE%20' + compounds[
i2] + '%3B' + compounds[i1] + '.php'
return url
try:
if compound1 == compound2:
st.warning('Choose different compounds')
else:
url = link_generator(i1, i2)
if requests.get(url).status_code == 404:
st.error(
"VLE data for this pair of compounds doesn't exist at DDBST.")
dataframes = pd.read_html(url)
isothermal_vledata = []
T = []
for i, data in enumerate(dataframes):
col = data.columns
if col.dtype == object:
if len(col) == 3 and 'P' in col[0] and 'x1' in col[
1] and 'y1' in col[2]:
T.append(float(dataframes[i - 1][1]))
isothermal_vledata.append(dataframes[i])
if isothermal_vledata == []:
st.error(
'There is no isothermal data available for this pair of compounds at DDBST'
)
else:
for i in range(len(T)):
st.write('%d)' % (i + 1), 'T = ', T[i], 'K')
st.write(isothermal_vledata[i])
if len(T) == 1:
choice = 1
else:
choice = st.number_input('Choose a dataset',
value=1,
min_value=1,
max_value=len(T))
st.info('Analysing dataset %d ...' % choice)
P = isothermal_vledata[choice - 1]['P [kPa]']
x1 = isothermal_vledata[choice - 1]['x1 [mol/mol]']
y1 = isothermal_vledata[choice - 1]['y1 [mol/mol]']
T = T[choice - 1]
st.write(r'$T = %0.2f K$' % T)
p1sat = get_psat(compounds[i1], T)
p2sat = get_psat(compounds[i2], T)
if p1sat > p2sat:
st.info('The more volatile component is %s' % menu_options[i1])
s1, s2 = compounds[i1], compounds[i2]
else:
st.info('The more volatile component is %s' % menu_options[i2])
s1, s2 = compounds[i2], compounds[i1]
p1_s = max(p1sat, p2sat)
p2_s = min(p1sat, p2sat)
st.write(r'$p_1^s = %0.3f kPa$' % p1_s)
st.write(r'$p_2^s = %0.3f kPa$' % p2_s)
x = np.linspace(0, 1, 50)
P_raoult = x * p1_s + (1 - x) * p2_s
y_raoult = x * p1_s / P_raoult
n_points = len(x1) - 1
try:
if x1[0] == 0 and x1[n_points] != 1:
x1, y1, P = x1[1:], y1[1:], P[1:]
if x1[0] != 0 and x1[n_points] == 1:
x1, y1, P = x1[:n_points], y1[:n_points], P[:n_points]
if x1[0] == 0 and x1[n_points] == 1:
x1, y1, P = x1[1:n_points], y1[1:n_points], P[1:n_points]
except KeyError:
pass
q1, q2 = get_volume(s1, T), get_volume(s2, T)
z1 = x1 * q1 / (x1 * q1 + (1 - x1) * q2)
gamma1 = np.divide(P * y1, x1 * p1_s)
gamma2 = np.divide(P * (1 - y1), ((1 - x1) * p2_s))
G_e = constants.R * T * (xlogy(x1, gamma1) + xlogy(1 - x1, gamma2))
alpha_gm = models.alphagm.get_alpha_gm(x1, y1)
if alpha_gm == 0:
pass
else:
st.success(r"$\alpha_{GM}=%0.3f$" % alpha_gm)
st.text("Try using this value in the McCabe-Thiele Plotter!")
model = st.selectbox("Choose a model", [
"Select", "Margules", "Redlich Kister", "van Laar",
"Truncated Wohls"
],
key='model')
if model == "Select":
st.info("Select a model")
else:
if model != "Truncated Wohls":
MODELS = {
"Margules": models.margules,
"Redlich Kister": models.redlichkister,
"van Laar": models.vanlaar
}
latest_iteration = st.empty()
bar = st.progress(0)
for i in range(100):
latest_iteration.text(f'{i + 1}%')
bar.progress(i + 1)
time.sleep(0.03)
A, acc, fig4, fig5, fig6 = MODELS[model].main(
x1, y1, P, G_e, x, p1_s, p2_s, T, P_raoult)
if model == "Margules":
st.write(r"$G^E = %0.3fx_1x_2$" % A)
if model == "Redlich Kister":
st.write(r"$G^E = x_1x_2(%0.3f + (%0.3f)(x_1-x_2))$" %
(A[0], A[1]))
if model == "van Laar":
st.write(
r"$\frac{x_1x_2}{G^E} = \frac{x_1}{%0.3f} + \frac{x_2}{%0.3f}$"
% (A[1], A[0]))
st.write(r"$R^2$ score = %0.3f" % acc)
st.write(fig4, fig5, fig6)
else:
latest_iteration = st.empty()
bar = st.progress(0)
for i in range(100):
latest_iteration.text(f'{i + 1}%')
bar.progress(i + 1)
time.sleep(0.03)
A, acc, fig4, fig5, fig6 = models.wohls.main(
x1, y1, P, G_e, T, s1, s2)
st.write(r"Molar volumes: $q_1=%0.3e$, $q_2=%0.3e$" %
(q1, q2))
st.write(
r"$\frac{G^E/RT}{x_1q_1 + x_2q_2} = 2(%0.3f)z_1z_2$" %
A)
st.write(r"$R^2$ score = %0.3f" % acc)
st.write(fig4, fig5, fig6)
except:
pass
st.sidebar.title("Note")
st.sidebar.info(""" The saturation pressures are obtained from
[DDBST's database](http://ddbonline.ddbst.com/AntoineCalculation/AntoineCalculationCGI.exe?component=Ethanol)."""
)
st.sidebar.info(""" The densities are obtained from
[DDBST's database](http://ddbonline.ddbst.de/DIPPR105DensityCalculation/DIPPR105CalculationCGI.exe?component=Diethyl%20ether)."""
)
st.sidebar.info(
"These models can only be used for binary isothermal vapor-liquid equilibrium data."
)