def test_bet(self):
# Carbon black reference material isotherm (N2 at 77K)
Qads = np.array([
4.39005, 4.67017, 4.79068, 4.9767, 5.14414, 5.31144, 5.47106,
5.63297, 5.80559, 5.96663, 6.13574, 6.31214, 6.49764, 6.67154,
6.85255, 7.04053, 7.22571, 7.40778, 7.59634, 7.7832, 7.96568,
8.1623, 8.34863, 8.54383, 8.74695, 8.94871, 9.16214, 9.38208,
9.61289, 9.8577, 10.12, 10.397, 10.6852, 11.0089, 11.3574, 11.7373,
12.1611, 12.6289, 13.1794, 13.819, 14.57, 15.4858, 16.6535, 18.2409
])
Prel = np.array([
0.0433547, 0.0672921, 0.0796994, 0.0999331, 0.119912, 0.140374,
0.159884, 0.179697, 0.200356, 0.219646, 0.239691, 0.259671,
0.280475, 0.299907, 0.320048, 0.340746, 0.360882, 0.380708,
0.400956, 0.421168, 0.440603, 0.460924, 0.480902, 0.500572,
0.521144, 0.540715, 0.560852, 0.580887, 0.600803, 0.62089, 0.64084,
0.66093, 0.68071, 0.70082, 0.72096, 0.74084, 0.76081, 0.78045,
0.80084, 0.82107, 0.84075, 0.86069, 0.88041, 0.90023
])
pmin = 0.05
pmax = 0.3
r = bet.bet(Prel, Qads, Pmin=pmin, Pmax=pmax, csa=0.162)
np.testing.assert_almost_equal(r.sa, 20.7049, 3)
np.testing.assert_almost_equal(r.sa_err, 0.0289, 4)
np.testing.assert_almost_equal(r.C, 149.959660, 2)
np.testing.assert_almost_equal(r.q_m, 4.7569, 4)
Pfit, Qfit = util.restrict_isotherm(Prel, Qads, pmin, pmax)
roq = bet.Isotherm2RoquerolBET(Pfit, Qfit)
np.testing.assert_almost_equal(roq[0], 4.3559, 4)
np.testing.assert_almost_equal(roq[6], 4.62074, 4)
def test_bet(self):
# Carbon black reference material isotherm (N2 at 77K)
Qads = np.array([4.39005, 4.67017, 4.79068, 4.9767, 5.14414, 5.31144,
5.47106, 5.63297, 5.80559, 5.96663, 6.13574, 6.31214,
6.49764, 6.67154, 6.85255, 7.04053, 7.22571, 7.40778,
7.59634, 7.7832, 7.96568, 8.1623, 8.34863, 8.54383,
8.74695, 8.94871, 9.16214, 9.38208, 9.61289, 9.8577,
10.12, 10.397, 10.6852, 11.0089, 11.3574, 11.7373,
12.1611, 12.6289, 13.1794, 13.819, 14.57, 15.4858,
16.6535, 18.2409])
Prel = np.array([0.0433547, 0.0672921, 0.0796994, 0.0999331, 0.119912,
0.140374, 0.159884, 0.179697, 0.200356, 0.219646,
0.239691, 0.259671, 0.280475, 0.299907, 0.320048,
0.340746, 0.360882, 0.380708, 0.400956, 0.421168,
0.440603, 0.460924, 0.480902, 0.500572, 0.521144,
0.540715, 0.560852, 0.580887, 0.600803, 0.62089,
0.64084, 0.66093, 0.68071, 0.70082, 0.72096, 0.74084,
0.76081, 0.78045, 0.80084, 0.82107, 0.84075, 0.86069,
0.88041, 0.90023])
pmin = 0.05
pmax = 0.3
r = bet.bet(Prel, Qads, Pmin = pmin, Pmax = pmax, csa=0.162)
np.testing.assert_almost_equal( r.sa, 20.7049, 3 )
np.testing.assert_almost_equal( r.sa_err, 0.0289, 4 )
np.testing.assert_almost_equal( r.C, 149.959660, 2 )
np.testing.assert_almost_equal( r.q_m, 4.7569, 4 )
Pfit, Qfit = util.restrict_isotherm(Prel,Qads,pmin,pmax)
roq = bet.Isotherm2RoquerolBET(Pfit,Qfit )
np.testing.assert_almost_equal( roq[0], 4.3559, 4 )
np.testing.assert_almost_equal( roq[6], 4.62074, 4 )
def test_restrict(self):
s = ex.carbon_black()
P,Q = util.restrict_isotherm( s.Prel, s.Qads, 0.05, 0.3 )
self.assertTrue( Q.shape == P.shape )
Qads = np.array([4.67017, 4.79068, 4.9767, 5.14414, 5.31144, 5.47106,
5.63297, 5.80559, 5.96663, 6.13574, 6.31214, 6.49764,
6.67154])
Prel = np.array([0.0672921, 0.0796994, 0.0999331, 0.119912, 0.140374,
0.159884, 0.179697, 0.200356, 0.219646, 0.239691,
0.259671, 0.280475, 0.299907])
self.assertTrue( (Qads == Q).all() )
self.assertTrue( (Prel == P).all() )
def test_restrict(self):
s = ex.carbon_black()
P, Q = util.restrict_isotherm(s.Prel, s.Qads, 0.05, 0.3)
self.assertTrue(Q.shape == P.shape)
Qads = np.array([
4.67017, 4.79068, 4.9767, 5.14414, 5.31144, 5.47106, 5.63297,
5.80559, 5.96663, 6.13574, 6.31214, 6.49764, 6.67154
])
Prel = np.array([
0.0672921, 0.0796994, 0.0999331, 0.119912, 0.140374, 0.159884,
0.179697, 0.200356, 0.219646, 0.239691, 0.259671, 0.280475,
0.299907
])
self.assertTrue((Qads == Q).all())
self.assertTrue((Prel == P).all())
def bjh_plot(x=None):
"""
This was created assuming the matplotlib inline backend is used (%matplotlib inline)
If %matplotlib notebook is used it may be more efficient to create a graph
once and use the ipywidgets to update/change the data
Takes an argument b/c wgt.observe calls BJHPlot(wgt.value) but we need the
value of all the wgts to do the calculation so we use global vars instead
"""
# Remove old graph but wait until new graph is made so the page won't change position
clear_output(wait=True)
fig = plt.figure(figsize=(15.5, 11))
# Adjust space between VolPlot and AreaPlot
fig.subplots_adjust(hspace=0.25)
# Create VolPlot
vol_plot = fig.add_subplot(2, 1, 1)
vol_plot.set_title('Cumulative Volume')
vol_plot.grid()
# Set x-axis (pore radius)
vol_plot.set_xlabel('Pore Radius [$\AA$]')
vol_plot.set_xscale('log')
vol_plot.xaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter())
vol_plot.set_xticks([5, 10, 50, 100, 500, 1000])
# Set y-axis 1 (cumulative pore volume)
vol_plot.set_ylabel('Pore Volume [cm3/g]')
# Create AreaPlot
area_plot = fig.add_subplot(2, 1, 2)
area_plot.set_title('Cumulative Area')
area_plot.grid()
# Set x-axis (pore radius)
area_plot.set_xlabel('Pore Radius [$\AA$]')
area_plot.set_xscale('log')
area_plot.xaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter())
area_plot.set_xticks([5, 10, 50, 100, 500, 1000])
# Set y-axis 1 (cumulative pore area)
area_plot.set_ylabel('Pore Area [m2/g]')
# Get currently selected widget values
selected_cor = cor_wgt.value
selected_thk = thk_wgt.value
selected_iso0 = iso_wgt.value[0]
selected_iso1 = iso_wgt.value[1]
# Restrict Prel if needed
if selected_iso0 == 0.0 and selected_iso1 == 1.0:
r_prel, r_qads = prel, qads
else:
r_prel, r_qads = util.restrict_isotherm(prel, qads, selected_iso0, selected_iso1)
# Run BJH Calculation
bjh_data = bjh_dict[selected_cor](r_prel, r_qads, APF, DCF, thk_dict[selected_thk], adsorption)
r, v, a = bjh_data.PoreRadii, bjh_data.cumV, bjh_data.cumA
# Plot Volume
vol_plot.plot(r, v, 'or-', alpha=0.7)
vol_plot.autoscale_view(True, True, True)
# Set Volume y-axis 2 (derivative)
d_vol_plot = vol_plot.twinx()
d_vol_plot.set_ylabel('Derivative [cm3/g]')
# Calculate Volume Derivative (this might differ from MicroActive)
smooth_v = intrp.splrep(np.log(r[::-1]), v[::-1], s=0.0002)
dV = intrp.splev(np.log(r[::-1]), smooth_v, der=1)
dV = -dV[::-1]*np.log(10)
# Plot Volume Derivative
d_vol_plot.plot(r, dV, 'oc-', alpha=0.7)
d_vol_plot.autoscale_view(True, True, True)
# Plot Area
area_plot.plot(r, a, 'or-', alpha=0.7)
area_plot.autoscale_view(True, True, True)
# Set Area y-axis 2 (derivative)
d_area_plot = area_plot.twinx()
d_area_plot.set_ylabel('Derivative [m2/g]')
# Calculate Area Derivative (this might differ from MicroActive)
smooth_a = intrp.splrep(np.log(r[::-1]), a[::-1], s=0.0002)
dA = intrp.splev(np.log(r[::-1]), smooth_a, der=1)
dA = -dA[::-1]*np.log(10)
# Plot Area Derivative
d_area_plot.plot(r, dA, 'oc-', alpha=0.7)
d_area_plot.autoscale_view(True, True, True)
