def test_fragility(self):
#in
"""
btype=1
Thresh=array([3.41376, 27.31008, 51.2064, 102.4128])
SDcr=0.900320457314158
beta=0.5
#out
0.00384212302089559 4.41097158798698e-012 3.33066907387547e-016 0
Pr1=array([0.00384212302089559, 4.41097158798698e-012,
3.33066907387547e-016, 0])
Pr_sd=array([0.996157876979104, 0.00384212301648462,
4.41063852107959e-012, 3.33066907387547e-016, 0])
"""
threshold = array([3.41376, 27.31008, 51.2064, 102.4128])
value = 0.900320457314158
beta = 0.5
cdf = cumulative_state_probability(threshold, beta, value)
cdf_matlab = array([0.00384212302089559, 4.41097158798698e-012,
3.33066907387547e-016, 0])
assert allclose(cdf, cdf_matlab)
reduce_cumulative_to_pdf(cdf)
pdf=cdf
pdf_matlab = array([0.996157876979104, 0.00384212301648462,
4.41063852107959e-012, 3.33066907387547e-016, 0])
assert allclose(pdf, pdf_matlab[1:])
f1 = array((0.02, 0.1, 0.5, 1.0)) # [newaxis, newaxis, :]
ci = 1.4516
Area = 4351.656708
cost = 113.9023
damage_matlab = array([0.00875252912708771, 5.02381651487638e-011,
1.89685350759916e-014, 0])
assert allclose(damage_matlab, f1*pdf*cost)
cost_matlab = 55.2885441482002
assert allclose(cost_matlab, (f1*pdf*cost*Area*ci).sum())
def test_fragility(self):
#in
"""
btype=1
Thresh=array([3.41376, 27.31008, 51.2064, 102.4128])
SDcr=0.900320457314158
beta=0.5
#out
0.00384212302089559 4.41097158798698e-012 3.33066907387547e-016 0
Pr1=array([0.00384212302089559, 4.41097158798698e-012,
3.33066907387547e-016, 0])
Pr_sd=array([0.996157876979104, 0.00384212301648462,
4.41063852107959e-012, 3.33066907387547e-016, 0])
"""
threshold = array([3.41376, 27.31008, 51.2064, 102.4128])
value = 0.900320457314158
beta = 0.5
cdf = cumulative_state_probability(threshold, beta, value)
cdf_matlab = array([0.00384212302089559, 4.41097158798698e-012,
3.33066907387547e-016, 0])
assert allclose(cdf, cdf_matlab)
reduce_cumulative_to_pdf(cdf)
pdf=cdf
pdf_matlab = array([0.996157876979104, 0.00384212301648462,
4.41063852107959e-012, 3.33066907387547e-016, 0])
assert allclose(pdf, pdf_matlab[1:])
f1 = array((0.02, 0.1, 0.5, 1.0)) # [newaxis, newaxis, :]
ci = 1.4516
Area = 4351.656708
cost = 113.9023
damage_matlab = array([0.00875252912708771, 5.02381651487638e-011,
1.89685350759916e-014, 0])
assert allclose(damage_matlab, f1*pdf*cost)
cost_matlab = 55.2885441482002
assert allclose(cost_matlab, (f1*pdf*cost*Area*ci).sum())
def test_cumulative_state_probability(self):
blocking_block_comments = True
"""Test that cumulative_state_probability works the same way
as matlab function.
Test that reduce_cumulative_to_pdf 'looks' right - it should
look a bit like a bell curve - zero at ends with a max
somewhere in the middle, and sum=one.
"""
# test against matlab implementation:
beta = 0.4
value = 5.0
threshold = array((0.000001, 0.00001, 1, 1.5, 2, 3, 4, 5, 10, 100, 1000))
oldsettings = seterr(divide='ignore')
x = (1/beta)*log(value/threshold)
seterr(**oldsettings)
# matlab:
# Pr11 = normcdf2(1/THE_VUN_T.('beta_nsd_d')*log(SDcrAll./Thresh))
root2 = sqrt(2)
y = 0.5*(1 + erf(x/root2))
# matlab:
# root2 = sqrt(2);
# y = 0.5*(1+erf(x/root2))
y2 = cumulative_state_probability(threshold, beta, value)
assert allclose(y, y2)
reduce_cumulative_to_pdf(y)
# y should now look a bit like a bell curve - zero at ends
# increasing to the middle, sum=one.
assert (y.sum() == 1.0)
assert y[0] == 0
assert y[1] < y[2]
assert y[-3] > y[-2]
def test_reduce_cumulative_to_pdf(self):
cumulative = array([1.0, 0.6, 0.3, 0.1])
non_cummulative = cumulative[:]
reduce_cumulative_to_pdf(non_cummulative)
actual = array([0.4, 0.3, 0.2, 0.1])
assert allclose(asarray(non_cummulative),asarray(actual))
def test_reduce_cumulative_to_pdf(self):
cumulative = array([1.0, 0.6, 0.3, 0.1])
non_cummulative = cumulative[:]
reduce_cumulative_to_pdf(non_cummulative)
actual = array([0.4, 0.3, 0.2, 0.1])
assert allclose(asarray(non_cummulative),asarray(actual))
