def run():
capacity = get_capacity('PC1', 'high', 7, 24, 2, 1990, 'very_poor', 'poor')
output = build_spectrum(
hazard,
pref_periods,
insert=[capacity['elastic_period'], capacity['ultimate_period']],
finish_val=0)
demand = make_demand_spectrum(output)
damped_demand = damp(demand, capacity, mag, r_rup)
fig1 = plt.figure()
plt.title('Input Hazard')
plt.plot([p['x'] for p in hazard], [p['y'] for p in hazard],
'o',
label='Input Hazard')
plt.plot([p['x'] for p in output], [p['y'] for p in output],
label='Expanded Hazard')
plt.xlabel('Period (s)')
plt.ylabel('Spectral Acceleration (%g)')
plt.legend()
fig2 = plt.figure()
plt.title('Demand Curve')
plt.plot([p['disp'] for p in demand], [p['y'] for p in demand],
label='Raw Demand')
plt.plot([p['disp'] for p in damped_demand],
[p['y'] for p in damped_demand],
label='Damped Demand')
plt.xlabel('Spectral Displacement (inches)')
plt.ylabel('Spectral Acceleration (%g)')
plt.legend()
return fig1, fig2
def run():
capacity = get_capacity('PC1', 'high', 7, 24, 2, 1990, 'very_poor', 'poor')
output = build_spectrum(
hazard,
pref_periods,
insert=[capacity['elastic_period'], capacity['ultimate_period']],
finish_val=0)
demand = make_demand_spectrum(output)
# damping
kappa = get_kappa(capacity['performance_rating'], capacity['year'], mag,
r_rup)
b_eff = get_b_eff(capacity, kappa)
beta = build_spectrum(b_eff, sanaz.t)
dsf = get_dsf(beta, mag, r_rup)
fig1 = plt.figure()
plt.title('Beta Effective comparison')
plt.plot([p['x'] for p in beta_check], [p['y'] for p in beta_check],
'o',
label='Verification Curve')
plt.plot([p['x'] for p in b_eff], [p['y'] for p in b_eff],
'o',
label='Calculated Beta')
plt.xlabel('Period (s)')
plt.ylabel('Beta Effective')
plt.xlim(0, 12)
plt.legend()
fig2 = plt.figure()
plt.title('DSF Comparison')
plt.plot([p['x'] for p in dsf_check], [p['y'] for p in dsf_check],
label='Verification Curve')
plt.plot([p['x'] for p in dsf], [p['y'] for p in dsf],
label='Calculated DSF')
plt.xlabel('Period (s)')
plt.ylabel('DSF')
plt.legend()
return fig1, fig2
def test_runs(self):
hazard = [{
'x': .03,
'y': 1.1377
}, {
'x': 1.0,
'y': .8302
}, {
'x': 3.0,
'y': .348
}]
mag = 6.7
r_rup = 20
capacity = get_capacity('C2', 'high', 1, 24, 2, 1990, 'very_poor',
'poor')
demand = make_demand_spectrum(hazard)
damped_demand = damp(demand, capacity, mag, r_rup)
for i in range(len(damped_demand) - 1):
self.assertTrue(damped_demand[i]['acc'] <= demand[i]['acc'])
def test_Runs(self):
# run shakecast example
hazard = [{
'x': .03,
'y': 1.1377
}, {
'x': 1.0,
'y': .8302
}, {
'x': 3.0,
'y': .348
}]
hazard_beta = .5
mag = 6.7
r_rup = 20
capacity = get_capacity('C2', 'high', 1, 24, 2, 1990, 'very_poor',
'poor')
results = run(capacity, hazard, hazard_beta, mag, r_rup)
for result in results:
self.assertIsNotNone(result)
def test_LargeHazardSmallCapacity(self):
# run shakecast example
hazard = [{
'x': .03,
'y': 3.0
}, {
'x': 1.0,
'y': 2.5
}, {
'x': 3.0,
'y': 1.4
}]
hazard_beta = .5
mag = 6.7
r_rup = 20
capacity = get_capacity('C2', 'low', 1, 24, 2, 1990, 'very_poor',
'poor')
results = run(capacity, hazard, hazard_beta, mag, r_rup)
for result in results:
self.assertIsNotNone(result)
def run():
# run shakecast example
hazard = [{
'x': .01,
'y': .46
}, {
'x': .3,
'y': 1.1377
}, {
'x': 1.0,
'y': .8302
}, {
'x': 3.0,
'y': .348
}]
hazard_beta = .5
mag = 6.7
r_rup = 20
capacity = get_capacity('C2', 'high', 1, 24, 2, 1990, 'very_poor', 'poor')
output = build_spectrum(
hazard,
pref_periods,
insert=[capacity['elastic_period'], capacity['ultimate_period']],
finish_val=0)
undamped_demand = make_demand_spectrum(output)
damped_demand = damp(undamped_demand, capacity, mag, r_rup)
kappa = get_kappa(capacity['performance_rating'], capacity['year'], mag,
r_rup)
b_eff = get_b_eff(capacity, kappa)
beta = build_spectrum(b_eff, sanaz_t)
dsf = get_dsf(beta, mag, r_rup)
(damage_probs, capacity, demand, lower_demand, upper_demand,
med_intersections, lower_intersections,
upper_intersections) = run_aebm(capacity, hazard, hazard_beta, mag, r_rup)
cap_fig = plt.figure()
plt.plot([p['disp'] for p in capacity['curve']],
[p['acc'] for p in capacity['curve']],
'b',
label='Capacity Curve')
plt.xlabel('Spectral Displacement (inches)')
plt.ylabel('Spectral Acceleration (%g)')
plt.title('Capacity Curve')
plt.xlim(0, 10)
haz_fig = plt.figure()
plt.title('Input Hazard')
plt.plot([p['x'] for p in hazard], [p['y'] for p in hazard],
'o',
label='Input Hazard')
plt.plot([p['x'] for p in output], [p['y'] for p in output],
label='Expanded Hazard')
plt.xlabel('Period (s)')
plt.ylabel('Spectral Acceleration (%g)')
plt.legend()
dsf_fig = plt.figure()
plt.title('DSF Curve')
plt.plot([p['x'] for p in dsf], [p['y'] for p in dsf])
plt.xlabel('Period (s)')
plt.ylabel('DSF')
plt.legend()
dem_fig = plt.figure()
plt.title('Demand Curve')
plt.plot([p['disp'] for p in undamped_demand],
[p['acc'] for p in undamped_demand],
label='Raw Demand')
plt.plot([p['disp'] for p in damped_demand],
[p['acc'] for p in damped_demand],
label='Damped Demand')
plt.xlabel('Spectral Displacement (inches)')
plt.ylabel('Spectral Acceleration (%g)')
plt.xlim(0, xmax=demand[-1]['period'] * 2)
plt.legend()
pp_fig = plt.figure()
plt.plot([p['disp'] for p in demand], [p['acc'] for p in demand],
'-r',
label='Median Demand')
plt.plot([p['disp'] for p in upper_demand],
[p['acc'] for p in upper_demand],
label='Upper bound demand')
plt.plot([p['disp'] for p in lower_demand],
[p['acc'] for p in lower_demand],
label='Lower bound demand')
plt.plot([p['disp'] for p in capacity['curve']],
[p['acc'] for p in capacity['curve']],
'b',
label='Capacity Curve')
intersections = [
med_intersections, lower_intersections, upper_intersections
]
# intersections
plt.plot([p['disp'] for p in intersections],
[p['acc'] for p in intersections],
'yo',
label='Intersections')
plt.xlim(0, 20)
plt.title('Performance Point Calculation\nDemand: {0:.2f} in, \
Spectral Acceleration: {1:.2f} %g'.format(
med_intersections['disp'], med_intersections['acc']))
plt.xlabel('Spectral Displacement (inches)')
plt.ylabel('Spectral Acceleration (%g)')
plt.legend()
impact_fig = plt.figure()
plt.ylim(0, 100)
n, s, m, e, c = plt.bar([1, 2, 3, 4, 5], [
damage_probs['none'] * 100, damage_probs['slight'] * 100,
damage_probs['moderate'] * 100, damage_probs['extensive'] * 100,
damage_probs['complete'] * 100
])
n.set_facecolor('gray')
s.set_facecolor('g')
m.set_facecolor('gold')
e.set_facecolor('orange')
c.set_facecolor('r')
plt.ylabel('Probability of Exceedance (%)')
plt.title('Potential Impact')
plt.xticks([1, 2, 3, 4, 5],
['None', 'Slight', 'Moderate', 'Extensive', 'Complete'])
return cap_fig, haz_fig, dsf_fig, dem_fig, pp_fig, impact_fig