def harness_plot_pml():
'''Harness to run plot_pml.py with demo data.
Input:
root_eqrm_dir path to the root directory for EQRM
'''
# get EQRM root path
eqrm_path = util.determine_eqrm_path()
# create paths to various sub-directories
datadir = os.path.join(eqrm_path, eqrm_filesystem.Resources_Data_Path)
demodir = os.path.join(eqrm_path,
eqrm_filesystem.Demo_Output_ProbRisk_Path)
outputdir = os.path.join(eqrm_path, eqrm_filesystem.Postprocessing_Path)
# get input file data into memory
saved_ecbval2 = \
load_data(os.path.join(eqrm_path,
eqrm_filesystem.Demo_Output_ProbRisk_Path,
site_tag + '_bval.txt'))
saved_ecloss = \
load_data(os.path.join(eqrm_path,
eqrm_filesystem.Demo_Output_ProbRisk_Path,
site_tag + '_total_building_loss.txt'))
saved_ecloss = saved_ecloss[1:]
saved_nu = load_data(
os.path.join(eqrm_path, eqrm_filesystem.Demo_Output_ProbRisk_Path,
site_tag + '_event_set.txt'))
saved_nu = saved_nu[:, 8]
# plot the data
out_file = 'harness_plot_pml.png'
pml_curve = plot_pml.plot_pml(saved_ecloss,
saved_ecbval2,
saved_nu,
outputdir,
title='Harness plot_pml()',
output_file=out_file,
grid=True,
datestamp=True,
show_graph=True)
# draw curve data on screen
print('pml_curve=%s' % str(pml_curve))
def harness_plot_pml():
'''Harness to run plot_pml.py with demo data.
Input:
root_eqrm_dir path to the root directory for EQRM
'''
# get EQRM root path
eqrm_path = util.determine_eqrm_path()
# create paths to various sub-directories
datadir = os.path.join(eqrm_path, eqrm_filesystem.Resources_Data_Path)
demodir= os.path.join(eqrm_path,
eqrm_filesystem.Demo_Output_ProbRisk_Path)
outputdir = os.path.join(eqrm_path, eqrm_filesystem.Postprocessing_Path)
# get input file data into memory
saved_ecbval2 = \
load_data(os.path.join(eqrm_path,
eqrm_filesystem.Demo_Output_ProbRisk_Path,
site_tag + '_bval.txt'))
saved_ecloss = \
load_data(os.path.join(eqrm_path,
eqrm_filesystem.Demo_Output_ProbRisk_Path,
site_tag + '_total_building_loss.txt'))
saved_ecloss = saved_ecloss[1:]
saved_nu = load_data(os.path.join(eqrm_path,
eqrm_filesystem.Demo_Output_ProbRisk_Path,
site_tag + '_event_set.txt'))
saved_nu = saved_nu[:, 8]
# plot the data
out_file = 'harness_plot_pml.png'
pml_curve = plot_pml.plot_pml(saved_ecloss, saved_ecbval2, saved_nu,
outputdir, title='Harness plot_pml()',
output_file=out_file, grid=True,
datestamp=True, show_graph=True)
# draw curve data on screen
print('pml_curve=%s' % str(pml_curve))
def test_real_world_small(self, show_graph=False):
# don't test if DISPLAY environment variable undefined
if sys.platform != 'win32':
try:
display = os.environ['DISPLAY']
except KeyError:
return
# create function input values.
# values are from MatLab execution of calc_pml.m
# with 'format long e' controlling display precision
# and a thinned data set.
saved_ecloss = [[3.6033316e+002, 0, 0, 0],
[4.3791719e+003, 5.4416445e+003, 1.5130650e+002, 1.1839250e+003],
[4.3966499e+001, 4.6514600e+003, 4.4796530e+002, 0],
[3.2344959e+004, 7.7890422e+004, 0, 2.1740315e+003],
[3.7524958e+003, 8.5406250e+003, 2.0106344e+004, 4.2080391e+004],
[ 0, 0, 0, 0],
[ 0, 0, 0, 0],
[6.8155151e+003, 7.4778169e+003, 4.0293285e+004, 1.8229467e+005],
[4.7653789e+004, 5.7597363e+005, 4.2247324e+004, 3.2269982e+004],
[ 0, 1.1022014e+004, 0, 0],
[ 0, 0, 0, 0],
[ 0, 0, 0, 0]]
saved_ecbval2 = [1.657488601000000e+006,
1.297431077000000e+006,
1.559989253000000e+006,
7.915145563000001e+005]
nu = [4.630686287680000e-003,
1.060828942660000e-002,
5.316739231220000e-003,
4.033159153080000e-003,
3.059464090050000e-003,
1.267327623180000e-001,
4.828586998700000e-002,
4.916231738260000e-004,
1.801492034580000e-004,
5.811644129970000e-004,
1.804422031540000e-002,
1.336441244060000e-002]
outputdir = tempfile.mkdtemp(prefix='test_plot_pml_')
expected_pml_curve=[scipy.array([9.516258196404048e-002, 6.001990726997575e-002, 3.758725213253833e-002,
2.343477581582976e-002, 1.457079605966405e-002, 9.044030246264989e-003,
5.607631459973006e-003, 3.474649972298738e-003, 2.152115561384393e-003,
1.332632687555568e-003, 8.250636329376615e-004, 5.107664918124755e-004,
3.161777712868963e-004, 1.957150234642713e-004, 1.211454271629053e-004,
7.498660929694534e-005, 4.641481113543122e-005, 2.872943563536623e-005,
1.778263598739560e-005, 1.100688113631065e-005, 6.812897482721958e-006,
4.216956142943928e-006, 2.610153809201599e-006, 1.615596793347329e-006,
9.999994999843054e-007]),
scipy.array([1.581815795898438e+002, 2.665191955566406e+002, 3.335764770507813e+002,
5.759092285156250e+003, 3.321068750000000e+004, 6.659575781250000e+004,
1.010225859375000e+005, 1.501917812500000e+005, 1.911231718750000e+005,
2.164582968750000e+005, 2.321398750000000e+005, 3.878219687500000e+005,
5.704695625000000e+005, 6.835221875000000e+005, 6.981447500000000e+005,
6.981447500000000e+005, 6.981447500000000e+005, 6.981447500000000e+005,
6.981447500000000e+005, 6.981447500000000e+005, 6.981447500000000e+005,
6.981447500000000e+005, 6.981447500000000e+005, 6.981447500000000e+005,
6.981447500000000e+005]),
scipy.array([2.980945263197025e-003, 5.022576810812553e-003, 6.286276959408505e-003,
1.085305818304859e-001, 6.258582184306246e-001, 1.255002695730662e+000,
1.903779187229967e+000, 2.830376836855518e+000, 3.601732359515218e+000,
4.079174935680411e+000, 4.374695603462225e+000, 7.308537844334971e+000,
1.075054721631848e+001, 1.288103350846180e+001, 1.315659693710628e+001,
1.315659693710628e+001, 1.315659693710628e+001, 1.315659693710628e+001,
1.315659693710628e+001, 1.315659693710628e+001, 1.315659693710628e+001,
1.315659693710628e+001, 1.315659693710628e+001, 1.315659693710628e+001,
1.315659693710628e+001])]
# call function
output_file = os.path.join(outputdir, 'test.eps')
plot_pml.plot_pml(expected_pml_curve, title='Test plot_pml()',
output_file=output_file, grid=True,
show_graph=show_graph)
# ensure file expected was generated
self.failUnless(os.path.isfile(output_file))
# clean up
shutil.rmtree(outputdir, ignore_errors=True)
def test_real_world_small(self, show_graph=False):
# don't test if DISPLAY environment variable undefined
if sys.platform != 'win32':
try:
display = os.environ['DISPLAY']
except KeyError:
return
# create function input values.
# values are from MatLab execution of calc_pml.m
# with 'format long e' controlling display precision
# and a thinned data set.
saved_ecloss = [
[3.6033316e+002, 0, 0, 0],
[4.3791719e+003, 5.4416445e+003, 1.5130650e+002, 1.1839250e+003],
[4.3966499e+001, 4.6514600e+003, 4.4796530e+002, 0],
[3.2344959e+004, 7.7890422e+004, 0, 2.1740315e+003],
[3.7524958e+003, 8.5406250e+003, 2.0106344e+004, 4.2080391e+004],
[0, 0, 0, 0], [0, 0, 0, 0],
[6.8155151e+003, 7.4778169e+003, 4.0293285e+004, 1.8229467e+005],
[4.7653789e+004, 5.7597363e+005, 4.2247324e+004, 3.2269982e+004],
[0, 1.1022014e+004, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]
]
saved_ecbval2 = [
1.657488601000000e+006, 1.297431077000000e+006,
1.559989253000000e+006, 7.915145563000001e+005
]
nu = [
4.630686287680000e-003, 1.060828942660000e-002,
5.316739231220000e-003, 4.033159153080000e-003,
3.059464090050000e-003, 1.267327623180000e-001,
4.828586998700000e-002, 4.916231738260000e-004,
1.801492034580000e-004, 5.811644129970000e-004,
1.804422031540000e-002, 1.336441244060000e-002
]
outputdir = tempfile.mkdtemp(prefix='test_plot_pml_')
expected_pml_curve = [
scipy.array([
9.516258196404048e-002, 6.001990726997575e-002,
3.758725213253833e-002, 2.343477581582976e-002,
1.457079605966405e-002, 9.044030246264989e-003,
5.607631459973006e-003, 3.474649972298738e-003,
2.152115561384393e-003, 1.332632687555568e-003,
8.250636329376615e-004, 5.107664918124755e-004,
3.161777712868963e-004, 1.957150234642713e-004,
1.211454271629053e-004, 7.498660929694534e-005,
4.641481113543122e-005, 2.872943563536623e-005,
1.778263598739560e-005, 1.100688113631065e-005,
6.812897482721958e-006, 4.216956142943928e-006,
2.610153809201599e-006, 1.615596793347329e-006,
9.999994999843054e-007
]),
scipy.array([
1.581815795898438e+002, 2.665191955566406e+002,
3.335764770507813e+002, 5.759092285156250e+003,
3.321068750000000e+004, 6.659575781250000e+004,
1.010225859375000e+005, 1.501917812500000e+005,
1.911231718750000e+005, 2.164582968750000e+005,
2.321398750000000e+005, 3.878219687500000e+005,
5.704695625000000e+005, 6.835221875000000e+005,
6.981447500000000e+005, 6.981447500000000e+005,
6.981447500000000e+005, 6.981447500000000e+005,
6.981447500000000e+005, 6.981447500000000e+005,
6.981447500000000e+005, 6.981447500000000e+005,
6.981447500000000e+005, 6.981447500000000e+005,
6.981447500000000e+005
]),
scipy.array([
2.980945263197025e-003, 5.022576810812553e-003,
6.286276959408505e-003, 1.085305818304859e-001,
6.258582184306246e-001, 1.255002695730662e+000,
1.903779187229967e+000, 2.830376836855518e+000,
3.601732359515218e+000, 4.079174935680411e+000,
4.374695603462225e+000, 7.308537844334971e+000,
1.075054721631848e+001, 1.288103350846180e+001,
1.315659693710628e+001, 1.315659693710628e+001,
1.315659693710628e+001, 1.315659693710628e+001,
1.315659693710628e+001, 1.315659693710628e+001,
1.315659693710628e+001, 1.315659693710628e+001,
1.315659693710628e+001, 1.315659693710628e+001,
1.315659693710628e+001
])
]
# call function
output_file = os.path.join(outputdir, 'test.eps')
plot_pml.plot_pml(expected_pml_curve,
title='Test plot_pml()',
output_file=output_file,
grid=True,
show_graph=show_graph)
# ensure file expected was generated
self.failUnless(os.path.isfile(output_file))
# clean up
shutil.rmtree(outputdir, ignore_errors=True)
