def test_calc_annloss_deagg_distmagII(self):
# See documentation/annualised_loss_calc.xls
# for the calculations of the expected values.
#
bldg_value = scipy.array([2020, 2030])
saved_ecloss = scipy.array([[2000, 5],
[10, 2001],
[2000, 5],
[10, 2001]])
nu = scipy.array([0.01, 0.001, 0.01, 0.001])
saved_rjb = scipy.array([[5, 15],[5, 15],[5, 15],[5, 15]])
aus_mag = scipy.array([6.2, 6.7, 7.2, 7.7])
momag_labels = momag_bin = scipy.array([6.0,7.0, 8.0])
R_bin = [ 0, 10, 20]
Zlim = None
NormDeAggLoss = cadd.calc_annloss_deagg_distmag(
bldg_value, saved_ecloss, nu, saved_rjb,
aus_mag, momag_bin, R_bin, Zlim, R_extend_flag=False)
#print "NormDeAggLoss", NormDeAggLoss
expected_NormDeAggLoss = scipy.array([[2.387182086, 23.93186447],
[2.387182086, 23.93186447]])
# test return values
self.failUnless(scipy.allclose(NormDeAggLoss, expected_NormDeAggLoss))
def test_calc_annloss_deagg_distmagII(self):
# See documentation/annualised_loss_calc.xls
# for the calculations of the expected values.
#
bldg_value = scipy.array([2020, 2030])
saved_ecloss = scipy.array([[2000, 5], [10, 2001], [2000, 5],
[10, 2001]])
nu = scipy.array([0.01, 0.001, 0.01, 0.001])
saved_rjb = scipy.array([[5, 15], [5, 15], [5, 15], [5, 15]])
aus_mag = scipy.array([6.2, 6.7, 7.2, 7.7])
momag_labels = momag_bin = scipy.array([6.0, 7.0, 8.0])
R_bin = [0, 10, 20]
Zlim = None
NormDeAggLoss = cadd.calc_annloss_deagg_distmag(bldg_value,
saved_ecloss,
nu,
saved_rjb,
aus_mag,
momag_bin,
R_bin,
Zlim,
R_extend_flag=False)
#print "NormDeAggLoss", NormDeAggLoss
expected_NormDeAggLoss = scipy.array([[2.387182086, 23.93186447],
[2.387182086, 23.93186447]])
# test return values
self.failUnless(scipy.allclose(NormDeAggLoss, expected_NormDeAggLoss))
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_annloss.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
]
aus_mag = [
5.028646345960000e+000, 4.666194309470000e+000,
4.960717775140000e+000, 5.066894448200000e+000,
5.213679185270000e+000, 4.750346609700000e+000,
5.137028659350000e+000, 5.846435787360000e+000,
6.332776147850000e+000, 5.831299749560000e+000,
5.704055389670000e+000, 5.678562864290000e+000
]
saved_rjb = [
[4.7861000e+001, 5.3972000e+001, 4.7769001e+001, 5.2328999e+001],
[2.0844000e+001, 1.5184000e+001, 2.0931999e+001, 1.6514999e+001],
[4.1341999e+001, 4.7893002e+001, 4.1130001e+001, 4.6007000e+001],
[1.0091000e+001, 4.7954001e+000, 1.0547000e+001, 6.5434999e+000],
[1.1430000e+001, 7.0029001e+000, 1.1938000e+001, 8.7105999e+000],
[2.6070001e+002, 2.5430000e+002, 2.6089001e+002, 2.5610001e+002],
[1.8946001e+002, 1.9613000e+002, 1.8919000e+002, 1.9414999e+002],
[1.3391000e+001, 7.5120001e+000, 1.3538000e+001, 8.9108000e+000],
[1.4840000e+001, 1.1531000e+001, 1.4634000e+001, 1.2035000e+001],
[5.5563999e+001, 5.8155998e+001, 5.5868000e+001, 5.7826000e+001],
[2.1272000e+002, 2.0710001e+002, 2.1275999e+002, 2.0850000e+002],
[2.7423001e+002, 2.8089999e+002, 2.7397000e+002, 2.7894000e+002]
]
momag_bin = [
4.500000000000000e+000, 5.000000000000000e+000,
5.500000000000000e+000, 6.000000000000000e+000,
6.500000000000000e+000
]
momag_labels = [
4.500000000000000e+000, 5.000000000000000e+000,
5.500000000000000e+000, 6.000000000000000e+000,
6.500000000000000e+000
]
R_bin = [
0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80,
85, 90, 95, 100
]
Zlim = [0, 8]
outputdir = tempfile.mkdtemp(prefix='test_calc_annloss_')
expected_NormDeAggLoss = \
scipy.array([[ 0, 0,
0, 9.953154884489259e-001,
6.805837966835917e-001, 0,
0, 0,
1.478403601668658e-001, 1.397904170353129e+000,
0, 0,
0, 0,
0, 0,
0, 0,
0, 0,
0],
[1.026185781801060e+001, 6.351341814315093e+000,
8.029817774010610e+000, 0,
0, 0,
0, 0,
0, 4.133484588328035e-002,
0, 0,
0, 0,
0, 0,
0, 0,
0, 0,
0],
[ 0, 3.155337893093759e+000,
7.832757193963517e-001, 0,
0, 0,
0, 0,
0, 0,
0, 1.550199002799408e-001,
0, 0,
0, 0,
0, 0,
0, 0,
0],
[ 0, 0,
0, 0,
0, 0,
0, 0,
0, 0,
0, 0,
0, 0,
0, 0,
0, 0,
0, 0,
0]])
# call function
NormDeAggLoss = cadd.calc_annloss_deagg_distmag(saved_ecbval2,
saved_ecloss,
nu,
saved_rjb,
aus_mag,
momag_bin,
R_bin,
Zlim,
R_extend_flag=True)
# plot the data
o_file = os.path.join(outputdir, 'test.png')
padd.plot_annloss_deagg_distmag(
NormDeAggLoss,
momag_labels,
R_bin,
Zlim,
output_file=o_file,
title='test_plot_calc_annloss_deagg_distmag.py',
show_graph=show_graph,
grid=False,
annotate=[])
# make sure output file was created
self.failUnless(os.path.isfile(o_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_annloss.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]
aus_mag = [5.028646345960000e+000, 4.666194309470000e+000,
4.960717775140000e+000, 5.066894448200000e+000,
5.213679185270000e+000, 4.750346609700000e+000,
5.137028659350000e+000, 5.846435787360000e+000,
6.332776147850000e+000, 5.831299749560000e+000,
5.704055389670000e+000, 5.678562864290000e+000]
saved_rjb = [[4.7861000e+001, 5.3972000e+001, 4.7769001e+001, 5.2328999e+001],
[2.0844000e+001, 1.5184000e+001, 2.0931999e+001, 1.6514999e+001],
[4.1341999e+001, 4.7893002e+001, 4.1130001e+001, 4.6007000e+001],
[1.0091000e+001, 4.7954001e+000, 1.0547000e+001, 6.5434999e+000],
[1.1430000e+001, 7.0029001e+000, 1.1938000e+001, 8.7105999e+000],
[2.6070001e+002, 2.5430000e+002, 2.6089001e+002, 2.5610001e+002],
[1.8946001e+002, 1.9613000e+002, 1.8919000e+002, 1.9414999e+002],
[1.3391000e+001, 7.5120001e+000, 1.3538000e+001, 8.9108000e+000],
[1.4840000e+001, 1.1531000e+001, 1.4634000e+001, 1.2035000e+001],
[5.5563999e+001, 5.8155998e+001, 5.5868000e+001, 5.7826000e+001],
[2.1272000e+002, 2.0710001e+002, 2.1275999e+002, 2.0850000e+002],
[2.7423001e+002, 2.8089999e+002, 2.7397000e+002, 2.7894000e+002]]
momag_bin = [4.500000000000000e+000, 5.000000000000000e+000,
5.500000000000000e+000, 6.000000000000000e+000,
6.500000000000000e+000]
momag_labels = [4.500000000000000e+000, 5.000000000000000e+000,
5.500000000000000e+000, 6.000000000000000e+000,
6.500000000000000e+000]
R_bin = [ 0, 5, 10, 15, 20, 25, 30, 35, 40, 45,
50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100]
Zlim = [0, 8]
outputdir = tempfile.mkdtemp(prefix='test_calc_annloss_')
expected_NormDeAggLoss = \
scipy.array([[ 0, 0,
0, 9.953154884489259e-001,
6.805837966835917e-001, 0,
0, 0,
1.478403601668658e-001, 1.397904170353129e+000,
0, 0,
0, 0,
0, 0,
0, 0,
0, 0,
0],
[1.026185781801060e+001, 6.351341814315093e+000,
8.029817774010610e+000, 0,
0, 0,
0, 0,
0, 4.133484588328035e-002,
0, 0,
0, 0,
0, 0,
0, 0,
0, 0,
0],
[ 0, 3.155337893093759e+000,
7.832757193963517e-001, 0,
0, 0,
0, 0,
0, 0,
0, 1.550199002799408e-001,
0, 0,
0, 0,
0, 0,
0, 0,
0],
[ 0, 0,
0, 0,
0, 0,
0, 0,
0, 0,
0, 0,
0, 0,
0, 0,
0, 0,
0, 0,
0]])
# call function
NormDeAggLoss = cadd.calc_annloss_deagg_distmag(saved_ecbval2, saved_ecloss,
nu, saved_rjb, aus_mag,
momag_bin, R_bin, Zlim,
R_extend_flag=True)
# plot the data
o_file = os.path.join(outputdir, 'test.png')
padd.plot_annloss_deagg_distmag(NormDeAggLoss, momag_labels, R_bin, Zlim,
output_file=o_file,
title='test_plot_calc_annloss_deagg_distmag.py',
show_graph=show_graph, grid=False, annotate=[])
# make sure output file was created
self.failUnless(os.path.isfile(o_file))
# clean up
shutil.rmtree(outputdir, ignore_errors=True)
