def write_gmm_data_file(model_name, mag, dist, result_type,
periods, file_out,
component_type="AVERAGE_HORIZONTAL",):
"""
Create a file of input and output parameters for the sommerville GMM.
params:
model_name: The ground motion model, as a string.
mag: dictionary, key - the mag column name, values, the mag vectors,
as a list
dist: dictionary, key - the distance column name, value,
the distance vectors, as a list.
result_type: MEAN or TOTAL_STDDEV
periods: A list of periods requiring SA values.
The first value has to be 0.0.
Mag, distance and periods will be iterated over to give a single SA for
each combination.
file_out: The file name and location of the produced data file.
"""
assert periods[0] == 0.0
handle = open(file_out, 'wb')
writer = csv.writer(handle, delimiter=',', quoting=csv.QUOTE_NONE)
# write title
title = [mag[0], dist[0], 'result_type', 'component_type'] + periods[1:] + \
['pga']
writer.writerow(title)
# prepare the coefficients
model = Ground_motion_specification(model_name)
coeff = model.calc_coefficient(periods)
coeff = reshape(coeff, (coeff.shape[0], 1, 1, coeff.shape[1]))
sigma_coeff = model.calc_sigma_coefficient(periods)
sigma_coeff = reshape(sigma_coeff, (sigma_coeff.shape[0], 1, 1,
sigma_coeff.shape[1]))
# Iterate
for magi in mag[1]:
for disti in dist[1]:
dist_args = {'mag': array([[[magi]]]),
dist[0]: array([[[disti]]]),
'coefficient': coeff,
'sigma_coefficient': sigma_coeff}
log_mean, log_sigma = model.distribution(**dist_args)
sa_mod = list(log_mean.reshape(-1))
sa_mod = [math.exp(x) for x in sa_mod]
sigma_mod = list(log_sigma.reshape(-1))
if result_type == 'MEAN':
row = [magi, disti, result_type, component_type] + \
sa_mod[1:] + \
[sa_mod[0]]
else:
row = [magi, disti, result_type, component_type] + \
sigma_mod[1:] + \
[sigma_mod[0]]
writer.writerow(row)
handle.close()
def write_gmm_data_file_depth(
model_name, mag, dist, depth, result_type, periods, file_out, component_type="AVERAGE_HORIZONTAL"
):
"""
Create a file of input and output parameters for the sommerville GMM.
params:
model_name: The ground motion model, as a string.
mag: dictionary, key - the mag column name, values, the mag vectors,
as a list
dist: dictionary, key - the distance column name, value,
the distance vectors, as a list.
depth: depth in km.
result_type: MEAN or TOTAL_STDDEV
periods: A list of periods requiring SA values.
The first value has to be 0.0.
Mag, distance and periods will be iterated over to give a single SA for
each combination.
file_out: The file name and location of the produced data file.
"""
assert periods[0] == 0.0
handle = open(file_out, "wb")
writer = csv.writer(handle, delimiter=",", quoting=csv.QUOTE_NONE)
# write title
title = [depth[0], mag[0], dist[0], "result_type", "component_type"] + periods[1:] + ["pga"]
writer.writerow(title)
# prepare the coefficients
model = Ground_motion_specification(model_name)
coeff = model.calc_coefficient(periods)
coeff = reshape(coeff, (coeff.shape[0], 1, 1, coeff.shape[1]))
sigma_coeff = model.calc_sigma_coefficient(periods)
sigma_coeff = reshape(sigma_coeff, (sigma_coeff.shape[0], 1, 1, sigma_coeff.shape[1]))
# Iterate
for depi in depth[1]:
for magi in mag[1]:
for disti in dist[1]:
dist_args = {
"mag": array([[[magi]]]),
dist[0]: array([[[disti]]]),
"depth": array([[[depi]]]),
"coefficient": coeff,
"sigma_coefficient": sigma_coeff,
}
log_mean, log_sigma = model.distribution(**dist_args)
sa_mod = list(log_mean.reshape(-1))
sa_mod = [math.exp(x) for x in sa_mod]
sigma_mod = list(log_sigma.reshape(-1))
if result_type == "MEAN":
row = [depi, magi, disti, result_type, component_type] + sa_mod[1:] + [sa_mod[0]]
else:
row = [depi, magi, disti, result_type, component_type] + sigma_mod[1:] + [sigma_mod[0]]
writer.writerow(row)
handle.close()
def test_Boore_08(self):
c_tab = array([
#T(s) e1 e2 e3 e4 e5 e6 e7 Mh c1 c2 c3 Mref Rref h blin Vref b1 b2 V1 V2 a1 pga_low a2 Sig TauU SigTU TauM SigTM
[0.010,-0.52883,-0.49429,-0.74551,-0.49966,0.28897,-0.10019,0.00000,6.75,-0.66220, 0.12000,-0.01151,4.5, 1.0, 1.35,-0.360,760, -0.640,-0.14,180,300,0.03,0.06, 0.09,0.502,0.267,0.569,0.262,0.566],
[0.020,-0.52192,-0.48508,-0.73906,-0.48895,0.25144,-0.11006,0.00000,6.75,-0.66600, 0.12280,-0.01151,4.5, 1.0, 1.35,-0.340,760, -0.630,-0.12,180,300,0.03,0.06, 0.09,0.502,0.267,0.569,0.262,0.566],
[0.030,-0.45285,-0.41831,-0.66722,-0.42229,0.17976,-0.12858,0.00000,6.75,-0.69010, 0.12830,-0.01151,4.5, 1.0, 1.35,-0.330,760, -0.620,-0.11,180,300,0.03,0.06, 0.09,0.507,0.276,0.578,0.274,0.576],
[0.050,-0.28476,-0.25022,-0.48462,-0.26092,0.06369,-0.15752,0.00000,6.75,-0.71700, 0.13170,-0.01151,4.5, 1.0, 1.35,-0.290,760, -0.640,-0.11,180,300,0.03,0.06, 0.09,0.516,0.286,0.589,0.286,0.589],
[0.075, 0.00767, 0.04912,-0.20578, 0.02706,0.01170,-0.17051,0.00000,6.75,-0.72050, 0.12370,-0.01151,4.5, 1.0, 1.55,-0.230,760, -0.640,-0.11,180,300,0.03,0.06, 0.09,0.513,0.322,0.606,0.320,0.606],
[0.10, 0.20109, 0.23102, 0.03058, 0.22193,0.04697,-0.15948,0.00000,6.75,-0.70810, 0.11170,-0.01151,4.5, 1.0, 1.68,-0.250,760, -0.600,-0.13,180,300,0.03,0.06, 0.09,0.520,0.313,0.608,0.318,0.608],
[0.15, 0.46128, 0.48661, 0.30185, 0.49328,0.17990,-0.14539,0.00000,6.75,-0.69610, 0.09884,-0.01113,4.5, 1.0, 1.86,-0.280,760, -0.530,-0.18,180,300,0.03,0.06, 0.09,0.518,0.288,0.592,0.290,0.594],
[0.20, 0.57180, 0.59253, 0.40860, 0.61472,0.52729,-0.12964,0.00102,6.75,-0.58300, 0.04273,-0.00952,4.5, 1.0, 1.98,-0.310,760, -0.520,-0.19,180,300,0.03,0.06, 0.09,0.523,0.283,0.596,0.288,0.596],
[0.25, 0.51884, 0.53496, 0.33880, 0.57747,0.60880,-0.13843,0.08607,6.75,-0.57260, 0.02977,-0.00837,4.5, 1.0, 2.07,-0.390,760, -0.520,-0.16,180,300,0.03,0.06, 0.09,0.527,0.267,0.592,0.267,0.592],
[0.30, 0.43825, 0.44516, 0.25356, 0.51990,0.64472,-0.15694,0.10601,6.75,-0.55430, 0.01955,-0.00750,4.5, 1.0, 2.14,-0.440,760, -0.520,-0.14,180,300,0.03,0.06, 0.09,0.546,0.272,0.608,0.269,0.608],
[0.40, 0.39220, 0.40602, 0.21398, 0.46080,0.78610,-0.07843,0.02262,6.75,-0.64430, 0.04394,-0.00626,4.5, 1.0, 2.24,-0.500,760, -0.510,-0.10,180,300,0.03,0.06, 0.09,0.541,0.267,0.603,0.267,0.603],
[0.50, 0.18957, 0.19878, 0.00967, 0.26337,0.76837,-0.09054,0.00000,6.75,-0.69140, 0.06080,-0.00540,4.5, 1.0, 2.32,-0.600,760, -0.500,-0.06,180,300,0.03,0.06, 0.09,0.555,0.265,0.615,0.265,0.615],
[0.75, -0.21338,-0.19496,-0.49176,-0.10813,0.75179,-0.14053,0.10302,6.75,-0.74080, 0.07518,-0.00409,4.5, 1.0, 2.46,-0.690,760, -0.470, 0.00,180,300,0.03,0.06, 0.09,0.571,0.311,0.649,0.299,0.645],
[1.00, -0.46896,-0.43443,-0.78465,-0.39330,0.67880,-0.18257,0.05393,6.75,-0.81830, 0.10270,-0.00334,4.5, 1.0, 2.54,-0.700,760, -0.440, 0.00,180,300,0.03,0.06, 0.09,0.573,0.318,0.654,0.302,0.647],
[1.50, -0.86271,-0.79593,-1.20902,-0.88085,0.70689,-0.25950,0.19082,6.75,-0.83030, 0.09793,-0.00255,4.5, 1.0, 2.66,-0.720,760, -0.400, 0.00,180,300,0.03,0.06, 0.09,0.566,0.382,0.684,0.373,0.679],
[2.00, -1.22652,-1.15514,-1.57697,-1.27669,0.77989,-0.29657,0.29888,6.75,-0.82850, 0.09432,-0.00217,4.5, 1.0, 2.73,-0.730,760, -0.380, 0.00,180,300,0.03,0.06, 0.09,0.580,0.398,0.702,0.389,0.700],
[3.00, -1.82979,-1.74690,-2.22584,-1.91814,0.77966,-0.45384,0.67466,6.75,-0.78440, 0.07282,-0.00191,4.5, 1.0, 2.83,-0.740,760, -0.340, 0.00,180,300,0.03,0.06, 0.09,0.566,0.410,0.700,0.401,0.695],
[4.00, -2.24656,-2.15906,-2.58228,-2.38168,1.24961,-0.35874,0.79508,6.75,-0.68540, 0.03758,-0.00191,4.5, 1.0, 2.89,-0.750,760, -0.310, 0.00,180,300,0.03,0.06, 0.09,0.583,0.394,0.702,0.385,0.698],
[5.00, -1.28408,-1.21270,-1.50904,-1.41093,0.14271,-0.39006,0.00000,8.50,-0.50960,-0.02391,-0.00191,4.5, 1.0, 2.93,-0.750,760, -0.291, 0.00,180,300,0.03,0.06, 0.09,0.601,0.414,0.730,0.437,0.744],
[7.50, -1.43145,-1.31632,-1.81022,-1.59217,0.52407,-0.37578,0.00000,8.50,-0.37240,-0.06568,-0.00191,4.5, 1.0, 3.00,-0.692,760, -0.247, 0.00,180,300,0.03,0.06, 0.09,0.626,0.465,0.781,0.477,0.787],
[10.0, -2.15446,-2.16137,-2.53323,-2.14635,0.40387,-0.48492,0.00000,8.50,-0.09824,-0.13800,-0.00191,4.5, 1.0, 3.04,-0.650,760, -0.215, 0.00,180,300,0.03,0.06, 0.09,0.645,0.355,0.735,0.477,0.801],
[ 0.0, -0.53804,-0.50350,-0.75472,-0.50970,0.28805,-0.10164,0.00000,6.75,-0.66050, 0.11970,-0.01151,4.5, 1.0, 1.35,-0.360,760, -0.640,-0.14,180,300,0.03,0.06, 0.09,0.502,0.265,0.566,0.260,0.564],
[-1.0, 5.00121, 5.04727, 4.63188, 5.08210,0.18322,-0.12736,0.00000,8.50,-0.87370, 0.10060,-0.00334,4.5, 1.0, 2.54,-0.600,760, -0.500,-0.06,180,300,0.03,0.06, 0.09,0.500,0.286,0.576,0.256,0.560],
])
# generate coefficients for varying periods
#[c1, c2, c3, h, e1, e2, e3, e4, e5, e6, e7, mh, sig, tu, sigtu, tm, sigtm, blin, b1, b2]
# 9 10 11 14 1 2 3 4 5 6 7 8 24 25 26 27 28 15 17 18
# 0.01
c_dict = {}
periods = [('0.01', 0), ('0.20', 7), ('1.00', 13), ('3.00', 16)]
for (T, i) in periods:
c = array([c_tab[i][9], c_tab[i][10], c_tab[i][11], c_tab[i][14], c_tab[i][1],
c_tab[i][2], c_tab[i][3], c_tab[i][4], c_tab[i][5], c_tab[i][6],
c_tab[i][7], c_tab[i][8], c_tab[i][24], c_tab[i][25], c_tab[i][26],
c_tab[i][27], c_tab[i][28], c_tab[i][15], c_tab[i][17], c_tab[i][18]])
c.shape = (-1, 1, 1, 1)
c_dict[T] = c
s_tab = array([[0.566, 0.569, 0.569, 0.578, 0.589, 0.606, 0.608, 0.592, 0.596, 0.592, 0.608,
0.603, 0.615, 0.649, 0.654, 0.684, 0.702, 0.7, 0.702, 0.73, 0.781, 0.735]])
s_dict = {}
periods = [('0.01', 0), ('0.20', 0), ('1.00', 0), ('3.00', 0)]
for (T, i) in periods:
s = s_tab[i]
s.shape = (-1, 1, 1, 1)
s_dict[T] = s
mrtol = 1.0e-3
matol = 1.0e-3
srtol = 5.0e-3
satol = 5.0e-3
# now run all tests
model_name = 'Boore_08'
model = Ground_motion_specification(model_name)
# T=0.01, M=4.0, R=5.0, Vs30=600.0
R = array([[[5.000000]]])
M = array([[[4.000000]]])
Vs30 = array([[[600.000000]]])
fault_type = array([[[2]]])
coefficient = c_dict['0.01']
sigma_coefficient = s_dict['0.01']
(log_mean, sigma) = model.distribution(mag=M, Joyner_Boore=R, Vs30=Vs30,
coefficient=coefficient, sigma_coefficient=sigma_coefficient,
fault_type=fault_type)
msg1 = 'log_mean: T=0.01, M=4.0, R=5.0, Vs30=600.0: got=%s, expected=[[[-3.179415]]]' % str(log_mean)
msg2 = 'abs_delta=%f, rel_delta=%f' % (abs(log_mean--3.179415), abs((log_mean--3.179415)/log_mean))
self.failUnless(allclose(log_mean, array([[[-3.179415]]]), atol=matol, rtol=mrtol), msg1+'\n'+msg2)
msg1 = 'sigma: T=0.01, M=4.0, R=5.0, Vs30=600.0: got=%s, expected=[[[0.566000]]]' % str(sigma)
msg2 = 'abs_delta=%f, rel_delta=%f' % (abs(sigma-0.566000), abs((sigma-0.566000)/sigma))
self.failUnless(allclose(sigma, array([[[0.566000]]]), atol=satol, rtol=srtol), msg1+'\n'+msg2)
# T=0.01, M=4.0, R=10.0, Vs30=600.0
R = array([[[10.000000]]])
M = array([[[4.000000]]])
Vs30 = array([[[600.000000]]])
coefficient = c_dict['0.01']
sigma_coefficient = s_dict['0.01']
(log_mean, sigma) = model.distribution(mag=M, Joyner_Boore=R, Vs30=Vs30,
coefficient=coefficient, sigma_coefficient=sigma_coefficient,
fault_type=fault_type)
msg1 = 'log_mean: T=0.01, M=4.0, R=10.0, Vs30=600.0: got=%s, expected=[[[-3.717279]]]' % str(log_mean)
msg2 = 'abs_delta=%f, rel_delta=%f' % (abs(log_mean--3.717279), abs((log_mean--3.717279)/log_mean))
self.failUnless(allclose(log_mean, array([[[-3.717279]]]), atol=matol, rtol=mrtol), msg1+'\n'+msg2)
msg1 = 'sigma: T=0.01, M=4.0, R=10.0, Vs30=600.0: got=%s, expected=[[[0.566000]]]' % str(sigma)
msg2 = 'abs_delta=%f, rel_delta=%f' % (abs(sigma-0.566000), abs((sigma-0.566000)/sigma))
self.failUnless(allclose(sigma, array([[[0.566000]]]), atol=satol, rtol=srtol), msg1+'\n'+msg2)
# T=0.01, M=4.0, R=25.0, Vs30=600.0
R = array([[[25.000000]]])
M = array([[[4.000000]]])
Vs30 = array([[[600.000000]]])
coefficient = c_dict['0.01']
sigma_coefficient = s_dict['0.01']
(log_mean, sigma) = model.distribution(mag=M, Joyner_Boore=R, Vs30=Vs30,
coefficient=coefficient, sigma_coefficient=sigma_coefficient,
fault_type=fault_type)
msg1 = 'log_mean: T=0.01, M=4.0, R=25.0, Vs30=600.0: got=%s, expected=[[[-4.545958]]]' % str(log_mean)
msg2 = 'abs_delta=%f, rel_delta=%f' % (abs(log_mean--4.545958), abs((log_mean--4.545958)/log_mean))
self.failUnless(allclose(log_mean, array([[[-4.545958]]]), atol=matol, rtol=mrtol), msg1+'\n'+msg2)
msg1 = 'sigma: T=0.01, M=4.0, R=25.0, Vs30=600.0: got=%s, expected=[[[0.566000]]]' % str(sigma)
msg2 = 'abs_delta=%f, rel_delta=%f' % (abs(sigma-0.566000), abs((sigma-0.566000)/sigma))
self.failUnless(allclose(sigma, array([[[0.566000]]]), atol=satol, rtol=srtol), msg1+'\n'+msg2)
# T=0.20, M=8.0, R=5.0, Vs30=600.0
R = array([[[5.000000]]])
M = array([[[8.000000]]])
Vs30 = array([[[600.000000]]])
coefficient = c_dict['0.20']
sigma_coefficient = s_dict['0.20']
(log_mean, sigma) = model.distribution(mag=M, Joyner_Boore=R, Vs30=Vs30,
coefficient=coefficient, sigma_coefficient=sigma_coefficient,
fault_type=fault_type)
msg1 = 'log_mean: T=0.20, M=8.0, R=5.0, Vs30=600.0: got=%s, expected=[[[-0.169129]]]' % str(log_mean)
msg2 = 'abs_delta=%f, rel_delta=%f' % (abs(log_mean--0.169129), abs((log_mean--0.169129)/log_mean))
self.failUnless(allclose(log_mean, array([[[-0.169129]]]), atol=matol, rtol=mrtol), msg1+'\n'+msg2)
msg1 = 'sigma: T=0.20, M=8.0, R=5.0, Vs30=600.0: got=%s, expected=[[[0.596000]]]' % str(sigma)
msg2 = 'abs_delta=%f, rel_delta=%f' % (abs(sigma-0.596000), abs((sigma-0.596000)/sigma))
self.failUnless(allclose(sigma, array([[[0.596000]]]), atol=satol, rtol=srtol), msg1+'\n'+msg2)
# T=0.20, M=4.0, R=5.0, Vs30=600.0
R = array([[[5.000000]]])
M = array([[[4.000000]]])
Vs30 = array([[[600.000000]]])
coefficient = c_dict['0.20']
sigma_coefficient = s_dict['0.20']
(log_mean, sigma) = model.distribution(mag=M, Joyner_Boore=R, Vs30=Vs30,
coefficient=coefficient, sigma_coefficient=sigma_coefficient,
fault_type=fault_type)
msg1 = 'log_mean: T=0.20, M=4.0, R=5.0, Vs30=600.0: got=%s, expected=[[[-2.798686]]]' % str(log_mean)
msg2 = 'abs_delta=%f, rel_delta=%f' % (abs(log_mean--2.798686), abs((log_mean--2.798686)/log_mean))
self.failUnless(allclose(log_mean, array([[[-2.798686]]]), atol=matol, rtol=mrtol), msg1+'\n'+msg2)
msg1 = 'sigma: T=0.20, M=4.0, R=5.0, Vs30=600.0: got=%s, expected=[[[0.596000]]]' % str(sigma)
msg2 = 'abs_delta=%f, rel_delta=%f' % (abs(sigma-0.596000), abs((sigma-0.596000)/sigma))
self.failUnless(allclose(sigma, array([[[0.596000]]]), atol=satol, rtol=srtol), msg1+'\n'+msg2)
# T=0.20, M=4.0, R=25.0, Vs30=600.0
R = array([[[25.000000]]])
M = array([[[4.000000]]])
Vs30 = array([[[600.000000]]])
coefficient = c_dict['0.20']
sigma_coefficient = s_dict['0.20']
(log_mean, sigma) = model.distribution(mag=M, Joyner_Boore=R, Vs30=Vs30,
coefficient=coefficient, sigma_coefficient=sigma_coefficient,
fault_type=fault_type)
msg1 = 'log_mean: T=0.20, M=4.0, R=25.0, Vs30=600.0: got=%s, expected=[[[-3.916533]]]' % str(log_mean)
msg2 = 'abs_delta=%f, rel_delta=%f' % (abs(log_mean--3.916533), abs((log_mean--3.916533)/log_mean))
self.failUnless(allclose(log_mean, array([[[-3.916533]]]), atol=matol, rtol=mrtol), msg1+'\n'+msg2)
msg1 = 'sigma: T=0.20, M=4.0, R=25.0, Vs30=600.0: got=%s, expected=[[[0.596000]]]' % str(sigma)
msg2 = 'abs_delta=%f, rel_delta=%f' % (abs(sigma-0.596000), abs((sigma-0.596000)/sigma))
self.failUnless(allclose(sigma, array([[[0.596000]]]), atol=satol, rtol=srtol), msg1+'\n'+msg2)
# T=1.00, M=4.0, R=5.0, Vs30=600.0
R = array([[[5.000000]]])
M = array([[[4.000000]]])
Vs30 = array([[[600.000000]]])
coefficient = c_dict['1.00']
sigma_coefficient = s_dict['1.00']
(log_mean, sigma) = model.distribution(mag=M, Joyner_Boore=R, Vs30=Vs30,
coefficient=coefficient, sigma_coefficient=sigma_coefficient,
fault_type=fault_type)
msg1 = 'log_mean: T=1.00, M=4.0, R=5.0, Vs30=600.0: got=%s, expected=[[[-5.031195]]]' % str(log_mean)
msg2 = 'abs_delta=%f, rel_delta=%f' % (abs(log_mean--5.031195), abs((log_mean--5.031195)/log_mean))
self.failUnless(allclose(log_mean, array([[[-5.031195]]]), atol=matol, rtol=mrtol), msg1+'\n'+msg2)
msg1 = 'sigma: T=1.00, M=4.0, R=5.0, Vs30=600.0: got=%s, expected=[[[0.647000]]]' % str(sigma)
msg2 = 'abs_delta=%f, rel_delta=%f' % (abs(sigma-0.647000), abs((sigma-0.647000)/sigma))
self.failUnless(allclose(sigma, array([[[0.647000]]]), atol=satol, rtol=srtol), msg1+'\n'+msg2)
# T=1.00, M=4.0, R=10.0, Vs30=600.0
R = array([[[10.000000]]])
M = array([[[4.000000]]])
Vs30 = array([[[600.000000]]])
coefficient = c_dict['1.00']
sigma_coefficient = s_dict['1.00']
(log_mean, sigma) = model.distribution(mag=M, Joyner_Boore=R, Vs30=Vs30,
coefficient=coefficient, sigma_coefficient=sigma_coefficient,
fault_type=fault_type)
msg1 = 'log_mean: T=1.00, M=4.0, R=10.0, Vs30=600.0: got=%s, expected=[[[-5.576974]]]' % str(log_mean)
msg2 = 'abs_delta=%f, rel_delta=%f' % (abs(log_mean--5.576974), abs((log_mean--5.576974)/log_mean))
self.failUnless(allclose(log_mean, array([[[-5.576974]]]), atol=matol, rtol=mrtol), msg1+'\n'+msg2)
msg1 = 'sigma: T=1.00, M=4.0, R=10.0, Vs30=600.0: got=%s, expected=[[[0.647000]]]' % str(sigma)
msg2 = 'abs_delta=%f, rel_delta=%f' % (abs(sigma-0.647000), abs((sigma-0.647000)/sigma))
self.failUnless(allclose(sigma, array([[[0.647000]]]), atol=satol, rtol=srtol), msg1+'\n'+msg2)
# T=1.00, M=4.0, R=25.0, Vs30=600.0
R = array([[[25.000000]]])
M = array([[[4.000000]]])
Vs30 = array([[[600.000000]]])
coefficient = c_dict['1.00']
sigma_coefficient = s_dict['1.00']
(log_mean, sigma) = model.distribution(mag=M, Joyner_Boore=R, Vs30=Vs30,
coefficient=coefficient, sigma_coefficient=sigma_coefficient,
fault_type=fault_type)
msg1 = 'log_mean: T=1.00, M=4.0, R=25.0, Vs30=600.0: got=%s, expected=[[[-6.400938]]]' % str(log_mean)
msg2 = 'abs_delta=%f, rel_delta=%f' % (abs(log_mean--6.400938), abs((log_mean--6.400938)/log_mean))
self.failUnless(allclose(log_mean, array([[[-6.400938]]]), atol=matol, rtol=mrtol), msg1+'\n'+msg2)
msg1 = 'sigma: T=1.00, M=4.0, R=25.0, Vs30=600.0: got=%s, expected=[[[0.647000]]]' % str(sigma)
msg2 = 'abs_delta=%f, rel_delta=%f' % (abs(sigma-0.647000), abs((sigma-0.647000)/sigma))
self.failUnless(allclose(sigma, array([[[0.647000]]]), atol=satol, rtol=srtol), msg1+'\n'+msg2)
def __init__(self, ground_motion_model_name, periods):
"""
Args:
ground_motion_model_name: A string, naming the ground motion model
periods: The periods that will be used for this simulation. Used
to calculate coefficient and sigma_coefficient.
RESIZING NOTES
Adding lots of extra dimensions.
'distance' had dimension [site]*[events]
'magnitude' had dimension [events]
'coefficient' had dimension [number of coefficients]*[Period]
'sigma_coefficient' had dimension [number of coefficients]*[Period]
Now 'distance' and 'magnitude' have dimension:
[bonus dimension!]*[site]*[events]
once 'coefficient' and 'sigma_coefficient are unpacked (ie
c1,c2,c4,c6,c7,c10=c), they have dimension:
[bonus dimension!]*[site]*[events]
Note that some of these dimensions are degenerate (newaxises),
such as [site] for 'magnitude'.
newaxis is used to broadcast arrays into higher dimensions:
a=array([1,2,3])
b=array([0,1])
a=a[...,newaxis]
print a
>[[1]
> [2]
> [3]]
# if a is added to a 1D array of length n; a will act as:
# [[1, 1, ... (n times],
# [2, 2, ... (n times],
# [3, 3, ... (n times]]
# (this is just broadcasting rules)
a+b
>array([[1, 2],
> [2, 3],
> [3, 4]])
Note that all [bonus dimensions] are degenerate.
They are there because once the distribution is sampled,
I want it to maintain the same number of dimension. I don't
want it to add an extra dimension for the spawnings. If that
happens, then it is hard to address futher samplings (from
soil) or multi-models in a uniform manner.
so ground_motion_from_toro =[gmd_torro]
sampled ground_motion_from_toro = [gmd1,gmd2,gmd3, ... (n samples)]
Uniform behaviour.
"""
self.GM_spec = Ground_motion_specification(ground_motion_model_name)
periods = asarray(periods)
# calc the coefficient and sigma_coefficient for the input periods
coefficient = self.GM_spec.calc_coefficient(periods)
sigma_coefficient = self.GM_spec.calc_sigma_coefficient(periods)
# Adding extra dimensions.
self.coefficient = coefficient[:, newaxis, newaxis, :]
self.sigma_coefficient = sigma_coefficient[:, newaxis, newaxis, :]
class Ground_motion_calculator(object):
"""Ground_motion_calculator instances are used to calculate the ground
motion given a ground motion specifiction.
Attributes:
GM_spec: instance of Ground_motion_specification
coefficient: ground motion coefficient for the given periods
sigma_coefficient: ground motion sigma coefficient for the given periods
periods: the periods
"""
def __init__(self, ground_motion_model_name, periods):
"""
Args:
ground_motion_model_name: A string, naming the ground motion model
periods: The periods that will be used for this simulation. Used
to calculate coefficient and sigma_coefficient.
RESIZING NOTES
Adding lots of extra dimensions.
'distance' had dimension [site]*[events]
'magnitude' had dimension [events]
'coefficient' had dimension [number of coefficients]*[Period]
'sigma_coefficient' had dimension [number of coefficients]*[Period]
Now 'distance' and 'magnitude' have dimension:
[bonus dimension!]*[site]*[events]
once 'coefficient' and 'sigma_coefficient are unpacked (ie
c1,c2,c4,c6,c7,c10=c), they have dimension:
[bonus dimension!]*[site]*[events]
Note that some of these dimensions are degenerate (newaxises),
such as [site] for 'magnitude'.
newaxis is used to broadcast arrays into higher dimensions:
a=array([1,2,3])
b=array([0,1])
a=a[...,newaxis]
print a
>[[1]
> [2]
> [3]]
# if a is added to a 1D array of length n; a will act as:
# [[1, 1, ... (n times],
# [2, 2, ... (n times],
# [3, 3, ... (n times]]
# (this is just broadcasting rules)
a+b
>array([[1, 2],
> [2, 3],
> [3, 4]])
Note that all [bonus dimensions] are degenerate.
They are there because once the distribution is sampled,
I want it to maintain the same number of dimension. I don't
want it to add an extra dimension for the spawnings. If that
happens, then it is hard to address futher samplings (from
soil) or multi-models in a uniform manner.
so ground_motion_from_toro =[gmd_torro]
sampled ground_motion_from_toro = [gmd1,gmd2,gmd3, ... (n samples)]
Uniform behaviour.
"""
self.GM_spec = Ground_motion_specification(ground_motion_model_name)
periods = asarray(periods)
# calc the coefficient and sigma_coefficient for the input periods
coefficient = self.GM_spec.calc_coefficient(periods)
sigma_coefficient = self.GM_spec.calc_sigma_coefficient(periods)
# Adding extra dimensions.
self.coefficient = coefficient[:, newaxis, newaxis, :]
self.sigma_coefficient = sigma_coefficient[:, newaxis, newaxis, :]
def distribution_function(self,
dist_object,
dist_types,
mag_dict,
periods=None,
depth=None,
depth_to_top=None,
fault_type=None,
Vs30=None,
mag_type=None,
Z25=None,
dip=None,
width=None,
event_activity=None):
"""
dist_object must give distance info if dist_object.distance(dist_type)
is called. The distance info must be an array.
Returns:
log_mean - dimensions are
log_sigma
FIXME: Why should we let depth be None?
"""
# dist_type and mag_type are attributes of self.GM_spec
# we shouldn't pass them around.
distances = {}
for dist_type in dist_types:
distances[dist_type] = dist_object.distance(dist_type)
mag = mag_dict[mag_type]
if depth is not None:
depth = asarray(depth)
(mag, depth, depth_to_top, fault_type, dip,
width) = self.resize_mag_depth(mag, depth, depth_to_top, fault_type,
dip, width)
for dist_type in dist_types:
distances[dist_type] = self.resize_dist(distances[dist_type],
mag.size)
# This is calling the distribution functions described in the
# ground_motion_interface module.
# We add the new 'dist_object' parameter to cater to models that
# require more than one distance. Once all existing models use
# the new parameter we can remove the 'distance' parameter.
distribution_args = {
'mag': mag,
'coefficient': self.coefficient,
'sigma_coefficient': self.sigma_coefficient,
'depth': depth,
'depth_to_top': depth_to_top,
'fault_type': fault_type,
'Vs30': Vs30,
'Z25': Z25,
'dip': dip,
'width': width,
'periods': periods
}
for dist_type in dist_types:
distribution_args[dist_type] = distances[dist_type]
(log_mean, log_sigma) = self.GM_spec.distribution(**distribution_args)
# FIXME when will this fail? Maybe let it fail then?
# If it does not fail here it fails in analysis.py"
#, line 427, in main
# assert isfinite(bedrock_SA).all()
# example of log_mean when this failed log_mean [[[ NaN NaN NaN]]]
# An Mw of 0.0 caused it.
# this is a good place to catch this error
assert isfinite(log_mean).all()
assert isfinite(log_sigma).all()
return (log_mean, log_sigma)
def resize_mag_depth(self, mag, depth, depth_to_top, fault_type, dip,
width):
"""
Warning, Toro_1997_midcontinent_distribution assumes
that this occurs. So if resizing is changed,
Toro_1997_midcontinent_distribution needs to be changed as well
"""
if mag.size == 1:
mag = mag.reshape([1])
if depth is not None:
depth = depth.reshape([1]) # Don't know if we have to do this
if depth_to_top is not None:
depth_to_top = depth_to_top.reshape([1])
if fault_type is not None:
fault_type = fault_type.reshape([1])
# resize depth, depth_to_top, etc
if depth is not None:
depth = depth[newaxis, :, newaxis]
# collapsed arrays are a bad idea...
if depth_to_top is not None:
depth_to_top = array(depth_to_top)[newaxis, :, newaxis]
if fault_type is not None:
fault_type = array(fault_type)[newaxis, :, newaxis]
if dip is not None:
dip = array(dip)[newaxis, :, newaxis]
if width is not None:
width = array(width)[newaxis, :, newaxis]
assert len(mag.shape) == 1
mag = mag[newaxis, :, newaxis]
return (mag, depth, depth_to_top, fault_type, dip, width)
def resize_dist(self, dist, mag_size):
"""
Warning, Toro_1997_midcontinent_distribution assumes
that this occurs. So if resizing is changed,
Toro_1997_midcontinent_distribution needs to be changed as well
"""
# [4.5,5.5,6.0] => site * mag * T
assert len(dist.shape) < 3
if not len(dist.shape) == 2:
# if distances is collapsed
if dist.size == 1:
# if distances is size 1
dist = dist.reshape((1, 1))
else:
assert len(dist.shape) == 1
if mag_size > 1:
assert dist.size == mag_size
# therefore distance is 1 site * n magnitudes
dist = dist.reshape((1, mag_size))
else:
# therefore distance is n site * 1 magnitudes
dist = dist.reshape((dist.size, 1))
# collapsed arrays are a bad idea...
dist = dist[:, :, newaxis]
# [[30.0,35.0],[45.0,20.0]]=> [site*mag] * T
return dist
def __init__(self, ground_motion_model_name, periods):
"""
Args:
ground_motion_model_name: A string, naming the ground motion model
periods: The periods that will be used for this simulation. Used
to calculate coefficient and sigma_coefficient.
RESIZING NOTES
Adding lots of extra dimensions.
'distance' had dimension [site]*[events]
'magnitude' had dimension [events]
'coefficient' had dimension [number of coefficients]*[Period]
'sigma_coefficient' had dimension [number of coefficients]*[Period]
Now 'distance' and 'magnitude' have dimension:
[bonus dimension!]*[site]*[events]
once 'coefficient' and 'sigma_coefficient are unpacked (ie
c1,c2,c4,c6,c7,c10=c), they have dimension:
[bonus dimension!]*[site]*[events]
Note that some of these dimensions are degenerate (newaxises),
such as [site] for 'magnitude'.
newaxis is used to broadcast arrays into higher dimensions:
a=array([1,2,3])
b=array([0,1])
a=a[...,newaxis]
print a
>[[1]
> [2]
> [3]]
# if a is added to a 1D array of length n; a will act as:
# [[1, 1, ... (n times],
# [2, 2, ... (n times],
# [3, 3, ... (n times]]
# (this is just broadcasting rules)
a+b
>array([[1, 2],
> [2, 3],
> [3, 4]])
Note that all [bonus dimensions] are degenerate.
They are there because once the distribution is sampled,
I want it to maintain the same number of dimension. I don't
want it to add an extra dimension for the spawnings. If that
happens, then it is hard to address futher samplings (from
soil) or multi-models in a uniform manner.
so ground_motion_from_toro =[gmd_torro]
sampled ground_motion_from_toro = [gmd1,gmd2,gmd3, ... (n samples)]
Uniform behaviour.
"""
self.GM_spec = Ground_motion_specification(ground_motion_model_name)
periods = asarray(periods)
# calc the coefficient and sigma_coefficient for the input periods
coefficient = self.GM_spec.calc_coefficient(periods)
sigma_coefficient = self.GM_spec.calc_sigma_coefficient(periods)
# Adding extra dimensions.
self.coefficient = coefficient[:, newaxis, newaxis, :]
self.sigma_coefficient = sigma_coefficient[:, newaxis, newaxis, :]
class Ground_motion_calculator(object):
"""Ground_motion_calculator instances are used to calculate the ground
motion given a ground motion specifiction.
Attributes:
GM_spec: instance of Ground_motion_specification
coefficient: ground motion coefficient for the given periods
sigma_coefficient: ground motion sigma coefficient for the given periods
periods: the periods
"""
def __init__(self, ground_motion_model_name, periods):
"""
Args:
ground_motion_model_name: A string, naming the ground motion model
periods: The periods that will be used for this simulation. Used
to calculate coefficient and sigma_coefficient.
RESIZING NOTES
Adding lots of extra dimensions.
'distance' had dimension [site]*[events]
'magnitude' had dimension [events]
'coefficient' had dimension [number of coefficients]*[Period]
'sigma_coefficient' had dimension [number of coefficients]*[Period]
Now 'distance' and 'magnitude' have dimension:
[bonus dimension!]*[site]*[events]
once 'coefficient' and 'sigma_coefficient are unpacked (ie
c1,c2,c4,c6,c7,c10=c), they have dimension:
[bonus dimension!]*[site]*[events]
Note that some of these dimensions are degenerate (newaxises),
such as [site] for 'magnitude'.
newaxis is used to broadcast arrays into higher dimensions:
a=array([1,2,3])
b=array([0,1])
a=a[...,newaxis]
print a
>[[1]
> [2]
> [3]]
# if a is added to a 1D array of length n; a will act as:
# [[1, 1, ... (n times],
# [2, 2, ... (n times],
# [3, 3, ... (n times]]
# (this is just broadcasting rules)
a+b
>array([[1, 2],
> [2, 3],
> [3, 4]])
Note that all [bonus dimensions] are degenerate.
They are there because once the distribution is sampled,
I want it to maintain the same number of dimension. I don't
want it to add an extra dimension for the spawnings. If that
happens, then it is hard to address futher samplings (from
soil) or multi-models in a uniform manner.
so ground_motion_from_toro =[gmd_torro]
sampled ground_motion_from_toro = [gmd1,gmd2,gmd3, ... (n samples)]
Uniform behaviour.
"""
self.GM_spec = Ground_motion_specification(ground_motion_model_name)
periods = asarray(periods)
# calc the coefficient and sigma_coefficient for the input periods
coefficient = self.GM_spec.calc_coefficient(periods)
sigma_coefficient = self.GM_spec.calc_sigma_coefficient(periods)
# Adding extra dimensions.
self.coefficient = coefficient[:, newaxis, newaxis, :]
self.sigma_coefficient = sigma_coefficient[:, newaxis, newaxis, :]
def distribution_function(self, dist_object, dist_types, mag_dict,
periods=None, depth=None, depth_to_top=None,
fault_type=None, Vs30=None, mag_type=None,
Z25=None, dip=None, width=None,
event_activity=None):
"""
dist_object must give distance info if dist_object.distance(dist_type)
is called. The distance info must be an array.
Returns:
log_mean - dimensions are
log_sigma
FIXME: Why should we let depth be None?
"""
# dist_type and mag_type are attributes of self.GM_spec
# we shouldn't pass them around.
distances = {}
for dist_type in dist_types:
distances[dist_type] = dist_object.distance(dist_type)
mag = mag_dict[mag_type]
if depth is not None:
depth = asarray(depth)
(mag, depth, depth_to_top, fault_type,
dip, width) = self.resize_mag_depth(mag, depth, depth_to_top,
fault_type, dip, width)
for dist_type in dist_types:
distances[dist_type] = self.resize_dist(distances[dist_type],
mag.size)
# This is calling the distribution functions described in the
# ground_motion_interface module.
# We add the new 'dist_object' parameter to cater to models that
# require more than one distance. Once all existing models use
# the new parameter we can remove the 'distance' parameter.
distribution_args = {'mag': mag,
'coefficient': self.coefficient,
'sigma_coefficient': self.sigma_coefficient,
'depth': depth,
'depth_to_top': depth_to_top,
'fault_type': fault_type,
'Vs30': Vs30,
'Z25': Z25,
'dip': dip,
'width': width,
'periods': periods}
for dist_type in dist_types:
distribution_args[dist_type] = distances[dist_type]
(log_mean, log_sigma) = self.GM_spec.distribution(**distribution_args)
# FIXME when will this fail? Maybe let it fail then?
# If it does not fail here it fails in analysis.py"
#, line 427, in main
# assert isfinite(bedrock_SA).all()
# example of log_mean when this failed log_mean [[[ NaN NaN NaN]]]
# An Mw of 0.0 caused it.
# this is a good place to catch this error
assert isfinite(log_mean).all()
assert isfinite(log_sigma).all()
return (log_mean, log_sigma)
def resize_mag_depth(self, mag, depth, depth_to_top,
fault_type, dip, width):
"""
Warning, Toro_1997_midcontinent_distribution assumes
that this occurs. So if resizing is changed,
Toro_1997_midcontinent_distribution needs to be changed as well
"""
if mag.size == 1:
mag = mag.reshape([1])
if depth is not None:
depth = depth.reshape([1]) # Don't know if we have to do this
if depth_to_top is not None:
depth_to_top = depth_to_top.reshape([1])
if fault_type is not None:
fault_type = fault_type.reshape([1])
# resize depth, depth_to_top, etc
if depth is not None:
depth = depth[newaxis, :, newaxis]
# collapsed arrays are a bad idea...
if depth_to_top is not None:
depth_to_top = array(depth_to_top)[newaxis, :, newaxis]
if fault_type is not None:
fault_type = array(fault_type)[newaxis, :, newaxis]
if dip is not None:
dip = array(dip)[newaxis, :, newaxis]
if width is not None:
width = array(width)[newaxis, :, newaxis]
assert len(mag.shape) == 1
mag = mag[newaxis, :, newaxis]
return (mag, depth, depth_to_top, fault_type, dip, width)
def resize_dist(self, dist, mag_size):
"""
Warning, Toro_1997_midcontinent_distribution assumes
that this occurs. So if resizing is changed,
Toro_1997_midcontinent_distribution needs to be changed as well
"""
# [4.5,5.5,6.0] => site * mag * T
assert len(dist.shape) < 3
if not len(dist.shape) == 2:
# if distances is collapsed
if dist.size == 1:
# if distances is size 1
dist = dist.reshape((1, 1))
else:
assert len(dist.shape) == 1
if mag_size > 1:
assert dist.size == mag_size
# therefore distance is 1 site * n magnitudes
dist = dist.reshape((1, mag_size))
else:
# therefore distance is n site * 1 magnitudes
dist = dist.reshape((dist.size, 1))
# collapsed arrays are a bad idea...
dist = dist[:, :, newaxis]
# [[30.0,35.0],[45.0,20.0]]=> [site*mag] * T
return dist
