def generate_normal_ss_data(avg_strike, avg_dip, n=10, noise_std=5, porp=2):
"""Generate test data: several conjugate normal and s/s faults with noisy
strike/dip"""
def append(*args):
return np.hstack(args)
def filter(strike, dip):
"""Ensure strike & dip are in the correct ranges"""
strike[dip > 90] -= 180
dip[dip > 90] = 180 - dip[dip > 90]
strike[dip < 0] -= 180
dip[dip < 0] *= -1
strike[strike < 0] += 360
strike[strike > 360] -= 360
return strike, dip
# Generate conjugate normal faults
norm_strike = avg_strike * np.ones(n)
norm_strike[n // porp:] = avg_strike + 180
norm_dip = avg_dip * np.ones(n)
# Generate strike slip faults @ +- 30 deg of normal strike
ss_strike = (avg_strike - 30) * np.ones(n)
ss_strike[n // porp:] = avg_strike + 30
ss_dip = 90 * np.ones(n)
# Add noise
for item in [norm_strike, norm_dip, ss_strike, ss_dip]:
item += noise_std * np.random.randn(item.size)
norm_strike, norm_dip = filter(norm_strike, norm_dip)
ss_strike, ss_dip = filter(ss_strike, ss_dip)
# Calculate slip
norm_normal = geometric_functions.plane2normal(norm_strike, norm_dip)
norm_slip = geometric_functions.normal_slip(*norm_normal)
ll_ss_normal = geometric_functions.plane2normal(ss_strike[n // 2:],
ss_dip[n // 2:])
ll_ss_slip = geometric_functions.left_slip(*ll_ss_normal)
rl_ss_normal = geometric_functions.plane2normal(ss_strike[:n - n // 2],
ss_dip[:n - n // 2])
rl_ss_slip = geometric_functions.left_slip(*rl_ss_normal)
strike, dip = append(norm_strike, ss_strike), append(norm_dip, ss_dip)
normal = append(norm_normal, rl_ss_normal, ll_ss_normal)
slip = append(norm_slip, rl_ss_slip, ll_ss_slip)
return strike, dip, normal, slip
def generate_normal_ss_data(avg_strike, avg_dip, n=10, noise_std=5, porp=2):
"""Generate test data: several conjugate normal and s/s faults with noisy
strike/dip"""
def append(*args):
return np.hstack(args)
def filter(strike, dip):
"""Ensure strike & dip are in the correct ranges"""
strike[dip > 90] -= 180
dip[dip > 90] = 180 - dip[dip>90]
strike[dip < 0] -= 180
dip[dip < 0] *= -1
strike[strike < 0] += 360
strike[strike > 360] -= 360
return strike, dip
# Generate conjugate normal faults
norm_strike = avg_strike * np.ones(n)
norm_strike[n//porp:] = avg_strike + 180
norm_dip = avg_dip * np.ones(n)
# Generate strike slip faults @ +- 30 deg of normal strike
ss_strike = (avg_strike - 30) * np.ones(n)
ss_strike[n//porp:] = avg_strike + 30
ss_dip = 90 * np.ones(n)
# Add noise
for item in [norm_strike, norm_dip, ss_strike, ss_dip]:
item += noise_std * np.random.randn(item.size)
norm_strike, norm_dip = filter(norm_strike, norm_dip)
ss_strike, ss_dip = filter(ss_strike, ss_dip)
# Calculate slip
norm_normal = geometric_functions.plane2normal(norm_strike, norm_dip)
norm_slip = geometric_functions.normal_slip(*norm_normal)
ll_ss_normal = geometric_functions.plane2normal(ss_strike[n//2:], ss_dip[n//2:])
ll_ss_slip = geometric_functions.left_slip(*ll_ss_normal)
rl_ss_normal = geometric_functions.plane2normal(ss_strike[:n-n//2], ss_dip[:n-n//2])
rl_ss_slip = geometric_functions.left_slip(*rl_ss_normal)
strike, dip = append(norm_strike, ss_strike), append(norm_dip, ss_dip)
normal = append(norm_normal, rl_ss_normal, ll_ss_normal)
slip = append(norm_slip, rl_ss_slip, ll_ss_slip)
return strike, dip, normal, slip
def generate_normal_data(avg_strike, avg_dip, n=10, noise_std=5, porp=2):
"""Generate several conjugate normal faults with noisy s/d"""
opp_strike = avg_strike + 180
if opp_strike > 360:
opp_strike -= 360
strike = avg_strike * np.ones(n)
strike[n//porp:] = opp_strike
dip = avg_dip * np.ones(n)
# Add noise
strike += noise_std * np.random.randn(n)
dip += noise_std * np.random.randn(n)
# Filter out things out of a reasonable range
strike[dip > 90] -= 180
dip[dip > 90] = 180 - dip[dip>90]
strike[dip < 0] -= 180
dip[dip < 0] *= -1
strike[strike < 0] += 360
strike[strike > 360] -= 360
normal = geometric_functions.plane2normal(strike, dip)
slip = geometric_functions.normal_slip(*normal)
return strike, dip, normal, slip
def generate_normal_data(avg_strike, avg_dip, n=10, noise_std=5, porp=2):
"""Generate several conjugate normal faults with noisy s/d"""
opp_strike = avg_strike + 180
if opp_strike > 360:
opp_strike -= 360
strike = avg_strike * np.ones(n)
strike[n // porp:] = opp_strike
dip = avg_dip * np.ones(n)
# Add noise
strike += noise_std * np.random.randn(n)
dip += noise_std * np.random.randn(n)
# Filter out things out of a reasonable range
strike[dip > 90] -= 180
dip[dip > 90] = 180 - dip[dip > 90]
strike[dip < 0] -= 180
dip[dip < 0] *= -1
strike[strike < 0] += 360
strike[strike > 360] -= 360
normal = geometric_functions.plane2normal(strike, dip)
slip = geometric_functions.normal_slip(*normal)
return strike, dip, normal, slip
def test_slip():
strike, dip = 90, 45
x, y, z = geometric_functions.plane2normal(strike, dip)
print 'Should be ~ [0, 1, -1]'
print[item * np.sqrt(2) for item in [x, y, z]]
print 'Should be 090/45 (s/d)', geometric_functions.normal2plane(x, y, z)
print 'Normal slip should be ~ [0, -1, -1]'
print[
item * np.sqrt(2) for item in geometric_functions.normal_slip(x, y, z)
]
print 'Reverse slip should be ~ [0, 1, 1]'
print[
item * np.sqrt(2)
for item in geometric_functions.reverse_slip(x, y, z)
]
print 'Now testing normal2plane'
print 'Should be 090/45 (s/d)', geometric_functions.normal2plane(0, -1, -1)
print 'Should be 45/180 (p/b)', geometric_functions.normal2plunge_bearing(
0, -1, -1)
def test_slip():
strike, dip = 90, 45
x,y,z = geometric_functions.plane2normal(strike, dip)
print 'Should be ~ [0, 1, -1]'
print [item * np.sqrt(2) for item in [x,y,z]]