def test_sdof():
"""
Create a plot of an elastic analysis, nonlinear analysis and closed form elastic
:return:
"""
record_filename = 'short_motion_dt0p01.txt'
asig = eqsig.load_asig(ap.MODULE_DATA_PATH + 'gms/' + record_filename, m=0.5)
period = 1.0
xi = 0.05
mass = 1.0
f_yield = 1.5 # Reduce this to make it nonlinear
r_post = 0.0
periods = np.array([period])
resp_u, resp_v, resp_a = sdof.response_series(motion=asig.values, dt=asig.dt, periods=periods, xi=xi)
k_spring = 4 * np.pi ** 2 * mass / period ** 2
outputs = get_elastic_response(mass, k_spring, asig.values, asig.dt, xi=xi, r_post=r_post)
acc_opensees_elastic = np.interp(asig.time, outputs["time"], outputs["rel_accel"]) - asig.values
time = asig.time
run = 1
if run:
import matplotlib.pyplot as plt
bf, sps = plt.subplots(nrows=3)
sps[0].plot(time, resp_u[0], lw=0.7, c='r')
sps[0].plot(outputs["time"], outputs["rel_disp"], ls='--')
sps[1].plot(outputs['rel_disp'], outputs['force'][:, 2])
sps[2].plot(time, resp_a[0], lw=0.7, c='r')
sps[2].plot(outputs["time"], outputs["rel_accel"], ls='--')
sps[2].plot(time, acc_opensees_elastic, ls='--', c='g')
plt.show()
def skip_plot2():
import matplotlib.pyplot as plt
asig = eqsig.load_asig(conftest.TEST_DATA_DIR + 'test_motion_dt0p01.txt')
travel_times = np.array([0.2, 0.5])
cases = eqsig.surface.calc_cum_abs_surface_energy(asig,
travel_times,
nodal=True,
up_red=1,
down_red=1,
stt=1.0,
trim=False,
start=True)
# plt.plot(tshifts, cases[:, -1])
# plt.plot(tshifts, expected_cases[:, -1])
plt.plot(cases[0])
plt.plot(cases[1])
cases = eqsig.surface.calc_cum_abs_surface_energy(asig,
travel_times,
nodal=True,
up_red=1,
down_red=1,
stt=1.0,
trim=False,
start=False)
plt.plot(cases[0])
plt.plot(cases[1])
# plt.plot(asig.time - 2 * travel_times[0], cases[0])
# plt.plot(asig.time - 2 * travel_times[1], cases[1])
# plt.plot(asig.time - 2 * travel_times[2], cases[2])
plt.show()
def run(show=0):
record_filename = 'test_motion_dt0p01.txt'
asig = eqsig.load_asig(ap.MODULE_DATA_PATH + 'gms/' + record_filename,
m=0.5)
period = 1.0
from eqsig import sdof
import matplotlib.pyplot as plt
import engformat as ef
bf, ax = plt.subplots(nrows=2)
xi = 0.05
outputs = gen_response(period, xi, asig, etype='implicit')
ax[0].plot(outputs['time'], outputs['rel_disp'], label='implicit', ls='--')
outputs = gen_response(period,
xi,
asig,
etype='central_difference',
fos_for_dt=5)
ax[0].plot(outputs['time'],
outputs['rel_disp'],
label='central_difference',
ls='--')
ef.text_at_rel_pos(ax[0], 0.05, 0.95, f'$\\xi$ = {xi*100:.0f}%')
periods = np.array([period])
# Compare closed form elastic solution
resp_u, resp_v, resp_a = sdof.response_series(motion=asig.values,
dt=asig.dt,
periods=periods,
xi=xi)
ax[0].plot(asig.time, resp_u[0], ls='-', label='Elastic', zorder=0)
xi = 0.0
outputs = gen_response(period, xi, asig, etype='implicit')
ax[1].plot(outputs['time'], outputs['rel_disp'], label='implicit', ls='--')
outputs = gen_response(period,
xi,
asig,
etype='central_difference',
fos_for_dt=5)
ax[1].plot(outputs['time'],
outputs['rel_disp'],
label='central_difference',
ls='--')
ef.text_at_rel_pos(ax[1], 0.05, 0.95, f'$\\xi$ = {xi*100:.0f}%')
periods = np.array([period])
# Compare closed form elastic solution
resp_u, resp_v, resp_a = sdof.response_series(motion=asig.values,
dt=asig.dt,
periods=periods,
xi=xi)
ax[1].plot(asig.time, resp_u[0], ls='-', label='Elastic', zorder=0)
plt.legend()
plt.show()
def create():
asig = eqsig.load_asig(conftest.TEST_DATA_DIR + 'test_motion_dt0p01.txt')
asig_small = eqsig.interp_to_approx_dt(asig, 0.001)
bf, sps = plt.subplots(nrows=2)
sps[0].plot(asig.time, asig.values)
sps[0].plot(asig_small.time, asig_small.values, ls='--')
sps[1].plot(asig.fa_frequencies, abs(asig.fa_spectrum))
sps[1].loglog(asig_small.fa_frequencies,
abs(asig_small.fa_spectrum),
ls='--')
plt.show()
def create():
"""
Run an SDOF using three different damping options
"""
dtypes = ['rot_dashpot', 'horz_dashpot', 'rayleigh']
# dtypes = ['rayleigh']
lss = ['-', '--', ':']
bd = sm.SDOFBuilding()
bd.mass_eff = 120.0e3
bd.h_eff = 10.0
bd.t_fixed = 0.7
bd.xi = 0.2 # use high damping to evaluate performance of diff damping options
bd.inputs += ['xi']
l_ph = 0.2
record_filename = 'test_motion_dt0p01.txt'
asig = eqsig.load_asig(ap.MODULE_DATA_PATH + 'gms/' + record_filename, m=0.5)
bf, sps = plt.subplots(nrows=3, figsize=(5, 8))
for c, dtype in enumerate(dtypes):
outputs = get_response(bd, asig, dtype=dtype, l_ph=l_ph)
# Time series plots
sps[0].plot(outputs['time'], outputs['deck_accel'] / 9.8, c=cbox(c), lw=0.7, label=dtype, ls=lss[c])
sps[1].plot(outputs['hinge_rotation'] * 1e3, outputs['col_moment'] / 1e3, c=cbox(c), label=dtype, ls=lss[c])
sps[2].plot(outputs['rel_deck_disp'] * 1e3, outputs['col_shear'] / 1e3, c=cbox(c), label=dtype, ls=lss[c])
if dtype == 'rot_dashpot':
sps[1].plot(outputs['hinge_rotation'] * 1e3, (outputs['col_moment'] - outputs['dashpot_force'][:, 2] / 2) / 1e3, c='r', label=dtype)
sps[2].plot(outputs['rel_deck_disp'] * 1e3, (outputs['col_shear'] - 3. / 2. * outputs['dashpot_force'][:, 2] / bd.h_eff) / 1e3, c='r', label=dtype)
sps[0].set_ylabel('Deck accel. [g]', fontsize=7)
sps[0].set_xlabel('Time [s]')
ef.text_at_rel_pos(sps[1], 0.1, 0.9, 'Column hinge')
sps[1].set_xlabel('Rotation [mrad]')
sps[1].set_ylabel('Moment [kNm]')
sps[1].set_ylabel('Shear [kN]')
sps[1].set_xlabel('Disp. [mm]')
ef.revamp_legend(sps[0])
ef.xy(sps[0], x_origin=True)
ef.xy(sps[0], x_axis=True, y_axis=True)
bf.tight_layout()
name = __file__.replace('.py', '')
name = name.split("fig_")[-1]
bf.savefig(f'figures/{name}.png', dpi=90)
plt.show()
def skip_plot():
import matplotlib.pyplot as plt
accsig = eqsig.load_asig(conftest.TEST_DATA_DIR + 'test_motion_dt0p01.txt')
accsig = eqsig.AccSignal(accsig.values[100:], accsig.dt)
travel_times = np.linspace(0, 2, 44)
travel_times = np.array([0.2, 0.5])
stt = 0.0
cases_w_s = eqsig.surface.calc_cum_abs_surface_energy(accsig,
travel_times,
nodal=True,
up_red=1,
down_red=1,
stt=stt,
trim=True,
start=True)
# plt.plot(tshifts, cases[:, -1])
# plt.plot(tshifts, expected_cases[:, -1])
from bwplot import cbox
plt.plot(accsig.time, cases_w_s[0], c=cbox(0))
plt.plot(accsig.time, cases_w_s[1], c='r')
cases = eqsig.surface.calc_cum_abs_surface_energy(accsig,
travel_times,
nodal=True,
up_red=1,
down_red=1,
stt=stt,
trim=True,
start=False)
plt.plot(accsig.time, cases[0], c=cbox(1), ls='--')
plt.plot(accsig.time, cases[1], c=cbox(1), ls='--')
plt.plot(accsig.time + (stt - travel_times[0]), cases[0], c='k', ls=':')
plt.plot(accsig.time + (stt - travel_times[1]), cases[1], c='k', ls=':')
case_interp_0 = np.interp(accsig.time + (stt - travel_times[0]),
accsig.time, cases_w_s[0])
diff0 = np.sum(abs(case_interp_0 - cases[0])) / cases[0][-1]
assert np.isclose(diff0, 0., atol=5.0e-3), diff0
case_interp_1 = np.interp(accsig.time + (stt - travel_times[1]),
accsig.time, cases_w_s[1])
diff1 = np.sum(abs(case_interp_1 - cases[1])) / cases[1][-1]
assert np.isclose(diff1, 0., atol=5.0e-3), diff1
case_interp_2 = np.interp(accsig.time,
accsig.time + (stt - travel_times[2]),
cases_w_s[2])
diff2 = np.sum(abs(case_interp_2 - cases[2])) / cases[2][-1]
assert np.isclose(diff2, 0., atol=5.0e-3), diff2
plt.show()
def create():
asig = eqsig.load_asig(conftest.TEST_DATA_DIR + 'test_motion_dt0p01.txt')
bf, sps = plt.subplots(nrows=2)
sps[0].plot(asig.time, asig.values, c='k', lw=1)
asig.gen_fa_spectrum(p2_plus=0)
sps[1].loglog(asig.fa_freqs, abs(asig.fa_spectrum), c='b', label='FAS')
sps[1].loglog(asig.smooth_fa_freqs, asig.smooth_fa_spectrum, c='r', label='band=40 (default)')
asig.gen_smooth_fa_spectrum(band=100)
sps[1].loglog(asig.smooth_fa_freqs, asig.smooth_fa_spectrum, c='orange', label='band=100')
asig.gen_smooth_fa_spectrum(band=40, smooth_fa_freqs=asig.fa_freqs[1:])
sps[1].loglog(asig.smooth_fa_freqs, asig.smooth_fa_spectrum, c='y', label='band=40 - full width')
# Using functions instead of inbuilt methods
smatrix = eqsig.calc_smoothing_matrix_konno_1998(asig.fa_freqs, band=40)
sps[1].loglog(asig.fa_freqs[1:], eqsig.calc_smooth_fa_spectrum_w_custom_matrix(asig, smatrix), c='g', label='band=40 (full w fns)', ls='--')
narrow_freqs = np.logspace(-1, 1, 100, base=10)
smatrix = eqsig.calc_smoothing_matrix_konno_1998(asig.fa_freqs, smooth_fa_frequencies=narrow_freqs, band=40)
sps[1].loglog(narrow_freqs, eqsig.calc_smooth_fa_spectrum_w_custom_matrix(asig, smatrix), c='m',
label='band=40 (narrow w fns)', ls=':')
plt.legend(prop={'size': 7})
plt.show()
def run(show):
period = 0.5
xi = 0.05
f_yield = 5.5 # Reduce this to make it nonlinear
f_yield = 1.5 # Reduce this to make it nonlinear
record_filename = 'test_motion_dt0p01.txt'
asig = eqsig.load_asig(ap.MODULE_DATA_PATH + 'gms/' + record_filename,
m=0.5)
etypes = [
'implicit', 'newmark_explicit', 'explicit_difference',
'central_difference'
]
cs = ['k', 'b', 'g', 'orange', 'm']
ls = ['-', '--', '-.', ':', '--']
for i in range(len(etypes)):
etype = etypes[i]
od = run_analysis(asig, period, xi, f_yield, etype=etype)
if show:
import matplotlib.pyplot as plt
plt.plot(od['time'],
od['rel_disp'],
label=etype,
c=cs[i],
ls=ls[i])
periods = np.array([period])
if show:
# Compare closed form elastic solution
from eqsig import sdof
resp_u, resp_v, resp_a = sdof.response_series(motion=asig.values,
dt=asig.dt,
periods=periods,
xi=xi)
plt.plot(asig.time, resp_u[0], ls='--', label='Elastic', c='r', lw=1)
plt.legend()
plt.show()
sps[0].legend()
# Validation against time-history analysis
# Load time history analysis solution
test_filename = 'test_motion_true_spectra_acc.csv'
data = np.loadtxt(TEST_DATA_DIR + test_filename, skiprows=1, delimiter=",")
periods = data[:, 0]
ss_s_a = data[:, 1]
# ## Load ground motion and compute response spectrum for same periods
record_filename = 'test_motion_dt0p01.txt'
# loaded as an eqsig.AccSignal object
asig = eqsig.load_asig(record_filename) # fast load of file for eqsig format
bf, sps = plt.subplots(nrows=2, figsize=(6, 6))
# plot THA
sps[0].plot(periods, ss_s_a, label="THA", c='k')
sps[1].plot(periods[:40], ss_s_a[:40], label="THA", c='k')
# plot response spectrum using default ratio on dt
asig.generate_response_spectrum(response_times=periods,
xi=0.05,
min_dt_ratio=2)
sps[0].plot(periods, asig.s_a / 9.81, label="eqsig - min dt ratio=2")
sps[1].plot(periods[:40], asig.s_a[:40] / 9.81, label="eqsig - min dt ratio=2")
# Plot response spectrum using lower ratio on dt (slower computation)
asig.generate_response_spectrum(response_times=periods,
def run():
sl = sm.Soil()
sl.o3_type = 'pimy'
vs = 250.
unit_mass = 1700.0
sl.cohesion = 120.0e3
sl.phi = 0.0
sl.g_mod = vs**2 * unit_mass
sl.poissons_ratio = 0.3
sl.unit_dry_weight = unit_mass * 9.8
sl.specific_gravity = 2.65
sl.xi = 0.03 # for linear analysis
sl.sra_type = 'linear'
assert np.isclose(vs, sl.get_shear_vel(saturated=False))
soil_profile = sm.SoilProfile()
soil_profile.add_layer(0, sl)
soil_profile.height = 30.0
sl_base = sm.Soil()
sl_base.o3_type = 'pimy'
vs = 450.
unit_mass = 1700.0
sl_base.g_mod = vs**2 * unit_mass
sl_base.poissons_ratio = 0.3
sl_base.cohesion = 120.0e3
sl_base.phi = 0.0
sl_base.unit_dry_weight = unit_mass * 9.8
sl_base.specific_gravity = 2.65
sl_base.xi = 0.03 # for linear analysis
sl_base.sra_type = 'linear'
soil_profile.add_layer(10.1, sl_base)
soil_profile.height = 20.0
gm_scale_factor = 1.5
record_filename = 'short_motion_dt0p01.txt'
in_sig = eqsig.load_asig(ap.MODULE_DATA_PATH + 'gms/' + record_filename,
m=gm_scale_factor)
# analysis with pysra
od = lq.sra.run_pysra(soil_profile, in_sig, odepths=np.array([0.0, 2.0]))
pysra_surf_sig = eqsig.AccSignal(od['ACCX'][0], in_sig.dt)
outputs = site_response(soil_profile, in_sig)
resp_dt = outputs['time'][2] - outputs['time'][1]
o3_surf_sig = eqsig.AccSignal(outputs['ACCX'][0], resp_dt)
o3_surf_vals = np.interp(pysra_surf_sig.time, o3_surf_sig.time,
o3_surf_sig.values)
show = 1
if show:
import matplotlib.pyplot as plt
from bwplot import cbox
in_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
o3_surf_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
pysra_surf_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
bf, sps = plt.subplots(nrows=3)
sps[0].plot(in_sig.time, in_sig.values, c='k', label='Input')
sps[0].plot(pysra_surf_sig.time, o3_surf_vals, c=cbox(0), label='o3')
sps[0].plot(pysra_surf_sig.time,
pysra_surf_sig.values,
c=cbox(1),
label='pysra')
sps[1].plot(in_sig.fa_frequencies, abs(in_sig.fa_spectrum), c='k')
sps[1].plot(o3_surf_sig.fa_frequencies,
abs(o3_surf_sig.fa_spectrum),
c=cbox(0))
sps[1].plot(pysra_surf_sig.fa_frequencies,
abs(pysra_surf_sig.fa_spectrum),
c=cbox(1))
sps[1].set_xlim([0, 20])
in_sig.smooth_fa_frequencies = in_sig.fa_frequencies
pysra_surf_sig.smooth_fa_frequencies = in_sig.fa_frequencies
o3_surf_sig.smooth_fa_frequencies = in_sig.fa_frequencies
h = o3_surf_sig.smooth_fa_spectrum / in_sig.smooth_fa_spectrum
sps[2].plot(o3_surf_sig.smooth_fa_frequencies, h, c=cbox(0))
pysra_h = pysra_surf_sig.smooth_fa_spectrum / in_sig.smooth_fa_spectrum
sps[2].plot(pysra_surf_sig.smooth_fa_frequencies, pysra_h, c=cbox(1))
sps[2].axhline(1, c='k', ls='--')
sps[0].legend()
name = __file__.replace('.py', '')
name = name.split("fig_")[-1]
bf.savefig(f'figs/{name}.png', dpi=90)
plt.show()
assert np.isclose(o3_surf_vals, pysra_surf_sig.values, atol=0.01,
rtol=100).all()
def run(show=0):
# Load a ground motion
record_filename = 'test_motion_dt0p01.txt'
asig = eqsig.load_asig(ap.MODULE_DATA_PATH + 'gms/' + record_filename,
m=0.5)
# Define inelastic SDOF
period = 1.0
xi = 0.05
mass = 1.0
f_yield = 1.5 # Reduce this to make it nonlinear
r_post = 0.0
# Initialise OpenSees instance
osi = o3.OpenSeesInstance(ndm=2, state=0)
# Establish nodes
bot_node = o3.node.Node(osi, 0, 0)
top_node = o3.node.Node(osi, 0, 0)
# Fix bottom node
o3.Fix3DOF(osi, top_node, o3.cc.FREE, o3.cc.FIXED, o3.cc.FIXED)
o3.Fix3DOF(osi, bot_node, o3.cc.FIXED, o3.cc.FIXED, o3.cc.FIXED)
# Set out-of-plane DOFs to be slaved
o3.EqualDOF(osi, top_node, bot_node, [o3.cc.Y, o3.cc.ROTZ])
# nodal mass (weight / g):
o3.Mass(osi, top_node, mass, 0., 0.)
# Define material
k_spring = 4 * np.pi**2 * mass / period**2
bilinear_mat = o3.uniaxial_material.Steel01(osi,
fy=f_yield,
e0=k_spring,
b=r_post)
# Assign zero length element, # Note: pass actual node and material objects into element
o3.element.ZeroLength(osi, [bot_node, top_node],
mats=[bilinear_mat],
dirs=[o3.cc.DOF2D_X],
r_flag=1)
# Define the dynamic analysis
# Define the dynamic analysis
acc_series = o3.time_series.Path(osi, dt=asig.dt, values=-1 *
asig.values) # should be negative
o3.pattern.UniformExcitation(osi, dir=o3.cc.X, accel_series=acc_series)
# set damping based on first eigen mode
angular_freq = o3.get_eigen(osi, solver='fullGenLapack', n=1)[0]**0.5
beta_k = 2 * xi / angular_freq
o3.rayleigh.Rayleigh(osi,
alpha_m=0.0,
beta_k=beta_k,
beta_k_init=0.0,
beta_k_comm=0.0)
# Run the dynamic analysis
o3.wipe_analysis(osi)
# Run the dynamic analysis
o3.algorithm.Newton(osi)
o3.system.SparseGeneral(osi)
o3.numberer.RCM(osi)
o3.constraints.Transformation(osi)
o3.integrator.Newmark(osi, gamma=0.5, beta=0.25)
o3.analysis.Transient(osi)
o3.test_check.EnergyIncr(osi, tol=1.0e-10, max_iter=10)
analysis_time = asig.time[-1]
analysis_dt = 0.001
outputs = {
"time": [],
"rel_disp": [],
"rel_accel": [],
"rel_vel": [],
"force": []
}
while o3.get_time(osi) < analysis_time:
o3.analyze(osi, 1, analysis_dt)
curr_time = o3.get_time(osi)
outputs["time"].append(curr_time)
outputs["rel_disp"].append(o3.get_node_disp(osi, top_node, o3.cc.X))
outputs["rel_vel"].append(o3.get_node_vel(osi, top_node, o3.cc.X))
outputs["rel_accel"].append(o3.get_node_accel(osi, top_node, o3.cc.X))
o3.gen_reactions(osi)
outputs["force"].append(-o3.get_node_reaction(
osi, bot_node, o3.cc.X)) # Negative since diff node
o3.wipe(osi)
for item in outputs:
outputs[item] = np.array(outputs[item])
if show:
import matplotlib.pyplot as plt
plt.plot(outputs['time'], outputs['rel_disp'], label='o3seespy')
periods = np.array([period])
# Compare closed form elastic solution
from eqsig import sdof
resp_u, resp_v, resp_a = sdof.response_series(motion=asig.values,
dt=asig.dt,
periods=periods,
xi=xi)
plt.plot(asig.time, resp_u[0], ls='--', label='Elastic')
plt.legend()
plt.show()
def run():
forder = 1.0e3
sl = sm.Soil()
sl.o3_type = 'pimy'
vs = 100.
xi = 0.03
unit_mass = 1700.0
sl.cohesion = 120.0e3
sl.phi = 0.0
sl.g_mod = vs ** 2 * unit_mass
sl.poissons_ratio = 0.0
sl.unit_dry_weight = unit_mass * 9.8
sl.specific_gravity = 2.65
sl.xi = 0.03 # for linear analysis
sl.xi_min = 0.03 # for eqlin analysis
sl.peak_strain = 0.1
sl.o3_mat = o3.nd_material.PressureIndependMultiYield(None, 2, unit_mass / forder, sl.g_mod / forder,
sl.bulk_mod / forder, sl.cohesion / forder, 0.1,
0.0, 101.0e3 / forder, 0.0, 25)
assert np.isclose(vs, sl.get_shear_vel(saturated=False))
soil_profile = sm.SoilProfile()
soil_profile.add_layer(0, sl)
sl_base = sm.Soil()
sl_base.o3_type = 'pimy'
vs = 150.
unit_mass = 1700.0
sl_base.g_mod = vs ** 2 * unit_mass
sl_base.poissons_ratio = 0.0
sl_base.cohesion = 120.0e3
sl_base.phi = 0.0
sl_base.unit_dry_weight = unit_mass * 9.8
sl_base.specific_gravity = 2.65
sl_base.xi = xi # for linear analysis
sl_base.xi_min = 0.03 # for eqlin analysis
sl_base.peak_strain = 0.1
e_mod = 2 * sl_base.g_mod * (1 + sl_base.poissons_ratio)
sl_base.o3_mat = o3.nd_material.PressureIndependMultiYield(None, 2, unit_mass / forder, sl_base.g_mod / forder,
sl_base.bulk_mod / forder, sl_base.cohesion / forder, 0.1, 0.0,
101.0e3 / forder, 0.0, 25)
soil_profile.add_layer(5.1, sl_base)
soil_profile.height = 10.0
gm_scale_factor = 0.5
record_filename = 'short_motion_dt0p01.txt'
in_sig = eqsig.load_asig(ap.MODULE_DATA_PATH + 'gms/' + record_filename, m=gm_scale_factor)
# analysis with pysra
sl.sra_type = 'hyperbolic'
sl_base.sra_type = 'hyperbolic'
od = lq.sra.run_pysra(soil_profile, in_sig, odepths=np.array([0.0]), wave_field='within')
pysra_sig = eqsig.AccSignal(od['ACCX'][0], in_sig.dt)
import matplotlib.pyplot as plt
from bwplot import cbox
bf, sps = plt.subplots(nrows=3, figsize=(6, 8))
in_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
pysra_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
sps[0].plot(in_sig.time, in_sig.values, c='k', label='Input')
sps[0].plot(pysra_sig.time, pysra_sig.values, c='r', label='pysra')
sps[1].plot(in_sig.fa_frequencies, abs(in_sig.fa_spectrum), c='k')
sps[1].semilogx(pysra_sig.fa_frequencies, abs(pysra_sig.fa_spectrum), c='r')
pysra_h = pysra_sig.smooth_fa_spectrum / in_sig.smooth_fa_spectrum
sps[2].semilogx(pysra_sig.smooth_fa_frequencies, pysra_h, c='r')
# analysis with O3
etypes = ['implicit', 'explicit_difference', 'central_difference', 'newmark_explicit']
# etypes = ['central_difference']
# etypes = ['implicit', 'explicit_difference']
ls = ['-', '--', ':', '-.']
for i, etype in enumerate(etypes):
outputs_exp = site_response(soil_profile, in_sig, freqs=(0.5, 10), xi=xi, etype=etype, forder=forder,
rec_dt=in_sig.dt, analysis_time=in_sig.time[-1] + 3)
resp_dt = (outputs_exp['time'][-1] - outputs_exp['time'][0]) / (len(outputs_exp['time']) - 1)
abs_surf_sig = outputs_exp['ACCX'][0] + np.interp(np.arange(len(outputs_exp['ACCX'][0])) * resp_dt, in_sig.time,
in_sig.values)
surf_sig = eqsig.AccSignal(abs_surf_sig, resp_dt)
surf_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
sps[0].plot(surf_sig.time, surf_sig.values, c=cbox(i), label=etype, ls=ls[i])
sps[1].plot(surf_sig.fa_frequencies, abs(surf_sig.fa_spectrum), c=cbox(i), ls=ls[i])
h = surf_sig.smooth_fa_spectrum / in_sig.smooth_fa_spectrum
sps[2].plot(surf_sig.smooth_fa_frequencies, h, c=cbox(i), ls=ls[i])
sps[2].axhline(1, c='k', ls='--')
sps[0].legend(prop={'size': 6})
name = __file__.replace('.py', '')
name = name.split("fig_")[-1]
bf.suptitle(name)
bf.savefig(f'figs/{name}.png', dpi=90)
plt.show()
def run():
sl = sm.Soil()
sl.type = 'pimy'
vs = 160.
unit_mass = 1700.0
sl.cohesion = 58.0e3
sl.phi = 0.0
sl.g_mod = vs**2 * unit_mass
sl.poissons_ratio = 0.0
sl.phi = 0.0
sl.unit_dry_weight = unit_mass * 9.8
sl.specific_gravity = 2.65
sl.strain_peak = 0.1 # set additional parameter required for PIMY model
sl.xi = 0.03 # for linear analysis
sl.sra_type = 'hyperbolic'
assert np.isclose(vs, sl.get_shear_vel(saturated=False))
soil_profile = sm.SoilProfile()
soil_profile.add_layer(0, sl)
sl = sm.Soil()
sl.type = 'pimy'
vs = 400.
unit_mass = 1700.0
sl.g_mod = vs**2 * unit_mass
sl.poissons_ratio = 0.0
sl.cohesion = 395.0e3
sl.phi = 0.0
sl.unit_dry_weight = unit_mass * 9.8
sl.specific_gravity = 2.65
sl.strain_peak = 0.1 # set additional parameter required for PIMY model
sl.xi = 0.03 # for linear analysis
sl.sra_type = 'hyperbolic'
soil_profile.add_layer(9.5, sl)
soil_profile.height = 20.0
ecp_out = sm.Output()
ecp_out.add_to_dict(soil_profile)
ofile = open('ecp.json', 'w')
ofile.write(json.dumps(ecp_out.to_dict(), indent=4))
ofile.close()
record_path = TEST_DATA_DIR
record_filename = 'short_motion_dt0p01.txt'
in_sig = eqsig.load_asig(record_filename)
# linear analysis with pysra
od = run_pysra(soil_profile, in_sig, odepths=np.array([0.0, 2.0]))
pysra_sig = eqsig.AccSignal(od['ACCX_d0'], in_sig.dt)
outputs = o3soil.sra.site_response(soil_profile, in_sig)
resp_dt = outputs['time'][2] - outputs['time'][1]
surf_sig = eqsig.AccSignal(outputs['ACCX'][0], resp_dt)
o3_surf_vals = np.interp(pysra_sig.time, surf_sig.time, surf_sig.values)
show = 1
if show:
import matplotlib.pyplot as plt
from bwplot import cbox
bf, sps = plt.subplots(nrows=3)
sps[0].plot(in_sig.time, in_sig.values, c='k', label='Input')
sps[0].plot(pysra_sig.time, o3_surf_vals, c=cbox(0), label='o3')
sps[0].plot(pysra_sig.time, pysra_sig.values, c=cbox(1), label='pysra')
sps[1].plot(in_sig.fa_frequencies, abs(in_sig.fa_spectrum), c='k')
sps[1].plot(surf_sig.fa_frequencies,
abs(surf_sig.fa_spectrum),
c=cbox(0))
sps[1].plot(pysra_sig.fa_frequencies,
abs(pysra_sig.fa_spectrum),
c=cbox(1))
sps[1].set_xlim([0, 20])
h = surf_sig.smooth_fa_spectrum / in_sig.smooth_fa_spectrum
sps[2].plot(surf_sig.smooth_fa_frequencies, h, c=cbox(0))
pysra_h = pysra_sig.smooth_fa_spectrum / in_sig.smooth_fa_spectrum
sps[2].plot(pysra_sig.smooth_fa_frequencies, pysra_h, c=cbox(1))
sps[2].axhline(1, c='k', ls='--')
sps[0].plot(pysra_sig.time, (o3_surf_vals - pysra_sig.values) * 10,
c='r',
label='Error x10')
sps[0].legend()
plt.show()
assert np.isclose(o3_surf_vals, pysra_sig.values, atol=0.01,
rtol=100).all()
def create():
"""
Run an SDOF using three different damping options
"""
dtypes = ['rot_link', 'horz_link'] #, 'rayleigh']
dtypes = ['rot_link'] # , 'rayleigh']
lss = ['-', '--', ':']
bd = sm.SDOFBuilding()
bd.mass_eff = 120.0e3
bd.h_eff = 10.0
bd.t_fixed = 0.7
bd.xi = 0.2 # use high damping to evaluate performance of diff damping options
bd.inputs += ['xi']
record_filename = 'test_motion_dt0p01.txt'
asig = eqsig.load_asig(ap.MODULE_DATA_PATH + 'gms/' + record_filename,
m=0.5)
bf, sps = plt.subplots(nrows=3, figsize=(5, 8))
c = 0
dtype = 'rot_link'
outputs = get_response(bd, dtype=dtype)
# Time series plots
sps[0].plot(outputs['time'],
outputs['deck_force'] / 1e3,
c=cbox(c),
lw=0.7,
label=dtype,
ls=lss[c])
sps[1].plot(outputs['hinge_rotation'] * 1e3,
outputs['col_moment'] / 1e3,
c=cbox(c),
label='Column',
ls=lss[c])
sps[1].plot(outputs['hinge_rotation'] * 1e3,
outputs['col_moment_end'] / 1e3,
c='purple',
label='Column',
ls=lss[c])
# sps[1].plot(outputs['hinge_rotation'] * 1e3, (outputs['col_moment'] - outputs['link_force'][:, 2] / 2) / 1e3, c='r', label=dtype)
sps[1].plot(outputs['hinge_rotation'] * 1e3,
(outputs['col_moment'] + outputs['link_force'][:, 2]) / 1e3,
c='b',
label=dtype)
sps[1].plot(outputs['hinge_rotation'] * 1e3,
(outputs['link_force'][:, 2]) / 1e3,
c='g',
label='Link',
ls='--')
sps[2].plot(outputs['rel_deck_disp'] * 1e3,
outputs['col_shear'] / 1e3,
c=cbox(c),
label=dtype,
ls=lss[c])
sps[2].plot(outputs['rel_deck_disp'] * 1e3,
outputs['link_force'][:, 2] / bd.h_eff / 1e3,
c='g',
label='Link equiv. shear',
ls='--')
sps[2].plot(
outputs['rel_deck_disp'] * 1e3,
(outputs['col_shear'] + outputs['link_force'][:, 2] / bd.h_eff) / 1e3,
c='orange',
label='Column and Link')
sps[1].plot(outputs['hinge_rotation'] * 1e3,
outputs['deck_force'] * bd.h_eff / 1e3,
c=cbox('mid grey'),
alpha=0.8,
lw=1.5,
label=dtype,
ls='-.')
sps[2].plot(outputs['rel_deck_disp'] * 1e3,
outputs['deck_force'] / 1e3,
c=cbox('mid grey'),
alpha=0.8,
lw=1.5,
label=dtype,
ls='-.')
sps[2].plot(outputs['rel_deck_disp'] * 1e3,
outputs['rel_deck_disp'] * bd.k_eff / 1e3,
c='k',
alpha=0.8,
lw=1.5,
label=dtype,
ls='-.')
sps[0].set_ylabel('Deck accel. [g]', fontsize=7)
sps[0].set_xlabel('Time [s]')
ef.text_at_rel_pos(sps[1], 0.1, 0.9, 'Column Element')
sps[1].set_xlabel('Rotation [mrad]')
sps[1].set_ylabel('Moment [kNm]')
ef.revamp_legend(sps[0])
ef.xy(sps[0], x_origin=True)
ef.xy(sps[0], x_axis=True, y_axis=True)
bf.tight_layout()
name = __file__.replace('.py', '')
name = name.split("fig_")[-1]
# bf.savefig(f'figures/{name}.png', dpi=90)
plt.show()
def run():
soil_profile = sm.SoilProfile()
soil_profile.height = 30.0
sl = sm.Soil()
vs = 250.
unit_mass = 1700.0
sl.g_mod = vs**2 * unit_mass
sl.poissons_ratio = 0.3
sl.unit_dry_weight = unit_mass * 9.8
sl.xi = 0.01 # for linear analysis
soil_profile.add_layer(0, sl)
sl_base = sm.Soil()
vs = 350.
unit_mass = 1700.0
sl_base.g_mod = vs**2 * unit_mass
sl_base.poissons_ratio = 0.3
sl_base.unit_dry_weight = unit_mass * 9.8
sl_base.xi = 0.01 # for linear analysis
soil_profile.add_layer(19., sl_base)
soil_profile.height = 20.0
record_filename = 'short_motion_dt0p01.txt'
in_sig = eqsig.load_asig(ap.MODULE_DATA_PATH + 'gms/' + record_filename,
m=0.5)
# analysis with pysra
od = lq.sra.run_pysra(soil_profile,
in_sig,
odepths=np.array([0.0]),
wave_field='outcrop')
pysra_sig = eqsig.AccSignal(od['ACCX'][0], in_sig.dt)
bf, sps = plt.subplots(nrows=3)
sps[0].plot(in_sig.time, in_sig.values, c='k', label='Input')
sps[0].plot(pysra_sig.time,
pysra_sig.values,
c=cbox('mid grey'),
label='pysra')
sps[1].plot(in_sig.fa_frequencies, abs(in_sig.fa_spectrum), c='k')
sps[1].plot(pysra_sig.fa_frequencies,
abs(pysra_sig.fa_spectrum),
c=cbox('mid grey'))
sps[1].set_xlim([0, 20])
in_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
pysra_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
pysra_h = pysra_sig.smooth_fa_spectrum / in_sig.smooth_fa_spectrum
sps[2].plot(pysra_sig.smooth_fa_frequencies, pysra_h, c=cbox('mid grey'))
# Normal 1d analysis with tied boundaries
outputs = site_response(soil_profile,
in_sig,
freqs=(0.5, 10),
xi=0.02,
analysis_dt=0.005,
x_eles=1,
ff_bc=False)
resp_dt = outputs['time'][2] - outputs['time'][1]
surf_sig = eqsig.AccSignal(outputs['ACCX'][0], resp_dt)
sps[0].plot(surf_sig.time, surf_sig.values, c=cbox(0), label='o3 - 1D')
sps[1].plot(surf_sig.fa_frequencies, abs(surf_sig.fa_spectrum), c=cbox(0))
surf_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
h = surf_sig.smooth_fa_spectrum / in_sig.smooth_fa_spectrum
sps[2].plot(surf_sig.smooth_fa_frequencies, h, c=cbox(0))
# 2d analysis with tied boundaries
outputs = site_response(soil_profile,
in_sig,
freqs=(0.5, 10),
xi=0.02,
analysis_dt=0.005,
x_eles=20,
ff_bc=False)
resp_dt = outputs['time'][2] - outputs['time'][1]
surf_sig = eqsig.AccSignal(outputs['ACCX'][0], resp_dt)
sps[0].plot(surf_sig.time,
surf_sig.values,
c=cbox(1),
label='o3 - 2D tied',
ls='--')
sps[1].plot(surf_sig.fa_frequencies,
abs(surf_sig.fa_spectrum),
c=cbox(1),
ls='--')
surf_sig.smooth_fa_frequencies = in_sig.fa_frequencies
h = surf_sig.smooth_fa_spectrum / in_sig.smooth_fa_spectrum
sps[2].plot(surf_sig.smooth_fa_frequencies, h, c=cbox(1), ls='--')
# 2d analysis with free-field columns
outputs = site_response(soil_profile,
in_sig,
freqs=(0.5, 10),
xi=0.02,
analysis_dt=0.005,
x_eles=20,
ff_bc=True)
resp_dt = outputs['time'][2] - outputs['time'][1]
surf_sig = eqsig.AccSignal(outputs['ACCX'][0], resp_dt)
sps[0].plot(surf_sig.time,
surf_sig.values,
c=cbox(2),
label='o3 - 2D FF columns',
ls=':')
sps[1].plot(surf_sig.fa_frequencies,
abs(surf_sig.fa_spectrum),
c=cbox(2),
ls=':')
surf_sig.smooth_fa_frequencies = in_sig.fa_frequencies
h = surf_sig.smooth_fa_spectrum / in_sig.smooth_fa_spectrum
sps[2].plot(surf_sig.smooth_fa_frequencies, h, c=cbox(2), ls=':')
sps[0].set_ylabel('Acc [m/s2]')
sps[1].set_ylabel('FA')
sps[2].set_ylabel('H')
sps[2].set_xlabel('Freq [Hz]')
sps[2].axhline(1, c='k', ls='--')
sps[0].legend()
plt.show()
def run():
soil_profile = sm.SoilProfile()
soil_profile.height = 30.0
xi = 0.03
sl = sm.Soil()
sl.o3_type = 'lin' # Use linear soil model
vs = 250.
unit_mass = 1700.0
sl.g_mod = vs**2 * unit_mass
sl.poissons_ratio = 0.3
sl.unit_dry_weight = unit_mass * 9.8
sl.xi = xi # for linear analysis
soil_profile.add_layer(0, sl)
sl_base = sm.Soil()
vs = 350.
unit_mass = 1700.0
sl_base.g_mod = vs**2 * unit_mass
sl_base.poissons_ratio = 0.3
sl_base.unit_dry_weight = unit_mass * 9.8
sl_base.xi = xi # for linear analysis
soil_profile.add_layer(10., sl_base)
soil_profile.height = 20.0
gm_scale_factor = 1.5
record_filename = 'short_motion_dt0p01.txt'
in_sig = eqsig.load_asig(ap.MODULE_DATA_PATH + 'gms/' + record_filename,
m=gm_scale_factor)
# analysis with pysra
od = lq.sra.run_pysra(soil_profile,
in_sig,
odepths=np.array([0.0]),
wave_field='outcrop')
pysra_sig = eqsig.AccSignal(od['ACCX'][0], in_sig.dt)
freqs = (0.5, 10)
# outputs_imp = site_response(soil_profile, in_sig, freqs=(0.5, 10), xi=xi, analysis_dt=0.001)
# resp_dt = outputs_imp['time'][2] - outputs_imp['time'][1]
# surf_sig_imp = eqsig.AccSignal(outputs_imp['ACCX'][0], resp_dt)
outputs_exp = site_response(soil_profile,
in_sig,
freqs=freqs,
xi=xi,
analysis_dt=0.0001,
use_explicit=1)
resp_dt = outputs_exp['time'][2] - outputs_exp['time'][1]
surf_sig = eqsig.AccSignal(outputs_exp['ACCX'][0], resp_dt)
surf_sig_imp = surf_sig
show = 1
if show:
import matplotlib.pyplot as plt
from bwplot import cbox
in_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
surf_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
pysra_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
surf_sig_imp.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
bf, sps = plt.subplots(nrows=3)
sps[0].plot(in_sig.time, in_sig.values, c='k', label='Input')
sps[0].plot(pysra_sig.time, pysra_sig.values, c=cbox(1), label='pysra')
sps[0].plot(surf_sig_imp.time,
surf_sig_imp.values,
c=cbox(0),
label='o3-imp')
sps[0].plot(surf_sig.time,
surf_sig.values,
c=cbox(2),
label='o3-exp',
ls='--')
sps[1].plot(in_sig.fa_frequencies, abs(in_sig.fa_spectrum), c='k')
sps[1].plot(pysra_sig.fa_frequencies,
abs(pysra_sig.fa_spectrum),
c=cbox(1))
sps[1].plot(surf_sig_imp.fa_frequencies,
abs(surf_sig_imp.fa_spectrum),
c=cbox(0))
sps[1].plot(surf_sig.fa_frequencies,
abs(surf_sig.fa_spectrum),
c=cbox(2),
ls='--')
sps[1].set_xlim([0, 20])
pysra_h = pysra_sig.smooth_fa_spectrum / in_sig.smooth_fa_spectrum
sps[2].plot(pysra_sig.smooth_fa_frequencies, pysra_h, c=cbox(1))
h = surf_sig_imp.smooth_fa_spectrum / in_sig.smooth_fa_spectrum
sps[2].plot(in_sig.smooth_fa_frequencies, h, c=cbox(0))
h = surf_sig.smooth_fa_spectrum / in_sig.smooth_fa_spectrum
sps[2].plot(surf_sig.smooth_fa_frequencies, h, c=cbox(2), ls='--')
omega_1 = 2 * np.pi * freqs[0]
omega_2 = 2 * np.pi * freqs[1]
a0 = 2 * xi * omega_1 * omega_2 / (omega_1 + omega_2)
a1 = 2 * xi / (omega_1 + omega_2)
xi_min, fmin = lq.num.flac.calc_rayleigh_damping_params(f1=freqs[0],
f2=freqs[1],
d=xi)
omega_min = 2 * np.pi * fmin
alpha = xi_min * 2 * np.pi * fmin # mass proportional term
beta = xi_min / omega_min # stiffness proportional term
fs = np.logspace(-1, 1.3, 30)
omgs = 2 * np.pi * fs
xi_vals = 0.5 * (alpha / omgs + beta * omgs)
sps[2].plot(fs, xi_vals * 100, c='k')
sps[2].axhline(1, c='k', ls='--')
sps[2].set_ylim([0, 2])
sps[0].legend()
plt.show()
def run():
xi = 0.03
sl = sm.Soil()
vs = 250.
unit_mass = 1700.0
sl.g_mod = vs**2 * unit_mass
sl.poissons_ratio = 0.3
sl.unit_dry_weight = unit_mass * 9.8
sl.xi = xi # for linear analysis
sl_base = sm.Soil()
vs = 350.
unit_mass = 1700.0
sl_base.g_mod = vs**2 * unit_mass
sl_base.poissons_ratio = 0.3
sl_base.unit_dry_weight = unit_mass * 9.8
sl_base.xi = xi # for linear analysis
soil_profile = sm.SoilProfile()
soil_profile.add_layer(0, sl)
soil_profile.add_layer(10., sl_base)
soil_profile.height = 20.0
gm_scale_factor = 1.0
record_filename = 'short_motion_dt0p01.txt'
in_sig = eqsig.load_asig(ap.MODULE_DATA_PATH + 'gms/' + record_filename,
m=gm_scale_factor)
# in_sig = eqsig.AccSignal(np.pad(in_sig.values, (500, 500), mode='constant', constant_values=0), in_sig.dt)
# in_sig.butter_pass([0.1, None])
# analysis with pysra
sl.sra_type = 'linear'
sl_base.sra_type = 'linear'
od = lq.sra.run_pysra(soil_profile,
in_sig,
odepths=np.array([0.0]),
wave_field='within')
pysra_sig = eqsig.AccSignal(od['ACCX'][0], in_sig.dt)
import matplotlib.pyplot as plt
from bwplot import cbox
bf, sps = plt.subplots(nrows=3, figsize=(6, 8))
in_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
pysra_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
sps[0].plot(in_sig.time, in_sig.values, c='k', label='Input')
sps[0].plot(pysra_sig.time, pysra_sig.values, c='r', label='pysra')
sps[1].plot(in_sig.fa_frequencies, abs(in_sig.fa_spectrum), c='k')
sps[1].semilogx(pysra_sig.fa_frequencies,
abs(pysra_sig.fa_spectrum),
c='r')
pysra_h = pysra_sig.smooth_fa_spectrum / in_sig.smooth_fa_spectrum
sps[2].semilogx(pysra_sig.smooth_fa_frequencies, pysra_h, c='r')
# analysis with O3
etypes = [
'implicit', 'explicit_difference', 'central_difference',
'newmark_explicit'
]
# etypes = ['implicit']
ls = ['-', '--', ':', '-.']
for i, etype in enumerate(etypes):
outputs_exp = site_response(soil_profile,
in_sig,
freqs=(0.5, 10),
xi=xi,
etype=etype,
rec_dt=0.01)
resp_dt = (outputs_exp['TIME'][-1] -
outputs_exp['TIME'][0]) / (len(outputs_exp['TIME']) - 1)
abs_surf_sig = outputs_exp['ACCX'][0] + np.interp(
np.arange(len(outputs_exp['ACCX'][0])) * resp_dt, in_sig.time,
in_sig.values)
surf_sig = eqsig.AccSignal(abs_surf_sig, resp_dt)
surf_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
sps[0].plot(surf_sig.time,
surf_sig.values,
c=cbox(i),
label=etype,
ls=ls[i])
sps[1].plot(surf_sig.fa_frequencies,
abs(surf_sig.fa_spectrum),
c=cbox(i),
ls=ls[i])
h = surf_sig.smooth_fa_spectrum / in_sig.smooth_fa_spectrum
sps[2].plot(surf_sig.smooth_fa_frequencies, h, c=cbox(i), ls=ls[i])
sps[2].axhline(1, c='k', ls='--')
sps[0].legend(prop={'size': 6})
name = __file__.replace('.py', '')
name = name.split("fig_")[-1]
bf.suptitle(name)
bf.savefig(f'figs/{name}.png', dpi=90)
plt.show()
def run():
vs = 250.
sp = sm.SoilProfile()
sp.height = 30.
sl = sm.Soil()
sl.type = 'elastic'
sl.poissons_ratio = 0.
sl.unit_dry_weight = 2.202e3 * 9.8
sl.g_mod = vs ** 2 * sl.unit_dry_mass
sl.xi = 0.01 # for linear analysis
sp.add_layer(0, sl)
rock = sm.Soil()
vs = 450.
unit_mass = 1700.0
rock.g_mod = vs ** 2 * unit_mass
rock.unit_dry_weight = unit_mass * 9.8
rock.poissons_ratio = 0.
rock.xi = 0.01
sp.add_layer(sp.height, rock)
sp.height = sp.height + 1
in_sig = eqsig.load_asig(ap.MODULE_DATA_PATH + 'gms/short_motion_dt0p01.txt', m=0.05)
opfile = __file__[:-3] + 'op.py'
fixed_base = 0
if fixed_base:
base_imp = -1
wave_field = 'within'
else:
base_imp = 0
wave_field = 'outcrop'
outputs = o3soil.sra.site_response(sp, in_sig, outs={'ACCX': 'all'}, dy=0.5, opfile=opfile,
base_imp=base_imp, freqs=(0.5, 20), xi=0.02, analysis_dt=0.005)
resp_dt = outputs['time'][2] - outputs['time'][1]
o3_surf_sig = eqsig.AccSignal(outputs['ACCX'][0], resp_dt)
pysra_outs_full = lq.sra.run_pysra(sp, in_sig, [0], wave_field=wave_field)
pysra_surf_sig = eqsig.AccSignal(pysra_outs_full['ACCX'][0], in_sig.dt)
show = 1
if show:
import matplotlib.pyplot as plt
from bwplot import cbox
bf, sps = plt.subplots(nrows=3)
sps[0].plot(in_sig.time, in_sig.values, c='k', label='Input', lw=1)
sps[0].plot(o3_surf_sig.time, o3_surf_sig.values, c=cbox(0), label='o3-surf-direct', lw=1)
sps[0].plot(pysra_surf_sig.time, pysra_surf_sig.values, c=cbox(4), label='pysra-surf-direct', lw=1, ls='--')
sps[1].loglog(o3_surf_sig.fa_frequencies, abs(o3_surf_sig.fa_spectrum), c=cbox(0), lw=1)
sps[1].loglog(pysra_surf_sig.fa_frequencies, abs(pysra_surf_sig.fa_spectrum), c=cbox(4), lw=1)
in_sig.smooth_fa_frequencies = in_sig.fa_frequencies
pysra_surf_sig.smooth_fa_frequencies = in_sig.fa_frequencies
o3_surf_sig.smooth_fa_frequencies = in_sig.fa_frequencies
in_sig.generate_smooth_fa_spectrum(band=80)
pysra_surf_sig.generate_smooth_fa_spectrum(band=80)
o3_surf_sig.generate_smooth_fa_spectrum(band=80)
sps[2].plot(in_sig.smooth_fa_frequencies, o3_surf_sig.smooth_fa_spectrum / in_sig.smooth_fa_spectrum,
c=cbox(0), label='O3 - smoothed')
sps[2].plot(in_sig.smooth_fa_frequencies, pysra_surf_sig.smooth_fa_spectrum / in_sig.smooth_fa_spectrum,
c=cbox(4), label='Pysra - smoothed')
pysra_sp = lq.sra.sm_profile_to_pysra(sp)
freqs, tfs = lq.sra.calc_pysra_tf(pysra_sp, in_sig.fa_frequencies, absolute=True)
sps[2].plot(freqs, tfs, c='k', label='Pysra - exact')
sps[0].legend(loc='lower right')
vs_damped = lq.sra.theory.calc_damped_vs_dormieux_1990(sl.get_shear_vel(saturated=False), sl.xi)
vs_br_damped = lq.sra.theory.calc_damped_vs_dormieux_1990(rock.get_shear_vel(saturated=False), rock.xi)
h = sp.height
omega = in_sig.fa_frequencies * np.pi * 2
imp = lq.sra.theory.calc_impedance(sl.unit_dry_mass, vs_damped, rock.unit_dry_mass, vs_br_damped)
tfs = lq.sra.theory.calc_tf_elastic_br(h, vs_damped, omega, imp, absolute=True)
sps[2].semilogx(in_sig.fa_frequencies, tfs, label='Theoretical Elastic BR', c='r', ls='--')
sps[2].set_xlim([0.1, 30])
sps[0].set_ylabel('Acc [m/s2]')
sps[1].set_ylabel('FAS')
sps[2].set_ylabel('H')
sps[0].set_xlabel('Time [s]')
sps[1].set_xlabel('Freq [Hz]')
sps[2].set_xlabel('Freq [Hz]')
sps[2].legend()
plt.show()
def run():
soil_profile = sm.SoilProfile()
soil_profile.height = 30.0
xi = 0.03
sl = sm.Soil()
vs = 250.
unit_mass = 1700.0
sl.g_mod = vs**2 * unit_mass
sl.poissons_ratio = 0.3
sl.unit_dry_weight = unit_mass * 9.8
sl.xi = xi # for linear analysis
sl.sra_type = 'linear'
soil_profile.add_layer(0, sl)
sl_base = sm.Soil()
vs = 350.
unit_mass = 1700.0
sl_base.g_mod = vs**2 * unit_mass
sl_base.poissons_ratio = 0.0
sl_base.unit_dry_weight = unit_mass * 9.8
sl_base.xi = xi # for linear analysis
sl_base.sra_type = 'linear'
soil_profile.add_layer(10., sl_base)
soil_profile.height = 20.0
gm_scale_factor = 1.5
record_filename = 'test_motion_dt0p01.txt'
in_sig = eqsig.load_asig(ap.MODULE_DATA_PATH + 'gms/' + record_filename,
m=gm_scale_factor)
# analysis with pysra
od = lq.sra.run_pysra(soil_profile,
in_sig,
odepths=np.array([0.0]),
wave_field='within')
ind = in_sig.npts
pysra_sig = eqsig.AccSignal(od['ACCX'][0][:ind], in_sig.dt)
dtype = 'rayleigh'
dtype = 'modal'
outputs = site_response(soil_profile,
in_sig,
freqs=(0.5, 10),
xi=xi,
analysis_dt=0.005,
dtype=dtype)
resp_dt = outputs['time'][2] - outputs['time'][1]
surf_sig = eqsig.AccSignal(outputs['ACCX'][0], resp_dt)
o3_surf_vals = np.interp(pysra_sig.time, surf_sig.time,
surf_sig.values) + in_sig.values
surf_sig = eqsig.AccSignal(
surf_sig.values + np.interp(surf_sig.time, in_sig.time, in_sig.values),
surf_sig.dt)
show = 1
if show:
import matplotlib.pyplot as plt
from bwplot import cbox
in_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
surf_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
pysra_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
bf, sps = plt.subplots(nrows=3)
sps[0].plot(in_sig.time, in_sig.values, c='k', label='Input')
sps[0].plot(surf_sig.time, surf_sig.values, c=cbox(0), label='o3')
sps[0].plot(pysra_sig.time, pysra_sig.values, c=cbox(1), label='pysra')
sps[1].plot(in_sig.fa_frequencies, abs(in_sig.fa_spectrum), c='k')
sps[1].plot(surf_sig.fa_frequencies,
abs(surf_sig.fa_spectrum),
c=cbox(0))
sps[1].plot(pysra_sig.fa_frequencies,
abs(pysra_sig.fa_spectrum),
c=cbox(1))
h = surf_sig.smooth_fa_spectrum / in_sig.smooth_fa_spectrum
sps[2].plot(surf_sig.smooth_fa_frequencies, h, c=cbox(0))
pysra_h = pysra_sig.smooth_fa_spectrum / in_sig.smooth_fa_spectrum
sps[2].plot(pysra_sig.smooth_fa_frequencies, pysra_h, c=cbox(1))
sps[2].axhline(1, c='k', ls='--')
sps[1].set_xlim([0, 20])
sps[2].set_xlim([0, 20])
sps[0].legend()
name = __file__.replace('.py', '')
name = name.split("fig_")[-1]
bf.suptitle(name)
bf.savefig(f'figs/{name}.png', dpi=90)
plt.show()
assert np.isclose(o3_surf_vals, pysra_sig.values, atol=0.01,
rtol=100).all()
def create():
"""
Run an SDOF using three different damping options
"""
lss = ['-', '--', ':']
bd = sm.SDOFBuilding()
bd.mass_eff = 120.0e3
bd.h_eff = 10.0
bd.t_fixed = 0.7
bd.xi = 0.2 # use high damping to evaluate performance of diff damping options
bd.inputs += ['xi']
l_ph = 0.2
record_filename = 'test_motion_dt0p01.txt'
asig = eqsig.load_asig(ap.MODULE_DATA_PATH + 'gms/' + record_filename,
m=0.5)
bf, sps = plt.subplots(nrows=3, figsize=(5, 8))
dtype = 'Column'
c = 0
outputs = get_response(bd, asig, l_ph=l_ph)
# Time series plots
sps[0].plot(outputs['time'],
outputs['deck_accel'] / 9.8,
c=cbox(c),
lw=0.7,
label=dtype,
ls=lss[c])
sps[1].plot(outputs['hinge_rotation'] * 1e3,
outputs['col_moment'] / 1e3,
c=cbox(c),
label=dtype,
ls=lss[c])
sps[1].plot(outputs['hinge_rotation1'] * 1e3,
outputs['col_moment'] / 1e3,
c='m',
label=dtype,
ls=lss[c])
sps[1].plot(outputs['rel_deck_disp'] / bd.h_eff * 1e3,
outputs['col_moment'] / 1e3,
c='g',
label=dtype)
sps[2].plot(outputs['rel_deck_disp'] * 1e3,
outputs['col_shear'] / 1e3,
c=cbox(c),
label=dtype,
ls=lss[c])
ei = bd.k_eff * bd.h_eff**3 / 3
moms = np.array([-1000e3, 1000e3])
k_offset = ei / (bd.h_eff * 1. / 3) # deflection / length
sps[1].plot(moms / k_offset * 1e3, moms / 1e3, c='k', label='Predicted')
k_offset = ei / (bd.h_eff * 1. /
3) * 2 # end_slope * length - (deflection / length)
sps[1].plot(moms / k_offset * 1e3,
moms / 1e3,
c='r',
label='Predicted - end2')
sps[0].set_ylabel('Deck accel. [g]', fontsize=7)
sps[0].set_xlabel('Time [s]')
sps[1].set_xlabel('Rotation [mrad]')
sps[1].set_ylabel('Moment [kNm]')
sps[1].set_ylabel('Shear [kN]')
sps[1].set_xlabel('Disp. [mm]')
ef.revamp_legend(sps[1])
ef.xy(sps[0], x_origin=True)
ef.xy(sps[0], x_axis=True, y_axis=True)
bf.tight_layout()
name = __file__.replace('.py', '')
name = name.split("fig_")[-1]
bf.savefig(f'figures/{name}.png', dpi=90)
plt.show()
def run():
soil_profile = sm.SoilProfile()
soil_profile.height = 30.0
xi = 0.03
sl = sm.Soil()
vs = 250.
unit_mass = 1700.0
sl.g_mod = vs**2 * unit_mass
sl.poissons_ratio = 0.3
sl.unit_dry_weight = unit_mass * 9.8
sl.xi = xi # for linear analysis
soil_profile.add_layer(0, sl)
sl_base = sm.Soil()
vs = 350.
unit_mass = 1700.0
sl_base.g_mod = vs**2 * unit_mass
sl_base.poissons_ratio = 0.3
sl_base.unit_dry_weight = unit_mass * 9.8
sl_base.xi = xi # for linear analysis
soil_profile.add_layer(19., sl_base)
soil_profile.height = 20.0
gm_scale_factor = 1.5
record_filename = 'short_motion_dt0p01.txt'
in_sig = eqsig.load_asig(ap.MODULE_DATA_PATH + 'gms/' + record_filename,
m=gm_scale_factor)
# analysis with pysra
od = lq.sra.run_pysra(soil_profile,
in_sig,
odepths=np.array([0.0]),
wave_field='within')
pysra_sig = eqsig.AccSignal(od['ACCX'][0], in_sig.dt)
# Implicit analysis with O3
outputs_imp = site_response(soil_profile,
in_sig,
freqs=(0.5, 10),
xi=xi,
analysis_dt=0.001)
resp_dt = outputs_imp['time'][2] - outputs_imp['time'][1]
surf_sig_imp = eqsig.AccSignal(outputs_imp['ACCX'][0], resp_dt)
# Explicit analysis with O3
outputs_exp = site_response(soil_profile,
in_sig,
freqs=(0.5, 10),
xi=xi,
analysis_dt=0.0001,
use_explicit=1)
resp_dt = outputs_exp['time'][2] - outputs_exp['time'][1]
surf_sig = eqsig.AccSignal(outputs_exp['ACCX'][0], resp_dt)
show = 1
if show:
import matplotlib.pyplot as plt
from bwplot import cbox
in_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
surf_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
pysra_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
surf_sig_imp.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
bf, sps = plt.subplots(nrows=3)
sps[0].plot(in_sig.time, in_sig.values, c='k', label='Input')
sps[0].plot(pysra_sig.time, pysra_sig.values, c=cbox(1), label='pysra')
sps[0].plot(surf_sig_imp.time,
surf_sig_imp.values,
c=cbox(0),
label='o3-imp')
sps[0].plot(surf_sig.time,
surf_sig.values,
c=cbox(2),
label='o3-exp',
ls='--')
sps[1].plot(in_sig.fa_frequencies, abs(in_sig.fa_spectrum), c='k')
sps[1].plot(pysra_sig.fa_frequencies,
abs(pysra_sig.fa_spectrum),
c=cbox(1))
sps[1].plot(surf_sig_imp.fa_frequencies,
abs(surf_sig_imp.fa_spectrum),
c=cbox(0))
sps[1].plot(surf_sig.fa_frequencies,
abs(surf_sig.fa_spectrum),
c=cbox(2),
ls='--')
sps[1].set_xlim([0, 20])
pysra_h = pysra_sig.smooth_fa_spectrum / in_sig.smooth_fa_spectrum
sps[2].plot(pysra_sig.smooth_fa_frequencies, pysra_h, c=cbox(1))
h = surf_sig_imp.smooth_fa_spectrum / in_sig.smooth_fa_spectrum
sps[2].plot(in_sig.smooth_fa_frequencies, h, c=cbox(0))
h = surf_sig.smooth_fa_spectrum / in_sig.smooth_fa_spectrum
sps[2].plot(surf_sig.smooth_fa_frequencies, h, c=cbox(2), ls='--')
sps[2].axhline(1, c='k', ls='--')
sps[0].legend()
plt.show()
def run(out_folder, dytime=None):
xi = 0.05
sl = sm.Soil()
sl.type = 'pimy'
vs = 160.
unit_mass = 1700.0
sl.cohesion = 58.0e3
sl.phi = 0.0
sl.g_mod = vs**2 * unit_mass
sl.poissons_ratio = 0.0
sl.phi = 0.0
sl.unit_dry_weight = unit_mass * 9.8
sl.specific_gravity = 2.65
sl.peak_strain = 0.01 # set additional parameter required for PIMY model
ref_press = 100.e3
sl.xi = 0.03 # for linear analysis
sl.sra_type = 'hyperbolic'
o3soil.backbone.set_params_from_op_pimy_model(sl, ref_press)
sl.inputs += [
'strain_curvature', 'xi_min', 'sra_type', 'strain_ref', 'peak_strain'
]
assert np.isclose(vs, sl.get_shear_vel(saturated=False))
sp = sm.SoilProfile()
sp.add_layer(0, sl)
sl = sm.Soil()
sl.type = 'pimy'
vs = 350.
unit_mass = 1700.0
sl.g_mod = vs**2 * unit_mass
sl.poissons_ratio = 0.0
sl.cohesion = 395.0e3
sl.phi = 0.0
sl.unit_dry_weight = unit_mass * 9.8
sl.specific_gravity = 2.65
sl.peak_strain = 0.1 # set additional parameter required for PIMY model
sl.xi = 0.03 # for linear analysis
sl.sra_type = 'hyperbolic'
o3soil.backbone.set_params_from_op_pimy_model(sl, ref_press)
sl.inputs += [
'strain_curvature', 'xi_min', 'sra_type', 'strain_ref', 'peak_strain'
]
sp.add_layer(8.5, sl)
sp.height = 14.0
ecp_out = sm.Output()
ecp_out.add_to_dict(sp)
ecp_out.to_file(out_folder + 'ecp.json')
asig = eqsig.load_asig(ap.MODULE_DATA_PATH + 'gms/short_motion_dt0p01.txt',
m=2.5)
if dytime is None:
ind = None
else:
ind = int(dytime / asig.dt)
asig = eqsig.AccSignal(asig.values[:ind], asig.dt)
outs = {'ACCX': 'all', 'TAU': 'all', 'STRS': 'all'}
# cache = 0
# if not cache:
# outputs = site_response(soil_profile, asig, xi=xi, out_folder=out_folder, outs=outs, rec_dt=asig.dt, analysis_time=dytime, fixed_base=1)
# else:
# o3sra_outs = o3ptools.O3SRAOutputs()
# outputs = o3sra_outs.load_results_from_files()
show = 1
if show:
import matplotlib.pyplot as plt
bf, sps = plt.subplots(nrows=3)
# TODO: Show loads on playback
# TODO: add material to playback, and show legend with material type, set material.__str__ as basetype, params[2:]
sra1d = o3soil.sra.run_sra(sp,
asig,
xi=xi,
cache_path=out_folder,
outs=outs,
analysis_time=dytime,
base_imp=-1,
playback=True)
outputs = sra1d.out_dict
import pandas as pd
df = pd.DataFrame.from_dict(outputs['TAU'])
df.to_csv('tau.csv', index=False)
ind_3m = sra1d.get_nearest_ele_layer_at_depth(3)
ind_6m = sra1d.get_nearest_ele_layer_at_depth(6)
ind_12m = sra1d.get_nearest_ele_layer_at_depth(12)
sps[0].plot(outputs["time"], outputs["TAU"][ind_3m], ls='--')
sps[0].plot(outputs["time"], outputs["TAU"][ind_6m], ls='--', c='r')
sps[0].plot(outputs["time"], outputs["TAU"][ind_12m], ls='--')
sps[2].plot(outputs["time"], outputs["STRS"][ind_6m], ls='--', c='r')
sps[1].plot(outputs['STRS'][ind_6m], outputs['TAU'][ind_6m], c='r')
# sps[1].plot(outputs['STRS'][3], sl.g_mod / 1e3 * outputs['STRS'][3], ls='--')
# sps[2].plot(outputs["time"], outputs["ACCX"][5], ls='--')
plt.show()
def run():
soil_profile = sm.SoilProfile()
soil_profile.height = 30.0
xi = 0.03
sl = sm.Soil()
vs = 250.
unit_mass = 1700.0
sl.g_mod = vs**2 * unit_mass
sl.poissons_ratio = 0.3
sl.unit_dry_weight = unit_mass * 9.8
sl.xi = xi # for linear analysis
soil_profile.add_layer(0, sl)
sl_base = sm.Soil()
vs = 350.
unit_mass = 1700.0
sl_base.g_mod = vs**2 * unit_mass
sl_base.poissons_ratio = 0.0
sl_base.unit_dry_weight = unit_mass * 9.8
sl_base.xi = xi # for linear analysis
soil_profile.add_layer(19., sl_base)
soil_profile.height = 20.0
gm_scale_factor = 1.5
record_filename = 'short_motion_dt0p01.txt'
in_sig = eqsig.load_asig(ap.MODULE_DATA_PATH + 'gms/' + record_filename,
m=gm_scale_factor)
# analysis with pysra
sl.sra_type = 'linear'
sl_base.sra_type = 'linear'
od = run_pysra(soil_profile,
in_sig,
odepths=np.array([0.0, 2.0]),
wave_field='outcrop')
pysra_sig = eqsig.AccSignal(od['ACCX'][0], in_sig.dt)
dtype = 'rayleigh'
dtype = 'modal'
outputs = site_response(soil_profile,
in_sig,
freqs=(0.5, 10),
xi=xi,
analysis_dt=0.001,
dtype=dtype)
resp_dt = (outputs['TIME'][-1] -
outputs['TIME'][0]) / (len(outputs['TIME']) - 1)
surf_sig = eqsig.AccSignal(outputs['ACCX'][0], resp_dt)
o3_surf_vals = np.interp(pysra_sig.time, surf_sig.time, surf_sig.values)
show = 1
if show:
import matplotlib.pyplot as plt
from bwplot import cbox
in_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
surf_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
pysra_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
bf, sps = plt.subplots(nrows=3)
sps[0].plot(in_sig.time, in_sig.values, c='k', label='Input')
sps[0].plot(surf_sig.time, surf_sig.values, c=cbox(0), label='o3')
sps[0].plot(pysra_sig.time, pysra_sig.values, c=cbox(1), label='pysra')
sps[0].plot(pysra_sig.time, (o3_surf_vals - pysra_sig.values) * 10,
c='r',
label='Error x10')
sps[1].plot(in_sig.fa_frequencies, abs(in_sig.fa_spectrum), c='k')
sps[1].plot(surf_sig.fa_frequencies,
abs(surf_sig.fa_spectrum),
c=cbox(0))
sps[1].plot(pysra_sig.fa_frequencies,
abs(pysra_sig.fa_spectrum),
c=cbox(1))
sps[1].set_xlim([0, 20])
h = surf_sig.smooth_fa_spectrum / in_sig.smooth_fa_spectrum
sps[2].plot(surf_sig.smooth_fa_frequencies, h, c=cbox(0))
pysra_h = pysra_sig.smooth_fa_spectrum / in_sig.smooth_fa_spectrum
sps[2].plot(pysra_sig.smooth_fa_frequencies, pysra_h, c=cbox(1))
sps[2].axhline(1, c='k', ls='--')
sps[0].legend()
plt.show()
assert np.isclose(o3_surf_vals, pysra_sig.values, atol=0.01,
rtol=100).all()
import all_paths as ap import eqsig import matplotlib.pyplot as plt record_filename = 'short_motion_dt0p01.txt' in_sig = eqsig.load_asig(ap.MODULE_DATA_PATH + 'gms/' + record_filename, m=1.0) bf, ax = plt.subplots(nrows=3) ax[0].plot(in_sig.time, in_sig.values) ax[1].plot(in_sig.time, in_sig.velocity) ax[2].plot(in_sig.time, in_sig.displacement) print(in_sig.velocity[-1]) plt.show()
def run():
soil_profile = sm.SoilProfile()
soil_profile.height = 10.0
xi = 0.03
sl = gen_mz_toyoura_sand_dafalias_and_manzari_2004()
sl.relative_density = 0.6
sl.sra_type = 'linear' # set hyperbolic
soil_profile.add_layer(0.0, sl)
sl.xi = xi
# Define o3 soil object
nu_init = 0.05
f_order = 1.0
soil_mat = o3.nd_material.ManzariDafalias(None,
g0=sl.g0,
nu=nu_init,
e_init=sl.e_curr,
m_c=sl.m_c,
c_c=sl.c_c,
lambda_c=sl.lambda_c,
e_0=sl.e_0,
ksi=sl.ksi,
p_atm=sl.p_atm / f_order,
m_yield=sl.m_yield,
h_0=sl.h_0,
c_h=sl.c_h,
n_b=sl.n_b,
a_0=sl.a_0,
n_d=sl.n_d,
z_max=sl.z_max,
c_z=sl.c_z,
den=sl.unit_dry_mass / f_order)
sl.o3_mat = soil_mat
sl_base = sm.Soil()
vs = 350.
unit_mass = 1700.0
sl_base.g_mod = vs**2 * unit_mass
sl_base.poissons_ratio = 0.0
sl_base.unit_dry_weight = unit_mass * 9.8
sl_base.xi = xi # for linear analysis
e_mod = 2 * sl_base.g_mod * (1 + sl_base.poissons_ratio)
sl_base.sra_type = 'linear'
sl_base.o3_mat = o3.nd_material.ElasticIsotropic(None,
e_mod=e_mod,
nu=sl_base.poissons_ratio,
rho=sl_base.unit_dry_mass)
soil_profile.add_layer(8., sl_base)
gm_scale_factor = 0.5
record_filename = 'short_motion_dt0p01.txt'
in_sig = eqsig.load_asig(ap.MODULE_DATA_PATH + 'gms/' + record_filename,
m=gm_scale_factor)
# analysis with pysra
od = lq.sra.run_pysra(soil_profile,
in_sig,
odepths=np.array([0.0]),
wave_field='within')
pysra_sig = eqsig.AccSignal(od['ACCX'][0], in_sig.dt)
import matplotlib.pyplot as plt
from bwplot import cbox
bf, sps = plt.subplots(nrows=3, figsize=(6, 8))
in_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
pysra_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
sps[0].plot(in_sig.time, in_sig.values, c='k', label='Input')
sps[0].plot(pysra_sig.time, pysra_sig.values, c='r', label='pysra')
sps[1].plot(in_sig.fa_frequencies, abs(in_sig.fa_spectrum), c='k')
sps[1].plot(pysra_sig.fa_frequencies, abs(pysra_sig.fa_spectrum), c='r')
# pysra_h = pysra_sig.smooth_fa_spectrum / in_sig.smooth_fa_spectrum
# sps[2].plot(pysra_sig.smooth_fa_frequencies, pysra_h, c='r')
# analysis with O3
etypes = [
'implicit', 'explicit_difference', 'central_difference',
'newmark_explicit'
]
# etypes = ['central_difference']
etypes = ['implicit']
# etypes = ['explicit_difference']
ls = ['-', '--', ':', '-.']
outs = {'ACCX': [0], 'TAUXY': 'all', 'STRS': 'all'}
for i, etype in enumerate(etypes):
outputs_exp = site_response(soil_profile,
in_sig,
freqs=(0.5, 10),
xi=xi,
etype=etype,
outs=outs)
resp_dt = outputs_exp['time'][2] - outputs_exp['time'][1]
surf_sig = eqsig.AccSignal(outputs_exp['ACCX'][0], resp_dt)
surf_sig.smooth_fa_frequencies = in_sig.fa_frequencies[1:]
sps[0].plot(surf_sig.time,
surf_sig.values,
c=cbox(i),
label=etype,
ls=ls[i])
sps[1].plot(surf_sig.fa_frequencies,
abs(surf_sig.fa_spectrum),
c=cbox(i),
ls=ls[i])
# h = surf_sig.smooth_fa_spectrum / in_sig.smooth_fa_spectrum
# sps[2].plot(surf_sig.smooth_fa_frequencies, h, c=cbox(i), ls=ls[i])
sps[2].plot(outputs_exp['STRS'][6], outputs_exp['TAUXY'][6])
# sps[2].axhline(1, c='k', ls='--')
sps[1].set_xlim([0, 20])
sps[0].legend(prop={'size': 6})
plt.show()
