def test_write_obj_to_fis_str():
sl = lq.num.flac.PM4Sand()
sl.name = 'layer_1'
sl.phi = 30.
sl.g_mod = 50.0e3
sl.specific_gravity = 2.65
sl.e_curr = 0.55
sl.poissons_ratio = 0.3
sl.dilation_angle = 0.0
sl.cohesion = 0.0
sl.permeability = 1.0e-4
sl.set_g0_mod_from_g_mod_at_v_eff_stress(20.0e6, 50.0e3)
with pytest.raises(sm.ModelError):
lq.num.flac.write_obj_to_fis_str(sl, sl.inputs, sl.required_parameters)
sl.h_po = 0.5
sl.relative_density = 0.5
p_str = ''.join(
lq.num.flac.write_obj_to_fis_str(sl, sl.inputs,
sl.required_parameters))
assert 'layer_1_relative_density=0.5' in p_str
sp = sm.SoilProfile()
sp.add_layer(0, sl)
sp.height = 10.
p_str = ''.join(lq.num.flac.write_soil_profile_obj_to_fis_str(sp))
assert 'layer_1_relative_density=0.5' in p_str
def build_soil_profile_from_bi2014_w_layer_dict(bi2014, layers_dict):
"""
Defined soil profile properties based on an equivalent soil profile define
Parameters
----------
bi2014: liquepy.trigger.BoulangerIdriss2014CPT
esp: equivalent_soil object
Equivalent soil profile of the triggering analysis
Returns
-------
sm.SoilProfile
"""
assert isinstance(bi2014, lq.trigger.BoulangerIdriss2014CPT)
d_inc = bi2014.depth[3] - bi2014.depth[2]
sp = sm.SoilProfile()
sp.gwl = bi2014.gwl
sp.height = bi2014.depth[-1]
s_depths = []
i_ths = []
csr_n15 = []
for crr in layers_dict:
for depth in layers_dict[crr]:
s_depths.append(depth)
csr_n15.append(crr)
i_ths.append((np.abs(bi2014.depth - depth - d_inc)).argmin())
i_ths.append(len(bi2014.depth) - 1)
s_depths.append(sp.height)
csr_n15.append(-1) # make same length
i_ths = np.array(i_ths)
s_depths = np.array(s_depths)
csr_n15 = np.array(csr_n15)
arr_inds = s_depths.argsort()
i_ths = i_ths[arr_inds]
s_depths = s_depths[arr_inds]
csr_n15 = csr_n15[arr_inds]
s_depths[0] = 0
crr_values = lq.trigger.boulanger_and_idriss_2014.calc_crr_m7p5_from_qc1ncs(
bi2014.q_c1n_cs)
crr_values = np.clip(crr_values, None, 0.6)
for i in range(len(s_depths) - 1):
sl = sm.Soil()
set_soil_props(sl,
bi2014,
i_top=i_ths[i] - 1,
i_bot=i_ths[i + 1],
gwl=sp.gwl)
# if csr_n15[i]:
# sl.csr_n15 = csr_n15[i]
# sl.inputs.append("csr_n15")
sl.csr_n15 = np.mean(crr_values[i_ths[i] - 1:i_ths[i + 1]])
sl.inputs.append("csr_n15")
sp.add_layer(s_depths[i], sl)
sp.set_soil_ids_to_layers()
return sp, i_ths
def test_two_d_mesh_w_1_profile():
rho = 1.8
sl1 = sm.Soil(g_mod=50, unit_dry_weight=rho * 9.8, poissons_ratio=0.3)
sl2 = sm.Soil(g_mod=100, unit_dry_weight=rho * 9.8, poissons_ratio=0.3)
sl3 = sm.Soil(g_mod=400, unit_dry_weight=rho * 9.8, poissons_ratio=0.3)
sp = sm.SoilProfile()
sp.add_layer(0, sl1)
sp.add_layer(5, sl2)
sp.add_layer(12, sl3)
sp.x_angles = [0.0, 0.0, 0.0]
sp.height = 18
sp.x = 0
tds = sm.TwoDSystem(4, 15)
tds.add_sp(sp, x=0)
tds.x_surf = np.array([0])
tds.y_surf = np.array([0])
x_scale_pos = np.array([0])
x_scale_vals = np.array([2.])
fc = sm.num.mesh.FiniteElementOrth2DMeshConstructor(
tds, 0.3, x_scale_pos=x_scale_pos, x_scale_vals=x_scale_vals)
femesh = fc.femesh
ind = np.argmin(abs(femesh.y_nodes + 5.0))
assert np.isclose(-5.0, femesh.y_nodes[ind])
ind = np.argmin(abs(femesh.y_nodes + 12.0))
assert np.isclose(-12.0, femesh.y_nodes[ind])
ind = np.argmin(abs(femesh.y_nodes + 15.0))
assert np.isclose(-15.0, femesh.y_nodes[ind])
def test_loc_to_dict():
cpt = lq.field.load_mpa_cpt_file(TEST_DATA_DIR + "standard_1.csv")
sp = sm.SoilProfile()
loc = lq.field.transect.Loc(cpt=cpt)
loc.soil_profile = sp
pdict = loc.to_dict()
assert 'id' in pdict
assert 'soil_profile_id' in pdict
def _example_run():
vs = 150.0
rho = 1.8
g_mod = vs**2 * rho
import sfsimodels as sm
sl = sm.Soil(g_mod=g_mod, unit_dry_weight=rho * 9.8, poissons_ratio=0.3)
sp = sm.SoilProfile()
sp.add_layer(0, sl)
sp.add_layer(5, sl)
sp.add_layer(12, sl)
sp.height = 18
sp.x = 0
sp2 = sm.SoilProfile()
sp2.add_layer(0, sl)
sp2.add_layer(7, sl)
sp2.add_layer(12, sl)
sp2.height = 20
sp.x_angles = [0.05, 0.0, 0.0]
sp2.x_angles = [0.0, 0.00, 0.0]
fd = sm.RaftFoundation()
fd.width = 2
fd.depth = 0
fd.length = 100
fd.ip_axis = 'width'
tds = TwoDSystem(width=40, height=15)
tds.add_sp(sp, x=0)
tds.add_sp(sp2, x=14)
tds.x_surf = np.array([0, 10, 12, 40])
tds.y_surf = np.array([0, 0, 2, 2])
bd = sm.NullBuilding()
bd.set_foundation(fd, x=0.0)
tds.add_bd(bd, x=8)
x_scale_pos = np.array([0, 5, 15, 30])
x_scale_vals = np.array([2., 1.0, 2.0, 3.0])
fc = FiniteElementOrth2DMeshConstructor(tds,
0.3,
x_scale_pos=x_scale_pos,
x_scale_vals=x_scale_vals)
femesh = fc.femesh
def run():
rho = 1.8
sl1 = sm.Soil(g_mod=50, unit_dry_weight=rho * 9.8, poissons_ratio=0.3)
sl2 = sm.Soil(g_mod=100, unit_dry_weight=rho * 9.8, poissons_ratio=0.3)
sl3 = sm.Soil(g_mod=400, unit_dry_weight=rho * 9.8, poissons_ratio=0.3)
sl4 = sm.Soil(g_mod=600, unit_dry_weight=rho * 9.8, poissons_ratio=0.3)
sp = sm.SoilProfile()
sp.add_layer(0, sl1)
sp.add_layer(5, sl2)
sp.add_layer(12, sl3)
sp.height = 18
sp.x = 0
sp.x_angles = [0.0, 0.0, 0.0]
fd = sm.RaftFoundation()
fd.width = 4
fd.height = 1.0
fd.depth = 0.5
fd.length = 100
tds = sm.TwoDSystem(width=10, height=15)
tds.add_sp(sp, x=0)
tds.x_surf = np.array([0, 40])
tds.y_surf = np.array([0, 0])
bd = sm.NullBuilding()
bd.set_foundation(fd, x=0)
tds.add_bd(bd, x=5)
x_scale_pos = np.array([0, 5, 15, 30])
x_scale_vals = np.array([2., 1.0, 2.0, 3.0])
femesh = sm.num.mesh.FiniteElement2DMesh(tds,
0.3,
x_scale_pos=x_scale_pos,
x_scale_vals=x_scale_vals)
import sys
import pyqtgraph as pg
from pyqtgraph.Qt import QtGui, QtCore
import o3plot
win = pg.plot()
win.setWindowTitle('ECP definition')
win.setXRange(0, femesh.tds.width)
win.setYRange(-femesh.tds.height, max(femesh.tds.y_surf))
o3plot.plot_finite_element_mesh(win, femesh)
# o3_plot.plot_two_d_system(win, tds)
if (sys.flags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'):
QtGui.QApplication.instance().exec_()
def test_save_and_load_2d_system():
vs = 250.0
sl = sm.Soil()
unit_mass = 1700.0
sl.g_mod = vs**2 * unit_mass
sl.poissons_ratio = 0.0
# sl.cohesion = sl.g_mod / 1000
sl.cohesion = 30.0e3
sl.id = 1
sl.phi = 0.0
sl.unit_dry_weight = unit_mass * 9.8
sl.specific_gravity = 2.65
sl.xi = 0.03 # for linear analysis
sl.sra_type = 'hyperbolic'
sl.inputs += ['sra_type', 'xi']
sp = sm.SoilProfile()
sp.add_layer(0, sl)
sp.height = 1.0e3
sp.id = 1
sp.x_angles = [0.0, 0.0]
fd = sm.RaftFoundation()
fd.width = 2
fd.depth = 0.8
fd.height = 1
fd.length = 100
fd.ip_axis = 'width'
bd = sm.SDOFBuilding()
bd.h_eff = 8.
bd.set_foundation(fd, x=0.0)
sys_width = 6 * fd.width
tds = sm.TwoDSystem(width=sys_width, height=10)
tds.add_sp(sp, 0)
tds.add_bd(bd, sys_width / 2)
tds.id = 1
ecp_out = sm.Output()
ecp_out.add_to_dict(tds)
# ecp_out.add_to_dict(sp)
p_str = json.dumps(ecp_out.to_dict(), skipkeys=["__repr__"], indent=4)
objs = sm.loads_json(p_str)
building = objs["building"][1]
system = objs["system"][1]
assert np.isclose(building.h_eff, 8)
assert np.isclose(building.fd.height, 1)
assert isinstance(system, sm.TwoDSystem)
assert system.sps[0].layer(1).xi == 0.03
def capacity_meyerhof_and_hanna_1978(sl_0, sl_1, h0, fd, gwl=1e6, verbose=0):
"""
Calculates the two-layered foundation capacity according Meyerhof and Hanna (1978)
:param sl_0: Top Soil object
:param sl_1: Base Soil object
:param h0: Height of top soil layer
:param fd: Foundation object
:param wtl: water table level
:param verbose: verbosity
:return: ultimate bearing stress
"""
sp = sm.SoilProfile()
sp.add_layer(0, sl_0)
sp.add_layer(h0, sl_1)
sp.gwl = gwl
return capacity_sp_meyerhof_and_hanna_1978(sp, fd)
def build_soil_profile_from_bi2014(esp, bi2014):
"""
Defined soil profile properties based on an equivalent soil profile define
Parameters
----------
bi2014: liquepy.trigger.BoulangerIdriss2014CPT
esp: equivalent_soil object
Equivalent soil profile of the triggering analysis
Returns
-------
sm.SoilProfile
"""
assert isinstance(bi2014, lq.trigger.BoulangerIdriss2014CPT)
sp = sm.SoilProfile()
sp.gwl = bi2014.gwl
sp.height = bi2014.depth[-1]
s_depths = [0, esp.h_crust]
csr_n15 = [0]
if esp.n_layers == 3:
csr_n15 += [esp.csr_n15, 0]
s_depths.append(esp.d_nonliq)
else:
s_depths.append(esp.d_nonliqs[0])
s_depths.append(esp.d_liqs[1])
s_depths.append(esp.d_nonliqs[1])
csr_n15 += [esp.csr_n15s[0], 0, esp.csr_n15s[1], 0]
s_depths.append(sp.height)
for i in range(len(s_depths) - 1):
if s_depths[i + 1] - s_depths[i] > 0:
sl = sm.Soil()
set_soil_props(sl,
bi2014,
i_top=esp.ith(s_depths[i]),
i_bot=esp.ith(s_depths[i + 1]),
gwl=sp.gwl)
if csr_n15[i]:
sl.csr_n15 = csr_n15[i]
sl.inputs.append("csr_n15")
sp.add_layer(s_depths[i], sl)
sp.set_soil_ids_to_layers()
return sp
def test_est_nses_millen_et_al_2019():
# Define soil profile properties
damp = 0.03
sp = sm.SoilProfile() # Soil profile object
# Top layer
sl1 = sm.Soil(g_mod=40.0e6, unit_dry_weight=20.0e3)
sl1.xi = damp
sp.add_layer(depth=0, soil=sl1)
# Middle layer - with lower shear modulus
sl2 = sm.Soil(g_mod=30.0e6, unit_dry_weight=20.0e3)
sl2.xi = damp
sp.add_layer(depth=10, soil=sl2)
# Bottom layer
sl3 = sm.Soil(g_mod=40.0e6, unit_dry_weight=20.0e3)
sl3.xi = damp
sp.add_layer(depth=20, soil=sl3)
sp.height = 30 # m
# Load ground motion
acc = np.loadtxt(conftest.TEST_DATA_DIR + 'test_motion_dt0p01.txt',
skiprows=2)
dt = 0.01
in_signal = eqsig.AccSignal(acc / 2, dt)
rho = sp.layer(3).unit_dry_weight / 9.8
in_uke = eqsig.im.calc_unit_kinetic_energy(in_signal)[-1]
in_cake = in_uke * rho
# Estimate CASE and CAKE using the input motion with method from Millen et al. (2019)
odepths = np.array([4.])
pred_case = nses.est_case_1d_millen_et_al_2019(sp,
in_signal,
odepths,
xi=damp)[0, -1]
pred_cake = nses.est_case_1d_millen_et_al_2019(sp,
in_signal,
odepths,
xi=damp,
nodal=False)[0, -1]
assert np.isclose(pred_cake, 729.00213882), pred_cake # v0.6.3
assert np.isclose(pred_case, 40.094265976), pred_case # v0.6.3
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
import matplotlib.pyplot as plt
from bwplot import cbox
bf, sps = plt.subplots(ncols=3, figsize=(6, 8))
ys = site_response(soil_profile, static=0)
sps[0].plot(ys, c=cbox(0))
ys = site_response(soil_profile, static=1)
sps[0].plot(ys, c=cbox(1))
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 test_mesh_vary_y():
vs = 150.0
rho = 1.8
g_mod = vs**2 * rho
sl0 = sm.Soil(g_mod=g_mod, unit_dry_weight=rho * 9.8, poissons_ratio=0.31)
sl1 = sm.Soil(g_mod=g_mod, unit_dry_weight=rho * 9.8, poissons_ratio=0.32)
sl2 = sm.Soil(g_mod=g_mod, unit_dry_weight=rho * 9.8, poissons_ratio=0.33)
sl3 = sm.Soil(g_mod=g_mod, unit_dry_weight=rho * 9.8, poissons_ratio=0.34)
sl4 = sm.Soil(g_mod=g_mod, unit_dry_weight=rho * 9.8, poissons_ratio=0.35)
sl5 = sm.Soil(g_mod=g_mod, unit_dry_weight=rho * 9.8, poissons_ratio=0.36)
sp = sm.SoilProfile()
sp.add_layer(0, sl1)
sp.add_layer(5, sl2)
sp.add_layer(12, sl3)
sp.height = 18
sp.x = 0
sp2 = sm.SoilProfile()
sp2.add_layer(0, sl4)
sp2.add_layer(7, sl5)
sp2.add_layer(12, sl0)
sp2.height = 20
sp.x_angles = [None, 0.01, 0.05]
sp2.x_angles = [None, 0.00, 0.0]
fd = sm.RaftFoundation()
fd.width = 2
fd.depth = 0
fd.ip_axis = 'width'
fd.height = 1
fd.length = 100
tds = sm.TwoDSystem(width=40, height=15)
tds.add_sp(sp, x=0)
tds.add_sp(sp2, x=14)
tds.x_surf = np.array([0, 10, 12, 25, 40])
tds.y_surf = np.array([0, 0, 2, 2.5, 2])
bd = sm.NullBuilding()
bd.set_foundation(fd, x=0.0)
tds.add_bd(bd, x=8)
x_scale_pos = np.array([0, 5, 15, 30])
x_scale_vals = np.array([2., 1.0, 2.0, 3.0])
fc = mesh2d_vary_y.FiniteElementVary2DMeshConstructor(
tds, 0.3, x_scale_pos=x_scale_pos, x_scale_vals=x_scale_vals)
show = 0
femesh = fc.femesh
if show:
import sys
import pyqtgraph as pg
from pyqtgraph.Qt import QtGui, QtCore
import o3plot
win = pg.plot()
win.setWindowTitle('ECP definition')
win.setXRange(0, tds.width)
win.setYRange(-tds.height, max(tds.y_surf))
o3plot.plot_finite_element_mesh_onto_win(win, femesh)
xcs = list(fc.yd)
xcs.sort()
xcs = np.array(xcs)
for i in range(len(xcs)):
win.addItem(pg.InfiniteLine(xcs[i], angle=90,
pen=(0, 255, 0, 100)))
o3plot.plot_two_d_system(tds, win)
for i, sd in enumerate(fc.sds):
x0 = sd[0][0]
x1 = sd[0][1]
y0 = sd[1][0]
y1 = sd[1][1]
win.plot(sd[0], sd[1], pen='r')
x0_ind = femesh.get_nearest_node_index_at_x(x0)
x1_ind = femesh.get_nearest_node_index_at_x(x1)
y0_ind = femesh.get_nearest_node_index_at_depth(y0, x0)
y1_ind = femesh.get_nearest_node_index_at_depth(y1, x1)
win.plot([femesh.x_nodes[x0_ind], femesh.x_nodes[x1_ind]], [
femesh.y_nodes[x0_ind][y0_ind], femesh.y_nodes[x1_ind][y1_ind]
],
pen='b')
if (sys.flags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'):
QtGui.QApplication.instance().exec_()
dhs = np.diff(femesh.y_nodes, axis=1)
assert np.min(-dhs) > 0.3 * 0.5, np.min(-dhs)
assert np.max(-dhs) < 0.3 * 2, np.min(-dhs)
sds = fc.sds
for sd in sds:
x0 = sd[0][0]
x1 = sd[0][1]
y0 = sd[1][0]
y1 = sd[1][1]
y0_ind = femesh.get_nearest_node_index_at_depth(y0, x0)
y1_ind = femesh.get_nearest_node_index_at_depth(y1, x1)
if y0 == 0 and y1 == 2:
assert y0_ind - 5 == y1_ind, (sd, y0_ind - 5, y1_ind)
else:
assert y0_ind == y1_ind, (sd, y0_ind, y1_ind)
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():
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():
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()
import sys import pyqtgraph as pg from pyqtgraph.Qt import QtGui, QtCore import o3plot #%% vs = 150.0 rho = 1.8 g_mod = vs ** 2 * rho sl1 = sm.Soil(g_mod=g_mod, unit_dry_weight=rho * 9.8, poissons_ratio=0.32) sl2 = sm.Soil(g_mod=g_mod, unit_dry_weight=rho * 9.8, poissons_ratio=0.33) sl3 = sm.Soil(g_mod=g_mod, unit_dry_weight=rho * 9.8, poissons_ratio=0.34) sl4 = sm.Soil(g_mod=g_mod, unit_dry_weight=rho * 9.8, poissons_ratio=0.35) sl5 = sm.Soil(g_mod=g_mod, unit_dry_weight=rho * 9.8, poissons_ratio=0.36) sp = sm.SoilProfile() h_face = 1.8 sp.add_layer(0, sl1) sp.add_layer(3.4, sl2) sp.add_layer(5.7, sl3) sp2 = sm.SoilProfile() sp2.add_layer(0, sl4) sp2.add_layer(3.9, sl2) sp2.add_layer(6.5, sl5) sp2.height = 20 sp.x_angles = [None, 0.07, 0.0] sp2.x_angles = [None, 0.00, 0.0] tds = sm.TwoDSystem(width=45, height=7.5) tds.add_sp(sp, x=0)
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():
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()
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
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():
sl = sm.Soil()
vs = 200.
unit_mass = 1700.0
sl.cohesion = 68.0e3
# sl.cohesion = 0.0
sl.phi = 0.0
sl.g_mod = 68.0e6
print('G_mod: ', sl.g_mod)
sl.unit_dry_weight = unit_mass * 9.8
sl.specific_gravity = 2.65
k0 = 1.0
# sl.poissons_ratio = k0 / (1 + k0) - 0.01
sl.poissons_ratio = 0.3
strain_max = 0.01
strains = np.logspace(-4, -1.5, 40)
esig_v0 = 100.0e3
ref_press = 100.e3
ndm = 2
# TODO: phi and cohesion are not used as you would expect for user defined surfaces !!! recalculated: tau_max calculated the
set_params_from_op_pimy_model(sl, strain_max, ref_press)
sl.inputs += ['strain_curvature', 'xi_min', 'sra_type', 'strain_ref']
sp = sm.SoilProfile()
sp.id = 1
sp.add_layer(0, sl)
sp.height = 30.0
ecp_out = sm.Output()
ecp_out.add_to_dict(sp)
ofile = open('ecp.json', 'w')
# ofile.write(json.dumps(ecp_out.to_dict(), indent=4))
ofile.close()
compare_backbone = 1
if compare_backbone:
dss_eq = np.sqrt(3. / 2)
pysra_sl = pysra.site.ModifiedHyperbolicSoilType(
"",
1,
strain_ref=sl.strain_ref * dss_eq,
curvature=sl.strain_curvature,
damping_min=sl.xi_min,
strains=strains)
pysra_tau = pysra_sl.mod_reduc.values * sl.g_mod * pysra_sl.mod_reduc.strains
taus = calc_backbone_op_pimy_model(sl,
strain_max,
strains,
ref_press,
ndm=ndm,
esig_v0=esig_v0)
osi = o3.OpenSeesInstance(ndm=2, ndf=2, state=3)
# See example: https://opensees.berkeley.edu/wiki/index.php/PressureIndependMultiYield_Material
base_mat = o3.nd_material.PressureIndependMultiYield(
osi,
nd=2,
rho=sl.unit_sat_mass,
g_mod_ref=sl.g_mod_ref,
bulk_mod_ref=sl.bulk_mod_ref,
peak_strain=strain_max,
cohesion=sl.cohesion,
phi=sl.phi,
p_ref=sl.p_ref,
d=0.0,
n_surf=25)
disps = np.array(
[0.0, 0.00003, -0.00003, 0.0004, 0.0001, 0.0009, -0.0009]) * 10
disps = np.linspace(0.0, 0.02, 100)
k0_init = sl.poissons_ratio / (1 - sl.poissons_ratio)
print(k0_init)
stress, strain, v_eff, h_eff, exit_code = d2d.run_2d_strain_driver_iso(
osi,
base_mat,
esig_v0=esig_v0,
disps=disps,
handle='warn',
verbose=1,
target_d_inc=0.00001)
bf, sps = plt.subplots(nrows=2)
sps[0].plot(strain, stress, c='r')
sps[0].plot(strains, taus, label='approx PIMY')
sps[0].plot(pysra_sl.mod_reduc.strains,
pysra_tau,
ls='--',
label='PySRA Hyperbolic')
# sps[1].plot(stress)
sps[1].plot(v_eff)
sps[1].plot(h_eff)
sps[0].axhline(sl.cohesion, c='k')
plt.legend()
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():
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(show=0, export=0):
sl = sm.Soil()
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.strain_peak = 0.1 # set additional parameter required for PIMY model
sl.xi = 0.03 # for linear analysis
assert np.isclose(vs, sl.get_shear_vel(saturated=False))
soil_profile = sm.SoilProfile()
soil_profile.add_layer(0, sl)
sl = sm.Soil()
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.strain_peak = 0.1 # set additional parameter required for PIMY model
sl.xi = 0.03 # for linear analysis
soil_profile.add_layer(9.5, sl)
soil_profile.height = 20.0
ecp_out = sm.Output()
ecp_out.add_to_dict(soil_profile)
if export:
import json
ofile = open('ecp.json', 'w')
ofile.write(json.dumps(ecp_out.to_dict(), indent=4))
ofile.close()
from tests.conftest import TEST_DATA_DIR
record_path = TEST_DATA_DIR
record_filename = 'test_motion_dt0p01.txt'
dt = 0.01
rec = np.loadtxt(record_path + record_filename) / 2
acc_signal = eqsig.AccSignal(rec, dt)
outputs = site_response(soil_profile, acc_signal, linear=0)
tot_acc = np.sum(abs(outputs['rel_accel']))
assert np.isclose(tot_acc, 515.76262984), tot_acc # v3.1.0.11
resp_dt = outputs['time'][2] - outputs['time'][1]
surf_sig = eqsig.AccSignal(outputs['rel_accel'], resp_dt)
if show:
lw = 0.7
import matplotlib.pyplot as plt
from bwplot import cbox
bf, sps = plt.subplots(nrows=3)
# linear analysis with pysra
od = run_pysra(soil_profile, acc_signal, odepths=np.array([0.0, 2.0]))
pysra_sig = eqsig.AccSignal(od['ACCX_d0'], acc_signal.dt)
sps[0].plot(acc_signal.time, acc_signal.values, c='k', lw=lw)
sps[0].plot(surf_sig.time, surf_sig.values, c=cbox(0), lw=lw)
sps[0].plot(acc_signal.time, pysra_sig.values, c=cbox(1), lw=lw)
sps[1].plot(acc_signal.fa_frequencies, abs(acc_signal.fa_spectrum), c='k', label='Input', lw=lw)
sps[1].plot(surf_sig.fa_frequencies, abs(surf_sig.fa_spectrum), c=cbox(0), label='O3', lw=lw)
sps[1].plot(pysra_sig.fa_frequencies, abs(pysra_sig.fa_spectrum), c=cbox(1), label='pysra', lw=lw)
sps[1].set_xlim([0, 20])
h = surf_sig.smooth_fa_spectrum / acc_signal.smooth_fa_spectrum
sps[2].plot(surf_sig.smooth_fa_frequencies, h, c=cbox(0))
pysra_h = pysra_sig.smooth_fa_spectrum / acc_signal.smooth_fa_spectrum
sps[2].plot(pysra_sig.smooth_fa_frequencies, pysra_h, c=cbox(1))
# o3_nl_h = nl_surf_sig.smooth_fa_spectrum / acc_signal.smooth_fa_spectrum
# sps[2].plot(nl_surf_sig.smooth_fa_frequencies, o3_nl_h, c=cbox(2))
sps[2].axhline(1, c='k', ls='--')
sps[1].legend()
plt.show()
print(outputs)
def test_two_d_mesh():
rho = 1.8
sl1 = sm.Soil(g_mod=50, unit_dry_weight=rho * 9.8, poissons_ratio=0.3)
sl2 = sm.Soil(g_mod=100, unit_dry_weight=rho * 9.8, poissons_ratio=0.3)
sl3 = sm.Soil(g_mod=400, unit_dry_weight=rho * 9.8, poissons_ratio=0.3)
sl4 = sm.Soil(g_mod=600, unit_dry_weight=rho * 9.8, poissons_ratio=0.3)
sp = sm.SoilProfile()
sp.add_layer(0, sl1)
sp.add_layer(5, sl2)
sp.add_layer(12, sl3)
sp.height = 18
sp.x = 0
sp2 = sm.SoilProfile()
sp2.add_layer(0, sl1)
sp2.add_layer(7, sl4)
sp2.add_layer(12, sl3)
sp2.height = 20
sp.x_angles = [0.0, 0.05, 0.0]
sp2.x_angles = [0.0, 0.00, 0.0]
fd = sm.RaftFoundation()
fd.width = 2
fd.depth = 0
fd.height = 0
fd.length = 100
fd.ip_axis = 'width'
tds = sm.TwoDSystem(40, 15)
tds.add_sp(sp, x=0)
tds.add_sp(sp2, x=14)
tds.x_surf = np.array([0, 10, 12, 40])
tds.y_surf = np.array([0, 0, 2, 2])
bd = sm.NullBuilding()
bd.set_foundation(fd, x=0.0)
bd_x = 8.
tds.add_bd(bd, x=bd_x)
x_scale_pos = np.array([0, 5, 15, 30])
x_scale_vals = np.array([2., 1.0, 2.0, 3.0])
fc = sm.num.mesh.FiniteElementOrth2DMeshConstructor(
tds, 0.3, x_scale_pos=x_scale_pos, x_scale_vals=x_scale_vals)
femesh = fc.femesh
x_ind = femesh.get_indexes_at_xs([4.])[0]
y_ind = femesh.get_indexes_at_depths([1.99])[0]
sl_ind = femesh.soil_grid[x_ind][y_ind]
assert sl_ind == 1000000
assert femesh.get_active_nodes()[x_ind][y_ind] == 0
p_ind = fc.x_index_to_sp_index[x_ind]
assert p_ind == 0
y_ind = femesh.get_indexes_at_depths([-3])[0]
sl_ind = femesh.soil_grid[x_ind][y_ind]
assert femesh.soils[sl_ind].g_mod == 50
y_ind = femesh.get_indexes_at_depths([-8])[0]
sl_ind = femesh.soil_grid[x_ind][y_ind]
assert femesh.soils[sl_ind].g_mod == 100
y_ind = femesh.get_indexes_at_depths([-12.5])[0]
sl_ind = femesh.soil_grid[x_ind][y_ind]
assert femesh.soils[sl_ind].g_mod == 400
assert femesh.get_active_nodes()[x_ind][y_ind] == 1
x_ind = femesh.get_indexes_at_xs([16.])[0]
p_ind = fc.x_index_to_sp_index[x_ind]
assert p_ind == 1
y_ind = femesh.get_indexes_at_depths([1.99])[0]
sl_ind = femesh.soil_grid[x_ind][y_ind]
assert femesh.soils[sl_ind].g_mod == 50
y_ind = femesh.get_indexes_at_depths([-3])[0]
sl_ind = femesh.soil_grid[x_ind][y_ind]
assert femesh.soils[sl_ind].g_mod == 50
y_ind = femesh.get_indexes_at_depths([-8])[0]
sl_ind = femesh.soil_grid[x_ind][y_ind]
assert femesh.soils[sl_ind].g_mod == 600
y_ind = femesh.get_indexes_at_depths([-12.5])[0]
sl_ind = femesh.soil_grid[x_ind][y_ind]
assert femesh.soils[sl_ind].g_mod == 400
# check foundation
lhs_ind = np.argmin(abs(femesh.x_nodes - (bd_x - fd.width / 2)))
assert np.isclose(bd_x - fd.width / 2,
femesh.x_nodes[lhs_ind]), (bd_x - fd.width / 2,
femesh.x_nodes[lhs_ind])
rhs_ind = np.argmin(abs(femesh.x_nodes - (bd_x + fd.width / 2)))
assert np.isclose(bd_x + fd.width / 2, femesh.x_nodes[rhs_ind])
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():
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()
