mags = ['m4.5', 'm5.0', 'm5.5', 'm6.0', 'm6.5', 'm7.0']
xl = [1, 50]
### PGA
plt.figure(figsize=(13, 8))
####### M4.5 ##########
k = 0
sim_path = path + sims[k]
lonlat = genfromtxt(sim_path + '/param_files/eew_test.stl', usecols=[0, 1])
stlon = lonlat[:, 0]
stlat = lonlat[:, 1]
#Get event hypocenter
srf_file = sim_path + '/template_' + mags[k] + '.srf'
xyz, slip, tinit, stf_all, rise_time, hypocenter = bbptools.read_srf(srf_file)
#Get station to event distances
evlon = ones(len(stlon)) * hypocenter[0]
evlat = ones(len(stlat)) * hypocenter[1]
g = Geod(ellps='WGS84')
az, baz, dist = g.inv(stlon, stlat, evlon, evlat)
dist = dist / 1000
#get predicted data
mag = 4.5
delta = 0.5
R_pred = logspace(-1, 2)
pred_pgd = 10**(-4.434 + 1.047 * mag - 0.138 * mag * log10(R_pred))
pred_pgd_minus = 10**(-4.434 + 1.047 * mag - delta - 0.138 *
(mag - delta) * log10(R_pred))
def plot_srf(srf_file,
src_file,
contour_interval=1.0,
highlight=3.0,
UTM_zone='10S',
figsize=(8, 4),
xlim=[-20, 20],
save=False,
fout=None):
'''
plot srf file
'''
from matplotlib import pyplot as plt
import bbptools
from pyproj import Proj, Geod
from numpy import linspace, ones, r_, zeros, argmin, where, arange
from mudpy.viewFQ import pqlx
import matplotlib.tri as tri
from matplotlib import ticker
#Parse src file
f = open(src_file)
while True:
line = f.readline()
if 'FAULT_LENGTH' in line:
length = float(line.split('=')[1]) * 1000
if 'STRIKE' in line:
strike = float(line.split('=')[1])
break
xyz, slip, tinit, stf_all, rise_time, hypocenter = bbptools.read_srf(
srf_file)
#Project fault coordiantes to strike direction
p = Proj(ellps='WGS84', proj='utm', zone=UTM_zone)
g = Geod(ellps='WGS84')
#First make straight line to be projected onto
npts = 10000
lon_orig = hypocenter[0] * ones(npts)
lat_orig = hypocenter[1] * ones(npts)
strike = r_[(strike - 180) * ones(npts / 2), strike * ones(npts / 2)]
L = r_[linspace(-1.2 * length, -0.1, npts / 2),
linspace(0.1, 1.1 * length, npts / 2)]
lon_line, lat_line, foo = g.fwd(lon_orig, lat_orig, strike, L)
lat_line = linspace(xyz[:, 1].min() - 1.0, xyz[:, 1].max() + 1.0, 10000)
#Convert everything to UTM
x_line, y_line = p(lon_line, lat_line)
x_hypo, y_hypo = p(hypocenter[0], hypocenter[1])
x_source, y_source = p(xyz[:, 0], xyz[:, 1])
# Get closest coordinates on line
x_proj = zeros(len(x_source))
y_proj = zeros(len(x_source))
for k in range(len(x_source)):
dist = ((x_source[k] - x_line)**2 + (y_source[k] - y_line)**2)**0.5
i = argmin(dist)
x_proj[k] = x_line[i]
y_proj[k] = y_line[i]
z_proj = -xyz[:, 2]
#hypocenter projection
i = where(tinit > 0)[0]
imin = argmin(tinit[i])
x_hypo_proj = x_proj[i[imin]]
y_hypo_proj = y_proj[i[imin]]
z_hypo_proj = -xyz[i[imin], 2]
#Convert to along strike and down dip distances
strike_dist = ((x_proj - x_hypo_proj)**2 + (y_proj - y_hypo_proj)**2)**0.5
i = where(y_proj > y_hypo_proj)[0]
strike_dist[i] = -strike_dist[i]
#strike_dist[i]=strike_dist[i]
strike_dist /= 1000
dip_dist = (((z_proj - z_hypo_proj) * 1000)**2)**0.5
i = where(z_proj < z_hypo_proj)[0]
dip_dist[i] = -dip_dist[i]
dip_dist /= 1000
# Make plot
triang = tri.Triangulation(strike_dist, dip_dist)
xl = xlim
plt.figure(figsize=figsize)
plt.subplot(211)
plt.tripcolor(triang, slip, shading='flat', cmap=whitejet)
plt.ylabel('Down dip (km)')
cb = plt.colorbar(shrink=0.85)
cb.set_label('Slip (cm)')
tick_locator = ticker.MaxNLocator(nbins=5)
cb.locator = tick_locator
cb.update_ticks()
yl = [dip_dist.min(), dip_dist.max()]
plt.ylim(yl)
plt.xlim(xl)
#Rupture onset contours
plt.tricontour(triang,
tinit,
levels=arange(0,
tinit.max() + contour_interval,
contour_interval),
colors='k',
linewidths=1)
plt.tricontour(triang, tinit, levels=[highlight], colors='k', linewidths=3)
plt.scatter(0, 0, c='w', marker='*', lw=1, s=250)
ax = plt.gca()
#ax.set_aspect('equal', adjustable='box')
plt.subplot(212)
plt.tripcolor(triang, rise_time, shading='flat', cmap=plt.cm.magma_r)
plt.ylabel('Down dip (km)')
plt.xlabel('Along Strike (km)')
cb = plt.colorbar(shrink=0.85)
cb.set_label('Rise time (s)')
tick_locator = ticker.MaxNLocator(nbins=5)
cb.locator = tick_locator
cb.update_ticks()
yl = [dip_dist.min(), dip_dist.max()]
plt.ylim(yl)
plt.xlim(xl)
#Rupture onset contours
plt.tricontour(triang,
tinit,
levels=arange(0,
tinit.max() + contour_interval,
contour_interval),
colors='k',
linewidths=1)
plt.tricontour(triang, tinit, levels=[highlight], colors='k', linewidths=3)
plt.scatter(0, 0, c='w', marker='*', lw=1, s=250)
ax = plt.gca()
#ax.set_aspect('equal', adjustable='box')
plt.xticks(rotation=0)
plt.subplots_adjust(hspace=0,
wspace=0,
top=0.96,
bottom=0.11,
left=0.1,
right=0.98)
plt.show()
if save == True:
plt.savefig(fout)
def plot_allP(sim_path, sim_list):
'''
Extract all P-values and scatter plot
'''
from glob import glob
from numpy import genfromtxt, where, log10, r_, ones, array, logspace, argmin
import bbptools
from scipy.integrate import cumtrapz
from pyproj import Geod
from matplotlib import pyplot as plt
#Initalize
observed_Pd = []
predicted_Pd = []
M = array([])
R = array([])
#Loop over sim_list
for ksim in sim_list:
print ksim
stations = genfromtxt(glob(sim_path + ksim + '/param_files/*.stl')[0],
usecols=2,
dtype='S')
lonlat = genfromtxt(glob(sim_path + ksim + '/param_files/*.stl')[0],
usecols=[0, 1])
#Get ID#
files = glob(sim_path + ksim + '/*.bbp')
ID = files[0].split('/')[-1].split('.')[0]
for kstation in range(len(stations)):
current_station = stations[kstation]
v = genfromtxt(sim_path + ksim + '/' + ID + '.' + current_station +
'.vel.bbp')
t = v[:, 0]
vz = v[:, 3]
#Get displacement
dz = cumtrapz(vz, t, initial=0)
#Read meta data get magnitude and hypocenter
if kstation == 0:
#Get hypocenter
srf_file = glob(sim_path + ksim + '/*.srf')[0]
xyz, slip, tinit, stf_all = bbptools.read_srf(srf_file)
i = argmin(tinit)
hypocenter = xyz[i, :]
#get magnitude
metadata = glob(sim_path + ksim + '/param_files/*.src')[0]
f = open(metadata, 'r')
line = f.readline()
mag = float(line.split()[-1])
f.close()
M = r_[M, mag * ones(len(stations))]
#Get P,S arrivals
ptime, stime = arrivals(hypocenter, lonlat[kstation, 0],
lonlat[kstation, 1])
#Get observed Pd
ptime = ptime - 1
i = where((t >= ptime) & (t <= ptime + 3))[0]
Pd = max(abs(dz[i]))
observed_Pd.append(Pd)
#Get predicted Pd
#Station to hypo distance
g = Geod(ellps='WGS84')
az, baz, dist = g.inv(hypocenter[0], hypocenter[1],
lonlat[kstation, 0], lonlat[kstation, 1])
#Convert distance from m to degrees and km
dist_km = dist / 1000
R = r_[R, dist_km]
#Finally, actually get Pd
Pd = 10**((mag - 5.39 - 1.38 * log10(dist_km)) / 1.23)
predicted_Pd.append(Pd)
observed_Pd = array(observed_Pd)
predicted_Pd = array(predicted_Pd)
#Make plot
plt.figure()
i = where(M == 4.5)[0]
plt.scatter(R[i], observed_Pd[i], lw=0.5, s=40, c='#DC143C')
i = where(M == 5.0)[0]
plt.scatter(R[i], observed_Pd[i], lw=0.5, s=40, c='#32CD32')
i = where(M == 5.5)[0]
plt.scatter(R[i], observed_Pd[i], lw=0.5, s=40, c='#0000CD')
i = where(M == 6.0)[0]
plt.scatter(R[i], observed_Pd[i], lw=0.5, s=40, c='#DAA520')
i = where(M == 6.5)[0]
plt.scatter(R[i], observed_Pd[i], lw=0.5, s=40, c='#9932CC')
i = where(M == 7.0)[0]
plt.scatter(R[i], observed_Pd[i], lw=0.5, s=40, c='#202020')
ax = plt.gca()
ax.set_yscale('log')
ax.set_xscale('log')
#Reference lines
Rref = logspace(0, 2)
Pd45 = 10**((4.5 - 5.39 - 1.38 * log10(Rref)) / 1.23)
Pd50 = 10**((5.0 - 5.39 - 1.38 * log10(Rref)) / 1.23)
Pd55 = 10**((5.5 - 5.39 - 1.38 * log10(Rref)) / 1.23)
Pd60 = 10**((6.0 - 5.39 - 1.38 * log10(Rref)) / 1.23)
Pd65 = 10**((6.5 - 5.39 - 1.38 * log10(Rref)) / 1.23)
Pd70 = 10**((7.0 - 5.39 - 1.38 * log10(Rref)) / 1.23)
plt.plot(Rref, Pd45, '--', lw=2, c='#DC143C')
plt.plot(Rref, Pd50, '--', lw=2, c='#32CD32')
plt.plot(Rref, Pd55, '--', lw=2, c='#0000CD')
plt.plot(Rref, Pd60, '--', lw=2, c='#DAA520')
plt.plot(Rref, Pd65, '--', lw=2, c='#9932CC')
plt.plot(Rref, Pd70, '--', lw=2, c='#202020')
ax.set_xlim([1e-4, 1e1])
ax.set_xlim([1e0, 1e2])
plt.xlabel('Distance (km)')
plt.ylabel('Pd (cm)')
plt.legend(['M4.5', 'M5.0', 'M5.5', 'M6.0', 'M6.5', '7.0'], loc=3)
plt.figure(figsize=(10, 6))
plt.subplot(231)
i = where(M == 4.5)[0]
plt.scatter(R[i], observed_Pd[i], lw=0.5, s=40, c='#DC143C')
plt.plot(Rref, Pd45, '--', lw=2, c='#DC143C')
plt.legend(['M4.5'], frameon=False, loc=3)
ax = plt.gca()
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_xlim([1e0, 1e2])
plt.ylabel('Pd (cm)')
plt.subplot(232)
i = where(M == 5.0)[0]
plt.scatter(R[i], observed_Pd[i], lw=0.5, s=40, c='#32CD32')
plt.plot(Rref, Pd50, '--', lw=2, c='#32CD32')
plt.legend(['M5.0'], frameon=False, loc=3)
ax = plt.gca()
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_xlim([1e0, 1e2])
plt.subplot(233)
i = where(M == 5.5)[0]
plt.scatter(R[i], observed_Pd[i], lw=0.5, s=40, c='#0000CD')
plt.plot(Rref, Pd55, '--', lw=2, c='#0000CD')
plt.legend(['M5.5'], frameon=False, loc=3)
ax = plt.gca()
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_xlim([1e0, 1e2])
plt.subplot(234)
i = where(M == 6.0)[0]
plt.scatter(R[i], observed_Pd[i], lw=0.5, s=40, c='#DAA520')
plt.plot(Rref, Pd60, '--', lw=2, c='#DAA520')
plt.legend(['M6.0'], frameon=False, loc=3)
ax = plt.gca()
ax.set_yscale('log')
ax.set_xscale('log')
plt.xlabel('Distance (km)')
plt.ylabel('Pd (cm)')
ax.set_xlim([1e0, 1e2])
plt.subplot(235)
i = where(M == 6.5)[0]
plt.scatter(R[i], observed_Pd[i], lw=0.5, s=40, c='#9932CC')
plt.plot(Rref, Pd65, '--', lw=2, c='#9932CC')
plt.legend(['M6.5'], frameon=False, loc=3)
ax = plt.gca()
ax.set_yscale('log')
ax.set_xscale('log')
plt.xlabel('Distance (km)')
ax.set_xlim([1e0, 1e2])
plt.subplot(236)
i = where(M == 7.0)[0]
plt.scatter(R[i], observed_Pd[i], lw=0.5, s=40, c='#202020')
plt.plot(Rref, Pd70, '--', lw=2, c='#202020')
plt.legend(['M7.0'], frameon=False, loc=3)
ax = plt.gca()
ax.set_yscale('log')
ax.set_xscale('log')
plt.xlabel('Distance (km)')
ax.set_xlim([1e0, 1e2])
plt.show()
def plot_pwave(sim_path,
staname,
stafile,
tlims=[0, 30],
ylims=[-2, 2],
filter=True,
corner=1. / 13):
'''
Plot one station and it's predicted arrivals from ray tracing
'''
from matplotlib import pyplot as plt
from obspy.taup import TauPyModel
from numpy import genfromtxt, where, rad2deg, ones, log10, argmin
from scipy.integrate import cumtrapz
from glob import glob
from pyproj import Geod
import bbptools
from mudpy.forward import highpass
#Get hypocenter
srf_file = glob(sim_path + '*.srf')[0]
xyz, slip, tinit, stf_all, rise_time, hypocenter = bbptools.read_srf(
srf_file)
i = argmin(tinit)
hypocenter = xyz[i, :]
#load data
vfile = glob(sim_path + '*.' + staname + '.vel*')[0]
afile = glob(sim_path + '*.' + staname + '.acc*')[0]
a = genfromtxt(afile)
v = genfromtxt(vfile)
t = v[:, 0]
vz = v[:, 3]
dt = t[1] - t[0]
accz = a[:, 3]
ta = a[:, 0]
#Get displacement
dz = cumtrapz(vz, t, initial=0)
#Filter?
if filter == True:
dfil = highpass(dz, corner, 1 / dt, 4)
dz = dfil.copy()
#Station coordinates
all_stations = genfromtxt(sim_path + 'param_files/' + stafile,
usecols=[2],
dtype='S')
i = where(all_stations == staname)[0]
lonlat = genfromtxt(sim_path + 'param_files/' + stafile, usecols=[0,
1])[i][0]
#Station to hypo distance
g = Geod(ellps='WGS84')
az, baz, dist = g.inv(hypocenter[0], hypocenter[1], lonlat[0], lonlat[1])
#Convert distance from m to degrees and km
dist_deg = rad2deg(dist / 6371e3)
dist_km = dist / 1000
#Calculate theoretical arrivals
mod = TauPyModel(model='Nocal')
arrivals = mod.get_travel_times(source_depth_in_km=hypocenter[2],
distance_in_degree=dist_deg,
phase_list=('P', 'p', 'S', 's'))
#Parse arrivals
for k in range(len(arrivals)):
if arrivals[k].name == 'p' or arrivals[k].name == 'P':
ptime = arrivals[k].time
if arrivals[k].name == 's' or arrivals[k].name == 'S':
stime = arrivals[k].time
#Get expected value of Pd
metadata = glob(sim_path + 'param_files/*.src')[0]
f = open(metadata, 'r')
line = f.readline()
mag = float(line.split()[-1])
f.close()
Pd = 10**((mag - 5.39 - 1.38 * log10(dist_km)) / 1.23)
#Assign ylims
ylims = [-2 * Pd, 2 * Pd]
#Plot the thingamajig
plt.figure(figsize=(9, 4.5))
plt.subplot(212)
plt.plot(t, dz, 'k')
plt.plot(t, Pd * ones(len(t)), '--')
plt.plot(t, -Pd * ones(len(t)), '--')
plt.scatter(ptime, 0, marker='|', c='b', s=100, lw=2)
plt.scatter(stime, 0, marker='|', c='r', s=100, lw=2)
plt.ylabel('Vertical (cm)')
plt.xlabel('Seconds after OT')
plt.xlim(tlims)
plt.ylim(ylims)
plt.subplot(211)
plt.plot(ta, accz, 'k')
plt.scatter(ptime, 0, marker='|', c='b', s=100, lw=2)
plt.scatter(stime, 0, marker='|', c='r', s=100, lw=2)
plt.ylabel('Vertical (cm/s/s)')
plt.xlim(tlims)
plt.subplots_adjust(left=0.12,
right=0.97,
bottom=0.12,
top=0.96,
hspace=0.12)
plt.show()
v = genfromtxt(ksim + '/' + ID + '.' + current_station + '.vel.bbp')
t = v[:, 0]
vz = v[:, 3]
#Get displacement
dz = cumtrapz(vz, t, initial=0)
dt = t[1] - t[0]
if filter == True:
dfil = highpass(dz, corner, 1 / dt, 4)
dz = dfil.copy()
#Read meta data get magnitude and hypocenter
if kstation == 0:
#Get hypocenter
srf_file = glob(ksim + '/*.srf')[0]
xyz, slip, tinit, stf_all = bbptools.read_srf(srf_file)
i = argmin(tinit)
hypocenter = xyz[i, :]
print hypocenter
#get magnitude
metadata = glob(ksim + '/param_files/*.src')[0]
f = open(metadata, 'r')
line = f.readline()
mag = float(line.split()[-1])
f.close()
M = r_[M, mag * ones(len(stations))]
#Get P,S arrivals
ptime, stime = bbptools.arrivals(hypocenter, lonlat[kstation, 0],
lonlat[kstation, 1])
u'/Users/dmelgar/code/BBP/bbp/bbp_data/finished/fake_nocal/param_files/fake_nocal.stl',
usecols=[2],
dtype='S')
lonlat = genfromtxt(
u'/Users/dmelgar/code/BBP/bbp/bbp_data/finished/fake_nocal/param_files/fake_nocal.stl',
usecols=[0, 1])
#Station miniSEED
st = read(
u'/Users/dmelgar/code/BBP/bbp/bbp_data/finished/rawdata/fake_nocal/_adjusted/fake_nocal_pfix.mseed'
)
#Station to hypo distance
g = Geod(ellps='WGS84')
xyz, slip, tinit, stf_all, rise_time, hypocenter = bbptools.read_srf(
u'/Users/dmelgar/code/BBP/bbp/bbp_data/finished/fake_nocal/large_eew_m5.0_frac0.5.srf'
)
xl = [-0.02, 13]
t0 = UTCDateTime('2016-09-07T14:42:26')
fig, axarr = plt.subplots(20, 1, figsize=(8, 16))
for k in range(len(stations)):
a = genfromtxt(
u'/Users/dmelgar/code/BBP/bbp/bbp_data/finished/fake_nocal/111.' +
stations[k].lower() + '.vel.bbp')
trigger_time = UTCDateTime(triggers[k]) - t0
#Find current station
i = where(all_stations == stations[k].lower())[0]
az, baz, dist = g.inv(hypocenter[0], hypocenter[1], lonlat[i, 0],