def filter_data(psignal, fmin, fmax):
"""
This function is used to filter with a bandpass filter between fmin/fmax
"""
if not isinstance(psignal, seism_psignal):
print("[ERROR]: found error filtering psignal.")
return False
delta_t = psignal.dt
psignal.accel = s_filter(psignal.accel, delta_t, type='bandpass',
family='butter', fmin=fmin, fmax=fmax,
N=4, rp=0.1, rs=100)
psignal.velo = s_filter(psignal.velo, delta_t, type='bandpass',
family='butter', fmin=fmin, fmax=fmax,
N=4, rp=0.1, rs=100)
psignal.displ = s_filter(psignal.displ, delta_t, type='bandpass',
family='butter', fmin=fmin, fmax=fmax,
N=4, rp=0.1, rs=100)
psignal.data = np.c_[psignal.displ, psignal.velo, psignal.accel]
return psignal
def filter_data(psignal, fmin, fmax):
"""
This function is used to filter with a bandpass filter between fmin/fmax
"""
if not isinstance(psignal, seism_psignal):
print("[ERROR]: found error filtering psignal.")
return False
delta_t = psignal.dt
psignal.accel = s_filter(psignal.accel,
delta_t,
type='bandpass',
family='butter',
fmin=fmin,
fmax=fmax,
N=4,
rp=0.1,
rs=100)
psignal.velo = s_filter(psignal.velo,
delta_t,
type='bandpass',
family='butter',
fmin=fmin,
fmax=fmax,
N=4,
rp=0.1,
rs=100)
psignal.displ = s_filter(psignal.displ,
delta_t,
type='bandpass',
family='butter',
fmin=fmin,
fmax=fmax,
N=4,
rp=0.1,
rs=100)
psignal.data = np.c_[psignal.displ, psignal.velo, psignal.accel]
return psignal
def process_signal_dt(signal, dt, fmax):
"""
Processes signal with common dt and fmax.
"""
# call low_pass filter at fmax
signal.accel = s_filter(signal.accel,
signal.dt,
type='lowpass',
family='butter',
fmax=fmax,
N=4,
rp=0.1,
rs=100)
signal.velo = s_filter(signal.velo,
signal.dt,
type='lowpass',
family='butter',
fmax=fmax,
N=4,
rp=0.1,
rs=100)
signal.displ = s_filter(signal.displ,
signal.dt,
type='lowpass',
family='butter',
fmax=fmax,
N=4,
rp=0.1,
rs=100)
# interpolate
signal.accel = interp(signal.accel, signal.samples, signal.dt, dt)
signal.velo = interp(signal.velo, signal.samples, signal.dt, dt)
signal.displ = interp(signal.displ, signal.samples, signal.dt, dt)
signal.samples = signal.accel.size
signal.dt = dt
return signal
def process_signal_dt(signal, dt, fmax):
"""
Processes signal with common dt and fmax.
"""
# call low_pass filter at fmax
signal.accel = s_filter(signal.accel, signal.dt, type='lowpass',
family='butter', fmax=fmax,
N=4, rp=0.1, rs=100)
signal.velo = s_filter(signal.velo, signal.dt, type='lowpass',
family='butter', fmax=fmax,
N=4, rp=0.1, rs=100)
signal.displ = s_filter(signal.displ, signal.dt, type='lowpass',
family='butter', fmax=fmax,
N=4, rp=0.1, rs=100)
# interpolate
signal.accel = interp(signal.accel, signal.samples, signal.dt, dt)
signal.velo = interp(signal.velo, signal.samples, signal.dt, dt)
signal.displ = interp(signal.displ, signal.samples, signal.dt, dt)
signal.samples = signal.accel.size
signal.dt = dt
return signal
def process(signal):
"""
The function takes a signal, use its's current data to get
acceleration, velocity, and displacement.
Then return a psignal.
"""
if not isinstance(signal, seism_signal):
print("[ERROR]: instance error; process signal objects only.")
return
# Correction of base lines was commented out here to avoid problems with synchronization
# correct_baseline(signal)
acc = np.array([], float)
vel = np.array([], float)
dis = np.array([], float)
dt = signal.dt
if signal.type == 'a':
acc = signal.data
acc = s_filter(acc, signal.dt, type='highpass',
family='butter', fmin=0.05, N=5)
vel = integrate(acc, dt)
vel = s_filter(vel, signal.dt, type='highpass',
family='butter', fmin=0.05, N=5)
dis = integrate(vel, dt)
dis = s_filter(dis, signal.dt, type='highpass',
family='butter', fmin=0.05, N=5)
elif signal.type == 'v':
vel = signal.data
vel = s_filter(vel, signal.dt, type='highpass',
family='butter', fmin=0.05, N=5)
acc = derivative(vel, dt)
dis = integrate(vel, dt)
dis = s_filter(dis, signal.dt, type='highpass',
family='butter', fmin=0.05, N=5)
elif signal.type == 'd':
dis = signal.data
dis = s_filter(dis, signal.dt, type='highpass',
family='butter', fmin=0.05, N=5)
vel = derivative(dis, dt)
acc = derivative(vel, dt)
else:
pass
psignal = seism_psignal(signal.samples, signal.dt,
np.c_[dis, vel, acc], 'c', acc, vel, dis)
return psignal
def simple_plot(parameter, filenames, stations, output_file, plot_title=None):
"""
plotting velocity for data and FAS only acceleration for Response
"""
all_styles = [
'k', 'r', 'b', 'm', 'g', 'c', 'y', 'brown', 'gold', 'blueviolet',
'grey', 'pink'
]
orientation = ['N/S', 'E/W', 'Up/Down']
# Check number of input timeseries
if len(stations) > len(all_styles):
print("[ERROR]: Too many timeseries to plot!")
sys.exit(-1)
delta_ts = [station[0].dt for station in stations]
files = [os.path.basename(filename) for filename in filenames]
xtmin = parameter[0]
xtmax = parameter[1]
xfmin = parameter[2]
xfmax = parameter[3]
filter_flag = parameter[4]
fmin = parameter[5]
fmax = parameter[6]
tmin = parameter[7]
tmax = parameter[8]
cut_flag = parameter[9]
min_is = [int(xtmin / delta_t) for delta_t in delta_ts]
max_is = [int(xtmax / delta_t) for delta_t in delta_ts]
period = get_period(tmin, tmax)
f, axarr = plt.subplots(nrows=3, ncols=3, figsize=(14, 9))
for i in range(0, 3):
title = orientation[i]
signals = [station[i] for station in stations]
samples = [signal.samples for signal in signals]
vels = [signal.velo for signal in signals]
accs = [signal.accel for signal in signals]
for sample, max_i, delta_t in zip(samples, max_is, delta_ts):
if sample - 1 < max_i:
print("[ERROR]: t_max has to be under %f" %
((sample - 1) * delta_t))
sys.exit(1)
# filtering data
if filter_flag:
vels = [
s_filter(vel,
delta_t,
type='bandpass',
family='ellip',
fmin=fmin,
fmax=fmax,
N=3,
rp=0.1,
rs=100) for vel, delta_t in zip(vels, delta_ts)
]
accs = [
s_filter(acc,
delta_t,
type='bandpass',
family='ellip',
fmin=fmin,
fmax=fmax,
N=3,
rp=0.1,
rs=100) for acc, delta_t in zip(accs, delta_ts)
]
# cutting signal by bounds
c_vels = [
vel[min_i:max_i]
for vel, min_i, max_i in zip(vels, min_is, max_is)
]
c_accs = [
acc[min_i:max_i]
for acc, min_i, max_i in zip(accs, min_is, max_is)
]
times = [np.arange(xtmin, xtmax, delta_t) for delta_t in delta_ts]
points = get_points(samples)
if cut_flag:
freqs, fas_s = zip(*[
FAS(c_vel, delta_t, points, xfmin, xfmax, 3)
for c_vel, delta_t in zip(c_vels, delta_ts)
])
rsps = cal_acc_response(period, c_accs, delta_ts)
else:
freqs, fas_s = zip(*[
FAS(vel, delta_t, points, xfmin, xfmax, 3)
for vel, delta_t in zip(vels, delta_ts)
])
rsps = cal_acc_response(period, accs, delta_ts)
axarr[i][0] = plt.subplot2grid((3, 4), (i, 0), colspan=2, rowspan=1)
axarr[i][0].set_title(title)
axarr[i][0].grid(True)
styles = all_styles[0:len(times)]
for timeseries, c_vel, style in zip(times, c_vels, styles):
axarr[i][0].plot(timeseries, c_vel, style)
if i == 0:
plt.legend(files, prop={'size': 8})
plt.xlim(xtmin, xtmax)
axarr[i][1] = plt.subplot2grid((3, 4), (i, 2), rowspan=1, colspan=1)
axarr[i][1].set_title('Fourier Amplitude Spectra')
axarr[i][1].grid(True, which='both')
axarr[i][1].set_xscale('log')
axarr[i][1].set_yscale('log')
for freq, fas, style in zip(freqs, fas_s, styles):
axarr[i][1].plot(freq, fas, style)
tmp_xfmin = 0
if xfmin < 0.5:
tmp_xfmin = 0
else:
tmp_xfmin = xfmin
plt.xlim(tmp_xfmin, xfmax)
axarr[i][2] = plt.subplot2grid((3, 4), (i, 3), rowspan=1, colspan=1)
axarr[i][2].set_title("Response Spectra")
axarr[i][2].set_xscale('log')
axarr[i][2].grid(True)
for rsp, style in zip(rsps, styles):
axarr[i][2].plot(period, rsp, style)
plt.xlim(tmin, tmax)
# Make nice plots with tight_layout
f.tight_layout()
# Add overall title if provided
if plot_title is not None:
st = plt.suptitle(plot_title, fontsize=16)
# shift subplots down:
#st.set_y(0.95)
f.subplots_adjust(top=0.92)
# All done, save plot
if output_file.lower().endswith(".png"):
fmt = 'png'
elif output_file.lower().endswith(".pdf"):
fmt = 'pdf'
else:
print("ERROR: Unknown format!")
sys.exit(-1)
plt.savefig(output_file, format=fmt, transparent=False, dpi=300)
def plot_stations(parameter, filenames, station1, station2):
"""
This function plots two lists of psignals with Fourier Amplitude Spectra.
station = a list of 3 psignals for three orientation.
"""
dtype = ['Displacement', 'Velocity', 'Acceleration']
orientation = ['N/S', 'E/W', 'Up/Down']
dt1 = station1[0].dt
dt2 = station2[0].dt
file1 = filenames[0]
file2 = filenames[1]
xtmin = parameter[0]
xtmax = parameter[1]
xfmin = parameter[2]
xfmax = parameter[3]
filter_flag = parameter[4]
fmin = parameter[5]
fmax = parameter[6]
tmin = parameter[7]
tmax = parameter[8]
cut_flag = parameter[9]
min_i1 = int(xtmin / dt1)
max_i1 = int(xtmax / dt1)
min_i2 = int(xtmin / dt2)
max_i2 = int(xtmax / dt2)
# calculating Response Spectra
rsp1 = []
rsp2 = []
period = get_period(tmin, tmax)
for i in range(0, 3):
signal1 = station1[i]
signal2 = station2[i]
(acc_rsp1, acc_rsp2, vel_rsp1, vel_rsp2, dis_rsp1,
dis_rsp2) = [], [], [], [], [], []
if cut_flag:
acc1 = signal1.accel[min_i1:max_i1]
acc2 = signal2.accel[min_i2:max_i2]
else:
acc1 = signal1.accel
acc2 = signal2.accel
for p in period:
rsp = max_osc_response(acc1, dt1, 0.05, p, 0, 0)
dis_rsp1.append(rsp[0])
vel_rsp1.append(rsp[1])
acc_rsp1.append(rsp[2])
rsp = max_osc_response(acc2, dt2, 0.05, p, 0, 0)
dis_rsp2.append(rsp[0])
vel_rsp2.append(rsp[1])
acc_rsp2.append(rsp[2])
rsp1.append([dis_rsp1, vel_rsp1, acc_rsp1])
rsp2.append([dis_rsp2, vel_rsp2, acc_rsp2])
# from displacement to velocity to acceleration
for i in range(0, 3):
f, axarr = plt.subplots(nrows=3, ncols=3, figsize=(12, 9))
# iterative through psignals in each station
for j in range(0, 3):
title = dtype[i] + ' in ' + orientation[j]
signal1 = station1[j]
signal2 = station2[j]
if ((not isinstance(signal1, seism_psignal)) or \
(not isinstance(signal2, seism_psignal))):
print("[PLOT ERROR]: Invalid instance type: "
"can only plot psignal objects.")
return
if signal1.data.shape[1] != 3 or signal2.data.shape[1] != 3:
print("[PLOT ERROR]: psignal's data must be 3-column "
"numpy array for displ, velo, accel.")
return
samples1 = signal1.samples
samples2 = signal2.samples
# psignal.data = [displ, velo, accel]
data1 = signal1.data[:, i]
data2 = signal2.data[:, i]
# filtering data
if filter_flag:
data1 = s_filter(data1,
dt1,
type='bandpass',
family='ellip',
fmin=fmin,
fmax=fmax,
N=3,
rp=0.1,
rs=100)
data2 = s_filter(data2,
dt2,
type='bandpass',
family='ellip',
fmin=fmin,
fmax=fmax,
N=3,
rp=0.1,
rs=100)
# cutting signal by bounds
c_data1 = data1[min_i1:max_i1]
c_data2 = data2[min_i2:max_i2]
t1 = np.arange(xtmin, xtmax, dt1)
t2 = np.arange(xtmin, xtmax, dt2)
points = get_points([samples1, samples2])
if not cut_flag:
# if user chooses not to cut; use original data to calculate FAS and Response
freq1, fas1 = FAS(data1, dt1, points, xfmin, xfmax, 3)
freq2, fas2 = FAS(data2, dt2, points, xfmin, xfmax, 3)
else:
# use cutted signals to calculate FAS
freq1, fas1 = FAS(c_data1, dt1, points, xfmin, xfmax, 3)
freq2, fas2 = FAS(c_data2, dt2, points, xfmin, xfmax, 3)
axarr[j][0] = plt.subplot2grid((3, 4), (j, 0),
colspan=2,
rowspan=1)
axarr[j][0].set_title(title)
axarr[j][0].plot(t1, c_data1, 'r', t2, c_data2, 'b')
if j == 0:
plt.legend([file1, file2])
plt.xlim(xtmin, xtmax)
axarr[j][1] = plt.subplot2grid((3, 4), (j, 2),
rowspan=1,
colspan=1)
axarr[j][1].set_title('Fourier Amplitude Spectra')
axarr[j][1].plot(freq1, fas1, 'r', freq2, fas2, 'b')
tmp_xfmin = 0
if xfmin < 0.5:
tmp_xfmin = 0
else:
tmp_xfmin = xfmin
plt.xlim(tmp_xfmin, xfmax)
axarr[j][2] = plt.subplot2grid((3, 4), (j, 3),
rowspan=1,
colspan=1)
axarr[j][2].set_title("Response Spectra")
axarr[j][2].set_xscale('log')
axarr[j][2].plot(period, rsp1[j][i], 'r', period, rsp2[j][i], 'b')
plt.xlim(tmin, tmax)
f.tight_layout()
plt.show()
def plot_signals(parameter, filenames, signal1, signal2):
"""
This function is to plot Signals with Fourier Amplitude Spectura.
"""
plt.close('all')
file1 = filenames[0]
file2 = filenames[1]
xtmin = parameter[0]
xtmax = parameter[1]
xfmin = parameter[2]
xfmax = parameter[3]
filter_flag = parameter[4]
fmin = parameter[5]
fmax = parameter[6]
tmin = parameter[7]
tmax = parameter[8]
cut_flag = parameter[9]
title = 'Acceleration ONLY: '
samples1 = signal1.samples
samples2 = signal2.samples
data1 = signal1.data
data2 = signal2.data
dt1 = signal1.dt
dt2 = signal2.dt
# filtering data
if filter_flag:
data1 = s_filter(data1,
dt1,
type='bandpass',
family='ellip',
fmin=fmin,
fmax=fmax,
N=3,
rp=0.1,
rs=100)
data2 = s_filter(data2,
dt2,
type='bandpass',
family='ellip',
fmin=fmin,
fmax=fmax,
N=3,
rp=0.1,
rs=100)
# cutting signals by bounds
min_i = int(xtmin / dt1)
max_i = int(xtmax / dt1)
c_data1 = data1[min_i:max_i]
min_i = int(xtmin / dt2)
max_i = int(xtmax / dt2)
c_data2 = data2[min_i:max_i]
t1 = np.arange(xtmin, xtmax, dt1)
t2 = np.arange(xtmin, xtmax, dt2)
points = get_points([samples1, samples2])
# setting period for Response
period = get_period(tmin, tmax)
if not cut_flag:
# if user chooses not to cut; use origina/filted data for FAS and Response
freq1, fas1 = FAS(data1, dt1, points, xfmin, xfmax, 3)
freq2, fas2 = FAS(data2, dt2, points, xfmin, xfmax, 3)
rsp1, rsp2 = cal_acc_response(period, [data1, data2], [dt1, dt2])
else:
# else uses cutted data for FAS and Response
freq1, fas1 = FAS(c_data1, dt1, points, xfmin, xfmax, 3)
freq2, fas2 = FAS(c_data2, dt2, points, xfmin, xfmax, 3)
rsp1, rsp2 = cal_acc_response(period, [c_data1, c_data2], [dt1, dt2])
f, axarr = plt.subplots(nrows=1, ncols=3, figsize=(12, 3))
axarr[0] = plt.subplot2grid((1, 4), (0, 0), colspan=2)
axarr[0].set_title(title)
axarr[0].plot(t1, c_data1, 'r', t2, c_data2, 'b')
plt.legend([file1, file2])
plt.xlim(xtmin, xtmax)
axarr[1] = plt.subplot2grid((1, 4), (0, 2), colspan=1)
axarr[1].set_title('Fourier Amplitude Spectra')
axarr[1].plot(freq1, fas1, 'r', freq2, fas2, 'b')
tmp_xfmin = 0
if xfmin < 0.5:
tmp_xfmin = 0
else:
tmp_xfmin = xfmin
plt.xlim(tmp_xfmin, xfmax)
axarr[2] = plt.subplot2grid((1, 4), (0, 3))
axarr[2].set_title("Response Spectra")
axarr[2].set_xscale('log')
axarr[2].plot(period, rsp1, 'r', period, rsp2, 'b')
plt.xlim(tmin, tmax)
f.tight_layout()
plt.show()
def simple_plot(parameter, filenames, stations,
output_file, plot_title=None):
"""
plotting velocity for data and FAS only acceleration for Response
"""
all_styles = ['k', 'r', 'b', 'm', 'g', 'c', 'y', 'brown',
'gold', 'blueviolet', 'grey', 'pink']
orientation = ['N/S', 'E/W', 'Up/Down']
# Check number of input timeseries
if len(stations) > len(all_styles):
print("[ERROR]: Too many timeseries to plot!")
sys.exit(-1)
delta_ts = [station[0].dt for station in stations]
files = [os.path.basename(filename) for filename in filenames]
xtmin = parameter[0]
xtmax = parameter[1]
xfmin = parameter[2]
xfmax = parameter[3]
filter_flag = parameter[4]
fmin = parameter[5]
fmax = parameter[6]
tmin = parameter[7]
tmax = parameter[8]
cut_flag = parameter[9]
min_is = [int(xtmin/delta_t) for delta_t in delta_ts]
max_is = [int(xtmax/delta_t) for delta_t in delta_ts]
period = get_period(tmin, tmax)
f, axarr = plt.subplots(nrows=3, ncols=3, figsize=(14, 9))
for i in range(0, 3):
title = orientation[i]
signals = [station[i] for station in stations]
samples = [signal.samples for signal in signals]
vels = [signal.velo for signal in signals]
accs = [signal.accel for signal in signals]
for sample, max_i, delta_t in zip(samples, max_is, delta_ts):
if sample - 1 < max_i:
print("[ERROR]: t_max has to be under %f" %
((sample - 1) * delta_t))
sys.exit(1)
# filtering data
if filter_flag:
vels = [s_filter(vel,
delta_t,
type='bandpass',
family='ellip',
fmin=fmin, fmax=fmax,
N=3, rp=0.1,
rs=100) for vel, delta_t in zip(vels, delta_ts)]
accs = [s_filter(acc,
delta_t,
type='bandpass',
family='ellip',
fmin=fmin, fmax=fmax,
N=3, rp=0.1,
rs=100) for acc, delta_t in zip(accs, delta_ts)]
# cutting signal by bounds
c_vels = [vel[min_i:max_i] for vel, min_i, max_i in zip(vels,
min_is,
max_is)]
c_accs = [acc[min_i:max_i] for acc, min_i, max_i in zip(accs,
min_is,
max_is)]
times = [np.arange(xtmin, xtmax, delta_t) for delta_t in delta_ts]
points = get_points(samples)
if cut_flag:
freqs, fas_s = zip(*[FAS(c_vel,
delta_t,
points,
xfmin,
xfmax,
3) for c_vel, delta_t in zip(c_vels,
delta_ts)])
rsps = cal_acc_response(period, c_accs, delta_ts)
else:
freqs, fas_s = zip(*[FAS(vel,
delta_t,
points,
xfmin,
xfmax,
3) for vel, delta_t in zip(vels,
delta_ts)])
rsps = cal_acc_response(period, accs, delta_ts)
axarr[i][0] = plt.subplot2grid((3, 4), (i, 0), colspan=2, rowspan=1)
axarr[i][0].set_title(title)
axarr[i][0].grid(True)
styles = all_styles[0:len(times)]
for timeseries, c_vel, style in zip(times, c_vels, styles):
axarr[i][0].plot(timeseries, c_vel, style)
if i == 0:
plt.legend(files, prop={'size':8})
plt.xlim(xtmin, xtmax)
axarr[i][1] = plt.subplot2grid((3, 4), (i, 2), rowspan=1, colspan=1)
axarr[i][1].set_title('Fourier Amplitude Spectra')
axarr[i][1].grid(True, which='both')
axarr[i][1].set_xscale('log')
axarr[i][1].set_yscale('log')
for freq, fas, style in zip(freqs, fas_s, styles):
axarr[i][1].plot(freq, fas, style)
tmp_xfmin = 0
if xfmin < 0.5:
tmp_xfmin = 0
else:
tmp_xfmin = xfmin
plt.xlim(tmp_xfmin, xfmax)
axarr[i][2] = plt.subplot2grid((3, 4), (i, 3), rowspan=1, colspan=1)
axarr[i][2].set_title("Response Spectra")
axarr[i][2].set_xscale('log')
axarr[i][2].grid(True)
for rsp, style in zip(rsps, styles):
axarr[i][2].plot(period, rsp, style)
plt.xlim(tmin, tmax)
# Make nice plots with tight_layout
f.tight_layout()
# Add overall title if provided
if plot_title is not None:
st = plt.suptitle(plot_title, fontsize=16)
# shift subplots down:
#st.set_y(0.95)
f.subplots_adjust(top=0.92)
# All done, save plot
if output_file.lower().endswith(".png"):
fmt = 'png'
elif output_file.lower().endswith(".pdf"):
fmt = 'pdf'
else:
print("ERROR: Unknown format!")
sys.exit(-1)
plt.savefig(output_file, format=fmt,
transparent=False, dpi=300)
def plot_stations(parameter, filenames, station1, station2):
"""
This function plots two lists of psignals with Fourier Amplitude Spectra.
station = a list of 3 psignals for three orientation.
"""
dtype = ['Displacement', 'Velocity', 'Acceleration']
orientation = ['N/S', 'E/W', 'Up/Down']
dt1 = station1[0].dt
dt2 = station2[0].dt
file1 = filenames[0]
file2 = filenames[1]
xtmin = parameter[0]
xtmax = parameter[1]
xfmin = parameter[2]
xfmax = parameter[3]
filter_flag = parameter[4]
fmin = parameter[5]
fmax = parameter[6]
tmin = parameter[7]
tmax = parameter[8]
cut_flag = parameter[9]
min_i1 = int(xtmin/dt1)
max_i1 = int(xtmax/dt1)
min_i2 = int(xtmin/dt2)
max_i2 = int(xtmax/dt2)
# calculating Response Spectra
rsp1 = []
rsp2 = []
period = get_period(tmin, tmax)
for i in range(0, 3):
signal1 = station1[i]
signal2 = station2[i]
(acc_rsp1, acc_rsp2,
vel_rsp1, vel_rsp2,
dis_rsp1, dis_rsp2) = [], [], [], [], [], []
if cut_flag:
acc1 = signal1.accel[min_i1:max_i1]
acc2 = signal2.accel[min_i2:max_i2]
else:
acc1 = signal1.accel
acc2 = signal2.accel
for p in period:
rsp = max_osc_response(acc1, dt1, 0.05, p, 0, 0)
dis_rsp1.append(rsp[0])
vel_rsp1.append(rsp[1])
acc_rsp1.append(rsp[2])
rsp = max_osc_response(acc2, dt2, 0.05, p, 0, 0)
dis_rsp2.append(rsp[0])
vel_rsp2.append(rsp[1])
acc_rsp2.append(rsp[2])
rsp1.append([dis_rsp1, vel_rsp1, acc_rsp1])
rsp2.append([dis_rsp2, vel_rsp2, acc_rsp2])
# from displacement to velocity to acceleration
for i in range(0, 3):
f, axarr = plt.subplots(nrows=3, ncols=3, figsize=(12, 9))
# iterative through psignals in each station
for j in range(0, 3):
title = dtype[i] + ' in ' + orientation[j]
signal1 = station1[j]
signal2 = station2[j]
if ((not isinstance(signal1, seism_psignal)) or \
(not isinstance(signal2, seism_psignal))):
print("[PLOT ERROR]: Invalid instance type: "
"can only plot psignal objects.")
return
if signal1.data.shape[1] != 3 or signal2.data.shape[1] != 3:
print("[PLOT ERROR]: psignal's data must be 3-column "
"numpy array for displ, velo, accel.")
return
samples1 = signal1.samples
samples2 = signal2.samples
# psignal.data = [displ, velo, accel]
data1 = signal1.data[:, i]
data2 = signal2.data[:, i]
# filtering data
if filter_flag:
data1 = s_filter(data1, dt1, type='bandpass', family='ellip',
fmin=fmin, fmax=fmax, N=3, rp=0.1, rs=100)
data2 = s_filter(data2, dt2, type='bandpass', family='ellip',
fmin=fmin, fmax=fmax, N=3, rp=0.1, rs=100)
# cutting signal by bounds
c_data1 = data1[min_i1:max_i1]
c_data2 = data2[min_i2:max_i2]
t1 = np.arange(xtmin, xtmax, dt1)
t2 = np.arange(xtmin, xtmax, dt2)
points = get_points([samples1, samples2])
if not cut_flag:
# if user chooses not to cut; use original data to calculate FAS and Response
freq1, fas1 = FAS(data1, dt1, points, xfmin, xfmax, 3)
freq2, fas2 = FAS(data2, dt2, points, xfmin, xfmax, 3)
else:
# use cutted signals to calculate FAS
freq1, fas1 = FAS(c_data1, dt1, points, xfmin, xfmax, 3)
freq2, fas2 = FAS(c_data2, dt2, points, xfmin, xfmax, 3)
axarr[j][0] = plt.subplot2grid((3, 4), (j, 0), colspan=2, rowspan=1)
axarr[j][0].set_title(title)
axarr[j][0].plot(t1, c_data1, 'r', t2, c_data2, 'b')
if j == 0:
plt.legend([file1, file2])
plt.xlim(xtmin, xtmax)
axarr[j][1] = plt.subplot2grid((3, 4), (j, 2), rowspan=1, colspan=1)
axarr[j][1].set_title('Fourier Amplitude Spectra')
axarr[j][1].plot(freq1, fas1, 'r', freq2, fas2, 'b')
tmp_xfmin = 0
if xfmin < 0.5:
tmp_xfmin = 0
else:
tmp_xfmin = xfmin
plt.xlim(tmp_xfmin, xfmax)
axarr[j][2] = plt.subplot2grid((3, 4), (j, 3), rowspan=1, colspan=1)
axarr[j][2].set_title("Response Spectra")
axarr[j][2].set_xscale('log')
axarr[j][2].plot(period, rsp1[j][i], 'r', period, rsp2[j][i], 'b')
plt.xlim(tmin, tmax)
f.tight_layout()
plt.show()
def plot_signals(parameter, filenames, signal1, signal2):
"""
This function is to plot Signals with Fourier Amplitude Spectura.
"""
plt.close('all')
file1 = filenames[0]
file2 = filenames[1]
xtmin = parameter[0]
xtmax = parameter[1]
xfmin = parameter[2]
xfmax = parameter[3]
filter_flag = parameter[4]
fmin = parameter[5]
fmax = parameter[6]
tmin = parameter[7]
tmax = parameter[8]
cut_flag = parameter[9]
title = 'Acceleration ONLY: '
samples1 = signal1.samples
samples2 = signal2.samples
data1 = signal1.data
data2 = signal2.data
dt1 = signal1.dt
dt2 = signal2.dt
# filtering data
if filter_flag:
data1 = s_filter(data1, dt1, type='bandpass', family='ellip',
fmin=fmin, fmax=fmax, N=3, rp=0.1, rs=100)
data2 = s_filter(data2, dt2, type='bandpass', family='ellip',
fmin=fmin, fmax=fmax, N=3, rp=0.1, rs=100)
# cutting signals by bounds
min_i = int(xtmin/dt1)
max_i = int(xtmax/dt1)
c_data1 = data1[min_i:max_i]
min_i = int(xtmin/dt2)
max_i = int(xtmax/dt2)
c_data2 = data2[min_i:max_i]
t1 = np.arange(xtmin, xtmax, dt1)
t2 = np.arange(xtmin, xtmax, dt2)
points = get_points([samples1, samples2])
# setting period for Response
period = get_period(tmin, tmax)
if not cut_flag:
# if user chooses not to cut; use origina/filted data for FAS and Response
freq1, fas1 = FAS(data1, dt1, points, xfmin, xfmax, 3)
freq2, fas2 = FAS(data2, dt2, points, xfmin, xfmax, 3)
rsp1, rsp2 = cal_acc_response(period, [data1, data2], [dt1, dt2])
else:
# else uses cutted data for FAS and Response
freq1, fas1 = FAS(c_data1, dt1, points, xfmin, xfmax, 3)
freq2, fas2 = FAS(c_data2, dt2, points, xfmin, xfmax, 3)
rsp1, rsp2 = cal_acc_response(period, [c_data1, c_data2], [dt1, dt2])
f, axarr = plt.subplots(nrows=1, ncols=3, figsize=(12, 3))
axarr[0] = plt.subplot2grid((1, 4), (0, 0), colspan=2)
axarr[0].set_title(title)
axarr[0].plot(t1, c_data1, 'r', t2, c_data2, 'b')
plt.legend([file1, file2])
plt.xlim(xtmin, xtmax)
axarr[1] = plt.subplot2grid((1, 4), (0, 2), colspan=1)
axarr[1].set_title('Fourier Amplitude Spectra')
axarr[1].plot(freq1, fas1, 'r', freq2, fas2, 'b')
tmp_xfmin = 0
if xfmin < 0.5:
tmp_xfmin = 0
else:
tmp_xfmin = xfmin
plt.xlim(tmp_xfmin, xfmax)
axarr[2] = plt.subplot2grid((1, 4), (0, 3))
axarr[2].set_title("Response Spectra")
axarr[2].set_xscale('log')
axarr[2].plot(period, rsp1, 'r', period, rsp2, 'b')
plt.xlim(tmin, tmax)
f.tight_layout()
plt.show()
def process(signal):
"""
The function takes a signal, use its's current data to get
acceleration, velocity, and displacement.
Then return a psignal.
"""
if not isinstance(signal, seism_signal):
print("[ERROR]: instance error; process signal objects only.")
return
# Correction of base lines was commented out here to avoid problems with synchronization
# correct_baseline(signal)
acc = np.array([], float)
vel = np.array([], float)
dis = np.array([], float)
dt = signal.dt
if signal.type == 'a':
acc = signal.data
acc = s_filter(acc,
signal.dt,
type='highpass',
family='butter',
fmin=0.05,
N=5)
vel = integrate(acc, dt)
vel = s_filter(vel,
signal.dt,
type='highpass',
family='butter',
fmin=0.05,
N=5)
dis = integrate(vel, dt)
dis = s_filter(dis,
signal.dt,
type='highpass',
family='butter',
fmin=0.05,
N=5)
elif signal.type == 'v':
vel = signal.data
vel = s_filter(vel,
signal.dt,
type='highpass',
family='butter',
fmin=0.05,
N=5)
acc = derivative(vel, dt)
dis = integrate(vel, dt)
dis = s_filter(dis,
signal.dt,
type='highpass',
family='butter',
fmin=0.05,
N=5)
elif signal.type == 'd':
dis = signal.data
dis = s_filter(dis,
signal.dt,
type='highpass',
family='butter',
fmin=0.05,
N=5)
vel = derivative(dis, dt)
acc = derivative(vel, dt)
else:
pass
psignal = seism_psignal(signal.samples, signal.dt, np.c_[dis, vel, acc],
'c', acc, vel, dis)
return psignal