def test_cumulative_absolute_velocity():
record_path = TEST_DATA_DIR
record_filename = 'test_motion_dt0p01.txt'
motion_step = 0.01
rec = np.loadtxt(record_path + record_filename)
acc_signal = AccSignal(rec, motion_step)
acc_signal.generate_cumulative_stats()
true_cav = 8.53872
assert np.isclose(acc_signal.cav, true_cav, rtol=0.0001)
def test_arias_intensity():
record_path = TEST_DATA_DIR
record_filename = 'test_motion_dt0p01.txt'
motion_step = 0.01
rec = np.loadtxt(record_path + record_filename)
acc_signal = AccSignal(rec, motion_step)
acc_signal.generate_cumulative_stats()
true_arias_intensity = 0.63398
assert np.isclose(acc_signal.arias_intensity,
true_arias_intensity,
rtol=0.0001)
def show_test_motion():
record_path = TEST_DATA_DIR
record_filename = 'test_motion_dt0p01.txt'
motion_step = 0.01
rec = np.loadtxt(record_path + record_filename)
acc_signal = AccSignal(rec, motion_step)
acc_signal.generate_displacement_and_velocity_series()
bf, sp = plt.subplots(3)
sp[0].plot(acc_signal.time, acc_signal.values)
sp[1].plot(acc_signal.time, acc_signal.velocity)
sp[2].plot(acc_signal.time, acc_signal.displacement)
plt.show()
def test_duration_stats():
record_path = TEST_DATA_DIR
record_filename = 'test_motion_dt0p01.txt'
motion_step = 0.01
rec = np.loadtxt(record_path + record_filename)
acc_signal = AccSignal(rec, motion_step)
acc_signal.generate_duration_stats()
assert np.isclose(acc_signal.t_595, 20.99) # eqsig==0.5.0
assert np.isclose(acc_signal.t_b01, 38.27) # eqsig==0.5.0
assert np.isclose(acc_signal.t_b05, 15.41) # eqsig==0.5.0
assert np.isclose(acc_signal.t_b10, 8.41) # eqsig==0.5.0
def test_stockwell_transform_then_inverse():
record_path = TEST_DATA_DIR
record_filename = 'test_motion_dt0p01.txt'
motion_step = 0.01
rec = np.loadtxt(record_path + record_filename, skiprows=2)
acc_signal = AccSignal(rec, motion_step)
acc2_signal = interp_to_approx_dt(acc_signal, 0.1)
acc2_signal.swtf = stockwell.transform(acc2_signal.values)
assert len(acc2_signal.swtf[0]) == acc2_signal.npts
inv_signal = AccSignal(stockwell.itransform(acc2_signal.swtf), acc2_signal.dt)
norm_abs_error = np.sum(abs(acc2_signal.values - inv_signal.values)) / acc2_signal.npts
assert norm_abs_error < 0.008
def test_peak_values():
record_path = TEST_DATA_DIR
record_filename = 'test_motion_dt0p01.txt'
motion_step = 0.01
rec = np.loadtxt(record_path + record_filename)
acc_signal = AccSignal(rec, motion_step)
acc_signal.generate_peak_values()
true_pga = 1.41
true_pgv = 0.26006
true_pgd = 0.07278134 # eqsig==0.4.12
assert np.isclose(acc_signal.pga, true_pga, rtol=0.001)
assert np.isclose(acc_signal.pgv, true_pgv, rtol=0.0001), acc_signal.pgv
assert np.isclose(acc_signal.pgd, true_pgd, rtol=0.0001), acc_signal.pgd
def __init__(self, values, dt, names=[], master_index=0, stypes="custom", **kwargs):
self.freq_range = np.array(kwargs.get('freq_range', [0.1, 20]))
lvt = np.log10(1.0 / self.freq_range)
if stypes == "custom" or stypes == "acc":
stypes = [stypes] * len(values)
self.response_times = kwargs.get('resp_times', np.logspace(lvt[1], lvt[0], 31, base=10))
if master_index < 0:
raise ValueError("master_index must be positive")
if master_index > len(values) - 1:
raise ValueError("master_index: %i, out of bounds, maximum value: %i" % (master_index, len(signals) - 1))
self.master_index = master_index
# if len(signals) != len(steps):
# raise ValueError("Length of signals: %i, must match length of steps: %i" % (len(signals), len(steps)))
# self.master = Record(master_motion, master_step, response_times=self.response_times)
shortage = len(values) - len(names)
self.names = list(names)
for i in range(shortage):
self.names.append("m%i" % (len(names) + i))
self.master = self.names[master_index]
self.dt = dt
self.signals = OrderedDict()
for s in range(len(values)):
if stypes[s] == "acc":
self.signals[self.names[s]] = AccSignal(values[s], dt, self.names[s], response_times=self.response_times)
else:
self.signals[self.names[s]] = Signal(values[s], dt, self.names[s])
def test_fourier_spectra_with_motion():
record_path = TEST_DATA_DIR
record_filename = 'test_motion_dt0p01.txt'
motion_dt = 0.01
rec = np.loadtxt(record_path + record_filename, skiprows=2)
rec2 = np.zeros(2**13)
rec2[:len(rec)] = rec
asig = AccSignal(-rec, motion_dt)
nfreq = len(asig.fa_spectrum)
test_filename = 'test_motion_true_fourier_spectra.csv'
data = np.loadtxt(record_path + test_filename, skiprows=1, delimiter=",")
freqs = data[:nfreq - 1, 0]
fa = data[:nfreq - 1, 1]
phase = data[:nfreq - 1, 2]
fa_eqsig = abs(asig.fa_spectrum)
freq_eqsig = asig.fa_frequencies
org_phases = np.angle(asig.fa_spectrum)
ss_phases = np.angle(np.fft.rfft(rec2))[:len(org_phases)] + 0.0001
assert np.isclose(freqs[0], freq_eqsig[1], rtol=0.001), freqs[0]
assert np.isclose(freqs[20], freq_eqsig[21], rtol=0.0001)
assert np.isclose(freqs[-1], freq_eqsig[-1], rtol=0.001)
for i in range(len(fa)):
assert np.isclose(fa[i], fa_eqsig[i + 1], atol=0.00001), i
def fas2signal(fas, dt, stype="signal"):
"""
Convert a fourier spectrum to time series signal
Parameters
----------
fas: array_like of img floats
Positive part only
dt: float
time step of time series
stype: str
If 'signal' then return Signal, else return AccSignal
"""
from eqsig.single import Signal, AccSignal
n = 2 * len(fas)
a = np.zeros(2 * len(fas), dtype=complex)
a[1:n // 2] = fas[1:]
a[n // 2 + 1:] = np.flip(np.conj(fas[1:]), axis=0)
a /= dt
s = np.fft.ifft(a)
npts = int(2 ** (np.log(n) / np.log(2)))
s = s[:npts]
if stype == 'signal':
return Signal(s, dt)
else:
return AccSignal(s, dt)
def show_response_spectra_at_high_frequencies():
record_path = TEST_DATA_DIR
test_filename = 'test_motion_true_spectra_acc.csv'
data = np.loadtxt(record_path + test_filename, skiprows=1, delimiter=",")
times = data[:40, 0]
ss_s_a = data[:40, 1]
record_filename = 'test_motion_dt0p01.txt'
motion_step = 0.01
rec = np.loadtxt(record_path + record_filename)
# acc_signal = AccSignal(rec, motion_step, response_times=times)
# s_a = acc_signal.s_a
#
# a_times = acc_signal.response_times
# s_d, s_v, s_a = dh.pseudo_response_spectra(rec, motion_step, times, xi=0.05)
# s_d, s_v, s_a = dh.true_response_spectra(rec, motion_step, times, xi=0.05)
acc_signal = AccSignal(rec, motion_step, response_times=times)
s_a = acc_signal.s_a
s_a_in_g = s_a / 9.81
# srss1 = sum(abs(s_a_in_g - ss_s_a))
plt.plot(times, s_a_in_g, label="eqsig")
plt.plot(times, ss_s_a, label="true-ish")
plt.legend()
plt.show()
def load_asig(ffp, load_label=False, m=1.0):
"""
Loads an ``AccSignal`` that was saved in eqsig input format.
Parameters
----------
ffp: str
Full file path to output file
load_label: bool
if true then get label from file
m: float (default=1.0)
Scale factor to apply to time series data when loading
Returns
-------
asig: eqsig.AccSignal
"""
vals, dt = load_values_and_dt(ffp)
if load_label:
a = open(ffp)
label = a.read().splitlines()[0]
a.close()
else:
label = 'm1'
return AccSignal(vals * m, dt, label=label)
def test_fourier_spectra_stable_against_aliasing():
record_path = TEST_DATA_DIR
record_filename = 'test_motion_dt0p01.txt'
motion_step = 0.01
rec = np.loadtxt(record_path + record_filename, skiprows=2)
rec2 = np.zeros(2**13)
rec2[:len(rec)] = rec
org_signal = AccSignal(rec, motion_step)
extended_signal = AccSignal(rec2, motion_step)
rec_split = []
for i in range(int(len(rec2) / 2)):
rec_split.append(rec2[i * 2])
acc_split = AccSignal(rec_split, motion_step * 2)
org_fa = abs(org_signal.fa_spectrum)
split_fa = abs(acc_split.fa_spectrum)
ext_fa = abs(extended_signal.fa_spectrum)
org_freq = abs(org_signal.fa_frequencies)
split_freq = abs(acc_split.fa_frequencies)
ext_freq = abs(extended_signal.fa_frequencies)
for i in range(len(org_signal.fa_spectrum)):
if i > 1830:
abs_tol = 0.03
else:
abs_tol = 0.02
assert np.isclose(org_freq[i], ext_freq[i])
assert np.isclose(org_fa[i], ext_fa[i])
if i < 2048:
assert np.isclose(org_freq[i], split_freq[i])
assert np.isclose(org_fa[i], split_fa[i], atol=abs_tol), i
def show_fourier_spectra_stable_against_aliasing():
record_path = TEST_DATA_DIR
record_filename = 'test_motion_dt0p01.txt'
motion_step = 0.01
rec = np.loadtxt(record_path + record_filename)
rec2 = np.zeros(2**13)
rec2[:len(rec)] = rec
org_signal = AccSignal(rec, motion_step)
extended_signal = AccSignal(rec2, motion_step)
rec_split = []
for i in range(int(len(rec2) / 2)):
rec_split.append(rec2[i * 2])
acc_split = AccSignal(rec_split, motion_step * 2)
bf, sp = plt.subplots(2)
sp[0].plot(org_signal.time, org_signal.values)
sp[0].plot(extended_signal.time, extended_signal.values)
sp[0].plot(acc_split.time, acc_split.values)
sp[1].plot(org_signal.fa_frequencies,
abs(org_signal.fa_spectrum),
lw=0.7,
label="original")
sp[1].plot(acc_split.fa_frequencies,
abs(acc_split.fa_spectrum),
lw=0.7,
label="split")
sp[1].plot(extended_signal.fa_frequencies,
abs(extended_signal.fa_spectrum),
lw=0.7,
label="full")
plt.legend()
plt.show()
def load_test_record_from_file(record_path, record_filename, scale=1):
a = open(record_path + record_filename, 'r')
b = a.readlines()
a.close()
acc = []
motion_step = float(b[0].split("=")[1])
print('values dt: ', motion_step)
for i in range(len(b)):
if i > 3:
dat = b[i].split()
for j in range(len(dat)):
acc.append(float(dat[j]) * scale)
rec = AccSignal(acc, motion_step)
return rec
def rewrite_fourier_spectra_test_file():
record_path = TEST_DATA_DIR
record_filename = 'test_motion_dt0p01.txt'
motion_step = 0.01
rec = np.loadtxt(record_path + record_filename)
acc_signal = AccSignal(rec, motion_step)
fa_amplitudes = abs(acc_signal.fa_spectrum)
fa_phases = np.angle(acc_signal.fa_spectrum)
paras = []
for i in range(len(acc_signal.fa_frequencies)):
paras.append("%.5f,%.5f,%.5f" % (acc_signal.fa_frequencies[i], fa_amplitudes[i], fa_phases[i]))
outfile_name = record_path + "test_motion_dt0p01_fas.txt"
outfile = open(outfile_name, "w")
outfile.write("\n".join(paras))
outfile.close()
def test_response_spectra_at_high_frequencies():
record_path = TEST_DATA_DIR
test_filename = 'test_motion_true_spectra_acc.csv'
data = np.loadtxt(record_path + test_filename, skiprows=1, delimiter=",")
times = data[:40, 0]
ss_s_a = data[:40, 1]
record_filename = 'test_motion_dt0p01.txt'
motion_step = 0.01
rec = np.loadtxt(record_path + record_filename)
acc_signal = AccSignal(rec, motion_step, response_times=times)
s_a = acc_signal.s_a
s_a_in_g = s_a / 9.81
a_times = acc_signal.response_times
assert len(times) == len(a_times)
srss1 = sum(abs(s_a_in_g - ss_s_a))
assert srss1 < 0.01 * 40, srss1
def rewrite_response_spectra_eqsig_test_file():
record_path = TEST_DATA_DIR
record_filename = 'test_motion_dt0p01.txt'
motion_step = 0.01
rec = np.loadtxt(record_path + record_filename)
acc_signal = AccSignal(rec, motion_step, response_times=(0.1, 5.))
s_a = acc_signal.s_a
s_d = acc_signal.s_d
times = acc_signal.response_times
paras = []
for i in range(len(times)):
paras.append("%.5f,%.5f,%.5f" % (times[i], s_a[i], s_d[i]))
outfile_name = record_path + "test_motion_dt0p01_rs.txt"
outfile = open(outfile_name, "w")
outfile.write("\n".join(paras))
outfile.close()
def test_fourier_spectra():
record_path = TEST_DATA_DIR
record_filename = 'test_motion_dt0p01.txt'
motion_step = 0.01
rec = np.loadtxt(record_path + record_filename)
acc_signal = AccSignal(rec, motion_step)
fa_amplitudes = abs(acc_signal.fa_spectrum)
fa_phases = np.angle(acc_signal.fa_spectrum)
paras = []
for i in range(len(acc_signal.fa_frequencies)):
paras.append("%.5f,%.5f,%.5f" % (acc_signal.fa_frequencies[i], fa_amplitudes[i], fa_phases[i]))
testfile_name = record_path + "test_motion_dt0p01_fas.txt"
testfile = open(testfile_name, "r")
test_lines = testfile.readlines()
for i, line in enumerate(test_lines):
line = line.replace("\n", "")
assert line == paras[i], i
def fas2signal(fas, dt, stype="signal"):
"""
Convert a fourier spectrum to time series signal
:param fas: positive part only
:param dt: time step of time series
:return:
"""
n = 2 * len(fas)
a = np.zeros(2 * len(fas), dtype=complex)
a[1:n // 2] = fas[1:]
a[n // 2 + 1:] = np.flip(np.conj(fas[1:]), axis=0)
a /= dt
s = np.fft.ifft(a)
npts = int(2**(np.log(n) / np.log(2)))
s = s[:npts]
if stype == 'signal':
return Signal(s, dt)
else:
return AccSignal(s, dt)
def test_response_spectra_versus_old_eqsig_version():
record_path = TEST_DATA_DIR
record_filename = 'test_motion_dt0p01.txt'
motion_step = 0.01
rec = np.loadtxt(record_path + record_filename)
acc_signal = AccSignal(rec, motion_step)
s_a = acc_signal.s_a
s_d = acc_signal.s_d
times = acc_signal.response_times
paras = []
for i in range(len(times)):
paras.append("%.5f,%.5f,%.5f" % (times[i], s_a[i], s_d[i]))
testfile_name = record_path + "test_motion_dt0p01_rs.txt"
testfile = open(testfile_name, "r")
test_lines = testfile.readlines()
for i, line in enumerate(test_lines):
line = line.replace("\n", "")
assert line == paras[i], i
def test_displacement_velocity():
record_path = TEST_DATA_DIR
record_filename = 'test_motion_dt0p01.txt'
motion_step = 0.01
rec = np.loadtxt(record_path + record_filename)
acc_signal = AccSignal(rec, motion_step)
# Compare time series
test_filename = 'test_motion_avd.csv'
data = np.loadtxt(record_path + test_filename, skiprows=1, delimiter=",")
time = data[:, 0]
velocity = data[:, 2]
displacement = data[:, 3]
assert len(time) == len(acc_signal.time)
abs_velocity_diff = abs(acc_signal.velocity - velocity)
cum_velocity_diff = sum(abs_velocity_diff)
max_velocity_diff = max(abs_velocity_diff)
assert cum_velocity_diff < 0.03, cum_velocity_diff
assert max_velocity_diff < 0.00006, max_velocity_diff
abs_disp_diff = abs(acc_signal.displacement - displacement)
cum_disp_diff = sum(abs_disp_diff)
max_disp_diff = max(abs_disp_diff)
assert cum_disp_diff < 0.02, cum_disp_diff
assert max_disp_diff < 0.00002, max_disp_diff
# Compare time series versus true
test_filename = 'test_motion_avd.csv'
data = np.loadtxt(record_path + test_filename, skiprows=1, delimiter=",")
time = data[:, 0]
velocity = data[:, 2]
displacement = data[:, 3]
assert len(time) == len(acc_signal.time)
abs_velocity_diff = abs(acc_signal.velocity - velocity)
cum_velocity_diff = sum(abs_velocity_diff)
max_velocity_diff = max(abs_velocity_diff)
assert cum_velocity_diff < 0.03, cum_velocity_diff
assert max_velocity_diff < 0.00006, max_velocity_diff
abs_disp_diff = abs(acc_signal.displacement - displacement)
cum_disp_diff = sum(abs_disp_diff)
max_disp_diff = max(abs_disp_diff)
assert cum_disp_diff < 0.02, cum_disp_diff
assert max_disp_diff < 0.00002, max_disp_diff
def combine_at_angle(acc_sig_ns, acc_sig_we, angle):
off_rad = np.radians(angle)
combo = acc_sig_ns.values * np.cos(off_rad) + acc_sig_we.values * np.sin(
off_rad)
new_sig = AccSignal(combo, acc_sig_ns.dt)
return new_sig
def load_signal(ffp, astype='sig'):
vals, dt = load_values_and_dt(ffp)
if astype == "signal":
return Signal(vals, dt)
elif astype == "acc_sig":
return AccSignal(vals, dt)