def get_polarization_stuff(st, env):
Ztrace = st.select(component="Z")[0]
Ntrace = st.select(component="N")[0]
Etrace = st.select(component="E")[0]
smooth_env = sp.smooth(env)
imax = np.argmax(smooth_env)
end_window = int(np.round(imax / 3.))
# normalise to get decent numbers
maxZ = max(abs(Ztrace.data))
xP = Etrace.data[0:end_window] / maxZ
yP = Ntrace.data[0:end_window] / maxZ
zP = Ztrace.data[0:end_window] / maxZ
try:
MP = np.cov(np.array([xP, yP, zP]))
w, v = np.linalg.eig(MP)
except np.linalg.linalg.LinAlgError:
return np.NaN, np.NaN, np.NaN, np.NaN
indexes = np.argsort(w)
DP = w[indexes]
pP = v[:, indexes]
rectilinP = 1 - ((DP[0] + DP[1]) / (2 * DP[2]))
azimuthP = np.arctan(pP[1, 2] / pP[0, 2]) * 180. / np.pi
dipP = np.arctan(
pP[2, 2] / np.sqrt(pP[1, 2]**2 + pP[0, 2]**2)) * 180 / np.pi
Plani = 1 - (2 * DP[0]) / (DP[1] + DP[2])
return rectilinP, azimuthP, dipP, Plani
def get_CorrStuff(tr):
min_peak_height = 0.4
# This value is sligtly greater than the matlab one, to account for
# differences in floating precision
cor = np.correlate(tr.data, tr.data, mode='full')
cor = cor / np.max(cor)
# find number of peaks
cor_env = np.abs(hilbert(cor))
cor_smooth = sp.smooth(cor_env)
npts = len(cor_smooth)
max_cor = np.max(cor_smooth)
CorPeakNumber, itriggers = sp.find_peaks(cor_smooth, min_peak_height)
# integrate over bands
ilag_0 = np.argmax(cor_smooth) + 1
ilag_third = ilag_0 + npts / 6
# note that these integrals are flase really (dt is not correct)
int1 = np.trapz(cor_smooth[ilag_0:ilag_third + 1] / max_cor)
int2 = np.trapz(cor_smooth[ilag_third:] / max_cor)
int_ratio = int1 / int2
return CorPeakNumber, int_ratio
def test_smooth(self):
imax_fraction = 0.2
max_amp = 300
tr, modulation = syn.modulate_trace_triangle(self.tr, imax_fraction,
max_amp)
smooth_modulation = sp.smooth(modulation)
self.assertEqual(len(modulation), len(smooth_modulation))
self.assertEqual(np.argmax(modulation), np.argmax(smooth_modulation))
def get_RappStuff(tr, env=None):
# get max over mean / median / std
if env is None:
env = envelope(tr.data)
max_env = max(env)
smooth_norm_env = sp.smooth(env / max_env)
RappMaxMean = 1. / np.mean(smooth_norm_env)
RappMaxMedian = 1. / np.median(smooth_norm_env)
RappMaxStd = 1. / np.std(smooth_norm_env)
return RappMaxMean, RappMaxMedian, RappMaxStd
def get_AsDec(tr, env=None):
# smooth data using a filter of the same length as the sampling rate
# to give 1-second smoothing window
if env is None:
env = envelope(tr.data)
smooth_env = sp.smooth(env)
imax = np.argmax(smooth_env)
AsDec = (imax + 1) / float(len(tr.data) + 1 - (imax + 1))
# note : the +1s are to avoid zeros or division by zero
return AsDec
def get_KurtoSkew(tr, env=None):
if env is None:
env = envelope(tr.data)
max_env = max(env)
smooth_norm_env = sp.smooth(env / max_env)
max_sig = max(tr.data)
norm_sig = tr.data / max_sig
KurtoEnv = kurtosis(smooth_norm_env, fisher=False)
KurtoSig = kurtosis(norm_sig, fisher=False)
SkewnessEnv = skew(smooth_norm_env)
SkewnessSig = skew(norm_sig)
return KurtoEnv, KurtoSig, SkewnessEnv, SkewnessSig
import numpy as np from copy import deepcopy from obspy.signal.filter import envelope from scipy.signal import spline_filter, hilbert #sig1 = syn.gen_gaussian_noise(0.01, 54321, 100) dt = 0.01 npts = 3000 t_vect = np.arange(npts) * dt sig1 = syn.gen_sweep_poly(dt, npts, [0.05, -0.75, 0.1, 1.0]) sig2 = sig1.copy() sig2, modulation = syn.modulate_trace_triangle(sig2, 0.2, 300) env = envelope(sig2.data) smooth_env1 = sp.smooth(env, window='hanning') smooth_env2 = sp.smooth(env, window='flat') assert (len(env) == len(smooth_env1)) rep = syn.gen_repeat_signal(dt, npts, 100, 5) cor = np.correlate(rep.data, rep.data, mode='full') t_vect_cor = np.arange(len(cor)) * dt - len(cor) / 2 * dt cor_env = np.abs(hilbert(cor)) cor_smooth = sp.smooth(cor_env) max_cor = np.max(cor_smooth) cor_smooth2 = sp.smooth(cor_smooth / max_cor) NyF = 1 / (2.0 * dt) sig3 = syn.gen_gaussian_noise(dt, npts, 100) FFI = [0.1, 1, 3, 10, 20] nf = len(FFI)
def get_full_spectrum_stuff(tr):
sps = tr.stats.sampling_rate
NyF = sps / 2.0
data = tr.data
npts = tr.stats.npts
n = sp.nextpow2(2 * npts - 1)
df = n / 2 * NyF
Freq1 = np.linspace(0, 1, n / 2) * NyF
FFTdata = 2 * np.abs(np.fft.fft(data, n=n)) / float(npts * npts)
FFTsmooth = sp.smooth(FFTdata[0:len(FFTdata) / 2])
FFTsmooth_norm = FFTsmooth / max(FFTsmooth)
MeanFFT = np.mean(FFTsmooth_norm)
MedianFFT = np.median(FFTsmooth_norm)
VarFFT = np.var(FFTsmooth_norm, ddof=1)
MaxFFT = np.max(FFTsmooth)
iMaxFFT = np.argmax(FFTsmooth)
FmaxFFT = Freq1[iMaxFFT]
xCenterFFT = np.sum((np.arange(len(FFTsmooth_norm))) *
FFTsmooth_norm) / np.sum(FFTsmooth_norm)
i_xCenterFFT = int(np.round(xCenterFFT))
xCenterFFT_1quart = np.sum((np.arange(i_xCenterFFT+1)) *
FFTsmooth_norm[0:i_xCenterFFT+1]) /\
np.sum(FFTsmooth_norm[0:i_xCenterFFT+1])
i_xCenterFFT_1quart = int(np.round(xCenterFFT_1quart))
xCenterFFT_3quart = np.sum((np.arange(len(FFTsmooth_norm) -
i_xCenterFFT)) *
FFTsmooth_norm[i_xCenterFFT:]) /\
np.sum(FFTsmooth_norm[i_xCenterFFT:]) + i_xCenterFFT+1
i_xCenterFFT_3quart = int(np.round(xCenterFFT_3quart))
FCentroid = Freq1[i_xCenterFFT]
Fquart1 = Freq1[i_xCenterFFT_1quart]
Fquart3 = Freq1[i_xCenterFFT_3quart]
min_peak_height = 0.75
NpeakFFT, ipeaks = sp.find_peaks(FFTsmooth_norm, min_peak_height)
npeaks, nb = ipeaks.shape
sum_peaks = 0.
for i in xrange(npeaks):
i1 = ipeaks[i, 0]
i2 = ipeaks[i, 1]
if i2 > i1:
sum_peaks += max(FFTsmooth_norm[i1:i2])
MeanPeaksFFT = sum_peaks / float(npeaks)
npts = len(FFTsmooth_norm)
E1FFT = np.trapz(FFTsmooth_norm[0:npts / 4], dx=df)
E2FFT = np.trapz(FFTsmooth_norm[npts / 4 - 1:2 * npts / 4], dx=df)
E3FFT = np.trapz(FFTsmooth_norm[2 * npts / 4 - 1:3 * npts / 4], dx=df)
E4FFT = np.trapz(FFTsmooth_norm[3 * npts / 4 - 1:npts], dx=df)
moment = np.empty(3, dtype=float)
for j in xrange(3):
moment[j] = np.sum(Freq1**j * FFTsmooth_norm[0:n / 2]**2)
gamma1 = moment[1] / moment[0]
gamma2 = np.sqrt(moment[2] / moment[0])
gammas = np.sqrt(np.abs(gamma1**2 - gamma2**2))
return MeanFFT, MaxFFT, FmaxFFT, MedianFFT, VarFFT, FCentroid, Fquart1,\
Fquart3, NpeakFFT, MeanPeaksFFT, E1FFT, E2FFT, E3FFT, E4FFT, gamma1,\
gamma2, gammas