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test_polarization.py
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test_polarization.py
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#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
The polarization.core test suite.
"""
import numpy as np
import pytest
from scipy import signal
import obspy
from obspy.signal import polarization, util
def _create_test_data():
"""
Test data used for some polarization tests.
:return:
"""
x = np.arange(0, 2048 / 20.0, 1.0 / 20.0)
x *= 2. * np.pi
y = np.cos(x)
tr_z = obspy.Trace(data=y)
tr_z.stats.sampling_rate = 20.
tr_z.stats.starttime = obspy.UTCDateTime('2014-03-01T00:00')
tr_z.stats.station = 'POLT'
tr_z.stats.channel = 'HHZ'
tr_z.stats.network = 'XX'
tr_n = tr_z.copy()
tr_n.data *= 2.
tr_n.stats.channel = 'HHN'
tr_e = tr_z.copy()
tr_e.stats.channel = 'HHE'
sz = obspy.Stream()
sz.append(tr_z)
sz.append(tr_n)
sz.append(tr_e)
sz.sort(reverse=True)
return sz
class TestPolarization():
"""
Test cases for polarization analysis
"""
@pytest.fixture(autouse=True, scope="function")
def setup_data(self, testdata):
# setting up sliding window data
data_z = np.loadtxt(testdata['MBGA_Z.ASC'])
data_e = np.loadtxt(testdata['MBGA_E.ASC'])
data_n = np.loadtxt(testdata['MBGA_N.ASC'])
n = 256
fs = 75
inc = int(0.05 * fs)
self.data_win_z, self.nwin, self.no_win = \
util.enframe(data_z, signal.hamming(n), inc)
self.data_win_e, self.nwin, self.no_win = \
util.enframe(data_e, signal.hamming(n), inc)
self.data_win_n, self.nwin, self.no_win = \
util.enframe(data_n, signal.hamming(n), inc)
# global test input
self.fk = [2, 1, 0, -1, -2]
self.norm = pow(np.max(data_z), 2)
self.res = np.loadtxt(testdata['3cssan.hy.1.MBGA_Z'])
def test_polarization(self):
"""
windowed data
"""
pol = polarization.eigval(self.data_win_e, self.data_win_n,
self.data_win_z, self.fk, self.norm)
rms = np.sqrt(np.sum((pol[0] - self.res[:, 34]) ** 2) /
np.sum(self.res[:, 34] ** 2))
assert rms < 1.0e-5
rms = np.sqrt(np.sum((pol[1] - self.res[:, 35]) ** 2) /
np.sum(self.res[:, 35] ** 2))
assert rms < 1.0e-5
rms = np.sqrt(np.sum((pol[2] - self.res[:, 36]) ** 2) /
np.sum(self.res[:, 36] ** 2))
assert rms < 1.0e-5
rms = np.sqrt(np.sum((pol[3] - self.res[:, 40]) ** 2) /
np.sum(self.res[:, 40] ** 2))
assert rms < 1.0e-5
rms = np.sqrt(np.sum((pol[4] - self.res[:, 42]) ** 2) /
np.sum(self.res[:, 42] ** 2))
assert rms < 1.0e-5
rms = np.sqrt(np.sum((pol[5][:, 0] - self.res[:, 37]) ** 2) /
np.sum(self.res[:, 37] ** 2))
assert rms < 1.0e-5
rms = np.sqrt(np.sum((pol[5][:, 1] - self.res[:, 38]) ** 2) /
np.sum(self.res[:, 38] ** 2))
assert rms < 1.0e-5
rms = np.sqrt(np.sum((pol[5][:, 2] - self.res[:, 39]) ** 2) /
np.sum(self.res[:, 39] ** 2))
assert rms < 1.0e-5
rms = np.sqrt(np.sum((pol[6] - self.res[:, 41]) ** 2) /
np.sum(self.res[:, 41] ** 2))
assert rms < 1.0e-5
rms = np.sqrt(np.sum((pol[7] - self.res[:, 43]) ** 2) /
np.sum(self.res[:, 43] ** 2))
assert rms < 1.0e-5
def test_polarization_1d(self):
"""
1 dimenstional input --- regression test case for bug #919
"""
pol = polarization.eigval(self.data_win_e[100, :],
self.data_win_n[100, :],
self.data_win_z[100, :],
self.fk, self.norm)
pol_5_ref = [2.81387533e-04, 3.18409580e-04, 6.74030846e-04,
5.55067015e-01, 4.32938188e-01]
assert np.allclose(np.concatenate(pol[:5]), pol_5_ref)
def test_polarization_pm(self):
st = _create_test_data()
t = st[0].stats.starttime
e = st[0].stats.endtime
wlen = 10.0
wfrac = 0.1
out = polarization.polarization_analysis(
st, win_len=wlen, win_frac=wfrac, frqlow=1.0, frqhigh=5.0,
verbose=False, stime=t, etime=e, method="pm",
var_noise=0.0)
# all values should be equal for the test data, so check first value
# and make sure all values are almost equal
assert out["timestamp"][0] == 1393632005.0
assert out["timestamp"][0] == t + wlen / 2.0
assert round(abs(out["azimuth"][0]-26.56505117707799), 7) == 0
assert round(abs(out["incidence"][0]-65.905157447889309), 7) == 0
assert round(abs(out["azimuth_error"][0]-0.000000), 7) == 0
assert round(abs(out["incidence_error"][0]-0.000000), 7) == 0
for key in ["azimuth", "incidence"]:
got = out[key]
assert np.allclose(got / got[0], np.ones_like(got), rtol=1e-4)
for key in ["azimuth_error", "incidence_error"]:
got = out[key]
expected = np.empty_like(got)
expected.fill(got[0])
assert np.allclose(got, expected, rtol=1e-4, atol=1e-16)
assert np.allclose(out["timestamp"] - out["timestamp"][0],
np.arange(0, 92, 1))
def test_polarization_flinn(self):
st = _create_test_data()
t = st[0].stats.starttime
e = st[0].stats.endtime
wlen = 10.0
wfrac = 0.1
out = polarization.polarization_analysis(
st, win_len=wlen, win_frac=wfrac, frqlow=1.0, frqhigh=5.0,
verbose=False, stime=t, etime=e,
method="flinn", var_noise=0.0)
# all values should be equal for the test data, so check first value
# and make sure all values are almost equal
assert out["timestamp"][0] == 1393632005.0
assert out["timestamp"][0] == t + wlen / 2.0
assert round(abs(out["azimuth"][0]-26.56505117707799), 7) == 0
assert round(abs(out["incidence"][0]-65.905157447889309), 7) == 0
assert round(abs(out["rectilinearity"][0]-1.000000), 7) == 0
assert round(abs(out["planarity"][0]-1.000000), 7) == 0
for key in ["azimuth", "incidence", "rectilinearity", "planarity"]:
got = out[key]
assert np.allclose(got / got[0], np.ones_like(got), rtol=1e-4)
assert np.allclose(out["timestamp"] - out["timestamp"][0],
np.arange(0, 92, 1))
def test_polarization_vidale(self):
st = _create_test_data()
t = st[0].stats.starttime
e = st[0].stats.endtime
out = polarization.polarization_analysis(
st, win_len=10.0, win_frac=0.1, frqlow=1.0, frqhigh=5.0,
verbose=False, stime=t, etime=e,
method="vidale", var_noise=0.0)
# all values should be equal for the test data, so check first value
# and make sure all values are almost equal
assert out["timestamp"][0] == 1393632003.0
assert round(abs(out["azimuth"][0]-26.56505117707799), 7) == 0
assert round(abs(out["incidence"][0]-65.905157447889309), 7) == 0
assert round(abs(out["rectilinearity"][0]-1.000000), 7) == 0
assert round(abs(out["planarity"][0]-1.000000), 7) == 0
assert round(abs(out["ellipticity"][0]-3.8195545129768958e-06), 7) == 0
for key in ["azimuth", "incidence", "rectilinearity", "planarity",
"ellipticity"]:
got = out[key]
assert np.allclose(got / got[0], np.ones_like(got), rtol=1e-4)
assert np.allclose(out["timestamp"] - out["timestamp"][0],
np.arange(0, 97.85, 0.05), rtol=1e-5)