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test_invsim.py
526 lines (475 loc) · 21.3 KB
/
test_invsim.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
The InvSim test suite.
"""
import ctypes as C # NOQA
import gzip
import io
import os
import unittest
import platform
import numpy as np
from obspy import Trace, UTCDateTime, read, read_inventory
from obspy.core.util.base import NamedTemporaryFile
from obspy.core.util.misc import SuppressOutput
from obspy.core.util.testing import traces_almost_equal
from obspy.io.sac import attach_paz
from obspy.signal.headers import clibevresp
from obspy.signal.invsim import (
cosine_taper, estimate_magnitude, evalresp, simulate_seismometer,
evalresp_for_frequencies)
# Seismometers defined as in Pitsa with one zero less. The corrected
# signals are in velocity, thus must be integrated to offset and take one
# zero less than pitsa (remove 1/w in frequency domain)
PAZ_WOOD_ANDERSON = {'poles': [-6.2832 - 4.7124j,
-6.2832 + 4.7124j],
'zeros': [0.0 + 0.0j] * 1,
'sensitivity': 1.0,
'gain': 1. / 2.25}
PAZ_WWSSN_SP = {'poles': [-4.0093 - 4.0093j,
-4.0093 + 4.0093j,
-4.6077 - 6.9967j,
-4.6077 + 6.9967j],
'zeros': [0.0 + 0.0j] * 2,
'sensitivity': 1.0,
'gain': 1. / 1.0413}
PAZ_WWSSN_LP = {'poles': [-0.4189 + 0.0j,
-0.4189 + 0.0j,
-0.0628 + 0.0j,
-0.0628 + 0.0j],
'zeros': [0.0 + 0.0j] * 2,
'sensitivity': 1.0,
'gain': 1. / 0.0271}
PAZ_KIRNOS = {'poles': [-0.1257 - 0.2177j,
-0.1257 + 0.2177j,
-83.4473 + 0.0j,
-0.3285 + 0.0j],
'zeros': [0.0 + 0.0j] * 2,
'sensitivity': 1.0,
'gain': 1. / 1.61}
INSTRUMENTS = {'None': None,
'kirnos': PAZ_KIRNOS,
'wood_anderson': PAZ_WOOD_ANDERSON,
'wwssn_lp': PAZ_WWSSN_LP,
'wwssn_sp': PAZ_WWSSN_SP}
class InvSimTestCase(unittest.TestCase):
"""
Test cases for InvSim.
"""
def setUp(self):
# directory where the test files are located
self.path = os.path.join(os.path.dirname(__file__), 'data')
def test_seis_sim_vs_pitsa1(self):
"""
Test simulate_seismometer seismometer simulation against seismometer
simulation of Pitsa - LE3D seismometer.
"""
# load test file
filename = os.path.join(self.path, 'rjob_20051006.gz')
with gzip.open(filename) as f:
data = np.loadtxt(f)
# paz of test file
samp_rate = 200.0
paz_le3d = {'poles': [-4.21 + 4.66j,
-4.21 - 4.66j,
-2.105 + 0.0j],
'zeros': [0.0 + 0.0j] * 3,
'sensitivity': 1.0,
'gain': 0.4}
for id, paz in INSTRUMENTS.items():
# simulate instrument
datcorr = simulate_seismometer(
data, samp_rate, paz_remove=paz_le3d, paz_simulate=paz,
water_level=600.0, zero_mean=False, nfft_pow2=True)
# load pitsa file
filename = os.path.join(self.path, 'rjob_20051006_%s.gz' % id)
with gzip.open(filename) as f:
data_pitsa = np.loadtxt(f)
# calculate normalized rms
rms = np.sqrt(np.sum((datcorr - data_pitsa) ** 2) /
np.sum(data_pitsa ** 2))
self.assertTrue(rms < 1.1e-05)
def test_seis_sim_vs_pitsa_2(self):
"""
Test simulate_seismometer seismometer simulation against seismometer
simulation of Pitsa - STS-2 seismometer.
"""
# load test file
file = os.path.join(self.path, 'rotz_20081028.gz')
with gzip.open(file) as f:
data = np.loadtxt(f)
# paz of test file
samp_rate = 200.0
paz_sts2 = {'poles': [-0.03736 - 0.03617j,
-0.03736 + 0.03617j],
'zeros': [0.0 + 0.0j] * 2,
'sensitivity': 1.0,
'gain': 1.5}
for id, paz in INSTRUMENTS.items():
# simulate instrument
datcorr = simulate_seismometer(
data, samp_rate, paz_remove=paz_sts2, paz_simulate=paz,
water_level=600.0, zero_mean=False, nfft_pow2=True)
# load pitsa file
filename = os.path.join(self.path, 'rotz_20081028_%s.gz' % id)
with gzip.open(filename) as f:
data_pitsa = np.loadtxt(f)
# calculate normalized rms
rms = np.sqrt(np.sum((datcorr - data_pitsa) ** 2) /
np.sum(data_pitsa ** 2))
self.assertTrue(rms < 1e-04)
def test_estimate_magnitude(self):
"""
Tests against PITSA. Note that PITSA displays microvolt, that is
the amplitude values must be computed back into counts (for this
stations .596microvolt/count was used). Pitsa internally calculates
with the sensitivity 2800 of the WA. Using this we get for the
following for event 2009-07-19 23:03::
RTSH PITSA 2.263 ObsPy 2.294
RTBE PITSA 1.325 ObsPy 1.363
RMOA PITSA 1.629 ObsPy 1.675
"""
# the poles/zeros are the same for all three stations but the overall
# sensitivity differs, this was probably not taken into account when
# implementing this test (the specified 'sensitivity' is for RTSH), so
# below we use the response for station RTSH for the test
paz = {'poles': [-4.444 + 4.444j, -4.444 - 4.444j, -1.083 + 0j],
'zeros': [0 + 0j, 0 + 0j, 0 + 0j],
'gain': 1.0,
'sensitivity': 671140000.0}
mag_rtsh = estimate_magnitude(paz, 3.34e6, 0.065, 0.255)
self.assertAlmostEqual(mag_rtsh, 2.1328727151723488)
mag_rtbe = estimate_magnitude(paz, 3.61e4, 0.08, 2.197)
self.assertAlmostEqual(mag_rtbe, 1.1962687721890191)
mag_rnon = estimate_magnitude(paz, 6.78e4, 0.125, 1.538)
self.assertAlmostEqual(mag_rnon, 1.4995311686507182)
# now also test using Response object to calculate amplitude
# (use RTSH response for all three measurements, see above comment)
# response calculated using all stages is slightly different from the
# PAZ + overall sensitivity used above, so we get slightly different
# values here..
response = read_inventory(os.path.join(self.path, 'BW_RTSH.xml'),
format='STATIONXML')[0][0][0].response
mag_rtsh = estimate_magnitude(response, 3.34e6, 0.065, 0.255)
self.assertAlmostEqual(mag_rtsh, 2.1179529876187635)
mag_rtbe = estimate_magnitude(response, 3.61e4, 0.08, 2.197)
self.assertAlmostEqual(mag_rtbe, 1.1832677953138184)
mag_rnon = estimate_magnitude(response, 6.78e4, 0.125, 1.538)
self.assertAlmostEqual(mag_rnon, 1.4895395665022975)
# XXX: Test for really big signal is missing, where the water level is
# actually acting
# def test_seisSimVsPitsa2(self):
# from obspy.io.mseed import tests as tests_
# path = os.path.dirname(__file__)
# file = os.path.join(path, 'data', 'BW.BGLD..EHE.D.2008.001')
# g = Trace()
# g.read(file,format='MSEED')
# # paz of test file
# samp_rate = 200.0
def test_sac_instrument_correction(self):
# SAC recommends to taper the transfer function if a pure
# deconvolution is done instead of simulating a different
# instrument. This test checks the difference between the
# result from removing the instrument response using SAC or
# ObsPy. Visual inspection shows that the traces are pretty
# much identical but differences remain (rms ~ 0.042). Haven't
# found the cause for those, yet. One possible reason is the
# floating point arithmetic of SAC vs. the double precision
# arithmetic of Python. However differences still seem to be
# too big for that.
pzf = os.path.join(self.path, 'SAC_PZs_KARC_BHZ')
sacf = os.path.join(self.path, 'KARC.LHZ.SAC.asc.gz')
testsacf = os.path.join(self.path, 'KARC_corrected.sac.asc.gz')
plow = 160.
phigh = 4.
fl1 = 1.0 / (plow + 0.0625 * plow)
fl2 = 1.0 / plow
fl3 = 1.0 / phigh
fl4 = 1.0 / (phigh - 0.25 * phigh)
# Uncomment the following to run the sac-commands
# that created the testing file
# if 1:
# import subprocess as sp
# p = sp.Popen('sac',shell=True,stdin=sp.PIPE)
# cd1 = p.stdin
# print("r %s"%sacf, file=cd1)
# print("rmean", file=cd1)
# print("rtrend", file=cd1)
# print("taper type cosine width 0.03", file=cd1)
# print("transfer from polezero subtype %s to none \
# freqlimits %f %f %f %f" % (pzf, fl1, fl2, fl3, fl4), file=cd1)
# print("w over ./data/KARC_corrected.sac", file=cd1)
# print("quit", file=cd1)
# cd1.close()
# p.wait()
stats = {'network': 'KA', 'delta': 0.99999988079072466,
'station': 'KARC', 'location': 'S1',
'starttime': UTCDateTime(2001, 2, 13, 0, 0, 0, 993700),
'calib': 1.00868e+09, 'channel': 'BHZ'}
with gzip.open(sacf) as f:
tr = Trace(np.loadtxt(f), stats)
attach_paz(tr, pzf, tovel=False)
tr.data = simulate_seismometer(
tr.data, tr.stats.sampling_rate, paz_remove=tr.stats.paz,
remove_sensitivity=False, pre_filt=(fl1, fl2, fl3, fl4))
with gzip.open(testsacf) as f:
data = np.loadtxt(f)
# import matplotlib.pyplot as plt
# plt.plot(tr.data)
# plt.plot(data)
# plt.show()
rms = np.sqrt(np.sum((tr.data - data) ** 2) /
np.sum(tr.data ** 2))
self.assertTrue(rms < 0.0421)
def test_evalresp_vs_obspy(self):
"""
Compare results from removing instrument response using
evalresp in SAC and ObsPy. Visual inspection shows that the traces are
pretty much identical but differences remain (rms ~ 0.042). Haven't
found the cause for those, yet.
"""
evalrespf = os.path.join(self.path, 'CRLZ.HHZ.10.NZ.SAC_resp')
rawf = os.path.join(self.path, 'CRLZ.HHZ.10.NZ.SAC')
respf = os.path.join(self.path, 'RESP.NZ.CRLZ.10.HHZ')
fl1 = 0.00588
fl2 = 0.00625
fl3 = 30.
fl4 = 35.
# #Set the following if-clause to True to run
# #the sac-commands that created the testing file
# if False:
# import subprocess as sp
# p = sp.Popen('sac', stdin=sp.PIPE)
# cd1 = p.stdin
# print("r %s" % rawf, file=cd1)
# print("rmean", file=cd1)
# print("taper type cosine width 0.05", file=cd1)
# print("transfer from evalresp fname %s to vel freqlimits\
# %f %f %f %f" % (respf, fl1, fl2, fl3, fl4), file=cd1)
# print("w over %s" % evalrespf, file=cd1)
# print("quit", file=cd1)
# cd1.close()
# p.wait()
tr = read(rawf)[0]
trtest = read(evalrespf)[0]
date = UTCDateTime(2003, 11, 1, 0, 0, 0)
seedresp = {'filename': respf, 'date': date, 'units': 'VEL',
'network': 'NZ', 'station': 'CRLZ', 'location': '10',
'channel': 'HHZ'}
tr.data = simulate_seismometer(
tr.data, tr.stats.sampling_rate, paz_remove=None,
pre_filt=(fl1, fl2, fl3, fl4), seedresp=seedresp,
taper_fraction=0.1, pitsasim=False, sacsim=True)
tr.data *= 1e9
rms = np.sqrt(np.sum((tr.data - trtest.data) ** 2) /
np.sum(trtest.data ** 2))
self.assertTrue(rms < 0.0094)
# import matplotlib.pyplot as plt #plt.plot(tr.data-trtest.data,'b')
# plt.plot(trtest.data,'g')
# plt.figure()
# plt.psd(tr.data,Fs=100.,NFFT=32768)
# plt.psd(trtest.data,Fs=100.,NFFT=32768)
# plt.figure()
# plt.psd(tr.data - trtest.data, Fs=100., NFFT=32768)
# plt.show()
def test_cosine_taper(self):
# SAC trace was generated with:
# taper type cosine width 0.05
for i in [99, 100]:
sac_taper = os.path.join(self.path,
'ones_trace_%d_tapered.sac' % i)
tr = read(sac_taper)[0]
tap = cosine_taper(i, p=0.1, halfcosine=False, sactaper=True)
np.testing.assert_array_almost_equal(tap, tr.data, decimal=6)
# The following lines compare the cosine_taper result with
# the result of the algorithm used by SAC in its taper routine
# (taper.c)
# freqs = np.fft.fftfreq(2**15,0.01)
# fl1 = 0.00588
# fl2 = 0.00625
# fl3 = 30.0
# fl4 = 35.0
# npts = freqs.size
# tap = cosine_taper(freqs.size, freqs=freqs, flimit=(fl1, fl2,
# fl3, fl4))
# tap2 = cosine_sac_taper(freqs, flimit=(fl1, fl2, fl3, fl4))
# import matplotlib.pyplot as plt
# plt.plot(tap,'b')
# plt.plot(tap2,'g--')
# plt.show()
def test_evalresp_using_different_line_separator(self):
"""
The evalresp needs a file with correct line separator, so '\n' for
POSIX, '\r' for Mac OS, or '\r\n' for Windows. Here we check that
evalresp reads all three formats.
This test only checks the parsing capabilities of evalresp,
the number of fft points used (nfft) can therefore be chosen
small.
"""
dt = UTCDateTime(2003, 11, 1, 0, 0, 0)
nfft = 8
# Linux
respf = os.path.join(self.path, 'RESP.NZ.CRLZ.10.HHZ')
evalresp(0.01, nfft, respf, dt)
# Mac
respf = os.path.join(self.path, 'RESP.NZ.CRLZ.10.HHZ.mac')
evalresp(0.01, nfft, respf, dt)
# Windows
respf = os.path.join(self.path, 'RESP.NZ.CRLZ.10.HHZ.windows')
evalresp(0.01, nfft, respf, dt)
def test_evalresp_bug_395(self):
"""
Was a bug due to inconstistent numerical range
"""
resp = os.path.join(self.path, 'RESP.CH._.HHZ.gz')
with NamedTemporaryFile() as fh:
tmpfile = fh.name
with gzip.open(resp) as f:
fh.write(f.read())
samprate = 120.0
nfft = 56328
args = [1.0 / samprate, nfft, tmpfile,
UTCDateTime(2012, 9, 4, 5, 12, 15, 863300)]
kwargs = {'units': 'VEL', 'freq': True}
_h, f = evalresp(*args, **kwargs)
self.assertEqual(len(f), nfft // 2 + 1)
def test_evalresp_specific_frequencies(self):
"""
Test getting response for specific frequencies from evalresp
"""
resp = os.path.join(self.path, 'RESP.CH._.HHZ.gz')
# test some frequencies (results taken from routine
# test_evalresp_bug_395)
freqs = [0.0, 0.0021303792075, 0.21303792075, 0.63911376225,
2.1303792075, 21.303792075, 59.9978696208, 60.0]
expected = [0j, -38033660.9731 + 14722854.5862j,
623756964.698 + 34705336.5587j,
625815840.91 + 11748438.5949j,
634173301.327 - 2261888.45356j,
689435074.739 - 216615642.231j,
-105.682658137 - 4360.67242023j,
-101.693155157 - 4172.61059939j,
]
with NamedTemporaryFile() as fh:
tmpfile = fh.name
with gzip.open(resp) as f:
fh.write(f.read())
samprate = 120.0
t = UTCDateTime(2012, 9, 4, 5, 12, 15, 863300)
h = evalresp_for_frequencies(
1.0 / samprate, freqs, tmpfile, t, units='VEL')
np.testing.assert_allclose(h, expected)
# this test seems to fail sometimes with almost same numbers but slight
# differences on Appveyor and we could not reproduce it on a local
# machine.. so skip on windows.. (e.g. http://tests.obspy.org/101648/#1,
# https://ci.appveyor.com/project/obspy/obspy/build/1.0.6561-master)
@unittest.skipIf(platform.system() == "Windows",
'unreproducible test fail encountered on Appveyor '
'sometimes.')
def test_evalresp_file_like_object(self):
"""
Test evalresp with file like object
"""
rawf = os.path.join(self.path, 'CRLZ.HHZ.10.NZ.SAC')
respf = os.path.join(self.path, 'RESP.NZ.CRLZ.10.HHZ')
tr1 = read(rawf)[0]
tr2 = read(rawf)[0]
date = UTCDateTime(2003, 11, 1, 0, 0, 0)
seedresp = {'filename': respf, 'date': date, 'units': 'VEL',
'network': 'NZ', 'station': 'CRLZ', 'location': '10',
'channel': 'HHZ'}
tr1.data = simulate_seismometer(
tr1.data, tr1.stats.sampling_rate, seedresp=seedresp)
with open(respf, 'rb') as fh:
stringio = io.BytesIO(fh.read())
seedresp['filename'] = stringio
tr2.data = simulate_seismometer(tr2.data, tr2.stats.sampling_rate,
seedresp=seedresp)
self.assertTrue(traces_almost_equal(tr1, tr2))
def test_segfaulting_resp_file(self):
"""
Test case for a file that segfaults when compiled with clang and
active optimization.
As long as the test does not segfault it is ok.
"""
filename = os.path.join(self.path, "segfaulting_RESPs",
"RESP.IE.LLRI..EHZ")
date = UTCDateTime(2003, 11, 1, 0, 0, 0)
# raises C-level EVRESP ERROR
with SuppressOutput():
self.assertRaises(ValueError, evalresp, t_samp=10.0, nfft=256,
filename=filename, date=date, station="LLRI",
channel="EHZ", network="IE", locid="*",
units="VEL")
def test_evalresp_seed_identifiers_work(self):
"""
Asserts that the network, station, location and channel identifiers can
be used to select difference responses.
"""
kwargs = {"filename": os.path.join(self.path, "RESP.OB.AAA._.BH_"),
"t_samp": 0.1, "nfft": 1024, "units": "VEL",
"date": UTCDateTime(2013, 1, 1), "network": "OP",
"station": "AAA", "locid": "", "freq": False, "debug": False}
# Get the response for the first channel
kwargs["channel"] = "BHE"
response_1 = evalresp(**kwargs)
# Get the second one. Should be different.
kwargs["channel"] = "BHN"
response_2 = evalresp(**kwargs)
# The only thing that changed was the channel code. This should change
# the response.
rel_diff = np.abs(response_2 - response_1).ptp() / \
max(np.abs(response_1).ptp(), np.abs(response_2).ptp())
self.assertGreater(rel_diff, 1E-3)
# The RESP file only contains two channels.
kwargs["channel"] = "BHZ"
# suppress a C-level "no response found" warning
with SuppressOutput():
self.assertRaises(ValueError, evalresp, **kwargs)
def test_evalresp_spline(self):
"""
evr_spline was based on GPL plotutils, now replaced by LGPL spline
library. Unittest for this function.
"""
# char *evr_spline(int num_points, double *t, double *y,
# double tension, double k,
# double *xvals_arr, int num_xvals,
# double **p_retvals_arr, int *p_num_retvals)
clibevresp.evr_spline.argtypes = [
C.c_int, # num_points
np.ctypeslib.ndpointer(dtype=np.float64, ndim=1,
flags='C_CONTIGUOUS'),
np.ctypeslib.ndpointer(dtype=np.float64, ndim=1,
flags='C_CONTIGUOUS'),
C.c_double, # tension
C.c_double, # k
np.ctypeslib.ndpointer(dtype=np.float64, ndim=1,
flags='C_CONTIGUOUS'),
C.c_int, # num_xvals
C.POINTER(C.POINTER(C.c_double)),
C.POINTER(C.c_int)
]
clibevresp.evr_spline.restype = C.c_char_p
x = np.arange(1.2, 2.0, .1)
n = len(x)
y = np.sin(x)
xi = x[:-1] + .05
ni = len(xi)
p_num_retvals = C.c_int(0)
p_retvals_arr = C.POINTER(C.c_double)()
res = clibevresp.evr_spline(n, x, y, 0.0, 1.0, xi, ni,
C.byref(p_retvals_arr),
C.byref(p_num_retvals))
self.assertEqual(res, None)
self.assertEqual(ni, p_num_retvals.value)
yi = np.array([p_retvals_arr[i] for i in range(ni)])
if False: # visually verify
import matplotlib.pyplot as plt
plt.plot(x, y, 'bo-', 'Orig values')
plt.plot(xi, yi, 'ro-', 'Cubic Spline interpolated values')
plt.legend()
plt.show()
yi_ref = [0.94899576, 0.97572004, 0.9927136, 0.99978309, 0.99686554,
0.98398301, 0.96128491]
self.assertTrue(np.allclose(yi, yi_ref, rtol=1e-7, atol=0))