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#!/usr/bin/env python 

# -*- coding: utf-8 -*- 

""" 

The Filter test suite. 

""" 

 

from obspy.signal import bandpass, lowpass, highpass 

from obspy.signal.filter import envelope, lowpassCheby2 

import os 

import unittest 

import gzip 

import numpy as np 

import scipy.signal as sg 

 

 

class FilterTestCase(unittest.TestCase): 

    """ 

    Test cases for Filter. 

    """ 

    def setUp(self): 

        # directory where the test files are located 

        self.path = os.path.join(os.path.dirname(__file__), 'data') 

 

    def test_bandpassVsPitsa(self): 

        """ 

        Test Butterworth bandpass filter against Butterworth bandpass filter 

        of PITSA. Note that the corners value is twice the value of the filter 

        sections in PITSA. The rms of the difference between ObsPy and PITSA 

        tends to get bigger with higher order filtering. 

        """ 

        # load test file 

        file = os.path.join(self.path, 'rjob_20051006.gz') 

        f = gzip.open(file) 

        data = np.loadtxt(f) 

        f.close() 

        # parameters for the test 

        samp_rate = 200.0 

        freq1 = 5 

        freq2 = 10 

        corners = 4 

        # filter trace 

        datcorr = bandpass(data, freq1, freq2, df=samp_rate, corners=corners) 

        # load pitsa file 

        file = os.path.join(self.path, 'rjob_20051006_bandpass.gz') 

        f = gzip.open(file) 

        data_pitsa = np.loadtxt(f) 

        f.close() 

        # calculate normalized rms 

        rms = np.sqrt(np.sum((datcorr - data_pitsa) ** 2) / \ 

                      np.sum(data_pitsa ** 2)) 

        self.assertEqual(rms < 1.0e-05, True) 

 

    def test_bandpassZPHSHVsPitsa(self): 

        """ 

        Test Butterworth zero-phase bandpass filter against Butterworth 

        zero-phase bandpass filter of PITSA. Note that the corners value is 

        twice the value of the filter sections in PITSA. The rms of the 

        difference between ObsPy and PITSA tends to get bigger with higher 

        order filtering. 

        Note: The Zero-Phase filters deviate from PITSA's zero-phase filters 

        at the end of the trace! The rms for the test is calculated omitting 

        the last 200 samples, as this part of the trace is assumed to 

        generally be of low interest/importance. 

        """ 

        # load test file 

        file = os.path.join(self.path, 'rjob_20051006.gz') 

        f = gzip.open(file) 

        data = np.loadtxt(f) 

        f.close() 

        # parameters for the test 

        samp_rate = 200.0 

        freq1 = 5 

        freq2 = 10 

        corners = 2 

        # filter trace 

        datcorr = bandpass(data, freq1, freq2, df=samp_rate, 

                           corners=corners, zerophase=True) 

        # load pitsa file 

        file = os.path.join(self.path, 'rjob_20051006_bandpassZPHSH.gz') 

        f = gzip.open(file) 

        data_pitsa = np.loadtxt(f) 

        f.close() 

        # calculate normalized rms 

        rms = np.sqrt(np.sum((datcorr[:-200] - data_pitsa[:-200]) ** 2) / \ 

                      np.sum(data_pitsa[:-200] ** 2)) 

        self.assertEqual(rms < 1.0e-05, True) 

 

    def test_lowpassVsPitsa(self): 

        """ 

        Test Butterworth lowpass filter against Butterworth lowpass filter of 

        PITSA. Note that the corners value is twice the value of the filter 

        sections in PITSA. The rms of the difference between ObsPy and PITSA 

        tends to get bigger with higher order filtering. 

        """ 

        # load test file 

        file = os.path.join(self.path, 'rjob_20051006.gz') 

        f = gzip.open(file) 

        data = np.loadtxt(f) 

        f.close() 

        # parameters for the test 

        samp_rate = 200.0 

        freq = 5 

        corners = 4 

        # filter trace 

        datcorr = lowpass(data, freq, df=samp_rate, corners=corners) 

        # load pitsa file 

        file = os.path.join(self.path, 'rjob_20051006_lowpass.gz') 

        f = gzip.open(file) 

        data_pitsa = np.loadtxt(f) 

        f.close() 

        # calculate normalized rms 

        rms = np.sqrt(np.sum((datcorr - data_pitsa) ** 2) / 

                      np.sum(data_pitsa ** 2)) 

        self.assertEqual(rms < 1.0e-05, True) 

 

    def test_lowpassZPHSHVsPitsa(self): 

        """ 

        Test Butterworth zero-phase lowpass filter against Butterworth 

        zero-phase lowpass filter of PITSA. Note that the corners value is 

        twice the value of the filter sections in PITSA. The rms of the 

        difference between ObsPy and PITSA tends to get bigger with higher 

        order filtering. 

        Note: The Zero-Phase filters deviate from PITSA's zero-phase filters 

        at the end of the trace! The rms for the test is calculated omitting 

        the last 200 samples, as this part of the trace is assumed to 

        generally be of low interest/importance. 

        """ 

        # load test file 

        file = os.path.join(self.path, 'rjob_20051006.gz') 

        f = gzip.open(file) 

        data = np.loadtxt(f) 

        f.close() 

        # parameters for the test 

        samp_rate = 200.0 

        freq = 5 

        corners = 2 

        # filter trace 

        datcorr = lowpass(data, freq, df=samp_rate, corners=corners, 

                          zerophase=True) 

        # load pitsa file 

        file = os.path.join(self.path, 'rjob_20051006_lowpassZPHSH.gz') 

        f = gzip.open(file) 

        data_pitsa = np.loadtxt(f) 

        f.close() 

        # calculate normalized rms 

        rms = np.sqrt(np.sum((datcorr[:-200] - data_pitsa[:-200]) ** 2) / \ 

                      np.sum(data_pitsa[:-200] ** 2)) 

        self.assertEqual(rms < 1.0e-05, True) 

 

    def test_highpassVsPitsa(self): 

        """ 

        Test Butterworth highpass filter against Butterworth highpass filter 

        of PITSA. Note that the corners value is twice the value of the filter 

        sections in PITSA. The rms of the difference between ObsPy and PITSA 

        tends to get bigger with higher order filtering. 

        """ 

        # load test file 

        file = os.path.join(self.path, 'rjob_20051006.gz') 

        f = gzip.open(file) 

        data = np.loadtxt(f) 

        f.close() 

        # parameters for the test 

        samp_rate = 200.0 

        freq = 10 

        corners = 4 

        # filter trace 

        datcorr = highpass(data, freq, df=samp_rate, corners=corners) 

        # load pitsa file 

        file = os.path.join(self.path, 'rjob_20051006_highpass.gz') 

        f = gzip.open(file) 

        data_pitsa = np.loadtxt(f) 

        f.close() 

        # calculate normalized rms 

        rms = np.sqrt(np.sum((datcorr - data_pitsa) ** 2) / \ 

                      np.sum(data_pitsa ** 2)) 

        self.assertEqual(rms < 1.0e-05, True) 

 

    def test_highpassZPHSHVsPitsa(self): 

        """ 

        Test Butterworth zero-phase highpass filter against Butterworth 

        zero-phase highpass filter of PITSA. Note that the corners value is 

        twice the value of the filter sections in PITSA. The rms of the 

        difference between ObsPy and PITSA tends to get bigger with higher 

        order filtering. 

        Note: The Zero-Phase filters deviate from PITSA's zero-phase filters 

        at the end of the trace! The rms for the test is calculated omitting 

        the last 200 samples, as this part of the trace is assumed to 

        generally be of low interest/importance. 

        """ 

        # load test file 

        file = os.path.join(self.path, 'rjob_20051006.gz') 

        f = gzip.open(file) 

        data = np.loadtxt(f) 

        f.close() 

        # parameters for the test 

        samp_rate = 200.0 

        freq = 10 

        corners = 2 

        # filter trace 

        datcorr = highpass(data, freq, df=samp_rate, corners=corners, 

                           zerophase=True) 

        # load pitsa file 

        file = os.path.join(self.path, 'rjob_20051006_highpassZPHSH.gz') 

        f = gzip.open(file) 

        data_pitsa = np.loadtxt(f) 

        f.close() 

        # calculate normalized rms 

        rms = np.sqrt(np.sum((datcorr[:-200] - data_pitsa[:-200]) ** 2) / \ 

                      np.sum(data_pitsa[:-200] ** 2)) 

        self.assertEqual(rms < 1.0e-05, True) 

 

    def test_envelopeVsPitsa(self): 

        """ 

        Test Envelope filter against PITSA. 

        The rms is not so good, but the fit is still good in most parts. 

        """ 

        # load test file 

        file = os.path.join(self.path, 'rjob_20051006.gz') 

        f = gzip.open(file) 

        data = np.loadtxt(f) 

        f.close() 

        # filter trace 

        datcorr = envelope(data) 

        # load pitsa file 

        file = os.path.join(self.path, 'rjob_20051006_envelope.gz') 

        f = gzip.open(file) 

        data_pitsa = np.loadtxt(f) 

        f.close() 

        # calculate normalized rms 

        rms = np.sqrt(np.sum((datcorr - data_pitsa) ** 2) / \ 

                      np.sum(data_pitsa ** 2)) 

        self.assertEqual(rms < 1.0e-02, True) 

 

    def test_lowpassCheby2(self): 

        """ 

        Check magnitudes of basic lowpass cheby2 

        """ 

        df = 200  # Hz 

        b, a = lowpassCheby2(data=None, freq=50, 

            df=df, maxorder=12, ba=True) 

        nyquist = 200 * 0.5 

        # calculate frequency response 

        w, h = sg.freqz(b, a, int(nyquist)) 

        freq = w / np.pi * nyquist 

        h_db = 20 * np.log10(abs(h)) 

        # be smaller than -96dB above lowpass frequency 

        self.assertTrue(h_db[freq > 50].max() < -96) 

        # be 0 (1dB ripple) before filter ramp 

        self.assertTrue(h_db[freq < 25].min() > -1) 

 

 

def suite(): 

    return unittest.makeSuite(FilterTestCase, 'test') 

 

 

if __name__ == '__main__': 

    unittest.main(defaultTest='suite')