Python PPSD.get_percentile方法代码示例

# 需要导入模块: from obspy.signal.spectral_estimation import PPSD [as 别名]
# 或者: from obspy.signal.spectral_estimation.PPSD import get_percentile [as 别名]
    def test_PPSD_w_IRIS(self):
        # Bands to be used this is the upper and lower frequency band pairs
        fres = zip([0.1, 0.05], [0.2, 0.1])

        file_dataANMO = os.path.join(self.path, 'IUANMO.seed')
        # Read in ANMO data for one day
        st = read(file_dataANMO)

        # Use a canned ANMO response which will stay static
        paz = {'gain': 86298.5, 'zeros': [0, 0],
               'poles': [-59.4313, -22.7121 + 27.1065j, -22.7121 + 27.1065j,
                         -0.0048004, -0.073199], 'sensitivity': 3.3554*10**9}

        # Make an empty PPSD and add the data
        ppsd = PPSD(st[0].stats, paz)
        ppsd.add(st)
        ppsd.calculate_histogram()

        # Get the 50th percentile from the PPSD
        (per, perval) = ppsd.get_percentile(percentile=50)

        # Read in the results obtained from a Mustang flat file
        file_dataIRIS = os.path.join(self.path, 'IRISpdfExample')
        freq, power, hits = np.genfromtxt(file_dataIRIS, comments='#',
                                          delimiter=',', unpack=True)

        # For each frequency pair we want to compare the mean of the bands
        for fre in fres:
            pervalGoodOBSPY = []

            # Get the values for the bands from the PPSD
            perinv = 1 / per
            mask = (fre[0] < perinv) & (perinv < fre[1])
            pervalGoodOBSPY = perval[mask]

            # Now we sort out all of the data from the IRIS flat file
            mask = (fre[0] < freq) & (freq < fre[1])
            triples = list(zip(freq[mask], hits[mask], power[mask]))
            # We now have all of the frequency values of interest
            # We will get the distinct frequency values
            freqdistinct = sorted(list(set(freq[mask])), reverse=True)
            percenlist = []
            # We will loop through the frequency values and compute a
            # 50th percentile
            for curfreq in freqdistinct:
                tempvalslist = []
                for triple in triples:
                    if np.isclose(curfreq, triple[0], atol=1e-3, rtol=0.0):
                        tempvalslist += [int(triple[2])] * int(triple[1])
                percenlist.append(np.percentile(tempvalslist, 50))
            # Here is the actual test
            np.testing.assert_allclose(np.mean(pervalGoodOBSPY),
                                       np.mean(percenlist), rtol=0.0, atol=1.0)

# 需要导入模块: from obspy.signal.spectral_estimation import PPSD [as 别名]
# 或者: from obspy.signal.spectral_estimation.PPSD import get_percentile [as 别名]
    def test_ppsd_w_iris(self):
        # Bands to be used this is the upper and lower frequency band pairs
        fres = zip([0.1, 0.05], [0.2, 0.1])

        file_data_anmo = os.path.join(self.path, 'IUANMO.seed')
        # Read in ANMO data for one day
        st = read(file_data_anmo)

        # Use a canned ANMO response which will stay static
        paz = {'gain': 86298.5, 'zeros': [0, 0],
               'poles': [-59.4313, -22.7121 + 27.1065j, -22.7121 + 27.1065j,
                         -0.0048004, -0.073199],
               'sensitivity': 3.3554 * 10 ** 9}

        # Make an empty PPSD and add the data
        # use highest frequency given by IRIS Mustang noise-pdf web service
        # (0.475683 Hz == 2.10224036 s) as center of first bin, so that we
        # end up with the same bins.
        ppsd = PPSD(st[0].stats, paz, period_limits=(2.10224036, 1400))
        ppsd.add(st)
        ppsd.calculate_histogram()

        # Get the 50th percentile from the PPSD
        (per, perval) = ppsd.get_percentile(percentile=50)
        perinv = 1 / per

        # Read in the results obtained from a Mustang flat file
        file_data_iris = os.path.join(self.path, 'IRISpdfExample')
        data = np.genfromtxt(
            file_data_iris, comments='#', delimiter=',',
            dtype=[(native_str("freq"), np.float64),
                   (native_str("power"), np.int32),
                   (native_str("hits"), np.int32)])
        freq = data["freq"]
        power = data["power"]
        hits = data["hits"]
        # cut data to same period range as in the ppsd we computed
        # (Mustang returns more long periods, probably due to some zero padding
        # or longer nfft in psd)
        num_periods = len(ppsd.period_bin_centers)
        freqdistinct = np.array(sorted(set(freq), reverse=True)[:num_periods])
        # just make sure that we compare the same periods in the following
        # (as we access both frequency arrays by indices from now on)
        np.testing.assert_allclose(freqdistinct, 1 / ppsd.period_bin_centers,
                                   rtol=1e-4)

        # For each frequency pair we want to compare the mean of the bands
        for fre in fres:
            # determine which bins we want to compare
            mask = (fre[0] < perinv) & (perinv < fre[1])

            # Get the values for the bands from the PPSD
            per_val_good_obspy = perval[mask]

            percenlist = []
            # Now we sort out all of the data from the IRIS flat file
            # We will loop through the frequency values and compute a
            # 50th percentile
            for curfreq in freqdistinct[mask]:
                mask_ = curfreq == freq
                tempvalslist = np.repeat(power[mask_], hits[mask_])
                percenlist.append(np.percentile(tempvalslist, 50))
            # Here is the actual test
            np.testing.assert_allclose(np.mean(per_val_good_obspy),
                                       np.mean(percenlist), rtol=0.0, atol=1.2)