Python PPSD.calculate_histogram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 calculate_histogram [as 别名]
def _internal_get_ppsd():
    """
    Returns ready computed ppsd for testing purposes.
    """
    tr, paz = _get_sample_data()
    st = Stream([tr])
    ppsd = PPSD(tr.stats, paz, db_bins=(-200, -50, 0.5))
    ppsd.add(st)
    ppsd.calculate_histogram()
    return ppsd

# 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 calculate_histogram [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 calculate_histogram [as 别名]
    def test_ppsd_restricted_stacks(self):
        """
        Test PPSD.calculate_histogram() with restrictions to what data should
        be stacked. Also includes image tests.
        """
        # set up a bogus PPSD, with fixed random psds but with real start times
        # of psd pieces, to facilitate testing the stack selection.
        ppsd = PPSD(stats=Stats(dict(sampling_rate=150)), metadata=None,
                    db_bins=(-200, -50, 20.), period_step_octaves=1.4)
        ppsd._times_processed = np.load(
            os.path.join(self.path, "ppsd_times_processed.npy")).tolist()
        np.random.seed(1234)
        ppsd._binned_psds = [
            arr for arr in np.random.uniform(
                -200, -50,
                (len(ppsd._times_processed), len(ppsd.period_bin_centers)))]

        # Test callback function that selects a fixed random set of the
        # timestamps.  Also checks that we get passed the type we expect,
        # which is 1D numpy ndarray of float type.
        def callback(t_array):
            self.assertIsInstance(t_array, np.ndarray)
            self.assertEqual(t_array.shape, (len(ppsd._times_processed),))
            self.assertEqual(t_array.dtype, np.float64)
            np.random.seed(1234)
            res = np.random.randint(0, 2, len(t_array)).astype(np.bool)
            return res

        # test several different sets of stack criteria, should cover
        # everything, even with lots of combined criteria
        stack_criteria_list = [
            dict(starttime=UTCDateTime(2015, 3, 8), month=[2, 3, 5, 7, 8]),
            dict(endtime=UTCDateTime(2015, 6, 7), year=[2015],
                 time_of_weekday=[(1, 0, 24), (2, 0, 24), (-1, 0, 11)]),
            dict(year=[2013, 2014, 2016, 2017], month=[2, 3, 4]),
            dict(month=[1, 2, 5, 6, 8], year=2015),
            dict(isoweek=[4, 5, 6, 13, 22, 23, 24, 44, 45]),
            dict(time_of_weekday=[(5, 22, 24), (6, 0, 2), (6, 22, 24)]),
            dict(callback=callback, month=[1, 3, 5, 7]),
            dict(callback=callback)]
        expected_selections = np.load(
            os.path.join(self.path, "ppsd_stack_selections.npy"))

        # test every set of criteria
        for stack_criteria, expected_selection in zip(
                stack_criteria_list, expected_selections):
            selection_got = ppsd._stack_selection(**stack_criteria)
            np.testing.assert_array_equal(selection_got, expected_selection)

        # test one particular selection as an image test
        plot_kwargs = dict(max_percentage=15, xaxis_frequency=True,
                           period_lim=(0.01, 50))
        ppsd.calculate_histogram(**stack_criteria_list[1])
        with ImageComparison(self.path_images,
                             'ppsd_restricted_stack.png', reltol=1.5) as ic:
            fig = ppsd.plot(show=False, **plot_kwargs)
            # some matplotlib/Python version combinations lack the left-most
            # tick/label "Jan 2015". Try to circumvent and get the (otherwise
            # OK) test by changing the left x limit a bit further out (by two
            # days, axis is in mpl days). See e.g.
            # https://tests.obspy.org/30657/#1
            fig.axes[1].set_xlim(left=fig.axes[1].get_xlim()[0] - 2)
            with np.errstate(under='ignore'):
                fig.savefig(ic.name)

        # test it again, checking that updating an existing plot with different
        # stack selection works..
        #  a) we start with the stack for the expected image and test that it
        #     matches (like above):
        ppsd.calculate_histogram(**stack_criteria_list[1])
        with ImageComparison(self.path_images,
                             'ppsd_restricted_stack.png', reltol=1.5,
                             plt_close_all_exit=False) as ic:
            fig = ppsd.plot(show=False, **plot_kwargs)
            # some matplotlib/Python version combinations lack the left-most
            # tick/label "Jan 2015". Try to circumvent and get the (otherwise
            # OK) test by changing the left x limit a bit further out (by two
            # days, axis is in mpl days). See e.g.
            # https://tests.obspy.org/30657/#1
            fig.axes[1].set_xlim(left=fig.axes[1].get_xlim()[0] - 2)
            with np.errstate(under='ignore'):
                fig.savefig(ic.name)
        #  b) now reuse figure and set the histogram with a different stack,
        #     image test should fail:
        ppsd.calculate_histogram(**stack_criteria_list[3])
        try:
            with ImageComparison(self.path_images,
                                 'ppsd_restricted_stack.png',
                                 adjust_tolerance=False,
                                 plt_close_all_enter=False,
                                 plt_close_all_exit=False) as ic:
                # rms of the valid comparison above is ~31,
                # rms of the invalid comparison we test here is ~36
                if MATPLOTLIB_VERSION == [1, 1, 1]:
                    ic.tol = 33
                ppsd._plot_histogram(fig=fig, draw=True)
                with np.errstate(under='ignore'):
                    fig.savefig(ic.name)
        except ImageComparisonException:
            pass
#.........这里部分代码省略.........

# 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 calculate_histogram [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)