Python spectral_estimation.PPSD类代码示例

def _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)
    return ppsd

    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)

        # 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)

    def test_ppsd_w_iris_against_obspy_results(self):
        """
        Test against results obtained after merging of #1108.
        """
        # Read in ANMO data for one day
        st = read(os.path.join(self.path, "IUANMO.seed"))

        # Read in metadata in various different formats
        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,
        }
        resp = os.path.join(self.path, "IUANMO.resp")
        parser = Parser(os.path.join(self.path, "IUANMO.dataless"))
        inv = read_inventory(os.path.join(self.path, "IUANMO.xml"))

        # load expected results, for both only PAZ and full response
        filename_paz = os.path.join(self.path, "IUANMO_ppsd_paz.npz")
        results_paz = PPSD.load_npz(filename_paz, metadata=None)
        filename_full = os.path.join(self.path, "IUANMO_ppsd_fullresponse.npz")
        results_full = PPSD.load_npz(filename_full, metadata=None)

        # Calculate the PPSDs and test against expected results
        # first: only PAZ
        ppsd = PPSD(st[0].stats, paz)
        ppsd.add(st)
        # commented code to generate the test data:
        # ## np.savez(filename_paz,
        # ##          **dict([(k, getattr(ppsd, k))
        # ##                  for k in PPSD.NPZ_STORE_KEYS]))
        for key in PPSD.NPZ_STORE_KEYS_ARRAY_TYPES:
            np.testing.assert_allclose(getattr(ppsd, key), getattr(results_paz, key), rtol=1e-5)
        for key in PPSD.NPZ_STORE_KEYS_LIST_TYPES:
            for got, expected in zip(getattr(ppsd, key), getattr(results_paz, key)):
                np.testing.assert_allclose(got, expected, rtol=1e-5)
        for key in PPSD.NPZ_STORE_KEYS_SIMPLE_TYPES:
            if key in ["obspy_version", "numpy_version", "matplotlib_version"]:
                continue
            self.assertEqual(getattr(ppsd, key), getattr(results_paz, key))
        # second: various methods for full response
        for metadata in [parser, inv, resp]:
            ppsd = PPSD(st[0].stats, metadata)
            ppsd.add(st)
            # commented code to generate the test data:
            # ## np.savez(filename_full,
            # ##          **dict([(k, getattr(ppsd, k))
            # ##                  for k in PPSD.NPZ_STORE_KEYS]))
            for key in PPSD.NPZ_STORE_KEYS_ARRAY_TYPES:
                np.testing.assert_allclose(getattr(ppsd, key), getattr(results_full, key), rtol=1e-5)
            for key in PPSD.NPZ_STORE_KEYS_LIST_TYPES:
                for got, expected in zip(getattr(ppsd, key), getattr(results_full, key)):
                    np.testing.assert_allclose(got, expected, rtol=1e-5)
            for key in PPSD.NPZ_STORE_KEYS_SIMPLE_TYPES:
                if key in ["obspy_version", "numpy_version", "matplotlib_version"]:
                    continue
                self.assertEqual(getattr(ppsd, key), getattr(results_full, key))

    def test_ppsd_temporal_plot(self):
        """
        Test plot of several period bins over time
        """
        ppsd = PPSD.load_npz(self.example_ppsd_npz)

        restrictions = {'starttime': UTCDateTime(2011, 2, 6, 1, 1),
                        'endtime': UTCDateTime(2011, 2, 7, 21, 12),
                        'year': [2011],
                        'time_of_weekday': [(-1, 2, 23)]}

        # add some gaps in the middle
        for i in sorted(list(range(30, 40)) + list(range(8, 18)) + [4])[::-1]:
            ppsd._times_processed.pop(i)
            ppsd._binned_psds.pop(i)

        with ImageComparison(self.path_images, 'ppsd_temporal.png',
                             reltol=1.5) as ic:
            fig = ppsd.plot_temporal([0.1, 1, 10], filename=None, show=False,
                                     **restrictions)
            fig.savefig(ic.name)
        with ImageComparison(self.path_images, 'ppsd_temporal.png',
                             reltol=1.5) as ic:
            ppsd.plot_temporal([0.1, 1, 10], filename=ic.name, show=False,
                               **restrictions)

    def test_ppsd(self):
        """
        Test PPSD routine with some real data.
        """
        # paths of the expected result data
        file_histogram = os.path.join(
            self.path,
            'BW.KW1._.EHZ.D.2011.090_downsampled__ppsd_hist_stack.npy')
        file_binning = os.path.join(
            self.path, 'BW.KW1._.EHZ.D.2011.090_downsampled__ppsd_mixed.npz')
        file_mode_mean = os.path.join(
            self.path,
            'BW.KW1._.EHZ.D.2011.090_downsampled__ppsd_mode_mean.npz')
        tr, _paz = _get_sample_data()
        st = Stream([tr])
        ppsd = _get_ppsd()
        # read results and compare
        result_hist = np.load(file_histogram)
        self.assertEqual(len(ppsd.times_processed), 4)
        self.assertEqual(ppsd.nfft, 65536)
        self.assertEqual(ppsd.nlap, 49152)
        np.testing.assert_array_equal(ppsd.current_histogram, result_hist)
        # add the same data a second time (which should do nothing at all) and
        # test again - but it will raise UserWarnings, which we omit for now
        with warnings.catch_warnings(record=True):
            warnings.simplefilter('ignore', UserWarning)
            ppsd.add(st)
            np.testing.assert_array_equal(ppsd.current_histogram, result_hist)
        # test the binning arrays
        binning = np.load(file_binning)
        np.testing.assert_array_equal(ppsd.db_bin_edges, binning['spec_bins'])
        np.testing.assert_array_equal(ppsd.period_bin_centers,
                                      binning['period_bins'])

        # test the mode/mean getter functions
        per_mode, mode = ppsd.get_mode()
        per_mean, mean = ppsd.get_mean()
        result_mode_mean = np.load(file_mode_mean)
        np.testing.assert_array_equal(per_mode, result_mode_mean['per_mode'])
        np.testing.assert_array_equal(mode, result_mode_mean['mode'])
        np.testing.assert_array_equal(per_mean, result_mode_mean['per_mean'])
        np.testing.assert_array_equal(mean, result_mode_mean['mean'])

        # test saving and loading of the PPSD (using a temporary file)
        with NamedTemporaryFile(suffix=".npz") as tf:
            filename = tf.name
            # test saving and loading to npz
            ppsd.save_npz(filename)
            ppsd_loaded = PPSD.load_npz(filename)
            ppsd_loaded.calculate_histogram()
            self.assertEqual(len(ppsd_loaded.times_processed), 4)
            self.assertEqual(ppsd_loaded.nfft, 65536)
            self.assertEqual(ppsd_loaded.nlap, 49152)
            np.testing.assert_array_equal(ppsd_loaded.current_histogram,
                                          result_hist)
            np.testing.assert_array_equal(ppsd_loaded.db_bin_edges,
                                          binning['spec_bins'])
            np.testing.assert_array_equal(ppsd_loaded.period_bin_centers,
                                          binning['period_bins'])

 def test_issue1216(self):
     tr, paz = _get_sample_data()
     st = Stream([tr])
     ppsd = PPSD(tr.stats, paz, db_bins=(-200, -50, 0.5))
     ppsd.add(st)
     # After adding data internal representation of hist stack is None
     self.assertIsNone(ppsd._current_hist_stack)
     # Accessing the current_histogram property calculates the default stack
     self.assertIsNotNone(ppsd.current_histogram)
     self.assertIsNotNone(ppsd._current_hist_stack)
     # Adding the same data again does not invalidate the internal stack
     # but raises "UserWarning: Already covered time spans detected"
     with warnings.catch_warnings(record=True) as w:
         warnings.simplefilter('always', UserWarning)
         ppsd.add(st)
         self.assertEqual(len(w), 4)
         for w_ in w:
             self.assertTrue(str(w_.message).startswith(
                 "Already covered time spans detected"))
     self.assertIsNotNone(ppsd._current_hist_stack)
     # Adding new data invalidates the internal stack
     tr.stats.starttime += 3600
     st2 = Stream([tr])
     # raises "UserWarning: Already covered time spans detected"
     with warnings.catch_warnings(record=True) as w:
         warnings.simplefilter('always', UserWarning)
         ppsd.add(st2)
         self.assertEqual(len(w), 2)
         for w_ in w:
             self.assertTrue(str(w_.message).startswith(
                 "Already covered time spans detected"))
     self.assertIsNone(ppsd._current_hist_stack)
     # Accessing current_histogram again calculates the stack
     self.assertIsNotNone(ppsd.current_histogram)
     self.assertIsNotNone(ppsd._current_hist_stack)

    def test_exclude_last_sample(self):
        start = UTCDateTime("2017-01-01T00:00:00")
        header = {
            "starttime": start,
            "network": "GR",
            "station": "FUR",
            "channel": "BHZ"
        }
        # 49 segments of 30 minutes to allow 30 minutes overlap in next day
        tr = Trace(data=np.arange(30 * 60 * 4, dtype=np.int32), header=header)

        ppsd = PPSD(tr.stats, read_inventory())
        ppsd.add(tr)

        self.assertEqual(3, len(ppsd._times_processed))
        self.assertEqual(3600, ppsd.len)
        for i, time in enumerate(ppsd._times_processed):
            current = start.ns + (i * 30 * 60) * 1e9
            self.assertTrue(time == current)

    def test_PPSD_w_IRIS_against_obspy_results(self):
        """
        Test against results obtained after merging of #1108.
        """
        # Read in ANMO data for one day
        st = read(os.path.join(self.path, 'IUANMO.seed'))

        # Read in metadata in various different formats
        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}
        resp = os.path.join(self.path, 'IUANMO.resp')
        parser = Parser(os.path.join(self.path, 'IUANMO.dataless'))
        inv = read_inventory(os.path.join(self.path, 'IUANMO.xml'))

        # load expected results, for both only PAZ and full response
        filename_paz = os.path.join(self.path, 'IUANMO_ppsd_paz.npz')
        results_paz = np.load(filename_paz)
        filename_full = os.path.join(self.path, 'IUANMO_ppsd_fullresponse.npz')
        results_full = np.load(filename_full)
        arrays_to_check = ['_times_data', '_times_processed', '_times_gaps',
                           '_spec_octaves', 'per_octaves', 'per_octaves_right',
                           'per_octaves_left', 'period_bin_centers',
                           'spec_bins', 'period_bins']
        arrays_to_check = [native_str(key) for key in arrays_to_check]

        # Calculate the PPSDs and test against expected results
        # first: only PAZ
        ppsd = PPSD(st[0].stats, paz)
        ppsd.add(st)
        # commented code to generate the test data:
        # ## np.savez(filename_paz,
        # ##          **dict([(k, getattr(ppsd, k)) for k in arrays_to_check]))
        for key in arrays_to_check:
            self.assertTrue(np.allclose(
                getattr(ppsd, key), results_paz[key], rtol=1e-5))
        # second: various methods for full response
        # (also test various means of initialization, basically testing the
        #  decorator that maps the deprecated keywords)
        for metadata in [parser, inv, resp]:
            ppsd = PPSD(st[0].stats, paz=metadata)
            ppsd = PPSD(st[0].stats, parser=metadata)
            ppsd = PPSD(st[0].stats, metadata)
            ppsd.add(st)
            # commented code to generate the test data:
            # ## np.savez(filename_full,
            # ##          **dict([(k, getattr(ppsd, k))
            # ##                  for k in arrays_to_check]))
            for key in arrays_to_check:
                self.assertTrue(np.allclose(
                    getattr(ppsd, key), results_full[key], rtol=1e-5))

 def test_exception_reading_newer_npz(self):
     """
     Checks that an exception is properly raised when trying to read a npz
     that was written on a more recent ObsPy version (specifically that has
     a higher 'ppsd_version' number which is used to keep track of changes
     in PPSD and the npz file used for serialization).
     """
     msg = ("Trying to read/add a PPSD npz with 'ppsd_version=100'. This "
            "file was written on a more recent ObsPy version that very "
            "likely has incompatible changes in PPSD internal structure "
            "and npz serialization. It can not safely be read with this "
            "ObsPy version (current 'ppsd_version' is {!s}). Please "
            "consider updating your ObsPy installation.".format(
                PPSD(stats=Stats(), metadata=None).ppsd_version))
     # 1 - loading a npz
     data = np.load(self.example_ppsd_npz)
     # we have to load, modify 'ppsd_version' and save the npz file for the
     # test..
     items = {key: data[key] for key in data.files}
     # deliberately set a higher ppsd_version number
     items['ppsd_version'] = items['ppsd_version'].copy()
     items['ppsd_version'].fill(100)
     with NamedTemporaryFile() as tf:
         filename = tf.name
         with open(filename, 'wb') as fh:
             np.savez(fh, **items)
         with self.assertRaises(ObsPyException) as e:
             PPSD.load_npz(filename)
     self.assertEqual(str(e.exception), msg)
     # 2 - adding a npz
     ppsd = PPSD.load_npz(self.example_ppsd_npz)
     for method in (ppsd.add_npz, ppsd._add_npz):
         with NamedTemporaryFile() as tf:
             filename = tf.name
             with open(filename, 'wb') as fh:
                 np.savez(fh, **items)
             with self.assertRaises(ObsPyException) as e:
                 method(filename)
             self.assertEqual(str(e.exception), msg)

 def test_wrong_trace_id_message(self):
     """
     Test that we get the expected warning message on waveform/metadata
     mismatch.
     """
     tr, _paz = _get_sample_data()
     inv = read_inventory(os.path.join(self.path, 'IUANMO.xml'))
     st = Stream([tr])
     ppsd = PPSD(tr.stats, inv)
     # metadata doesn't fit the trace ID specified via stats
     # should show a warning..
     with warnings.catch_warnings(record=True) as w:
         warnings.simplefilter('always')
         ret = ppsd.add(st)
         # the trace is sliced into four segments, so we get the warning
         # message four times..
         self.assertEqual(len(w), 4)
         for w_ in w:
             self.assertTrue(str(w_.message).startswith(
                 "Error getting response from provided metadata"))
     # should not add the data to the ppsd
     self.assertFalse(ret)

 def test_ppsd_time_checks(self):
     """
     Some tests that make sure checking if a new PSD slice to be addded to
     existing PPSD has an invalid overlap or not works as expected.
     """
     ppsd = PPSD(Stats(), Response())
     one_second = 1000000000
     t0 = 946684800000000000  # 2000-01-01T00:00:00
     time_diffs = [
         0, one_second, one_second * 2, one_second * 3,
         one_second * 8, one_second * 9, one_second * 10]
     ppsd._times_processed = [t0 + td for td in time_diffs]
     ppsd.ppsd_length = 2
     ppsd.overlap = 0.5
     # valid time stamps to insert data for (i.e. data that overlaps with
     # existing data at most "overlap" times "ppsd_length")
     ns_ok = [
         t0 - 3 * one_second,
         t0 - 1.01 * one_second,
         t0 - one_second,
         t0 + 4 * one_second,
         t0 + 4.01 * one_second,
         t0 + 6 * one_second,
         t0 + 7 * one_second,
         t0 + 6.99 * one_second,
         t0 + 11 * one_second,
         t0 + 11.01 * one_second,
         t0 + 15 * one_second,
         ]
     for ns in ns_ok:
         t = UTCDateTime(ns=int(ns))
         # getting False means time is not present yet and a PSD slice would
         # be added to the PPSD data
         self.assertFalse(ppsd._PPSD__check_time_present(t))
     # invalid time stamps to insert data for (i.e. data that overlaps with
     # existing data more than "overlap" times "ppsd_length")
     ns_bad = [
         t0 - 0.99 * one_second,
         t0 - 0.5 * one_second,
         t0,
         t0 + 1.1 * one_second,
         t0 + 3.99 * one_second,
         t0 + 7.01 * one_second,
         t0 + 7.5 * one_second,
         t0 + 8 * one_second,
         t0 + 8.8 * one_second,
         t0 + 10 * one_second,
         t0 + 10.99 * one_second,
         ]
     for ns in ns_bad:
         t = UTCDateTime(ns=int(ns))
         # getting False means time is not present yet and a PSD slice would
         # be added to the PPSD data
         self.assertTrue(ppsd._PPSD__check_time_present(t))

    def test_PPSD_save_and_load_npz(self):
        """
        Test PPSD.load_npz() and PPSD.save_npz()
        """
        _, paz = _get_sample_data()
        ppsd = _get_ppsd()

        # save results to npz file
        with NamedTemporaryFile(suffix=".npz") as tf:
            filename = tf.name
            # test saving and loading an uncompressed file
            ppsd.save_npz(filename)
            ppsd_loaded = PPSD.load_npz(filename, metadata=paz)

        for key in NPZ_STORE_KEYS:
            np.testing.assert_equal(getattr(ppsd, key),
                                    getattr(ppsd_loaded, key))

    def test_ppsd_spectrogram_plot(self):
        """
        Test spectrogram type plot of PPSD
        """
        ppsd = PPSD.load_npz(self.example_ppsd_npz)

        # add some gaps in the middle
        for i in sorted(list(range(30, 40)) + list(range(8, 18)) + [4])[::-1]:
            ppsd._times_processed.pop(i)
            ppsd._binned_psds.pop(i)

        with ImageComparison(self.path_images, 'ppsd_spectrogram.png',
                             reltol=1.5) as ic:
            fig = ppsd.plot_spectrogram(filename=None, show=False)
            fig.savefig(ic.name)
        with ImageComparison(self.path_images, 'ppsd_spectrogram.png',
                             reltol=1.5) as ic:
            ppsd.plot_spectrogram(filename=ic.name, show=False)

    def test_ppsd_save_and_load_npz(self):
        """
        Test PPSD.load_npz() and PPSD.save_npz()
        """
        _, paz = _get_sample_data()
        ppsd = _get_ppsd()

        # save results to npz file
        with NamedTemporaryFile(suffix=".npz") as tf:
            filename = tf.name
            # test saving and loading an uncompressed file
            ppsd.save_npz(filename)
            ppsd_loaded = PPSD.load_npz(filename, metadata=paz)

        for key in PPSD.NPZ_STORE_KEYS:
            if isinstance(getattr(ppsd, key), np.ndarray) or key == "_binned_psds":
                np.testing.assert_equal(getattr(ppsd, key), getattr(ppsd_loaded, key))
            else:
                self.assertEqual(getattr(ppsd, key), getattr(ppsd_loaded, key))

    def test_ppsd_spectrogram_plot(self):
        """
        Test spectrogram type plot of PPSD

        Matplotlib version 3 shifts the x-axis labels but everything else looks
        the same. Skipping test for matplotlib >= 3 on 05/12/2018.
        """
        ppsd = PPSD.load_npz(self.example_ppsd_npz)

        # add some gaps in the middle
        for i in sorted(list(range(30, 40)) + list(range(8, 18)) + [4])[::-1]:
            ppsd._times_processed.pop(i)
            ppsd._binned_psds.pop(i)

        with ImageComparison(self.path_images, 'ppsd_spectrogram.png',
                             reltol=1.5) as ic:
            fig = ppsd.plot_spectrogram(filename=None, show=False)
            fig.savefig(ic.name)
        with ImageComparison(self.path_images, 'ppsd_spectrogram.png',
                             reltol=1.5) as ic:
            ppsd.plot_spectrogram(filename=ic.name, show=False)