Python PPSD._times_processed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 _times_processed [as 别名]
    def test_ppsd_add_npz(self):
        """
        Test PPSD.add_npz().
        """
        # 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)
        _times_processed = np.load(
            os.path.join(self.path, "ppsd_times_processed.npy")).tolist()
        # change data to nowadays used nanoseconds POSIX timestamp
        _times_processed = [UTCDateTime(t)._ns for t in _times_processed]
        np.random.seed(1234)
        _binned_psds = [
            arr for arr in np.random.uniform(
                -200, -50,
                (len(_times_processed), len(ppsd.period_bin_centers)))]

        with NamedTemporaryFile(suffix=".npz") as tf1, \
                NamedTemporaryFile(suffix=".npz") as tf2, \
                NamedTemporaryFile(suffix=".npz") as tf3:
            # save data split up over three separate temporary files
            ppsd._times_processed = _times_processed[:200]
            ppsd._binned_psds = _binned_psds[:200]
            ppsd.save_npz(tf1.name)
            ppsd._times_processed = _times_processed[200:400]
            ppsd._binned_psds = _binned_psds[200:400]
            ppsd.save_npz(tf2.name)
            ppsd._times_processed = _times_processed[400:]
            ppsd._binned_psds = _binned_psds[400:]
            ppsd.matplotlib_version = "X.X.X"
            ppsd.save_npz(tf3.name)
            # now load these saved npz files and check if all data is present
            ppsd = PPSD.load_npz(tf1.name, metadata=None)
            ppsd.add_npz(tf2.name)
            # we changed a version number so this should emit a warning
            with warnings.catch_warnings(record=True) as w:
                warnings.simplefilter('always')
                ppsd.add_npz(tf3.name)
                self.assertEqual(len(w), 1)
            np.testing.assert_array_equal(_binned_psds, ppsd._binned_psds)
            np.testing.assert_array_equal(_times_processed,
                                          ppsd._times_processed)
            # adding data already present should also emit a warning and the
            # PPSD should not be changed
            with warnings.catch_warnings(record=True) as w:
                warnings.simplefilter('always')
                ppsd.add_npz(tf2.name)
                self.assertEqual(len(w), 1)
            np.testing.assert_array_equal(_binned_psds, ppsd._binned_psds)
            np.testing.assert_array_equal(_times_processed,
                                          ppsd._times_processed)

# 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 _times_processed [as 别名]
 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))

# 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 _times_processed [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.........