def test_select_within_magnitude_range(self): ''' Tests the function to select within the magnitude range ''' # Setup function self.catalogue = Catalogue() self.catalogue.data['magnitude'] = np.array([4., 5., 6., 7., 8.]) selector0 = CatalogueSelector(self.catalogue) # Test case 1: No limits specified - all catalogue valid test_cat_1 = selector0.within_magnitude_range() np.testing.assert_array_almost_equal(test_cat_1.data['magnitude'], self.catalogue.data['magnitude']) # Test case 2: Lower depth limit specfied only test_cat_1 = selector0.within_magnitude_range(lower_mag=5.5) np.testing.assert_array_almost_equal(test_cat_1.data['magnitude'], np.array([6., 7., 8.])) # Test case 3: Upper depth limit specified only test_cat_1 = selector0.within_magnitude_range(upper_mag=5.5) np.testing.assert_array_almost_equal(test_cat_1.data['magnitude'], np.array([4., 5.])) # Test case 4: Both depth limits specified test_cat_1 = selector0.within_magnitude_range(upper_mag=7.5, lower_mag=5.5) np.testing.assert_array_almost_equal(test_cat_1.data['magnitude'], np.array([6., 7.]))
def test_select_within_magnitude_range(self): ''' Tests the function to select within the magnitude range ''' # Setup function self.catalogue = Catalogue() self.catalogue.data['magnitude'] = np.array([4., 5., 6., 7., 8.]) selector0 = CatalogueSelector(self.catalogue) # Test case 1: No limits specified - all catalogue valid test_cat_1 = selector0.within_magnitude_range() np.testing.assert_array_almost_equal(test_cat_1.data['magnitude'], self.catalogue.data['magnitude']) # Test case 2: Lower depth limit specfied only test_cat_1 = selector0.within_magnitude_range(lower_mag=5.5) np.testing.assert_array_almost_equal(test_cat_1.data['magnitude'], np.array([6., 7., 8.])) # Test case 3: Upper depth limit specified only test_cat_1 = selector0.within_magnitude_range(upper_mag=5.5) np.testing.assert_array_almost_equal(test_cat_1.data['magnitude'], np.array([4., 5.])) # Test case 4: Both depth limits specified test_cat_1 = selector0.within_magnitude_range(upper_mag=7.5, lower_mag=5.5) np.testing.assert_array_almost_equal(test_cat_1.data['magnitude'], np.array([6., 7.]))
def plot_mfd(catalogue_fname, grd, label, store, tr_fname, compl_table=None, mwid=0.1, upper_mag=11., title='', xlim=None, ylim=None, figsize=None): """ This function plots the incremental and complementary cumulative distribution of the earthquakes included in a catalogue file. :param catalogue_fname: Full path to the hmtk formatted catalogue :param label: If the user provides a tectonic regionalisation file, this label (or list of labels with the format LAB1, LAB2) defines the tectonic regions to be selected. :param tr_fname: Full path to the .hdf5 file containing the TR :param compl_table: A :class:`numpy.ndarray` instance of shape (2, n) where the first column contains years in a decreasing order and the second column contains magnitude (generally) in an increasing order :param grd: A boolean indicating the need to compute GR parameters :param upper_mag: The upper magnitude threshold used to filter the catalogue. This is useful for example in cases when it is interesting to fit only the exponential component of a magnitude-frequency distribution. :return: A tuple containing the output of the Weichert method in the following order: gr_pars """ mwid = float(mwid) # # loading catalogue cat = _load_catalogue(catalogue_fname) # # select earthquakes belonging to a given TR idx = numpy.full(cat.data['magnitude'].shape, True, dtype=bool) if label is not None and tr_fname is not None: f = h5py.File(tr_fname, 'r') idx = f[label][:] f.close() # # select catalogue sel = CatalogueSelector(cat, create_copy=False) sel.select_catalogue(idx) sel.within_magnitude_range(-1, upper_mag) tmps = 'Selecting earthquakes below magnitude {:.2f}' logging.info(tmps.format(upper_mag)) # # find rounded min and max magnitude mmin, mmax = _get_extremes(cat.data['magnitude'], mwid) tmin, tmax = _get_extremes(cat.data['year'], 10) # # compute histogram bins = numpy.arange(mmin, mmax + mwid * 0.01, mwid) his, _ = numpy.histogram(cat.data['magnitude'], bins) # # plotting if figsize is None: figsize = (8, 6) fig = plt.figure(figsize=figsize) ax = fig.add_subplot(1, 1, 1) # # add cumulative plot cs = numpy.cumsum(his[::-1]) plt.bar(bins[:-1], cs[::-1], mwid, align='edge', ec='cyan', fc='none') plt.plot(bins[:-1] + mwid / 2, cs[::-1], '-r', label='cumulative') # # add incremental plot plt.bar(bins[:-1], his, mwid, align='edge', ec='orange', fc='none') plt.plot(bins[:-1] + mwid / 2, his, '-b', label='incremental') # # if grd: if compl_table is None: compl_table = numpy.array([[tmin, mmin]]) agr, bgr, asig, bsig = _compute_mfd(cat, compl_table, mwid) # # info num = len(cat.data['magnitude']) print('Number of earthquakes in the catalogue : {:d}'.format(num)) num = max(cs) print('Maximum value in the c. cumulative distribution : {:d}'.format(num)) # # finish plot plt.legend() plt.yscale('log') plt.ylabel('Number of earthquakes') plt.xlabel('Magnitude') if label is not None: plt.title('label: {:s}'.format(label)) plt.grid(linestyle='-') if grd: plt.text(0.65, 0.70, 'bval: %.3f (sigma=%.3f)' % (bgr, bsig), horizontalalignment='left', verticalalignment='center', fontsize=8, transform=ax.transAxes) plt.text(0.65, 0.75, 'aval: %.3f (sigma=%.3f)' % (agr, asig), horizontalalignment='left', verticalalignment='center', fontsize=8, transform=ax.transAxes) # # ascaled = numpy.log10(10**agr * (tmax - tmin)) v = 10.**(-bins * bgr + ascaled) plt.plot(bins, v, '--g', lw=2) # # set limits if xlim is not None: plt.xlim(xlim) if ylim is not None: plt.ylim(ylim) # # Set title plt.title(title) # # Storing figure if store is not None: lbl = '' ext = 'png' if label is not None: lbl = label figure_fname = 'fig_mfd_{:s}.{:s}'.format(lbl, ext) plt.savefig(figure_fname, format=ext) else: plt.show() out = (bins[:-1] + mwid / 2, numpy.array([float(h) for h in his])) if grd: return out, (agr, bgr, asig, bsig) else: return out, None