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
