def test_select_within_time(self):
# Tests the function to select within a time period
self.catalogue.data['year'] = np.arange(1900, 2010, 20)
self.catalogue.data['month'] = np.arange(1, 12, 2)
self.catalogue.data['day'] = np.ones(6, dtype=int)
self.catalogue.data['hour'] = np.ones(6, dtype=int)
self.catalogue.data['minute'] = np.zeros(6, dtype=int)
self.catalogue.data['second'] = np.ones(6, dtype=float)
selector0 = CatalogueSelector(self.catalogue)
# Start time and End time not defined
test_cat_1 = selector0.within_time_period()
self._compare_time_data_dictionaries(test_cat_1.data,
self.catalogue.data)
# Start time defined - end time not defined
begin_time = datetime.datetime(1975, 1, 1, 0, 0, 0, 0)
expected_data = {
'year': np.array([1980, 2000]),
'month': np.array([9, 11]),
'day': np.array([1, 1]),
'hour': np.array([1, 1]),
'minute': np.array([0, 0]),
'second': np.array([1., 1.])
}
test_cat_1 = selector0.within_time_period(start_time=begin_time)
self._compare_time_data_dictionaries(expected_data, test_cat_1.data)
# Test 3 - Start time not defined, end-time defined
finish_time = datetime.datetime(1965, 1, 1, 0, 0, 0, 0)
expected_data = {
'year': np.array([1900, 1920, 1940, 1960]),
'month': np.array([1, 3, 5, 7]),
'day': np.array([1, 1, 1, 1]),
'hour': np.array([1, 1, 1, 1]),
'minute': np.array([0, 0, 0, 0]),
'second': np.array([1., 1., 1., 1.])
}
test_cat_1 = selector0.within_time_period(end_time=finish_time)
self._compare_time_data_dictionaries(expected_data, test_cat_1.data)
# Test 4 - both start time and end-time defined
begin_time = datetime.datetime(1935, 1, 1, 0, 0, 0, 0)
finish_time = datetime.datetime(1995, 1, 1, 0, 0, 0, 0)
expected_data = {
'year': np.array([1940, 1960, 1980]),
'month': np.array([5, 7, 9]),
'day': np.array([1, 1, 1]),
'hour': np.array([1, 1, 1]),
'minute': np.array([0, 0, 0]),
'second': np.array([1., 1., 1.])
}
test_cat_1 = selector0.within_time_period(begin_time, finish_time)
self._compare_time_data_dictionaries(expected_data, test_cat_1.data)
def test_select_within_time(self):
# Tests the function to select within a time period
self.catalogue.data['year'] = np.arange(1900, 2010, 20)
self.catalogue.data['month'] = np.arange(1, 12, 2)
self.catalogue.data['day'] = np.ones(6, dtype=int)
self.catalogue.data['hour'] = np.ones(6, dtype=int)
self.catalogue.data['minute'] = np.zeros(6, dtype=int)
self.catalogue.data['second'] = np.ones(6, dtype=float)
selector0 = CatalogueSelector(self.catalogue)
# Start time and End time not defined
test_cat_1 = selector0.within_time_period()
self._compare_time_data_dictionaries(test_cat_1.data,
self.catalogue.data)
# Start time defined - end time not defined
begin_time = datetime.datetime(1975, 1, 1, 0, 0, 0, 0)
expected_data = {'year': np.array([1980, 2000]),
'month': np.array([9, 11]),
'day': np.array([1, 1]),
'hour': np.array([1, 1]),
'minute': np.array([0, 0]),
'second': np.array([1., 1.])}
test_cat_1 = selector0.within_time_period(start_time=begin_time)
self._compare_time_data_dictionaries(expected_data, test_cat_1.data)
# Test 3 - Start time not defined, end-time defined
finish_time = datetime.datetime(1965, 1, 1, 0, 0, 0, 0)
expected_data = {'year': np.array([1900, 1920, 1940, 1960]),
'month': np.array([1, 3, 5, 7]),
'day': np.array([1, 1, 1, 1]),
'hour': np.array([1, 1, 1, 1]),
'minute': np.array([0, 0, 0, 0]),
'second': np.array([1., 1., 1., 1.])}
test_cat_1 = selector0.within_time_period(end_time=finish_time)
self._compare_time_data_dictionaries(expected_data, test_cat_1.data)
# Test 4 - both start time and end-time defined
begin_time = datetime.datetime(1935, 1, 1, 0, 0, 0, 0)
finish_time = datetime.datetime(1995, 1, 1, 0, 0, 0, 0)
expected_data = {'year': np.array([1940, 1960, 1980]),
'month': np.array([5, 7, 9]),
'day': np.array([1, 1, 1]),
'hour': np.array([1, 1, 1]),
'minute': np.array([0, 0, 0]),
'second': np.array([1., 1., 1.])}
test_cat_1 = selector0.within_time_period(begin_time, finish_time)
self._compare_time_data_dictionaries(expected_data, test_cat_1.data)
def create_mtd(catalogue_fname,
label,
tr_fname,
cumulative,
store,
mwid=0.1,
twid=20.,
pmint=None,
pmaxt=None,
ylim=None):
"""
:param catalogue_fname:
:param label:
:param tr_fname:
"""
mwid = float(mwid)
twid = float(twid)
if pmint is not None:
pmint = int(pmint)
if pmaxt is not None:
pmaxt = int(pmaxt)
#
# loading catalogue
if isinstance(catalogue_fname, str):
cat = _load_catalogue(catalogue_fname)
elif isinstance(catalogue_fname, Catalogue):
cat = catalogue_fname
else:
raise ValueError('Unknown instance')
# Check catalogue
if cat is None or len(cat.data['magnitude']) < 1:
return None
# 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)
start = datetime.datetime(pmint, 1, 1) if pmint is not None else None
stop = datetime.datetime(pmaxt, 12, 31) if pmaxt is not None else None
sel.within_time_period(start, stop)
# Check if the catalogue contains earthquakes
if len(cat.data['magnitude']) < 1:
return None
#
# find rounded min and max magnitude
mmin, mmax = _get_extremes(cat.data['magnitude'], mwid)
tmin, tmax = _get_extremes(cat.data['year'], twid)
if ylim is not None:
mmin = ylim[0]
mmax = ylim[1]
#
#
if pmint is None:
pmint = tmin
if pmaxt is None:
pmaxt = tmax
#
# histogram
bins_ma = numpy.arange(mmin, mmax + mwid * 0.01, mwid)
bins_time = numpy.arange(tmin, tmax + twid * 0.01, twid)
his, _, _ = numpy.histogram2d(cat.data['year'],
cat.data['magnitude'],
bins=(bins_time, bins_ma))
his = his.T
#
# complementary cumulative
if cumulative:
ccu = numpy.zeros_like(his)
for i in range(his.shape[1]):
cc = numpy.cumsum(his[::-1, i])
ccu[:, i] = cc[::-1]
#
# plotting
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
#
#
X, Y = numpy.meshgrid(bins_time, bins_ma)
if cumulative:
his = ccu
pcm = ax.pcolormesh(X,
Y,
his,
norm=colors.LogNorm(vmin=1e-1, vmax=his.max()),
cmap='BuGn')
#
# plotting number of occurrences
for it, vt in enumerate(bins_time[:-1]):
for im, vm in enumerate(bins_ma[:-1]):
ax.text(vt + twid / 2,
vm + mwid / 2,
'{:.0f}'.format(his[im, it]),
fontsize=7,
ha='center')
#
# plotting colorbar
cb = fig.colorbar(pcm, ax=ax, extend='max')
cb.set_label('Number of occurrences')
#
# finishing the plot
plt.ylabel('Magnitude')
plt.xlabel('Time')
if label is not None:
plt.title('label: {:s}'.format(label))
plt.grid(linestyle='-')
return fig
def create_mtd(catalogue_fname,
label,
tr_fname,
cumulative,
store,
mwid=0.1,
twid=20.,
pmint=None,
pmaxt=None,
ylim=None):
"""
:param catalogue_fname:
:param label:
:param tr_fname:
"""
mwid = float(mwid)
twid = float(twid)
if pmint is not None:
pmint = int(pmint)
if pmaxt is not None:
pmaxt = int(pmaxt)
#
# loading catalogue
if isinstance(catalogue_fname, str):
cat = _load_catalogue(catalogue_fname)
elif isinstance(catalogue_fname, Catalogue):
cat = catalogue_fname
else:
raise ValueError('Unknown instance')
# Check catalogue
if cat is None or len(cat.data['magnitude']) < 1:
return None
# 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)
start = datetime.datetime(pmint, 1, 1) if pmint is not None else None
stop = datetime.datetime(pmaxt, 12, 31) if pmaxt is not None else None
sel.within_time_period(start, stop)
# Check if the catalogue contains earthquakes
if len(cat.data['magnitude']) < 1:
return None
# Get matrix
bins_time, bins_ma, his = get_mtd(cat, mwid, twid, ylim, cumulative)
# Plotting
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
X, Y = numpy.meshgrid(bins_time, bins_ma)
tmp_col = colors.LogNorm(vmin=1e-1, vmax=his.max())
pcm = ax.pcolormesh(X, Y, his, norm=tmp_col, cmap='BuGn')
# Plotting number of occurrences
for it, vt in enumerate(bins_time[:-1]):
for im, vm in enumerate(bins_ma[:-1]):
ax.text(vt + twid / 2,
vm + mwid / 2,
'{:.0f}'.format(his[im, it]),
fontsize=7,
ha='center')
# Plotting colorbar
cb = fig.colorbar(pcm, ax=ax, extend='max')
cb.set_label('Number of occurrences')
# Finishing the plot
plt.ylabel('Magnitude')
plt.xlabel('Time')
if label is not None:
plt.title('label: {:s}'.format(label))
plt.grid(linestyle='-')
return fig