def test_residuals_execution(self):
"""
Tests basic execution of residuals - not correctness of values -
to be the same when created from sm_database.GroundMotionDatabase and
sm_table.GroundMotionTable
"""
residuals1 = res.Residuals(self.gsims, self.imts)
residuals1.get_residuals(self.database, component="Geometric")
self._check_residual_dictionary_correctness(residuals1.residuals)
stats1 = residuals1.get_residual_statistics()
# yes, we said we do not check for correctness of values.
# However, this simple check warn us in case
# code modification will unexpectedly change residuals calculations:
expected_res_obs = {
'PGA':
np.array([
2.02129254e-04, 1.05953800e-03, 1.89631889e-04, 7.53992832e-05
]),
'SA(1.0)':
np.array([0.00012647, 0.00073836, 0.00019964, 0.00014166])
}
for imtx in expected_res_obs:
assert np.allclose(residuals1.contexts[0]['Observations'][imtx],
expected_res_obs[imtx],
rtol=.5e-3)
residuals2 = res.Residuals(self.gsims, self.imts)
residuals2.get_residuals(self.dbtable, component="Geometric")
self._check_residual_dictionary_correctness(residuals2.residuals)
stats2 = residuals2.get_residual_statistics()
self._check_new_context_vs_old(residuals1.contexts,
residuals2.contexts)
self._cmp_objects(stats1, stats2)
def tests_residual_plotter(self, mock_pyplot, mock_pyplot_subplot):
"""
Tests basic execution of residual plot.
Simply tests pyplot show is called by mocking its `show` method
"""
# setup a mock which will handle all calls to matplotlib Axes calls
# (e.g., bar, plot or semilogx) so we can test what has been called:
mocked_axes_obj = MagicMock()
mock_pyplot_subplot.side_effect = lambda *a, **v: mocked_axes_obj
residuals = res.Residuals(self.gsims, self.imts)
residuals.get_residuals(self.database, component="Geometric")
for gsim in self.gsims:
for imt in self.imts:
ResidualPlot(residuals, gsim, imt, bin_width=0.1)
# assert we called pyplot show:
self.assertTrue(mock_pyplot.show.call_count == 1)
ResidualPlot(residuals, gsim, imt, bin_width=0.1, show=False)
# assert we did NOT call pyplot show (call count still 1):
self.assertTrue(mock_pyplot.show.call_count == 1)
# reset mock:
mock_pyplot.show.reset_mock()
# assert we called the right matplotlib plotting functions:
self.assertTrue(mocked_axes_obj.bar.called)
self.assertTrue(mocked_axes_obj.plot.called)
self.assertFalse(mocked_axes_obj.semilogx.called)
# reset mock:
mocked_axes_obj.reset_mock()
def test_edr_execution(self):
"""
Tests execution of EDR - not correctness of values -
to be the same when created from sm_database.GroundMotionDatabase and
sm_table.GroundMotionTable
"""
edr1 = res.Residuals(self.gsims, self.imts)
edr1.get_residuals(self.database, component="Geometric")
self._check_residual_dictionary_correctness(edr1.residuals)
edr1.get_edr_values()
edr2 = res.Residuals(self.gsims, self.imts)
edr2.get_residuals(self.dbtable, component="Geometric")
self._check_residual_dictionary_correctness(edr2.residuals)
edr2.get_edr_values()
self._check_new_context_vs_old(edr1.contexts, edr2.contexts)
def test_llh_execution(self):
"""
Tests execution of LLH - not correctness of values -
to be the same when created from sm_database.GroundMotionDatabase and
sm_table.GroundMotionTable
"""
llh1 = res.Residuals(self.gsims, self.imts)
llh1.get_residuals(self.database, component="Geometric")
self._check_residual_dictionary_correctness(llh1.residuals)
llh1.get_loglikelihood_values(self.imts)
llh2 = res.Residuals(self.gsims, self.imts)
llh2.get_residuals(self.dbtable, component="Geometric")
self._check_residual_dictionary_correctness(llh2.residuals)
llh2.get_loglikelihood_values(self.imts)
self._check_new_context_vs_old(llh1.contexts, llh2.contexts)
def tests_residuals_execution(self):
"""
Tests basic execution of residuals - not correctness of values
"""
residuals = res.Residuals(self.gsims, self.imts)
residuals.get_residuals(self.database, component="Geometric")
self._check_residual_dictionary_correctness(residuals.residuals)
_ = residuals.get_residual_statistics()
def test_edr_execution(self):
"""
Tests execution of EDR - not correctness of values
"""
edr = res.Residuals(self.gsims, self.imts)
edr.get_residuals(self.database, component="Geometric")
self._check_residual_dictionary_correctness(edr.residuals)
edr.get_edr_values()
def test_multivariate_llh_execution(self):
"""
Tests execution of multivariate llh - not correctness of values
"""
multi_llh = res.Residuals(self.gsims, self.imts)
multi_llh.get_residuals(self.database, component="Geometric")
self._check_residual_dictionary_correctness(multi_llh.residuals)
multi_llh.get_multivariate_loglikelihood_values()
def test_likelihood_execution(self):
"""
Tests basic execution of residuals - not correctness of values
"""
lkh = res.Residuals(self.gsims, self.imts)
lkh.get_residuals(self.database, component="Geometric")
self._check_residual_dictionary_correctness(lkh.residuals)
lkh.get_likelihood_values()
def tests_with_distance(self, mock_pyplot, mock_pyplot_subplot):
"""
Tests basic execution of residual with distance plots.
Simply tests pyplot show is called by mocking its `show` method
"""
# setup a mock which will handle all calls to matplotlib Axes calls
# (e.g., bar, plot or semilogx) so we can test what has been called:
mocked_axes_obj = MagicMock()
mock_pyplot_subplot.side_effect = lambda *a, **v: mocked_axes_obj
residuals = res.Residuals(self.gsims, self.imts)
residuals.get_residuals(self.database, component="Geometric")
for gsim in self.gsims:
for imt in self.imts:
for dist in DISTANCES.keys():
if dist == 'r_x':
# as for residual_plots_test, we should confirm
# with scientific expertise that this is the case:
with self.assertRaises(AttributeError):
ResidualWithDistance(residuals,
gsim,
imt,
distance_type=dist,
show=True)
continue
ResidualWithDistance(residuals, gsim, imt, bin_width=0.1)
# assert we called pyplot show:
self.assertTrue(mock_pyplot.show.call_count == 1)
ResidualWithDistance(residuals,
gsim,
imt,
bin_width=0.1,
show=False)
# assert we did NOT call pyplot show (call count still 1):
self.assertTrue(mock_pyplot.show.call_count == 1)
# reset mock:
mock_pyplot.show.reset_mock()
# assert we called the right matplotlib plotting functions:
self.assertFalse(mocked_axes_obj.bar.called)
self.assertFalse(mocked_axes_obj.plot.called)
self.assertTrue(mocked_axes_obj.semilogx.called)
# check plot type:
ResidualWithDistance(residuals,
gsim,
imt,
plot_type='',
bin_width=0.1,
show=False)
self.assertTrue(mocked_axes_obj.plot.called)
# reset mock:
mocked_axes_obj.reset_mock()
def test_multivariate_llh_execution(self):
"""
Tests execution of multivariate llh - not correctness of values -
to be the same when created from sm_database.GroundMotionDatabase and
sm_table.GroundMotionTable
"""
multi_llh1 = res.Residuals(self.gsims, self.imts)
multi_llh1.get_residuals(self.database, component="Geometric")
self._check_residual_dictionary_correctness(multi_llh1.residuals)
multi_llh1.get_multivariate_loglikelihood_values()
def test_likelihood_execution(self):
"""
Tests basic execution of residuals - not correctness of values -
to be the same when created from sm_database.GroundMotionDatabase and
sm_table.GroundMotionTable
"""
lkh1 = res.Residuals(self.gsims, self.imts)
lkh1.get_residuals(self.database, component="Geometric")
self._check_residual_dictionary_correctness(lkh1.residuals)
lkh1.get_likelihood_values()
def test_multiple_metrics(self):
"""
Tests the execution running multiple metrics in one call
"""
residuals = res.Residuals(self.gsims, self.imts)
residuals.get_residuals(self.database, component="Geometric")
config = {}
for key in [
"Residuals", "Likelihood", "LLH", "MultivariateLLH", "EDR"
]:
_ = res.GSIM_MODEL_DATA_TESTS[key](residuals, config)
def test_multiple_metrics(self):
"""
Tests the execution running multiple metrics in one call
with sm_table.GroundMotionTable instead of
sm_database.GroundMotionDatabase
"""
# OLD CODE:
# residuals = res.Residuals(self.gsims, self.imts)
# residuals.get_residuals(self.database, component="Geometric")
# config = {}
# for key in ["Residuals", "Likelihood", "LLH",
# "MultivariateLLH", "EDR"]:
# _ = res.GSIM_MODEL_DATA_TESTS[key](residuals, config)
residuals = res.Residuals(self.gsims, self.imts)
residuals.get_residuals(self.dbtable, component="Geometric")
config = {}
for key in ["Residuals", "Likelihood", "LLH",
"MultivariateLLH", "EDR"]:
_ = res.GSIM_MODEL_DATA_TESTS[key](residuals, config)
def test_likelihood_density_distribution(self):
"""
Tests basic execution of Likelihood plot data.
Does not test correctness of values
"""
residuals = res.Residuals(self.gsims, self.imts)
residuals.get_residuals(self.database, component="Geometric")
additional_keys = ['median']
for gsim in self.gsims:
for imt in self.imts:
for as_json in (True, False):
bin_w1, bin_w2 = 0.1, 0.2
data1 = likelihood(residuals,
gsim,
imt,
bin_width=bin_w1,
as_json=as_json)
self._plot_data_check(data1, as_json, "LH (%s)" % imt,
"Frequency", additional_keys)
data2 = likelihood(residuals,
gsim,
imt,
bin_width=bin_w2,
as_json=as_json)
self._plot_data_check(data2, as_json, "LH (%s)" % imt,
"Frequency", additional_keys)
# assert histogram data is ok:
self._hist_data_check(residuals, gsim, imt, data1, bin_w1)
self._hist_data_check(residuals, gsim, imt, data2, bin_w2)
# assert bin width did its job:
for res_type in data1:
self.assertTrue(
len(data1[res_type]['x']) > len(data2[res_type]['x']))
