def plot_observed_recurrence(catalogue,
completeness,
dmag,
end_year=None,
figure_size=DEFAULT_SIZE,
filename=None,
filetype='png',
dpi=300):
"""
Plots the observed recurrence taking into account the completeness
"""
# Get completeness adjusted recurrence table
if isinstance(completeness, float):
# Unique completeness
completeness = np.array(
[[np.min(catalogue.data['year']), completeness]])
if not end_year:
end_year = catalogue.update_end_year()
catalogue.data["dtime"] = catalogue.get_decimal_time()
cent_mag, t_per, n_obs = get_completeness_counts(catalogue, completeness,
dmag)
obs_rates = n_obs / t_per
cum_obs_rates = np.array(
[np.sum(obs_rates[i:]) for i in range(len(obs_rates))])
plt.figure(figsize=figure_size)
plt.semilogy(cent_mag, obs_rates, 'bo', label="Incremental")
plt.semilogy(cent_mag, cum_obs_rates, 'rs', label="Cumulative")
plt.xlim([cent_mag[0] - 0.1, cent_mag[-1] + 0.1])
plt.xlabel('Magnitude', fontsize=16)
plt.ylabel('Annual Rate', fontsize=16)
plt.legend(fontsize=14)
plt.tick_params(labelsize=12)
_save_image(filename, filetype, dpi)
plt.show()
def plot_recurrence_model(input_model, catalogue, completeness, dmag,
figure_size=(10, 8), filename=None, filetype='png', dpi=300):
"""
Plot a calculated recurrence model over an observed catalogue, adjusted for
time-varying completeness
"""
if figure_size is None:
figure_size=(10, 8)
if dmag is None:
dmag = 0.1
annual_rates, cumulative_rates = _get_recurrence_model(input_model)
# Get observed annual recurrence
if not catalogue.end_year:
catalogue.update_end_year()
cent_mag, t_per, n_obs = get_completeness_counts(catalogue,
completeness,
dmag)
obs_rates = n_obs / t_per
cum_obs_rates = np.array([np.sum(obs_rates[i:])
for i in range(len(obs_rates))])
# Create plot
plt.figure(figsize=figure_size)
plt.semilogy(cent_mag, obs_rates, 'bo')
plt.semilogy(annual_rates[:, 0], annual_rates[:, 1], 'b-')
plt.semilogy(cent_mag, cum_obs_rates, 'rs')
plt.semilogy(annual_rates[:, 0], cumulative_rates, 'r-')
plt.grid(which='both')
plt.xlabel('Magnitude', fontsize='16')
plt.ylabel('Annual Rate', fontsize='16')
plt.legend(['Observed Incremental Rate',
'Model Incremental Rate',
'Observed Cumulative Rate',
'Model Cumulative Rate'], fontsize=14)
plt.tick_params(labelsize=12)
_save_image(filename, filetype, dpi)
def calculate(self, catalogue, config, completeness=None):
'''Calculates recurrence using the Weichert (1980) method'''
# Input checks
cmag, ctime, ref_mag, _, config = input_checks(catalogue, config,
completeness)
if not "dtime" in catalogue.data.keys() or not\
len(catalogue.data["dtime"]):
catalogue.data["dtime"] = catalogue.get_decimal_time()
if not catalogue.end_year:
catalogue.update_end_year()
if completeness is None:
start_year = float(np.min(catalogue.data["year"]))
completeness = np.column_stack([ctime, cmag])
# Apply Weichert preparation
cent_mag, t_per, n_obs = get_completeness_counts(
catalogue, completeness, config["magnitude_interval"])
# A few more Weichert checks
key_list = config.keys()
if (not 'bvalue' in key_list) or (not config['bvalue']):
config['bvalue'] = 1.0
if (not 'itstab' in key_list) or (not config['itstab']):
config['itstab'] = 1E-5
if (not 'maxiter' in key_list) or (not config['maxiter']):
config['maxiter'] = 1000
bval, sigma_b, rate, sigma_rate, aval, sigma_a = \
self.weichert_algorithm(t_per, cent_mag, n_obs, ref_mag,
config['bvalue'], config['itstab'], config['maxiter'])
if not config['reference_magnitude']:
rate = np.log10(aval)
sigma_rate = np.log10(aval + sigma_a) - np.log10(aval)
return bval, sigma_b, rate, sigma_rate
def calculate(self, catalogue, config, completeness=None):
'''Calculates recurrence using the Weichert (1980) method'''
# Input checks
cmag, ctime, ref_mag, _, config = input_checks(catalogue,
config,
completeness)
if not "dtime" in catalogue.data.keys() or not\
len(catalogue.data["dtime"]):
catalogue.data["dtime"] = catalogue.get_decimal_time()
if not catalogue.end_year:
catalogue.update_end_year()
if completeness is None:
start_year = float(np.min(catalogue.data["year"]))
completeness = np.column_stack([ctime, cmag])
# Apply Weichert preparation
cent_mag, t_per, n_obs = get_completeness_counts(
catalogue, completeness, config["magnitude_interval"])
# A few more Weichert checks
key_list = config.keys()
if (not 'bvalue' in key_list) or (not config['bvalue']):
config['bvalue'] = 1.0
if (not 'itstab' in key_list) or (not config['itstab']):
config['itstab'] = 1E-5
if (not 'maxiter' in key_list) or (not config['maxiter']):
config['maxiter'] = 1000
bval, sigma_b, rate, sigma_rate, aval, sigma_a = \
self.weichert_algorithm(t_per, cent_mag, n_obs, ref_mag,
config['bvalue'], config['itstab'], config['maxiter'])
if not config['reference_magnitude']:
rate = np.log10(aval)
sigma_rate = np.log10(aval + sigma_a) - np.log10(aval)
return bval, sigma_b, rate, sigma_rate
def plot_observed_recurrence(catalogue, completeness, dmag, end_year=None,
figure_size=DEFAULT_SIZE, filename=None, filetype='png', dpi=300):
"""
Plots the observed recurrence taking into account the completeness
"""
# Get completeness adjusted recurrence table
if isinstance(completeness, float):
# Unique completeness
completeness = np.array([[np.min(catalogue.data['year']),
completeness]])
if not end_year:
end_year = catalogue.update_end_year()
catalogue.data["dtime"] = catalogue.get_decimal_time()
cent_mag, t_per, n_obs = get_completeness_counts(catalogue,
completeness,
dmag)
obs_rates = n_obs / t_per
cum_obs_rates = np.array([np.sum(obs_rates[i:])
for i in range(len(obs_rates))])
plt.figure(figsize=figure_size)
plt.semilogy(cent_mag, obs_rates, 'bo', label="Incremental")
plt.semilogy(cent_mag, cum_obs_rates, 'rs', label="Cumulative")
plt.xlim([cent_mag[0] - 0.1, cent_mag[-1] + 0.1])
plt.xlabel('Magnitude', fontsize=16)
plt.ylabel('Annual Rate', fontsize=16)
plt.legend(fontsize=14)
plt.tick_params(labelsize=12)
_save_image(filename, filetype, dpi)
plt.show()
def test_completeness_counts(self):
"""
Assert that the correct counts are returned
"""
expected_data = np.array([[3.25, 20.0, 1281.0], [3.75, 20.0, 468.0],
[4.25, 35.0, 275.0], [4.75, 35.0, 116.0],
[5.25, 50.0, 55.0], [5.75, 50.0, 17.0],
[6.25, 80.0, 11.0], [6.75, 80.0, 2.0],
[7.25, 100.0, 1.0]])
cent_mag, t_per, n_obs = rec_utils.get_completeness_counts(
self.catalogue, self.completeness, 0.5)
np.testing.assert_array_almost_equal(cent_mag, expected_data[:, 0])
np.testing.assert_array_almost_equal(t_per, expected_data[:, 1])
np.testing.assert_array_almost_equal(n_obs, expected_data[:, 2])
self.assertEqual(self.catalogue.get_number_events(),
int(np.sum(n_obs)))
def test_completeness_counts(self):
"""
Assert that the correct counts are returned
"""
expected_data = np.array([[3.25, 20.0, 1281.0],
[3.75, 20.0, 468.0],
[4.25, 35.0, 275.0],
[4.75, 35.0, 116.0],
[5.25, 50.0, 55.0],
[5.75, 50.0, 17.0],
[6.25, 80.0, 11.0],
[6.75, 80.0, 2.0],
[7.25,100.0, 1.0]])
cent_mag, t_per, n_obs = rec_utils.get_completeness_counts(
self.catalogue, self.completeness, 0.5)
np.testing.assert_array_almost_equal(cent_mag, expected_data[:, 0])
np.testing.assert_array_almost_equal(t_per, expected_data[:, 1])
np.testing.assert_array_almost_equal(n_obs, expected_data[:, 2])
self.assertEqual(self.catalogue.get_number_events(),
int(np.sum(n_obs)))
