# -*- coding: utf-8 -*-
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# D. Monelli.
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"""
Simple plots for the recurrence model
"""
import numpy as np
import matplotlib.pyplot as plt
from openquake.hmtk.plotting.seismicity.catalogue_plots import _save_image
from openquake.hmtk.seismicity.occurrence.utils import get_completeness_counts
from openquake.hazardlib.mfd.truncated_gr import TruncatedGRMFD
from openquake.hazardlib.mfd.evenly_discretized import EvenlyDiscretizedMFD
from openquake.hazardlib.mfd.youngs_coppersmith_1985 import\
YoungsCoppersmith1985MFD
def _get_recurrence_model(input_model):
"""
Returns the annual and cumulative recurrence rates predicted by the
recurrence model
"""
if not isinstance(input_model, (TruncatedGRMFD,
EvenlyDiscretizedMFD,
YoungsCoppersmith1985MFD)):
raise ValueError('Recurrence model not recognised')
# Get model annual occurrence rates
annual_rates = input_model.get_annual_occurrence_rates()
annual_rates = np.array([[val[0], val[1]] for val in annual_rates])
# Get cumulative rates
cumulative_rates = np.array([np.sum(annual_rates[iloc:, 1])
for iloc in range(0, len(annual_rates), 1)])
return annual_rates, cumulative_rates
def _check_completeness_table(completeness, catalogue):
"""
Generates the completeness table according to different instances
"""
if isinstance(completeness, np.ndarray) and np.shape(completeness)[1] == 2:
return completeness
elif isinstance(completeness, float):
return np.array([[float(np.min(catalogue.data['year'])),
completeness]])
elif completeness is None:
return np.array([[float(np.min(catalogue.data['year'])),
np.min(catalogue.data['magnitude'])]])
else:
raise ValueError('Completeness representation not recognised')
[docs]def plot_recurrence_model(
input_model, catalogue, completeness, dmag=0.1, filename=None,
figure_size=(8, 6), filetype='png', dpi=300, ax=None):
"""
Plot a calculated recurrence model over an observed catalogue, adjusted for
time-varying completeness
"""
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))])
if ax is None:
fig, ax = plt.subplots(figsize=figure_size)
else:
fig = ax.get_figure()
ax.semilogy(cent_mag, obs_rates, 'bo')
ax.semilogy(annual_rates[:, 0], annual_rates[:, 1], 'b-')
ax.semilogy(cent_mag, cum_obs_rates, 'rs')
ax.semilogy(annual_rates[:, 0], cumulative_rates, 'r-')
ax.grid(which='both')
ax.set_xlabel('Magnitude')
ax.set_ylabel('Annual Rate')
ax.legend(['Observed Incremental Rate',
'Model Incremental Rate',
'Observed Cumulative Rate',
'Model Cumulative Rate'])
ax.tick_params(labelsize=12)
_save_image(fig, filename, filetype, dpi)
[docs]def plot_trunc_gr_model(
aval, bval, min_mag, max_mag, dmag,
catalogue=None, completeness=None, filename=None,
figure_size=(8, 6), filetype='png', dpi=300, ax=None):
"""
Plots a Gutenberg-Richter model
"""
input_model = TruncatedGRMFD(min_mag, max_mag, dmag, aval, bval)
if not catalogue:
# Plot only the modelled recurrence
annual_rates, cumulative_rates = _get_recurrence_model(input_model)
if ax is None:
fig, ax = plt.subplots(figsize=figure_size)
else:
fig = ax.get_figure()
ax.semilogy(annual_rates[:, 0], annual_rates[:, 1], 'b-')
ax.semilogy(annual_rates[:, 0], cumulative_rates, 'r-')
ax.xlabel('Magnitude')
ax.set_ylabel('Annual Rate')
ax.set_legend(['Incremental Rate', 'Cumulative Rate'])
_save_image(fig, filename, filetype, dpi)
else:
completeness = _check_completeness_table(completeness, catalogue)
plot_recurrence_model(
input_model, catalogue, completeness, dmag, filename=filename,
figure_size=figure_size, filetype=filetype, dpi=dpi, ax=ax)