# -*- coding: utf-8 -*-
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#
# Copyright (C) 2010-2023 GEM Foundation, G. Weatherill, M. Pagani,
# D. Monelli.
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# The Hazard Modeller's Toolkit is free software: you can redistribute
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# Earthquake Model).
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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,
)
