# ------------------- The OpenQuake Model Building Toolkit --------------------
# Copyright (C) 2022 GEM Foundation
# _______ _______ __ __ _______ _______ ___ _
# | || | | |_| || _ || || | | |
# | _ || _ | ____ | || |_| ||_ _|| |_| |
# | | | || | | ||____|| || | | | | _|
# | |_| || |_| | | || _ | | | | |_
# | || | | ||_|| || |_| | | | | _ |
# |_______||____||_| |_| |_||_______| |___| |___| |_|
#
# This program is free software: you can redistribute it and/or modify it under
# the terms of the GNU Affero General Public License as published by the Free
# Software Foundation, either version 3 of the License, or (at your option) any
# later version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more
# details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
# -----------------------------------------------------------------------------
# vim: tabstop=4 shiftwidth=4 softtabstop=4
# coding: utf-8
import os
import unittest
import tempfile
import numpy as np
import pandas as pd
from openquake.wkf.seismicity.mmax_epri import (
get_mmax_pmf, get_composite_likelihood)
DATA_PATH = os.path.join(os.path.dirname(__file__), 'data')
[docs]
class MmaxEPRITest(unittest.TestCase):
""" Tests the calculation of the EPRI mmax distribution """
[docs]
def test_01(self):
"""
Tests the PMF described in Figure 5.2.1-1 of the CEUS-SSC report. See
page 432. Since I did not manage to find it in the report, I assume a
value of bGR = 1.0.
"""
n_gt_n0 = 2.0
mag0 = 4.5
mmaxobs = 5.3
pri_mean = 6.4
pri_std = 0.85
bgr = 1.0
wdt = 0.5
bins = np.arange(5.25, 9.5, wdt)
# Expected results manually digitized
fname = os.path.join(DATA_PATH, 'ceus_fig_5.2.1-1.csv')
expected = np.loadtxt(fname, delimiter=',')
wei, mag = get_mmax_pmf(
pri_mean, pri_std, bins, mmaxobs=mmaxobs, mag0=mag0,
n_gt_n0=n_gt_n0, bgr=bgr)
np.testing.assert_almost_equal(wei, expected[:, 1], decimal=2)
[docs]
class CompositeLikelihoodTest(unittest.TestCase):
""" Tests the calculation of a composite likelihood"""
[docs]
def test_clikl(self):
fname_cat = os.path.join(DATA_PATH, 'ctlg_composite_prior.csv')
dfc = pd.read_csv(fname_cat)
ccomp = [[2000.0, 3.9], [1950, 5.5]]
bgr = 1.0
wdt = 0.5
mag0 = np.ceil(np.min(dfc.magnitude)/0.1)*0.1
bins = np.arange(mag0, 9.5, wdt)
mupp, lkl = get_composite_likelihood(dfc, ccomp, bgr)
pri_mean = 6.4
pri_std = 0.85
wei, mag = get_mmax_pmf(pri_mean, pri_std, bins, mupp=mupp,
likelihood=lkl,
fig_name=os.path.join(
tempfile.mkdtemp(), 'mmax.png'))