def test_weichert_full(self):
"""
Tests the Weichert function for the synthetic catalogue
"""
wchrt = Weichert()
bval, sigmab, rate, sigma_rate = wchrt.calculate(
self.catalogue, self.config, self.completeness)
self.assertAlmostEqual(bval, 0.890, 3)
self.assertAlmostEqual(sigmab, 0.015, 3)
self.assertAlmostEqual(rate, 100.1078, 4)
self.assertAlmostEqual(sigma_rate, 2.1218, 4)
def test_weichert_full(self):
"""
Tests the Weichert function for the synthetic catalogue
"""
wchrt = Weichert()
bval, sigmab, rate, sigma_rate = wchrt.calculate(self.catalogue,
self.config,
self.completeness)
self.assertAlmostEqual(bval, 0.890, 3)
self.assertAlmostEqual(sigmab, 0.015, 3)
self.assertAlmostEqual(rate, 100.1078, 4)
self.assertAlmostEqual(sigma_rate, 2.1218, 4)
def _compute_mfd(cat, compl_table, mwid):
"""
"""
cat = _add_defaults(cat)
weichert_config = {'magnitude_interval': mwid, 'reference_magnitude': 0.0}
weichert = Weichert()
bval_wei, sigmab, aval_wei, sigmaa = weichert.calculate(
cat, weichert_config, compl_table)
#
# info
print('bval: %.6f (sigma=%.3f)' % (bval_wei, sigmab))
print('aval: %.6f (sigma=%.3f)' % (aval_wei, sigmaa))
return aval_wei, bval_wei, sigmaa, sigmab
def completeness_analysis(fname,
idxs,
years,
mags,
binw,
ref_mag,
bgrlim,
src_id,
folder_out_figs,
rewrite=False):
tcat = _load_catalogue(fname)
tcat = _add_defaults(tcat)
tcat.data["dtime"] = tcat.get_decimal_time()
print('\nSOURCE:', src_id)
print('Catalogue contains {:d} events'.format(len(tcat.data['magnitude'])))
# See http://shorturl.at/adsvA
fname_disp = 'dispositions.npy'
perms = np.load(fname_disp)
mags = np.flipud(np.load('mags.npy'))
years = np.load('years.npy')
wei_conf = {'magnitude_interval': binw, 'reference_magnitude': 0.0}
weichert = Weichert()
rate = -1e10
save = []
mags = np.array(mags)
for prm in perms:
idx = prm.astype(int)
tmp = np.array([(y, m) for y, m in zip(years, mags[idx])])
ctab = clean_completeness(tmp)
try:
cent_mag, t_per, n_obs = get_completeness_counts(tcat, ctab, binw)
bval, sigb, aval, siga = weichert.calculate(tcat, wei_conf, ctab)
tmp_rate = 10**(-bval * ref_mag + aval)
if tmp_rate > rate and bval <= bgrlim[1] and bval >= bgrlim[0]:
rate = tmp_rate
save = [aval, bval, rate, ctab]
gwci = get_weichert_confidence_intervals
lcl, ucl, ex_rates, ex_rates_scaled = gwci(
cent_mag, n_obs, t_per, bval)
mmax = max(tcat.data['magnitude'])
wei = [
cent_mag, n_obs, binw, t_per, ex_rates_scaled, lcl, ucl,
mmax, aval, bval
]
except RuntimeWarning:
logging.debug('Skipping', ctab)
except UserWarning:
logging.debug('Skipping', ctab)
except:
logging.debug('Skipping', ctab)
if True:
fmt = 'Maximum annual rate for {:.1f}: {:.4f}'
print(fmt.format(ref_mag, save[2]))
fmt = 'GR a and b : {:.4f} {:.4f}'
print(fmt.format(save[0], save[1]))
print('Completeness:\n', save[3])
_weichert_plot(wei[0],
wei[1],
wei[2],
wei[3],
wei[4],
wei[5],
wei[6],
wei[7],
wei[8],
wei[9],
src_id=src_id)
# Saving figure
if folder_out_figs is not None:
ext = 'png'
fmt = 'fig_mfd_{:s}.{:s}'
figure_fname = os.path.join(folder_out_figs, fmt.format(src_id, ext))
plt.savefig(figure_fname, format=ext)
plt.close()
return save
def weichert_analysis(fname_input_pattern,
fname_config,
folder_out=None,
folder_out_figs=None,
skip=[],
binw=None,
plt_show=False):
"""
Computes GR parameters for a set of catalogues stored in a .csv file
:param fname_input_pattern:
It can be either a string (definining a pattern) or a list of
.csv files. The file names must have the source ID at the end. The
delimiter of the source ID on the left is `_`
:param fname_config:
The name of the .toml configuration file
:param folder_out:
The folder where to store the files with the counting of occurrences
:param folder_out_figs:
The folder where to store the figures
:param skip:
A list with the IDs of the sources to skip
"""
if folder_out is not None:
create_folder(folder_out)
if folder_out_figs is not None:
create_folder(folder_out_figs)
# Parsing config
if fname_config is not None:
model = toml.load(fname_config)
if binw is None and fname_config is not None:
binw = model['bin_width']
else:
binw = 0.1
if isinstance(fname_input_pattern, str):
fname_list = [f for f in glob(fname_input_pattern)]
else:
fname_list = fname_input_pattern
# Processing files
for fname in sorted(fname_list):
print(fname)
# Get source ID
src_id = _get_src_id(fname)
if src_id in skip:
print(" skipping")
continue
if 'sources' in model:
if (src_id in model['sources']
and 'mmax' in model['sources'][src_id]):
mmax = model['sources'][src_id]['mmax']
else:
mmax = model['default']['mmax']
if (src_id in model['sources']
and 'completeness_table' in model['sources'][src_id]):
key_tmp = 'completeness_table'
ctab = numpy.array(model['sources'][src_id][key_tmp])
print('Using source specific completeness')
else:
ctab = numpy.array(model['default']['completeness_table'])
else:
mmax = model['default']['mmax']
ctab = numpy.array(model['default']['completeness_table'])
# Processing catalogue
tcat = _load_catalogue(fname)
if tcat is None or len(tcat.data['magnitude']) < 2:
print(' Source {:s} has less than 2 eqks'.format(src_id))
continue
tcat.data["dtime"] = tcat.get_decimal_time()
cent_mag, t_per, n_obs = get_completeness_counts(tcat, ctab, binw)
if folder_out is not None:
df = pd.DataFrame()
df['mag'] = cent_mag
df['deltaT'] = t_per
df['nobs'] = n_obs
fmt = 'occ_count_zone_{:s}'
fout = os.path.join(folder_out, fmt.format(src_id))
df.to_csv(fout, index=False)
# Computing GR a and b
tcat = _add_defaults(tcat)
weichert_config = {
'magnitude_interval': binw,
'reference_magnitude': 0.0
}
weichert = Weichert()
bval_wei, sigmab, aval_wei, sigmaa = weichert.calculate(
tcat, weichert_config, ctab)
# Computing confidence intervals
gwci = get_weichert_confidence_intervals
lcl, ucl, ex_rates, ex_rates_scaled = gwci(cent_mag, n_obs, t_per,
bval_wei)
if 'sources' not in model:
model['sources'] = {}
if src_id not in model['sources']:
model['sources'][src_id] = {}
tmp = "{:.5e}".format(aval_wei)
model['sources'][src_id]['agr_weichert'] = float(tmp)
tmp = "{:.3f}".format(bval_wei)
model['sources'][src_id]['bgr_weichert'] = float(tmp)
_ = plt.figure()
ax = plt.gca()
plt.plot(cent_mag, n_obs / t_per, 'o', markerfacecolor='none')
plt.plot(cent_mag - binw / 2,
ex_rates_scaled,
's',
markerfacecolor='none',
color='red')
plt.plot(cent_mag - binw / 2, lcl, '--', color='darkgrey')
plt.plot(cent_mag - binw / 2, ucl, '--', color='darkgrey')
xmag = numpy.arange(cent_mag[0] - binw / 2, mmax - 0.01 * binw,
binw / 2)
exra = (10.0**(aval_wei - bval_wei * xmag) -
10.0**(aval_wei - bval_wei * mmax))
plt.plot(xmag, exra, '--', lw=3, color='green')
plt.yscale('log')
plt.xlabel('Magnitude')
plt.ylabel('Annual rate of exceedance')
plt.text(0.75,
0.95,
'b_GR = {:.2f}'.format(bval_wei),
transform=ax.transAxes)
plt.grid(which='major', color='grey')
plt.grid(which='minor', linestyle='--', color='lightgrey')
plt.title(src_id)
if plt_show:
plt.show()
# Saving figures
if folder_out_figs is not None:
ext = 'png'
fmt = 'fig_mfd_{:s}.{:s}'
figure_fname = os.path.join(folder_out_figs,
fmt.format(src_id, ext))
plt.savefig(figure_fname, format=ext)
plt.close()
# Saving results into the config file
if fname_config is not None:
with open(fname_config, 'w') as f:
f.write(toml.dumps(model))
print('Updated {:s}'.format(fname_config))