def plot_mags(result,
fname=None,
title=None,
xlim=None,
ylim=None,
figsize=None):
"""Plot Qopen moment magnitudes versus catalogue magnitudes"""
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
temp = [(r['Mcat'], r['Mw']) for r in result['events'].values()
if r.get('Mcat') is not None and r.get('Mw') is not None]
if len(temp) == 0:
return
Mcat, Mw = zip(*temp)
ax.plot(Mcat, Mw, 'ok', ms=MS)
if xlim is not None:
mmin, mmax = xlim
else:
mmin, mmax = np.min(Mcat), np.max(Mcat)
m = np.linspace(mmin, mmax, 100)
if len(Mw) > 2:
a, b = linear_fit(Mw, Mcat)
ax.plot(m, a * m + b, '-m', label='%.2fM %+.2f' % (a, b))
if len(Mw) > 3:
_, b2 = linear_fit(Mw, Mcat, m=1)
ax.plot(m, m + b2, '--m', label='M %+.2f' % (b2, ))
if len(Mw) > 2:
ax.legend(loc='lower right')
if xlim:
ax.set_xlim(xlim)
ax.set_xlabel('M from catalog')
ax.set_ylabel('Mw from inversion')
_savefig(fig, fname=fname, title=title)
def plot_mags(result, xlim=None, ylim=None, plot_only_ids=None, **kwargs):
"""Plot Qopen moment magnitudes versus catalogue magnitudes"""
fig = plt.figure()
ax = fig.add_subplot(111)
temp = [(r['Mcat'], r['Mw']) for id_, r in result['events'].items()
if r.get('Mcat') is not None and r.get('Mw') is not None and (
plot_only_ids is None or id_ in plot_only_ids)]
if len(temp) == 0:
return
Mcat, Mw = zip(*temp)
ax.plot(Mcat, Mw, 'ok', ms=MS)
if xlim is not None:
mmin, mmax = xlim
else:
mmin, mmax = np.min(Mcat), np.max(Mcat)
m = np.linspace(mmin, mmax, 100)
if len(Mw) > 3:
a, b = linear_fit(Mw, Mcat)
ax.plot(m, a * m + b, '-m', label='%.2fM %+.2f' % (a, b))
# if len(Mw) > 3:
# _, b2 = linear_fit(Mw, Mcat, m=1)
# ax.plot(m, m + b2, '--m', label='M %+.2f' % (b2,))
if len(Mw) > 3:
ax.legend(loc='lower right')
if xlim:
ax.set_xlim(xlim)
ax.set_xlabel('M from catalog')
ax.set_ylabel('Mw from inversion')
_savefig(fig, **kwargs)
def plot_mean():
import seaborn as sns
sns.set_style('ticks')
d = [[] for _ in range(9)]
for i in range(len(FREQ)):
lons1, lats1, data1 = collect_data('Qi', i)
lons2, lats2, data2 = collect_data('Qsc', i)
data3 = Qtot(np.array(data1), np.array(data2))
d[0].append(np.mean(data1))
d[1].append(np.mean(data2))
d[2].append(np.mean(data3))
ind = np.array(lons1) < -103
d[3].append(np.mean(np.array(data1)[ind]))
d[4].append(np.mean(np.array(data1)[~ind]))
d[5].append(np.mean(np.array(data2)[ind]))
d[6].append(np.mean(np.array(data2)[~ind]))
d[7].append(np.mean(np.array(data3)[ind]))
d[8].append(np.mean(np.array(data3)[~ind]))
fs = 100 / 25.4
fig = plt.figure(figsize=(fs, 0.9 * fs))
ax = fig.add_subplot(111)
d = [10**np.array(dx) for dx in d]
c1, c2 = '#e41a1c #275982'.split()
kw = {'lw': 2, 'dash_capstyle': 'round'}
ax.loglog(FREQ, d[2], color='#4E4E4E', label='total', **kw)
kw['dashes'] = (3, 2)
ax.loglog(FREQ, d[0], '--', color='#4E4E4E', label='intrinsic', **kw)
ax.loglog(FREQ, d[3], '--', color='#CF0000', alpha=0.5, **kw)
ax.loglog(FREQ, d[4], '--', color='#006AFF', alpha=0.5, **kw)
kw['dashes'] = (2, 2, 0.1, 2)
ax.loglog(FREQ, d[1], '-.', color='#4E4E4E', label='scattering', **kw)
ax.loglog(FREQ, d[5], '-.', color='#CF0000', alpha=0.5, **kw)
ax.loglog(FREQ, d[6], '-.', color='#006AFF', alpha=0.5, **kw)
kw.pop('dashes')
ax.loglog(FREQ, d[7], color='#CF0000', label='west', alpha=0.5, **kw)
ax.loglog(FREQ, d[8], color='#006AFF', label='east', alpha=0.5, **kw)
ax.legend(fontsize='small',
labelspacing=0.3,
handlelength=1.8,
bbox_to_anchor=(1.1, 0.9))
ax.set_xlim(1.5 / 1.2, 17 * 1.2)
ax.set_ylim(4e-4, 2e-2)
ax.set_xticks([1.5, 3, 6, 12])
ax.set_xticklabels([1.5, 3., 6., 12.])
ax.set_xticks([2.1, 4.2, 8.5, 17.], minor=True)
ax.set_xlabel('frequency (Hz)')
ax.set_ylabel('attenuation Q$^{-1}$')
slope, intercept = linear_fit(np.log10(d[2]), np.log10(FREQ))
l = r'$Q_{\mathrm{tot}}=%.0f\times f^{%.1f}$' % (10**(-intercept), -slope)
ax.annotate(l, (1.7, 1.3e-2))
sns.despine()
fig.tight_layout()
fig.savefig(FIGOUT2 + 'Qmean.pdf', bbox_inches='tight')
plt.close(fig)
def collect_results(correct=True, events=False):
with open(CONF_FNAME) as f:
conf = json.load(f, cls=ConfigJSONDecoder)
freq = get_freqs(**conf['freqs'])
fbands = list(freq.values())
freq = list(freq.keys())
fa = np.asarray(freq)
print('copy results from shove')
r = {'freq': freq, 'events': {}, 'config': conf}
if GENERATE_TEST_FILE:
nobs_station = defaultdict(int)
with JsonShove(SHOVE_FNAME, compress=True) as sh:
msg = '%d in shove / %d events processed'
print(msg % (len(sh), len(glob(EVENT_GLOB))))
for evid in sh:
if sh[evid] is None:
continue
if GENERATE_TEST_FILE:
stations = sh[evid]['R'].keys()
if min(nobs_station[s] for s in stations) >= MAX_NOBS:
continue
for s in stations:
nobs_station[s] = nobs_station[s] + 1
r['events'][evid] = sh[evid]
if GENERATE_TEST_FILE:
i = 6
for evid in r['events']:
for sta in r['events'][evid]['R']:
r['events'][evid]['R'][sta] = \
r['events'][evid]['R'][sta][i:i + 1]
r['events'][evid] = {
'W': r['events'][evid]['W'][i:i + 1],
'R': r['events'][evid]['R']
}
r['freq'] = r['freq'][i:i + 1]
del r['config']
with open(TMP + 'usarray_dataset.json', 'wb') as f:
json.dump(r, f)
return
if IGNORE_STATIONS is not None:
print('remove stations %s' % IGNORE_STATIONS)
sh = r['events']
for evid in sh.keys():
for station in sh[evid]['R'].keys()[:]:
if station in IGNORE_STATIONS:
sh[evid]['R'].pop(station)
if len(sh[evid]['R']) == 0:
sh.pop(evid)
Nres = len(r['events'])
print("number of events with results: %d" % Nres)
rorig = r
if correct:
rorig = deepcopy(r)
print('correct sensitivities')
# r = align_site_responses(r, station=BOREHOLE_STATIONS, response=0.25)
r = align_site_responses(r)
if events:
print('collect magnitudes')
eventresults2json(r)
print('collect results')
data = defaultdict(lambda: defaultdict(lambda: [[] for _ in freq]))
data['v0'] = defaultdict(list)
results = defaultdict(lambda: defaultdict(dict))
sh = r['events']
sh2 = rorig['events']
for evid in sh:
for station in sh2[evid]['R']:
v0 = sh2[evid]['v0']
data['v0'][station].append(v0)
for i in range(len(sh2[evid]['R'][station])):
R1 = sh[evid]['R'][station][i]
R2 = sh2[evid]['R'][station][i]
b = sh[evid]['b'][i]
g0 = sh[evid]['g0'][i]
if R2 is not None:
if R1 is not None:
data['R'][station][i].append(R1)
Qi = calc_dependent('Qi', b, freq[i], v0)
data['Qi'][station][i].append(Qi)
Qsc = calc_dependent('Qsc', g0, freq[i], v0)
data['Qsc'][station][i].append(Qsc)
print('calc mean, err and slope')
for what in ('v0', 'R', 'Qsc', 'Qi'):
for station in data[what]:
d = data[what][station]
if what == 'v0':
results[what][station] = np.mean(d)
continue
mean, err, nobs = [], [], []
for i in range(len(freq)):
da = np.asarray(d[i], dtype=np.float)
m, e1, e2 = gerr(da)
e = np.log10(m) - np.log10(e1)
m = np.log10(m)
mean.append(None if not np.isscalar(m) or np.isnan(m) else m)
err.append(None if not np.isscalar(e) or np.isnan(e) else e)
nobs.append(np.count_nonzero(np.logical_not(np.isnan(da))))
results[what][station]['mean'] = mean
results[what][station]['error'] = err
if what == 'Qsc':
results['nobs'][station] = nobs
mean = np.asarray(mean, dtype=np.float)
ind = np.logical_not(np.isnan(mean))
if what == 'R':
continue
elif np.count_nonzero(ind) < 3:
slope, intercept = None, None
else:
slope, intercept = linear_fit(mean[ind], np.log10(fa[ind]))
results[what][station]['slope'] = slope
results[what][station]['intercept'] = intercept
print('convert to OrderedDict and save json')
for key in results:
if key not in ('v0', 'nobs'):
for sta in results[key]:
results[key][sta] = sort_dict(results[key][sta], order=ORDER)
results[key] = OrderedDict(sorted(results[key].items()))
results['freqs'] = freq
results['freq_bands'] = fbands
results['stations'] = collect_stations(False) # get coordinates
results['title'] = TITLE
results['description'] = DESC
results['author'] = 'Tom Eulenfeld'
# results['copyright'] = COPYR
results['license'] = LICENSE
results = sort_dict(results, order=ORDER)
with open(RESULTSJSON, 'w') as f:
f.write(to_json(results, nl_after_str=True))