def plot_sds(freq,
result,
ax=None,
fname=None,
annotate=False,
seismic_moment_method=None,
seismic_moment_options={}):
"""Plot source displacement spectrum and fitted source model"""
freq = np.array(freq)
omM = np.array(result['sds'], dtype=np.float)
if all(np.isnan(omM)):
return
fig = None
obs = ('M0', 'fc', 'n', 'gamma')
smo = seismic_moment_options
def _get(k):
return smo.get(k) or result.get(k)
smo = {k: _get(k) for k in obs if _get(k) is not None}
M0 = smo.get('M0')
fc = smo.get('fc')
if ax is None:
fig = plt.figure()
ax = fig.add_subplot(111)
if seismic_moment_method == 'mean':
ax.loglog(freq, omM, 'o-', color='gray', mec='gray')
ax.loglog(freq[freq < fc], omM[freq < fc], 'o-k')
elif seismic_moment_method in ('fit', 'robust_fit'):
ax.loglog(freq, omM, 'ok')
if M0 and fc:
f = np.linspace(freq[0] / 1.5, freq[-1] * 1.5, 100)
omM2 = source_model(f, **smo)
ax.loglog(f, omM2, '-k')
else:
ax.loglog(freq, omM, 'o-k')
if M0:
ax.axhline(M0, ls='--', color='k')
labels = OrderedDict(
(('M0', r'M$_0$=%.1e Nm'), ('fc', r'f$_{\rm{c}}$=%.1f Hz'),
('n', 'n=%.1f'), ('gamma', r'$\gamma$=%.2f'), ('fit_error',
'err=%.2f')))
labels = [
labels[key] % np.float32(result[key]) for key in labels
if key in result
]
if len(labels) > 0 and annotate:
va = annotate if annotate in ('top', 'bottom') else 'top'
ypos = 1 if annotate == 'top' else 0
ax.annotate('\n'.join(labels), (1, ypos), (-5, 5 - 10 * ypos),
'axes fraction',
'offset points',
ha='right',
va=va,
size='x-small')
if fig and fname:
_savefig(fig, fname=fname)
def plot_sds(freq,
result,
ax=None,
annotate=False,
va='bottom',
seismic_moment_method=None,
seismic_moment_options={},
cmap='viridis_r',
vmin=None,
vmax=None,
max_nobs=None,
**kwargs):
"""Plot source displacement spectrum and fitted source model"""
kw = {
's': 4 * MS**2,
'marker': 'o',
'zorder': 10, #'linewidth': 0.5,
'c': 'k'
}
if 'R' in result and cmap is not None:
Rvals = np.array(list(result['R'].values()), dtype='float')
nobs = np.sum(~np.isnan(Rvals), axis=0)
if max_nobs is None:
max_nobs = np.max(nobs)
cmap = plt.get_cmap(cmap, max_nobs)
if vmax is None:
vmax = max_nobs + 0.5
if vmin is None:
vmin = 0.5
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
kw.update({'c': nobs, 'norm': norm, 'cmap': cmap})
freq = np.array(freq)
omM = np.array(result['sds'], dtype=np.float)
if all(np.isnan(omM)):
return
fig = None
obs = ('M0', 'fc', 'n', 'gamma')
smo = seismic_moment_options
def _get(k):
return smo.get(k) or result.get(k)
smo = {k: _get(k) for k in obs if _get(k) is not None}
M0 = smo.get('M0')
fc = smo.get('fc')
if ax is None:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xscale('log')
ax.set_yscale('log')
if seismic_moment_method == 'mean':
ax.plot(freq, omM, 'o-', color='gray', mec='gray')
ax.plot(freq[freq < fc], omM[freq < fc], 'o-k')
kw['c'] = nobs[freq < fc]
kw['linewidth'] = 0.5
sc = ax.scatter(freq[freq < fc], freq[freq < fc], **kw)
elif seismic_moment_method in ('fit', 'robust_fit'):
sc = ax.scatter(freq, omM, **kw)
if M0 and fc:
f = np.linspace(freq[0] / 1.5, freq[-1] * 1.5, 100)
omM2 = source_model(f, **smo)
ax.plot(f, omM2, '-k')
else:
kw['linewidth'] = 0.5
sc = ax.scatter(freq, omM, **kw)
if M0:
ax.axhline(M0, ls='--', color='k')
labels = OrderedDict(
(('M0', r'M$_0$=%.1e Nm'), ('fc', r'f$_{\rm{c}}$=%.1f Hz'),
('n', 'n=%.1f'), ('gamma', r'$\gamma$=%.2f'), ('fit_error',
'err=%.2f')))
labels = [
labels[key] % np.float32(result[key]) for key in labels
if key in result
]
if len(labels) > 0 and annotate:
ypos = 1 if va == 'top' else 0
ax.annotate('\n'.join(labels), (1, ypos), (-5, 5 - 10 * ypos),
'axes fraction',
'offset points',
ha='right',
va=va,
size='x-small')
if fig:
_savefig(fig, **kwargs)
return sc