def plot_energies(energies,
bulk_window=None, coda_window=None, downsample_to=None,
xlim_lin=None, xlim_log=None,
figsize=None, **kwargs):
gs = gridspec.GridSpec(2 * len(energies), 2)
gs.update(wspace=0.05)
fig = plt.figure(figsize=figsize)
sax1 = sax3 = None
for i, tr in enumerate(energies):
pair = get_pair(tr)
otime = tr.stats.origintime
if downsample_to is None:
d = 1
else:
d = tr.stats.sampling_rate // downsample_to
ts = np.arange(len(tr)) * tr.stats.delta
ts = ts - (otime - tr.stats.starttime)
c = 'k'
ax2 = plt.subplot(gs[2 * i + 1, 0], sharex=sax1, sharey=sax1)
ax1 = plt.subplot(gs[2 * i, 0], sharex=ax2)
ax3 = plt.subplot(gs[2 * i:2 * i + 2, 1], sharex=sax3, sharey=sax3)
ax1.annotate('%s' % pair[1], (1, 0.5), (-10, 0), 'axes fraction',
'offset points', size='small', ha='right', va='center')
ax3.annotate('%s' % pair[0], (0, 1), (10, -5), 'axes fraction',
'offset points', size='small', ha='left', va='top')
ax1.plot(ts[::d], tr.data[::d], color=c)
ax2.semilogy(ts[::d], tr.data[::d], color=c)
ax3.loglog(ts[::d], tr.data[::d], color=c)
for ax in (ax1, ax2, ax3):
plt.setp(ax.get_xticklabels(), visible=False)
ax.set_yticklabels([])
if 'ponset' in tr.stats:
tponset = tr.stats.ponset - otime
ax.axvline(tponset, color='green', alpha=0.5)
if 'sonset' in tr.stats:
tsonset = tr.stats.sonset - otime
ax.axvline(tsonset, color='b', alpha=0.5)
for ax in (ax2, ax3):
if bulk_window and coda_window:
c = ('b', 'k')
wins = (bulk_window[pair], coda_window[pair])
for i, win in enumerate(wins):
ax.axvspan(win[0] - otime, win[1] - otime,
0.05, 0.08, color=c[i], alpha=0.5)
if sax1 is None:
sax1 = ax2
sax3 = ax3
if xlim_lin:
ax1.set_xlim(xlim_lin)
if xlim_log:
ax3.set_xlim(xlim_log)
loglocator = mpl.ticker.LogLocator(base=100)
ax2.yaxis.set_major_locator(loglocator)
ax3.yaxis.set_major_locator(loglocator)
ax2.yaxis.set_minor_locator(mpl.ticker.NullLocator())
ax3.yaxis.set_minor_locator(mpl.ticker.NullLocator())
plt.setp(ax2.get_xticklabels(), visible=True)
plt.setp(ax3.get_xticklabels(), visible=True)
_savefig(fig, **kwargs)
def plot_energies(energies,
bulk_window=None, coda_window=None, downsample_to=None,
xlim_lin=None, xlim_log=None,
figsize=None, **kwargs):
gs = gridspec.GridSpec(2 * len(energies), 2)
gs.update(wspace=0.05)
fig = plt.figure(figsize=figsize)
sax1 = sax3 = None
for i, tr in enumerate(energies):
pair = get_pair(tr)
otime = tr.stats.origintime
if downsample_to is None:
d = 1
else:
d = tr.stats.sampling_rate // downsample_to
ts = np.arange(len(tr)) * tr.stats.delta
ts = ts - (otime - tr.stats.starttime)
c = 'k'
ax2 = plt.subplot(gs[2 * i + 1, 0], sharex=sax1, sharey=sax1)
ax1 = plt.subplot(gs[2 * i, 0], sharex=ax2)
ax3 = plt.subplot(gs[2 * i:2 * i + 2, 1], sharex=sax3, sharey=sax3)
ax1.annotate('%s' % pair[1], (1, 0.5), (-10, 0), 'axes fraction',
'offset points', size='small', ha='right', va='center')
ax3.annotate('%s' % pair[0], (0, 1), (10, -5), 'axes fraction',
'offset points', size='small', ha='left', va='top')
ax1.plot(ts[::d], tr.data[::d], color=c)
ax2.semilogy(ts[::d], tr.data[::d], color=c)
ax3.loglog(ts[::d], tr.data[::d], color=c)
for ax in (ax1, ax2, ax3):
plt.setp(ax.get_xticklabels(), visible=False)
ax.set_yticklabels([])
if 'ponset' in tr.stats:
tponset = tr.stats.ponset - otime
ax.axvline(tponset, color='green', alpha=0.5)
if 'sonset' in tr.stats:
tsonset = tr.stats.sonset - otime
ax.axvline(tsonset, color='b', alpha=0.5)
for ax in (ax2, ax3):
if bulk_window and coda_window:
c = ('b', 'k')
wins = (bulk_window[pair], coda_window[pair])
for i, win in enumerate(wins):
ax.axvspan(win[0] - otime, win[1] - otime,
0.05, 0.08, color=c[i], alpha=0.5)
if sax1 is None:
sax1 = ax2
sax3 = ax3
if xlim_lin:
ax1.set_xlim(xlim_lin)
if xlim_log:
ax3.set_xlim(xlim_log)
loglocator = mpl.ticker.LogLocator(base=100)
ax2.yaxis.set_major_locator(loglocator)
ax3.yaxis.set_major_locator(loglocator)
ax2.yaxis.set_minor_locator(mpl.ticker.NullLocator())
ax3.yaxis.set_minor_locator(mpl.ticker.NullLocator())
plt.setp(ax2.get_xticklabels(), visible=True)
plt.setp(ax3.get_xticklabels(), visible=True)
_savefig(fig, **kwargs)
def plot_fits(energies, g0, b, W, R, v0, info, G_func,
smooth=None, smooth_window='bartlett',
xlim=None, ylim=None, fname=None, title=None, figsize=None,
**kwargs):
tcoda, tbulk, Ecoda, Ebulk, Gcoda, Gbulk = info
N = len(energies)
n = int(np.ceil(np.sqrt(N)))
fig = plt.figure(figsize=figsize)
gs = gridspec.GridSpec(n, n)
share = None
if b is None:
b = 0
tmaxs = []
ymaxs = []
ymins = []
c1 = 'mediumblue'
c2 = 'darkred'
c1l = '#8181CD' # 37.25% #'#8686CD'
c2l = '#8B6969' # 25% #'#8B6161'
for i, energy in enumerate(energies):
evid, station = get_pair(energy)
ax = plt.subplot(gs[i // n, i % n], sharex=share, sharey=share)
plot = ax.semilogy
def get_Emod(G, t):
#return FS * R[station] * W[evid] * G * np.exp(-b * t)
return R[station] * W[evid] * G * np.exp(-b * t)
st = energy.stats
r = st.distance
#ax.axvline(st.starttime - st.origintime + r / v0, ls = '--', c='gray')
#ax.axvline(r / v0, ls='--', c='gray')
t = _get_times(energy) + r / v0 - (st.sonset - st.origintime)
if smooth:
plot(t, energy.data_unsmoothed, color='0.7')
plot(t, energy.data, color=c1l)
G_of_t = lambda tt: G_func(r, tt, v0, g0)
G_ = smooth_func(G_of_t, t, smooth, window=smooth_window)
Emod = get_Emod(G_, t)
index = np.argwhere(Emod<1e-30)[-1]
Emod[index] = 1e-30
plot(t, Emod, color=c2l)
plot(tcoda[i], Ecoda[i], color=c1)
Emodcoda = get_Emod(Gcoda[i], tcoda[i])
plot(tcoda[i], Emodcoda, color=c2)
if tbulk and len(tbulk) > 0:
plot(tbulk[i], Ebulk[i], 'o', color=c1, mec=c1, ms=MS)
Emodbulk = get_Emod(Gbulk[i], tbulk[i])
plot(tbulk[i], Emodbulk, 'o', ms=MS - 1,
color=c2, mec=c2)
l = '%s\n%s' % (evid, station)
l = l + '\nr=%dkm' % (r / 1000)
ax.annotate(l, (1, 1), (-5, -5), 'axes fraction',
'offset points', ha='right', va='top', size='x-small')
_set_gridlabels(ax, i, n, n, N, xlabel='time (s)',
ylabel=r'E (Jm$^{-3}$Hz$^{-1}$)')
tmaxs.append(t[-1])
ymaxs.append(max(np.max(Emod), np.max(energy.data)))
ymins.append(min(np.min(Emodcoda), np.max(Ecoda[i])))
if share is None:
share = ax
# if True:
# save = {'t': t, 'data': energy.stats.orig_data,
# 'Eobs': energy.data, 'Emod': Emod,
# 'tcoda': tcoda[i], 'Eobscoda': Ecoda[i],
# 'Emodcoda': Emodcoda}
# if tbulk:
# save.update({'tbulk': tbulk[i], 'Eobsbulk': Ebulk[i],
# 'Emodbulk': Emodbulk})
# np.savez(fname.replace('png', '') + station + '.npz', **save)
ax.locator_params(axis='x', nbins=5, prune='upper')
loglocator = mpl.ticker.LogLocator(base=100)
ax.yaxis.set_major_locator(loglocator)
ax.yaxis.set_minor_locator(mpl.ticker.NullLocator())
ax.set_xlim(xlim or (0, max(tmaxs)))
ax.set_ylim(ylim or (0.1 * min(ymins), 1.5 * max(ymaxs)))
_savefig(fig, fname=fname, title=title, **kwargs)
def plot_fits(energies,
g0,
b,
W,
R,
v0,
info,
G_func,
smooth=None,
smooth_window='bartlett',
xlim=None,
ylim=None,
fname=None,
title=None,
figsize=None,
**kwargs):
"""Plot fits of spectral energy densities"""
tcoda, tbulk, Ecoda, Ebulk, Gcoda, Gbulk = info
N = len(energies)
n = int(np.ceil(np.sqrt(N)))
fig = plt.figure(figsize=figsize)
gs = gridspec.GridSpec(n, n)
share = None
if b is None:
b = 0
tmaxs = []
ymaxs = []
ymins = []
c1 = 'mediumblue'
c2 = 'darkred'
c1l = '#8181CD' # 37.25% #'#8686CD'
c2l = '#8B6969' # 25% #'#8B6161'
for i, energy in enumerate(energies):
evid, station = get_pair(energy)
ax = plt.subplot(gs[i // n, i % n], sharex=share, sharey=share)
plot = ax.semilogy
def get_Emod(G, t):
return R[station] * W[evid] * G * np.exp(-b * t)
# return FS * R[station] * W[evid] * G * np.exp(-b * t)
st = energy.stats
r = st.distance
# ax.axvline(st.starttime - st.origintime + r / v0, ls = '--', c='gray')
# ax.axvline(r / v0, ls='--', c='gray')
t = _get_times(energy) + r / v0 - (st.sonset - st.origintime)
if smooth:
plot(t, energy.data_unsmoothed, color='0.7')
plot(t, energy.data, color=c1l)
G_ = smooth_func(lambda t_: G_func(r, t_, v0, g0),
t,
smooth,
window=smooth_window)
Emod = get_Emod(G_, t)
index = np.argwhere(Emod < 1e-30)[-1]
Emod[index] = 1e-30
plot(t, Emod, color=c2l)
plot(tcoda[i], Ecoda[i], color=c1)
Emodcoda = get_Emod(Gcoda[i], tcoda[i])
plot(tcoda[i], Emodcoda, color=c2)
if tbulk and len(tbulk) > 0:
plot(tbulk[i], Ebulk[i], 'o', color=c1, mec=c1, ms=MS)
Emodbulk = get_Emod(Gbulk[i], tbulk[i])
plot(tbulk[i], Emodbulk, 'o', ms=MS - 1, color=c2, mec=c2)
l = '%s\n%s' % (evid, station)
l = l + '\nr=%dkm' % (r / 1000)
ax.annotate(l, (1, 1), (-5, -5),
'axes fraction',
'offset points',
ha='right',
va='top',
size='x-small')
_set_gridlabels(ax,
i,
n,
n,
N,
xlabel='time (s)',
ylabel=r'E (Jm$^{-3}$Hz$^{-1}$)')
tmaxs.append(t[-1])
ymaxs.append(max(np.max(Emod), np.max(energy.data)))
ymins.append(min(np.min(Emodcoda), np.max(Ecoda[i])))
if share is None:
share = ax
# if True:
# save = {'t': t, 'data': energy.stats.orig_data,
# 'Eobs': energy.data, 'Emod': Emod,
# 'tcoda': tcoda[i], 'Eobscoda': Ecoda[i],
# 'Emodcoda': Emodcoda}
# if tbulk:
# save.update({'tbulk': tbulk[i], 'Eobsbulk': Ebulk[i],
# 'Emodbulk': Emodbulk})
# np.savez(fname.replace('png', '') + station + '.npz', **save)
ax.locator_params(axis='x', nbins=5, prune='upper')
loglocator = mpl.ticker.LogLocator(base=100)
ax.yaxis.set_major_locator(loglocator)
ax.yaxis.set_minor_locator(mpl.ticker.NullLocator())
ax.set_xlim(xlim or (0, max(tmaxs)))
ax.set_ylim(ylim or (0.1 * min(ymins), 1.5 * max(ymaxs)))
_savefig(fig, fname=fname, title=title, **kwargs)
def plot_fits(energies,
g0,
b,
W,
R,
v0,
info,
smooth=None,
smooth_window='bartlett',
title=None,
mag=None):
fs = 150 / 25.4
plt.figure(figsize=(fs, 0.6 * fs))
tcoda, tbulk, Ecoda, Ebulk, Gcoda, Gbulk = info
N = len(energies)
nx, ny = 3, 2
gs = gridspec.GridSpec(ny, nx, wspace=0.05, hspace=0.05)
share = None
if b is None:
b = 0
tmaxs = []
ymaxs = []
ymins = []
c1 = 'mediumblue'
c2 = 'darkred'
c1l = '#8181CD'
c2l = '#8B6969'
for i, energy in enumerate(energies):
evid, station = get_pair(energy)
ax = plt.subplot(gs[i // nx, i % nx], sharex=share, sharey=share)
plot = ax.semilogy
def get_Emod(G, t):
return R[station] * W[evid] * G * np.exp(-b * t)
st = energy.stats
r = st.distance
t = _get_times(energy) + r / v0 - (st.sonset - st.origintime)
if smooth:
plot(t, energy.data_unsmoothed, color='0.7')
plot(t, energy.data, color=c1l)
G_ = Gsmooth(G_func,
r,
t,
v0,
g0,
smooth=smooth,
smooth_window=smooth_window)
Emod = get_Emod(G_, t)
index = np.argwhere(Emod < 1e-30)[-1]
Emod[index] = 1e-30
plot(t, Emod, color=c2l)
plot(tcoda[i], Ecoda[i], color=c1)
Emodcoda = get_Emod(Gcoda[i], tcoda[i])
plot(tcoda[i], Emodcoda, color=c2)
if tbulk and len(tbulk) > 0:
plot(tbulk[i], Ebulk[i], 'o', color=c1, mec=c1, ms=4)
Emodbulk = get_Emod(Gbulk[i], tbulk[i])
plot(tbulk[i], Emodbulk, 'o', ms=3, color=c2, mec=c2)
l = '%s\nr=%dkm' % (station, r / 1000)
ax.annotate(l, (1, 1), (-5, -5),
'axes fraction',
'offset points',
ha='right',
va='top',
size='x-small')
set_gridlabels(ax,
i,
nx,
N,
xlabel='time (s)',
ylabel=r'E (Jm$^{-3}$Hz$^{-1}$)')
kw = dict(color='darkgreen', alpha=0.5, lw=0, zorder=10000)
ax.axvspan(tcoda[i][0] - 10, tcoda[i][0] - 0.8, 0.05, 0.08, **kw)
ax.axvspan(tcoda[i][0] + 0.8, tcoda[i][-1], 0.05, 0.08, **kw)
tmaxs.append(t[-1])
ymaxs.append(max(np.max(Emod), np.max(energy.data)))
ymins.append(min(np.min(Emodcoda), np.max(Ecoda[i])))
if share is None:
share = ax
f = 6
lmag = (r' $M_{\mathrm{R}}%.1f$' % mag) if mag else ''
l = ('id %s' + lmag + '\n'
r'$Q^{-1}_{\mathrm{sc}} = %.1f\times 10^{%d}$' + '\n'
r'$Q^{-1}_{\mathrm{i}} = %.1f\times 10^{%d}$')
l = l % (evid, g0 * v0 / (2 * np.pi * f) * 1e3, -3, b /
(2 * np.pi * f) * 1e3, -3)
ax.annotate(title, (0.715, 0.43),
xycoords='figure fraction',
ha='left',
va='top',
annotation_clip=False,
size='large')
ax.annotate(l, (0.715, 0.34),
xycoords='figure fraction',
ha='left',
va='top',
annotation_clip=False)
ax.yaxis.set_minor_locator(mpl.ticker.NullLocator())
ax.set_yticks(10.**np.arange(-18, -7, 2))
ax.set_xlim((-5, 120))
ax.set_ylim((1e-15 / 1.5, 1e-7 * 1.5))
