def draw_figures(self, history):
problem = history.problem
by_cluster = history.imodels_by_cluster(
self.cluster_attribute)
for icluster, percentage, imodels in by_cluster:
misfits = history.misfits[imodels]
models = history.models[imodels]
mts = []
for ix, x in enumerate(models):
source = problem.get_source(x)
mts.append(source.pyrocko_moment_tensor())
best_mt = stats.get_best_source(
problem, models, misfits).pyrocko_moment_tensor()
fig = plt.figure(figsize=self.size_inch)
fig.subplots_adjust(left=0., right=1., bottom=0., top=1.)
axes = fig.add_subplot(1, 1, 1, aspect=1.0)
if self.cluster_attribute is not None:
color = cluster_color(icluster)
else:
color = 'black'
beachball.plot_fuzzy_beachball_mpl_pixmap(
mts, axes, best_mt,
beachball_type='full',
size=8.*math.sqrt(percentage/100.),
position=(5., 5.),
color_t=color,
edgecolor='black',
best_color=mpl_color('scarletred2'))
if self.cluster_attribute is not None:
axes.annotate(
cluster_label(icluster, percentage),
xy=(5., 0.),
xycoords='data',
xytext=(0., self.font_size/2.),
textcoords='offset points',
ha='center',
va='bottom',
color='black',
fontsize=self.font_size)
axes.set_xlim(0., 10.)
axes.set_ylim(0., 10.)
axes.set_axis_off()
item = PlotItem(
name=(
'cluster_%i' % icluster
if icluster >= 0
else 'unclustered'))
yield [item, fig]
def plot_pred_bayesian_mt_hist(pred_mts, best_mt=None, hist=True):
fig = plt.figure(figsize=(4., 4.))
fig.subplots_adjust(left=0., right=1., bottom=0., top=1.)
gs = GridSpec(3, 5)
axes = fig.add_subplot(gs[0:2, :])
plot_kwargs = {
'beachball_type': 'full',
'size': 3,
'position': (5, 5),
'color_t': 'black',
'edgecolor': 'black'
}
beachball.plot_fuzzy_beachball_mpl_pixmap(pred_mts, axes, best_mt,
**plot_kwargs)
axes.set_xlim(0., 10.)
axes.set_ylim(0., 10.)
axes.set_axis_off()
plt.axis('off')
if hist is True:
omegas = []
kagans = []
for mt in pred_mts:
omega = omega_angle(mt.m6(), best_mt.m6())
kagan = mtm.kagan_angle(mt, best_mt)
omegas.append(omega)
kagans.append(kagan)
ax = fig.add_subplot(gs[2, 1])
sns.distplot(omegas, kde=False)
plt.xlabel('Omega angle (°)', fontsize=12)
plt.ylabel('#', fontsize=12)
xmin, xmax = ax.get_xlim()
ax.set_xticks(np.round(np.linspace(xmin, xmax, 2), 2))
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
ax = fig.add_subplot(gs[2, 3])
sns.distplot(kagans, color="orange", kde=False)
plt.xlabel('Kagan angle (°)', fontsize=12)
plt.ylabel('#', fontsize=12)
xmin, xmax = ax.get_xlim()
ax.set_xticks(np.round(np.linspace(xmin, xmax, 2), 2))
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.show()
return fig
def draw_figures(self, history):
problem = history.problem
fig = plt.figure(figsize=self.size_inch)
axes = fig.gca()
gms = problem.combine_misfits(history.misfits)
isort = num.argsort(gms)[::-1]
gms = gms[isort]
models = history.models[isort, :]
kwargs = {
'beachball_type': 'full',
'size': 8,
'position': (5, 5),
'color_t': 'black',
'edgecolor': 'black'
}
mts = []
for ix, x in enumerate(models):
if random.random() < 0.1:
source = problem.get_source(x)
mts.append(source.pyrocko_moment_tensor())
best_mt = mts[-1]
beachball.plot_fuzzy_beachball_mpl_pixmap(mts, axes, best_mt, **kwargs)
axes.set_xlim(0., 10.)
axes.set_ylim(0., 10.)
axes.set_axis_off()
item = PlotItem(
name='main',
title='Fuzzy Moment Tensor',
description='The opaqueness illustrates the propability'
' of all combined moment tensor solution.'
'The red lines indicate the global best solution.')
return [[item, fig]]
def plot_pred_bayesian_mt(pred_mts, best_mt=None):
fig = plt.figure(figsize=(4., 4.))
fig.subplots_adjust(left=0., right=1., bottom=0., top=1.)
axes = fig.add_subplot(1, 1, 1)
plot_kwargs = {
'beachball_type': 'full',
'size': 3,
'position': (5, 5),
'color_t': 'black',
'edgecolor': 'black'
}
beachball.plot_fuzzy_beachball_mpl_pixmap(pred_mts, axes, best_mt,
**plot_kwargs)
axes.set_xlim(0., 10.)
axes.set_ylim(0., 10.)
axes.set_axis_off()
plt.show()
def test_fuzzy_beachball(self):
from matplotlib import pyplot as plt
def get_random_uniform(lower, upper, dimension=1):
return (upper - lower) * num.random.rand(dimension) + lower
fig = plt.figure(figsize=(4., 4.))
fig.subplots_adjust(left=0., right=1., bottom=0., top=1.)
axes = fig.add_subplot(1, 1, 1)
strike = 135.
dip = 65.
rake = 15.
best_mt = mtm.MomentTensor.from_values((strike, dip, rake))
n_balls = 1000
mts = []
for i in range(n_balls):
strike_dev = get_random_uniform(-15., 15.)
mts.append(
mtm.MomentTensor.from_values((strike + strike_dev, dip, rake)))
kwargs = {
'beachball_type': 'full',
'size': 8,
'position': (5, 5),
'color_t': 'black',
'edgecolor': 'black'
}
beachball.plot_fuzzy_beachball_mpl_pixmap(mts, axes, best_mt, **kwargs)
axes.set_xlim(0., 10.)
axes.set_ylim(0., 10.)
axes.set_axis_off()
def test_fuzzy_beachball(self):
from matplotlib import pyplot as plt
def get_random_uniform(lower, upper, dimension=1):
return (upper - lower) * num.random.rand(dimension) + lower
fig = plt.figure(figsize=(4., 4.))
fig.subplots_adjust(left=0., right=1., bottom=0., top=1.)
axes = fig.add_subplot(1, 1, 1)
strike = 135.
dip = 65.
rake = 15.
best_mt = mtm.MomentTensor.from_values((strike, dip, rake))
n_balls = 1000
mts = []
for i in range(n_balls):
strike_dev = get_random_uniform(-15., 15.)
mts.append(mtm.MomentTensor.from_values(
(strike + strike_dev, dip, rake)))
kwargs = {
'beachball_type': 'full',
'size': 8,
'position': (5, 5),
'color_t': 'black',
'edgecolor': 'black'}
beachball.plot_fuzzy_beachball_mpl_pixmap(mts, axes, best_mt, **kwargs)
axes.set_xlim(0., 10.)
axes.set_ylim(0., 10.)
axes.set_axis_off()
def plot_map_basemap(stations=None,
east_min=-119.2,
east_max=-116,
north_min=34.5,
north_max=37.501,
events=None,
savename=None,
preds=None,
best_mts=None,
pred_events=None,
rect_lats=None,
rect_lons=None,
ticks=0.01,
kmscale=5,
add_grid=True,
overview=False):
try:
from mpl_toolkits.basemap import Basemap
use_basemap = True
except:
import cartopy.crs as ccrs
import cartopy
import cartopy.geodesic as cgeo
from cartopy.io import srtm
from cartopy.io import PostprocessedRasterSource, LocatedImage
from cartopy.io.srtm import SRTM3Source, SRTM1Source
use_basemap = False
from matplotlib import pyplot as plt
from pyrocko.plot import beachball
from pyrocko import plot
from obspy.imaging.beachball import beach
fig, ax = plt.subplots(figsize=(12, 12))
map = Basemap(projection='merc',
llcrnrlon=east_min,
llcrnrlat=north_min,
urcrnrlon=east_max,
urcrnrlat=north_max,
resolution='h',
epsg=3395,
ax=ax)
xpixels = 1000
map.arcgisimage(service='World_Shaded_Relief',
xpixels=xpixels,
verbose=False,
zorder=-3,
colormap="gray",
cmap="gray")
if overview is True:
map.drawmapscale(east_min + 0.35, north_min + 0.31, east_min + 0.65,
north_min + 0.5, kmscale)
else:
map.drawmapscale(east_min + 0.05, north_min + 0.01, east_min + 0.55,
north_min + 0.2, kmscale)
parallels = np.arange(north_min, north_max, ticks)
meridians = np.arange(east_min, east_max, ticks)
if overview is True:
map.drawparallels(np.round(parallels, 1),
labels=[1, 0, 0, 0],
fontsize=12)
map.drawmeridians(np.round(meridians, 1),
labels=[1, 1, 0, 1],
fontsize=12,
rotation=45)
else:
map.drawparallels(parallels, labels=[1, 0, 0, 0], fontsize=12)
map.drawmeridians(meridians,
labels=[1, 1, 0, 1],
fontsize=12,
rotation=45)
if events is not None:
for event in events:
mt = event.moment_tensor
if overview is False:
if event.lat > 35.845:
x, y = map(event.lon, event.lat)
else:
x, y = map(event.lon, event.lat)
if overview is True:
size = 12
else:
size = 20
beachball.plot_beachball_mpl(mt,
ax,
beachball_type='full',
size=size,
position=(x, y),
color_t=plot.mpl_color('scarletred2'),
linewidth=1.0,
zorder=1)
if stations is not None:
lats = [s.lat for s in stations]
lons = [s.lon for s in stations]
labels = ['.'.join(s.nsl()) for s in stations]
x_station, y_station = map(lons, lats)
map.scatter(x_station, y_station, marker="^", s=36, c="g", zorder=8)
for k, label in enumerate(labels):
plt.text(x_station[k], y_station[k], str(label), fontsize=12)
if rect_lats is not None:
import matplotlib.patches as patches
if add_grid is True:
for lat in rect_lats:
for lon in rect_lons:
x, y = map(lon, lat)
rect = patches.Rectangle((x, y),
1800,
2200,
linewidth=1,
edgecolor='r',
facecolor='none')
# Add the patch to the Axes
ax.add_patch(rect)
max_lat_rect = np.max(rect_lats)
min_lat_rect = np.min(rect_lats)
max_lon_rect = np.max(rect_lons)
min_lon_rect = np.min(rect_lons)
xmin, ymin = map(min_lon_rect, max_lat_rect)
xmax, ymax = map(max_lon_rect, min_lat_rect)
width = xmax - xmin
length = ymin - ymax
if overview is True:
rect = patches.Rectangle(
(xmin, ymax),
width,
length,
linewidth=5,
edgecolor='k',
facecolor='none',
zorder=7,
)
# Add the patch to the Axes
ax.add_patch(rect)
if preds is None and best_mts is not None:
k = 1
for best_mt, ev in zip(best_mts, pred_events):
mt = ev.moment_tensor
x, y = map(ev.lon, ev.lat)
plt.text(x, y + 0.02, str(k), fontsize=42, zorder=9, color="k")
k = k + 1
beachball.plot_beachball_mpl(mt,
ax,
beachball_type='full',
size=22.,
position=(x, y),
color_t=plot.mpl_color('blue'),
linewidth=1.0,
zorder=3)
if preds is not None:
for pred_mts, best_mt, ev in zip(preds, best_mts, pred_events):
x, y = map(ev.lon, ev.lat)
plot_kwargs = {
'beachball_type': 'full',
'size': 500,
'position': (x, y),
'color_t': 'black',
'edgecolor': 'black',
'zorder': 3,
}
beachball.plot_fuzzy_beachball_mpl_pixmap(pred_mts, ax, best_mt,
**plot_kwargs)
plt.show()
best_mt = mtm.MomentTensor.from_values((strike, dip, rake))
def get_random_uniform(lower, upper, dimension=1):
''' Help function to pertubate the best solution '''
return (upper - lower) * num.random.rand(dimension) + lower
mts = []
for i in range(n_balls):
strike_dev = get_random_uniform(-15., 15.)
mts.append(mtm.MomentTensor.from_values((strike + strike_dev, dip, rake)))
plot_kwargs = {
'beachball_type': 'full',
'size': 8,
'position': (5, 5),
'color_t': 'black',
'edgecolor': 'black'
}
beachball.plot_fuzzy_beachball_mpl_pixmap(mts, axes, best_mt, **plot_kwargs)
# Decorate the axes
axes.set_xlim(0., 10.)
axes.set_ylim(0., 10.)
axes.set_axis_off()
plt.show()
best_mt = mtm.MomentTensor.from_values((strike, dip, rake))
def get_random_uniform(lower, upper, dimension=1):
''' Help function to pertubate the best solution '''
return (upper - lower) * num.random.rand(dimension) + lower
mts = []
for i in range(n_balls):
strike_dev = get_random_uniform(-15., 15.)
mts.append(mtm.MomentTensor.from_values(
(strike + strike_dev, dip, rake)))
plot_kwargs = {
'beachball_type': 'full',
'size': 8,
'position': (5, 5),
'color_t': 'black',
'edgecolor': 'black'
}
beachball.plot_fuzzy_beachball_mpl_pixmap(mts, axes, best_mt, **plot_kwargs)
# Decorate the axes
axes.set_xlim(0., 10.)
axes.set_ylim(0., 10.)
axes.set_axis_off()
plt.show()
