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import math 

import logging 

import random 

 

import numpy as num 

from matplotlib import cm, patches, colors as mcolors 

 

from pyrocko.guts import Tuple, Float, Int, String, List, Bool, StringChoice 

 

from pyrocko.plot import mpl_margins, mpl_color, mpl_init, mpl_graph_color 

from pyrocko.plot import beachball, hudson 

 

from grond.plot.config import PlotConfig 

from grond.plot.section import SectionPlotConfig, SectionPlot 

from grond.plot.collection import PlotItem 

from grond import meta, core, stats 

from matplotlib import pyplot as plt 

 

logger = logging.getLogger('grond.problem.plot') 

 

guts_prefix = 'grond' 

 

 

def cluster_label(icluster, perc): 

if icluster == -1: 

return 'Unclust. (%.0f%%)' % perc 

else: 

return 'Cluster %i (%.0f%%)' % (icluster, perc) 

 

 

def cluster_color(icluster): 

if icluster == -1: 

return mpl_color('aluminium3') 

else: 

return mpl_graph_color(icluster) 

 

 

def fixlim(lo, hi): 

if lo == hi: 

return lo - 1.0, hi + 1.0 

else: 

return lo, hi 

 

 

def eigh_sorted(mat): 

evals, evecs = num.linalg.eigh(mat) 

iorder = num.argsort(evals) 

return evals[iorder], evecs[:, iorder] 

 

 

class JointparPlot(PlotConfig): 

''' 

Source problem parameter's tradeoff plots. 

''' 

 

name = 'jointpar' 

size_cm = Tuple.T(2, Float.T(), default=(20., 20.)) 

misfit_cutoff = Float.T(optional=True) 

ibootstrap = Int.T(optional=True) 

color_parameter = String.T(default='misfit') 

exclude = List.T(String.T()) 

include = List.T(String.T()) 

show_ellipses = Bool.T(default=False) 

nsubplots = Int.T(default=6) 

show_ticks = Bool.T(default=False) 

show_reference = Bool.T(default=True) 

 

def make(self, environ): 

cm = environ.get_plot_collection_manager() 

history = environ.get_history(subset='harvest') 

optimiser = environ.get_optimiser() 

sref = 'Dark gray boxes mark reference solution.' \ 

if self.show_reference else '' 

 

mpl_init(fontsize=self.font_size) 

cm.create_group_mpl( 

self, 

self.draw_figures(history, optimiser), 

title=u'Jointpar Plot', 

section='solution', 

feather_icon='crosshair', 

description=u''' 

Source problem parameter's scatter plots, to evaluate the resolution of source 

parameters and possible trade-offs between pairs of model parameters. 

 

A subset of model solutions (from harvest) is shown in two dimensions for all 

possible parameter pairs as points. The point color indicates the misfit for 

the model solution with cold colors (blue) for high misfit models and warm 

colors (red) for low misfit models. The plot ranges are defined by the given 

parameter bounds and shows the model space of the optimsation. %s''' % sref) 

 

def draw_figures(self, history, optimiser): 

 

color_parameter = self.color_parameter 

exclude = self.exclude 

include = self.include 

nsubplots = self.nsubplots 

figsize = self.size_inch 

ibootstrap = 0 if self.ibootstrap is None else self.ibootstrap 

misfit_cutoff = self.misfit_cutoff 

show_ellipses = self.show_ellipses 

msize = 1.5 

cmap = 'coolwarm' 

 

problem = history.problem 

if not problem: 

return [] 

 

models = history.models 

 

exclude = list(exclude) 

bounds = problem.get_combined_bounds() 

for ipar in range(problem.ncombined): 

par = problem.combined[ipar] 

lo, hi = bounds[ipar] 

if lo == hi: 

exclude.append(par.name) 

 

xref = problem.get_reference_model() 

 

isort = history.get_sorted_misfits_idx(chain=ibootstrap)[::-1] 

models = history.get_sorted_models(chain=ibootstrap)[::-1] 

nmodels = history.nmodels 

 

gms = history.get_sorted_misfits(chain=ibootstrap)[::-1] 

if misfit_cutoff is not None: 

ibest = gms < misfit_cutoff 

gms = gms[ibest] 

models = models[ibest] 

 

kwargs = {} 

 

if color_parameter == 'dist': 

mx = num.mean(models, axis=0) 

cov = num.cov(models.T) 

mdists = core.mahalanobis_distance(models, mx, cov) 

icolor = meta.ordersort(mdists) 

 

elif color_parameter == 'misfit': 

iorder = num.arange(nmodels) 

icolor = iorder 

 

elif color_parameter in problem.parameter_names: 

ind = problem.name_to_index(color_parameter) 

icolor = problem.extract(models, ind) 

 

elif color_parameter in history.attribute_names: 

icolor = history.get_attribute(color_parameter)[isort] 

icolor_need = num.unique(icolor) 

 

colors = [] 

for i in range(icolor_need[-1]+1): 

colors.append(mpl_graph_color(i)) 

 

cmap = mcolors.ListedColormap(colors) 

cmap.set_under(mpl_color('aluminium3')) 

kwargs.update(dict(vmin=0, vmax=icolor_need[-1])) 

else: 

raise meta.GrondError( 

'Invalid color_parameter: %s' % color_parameter) 

 

smap = {} 

iselected = 0 

for ipar in range(problem.ncombined): 

par = problem.combined[ipar] 

if exclude and par.name in exclude or \ 

include and par.name not in include: 

continue 

 

smap[iselected] = ipar 

iselected += 1 

 

nselected = iselected 

 

if nselected < 2: 

logger.warn('Cannot draw joinpar figures with less than two ' 

'parameters selected.') 

return [] 

 

nfig = (nselected - 2) // nsubplots + 1 

 

figs = [] 

for ifig in range(nfig): 

figs_row = [] 

for jfig in range(nfig): 

if ifig >= jfig: 

item = PlotItem(name='fig_%i_%i' % (ifig, jfig)) 

item.attributes['parameters'] = [] 

figs_row.append((item, plt.figure(figsize=figsize))) 

else: 

figs_row.append(None) 

 

figs.append(figs_row) 

 

for iselected in range(nselected): 

ipar = smap[iselected] 

ypar = problem.combined[ipar] 

for jselected in range(iselected): 

jpar = smap[jselected] 

xpar = problem.combined[jpar] 

 

ixg = (iselected - 1) 

iyg = jselected 

 

ix = ixg % nsubplots 

iy = iyg % nsubplots 

 

ifig = ixg // nsubplots 

jfig = iyg // nsubplots 

 

aind = (nsubplots, nsubplots, (ix * nsubplots) + iy + 1) 

 

item, fig = figs[ifig][jfig] 

 

tlist = item.attributes['parameters'] 

if xpar.name not in tlist: 

tlist.append(xpar.name) 

if ypar.name not in tlist: 

tlist.append(ypar.name) 

 

axes = fig.add_subplot(*aind) 

 

axes.axvline(0., color=mpl_color('aluminium3'), lw=0.5) 

axes.axhline(0., color=mpl_color('aluminium3'), lw=0.5) 

for spine in axes.spines.values(): 

spine.set_edgecolor(mpl_color('aluminium5')) 

spine.set_linewidth(0.5) 

 

xmin, xmax = fixlim(*xpar.scaled(bounds[jpar])) 

ymin, ymax = fixlim(*ypar.scaled(bounds[ipar])) 

 

if ix == 0 or jselected + 1 == iselected: 

for (xpos, xoff, x) in [ 

(0.0, 10., xmin), 

(1.0, -10., xmax)]: 

 

axes.annotate( 

'%.3g%s' % (x, xpar.get_unit_suffix()), 

xy=(xpos, 1.05), 

xycoords='axes fraction', 

xytext=(xoff, 5.), 

textcoords='offset points', 

verticalalignment='bottom', 

horizontalalignment='left', 

rotation=45.) 

 

if iy == nsubplots - 1 or jselected + 1 == iselected: 

for (ypos, yoff, y) in [ 

(0., 10., ymin), 

(1.0, -10., ymax)]: 

 

axes.annotate( 

'%.3g%s' % (y, ypar.get_unit_suffix()), 

xy=(1.0, ypos), 

xycoords='axes fraction', 

xytext=(5., yoff), 

textcoords='offset points', 

verticalalignment='bottom', 

horizontalalignment='left', 

rotation=45.) 

 

axes.set_xlim(xmin, xmax) 

axes.set_ylim(ymin, ymax) 

 

if not self.show_ticks: 

axes.get_xaxis().set_ticks([]) 

axes.get_yaxis().set_ticks([]) 

else: 

axes.tick_params(length=4, which='both') 

axes.get_yaxis().set_ticklabels([]) 

axes.get_xaxis().set_ticklabels([]) 

 

if iselected == nselected - 1 or ix == nsubplots - 1: 

axes.annotate( 

xpar.get_label(with_unit=False), 

xy=(0.5, -0.05), 

xycoords='axes fraction', 

verticalalignment='top', 

horizontalalignment='right', 

rotation=45.) 

 

if iy == 0: 

axes.annotate( 

ypar.get_label(with_unit=False), 

xy=(-0.05, 0.5), 

xycoords='axes fraction', 

verticalalignment='top', 

horizontalalignment='right', 

rotation=45.) 

 

fx = problem.extract(models, jpar) 

fy = problem.extract(models, ipar) 

 

axes.scatter( 

xpar.scaled(fx), 

ypar.scaled(fy), 

c=icolor, 

s=msize, alpha=0.5, cmap=cmap, edgecolors='none', **kwargs) 

 

if show_ellipses: 

cov = num.cov((xpar.scaled(fx), ypar.scaled(fy))) 

evals, evecs = eigh_sorted(cov) 

evals = num.sqrt(evals) 

ell = patches.Ellipse( 

xy=( 

num.mean(xpar.scaled(fx)), 

num.mean(ypar.scaled(fy))), 

width=evals[0] * 2, 

height=evals[1] * 2, 

angle=num.rad2deg( 

num.arctan2(evecs[1][0], evecs[0][0]))) 

 

ell.set_facecolor('none') 

axes.add_artist(ell) 

 

if self.show_reference: 

fx = problem.extract(xref, jpar) 

fy = problem.extract(xref, ipar) 

 

ref_color = mpl_color('aluminium6') 

ref_color_light = 'none' 

axes.plot( 

xpar.scaled(fx), ypar.scaled(fy), 's', 

mew=1.5, ms=5, mfc=ref_color_light, mec=ref_color) 

 

figs_flat = [] 

for figs_row in figs: 

figs_flat.extend( 

item_fig for item_fig in figs_row if item_fig is not None) 

 

return figs_flat 

 

 

class HistogramPlot(PlotConfig): 

''' 

Histograms or Gaussian kernel densities (default) of all parameters 

(marginal distributions of model parameters). 

 

The histograms (by default shown as Gaussian kernel densities) show (red 

curved solid line) the distributions of the parameters (marginals) along 

with some characteristics: The red solid vertical line gives the median of 

the distribution and the dashed red vertical line the mean value. Dark gray 

vertical lines show reference values (given in the event.txt file). The 

overlapping red-shaded areas show the 68% confidence intervals (innermost 

area), the 90% confidence intervals (middle area) and the minimum and 

maximum values (widest area). The plot ranges are defined by the given 

parameter bounds and show the model space. Well resolved model parameters 

show peaked distributions. 

''' 

 

name = 'histogram' 

size_cm = Tuple.T(2, Float.T(), default=(12.5, 7.5)) 

exclude = List.T(String.T()) 

include = List.T(String.T()) 

method = StringChoice.T( 

choices=['gaussian_kde', 'histogram'], 

default='gaussian_kde') 

show_reference = Bool.T(default=True) 

 

def make(self, environ): 

cm = environ.get_plot_collection_manager() 

history = environ.get_history(subset='harvest') 

 

mpl_init(fontsize=self.font_size) 

cm.create_group_mpl( 

self, 

self.draw_figures(history), 

title=u'Histogram', 

section='solution', 

feather_icon='bar-chart-2', 

description=u''' 

Distribution of the problem's parameters. 

 

The histograms are shown either as Gaussian kernel densities (red curved solid 

line) or as bar plots the distributions of the parameters (marginals) along 

with some characteristics: 

 

The red solid vertical line gives the median of the distribution and the dashed 

red vertical line the mean value. Dark gray vertical lines show reference 

parameter values if given in the event.txt file. The overlapping red-shaded 

areas show the 68% confidence intervals (innermost area), the 90% confidence 

intervals (middle area) and the minimum and maximum values (widest area). The 

plot ranges are defined by the given parameter bounds and show the model 

space. 

''') 

 

def draw_figures(self, history): 

 

import scipy.stats 

from grond.core import make_stats 

 

exclude = self.exclude 

include = self.include 

figsize = self.size_inch 

fontsize = self.font_size 

method = self.method 

ref_color = mpl_color('aluminium6') 

stats_color = mpl_color('scarletred2') 

bar_color = mpl_color('scarletred1') 

stats_color3 = mpl_color('scarletred3') 

 

problem = history.problem 

 

models = history.models 

 

bounds = problem.get_combined_bounds() 

exclude = list(exclude) 

for ipar in range(problem.ncombined): 

par = problem.combined[ipar] 

vmin, vmax = bounds[ipar] 

if vmin == vmax: 

exclude.append(par.name) 

 

xref = problem.get_reference_model() 

 

smap = {} 

iselected = 0 

for ipar in range(problem.ncombined): 

par = problem.combined[ipar] 

if exclude and par.name in exclude or \ 

include and par.name not in include: 

continue 

 

smap[iselected] = ipar 

iselected += 1 

 

nselected = iselected 

del iselected 

 

pnames = [ 

problem.combined[smap[iselected]].name 

for iselected in range(nselected)] 

 

rstats = make_stats(problem, models, 

history.get_primary_chain_misfits(), 

pnames=pnames) 

 

for iselected in range(nselected): 

ipar = smap[iselected] 

par = problem.combined[ipar] 

vs = problem.extract(models, ipar) 

vmin, vmax = bounds[ipar] 

 

fig = plt.figure(figsize=figsize) 

labelpos = mpl_margins( 

fig, nw=1, nh=1, w=7., bottom=5., top=1, units=fontsize) 

 

axes = fig.add_subplot(1, 1, 1) 

labelpos(axes, 2.5, 2.0) 

axes.set_xlabel(par.get_label()) 

axes.set_ylabel('PDF') 

axes.set_xlim(*fixlim(*par.scaled((vmin, vmax)))) 

 

if method == 'gaussian_kde': 

try: 

kde = scipy.stats.gaussian_kde(vs) 

except Exception: 

logger.warn( 

'Cannot create plot histogram with gaussian_kde: ' 

'possibly all samples have the same value.') 

continue 

 

vps = num.linspace(vmin, vmax, 600) 

pps = kde(vps) 

 

axes.plot( 

par.scaled(vps), par.inv_scaled(pps), color=stats_color) 

 

elif method == 'histogram': 

pps, edges = num.histogram( 

vs, 

bins=num.linspace(vmin, vmax, num=40), 

density=True) 

vps = 0.5 * (edges[:-1] + edges[1:]) 

 

axes.bar(par.scaled(vps), par.inv_scaled(pps), 

par.scaled(2.*(vps - edges[:-1])), 

color=bar_color) 

 

pstats = rstats.parameter_stats_list[iselected] 

 

axes.axvspan( 

par.scaled(pstats.minimum), 

par.scaled(pstats.maximum), 

color=stats_color, alpha=0.1) 

axes.axvspan( 

par.scaled(pstats.percentile16), 

par.scaled(pstats.percentile84), 

color=stats_color, alpha=0.1) 

axes.axvspan( 

par.scaled(pstats.percentile5), 

par.scaled(pstats.percentile95), 

color=stats_color, alpha=0.1) 

 

axes.axvline( 

par.scaled(pstats.median), 

color=stats_color3, alpha=0.5) 

axes.axvline( 

par.scaled(pstats.mean), 

color=stats_color3, ls=':', alpha=0.5) 

 

if self.show_reference: 

axes.axvline( 

par.scaled(problem.extract(xref, ipar)), 

color=ref_color) 

 

item = PlotItem(name=par.name) 

item.attributes['parameters'] = [par.name] 

yield item, fig 

 

 

class MTDecompositionPlot(PlotConfig): 

''' 

Moment tensor decomposition plot. 

''' 

 

name = 'mt_decomposition' 

size_cm = Tuple.T(2, Float.T(), default=(15., 5.)) 

cluster_attribute = meta.StringID.T( 

optional=True, 

help='name of attribute to use as cluster IDs') 

show_reference = Bool.T(default=True) 

 

def make(self, environ): 

cm = environ.get_plot_collection_manager() 

history = environ.get_history(subset='harvest') 

mpl_init(fontsize=self.font_size) 

cm.create_group_mpl( 

self, 

self.draw_figures(history), 

title=u'MT Decomposition', 

section='solution', 

feather_icon='sun', 

description=u''' 

Moment tensor decomposition of the best-fitting solution into isotropic, 

deviatoric and best double couple components. 

 

Shown are the ensemble best, the ensemble mean%s and, if available, a reference 

mechanism. The symbol size indicates the relative strength of the components. 

The inversion result is consistent and stable if ensemble mean and ensemble 

best have similar symbol size and patterns. 

''' % (', cluster results' if self.cluster_attribute else '')) 

 

def draw_figures(self, history): 

 

fontsize = self.font_size 

 

fig = plt.figure(figsize=self.size_inch) 

axes = fig.add_subplot(1, 1, 1, aspect=1.0) 

fig.subplots_adjust(left=0., right=1., bottom=0., top=1.) 

 

problem = history.problem 

models = history.models 

 

if models.size == 0: 

logger.warn('Empty models vector.') 

return [] 

 

# gms = problem.combine_misfits(history.misfits) 

# isort = num.argsort(gms) 

# iorder = num.empty_like(isort) 

# iorder[isort] = num.arange(iorder.size)[::-1] 

 

ref_source = problem.base_source 

 

mean_source = stats.get_mean_source( 

problem, history.models) 

 

best_source = history.get_best_source() 

 

nlines_max = int(round(self.size_cm[1] / 5. * 4. - 1.0)) 

 

if self.cluster_attribute: 

cluster_sources = history.mean_sources_by_cluster( 

self.cluster_attribute) 

else: 

cluster_sources = [] 

 

def get_deco(source): 

mt = source.pyrocko_moment_tensor() 

return mt.standard_decomposition() 

 

lines = [] 

lines.append( 

('Ensemble best', get_deco(best_source), mpl_color('aluminium5'))) 

 

lines.append( 

('Ensemble mean', get_deco(mean_source), mpl_color('aluminium5'))) 

 

for (icluster, perc, source) in cluster_sources: 

if len(lines) < nlines_max - int(self.show_reference): 

lines.append( 

(cluster_label(icluster, perc), 

get_deco(source), 

cluster_color(icluster))) 

else: 

logger.warn( 

'Skipping display of cluster %i because figure height is ' 

'too small. Figure height should be at least %g cm.' % ( 

icluster, (3 + len(cluster_sources) 

+ int(self.show_reference)) * 5/4.)) 

 

if self.show_reference: 

lines.append( 

('Reference', get_deco(ref_source), mpl_color('aluminium3'))) 

 

moment_full_max = max(deco[-1][0] for (_, deco, _) in lines) 

 

for xpos, label in [ 

(0., 'Full'), 

(2., 'Isotropic'), 

(4., 'Deviatoric'), 

(6., 'CLVD'), 

(8., 'DC')]: 

 

axes.annotate( 

label, 

xy=(1 + xpos, nlines_max), 

xycoords='data', 

xytext=(0., 0.), 

textcoords='offset points', 

ha='center', 

va='center', 

color='black', 

fontsize=fontsize) 

 

for i, (label, deco, color_t) in enumerate(lines): 

ypos = nlines_max - i - 1.0 

 

[(moment_iso, ratio_iso, m_iso), 

(moment_dc, ratio_dc, m_dc), 

(moment_clvd, ratio_clvd, m_clvd), 

(moment_devi, ratio_devi, m_devi), 

(moment_full, ratio_full, m_full)] = deco 

 

size0 = moment_full / moment_full_max 

 

axes.annotate( 

label, 

xy=(-2., ypos), 

xycoords='data', 

xytext=(0., 0.), 

textcoords='offset points', 

ha='left', 

va='center', 

color='black', 

fontsize=fontsize) 

 

for xpos, mt_part, ratio, ops in [ 

(0., m_full, ratio_full, '-'), 

(2., m_iso, ratio_iso, '='), 

(4., m_devi, ratio_devi, '='), 

(6., m_clvd, ratio_clvd, '+'), 

(8., m_dc, ratio_dc, None)]: 

 

if ratio > 1e-4: 

try: 

beachball.plot_beachball_mpl( 

mt_part, axes, 

beachball_type='full', 

position=(1. + xpos, ypos), 

size=0.9 * size0 * math.sqrt(ratio), 

size_units='data', 

color_t=color_t, 

linewidth=1.0) 

 

except beachball.BeachballError as e: 

logger.warn(str(e)) 

 

axes.annotate( 

'ERROR', 

xy=(1. + xpos, ypos), 

ha='center', 

va='center', 

color='red', 

fontsize=fontsize) 

 

else: 

axes.annotate( 

'N/A', 

xy=(1. + xpos, ypos), 

ha='center', 

va='center', 

color='black', 

fontsize=fontsize) 

 

if ops is not None: 

axes.annotate( 

ops, 

xy=(2. + xpos, ypos), 

ha='center', 

va='center', 

color='black', 

fontsize=fontsize) 

 

axes.axison = False 

axes.set_xlim(-2.25, 9.75) 

axes.set_ylim(-0.5, nlines_max+0.5) 

 

item = PlotItem(name='main') 

return [[item, fig]] 

 

 

class MTLocationPlot(SectionPlotConfig): 

''' MT location plot of the best solutions in three cross-sections. ''' 

name = 'location_mt' 

beachball_type = StringChoice.T( 

choices=['full', 'deviatoric', 'dc'], 

default='dc') 

normalisation_gamma = Float.T( 

default=3., 

help='Normalisation of colors and alpha as :math:`x^\\gamma`.' 

'A linear colormap/alpha with :math:`\\gamma=1`.') 

 

def make(self, environ): 

environ.setup_modelling() 

cm = environ.get_plot_collection_manager() 

history = environ.get_history(subset='harvest') 

mpl_init(fontsize=self.font_size) 

self._to_be_closed = [] 

cm.create_group_mpl( 

self, 

self.draw_figures(history), 

title=u'MT Location', 

section='solution', 

feather_icon='target', 

description=u''' 

Location plot of the ensemble of best solutions in three cross-sections. 

 

The coordinate range is defined by the search space given in the config file. 

Symbols show best double-couple mechanisms, and colors indicate low (red) and 

high (blue) misfit. 

''') 

for obj in self._to_be_closed: 

obj.close() 

 

def draw_figures(self, history, color_p_axis=False): 

from matplotlib import colors 

 

color = 'black' 

fontsize = self.font_size 

markersize = fontsize * 1.5 

beachballsize_small = markersize * 0.5 

beachball_type = self.beachball_type 

 

problem = history.problem 

sp = SectionPlot(config=self) 

self._to_be_closed.append(sp) 

 

fig = sp.fig 

axes_en = sp.axes_xy 

axes_dn = sp.axes_zy 

axes_ed = sp.axes_xz 

 

bounds = problem.get_combined_bounds() 

 

models = history.get_sorted_primary_models()[::-1] 

 

iorder = num.arange(history.nmodels) 

 

for parname, set_label, set_lim in [ 

['east_shift', sp.set_xlabel, sp.set_xlim], 

['north_shift', sp.set_ylabel, sp.set_ylim], 

['depth', sp.set_zlabel, sp.set_zlim]]: 

 

ipar = problem.name_to_index(parname) 

par = problem.combined[ipar] 

set_label(par.get_label()) 

xmin, xmax = fixlim(*par.scaled(bounds[ipar])) 

set_lim(xmin, xmax) 

 

if 'volume_change' in problem.parameter_names: 

volumes = models[:, problem.name_to_index('volume_change')] 

volume_max = volumes.max() 

volume_min = volumes.min() 

 

def scale_size(source): 

if not hasattr(source, 'volume_change'): 

return beachballsize_small 

 

volume_change = source.volume_change 

fac = (volume_change - volume_min) / (volume_max - volume_min) 

return markersize * .25 + markersize * .5 * fac 

 

for axes, xparname, yparname in [ 

(axes_en, 'east_shift', 'north_shift'), 

(axes_dn, 'depth', 'north_shift'), 

(axes_ed, 'east_shift', 'depth')]: 

 

ixpar = problem.name_to_index(xparname) 

iypar = problem.name_to_index(yparname) 

 

xpar = problem.combined[ixpar] 

ypar = problem.combined[iypar] 

 

xmin, xmax = fixlim(*xpar.scaled(bounds[ixpar])) 

ymin, ymax = fixlim(*ypar.scaled(bounds[iypar])) 

 

try: 

axes.set_facecolor(mpl_color('aluminium1')) 

except AttributeError: 

axes.patch.set_facecolor(mpl_color('aluminium1')) 

 

rect = patches.Rectangle( 

(xmin, ymin), xmax-xmin, ymax-ymin, 

facecolor=mpl_color('white'), 

edgecolor=mpl_color('aluminium2')) 

 

axes.add_patch(rect) 

 

# fxs = xpar.scaled(problem.extract(models, ixpar)) 

# fys = ypar.scaled(problem.extract(models, iypar)) 

 

# axes.set_xlim(*fixlim(num.min(fxs), num.max(fxs))) 

# axes.set_ylim(*fixlim(num.min(fys), num.max(fys))) 

 

cmap = cm.ScalarMappable( 

norm=colors.PowerNorm( 

gamma=self.normalisation_gamma, 

vmin=iorder.min(), 

vmax=iorder.max()), 

 

cmap=plt.get_cmap('coolwarm')) 

 

for ix, x in enumerate(models): 

 

source = problem.get_source(x) 

mt = source.pyrocko_moment_tensor( 

store=problem.get_gf_store(problem.targets[0]), 

target=problem.targets[0]) 

fx = problem.extract(x, ixpar) 

fy = problem.extract(x, iypar) 

sx, sy = xpar.scaled(fx), ypar.scaled(fy) 

 

# TODO: Add rotation in cross-sections 

color = cmap.to_rgba(iorder[ix]) 

 

alpha = (iorder[ix] - iorder.min()) / \ 

float(iorder.max() - iorder.min()) 

alpha = alpha**self.normalisation_gamma 

 

try: 

beachball.plot_beachball_mpl( 

mt, axes, 

beachball_type=beachball_type, 

position=(sx, sy), 

size=scale_size(source), 

color_t=color, 

color_p=color if color_p_axis else 'white', 

alpha=alpha, 

zorder=1, 

linewidth=0.25) 

 

except beachball.BeachballError as e: 

logger.warn(str(e)) 

 

item = PlotItem(name='main') 

return [[item, fig]] 

 

 

class MTFuzzyPlot(PlotConfig): 

'''Fuzzy, propabalistic moment tensor plot ''' 

 

name = 'mt_fuzzy' 

size_cm = Tuple.T(2, Float.T(), default=(10., 10.)) 

cluster_attribute = meta.StringID.T( 

optional=True, 

help='name of attribute to use as cluster IDs') 

 

def make(self, environ): 

cm = environ.get_plot_collection_manager() 

history = environ.get_history(subset='harvest') 

mpl_init(fontsize=self.font_size) 

cm.create_group_mpl( 

self, 

self.draw_figures(history), 

title=u'Fuzzy MT', 

section='solution', 

feather_icon='wind', 

description=u''' 

A fuzzy moment tensor, illustrating the solution's uncertainty. 

 

The P wave radiation pattern strength of every ensemble solution is stacked for 

all ray spokes. The projection shows the stacked radiation pattern. If the 

variability of the ensemble solutions is small, the fuzzy plot has clearly 

separated black and white fields, consistent with the nodal lines of the %s 

best solution (indicated in red). 

''' % ('cluster' if self.cluster_attribute is not None else 'global')) 

 

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] 

 

 

class HudsonPlot(PlotConfig): 

 

''' 

Illustration of the solution distribution of decomposed moment tensor. 

''' 

 

name = 'hudson' 

size_cm = Tuple.T(2, Float.T(), default=(17.5, 17.5*(3./4.))) 

beachball_type = StringChoice.T( 

choices=['full', 'deviatoric', 'dc'], 

default='dc') 

show_reference = Bool.T(default=True) 

 

def make(self, environ): 

cm = environ.get_plot_collection_manager() 

history = environ.get_history(subset='harvest') 

mpl_init(fontsize=self.font_size) 

cm.create_group_mpl( 

self, 

self.draw_figures(history), 

title=u'Hudson Plot', 

section='solution', 

feather_icon='box', 

description=u''' 

Hudson's source type plot with the ensemble of bootstrap solutions. 

 

For about 10% of the solutions (randomly chosen), the focal mechanism is 

depicted, others are represented as dots. The square marks the global best 

fitting solution. 

''') 

 

def draw_figures(self, history): 

 

color = 'black' 

fontsize = self.font_size 

markersize = fontsize * 1.5 

markersize_small = markersize * 0.2 

beachballsize = markersize 

beachballsize_small = beachballsize * 0.5 

beachball_type = self.beachball_type 

 

problem = history.problem 

best_source = history.get_best_source() 

mean_source = history.get_mean_source() 

 

fig = plt.figure(figsize=self.size_inch) 

axes = fig.add_subplot(1, 1, 1) 

 

data = [] 

for ix, x in enumerate(history.models): 

source = problem.get_source(x) 

mt = source.pyrocko_moment_tensor() 

u, v = hudson.project(mt) 

 

if random.random() < 0.1: 

try: 

beachball.plot_beachball_mpl( 

mt, axes, 

beachball_type=beachball_type, 

position=(u, v), 

size=beachballsize_small, 

color_t=color, 

alpha=0.5, 

zorder=1, 

linewidth=0.25) 

except beachball.BeachballError as e: 

logger.warn(str(e)) 

 

else: 

data.append((u, v)) 

 

if data: 

u, v = num.array(data).T 

axes.plot( 

u, v, 'o', 

color=color, 

ms=markersize_small, 

mec='none', 

mew=0, 

alpha=0.25, 

zorder=0) 

 

hudson.draw_axes(axes) 

 

mt = mean_source.pyrocko_moment_tensor() 

u, v = hudson.project(mt) 

 

try: 

beachball.plot_beachball_mpl( 

mt, axes, 

beachball_type=beachball_type, 

position=(u, v), 

size=beachballsize, 

color_t=color, 

zorder=2, 

linewidth=0.5) 

except beachball.BeachballError as e: 

logger.warn(str(e)) 

 

mt = best_source.pyrocko_moment_tensor() 

u, v = hudson.project(mt) 

 

axes.plot( 

u, v, 's', 

markersize=markersize, 

mew=1, 

mec='black', 

mfc='none', 

zorder=-2) 

 

if self.show_reference: 

mt = problem.base_source.pyrocko_moment_tensor() 

u, v = hudson.project(mt) 

 

try: 

beachball.plot_beachball_mpl( 

mt, axes, 

beachball_type=beachball_type, 

position=(u, v), 

size=beachballsize, 

color_t='red', 

zorder=2, 

linewidth=0.5) 

except beachball.BeachballError as e: 

logger.warn(str(e)) 

 

item = PlotItem( 

name='main') 

return [[item, fig]] 

 

 

def get_plot_classes(): 

return [ 

JointparPlot, 

HistogramPlot, 

]