def plot(self, V):
f_source = df.interpolate(self, V)
from util import matplot4dolfin
matplot = matplot4dolfin()
fig = matplot.plot(f_source)
# matplot.show()
return fig
def _plot_mpl(self, SAVE=False):
import matplotlib.pyplot as plt
from util import matplot4dolfin
matplot = matplot4dolfin()
# codes for plotting solutions
import matplotlib as mpl
for i, titl in enumerate(self.titles):
# get solution
try:
soln = getattr(self, '_'.join(['states', self.sols[i]]))
except AttributeError:
print(self.sols_names[i] + 'solution not found!')
pass
else:
soln = soln.split(True)
fig, axes = plt.subplots(nrows=2,
ncols=2,
sharex=True,
sharey=True,
num=i,
figsize=(10, 6))
for j, ax in enumerate(axes.flat):
plt.axes(ax)
sub_fig = matplot.plot(soln[j])
plt.axis('tight')
ax.set_title(self.sub_titles[j])
cax, kw = mpl.colorbar.make_axes([ax for ax in axes.flat])
plt.colorbar(sub_fig, cax=cax,
**kw) # TODO: fix the issue of common color range
# set common titles
fig.suptitle(titl)
if SAVE:
matplot.save(self.savepath,
self.sols_names[i] + '.png',
bbox_inches='tight')
def plot_soln(self,soln_f):
"""
Plot solution function.
"""
from util import matplot4dolfin
matplot=matplot4dolfin()
fig=matplot.plot(soln_f)
return fig
def plot(self, V):
u_coeff = df.interpolate(self, V)
from util import matplot4dolfin
matplot = matplot4dolfin(
overloaded=False
) # switch overloaded to use the default or the manually coded plotting function
fig = matplot.plot(u_coeff)
# matplot.show()
return fig
def plot_data(self):
"""
Plot the data information.
"""
import matplotlib.pyplot as plt
from util import matplot4dolfin
matplot = matplot4dolfin()
fig = matplot.plot(self.pde.mesh)
plt.axis('tight')
# plt.plot(self.loc[:,0],self.loc[:,1],'bo',markersize=10)
plt.scatter(self.loc[:, 0],
self.loc[:, 1],
c=self.obs,
s=200,
zorder=2)
# plt.xlim(0,1); plt.ylim(0,1)
# plt.axis('tight')
# plt.xlabel('x',fontsize=12); plt.ylabel('y',fontsize=12,rotation=0)
# plt.title('Observations on selected locations',fontsize=12)
# matplot.show()
return fig
Shiwei Lan @ U of Warwick, 2016
"""
import os
import dolfin as df
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mp
from Elliptic_dili import Elliptic
import sys
sys.path.append("../")
from util import matplot4dolfin
matplot = matplot4dolfin()
# define the inverse problem
np.random.seed(2017)
SNR = 100
elliptic = Elliptic(nx=40, ny=40, SNR=SNR)
# algorithms
algs = ('pCN', 'infMALA', 'infHMC', 'DRinfmMALA', 'DRinfmHMC', 'DILI',
'aDRinfmMALA', 'aDRinfmHMC')
alg_names = ('pCN', '$\infty$-MALA', '$\infty$-HMC', 'DR-$\infty$-mMALA',
'DR-$\infty$-mHMC', 'DILI', 'aDR-$\infty$-mMALA',
'aDR-$\infty$-mHMC')
num_algs = len(algs)
# preparation for estimates
folder = './analysis_f_SNR' + str(SNR)
