def showFit(self, axs=None, **kwargs):
"""Show data and the inversion result model response."""
orientation = 'vertical'
if axs is None:
fig, axs = pg.plt.subplots(nrows=1, ncols=2)
orientation = 'horizontal'
self.showData(data=self.inv.dataVals,
orientation=orientation,
ax=axs[0],
**kwargs)
axs[0].text(0.0,
1.03,
"Data",
transform=axs[0].transAxes,
horizontalalignment='left',
verticalalignment='center')
resp = None
data = None
if 'model' in kwargs:
resp = self.fop.response(kwargs['model'])
data = self._ensureData(self.fop.data)
else:
resp = self.inv.response
data = self.fw.dataVals
self.showData(data=resp, orientation=orientation, ax=axs[1], **kwargs)
axs[1].text(0.0,
1.03,
"Response",
transform=axs[1].transAxes,
horizontalalignment='left',
verticalalignment='center')
axs[1].text(1.0,
1.03,
r"rrms: {0}%, $\chi^2$: {1}".format(
pg.pf(pg.utils.rrms(data, resp) * 100),
pg.pf(self.fw.chi2History[-1])),
transform=axs[1].transAxes,
horizontalalignment='right',
verticalalignment='center')
# if not kwargs.pop('hideFittingAnnotation', False):
# axs[0].text(0.01, 1.0025, "rrms: {0}, $\chi^2$: {1}"
# .format(pg.utils.prettyFloat(self.fw.inv.relrms()),
# pg.utils.prettyFloat(self.fw.inv.chi2())),
# transform=axs[0].transAxes,
# horizontalalignment='left',
# verticalalignment='bottom')
return axs
def showFit(self, axs=None, **kwargs):
"""Show data and the inversion result model response."""
orientation = 'vertical'
if axs is None:
fig, axs = pg.plt.subplots(nrows=1, ncols=2)
orientation = 'horizontal'
self.showData(data=self.inv.dataVals,
orientation=orientation,
ax=axs[0],
**kwargs)
axs[0].text(0.0,
1.03,
"Data",
transform=axs[0].transAxes,
horizontalalignment='left',
verticalalignment='center')
resp = None
data = None
if 'model' in kwargs:
resp = self.fop.response(kwargs['model'])
data = self._ensureData(self.fop.data)
else:
resp = self.inv.response
data = self.fw.dataVals
self.showData(data=resp, orientation=orientation, ax=axs[1], **kwargs)
axs[1].text(0.0,
1.03,
"Response",
transform=axs[1].transAxes,
horizontalalignment='left',
verticalalignment='center')
fittext = r"rrms: {0}%, $\chi^2$: {1}".format(
pg.pf(pg.utils.rrms(data, resp) * 100),
pg.pf(self.fw.chi2History[-1]))
axs[1].text(1.0,
1.03,
fittext,
transform=axs[1].transAxes,
horizontalalignment='right',
verticalalignment='center')
return axs
def flipImagPart(self, v):
z = pg.utils.toComplex(v)
pg.warn('pre min/max={0} / {1} im: {2} / {3}'.format(
pf(min(z.real)), pf(max(z.real)), pf(min(z.imag)),
pf(max(z.imag))))
v = pg.utils.squeezeComplex(pg.utils.toComplex(v), conj=self._conjImag)
z = pg.utils.toComplex(v)
pg.warn('pos min/max={0} / {1} im: {2} / {3}'.format(
pf(min(z.real)), pf(max(z.real)), pf(min(z.imag)),
pf(max(z.imag))))
return v
def __ElementMatrix_str(self):
"""Show entries of an ElementMatrix."""
import pygimli as pg
if self.mat().cols() == 0 and self.mat().rows() == 0:
return 'Empty ElementMatrix\n'
maxRowID = int(np.log10(max(self.rowIDs()))) + 2
s = '\n ' + ' ' * maxRowID
# print(self.mat())
# print(self.colIDs())
# print(self.rowIDs())
for i in range(self.mat().cols()):
s += str(self.colIDs()[i]).rjust(9)
s += '\n'
s += ' ' + '-' * self.mat().cols() * (9 + maxRowID) + '-\n'
for i in range(self.mat().rows()):
s += str(self.rowIDs()[i]).rjust(maxRowID) + " :"
for v in self.row(i):
s += pg.pf(v).rjust(9)
s += '\n'
return s
# differences.
#
uAna = pg.Vector(
list(
map(lambda p__: uAnalytical(p__, sourcePosA, k, sigma),
mesh.positions())))
uAna -= pg.Vector(
list(
map(lambda p__: uAnalytical(p__, sourcePosB, k, sigma),
mesh.positions())))
ax = pg.show(mesh,
data=pg.abs(uAna - u),
cMap="Reds",
orientation='horizontal',
label='|$u_{exact}$ -$u$|',
logScale=True,
cMin=1e-7,
cMax=1e-1,
contourLines=False,
nCols=12,
nLevs=7,
showMesh=True)[0]
#print('l2:', pg.pf(pg.solver.normL2(uAna-u)))
print('L2:', pg.pf(pg.solver.normL2(uAna - u, mesh)))
print('H1:', pg.pf(pg.solver.normH1(uAna - u, mesh)))
np.testing.assert_approx_equal(pg.solver.normL2(uAna - u, mesh),
0.02415,
significant=3)