###############################################################################
# The data container has no apparent resistivities (token field 'rhoa') yet.
# We can let the Manager fix this later for us (as we now have the 'k' field),
# or we do it manually.
mgr.checkData(data)
print(data)
###############################################################################
# The data container does not necessarily contain data errors data errors
# (token field 'err'), requiring us to enter data errors. We can let the
# manager guess some defaults for us automaticly or set them manually
data['err'] = ert.estimateError(data, absoluteUError=5e-5, relativeError=0.03)
# or manually:
# data['err'] = data_errors # somehow
ert.show(data, data['err'] * 100)
###############################################################################
# Now the data have all necessary fields ('rhoa', 'err' and 'k') so we can run
# the inversion. The inversion mesh will be created with some optional values
# for the parametric mesh generation.
#
mod = mgr.invert(data,
lam=10,
verbose=True,
paraDX=0.3,
paraMaxCellSize=10,
paraDepth=20,
quality=33.6)
mgr.showResult()
pg.info('Selected data noise %(min/max)',
min(data['err']) * 100,
max(data['err']) * 100)
###############################################################################
# Optional: you can filter all values and tokens in the data container.
# Its possible that there are some negative data values due to noise and
# huge geometric factors. So we need to remove them.
data.remove(data['rhoa'] < 0)
pg.info('Filtered rhoa (min/max)', min(data['rhoa']), max(data['rhoa']))
# You can save the data for further use
data.save('simple.dat')
# You can take a look at the data
ert.show(data)
###############################################################################
# Initialize the ERTManager, e.g. with a data container or a filename.
#
mgr = ert.ERTManager('simple.dat')
###############################################################################
# Run the inversion with the preset data. The Inversion mesh will be created
# with default settings.
inv = mgr.invert(lam=20, verbose=True)
# np.testing.assert_approx_equal(mgr.inv.chi2(), 1.049145, significant=3)
###############################################################################
# Let the ERTManger show you the model of the last successful run and how it
# fits the data. Shows data, model response, and model.
#
schemes = ['wa', 'wb', 'pp', 'pd', 'dd', 'slm', 'gr', 'hw']
fig, ax = pg.plt.subplots(3, 3)
kw = dict(cMin=10,
cMax=1000,
logScale=True,
colorBar=False,
cMap="viridis")
for i, schemeName in enumerate(schemes):
shm = ert.createData(elecs=41, schemeName=schemeName)
print(schemeName, shm)
k = ert.geometricFactor(shm)
mg = DataSchemeManager()
longname = mg.scheme(schemeName).name
ert.show(shm,
vals=np.abs(k),
ax=ax.flat[i],
colorBar=1,
logScale=0,
label='k ' + longname + ')-' + schemeName)
createColorBarOnly(**kw, ax=ax.flat[-1], aspect=0.1)
pg.plt.show()
# %%
# import matplotlib.pyplot as plt
# from pygimli.physics import ert
# schemes = ['wa', 'wb', 'pp', 'pd', 'dd', 'slm', 'hw', 'gr']
# fig, ax = plt.subplots(3,3)
# for i, schemeName in enumerate(schemes):
# s = ert.createData(elecs=41, schemeName=schemeName)
# k = ert.geometricFactors(s)