def showCoverage(self, ax=None, name='coverage', **kwargs):
"""Show the ray coverage in log-scale."""
if ax is None:
fig, ax = plt.subplots()
cov = self.rayCoverage()
return pg.show(self.fop.paraDomain,
pg.log10(cov+min(cov[cov > 0])*.5), ax=ax,
coverage=self.standardizedCoverage(), **kwargs)
def showCoverage(self, ax=None, name='coverage'):
"""shows the ray coverage in logscale"""
if ax is None:
fig, ax = plt.subplots()
self.figs[name] = fig
self.axs[name] = ax
cov = self.rayCoverage()
pg.show(self.mesh, pg.log10(cov+min(cov[cov > 0])*.5), axes=ax,
coverage=self.standardizedCoverage())
def showCoverage(self, ax=None, name='coverage', **kwargs):
"""shows the ray coverage in logscale"""
if ax is None:
fig, ax = plt.subplots()
self.figs[name] = fig
self.axs[name] = ax
cov = self.rayCoverage()
pg.show(self.mesh, pg.log10(cov+min(cov[cov > 0])*.5), ax=ax,
coverage=self.standardizedCoverage(), **kwargs)
def logDropTol( p, droptol = 1e-3 ):
""""""
tmp = pg.RVector( p );
tmp = pg.abs( tmp / droptol )
tmp.setVal( 1.0, pg.find( tmp < 1.0 ) )
#for i, v in enumerate( tmp ):
#tmp[ i ] = abs( tmp[ i ] / droptol );
#if tmp[ i ] < 1.0: tmp[ i ] = 1.0;
tmp = pg.log10( tmp );
tmp *= pg.sign( p );
return tmp;
def logDropTol(p, droptol=1e-3):
"""
Example
-------
>>> from pygimli.utils import logDropTol
>>> x = logDropTol((-10,-1,0,1,100))
>>> print(x.array())
[-4. -3. 0. 3. 5.]
"""
tmp = pg.RVector(p)
tmp = pg.abs(tmp / droptol)
tmp.setVal(1.0, pg.find(tmp < 1.0))
tmp = pg.log10(tmp)
tmp *= pg.sign(p)
return tmp
def logDropTol(p, droptol=1e-3):
"""Create logarithmic scaled copy of p.
Examples
--------
>>> from pygimli.utils import logDropTol
>>> x = logDropTol((-10, -1, 0, 1, 100))
>>> print(x.array())
[-4. -3. 0. 3. 5.]
"""
tmp = pg.RVector(p)
tmp = pg.abs(tmp / droptol)
tmp.setVal(1.0, pg.find(tmp < 1.0))
tmp = pg.log10(tmp)
tmp *= pg.sign(p)
return tmp
# %% # as desired for a roughness operator. Therefore, an additional matrix called # :py:func:`pg.matrix.GeostatisticalConstraintsMatrix` # was implemented where this spur is corrected for. # It is, like the correlation matrix, created by a mesh, a list of correlation # lengths I, a dip angle# that distorts the x/y plane and a strike angle # towards the third direction. # C = pg.matrix.GeostatisticConstraintsMatrix(mesh=mesh, I=5) # %% # In order to extract a certain column, we generate a vector with a single 1 vec = pg.Vector(mesh.cellCount()) vec[ind] = 1.0 pg.show(mesh, pg.log10(pg.abs(C * vec)), cMin=-6, cMax=0, cMap="magma_r") # %% # The constraints have a rather small footprint compared to the correlation # (note the logarithmic scale) but still to the whole mesh unlike the classical # constraint matrices that only include relations to neighboring cells. # %% # Such a matrix can also be defined for different ranges and a dip angle Cdip = pg.matrix.GeostatisticConstraintsMatrix(mesh=mesh, I=[10, 3], dip=-25) pg.show(mesh, pg.log10(pg.abs(Cdip * vec)), cMin=-6, cMax=0, cMap="magma_r") # %% # In order to illustrate the role of the constraints, we use a very simple # mapping forward operator that retrieves the values in the mesh at some given # positions. The constraints are therefore used as interpolation operators.
# %%
# as desired for a roughness operator. Therefore, an additional matrix called
# :py:mod:`pg.matrix.GeostatisticalConstraintsMatrix`
# was implemented where this spur is corrected for.
# It is, like the correlation matrix, created by a mesh, a list of correlation
# lengths I, a dip angle# that distorts the x/y plane and a strike angle
# towards the third direction.
#
C = pg.matrix.GeostatisticConstraintsMatrix(mesh=mesh, I=5)
# %%
# In order to extract a certain column, we generate a vector with a single 1
vec = pg.Vector(mesh.cellCount())
vec[ind] = 1.0
ax, cb = pg.show(mesh,
pg.log10(pg.abs(C * vec)),
cMin=-6,
cMax=0,
cMap="magma_r")
# %%
# The constraints have a rather small footprint compared to the correlation
# (note the logarithmic scale) but still to the whole mesh unlike the classical
# constraint matrices that only include relations to neighboring cells.
# %%
# Such a matrix can also be defined for different ranges and dip angles, e.g.
Cdip = pg.matrix.GeostatisticConstraintsMatrix(mesh=mesh, I=[10, 3], dip=-25)
ax, cb = pg.show(mesh,
pg.log10(pg.abs(Cdip * vec)),
cMin=-6,
