Remove LCurve usage from the cookbook

The recipes should do normal inversion. There should be a separate
document showing what LCurve can do

cookbook/seismic_profile_vertical_smooth.py

@@ -5,7 +5,7 @@
 import numpy
 from fatiando import utils
 from fatiando.seismic.profile import layered_straight_ray, LayeredStraight
-from fatiando.inversion.regularization import Smoothness1D
+from fatiando.inversion import Smoothness1D
 from fatiando.vis import mpl
 
 # Make a layered model

cookbook/seismic_srtomo_damped.py

@@ -4,7 +4,7 @@
 import numpy as np
 from fatiando.mesher import SquareMesh
 from fatiando.seismic import ttime2d, srtomo
-from fatiando.inversion import Damping, LCurve
+from fatiando.inversion import Damping
 from fatiando.vis import mpl
 from fatiando import utils
 
@@ -26,19 +26,11 @@
 # Make the mesh
 mesh = SquareMesh(area, shape)
 # and run the inversion
-misfit = srtomo.SRTomo(tts, srcs, recs, mesh)
-regularization = Damping(mesh.size)
-# Will use the l-curve criterion to find the best regularization parameter
-tomo = LCurve(misfit, regularization,
-              [10.**i for i in np.arange(0, 12, 1)], njobs=4).fit()
+tomo = (srtomo.SRTomo(tts, srcs, recs, mesh) +
+        1e8*Damping(mesh.size))
+tomo.fit()
 mesh.addprop('vp', tomo.estimate_)
 
-# Plot the L-curve annd print the regularization parameter estimated
-mpl.figure()
-mpl.title('L-curve: triangle marks the best solution')
-tomo.plot_lcurve()
-print "Estimated regularization parameter: %g" % (tomo.regul_param_)
-
 # Calculate and print the standard deviation of the residuals
 # Should be close to the data error if the inversion was able to fit the data
 residuals = tomo[0].residuals()

cookbook/seismic_srtomo_smooth.py

@@ -4,7 +4,7 @@
 import numpy as np
 from fatiando.mesher import SquareMesh
 from fatiando.seismic import ttime2d, srtomo
-from fatiando.inversion import Smoothness2D, LCurve
+from fatiando.inversion import Smoothness2D
 from fatiando.vis import mpl
 from fatiando import utils
 
@@ -26,18 +26,11 @@
 # Make the mesh
 mesh = SquareMesh(area, shape)
 # and run the inversion
-misfit = srtomo.SRTomo(tts, srcs, recs, mesh)
-regularization = Smoothness2D(mesh.shape)
-# Will use the l-curve criterion to find the best regularization parameter
-tomo = LCurve(misfit, regularization, np.logspace(0, 12, 10), njobs=4).fit()
+tomo = (srtomo.SRTomo(tts, srcs, recs, mesh) +
+        1e8*Smoothness2D(mesh.shape))
+tomo.fit()
 mesh.addprop('vp', tomo.estimate_)
 
-# Plot the L-curve annd print the regularization parameter estimated
-mpl.figure()
-mpl.title('L-curve: triangle marks the best solution')
-tomo.plot_lcurve()
-print "Estimated regularization parameter: %g" % (tomo.regul_param_)
-
 # Calculate and print the standard deviation of the residuals
 # Should be close to the data error if the inversion was able to fit the data
 residuals = tomo[0].residuals()