from fatiando.vis import mpl
# Make synthetic data
props = {'density':1000}
model = [mesher.Prism(-500, 500, -1000, 1000, 500, 4000, props)]
shape = (25, 25)
x, y, z = gridder.regular([-5000, 5000, -5000, 5000], shape, z=0)
gz = utils.contaminate(prism.gz(x, y, z, model), 0.1)
# Setup the layer
layer = mesher.PointGrid([-6000, 6000, -6000, 6000], 500, (50, 50))
# Estimate the density
# Need to apply enough damping so that won't try to fit the error as well
solver = EQLGravity(x, y, z, gz, layer) + 10**-24*Damping(layer.size)
solver.fit()
layer.addprop('density', solver.estimate_)
residuals = solver.residuals()
print "Residuals:"
print "mean:", residuals.mean()
print "stddev:", residuals.std()
# Plot the layer and the fit
mpl.figure(figsize=(14, 4))
mpl.subplot(1, 3, 1)
mpl.axis('scaled')
mpl.title('Layer (kg.m^-3)')
mpl.pcolor(layer.y, layer.x, layer.props['density'], layer.shape)
mpl.colorbar()
mpl.m2km()
mpl.subplot(1, 3, 2)
mpl.axis('scaled')
mpl.title('Fit (mGal)')
