# Clipping Surface and Contours¶

The function is given on the Wikipedia page Test functions for constrained optimization.

The function and constraint are given by:

This example is a surface which is clipped using a functional operation, which itself is a function of a surface. The above ‘constrained optimization’ surface is clipped at the boundary intersection of a plane and ‘constraint’ surface. The ‘subjected constraint’ is visualized in the plot below.

The constrained optimization surface script is given below.

```import numpy as np
import matplotlib.pyplot as plt
import s3dlib.surface as s3d

#.. Contours Projection and using a clipping function.

# 1. Define function to examine ....................................

def optimization(xyz) :
x,y,z = xyz
Z = 4*x**2 - 2.1*x**4 + x**6/3 + x*y - 4*y**2 + 4*y**4
return x,y,Z

def constraint(xyz) :
x,y,z = xyz
Z = 2*(np.sin(2*np.pi*y))**2 - np.sin(4*np.pi*x)
return x,y,Z

def criterion(xyz) :  return np.less_equal( constraint(xyz)[2] , 1.5)

# 2. Setup and map surface .........................................
rez=7

surface = s3d.PlanarSurface(rez,name='constrained optimization').domain( (-1,0.75) )
surface.map_geom_from_op(optimization)
surface.map_cmap_from_op( lambda xyz : xyz[2], 'jet')
contours = surface.contourLineSet(20)       #    | REQUIRED: Order Of Operation.
surface.clip(criterion)              # note-->   | Clip surface 'after' contours
contours.clip(criterion)             #           | are created, 'then' clip contours,
contours.map_to_plane(-5)            #           | followed by projection.
contours.set_linewidth(0.75)

# ..... following is to form the black edges on the clipped contours...
c_surface = s3d.PlanarSurface(rez).domain( (-1,0.75) )  #.. note: not rendered
c_surface.map_geom_from_op(constraint)
c_contours = c_surface.contourLines(1.5, color='k')
c_contours.map_to_plane(-5)
c_contours.set_linewidth(0.75)

# ..... front grid ....................................................
vbox = [ [-1,-1,3],[0.75,-1,3],[0.75,1.0,3],[0.75,-1,-5] ]
ibox = [ [0,1,2],[1,3]]
box= s3d.ColorLine3DCollection(vbox,ibox,color='grey',linewidth=1)

# 3. Construct figure, add surface plot ............................

fig = plt.figure()
fig.text(0.975,0.975,surface.name, ha='right', va='top', fontsize='large')
sID = 'Gomez and Levy function\n(modified)'
fig.text(0.025,0.975,sID, ha='left', va='top', fontsize='large')
ax = plt.axes(projection='3d', proj_type='ortho')
ax.set(xlim=(-1,0.75), ylim=(-1,1), zlim=(-5,3) )
ax.view_init(48,-50)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_xticks( (-1,-.75,-.50,-.25,0.0,0.25,0.5,0.75) )
ax.set_zticks( (-1,-.75,-.50,-.25,0.0,0.25,0.5,0.75,-1.0) )
ax.set_zticks( (-1,0,1,2,3) )

fig.tight_layout()
plt.show()
```

The addition script for visualizing the constraint criterion is given below.

```surface = s3d.PlanarSurface(rez, color='lightsteelblue' ).domain( (-1,0.75) )
surface.map_geom_from_op(constraint)
plane = s3d.PlanarSurface(rez, color='khaki').domain( (-1,0.75) )
plane.map_geom_from_op(lambda A : [ A[0], A[1], np.full(A.shape[1],1.5) ])
c_contours = surface.contourLines(1.5, color='k')
c_contours.map_to_plane(-5)
c_contours.set_linewidth(0.75)

surface = surface + plane
surface.name = 'subjected constraint'

vbox = [ [-1,-1,3],[0.75,-1,3],[0.75,1.0,3],[0.75,-1,-5] ]
ibox = [ [0,1,2],[1,3]]
box= s3d.ColorLine3DCollection(vbox,ibox,color='grey',linewidth=1)

fig = plt.figure()
fig.text(0.975,0.975,surface.name, ha='right', va='top',
fontsize='large', multialignment='right')
ax = plt.axes(projection='3d', proj_type='ortho')
ax.set(xlim=(-1,0.75), ylim=(-1,1), zlim=(-5,3) )
ax.view_init(48,-50)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_xticks( (-1,-.75,-.50,-.25,0.0,0.25,0.5,0.75) )
ax.set_zticks( (-1,-.75,-.50,-.25,0.0,0.25,0.5,0.75,-1.0) )
ax.set_zticks( (-1,0,1,2,3) )