Genie2D
Module Contents
Classes
Base class for 2D geometric shape representation. |
- class Genie2D.Genie2D(verbose=False)
Bases:
lsdo_genie.bsplines.BsplineSurface.BsplineSurfaceBase class for 2D geometric shape representation.
- Attributes:
- verbosebool
Prints out information for troubleshooting
- compute_errors()
Compute error values and print to terminal
- compute_phi(pts: numpy.ndarray)
Compute the phi value at a set of points
- Parameters:
- ptsnp.ndarray(N,2)
Set of points to be evaluated
- Returns:
- phinp.ndarray(N,)
The phi values at each input point
- config(domain: numpy.ndarray, max_control_points: int, min_ratio: float = 0.5, order: int = 4)
Set up the Bspline for non-interference constraints
- Parameters:
- domainnp.ndarray(2,2)
Lower and upper bounds in each dimension for the domain of interest
- max_control_pointsint
Maximum number of control points along the longest dimension
- min_ratiofloat
Minimum ratio from the shortest dimension to the longest dimension to maintain uniform control point spacing
- orderint
Polynomial order of the Bspline
- get_basis(loc='surf', du=0, dv=0)
Convenience function for building the basis matrix for different points
- Parameters:
- locstr
‘surf’ for surface points or ‘hess’ for quadrature points to evaluate the hessian
- duint
Derivative in the ‘u’ direction
- dvint
Derivative in the ‘v’ direction
- Returns:
- basissps.csc_matrix(N,Ncp)
The basis matrix that can be multiplied with the control points to get (N,) output values
- gradient_phi(pts: numpy.ndarray)
Compute the phi value at a set of points
- Parameters:
- ptsnp.ndarray(N,2)
Set of points to be evaluated
- Returns:
- dpdxnp.ndarray(N,)
The derivative of phi with respect to the x-coordinate
- dpdynp.ndarray(N,)
The derivative of phi with respect to the y-coordinate
- initialize_control_points(k: int = 6, rho: float = 10)
Initialize the control points using Hicken and Kaur’s explicit method
- Parameters:
- kint
Numer of nearest neighbors to include in the function
- rhofloat
Smoothing parameter
- Returns:
- cpsnp.ndarray(Ncp,3)
The initial control points with (x,y,phi) values at each point
- input_point_cloud(surface_points: numpy.ndarray = None, surface_normals: numpy.ndarray = None)
Input the point cloud data
- Parameters:
- surface_pointsnp.ndarray(N,2)
Positional data of the points in a point cloud
- surface_normalsnp.ndarray(N,2)
Normal vectors of the points in a point cloud
- solve_energy_minimization(Lp: float = 1.0, Ln: float = 1.0, Lr: float = 0.001, maxiter=None)
Solve the energy minimization problem
- Parameters:
- Lpfloat
Depricated weighting parameter for zero level set point energy term
- Lnfloat
Weighting parameter for the normal vectors energy term
- Lrfloat
Weighting parameter for the regularization energy term
- spatial_to_parametric(pts: numpy.ndarray)
Convert (x,y) coordinates to (u,v) coordinates
- Parameters:
- ptsnp.ndarray(N,2)
The points to convert to parametric coordinates
- Returns:
- unp.ndarray(N,)
The ‘u’ parametric coordinates
- vnp.ndarray(N,)
The ‘v’ parametric coordinates
- visualize(res: int = 300)
Visualize the isocontours, control points in 3D, and phi values along the x-y axes
- Parameters:
- resint
The resolution in each dimension to evaluate the function for visualization