lsdo_genie.utils

Subpackages

Submodules

Package Contents

Classes

Genie2DCSDLModel

Computational System Design Language (CSDL) interface for using lsdo_genie in gradient-based

Genie3DCSDLModel

Computational System Design Language (CSDL) interface for using lsdo_genie in gradient-based

Functions

enlarged_bbox(surface_points[, percent])

Calcuate an enlarged bounding box of a given point cloud

explicit_lsf(pts, data_points, data_normals, k, rho)

Explicit level set function derived by Hicken and Kaur which interpolates

extract_stl_data(filename[, verbose])

Extract the point cloud data from a .stl file

midpoint_normal_approx(vertices)

Approximate an arbitrary closed polygon by using the midpoints of the edges formed by an input set of vertices

vertex_normal_approx(vertices)

A quick way to approximate the normal vectors from an arbitrary closed polygon.

visualize_2Dptcloud(points, normals[, show_normals, ...])

Plots and shows a 2D point cloud with normal vectors

visualize_3Dptcloud(points, normals[, show_normals, ...])

Plots and shows a 3D point cloud with normal vectors
class lsdo_genie.utils.Genie2DCSDLModel

Bases: csdl.CustomExplicitOperation

Computational System Design Language (CSDL) interface for using lsdo_genie in gradient-based optimization for 2D geometric shapes

Parameters:
num_ptsint

Number of points in the input

x_namestr

CSDL variable name of the x-coordinate of the input points

y_namestr

CSDL variable name of the y-coordinate of the input points

out_namestr

CSDL variable name of the output phi

genie_objectlsdo_genie.Genie2D

The genie object to be used in the model to compute phi

compute(inputs, outputs)

compute

compute_derivatives(inputs, derivatives)

compute_derivatives

define()

define

initialize()

initialize

class lsdo_genie.utils.Genie3DCSDLModel

Bases: csdl.CustomExplicitOperation

Computational System Design Language (CSDL) interface for using lsdo_genie in gradient-based optimization for 3D geometric shapes

Parameters:
num_ptsint

Number of points in the input

x_namestr

CSDL variable name of the x-coordinate of the input points

y_namestr

CSDL variable name of the y-coordinate of the input points

z_namestr

CSDL variable name of the z-coordinate of the input points

out_namestr

CSDL variable name of the output phi

genie_objectlsdo_genie.Genie2D

The genie object to be used in the model to compute phi

compute(inputs, outputs)

compute

compute_derivatives(inputs, derivatives)

compute_derivatives

define()

define

initialize()

initialize

lsdo_genie.utils.enlarged_bbox(surface_points: numpy.ndarray, percent: float = 10.0)

Calcuate an enlarged bounding box of a given point cloud

Parameters:
surface_pointsnp.ndarray(Ngamma,d)

The positions of points in a point cloud in ‘d’ dimensions

percentfloat

The percentage to increase the bounding box from the minimum bounding box

Returns:
domainnp.ndarray(d,2)

The lower and upper bounds along each dimension

lsdo_genie.utils.explicit_lsf(pts, data_points: scipy.spatial.KDTree, data_normals, k, rho)

Explicit level set function derived by Hicken and Kaur which interpolates data points via piecewise linear signed distance functions defined by local hyperplanes. The function is defined by these piecewise functions with KS-aggregation, but is non-differentiable if only using the k-nearest neighbors.

“An Explicit Level-Set Formula to Approximate Geometries” Jason E. Hicken and Sharanjeet Kaur doi:10.2514/6.2022-1862

Parameters:
pointsnp.ndarray(N,d)

Points to evaluate the level set funcion

data_pointsscipy.spatial.KDTree

KDTree structure of the point cloud

data_normalsnp.ndarray(Ngamma,d)

Normal vectors of the point cloud

kint

Numer of nearest neighbors to include in the function

rhofloat

Smoothing parameter

Returns:
phinp.ndarray(N,)

Approximate signed distance value of the points

lsdo_genie.utils.extract_stl_data(filename: str, verbose: bool = False)

Extract the point cloud data from a .stl file

Parameters:
filenamestr

The location of your file relative to your working directory when running

verbosebool

Prints the number of points loaded to the terminal

Returns:
centroidsnp.ndarray(N,3)

The centroids of the triangulation

normalsnp.ndarray(N,3)

The normal vectors of the associated centroids

lsdo_genie.utils.midpoint_normal_approx(vertices: numpy.ndarray)

Approximate an arbitrary closed polygon by using the midpoints of the edges formed by an input set of vertices Normal direction is determined by the direction of the points given in the input: Clockwise : normals point outwards Counter-Clockwise : normals point inwards

Parameters:
verticesnp.ndarray(num_pts,2)

The input points

Returns:
midpoints: np.ndarray(num_pts,2)

Midpoints between input points

normalsnp.ndarray(num_pts,2)

Normal vectors at the midpoints

lsdo_genie.utils.vertex_normal_approx(vertices: numpy.ndarray)

A quick way to approximate the normal vectors from an arbitrary closed polygon. Normals are an average between the 2 edge normals adjacent to a point and follow a covention: Clockwise : normals point outwards Counter-Clockwise : normals point inwards

Parameters:
verticesnp.ndarray(num_pts,2)

The input points

Returns:
normalsnp.ndarray(num_pts,2)

Approximate normal vectors at the input points

lsdo_genie.utils.visualize_2Dptcloud(points: numpy.ndarray, normals: numpy.ndarray, show_normals: bool = True, title: str = '2D Point cloud with normals', perc_normal_length: float = 5.0)

Plots and shows a 2D point cloud with normal vectors

Parameters:
pointsnp.ndarray(N,2)

The points in the poind cloud

normalsnp.ndarray(N,2)

The normal vectors of the point cloud

show_normalsbool

Show the normal vectors with a blue arrow

titlestr

Title of the plot

lsdo_genie.utils.visualize_3Dptcloud(points: numpy.ndarray, normals: numpy.ndarray, show_normals: bool = True, title: str = '3D Point cloud with normals', perc_normal_length: float = 5.0)

Plots and shows a 3D point cloud with normal vectors

Parameters:
pointsnp.ndarray(N,3)

The points in the poind cloud

normalsnp.ndarray(N,3)

The normal vectors of the point cloud

show_normalsbool

Show the normal vectors with a blue arrow

titlestr

Title of the plot