miv_simulator.geometry.point_fit

Procedures to fit 3d point clouds to an alpha shape

Functions

PCA(data[, correlation, sort])	Applies Principal Component Analysis to the data
points_plane_fit(points[, equation])	Computes the best fitting plane of the given points
rotvector3d(A, B)
ssc(v)

miv_simulator.geometry.point_fit.PCA(data, correlation=False, sort=True)

Applies Principal Component Analysis to the data

Parameters:

data: array

The array containing the data. The array must have NxM dimensions, where each

of the N rows represents a different individual record and each of the M columns

represents a different variable recorded for that individual record.

array([ [V11, … , V1m], …, [Vn1, … , Vnm]])

correlation(Optional)bool

Set the type of matrix to be computed (see Notes):

If True compute the correlation matrix. If False(Default) compute the covariance matrix.

sort(Optional)bool

Set the order that the eigenvalues/vectors will have

If True(Default) they will be sorted (from higher value to less). If False they won’t.

Returns

——-

eigenvalues: (1,M) array

The eigenvalues of the corresponding matrix.

eigenvector: (M,M) array

The eigenvectors of the corresponding matrix.

Notes

The correlation matrix is a better choice when there are different magnitudes representing the M variables. Use covariance matrix in other cases.

miv_simulator.geometry.point_fit.points_plane_fit(points, equation=False)

Computes the best fitting plane of the given points

Parameters:

points: array

The x,y,z coordinates corresponding to the points from which we want

to define the best fitting plane. Expected format:

array([ [x1,y1,z1], …, [xn,yn,zn]])

equation(Optional)bool

Set the oputput plane format:

If True return the a,b,c,d coefficients of the plane. If False(Default) return 1 Point and 1 Normal vector.

Returns

——-

a, b, c, dfloat

The coefficients solving the plane equation.

or

point, normal: array

The plane defined by 1 Point and 1 Normal vector. With format:

array([Px,Py,Pz]), array([Nx,Ny,Nz])