Utility Functions¶
These modules contain common utility and helper functions for BSpline / NURBS curve and surface evaluation operations.
Utilities¶
The utilities
module contains common utility functions for NURBSPython library and its extensions.

geomdl.utilities.
check_params
(params)¶ Checks if the parameters are defined in the domain [0, 1].
Parameters: params (list, tuple) – parameters (u, v, w) Returns: True if defined in the domain [0, 1]. False, otherwise. Return type: bool

geomdl.utilities.
color_generator
(seed=None)¶ Generates random colors for control and evaluated curve/surface points plots.
The
seed
argument is used to set the random seed by directly passing the value torandom.seed()
function. Please see the Python documentation for more details on therandom
module .Inspired from https://stackoverflow.com/a/14019260
Parameters: seed – Sets the random seed Returns: list of color strings in hex format Return type: list

geomdl.utilities.
evaluate_bounding_box
(ctrlpts)¶ Computes the minimum bounding box of the point set.
The (minimum) bounding box is the smallest enclosure in which all the input points lie.
Parameters: ctrlpts (list, tuple) – points Returns: bounding box in the format [min, max] Return type: tuple

geomdl.utilities.
make_quad
(points, size_u, size_v)¶ Converts linear sequence of input points into a quad structure.
Parameters:  points (list, tuple) – list of points to be ordered
 size_v (int) – number of elements in a row
 size_u (int) – number of elements in a column
Returns: reordered points
Return type: list

geomdl.utilities.
make_quadtree
(points, size_u, size_v, **kwargs)¶ Generates a quadtreelike structure from surface control points.
This function generates a 2dimensional list of control point coordinates. Considering the objectoriented representation of a quadtree data structure, first dimension of the generated list corresponds to a list of QuadTree classes. Second dimension of the generated list corresponds to a QuadTree data structure. The first element of the 2nd dimension is the midpoint of the bounding box and the remaining elements are corner points of the bounding box organized in counterclockwise order.
To maintain stability for the data structure on the edges and corners, the function accepts
extrapolate
keyword argument. If it is True, then the function extrapolates the surface on the corners and edges to complete the quadlike structure for each control point. If it is False, no extrapolation will be applied. By default,extrapolate
is set to True.Please note that this function’s intention is not generating a real quadtree structure but reorganizing the control points in a very similar fashion to make them available for various geometric operations.
Parameters:  points (list, tuple) – 1dimensional array of surface control points
 size_u (int) – number of control points on the udirection
 size_v (int) – number of control points on the vdirection
Returns: control points organized in a quadtreelike structure
Return type: tuple

geomdl.utilities.
make_zigzag
(points, num_cols)¶ Converts linear sequence of points into a zigzag shape.
This function is designed to create input for the visualization software. It orders the points to draw a zigzag shape which enables generating properly connected lines without any scanlines. Please see the below sketch on the functionality of the
num_cols
parameter:num cols <=============> >> << >> <<
Please note that this function does not detect the ordering of the input points to detect the input points have already been processed to generate a zigzag shape.
Parameters:  points (list) – list of points to be ordered
 num_cols (int) – number of elements in a row which the zigzag is generated
Returns: reordered points
Return type: list
Helpers¶
The helpers
module contains common functions required for evaluating both surfaces and curves, such as basis
function computations, knot vector span finding, etc.

geomdl.helpers.
basis_function
(degree, knot_vector, span, knot)¶ Computes the nonvanishing basis functions for a single parameter.
Implementation of Algorithm A2.2 from The NURBS Book by Piegl & Tiller. Uses recurrence to compute the basis functions, also known as Cox  de Boor recursion formula.
Parameters:  degree (int) – degree,
 knot_vector (list, tuple) – knot vector,
 span (int) – knot span,
 knot (float) – knot or parameter,
Returns: basis functions
Return type: list

geomdl.helpers.
basis_function_all
(degree, knot_vector, span, knot)¶ Computes all nonzero basis functions of all degrees from 0 up to the input degree for a single parameter.
A slightly modified version of Algorithm A2.2 from The NURBS Book by Piegl & Tiller. Wrapper for
helpers.basis_function()
to compute multiple basis functions. Uses recurrence to compute the basis functions, also known as Cox  de Boor recursion formula.For instance; if
degree = 2
, then this function will compute the basis function values of degrees 0, 1 and 2 for theknot
value at the input knotspan
of theknot_vector
.Parameters:  degree (int) – degree,
 knot_vector (list, tuple) – knot vector,
 span (int) – knot span,
 knot (float) – knot or parameter,
Returns: basis functions
Return type: list

geomdl.helpers.
basis_function_ders
(degree, knot_vector, span, knot, order)¶ Computes derivatives of the basis functions for a single parameter.
Implementation of Algorithm A2.3 from The NURBS Book by Piegl & Tiller.
Parameters:  degree (int) – degree,
 knot_vector (list, tuple) – knot vector,
 span (int) – knot span,
 knot (float) – knot or parameter,
 order (int) – order of the derivative
Returns: derivatives of the basis functions
Return type: list

geomdl.helpers.
basis_function_ders_one
(degree, knot_vector, span, knot, order)¶ Computes the derivative of one basis functions for a single parameter.
Implementation of Algorithm A2.5 from The NURBS Book by Piegl & Tiller.
Parameters:  degree (int) – degree,
 knot_vector (list, tuple) – knot_vector,
 span (int) – knot span,
 knot (float) – knot or parameter,
 order (int) – order of the derivative
Returns: basis function derivatives
Return type: list

geomdl.helpers.
basis_function_one
(degree, knot_vector, span, knot)¶ Computes the value of a basis function for a single parameter.
Implementation of Algorithm 2.4 from The NURBS Book by Piegl & Tiller.
Parameters:  degree (int) – degree,
 knot_vector (list, tuple) – knot vector
 span (int) – knot span,
 knot (float) – knot or parameter,
Returns: basis function,
Return type: float

geomdl.helpers.
basis_functions
(degree, knot_vector, spans, knots)¶ Computes the nonvanishing basis functions for a list of parameters.
Wrapper for
helpers.basis_function()
to process multiple span and knot values. Uses recurrence to compute the basis functions, also known as Cox  de Boor recursion formula.Parameters:  degree (int) – degree,
 knot_vector (list, tuple) – knot vector,
 spans (list, tuple) – list of knot spans
 knots (list, tuple) – list of knots or parameters
Returns: basis functions
Return type: list

geomdl.helpers.
basis_functions_ders
(degree, knot_vector, spans, knots, order)¶ Computes derivatives of the basis functions for a list of parameters.
Wrapper for
helpers.basis_function_ders()
to process multiple span and knot values.Parameters:  degree (int) – degree,
 knot_vector (list, tuple) – knot vector,
 spans (list, tuple) – list of knot spans
 knots (list, tuple) – list of knots or parameters
 order (int) – order of the derivative
Returns: derivatives of the basis functions
Return type: list

geomdl.helpers.
degree_elevation
(degree, ctrlpts, **kwargs)¶ Computes the control points of the rational/nonrational spline after degree elevation.
Implementation of Eq. 5.36 of The NURBS Book by Piegl & Tiller, 2nd Edition, p.205
 Keyword Arguments:
num
: number of degree elevations
Please note that degree elevation algorithm can only operate on Bezier shapes, i.e. curves, surfaces, volumes.
Parameters:  degree (int) – degree
 ctrlpts (list, tuple) – control points
Returns: control points of the degreeelevated shape
Return type: list

geomdl.helpers.
degree_reduction
(degree, ctrlpts, **kwargs)¶ Computes the control points of the rational/nonrational spline after degree reduction.
Implementation of Eqs. 5.41 and 5.42 of The NURBS Book by Piegl & Tiller, 2nd Edition, p.220
Please note that degree reduction algorithm can only operate on Bezier shapes, i.e. curves, surfaces, volumes and this implementation does NOT compute the maximum error tolerance as described via Eqs. 5.45 and 5.46 of The NURBS Book by Piegl & Tiller, 2nd Edition, p.221 to determine whether the shape is degree reducible or not.
Parameters:  degree (int) – degree
 ctrlpts (list, tuple) – control points
Returns: control points of the degreereduced shape
Return type: list

geomdl.helpers.
find_multiplicity
(knot, knot_vector, **kwargs)¶ Finds knot multiplicity over the knot vector.
 Keyword Arguments:
tol
: tolerance (delta) value for equality checking
Parameters:  knot (float) – knot or parameter,
 knot_vector (list, tuple) – knot vector,
Returns: knot multiplicity,
Return type: int

geomdl.helpers.
find_span_binsearch
(degree, knot_vector, num_ctrlpts, knot, **kwargs)¶ Finds the span of the knot over the input knot vector using binary search.
Implementation of Algorithm A2.1 from The NURBS Book by Piegl & Tiller.
The NURBS Book states that the knot span index always starts from zero, i.e. for a knot vector [0, 0, 1, 1]; if FindSpan returns 1, then the knot is between the halfopen interval [0, 1).
Parameters:  degree (int) – degree,
 knot_vector (list, tuple) – knot vector,
 num_ctrlpts (int) – number of control points,
 knot (float) – knot or parameter,
Returns: knot span
Return type: int

geomdl.helpers.
find_span_linear
(degree, knot_vector, num_ctrlpts, knot, **kwargs)¶ Finds the span of a single knot over the knot vector using linear search.
Alternative implementation for the Algorithm A2.1 from The NURBS Book by Piegl & Tiller.
Parameters:  degree (int) – degree,
 knot_vector (list, tuple) – knot vector,
 num_ctrlpts (int) – number of control points,
 knot (float) – knot or parameter,
Returns: knot span
Return type: int

geomdl.helpers.
find_spans
(degree, knot_vector, num_ctrlpts, knots, func=<function find_span_linear>)¶ Finds spans of a list of knots over the knot vector.
Parameters:  degree (int) – degree,
 knot_vector (list, tuple) – knot vector,
 num_ctrlpts (int) – number of control points,
 knots (list, tuple) – list of knots or parameters
 func – function for span finding, e.g. linear or binary search
Returns: list of spans
Return type: list

geomdl.helpers.
knot_insertion
(degree, knotvector, ctrlpts, u, **kwargs)¶ Computes the control points of the rational/nonrational spline after knot insertion.
Part of Algorithm A5.1 of The NURBS Book by Piegl & Tiller, 2nd Edition.
 Keyword Arguments:
num
: number of knot insertions. Default: 1s
: multiplicity of the knot. Default: computed via :func:`.find_multiplicity`span
: knot span. Default: computed via :func:`.find_span_linear`
Parameters:  degree (int) – degree
 knotvector (list, tuple) – knot vector
 ctrlpts (list) – control points
 u (float) – knot to be inserted
Returns: updated control points
Return type: list

geomdl.helpers.
knot_insertion_alpha
¶ Computes coefficient for knot insertion algorithm.
Parameters:  u (float) – knot
 knotvector (tuple) – knot vector
 span (int) – knot span
 idx (int) – index value (degreedependent)
 leg (int) – ith leg of the control points polygon
Returns: coefficient value
Return type: float

geomdl.helpers.
knot_insertion_kv
(knotvector, u, span, r)¶ Computes the knot vector of the rational/nonrational spline after knot insertion.
Part of Algorithm A5.1 of The NURBS Book by Piegl & Tiller, 2nd Edition.
Parameters:  knotvector (list, tuple) – knot vector
 u (float) – knot
 span (int) – knot span
 r (int) – number of knot insertions
Returns: updated knot vector
Return type: list

geomdl.helpers.
knot_refinement
(degree, knotvector, ctrlpts, **kwargs)¶ Computes the knot vector and the control points of the rational/nonrational spline after knot refinement.
Implementation of Algorithm A5.4 of The NURBS Book by Piegl & Tiller, 2nd Edition.
The algorithm automatically find the knots to be refined, i.e. the middle knots in the knot vector, and their multiplicities, i.e. number of same knots in the knot vector. This is the basis of knot refinement algorithm. This operation can be overridden by providing a list of knots via
knot_list
argument. In addition, users can provide a list of additional knots to be inserted in the knot vector viaadd_knot_list
argument.Moreover, a numerical
density
argument can be used to automate extra knot insertions. Ifdensity
is bigger than 1, then the algorithm finds the middle knots in each internal knot span to increase the number of knots to be refined.Example: Let the knot vector to be refined is
[0, 2, 4]
with the superfluous knots from the start and end are removed: If
density
is 1, knot vector to be refined is[0, 2, 4]
 If
density
is 2, knot vector to be refined is[0, 1, 2, 3, 4]
 If
density
is 3, knot vector to be refined is[0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4]
 Keyword Arguments:
knot_list
: knot list to be refined. Default: list of internal knotsadd_knot_list
: additional list of knots to be refined. Default: []density
: Density of the knots. Default: 1
Parameters:  degree (int) – degree
 knotvector (list, tuple) – knot vector
 ctrlpts – control points
Returns: updated control points and knot vector
Return type: tuple
 If

geomdl.helpers.
knot_removal
(degree, knotvector, ctrlpts, u, **kwargs)¶ Computes the control points of the rational/nonrational spline after knot removal.
Implementation based on Algorithm A5.8 and Equation 5.28 of The NURBS Book by Piegl & Tiller
 Keyword Arguments:
num
: number of knot removals
Parameters:  degree (int) – degree
 knotvector (list, tuple) – knot vector
 ctrlpts (list) – control points
 u (float) – knot to be removed
Returns: updated control points
Return type: list

geomdl.helpers.
knot_removal_alpha_i
¶ Computes coefficient for knot removal algorithm.
Please refer to Eq. 5.29 of The NURBS Book by Piegl & Tiller, 2nd Edition, p.184 for details.
Parameters:  u (float) – knot
 degree (int) – degree
 knotvector (tuple) – knot vector
 num (int) – knot removal index
 idx (int) – iterator index
Returns: coefficient value
Return type: float

geomdl.helpers.
knot_removal_alpha_j
¶ Computes coefficient for knot removal algorithm.
Please refer to Eq. 5.29 of The NURBS Book by Piegl & Tiller, 2nd Edition, p.184 for details.
Parameters:  u (float) – knot
 degree (int) – degree
 knotvector (tuple) – knot vector
 num (int) – knot removal index
 idx (int) – iterator index
Returns: coefficient value
Return type: float

geomdl.helpers.
knot_removal_kv
(knotvector, span, r)¶ Computes the knot vector of the rational/nonrational spline after knot removal.
Part of Algorithm A5.8 of The NURBS Book by Piegl & Tiller, 2nd Edition.
Parameters:  knotvector (list, tuple) – knot vector
 span (int) – knot span
 r (int) – number of knot removals
Returns: updated knot vector
Return type: list
Linear Algebra¶
The linalg
module contains some basic functions for point, vector and matrix operations.
Although most of the functions are designed for internal usage, the users can still use some of the functions for their
advantage, especially the point and vector manipulation and generation functions. Functions related to point
manipulation have point_
prefix and the ones related to vectors have vector_
prefix.

geomdl.linalg.
backward_substitution
(matrix_u, matrix_y)¶ Backward substitution method for the solution of linear systems.
Solves the equation using backward substitution method where is a upper triangular matrix and is a column matrix.
Parameters:  matrix_u (list, tuple) – U, upper triangular matrix
 matrix_y (list, tuple) – y, column matrix
Returns: x, column matrix
Return type: list

geomdl.linalg.
binomial_coefficient
¶ Computes the binomial coefficient (denoted by k choose i).
Please see the following website for details: http://mathworld.wolfram.com/BinomialCoefficient.html
Parameters:  k (int) – size of the set of distinct elements
 i (int) – size of the subsets
Returns: combination of k and i
Return type: float

geomdl.linalg.
convex_hull
(points)¶ Returns points on convex hull in counterclockwise order according to Graham’s scan algorithm.
Reference: https://gist.github.com/arthure/5cf52962341310f438e96c1f3c3398b8
Note
This implementation only works in 2dimensional space.
Parameters: points (list, tuple) – list of 2dimensional points Returns: convex hull of the input points Return type: list

geomdl.linalg.
forward_substitution
(matrix_l, matrix_b)¶ Forward substitution method for the solution of linear systems.
Solves the equation using forward substitution method where is a lower triangular matrix and is a column matrix.
Parameters:  matrix_l (list, tuple) – L, lower triangular matrix
 matrix_b (list, tuple) – b, column matrix
Returns: y, column matrix
Return type: list

geomdl.linalg.
frange
(start, stop, step=1.0)¶ Implementation of Python’s
range()
function which works with floats.Reference to this implementation: https://stackoverflow.com/a/36091634
Parameters:  start (float) – start value
 stop (float) – end value
 step (float) – increment
Returns: float
Return type: generator

geomdl.linalg.
is_left
(point0, point1, point2)¶ Tests if a point is LeftOnRight of an infinite line.
Ported from the C++ version: on http://geomalgorithms.com/a03_inclusion.html
Note
This implementation only works in 2dimensional space.
Parameters:  point0 – Point P0
 point1 – Point P1
 point2 – Point P2
Returns: >0 for P2 left of the line through P0 and P1 =0 for P2 on the line <0 for P2 right of the line

geomdl.linalg.
linspace
(start, stop, num, decimals=18)¶ Returns a list of evenly spaced numbers over a specified interval.
Inspired from Numpy’s linspace function: https://github.com/numpy/numpy/blob/master/numpy/core/function_base.py
Parameters:  start (float) – starting value
 stop (float) – end value
 num (int) – number of samples to generate
 decimals (int) – number of significands
Returns: a list of equally spaced numbers
Return type: list

geomdl.linalg.
lu_decomposition
(matrix_a)¶ LUFactorization method using Doolittle’s Method for solution of linear systems.
Decomposes the matrix such that .
The input matrix is represented by a list or a tuple. The input matrix is 2dimensional, i.e. list of lists of integers and/or floats.
Parameters: matrix_a (list, tuple) – Input matrix (must be a square matrix) Returns: a tuple containing matrices L and U Return type: tuple

geomdl.linalg.
lu_factor
(matrix_a, b)¶ Computes the solution to a system of linear equations with partial pivoting.
This function solves using LUP decomposition. is a matrix, is matrix of column vectors. Each column of is a solution for corresponding column of .
Parameters:  matrix_a – matrix A
 b (list) – matrix of M column vectors
Returns: x, the solution matrix
Return type: list

geomdl.linalg.
lu_solve
(matrix_a, b)¶ Computes the solution to a system of linear equations.
This function solves using LU decomposition. is a matrix, is matrix of column vectors. Each column of is a solution for corresponding column of .
Parameters:  matrix_a – matrix A
 b (list) – matrix of M column vectors
Returns: x, the solution matrix
Return type: list

geomdl.linalg.
matrix_determinant
(m)¶ Computes the determinant of the square matrix via LUP decomposition.
Parameters: m (list, tuple) – input matrix Returns: determinant of the matrix Return type: float

geomdl.linalg.
matrix_identity
¶ Generates a identity matrix.
Parameters: n (int) – size of the matrix Returns: identity matrix Return type: list

geomdl.linalg.
matrix_inverse
(m)¶ Computes the inverse of the matrix via LUP decomposition.
Parameters: m (list, tuple) – input matrix Returns: inverse of the matrix Return type: list

geomdl.linalg.
matrix_multiply
(m1, m2)¶ Matrix multiplication (iterative algorithm).
The running time of the iterative matrix multiplication algorithm is .
Parameters:  m1 (list, tuple) – 1st matrix with dimensions
 m2 (list, tuple) – 2nd matrix with dimensions
Returns: resultant matrix with dimensions
Return type: list

geomdl.linalg.
matrix_pivot
(m, sign=False)¶ Computes the pivot matrix for M, a square matrix.
This function computes
 the permutation matrix,
 the product of M and P,
 determinant of P, if
sign = True
Parameters:  m (list, tuple) – input matrix
 sign (bool) – flag to return the determinant of the permutation matrix, P
Returns: a tuple containing the matrix product of M x P, P and det(P)
Return type: tuple

geomdl.linalg.
matrix_scalar
(m, sc)¶ Matrix multiplication by a scalar value (iterative algorithm).
The running time of the iterative matrix multiplication algorithm is .
Parameters:  m (list, tuple) – input matrix
 sc (int, float) – scalar value
Returns: resultant matrix
Return type: list

geomdl.linalg.
matrix_transpose
(m)¶ Transposes the input matrix.
The input matrix is a 2dimensional array.
Parameters: m (list, tuple) – input matrix with dimensions Returns: transpose matrix with dimensions Return type: list

geomdl.linalg.
point_distance
(pt1, pt2)¶ Computes distance between two points.
Parameters:  pt1 (list, tuple) – point 1
 pt2 (list, tuple) – point 2
Returns: distance between input points
Return type: float

geomdl.linalg.
point_mid
(pt1, pt2)¶ Computes the midpoint of the input points.
Parameters:  pt1 (list, tuple) – point 1
 pt2 (list, tuple) – point 2
Returns: midpoint
Return type: list

geomdl.linalg.
point_translate
(point_in, vector_in)¶ Translates the input points using the input vector.
Parameters:  point_in (list, tuple) – input point
 vector_in (list, tuple) – input vector
Returns: translated point
Return type: list

geomdl.linalg.
triangle_center
(tri, uv=False)¶ Computes the center of mass of the input triangle.
Parameters:  tri (elements.Triangle) – triangle object
 uv (bool) – if True, then finds parametric position of the center of mass
Returns: center of mass of the triangle
Return type: tuple

geomdl.linalg.
triangle_normal
(tri)¶ Computes the (approximate) normal vector of the input triangle.
Parameters: tri (elements.Triangle) – triangle object Returns: normal vector of the triangle Return type: tuple

geomdl.linalg.
vector_angle_between
(vector1, vector2, **kwargs)¶ Computes the angle between the two input vectors.
If the keyword argument
degrees
is set to True, then the angle will be in degrees. Otherwise, it will be in radians. By default,degrees
is set to True.Parameters:  vector1 (list, tuple) – vector
 vector2 (list, tuple) – vector
Returns: angle between the vectors
Return type: float

geomdl.linalg.
vector_cross
(vector1, vector2)¶ Computes the crossproduct of the input vectors.
Parameters:  vector1 (list, tuple) – input vector 1
 vector2 (list, tuple) – input vector 2
Returns: result of the cross product
Return type: tuple

geomdl.linalg.
vector_dot
(vector1, vector2)¶ Computes the dotproduct of the input vectors.
Parameters:  vector1 (list, tuple) – input vector 1
 vector2 (list, tuple) – input vector 2
Returns: result of the dot product
Return type: float

geomdl.linalg.
vector_generate
(start_pt, end_pt, normalize=False)¶ Generates a vector from 2 input points.
Parameters:  start_pt (list, tuple) – start point of the vector
 end_pt (list, tuple) – end point of the vector
 normalize (bool) – if True, the generated vector is normalized
Returns: a vector from start_pt to end_pt
Return type: list

geomdl.linalg.
vector_is_zero
(vector_in, tol=1e07)¶ Checks if the input vector is a zero vector.
Parameters:  vector_in (list, tuple) – input vector
 tol (float) – tolerance value
Returns: True if the input vector is zero, False otherwise
Return type: bool

geomdl.linalg.
vector_magnitude
(vector_in)¶ Computes the magnitude of the input vector.
Parameters: vector_in (list, tuple) – input vector Returns: magnitude of the vector Return type: float

geomdl.linalg.
vector_mean
(*args)¶ Computes the mean (average) of a list of vectors.
The function computes the arithmetic mean of a list of vectors, which are also organized as a list of integers or floating point numbers.
1 2 3 4 5 6 7 8 9 10 11
# Import geomdl.utilities module from geomdl import utilities # Create a list of vectors as an example vector_list = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] # Compute mean vector mean_vector = utilities.vector_mean(*vector_list) # Alternative usage example (same as above): mean_vector = utilities.vector_mean([1, 2, 3], [4, 5, 6], [7, 8, 9])
Parameters: args (list, tuple) – list of vectors Returns: mean vector Return type: list

geomdl.linalg.
vector_multiply
(vector_in, scalar)¶ Multiplies the vector with a scalar value.
This operation is also called vector scaling.
Parameters:  vector_in (list, tuple) – vector
 scalar (int, float) – scalar value
Returns: updated vector
Return type: tuple

geomdl.linalg.
vector_normalize
(vector_in, decimals=18)¶ Generates a unit vector from the input.
Parameters:  vector_in (list, tuple) – vector to be normalized
 decimals (int) – number of significands
Returns: the normalized vector (i.e. the unit vector)
Return type: list

geomdl.linalg.
vector_sum
(vector1, vector2, coeff=1.0)¶ Sums the vectors.
This function computes the result of the vector operation , where is
vector1
, isvector2
and iscoeff
.Parameters:  vector1 (list, tuple) – vector 1
 vector2 (list, tuple) – vector 2
 coeff (float) – multiplier for vector 2
Returns: updated vector
Return type: list

geomdl.linalg.
wn_poly
(point, vertices)¶ Winding number test for a point in a polygon.
Ported from the C++ version: http://geomalgorithms.com/a03_inclusion.html
Note
This implementation only works in 2dimensional space.
Parameters:  point (list, tuple) – point to be tested
 vertices (list, tuple) – vertex points of a polygon vertices[n+1] with vertices[n] = vertices[0]
Returns: True if the point is inside the input polygon, False otherwise
Return type: bool