yade.minieigenHP module

When yade uses high-precision number as Real type the usual (old):

from minieigen import *

has to be replaced with:

from yade.minieigenHP import *

This command ensures backward compatibility between both. It is then guaranteed that python uses the same number of decimal places as yade is using everywhere else.

Please note that used precision can be very arbitrary, because cpp_bin_float or mpfr take it as a compile-time argument. Hence such yade.minieigenHP cannot be separately precompiled as a package. Though it could be precompiled for some special types such as boost::multiprecision::float128.

The RealHP<n> higher precision vectors and matrices can be accessed in python by using the .HPn module scope. For example:

import yade.minieigenHP as mne
mne.HP2.Vector3(1,2,3) # produces Vector3 using RealHP<2> precision
mne.Vector3(1,2,3)     # without using HPn module scope it defaults to RealHP<1>

miniEigen is wrapper for a small part of the Eigen library. Refer to its documentation for details. All classes in this module support pickling.

class yade._minieigenHP.AlignedBox2

Axis-aligned box object in 2d, defined by its minimum and maximum corners

__init__((object)arg1) → None

__init__( (object)arg1, (AlignedBox2)other) -> None

__init__( (object)arg1, (Vector2)min, (Vector2)max) -> None

center((AlignedBox2)arg1) → Vector2

clamp((AlignedBox2)arg1, (AlignedBox2)arg2) → None

contains((AlignedBox2)arg1, (Vector2)arg2) → bool

contains( (AlignedBox2)arg1, (AlignedBox2)arg2) -> bool

empty((AlignedBox2)arg1) → bool

extend((AlignedBox2)arg1, (Vector2)arg2) → None

extend( (AlignedBox2)arg1, (AlignedBox2)arg2) -> None

intersection((AlignedBox2)arg1, (AlignedBox2)arg2) → AlignedBox2

property max

merged((AlignedBox2)arg1, (AlignedBox2)arg2) → AlignedBox2

property min

sizes((AlignedBox2)arg1) → Vector2

volume((AlignedBox2)arg1) → float

class yade._minieigenHP.AlignedBox3

Axis-aligned box object, defined by its minimum and maximum corners

__init__((object)arg1) → None

__init__( (object)arg1, (AlignedBox3)other) -> None

__init__( (object)arg1, (Vector3)min, (Vector3)max) -> None

center((AlignedBox3)arg1) → Vector3

clamp((AlignedBox3)arg1, (AlignedBox3)arg2) → None

contains((AlignedBox3)arg1, (Vector3)arg2) → bool

contains( (AlignedBox3)arg1, (AlignedBox3)arg2) -> bool

empty((AlignedBox3)arg1) → bool

extend((AlignedBox3)arg1, (Vector3)arg2) → None

extend( (AlignedBox3)arg1, (AlignedBox3)arg2) -> None

intersection((AlignedBox3)arg1, (AlignedBox3)arg2) → AlignedBox3

property max

merged((AlignedBox3)arg1, (AlignedBox3)arg2) → AlignedBox3

property min

sizes((AlignedBox3)arg1) → Vector3

volume((AlignedBox3)arg1) → float

class yade._minieigenHP.HP1

class AlignedBox2

Axis-aligned box object in 2d, defined by its minimum and maximum corners

__init__((object)arg1) → None

__init__( (object)arg1, (AlignedBox2)other) -> None

__init__( (object)arg1, (Vector2)min, (Vector2)max) -> None

center((AlignedBox2)arg1) → Vector2

clamp((AlignedBox2)arg1, (AlignedBox2)arg2) → None

contains((AlignedBox2)arg1, (Vector2)arg2) → bool

contains( (AlignedBox2)arg1, (AlignedBox2)arg2) -> bool

empty((AlignedBox2)arg1) → bool

extend((AlignedBox2)arg1, (Vector2)arg2) → None

extend( (AlignedBox2)arg1, (AlignedBox2)arg2) -> None

intersection((AlignedBox2)arg1, (AlignedBox2)arg2) → AlignedBox2

property max

merged((AlignedBox2)arg1, (AlignedBox2)arg2) → AlignedBox2

property min

sizes((AlignedBox2)arg1) → Vector2

volume((AlignedBox2)arg1) → float

class AlignedBox3

Axis-aligned box object, defined by its minimum and maximum corners

__init__((object)arg1) → None

__init__( (object)arg1, (AlignedBox3)other) -> None

__init__( (object)arg1, (Vector3)min, (Vector3)max) -> None

center((AlignedBox3)arg1) → Vector3

clamp((AlignedBox3)arg1, (AlignedBox3)arg2) → None

contains((AlignedBox3)arg1, (Vector3)arg2) → bool

contains( (AlignedBox3)arg1, (AlignedBox3)arg2) -> bool

empty((AlignedBox3)arg1) → bool

extend((AlignedBox3)arg1, (Vector3)arg2) → None

extend( (AlignedBox3)arg1, (AlignedBox3)arg2) -> None

intersection((AlignedBox3)arg1, (AlignedBox3)arg2) → AlignedBox3

property max

merged((AlignedBox3)arg1, (AlignedBox3)arg2) → AlignedBox3

property min

sizes((AlignedBox3)arg1) → Vector3

volume((AlignedBox3)arg1) → float

class Matrix3

3x3 float matrix.

Supported operations (m is a Matrix3, f if a float/int, v is a Vector3): -m, m+m, m+=m, m-m, m-=m, m*f, f*m, m*=f, m/f, m/=f, m*m, m*=m, m*v, v*m, m==m, m!=m.

Static attributes: Zero, Ones, Identity.

Identity = Matrix3(1,0,0, 0,1,0, 0,0,1)

Ones = Matrix3(1,1,1, 1,1,1, 1,1,1)

static Random() → Matrix3 :

Return an object where all elements are randomly set to values between 0 and 1.

Zero = Matrix3(0,0,0, 0,0,0, 0,0,0)

__init__((object)arg1) → None

__init__( (object)arg1, (Quaternion)q) -> None

__init__( (object)arg1, (Matrix3)other) -> None

__init__( (object)arg1, (Vector3)diag) -> object

__init__( (object)arg1, (float)m00, (float)m01, (float)m02, (float)m10, (float)m11, (float)m12, (float)m20, (float)m21, (float)m22) -> object

__init__( (object)arg1, (Vector3)r0, (Vector3)r1, (Vector3)r2 [, (bool)cols=False]) -> object

col((Matrix3)arg1, (int)col) → Vector3 :

Return column as vector.

cols((Matrix3)arg1) → int :

Number of columns.

computeUnitaryPositive((Matrix3)arg1) → tuple :

Compute polar decomposition (unitary matrix U and positive semi-definite symmetric matrix P such that self=U*P).

determinant((Matrix3)arg1) → float :

Return matrix determinant.

diagonal((Matrix3)arg1) → Vector3 :

Return diagonal as vector.

inverse((Matrix3)arg1) → Matrix3 :

Return inverted matrix.

isApprox((Matrix3)arg1, (Matrix3)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

jacobiSVD((Matrix3)arg1) → tuple :

Compute SVD decomposition of square matrix, retuns (U,S,V) such that self=U*S*V.transpose()

maxAbsCoeff((Matrix3)arg1) → float :

Maximum absolute value over all elements.

maxCoeff((Matrix3)arg1) → float :

Maximum value over all elements.

mean((Matrix3)arg1) → float :

Mean value over all elements.

minCoeff((Matrix3)arg1) → float :

Minimum value over all elements.

norm((Matrix3)arg1) → float :

Euclidean norm.

normalize((Matrix3)arg1) → None :

Normalize this object in-place.

normalized((Matrix3)arg1) → Matrix3 :

Return normalized copy of this object

polarDecomposition((Matrix3)arg1) → tuple :

Alias for computeUnitaryPositive.

prod((Matrix3)arg1) → float :

Product of all elements.

pruned((Matrix3)arg1[, (float)absTol=1e-06]) → Matrix3 :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

row((Matrix3)arg1, (int)row) → Vector3 :

Return row as vector.

rows((Matrix3)arg1) → int :

Number of rows.

selfAdjointEigenDecomposition((Matrix3)arg1) → tuple :

Compute eigen (spectral) decomposition of symmetric matrix, returns (eigVecs,eigVals). eigVecs is orthogonal Matrix3 with columns ar normalized eigenvectors, eigVals is Vector3 with corresponding eigenvalues. self=eigVecs*diag(eigVals)*eigVecs.transpose().

spectralDecomposition((Matrix3)arg1) → tuple :

Alias for selfAdjointEigenDecomposition.

squaredNorm((Matrix3)arg1) → float :

Square of the Euclidean norm.

sum((Matrix3)arg1) → float :

Sum of all elements.

svd((Matrix3)arg1) → tuple :

Alias for jacobiSVD.

trace((Matrix3)arg1) → float :

Return sum of diagonal elements.

transpose((Matrix3)arg1) → Matrix3 :

Return transposed matrix.

class Matrix3c

/TODO/

Identity = Matrix3c(1,0,0, 0,1,0, 0,0,1)

Ones = Matrix3c(1,1,1, 1,1,1, 1,1,1)

static Random() → Matrix3c :

Return an object where all elements are randomly set to values between 0 and 1.

Zero = Matrix3c(0,0,0, 0,0,0, 0,0,0)

__init__((object)arg1) → None

__init__( (object)arg1, (Matrix3c)other) -> None

__init__( (object)arg1, (Vector3c)diag) -> object

__init__( (object)arg1, (complex)m00, (complex)m01, (complex)m02, (complex)m10, (complex)m11, (complex)m12, (complex)m20, (complex)m21, (complex)m22) -> object

__init__( (object)arg1, (Vector3c)r0, (Vector3c)r1, (Vector3c)r2 [, (bool)cols=False]) -> object

col((Matrix3c)arg1, (int)col) → Vector3c :

Return column as vector.

cols((Matrix3c)arg1) → int :

Number of columns.

determinant((Matrix3c)arg1) → complex :

Return matrix determinant.

diagonal((Matrix3c)arg1) → Vector3c :

Return diagonal as vector.

inverse((Matrix3c)arg1) → Matrix3c :

Return inverted matrix.

isApprox((Matrix3c)arg1, (Matrix3c)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Matrix3c)arg1) → float :

Maximum absolute value over all elements.

mean((Matrix3c)arg1) → complex :

Mean value over all elements.

norm((Matrix3c)arg1) → float :

Euclidean norm.

normalize((Matrix3c)arg1) → None :

Normalize this object in-place.

normalized((Matrix3c)arg1) → Matrix3c :

Return normalized copy of this object

prod((Matrix3c)arg1) → complex :

Product of all elements.

pruned((Matrix3c)arg1[, (float)absTol=1e-06]) → Matrix3c :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

row((Matrix3c)arg1, (int)row) → Vector3c :

Return row as vector.

rows((Matrix3c)arg1) → int :

Number of rows.

squaredNorm((Matrix3c)arg1) → float :

Square of the Euclidean norm.

sum((Matrix3c)arg1) → complex :

Sum of all elements.

trace((Matrix3c)arg1) → complex :

Return sum of diagonal elements.

transpose((Matrix3c)arg1) → Matrix3c :

Return transposed matrix.

class Matrix6

6x6 float matrix. Constructed from 4 3x3 sub-matrices, from 6xVector6 (rows).

Supported operations (m is a Matrix6, f if a float/int, v is a Vector6): -m, m+m, m+=m, m-m, m-=m, m*f, f*m, m*=f, m/f, m/=f, m*m, m*=m, m*v, v*m, m==m, m!=m.

Static attributes: Zero, Ones, Identity.

Identity = Matrix6( (1,0,0,0,0,0), (0,1,0,0,0,0), (0,0,1,0,0,0), (0,0,0,1,0,0), (0,0,0,0,1,0), (0,0,0,0,0,1) )

Ones = Matrix6( (1,1,1,1,1,1), (1,1,1,1,1,1), (1,1,1,1,1,1), (1,1,1,1,1,1), (1,1,1,1,1,1), (1,1,1,1,1,1) )

static Random() → Matrix6 :

Return an object where all elements are randomly set to values between 0 and 1.

Zero = Matrix6( (0,0,0,0,0,0), (0,0,0,0,0,0), (0,0,0,0,0,0), (0,0,0,0,0,0), (0,0,0,0,0,0), (0,0,0,0,0,0) )

__init__((object)arg1) → None

__init__( (object)arg1, (Matrix6)other) -> None

__init__( (object)arg1, (Vector6)diag) -> object

__init__( (object)arg1, (Matrix3)ul, (Matrix3)ur, (Matrix3)ll, (Matrix3)lr) -> object

__init__( (object)arg1, (Vector6)l0, (Vector6)l1, (Vector6)l2, (Vector6)l3, (Vector6)l4, (Vector6)l5 [, (bool)cols=False]) -> object

col((Matrix6)arg1, (int)col) → Vector6 :

Return column as vector.

cols((Matrix6)arg1) → int :

Number of columns.

computeUnitaryPositive((Matrix6)arg1) → tuple :

Compute polar decomposition (unitary matrix U and positive semi-definite symmetric matrix P such that self=U*P).

determinant((Matrix6)arg1) → float :

Return matrix determinant.

diagonal((Matrix6)arg1) → Vector6 :

Return diagonal as vector.

inverse((Matrix6)arg1) → Matrix6 :

Return inverted matrix.

isApprox((Matrix6)arg1, (Matrix6)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

jacobiSVD((Matrix6)arg1) → tuple :

Compute SVD decomposition of square matrix, retuns (U,S,V) such that self=U*S*V.transpose()

ll((Matrix6)arg1) → Matrix3 :

Return lower-left 3x3 block

lr((Matrix6)arg1) → Matrix3 :

Return lower-right 3x3 block

maxAbsCoeff((Matrix6)arg1) → float :

Maximum absolute value over all elements.

maxCoeff((Matrix6)arg1) → float :

Maximum value over all elements.

mean((Matrix6)arg1) → float :

Mean value over all elements.

minCoeff((Matrix6)arg1) → float :

Minimum value over all elements.

norm((Matrix6)arg1) → float :

Euclidean norm.

normalize((Matrix6)arg1) → None :

Normalize this object in-place.

normalized((Matrix6)arg1) → Matrix6 :

Return normalized copy of this object

polarDecomposition((Matrix6)arg1) → tuple :

Alias for computeUnitaryPositive.

prod((Matrix6)arg1) → float :

Product of all elements.

pruned((Matrix6)arg1[, (float)absTol=1e-06]) → Matrix6 :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

row((Matrix6)arg1, (int)row) → Vector6 :

Return row as vector.

rows((Matrix6)arg1) → int :

Number of rows.

selfAdjointEigenDecomposition((Matrix6)arg1) → tuple :

Compute eigen (spectral) decomposition of symmetric matrix, returns (eigVecs,eigVals). eigVecs is orthogonal Matrix3 with columns ar normalized eigenvectors, eigVals is Vector3 with corresponding eigenvalues. self=eigVecs*diag(eigVals)*eigVecs.transpose().

spectralDecomposition((Matrix6)arg1) → tuple :

Alias for selfAdjointEigenDecomposition.

squaredNorm((Matrix6)arg1) → float :

Square of the Euclidean norm.

sum((Matrix6)arg1) → float :

Sum of all elements.

svd((Matrix6)arg1) → tuple :

Alias for jacobiSVD.

trace((Matrix6)arg1) → float :

Return sum of diagonal elements.

transpose((Matrix6)arg1) → Matrix6 :

Return transposed matrix.

ul((Matrix6)arg1) → Matrix3 :

Return upper-left 3x3 block

ur((Matrix6)arg1) → Matrix3 :

Return upper-right 3x3 block

class Matrix6c

/TODO/

Identity = Matrix6c( (1,0,0,0,0,0), (0,1,0,0,0,0), (0,0,1,0,0,0), (0,0,0,1,0,0), (0,0,0,0,1,0), (0,0,0,0,0,1) )

Ones = Matrix6c( (1,1,1,1,1,1), (1,1,1,1,1,1), (1,1,1,1,1,1), (1,1,1,1,1,1), (1,1,1,1,1,1), (1,1,1,1,1,1) )

static Random() → Matrix6c :

Return an object where all elements are randomly set to values between 0 and 1.

Zero = Matrix6c( (0,0,0,0,0,0), (0,0,0,0,0,0), (0,0,0,0,0,0), (0,0,0,0,0,0), (0,0,0,0,0,0), (0,0,0,0,0,0) )

__init__((object)arg1) → None

__init__( (object)arg1, (Matrix6c)other) -> None

__init__( (object)arg1, (Vector6c)diag) -> object

__init__( (object)arg1, (Matrix3c)ul, (Matrix3c)ur, (Matrix3c)ll, (Matrix3c)lr) -> object

__init__( (object)arg1, (Vector6c)l0, (Vector6c)l1, (Vector6c)l2, (Vector6c)l3, (Vector6c)l4, (Vector6c)l5 [, (bool)cols=False]) -> object

col((Matrix6c)arg1, (int)col) → Vector6c :

Return column as vector.

cols((Matrix6c)arg1) → int :

Number of columns.

determinant((Matrix6c)arg1) → complex :

Return matrix determinant.

diagonal((Matrix6c)arg1) → Vector6c :

Return diagonal as vector.

inverse((Matrix6c)arg1) → Matrix6c :

Return inverted matrix.

isApprox((Matrix6c)arg1, (Matrix6c)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

ll((Matrix6c)arg1) → Matrix3c :

Return lower-left 3x3 block

lr((Matrix6c)arg1) → Matrix3c :

Return lower-right 3x3 block

maxAbsCoeff((Matrix6c)arg1) → float :

Maximum absolute value over all elements.

mean((Matrix6c)arg1) → complex :

Mean value over all elements.

norm((Matrix6c)arg1) → float :

Euclidean norm.

normalize((Matrix6c)arg1) → None :

Normalize this object in-place.

normalized((Matrix6c)arg1) → Matrix6c :

Return normalized copy of this object

prod((Matrix6c)arg1) → complex :

Product of all elements.

pruned((Matrix6c)arg1[, (float)absTol=1e-06]) → Matrix6c :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

row((Matrix6c)arg1, (int)row) → Vector6c :

Return row as vector.

rows((Matrix6c)arg1) → int :

Number of rows.

squaredNorm((Matrix6c)arg1) → float :

Square of the Euclidean norm.

sum((Matrix6c)arg1) → complex :

Sum of all elements.

trace((Matrix6c)arg1) → complex :

Return sum of diagonal elements.

transpose((Matrix6c)arg1) → Matrix6c :

Return transposed matrix.

ul((Matrix6c)arg1) → Matrix3c :

Return upper-left 3x3 block

ur((Matrix6c)arg1) → Matrix3c :

Return upper-right 3x3 block

class MatrixX

XxX (dynamic-sized) float matrix. Constructed from list of rows (as VectorX).

Supported operations (m is a MatrixX, f if a float/int, v is a VectorX): -m, m+m, m+=m, m-m, m-=m, m*f, f*m, m*=f, m/f, m/=f, m*m, m*=m, m*v, v*m, m==m, m!=m.

static Identity((int)arg1, (int)rank) → MatrixX :

Create identity matrix with given rank (square).

static Ones((int)rows, (int)cols) → MatrixX :

Create matrix of given dimensions where all elements are set to 1.

static Random((int)rows, (int)cols) → MatrixX :

Create matrix with given dimensions where all elements are set to number between 0 and 1 (uniformly-distributed).

static Zero((int)rows, (int)cols) → MatrixX :

Create zero matrix of given dimensions

__init__((object)arg1) → None

__init__( (object)arg1, (MatrixX)other) -> None

__init__( (object)arg1, (VectorX)diag) -> object

__init__( (object)arg1 [, (VectorX)r0=VectorX() [, (VectorX)r1=VectorX() [, (VectorX)r2=VectorX() [, (VectorX)r3=VectorX() [, (VectorX)r4=VectorX() [, (VectorX)r5=VectorX() [, (VectorX)r6=VectorX() [, (VectorX)r7=VectorX() [, (VectorX)r8=VectorX() [, (VectorX)r9=VectorX() [, (bool)cols=False]]]]]]]]]]]) -> object

__init__( (object)arg1, (object)rows [, (bool)cols=False]) -> object

col((MatrixX)arg1, (int)col) → VectorX :

Return column as vector.

cols((MatrixX)arg1) → int :

Number of columns.

computeUnitaryPositive((MatrixX)arg1) → tuple :

Compute polar decomposition (unitary matrix U and positive semi-definite symmetric matrix P such that self=U*P).

determinant((MatrixX)arg1) → float :

Return matrix determinant.

diagonal((MatrixX)arg1) → VectorX :

Return diagonal as vector.

inverse((MatrixX)arg1) → MatrixX :

Return inverted matrix.

isApprox((MatrixX)arg1, (MatrixX)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

jacobiSVD((MatrixX)arg1) → tuple :

Compute SVD decomposition of square matrix, retuns (U,S,V) such that self=U*S*V.transpose()

maxAbsCoeff((MatrixX)arg1) → float :

Maximum absolute value over all elements.

maxCoeff((MatrixX)arg1) → float :

Maximum value over all elements.

mean((MatrixX)arg1) → float :

Mean value over all elements.

minCoeff((MatrixX)arg1) → float :

Minimum value over all elements.

norm((MatrixX)arg1) → float :

Euclidean norm.

normalize((MatrixX)arg1) → None :

Normalize this object in-place.

normalized((MatrixX)arg1) → MatrixX :

Return normalized copy of this object

polarDecomposition((MatrixX)arg1) → tuple :

Alias for computeUnitaryPositive.

prod((MatrixX)arg1) → float :

Product of all elements.

pruned((MatrixX)arg1[, (float)absTol=1e-06]) → MatrixX :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

resize((MatrixX)arg1, (int)rows, (int)cols) → None :

Change size of the matrix, keep values of elements which exist in the new matrix

row((MatrixX)arg1, (int)row) → VectorX :

Return row as vector.

rows((MatrixX)arg1) → int :

Number of rows.

selfAdjointEigenDecomposition((MatrixX)arg1) → tuple :

Compute eigen (spectral) decomposition of symmetric matrix, returns (eigVecs,eigVals). eigVecs is orthogonal Matrix3 with columns ar normalized eigenvectors, eigVals is Vector3 with corresponding eigenvalues. self=eigVecs*diag(eigVals)*eigVecs.transpose().

spectralDecomposition((MatrixX)arg1) → tuple :

Alias for selfAdjointEigenDecomposition.

squaredNorm((MatrixX)arg1) → float :

Square of the Euclidean norm.

sum((MatrixX)arg1) → float :

Sum of all elements.

svd((MatrixX)arg1) → tuple :

Alias for jacobiSVD.

trace((MatrixX)arg1) → float :

Return sum of diagonal elements.

transpose((MatrixX)arg1) → MatrixX :

Return transposed matrix.

class MatrixXc

/TODO/

static Identity((int)arg1, (int)rank) → MatrixXc :

Create identity matrix with given rank (square).

static Ones((int)rows, (int)cols) → MatrixXc :

Create matrix of given dimensions where all elements are set to 1.

static Random((int)rows, (int)cols) → MatrixXc :

Create matrix with given dimensions where all elements are set to number between 0 and 1 (uniformly-distributed).

static Zero((int)rows, (int)cols) → MatrixXc :

Create zero matrix of given dimensions

__init__((object)arg1) → None

__init__( (object)arg1, (MatrixXc)other) -> None

__init__( (object)arg1, (VectorXc)diag) -> object

__init__( (object)arg1 [, (VectorXc)r0=VectorXc() [, (VectorXc)r1=VectorXc() [, (VectorXc)r2=VectorXc() [, (VectorXc)r3=VectorXc() [, (VectorXc)r4=VectorXc() [, (VectorXc)r5=VectorXc() [, (VectorXc)r6=VectorXc() [, (VectorXc)r7=VectorXc() [, (VectorXc)r8=VectorXc() [, (VectorXc)r9=VectorXc() [, (bool)cols=False]]]]]]]]]]]) -> object

__init__( (object)arg1, (object)rows [, (bool)cols=False]) -> object

col((MatrixXc)arg1, (int)col) → VectorXc :

Return column as vector.

cols((MatrixXc)arg1) → int :

Number of columns.

determinant((MatrixXc)arg1) → complex :

Return matrix determinant.

diagonal((MatrixXc)arg1) → VectorXc :

Return diagonal as vector.

inverse((MatrixXc)arg1) → MatrixXc :

Return inverted matrix.

isApprox((MatrixXc)arg1, (MatrixXc)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((MatrixXc)arg1) → float :

Maximum absolute value over all elements.

mean((MatrixXc)arg1) → complex :

Mean value over all elements.

norm((MatrixXc)arg1) → float :

Euclidean norm.

normalize((MatrixXc)arg1) → None :

Normalize this object in-place.

normalized((MatrixXc)arg1) → MatrixXc :

Return normalized copy of this object

prod((MatrixXc)arg1) → complex :

Product of all elements.

pruned((MatrixXc)arg1[, (float)absTol=1e-06]) → MatrixXc :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

resize((MatrixXc)arg1, (int)rows, (int)cols) → None :

Change size of the matrix, keep values of elements which exist in the new matrix

row((MatrixXc)arg1, (int)row) → VectorXc :

Return row as vector.

rows((MatrixXc)arg1) → int :

Number of rows.

squaredNorm((MatrixXc)arg1) → float :

Square of the Euclidean norm.

sum((MatrixXc)arg1) → complex :

Sum of all elements.

trace((MatrixXc)arg1) → complex :

Return sum of diagonal elements.

transpose((MatrixXc)arg1) → MatrixXc :

Return transposed matrix.

class Quaternion

Quaternion representing rotation.

Supported operations (q is a Quaternion, v is a Vector3): q*q (rotation composition), q*=q, q*v (rotating v by q), q==q, q!=q.

Static attributes: Identity.

Note

Quaternion is represented as axis-angle when printed (e.g. Identity is Quaternion((1,0,0),0), and can also be constructed from the axis-angle representation. This is however different from the data stored inside, which can be accessed by indices [0] (\(x\)), [1] (\(y\)), [2] (\(z\)), [3] (\(w\)). To obtain axis-angle programatically, use Quaternion.toAxisAngle which returns the tuple.

Identity = Quaternion((1,0,0),0)

Rotate((Quaternion)arg1, (Vector3)v) → Vector3

__init__((object)arg1) → None

__init__( (object)arg1, (Vector3)axis, (float)angle) -> object

__init__( (object)arg1, (float)angle, (Vector3)axis) -> object

__init__( (object)arg1, (Vector3)u, (Vector3)v) -> object

__init__( (object)arg1, (float)w, (float)x, (float)y, (float)z) -> None :

Initialize from coefficients.

Note

The order of coefficients is w, x, y, z. The [] operator numbers them differently, 0…4 for x y z w!

__init__( (object)arg1, (Matrix3)rotMatrix) -> None

__init__( (object)arg1, (Quaternion)other) -> None

angularDistance((Quaternion)arg1, (Quaternion)arg2) → float

conjugate((Quaternion)arg1) → Quaternion

inverse((Quaternion)arg1) → Quaternion

norm((Quaternion)arg1) → float

normalize((Quaternion)arg1) → None

normalized((Quaternion)arg1) → Quaternion

setFromTwoVectors((Quaternion)arg1, (Vector3)u, (Vector3)v) → None

slerp((Quaternion)arg1, (float)t, (Quaternion)other) → Quaternion

toAngleAxis((Quaternion)arg1) → tuple

toAxisAngle((Quaternion)arg1) → tuple

toRotationMatrix((Quaternion)arg1) → Matrix3

toRotationVector((Quaternion)arg1) → Vector3

class Vector2

3-dimensional float vector.

Supported operations (f if a float/int, v is a Vector3): -v, v+v, v+=v, v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v.

Implicit conversion from sequence (list, tuple, …) of 2 floats.

Static attributes: Zero, Ones, UnitX, UnitY.

Identity = Vector2(1,0)

Ones = Vector2(1,1)

static Random() → Vector2 :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector2

UnitX = Vector2(1,0)

UnitY = Vector2(0,1)

Zero = Vector2(0,0)

__init__((object)arg1) → None

__init__( (object)arg1, (Vector2)other) -> None

__init__( (object)arg1, (float)x, (float)y) -> None

asDiagonal((Vector2)arg1) → object :

Return diagonal matrix with this vector on the diagonal.

cols((Vector2)arg1) → int :

Number of columns.

dot((Vector2)arg1, (Vector2)other) → float :

Dot product with other.

isApprox((Vector2)arg1, (Vector2)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector2)arg1) → float :

Maximum absolute value over all elements.

maxCoeff((Vector2)arg1) → float :

Maximum value over all elements.

mean((Vector2)arg1) → float :

Mean value over all elements.

minCoeff((Vector2)arg1) → float :

Minimum value over all elements.

norm((Vector2)arg1) → float :

Euclidean norm.

normalize((Vector2)arg1) → None :

Normalize this object in-place.

normalized((Vector2)arg1) → Vector2 :

Return normalized copy of this object

outer((Vector2)arg1, (Vector2)other) → object :

Outer product with other.

prod((Vector2)arg1) → float :

Product of all elements.

pruned((Vector2)arg1[, (float)absTol=1e-06]) → Vector2 :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

rows((Vector2)arg1) → int :

Number of rows.

squaredNorm((Vector2)arg1) → float :

Square of the Euclidean norm.

sum((Vector2)arg1) → float :

Sum of all elements.

class Vector2c

/TODO/

Identity = Vector2c(1,0)

Ones = Vector2c(1,1)

static Random() → Vector2c :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector2c

UnitX = Vector2c(1,0)

UnitY = Vector2c(0,1)

Zero = Vector2c(0,0)

__init__((object)arg1) → None

__init__( (object)arg1, (Vector2c)other) -> None

__init__( (object)arg1, (complex)x, (complex)y) -> None

asDiagonal((Vector2c)arg1) → object :

Return diagonal matrix with this vector on the diagonal.

cols((Vector2c)arg1) → int :

Number of columns.

dot((Vector2c)arg1, (Vector2c)other) → complex :

Dot product with other.

isApprox((Vector2c)arg1, (Vector2c)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector2c)arg1) → float :

Maximum absolute value over all elements.

mean((Vector2c)arg1) → complex :

Mean value over all elements.

norm((Vector2c)arg1) → float :

Euclidean norm.

normalize((Vector2c)arg1) → None :

Normalize this object in-place.

normalized((Vector2c)arg1) → Vector2c :

Return normalized copy of this object

outer((Vector2c)arg1, (Vector2c)other) → object :

Outer product with other.

prod((Vector2c)arg1) → complex :

Product of all elements.

pruned((Vector2c)arg1[, (float)absTol=1e-06]) → Vector2c :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

rows((Vector2c)arg1) → int :

Number of rows.

squaredNorm((Vector2c)arg1) → float :

Square of the Euclidean norm.

sum((Vector2c)arg1) → complex :

Sum of all elements.

class Vector2i

2-dimensional integer vector.

Supported operations (i if an int, v is a Vector2i): -v, v+v, v+=v, v-v, v-=v, v*i, i*v, v*=i, v==v, v!=v.

Implicit conversion from sequence (list, tuple, …) of 2 integers.

Static attributes: Zero, Ones, UnitX, UnitY.

Identity = Vector2i(1,0)

Ones = Vector2i(1,1)

static Random() → Vector2i :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector2i

UnitX = Vector2i(1,0)

UnitY = Vector2i(0,1)

Zero = Vector2i(0,0)

__init__((object)arg1) → None

__init__( (object)arg1, (Vector2i)other) -> None

__init__( (object)arg1, (int)x, (int)y) -> None

asDiagonal((Vector2i)arg1) → object :

Return diagonal matrix with this vector on the diagonal.

cols((Vector2i)arg1) → int :

Number of columns.

dot((Vector2i)arg1, (Vector2i)other) → int :

Dot product with other.

isApprox((Vector2i)arg1, (Vector2i)other[, (int)prec=0]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector2i)arg1) → int :

Maximum absolute value over all elements.

maxCoeff((Vector2i)arg1) → int :

Maximum value over all elements.

mean((Vector2i)arg1) → int :

Mean value over all elements.

minCoeff((Vector2i)arg1) → int :

Minimum value over all elements.

outer((Vector2i)arg1, (Vector2i)other) → object :

Outer product with other.

prod((Vector2i)arg1) → int :

Product of all elements.

rows((Vector2i)arg1) → int :

Number of rows.

sum((Vector2i)arg1) → int :

Sum of all elements.

class Vector3

3-dimensional float vector.

Supported operations (f if a float/int, v is a Vector3): -v, v+v, v+=v, v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v, plus operations with Matrix3 and Quaternion.

Implicit conversion from sequence (list, tuple, …) of 3 floats.

Static attributes: Zero, Ones, UnitX, UnitY, UnitZ.

Identity = Vector3(1,0,0)

Ones = Vector3(1,1,1)

static Random() → Vector3 :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector3

UnitX = Vector3(1,0,0)

UnitY = Vector3(0,1,0)

UnitZ = Vector3(0,0,1)

Zero = Vector3(0,0,0)

__init__((object)arg1) → None

__init__( (object)arg1, (Vector3)other) -> None

__init__( (object)arg1 [, (float)x=0.0 [, (float)y=0.0 [, (float)z=0.0]]]) -> None

asDiagonal((Vector3)arg1) → Matrix3 :

Return diagonal matrix with this vector on the diagonal.

cols((Vector3)arg1) → int :

Number of columns.

cross((Vector3)arg1, (Vector3)arg2) → Vector3

dot((Vector3)arg1, (Vector3)other) → float :

Dot product with other.

isApprox((Vector3)arg1, (Vector3)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector3)arg1) → float :

Maximum absolute value over all elements.

maxCoeff((Vector3)arg1) → float :

Maximum value over all elements.

mean((Vector3)arg1) → float :

Mean value over all elements.

minCoeff((Vector3)arg1) → float :

Minimum value over all elements.

norm((Vector3)arg1) → float :

Euclidean norm.

normalize((Vector3)arg1) → None :

Normalize this object in-place.

normalized((Vector3)arg1) → Vector3 :

Return normalized copy of this object

outer((Vector3)arg1, (Vector3)other) → Matrix3 :

Outer product with other.

prod((Vector3)arg1) → float :

Product of all elements.

pruned((Vector3)arg1[, (float)absTol=1e-06]) → Vector3 :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

rows((Vector3)arg1) → int :

Number of rows.

squaredNorm((Vector3)arg1) → float :

Square of the Euclidean norm.

sum((Vector3)arg1) → float :

Sum of all elements.

xy((Vector3)arg1) → Vector2

xz((Vector3)arg1) → Vector2

yx((Vector3)arg1) → Vector2

yz((Vector3)arg1) → Vector2

zx((Vector3)arg1) → Vector2

zy((Vector3)arg1) → Vector2

class Vector3c

/TODO/

Identity = Vector3c(1,0,0)

Ones = Vector3c(1,1,1)

static Random() → Vector3c :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector3c

UnitX = Vector3c(1,0,0)

UnitY = Vector3c(0,1,0)

UnitZ = Vector3c(0,0,1)

Zero = Vector3c(0,0,0)

__init__((object)arg1) → None

__init__( (object)arg1, (Vector3c)other) -> None

__init__( (object)arg1 [, (complex)x=0j [, (complex)y=0j [, (complex)z=0j]]]) -> None

asDiagonal((Vector3c)arg1) → Matrix3c :

Return diagonal matrix with this vector on the diagonal.

cols((Vector3c)arg1) → int :

Number of columns.

cross((Vector3c)arg1, (Vector3c)arg2) → Vector3c

dot((Vector3c)arg1, (Vector3c)other) → complex :

Dot product with other.

isApprox((Vector3c)arg1, (Vector3c)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector3c)arg1) → float :

Maximum absolute value over all elements.

mean((Vector3c)arg1) → complex :

Mean value over all elements.

norm((Vector3c)arg1) → float :

Euclidean norm.

normalize((Vector3c)arg1) → None :

Normalize this object in-place.

normalized((Vector3c)arg1) → Vector3c :

Return normalized copy of this object

outer((Vector3c)arg1, (Vector3c)other) → Matrix3c :

Outer product with other.

prod((Vector3c)arg1) → complex :

Product of all elements.

pruned((Vector3c)arg1[, (float)absTol=1e-06]) → Vector3c :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

rows((Vector3c)arg1) → int :

Number of rows.

squaredNorm((Vector3c)arg1) → float :

Square of the Euclidean norm.

sum((Vector3c)arg1) → complex :

Sum of all elements.

xy((Vector3c)arg1) → Vector2c

xz((Vector3c)arg1) → Vector2c

yx((Vector3c)arg1) → Vector2c

yz((Vector3c)arg1) → Vector2c

zx((Vector3c)arg1) → Vector2c

zy((Vector3c)arg1) → Vector2c

class Vector3i

3-dimensional integer vector.

Supported operations (i if an int, v is a Vector3i): -v, v+v, v+=v, v-v, v-=v, v*i, i*v, v*=i, v==v, v!=v.

Implicit conversion from sequence (list, tuple, …) of 3 integers.

Static attributes: Zero, Ones, UnitX, UnitY, UnitZ.

Identity = Vector3i(1,0,0)

Ones = Vector3i(1,1,1)

static Random() → Vector3i :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector3i

UnitX = Vector3i(1,0,0)

UnitY = Vector3i(0,1,0)

UnitZ = Vector3i(0,0,1)

Zero = Vector3i(0,0,0)

__init__((object)arg1) → None

__init__( (object)arg1, (Vector3i)other) -> None

__init__( (object)arg1 [, (int)x=0 [, (int)y=0 [, (int)z=0]]]) -> None

asDiagonal((Vector3i)arg1) → object :

Return diagonal matrix with this vector on the diagonal.

cols((Vector3i)arg1) → int :

Number of columns.

cross((Vector3i)arg1, (Vector3i)arg2) → Vector3i

dot((Vector3i)arg1, (Vector3i)other) → int :

Dot product with other.

isApprox((Vector3i)arg1, (Vector3i)other[, (int)prec=0]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector3i)arg1) → int :

Maximum absolute value over all elements.

maxCoeff((Vector3i)arg1) → int :

Maximum value over all elements.

mean((Vector3i)arg1) → int :

Mean value over all elements.

minCoeff((Vector3i)arg1) → int :

Minimum value over all elements.

outer((Vector3i)arg1, (Vector3i)other) → object :

Outer product with other.

prod((Vector3i)arg1) → int :

Product of all elements.

rows((Vector3i)arg1) → int :

Number of rows.

sum((Vector3i)arg1) → int :

Sum of all elements.

xy((Vector3i)arg1) → Vector2i

xz((Vector3i)arg1) → Vector2i

yx((Vector3i)arg1) → Vector2i

yz((Vector3i)arg1) → Vector2i

zx((Vector3i)arg1) → Vector2i

zy((Vector3i)arg1) → Vector2i

class Vector4

4-dimensional float vector.

Supported operations (f if a float/int, v is a Vector3): -v, v+v, v+=v, v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v.

Implicit conversion from sequence (list, tuple, …) of 4 floats.

Static attributes: Zero, Ones.

Identity = Vector4(1,0,0, 0)

Ones = Vector4(1,1,1, 1)

static Random() → Vector4 :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector4

Zero = Vector4(0,0,0, 0)

__init__((object)arg1) → None

__init__( (object)arg1, (Vector4)other) -> None

__init__( (object)arg1, (float)v0, (float)v1, (float)v2, (float)v3) -> None

asDiagonal((Vector4)arg1) → object :

Return diagonal matrix with this vector on the diagonal.

cols((Vector4)arg1) → int :

Number of columns.

dot((Vector4)arg1, (Vector4)other) → float :

Dot product with other.

isApprox((Vector4)arg1, (Vector4)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector4)arg1) → float :

Maximum absolute value over all elements.

maxCoeff((Vector4)arg1) → float :

Maximum value over all elements.

mean((Vector4)arg1) → float :

Mean value over all elements.

minCoeff((Vector4)arg1) → float :

Minimum value over all elements.

norm((Vector4)arg1) → float :

Euclidean norm.

normalize((Vector4)arg1) → None :

Normalize this object in-place.

normalized((Vector4)arg1) → Vector4 :

Return normalized copy of this object

outer((Vector4)arg1, (Vector4)other) → object :

Outer product with other.

prod((Vector4)arg1) → float :

Product of all elements.

pruned((Vector4)arg1[, (float)absTol=1e-06]) → Vector4 :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

rows((Vector4)arg1) → int :

Number of rows.

squaredNorm((Vector4)arg1) → float :

Square of the Euclidean norm.

sum((Vector4)arg1) → float :

Sum of all elements.

class Vector6

6-dimensional float vector.

Supported operations (f if a float/int, v is a Vector6): -v, v+v, v+=v, v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v.

Implicit conversion from sequence (list, tuple, …) of 6 floats.

Static attributes: Zero, Ones.

Identity = Vector6(1,0,0, 0,0,0)

Ones = Vector6(1,1,1, 1,1,1)

static Random() → Vector6 :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector6

Zero = Vector6(0,0,0, 0,0,0)

__init__((object)arg1) → None

__init__( (object)arg1, (Vector6)other) -> None

__init__( (object)arg1, (float)v0, (float)v1, (float)v2, (float)v3, (float)v4, (float)v5) -> object

__init__( (object)arg1, (Vector3)head, (Vector3)tail) -> object

asDiagonal((Vector6)arg1) → Matrix6 :

Return diagonal matrix with this vector on the diagonal.

cols((Vector6)arg1) → int :

Number of columns.

dot((Vector6)arg1, (Vector6)other) → float :

Dot product with other.

head((Vector6)arg1) → Vector3

isApprox((Vector6)arg1, (Vector6)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector6)arg1) → float :

Maximum absolute value over all elements.

maxCoeff((Vector6)arg1) → float :

Maximum value over all elements.

mean((Vector6)arg1) → float :

Mean value over all elements.

minCoeff((Vector6)arg1) → float :

Minimum value over all elements.

norm((Vector6)arg1) → float :

Euclidean norm.

normalize((Vector6)arg1) → None :

Normalize this object in-place.

normalized((Vector6)arg1) → Vector6 :

Return normalized copy of this object

outer((Vector6)arg1, (Vector6)other) → Matrix6 :

Outer product with other.

prod((Vector6)arg1) → float :

Product of all elements.

pruned((Vector6)arg1[, (float)absTol=1e-06]) → Vector6 :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

rows((Vector6)arg1) → int :

Number of rows.

squaredNorm((Vector6)arg1) → float :

Square of the Euclidean norm.

sum((Vector6)arg1) → float :

Sum of all elements.

tail((Vector6)arg1) → Vector3

class Vector6c

/TODO/

Identity = Vector6c(1,0,0, 0,0,0)

Ones = Vector6c(1,1,1, 1,1,1)

static Random() → Vector6c :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector6c

Zero = Vector6c(0,0,0, 0,0,0)

__init__((object)arg1) → None

__init__( (object)arg1, (Vector6c)other) -> None

__init__( (object)arg1, (complex)v0, (complex)v1, (complex)v2, (complex)v3, (complex)v4, (complex)v5) -> object

__init__( (object)arg1, (Vector3c)head, (Vector3c)tail) -> object

asDiagonal((Vector6c)arg1) → Matrix6c :

Return diagonal matrix with this vector on the diagonal.

cols((Vector6c)arg1) → int :

Number of columns.

dot((Vector6c)arg1, (Vector6c)other) → complex :

Dot product with other.

head((Vector6c)arg1) → Vector3c

isApprox((Vector6c)arg1, (Vector6c)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector6c)arg1) → float :

Maximum absolute value over all elements.

mean((Vector6c)arg1) → complex :

Mean value over all elements.

norm((Vector6c)arg1) → float :

Euclidean norm.

normalize((Vector6c)arg1) → None :

Normalize this object in-place.

normalized((Vector6c)arg1) → Vector6c :

Return normalized copy of this object

outer((Vector6c)arg1, (Vector6c)other) → Matrix6c :

Outer product with other.

prod((Vector6c)arg1) → complex :

Product of all elements.

pruned((Vector6c)arg1[, (float)absTol=1e-06]) → Vector6c :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

rows((Vector6c)arg1) → int :

Number of rows.

squaredNorm((Vector6c)arg1) → float :

Square of the Euclidean norm.

sum((Vector6c)arg1) → complex :

Sum of all elements.

tail((Vector6c)arg1) → Vector3c

class Vector6i

6-dimensional float vector.

Supported operations (f if a float/int, v is a Vector6): -v, v+v, v+=v, v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v.

Implicit conversion from sequence (list, tuple, …) of 6 ints.

Static attributes: Zero, Ones.

Identity = Vector6i(1,0,0, 0,0,0)

Ones = Vector6i(1,1,1, 1,1,1)

static Random() → Vector6i :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector6i

Zero = Vector6i(0,0,0, 0,0,0)

__init__((object)arg1) → None

__init__( (object)arg1, (Vector6i)other) -> None

__init__( (object)arg1, (int)v0, (int)v1, (int)v2, (int)v3, (int)v4, (int)v5) -> object

__init__( (object)arg1, (Vector3i)head, (Vector3i)tail) -> object

asDiagonal((Vector6i)arg1) → object :

Return diagonal matrix with this vector on the diagonal.

cols((Vector6i)arg1) → int :

Number of columns.

dot((Vector6i)arg1, (Vector6i)other) → int :

Dot product with other.

head((Vector6i)arg1) → Vector3i

isApprox((Vector6i)arg1, (Vector6i)other[, (int)prec=0]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector6i)arg1) → int :

Maximum absolute value over all elements.

maxCoeff((Vector6i)arg1) → int :

Maximum value over all elements.

mean((Vector6i)arg1) → int :

Mean value over all elements.

minCoeff((Vector6i)arg1) → int :

Minimum value over all elements.

outer((Vector6i)arg1, (Vector6i)other) → object :

Outer product with other.

prod((Vector6i)arg1) → int :

Product of all elements.

rows((Vector6i)arg1) → int :

Number of rows.

sum((Vector6i)arg1) → int :

Sum of all elements.

tail((Vector6i)arg1) → Vector3i

class VectorX

Dynamic-sized float vector.

Supported operations (f if a float/int, v is a VectorX): -v, v+v, v+=v, v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v.

Implicit conversion from sequence (list, tuple, …) of X floats.

static Ones((int)arg1) → VectorX

static Random((int)len) → VectorX :

Return vector of given length with all elements set to values between 0 and 1 randomly.

static Unit((int)arg1, (int)arg2) → VectorX

static Zero((int)arg1) → VectorX

__init__((object)arg1) → None

__init__( (object)arg1, (VectorX)other) -> None

__init__( (object)arg1, (object)vv) -> object

asDiagonal((VectorX)arg1) → MatrixX :

Return diagonal matrix with this vector on the diagonal.

cols((VectorX)arg1) → int :

Number of columns.

dot((VectorX)arg1, (VectorX)other) → float :

Dot product with other.

isApprox((VectorX)arg1, (VectorX)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((VectorX)arg1) → float :

Maximum absolute value over all elements.

maxCoeff((VectorX)arg1) → float :

Maximum value over all elements.

mean((VectorX)arg1) → float :

Mean value over all elements.

minCoeff((VectorX)arg1) → float :

Minimum value over all elements.

norm((VectorX)arg1) → float :

Euclidean norm.

normalize((VectorX)arg1) → None :

Normalize this object in-place.

normalized((VectorX)arg1) → VectorX :

Return normalized copy of this object

outer((VectorX)arg1, (VectorX)other) → MatrixX :

Outer product with other.

prod((VectorX)arg1) → float :

Product of all elements.

pruned((VectorX)arg1[, (float)absTol=1e-06]) → VectorX :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

resize((VectorX)arg1, (int)arg2) → None

rows((VectorX)arg1) → int :

Number of rows.

squaredNorm((VectorX)arg1) → float :

Square of the Euclidean norm.

sum((VectorX)arg1) → float :

Sum of all elements.

class VectorXc

/TODO/

static Ones((int)arg1) → VectorXc

static Random((int)len) → VectorXc :

Return vector of given length with all elements set to values between 0 and 1 randomly.

static Unit((int)arg1, (int)arg2) → VectorXc

static Zero((int)arg1) → VectorXc

__init__((object)arg1) → None

__init__( (object)arg1, (VectorXc)other) -> None

__init__( (object)arg1, (object)vv) -> object

asDiagonal((VectorXc)arg1) → MatrixXc :

Return diagonal matrix with this vector on the diagonal.

cols((VectorXc)arg1) → int :

Number of columns.

dot((VectorXc)arg1, (VectorXc)other) → complex :

Dot product with other.

isApprox((VectorXc)arg1, (VectorXc)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((VectorXc)arg1) → float :

Maximum absolute value over all elements.

mean((VectorXc)arg1) → complex :

Mean value over all elements.

norm((VectorXc)arg1) → float :

Euclidean norm.

normalize((VectorXc)arg1) → None :

Normalize this object in-place.

normalized((VectorXc)arg1) → VectorXc :

Return normalized copy of this object

outer((VectorXc)arg1, (VectorXc)other) → MatrixXc :

Outer product with other.

prod((VectorXc)arg1) → complex :

Product of all elements.

pruned((VectorXc)arg1[, (float)absTol=1e-06]) → VectorXc :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

resize((VectorXc)arg1, (int)arg2) → None

rows((VectorXc)arg1) → int :

Number of rows.

squaredNorm((VectorXc)arg1) → float :

Square of the Euclidean norm.

sum((VectorXc)arg1) → complex :

Sum of all elements.

vectorize = False

class yade._minieigenHP.HP2

class AlignedBox2

Axis-aligned box object in 2d, defined by its minimum and maximum corners

__init__((object)arg1) → None

__init__( (object)arg1, (AlignedBox2)other) -> None

__init__( (object)arg1, (Vector2)min, (Vector2)max) -> None

center((AlignedBox2)arg1) → Vector2

clamp((AlignedBox2)arg1, (AlignedBox2)arg2) → None

contains((AlignedBox2)arg1, (Vector2)arg2) → bool

contains( (AlignedBox2)arg1, (AlignedBox2)arg2) -> bool

empty((AlignedBox2)arg1) → bool

extend((AlignedBox2)arg1, (Vector2)arg2) → None

extend( (AlignedBox2)arg1, (AlignedBox2)arg2) -> None

intersection((AlignedBox2)arg1, (AlignedBox2)arg2) → AlignedBox2

property max

merged((AlignedBox2)arg1, (AlignedBox2)arg2) → AlignedBox2

property min

sizes((AlignedBox2)arg1) → Vector2

volume((AlignedBox2)arg1) → object

class AlignedBox3

Axis-aligned box object, defined by its minimum and maximum corners

__init__((object)arg1) → None

__init__( (object)arg1, (AlignedBox3)other) -> None

__init__( (object)arg1, (Vector3)min, (Vector3)max) -> None

center((AlignedBox3)arg1) → Vector3

clamp((AlignedBox3)arg1, (AlignedBox3)arg2) → None

contains((AlignedBox3)arg1, (Vector3)arg2) → bool

contains( (AlignedBox3)arg1, (AlignedBox3)arg2) -> bool

empty((AlignedBox3)arg1) → bool

extend((AlignedBox3)arg1, (Vector3)arg2) → None

extend( (AlignedBox3)arg1, (AlignedBox3)arg2) -> None

intersection((AlignedBox3)arg1, (AlignedBox3)arg2) → AlignedBox3

property max

merged((AlignedBox3)arg1, (AlignedBox3)arg2) → AlignedBox3

property min

sizes((AlignedBox3)arg1) → Vector3

volume((AlignedBox3)arg1) → object

class Matrix3

3x3 float matrix.

Supported operations (m is a Matrix3, f if a float/int, v is a Vector3): -m, m+m, m+=m, m-m, m-=m, m*f, f*m, m*=f, m/f, m/=f, m*m, m*=m, m*v, v*m, m==m, m!=m.

Static attributes: Zero, Ones, Identity.

Identity = Matrix3("1","0","0", "0","1","0", "0","0","1")

Ones = Matrix3("1","1","1", "1","1","1", "1","1","1")

static Random() → Matrix3 :

Return an object where all elements are randomly set to values between 0 and 1.

Zero = Matrix3("0","0","0", "0","0","0", "0","0","0")

__init__((object)arg1) → None

__init__( (object)arg1, (Quaternion)q) -> None

__init__( (object)arg1, (Matrix3)other) -> None

__init__( (object)arg1, (Vector3)diag) -> object

__init__( (object)arg1, (object)m00, (object)m01, (object)m02, (object)m10, (object)m11, (object)m12, (object)m20, (object)m21, (object)m22) -> object

__init__( (object)arg1, (Vector3)r0, (Vector3)r1, (Vector3)r2 [, (bool)cols=False]) -> object

col((Matrix3)arg1, (int)col) → Vector3 :

Return column as vector.

cols((Matrix3)arg1) → int :

Number of columns.

computeUnitaryPositive((Matrix3)arg1) → tuple :

Compute polar decomposition (unitary matrix U and positive semi-definite symmetric matrix P such that self=U*P).

determinant((Matrix3)arg1) → object :

Return matrix determinant.

diagonal((Matrix3)arg1) → Vector3 :

Return diagonal as vector.

inverse((Matrix3)arg1) → Matrix3 :

Return inverted matrix.

isApprox((Matrix3)arg1, (Matrix3)other[, (object)prec=mpf('3.842735439305961757e-31')]) → bool :

Approximate comparison with precision prec.

jacobiSVD((Matrix3)arg1) → tuple :

Compute SVD decomposition of square matrix, retuns (U,S,V) such that self=U*S*V.transpose()

maxAbsCoeff((Matrix3)arg1) → object :

Maximum absolute value over all elements.

maxCoeff((Matrix3)arg1) → object :

Maximum value over all elements.

mean((Matrix3)arg1) → object :

Mean value over all elements.

minCoeff((Matrix3)arg1) → object :

Minimum value over all elements.

norm((Matrix3)arg1) → object :

Euclidean norm.

normalize((Matrix3)arg1) → None :

Normalize this object in-place.

normalized((Matrix3)arg1) → Matrix3 :

Return normalized copy of this object

polarDecomposition((Matrix3)arg1) → tuple :

Alias for computeUnitaryPositive.

prod((Matrix3)arg1) → object :

Product of all elements.

pruned((Matrix3)arg1[, (float)absTol=1e-06]) → Matrix3 :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

row((Matrix3)arg1, (int)row) → Vector3 :

Return row as vector.

rows((Matrix3)arg1) → int :

Number of rows.

selfAdjointEigenDecomposition((Matrix3)arg1) → tuple :

Compute eigen (spectral) decomposition of symmetric matrix, returns (eigVecs,eigVals). eigVecs is orthogonal Matrix3 with columns ar normalized eigenvectors, eigVals is Vector3 with corresponding eigenvalues. self=eigVecs*diag(eigVals)*eigVecs.transpose().

spectralDecomposition((Matrix3)arg1) → tuple :

Alias for selfAdjointEigenDecomposition.

squaredNorm((Matrix3)arg1) → object :

Square of the Euclidean norm.

sum((Matrix3)arg1) → object :

Sum of all elements.

svd((Matrix3)arg1) → tuple :

Alias for jacobiSVD.

trace((Matrix3)arg1) → object :

Return sum of diagonal elements.

transpose((Matrix3)arg1) → Matrix3 :

Return transposed matrix.

class Matrix3c

/TODO/

Identity = Matrix3c(mpc("1","0"),mpc("0","0"),mpc("0","0"), mpc("0","0"),mpc("1","0"),mpc("0","0"), mpc("0","0"),mpc("0","0"),mpc("1","0"))

Ones = Matrix3c(mpc("1","0"),mpc("1","0"),mpc("1","0"), mpc("1","0"),mpc("1","0"),mpc("1","0"), mpc("1","0"),mpc("1","0"),mpc("1","0"))

static Random() → Matrix3c :

Return an object where all elements are randomly set to values between 0 and 1.

Zero = Matrix3c(mpc("0","0"),mpc("0","0"),mpc("0","0"), mpc("0","0"),mpc("0","0"),mpc("0","0"), mpc("0","0"),mpc("0","0"),mpc("0","0"))

__init__((object)arg1) → None

__init__( (object)arg1, (Matrix3c)other) -> None

__init__( (object)arg1, (Vector3c)diag) -> object

__init__( (object)arg1, (object)m00, (object)m01, (object)m02, (object)m10, (object)m11, (object)m12, (object)m20, (object)m21, (object)m22) -> object

__init__( (object)arg1, (Vector3c)r0, (Vector3c)r1, (Vector3c)r2 [, (bool)cols=False]) -> object

col((Matrix3c)arg1, (int)col) → Vector3c :

Return column as vector.

cols((Matrix3c)arg1) → int :

Number of columns.

determinant((Matrix3c)arg1) → object :

Return matrix determinant.

diagonal((Matrix3c)arg1) → Vector3c :

Return diagonal as vector.

inverse((Matrix3c)arg1) → Matrix3c :

Return inverted matrix.

isApprox((Matrix3c)arg1, (Matrix3c)other[, (object)prec=mpc(real='3.842735439305961757e-31', imag='0.0')]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Matrix3c)arg1) → object :

Maximum absolute value over all elements.

mean((Matrix3c)arg1) → object :

Mean value over all elements.

norm((Matrix3c)arg1) → object :

Euclidean norm.

normalize((Matrix3c)arg1) → None :

Normalize this object in-place.

normalized((Matrix3c)arg1) → Matrix3c :

Return normalized copy of this object

prod((Matrix3c)arg1) → object :

Product of all elements.

pruned((Matrix3c)arg1[, (float)absTol=1e-06]) → Matrix3c :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

row((Matrix3c)arg1, (int)row) → Vector3c :

Return row as vector.

rows((Matrix3c)arg1) → int :

Number of rows.

squaredNorm((Matrix3c)arg1) → object :

Square of the Euclidean norm.

sum((Matrix3c)arg1) → object :

Sum of all elements.

trace((Matrix3c)arg1) → object :

Return sum of diagonal elements.

transpose((Matrix3c)arg1) → Matrix3c :

Return transposed matrix.

class Matrix6

6x6 float matrix. Constructed from 4 3x3 sub-matrices, from 6xVector6 (rows).

Supported operations (m is a Matrix6, f if a float/int, v is a Vector6): -m, m+m, m+=m, m-m, m-=m, m*f, f*m, m*=f, m/f, m/=f, m*m, m*=m, m*v, v*m, m==m, m!=m.

Static attributes: Zero, Ones, Identity.

Identity = Matrix6( ("1","0","0","0","0","0"), ("0","1","0","0","0","0"), ("0","0","1","0","0","0"), ("0","0","0","1","0","0"), ("0","0","0","0","1","0"), ("0","0","0","0","0","1") )

Ones = Matrix6( ("1","1","1","1","1","1"), ("1","1","1","1","1","1"), ("1","1","1","1","1","1"), ("1","1","1","1","1","1"), ("1","1","1","1","1","1"), ("1","1","1","1","1","1") )

static Random() → Matrix6 :

Return an object where all elements are randomly set to values between 0 and 1.

Zero = Matrix6( ("0","0","0","0","0","0"), ("0","0","0","0","0","0"), ("0","0","0","0","0","0"), ("0","0","0","0","0","0"), ("0","0","0","0","0","0"), ("0","0","0","0","0","0") )

__init__((object)arg1) → None

__init__( (object)arg1, (Matrix6)other) -> None

__init__( (object)arg1, (Vector6)diag) -> object

__init__( (object)arg1, (Matrix3)ul, (Matrix3)ur, (Matrix3)ll, (Matrix3)lr) -> object

__init__( (object)arg1, (Vector6)l0, (Vector6)l1, (Vector6)l2, (Vector6)l3, (Vector6)l4, (Vector6)l5 [, (bool)cols=False]) -> object

col((Matrix6)arg1, (int)col) → Vector6 :

Return column as vector.

cols((Matrix6)arg1) → int :

Number of columns.

computeUnitaryPositive((Matrix6)arg1) → tuple :

Compute polar decomposition (unitary matrix U and positive semi-definite symmetric matrix P such that self=U*P).

determinant((Matrix6)arg1) → object :

Return matrix determinant.

diagonal((Matrix6)arg1) → Vector6 :

Return diagonal as vector.

inverse((Matrix6)arg1) → Matrix6 :

Return inverted matrix.

isApprox((Matrix6)arg1, (Matrix6)other[, (object)prec=mpf('3.842735439305961757e-31')]) → bool :

Approximate comparison with precision prec.

jacobiSVD((Matrix6)arg1) → tuple :

Compute SVD decomposition of square matrix, retuns (U,S,V) such that self=U*S*V.transpose()

ll((Matrix6)arg1) → Matrix3 :

Return lower-left 3x3 block

lr((Matrix6)arg1) → Matrix3 :

Return lower-right 3x3 block

maxAbsCoeff((Matrix6)arg1) → object :

Maximum absolute value over all elements.

maxCoeff((Matrix6)arg1) → object :

Maximum value over all elements.

mean((Matrix6)arg1) → object :

Mean value over all elements.

minCoeff((Matrix6)arg1) → object :

Minimum value over all elements.

norm((Matrix6)arg1) → object :

Euclidean norm.

normalize((Matrix6)arg1) → None :

Normalize this object in-place.

normalized((Matrix6)arg1) → Matrix6 :

Return normalized copy of this object

polarDecomposition((Matrix6)arg1) → tuple :

Alias for computeUnitaryPositive.

prod((Matrix6)arg1) → object :

Product of all elements.

pruned((Matrix6)arg1[, (float)absTol=1e-06]) → Matrix6 :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

row((Matrix6)arg1, (int)row) → Vector6 :

Return row as vector.

rows((Matrix6)arg1) → int :

Number of rows.

selfAdjointEigenDecomposition((Matrix6)arg1) → tuple :

Compute eigen (spectral) decomposition of symmetric matrix, returns (eigVecs,eigVals). eigVecs is orthogonal Matrix3 with columns ar normalized eigenvectors, eigVals is Vector3 with corresponding eigenvalues. self=eigVecs*diag(eigVals)*eigVecs.transpose().

spectralDecomposition((Matrix6)arg1) → tuple :

Alias for selfAdjointEigenDecomposition.

squaredNorm((Matrix6)arg1) → object :

Square of the Euclidean norm.

sum((Matrix6)arg1) → object :

Sum of all elements.

svd((Matrix6)arg1) → tuple :

Alias for jacobiSVD.

trace((Matrix6)arg1) → object :

Return sum of diagonal elements.

transpose((Matrix6)arg1) → Matrix6 :

Return transposed matrix.

ul((Matrix6)arg1) → Matrix3 :

Return upper-left 3x3 block

ur((Matrix6)arg1) → Matrix3 :

Return upper-right 3x3 block

class Matrix6c

/TODO/

Identity = Matrix6c( (mpc("1","0"),mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("0","0")), (mpc("0","0"),mpc("1","0"),mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("0","0")), (mpc("0","0"),mpc("0","0"),mpc("1","0"),mpc("0","0"),mpc("0","0"),mpc("0","0")), (mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("1","0"),mpc("0","0"),mpc("0","0")), (mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("1","0"),mpc("0","0")), (mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("1","0")) )

Ones = Matrix6c( (mpc("1","0"),mpc("1","0"),mpc("1","0"),mpc("1","0"),mpc("1","0"),mpc("1","0")), (mpc("1","0"),mpc("1","0"),mpc("1","0"),mpc("1","0"),mpc("1","0"),mpc("1","0")), (mpc("1","0"),mpc("1","0"),mpc("1","0"),mpc("1","0"),mpc("1","0"),mpc("1","0")), (mpc("1","0"),mpc("1","0"),mpc("1","0"),mpc("1","0"),mpc("1","0"),mpc("1","0")), (mpc("1","0"),mpc("1","0"),mpc("1","0"),mpc("1","0"),mpc("1","0"),mpc("1","0")), (mpc("1","0"),mpc("1","0"),mpc("1","0"),mpc("1","0"),mpc("1","0"),mpc("1","0")) )

static Random() → Matrix6c :

Return an object where all elements are randomly set to values between 0 and 1.

Zero = Matrix6c( (mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("0","0")), (mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("0","0")), (mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("0","0")), (mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("0","0")), (mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("0","0")), (mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("0","0"),mpc("0","0")) )

__init__((object)arg1) → None

__init__( (object)arg1, (Matrix6c)other) -> None

__init__( (object)arg1, (Vector6c)diag) -> object

__init__( (object)arg1, (Matrix3c)ul, (Matrix3c)ur, (Matrix3c)ll, (Matrix3c)lr) -> object

__init__( (object)arg1, (Vector6c)l0, (Vector6c)l1, (Vector6c)l2, (Vector6c)l3, (Vector6c)l4, (Vector6c)l5 [, (bool)cols=False]) -> object

col((Matrix6c)arg1, (int)col) → Vector6c :

Return column as vector.

cols((Matrix6c)arg1) → int :

Number of columns.

determinant((Matrix6c)arg1) → object :

Return matrix determinant.

diagonal((Matrix6c)arg1) → Vector6c :

Return diagonal as vector.

inverse((Matrix6c)arg1) → Matrix6c :

Return inverted matrix.

isApprox((Matrix6c)arg1, (Matrix6c)other[, (object)prec=mpc(real='3.842735439305961757e-31', imag='0.0')]) → bool :

Approximate comparison with precision prec.

ll((Matrix6c)arg1) → Matrix3c :

Return lower-left 3x3 block

lr((Matrix6c)arg1) → Matrix3c :

Return lower-right 3x3 block

maxAbsCoeff((Matrix6c)arg1) → object :

Maximum absolute value over all elements.

mean((Matrix6c)arg1) → object :

Mean value over all elements.

norm((Matrix6c)arg1) → object :

Euclidean norm.

normalize((Matrix6c)arg1) → None :

Normalize this object in-place.

normalized((Matrix6c)arg1) → Matrix6c :

Return normalized copy of this object

prod((Matrix6c)arg1) → object :

Product of all elements.

pruned((Matrix6c)arg1[, (float)absTol=1e-06]) → Matrix6c :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

row((Matrix6c)arg1, (int)row) → Vector6c :

Return row as vector.

rows((Matrix6c)arg1) → int :

Number of rows.

squaredNorm((Matrix6c)arg1) → object :

Square of the Euclidean norm.

sum((Matrix6c)arg1) → object :

Sum of all elements.

trace((Matrix6c)arg1) → object :

Return sum of diagonal elements.

transpose((Matrix6c)arg1) → Matrix6c :

Return transposed matrix.

ul((Matrix6c)arg1) → Matrix3c :

Return upper-left 3x3 block

ur((Matrix6c)arg1) → Matrix3c :

Return upper-right 3x3 block

class MatrixX

XxX (dynamic-sized) float matrix. Constructed from list of rows (as VectorX).

Supported operations (m is a MatrixX, f if a float/int, v is a VectorX): -m, m+m, m+=m, m-m, m-=m, m*f, f*m, m*=f, m/f, m/=f, m*m, m*=m, m*v, v*m, m==m, m!=m.

static Identity((int)arg1, (int)rank) → MatrixX :

Create identity matrix with given rank (square).

static Ones((int)rows, (int)cols) → MatrixX :

Create matrix of given dimensions where all elements are set to 1.

static Random((int)rows, (int)cols) → MatrixX :

Create matrix with given dimensions where all elements are set to number between 0 and 1 (uniformly-distributed).

static Zero((int)rows, (int)cols) → MatrixX :

Create zero matrix of given dimensions

__init__((object)arg1) → None

__init__( (object)arg1, (MatrixX)other) -> None

__init__( (object)arg1, (VectorX)diag) -> object

__init__( (object)arg1 [, (VectorX)r0=VectorX() [, (VectorX)r1=VectorX() [, (VectorX)r2=VectorX() [, (VectorX)r3=VectorX() [, (VectorX)r4=VectorX() [, (VectorX)r5=VectorX() [, (VectorX)r6=VectorX() [, (VectorX)r7=VectorX() [, (VectorX)r8=VectorX() [, (VectorX)r9=VectorX() [, (bool)cols=False]]]]]]]]]]]) -> object

__init__( (object)arg1, (object)rows [, (bool)cols=False]) -> object

col((MatrixX)arg1, (int)col) → VectorX :

Return column as vector.

cols((MatrixX)arg1) → int :

Number of columns.

computeUnitaryPositive((MatrixX)arg1) → tuple :

Compute polar decomposition (unitary matrix U and positive semi-definite symmetric matrix P such that self=U*P).

determinant((MatrixX)arg1) → object :

Return matrix determinant.

diagonal((MatrixX)arg1) → VectorX :

Return diagonal as vector.

inverse((MatrixX)arg1) → MatrixX :

Return inverted matrix.

isApprox((MatrixX)arg1, (MatrixX)other[, (object)prec=mpf('3.842735439305961757e-31')]) → bool :

Approximate comparison with precision prec.

jacobiSVD((MatrixX)arg1) → tuple :

Compute SVD decomposition of square matrix, retuns (U,S,V) such that self=U*S*V.transpose()

maxAbsCoeff((MatrixX)arg1) → object :

Maximum absolute value over all elements.

maxCoeff((MatrixX)arg1) → object :

Maximum value over all elements.

mean((MatrixX)arg1) → object :

Mean value over all elements.

minCoeff((MatrixX)arg1) → object :

Minimum value over all elements.

norm((MatrixX)arg1) → object :

Euclidean norm.

normalize((MatrixX)arg1) → None :

Normalize this object in-place.

normalized((MatrixX)arg1) → MatrixX :

Return normalized copy of this object

polarDecomposition((MatrixX)arg1) → tuple :

Alias for computeUnitaryPositive.

prod((MatrixX)arg1) → object :

Product of all elements.

pruned((MatrixX)arg1[, (float)absTol=1e-06]) → MatrixX :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

resize((MatrixX)arg1, (int)rows, (int)cols) → None :

Change size of the matrix, keep values of elements which exist in the new matrix

row((MatrixX)arg1, (int)row) → VectorX :

Return row as vector.

rows((MatrixX)arg1) → int :

Number of rows.

selfAdjointEigenDecomposition((MatrixX)arg1) → tuple :

Compute eigen (spectral) decomposition of symmetric matrix, returns (eigVecs,eigVals). eigVecs is orthogonal Matrix3 with columns ar normalized eigenvectors, eigVals is Vector3 with corresponding eigenvalues. self=eigVecs*diag(eigVals)*eigVecs.transpose().

spectralDecomposition((MatrixX)arg1) → tuple :

Alias for selfAdjointEigenDecomposition.

squaredNorm((MatrixX)arg1) → object :

Square of the Euclidean norm.

sum((MatrixX)arg1) → object :

Sum of all elements.

svd((MatrixX)arg1) → tuple :

Alias for jacobiSVD.

trace((MatrixX)arg1) → object :

Return sum of diagonal elements.

transpose((MatrixX)arg1) → MatrixX :

Return transposed matrix.

class MatrixXc

/TODO/

static Identity((int)arg1, (int)rank) → MatrixXc :

Create identity matrix with given rank (square).

static Ones((int)rows, (int)cols) → MatrixXc :

Create matrix of given dimensions where all elements are set to 1.

static Random((int)rows, (int)cols) → MatrixXc :

Create matrix with given dimensions where all elements are set to number between 0 and 1 (uniformly-distributed).

static Zero((int)rows, (int)cols) → MatrixXc :

Create zero matrix of given dimensions

__init__((object)arg1) → None

__init__( (object)arg1, (MatrixXc)other) -> None

__init__( (object)arg1, (VectorXc)diag) -> object

__init__( (object)arg1 [, (VectorXc)r0=VectorXc() [, (VectorXc)r1=VectorXc() [, (VectorXc)r2=VectorXc() [, (VectorXc)r3=VectorXc() [, (VectorXc)r4=VectorXc() [, (VectorXc)r5=VectorXc() [, (VectorXc)r6=VectorXc() [, (VectorXc)r7=VectorXc() [, (VectorXc)r8=VectorXc() [, (VectorXc)r9=VectorXc() [, (bool)cols=False]]]]]]]]]]]) -> object

__init__( (object)arg1, (object)rows [, (bool)cols=False]) -> object

col((MatrixXc)arg1, (int)col) → VectorXc :

Return column as vector.

cols((MatrixXc)arg1) → int :

Number of columns.

determinant((MatrixXc)arg1) → object :

Return matrix determinant.

diagonal((MatrixXc)arg1) → VectorXc :

Return diagonal as vector.

inverse((MatrixXc)arg1) → MatrixXc :

Return inverted matrix.

isApprox((MatrixXc)arg1, (MatrixXc)other[, (object)prec=mpc(real='3.842735439305961757e-31', imag='0.0')]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((MatrixXc)arg1) → object :

Maximum absolute value over all elements.

mean((MatrixXc)arg1) → object :

Mean value over all elements.

norm((MatrixXc)arg1) → object :

Euclidean norm.

normalize((MatrixXc)arg1) → None :

Normalize this object in-place.

normalized((MatrixXc)arg1) → MatrixXc :

Return normalized copy of this object

prod((MatrixXc)arg1) → object :

Product of all elements.

pruned((MatrixXc)arg1[, (float)absTol=1e-06]) → MatrixXc :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

resize((MatrixXc)arg1, (int)rows, (int)cols) → None :

Change size of the matrix, keep values of elements which exist in the new matrix

row((MatrixXc)arg1, (int)row) → VectorXc :

Return row as vector.

rows((MatrixXc)arg1) → int :

Number of rows.

squaredNorm((MatrixXc)arg1) → object :

Square of the Euclidean norm.

sum((MatrixXc)arg1) → object :

Sum of all elements.

trace((MatrixXc)arg1) → object :

Return sum of diagonal elements.

transpose((MatrixXc)arg1) → MatrixXc :

Return transposed matrix.

class Quaternion

Quaternion representing rotation.

Supported operations (q is a Quaternion, v is a Vector3): q*q (rotation composition), q*=q, q*v (rotating v by q), q==q, q!=q.

Static attributes: Identity.

Note

Quaternion is represented as axis-angle when printed (e.g. Identity is Quaternion((1,0,0),0), and can also be constructed from the axis-angle representation. This is however different from the data stored inside, which can be accessed by indices [0] (\(x\)), [1] (\(y\)), [2] (\(z\)), [3] (\(w\)). To obtain axis-angle programatically, use Quaternion.toAxisAngle which returns the tuple.

Identity = Quaternion(("1","0","0"),"0")

Rotate((Quaternion)arg1, (Vector3)v) → Vector3

__init__((object)arg1) → None

__init__( (object)arg1, (Vector3)axis, (object)angle) -> object

__init__( (object)arg1, (object)angle, (Vector3)axis) -> object

__init__( (object)arg1, (Vector3)u, (Vector3)v) -> object

__init__( (object)arg1, (object)w, (object)x, (object)y, (object)z) -> None :

Initialize from coefficients.

Note

The order of coefficients is w, x, y, z. The [] operator numbers them differently, 0…4 for x y z w!

__init__( (object)arg1, (Matrix3)rotMatrix) -> None

__init__( (object)arg1, (Quaternion)other) -> None

angularDistance((Quaternion)arg1, (Quaternion)arg2) → object

conjugate((Quaternion)arg1) → Quaternion

inverse((Quaternion)arg1) → Quaternion

norm((Quaternion)arg1) → object

normalize((Quaternion)arg1) → None

normalized((Quaternion)arg1) → Quaternion

setFromTwoVectors((Quaternion)arg1, (Vector3)u, (Vector3)v) → None

slerp((Quaternion)arg1, (object)t, (Quaternion)other) → Quaternion

toAngleAxis((Quaternion)arg1) → tuple

toAxisAngle((Quaternion)arg1) → tuple

toRotationMatrix((Quaternion)arg1) → Matrix3

toRotationVector((Quaternion)arg1) → Vector3

class Vector2

3-dimensional float vector.

Supported operations (f if a float/int, v is a Vector3): -v, v+v, v+=v, v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v.

Implicit conversion from sequence (list, tuple, …) of 2 floats.

Static attributes: Zero, Ones, UnitX, UnitY.

Identity = Vector2("1","0")

Ones = Vector2("1","1")

static Random() → Vector2 :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector2

UnitX = Vector2("1","0")

UnitY = Vector2("0","1")

Zero = Vector2("0","0")

__init__((object)arg1) → None

__init__( (object)arg1, (Vector2)other) -> None

__init__( (object)arg1, (object)x, (object)y) -> None

asDiagonal((Vector2)arg1) → object :

Return diagonal matrix with this vector on the diagonal.

cols((Vector2)arg1) → int :

Number of columns.

dot((Vector2)arg1, (Vector2)other) → object :

Dot product with other.

isApprox((Vector2)arg1, (Vector2)other[, (object)prec=mpf('3.842735439305961757e-31')]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector2)arg1) → object :

Maximum absolute value over all elements.

maxCoeff((Vector2)arg1) → object :

Maximum value over all elements.

mean((Vector2)arg1) → object :

Mean value over all elements.

minCoeff((Vector2)arg1) → object :

Minimum value over all elements.

norm((Vector2)arg1) → object :

Euclidean norm.

normalize((Vector2)arg1) → None :

Normalize this object in-place.

normalized((Vector2)arg1) → Vector2 :

Return normalized copy of this object

outer((Vector2)arg1, (Vector2)other) → object :

Outer product with other.

prod((Vector2)arg1) → object :

Product of all elements.

pruned((Vector2)arg1[, (float)absTol=1e-06]) → Vector2 :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

rows((Vector2)arg1) → int :

Number of rows.

squaredNorm((Vector2)arg1) → object :

Square of the Euclidean norm.

sum((Vector2)arg1) → object :

Sum of all elements.

class Vector2c

/TODO/

Identity = Vector2c(mpc("1","0"),mpc("0","0"))

Ones = Vector2c(mpc("1","0"),mpc("1","0"))

static Random() → Vector2c :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector2c

UnitX = Vector2c(mpc("1","0"),mpc("0","0"))

UnitY = Vector2c(mpc("0","0"),mpc("1","0"))

Zero = Vector2c(mpc("0","0"),mpc("0","0"))

__init__((object)arg1) → None

__init__( (object)arg1, (Vector2c)other) -> None

__init__( (object)arg1, (object)x, (object)y) -> None

asDiagonal((Vector2c)arg1) → object :

Return diagonal matrix with this vector on the diagonal.

cols((Vector2c)arg1) → int :

Number of columns.

dot((Vector2c)arg1, (Vector2c)other) → object :

Dot product with other.

isApprox((Vector2c)arg1, (Vector2c)other[, (object)prec=mpc(real='3.842735439305961757e-31', imag='0.0')]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector2c)arg1) → object :

Maximum absolute value over all elements.

mean((Vector2c)arg1) → object :

Mean value over all elements.

norm((Vector2c)arg1) → object :

Euclidean norm.

normalize((Vector2c)arg1) → None :

Normalize this object in-place.

normalized((Vector2c)arg1) → Vector2c :

Return normalized copy of this object

outer((Vector2c)arg1, (Vector2c)other) → object :

Outer product with other.

prod((Vector2c)arg1) → object :

Product of all elements.

pruned((Vector2c)arg1[, (float)absTol=1e-06]) → Vector2c :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

rows((Vector2c)arg1) → int :

Number of rows.

squaredNorm((Vector2c)arg1) → object :

Square of the Euclidean norm.

sum((Vector2c)arg1) → object :

Sum of all elements.

class Vector2i

2-dimensional integer vector.

Supported operations (i if an int, v is a Vector2i): -v, v+v, v+=v, v-v, v-=v, v*i, i*v, v*=i, v==v, v!=v.

Implicit conversion from sequence (list, tuple, …) of 2 integers.

Static attributes: Zero, Ones, UnitX, UnitY.

Identity = Vector2i(1,0)

Ones = Vector2i(1,1)

static Random() → Vector2i :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector2i

UnitX = Vector2i(1,0)

UnitY = Vector2i(0,1)

Zero = Vector2i(0,0)

__init__((object)arg1) → None

__init__( (object)arg1, (Vector2i)other) -> None

__init__( (object)arg1, (int)x, (int)y) -> None

asDiagonal((Vector2i)arg1) → object :

Return diagonal matrix with this vector on the diagonal.

cols((Vector2i)arg1) → int :

Number of columns.

dot((Vector2i)arg1, (Vector2i)other) → int :

Dot product with other.

isApprox((Vector2i)arg1, (Vector2i)other[, (int)prec=0]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector2i)arg1) → int :

Maximum absolute value over all elements.

maxCoeff((Vector2i)arg1) → int :

Maximum value over all elements.

mean((Vector2i)arg1) → int :

Mean value over all elements.

minCoeff((Vector2i)arg1) → int :

Minimum value over all elements.

outer((Vector2i)arg1, (Vector2i)other) → object :

Outer product with other.

prod((Vector2i)arg1) → int :

Product of all elements.

rows((Vector2i)arg1) → int :

Number of rows.

sum((Vector2i)arg1) → int :

Sum of all elements.

class Vector3

3-dimensional float vector.

Supported operations (f if a float/int, v is a Vector3): -v, v+v, v+=v, v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v, plus operations with Matrix3 and Quaternion.

Implicit conversion from sequence (list, tuple, …) of 3 floats.

Static attributes: Zero, Ones, UnitX, UnitY, UnitZ.

Identity = Vector3("1","0","0")

Ones = Vector3("1","1","1")

static Random() → Vector3 :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector3

UnitX = Vector3("1","0","0")

UnitY = Vector3("0","1","0")

UnitZ = Vector3("0","0","1")

Zero = Vector3("0","0","0")

__init__((object)arg1) → None

__init__( (object)arg1, (Vector3)other) -> None

__init__( (object)arg1 [, (object)x=mpf(‘0.0’) [, (object)y=mpf(‘0.0’) [, (object)z=mpf(‘0.0’)]]]) -> None

asDiagonal((Vector3)arg1) → Matrix3 :

Return diagonal matrix with this vector on the diagonal.

cols((Vector3)arg1) → int :

Number of columns.

cross((Vector3)arg1, (Vector3)arg2) → Vector3

dot((Vector3)arg1, (Vector3)other) → object :

Dot product with other.

isApprox((Vector3)arg1, (Vector3)other[, (object)prec=mpf('3.842735439305961757e-31')]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector3)arg1) → object :

Maximum absolute value over all elements.

maxCoeff((Vector3)arg1) → object :

Maximum value over all elements.

mean((Vector3)arg1) → object :

Mean value over all elements.

minCoeff((Vector3)arg1) → object :

Minimum value over all elements.

norm((Vector3)arg1) → object :

Euclidean norm.

normalize((Vector3)arg1) → None :

Normalize this object in-place.

normalized((Vector3)arg1) → Vector3 :

Return normalized copy of this object

outer((Vector3)arg1, (Vector3)other) → Matrix3 :

Outer product with other.

prod((Vector3)arg1) → object :

Product of all elements.

pruned((Vector3)arg1[, (float)absTol=1e-06]) → Vector3 :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

rows((Vector3)arg1) → int :

Number of rows.

squaredNorm((Vector3)arg1) → object :

Square of the Euclidean norm.

sum((Vector3)arg1) → object :

Sum of all elements.

xy((Vector3)arg1) → Vector2

xz((Vector3)arg1) → Vector2

yx((Vector3)arg1) → Vector2

yz((Vector3)arg1) → Vector2

zx((Vector3)arg1) → Vector2

zy((Vector3)arg1) → Vector2

class Vector3c

/TODO/

Identity = Vector3c(mpc("1","0"),mpc("0","0"),mpc("0","0"))

Ones = Vector3c(mpc("1","0"),mpc("1","0"),mpc("1","0"))

static Random() → Vector3c :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector3c

UnitX = Vector3c(mpc("1","0"),mpc("0","0"),mpc("0","0"))

UnitY = Vector3c(mpc("0","0"),mpc("1","0"),mpc("0","0"))

UnitZ = Vector3c(mpc("0","0"),mpc("0","0"),mpc("1","0"))

Zero = Vector3c(mpc("0","0"),mpc("0","0"),mpc("0","0"))

__init__((object)arg1) → None

__init__( (object)arg1, (Vector3c)other) -> None

__init__( (object)arg1 [, (object)x=mpc(real=’0.0’, imag=’0.0’) [, (object)y=mpc(real=’0.0’, imag=’0.0’) [, (object)z=mpc(real=’0.0’, imag=’0.0’)]]]) -> None

asDiagonal((Vector3c)arg1) → Matrix3c :

Return diagonal matrix with this vector on the diagonal.

cols((Vector3c)arg1) → int :

Number of columns.

cross((Vector3c)arg1, (Vector3c)arg2) → Vector3c

dot((Vector3c)arg1, (Vector3c)other) → object :

Dot product with other.

isApprox((Vector3c)arg1, (Vector3c)other[, (object)prec=mpc(real='3.842735439305961757e-31', imag='0.0')]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector3c)arg1) → object :

Maximum absolute value over all elements.

mean((Vector3c)arg1) → object :

Mean value over all elements.

norm((Vector3c)arg1) → object :

Euclidean norm.

normalize((Vector3c)arg1) → None :

Normalize this object in-place.

normalized((Vector3c)arg1) → Vector3c :

Return normalized copy of this object

outer((Vector3c)arg1, (Vector3c)other) → Matrix3c :

Outer product with other.

prod((Vector3c)arg1) → object :

Product of all elements.

pruned((Vector3c)arg1[, (float)absTol=1e-06]) → Vector3c :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

rows((Vector3c)arg1) → int :

Number of rows.

squaredNorm((Vector3c)arg1) → object :

Square of the Euclidean norm.

sum((Vector3c)arg1) → object :

Sum of all elements.

xy((Vector3c)arg1) → Vector2c

xz((Vector3c)arg1) → Vector2c

yx((Vector3c)arg1) → Vector2c

yz((Vector3c)arg1) → Vector2c

zx((Vector3c)arg1) → Vector2c

zy((Vector3c)arg1) → Vector2c

class Vector3i

3-dimensional integer vector.

Supported operations (i if an int, v is a Vector3i): -v, v+v, v+=v, v-v, v-=v, v*i, i*v, v*=i, v==v, v!=v.

Implicit conversion from sequence (list, tuple, …) of 3 integers.

Static attributes: Zero, Ones, UnitX, UnitY, UnitZ.

Identity = Vector3i(1,0,0)

Ones = Vector3i(1,1,1)

static Random() → Vector3i :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector3i

UnitX = Vector3i(1,0,0)

UnitY = Vector3i(0,1,0)

UnitZ = Vector3i(0,0,1)

Zero = Vector3i(0,0,0)

__init__((object)arg1) → None

__init__( (object)arg1, (Vector3i)other) -> None

__init__( (object)arg1 [, (int)x=0 [, (int)y=0 [, (int)z=0]]]) -> None

asDiagonal((Vector3i)arg1) → object :

Return diagonal matrix with this vector on the diagonal.

cols((Vector3i)arg1) → int :

Number of columns.

cross((Vector3i)arg1, (Vector3i)arg2) → Vector3i

dot((Vector3i)arg1, (Vector3i)other) → int :

Dot product with other.

isApprox((Vector3i)arg1, (Vector3i)other[, (int)prec=0]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector3i)arg1) → int :

Maximum absolute value over all elements.

maxCoeff((Vector3i)arg1) → int :

Maximum value over all elements.

mean((Vector3i)arg1) → int :

Mean value over all elements.

minCoeff((Vector3i)arg1) → int :

Minimum value over all elements.

outer((Vector3i)arg1, (Vector3i)other) → object :

Outer product with other.

prod((Vector3i)arg1) → int :

Product of all elements.

rows((Vector3i)arg1) → int :

Number of rows.

sum((Vector3i)arg1) → int :

Sum of all elements.

xy((Vector3i)arg1) → Vector2i

xz((Vector3i)arg1) → Vector2i

yx((Vector3i)arg1) → Vector2i

yz((Vector3i)arg1) → Vector2i

zx((Vector3i)arg1) → Vector2i

zy((Vector3i)arg1) → Vector2i

class Vector4

4-dimensional float vector.

Supported operations (f if a float/int, v is a Vector3): -v, v+v, v+=v, v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v.

Implicit conversion from sequence (list, tuple, …) of 4 floats.

Static attributes: Zero, Ones.

Identity = Vector4("1","0","0", "0")

Ones = Vector4("1","1","1", "1")

static Random() → Vector4 :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector4

Zero = Vector4("0","0","0", "0")

__init__((object)arg1) → None

__init__( (object)arg1, (Vector4)other) -> None

__init__( (object)arg1, (object)v0, (object)v1, (object)v2, (object)v3) -> None

asDiagonal((Vector4)arg1) → object :

Return diagonal matrix with this vector on the diagonal.

cols((Vector4)arg1) → int :

Number of columns.

dot((Vector4)arg1, (Vector4)other) → object :

Dot product with other.

isApprox((Vector4)arg1, (Vector4)other[, (object)prec=mpf('3.842735439305961757e-31')]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector4)arg1) → object :

Maximum absolute value over all elements.

maxCoeff((Vector4)arg1) → object :

Maximum value over all elements.

mean((Vector4)arg1) → object :

Mean value over all elements.

minCoeff((Vector4)arg1) → object :

Minimum value over all elements.

norm((Vector4)arg1) → object :

Euclidean norm.

normalize((Vector4)arg1) → None :

Normalize this object in-place.

normalized((Vector4)arg1) → Vector4 :

Return normalized copy of this object

outer((Vector4)arg1, (Vector4)other) → object :

Outer product with other.

prod((Vector4)arg1) → object :

Product of all elements.

pruned((Vector4)arg1[, (float)absTol=1e-06]) → Vector4 :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

rows((Vector4)arg1) → int :

Number of rows.

squaredNorm((Vector4)arg1) → object :

Square of the Euclidean norm.

sum((Vector4)arg1) → object :

Sum of all elements.

class Vector6

6-dimensional float vector.

Supported operations (f if a float/int, v is a Vector6): -v, v+v, v+=v, v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v.

Implicit conversion from sequence (list, tuple, …) of 6 floats.

Static attributes: Zero, Ones.

Identity = Vector6("1","0","0", "0","0","0")

Ones = Vector6("1","1","1", "1","1","1")

static Random() → Vector6 :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector6

Zero = Vector6("0","0","0", "0","0","0")

__init__((object)arg1) → None

__init__( (object)arg1, (Vector6)other) -> None

__init__( (object)arg1, (object)v0, (object)v1, (object)v2, (object)v3, (object)v4, (object)v5) -> object

__init__( (object)arg1, (Vector3)head, (Vector3)tail) -> object

asDiagonal((Vector6)arg1) → Matrix6 :

Return diagonal matrix with this vector on the diagonal.

cols((Vector6)arg1) → int :

Number of columns.

dot((Vector6)arg1, (Vector6)other) → object :

Dot product with other.

head((Vector6)arg1) → Vector3

isApprox((Vector6)arg1, (Vector6)other[, (object)prec=mpf('3.842735439305961757e-31')]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector6)arg1) → object :

Maximum absolute value over all elements.

maxCoeff((Vector6)arg1) → object :

Maximum value over all elements.

mean((Vector6)arg1) → object :

Mean value over all elements.

minCoeff((Vector6)arg1) → object :

Minimum value over all elements.

norm((Vector6)arg1) → object :

Euclidean norm.

normalize((Vector6)arg1) → None :

Normalize this object in-place.

normalized((Vector6)arg1) → Vector6 :

Return normalized copy of this object

outer((Vector6)arg1, (Vector6)other) → Matrix6 :

Outer product with other.

prod((Vector6)arg1) → object :

Product of all elements.

pruned((Vector6)arg1[, (float)absTol=1e-06]) → Vector6 :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

rows((Vector6)arg1) → int :

Number of rows.

squaredNorm((Vector6)arg1) → object :

Square of the Euclidean norm.

sum((Vector6)arg1) → object :

Sum of all elements.

tail((Vector6)arg1) → Vector3

class Vector6c

/TODO/

Identity = Vector6c(mpc("1","0"),mpc("0","0"),mpc("0","0"), mpc("0","0"),mpc("0","0"),mpc("0","0"))

Ones = Vector6c(mpc("1","0"),mpc("1","0"),mpc("1","0"), mpc("1","0"),mpc("1","0"),mpc("1","0"))

static Random() → Vector6c :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector6c

Zero = Vector6c(mpc("0","0"),mpc("0","0"),mpc("0","0"), mpc("0","0"),mpc("0","0"),mpc("0","0"))

__init__((object)arg1) → None

__init__( (object)arg1, (Vector6c)other) -> None

__init__( (object)arg1, (object)v0, (object)v1, (object)v2, (object)v3, (object)v4, (object)v5) -> object

__init__( (object)arg1, (Vector3c)head, (Vector3c)tail) -> object

asDiagonal((Vector6c)arg1) → Matrix6c :

Return diagonal matrix with this vector on the diagonal.

cols((Vector6c)arg1) → int :

Number of columns.

dot((Vector6c)arg1, (Vector6c)other) → object :

Dot product with other.

head((Vector6c)arg1) → Vector3c

isApprox((Vector6c)arg1, (Vector6c)other[, (object)prec=mpc(real='3.842735439305961757e-31', imag='0.0')]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector6c)arg1) → object :

Maximum absolute value over all elements.

mean((Vector6c)arg1) → object :

Mean value over all elements.

norm((Vector6c)arg1) → object :

Euclidean norm.

normalize((Vector6c)arg1) → None :

Normalize this object in-place.

normalized((Vector6c)arg1) → Vector6c :

Return normalized copy of this object

outer((Vector6c)arg1, (Vector6c)other) → Matrix6c :

Outer product with other.

prod((Vector6c)arg1) → object :

Product of all elements.

pruned((Vector6c)arg1[, (float)absTol=1e-06]) → Vector6c :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

rows((Vector6c)arg1) → int :

Number of rows.

squaredNorm((Vector6c)arg1) → object :

Square of the Euclidean norm.

sum((Vector6c)arg1) → object :

Sum of all elements.

tail((Vector6c)arg1) → Vector3c

class Vector6i

6-dimensional float vector.

Supported operations (f if a float/int, v is a Vector6): -v, v+v, v+=v, v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v.

Implicit conversion from sequence (list, tuple, …) of 6 ints.

Static attributes: Zero, Ones.

Identity = Vector6i(1,0,0, 0,0,0)

Ones = Vector6i(1,1,1, 1,1,1)

static Random() → Vector6i :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector6i

Zero = Vector6i(0,0,0, 0,0,0)

__init__((object)arg1) → None

__init__( (object)arg1, (Vector6i)other) -> None

__init__( (object)arg1, (int)v0, (int)v1, (int)v2, (int)v3, (int)v4, (int)v5) -> object

__init__( (object)arg1, (Vector3i)head, (Vector3i)tail) -> object

asDiagonal((Vector6i)arg1) → object :

Return diagonal matrix with this vector on the diagonal.

cols((Vector6i)arg1) → int :

Number of columns.

dot((Vector6i)arg1, (Vector6i)other) → int :

Dot product with other.

head((Vector6i)arg1) → Vector3i

isApprox((Vector6i)arg1, (Vector6i)other[, (int)prec=0]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector6i)arg1) → int :

Maximum absolute value over all elements.

maxCoeff((Vector6i)arg1) → int :

Maximum value over all elements.

mean((Vector6i)arg1) → int :

Mean value over all elements.

minCoeff((Vector6i)arg1) → int :

Minimum value over all elements.

outer((Vector6i)arg1, (Vector6i)other) → object :

Outer product with other.

prod((Vector6i)arg1) → int :

Product of all elements.

rows((Vector6i)arg1) → int :

Number of rows.

sum((Vector6i)arg1) → int :

Sum of all elements.

tail((Vector6i)arg1) → Vector3i

class VectorX

Dynamic-sized float vector.

Supported operations (f if a float/int, v is a VectorX): -v, v+v, v+=v, v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v.

Implicit conversion from sequence (list, tuple, …) of X floats.

static Ones((int)arg1) → VectorX

static Random((int)len) → VectorX :

Return vector of given length with all elements set to values between 0 and 1 randomly.

static Unit((int)arg1, (int)arg2) → VectorX

static Zero((int)arg1) → VectorX

__init__((object)arg1) → None

__init__( (object)arg1, (VectorX)other) -> None

__init__( (object)arg1, (object)vv) -> object

asDiagonal((VectorX)arg1) → MatrixX :

Return diagonal matrix with this vector on the diagonal.

cols((VectorX)arg1) → int :

Number of columns.

dot((VectorX)arg1, (VectorX)other) → object :

Dot product with other.

isApprox((VectorX)arg1, (VectorX)other[, (object)prec=mpf('3.842735439305961757e-31')]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((VectorX)arg1) → object :

Maximum absolute value over all elements.

maxCoeff((VectorX)arg1) → object :

Maximum value over all elements.

mean((VectorX)arg1) → object :

Mean value over all elements.

minCoeff((VectorX)arg1) → object :

Minimum value over all elements.

norm((VectorX)arg1) → object :

Euclidean norm.

normalize((VectorX)arg1) → None :

Normalize this object in-place.

normalized((VectorX)arg1) → VectorX :

Return normalized copy of this object

outer((VectorX)arg1, (VectorX)other) → MatrixX :

Outer product with other.

prod((VectorX)arg1) → object :

Product of all elements.

pruned((VectorX)arg1[, (float)absTol=1e-06]) → VectorX :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

resize((VectorX)arg1, (int)arg2) → None

rows((VectorX)arg1) → int :

Number of rows.

squaredNorm((VectorX)arg1) → object :

Square of the Euclidean norm.

sum((VectorX)arg1) → object :

Sum of all elements.

class VectorXc

/TODO/

static Ones((int)arg1) → VectorXc

static Random((int)len) → VectorXc :

Return vector of given length with all elements set to values between 0 and 1 randomly.

static Unit((int)arg1, (int)arg2) → VectorXc

static Zero((int)arg1) → VectorXc

__init__((object)arg1) → None

__init__( (object)arg1, (VectorXc)other) -> None

__init__( (object)arg1, (object)vv) -> object

asDiagonal((VectorXc)arg1) → MatrixXc :

Return diagonal matrix with this vector on the diagonal.

cols((VectorXc)arg1) → int :

Number of columns.

dot((VectorXc)arg1, (VectorXc)other) → object :

Dot product with other.

isApprox((VectorXc)arg1, (VectorXc)other[, (object)prec=mpc(real='3.842735439305961757e-31', imag='0.0')]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((VectorXc)arg1) → object :

Maximum absolute value over all elements.

mean((VectorXc)arg1) → object :

Mean value over all elements.

norm((VectorXc)arg1) → object :

Euclidean norm.

normalize((VectorXc)arg1) → None :

Normalize this object in-place.

normalized((VectorXc)arg1) → VectorXc :

Return normalized copy of this object

outer((VectorXc)arg1, (VectorXc)other) → MatrixXc :

Outer product with other.

prod((VectorXc)arg1) → object :

Product of all elements.

pruned((VectorXc)arg1[, (float)absTol=1e-06]) → VectorXc :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

resize((VectorXc)arg1, (int)arg2) → None

rows((VectorXc)arg1) → int :

Number of rows.

squaredNorm((VectorXc)arg1) → object :

Square of the Euclidean norm.

sum((VectorXc)arg1) → object :

Sum of all elements.

vectorize = False

class yade._minieigenHP.Matrix3

3x3 float matrix.

Supported operations (m is a Matrix3, f if a float/int, v is a Vector3): -m, m+m, m+=m, m-m, m-=m, m*f, f*m, m*=f, m/f, m/=f, m*m, m*=m, m*v, v*m, m==m, m!=m.

Static attributes: Zero, Ones, Identity.

Identity = Matrix3(1,0,0, 0,1,0, 0,0,1)

Ones = Matrix3(1,1,1, 1,1,1, 1,1,1)

static Random() → Matrix3 :

Return an object where all elements are randomly set to values between 0 and 1.

Zero = Matrix3(0,0,0, 0,0,0, 0,0,0)

__init__((object)arg1) → None

__init__( (object)arg1, (Quaternion)q) -> None

__init__( (object)arg1, (Matrix3)other) -> None

__init__( (object)arg1, (Vector3)diag) -> object

__init__( (object)arg1, (float)m00, (float)m01, (float)m02, (float)m10, (float)m11, (float)m12, (float)m20, (float)m21, (float)m22) -> object

__init__( (object)arg1, (Vector3)r0, (Vector3)r1, (Vector3)r2 [, (bool)cols=False]) -> object

col((Matrix3)arg1, (int)col) → Vector3 :

Return column as vector.

cols((Matrix3)arg1) → int :

Number of columns.

computeUnitaryPositive((Matrix3)arg1) → tuple :

Compute polar decomposition (unitary matrix U and positive semi-definite symmetric matrix P such that self=U*P).

determinant((Matrix3)arg1) → float :

Return matrix determinant.

diagonal((Matrix3)arg1) → Vector3 :

Return diagonal as vector.

inverse((Matrix3)arg1) → Matrix3 :

Return inverted matrix.

isApprox((Matrix3)arg1, (Matrix3)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

jacobiSVD((Matrix3)arg1) → tuple :

Compute SVD decomposition of square matrix, retuns (U,S,V) such that self=U*S*V.transpose()

maxAbsCoeff((Matrix3)arg1) → float :

Maximum absolute value over all elements.

maxCoeff((Matrix3)arg1) → float :

Maximum value over all elements.

mean((Matrix3)arg1) → float :

Mean value over all elements.

minCoeff((Matrix3)arg1) → float :

Minimum value over all elements.

norm((Matrix3)arg1) → float :

Euclidean norm.

normalize((Matrix3)arg1) → None :

Normalize this object in-place.

normalized((Matrix3)arg1) → Matrix3 :

Return normalized copy of this object

polarDecomposition((Matrix3)arg1) → tuple :

Alias for computeUnitaryPositive.

prod((Matrix3)arg1) → float :

Product of all elements.

pruned((Matrix3)arg1[, (float)absTol=1e-06]) → Matrix3 :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

row((Matrix3)arg1, (int)row) → Vector3 :

Return row as vector.

rows((Matrix3)arg1) → int :

Number of rows.

selfAdjointEigenDecomposition((Matrix3)arg1) → tuple :

Compute eigen (spectral) decomposition of symmetric matrix, returns (eigVecs,eigVals). eigVecs is orthogonal Matrix3 with columns ar normalized eigenvectors, eigVals is Vector3 with corresponding eigenvalues. self=eigVecs*diag(eigVals)*eigVecs.transpose().

spectralDecomposition((Matrix3)arg1) → tuple :

Alias for selfAdjointEigenDecomposition.

squaredNorm((Matrix3)arg1) → float :

Square of the Euclidean norm.

sum((Matrix3)arg1) → float :

Sum of all elements.

svd((Matrix3)arg1) → tuple :

Alias for jacobiSVD.

trace((Matrix3)arg1) → float :

Return sum of diagonal elements.

transpose((Matrix3)arg1) → Matrix3 :

Return transposed matrix.

class yade._minieigenHP.Matrix3c

/TODO/

Identity = Matrix3c(1,0,0, 0,1,0, 0,0,1)

Ones = Matrix3c(1,1,1, 1,1,1, 1,1,1)

static Random() → Matrix3c :

Return an object where all elements are randomly set to values between 0 and 1.

Zero = Matrix3c(0,0,0, 0,0,0, 0,0,0)

__init__((object)arg1) → None

__init__( (object)arg1, (Matrix3c)other) -> None

__init__( (object)arg1, (Vector3c)diag) -> object

__init__( (object)arg1, (complex)m00, (complex)m01, (complex)m02, (complex)m10, (complex)m11, (complex)m12, (complex)m20, (complex)m21, (complex)m22) -> object

__init__( (object)arg1, (Vector3c)r0, (Vector3c)r1, (Vector3c)r2 [, (bool)cols=False]) -> object

col((Matrix3c)arg1, (int)col) → Vector3c :

Return column as vector.

cols((Matrix3c)arg1) → int :

Number of columns.

determinant((Matrix3c)arg1) → complex :

Return matrix determinant.

diagonal((Matrix3c)arg1) → Vector3c :

Return diagonal as vector.

inverse((Matrix3c)arg1) → Matrix3c :

Return inverted matrix.

isApprox((Matrix3c)arg1, (Matrix3c)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Matrix3c)arg1) → float :

Maximum absolute value over all elements.

mean((Matrix3c)arg1) → complex :

Mean value over all elements.

norm((Matrix3c)arg1) → float :

Euclidean norm.

normalize((Matrix3c)arg1) → None :

Normalize this object in-place.

normalized((Matrix3c)arg1) → Matrix3c :

Return normalized copy of this object

prod((Matrix3c)arg1) → complex :

Product of all elements.

pruned((Matrix3c)arg1[, (float)absTol=1e-06]) → Matrix3c :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

row((Matrix3c)arg1, (int)row) → Vector3c :

Return row as vector.

rows((Matrix3c)arg1) → int :

Number of rows.

squaredNorm((Matrix3c)arg1) → float :

Square of the Euclidean norm.

sum((Matrix3c)arg1) → complex :

Sum of all elements.

trace((Matrix3c)arg1) → complex :

Return sum of diagonal elements.

transpose((Matrix3c)arg1) → Matrix3c :

Return transposed matrix.

class yade._minieigenHP.Matrix6

6x6 float matrix. Constructed from 4 3x3 sub-matrices, from 6xVector6 (rows).

Supported operations (m is a Matrix6, f if a float/int, v is a Vector6): -m, m+m, m+=m, m-m, m-=m, m*f, f*m, m*=f, m/f, m/=f, m*m, m*=m, m*v, v*m, m==m, m!=m.

Static attributes: Zero, Ones, Identity.

Identity = Matrix6( (1,0,0,0,0,0), (0,1,0,0,0,0), (0,0,1,0,0,0), (0,0,0,1,0,0), (0,0,0,0,1,0), (0,0,0,0,0,1) )

Ones = Matrix6( (1,1,1,1,1,1), (1,1,1,1,1,1), (1,1,1,1,1,1), (1,1,1,1,1,1), (1,1,1,1,1,1), (1,1,1,1,1,1) )

static Random() → Matrix6 :

Return an object where all elements are randomly set to values between 0 and 1.

Zero = Matrix6( (0,0,0,0,0,0), (0,0,0,0,0,0), (0,0,0,0,0,0), (0,0,0,0,0,0), (0,0,0,0,0,0), (0,0,0,0,0,0) )

__init__((object)arg1) → None

__init__( (object)arg1, (Matrix6)other) -> None

__init__( (object)arg1, (Vector6)diag) -> object

__init__( (object)arg1, (Matrix3)ul, (Matrix3)ur, (Matrix3)ll, (Matrix3)lr) -> object

__init__( (object)arg1, (Vector6)l0, (Vector6)l1, (Vector6)l2, (Vector6)l3, (Vector6)l4, (Vector6)l5 [, (bool)cols=False]) -> object

col((Matrix6)arg1, (int)col) → Vector6 :

Return column as vector.

cols((Matrix6)arg1) → int :

Number of columns.

computeUnitaryPositive((Matrix6)arg1) → tuple :

Compute polar decomposition (unitary matrix U and positive semi-definite symmetric matrix P such that self=U*P).

determinant((Matrix6)arg1) → float :

Return matrix determinant.

diagonal((Matrix6)arg1) → Vector6 :

Return diagonal as vector.

inverse((Matrix6)arg1) → Matrix6 :

Return inverted matrix.

isApprox((Matrix6)arg1, (Matrix6)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

jacobiSVD((Matrix6)arg1) → tuple :

Compute SVD decomposition of square matrix, retuns (U,S,V) such that self=U*S*V.transpose()

ll((Matrix6)arg1) → Matrix3 :

Return lower-left 3x3 block

lr((Matrix6)arg1) → Matrix3 :

Return lower-right 3x3 block

maxAbsCoeff((Matrix6)arg1) → float :

Maximum absolute value over all elements.

maxCoeff((Matrix6)arg1) → float :

Maximum value over all elements.

mean((Matrix6)arg1) → float :

Mean value over all elements.

minCoeff((Matrix6)arg1) → float :

Minimum value over all elements.

norm((Matrix6)arg1) → float :

Euclidean norm.

normalize((Matrix6)arg1) → None :

Normalize this object in-place.

normalized((Matrix6)arg1) → Matrix6 :

Return normalized copy of this object

polarDecomposition((Matrix6)arg1) → tuple :

Alias for computeUnitaryPositive.

prod((Matrix6)arg1) → float :

Product of all elements.

pruned((Matrix6)arg1[, (float)absTol=1e-06]) → Matrix6 :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

row((Matrix6)arg1, (int)row) → Vector6 :

Return row as vector.

rows((Matrix6)arg1) → int :

Number of rows.

selfAdjointEigenDecomposition((Matrix6)arg1) → tuple :

Compute eigen (spectral) decomposition of symmetric matrix, returns (eigVecs,eigVals). eigVecs is orthogonal Matrix3 with columns ar normalized eigenvectors, eigVals is Vector3 with corresponding eigenvalues. self=eigVecs*diag(eigVals)*eigVecs.transpose().

spectralDecomposition((Matrix6)arg1) → tuple :

Alias for selfAdjointEigenDecomposition.

squaredNorm((Matrix6)arg1) → float :

Square of the Euclidean norm.

sum((Matrix6)arg1) → float :

Sum of all elements.

svd((Matrix6)arg1) → tuple :

Alias for jacobiSVD.

trace((Matrix6)arg1) → float :

Return sum of diagonal elements.

transpose((Matrix6)arg1) → Matrix6 :

Return transposed matrix.

ul((Matrix6)arg1) → Matrix3 :

Return upper-left 3x3 block

ur((Matrix6)arg1) → Matrix3 :

Return upper-right 3x3 block

class yade._minieigenHP.Matrix6c

/TODO/

Identity = Matrix6c( (1,0,0,0,0,0), (0,1,0,0,0,0), (0,0,1,0,0,0), (0,0,0,1,0,0), (0,0,0,0,1,0), (0,0,0,0,0,1) )

Ones = Matrix6c( (1,1,1,1,1,1), (1,1,1,1,1,1), (1,1,1,1,1,1), (1,1,1,1,1,1), (1,1,1,1,1,1), (1,1,1,1,1,1) )

static Random() → Matrix6c :

Return an object where all elements are randomly set to values between 0 and 1.

Zero = Matrix6c( (0,0,0,0,0,0), (0,0,0,0,0,0), (0,0,0,0,0,0), (0,0,0,0,0,0), (0,0,0,0,0,0), (0,0,0,0,0,0) )

__init__((object)arg1) → None

__init__( (object)arg1, (Matrix6c)other) -> None

__init__( (object)arg1, (Vector6c)diag) -> object

__init__( (object)arg1, (Matrix3c)ul, (Matrix3c)ur, (Matrix3c)ll, (Matrix3c)lr) -> object

__init__( (object)arg1, (Vector6c)l0, (Vector6c)l1, (Vector6c)l2, (Vector6c)l3, (Vector6c)l4, (Vector6c)l5 [, (bool)cols=False]) -> object

col((Matrix6c)arg1, (int)col) → Vector6c :

Return column as vector.

cols((Matrix6c)arg1) → int :

Number of columns.

determinant((Matrix6c)arg1) → complex :

Return matrix determinant.

diagonal((Matrix6c)arg1) → Vector6c :

Return diagonal as vector.

inverse((Matrix6c)arg1) → Matrix6c :

Return inverted matrix.

isApprox((Matrix6c)arg1, (Matrix6c)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

ll((Matrix6c)arg1) → Matrix3c :

Return lower-left 3x3 block

lr((Matrix6c)arg1) → Matrix3c :

Return lower-right 3x3 block

maxAbsCoeff((Matrix6c)arg1) → float :

Maximum absolute value over all elements.

mean((Matrix6c)arg1) → complex :

Mean value over all elements.

norm((Matrix6c)arg1) → float :

Euclidean norm.

normalize((Matrix6c)arg1) → None :

Normalize this object in-place.

normalized((Matrix6c)arg1) → Matrix6c :

Return normalized copy of this object

prod((Matrix6c)arg1) → complex :

Product of all elements.

pruned((Matrix6c)arg1[, (float)absTol=1e-06]) → Matrix6c :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

row((Matrix6c)arg1, (int)row) → Vector6c :

Return row as vector.

rows((Matrix6c)arg1) → int :

Number of rows.

squaredNorm((Matrix6c)arg1) → float :

Square of the Euclidean norm.

sum((Matrix6c)arg1) → complex :

Sum of all elements.

trace((Matrix6c)arg1) → complex :

Return sum of diagonal elements.

transpose((Matrix6c)arg1) → Matrix6c :

Return transposed matrix.

ul((Matrix6c)arg1) → Matrix3c :

Return upper-left 3x3 block

ur((Matrix6c)arg1) → Matrix3c :

Return upper-right 3x3 block

class yade._minieigenHP.MatrixX

XxX (dynamic-sized) float matrix. Constructed from list of rows (as VectorX).

Supported operations (m is a MatrixX, f if a float/int, v is a VectorX): -m, m+m, m+=m, m-m, m-=m, m*f, f*m, m*=f, m/f, m/=f, m*m, m*=m, m*v, v*m, m==m, m!=m.

static Identity((int)arg1, (int)rank) → MatrixX :

Create identity matrix with given rank (square).

static Ones((int)rows, (int)cols) → MatrixX :

Create matrix of given dimensions where all elements are set to 1.

static Random((int)rows, (int)cols) → MatrixX :

Create matrix with given dimensions where all elements are set to number between 0 and 1 (uniformly-distributed).

static Zero((int)rows, (int)cols) → MatrixX :

Create zero matrix of given dimensions

__init__((object)arg1) → None

__init__( (object)arg1, (MatrixX)other) -> None

__init__( (object)arg1, (VectorX)diag) -> object

__init__( (object)arg1 [, (VectorX)r0=VectorX() [, (VectorX)r1=VectorX() [, (VectorX)r2=VectorX() [, (VectorX)r3=VectorX() [, (VectorX)r4=VectorX() [, (VectorX)r5=VectorX() [, (VectorX)r6=VectorX() [, (VectorX)r7=VectorX() [, (VectorX)r8=VectorX() [, (VectorX)r9=VectorX() [, (bool)cols=False]]]]]]]]]]]) -> object

__init__( (object)arg1, (object)rows [, (bool)cols=False]) -> object

col((MatrixX)arg1, (int)col) → VectorX :

Return column as vector.

cols((MatrixX)arg1) → int :

Number of columns.

computeUnitaryPositive((MatrixX)arg1) → tuple :

Compute polar decomposition (unitary matrix U and positive semi-definite symmetric matrix P such that self=U*P).

determinant((MatrixX)arg1) → float :

Return matrix determinant.

diagonal((MatrixX)arg1) → VectorX :

Return diagonal as vector.

inverse((MatrixX)arg1) → MatrixX :

Return inverted matrix.

isApprox((MatrixX)arg1, (MatrixX)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

jacobiSVD((MatrixX)arg1) → tuple :

Compute SVD decomposition of square matrix, retuns (U,S,V) such that self=U*S*V.transpose()

maxAbsCoeff((MatrixX)arg1) → float :

Maximum absolute value over all elements.

maxCoeff((MatrixX)arg1) → float :

Maximum value over all elements.

mean((MatrixX)arg1) → float :

Mean value over all elements.

minCoeff((MatrixX)arg1) → float :

Minimum value over all elements.

norm((MatrixX)arg1) → float :

Euclidean norm.

normalize((MatrixX)arg1) → None :

Normalize this object in-place.

normalized((MatrixX)arg1) → MatrixX :

Return normalized copy of this object

polarDecomposition((MatrixX)arg1) → tuple :

Alias for computeUnitaryPositive.

prod((MatrixX)arg1) → float :

Product of all elements.

pruned((MatrixX)arg1[, (float)absTol=1e-06]) → MatrixX :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

resize((MatrixX)arg1, (int)rows, (int)cols) → None :

Change size of the matrix, keep values of elements which exist in the new matrix

row((MatrixX)arg1, (int)row) → VectorX :

Return row as vector.

rows((MatrixX)arg1) → int :

Number of rows.

selfAdjointEigenDecomposition((MatrixX)arg1) → tuple :

Compute eigen (spectral) decomposition of symmetric matrix, returns (eigVecs,eigVals). eigVecs is orthogonal Matrix3 with columns ar normalized eigenvectors, eigVals is Vector3 with corresponding eigenvalues. self=eigVecs*diag(eigVals)*eigVecs.transpose().

spectralDecomposition((MatrixX)arg1) → tuple :

Alias for selfAdjointEigenDecomposition.

squaredNorm((MatrixX)arg1) → float :

Square of the Euclidean norm.

sum((MatrixX)arg1) → float :

Sum of all elements.

svd((MatrixX)arg1) → tuple :

Alias for jacobiSVD.

trace((MatrixX)arg1) → float :

Return sum of diagonal elements.

transpose((MatrixX)arg1) → MatrixX :

Return transposed matrix.

class yade._minieigenHP.MatrixXc

/TODO/

static Identity((int)arg1, (int)rank) → MatrixXc :

Create identity matrix with given rank (square).

static Ones((int)rows, (int)cols) → MatrixXc :

Create matrix of given dimensions where all elements are set to 1.

static Random((int)rows, (int)cols) → MatrixXc :

Create matrix with given dimensions where all elements are set to number between 0 and 1 (uniformly-distributed).

static Zero((int)rows, (int)cols) → MatrixXc :

Create zero matrix of given dimensions

__init__((object)arg1) → None

__init__( (object)arg1, (MatrixXc)other) -> None

__init__( (object)arg1, (VectorXc)diag) -> object

__init__( (object)arg1 [, (VectorXc)r0=VectorXc() [, (VectorXc)r1=VectorXc() [, (VectorXc)r2=VectorXc() [, (VectorXc)r3=VectorXc() [, (VectorXc)r4=VectorXc() [, (VectorXc)r5=VectorXc() [, (VectorXc)r6=VectorXc() [, (VectorXc)r7=VectorXc() [, (VectorXc)r8=VectorXc() [, (VectorXc)r9=VectorXc() [, (bool)cols=False]]]]]]]]]]]) -> object

__init__( (object)arg1, (object)rows [, (bool)cols=False]) -> object

col((MatrixXc)arg1, (int)col) → VectorXc :

Return column as vector.

cols((MatrixXc)arg1) → int :

Number of columns.

determinant((MatrixXc)arg1) → complex :

Return matrix determinant.

diagonal((MatrixXc)arg1) → VectorXc :

Return diagonal as vector.

inverse((MatrixXc)arg1) → MatrixXc :

Return inverted matrix.

isApprox((MatrixXc)arg1, (MatrixXc)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((MatrixXc)arg1) → float :

Maximum absolute value over all elements.

mean((MatrixXc)arg1) → complex :

Mean value over all elements.

norm((MatrixXc)arg1) → float :

Euclidean norm.

normalize((MatrixXc)arg1) → None :

Normalize this object in-place.

normalized((MatrixXc)arg1) → MatrixXc :

Return normalized copy of this object

prod((MatrixXc)arg1) → complex :

Product of all elements.

pruned((MatrixXc)arg1[, (float)absTol=1e-06]) → MatrixXc :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

resize((MatrixXc)arg1, (int)rows, (int)cols) → None :

Change size of the matrix, keep values of elements which exist in the new matrix

row((MatrixXc)arg1, (int)row) → VectorXc :

Return row as vector.

rows((MatrixXc)arg1) → int :

Number of rows.

squaredNorm((MatrixXc)arg1) → float :

Square of the Euclidean norm.

sum((MatrixXc)arg1) → complex :

Sum of all elements.

trace((MatrixXc)arg1) → complex :

Return sum of diagonal elements.

transpose((MatrixXc)arg1) → MatrixXc :

Return transposed matrix.

class yade._minieigenHP.Quaternion

Quaternion representing rotation.

Supported operations (q is a Quaternion, v is a Vector3): q*q (rotation composition), q*=q, q*v (rotating v by q), q==q, q!=q.

Static attributes: Identity.

Note

Quaternion is represented as axis-angle when printed (e.g. Identity is Quaternion((1,0,0),0), and can also be constructed from the axis-angle representation. This is however different from the data stored inside, which can be accessed by indices [0] (\(x\)), [1] (\(y\)), [2] (\(z\)), [3] (\(w\)). To obtain axis-angle programatically, use Quaternion.toAxisAngle which returns the tuple.

Identity = Quaternion((1,0,0),0)

Rotate((Quaternion)arg1, (Vector3)v) → Vector3

__init__((object)arg1) → None

__init__( (object)arg1, (Vector3)axis, (float)angle) -> object

__init__( (object)arg1, (float)angle, (Vector3)axis) -> object

__init__( (object)arg1, (Vector3)u, (Vector3)v) -> object

__init__( (object)arg1, (float)w, (float)x, (float)y, (float)z) -> None :

Initialize from coefficients.

Note

The order of coefficients is w, x, y, z. The [] operator numbers them differently, 0…4 for x y z w!

__init__( (object)arg1, (Matrix3)rotMatrix) -> None

__init__( (object)arg1, (Quaternion)other) -> None

angularDistance((Quaternion)arg1, (Quaternion)arg2) → float

conjugate((Quaternion)arg1) → Quaternion

inverse((Quaternion)arg1) → Quaternion

norm((Quaternion)arg1) → float

normalize((Quaternion)arg1) → None

normalized((Quaternion)arg1) → Quaternion

setFromTwoVectors((Quaternion)arg1, (Vector3)u, (Vector3)v) → None

slerp((Quaternion)arg1, (float)t, (Quaternion)other) → Quaternion

toAngleAxis((Quaternion)arg1) → tuple

toAxisAngle((Quaternion)arg1) → tuple

toRotationMatrix((Quaternion)arg1) → Matrix3

toRotationVector((Quaternion)arg1) → Vector3

class yade._minieigenHP.Vector2

3-dimensional float vector.

Supported operations (f if a float/int, v is a Vector3): -v, v+v, v+=v, v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v.

Implicit conversion from sequence (list, tuple, …) of 2 floats.

Static attributes: Zero, Ones, UnitX, UnitY.

Identity = Vector2(1,0)

Ones = Vector2(1,1)

static Random() → Vector2 :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector2

UnitX = Vector2(1,0)

UnitY = Vector2(0,1)

Zero = Vector2(0,0)

__init__((object)arg1) → None

__init__( (object)arg1, (Vector2)other) -> None

__init__( (object)arg1, (float)x, (float)y) -> None

asDiagonal((Vector2)arg1) → object :

Return diagonal matrix with this vector on the diagonal.

cols((Vector2)arg1) → int :

Number of columns.

dot((Vector2)arg1, (Vector2)other) → float :

Dot product with other.

isApprox((Vector2)arg1, (Vector2)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector2)arg1) → float :

Maximum absolute value over all elements.

maxCoeff((Vector2)arg1) → float :

Maximum value over all elements.

mean((Vector2)arg1) → float :

Mean value over all elements.

minCoeff((Vector2)arg1) → float :

Minimum value over all elements.

norm((Vector2)arg1) → float :

Euclidean norm.

normalize((Vector2)arg1) → None :

Normalize this object in-place.

normalized((Vector2)arg1) → Vector2 :

Return normalized copy of this object

outer((Vector2)arg1, (Vector2)other) → object :

Outer product with other.

prod((Vector2)arg1) → float :

Product of all elements.

pruned((Vector2)arg1[, (float)absTol=1e-06]) → Vector2 :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

rows((Vector2)arg1) → int :

Number of rows.

squaredNorm((Vector2)arg1) → float :

Square of the Euclidean norm.

sum((Vector2)arg1) → float :

Sum of all elements.

class yade._minieigenHP.Vector2c

/TODO/

Identity = Vector2c(1,0)

Ones = Vector2c(1,1)

static Random() → Vector2c :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector2c

UnitX = Vector2c(1,0)

UnitY = Vector2c(0,1)

Zero = Vector2c(0,0)

__init__((object)arg1) → None

__init__( (object)arg1, (Vector2c)other) -> None

__init__( (object)arg1, (complex)x, (complex)y) -> None

asDiagonal((Vector2c)arg1) → object :

Return diagonal matrix with this vector on the diagonal.

cols((Vector2c)arg1) → int :

Number of columns.

dot((Vector2c)arg1, (Vector2c)other) → complex :

Dot product with other.

isApprox((Vector2c)arg1, (Vector2c)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector2c)arg1) → float :

Maximum absolute value over all elements.

mean((Vector2c)arg1) → complex :

Mean value over all elements.

norm((Vector2c)arg1) → float :

Euclidean norm.

normalize((Vector2c)arg1) → None :

Normalize this object in-place.

normalized((Vector2c)arg1) → Vector2c :

Return normalized copy of this object

outer((Vector2c)arg1, (Vector2c)other) → object :

Outer product with other.

prod((Vector2c)arg1) → complex :

Product of all elements.

pruned((Vector2c)arg1[, (float)absTol=1e-06]) → Vector2c :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

rows((Vector2c)arg1) → int :

Number of rows.

squaredNorm((Vector2c)arg1) → float :

Square of the Euclidean norm.

sum((Vector2c)arg1) → complex :

Sum of all elements.

class yade._minieigenHP.Vector2i

2-dimensional integer vector.

Supported operations (i if an int, v is a Vector2i): -v, v+v, v+=v, v-v, v-=v, v*i, i*v, v*=i, v==v, v!=v.

Implicit conversion from sequence (list, tuple, …) of 2 integers.

Static attributes: Zero, Ones, UnitX, UnitY.

Identity = Vector2i(1,0)

Ones = Vector2i(1,1)

static Random() → Vector2i :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector2i

UnitX = Vector2i(1,0)

UnitY = Vector2i(0,1)

Zero = Vector2i(0,0)

__init__((object)arg1) → None

__init__( (object)arg1, (Vector2i)other) -> None

__init__( (object)arg1, (int)x, (int)y) -> None

asDiagonal((Vector2i)arg1) → object :

Return diagonal matrix with this vector on the diagonal.

cols((Vector2i)arg1) → int :

Number of columns.

dot((Vector2i)arg1, (Vector2i)other) → int :

Dot product with other.

isApprox((Vector2i)arg1, (Vector2i)other[, (int)prec=0]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector2i)arg1) → int :

Maximum absolute value over all elements.

maxCoeff((Vector2i)arg1) → int :

Maximum value over all elements.

mean((Vector2i)arg1) → int :

Mean value over all elements.

minCoeff((Vector2i)arg1) → int :

Minimum value over all elements.

outer((Vector2i)arg1, (Vector2i)other) → object :

Outer product with other.

prod((Vector2i)arg1) → int :

Product of all elements.

rows((Vector2i)arg1) → int :

Number of rows.

sum((Vector2i)arg1) → int :

Sum of all elements.

class yade._minieigenHP.Vector3

3-dimensional float vector.

Supported operations (f if a float/int, v is a Vector3): -v, v+v, v+=v, v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v, plus operations with Matrix3 and Quaternion.

Implicit conversion from sequence (list, tuple, …) of 3 floats.

Static attributes: Zero, Ones, UnitX, UnitY, UnitZ.

Identity = Vector3(1,0,0)

Ones = Vector3(1,1,1)

static Random() → Vector3 :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector3

UnitX = Vector3(1,0,0)

UnitY = Vector3(0,1,0)

UnitZ = Vector3(0,0,1)

Zero = Vector3(0,0,0)

__init__((object)arg1) → None

__init__( (object)arg1, (Vector3)other) -> None

__init__( (object)arg1 [, (float)x=0.0 [, (float)y=0.0 [, (float)z=0.0]]]) -> None

asDiagonal((Vector3)arg1) → Matrix3 :

Return diagonal matrix with this vector on the diagonal.

cols((Vector3)arg1) → int :

Number of columns.

cross((Vector3)arg1, (Vector3)arg2) → Vector3

dot((Vector3)arg1, (Vector3)other) → float :

Dot product with other.

isApprox((Vector3)arg1, (Vector3)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector3)arg1) → float :

Maximum absolute value over all elements.

maxCoeff((Vector3)arg1) → float :

Maximum value over all elements.

mean((Vector3)arg1) → float :

Mean value over all elements.

minCoeff((Vector3)arg1) → float :

Minimum value over all elements.

norm((Vector3)arg1) → float :

Euclidean norm.

normalize((Vector3)arg1) → None :

Normalize this object in-place.

normalized((Vector3)arg1) → Vector3 :

Return normalized copy of this object

outer((Vector3)arg1, (Vector3)other) → Matrix3 :

Outer product with other.

prod((Vector3)arg1) → float :

Product of all elements.

pruned((Vector3)arg1[, (float)absTol=1e-06]) → Vector3 :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

rows((Vector3)arg1) → int :

Number of rows.

squaredNorm((Vector3)arg1) → float :

Square of the Euclidean norm.

sum((Vector3)arg1) → float :

Sum of all elements.

xy((Vector3)arg1) → Vector2

xz((Vector3)arg1) → Vector2

yx((Vector3)arg1) → Vector2

yz((Vector3)arg1) → Vector2

zx((Vector3)arg1) → Vector2

zy((Vector3)arg1) → Vector2

class yade._minieigenHP.Vector3c

/TODO/

Identity = Vector3c(1,0,0)

Ones = Vector3c(1,1,1)

static Random() → Vector3c :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector3c

UnitX = Vector3c(1,0,0)

UnitY = Vector3c(0,1,0)

UnitZ = Vector3c(0,0,1)

Zero = Vector3c(0,0,0)

__init__((object)arg1) → None

__init__( (object)arg1, (Vector3c)other) -> None

__init__( (object)arg1 [, (complex)x=0j [, (complex)y=0j [, (complex)z=0j]]]) -> None

asDiagonal((Vector3c)arg1) → Matrix3c :

Return diagonal matrix with this vector on the diagonal.

cols((Vector3c)arg1) → int :

Number of columns.

cross((Vector3c)arg1, (Vector3c)arg2) → Vector3c

dot((Vector3c)arg1, (Vector3c)other) → complex :

Dot product with other.

isApprox((Vector3c)arg1, (Vector3c)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector3c)arg1) → float :

Maximum absolute value over all elements.

mean((Vector3c)arg1) → complex :

Mean value over all elements.

norm((Vector3c)arg1) → float :

Euclidean norm.

normalize((Vector3c)arg1) → None :

Normalize this object in-place.

normalized((Vector3c)arg1) → Vector3c :

Return normalized copy of this object

outer((Vector3c)arg1, (Vector3c)other) → Matrix3c :

Outer product with other.

prod((Vector3c)arg1) → complex :

Product of all elements.

pruned((Vector3c)arg1[, (float)absTol=1e-06]) → Vector3c :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

rows((Vector3c)arg1) → int :

Number of rows.

squaredNorm((Vector3c)arg1) → float :

Square of the Euclidean norm.

sum((Vector3c)arg1) → complex :

Sum of all elements.

xy((Vector3c)arg1) → Vector2c

xz((Vector3c)arg1) → Vector2c

yx((Vector3c)arg1) → Vector2c

yz((Vector3c)arg1) → Vector2c

zx((Vector3c)arg1) → Vector2c

zy((Vector3c)arg1) → Vector2c

class yade._minieigenHP.Vector3i

3-dimensional integer vector.

Supported operations (i if an int, v is a Vector3i): -v, v+v, v+=v, v-v, v-=v, v*i, i*v, v*=i, v==v, v!=v.

Implicit conversion from sequence (list, tuple, …) of 3 integers.

Static attributes: Zero, Ones, UnitX, UnitY, UnitZ.

Identity = Vector3i(1,0,0)

Ones = Vector3i(1,1,1)

static Random() → Vector3i :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector3i

UnitX = Vector3i(1,0,0)

UnitY = Vector3i(0,1,0)

UnitZ = Vector3i(0,0,1)

Zero = Vector3i(0,0,0)

__init__((object)arg1) → None

__init__( (object)arg1, (Vector3i)other) -> None

__init__( (object)arg1 [, (int)x=0 [, (int)y=0 [, (int)z=0]]]) -> None

asDiagonal((Vector3i)arg1) → object :

Return diagonal matrix with this vector on the diagonal.

cols((Vector3i)arg1) → int :

Number of columns.

cross((Vector3i)arg1, (Vector3i)arg2) → Vector3i

dot((Vector3i)arg1, (Vector3i)other) → int :

Dot product with other.

isApprox((Vector3i)arg1, (Vector3i)other[, (int)prec=0]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector3i)arg1) → int :

Maximum absolute value over all elements.

maxCoeff((Vector3i)arg1) → int :

Maximum value over all elements.

mean((Vector3i)arg1) → int :

Mean value over all elements.

minCoeff((Vector3i)arg1) → int :

Minimum value over all elements.

outer((Vector3i)arg1, (Vector3i)other) → object :

Outer product with other.

prod((Vector3i)arg1) → int :

Product of all elements.

rows((Vector3i)arg1) → int :

Number of rows.

sum((Vector3i)arg1) → int :

Sum of all elements.

xy((Vector3i)arg1) → Vector2i

xz((Vector3i)arg1) → Vector2i

yx((Vector3i)arg1) → Vector2i

yz((Vector3i)arg1) → Vector2i

zx((Vector3i)arg1) → Vector2i

zy((Vector3i)arg1) → Vector2i

class yade._minieigenHP.Vector4

4-dimensional float vector.

Supported operations (f if a float/int, v is a Vector3): -v, v+v, v+=v, v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v.

Implicit conversion from sequence (list, tuple, …) of 4 floats.

Static attributes: Zero, Ones.

Identity = Vector4(1,0,0, 0)

Ones = Vector4(1,1,1, 1)

static Random() → Vector4 :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector4

Zero = Vector4(0,0,0, 0)

__init__((object)arg1) → None

__init__( (object)arg1, (Vector4)other) -> None

__init__( (object)arg1, (float)v0, (float)v1, (float)v2, (float)v3) -> None

asDiagonal((Vector4)arg1) → object :

Return diagonal matrix with this vector on the diagonal.

cols((Vector4)arg1) → int :

Number of columns.

dot((Vector4)arg1, (Vector4)other) → float :

Dot product with other.

isApprox((Vector4)arg1, (Vector4)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector4)arg1) → float :

Maximum absolute value over all elements.

maxCoeff((Vector4)arg1) → float :

Maximum value over all elements.

mean((Vector4)arg1) → float :

Mean value over all elements.

minCoeff((Vector4)arg1) → float :

Minimum value over all elements.

norm((Vector4)arg1) → float :

Euclidean norm.

normalize((Vector4)arg1) → None :

Normalize this object in-place.

normalized((Vector4)arg1) → Vector4 :

Return normalized copy of this object

outer((Vector4)arg1, (Vector4)other) → object :

Outer product with other.

prod((Vector4)arg1) → float :

Product of all elements.

pruned((Vector4)arg1[, (float)absTol=1e-06]) → Vector4 :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

rows((Vector4)arg1) → int :

Number of rows.

squaredNorm((Vector4)arg1) → float :

Square of the Euclidean norm.

sum((Vector4)arg1) → float :

Sum of all elements.

class yade._minieigenHP.Vector6

6-dimensional float vector.

Supported operations (f if a float/int, v is a Vector6): -v, v+v, v+=v, v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v.

Implicit conversion from sequence (list, tuple, …) of 6 floats.

Static attributes: Zero, Ones.

Identity = Vector6(1,0,0, 0,0,0)

Ones = Vector6(1,1,1, 1,1,1)

static Random() → Vector6 :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector6

Zero = Vector6(0,0,0, 0,0,0)

__init__((object)arg1) → None

__init__( (object)arg1, (Vector6)other) -> None

__init__( (object)arg1, (float)v0, (float)v1, (float)v2, (float)v3, (float)v4, (float)v5) -> object

__init__( (object)arg1, (Vector3)head, (Vector3)tail) -> object

asDiagonal((Vector6)arg1) → Matrix6 :

Return diagonal matrix with this vector on the diagonal.

cols((Vector6)arg1) → int :

Number of columns.

dot((Vector6)arg1, (Vector6)other) → float :

Dot product with other.

head((Vector6)arg1) → Vector3

isApprox((Vector6)arg1, (Vector6)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector6)arg1) → float :

Maximum absolute value over all elements.

maxCoeff((Vector6)arg1) → float :

Maximum value over all elements.

mean((Vector6)arg1) → float :

Mean value over all elements.

minCoeff((Vector6)arg1) → float :

Minimum value over all elements.

norm((Vector6)arg1) → float :

Euclidean norm.

normalize((Vector6)arg1) → None :

Normalize this object in-place.

normalized((Vector6)arg1) → Vector6 :

Return normalized copy of this object

outer((Vector6)arg1, (Vector6)other) → Matrix6 :

Outer product with other.

prod((Vector6)arg1) → float :

Product of all elements.

pruned((Vector6)arg1[, (float)absTol=1e-06]) → Vector6 :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

rows((Vector6)arg1) → int :

Number of rows.

squaredNorm((Vector6)arg1) → float :

Square of the Euclidean norm.

sum((Vector6)arg1) → float :

Sum of all elements.

tail((Vector6)arg1) → Vector3

class yade._minieigenHP.Vector6c

/TODO/

Identity = Vector6c(1,0,0, 0,0,0)

Ones = Vector6c(1,1,1, 1,1,1)

static Random() → Vector6c :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector6c

Zero = Vector6c(0,0,0, 0,0,0)

__init__((object)arg1) → None

__init__( (object)arg1, (Vector6c)other) -> None

__init__( (object)arg1, (complex)v0, (complex)v1, (complex)v2, (complex)v3, (complex)v4, (complex)v5) -> object

__init__( (object)arg1, (Vector3c)head, (Vector3c)tail) -> object

asDiagonal((Vector6c)arg1) → Matrix6c :

Return diagonal matrix with this vector on the diagonal.

cols((Vector6c)arg1) → int :

Number of columns.

dot((Vector6c)arg1, (Vector6c)other) → complex :

Dot product with other.

head((Vector6c)arg1) → Vector3c

isApprox((Vector6c)arg1, (Vector6c)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector6c)arg1) → float :

Maximum absolute value over all elements.

mean((Vector6c)arg1) → complex :

Mean value over all elements.

norm((Vector6c)arg1) → float :

Euclidean norm.

normalize((Vector6c)arg1) → None :

Normalize this object in-place.

normalized((Vector6c)arg1) → Vector6c :

Return normalized copy of this object

outer((Vector6c)arg1, (Vector6c)other) → Matrix6c :

Outer product with other.

prod((Vector6c)arg1) → complex :

Product of all elements.

pruned((Vector6c)arg1[, (float)absTol=1e-06]) → Vector6c :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

rows((Vector6c)arg1) → int :

Number of rows.

squaredNorm((Vector6c)arg1) → float :

Square of the Euclidean norm.

sum((Vector6c)arg1) → complex :

Sum of all elements.

tail((Vector6c)arg1) → Vector3c

class yade._minieigenHP.Vector6i

6-dimensional float vector.

Supported operations (f if a float/int, v is a Vector6): -v, v+v, v+=v, v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v.

Implicit conversion from sequence (list, tuple, …) of 6 ints.

Static attributes: Zero, Ones.

Identity = Vector6i(1,0,0, 0,0,0)

Ones = Vector6i(1,1,1, 1,1,1)

static Random() → Vector6i :

Return an object where all elements are randomly set to values between 0 and 1.

static Unit((int)arg1) → Vector6i

Zero = Vector6i(0,0,0, 0,0,0)

__init__((object)arg1) → None

__init__( (object)arg1, (Vector6i)other) -> None

__init__( (object)arg1, (int)v0, (int)v1, (int)v2, (int)v3, (int)v4, (int)v5) -> object

__init__( (object)arg1, (Vector3i)head, (Vector3i)tail) -> object

asDiagonal((Vector6i)arg1) → object :

Return diagonal matrix with this vector on the diagonal.

cols((Vector6i)arg1) → int :

Number of columns.

dot((Vector6i)arg1, (Vector6i)other) → int :

Dot product with other.

head((Vector6i)arg1) → Vector3i

isApprox((Vector6i)arg1, (Vector6i)other[, (int)prec=0]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((Vector6i)arg1) → int :

Maximum absolute value over all elements.

maxCoeff((Vector6i)arg1) → int :

Maximum value over all elements.

mean((Vector6i)arg1) → int :

Mean value over all elements.

minCoeff((Vector6i)arg1) → int :

Minimum value over all elements.

outer((Vector6i)arg1, (Vector6i)other) → object :

Outer product with other.

prod((Vector6i)arg1) → int :

Product of all elements.

rows((Vector6i)arg1) → int :

Number of rows.

sum((Vector6i)arg1) → int :

Sum of all elements.

tail((Vector6i)arg1) → Vector3i

class yade._minieigenHP.VectorX

Dynamic-sized float vector.

Supported operations (f if a float/int, v is a VectorX): -v, v+v, v+=v, v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v.

Implicit conversion from sequence (list, tuple, …) of X floats.

static Ones((int)arg1) → VectorX

static Random((int)len) → VectorX :

Return vector of given length with all elements set to values between 0 and 1 randomly.

static Unit((int)arg1, (int)arg2) → VectorX

static Zero((int)arg1) → VectorX

__init__((object)arg1) → None

__init__( (object)arg1, (VectorX)other) -> None

__init__( (object)arg1, (object)vv) -> object

asDiagonal((VectorX)arg1) → MatrixX :

Return diagonal matrix with this vector on the diagonal.

cols((VectorX)arg1) → int :

Number of columns.

dot((VectorX)arg1, (VectorX)other) → float :

Dot product with other.

isApprox((VectorX)arg1, (VectorX)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((VectorX)arg1) → float :

Maximum absolute value over all elements.

maxCoeff((VectorX)arg1) → float :

Maximum value over all elements.

mean((VectorX)arg1) → float :

Mean value over all elements.

minCoeff((VectorX)arg1) → float :

Minimum value over all elements.

norm((VectorX)arg1) → float :

Euclidean norm.

normalize((VectorX)arg1) → None :

Normalize this object in-place.

normalized((VectorX)arg1) → VectorX :

Return normalized copy of this object

outer((VectorX)arg1, (VectorX)other) → MatrixX :

Outer product with other.

prod((VectorX)arg1) → float :

Product of all elements.

pruned((VectorX)arg1[, (float)absTol=1e-06]) → VectorX :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

resize((VectorX)arg1, (int)arg2) → None

rows((VectorX)arg1) → int :

Number of rows.

squaredNorm((VectorX)arg1) → float :

Square of the Euclidean norm.

sum((VectorX)arg1) → float :

Sum of all elements.

class yade._minieigenHP.VectorXc

/TODO/

static Ones((int)arg1) → VectorXc

static Random((int)len) → VectorXc :

Return vector of given length with all elements set to values between 0 and 1 randomly.

static Unit((int)arg1, (int)arg2) → VectorXc

static Zero((int)arg1) → VectorXc

__init__((object)arg1) → None

__init__( (object)arg1, (VectorXc)other) -> None

__init__( (object)arg1, (object)vv) -> object

asDiagonal((VectorXc)arg1) → MatrixXc :

Return diagonal matrix with this vector on the diagonal.

cols((VectorXc)arg1) → int :

Number of columns.

dot((VectorXc)arg1, (VectorXc)other) → complex :

Dot product with other.

isApprox((VectorXc)arg1, (VectorXc)other[, (float)prec=1e-12]) → bool :

Approximate comparison with precision prec.

maxAbsCoeff((VectorXc)arg1) → float :

Maximum absolute value over all elements.

mean((VectorXc)arg1) → complex :

Mean value over all elements.

norm((VectorXc)arg1) → float :

Euclidean norm.

normalize((VectorXc)arg1) → None :

Normalize this object in-place.

normalized((VectorXc)arg1) → VectorXc :

Return normalized copy of this object

outer((VectorXc)arg1, (VectorXc)other) → MatrixXc :

Outer product with other.

prod((VectorXc)arg1) → complex :

Product of all elements.

pruned((VectorXc)arg1[, (float)absTol=1e-06]) → VectorXc :

Zero all elements which are greater than absTol. Negative zeros are not pruned.

resize((VectorXc)arg1, (int)arg2) → None

rows((VectorXc)arg1) → int :

Number of rows.

squaredNorm((VectorXc)arg1) → float :

Square of the Euclidean norm.

sum((VectorXc)arg1) → complex :

Sum of all elements.