{"diffoscope-json-version": 1, "source1": "/srv/reproducible-results/rbuild-debian/r-b-build.xrmmN4w7/b1/minieigen_0.50.3+dfsg1-13_armhf.changes", "source2": "/srv/reproducible-results/rbuild-debian/r-b-build.xrmmN4w7/b2/minieigen_0.50.3+dfsg1-13_armhf.changes", "unified_diff": null, "details": [{"source1": "Files", "source2": "Files", "unified_diff": "@@ -1,3 +1,3 @@\n \n f204631f68d537b2b8588b18255c149b 26523876 debug optional python3-minieigen-dbgsym_0.50.3+dfsg1-13_armhf.deb\n- 3643d009b5a247f1c8c177c77b931595 745612 python optional python3-minieigen_0.50.3+dfsg1-13_armhf.deb\n+ a1c27fe4394015b682a638950ae95ca0 726500 python optional python3-minieigen_0.50.3+dfsg1-13_armhf.deb\n"}, {"source1": "python3-minieigen_0.50.3+dfsg1-13_armhf.deb", "source2": "python3-minieigen_0.50.3+dfsg1-13_armhf.deb", "unified_diff": null, "details": [{"source1": "file list", "source2": "file list", "unified_diff": "@@ -1,3 +1,3 @@\n -rw-r--r-- 0 0 0 4 2021-11-08 17:29:32.000000 debian-binary\n--rw-r--r-- 0 0 0 1572 2021-11-08 17:29:32.000000 control.tar.xz\n--rw-r--r-- 0 0 0 743848 2021-11-08 17:29:32.000000 data.tar.xz\n+-rw-r--r-- 0 0 0 1576 2021-11-08 17:29:32.000000 control.tar.xz\n+-rw-r--r-- 0 0 0 724732 2021-11-08 17:29:32.000000 data.tar.xz\n"}, {"source1": "control.tar.xz", "source2": "control.tar.xz", "unified_diff": null, "details": [{"source1": "control.tar", "source2": "control.tar", "unified_diff": null, "details": [{"source1": "./control", "source2": "./control", "unified_diff": "@@ -1,13 +1,13 @@\n Package: python3-minieigen\n Source: minieigen\n Version: 0.50.3+dfsg1-13\n Architecture: armhf\n Maintainer: Debian Science Maintainers \n-Installed-Size: 6529\n+Installed-Size: 5918\n Depends: python3 (<< 3.13), python3 (>= 3.11~), libboost-python1.83.0 (>= 1.83.0), libboost-python1.83.0-py311, libboost-python1.83.0-py312, libc6 (>= 2.32), libdouble-conversion3 (>= 2.0.0), libgcc-s1 (>= 4.0), libstdc++6 (>= 5.2), libjs-sphinxdoc (>= 7.2.2)\n Recommends: libeigen3-dev\n Section: python\n Priority: optional\n Homepage: http://www.launchpad.net/minieigen\n Description: Wrapper of parts of the Eigen library (Python 3)\n Small wrapper for core parts of Eigen, c++ library for linear algebra.\n"}, {"source1": "./md5sums", "source2": "./md5sums", "unified_diff": null, "details": [{"source1": "./md5sums", "source2": "./md5sums", "comments": ["Files differ"], "unified_diff": null}]}]}]}, {"source1": "data.tar.xz", "source2": "data.tar.xz", "unified_diff": null, "details": [{"source1": "data.tar", "source2": "data.tar", "unified_diff": null, "details": [{"source1": "file list", "source2": "file list", "unified_diff": "@@ -22,19 +22,19 @@\n -rw-r--r-- 0 root (0) root (0) 15094 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/_static/basic.css\n -rw-r--r-- 0 root (0) root (0) 4302 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/_static/classic.css\n -rw-r--r-- 0 root (0) root (0) 328 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/_static/documentation_options.js\n -rw-r--r-- 0 root (0) root (0) 286 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/_static/file.png\n -rw-r--r-- 0 root (0) root (0) 90 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/_static/minus.png\n -rw-r--r-- 0 root (0) root (0) 90 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/_static/plus.png\n -rw-r--r-- 0 root (0) root (0) 4929 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/_static/pygments.css\n--rw-r--r-- 0 root (0) root (0) 60738 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/genindex.html\n--rw-r--r-- 0 root (0) root (0) 520343 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/index.html\n--rw-r--r-- 0 root (0) root (0) 2125 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/objects.inv\n+-rw-r--r-- 0 root (0) root (0) 2823 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/genindex.html\n+-rw-r--r-- 0 root (0) root (0) 11289 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/index.html\n+-rw-r--r-- 0 root (0) root (0) 246 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/objects.inv\n -rw-r--r-- 0 root (0) root (0) 3126 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/search.html\n--rw-r--r-- 0 root (0) root (0) 58645 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/searchindex.js\n+-rw-r--r-- 0 root (0) root (0) 2980 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/searchindex.js\n drwxr-xr-x 0 root (0) root (0) 0 2021-11-08 17:29:32.000000 ./usr/share/doc-base/\n -rw-r--r-- 0 root (0) root (0) 262 2020-02-19 22:00:59.000000 ./usr/share/doc-base/python3-minieigen.python3-minieigen\n lrwxrwxrwx 0 root (0) root (0) 0 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/_static/doctools.js -> ../../../../javascript/sphinxdoc/1.0/doctools.js\n lrwxrwxrwx 0 root (0) root (0) 0 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/_static/language_data.js -> ../../../../javascript/sphinxdoc/1.0/language_data.js\n lrwxrwxrwx 0 root (0) root (0) 0 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/_static/searchtools.js -> ../../../../javascript/sphinxdoc/1.0/searchtools.js\n lrwxrwxrwx 0 root (0) root (0) 0 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/_static/sidebar.js -> ../../../../javascript/sphinxdoc/1.0/sidebar.js\n lrwxrwxrwx 0 root (0) root (0) 0 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/_static/sphinx_highlight.js -> ../../../../javascript/sphinxdoc/1.0/sphinx_highlight.js\n"}, {"source1": "./usr/share/doc/python3-minieigen/html/genindex.html", "source2": "./usr/share/doc/python3-minieigen/html/genindex.html", "unified_diff": "@@ -31,1376 +31,16 @@\n
\n
\n \n \n

Index

\n \n
\n- A\n- | C\n- | D\n- | E\n- | F\n- | H\n- | I\n- | J\n- | L\n- | M\n- | N\n- | O\n- | P\n- | Q\n- | R\n- | S\n- | T\n- | U\n- | V\n- | X\n- | Y\n- | Z\n \n
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R

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\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -3,468 +3,14 @@\n \n \n **** Navigation ****\n * index\n * minieigen_0.4-1_documentation \u00bb\n * Index\n ****** Index ******\n-A | C | D | E | F | H | I | J | L | M | N | O | P | Q | R | S | T | U | V | X |\n-Y | Z\n-***** A *****\n- * AlignedBox2_(class_in_minieigen)\n- * AlignedBox3_(class_in_minieigen)\n- * angularDistance()_(minieigen.Quaternion_method)\n- * asDiagonal()_(minieigen.Vector2_method)\n- o (minieigen.Vector2c_method)\n- o (minieigen.Vector2i_method)\n- o (minieigen.Vector3_method)\n- o (minieigen.Vector3c_method)\n- o (minieigen.Vector3i_method)\n- o (minieigen.Vector4_method)\n- o (minieigen.Vector6_method)\n- o (minieigen.Vector6c_method)\n- o (minieigen.Vector6i_method)\n- o (minieigen.VectorX_method)\n- o (minieigen.VectorXc_method)\n-***** C *****\n- * center()_(minieigen.AlignedBox2\n- method)\n- o (minieigen.AlignedBox3\n- method)\n- * clamp()_(minieigen.AlignedBox2\n- method)\n- o (minieigen.AlignedBox3\n- method)\n- * col()_(minieigen.Matrix3_method)\n- o (minieigen.Matrix3c_method) * computeUnitaryPositive()_\n- o (minieigen.Matrix6_method) (minieigen.Matrix3_method)\n- o (minieigen.Matrix6c_method) o (minieigen.Matrix6_method)\n- o (minieigen.MatrixX_method) o (minieigen.MatrixX_method)\n- o (minieigen.MatrixXc_method) * conjugate()_(minieigen.Quaternion\n- * cols()_(minieigen.Matrix3_method) method)\n- o (minieigen.Matrix3c_method) * contains()_(minieigen.AlignedBox2\n- o (minieigen.Matrix6_method) method)\n- o (minieigen.Matrix6c_method) o (minieigen.AlignedBox3\n- o (minieigen.MatrixX_method) method)\n- o (minieigen.MatrixXc_method) * cross()_(minieigen.Vector3\n- o (minieigen.Vector2_method) method)\n- o (minieigen.Vector2c_method) o (minieigen.Vector3c_method)\n- o (minieigen.Vector2i_method) o (minieigen.Vector3i_method)\n- o (minieigen.Vector3_method)\n- o (minieigen.Vector3c_method)\n- o (minieigen.Vector3i_method)\n- o (minieigen.Vector4_method)\n- o (minieigen.Vector6_method)\n- o (minieigen.Vector6c_method)\n- o (minieigen.Vector6i_method)\n- o (minieigen.VectorX_method)\n- o (minieigen.VectorXc_method)\n-***** D *****\n- * determinant()_(minieigen.Matrix3\n- method) * dot()_(minieigen.Vector2_method)\n- o (minieigen.Matrix3c_method) o (minieigen.Vector2c_method)\n- o (minieigen.Matrix6_method) o (minieigen.Vector2i_method)\n- o (minieigen.Matrix6c_method) o (minieigen.Vector3_method)\n- o (minieigen.MatrixX_method) o (minieigen.Vector3c_method)\n- o (minieigen.MatrixXc_method) o (minieigen.Vector3i_method)\n- * diagonal()_(minieigen.Matrix3 o (minieigen.Vector4_method)\n- method) o (minieigen.Vector6_method)\n- o (minieigen.Matrix3c_method) o (minieigen.Vector6c_method)\n- o (minieigen.Matrix6_method) o (minieigen.Vector6i_method)\n- o (minieigen.Matrix6c_method) o (minieigen.VectorX_method)\n- o (minieigen.MatrixX_method) o (minieigen.VectorXc_method)\n- o (minieigen.MatrixXc_method)\n-***** E *****\n- * empty()_(minieigen.AlignedBox2 * extend()_(minieigen.AlignedBox2\n- method) method)\n- o (minieigen.AlignedBox3 o (minieigen.AlignedBox3_method)\n- method)\n-***** F *****\n- * float2str()_(in_module_minieigen)\n-***** H *****\n- * head()_(minieigen.Vector6_method)\n- o (minieigen.Vector6c_method)\n- o (minieigen.Vector6i_method)\n-***** I *****\n- * Identity_(minieigen.Matrix3\n- attribute)\n- o (minieigen.Matrix3c\n- attribute)\n- o (minieigen.Matrix6\n- attribute)\n- o (minieigen.Matrix6c\n- attribute)\n- o (minieigen.Quaternion\n- attribute)\n- o (minieigen.Vector2\n- attribute)\n- o (minieigen.Vector2c\n- attribute)\n- o (minieigen.Vector2i * isApprox()_(minieigen.Matrix3\n- attribute) method)\n- o (minieigen.Vector3 o (minieigen.Matrix3c_method)\n- attribute) o (minieigen.Matrix6_method)\n- o (minieigen.Vector3c o (minieigen.Matrix6c_method)\n- attribute) o (minieigen.MatrixX_method)\n- o (minieigen.Vector3i o (minieigen.MatrixXc_method)\n- attribute) o (minieigen.Vector2_method)\n- o (minieigen.Vector4 o (minieigen.Vector2c_method)\n- attribute) o (minieigen.Vector2i_method)\n- o (minieigen.Vector6 o (minieigen.Vector3_method)\n- attribute) o (minieigen.Vector3c_method)\n- o (minieigen.Vector6c o (minieigen.Vector3i_method)\n- attribute) o (minieigen.Vector4_method)\n- o (minieigen.Vector6i o (minieigen.Vector6_method)\n- attribute) o (minieigen.Vector6c_method)\n- * Identity()_(minieigen.MatrixX o (minieigen.Vector6i_method)\n- static_method) o (minieigen.VectorX_method)\n- o (minieigen.MatrixXc_static o (minieigen.VectorXc_method)\n- method)\n- * intersection()_\n- (minieigen.AlignedBox2_method)\n- o (minieigen.AlignedBox3\n- method)\n- * inverse()_(minieigen.Matrix3\n- method)\n- o (minieigen.Matrix3c_method)\n- o (minieigen.Matrix6_method)\n- o (minieigen.Matrix6c_method)\n- o (minieigen.MatrixX_method)\n- o (minieigen.MatrixXc_method)\n- o (minieigen.Quaternion\n- method)\n-***** J *****\n- * jacobiSVD()_(minieigen.Matrix3_method)\n- o (minieigen.Matrix6_method)\n- o (minieigen.MatrixX_method)\n-***** L *****\n- * ll()_(minieigen.Matrix6_method) * lr()_(minieigen.Matrix6_method)\n- o (minieigen.Matrix6c_method) o (minieigen.Matrix6c_method)\n-***** M *****\n- * Matrix3_(class_in_minieigen) * mean()_(minieigen.Matrix3_method)\n- * Matrix3c_(class_in_minieigen) o (minieigen.Matrix3c_method)\n- * Matrix6_(class_in_minieigen) o (minieigen.Matrix6_method)\n- * Matrix6c_(class_in_minieigen) o (minieigen.Matrix6c_method)\n- * MatrixX_(class_in_minieigen) o (minieigen.MatrixX_method)\n- * MatrixXc_(class_in_minieigen) o (minieigen.MatrixXc_method)\n- * max_(minieigen.AlignedBox2 o (minieigen.Vector2_method)\n- property) o (minieigen.Vector2c_method)\n- o (minieigen.AlignedBox3 o (minieigen.Vector2i_method)\n- property) o (minieigen.Vector3_method)\n- * maxAbsCoeff()_(minieigen.Matrix3 o (minieigen.Vector3c_method)\n- method) o (minieigen.Vector3i_method)\n- o (minieigen.Matrix3c_method) o (minieigen.Vector4_method)\n- o (minieigen.Matrix6_method) o (minieigen.Vector6_method)\n- o (minieigen.Matrix6c_method) o (minieigen.Vector6c_method)\n- o (minieigen.MatrixX_method) o (minieigen.Vector6i_method)\n- o (minieigen.MatrixXc_method) o (minieigen.VectorX_method)\n- o (minieigen.Vector2_method) o (minieigen.VectorXc_method)\n- o (minieigen.Vector2c_method) * merged()_(minieigen.AlignedBox2\n- o (minieigen.Vector2i_method) method)\n- o (minieigen.Vector3_method) o (minieigen.AlignedBox3\n- o (minieigen.Vector3c_method) method)\n- o (minieigen.Vector3i_method) * min_(minieigen.AlignedBox2\n- o (minieigen.Vector4_method) property)\n- o (minieigen.Vector6_method) o (minieigen.AlignedBox3\n- o (minieigen.Vector6c_method) property)\n- o (minieigen.Vector6i_method) * minCoeff()_(minieigen.Matrix3\n- o (minieigen.VectorX_method) method)\n- o (minieigen.VectorXc_method) o (minieigen.Matrix6_method)\n- * maxCoeff()_(minieigen.Matrix3 o (minieigen.MatrixX_method)\n- method) o (minieigen.Vector2_method)\n- o (minieigen.Matrix6_method) o (minieigen.Vector2i_method)\n- o (minieigen.MatrixX_method) o (minieigen.Vector3_method)\n- o (minieigen.Vector2_method) o (minieigen.Vector3i_method)\n- o (minieigen.Vector2i_method) o (minieigen.Vector4_method)\n- o (minieigen.Vector3_method) o (minieigen.Vector6_method)\n- o (minieigen.Vector3i_method) o (minieigen.Vector6i_method)\n- o (minieigen.Vector4_method) o (minieigen.VectorX_method)\n- o (minieigen.Vector6_method) * minieigen\n- o (minieigen.Vector6i_method) o module\n- o (minieigen.VectorX_method) * module\n- o minieigen\n-***** N *****\n- * norm()_(minieigen.Matrix3_method)\n- o (minieigen.Matrix3c_method)\n- o (minieigen.Matrix6_method)\n- o (minieigen.Matrix6c_method)\n- o (minieigen.MatrixX_method)\n- o (minieigen.MatrixXc_method)\n- o (minieigen.Quaternion\n- method)\n- o (minieigen.Vector2_method) * normalized()_(minieigen.Matrix3\n- o (minieigen.Vector2c_method) method)\n- o (minieigen.Vector3_method) o (minieigen.Matrix3c_method)\n- o (minieigen.Vector3c_method) o (minieigen.Matrix6_method)\n- o (minieigen.Vector4_method) o (minieigen.Matrix6c_method)\n- o (minieigen.Vector6_method) o (minieigen.MatrixX_method)\n- o (minieigen.Vector6c_method) o (minieigen.MatrixXc_method)\n- o (minieigen.VectorX_method) o (minieigen.Quaternion\n- o (minieigen.VectorXc_method) method)\n- * normalize()_(minieigen.Matrix3 o (minieigen.Vector2_method)\n- method) o (minieigen.Vector2c_method)\n- o (minieigen.Matrix3c_method) o (minieigen.Vector3_method)\n- o (minieigen.Matrix6_method) o (minieigen.Vector3c_method)\n- o (minieigen.Matrix6c_method) o (minieigen.Vector4_method)\n- o (minieigen.MatrixX_method) o (minieigen.Vector6_method)\n- o (minieigen.MatrixXc_method) o (minieigen.Vector6c_method)\n- o (minieigen.Quaternion o (minieigen.VectorX_method)\n- method) o (minieigen.VectorXc_method)\n- o (minieigen.Vector2_method)\n- o (minieigen.Vector2c_method)\n- o (minieigen.Vector3_method)\n- o (minieigen.Vector3c_method)\n- o (minieigen.Vector4_method)\n- o (minieigen.Vector6_method)\n- o (minieigen.Vector6c_method)\n- o (minieigen.VectorX_method)\n- o (minieigen.VectorXc_method)\n-***** O *****\n- * Ones_(minieigen.Matrix3\n- attribute)\n- o (minieigen.Matrix3c\n- attribute)\n- o (minieigen.Matrix6\n- attribute)\n- o (minieigen.Matrix6c\n- attribute)\n- o (minieigen.Vector2\n- attribute)\n- o (minieigen.Vector2c\n- attribute)\n- o (minieigen.Vector2i * outer()_(minieigen.Vector2_method)\n- attribute) o (minieigen.Vector2c_method)\n- o (minieigen.Vector3 o (minieigen.Vector2i_method)\n- attribute) o (minieigen.Vector3_method)\n- o (minieigen.Vector3c o (minieigen.Vector3c_method)\n- attribute) o (minieigen.Vector3i_method)\n- o (minieigen.Vector3i o (minieigen.Vector4_method)\n- attribute) o (minieigen.Vector6_method)\n- o (minieigen.Vector4 o (minieigen.Vector6c_method)\n- attribute) o (minieigen.Vector6i_method)\n- o (minieigen.Vector6 o (minieigen.VectorX_method)\n- attribute) o (minieigen.VectorXc_method)\n- o (minieigen.Vector6c\n- attribute)\n- o (minieigen.Vector6i\n- attribute)\n- * Ones()_(minieigen.MatrixX_static\n- method)\n- o (minieigen.MatrixXc_static\n- method)\n- o (minieigen.VectorX_static\n- method)\n- o (minieigen.VectorXc_static\n- method)\n-***** P *****\n- * polarDecomposition()_\n- (minieigen.Matrix3_method)\n- o (minieigen.Matrix6_method)\n- o (minieigen.MatrixX_method) * pruned()_(minieigen.Matrix3\n- * prod()_(minieigen.Matrix3_method) method)\n- o (minieigen.Matrix3c_method) o (minieigen.Matrix3c_method)\n- o (minieigen.Matrix6_method) o (minieigen.Matrix6_method)\n- o (minieigen.Matrix6c_method) o (minieigen.Matrix6c_method)\n- o (minieigen.MatrixX_method) o (minieigen.MatrixX_method)\n- o (minieigen.MatrixXc_method) o (minieigen.MatrixXc_method)\n- o (minieigen.Vector2_method) o (minieigen.Vector2_method)\n- o (minieigen.Vector2c_method) o (minieigen.Vector2c_method)\n- o (minieigen.Vector2i_method) o (minieigen.Vector3_method)\n- o (minieigen.Vector3_method) o (minieigen.Vector3c_method)\n- o (minieigen.Vector3c_method) o (minieigen.Vector4_method)\n- o (minieigen.Vector3i_method) o (minieigen.Vector6_method)\n- o (minieigen.Vector4_method) o (minieigen.Vector6c_method)\n- o (minieigen.Vector6_method) o (minieigen.VectorX_method)\n- o (minieigen.Vector6c_method) o (minieigen.VectorXc_method)\n- o (minieigen.Vector6i_method)\n- o (minieigen.VectorX_method)\n- o (minieigen.VectorXc_method)\n-***** Q *****\n- * Quaternion_(class_in_minieigen)\n-***** R *****\n- * Random()_(minieigen.Matrix3\n- static_method)\n- o (minieigen.Matrix3c_static\n- method)\n- o (minieigen.Matrix6_static\n- method)\n- o (minieigen.Matrix6c_static\n- method)\n- o (minieigen.MatrixX_static\n- method) * row()_(minieigen.Matrix3_method)\n- o (minieigen.MatrixXc_static o (minieigen.Matrix3c_method)\n- method) o (minieigen.Matrix6_method)\n- o (minieigen.Vector2_static o (minieigen.Matrix6c_method)\n- method) o (minieigen.MatrixX_method)\n- o (minieigen.Vector2c_static o (minieigen.MatrixXc_method)\n- method) * rows()_(minieigen.Matrix3_method)\n- o (minieigen.Vector2i_static o (minieigen.Matrix3c_method)\n- method) o (minieigen.Matrix6_method)\n- o (minieigen.Vector3_static o (minieigen.Matrix6c_method)\n- method) o (minieigen.MatrixX_method)\n- o (minieigen.Vector3c_static o (minieigen.MatrixXc_method)\n- method) o (minieigen.Vector2_method)\n- o (minieigen.Vector3i_static o (minieigen.Vector2c_method)\n- method) o (minieigen.Vector2i_method)\n- o (minieigen.Vector4_static o (minieigen.Vector3_method)\n- method) o (minieigen.Vector3c_method)\n- o (minieigen.Vector6_static o (minieigen.Vector3i_method)\n- method) o (minieigen.Vector4_method)\n- o (minieigen.Vector6c_static o (minieigen.Vector6_method)\n- method) o (minieigen.Vector6c_method)\n- o (minieigen.Vector6i_static o (minieigen.Vector6i_method)\n- method) o (minieigen.VectorX_method)\n- o (minieigen.VectorX_static o (minieigen.VectorXc_method)\n- method)\n- o (minieigen.VectorXc_static\n- method)\n- * resize()_(minieigen.MatrixX\n- method)\n- o (minieigen.MatrixXc_method)\n- o (minieigen.VectorX_method)\n- o (minieigen.VectorXc_method)\n- * Rotate()_(minieigen.Quaternion\n- method)\n-***** S *****\n- * selfAdjointEigenDecomposition()_\n- (minieigen.Matrix3_method)\n- o (minieigen.Matrix6_method)\n- o (minieigen.MatrixX_method)\n- * setFromTwoVectors()_\n- (minieigen.Quaternion_method) * sum()_(minieigen.Matrix3_method)\n- * sizes()_(minieigen.AlignedBox2 o (minieigen.Matrix3c_method)\n- method) o (minieigen.Matrix6_method)\n- o (minieigen.AlignedBox3 o (minieigen.Matrix6c_method)\n- method) o (minieigen.MatrixX_method)\n- * slerp()_(minieigen.Quaternion o (minieigen.MatrixXc_method)\n- method) o (minieigen.Vector2_method)\n- * spectralDecomposition()_ o (minieigen.Vector2c_method)\n- (minieigen.Matrix3_method) o (minieigen.Vector2i_method)\n- o (minieigen.Matrix6_method) o (minieigen.Vector3_method)\n- o (minieigen.MatrixX_method) o (minieigen.Vector3c_method)\n- * squaredNorm()_(minieigen.Matrix3 o (minieigen.Vector3i_method)\n- method) o (minieigen.Vector4_method)\n- o (minieigen.Matrix3c_method) o (minieigen.Vector6_method)\n- o (minieigen.Matrix6_method) o (minieigen.Vector6c_method)\n- o (minieigen.Matrix6c_method) o (minieigen.Vector6i_method)\n- o (minieigen.MatrixX_method) o (minieigen.VectorX_method)\n- o (minieigen.MatrixXc_method) o (minieigen.VectorXc_method)\n- o (minieigen.Vector2_method) * svd()_(minieigen.Matrix3_method)\n- o (minieigen.Vector2c_method) o (minieigen.Matrix6_method)\n- o (minieigen.Vector3_method) o (minieigen.MatrixX_method)\n- o (minieigen.Vector3c_method)\n- o (minieigen.Vector4_method)\n- o (minieigen.Vector6_method)\n- o (minieigen.Vector6c_method)\n- o (minieigen.VectorX_method)\n- o (minieigen.VectorXc_method)\n-***** T *****\n- * tail()_(minieigen.Vector6_method)\n- o (minieigen.Vector6c_method)\n- o (minieigen.Vector6i_method)\n- * toAngleAxis()_\n- (minieigen.Quaternion_method)\n- * toAxisAngle()_ * transpose()_(minieigen.Matrix3\n- (minieigen.Quaternion_method) method)\n- * toRotationMatrix()_ o (minieigen.Matrix3c_method)\n- (minieigen.Quaternion_method) o (minieigen.Matrix6_method)\n- * toRotationVector()_ o (minieigen.Matrix6c_method)\n- (minieigen.Quaternion_method) o (minieigen.MatrixX_method)\n- * trace()_(minieigen.Matrix3 o (minieigen.MatrixXc_method)\n- method)\n- o (minieigen.Matrix3c_method)\n- o (minieigen.Matrix6_method)\n- o (minieigen.Matrix6c_method)\n- o (minieigen.MatrixX_method)\n- o (minieigen.MatrixXc_method)\n-***** U *****\n- * ul()_(minieigen.Matrix6_method)\n- o (minieigen.Matrix6c_method)\n- * Unit()_(minieigen.Vector2_static\n- method)\n- o (minieigen.Vector2c_static\n- method)\n- o (minieigen.Vector2i_static\n- method)\n- o (minieigen.Vector3_static\n- method) * UnitY_(minieigen.Vector2\n- o (minieigen.Vector3c_static attribute)\n- method) o (minieigen.Vector2c\n- o (minieigen.Vector3i_static attribute)\n- method) o (minieigen.Vector2i\n- o (minieigen.Vector4_static attribute)\n- method) o (minieigen.Vector3\n- o (minieigen.Vector6_static attribute)\n- method) o (minieigen.Vector3c\n- o (minieigen.Vector6c_static attribute)\n- method) o (minieigen.Vector3i\n- o (minieigen.Vector6i_static attribute)\n- method) * UnitZ_(minieigen.Vector3\n- o (minieigen.VectorX_static attribute)\n- method) o (minieigen.Vector3c\n- o (minieigen.VectorXc_static attribute)\n- method) o (minieigen.Vector3i\n- * UnitX_(minieigen.Vector2 attribute)\n- attribute) * ur()_(minieigen.Matrix6_method)\n- o (minieigen.Vector2c o (minieigen.Matrix6c_method)\n- attribute)\n- o (minieigen.Vector2i\n- attribute)\n- o (minieigen.Vector3\n- attribute)\n- o (minieigen.Vector3c\n- attribute)\n- o (minieigen.Vector3i\n- attribute)\n-***** V *****\n- * Vector2_(class_in_minieigen) * Vector6_(class_in_minieigen)\n- * Vector2c_(class_in_minieigen) * Vector6c_(class_in_minieigen)\n- * Vector2i_(class_in_minieigen) * Vector6i_(class_in_minieigen)\n- * Vector3_(class_in_minieigen) * VectorX_(class_in_minieigen)\n- * Vector3c_(class_in_minieigen) * VectorXc_(class_in_minieigen)\n- * Vector3i_(class_in_minieigen) * volume()_(minieigen.AlignedBox2\n- * Vector4_(class_in_minieigen) method)\n- o (minieigen.AlignedBox3_method)\n-***** X *****\n- * xy()_(minieigen.Vector3_method) * xz()_(minieigen.Vector3_method)\n- o (minieigen.Vector3c_method) o (minieigen.Vector3c_method)\n- o (minieigen.Vector3i_method) o (minieigen.Vector3i_method)\n-***** Y *****\n- * yx()_(minieigen.Vector3_method) * yz()_(minieigen.Vector3_method)\n- o (minieigen.Vector3c_method) o (minieigen.Vector3c_method)\n- o (minieigen.Vector3i_method) o (minieigen.Vector3i_method)\n-***** Z *****\n- * Zero_(minieigen.Matrix3\n- attribute)\n- o (minieigen.Matrix3c\n- attribute)\n- o (minieigen.Matrix6\n- attribute)\n- o (minieigen.Matrix6c\n- attribute) * Zero()_(minieigen.MatrixX_static\n- o (minieigen.Vector2 method)\n- attribute) o (minieigen.MatrixXc_static\n- o (minieigen.Vector2c method)\n- attribute) o (minieigen.VectorX_static\n- o (minieigen.Vector2i method)\n- attribute) o (minieigen.VectorXc_static\n- o (minieigen.Vector3 method)\n- attribute) * zx()_(minieigen.Vector3_method)\n- o (minieigen.Vector3c o (minieigen.Vector3c_method)\n- attribute) o (minieigen.Vector3i_method)\n- o (minieigen.Vector3i * zy()_(minieigen.Vector3_method)\n- attribute) o (minieigen.Vector3c_method)\n- o (minieigen.Vector4 o (minieigen.Vector3i_method)\n- attribute)\n- o (minieigen.Vector6\n- attribute)\n- o (minieigen.Vector6c\n- attribute)\n- o (minieigen.Vector6i\n- attribute)\n **** Quick search ****\n [q ] [Go]\n **** Navigation ****\n * index\n * minieigen_0.4-1_documentation \u00bb\n * Index\n \u00a9 Copyright 2012\u00e2\u0088\u00922015, V\u00c3\u00a1clav \u00c5\u00a0milauer. Created using Sphinx 7.2.6.\n"}]}, {"source1": "./usr/share/doc/python3-minieigen/html/index.html", "source2": "./usr/share/doc/python3-minieigen/html/index.html", "unified_diff": "@@ -41,49 +41,49 @@\n

Something concise here.

\n
\n \n
\n

Examples\u00b6

\n
\n

Todo

\n-

Some examples of what can be done with minieigen.

\n+

Some examples of what can be done with minieigen.

\n
\n
\n
\n

Naming conventions\u00b6

\n \n
\n
\n

Limitations\u00b6

\n@@ -112,2989 +112,14 @@\n
\n
\n

Documentation\u00b6

\n \n-

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

\n-
\n-
\n-class minieigen.AlignedBox2\u00b6
\n-

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

\n-
\n-
\n-center((AlignedBox2)arg1) → Vector2[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-clamp((AlignedBox2)arg1, (AlignedBox2)arg2) → None[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-contains((AlignedBox2)arg1, (Vector2)arg2) → bool[STATIC]\u00b6
\n-

contains( (AlignedBox2)arg1, (AlignedBox2)arg2) \u2192 bool

\n-
\n-\n-
\n-
\n-empty((AlignedBox2)arg1) → bool[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-extend((AlignedBox2)arg1, (Vector2)arg2) → None[STATIC]\u00b6
\n-

extend( (AlignedBox2)arg1, (AlignedBox2)arg2) \u2192 None

\n-
\n-\n-
\n-
\n-intersection((AlignedBox2)arg1, (AlignedBox2)arg2) → AlignedBox2[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-property max\u00b6
\n-
\n-\n-
\n-
\n-merged((AlignedBox2)arg1, (AlignedBox2)arg2) → AlignedBox2[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-property min\u00b6
\n-
\n-\n-
\n-
\n-sizes((AlignedBox2)arg1) → Vector2[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-volume((AlignedBox2)arg1) → float[STATIC]\u00b6
\n-
\n-\n-
\n-\n-
\n-
\n-class minieigen.AlignedBox3\u00b6
\n-

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

\n-
\n-
\n-center((AlignedBox3)arg1) → Vector3[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-clamp((AlignedBox3)arg1, (AlignedBox3)arg2) → None[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-contains((AlignedBox3)arg1, (Vector3)arg2) → bool[STATIC]\u00b6
\n-

contains( (AlignedBox3)arg1, (AlignedBox3)arg2) \u2192 bool

\n-
\n-\n-
\n-
\n-empty((AlignedBox3)arg1) → bool[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-extend((AlignedBox3)arg1, (Vector3)arg2) → None[STATIC]\u00b6
\n-

extend( (AlignedBox3)arg1, (AlignedBox3)arg2) \u2192 None

\n-
\n-\n-
\n-
\n-intersection((AlignedBox3)arg1, (AlignedBox3)arg2) → AlignedBox3[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-property max\u00b6
\n-
\n-\n-
\n-
\n-merged((AlignedBox3)arg1, (AlignedBox3)arg2) → AlignedBox3[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-property min\u00b6
\n-
\n-\n-
\n-
\n-sizes((AlignedBox3)arg1) → Vector3[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-volume((AlignedBox3)arg1) → float[STATIC]\u00b6
\n-
\n-\n-
\n-\n-
\n-
\n-class minieigen.Matrix3\u00b6
\n-

3x3 float matrix.

\n-

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.

\n-

Static attributes: Zero, Ones, Identity.

\n-
\n-
\n-Identity = Matrix3(1,0,0, 0,1,0, 0,0,1)\u00b6
\n-
\n-\n-
\n-
\n-Ones = Matrix3(1,1,1, 1,1,1, 1,1,1)\u00b6
\n-
\n-\n-
\n-
\n-static Random() → Matrix3[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-Zero = Matrix3(0,0,0, 0,0,0, 0,0,0)\u00b6
\n-
\n-\n-
\n-
\n-col((Matrix3)arg1, (int)col) → Vector3[STATIC]\u00b6
\n-

Return column as vector.

\n-
\n-\n-
\n-
\n-cols((Matrix3)arg1) → int[STATIC]\u00b6
\n-

Number of columns.

\n-
\n-\n-
\n-
\n-computeUnitaryPositive((Matrix3)arg1) → tuple[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-determinant((Matrix3)arg1) → float[STATIC]\u00b6
\n-

Return matrix determinant.

\n-
\n-\n-
\n-
\n-diagonal((Matrix3)arg1) → Vector3[STATIC]\u00b6
\n-

Return diagonal as vector.

\n-
\n-\n-
\n-
\n-inverse((Matrix3)arg1) → Matrix3[STATIC]\u00b6
\n-

Return inverted matrix.

\n-
\n-\n-
\n-
\n-isApprox((Matrix3)arg1, (Matrix3)other[, (float)prec=1e-12]) → bool[STATIC]\u00b6
\n-

Approximate comparison with precision prec.

\n-
\n-\n-
\n-
\n-jacobiSVD((Matrix3)arg1) → tuple[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-maxAbsCoeff((Matrix3)arg1) → float[STATIC]\u00b6
\n-

Maximum absolute value over all elements.

\n-
\n-\n-
\n-
\n-maxCoeff((Matrix3)arg1) → float[STATIC]\u00b6
\n-

Maximum value over all elements.

\n-
\n-\n-
\n-
\n-mean((Matrix3)arg1) → float[STATIC]\u00b6
\n-

Mean value over all elements.

\n-
\n-\n-
\n-
\n-minCoeff((Matrix3)arg1) → float[STATIC]\u00b6
\n-

Minimum value over all elements.

\n-
\n-\n-
\n-
\n-norm((Matrix3)arg1) → float[STATIC]\u00b6
\n-

Euclidean norm.

\n-
\n-\n-
\n-
\n-normalize((Matrix3)arg1) → None[STATIC]\u00b6
\n-

Normalize this object in-place.

\n-
\n-\n-
\n-
\n-normalized((Matrix3)arg1) → Matrix3[STATIC]\u00b6
\n-

Return normalized copy of this object

\n-
\n-\n-
\n-
\n-polarDecomposition((Matrix3)arg1) → tuple[STATIC]\u00b6
\n-

Alias for computeUnitaryPositive.

\n-
\n-\n-
\n-
\n-prod((Matrix3)arg1) → float[STATIC]\u00b6
\n-

Product of all elements.

\n-
\n-\n-
\n-
\n-pruned((Matrix3)arg1[, (float)absTol=1e-06]) → Matrix3[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-row((Matrix3)arg1, (int)row) → Vector3[STATIC]\u00b6
\n-

Return row as vector.

\n-
\n-\n-
\n-
\n-rows((Matrix3)arg1) → int[STATIC]\u00b6
\n-

Number of rows.

\n-
\n-\n-
\n-
\n-selfAdjointEigenDecomposition((Matrix3)arg1) → tuple[STATIC]\u00b6
\n-

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().

\n-
\n-\n-
\n-
\n-spectralDecomposition((Matrix3)arg1) → tuple[STATIC]\u00b6
\n-

Alias for selfAdjointEigenDecomposition.

\n-
\n-\n-
\n-
\n-squaredNorm((Matrix3)arg1) → float[STATIC]\u00b6
\n-

Square of the Euclidean norm.

\n-
\n-\n-
\n-
\n-sum((Matrix3)arg1) → float[STATIC]\u00b6
\n-

Sum of all elements.

\n-
\n-\n-
\n-
\n-svd((Matrix3)arg1) → tuple[STATIC]\u00b6
\n-

Alias for jacobiSVD.

\n-
\n-\n-
\n-
\n-trace((Matrix3)arg1) → float[STATIC]\u00b6
\n-

Return sum of diagonal elements.

\n-
\n-\n-
\n-
\n-transpose((Matrix3)arg1) → Matrix3[STATIC]\u00b6
\n-

Return transposed matrix.

\n-
\n-\n-
\n-\n-
\n-
\n-class minieigen.Matrix3c\u00b6
\n-

/TODO/

\n-
\n-
\n-Identity = Matrix3c(1,0,0, 0,1,0, 0,0,1)\u00b6
\n-
\n-\n-
\n-
\n-Ones = Matrix3c(1,1,1, 1,1,1, 1,1,1)\u00b6
\n-
\n-\n-
\n-
\n-static Random() → Matrix3c[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-Zero = Matrix3c(0,0,0, 0,0,0, 0,0,0)\u00b6
\n-
\n-\n-
\n-
\n-col((Matrix3c)arg1, (int)col) → Vector3c[STATIC]\u00b6
\n-

Return column as vector.

\n-
\n-\n-
\n-
\n-cols((Matrix3c)arg1) → int[STATIC]\u00b6
\n-

Number of columns.

\n-
\n-\n-
\n-
\n-determinant((Matrix3c)arg1) → complex[STATIC]\u00b6
\n-

Return matrix determinant.

\n-
\n-\n-
\n-
\n-diagonal((Matrix3c)arg1) → Vector3c[STATIC]\u00b6
\n-

Return diagonal as vector.

\n-
\n-\n-
\n-
\n-inverse((Matrix3c)arg1) → Matrix3c[STATIC]\u00b6
\n-

Return inverted matrix.

\n-
\n-\n-
\n-
\n-isApprox((Matrix3c)arg1, (Matrix3c)other[, (float)prec=1e-12]) → bool[STATIC]\u00b6
\n-

Approximate comparison with precision prec.

\n-
\n-\n-
\n-
\n-maxAbsCoeff((Matrix3c)arg1) → float[STATIC]\u00b6
\n-

Maximum absolute value over all elements.

\n-
\n-\n-
\n-
\n-mean((Matrix3c)arg1) → complex[STATIC]\u00b6
\n-

Mean value over all elements.

\n-
\n-\n-
\n-
\n-norm((Matrix3c)arg1) → float[STATIC]\u00b6
\n-

Euclidean norm.

\n-
\n-\n-
\n-
\n-normalize((Matrix3c)arg1) → None[STATIC]\u00b6
\n-

Normalize this object in-place.

\n-
\n-\n-
\n-
\n-normalized((Matrix3c)arg1) → Matrix3c[STATIC]\u00b6
\n-

Return normalized copy of this object

\n-
\n-\n-
\n-
\n-prod((Matrix3c)arg1) → complex[STATIC]\u00b6
\n-

Product of all elements.

\n-
\n-\n-
\n-
\n-pruned((Matrix3c)arg1[, (float)absTol=1e-06]) → Matrix3c[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-row((Matrix3c)arg1, (int)row) → Vector3c[STATIC]\u00b6
\n-

Return row as vector.

\n-
\n-\n-
\n-
\n-rows((Matrix3c)arg1) → int[STATIC]\u00b6
\n-

Number of rows.

\n-
\n-\n-
\n-
\n-squaredNorm((Matrix3c)arg1) → float[STATIC]\u00b6
\n-

Square of the Euclidean norm.

\n-
\n-\n-
\n-
\n-sum((Matrix3c)arg1) → complex[STATIC]\u00b6
\n-

Sum of all elements.

\n-
\n-\n-
\n-
\n-trace((Matrix3c)arg1) → complex[STATIC]\u00b6
\n-

Return sum of diagonal elements.

\n-
\n-\n-
\n-
\n-transpose((Matrix3c)arg1) → Matrix3c[STATIC]\u00b6
\n-

Return transposed matrix.

\n-
\n-\n-
\n-\n-
\n-
\n-class minieigen.Matrix6\u00b6
\n-

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

\n-

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.

\n-

Static attributes: Zero, Ones, Identity.

\n-
\n-
\n-Identity = Matrix6( \t(      1,      0,      0,      0,      0,      0), \t(      0,      1,      0,      0,      0,      0), \t(      0,      0,      1,      0,      0,      0), \t(      0,      0,      0,      1,      0,      0), \t(      0,      0,      0,      0,      1,      0), \t(      0,      0,      0,      0,      0,      1) )\u00b6
\n-
\n-\n-
\n-
\n-Ones = Matrix6( \t(      1,      1,      1,      1,      1,      1), \t(      1,      1,      1,      1,      1,      1), \t(      1,      1,      1,      1,      1,      1), \t(      1,      1,      1,      1,      1,      1), \t(      1,      1,      1,      1,      1,      1), \t(      1,      1,      1,      1,      1,      1) )\u00b6
\n-
\n-\n-
\n-
\n-static Random() → Matrix6[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-Zero = Matrix6( \t(      0,      0,      0,      0,      0,      0), \t(      0,      0,      0,      0,      0,      0), \t(      0,      0,      0,      0,      0,      0), \t(      0,      0,      0,      0,      0,      0), \t(      0,      0,      0,      0,      0,      0), \t(      0,      0,      0,      0,      0,      0) )\u00b6
\n-
\n-\n-
\n-
\n-col((Matrix6)arg1, (int)col) → Vector6[STATIC]\u00b6
\n-

Return column as vector.

\n-
\n-\n-
\n-
\n-cols((Matrix6)arg1) → int[STATIC]\u00b6
\n-

Number of columns.

\n-
\n-\n-
\n-
\n-computeUnitaryPositive((Matrix6)arg1) → tuple[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-determinant((Matrix6)arg1) → float[STATIC]\u00b6
\n-

Return matrix determinant.

\n-
\n-\n-
\n-
\n-diagonal((Matrix6)arg1) → Vector6[STATIC]\u00b6
\n-

Return diagonal as vector.

\n-
\n-\n-
\n-
\n-inverse((Matrix6)arg1) → Matrix6[STATIC]\u00b6
\n-

Return inverted matrix.

\n-
\n-\n-
\n-
\n-isApprox((Matrix6)arg1, (Matrix6)other[, (float)prec=1e-12]) → bool[STATIC]\u00b6
\n-

Approximate comparison with precision prec.

\n-
\n-\n-
\n-
\n-jacobiSVD((Matrix6)arg1) → tuple[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-ll((Matrix6)arg1) → Matrix3[STATIC]\u00b6
\n-

Return lower-left 3x3 block

\n-
\n-\n-
\n-
\n-lr((Matrix6)arg1) → Matrix3[STATIC]\u00b6
\n-

Return lower-right 3x3 block

\n-
\n-\n-
\n-
\n-maxAbsCoeff((Matrix6)arg1) → float[STATIC]\u00b6
\n-

Maximum absolute value over all elements.

\n-
\n-\n-
\n-
\n-maxCoeff((Matrix6)arg1) → float[STATIC]\u00b6
\n-

Maximum value over all elements.

\n-
\n-\n-
\n-
\n-mean((Matrix6)arg1) → float[STATIC]\u00b6
\n-

Mean value over all elements.

\n-
\n-\n-
\n-
\n-minCoeff((Matrix6)arg1) → float[STATIC]\u00b6
\n-

Minimum value over all elements.

\n-
\n-\n-
\n-
\n-norm((Matrix6)arg1) → float[STATIC]\u00b6
\n-

Euclidean norm.

\n-
\n-\n-
\n-
\n-normalize((Matrix6)arg1) → None[STATIC]\u00b6
\n-

Normalize this object in-place.

\n-
\n-\n-
\n-
\n-normalized((Matrix6)arg1) → Matrix6[STATIC]\u00b6
\n-

Return normalized copy of this object

\n-
\n-\n-
\n-
\n-polarDecomposition((Matrix6)arg1) → tuple[STATIC]\u00b6
\n-

Alias for computeUnitaryPositive.

\n-
\n-\n-
\n-
\n-prod((Matrix6)arg1) → float[STATIC]\u00b6
\n-

Product of all elements.

\n-
\n-\n-
\n-
\n-pruned((Matrix6)arg1[, (float)absTol=1e-06]) → Matrix6[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-row((Matrix6)arg1, (int)row) → Vector6[STATIC]\u00b6
\n-

Return row as vector.

\n-
\n-\n-
\n-
\n-rows((Matrix6)arg1) → int[STATIC]\u00b6
\n-

Number of rows.

\n-
\n-\n-
\n-
\n-selfAdjointEigenDecomposition((Matrix6)arg1) → tuple[STATIC]\u00b6
\n-

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().

\n-
\n-\n-
\n-
\n-spectralDecomposition((Matrix6)arg1) → tuple[STATIC]\u00b6
\n-

Alias for selfAdjointEigenDecomposition.

\n-
\n-\n-
\n-
\n-squaredNorm((Matrix6)arg1) → float[STATIC]\u00b6
\n-

Square of the Euclidean norm.

\n-
\n-\n-
\n-
\n-sum((Matrix6)arg1) → float[STATIC]\u00b6
\n-

Sum of all elements.

\n-
\n-\n-
\n-
\n-svd((Matrix6)arg1) → tuple[STATIC]\u00b6
\n-

Alias for jacobiSVD.

\n-
\n-\n-
\n-
\n-trace((Matrix6)arg1) → float[STATIC]\u00b6
\n-

Return sum of diagonal elements.

\n-
\n-\n-
\n-
\n-transpose((Matrix6)arg1) → Matrix6[STATIC]\u00b6
\n-

Return transposed matrix.

\n-
\n-\n-
\n-
\n-ul((Matrix6)arg1) → Matrix3[STATIC]\u00b6
\n-

Return upper-left 3x3 block

\n-
\n-\n-
\n-
\n-ur((Matrix6)arg1) → Matrix3[STATIC]\u00b6
\n-

Return upper-right 3x3 block

\n-
\n-\n-
\n-\n-
\n-
\n-class minieigen.Matrix6c\u00b6
\n-

/TODO/

\n-
\n-
\n-Identity = Matrix6c( \t(      1,      0,      0,      0,      0,      0), \t(      0,      1,      0,      0,      0,      0), \t(      0,      0,      1,      0,      0,      0), \t(      0,      0,      0,      1,      0,      0), \t(      0,      0,      0,      0,      1,      0), \t(      0,      0,      0,      0,      0,      1) )\u00b6
\n-
\n-\n-
\n-
\n-Ones = Matrix6c( \t(      1,      1,      1,      1,      1,      1), \t(      1,      1,      1,      1,      1,      1), \t(      1,      1,      1,      1,      1,      1), \t(      1,      1,      1,      1,      1,      1), \t(      1,      1,      1,      1,      1,      1), \t(      1,      1,      1,      1,      1,      1) )\u00b6
\n-
\n-\n-
\n-
\n-static Random() → Matrix6c[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-Zero = Matrix6c( \t(      0,      0,      0,      0,      0,      0), \t(      0,      0,      0,      0,      0,      0), \t(      0,      0,      0,      0,      0,      0), \t(      0,      0,      0,      0,      0,      0), \t(      0,      0,      0,      0,      0,      0), \t(      0,      0,      0,      0,      0,      0) )\u00b6
\n-
\n-\n-
\n-
\n-col((Matrix6c)arg1, (int)col) → Vector6c[STATIC]\u00b6
\n-

Return column as vector.

\n-
\n-\n-
\n-
\n-cols((Matrix6c)arg1) → int[STATIC]\u00b6
\n-

Number of columns.

\n-
\n-\n-
\n-
\n-determinant((Matrix6c)arg1) → complex[STATIC]\u00b6
\n-

Return matrix determinant.

\n-
\n-\n-
\n-
\n-diagonal((Matrix6c)arg1) → Vector6c[STATIC]\u00b6
\n-

Return diagonal as vector.

\n-
\n-\n-
\n-
\n-inverse((Matrix6c)arg1) → Matrix6c[STATIC]\u00b6
\n-

Return inverted matrix.

\n-
\n-\n-
\n-
\n-isApprox((Matrix6c)arg1, (Matrix6c)other[, (float)prec=1e-12]) → bool[STATIC]\u00b6
\n-

Approximate comparison with precision prec.

\n-
\n-\n-
\n-
\n-ll((Matrix6c)arg1) → Matrix3c[STATIC]\u00b6
\n-

Return lower-left 3x3 block

\n-
\n-\n-
\n-
\n-lr((Matrix6c)arg1) → Matrix3c[STATIC]\u00b6
\n-

Return lower-right 3x3 block

\n-
\n-\n-
\n-
\n-maxAbsCoeff((Matrix6c)arg1) → float[STATIC]\u00b6
\n-

Maximum absolute value over all elements.

\n-
\n-\n-
\n-
\n-mean((Matrix6c)arg1) → complex[STATIC]\u00b6
\n-

Mean value over all elements.

\n-
\n-\n-
\n-
\n-norm((Matrix6c)arg1) → float[STATIC]\u00b6
\n-

Euclidean norm.

\n-
\n-\n-
\n-
\n-normalize((Matrix6c)arg1) → None[STATIC]\u00b6
\n-

Normalize this object in-place.

\n-
\n-\n-
\n-
\n-normalized((Matrix6c)arg1) → Matrix6c[STATIC]\u00b6
\n-

Return normalized copy of this object

\n-
\n-\n-
\n-
\n-prod((Matrix6c)arg1) → complex[STATIC]\u00b6
\n-

Product of all elements.

\n-
\n-\n-
\n-
\n-pruned((Matrix6c)arg1[, (float)absTol=1e-06]) → Matrix6c[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-row((Matrix6c)arg1, (int)row) → Vector6c[STATIC]\u00b6
\n-

Return row as vector.

\n-
\n-\n-
\n-
\n-rows((Matrix6c)arg1) → int[STATIC]\u00b6
\n-

Number of rows.

\n-
\n-\n-
\n-
\n-squaredNorm((Matrix6c)arg1) → float[STATIC]\u00b6
\n-

Square of the Euclidean norm.

\n-
\n-\n-
\n-
\n-sum((Matrix6c)arg1) → complex[STATIC]\u00b6
\n-

Sum of all elements.

\n-
\n-\n-
\n-
\n-trace((Matrix6c)arg1) → complex[STATIC]\u00b6
\n-

Return sum of diagonal elements.

\n-
\n-\n-
\n-
\n-transpose((Matrix6c)arg1) → Matrix6c[STATIC]\u00b6
\n-

Return transposed matrix.

\n-
\n-\n-
\n-
\n-ul((Matrix6c)arg1) → Matrix3c[STATIC]\u00b6
\n-

Return upper-left 3x3 block

\n-
\n-\n-
\n-
\n-ur((Matrix6c)arg1) → Matrix3c[STATIC]\u00b6
\n-

Return upper-right 3x3 block

\n-
\n-\n-
\n-\n-
\n-
\n-class minieigen.MatrixX\u00b6
\n-

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

\n-

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.

\n-
\n-
\n-static Identity((int)arg1, (int)rank) → MatrixX[STATIC]\u00b6
\n-

Create identity matrix with given rank (square).

\n-
\n-\n-
\n-
\n-static Ones((int)rows, (int)cols) → MatrixX[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-static Random((int)rows, (int)cols) → MatrixX[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-static Zero((int)rows, (int)cols) → MatrixX[STATIC]\u00b6
\n-

Create zero matrix of given dimensions

\n-
\n-\n-
\n-
\n-col((MatrixX)arg1, (int)col) → VectorX[STATIC]\u00b6
\n-

Return column as vector.

\n-
\n-\n-
\n-
\n-cols((MatrixX)arg1) → int[STATIC]\u00b6
\n-

Number of columns.

\n-
\n-\n-
\n-
\n-computeUnitaryPositive((MatrixX)arg1) → tuple[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-determinant((MatrixX)arg1) → float[STATIC]\u00b6
\n-

Return matrix determinant.

\n-
\n-\n-
\n-
\n-diagonal((MatrixX)arg1) → VectorX[STATIC]\u00b6
\n-

Return diagonal as vector.

\n-
\n-\n-
\n-
\n-inverse((MatrixX)arg1) → MatrixX[STATIC]\u00b6
\n-

Return inverted matrix.

\n-
\n-\n-
\n-
\n-isApprox((MatrixX)arg1, (MatrixX)other[, (float)prec=1e-12]) → bool[STATIC]\u00b6
\n-

Approximate comparison with precision prec.

\n-
\n-\n-
\n-
\n-jacobiSVD((MatrixX)arg1) → tuple[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-maxAbsCoeff((MatrixX)arg1) → float[STATIC]\u00b6
\n-

Maximum absolute value over all elements.

\n-
\n-\n-
\n-
\n-maxCoeff((MatrixX)arg1) → float[STATIC]\u00b6
\n-

Maximum value over all elements.

\n-
\n-\n-
\n-
\n-mean((MatrixX)arg1) → float[STATIC]\u00b6
\n-

Mean value over all elements.

\n-
\n-\n-
\n-
\n-minCoeff((MatrixX)arg1) → float[STATIC]\u00b6
\n-

Minimum value over all elements.

\n-
\n-\n-
\n-
\n-norm((MatrixX)arg1) → float[STATIC]\u00b6
\n-

Euclidean norm.

\n-
\n-\n-
\n-
\n-normalize((MatrixX)arg1) → None[STATIC]\u00b6
\n-

Normalize this object in-place.

\n-
\n-\n-
\n-
\n-normalized((MatrixX)arg1) → MatrixX[STATIC]\u00b6
\n-

Return normalized copy of this object

\n-
\n-\n-
\n-
\n-polarDecomposition((MatrixX)arg1) → tuple[STATIC]\u00b6
\n-

Alias for computeUnitaryPositive.

\n-
\n-\n-
\n-
\n-prod((MatrixX)arg1) → float[STATIC]\u00b6
\n-

Product of all elements.

\n-
\n-\n-
\n-
\n-pruned((MatrixX)arg1[, (float)absTol=1e-06]) → MatrixX[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-resize((MatrixX)arg1, (int)rows, (int)cols) → None[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-row((MatrixX)arg1, (int)row) → VectorX[STATIC]\u00b6
\n-

Return row as vector.

\n-
\n-\n-
\n-
\n-rows((MatrixX)arg1) → int[STATIC]\u00b6
\n-

Number of rows.

\n-
\n-\n-
\n-
\n-selfAdjointEigenDecomposition((MatrixX)arg1) → tuple[STATIC]\u00b6
\n-

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().

\n-
\n-\n-
\n-
\n-spectralDecomposition((MatrixX)arg1) → tuple[STATIC]\u00b6
\n-

Alias for selfAdjointEigenDecomposition.

\n-
\n-\n-
\n-
\n-squaredNorm((MatrixX)arg1) → float[STATIC]\u00b6
\n-

Square of the Euclidean norm.

\n-
\n-\n-
\n-
\n-sum((MatrixX)arg1) → float[STATIC]\u00b6
\n-

Sum of all elements.

\n-
\n-\n-
\n-
\n-svd((MatrixX)arg1) → tuple[STATIC]\u00b6
\n-

Alias for jacobiSVD.

\n-
\n-\n-
\n-
\n-trace((MatrixX)arg1) → float[STATIC]\u00b6
\n-

Return sum of diagonal elements.

\n-
\n-\n-
\n-
\n-transpose((MatrixX)arg1) → MatrixX[STATIC]\u00b6
\n-

Return transposed matrix.

\n-
\n-\n-
\n-\n-
\n-
\n-class minieigen.MatrixXc\u00b6
\n-

/TODO/

\n-
\n-
\n-static Identity((int)arg1, (int)rank) → MatrixXc[STATIC]\u00b6
\n-

Create identity matrix with given rank (square).

\n-
\n-\n-
\n-
\n-static Ones((int)rows, (int)cols) → MatrixXc[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-static Random((int)rows, (int)cols) → MatrixXc[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-static Zero((int)rows, (int)cols) → MatrixXc[STATIC]\u00b6
\n-

Create zero matrix of given dimensions

\n-
\n-\n-
\n-
\n-col((MatrixXc)arg1, (int)col) → VectorXc[STATIC]\u00b6
\n-

Return column as vector.

\n-
\n-\n-
\n-
\n-cols((MatrixXc)arg1) → int[STATIC]\u00b6
\n-

Number of columns.

\n-
\n-\n-
\n-
\n-determinant((MatrixXc)arg1) → complex[STATIC]\u00b6
\n-

Return matrix determinant.

\n-
\n-\n-
\n-
\n-diagonal((MatrixXc)arg1) → VectorXc[STATIC]\u00b6
\n-

Return diagonal as vector.

\n-
\n-\n-
\n-
\n-inverse((MatrixXc)arg1) → MatrixXc[STATIC]\u00b6
\n-

Return inverted matrix.

\n-
\n-\n-
\n-
\n-isApprox((MatrixXc)arg1, (MatrixXc)other[, (float)prec=1e-12]) → bool[STATIC]\u00b6
\n-

Approximate comparison with precision prec.

\n-
\n-\n-
\n-
\n-maxAbsCoeff((MatrixXc)arg1) → float[STATIC]\u00b6
\n-

Maximum absolute value over all elements.

\n-
\n-\n-
\n-
\n-mean((MatrixXc)arg1) → complex[STATIC]\u00b6
\n-

Mean value over all elements.

\n-
\n-\n-
\n-
\n-norm((MatrixXc)arg1) → float[STATIC]\u00b6
\n-

Euclidean norm.

\n-
\n-\n-
\n-
\n-normalize((MatrixXc)arg1) → None[STATIC]\u00b6
\n-

Normalize this object in-place.

\n-
\n-\n-
\n-
\n-normalized((MatrixXc)arg1) → MatrixXc[STATIC]\u00b6
\n-

Return normalized copy of this object

\n-
\n-\n-
\n-
\n-prod((MatrixXc)arg1) → complex[STATIC]\u00b6
\n-

Product of all elements.

\n-
\n-\n-
\n-
\n-pruned((MatrixXc)arg1[, (float)absTol=1e-06]) → MatrixXc[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-resize((MatrixXc)arg1, (int)rows, (int)cols) → None[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-row((MatrixXc)arg1, (int)row) → VectorXc[STATIC]\u00b6
\n-

Return row as vector.

\n-
\n-\n-
\n-
\n-rows((MatrixXc)arg1) → int[STATIC]\u00b6
\n-

Number of rows.

\n-
\n-\n-
\n-
\n-squaredNorm((MatrixXc)arg1) → float[STATIC]\u00b6
\n-

Square of the Euclidean norm.

\n-
\n-\n-
\n-
\n-sum((MatrixXc)arg1) → complex[STATIC]\u00b6
\n-

Sum of all elements.

\n-
\n-\n-
\n-
\n-trace((MatrixXc)arg1) → complex[STATIC]\u00b6
\n-

Return sum of diagonal elements.

\n-
\n-\n-
\n-
\n-transpose((MatrixXc)arg1) → MatrixXc[STATIC]\u00b6
\n-

Return transposed matrix.

\n-
\n-\n-
\n-\n-
\n-
\n-class minieigen.Quaternion\u00b6
\n-

Quaternion representing rotation.

\n-

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.

\n-

Static attributes: Identity.

\n-
\n-
\n-Identity = Quaternion((1,0,0),0)\u00b6
\n-
\n-\n-
\n-
\n-Rotate((Quaternion)arg1, (Vector3)v) → Vector3[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-angularDistance((Quaternion)arg1, (Quaternion)arg2) → float[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-conjugate((Quaternion)arg1) → Quaternion[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-inverse((Quaternion)arg1) → Quaternion[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-norm((Quaternion)arg1) → float[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-normalize((Quaternion)arg1) → None[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-normalized((Quaternion)arg1) → Quaternion[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-setFromTwoVectors((Quaternion)arg1, (Vector3)u, (Vector3)v) → None[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-slerp((Quaternion)arg1, (float)t, (Quaternion)other) → Quaternion[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-toAngleAxis((Quaternion)arg1) → tuple[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-toAxisAngle((Quaternion)arg1) → tuple[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-toRotationMatrix((Quaternion)arg1) → Matrix3[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-toRotationVector((Quaternion)arg1) → Vector3[STATIC]\u00b6
\n-
\n-\n-
\n-\n-
\n-
\n-class minieigen.Vector2\u00b6
\n-

3-dimensional float vector.

\n-

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.

\n-

Implicit conversion from sequence (list, tuple, \u2026) of 2 floats.

\n-

Static attributes: Zero, Ones, UnitX, UnitY.

\n-
\n-
\n-Identity = Vector2(1,0)\u00b6
\n-
\n-\n-
\n-
\n-Ones = Vector2(1,1)\u00b6
\n-
\n-\n-
\n-
\n-static Random() → Vector2[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-static Unit((int)arg1) → Vector2[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-UnitX = Vector2(1,0)\u00b6
\n-
\n-\n-
\n-
\n-UnitY = Vector2(0,1)\u00b6
\n-
\n-\n-
\n-
\n-Zero = Vector2(0,0)\u00b6
\n-
\n-\n-
\n-
\n-asDiagonal((Vector2)arg1) → object[STATIC]\u00b6
\n-

Return diagonal matrix with this vector on the diagonal.

\n-
\n-\n-
\n-
\n-cols((Vector2)arg1) → int[STATIC]\u00b6
\n-

Number of columns.

\n-
\n-\n-
\n-
\n-dot((Vector2)arg1, (Vector2)other) → float[STATIC]\u00b6
\n-

Dot product with other.

\n-
\n-\n-
\n-
\n-isApprox((Vector2)arg1, (Vector2)other[, (float)prec=1e-12]) → bool[STATIC]\u00b6
\n-

Approximate comparison with precision prec.

\n-
\n-\n-
\n-
\n-maxAbsCoeff((Vector2)arg1) → float[STATIC]\u00b6
\n-

Maximum absolute value over all elements.

\n-
\n-\n-
\n-
\n-maxCoeff((Vector2)arg1) → float[STATIC]\u00b6
\n-

Maximum value over all elements.

\n-
\n-\n-
\n-
\n-mean((Vector2)arg1) → float[STATIC]\u00b6
\n-

Mean value over all elements.

\n-
\n-\n-
\n-
\n-minCoeff((Vector2)arg1) → float[STATIC]\u00b6
\n-

Minimum value over all elements.

\n-
\n-\n-
\n-
\n-norm((Vector2)arg1) → float[STATIC]\u00b6
\n-

Euclidean norm.

\n-
\n-\n-
\n-
\n-normalize((Vector2)arg1) → None[STATIC]\u00b6
\n-

Normalize this object in-place.

\n-
\n-\n-
\n-
\n-normalized((Vector2)arg1) → Vector2[STATIC]\u00b6
\n-

Return normalized copy of this object

\n-
\n-\n-
\n-
\n-outer((Vector2)arg1, (Vector2)other) → object[STATIC]\u00b6
\n-

Outer product with other.

\n-
\n-\n-
\n-
\n-prod((Vector2)arg1) → float[STATIC]\u00b6
\n-

Product of all elements.

\n-
\n-\n-
\n-
\n-pruned((Vector2)arg1[, (float)absTol=1e-06]) → Vector2[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-rows((Vector2)arg1) → int[STATIC]\u00b6
\n-

Number of rows.

\n-
\n-\n-
\n-
\n-squaredNorm((Vector2)arg1) → float[STATIC]\u00b6
\n-

Square of the Euclidean norm.

\n-
\n-\n-
\n-
\n-sum((Vector2)arg1) → float[STATIC]\u00b6
\n-

Sum of all elements.

\n-
\n-\n-
\n-\n-
\n-
\n-class minieigen.Vector2c\u00b6
\n-

/TODO/

\n-
\n-
\n-Identity = Vector2c(1,0)\u00b6
\n-
\n-\n-
\n-
\n-Ones = Vector2c(1,1)\u00b6
\n-
\n-\n-
\n-
\n-static Random() → Vector2c[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-static Unit((int)arg1) → Vector2c[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-UnitX = Vector2c(1,0)\u00b6
\n-
\n-\n-
\n-
\n-UnitY = Vector2c(0,1)\u00b6
\n-
\n-\n-
\n-
\n-Zero = Vector2c(0,0)\u00b6
\n-
\n-\n-
\n-
\n-asDiagonal((Vector2c)arg1) → object[STATIC]\u00b6
\n-

Return diagonal matrix with this vector on the diagonal.

\n-
\n-\n-
\n-
\n-cols((Vector2c)arg1) → int[STATIC]\u00b6
\n-

Number of columns.

\n-
\n-\n-
\n-
\n-dot((Vector2c)arg1, (Vector2c)other) → complex[STATIC]\u00b6
\n-

Dot product with other.

\n-
\n-\n-
\n-
\n-isApprox((Vector2c)arg1, (Vector2c)other[, (float)prec=1e-12]) → bool[STATIC]\u00b6
\n-

Approximate comparison with precision prec.

\n-
\n-\n-
\n-
\n-maxAbsCoeff((Vector2c)arg1) → float[STATIC]\u00b6
\n-

Maximum absolute value over all elements.

\n-
\n-\n-
\n-
\n-mean((Vector2c)arg1) → complex[STATIC]\u00b6
\n-

Mean value over all elements.

\n-
\n-\n-
\n-
\n-norm((Vector2c)arg1) → float[STATIC]\u00b6
\n-

Euclidean norm.

\n-
\n-\n-
\n-
\n-normalize((Vector2c)arg1) → None[STATIC]\u00b6
\n-

Normalize this object in-place.

\n-
\n-\n-
\n-
\n-normalized((Vector2c)arg1) → Vector2c[STATIC]\u00b6
\n-

Return normalized copy of this object

\n-
\n-\n-
\n-
\n-outer((Vector2c)arg1, (Vector2c)other) → object[STATIC]\u00b6
\n-

Outer product with other.

\n-
\n-\n-
\n-
\n-prod((Vector2c)arg1) → complex[STATIC]\u00b6
\n-

Product of all elements.

\n-
\n-\n-
\n-
\n-pruned((Vector2c)arg1[, (float)absTol=1e-06]) → Vector2c[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-rows((Vector2c)arg1) → int[STATIC]\u00b6
\n-

Number of rows.

\n-
\n-\n-
\n-
\n-squaredNorm((Vector2c)arg1) → float[STATIC]\u00b6
\n-

Square of the Euclidean norm.

\n-
\n-\n-
\n-
\n-sum((Vector2c)arg1) → complex[STATIC]\u00b6
\n-

Sum of all elements.

\n-
\n-\n-
\n-\n-
\n-
\n-class minieigen.Vector2i\u00b6
\n-

2-dimensional integer vector.

\n-

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.

\n-

Implicit conversion from sequence (list, tuple, \u2026) of 2 integers.

\n-

Static attributes: Zero, Ones, UnitX, UnitY.

\n-
\n-
\n-Identity = Vector2i(1,0)\u00b6
\n-
\n-\n-
\n-
\n-Ones = Vector2i(1,1)\u00b6
\n-
\n-\n-
\n-
\n-static Random() → Vector2i[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-static Unit((int)arg1) → Vector2i[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-UnitX = Vector2i(1,0)\u00b6
\n-
\n-\n-
\n-
\n-UnitY = Vector2i(0,1)\u00b6
\n-
\n-\n-
\n-
\n-Zero = Vector2i(0,0)\u00b6
\n-
\n-\n-
\n-
\n-asDiagonal((Vector2i)arg1) → object[STATIC]\u00b6
\n-

Return diagonal matrix with this vector on the diagonal.

\n-
\n-\n-
\n-
\n-cols((Vector2i)arg1) → int[STATIC]\u00b6
\n-

Number of columns.

\n-
\n-\n-
\n-
\n-dot((Vector2i)arg1, (Vector2i)other) → int[STATIC]\u00b6
\n-

Dot product with other.

\n-
\n-\n-
\n-
\n-isApprox((Vector2i)arg1, (Vector2i)other[, (int)prec=0]) → bool[STATIC]\u00b6
\n-

Approximate comparison with precision prec.

\n-
\n-\n-
\n-
\n-maxAbsCoeff((Vector2i)arg1) → int[STATIC]\u00b6
\n-

Maximum absolute value over all elements.

\n-
\n-\n-
\n-
\n-maxCoeff((Vector2i)arg1) → int[STATIC]\u00b6
\n-

Maximum value over all elements.

\n-
\n-\n-
\n-
\n-mean((Vector2i)arg1) → int[STATIC]\u00b6
\n-

Mean value over all elements.

\n-
\n-\n-
\n-
\n-minCoeff((Vector2i)arg1) → int[STATIC]\u00b6
\n-

Minimum value over all elements.

\n-
\n-\n-
\n-
\n-outer((Vector2i)arg1, (Vector2i)other) → object[STATIC]\u00b6
\n-

Outer product with other.

\n-
\n-\n-
\n-
\n-prod((Vector2i)arg1) → int[STATIC]\u00b6
\n-

Product of all elements.

\n-
\n-\n-
\n-
\n-rows((Vector2i)arg1) → int[STATIC]\u00b6
\n-

Number of rows.

\n-
\n-\n-
\n-
\n-sum((Vector2i)arg1) → int[STATIC]\u00b6
\n-

Sum of all elements.

\n-
\n-\n-
\n-\n-
\n-
\n-class minieigen.Vector3\u00b6
\n-

3-dimensional float vector.

\n-

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.

\n-

Implicit conversion from sequence (list, tuple, \u2026) of 3 floats.

\n-

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

\n-
\n-
\n-Identity = Vector3(1,0,0)\u00b6
\n-
\n-\n-
\n-
\n-Ones = Vector3(1,1,1)\u00b6
\n-
\n-\n-
\n-
\n-static Random() → Vector3[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-static Unit((int)arg1) → Vector3[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-UnitX = Vector3(1,0,0)\u00b6
\n-
\n-\n-
\n-
\n-UnitY = Vector3(0,1,0)\u00b6
\n-
\n-\n-
\n-
\n-UnitZ = Vector3(0,0,1)\u00b6
\n-
\n-\n-
\n-
\n-Zero = Vector3(0,0,0)\u00b6
\n-
\n-\n-
\n-
\n-asDiagonal((Vector3)arg1) → Matrix3[STATIC]\u00b6
\n-

Return diagonal matrix with this vector on the diagonal.

\n-
\n-\n-
\n-
\n-cols((Vector3)arg1) → int[STATIC]\u00b6
\n-

Number of columns.

\n-
\n-\n-
\n-
\n-cross((Vector3)arg1, (Vector3)arg2) → Vector3[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-dot((Vector3)arg1, (Vector3)other) → float[STATIC]\u00b6
\n-

Dot product with other.

\n-
\n-\n-
\n-
\n-isApprox((Vector3)arg1, (Vector3)other[, (float)prec=1e-12]) → bool[STATIC]\u00b6
\n-

Approximate comparison with precision prec.

\n-
\n-\n-
\n-
\n-maxAbsCoeff((Vector3)arg1) → float[STATIC]\u00b6
\n-

Maximum absolute value over all elements.

\n-
\n-\n-
\n-
\n-maxCoeff((Vector3)arg1) → float[STATIC]\u00b6
\n-

Maximum value over all elements.

\n-
\n-\n-
\n-
\n-mean((Vector3)arg1) → float[STATIC]\u00b6
\n-

Mean value over all elements.

\n-
\n-\n-
\n-
\n-minCoeff((Vector3)arg1) → float[STATIC]\u00b6
\n-

Minimum value over all elements.

\n-
\n-\n-
\n-
\n-norm((Vector3)arg1) → float[STATIC]\u00b6
\n-

Euclidean norm.

\n-
\n-\n-
\n-
\n-normalize((Vector3)arg1) → None[STATIC]\u00b6
\n-

Normalize this object in-place.

\n-
\n-\n-
\n-
\n-normalized((Vector3)arg1) → Vector3[STATIC]\u00b6
\n-

Return normalized copy of this object

\n-
\n-\n-
\n-
\n-outer((Vector3)arg1, (Vector3)other) → Matrix3[STATIC]\u00b6
\n-

Outer product with other.

\n-
\n-\n-
\n-
\n-prod((Vector3)arg1) → float[STATIC]\u00b6
\n-

Product of all elements.

\n-
\n-\n-
\n-
\n-pruned((Vector3)arg1[, (float)absTol=1e-06]) → Vector3[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-rows((Vector3)arg1) → int[STATIC]\u00b6
\n-

Number of rows.

\n-
\n-\n-
\n-
\n-squaredNorm((Vector3)arg1) → float[STATIC]\u00b6
\n-

Square of the Euclidean norm.

\n-
\n-\n-
\n-
\n-sum((Vector3)arg1) → float[STATIC]\u00b6
\n-

Sum of all elements.

\n-
\n-\n-
\n-
\n-xy((Vector3)arg1) → Vector2[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-xz((Vector3)arg1) → Vector2[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-yx((Vector3)arg1) → Vector2[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-yz((Vector3)arg1) → Vector2[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-zx((Vector3)arg1) → Vector2[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-zy((Vector3)arg1) → Vector2[STATIC]\u00b6
\n-
\n-\n-
\n-\n-
\n-
\n-class minieigen.Vector3c\u00b6
\n-

/TODO/

\n-
\n-
\n-Identity = Vector3c(1,0,0)\u00b6
\n-
\n-\n-
\n-
\n-Ones = Vector3c(1,1,1)\u00b6
\n-
\n-\n-
\n-
\n-static Random() → Vector3c[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-static Unit((int)arg1) → Vector3c[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-UnitX = Vector3c(1,0,0)\u00b6
\n-
\n-\n-
\n-
\n-UnitY = Vector3c(0,1,0)\u00b6
\n-
\n-\n-
\n-
\n-UnitZ = Vector3c(0,0,1)\u00b6
\n-
\n-\n-
\n-
\n-Zero = Vector3c(0,0,0)\u00b6
\n-
\n-\n-
\n-
\n-asDiagonal((Vector3c)arg1) → Matrix3c[STATIC]\u00b6
\n-

Return diagonal matrix with this vector on the diagonal.

\n-
\n-\n-
\n-
\n-cols((Vector3c)arg1) → int[STATIC]\u00b6
\n-

Number of columns.

\n-
\n-\n-
\n-
\n-cross((Vector3c)arg1, (Vector3c)arg2) → Vector3c[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-dot((Vector3c)arg1, (Vector3c)other) → complex[STATIC]\u00b6
\n-

Dot product with other.

\n-
\n-\n-
\n-
\n-isApprox((Vector3c)arg1, (Vector3c)other[, (float)prec=1e-12]) → bool[STATIC]\u00b6
\n-

Approximate comparison with precision prec.

\n-
\n-\n-
\n-
\n-maxAbsCoeff((Vector3c)arg1) → float[STATIC]\u00b6
\n-

Maximum absolute value over all elements.

\n-
\n-\n-
\n-
\n-mean((Vector3c)arg1) → complex[STATIC]\u00b6
\n-

Mean value over all elements.

\n-
\n-\n-
\n-
\n-norm((Vector3c)arg1) → float[STATIC]\u00b6
\n-

Euclidean norm.

\n-
\n-\n-
\n-
\n-normalize((Vector3c)arg1) → None[STATIC]\u00b6
\n-

Normalize this object in-place.

\n-
\n-\n-
\n-
\n-normalized((Vector3c)arg1) → Vector3c[STATIC]\u00b6
\n-

Return normalized copy of this object

\n-
\n-\n-
\n-
\n-outer((Vector3c)arg1, (Vector3c)other) → Matrix3c[STATIC]\u00b6
\n-

Outer product with other.

\n-
\n-\n-
\n-
\n-prod((Vector3c)arg1) → complex[STATIC]\u00b6
\n-

Product of all elements.

\n-
\n-\n-
\n-
\n-pruned((Vector3c)arg1[, (float)absTol=1e-06]) → Vector3c[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-rows((Vector3c)arg1) → int[STATIC]\u00b6
\n-

Number of rows.

\n-
\n-\n-
\n-
\n-squaredNorm((Vector3c)arg1) → float[STATIC]\u00b6
\n-

Square of the Euclidean norm.

\n-
\n-\n-
\n-
\n-sum((Vector3c)arg1) → complex[STATIC]\u00b6
\n-

Sum of all elements.

\n-
\n-\n-
\n-
\n-xy((Vector3c)arg1) → Vector2c[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-xz((Vector3c)arg1) → Vector2c[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-yx((Vector3c)arg1) → Vector2c[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-yz((Vector3c)arg1) → Vector2c[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-zx((Vector3c)arg1) → Vector2c[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-zy((Vector3c)arg1) → Vector2c[STATIC]\u00b6
\n-
\n-\n-
\n-\n-
\n-
\n-class minieigen.Vector3i\u00b6
\n-

3-dimensional integer vector.

\n-

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.

\n-

Implicit conversion from sequence (list, tuple, \u2026) of 3 integers.

\n-

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

\n-
\n-
\n-Identity = Vector3i(1,0,0)\u00b6
\n-
\n-\n-
\n-
\n-Ones = Vector3i(1,1,1)\u00b6
\n-
\n-\n-
\n-
\n-static Random() → Vector3i[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-static Unit((int)arg1) → Vector3i[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-UnitX = Vector3i(1,0,0)\u00b6
\n-
\n-\n-
\n-
\n-UnitY = Vector3i(0,1,0)\u00b6
\n-
\n-\n-
\n-
\n-UnitZ = Vector3i(0,0,1)\u00b6
\n-
\n-\n-
\n-
\n-Zero = Vector3i(0,0,0)\u00b6
\n-
\n-\n-
\n-
\n-asDiagonal((Vector3i)arg1) → object[STATIC]\u00b6
\n-

Return diagonal matrix with this vector on the diagonal.

\n-
\n-\n-
\n-
\n-cols((Vector3i)arg1) → int[STATIC]\u00b6
\n-

Number of columns.

\n-
\n-\n-
\n-
\n-cross((Vector3i)arg1, (Vector3i)arg2) → Vector3i[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-dot((Vector3i)arg1, (Vector3i)other) → int[STATIC]\u00b6
\n-

Dot product with other.

\n-
\n-\n-
\n-
\n-isApprox((Vector3i)arg1, (Vector3i)other[, (int)prec=0]) → bool[STATIC]\u00b6
\n-

Approximate comparison with precision prec.

\n-
\n-\n-
\n-
\n-maxAbsCoeff((Vector3i)arg1) → int[STATIC]\u00b6
\n-

Maximum absolute value over all elements.

\n-
\n-\n-
\n-
\n-maxCoeff((Vector3i)arg1) → int[STATIC]\u00b6
\n-

Maximum value over all elements.

\n-
\n-\n-
\n-
\n-mean((Vector3i)arg1) → int[STATIC]\u00b6
\n-

Mean value over all elements.

\n-
\n-\n-
\n-
\n-minCoeff((Vector3i)arg1) → int[STATIC]\u00b6
\n-

Minimum value over all elements.

\n-
\n-\n-
\n-
\n-outer((Vector3i)arg1, (Vector3i)other) → object[STATIC]\u00b6
\n-

Outer product with other.

\n-
\n-\n-
\n-
\n-prod((Vector3i)arg1) → int[STATIC]\u00b6
\n-

Product of all elements.

\n-
\n-\n-
\n-
\n-rows((Vector3i)arg1) → int[STATIC]\u00b6
\n-

Number of rows.

\n-
\n-\n-
\n-
\n-sum((Vector3i)arg1) → int[STATIC]\u00b6
\n-

Sum of all elements.

\n-
\n-\n-
\n-
\n-xy((Vector3i)arg1) → Vector2i[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-xz((Vector3i)arg1) → Vector2i[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-yx((Vector3i)arg1) → Vector2i[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-yz((Vector3i)arg1) → Vector2i[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-zx((Vector3i)arg1) → Vector2i[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-zy((Vector3i)arg1) → Vector2i[STATIC]\u00b6
\n-
\n-\n-
\n-\n-
\n-
\n-class minieigen.Vector4\u00b6
\n-

4-dimensional float vector.

\n-

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.

\n-

Implicit conversion from sequence (list, tuple, \u2026) of 4 floats.

\n-

Static attributes: Zero, Ones.

\n-
\n-
\n-Identity = Vector4(1,0,0, 0)\u00b6
\n-
\n-\n-
\n-
\n-Ones = Vector4(1,1,1, 1)\u00b6
\n-
\n-\n-
\n-
\n-static Random() → Vector4[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-static Unit((int)arg1) → Vector4[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-Zero = Vector4(0,0,0, 0)\u00b6
\n-
\n-\n-
\n-
\n-asDiagonal((Vector4)arg1) → object[STATIC]\u00b6
\n-

Return diagonal matrix with this vector on the diagonal.

\n-
\n-\n-
\n-
\n-cols((Vector4)arg1) → int[STATIC]\u00b6
\n-

Number of columns.

\n-
\n-\n-
\n-
\n-dot((Vector4)arg1, (Vector4)other) → float[STATIC]\u00b6
\n-

Dot product with other.

\n-
\n-\n-
\n-
\n-isApprox((Vector4)arg1, (Vector4)other[, (float)prec=1e-12]) → bool[STATIC]\u00b6
\n-

Approximate comparison with precision prec.

\n-
\n-\n-
\n-
\n-maxAbsCoeff((Vector4)arg1) → float[STATIC]\u00b6
\n-

Maximum absolute value over all elements.

\n-
\n-\n-
\n-
\n-maxCoeff((Vector4)arg1) → float[STATIC]\u00b6
\n-

Maximum value over all elements.

\n-
\n-\n-
\n-
\n-mean((Vector4)arg1) → float[STATIC]\u00b6
\n-

Mean value over all elements.

\n-
\n-\n-
\n-
\n-minCoeff((Vector4)arg1) → float[STATIC]\u00b6
\n-

Minimum value over all elements.

\n-
\n-\n-
\n-
\n-norm((Vector4)arg1) → float[STATIC]\u00b6
\n-

Euclidean norm.

\n-
\n-\n-
\n-
\n-normalize((Vector4)arg1) → None[STATIC]\u00b6
\n-

Normalize this object in-place.

\n-
\n-\n-
\n-
\n-normalized((Vector4)arg1) → Vector4[STATIC]\u00b6
\n-

Return normalized copy of this object

\n-
\n-\n-
\n-
\n-outer((Vector4)arg1, (Vector4)other) → object[STATIC]\u00b6
\n-

Outer product with other.

\n-
\n-\n-
\n-
\n-prod((Vector4)arg1) → float[STATIC]\u00b6
\n-

Product of all elements.

\n-
\n-\n-
\n-
\n-pruned((Vector4)arg1[, (float)absTol=1e-06]) → Vector4[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-rows((Vector4)arg1) → int[STATIC]\u00b6
\n-

Number of rows.

\n-
\n-\n-
\n-
\n-squaredNorm((Vector4)arg1) → float[STATIC]\u00b6
\n-

Square of the Euclidean norm.

\n-
\n-\n-
\n-
\n-sum((Vector4)arg1) → float[STATIC]\u00b6
\n-

Sum of all elements.

\n-
\n-\n-
\n-\n-
\n-
\n-class minieigen.Vector6\u00b6
\n-

6-dimensional float vector.

\n-

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.

\n-

Implicit conversion from sequence (list, tuple, \u2026) of 6 floats.

\n-

Static attributes: Zero, Ones.

\n-
\n-
\n-Identity = Vector6(1,0,0, 0,0,0)\u00b6
\n-
\n-\n-
\n-
\n-Ones = Vector6(1,1,1, 1,1,1)\u00b6
\n-
\n-\n-
\n-
\n-static Random() → Vector6[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-static Unit((int)arg1) → Vector6[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-Zero = Vector6(0,0,0, 0,0,0)\u00b6
\n-
\n-\n-
\n-
\n-asDiagonal((Vector6)arg1) → Matrix6[STATIC]\u00b6
\n-

Return diagonal matrix with this vector on the diagonal.

\n-
\n-\n-
\n-
\n-cols((Vector6)arg1) → int[STATIC]\u00b6
\n-

Number of columns.

\n-
\n-\n-
\n-
\n-dot((Vector6)arg1, (Vector6)other) → float[STATIC]\u00b6
\n-

Dot product with other.

\n-
\n-\n-
\n-
\n-head((Vector6)arg1) → Vector3[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-isApprox((Vector6)arg1, (Vector6)other[, (float)prec=1e-12]) → bool[STATIC]\u00b6
\n-

Approximate comparison with precision prec.

\n-
\n-\n-
\n-
\n-maxAbsCoeff((Vector6)arg1) → float[STATIC]\u00b6
\n-

Maximum absolute value over all elements.

\n-
\n-\n-
\n-
\n-maxCoeff((Vector6)arg1) → float[STATIC]\u00b6
\n-

Maximum value over all elements.

\n-
\n-\n-
\n-
\n-mean((Vector6)arg1) → float[STATIC]\u00b6
\n-

Mean value over all elements.

\n-
\n-\n-
\n-
\n-minCoeff((Vector6)arg1) → float[STATIC]\u00b6
\n-

Minimum value over all elements.

\n-
\n-\n-
\n-
\n-norm((Vector6)arg1) → float[STATIC]\u00b6
\n-

Euclidean norm.

\n-
\n-\n-
\n-
\n-normalize((Vector6)arg1) → None[STATIC]\u00b6
\n-

Normalize this object in-place.

\n-
\n-\n-
\n-
\n-normalized((Vector6)arg1) → Vector6[STATIC]\u00b6
\n-

Return normalized copy of this object

\n-
\n-\n-
\n-
\n-outer((Vector6)arg1, (Vector6)other) → Matrix6[STATIC]\u00b6
\n-

Outer product with other.

\n-
\n-\n-
\n-
\n-prod((Vector6)arg1) → float[STATIC]\u00b6
\n-

Product of all elements.

\n-
\n-\n-
\n-
\n-pruned((Vector6)arg1[, (float)absTol=1e-06]) → Vector6[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-rows((Vector6)arg1) → int[STATIC]\u00b6
\n-

Number of rows.

\n-
\n-\n-
\n-
\n-squaredNorm((Vector6)arg1) → float[STATIC]\u00b6
\n-

Square of the Euclidean norm.

\n-
\n-\n-
\n-
\n-sum((Vector6)arg1) → float[STATIC]\u00b6
\n-

Sum of all elements.

\n-
\n-\n-
\n-
\n-tail((Vector6)arg1) → Vector3[STATIC]\u00b6
\n-
\n-\n-
\n-\n-
\n-
\n-class minieigen.Vector6c\u00b6
\n-

/TODO/

\n-
\n-
\n-Identity = Vector6c(1,0,0, 0,0,0)\u00b6
\n-
\n-\n-
\n-
\n-Ones = Vector6c(1,1,1, 1,1,1)\u00b6
\n-
\n-\n-
\n-
\n-static Random() → Vector6c[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-static Unit((int)arg1) → Vector6c[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-Zero = Vector6c(0,0,0, 0,0,0)\u00b6
\n-
\n-\n-
\n-
\n-asDiagonal((Vector6c)arg1) → Matrix6c[STATIC]\u00b6
\n-

Return diagonal matrix with this vector on the diagonal.

\n-
\n-\n-
\n-
\n-cols((Vector6c)arg1) → int[STATIC]\u00b6
\n-

Number of columns.

\n-
\n-\n-
\n-
\n-dot((Vector6c)arg1, (Vector6c)other) → complex[STATIC]\u00b6
\n-

Dot product with other.

\n-
\n-\n-
\n-
\n-head((Vector6c)arg1) → Vector3c[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-isApprox((Vector6c)arg1, (Vector6c)other[, (float)prec=1e-12]) → bool[STATIC]\u00b6
\n-

Approximate comparison with precision prec.

\n-
\n-\n-
\n-
\n-maxAbsCoeff((Vector6c)arg1) → float[STATIC]\u00b6
\n-

Maximum absolute value over all elements.

\n-
\n-\n-
\n-
\n-mean((Vector6c)arg1) → complex[STATIC]\u00b6
\n-

Mean value over all elements.

\n-
\n-\n-
\n-
\n-norm((Vector6c)arg1) → float[STATIC]\u00b6
\n-

Euclidean norm.

\n-
\n-\n-
\n-
\n-normalize((Vector6c)arg1) → None[STATIC]\u00b6
\n-

Normalize this object in-place.

\n-
\n-\n-
\n-
\n-normalized((Vector6c)arg1) → Vector6c[STATIC]\u00b6
\n-

Return normalized copy of this object

\n-
\n-\n-
\n-
\n-outer((Vector6c)arg1, (Vector6c)other) → Matrix6c[STATIC]\u00b6
\n-

Outer product with other.

\n-
\n-\n-
\n-
\n-prod((Vector6c)arg1) → complex[STATIC]\u00b6
\n-

Product of all elements.

\n-
\n-\n-
\n-
\n-pruned((Vector6c)arg1[, (float)absTol=1e-06]) → Vector6c[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-rows((Vector6c)arg1) → int[STATIC]\u00b6
\n-

Number of rows.

\n-
\n-\n-
\n-
\n-squaredNorm((Vector6c)arg1) → float[STATIC]\u00b6
\n-

Square of the Euclidean norm.

\n-
\n-\n-
\n-
\n-sum((Vector6c)arg1) → complex[STATIC]\u00b6
\n-

Sum of all elements.

\n-
\n-\n-
\n-
\n-tail((Vector6c)arg1) → Vector3c[STATIC]\u00b6
\n-
\n-\n-
\n-\n-
\n-
\n-class minieigen.Vector6i\u00b6
\n-

6-dimensional float vector.

\n-

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.

\n-

Implicit conversion from sequence (list, tuple, \u2026) of 6 floats.

\n-

Static attributes: Zero, Ones.

\n-
\n-
\n-Identity = Vector6i(1,0,0, 0,0,0)\u00b6
\n-
\n-\n-
\n-
\n-Ones = Vector6i(1,1,1, 1,1,1)\u00b6
\n-
\n-\n-
\n-
\n-static Random() → Vector6i[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-static Unit((int)arg1) → Vector6i[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-Zero = Vector6i(0,0,0, 0,0,0)\u00b6
\n-
\n-\n-
\n-
\n-asDiagonal((Vector6i)arg1) → object[STATIC]\u00b6
\n-

Return diagonal matrix with this vector on the diagonal.

\n-
\n-\n-
\n-
\n-cols((Vector6i)arg1) → int[STATIC]\u00b6
\n-

Number of columns.

\n-
\n-\n-
\n-
\n-dot((Vector6i)arg1, (Vector6i)other) → int[STATIC]\u00b6
\n-

Dot product with other.

\n-
\n-\n-
\n-
\n-head((Vector6i)arg1) → Vector3i[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-isApprox((Vector6i)arg1, (Vector6i)other[, (int)prec=0]) → bool[STATIC]\u00b6
\n-

Approximate comparison with precision prec.

\n-
\n-\n-
\n-
\n-maxAbsCoeff((Vector6i)arg1) → int[STATIC]\u00b6
\n-

Maximum absolute value over all elements.

\n-
\n-\n-
\n-
\n-maxCoeff((Vector6i)arg1) → int[STATIC]\u00b6
\n-

Maximum value over all elements.

\n-
\n-\n-
\n-
\n-mean((Vector6i)arg1) → int[STATIC]\u00b6
\n-

Mean value over all elements.

\n-
\n-\n-
\n-
\n-minCoeff((Vector6i)arg1) → int[STATIC]\u00b6
\n-

Minimum value over all elements.

\n-
\n-\n-
\n-
\n-outer((Vector6i)arg1, (Vector6i)other) → object[STATIC]\u00b6
\n-

Outer product with other.

\n-
\n-\n-
\n-
\n-prod((Vector6i)arg1) → int[STATIC]\u00b6
\n-

Product of all elements.

\n-
\n-\n-
\n-
\n-rows((Vector6i)arg1) → int[STATIC]\u00b6
\n-

Number of rows.

\n-
\n-\n-
\n-
\n-sum((Vector6i)arg1) → int[STATIC]\u00b6
\n-

Sum of all elements.

\n-
\n-\n-
\n-
\n-tail((Vector6i)arg1) → Vector3i[STATIC]\u00b6
\n-
\n-\n-
\n-\n-
\n-
\n-class minieigen.VectorX\u00b6
\n-

Dynamic-sized float vector.

\n-

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.

\n-

Implicit conversion from sequence (list, tuple, \u2026) of X floats.

\n-
\n-
\n-static Ones((int)arg1) → VectorX[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-static Random((int)len) → VectorX[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-static Unit((int)arg1, (int)arg2) → VectorX[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-static Zero((int)arg1) → VectorX[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-asDiagonal((VectorX)arg1) → MatrixX[STATIC]\u00b6
\n-

Return diagonal matrix with this vector on the diagonal.

\n-
\n-\n-
\n-
\n-cols((VectorX)arg1) → int[STATIC]\u00b6
\n-

Number of columns.

\n-
\n-\n-
\n-
\n-dot((VectorX)arg1, (VectorX)other) → float[STATIC]\u00b6
\n-

Dot product with other.

\n-
\n-\n-
\n-
\n-isApprox((VectorX)arg1, (VectorX)other[, (float)prec=1e-12]) → bool[STATIC]\u00b6
\n-

Approximate comparison with precision prec.

\n-
\n-\n-
\n-
\n-maxAbsCoeff((VectorX)arg1) → float[STATIC]\u00b6
\n-

Maximum absolute value over all elements.

\n-
\n-\n-
\n-
\n-maxCoeff((VectorX)arg1) → float[STATIC]\u00b6
\n-

Maximum value over all elements.

\n-
\n-\n-
\n-
\n-mean((VectorX)arg1) → float[STATIC]\u00b6
\n-

Mean value over all elements.

\n-
\n-\n-
\n-
\n-minCoeff((VectorX)arg1) → float[STATIC]\u00b6
\n-

Minimum value over all elements.

\n-
\n-\n-
\n-
\n-norm((VectorX)arg1) → float[STATIC]\u00b6
\n-

Euclidean norm.

\n-
\n-\n-
\n-
\n-normalize((VectorX)arg1) → None[STATIC]\u00b6
\n-

Normalize this object in-place.

\n-
\n-\n-
\n-
\n-normalized((VectorX)arg1) → VectorX[STATIC]\u00b6
\n-

Return normalized copy of this object

\n-
\n-\n-
\n-
\n-outer((VectorX)arg1, (VectorX)other) → MatrixX[STATIC]\u00b6
\n-

Outer product with other.

\n-
\n-\n-
\n-
\n-prod((VectorX)arg1) → float[STATIC]\u00b6
\n-

Product of all elements.

\n-
\n-\n-
\n-
\n-pruned((VectorX)arg1[, (float)absTol=1e-06]) → VectorX[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-resize((VectorX)arg1, (int)arg2) → None[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-rows((VectorX)arg1) → int[STATIC]\u00b6
\n-

Number of rows.

\n-
\n-\n-
\n-
\n-squaredNorm((VectorX)arg1) → float[STATIC]\u00b6
\n-

Square of the Euclidean norm.

\n-
\n-\n-
\n-
\n-sum((VectorX)arg1) → float[STATIC]\u00b6
\n-

Sum of all elements.

\n-
\n-\n-
\n-\n-
\n-
\n-class minieigen.VectorXc\u00b6
\n-

/TODO/

\n-
\n-
\n-static Ones((int)arg1) → VectorXc[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-static Random((int)len) → VectorXc[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-static Unit((int)arg1, (int)arg2) → VectorXc[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-static Zero((int)arg1) → VectorXc[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-asDiagonal((VectorXc)arg1) → MatrixXc[STATIC]\u00b6
\n-

Return diagonal matrix with this vector on the diagonal.

\n-
\n-\n-
\n-
\n-cols((VectorXc)arg1) → int[STATIC]\u00b6
\n-

Number of columns.

\n-
\n-\n-
\n-
\n-dot((VectorXc)arg1, (VectorXc)other) → complex[STATIC]\u00b6
\n-

Dot product with other.

\n-
\n-\n-
\n-
\n-isApprox((VectorXc)arg1, (VectorXc)other[, (float)prec=1e-12]) → bool[STATIC]\u00b6
\n-

Approximate comparison with precision prec.

\n-
\n-\n-
\n-
\n-maxAbsCoeff((VectorXc)arg1) → float[STATIC]\u00b6
\n-

Maximum absolute value over all elements.

\n-
\n-\n-
\n-
\n-mean((VectorXc)arg1) → complex[STATIC]\u00b6
\n-

Mean value over all elements.

\n-
\n-\n-
\n-
\n-norm((VectorXc)arg1) → float[STATIC]\u00b6
\n-

Euclidean norm.

\n-
\n-\n-
\n-
\n-normalize((VectorXc)arg1) → None[STATIC]\u00b6
\n-

Normalize this object in-place.

\n-
\n-\n-
\n-
\n-normalized((VectorXc)arg1) → VectorXc[STATIC]\u00b6
\n-

Return normalized copy of this object

\n-
\n-\n-
\n-
\n-outer((VectorXc)arg1, (VectorXc)other) → MatrixXc[STATIC]\u00b6
\n-

Outer product with other.

\n-
\n-\n-
\n-
\n-prod((VectorXc)arg1) → complex[STATIC]\u00b6
\n-

Product of all elements.

\n-
\n-\n-
\n-
\n-pruned((VectorXc)arg1[, (float)absTol=1e-06]) → VectorXc[STATIC]\u00b6
\n-

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

\n-
\n-\n-
\n-
\n-resize((VectorXc)arg1, (int)arg2) → None[STATIC]\u00b6
\n-
\n-\n-
\n-
\n-rows((VectorXc)arg1) → int[STATIC]\u00b6
\n-

Number of rows.

\n-
\n-\n-
\n-
\n-squaredNorm((VectorXc)arg1) → float[STATIC]\u00b6
\n-

Square of the Euclidean norm.

\n-
\n-\n-
\n-
\n-sum((VectorXc)arg1) → complex[STATIC]\u00b6
\n-

Sum of all elements.

\n-
\n-\n-
\n-\n-
\n-
\n-minieigen.float2str((float)f[, (int)pad=0]) → str\u00b6
\n-

Return the shortest string representation of f which will is equal to f when converted back to float. This function is only useful in Python prior to 3.0; starting from that version, standard string conversion does just that.

\n-
\n-\n
\n \n \n \n
\n \n \n@@ -3106,572 +131,15 @@\n \n \n \n
\n

Quick search

\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -74,1557 +74,23 @@\n * packages:\n o Debian\n o Ubuntu: distribution, PPA\n \n ***** Documentation\u00c2\u00b6 *****\n * Index\n * Search_Page\n-miniEigen is wrapper for a small part of the Eigen library. Refer to its\n-documentation for details. All classes in this module support pickling.\n- classminieigen.AlignedBox2\u00c2\u00b6\n- Axis-aligned box object in 2d, defined by its minimum and maximum corners\n- center((AlignedBox2)arg1) → Vector2[STATIC]\u00c2\u00b6\n- clamp((AlignedBox2)arg1, (AlignedBox2)arg2) → None[STATIC]\u00c2\u00b6\n- contains((AlignedBox2)arg1, (Vector2)arg2) → bool[STATIC]\u00c2\u00b6\n- contains( (AlignedBox2)arg1, (AlignedBox2)arg2) \u00e2\u0086\u0092 bool\n- empty((AlignedBox2)arg1) → bool[STATIC]\u00c2\u00b6\n- extend((AlignedBox2)arg1, (Vector2)arg2) → None[STATIC]\u00c2\u00b6\n- extend( (AlignedBox2)arg1, (AlignedBox2)arg2) \u00e2\u0086\u0092 None\n- intersection((AlignedBox2)arg1, (AlignedBox2)arg2) → AlignedBox2\n- [STATIC]\u00c2\u00b6\n- propertymax\u00c2\u00b6\n- merged((AlignedBox2)arg1, (AlignedBox2)arg2) → AlignedBox2\n- [STATIC]\u00c2\u00b6\n- propertymin\u00c2\u00b6\n- sizes((AlignedBox2)arg1) → Vector2[STATIC]\u00c2\u00b6\n- volume((AlignedBox2)arg1) → float[STATIC]\u00c2\u00b6\n- classminieigen.AlignedBox3\u00c2\u00b6\n- Axis-aligned box object, defined by its minimum and maximum corners\n- center((AlignedBox3)arg1) → Vector3[STATIC]\u00c2\u00b6\n- clamp((AlignedBox3)arg1, (AlignedBox3)arg2) → None[STATIC]\u00c2\u00b6\n- contains((AlignedBox3)arg1, (Vector3)arg2) → bool[STATIC]\u00c2\u00b6\n- contains( (AlignedBox3)arg1, (AlignedBox3)arg2) \u00e2\u0086\u0092 bool\n- empty((AlignedBox3)arg1) → bool[STATIC]\u00c2\u00b6\n- extend((AlignedBox3)arg1, (Vector3)arg2) → None[STATIC]\u00c2\u00b6\n- extend( (AlignedBox3)arg1, (AlignedBox3)arg2) \u00e2\u0086\u0092 None\n- intersection((AlignedBox3)arg1, (AlignedBox3)arg2) → AlignedBox3\n- [STATIC]\u00c2\u00b6\n- propertymax\u00c2\u00b6\n- merged((AlignedBox3)arg1, (AlignedBox3)arg2) → AlignedBox3\n- [STATIC]\u00c2\u00b6\n- propertymin\u00c2\u00b6\n- sizes((AlignedBox3)arg1) → Vector3[STATIC]\u00c2\u00b6\n- volume((AlignedBox3)arg1) → float[STATIC]\u00c2\u00b6\n- classminieigen.Matrix3\u00c2\u00b6\n- 3x3 float matrix.\n- Supported operations (m is a Matrix3, f if a float/int, v is a Vector3):\n- -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,\n- m==m, m!=m.\n- Static attributes: Zero, Ones, Identity.\n- Identity= Matrix3(1,0,0, 0,1,0, 0,0,1)\u00c2\u00b6\n- Ones= Matrix3(1,1,1, 1,1,1, 1,1,1)\u00c2\u00b6\n- staticRandom() → Matrix3[STATIC]\u00c2\u00b6\n- Return an object where all elements are randomly set to values\n- between 0 and 1.\n- Zero= Matrix3(0,0,0, 0,0,0, 0,0,0)\u00c2\u00b6\n- col((Matrix3)arg1, (int)col) → Vector3[STATIC]\u00c2\u00b6\n- Return column as vector.\n- cols((Matrix3)arg1) → int[STATIC]\u00c2\u00b6\n- Number of columns.\n- computeUnitaryPositive((Matrix3)arg1) → tuple[STATIC]\u00c2\u00b6\n- Compute polar decomposition (unitary matrix U and positive semi-\n- definite symmetric matrix P such that self=U*P).\n- determinant((Matrix3)arg1) → float[STATIC]\u00c2\u00b6\n- Return matrix determinant.\n- diagonal((Matrix3)arg1) → Vector3[STATIC]\u00c2\u00b6\n- Return diagonal as vector.\n- inverse((Matrix3)arg1) → Matrix3[STATIC]\u00c2\u00b6\n- Return inverted matrix.\n- isApprox((Matrix3)arg1, (Matrix3)other[, (float)prec=1e-12]) →\n- bool[STATIC]\u00c2\u00b6\n- Approximate comparison with precision prec.\n- jacobiSVD((Matrix3)arg1) → tuple[STATIC]\u00c2\u00b6\n- Compute SVD decomposition of square matrix, retuns (U,S,V) such\n- that self=U*S*V.transpose()\n- maxAbsCoeff((Matrix3)arg1) → float[STATIC]\u00c2\u00b6\n- Maximum absolute value over all elements.\n- maxCoeff((Matrix3)arg1) → float[STATIC]\u00c2\u00b6\n- Maximum value over all elements.\n- mean((Matrix3)arg1) → float[STATIC]\u00c2\u00b6\n- Mean value over all elements.\n- minCoeff((Matrix3)arg1) → float[STATIC]\u00c2\u00b6\n- Minimum value over all elements.\n- norm((Matrix3)arg1) → float[STATIC]\u00c2\u00b6\n- Euclidean norm.\n- normalize((Matrix3)arg1) → None[STATIC]\u00c2\u00b6\n- Normalize this object in-place.\n- normalized((Matrix3)arg1) → Matrix3[STATIC]\u00c2\u00b6\n- Return normalized copy of this object\n- polarDecomposition((Matrix3)arg1) → tuple[STATIC]\u00c2\u00b6\n- Alias for computeUnitaryPositive.\n- prod((Matrix3)arg1) → float[STATIC]\u00c2\u00b6\n- Product of all elements.\n- pruned((Matrix3)arg1[, (float)absTol=1e-06]) → Matrix3[STATIC]\u00c2\u00b6\n- Zero all elements which are greater than absTol. Negative zeros are\n- not pruned.\n- row((Matrix3)arg1, (int)row) → Vector3[STATIC]\u00c2\u00b6\n- Return row as vector.\n- rows((Matrix3)arg1) → int[STATIC]\u00c2\u00b6\n- Number of rows.\n- selfAdjointEigenDecomposition((Matrix3)arg1) → tuple[STATIC]\u00c2\u00b6\n- Compute eigen (spectral) decomposition of symmetric matrix, returns\n- (eigVecs,eigVals). eigVecs is orthogonal Matrix3 with columns ar\n- normalized eigenvectors, eigVals is Vector3 with corresponding\n- eigenvalues. self=eigVecs*diag(eigVals)*eigVecs.transpose().\n- spectralDecomposition((Matrix3)arg1) → tuple[STATIC]\u00c2\u00b6\n- Alias for selfAdjointEigenDecomposition.\n- squaredNorm((Matrix3)arg1) → float[STATIC]\u00c2\u00b6\n- Square of the Euclidean norm.\n- sum((Matrix3)arg1) → float[STATIC]\u00c2\u00b6\n- Sum of all elements.\n- svd((Matrix3)arg1) → tuple[STATIC]\u00c2\u00b6\n- Alias for jacobiSVD.\n- trace((Matrix3)arg1) → float[STATIC]\u00c2\u00b6\n- Return sum of diagonal elements.\n- transpose((Matrix3)arg1) → Matrix3[STATIC]\u00c2\u00b6\n- Return transposed matrix.\n- classminieigen.Matrix3c\u00c2\u00b6\n- /TODO/\n- Identity= Matrix3c(1,0,0, 0,1,0, 0,0,1)\u00c2\u00b6\n- Ones= Matrix3c(1,1,1, 1,1,1, 1,1,1)\u00c2\u00b6\n- staticRandom() → Matrix3c[STATIC]\u00c2\u00b6\n- Return an object where all elements are randomly set to values\n- between 0 and 1.\n- Zero= Matrix3c(0,0,0, 0,0,0, 0,0,0)\u00c2\u00b6\n- col((Matrix3c)arg1, (int)col) → Vector3c[STATIC]\u00c2\u00b6\n- Return column as vector.\n- cols((Matrix3c)arg1) → int[STATIC]\u00c2\u00b6\n- Number of columns.\n- determinant((Matrix3c)arg1) → complex[STATIC]\u00c2\u00b6\n- Return matrix determinant.\n- diagonal((Matrix3c)arg1) → Vector3c[STATIC]\u00c2\u00b6\n- Return diagonal as vector.\n- inverse((Matrix3c)arg1) → Matrix3c[STATIC]\u00c2\u00b6\n- Return inverted matrix.\n- isApprox((Matrix3c)arg1, (Matrix3c)other[, (float)prec=1e-12]) →\n- bool[STATIC]\u00c2\u00b6\n- Approximate comparison with precision prec.\n- maxAbsCoeff((Matrix3c)arg1) → float[STATIC]\u00c2\u00b6\n- Maximum absolute value over all elements.\n- mean((Matrix3c)arg1) → complex[STATIC]\u00c2\u00b6\n- Mean value over all elements.\n- norm((Matrix3c)arg1) → float[STATIC]\u00c2\u00b6\n- Euclidean norm.\n- normalize((Matrix3c)arg1) → None[STATIC]\u00c2\u00b6\n- Normalize this object in-place.\n- normalized((Matrix3c)arg1) → Matrix3c[STATIC]\u00c2\u00b6\n- Return normalized copy of this object\n- prod((Matrix3c)arg1) → complex[STATIC]\u00c2\u00b6\n- Product of all elements.\n- pruned((Matrix3c)arg1[, (float)absTol=1e-06]) → Matrix3c\n- [STATIC]\u00c2\u00b6\n- Zero all elements which are greater than absTol. Negative zeros are\n- not pruned.\n- row((Matrix3c)arg1, (int)row) → Vector3c[STATIC]\u00c2\u00b6\n- Return row as vector.\n- rows((Matrix3c)arg1) → int[STATIC]\u00c2\u00b6\n- Number of rows.\n- squaredNorm((Matrix3c)arg1) → float[STATIC]\u00c2\u00b6\n- Square of the Euclidean norm.\n- sum((Matrix3c)arg1) → complex[STATIC]\u00c2\u00b6\n- Sum of all elements.\n- trace((Matrix3c)arg1) → complex[STATIC]\u00c2\u00b6\n- Return sum of diagonal elements.\n- transpose((Matrix3c)arg1) → Matrix3c[STATIC]\u00c2\u00b6\n- Return transposed matrix.\n- classminieigen.Matrix6\u00c2\u00b6\n- 6x6 float matrix. Constructed from 4 3x3 sub-matrices, from 6xVector6\n- (rows).\n- Supported operations (m is a Matrix6, f if a float/int, v is a Vector6):\n- -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,\n- m==m, m!=m.\n- Static attributes: Zero, Ones, Identity.\n- staticRandom() → Matrix6[STATIC]\u00c2\u00b6\n- Return an object where all elements are randomly set to values\n- between 0 and 1.\n- col((Matrix6)arg1, (int)col) → Vector6[STATIC]\u00c2\u00b6\n- Return column as vector.\n- cols((Matrix6)arg1) → int[STATIC]\u00c2\u00b6\n- Number of columns.\n- computeUnitaryPositive((Matrix6)arg1) → tuple[STATIC]\u00c2\u00b6\n- Compute polar decomposition (unitary matrix U and positive semi-\n- definite symmetric matrix P such that self=U*P).\n- determinant((Matrix6)arg1) → float[STATIC]\u00c2\u00b6\n- Return matrix determinant.\n- diagonal((Matrix6)arg1) → Vector6[STATIC]\u00c2\u00b6\n- Return diagonal as vector.\n- inverse((Matrix6)arg1) → Matrix6[STATIC]\u00c2\u00b6\n- Return inverted matrix.\n- isApprox((Matrix6)arg1, (Matrix6)other[, (float)prec=1e-12]) →\n- bool[STATIC]\u00c2\u00b6\n- Approximate comparison with precision prec.\n- jacobiSVD((Matrix6)arg1) → tuple[STATIC]\u00c2\u00b6\n- Compute SVD decomposition of square matrix, retuns (U,S,V) such\n- that self=U*S*V.transpose()\n- ll((Matrix6)arg1) → Matrix3[STATIC]\u00c2\u00b6\n- Return lower-left 3x3 block\n- lr((Matrix6)arg1) → Matrix3[STATIC]\u00c2\u00b6\n- Return lower-right 3x3 block\n- maxAbsCoeff((Matrix6)arg1) → float[STATIC]\u00c2\u00b6\n- Maximum absolute value over all elements.\n- maxCoeff((Matrix6)arg1) → float[STATIC]\u00c2\u00b6\n- Maximum value over all elements.\n- mean((Matrix6)arg1) → float[STATIC]\u00c2\u00b6\n- Mean value over all elements.\n- minCoeff((Matrix6)arg1) → float[STATIC]\u00c2\u00b6\n- Minimum value over all elements.\n- norm((Matrix6)arg1) → float[STATIC]\u00c2\u00b6\n- Euclidean norm.\n- normalize((Matrix6)arg1) → None[STATIC]\u00c2\u00b6\n- Normalize this object in-place.\n- normalized((Matrix6)arg1) → Matrix6[STATIC]\u00c2\u00b6\n- Return normalized copy of this object\n- polarDecomposition((Matrix6)arg1) → tuple[STATIC]\u00c2\u00b6\n- Alias for computeUnitaryPositive.\n- prod((Matrix6)arg1) → float[STATIC]\u00c2\u00b6\n- Product of all elements.\n- pruned((Matrix6)arg1[, (float)absTol=1e-06]) → Matrix6[STATIC]\u00c2\u00b6\n- Zero all elements which are greater than absTol. Negative zeros are\n- not pruned.\n- row((Matrix6)arg1, (int)row) → Vector6[STATIC]\u00c2\u00b6\n- Return row as vector.\n- rows((Matrix6)arg1) → int[STATIC]\u00c2\u00b6\n- Number of rows.\n- selfAdjointEigenDecomposition((Matrix6)arg1) → tuple[STATIC]\u00c2\u00b6\n- Compute eigen (spectral) decomposition of symmetric matrix, returns\n- (eigVecs,eigVals). eigVecs is orthogonal Matrix3 with columns ar\n- normalized eigenvectors, eigVals is Vector3 with corresponding\n- eigenvalues. self=eigVecs*diag(eigVals)*eigVecs.transpose().\n- spectralDecomposition((Matrix6)arg1) → tuple[STATIC]\u00c2\u00b6\n- Alias for selfAdjointEigenDecomposition.\n- squaredNorm((Matrix6)arg1) → float[STATIC]\u00c2\u00b6\n- Square of the Euclidean norm.\n- sum((Matrix6)arg1) → float[STATIC]\u00c2\u00b6\n- Sum of all elements.\n- svd((Matrix6)arg1) → tuple[STATIC]\u00c2\u00b6\n- Alias for jacobiSVD.\n- trace((Matrix6)arg1) → float[STATIC]\u00c2\u00b6\n- Return sum of diagonal elements.\n- transpose((Matrix6)arg1) → Matrix6[STATIC]\u00c2\u00b6\n- Return transposed matrix.\n- ul((Matrix6)arg1) → Matrix3[STATIC]\u00c2\u00b6\n- Return upper-left 3x3 block\n- ur((Matrix6)arg1) → Matrix3[STATIC]\u00c2\u00b6\n- Return upper-right 3x3 block\n- classminieigen.Matrix6c\u00c2\u00b6\n- /TODO/\n- staticRandom() → Matrix6c[STATIC]\u00c2\u00b6\n- Return an object where all elements are randomly set to values\n- between 0 and 1.\n- col((Matrix6c)arg1, (int)col) → Vector6c[STATIC]\u00c2\u00b6\n- Return column as vector.\n- cols((Matrix6c)arg1) → int[STATIC]\u00c2\u00b6\n- Number of columns.\n- determinant((Matrix6c)arg1) → complex[STATIC]\u00c2\u00b6\n- Return matrix determinant.\n- diagonal((Matrix6c)arg1) → Vector6c[STATIC]\u00c2\u00b6\n- Return diagonal as vector.\n- inverse((Matrix6c)arg1) → Matrix6c[STATIC]\u00c2\u00b6\n- Return inverted matrix.\n- isApprox((Matrix6c)arg1, (Matrix6c)other[, (float)prec=1e-12]) →\n- bool[STATIC]\u00c2\u00b6\n- Approximate comparison with precision prec.\n- ll((Matrix6c)arg1) → Matrix3c[STATIC]\u00c2\u00b6\n- Return lower-left 3x3 block\n- lr((Matrix6c)arg1) → Matrix3c[STATIC]\u00c2\u00b6\n- Return lower-right 3x3 block\n- maxAbsCoeff((Matrix6c)arg1) → float[STATIC]\u00c2\u00b6\n- Maximum absolute value over all elements.\n- mean((Matrix6c)arg1) → complex[STATIC]\u00c2\u00b6\n- Mean value over all elements.\n- norm((Matrix6c)arg1) → float[STATIC]\u00c2\u00b6\n- Euclidean norm.\n- normalize((Matrix6c)arg1) → None[STATIC]\u00c2\u00b6\n- Normalize this object in-place.\n- normalized((Matrix6c)arg1) → Matrix6c[STATIC]\u00c2\u00b6\n- Return normalized copy of this object\n- prod((Matrix6c)arg1) → complex[STATIC]\u00c2\u00b6\n- Product of all elements.\n- pruned((Matrix6c)arg1[, (float)absTol=1e-06]) → Matrix6c\n- [STATIC]\u00c2\u00b6\n- Zero all elements which are greater than absTol. Negative zeros are\n- not pruned.\n- row((Matrix6c)arg1, (int)row) → Vector6c[STATIC]\u00c2\u00b6\n- Return row as vector.\n- rows((Matrix6c)arg1) → int[STATIC]\u00c2\u00b6\n- Number of rows.\n- squaredNorm((Matrix6c)arg1) → float[STATIC]\u00c2\u00b6\n- Square of the Euclidean norm.\n- sum((Matrix6c)arg1) → complex[STATIC]\u00c2\u00b6\n- Sum of all elements.\n- trace((Matrix6c)arg1) → complex[STATIC]\u00c2\u00b6\n- Return sum of diagonal elements.\n- transpose((Matrix6c)arg1) → Matrix6c[STATIC]\u00c2\u00b6\n- Return transposed matrix.\n- ul((Matrix6c)arg1) → Matrix3c[STATIC]\u00c2\u00b6\n- Return upper-left 3x3 block\n- ur((Matrix6c)arg1) → Matrix3c[STATIC]\u00c2\u00b6\n- Return upper-right 3x3 block\n- classminieigen.MatrixX\u00c2\u00b6\n- XxX (dynamic-sized) float matrix. Constructed from list of rows (as\n- VectorX).\n- Supported operations (m is a MatrixX, f if a float/int, v is a VectorX):\n- -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,\n- m==m, m!=m.\n- staticIdentity((int)arg1, (int)rank) → MatrixX[STATIC]\u00c2\u00b6\n- Create identity matrix with given rank (square).\n- staticOnes((int)rows, (int)cols) → MatrixX[STATIC]\u00c2\u00b6\n- Create matrix of given dimensions where all elements are set to 1.\n- staticRandom((int)rows, (int)cols) → MatrixX[STATIC]\u00c2\u00b6\n- Create matrix with given dimensions where all elements are set to\n- number between 0 and 1 (uniformly-distributed).\n- staticZero((int)rows, (int)cols) → MatrixX[STATIC]\u00c2\u00b6\n- Create zero matrix of given dimensions\n- col((MatrixX)arg1, (int)col) → VectorX[STATIC]\u00c2\u00b6\n- Return column as vector.\n- cols((MatrixX)arg1) → int[STATIC]\u00c2\u00b6\n- Number of columns.\n- computeUnitaryPositive((MatrixX)arg1) → tuple[STATIC]\u00c2\u00b6\n- Compute polar decomposition (unitary matrix U and positive semi-\n- definite symmetric matrix P such that self=U*P).\n- determinant((MatrixX)arg1) → float[STATIC]\u00c2\u00b6\n- Return matrix determinant.\n- diagonal((MatrixX)arg1) → VectorX[STATIC]\u00c2\u00b6\n- Return diagonal as vector.\n- inverse((MatrixX)arg1) → MatrixX[STATIC]\u00c2\u00b6\n- Return inverted matrix.\n- isApprox((MatrixX)arg1, (MatrixX)other[, (float)prec=1e-12]) →\n- bool[STATIC]\u00c2\u00b6\n- Approximate comparison with precision prec.\n- jacobiSVD((MatrixX)arg1) → tuple[STATIC]\u00c2\u00b6\n- Compute SVD decomposition of square matrix, retuns (U,S,V) such\n- that self=U*S*V.transpose()\n- maxAbsCoeff((MatrixX)arg1) → float[STATIC]\u00c2\u00b6\n- Maximum absolute value over all elements.\n- maxCoeff((MatrixX)arg1) → float[STATIC]\u00c2\u00b6\n- Maximum value over all elements.\n- mean((MatrixX)arg1) → float[STATIC]\u00c2\u00b6\n- Mean value over all elements.\n- minCoeff((MatrixX)arg1) → float[STATIC]\u00c2\u00b6\n- Minimum value over all elements.\n- norm((MatrixX)arg1) → float[STATIC]\u00c2\u00b6\n- Euclidean norm.\n- normalize((MatrixX)arg1) → None[STATIC]\u00c2\u00b6\n- Normalize this object in-place.\n- normalized((MatrixX)arg1) → MatrixX[STATIC]\u00c2\u00b6\n- Return normalized copy of this object\n- polarDecomposition((MatrixX)arg1) → tuple[STATIC]\u00c2\u00b6\n- Alias for computeUnitaryPositive.\n- prod((MatrixX)arg1) → float[STATIC]\u00c2\u00b6\n- Product of all elements.\n- pruned((MatrixX)arg1[, (float)absTol=1e-06]) → MatrixX[STATIC]\u00c2\u00b6\n- Zero all elements which are greater than absTol. Negative zeros are\n- not pruned.\n- resize((MatrixX)arg1, (int)rows, (int)cols) → None[STATIC]\u00c2\u00b6\n- Change size of the matrix, keep values of elements which exist in\n- the new matrix\n- row((MatrixX)arg1, (int)row) → VectorX[STATIC]\u00c2\u00b6\n- Return row as vector.\n- rows((MatrixX)arg1) → int[STATIC]\u00c2\u00b6\n- Number of rows.\n- selfAdjointEigenDecomposition((MatrixX)arg1) → tuple[STATIC]\u00c2\u00b6\n- Compute eigen (spectral) decomposition of symmetric matrix, returns\n- (eigVecs,eigVals). eigVecs is orthogonal Matrix3 with columns ar\n- normalized eigenvectors, eigVals is Vector3 with corresponding\n- eigenvalues. self=eigVecs*diag(eigVals)*eigVecs.transpose().\n- spectralDecomposition((MatrixX)arg1) → tuple[STATIC]\u00c2\u00b6\n- Alias for selfAdjointEigenDecomposition.\n- squaredNorm((MatrixX)arg1) → float[STATIC]\u00c2\u00b6\n- Square of the Euclidean norm.\n- sum((MatrixX)arg1) → float[STATIC]\u00c2\u00b6\n- Sum of all elements.\n- svd((MatrixX)arg1) → tuple[STATIC]\u00c2\u00b6\n- Alias for jacobiSVD.\n- trace((MatrixX)arg1) → float[STATIC]\u00c2\u00b6\n- Return sum of diagonal elements.\n- transpose((MatrixX)arg1) → MatrixX[STATIC]\u00c2\u00b6\n- Return transposed matrix.\n- classminieigen.MatrixXc\u00c2\u00b6\n- /TODO/\n- staticIdentity((int)arg1, (int)rank) → MatrixXc[STATIC]\u00c2\u00b6\n- Create identity matrix with given rank (square).\n- staticOnes((int)rows, (int)cols) → MatrixXc[STATIC]\u00c2\u00b6\n- Create matrix of given dimensions where all elements are set to 1.\n- staticRandom((int)rows, (int)cols) → MatrixXc[STATIC]\u00c2\u00b6\n- Create matrix with given dimensions where all elements are set to\n- number between 0 and 1 (uniformly-distributed).\n- staticZero((int)rows, (int)cols) → MatrixXc[STATIC]\u00c2\u00b6\n- Create zero matrix of given dimensions\n- col((MatrixXc)arg1, (int)col) → VectorXc[STATIC]\u00c2\u00b6\n- Return column as vector.\n- cols((MatrixXc)arg1) → int[STATIC]\u00c2\u00b6\n- Number of columns.\n- determinant((MatrixXc)arg1) → complex[STATIC]\u00c2\u00b6\n- Return matrix determinant.\n- diagonal((MatrixXc)arg1) → VectorXc[STATIC]\u00c2\u00b6\n- Return diagonal as vector.\n- inverse((MatrixXc)arg1) → MatrixXc[STATIC]\u00c2\u00b6\n- Return inverted matrix.\n- isApprox((MatrixXc)arg1, (MatrixXc)other[, (float)prec=1e-12]) →\n- bool[STATIC]\u00c2\u00b6\n- Approximate comparison with precision prec.\n- maxAbsCoeff((MatrixXc)arg1) → float[STATIC]\u00c2\u00b6\n- Maximum absolute value over all elements.\n- mean((MatrixXc)arg1) → complex[STATIC]\u00c2\u00b6\n- Mean value over all elements.\n- norm((MatrixXc)arg1) → float[STATIC]\u00c2\u00b6\n- Euclidean norm.\n- normalize((MatrixXc)arg1) → None[STATIC]\u00c2\u00b6\n- Normalize this object in-place.\n- normalized((MatrixXc)arg1) → MatrixXc[STATIC]\u00c2\u00b6\n- Return normalized copy of this object\n- prod((MatrixXc)arg1) → complex[STATIC]\u00c2\u00b6\n- Product of all elements.\n- pruned((MatrixXc)arg1[, (float)absTol=1e-06]) → MatrixXc\n- [STATIC]\u00c2\u00b6\n- Zero all elements which are greater than absTol. Negative zeros are\n- not pruned.\n- resize((MatrixXc)arg1, (int)rows, (int)cols) → None[STATIC]\u00c2\u00b6\n- Change size of the matrix, keep values of elements which exist in\n- the new matrix\n- row((MatrixXc)arg1, (int)row) → VectorXc[STATIC]\u00c2\u00b6\n- Return row as vector.\n- rows((MatrixXc)arg1) → int[STATIC]\u00c2\u00b6\n- Number of rows.\n- squaredNorm((MatrixXc)arg1) → float[STATIC]\u00c2\u00b6\n- Square of the Euclidean norm.\n- sum((MatrixXc)arg1) → complex[STATIC]\u00c2\u00b6\n- Sum of all elements.\n- trace((MatrixXc)arg1) → complex[STATIC]\u00c2\u00b6\n- Return sum of diagonal elements.\n- transpose((MatrixXc)arg1) → MatrixXc[STATIC]\u00c2\u00b6\n- Return transposed matrix.\n- classminieigen.Quaternion\u00c2\u00b6\n- Quaternion representing rotation.\n- Supported operations (q is a Quaternion, v is a Vector3): q*q (rotation\n- composition), q*=q, q*v (rotating v by q), q==q, q!=q.\n- Static attributes: Identity.\n- Identity= Quaternion((1,0,0),0)\u00c2\u00b6\n- Rotate((Quaternion)arg1, (Vector3)v) → Vector3[STATIC]\u00c2\u00b6\n- angularDistance((Quaternion)arg1, (Quaternion)arg2) → float\n- [STATIC]\u00c2\u00b6\n- conjugate((Quaternion)arg1) → Quaternion[STATIC]\u00c2\u00b6\n- inverse((Quaternion)arg1) → Quaternion[STATIC]\u00c2\u00b6\n- norm((Quaternion)arg1) → float[STATIC]\u00c2\u00b6\n- normalize((Quaternion)arg1) → None[STATIC]\u00c2\u00b6\n- normalized((Quaternion)arg1) → Quaternion[STATIC]\u00c2\u00b6\n- setFromTwoVectors((Quaternion)arg1, (Vector3)u, (Vector3)v) →\n- None[STATIC]\u00c2\u00b6\n- slerp((Quaternion)arg1, (float)t, (Quaternion)other) →\n- Quaternion[STATIC]\u00c2\u00b6\n- toAngleAxis((Quaternion)arg1) → tuple[STATIC]\u00c2\u00b6\n- toAxisAngle((Quaternion)arg1) → tuple[STATIC]\u00c2\u00b6\n- toRotationMatrix((Quaternion)arg1) → Matrix3[STATIC]\u00c2\u00b6\n- toRotationVector((Quaternion)arg1) → Vector3[STATIC]\u00c2\u00b6\n- classminieigen.Vector2\u00c2\u00b6\n- 3-dimensional float vector.\n- Supported operations (f if a float/int, v is a Vector3): -v, v+v, v+=v,\n- v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v.\n- Implicit conversion from sequence (list, tuple, \u00e2\u0080\u00a6) of 2 floats.\n- Static attributes: Zero, Ones, UnitX, UnitY.\n- Identity= Vector2(1,0)\u00c2\u00b6\n- Ones= Vector2(1,1)\u00c2\u00b6\n- staticRandom() → Vector2[STATIC]\u00c2\u00b6\n- Return an object where all elements are randomly set to values\n- between 0 and 1.\n- staticUnit((int)arg1) → Vector2[STATIC]\u00c2\u00b6\n- UnitX= Vector2(1,0)\u00c2\u00b6\n- UnitY= Vector2(0,1)\u00c2\u00b6\n- Zero= Vector2(0,0)\u00c2\u00b6\n- asDiagonal((Vector2)arg1) → object[STATIC]\u00c2\u00b6\n- Return diagonal matrix with this vector on the diagonal.\n- cols((Vector2)arg1) → int[STATIC]\u00c2\u00b6\n- Number of columns.\n- dot((Vector2)arg1, (Vector2)other) → float[STATIC]\u00c2\u00b6\n- Dot product with other.\n- isApprox((Vector2)arg1, (Vector2)other[, (float)prec=1e-12]) →\n- bool[STATIC]\u00c2\u00b6\n- Approximate comparison with precision prec.\n- maxAbsCoeff((Vector2)arg1) → float[STATIC]\u00c2\u00b6\n- Maximum absolute value over all elements.\n- maxCoeff((Vector2)arg1) → float[STATIC]\u00c2\u00b6\n- Maximum value over all elements.\n- mean((Vector2)arg1) → float[STATIC]\u00c2\u00b6\n- Mean value over all elements.\n- minCoeff((Vector2)arg1) → float[STATIC]\u00c2\u00b6\n- Minimum value over all elements.\n- norm((Vector2)arg1) → float[STATIC]\u00c2\u00b6\n- Euclidean norm.\n- normalize((Vector2)arg1) → None[STATIC]\u00c2\u00b6\n- Normalize this object in-place.\n- normalized((Vector2)arg1) → Vector2[STATIC]\u00c2\u00b6\n- Return normalized copy of this object\n- outer((Vector2)arg1, (Vector2)other) → object[STATIC]\u00c2\u00b6\n- Outer product with other.\n- prod((Vector2)arg1) → float[STATIC]\u00c2\u00b6\n- Product of all elements.\n- pruned((Vector2)arg1[, (float)absTol=1e-06]) → Vector2[STATIC]\u00c2\u00b6\n- Zero all elements which are greater than absTol. Negative zeros are\n- not pruned.\n- rows((Vector2)arg1) → int[STATIC]\u00c2\u00b6\n- Number of rows.\n- squaredNorm((Vector2)arg1) → float[STATIC]\u00c2\u00b6\n- Square of the Euclidean norm.\n- sum((Vector2)arg1) → float[STATIC]\u00c2\u00b6\n- Sum of all elements.\n- classminieigen.Vector2c\u00c2\u00b6\n- /TODO/\n- Identity= Vector2c(1,0)\u00c2\u00b6\n- Ones= Vector2c(1,1)\u00c2\u00b6\n- staticRandom() → Vector2c[STATIC]\u00c2\u00b6\n- Return an object where all elements are randomly set to values\n- between 0 and 1.\n- staticUnit((int)arg1) → Vector2c[STATIC]\u00c2\u00b6\n- UnitX= Vector2c(1,0)\u00c2\u00b6\n- UnitY= Vector2c(0,1)\u00c2\u00b6\n- Zero= Vector2c(0,0)\u00c2\u00b6\n- asDiagonal((Vector2c)arg1) → object[STATIC]\u00c2\u00b6\n- Return diagonal matrix with this vector on the diagonal.\n- cols((Vector2c)arg1) → int[STATIC]\u00c2\u00b6\n- Number of columns.\n- dot((Vector2c)arg1, (Vector2c)other) → complex[STATIC]\u00c2\u00b6\n- Dot product with other.\n- isApprox((Vector2c)arg1, (Vector2c)other[, (float)prec=1e-12]) →\n- bool[STATIC]\u00c2\u00b6\n- Approximate comparison with precision prec.\n- maxAbsCoeff((Vector2c)arg1) → float[STATIC]\u00c2\u00b6\n- Maximum absolute value over all elements.\n- mean((Vector2c)arg1) → complex[STATIC]\u00c2\u00b6\n- Mean value over all elements.\n- norm((Vector2c)arg1) → float[STATIC]\u00c2\u00b6\n- Euclidean norm.\n- normalize((Vector2c)arg1) → None[STATIC]\u00c2\u00b6\n- Normalize this object in-place.\n- normalized((Vector2c)arg1) → Vector2c[STATIC]\u00c2\u00b6\n- Return normalized copy of this object\n- outer((Vector2c)arg1, (Vector2c)other) → object[STATIC]\u00c2\u00b6\n- Outer product with other.\n- prod((Vector2c)arg1) → complex[STATIC]\u00c2\u00b6\n- Product of all elements.\n- pruned((Vector2c)arg1[, (float)absTol=1e-06]) → Vector2c\n- [STATIC]\u00c2\u00b6\n- Zero all elements which are greater than absTol. Negative zeros are\n- not pruned.\n- rows((Vector2c)arg1) → int[STATIC]\u00c2\u00b6\n- Number of rows.\n- squaredNorm((Vector2c)arg1) → float[STATIC]\u00c2\u00b6\n- Square of the Euclidean norm.\n- sum((Vector2c)arg1) → complex[STATIC]\u00c2\u00b6\n- Sum of all elements.\n- classminieigen.Vector2i\u00c2\u00b6\n- 2-dimensional integer vector.\n- Supported operations (i if an int, v is a Vector2i): -v, v+v, v+=v, v-v,\n- v-=v, v*i, i*v, v*=i, v==v, v!=v.\n- Implicit conversion from sequence (list, tuple, \u00e2\u0080\u00a6) of 2 integers.\n- Static attributes: Zero, Ones, UnitX, UnitY.\n- Identity= Vector2i(1,0)\u00c2\u00b6\n- Ones= Vector2i(1,1)\u00c2\u00b6\n- staticRandom() → Vector2i[STATIC]\u00c2\u00b6\n- Return an object where all elements are randomly set to values\n- between 0 and 1.\n- staticUnit((int)arg1) → Vector2i[STATIC]\u00c2\u00b6\n- UnitX= Vector2i(1,0)\u00c2\u00b6\n- UnitY= Vector2i(0,1)\u00c2\u00b6\n- Zero= Vector2i(0,0)\u00c2\u00b6\n- asDiagonal((Vector2i)arg1) → object[STATIC]\u00c2\u00b6\n- Return diagonal matrix with this vector on the diagonal.\n- cols((Vector2i)arg1) → int[STATIC]\u00c2\u00b6\n- Number of columns.\n- dot((Vector2i)arg1, (Vector2i)other) → int[STATIC]\u00c2\u00b6\n- Dot product with other.\n- isApprox((Vector2i)arg1, (Vector2i)other[, (int)prec=0]) → bool\n- [STATIC]\u00c2\u00b6\n- Approximate comparison with precision prec.\n- maxAbsCoeff((Vector2i)arg1) → int[STATIC]\u00c2\u00b6\n- Maximum absolute value over all elements.\n- maxCoeff((Vector2i)arg1) → int[STATIC]\u00c2\u00b6\n- Maximum value over all elements.\n- mean((Vector2i)arg1) → int[STATIC]\u00c2\u00b6\n- Mean value over all elements.\n- minCoeff((Vector2i)arg1) → int[STATIC]\u00c2\u00b6\n- Minimum value over all elements.\n- outer((Vector2i)arg1, (Vector2i)other) → object[STATIC]\u00c2\u00b6\n- Outer product with other.\n- prod((Vector2i)arg1) → int[STATIC]\u00c2\u00b6\n- Product of all elements.\n- rows((Vector2i)arg1) → int[STATIC]\u00c2\u00b6\n- Number of rows.\n- sum((Vector2i)arg1) → int[STATIC]\u00c2\u00b6\n- Sum of all elements.\n- classminieigen.Vector3\u00c2\u00b6\n- 3-dimensional float vector.\n- Supported operations (f if a float/int, v is a Vector3): -v, v+v, v+=v,\n- v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v, plus operations with\n- Matrix3 and Quaternion.\n- Implicit conversion from sequence (list, tuple, \u00e2\u0080\u00a6) of 3 floats.\n- Static attributes: Zero, Ones, UnitX, UnitY, UnitZ.\n- Identity= Vector3(1,0,0)\u00c2\u00b6\n- Ones= Vector3(1,1,1)\u00c2\u00b6\n- staticRandom() → Vector3[STATIC]\u00c2\u00b6\n- Return an object where all elements are randomly set to values\n- between 0 and 1.\n- staticUnit((int)arg1) → Vector3[STATIC]\u00c2\u00b6\n- UnitX= Vector3(1,0,0)\u00c2\u00b6\n- UnitY= Vector3(0,1,0)\u00c2\u00b6\n- UnitZ= Vector3(0,0,1)\u00c2\u00b6\n- Zero= Vector3(0,0,0)\u00c2\u00b6\n- asDiagonal((Vector3)arg1) → Matrix3[STATIC]\u00c2\u00b6\n- Return diagonal matrix with this vector on the diagonal.\n- cols((Vector3)arg1) → int[STATIC]\u00c2\u00b6\n- Number of columns.\n- cross((Vector3)arg1, (Vector3)arg2) → Vector3[STATIC]\u00c2\u00b6\n- dot((Vector3)arg1, (Vector3)other) → float[STATIC]\u00c2\u00b6\n- Dot product with other.\n- isApprox((Vector3)arg1, (Vector3)other[, (float)prec=1e-12]) →\n- bool[STATIC]\u00c2\u00b6\n- Approximate comparison with precision prec.\n- maxAbsCoeff((Vector3)arg1) → float[STATIC]\u00c2\u00b6\n- Maximum absolute value over all elements.\n- maxCoeff((Vector3)arg1) → float[STATIC]\u00c2\u00b6\n- Maximum value over all elements.\n- mean((Vector3)arg1) → float[STATIC]\u00c2\u00b6\n- Mean value over all elements.\n- minCoeff((Vector3)arg1) → float[STATIC]\u00c2\u00b6\n- Minimum value over all elements.\n- norm((Vector3)arg1) → float[STATIC]\u00c2\u00b6\n- Euclidean norm.\n- normalize((Vector3)arg1) → None[STATIC]\u00c2\u00b6\n- Normalize this object in-place.\n- normalized((Vector3)arg1) → Vector3[STATIC]\u00c2\u00b6\n- Return normalized copy of this object\n- outer((Vector3)arg1, (Vector3)other) → Matrix3[STATIC]\u00c2\u00b6\n- Outer product with other.\n- prod((Vector3)arg1) → float[STATIC]\u00c2\u00b6\n- Product of all elements.\n- pruned((Vector3)arg1[, (float)absTol=1e-06]) → Vector3[STATIC]\u00c2\u00b6\n- Zero all elements which are greater than absTol. Negative zeros are\n- not pruned.\n- rows((Vector3)arg1) → int[STATIC]\u00c2\u00b6\n- Number of rows.\n- squaredNorm((Vector3)arg1) → float[STATIC]\u00c2\u00b6\n- Square of the Euclidean norm.\n- sum((Vector3)arg1) → float[STATIC]\u00c2\u00b6\n- Sum of all elements.\n- xy((Vector3)arg1) → Vector2[STATIC]\u00c2\u00b6\n- xz((Vector3)arg1) → Vector2[STATIC]\u00c2\u00b6\n- yx((Vector3)arg1) → Vector2[STATIC]\u00c2\u00b6\n- yz((Vector3)arg1) → Vector2[STATIC]\u00c2\u00b6\n- zx((Vector3)arg1) → Vector2[STATIC]\u00c2\u00b6\n- zy((Vector3)arg1) → Vector2[STATIC]\u00c2\u00b6\n- classminieigen.Vector3c\u00c2\u00b6\n- /TODO/\n- Identity= Vector3c(1,0,0)\u00c2\u00b6\n- Ones= Vector3c(1,1,1)\u00c2\u00b6\n- staticRandom() → Vector3c[STATIC]\u00c2\u00b6\n- Return an object where all elements are randomly set to values\n- between 0 and 1.\n- staticUnit((int)arg1) → Vector3c[STATIC]\u00c2\u00b6\n- UnitX= Vector3c(1,0,0)\u00c2\u00b6\n- UnitY= Vector3c(0,1,0)\u00c2\u00b6\n- UnitZ= Vector3c(0,0,1)\u00c2\u00b6\n- Zero= Vector3c(0,0,0)\u00c2\u00b6\n- asDiagonal((Vector3c)arg1) → Matrix3c[STATIC]\u00c2\u00b6\n- Return diagonal matrix with this vector on the diagonal.\n- cols((Vector3c)arg1) → int[STATIC]\u00c2\u00b6\n- Number of columns.\n- cross((Vector3c)arg1, (Vector3c)arg2) → Vector3c[STATIC]\u00c2\u00b6\n- dot((Vector3c)arg1, (Vector3c)other) → complex[STATIC]\u00c2\u00b6\n- Dot product with other.\n- isApprox((Vector3c)arg1, (Vector3c)other[, (float)prec=1e-12]) →\n- bool[STATIC]\u00c2\u00b6\n- Approximate comparison with precision prec.\n- maxAbsCoeff((Vector3c)arg1) → float[STATIC]\u00c2\u00b6\n- Maximum absolute value over all elements.\n- mean((Vector3c)arg1) → complex[STATIC]\u00c2\u00b6\n- Mean value over all elements.\n- norm((Vector3c)arg1) → float[STATIC]\u00c2\u00b6\n- Euclidean norm.\n- normalize((Vector3c)arg1) → None[STATIC]\u00c2\u00b6\n- Normalize this object in-place.\n- normalized((Vector3c)arg1) → Vector3c[STATIC]\u00c2\u00b6\n- Return normalized copy of this object\n- outer((Vector3c)arg1, (Vector3c)other) → Matrix3c[STATIC]\u00c2\u00b6\n- Outer product with other.\n- prod((Vector3c)arg1) → complex[STATIC]\u00c2\u00b6\n- Product of all elements.\n- pruned((Vector3c)arg1[, (float)absTol=1e-06]) → Vector3c\n- [STATIC]\u00c2\u00b6\n- Zero all elements which are greater than absTol. Negative zeros are\n- not pruned.\n- rows((Vector3c)arg1) → int[STATIC]\u00c2\u00b6\n- Number of rows.\n- squaredNorm((Vector3c)arg1) → float[STATIC]\u00c2\u00b6\n- Square of the Euclidean norm.\n- sum((Vector3c)arg1) → complex[STATIC]\u00c2\u00b6\n- Sum of all elements.\n- xy((Vector3c)arg1) → Vector2c[STATIC]\u00c2\u00b6\n- xz((Vector3c)arg1) → Vector2c[STATIC]\u00c2\u00b6\n- yx((Vector3c)arg1) → Vector2c[STATIC]\u00c2\u00b6\n- yz((Vector3c)arg1) → Vector2c[STATIC]\u00c2\u00b6\n- zx((Vector3c)arg1) → Vector2c[STATIC]\u00c2\u00b6\n- zy((Vector3c)arg1) → Vector2c[STATIC]\u00c2\u00b6\n- classminieigen.Vector3i\u00c2\u00b6\n- 3-dimensional integer vector.\n- Supported operations (i if an int, v is a Vector3i): -v, v+v, v+=v, v-v,\n- v-=v, v*i, i*v, v*=i, v==v, v!=v.\n- Implicit conversion from sequence (list, tuple, \u00e2\u0080\u00a6) of 3 integers.\n- Static attributes: Zero, Ones, UnitX, UnitY, UnitZ.\n- Identity= Vector3i(1,0,0)\u00c2\u00b6\n- Ones= Vector3i(1,1,1)\u00c2\u00b6\n- staticRandom() → Vector3i[STATIC]\u00c2\u00b6\n- Return an object where all elements are randomly set to values\n- between 0 and 1.\n- staticUnit((int)arg1) → Vector3i[STATIC]\u00c2\u00b6\n- UnitX= Vector3i(1,0,0)\u00c2\u00b6\n- UnitY= Vector3i(0,1,0)\u00c2\u00b6\n- UnitZ= Vector3i(0,0,1)\u00c2\u00b6\n- Zero= Vector3i(0,0,0)\u00c2\u00b6\n- asDiagonal((Vector3i)arg1) → object[STATIC]\u00c2\u00b6\n- Return diagonal matrix with this vector on the diagonal.\n- cols((Vector3i)arg1) → int[STATIC]\u00c2\u00b6\n- Number of columns.\n- cross((Vector3i)arg1, (Vector3i)arg2) → Vector3i[STATIC]\u00c2\u00b6\n- dot((Vector3i)arg1, (Vector3i)other) → int[STATIC]\u00c2\u00b6\n- Dot product with other.\n- isApprox((Vector3i)arg1, (Vector3i)other[, (int)prec=0]) → bool\n- [STATIC]\u00c2\u00b6\n- Approximate comparison with precision prec.\n- maxAbsCoeff((Vector3i)arg1) → int[STATIC]\u00c2\u00b6\n- Maximum absolute value over all elements.\n- maxCoeff((Vector3i)arg1) → int[STATIC]\u00c2\u00b6\n- Maximum value over all elements.\n- mean((Vector3i)arg1) → int[STATIC]\u00c2\u00b6\n- Mean value over all elements.\n- minCoeff((Vector3i)arg1) → int[STATIC]\u00c2\u00b6\n- Minimum value over all elements.\n- outer((Vector3i)arg1, (Vector3i)other) → object[STATIC]\u00c2\u00b6\n- Outer product with other.\n- prod((Vector3i)arg1) → int[STATIC]\u00c2\u00b6\n- Product of all elements.\n- rows((Vector3i)arg1) → int[STATIC]\u00c2\u00b6\n- Number of rows.\n- sum((Vector3i)arg1) → int[STATIC]\u00c2\u00b6\n- Sum of all elements.\n- xy((Vector3i)arg1) → Vector2i[STATIC]\u00c2\u00b6\n- xz((Vector3i)arg1) → Vector2i[STATIC]\u00c2\u00b6\n- yx((Vector3i)arg1) → Vector2i[STATIC]\u00c2\u00b6\n- yz((Vector3i)arg1) → Vector2i[STATIC]\u00c2\u00b6\n- zx((Vector3i)arg1) → Vector2i[STATIC]\u00c2\u00b6\n- zy((Vector3i)arg1) → Vector2i[STATIC]\u00c2\u00b6\n- classminieigen.Vector4\u00c2\u00b6\n- 4-dimensional float vector.\n- Supported operations (f if a float/int, v is a Vector3): -v, v+v, v+=v,\n- v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v.\n- Implicit conversion from sequence (list, tuple, \u00e2\u0080\u00a6) of 4 floats.\n- Static attributes: Zero, Ones.\n- Identity= Vector4(1,0,0, 0)\u00c2\u00b6\n- Ones= Vector4(1,1,1, 1)\u00c2\u00b6\n- staticRandom() → Vector4[STATIC]\u00c2\u00b6\n- Return an object where all elements are randomly set to values\n- between 0 and 1.\n- staticUnit((int)arg1) → Vector4[STATIC]\u00c2\u00b6\n- Zero= Vector4(0,0,0, 0)\u00c2\u00b6\n- asDiagonal((Vector4)arg1) → object[STATIC]\u00c2\u00b6\n- Return diagonal matrix with this vector on the diagonal.\n- cols((Vector4)arg1) → int[STATIC]\u00c2\u00b6\n- Number of columns.\n- dot((Vector4)arg1, (Vector4)other) → float[STATIC]\u00c2\u00b6\n- Dot product with other.\n- isApprox((Vector4)arg1, (Vector4)other[, (float)prec=1e-12]) →\n- bool[STATIC]\u00c2\u00b6\n- Approximate comparison with precision prec.\n- maxAbsCoeff((Vector4)arg1) → float[STATIC]\u00c2\u00b6\n- Maximum absolute value over all elements.\n- maxCoeff((Vector4)arg1) → float[STATIC]\u00c2\u00b6\n- Maximum value over all elements.\n- mean((Vector4)arg1) → float[STATIC]\u00c2\u00b6\n- Mean value over all elements.\n- minCoeff((Vector4)arg1) → float[STATIC]\u00c2\u00b6\n- Minimum value over all elements.\n- norm((Vector4)arg1) → float[STATIC]\u00c2\u00b6\n- Euclidean norm.\n- normalize((Vector4)arg1) → None[STATIC]\u00c2\u00b6\n- Normalize this object in-place.\n- normalized((Vector4)arg1) → Vector4[STATIC]\u00c2\u00b6\n- Return normalized copy of this object\n- outer((Vector4)arg1, (Vector4)other) → object[STATIC]\u00c2\u00b6\n- Outer product with other.\n- prod((Vector4)arg1) → float[STATIC]\u00c2\u00b6\n- Product of all elements.\n- pruned((Vector4)arg1[, (float)absTol=1e-06]) → Vector4[STATIC]\u00c2\u00b6\n- Zero all elements which are greater than absTol. Negative zeros are\n- not pruned.\n- rows((Vector4)arg1) → int[STATIC]\u00c2\u00b6\n- Number of rows.\n- squaredNorm((Vector4)arg1) → float[STATIC]\u00c2\u00b6\n- Square of the Euclidean norm.\n- sum((Vector4)arg1) → float[STATIC]\u00c2\u00b6\n- Sum of all elements.\n- classminieigen.Vector6\u00c2\u00b6\n- 6-dimensional float vector.\n- Supported operations (f if a float/int, v is a Vector6): -v, v+v, v+=v,\n- v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v.\n- Implicit conversion from sequence (list, tuple, \u00e2\u0080\u00a6) of 6 floats.\n- Static attributes: Zero, Ones.\n- Identity= Vector6(1,0,0, 0,0,0)\u00c2\u00b6\n- Ones= Vector6(1,1,1, 1,1,1)\u00c2\u00b6\n- staticRandom() → Vector6[STATIC]\u00c2\u00b6\n- Return an object where all elements are randomly set to values\n- between 0 and 1.\n- staticUnit((int)arg1) → Vector6[STATIC]\u00c2\u00b6\n- Zero= Vector6(0,0,0, 0,0,0)\u00c2\u00b6\n- asDiagonal((Vector6)arg1) → Matrix6[STATIC]\u00c2\u00b6\n- Return diagonal matrix with this vector on the diagonal.\n- cols((Vector6)arg1) → int[STATIC]\u00c2\u00b6\n- Number of columns.\n- dot((Vector6)arg1, (Vector6)other) → float[STATIC]\u00c2\u00b6\n- Dot product with other.\n- head((Vector6)arg1) → Vector3[STATIC]\u00c2\u00b6\n- isApprox((Vector6)arg1, (Vector6)other[, (float)prec=1e-12]) →\n- bool[STATIC]\u00c2\u00b6\n- Approximate comparison with precision prec.\n- maxAbsCoeff((Vector6)arg1) → float[STATIC]\u00c2\u00b6\n- Maximum absolute value over all elements.\n- maxCoeff((Vector6)arg1) → float[STATIC]\u00c2\u00b6\n- Maximum value over all elements.\n- mean((Vector6)arg1) → float[STATIC]\u00c2\u00b6\n- Mean value over all elements.\n- minCoeff((Vector6)arg1) → float[STATIC]\u00c2\u00b6\n- Minimum value over all elements.\n- norm((Vector6)arg1) → float[STATIC]\u00c2\u00b6\n- Euclidean norm.\n- normalize((Vector6)arg1) → None[STATIC]\u00c2\u00b6\n- Normalize this object in-place.\n- normalized((Vector6)arg1) → Vector6[STATIC]\u00c2\u00b6\n- Return normalized copy of this object\n- outer((Vector6)arg1, (Vector6)other) → Matrix6[STATIC]\u00c2\u00b6\n- Outer product with other.\n- prod((Vector6)arg1) → float[STATIC]\u00c2\u00b6\n- Product of all elements.\n- pruned((Vector6)arg1[, (float)absTol=1e-06]) → Vector6[STATIC]\u00c2\u00b6\n- Zero all elements which are greater than absTol. Negative zeros are\n- not pruned.\n- rows((Vector6)arg1) → int[STATIC]\u00c2\u00b6\n- Number of rows.\n- squaredNorm((Vector6)arg1) → float[STATIC]\u00c2\u00b6\n- Square of the Euclidean norm.\n- sum((Vector6)arg1) → float[STATIC]\u00c2\u00b6\n- Sum of all elements.\n- tail((Vector6)arg1) → Vector3[STATIC]\u00c2\u00b6\n- classminieigen.Vector6c\u00c2\u00b6\n- /TODO/\n- Identity= Vector6c(1,0,0, 0,0,0)\u00c2\u00b6\n- Ones= Vector6c(1,1,1, 1,1,1)\u00c2\u00b6\n- staticRandom() → Vector6c[STATIC]\u00c2\u00b6\n- Return an object where all elements are randomly set to values\n- between 0 and 1.\n- staticUnit((int)arg1) → Vector6c[STATIC]\u00c2\u00b6\n- Zero= Vector6c(0,0,0, 0,0,0)\u00c2\u00b6\n- asDiagonal((Vector6c)arg1) → Matrix6c[STATIC]\u00c2\u00b6\n- Return diagonal matrix with this vector on the diagonal.\n- cols((Vector6c)arg1) → int[STATIC]\u00c2\u00b6\n- Number of columns.\n- dot((Vector6c)arg1, (Vector6c)other) → complex[STATIC]\u00c2\u00b6\n- Dot product with other.\n- head((Vector6c)arg1) → Vector3c[STATIC]\u00c2\u00b6\n- isApprox((Vector6c)arg1, (Vector6c)other[, (float)prec=1e-12]) →\n- bool[STATIC]\u00c2\u00b6\n- Approximate comparison with precision prec.\n- maxAbsCoeff((Vector6c)arg1) → float[STATIC]\u00c2\u00b6\n- Maximum absolute value over all elements.\n- mean((Vector6c)arg1) → complex[STATIC]\u00c2\u00b6\n- Mean value over all elements.\n- norm((Vector6c)arg1) → float[STATIC]\u00c2\u00b6\n- Euclidean norm.\n- normalize((Vector6c)arg1) → None[STATIC]\u00c2\u00b6\n- Normalize this object in-place.\n- normalized((Vector6c)arg1) → Vector6c[STATIC]\u00c2\u00b6\n- Return normalized copy of this object\n- outer((Vector6c)arg1, (Vector6c)other) → Matrix6c[STATIC]\u00c2\u00b6\n- Outer product with other.\n- prod((Vector6c)arg1) → complex[STATIC]\u00c2\u00b6\n- Product of all elements.\n- pruned((Vector6c)arg1[, (float)absTol=1e-06]) → Vector6c\n- [STATIC]\u00c2\u00b6\n- Zero all elements which are greater than absTol. Negative zeros are\n- not pruned.\n- rows((Vector6c)arg1) → int[STATIC]\u00c2\u00b6\n- Number of rows.\n- squaredNorm((Vector6c)arg1) → float[STATIC]\u00c2\u00b6\n- Square of the Euclidean norm.\n- sum((Vector6c)arg1) → complex[STATIC]\u00c2\u00b6\n- Sum of all elements.\n- tail((Vector6c)arg1) → Vector3c[STATIC]\u00c2\u00b6\n- classminieigen.Vector6i\u00c2\u00b6\n- 6-dimensional float vector.\n- Supported operations (f if a float/int, v is a Vector6): -v, v+v, v+=v,\n- v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v.\n- Implicit conversion from sequence (list, tuple, \u00e2\u0080\u00a6) of 6 floats.\n- Static attributes: Zero, Ones.\n- Identity= Vector6i(1,0,0, 0,0,0)\u00c2\u00b6\n- Ones= Vector6i(1,1,1, 1,1,1)\u00c2\u00b6\n- staticRandom() → Vector6i[STATIC]\u00c2\u00b6\n- Return an object where all elements are randomly set to values\n- between 0 and 1.\n- staticUnit((int)arg1) → Vector6i[STATIC]\u00c2\u00b6\n- Zero= Vector6i(0,0,0, 0,0,0)\u00c2\u00b6\n- asDiagonal((Vector6i)arg1) → object[STATIC]\u00c2\u00b6\n- Return diagonal matrix with this vector on the diagonal.\n- cols((Vector6i)arg1) → int[STATIC]\u00c2\u00b6\n- Number of columns.\n- dot((Vector6i)arg1, (Vector6i)other) → int[STATIC]\u00c2\u00b6\n- Dot product with other.\n- head((Vector6i)arg1) → Vector3i[STATIC]\u00c2\u00b6\n- isApprox((Vector6i)arg1, (Vector6i)other[, (int)prec=0]) → bool\n- [STATIC]\u00c2\u00b6\n- Approximate comparison with precision prec.\n- maxAbsCoeff((Vector6i)arg1) → int[STATIC]\u00c2\u00b6\n- Maximum absolute value over all elements.\n- maxCoeff((Vector6i)arg1) → int[STATIC]\u00c2\u00b6\n- Maximum value over all elements.\n- mean((Vector6i)arg1) → int[STATIC]\u00c2\u00b6\n- Mean value over all elements.\n- minCoeff((Vector6i)arg1) → int[STATIC]\u00c2\u00b6\n- Minimum value over all elements.\n- outer((Vector6i)arg1, (Vector6i)other) → object[STATIC]\u00c2\u00b6\n- Outer product with other.\n- prod((Vector6i)arg1) → int[STATIC]\u00c2\u00b6\n- Product of all elements.\n- rows((Vector6i)arg1) → int[STATIC]\u00c2\u00b6\n- Number of rows.\n- sum((Vector6i)arg1) → int[STATIC]\u00c2\u00b6\n- Sum of all elements.\n- tail((Vector6i)arg1) → Vector3i[STATIC]\u00c2\u00b6\n- classminieigen.VectorX\u00c2\u00b6\n- Dynamic-sized float vector.\n- Supported operations (f if a float/int, v is a VectorX): -v, v+v, v+=v,\n- v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v.\n- Implicit conversion from sequence (list, tuple, \u00e2\u0080\u00a6) of X floats.\n- staticOnes((int)arg1) → VectorX[STATIC]\u00c2\u00b6\n- staticRandom((int)len) → VectorX[STATIC]\u00c2\u00b6\n- Return vector of given length with all elements set to values\n- between 0 and 1 randomly.\n- staticUnit((int)arg1, (int)arg2) → VectorX[STATIC]\u00c2\u00b6\n- staticZero((int)arg1) → VectorX[STATIC]\u00c2\u00b6\n- asDiagonal((VectorX)arg1) → MatrixX[STATIC]\u00c2\u00b6\n- Return diagonal matrix with this vector on the diagonal.\n- cols((VectorX)arg1) → int[STATIC]\u00c2\u00b6\n- Number of columns.\n- dot((VectorX)arg1, (VectorX)other) → float[STATIC]\u00c2\u00b6\n- Dot product with other.\n- isApprox((VectorX)arg1, (VectorX)other[, (float)prec=1e-12]) →\n- bool[STATIC]\u00c2\u00b6\n- Approximate comparison with precision prec.\n- maxAbsCoeff((VectorX)arg1) → float[STATIC]\u00c2\u00b6\n- Maximum absolute value over all elements.\n- maxCoeff((VectorX)arg1) → float[STATIC]\u00c2\u00b6\n- Maximum value over all elements.\n- mean((VectorX)arg1) → float[STATIC]\u00c2\u00b6\n- Mean value over all elements.\n- minCoeff((VectorX)arg1) → float[STATIC]\u00c2\u00b6\n- Minimum value over all elements.\n- norm((VectorX)arg1) → float[STATIC]\u00c2\u00b6\n- Euclidean norm.\n- normalize((VectorX)arg1) → None[STATIC]\u00c2\u00b6\n- Normalize this object in-place.\n- normalized((VectorX)arg1) → VectorX[STATIC]\u00c2\u00b6\n- Return normalized copy of this object\n- outer((VectorX)arg1, (VectorX)other) → MatrixX[STATIC]\u00c2\u00b6\n- Outer product with other.\n- prod((VectorX)arg1) → float[STATIC]\u00c2\u00b6\n- Product of all elements.\n- pruned((VectorX)arg1[, (float)absTol=1e-06]) → VectorX[STATIC]\u00c2\u00b6\n- Zero all elements which are greater than absTol. Negative zeros are\n- not pruned.\n- resize((VectorX)arg1, (int)arg2) → None[STATIC]\u00c2\u00b6\n- rows((VectorX)arg1) → int[STATIC]\u00c2\u00b6\n- Number of rows.\n- squaredNorm((VectorX)arg1) → float[STATIC]\u00c2\u00b6\n- Square of the Euclidean norm.\n- sum((VectorX)arg1) → float[STATIC]\u00c2\u00b6\n- Sum of all elements.\n- classminieigen.VectorXc\u00c2\u00b6\n- /TODO/\n- staticOnes((int)arg1) → VectorXc[STATIC]\u00c2\u00b6\n- staticRandom((int)len) → VectorXc[STATIC]\u00c2\u00b6\n- Return vector of given length with all elements set to values\n- between 0 and 1 randomly.\n- staticUnit((int)arg1, (int)arg2) → VectorXc[STATIC]\u00c2\u00b6\n- staticZero((int)arg1) → VectorXc[STATIC]\u00c2\u00b6\n- asDiagonal((VectorXc)arg1) → MatrixXc[STATIC]\u00c2\u00b6\n- Return diagonal matrix with this vector on the diagonal.\n- cols((VectorXc)arg1) → int[STATIC]\u00c2\u00b6\n- Number of columns.\n- dot((VectorXc)arg1, (VectorXc)other) → complex[STATIC]\u00c2\u00b6\n- Dot product with other.\n- isApprox((VectorXc)arg1, (VectorXc)other[, (float)prec=1e-12]) →\n- bool[STATIC]\u00c2\u00b6\n- Approximate comparison with precision prec.\n- maxAbsCoeff((VectorXc)arg1) → float[STATIC]\u00c2\u00b6\n- Maximum absolute value over all elements.\n- mean((VectorXc)arg1) → complex[STATIC]\u00c2\u00b6\n- Mean value over all elements.\n- norm((VectorXc)arg1) → float[STATIC]\u00c2\u00b6\n- Euclidean norm.\n- normalize((VectorXc)arg1) → None[STATIC]\u00c2\u00b6\n- Normalize this object in-place.\n- normalized((VectorXc)arg1) → VectorXc[STATIC]\u00c2\u00b6\n- Return normalized copy of this object\n- outer((VectorXc)arg1, (VectorXc)other) → MatrixXc[STATIC]\u00c2\u00b6\n- Outer product with other.\n- prod((VectorXc)arg1) → complex[STATIC]\u00c2\u00b6\n- Product of all elements.\n- pruned((VectorXc)arg1[, (float)absTol=1e-06]) → VectorXc\n- [STATIC]\u00c2\u00b6\n- Zero all elements which are greater than absTol. Negative zeros are\n- not pruned.\n- resize((VectorXc)arg1, (int)arg2) → None[STATIC]\u00c2\u00b6\n- rows((VectorXc)arg1) → int[STATIC]\u00c2\u00b6\n- Number of rows.\n- squaredNorm((VectorXc)arg1) → float[STATIC]\u00c2\u00b6\n- Square of the Euclidean norm.\n- sum((VectorXc)arg1) → complex[STATIC]\u00c2\u00b6\n- Sum of all elements.\n- minieigen.float2str((float)f[, (int)pad=0]) → str\u00c2\u00b6\n- Return the shortest string representation of f which will is equal to f\n- when converted back to float. This function is only useful in Python\n- prior to 3.0; starting from that version, standard string conversion does\n- just that.\n \n **** Table_of_Contents ****\n * minieigen_documentation\n o Overview\n o Examples\n o Naming_conventions\n o Limitations\n o Links\n o Documentation\n- # AlignedBox2\n- # AlignedBox2.center()\n- # AlignedBox2.clamp()\n- # AlignedBox2.contains()\n- # AlignedBox2.empty()\n- # AlignedBox2.extend()\n- # AlignedBox2.intersection()\n- # AlignedBox2.max\n- # AlignedBox2.merged()\n- # AlignedBox2.min\n- # AlignedBox2.sizes()\n- # AlignedBox2.volume()\n- # AlignedBox3\n- # AlignedBox3.center()\n- # AlignedBox3.clamp()\n- # AlignedBox3.contains()\n- # AlignedBox3.empty()\n- # AlignedBox3.extend()\n- # AlignedBox3.intersection()\n- # AlignedBox3.max\n- # AlignedBox3.merged()\n- # AlignedBox3.min\n- # AlignedBox3.sizes()\n- # AlignedBox3.volume()\n- # Matrix3\n- # Matrix3.Identity\n- # Matrix3.Ones\n- # Matrix3.Random()\n- # Matrix3.Zero\n- # Matrix3.col()\n- # Matrix3.cols()\n- # Matrix3.computeUnitaryPositive()\n- # Matrix3.determinant()\n- # Matrix3.diagonal()\n- # Matrix3.inverse()\n- # Matrix3.isApprox()\n- # Matrix3.jacobiSVD()\n- # Matrix3.maxAbsCoeff()\n- # Matrix3.maxCoeff()\n- # Matrix3.mean()\n- # Matrix3.minCoeff()\n- # Matrix3.norm()\n- # Matrix3.normalize()\n- # Matrix3.normalized()\n- # Matrix3.polarDecomposition()\n- # Matrix3.prod()\n- # Matrix3.pruned()\n- # Matrix3.row()\n- # Matrix3.rows()\n- # Matrix3.selfAdjointEigenDecomposition()\n- # Matrix3.spectralDecomposition()\n- # Matrix3.squaredNorm()\n- # Matrix3.sum()\n- # Matrix3.svd()\n- # Matrix3.trace()\n- # Matrix3.transpose()\n- # Matrix3c\n- # Matrix3c.Identity\n- # Matrix3c.Ones\n- # Matrix3c.Random()\n- # Matrix3c.Zero\n- # Matrix3c.col()\n- # Matrix3c.cols()\n- # Matrix3c.determinant()\n- # Matrix3c.diagonal()\n- # Matrix3c.inverse()\n- # Matrix3c.isApprox()\n- # Matrix3c.maxAbsCoeff()\n- # Matrix3c.mean()\n- # Matrix3c.norm()\n- # Matrix3c.normalize()\n- # Matrix3c.normalized()\n- # Matrix3c.prod()\n- # Matrix3c.pruned()\n- # Matrix3c.row()\n- # Matrix3c.rows()\n- # Matrix3c.squaredNorm()\n- # Matrix3c.sum()\n- # Matrix3c.trace()\n- # Matrix3c.transpose()\n- # Matrix6\n- # Matrix6.Identity\n- # Matrix6.Ones\n- # Matrix6.Random()\n- # Matrix6.Zero\n- # Matrix6.col()\n- # Matrix6.cols()\n- # Matrix6.computeUnitaryPositive()\n- # Matrix6.determinant()\n- # Matrix6.diagonal()\n- # Matrix6.inverse()\n- # Matrix6.isApprox()\n- # Matrix6.jacobiSVD()\n- # Matrix6.ll()\n- # Matrix6.lr()\n- # Matrix6.maxAbsCoeff()\n- # Matrix6.maxCoeff()\n- # Matrix6.mean()\n- # Matrix6.minCoeff()\n- # Matrix6.norm()\n- # Matrix6.normalize()\n- # Matrix6.normalized()\n- # Matrix6.polarDecomposition()\n- # Matrix6.prod()\n- # Matrix6.pruned()\n- # Matrix6.row()\n- # Matrix6.rows()\n- # Matrix6.selfAdjointEigenDecomposition()\n- # Matrix6.spectralDecomposition()\n- # Matrix6.squaredNorm()\n- # Matrix6.sum()\n- # Matrix6.svd()\n- # Matrix6.trace()\n- # Matrix6.transpose()\n- # Matrix6.ul()\n- # Matrix6.ur()\n- # Matrix6c\n- # Matrix6c.Identity\n- # Matrix6c.Ones\n- # Matrix6c.Random()\n- # Matrix6c.Zero\n- # Matrix6c.col()\n- # Matrix6c.cols()\n- # Matrix6c.determinant()\n- # Matrix6c.diagonal()\n- # Matrix6c.inverse()\n- # Matrix6c.isApprox()\n- # Matrix6c.ll()\n- # Matrix6c.lr()\n- # Matrix6c.maxAbsCoeff()\n- # Matrix6c.mean()\n- # Matrix6c.norm()\n- # Matrix6c.normalize()\n- # Matrix6c.normalized()\n- # Matrix6c.prod()\n- # Matrix6c.pruned()\n- # Matrix6c.row()\n- # Matrix6c.rows()\n- # Matrix6c.squaredNorm()\n- # Matrix6c.sum()\n- # Matrix6c.trace()\n- # Matrix6c.transpose()\n- # Matrix6c.ul()\n- # Matrix6c.ur()\n- # MatrixX\n- # MatrixX.Identity()\n- # MatrixX.Ones()\n- # MatrixX.Random()\n- # MatrixX.Zero()\n- # MatrixX.col()\n- # MatrixX.cols()\n- # MatrixX.computeUnitaryPositive()\n- # MatrixX.determinant()\n- # MatrixX.diagonal()\n- # MatrixX.inverse()\n- # MatrixX.isApprox()\n- # MatrixX.jacobiSVD()\n- # MatrixX.maxAbsCoeff()\n- # MatrixX.maxCoeff()\n- # MatrixX.mean()\n- # MatrixX.minCoeff()\n- # MatrixX.norm()\n- # MatrixX.normalize()\n- # MatrixX.normalized()\n- # MatrixX.polarDecomposition()\n- # MatrixX.prod()\n- # MatrixX.pruned()\n- # MatrixX.resize()\n- # MatrixX.row()\n- # MatrixX.rows()\n- # MatrixX.selfAdjointEigenDecomposition()\n- # MatrixX.spectralDecomposition()\n- # MatrixX.squaredNorm()\n- # MatrixX.sum()\n- # MatrixX.svd()\n- # MatrixX.trace()\n- # MatrixX.transpose()\n- # MatrixXc\n- # MatrixXc.Identity()\n- # MatrixXc.Ones()\n- # MatrixXc.Random()\n- # MatrixXc.Zero()\n- # MatrixXc.col()\n- # MatrixXc.cols()\n- # MatrixXc.determinant()\n- # MatrixXc.diagonal()\n- # MatrixXc.inverse()\n- # MatrixXc.isApprox()\n- # MatrixXc.maxAbsCoeff()\n- # MatrixXc.mean()\n- # MatrixXc.norm()\n- # MatrixXc.normalize()\n- # MatrixXc.normalized()\n- # MatrixXc.prod()\n- # MatrixXc.pruned()\n- # MatrixXc.resize()\n- # MatrixXc.row()\n- # MatrixXc.rows()\n- # MatrixXc.squaredNorm()\n- # MatrixXc.sum()\n- # MatrixXc.trace()\n- # MatrixXc.transpose()\n- # Quaternion\n- # Quaternion.Identity\n- # Quaternion.Rotate()\n- # Quaternion.angularDistance()\n- # Quaternion.conjugate()\n- # Quaternion.inverse()\n- # Quaternion.norm()\n- # Quaternion.normalize()\n- # Quaternion.normalized()\n- # Quaternion.setFromTwoVectors()\n- # Quaternion.slerp()\n- # Quaternion.toAngleAxis()\n- # Quaternion.toAxisAngle()\n- # Quaternion.toRotationMatrix()\n- # Quaternion.toRotationVector()\n- # Vector2\n- # Vector2.Identity\n- # Vector2.Ones\n- # Vector2.Random()\n- # Vector2.Unit()\n- # Vector2.UnitX\n- # Vector2.UnitY\n- # Vector2.Zero\n- # Vector2.asDiagonal()\n- # Vector2.cols()\n- # Vector2.dot()\n- # Vector2.isApprox()\n- # Vector2.maxAbsCoeff()\n- # Vector2.maxCoeff()\n- # Vector2.mean()\n- # Vector2.minCoeff()\n- # Vector2.norm()\n- # Vector2.normalize()\n- # Vector2.normalized()\n- # Vector2.outer()\n- # Vector2.prod()\n- # Vector2.pruned()\n- # Vector2.rows()\n- # Vector2.squaredNorm()\n- # Vector2.sum()\n- # Vector2c\n- # Vector2c.Identity\n- # Vector2c.Ones\n- # Vector2c.Random()\n- # Vector2c.Unit()\n- # Vector2c.UnitX\n- # Vector2c.UnitY\n- # Vector2c.Zero\n- # Vector2c.asDiagonal()\n- # Vector2c.cols()\n- # Vector2c.dot()\n- # Vector2c.isApprox()\n- # Vector2c.maxAbsCoeff()\n- # Vector2c.mean()\n- # Vector2c.norm()\n- # Vector2c.normalize()\n- # Vector2c.normalized()\n- # Vector2c.outer()\n- # Vector2c.prod()\n- # Vector2c.pruned()\n- # Vector2c.rows()\n- # Vector2c.squaredNorm()\n- # Vector2c.sum()\n- # Vector2i\n- # Vector2i.Identity\n- # Vector2i.Ones\n- # Vector2i.Random()\n- # Vector2i.Unit()\n- # Vector2i.UnitX\n- # Vector2i.UnitY\n- # Vector2i.Zero\n- # Vector2i.asDiagonal()\n- # Vector2i.cols()\n- # Vector2i.dot()\n- # Vector2i.isApprox()\n- # Vector2i.maxAbsCoeff()\n- # Vector2i.maxCoeff()\n- # Vector2i.mean()\n- # Vector2i.minCoeff()\n- # Vector2i.outer()\n- # Vector2i.prod()\n- # Vector2i.rows()\n- # Vector2i.sum()\n- # Vector3\n- # Vector3.Identity\n- # Vector3.Ones\n- # Vector3.Random()\n- # Vector3.Unit()\n- # Vector3.UnitX\n- # Vector3.UnitY\n- # Vector3.UnitZ\n- # Vector3.Zero\n- # Vector3.asDiagonal()\n- # Vector3.cols()\n- # Vector3.cross()\n- # Vector3.dot()\n- # Vector3.isApprox()\n- # Vector3.maxAbsCoeff()\n- # Vector3.maxCoeff()\n- # Vector3.mean()\n- # Vector3.minCoeff()\n- # Vector3.norm()\n- # Vector3.normalize()\n- # Vector3.normalized()\n- # Vector3.outer()\n- # Vector3.prod()\n- # Vector3.pruned()\n- # Vector3.rows()\n- # Vector3.squaredNorm()\n- # Vector3.sum()\n- # Vector3.xy()\n- # Vector3.xz()\n- # Vector3.yx()\n- # Vector3.yz()\n- # Vector3.zx()\n- # Vector3.zy()\n- # Vector3c\n- # Vector3c.Identity\n- # Vector3c.Ones\n- # Vector3c.Random()\n- # Vector3c.Unit()\n- # Vector3c.UnitX\n- # Vector3c.UnitY\n- # Vector3c.UnitZ\n- # Vector3c.Zero\n- # Vector3c.asDiagonal()\n- # Vector3c.cols()\n- # Vector3c.cross()\n- # Vector3c.dot()\n- # Vector3c.isApprox()\n- # Vector3c.maxAbsCoeff()\n- # Vector3c.mean()\n- # Vector3c.norm()\n- # Vector3c.normalize()\n- # Vector3c.normalized()\n- # Vector3c.outer()\n- # Vector3c.prod()\n- # Vector3c.pruned()\n- # Vector3c.rows()\n- # Vector3c.squaredNorm()\n- # Vector3c.sum()\n- # Vector3c.xy()\n- # Vector3c.xz()\n- # Vector3c.yx()\n- # Vector3c.yz()\n- # Vector3c.zx()\n- # Vector3c.zy()\n- # Vector3i\n- # Vector3i.Identity\n- # Vector3i.Ones\n- # Vector3i.Random()\n- # Vector3i.Unit()\n- # Vector3i.UnitX\n- # Vector3i.UnitY\n- # Vector3i.UnitZ\n- # Vector3i.Zero\n- # Vector3i.asDiagonal()\n- # Vector3i.cols()\n- # Vector3i.cross()\n- # Vector3i.dot()\n- # Vector3i.isApprox()\n- # Vector3i.maxAbsCoeff()\n- # Vector3i.maxCoeff()\n- # Vector3i.mean()\n- # Vector3i.minCoeff()\n- # Vector3i.outer()\n- # Vector3i.prod()\n- # Vector3i.rows()\n- # Vector3i.sum()\n- # Vector3i.xy()\n- # Vector3i.xz()\n- # Vector3i.yx()\n- # Vector3i.yz()\n- # Vector3i.zx()\n- # Vector3i.zy()\n- # Vector4\n- # Vector4.Identity\n- # Vector4.Ones\n- # Vector4.Random()\n- # Vector4.Unit()\n- # Vector4.Zero\n- # Vector4.asDiagonal()\n- # Vector4.cols()\n- # Vector4.dot()\n- # Vector4.isApprox()\n- # Vector4.maxAbsCoeff()\n- # Vector4.maxCoeff()\n- # Vector4.mean()\n- # Vector4.minCoeff()\n- # Vector4.norm()\n- # Vector4.normalize()\n- # Vector4.normalized()\n- # Vector4.outer()\n- # Vector4.prod()\n- # Vector4.pruned()\n- # Vector4.rows()\n- # Vector4.squaredNorm()\n- # Vector4.sum()\n- # Vector6\n- # Vector6.Identity\n- # Vector6.Ones\n- # Vector6.Random()\n- # Vector6.Unit()\n- # Vector6.Zero\n- # Vector6.asDiagonal()\n- # Vector6.cols()\n- # Vector6.dot()\n- # Vector6.head()\n- # Vector6.isApprox()\n- # Vector6.maxAbsCoeff()\n- # Vector6.maxCoeff()\n- # Vector6.mean()\n- # Vector6.minCoeff()\n- # Vector6.norm()\n- # Vector6.normalize()\n- # Vector6.normalized()\n- # Vector6.outer()\n- # Vector6.prod()\n- # Vector6.pruned()\n- # Vector6.rows()\n- # Vector6.squaredNorm()\n- # Vector6.sum()\n- # Vector6.tail()\n- # Vector6c\n- # Vector6c.Identity\n- # Vector6c.Ones\n- # Vector6c.Random()\n- # Vector6c.Unit()\n- # Vector6c.Zero\n- # Vector6c.asDiagonal()\n- # Vector6c.cols()\n- # Vector6c.dot()\n- # Vector6c.head()\n- # Vector6c.isApprox()\n- # Vector6c.maxAbsCoeff()\n- # Vector6c.mean()\n- # Vector6c.norm()\n- # Vector6c.normalize()\n- # Vector6c.normalized()\n- # Vector6c.outer()\n- # Vector6c.prod()\n- # Vector6c.pruned()\n- # Vector6c.rows()\n- # Vector6c.squaredNorm()\n- # Vector6c.sum()\n- # Vector6c.tail()\n- # Vector6i\n- # Vector6i.Identity\n- # Vector6i.Ones\n- # Vector6i.Random()\n- # Vector6i.Unit()\n- # Vector6i.Zero\n- # Vector6i.asDiagonal()\n- # Vector6i.cols()\n- # Vector6i.dot()\n- # Vector6i.head()\n- # Vector6i.isApprox()\n- # Vector6i.maxAbsCoeff()\n- # Vector6i.maxCoeff()\n- # Vector6i.mean()\n- # Vector6i.minCoeff()\n- # Vector6i.outer()\n- # Vector6i.prod()\n- # Vector6i.rows()\n- # Vector6i.sum()\n- # Vector6i.tail()\n- # VectorX\n- # VectorX.Ones()\n- # VectorX.Random()\n- # VectorX.Unit()\n- # VectorX.Zero()\n- # VectorX.asDiagonal()\n- # VectorX.cols()\n- # VectorX.dot()\n- # VectorX.isApprox()\n- # VectorX.maxAbsCoeff()\n- # VectorX.maxCoeff()\n- # VectorX.mean()\n- # VectorX.minCoeff()\n- # VectorX.norm()\n- # VectorX.normalize()\n- # VectorX.normalized()\n- # VectorX.outer()\n- # VectorX.prod()\n- # VectorX.pruned()\n- # VectorX.resize()\n- # VectorX.rows()\n- # VectorX.squaredNorm()\n- # VectorX.sum()\n- # VectorXc\n- # VectorXc.Ones()\n- # VectorXc.Random()\n- # VectorXc.Unit()\n- # VectorXc.Zero()\n- # VectorXc.asDiagonal()\n- # VectorXc.cols()\n- # VectorXc.dot()\n- # VectorXc.isApprox()\n- # VectorXc.maxAbsCoeff()\n- # VectorXc.mean()\n- # VectorXc.norm()\n- # VectorXc.normalize()\n- # VectorXc.normalized()\n- # VectorXc.outer()\n- # VectorXc.prod()\n- # VectorXc.pruned()\n- # VectorXc.resize()\n- # VectorXc.rows()\n- # VectorXc.squaredNorm()\n- # VectorXc.sum()\n- # float2str()\n **** Quick search ****\n [q ] [Go]\n **** Navigation ****\n * index\n * minieigen_0.4-1_documentation \u00bb\n * minieigen documentation\n \u00a9 Copyright 2012\u00e2\u0088\u00922015, V\u00c3\u00a1clav \u00c5\u00a0milauer. Created using Sphinx 7.2.6.\n"}]}, {"source1": "./usr/share/doc/python3-minieigen/html/objects.inv", "source2": "./usr/share/doc/python3-minieigen/html/objects.inv", "unified_diff": null, "details": [{"source1": "Sphinx inventory", "source2": "Sphinx inventory", "unified_diff": "@@ -1,524 +1,10 @@\n # Sphinx inventory version 2\n # Project: minieigen\n # Version: 0.4\n # The remainder of this file is compressed using zlib.\n \n-minieigen py:module 0 index.html#module-$ -\n-minieigen.AlignedBox2 py:class 1 index.html#$ -\n-minieigen.AlignedBox2.center py:method 1 index.html#$ -\n-minieigen.AlignedBox2.clamp py:method 1 index.html#$ -\n-minieigen.AlignedBox2.contains py:method 1 index.html#$ -\n-minieigen.AlignedBox2.empty py:method 1 index.html#$ -\n-minieigen.AlignedBox2.extend py:method 1 index.html#$ -\n-minieigen.AlignedBox2.intersection py:method 1 index.html#$ -\n-minieigen.AlignedBox2.max py:property 1 index.html#$ -\n-minieigen.AlignedBox2.merged py:method 1 index.html#$ 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