{"diffoscope-json-version": 1, "source1": "/srv/reproducible-results/rbuild-debian/r-b-build.o5LxNVBK/b1/minieigen_0.50.3+dfsg1-13_amd64.changes", "source2": "/srv/reproducible-results/rbuild-debian/r-b-build.o5LxNVBK/b2/minieigen_0.50.3+dfsg1-13_amd64.changes", "unified_diff": null, "details": [{"source1": "Files", "source2": "Files", "unified_diff": "@@ -1,3 +1,3 @@\n \n 758823bbe85717e548e84436c095fe40 27918776 debug optional python3-minieigen-dbgsym_0.50.3+dfsg1-13_amd64.deb\n- 4e5e8abeb3df91e48c5f813e7bcfc56f 865032 python optional python3-minieigen_0.50.3+dfsg1-13_amd64.deb\n+ 7ff3bfce100b05e7469d48f82d593349 884120 python optional python3-minieigen_0.50.3+dfsg1-13_amd64.deb\n"}, {"source1": "python3-minieigen_0.50.3+dfsg1-13_amd64.deb", "source2": "python3-minieigen_0.50.3+dfsg1-13_amd64.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 1576 2021-11-08 17:29:32.000000 control.tar.xz\n--rw-r--r-- 0 0 0 863264 2021-11-08 17:29:32.000000 data.tar.xz\n+-rw-r--r-- 0 0 0 882352 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: amd64\n Maintainer: Debian Science Maintainers \n-Installed-Size: 7840\n+Installed-Size: 8451\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) 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) 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) 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) 2980 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/searchindex.js\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 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": "@@ -31,16 +31,1376 @@\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
\n+

A

\n+\n+ \n+
\n+\n+

C

\n+\n+ \n+ \n+
\n+\n+

D

\n+\n+ \n+ \n+
\n+\n+

E

\n+\n+ \n+ \n+
\n+\n+

F

\n+\n+ \n+
\n+\n+

H

\n+\n+ \n+
\n+\n+

I

\n+\n+ \n+ \n+
\n+\n+

J

\n+\n+ \n+
\n+\n+

L

\n+\n+ \n+ \n+
\n+\n+

M

\n+\n+ \n+ \n+
\n+\n+

N

\n+\n+ \n+ \n+
\n+\n+

O

\n+\n+ \n+ \n+
\n+\n+

P

\n+\n+ \n+ \n+
\n+\n+

Q

\n+\n+ \n+
\n+\n+

R

\n+\n+ \n+ \n+
\n+\n+

S

\n+\n+ \n+ \n+
\n+\n+

T

\n+\n+ \n+ \n+
\n+\n+

U

\n+\n+ \n+ \n+
\n+\n+

V

\n+\n+ \n+ \n+
\n+\n+

X

\n+\n+ \n+ \n+
\n+\n+

Y

\n+\n+ \n+ \n+
\n+\n+

Z

\n+\n+ \n+ \n+
\n+\n \n \n
\n
\n
\n \n
\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -1,12 +1,466 @@\n *\b**\b**\b**\b* N\bNa\bav\bvi\big\bga\bat\bti\bio\bon\bn *\b**\b**\b**\b*\n * _\bi_\bn_\bd_\be_\bx\n * _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b _\b0_\b._\b4_\b-_\b1_\b _\bd_\bo_\bc_\bu_\bm_\be_\bn_\bt_\ba_\bt_\bi_\bo_\bn \u00bb\n * Index\n *\b**\b**\b**\b**\b**\b* I\bIn\bnd\bde\bex\bx *\b**\b**\b**\b**\b**\b*\n+_\bA\bA | _\bC\bC | _\bD\bD | _\bE\bE | _\bF\bF | _\bH\bH | _\bI\bI | _\bJ\bJ | _\bL\bL | _\bM\bM | _\bN\bN | _\bO\bO | _\bP\bP | _\bQ\bQ | _\bR\bR | _\bS\bS | _\bT\bT | _\bU\bU | _\bV\bV | _\bX\bX |\n+_\bY\bY | _\bZ\bZ\n+*\b**\b**\b**\b**\b* A\bA *\b**\b**\b**\b**\b*\n+ * _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2_\b _\b(_\bc_\bl_\ba_\bs_\bs_\b _\bi_\bn_\b _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b)\n+ * _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3_\b _\b(_\bc_\bl_\ba_\bs_\bs_\b _\bi_\bn_\b _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b)\n+ * _\ba_\bn_\bg_\bu_\bl_\ba_\br_\bD_\bi_\bs_\bt_\ba_\bn_\bc_\be_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\ba_\bs_\bD_\bi_\ba_\bg_\bo_\bn_\ba_\bl_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b4_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+*\b**\b**\b**\b**\b* C\bC *\b**\b**\b**\b**\b*\n+ * _\bc_\be_\bn_\bt_\be_\br_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2 * _\bc_\bo_\bm_\bp_\bu_\bt_\be_\bU_\bn_\bi_\bt_\ba_\br_\by_\bP_\bo_\bs_\bi_\bt_\bi_\bv_\be_\b(_\b)_\b \n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3 o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bc_\bl_\ba_\bm_\bp_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2 * _\bc_\bo_\bn_\bj_\bu_\bg_\ba_\bt_\be_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3 * _\bc_\bo_\bn_\bt_\ba_\bi_\bn_\bs_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bc_\bo_\bl_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) * _\bc_\br_\bo_\bs_\bs_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bc_\bo_\bl_\bs_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b4_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+*\b**\b**\b**\b**\b* D\bD *\b**\b**\b**\b**\b*\n+ * _\bd_\be_\bt_\be_\br_\bm_\bi_\bn_\ba_\bn_\bt_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3 * _\bd_\bo_\bt_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b4_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bd_\bi_\ba_\bg_\bo_\bn_\ba_\bl_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3 o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+*\b**\b**\b**\b**\b* E\bE *\b**\b**\b**\b**\b*\n+ * _\be_\bm_\bp_\bt_\by_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2 * _\be_\bx_\bt_\be_\bn_\bd_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3 o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+*\b**\b**\b**\b**\b* F\bF *\b**\b**\b**\b**\b*\n+ * _\bf_\bl_\bo_\ba_\bt_\b2_\bs_\bt_\br_\b(_\b)_\b _\b(_\bi_\bn_\b _\bm_\bo_\bd_\bu_\bl_\be_\b _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b)\n+*\b**\b**\b**\b**\b* H\bH *\b**\b**\b**\b**\b*\n+ * _\bh_\be_\ba_\bd_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+*\b**\b**\b**\b**\b* I\bI *\b**\b**\b**\b**\b*\n+ * _\bI_\bd_\be_\bn_\bt_\bi_\bt_\by_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3 * _\bi_\bs_\bA_\bp_\bp_\br_\bo_\bx_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b) _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6 o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2 o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b4_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bi o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3 o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b4\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bc\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bi\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ * _\bI_\bd_\be_\bn_\bt_\bi_\bt_\by_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX\n+ _\bs_\bt_\ba_\bt_\bi_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b _\bs_\bt_\ba_\bt_\bi_\bc\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bi_\bn_\bt_\be_\br_\bs_\be_\bc_\bt_\bi_\bo_\bn_\b(_\b)_\b \n+ _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bi_\bn_\bv_\be_\br_\bs_\be_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+*\b**\b**\b**\b**\b* J\bJ *\b**\b**\b**\b**\b*\n+ * _\bj_\ba_\bc_\bo_\bb_\bi_\bS_\bV_\bD_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+*\b**\b**\b**\b**\b* L\bL *\b**\b**\b**\b**\b*\n+ * _\bl_\bl_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) * _\bl_\br_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+*\b**\b**\b**\b**\b* M\bM *\b**\b**\b**\b**\b*\n+ * _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b _\b(_\bc_\bl_\ba_\bs_\bs_\b _\bi_\bn_\b _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b) * _\bm_\be_\ba_\bn_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b _\b(_\bc_\bl_\ba_\bs_\bs_\b _\bi_\bn_\b _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\b(_\bc_\bl_\ba_\bs_\bs_\b _\bi_\bn_\b _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b _\b(_\bc_\bl_\ba_\bs_\bs_\b _\bi_\bn_\b _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\b(_\bc_\bl_\ba_\bs_\bs_\b _\bi_\bn_\b _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b _\b(_\bc_\bl_\ba_\bs_\bs_\b _\bi_\bn_\b _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bm_\ba_\bx_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2 o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bp_\br_\bo_\bp_\be_\br_\bt_\by_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3 o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bp_\br_\bo_\bp_\be_\br_\bt_\by_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bm_\ba_\bx_\bA_\bb_\bs_\bC_\bo_\be_\bf_\bf_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3 o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b4_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) * _\bm_\be_\br_\bg_\be_\bd_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) * _\bm_\bi_\bn_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b4_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) _\bp_\br_\bo_\bp_\be_\br_\bt_\by_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) _\bp_\br_\bo_\bp_\be_\br_\bt_\by_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) * _\bm_\bi_\bn_\bC_\bo_\be_\bf_\bf_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bm_\ba_\bx_\bC_\bo_\be_\bf_\bf_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3 o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b4_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b4_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) * minieigen\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\bm_\bo_\bd_\bu_\bl_\be\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) * module\n+ o _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn\n+*\b**\b**\b**\b**\b* N\bN *\b**\b**\b**\b**\b*\n+ * _\bn_\bo_\br_\bm_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) * _\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\bd_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b4_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b4_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3 o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b4_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+*\b**\b**\b**\b**\b* O\bO *\b**\b**\b**\b**\b*\n+ * _\bO_\bn_\be_\bs_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3 * _\bo_\bu_\bt_\be_\br_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6 o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b4_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2 o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bi\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b4\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bc\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bi\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ * _\bO_\bn_\be_\bs_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bs_\bt_\ba_\bt_\bi_\bc\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b _\bs_\bt_\ba_\bt_\bi_\bc\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\b _\bs_\bt_\ba_\bt_\bi_\bc\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b _\bs_\bt_\ba_\bt_\bi_\bc\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+*\b**\b**\b**\b**\b* P\bP *\b**\b**\b**\b**\b*\n+ * _\bp_\bo_\bl_\ba_\br_\bD_\be_\bc_\bo_\bm_\bp_\bo_\bs_\bi_\bt_\bi_\bo_\bn_\b(_\b)_\b * _\bp_\br_\bu_\bn_\be_\bd_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3\n+ _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bp_\br_\bo_\bd_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b4_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b4_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+*\b**\b**\b**\b**\b* Q\bQ *\b**\b**\b**\b**\b*\n+ * _\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn_\b _\b(_\bc_\bl_\ba_\bs_\bs_\b _\bi_\bn_\b _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b)\n+*\b**\b**\b**\b**\b* R\bR *\b**\b**\b**\b**\b*\n+ * _\bR_\ba_\bn_\bd_\bo_\bm_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3 * _\br_\bo_\bw_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bs_\bt_\ba_\bt_\bi_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b _\bs_\bt_\ba_\bt_\bi_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bs_\bt_\ba_\bt_\bi_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b _\bs_\bt_\ba_\bt_\bi_\bc * _\br_\bo_\bw_\bs_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bs_\bt_\ba_\bt_\bi_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b _\bs_\bt_\ba_\bt_\bi_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\b _\bs_\bt_\ba_\bt_\bi_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b _\bs_\bt_\ba_\bt_\bi_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b _\bs_\bt_\ba_\bt_\bi_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\bs_\bt_\ba_\bt_\bi_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b4_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b _\bs_\bt_\ba_\bt_\bi_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b _\bs_\bt_\ba_\bt_\bi_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b4_\b _\bs_\bt_\ba_\bt_\bi_\bc\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\b _\bs_\bt_\ba_\bt_\bi_\bc\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b _\bs_\bt_\ba_\bt_\bi_\bc\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b _\bs_\bt_\ba_\bt_\bi_\bc\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\b _\bs_\bt_\ba_\bt_\bi_\bc\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b _\bs_\bt_\ba_\bt_\bi_\bc\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\br_\be_\bs_\bi_\bz_\be_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bR_\bo_\bt_\ba_\bt_\be_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+*\b**\b**\b**\b**\b* S\bS *\b**\b**\b**\b**\b*\n+ * _\bs_\be_\bl_\bf_\bA_\bd_\bj_\bo_\bi_\bn_\bt_\bE_\bi_\bg_\be_\bn_\bD_\be_\bc_\bo_\bm_\bp_\bo_\bs_\bi_\bt_\bi_\bo_\bn_\b(_\b)_\b * _\bs_\bu_\bm_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bs_\be_\bt_\bF_\br_\bo_\bm_\bT_\bw_\bo_\bV_\be_\bc_\bt_\bo_\br_\bs_\b(_\b)_\b o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bs_\bi_\bz_\be_\bs_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2 o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3 o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bs_\bl_\be_\br_\bp_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bs_\bp_\be_\bc_\bt_\br_\ba_\bl_\bD_\be_\bc_\bo_\bm_\bp_\bo_\bs_\bi_\bt_\bi_\bo_\bn_\b(_\b)_\b o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b4_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bs_\bq_\bu_\ba_\br_\be_\bd_\bN_\bo_\br_\bm_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3 o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) * _\bs_\bv_\bd_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b4_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+*\b**\b**\b**\b**\b* T\bT *\b**\b**\b**\b**\b*\n+ * _\bt_\ba_\bi_\bl_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) * _\bt_\br_\ba_\bn_\bs_\bp_\bo_\bs_\be_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bt_\bo_\bA_\bn_\bg_\bl_\be_\bA_\bx_\bi_\bs_\b(_\b)_\b o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bt_\bo_\bA_\bx_\bi_\bs_\bA_\bn_\bg_\bl_\be_\b(_\b)_\b o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bt_\bo_\bR_\bo_\bt_\ba_\bt_\bi_\bo_\bn_\bM_\ba_\bt_\br_\bi_\bx_\b(_\b)_\b \n+ _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bt_\bo_\bR_\bo_\bt_\ba_\bt_\bi_\bo_\bn_\bV_\be_\bc_\bt_\bo_\br_\b(_\b)_\b \n+ _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bt_\br_\ba_\bc_\be_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+*\b**\b**\b**\b**\b* U\bU *\b**\b**\b**\b**\b*\n+ * _\bu_\bl_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) * _\bU_\bn_\bi_\bt_\bY_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ * _\bU_\bn_\bi_\bt_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\b _\bs_\bt_\ba_\bt_\bi_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bc\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b _\bs_\bt_\ba_\bt_\bi_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bi\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b _\bs_\bt_\ba_\bt_\bi_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\bs_\bt_\ba_\bt_\bi_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b _\bs_\bt_\ba_\bt_\bi_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b _\bs_\bt_\ba_\bt_\bi_\bc * _\bU_\bn_\bi_\bt_\bZ_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b4_\b _\bs_\bt_\ba_\bt_\bi_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\b _\bs_\bt_\ba_\bt_\bi_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b _\bs_\bt_\ba_\bt_\bi_\bc * _\bu_\br_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b _\bs_\bt_\ba_\bt_\bi_\bc\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\b _\bs_\bt_\ba_\bt_\bi_\bc\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b _\bs_\bt_\ba_\bt_\bi_\bc\n+ _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ * _\bU_\bn_\bi_\bt_\bX_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bc\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bi\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+*\b**\b**\b**\b**\b* V\bV *\b**\b**\b**\b**\b*\n+ * _\bV_\be_\bc_\bt_\bo_\br_\b2_\b _\b(_\bc_\bl_\ba_\bs_\bs_\b _\bi_\bn_\b _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b) * _\bV_\be_\bc_\bt_\bo_\br_\b6_\b _\b(_\bc_\bl_\ba_\bs_\bs_\b _\bi_\bn_\b _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b)\n+ * _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b _\b(_\bc_\bl_\ba_\bs_\bs_\b _\bi_\bn_\b _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b) * _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b _\b(_\bc_\bl_\ba_\bs_\bs_\b _\bi_\bn_\b _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b)\n+ * _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b _\b(_\bc_\bl_\ba_\bs_\bs_\b _\bi_\bn_\b _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b) * _\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b _\b(_\bc_\bl_\ba_\bs_\bs_\b _\bi_\bn_\b _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b)\n+ * _\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\b(_\bc_\bl_\ba_\bs_\bs_\b _\bi_\bn_\b _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b) * _\bV_\be_\bc_\bt_\bo_\br_\bX_\b _\b(_\bc_\bl_\ba_\bs_\bs_\b _\bi_\bn_\b _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b)\n+ * _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b _\b(_\bc_\bl_\ba_\bs_\bs_\b _\bi_\bn_\b _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b) * _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b _\b(_\bc_\bl_\ba_\bs_\bs_\b _\bi_\bn_\b _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b)\n+ * _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b _\b(_\bc_\bl_\ba_\bs_\bs_\b _\bi_\bn_\b _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b) * _\bv_\bo_\bl_\bu_\bm_\be_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2\n+ * _\bV_\be_\bc_\bt_\bo_\br_\b4_\b _\b(_\bc_\bl_\ba_\bs_\bs_\b _\bi_\bn_\b _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b) _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+*\b**\b**\b**\b**\b* X\bX *\b**\b**\b**\b**\b*\n+ * _\bx_\by_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) * _\bx_\bz_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+*\b**\b**\b**\b**\b* Y\bY *\b**\b**\b**\b**\b*\n+ * _\by_\bx_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) * _\by_\bz_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+*\b**\b**\b**\b**\b* Z\bZ *\b**\b**\b**\b**\b*\n+ * _\bZ_\be_\br_\bo_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3 * _\bZ_\be_\br_\bo_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\b _\bs_\bt_\ba_\bt_\bi_\bc\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b) _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b _\bs_\bt_\ba_\bt_\bi_\bc\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b) _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6 o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\b _\bs_\bt_\ba_\bt_\bi_\bc\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b) _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b _\bs_\bt_\ba_\bt_\bi_\bc\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b) _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2 * _\bz_\bx_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bc o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b) * _\bz_\by_\b(_\b)_\b _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b2_\bi o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b) o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b _\bm_\be_\bt_\bh_\bo_\bd_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bc\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\bi\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b4\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bc\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n+ o _\b(_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b6_\bi\n+ _\ba_\bt_\bt_\br_\bi_\bb_\bu_\bt_\be_\b)\n *\b**\b**\b**\b* Q\bQu\bui\bic\bck\bk s\bse\bea\bar\brc\bch\bh *\b**\b**\b**\b*\n [q ][Go]\n *\b**\b**\b**\b* N\bNa\bav\bvi\big\bga\bat\bti\bio\bon\bn *\b**\b**\b**\b*\n * _\bi_\bn_\bd_\be_\bx\n * _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b _\b0_\b._\b4_\b-_\b1_\b _\bd_\bo_\bc_\bu_\bm_\be_\bn_\bt_\ba_\bt_\bi_\bo_\bn \u00bb\n * Index\n \u00a9 Copyright 2012\u22122015, V\u00e1clav \u0160milauer. Created using _\bS_\bp_\bh_\bi_\bn_\bx 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@@ -112,14 +112,2989 @@\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@@ -131,15 +3106,572 @@\n \n \n \n
\n

Quick search

\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -4,47 +4,47 @@\n * minieigen documentation\n *\b**\b**\b**\b**\b**\b* m\bmi\bin\bni\bie\bei\big\bge\ben\bn d\bdo\boc\bcu\bum\bme\ben\bnt\bta\bat\bti\bio\bon\bn_\b?\b\u00b6 *\b**\b**\b**\b**\b**\b*\n *\b**\b**\b**\b**\b* O\bOv\bve\ber\brv\bvi\bie\bew\bw_\b?\b\u00b6 *\b**\b**\b**\b**\b*\n Todo\n Something concise here.\n *\b**\b**\b**\b**\b* E\bEx\bxa\bam\bmp\bpl\ble\bes\bs_\b?\b\u00b6 *\b**\b**\b**\b**\b*\n Todo\n-Some examples of what can be done with minieigen.\n+Some examples of what can be done with _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn.\n *\b**\b**\b**\b**\b* N\bNa\bam\bmi\bin\bng\bg c\bco\bon\bnv\bve\ben\bnt\bti\bio\bon\bns\bs_\b?\b\u00b6 *\b**\b**\b**\b**\b*\n * Classes are suffixed with number indicating size where it makes sense (it\n- does not make sense for minieigen.Quaternion):\n- o minieigen.Vector3 is a 3-vector (column vector);\n- o minieigen.Matrix3 is a 3\u00d73 matrix;\n- o minieigen.AlignedBox3 is aligned box in 3d;\n- o X indicates dynamic-sized types, such as minieigen.VectorX or\n- minieigen.MatrixX.\n+ does not make sense for _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn):\n+ o _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3 is a 3-vector (column vector);\n+ o _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3 is a 3\u00d73 matrix;\n+ o _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3 is aligned box in 3d;\n+ o X indicates dynamic-sized types, such as _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\bX or\n+ _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\bX.\n * Scalar (element) type is suffixed at the end:\n- o nothing is suffixed for floats (minieigen.Matrix3);\n+ o nothing is suffixed for floats (_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3);\n o i indicates integers (minieigen.Matrix3i);\n- o c indicates complex numbers (minieigen.Matrix3c).\n+ o c indicates complex numbers (_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc).\n * Methods are named as follows:\n o static methods are upper-case (as in c++), e.g.\n- minieigen.Matrix3.Random;\n+ _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bR_\ba_\bn_\bd_\bo_\bm;\n # nullary static methods are exposed as properties, if they\n- return a constant (e.g. minieigen.Matrix3.Identity); if they\n+ return a constant (e.g. _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bI_\bd_\be_\bn_\bt_\bi_\bt_\by); if they\n don\u2019t, they are exposed as methods\n- (minieigen.Matrix3.Random); the idea is that the necessity to\n+ (_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bR_\ba_\bn_\bd_\bo_\bm); the idea is that the necessity to\n call the method (Matrix3.Random()) singifies that there is\n some computation going on, whereas constants behave like\n immutable singletons.\n o non-static methods are lower-case (as in c++), e.g.\n- minieigen.Matrix3.inverse.\n+ _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bi_\bn_\bv_\be_\br_\bs_\be.\n * Return types:\n o methods modifying the instance in-place return None (e.g.\n- minieigen.Vector3.normalize); some methods in c++ (e.g.\n+ _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be); some methods in c++ (e.g.\n _\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn_\b:_\b:_\bs_\be_\bt_\bF_\br_\bo_\bm_\bT_\bw_\bo_\bV_\be_\bc_\bt_\bo_\br_\bs) both modify the instance a\ban\bnd\bd return\n the reference to it, which we don\u2019t want to do in Python\n- (minieigen.Quaternion.setFromTwoVectors);\n+ (_\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn_\b._\bs_\be_\bt_\bF_\br_\bo_\bm_\bT_\bw_\bo_\bV_\be_\bc_\bt_\bo_\br_\bs);\n o methods returning another object (e.g.\n- minieigen.Vector3.normalized) do not modify the instance;\n+ _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b._\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\bd) do not modify the instance;\n o methods returning (non-const) references return by value in python\n *\b**\b**\b**\b**\b* L\bLi\bim\bmi\bit\bta\bat\bti\bio\bon\bns\bs_\b?\b\u00b6 *\b**\b**\b**\b**\b*\n * Type conversions (e.g. float to complex) are not supported.\n * Methods returning references in c++ return values in Python (so e.g.\n Matrix3().diagonal()[2]=0 would zero the last diagonal element in c++ but\n not in Python).\n * Many methods are not wrapped, though they are fairly easy to add.\n@@ -65,22 +65,1546 @@\n by easy_install)\n * packages:\n o _\bD_\be_\bb_\bi_\ba_\bn\n o Ubuntu: _\bd_\bi_\bs_\bt_\br_\bi_\bb_\bu_\bt_\bi_\bo_\bn, _\bP_\bP_\bA\n *\b**\b**\b**\b**\b* D\bDo\boc\bcu\bum\bme\ben\bnt\bta\bat\bti\bio\bon\bn_\b?\b\u00b6 *\b**\b**\b**\b**\b*\n * _\bI_\bn_\bd_\be_\bx\n * _\bS_\be_\ba_\br_\bc_\bh_\b _\bP_\ba_\bg_\be\n+miniEigen is wrapper for a small part of the _\bE_\bi_\bg_\be_\bn library. Refer to its\n+documentation for details. All classes in this module support pickling.\n+ c\bcl\bla\bas\bss\bs minieigen.AlignedBox2_\b\u00b6\n+ Axis-aligned box object in 2d, defined by its minimum and maximum corners\n+ center((\b(A\bAl\bli\big\bgn\bne\bed\bdB\bBo\box\bx2\b2)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2[STATIC]_\b\u00b6\n+ clamp((\b(A\bAl\bli\big\bgn\bne\bed\bdB\bBo\box\bx2\b2)\b)a\bar\brg\bg1\b1, (\b(A\bAl\bli\big\bgn\bne\bed\bdB\bBo\box\bx2\b2)\b)a\bar\brg\bg2\b2) \u2192 None[STATIC]_\b\u00b6\n+ contains((\b(A\bAl\bli\big\bgn\bne\bed\bdB\bBo\box\bx2\b2)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br2\b2)\b)a\bar\brg\bg2\b2) \u2192 bool[STATIC]_\b\u00b6\n+ contains( (AlignedBox2)arg1, (AlignedBox2)arg2) \u2192 bool\n+ empty((\b(A\bAl\bli\big\bgn\bne\bed\bdB\bBo\box\bx2\b2)\b)a\bar\brg\bg1\b1) \u2192 bool[STATIC]_\b\u00b6\n+ extend((\b(A\bAl\bli\big\bgn\bne\bed\bdB\bBo\box\bx2\b2)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br2\b2)\b)a\bar\brg\bg2\b2) \u2192 None[STATIC]_\b\u00b6\n+ extend( (AlignedBox2)arg1, (AlignedBox2)arg2) \u2192 None\n+ intersection((\b(A\bAl\bli\big\bgn\bne\bed\bdB\bBo\box\bx2\b2)\b)a\bar\brg\bg1\b1, (\b(A\bAl\bli\big\bgn\bne\bed\bdB\bBo\box\bx2\b2)\b)a\bar\brg\bg2\b2) \u2192 _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2\n+ [STATIC]_\b\u00b6\n+ p\bpr\bro\bop\bpe\ber\brt\bty\by max_\b\u00b6\n+ merged((\b(A\bAl\bli\big\bgn\bne\bed\bdB\bBo\box\bx2\b2)\b)a\bar\brg\bg1\b1, (\b(A\bAl\bli\big\bgn\bne\bed\bdB\bBo\box\bx2\b2)\b)a\bar\brg\bg2\b2) \u2192 _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2[STATIC]_\b\u00b6\n+ p\bpr\bro\bop\bpe\ber\brt\bty\by min_\b\u00b6\n+ sizes((\b(A\bAl\bli\big\bgn\bne\bed\bdB\bBo\box\bx2\b2)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2[STATIC]_\b\u00b6\n+ volume((\b(A\bAl\bli\big\bgn\bne\bed\bdB\bBo\box\bx2\b2)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ c\bcl\bla\bas\bss\bs minieigen.AlignedBox3_\b\u00b6\n+ Axis-aligned box object, defined by its minimum and maximum corners\n+ center((\b(A\bAl\bli\big\bgn\bne\bed\bdB\bBo\box\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3[STATIC]_\b\u00b6\n+ clamp((\b(A\bAl\bli\big\bgn\bne\bed\bdB\bBo\box\bx3\b3)\b)a\bar\brg\bg1\b1, (\b(A\bAl\bli\big\bgn\bne\bed\bdB\bBo\box\bx3\b3)\b)a\bar\brg\bg2\b2) \u2192 None[STATIC]_\b\u00b6\n+ contains((\b(A\bAl\bli\big\bgn\bne\bed\bdB\bBo\box\bx3\b3)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg2\b2) \u2192 bool[STATIC]_\b\u00b6\n+ contains( (AlignedBox3)arg1, (AlignedBox3)arg2) \u2192 bool\n+ empty((\b(A\bAl\bli\big\bgn\bne\bed\bdB\bBo\box\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 bool[STATIC]_\b\u00b6\n+ extend((\b(A\bAl\bli\big\bgn\bne\bed\bdB\bBo\box\bx3\b3)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg2\b2) \u2192 None[STATIC]_\b\u00b6\n+ extend( (AlignedBox3)arg1, (AlignedBox3)arg2) \u2192 None\n+ intersection((\b(A\bAl\bli\big\bgn\bne\bed\bdB\bBo\box\bx3\b3)\b)a\bar\brg\bg1\b1, (\b(A\bAl\bli\big\bgn\bne\bed\bdB\bBo\box\bx3\b3)\b)a\bar\brg\bg2\b2) \u2192 _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3\n+ [STATIC]_\b\u00b6\n+ p\bpr\bro\bop\bpe\ber\brt\bty\by max_\b\u00b6\n+ merged((\b(A\bAl\bli\big\bgn\bne\bed\bdB\bBo\box\bx3\b3)\b)a\bar\brg\bg1\b1, (\b(A\bAl\bli\big\bgn\bne\bed\bdB\bBo\box\bx3\b3)\b)a\bar\brg\bg2\b2) \u2192 _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3[STATIC]_\b\u00b6\n+ p\bpr\bro\bop\bpe\ber\brt\bty\by min_\b\u00b6\n+ sizes((\b(A\bAl\bli\big\bgn\bne\bed\bdB\bBo\box\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3[STATIC]_\b\u00b6\n+ volume((\b(A\bAl\bli\big\bgn\bne\bed\bdB\bBo\box\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ c\bcl\bla\bas\bss\bs minieigen.Matrix3_\b\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 =\b= M\bMa\bat\btr\bri\bix\bx3\b3(\b(1\b1,\b,0\b0,\b,0\b0,\b, 0\b0,\b,1\b1,\b,0\b0,\b, 0\b0,\b,0\b0,\b,1\b1)\b)_\b\u00b6\n+ Ones =\b= M\bMa\bat\btr\bri\bix\bx3\b3(\b(1\b1,\b,1\b1,\b,1\b1,\b, 1\b1,\b,1\b1,\b,1\b1,\b, 1\b1,\b,1\b1,\b,1\b1)\b)_\b\u00b6\n+ s\bst\bta\bat\bti\bic\bc Random() \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b3[STATIC]_\b\u00b6\n+ Return an object where all elements are randomly set to values\n+ between 0 and 1.\n+ Zero =\b= M\bMa\bat\btr\bri\bix\bx3\b3(\b(0\b0,\b,0\b0,\b,0\b0,\b, 0\b0,\b,0\b0,\b,0\b0,\b, 0\b0,\b,0\b0,\b,0\b0)\b)_\b\u00b6\n+ col((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1, (\b(i\bin\bnt\bt)\b)c\bco\bol\bl) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3[STATIC]_\b\u00b6\n+ Return column as vector.\n+ cols((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of columns.\n+ computeUnitaryPositive((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 tuple[STATIC]_\b\u00b6\n+ Compute polar decomposition (unitary matrix U and positive semi-\n+ definite symmetric matrix P such that self=U*P).\n+ determinant((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Return matrix determinant.\n+ diagonal((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3[STATIC]_\b\u00b6\n+ Return diagonal as vector.\n+ inverse((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b3[STATIC]_\b\u00b6\n+ Return inverted matrix.\n+ isApprox((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1, (\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)o\bot\bth\bhe\ber\br[, (\b(f\bfl\blo\boa\bat\bt)\b)p\bpr\bre\bec\bc=\b=1\b1e\be-\b-1\b12\b2]) \u2192 bool\n+ [STATIC]_\b\u00b6\n+ Approximate comparison with precision p\bpr\bre\bec\bc.\n+ jacobiSVD((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 tuple[STATIC]_\b\u00b6\n+ Compute SVD decomposition of square matrix, retuns (U,S,V) such\n+ that self=U*S*V.transpose()\n+ maxAbsCoeff((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Maximum absolute value over all elements.\n+ maxCoeff((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Maximum value over all elements.\n+ mean((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Mean value over all elements.\n+ minCoeff((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Minimum value over all elements.\n+ norm((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Euclidean norm.\n+ normalize((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 None[STATIC]_\b\u00b6\n+ Normalize this object in-place.\n+ normalized((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b3[STATIC]_\b\u00b6\n+ Return normalized copy of this object\n+ polarDecomposition((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 tuple[STATIC]_\b\u00b6\n+ Alias for _\bc_\bo_\bm_\bp_\bu_\bt_\be_\bU_\bn_\bi_\bt_\ba_\br_\by_\bP_\bo_\bs_\bi_\bt_\bi_\bv_\be.\n+ prod((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Product of all elements.\n+ pruned((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1[, (\b(f\bfl\blo\boa\bat\bt)\b)a\bab\bbs\bsT\bTo\bol\bl=\b=1\b1e\be-\b-0\b06\b6]) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b3[STATIC]_\b\u00b6\n+ Zero all elements which are greater than a\bab\bbs\bsT\bTo\bol\bl. Negative zeros are\n+ not pruned.\n+ row((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1, (\b(i\bin\bnt\bt)\b)r\bro\bow\bw) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3[STATIC]_\b\u00b6\n+ Return row as vector.\n+ rows((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of rows.\n+ selfAdjointEigenDecomposition((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 tuple[STATIC]_\b\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((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 tuple[STATIC]_\b\u00b6\n+ Alias for _\bs_\be_\bl_\bf_\bA_\bd_\bj_\bo_\bi_\bn_\bt_\bE_\bi_\bg_\be_\bn_\bD_\be_\bc_\bo_\bm_\bp_\bo_\bs_\bi_\bt_\bi_\bo_\bn.\n+ squaredNorm((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Square of the Euclidean norm.\n+ sum((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Sum of all elements.\n+ svd((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 tuple[STATIC]_\b\u00b6\n+ Alias for _\bj_\ba_\bc_\bo_\bb_\bi_\bS_\bV_\bD.\n+ trace((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Return sum of diagonal elements.\n+ transpose((\b(M\bMa\bat\btr\bri\bix\bx3\b3)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b3[STATIC]_\b\u00b6\n+ Return transposed matrix.\n+ c\bcl\bla\bas\bss\bs minieigen.Matrix3c_\b\u00b6\n+ /T\bTO\bOD\bDO\bO/\n+ Identity =\b= M\bMa\bat\btr\bri\bix\bx3\b3c\bc(\b(1\b1,\b,0\b0,\b,0\b0,\b, 0\b0,\b,1\b1,\b,0\b0,\b, 0\b0,\b,0\b0,\b,1\b1)\b)_\b\u00b6\n+ Ones =\b= M\bMa\bat\btr\bri\bix\bx3\b3c\bc(\b(1\b1,\b,1\b1,\b,1\b1,\b, 1\b1,\b,1\b1,\b,1\b1,\b, 1\b1,\b,1\b1,\b,1\b1)\b)_\b\u00b6\n+ s\bst\bta\bat\bti\bic\bc Random() \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc[STATIC]_\b\u00b6\n+ Return an object where all elements are randomly set to values\n+ between 0 and 1.\n+ Zero =\b= M\bMa\bat\btr\bri\bix\bx3\b3c\bc(\b(0\b0,\b,0\b0,\b,0\b0,\b, 0\b0,\b,0\b0,\b,0\b0,\b, 0\b0,\b,0\b0,\b,0\b0)\b)_\b\u00b6\n+ col((\b(M\bMa\bat\btr\bri\bix\bx3\b3c\bc)\b)a\bar\brg\bg1\b1, (\b(i\bin\bnt\bt)\b)c\bco\bol\bl) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc[STATIC]_\b\u00b6\n+ Return column as vector.\n+ cols((\b(M\bMa\bat\btr\bri\bix\bx3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of columns.\n+ determinant((\b(M\bMa\bat\btr\bri\bix\bx3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Return matrix determinant.\n+ diagonal((\b(M\bMa\bat\btr\bri\bix\bx3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc[STATIC]_\b\u00b6\n+ Return diagonal as vector.\n+ inverse((\b(M\bMa\bat\btr\bri\bix\bx3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc[STATIC]_\b\u00b6\n+ Return inverted matrix.\n+ isApprox((\b(M\bMa\bat\btr\bri\bix\bx3\b3c\bc)\b)a\bar\brg\bg1\b1, (\b(M\bMa\bat\btr\bri\bix\bx3\b3c\bc)\b)o\bot\bth\bhe\ber\br[, (\b(f\bfl\blo\boa\bat\bt)\b)p\bpr\bre\bec\bc=\b=1\b1e\be-\b-1\b12\b2]) \u2192 bool\n+ [STATIC]_\b\u00b6\n+ Approximate comparison with precision p\bpr\bre\bec\bc.\n+ maxAbsCoeff((\b(M\bMa\bat\btr\bri\bix\bx3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Maximum absolute value over all elements.\n+ mean((\b(M\bMa\bat\btr\bri\bix\bx3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Mean value over all elements.\n+ norm((\b(M\bMa\bat\btr\bri\bix\bx3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Euclidean norm.\n+ normalize((\b(M\bMa\bat\btr\bri\bix\bx3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 None[STATIC]_\b\u00b6\n+ Normalize this object in-place.\n+ normalized((\b(M\bMa\bat\btr\bri\bix\bx3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc[STATIC]_\b\u00b6\n+ Return normalized copy of this object\n+ prod((\b(M\bMa\bat\btr\bri\bix\bx3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Product of all elements.\n+ pruned((\b(M\bMa\bat\btr\bri\bix\bx3\b3c\bc)\b)a\bar\brg\bg1\b1[, (\b(f\bfl\blo\boa\bat\bt)\b)a\bab\bbs\bsT\bTo\bol\bl=\b=1\b1e\be-\b-0\b06\b6]) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc[STATIC]_\b\u00b6\n+ Zero all elements which are greater than a\bab\bbs\bsT\bTo\bol\bl. Negative zeros are\n+ not pruned.\n+ row((\b(M\bMa\bat\btr\bri\bix\bx3\b3c\bc)\b)a\bar\brg\bg1\b1, (\b(i\bin\bnt\bt)\b)r\bro\bow\bw) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc[STATIC]_\b\u00b6\n+ Return row as vector.\n+ rows((\b(M\bMa\bat\btr\bri\bix\bx3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of rows.\n+ squaredNorm((\b(M\bMa\bat\btr\bri\bix\bx3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Square of the Euclidean norm.\n+ sum((\b(M\bMa\bat\btr\bri\bix\bx3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Sum of all elements.\n+ trace((\b(M\bMa\bat\btr\bri\bix\bx3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Return sum of diagonal elements.\n+ transpose((\b(M\bMa\bat\btr\bri\bix\bx3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc[STATIC]_\b\u00b6\n+ Return transposed matrix.\n+ c\bcl\bla\bas\bss\bs minieigen.Matrix6_\b\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+ s\bst\bta\bat\bti\bic\bc Random() \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b6[STATIC]_\b\u00b6\n+ Return an object where all elements are randomly set to values\n+ between 0 and 1.\n+ col((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1, (\b(i\bin\bnt\bt)\b)c\bco\bol\bl) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b6[STATIC]_\b\u00b6\n+ Return column as vector.\n+ cols((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of columns.\n+ computeUnitaryPositive((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 tuple[STATIC]_\b\u00b6\n+ Compute polar decomposition (unitary matrix U and positive semi-\n+ definite symmetric matrix P such that self=U*P).\n+ determinant((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Return matrix determinant.\n+ diagonal((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b6[STATIC]_\b\u00b6\n+ Return diagonal as vector.\n+ inverse((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b6[STATIC]_\b\u00b6\n+ Return inverted matrix.\n+ isApprox((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1, (\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)o\bot\bth\bhe\ber\br[, (\b(f\bfl\blo\boa\bat\bt)\b)p\bpr\bre\bec\bc=\b=1\b1e\be-\b-1\b12\b2]) \u2192 bool\n+ [STATIC]_\b\u00b6\n+ Approximate comparison with precision p\bpr\bre\bec\bc.\n+ jacobiSVD((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 tuple[STATIC]_\b\u00b6\n+ Compute SVD decomposition of square matrix, retuns (U,S,V) such\n+ that self=U*S*V.transpose()\n+ ll((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b3[STATIC]_\b\u00b6\n+ Return lower-left 3x3 block\n+ lr((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b3[STATIC]_\b\u00b6\n+ Return lower-right 3x3 block\n+ maxAbsCoeff((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Maximum absolute value over all elements.\n+ maxCoeff((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Maximum value over all elements.\n+ mean((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Mean value over all elements.\n+ minCoeff((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Minimum value over all elements.\n+ norm((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Euclidean norm.\n+ normalize((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 None[STATIC]_\b\u00b6\n+ Normalize this object in-place.\n+ normalized((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b6[STATIC]_\b\u00b6\n+ Return normalized copy of this object\n+ polarDecomposition((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 tuple[STATIC]_\b\u00b6\n+ Alias for _\bc_\bo_\bm_\bp_\bu_\bt_\be_\bU_\bn_\bi_\bt_\ba_\br_\by_\bP_\bo_\bs_\bi_\bt_\bi_\bv_\be.\n+ prod((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Product of all elements.\n+ pruned((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1[, (\b(f\bfl\blo\boa\bat\bt)\b)a\bab\bbs\bsT\bTo\bol\bl=\b=1\b1e\be-\b-0\b06\b6]) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b6[STATIC]_\b\u00b6\n+ Zero all elements which are greater than a\bab\bbs\bsT\bTo\bol\bl. Negative zeros are\n+ not pruned.\n+ row((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1, (\b(i\bin\bnt\bt)\b)r\bro\bow\bw) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b6[STATIC]_\b\u00b6\n+ Return row as vector.\n+ rows((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of rows.\n+ selfAdjointEigenDecomposition((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 tuple[STATIC]_\b\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((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 tuple[STATIC]_\b\u00b6\n+ Alias for _\bs_\be_\bl_\bf_\bA_\bd_\bj_\bo_\bi_\bn_\bt_\bE_\bi_\bg_\be_\bn_\bD_\be_\bc_\bo_\bm_\bp_\bo_\bs_\bi_\bt_\bi_\bo_\bn.\n+ squaredNorm((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Square of the Euclidean norm.\n+ sum((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Sum of all elements.\n+ svd((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 tuple[STATIC]_\b\u00b6\n+ Alias for _\bj_\ba_\bc_\bo_\bb_\bi_\bS_\bV_\bD.\n+ trace((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Return sum of diagonal elements.\n+ transpose((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b6[STATIC]_\b\u00b6\n+ Return transposed matrix.\n+ ul((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b3[STATIC]_\b\u00b6\n+ Return upper-left 3x3 block\n+ ur((\b(M\bMa\bat\btr\bri\bix\bx6\b6)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b3[STATIC]_\b\u00b6\n+ Return upper-right 3x3 block\n+ c\bcl\bla\bas\bss\bs minieigen.Matrix6c_\b\u00b6\n+ /T\bTO\bOD\bDO\bO/\n+ s\bst\bta\bat\bti\bic\bc Random() \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc[STATIC]_\b\u00b6\n+ Return an object where all elements are randomly set to values\n+ between 0 and 1.\n+ col((\b(M\bMa\bat\btr\bri\bix\bx6\b6c\bc)\b)a\bar\brg\bg1\b1, (\b(i\bin\bnt\bt)\b)c\bco\bol\bl) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc[STATIC]_\b\u00b6\n+ Return column as vector.\n+ cols((\b(M\bMa\bat\btr\bri\bix\bx6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of columns.\n+ determinant((\b(M\bMa\bat\btr\bri\bix\bx6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Return matrix determinant.\n+ diagonal((\b(M\bMa\bat\btr\bri\bix\bx6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc[STATIC]_\b\u00b6\n+ Return diagonal as vector.\n+ inverse((\b(M\bMa\bat\btr\bri\bix\bx6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc[STATIC]_\b\u00b6\n+ Return inverted matrix.\n+ isApprox((\b(M\bMa\bat\btr\bri\bix\bx6\b6c\bc)\b)a\bar\brg\bg1\b1, (\b(M\bMa\bat\btr\bri\bix\bx6\b6c\bc)\b)o\bot\bth\bhe\ber\br[, (\b(f\bfl\blo\boa\bat\bt)\b)p\bpr\bre\bec\bc=\b=1\b1e\be-\b-1\b12\b2]) \u2192 bool\n+ [STATIC]_\b\u00b6\n+ Approximate comparison with precision p\bpr\bre\bec\bc.\n+ ll((\b(M\bMa\bat\btr\bri\bix\bx6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc[STATIC]_\b\u00b6\n+ Return lower-left 3x3 block\n+ lr((\b(M\bMa\bat\btr\bri\bix\bx6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc[STATIC]_\b\u00b6\n+ Return lower-right 3x3 block\n+ maxAbsCoeff((\b(M\bMa\bat\btr\bri\bix\bx6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Maximum absolute value over all elements.\n+ mean((\b(M\bMa\bat\btr\bri\bix\bx6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Mean value over all elements.\n+ norm((\b(M\bMa\bat\btr\bri\bix\bx6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Euclidean norm.\n+ normalize((\b(M\bMa\bat\btr\bri\bix\bx6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 None[STATIC]_\b\u00b6\n+ Normalize this object in-place.\n+ normalized((\b(M\bMa\bat\btr\bri\bix\bx6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc[STATIC]_\b\u00b6\n+ Return normalized copy of this object\n+ prod((\b(M\bMa\bat\btr\bri\bix\bx6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Product of all elements.\n+ pruned((\b(M\bMa\bat\btr\bri\bix\bx6\b6c\bc)\b)a\bar\brg\bg1\b1[, (\b(f\bfl\blo\boa\bat\bt)\b)a\bab\bbs\bsT\bTo\bol\bl=\b=1\b1e\be-\b-0\b06\b6]) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc[STATIC]_\b\u00b6\n+ Zero all elements which are greater than a\bab\bbs\bsT\bTo\bol\bl. Negative zeros are\n+ not pruned.\n+ row((\b(M\bMa\bat\btr\bri\bix\bx6\b6c\bc)\b)a\bar\brg\bg1\b1, (\b(i\bin\bnt\bt)\b)r\bro\bow\bw) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc[STATIC]_\b\u00b6\n+ Return row as vector.\n+ rows((\b(M\bMa\bat\btr\bri\bix\bx6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of rows.\n+ squaredNorm((\b(M\bMa\bat\btr\bri\bix\bx6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Square of the Euclidean norm.\n+ sum((\b(M\bMa\bat\btr\bri\bix\bx6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Sum of all elements.\n+ trace((\b(M\bMa\bat\btr\bri\bix\bx6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Return sum of diagonal elements.\n+ transpose((\b(M\bMa\bat\btr\bri\bix\bx6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc[STATIC]_\b\u00b6\n+ Return transposed matrix.\n+ ul((\b(M\bMa\bat\btr\bri\bix\bx6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc[STATIC]_\b\u00b6\n+ Return upper-left 3x3 block\n+ ur((\b(M\bMa\bat\btr\bri\bix\bx6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc[STATIC]_\b\u00b6\n+ Return upper-right 3x3 block\n+ c\bcl\bla\bas\bss\bs minieigen.MatrixX_\b\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+ s\bst\bta\bat\bti\bic\bc Identity((\b(i\bin\bnt\bt)\b)a\bar\brg\bg1\b1, (\b(i\bin\bnt\bt)\b)r\bra\ban\bnk\bk) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\bX[STATIC]_\b\u00b6\n+ Create identity matrix with given rank (square).\n+ s\bst\bta\bat\bti\bic\bc Ones((\b(i\bin\bnt\bt)\b)r\bro\bow\bws\bs, (\b(i\bin\bnt\bt)\b)c\bco\bol\bls\bs) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\bX[STATIC]_\b\u00b6\n+ Create matrix of given dimensions where all elements are set to 1.\n+ s\bst\bta\bat\bti\bic\bc Random((\b(i\bin\bnt\bt)\b)r\bro\bow\bws\bs, (\b(i\bin\bnt\bt)\b)c\bco\bol\bls\bs) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\bX[STATIC]_\b\u00b6\n+ Create matrix with given dimensions where all elements are set to\n+ number between 0 and 1 (uniformly-distributed).\n+ s\bst\bta\bat\bti\bic\bc Zero((\b(i\bin\bnt\bt)\b)r\bro\bow\bws\bs, (\b(i\bin\bnt\bt)\b)c\bco\bol\bls\bs) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\bX[STATIC]_\b\u00b6\n+ Create zero matrix of given dimensions\n+ col((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1, (\b(i\bin\bnt\bt)\b)c\bco\bol\bl) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\bX[STATIC]_\b\u00b6\n+ Return column as vector.\n+ cols((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of columns.\n+ computeUnitaryPositive((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1) \u2192 tuple[STATIC]_\b\u00b6\n+ Compute polar decomposition (unitary matrix U and positive semi-\n+ definite symmetric matrix P such that self=U*P).\n+ determinant((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Return matrix determinant.\n+ diagonal((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\bX[STATIC]_\b\u00b6\n+ Return diagonal as vector.\n+ inverse((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\bX[STATIC]_\b\u00b6\n+ Return inverted matrix.\n+ isApprox((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1, (\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)o\bot\bth\bhe\ber\br[, (\b(f\bfl\blo\boa\bat\bt)\b)p\bpr\bre\bec\bc=\b=1\b1e\be-\b-1\b12\b2]) \u2192 bool\n+ [STATIC]_\b\u00b6\n+ Approximate comparison with precision p\bpr\bre\bec\bc.\n+ jacobiSVD((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1) \u2192 tuple[STATIC]_\b\u00b6\n+ Compute SVD decomposition of square matrix, retuns (U,S,V) such\n+ that self=U*S*V.transpose()\n+ maxAbsCoeff((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Maximum absolute value over all elements.\n+ maxCoeff((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Maximum value over all elements.\n+ mean((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Mean value over all elements.\n+ minCoeff((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Minimum value over all elements.\n+ norm((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Euclidean norm.\n+ normalize((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1) \u2192 None[STATIC]_\b\u00b6\n+ Normalize this object in-place.\n+ normalized((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\bX[STATIC]_\b\u00b6\n+ Return normalized copy of this object\n+ polarDecomposition((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1) \u2192 tuple[STATIC]_\b\u00b6\n+ Alias for _\bc_\bo_\bm_\bp_\bu_\bt_\be_\bU_\bn_\bi_\bt_\ba_\br_\by_\bP_\bo_\bs_\bi_\bt_\bi_\bv_\be.\n+ prod((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Product of all elements.\n+ pruned((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1[, (\b(f\bfl\blo\boa\bat\bt)\b)a\bab\bbs\bsT\bTo\bol\bl=\b=1\b1e\be-\b-0\b06\b6]) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\bX[STATIC]_\b\u00b6\n+ Zero all elements which are greater than a\bab\bbs\bsT\bTo\bol\bl. Negative zeros are\n+ not pruned.\n+ resize((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1, (\b(i\bin\bnt\bt)\b)r\bro\bow\bws\bs, (\b(i\bin\bnt\bt)\b)c\bco\bol\bls\bs) \u2192 None[STATIC]_\b\u00b6\n+ Change size of the matrix, keep values of elements which exist in\n+ the new matrix\n+ row((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1, (\b(i\bin\bnt\bt)\b)r\bro\bow\bw) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\bX[STATIC]_\b\u00b6\n+ Return row as vector.\n+ rows((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of rows.\n+ selfAdjointEigenDecomposition((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1) \u2192 tuple[STATIC]_\b\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((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1) \u2192 tuple[STATIC]_\b\u00b6\n+ Alias for _\bs_\be_\bl_\bf_\bA_\bd_\bj_\bo_\bi_\bn_\bt_\bE_\bi_\bg_\be_\bn_\bD_\be_\bc_\bo_\bm_\bp_\bo_\bs_\bi_\bt_\bi_\bo_\bn.\n+ squaredNorm((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Square of the Euclidean norm.\n+ sum((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Sum of all elements.\n+ svd((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1) \u2192 tuple[STATIC]_\b\u00b6\n+ Alias for _\bj_\ba_\bc_\bo_\bb_\bi_\bS_\bV_\bD.\n+ trace((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Return sum of diagonal elements.\n+ transpose((\b(M\bMa\bat\btr\bri\bix\bxX\bX)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\bX[STATIC]_\b\u00b6\n+ Return transposed matrix.\n+ c\bcl\bla\bas\bss\bs minieigen.MatrixXc_\b\u00b6\n+ /T\bTO\bOD\bDO\bO/\n+ s\bst\bta\bat\bti\bic\bc Identity((\b(i\bin\bnt\bt)\b)a\bar\brg\bg1\b1, (\b(i\bin\bnt\bt)\b)r\bra\ban\bnk\bk) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc[STATIC]_\b\u00b6\n+ Create identity matrix with given rank (square).\n+ s\bst\bta\bat\bti\bic\bc Ones((\b(i\bin\bnt\bt)\b)r\bro\bow\bws\bs, (\b(i\bin\bnt\bt)\b)c\bco\bol\bls\bs) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc[STATIC]_\b\u00b6\n+ Create matrix of given dimensions where all elements are set to 1.\n+ s\bst\bta\bat\bti\bic\bc Random((\b(i\bin\bnt\bt)\b)r\bro\bow\bws\bs, (\b(i\bin\bnt\bt)\b)c\bco\bol\bls\bs) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc[STATIC]_\b\u00b6\n+ Create matrix with given dimensions where all elements are set to\n+ number between 0 and 1 (uniformly-distributed).\n+ s\bst\bta\bat\bti\bic\bc Zero((\b(i\bin\bnt\bt)\b)r\bro\bow\bws\bs, (\b(i\bin\bnt\bt)\b)c\bco\bol\bls\bs) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc[STATIC]_\b\u00b6\n+ Create zero matrix of given dimensions\n+ col((\b(M\bMa\bat\btr\bri\bix\bxX\bXc\bc)\b)a\bar\brg\bg1\b1, (\b(i\bin\bnt\bt)\b)c\bco\bol\bl) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc[STATIC]_\b\u00b6\n+ Return column as vector.\n+ cols((\b(M\bMa\bat\btr\bri\bix\bxX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of columns.\n+ determinant((\b(M\bMa\bat\btr\bri\bix\bxX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Return matrix determinant.\n+ diagonal((\b(M\bMa\bat\btr\bri\bix\bxX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc[STATIC]_\b\u00b6\n+ Return diagonal as vector.\n+ inverse((\b(M\bMa\bat\btr\bri\bix\bxX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc[STATIC]_\b\u00b6\n+ Return inverted matrix.\n+ isApprox((\b(M\bMa\bat\btr\bri\bix\bxX\bXc\bc)\b)a\bar\brg\bg1\b1, (\b(M\bMa\bat\btr\bri\bix\bxX\bXc\bc)\b)o\bot\bth\bhe\ber\br[, (\b(f\bfl\blo\boa\bat\bt)\b)p\bpr\bre\bec\bc=\b=1\b1e\be-\b-1\b12\b2]) \u2192 bool\n+ [STATIC]_\b\u00b6\n+ Approximate comparison with precision p\bpr\bre\bec\bc.\n+ maxAbsCoeff((\b(M\bMa\bat\btr\bri\bix\bxX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Maximum absolute value over all elements.\n+ mean((\b(M\bMa\bat\btr\bri\bix\bxX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Mean value over all elements.\n+ norm((\b(M\bMa\bat\btr\bri\bix\bxX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Euclidean norm.\n+ normalize((\b(M\bMa\bat\btr\bri\bix\bxX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 None[STATIC]_\b\u00b6\n+ Normalize this object in-place.\n+ normalized((\b(M\bMa\bat\btr\bri\bix\bxX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc[STATIC]_\b\u00b6\n+ Return normalized copy of this object\n+ prod((\b(M\bMa\bat\btr\bri\bix\bxX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Product of all elements.\n+ pruned((\b(M\bMa\bat\btr\bri\bix\bxX\bXc\bc)\b)a\bar\brg\bg1\b1[, (\b(f\bfl\blo\boa\bat\bt)\b)a\bab\bbs\bsT\bTo\bol\bl=\b=1\b1e\be-\b-0\b06\b6]) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc[STATIC]_\b\u00b6\n+ Zero all elements which are greater than a\bab\bbs\bsT\bTo\bol\bl. Negative zeros are\n+ not pruned.\n+ resize((\b(M\bMa\bat\btr\bri\bix\bxX\bXc\bc)\b)a\bar\brg\bg1\b1, (\b(i\bin\bnt\bt)\b)r\bro\bow\bws\bs, (\b(i\bin\bnt\bt)\b)c\bco\bol\bls\bs) \u2192 None[STATIC]_\b\u00b6\n+ Change size of the matrix, keep values of elements which exist in\n+ the new matrix\n+ row((\b(M\bMa\bat\btr\bri\bix\bxX\bXc\bc)\b)a\bar\brg\bg1\b1, (\b(i\bin\bnt\bt)\b)r\bro\bow\bw) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc[STATIC]_\b\u00b6\n+ Return row as vector.\n+ rows((\b(M\bMa\bat\btr\bri\bix\bxX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of rows.\n+ squaredNorm((\b(M\bMa\bat\btr\bri\bix\bxX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Square of the Euclidean norm.\n+ sum((\b(M\bMa\bat\btr\bri\bix\bxX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Sum of all elements.\n+ trace((\b(M\bMa\bat\btr\bri\bix\bxX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Return sum of diagonal elements.\n+ transpose((\b(M\bMa\bat\btr\bri\bix\bxX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc[STATIC]_\b\u00b6\n+ Return transposed matrix.\n+ c\bcl\bla\bas\bss\bs minieigen.Quaternion_\b\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 =\b= Q\bQu\bua\bat\bte\ber\brn\bni\bio\bon\bn(\b((\b(1\b1,\b,0\b0,\b,0\b0)\b),\b,0\b0)\b)_\b\u00b6\n+ Rotate((\b(Q\bQu\bua\bat\bte\ber\brn\bni\bio\bon\bn)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)v\bv) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3[STATIC]_\b\u00b6\n+ angularDistance((\b(Q\bQu\bua\bat\bte\ber\brn\bni\bio\bon\bn)\b)a\bar\brg\bg1\b1, (\b(Q\bQu\bua\bat\bte\ber\brn\bni\bio\bon\bn)\b)a\bar\brg\bg2\b2) \u2192 float[STATIC]_\b\u00b6\n+ conjugate((\b(Q\bQu\bua\bat\bte\ber\brn\bni\bio\bon\bn)\b)a\bar\brg\bg1\b1) \u2192 _\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn[STATIC]_\b\u00b6\n+ inverse((\b(Q\bQu\bua\bat\bte\ber\brn\bni\bio\bon\bn)\b)a\bar\brg\bg1\b1) \u2192 _\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn[STATIC]_\b\u00b6\n+ norm((\b(Q\bQu\bua\bat\bte\ber\brn\bni\bio\bon\bn)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ normalize((\b(Q\bQu\bua\bat\bte\ber\brn\bni\bio\bon\bn)\b)a\bar\brg\bg1\b1) \u2192 None[STATIC]_\b\u00b6\n+ normalized((\b(Q\bQu\bua\bat\bte\ber\brn\bni\bio\bon\bn)\b)a\bar\brg\bg1\b1) \u2192 _\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn[STATIC]_\b\u00b6\n+ setFromTwoVectors((\b(Q\bQu\bua\bat\bte\ber\brn\bni\bio\bon\bn)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)u\bu, (\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)v\bv) \u2192 None\n+ [STATIC]_\b\u00b6\n+ slerp((\b(Q\bQu\bua\bat\bte\ber\brn\bni\bio\bon\bn)\b)a\bar\brg\bg1\b1, (\b(f\bfl\blo\boa\bat\bt)\b)t\bt, (\b(Q\bQu\bua\bat\bte\ber\brn\bni\bio\bon\bn)\b)o\bot\bth\bhe\ber\br) \u2192 _\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn\n+ [STATIC]_\b\u00b6\n+ toAngleAxis((\b(Q\bQu\bua\bat\bte\ber\brn\bni\bio\bon\bn)\b)a\bar\brg\bg1\b1) \u2192 tuple[STATIC]_\b\u00b6\n+ toAxisAngle((\b(Q\bQu\bua\bat\bte\ber\brn\bni\bio\bon\bn)\b)a\bar\brg\bg1\b1) \u2192 tuple[STATIC]_\b\u00b6\n+ toRotationMatrix((\b(Q\bQu\bua\bat\bte\ber\brn\bni\bio\bon\bn)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b3[STATIC]_\b\u00b6\n+ toRotationVector((\b(Q\bQu\bua\bat\bte\ber\brn\bni\bio\bon\bn)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3[STATIC]_\b\u00b6\n+ c\bcl\bla\bas\bss\bs minieigen.Vector2_\b\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, \u2026) of 2 floats.\n+ Static attributes: Zero, Ones, UnitX, UnitY.\n+ Identity =\b= V\bVe\bec\bct\bto\bor\br2\b2(\b(1\b1,\b,0\b0)\b)_\b\u00b6\n+ Ones =\b= V\bVe\bec\bct\bto\bor\br2\b2(\b(1\b1,\b,1\b1)\b)_\b\u00b6\n+ s\bst\bta\bat\bti\bic\bc Random() \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2[STATIC]_\b\u00b6\n+ Return an object where all elements are randomly set to values\n+ between 0 and 1.\n+ s\bst\bta\bat\bti\bic\bc Unit((\b(i\bin\bnt\bt)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2[STATIC]_\b\u00b6\n+ UnitX =\b= V\bVe\bec\bct\bto\bor\br2\b2(\b(1\b1,\b,0\b0)\b)_\b\u00b6\n+ UnitY =\b= V\bVe\bec\bct\bto\bor\br2\b2(\b(0\b0,\b,1\b1)\b)_\b\u00b6\n+ Zero =\b= V\bVe\bec\bct\bto\bor\br2\b2(\b(0\b0,\b,0\b0)\b)_\b\u00b6\n+ asDiagonal((\b(V\bVe\bec\bct\bto\bor\br2\b2)\b)a\bar\brg\bg1\b1) \u2192 object[STATIC]_\b\u00b6\n+ Return diagonal matrix with this vector on the diagonal.\n+ cols((\b(V\bVe\bec\bct\bto\bor\br2\b2)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of columns.\n+ dot((\b(V\bVe\bec\bct\bto\bor\br2\b2)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br2\b2)\b)o\bot\bth\bhe\ber\br) \u2192 float[STATIC]_\b\u00b6\n+ Dot product with o\bot\bth\bhe\ber\br.\n+ isApprox((\b(V\bVe\bec\bct\bto\bor\br2\b2)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br2\b2)\b)o\bot\bth\bhe\ber\br[, (\b(f\bfl\blo\boa\bat\bt)\b)p\bpr\bre\bec\bc=\b=1\b1e\be-\b-1\b12\b2]) \u2192 bool\n+ [STATIC]_\b\u00b6\n+ Approximate comparison with precision p\bpr\bre\bec\bc.\n+ maxAbsCoeff((\b(V\bVe\bec\bct\bto\bor\br2\b2)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Maximum absolute value over all elements.\n+ maxCoeff((\b(V\bVe\bec\bct\bto\bor\br2\b2)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Maximum value over all elements.\n+ mean((\b(V\bVe\bec\bct\bto\bor\br2\b2)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Mean value over all elements.\n+ minCoeff((\b(V\bVe\bec\bct\bto\bor\br2\b2)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Minimum value over all elements.\n+ norm((\b(V\bVe\bec\bct\bto\bor\br2\b2)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Euclidean norm.\n+ normalize((\b(V\bVe\bec\bct\bto\bor\br2\b2)\b)a\bar\brg\bg1\b1) \u2192 None[STATIC]_\b\u00b6\n+ Normalize this object in-place.\n+ normalized((\b(V\bVe\bec\bct\bto\bor\br2\b2)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2[STATIC]_\b\u00b6\n+ Return normalized copy of this object\n+ outer((\b(V\bVe\bec\bct\bto\bor\br2\b2)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br2\b2)\b)o\bot\bth\bhe\ber\br) \u2192 object[STATIC]_\b\u00b6\n+ Outer product with o\bot\bth\bhe\ber\br.\n+ prod((\b(V\bVe\bec\bct\bto\bor\br2\b2)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Product of all elements.\n+ pruned((\b(V\bVe\bec\bct\bto\bor\br2\b2)\b)a\bar\brg\bg1\b1[, (\b(f\bfl\blo\boa\bat\bt)\b)a\bab\bbs\bsT\bTo\bol\bl=\b=1\b1e\be-\b-0\b06\b6]) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2[STATIC]_\b\u00b6\n+ Zero all elements which are greater than a\bab\bbs\bsT\bTo\bol\bl. Negative zeros are\n+ not pruned.\n+ rows((\b(V\bVe\bec\bct\bto\bor\br2\b2)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of rows.\n+ squaredNorm((\b(V\bVe\bec\bct\bto\bor\br2\b2)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Square of the Euclidean norm.\n+ sum((\b(V\bVe\bec\bct\bto\bor\br2\b2)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Sum of all elements.\n+ c\bcl\bla\bas\bss\bs minieigen.Vector2c_\b\u00b6\n+ /T\bTO\bOD\bDO\bO/\n+ Identity =\b= V\bVe\bec\bct\bto\bor\br2\b2c\bc(\b(1\b1,\b,0\b0)\b)_\b\u00b6\n+ Ones =\b= V\bVe\bec\bct\bto\bor\br2\b2c\bc(\b(1\b1,\b,1\b1)\b)_\b\u00b6\n+ s\bst\bta\bat\bti\bic\bc Random() \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc[STATIC]_\b\u00b6\n+ Return an object where all elements are randomly set to values\n+ between 0 and 1.\n+ s\bst\bta\bat\bti\bic\bc Unit((\b(i\bin\bnt\bt)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc[STATIC]_\b\u00b6\n+ UnitX =\b= V\bVe\bec\bct\bto\bor\br2\b2c\bc(\b(1\b1,\b,0\b0)\b)_\b\u00b6\n+ UnitY =\b= V\bVe\bec\bct\bto\bor\br2\b2c\bc(\b(0\b0,\b,1\b1)\b)_\b\u00b6\n+ Zero =\b= V\bVe\bec\bct\bto\bor\br2\b2c\bc(\b(0\b0,\b,0\b0)\b)_\b\u00b6\n+ asDiagonal((\b(V\bVe\bec\bct\bto\bor\br2\b2c\bc)\b)a\bar\brg\bg1\b1) \u2192 object[STATIC]_\b\u00b6\n+ Return diagonal matrix with this vector on the diagonal.\n+ cols((\b(V\bVe\bec\bct\bto\bor\br2\b2c\bc)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of columns.\n+ dot((\b(V\bVe\bec\bct\bto\bor\br2\b2c\bc)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br2\b2c\bc)\b)o\bot\bth\bhe\ber\br) \u2192 complex[STATIC]_\b\u00b6\n+ Dot product with o\bot\bth\bhe\ber\br.\n+ isApprox((\b(V\bVe\bec\bct\bto\bor\br2\b2c\bc)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br2\b2c\bc)\b)o\bot\bth\bhe\ber\br[, (\b(f\bfl\blo\boa\bat\bt)\b)p\bpr\bre\bec\bc=\b=1\b1e\be-\b-1\b12\b2]) \u2192 bool\n+ [STATIC]_\b\u00b6\n+ Approximate comparison with precision p\bpr\bre\bec\bc.\n+ maxAbsCoeff((\b(V\bVe\bec\bct\bto\bor\br2\b2c\bc)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Maximum absolute value over all elements.\n+ mean((\b(V\bVe\bec\bct\bto\bor\br2\b2c\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Mean value over all elements.\n+ norm((\b(V\bVe\bec\bct\bto\bor\br2\b2c\bc)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Euclidean norm.\n+ normalize((\b(V\bVe\bec\bct\bto\bor\br2\b2c\bc)\b)a\bar\brg\bg1\b1) \u2192 None[STATIC]_\b\u00b6\n+ Normalize this object in-place.\n+ normalized((\b(V\bVe\bec\bct\bto\bor\br2\b2c\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc[STATIC]_\b\u00b6\n+ Return normalized copy of this object\n+ outer((\b(V\bVe\bec\bct\bto\bor\br2\b2c\bc)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br2\b2c\bc)\b)o\bot\bth\bhe\ber\br) \u2192 object[STATIC]_\b\u00b6\n+ Outer product with o\bot\bth\bhe\ber\br.\n+ prod((\b(V\bVe\bec\bct\bto\bor\br2\b2c\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Product of all elements.\n+ pruned((\b(V\bVe\bec\bct\bto\bor\br2\b2c\bc)\b)a\bar\brg\bg1\b1[, (\b(f\bfl\blo\boa\bat\bt)\b)a\bab\bbs\bsT\bTo\bol\bl=\b=1\b1e\be-\b-0\b06\b6]) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc[STATIC]_\b\u00b6\n+ Zero all elements which are greater than a\bab\bbs\bsT\bTo\bol\bl. Negative zeros are\n+ not pruned.\n+ rows((\b(V\bVe\bec\bct\bto\bor\br2\b2c\bc)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of rows.\n+ squaredNorm((\b(V\bVe\bec\bct\bto\bor\br2\b2c\bc)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Square of the Euclidean norm.\n+ sum((\b(V\bVe\bec\bct\bto\bor\br2\b2c\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Sum of all elements.\n+ c\bcl\bla\bas\bss\bs minieigen.Vector2i_\b\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, \u2026) of 2 integers.\n+ Static attributes: Zero, Ones, UnitX, UnitY.\n+ Identity =\b= V\bVe\bec\bct\bto\bor\br2\b2i\bi(\b(1\b1,\b,0\b0)\b)_\b\u00b6\n+ Ones =\b= V\bVe\bec\bct\bto\bor\br2\b2i\bi(\b(1\b1,\b,1\b1)\b)_\b\u00b6\n+ s\bst\bta\bat\bti\bic\bc Random() \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi[STATIC]_\b\u00b6\n+ Return an object where all elements are randomly set to values\n+ between 0 and 1.\n+ s\bst\bta\bat\bti\bic\bc Unit((\b(i\bin\bnt\bt)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi[STATIC]_\b\u00b6\n+ UnitX =\b= V\bVe\bec\bct\bto\bor\br2\b2i\bi(\b(1\b1,\b,0\b0)\b)_\b\u00b6\n+ UnitY =\b= V\bVe\bec\bct\bto\bor\br2\b2i\bi(\b(0\b0,\b,1\b1)\b)_\b\u00b6\n+ Zero =\b= V\bVe\bec\bct\bto\bor\br2\b2i\bi(\b(0\b0,\b,0\b0)\b)_\b\u00b6\n+ asDiagonal((\b(V\bVe\bec\bct\bto\bor\br2\b2i\bi)\b)a\bar\brg\bg1\b1) \u2192 object[STATIC]_\b\u00b6\n+ Return diagonal matrix with this vector on the diagonal.\n+ cols((\b(V\bVe\bec\bct\bto\bor\br2\b2i\bi)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of columns.\n+ dot((\b(V\bVe\bec\bct\bto\bor\br2\b2i\bi)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br2\b2i\bi)\b)o\bot\bth\bhe\ber\br) \u2192 int[STATIC]_\b\u00b6\n+ Dot product with o\bot\bth\bhe\ber\br.\n+ isApprox((\b(V\bVe\bec\bct\bto\bor\br2\b2i\bi)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br2\b2i\bi)\b)o\bot\bth\bhe\ber\br[, (\b(i\bin\bnt\bt)\b)p\bpr\bre\bec\bc=\b=0\b0]) \u2192 bool\n+ [STATIC]_\b\u00b6\n+ Approximate comparison with precision p\bpr\bre\bec\bc.\n+ maxAbsCoeff((\b(V\bVe\bec\bct\bto\bor\br2\b2i\bi)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Maximum absolute value over all elements.\n+ maxCoeff((\b(V\bVe\bec\bct\bto\bor\br2\b2i\bi)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Maximum value over all elements.\n+ mean((\b(V\bVe\bec\bct\bto\bor\br2\b2i\bi)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Mean value over all elements.\n+ minCoeff((\b(V\bVe\bec\bct\bto\bor\br2\b2i\bi)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Minimum value over all elements.\n+ outer((\b(V\bVe\bec\bct\bto\bor\br2\b2i\bi)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br2\b2i\bi)\b)o\bot\bth\bhe\ber\br) \u2192 object[STATIC]_\b\u00b6\n+ Outer product with o\bot\bth\bhe\ber\br.\n+ prod((\b(V\bVe\bec\bct\bto\bor\br2\b2i\bi)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Product of all elements.\n+ rows((\b(V\bVe\bec\bct\bto\bor\br2\b2i\bi)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of rows.\n+ sum((\b(V\bVe\bec\bct\bto\bor\br2\b2i\bi)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Sum of all elements.\n+ c\bcl\bla\bas\bss\bs minieigen.Vector3_\b\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, \u2026) of 3 floats.\n+ Static attributes: Zero, Ones, UnitX, UnitY, UnitZ.\n+ Identity =\b= V\bVe\bec\bct\bto\bor\br3\b3(\b(1\b1,\b,0\b0,\b,0\b0)\b)_\b\u00b6\n+ Ones =\b= V\bVe\bec\bct\bto\bor\br3\b3(\b(1\b1,\b,1\b1,\b,1\b1)\b)_\b\u00b6\n+ s\bst\bta\bat\bti\bic\bc Random() \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3[STATIC]_\b\u00b6\n+ Return an object where all elements are randomly set to values\n+ between 0 and 1.\n+ s\bst\bta\bat\bti\bic\bc Unit((\b(i\bin\bnt\bt)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3[STATIC]_\b\u00b6\n+ UnitX =\b= V\bVe\bec\bct\bto\bor\br3\b3(\b(1\b1,\b,0\b0,\b,0\b0)\b)_\b\u00b6\n+ UnitY =\b= V\bVe\bec\bct\bto\bor\br3\b3(\b(0\b0,\b,1\b1,\b,0\b0)\b)_\b\u00b6\n+ UnitZ =\b= V\bVe\bec\bct\bto\bor\br3\b3(\b(0\b0,\b,0\b0,\b,1\b1)\b)_\b\u00b6\n+ Zero =\b= V\bVe\bec\bct\bto\bor\br3\b3(\b(0\b0,\b,0\b0,\b,0\b0)\b)_\b\u00b6\n+ asDiagonal((\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b3[STATIC]_\b\u00b6\n+ Return diagonal matrix with this vector on the diagonal.\n+ cols((\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of columns.\n+ cross((\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg2\b2) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3[STATIC]_\b\u00b6\n+ dot((\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)o\bot\bth\bhe\ber\br) \u2192 float[STATIC]_\b\u00b6\n+ Dot product with o\bot\bth\bhe\ber\br.\n+ isApprox((\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)o\bot\bth\bhe\ber\br[, (\b(f\bfl\blo\boa\bat\bt)\b)p\bpr\bre\bec\bc=\b=1\b1e\be-\b-1\b12\b2]) \u2192 bool\n+ [STATIC]_\b\u00b6\n+ Approximate comparison with precision p\bpr\bre\bec\bc.\n+ maxAbsCoeff((\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Maximum absolute value over all elements.\n+ maxCoeff((\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Maximum value over all elements.\n+ mean((\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Mean value over all elements.\n+ minCoeff((\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Minimum value over all elements.\n+ norm((\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Euclidean norm.\n+ normalize((\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg1\b1) \u2192 None[STATIC]_\b\u00b6\n+ Normalize this object in-place.\n+ normalized((\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3[STATIC]_\b\u00b6\n+ Return normalized copy of this object\n+ outer((\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)o\bot\bth\bhe\ber\br) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b3[STATIC]_\b\u00b6\n+ Outer product with o\bot\bth\bhe\ber\br.\n+ prod((\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Product of all elements.\n+ pruned((\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg1\b1[, (\b(f\bfl\blo\boa\bat\bt)\b)a\bab\bbs\bsT\bTo\bol\bl=\b=1\b1e\be-\b-0\b06\b6]) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3[STATIC]_\b\u00b6\n+ Zero all elements which are greater than a\bab\bbs\bsT\bTo\bol\bl. Negative zeros are\n+ not pruned.\n+ rows((\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of rows.\n+ squaredNorm((\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Square of the Euclidean norm.\n+ sum((\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Sum of all elements.\n+ xy((\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2[STATIC]_\b\u00b6\n+ xz((\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2[STATIC]_\b\u00b6\n+ yx((\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2[STATIC]_\b\u00b6\n+ yz((\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2[STATIC]_\b\u00b6\n+ zx((\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2[STATIC]_\b\u00b6\n+ zy((\b(V\bVe\bec\bct\bto\bor\br3\b3)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2[STATIC]_\b\u00b6\n+ c\bcl\bla\bas\bss\bs minieigen.Vector3c_\b\u00b6\n+ /T\bTO\bOD\bDO\bO/\n+ Identity =\b= V\bVe\bec\bct\bto\bor\br3\b3c\bc(\b(1\b1,\b,0\b0,\b,0\b0)\b)_\b\u00b6\n+ Ones =\b= V\bVe\bec\bct\bto\bor\br3\b3c\bc(\b(1\b1,\b,1\b1,\b,1\b1)\b)_\b\u00b6\n+ s\bst\bta\bat\bti\bic\bc Random() \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc[STATIC]_\b\u00b6\n+ Return an object where all elements are randomly set to values\n+ between 0 and 1.\n+ s\bst\bta\bat\bti\bic\bc Unit((\b(i\bin\bnt\bt)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc[STATIC]_\b\u00b6\n+ UnitX =\b= V\bVe\bec\bct\bto\bor\br3\b3c\bc(\b(1\b1,\b,0\b0,\b,0\b0)\b)_\b\u00b6\n+ UnitY =\b= V\bVe\bec\bct\bto\bor\br3\b3c\bc(\b(0\b0,\b,1\b1,\b,0\b0)\b)_\b\u00b6\n+ UnitZ =\b= V\bVe\bec\bct\bto\bor\br3\b3c\bc(\b(0\b0,\b,0\b0,\b,1\b1)\b)_\b\u00b6\n+ Zero =\b= V\bVe\bec\bct\bto\bor\br3\b3c\bc(\b(0\b0,\b,0\b0,\b,0\b0)\b)_\b\u00b6\n+ asDiagonal((\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc[STATIC]_\b\u00b6\n+ Return diagonal matrix with this vector on the diagonal.\n+ cols((\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of columns.\n+ cross((\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)a\bar\brg\bg2\b2) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc[STATIC]_\b\u00b6\n+ dot((\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)o\bot\bth\bhe\ber\br) \u2192 complex[STATIC]_\b\u00b6\n+ Dot product with o\bot\bth\bhe\ber\br.\n+ isApprox((\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)o\bot\bth\bhe\ber\br[, (\b(f\bfl\blo\boa\bat\bt)\b)p\bpr\bre\bec\bc=\b=1\b1e\be-\b-1\b12\b2]) \u2192 bool\n+ [STATIC]_\b\u00b6\n+ Approximate comparison with precision p\bpr\bre\bec\bc.\n+ maxAbsCoeff((\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Maximum absolute value over all elements.\n+ mean((\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Mean value over all elements.\n+ norm((\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Euclidean norm.\n+ normalize((\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 None[STATIC]_\b\u00b6\n+ Normalize this object in-place.\n+ normalized((\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc[STATIC]_\b\u00b6\n+ Return normalized copy of this object\n+ outer((\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)o\bot\bth\bhe\ber\br) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc[STATIC]_\b\u00b6\n+ Outer product with o\bot\bth\bhe\ber\br.\n+ prod((\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Product of all elements.\n+ pruned((\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)a\bar\brg\bg1\b1[, (\b(f\bfl\blo\boa\bat\bt)\b)a\bab\bbs\bsT\bTo\bol\bl=\b=1\b1e\be-\b-0\b06\b6]) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc[STATIC]_\b\u00b6\n+ Zero all elements which are greater than a\bab\bbs\bsT\bTo\bol\bl. Negative zeros are\n+ not pruned.\n+ rows((\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of rows.\n+ squaredNorm((\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Square of the Euclidean norm.\n+ sum((\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Sum of all elements.\n+ xy((\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc[STATIC]_\b\u00b6\n+ xz((\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc[STATIC]_\b\u00b6\n+ yx((\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc[STATIC]_\b\u00b6\n+ yz((\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc[STATIC]_\b\u00b6\n+ zx((\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc[STATIC]_\b\u00b6\n+ zy((\b(V\bVe\bec\bct\bto\bor\br3\b3c\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc[STATIC]_\b\u00b6\n+ c\bcl\bla\bas\bss\bs minieigen.Vector3i_\b\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, \u2026) of 3 integers.\n+ Static attributes: Zero, Ones, UnitX, UnitY, UnitZ.\n+ Identity =\b= V\bVe\bec\bct\bto\bor\br3\b3i\bi(\b(1\b1,\b,0\b0,\b,0\b0)\b)_\b\u00b6\n+ Ones =\b= V\bVe\bec\bct\bto\bor\br3\b3i\bi(\b(1\b1,\b,1\b1,\b,1\b1)\b)_\b\u00b6\n+ s\bst\bta\bat\bti\bic\bc Random() \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi[STATIC]_\b\u00b6\n+ Return an object where all elements are randomly set to values\n+ between 0 and 1.\n+ s\bst\bta\bat\bti\bic\bc Unit((\b(i\bin\bnt\bt)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi[STATIC]_\b\u00b6\n+ UnitX =\b= V\bVe\bec\bct\bto\bor\br3\b3i\bi(\b(1\b1,\b,0\b0,\b,0\b0)\b)_\b\u00b6\n+ UnitY =\b= V\bVe\bec\bct\bto\bor\br3\b3i\bi(\b(0\b0,\b,1\b1,\b,0\b0)\b)_\b\u00b6\n+ UnitZ =\b= V\bVe\bec\bct\bto\bor\br3\b3i\bi(\b(0\b0,\b,0\b0,\b,1\b1)\b)_\b\u00b6\n+ Zero =\b= V\bVe\bec\bct\bto\bor\br3\b3i\bi(\b(0\b0,\b,0\b0,\b,0\b0)\b)_\b\u00b6\n+ asDiagonal((\b(V\bVe\bec\bct\bto\bor\br3\b3i\bi)\b)a\bar\brg\bg1\b1) \u2192 object[STATIC]_\b\u00b6\n+ Return diagonal matrix with this vector on the diagonal.\n+ cols((\b(V\bVe\bec\bct\bto\bor\br3\b3i\bi)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of columns.\n+ cross((\b(V\bVe\bec\bct\bto\bor\br3\b3i\bi)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br3\b3i\bi)\b)a\bar\brg\bg2\b2) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi[STATIC]_\b\u00b6\n+ dot((\b(V\bVe\bec\bct\bto\bor\br3\b3i\bi)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br3\b3i\bi)\b)o\bot\bth\bhe\ber\br) \u2192 int[STATIC]_\b\u00b6\n+ Dot product with o\bot\bth\bhe\ber\br.\n+ isApprox((\b(V\bVe\bec\bct\bto\bor\br3\b3i\bi)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br3\b3i\bi)\b)o\bot\bth\bhe\ber\br[, (\b(i\bin\bnt\bt)\b)p\bpr\bre\bec\bc=\b=0\b0]) \u2192 bool\n+ [STATIC]_\b\u00b6\n+ Approximate comparison with precision p\bpr\bre\bec\bc.\n+ maxAbsCoeff((\b(V\bVe\bec\bct\bto\bor\br3\b3i\bi)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Maximum absolute value over all elements.\n+ maxCoeff((\b(V\bVe\bec\bct\bto\bor\br3\b3i\bi)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Maximum value over all elements.\n+ mean((\b(V\bVe\bec\bct\bto\bor\br3\b3i\bi)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Mean value over all elements.\n+ minCoeff((\b(V\bVe\bec\bct\bto\bor\br3\b3i\bi)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Minimum value over all elements.\n+ outer((\b(V\bVe\bec\bct\bto\bor\br3\b3i\bi)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br3\b3i\bi)\b)o\bot\bth\bhe\ber\br) \u2192 object[STATIC]_\b\u00b6\n+ Outer product with o\bot\bth\bhe\ber\br.\n+ prod((\b(V\bVe\bec\bct\bto\bor\br3\b3i\bi)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Product of all elements.\n+ rows((\b(V\bVe\bec\bct\bto\bor\br3\b3i\bi)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of rows.\n+ sum((\b(V\bVe\bec\bct\bto\bor\br3\b3i\bi)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Sum of all elements.\n+ xy((\b(V\bVe\bec\bct\bto\bor\br3\b3i\bi)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi[STATIC]_\b\u00b6\n+ xz((\b(V\bVe\bec\bct\bto\bor\br3\b3i\bi)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi[STATIC]_\b\u00b6\n+ yx((\b(V\bVe\bec\bct\bto\bor\br3\b3i\bi)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi[STATIC]_\b\u00b6\n+ yz((\b(V\bVe\bec\bct\bto\bor\br3\b3i\bi)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi[STATIC]_\b\u00b6\n+ zx((\b(V\bVe\bec\bct\bto\bor\br3\b3i\bi)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi[STATIC]_\b\u00b6\n+ zy((\b(V\bVe\bec\bct\bto\bor\br3\b3i\bi)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi[STATIC]_\b\u00b6\n+ c\bcl\bla\bas\bss\bs minieigen.Vector4_\b\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, \u2026) of 4 floats.\n+ Static attributes: Zero, Ones.\n+ Identity =\b= V\bVe\bec\bct\bto\bor\br4\b4(\b(1\b1,\b,0\b0,\b,0\b0,\b, 0\b0)\b)_\b\u00b6\n+ Ones =\b= V\bVe\bec\bct\bto\bor\br4\b4(\b(1\b1,\b,1\b1,\b,1\b1,\b, 1\b1)\b)_\b\u00b6\n+ s\bst\bta\bat\bti\bic\bc Random() \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b4[STATIC]_\b\u00b6\n+ Return an object where all elements are randomly set to values\n+ between 0 and 1.\n+ s\bst\bta\bat\bti\bic\bc Unit((\b(i\bin\bnt\bt)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b4[STATIC]_\b\u00b6\n+ Zero =\b= V\bVe\bec\bct\bto\bor\br4\b4(\b(0\b0,\b,0\b0,\b,0\b0,\b, 0\b0)\b)_\b\u00b6\n+ asDiagonal((\b(V\bVe\bec\bct\bto\bor\br4\b4)\b)a\bar\brg\bg1\b1) \u2192 object[STATIC]_\b\u00b6\n+ Return diagonal matrix with this vector on the diagonal.\n+ cols((\b(V\bVe\bec\bct\bto\bor\br4\b4)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of columns.\n+ dot((\b(V\bVe\bec\bct\bto\bor\br4\b4)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br4\b4)\b)o\bot\bth\bhe\ber\br) \u2192 float[STATIC]_\b\u00b6\n+ Dot product with o\bot\bth\bhe\ber\br.\n+ isApprox((\b(V\bVe\bec\bct\bto\bor\br4\b4)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br4\b4)\b)o\bot\bth\bhe\ber\br[, (\b(f\bfl\blo\boa\bat\bt)\b)p\bpr\bre\bec\bc=\b=1\b1e\be-\b-1\b12\b2]) \u2192 bool\n+ [STATIC]_\b\u00b6\n+ Approximate comparison with precision p\bpr\bre\bec\bc.\n+ maxAbsCoeff((\b(V\bVe\bec\bct\bto\bor\br4\b4)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Maximum absolute value over all elements.\n+ maxCoeff((\b(V\bVe\bec\bct\bto\bor\br4\b4)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Maximum value over all elements.\n+ mean((\b(V\bVe\bec\bct\bto\bor\br4\b4)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Mean value over all elements.\n+ minCoeff((\b(V\bVe\bec\bct\bto\bor\br4\b4)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Minimum value over all elements.\n+ norm((\b(V\bVe\bec\bct\bto\bor\br4\b4)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Euclidean norm.\n+ normalize((\b(V\bVe\bec\bct\bto\bor\br4\b4)\b)a\bar\brg\bg1\b1) \u2192 None[STATIC]_\b\u00b6\n+ Normalize this object in-place.\n+ normalized((\b(V\bVe\bec\bct\bto\bor\br4\b4)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b4[STATIC]_\b\u00b6\n+ Return normalized copy of this object\n+ outer((\b(V\bVe\bec\bct\bto\bor\br4\b4)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br4\b4)\b)o\bot\bth\bhe\ber\br) \u2192 object[STATIC]_\b\u00b6\n+ Outer product with o\bot\bth\bhe\ber\br.\n+ prod((\b(V\bVe\bec\bct\bto\bor\br4\b4)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Product of all elements.\n+ pruned((\b(V\bVe\bec\bct\bto\bor\br4\b4)\b)a\bar\brg\bg1\b1[, (\b(f\bfl\blo\boa\bat\bt)\b)a\bab\bbs\bsT\bTo\bol\bl=\b=1\b1e\be-\b-0\b06\b6]) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b4[STATIC]_\b\u00b6\n+ Zero all elements which are greater than a\bab\bbs\bsT\bTo\bol\bl. Negative zeros are\n+ not pruned.\n+ rows((\b(V\bVe\bec\bct\bto\bor\br4\b4)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of rows.\n+ squaredNorm((\b(V\bVe\bec\bct\bto\bor\br4\b4)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Square of the Euclidean norm.\n+ sum((\b(V\bVe\bec\bct\bto\bor\br4\b4)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Sum of all elements.\n+ c\bcl\bla\bas\bss\bs minieigen.Vector6_\b\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, \u2026) of 6 floats.\n+ Static attributes: Zero, Ones.\n+ Identity =\b= V\bVe\bec\bct\bto\bor\br6\b6(\b(1\b1,\b,0\b0,\b,0\b0,\b, 0\b0,\b,0\b0,\b,0\b0)\b)_\b\u00b6\n+ Ones =\b= V\bVe\bec\bct\bto\bor\br6\b6(\b(1\b1,\b,1\b1,\b,1\b1,\b, 1\b1,\b,1\b1,\b,1\b1)\b)_\b\u00b6\n+ s\bst\bta\bat\bti\bic\bc Random() \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b6[STATIC]_\b\u00b6\n+ Return an object where all elements are randomly set to values\n+ between 0 and 1.\n+ s\bst\bta\bat\bti\bic\bc Unit((\b(i\bin\bnt\bt)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b6[STATIC]_\b\u00b6\n+ Zero =\b= V\bVe\bec\bct\bto\bor\br6\b6(\b(0\b0,\b,0\b0,\b,0\b0,\b, 0\b0,\b,0\b0,\b,0\b0)\b)_\b\u00b6\n+ asDiagonal((\b(V\bVe\bec\bct\bto\bor\br6\b6)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b6[STATIC]_\b\u00b6\n+ Return diagonal matrix with this vector on the diagonal.\n+ cols((\b(V\bVe\bec\bct\bto\bor\br6\b6)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of columns.\n+ dot((\b(V\bVe\bec\bct\bto\bor\br6\b6)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br6\b6)\b)o\bot\bth\bhe\ber\br) \u2192 float[STATIC]_\b\u00b6\n+ Dot product with o\bot\bth\bhe\ber\br.\n+ head((\b(V\bVe\bec\bct\bto\bor\br6\b6)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3[STATIC]_\b\u00b6\n+ isApprox((\b(V\bVe\bec\bct\bto\bor\br6\b6)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br6\b6)\b)o\bot\bth\bhe\ber\br[, (\b(f\bfl\blo\boa\bat\bt)\b)p\bpr\bre\bec\bc=\b=1\b1e\be-\b-1\b12\b2]) \u2192 bool\n+ [STATIC]_\b\u00b6\n+ Approximate comparison with precision p\bpr\bre\bec\bc.\n+ maxAbsCoeff((\b(V\bVe\bec\bct\bto\bor\br6\b6)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Maximum absolute value over all elements.\n+ maxCoeff((\b(V\bVe\bec\bct\bto\bor\br6\b6)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Maximum value over all elements.\n+ mean((\b(V\bVe\bec\bct\bto\bor\br6\b6)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Mean value over all elements.\n+ minCoeff((\b(V\bVe\bec\bct\bto\bor\br6\b6)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Minimum value over all elements.\n+ norm((\b(V\bVe\bec\bct\bto\bor\br6\b6)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Euclidean norm.\n+ normalize((\b(V\bVe\bec\bct\bto\bor\br6\b6)\b)a\bar\brg\bg1\b1) \u2192 None[STATIC]_\b\u00b6\n+ Normalize this object in-place.\n+ normalized((\b(V\bVe\bec\bct\bto\bor\br6\b6)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b6[STATIC]_\b\u00b6\n+ Return normalized copy of this object\n+ outer((\b(V\bVe\bec\bct\bto\bor\br6\b6)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br6\b6)\b)o\bot\bth\bhe\ber\br) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b6[STATIC]_\b\u00b6\n+ Outer product with o\bot\bth\bhe\ber\br.\n+ prod((\b(V\bVe\bec\bct\bto\bor\br6\b6)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Product of all elements.\n+ pruned((\b(V\bVe\bec\bct\bto\bor\br6\b6)\b)a\bar\brg\bg1\b1[, (\b(f\bfl\blo\boa\bat\bt)\b)a\bab\bbs\bsT\bTo\bol\bl=\b=1\b1e\be-\b-0\b06\b6]) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b6[STATIC]_\b\u00b6\n+ Zero all elements which are greater than a\bab\bbs\bsT\bTo\bol\bl. Negative zeros are\n+ not pruned.\n+ rows((\b(V\bVe\bec\bct\bto\bor\br6\b6)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of rows.\n+ squaredNorm((\b(V\bVe\bec\bct\bto\bor\br6\b6)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Square of the Euclidean norm.\n+ sum((\b(V\bVe\bec\bct\bto\bor\br6\b6)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Sum of all elements.\n+ tail((\b(V\bVe\bec\bct\bto\bor\br6\b6)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3[STATIC]_\b\u00b6\n+ c\bcl\bla\bas\bss\bs minieigen.Vector6c_\b\u00b6\n+ /T\bTO\bOD\bDO\bO/\n+ Identity =\b= V\bVe\bec\bct\bto\bor\br6\b6c\bc(\b(1\b1,\b,0\b0,\b,0\b0,\b, 0\b0,\b,0\b0,\b,0\b0)\b)_\b\u00b6\n+ Ones =\b= V\bVe\bec\bct\bto\bor\br6\b6c\bc(\b(1\b1,\b,1\b1,\b,1\b1,\b, 1\b1,\b,1\b1,\b,1\b1)\b)_\b\u00b6\n+ s\bst\bta\bat\bti\bic\bc Random() \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc[STATIC]_\b\u00b6\n+ Return an object where all elements are randomly set to values\n+ between 0 and 1.\n+ s\bst\bta\bat\bti\bic\bc Unit((\b(i\bin\bnt\bt)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc[STATIC]_\b\u00b6\n+ Zero =\b= V\bVe\bec\bct\bto\bor\br6\b6c\bc(\b(0\b0,\b,0\b0,\b,0\b0,\b, 0\b0,\b,0\b0,\b,0\b0)\b)_\b\u00b6\n+ asDiagonal((\b(V\bVe\bec\bct\bto\bor\br6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc[STATIC]_\b\u00b6\n+ Return diagonal matrix with this vector on the diagonal.\n+ cols((\b(V\bVe\bec\bct\bto\bor\br6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of columns.\n+ dot((\b(V\bVe\bec\bct\bto\bor\br6\b6c\bc)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br6\b6c\bc)\b)o\bot\bth\bhe\ber\br) \u2192 complex[STATIC]_\b\u00b6\n+ Dot product with o\bot\bth\bhe\ber\br.\n+ head((\b(V\bVe\bec\bct\bto\bor\br6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc[STATIC]_\b\u00b6\n+ isApprox((\b(V\bVe\bec\bct\bto\bor\br6\b6c\bc)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br6\b6c\bc)\b)o\bot\bth\bhe\ber\br[, (\b(f\bfl\blo\boa\bat\bt)\b)p\bpr\bre\bec\bc=\b=1\b1e\be-\b-1\b12\b2]) \u2192 bool\n+ [STATIC]_\b\u00b6\n+ Approximate comparison with precision p\bpr\bre\bec\bc.\n+ maxAbsCoeff((\b(V\bVe\bec\bct\bto\bor\br6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Maximum absolute value over all elements.\n+ mean((\b(V\bVe\bec\bct\bto\bor\br6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Mean value over all elements.\n+ norm((\b(V\bVe\bec\bct\bto\bor\br6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Euclidean norm.\n+ normalize((\b(V\bVe\bec\bct\bto\bor\br6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 None[STATIC]_\b\u00b6\n+ Normalize this object in-place.\n+ normalized((\b(V\bVe\bec\bct\bto\bor\br6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc[STATIC]_\b\u00b6\n+ Return normalized copy of this object\n+ outer((\b(V\bVe\bec\bct\bto\bor\br6\b6c\bc)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br6\b6c\bc)\b)o\bot\bth\bhe\ber\br) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc[STATIC]_\b\u00b6\n+ Outer product with o\bot\bth\bhe\ber\br.\n+ prod((\b(V\bVe\bec\bct\bto\bor\br6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Product of all elements.\n+ pruned((\b(V\bVe\bec\bct\bto\bor\br6\b6c\bc)\b)a\bar\brg\bg1\b1[, (\b(f\bfl\blo\boa\bat\bt)\b)a\bab\bbs\bsT\bTo\bol\bl=\b=1\b1e\be-\b-0\b06\b6]) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc[STATIC]_\b\u00b6\n+ Zero all elements which are greater than a\bab\bbs\bsT\bTo\bol\bl. Negative zeros are\n+ not pruned.\n+ rows((\b(V\bVe\bec\bct\bto\bor\br6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of rows.\n+ squaredNorm((\b(V\bVe\bec\bct\bto\bor\br6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Square of the Euclidean norm.\n+ sum((\b(V\bVe\bec\bct\bto\bor\br6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Sum of all elements.\n+ tail((\b(V\bVe\bec\bct\bto\bor\br6\b6c\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc[STATIC]_\b\u00b6\n+ c\bcl\bla\bas\bss\bs minieigen.Vector6i_\b\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, \u2026) of 6 floats.\n+ Static attributes: Zero, Ones.\n+ Identity =\b= V\bVe\bec\bct\bto\bor\br6\b6i\bi(\b(1\b1,\b,0\b0,\b,0\b0,\b, 0\b0,\b,0\b0,\b,0\b0)\b)_\b\u00b6\n+ Ones =\b= V\bVe\bec\bct\bto\bor\br6\b6i\bi(\b(1\b1,\b,1\b1,\b,1\b1,\b, 1\b1,\b,1\b1,\b,1\b1)\b)_\b\u00b6\n+ s\bst\bta\bat\bti\bic\bc Random() \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b6_\bi[STATIC]_\b\u00b6\n+ Return an object where all elements are randomly set to values\n+ between 0 and 1.\n+ s\bst\bta\bat\bti\bic\bc Unit((\b(i\bin\bnt\bt)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b6_\bi[STATIC]_\b\u00b6\n+ Zero =\b= V\bVe\bec\bct\bto\bor\br6\b6i\bi(\b(0\b0,\b,0\b0,\b,0\b0,\b, 0\b0,\b,0\b0,\b,0\b0)\b)_\b\u00b6\n+ asDiagonal((\b(V\bVe\bec\bct\bto\bor\br6\b6i\bi)\b)a\bar\brg\bg1\b1) \u2192 object[STATIC]_\b\u00b6\n+ Return diagonal matrix with this vector on the diagonal.\n+ cols((\b(V\bVe\bec\bct\bto\bor\br6\b6i\bi)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of columns.\n+ dot((\b(V\bVe\bec\bct\bto\bor\br6\b6i\bi)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br6\b6i\bi)\b)o\bot\bth\bhe\ber\br) \u2192 int[STATIC]_\b\u00b6\n+ Dot product with o\bot\bth\bhe\ber\br.\n+ head((\b(V\bVe\bec\bct\bto\bor\br6\b6i\bi)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi[STATIC]_\b\u00b6\n+ isApprox((\b(V\bVe\bec\bct\bto\bor\br6\b6i\bi)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br6\b6i\bi)\b)o\bot\bth\bhe\ber\br[, (\b(i\bin\bnt\bt)\b)p\bpr\bre\bec\bc=\b=0\b0]) \u2192 bool\n+ [STATIC]_\b\u00b6\n+ Approximate comparison with precision p\bpr\bre\bec\bc.\n+ maxAbsCoeff((\b(V\bVe\bec\bct\bto\bor\br6\b6i\bi)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Maximum absolute value over all elements.\n+ maxCoeff((\b(V\bVe\bec\bct\bto\bor\br6\b6i\bi)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Maximum value over all elements.\n+ mean((\b(V\bVe\bec\bct\bto\bor\br6\b6i\bi)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Mean value over all elements.\n+ minCoeff((\b(V\bVe\bec\bct\bto\bor\br6\b6i\bi)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Minimum value over all elements.\n+ outer((\b(V\bVe\bec\bct\bto\bor\br6\b6i\bi)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\br6\b6i\bi)\b)o\bot\bth\bhe\ber\br) \u2192 object[STATIC]_\b\u00b6\n+ Outer product with o\bot\bth\bhe\ber\br.\n+ prod((\b(V\bVe\bec\bct\bto\bor\br6\b6i\bi)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Product of all elements.\n+ rows((\b(V\bVe\bec\bct\bto\bor\br6\b6i\bi)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of rows.\n+ sum((\b(V\bVe\bec\bct\bto\bor\br6\b6i\bi)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Sum of all elements.\n+ tail((\b(V\bVe\bec\bct\bto\bor\br6\b6i\bi)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi[STATIC]_\b\u00b6\n+ c\bcl\bla\bas\bss\bs minieigen.VectorX_\b\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, \u2026) of X floats.\n+ s\bst\bta\bat\bti\bic\bc Ones((\b(i\bin\bnt\bt)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\bX[STATIC]_\b\u00b6\n+ s\bst\bta\bat\bti\bic\bc Random((\b(i\bin\bnt\bt)\b)l\ble\ben\bn) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\bX[STATIC]_\b\u00b6\n+ Return vector of given length with all elements set to values\n+ between 0 and 1 randomly.\n+ s\bst\bta\bat\bti\bic\bc Unit((\b(i\bin\bnt\bt)\b)a\bar\brg\bg1\b1, (\b(i\bin\bnt\bt)\b)a\bar\brg\bg2\b2) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\bX[STATIC]_\b\u00b6\n+ s\bst\bta\bat\bti\bic\bc Zero((\b(i\bin\bnt\bt)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\bX[STATIC]_\b\u00b6\n+ asDiagonal((\b(V\bVe\bec\bct\bto\bor\brX\bX)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\bX[STATIC]_\b\u00b6\n+ Return diagonal matrix with this vector on the diagonal.\n+ cols((\b(V\bVe\bec\bct\bto\bor\brX\bX)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of columns.\n+ dot((\b(V\bVe\bec\bct\bto\bor\brX\bX)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\brX\bX)\b)o\bot\bth\bhe\ber\br) \u2192 float[STATIC]_\b\u00b6\n+ Dot product with o\bot\bth\bhe\ber\br.\n+ isApprox((\b(V\bVe\bec\bct\bto\bor\brX\bX)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\brX\bX)\b)o\bot\bth\bhe\ber\br[, (\b(f\bfl\blo\boa\bat\bt)\b)p\bpr\bre\bec\bc=\b=1\b1e\be-\b-1\b12\b2]) \u2192 bool\n+ [STATIC]_\b\u00b6\n+ Approximate comparison with precision p\bpr\bre\bec\bc.\n+ maxAbsCoeff((\b(V\bVe\bec\bct\bto\bor\brX\bX)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Maximum absolute value over all elements.\n+ maxCoeff((\b(V\bVe\bec\bct\bto\bor\brX\bX)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Maximum value over all elements.\n+ mean((\b(V\bVe\bec\bct\bto\bor\brX\bX)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Mean value over all elements.\n+ minCoeff((\b(V\bVe\bec\bct\bto\bor\brX\bX)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Minimum value over all elements.\n+ norm((\b(V\bVe\bec\bct\bto\bor\brX\bX)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Euclidean norm.\n+ normalize((\b(V\bVe\bec\bct\bto\bor\brX\bX)\b)a\bar\brg\bg1\b1) \u2192 None[STATIC]_\b\u00b6\n+ Normalize this object in-place.\n+ normalized((\b(V\bVe\bec\bct\bto\bor\brX\bX)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\bX[STATIC]_\b\u00b6\n+ Return normalized copy of this object\n+ outer((\b(V\bVe\bec\bct\bto\bor\brX\bX)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\brX\bX)\b)o\bot\bth\bhe\ber\br) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\bX[STATIC]_\b\u00b6\n+ Outer product with o\bot\bth\bhe\ber\br.\n+ prod((\b(V\bVe\bec\bct\bto\bor\brX\bX)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Product of all elements.\n+ pruned((\b(V\bVe\bec\bct\bto\bor\brX\bX)\b)a\bar\brg\bg1\b1[, (\b(f\bfl\blo\boa\bat\bt)\b)a\bab\bbs\bsT\bTo\bol\bl=\b=1\b1e\be-\b-0\b06\b6]) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\bX[STATIC]_\b\u00b6\n+ Zero all elements which are greater than a\bab\bbs\bsT\bTo\bol\bl. Negative zeros are\n+ not pruned.\n+ resize((\b(V\bVe\bec\bct\bto\bor\brX\bX)\b)a\bar\brg\bg1\b1, (\b(i\bin\bnt\bt)\b)a\bar\brg\bg2\b2) \u2192 None[STATIC]_\b\u00b6\n+ rows((\b(V\bVe\bec\bct\bto\bor\brX\bX)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of rows.\n+ squaredNorm((\b(V\bVe\bec\bct\bto\bor\brX\bX)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Square of the Euclidean norm.\n+ sum((\b(V\bVe\bec\bct\bto\bor\brX\bX)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Sum of all elements.\n+ c\bcl\bla\bas\bss\bs minieigen.VectorXc_\b\u00b6\n+ /T\bTO\bOD\bDO\bO/\n+ s\bst\bta\bat\bti\bic\bc Ones((\b(i\bin\bnt\bt)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc[STATIC]_\b\u00b6\n+ s\bst\bta\bat\bti\bic\bc Random((\b(i\bin\bnt\bt)\b)l\ble\ben\bn) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc[STATIC]_\b\u00b6\n+ Return vector of given length with all elements set to values\n+ between 0 and 1 randomly.\n+ s\bst\bta\bat\bti\bic\bc Unit((\b(i\bin\bnt\bt)\b)a\bar\brg\bg1\b1, (\b(i\bin\bnt\bt)\b)a\bar\brg\bg2\b2) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc[STATIC]_\b\u00b6\n+ s\bst\bta\bat\bti\bic\bc Zero((\b(i\bin\bnt\bt)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc[STATIC]_\b\u00b6\n+ asDiagonal((\b(V\bVe\bec\bct\bto\bor\brX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc[STATIC]_\b\u00b6\n+ Return diagonal matrix with this vector on the diagonal.\n+ cols((\b(V\bVe\bec\bct\bto\bor\brX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of columns.\n+ dot((\b(V\bVe\bec\bct\bto\bor\brX\bXc\bc)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\brX\bXc\bc)\b)o\bot\bth\bhe\ber\br) \u2192 complex[STATIC]_\b\u00b6\n+ Dot product with o\bot\bth\bhe\ber\br.\n+ isApprox((\b(V\bVe\bec\bct\bto\bor\brX\bXc\bc)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\brX\bXc\bc)\b)o\bot\bth\bhe\ber\br[, (\b(f\bfl\blo\boa\bat\bt)\b)p\bpr\bre\bec\bc=\b=1\b1e\be-\b-1\b12\b2]) \u2192 bool\n+ [STATIC]_\b\u00b6\n+ Approximate comparison with precision p\bpr\bre\bec\bc.\n+ maxAbsCoeff((\b(V\bVe\bec\bct\bto\bor\brX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Maximum absolute value over all elements.\n+ mean((\b(V\bVe\bec\bct\bto\bor\brX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Mean value over all elements.\n+ norm((\b(V\bVe\bec\bct\bto\bor\brX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Euclidean norm.\n+ normalize((\b(V\bVe\bec\bct\bto\bor\brX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 None[STATIC]_\b\u00b6\n+ Normalize this object in-place.\n+ normalized((\b(V\bVe\bec\bct\bto\bor\brX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc[STATIC]_\b\u00b6\n+ Return normalized copy of this object\n+ outer((\b(V\bVe\bec\bct\bto\bor\brX\bXc\bc)\b)a\bar\brg\bg1\b1, (\b(V\bVe\bec\bct\bto\bor\brX\bXc\bc)\b)o\bot\bth\bhe\ber\br) \u2192 _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc[STATIC]_\b\u00b6\n+ Outer product with o\bot\bth\bhe\ber\br.\n+ prod((\b(V\bVe\bec\bct\bto\bor\brX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Product of all elements.\n+ pruned((\b(V\bVe\bec\bct\bto\bor\brX\bXc\bc)\b)a\bar\brg\bg1\b1[, (\b(f\bfl\blo\boa\bat\bt)\b)a\bab\bbs\bsT\bTo\bol\bl=\b=1\b1e\be-\b-0\b06\b6]) \u2192 _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc[STATIC]_\b\u00b6\n+ Zero all elements which are greater than a\bab\bbs\bsT\bTo\bol\bl. Negative zeros are\n+ not pruned.\n+ resize((\b(V\bVe\bec\bct\bto\bor\brX\bXc\bc)\b)a\bar\brg\bg1\b1, (\b(i\bin\bnt\bt)\b)a\bar\brg\bg2\b2) \u2192 None[STATIC]_\b\u00b6\n+ rows((\b(V\bVe\bec\bct\bto\bor\brX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 int[STATIC]_\b\u00b6\n+ Number of rows.\n+ squaredNorm((\b(V\bVe\bec\bct\bto\bor\brX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 float[STATIC]_\b\u00b6\n+ Square of the Euclidean norm.\n+ sum((\b(V\bVe\bec\bct\bto\bor\brX\bXc\bc)\b)a\bar\brg\bg1\b1) \u2192 complex[STATIC]_\b\u00b6\n+ Sum of all elements.\n+ minieigen.float2str((\b(f\bfl\blo\boa\bat\bt)\b)f\bf[, (\b(i\bin\bnt\bt)\b)p\bpa\bad\bd=\b=0\b0]) \u2192 str_\b\u00b6\n+ Return the shortest string representation of f\bf which will is equal to f\bf\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 *\b**\b**\b**\b* _\bT\bT_\ba\ba_\bb\bb_\bl\bl_\be\be_\b _\bo\bo_\bf\bf_\b _\bC\bC_\bo\bo_\bn\bn_\bt\bt_\be\be_\bn\bn_\bt\bt_\bs\bs *\b**\b**\b**\b*\n * _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b _\bd_\bo_\bc_\bu_\bm_\be_\bn_\bt_\ba_\bt_\bi_\bo_\bn\n o _\bO_\bv_\be_\br_\bv_\bi_\be_\bw\n o _\bE_\bx_\ba_\bm_\bp_\bl_\be_\bs\n o _\bN_\ba_\bm_\bi_\bn_\bg_\b _\bc_\bo_\bn_\bv_\be_\bn_\bt_\bi_\bo_\bn_\bs\n o _\bL_\bi_\bm_\bi_\bt_\ba_\bt_\bi_\bo_\bn_\bs\n o _\bL_\bi_\bn_\bk_\bs\n o _\bD_\bo_\bc_\bu_\bm_\be_\bn_\bt_\ba_\bt_\bi_\bo_\bn\n+ # _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2\n+ # _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2_\b._\bc_\be_\bn_\bt_\be_\br_\b(_\b)\n+ # _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2_\b._\bc_\bl_\ba_\bm_\bp_\b(_\b)\n+ # _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2_\b._\bc_\bo_\bn_\bt_\ba_\bi_\bn_\bs_\b(_\b)\n+ # _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2_\b._\be_\bm_\bp_\bt_\by_\b(_\b)\n+ # _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2_\b._\be_\bx_\bt_\be_\bn_\bd_\b(_\b)\n+ # _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2_\b._\bi_\bn_\bt_\be_\br_\bs_\be_\bc_\bt_\bi_\bo_\bn_\b(_\b)\n+ # _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2_\b._\bm_\ba_\bx\n+ # _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2_\b._\bm_\be_\br_\bg_\be_\bd_\b(_\b)\n+ # _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2_\b._\bm_\bi_\bn\n+ # _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2_\b._\bs_\bi_\bz_\be_\bs_\b(_\b)\n+ # _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b2_\b._\bv_\bo_\bl_\bu_\bm_\be_\b(_\b)\n+ # _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3\n+ # _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3_\b._\bc_\be_\bn_\bt_\be_\br_\b(_\b)\n+ # _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3_\b._\bc_\bl_\ba_\bm_\bp_\b(_\b)\n+ # _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3_\b._\bc_\bo_\bn_\bt_\ba_\bi_\bn_\bs_\b(_\b)\n+ # _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3_\b._\be_\bm_\bp_\bt_\by_\b(_\b)\n+ # _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3_\b._\be_\bx_\bt_\be_\bn_\bd_\b(_\b)\n+ # _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3_\b._\bi_\bn_\bt_\be_\br_\bs_\be_\bc_\bt_\bi_\bo_\bn_\b(_\b)\n+ # _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3_\b._\bm_\ba_\bx\n+ # _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3_\b._\bm_\be_\br_\bg_\be_\bd_\b(_\b)\n+ # _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3_\b._\bm_\bi_\bn\n+ # _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3_\b._\bs_\bi_\bz_\be_\bs_\b(_\b)\n+ # _\bA_\bl_\bi_\bg_\bn_\be_\bd_\bB_\bo_\bx_\b3_\b._\bv_\bo_\bl_\bu_\bm_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bI_\bd_\be_\bn_\bt_\bi_\bt_\by\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bO_\bn_\be_\bs\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bR_\ba_\bn_\bd_\bo_\bm_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bZ_\be_\br_\bo\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bc_\bo_\bl_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bc_\bo_\bl_\bs_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bc_\bo_\bm_\bp_\bu_\bt_\be_\bU_\bn_\bi_\bt_\ba_\br_\by_\bP_\bo_\bs_\bi_\bt_\bi_\bv_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bd_\be_\bt_\be_\br_\bm_\bi_\bn_\ba_\bn_\bt_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bd_\bi_\ba_\bg_\bo_\bn_\ba_\bl_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bi_\bn_\bv_\be_\br_\bs_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bi_\bs_\bA_\bp_\bp_\br_\bo_\bx_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bj_\ba_\bc_\bo_\bb_\bi_\bS_\bV_\bD_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bm_\ba_\bx_\bA_\bb_\bs_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bm_\ba_\bx_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bm_\be_\ba_\bn_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bm_\bi_\bn_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bn_\bo_\br_\bm_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\bd_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bp_\bo_\bl_\ba_\br_\bD_\be_\bc_\bo_\bm_\bp_\bo_\bs_\bi_\bt_\bi_\bo_\bn_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bp_\br_\bo_\bd_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bp_\br_\bu_\bn_\be_\bd_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\br_\bo_\bw_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\br_\bo_\bw_\bs_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bs_\be_\bl_\bf_\bA_\bd_\bj_\bo_\bi_\bn_\bt_\bE_\bi_\bg_\be_\bn_\bD_\be_\bc_\bo_\bm_\bp_\bo_\bs_\bi_\bt_\bi_\bo_\bn_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bs_\bp_\be_\bc_\bt_\br_\ba_\bl_\bD_\be_\bc_\bo_\bm_\bp_\bo_\bs_\bi_\bt_\bi_\bo_\bn_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bs_\bq_\bu_\ba_\br_\be_\bd_\bN_\bo_\br_\bm_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bs_\bu_\bm_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bs_\bv_\bd_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bt_\br_\ba_\bc_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\b._\bt_\br_\ba_\bn_\bs_\bp_\bo_\bs_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b._\bI_\bd_\be_\bn_\bt_\bi_\bt_\by\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b._\bO_\bn_\be_\bs\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b._\bR_\ba_\bn_\bd_\bo_\bm_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b._\bZ_\be_\br_\bo\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b._\bc_\bo_\bl_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b._\bc_\bo_\bl_\bs_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b._\bd_\be_\bt_\be_\br_\bm_\bi_\bn_\ba_\bn_\bt_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b._\bd_\bi_\ba_\bg_\bo_\bn_\ba_\bl_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b._\bi_\bn_\bv_\be_\br_\bs_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b._\bi_\bs_\bA_\bp_\bp_\br_\bo_\bx_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b._\bm_\ba_\bx_\bA_\bb_\bs_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b._\bm_\be_\ba_\bn_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b._\bn_\bo_\br_\bm_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\bd_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b._\bp_\br_\bo_\bd_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b._\bp_\br_\bu_\bn_\be_\bd_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b._\br_\bo_\bw_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b._\br_\bo_\bw_\bs_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b._\bs_\bq_\bu_\ba_\br_\be_\bd_\bN_\bo_\br_\bm_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b._\bs_\bu_\bm_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b._\bt_\br_\ba_\bc_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b3_\bc_\b._\bt_\br_\ba_\bn_\bs_\bp_\bo_\bs_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bI_\bd_\be_\bn_\bt_\bi_\bt_\by\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bO_\bn_\be_\bs\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bR_\ba_\bn_\bd_\bo_\bm_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bZ_\be_\br_\bo\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bc_\bo_\bl_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bc_\bo_\bl_\bs_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bc_\bo_\bm_\bp_\bu_\bt_\be_\bU_\bn_\bi_\bt_\ba_\br_\by_\bP_\bo_\bs_\bi_\bt_\bi_\bv_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bd_\be_\bt_\be_\br_\bm_\bi_\bn_\ba_\bn_\bt_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bd_\bi_\ba_\bg_\bo_\bn_\ba_\bl_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bi_\bn_\bv_\be_\br_\bs_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bi_\bs_\bA_\bp_\bp_\br_\bo_\bx_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bj_\ba_\bc_\bo_\bb_\bi_\bS_\bV_\bD_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bl_\bl_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bl_\br_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bm_\ba_\bx_\bA_\bb_\bs_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bm_\ba_\bx_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bm_\be_\ba_\bn_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bm_\bi_\bn_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bn_\bo_\br_\bm_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\bd_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bp_\bo_\bl_\ba_\br_\bD_\be_\bc_\bo_\bm_\bp_\bo_\bs_\bi_\bt_\bi_\bo_\bn_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bp_\br_\bo_\bd_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bp_\br_\bu_\bn_\be_\bd_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\br_\bo_\bw_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\br_\bo_\bw_\bs_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bs_\be_\bl_\bf_\bA_\bd_\bj_\bo_\bi_\bn_\bt_\bE_\bi_\bg_\be_\bn_\bD_\be_\bc_\bo_\bm_\bp_\bo_\bs_\bi_\bt_\bi_\bo_\bn_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bs_\bp_\be_\bc_\bt_\br_\ba_\bl_\bD_\be_\bc_\bo_\bm_\bp_\bo_\bs_\bi_\bt_\bi_\bo_\bn_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bs_\bq_\bu_\ba_\br_\be_\bd_\bN_\bo_\br_\bm_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bs_\bu_\bm_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bs_\bv_\bd_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bt_\br_\ba_\bc_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bt_\br_\ba_\bn_\bs_\bp_\bo_\bs_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bu_\bl_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\b._\bu_\br_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\bI_\bd_\be_\bn_\bt_\bi_\bt_\by\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\bO_\bn_\be_\bs\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\bR_\ba_\bn_\bd_\bo_\bm_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\bZ_\be_\br_\bo\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\bc_\bo_\bl_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\bc_\bo_\bl_\bs_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\bd_\be_\bt_\be_\br_\bm_\bi_\bn_\ba_\bn_\bt_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\bd_\bi_\ba_\bg_\bo_\bn_\ba_\bl_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\bi_\bn_\bv_\be_\br_\bs_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\bi_\bs_\bA_\bp_\bp_\br_\bo_\bx_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\bl_\bl_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\bl_\br_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\bm_\ba_\bx_\bA_\bb_\bs_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\bm_\be_\ba_\bn_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\bn_\bo_\br_\bm_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\bd_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\bp_\br_\bo_\bd_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\bp_\br_\bu_\bn_\be_\bd_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\br_\bo_\bw_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\br_\bo_\bw_\bs_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\bs_\bq_\bu_\ba_\br_\be_\bd_\bN_\bo_\br_\bm_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\bs_\bu_\bm_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\bt_\br_\ba_\bc_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\bt_\br_\ba_\bn_\bs_\bp_\bo_\bs_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\bu_\bl_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\b6_\bc_\b._\bu_\br_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bI_\bd_\be_\bn_\bt_\bi_\bt_\by_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bO_\bn_\be_\bs_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bR_\ba_\bn_\bd_\bo_\bm_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bZ_\be_\br_\bo_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bc_\bo_\bl_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bc_\bo_\bl_\bs_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bc_\bo_\bm_\bp_\bu_\bt_\be_\bU_\bn_\bi_\bt_\ba_\br_\by_\bP_\bo_\bs_\bi_\bt_\bi_\bv_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bd_\be_\bt_\be_\br_\bm_\bi_\bn_\ba_\bn_\bt_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bd_\bi_\ba_\bg_\bo_\bn_\ba_\bl_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bi_\bn_\bv_\be_\br_\bs_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bi_\bs_\bA_\bp_\bp_\br_\bo_\bx_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bj_\ba_\bc_\bo_\bb_\bi_\bS_\bV_\bD_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bm_\ba_\bx_\bA_\bb_\bs_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bm_\ba_\bx_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bm_\be_\ba_\bn_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bm_\bi_\bn_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bn_\bo_\br_\bm_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\bd_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bp_\bo_\bl_\ba_\br_\bD_\be_\bc_\bo_\bm_\bp_\bo_\bs_\bi_\bt_\bi_\bo_\bn_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bp_\br_\bo_\bd_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bp_\br_\bu_\bn_\be_\bd_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\br_\be_\bs_\bi_\bz_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\br_\bo_\bw_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\br_\bo_\bw_\bs_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bs_\be_\bl_\bf_\bA_\bd_\bj_\bo_\bi_\bn_\bt_\bE_\bi_\bg_\be_\bn_\bD_\be_\bc_\bo_\bm_\bp_\bo_\bs_\bi_\bt_\bi_\bo_\bn_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bs_\bp_\be_\bc_\bt_\br_\ba_\bl_\bD_\be_\bc_\bo_\bm_\bp_\bo_\bs_\bi_\bt_\bi_\bo_\bn_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bs_\bq_\bu_\ba_\br_\be_\bd_\bN_\bo_\br_\bm_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bs_\bu_\bm_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bs_\bv_\bd_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bt_\br_\ba_\bc_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\b._\bt_\br_\ba_\bn_\bs_\bp_\bo_\bs_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b._\bI_\bd_\be_\bn_\bt_\bi_\bt_\by_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b._\bO_\bn_\be_\bs_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b._\bR_\ba_\bn_\bd_\bo_\bm_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b._\bZ_\be_\br_\bo_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b._\bc_\bo_\bl_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b._\bc_\bo_\bl_\bs_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b._\bd_\be_\bt_\be_\br_\bm_\bi_\bn_\ba_\bn_\bt_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b._\bd_\bi_\ba_\bg_\bo_\bn_\ba_\bl_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b._\bi_\bn_\bv_\be_\br_\bs_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b._\bi_\bs_\bA_\bp_\bp_\br_\bo_\bx_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b._\bm_\ba_\bx_\bA_\bb_\bs_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b._\bm_\be_\ba_\bn_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b._\bn_\bo_\br_\bm_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\bd_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b._\bp_\br_\bo_\bd_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b._\bp_\br_\bu_\bn_\be_\bd_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b._\br_\be_\bs_\bi_\bz_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b._\br_\bo_\bw_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b._\br_\bo_\bw_\bs_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b._\bs_\bq_\bu_\ba_\br_\be_\bd_\bN_\bo_\br_\bm_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b._\bs_\bu_\bm_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b._\bt_\br_\ba_\bc_\be_\b(_\b)\n+ # _\bM_\ba_\bt_\br_\bi_\bx_\bX_\bc_\b._\bt_\br_\ba_\bn_\bs_\bp_\bo_\bs_\be_\b(_\b)\n+ # _\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn\n+ # _\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn_\b._\bI_\bd_\be_\bn_\bt_\bi_\bt_\by\n+ # _\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn_\b._\bR_\bo_\bt_\ba_\bt_\be_\b(_\b)\n+ # _\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn_\b._\ba_\bn_\bg_\bu_\bl_\ba_\br_\bD_\bi_\bs_\bt_\ba_\bn_\bc_\be_\b(_\b)\n+ # _\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn_\b._\bc_\bo_\bn_\bj_\bu_\bg_\ba_\bt_\be_\b(_\b)\n+ # _\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn_\b._\bi_\bn_\bv_\be_\br_\bs_\be_\b(_\b)\n+ # _\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn_\b._\bn_\bo_\br_\bm_\b(_\b)\n+ # _\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\b(_\b)\n+ # _\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\bd_\b(_\b)\n+ # _\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn_\b._\bs_\be_\bt_\bF_\br_\bo_\bm_\bT_\bw_\bo_\bV_\be_\bc_\bt_\bo_\br_\bs_\b(_\b)\n+ # _\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn_\b._\bs_\bl_\be_\br_\bp_\b(_\b)\n+ # _\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn_\b._\bt_\bo_\bA_\bn_\bg_\bl_\be_\bA_\bx_\bi_\bs_\b(_\b)\n+ # _\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn_\b._\bt_\bo_\bA_\bx_\bi_\bs_\bA_\bn_\bg_\bl_\be_\b(_\b)\n+ # _\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn_\b._\bt_\bo_\bR_\bo_\bt_\ba_\bt_\bi_\bo_\bn_\bM_\ba_\bt_\br_\bi_\bx_\b(_\b)\n+ # _\bQ_\bu_\ba_\bt_\be_\br_\bn_\bi_\bo_\bn_\b._\bt_\bo_\bR_\bo_\bt_\ba_\bt_\bi_\bo_\bn_\bV_\be_\bc_\bt_\bo_\br_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\b._\bI_\bd_\be_\bn_\bt_\bi_\bt_\by\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\b._\bO_\bn_\be_\bs\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\b._\bR_\ba_\bn_\bd_\bo_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\b._\bU_\bn_\bi_\bt_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\b._\bU_\bn_\bi_\bt_\bX\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\b._\bU_\bn_\bi_\bt_\bY\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\b._\bZ_\be_\br_\bo\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\b._\ba_\bs_\bD_\bi_\ba_\bg_\bo_\bn_\ba_\bl_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\b._\bc_\bo_\bl_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\b._\bd_\bo_\bt_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\b._\bi_\bs_\bA_\bp_\bp_\br_\bo_\bx_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\b._\bm_\ba_\bx_\bA_\bb_\bs_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\b._\bm_\ba_\bx_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\b._\bm_\be_\ba_\bn_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\b._\bm_\bi_\bn_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\b._\bn_\bo_\br_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\b._\bo_\bu_\bt_\be_\br_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\b._\bp_\br_\bo_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\b._\bp_\br_\bu_\bn_\be_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\b._\br_\bo_\bw_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\b._\bs_\bq_\bu_\ba_\br_\be_\bd_\bN_\bo_\br_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\b._\bs_\bu_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b._\bI_\bd_\be_\bn_\bt_\bi_\bt_\by\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b._\bO_\bn_\be_\bs\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b._\bR_\ba_\bn_\bd_\bo_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b._\bU_\bn_\bi_\bt_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b._\bU_\bn_\bi_\bt_\bX\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b._\bU_\bn_\bi_\bt_\bY\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b._\bZ_\be_\br_\bo\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b._\ba_\bs_\bD_\bi_\ba_\bg_\bo_\bn_\ba_\bl_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b._\bc_\bo_\bl_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b._\bd_\bo_\bt_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b._\bi_\bs_\bA_\bp_\bp_\br_\bo_\bx_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b._\bm_\ba_\bx_\bA_\bb_\bs_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b._\bm_\be_\ba_\bn_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b._\bn_\bo_\br_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b._\bo_\bu_\bt_\be_\br_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b._\bp_\br_\bo_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b._\bp_\br_\bu_\bn_\be_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b._\br_\bo_\bw_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b._\bs_\bq_\bu_\ba_\br_\be_\bd_\bN_\bo_\br_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bc_\b._\bs_\bu_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b._\bI_\bd_\be_\bn_\bt_\bi_\bt_\by\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b._\bO_\bn_\be_\bs\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b._\bR_\ba_\bn_\bd_\bo_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b._\bU_\bn_\bi_\bt_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b._\bU_\bn_\bi_\bt_\bX\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b._\bU_\bn_\bi_\bt_\bY\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b._\bZ_\be_\br_\bo\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b._\ba_\bs_\bD_\bi_\ba_\bg_\bo_\bn_\ba_\bl_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b._\bc_\bo_\bl_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b._\bd_\bo_\bt_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b._\bi_\bs_\bA_\bp_\bp_\br_\bo_\bx_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b._\bm_\ba_\bx_\bA_\bb_\bs_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b._\bm_\ba_\bx_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b._\bm_\be_\ba_\bn_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b._\bm_\bi_\bn_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b._\bo_\bu_\bt_\be_\br_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b._\bp_\br_\bo_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b._\br_\bo_\bw_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b2_\bi_\b._\bs_\bu_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bI_\bd_\be_\bn_\bt_\bi_\bt_\by\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bO_\bn_\be_\bs\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bR_\ba_\bn_\bd_\bo_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bU_\bn_\bi_\bt_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bU_\bn_\bi_\bt_\bX\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bU_\bn_\bi_\bt_\bY\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bU_\bn_\bi_\bt_\bZ\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bZ_\be_\br_\bo\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\ba_\bs_\bD_\bi_\ba_\bg_\bo_\bn_\ba_\bl_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bc_\bo_\bl_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bc_\br_\bo_\bs_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bd_\bo_\bt_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bi_\bs_\bA_\bp_\bp_\br_\bo_\bx_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bm_\ba_\bx_\bA_\bb_\bs_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bm_\ba_\bx_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bm_\be_\ba_\bn_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bm_\bi_\bn_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bn_\bo_\br_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bo_\bu_\bt_\be_\br_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bp_\br_\bo_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bp_\br_\bu_\bn_\be_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\br_\bo_\bw_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bs_\bq_\bu_\ba_\br_\be_\bd_\bN_\bo_\br_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bs_\bu_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bx_\by_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bx_\bz_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\by_\bx_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\by_\bz_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bz_\bx_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\b._\bz_\by_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bI_\bd_\be_\bn_\bt_\bi_\bt_\by\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bO_\bn_\be_\bs\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bR_\ba_\bn_\bd_\bo_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bU_\bn_\bi_\bt_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bU_\bn_\bi_\bt_\bX\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bU_\bn_\bi_\bt_\bY\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bU_\bn_\bi_\bt_\bZ\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bZ_\be_\br_\bo\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\ba_\bs_\bD_\bi_\ba_\bg_\bo_\bn_\ba_\bl_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bc_\bo_\bl_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bc_\br_\bo_\bs_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bd_\bo_\bt_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bi_\bs_\bA_\bp_\bp_\br_\bo_\bx_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bm_\ba_\bx_\bA_\bb_\bs_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bm_\be_\ba_\bn_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bn_\bo_\br_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bo_\bu_\bt_\be_\br_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bp_\br_\bo_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bp_\br_\bu_\bn_\be_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\br_\bo_\bw_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bs_\bq_\bu_\ba_\br_\be_\bd_\bN_\bo_\br_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bs_\bu_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bx_\by_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bx_\bz_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\by_\bx_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\by_\bz_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bz_\bx_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bc_\b._\bz_\by_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\bI_\bd_\be_\bn_\bt_\bi_\bt_\by\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\bO_\bn_\be_\bs\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\bR_\ba_\bn_\bd_\bo_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\bU_\bn_\bi_\bt_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\bU_\bn_\bi_\bt_\bX\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\bU_\bn_\bi_\bt_\bY\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\bU_\bn_\bi_\bt_\bZ\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\bZ_\be_\br_\bo\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\ba_\bs_\bD_\bi_\ba_\bg_\bo_\bn_\ba_\bl_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\bc_\bo_\bl_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\bc_\br_\bo_\bs_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\bd_\bo_\bt_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\bi_\bs_\bA_\bp_\bp_\br_\bo_\bx_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\bm_\ba_\bx_\bA_\bb_\bs_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\bm_\ba_\bx_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\bm_\be_\ba_\bn_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\bm_\bi_\bn_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\bo_\bu_\bt_\be_\br_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\bp_\br_\bo_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\br_\bo_\bw_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\bs_\bu_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\bx_\by_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\bx_\bz_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\by_\bx_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\by_\bz_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\bz_\bx_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b3_\bi_\b._\bz_\by_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b4\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b4_\b._\bI_\bd_\be_\bn_\bt_\bi_\bt_\by\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b4_\b._\bO_\bn_\be_\bs\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b4_\b._\bR_\ba_\bn_\bd_\bo_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b4_\b._\bU_\bn_\bi_\bt_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b4_\b._\bZ_\be_\br_\bo\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b4_\b._\ba_\bs_\bD_\bi_\ba_\bg_\bo_\bn_\ba_\bl_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b4_\b._\bc_\bo_\bl_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b4_\b._\bd_\bo_\bt_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b4_\b._\bi_\bs_\bA_\bp_\bp_\br_\bo_\bx_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b4_\b._\bm_\ba_\bx_\bA_\bb_\bs_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b4_\b._\bm_\ba_\bx_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b4_\b._\bm_\be_\ba_\bn_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b4_\b._\bm_\bi_\bn_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b4_\b._\bn_\bo_\br_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b4_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b4_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b4_\b._\bo_\bu_\bt_\be_\br_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b4_\b._\bp_\br_\bo_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b4_\b._\bp_\br_\bu_\bn_\be_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b4_\b._\br_\bo_\bw_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b4_\b._\bs_\bq_\bu_\ba_\br_\be_\bd_\bN_\bo_\br_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b4_\b._\bs_\bu_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\b._\bI_\bd_\be_\bn_\bt_\bi_\bt_\by\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\b._\bO_\bn_\be_\bs\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\b._\bR_\ba_\bn_\bd_\bo_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\b._\bU_\bn_\bi_\bt_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\b._\bZ_\be_\br_\bo\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\b._\ba_\bs_\bD_\bi_\ba_\bg_\bo_\bn_\ba_\bl_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\b._\bc_\bo_\bl_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\b._\bd_\bo_\bt_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\b._\bh_\be_\ba_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\b._\bi_\bs_\bA_\bp_\bp_\br_\bo_\bx_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\b._\bm_\ba_\bx_\bA_\bb_\bs_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\b._\bm_\ba_\bx_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\b._\bm_\be_\ba_\bn_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\b._\bm_\bi_\bn_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\b._\bn_\bo_\br_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\b._\bo_\bu_\bt_\be_\br_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\b._\bp_\br_\bo_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\b._\bp_\br_\bu_\bn_\be_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\b._\br_\bo_\bw_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\b._\bs_\bq_\bu_\ba_\br_\be_\bd_\bN_\bo_\br_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\b._\bs_\bu_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\b._\bt_\ba_\bi_\bl_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b._\bI_\bd_\be_\bn_\bt_\bi_\bt_\by\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b._\bO_\bn_\be_\bs\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b._\bR_\ba_\bn_\bd_\bo_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b._\bU_\bn_\bi_\bt_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b._\bZ_\be_\br_\bo\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b._\ba_\bs_\bD_\bi_\ba_\bg_\bo_\bn_\ba_\bl_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b._\bc_\bo_\bl_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b._\bd_\bo_\bt_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b._\bh_\be_\ba_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b._\bi_\bs_\bA_\bp_\bp_\br_\bo_\bx_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b._\bm_\ba_\bx_\bA_\bb_\bs_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b._\bm_\be_\ba_\bn_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b._\bn_\bo_\br_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b._\bo_\bu_\bt_\be_\br_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b._\bp_\br_\bo_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b._\bp_\br_\bu_\bn_\be_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b._\br_\bo_\bw_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b._\bs_\bq_\bu_\ba_\br_\be_\bd_\bN_\bo_\br_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b._\bs_\bu_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bc_\b._\bt_\ba_\bi_\bl_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bi\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b._\bI_\bd_\be_\bn_\bt_\bi_\bt_\by\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b._\bO_\bn_\be_\bs\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b._\bR_\ba_\bn_\bd_\bo_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b._\bU_\bn_\bi_\bt_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b._\bZ_\be_\br_\bo\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b._\ba_\bs_\bD_\bi_\ba_\bg_\bo_\bn_\ba_\bl_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b._\bc_\bo_\bl_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b._\bd_\bo_\bt_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b._\bh_\be_\ba_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b._\bi_\bs_\bA_\bp_\bp_\br_\bo_\bx_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b._\bm_\ba_\bx_\bA_\bb_\bs_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b._\bm_\ba_\bx_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b._\bm_\be_\ba_\bn_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b._\bm_\bi_\bn_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b._\bo_\bu_\bt_\be_\br_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b._\bp_\br_\bo_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b._\br_\bo_\bw_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b._\bs_\bu_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\b6_\bi_\b._\bt_\ba_\bi_\bl_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\b._\bO_\bn_\be_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\b._\bR_\ba_\bn_\bd_\bo_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\b._\bU_\bn_\bi_\bt_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\b._\bZ_\be_\br_\bo_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\b._\ba_\bs_\bD_\bi_\ba_\bg_\bo_\bn_\ba_\bl_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\b._\bc_\bo_\bl_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\b._\bd_\bo_\bt_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\b._\bi_\bs_\bA_\bp_\bp_\br_\bo_\bx_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\b._\bm_\ba_\bx_\bA_\bb_\bs_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\b._\bm_\ba_\bx_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\b._\bm_\be_\ba_\bn_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\b._\bm_\bi_\bn_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\b._\bn_\bo_\br_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\b._\bo_\bu_\bt_\be_\br_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\b._\bp_\br_\bo_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\b._\bp_\br_\bu_\bn_\be_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\b._\br_\be_\bs_\bi_\bz_\be_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\b._\br_\bo_\bw_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\b._\bs_\bq_\bu_\ba_\br_\be_\bd_\bN_\bo_\br_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\b._\bs_\bu_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b._\bO_\bn_\be_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b._\bR_\ba_\bn_\bd_\bo_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b._\bU_\bn_\bi_\bt_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b._\bZ_\be_\br_\bo_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b._\ba_\bs_\bD_\bi_\ba_\bg_\bo_\bn_\ba_\bl_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b._\bc_\bo_\bl_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b._\bd_\bo_\bt_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b._\bi_\bs_\bA_\bp_\bp_\br_\bo_\bx_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b._\bm_\ba_\bx_\bA_\bb_\bs_\bC_\bo_\be_\bf_\bf_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b._\bm_\be_\ba_\bn_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b._\bn_\bo_\br_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b._\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\be_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b._\bo_\bu_\bt_\be_\br_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b._\bp_\br_\bo_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b._\bp_\br_\bu_\bn_\be_\bd_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b._\br_\be_\bs_\bi_\bz_\be_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b._\br_\bo_\bw_\bs_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b._\bs_\bq_\bu_\ba_\br_\be_\bd_\bN_\bo_\br_\bm_\b(_\b)\n+ # _\bV_\be_\bc_\bt_\bo_\br_\bX_\bc_\b._\bs_\bu_\bm_\b(_\b)\n+ # _\bf_\bl_\bo_\ba_\bt_\b2_\bs_\bt_\br_\b(_\b)\n *\b**\b**\b**\b* Q\bQu\bui\bic\bck\bk s\bse\bea\bar\brc\bch\bh *\b**\b**\b**\b*\n [q ][Go]\n *\b**\b**\b**\b* N\bNa\bav\bvi\big\bga\bat\bti\bio\bon\bn *\b**\b**\b**\b*\n * _\bi_\bn_\bd_\be_\bx\n * _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn_\b _\b0_\b._\b4_\b-_\b1_\b _\bd_\bo_\bc_\bu_\bm_\be_\bn_\bt_\ba_\bt_\bi_\bo_\bn \u00bb\n * minieigen documentation\n \u00a9 Copyright 2012\u22122015, V\u00e1clav \u0160milauer. Created using _\bS_\bp_\bh_\bi_\bn_\bx 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": "@@ -1,10 +1,524 @@\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#$ -\n+minieigen.AlignedBox2.min py:property 1 index.html#$ -\n+minieigen.AlignedBox2.sizes py:method 1 index.html#$ -\n+minieigen.AlignedBox2.volume py:method 1 index.html#$ -\n+minieigen.AlignedBox3 py:class 1 index.html#$ -\n+minieigen.AlignedBox3.center py:method 1 index.html#$ -\n+minieigen.AlignedBox3.clamp py:method 1 index.html#$ -\n+minieigen.AlignedBox3.contains py:method 1 index.html#$ -\n+minieigen.AlignedBox3.empty py:method 1 index.html#$ -\n+minieigen.AlignedBox3.extend py:method 1 index.html#$ -\n+minieigen.AlignedBox3.intersection py:method 1 index.html#$ -\n+minieigen.AlignedBox3.max py:property 1 index.html#$ -\n+minieigen.AlignedBox3.merged py:method 1 index.html#$ -\n+minieigen.AlignedBox3.min py:property 1 index.html#$ -\n+minieigen.AlignedBox3.sizes py:method 1 index.html#$ -\n+minieigen.AlignedBox3.volume py:method 1 index.html#$ -\n+minieigen.Matrix3 py:class 1 index.html#$ -\n+minieigen.Matrix3.Identity py:attribute 1 index.html#$ -\n+minieigen.Matrix3.Ones py:attribute 1 index.html#$ -\n+minieigen.Matrix3.Random py:method 1 index.html#$ -\n+minieigen.Matrix3.Zero py:attribute 1 index.html#$ -\n+minieigen.Matrix3.col py:method 1 index.html#$ -\n+minieigen.Matrix3.cols py:method 1 index.html#$ -\n+minieigen.Matrix3.computeUnitaryPositive py:method 1 index.html#$ -\n+minieigen.Matrix3.determinant py:method 1 index.html#$ -\n+minieigen.Matrix3.diagonal py:method 1 index.html#$ -\n+minieigen.Matrix3.inverse py:method 1 index.html#$ -\n+minieigen.Matrix3.isApprox py:method 1 index.html#$ -\n+minieigen.Matrix3.jacobiSVD py:method 1 index.html#$ -\n+minieigen.Matrix3.maxAbsCoeff py:method 1 index.html#$ -\n+minieigen.Matrix3.maxCoeff py:method 1 index.html#$ -\n+minieigen.Matrix3.mean py:method 1 index.html#$ -\n+minieigen.Matrix3.minCoeff py:method 1 index.html#$ -\n+minieigen.Matrix3.norm py:method 1 index.html#$ -\n+minieigen.Matrix3.normalize py:method 1 index.html#$ -\n+minieigen.Matrix3.normalized py:method 1 index.html#$ -\n+minieigen.Matrix3.polarDecomposition py:method 1 index.html#$ -\n+minieigen.Matrix3.prod py:method 1 index.html#$ -\n+minieigen.Matrix3.pruned py:method 1 index.html#$ -\n+minieigen.Matrix3.row py:method 1 index.html#$ -\n+minieigen.Matrix3.rows py:method 1 index.html#$ -\n+minieigen.Matrix3.selfAdjointEigenDecomposition py:method 1 index.html#$ -\n+minieigen.Matrix3.spectralDecomposition py:method 1 index.html#$ -\n+minieigen.Matrix3.squaredNorm py:method 1 index.html#$ -\n+minieigen.Matrix3.sum py:method 1 index.html#$ -\n+minieigen.Matrix3.svd py:method 1 index.html#$ -\n+minieigen.Matrix3.trace py:method 1 index.html#$ -\n+minieigen.Matrix3.transpose py:method 1 index.html#$ -\n+minieigen.Matrix3c py:class 1 index.html#$ -\n+minieigen.Matrix3c.Identity py:attribute 1 index.html#$ -\n+minieigen.Matrix3c.Ones py:attribute 1 index.html#$ -\n+minieigen.Matrix3c.Random py:method 1 index.html#$ -\n+minieigen.Matrix3c.Zero py:attribute 1 index.html#$ -\n+minieigen.Matrix3c.col py:method 1 index.html#$ -\n+minieigen.Matrix3c.cols py:method 1 index.html#$ -\n+minieigen.Matrix3c.determinant py:method 1 index.html#$ -\n+minieigen.Matrix3c.diagonal py:method 1 index.html#$ -\n+minieigen.Matrix3c.inverse py:method 1 index.html#$ -\n+minieigen.Matrix3c.isApprox py:method 1 index.html#$ -\n+minieigen.Matrix3c.maxAbsCoeff py:method 1 index.html#$ -\n+minieigen.Matrix3c.mean py:method 1 index.html#$ -\n+minieigen.Matrix3c.norm py:method 1 index.html#$ -\n+minieigen.Matrix3c.normalize py:method 1 index.html#$ -\n+minieigen.Matrix3c.normalized py:method 1 index.html#$ -\n+minieigen.Matrix3c.prod py:method 1 index.html#$ -\n+minieigen.Matrix3c.pruned py:method 1 index.html#$ -\n+minieigen.Matrix3c.row py:method 1 index.html#$ -\n+minieigen.Matrix3c.rows py:method 1 index.html#$ -\n+minieigen.Matrix3c.squaredNorm py:method 1 index.html#$ -\n+minieigen.Matrix3c.sum py:method 1 index.html#$ -\n+minieigen.Matrix3c.trace py:method 1 index.html#$ -\n+minieigen.Matrix3c.transpose py:method 1 index.html#$ -\n+minieigen.Matrix6 py:class 1 index.html#$ -\n+minieigen.Matrix6.Identity py:attribute 1 index.html#$ -\n+minieigen.Matrix6.Ones py:attribute 1 index.html#$ -\n+minieigen.Matrix6.Random py:method 1 index.html#$ -\n+minieigen.Matrix6.Zero py:attribute 1 index.html#$ -\n+minieigen.Matrix6.col py:method 1 index.html#$ -\n+minieigen.Matrix6.cols py:method 1 index.html#$ -\n+minieigen.Matrix6.computeUnitaryPositive py:method 1 index.html#$ -\n+minieigen.Matrix6.determinant py:method 1 index.html#$ -\n+minieigen.Matrix6.diagonal py:method 1 index.html#$ -\n+minieigen.Matrix6.inverse py:method 1 index.html#$ -\n+minieigen.Matrix6.isApprox py:method 1 index.html#$ -\n+minieigen.Matrix6.jacobiSVD py:method 1 index.html#$ -\n+minieigen.Matrix6.ll py:method 1 index.html#$ -\n+minieigen.Matrix6.lr py:method 1 index.html#$ -\n+minieigen.Matrix6.maxAbsCoeff py:method 1 index.html#$ -\n+minieigen.Matrix6.maxCoeff py:method 1 index.html#$ -\n+minieigen.Matrix6.mean py:method 1 index.html#$ -\n+minieigen.Matrix6.minCoeff py:method 1 index.html#$ -\n+minieigen.Matrix6.norm py:method 1 index.html#$ -\n+minieigen.Matrix6.normalize py:method 1 index.html#$ -\n+minieigen.Matrix6.normalized py:method 1 index.html#$ -\n+minieigen.Matrix6.polarDecomposition py:method 1 index.html#$ -\n+minieigen.Matrix6.prod py:method 1 index.html#$ -\n+minieigen.Matrix6.pruned py:method 1 index.html#$ -\n+minieigen.Matrix6.row py:method 1 index.html#$ -\n+minieigen.Matrix6.rows py:method 1 index.html#$ -\n+minieigen.Matrix6.selfAdjointEigenDecomposition py:method 1 index.html#$ -\n+minieigen.Matrix6.spectralDecomposition py:method 1 index.html#$ -\n+minieigen.Matrix6.squaredNorm py:method 1 index.html#$ -\n+minieigen.Matrix6.sum py:method 1 index.html#$ -\n+minieigen.Matrix6.svd py:method 1 index.html#$ -\n+minieigen.Matrix6.trace py:method 1 index.html#$ -\n+minieigen.Matrix6.transpose py:method 1 index.html#$ -\n+minieigen.Matrix6.ul py:method 1 index.html#$ -\n+minieigen.Matrix6.ur py:method 1 index.html#$ -\n+minieigen.Matrix6c py:class 1 index.html#$ -\n+minieigen.Matrix6c.Identity py:attribute 1 index.html#$ -\n+minieigen.Matrix6c.Ones py:attribute 1 index.html#$ -\n+minieigen.Matrix6c.Random py:method 1 index.html#$ -\n+minieigen.Matrix6c.Zero py:attribute 1 index.html#$ -\n+minieigen.Matrix6c.col py:method 1 index.html#$ -\n+minieigen.Matrix6c.cols py:method 1 index.html#$ -\n+minieigen.Matrix6c.determinant py:method 1 index.html#$ -\n+minieigen.Matrix6c.diagonal py:method 1 index.html#$ -\n+minieigen.Matrix6c.inverse py:method 1 index.html#$ -\n+minieigen.Matrix6c.isApprox py:method 1 index.html#$ -\n+minieigen.Matrix6c.ll py:method 1 index.html#$ -\n+minieigen.Matrix6c.lr py:method 1 index.html#$ -\n+minieigen.Matrix6c.maxAbsCoeff py:method 1 index.html#$ -\n+minieigen.Matrix6c.mean py:method 1 index.html#$ -\n+minieigen.Matrix6c.norm py:method 1 index.html#$ -\n+minieigen.Matrix6c.normalize py:method 1 index.html#$ -\n+minieigen.Matrix6c.normalized py:method 1 index.html#$ -\n+minieigen.Matrix6c.prod py:method 1 index.html#$ -\n+minieigen.Matrix6c.pruned py:method 1 index.html#$ -\n+minieigen.Matrix6c.row py:method 1 index.html#$ -\n+minieigen.Matrix6c.rows py:method 1 index.html#$ -\n+minieigen.Matrix6c.squaredNorm py:method 1 index.html#$ -\n+minieigen.Matrix6c.sum py:method 1 index.html#$ -\n+minieigen.Matrix6c.trace py:method 1 index.html#$ -\n+minieigen.Matrix6c.transpose py:method 1 index.html#$ -\n+minieigen.Matrix6c.ul py:method 1 index.html#$ -\n+minieigen.Matrix6c.ur py:method 1 index.html#$ -\n+minieigen.MatrixX py:class 1 index.html#$ -\n+minieigen.MatrixX.Identity py:method 1 index.html#$ -\n+minieigen.MatrixX.Ones py:method 1 index.html#$ -\n+minieigen.MatrixX.Random py:method 1 index.html#$ -\n+minieigen.MatrixX.Zero py:method 1 index.html#$ -\n+minieigen.MatrixX.col py:method 1 index.html#$ -\n+minieigen.MatrixX.cols py:method 1 index.html#$ -\n+minieigen.MatrixX.computeUnitaryPositive py:method 1 index.html#$ -\n+minieigen.MatrixX.determinant py:method 1 index.html#$ -\n+minieigen.MatrixX.diagonal py:method 1 index.html#$ -\n+minieigen.MatrixX.inverse py:method 1 index.html#$ -\n+minieigen.MatrixX.isApprox py:method 1 index.html#$ -\n+minieigen.MatrixX.jacobiSVD py:method 1 index.html#$ -\n+minieigen.MatrixX.maxAbsCoeff py:method 1 index.html#$ -\n+minieigen.MatrixX.maxCoeff py:method 1 index.html#$ -\n+minieigen.MatrixX.mean py:method 1 index.html#$ -\n+minieigen.MatrixX.minCoeff py:method 1 index.html#$ -\n+minieigen.MatrixX.norm py:method 1 index.html#$ -\n+minieigen.MatrixX.normalize py:method 1 index.html#$ -\n+minieigen.MatrixX.normalized py:method 1 index.html#$ -\n+minieigen.MatrixX.polarDecomposition py:method 1 index.html#$ -\n+minieigen.MatrixX.prod py:method 1 index.html#$ -\n+minieigen.MatrixX.pruned py:method 1 index.html#$ -\n+minieigen.MatrixX.resize py:method 1 index.html#$ -\n+minieigen.MatrixX.row py:method 1 index.html#$ -\n+minieigen.MatrixX.rows py:method 1 index.html#$ -\n+minieigen.MatrixX.selfAdjointEigenDecomposition py:method 1 index.html#$ -\n+minieigen.MatrixX.spectralDecomposition py:method 1 index.html#$ -\n+minieigen.MatrixX.squaredNorm py:method 1 index.html#$ -\n+minieigen.MatrixX.sum py:method 1 index.html#$ -\n+minieigen.MatrixX.svd py:method 1 index.html#$ -\n+minieigen.MatrixX.trace py:method 1 index.html#$ -\n+minieigen.MatrixX.transpose py:method 1 index.html#$ -\n+minieigen.MatrixXc py:class 1 index.html#$ -\n+minieigen.MatrixXc.Identity py:method 1 index.html#$ -\n+minieigen.MatrixXc.Ones py:method 1 index.html#$ -\n+minieigen.MatrixXc.Random py:method 1 index.html#$ -\n+minieigen.MatrixXc.Zero py:method 1 index.html#$ -\n+minieigen.MatrixXc.col py:method 1 index.html#$ -\n+minieigen.MatrixXc.cols py:method 1 index.html#$ -\n+minieigen.MatrixXc.determinant py:method 1 index.html#$ -\n+minieigen.MatrixXc.diagonal py:method 1 index.html#$ -\n+minieigen.MatrixXc.inverse py:method 1 index.html#$ -\n+minieigen.MatrixXc.isApprox py:method 1 index.html#$ -\n+minieigen.MatrixXc.maxAbsCoeff py:method 1 index.html#$ -\n+minieigen.MatrixXc.mean py:method 1 index.html#$ -\n+minieigen.MatrixXc.norm py:method 1 index.html#$ -\n+minieigen.MatrixXc.normalize py:method 1 index.html#$ -\n+minieigen.MatrixXc.normalized py:method 1 index.html#$ -\n+minieigen.MatrixXc.prod py:method 1 index.html#$ -\n+minieigen.MatrixXc.pruned py:method 1 index.html#$ -\n+minieigen.MatrixXc.resize py:method 1 index.html#$ -\n+minieigen.MatrixXc.row py:method 1 index.html#$ -\n+minieigen.MatrixXc.rows py:method 1 index.html#$ -\n+minieigen.MatrixXc.squaredNorm py:method 1 index.html#$ -\n+minieigen.MatrixXc.sum py:method 1 index.html#$ -\n+minieigen.MatrixXc.trace py:method 1 index.html#$ -\n+minieigen.MatrixXc.transpose py:method 1 index.html#$ -\n+minieigen.Quaternion py:class 1 index.html#$ -\n+minieigen.Quaternion.Identity py:attribute 1 index.html#$ -\n+minieigen.Quaternion.Rotate py:method 1 index.html#$ -\n+minieigen.Quaternion.angularDistance py:method 1 index.html#$ -\n+minieigen.Quaternion.conjugate py:method 1 index.html#$ -\n+minieigen.Quaternion.inverse py:method 1 index.html#$ -\n+minieigen.Quaternion.norm py:method 1 index.html#$ -\n+minieigen.Quaternion.normalize py:method 1 index.html#$ -\n+minieigen.Quaternion.normalized py:method 1 index.html#$ -\n+minieigen.Quaternion.setFromTwoVectors py:method 1 index.html#$ -\n+minieigen.Quaternion.slerp py:method 1 index.html#$ -\n+minieigen.Quaternion.toAngleAxis py:method 1 index.html#$ -\n+minieigen.Quaternion.toAxisAngle py:method 1 index.html#$ -\n+minieigen.Quaternion.toRotationMatrix py:method 1 index.html#$ -\n+minieigen.Quaternion.toRotationVector py:method 1 index.html#$ -\n+minieigen.Vector2 py:class 1 index.html#$ -\n+minieigen.Vector2.Identity py:attribute 1 index.html#$ -\n+minieigen.Vector2.Ones py:attribute 1 index.html#$ -\n+minieigen.Vector2.Random py:method 1 index.html#$ -\n+minieigen.Vector2.Unit py:method 1 index.html#$ -\n+minieigen.Vector2.UnitX py:attribute 1 index.html#$ -\n+minieigen.Vector2.UnitY py:attribute 1 index.html#$ -\n+minieigen.Vector2.Zero py:attribute 1 index.html#$ -\n+minieigen.Vector2.asDiagonal py:method 1 index.html#$ -\n+minieigen.Vector2.cols py:method 1 index.html#$ -\n+minieigen.Vector2.dot py:method 1 index.html#$ -\n+minieigen.Vector2.isApprox py:method 1 index.html#$ -\n+minieigen.Vector2.maxAbsCoeff py:method 1 index.html#$ -\n+minieigen.Vector2.maxCoeff py:method 1 index.html#$ -\n+minieigen.Vector2.mean py:method 1 index.html#$ -\n+minieigen.Vector2.minCoeff py:method 1 index.html#$ -\n+minieigen.Vector2.norm py:method 1 index.html#$ -\n+minieigen.Vector2.normalize py:method 1 index.html#$ -\n+minieigen.Vector2.normalized py:method 1 index.html#$ -\n+minieigen.Vector2.outer py:method 1 index.html#$ -\n+minieigen.Vector2.prod py:method 1 index.html#$ -\n+minieigen.Vector2.pruned py:method 1 index.html#$ -\n+minieigen.Vector2.rows py:method 1 index.html#$ -\n+minieigen.Vector2.squaredNorm py:method 1 index.html#$ -\n+minieigen.Vector2.sum py:method 1 index.html#$ -\n+minieigen.Vector2c py:class 1 index.html#$ -\n+minieigen.Vector2c.Identity py:attribute 1 index.html#$ -\n+minieigen.Vector2c.Ones py:attribute 1 index.html#$ -\n+minieigen.Vector2c.Random py:method 1 index.html#$ -\n+minieigen.Vector2c.Unit py:method 1 index.html#$ -\n+minieigen.Vector2c.UnitX py:attribute 1 index.html#$ -\n+minieigen.Vector2c.UnitY py:attribute 1 index.html#$ -\n+minieigen.Vector2c.Zero py:attribute 1 index.html#$ -\n+minieigen.Vector2c.asDiagonal py:method 1 index.html#$ -\n+minieigen.Vector2c.cols py:method 1 index.html#$ -\n+minieigen.Vector2c.dot py:method 1 index.html#$ -\n+minieigen.Vector2c.isApprox py:method 1 index.html#$ -\n+minieigen.Vector2c.maxAbsCoeff py:method 1 index.html#$ -\n+minieigen.Vector2c.mean py:method 1 index.html#$ -\n+minieigen.Vector2c.norm py:method 1 index.html#$ -\n+minieigen.Vector2c.normalize py:method 1 index.html#$ -\n+minieigen.Vector2c.normalized py:method 1 index.html#$ -\n+minieigen.Vector2c.outer py:method 1 index.html#$ -\n+minieigen.Vector2c.prod py:method 1 index.html#$ -\n+minieigen.Vector2c.pruned py:method 1 index.html#$ -\n+minieigen.Vector2c.rows py:method 1 index.html#$ -\n+minieigen.Vector2c.squaredNorm py:method 1 index.html#$ -\n+minieigen.Vector2c.sum py:method 1 index.html#$ -\n+minieigen.Vector2i py:class 1 index.html#$ -\n+minieigen.Vector2i.Identity py:attribute 1 index.html#$ -\n+minieigen.Vector2i.Ones py:attribute 1 index.html#$ -\n+minieigen.Vector2i.Random py:method 1 index.html#$ -\n+minieigen.Vector2i.Unit py:method 1 index.html#$ -\n+minieigen.Vector2i.UnitX py:attribute 1 index.html#$ -\n+minieigen.Vector2i.UnitY py:attribute 1 index.html#$ -\n+minieigen.Vector2i.Zero py:attribute 1 index.html#$ -\n+minieigen.Vector2i.asDiagonal py:method 1 index.html#$ -\n+minieigen.Vector2i.cols py:method 1 index.html#$ -\n+minieigen.Vector2i.dot py:method 1 index.html#$ -\n+minieigen.Vector2i.isApprox py:method 1 index.html#$ -\n+minieigen.Vector2i.maxAbsCoeff py:method 1 index.html#$ -\n+minieigen.Vector2i.maxCoeff py:method 1 index.html#$ -\n+minieigen.Vector2i.mean py:method 1 index.html#$ -\n+minieigen.Vector2i.minCoeff py:method 1 index.html#$ -\n+minieigen.Vector2i.outer py:method 1 index.html#$ -\n+minieigen.Vector2i.prod py:method 1 index.html#$ -\n+minieigen.Vector2i.rows py:method 1 index.html#$ -\n+minieigen.Vector2i.sum py:method 1 index.html#$ -\n+minieigen.Vector3 py:class 1 index.html#$ -\n+minieigen.Vector3.Identity py:attribute 1 index.html#$ -\n+minieigen.Vector3.Ones py:attribute 1 index.html#$ -\n+minieigen.Vector3.Random py:method 1 index.html#$ -\n+minieigen.Vector3.Unit py:method 1 index.html#$ -\n+minieigen.Vector3.UnitX py:attribute 1 index.html#$ -\n+minieigen.Vector3.UnitY py:attribute 1 index.html#$ -\n+minieigen.Vector3.UnitZ py:attribute 1 index.html#$ -\n+minieigen.Vector3.Zero py:attribute 1 index.html#$ -\n+minieigen.Vector3.asDiagonal py:method 1 index.html#$ -\n+minieigen.Vector3.cols py:method 1 index.html#$ -\n+minieigen.Vector3.cross py:method 1 index.html#$ -\n+minieigen.Vector3.dot py:method 1 index.html#$ -\n+minieigen.Vector3.isApprox py:method 1 index.html#$ -\n+minieigen.Vector3.maxAbsCoeff py:method 1 index.html#$ -\n+minieigen.Vector3.maxCoeff py:method 1 index.html#$ -\n+minieigen.Vector3.mean py:method 1 index.html#$ -\n+minieigen.Vector3.minCoeff py:method 1 index.html#$ -\n+minieigen.Vector3.norm py:method 1 index.html#$ -\n+minieigen.Vector3.normalize py:method 1 index.html#$ -\n+minieigen.Vector3.normalized py:method 1 index.html#$ -\n+minieigen.Vector3.outer py:method 1 index.html#$ -\n+minieigen.Vector3.prod py:method 1 index.html#$ -\n+minieigen.Vector3.pruned py:method 1 index.html#$ -\n+minieigen.Vector3.rows py:method 1 index.html#$ -\n+minieigen.Vector3.squaredNorm py:method 1 index.html#$ -\n+minieigen.Vector3.sum py:method 1 index.html#$ -\n+minieigen.Vector3.xy py:method 1 index.html#$ -\n+minieigen.Vector3.xz py:method 1 index.html#$ -\n+minieigen.Vector3.yx py:method 1 index.html#$ -\n+minieigen.Vector3.yz py:method 1 index.html#$ -\n+minieigen.Vector3.zx py:method 1 index.html#$ -\n+minieigen.Vector3.zy py:method 1 index.html#$ -\n+minieigen.Vector3c py:class 1 index.html#$ -\n+minieigen.Vector3c.Identity py:attribute 1 index.html#$ -\n+minieigen.Vector3c.Ones py:attribute 1 index.html#$ -\n+minieigen.Vector3c.Random py:method 1 index.html#$ -\n+minieigen.Vector3c.Unit py:method 1 index.html#$ -\n+minieigen.Vector3c.UnitX py:attribute 1 index.html#$ -\n+minieigen.Vector3c.UnitY py:attribute 1 index.html#$ -\n+minieigen.Vector3c.UnitZ py:attribute 1 index.html#$ -\n+minieigen.Vector3c.Zero py:attribute 1 index.html#$ -\n+minieigen.Vector3c.asDiagonal py:method 1 index.html#$ -\n+minieigen.Vector3c.cols py:method 1 index.html#$ -\n+minieigen.Vector3c.cross py:method 1 index.html#$ -\n+minieigen.Vector3c.dot py:method 1 index.html#$ -\n+minieigen.Vector3c.isApprox py:method 1 index.html#$ -\n+minieigen.Vector3c.maxAbsCoeff py:method 1 index.html#$ -\n+minieigen.Vector3c.mean py:method 1 index.html#$ -\n+minieigen.Vector3c.norm py:method 1 index.html#$ -\n+minieigen.Vector3c.normalize py:method 1 index.html#$ -\n+minieigen.Vector3c.normalized py:method 1 index.html#$ -\n+minieigen.Vector3c.outer py:method 1 index.html#$ -\n+minieigen.Vector3c.prod py:method 1 index.html#$ -\n+minieigen.Vector3c.pruned py:method 1 index.html#$ -\n+minieigen.Vector3c.rows py:method 1 index.html#$ -\n+minieigen.Vector3c.squaredNorm py:method 1 index.html#$ -\n+minieigen.Vector3c.sum py:method 1 index.html#$ -\n+minieigen.Vector3c.xy py:method 1 index.html#$ -\n+minieigen.Vector3c.xz py:method 1 index.html#$ -\n+minieigen.Vector3c.yx py:method 1 index.html#$ -\n+minieigen.Vector3c.yz py:method 1 index.html#$ -\n+minieigen.Vector3c.zx py:method 1 index.html#$ -\n+minieigen.Vector3c.zy py:method 1 index.html#$ -\n+minieigen.Vector3i py:class 1 index.html#$ -\n+minieigen.Vector3i.Identity py:attribute 1 index.html#$ -\n+minieigen.Vector3i.Ones py:attribute 1 index.html#$ -\n+minieigen.Vector3i.Random py:method 1 index.html#$ -\n+minieigen.Vector3i.Unit py:method 1 index.html#$ -\n+minieigen.Vector3i.UnitX py:attribute 1 index.html#$ -\n+minieigen.Vector3i.UnitY py:attribute 1 index.html#$ -\n+minieigen.Vector3i.UnitZ py:attribute 1 index.html#$ -\n+minieigen.Vector3i.Zero py:attribute 1 index.html#$ -\n+minieigen.Vector3i.asDiagonal py:method 1 index.html#$ -\n+minieigen.Vector3i.cols py:method 1 index.html#$ -\n+minieigen.Vector3i.cross py:method 1 index.html#$ -\n+minieigen.Vector3i.dot py:method 1 index.html#$ -\n+minieigen.Vector3i.isApprox py:method 1 index.html#$ -\n+minieigen.Vector3i.maxAbsCoeff py:method 1 index.html#$ -\n+minieigen.Vector3i.maxCoeff py:method 1 index.html#$ -\n+minieigen.Vector3i.mean py:method 1 index.html#$ -\n+minieigen.Vector3i.minCoeff py:method 1 index.html#$ -\n+minieigen.Vector3i.outer py:method 1 index.html#$ -\n+minieigen.Vector3i.prod py:method 1 index.html#$ -\n+minieigen.Vector3i.rows py:method 1 index.html#$ -\n+minieigen.Vector3i.sum py:method 1 index.html#$ -\n+minieigen.Vector3i.xy py:method 1 index.html#$ -\n+minieigen.Vector3i.xz py:method 1 index.html#$ -\n+minieigen.Vector3i.yx py:method 1 index.html#$ -\n+minieigen.Vector3i.yz py:method 1 index.html#$ -\n+minieigen.Vector3i.zx py:method 1 index.html#$ -\n+minieigen.Vector3i.zy py:method 1 index.html#$ -\n+minieigen.Vector4 py:class 1 index.html#$ -\n+minieigen.Vector4.Identity py:attribute 1 index.html#$ -\n+minieigen.Vector4.Ones py:attribute 1 index.html#$ -\n+minieigen.Vector4.Random py:method 1 index.html#$ -\n+minieigen.Vector4.Unit py:method 1 index.html#$ -\n+minieigen.Vector4.Zero py:attribute 1 index.html#$ -\n+minieigen.Vector4.asDiagonal py:method 1 index.html#$ -\n+minieigen.Vector4.cols py:method 1 index.html#$ -\n+minieigen.Vector4.dot py:method 1 index.html#$ -\n+minieigen.Vector4.isApprox py:method 1 index.html#$ -\n+minieigen.Vector4.maxAbsCoeff py:method 1 index.html#$ -\n+minieigen.Vector4.maxCoeff py:method 1 index.html#$ -\n+minieigen.Vector4.mean py:method 1 index.html#$ -\n+minieigen.Vector4.minCoeff py:method 1 index.html#$ -\n+minieigen.Vector4.norm py:method 1 index.html#$ -\n+minieigen.Vector4.normalize py:method 1 index.html#$ -\n+minieigen.Vector4.normalized py:method 1 index.html#$ -\n+minieigen.Vector4.outer py:method 1 index.html#$ -\n+minieigen.Vector4.prod py:method 1 index.html#$ -\n+minieigen.Vector4.pruned py:method 1 index.html#$ -\n+minieigen.Vector4.rows py:method 1 index.html#$ -\n+minieigen.Vector4.squaredNorm py:method 1 index.html#$ -\n+minieigen.Vector4.sum py:method 1 index.html#$ -\n+minieigen.Vector6 py:class 1 index.html#$ -\n+minieigen.Vector6.Identity py:attribute 1 index.html#$ -\n+minieigen.Vector6.Ones py:attribute 1 index.html#$ -\n+minieigen.Vector6.Random py:method 1 index.html#$ -\n+minieigen.Vector6.Unit py:method 1 index.html#$ -\n+minieigen.Vector6.Zero py:attribute 1 index.html#$ -\n+minieigen.Vector6.asDiagonal py:method 1 index.html#$ -\n+minieigen.Vector6.cols py:method 1 index.html#$ -\n+minieigen.Vector6.dot py:method 1 index.html#$ -\n+minieigen.Vector6.head py:method 1 index.html#$ -\n+minieigen.Vector6.isApprox py:method 1 index.html#$ -\n+minieigen.Vector6.maxAbsCoeff py:method 1 index.html#$ -\n+minieigen.Vector6.maxCoeff py:method 1 index.html#$ -\n+minieigen.Vector6.mean py:method 1 index.html#$ -\n+minieigen.Vector6.minCoeff py:method 1 index.html#$ -\n+minieigen.Vector6.norm py:method 1 index.html#$ -\n+minieigen.Vector6.normalize py:method 1 index.html#$ -\n+minieigen.Vector6.normalized py:method 1 index.html#$ -\n+minieigen.Vector6.outer py:method 1 index.html#$ -\n+minieigen.Vector6.prod py:method 1 index.html#$ -\n+minieigen.Vector6.pruned py:method 1 index.html#$ -\n+minieigen.Vector6.rows py:method 1 index.html#$ -\n+minieigen.Vector6.squaredNorm py:method 1 index.html#$ -\n+minieigen.Vector6.sum py:method 1 index.html#$ -\n+minieigen.Vector6.tail py:method 1 index.html#$ -\n+minieigen.Vector6c py:class 1 index.html#$ -\n+minieigen.Vector6c.Identity py:attribute 1 index.html#$ -\n+minieigen.Vector6c.Ones py:attribute 1 index.html#$ -\n+minieigen.Vector6c.Random py:method 1 index.html#$ -\n+minieigen.Vector6c.Unit py:method 1 index.html#$ -\n+minieigen.Vector6c.Zero py:attribute 1 index.html#$ -\n+minieigen.Vector6c.asDiagonal py:method 1 index.html#$ -\n+minieigen.Vector6c.cols py:method 1 index.html#$ -\n+minieigen.Vector6c.dot py:method 1 index.html#$ -\n+minieigen.Vector6c.head py:method 1 index.html#$ -\n+minieigen.Vector6c.isApprox py:method 1 index.html#$ -\n+minieigen.Vector6c.maxAbsCoeff py:method 1 index.html#$ -\n+minieigen.Vector6c.mean py:method 1 index.html#$ -\n+minieigen.Vector6c.norm py:method 1 index.html#$ -\n+minieigen.Vector6c.normalize py:method 1 index.html#$ -\n+minieigen.Vector6c.normalized py:method 1 index.html#$ -\n+minieigen.Vector6c.outer py:method 1 index.html#$ -\n+minieigen.Vector6c.prod py:method 1 index.html#$ -\n+minieigen.Vector6c.pruned py:method 1 index.html#$ -\n+minieigen.Vector6c.rows py:method 1 index.html#$ -\n+minieigen.Vector6c.squaredNorm py:method 1 index.html#$ -\n+minieigen.Vector6c.sum py:method 1 index.html#$ -\n+minieigen.Vector6c.tail py:method 1 index.html#$ -\n+minieigen.Vector6i py:class 1 index.html#$ -\n+minieigen.Vector6i.Identity py:attribute 1 index.html#$ -\n+minieigen.Vector6i.Ones py:attribute 1 index.html#$ -\n+minieigen.Vector6i.Random py:method 1 index.html#$ -\n+minieigen.Vector6i.Unit py:method 1 index.html#$ -\n+minieigen.Vector6i.Zero py:attribute 1 index.html#$ -\n+minieigen.Vector6i.asDiagonal py:method 1 index.html#$ -\n+minieigen.Vector6i.cols py:method 1 index.html#$ -\n+minieigen.Vector6i.dot py:method 1 index.html#$ -\n+minieigen.Vector6i.head py:method 1 index.html#$ -\n+minieigen.Vector6i.isApprox py:method 1 index.html#$ -\n+minieigen.Vector6i.maxAbsCoeff py:method 1 index.html#$ -\n+minieigen.Vector6i.maxCoeff py:method 1 index.html#$ -\n+minieigen.Vector6i.mean py:method 1 index.html#$ -\n+minieigen.Vector6i.minCoeff py:method 1 index.html#$ -\n+minieigen.Vector6i.outer py:method 1 index.html#$ -\n+minieigen.Vector6i.prod py:method 1 index.html#$ -\n+minieigen.Vector6i.rows py:method 1 index.html#$ -\n+minieigen.Vector6i.sum py:method 1 index.html#$ -\n+minieigen.Vector6i.tail py:method 1 index.html#$ -\n+minieigen.VectorX py:class 1 index.html#$ -\n+minieigen.VectorX.Ones py:method 1 index.html#$ -\n+minieigen.VectorX.Random py:method 1 index.html#$ -\n+minieigen.VectorX.Unit py:method 1 index.html#$ -\n+minieigen.VectorX.Zero py:method 1 index.html#$ -\n+minieigen.VectorX.asDiagonal py:method 1 index.html#$ -\n+minieigen.VectorX.cols py:method 1 index.html#$ -\n+minieigen.VectorX.dot py:method 1 index.html#$ -\n+minieigen.VectorX.isApprox py:method 1 index.html#$ -\n+minieigen.VectorX.maxAbsCoeff py:method 1 index.html#$ -\n+minieigen.VectorX.maxCoeff py:method 1 index.html#$ -\n+minieigen.VectorX.mean py:method 1 index.html#$ -\n+minieigen.VectorX.minCoeff py:method 1 index.html#$ -\n+minieigen.VectorX.norm py:method 1 index.html#$ -\n+minieigen.VectorX.normalize py:method 1 index.html#$ -\n+minieigen.VectorX.normalized py:method 1 index.html#$ -\n+minieigen.VectorX.outer py:method 1 index.html#$ -\n+minieigen.VectorX.prod py:method 1 index.html#$ -\n+minieigen.VectorX.pruned py:method 1 index.html#$ -\n+minieigen.VectorX.resize py:method 1 index.html#$ -\n+minieigen.VectorX.rows py:method 1 index.html#$ -\n+minieigen.VectorX.squaredNorm py:method 1 index.html#$ -\n+minieigen.VectorX.sum py:method 1 index.html#$ -\n+minieigen.VectorXc py:class 1 index.html#$ -\n+minieigen.VectorXc.Ones py:method 1 index.html#$ -\n+minieigen.VectorXc.Random py:method 1 index.html#$ -\n+minieigen.VectorXc.Unit py:method 1 index.html#$ -\n+minieigen.VectorXc.Zero py:method 1 index.html#$ -\n+minieigen.VectorXc.asDiagonal py:method 1 index.html#$ -\n+minieigen.VectorXc.cols py:method 1 index.html#$ -\n+minieigen.VectorXc.dot py:method 1 index.html#$ -\n+minieigen.VectorXc.isApprox py:method 1 index.html#$ -\n+minieigen.VectorXc.maxAbsCoeff py:method 1 index.html#$ -\n+minieigen.VectorXc.mean py:method 1 index.html#$ -\n+minieigen.VectorXc.norm py:method 1 index.html#$ -\n+minieigen.VectorXc.normalize py:method 1 index.html#$ -\n+minieigen.VectorXc.normalized py:method 1 index.html#$ -\n+minieigen.VectorXc.outer py:method 1 index.html#$ -\n+minieigen.VectorXc.prod py:method 1 index.html#$ -\n+minieigen.VectorXc.pruned py:method 1 index.html#$ -\n+minieigen.VectorXc.resize py:method 1 index.html#$ -\n+minieigen.VectorXc.rows py:method 1 index.html#$ -\n+minieigen.VectorXc.squaredNorm py:method 1 index.html#$ -\n+minieigen.VectorXc.sum py:method 1 index.html#$ -\n+minieigen.float2str py:function 1 index.html#$ -\n genindex std:label -1 genindex.html Index\n index std:doc -1 index.html minieigen documentation\n modindex std:label -1 py-modindex.html Module Index\n py-modindex std:label -1 py-modindex.html Python Module Index\n search std:label -1 search.html Search Page\n"}]}, {"source1": "./usr/share/doc/python3-minieigen/html/searchindex.js", "source2": "./usr/share/doc/python3-minieigen/html/searchindex.js", "unified_diff": null, "details": [{"source1": "js-beautify {}", "source2": "js-beautify {}", "unified_diff": "@@ -163,19 +163,784 @@\n \"page\": 0,\n \"us\": 0,\n \"easy_instal\": 0,\n \"debian\": 0,\n \"ubuntu\": 0,\n \"distribut\": 0,\n \"ppa\": 0,\n- \"search\": 0\n+ \"search\": 0,\n+ \"wrapper\": 0,\n+ \"small\": 0,\n+ \"part\": 0,\n+ \"librari\": 0,\n+ \"its\": 0,\n+ \"detail\": 0,\n+ \"all\": 0,\n+ \"thi\": 0,\n+ \"modul\": 0,\n+ \"pickl\": 0,\n+ \"alignedbox2\": 0,\n+ \"axi\": 0,\n+ \"2d\": 0,\n+ \"defin\": 0,\n+ \"minimum\": 0,\n+ \"maximum\": 0,\n+ \"corner\": 0,\n+ \"center\": 0,\n+ \"arg1\": 0,\n+ \"vector2\": 0,\n+ \"clamp\": 0,\n+ \"arg2\": 0,\n+ \"contain\": 0,\n+ \"bool\": 0,\n+ \"empti\": 0,\n+ \"extend\": 0,\n+ \"intersect\": 0,\n+ \"max\": 0,\n+ \"merg\": 0,\n+ \"min\": 0,\n+ \"volum\": 0,\n+ \"3x3\": 0,\n+ \"oper\": 0,\n+ \"m\": 0,\n+ \"f\": 0,\n+ \"int\": 0,\n+ \"v\": 0,\n+ \"attribut\": 0,\n+ \"Ones\": 0,\n+ \"an\": 0,\n+ \"randomli\": 0,\n+ \"between\": 0,\n+ \"col\": 0,\n+ \"computeunitaryposit\": 0,\n+ \"tupl\": 0,\n+ \"polar\": 0,\n+ \"decomposit\": 0,\n+ \"unitari\": 0,\n+ \"u\": 0,\n+ \"posit\": 0,\n+ \"semi\": 0,\n+ \"definit\": 0,\n+ \"symmetr\": 0,\n+ \"p\": 0,\n+ \"self\": 0,\n+ \"determin\": 0,\n+ \"invert\": 0,\n+ \"isapprox\": 0,\n+ \"other\": 0,\n+ \"prec\": 0,\n+ \"1e\": 0,\n+ \"12\": 0,\n+ \"approxim\": 0,\n+ \"comparison\": 0,\n+ \"precis\": 0,\n+ \"jacobisvd\": 0,\n+ \"svd\": 0,\n+ \"squar\": 0,\n+ \"retun\": 0,\n+ \"\": 0,\n+ \"transpos\": 0,\n+ \"maxabscoeff\": 0,\n+ \"absolut\": 0,\n+ \"over\": 0,\n+ \"maxcoeff\": 0,\n+ \"mean\": 0,\n+ \"mincoeff\": 0,\n+ \"norm\": 0,\n+ \"euclidean\": 0,\n+ \"copi\": 0,\n+ \"polardecomposit\": 0,\n+ \"alia\": 0,\n+ \"prod\": 0,\n+ \"product\": 0,\n+ \"prune\": 0,\n+ \"abstol\": 0,\n+ \"06\": 0,\n+ \"greater\": 0,\n+ \"than\": 0,\n+ \"neg\": 0,\n+ \"row\": 0,\n+ \"selfadjointeigendecomposit\": 0,\n+ \"spectral\": 0,\n+ \"eigvec\": 0,\n+ \"eigval\": 0,\n+ \"orthogon\": 0,\n+ \"eigenvector\": 0,\n+ \"correspond\": 0,\n+ \"eigenvalu\": 0,\n+ \"diag\": 0,\n+ \"spectraldecomposit\": 0,\n+ \"squarednorm\": 0,\n+ \"sum\": 0,\n+ \"trace\": 0,\n+ \"vector3c\": 0,\n+ \"matrix6\": 0,\n+ \"6x6\": 0,\n+ \"construct\": 0,\n+ \"4\": 0,\n+ \"sub\": 0,\n+ \"6xvector6\": 0,\n+ \"vector6\": 0,\n+ \"ll\": 0,\n+ \"left\": 0,\n+ \"block\": 0,\n+ \"lr\": 0,\n+ \"right\": 0,\n+ \"ul\": 0,\n+ \"ur\": 0,\n+ \"matrix6c\": 0,\n+ \"vector6c\": 0,\n+ \"xxx\": 0,\n+ \"list\": 0,\n+ \"rank\": 0,\n+ \"creat\": 0,\n+ \"given\": 0,\n+ \"dimens\": 0,\n+ \"uniformli\": 0,\n+ \"resiz\": 0,\n+ \"chang\": 0,\n+ \"keep\": 0,\n+ \"exist\": 0,\n+ \"new\": 0,\n+ \"matrixxc\": 0,\n+ \"vectorxc\": 0,\n+ \"repres\": 0,\n+ \"rotat\": 0,\n+ \"q\": 0,\n+ \"composit\": 0,\n+ \"angulardist\": 0,\n+ \"conjug\": 0,\n+ \"slerp\": 0,\n+ \"toangleaxi\": 0,\n+ \"toaxisangl\": 0,\n+ \"torotationmatrix\": 0,\n+ \"torotationvector\": 0,\n+ \"dimension\": 0,\n+ \"implicit\": 0,\n+ \"sequenc\": 0,\n+ \"unitx\": 0,\n+ \"uniti\": 0,\n+ \"unit\": 0,\n+ \"asdiagon\": 0,\n+ \"dot\": 0,\n+ \"outer\": 0,\n+ \"vector2c\": 0,\n+ \"vector2i\": 0,\n+ \"plu\": 0,\n+ \"unitz\": 0,\n+ \"cross\": 0,\n+ \"xy\": 0,\n+ \"xz\": 0,\n+ \"yx\": 0,\n+ \"yz\": 0,\n+ \"zx\": 0,\n+ \"zy\": 0,\n+ \"vector3i\": 0,\n+ \"vector4\": 0,\n+ \"6\": 0,\n+ \"head\": 0,\n+ \"tail\": 0,\n+ \"vector6i\": 0,\n+ \"len\": 0,\n+ \"length\": 0,\n+ \"float2str\": 0,\n+ \"pad\": 0,\n+ \"str\": 0,\n+ \"shortest\": 0,\n+ \"string\": 0,\n+ \"represent\": 0,\n+ \"equal\": 0,\n+ \"convert\": 0,\n+ \"back\": 0,\n+ \"function\": 0,\n+ \"onli\": 0,\n+ \"prior\": 0,\n+ \"start\": 0,\n+ \"version\": 0,\n+ \"standard\": 0,\n+ \"just\": 0\n+ },\n+ \"objects\": {\n+ \"\": [\n+ [0, 0, 0, \"-\", \"minieigen\"]\n+ ],\n+ \"minieigen\": [\n+ [0, 1, 1, \"\", \"AlignedBox2\"],\n+ [0, 1, 1, \"\", \"AlignedBox3\"],\n+ [0, 1, 1, \"\", \"Matrix3\"],\n+ [0, 1, 1, \"\", \"Matrix3c\"],\n+ [0, 1, 1, \"\", \"Matrix6\"],\n+ [0, 1, 1, \"\", \"Matrix6c\"],\n+ [0, 1, 1, \"\", \"MatrixX\"],\n+ [0, 1, 1, \"\", \"MatrixXc\"],\n+ [0, 1, 1, \"\", \"Quaternion\"],\n+ [0, 1, 1, \"\", \"Vector2\"],\n+ [0, 1, 1, \"\", \"Vector2c\"],\n+ [0, 1, 1, \"\", \"Vector2i\"],\n+ [0, 1, 1, \"\", \"Vector3\"],\n+ [0, 1, 1, \"\", \"Vector3c\"],\n+ [0, 1, 1, \"\", \"Vector3i\"],\n+ [0, 1, 1, \"\", \"Vector4\"],\n+ [0, 1, 1, \"\", \"Vector6\"],\n+ [0, 1, 1, \"\", \"Vector6c\"],\n+ [0, 1, 1, \"\", \"Vector6i\"],\n+ [0, 1, 1, \"\", \"VectorX\"],\n+ [0, 1, 1, \"\", \"VectorXc\"],\n+ [0, 5, 1, \"\", \"float2str\"]\n+ ],\n+ \"minieigen.AlignedBox2\": [\n+ [0, 2, 1, \"\", \"center\"],\n+ [0, 2, 1, \"\", \"clamp\"],\n+ [0, 2, 1, \"\", \"contains\"],\n+ [0, 2, 1, \"\", \"empty\"],\n+ [0, 2, 1, \"\", \"extend\"],\n+ [0, 2, 1, \"\", \"intersection\"],\n+ [0, 3, 1, \"\", \"max\"],\n+ [0, 2, 1, \"\", \"merged\"],\n+ [0, 3, 1, \"\", \"min\"],\n+ [0, 2, 1, \"\", \"sizes\"],\n+ [0, 2, 1, \"\", \"volume\"]\n+ ],\n+ \"minieigen.AlignedBox3\": [\n+ [0, 2, 1, \"\", \"center\"],\n+ [0, 2, 1, \"\", \"clamp\"],\n+ [0, 2, 1, \"\", \"contains\"],\n+ [0, 2, 1, \"\", \"empty\"],\n+ [0, 2, 1, \"\", \"extend\"],\n+ [0, 2, 1, \"\", \"intersection\"],\n+ [0, 3, 1, \"\", \"max\"],\n+ [0, 2, 1, \"\", \"merged\"],\n+ [0, 3, 1, \"\", \"min\"],\n+ [0, 2, 1, \"\", \"sizes\"],\n+ [0, 2, 1, \"\", \"volume\"]\n+ ],\n+ \"minieigen.Matrix3\": [\n+ [0, 4, 1, \"\", \"Identity\"],\n+ [0, 4, 1, \"\", \"Ones\"],\n+ [0, 2, 1, \"\", \"Random\"],\n+ [0, 4, 1, \"\", \"Zero\"],\n+ [0, 2, 1, \"\", \"col\"],\n+ [0, 2, 1, \"\", \"cols\"],\n+ [0, 2, 1, \"\", \"computeUnitaryPositive\"],\n+ [0, 2, 1, \"\", \"determinant\"],\n+ [0, 2, 1, \"\", \"diagonal\"],\n+ [0, 2, 1, \"\", \"inverse\"],\n+ [0, 2, 1, \"\", \"isApprox\"],\n+ [0, 2, 1, \"\", \"jacobiSVD\"],\n+ [0, 2, 1, \"\", \"maxAbsCoeff\"],\n+ [0, 2, 1, \"\", \"maxCoeff\"],\n+ [0, 2, 1, \"\", \"mean\"],\n+ [0, 2, 1, \"\", \"minCoeff\"],\n+ [0, 2, 1, \"\", \"norm\"],\n+ [0, 2, 1, \"\", \"normalize\"],\n+ [0, 2, 1, \"\", \"normalized\"],\n+ [0, 2, 1, \"\", \"polarDecomposition\"],\n+ [0, 2, 1, \"\", \"prod\"],\n+ [0, 2, 1, \"\", \"pruned\"],\n+ [0, 2, 1, \"\", \"row\"],\n+ [0, 2, 1, \"\", \"rows\"],\n+ [0, 2, 1, \"\", \"selfAdjointEigenDecomposition\"],\n+ [0, 2, 1, \"\", \"spectralDecomposition\"],\n+ [0, 2, 1, \"\", \"squaredNorm\"],\n+ [0, 2, 1, \"\", \"sum\"],\n+ [0, 2, 1, \"\", \"svd\"],\n+ [0, 2, 1, \"\", \"trace\"],\n+ [0, 2, 1, \"\", \"transpose\"]\n+ ],\n+ \"minieigen.Matrix3c\": [\n+ [0, 4, 1, \"\", \"Identity\"],\n+ [0, 4, 1, \"\", \"Ones\"],\n+ [0, 2, 1, \"\", \"Random\"],\n+ [0, 4, 1, \"\", \"Zero\"],\n+ [0, 2, 1, \"\", \"col\"],\n+ [0, 2, 1, \"\", \"cols\"],\n+ [0, 2, 1, \"\", \"determinant\"],\n+ [0, 2, 1, \"\", \"diagonal\"],\n+ [0, 2, 1, \"\", \"inverse\"],\n+ [0, 2, 1, \"\", \"isApprox\"],\n+ [0, 2, 1, \"\", \"maxAbsCoeff\"],\n+ [0, 2, 1, \"\", \"mean\"],\n+ [0, 2, 1, \"\", \"norm\"],\n+ [0, 2, 1, \"\", \"normalize\"],\n+ [0, 2, 1, \"\", \"normalized\"],\n+ [0, 2, 1, \"\", \"prod\"],\n+ [0, 2, 1, \"\", \"pruned\"],\n+ [0, 2, 1, \"\", \"row\"],\n+ [0, 2, 1, \"\", \"rows\"],\n+ [0, 2, 1, \"\", \"squaredNorm\"],\n+ [0, 2, 1, \"\", \"sum\"],\n+ [0, 2, 1, \"\", \"trace\"],\n+ [0, 2, 1, \"\", \"transpose\"]\n+ ],\n+ \"minieigen.Matrix6\": [\n+ [0, 4, 1, \"\", \"Identity\"],\n+ [0, 4, 1, \"\", \"Ones\"],\n+ [0, 2, 1, \"\", \"Random\"],\n+ [0, 4, 1, \"\", \"Zero\"],\n+ [0, 2, 1, \"\", \"col\"],\n+ [0, 2, 1, \"\", \"cols\"],\n+ [0, 2, 1, \"\", \"computeUnitaryPositive\"],\n+ [0, 2, 1, \"\", \"determinant\"],\n+ [0, 2, 1, \"\", \"diagonal\"],\n+ [0, 2, 1, \"\", \"inverse\"],\n+ [0, 2, 1, \"\", \"isApprox\"],\n+ [0, 2, 1, \"\", \"jacobiSVD\"],\n+ [0, 2, 1, \"\", \"ll\"],\n+ [0, 2, 1, \"\", \"lr\"],\n+ [0, 2, 1, \"\", \"maxAbsCoeff\"],\n+ [0, 2, 1, \"\", \"maxCoeff\"],\n+ [0, 2, 1, \"\", \"mean\"],\n+ [0, 2, 1, \"\", \"minCoeff\"],\n+ [0, 2, 1, \"\", \"norm\"],\n+ [0, 2, 1, \"\", \"normalize\"],\n+ [0, 2, 1, \"\", \"normalized\"],\n+ [0, 2, 1, \"\", \"polarDecomposition\"],\n+ [0, 2, 1, \"\", \"prod\"],\n+ [0, 2, 1, \"\", \"pruned\"],\n+ [0, 2, 1, \"\", \"row\"],\n+ [0, 2, 1, \"\", \"rows\"],\n+ [0, 2, 1, \"\", \"selfAdjointEigenDecomposition\"],\n+ [0, 2, 1, \"\", \"spectralDecomposition\"],\n+ [0, 2, 1, \"\", \"squaredNorm\"],\n+ [0, 2, 1, \"\", \"sum\"],\n+ [0, 2, 1, \"\", \"svd\"],\n+ [0, 2, 1, \"\", \"trace\"],\n+ [0, 2, 1, \"\", \"transpose\"],\n+ [0, 2, 1, \"\", \"ul\"],\n+ [0, 2, 1, \"\", \"ur\"]\n+ ],\n+ \"minieigen.Matrix6c\": [\n+ [0, 4, 1, \"\", \"Identity\"],\n+ [0, 4, 1, \"\", \"Ones\"],\n+ [0, 2, 1, \"\", \"Random\"],\n+ [0, 4, 1, \"\", \"Zero\"],\n+ [0, 2, 1, \"\", \"col\"],\n+ [0, 2, 1, \"\", \"cols\"],\n+ [0, 2, 1, \"\", \"determinant\"],\n+ [0, 2, 1, \"\", \"diagonal\"],\n+ [0, 2, 1, \"\", \"inverse\"],\n+ [0, 2, 1, \"\", \"isApprox\"],\n+ [0, 2, 1, \"\", \"ll\"],\n+ [0, 2, 1, \"\", \"lr\"],\n+ [0, 2, 1, \"\", \"maxAbsCoeff\"],\n+ [0, 2, 1, \"\", \"mean\"],\n+ [0, 2, 1, \"\", \"norm\"],\n+ [0, 2, 1, \"\", \"normalize\"],\n+ [0, 2, 1, \"\", \"normalized\"],\n+ [0, 2, 1, \"\", \"prod\"],\n+ [0, 2, 1, \"\", \"pruned\"],\n+ [0, 2, 1, \"\", \"row\"],\n+ [0, 2, 1, \"\", \"rows\"],\n+ [0, 2, 1, \"\", \"squaredNorm\"],\n+ [0, 2, 1, \"\", \"sum\"],\n+ [0, 2, 1, \"\", \"trace\"],\n+ [0, 2, 1, \"\", \"transpose\"],\n+ [0, 2, 1, \"\", \"ul\"],\n+ [0, 2, 1, \"\", \"ur\"]\n+ ],\n+ \"minieigen.MatrixX\": [\n+ [0, 2, 1, \"\", \"Identity\"],\n+ [0, 2, 1, \"\", \"Ones\"],\n+ [0, 2, 1, \"\", \"Random\"],\n+ [0, 2, 1, \"\", \"Zero\"],\n+ [0, 2, 1, \"\", \"col\"],\n+ [0, 2, 1, \"\", \"cols\"],\n+ [0, 2, 1, \"\", \"computeUnitaryPositive\"],\n+ [0, 2, 1, \"\", \"determinant\"],\n+ [0, 2, 1, \"\", \"diagonal\"],\n+ [0, 2, 1, \"\", \"inverse\"],\n+ [0, 2, 1, \"\", \"isApprox\"],\n+ [0, 2, 1, \"\", \"jacobiSVD\"],\n+ [0, 2, 1, \"\", \"maxAbsCoeff\"],\n+ [0, 2, 1, \"\", \"maxCoeff\"],\n+ [0, 2, 1, \"\", \"mean\"],\n+ [0, 2, 1, \"\", \"minCoeff\"],\n+ [0, 2, 1, \"\", \"norm\"],\n+ [0, 2, 1, \"\", \"normalize\"],\n+ [0, 2, 1, \"\", \"normalized\"],\n+ [0, 2, 1, \"\", \"polarDecomposition\"],\n+ [0, 2, 1, \"\", \"prod\"],\n+ [0, 2, 1, \"\", \"pruned\"],\n+ [0, 2, 1, \"\", \"resize\"],\n+ [0, 2, 1, \"\", \"row\"],\n+ [0, 2, 1, \"\", \"rows\"],\n+ [0, 2, 1, \"\", \"selfAdjointEigenDecomposition\"],\n+ [0, 2, 1, \"\", \"spectralDecomposition\"],\n+ [0, 2, 1, \"\", \"squaredNorm\"],\n+ [0, 2, 1, \"\", \"sum\"],\n+ [0, 2, 1, \"\", \"svd\"],\n+ [0, 2, 1, \"\", \"trace\"],\n+ [0, 2, 1, \"\", \"transpose\"]\n+ ],\n+ \"minieigen.MatrixXc\": [\n+ [0, 2, 1, \"\", \"Identity\"],\n+ [0, 2, 1, \"\", \"Ones\"],\n+ [0, 2, 1, \"\", \"Random\"],\n+ [0, 2, 1, \"\", \"Zero\"],\n+ [0, 2, 1, \"\", \"col\"],\n+ [0, 2, 1, \"\", \"cols\"],\n+ [0, 2, 1, \"\", \"determinant\"],\n+ [0, 2, 1, \"\", \"diagonal\"],\n+ [0, 2, 1, \"\", \"inverse\"],\n+ [0, 2, 1, \"\", \"isApprox\"],\n+ [0, 2, 1, \"\", \"maxAbsCoeff\"],\n+ [0, 2, 1, \"\", \"mean\"],\n+ [0, 2, 1, \"\", \"norm\"],\n+ [0, 2, 1, \"\", \"normalize\"],\n+ [0, 2, 1, \"\", \"normalized\"],\n+ [0, 2, 1, \"\", \"prod\"],\n+ [0, 2, 1, \"\", \"pruned\"],\n+ [0, 2, 1, \"\", \"resize\"],\n+ [0, 2, 1, \"\", \"row\"],\n+ [0, 2, 1, \"\", \"rows\"],\n+ [0, 2, 1, \"\", \"squaredNorm\"],\n+ [0, 2, 1, \"\", \"sum\"],\n+ [0, 2, 1, \"\", \"trace\"],\n+ [0, 2, 1, \"\", \"transpose\"]\n+ ],\n+ \"minieigen.Quaternion\": [\n+ [0, 4, 1, \"\", \"Identity\"],\n+ [0, 2, 1, \"\", \"Rotate\"],\n+ [0, 2, 1, \"\", \"angularDistance\"],\n+ [0, 2, 1, \"\", \"conjugate\"],\n+ [0, 2, 1, \"\", \"inverse\"],\n+ [0, 2, 1, \"\", \"norm\"],\n+ [0, 2, 1, \"\", \"normalize\"],\n+ [0, 2, 1, \"\", \"normalized\"],\n+ [0, 2, 1, \"\", \"setFromTwoVectors\"],\n+ [0, 2, 1, \"\", \"slerp\"],\n+ [0, 2, 1, \"\", \"toAngleAxis\"],\n+ [0, 2, 1, \"\", \"toAxisAngle\"],\n+ [0, 2, 1, \"\", \"toRotationMatrix\"],\n+ [0, 2, 1, \"\", \"toRotationVector\"]\n+ ],\n+ \"minieigen.Vector2\": [\n+ [0, 4, 1, \"\", \"Identity\"],\n+ [0, 4, 1, \"\", \"Ones\"],\n+ [0, 2, 1, \"\", \"Random\"],\n+ [0, 2, 1, \"\", \"Unit\"],\n+ [0, 4, 1, \"\", \"UnitX\"],\n+ [0, 4, 1, \"\", \"UnitY\"],\n+ [0, 4, 1, \"\", \"Zero\"],\n+ [0, 2, 1, \"\", \"asDiagonal\"],\n+ [0, 2, 1, \"\", \"cols\"],\n+ [0, 2, 1, \"\", \"dot\"],\n+ [0, 2, 1, \"\", \"isApprox\"],\n+ [0, 2, 1, \"\", \"maxAbsCoeff\"],\n+ [0, 2, 1, \"\", \"maxCoeff\"],\n+ [0, 2, 1, \"\", \"mean\"],\n+ [0, 2, 1, \"\", \"minCoeff\"],\n+ [0, 2, 1, \"\", \"norm\"],\n+ [0, 2, 1, \"\", \"normalize\"],\n+ [0, 2, 1, \"\", \"normalized\"],\n+ [0, 2, 1, \"\", \"outer\"],\n+ [0, 2, 1, \"\", \"prod\"],\n+ [0, 2, 1, \"\", \"pruned\"],\n+ [0, 2, 1, \"\", \"rows\"],\n+ [0, 2, 1, \"\", \"squaredNorm\"],\n+ [0, 2, 1, \"\", \"sum\"]\n+ ],\n+ \"minieigen.Vector2c\": [\n+ [0, 4, 1, \"\", \"Identity\"],\n+ [0, 4, 1, \"\", \"Ones\"],\n+ [0, 2, 1, \"\", \"Random\"],\n+ [0, 2, 1, \"\", \"Unit\"],\n+ [0, 4, 1, \"\", \"UnitX\"],\n+ [0, 4, 1, \"\", \"UnitY\"],\n+ [0, 4, 1, \"\", \"Zero\"],\n+ [0, 2, 1, \"\", \"asDiagonal\"],\n+ [0, 2, 1, \"\", \"cols\"],\n+ [0, 2, 1, \"\", \"dot\"],\n+ [0, 2, 1, \"\", \"isApprox\"],\n+ [0, 2, 1, \"\", \"maxAbsCoeff\"],\n+ [0, 2, 1, \"\", \"mean\"],\n+ [0, 2, 1, \"\", \"norm\"],\n+ [0, 2, 1, \"\", \"normalize\"],\n+ [0, 2, 1, \"\", \"normalized\"],\n+ [0, 2, 1, \"\", \"outer\"],\n+ [0, 2, 1, \"\", \"prod\"],\n+ [0, 2, 1, \"\", \"pruned\"],\n+ [0, 2, 1, \"\", \"rows\"],\n+ [0, 2, 1, \"\", \"squaredNorm\"],\n+ [0, 2, 1, \"\", \"sum\"]\n+ ],\n+ \"minieigen.Vector2i\": [\n+ [0, 4, 1, \"\", \"Identity\"],\n+ [0, 4, 1, \"\", \"Ones\"],\n+ [0, 2, 1, \"\", \"Random\"],\n+ [0, 2, 1, \"\", \"Unit\"],\n+ [0, 4, 1, \"\", \"UnitX\"],\n+ [0, 4, 1, \"\", \"UnitY\"],\n+ [0, 4, 1, \"\", \"Zero\"],\n+ [0, 2, 1, \"\", \"asDiagonal\"],\n+ [0, 2, 1, \"\", \"cols\"],\n+ [0, 2, 1, \"\", \"dot\"],\n+ [0, 2, 1, \"\", \"isApprox\"],\n+ [0, 2, 1, \"\", \"maxAbsCoeff\"],\n+ [0, 2, 1, \"\", \"maxCoeff\"],\n+ [0, 2, 1, \"\", \"mean\"],\n+ [0, 2, 1, \"\", \"minCoeff\"],\n+ [0, 2, 1, \"\", \"outer\"],\n+ [0, 2, 1, \"\", \"prod\"],\n+ [0, 2, 1, \"\", \"rows\"],\n+ [0, 2, 1, \"\", \"sum\"]\n+ ],\n+ \"minieigen.Vector3\": [\n+ [0, 4, 1, \"\", \"Identity\"],\n+ [0, 4, 1, \"\", \"Ones\"],\n+ [0, 2, 1, \"\", \"Random\"],\n+ [0, 2, 1, \"\", \"Unit\"],\n+ [0, 4, 1, \"\", \"UnitX\"],\n+ [0, 4, 1, \"\", \"UnitY\"],\n+ [0, 4, 1, \"\", \"UnitZ\"],\n+ [0, 4, 1, \"\", \"Zero\"],\n+ [0, 2, 1, \"\", \"asDiagonal\"],\n+ [0, 2, 1, \"\", \"cols\"],\n+ [0, 2, 1, \"\", \"cross\"],\n+ [0, 2, 1, \"\", \"dot\"],\n+ [0, 2, 1, \"\", \"isApprox\"],\n+ [0, 2, 1, \"\", \"maxAbsCoeff\"],\n+ [0, 2, 1, \"\", \"maxCoeff\"],\n+ [0, 2, 1, \"\", \"mean\"],\n+ [0, 2, 1, \"\", \"minCoeff\"],\n+ [0, 2, 1, \"\", \"norm\"],\n+ [0, 2, 1, \"\", \"normalize\"],\n+ [0, 2, 1, \"\", \"normalized\"],\n+ [0, 2, 1, \"\", \"outer\"],\n+ [0, 2, 1, \"\", \"prod\"],\n+ [0, 2, 1, \"\", \"pruned\"],\n+ [0, 2, 1, \"\", \"rows\"],\n+ [0, 2, 1, \"\", \"squaredNorm\"],\n+ [0, 2, 1, \"\", \"sum\"],\n+ [0, 2, 1, \"\", \"xy\"],\n+ [0, 2, 1, \"\", \"xz\"],\n+ [0, 2, 1, \"\", \"yx\"],\n+ [0, 2, 1, \"\", \"yz\"],\n+ [0, 2, 1, \"\", \"zx\"],\n+ [0, 2, 1, \"\", \"zy\"]\n+ ],\n+ \"minieigen.Vector3c\": [\n+ [0, 4, 1, \"\", \"Identity\"],\n+ [0, 4, 1, \"\", \"Ones\"],\n+ [0, 2, 1, \"\", \"Random\"],\n+ [0, 2, 1, \"\", \"Unit\"],\n+ [0, 4, 1, \"\", \"UnitX\"],\n+ [0, 4, 1, \"\", \"UnitY\"],\n+ [0, 4, 1, \"\", \"UnitZ\"],\n+ [0, 4, 1, \"\", \"Zero\"],\n+ [0, 2, 1, \"\", \"asDiagonal\"],\n+ [0, 2, 1, \"\", \"cols\"],\n+ [0, 2, 1, \"\", \"cross\"],\n+ [0, 2, 1, \"\", \"dot\"],\n+ [0, 2, 1, \"\", \"isApprox\"],\n+ [0, 2, 1, \"\", \"maxAbsCoeff\"],\n+ [0, 2, 1, \"\", \"mean\"],\n+ [0, 2, 1, \"\", \"norm\"],\n+ [0, 2, 1, \"\", \"normalize\"],\n+ [0, 2, 1, \"\", \"normalized\"],\n+ [0, 2, 1, \"\", \"outer\"],\n+ [0, 2, 1, \"\", \"prod\"],\n+ [0, 2, 1, \"\", \"pruned\"],\n+ [0, 2, 1, \"\", \"rows\"],\n+ [0, 2, 1, \"\", \"squaredNorm\"],\n+ [0, 2, 1, \"\", \"sum\"],\n+ [0, 2, 1, \"\", \"xy\"],\n+ [0, 2, 1, \"\", \"xz\"],\n+ [0, 2, 1, \"\", \"yx\"],\n+ [0, 2, 1, \"\", \"yz\"],\n+ [0, 2, 1, \"\", \"zx\"],\n+ [0, 2, 1, \"\", \"zy\"]\n+ ],\n+ \"minieigen.Vector3i\": [\n+ [0, 4, 1, \"\", \"Identity\"],\n+ [0, 4, 1, \"\", \"Ones\"],\n+ [0, 2, 1, \"\", \"Random\"],\n+ [0, 2, 1, \"\", \"Unit\"],\n+ [0, 4, 1, \"\", \"UnitX\"],\n+ [0, 4, 1, \"\", \"UnitY\"],\n+ [0, 4, 1, \"\", \"UnitZ\"],\n+ [0, 4, 1, \"\", \"Zero\"],\n+ [0, 2, 1, \"\", \"asDiagonal\"],\n+ [0, 2, 1, \"\", \"cols\"],\n+ [0, 2, 1, \"\", \"cross\"],\n+ [0, 2, 1, \"\", \"dot\"],\n+ [0, 2, 1, \"\", \"isApprox\"],\n+ [0, 2, 1, \"\", \"maxAbsCoeff\"],\n+ [0, 2, 1, \"\", \"maxCoeff\"],\n+ [0, 2, 1, \"\", \"mean\"],\n+ [0, 2, 1, \"\", \"minCoeff\"],\n+ [0, 2, 1, \"\", \"outer\"],\n+ [0, 2, 1, \"\", \"prod\"],\n+ [0, 2, 1, \"\", \"rows\"],\n+ [0, 2, 1, \"\", \"sum\"],\n+ [0, 2, 1, \"\", \"xy\"],\n+ [0, 2, 1, \"\", \"xz\"],\n+ [0, 2, 1, \"\", \"yx\"],\n+ [0, 2, 1, \"\", \"yz\"],\n+ [0, 2, 1, \"\", \"zx\"],\n+ [0, 2, 1, \"\", \"zy\"]\n+ ],\n+ \"minieigen.Vector4\": [\n+ [0, 4, 1, \"\", \"Identity\"],\n+ [0, 4, 1, \"\", \"Ones\"],\n+ [0, 2, 1, \"\", \"Random\"],\n+ [0, 2, 1, \"\", \"Unit\"],\n+ [0, 4, 1, \"\", \"Zero\"],\n+ [0, 2, 1, \"\", \"asDiagonal\"],\n+ [0, 2, 1, \"\", \"cols\"],\n+ [0, 2, 1, \"\", \"dot\"],\n+ [0, 2, 1, \"\", \"isApprox\"],\n+ [0, 2, 1, \"\", \"maxAbsCoeff\"],\n+ [0, 2, 1, \"\", \"maxCoeff\"],\n+ [0, 2, 1, \"\", \"mean\"],\n+ [0, 2, 1, \"\", \"minCoeff\"],\n+ [0, 2, 1, \"\", \"norm\"],\n+ [0, 2, 1, \"\", \"normalize\"],\n+ [0, 2, 1, \"\", \"normalized\"],\n+ [0, 2, 1, \"\", \"outer\"],\n+ [0, 2, 1, \"\", \"prod\"],\n+ [0, 2, 1, \"\", \"pruned\"],\n+ [0, 2, 1, \"\", \"rows\"],\n+ [0, 2, 1, \"\", \"squaredNorm\"],\n+ [0, 2, 1, \"\", \"sum\"]\n+ ],\n+ \"minieigen.Vector6\": [\n+ [0, 4, 1, \"\", \"Identity\"],\n+ [0, 4, 1, \"\", \"Ones\"],\n+ [0, 2, 1, \"\", \"Random\"],\n+ [0, 2, 1, \"\", \"Unit\"],\n+ [0, 4, 1, \"\", \"Zero\"],\n+ [0, 2, 1, \"\", \"asDiagonal\"],\n+ [0, 2, 1, \"\", \"cols\"],\n+ [0, 2, 1, \"\", \"dot\"],\n+ [0, 2, 1, \"\", \"head\"],\n+ [0, 2, 1, \"\", \"isApprox\"],\n+ [0, 2, 1, \"\", \"maxAbsCoeff\"],\n+ [0, 2, 1, \"\", \"maxCoeff\"],\n+ [0, 2, 1, \"\", \"mean\"],\n+ [0, 2, 1, \"\", \"minCoeff\"],\n+ [0, 2, 1, \"\", \"norm\"],\n+ [0, 2, 1, \"\", \"normalize\"],\n+ [0, 2, 1, \"\", \"normalized\"],\n+ [0, 2, 1, \"\", \"outer\"],\n+ [0, 2, 1, \"\", \"prod\"],\n+ [0, 2, 1, \"\", \"pruned\"],\n+ [0, 2, 1, \"\", \"rows\"],\n+ [0, 2, 1, \"\", \"squaredNorm\"],\n+ [0, 2, 1, \"\", \"sum\"],\n+ [0, 2, 1, \"\", \"tail\"]\n+ ],\n+ \"minieigen.Vector6c\": [\n+ [0, 4, 1, \"\", \"Identity\"],\n+ [0, 4, 1, \"\", \"Ones\"],\n+ [0, 2, 1, \"\", \"Random\"],\n+ [0, 2, 1, \"\", \"Unit\"],\n+ [0, 4, 1, \"\", \"Zero\"],\n+ [0, 2, 1, \"\", \"asDiagonal\"],\n+ [0, 2, 1, \"\", \"cols\"],\n+ [0, 2, 1, \"\", \"dot\"],\n+ [0, 2, 1, \"\", \"head\"],\n+ [0, 2, 1, \"\", \"isApprox\"],\n+ [0, 2, 1, \"\", \"maxAbsCoeff\"],\n+ [0, 2, 1, \"\", \"mean\"],\n+ [0, 2, 1, \"\", \"norm\"],\n+ [0, 2, 1, \"\", \"normalize\"],\n+ [0, 2, 1, \"\", \"normalized\"],\n+ [0, 2, 1, \"\", \"outer\"],\n+ [0, 2, 1, \"\", \"prod\"],\n+ [0, 2, 1, \"\", \"pruned\"],\n+ [0, 2, 1, \"\", \"rows\"],\n+ [0, 2, 1, \"\", \"squaredNorm\"],\n+ [0, 2, 1, \"\", \"sum\"],\n+ [0, 2, 1, \"\", \"tail\"]\n+ ],\n+ \"minieigen.Vector6i\": [\n+ [0, 4, 1, \"\", \"Identity\"],\n+ [0, 4, 1, \"\", \"Ones\"],\n+ [0, 2, 1, \"\", \"Random\"],\n+ [0, 2, 1, \"\", \"Unit\"],\n+ [0, 4, 1, \"\", \"Zero\"],\n+ [0, 2, 1, \"\", \"asDiagonal\"],\n+ [0, 2, 1, \"\", \"cols\"],\n+ [0, 2, 1, \"\", \"dot\"],\n+ [0, 2, 1, \"\", \"head\"],\n+ [0, 2, 1, \"\", \"isApprox\"],\n+ [0, 2, 1, \"\", \"maxAbsCoeff\"],\n+ [0, 2, 1, \"\", \"maxCoeff\"],\n+ [0, 2, 1, \"\", \"mean\"],\n+ [0, 2, 1, \"\", \"minCoeff\"],\n+ [0, 2, 1, \"\", \"outer\"],\n+ [0, 2, 1, \"\", \"prod\"],\n+ [0, 2, 1, \"\", \"rows\"],\n+ [0, 2, 1, \"\", \"sum\"],\n+ [0, 2, 1, \"\", \"tail\"]\n+ ],\n+ \"minieigen.VectorX\": [\n+ [0, 2, 1, \"\", \"Ones\"],\n+ [0, 2, 1, \"\", \"Random\"],\n+ [0, 2, 1, \"\", \"Unit\"],\n+ [0, 2, 1, \"\", \"Zero\"],\n+ [0, 2, 1, \"\", \"asDiagonal\"],\n+ [0, 2, 1, \"\", \"cols\"],\n+ [0, 2, 1, \"\", \"dot\"],\n+ [0, 2, 1, \"\", \"isApprox\"],\n+ [0, 2, 1, \"\", \"maxAbsCoeff\"],\n+ [0, 2, 1, \"\", \"maxCoeff\"],\n+ [0, 2, 1, \"\", \"mean\"],\n+ [0, 2, 1, \"\", \"minCoeff\"],\n+ [0, 2, 1, \"\", \"norm\"],\n+ [0, 2, 1, \"\", \"normalize\"],\n+ [0, 2, 1, \"\", \"normalized\"],\n+ [0, 2, 1, \"\", \"outer\"],\n+ [0, 2, 1, \"\", \"prod\"],\n+ [0, 2, 1, \"\", \"pruned\"],\n+ [0, 2, 1, \"\", \"resize\"],\n+ [0, 2, 1, \"\", \"rows\"],\n+ [0, 2, 1, \"\", \"squaredNorm\"],\n+ [0, 2, 1, \"\", \"sum\"]\n+ ],\n+ \"minieigen.VectorXc\": [\n+ [0, 2, 1, \"\", \"Ones\"],\n+ [0, 2, 1, \"\", \"Random\"],\n+ [0, 2, 1, \"\", \"Unit\"],\n+ [0, 2, 1, \"\", \"Zero\"],\n+ [0, 2, 1, \"\", \"asDiagonal\"],\n+ [0, 2, 1, \"\", \"cols\"],\n+ [0, 2, 1, \"\", \"dot\"],\n+ [0, 2, 1, \"\", \"isApprox\"],\n+ [0, 2, 1, \"\", \"maxAbsCoeff\"],\n+ [0, 2, 1, \"\", \"mean\"],\n+ [0, 2, 1, \"\", \"norm\"],\n+ [0, 2, 1, \"\", \"normalize\"],\n+ [0, 2, 1, \"\", \"normalized\"],\n+ [0, 2, 1, \"\", \"outer\"],\n+ [0, 2, 1, \"\", \"prod\"],\n+ [0, 2, 1, \"\", \"pruned\"],\n+ [0, 2, 1, \"\", \"resize\"],\n+ [0, 2, 1, \"\", \"rows\"],\n+ [0, 2, 1, \"\", \"squaredNorm\"],\n+ [0, 2, 1, \"\", \"sum\"]\n+ ]\n+ },\n+ \"objtypes\": {\n+ \"0\": \"py:module\",\n+ \"1\": \"py:class\",\n+ \"2\": \"py:method\",\n+ \"3\": \"py:property\",\n+ \"4\": \"py:attribute\",\n+ \"5\": \"py:function\"\n+ },\n+ \"objnames\": {\n+ \"0\": [\"py\", \"module\", \"Python module\"],\n+ \"1\": [\"py\", \"class\", \"Python class\"],\n+ \"2\": [\"py\", \"method\", \"Python method\"],\n+ \"3\": [\"py\", \"property\", \"Python property\"],\n+ \"4\": [\"py\", \"attribute\", \"Python attribute\"],\n+ \"5\": [\"py\", \"function\", \"Python function\"]\n },\n- \"objects\": {},\n- \"objtypes\": {},\n- \"objnames\": {},\n \"titleterms\": {\n \"minieigen\": 0,\n \"document\": 0,\n \"overview\": 0,\n \"todo\": 0,\n \"exampl\": 0,\n \"name\": 0,\n@@ -220,9 +985,1555 @@\n \"Links\": [\n [0, \"links\"]\n ],\n \"Documentation\": [\n [0, \"documentation\"]\n ]\n },\n- \"indexentries\": {}\n+ \"indexentries\": {\n+ \"alignedbox2 (class in minieigen)\": [\n+ [0, \"minieigen.AlignedBox2\"]\n+ ],\n+ \"alignedbox3 (class in minieigen)\": [\n+ [0, \"minieigen.AlignedBox3\"]\n+ ],\n+ \"identity (minieigen.matrix3 attribute)\": [\n+ [0, \"minieigen.Matrix3.Identity\"]\n+ ],\n+ \"identity (minieigen.matrix3c attribute)\": [\n+ [0, \"minieigen.Matrix3c.Identity\"]\n+ ],\n+ \"identity (minieigen.matrix6 attribute)\": [\n+ [0, \"minieigen.Matrix6.Identity\"]\n+ ],\n+ \"identity (minieigen.matrix6c attribute)\": [\n+ [0, \"minieigen.Matrix6c.Identity\"]\n+ ],\n+ \"identity (minieigen.quaternion attribute)\": [\n+ [0, \"minieigen.Quaternion.Identity\"]\n+ ],\n+ \"identity (minieigen.vector2 attribute)\": [\n+ [0, \"minieigen.Vector2.Identity\"]\n+ ],\n+ \"identity (minieigen.vector2c attribute)\": [\n+ [0, \"minieigen.Vector2c.Identity\"]\n+ ],\n+ \"identity (minieigen.vector2i attribute)\": [\n+ [0, \"minieigen.Vector2i.Identity\"]\n+ ],\n+ \"identity (minieigen.vector3 attribute)\": [\n+ [0, \"minieigen.Vector3.Identity\"]\n+ ],\n+ \"identity (minieigen.vector3c attribute)\": [\n+ [0, \"minieigen.Vector3c.Identity\"]\n+ ],\n+ \"identity (minieigen.vector3i attribute)\": [\n+ [0, \"minieigen.Vector3i.Identity\"]\n+ ],\n+ \"identity (minieigen.vector4 attribute)\": [\n+ [0, \"minieigen.Vector4.Identity\"]\n+ ],\n+ \"identity (minieigen.vector6 attribute)\": [\n+ [0, \"minieigen.Vector6.Identity\"]\n+ ],\n+ \"identity (minieigen.vector6c attribute)\": [\n+ [0, \"minieigen.Vector6c.Identity\"]\n+ ],\n+ \"identity (minieigen.vector6i attribute)\": [\n+ [0, \"minieigen.Vector6i.Identity\"]\n+ ],\n+ \"identity() (minieigen.matrixx static method)\": [\n+ [0, \"minieigen.MatrixX.Identity\"]\n+ ],\n+ \"identity() (minieigen.matrixxc static method)\": [\n+ [0, \"minieigen.MatrixXc.Identity\"]\n+ ],\n+ \"matrix3 (class in minieigen)\": [\n+ [0, \"minieigen.Matrix3\"]\n+ ],\n+ \"matrix3c (class in minieigen)\": [\n+ [0, \"minieigen.Matrix3c\"]\n+ ],\n+ \"matrix6 (class in minieigen)\": [\n+ [0, \"minieigen.Matrix6\"]\n+ ],\n+ \"matrix6c (class in minieigen)\": [\n+ [0, \"minieigen.Matrix6c\"]\n+ ],\n+ \"matrixx (class in minieigen)\": [\n+ [0, \"minieigen.MatrixX\"]\n+ ],\n+ \"matrixxc (class in minieigen)\": [\n+ [0, \"minieigen.MatrixXc\"]\n+ ],\n+ \"ones (minieigen.matrix3 attribute)\": [\n+ [0, \"minieigen.Matrix3.Ones\"]\n+ ],\n+ \"ones (minieigen.matrix3c attribute)\": [\n+ [0, \"minieigen.Matrix3c.Ones\"]\n+ ],\n+ \"ones (minieigen.matrix6 attribute)\": [\n+ [0, \"minieigen.Matrix6.Ones\"]\n+ ],\n+ \"ones (minieigen.matrix6c attribute)\": [\n+ [0, \"minieigen.Matrix6c.Ones\"]\n+ ],\n+ \"ones (minieigen.vector2 attribute)\": [\n+ [0, \"minieigen.Vector2.Ones\"]\n+ ],\n+ \"ones (minieigen.vector2c attribute)\": [\n+ [0, \"minieigen.Vector2c.Ones\"]\n+ ],\n+ \"ones (minieigen.vector2i attribute)\": [\n+ [0, \"minieigen.Vector2i.Ones\"]\n+ ],\n+ \"ones (minieigen.vector3 attribute)\": [\n+ [0, \"minieigen.Vector3.Ones\"]\n+ ],\n+ \"ones (minieigen.vector3c attribute)\": [\n+ [0, \"minieigen.Vector3c.Ones\"]\n+ ],\n+ \"ones (minieigen.vector3i attribute)\": [\n+ [0, \"minieigen.Vector3i.Ones\"]\n+ ],\n+ \"ones (minieigen.vector4 attribute)\": [\n+ [0, \"minieigen.Vector4.Ones\"]\n+ ],\n+ \"ones (minieigen.vector6 attribute)\": [\n+ [0, \"minieigen.Vector6.Ones\"]\n+ ],\n+ \"ones (minieigen.vector6c attribute)\": [\n+ [0, \"minieigen.Vector6c.Ones\"]\n+ ],\n+ \"ones (minieigen.vector6i attribute)\": [\n+ [0, \"minieigen.Vector6i.Ones\"]\n+ ],\n+ \"ones() (minieigen.matrixx static method)\": [\n+ [0, \"minieigen.MatrixX.Ones\"]\n+ ],\n+ \"ones() (minieigen.matrixxc static method)\": [\n+ [0, \"minieigen.MatrixXc.Ones\"]\n+ ],\n+ \"ones() (minieigen.vectorx static method)\": [\n+ [0, \"minieigen.VectorX.Ones\"]\n+ ],\n+ \"ones() (minieigen.vectorxc static method)\": [\n+ [0, \"minieigen.VectorXc.Ones\"]\n+ ],\n+ \"quaternion (class in minieigen)\": [\n+ [0, \"minieigen.Quaternion\"]\n+ ],\n+ \"random() (minieigen.matrix3 static method)\": [\n+ [0, \"minieigen.Matrix3.Random\"]\n+ ],\n+ \"random() (minieigen.matrix3c static method)\": [\n+ [0, \"minieigen.Matrix3c.Random\"]\n+ ],\n+ \"random() (minieigen.matrix6 static method)\": [\n+ [0, \"minieigen.Matrix6.Random\"]\n+ ],\n+ \"random() (minieigen.matrix6c static method)\": [\n+ [0, \"minieigen.Matrix6c.Random\"]\n+ ],\n+ \"random() (minieigen.matrixx static method)\": [\n+ [0, \"minieigen.MatrixX.Random\"]\n+ ],\n+ \"random() (minieigen.matrixxc static method)\": [\n+ [0, \"minieigen.MatrixXc.Random\"]\n+ ],\n+ \"random() (minieigen.vector2 static method)\": [\n+ [0, \"minieigen.Vector2.Random\"]\n+ ],\n+ \"random() (minieigen.vector2c static method)\": [\n+ [0, \"minieigen.Vector2c.Random\"]\n+ ],\n+ \"random() (minieigen.vector2i static method)\": [\n+ [0, \"minieigen.Vector2i.Random\"]\n+ ],\n+ \"random() (minieigen.vector3 static method)\": [\n+ [0, \"minieigen.Vector3.Random\"]\n+ ],\n+ \"random() (minieigen.vector3c static method)\": [\n+ [0, \"minieigen.Vector3c.Random\"]\n+ ],\n+ \"random() (minieigen.vector3i static method)\": [\n+ [0, \"minieigen.Vector3i.Random\"]\n+ ],\n+ \"random() (minieigen.vector4 static method)\": [\n+ [0, \"minieigen.Vector4.Random\"]\n+ ],\n+ \"random() (minieigen.vector6 static method)\": [\n+ [0, \"minieigen.Vector6.Random\"]\n+ ],\n+ \"random() (minieigen.vector6c static method)\": [\n+ [0, \"minieigen.Vector6c.Random\"]\n+ ],\n+ \"random() (minieigen.vector6i static method)\": [\n+ [0, \"minieigen.Vector6i.Random\"]\n+ ],\n+ \"random() (minieigen.vectorx static method)\": [\n+ [0, \"minieigen.VectorX.Random\"]\n+ ],\n+ \"random() (minieigen.vectorxc static method)\": [\n+ [0, \"minieigen.VectorXc.Random\"]\n+ ],\n+ \"rotate() (minieigen.quaternion method)\": [\n+ [0, \"minieigen.Quaternion.Rotate\"]\n+ ],\n+ \"unit() (minieigen.vector2 static method)\": [\n+ [0, \"minieigen.Vector2.Unit\"]\n+ ],\n+ \"unit() (minieigen.vector2c static method)\": [\n+ [0, \"minieigen.Vector2c.Unit\"]\n+ ],\n+ \"unit() (minieigen.vector2i static method)\": [\n+ [0, \"minieigen.Vector2i.Unit\"]\n+ ],\n+ \"unit() (minieigen.vector3 static method)\": [\n+ [0, \"minieigen.Vector3.Unit\"]\n+ ],\n+ \"unit() (minieigen.vector3c static method)\": [\n+ [0, \"minieigen.Vector3c.Unit\"]\n+ ],\n+ \"unit() (minieigen.vector3i static method)\": [\n+ [0, \"minieigen.Vector3i.Unit\"]\n+ ],\n+ \"unit() (minieigen.vector4 static method)\": [\n+ [0, \"minieigen.Vector4.Unit\"]\n+ ],\n+ \"unit() (minieigen.vector6 static method)\": [\n+ [0, \"minieigen.Vector6.Unit\"]\n+ ],\n+ \"unit() (minieigen.vector6c static method)\": [\n+ [0, \"minieigen.Vector6c.Unit\"]\n+ ],\n+ \"unit() (minieigen.vector6i static method)\": [\n+ [0, \"minieigen.Vector6i.Unit\"]\n+ ],\n+ \"unit() (minieigen.vectorx static method)\": [\n+ [0, \"minieigen.VectorX.Unit\"]\n+ ],\n+ \"unit() (minieigen.vectorxc static method)\": [\n+ [0, \"minieigen.VectorXc.Unit\"]\n+ ],\n+ \"unitx (minieigen.vector2 attribute)\": [\n+ [0, \"minieigen.Vector2.UnitX\"]\n+ ],\n+ \"unitx (minieigen.vector2c attribute)\": [\n+ [0, \"minieigen.Vector2c.UnitX\"]\n+ ],\n+ \"unitx (minieigen.vector2i attribute)\": [\n+ [0, \"minieigen.Vector2i.UnitX\"]\n+ ],\n+ \"unitx (minieigen.vector3 attribute)\": [\n+ [0, \"minieigen.Vector3.UnitX\"]\n+ ],\n+ \"unitx (minieigen.vector3c attribute)\": [\n+ [0, \"minieigen.Vector3c.UnitX\"]\n+ ],\n+ \"unitx (minieigen.vector3i attribute)\": [\n+ [0, \"minieigen.Vector3i.UnitX\"]\n+ ],\n+ \"unity (minieigen.vector2 attribute)\": [\n+ [0, \"minieigen.Vector2.UnitY\"]\n+ ],\n+ \"unity (minieigen.vector2c attribute)\": [\n+ [0, \"minieigen.Vector2c.UnitY\"]\n+ ],\n+ \"unity (minieigen.vector2i attribute)\": [\n+ [0, \"minieigen.Vector2i.UnitY\"]\n+ ],\n+ \"unity (minieigen.vector3 attribute)\": [\n+ [0, \"minieigen.Vector3.UnitY\"]\n+ ],\n+ \"unity (minieigen.vector3c attribute)\": [\n+ [0, \"minieigen.Vector3c.UnitY\"]\n+ ],\n+ \"unity (minieigen.vector3i attribute)\": [\n+ [0, \"minieigen.Vector3i.UnitY\"]\n+ ],\n+ \"unitz (minieigen.vector3 attribute)\": [\n+ [0, \"minieigen.Vector3.UnitZ\"]\n+ ],\n+ \"unitz (minieigen.vector3c attribute)\": [\n+ [0, \"minieigen.Vector3c.UnitZ\"]\n+ ],\n+ \"unitz (minieigen.vector3i attribute)\": [\n+ [0, \"minieigen.Vector3i.UnitZ\"]\n+ ],\n+ \"vector2 (class in minieigen)\": [\n+ [0, \"minieigen.Vector2\"]\n+ ],\n+ \"vector2c (class in minieigen)\": [\n+ [0, \"minieigen.Vector2c\"]\n+ ],\n+ \"vector2i (class in minieigen)\": [\n+ [0, \"minieigen.Vector2i\"]\n+ ],\n+ \"vector3 (class in minieigen)\": [\n+ [0, \"minieigen.Vector3\"]\n+ ],\n+ \"vector3c (class in minieigen)\": [\n+ [0, \"minieigen.Vector3c\"]\n+ ],\n+ \"vector3i (class in minieigen)\": [\n+ [0, \"minieigen.Vector3i\"]\n+ ],\n+ \"vector4 (class in minieigen)\": [\n+ [0, \"minieigen.Vector4\"]\n+ ],\n+ \"vector6 (class in minieigen)\": [\n+ [0, \"minieigen.Vector6\"]\n+ ],\n+ \"vector6c (class in minieigen)\": [\n+ [0, \"minieigen.Vector6c\"]\n+ ],\n+ \"vector6i (class in minieigen)\": [\n+ [0, \"minieigen.Vector6i\"]\n+ ],\n+ \"vectorx (class in minieigen)\": [\n+ [0, \"minieigen.VectorX\"]\n+ ],\n+ \"vectorxc (class in minieigen)\": [\n+ [0, \"minieigen.VectorXc\"]\n+ ],\n+ \"zero (minieigen.matrix3 attribute)\": [\n+ [0, \"minieigen.Matrix3.Zero\"]\n+ ],\n+ \"zero (minieigen.matrix3c attribute)\": [\n+ [0, \"minieigen.Matrix3c.Zero\"]\n+ ],\n+ \"zero (minieigen.matrix6 attribute)\": [\n+ [0, \"minieigen.Matrix6.Zero\"]\n+ ],\n+ \"zero (minieigen.matrix6c attribute)\": [\n+ [0, \"minieigen.Matrix6c.Zero\"]\n+ ],\n+ \"zero (minieigen.vector2 attribute)\": [\n+ [0, \"minieigen.Vector2.Zero\"]\n+ ],\n+ \"zero (minieigen.vector2c attribute)\": [\n+ [0, \"minieigen.Vector2c.Zero\"]\n+ ],\n+ \"zero (minieigen.vector2i attribute)\": [\n+ [0, \"minieigen.Vector2i.Zero\"]\n+ ],\n+ \"zero (minieigen.vector3 attribute)\": [\n+ [0, \"minieigen.Vector3.Zero\"]\n+ ],\n+ \"zero (minieigen.vector3c attribute)\": [\n+ [0, \"minieigen.Vector3c.Zero\"]\n+ ],\n+ \"zero (minieigen.vector3i attribute)\": [\n+ [0, \"minieigen.Vector3i.Zero\"]\n+ ],\n+ \"zero (minieigen.vector4 attribute)\": [\n+ [0, \"minieigen.Vector4.Zero\"]\n+ ],\n+ \"zero (minieigen.vector6 attribute)\": [\n+ [0, \"minieigen.Vector6.Zero\"]\n+ ],\n+ \"zero (minieigen.vector6c attribute)\": [\n+ [0, \"minieigen.Vector6c.Zero\"]\n+ ],\n+ \"zero (minieigen.vector6i attribute)\": [\n+ [0, \"minieigen.Vector6i.Zero\"]\n+ ],\n+ \"zero() (minieigen.matrixx static method)\": [\n+ [0, \"minieigen.MatrixX.Zero\"]\n+ ],\n+ \"zero() (minieigen.matrixxc static method)\": [\n+ [0, \"minieigen.MatrixXc.Zero\"]\n+ ],\n+ \"zero() (minieigen.vectorx static method)\": [\n+ [0, \"minieigen.VectorX.Zero\"]\n+ ],\n+ \"zero() (minieigen.vectorxc static method)\": [\n+ [0, \"minieigen.VectorXc.Zero\"]\n+ ],\n+ \"angulardistance() (minieigen.quaternion method)\": [\n+ [0, \"minieigen.Quaternion.angularDistance\"]\n+ ],\n+ \"asdiagonal() (minieigen.vector2 method)\": [\n+ [0, \"minieigen.Vector2.asDiagonal\"]\n+ ],\n+ \"asdiagonal() (minieigen.vector2c method)\": [\n+ [0, \"minieigen.Vector2c.asDiagonal\"]\n+ ],\n+ \"asdiagonal() (minieigen.vector2i method)\": [\n+ [0, \"minieigen.Vector2i.asDiagonal\"]\n+ ],\n+ \"asdiagonal() (minieigen.vector3 method)\": [\n+ [0, \"minieigen.Vector3.asDiagonal\"]\n+ ],\n+ \"asdiagonal() (minieigen.vector3c method)\": [\n+ [0, \"minieigen.Vector3c.asDiagonal\"]\n+ ],\n+ \"asdiagonal() (minieigen.vector3i method)\": [\n+ [0, \"minieigen.Vector3i.asDiagonal\"]\n+ ],\n+ \"asdiagonal() (minieigen.vector4 method)\": [\n+ [0, \"minieigen.Vector4.asDiagonal\"]\n+ ],\n+ \"asdiagonal() (minieigen.vector6 method)\": [\n+ [0, \"minieigen.Vector6.asDiagonal\"]\n+ ],\n+ \"asdiagonal() (minieigen.vector6c method)\": [\n+ [0, \"minieigen.Vector6c.asDiagonal\"]\n+ ],\n+ \"asdiagonal() (minieigen.vector6i method)\": [\n+ [0, \"minieigen.Vector6i.asDiagonal\"]\n+ ],\n+ \"asdiagonal() (minieigen.vectorx method)\": [\n+ [0, \"minieigen.VectorX.asDiagonal\"]\n+ ],\n+ \"asdiagonal() (minieigen.vectorxc method)\": [\n+ [0, \"minieigen.VectorXc.asDiagonal\"]\n+ ],\n+ \"center() (minieigen.alignedbox2 method)\": [\n+ [0, \"minieigen.AlignedBox2.center\"]\n+ ],\n+ \"center() (minieigen.alignedbox3 method)\": [\n+ [0, \"minieigen.AlignedBox3.center\"]\n+ ],\n+ \"clamp() (minieigen.alignedbox2 method)\": [\n+ [0, \"minieigen.AlignedBox2.clamp\"]\n+ ],\n+ \"clamp() (minieigen.alignedbox3 method)\": [\n+ [0, \"minieigen.AlignedBox3.clamp\"]\n+ ],\n+ \"col() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.col\"]\n+ ],\n+ \"col() (minieigen.matrix3c method)\": [\n+ [0, \"minieigen.Matrix3c.col\"]\n+ ],\n+ \"col() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.col\"]\n+ ],\n+ \"col() (minieigen.matrix6c method)\": [\n+ [0, \"minieigen.Matrix6c.col\"]\n+ ],\n+ \"col() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.col\"]\n+ ],\n+ \"col() (minieigen.matrixxc method)\": [\n+ [0, \"minieigen.MatrixXc.col\"]\n+ ],\n+ \"cols() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.cols\"]\n+ ],\n+ \"cols() (minieigen.matrix3c method)\": [\n+ [0, \"minieigen.Matrix3c.cols\"]\n+ ],\n+ \"cols() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.cols\"]\n+ ],\n+ \"cols() (minieigen.matrix6c method)\": [\n+ [0, \"minieigen.Matrix6c.cols\"]\n+ ],\n+ \"cols() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.cols\"]\n+ ],\n+ \"cols() (minieigen.matrixxc method)\": [\n+ [0, \"minieigen.MatrixXc.cols\"]\n+ ],\n+ \"cols() (minieigen.vector2 method)\": [\n+ [0, \"minieigen.Vector2.cols\"]\n+ ],\n+ \"cols() (minieigen.vector2c method)\": [\n+ [0, \"minieigen.Vector2c.cols\"]\n+ ],\n+ \"cols() (minieigen.vector2i method)\": [\n+ [0, \"minieigen.Vector2i.cols\"]\n+ ],\n+ \"cols() (minieigen.vector3 method)\": [\n+ [0, \"minieigen.Vector3.cols\"]\n+ ],\n+ \"cols() (minieigen.vector3c method)\": [\n+ [0, \"minieigen.Vector3c.cols\"]\n+ ],\n+ \"cols() (minieigen.vector3i method)\": [\n+ [0, \"minieigen.Vector3i.cols\"]\n+ ],\n+ \"cols() (minieigen.vector4 method)\": [\n+ [0, \"minieigen.Vector4.cols\"]\n+ ],\n+ \"cols() (minieigen.vector6 method)\": [\n+ [0, \"minieigen.Vector6.cols\"]\n+ ],\n+ \"cols() (minieigen.vector6c method)\": [\n+ [0, \"minieigen.Vector6c.cols\"]\n+ ],\n+ \"cols() (minieigen.vector6i method)\": [\n+ [0, \"minieigen.Vector6i.cols\"]\n+ ],\n+ \"cols() (minieigen.vectorx method)\": [\n+ [0, \"minieigen.VectorX.cols\"]\n+ ],\n+ \"cols() (minieigen.vectorxc method)\": [\n+ [0, \"minieigen.VectorXc.cols\"]\n+ ],\n+ \"computeunitarypositive() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.computeUnitaryPositive\"]\n+ ],\n+ \"computeunitarypositive() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.computeUnitaryPositive\"]\n+ ],\n+ \"computeunitarypositive() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.computeUnitaryPositive\"]\n+ ],\n+ \"conjugate() (minieigen.quaternion method)\": [\n+ [0, \"minieigen.Quaternion.conjugate\"]\n+ ],\n+ \"contains() (minieigen.alignedbox2 method)\": [\n+ [0, \"minieigen.AlignedBox2.contains\"]\n+ ],\n+ \"contains() (minieigen.alignedbox3 method)\": [\n+ [0, \"minieigen.AlignedBox3.contains\"]\n+ ],\n+ \"cross() (minieigen.vector3 method)\": [\n+ [0, \"minieigen.Vector3.cross\"]\n+ ],\n+ \"cross() (minieigen.vector3c method)\": [\n+ [0, \"minieigen.Vector3c.cross\"]\n+ ],\n+ \"cross() (minieigen.vector3i method)\": [\n+ [0, \"minieigen.Vector3i.cross\"]\n+ ],\n+ \"determinant() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.determinant\"]\n+ ],\n+ \"determinant() (minieigen.matrix3c method)\": [\n+ [0, \"minieigen.Matrix3c.determinant\"]\n+ ],\n+ \"determinant() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.determinant\"]\n+ ],\n+ \"determinant() (minieigen.matrix6c method)\": [\n+ [0, \"minieigen.Matrix6c.determinant\"]\n+ ],\n+ \"determinant() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.determinant\"]\n+ ],\n+ \"determinant() (minieigen.matrixxc method)\": [\n+ [0, \"minieigen.MatrixXc.determinant\"]\n+ ],\n+ \"diagonal() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.diagonal\"]\n+ ],\n+ \"diagonal() (minieigen.matrix3c method)\": [\n+ [0, \"minieigen.Matrix3c.diagonal\"]\n+ ],\n+ \"diagonal() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.diagonal\"]\n+ ],\n+ \"diagonal() (minieigen.matrix6c method)\": [\n+ [0, \"minieigen.Matrix6c.diagonal\"]\n+ ],\n+ \"diagonal() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.diagonal\"]\n+ ],\n+ \"diagonal() (minieigen.matrixxc method)\": [\n+ [0, \"minieigen.MatrixXc.diagonal\"]\n+ ],\n+ \"dot() (minieigen.vector2 method)\": [\n+ [0, \"minieigen.Vector2.dot\"]\n+ ],\n+ \"dot() (minieigen.vector2c method)\": [\n+ [0, \"minieigen.Vector2c.dot\"]\n+ ],\n+ \"dot() (minieigen.vector2i method)\": [\n+ [0, \"minieigen.Vector2i.dot\"]\n+ ],\n+ \"dot() (minieigen.vector3 method)\": [\n+ [0, \"minieigen.Vector3.dot\"]\n+ ],\n+ \"dot() (minieigen.vector3c method)\": [\n+ [0, \"minieigen.Vector3c.dot\"]\n+ ],\n+ \"dot() (minieigen.vector3i method)\": [\n+ [0, \"minieigen.Vector3i.dot\"]\n+ ],\n+ \"dot() (minieigen.vector4 method)\": [\n+ [0, \"minieigen.Vector4.dot\"]\n+ ],\n+ \"dot() (minieigen.vector6 method)\": [\n+ [0, \"minieigen.Vector6.dot\"]\n+ ],\n+ \"dot() (minieigen.vector6c method)\": [\n+ [0, \"minieigen.Vector6c.dot\"]\n+ ],\n+ \"dot() (minieigen.vector6i method)\": [\n+ [0, \"minieigen.Vector6i.dot\"]\n+ ],\n+ \"dot() (minieigen.vectorx method)\": [\n+ [0, \"minieigen.VectorX.dot\"]\n+ ],\n+ \"dot() (minieigen.vectorxc method)\": [\n+ [0, \"minieigen.VectorXc.dot\"]\n+ ],\n+ \"empty() (minieigen.alignedbox2 method)\": [\n+ [0, \"minieigen.AlignedBox2.empty\"]\n+ ],\n+ \"empty() (minieigen.alignedbox3 method)\": [\n+ [0, \"minieigen.AlignedBox3.empty\"]\n+ ],\n+ \"extend() (minieigen.alignedbox2 method)\": [\n+ [0, \"minieigen.AlignedBox2.extend\"]\n+ ],\n+ \"extend() (minieigen.alignedbox3 method)\": [\n+ [0, \"minieigen.AlignedBox3.extend\"]\n+ ],\n+ \"float2str() (in module minieigen)\": [\n+ [0, \"minieigen.float2str\"]\n+ ],\n+ \"head() (minieigen.vector6 method)\": [\n+ [0, \"minieigen.Vector6.head\"]\n+ ],\n+ \"head() (minieigen.vector6c method)\": [\n+ [0, \"minieigen.Vector6c.head\"]\n+ ],\n+ \"head() (minieigen.vector6i method)\": [\n+ [0, \"minieigen.Vector6i.head\"]\n+ ],\n+ \"intersection() (minieigen.alignedbox2 method)\": [\n+ [0, \"minieigen.AlignedBox2.intersection\"]\n+ ],\n+ \"intersection() (minieigen.alignedbox3 method)\": [\n+ [0, \"minieigen.AlignedBox3.intersection\"]\n+ ],\n+ \"inverse() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.inverse\"]\n+ ],\n+ \"inverse() (minieigen.matrix3c method)\": [\n+ [0, \"minieigen.Matrix3c.inverse\"]\n+ ],\n+ \"inverse() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.inverse\"]\n+ ],\n+ \"inverse() (minieigen.matrix6c method)\": [\n+ [0, \"minieigen.Matrix6c.inverse\"]\n+ ],\n+ \"inverse() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.inverse\"]\n+ ],\n+ \"inverse() (minieigen.matrixxc method)\": [\n+ [0, \"minieigen.MatrixXc.inverse\"]\n+ ],\n+ \"inverse() (minieigen.quaternion method)\": [\n+ [0, \"minieigen.Quaternion.inverse\"]\n+ ],\n+ \"isapprox() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.isApprox\"]\n+ ],\n+ \"isapprox() (minieigen.matrix3c method)\": [\n+ [0, \"minieigen.Matrix3c.isApprox\"]\n+ ],\n+ \"isapprox() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.isApprox\"]\n+ ],\n+ \"isapprox() (minieigen.matrix6c method)\": [\n+ [0, \"minieigen.Matrix6c.isApprox\"]\n+ ],\n+ \"isapprox() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.isApprox\"]\n+ ],\n+ \"isapprox() (minieigen.matrixxc method)\": [\n+ [0, \"minieigen.MatrixXc.isApprox\"]\n+ ],\n+ \"isapprox() (minieigen.vector2 method)\": [\n+ [0, \"minieigen.Vector2.isApprox\"]\n+ ],\n+ \"isapprox() (minieigen.vector2c method)\": [\n+ [0, \"minieigen.Vector2c.isApprox\"]\n+ ],\n+ \"isapprox() (minieigen.vector2i method)\": [\n+ [0, \"minieigen.Vector2i.isApprox\"]\n+ ],\n+ \"isapprox() (minieigen.vector3 method)\": [\n+ [0, \"minieigen.Vector3.isApprox\"]\n+ ],\n+ \"isapprox() (minieigen.vector3c method)\": [\n+ [0, \"minieigen.Vector3c.isApprox\"]\n+ ],\n+ \"isapprox() (minieigen.vector3i method)\": [\n+ [0, \"minieigen.Vector3i.isApprox\"]\n+ ],\n+ \"isapprox() (minieigen.vector4 method)\": [\n+ [0, \"minieigen.Vector4.isApprox\"]\n+ ],\n+ \"isapprox() (minieigen.vector6 method)\": [\n+ [0, \"minieigen.Vector6.isApprox\"]\n+ ],\n+ \"isapprox() (minieigen.vector6c method)\": [\n+ [0, \"minieigen.Vector6c.isApprox\"]\n+ ],\n+ \"isapprox() (minieigen.vector6i method)\": [\n+ [0, \"minieigen.Vector6i.isApprox\"]\n+ ],\n+ \"isapprox() (minieigen.vectorx method)\": [\n+ [0, \"minieigen.VectorX.isApprox\"]\n+ ],\n+ \"isapprox() (minieigen.vectorxc method)\": [\n+ [0, \"minieigen.VectorXc.isApprox\"]\n+ ],\n+ \"jacobisvd() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.jacobiSVD\"]\n+ ],\n+ \"jacobisvd() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.jacobiSVD\"]\n+ ],\n+ \"jacobisvd() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.jacobiSVD\"]\n+ ],\n+ \"ll() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.ll\"]\n+ ],\n+ \"ll() (minieigen.matrix6c method)\": [\n+ [0, \"minieigen.Matrix6c.ll\"]\n+ ],\n+ \"lr() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.lr\"]\n+ ],\n+ \"lr() (minieigen.matrix6c method)\": [\n+ [0, \"minieigen.Matrix6c.lr\"]\n+ ],\n+ \"max (minieigen.alignedbox2 property)\": [\n+ [0, \"minieigen.AlignedBox2.max\"]\n+ ],\n+ \"max (minieigen.alignedbox3 property)\": [\n+ [0, \"minieigen.AlignedBox3.max\"]\n+ ],\n+ \"maxabscoeff() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.maxAbsCoeff\"]\n+ ],\n+ \"maxabscoeff() (minieigen.matrix3c method)\": [\n+ [0, \"minieigen.Matrix3c.maxAbsCoeff\"]\n+ ],\n+ \"maxabscoeff() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.maxAbsCoeff\"]\n+ ],\n+ \"maxabscoeff() (minieigen.matrix6c method)\": [\n+ [0, \"minieigen.Matrix6c.maxAbsCoeff\"]\n+ ],\n+ \"maxabscoeff() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.maxAbsCoeff\"]\n+ ],\n+ \"maxabscoeff() (minieigen.matrixxc method)\": [\n+ [0, \"minieigen.MatrixXc.maxAbsCoeff\"]\n+ ],\n+ \"maxabscoeff() (minieigen.vector2 method)\": [\n+ [0, \"minieigen.Vector2.maxAbsCoeff\"]\n+ ],\n+ \"maxabscoeff() (minieigen.vector2c method)\": [\n+ [0, \"minieigen.Vector2c.maxAbsCoeff\"]\n+ ],\n+ \"maxabscoeff() (minieigen.vector2i method)\": [\n+ [0, \"minieigen.Vector2i.maxAbsCoeff\"]\n+ ],\n+ \"maxabscoeff() (minieigen.vector3 method)\": [\n+ [0, \"minieigen.Vector3.maxAbsCoeff\"]\n+ ],\n+ \"maxabscoeff() (minieigen.vector3c method)\": [\n+ [0, \"minieigen.Vector3c.maxAbsCoeff\"]\n+ ],\n+ \"maxabscoeff() (minieigen.vector3i method)\": [\n+ [0, \"minieigen.Vector3i.maxAbsCoeff\"]\n+ ],\n+ \"maxabscoeff() (minieigen.vector4 method)\": [\n+ [0, \"minieigen.Vector4.maxAbsCoeff\"]\n+ ],\n+ \"maxabscoeff() (minieigen.vector6 method)\": [\n+ [0, \"minieigen.Vector6.maxAbsCoeff\"]\n+ ],\n+ \"maxabscoeff() (minieigen.vector6c method)\": [\n+ [0, \"minieigen.Vector6c.maxAbsCoeff\"]\n+ ],\n+ \"maxabscoeff() (minieigen.vector6i method)\": [\n+ [0, \"minieigen.Vector6i.maxAbsCoeff\"]\n+ ],\n+ \"maxabscoeff() (minieigen.vectorx method)\": [\n+ [0, \"minieigen.VectorX.maxAbsCoeff\"]\n+ ],\n+ \"maxabscoeff() (minieigen.vectorxc method)\": [\n+ [0, \"minieigen.VectorXc.maxAbsCoeff\"]\n+ ],\n+ \"maxcoeff() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.maxCoeff\"]\n+ ],\n+ \"maxcoeff() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.maxCoeff\"]\n+ ],\n+ \"maxcoeff() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.maxCoeff\"]\n+ ],\n+ \"maxcoeff() (minieigen.vector2 method)\": [\n+ [0, \"minieigen.Vector2.maxCoeff\"]\n+ ],\n+ \"maxcoeff() (minieigen.vector2i method)\": [\n+ [0, \"minieigen.Vector2i.maxCoeff\"]\n+ ],\n+ \"maxcoeff() (minieigen.vector3 method)\": [\n+ [0, \"minieigen.Vector3.maxCoeff\"]\n+ ],\n+ \"maxcoeff() (minieigen.vector3i method)\": [\n+ [0, \"minieigen.Vector3i.maxCoeff\"]\n+ ],\n+ \"maxcoeff() (minieigen.vector4 method)\": [\n+ [0, \"minieigen.Vector4.maxCoeff\"]\n+ ],\n+ \"maxcoeff() (minieigen.vector6 method)\": [\n+ [0, \"minieigen.Vector6.maxCoeff\"]\n+ ],\n+ \"maxcoeff() (minieigen.vector6i method)\": [\n+ [0, \"minieigen.Vector6i.maxCoeff\"]\n+ ],\n+ \"maxcoeff() (minieigen.vectorx method)\": [\n+ [0, \"minieigen.VectorX.maxCoeff\"]\n+ ],\n+ \"mean() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.mean\"]\n+ ],\n+ \"mean() (minieigen.matrix3c method)\": [\n+ [0, \"minieigen.Matrix3c.mean\"]\n+ ],\n+ \"mean() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.mean\"]\n+ ],\n+ \"mean() (minieigen.matrix6c method)\": [\n+ [0, \"minieigen.Matrix6c.mean\"]\n+ ],\n+ \"mean() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.mean\"]\n+ ],\n+ \"mean() (minieigen.matrixxc method)\": [\n+ [0, \"minieigen.MatrixXc.mean\"]\n+ ],\n+ \"mean() (minieigen.vector2 method)\": [\n+ [0, \"minieigen.Vector2.mean\"]\n+ ],\n+ \"mean() (minieigen.vector2c method)\": [\n+ [0, \"minieigen.Vector2c.mean\"]\n+ ],\n+ \"mean() (minieigen.vector2i method)\": [\n+ [0, \"minieigen.Vector2i.mean\"]\n+ ],\n+ \"mean() (minieigen.vector3 method)\": [\n+ [0, \"minieigen.Vector3.mean\"]\n+ ],\n+ \"mean() (minieigen.vector3c method)\": [\n+ [0, \"minieigen.Vector3c.mean\"]\n+ ],\n+ \"mean() (minieigen.vector3i method)\": [\n+ [0, \"minieigen.Vector3i.mean\"]\n+ ],\n+ \"mean() (minieigen.vector4 method)\": [\n+ [0, \"minieigen.Vector4.mean\"]\n+ ],\n+ \"mean() (minieigen.vector6 method)\": [\n+ [0, \"minieigen.Vector6.mean\"]\n+ ],\n+ \"mean() (minieigen.vector6c method)\": [\n+ [0, \"minieigen.Vector6c.mean\"]\n+ ],\n+ \"mean() (minieigen.vector6i method)\": [\n+ [0, \"minieigen.Vector6i.mean\"]\n+ ],\n+ \"mean() (minieigen.vectorx method)\": [\n+ [0, \"minieigen.VectorX.mean\"]\n+ ],\n+ \"mean() (minieigen.vectorxc method)\": [\n+ [0, \"minieigen.VectorXc.mean\"]\n+ ],\n+ \"merged() (minieigen.alignedbox2 method)\": [\n+ [0, \"minieigen.AlignedBox2.merged\"]\n+ ],\n+ \"merged() (minieigen.alignedbox3 method)\": [\n+ [0, \"minieigen.AlignedBox3.merged\"]\n+ ],\n+ \"min (minieigen.alignedbox2 property)\": [\n+ [0, \"minieigen.AlignedBox2.min\"]\n+ ],\n+ \"min (minieigen.alignedbox3 property)\": [\n+ [0, \"minieigen.AlignedBox3.min\"]\n+ ],\n+ \"mincoeff() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.minCoeff\"]\n+ ],\n+ \"mincoeff() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.minCoeff\"]\n+ ],\n+ \"mincoeff() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.minCoeff\"]\n+ ],\n+ \"mincoeff() (minieigen.vector2 method)\": [\n+ [0, \"minieigen.Vector2.minCoeff\"]\n+ ],\n+ \"mincoeff() (minieigen.vector2i method)\": [\n+ [0, \"minieigen.Vector2i.minCoeff\"]\n+ ],\n+ \"mincoeff() (minieigen.vector3 method)\": [\n+ [0, \"minieigen.Vector3.minCoeff\"]\n+ ],\n+ \"mincoeff() (minieigen.vector3i method)\": [\n+ [0, \"minieigen.Vector3i.minCoeff\"]\n+ ],\n+ \"mincoeff() (minieigen.vector4 method)\": [\n+ [0, \"minieigen.Vector4.minCoeff\"]\n+ ],\n+ \"mincoeff() (minieigen.vector6 method)\": [\n+ [0, \"minieigen.Vector6.minCoeff\"]\n+ ],\n+ \"mincoeff() (minieigen.vector6i method)\": [\n+ [0, \"minieigen.Vector6i.minCoeff\"]\n+ ],\n+ \"mincoeff() (minieigen.vectorx method)\": [\n+ [0, \"minieigen.VectorX.minCoeff\"]\n+ ],\n+ \"minieigen\": [\n+ [0, \"module-minieigen\"]\n+ ],\n+ \"module\": [\n+ [0, \"module-minieigen\"]\n+ ],\n+ \"norm() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.norm\"]\n+ ],\n+ \"norm() (minieigen.matrix3c method)\": [\n+ [0, \"minieigen.Matrix3c.norm\"]\n+ ],\n+ \"norm() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.norm\"]\n+ ],\n+ \"norm() (minieigen.matrix6c method)\": [\n+ [0, \"minieigen.Matrix6c.norm\"]\n+ ],\n+ \"norm() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.norm\"]\n+ ],\n+ \"norm() (minieigen.matrixxc method)\": [\n+ [0, \"minieigen.MatrixXc.norm\"]\n+ ],\n+ \"norm() (minieigen.quaternion method)\": [\n+ [0, \"minieigen.Quaternion.norm\"]\n+ ],\n+ \"norm() (minieigen.vector2 method)\": [\n+ [0, \"minieigen.Vector2.norm\"]\n+ ],\n+ \"norm() (minieigen.vector2c method)\": [\n+ [0, \"minieigen.Vector2c.norm\"]\n+ ],\n+ \"norm() (minieigen.vector3 method)\": [\n+ [0, \"minieigen.Vector3.norm\"]\n+ ],\n+ \"norm() (minieigen.vector3c method)\": [\n+ [0, \"minieigen.Vector3c.norm\"]\n+ ],\n+ \"norm() (minieigen.vector4 method)\": [\n+ [0, \"minieigen.Vector4.norm\"]\n+ ],\n+ \"norm() (minieigen.vector6 method)\": [\n+ [0, \"minieigen.Vector6.norm\"]\n+ ],\n+ \"norm() (minieigen.vector6c method)\": [\n+ [0, \"minieigen.Vector6c.norm\"]\n+ ],\n+ \"norm() (minieigen.vectorx method)\": [\n+ [0, \"minieigen.VectorX.norm\"]\n+ ],\n+ \"norm() (minieigen.vectorxc method)\": [\n+ [0, \"minieigen.VectorXc.norm\"]\n+ ],\n+ \"normalize() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.normalize\"]\n+ ],\n+ \"normalize() (minieigen.matrix3c method)\": [\n+ [0, \"minieigen.Matrix3c.normalize\"]\n+ ],\n+ \"normalize() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.normalize\"]\n+ ],\n+ \"normalize() (minieigen.matrix6c method)\": [\n+ [0, \"minieigen.Matrix6c.normalize\"]\n+ ],\n+ \"normalize() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.normalize\"]\n+ ],\n+ \"normalize() (minieigen.matrixxc method)\": [\n+ [0, \"minieigen.MatrixXc.normalize\"]\n+ ],\n+ \"normalize() (minieigen.quaternion method)\": [\n+ [0, \"minieigen.Quaternion.normalize\"]\n+ ],\n+ \"normalize() (minieigen.vector2 method)\": [\n+ [0, \"minieigen.Vector2.normalize\"]\n+ ],\n+ \"normalize() (minieigen.vector2c method)\": [\n+ [0, \"minieigen.Vector2c.normalize\"]\n+ ],\n+ \"normalize() (minieigen.vector3 method)\": [\n+ [0, \"minieigen.Vector3.normalize\"]\n+ ],\n+ \"normalize() (minieigen.vector3c method)\": [\n+ [0, \"minieigen.Vector3c.normalize\"]\n+ ],\n+ \"normalize() (minieigen.vector4 method)\": [\n+ [0, \"minieigen.Vector4.normalize\"]\n+ ],\n+ \"normalize() (minieigen.vector6 method)\": [\n+ [0, \"minieigen.Vector6.normalize\"]\n+ ],\n+ \"normalize() (minieigen.vector6c method)\": [\n+ [0, \"minieigen.Vector6c.normalize\"]\n+ ],\n+ \"normalize() (minieigen.vectorx method)\": [\n+ [0, \"minieigen.VectorX.normalize\"]\n+ ],\n+ \"normalize() (minieigen.vectorxc method)\": [\n+ [0, \"minieigen.VectorXc.normalize\"]\n+ ],\n+ \"normalized() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.normalized\"]\n+ ],\n+ \"normalized() (minieigen.matrix3c method)\": [\n+ [0, \"minieigen.Matrix3c.normalized\"]\n+ ],\n+ \"normalized() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.normalized\"]\n+ ],\n+ \"normalized() (minieigen.matrix6c method)\": [\n+ [0, \"minieigen.Matrix6c.normalized\"]\n+ ],\n+ \"normalized() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.normalized\"]\n+ ],\n+ \"normalized() (minieigen.matrixxc method)\": [\n+ [0, \"minieigen.MatrixXc.normalized\"]\n+ ],\n+ \"normalized() (minieigen.quaternion method)\": [\n+ [0, \"minieigen.Quaternion.normalized\"]\n+ ],\n+ \"normalized() (minieigen.vector2 method)\": [\n+ [0, \"minieigen.Vector2.normalized\"]\n+ ],\n+ \"normalized() (minieigen.vector2c method)\": [\n+ [0, \"minieigen.Vector2c.normalized\"]\n+ ],\n+ \"normalized() (minieigen.vector3 method)\": [\n+ [0, \"minieigen.Vector3.normalized\"]\n+ ],\n+ \"normalized() (minieigen.vector3c method)\": [\n+ [0, \"minieigen.Vector3c.normalized\"]\n+ ],\n+ \"normalized() (minieigen.vector4 method)\": [\n+ [0, \"minieigen.Vector4.normalized\"]\n+ ],\n+ \"normalized() (minieigen.vector6 method)\": [\n+ [0, \"minieigen.Vector6.normalized\"]\n+ ],\n+ \"normalized() (minieigen.vector6c method)\": [\n+ [0, \"minieigen.Vector6c.normalized\"]\n+ ],\n+ \"normalized() (minieigen.vectorx method)\": [\n+ [0, \"minieigen.VectorX.normalized\"]\n+ ],\n+ \"normalized() (minieigen.vectorxc method)\": [\n+ [0, \"minieigen.VectorXc.normalized\"]\n+ ],\n+ \"outer() (minieigen.vector2 method)\": [\n+ [0, \"minieigen.Vector2.outer\"]\n+ ],\n+ \"outer() (minieigen.vector2c method)\": [\n+ [0, \"minieigen.Vector2c.outer\"]\n+ ],\n+ \"outer() (minieigen.vector2i method)\": [\n+ [0, \"minieigen.Vector2i.outer\"]\n+ ],\n+ \"outer() (minieigen.vector3 method)\": [\n+ [0, \"minieigen.Vector3.outer\"]\n+ ],\n+ \"outer() (minieigen.vector3c method)\": [\n+ [0, \"minieigen.Vector3c.outer\"]\n+ ],\n+ \"outer() (minieigen.vector3i method)\": [\n+ [0, \"minieigen.Vector3i.outer\"]\n+ ],\n+ \"outer() (minieigen.vector4 method)\": [\n+ [0, \"minieigen.Vector4.outer\"]\n+ ],\n+ \"outer() (minieigen.vector6 method)\": [\n+ [0, \"minieigen.Vector6.outer\"]\n+ ],\n+ \"outer() (minieigen.vector6c method)\": [\n+ [0, \"minieigen.Vector6c.outer\"]\n+ ],\n+ \"outer() (minieigen.vector6i method)\": [\n+ [0, \"minieigen.Vector6i.outer\"]\n+ ],\n+ \"outer() (minieigen.vectorx method)\": [\n+ [0, \"minieigen.VectorX.outer\"]\n+ ],\n+ \"outer() (minieigen.vectorxc method)\": [\n+ [0, \"minieigen.VectorXc.outer\"]\n+ ],\n+ \"polardecomposition() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.polarDecomposition\"]\n+ ],\n+ \"polardecomposition() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.polarDecomposition\"]\n+ ],\n+ \"polardecomposition() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.polarDecomposition\"]\n+ ],\n+ \"prod() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.prod\"]\n+ ],\n+ \"prod() (minieigen.matrix3c method)\": [\n+ [0, \"minieigen.Matrix3c.prod\"]\n+ ],\n+ \"prod() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.prod\"]\n+ ],\n+ \"prod() (minieigen.matrix6c method)\": [\n+ [0, \"minieigen.Matrix6c.prod\"]\n+ ],\n+ \"prod() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.prod\"]\n+ ],\n+ \"prod() (minieigen.matrixxc method)\": [\n+ [0, \"minieigen.MatrixXc.prod\"]\n+ ],\n+ \"prod() (minieigen.vector2 method)\": [\n+ [0, \"minieigen.Vector2.prod\"]\n+ ],\n+ \"prod() (minieigen.vector2c method)\": [\n+ [0, \"minieigen.Vector2c.prod\"]\n+ ],\n+ \"prod() (minieigen.vector2i method)\": [\n+ [0, \"minieigen.Vector2i.prod\"]\n+ ],\n+ \"prod() (minieigen.vector3 method)\": [\n+ [0, \"minieigen.Vector3.prod\"]\n+ ],\n+ \"prod() (minieigen.vector3c method)\": [\n+ [0, \"minieigen.Vector3c.prod\"]\n+ ],\n+ \"prod() (minieigen.vector3i method)\": [\n+ [0, \"minieigen.Vector3i.prod\"]\n+ ],\n+ \"prod() (minieigen.vector4 method)\": [\n+ [0, \"minieigen.Vector4.prod\"]\n+ ],\n+ \"prod() (minieigen.vector6 method)\": [\n+ [0, \"minieigen.Vector6.prod\"]\n+ ],\n+ \"prod() (minieigen.vector6c method)\": [\n+ [0, \"minieigen.Vector6c.prod\"]\n+ ],\n+ \"prod() (minieigen.vector6i method)\": [\n+ [0, \"minieigen.Vector6i.prod\"]\n+ ],\n+ \"prod() (minieigen.vectorx method)\": [\n+ [0, \"minieigen.VectorX.prod\"]\n+ ],\n+ \"prod() (minieigen.vectorxc method)\": [\n+ [0, \"minieigen.VectorXc.prod\"]\n+ ],\n+ \"pruned() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.pruned\"]\n+ ],\n+ \"pruned() (minieigen.matrix3c method)\": [\n+ [0, \"minieigen.Matrix3c.pruned\"]\n+ ],\n+ \"pruned() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.pruned\"]\n+ ],\n+ \"pruned() (minieigen.matrix6c method)\": [\n+ [0, \"minieigen.Matrix6c.pruned\"]\n+ ],\n+ \"pruned() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.pruned\"]\n+ ],\n+ \"pruned() (minieigen.matrixxc method)\": [\n+ [0, \"minieigen.MatrixXc.pruned\"]\n+ ],\n+ \"pruned() (minieigen.vector2 method)\": [\n+ [0, \"minieigen.Vector2.pruned\"]\n+ ],\n+ \"pruned() (minieigen.vector2c method)\": [\n+ [0, \"minieigen.Vector2c.pruned\"]\n+ ],\n+ \"pruned() (minieigen.vector3 method)\": [\n+ [0, \"minieigen.Vector3.pruned\"]\n+ ],\n+ \"pruned() (minieigen.vector3c method)\": [\n+ [0, \"minieigen.Vector3c.pruned\"]\n+ ],\n+ \"pruned() (minieigen.vector4 method)\": [\n+ [0, \"minieigen.Vector4.pruned\"]\n+ ],\n+ \"pruned() (minieigen.vector6 method)\": [\n+ [0, \"minieigen.Vector6.pruned\"]\n+ ],\n+ \"pruned() (minieigen.vector6c method)\": [\n+ [0, \"minieigen.Vector6c.pruned\"]\n+ ],\n+ \"pruned() (minieigen.vectorx method)\": [\n+ [0, \"minieigen.VectorX.pruned\"]\n+ ],\n+ \"pruned() (minieigen.vectorxc method)\": [\n+ [0, \"minieigen.VectorXc.pruned\"]\n+ ],\n+ \"resize() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.resize\"]\n+ ],\n+ \"resize() (minieigen.matrixxc method)\": [\n+ [0, \"minieigen.MatrixXc.resize\"]\n+ ],\n+ \"resize() (minieigen.vectorx method)\": [\n+ [0, \"minieigen.VectorX.resize\"]\n+ ],\n+ \"resize() (minieigen.vectorxc method)\": [\n+ [0, \"minieigen.VectorXc.resize\"]\n+ ],\n+ \"row() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.row\"]\n+ ],\n+ \"row() (minieigen.matrix3c method)\": [\n+ [0, \"minieigen.Matrix3c.row\"]\n+ ],\n+ \"row() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.row\"]\n+ ],\n+ \"row() (minieigen.matrix6c method)\": [\n+ [0, \"minieigen.Matrix6c.row\"]\n+ ],\n+ \"row() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.row\"]\n+ ],\n+ \"row() (minieigen.matrixxc method)\": [\n+ [0, \"minieigen.MatrixXc.row\"]\n+ ],\n+ \"rows() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.rows\"]\n+ ],\n+ \"rows() (minieigen.matrix3c method)\": [\n+ [0, \"minieigen.Matrix3c.rows\"]\n+ ],\n+ \"rows() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.rows\"]\n+ ],\n+ \"rows() (minieigen.matrix6c method)\": [\n+ [0, \"minieigen.Matrix6c.rows\"]\n+ ],\n+ \"rows() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.rows\"]\n+ ],\n+ \"rows() (minieigen.matrixxc method)\": [\n+ [0, \"minieigen.MatrixXc.rows\"]\n+ ],\n+ \"rows() (minieigen.vector2 method)\": [\n+ [0, \"minieigen.Vector2.rows\"]\n+ ],\n+ \"rows() (minieigen.vector2c method)\": [\n+ [0, \"minieigen.Vector2c.rows\"]\n+ ],\n+ \"rows() (minieigen.vector2i method)\": [\n+ [0, \"minieigen.Vector2i.rows\"]\n+ ],\n+ \"rows() (minieigen.vector3 method)\": [\n+ [0, \"minieigen.Vector3.rows\"]\n+ ],\n+ \"rows() (minieigen.vector3c method)\": [\n+ [0, \"minieigen.Vector3c.rows\"]\n+ ],\n+ \"rows() (minieigen.vector3i method)\": [\n+ [0, \"minieigen.Vector3i.rows\"]\n+ ],\n+ \"rows() (minieigen.vector4 method)\": [\n+ [0, \"minieigen.Vector4.rows\"]\n+ ],\n+ \"rows() (minieigen.vector6 method)\": [\n+ [0, \"minieigen.Vector6.rows\"]\n+ ],\n+ \"rows() (minieigen.vector6c method)\": [\n+ [0, \"minieigen.Vector6c.rows\"]\n+ ],\n+ \"rows() (minieigen.vector6i method)\": [\n+ [0, \"minieigen.Vector6i.rows\"]\n+ ],\n+ \"rows() (minieigen.vectorx method)\": [\n+ [0, \"minieigen.VectorX.rows\"]\n+ ],\n+ \"rows() (minieigen.vectorxc method)\": [\n+ [0, \"minieigen.VectorXc.rows\"]\n+ ],\n+ \"selfadjointeigendecomposition() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.selfAdjointEigenDecomposition\"]\n+ ],\n+ \"selfadjointeigendecomposition() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.selfAdjointEigenDecomposition\"]\n+ ],\n+ \"selfadjointeigendecomposition() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.selfAdjointEigenDecomposition\"]\n+ ],\n+ \"setfromtwovectors() (minieigen.quaternion method)\": [\n+ [0, \"minieigen.Quaternion.setFromTwoVectors\"]\n+ ],\n+ \"sizes() (minieigen.alignedbox2 method)\": [\n+ [0, \"minieigen.AlignedBox2.sizes\"]\n+ ],\n+ \"sizes() (minieigen.alignedbox3 method)\": [\n+ [0, \"minieigen.AlignedBox3.sizes\"]\n+ ],\n+ \"slerp() (minieigen.quaternion method)\": [\n+ [0, \"minieigen.Quaternion.slerp\"]\n+ ],\n+ \"spectraldecomposition() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.spectralDecomposition\"]\n+ ],\n+ \"spectraldecomposition() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.spectralDecomposition\"]\n+ ],\n+ \"spectraldecomposition() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.spectralDecomposition\"]\n+ ],\n+ \"squarednorm() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.squaredNorm\"]\n+ ],\n+ \"squarednorm() (minieigen.matrix3c method)\": [\n+ [0, \"minieigen.Matrix3c.squaredNorm\"]\n+ ],\n+ \"squarednorm() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.squaredNorm\"]\n+ ],\n+ \"squarednorm() (minieigen.matrix6c method)\": [\n+ [0, \"minieigen.Matrix6c.squaredNorm\"]\n+ ],\n+ \"squarednorm() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.squaredNorm\"]\n+ ],\n+ \"squarednorm() (minieigen.matrixxc method)\": [\n+ [0, \"minieigen.MatrixXc.squaredNorm\"]\n+ ],\n+ \"squarednorm() (minieigen.vector2 method)\": [\n+ [0, \"minieigen.Vector2.squaredNorm\"]\n+ ],\n+ \"squarednorm() (minieigen.vector2c method)\": [\n+ [0, \"minieigen.Vector2c.squaredNorm\"]\n+ ],\n+ \"squarednorm() (minieigen.vector3 method)\": [\n+ [0, \"minieigen.Vector3.squaredNorm\"]\n+ ],\n+ \"squarednorm() (minieigen.vector3c method)\": [\n+ [0, \"minieigen.Vector3c.squaredNorm\"]\n+ ],\n+ \"squarednorm() (minieigen.vector4 method)\": [\n+ [0, \"minieigen.Vector4.squaredNorm\"]\n+ ],\n+ \"squarednorm() (minieigen.vector6 method)\": [\n+ [0, \"minieigen.Vector6.squaredNorm\"]\n+ ],\n+ \"squarednorm() (minieigen.vector6c method)\": [\n+ [0, \"minieigen.Vector6c.squaredNorm\"]\n+ ],\n+ \"squarednorm() (minieigen.vectorx method)\": [\n+ [0, \"minieigen.VectorX.squaredNorm\"]\n+ ],\n+ \"squarednorm() (minieigen.vectorxc method)\": [\n+ [0, \"minieigen.VectorXc.squaredNorm\"]\n+ ],\n+ \"sum() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.sum\"]\n+ ],\n+ \"sum() (minieigen.matrix3c method)\": [\n+ [0, \"minieigen.Matrix3c.sum\"]\n+ ],\n+ \"sum() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.sum\"]\n+ ],\n+ \"sum() (minieigen.matrix6c method)\": [\n+ [0, \"minieigen.Matrix6c.sum\"]\n+ ],\n+ \"sum() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.sum\"]\n+ ],\n+ \"sum() (minieigen.matrixxc method)\": [\n+ [0, \"minieigen.MatrixXc.sum\"]\n+ ],\n+ \"sum() (minieigen.vector2 method)\": [\n+ [0, \"minieigen.Vector2.sum\"]\n+ ],\n+ \"sum() (minieigen.vector2c method)\": [\n+ [0, \"minieigen.Vector2c.sum\"]\n+ ],\n+ \"sum() (minieigen.vector2i method)\": [\n+ [0, \"minieigen.Vector2i.sum\"]\n+ ],\n+ \"sum() (minieigen.vector3 method)\": [\n+ [0, \"minieigen.Vector3.sum\"]\n+ ],\n+ \"sum() (minieigen.vector3c method)\": [\n+ [0, \"minieigen.Vector3c.sum\"]\n+ ],\n+ \"sum() (minieigen.vector3i method)\": [\n+ [0, \"minieigen.Vector3i.sum\"]\n+ ],\n+ \"sum() (minieigen.vector4 method)\": [\n+ [0, \"minieigen.Vector4.sum\"]\n+ ],\n+ \"sum() (minieigen.vector6 method)\": [\n+ [0, \"minieigen.Vector6.sum\"]\n+ ],\n+ \"sum() (minieigen.vector6c method)\": [\n+ [0, \"minieigen.Vector6c.sum\"]\n+ ],\n+ \"sum() (minieigen.vector6i method)\": [\n+ [0, \"minieigen.Vector6i.sum\"]\n+ ],\n+ \"sum() (minieigen.vectorx method)\": [\n+ [0, \"minieigen.VectorX.sum\"]\n+ ],\n+ \"sum() (minieigen.vectorxc method)\": [\n+ [0, \"minieigen.VectorXc.sum\"]\n+ ],\n+ \"svd() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.svd\"]\n+ ],\n+ \"svd() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.svd\"]\n+ ],\n+ \"svd() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.svd\"]\n+ ],\n+ \"tail() (minieigen.vector6 method)\": [\n+ [0, \"minieigen.Vector6.tail\"]\n+ ],\n+ \"tail() (minieigen.vector6c method)\": [\n+ [0, \"minieigen.Vector6c.tail\"]\n+ ],\n+ \"tail() (minieigen.vector6i method)\": [\n+ [0, \"minieigen.Vector6i.tail\"]\n+ ],\n+ \"toangleaxis() (minieigen.quaternion method)\": [\n+ [0, \"minieigen.Quaternion.toAngleAxis\"]\n+ ],\n+ \"toaxisangle() (minieigen.quaternion method)\": [\n+ [0, \"minieigen.Quaternion.toAxisAngle\"]\n+ ],\n+ \"torotationmatrix() (minieigen.quaternion method)\": [\n+ [0, \"minieigen.Quaternion.toRotationMatrix\"]\n+ ],\n+ \"torotationvector() (minieigen.quaternion method)\": [\n+ [0, \"minieigen.Quaternion.toRotationVector\"]\n+ ],\n+ \"trace() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.trace\"]\n+ ],\n+ \"trace() (minieigen.matrix3c method)\": [\n+ [0, \"minieigen.Matrix3c.trace\"]\n+ ],\n+ \"trace() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.trace\"]\n+ ],\n+ \"trace() (minieigen.matrix6c method)\": [\n+ [0, \"minieigen.Matrix6c.trace\"]\n+ ],\n+ \"trace() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.trace\"]\n+ ],\n+ \"trace() (minieigen.matrixxc method)\": [\n+ [0, \"minieigen.MatrixXc.trace\"]\n+ ],\n+ \"transpose() (minieigen.matrix3 method)\": [\n+ [0, \"minieigen.Matrix3.transpose\"]\n+ ],\n+ \"transpose() (minieigen.matrix3c method)\": [\n+ [0, \"minieigen.Matrix3c.transpose\"]\n+ ],\n+ \"transpose() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.transpose\"]\n+ ],\n+ \"transpose() (minieigen.matrix6c method)\": [\n+ [0, \"minieigen.Matrix6c.transpose\"]\n+ ],\n+ \"transpose() (minieigen.matrixx method)\": [\n+ [0, \"minieigen.MatrixX.transpose\"]\n+ ],\n+ \"transpose() (minieigen.matrixxc method)\": [\n+ [0, \"minieigen.MatrixXc.transpose\"]\n+ ],\n+ \"ul() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.ul\"]\n+ ],\n+ \"ul() (minieigen.matrix6c method)\": [\n+ [0, \"minieigen.Matrix6c.ul\"]\n+ ],\n+ \"ur() (minieigen.matrix6 method)\": [\n+ [0, \"minieigen.Matrix6.ur\"]\n+ ],\n+ \"ur() (minieigen.matrix6c method)\": [\n+ [0, \"minieigen.Matrix6c.ur\"]\n+ ],\n+ \"volume() (minieigen.alignedbox2 method)\": [\n+ [0, \"minieigen.AlignedBox2.volume\"]\n+ ],\n+ \"volume() (minieigen.alignedbox3 method)\": [\n+ [0, \"minieigen.AlignedBox3.volume\"]\n+ ],\n+ \"xy() (minieigen.vector3 method)\": [\n+ [0, \"minieigen.Vector3.xy\"]\n+ ],\n+ \"xy() (minieigen.vector3c method)\": [\n+ [0, \"minieigen.Vector3c.xy\"]\n+ ],\n+ \"xy() (minieigen.vector3i method)\": [\n+ [0, \"minieigen.Vector3i.xy\"]\n+ ],\n+ \"xz() (minieigen.vector3 method)\": [\n+ [0, \"minieigen.Vector3.xz\"]\n+ ],\n+ \"xz() (minieigen.vector3c method)\": [\n+ [0, \"minieigen.Vector3c.xz\"]\n+ ],\n+ \"xz() (minieigen.vector3i method)\": [\n+ [0, \"minieigen.Vector3i.xz\"]\n+ ],\n+ \"yx() (minieigen.vector3 method)\": [\n+ [0, \"minieigen.Vector3.yx\"]\n+ ],\n+ \"yx() (minieigen.vector3c method)\": [\n+ [0, \"minieigen.Vector3c.yx\"]\n+ ],\n+ \"yx() (minieigen.vector3i method)\": [\n+ [0, \"minieigen.Vector3i.yx\"]\n+ ],\n+ \"yz() (minieigen.vector3 method)\": [\n+ [0, \"minieigen.Vector3.yz\"]\n+ ],\n+ \"yz() (minieigen.vector3c method)\": [\n+ [0, \"minieigen.Vector3c.yz\"]\n+ ],\n+ \"yz() (minieigen.vector3i method)\": [\n+ [0, \"minieigen.Vector3i.yz\"]\n+ ],\n+ \"zx() (minieigen.vector3 method)\": [\n+ [0, \"minieigen.Vector3.zx\"]\n+ ],\n+ \"zx() (minieigen.vector3c method)\": [\n+ [0, \"minieigen.Vector3c.zx\"]\n+ ],\n+ \"zx() (minieigen.vector3i method)\": [\n+ [0, \"minieigen.Vector3i.zx\"]\n+ ],\n+ \"zy() (minieigen.vector3 method)\": [\n+ [0, \"minieigen.Vector3.zy\"]\n+ ],\n+ \"zy() (minieigen.vector3c method)\": [\n+ [0, \"minieigen.Vector3c.zy\"]\n+ ],\n+ \"zy() (minieigen.vector3i method)\": [\n+ [0, \"minieigen.Vector3i.zy\"]\n+ ]\n+ }\n })\n"}]}]}, {"source1": "xz --list", "source2": "xz --list", "unified_diff": "@@ -1,13 +1,13 @@\n Streams: 1\n Blocks: 1\n- Compressed size: 843.0 KiB (863264 B)\n- Uncompressed size: 7840.0 KiB (8028160 B)\n- Ratio: 0.108\n+ Compressed size: 861.7 KiB (882352 B)\n+ Uncompressed size: 8450.0 KiB (8652800 B)\n+ Ratio: 0.102\n Check: CRC64\n Stream Padding: 0 B\n Streams:\n Stream Blocks CompOffset UncompOffset CompSize UncompSize Ratio Check Padding\n- 1 1 0 0 863264 8028160 0.108 CRC64 0\n+ 1 1 0 0 882352 8652800 0.102 CRC64 0\n Blocks:\n Stream Block CompOffset UncompOffset TotalSize UncompSize Ratio Check\n- 1 1 12 0 863224 8028160 0.108 CRC64\n+ 1 1 12 0 882312 8652800 0.102 CRC64\n"}]}]}]}