{"diffoscope-json-version": 1, "source1": "/srv/reproducible-results/rbuild-debian/r-b-build.JXToCBxm/b1/minieigen_0.50.3+dfsg1-13_armhf.changes", "source2": "/srv/reproducible-results/rbuild-debian/r-b-build.JXToCBxm/b2/minieigen_0.50.3+dfsg1-13_armhf.changes", "unified_diff": null, "details": [{"source1": "Files", "source2": "Files", "unified_diff": "@@ -1,3 +1,3 @@\n \n 761779789ea0f857a3ae61ae3ce17150 27520888 debug optional python3-minieigen-dbgsym_0.50.3+dfsg1-13_armhf.deb\n- a84fca97106eb976f626050ccef36832 734032 python optional python3-minieigen_0.50.3+dfsg1-13_armhf.deb\n+ 3c8a73a31ff74c63866a141a833b0250 714764 python optional python3-minieigen_0.50.3+dfsg1-13_armhf.deb\n"}, {"source1": "python3-minieigen_0.50.3+dfsg1-13_armhf.deb", "source2": "python3-minieigen_0.50.3+dfsg1-13_armhf.deb", "unified_diff": null, "details": [{"source1": "file list", "source2": "file list", "unified_diff": "@@ -1,3 +1,3 @@\n -rw-r--r-- 0 0 0 4 2021-11-08 17:29:32.000000 debian-binary\n -rw-r--r-- 0 0 0 1576 2021-11-08 17:29:32.000000 control.tar.xz\n--rw-r--r-- 0 0 0 732264 2021-11-08 17:29:32.000000 data.tar.xz\n+-rw-r--r-- 0 0 0 712996 2021-11-08 17:29:32.000000 data.tar.xz\n"}, {"source1": "control.tar.xz", "source2": "control.tar.xz", "unified_diff": null, "details": [{"source1": "control.tar", "source2": "control.tar", "unified_diff": null, "details": [{"source1": "./control", "source2": "./control", "unified_diff": "@@ -1,13 +1,13 @@\n Package: python3-minieigen\n Source: minieigen\n Version: 0.50.3+dfsg1-13\n Architecture: armhf\n Maintainer: Debian Science Maintainers \n-Installed-Size: 7024\n+Installed-Size: 6409\n Depends: python3 (<< 3.14), python3 (>= 3.12~), libboost-python1.83.0 (>= 1.83.0), libboost-python1.83.0-py312, libboost-python1.83.0-py313, libc6 (>= 2.32), libdouble-conversion3 (>= 2.0.0), libgcc-s1 (>= 4.0), libstdc++6 (>= 5.2), libjs-sphinxdoc (>= 7.4)\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) 60711 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/genindex.html\n--rw-r--r-- 0 root (0) root (0) 520316 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) 2796 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/genindex.html\n+-rw-r--r-- 0 root (0) root (0) 11262 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) 3141 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/search.html\n--rw-r--r-- 0 root (0) root (0) 62229 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/searchindex.js\n+-rw-r--r-- 0 root (0) root (0) 2959 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/searchindex.js\n drwxr-xr-x 0 root (0) root (0) 0 2021-11-08 17:29:32.000000 ./usr/share/doc-base/\n -rw-r--r-- 0 root (0) root (0) 262 2020-02-19 22:00:59.000000 ./usr/share/doc-base/python3-minieigen.python3-minieigen\n lrwxrwxrwx 0 root (0) root (0) 0 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/_static/doctools.js -> ../../../../javascript/sphinxdoc/1.0/doctools.js\n lrwxrwxrwx 0 root (0) root (0) 0 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/_static/language_data.js -> ../../../../javascript/sphinxdoc/1.0/language_data.js\n lrwxrwxrwx 0 root (0) root (0) 0 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/_static/searchtools.js -> ../../../../javascript/sphinxdoc/1.0/searchtools.js\n lrwxrwxrwx 0 root (0) root (0) 0 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/_static/sidebar.js -> ../../../../javascript/sphinxdoc/1.0/sidebar.js\n lrwxrwxrwx 0 root (0) root (0) 0 2021-11-08 17:29:32.000000 ./usr/share/doc/python3-minieigen/html/_static/sphinx_highlight.js -> ../../../../javascript/sphinxdoc/1.0/sphinx_highlight.js\n"}, {"source1": "./usr/share/doc/python3-minieigen/html/genindex.html", "source2": "./usr/share/doc/python3-minieigen/html/genindex.html", "unified_diff": "@@ -31,1376 +31,16 @@\n
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\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -1,466 +1,12 @@\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- * 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_\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 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_\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 7.4.7.\n"}]}, {"source1": "./usr/share/doc/python3-minieigen/html/index.html", "source2": "./usr/share/doc/python3-minieigen/html/index.html", "unified_diff": "@@ -41,49 +41,49 @@\n

Something concise here.

\n
\n \n
\n

Examples\u00b6

\n
\n

Todo

\n-

Some examples of what can be done with minieigen.

\n+

Some examples of what can be done with minieigen.

\n
\n
\n
\n

Naming conventions\u00b6

\n \n
\n
\n

Limitations\u00b6

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

Documentation\u00b6

\n \n-

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

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

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

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

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

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

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

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

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

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

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

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

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

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

3x3 float matrix.

\n-

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

\n-

Static attributes: Zero, Ones, Identity.

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

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

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

Return column as vector.

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

Number of columns.

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

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

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

Return matrix determinant.

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

Return diagonal as vector.

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

Return inverted matrix.

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

Approximate comparison with precision prec.

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

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

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

Maximum absolute value over all elements.

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

Maximum value over all elements.

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

Mean value over all elements.

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

Minimum value over all elements.

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

Euclidean norm.

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

Normalize this object in-place.

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

Return normalized copy of this object

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

Alias for computeUnitaryPositive.

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

Product of all elements.

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

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

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

Return row as vector.

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

Number of rows.

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

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

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

Alias for selfAdjointEigenDecomposition.

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

Square of the Euclidean norm.

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

Sum of all elements.

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

Alias for jacobiSVD.

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

Return sum of diagonal elements.

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

Return transposed matrix.

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

/TODO/

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

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

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

Return column as vector.

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

Number of columns.

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

Return matrix determinant.

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

Return diagonal as vector.

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

Return inverted matrix.

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

Approximate comparison with precision prec.

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

Maximum absolute value over all elements.

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

Mean value over all elements.

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

Euclidean norm.

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

Normalize this object in-place.

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

Return normalized copy of this object

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

Product of all elements.

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

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

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

Return row as vector.

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

Number of rows.

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

Square of the Euclidean norm.

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

Sum of all elements.

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

Return sum of diagonal elements.

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

Return transposed matrix.

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

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

\n-

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

\n-

Static attributes: Zero, Ones, Identity.

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

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

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

Return column as vector.

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

Number of columns.

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

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

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

Return matrix determinant.

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

Return diagonal as vector.

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

Return inverted matrix.

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

Approximate comparison with precision prec.

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

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

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

Return lower-left 3x3 block

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

Return lower-right 3x3 block

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

Maximum absolute value over all elements.

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

Maximum value over all elements.

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

Mean value over all elements.

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

Minimum value over all elements.

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

Euclidean norm.

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

Normalize this object in-place.

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

Return normalized copy of this object

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

Alias for computeUnitaryPositive.

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

Product of all elements.

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

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

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

Return row as vector.

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

Number of rows.

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

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

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

Alias for selfAdjointEigenDecomposition.

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

Square of the Euclidean norm.

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

Sum of all elements.

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

Alias for jacobiSVD.

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

Return sum of diagonal elements.

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

Return transposed matrix.

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

Return upper-left 3x3 block

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

Return upper-right 3x3 block

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

/TODO/

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

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

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

Return column as vector.

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

Number of columns.

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

Return matrix determinant.

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

Return diagonal as vector.

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

Return inverted matrix.

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

Approximate comparison with precision prec.

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

Return lower-left 3x3 block

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

Return lower-right 3x3 block

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

Maximum absolute value over all elements.

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

Mean value over all elements.

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

Euclidean norm.

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

Normalize this object in-place.

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

Return normalized copy of this object

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

Product of all elements.

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

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

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

Return row as vector.

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

Number of rows.

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

Square of the Euclidean norm.

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

Sum of all elements.

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

Return sum of diagonal elements.

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

Return transposed matrix.

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

Return upper-left 3x3 block

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

Return upper-right 3x3 block

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

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

\n-

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

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

Create identity matrix with given rank (square).

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

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

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

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

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

Create zero matrix of given dimensions

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

Return column as vector.

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

Number of columns.

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

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

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

Return matrix determinant.

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

Return diagonal as vector.

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

Return inverted matrix.

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

Approximate comparison with precision prec.

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

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

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

Maximum absolute value over all elements.

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

Maximum value over all elements.

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

Mean value over all elements.

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

Minimum value over all elements.

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

Euclidean norm.

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

Normalize this object in-place.

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

Return normalized copy of this object

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

Alias for computeUnitaryPositive.

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

Product of all elements.

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

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

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

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

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

Return row as vector.

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

Number of rows.

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

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

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

Alias for selfAdjointEigenDecomposition.

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

Square of the Euclidean norm.

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

Sum of all elements.

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

Alias for jacobiSVD.

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

Return sum of diagonal elements.

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

Return transposed matrix.

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

/TODO/

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

Create identity matrix with given rank (square).

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

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

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

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

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

Create zero matrix of given dimensions

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

Return column as vector.

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

Number of columns.

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

Return matrix determinant.

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

Return diagonal as vector.

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

Return inverted matrix.

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

Approximate comparison with precision prec.

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

Maximum absolute value over all elements.

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

Mean value over all elements.

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

Euclidean norm.

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

Normalize this object in-place.

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

Return normalized copy of this object

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

Product of all elements.

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

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

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

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

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

Return row as vector.

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

Number of rows.

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

Square of the Euclidean norm.

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

Sum of all elements.

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

Return sum of diagonal elements.

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

Return transposed matrix.

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

Quaternion representing rotation.

\n-

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

\n-

Static attributes: Identity.

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

3-dimensional float vector.

\n-

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

\n-

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

\n-

Static attributes: Zero, Ones, UnitX, UnitY.

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

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

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

Return diagonal matrix with this vector on the diagonal.

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

Number of columns.

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

Dot product with other.

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

Approximate comparison with precision prec.

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

Maximum absolute value over all elements.

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

Maximum value over all elements.

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

Mean value over all elements.

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

Minimum value over all elements.

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

Euclidean norm.

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

Normalize this object in-place.

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

Return normalized copy of this object

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

Outer product with other.

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

Product of all elements.

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

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

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

Number of rows.

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

Square of the Euclidean norm.

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

Sum of all elements.

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

/TODO/

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

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

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

Return diagonal matrix with this vector on the diagonal.

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

Number of columns.

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

Dot product with other.

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

Approximate comparison with precision prec.

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

Maximum absolute value over all elements.

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

Mean value over all elements.

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

Euclidean norm.

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

Normalize this object in-place.

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

Return normalized copy of this object

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

Outer product with other.

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

Product of all elements.

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

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

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

Number of rows.

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

Square of the Euclidean norm.

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

Sum of all elements.

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

2-dimensional integer vector.

\n-

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

\n-

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

\n-

Static attributes: Zero, Ones, UnitX, UnitY.

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

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

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

Return diagonal matrix with this vector on the diagonal.

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

Number of columns.

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

Dot product with other.

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

Approximate comparison with precision prec.

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

Maximum absolute value over all elements.

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

Maximum value over all elements.

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

Mean value over all elements.

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

Minimum value over all elements.

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

Outer product with other.

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

Product of all elements.

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

Number of rows.

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

Sum of all elements.

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

3-dimensional float vector.

\n-

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

\n-

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

\n-

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

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

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

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

Return diagonal matrix with this vector on the diagonal.

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

Number of columns.

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

Dot product with other.

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

Approximate comparison with precision prec.

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

Maximum absolute value over all elements.

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

Maximum value over all elements.

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

Mean value over all elements.

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

Minimum value over all elements.

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

Euclidean norm.

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

Normalize this object in-place.

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

Return normalized copy of this object

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

Outer product with other.

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

Product of all elements.

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

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

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

Number of rows.

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

Square of the Euclidean norm.

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

Sum of all elements.

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

/TODO/

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

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

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

Return diagonal matrix with this vector on the diagonal.

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

Number of columns.

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

Dot product with other.

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

Approximate comparison with precision prec.

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

Maximum absolute value over all elements.

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

Mean value over all elements.

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

Euclidean norm.

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

Normalize this object in-place.

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

Return normalized copy of this object

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

Outer product with other.

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

Product of all elements.

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

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

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

Number of rows.

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

Square of the Euclidean norm.

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

Sum of all elements.

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

3-dimensional integer vector.

\n-

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

\n-

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

\n-

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

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

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

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

Return diagonal matrix with this vector on the diagonal.

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

Number of columns.

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

Dot product with other.

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

Approximate comparison with precision prec.

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

Maximum absolute value over all elements.

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

Maximum value over all elements.

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

Mean value over all elements.

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

Minimum value over all elements.

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

Outer product with other.

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

Product of all elements.

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

Number of rows.

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

Sum of all elements.

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

4-dimensional float vector.

\n-

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

\n-

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

\n-

Static attributes: Zero, Ones.

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

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

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

Return diagonal matrix with this vector on the diagonal.

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

Number of columns.

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

Dot product with other.

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

Approximate comparison with precision prec.

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

Maximum absolute value over all elements.

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

Maximum value over all elements.

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

Mean value over all elements.

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

Minimum value over all elements.

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

Euclidean norm.

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

Normalize this object in-place.

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

Return normalized copy of this object

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

Outer product with other.

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

Product of all elements.

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

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

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

Number of rows.

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

Square of the Euclidean norm.

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

Sum of all elements.

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

6-dimensional float vector.

\n-

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

\n-

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

\n-

Static attributes: Zero, Ones.

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

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

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

Return diagonal matrix with this vector on the diagonal.

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

Number of columns.

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

Dot product with other.

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

Approximate comparison with precision prec.

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

Maximum absolute value over all elements.

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

Maximum value over all elements.

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

Mean value over all elements.

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

Minimum value over all elements.

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

Euclidean norm.

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

Normalize this object in-place.

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

Return normalized copy of this object

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

Outer product with other.

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

Product of all elements.

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

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

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

Number of rows.

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

Square of the Euclidean norm.

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

Sum of all elements.

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

/TODO/

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

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

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

Return diagonal matrix with this vector on the diagonal.

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

Number of columns.

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

Dot product with other.

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

Approximate comparison with precision prec.

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

Maximum absolute value over all elements.

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

Mean value over all elements.

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

Euclidean norm.

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

Normalize this object in-place.

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

Return normalized copy of this object

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

Outer product with other.

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

Product of all elements.

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

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

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

Number of rows.

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

Square of the Euclidean norm.

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

Sum of all elements.

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

6-dimensional float vector.

\n-

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

\n-

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

\n-

Static attributes: Zero, Ones.

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

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

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

Return diagonal matrix with this vector on the diagonal.

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

Number of columns.

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

Dot product with other.

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

Approximate comparison with precision prec.

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

Maximum absolute value over all elements.

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

Maximum value over all elements.

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

Mean value over all elements.

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

Minimum value over all elements.

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

Outer product with other.

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

Product of all elements.

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

Number of rows.

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

Sum of all elements.

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

Dynamic-sized float vector.

\n-

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

\n-

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

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

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

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

Return diagonal matrix with this vector on the diagonal.

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

Number of columns.

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

Dot product with other.

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

Approximate comparison with precision prec.

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

Maximum absolute value over all elements.

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

Maximum value over all elements.

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

Mean value over all elements.

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

Minimum value over all elements.

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

Euclidean norm.

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

Normalize this object in-place.

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

Return normalized copy of this object

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

Outer product with other.

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

Product of all elements.

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

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

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

Number of rows.

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

Square of the Euclidean norm.

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

Sum of all elements.

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

/TODO/

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

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

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

Return diagonal matrix with this vector on the diagonal.

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

Number of columns.

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

Dot product with other.

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

Approximate comparison with precision prec.

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

Maximum absolute value over all elements.

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

Mean value over all elements.

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

Euclidean norm.

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

Normalize this object in-place.

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

Return normalized copy of this object

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

Outer product with other.

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

Product of all elements.

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

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

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

Number of rows.

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

Square of the Euclidean norm.

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

Sum of all elements.

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

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

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

Quick search

\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -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 _\bm_\bi_\bn_\bi_\be_\bi_\bg_\be_\bn.\n+Some examples of what can be done with minieigen.\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 _\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+ 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 * Scalar (element) type is suffixed at the end:\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 nothing is suffixed for floats (minieigen.Matrix3);\n o i indicates integers (minieigen.Matrix3i);\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+ o c indicates complex numbers (minieigen.Matrix3c).\n * Methods are named as follows:\n o static methods are upper-case (as in c++), e.g.\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+ minieigen.Matrix3.Random;\n # nullary static methods are exposed as properties, 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+ return a constant (e.g. minieigen.Matrix3.Identity); if they\n don\u2019t, they are exposed as methods\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+ (minieigen.Matrix3.Random); 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- _\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+ minieigen.Matrix3.inverse.\n * Return types:\n o methods modifying the instance in-place return None (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+ minieigen.Vector3.normalize); 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- (_\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+ (minieigen.Quaternion.setFromTwoVectors);\n o methods returning another object (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_\bd) do not modify the instance;\n+ minieigen.Vector3.normalized) 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@@ -65,1546 +65,22 @@\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- # 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_\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 7.4.7.\n"}]}, {"source1": "./usr/share/doc/python3-minieigen/html/objects.inv", "source2": "./usr/share/doc/python3-minieigen/html/objects.inv", "unified_diff": null, "details": [{"source1": "Sphinx inventory", "source2": "Sphinx inventory", "unified_diff": "@@ -1,524 +1,10 @@\n # Sphinx inventory version 2\n # Project: minieigen\n # Version: 0.4\n # The remainder of this file is compressed using zlib.\n \n-minieigen py:module 0 index.html#module-$ -\n-minieigen.AlignedBox2 py:class 1 index.html#$ -\n-minieigen.AlignedBox2.center py:method 1 index.html#$ -\n-minieigen.AlignedBox2.clamp py:method 1 index.html#$ -\n-minieigen.AlignedBox2.contains py:method 1 index.html#$ -\n-minieigen.AlignedBox2.empty py:method 1 index.html#$ -\n-minieigen.AlignedBox2.extend py:method 1 index.html#$ -\n-minieigen.AlignedBox2.intersection py:method 1 index.html#$ -\n-minieigen.AlignedBox2.max py:property 1 index.html#$ -\n-minieigen.AlignedBox2.merged py:method 1 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