diff --git a/.gitattributes b/.gitattributes index 6c2000fe2d5d70feb699f5b24c60dfa73bc446ca..0e7ab0e676bfb126b9293587f9fcebdd4b385500 100644 --- a/.gitattributes +++ b/.gitattributes @@ -8,3 +8,5 @@ examples/mof/classification/MACE-MPA.pkl filter=lfs diff=lfs merge=lfs -text examples/mof/classification/MACE-MP(M).pkl filter=lfs diff=lfs merge=lfs -text examples/mof/classification/MatterSim.pkl filter=lfs diff=lfs merge=lfs -text examples/mof/classification/ORBv2.pkl filter=lfs diff=lfs merge=lfs -text +examples/c2db/*.db filter=lfs diff=lfs merge=lfs -text +examples/c2db/*.parquet filter=lfs diff=lfs merge=lfs -text diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index 87f489ac62f76c2e11fcf87fae1d516f22fe2d8e..f41dd9214ea44f973074c687e247fcbb1e26dd4f 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -21,13 +21,11 @@ jobs: - name: Checkout PR with full history uses: actions/checkout@v4 with: - lfs: true fetch-depth: 0 - name: Install uv uses: astral-sh/setup-uv@v6 with: - version: "latest" enable-cache: true cache-dependency-glob: "pyproject.toml" @@ -51,14 +49,14 @@ jobs: env: PREFECT_API_KEY: ${{ secrets.PREFECT_API_KEY }} PREFECT_API_URL: ${{ secrets.PREFECT_API_URL }} - run: pytest -vra -n 5 --dist=loadscope tests + run: pytest -vra -n 5 --basetemp=./.pytest_temp tests - name: Squash commits and trial push to Hugging Face if: github.event_name == 'pull_request' id: trial_push env: HF_TOKEN: ${{ secrets.HF_TOKEN }} - TRIAL_BRANCH: trial-sync-${{ github.sha }} + TRIAL_BRANCH: trial-sync-${{ github.sha }}-${{ matrix.python-version }} run: | # Configure Git user identity git config user.name "github-actions[ci]" @@ -69,9 +67,15 @@ jobs: git reset --soft $BASE git commit -m "Squashed commit from PR #${{ github.event.pull_request.number }}" + # Install Git LFS + sudo apt-get update + sudo apt-get install -y git-lfs + git lfs install + # Setup LFS git lfs fetch git lfs checkout + # git config lfs.allowincompletepush true # Push to temporary branch on Hugging Face git push -f https://HF_USERNAME:$HF_TOKEN@huggingface.co/spaces/atomind/mlip-arena HEAD:refs/heads/$TRIAL_BRANCH @@ -80,6 +84,6 @@ jobs: if: steps.trial_push.outcome == 'success' env: HF_TOKEN: ${{ secrets.HF_TOKEN }} - TRIAL_BRANCH: trial-sync-${{ github.sha }} + TRIAL_BRANCH: trial-sync-${{ github.sha }}-${{ matrix.python-version }} run: | git push https://HF_USERNAME:$HF_TOKEN@huggingface.co/spaces/atomind/mlip-arena --delete $TRIAL_BRANCH || true diff --git a/examples/bzo/dft.ipynb b/examples/bzo/dft.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..628cae23a8b19aa4d1f2f6308e3762884df9cb5a --- /dev/null +++ b/examples/bzo/dft.ipynb @@ -0,0 +1,340 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/pscratch/sd/c/cyrusyc/.conda/mlip-arena/lib/python3.11/site-packages/torchani/aev.py:16: UserWarning: cuaev not installed\n", + " warnings.warn(\"cuaev not installed\")\n", + "\u001b[32m2025-03-18 22:33:32.183\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mmlip_arena.models\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m34\u001b[0m - \u001b[33m\u001b[1mNo module named 'deepmd'\u001b[0m\n" + ] + } + ], + "source": [ + "from mlip_arena.tasks import MD, PHONON, OPT\n", + "from mlip_arena.tasks.utils import get_calculator\n", + "from ase.build import bulk\n", + "from ase.io import read\n", + "import numpy as np\n", + "\n", + "atoms = read('BZO_cubic_prim.xyz')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
22:33:35.208 | INFO    | prefect - Starting temporary server on http://127.0.0.1:8224\n",
+       "See https://docs.prefect.io/3.0/manage/self-host#self-host-a-prefect-server for more information on running a dedicated Prefect server.\n",
+       "
\n" + ], + "text/plain": [ + "22:33:35.208 | \u001b[36mINFO\u001b[0m | prefect - Starting temporary server on \u001b[94mhttp://127.0.0.1:8224\u001b[0m\n", + "See \u001b[94mhttps://docs.prefect.io/3.0/manage/self-host#self-host-a-prefect-server\u001b[0m for more information on running a dedicated Prefect server.\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
22:33:41.734 | INFO    | Task run 'OPT: BaO3Zr - <ase.calculators.vasp.vasp.Vasp object at 0x7f582ccbee10>' - Finished in state Cached(type=COMPLETED)\n",
+       "
\n" + ], + "text/plain": [ + "22:33:41.734 | \u001b[36mINFO\u001b[0m | Task run 'OPT: BaO3Zr - ' - Finished in state \u001b[94mCached\u001b[0m(type=COMPLETED)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
22:33:41.851 | INFO    | Task run 'get_phonopy' - Finished in state Completed()\n",
+       "
\n" + ], + "text/plain": [ + "22:33:41.851 | \u001b[36mINFO\u001b[0m | Task run 'get_phonopy' - Finished in state \u001b[32mCompleted\u001b[0m()\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/pscratch/sd/c/cyrusyc/.conda/mlip-arena/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_lattice']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/pscratch/sd/c/cyrusyc/.conda/mlip-arena/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_positions']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/pscratch/sd/c/cyrusyc/.conda/mlip-arena/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_types']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/pscratch/sd/c/cyrusyc/.conda/mlip-arena/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/pscratch/sd/c/cyrusyc/.conda/mlip-arena/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['transformation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/pscratch/sd/c/cyrusyc/.conda/mlip-arena/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n", + "/pscratch/sd/c/cyrusyc/.conda/mlip-arena/lib/python3.11/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['std_rotation_matrix']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "
22:38:40.064 | INFO    | Task run 'PHONON: BaO3Zr - <ase.calculators.vasp.vasp.Vasp object at 0x7f582ccbee10>' - Finished in state Completed()\n",
+       "
\n" + ], + "text/plain": [ + "22:38:40.064 | \u001b[36mINFO\u001b[0m | Task run 'PHONON: BaO3Zr - ' - Finished in state \u001b[32mCompleted\u001b[0m()\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from ase.visualize.plot import plot_atoms\n", + "import matplotlib.pyplot as plt\n", + "from ase.io import write\n", + "\n", + "from ase.calculators.vasp import Vasp\n", + "\n", + "replicas = (2, 2, 2)\n", + "\n", + "atoms = read('BZO_cubic_prim.xyz')\n", + "atoms.set_cell(cell=[4.0, 4.0, 4.0], scale_atoms=True)\n", + "atoms.set_constraint()\n", + "\n", + "calc = Vasp(\n", + " kpts=(3, 3, 3),\n", + " xc='PBE',\n", + " encut=500,\n", + " ediff=1e-7,\n", + " ibrion=-1,\n", + " ismear=0,\n", + " sigma=0.01,\n", + " isif=2,\n", + " isym=0,\n", + " nsw=0,\n", + " lreal=False,\n", + " lwave=False,\n", + " lcharg=False,\n", + " lmaxmix=4,\n", + " command='vasp_std',\n", + " directory='pbe',\n", + ")\n", + "\n", + "atoms = OPT(\n", + " atoms=atoms,\n", + " calculator=calc,\n", + " optimizer=\"FIRE2\",\n", + " # filter=\"FrechetCell\",\n", + " criterion=dict(fmax=1e-3),\n", + " symmetry=True,\n", + ")[\"atoms\"]\n", + "\n", + "atoms.set_constraint()\n", + "\n", + "result = PHONON(\n", + " atoms=atoms.copy(),\n", + " calculator=calc,\n", + " supercell_matrix=np.eye(3) * 2,\n", + " distance=0.01,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ULm3bMgbjr7/Y0AuORSkGUi9fDixbJmvJWENEBJ/B3r35d2wsPTCHDzPW7Nw5/r52LcWOtn6Y4DiaNmW7ceaMqy0R7IQIGFdiMABffMHy5Xfu0EsyciRFyLVrFCABAUzl1OkoQrQRmb8//xccDISGsuCWJ4uU3Khfn9MXbdsyhXf5cmaGCI7jww8ZSD17NuM6BNsJCwNatuQrK1JSKHISEihmkpP5nrY+UOY1ghISOK0nWMaJE0Dnzub6VkK+QASMq9i1i0WxTp1i2vCoUe4VROuONGlCT0CXLgyg/P77/Ft/xNXMns3YqfHjgX79XG1N/sfPj16biAjLttfrHWtPfmL3bqBTJ57bVavcepFOwTry8ZDdjZkxg6mXhQoxtW/qVBEvlvLQQ+xcZ81i5yrYn3XrKKpfeIGrMguCpxIfz0VGK1RgRmOZMq62SLAj4oFxJkpxMcNPP2VmwuTJHHkJ1tGnD4vtvfsu43ueeMLVFuUfTpwAevYE2rShsBYPl+DJjB/P6fj161nlW8hXiIBxFiYTRcv06Yx7ee01V1vk2bz3HmtwPPccUKVK3heYFBiH1a0bs2mWLJEYI8GzUYrlIl58kbEvQr5DppCcgVKs3jljBjBzpogXe6DTcRqpZk2ge3euni3YjtEIPPUUM2J++01S1QXP5/BhlpXo2tXVlggOQgSMM5g4Efjf/1gLYsAAV1uTfwgMBH75hRkZTz/NTliwjbffBtasYTp/5cqutkYQ8s7atczWzC7zS/B4RMA4miVL2DmMHcvMGcG+lC3LTnfdOmbNCNazdCkXKPzkEwZJC0J+YO1aJksEBrraEsFBiIBxJPv2MQX1mWcoYATH0LYtK2uOG8daMYLlHD4M9O/P6aPhw11tjSDYB5OJ1aPbtHG1JYIDEQHjKG7fZvpejRpSr8QZvPUWC1U9+yznvYXc0euZcVS+vNyjQv7iwgXe3/fd52pLBAciAsYRKMVYl9u3gZ9+EhemM/DyAubN45x3794SD5MbSnHBwcuXGUckSzMI+YmTJ/mzenXX2iE4FBEwjuCbb4BffwXmzGEBJcE5hIcDCxeyYJUUucuZqVMZ+zJ7NlC1qqutEQT7cvIkl1opW9bVlggORASMvTl8GBgxgjVfHn3U1dYUPFq1YoG7Dz4ANm1ytTXuyc6djHd59VVOIQlCfuPsWdZ+yc/rwwnuIWAmTJiA++67DyEhIShevDi6d++O48ePZ9imdevW0Ol0GV4vu1tWT0oKYzAqVmS1XcE1jBkDNG/Oir22rOydn7lzB3jySaBBA7lHhfzLhQuyPEsBwC0EzIYNGzB48GBs27YNa9asQWpqKjp06ICEhIQM273wwgu4cuXK3dcnn3ziIouzYfx44OBBVn+UuBfX4eMDLFjAGKTBg11tjfugFDBwIEXM4sWyjIWQf4mOFgFTAHCLpQRWZ0p9nTNnDooXL47du3ejVatWd98PCgpCZGSks82zjL17KWDGjAEaNXK1NUL58oxF6tOHlTifftrVFrmeb75hwO4vv/D8CEJ+5fJliX8pALiFgMlM7H9u/6JFi2Z4/4cffsCCBQsQGRmJbt264d1330VQUFCO+9JnWnbe398f/v7+9jU4NZVZR7VqsWid4B707g38/jswaBDQogUQFeVqi1zH3r2Mexk6FOjRw9XWCFZiMBhgMBju/p25XbMEp7SFdsAex4rbt5EUFobU/z7rrscqEJuvuXIzjEaj6tq1q3rggQcyvD9jxgy1evVqdeDAAbVgwQJVunRp1aNHj2z3ExsbqwDc8xo7dqz9jR4/Xilvb6V27bL/voW8ceuWUqVLK9WunVJGo6utcQ1xcUpVqaJUgwZKJSe72hrBBsaOHZtlexYbG5vrZ53aFtoBexxrLKC6eMCxCsTWa65TSqk8CCe7M2jQIPzxxx/YtGkTypQpk+1269atQ7t27XDq1ClUqlTpnv/r9XqEhYUhOjoaoaGhd9+3uxI/cQKoW5cLNE6aZL/9CvZj7VqgfXtgyhTg9dddbY3zee454OefgT17JGXaQ8lqhBoVFYXY2NgM7VtWOK0ttBP2ONZYAF7r18PUsCEA9z1Wgdh6zd1qCmnIkCFYuXIlNm7cmKN4AYCmTZsCQLYCRiM0NDTXm95mlAJeegkoXVqWCnBn2rWjwHzrLaBDB65gXVCYN8/8EvHisdijA3ZoW2hH7CU2gitVAjzgeAXbr7lbZCEppTBkyBAsW7YM69atQwULir/t27cPAFCyZEkHW5cDc+cC//wDTJ8O5BKLI7iYCROY3v7ss0x3LwicOAG88grQty+PWxAKEuHhrrZAcDBuIWAGDx6MBQsWYOHChQgJCcHVq1dx9epVJCUlAQBOnz6Njz76CLt378a5c+ewYsUK9O3bF61atULdunVdY/SNG8DIkQwUlRV83Z/AQKa3HzjAhR/zOwYDF2gsXZpVdwWhIOHjw0q8Qr7GLQTMtGnTEBsbi9atW6NkyZJ3X4sXLwYA+Pn54e+//0aHDh1QvXp1jBgxAj179sRvv/3mOqNHjuSKp5Mnu84GwToaNQLeew/4+GNg2zZXW+NYRo1iVehFi4DgYFdbIwjOJSREFictALhFDExuccRRUVHYsGGDk6yxgA0bOH307bdA8eKutkawhtGjgZUrOaWyb1/+XMRwxQrgf//jq0EDV1sjCM5HYl8KBG7hgfEoDAbg5ZeB++9nVVPBs/Dx4VTS5cv0ouU3LlwA+vXjOlxDhrjaGkFwDSEhrrZAcAIiYKzl00+BU6cYuCsLhXkmVasCn3/Oa/j77662xn6kprLicEgIMGuWuNCFgosImAKB9MDWcOoUMG4cV5uuU8fV1gh54aWXgC5dWEH52jVXW2MfxowBduxg3EumKtaCUKCQuK8CgQgYS1GKJelLlgTefdfV1gh5Raejl0Jb4NC96jlaz8qVwCefMF28eXNXWyMIrkViYAoEImAs5YcfgL//Zkpqfgz8LIiUKAHMnMnO/5tvXG2N7Zw7x1ovjzxC76AgFHRkCqlAIALGEm7cAIYNA3r14rSDkH/o1g0YPJgd/8GDrrbGepKTgccfBwoXBubMkbgXQQBEwBQQRMBYwogRgNEIfPmlqy0RHMGnnzKw96mngMREV1tjHUOGAIcOAT/9BBQp4mprBME9EAFTIBABkxurV3Mdmc8/55SDkP8IDAQWL+ZUzNChrrbGcmbM4BTYtGnAf4vWCYIAiYEpIIiAyQm9HnjxRS4V0K+fq60RHEmNGoxvmjWLRQrdnU2bKLZeeQXo39/V1giCe3H8OPDvv8D580BamqutERyEW1TidVuGDwdu3wa++05iC9KjFAv6JSQwBiMlhS+jkcsrXL3KmiRRUayV4+3NAnK+voCfH+DvT6+Hv797ndd+/YCNG5ltVr8+UK+eqy3KmvPngZ49WUzxiy9cbY0g5A2tPUlKytiepKWxPYmLs36fs2fzBbANKl0aKFs2+1dYmHu1RYJFiIDJjpUr6Z7/9lugXDlXW+NclAIuXgT27uV6OqdOsdO8cgW4fp2izh6jGi8v1msIDWUDUrQoEBHBqbqSJYEyZXjuK1akGPL2zvt35sbUqTzuxx4Ddu50v3oqej0Dj4OCGPfi6+tqiwSBJCSwfbh+nYkP2uvmTbYZt27x5507vI/1eoqT+HgKFXty8SIQG8vK1Jlf27bx/6mp5u1DQihkKlUCKlfO+IqK4gBMcDvkqmTFtWusDdKlC/D88662xjno9cCqVXytX88HHGB2S5UqFBK1anHtpyJFKDoKFeKKr35+7Ei9vSlKLl6kwClfnmIoLY3emdRUjqySkznaSkxkA6bXs1G7dYuN35kzLPWfvsCcvz8DbWvVonekYUOgcWP7B64GBgK//MJ9P/00K/W6S+OVmspMuPPngS1bgPBwV1sk5Ec0r8edOxQcmvi4eTOjMNHEivZKSrp3X0FBQLFiHAgULcrntXRpDlhCQvgKDuZ2QUF8zgMC2J74+LBNSUwE2re37hhCQvg9NWtm/X+jEYiJAaKjzcLm3Dng9Gngt9+As2fNgzRfX6BCBbOg0UROs2buN8ApYLhJy+xGmEyMKVCKHpj87FY0mYC1azlFtmIF3bj16rGTbNGCnXjp0q47BwYDG5bTp4ETJ4CjR5nq/PvvZrdyzZpAy5ZA69ZAu3b04OSVChWAJUuAjh2ZgeYO2WdKsXrw338zsLxWLVdblDNpaewcoqPpubt6lR3gnTtm0ZqUZJ420MRtaio7F+2llHl0fu0aO7RixXhP6nQUzNpPb2/zS+v8tKnL9PewJrR9fc0dZqFC7EiLFOH+ixc3TzuUKeMc75+jqVHDfL60F8BzrA0wkpLoEcmqsKNOxw47PJzPWXg4BxIREeZX8eJ8X3sFBubdbr0+7/vIjLc3UKoUX02b3vv/tDS2PadOmV///MMBnkaPHhzsCC5DBExmPv/c7ImIjHS1NY4hNRVYsIDpw0ePArVrc4mEJ59kg+0u+PvT+1OlCtCpk/l9kwk4eRLYvh3YvJmrg8+Ywf81akTP2SOP8HdbxVe7dsBXXzFItnJl12YnKQW89Rbn9OfPp22uIjER2LWL02xHj3LUevkyR+hxcWYxYkll4/QiJLtX+o5WEzRJSbwHlDJ3vtoru+/296dI0YRKoUIc8SvFY4qONnsCb97kcWj4+fEerF2bHXbTpsB99/HznkRSEs+lds7S0ni+0k+lAPSOlC0LVKvG433wQaB6db6fH4ScJfj4cOq6RAmWKfj3X97zPj5AmzZA9+7AE0+42soCjwiY9GzcCIweDYwaBXTu7Gpr7I/JBPz4I/Dee5ymefRRdvwtWniWp8nLi41rtWqsQAuwE9W8E199BXz0EUfOPXuyoWne3PrFNwcN4sjrtdcYk/P44/Y/FksYN47LBEyZAvTp4/jvM5mAAweAv/7i2krHj/P86vX3xj7pdOzgCxUyTxMUK2aOZYqMzPhTG6H7+TnO/sRE83TktWu0/eJFipSzZ+nRO37c7NkpWZLTkm3bUpg0bUrPwaVLnK47dQo4dszs/YuPp/emeXN66bp1o7hx92fo3Lms04sNBnrItPNz/Dg77e3bGWcF0HvTqRM77gceyP9CJjmZbeOECRS03buzmGmXLpxWF9wDlU+JjY1VAFRsbKxlHzh/XqmICKVat1YqNdWxxrmCLVuUatyYY9ZHH1Vq/35XW+Q4UlKUWrdOqSFDlCpZksdctqxSb76p1MGD1u3LaFTq6aeV8vNTavVqx9ibE+PH0/6PPnLM/lNTlfr9d6VeflmpRo2UKlxYKZ1O823w5e+vVKlSSt13n1LPPKPUxx8rtWqVUtevO8YmZ5CczHth0SKl3n5bqS5dlCpRwnzMlSsr1a+fUnPmKHXhgvlzaWlK7dun1P/+p9QjjyhVqBC3r1JFqXffVer4caeYb037ZnVbmJ5Ll3iOBg40P0slSyo1cqRSR47YYLn1OO1YNW7e5PX39laqf3+lzpyxfV+CTVh6HUXAcGOl6tRRqnx5z26Us+L6dT6EgFINGyq1caOrLXIuRqNSGzYo9dJLShUpwvPQoIFSX3yh1LVrlu3DYFDq4YeVCghQas0ax9qrYTIp9c47tPeDD+y333PnKIZatlSqaNGMQsXHh0KlTRulRo2iYEtKst93uzsmE8/PokUUv3XrmsVc1apKDR1KsZeYaP5McjLf69dPqdBQbtuihVLz5jn03Dm9U1eKz9LmzTwPxYrxWFu3VurXX/k/B+H0Y+3Vi0L+8GHb9yHkCREwlt7ISUlKtW2rVFiYUocOOcU2p2AyKTV7NhuawoWVmjaNo8eCjMGg1LJlSnXvrpSvL189eii1YgW9NjmRlKRU5870Rvz2m2PtTEnhiBdQ6tNP87avQ4eUGj6cnXFAgFms6HT0OLRvr9SECTLKzI4bN5RaupQCuFw5nrvAQAraGTOUunzZvG1iolI//sj2BFAqPJzenYsX7W6WSwRMepKTeaz3328WeLNm5f4c2YBTj/XaNR7P1Km2fV6wCyJgLDkBSUlKde3Khj0/eSZOnjQ3or17KxUT42qL3I/r15X68kul6tXjeSpRgh39gQPZfyY5mYLH25udl6PsatOG4mrePOs/f+qUUm+8QY+iv39G70qVKvTGrV7t0BFzvsVk4rTJJ58o1aqVUl5eFILNmys1aRKfO43jx5V67TWlQkJ47vv04dSTnXC5gEnP1q0cFABKVajAgZMdp+Gdeqzx8TyOuXNt+7xgF0TA5HYC9HqOPgMClPrzT+ca5yiSk5UaN44dV/nyronZ8ET27mVnEx7Oxqt+faU++yzrkXNamlKDB3O7wYN5zu3Fxo1KlSnDWKx//rHsM+fPM/aiUSN6BtILlqpVlRo0SKmdO+1no2Dmxg12dN27m899nTpKjR3LGDOTidPTkyczBgtQ6qGHlPrrL/4vD7iVgNHYv1+pxx7jcVarptRPP+X5OJVy8rGaTHz+77/fLrYLtiECJqcTcOKEUrVrc9rI0o7C3fnrL6WqV6d3YNQopRISXG2R52EwKLV8uVKPP04RqNNxpP3VV/eKmenTGdjboEHOXhtLiItTasQIfl+LFhmDRjNz7JhSo0dTsAQFmQWLt7dSFSsq9fzzDNgWnEt8vFI//0yPZ1gYr0mlSryumzZR6P7wA+8XgJ6/uXNtFsBuKWA0du1SqkMHHud99zGgPg84/Vj//pu2P/lkzs+i4DBEwGR1AtLSGAsSHEx3en4I0jp8WKlu3fjAtWyZ985UILdv0xXeqRO9GQBFw3vvMZAxJUWpPXuUqlmT/x8xgtkL1mAwKPXddwycDQjgNET6OKW4OKUWLGAWVJUqFEzpBUv58kr17avU2rUyJeROGAxK/fGHUi++aM5siohgoO/SpRTJXbqYpy7ffVep6GirvsKtBYzGunUUMAAFza5dNu3GJcc6fz6vTVCQUmPGcIB44YI8Z07C0uuoU8qSilOeh16vR1hYGGJjYxHq78+KiZMmAfv3c5mAKVNYbtpTOXQImDiRdV3KluXvTz7p/rUoPJE7d1j/Y8UKYM0allYvVIh1QBo3Zv2MZctYZ6Z/f+C551gALKtroRTXl1qyhJWeL19mPZ7OnVmbZ/9+Vh2+ciVjMbXAQBbWatUK6N2btTgE98dk4to7K1awRP2RIyyG1rw5q15fvMj6RYmJvAeeew54+OFcK9hmaN+yqu1i47Z2Rym2vWPGsJZOjx7A2LFWLZTqsmPV61lP6uuvzc9ioUKsP1W9esZXlSqs6CzYBUuvY/4XMCEhCE1IYEMSFsb1eYoVYyPi52deHTkggI2GVqlTW2RQW2iwWDFzga6gIOuLotnnoNgQzp4NrFvHQm1vvcX1mvz9nW9PfiQ1lYXK4uO5OF1iIn9qZe8TE1mB9tAhVgM+d47bAhlLs3t78z4xmfiZsDAWgUtKYhVUgPdQVovYFSrEa1u3LteAeewxWfcov3DuHIst/vkn1xyLjeWzGxXF++jqVd43XbqwQF67dlzSIBMeI2A00tKAH34APvyQQv3hh4E33uAyILkMulx+rEYjCxoeO3bv6/p1bqPTsW+ZPh3o0ME+31uAEQGjnQAAoVpJcsBcZlxzxueF9Ouv+Pqa11bRBJEmirRXUFDGvwMCuK2vL4WUtp+bNzlSq1OHyv/8ef598CAbgkaNgGeeYbn84GDuIzCQPwuyB8ZkosjTFqDTVr5N/3tsrPmnXm/+GRfHl8Fg+fdp106rSqqt25OayuuUfh0fwHy/BASwmmfRoqxQW7Eir3XLllzbyRXiWHA+RiOwbx8rgG/dSk9NdDT/p5X8B8wrJVeowJ8lSkDv54ew0aM9R8BopKbSazxpEtu0+vWBF17g+mvFimX5EZcLmKxISWHbcfYsvd/LlnHB2xUrOPAQ8oQIGGtv5JQUc4d2+7b5d62zy9zRxcVxZJ5+dJ6czP1kXpTOZLL/cvHZoQmloCDz2i/pvUraCrDpV4INCaGnKf022u+FCjm27LuGUhQP8fEZz3Pm65BekGQWKnfuZH+eQ0MpGrSX5lkLC+Pvmc9L+nOXXnxqwjTzAoGCYA+uX+c04uHD/Llnj3mdpnTLOOgBhAHu1albg8nE6dhvvuH0rE7HNYY6d+Y0ae3ad73KdhUwSpnbmPTtela/5/S/9AMdLy8uN/Lhh2wzhTxj6TWXtZA0/PzMK6g6krS0jFMUmvjRFlVLS+M6LqdO0dNSqhSnu0wm/k9bsE4TSgYDX5qA0qY5NHGlveLj6dnRpjw0cRAfb57SyA4fn4yduOZhyuw90lb5PXuWtlWrZhZv2sJx6W3ObG9OIs/bm2KjSBEKEO1nhQr8vUgRejS0/xUtat6mIC1CJ3g2ERGcNmzf/t7/paZS4Fy6xFePHs63z154eXEdqY4dgZgYYOlSei/efpvtgrc3p2RKlrQttmTgQLYrmQWIXp9zO5M+bEB7RURwQVdtsJN+4KOFJVSoYOuZEPKACBhn4+Nj9gK4A0qxwdA8Hpq40QSW9kpMNIuN5GQKkOy8TZrgCggwryrs45P1NJsmjDRvR2avkNZYBAWJx0Mo2Pj6ckBTqhQHB/mFEiWAIUP4Sk7mqs/793PRzZgYelit5epVTklVqJBRjGQlQNJ7YWX61qMQAVPQ0enMMTnFi7vaGkEQCjIBAczQat7c/J5eT5FhDb//nvXK20K+QuSmIAiCIAgeh1sImAkTJuC+++5DSEgIihcvju7du+P48eMZtklOTsbgwYNRrFgxBAcHo2fPnoiJiXGRxYIgCIIguBK3EDAbNmzA4MGDsW3bNqxZswapqano0KEDEhIS7m4zbNgw/Pbbb1i6dCk2bNiAy5cv47HHHnOh1YIgCIIguAq3iIFZvXp1hr/nzJmD4sWLY/fu3WjVqhViY2Mxc+ZMLFy4EG3btgUAzJ49GzVq1MC2bdvQrFkzV5gtCIIgCIKLcAsBk5nY2FgAQNGiRQEAu3fvRmpqKtqnSy2sXr06ypYti61bt+YoYPR6fYa//f394S9VawVB8CAMBgMM6WqPZG7XLMFT2sKCdKwCsfWau8UUUnpMJhNef/11PPDAA6hduzYA4OrVq/Dz80PhTKnHJUqUwNWrV3PcX1RUFMLCwu6+JkyY4CjTBUEQHMKECRMytGNRUVFW78NT2sKCdKwCsfWau50HZvDgwTh06BA2bdpkl/1FR0dnqOQnKlwQBE9j9OjRGD58+N2/9Xq91R27p7SFBelYBWLrNXcrATNkyBCsXLkSGzduRJkyZe6+HxkZiZSUFNy5cyeDFyYmJgaRkZE57jM0NNR9ymcLgiDYgD2mQDylLSxIxyoQW6+5W0whKaUwZMgQLFu2DOvWrUOFTGWZGzVqBF9fX6xdu/bue8ePH8eFCxfQPH3BI0EQBEEQCgRu4YEZPHgwFi5ciOXLlyMkJORuXEtYWBgCAwMRFhaGgQMHYvjw4ShatChCQ0MxdOhQNG/eXDKQBEEQBKEA4hYCZtq0aQCA1q1bZ3h/9uzZ6NevHwBgypQp8PLyQs+ePWEwGNCxY0d88803TrZUEARBEAR3wC0EjFIq120CAgIwdepUTJ061QkWCYIgCILgzrhFDIwgCIIgCII1iIARBEEQBMHjEAEjCIIgCILHIQJGEARBEASPQwSMIAiCIAgehwgYQRAEQRA8DhEwgiAIgiB4HCJgBEEQBEHwOETACIIgCILgcYiAEQRBEATB4xABIwiCIAiCxyECRhAEQRAEj0MEjCAIgiAIHocIGEEQBEEQPA4RMIIgCIIgeBwiYARBEARB8DhEwAiCIAiC4HGIgBEEQRAEweMQASMIgiAIgsdhk4A5c+aMve0QBEEQBEGwGJsETOXKldGmTRssWLAAycnJ9rZJEARBEAQhR2wSMHv27EHdunUxfPhwREZG4qWXXsKOHTvsbZsgCIIgCEKW2CRg6tevjy+//BKXL1/GrFmzcOXKFbRo0QK1a9fG5MmTcf36dXvbKQiCIAiCcJc8BfH6+Pjgsccew9KlSzFp0iScOnUKI0eORFRUFPr27YsrV67Yy05BEARBEIS75EnA7Nq1C6+88gpKliyJyZMnY+TIkTh9+jTWrFmDy5cv49FHH7WXnYIgCIIgCHfxseVDkydPxuzZs3H8+HF06dIF8+bNQ5cuXeDlRT1UoUIFzJkzB+XLl7enrYIgCIIgCABsFDDTpk3DgAED0K9fP5QsWTLLbYoXL46ZM2fmyThBEARBEISssEnAnDx5Mtdt/Pz88Nxzz9mye0EQBEEQhByxKQZm9uzZWLp06T3vL126FHPnzs2zUYIgCIIgCDlhk4CZMGECwsPD73m/ePHi+Pjjj/NslCAIgiAIQk7YJGAuXLiAChUq3PN+uXLlcOHCBZsM2bhxI7p164ZSpUpBp9Ph119/zfD/fv36QafTZXh16tTJpu8SBEEQBMGzsUnAFC9eHAcOHLjn/f3796NYsWI2GZKQkIB69eph6tSp2W7TqVMnXLly5e7rxx9/tOm7BEEQBEHwbGwK4n366afx6quvIiQkBK1atQIAbNiwAa+99hqeeuopmwzp3LkzOnfunOM2/v7+iIyMtGq/er3+nn34+/tbbZ8gCIKrMBgMMBgMd//O3K5Zgqe0hQXpWAVi6zW3yQPz0UcfoWnTpmjXrh0CAwMRGBiIDh06oG3btg6Ngfnnn39QvHhxVKtWDYMGDcLNmzdz/UxUVBTCwsLuviZMmOAw+wRBEBzBhAkTMrRjUVFRVu/DU9rCgnSsArH1muuUUsrWLz1x4gT279+PwMBA1KlTB+XKlbN1VxmN0umwbNkydO/e/e57ixYtQlBQECpUqIDTp0/j7bffRnBwMLZu3Qpvb+979qHX6xEWFobo6GiEhobefV+UuCAInkZWI9SoqCjExsZmaN+ywtPawoJ0rAKx9ZrbNIWkUbVqVVStWjUvu7CY9FNTderUQd26dVGpUiX8888/aNeuXbafCw0NzfWmFwRBcGfs0QF7SltYkI5VILZec5sEjNFoxJw5c7B27Vpcu3YNJpMpw//XrVtny26tomLFiggPD8epU6dyFDCCIAiCIOQ/bBIwr732GubMmYOuXbuidu3a0Ol09rYrVy5evIibN29mu5SBIAiCIAj5F5sEzKJFi7BkyRJ06dLFbobEx8fj1KlTd/8+e/Ys9u3bh6JFi6Jo0aL44IMP0LNnT0RGRuL06dMYNWoUKleujI4dO9rNBkEQBEEQPAObBIyfnx8qV65sV0N27dqFNm3a3P17+PDhAIDnnnsO06ZNw4EDBzB37lzcuXMHpUqVQocOHfDRRx9JYJYgCIIgFEBsEjAjRozAl19+ia+//tpu00etW7dGTglRf/75p12+RxAEQRAEz8cmAbNp0yasX78ef/zxB2rVqgVfX98M///ll1/sYpwgCIIgCEJW2CRgChcujB49etjbFkEQBEEQBIuwScDMnj3b3nYIgiAIgiBYjE1LCQBAWloa/v77b8yYMQNxcXEAgMuXLyM+Pt5uxgmCIAiCIGSFTR6Y8+fPo1OnTrhw4QIMBgMeeughhISEYNKkSTAYDJg+fbq97RQEQRAEQbiLTR6Y1157DY0bN8bt27cRGBh49/0ePXpg7dq1djNOEARBEAQhK2zywPz777/YsmUL/Pz8Mrxfvnx5XLp0yS6GCYIgCIIgZIdNHhiTyQSj0XjP+xcvXkRISEiejRIEQRAEQcgJmwRMhw4d8MUXX9z9W6fTIT4+HmPHjrXr8gKCIAiCIAhZYdMU0ueff46OHTuiZs2aSE5OxjPPPIOTJ08iPDwcP/74o71tLBhcvw789huwZQuwdy9w7BhQvToQEgIEBwNFigAREUCpUkBUFFCxIlClClC4sKstB9LSgBs3gNu3gYQE4NQpIDWV9hcqRNvDw4FMBQ8FQcgCpfg8HT8OnDkDREcDV67wvTt3+IwlJbnaSsehFHDrFnDzJqDXs12xlr/+Arp1A2SpmXyNTQKmTJky2L9/PxYtWoQDBw4gPj4eAwcORO/evTME9QoWcPw48NFHwOLFgNEI1K1LkZKUBNSowYc5Ph44dw7YuRO4dIl/a5QoAdSsCdSpw8/WrQvUrg3Y+zoYjRQmhw4Bhw8DJ07w7/PngZgY2pkTOh0QGQlUqkR7GzQAmjWj3d7e9rVVEDwFpYALF4Bt2/h879kDHDxIsaJRrBjbhIgIDliKF+fztHu3y8y2GzdvAlu38tgPHODA7dw5IDk5b/t94gkO/Nq3B7p0ATp3BsqUsYvJgvugUzktQOTB6PV6hIWFITY2FqGhoa42514MBuCDD4BPPwVKlgSGDQP69GEjlRu3b3NkdvIkcPQoBcXBgxQUJhPg5QVUq0ZxULs2hVDVqvTaBAfnvO/UVDaoJ08CR45QsBw8yJ9ao1KsGL0rVaoA5csDpUuzUS1alB6XO3coeMLDOVq8fRu4epUjyZMnua+jR+m5KVoU6NAB6NEDePhhICgor2dWENwXpYDTp4F164D164F//+WgBADKlQMaNgTq1QNq1eIzXLEin6lMWNO+uVVbmJYGbNoE/P47vSQHDvD98HCgfn22VZUqUWxERAAhIdCnpCCsWTPrjnXzZoRu2ACsWkWvtsnEtrBdO6BNG+DBB93Dey1kiaX3rE0CZt68eTn+v2/fvtbu0u641UObmXPngJ49KQzefRd44w0gICDv+01MpDjYv58vTXjcumXeJiyMXpuwMHppvLwoWvR6TmNdu8aHHeD/M3t36tShWLGHrTt2sCFfuZLTZsHBwDPPAEOG8HsEIT8QGwusXQusXs1O+/x5eh0bNQJatQJatqQ30ornyqMEjMkEbNwILFwI/PILvS6RkUDHjhQULVpwIJTNwsB5PtZbt4A1a4A//6RoPHeO7V6DBhQzDz3El50WJhbyjkMFTJEiRTL8nZqaisTERPj5+SEoKAi30neYLsLlD2127NoFdO3KUdXPP/MhcjQ3bnDK59w54OJFipQ7dzhNpRTg50fxEB5Ob0r58kDlyhwRetlcrNk6Tp8G5s0DZs7kiLRzZ3qo7rvPOd8vCPZCKXoYf/+dr82b6XmoVo3exoceogcgD+2SRwiY8+eBWbOAOXPo1a1QAXjySQ7eGjWyuG2x+7GePUshs349Rc3162yXGzWy8gAFR2HxNVd24sSJE6pdu3Zq9erV9tplnoiNjVUAVGxsrKtNMbNpk1IhIUo1a6bUtWuutsY9SUlRav58pWrUUApQqlcvpS5ccLVVgpAzyclKrV6t1JAhSpUvz3s3MFCphx9W6ptvlDp71q5fZ0375tS20GhUatUqpbp2VUqnUyo4WKnnn2fbZzLZtEuHHavRqFTTpko1aKBUWppNtgmOwdLraFMQb1ZUqVIFEydORJ8+fXDs2DF77Tb/sHs3vQoNG3LKJLdYlIKKry9jgZ5+mh6Zt9/mvPjHH3NqyVkeIUHIjStXGGOxciWnKBISgLJlGcv18MNA69b2D6Z3V+Li6Gn53/8Yi9egATBjBp9jd23rfvwR2L4d+OcfSSTwUOwmYADAx8cHly9ftucu8wfnzjESvkYNES+W4u0N9O9Pd/PbbwOvvQYsWwYsWMBpLkFwNkYjpxpWreLU0O7dFNTNmgHvvEPRUrt2wYqlOH+eouX77xnX9vjjwNy5QPPm7n0ejEa2K489xuk8wSOxScCsWLEiw99KKVy5cgVff/01HnjgAbsYlm+Ij2c9guBgES+2EBoKfP01hcyzzzJT4ccfmR4pCI7m+nUG3v7xB+MlbtxgXaMOHSiqO3dm7FhBQimmPk+ZwqDcsDBg0CB6SD0lVXntWsbl/PSTqy0R8oBNAqZ79+4Z/tbpdIiIiEDbtm3x+eef28Ou/IFSwPPP0wOzfbtlKdJC1rRpA+zbB/TuzeyFL74Ahg51tVVCfiMlhZ3zX3/xtXs3n+P69YEXX6RgadYM8LGr89ozSE4Gli4FvvqKdVuqVOHvzz2XZaq326IUy1fUqgU0buxqa4Q8YNNTaNLSbIWcmTGDBeqWLGE6spA3wsPpvh81Cnj1VWYTfPaZxMUItmMysRbJunUclW/YwFiWYsXoZRkyhD9LlnS1pa7j1Cngu++YUXTjBrOoVq6kmPPEZ+/334G//2blc3ee5hJypQAOI5zEkSMsTjdoEKtCCvbB2xv4/HMW+Bo6lDUlZs4smCNiwXrS0ujJ27SJYmXjRtYJCQgAHngAGDOGHXSDBp7ZOduL27dZ5mH+fJ6jwoXpaRk0iOngnkpqKjByJOvPdO3qamuEPGJTqz98+HCLt508ebItX+HZpKYCffuynspnn7namvzJ4MEcJffpw/M9f75kEggZUYpeuj17OOWxYwd/JiRwjZxmzehhadOGv9ujmKQnc/48i+0tX04PRVoaO/oFCxjs6ukZVdeuAS+8wJpYixeL9yUfYJOA2bt3L/bu3YvU1FRU+0+NnzhxAt7e3mjYsOHd7XQF9Qb59FNWlt22TUrjO5KnnmLada9eLMY3a1bBHjW7Er2ey0ScP29eeFCvZ7HElBSKCW9vXq/AQMZMhIRwZF+4MANj079CQy27lkYjO6ZLl/jdp0+zgzpyhEts6PXcrkwZoEkTYOxYeloaNSq4C/2ZTFy/7MQJVuveuZMeqTNneI1atODA6/HHuQZTfuDvv5nSfeMGg699fem9LVJE2gwPxiYB061bN4SEhGDu3Ll3q/Levn0b/fv3R8uWLTFixAi7GulRnDgBfPgh3ZSeXkU2JYWVe69e5cMeG8vRa3IyvR5paWwMTSau7XT5Mrfbv5+fbdSIgctVqrDzqFOHcSz2FLY9e7JeTJ8+bIwmT5aRlaNRih3f2rXs+HbtYkaHhq8vr3NoKAW8nx+vidHI+yIpifdRXJxZYGRF5uuoFQ1P/37mQuI+Plxfq0QJluivXp33Yc2a7Iztff95EmFhPPb050yn4/mqUoVT3c8+y+BWe5GSQtFw7Rozum7dYhXw2FhmaMbHM/06KYltiMHAtsVo5MtkYjuTV9asMS+C++WXfAG8XyIieL+UKMHlHLTfM78XESFT1W6GTUsJlC5dGn/99RdqZbrRDx06hA4dOrhFLRiXlM9WivPnZ8+ygfck74vJxNiAtWuZhXHgAI8jc8C2TkdXu58fH+aUFLOgsYTAQDaQHTuy0NcDD9jHNf3NN5xW+vxzwIopTsEK9u2jWPzpJy7MGRBAYdqkCRcgrFaN06aZRYLJxGJhv/7KaZwzZxhjkVXH5O3N+8rHh797eXFfOh07Pi8vCiOlzJ1bairvQw3t80bjvfelvz+XyKhUiQuc1qhhXuvLg0oc2FRev0gRhALZn7f0+Pry+gYG8qe/P8+pTsfPawIjJYUvbV9GI/dtNOa+Qn1OpLt/9ADClMr7UgJKUTTHxFBQxcSYX5n/jonhtpltKlaMJRx+/NH2YxNyxdL72yY5qdfrcf369Xvev379OuIyX/SCxOLFFACrVnmOeDl2jEWoFi2iGz4oiB3So49y9Fq+PDMwwsM5ggsIYEP19dfApEkc1bRrxznyli3Zifn6mvd/5w5TUTdu5GvXLr727AHGj2cD+dBDHP117257J/LKK+xUR45kgG+mVH/BRtLSmDo7ZQqnGooX57V69FFe7+ziRtasYebK5s2cUtI6M29vjvi1lYfr1ePvDRrwfVtJTaUwOnSI99eWLZzCBbgGz4MPMmU2LY1lDU6fZrzH11+zs9XpaE+zZlxgsU0bVtXNT5w7l/X6S/HxFKf79gHHj3PgcvUqRWZCAr1kmrdVKbOg1MSmry+fW03wBAXx75AQfl9YGF9Fi9JLqk0ZFi5s/l9wcPbeDb2e2+QVnY72hIbS45QbSUkUNgcPAiNG0LuelsYifYJbYJMHpm/fvvj333/x+eefo0mTJgCA7du344033kDLli0xd+5cuxtqLU73wCQksPO+7z5WjHV3Nm8Gxo1jIx4ezniSxx6jR8TPL/vPbdjA2jZnzwIDBgBvvsnRrKWYTOxc5swBfviBHU94OEc8hQoxnmXIENsWuTSZuFjc6tX0IsmK1rZjMlGQv/ce02jbt+d16do1647GZGIg9ddfcwpR83yEhvI6dOjAa2tJBsuJExzh7tjBqalbt+jlu3OHnVDhwuwow8JYlblyZd47vr68d/39+dPLi/fpgQPm4N0GDVjhuXt3jqa9vCjitUDfzZspgpSigO/aFejRg52WG8VK2OSBKVwYof7+ZoGhnatChXguIyP5LDduzEFMTu2AE3H5wpUvvQR8+y2nqb/+2j5iSsgRhy7mmJCQoAYNGqT8/f2Vl5eX8vLyUn5+fmrQoEEqPj7ell3aHacv5vjee0r5+yt1+rRzvs9Wjh9Xqls3LjZXu7ZS8+ZxIbrcSE1V6q23uEBbixZKHT6cd1tu3VJq/HilihVTys9PqQceUKpMGdrWurVSa9ZYvwBcfLxS9eopVbEi9y9Yz65dXOQO4L2ye3f22+7erVT79kr5+nJ7nU6patWUGjNGqStXcv+uO3eU+vxzperW5eKHlA73vnS6rH/XXl5eSgUEKFWkiFJFiypVqJBSPj7Z7097+fsrVbo0F/Tr1EmpAQOUGjVKqUGDlOrSRamICG5XurRSb7xhn/veDti0wGFW58zbO+vzCfAc1q/PaxkT44SjysV+Vy1ceekS74/gYC5KKTgcS69jnlajjo+PV/v371f79+93G+Gi4VQBc/EiG98333T8d9lKcjJFlp8fV8v98UeuxmoJt24p1a4dG7tJkyz/nKXo9bQtMFCpEiWUGjZMqcaN2Yi2bKnU1q3W7e/MGXZk3brZvAJugSQpSamRI9mx1a2r1D//ZL/t998rFRVl7uwqVlRqwgTuIydMJgqkJ5/kyuyZO9SICKVateL9sHt3zvfa+fNKTZ+uVI8eSpUsad6Pnx/Fx6FDFN56vVJXryp16pRS+/bR9mbNuG2JEko99ZRSL7+sVPfuSjVpolRkZEa7tPsyIIB/N2jA5yc11bbzbAds7tQNBj5Po0crVaoUj6dnT6VOnlQqOporSY8bp9SjjypVoUJGEVi6NMVMbtfYzrhcwCjFZwFQ6rvv7LtfIUucImBOnjypVq9erRITE5VSSpny0Fls2LBBPfzww6pkyZIKgFq2bFmG/5tMJvXuu++qyMhIFRAQoNq1a6dOnDiR7f6cKmAGDFAqPJyjSXdk/36l6tRhY/TOO0r9d70s4sIFpWrUoJdk/XqHmXj3u3r0YEPxyCNKLVhAbwqg1DPPcCRkKStX8nOffeYwc/MVhw/TI+fnp9TEidl3zlOmKFW4MM+try87v/Pnc963yUQxMnDgvV6W8HCl+ve3j+cyNpbCJ72wqlpVqd9+y1rI7tqlVJs23O7hh5U6e9b8v6QkpY4cUWr5cp6PPn2Uqlkzo7ciMJCiZ8cOpwtlu3TqKSlKzZxJr2dAgFKffpq1YPzrLwpC7dp5eyvVsSMHCk7A5QImNpaCvl49l4rWgoRDBcyNGzdU27ZtlU6nU15eXur0f41P//791fDhw23ZpVq1apV655131C+//JKlgJk4caIKCwtTv/76q9q/f7965JFHVIUKFVRSNqMBpwmYw4c5cvzf/xz7PbZgMtEuPz8KmH37rPv8qVNKlS1Lj83x446xMSt+/pkj8YgIpVasYCMbEcER+zffWO4BGjmSom3nTsfa6+ksXKhUUBA76AMHst+maFFzx/3GG7k35rdvK/Xll7x/0ouWYsXoAYiLs/uh3GX/fqUefND8neXLU9RmFhomk1I//cROPChIqa++yvn+SkhQasMGpYYMyeipKVKE007r1yuVlua44/oPu3bq8fFKDR9OcfbQQ0pdv579zhYupCjUjrt5c7YTDsSlAsZgoPc5LCz7Z0OwOw4VMM8++6zq2LGjio6OVsHBwXcFzOrVq1XNmjVt2WVGozIJGJPJpCIjI9Wnn3569707d+4of39/9eOPP2a5D6cJmB496Go1GBz7Pdai1yv1+ONsZF591bI4l/ScPcuRbJUqdC07m5gYpbp2pf1vvKHUtWtKPf88/27b1jKbDAalGjXiMbjZFKdbYDQq9fbbPKd9+rBzzsyxY4xp0aZmRo3KXUCePKnUK69w+/RxK23aKHXwoGOOJTvOnOH9otnRsCE9L5nR62kzwE788mXL9n/woNmLo023lCpF8XzokH2PJR0O6dTXrKFHrHx5ep9y4uBBTrdp57VDB6Vu3rTiCCzHpQLmjz94fH/9ZZ/9CRbhUAFTokQJte+/0Xx6AXP69GlVqFAhW3aZ0ahMAub06dMKgNq7d2+G7Vq1aqVeffXVLPehnYDo6GgVGxt795VsbUeeEzt38uaeO9d++7QHx44pVb06PRY//WT952NilKpcmXENFy/a3z5LMZk4BeTtzaDea9fYyJYuzRHvr7/mvo9jxziyHjTI8fZ6EgYDp+V0OqU++eRez4TRSMGo0/H1xBO5xz4cPKhUr17c3svL3Kn37083vCvZsoUDDa3DHTBAqRs37t3ur78YTxMRYV2ntXevWciUL8/7U/NQzJuX57iR5OTkDO1YdHS01Z26RW3huXNK1apFb9v27bkbdugQp1e0qaXhw/McI+e0Y7WEuXN5bPbsN4R7sPWa2yRggoOD78afpBcwO3fuVEWLFrVllxmNyiRgNm/erACoy5lGRU888YR68skns9yHdiNnfo0dOzbP9t2lc2cKBSe4jC1m1SqlQkNp17Fj1n8+IUGp++6je9xJc9y5smGDUsWLs2M4eJBBxVqsjCVTGVOnyigqPQkJvHf9/JRasuTe/+/da86+KV8+d0/C6dNmMaR5IXx8GBjrTp7JlBSlPvjAnH1TuDDjrDKLt2vX6FHQ6RiYbGl8S/rpqMBApZ57jhlaAO/f996zOZtn7NixWbZn1nTqFreFt25ReIWGKrVtm2UGrl7NY9SmCNeutfzgMuHUY82NDz6gV0pwKLZec5sETOfOndWYMWOUUhQwZ86cUUajUT3xxBOqZ8+etuwyo1F2FDAO88Bs3cqHNZspLKejeSu8vBiQaMuI12jktFNQUNZudldy/jyD6EJClPrzTx7v55+zI3roIcZbZIfJxHnsqCjXewJcTUICp1SCgujNysy775o9KO++m/O+7tzhVImvb8bpoqeeyno6yl3Ys0epSpXMYqtbt3tTvtPSmHEDKPX009Z5UPR6pYYO5Xm8/34K58GDec4DAjhVlT5g2AKc7pXQ61nWoHBhxhNZyrvv8pkEOAVsg4B1Kw/Mo48ylkpwKE71wBw8eFAVL15cderUSfn5+anHH39c1ahRQ5UoUUKdskNAlz2nkBwWA9OpE7Nz3MH7kpxMNz3AVG5bbfroI+4jUwC126DX03Pg46PU/Pl8b+1auuurV8/ZY3T2LOtavPKKU0x1S5KSKPYKFVJq48Z7/6fFNBQvnnMMhMlEz0WJEhQv2nRRkyauiZeyhbg4ChOAwiI8nBlHmVmyhKLj/vtzDm7Nio0bOQ1bqJBSs2dzyurDD/ldPj6cxrKxvXRKXEhsLGOGSpbMPdMsPTEx/Bz+qyXz++/Wfe89ZrgoBubWLQrzzz/P+74Eq3B4GvWdO3fUuHHj1BNPPKE6d+6s3nnnnXs8JLaSXRDvZ+lSYmNjY10XxKvFvixcaP99W8uVK2xc/f3Nnbot/PEHR4zvv28/2xxBairTcQGlvviC7x0/zhF1iRKc/siO//2PnyuIxahSUzmaDAi4Nx3+xAlzzMbDD+ccw3D2LKdXtGwkLQNn1SoHGu8gTCalJk/mfV+iBI9l6NB74x22b+eUWtWqjBGxBr3ePLh45hn+HR/PTrFECXorBg602iPjtE796lVOI9apQ9utYcYMc4HDxx6zOQXZZQJmzRra7swMTEEp5UABk5KSotq2bZtjDRZbiIuLU3v37lV79+5VANTkyZPV3r171fn/lP/EiRNV4cKF1fLly9WBAwfUo48+6ro06u7dmdniau/L1q0MaC1Z0vpib+mJjua8dZcu9i9S5whMJmbDAPQaKcW4hcaNOW+fnUBJS2OF2Zo13Ss2w9GYTEq98AJH/ZlHw6tW8X2djjVectrHjBmsRhocbM4sGjTIM+6ZnPjjDx5TmTIccd93370eh5MnGQRcunTuGTpZsXAhv6NaNXM134QECpnixdnRv/KKxbWOnNqpHz7Mqdvu3a2/1tevm4N8ixXLeYCRDS4TMMuW0e5r1/K+L8EqHOqBCQ8Pt7uAWb9+fZZBPM8995xSylzIrkSJEsrf31+1a9dOHc9BGTtMwBw6xJt61iz77tcaTCZ2Nr6+9L5YU+AtM2lprHxapkzWWRnujDblNWYMz4lez2yloCCl1q3L+jP793PU+/HHzrXVlYwfz/M0e3bG96dOpQjx82OgdHZcu2ZefkLzupQrZ1uQuLuybx8HAqVLMw06PPxeT9Xlyyz2FxFhXVyIxvHjzPApVEippUvN78fHs1he0aL0kA0blmuwr9M79RUreN1tfW7GjeNUo06Xe2xVJlwmYH74gcfszvFc+RSHCpjXX39dvenOZfOVAwVMnz4MBnXVCP7cObML//XX826H1rBkjonwFD75xCxilGJj06EDO9rsKgePGMH/Wzsd4IksXcrzkzkL4913+X5YWM7n4Z9/2KFrwkWno/crP3L2LD2rJUrQU+fjo9S0aRm3uXGD8R1Fi9rkTVDx8Uw11+7Z9B6NO3d4nUJDKcJHjMi2Ho1LOvV33mFbkZPYzYkjR8yZSnXr5hx4nw6XCZhvv6Wtnu5h9EAcKmCGDBmiQkNDVaNGjdSLL76ohg0bluHlDjhEwJw7x9H7l1/ab5+Wcvs2G7fAQI4S//gj7/vcudO8vIAno4mYDz/k34mJ5mDVzZvv3V6v5zns3t25djqbvXt5v/TqlTEV+NVXeb5Klsy+EzGZeF69vNiZAkytd2BxNrfg6lXGe4SHM5sKoEck/XTxrVucrixWzLbqrCYT07N1OsaGZC6yePMmxU1oKL1jAwYwcyodLunUU1PN3lpbi9ZpmY4AvU1ZBU5nwmUCZsoU3vuC03GIgDl9+rQyGo2qdevW2b7atGmTJ8PthUMEzNChbLQcWdU1JYXu4wMH+Fq0iPUkChVioO6oUdYH02VFYiKzqBo14nd6OuPGsVGcPJl/JyQw/TEsLOuR8o8/cvvVq51opBO5eZPBlw0bZnSBv/wyj7tixexTg+PjWbgOMKfEPv10wRmJ3rjBBRvDw7nkgZcX6w6lX0Ps1i1uExFh+1Ta8uV8rhs2zHoa+M4dTi2VLm32Wrz3nlJ//qli9+1zTaceHc2g7ccfz9v6T0uXsj0DeK/lcG+5TMCMH8/2XnA6DhEwXl5eKibd3OyTTz6prl69apuFDsbuAubGDarx996zz/5MJhZl+/prCpTGjc1rzWR+Va/O7KDMtSrywhtvcHSXn0bUb76ZMT4pNpYCrUSJexcLNJk4mqxePX8IuPQYjUw3L1Ys4/TQkCE8P1WqZJ8REh3NjlmrkeLvb1nF4/zGzZtK1a/PKY9vvqEnq0WLjB6r69cZ01KmjO3TkXv3UqCUKZN9XE1qKmNQnn76brZYrIWFvpRyQFu4eDHvjR9+yNt+rl+nt0sL8N2yJcvNXCZgxoxhuIDgdBwiYHQ6XQYBExIScrcKr7th94f2ww/p8sxLRLrJxAJxr71mXjHX15fipX9/ehFmzuRrzBgWwLKxcmeO7NjBUWV+C2Q1mZR66SUem9bpxsSww65S5d46Hvv2cVstHTu/MH48pyfSe5feeov3W6VK2YuXPXs4raSJl8qVra99kp/QBErp0kr98gsHGHXrZhxIXLrE7KQqVWx/Vi9epGgMCcm9WrTRqNTJkyp2+XLXCRilOL1WpIjla0blxLhxZk/fww/fs8inywTMiBFMnRecjlMETPplBNwNu97ISUkcib38sm2fT01l4a9GjcyxBEOGsKJsere0M0hJ4ainYcP8uTR8Whrd2wEB5nTqU6fo6r///nunTV58kdVGPS0DKzv+/ZeiLH1c02ef8b7LKfj8zz/pZdCK0vXr5xx73Z3Llyn6Kldm8GqpUvw7vcfl1Cl6+Ro1sn2F7bg4c5HGmTNz3dxlnbrGjRtsE3v0sM/+zp83e2N8fJTq2/euIHTZsWqDVkuXUxDshsOmkK6l80Boywi4I3a9kb/7jiNaa1PHjUbWf6hcmQ9mx45K/faba4XD+PEc7WQKCrSYuDieh3//ZZ2EWbO4Xshzz7E68f33M3agSBGup9KxIxujjz5iYag8LmpnEUlJjH8pUkSpo0f53rZtbIx69844dx8Tw2DJoUMdb5ejuXWLIqVFC/M9tmCB2UWfXee6cKFZuHh5uUeBRnfizBl6pho2ZFxahQpKlS2bsYru3r30oHTsaPuUZGoqBTXALLEcYkxcLmCUYpVigN4pe/HLLxSJ2vR5sWIqtkYN1xxrYiKXUyhWzD6eJsFiHOaB6dKli+rRo4fq0aOH8vHxUR06dLj7t/ZyB+x2IxuNDHa1NmNl71524Jpb1FbBYE9OnmRMwxtvZL+N0cjR5erVrFz76qu0v3ZtBsRmFaOT/qW5grWRlE537zZBQXTFjxjhuCqXt2+zYF2FCmbX/qJF/P7x4zNuO3EibfXkipsmE4MhCxc2F2HTvDFBQdnHT339tfm6FClivUgvKOzfT6H70EMUNFWrcmop/flau5ZTws89Z3uAq5ahBFBs26FQp8MEjMnE+kClS9snsSA9GzZwQFS8uIr193fdsb71Fgc+eam1JViNpddRp5RSsJD+/ftbtN3s2bMt3aXD0Ov1CAsLQ2xsLEJDQ23f0apVQNeuwL//Ai1a5L59SgrwwQfApElA9erAV18BbdrY/v32QimgY0fg5Eng0CEgKAi4cgU4cAA4eJDvHT4MHD0KJCbyM35+QMWKQKVKQPnygK8vt9+2jdtUq8Z9PvggUL8+UK4c4O1973cnJgJ79gBbtgBbtwL79wPR0UBaGv8fEgK0bQu8/TbQpIn9jvn8eaBZM9q1fj0QGAi8/z6vz/LlwCOPcLvkZF6r+vWBX3+13/c7k/nzgb59gcWLgSef5LFXrQqYTMC+fUCtWvd+Zvx4YMwY/t6gAa9PQIBTzfYo1q/n/d6nDzBuHNCuHaDXA//8A1Spwm1++IH/f/99YOxY279ryRLgued4XX75BYiMzPBva9o3u7WFWXH+PFCzJvDii8CUKfbd93+47FjXrgW6dAHefBP48MO87UuwCouvo1PklAuwmxJv146lxS0ZUR0/zmA8X1/On7pLdktCArOYAK6H06YN3aLayLtQIS7EN2AA4yV+/51ZO1rtiy1bOBoCONp69928eytMJu63Tx/Gp2i2FC3KFY7v3MnzYSulWOsmMNCcqmk0ct4+JMQ8vaSUearFEwv6XbhA70CfPvw7IYHnUafLfo2iYcPM51ziXSxn/nzzEhZXrjCLrXTpjFluWuXjefPy9l3bt3PqqlQpetPS4RYeGI2JE+l5taU6sQW45FjXraPnpXPne9fGEhyOQwvZeQJ2uZH372dDlM2CkRn4+Wd2ilWrKrV7t+3fmRdu3WKjN2+eUm+/zWmvypUzTuNUrsziWe+/z/nm06ezr8Fw7Ji5hHytWkybdJQoO3mSi91pRdO8vLg2kx1WN1e//GKOK1CK7u4aNdj5aK5vo5FBmM2a5a2+hbMxmZRq355puFqKrxYMmd0qus89Z74fvvrKWZbmHz74gOdu0SLGRlSpwqUVtKk7k4mDAV9fVjLOC5cvK9WyJZ+H0aPvBv27lYAxGLjGU6tWDnl2nH6st28z0aJdO+fE7An3IALGHjdyv34Misyp0zaZzN6NJ57I+1yw0cg59jVrKEZ+/VWpSZPY6dx/P4MHg4M54tHp+NPX15z6qr1KleJ8/euvs4MLCLBcWMXHs6aKjw+LoS1c6LwiZmlpXLOnbFnzsbRsmfd1d7S4Ak2MHjtGwZm+INfatdzmp5/y9l3OZNo02vznn/y7d29z/ERWaILU2zv7pRaEnDGZeH4DAviMXrjA56RyZXOwZ0qKUm3bMq4or97K1FR6fPz8KFQ/+0zF/vuv+wgYpZj+belgz0qcLmBGjmQbGx1t+z6EPCECJq838pUrbDA+/TT7bQwGuu0B1jKwdvSRlMQRcJs2TEnMLEJyegUGcpRQrRqnrRo04EhQW7PGx4fZKEOHcvQ2caJlNq1dy8DXgABOg7lyBLJuHb0k2jF36WJ7MJ3JxGsVEMBpJaXMnhmteq9SnCqrWtV9pv9y4tw5NrQvvMC/p0/n8dSunfX2LVqYi9MVhHWgHElSEr11JUuyjsuZMxw01K5tLrN/6xbv3ypV8p6mbzAwqL5cOdcXssuORx+lwLLz4odOFzD332+ejhVcggiYvN7I777L2JDs1oqJj2fKpJ8fXcnWMH8+M2S0qR2djjEpjRrRI9CrF+Nuhg+nwPntN8aLHDrEDjynBRzT0ljhd+pUTiH5+nL/tWvTk5NdNkpCgrlSa+vWnNJxF9asudtwK29vepVsqZ+TlMRYn1KlzCPlkSMp9rQ1k/bu5ffMmGEv6x2DyUQPW1QUKw7v3UuhGhp6bwdiNHIKEGAmmb0zRgoqV66ww77vPt6PR46whEDTpuaU9VOn+Gw/+KBtC68mJHBNqshIXr8GDZR6/XUV+/rr7idgTp0yx//ZEacLmPLlmX0kuAwRMHm5kRMT2RBlVxskNpaj2eBgeiwswWjkVFNIiLkjbtqUdVQctbK1Fpg6dixFUUAAO+uePTl9oHmM9u9nTEhAAEd57rjmjcmk1Pff85xrKb/Lllm/n0uXKGCaNWNwXkoKR1xlyphHyb17c2Rt55GkXZk1i+fhjz9oZ2goBUzmQMqkJPNaOiVL5s/iha5k1y4+N88+a660HRJCcakFf27cyI59wADLvbQmE2PZSpXiMztwYIZlP9wqBiY9I0dy4GfHuilOP9YyZey3ZIxgEyJg8nIjz5hBr0VWAaSxsez8wsKU2rrV8v1pHW9ICNW9o0SLRmwsR22PP25+7/ZtenRq1DCP5oYO5ZRC3bpKHT7sWJvsQXy8Us8/b/ZetWlj/RpRO3bwmPv1Y0cRHc1Rcpcu5hgkX1/Lp92czZUrFHDPPsu/GzbkuZg+PeN216+zLgzAxRvdUZjmB374ged4yhT+vX49768nnjBn8s2dy20mTcp9fydO0GMD8PnNotq52wqY27f5LA0caLddOv1YS5XiYFNwGSJgbL2RjUbOW2dVkC8ujpUZw8LYCebGsWOc/9ZiVj7+2HmdyLBhzOi5cOHe/5lMXByuZEnaVrgwp5wcLarsyf79LOmuxXTMnGldDJKWDqt1On/8kbGDGTKE5+XWLbubnmeeeIKp5zducMQLMLMsPceOmeOhPCWmx5MZMYJe1XXr+Pevv9IjNmiQ+b4cMybnIHGjkfdjQAAFZw7rIrmtgFGKXlwvL1YttgMuETCjR9v+eSHPiICx9Ub+7Tc2MlpMhEZSEtPqQkIsWxtj1Ch6CXQ6pgc7UxwcOMDGdMKErP9/6hRTbYOC2GE//jjtLFuW3iJPETJpaZwe08rgt25t3WKbI0fys1pH8dZbPG+bNyt19Spd4W++6RDTbWbFCh7rwoVK/f23uTZPemG8YYM5ILxqVeevt1UQSU1l+xARYR40fP89r4E2mjcaOZUbGMjspfRcvsxsQYDVr3OZvnRrAWMwMCOrSxe77M7px9qrF5MjPKmcQj5DBIytN/KDD3KKKD1pafTIBATkXtdBW/0Y4BSOg4o7ZYvRyJiO6tWzFiJ//EHPQuXKDPbVOHSID65OxyC277/3nFH74cPMnAI4Vffbb5Z9Li2NhaoKF2aqa0oKPWxRUfRujBnDa37xomPttxS9nvPznTvTMxQYyKmu9Ome8+ebp9cqVMg+CF2wP9evcxDQuLE5e+/jj3ktvvmGfycmcomR4sU5VakUBXREBNuL3Faj/g+3FjBKmddJsjRGMAecfqwff8xnyJnnS8iACBhbbuTt2/nQ/fyz+T2TiQuseXtz9JsTy5czKwn/VTd1RcyBNurTXNka2horOp1SXbtm37EdPEiPjBY3MXOm6z0yJhODbzdsYGDy5MksJvbOOwy2mzRJqW+/ZdaVlnL9wguWVdC8fZujrWrV+PuFC5zD79qVIqFoUV5/d2DoUHqFzp1jVhmQMQNu3DhzVlvp0rIAnSvYtYtTmloMiMmk1Guv8ZpoU0fXrnH6s1o1ev10OqU6dDCv2WUBbi9gTCZm/DVunGdPhlOPVa/nM9+/v22fF+yCCBhbbuSePemZ0ALvlDIXqZs1K+fParEI/v4sxe8KYmIY3Nm3b8b34+OVevJJ2vfOO5YJq/37GVcBcFT5v/9xP87g6lU29iNGsLpn5kUkg4I4Wi1Xjh115v9rHojwcMtGgCdO8Ly1b08vzKpV5niYzz6jeHX1Qo/btvG4Jk9mGjmg1NNPm//fv7+5/k94uHulwRc0Zs/OmIpvNPJa+fmZ78c9e9hRAqyabeVgx+0FjFL0VgP0xuQBpx5rXBzFZdmyZg+Z4HREwFh7Ix87xg7i22/N72mpquPGZf+51FR2sgCnHqwYRdmdp56i9yB9HIgW71KokG0VZg8eZAyPtzc7+TfesE95//RcvUpPwksvZSxcV7YsvUHjxzMo8vBhc32NzBgM7LRXrKCYDA0176dcOQqRnOJj1q9n5//CCxwxavEwf/3F6/rEE/Y9ZmtISeE1bNSIFXe1e01b26llS7OwK1TIdUtZCGYGDaJg0eLlDAZ6WUJC2KHXqcMpQH9/Vke2Mr3dIwSMUpzurFIlT9PRTj/W6GgOZGvUkMw9FyECxtobuW9fRp9r0w5//cUO7cUXs3eB3rzJjgRg3QdX3uy//ko75s83v7dqFUVH5ngXWzh7lh4RLS23RQt6ZawdpRiNFIvz5lGwaCndWszGo4+ygN+nn3Itn7FjGRD92msMbnz9dQbWfvQRM6d+/pmVdTNPiRmNLEao7dvLix1Kv37Znwtt5Pzxx+xQWrdWqkQJej2AewMvncXHH1NMbdyYMe4lNpbxSgBjKvz88r72jmAfDAbGupQuTYGuFMV3/fpmAXroEJ9RHx+mxFvRfniMgNm3L+sUfytwybFu2UK7FyzI234EmxABY82NfOoUO4gvvuDf+/dzpNSlS/YjoyNHzLVd3njDfobbwrVr7MC6daPY0rJztHgXe6YCJyYqNWcO13nRMl0iIyloevVS6uWXKeYefFCpV15hSe7Onek9KF2ana8mKgIC6DHw9s56uYTgYIrKypVZubhOHf6sVCnrpRdKlVLq4YfpMfv3X476Nm82i67ISG6jpR1nJWTGjuX/Z85kx1OqFDuimjUpaJydmXDiBEfpI0ea414WLmTnpxVFrFqV52LlSufaJuTMpUsUwK1a8V784w/zYqWlSpmXc1i0iM/qSy9ZfH95jIBRih5cWwpDGo1K/f23ih01yvnHajLRY6YN1pYvF2+MExEBY82N/OyzfMASE5lxUro0i7xlV3J9/Xpzif7cYmNyIjGR3oP58+lReOUVTgM9+CCzEgYM4JRBTo2aycROOzycBc6uXGEsh07Hkt7WPnRawOzatZzDf/NNzt+3bEkhoXWa1r68vOg9KF6cZe27dOF0zZgxLK63eDHP6+HDFGSWuNRNJmZ+7NzJYmKjR7PR0aaPQkMpqmbOZDAhQMH01lsMUNbpuEhm+iwjk4kizMtLqaVLOQXg50dRBjg3vslo5L1QsSKnJADeH4sWUfR5eVFceXnlOc5AcBCbNrGtaNOGIvPhhznVWbEihbi2tpc2Xf3yyzk/s/HxSs2apWKfftpzBMzp0zz27Mo6ZMZoZB0mf3+l4MJ1n4xGVvu+/35em7p1ZXVqJyECxtIbed8+dmRTp1Kw1KtH9252iwYuWMDtfX2tX803NpYLCA4ezO9J73kID+cD0qYNFX9kpHl6oHFj2pkVn3zCbVauZOdavDhHfX//nbs9JhOnc+bOZYZLixYZA2K9vGhDq1Ysrz9yJKd2Zs/miGTDBgYjHj+u1PnzFE/XrjEGY8cO/p6c7HyvRVoap3vef9/ssg8JMf+u09FL8/XXFIpBQYyz0aYP09I4avTx4RSVVkW1YkV6YpxVjl9baVrLLipblh0cQDHYoQOv0cKFzrFHsI1nnuE1a9fOfO+cPcuU+OrVzZWkv/+e92a/fvfeYyYTRU5EhFJeXiq2QQPPETBKsc2zpDDk/v3m4ObixZV6+20Vu36964+1b1+20Z5SWsLDEQFjyQkwmSgYqlenN6RTJ47as6sgOWmSeRR/5Ihlhty5w4anUyfz9EnFivSuzJjBjjY7T4/JRJFUty69AJm9PStXsgMbNozeDIDTNdqce1b7O3hQqS+/ZKxJsWJmsVK1Kr0VH3/MeJrjx12fPm0vjh9n9pU2faQVvuvUiedqxAiKlapVzVkiqak8H97eFDBaFVWAwsLRnD3LKTQte8XPzxzgXL48FxL19RXPi7vz1Vfm+C4fn4zlDU6c4D1Zvbo55f2HH7hdt27mrL+4OHNpg969lTp71rOmkJQyF4bMabp96VI+m15eGRIn3OJY69fn4EUWQnUKImAsOQHpF8R7+WU2HNkVktJWag4Pzz3TyGSid+KZZxjnodNxKuDLL7Nc1yRXkpPNAuXDD7n/f/6h5+C++zj9FRzMjjWztyM+nm7Q5583L+rn50d73n1XqdWrC06xs5QUNpKNGpnFSNGivCaHDpmzefr350gxLY31PAB6cwYM4LUMCXHsOTMaKazLlDHH72j1hR55hGsfFSrEaye4L5p4GT6c995DD/F6pluUUZ04wetcqRJFq1Jsj4KD2Wlu3syfISEZsgjdolO3lvfe47RQVsubaN6nwECurJ4OtzjWzZs5uL3vPno8z5+37/6FDIiAye0EHDvGRqJvXwbvAkp999292xmNnLfWPCc5BaIlJfFBrFOH21epwgUB7VHJ1WQyTyU89hg7NK1ze+SRjA/U7dvM8nn0UQoogEWzhg1jTI07r7LsLNauNa8FpdPRw2I0Mo0+NJT/++03nvcPPzSfZ+1eePBBx02NffaZWVxpQsvHh/FIxYuzw9uzxzHfLdiHb781ixftPrlzh97UUqUyliI4c4YCpmRJc+e9fz+nDLX7IJNX2C06dWvR682xfelZvNg8zZu+qvR/uM2x7tpFL4z2TJYpw/paX3zBODyZXrIbImByOgFXrrDBqF7dvOjaiBH3bmcwsMEBGCyZXXCdXs9YlMhIdobduim1Zo39O7hDh8zBpwCF0p9/8n/x8XQ/d+tmHq03b067XF2EzZ358EPz+Sxdmufq4kUGGQNsbGNjeZ8UKcJORpvKefpp+0+zbd3Kaav0GVaRkQw0Bxig7cpaQ0Lu/Pgj76nBg+9tA65c4VRlqVIZPQ1Xr5o9a8uW0StYurT5We7end6a/3CbTt1atIUetQzAf//l30FBWYoXpdzwWK9dY3swahSXHvkv2FiFhtq/RlYBRQRMdifg6FFm05QsyQyBYsUYT5C++q5S9GhERJg7qqzQ6xn8WbQo4xGef56eHVuJi+MDsHkzPSjLl1OAdOtmtgUwe15mzWKw7rPPsuEDuI7TlCnZNgZCFly5YvbGAExnjYujNy04mPELW7bQ9d2xozmIFuCIzJLFPS3hu+/uTSmvWJH3aHAwpyAlldO9+fPP3Ou6XLlCsRIQwGkVbUolPp7VwAF26JUrU0wvXEgx4+XFGLevv1axv/3mXp26paRf6PHSJXb+Pj4Zp9Uy4XYCJj1paSye6evL6SVnVSvP5+Q7ATN27FiF/9LptFe1atWy3f6eE2AwsGMvVIjF006eZMxDmTJcuC89CxeaA26zqsKbkEBhUawYR0iDB1smGE6coLvx6afZgJUsyYZKCyrN6VW4MEdh+/ezAdTSgrWpqo8+si2+RiAmkznOSRtNrVjBc9qsGYXFxx+zwfrhB4pWLeAQYEbQpk3We92OHOE0piZAAWaRAbwHvbyYlSKC1P3ZvZvXsUuX3KcTEhM5gtdG70WL0itcvrzZIwhkWyPJZanF9uCnn8xTt0Cui6+6nYC5coW1sLTK5wAzNd3l/OYDLL2OPvAgatWqhb///vvu3z4+Fpj/66/AgQPAokXAlSvASy8BkyYBkycDmzcD//wDFCvGbTduBF5/Hdi7FwgMBH7/HXjoIfO+UlOBmTOBDz8Erl8HBg4ExowBypS593tv3QJ++AFYtQrYvx+4dg0wGs3/9/UFQkP52eLF+SpaFChcGEhIAI4d43c3bgy0aMHP/PUXv3v5csDHB4iIAJKS+HeNGlaeTSEDOh3w1VfAs88CHTsCd+4AjzwCNGrEa754MfD228CmTcD8+bym99/P8x8fz2vz119ASAg/07w5ULcu763bt3mtLl0CDh3i/XX0KHD1Ku8pjVKlgLAw/k+nA/r3B0aOBKpUcdlpESzkwgWga1egVi1gyRI+3zkRGMh26K23gHXrgBMngJgYYOFCICgIeOwxtk3R0UDt2kDnzryvAgJ4v505A7z3nlMOze489hifi5s3gQ8+AB5+2NUWWcaGDewf9u0zv9eqFTBgANCjB9tzwal4lIDx8fFBZGSkdR967rmMf0+bBkyfznGMtzc7K6WAlBTAZOI27dsDP/9sviFNJgqg995jw9G7Nx+8ihXN+9XrgdmzgV9+4Q2u15v/V7gw0LAh0KwZ992+PRspjYQE7vfSJeDGDTZaN26wc1u5Evj4Y2D7dnaW9eoBn3/OjtbLi51ot278vybEBNtp0oTCon9/4Mcfgd27gfr1gRIlKEjWrKHobNIEiIwE9uwBgoN5PfV6IC6OHc8//+T+XTodhU3HjsCDDwIjRgCXL1MEnTpFUWstaWns4Ly8aJeXl/X7EKwjLo6dcEAAsGIFUKiQ5Z8tUgTo2RNITOR9kJYGbNtG0WIyAUuXAhMmAJ9+ynakZUsOVsLCHHY4DufbbyleAKBcOdfaYg1GI5/NypX5nCYmctC7cSPQrx/7i1KlzK+SJe/9vUwZwN/f1UeSb9AppZSrjbCE999/H59++inCwsIQEBCA5s2bY8KECShbtmyW2+v1eoSFheF6jx4IDQujGEhMhHdiIrw3beLNWKUKf3p70/vRpAnwyisUD7GxbCTOngXGjaMX5+GHKSbq1OEIffFiYNkyYNcu8wOp0/FmbdYMePJJKnM/P7NhSnH0vm4dPUB79gDnzmV90KGhQOnSbLAeeICjsMyelrNnaXfNmuxc03+XkDdWrgT69qUwMRqBsmX52rsXSE6meExMpMjp04edTlAQvWNXrwIXL3JkfeIER89RUUDVqvSoVanCzkunA7ZsAdq14z5DQ4Hz59lZZUdKCj2Lv/9OsXzpEjvRlJR7t9Xp2LGGhwPVqwNdurCxzWn/guWYTED37hSsW7fSA5MVV68Cf/xBcXL2LNuBGjU4GGrYkF6JdeuAtWvZdqRHKaRs2wa1YgW8d+6E16lTiLtxA4UTEhAbG4vQXEb+WlsYHR2dYVt/f3/4O7szvXABqFSJHqiHHwZWr+bzER5+dxODwQCDwXD3b71ej6ioKPc6VqX4zF2+zNeVK/x55gyf54MHuU1mypfn9RcyYPM1d8qElh1YtWqVWrJkidq/f79avXq1at68uSpbtqzSZ1NYSJtDy/za1Lw5YwvSZwAYjYxpSR/Imfnl48NAypCQjOv54L/aMF27MnYmqyqtJhPjIwYPNhdTCwhgKu6IEZxP3bSJgcOJidbHUWzaZF6o0NlVb/MbBgOvw4EDrH68axeDqLWYlIgIZplowZbjxrHCaokS5mJk1rBrlzkgODAw+1iXDRtYzKx48Yz3np8f79uGDRl78eyzDEJ+4QXO0bdrx6yp9BWWtcymV17hUgyC7YwZw1iOrJaY0ErRP/QQt9HpmDnYsydfWpp02bK8t3Ko65NVDCCsjAvJ/Bo7dqztx20rFSvymP/9l9l0hQvfk1btEcealsbYnffe43pv999/b//h58eMs44duRTIJ5/wuIV7sPWae4yAyczt27dVaGio+v7777P8v3YjR0dHq9jYWBUbG6sSlizhjfXpp+YNf/3VXG8jIMC86CD+y/Pv1IkBmtWrM2ArNJSCpXZtltbPqaDZ7dtcUblqVfP+XnuNmUNa2Xp7sWABv+Ojj+y73/xKWhrXXZo7l/VxHnqIS0ikD6DUXgEBDPwODDSnNz/2GD+H/+rDREYy2NeatVL27jUH7wYE3JuC+ddfFCBaLR8tmLt9ewaDW5tOnZrKmhtdupgXIgW4rIW1y2IIXBYEYK2n9JhMXIJCW2m9WTNmmGUWi2lpHPgAHNjkUO4gOTn5bjsWGxuroqOjre7U07eFsbGxKtnebVBujBrFY33hBfN706fzvXT3n1sf6/XrvN7lytHukiWZSv3ssxQzc+ZwsBEdLRmDVmDrNfdYAaOUUo0bN1ZvvfVWlv+7J4r50CGOQrt144114oS5xouvL1VytWr8u0ULLhvQty9TaLNbLVl7FSnClYqnTmWG0pUr9KwEB3PfTz/NEuKOvqG1gmt5WWAyvxIbyxHumDFcSTv9opSVKjHDa/RodjSrVrEey/bt/P2zz9jRpPe8eXlRWPTpQxFSvjz/fuQRy2rDbNxozkAJCTGvTLx7N/ehrVqsjdCHDct+fS5bWbuWnasm2sqX5/EKuXPsGK9bz54ZvZ5HjtCzCrBN2bo168+bTEq9/Ta3GzOGYqdYMabrW4DbZebkxrFjvM8iIzO2g0Yj29tKlbJNQXaLYzWZuFK9vz9f/fqxeJ3gEPJdGnVm4uLiVJEiRdSXX36Z5f8znIAjRzjCqVOHLnutgdG8IoUKsUOKiqKiTi9YgoLonh82jCPi69fZGW7bRpHTqROnDzKP3H196crXFmpzBiYTv9PLiyPtgsytW6yjM2wYr5+W7hwRQYEwYQJF5Z07lu/z9m2OGNNX4wR4/5QsSe+Mlxe9Odnt12jkSE37bLlyFBK9epnr+2gju+HDlbp50x5nI2euX+f0lHaOqlSxX22b/EhcHFdUr17dvDaO0Uih6+fH86cVmMyK9OLls8/43q1b7MgDA5m+nwtu0albQ4UKbCP377/3fydOsJ198cUsP+ryY9VWxwZ43WTa1eHkOwEzYsQI9c8//6izZ8+qzZs3q/bt26vw8HB17dq1LLe/ewKmTKEnpFgxc30NpKtBkFl4aIJlzBjzqDg3jEalvvnGLITSj9SLFKEHxtLFH/NKWhoXfPP2pjuzoHDnDhv+4cN5/bTrWrYsvSTffcdRoL1ihPbvp+jIHA+lvUJDlZo82TyllJhI4ZLe81OsmNkLo/3dv7/l9529iY1VqkcP87lr1CjHAmMFEpOJa5wVKsQpSKUoMjt35jkbPpzXOjtSUjjISC9eNBITef69vOjNzQGXd+rW8MEHPN7nn89+mxkzuM2PP97zL5cf68aNGQe8rVrRA/Phh5y637qV07kSf2g3LL2OHpOF9NRTT2Hjxo24efMmIiIi0KJFC4wfPx6VKlXKcnstGj0WQLYxzD4+TIWtU4epzc88w7+t4dAh4IUXmF3Qty9THkuVYnbIp58yyv7WLW5brBjw+OPA++9b/z3WYDSy3s3MmcAbbzCLKr9lJ926xZosGzbwtXcvM0LKlAHatAFat+bPChWs37dSrN0SHc3sguvXmWUWG8uU98REwGBg1k9qKjMrjh7lZ6x5nHQ6ZgPVqcNU2lq1mDpdqhSz4nQ66223B5cvMztGSwWvU4f30COP5G2/Wv2Sc+eYOXXtGjP+bt9mRkdcHH+Pj2fJAIOBacVpaTyvCQlMCw8M5E9vb9Zb8fdn9ldICJ+xIkWYQVi0KNPUtTT3QoX4CgnhKzSU24WEWJ5u/r//Aa+9xrIKvXoBx48zm0ar+9SpU/afPXuWZR22bgVmzGD9kMwYjUyn//JL4OWXgS++yDLt9m77ZkVmTmxQEEK18+bjYz5vYWHMAoqKAqpVY9mAmjXZRuU1Df/yZWbuhYXxOcpuf0oxk2/ZMj7P992Xt2O1YFuLMZmYWXTyJO/fM2eA06f58/p183aFCrG0RsWKbHe0e7FwYb6037WfAQGue8bdHEuvo8cIGGvJIGD8/HgzVa3KlOM2bVhPITjY9i8wGoFPPgHGjmVa4HffmQvOZeboUeCjj5j2qtWHqVABGDQIGDaMjYk1GAx8oLZupYA6f54dbFIS/x8QwGOLj+dDV6wYO8iHH2YNgwoVPKsWgdHIjmL7doqWf//lcQFsCKKi2PFHRPDYvbx4fCEhbJhLl2ajklX9jJgYpkHv3w8cPsxrdfIkO9P0BAbyuwoVYqMfEMDO09ub36c1zAkJTJ++fZvXI/3jpXW+2udNJoqh2FhzDSKNoCDWyKhcmXbXqQM0aMBUaG9ve57d7Dl9msUaN27kcfj78xmqXdvcQHt58Thv36YY0UTJrVu81+PjzULEFnQ680srBOntndHflVd0OoqZIkUoerRXsWJ8hYfzdfUq8OabTEP/4gveM488whpBK1dmrAulkZbG+2vBAuD773mfLljA9icnvvsOGDKE133ePAqKdNjUqXt5IVSns+7cac9SWBgHB1Wr0paGDVmsMbd0/GrVmCa9YQOLvuVEUhLQti2fv7//ppCy9VjtKWByIi6OwlQTNNrr3Dk+A7dvZyxWmR4/v5wFTna/awLd2n7DgxAB48gbOTqa3potW4BRoyhiAgIs++ymTfTAbNzIG9vbmw3BmDEsZJUdycmsI7F4MRvLhAQ2unXrsjOJiGDnqG0bF8eO5MwZdsrpcuzv1qqpUYMNTNWqfFWpwjoFrnowlKKgOHqU52nHDuDIEZ7v7BoBf392MmFh7PR9fbkfg4Ed6I0bGYsKRkXRwwFQaFy6xN/DwugBqVGD56FiRY4cS5Zkp2Pp9bX1uOPiOJqLiaHX58IFNownT/J8nD/PbUNCWH+mdWuO9uvVc/woTq+nZ/Hnn2lHVvVm0qON7gsV4j0aHMz7PDWVYi0mhvcowHNbrx5FUfXqFGzly1N0Wiqy09IoLs6f57k6dIhi9MQJXmNNHBYqxA5Z855p+PuzUwgN5XX28eFnDAZel/SDg8z4+lJoauLWz4/XIymJnztzhsdavDgweDAwfLjlA6c9e9jOnD1Lr8+bb94tVmnXTj0pid+xZw/rlxw7xnN59Sqvl3atskKn43NXrBg9NlFRfAUHA7/9RpHXvz8wa5Zlx3z7NiuQnzhBL9VTT0EfF+e+AiY3lOL5vXOHr9u3M/7M7nftZ2xs9kIzOJjtQUAAX4GBWf+09n85be+kwZMIGEfdyH/+yUalUCG6jHMbSWWHycRR1hdfsMEA2BDcfz89RN7e7GjPn6enZeNGjmarVeN0V/Pm7GSV4sg0/SstjT9NJvPfR49yVHPwIB8KgI2Plxe3024DrUEKDTWr/WLFKJDCw9nRe3llHMFlvoU0wfTfCOrutiYTbUlJMXfYV66wQ9OERmZPhLc3G//KlVkMrmZNdnCaEAkJydiBK8Xju3SJncexY2yYd+7k3+lt9fHheXz9dRYcdGd3bmwsj2PbNnqgNm6kiI2KYhG0Vq14zs+f57nUOlw/PzZ0RYuap6dKl+ZoOn01aGtITmal4JgYXq+gILOn69o1Fn3cv5+eh127eI0BisEmTbg8RuPGHMXbUm3YGgwGiuA9e2jLrl20LTWV179cOd7bfn48Z1olbM1jpNPxGbh1i+ewc2ce808/8Xgee4zbxcfTm5aaavZWFSvGwUXjxixOZ8vAIDERGD+eU0pGI7+/ZUvoIyMR9swzzunUTSazONy3j/ffgQMUONrzqj07mduCunXpRdKWPilblh7ratV47rM6J/HxwPPPc7BWvz70rVoh7H//80wBk1dMJraL2Ymd+Hg+j8nJvH+z+pnd/5KS7m1vc8PHx35iSPu9YkVzX/EfImDsfSMrxSmj0aM58l2wgA2aPbh1i1NMS5eaPQKegNZopXfzA+bGX2ucMoudrG45nc48HePjw5Gtjw9f2px9+r+9vfkZTcAZDOzQb9/OOLouVIielfr12ZE0acK/L1xgAzlzJt2/rVuzynLz5o44U/YnOZlrN33/Pb002jkNCqJIKVSI50fzIty6xc4wPUWLskPRxGCpUpwOiYhgpx0czAZGu45padyHXs/9adWGz56loDlxwvwd4eEUKI0aMZ6hSRMKHHfAYGBHvHMnX7t3s3PWGvOwMB5/WhpFWmoqRXOJEmbPRHo0kRMebnbvh4byGgQGUhz5+pqnGTUhbzDwOiYm8t6Nj+e10pakiI/n+1mgBxAGuLZTT0vjNd+/n+dz3z4KxRs3LN+HTsdz4+dn7tQCAvi3wQDcugV9QgLCUlLyn4DR2q7UVMteWsydPV9JSbzftHsu/U9bp32tpVixe+4ZETD2vJFTU4EXXwTmzOGCfh995Lg1ZpKT6Zn56Se6UitVYkOodepa553VK33n7uNjFgPpA/e099LHbmQlRLS4htu32dmfPMnG+9Ilek5u3+ZNrgW0pqbyhjeZzNM9WqOdPmgwMNA8tVC4sDnGQGvoM4sTkymjJ0l7GY38vyZ8/P3Z4RYuzA64dGmOfkuVytmzYjJxMcwPPmBD/NxzXGvKndeV+ucfLvK4ezenXXr2pBhZu5ZB44ULc8rhtdcyxvwkJDCo8tIlehkuXOBPbSpNC1i2dFTm78/zXL68eVRdsyZH3bmdd3cjKYneuuPHzV4sLSi8Y0ce1/XrQNOmFIk//cSG/upVvq5fp6jTYn80AZKUxI5HezaU4jOhddrac+3ry467UCHex9r0gPbSxJC3N5CQAP2FCwj74AP37NSvXOG00Z9/0ssSGspzq7ULfn58z9+f5yK95zjz820yQW8yIcxodK9jfecdit68Cg17owW2Z35pItpdXuntKVKE93g6RMDY60ZOSGAHsW4d53H79LGfkek5eZLenbVr6R50NzKLnMzvpd9OyxrJvCJv5uyHkBDz1IbWEVatSg9JuXLO7QCNRi7GOWoUO5L587k+kTuRmMig72+/5ZTERx/RxvTn6dw5CrDvvmOn9+67jL3IbXVkDZPJ7KaOi6Og1hpaH5+M2T6hoZ4lUvJCaioHFEePcvpOE7je3myMNTGSeTrTaDS77BMSKBgXL2aM18mT93rFLMQtPDDZceECn+Vy5czr/qSl8fdjx+i1OXWKU7rnz1NAZ+Npgk4Hvbc3wtLS3OtYBw3iAOLmTYpWa9vsIkXo0YuMNP9M/7u/v23CIJ88jyJg7HEjx8Zy8bsDB7h4niM6tJ9/pnDRsmoiIjiqjo+n+z0oyOyF0EYnKSnmKRPNnX/lCjsegPEiHTownkaLVwGyn8rJnJWQuaqJ9l5Wn0v/U+PYMdpXr17Gz2gjLYMh41RETIx5KkILGAwLM6/g3bIlM7wyqXSHcPky0+HXr2ca/LBh7tEoXLrELLLjx4HJk+kRzMkLePkyvUrff0/PyPTpuWeBCNnTty+njUNDzTFkWZF5ajM3dDpzSrf20tK+AwLYkWniKJ13Um8wIGzGDPfq1DUeeIAJDrt38xnODS2IPSaGgkBLq09MBFJSGMQ7cqR7HqtGWhpFzM2bfJ0/T5F26hSnqLWYMUt45hnGVxZgRMDk9UbW6+k6PnaMrtAmTexr4IwZHB1fv85GqX17jpyzW83WEmJiuCL1ggW0uV49YO5cs5Bwd0wmCpnDh1nXZedOBjDHxPAcNWvGIMZu3RgY6ChhYTTSRTxpEjB0KAOtHTVlaAmnTzO9VClmdlhzPQ8c4GhxyxZ6Yj755N7g3cRE1t9Yu5YC6cYNNsjBwRQ/AwfmnCGX3xk6FPj6aw4uXnqJWWpa8Lg2oNCmDbSpD8DszjcaGYR75Ajw9NNc8b5cOYp0LVbJStw2tfjoUU63NWvGZ9cOuM2xXr/O5+j0afP0upYqnf73O3eyjx/R6hMVKWJ+pf+7aFEO2KpXt6/tHoYImLzcyImJDNQ9cICNeqNG9jPsm2+YMn37NkdX/fpxRG1rRkh27NjBUfrx46wj8cQT9t2/s1CK3ql164C//qJAi49nfMvjj7OYWMOGjhEzM2aw8x8wgNM2rhAx58+zQQsM5DmwJRDWZAKmTuX0WIUKnMK4fJmd8pYt5kKLADtdrZaO5ukDGFfz5Zf0RBQk5sxhPEdUFEfR1haEvH2b4vPCBYpEO3nB3KZTz0yzZqzXdOIEhZ4dcMmxKsW2c/Nm8+vECf6vUCGz6MgsPnISJmFhzqvh5OFYfB0dUAXYLbC5pHRqKhd8DArKfiE2W5g1K+Oq1yNH8rscSWIilzHw8mLJ6/xAcjIXZXzpJa4KDnARzo8/VuriRft/37x5LK0/dKjzS4XfusX1dipUsM9CjvPmmVe/1l7h4VyocsaMrNd4uX5dqddfN6+I3aFDwVllNzqaS0X4+tp2byUlKXX//Vwi4sABu5rm8vL6WXH+PO+RJk3sulunH+vnn/OaAWw769VT6pVXlFq4kMcoOJx8txaStdh8Iw8ezHWE7LUq7/LlXJgP4Lo3zhAu6TEaub6OtzcXo8xPpKZSzPTuzUXwvLyUevhhpX7/3b6d7PTpvH6ffGK/feZGaioXhSxaVKnjx23fT1ycUq++yjW50q/3pS1MZ6koS0riYoMAF0UtCCJGO96vvrL+syYT1+AKCHDIwphuKWC09aD27rXrbp16rLGxfD4ef5wLcrpyAcwCjAgYW27kadP4AE6fnncDdu+mZwDgKsUvvaSUwZD3/dpCaiobl7AwpU6edI0NjiY2ltetQQOe84oVlZoyxX4N0Dvv0BNjwUrBduGttyg616617fOHDinVurV5henQUKVeeMG86NzYsXz/1VetEyN9+/JzLVrYZpensGYNj7N6dds8b1On8vMLF9rfNuWGAiYpifdr5cp237VTj3XBAl636GjbPi/YBREw1t7ImzdTaAwZkrcvvnJFqZYtzStd9+jhHir+zh02LvXqmVdIzo+YTJz6e+YZXs+wMKVGjeJ1yQtGo1Ldu1MInDhhF1OzZdUq3j+TJln/2X//VapWLbO3pVYtpX79Nettp03jPfryy9Z10ulXXs6PmExKlS/PY9y3z/rPHzhAb2te25IccDsBM3w4z9eiRXbftVOP9dVXlapUybbPCnZDBIw1N/KNG1wm/YEHuNy9LaSmKjVwoHnE27Sp+82X7tunlJ+fUsOGudoS5xAdrdQbbygVEsIOpXdvpXbssN0TFhurVNWqStWvz1gcRxATo1Tx4hQJ1nhGNm2ibZpwbtdOqVOncv/crFlmMWKpiDEalYqK4uccMD3icpYt47F17Gj9Zw0GperWVap2bYcOFNxOwBQpwufMATjtWJcv56Dn1VdtsFKwJyJgLL2RTSalHn2UQVu2ug2/+84cHBkVxVGwu/L55+zgNmxwtSWOw2Ti8Q0erFTNmjzezNVtypdX6qmneO2s8c7s3UsR+PrrjrG7e3elIiKUunrVss8cO0ZBpQmXRx6hCLKGr7/m5z/7zPLPnDlDsR4Rkf/iYSpW5Lm8cMH6z44dy07QznEgmXErAbN2Le+fl15yyO4deqwpKYyZ69uXwdo9e9o+iBXshggYS2/k777jw7d8ufVfcvAg3Y0Ag0inTrXNWGeSlsb4hcqVmaWUnzCZlPrlFwaZAkqVLctGdcYMpf74g1Mzzz3HID1vb6VKlWIn7OXFgNmffrIswHryZO7/77/ta782//7LL7lvGxdHsaIJsvbtrRcu6XnrLXbav/1m+WdGj+Z3jxpl+/e6G1u28Jg6dbL+s0ePshMcM8b+dmXCrQRMq1Y8Z1llsdkBhxzrv/8yJkzLDK1eXalx40S8uAkiYCw5AefOKRUcrNTzz1u3c4OBUeraqPeZZ1wXoGsLx47Ri/D22662xH6cP08RonXma9dmPyVy+zaPPTCQru+nn1aqeXN+tkIFCp6cGjKjkQGyZcsqpdfbx/6YGDamTz2V+7ZTpvD6aTEuR47k/fuNRnoiQ0Mtm3rSCA9npx0Xl3cb3AHtPjh92rrPmUyctqtUySkxZm4jYIxGepyqVrX/vv/DrsealkbBrT3ro0dzat3ZJRKEHBEBk9sJMJlY0yIqyrog2/nzzWmo1as7PqDTUbz3HjueY8dcbUneWblSqcKFGce0cqXln7t0iSnmOh2nmqZNU+rJJ/l35coMfs2uYTtzhvfB4MH2OYbevTmNee1a9tucOkW7AMYbLF5sn+/WiI1lB9y4seUj0Z9+oj19+9rXFleg1/Pa16lj/Wd//ZXnwZr7Lw+4jYCZM8f66UcrsduxJiYq1aULPa6ffy6ixY0RAZPbCZg/nw/e779btsMrV8wpugEBnHryZJKSGAfSubOrLckbX35pjv24dcu2fezeba758cQTrJfTsSP/7tIl+2DsKVP43XkteKil7M6Zk/02775rDhB/9lnH1RLasYPTa2PHWv6Z0qU5Cvf07LaRIy2fwktPaipLJjz0kNM6RbcRME2a8Blw94Dl1FS2EYGBrO8iuDUiYHI6AbduMdPjySct29m4cWzUAXb4CQn2NdZVaKNnTy1wN3487R85Mu+BpCYTY1BKlOC04pQpSv38MzvnkBBm62TunNLSlGrUiFkntgqK5GSlqlRR6sEHs+78YmLo6QMYMLtzp23fYw1jxtA7d/iwZdtrg4G33nKsXY6meHEG41srQmbP5vHv2uUQs7LCbQSMry/vTweS52M1mZQaMIBtuJM8ZELeEAGT0wkYOpSdVG7lwc+dY+cCMFZi/XqH2OoyTCaWOm/QwPMySb74gtflww/tu987d1g2XKdjKvzWrZxmAih4M99PO3dy2y++sO37xo2j9+LQoXv/99NP5liX3r2dd42Sknjft2tneWceHMxnxFM5e5bnuUcP6z6XlsZz1b27Q8zKDrcQMFr20ejR9t1vJvJ8rG+/TTvnznWglYI9EQGT3Qk4dIhKPLey8B99ZHbZ9+3reR28pWzcyGP86SdXW2I5y5ZRNLzxhuO+Y/NmpWrUoIAYP16pH39kgGuVKvd6Jl5+mf+zNPVZ48IFurRHjLj3f88/b56utHSa056sWGHdFOvAgZ5dF+bFF2m/tR4uzYu5Y4dj7MoGtxAwvXo5pWptno71r79o48SJDrVRsC8iYLJzJXbowEDI7LKGrlxhxwUwqNJTG2RraN+e0yCeENR29ChH+z17Ol5UJiVxWsTLi3P9f/3FAM/g4IzpxjduMINo4EDr9t+rl1KRkRm9OgkJ5jTwKlWUunnTPsdiLSYT44IaN7bsvoiONk+xeiJRUZw+spYHH3TJsgpuIWBKlqRwdzA2H6sW59emjWe0bcJdRMBkdQJWr2Yju2xZ1h+aNYtzugDTWfOr1yUz69bxmP/4w9WW5IzBwKJt1arZL33ZErZupZgICmLQcPfuFDXpF/n7+mt6hXbvtmyf//7Lcz57tvm9U6eYTQUwNd/VaMHFlta7KV3aNhHgagwGXrsHHrDuc0eO8Pz8+KNj7MoBtxAwXl5KNWtm331mgc3H+u23vK5HjzrcRsG+iIDJfALS0uhlaNnyXjVuMNAzA7AB9tSgVlsxmRiMakvxLmfywQeMF7FUJNiT+HgWxQMoYAYNMs//m0wM4q1Zk/VhchvtGY08340bm0Xy339TPOt0Sv3vf44/HkswmVgS39L4jn79eE6sqSPjDvzwA+22No5pzBgKTkctK5EDLhcwO3Y4rYihTcd65w5rJFkb0yS4BSJgMp+AefP4wGVOed2/n8GHAANa80uGkbXMnMnO09oCXs7izBnGozg4YDBXli3j/RIVxWBwgPETRiM9WED2iydqzJ3L7TZt4t/ffcdz7+dn++rTjuKbbzjStqTKr9apOWKZBUfy1FO025rpOpOJNXMGDHCcXTngcgHzxhs8Z04YTNh0rFpFZWsqSwtugwiY9CfAYOBcaGY1/skn7Di8vBxaiMkjSEhguvAHH7jakqx55hnOucfHu9oS1oW5/356g3r14v2j1WZ56CFWJc2uEFx8PKdanniCf7//vrkwnTsWRbx2jUHv06dbtr2vLz2dnkTNmhSP1nDihFML12XG5QKmVSve907ApmP98ENO+XpShXThLpZecy8UBL7/Hjh/Hhg3jn+npQFt2wKjRgFFigAHDgAjRrjWRlcTFAR06wb8/LOrLbmXs2eBRYuAMWOAQoVcbQ1Qtizwzz/AsGHA4sVAo0bAwoXAc88BEyYAJ0/ynsuKyZOB69eBiROBwYOB998HIiOBc+eAKlWceBAWEhEBtG5t+X0RHg5cvOhQk+zOhQtAiRLWfWb9esDbG2jVyjE2uTs3bgD+/q62Int27ACaNAH8/FxtieBA8r+ASUqicOndG6hZEzh9GihVig1Q69bAlStArVquttI9ePxxirkzZ1xtSUa+/hooXBjo18/Vlpjx9QU++QT49Vfg+HGgeHGKmS++AJ59lsIkPj7jZ65eBSZNAoYMAd57D/jmG6BSJQq0okVdcBAW0rYtsH07oFTu21asCNy543CT7EZ8PF/Vqln3ufXrgcaNgZAQx9jl7sTFAQEBrrYie44cAerXd7UVgoPJ/wJm1izg2jVg7FhgyRKgenWOHsaNYyMkCt1Ms2b8eeiQa+1Ij1Ic/T/9NL1E7sajjwK7dwPFigE+PsAPPwDJyUBsLPD55xm3HTuW99uxY9yubl3+7s4dAQA0aADo9RRauVG3LmAy0avhCZw4wZ/Wdna7dgH33293czyG+HggONjVVmTP+fNA5cqutkJwMPlfwEyZwpH7//4H9OrFkfPGjcA777jaMvcjMhIIDHQvD8zRo2yMunZ1tSXZU7kysHUrxYxSFMo1awKffUbxDPA4vv+enppVq4CmTYG9eyl63J0GDfhz797ct23Zkj/XrHGcPfbkyBH+rF3bus9dvQqULm1/ezyF5GT39j4ZjWzPhHyNxwmYqVOnonz58ggICEDTpk2xY8eOnD9w6xZHS199BZQvz/n5Fi2cYqvHodNxCsCdBMyff9JD0bq1qy3JmeBgxulMnMi/9+4FUlPNcVdvvUXvy/HjjJvYsgXw8pDHLzKSMSL79uW+bbt2/Ll7t0NNshvnz/NnpUqWfyYxkR4Ia+Nm8hMpKYwfdGciIlxtgeBgPKQFJYsXL8bw4cMxduxY7NmzB/Xq1UPHjh1xTRvlZoW3N7B/P/DII4x/cedYA3fA3QTM0aP0ZgQGutqS3NHpgDffBP74g6LLYACmTmVszIoVHLW2aQNs2OA54kWjZk2Kr9woXpw/L192rD324soV/ixf3vLPaO1NQRYwRiMDtt0Zd7dPyDMe1YpOnjwZL7zwAvr374+aNWti+vTpCAoKwqxZs7L/UEoKM0OWL/e8TsMVuJuAOX8eKFfO1VZYR6dOFM1FijAe5Jln+H7btsC6da61zVZKlzZ39rnh7c1MK08gJoY/NeHlqM/kJ/R6/nR3D4e7x5YJecYDJuBJSkoKdu/ejdGjR999z8vLC+3bt8fWrVuz/VzCwoWMn/jvofP394e/O6f/uRpNwJhM7iH4zp1z7/iX7KhalecxIoJp+yVLAmvXutoq2ylZknE+luDvz6lbT+DGDXrOrAnm1wSMEz0wBoMBBoPh7t96TURYQebP2NwWagOckiWt/6wF2ONYASAuMRFK2n2PwNZr7gY9lGXcuHEDRqMRJTI1GiVKlMDVq1ez/VypZ95GWNg4hIVFICzsE0yYMAHjxtHDPXMmsHMnwyx++YXxfP/7H7WOFrowbhzb4qlTmWG8YgXw++/Anj3AjBkcaKbfNjGRySfHjzOWc+1ahjvMmQNER2fc1mikc+j8eWD+fGDTJpYXWbSIpUQ+/ZSzDuk/c/Uq8N13tHv1amDZMiYNffUVE1/Sb3vnDjOQDx5ktu+qVQxN+PZbtsHpt01Kot2XgqoABgO2LI7G5s3AvHlMKBk/nppm3Dj+HD+e78+bB2zeTMfC4sW0+/PPzdnr2v5jYvi9u3fTjl9/pV1ff007028bG8vjOXRQwXjuAo4mlsPOnTzuq1czbpuczPN08iTP2z//8DzOn8/zOmECz3P6z0RH83ps2cLrs2QJr9fnn/P6pd/2+nVe5z17eN1XrOB9MHUq74v02+r1vH+OHOH99Of2wjgwfz8MAWG4/O/pDNsaDMzCPn0a+PFH2v3vv8CCBdRsEydS96T/zMWLwOzZwLZtjJFdupRJTFOmMCQj/bY3bgDTpzNsZeVK4Lff+Pu0acDNmxm3jYtj9veRI0z4+usvZk3PmsXnZNw4AKVKIfXCZaSmKEyaxD7shx8YD79xI38/c4ZZ4iqoEOIuxt7d/+XL3Nf27dz3zz/zu774gt+d3pabN2njvn20eeVK/j59ujl5UNs2Pp7HfuwYz8WaNTw3s2fzXKXfNi2N5/TcOZ7jf//lOb995hZMPr745BNek/Sfya6NWPvjNSidDuNnhDutjXjzzW8QFjYYYWFdEBb2CKKiXsvc1OVKVFRLu7SFq6YyG23fzTIOaQufeur3u8cZFvYioqIetPpYz6A8ytSZJ+3+r5a1+ydPsv1etw4uafc7d96MsLAohIW9g7Cw+xEV9bJF11mnlCXFHVzP5cuXUbp0aWzZsgXNmze/+/6oUaOwYcMGbN++PcP2er0eYWFhiI6ORmho6N33RYnnwrlzQIUKjOPo1Mm1tly9ylHer78yw0dwHYsXA089xVYnLCznbStVYgtv48jZqVSowJ4qNtbyz4wfT/XlxGmyrEaoUVFRiI2NzdC+ZYXd28IvvwRef509UufO1n8+F+xxrLEAdMePQ/2XiSTtvntj6zX3mCmk8PBweHt7I0Zz3/5HTEwMInNIlwsNDc31phfSUbYs660cOeJ6AaNliHhaDEx+pFQp/rx8OXcBU6QIh52eQFyc9dWdY2KcHsBrjw7Ybm2hFqBdsWLe95UF9hIbIWFhgLT9HoGt19xjppD8/PzQqFEjrE0XR2AymbB27doMHhkhj3h5sdifVh/DlWgF9azJEBEcgxbvYEl2UXg452w8gaQk6+uZXL5csGuMnDrFn9aknrsCT7kHBZvxGAEDAMOHD8d3332HuXPn4ujRoxg0aBASEhLQv39/V5uWv6hZk+nLrmbhQtZ/KVzY1ZYImoCxJBMpMpIF/ZKTHWuTPUhJsf7+2rWrYJepP3WKgdruXoQxJcXVFggOxs3vwIz06tUL169fx3vvvYerV6+ifv36WL169T2BvUIeue8+RqLdvMkS+a7g9Gku9ZBTirzgPAoVomfl2LHct9Uq1J46ZX2FW2diMHCUbk068KVLnNosyMsIXL7s/kXsABEwBQCP8sAAwJAhQ3D+/HkYDAZs374dTZs2dbVJ+Y+nn+YI+ocfXGfD2LEcyT/5pOtsEDLy0EMM3MwNLTbCksq9rkSzr2pVyz+zZQt/FmQBExvrGcsopKa62gLBwXicgBGcQEQE0L07sw1cMYrZsIHi6f333XMBx4JKt25cIuHixZy369GDP5ctc7xNeeH33/mzfXvLP/PXX0CVKgU3BiYxkcLAGtHnKjKvBi/kO0TACFnz3ntcfXjaNOd+782bXHyzZUvg+eed+91CznTuzNiHhQtz3q5oUWZ/ZCpt4HZoCQGWro1mMAA//cRFYQsqmzfzpyfEAJ0752oLBAcjAkbImtq1gZdfBt5+m1WKnEFSEtCzJ1Nb5893j0rAgpnChYEnnmBlKpMp523r1GGshDtnghw6ZBZblvDbb6yD8/TTDjXLrVm6lD/dvS5T2bKsVijka6SHELLnk0841/3oo8Dt2479rvh4Tlvt2MHCdVL7xT155RUGWP/2W87bPfUU46hmznSOXdZy/DgL7TVpYvlnvvySnsGaNR1nl7uzfj3XGKpWzdWW5EzXrmxHchPagkcjAkbInuBg1tCOiWEAZ06rfueFU6foxt+yhXEJlrr0BefTvDnQqhVrgOdUxPvll+lBmzHDebZZgyasXnjBsu03beJr2DDH2eQJnDvHOlHuTrdu9ADu2OFqSwQHIgJGyJnq1RkrcPEi06s3brTfvlNTuZhN/fpAQgIFTJs29tu/4Bg++IC1ULTphKzw8QFq1eKq3O5YD2bRIq6a3aVL7tsqxanU+vXdf+rEkSxfzilBTzgHzZqxWnJu8VqCRyMCRsid+vU5kilbFnjwQaB377xV6tXrGRxcvTowciSDdvfsYdyE4P60bg08/DDwxhsUntnxzjt04X/4odNMs4ijR7nUQdOmnA7JjSVLGE8xcWLBjsuaMoU/hw93rR2W4O3NdmXePGZOCfmSAvw0ClZRtiyXTP32W6Y516oFPPAA42S2bcu5kUhJ4ZKu337LIN3ISGDIEKBRI9bi+Ppr68u5C67liy84pfjee9lv06sXC+C52zSStgTu6NG5b3vrFhcu7N4d6NjRkVa5NyYTsHUra/x4yvpCL7zAmjVLlrjaEsFBeFQlXsHFeHuzUXjuOa5Dv3AhpxM08VKyJFC8OBv9tDQKlRs3WL3UZOLotUkTdnq9ewNRUa49HsF2KlWiZ+XNNzml0KpV1tsNGAB89RXr+vTu7VwbsyIujlNfoaG5r6SsFDBoENOnp051jn3uyjffcCDy8suO/Z6rVzkgOnQIOHOG8XfWrBSenkqVgAYNGHDer59dzRTcAxEwgvX4+THL5KmnzN6VgwcZ4Hf9OoudGQx00RcrRu9NjRqcihJPS/5h+HBg5UrgmWfoSQsPv3ebTz7hdOEbb7iHgPn0U8ZevfkmBXlOTJvG0fuSJebVuAsqn30G+PraP4hZKQqWn39mlWdtDbaiRVkwsEQJ2899dDTbpoED7Wev4FaIgBHyhp8f0LgxX0LBwtubnpWGDTldtHo1O7n0BASwA5kxg5k/ruxMEhLYEfv5UVDlxPr1wGuvcarziSecY5+7smkT13/q0cN+Czhevsx1zmbPpqelRAlmDo0dy2UaypQBdDpuq9fnHDCeHX/9Rc9v3772sVlwOyQGRhAE2ylThp3Lxo3A4MFZp1b/738UMq+95trCdm+/zWKJr76acxzH/v3srFu3BiZPdpp5bsuLL1JMTJ+e933t3MlCgOXKARMmcOpx/XpOM3/3HYVwVJRZvOSFM2d4f4rXN98iAkYQhLzx4IPsfL77Dnj33Xv/7+fHDJ6EBMbEuIJz5xjHEhzMUX52HDrEmkeVK3NaI7NHqaDx55+c1unalfFttqAUvSFt2jAGbscOesIuX6YHpnXr3KfzbOH0afPCokK+RASMIAh5p18/xpeMH8/A7syemNdeY0zD/PnMZnEmSgGPPw4YjaymGxyc9XY7drAzLV2a02Gekm3jKEwmoE8fiov5863/vFIshNm0KTO4EhK4ltSJE7wfwsLsb3N6zpxhIK+QbxEBIwiCfRg5Evj4Y64i/sYb95ZxX7OGnWHnzs4tbvfFF8Du3Uzb798/622WLaN4qVYNWLcu64DkgsbrrzOLcNQoroNlKSYTz2fDhsxQCwykB2b7dpZRcIS3JSvOnBEPTD5HBIwgCPZj9GimTU+ezCy19PWBypVjMbTYWE47OYMDByis/P3pDcgcW2E0ctrrscdYnO/vv4EiRZxjmzuzcyfrM0VFUZRagsnEeKj69Xk+ixRh7agNGzgtZ4+4Fku5eZOvypWd952C0xEBIwiCfRkyhPEjv//OjJJTp8z/GzqUo/IdOxwfD3PjBoWSyQT8+OO96bjnzgFt27KDHj8eWLyY3oKCjl4PtGvHuk1r1+a+fWoqp5hq1waefJIZRf/+S0+Ws4RqZrR0bKnuna8RASMIgv3p0YOxLomJLCY2e7Y5LuaXX7iMxOzZllXDtYX4eAaM3rkDvPUW7dEwGhnQW6cORcz69cxQcqaHwF1JS+P1iotj1lGVKtlvm5BAL03VqkxVrliR65mtWeP6BVmPHGEAdk72Cx6PCBhBEBxD3bqMPenZk96WLl2YGeLlxWKHZcowO+m11+z7vXo94y/OnuU0VvopkI0bKWyGDGEBvgMHsq8iXNAwmRgndOYMz8/zz2e93YULFIVRUYyTad6chQxXruTv7sDRoyyeWdCzyPI5ImAEQXAcISHAnDks537kCFCzJoNCExOB48eB8uVZJ6Zjx3uDfm3h/HmOuk+e5HTGwoX0rOzYwVTgBx+kgNqyhcX1HJ0J4ynEx9OTcuAAKyZ/9VXG/6elcUrw0UeBChVYpbhfP04PLlwI1KvnErOz5fBhTmkJ+RoRMIIgOJ6HH6aAGT2a6+qUL89063XrgJYtmaUSGclVyW1l1iyKl2vXgBEjGJfxyy/MLmralN6fH39kNoy7eArcgY0buY7Z6dPAK68ACxbwfaUo/EaMoLfl4YcpEKdOZeG5yZN5Hd2RPXvoaRPyNSJgBEFwDoUKMcX6zBl2lNOnM0skLIx1Wm7c4BRGly4UIZaybx9H2wMHMkV33Diu0RUVxf2mpbH+yOHDnFLykmYPAM93p070SiUnA99/z+q4K1bw+pQrR+G3YAGXU9i1i1N/L7+cfS0ddyE1lR43IV+jUyqr2t+ej16vR1hYGGJjYxFa0AtSCYI7EhfHznHOHI70fX1ZtTchgf+vVQt46SWufp75Gb56lVNAs2YxJgNgpdjkZMbAlC7NsvTPPcdYnHyGNe1bhm39/OiFmj6dqdJKUei1akUP2f79nMqrWJECoHt3/s9eayDZgE3H2rIlQjdudJKFgr2x9JqLgBEEwfWcOcOR/5o1rBuiiRgNnS7rdZY0/P05LdS6NQvlNW6crz0tNnXqALLc0tubWWH33Qc88ADPYaVKbpOVZdOxLlmC0IK+CKcHY+k1l9WoBUFwPRUrMqPl9dfpATh9mtMVv/7KTKYbN4Dbt9mpFi1Kb0vz5vQS1KjB2BdnVXj1VMLCWFG3enUWlqtUia8qVbjYZn7ioYdcbYHgBETACILgXnh5sVOtUoWZRIJ9uHCh4KzvlI+9b4IZucqCIAiCIHgcImAEQRAEQfA4RMAIgiAIguBxiIARBEEQBMHj8BgBU758eeh0ugyviRMnutosQRAEQRBcgEdlIX344Yd44YUX7v4dEhLiQmsEQRAEQXAVHiVgQkJCEBkZadVn9Hp9hr/9/f3h7+9vT7MEQRAcisFggMFguPt35nbNEjylLSxIxyoQW6+5x0whAcDEiRNRrFgxNGjQAJ9++inS0tJy/UxUVBTCwsLuviZMmOAESwVBEOzHhAkTMrRjUVFRVu/DU9rCgnSsArH1mnvMUgKTJ09Gw4YNUbRoUWzZsgWjR49G//79MXny5Cy310oRR0dHZyhFLEpcEARPI6sRalRUlFXl9T2lLSxIxyoQW6+5S6eQ3nrrLUyaNCnHbY4ePYrq1atj+PDhd9+rW7cu/Pz88NJLL2HChAk53pihoaGyFpIgCB6NPTpgT2kLC9KxCsTWa+5SATNixAj069cvx20qVqyY5ftNmzZFWloazp07h2rVqjnAOkEQBEEQ3BWXCpiIiAhERETY9Nl9+/bBy8sLxYsXt7NVgiAIgiC4Ox6RhbR161Zs374dbdq0QUhICLZu3Yphw4ahT58+KFKkiKvNEwRBEATByXiEgPH398eiRYvw/vvvw2AwoEKFChg2bFiGuBhBEARBEAoOHiFgGjZsiG3btrnaDEEQBEEQ3ASPqgMjCIIgCIIAiIARBEEQBMEDEQEjCIIgCILHIQJGEARBEASPQwSMIAiCIAgehwgYQRAEQRA8DhEwgiAIgiB4HCJgBEEQBEHwOETACIIgCILgcYiAEQRBEATB4xABIwiCIAiCxyECRhAEQRAEj0MEjCAIgiAIHocIGEEQBEEQPA4RMIIgCIIgeBwiYARBEARB8DhEwAiCIAiC4HGIgBEEQRAEweMQASMIgiAIgschAkYQBEEQBI9DBIwgCIIgCB5HvhUwBoMhw09PwmAw4P333/dI2wHPt9+eyLmwH3IuzVjTvnlyWwgUrGPN79j7GRYB44YYDAZ88MEHHmk74Pn22xM5F/ZDzqWZgtSpF6Rjze/Y+xnOtwJGEARBEIT8iwgYQRAEQRA8Dh9XG+AolFIAgLi4OOj1ehdbYx2avZ5mt4an229P5FzYDzmXZuLi4gCY27mc8OS2EChYx5rfsfQZ1v6f2zXPtwImNTUVAFCzZk0XW2I7UVFRrjYhT3i6/fZEzoX9kHNpRmvnLNnGk9tCoGAda37H0mc4Li4OYWFh2f5fpyyRtR6IyWTC5cuXERISAp1O52pzBEEQ7IZSCnFxcShVqhS8vHKOBPD0trAgHatALL3m+VbACIIgCIKQf5EgXkEQBEEQPA4RMIIgCIIgeBwiYARBEARB8DhEwAiCIAiC4HHkOwHTr18/6HS6e17PP/+8q03LEaPRiPvvvx+PPfZYhvdjY2MRFRWFd955x0WWWYZ23l9++eV7/jd48GDodDr069fP+Ya5EE+9F92J9OfQ19cXFSpUwKhRo5CcnOxq05yOtW2EJ587W9qTqVOnonz58ggICEDTpk2xY8cOJ1kr5Mb7779/TztYvXr1PO833wkYAOjUqROuXLmS4TV58mRXm5Uj3t7emDNnDlavXo0ffvjh7vtDhw5F0aJFMXbsWBdaZxlRUVFYtGgRkpKS7r6XnJyMhQsXomzZsi60zHV44r3obmjn8MyZM5gyZQpmzJjhEc+DvbGljfDkc2dNe7J48WIMHz4cY8eOxZ49e1CvXj107NgR165dc7bZQjbUqlUrQzu4adOmPO8zXwoYf39/REZGZniFhoa62qxcqVq1KiZOnIihQ4fiypUrWL58ORYtWoR58+bBz8/P1eblSsOGDREVFYVffvnl7nu//PILypYtiwYNGrjQMtfhqfeiO6Gdw6ioKHTv3h3t27fHmjVrXG2WS7C2jfDkc2dNezJ58mS88MIL6N+/P2rWrInp06cjKCgIs2bNcrbZQjb4+PhkaAfDw8PzvM98KWA8maFDh6JevXp49tln8eKLL+K9995DvXr1XG2WxQwYMACzZ8+++/esWbPQv39/F1ok5CcOHTqELVu2eISgdxS2thGeeO4saU9SUlKwe/dutG/f/u57Xl5eaN++PbZu3eo0W4WcOXnyJEqVKoWKFSuid+/euHDhQp73KQLGzdDpdJg2bRrWrl2LEiVK4K233nK1SVbRp08fbNq0CefPn8f58+exefNm9OnTx9VmCR7MypUrERwcjICAANSpUwfXrl3DG2+84WqzXIY1bYSnnztL2pMbN27AaDSiRIkSGd4vUaIErl696kxzhWxo2rTp3enPadOm4ezZs2jZsuXdda5sJd+uheTJzJo1C0FBQTh79iwuXryI8uXLu9oki4mIiEDXrl0xZ84cKKXQtWtXu7gKhYJLmzZtMG3aNCQkJGDKlCnw8fFBz549XW2WS7G0jfD0cyftSf6gc+fOd3+vW7cumjZtinLlymHJkiUYOHCgzfsVD4ybsWXLFkyZMgUrV65EkyZNMHDgQItWYXUnBgwYgDlz5mDu3LkYMGCAq80RPJxChQqhcuXKqFevHmbNmoXt27dj5syZrjbLZVjTRuSHc5dbexIeHg5vb2/ExMRkeD8mJgaRkZHOMlOwgsKFC6Nq1ao4depUnvYjAsaNSExMRL9+/TBo0CC0adMGM2fOxI4dOzB9+nRXm2YVnTp1QkpKClJTU9GxY0dXmyPkI7y8vPD2229jzJgxGbJTCgp5aSM89dzl1p74+fmhUaNGWLt27d33TCYT1q5di+bNmzvTVMFC4uPjcfr0aZQsWTJP+xEB40aMHj0aSilMnDgRAFC+fHl89tlnGDVqFM6dO+da46zA29sbR48exZEjR+Dt7e1qc4R8xhNPPAFvb29MnTrV1aY4nby2EZ547ixpT4YPH47vvvsOc+fOxdGjRzFo0CAkJCRIAoGbMHLkSGzYsAHnzp3Dli1b0KNHD3h7e+Ppp5/O035FwLgJGzZswNSpUzF79mwEBQXdff+ll17C/fff73FTSaGhoZIuLDgEHx8fDBkyBJ988gkSEhJcbY7TsEcb4annLrf2pFevXvjss8/w3nvvoX79+ti3bx9Wr159T2Cv4BouXryIp59+GtWqVcOTTz6JYsWKYdu2bYiIiMjTfnXKk3pFQRAEQRAEiAdGEARBEAQPRASMIAiCIAgehwgYQRAEQRA8DhEwgiAIgiB4HCJgBEEQBEHwOETACIIgCILgcYiAEQRBEATB4xABIwiCIAiCxyECRhAEQRAEj0MEjCAIgiAIHocIGEEQBEEQPI7/AyELd5CDX45EAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import pyplot as plt\n", + "\n", + "phonon = result['phonon']\n", + "phonon.save(settings={'force_constants': True}, filename='pbe/phonopy_params.yaml')\n", + "\n", + "with plt.style.context(\"default\"):\n", + "\n", + "\n", + " phonon.plot_band_structure_and_dos()\n", + " \n", + " plt.savefig('pbe/band_structure.png', dpi=300)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 -3.852752827221051\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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WH39McLNmjBs/nkWLFhkdTqoMHTqUf/75h8a9e4vk42BePj50//ln/LNnp1GTJoSHhxsd0lOJHpCQardu3aJAwYLIPj589vffZEpHRSKNlBgXx/jWrblx4QKHQkIoVaqU0SE908qVK3m1VStK161L9xkzxI2Kk1w9cYJxrVoRlD07ly5edPr7LnpAgkOoqkrlKlWIT0jg/V9/FcnHibz9/Hj/11+x+PlRvUYNYmJijA7pqS5cuEDb118nsEABunz3nUg+TpSnVCm6fvcdV69coVGjRkaH80TijPBw9i7F0759e/67cIHO48eTv1w5u7ymkHJZ8+Shx/Tp3Llzh7p16xodzhOpqkr16tWRzWZ6zpwpFpkaoELTprTo14+NGzfyzTff2O11RSkeIcXsWTZj48aNLFq8mNqdOlHllVfsEJ3wPIpUrcqrgwZx4MABJk+ebHQ4j9W9e3fCwsJ4a+JEj9jh1l017dOHErVqMfTrr7l06ZJdXlOU4hGczmaz0aZtWzLnzEmrQYOMDifdq9+tGwWDg+k/YAARERFGh/OAffv2MXPWLKq2akV5Fx7+SQ8kSdInCZlMNGzY0OhwHiESkJAi7du3Jyoyks7jx+OdIYPR4aR7sslE5wkTUBSFxi60pkZVVZq3aIFf5sy8PmyY0eEI6MO2bb/6ijNnzrhcpQSRgIRn2rhxI0uWLqV2584Ur17d6HCEZDkKF+aVTz/l4MGDLjMU1717d26Eh9NxzBj8AgKMDkdIVqN9e0rUqsWw4cPtNhRnDyIBCc/0Rrt2+tDbZ58ZHYrwkLtDcZ8MGEBcXJyhsZw5c4aZs2bxQuvWlGvQwNBYhAfdPxTX3IVKIJmNDkCwv4sXL7JmzRoiIyM5e/YsBQsW5ODBg+TMmZP69eunajrst99+y82ICLr//LMYenNBsslE+5Ej+bZFC9577z3mzp2b4u9VVZW9e/eyZ88eoqOjSUhIwM/Pj4CAgOcqtdKhQwfMXl60/vLL1P4YghNkzZOH5n37suzbb9mwYQMNnvMmQZTiER6waNEiZs6cyZEjR4gIDSVeUZ54rBkICAigcPHi1KtXjy+//PKJ77PNZiNzlixkL1qU/kuXijpvLmzWRx8RsmoV10JDCQwMfOwxSUlJjB07ltWrV3PqxAkib93C+pQ/f29ZJmtQEKXLlKFt27b06NHjiTcvO3fupGatWjT/6COaffSRXX4mwf6SEhIYWrs2mf38uPjffw5rJ6XXcZGA3FR0dDSfffYZ8377jajYWHyAirLMCyYTlU0myssyAZKEN2AD4oHzqsp+RWGforBHVQnVNCySROVq1Rg7diy1atV6oI2PP/6YSZMm0XfhQoq+8IIBP6WQUhGXLvF1/fo0atiQ1atXP/Bvx44do3///mxav55ERSGbJPGCLFPFZKKyLFNClvGTJLyARCBG0zimquxXVfYmny93gAxeXrRs04bx48eTO3fuB9ooVrw4oTduMHz7dtFTdnE7Fixg3qefMmfOHDp27OiQNkQC8lAxMTG0atWKLRs3YtU06ptM9LFYaGk2Y05lD+WUqjI1KYlfrFZigJxBQfw6axbNmjUjLi6OLNmyUbRaNXrNnu2YH0awq4VDhrBtzhxOnzpFkSJFCAkJ4bW2bblw/jxm4E2zmd5eXlSW5VT1ZjVNY6OiMDkpib8UBQkoX7EiK1euJHfu3Pz111+8+uqrvDZ0KPW6dnXUjyfYiWKzMaJhQ9SYGCJu3HBIGyIBeaD58+fzXrduJCQm0tNiobfFQkk77CQZq2nMt1oZmZTERU2j+csvkylTJubNn8+gVavI4wY1xwSIvnGDIbVqUSk4mIoVKzJj2jQySxKDvLzoarGQ1Q5DqFdUlZ+sVsYnJaHKMl+PGsV3339PnKoydPNmzF5edvhJBEcLWb2aGe+/z8SJE+nbt6/dX18kIA8SExNDixYt2LplC2Vkmd99fAhOYeK5lnzB6GGxkOsZkw/iNI0vExOZaLViMpko06AB3X/+2R4/guAkS775hk2//oqqKHQwm/nBx4dsDnh295+q0i0hgU3JzxvbjxxJrTfftHs7gmNomsaIRo2Q4+IIvXo1Vd8bEhJC3759mTRpEsHBwY89RhQj9RChoaEUyJuX7Vu2MMTLi/1+filOPgDXNI1hSUlcS8F9hp8kMcHHhx+8vFAUhbpduqQldMEAtTt1QlUU+pjNzPP1dUjyASgoy2zw9aWJyYSXnx9VW7VySDuCY0iSRL0uXbgWGsrevXtT9b2iFE86cfHiRUoWLUpiVBT/+voyxNsbLyfMRNuqaeQsUIDiNWo4vC3BvrIXKEDp2rXZ4YTzxArslyRqvPEG3n5+Dm9PsK+qrVtj8fGhf//+hsUgEpCLCg8Pp0KZMsjx8Wzx86O22TlLtq6rKktsNmp26SKmXbup2l26sN9qZe9TpuPbw2KbjQibjVqdOjm0HcExfDJm5MXXX2fnrl2Gbe0hEpALUlWViuXLkxQby1o/PyrZYaJBSs2wWpEtFqq1beu0NgX7KlO/Ptly5GBKUpJD25msKJSoVo2cRYs6tB3Bcep07ozNamXw4MGGtC8SkAvq1asXoWFhzPPx4YU0Jp9cksQQLy9ypaA3o2kav2oalV55RdTxcmOyyUT1Tp34Q1WJd9Aco3Oqyk6rlZqdOzvk9QXnyFW8OIUqVWJOKipo2LMSgkhALubAgQP88tNPvGk208piSfPr5ZJlhnp7P3MGHMAJVeWC1UrFFi3S3K5grOBmzUhQVTY4aBjuL5sNi8VCmZdecsjrC85TqUULbkZEcP369RQdHxwczKZNm544Ay41RAJyIaqq0rxpUzJLEt/7+Di9/b9sNry9vUXFaw+Qo0gRcuTNy182m0Nef7mqUqJGDTH5wAOUbdgQTdOYMGGC09sWCciFjB49mrAbN/jZ29th02efZrmmUbJOHSwGJD/BviRJokzTpizXNFQ7D8Pd0jS22WyUdaF9iITnl71AAYIKF2bJkiVOb1skIBfyw6RJlJFlWjlpxtv9wlWV3VYrZcUOlh6jXMOGhNts7FdVu77uPzYbiqZRVmy54DGCmzblv4sXSXLwxJWHiQTkIrZv38718HA+tFgMmf78t6KAJFFWjOl7jMJVqpAhY0a7D8P9ZbNRsHRpMufMadfXFYxTtkEDFJuNX375xantigTkIgYOHIgf8KYdJh7c75qqMjQxkWvPuAvearORr3hx/J9Qyl9wPyazmeK1a7PFzkNwmyWJ4vXq2fU1BWMVrFgRLz8/li9f/sxjQ0JCqFevHiEhIWluVyQgF6CqKvt27qSrxUJGO/d+UlqKZ68sk7dSJbu2LRgvX7lyHFBVuz0HClVVwqxW8pcvb5fXE1yDLMvkK1OGoykoryNK8XiY3bt3k6RpNDbg2Q9AvKZxwmolf9myhrQvOE6+cuWIURTO2CkB3X2eJM4Vz1OgQgVuOGh7hicRCcgFLFu2DIDKqdgq254OqSqKpom7Wg+Uv1w5APbbaT3QfkXBP1MmsuTJY5fXE1xH/nLlSEpM5MyZM05rUyQgF7B9+3YCJYncBtVe268omE0mchUvbkj7guNkyJyZ7Lly2S0B7dM08pUvL+oEeqB8yTcrixcvdlqbIgG5gNMnT/JCKnepTKmUlOI5oKrkKVYMi7e33dsXjJc3OJh9dnqt/UDeChXs9GqCK8lesCBefn5s2rTpqcfZsxSPMQ8dhAfE37lDYQcNv90txfM054HAYsUc0r5gvOyFC3PCDjc3iZrGdauVhoUK2SEqwdXIskxg/vxcunTpqcfdLcVjlzbt8ipCmqiqiq+B7V+VJDIFBRkYgeBIAUFBhFmtpHXz47szKQPEueKxsuTKxa1bt5zWnkhALkDTNEwGjqlfVxQCcuQwrH3BsQJy5CBJ00jrZeVeAhLniscKyJmT2Ph4p7UnEpALkGWZRAeVzX+WWE3jjqKIu1oPlil7dkBfw5MWd79f9JY9V0D27CQkJDitPZGAXIDJbCbKoLbFsIrny5zcY3nWYuRnuaZpmM1mMmTObIeoBFcUkCMHNqvVaTXhRAJyAdlz5+aAg/ZteVYpnmvirtbj+d/tAdkhAWXOlk1MwfZgmYKCQNM4efLkE48RpXg8yMWLFwE4qqokOWAY7lmleGKTP3tnyGD3tgXXYPH2xmwyEZfG8ytW08T+Px7u7nXgaRMRRCkeNxcTE8NHH31ElsAsFCxUkHPnzmEDjtm5bH5K3K2TbErj1t+Ca5NNJtJaE9sGyAaVixKc4+51IDEx0SntiQTkRLt376Z27dpkypKJ77//nsi8kTAA+BOQYK+DhuGexpZ8VyyJBOTRTCYT1jS+hg19wozgue7eYDhrIoK4nXEwm83G2LFj+e777wi7HgY+QGugDVD4/8fJ5eG341a64+XU+MRofvqgaVqa7zbFueL57q4Vc9aIiEhADqKqKp999hmTfpiENcEKhYBPgSbAYx63qK/D9i9VjloUytrxl/+sUjzm5K+rBvS+BOdRFQVzGnsvZsR54unU5M0LvZ9SPUWU4nFxEyZMYPDQwcTdiYOqwDtAME+/hawH5swwNdbKj/ZMQM8oxXN3+zvFmtYBGsFVaZqGzQ4JyII4TzydkoIEJErxuKj58+eTLXs2+vfvT1zuOPgRmAxU5NnjFxawtYGZipVIJy5KzZLcA4qLjHRam4JzJcTEoKoqWdM4fTqLJBEbZdSKNcEZ7l4HChYs6JT2RAKyg3379pG/QH7efPNNbnnfglHAbKBKKl+oDSRZYECi81Yi3x2aiwoPd1qbgnNFhYUBPLUiekrkkiSio6Lu3SULnicqLAxJlsmbN69T2hMJKA1UVeXtt9+m6otVuRx9GQYBC4GXeL4nttlB6QszrDbWOumPPIckISESkCeLTv7d5k7jEFxuWUbTNO5ERNgjLMEFRYWH4+Xt7bTZjiIBPad9+/aRI1cOZs6cCY3Rp1K3Iu1P1VqDXBm6WhOIcsJQnEWSyGax3LtLFjzP3ZsLe/SAAHGueLCo8PCnPv+xN5GAUklVVd59912qvliVCGsEjAOGApns1IAE6mAIN2n0SExATWv5lGeU4oHkoRXRA/JYUeHhZDSZyGCvBCTOFY8Vef06mTJmfOoxohSPQY4ePUqOXDn45Zdf9F7PQqC2AxrKBcoQWGC18WFiYpr2cXlWKR6APKpK5PXrz92G4Nqirl8npx1mVmaXJEySRJQ4VzzW7dBQsmXL9tRjRCkeA8yZM4cKlSoQkeiAXs/jNAA+gx+tVvokJqa5J/Q0pSWJ63Y4mQTXdO34cUrbocyTSZIoZrEQeuqUHaISXE1SQgI3L1+mdOnSTmvT7glIURQGDx5MoUKF8PX1pUiRIgwfPjzNuzEa6cMPP6Rzl86ohVWYg2N6PY/TGvhUT0JvJjrumVBlk4kb164RK6ZiexxN07hy9ChV7fRQuaqqcsUOQy+C6wk9cQJNVWnatKnT2rR7Aho9ejRTp05l8uTJnDhxgtGjRzNmzBh++OEHezflcDabjZo1a+qxNwJ+Bpy9a0EbYCQsMtkomRDLGgfMjqucPDxz+ehRu7+2YKybly8TExNz73ecVpVNJq6cOiUWpHqgS0ePIkkSrVq1clqbdk9AO3bs4NVXX6VFixYULFiQ1157jcaNG7Nnzx57N+VQV65cIXe+3OzYsQM+AIah13EzQgNQ/oDwYI2m8fG8mxCf4sWqzyrFA1BMkshoMnH5yBF7RSy4iEvJv9PKduoBVZZlrFYr186cscvrCa7j8pEj+GbIQKZMT3+2YM9SPHZPQDVq1GDDhg2cPn0agEOHDrFt2zaaNWv22OMTExOJjo5+4MNoBw4coHDxwtyIugETgM4YX4kxF6iTgc9gpmwjV1wMPRMSOPKM2lx3S/HkesIFKFrTmGq1oqJxUSQgj3P5yBEsZjO/WK2Ep/E50AVVZXFyD1z0lj3PfyEh5MuT55nH3S3FExwcnOY27Z6APvvsM9q3b0/JkiWxWCxUrFiRvn370rFjx8ceP2rUKAICAu595MuXz94hpcrOnTt5ocYLWDNYYSZQ09BwHiQBrUFdAglvw4yMVsrHxVEjIZapSUnsUxQSn9Ez0jSNi6rKEquV9xMSyBEXQx9rInG5VM4f2OvWz+qER50/uB9roI0vtSTyxMXSISGe+VYrp1X1mRNbbJrGEUVhptVK04Q4isTG8oPFipzFzIWDB530EwjOkBgby/WzZ6lcubJT27V7MdKFCxcyd+5c5s2bR5kyZQgJCaFv377kzp2bLl26PHL8oEGD6Nev373/j46ONiwJbdq0iQZNGqBmUWEakNuQMJ4tEHgPbN2AzbD7T5VdhxLREsEsQUmLTGVVJpMk4YO+j0s8cBaVvZpCVPJjJHMg2FoBrwLnILrvDa6dPk3uEiWM+bkEu4qLiuL8/v3wMaiNQF0FixbZ+OOKfgJkMEFFWaY0JvzQi40mATHAIRSOKCqJKiCBqRhor4PSGJhq48jGdWjaSLE9t4c4sXUrmqry3nvvObVduyegAQMG3OsFAZQrV46LFy8yatSoxyYgb29vp668fZJt27bpyScwOfnkMDqiFDADDUBtACQAZ8F2Eo6eVDl5RkWKBxKTj/MGay6gFFBS/7AF3vdamUHykzmyfr1IQB7i+ObNaDZVn7UZAHQAWwcgCjgJsSdh2wmVXZdUpCTACniB5gO2wtw7TygOyv1biNSGO39EcOXYMfKVLevsH0twgMPr1uHj50e9evWc2q7dE1BcXNwjdYRMJhOqAdtNp9TevXup17AeajZVn+mW3eiInoMPUDb5A1K//bIXaNVUQtatpknv3vaNTTDEkfXrkEuYUHM89JwwAKiW/MFznCvBIGc0cXjdOpGAPICqKBxZt46KFSo4vW27PwNq2bIl33zzDX///Tf//fcfS5cuZcKECbRu3dreTdnFsWPHqFGnBkqAovd83DH5PE0EMD3587PUgcshR0RZHg+gWK0c/XcDam0HbCBnBrWGwqH1q+3/2oLTXTh4kPjoaN5+++0UHe/SpXh++OEHXnvtNXr16kWpUqX45JNP6NGjB8OHD7d3U2kWExPDizVfxOZjg6lATqMjcoAIYAYpS0A1ABmObtzo2JgEhzu7dy+Jd+KgloMaqA2hx05xOzTUQQ0IznJk3TpMFgtdu3ZN0fEuXYrH39+fSZMmcfHiReLj4zl37hwjRozAy8vL3k2liaqqVKpciZiYGL20jnO2v3BtmUEKltm9bLHRkQhptHfZMuQcJv0ZjiNUB8lLYt/y5Q5qQHAGVVHYu2wZRQsXxmx2/gbZ6bYW3BtvvMGZ02fgC6Cc0dG4Dq2Vyrlde7l+9qzRoQjPKS4qin1/LUNtpThu/Zo/aA01Ns/7DfUZa9EE13Xs33+JCgvj888/N6T9dJmAxo4dy+Ili+FNoIXR0biY+iBnMbF1zhyjIxGe0+5Fi7DZrPr0ekdqC5GXr3FiyxYHNyQ4yubZs8ng789bb71lSPvpLgFt3LiRgYMGQlUgPUz2CgTeTf6cEl6gvqKwc9FCEmNjHRiY4AiqqrLp91lQT4OnV9VPuzIglzSx+ffZDm5IcIQbFy9ycutW2r/xRqq+z6VL8biyyMhImr7cVF/j8w0OmITugpIXraY4AQG0hqTYOPaK8X23c3rHDm7+dxlec0JjEqhtFY7/u5mIy5ed0KBgT9vmzsVkNjNmzJhUfZ9Ll+JxZY0bN8aaZNUnHThyLx93lwuoJfHvrF9cev2W8Kh/Z/6CXNgEwU5qsDFIGSS2/PabkxoU7CEhJobt8+cTXKECWbNmNSyOdJOAZsyYwd69e6EHUMToaNxAZ42w0+c4sGKF0ZEIKXThwAGObfgXtbMDJx88zAe0N1Q2/zZL7KrrRjbOmEFibCw//vijoXGkiwQUGRlJrz69oATw+JqowsMqgFRbYvn40diSkoyORngGTdNYOnokclETNHFy4x1B81FZ9d13Tm5YeB53bt5k3bRpVKpYkWrVqhkaS7pIQI0bN8ZqterbaKeH5z73S00lhIdoPTVuXw5lx4IF9o5KsLMTmzdzfvc+1J4K2GfvuZTLCGo3hZ0LFxB27pyTGxdSa83kyShWK/Pnz3+u73fpSgiu5oGht8JGR2OA1FRCeFgRoBn8/d0EEuPi7BuXYDeqqrJ0zEikCrJx24e0ASlQYsX4cQYFIKTEzcuX2fL777xUvz7FihV7rtdw6UoIriQuLk4MvaXVexAXFcnGGTOMjkR4gv1//cW146fReqvGbZzoDWp3hZBV//Cf2CvIZa0YPx5Zkpg7d67RoQAenoDefvttrAlW+JL0N/RmL7lBa6+xevL3ojqCC7pz8yZ/fj0E6kvg/GLGD2oGcgkTv3/6CdbERIODER52fNMm9i1bxpsdOhAUFGR0OIAHJ6Dw8HAWLl4ITYHiRkfj5t4FLZfG7P59UWypLt4vONDCrwaToMTAABfYydYE6mCFsPPnWf3DD0ZHI9wnPjqaOQMGkC1bNmbOnGl0OPd4bALq2LGjvr10D6Mj8QA++oXl8pGjYijOhRxctYqDf69C/URxfNWDlCoGdNNYO3UKFw8fNjoaIdniESOIuXWLFStWPLJfm5FcJxI7OnPmDOv/XQ9tcd1ttZ0ltaV4nqQc0AFWThgnhuJcwJ2bN5n/5SCoJ0Ejo6N5SFeQikrM7t9XDMW5gOObNrFr4UI6vvkm1atXT/Pr2bMUj6Rpmgv03f8vOjqagIAAoqKiyJTp+coVVKlShf1H98MywLhFvp4nAeS3TOQJKEm/P5dgcYGt1NMjTdOY3rMHR3duQJ3vQr2f+50Buko0fKc7rQYNMjqadCvm9m1GNm6MRVUJDw93Wu8npddxj+sB7dy5k/0H9sNbiORjbz6gDlG4cuI4CwYPxsXuXdKNtVOmcHj1WtRBLpp8QB+Ke19j/U8/cWDlSqOjSZcUq5UZ779P7O3bLjf0dpfrRZRGvXv3hgxAB6Mj8VBlQBuksWvhQjbPFlWQne3wunWsGDcO3gHqGx3NM3QCGsFvn/Tj8tGjRkeT7iwePpyze/Ywftw4uwy9OYJHJaDw8HAOHj4IbdCTkOAYLYA3YfHwrzm5bZvR0aQb106fZmbfPlAX/bmeq5OAL0EtqDD1vbeJvnHD6IjSjW3z5rHlt994s0MHPvroI6PDeSKPSkADBw4EBWhtdCQuJA2leJ6qN1BVY0bv97nx3392fnHhYTG3bzPl3a4ouawwRHOfv1wfUMcoxCTd5Of3u4u6gk5wds8eFnz5JWXKlHHIglNRiucxVFVlwaIFUAMx8+1+aSnF8zRm0EZoJAXEM7lrZ6LDw+3cgHBXYmws0959m6joMNSxCvgZHVEq5QB1tMLFwyH81v9jsYW3A4WeOsVP775LQEAAe/bscUgbohTPY8yePZuE2ATnbMQl6PxBnaRwOy6USR3bcefmTaMj8jhJCQlMfbcbF08eQh2vuO/NVTnQhmscWLWKuZ8OFPtMOUDYuXN81749kqKwb+9e/Pxc/07FYxLQiG9G6Dudvmh0JOlMHlAnK0TcusR3HduLJGRHSfHx/PTeO5w7uA9tggpljY4ojeoDQzR2L17M/EGfiSRkR2HnzzOpXTuUhAT27d1LkSLusemZRySgM2fOcP78eXgd55eiF6CAnoTCb5xnYvvXiBLDcWmWGBvLj93e4vS+nXryCTY6IjtpAnwFO/9cyO/9+4nSTnYQeuoUE9q2xRoby+5duyhVqpTRIaWYRySgUaNG6f/R3Ng40rXCoE5TiIi6xPjX24h9YdIg+sYNvuvUgfNH9qN9p0JloyOys+bA17D3r+X80run2OojDc7t28fE119HTUriwP79lC9f3uiQUsUjEtCq1augNK67KM9I9irFkxL59SQUKV1jTKuWHN+82QmNepbLR4/ybcvmXLl0DO1H1fgK147SCBitcWTLBsa1bcWtK1eMjsjt7FiwgO/atcNLljly6JDTej6iFM99bt26Rbbs2aAnevUDwXgxIH0lwU5o9fkXvPTOO0iSURvVuI/9K1bw+4D+KIVtaKNVcI2K+Y51FuQBJnwSMtJ92nSKvvCC0RG5PMVmY8nw4WyePZtSpUqxZ88eMmbMaHRYD0g3pXi+//57UIHaRkci3JMRtLEaWieNpSNG8Fv/flgTEoyOymWpqspfY8cys08fbPWS0Kamk+QDUBTUmQrxBe/w3Zvt2TZvntERubSY27eZ3Lkzm3/7jfbt23P8+HGXSz6p4fYJaMGCBZALKGh0JMIDTOiLVYfBvpXLGNP6Fa7YYd2Ap7l5+TLfd+zA2ik/wgfAUMDH4KCcLTNoP6hor6r88fnnzPzoQ2Ju3zY6Kpdzcts2RjVtyvm9exk/bhzz5883OqQ0c+sEZLPZOHXuFNTDuK2IhadrCtoMjTDrOUa/2pJVkyahWK1GR2U4VVXZ+vvvjGjSkPPn98H3QGfS73lsBgYCQ+HAxpUMb/QSh9euNTgo15AQE8P8QYOY3KkTstXKpn//pV+/fkaHZRdunYDmzp2LZtXE8NvTOKoUT2qUAHWWgtZZZdUP3/Htqy9z5fhxAwMy1t1ez4LBg7E2TkSdp4B49KFrBtp8lbiSkfzcvTszP/qQ2MhIo6MyzMlt2xjeoAE7Fyygffv2hIeFUatWLUNjEqV4ki1duhQseO5MIXtwVCme1LIA7wO/aoQlnWX0Ky+z/NtviYuKMjgw50lKSGD9Tz8xommj//d6BgHuO4TvGNlBG6fBEL039HWjl9i5cGG6KuFz+9o15nzyyb1ez7///sv8+fMxm81GhyZK8dwVEhICRdG774J7KJncG+qmsn7Wz3xVpybrpk0jyYMnKSg2GzsWLGBovdosG/st1mYJeq+nmtGRuTAJaK73hmIr3GbuwIGMaNqQQ2vWePQ+VLGRkSwbNYqhdeqwd9kyOnToQHhYGHXq1DE6NIdw6wR0NfwqpH0quuBsFuBd0BarJDSMYfm40QypW4sdf/zhUSvjNU0jZPVqRjRpwLxPPyW6XAT8ocEARK8npbID32gwC25kvsj0Hj0Y2+ZVzuzaZXRkdpUUH8+aH3/kq5o12Th9OjWrV+e/CxeYN2+eS/R6HMVtf7LQ0FBs8TYoaXQkwnMLRH/w3EHjzk8RzPvsM/758Xvqdu7Ki6+/TsYsWYyO8Lkkxsayd/lyNv02k+snzyBVk+AroKTn3rk7XCnQJquwB678eIzv2rencNXK1H2rKxWaNMHs5WV0hM/l1tWrbJ8/n21z5hAXFUW5cuWYO3cuZcu6e+G/lHHbBLR48WL9P0QCcn/5gBFAZ7g9KJTlo0bx99ixVGrZktpvvUWBChXcYiHr9bNn2TpnDjsXLSApLh5qStAHtCoi8djNC6BWUWA9/PfNQc732U9AlixU79SJmh06kCW365cLV1WVU9u2seW33zi6YQOSLFOmdGm+X7qUevXqGR2eU7ltAtqwYYM+lFPI6EhcnDNL8aTVTeAqTPCykAhMWbGCcUuWkL9UKSq1akW5hg3J4WJVfm9dvcqRDRs48PcKzu3ei5zVhPqaAq2AXCLxOIQMHAVzkspSX19WxcQwe8oU1k6eTJn69anQvDll6tfHP5vr1ObSNI3LR49yZN069ixZws0rV/DNkIEOHTowfvx4cubMaXSIKSZK8QCFChXiP9//YJbTQhMc6Q6Y34B6d0ys9fZFkiQUTWONovCzzcYaRSFBVcmZLx9lmjWjXMOGFKpUCZOTx8fvv5CErF/NteOnwSwhVZLQXlb1LQfcczTIfRwE3ocJ3t58nDz0dkfTmGu1MlNV2Wu1IkkShYODKdu0qWE3LtaEBE7v3MmR9es5tGYNdyIikM1mCubPzyeffEKPHj2QZbd+DP9EKb2Ou20C8vX3JaFBAnzuxOAEx/kJfGbBad8M5HvMH2W8prFBUfjLZmO5phFus+Hn50e+cuXIW6EC+cqWJX+5cgQWKGC3P2pN07gdGsrlI0e4dPQoF0NCuHj0MPGR0cj+JtQair4GrTpiUoETmd6CSudldnr7YXrM0GyYqvK3orBcUVibfOOSNTCQvMHB5CtfnvzlypGvbFkyZc9ut5gUm42wc+e4dPgwl44e5b+DBwk9eRJbUhJePj6UKVWKTp068f7777vFRnFp5fEJSPaS0bpo8J4TgxMcwwrml6F7rIUffZ5dh0bVNPapKuttNvarKnslicvJ1RX8/PzIWaQI/nnyEJAjBwFBQQQEBZEpKAi/gABMZjOyyQSShGqzoSoK8XfuEB0eTlR4OFFhYUSHhxMdGkrYuXPciY4GIMhiwU9RuGhW0cYCVXDjAWw3dhzoBn/5+tIyBb3fuzcu2xSFfZrGfk0jMnmmZdbAQAILFcI/V65HzhVvPz9kkwnZbAZNQ1UUFKuVmNu3Hz1Xrl4l9OxZkpKSACjm5cW1pCRsPj788ccftGzZ0mN7Ok+S0uu4W/4JJSUl6RUQXGeIV0iLzWCLhJ5+lhQdLksSL5hMvGD6/+6DEV5eHFBV9ttsnDpxgtATJwiVZY6oKrdTOLU7wGwmpyyTR9MorWkUkWUq+/pSWZbJLcucUlVKxsZCJG76l+MBFkNus0RzU8p2nvSVJF42m3k5OVlpmsZ/msY+ReFAdLTeUwkJ4aIkcV1RiE3BYlcJCLRYyCVJ5FFVqkoSpZPPlYomE5kkie8lib4JCRSwY4/cE7nln9Hxu2Vc7NeD9lwRwFKgNS47EUFeCNUtMmVTeFF5nEBZprEs0/gxd8WJmsZ1TSNK07ABNvQC6pbkj4ySRC5JwvcZM+1KyDL1LCa2LlRQmj53qMLzigJ5DXwgWx479JYSkiRRSJIoJMu8/ph/v6NpXNM04pPPFSv6nAcL+sUyqySRQ5IwP6P9tywWPk1MpH///vqEKQ8SEhJC3759mTRpEsHBwWl6LbdMQPdKQLjoBdWl3C3FUxvXfL+ug3oIevk47sm9tyRRwE7TuHubLGw6psAVIK9dXlJIqY2ADd5JYU/5efhLEv52OFcySxIdLRbmbdlih6hcy/2leNKagNyyb3jmzBn9P1zxgiqkTvK9xEtp6P04U/27PSyxs4TzHYeSZpkgNxnSeslkIt5m45zYnv6J3OM3+ZDz58/rA7HuuVBeuN9JCLJI5HSTi0o2SSKPRYKTRkeS/piPQnXc40YFoHLyTdW9RfPCI9zjr/4h165d06e9uuUAonA/6Ti8oLnXaVhNk5HT724SxkgE5T+o7CY3KgDFJAk/4N9//zU6FJflPr/N+yQlJYnFfinlypUQNJBPQFXZfe5qAarIJqRTgEstYPBw50BT/9+rcAeyJFFRljl69KjRodiVPSshuGUfwmaz4UY9cWMF4rprpRRQYiG/j3vdB+WXZZR4IJH0t322UW7qn/K7QU3A+xWSZY572J5XwcHBbNq0yS6v5V5/+cmsVqtIQJ4geQsgX2OjSLV78SYaGUU6k/xeP2uqvKvxlSSP2mLE3twyAWmapk9CENybqn9yt3uJe380qpFRpDPJ60Pd8VxxsWIzLsUtE5DZbBZ//J4g+Tmeu+2Feq/jI55DOo+3/inBzS7miZqGyY2eWzmb+yYg0at1f94gyXDbzS4qt+/2wL2NjiQdSa7fedvYKFLtlqZh8RYnypOIBOTpIoDpyZ9djQSmghCiuld3NkRVMefHTafwuKnk3RRCUlCrzZXsV1UKFC5sdBh2FRISQr169QgJCUnza7llAsqSJQvEIqbBpsTdUjyumIAAW1nYhXtdVHajYEsfOya7jmxgzqpf0N3FTU3jqqZRvXp1o0Oxq/tL8aSVWyagggUL6lUCY4yOREizUnDKphLvJsNwSZrGUZsqtoI3gK0M7NHc52Zlf3JvrWXLlgZH4rrcMgEVLVpU/48bxsYh2EFJUDQ46CZ3todVFZuGSEBGKAX7VAWbm9ys7FEUzED9+vWNDsVluWUCKlky+a//prFxCHZQHMxZ4PfkDeVc3W9WK+ZMQCmjI0mHasIdBf52g3U1qqbxq9VK7nz59GfWwmO55TtTvnx5/T9ED+jZXLkUD4AZbG1h1q9WRmveZErjQsMbqsppVSU0eV+XUE3jmqpyFYiUZWyShE3T0PSmsQD+mkZuTSO3JJFLlvXPkkQRWSaPJCElxxSjafyqWrG1Sf5GwblKgqkU/HDWyquWtP0CNE3jvKbxn6rq50ny52uaRqgkEStJ9/aOkvj/fkDZVJXcQG5ZJpckkVuSyC3LlJRlAu47dzcqChc0jR8GDkxTnK7InqV43HZLbsksQU+gs/NiExzkBkivwA8Wb3p7pXxxTbiqsl9V2a8o7FNV9kkSV+/rSXlZLARky0ZAzpz4586Nb6ZM+pbcyXekqqKg2mzE37nDnWvXuBMWRuSNGyQk/r/EQaDZTBWgiixzU1WZarPBMiCXnX52IXVWAcPgdIYMFEthYVJN0zinaexXFP1cAQ6oKlH3zajz8/MjIDCQTLly4Z8zp74l993t2wHVZsNmtRJ7+zYxoaFEhYURdesWtvteo7CXF1VVlSomE0utVg7KMjGJielyR1RDt+S+evUqn376Kf/88w9xcXEULVqUmTNnUqVKFbu1YfYyY4tw/a64kALZgbowaVsSPTTLE3ebVDWNvarKCpuNZZrGseRkkyFDBvKVK0epChVoUq4cuYoXJyBHDnwzZbrXe0mNhJgYosLCCDt3jktHj3L50CF2HjpEVGQkAHJvE2ptRd/kLxg3HUdwUw3ANB4mJybxnc+TC/HFaRobFIUVNhvLNY3w5GG7wKAg8gQHU7t8efKXLUv2QoUICArCyzf1BaFUVSUuMpLIa9cIPXWKS4cPs+/QIZYdP06iqoKqkjlLFqq/+CK9evWiZcuW6TIZPY3de0C3b9+mYsWK1K9fn549e5I9e3bOnDlDkSJFKFKkyDO/P6WZMzAokJvFb8IEe0YvGOYYSO/AaC9vBtzXC7JpGqsVheXJF5IbNhsZ/f0p3bAhpevVo0BwMIH58z9XokkNTdOIvH6dS4cPc2LLFkLWryYm7Cayvwm1ugJ10BOSKE7qeDNB/gn2+vpR6b4qA5GaxmKrleWqyjpFIUFVyZE3L2WaNaNkzZrkK1cO/2zZHB6eqiiEnT/PfwcPcnTDBo5v2oQ1MREvHx9KlyxJ165d6dOnj0cnoxSPZNk7AX322Wds376drVu3Ptf3pzTwmjVrsuP4DljzvJEKLud7sMyHQz5+ZJYkZlitTFNVQm02cuTLR9lmzSjXsCGFKlXCZPCDXU3TuHz0KEfWrSNk/WquHT+NHGBCfVmBNojtuh3JBqa3oPgliRDvDBxTVX60WpmrKCRpGoUrVqRskyaUa9iQHCm46XU0a0ICp3fu5Mj69RxavZo7N2/i7evLy82bM2HCBPLnz290iHZnWAIqXbo0TZo04cqVK2zevJk8efLQq1cv3nvv8XsCJCYmknjfmHt0dDT58uV7ZuADBw5k7NixsBJ9CEdwf/Fgeh0y3ZS4o4FssVCldWtqd+5MvrKuvfIz/MIFts2bx46F80mIikGqLqG11aAG7ldB0x0cBd6FPCYzV202sgQGUuOtt6jRvj0BQUFGR/dEmqZxZudOtsyZw6HVqwEoUbw4Q4cOpV27dgZHZz+GJSCf5HHZfv368frrr7N3714++ugjpk2bRpcuXR45fujQoQwbNuyRrz8r8O3bt1OrVi0Yhz70ITxeBLAUaI3rzoQD2AfSjzLacZXA/Pmp160bL7Rpg19AgNGRpUpSQgIHVqxg0++zuHL4GHJ+E+r7CryEqOBuDzbgL5BnmFBvKhSrXp16XbpQtmFDw3vFqRUZFsbOP/5gy++/cycigsDs2Zn8ww8un4hCQkLo27cvkyZNIjg4+LHHGJaAvLy8qFKlCjt27Lj3tQ8//JC9e/eyc+fOR45/3h6QqqqYvE3QFdfdcM0VnAS6ALNxzcWTJ0H6UULbo5E/uBwt+w2gZO3aDn+m4wz/hYSwctIETm7aglRKRuutQlWjo3JTKrAB5J9MqFdUqrZ6laYf9HGJIba0UqxWQlavZsXYsURcukT+AgWY+euvvPTSS0aH9lhz586lU6dOzJkzh44dOz72mJQmILs/BcuVKxelS5d+4GulSpXi0qVLjz3e29ubTJkyPfCRErIskzUgK5xIc8iCES4DX0jQBQJv5ue9adMYsPQvStWp4xHJB6BgcDAfzPqNj/74g3zeZeADkPrI+k2BkHK7Qe5mgi+hZLFaDFq1ii4TJ3lE8gEwWSxUbtmSwRs30mHUKG7HxtKgYUPKly/vcdt5P8zuCahmzZqcOnXqga+dPn2aAgUK2LspSpYoCWmvhyc4kwLMBamjTKajgXQcM4Yv12ygQtOmHpN4HlbsxRcZsPQv3vvpJwJv5YduwPe430ZIzhYFDAE+hHwZytF34UJ6/TqbPKU8swyFyWymZocODNu6lVcHDuT0+fNUCA6mV69eqG5Sqiq17J6APv74Y3bt2sXIkSM5e/Ys8+bN4+eff6Z37972boq6detCJHDV7i8tOMJFkHrI8INE/c5vM/TfLVR/4w23G7t/HpIkUaFJE75cvZ5XB36G6U8z8lsmOGJ0ZC5qC8gdTHjvyEDn8eP5ZNFSir7wgtFROYWXry+NevZk2NatVGzenKlTp5Inb14OHz5sdGh2Z/cEVLVqVZYuXcr8+fMpW7Ysw4cPZ9KkSU8cK0yLDz74QH+w+3wzvtMHVyjFc7fX01km6508fPznn7T58svnWvzn7kxmM43ef5/P/v6HPJlLQXdEb+h+d3s9A6BkhToMXreRam3bemzv+GkyZs1Ktx9+4N1p07gTH0/FypVdojckSvHcJzAokJv5bsJUJwQnpN4tkD6X0UI0XnrnHV7u3z9dJp7HUWw2Nk6fzooJ4yGvhjpWgXxGR2WgIyB/ZsKS5EO7ocOp2rp1ukw8jxNz6xYLv/qKAytXkr9AAfbv20dgoOtOazVsEoKzNXypIYQAd4yORHjEaf3hse+lTPT944902+t5EpPZTKOePRn09yqykAf5bRPsMToqg/wNUk+J/AXKM3jdRl5o00Ykn/tkzJqVtydPptsPPxB6/ToFCxVi9+7dRoeVZm6fgD7++GN9iuajM7wFI20A6T2ZXIHFGbRiFUWrVTM6IpeVq3hxPl22gmLB1aGvBAtIP7v92oDvgK+hWuvX+WjeH2TOkcPoqFxW5ZYt+WTJEswZMlCzdm1++ukno0NKE7dPQNWqVcM7gzdsMToSAdBvBn4GPofghs3o/+cSsuTObXRULs8vIIDeM2fz0tvv6vUNRwJJRkflYNEg9ZOQFsi8NnQoHUePxuLtbXRULi9vmTJ89vffFChfnp49e9KtWzejQ3pubp+AAF6s8iJsQ7+bEoyjACOAXyVaDhjA2z9MFkNuqSCbTLT54gs6jx+P/I8Zqb/suZMTboL8vgnvkxn44Lffqde1qxhySwX/wEA+nD+fGu3bM2vWLBo0aGB0SM/FIxJQz549IR7Y8cxD058IYHryZ0eyAUMkpNUyXSdNoknv3uKC8pyqtW1Ln9/nYD5qQeorQ6zREdlZOMg9TWSIycwni5dRomZNoyNyS2YvL9qPHMnLn3zCxo0bqVWrllNmyIWEhFCvXj1CQkLS/FoekYDatWuHr78vLDI6EhcUAczAsQnIBnwlIW+SeefHKVR59VUHNpY+FHvxRfr8Pg/LGR+kj2WIMzoiO7kBci8T/tZA+i1cQs6iRY2OyK1JkkTTDz6g9RdfsH37dmrUqOHwJHTs2DE2b97MsWNprwLgEQkI4M033oTdwBWjI0lnFGA4SJsl3pk8heCmTY2OyGMUrlyZD+fMw3LWG+kTDxiOuwXyByYy2rLx8YJFZHdAdZT0qsF779F28GB2797tVsNxHpOAxowZo5e9X2J0JOnMBJDWSnSb9D0VmjQxOhqPUzA4mN6zfsN03IL0uawnfHcUC/KHJvziAug7bwGB+dLzgifHqP/OO7wycCCbNm2iTZs2RoeTIh6TgLJmzUrl4MqwDPe/U3QXi4FF0P6bkVR6+WWjo/FYRapWpcdP02GnBlOMjuY5qMAwCXOoFx/+Pp+gQoWMjshjNe7Viwbdu7N06VJGjRpldDjP5DEJCGD06NH6A9sNRkfiQhxVimc/MEGiTpcu1OzQwc4vLjysVJ06tPniS5gDrDI6mlSaDmyBbt9PJneJEkZH4/Fe/fRTStety5eDB7Nmjf23jBaleJ4iW/Zs3Mp2S9//RkzCcoxQkLuaKFK2Kh/MnpMuiom6Ak3TmDNwAHuWL0abqoFrbxKrWw98Aa8MHEjjXr2MjibdiI+OZnTLltwJC+P0qVNO3/Y73ZTiedjHH30MpxBTsh0lFuRPTGQOyMk7k6eK5ONEkiTRfsQ35C9bHvlTE4QbHdEznAJpuEylV16mUc+eRkeTrvhmykTPmTPBZKJK1aokJbnmqmaPS0Cff/45/pn9YTL62LNgXxPBdN1CzxkzyZgli9HRpDsWb296TJtORnNWpCGy657jCSB/YSJ30RJ0Gj1WrAkzQI7ChXl36lQibtzgVRddGuFxCUiWZcZ+OxbOA2uNjsbD7ABWwOuDh5KreHGjo0m3MgUF0WX8JLQDquvO+pwCUrjM29//KKphGKhUnTo06N6dNWvW8NdffxkdziM8LgEB9OjRg6CcQfqMIavR0XiIGJBHmShRuybV27UzOpp0r0TNmtTs+CbSZBlCjY7mIQeBhfDKgE/JUbiw0dGkey0+/pjAAgXo2KkTCQmuNUXYIxMQwM/TfoYw9GnZ6Zm9SvFMAnOsFx2/HSOGU1xE60GfkylrENJwFxqKSwD5GxMFK1WkvhsXyfQkFh8f3powgdiYGFq3bp3m1xOleFLg1VdfpUjRIvrF11PKmDwPe5TiSR56e23wELLmyWOfuIQ088mYkbfGjHetobjkobfOY8cjm0xGRyMkK1Spkt2G4kQpnhSaPWs2RKNfgIXnYwV5nInitWqIoTcXVKJmTWq+2QFpigyRBgdzFn3o7ZOBYujNBbX4+GOy5c9PVxfqmXp0AqpZsyZNGjeBecARo6NxAg24DKxD35PnO+C35H9bCPyJ/j6kZhh4GaihKq9/NVQMvbmol/t/ggVvfe1bSlmBk+hD1FOAicAYYBIwDX2x63lSVfpHmiqRNV8e6nXtmopABGex+PjQ5ssvuX3rFt9++63R4QDg8Ys4li1bRtbsWYkfGg9zAR+jI7Kz28BKkHeAdBKU5OHGbGbIJEtoKvyHRvY1cHsV2DSQZDDlB1sFoCX6gsbH5ZY4kH81UaXNq2LWmwvzz5aNRt3fZ9XkSWjtNMj5hAPPA8vBfACU86DZ9DvQnBYJXwm8kEhEI1aDMKu+Pl32BqkYKFWBVjz5tUNA26bxyvefYrJY7PwTCvZSrmFDCgYHM+Kbb/jkk08wG7yOz6N7QAA+Pj78MfcPuIr+PMgTaOg9mSEgtwDLFGhx2MRQmxf/+PoSliEDEb7+nPfOyA5vP4Z4eXHIOwMxGTKyz8+PaRZv3r5qId8qCd4FU0f0O+H4h9qZD9yRePnjfk7+AYXUeundd/HNFKD3fO9nBdaD3B3oANkWQcfzZr4zebPdz4/ojBm56pORs94ZOe6dgXPeGbnuk5HbGTOy0deXb/GmzUkzGX4DqRVI/YFdPDjpQQPpR5ncZUqKmoAuTpIkWn3+ObExMfTv3/+5XkOU4nkOTZs2Zc3aNXoSKme3l3W+/8A0HJSjkN8i0Uf2opvFQrbnGB5TNY21isJkWxKrrAqyHygfot/pRoHURqZe+260HTzYzj+E4AibZ8/mz2FD9J5+YWAzmL8F2y2oZZHpY/KildmM13OcKzGaxjyrle9UK8etKub8YPsK/W9pK/AJ9P7tN0rVqWPfH0pwiCldu3J21y5u37yJn5+f3V8/pdfxdJOAEhISyBaUjbiAOPccilOA+SBPhQJI/GDxoZnJhGyn5zL/qSpfJyUy02pDrgJqAfBa7cuwLdvwz5bNLm0IjmVLSmJog7pEFr4O3hqsheYWE2Ms3pSx04w0TdPYoSh8aEvkoE1F6wDyXhOFAyvz0bwF4jmhm7h64gSjmjenW9eu/Prrr3Z//XRbC+5JfHx8mD9nvj4UN8noaFLpCpjeBekH6CtZOOaTgRZms92SD0BBWeZXH19W+/qS/SDIy03UbNdBJB83YvbyouE7PWALZFgPv/n4sNLb127JB/QhnJpmM7u9/fjW4oVpPqhnFBq/L7Zgdyd5SpWidJ06LPzzT6ds4/0k6SYBAbzyyit0easLLEXfy8YdnAbT25D3tMQ2Pz/G+/jg68A/9CZmM1+bvVFtCrU6dnRYO4JjVGvTBouXFz1NFjpbLA5LCmZJYqC3N81kE1nz5KFk7doOaUdwnDpduhAbE8Ps2amZPmlf6SoBAcyaNYsKFSrAOPQ9bVzZSTD1gLJxMvu8/ajxHHey11SVoYmJXEvFXc5MVaVk9erkKFIk1e0JxvLNlIkX2rRhrqZhdfDo+g1VZa2mUbdrV2Q53V1K3F7punXJnCsX33zzTaq+T1RCSKNdu3aROXNmGIjr1dG66z8wfQAVrDJbvP0IfM4/8GuaxrCkJK6l8GJ0UFHYZbVSu0uX52pPMF7tTp24ZrOxwmZzaDu/Wq1gMlHttdcc2o7gGLLJRN233uL8hQucOXMmxd8nKiGkkY+PD3t27cFkNUE/XK9UTwKYPoaiCRLrvP3I5MSx9alWK1kCAynbsKHT2hTsK2+ZMhQJDmaykopVpKmkahpTNI2Kr7wituVwYy++8QaSLNO3b19D2k+XCQigWLFiLF6wGC4BQ0jVim+H+wnk67DM4ktWJyYfq6axUFWp2q6d2GjOzb345ptssloJc9AD5t2qyiWrVZRncnP+2bJRoUkTNm/ZYkj76TYBgV6wdPDng2EL8C2uUVH4EDAfvrF4U9LJxRy3KwpRikKFxo2d2q5gf2Vfegkkib8d1Av6y2bDP1MmCleu7JDXF5ynfOPGxMbEsHfvXqe3na4TEMDXX39Njx494C9gPHqVAaMkgGkoVDXL9DOgnMlfNhuZs2YlXzl3XqkrAPgHBlK4QgWWOygBLdM0SjduLCpee4Ay9eohyTITJ050etvpPgEBTJs2jc6dO8Mi9AKeRiWh1aCGwmyLDyY7Db3lkiSGeHmR6xmvp2kay4AyTZqIGU0eomyTJqxTFOLtPBvurKpy0mqlvHhO6BH8AgIoUrUq6zdsSNHx9izFI640yX777TfatWun1z8bh/OH4zQwLYRmFhOl7HhXmUuWGertTa5nJJWTqsoFq5VyjRrZrW3BWOUaNSJeVdlo517QCpsNi8Ui1v54kPKNGhEREcH169efeWxwcDCbNm0iODg4ze2KBHSfP/74g7feekvvCY3EuRMTjoJyDj4wezmx0f9bqSh4e3tTvEYNQ9oX7C9HkSLkyJuXlXaejr1CVSlevTreGTLY9XUF45Rr1AhNVfn++++d2q5IQA+ZPXu2/kxoBTAAiHFSw4v04qJNDBpT36UoFKhQAS8fdyuSJzyJJEkUrlGDHXacSalqGntUlaLVq9vtNQXjZS9QAP9s2fj333+d2q5IQI8xbdo0hg0bpped74a+yZsj2UDeCN0li13ru6XGXkkinx261IJryV+uHMetVhLs9BzotKoSqyjkL1/eLq8nuI4CwcGpWpBqDyIBPcFXX33F33/9jTncDF0AR85QvAhqEtRyQO8nJaV4bmoal61W8pUta/f2BWPlK1cOm6ZxxE7rgfYnv444VzxPgfLliYyKemZxUlGKx0maN2/O6eOnyeqbFT4EFuCYGXIn9U8VHZGAUlCKZ3/yQ+r8Yvq1x8lTsiQmk+ne7zit9isKQblz4xcQYJfXE1xHvnLlUGw2duzY8dTjRCkeJypUqBBXL1+lcsXKMAEYwaM7h6bVSShkkZxacud++xUFPz8/AgsUMKR9wXEsPj7kLlz4Xs8lrfYBecRQrUfKn9yrXb58udPaFAkoBXx8fNi3bx/vvvsurALaAwfs9/qmE1BNM25BX4iqkrdMGbH+x0PlCQ5mvx1ubjRNI0RVxfCbh8oUFETGrFnZuXOn09oUV5xUmD59OmtXryVjfEboib5eyA69ITkSchi4mddFWSZL/vyGtS84Vra8eblih0kId4A7ikLWvHnTHpTgkrLly5eitUD2IhJQKjVq1IjbEbdp3bq1vqmdPXpDSeBjYAK6hn73I3imTEFBRFit2NKYhO4+RwwQ54rHypwrF5FRUU5rTySg52A2m1myZMmDvaGxgPN+byn2rFI8mqYRZrMRkCOHkyMTnCUgRw40IDytCSj5OZI4VzxXQI4cxMU/fVhHlOJxEQ/0hpYArYDZQEIqX8gLu63TeNizSvHcBhJVVdzVerC7v9vQNJ5joaIH5PECgoJISkx86jGiFI8LudsbOnLoCOWKlIOpQGtgKZDCCihqZghz8PbJTyLuaj3f3YSR0l1xn+SapuHr6ytK8HiwgKAgFJuN6Ohop7QnEpCdlC1blsOHD/Pvxn/Jnym/vr/QG8AGnrl2SCkFuyVjdsS7m/j8AwMNaV9wvIzZsiFJEtfTOBU7TFXJlDWrnaISXNHd68CJEyec0p5IQHZWr149Ll64yB9//EG2pGzwOfA6+iLWJ9WVKwkXrBrRBvSC7na2Ld7eTm9bcA7ZZMJkMpGUxtdJRJwnnu7u7zc2NtYp7YkE5CDt2rUjIjyCH3/8kbzkhYlAM2AUcPqhg0vqnw44YPOwZ5XiuTtKKDYW82wmkymlI8JPZANksVW7R7v7+018ynMgUYrHjfTq1YvLly6zccNGcmXLpVfZ7gy8DfwDxAIFQPbSt8S2t2eV4rk7NVckIM8my7JdEpAkFit7tLvXgaSkJ/eXRSkeN7Jx40aqVKnCS41e4tq1a1AF6Ar4AUOBJkB/UIvCz6oV1cnDcHd3XtXsVKpFcE2aqpLWWwwTgDhPPNrd64DFYnFKe6I/7SD79u2jU6dOnDp9CjIAb6LPjstz30GhwNbkj5NwSdVYoyg0c+Iwx93TTHFA70twHYqqYklj78WCOE88nZK8eaGPk/YFEwnIzi5cuED79u3Zs2+P3sv5CD3xPO73mRtol/wRDaZOMPlmkiEJyPaMuf+C+1JVFZvNhsUrbbvtWhDniaezJQ+9+fr6OqU9MQRnJxERETRt2pTCxQqz59AefZhtOdCBxyefh2UC5R34x6Zw3Il3mUHJQ3B3IiKc1qbgXLG3bqFp2r3f9fMKkiSib92yU1SCK7p7HShRooRT2hMJyA4GDBhAUO4g1qxb8/9FqD2AjKl8oSYg54Eu1gQUOz0LelYpnrtfjw4Pt0t7guuJSv7dPqkaRkrlkmXi4uJISkhtqQ/BXUSHhyObzWR9ynove5biEUNwaXDixAkaN2nMlctXoDbQF0hLoWAfUIbAvh4qE2QrA9I4ZAL/L8XzJNkkCYskEXXjRprbElzT3ZuL3GnsAeW+72YlUFRP90hR4eF4P+O6c7cUjz2IHtBzGjBgAGXKl+HK7SvwNXoxUntUqa8AdIAvrImccMJQnCxJBJnNRIWFObwtwRh3e0Bp3fLjbm9ZnCueKyo83GnPf0D0gFLt1KlTNGzckCuXrkAd4DMgm50b6QHqZmgVHs8OOQPZHLxVQ25JEkNwHiwqPJxsZjNeae0BJQ/hRYlzxWNFXrtGZiduty56QKkwatQoSpUtxZVbyb2eMdg/+YA+FDcBzvloNE6MI8rBa4PyqSq3Ll92aBuCcW5fvUoeOywgDQB8ZZlbV6+mPSjBJd26coUgJ1Y7d3gC+vbbb5Ekib59+zq6KYdRVZVWrVrx+eefo1XWYCH6AlJHdkwKgjIZDllU6iTGceM5FwA+qxQPQHlZJvToUTSDKnILjnX18GGC7bCAVJIkyplMXD1+3A5RCa4m5vZtosLDqVKlylOPc5tSPHv37uWnn36ifPnyjmzGoaKjoyleojjLly/XS+hMxDG9nscpCcpPcMxPpWpiHNttqS+m8qxSPACVTSbu3LnDbXFn63EUq5Wrp09T2U6llqoCVw4etMtrCa7l8tGjALz88stPPc4tSvHExMTQsWNHpk+fTpYsWRzVjEMdPnyY3Plyc+6/c3rZnA8gzfVMUqs4KL/CleIatePj6ZeQQJydeyqVk4dnLiWfgILnuHb6NFar9d7vOK0qm0xcv3SJhJgnlXYX3NXlI0eQTSYaNGjgtDYdloB69+5NixYtaNiwoaOacKg5c+ZQsUpFYk2x8DN6JWuj5AVlBmgfwndYKZsYy982m93qxuWSZbJbLFw6fNgurye4jktHjiADwXbqAVWSJDRN44od7n4F13Lx8GEyZcqE2YmVWBySgP744w8OHDjAqFGjnnlsYmIi0dHRD3wYbdSoUXTu0hm1iAq/A2lfb5V2JqAjqHPhUkmNl+PjKZQYy9ikJCKec3xf1TT+sdlokRDHDauVi4cO2TdmwXCXjhwBs5kmiXEssFpJes6blhhN46ekJDpYE5BkWX9dwaNcDAmhSOHCTm3T7qnu8uXLfPTRR6xbty5FBe1GjRrFsGHD7B3Gc/vqq68YPmI4VEffu8c5NflSrgAo04GjcGmRxmfrEvnCmkhT2cQLsonKJhOVZZmgxwy5JGoaR1SV/YrCflVlNTYuWzVMRYHccOHAARSrFZOTKuEKjndm7y7U8jZ2qrA9JIFsZmiB+d55EmwykeEx07MjNY0DyefJXkXhb81GvArUAq7KnN23h5fefdfpP4/gGJHXrxN5/To133jDqe1Kmp2nPi1btozWrVtjuq/LrygKkiQhyzKJiYkP/FtiYuIDmx9FR0eTL18+oqKiyJQpkz1De6aBAwcydtxYfX3PN/y/Uqcruw2sBHknSCdAidO/nNUM/rKEWdO4pYCXDDcVsGkgyWAqALYKwMtAWeAU0AX6zJtHiRo1DPtxBPuJuHyZobVrw0igAXAeWA6mg6CeA82mD4HksEj4SvrpngTEahBuTd4nyhuk4qBUAVoBOYFZYJntzZgDh7A4qWqy4Fhb58xhweDBnD93jkKFCj312JCQEPr27cukSZMIDg5+7DHR0dEEBAQ88zpu9x5QgwYNOPJQ97xbt26ULFmSTz/99IHkA+Dt7Y23C2zzO3jwYD35NACG4T5LdLMAnUHtDGjoWzychFvn4VaCpu+jbAa8gCCgJGhFwfbwdaMEyEEmjqxbJxKQhzi6fj2SRUJ7MfkeszDwMSig7y53HtSTcO2qpmceK3oW8gHyAyVBzc+jE29qg3VqImd27aJ0vXrO+WEEhzqybh1ZsmR5ZvIB+5bisftl1t/fn7Jlyz7wtQwZMpAtW7ZHvu4qvvnmG0Z8MwLq4l7J52ES+n5DedATaSq/V62tELJ+NW2/+grJwdUXBMc7tH4NVEbfj+phZqB48kdqFQY5j5kj69eLBOQBEmNjObVjBy1btHB62+m+EsLPP//Ml199qT/zGYH7Jh97qAWRl69x7fRpoyMR0iguKoqzu/eg1XLA4mIJ1Fo2QtatFouXPcDJrVtRrFb69Onj9Ladcrm1V3fN3nbv3s37vd/Xn4F8i3s883GkyiD5yhxet47cTtoPRHCM45s2odlUvUq7I9SBOwsiuHz0KPnLlXNQI4IzHF63Dh8/P1566SWnt51ue0ARERHUa1APLaum13Qz/jGUY0QA05M/P4s3aHVUdi76A9UOpVsE4+xctBCprEmfNOAIwSBnM7Fr0SIHNSA4Q/ydOxz4+2+qVa2a4u9xm1I8rkpVVSpWrkiCNQHGoz/I91QRwAxSloAA2sDN/65wavt2BwYlOFL4hQuc2rodra0Dt/Mwg/qKwq5FC0mMjXVcO4JD7Vm6FGtiIuPGjUvx97hFKR5X1rRpU307hWE830NYT1YB5KImtvw+2+hIhOe0be5c5ABT6ieipFZrSIqPZ++yZQ5uSHAETdPYPGsWeXLnfmYBUkdJdwnoiy++YN26dfAu4PwhT9cngdpW4ej6DdwODTU6GiGVkuLj2fHnH6gtFccPK+cAakls+n2WmIzghs7u3k34+fN8/PHHhsWQrhLQzp07GTl6pL7Q9B2jo3FhTQBfie3z5xsdiZBK+1esICE6Blo7qcG2GtdPnuH8/v1OalCwly2//Ya3r69IQM6gqiotWrbQd9X6ivTzkwei9/YCU/E9GUBrprJ5zmxR9diNqIrC+hk/Ib0o22d7+JR4AeT8Jtb/PM1JDQr2EHHpEiGrV9OiWTPkVFZKL1OmDHXr1qVMmbQXyUwvl2G6devG7Zu3YTDgb3Q0ThQIvEfqEhDAW5AQe4eNM2Y4ICjBEfYtX07Y6XNo7zhxBqMMajeFI2vXc+HAAee1K6TJinHjMJlM/PTTT6n+3ruVEJ5Uhic10kUC2rlzJ7/N/U2veyaqzKRMDtBe01g3fRp3IlI6hU4wijUxkeXjx0A9CZy9LKeJPnFl6eiR4lmQG7hy7Bj7//qLdm+8QWBgau9M7cvjE9ADQ299jY7GzXQFm5TEmh9/NDoS4Rm2z5tH1LXr8L4BCcAEak+F87v3cWLzZue3L6TK8tGj8fbxYYYLjG54fALq2rVr+hx6s4cA0DqpbJnzOzcvXzY6GuEJEmJiWPXDJGgBPLuWpGPUBKmCzNLRI8UiZhd2ZtcuTmzZQq+ePVO0XY6jeXQCOnHiBL/P+13/wxRDb8+nPZBJY9m3z95cUDDG2ilTiI+J1p/1GUUC7QOVaydOs1tUR3BJis3G4uHDyeDvn6qFp47k0QnozTff1Lch+MjoSAyUmlI8j+ML6ocKB/9exaHVq+0YmGAPFw8fZt1P09C6aPq6HCOVB5rCohHDiAwLMzgY4WEbp0/nyrFjTBg3LtUz3+4nSvGkwLZt2wg5FAJvoT//Sa9SW4rncZoAdSTmfvEZMbdv2ycuIc2siYn89snHSEUl6Gp0NMn6gdWSwLxBn4oJCS7k2pkzrBg/nipVqtC9e/c0vZYoxZMCXbp0gUxAB6Mj8QAS8KlGgvUOC4cMNjoaIdnqH34g/Px51MGK62wjEgDqZwrHN25i9+LFRkcjoA+9/davHxazmTVr1hgdzgM8MgEtXryY8+fPQ3fA1+hoPEQgqP0VDvy1UgzFuYCLhw+zduoUtLc1KGZ0NA+pAzSFP4d9ReT160ZHk+5tnD6dy0eOMPmHH8iaNavR4TzAIxNQz9499TL0rxodiYe5OxT3+aeiTpyB4u/cYdbHH+pDb12MjuYJ+oHVK5HZ/fui2GxGR5NuXTx8+N7Q27vvvmt0OI/wuAQ0depUboTdgF6IDebg+UrxPIkEfK6R4BXDtO5vkxQfb4cXFVJDVRRmfvQhEWGXUL92oaG3hwWA+rXCmV27WTZypNHRpEtR4eFMe+cd/Hx99QLMdmLPUjyS5mJPCqOjowkICCAqKopMmTKl+vtz583NNfkaLMQD06uLOA3SezLBDZvx9g+TkSTJ6IjSjb/GjGHt1CkwAfdYWvAnMA46jhlD9TfeMDqadMOakMDEN94g9MQJdu/aRaVKlZzafkqv4x51id63bx/XQq9BOzzsJ3MxxUEbonJw5d+snTLF6GjSjX3Ll+vv9we4R/IBeA14FeZ/8bmomO0kmqYx/4svuHz0KDOmT3d68kkNj7pMf/LJJ/q6n2ZGR5IOvAS8CyvGjuXw2rVGR+PxLh4+zO8DP9HP7Y5GR5MKEjAAtNIqP/V4Vzw7dIKNv/zCnsWLeb9HD302sAvzmAQUFxfHlh1b9KoHGY2OJp14B6gv8Uuf3pzeudPoaDzWtTNnmNylE2oxFQahX9TdiQW0b1XiLdF816kD0TduGB2Rx9q1aBFLv/mGatWqMcUNRic8JgENHjwYzapBG6MjSUdkYJiGWsHG1He6cm7fPqMj8jjhFy4w6c12JGaLRZvghF1OHSUrqD8o3Iq+wncd2xNz65bREXmc/StWMHfAAEoUL86OHTuMDidFPCYB/TLzF70MvautiTBaWkvxPIs3aGM1bCWtTO7SibN79jioofTn+tmzTGz/GvEZo1B/UNy/okc+UH9UuBHxH5PebCe2+bCjfcuXM+ujjyhYqBBHjx5NU6mdZxGleB6yZs0aom5HwetGR+KC7FGK51l8QJugYiuVxOQunTm1fbsDG0sfrp48yYR2rxGbMRL1RwVca/3g8yuoJ6HwG+eZ0P41UTPODnb9+Sez+valSOHCnDxxArPZsXPzRSmeh0yaNEmffFDX6EjSMV/QxqsowUn82LWLKMOSBie2bmXCG21JCLyDOkWBbEZHZGeFQZ2mcPPOZca2eZWrJ04YHZFb0jSN1T/8wJwBAyhZogQnTpzAy8vL6LBSxSMS0NadW+FFwPjtLdI3H9DGaKhNbfzevz9LvvkGVVGMjsptaJrGv7/8wpQub5FUJk7v+WQ2OioHyQ/qdIU7GW8wrm0rQkR5p1RJjIvjl969WTl+PPXq1ePYsWMO7/k4gtsnoGPHjhEbFavXnxKM5wV8AfSDjb9MZ8rbXYmLijI6KpdnTUxkzsABLB4+HO1NDW285vkbKOYA9WcFa40kZrz/PqsmTRKb2aXAratXGd+6NYdXr2bAgAH8+++/Dn3m40juGfV9xo8fr09LrWl0JC7KnqV4UkpCXww8CU4f3MHoVi25fvasEwNwL1Hh4Uzq8AZ7li+GIUAfwGR0VE7iA4zQ4H1YNWkSv/TqSUJMjNFRuayze/bwbYsW3PjvPxYsWMCYMWOcHoMoxXOfnLlzEpYlDGY6ITgh9S6DPMCEFCrz6oBPqdetG7IpvVxdn07TNPYtX86CIV+SZIlHHa1AWaOjMtBmkIbKZMoSxFtjJ1CihruUe3C8pIQE/p4wgY3TpxOQOTM7tm+nVKlSRof1ROmiFM+tW7cICwuDekZHIjxRPlBnKSitrCz5ZgQT3niNsPPnjY7KcNHh4fzU/V1m9+1LwguxqHPTefIBqAvaHJXoHOH88OabzP/ic9EbAi4cOMDIJk3YOH06TZo04VpoqEsnn9Rw6wQ0ZcoUUIHaRkciPJUP0A+YCpfCDjOyWRM2zpiRLicoaJrG3mXL+LrRSxzfvwm+RR+CymxwYK4iD2g/qvAJ7FjyB8ObNOSUmyyqtLekhASWjhzJhLZtSbh1i2XLlvHPP//g4+M5s63cegiuXr16bN6/GdbjfuVJ0qsEYAqwEHKXLkHrT7+gZO3a6aKi9sVDh1j67UjO7twNjST4RCSep7oK0ggZ7YBKxRbNafnJAIIKFTI6KodTVZX9K1bw15gxRIaG0qRJE5YuXepWiSel13G3TkBBOYO4kf8GTHZScIL9HAHpexntsErR6i/Q6tPPKRgcbHRUDhF27hx/jRvLoX9WIxc2ofZWoJbRUbkJFfgb5OkmtAiNGu3a0eyjvmTOkcPoyOxO0zSOb9rEslGjuHb6NDlz5eKXGTNo3ry50aGlWrp4BhRxKwJKGx2Fi3N0KZ7nVQ60n1UYB+fD9jOuVSt+fr87186cMToyu7kdGsq8zz5jRONGHDm4Dr4CdY5IPqkiAy1BXaig9VLZuXIhQ+vWZvno0cTcvm10dHahaRrn9u1j4uuvM7VbN+LCw5k2bRrXQkNdMvnYsxSP+61cShYSEqIXH/WMZ3GOc7cUT22cOxU7JSSgNqg1FFgNR3/ewOFGayn24ovU7dqVcg0bYnKzxXWqqnJ6xw62/D6bI+s3IGWU0PqoaG1w30KirsAH6ATqqwrqHIX1M39i4y8zqNLyFeq89RYFKlQwOsJUS4qPZ/+KFWyaNYurx4/jmyEDQ4cOZfDgwS69ruf+UjzBaRy1cK+/7vssWbJE/4+SxsYh2IEJaAFqfQXpTbiydy8zdu0iS2AgNTp3pnr79i4/5BIXFcXuRYvYNGc2Ny9cQi5sQuuvojUFMhgdnQfxB3qCVkdFfU/l+PLl7F68mAKlS1Ora1cqv/IKXi7+rCTs/Hm2zZ3Ljj/+IDE2luxBQQwZMoTPP//c7UrppJXbJqAtW7bo+/7kNDoSwW7+BK7Bv37eeANTo6L4/bvv+Oe77yhWtSplmzShXMOGBObPb3SkgL6A9OiGDRxat5ZT27ehKDaor8GnoAYrYmKMo6ggfw+5JYmj3t5ssVj48cwZ5g4cyNKhQyn10kuUa9SI0vXq4RdgfAlxTdMIPXWKI+vXc2j1ai4fPYrJbKZypUqMHj2aevXqGR2iYdw2AZ04eUIffhN/5J7hAsg/wSdeXlRLXqj6k8nEGE1jvtXKsgMHWLF3L4u//po8RYpQpkkTytSvT76yZfHy9XVKiLakJK6ePMmJLVs4tG41lw8dBRmk8jJad1XfrdTTCoe6oqWghsBsXx8CZJmWskxLs5mzXl7MsVpZvmYNs1auxGQyUbRKFco2bkzJ2rXJUaSI0xZBx0dH89+hQxzbsIGQNWuIvHYN2WQiZ86c9OzZk5EjR5I5c2anxOLK3HYWnFcGL6wtrPCJE4NzRxHAUqA1rvcM6H5fQ+41Eud8MuDzhCnZdzSNtTYbf9lsrARu2WzIkkTuQoXIU7Ei+cqVI3+5cuQqVgwff/80Te1OjIsj7Nw5Lh05wuUjR7gSEsLl06dRFQXJT0Z7UdWfq9XE/ffpcSc2MLeEN++Yme3z5BuPK6rKSpuN5arKRpuNJE3D29ubvKVKkTc4mHxly5K/XDmyFyiAJQ1DdpqmERsZSeiJE1w6epRLhw9z5eBBwq9eBcDi5UWJ4sVp3749ffr0SVF1F1cXEhJC3759mTRp0hOfAXn8NGzJIsF7QFenhSY4ShTILWCk5MWn3il7Uq9oGodUlf2Kwn5VZa8kcdRqJSn5dPb29iZzYCCZcuTAP3duAoKC8AsIQDabkU0mJElCsdlQFYX4O3eIDg/nzvXrRIeGEnXzJnFxcQCYJImSFgtVVZUoYJlqQ1uKGPo1yibgUzjo50dwCnszMZrGvuTzZL+isFeWOZuUdO/fM2bMSED27PjnykWmnDkJCArCy88P2WTCZDajaRqqoqBYrcTcvq2fK1evEh0eTuTNm1itVgB8ZZlgk4kXgLU2Gxe9vLgTF+fSEwocxaMT0N1j+Apo4dz4BAeYA+bJEOqXgexp+GNN0jSOqSqnVJVrmsY1VSVU0wiVJK5KElGahg09eamahlmSsEgSGSWJ3JpGHk0jlySRW5bJJUkUkWXKyzJ+yT2pW5pGrrgYknogbnwMIveCKodldvukbWZHlKYRoij8d995ck3TuCrLhALxmoYt+UOWJMyShBnIKknkVlXyALlkmdySRC5JorQsU0KWMSWfK8usVlonJDBv3jw6dOiQ5p/b3aQ0AbnlM6DDhw/r/yHG292fCuY/oZ3ZnKbkA+AlSVQ0majooHH+rJLEmyYzcxbZsHUm/VSsdhX/gbofPvRJ+0yxAEmirtnssD0sXzabySlJDBs6NF0moJRyy77h8ePH9f9w5WcaQsqcBdt1eNtsMTqSFHnbbMF2AzhtdCTp0BbwNcFrbrA2zCxJvG2xcP7MGbHH0VO4ZQI6e3dvmezGxuEWXLUSwl0n9YmML7jJFg1VTCZkCRC7SDvfSagky3i7Sd3A6iYTVk1j586dRodiV/ashOCWCejChQv68If7TyhxvLuVEFw4ARXx0p/DuANfSaKERYaTRkeS/liOwAtuNO5ZOXlIefny5QZHYl/3V0JIK7dMQDdv3tQXobrHNUt4CtMxeFF1n4sKwIuqjDntf3tCakSBNRwqu0lPGfRJCtklie3btxsdistyywRks9nAPR4ZCE+jgXYWh00acJRKJhPKBSD9bWdknORR94puNqW5qixz9rR4YPgk7vXbTGa1WsUMJE+QBKoNsrvJ8NtdgZKEpgCJRkeSjiRvjOpu50p2SSIpIcHoMFyW608neQybzSYSUGr999D/B/L4WYQRPPq86HHHPu641B7r88AnAK4lr+F5WC5JItdDd79pPfZxx6Xk2HvxJgJxpOz9gpS/t6k51h6/B0cca+/34IL+KVLTHjv3yJ6/36cdl9pjFUCx2R74WkhIyGOfn5QpU+aRygJpPfZxx6X1WHsOKbplAkoPu2faTSBQERjy0NffRa8k8bCl6JMWnnXs445L7bFv6Z/u/7P9yWpl2H2r1O8a4uXF0IeqJKT12Mcdl5Jj78UrA4tI2fsFKX9vU3OsPX4PjjjWQe/Br1Yrox4zZGvP3+/TjkvtsZVk+ZFH1X379mXz5s2PHFu3bl02bdpk12Mfd5w9jg0ICKBMmTKPHJtablkJoUaNGuw8txNWODk4d+Wqd94BQCuY5eNDF4v+UM8dekDzrFY6JiTAZvShIdEDcvx7cAD4Dg75+lL+MeuAXLUHNDIxkY1+ftyKjr73NXfvAT3t2Ls8uhRPnTp12Hp8K6xycnCCfWlgqgfDVS8GpbAGnCsYn5TEQC0RdTNu+hTVDR0G3oMDfn5uNWmlQVwcJ7JnJ/TaNaNDcSqP3pLby8sLrEZHIaSZBFoJ2K+613Sy/YqCVAw3/etxU0UBCQ4o7nOuaMlFUEuVLm10KC7LLf+EcuTIoQ99uM+5KDyBWgZ2Se5VqmSXpKCkffhbSA0/sOSFfW5U1uY/TSMafcRGeDy3TEAFCxYEFYg0OBAh7UrCVavGTdcaCX6iKE3jglUTW8EbwFoWdrvRXee+5N5a27ZtDY7EdbllAipevLj+H65aXkZIuVL6p60PTVV1VdvvDgGVMjaOdKk0HLGpRLnJzcpWRcFblilbtqzRobgst0xA96b/iQTk/vKBqRhMU9zjod40WxKmQkAhoyNJh+qDIsFvVtc/V+I0jVlWKxWqVDE6FJfmlgno3h2FSEDuTwLlDVhrVTjv4uP7l1SVlVYF5Q1EHUIjZAfqwfdqEi42efcRf9hs3AFGjx5tdCguzS0TkI+Pj74lt0hAnqExyH76Yj5X9rPViuwDNDU6kvRLew3OWjU2ufBsOE3T+D4piaDAQOrVq2d0OC7N7glo1KhRVK1aFX9/f4KCgmjVqhWnTp2ydzN4eXuJBOQpfEB5FaYpSS47GeG2pjFFTUJpCfgZHU06VgnM+WCUzXV7QRsVhUOqSs/evY0OxeXZPQFt3ryZ3r17s2vXLtatW4fVaqVx48bExsbatZ3smbPDObu+pGCkThDrAx8kumbhxg8TE4i2AJ2NjiSdk8D2IayzKsxxwYkrMZrG2wkJZPH358svvzQ6HJdn9wS0evVqunbtSpkyZahQoQKzZs3i0qVL7N+/367tlC1bVt8UzLUfGwgpFQjKQPjDamOZiw3FrbDZmGO1oXwC5DA6GoE6QBPobU0g1MWeG36amMgVTWPRsmWY3WDrcKM5/BlQVFQUAFmzZn3svycmJhIdHf3AR0rUr19fr0Z8yV6RCoZrAlIteNeW4DJDcbc1jXesCcgvAi2Mjka4pz/E+cN7SQkuMxS3yWZjitVKqzZteOmll4wOxy04NAGpqkrfvn2pWbPmE+fCjxo1ioCAgHsf+fLlS9Fr31vcJbZG9hwSaJ9DpA+0Town3uALS4Km0SYxnlteGuoXiJlvriQAlC9glVXh68dUoXa2s6rKG8lDbwsWLDA6HLfh0ATUu3dvjh49yh9//PHEYwYNGkRUVNS9j8uXL6fotYsUKYLJ2yQSkKfJBsoE2I7Ca4nxJBqUhJI0jXaJ8WxBQRkPBBkShvA0dYBeMDQpiQkGJqFLqkr9uDhiTCa27twpht5SwWEJ6IMPPmDlypX8+++/5M2b94nHeXt7kylTpgc+UipX9lzwaKVywd1VAHUcrNYUmifGE+PkJBSnabycGM9KVUH9Fqjk1OaF1Oiif/RPTOTrxESnD8edUlWqx8VxQ5b5d9s2u+yRk57YPQFpmsYHH3zA0qVL2bhxI4UKOW7JeNmyZeEUoiipJ3oR1O9hs6zwQmKs06ogH1IUqiXGsUFWUCcBtZzSrJAWPfWPIUlJtEtMIMIJExM0TWO+1Uq12FgizWa27dpFtWrVHN6up7F7Aurduzdz5sxh3rx5+Pv7c/36da5fv058fLy9m6JFixb6RIQTdn9pwRVUBuUXOJ1Po2p8HF8lJpLkoDtcq6YxLDGRyvFxnMijok4HXnBIU4K9SUBX4GtY4mWjRGIsSxw4k/K6qtIqPp43ExLIlDcvIceOUUWU3Hkudt+Q7knbZc+cOZOuXbs+8/tTupERQEJCAr7+vtAJ/S5I8ExWYBZIv0JJk8RYsw9NTSZMdtiaXdU01ioKA2yJHFNUtC7A24BXml9aMMJNkEaBthXaWswMsXhRzk4b2MVoGr9brQxKTCROkujz8ceMHz/eLq/taTx6R9T7FS5SmAvqBRATTzzfaTCNAOUU5DNLfCBbeNtiIfAx2y4/y01NY6bVymQ1iYtWDVMxfVaVqHLtATRgDZgmghIJNSwyfUxetDGb8XqOm5aTisJUq5VfrFZigYL587Nq9WpKlRIny5OkmwTUv39/JkyYAEuB3I6PTzCYBhwFFoG8Dkwa1DaZeEEyUVmWqWwyUVCSHuiJa5rGRU1jv6KwX1XZqylsURRsEqgNgdeAcohp1p7GCmwGeSGohyCLGepjoopsorJJ/8j2UEKyaRrHVZX9qso+RWEnCgetKibAP3NmVqxYQa1a4sHgs6SbBBQaGkqevHngY6Cd4+MTXMhtYBVwAMzHwXZL/7KXDD4yWNCvQQkqJCU/lzYDtkpADfSFpY9fHy14mvPAKpCPgHQKlORH0r4m8JbABCRqEK+CogESWPLom+CRFZgHf//9N82bNzfsR3An6SYBAWTLno1bBW7BFAcHJ7i2m+jrwi4DSckfXskfedH38OkAdEd/biikTypwBf1cuYk+kckGeKMXmi0MFAcyJB8/EkxrTCTFJiE/x3BvepTS67hHrJhq1KARC/5cAHcAf6OjEQyTDaj5jGNeALYgElB6JgP5kz+eRQU2Q3CZYJF8HMAj3tGBAwfee/AoCE/1EnAIvZckCM+yG4iE999/3+hIPJJHJKBKlSqRK3cuWIieiAThSRoAvsASowMR3MKf4J3Bm7ffftvoSDySRyQggP4f94eLQIjRkQguzQt9dsJywDW3HhJcRSiwA9q1bSeG3xzEY97Vjz/+GLOPGf40OhLBpYUA0UAssN7YUAQXtwyQYezYsUZH4rE8JgHJskzLZi3hX/SZLYLwOH+C2cdM1uxZ9SFbQXicJGApVChbgaAgUQrdUTwmAQFMnDhR/4/lxsYhuKibwL/QsllLevXopReyPW50UIJL+heIhpEjRxodiUfziHVA9ytRogSnI07rSUjU8xLuNx34Ff47/x+5cuXC198X9SUVhhkdmOBSNKAbBIQFEHkz0uho3FJKr+Me1QOC5F7QLWCx0ZEILuU2MAcqVqhIgQIF8PLyos0rbfSp++eNDk5wKVuBEzCg3wCjI/F4HtcDAiheojhnrp3RHyJmtGt4gruaBCyEk8dOUqJECQBiYmLIHJgZ5QUFxhkaneAqFKADBEQHcCvilpj99pzSbQ8IYO6cufosp3lGRyK4hGvAQnip7kv3kg9AxowZ6f52d/2O97Bh0QmuZDVwESaOnyiSjxN4ZA8I4IUXXmDv4b16lexs9otPcEPDQForEXo5lJw5cz7wTzabjQwBGUgqmgQ/Iypip2eJQFvI5ZWL0CuhRkfj1tJ1Dwhg/vz5eoHBmUZHIhjqHLAK2rZq+0jyATCbzXw+8HO9B7Td6dEJrmQJcANm/DzD6EjSDY/tAQE0adKEtRvW6otT89gnPsHN9APTPhOREZFkzPj4B4KqqpI5W2buZLkDc9Fr8wvpSwzwKhTPXZxTp04ZHY3bS/c9IIC5c+fq47ijEDXi0qPNwHbo1b3XE5MP6IuYJ46bCBcQO+umV5OBOJgzZ47RkaQrHp2AAgMDGfLlENiLWJya3kQB30BQjiAmTZr0zMPfeecdgoOD9T2lLjo4NsG17AGWQrvX21G1alWjo0lXPHoI7q7ixYtz5vIZ/e720ccAgicaDGyAg/sO6oklBSIiIsiZNydKUUVftCqG4jxfLNAeAqwBRIRHYDZ7xBZphhNDcPdZt24dsiLDCMRQXHqwGVgLPd7tkeLkA3qPedK4SXAMMRSXXvwARMBfy/4SyccA6SIBFShQQAzFpRf3Db1NmZL6Pdo/+OADMRSXXtw39FanTh2jo0mX0sUQ3F1iKC4deI6ht4dFRESQK28ubEVtYijOU4mhN4cSQ3CPsX79en0obiBiMzJPtBhYCz2793zu5AP6UNx3E77Th+JS34kSXJ0KDEUMvbmAdJWA8ufPz28zf4MzwDeI50Ge5AAwDsqVL/dcQ28P69WrF61atYI5wKo0v5zgSqYDW+DLz78UQ28GS1cJCKBjx458+MGHsBb94iK4v1BgAGTOnJk9u/fY7WUXL15M0aJF9ZuVY3Z7WcFIG4Bf9UXqw4cPNzqadC/dJSCA7777jho1auiLz0T5FfcWh17twGpiz649+Pj42O2lZVlm//79ZPDLAP2BG3Z7acEIp4AhkL9AflatEt1aV5AuExDA5s2byZ4zO3yBvgJecD8qMAS4CIsXLKZYsWJ2byJTpkzs2LoDOUaGT9ALVgru5xbQD3y8fdi/b7+odO0i0u1vwWw2c2DvASxYoB/6hmWCe/mJe2P5r776qsOaKV++PLN/nQ2ngeHoiU9wHwnAAJAiJbZs3EJgYKDREQnJ0m0CAsibNy9rV61FuiFBL/Q1JIJ7mA3MghYtWjhlLL9Tp07069sP1gGjERNY3EUies/1OPw89WdRasfFpOsEBFCvXj2W/rkULgF90KviCq5tPjBF/92tXLnSac2OHz+ed999V99pdwIiCbk6KzAI2A/jxyb/7gSXku4TEMCrr77KgrkL4CzwAXDH6IiEJ1oATIIXX3yRDRs2OL356dOn07FjR1ioxyGSkItKAj4HdsCIr0fQr18/oyMSHkMkoGRvvPEGv8/6XR/n74kYjnNFvwMToGrVqmzfvt2wB8lz5syhffv28AcwBvFMyNUkP/NhKwwdMpQvvvjC6IiEJxAJ6D6dOnXizz/+RLogQQ8gwuiIBEDvZUwHJkOdOnXYtWuX4bOY5s+fT9euXfVdNEeg774rGC8WfVLRHhj97WiGDBlidETCU4gE9JDXXnuNv5b+hXxFhs7ACaMjSucSgK+AGfriwc2bNxuefO6aOXMmPXv21Csl9EH0mo12FegGHITvJ33PwIEDjY5IeAbX+Et2MS+//DL79+zHz+oH7wFrjI4onQoDugProW/fvqxevdroiB4xZcoUvv/ue6TDErwFnDM6onRqH/AWmK+bWbF8BX369DE6IiEFRAJ6guDgYK5eukrBfAX1O/AfAcXgoNKTI8BbIF+QmTdnHhMnTjQ6oifq06cPW/7dgle0l34HvsXoiNIRDfgT6ANZvLNw+sRpXn75ZaOjElJIJKCnyJw5M+fOnNNP6N/Q1xOIadqO9zfQAzJqGTm0/xAdOnQwOqJnqlWrFhfPXSRXtlz6A/CZiBlyjmYFRgHjoGKFioReCaVQoUJGRyWkgkhAzyDLMitWrNAXO+4GuiKeCzlKHPoiz6+hWJFiXL10lbJlyxodVYrlzJmTSxcvUbduXZiGvu3HLaOj8lCXgfeBv6Bbt24cOHDArnUABecQCSiFvvzyS1atWIX3LW94G5iKvtZAsI/9QHtgGbRv356TJ07afUNCZzCbzWzatEl/AL4DeAO9ArNgHyr6WrAOYDpt4qdpP/Hrr78aHZXwnNLVjqj2EBcXR8uWLdn470bIDwwDShkdlRuLQ3++tgj8A/xZungpDRo0MDoquzh69CiNmzTmWug1qI/eI8pqdFRu7DLwNXAYypUrx9q1a8mZU2xt7IrEjqgO4ufnx4YNG/hz4Z94R4jeUJrc7fUsgTZt2nAr4pbHJB+AsmXLcuXyFT788EPYiugNPa/7ez0nTUycOJHDhw+L5OMBRA8oDR7oDeUDPgaqA5LBgbm6G8DPwF+e1+t5kgd6Q3XQSz4VMDoqN3AUmKh/Fr0e9yF6QE5wf28oY1RGPQG9jz6FWHhUNPpwW2vgb33Rr6f1ep7k/t6QvEvWe34jgXCjI3NRF9BnE74DXue8RK/HQ4kekJ2oqsrQoUMZPW40SfFJ+l1uL0DMCtWrGfyJPjU5HmpWr8m8efPInz+/wYEZIzIyki5duvDX33/pt4Ad0KtuuM/p7jhh6GWXVoJskXmnyztMnjwZLy8voyMTUiGl13GRgOwsKSmJDz/8kOkzp6NaVXgZfXFiHqMjM0Aiepman4HbUKZ0GebMmUNwcLCxcbmIS5cu0bFjR7bt2AY+6OdJGyCjwYEZ4SYwF1gAEhKtWrZi1qxZbnkNEEQCMlx0dDTdunVj6V9L0WwavAi8jv6MyGRwcI52Fb1I5zIgBvLlz8fMX2emi6G253H48GE6derEkaNHwAtoDrQF7L/DuGvRgBBgEfCv/v91atVh7ty55M2b19DQhLQRz4AMlilTJhYvXkz4tXC6du2Kz1Ef6A+0Qq+q4GlbgCvANuAj9Lv4+VC5RGXWrl3LpYuXRPJ5ivLly3P48GH27tlLver1kFZK0Al4B70OoafNsIwFFqM/B3sfzNvMtGrZivNnz7N582aRfNIR0QNyotmzZzN8xHDOnTunp/6GyR8voA/BuBsNvfjmZvTeTjj4ZPShw+sdGDNmDIGBgYaG567i4uIYMmQI03+dTtStKP3Z0KtAXaAM7nnbaAMOAhvRSy0lQs7cOenXtx/9+/d3mQrngn2IITgXdubMGfr168fqDauxxdvAgp6E6gK1gGzGxvdUVvQLyVZgE/osLhmKFCrC4MGD6dKli5HReZyVK1fy5ZdfcujYIf0iHoB+ntTG9W9cooGd6OfKNiBen1hQ88WajB8/nqpVqxobn+AwIgG5iX/++Ycff/yRLdu3cCcyeS/wkugXmXJACYydHWUD/kOvf7cL2I5+IfGSKV28NO3bt6dPnz7p4ndlpKSkJGbMmMGsWbM4eOQgtoT7blxqoPeMiqA/QzJKHPqOwsfQk84hQNV7xS9WeZHu3bvTrl070dtJB0QCckOnTp1i/Pjx/L3qb0Kvhf5/q+ec6BeYUskfJQB/BwRwN9mcRE84x4Ez6L0e9AtJ9arVee+998SFxGCPvXExoU/7L4N+E1MKxyWleOAU+rlyEj3pXEYflpUgW2A2GjVoRL9+/URPJx0SCcjNJSUlsXbtWlauXMnu3bs5e+EsMXdi/p+UMqDXFcsBZE/+yAYEok/jNaNfkMzo32NDnyiQhD7l9cZ9n8OTP0dy7/XNvmbyBOWhQoUKvPTSS7Rt21Y8HHZRt27dYsmSJaxdu5aQkBAuhl4kKTZ55oKE3oPODgQlfw5M/sgG+KKfJ3c/FP5/rsShb0t/9+PuuRIBJOc8JPDN6EuhfIWoWrUqzZo1o2XLlvj5+TnhJxdclUhAHuhuUvrnn384d+4c169fJyIigsiYSBISE1CSlP8nqGeQLBJe3l74+/qTNUtWcuTIQZ48eXjxxRdFsvEAd5PSli1buHz5MmFhYdy8dZPouGgSExPRklL4Zy/pw60+3j4EZAggW7Zs5MyZkwIFCtCoUSORbITHEgkoHVJVlcuXLxMaGkpiYiKJiYkkJCRgsVjw9vbG29ubjBkzUrJkSbF3SjqXlJTEmTNniIyMvHeeWK1WvLy88Pb2xtfXl8DAQIoUKSKGWoVUS+l13OzEmAQHk2WZAgUKUKCAqHIpPJ2XlxdlypQxOgwhnRO3NoIgCIIhRAISBEEQDOGwBPTjjz9SsGBBfHx8qFatGnv27HFUU4IgCIIbckgCWrBgAf369WPIkCEcOHCAChUq0KRJE8LDxeYngiAIgs4hCWjChAm89957dOvWjdKlSzNt2jT8/Pz49ddfHdGcIAiC4IbsPgsuKSmJ/fv3M2jQoHtfk2WZhg0bsnPnzkeOvztd+K6oqChAn8YnCIIguJ+71+9nrfKxewKKiIhAURRy5MjxwNdz5MjByZMnHzl+1KhRDBs27JGv58uXz96hCYIgCE50584dAgICnvjvhq8DGjRoEP369bv3/5GRkRQoUIBLly49NfD0Jjo6mnz58nH58mWxQDeZeE8eJd6TxxPvy6Mc+Z5omsadO3fInTv3U4+zewIKDAzEZDIRFhb2wNfDwsLImTPnI8ffXaH/sICAAHGiPEamTJnE+/IQ8Z48Srwnjyfel0c56j1JSQfC7pMQvLy8qFy5Mhs2bLj3NVVV2bBhA9WrV7d3c4IgCIKbcsgQXL9+/ejSpQtVqlThhRdeYNKkScTGxtKtWzdHNCcIgiC4IYckoHbt2nHjxg2++uorrl+/TnBwMKtXr35kYsLjeHt7M2TIkMcOy6Vn4n15lHhPHiXek8cT78ujXOE9cblq2IIgCEL6IGrBCYIgCIYQCUgQBEEwhEhAgiAIgiFEAhIEQRAM4XIJSGzj8H+jRo2iatWq+Pv7ExQURKtWrTh16pTRYbmUb7/9FkmS6Nu3r9GhGO7q1at06tSJbNmy4evrS7ly5di3b5/RYRlGURQGDx5MoUKF8PX1pUiRIgwfPvyZ9ck8zZYtW2jZsiW5c+dGkiSWLVv2wL9rmsb/2ruDkCbjMI7jP9rczA5qhLkVygJBbRrGUHCCh0kiHoIgCIYMOgQycRaYw6jbsil62AytDnXIFA9KJXQYtQYetGFbKUYbJOrB4aXYcBWxPR3CwUiCDu55Yc8H3sPe9/JlbP+HbS/737lzBzqdDkePHkVbWxui0WhO2hQ1gGQbh2yBQAB2ux1LS0vw+Xz49esXLly4gL29Pe40RQgGg3jw4AHq6+u5U9h9/foVZrMZBQUFePXqFdbX1zE6OorS0lLuNDZutxsTExMYHx/Hp0+f4Ha7MTw8DK/Xy52WU3t7ezh37hzu379/4PXh4WF4PB5MTk5ieXkZx44dQ3t7O378+HH4caQgjY2NZLfbM49TqRTp9XoaGhpirFKO3d1dAkCBQIA7hV0ikaCqqiry+XzU2tpKDoeDO4nVwMAAtbS0cGcoSmdnJ129ejXr3KVLl8hqtTIV8QNA8/PzmcfpdJrKy8tpZGQkc+7bt2+k1Wppenr60HsU8wlofxuHtra2zLl/beOQj/a3qjh+/DhzCT+73Y7Ozs6s10s+e/HiBUwmEy5fvoyysjI0NDTg0aNH3Fmsmpub8fr1a0QiEQDAhw8fsLi4iI6ODuYy5djY2EAsFst6HxUXF6OpqSkn6y77v2Hv+99tHPJNOp1GX18fzGYzjEYjdw6rmZkZvH//HsFgkDtFMb58+YKJiQncuHEDg4ODCAaD6O3thUajgc1m485j4XQ6EY/HUV1dDZVKhVQqBZfLBavVyp2mGLFYDAAOXHf3rx0mxQwg8W92ux1ra2tYXFzkTmG1vb0Nh8MBn8+HwsJC7hzFSKfTMJlMuHv3LgCgoaEBa2trmJyczNsBNDs7i6mpKTx79gxnz55FOBxGX18f9Hp93j4nSqOYr+D+dxuHfNLT04OFhQX4/X6cPn2aO4fVysoKdnd3cf78eajVaqjVagQCAXg8HqjVaqRSKe5EFjqdDrW1tVnnampqsLW1xVTEr7+/H06nE1euXEFdXR26urpw/fp1DA0Ncacpxv7ayrXuKmYAyTYOfyMi9PT0YH5+Hm/evIHBYOBOYmexWLC6uopwOJw5TCYTrFYrwuEwVCoVdyILs9n81y36kUgElZWVTEX8kskkjhzJXuJUKhXS6TRTkfIYDAaUl5dnrbvxeBzLy8u5WXcP/TaH/zAzM0NarZaePHlC6+vrdO3aNSopKaFYLMadxqK7u5uKi4vp7du3tLOzkzmSySR3mqLIXXBE7969I7VaTS6Xi6LRKE1NTVFRURE9ffqUO42NzWajU6dO0cLCAm1sbNDc3BydOHGCbt68yZ2WU4lEgkKhEIVCIQJAY2NjFAqFaHNzk4iI7t27RyUlJfT8+XP6+PEjXbx4kQwGA33//v3Q2xQ1gIiIvF4vVVRUkEajocbGRlpaWuJOYgPgwOPx48fcaYoiA+iPly9fktFoJK1WS9XV1fTw4UPuJFbxeJwcDgdVVFRQYWEhnTlzhm7dukU/f/7kTsspv99/4Dpis9mI6M+t2Ldv36aTJ0+SVqsli8VCnz9/zkmbbMcghBCChWJ+AxJCCJFfZAAJIYRgIQNICCEECxlAQgghWMgAEkIIwUIGkBBCCBYygIQQQrCQASSEEIKFDCAhhBAsZAAJIYRgIQNICCEECxlAQgghWPwGaJQ0hk4qMtoAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3 4.239601372763498\n", + "4 4.2396013727635\n", + "5 4.239601372763502\n", + "6 10.741792762297127\n", + "7 10.741792762297136\n", + "8 10.74179276229714\n", + "9 11.135561953696916\n", + "10 11.135561953696929\n", + "11 11.135561953696929\n", + "12 22.53713229271336\n", + "13 22.537132292713366\n", + "14 29.48031578745088\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "\n", + "freqs, eigvecs = phonon.get_frequencies_with_eigenvectors(q=[0.5, 0.5, 0.5])\n", + "\n", + "# write(\"undeformed.extxyz\", atoms * (2, 2, 2))\n", + "\n", + "for mode in range(len(eigvecs)):\n", + " # mode = 0\n", + " eigvec = eigvecs[:, mode].real.reshape(-1, 3)\n", + "\n", + " # atoms = read('BZO_cubic_prim.xyz')\n", + " cloned = atoms.copy() #* replicas\n", + " norm_eigvec = eigvec / np.linalg.norm(eigvec, axis=1).max() * 0.5\n", + " cloned.positions += norm_eigvec\n", + " cloned.set_constraint()\n", + "\n", + " print(mode, freq := freqs[mode])\n", + "\n", + " if freq >= 0:\n", + " continue\n", + " \n", + " # view(atoms * (5, 5, 5), viewer='x3d')\n", + "\n", + " plot_atoms(cloned * (2, 2, 2))\n", + " write(f\"pbe/mode-{mode}.extxyz\", cloned * (2, 2, 2))\n", + " plt.savefig(f\"pbe/mode-{mode}.png\", dpi=300)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-2.11439178e-18 -4.22304315e-17 -5.70003002e-16]\n", + " [-1.91068525e-17 6.38796468e-17 -3.43967329e-17]\n", + " [ 1.65178706e-02 1.86479850e-01 -6.38796468e-17]\n", + " [ 4.63923771e-01 -2.70260044e-17 -1.86479850e-01]\n", + " [ 9.68175896e-17 -4.63923771e-01 -1.65178706e-02]]\n" + ] + } + ], + "source": [ + "mode = 1\n", + "eigvec = eigvecs[:, mode].real.reshape(-1, 3)\n", + "\n", + "norm_eigvec = eigvec / np.linalg.norm(eigvec, axis=1).max() * 0.5\n", + "print(norm_eigvec)\n", + "\n", + "np.save(f'pbe/mode-{mode}.npy', norm_eigvec)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mlip-arena", + "language": "python", + "name": "mlip-arena" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/bzo/pbe/mode-1.npy b/examples/bzo/pbe/mode-1.npy new file mode 100644 index 0000000000000000000000000000000000000000..07f3da66ab9bc8bc87ad97094d5cbf038a653fd1 Binary files /dev/null and b/examples/bzo/pbe/mode-1.npy differ diff --git a/examples/bzo/pbe/phonopy_params.yaml b/examples/bzo/pbe/phonopy_params.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d22b1b1870cff83c075d4b56671d1d3ada248a57 --- /dev/null +++ b/examples/bzo/pbe/phonopy_params.yaml @@ -0,0 +1,6773 @@ +phonopy: + version: "2.30.1" + frequency_unit_conversion_factor: 15.633302 + symmetry_tolerance: 1.00000e-05 + +space_group: + type: "Pm-3m" + number: 221 + Hall_symbol: "-P 4 2 3" + +supercell_matrix: +- [ 2, 0, 0 ] +- [ 0, 2, 0 ] +- [ 0, 0, 2 ] + +primitive_cell: + lattice: + - [ 4.000000000000000, 0.000000000000000, 0.000000000000000 ] # a + - [ 0.000000000000000, 4.000000000000000, 0.000000000000000 ] # b + - [ 0.000000000000000, 0.000000000000000, 4.000000000000000 ] # c + points: + - symbol: Ba # 1 + coordinates: [ 0.000000000000000, 0.000000000000000, 0.000000000000000 ] + mass: 137.327000 + - symbol: Zr # 2 + coordinates: [ 0.500000000000000, 0.500000000000000, 0.500000000000000 ] + mass: 91.224000 + - symbol: O # 3 + coordinates: [ 0.500000000000000, 0.500000000000000, 0.000000000000000 ] + mass: 15.999000 + - symbol: O # 4 + coordinates: [ 0.500000000000000, 0.000000000000000, 0.500000000000000 ] + mass: 15.999000 + - symbol: O # 5 + coordinates: [ 0.000000000000000, 0.500000000000000, 0.500000000000000 ] + mass: 15.999000 + reciprocal_lattice: # without 2pi + - [ 0.250000000000000, 0.000000000000000, 0.000000000000000 ] # a* + - [ 0.000000000000000, 0.250000000000000, 0.000000000000000 ] # b* + - [ 0.000000000000000, 0.000000000000000, 0.250000000000000 ] # c* + +unit_cell: + lattice: + - [ 4.000000000000000, 0.000000000000000, 0.000000000000000 ] # a + - [ 0.000000000000000, 4.000000000000000, 0.000000000000000 ] # b + - [ 0.000000000000000, 0.000000000000000, 4.000000000000000 ] # c + points: + - symbol: Ba # 1 + coordinates: [ 0.000000000000000, 0.000000000000000, 0.000000000000000 ] + mass: 137.327000 + reduced_to: 1 + - symbol: Zr # 2 + coordinates: [ 0.500000000000000, 0.500000000000000, 0.500000000000000 ] + mass: 91.224000 + reduced_to: 2 + - symbol: O # 3 + coordinates: [ 0.500000000000000, 0.500000000000000, 0.000000000000000 ] + mass: 15.999000 + reduced_to: 3 + - symbol: O # 4 + coordinates: [ 0.500000000000000, 0.000000000000000, 0.500000000000000 ] + mass: 15.999000 + reduced_to: 4 + - symbol: O # 5 + coordinates: [ 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-0.107671250000000 ] + - [ -0.000000000000000, -0.107671250000000, -0.172916250000000 ] + - # (1, 38) + - [ 0.651338250000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.012134875000000, -0.132871375000000 ] + - [ -0.000000000000000, -0.132871375000000, -0.012134875000000 ] + - # (1, 39) + - [ -0.932719000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.172916250000000, 0.107671250000000 ] + - [ -0.000000000000000, 0.107671250000000, -0.172916250000000 ] + - # (1, 40) + - [ 0.651338250000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.012134875000000, 0.132871375000000 ] + - [ 0.000000000000000, 0.132871375000000, -0.012134875000000 ] + - # (2, 1) + - [ -2.475097000000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.704353500000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.704353500000000 ] + - # (2, 2) + - [ 9.720056000000001, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 9.720056000000001, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 9.720056000000001 ] + - # (2, 3) + - [ -0.597366500000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.597366500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.265738000000000 ] + - # (2, 4) + - [ 0.704353500000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -2.475097000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.704353500000000 ] + - # (2, 5) + - [ -0.597366500000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.265738000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.597366500000000 ] + - # (2, 6) + - [ 0.704353500000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.704353500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -2.475097000000000 ] + - # (2, 7) + - [ -0.245066000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.245066000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.245066000000000 ] + - # (2, 8) + - [ 0.265738000000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.597366500000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.597366500000000 ] + - # (2, 9) + - [ -0.609209250000000, 1.488184375000000, 1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ 1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (2, 10) + - [ -0.609209250000000, -1.488184375000000, -1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ -1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (2, 11) + - [ -0.609209250000000, -1.488184375000000, 1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ 1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (2, 12) + - [ -0.609209250000000, 1.488184375000000, -1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ -1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (2, 13) + - [ -0.609209250000000, 1.488184375000000, -1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ -1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (2, 14) + - [ -0.609209250000000, -1.488184375000000, 1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ 1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (2, 15) + - [ -0.609209250000000, -1.488184375000000, -1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ -1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (2, 16) + - [ -0.609209250000000, 1.488184375000000, 1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ 1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (2, 17) + - [ -0.172916250000000, -0.107671250000000, -0.000000000000000 ] + - [ -0.107671250000000, -0.172916250000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932719000000000 ] + - # (2, 18) + - [ -0.172916250000000, 0.107671250000000, 0.000000000000000 ] + - [ 0.107671250000000, -0.172916250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.932719000000000 ] + - # (2, 19) + - [ -0.172916250000000, 0.107671250000000, -0.000000000000000 ] + - [ 0.107671250000000, -0.172916250000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932719000000000 ] + - # (2, 20) + - [ -0.172916250000000, -0.107671250000000, -0.000000000000000 ] + - [ -0.107671250000000, -0.172916250000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.932719000000000 ] + - # (2, 21) + - [ -0.012134875000000, -0.132871375000000, -0.000000000000000 ] + - [ -0.132871375000000, -0.012134875000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.651338250000000 ] + - # (2, 22) + - [ -0.012134875000000, 0.132871375000000, 0.000000000000000 ] + - [ 0.132871375000000, -0.012134875000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.651338250000000 ] + - # (2, 23) + - [ -0.012134875000000, 0.132871375000000, -0.000000000000000 ] + - [ 0.132871375000000, -0.012134875000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.651338250000000 ] + - # (2, 24) + - [ -0.012134875000000, -0.132871375000000, -0.000000000000000 ] + - [ -0.132871375000000, -0.012134875000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.651338250000000 ] + - # (2, 25) + - [ -0.172916250000000, 0.000000000000000, -0.107671250000000 ] + - [ -0.000000000000000, -0.932719000000000, 0.000000000000000 ] + - [ -0.107671250000000, -0.000000000000000, -0.172916250000000 ] + - # (2, 26) + - [ -0.172916250000000, -0.000000000000000, 0.107671250000000 ] + - [ 0.000000000000000, -0.932719000000000, -0.000000000000000 ] + - [ 0.107671250000000, 0.000000000000000, -0.172916250000000 ] + - # (2, 27) + - [ -0.012134875000000, -0.000000000000000, -0.132871375000000 ] + - [ 0.000000000000000, 0.651338250000000, -0.000000000000000 ] + - [ -0.132871375000000, -0.000000000000000, -0.012134875000000 ] + - # (2, 28) + - [ -0.012134875000000, -0.000000000000000, 0.132871375000000 ] + - [ -0.000000000000000, 0.651338250000000, -0.000000000000000 ] + - [ 0.132871375000000, 0.000000000000000, -0.012134875000000 ] + - # (2, 29) + - [ -0.172916250000000, -0.000000000000000, 0.107671250000000 ] + - [ 0.000000000000000, -0.932719000000000, 0.000000000000000 ] + - [ 0.107671250000000, -0.000000000000000, -0.172916250000000 ] + - # (2, 30) + - [ -0.172916250000000, 0.000000000000000, -0.107671250000000 ] + - [ 0.000000000000000, -0.932719000000000, 0.000000000000000 ] + - [ -0.107671250000000, -0.000000000000000, -0.172916250000000 ] + - # (2, 31) + - [ -0.012134875000000, 0.000000000000000, 0.132871375000000 ] + - [ 0.000000000000000, 0.651338250000000, 0.000000000000000 ] + - [ 0.132871375000000, 0.000000000000000, -0.012134875000000 ] + - # (2, 32) + - [ -0.012134875000000, 0.000000000000000, -0.132871375000000 ] + - [ -0.000000000000000, 0.651338250000000, 0.000000000000000 ] + - [ -0.132871375000000, 0.000000000000000, -0.012134875000000 ] + - # (2, 33) + - [ 0.651338250000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.012134875000000, 0.132871375000000 ] + - [ 0.000000000000000, 0.132871375000000, -0.012134875000000 ] + - # (2, 34) + - [ -0.932719000000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.172916250000000, 0.107671250000000 ] + - [ 0.000000000000000, 0.107671250000000, -0.172916250000000 ] + - # (2, 35) + - [ 0.651338250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.012134875000000, -0.132871375000000 ] + - [ -0.000000000000000, -0.132871375000000, -0.012134875000000 ] + - # (2, 36) + - [ -0.932719000000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.172916250000000, -0.107671250000000 ] + - [ -0.000000000000000, -0.107671250000000, -0.172916250000000 ] + - # (2, 37) + - [ 0.651338250000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.012134875000000, -0.132871375000000 ] + - [ -0.000000000000000, -0.132871375000000, -0.012134875000000 ] + - # (2, 38) + - [ -0.932719000000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.172916250000000, -0.107671250000000 ] + - [ -0.000000000000000, -0.107671250000000, -0.172916250000000 ] + - # (2, 39) + - [ 0.651338250000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.012134875000000, 0.132871375000000 ] + - [ 0.000000000000000, 0.132871375000000, -0.012134875000000 ] + - # (2, 40) + - [ -0.932719000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.172916250000000, 0.107671250000000 ] + - [ -0.000000000000000, 0.107671250000000, -0.172916250000000 ] + - # (3, 1) + - [ 0.704353500000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -2.475097000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.704353500000000 ] + - # (3, 2) + - [ -0.597366500000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.597366500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.265738000000000 ] + - # (3, 3) + - [ 9.720056000000001, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 9.720056000000001, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 9.720056000000001 ] + - # (3, 4) + - [ -2.475097000000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.704353500000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.704353500000000 ] + - # (3, 5) + - [ 0.265738000000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.597366500000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.597366500000000 ] + - # (3, 6) + - [ -0.245066000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.245066000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.245066000000000 ] + - # (3, 7) + - [ 0.704353500000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.704353500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -2.475097000000000 ] + - # (3, 8) + - [ -0.597366500000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.265738000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.597366500000000 ] + - # (3, 9) + - [ -0.609209250000000, 1.488184375000000, -1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ -1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (3, 10) + - [ -0.609209250000000, -1.488184375000000, 1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ 1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (3, 11) + - [ -0.609209250000000, -1.488184375000000, -1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ -1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (3, 12) + - [ -0.609209250000000, 1.488184375000000, 1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ 1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (3, 13) + - [ -0.609209250000000, 1.488184375000000, 1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ 1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (3, 14) + - [ -0.609209250000000, -1.488184375000000, -1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ -1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (3, 15) + - [ -0.609209250000000, -1.488184375000000, 1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ 1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (3, 16) + - [ -0.609209250000000, 1.488184375000000, -1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ -1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (3, 17) + - [ -0.172916250000000, -0.107671250000000, -0.000000000000000 ] + - [ -0.107671250000000, -0.172916250000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.932719000000000 ] + - # (3, 18) + - [ -0.172916250000000, 0.107671250000000, -0.000000000000000 ] + - [ 0.107671250000000, -0.172916250000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932719000000000 ] + - # (3, 19) + - [ -0.172916250000000, 0.107671250000000, 0.000000000000000 ] + - [ 0.107671250000000, -0.172916250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.932719000000000 ] + - # (3, 20) + - [ -0.172916250000000, -0.107671250000000, -0.000000000000000 ] + - [ -0.107671250000000, -0.172916250000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932719000000000 ] + - # (3, 21) + - [ -0.012134875000000, -0.132871375000000, -0.000000000000000 ] + - [ -0.132871375000000, -0.012134875000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.651338250000000 ] + - # (3, 22) + - [ -0.012134875000000, 0.132871375000000, -0.000000000000000 ] + - [ 0.132871375000000, -0.012134875000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.651338250000000 ] + - # (3, 23) + - [ -0.012134875000000, 0.132871375000000, 0.000000000000000 ] + - [ 0.132871375000000, -0.012134875000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.651338250000000 ] + - # (3, 24) + - [ -0.012134875000000, -0.132871375000000, -0.000000000000000 ] + - [ -0.132871375000000, -0.012134875000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.651338250000000 ] + - # (3, 25) + - [ -0.012134875000000, -0.000000000000000, 0.132871375000000 ] + - [ -0.000000000000000, 0.651338250000000, -0.000000000000000 ] + - [ 0.132871375000000, 0.000000000000000, -0.012134875000000 ] + - # (3, 26) + - [ -0.012134875000000, -0.000000000000000, -0.132871375000000 ] + - [ 0.000000000000000, 0.651338250000000, -0.000000000000000 ] + - [ -0.132871375000000, -0.000000000000000, -0.012134875000000 ] + - # (3, 27) + - [ -0.172916250000000, -0.000000000000000, 0.107671250000000 ] + - [ 0.000000000000000, -0.932719000000000, -0.000000000000000 ] + - [ 0.107671250000000, 0.000000000000000, -0.172916250000000 ] + - # (3, 28) + - [ -0.172916250000000, 0.000000000000000, -0.107671250000000 ] + - [ -0.000000000000000, -0.932719000000000, 0.000000000000000 ] + - [ -0.107671250000000, -0.000000000000000, -0.172916250000000 ] + - # (3, 29) + - [ -0.012134875000000, 0.000000000000000, -0.132871375000000 ] + - [ -0.000000000000000, 0.651338250000000, 0.000000000000000 ] + - [ -0.132871375000000, 0.000000000000000, -0.012134875000000 ] + - # (3, 30) + - [ -0.012134875000000, 0.000000000000000, 0.132871375000000 ] + - [ 0.000000000000000, 0.651338250000000, 0.000000000000000 ] + - [ 0.132871375000000, 0.000000000000000, -0.012134875000000 ] + - # (3, 31) + - [ -0.172916250000000, 0.000000000000000, -0.107671250000000 ] + - [ 0.000000000000000, -0.932719000000000, 0.000000000000000 ] + - [ -0.107671250000000, -0.000000000000000, -0.172916250000000 ] + - # (3, 32) + - [ -0.172916250000000, -0.000000000000000, 0.107671250000000 ] + - [ 0.000000000000000, -0.932719000000000, 0.000000000000000 ] + - [ 0.107671250000000, -0.000000000000000, -0.172916250000000 ] + - # (3, 33) + - [ -0.932719000000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.172916250000000, -0.107671250000000 ] + - [ -0.000000000000000, -0.107671250000000, -0.172916250000000 ] + - # (3, 34) + - [ 0.651338250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.012134875000000, -0.132871375000000 ] + - [ -0.000000000000000, -0.132871375000000, -0.012134875000000 ] + - # (3, 35) + - [ -0.932719000000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.172916250000000, 0.107671250000000 ] + - [ 0.000000000000000, 0.107671250000000, -0.172916250000000 ] + - # (3, 36) + - [ 0.651338250000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.012134875000000, 0.132871375000000 ] + - [ 0.000000000000000, 0.132871375000000, -0.012134875000000 ] + - # (3, 37) + - [ -0.932719000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.172916250000000, 0.107671250000000 ] + - [ -0.000000000000000, 0.107671250000000, -0.172916250000000 ] + - # (3, 38) + - [ 0.651338250000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.012134875000000, 0.132871375000000 ] + - [ 0.000000000000000, 0.132871375000000, -0.012134875000000 ] + - # (3, 39) + - [ -0.932719000000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.172916250000000, -0.107671250000000 ] + - [ -0.000000000000000, -0.107671250000000, -0.172916250000000 ] + - # (3, 40) + - [ 0.651338250000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.012134875000000, -0.132871375000000 ] + - [ -0.000000000000000, -0.132871375000000, -0.012134875000000 ] + - # (4, 1) + - [ -0.597366500000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.597366500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.265738000000000 ] + - # (4, 2) + - [ 0.704353500000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -2.475097000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.704353500000000 ] + - # (4, 3) + - [ -2.475097000000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.704353500000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.704353500000000 ] + - # (4, 4) + - [ 9.720056000000001, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 9.720056000000001, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 9.720056000000001 ] + - # (4, 5) + - [ -0.245066000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.245066000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.245066000000000 ] + - # (4, 6) + - [ 0.265738000000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.597366500000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.597366500000000 ] + - # (4, 7) + - [ -0.597366500000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.265738000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.597366500000000 ] + - # (4, 8) + - [ 0.704353500000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.704353500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -2.475097000000000 ] + - # (4, 9) + - [ -0.609209250000000, -1.488184375000000, 1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ 1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (4, 10) + - [ -0.609209250000000, 1.488184375000000, -1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ -1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (4, 11) + - [ -0.609209250000000, 1.488184375000000, 1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ 1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (4, 12) + - [ -0.609209250000000, -1.488184375000000, -1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ -1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (4, 13) + - [ -0.609209250000000, -1.488184375000000, -1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ -1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (4, 14) + - [ -0.609209250000000, 1.488184375000000, 1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ 1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (4, 15) + - [ -0.609209250000000, 1.488184375000000, -1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ -1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (4, 16) + - [ -0.609209250000000, -1.488184375000000, 1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ 1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (4, 17) + - [ -0.172916250000000, 0.107671250000000, -0.000000000000000 ] + - [ 0.107671250000000, -0.172916250000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932719000000000 ] + - # (4, 18) + - [ -0.172916250000000, -0.107671250000000, -0.000000000000000 ] + - [ -0.107671250000000, -0.172916250000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.932719000000000 ] + - # (4, 19) + - [ -0.172916250000000, -0.107671250000000, -0.000000000000000 ] + - [ -0.107671250000000, -0.172916250000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932719000000000 ] + - # (4, 20) + - [ -0.172916250000000, 0.107671250000000, 0.000000000000000 ] + - [ 0.107671250000000, -0.172916250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.932719000000000 ] + - # (4, 21) + - [ -0.012134875000000, 0.132871375000000, -0.000000000000000 ] + - [ 0.132871375000000, -0.012134875000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.651338250000000 ] + - # (4, 22) + - [ -0.012134875000000, -0.132871375000000, -0.000000000000000 ] + - [ -0.132871375000000, -0.012134875000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.651338250000000 ] + - # (4, 23) + - [ -0.012134875000000, -0.132871375000000, -0.000000000000000 ] + - [ -0.132871375000000, -0.012134875000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.651338250000000 ] + - # (4, 24) + - [ -0.012134875000000, 0.132871375000000, 0.000000000000000 ] + - [ 0.132871375000000, -0.012134875000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.651338250000000 ] + - # (4, 25) + - [ -0.012134875000000, -0.000000000000000, -0.132871375000000 ] + - [ 0.000000000000000, 0.651338250000000, -0.000000000000000 ] + - [ -0.132871375000000, -0.000000000000000, -0.012134875000000 ] + - # (4, 26) + - [ -0.012134875000000, -0.000000000000000, 0.132871375000000 ] + - [ -0.000000000000000, 0.651338250000000, -0.000000000000000 ] + - [ 0.132871375000000, 0.000000000000000, -0.012134875000000 ] + - # (4, 27) + - [ -0.172916250000000, 0.000000000000000, -0.107671250000000 ] + - [ -0.000000000000000, -0.932719000000000, 0.000000000000000 ] + - [ -0.107671250000000, -0.000000000000000, -0.172916250000000 ] + - # (4, 28) + - [ -0.172916250000000, -0.000000000000000, 0.107671250000000 ] + - [ 0.000000000000000, -0.932719000000000, -0.000000000000000 ] + - [ 0.107671250000000, 0.000000000000000, -0.172916250000000 ] + - # (4, 29) + - [ -0.012134875000000, 0.000000000000000, 0.132871375000000 ] + - [ 0.000000000000000, 0.651338250000000, 0.000000000000000 ] + - [ 0.132871375000000, 0.000000000000000, -0.012134875000000 ] + - # (4, 30) + - [ -0.012134875000000, 0.000000000000000, -0.132871375000000 ] + - [ -0.000000000000000, 0.651338250000000, 0.000000000000000 ] + - [ -0.132871375000000, 0.000000000000000, -0.012134875000000 ] + - # (4, 31) + - [ -0.172916250000000, -0.000000000000000, 0.107671250000000 ] + - [ 0.000000000000000, -0.932719000000000, 0.000000000000000 ] + - [ 0.107671250000000, -0.000000000000000, -0.172916250000000 ] + - # (4, 32) + - [ -0.172916250000000, 0.000000000000000, -0.107671250000000 ] + - [ 0.000000000000000, -0.932719000000000, 0.000000000000000 ] + - [ -0.107671250000000, -0.000000000000000, -0.172916250000000 ] + - # (4, 33) + - [ 0.651338250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.012134875000000, -0.132871375000000 ] + - [ -0.000000000000000, -0.132871375000000, -0.012134875000000 ] + - # (4, 34) + - [ -0.932719000000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.172916250000000, -0.107671250000000 ] + - [ -0.000000000000000, -0.107671250000000, -0.172916250000000 ] + - # (4, 35) + - [ 0.651338250000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.012134875000000, 0.132871375000000 ] + - [ 0.000000000000000, 0.132871375000000, -0.012134875000000 ] + - # (4, 36) + - [ -0.932719000000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.172916250000000, 0.107671250000000 ] + - [ 0.000000000000000, 0.107671250000000, -0.172916250000000 ] + - # (4, 37) + - [ 0.651338250000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.012134875000000, 0.132871375000000 ] + - [ 0.000000000000000, 0.132871375000000, -0.012134875000000 ] + - # (4, 38) + - [ -0.932719000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.172916250000000, 0.107671250000000 ] + - [ -0.000000000000000, 0.107671250000000, -0.172916250000000 ] + - # (4, 39) + - [ 0.651338250000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.012134875000000, -0.132871375000000 ] + - [ -0.000000000000000, -0.132871375000000, -0.012134875000000 ] + - # (4, 40) + - [ -0.932719000000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.172916250000000, -0.107671250000000 ] + - [ -0.000000000000000, -0.107671250000000, -0.172916250000000 ] + - # (5, 1) + - [ 0.704353500000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.704353500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -2.475097000000000 ] + - # (5, 2) + - [ -0.597366500000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.265738000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.597366500000000 ] + - # (5, 3) + - [ 0.265738000000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.597366500000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.597366500000000 ] + - # (5, 4) + - [ -0.245066000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.245066000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.245066000000000 ] + - # (5, 5) + - [ 9.720056000000001, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 9.720056000000001, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 9.720056000000001 ] + - # (5, 6) + - [ -2.475097000000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.704353500000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.704353500000000 ] + - # (5, 7) + - [ 0.704353500000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -2.475097000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.704353500000000 ] + - # (5, 8) + - [ -0.597366500000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.597366500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.265738000000000 ] + - # (5, 9) + - [ -0.609209250000000, -1.488184375000000, 1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ 1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (5, 10) + - [ -0.609209250000000, 1.488184375000000, -1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ -1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (5, 11) + - [ -0.609209250000000, 1.488184375000000, 1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ 1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (5, 12) + - [ -0.609209250000000, -1.488184375000000, -1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ -1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (5, 13) + - [ -0.609209250000000, -1.488184375000000, -1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ -1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (5, 14) + - [ -0.609209250000000, 1.488184375000000, 1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ 1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (5, 15) + - [ -0.609209250000000, 1.488184375000000, -1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ -1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (5, 16) + - [ -0.609209250000000, -1.488184375000000, 1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ 1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (5, 17) + - [ -0.012134875000000, 0.132871375000000, 0.000000000000000 ] + - [ 0.132871375000000, -0.012134875000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.651338250000000 ] + - # (5, 18) + - [ -0.012134875000000, -0.132871375000000, -0.000000000000000 ] + - [ -0.132871375000000, -0.012134875000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.651338250000000 ] + - # (5, 19) + - [ -0.012134875000000, -0.132871375000000, -0.000000000000000 ] + - [ -0.132871375000000, -0.012134875000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.651338250000000 ] + - # (5, 20) + - [ -0.012134875000000, 0.132871375000000, -0.000000000000000 ] + - [ 0.132871375000000, -0.012134875000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.651338250000000 ] + - # (5, 21) + - [ -0.172916250000000, 0.107671250000000, 0.000000000000000 ] + - [ 0.107671250000000, -0.172916250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.932719000000000 ] + - # (5, 22) + - [ -0.172916250000000, -0.107671250000000, -0.000000000000000 ] + - [ -0.107671250000000, -0.172916250000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932719000000000 ] + - # (5, 23) + - [ -0.172916250000000, -0.107671250000000, -0.000000000000000 ] + - [ -0.107671250000000, -0.172916250000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.932719000000000 ] + - # (5, 24) + - [ -0.172916250000000, 0.107671250000000, -0.000000000000000 ] + - [ 0.107671250000000, -0.172916250000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932719000000000 ] + - # (5, 25) + - [ -0.172916250000000, 0.000000000000000, -0.107671250000000 ] + - [ 0.000000000000000, -0.932719000000000, 0.000000000000000 ] + - [ -0.107671250000000, -0.000000000000000, -0.172916250000000 ] + - # (5, 26) + - [ -0.172916250000000, -0.000000000000000, 0.107671250000000 ] + - [ 0.000000000000000, -0.932719000000000, 0.000000000000000 ] + - [ 0.107671250000000, -0.000000000000000, -0.172916250000000 ] + - # (5, 27) + - [ -0.012134875000000, 0.000000000000000, -0.132871375000000 ] + - [ -0.000000000000000, 0.651338250000000, 0.000000000000000 ] + - [ -0.132871375000000, 0.000000000000000, -0.012134875000000 ] + - # (5, 28) + - [ -0.012134875000000, 0.000000000000000, 0.132871375000000 ] + - [ 0.000000000000000, 0.651338250000000, 0.000000000000000 ] + - [ 0.132871375000000, 0.000000000000000, -0.012134875000000 ] + - # (5, 29) + - [ -0.172916250000000, -0.000000000000000, 0.107671250000000 ] + - [ 0.000000000000000, -0.932719000000000, -0.000000000000000 ] + - [ 0.107671250000000, 0.000000000000000, -0.172916250000000 ] + - # (5, 30) + - [ -0.172916250000000, 0.000000000000000, -0.107671250000000 ] + - [ -0.000000000000000, -0.932719000000000, 0.000000000000000 ] + - [ -0.107671250000000, -0.000000000000000, -0.172916250000000 ] + - # (5, 31) + - [ -0.012134875000000, -0.000000000000000, 0.132871375000000 ] + - [ -0.000000000000000, 0.651338250000000, -0.000000000000000 ] + - [ 0.132871375000000, 0.000000000000000, -0.012134875000000 ] + - # (5, 32) + - [ -0.012134875000000, -0.000000000000000, -0.132871375000000 ] + - [ 0.000000000000000, 0.651338250000000, -0.000000000000000 ] + - [ -0.132871375000000, -0.000000000000000, -0.012134875000000 ] + - # (5, 33) + - [ -0.932719000000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.172916250000000, -0.107671250000000 ] + - [ -0.000000000000000, -0.107671250000000, -0.172916250000000 ] + - # (5, 34) + - [ 0.651338250000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.012134875000000, -0.132871375000000 ] + - [ -0.000000000000000, -0.132871375000000, -0.012134875000000 ] + - # (5, 35) + - [ -0.932719000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.172916250000000, 0.107671250000000 ] + - [ -0.000000000000000, 0.107671250000000, -0.172916250000000 ] + - # (5, 36) + - [ 0.651338250000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.012134875000000, 0.132871375000000 ] + - [ 0.000000000000000, 0.132871375000000, -0.012134875000000 ] + - # (5, 37) + - [ -0.932719000000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.172916250000000, 0.107671250000000 ] + - [ 0.000000000000000, 0.107671250000000, -0.172916250000000 ] + - # (5, 38) + - [ 0.651338250000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.012134875000000, 0.132871375000000 ] + - [ 0.000000000000000, 0.132871375000000, -0.012134875000000 ] + - # (5, 39) + - [ -0.932719000000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.172916250000000, -0.107671250000000 ] + - [ -0.000000000000000, -0.107671250000000, -0.172916250000000 ] + - # (5, 40) + - [ 0.651338250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.012134875000000, -0.132871375000000 ] + - [ -0.000000000000000, -0.132871375000000, -0.012134875000000 ] + - # (6, 1) + - [ -0.597366500000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.265738000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.597366500000000 ] + - # (6, 2) + - [ 0.704353500000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.704353500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -2.475097000000000 ] + - # (6, 3) + - [ -0.245066000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.245066000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.245066000000000 ] + - # (6, 4) + - [ 0.265738000000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.597366500000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.597366500000000 ] + - # (6, 5) + - [ -2.475097000000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.704353500000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.704353500000000 ] + - # (6, 6) + - [ 9.720056000000001, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 9.720056000000001, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 9.720056000000001 ] + - # (6, 7) + - [ -0.597366500000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.597366500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.265738000000000 ] + - # (6, 8) + - [ 0.704353500000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -2.475097000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.704353500000000 ] + - # (6, 9) + - [ -0.609209250000000, 1.488184375000000, -1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ -1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (6, 10) + - [ -0.609209250000000, -1.488184375000000, 1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ 1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (6, 11) + - [ -0.609209250000000, -1.488184375000000, -1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ -1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (6, 12) + - [ -0.609209250000000, 1.488184375000000, 1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ 1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (6, 13) + - [ -0.609209250000000, 1.488184375000000, 1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ 1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (6, 14) + - [ -0.609209250000000, -1.488184375000000, -1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ -1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (6, 15) + - [ -0.609209250000000, -1.488184375000000, 1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ 1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (6, 16) + - [ -0.609209250000000, 1.488184375000000, -1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ -1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (6, 17) + - [ -0.012134875000000, -0.132871375000000, -0.000000000000000 ] + - [ -0.132871375000000, -0.012134875000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.651338250000000 ] + - # (6, 18) + - [ -0.012134875000000, 0.132871375000000, 0.000000000000000 ] + - [ 0.132871375000000, -0.012134875000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.651338250000000 ] + - # (6, 19) + - [ -0.012134875000000, 0.132871375000000, -0.000000000000000 ] + - [ 0.132871375000000, -0.012134875000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.651338250000000 ] + - # (6, 20) + - [ -0.012134875000000, -0.132871375000000, -0.000000000000000 ] + - [ -0.132871375000000, -0.012134875000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.651338250000000 ] + - # (6, 21) + - [ -0.172916250000000, -0.107671250000000, -0.000000000000000 ] + - [ -0.107671250000000, -0.172916250000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932719000000000 ] + - # (6, 22) + - [ -0.172916250000000, 0.107671250000000, 0.000000000000000 ] + - [ 0.107671250000000, -0.172916250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.932719000000000 ] + - # (6, 23) + - [ -0.172916250000000, 0.107671250000000, -0.000000000000000 ] + - [ 0.107671250000000, -0.172916250000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932719000000000 ] + - # (6, 24) + - [ -0.172916250000000, -0.107671250000000, -0.000000000000000 ] + - [ -0.107671250000000, -0.172916250000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.932719000000000 ] + - # (6, 25) + - [ -0.172916250000000, -0.000000000000000, 0.107671250000000 ] + - [ 0.000000000000000, -0.932719000000000, 0.000000000000000 ] + - [ 0.107671250000000, -0.000000000000000, -0.172916250000000 ] + - # (6, 26) + - [ -0.172916250000000, 0.000000000000000, -0.107671250000000 ] + - [ 0.000000000000000, -0.932719000000000, 0.000000000000000 ] + - [ -0.107671250000000, -0.000000000000000, -0.172916250000000 ] + - # (6, 27) + - [ -0.012134875000000, 0.000000000000000, 0.132871375000000 ] + - [ 0.000000000000000, 0.651338250000000, 0.000000000000000 ] + - [ 0.132871375000000, 0.000000000000000, -0.012134875000000 ] + - # (6, 28) + - [ -0.012134875000000, 0.000000000000000, -0.132871375000000 ] + - [ -0.000000000000000, 0.651338250000000, 0.000000000000000 ] + - [ -0.132871375000000, 0.000000000000000, -0.012134875000000 ] + - # (6, 29) + - [ -0.172916250000000, 0.000000000000000, -0.107671250000000 ] + - [ -0.000000000000000, -0.932719000000000, 0.000000000000000 ] + - [ -0.107671250000000, -0.000000000000000, -0.172916250000000 ] + - # (6, 30) + - [ -0.172916250000000, -0.000000000000000, 0.107671250000000 ] + - [ 0.000000000000000, -0.932719000000000, -0.000000000000000 ] + - [ 0.107671250000000, 0.000000000000000, -0.172916250000000 ] + - # (6, 31) + - [ -0.012134875000000, -0.000000000000000, -0.132871375000000 ] + - [ 0.000000000000000, 0.651338250000000, -0.000000000000000 ] + - [ -0.132871375000000, -0.000000000000000, -0.012134875000000 ] + - # (6, 32) + - [ -0.012134875000000, -0.000000000000000, 0.132871375000000 ] + - [ -0.000000000000000, 0.651338250000000, -0.000000000000000 ] + - [ 0.132871375000000, 0.000000000000000, -0.012134875000000 ] + - # (6, 33) + - [ 0.651338250000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.012134875000000, -0.132871375000000 ] + - [ -0.000000000000000, -0.132871375000000, -0.012134875000000 ] + - # (6, 34) + - [ -0.932719000000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.172916250000000, -0.107671250000000 ] + - [ -0.000000000000000, -0.107671250000000, -0.172916250000000 ] + - # (6, 35) + - [ 0.651338250000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.012134875000000, 0.132871375000000 ] + - [ 0.000000000000000, 0.132871375000000, -0.012134875000000 ] + - # (6, 36) + - [ -0.932719000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.172916250000000, 0.107671250000000 ] + - [ -0.000000000000000, 0.107671250000000, -0.172916250000000 ] + - # (6, 37) + - [ 0.651338250000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.012134875000000, 0.132871375000000 ] + - [ 0.000000000000000, 0.132871375000000, -0.012134875000000 ] + - # (6, 38) + - [ -0.932719000000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.172916250000000, 0.107671250000000 ] + - [ 0.000000000000000, 0.107671250000000, -0.172916250000000 ] + - # (6, 39) + - [ 0.651338250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.012134875000000, -0.132871375000000 ] + - [ -0.000000000000000, -0.132871375000000, -0.012134875000000 ] + - # (6, 40) + - [ -0.932719000000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.172916250000000, -0.107671250000000 ] + - [ -0.000000000000000, -0.107671250000000, -0.172916250000000 ] + - # (7, 1) + - [ 0.265738000000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.597366500000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.597366500000000 ] + - # (7, 2) + - [ -0.245066000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.245066000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.245066000000000 ] + - # (7, 3) + - [ 0.704353500000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.704353500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -2.475097000000000 ] + - # (7, 4) + - [ -0.597366500000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.265738000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.597366500000000 ] + - # (7, 5) + - [ 0.704353500000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -2.475097000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.704353500000000 ] + - # (7, 6) + - [ -0.597366500000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.597366500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.265738000000000 ] + - # (7, 7) + - [ 9.720056000000001, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 9.720056000000001, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 9.720056000000001 ] + - # (7, 8) + - [ -2.475097000000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.704353500000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.704353500000000 ] + - # (7, 9) + - [ -0.609209250000000, 1.488184375000000, 1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ 1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (7, 10) + - [ -0.609209250000000, -1.488184375000000, -1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ -1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (7, 11) + - [ -0.609209250000000, -1.488184375000000, 1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ 1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (7, 12) + - [ -0.609209250000000, 1.488184375000000, -1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ -1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (7, 13) + - [ -0.609209250000000, 1.488184375000000, -1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ -1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (7, 14) + - [ -0.609209250000000, -1.488184375000000, 1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ 1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (7, 15) + - [ -0.609209250000000, -1.488184375000000, -1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ -1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (7, 16) + - [ -0.609209250000000, 1.488184375000000, 1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ 1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (7, 17) + - [ -0.012134875000000, -0.132871375000000, -0.000000000000000 ] + - [ -0.132871375000000, -0.012134875000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.651338250000000 ] + - # (7, 18) + - [ -0.012134875000000, 0.132871375000000, -0.000000000000000 ] + - [ 0.132871375000000, -0.012134875000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.651338250000000 ] + - # (7, 19) + - [ -0.012134875000000, 0.132871375000000, 0.000000000000000 ] + - [ 0.132871375000000, -0.012134875000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.651338250000000 ] + - # (7, 20) + - [ -0.012134875000000, -0.132871375000000, -0.000000000000000 ] + - [ -0.132871375000000, -0.012134875000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.651338250000000 ] + - # (7, 21) + - [ -0.172916250000000, -0.107671250000000, -0.000000000000000 ] + - [ -0.107671250000000, -0.172916250000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.932719000000000 ] + - # (7, 22) + - [ -0.172916250000000, 0.107671250000000, -0.000000000000000 ] + - [ 0.107671250000000, -0.172916250000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932719000000000 ] + - # (7, 23) + - [ -0.172916250000000, 0.107671250000000, 0.000000000000000 ] + - [ 0.107671250000000, -0.172916250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.932719000000000 ] + - # (7, 24) + - [ -0.172916250000000, -0.107671250000000, -0.000000000000000 ] + - [ -0.107671250000000, -0.172916250000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932719000000000 ] + - # (7, 25) + - [ -0.012134875000000, 0.000000000000000, -0.132871375000000 ] + - [ -0.000000000000000, 0.651338250000000, 0.000000000000000 ] + - [ -0.132871375000000, 0.000000000000000, -0.012134875000000 ] + - # (7, 26) + - [ -0.012134875000000, 0.000000000000000, 0.132871375000000 ] + - [ 0.000000000000000, 0.651338250000000, 0.000000000000000 ] + - [ 0.132871375000000, 0.000000000000000, -0.012134875000000 ] + - # (7, 27) + - [ -0.172916250000000, 0.000000000000000, -0.107671250000000 ] + - [ 0.000000000000000, -0.932719000000000, 0.000000000000000 ] + - [ -0.107671250000000, -0.000000000000000, -0.172916250000000 ] + - # (7, 28) + - [ -0.172916250000000, -0.000000000000000, 0.107671250000000 ] + - [ 0.000000000000000, -0.932719000000000, 0.000000000000000 ] + - [ 0.107671250000000, -0.000000000000000, -0.172916250000000 ] + - # (7, 29) + - [ -0.012134875000000, -0.000000000000000, 0.132871375000000 ] + - [ -0.000000000000000, 0.651338250000000, -0.000000000000000 ] + - [ 0.132871375000000, 0.000000000000000, -0.012134875000000 ] + - # (7, 30) + - [ -0.012134875000000, -0.000000000000000, -0.132871375000000 ] + - [ 0.000000000000000, 0.651338250000000, -0.000000000000000 ] + - [ -0.132871375000000, -0.000000000000000, -0.012134875000000 ] + - # (7, 31) + - [ -0.172916250000000, -0.000000000000000, 0.107671250000000 ] + - [ 0.000000000000000, -0.932719000000000, -0.000000000000000 ] + - [ 0.107671250000000, 0.000000000000000, -0.172916250000000 ] + - # (7, 32) + - [ -0.172916250000000, 0.000000000000000, -0.107671250000000 ] + - [ -0.000000000000000, -0.932719000000000, 0.000000000000000 ] + - [ -0.107671250000000, -0.000000000000000, -0.172916250000000 ] + - # (7, 33) + - [ -0.932719000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.172916250000000, 0.107671250000000 ] + - [ -0.000000000000000, 0.107671250000000, -0.172916250000000 ] + - # (7, 34) + - [ 0.651338250000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.012134875000000, 0.132871375000000 ] + - [ 0.000000000000000, 0.132871375000000, -0.012134875000000 ] + - # (7, 35) + - [ -0.932719000000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.172916250000000, -0.107671250000000 ] + - [ -0.000000000000000, -0.107671250000000, -0.172916250000000 ] + - # (7, 36) + - [ 0.651338250000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.012134875000000, -0.132871375000000 ] + - [ -0.000000000000000, -0.132871375000000, -0.012134875000000 ] + - # (7, 37) + - [ -0.932719000000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.172916250000000, -0.107671250000000 ] + - [ -0.000000000000000, -0.107671250000000, -0.172916250000000 ] + - # (7, 38) + - [ 0.651338250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.012134875000000, -0.132871375000000 ] + - [ -0.000000000000000, -0.132871375000000, -0.012134875000000 ] + - # (7, 39) + - [ -0.932719000000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.172916250000000, 0.107671250000000 ] + - [ 0.000000000000000, 0.107671250000000, -0.172916250000000 ] + - # (7, 40) + - [ 0.651338250000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.012134875000000, 0.132871375000000 ] + - [ 0.000000000000000, 0.132871375000000, -0.012134875000000 ] + - # (8, 1) + - [ -0.245066000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.245066000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.245066000000000 ] + - # (8, 2) + - [ 0.265738000000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.597366500000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.597366500000000 ] + - # (8, 3) + - [ -0.597366500000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.265738000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.597366500000000 ] + - # (8, 4) + - [ 0.704353500000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.704353500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -2.475097000000000 ] + - # (8, 5) + - [ -0.597366500000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.597366500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.265738000000000 ] + - # (8, 6) + - [ 0.704353500000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -2.475097000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.704353500000000 ] + - # (8, 7) + - [ -2.475097000000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.704353500000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.704353500000000 ] + - # (8, 8) + - [ 9.720056000000001, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 9.720056000000001, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 9.720056000000001 ] + - # (8, 9) + - [ -0.609209250000000, -1.488184375000000, -1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ -1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (8, 10) + - [ -0.609209250000000, 1.488184375000000, 1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ 1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (8, 11) + - [ -0.609209250000000, 1.488184375000000, -1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ -1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (8, 12) + - [ -0.609209250000000, -1.488184375000000, 1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ 1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (8, 13) + - [ -0.609209250000000, -1.488184375000000, 1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ 1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (8, 14) + - [ -0.609209250000000, 1.488184375000000, -1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, 1.488184375000000 ] + - [ -1.488184375000000, 1.488184375000000, -0.609209250000000 ] + - # (8, 15) + - [ -0.609209250000000, 1.488184375000000, 1.488184375000000 ] + - [ 1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ 1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (8, 16) + - [ -0.609209250000000, -1.488184375000000, -1.488184375000000 ] + - [ -1.488184375000000, -0.609209250000000, -1.488184375000000 ] + - [ -1.488184375000000, -1.488184375000000, -0.609209250000000 ] + - # (8, 17) + - [ -0.012134875000000, 0.132871375000000, -0.000000000000000 ] + - [ 0.132871375000000, -0.012134875000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.651338250000000 ] + - # (8, 18) + - [ -0.012134875000000, -0.132871375000000, -0.000000000000000 ] + - [ -0.132871375000000, -0.012134875000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.651338250000000 ] + - # (8, 19) + - [ -0.012134875000000, -0.132871375000000, -0.000000000000000 ] + - [ -0.132871375000000, -0.012134875000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.651338250000000 ] + - # (8, 20) + - [ -0.012134875000000, 0.132871375000000, 0.000000000000000 ] + - [ 0.132871375000000, -0.012134875000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.651338250000000 ] + - # (8, 21) + - [ -0.172916250000000, 0.107671250000000, -0.000000000000000 ] + - [ 0.107671250000000, -0.172916250000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932719000000000 ] + - # (8, 22) + - [ -0.172916250000000, -0.107671250000000, -0.000000000000000 ] + - [ -0.107671250000000, -0.172916250000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.932719000000000 ] + - # (8, 23) + - [ -0.172916250000000, -0.107671250000000, -0.000000000000000 ] + - [ -0.107671250000000, -0.172916250000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932719000000000 ] + - # (8, 24) + - [ -0.172916250000000, 0.107671250000000, 0.000000000000000 ] + - [ 0.107671250000000, -0.172916250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.932719000000000 ] + - # (8, 25) + - [ -0.012134875000000, 0.000000000000000, 0.132871375000000 ] + - [ 0.000000000000000, 0.651338250000000, 0.000000000000000 ] + - [ 0.132871375000000, 0.000000000000000, -0.012134875000000 ] + - # (8, 26) + - [ -0.012134875000000, 0.000000000000000, -0.132871375000000 ] + - [ -0.000000000000000, 0.651338250000000, 0.000000000000000 ] + - [ -0.132871375000000, 0.000000000000000, -0.012134875000000 ] + - # (8, 27) + - [ -0.172916250000000, -0.000000000000000, 0.107671250000000 ] + - [ 0.000000000000000, -0.932719000000000, 0.000000000000000 ] + - [ 0.107671250000000, -0.000000000000000, -0.172916250000000 ] + - # (8, 28) + - [ -0.172916250000000, 0.000000000000000, -0.107671250000000 ] + - [ 0.000000000000000, -0.932719000000000, 0.000000000000000 ] + - [ -0.107671250000000, -0.000000000000000, -0.172916250000000 ] + - # (8, 29) + - [ -0.012134875000000, -0.000000000000000, -0.132871375000000 ] + - [ 0.000000000000000, 0.651338250000000, -0.000000000000000 ] + - [ -0.132871375000000, -0.000000000000000, -0.012134875000000 ] + - # (8, 30) + - [ -0.012134875000000, -0.000000000000000, 0.132871375000000 ] + - [ -0.000000000000000, 0.651338250000000, -0.000000000000000 ] + - [ 0.132871375000000, 0.000000000000000, -0.012134875000000 ] + - # (8, 31) + - [ -0.172916250000000, 0.000000000000000, -0.107671250000000 ] + - [ -0.000000000000000, -0.932719000000000, 0.000000000000000 ] + - [ -0.107671250000000, -0.000000000000000, -0.172916250000000 ] + - # (8, 32) + - [ -0.172916250000000, -0.000000000000000, 0.107671250000000 ] + - [ 0.000000000000000, -0.932719000000000, -0.000000000000000 ] + - [ 0.107671250000000, 0.000000000000000, -0.172916250000000 ] + - # (8, 33) + - [ 0.651338250000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.012134875000000, 0.132871375000000 ] + - [ 0.000000000000000, 0.132871375000000, -0.012134875000000 ] + - # (8, 34) + - [ -0.932719000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.172916250000000, 0.107671250000000 ] + - [ -0.000000000000000, 0.107671250000000, -0.172916250000000 ] + - # (8, 35) + - [ 0.651338250000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.012134875000000, -0.132871375000000 ] + - [ -0.000000000000000, -0.132871375000000, -0.012134875000000 ] + - # (8, 36) + - [ -0.932719000000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.172916250000000, -0.107671250000000 ] + - [ -0.000000000000000, -0.107671250000000, -0.172916250000000 ] + - # (8, 37) + - [ 0.651338250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.012134875000000, -0.132871375000000 ] + - [ -0.000000000000000, -0.132871375000000, -0.012134875000000 ] + - # (8, 38) + - [ -0.932719000000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.172916250000000, -0.107671250000000 ] + - [ -0.000000000000000, -0.107671250000000, -0.172916250000000 ] + - # (8, 39) + - [ 0.651338250000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.012134875000000, 0.132871375000000 ] + - [ 0.000000000000000, 0.132871375000000, -0.012134875000000 ] + - # (8, 40) + - [ -0.932719000000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.172916250000000, 0.107671250000000 ] + - [ 0.000000000000000, 0.107671250000000, -0.172916250000000 ] + - # (9, 1) + - [ -0.608242750000000, -1.488044437500000, -1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, -1.488044437500000 ] + - [ -1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (9, 2) + - [ -0.608242750000000, 1.488044437500000, 1.488044437500000 ] + - [ 1.488044437500001, -0.608242750000000, -1.488044437500000 ] + - [ 1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (9, 3) + - [ -0.608242750000000, 1.488044437500000, -1.488044437500000 ] + - [ 1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ -1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (9, 4) + - [ -0.608242750000000, -1.488044437500000, 1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ 1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (9, 5) + - [ -0.608242750000000, -1.488044437500000, 1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ 1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (9, 6) + - [ -0.608242750000000, 1.488044437500000, -1.488044437500000 ] + - [ 1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ -1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (9, 7) + - [ -0.608242750000000, 1.488044437500000, 1.488044437500001 ] + - [ 1.488044437500000, -0.608242750000000, -1.488044437500000 ] + - [ 1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (9, 8) + - [ -0.608242750000000, -1.488044437500000, -1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, -1.488044437500000 ] + - [ -1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (9, 9) + - [ 30.575393000000009, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 30.575393000000009, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 30.575393000000009 ] + - # (9, 10) + - [ -13.286822000000003, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.524178500000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.524178500000000 ] + - # (9, 11) + - [ 0.524178500000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -13.286822000000003, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.524178500000000 ] + - # (9, 12) + - [ -0.562291000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.562291000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.693991000000000 ] + - # (9, 13) + - [ 0.524178500000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.524178500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -13.286822000000001 ] + - # (9, 14) + - [ -0.562291000000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.693991000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.562291000000000 ] + - # (9, 15) + - [ 0.693991000000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.562291000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.562291000000000 ] + - # (9, 16) + - [ -0.685377000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.685377000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.685377000000000 ] + - # (9, 17) + - [ -0.260864250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260864250000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -8.618835499999999 ] + - # (9, 18) + - [ 1.460922500000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.338159250000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.025518500000000 ] + - # (9, 19) + - [ -0.338159250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.025518500000000 ] + - # (9, 20) + - [ 0.329333750000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.329333750000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.007826000000000 ] + - # (9, 21) + - [ -0.260864250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260864250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -8.618835499999999 ] + - # (9, 22) + - [ 1.460922500000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.338159250000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.025518500000000 ] + - # (9, 23) + - [ -0.338159250000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.025518500000000 ] + - # (9, 24) + - [ 0.329333750000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.329333750000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.007826000000000 ] + - # (9, 25) + - [ -0.260864250000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -8.618835499999999, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.260864250000000 ] + - # (9, 26) + - [ 1.460922500000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.025518500000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.338159250000000 ] + - # (9, 27) + - [ -0.260864250000000, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -8.618835500000003, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.260864250000000 ] + - # (9, 28) + - [ 1.460922500000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.025518500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.338159250000000 ] + - # (9, 29) + - [ -0.338159250000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.025518500000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 1.460922500000000 ] + - # (9, 30) + - [ 0.329333750000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.007826000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.329333750000000 ] + - # (9, 31) + - [ -0.338159250000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.025518500000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 1.460922500000000 ] + - # (9, 32) + - [ 0.329333750000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.007826000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.329333750000000 ] + - # (9, 33) + - [ -8.618835500000001, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.260864250000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.260864250000000 ] + - # (9, 34) + - [ -8.618835500000001, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.260864250000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.260864250000000 ] + - # (9, 35) + - [ 0.025518500000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.338159250000000 ] + - # (9, 36) + - [ 0.025518500000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.338159250000000 ] + - # (9, 37) + - [ 0.025518500000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.338159250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 1.460922500000000 ] + - # (9, 38) + - [ 0.025518500000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.338159250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 1.460922500000000 ] + - # (9, 39) + - [ 0.007826000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.329333750000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.329333750000000 ] + - # (9, 40) + - [ 0.007826000000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.329333750000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.329333750000000 ] + - # (10, 1) + - [ -0.608242750000000, 1.488044437500000, 1.488044437500000 ] + - [ 1.488044437500001, -0.608242750000000, -1.488044437500000 ] + - [ 1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (10, 2) + - [ -0.608242750000000, -1.488044437500000, -1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, -1.488044437500000 ] + - [ -1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (10, 3) + - [ -0.608242750000000, -1.488044437500000, 1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ 1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (10, 4) + - [ -0.608242750000000, 1.488044437500000, -1.488044437500000 ] + - [ 1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ -1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (10, 5) + - [ -0.608242750000000, 1.488044437500000, -1.488044437500000 ] + - [ 1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ -1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (10, 6) + - [ -0.608242750000000, -1.488044437500000, 1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ 1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (10, 7) + - [ -0.608242750000000, -1.488044437500000, -1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, -1.488044437500000 ] + - [ -1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (10, 8) + - [ -0.608242750000000, 1.488044437500000, 1.488044437500001 ] + - [ 1.488044437500000, -0.608242750000000, -1.488044437500000 ] + - [ 1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (10, 9) + - [ -13.286822000000003, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.524178500000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.524178500000000 ] + - # (10, 10) + - [ 30.575393000000009, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 30.575393000000009, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 30.575393000000009 ] + - # (10, 11) + - [ -0.562291000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.562291000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.693991000000000 ] + - # (10, 12) + - [ 0.524178500000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -13.286822000000003, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.524178500000000 ] + - # (10, 13) + - [ -0.562291000000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.693991000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.562291000000000 ] + - # (10, 14) + - [ 0.524178500000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.524178500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -13.286822000000001 ] + - # (10, 15) + - [ -0.685377000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.685377000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.685377000000000 ] + - # (10, 16) + - [ 0.693991000000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.562291000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.562291000000000 ] + - # (10, 17) + - [ 1.460922500000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.338159250000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.025518500000000 ] + - # (10, 18) + - [ -0.260864250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260864250000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -8.618835499999999 ] + - # (10, 19) + - [ 0.329333750000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.329333750000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.007826000000000 ] + - # (10, 20) + - [ -0.338159250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.025518500000000 ] + - # (10, 21) + - [ 1.460922500000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.338159250000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.025518500000000 ] + - # (10, 22) + - [ -0.260864250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260864250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -8.618835499999999 ] + - # (10, 23) + - [ 0.329333750000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.329333750000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.007826000000000 ] + - # (10, 24) + - [ -0.338159250000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.025518500000000 ] + - # (10, 25) + - [ 1.460922500000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.025518500000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.338159250000000 ] + - # (10, 26) + - [ -0.260864250000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -8.618835499999999, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.260864250000000 ] + - # (10, 27) + - [ 1.460922500000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.025518500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.338159250000000 ] + - # (10, 28) + - [ -0.260864250000000, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -8.618835500000003, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.260864250000000 ] + - # (10, 29) + - [ 0.329333750000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.007826000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.329333750000000 ] + - # (10, 30) + - [ -0.338159250000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.025518500000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 1.460922500000000 ] + - # (10, 31) + - [ 0.329333750000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.007826000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.329333750000000 ] + - # (10, 32) + - [ -0.338159250000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.025518500000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 1.460922500000000 ] + - # (10, 33) + - [ -8.618835500000001, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.260864250000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.260864250000000 ] + - # (10, 34) + - [ -8.618835500000001, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.260864250000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.260864250000000 ] + - # (10, 35) + - [ 0.025518500000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.338159250000000 ] + - # (10, 36) + - [ 0.025518500000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.338159250000000 ] + - # (10, 37) + - [ 0.025518500000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.338159250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 1.460922500000000 ] + - # (10, 38) + - [ 0.025518500000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.338159250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 1.460922500000000 ] + - # (10, 39) + - [ 0.007826000000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.329333750000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.329333750000000 ] + - # (10, 40) + - [ 0.007826000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.329333750000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.329333750000000 ] + - # (11, 1) + - [ -0.608242750000000, 1.488044437500000, -1.488044437500000 ] + - [ 1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ -1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (11, 2) + - [ -0.608242750000000, -1.488044437500000, 1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ 1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (11, 3) + - [ -0.608242750000000, -1.488044437500000, -1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, -1.488044437500000 ] + - [ -1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (11, 4) + - [ -0.608242750000000, 1.488044437500000, 1.488044437500000 ] + - [ 1.488044437500001, -0.608242750000000, -1.488044437500000 ] + - [ 1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (11, 5) + - [ -0.608242750000000, 1.488044437500000, 1.488044437500001 ] + - [ 1.488044437500000, -0.608242750000000, -1.488044437500000 ] + - [ 1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (11, 6) + - [ -0.608242750000000, -1.488044437500000, -1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, -1.488044437500000 ] + - [ -1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (11, 7) + - [ -0.608242750000000, -1.488044437500000, 1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ 1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (11, 8) + - [ -0.608242750000000, 1.488044437500000, -1.488044437500000 ] + - [ 1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ -1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (11, 9) + - [ 0.524178500000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -13.286822000000003, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.524178500000000 ] + - # (11, 10) + - [ -0.562291000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.562291000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.693991000000000 ] + - # (11, 11) + - [ 30.575393000000009, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 30.575393000000009, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 30.575393000000009 ] + - # (11, 12) + - [ -13.286822000000003, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.524178500000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.524178500000000 ] + - # (11, 13) + - [ 0.693991000000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.562291000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.562291000000000 ] + - # (11, 14) + - [ -0.685377000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.685377000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.685377000000000 ] + - # (11, 15) + - [ 0.524178500000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.524178500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -13.286822000000001 ] + - # (11, 16) + - [ -0.562291000000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.693991000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.562291000000000 ] + - # (11, 17) + - [ -0.338159250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.025518500000000 ] + - # (11, 18) + - [ 0.329333750000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.329333750000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.007826000000000 ] + - # (11, 19) + - [ -0.260864250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260864250000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -8.618835499999999 ] + - # (11, 20) + - [ 1.460922500000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.338159250000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.025518500000000 ] + - # (11, 21) + - [ -0.338159250000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.025518500000000 ] + - # (11, 22) + - [ 0.329333750000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.329333750000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.007826000000000 ] + - # (11, 23) + - [ -0.260864250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260864250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -8.618835499999999 ] + - # (11, 24) + - [ 1.460922500000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.338159250000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.025518500000000 ] + - # (11, 25) + - [ -0.260864250000000, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -8.618835500000003, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.260864250000000 ] + - # (11, 26) + - [ 1.460922500000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.025518500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.338159250000000 ] + - # (11, 27) + - [ -0.260864250000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -8.618835499999999, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.260864250000000 ] + - # (11, 28) + - [ 1.460922500000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.025518500000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.338159250000000 ] + - # (11, 29) + - [ -0.338159250000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.025518500000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 1.460922500000000 ] + - # (11, 30) + - [ 0.329333750000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.007826000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.329333750000000 ] + - # (11, 31) + - [ -0.338159250000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.025518500000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 1.460922500000000 ] + - # (11, 32) + - [ 0.329333750000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.007826000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.329333750000000 ] + - # (11, 33) + - [ 0.025518500000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.338159250000000 ] + - # (11, 34) + - [ 0.025518500000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.338159250000000 ] + - # (11, 35) + - [ -8.618835500000001, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.260864250000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.260864250000000 ] + - # (11, 36) + - [ -8.618835500000001, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.260864250000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.260864250000000 ] + - # (11, 37) + - [ 0.007826000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.329333750000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.329333750000000 ] + - # (11, 38) + - [ 0.007826000000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.329333750000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.329333750000000 ] + - # (11, 39) + - [ 0.025518500000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.338159250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 1.460922500000000 ] + - # (11, 40) + - [ 0.025518500000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.338159250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 1.460922500000000 ] + - # (12, 1) + - [ -0.608242750000000, -1.488044437500000, 1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ 1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (12, 2) + - [ -0.608242750000000, 1.488044437500000, -1.488044437500000 ] + - [ 1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ -1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (12, 3) + - [ -0.608242750000000, 1.488044437500000, 1.488044437500000 ] + - [ 1.488044437500001, -0.608242750000000, -1.488044437500000 ] + - [ 1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (12, 4) + - [ -0.608242750000000, -1.488044437500000, -1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, -1.488044437500000 ] + - [ -1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (12, 5) + - [ -0.608242750000000, -1.488044437500000, -1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, -1.488044437500000 ] + - [ -1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (12, 6) + - [ -0.608242750000000, 1.488044437500000, 1.488044437500001 ] + - [ 1.488044437500000, -0.608242750000000, -1.488044437500000 ] + - [ 1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (12, 7) + - [ -0.608242750000000, 1.488044437500000, -1.488044437500000 ] + - [ 1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ -1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (12, 8) + - [ -0.608242750000000, -1.488044437500000, 1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ 1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (12, 9) + - [ -0.562291000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.562291000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.693991000000000 ] + - # (12, 10) + - [ 0.524178500000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -13.286822000000003, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.524178500000000 ] + - # (12, 11) + - [ -13.286822000000003, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.524178500000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.524178500000000 ] + - # (12, 12) + - [ 30.575393000000009, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 30.575393000000009, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 30.575393000000009 ] + - # (12, 13) + - [ -0.685377000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.685377000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.685377000000000 ] + - # (12, 14) + - [ 0.693991000000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.562291000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.562291000000000 ] + - # (12, 15) + - [ -0.562291000000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.693991000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.562291000000000 ] + - # (12, 16) + - [ 0.524178500000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.524178500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -13.286822000000001 ] + - # (12, 17) + - [ 0.329333750000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.329333750000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.007826000000000 ] + - # (12, 18) + - [ -0.338159250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.025518500000000 ] + - # (12, 19) + - [ 1.460922500000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.338159250000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.025518500000000 ] + - # (12, 20) + - [ -0.260864250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260864250000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -8.618835499999999 ] + - # (12, 21) + - [ 0.329333750000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.329333750000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.007826000000000 ] + - # (12, 22) + - [ -0.338159250000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.025518500000000 ] + - # (12, 23) + - [ 1.460922500000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.338159250000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.025518500000000 ] + - # (12, 24) + - [ -0.260864250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260864250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -8.618835499999999 ] + - # (12, 25) + - [ 1.460922500000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.025518500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.338159250000000 ] + - # (12, 26) + - [ -0.260864250000000, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -8.618835500000003, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.260864250000000 ] + - # (12, 27) + - [ 1.460922500000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.025518500000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.338159250000000 ] + - # (12, 28) + - [ -0.260864250000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -8.618835499999999, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.260864250000000 ] + - # (12, 29) + - [ 0.329333750000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.007826000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.329333750000000 ] + - # (12, 30) + - [ -0.338159250000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.025518500000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 1.460922500000000 ] + - # (12, 31) + - [ 0.329333750000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.007826000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.329333750000000 ] + - # (12, 32) + - [ -0.338159250000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.025518500000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 1.460922500000000 ] + - # (12, 33) + - [ 0.025518500000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.338159250000000 ] + - # (12, 34) + - [ 0.025518500000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.338159250000000 ] + - # (12, 35) + - [ -8.618835500000001, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.260864250000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.260864250000000 ] + - # (12, 36) + - [ -8.618835500000001, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.260864250000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.260864250000000 ] + - # (12, 37) + - [ 0.007826000000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.329333750000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.329333750000000 ] + - # (12, 38) + - [ 0.007826000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.329333750000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.329333750000000 ] + - # (12, 39) + - [ 0.025518500000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.338159250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 1.460922500000000 ] + - # (12, 40) + - [ 0.025518500000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.338159250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 1.460922500000000 ] + - # (13, 1) + - [ -0.608242750000000, -1.488044437500000, 1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ 1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (13, 2) + - [ -0.608242750000000, 1.488044437500000, -1.488044437500000 ] + - [ 1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ -1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (13, 3) + - [ -0.608242750000000, 1.488044437500000, 1.488044437500001 ] + - [ 1.488044437500000, -0.608242750000000, -1.488044437500000 ] + - [ 1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (13, 4) + - [ -0.608242750000000, -1.488044437500000, -1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, -1.488044437500000 ] + - [ -1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (13, 5) + - [ -0.608242750000000, -1.488044437500000, -1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, -1.488044437500000 ] + - [ -1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (13, 6) + - [ -0.608242750000000, 1.488044437500000, 1.488044437500000 ] + - [ 1.488044437500001, -0.608242750000000, -1.488044437500000 ] + - [ 1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (13, 7) + - [ -0.608242750000000, 1.488044437500000, -1.488044437500000 ] + - [ 1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ -1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (13, 8) + - [ -0.608242750000000, -1.488044437500000, 1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ 1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (13, 9) + - [ 0.524178500000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.524178500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -13.286822000000001 ] + - # (13, 10) + - [ -0.562291000000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.693991000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.562291000000000 ] + - # (13, 11) + - [ 0.693991000000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.562291000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.562291000000000 ] + - # (13, 12) + - [ -0.685377000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.685377000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.685377000000000 ] + - # (13, 13) + - [ 30.575393000000009, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 30.575393000000009, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 30.575393000000009 ] + - # (13, 14) + - [ -13.286822000000003, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.524178500000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.524178500000000 ] + - # (13, 15) + - [ 0.524178500000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -13.286822000000003, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.524178500000000 ] + - # (13, 16) + - [ -0.562291000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.562291000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.693991000000000 ] + - # (13, 17) + - [ -0.260864250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260864250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -8.618835499999999 ] + - # (13, 18) + - [ 1.460922500000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.338159250000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.025518500000000 ] + - # (13, 19) + - [ -0.338159250000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.025518500000000 ] + - # (13, 20) + - [ 0.329333750000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.329333750000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.007826000000000 ] + - # (13, 21) + - [ -0.260864250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260864250000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -8.618835499999999 ] + - # (13, 22) + - [ 1.460922500000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.338159250000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.025518500000000 ] + - # (13, 23) + - [ -0.338159250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.025518500000000 ] + - # (13, 24) + - [ 0.329333750000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.329333750000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.007826000000000 ] + - # (13, 25) + - [ -0.338159250000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.025518500000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 1.460922500000000 ] + - # (13, 26) + - [ 0.329333750000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.007826000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.329333750000000 ] + - # (13, 27) + - [ -0.338159250000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.025518500000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 1.460922500000000 ] + - # (13, 28) + - [ 0.329333750000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.007826000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.329333750000000 ] + - # (13, 29) + - [ -0.260864250000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -8.618835499999999, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.260864250000000 ] + - # (13, 30) + - [ 1.460922500000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.025518500000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.338159250000000 ] + - # (13, 31) + - [ -0.260864250000000, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -8.618835500000003, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.260864250000000 ] + - # (13, 32) + - [ 1.460922500000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.025518500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.338159250000000 ] + - # (13, 33) + - [ 0.025518500000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.338159250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 1.460922500000000 ] + - # (13, 34) + - [ 0.025518500000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.338159250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 1.460922500000000 ] + - # (13, 35) + - [ 0.007826000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.329333750000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.329333750000000 ] + - # (13, 36) + - [ 0.007826000000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.329333750000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.329333750000000 ] + - # (13, 37) + - [ -8.618835500000001, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.260864250000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.260864250000000 ] + - # (13, 38) + - [ -8.618835500000001, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.260864250000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.260864250000000 ] + - # (13, 39) + - [ 0.025518500000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.338159250000000 ] + - # (13, 40) + - [ 0.025518500000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.338159250000000 ] + - # (14, 1) + - [ -0.608242750000000, 1.488044437500000, -1.488044437500000 ] + - [ 1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ -1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (14, 2) + - [ -0.608242750000000, -1.488044437500000, 1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ 1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (14, 3) + - [ -0.608242750000000, -1.488044437500000, -1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, -1.488044437500000 ] + - [ -1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (14, 4) + - [ -0.608242750000000, 1.488044437500000, 1.488044437500001 ] + - [ 1.488044437500000, -0.608242750000000, -1.488044437500000 ] + - [ 1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (14, 5) + - [ -0.608242750000000, 1.488044437500000, 1.488044437500000 ] + - [ 1.488044437500001, -0.608242750000000, -1.488044437500000 ] + - [ 1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (14, 6) + - [ -0.608242750000000, -1.488044437500000, -1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, -1.488044437500000 ] + - [ -1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (14, 7) + - [ -0.608242750000000, -1.488044437500000, 1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ 1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (14, 8) + - [ -0.608242750000000, 1.488044437500000, -1.488044437500000 ] + - [ 1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ -1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (14, 9) + - [ -0.562291000000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.693991000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.562291000000000 ] + - # (14, 10) + - [ 0.524178500000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.524178500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -13.286822000000001 ] + - # (14, 11) + - [ -0.685377000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.685377000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.685377000000000 ] + - # (14, 12) + - [ 0.693991000000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.562291000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.562291000000000 ] + - # (14, 13) + - [ -13.286822000000003, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.524178500000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.524178500000000 ] + - # (14, 14) + - [ 30.575393000000009, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 30.575393000000009, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 30.575393000000009 ] + - # (14, 15) + - [ -0.562291000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.562291000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.693991000000000 ] + - # (14, 16) + - [ 0.524178500000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -13.286822000000003, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.524178500000000 ] + - # (14, 17) + - [ 1.460922500000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.338159250000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.025518500000000 ] + - # (14, 18) + - [ -0.260864250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260864250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -8.618835499999999 ] + - # (14, 19) + - [ 0.329333750000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.329333750000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.007826000000000 ] + - # (14, 20) + - [ -0.338159250000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.025518500000000 ] + - # (14, 21) + - [ 1.460922500000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.338159250000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.025518500000000 ] + - # (14, 22) + - [ -0.260864250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260864250000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -8.618835499999999 ] + - # (14, 23) + - [ 0.329333750000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.329333750000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.007826000000000 ] + - # (14, 24) + - [ -0.338159250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.025518500000000 ] + - # (14, 25) + - [ 0.329333750000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.007826000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.329333750000000 ] + - # (14, 26) + - [ -0.338159250000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.025518500000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 1.460922500000000 ] + - # (14, 27) + - [ 0.329333750000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.007826000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.329333750000000 ] + - # (14, 28) + - [ -0.338159250000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.025518500000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 1.460922500000000 ] + - # (14, 29) + - [ 1.460922500000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.025518500000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.338159250000000 ] + - # (14, 30) + - [ -0.260864250000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -8.618835499999999, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.260864250000000 ] + - # (14, 31) + - [ 1.460922500000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.025518500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.338159250000000 ] + - # (14, 32) + - [ -0.260864250000000, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -8.618835500000003, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.260864250000000 ] + - # (14, 33) + - [ 0.025518500000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.338159250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 1.460922500000000 ] + - # (14, 34) + - [ 0.025518500000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.338159250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 1.460922500000000 ] + - # (14, 35) + - [ 0.007826000000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.329333750000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.329333750000000 ] + - # (14, 36) + - [ 0.007826000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.329333750000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.329333750000000 ] + - # (14, 37) + - [ -8.618835500000001, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.260864250000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.260864250000000 ] + - # (14, 38) + - [ -8.618835500000001, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.260864250000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.260864250000000 ] + - # (14, 39) + - [ 0.025518500000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.338159250000000 ] + - # (14, 40) + - [ 0.025518500000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.338159250000000 ] + - # (15, 1) + - [ -0.608242750000000, 1.488044437500000, 1.488044437500001 ] + - [ 1.488044437500000, -0.608242750000000, -1.488044437500000 ] + - [ 1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (15, 2) + - [ -0.608242750000000, -1.488044437500000, -1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, -1.488044437500000 ] + - [ -1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (15, 3) + - [ -0.608242750000000, -1.488044437500000, 1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ 1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (15, 4) + - [ -0.608242750000000, 1.488044437500000, -1.488044437500000 ] + - [ 1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ -1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (15, 5) + - [ -0.608242750000000, 1.488044437500000, -1.488044437500000 ] + - [ 1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ -1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (15, 6) + - [ -0.608242750000000, -1.488044437500000, 1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ 1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (15, 7) + - [ -0.608242750000000, -1.488044437500000, -1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, -1.488044437500000 ] + - [ -1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (15, 8) + - [ -0.608242750000000, 1.488044437500000, 1.488044437500000 ] + - [ 1.488044437500001, -0.608242750000000, -1.488044437500000 ] + - [ 1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (15, 9) + - [ 0.693991000000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.562291000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.562291000000000 ] + - # (15, 10) + - [ -0.685377000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.685377000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.685377000000000 ] + - # (15, 11) + - [ 0.524178500000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.524178500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -13.286822000000001 ] + - # (15, 12) + - [ -0.562291000000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.693991000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.562291000000000 ] + - # (15, 13) + - [ 0.524178500000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -13.286822000000003, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.524178500000000 ] + - # (15, 14) + - [ -0.562291000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.562291000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.693991000000000 ] + - # (15, 15) + - [ 30.575393000000009, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 30.575393000000009, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 30.575393000000009 ] + - # (15, 16) + - [ -13.286822000000003, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.524178500000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.524178500000000 ] + - # (15, 17) + - [ -0.338159250000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.025518500000000 ] + - # (15, 18) + - [ 0.329333750000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.329333750000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.007826000000000 ] + - # (15, 19) + - [ -0.260864250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260864250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -8.618835499999999 ] + - # (15, 20) + - [ 1.460922500000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.338159250000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.025518500000000 ] + - # (15, 21) + - [ -0.338159250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.025518500000000 ] + - # (15, 22) + - [ 0.329333750000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.329333750000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.007826000000000 ] + - # (15, 23) + - [ -0.260864250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260864250000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -8.618835499999999 ] + - # (15, 24) + - [ 1.460922500000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.338159250000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.025518500000000 ] + - # (15, 25) + - [ -0.338159250000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.025518500000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 1.460922500000000 ] + - # (15, 26) + - [ 0.329333750000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.007826000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.329333750000000 ] + - # (15, 27) + - [ -0.338159250000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.025518500000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 1.460922500000000 ] + - # (15, 28) + - [ 0.329333750000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.007826000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.329333750000000 ] + - # (15, 29) + - [ -0.260864250000000, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -8.618835500000003, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.260864250000000 ] + - # (15, 30) + - [ 1.460922500000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.025518500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.338159250000000 ] + - # (15, 31) + - [ -0.260864250000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -8.618835499999999, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.260864250000000 ] + - # (15, 32) + - [ 1.460922500000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.025518500000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.338159250000000 ] + - # (15, 33) + - [ 0.007826000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.329333750000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.329333750000000 ] + - # (15, 34) + - [ 0.007826000000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.329333750000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.329333750000000 ] + - # (15, 35) + - [ 0.025518500000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.338159250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 1.460922500000000 ] + - # (15, 36) + - [ 0.025518500000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.338159250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 1.460922500000000 ] + - # (15, 37) + - [ 0.025518500000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.338159250000000 ] + - # (15, 38) + - [ 0.025518500000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.338159250000000 ] + - # (15, 39) + - [ -8.618835500000001, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.260864250000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.260864250000000 ] + - # (15, 40) + - [ -8.618835500000001, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.260864250000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.260864250000000 ] + - # (16, 1) + - [ -0.608242750000000, -1.488044437500000, -1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, -1.488044437500000 ] + - [ -1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (16, 2) + - [ -0.608242750000000, 1.488044437500000, 1.488044437500001 ] + - [ 1.488044437500000, -0.608242750000000, -1.488044437500000 ] + - [ 1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (16, 3) + - [ -0.608242750000000, 1.488044437500000, -1.488044437500000 ] + - [ 1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ -1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (16, 4) + - [ -0.608242750000000, -1.488044437500000, 1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ 1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (16, 5) + - [ -0.608242750000000, -1.488044437500000, 1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ 1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (16, 6) + - [ -0.608242750000000, 1.488044437500000, -1.488044437500000 ] + - [ 1.488044437500000, -0.608242750000000, 1.488044437500000 ] + - [ -1.488044437500000, 1.488044437500000, -0.608242750000000 ] + - # (16, 7) + - [ -0.608242750000000, 1.488044437500000, 1.488044437500000 ] + - [ 1.488044437500001, -0.608242750000000, -1.488044437500000 ] + - [ 1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (16, 8) + - [ -0.608242750000000, -1.488044437500000, -1.488044437500000 ] + - [ -1.488044437500000, -0.608242750000000, -1.488044437500000 ] + - [ -1.488044437500000, -1.488044437500000, -0.608242750000000 ] + - # (16, 9) + - [ -0.685377000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.685377000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.685377000000000 ] + - # (16, 10) + - [ 0.693991000000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.562291000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.562291000000000 ] + - # (16, 11) + - [ -0.562291000000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.693991000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.562291000000000 ] + - # (16, 12) + - [ 0.524178500000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.524178500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -13.286822000000001 ] + - # (16, 13) + - [ -0.562291000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.562291000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.693991000000000 ] + - # (16, 14) + - [ 0.524178500000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -13.286822000000003, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.524178500000000 ] + - # (16, 15) + - [ -13.286822000000003, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.524178500000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.524178500000000 ] + - # (16, 16) + - [ 30.575393000000009, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 30.575393000000009, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 30.575393000000009 ] + - # (16, 17) + - [ 0.329333750000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.329333750000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.007826000000000 ] + - # (16, 18) + - [ -0.338159250000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.025518500000000 ] + - # (16, 19) + - [ 1.460922500000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.338159250000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.025518500000000 ] + - # (16, 20) + - [ -0.260864250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260864250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -8.618835499999999 ] + - # (16, 21) + - [ 0.329333750000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.329333750000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.007826000000000 ] + - # (16, 22) + - [ -0.338159250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.025518500000000 ] + - # (16, 23) + - [ 1.460922500000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.338159250000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.025518500000000 ] + - # (16, 24) + - [ -0.260864250000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260864250000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -8.618835499999999 ] + - # (16, 25) + - [ 0.329333750000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.007826000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.329333750000000 ] + - # (16, 26) + - [ -0.338159250000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.025518500000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 1.460922500000000 ] + - # (16, 27) + - [ 0.329333750000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.007826000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.329333750000000 ] + - # (16, 28) + - [ -0.338159250000000, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.025518500000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 1.460922500000000 ] + - # (16, 29) + - [ 1.460922500000000, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.025518500000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.338159250000000 ] + - # (16, 30) + - [ -0.260864250000000, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -8.618835500000003, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.260864250000000 ] + - # (16, 31) + - [ 1.460922500000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.025518500000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.338159250000000 ] + - # (16, 32) + - [ -0.260864250000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -8.618835499999999, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.260864250000000 ] + - # (16, 33) + - [ 0.007826000000000, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.329333750000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.329333750000000 ] + - # (16, 34) + - [ 0.007826000000000, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.329333750000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.329333750000000 ] + - # (16, 35) + - [ 0.025518500000000, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.338159250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 1.460922500000000 ] + - # (16, 36) + - [ 0.025518500000000, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.338159250000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 1.460922500000000 ] + - # (16, 37) + - [ 0.025518500000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.338159250000000 ] + - # (16, 38) + - [ 0.025518500000000, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 1.460922500000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.338159250000000 ] + - # (16, 39) + - [ -8.618835500000001, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.260864250000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.260864250000000 ] + - # (16, 40) + - [ -8.618835500000001, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.260864250000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.260864250000000 ] + - # (17, 1) + - [ -0.173171511372757, 0.107862775508977, 0.000000000000000 ] + - [ 0.107862775508977, -0.173171511372757, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.932441913360827 ] + - # (17, 2) + - [ -0.173171511372757, -0.107862775508977, -0.000000000000000 ] + - [ -0.107862775508977, -0.173171511372757, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932441913360827 ] + - # (17, 3) + - [ -0.173171511372757, -0.107862775508977, -0.000000000000000 ] + - [ -0.107862775508977, -0.173171511372757, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932441913360827 ] + - # (17, 4) + - [ -0.173171511372757, 0.107862775508977, -0.000000000000000 ] + - [ 0.107862775508977, -0.173171511372757, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.932441913360827 ] + - # (17, 5) + - [ -0.012499173017644, 0.132896830436715, 0.000000000000000 ] + - [ 0.132896830436715, -0.012499173017644, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.651612687445637 ] + - # (17, 6) + - [ -0.012499173017644, -0.132896830436715, 0.000000000000000 ] + - [ -0.132896830436715, -0.012499173017644, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.651612687445637 ] + - # (17, 7) + - [ -0.012499173017644, -0.132896830436715, 0.000000000000000 ] + - [ -0.132896830436715, -0.012499173017644, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.651612687445637 ] + - # (17, 8) + - [ -0.012499173017644, 0.132896830436715, -0.000000000000000 ] + - [ 0.132896830436715, -0.012499173017644, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.651612687445637 ] + - # (17, 9) + - [ -0.260271156912363, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260271156912363, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -8.614914553326718 ] + - # (17, 10) + - [ 1.459516479630155, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.339440953454353, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.024790810195010 ] + - # (17, 11) + - [ -0.339440953454353, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.459516479630155, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.024790810195010 ] + - # (17, 12) + - [ 0.327944811405822, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.327944811405822, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.007009549521902 ] + - # (17, 13) + - [ -0.260271156912363, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260271156912363, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -8.614914553326720 ] + - # (17, 14) + - [ 1.459516479630155, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.339440953454353, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.024790810195010 ] + - # (17, 15) + - [ -0.339440953454353, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 1.459516479630155, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.024790810195010 ] + - # (17, 16) + - [ 0.327944811405822, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.327944811405822, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.007009549521902 ] + - # (17, 17) + - [ 3.672650396086183, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 3.672650396086184, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 35.215363029350826 ] + - # (17, 18) + - [ -0.478200759767276, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.305535183296458, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.843556469645334 ] + - # (17, 19) + - [ 0.305535183296458, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.478200759767276, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.843556469645334 ] + - # (17, 20) + - [ -0.685255767434321, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.685255767434321, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.190504466346593 ] + - # (17, 21) + - [ -0.653682035440779, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.653682035440779, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -9.018059463804947 ] + - # (17, 22) + - [ -0.410048393982954, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.076376017649521, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.899532456657624 ] + - # (17, 23) + - [ 0.076376017649521, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.410048393982954, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.899532456657624 ] + - # (17, 24) + - [ -0.092597047209941, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.092597047209941, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.192960955304435 ] + - # (17, 25) + - [ 0.387611895815905, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.984988432484362, 1.253138880259487 ] + - [ -0.000000000000000, 2.195885460742546, -0.984288396770987 ] + - # (17, 26) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003301835114751, 0.117046324829637 ] + - [ 0.000000000000000, 0.225651385682165, 0.003922321315242 ] + - # (17, 27) + - [ 0.387611895815905, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.984988432484362, -1.253138880259487 ] + - [ -0.000000000000000, -2.195885460742546, -0.984288396770987 ] + - # (17, 28) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003301835114751, -0.117046324829637 ] + - [ 0.000000000000000, -0.225651385682165, 0.003922321315242 ] + - # (17, 29) + - [ 0.387611895815905, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984988432484362, -1.253138880259487 ] + - [ -0.000000000000000, -2.195885460742546, -0.984288396770987 ] + - # (17, 30) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.003301835114751, -0.117046324829637 ] + - [ 0.000000000000000, -0.225651385682165, 0.003922321315242 ] + - # (17, 31) + - [ 0.387611895815905, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.984988432484362, 1.253138880259487 ] + - [ -0.000000000000000, 2.195885460742546, -0.984288396770987 ] + - # (17, 32) + - [ -0.247822895582965, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.003301835114751, 0.117046324829637 ] + - [ 0.000000000000000, 0.225651385682165, 0.003922321315242 ] + - # (17, 33) + - [ -0.984988432484362, 0.000000000000000, 1.253138880259486 ] + - [ 0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ 2.195885460742546, 0.000000000000000, -0.984288396770987 ] + - # (17, 34) + - [ -0.984988432484362, -0.000000000000000, -1.253138880259486 ] + - [ 0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ -2.195885460742545, 0.000000000000000, -0.984288396770987 ] + - # (17, 35) + - [ 0.003301835114751, -0.000000000000000, 0.117046324829637 ] + - [ -0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ 0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (17, 36) + - [ 0.003301835114751, 0.000000000000000, -0.117046324829637 ] + - [ 0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ -0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (17, 37) + - [ -0.984988432484362, 0.000000000000000, -1.253138880259487 ] + - [ 0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ -2.195885460742546, 0.000000000000000, -0.984288396770987 ] + - # (17, 38) + - [ -0.984988432484362, -0.000000000000000, 1.253138880259487 ] + - [ -0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ 2.195885460742546, 0.000000000000000, -0.984288396770987 ] + - # (17, 39) + - [ 0.003301835114751, 0.000000000000000, -0.117046324829637 ] + - [ -0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ -0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (17, 40) + - [ 0.003301835114751, -0.000000000000000, 0.117046324829637 ] + - [ 0.000000000000000, -0.247822895582965, -0.000000000000000 ] + - [ 0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (18, 1) + - [ -0.173171511372757, -0.107862775508977, -0.000000000000000 ] + - [ -0.107862775508977, -0.173171511372757, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932441913360827 ] + - # (18, 2) + - [ -0.173171511372757, 0.107862775508977, 0.000000000000000 ] + - [ 0.107862775508977, -0.173171511372757, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.932441913360827 ] + - # (18, 3) + - [ -0.173171511372757, 0.107862775508977, -0.000000000000000 ] + - [ 0.107862775508977, -0.173171511372757, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.932441913360827 ] + - # (18, 4) + - [ -0.173171511372757, -0.107862775508977, -0.000000000000000 ] + - [ -0.107862775508977, -0.173171511372757, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932441913360827 ] + - # (18, 5) + - [ -0.012499173017644, -0.132896830436715, 0.000000000000000 ] + - [ -0.132896830436715, -0.012499173017644, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.651612687445637 ] + - # (18, 6) + - [ -0.012499173017644, 0.132896830436715, 0.000000000000000 ] + - [ 0.132896830436715, -0.012499173017644, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.651612687445637 ] + - # (18, 7) + - [ -0.012499173017644, 0.132896830436715, -0.000000000000000 ] + - [ 0.132896830436715, -0.012499173017644, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.651612687445637 ] + - # (18, 8) + - [ -0.012499173017644, -0.132896830436715, 0.000000000000000 ] + - [ -0.132896830436715, -0.012499173017644, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.651612687445637 ] + - # (18, 9) + - [ 1.459516479630155, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.339440953454353, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.024790810195010 ] + - # (18, 10) + - [ -0.260271156912363, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260271156912363, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -8.614914553326718 ] + - # (18, 11) + - [ 0.327944811405822, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.327944811405822, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.007009549521902 ] + - # (18, 12) + - [ -0.339440953454353, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.459516479630155, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.024790810195010 ] + - # (18, 13) + - [ 1.459516479630155, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.339440953454353, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.024790810195010 ] + - # (18, 14) + - [ -0.260271156912363, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260271156912363, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -8.614914553326720 ] + - # (18, 15) + - [ 0.327944811405822, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.327944811405822, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.007009549521902 ] + - # (18, 16) + - [ -0.339440953454353, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 1.459516479630155, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.024790810195010 ] + - # (18, 17) + - [ -0.478200759767276, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.305535183296458, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.843556469645334 ] + - # (18, 18) + - [ 3.672650396086183, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 3.672650396086184, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 35.215363029350826 ] + - # (18, 19) + - [ -0.685255767434321, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.685255767434321, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.190504466346593 ] + - # (18, 20) + - [ 0.305535183296458, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.478200759767276, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.843556469645334 ] + - # (18, 21) + - [ -0.410048393982954, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.076376017649521, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.899532456657624 ] + - # (18, 22) + - [ -0.653682035440779, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.653682035440779, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -9.018059463804947 ] + - # (18, 23) + - [ -0.092597047209941, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.092597047209941, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.192960955304435 ] + - # (18, 24) + - [ 0.076376017649521, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.410048393982954, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.899532456657624 ] + - # (18, 25) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003301835114751, 0.117046324829637 ] + - [ 0.000000000000000, 0.225651385682165, 0.003922321315242 ] + - # (18, 26) + - [ 0.387611895815905, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.984988432484362, 1.253138880259487 ] + - [ -0.000000000000000, 2.195885460742546, -0.984288396770987 ] + - # (18, 27) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003301835114751, -0.117046324829637 ] + - [ 0.000000000000000, -0.225651385682165, 0.003922321315242 ] + - # (18, 28) + - [ 0.387611895815905, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.984988432484362, -1.253138880259487 ] + - [ -0.000000000000000, -2.195885460742546, -0.984288396770987 ] + - # (18, 29) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.003301835114751, -0.117046324829637 ] + - [ 0.000000000000000, -0.225651385682165, 0.003922321315242 ] + - # (18, 30) + - [ 0.387611895815905, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984988432484362, -1.253138880259487 ] + - [ -0.000000000000000, -2.195885460742546, -0.984288396770987 ] + - # (18, 31) + - [ -0.247822895582965, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.003301835114751, 0.117046324829637 ] + - [ 0.000000000000000, 0.225651385682165, 0.003922321315242 ] + - # (18, 32) + - [ 0.387611895815905, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.984988432484362, 1.253138880259487 ] + - [ -0.000000000000000, 2.195885460742546, -0.984288396770987 ] + - # (18, 33) + - [ -0.984988432484362, -0.000000000000000, -1.253138880259486 ] + - [ 0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ -2.195885460742545, 0.000000000000000, -0.984288396770987 ] + - # (18, 34) + - [ -0.984988432484362, 0.000000000000000, 1.253138880259486 ] + - [ 0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ 2.195885460742546, 0.000000000000000, -0.984288396770987 ] + - # (18, 35) + - [ 0.003301835114751, 0.000000000000000, -0.117046324829637 ] + - [ 0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ -0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (18, 36) + - [ 0.003301835114751, -0.000000000000000, 0.117046324829637 ] + - [ -0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ 0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (18, 37) + - [ -0.984988432484362, -0.000000000000000, 1.253138880259487 ] + - [ -0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ 2.195885460742546, 0.000000000000000, -0.984288396770987 ] + - # (18, 38) + - [ -0.984988432484362, 0.000000000000000, -1.253138880259487 ] + - [ 0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ -2.195885460742546, 0.000000000000000, -0.984288396770987 ] + - # (18, 39) + - [ 0.003301835114751, -0.000000000000000, 0.117046324829637 ] + - [ 0.000000000000000, -0.247822895582965, -0.000000000000000 ] + - [ 0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (18, 40) + - [ 0.003301835114751, 0.000000000000000, -0.117046324829637 ] + - [ -0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ -0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (19, 1) + - [ -0.173171511372757, -0.107862775508977, -0.000000000000000 ] + - [ -0.107862775508977, -0.173171511372757, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932441913360827 ] + - # (19, 2) + - [ -0.173171511372757, 0.107862775508977, -0.000000000000000 ] + - [ 0.107862775508977, -0.173171511372757, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.932441913360827 ] + - # (19, 3) + - [ -0.173171511372757, 0.107862775508977, 0.000000000000000 ] + - [ 0.107862775508977, -0.173171511372757, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.932441913360827 ] + - # (19, 4) + - [ -0.173171511372757, -0.107862775508977, -0.000000000000000 ] + - [ -0.107862775508977, -0.173171511372757, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932441913360827 ] + - # (19, 5) + - [ -0.012499173017644, -0.132896830436715, 0.000000000000000 ] + - [ -0.132896830436715, -0.012499173017644, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.651612687445637 ] + - # (19, 6) + - [ -0.012499173017644, 0.132896830436715, -0.000000000000000 ] + - [ 0.132896830436715, -0.012499173017644, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.651612687445637 ] + - # (19, 7) + - [ -0.012499173017644, 0.132896830436715, 0.000000000000000 ] + - [ 0.132896830436715, -0.012499173017644, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.651612687445637 ] + - # (19, 8) + - [ -0.012499173017644, -0.132896830436715, 0.000000000000000 ] + - [ -0.132896830436715, -0.012499173017644, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.651612687445637 ] + - # (19, 9) + - [ -0.339440953454353, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.459516479630155, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.024790810195010 ] + - # (19, 10) + - [ 0.327944811405822, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.327944811405822, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.007009549521902 ] + - # (19, 11) + - [ -0.260271156912363, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260271156912363, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -8.614914553326718 ] + - # (19, 12) + - [ 1.459516479630155, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.339440953454353, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.024790810195010 ] + - # (19, 13) + - [ -0.339440953454353, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 1.459516479630155, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.024790810195010 ] + - # (19, 14) + - [ 0.327944811405822, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.327944811405822, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.007009549521902 ] + - # (19, 15) + - [ -0.260271156912363, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260271156912363, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -8.614914553326720 ] + - # (19, 16) + - [ 1.459516479630155, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.339440953454353, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.024790810195010 ] + - # (19, 17) + - [ 0.305535183296458, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.478200759767276, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.843556469645334 ] + - # (19, 18) + - [ -0.685255767434321, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.685255767434321, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.190504466346593 ] + - # (19, 19) + - [ 3.672650396086183, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 3.672650396086184, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 35.215363029350826 ] + - # (19, 20) + - [ -0.478200759767276, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.305535183296458, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.843556469645334 ] + - # (19, 21) + - [ 0.076376017649521, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.410048393982954, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.899532456657624 ] + - # (19, 22) + - [ -0.092597047209941, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.092597047209941, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.192960955304435 ] + - # (19, 23) + - [ -0.653682035440779, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.653682035440779, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -9.018059463804947 ] + - # (19, 24) + - [ -0.410048393982954, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.076376017649521, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.899532456657624 ] + - # (19, 25) + - [ 0.387611895815905, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.984988432484362, -1.253138880259487 ] + - [ -0.000000000000000, -2.195885460742546, -0.984288396770987 ] + - # (19, 26) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003301835114751, -0.117046324829637 ] + - [ 0.000000000000000, -0.225651385682165, 0.003922321315242 ] + - # (19, 27) + - [ 0.387611895815905, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.984988432484362, 1.253138880259487 ] + - [ -0.000000000000000, 2.195885460742546, -0.984288396770987 ] + - # (19, 28) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003301835114751, 0.117046324829637 ] + - [ 0.000000000000000, 0.225651385682165, 0.003922321315242 ] + - # (19, 29) + - [ 0.387611895815905, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.984988432484362, 1.253138880259487 ] + - [ -0.000000000000000, 2.195885460742546, -0.984288396770987 ] + - # (19, 30) + - [ -0.247822895582965, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.003301835114751, 0.117046324829637 ] + - [ 0.000000000000000, 0.225651385682165, 0.003922321315242 ] + - # (19, 31) + - [ 0.387611895815905, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984988432484362, -1.253138880259487 ] + - [ -0.000000000000000, -2.195885460742546, -0.984288396770987 ] + - # (19, 32) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.003301835114751, -0.117046324829637 ] + - [ 0.000000000000000, -0.225651385682165, 0.003922321315242 ] + - # (19, 33) + - [ 0.003301835114751, -0.000000000000000, 0.117046324829637 ] + - [ -0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ 0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (19, 34) + - [ 0.003301835114751, 0.000000000000000, -0.117046324829637 ] + - [ 0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ -0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (19, 35) + - [ -0.984988432484362, 0.000000000000000, 1.253138880259486 ] + - [ 0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ 2.195885460742546, 0.000000000000000, -0.984288396770987 ] + - # (19, 36) + - [ -0.984988432484362, -0.000000000000000, -1.253138880259486 ] + - [ 0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ -2.195885460742545, 0.000000000000000, -0.984288396770987 ] + - # (19, 37) + - [ 0.003301835114751, 0.000000000000000, -0.117046324829637 ] + - [ -0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ -0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (19, 38) + - [ 0.003301835114751, -0.000000000000000, 0.117046324829637 ] + - [ 0.000000000000000, -0.247822895582965, -0.000000000000000 ] + - [ 0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (19, 39) + - [ -0.984988432484362, 0.000000000000000, -1.253138880259487 ] + - [ 0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ -2.195885460742546, 0.000000000000000, -0.984288396770987 ] + - # (19, 40) + - [ -0.984988432484362, -0.000000000000000, 1.253138880259487 ] + - [ -0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ 2.195885460742546, 0.000000000000000, -0.984288396770987 ] + - # (20, 1) + - [ -0.173171511372757, 0.107862775508977, -0.000000000000000 ] + - [ 0.107862775508977, -0.173171511372757, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.932441913360827 ] + - # (20, 2) + - [ -0.173171511372757, -0.107862775508977, -0.000000000000000 ] + - [ -0.107862775508977, -0.173171511372757, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932441913360827 ] + - # (20, 3) + - [ -0.173171511372757, -0.107862775508977, -0.000000000000000 ] + - [ -0.107862775508977, -0.173171511372757, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932441913360827 ] + - # (20, 4) + - [ -0.173171511372757, 0.107862775508977, 0.000000000000000 ] + - [ 0.107862775508977, -0.173171511372757, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.932441913360827 ] + - # (20, 5) + - [ -0.012499173017644, 0.132896830436715, -0.000000000000000 ] + - [ 0.132896830436715, -0.012499173017644, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.651612687445637 ] + - # (20, 6) + - [ -0.012499173017644, -0.132896830436715, 0.000000000000000 ] + - [ -0.132896830436715, -0.012499173017644, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.651612687445637 ] + - # (20, 7) + - [ -0.012499173017644, -0.132896830436715, 0.000000000000000 ] + - [ -0.132896830436715, -0.012499173017644, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.651612687445637 ] + - # (20, 8) + - [ -0.012499173017644, 0.132896830436715, 0.000000000000000 ] + - [ 0.132896830436715, -0.012499173017644, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.651612687445637 ] + - # (20, 9) + - [ 0.327944811405822, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.327944811405822, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.007009549521902 ] + - # (20, 10) + - [ -0.339440953454353, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.459516479630155, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.024790810195010 ] + - # (20, 11) + - [ 1.459516479630155, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.339440953454353, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.024790810195010 ] + - # (20, 12) + - [ -0.260271156912363, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260271156912363, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -8.614914553326718 ] + - # (20, 13) + - [ 0.327944811405822, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.327944811405822, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.007009549521902 ] + - # (20, 14) + - [ -0.339440953454353, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 1.459516479630155, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.024790810195010 ] + - # (20, 15) + - [ 1.459516479630155, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.339440953454353, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.024790810195010 ] + - # (20, 16) + - [ -0.260271156912363, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260271156912363, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -8.614914553326720 ] + - # (20, 17) + - [ -0.685255767434321, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.685255767434321, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.190504466346593 ] + - # (20, 18) + - [ 0.305535183296458, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.478200759767276, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.843556469645334 ] + - # (20, 19) + - [ -0.478200759767276, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.305535183296458, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.843556469645334 ] + - # (20, 20) + - [ 3.672650396086183, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 3.672650396086184, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 35.215363029350826 ] + - # (20, 21) + - [ -0.092597047209941, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.092597047209941, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.192960955304435 ] + - # (20, 22) + - [ 0.076376017649521, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.410048393982954, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.899532456657624 ] + - # (20, 23) + - [ -0.410048393982954, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.076376017649521, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.899532456657624 ] + - # (20, 24) + - [ -0.653682035440779, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.653682035440779, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -9.018059463804947 ] + - # (20, 25) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003301835114751, -0.117046324829637 ] + - [ 0.000000000000000, -0.225651385682165, 0.003922321315242 ] + - # (20, 26) + - [ 0.387611895815905, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.984988432484362, -1.253138880259487 ] + - [ -0.000000000000000, -2.195885460742546, -0.984288396770987 ] + - # (20, 27) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003301835114751, 0.117046324829637 ] + - [ 0.000000000000000, 0.225651385682165, 0.003922321315242 ] + - # (20, 28) + - [ 0.387611895815905, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.984988432484362, 1.253138880259487 ] + - [ -0.000000000000000, 2.195885460742546, -0.984288396770987 ] + - # (20, 29) + - [ -0.247822895582965, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.003301835114751, 0.117046324829637 ] + - [ 0.000000000000000, 0.225651385682165, 0.003922321315242 ] + - # (20, 30) + - [ 0.387611895815905, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.984988432484362, 1.253138880259487 ] + - [ -0.000000000000000, 2.195885460742546, -0.984288396770987 ] + - # (20, 31) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.003301835114751, -0.117046324829637 ] + - [ 0.000000000000000, -0.225651385682165, 0.003922321315242 ] + - # (20, 32) + - [ 0.387611895815905, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984988432484362, -1.253138880259487 ] + - [ -0.000000000000000, -2.195885460742546, -0.984288396770987 ] + - # (20, 33) + - [ 0.003301835114751, 0.000000000000000, -0.117046324829637 ] + - [ 0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ -0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (20, 34) + - [ 0.003301835114751, -0.000000000000000, 0.117046324829637 ] + - [ -0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ 0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (20, 35) + - [ -0.984988432484362, -0.000000000000000, -1.253138880259486 ] + - [ 0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ -2.195885460742545, 0.000000000000000, -0.984288396770987 ] + - # (20, 36) + - [ -0.984988432484362, 0.000000000000000, 1.253138880259486 ] + - [ 0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ 2.195885460742546, 0.000000000000000, -0.984288396770987 ] + - # (20, 37) + - [ 0.003301835114751, -0.000000000000000, 0.117046324829637 ] + - [ 0.000000000000000, -0.247822895582965, -0.000000000000000 ] + - [ 0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (20, 38) + - [ 0.003301835114751, 0.000000000000000, -0.117046324829637 ] + - [ -0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ -0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (20, 39) + - [ -0.984988432484362, -0.000000000000000, 1.253138880259487 ] + - [ -0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ 2.195885460742546, 0.000000000000000, -0.984288396770987 ] + - # (20, 40) + - [ -0.984988432484362, 0.000000000000000, -1.253138880259487 ] + - [ 0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ -2.195885460742546, 0.000000000000000, -0.984288396770987 ] + - # (21, 1) + - [ -0.012499173017644, 0.132896830436715, 0.000000000000000 ] + - [ 0.132896830436715, -0.012499173017644, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.651612687445637 ] + - # (21, 2) + - [ -0.012499173017644, -0.132896830436715, 0.000000000000000 ] + - [ -0.132896830436715, -0.012499173017644, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.651612687445637 ] + - # (21, 3) + - [ -0.012499173017644, -0.132896830436715, 0.000000000000000 ] + - [ -0.132896830436715, -0.012499173017644, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.651612687445637 ] + - # (21, 4) + - [ -0.012499173017644, 0.132896830436715, -0.000000000000000 ] + - [ 0.132896830436715, -0.012499173017644, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.651612687445637 ] + - # (21, 5) + - [ -0.173171511372757, 0.107862775508977, 0.000000000000000 ] + - [ 0.107862775508977, -0.173171511372757, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.932441913360827 ] + - # (21, 6) + - [ -0.173171511372757, -0.107862775508977, -0.000000000000000 ] + - [ -0.107862775508977, -0.173171511372757, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932441913360827 ] + - # (21, 7) + - [ -0.173171511372757, -0.107862775508977, -0.000000000000000 ] + - [ -0.107862775508977, -0.173171511372757, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932441913360827 ] + - # (21, 8) + - [ -0.173171511372757, 0.107862775508977, -0.000000000000000 ] + - [ 0.107862775508977, -0.173171511372757, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.932441913360827 ] + - # (21, 9) + - [ -0.260271156912363, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260271156912363, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -8.614914553326720 ] + - # (21, 10) + - [ 1.459516479630155, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.339440953454353, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.024790810195010 ] + - # (21, 11) + - [ -0.339440953454353, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 1.459516479630155, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.024790810195010 ] + - # (21, 12) + - [ 0.327944811405822, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.327944811405822, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.007009549521902 ] + - # (21, 13) + - [ -0.260271156912363, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260271156912363, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -8.614914553326718 ] + - # (21, 14) + - [ 1.459516479630155, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.339440953454353, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.024790810195010 ] + - # (21, 15) + - [ -0.339440953454353, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.459516479630155, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.024790810195010 ] + - # (21, 16) + - [ 0.327944811405822, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.327944811405822, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.007009549521902 ] + - # (21, 17) + - [ -0.653682035440779, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.653682035440779, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -9.018059463804947 ] + - # (21, 18) + - [ -0.410048393982954, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.076376017649521, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.899532456657624 ] + - # (21, 19) + - [ 0.076376017649521, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.410048393982954, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.899532456657624 ] + - # (21, 20) + - [ -0.092597047209941, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.092597047209941, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.192960955304435 ] + - # (21, 21) + - [ 3.672650396086183, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 3.672650396086184, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 35.215363029350826 ] + - # (21, 22) + - [ -0.478200759767276, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.305535183296458, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.843556469645334 ] + - # (21, 23) + - [ 0.305535183296458, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.478200759767276, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.843556469645334 ] + - # (21, 24) + - [ -0.685255767434321, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.685255767434321, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.190504466346593 ] + - # (21, 25) + - [ 0.387611895815905, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984988432484362, -1.253138880259487 ] + - [ -0.000000000000000, -2.195885460742546, -0.984288396770987 ] + - # (21, 26) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.003301835114751, -0.117046324829637 ] + - [ 0.000000000000000, -0.225651385682165, 0.003922321315242 ] + - # (21, 27) + - [ 0.387611895815905, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.984988432484362, 1.253138880259487 ] + - [ -0.000000000000000, 2.195885460742546, -0.984288396770987 ] + - # (21, 28) + - [ -0.247822895582965, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.003301835114751, 0.117046324829637 ] + - [ 0.000000000000000, 0.225651385682165, 0.003922321315242 ] + - # (21, 29) + - [ 0.387611895815905, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.984988432484362, 1.253138880259487 ] + - [ -0.000000000000000, 2.195885460742546, -0.984288396770987 ] + - # (21, 30) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003301835114751, 0.117046324829637 ] + - [ 0.000000000000000, 0.225651385682165, 0.003922321315242 ] + - # (21, 31) + - [ 0.387611895815905, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.984988432484362, -1.253138880259487 ] + - [ -0.000000000000000, -2.195885460742546, -0.984288396770987 ] + - # (21, 32) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003301835114751, -0.117046324829637 ] + - [ 0.000000000000000, -0.225651385682165, 0.003922321315242 ] + - # (21, 33) + - [ -0.984988432484362, 0.000000000000000, -1.253138880259487 ] + - [ 0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ -2.195885460742546, 0.000000000000000, -0.984288396770987 ] + - # (21, 34) + - [ -0.984988432484362, -0.000000000000000, 1.253138880259487 ] + - [ -0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ 2.195885460742546, 0.000000000000000, -0.984288396770987 ] + - # (21, 35) + - [ 0.003301835114751, 0.000000000000000, -0.117046324829637 ] + - [ -0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ -0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (21, 36) + - [ 0.003301835114751, -0.000000000000000, 0.117046324829637 ] + - [ 0.000000000000000, -0.247822895582965, -0.000000000000000 ] + - [ 0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (21, 37) + - [ -0.984988432484362, 0.000000000000000, 1.253138880259486 ] + - [ 0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ 2.195885460742546, 0.000000000000000, -0.984288396770987 ] + - # (21, 38) + - [ -0.984988432484362, -0.000000000000000, -1.253138880259486 ] + - [ 0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ -2.195885460742545, 0.000000000000000, -0.984288396770987 ] + - # (21, 39) + - [ 0.003301835114751, -0.000000000000000, 0.117046324829637 ] + - [ -0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ 0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (21, 40) + - [ 0.003301835114751, 0.000000000000000, -0.117046324829637 ] + - [ 0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ -0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (22, 1) + - [ -0.012499173017644, -0.132896830436715, 0.000000000000000 ] + - [ -0.132896830436715, -0.012499173017644, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.651612687445637 ] + - # (22, 2) + - [ -0.012499173017644, 0.132896830436715, 0.000000000000000 ] + - [ 0.132896830436715, -0.012499173017644, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.651612687445637 ] + - # (22, 3) + - [ -0.012499173017644, 0.132896830436715, -0.000000000000000 ] + - [ 0.132896830436715, -0.012499173017644, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.651612687445637 ] + - # (22, 4) + - [ -0.012499173017644, -0.132896830436715, 0.000000000000000 ] + - [ -0.132896830436715, -0.012499173017644, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.651612687445637 ] + - # (22, 5) + - [ -0.173171511372757, -0.107862775508977, -0.000000000000000 ] + - [ -0.107862775508977, -0.173171511372757, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932441913360827 ] + - # (22, 6) + - [ -0.173171511372757, 0.107862775508977, 0.000000000000000 ] + - [ 0.107862775508977, -0.173171511372757, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.932441913360827 ] + - # (22, 7) + - [ -0.173171511372757, 0.107862775508977, -0.000000000000000 ] + - [ 0.107862775508977, -0.173171511372757, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.932441913360827 ] + - # (22, 8) + - [ -0.173171511372757, -0.107862775508977, -0.000000000000000 ] + - [ -0.107862775508977, -0.173171511372757, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932441913360827 ] + - # (22, 9) + - [ 1.459516479630155, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.339440953454353, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.024790810195010 ] + - # (22, 10) + - [ -0.260271156912363, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260271156912363, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -8.614914553326720 ] + - # (22, 11) + - [ 0.327944811405822, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.327944811405822, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.007009549521902 ] + - # (22, 12) + - [ -0.339440953454353, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 1.459516479630155, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.024790810195010 ] + - # (22, 13) + - [ 1.459516479630155, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.339440953454353, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.024790810195010 ] + - # (22, 14) + - [ -0.260271156912363, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260271156912363, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -8.614914553326718 ] + - # (22, 15) + - [ 0.327944811405822, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.327944811405822, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.007009549521902 ] + - # (22, 16) + - [ -0.339440953454353, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.459516479630155, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.024790810195010 ] + - # (22, 17) + - [ -0.410048393982954, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.076376017649521, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.899532456657624 ] + - # (22, 18) + - [ -0.653682035440779, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.653682035440779, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -9.018059463804947 ] + - # (22, 19) + - [ -0.092597047209941, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.092597047209941, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.192960955304435 ] + - # (22, 20) + - [ 0.076376017649521, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.410048393982954, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.899532456657624 ] + - # (22, 21) + - [ -0.478200759767276, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.305535183296458, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.843556469645334 ] + - # (22, 22) + - [ 3.672650396086183, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 3.672650396086184, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 35.215363029350826 ] + - # (22, 23) + - [ -0.685255767434321, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.685255767434321, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.190504466346593 ] + - # (22, 24) + - [ 0.305535183296458, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.478200759767276, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.843556469645334 ] + - # (22, 25) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.003301835114751, -0.117046324829637 ] + - [ 0.000000000000000, -0.225651385682165, 0.003922321315242 ] + - # (22, 26) + - [ 0.387611895815905, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984988432484362, -1.253138880259487 ] + - [ -0.000000000000000, -2.195885460742546, -0.984288396770987 ] + - # (22, 27) + - [ -0.247822895582965, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.003301835114751, 0.117046324829637 ] + - [ 0.000000000000000, 0.225651385682165, 0.003922321315242 ] + - # (22, 28) + - [ 0.387611895815905, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.984988432484362, 1.253138880259487 ] + - [ -0.000000000000000, 2.195885460742546, -0.984288396770987 ] + - # (22, 29) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003301835114751, 0.117046324829637 ] + - [ 0.000000000000000, 0.225651385682165, 0.003922321315242 ] + - # (22, 30) + - [ 0.387611895815905, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.984988432484362, 1.253138880259487 ] + - [ -0.000000000000000, 2.195885460742546, -0.984288396770987 ] + - # (22, 31) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003301835114751, -0.117046324829637 ] + - [ 0.000000000000000, -0.225651385682165, 0.003922321315242 ] + - # (22, 32) + - [ 0.387611895815905, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.984988432484362, -1.253138880259487 ] + - [ -0.000000000000000, -2.195885460742546, -0.984288396770987 ] + - # (22, 33) + - [ -0.984988432484362, -0.000000000000000, 1.253138880259487 ] + - [ -0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ 2.195885460742546, 0.000000000000000, -0.984288396770987 ] + - # (22, 34) + - [ -0.984988432484362, 0.000000000000000, -1.253138880259487 ] + - [ 0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ -2.195885460742546, 0.000000000000000, -0.984288396770987 ] + - # (22, 35) + - [ 0.003301835114751, -0.000000000000000, 0.117046324829637 ] + - [ 0.000000000000000, -0.247822895582965, -0.000000000000000 ] + - [ 0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (22, 36) + - [ 0.003301835114751, 0.000000000000000, -0.117046324829637 ] + - [ -0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ -0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (22, 37) + - [ -0.984988432484362, -0.000000000000000, -1.253138880259486 ] + - [ 0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ -2.195885460742545, 0.000000000000000, -0.984288396770987 ] + - # (22, 38) + - [ -0.984988432484362, 0.000000000000000, 1.253138880259486 ] + - [ 0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ 2.195885460742546, 0.000000000000000, -0.984288396770987 ] + - # (22, 39) + - [ 0.003301835114751, 0.000000000000000, -0.117046324829637 ] + - [ 0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ -0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (22, 40) + - [ 0.003301835114751, -0.000000000000000, 0.117046324829637 ] + - [ -0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ 0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (23, 1) + - [ -0.012499173017644, -0.132896830436715, 0.000000000000000 ] + - [ -0.132896830436715, -0.012499173017644, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.651612687445637 ] + - # (23, 2) + - [ -0.012499173017644, 0.132896830436715, -0.000000000000000 ] + - [ 0.132896830436715, -0.012499173017644, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.651612687445637 ] + - # (23, 3) + - [ -0.012499173017644, 0.132896830436715, 0.000000000000000 ] + - [ 0.132896830436715, -0.012499173017644, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.651612687445637 ] + - # (23, 4) + - [ -0.012499173017644, -0.132896830436715, 0.000000000000000 ] + - [ -0.132896830436715, -0.012499173017644, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.651612687445637 ] + - # (23, 5) + - [ -0.173171511372757, -0.107862775508977, -0.000000000000000 ] + - [ -0.107862775508977, -0.173171511372757, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932441913360827 ] + - # (23, 6) + - [ -0.173171511372757, 0.107862775508977, -0.000000000000000 ] + - [ 0.107862775508977, -0.173171511372757, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.932441913360827 ] + - # (23, 7) + - [ -0.173171511372757, 0.107862775508977, 0.000000000000000 ] + - [ 0.107862775508977, -0.173171511372757, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.932441913360827 ] + - # (23, 8) + - [ -0.173171511372757, -0.107862775508977, -0.000000000000000 ] + - [ -0.107862775508977, -0.173171511372757, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932441913360827 ] + - # (23, 9) + - [ -0.339440953454353, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 1.459516479630155, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.024790810195010 ] + - # (23, 10) + - [ 0.327944811405822, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.327944811405822, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.007009549521902 ] + - # (23, 11) + - [ -0.260271156912363, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260271156912363, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -8.614914553326720 ] + - # (23, 12) + - [ 1.459516479630155, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.339440953454353, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.024790810195010 ] + - # (23, 13) + - [ -0.339440953454353, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.459516479630155, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.024790810195010 ] + - # (23, 14) + - [ 0.327944811405822, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.327944811405822, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.007009549521902 ] + - # (23, 15) + - [ -0.260271156912363, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260271156912363, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -8.614914553326718 ] + - # (23, 16) + - [ 1.459516479630155, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.339440953454353, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.024790810195010 ] + - # (23, 17) + - [ 0.076376017649521, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.410048393982954, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.899532456657624 ] + - # (23, 18) + - [ -0.092597047209941, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.092597047209941, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.192960955304435 ] + - # (23, 19) + - [ -0.653682035440779, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.653682035440779, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -9.018059463804947 ] + - # (23, 20) + - [ -0.410048393982954, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.076376017649521, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.899532456657624 ] + - # (23, 21) + - [ 0.305535183296458, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.478200759767276, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.843556469645334 ] + - # (23, 22) + - [ -0.685255767434321, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.685255767434321, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.190504466346593 ] + - # (23, 23) + - [ 3.672650396086183, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 3.672650396086184, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 35.215363029350826 ] + - # (23, 24) + - [ -0.478200759767276, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.305535183296458, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.843556469645334 ] + - # (23, 25) + - [ 0.387611895815905, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.984988432484362, 1.253138880259487 ] + - [ -0.000000000000000, 2.195885460742546, -0.984288396770987 ] + - # (23, 26) + - [ -0.247822895582965, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.003301835114751, 0.117046324829637 ] + - [ 0.000000000000000, 0.225651385682165, 0.003922321315242 ] + - # (23, 27) + - [ 0.387611895815905, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984988432484362, -1.253138880259487 ] + - [ -0.000000000000000, -2.195885460742546, -0.984288396770987 ] + - # (23, 28) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.003301835114751, -0.117046324829637 ] + - [ 0.000000000000000, -0.225651385682165, 0.003922321315242 ] + - # (23, 29) + - [ 0.387611895815905, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.984988432484362, -1.253138880259487 ] + - [ -0.000000000000000, -2.195885460742546, -0.984288396770987 ] + - # (23, 30) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003301835114751, -0.117046324829637 ] + - [ 0.000000000000000, -0.225651385682165, 0.003922321315242 ] + - # (23, 31) + - [ 0.387611895815905, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.984988432484362, 1.253138880259487 ] + - [ -0.000000000000000, 2.195885460742546, -0.984288396770987 ] + - # (23, 32) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003301835114751, 0.117046324829637 ] + - [ 0.000000000000000, 0.225651385682165, 0.003922321315242 ] + - # (23, 33) + - [ 0.003301835114751, 0.000000000000000, -0.117046324829637 ] + - [ -0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ -0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (23, 34) + - [ 0.003301835114751, -0.000000000000000, 0.117046324829637 ] + - [ 0.000000000000000, -0.247822895582965, -0.000000000000000 ] + - [ 0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (23, 35) + - [ -0.984988432484362, 0.000000000000000, -1.253138880259487 ] + - [ 0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ -2.195885460742546, 0.000000000000000, -0.984288396770987 ] + - # (23, 36) + - [ -0.984988432484362, -0.000000000000000, 1.253138880259487 ] + - [ -0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ 2.195885460742546, 0.000000000000000, -0.984288396770987 ] + - # (23, 37) + - [ 0.003301835114751, -0.000000000000000, 0.117046324829637 ] + - [ -0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ 0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (23, 38) + - [ 0.003301835114751, 0.000000000000000, -0.117046324829637 ] + - [ 0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ -0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (23, 39) + - [ -0.984988432484362, 0.000000000000000, 1.253138880259486 ] + - [ 0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ 2.195885460742546, 0.000000000000000, -0.984288396770987 ] + - # (23, 40) + - [ -0.984988432484362, -0.000000000000000, -1.253138880259486 ] + - [ 0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ -2.195885460742545, 0.000000000000000, -0.984288396770987 ] + - # (24, 1) + - [ -0.012499173017644, 0.132896830436715, -0.000000000000000 ] + - [ 0.132896830436715, -0.012499173017644, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.651612687445637 ] + - # (24, 2) + - [ -0.012499173017644, -0.132896830436715, 0.000000000000000 ] + - [ -0.132896830436715, -0.012499173017644, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.651612687445637 ] + - # (24, 3) + - [ -0.012499173017644, -0.132896830436715, 0.000000000000000 ] + - [ -0.132896830436715, -0.012499173017644, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.651612687445637 ] + - # (24, 4) + - [ -0.012499173017644, 0.132896830436715, 0.000000000000000 ] + - [ 0.132896830436715, -0.012499173017644, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.651612687445637 ] + - # (24, 5) + - [ -0.173171511372757, 0.107862775508977, -0.000000000000000 ] + - [ 0.107862775508977, -0.173171511372757, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.932441913360827 ] + - # (24, 6) + - [ -0.173171511372757, -0.107862775508977, -0.000000000000000 ] + - [ -0.107862775508977, -0.173171511372757, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932441913360827 ] + - # (24, 7) + - [ -0.173171511372757, -0.107862775508977, -0.000000000000000 ] + - [ -0.107862775508977, -0.173171511372757, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.932441913360827 ] + - # (24, 8) + - [ -0.173171511372757, 0.107862775508977, 0.000000000000000 ] + - [ 0.107862775508977, -0.173171511372757, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.932441913360827 ] + - # (24, 9) + - [ 0.327944811405822, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.327944811405822, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.007009549521902 ] + - # (24, 10) + - [ -0.339440953454353, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 1.459516479630155, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.024790810195010 ] + - # (24, 11) + - [ 1.459516479630155, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.339440953454353, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.024790810195010 ] + - # (24, 12) + - [ -0.260271156912363, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260271156912363, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -8.614914553326720 ] + - # (24, 13) + - [ 0.327944811405822, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.327944811405822, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.007009549521902 ] + - # (24, 14) + - [ -0.339440953454353, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.459516479630155, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.024790810195010 ] + - # (24, 15) + - [ 1.459516479630155, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.339440953454353, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.024790810195010 ] + - # (24, 16) + - [ -0.260271156912363, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260271156912363, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -8.614914553326718 ] + - # (24, 17) + - [ -0.092597047209941, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.092597047209941, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.192960955304435 ] + - # (24, 18) + - [ 0.076376017649521, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.410048393982954, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.899532456657624 ] + - # (24, 19) + - [ -0.410048393982954, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.076376017649521, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.899532456657624 ] + - # (24, 20) + - [ -0.653682035440779, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.653682035440779, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -9.018059463804947 ] + - # (24, 21) + - [ -0.685255767434321, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.685255767434321, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.190504466346593 ] + - # (24, 22) + - [ 0.305535183296458, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.478200759767276, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.843556469645334 ] + - # (24, 23) + - [ -0.478200759767276, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.305535183296458, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.843556469645334 ] + - # (24, 24) + - [ 3.672650396086183, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 3.672650396086184, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 35.215363029350826 ] + - # (24, 25) + - [ -0.247822895582965, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.003301835114751, 0.117046324829637 ] + - [ 0.000000000000000, 0.225651385682165, 0.003922321315242 ] + - # (24, 26) + - [ 0.387611895815905, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.984988432484362, 1.253138880259487 ] + - [ -0.000000000000000, 2.195885460742546, -0.984288396770987 ] + - # (24, 27) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.003301835114751, -0.117046324829637 ] + - [ 0.000000000000000, -0.225651385682165, 0.003922321315242 ] + - # (24, 28) + - [ 0.387611895815905, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984988432484362, -1.253138880259487 ] + - [ -0.000000000000000, -2.195885460742546, -0.984288396770987 ] + - # (24, 29) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003301835114751, -0.117046324829637 ] + - [ 0.000000000000000, -0.225651385682165, 0.003922321315242 ] + - # (24, 30) + - [ 0.387611895815905, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.984988432484362, -1.253138880259487 ] + - [ -0.000000000000000, -2.195885460742546, -0.984288396770987 ] + - # (24, 31) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003301835114751, 0.117046324829637 ] + - [ 0.000000000000000, 0.225651385682165, 0.003922321315242 ] + - # (24, 32) + - [ 0.387611895815905, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.984988432484362, 1.253138880259487 ] + - [ -0.000000000000000, 2.195885460742546, -0.984288396770987 ] + - # (24, 33) + - [ 0.003301835114751, -0.000000000000000, 0.117046324829637 ] + - [ 0.000000000000000, -0.247822895582965, -0.000000000000000 ] + - [ 0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (24, 34) + - [ 0.003301835114751, 0.000000000000000, -0.117046324829637 ] + - [ -0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ -0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (24, 35) + - [ -0.984988432484362, -0.000000000000000, 1.253138880259487 ] + - [ -0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ 2.195885460742546, 0.000000000000000, -0.984288396770987 ] + - # (24, 36) + - [ -0.984988432484362, 0.000000000000000, -1.253138880259487 ] + - [ 0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ -2.195885460742546, 0.000000000000000, -0.984288396770987 ] + - # (24, 37) + - [ 0.003301835114751, 0.000000000000000, -0.117046324829637 ] + - [ 0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ -0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (24, 38) + - [ 0.003301835114751, -0.000000000000000, 0.117046324829637 ] + - [ -0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ 0.225651385682165, -0.000000000000000, 0.003922321315242 ] + - # (24, 39) + - [ -0.984988432484362, -0.000000000000000, -1.253138880259486 ] + - [ 0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ -2.195885460742545, 0.000000000000000, -0.984288396770987 ] + - # (24, 40) + - [ -0.984988432484362, 0.000000000000000, 1.253138880259486 ] + - [ 0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ 2.195885460742546, 0.000000000000000, -0.984288396770987 ] + - # (25, 1) + - [ -0.173171511372757, -0.000000000000000, 0.107862775508977 ] + - [ -0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ 0.107862775508977, 0.000000000000000, -0.173171511372757 ] + - # (25, 2) + - [ -0.173171511372757, 0.000000000000000, -0.107862775508977 ] + - [ 0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ -0.107862775508977, -0.000000000000000, -0.173171511372757 ] + - # (25, 3) + - [ -0.012499173017644, 0.000000000000000, 0.132896830436715 ] + - [ -0.000000000000000, 0.651612687445637, 0.000000000000000 ] + - [ 0.132896830436715, 0.000000000000000, -0.012499173017644 ] + - # (25, 4) + - [ -0.012499173017644, 0.000000000000000, -0.132896830436715 ] + - [ 0.000000000000000, 0.651612687445637, -0.000000000000000 ] + - [ -0.132896830436715, 0.000000000000000, -0.012499173017644 ] + - # (25, 5) + - [ -0.173171511372757, -0.000000000000000, -0.107862775508977 ] + - [ 0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ -0.107862775508977, -0.000000000000000, -0.173171511372757 ] + - # (25, 6) + - [ -0.173171511372757, 0.000000000000000, 0.107862775508977 ] + - [ -0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ 0.107862775508977, -0.000000000000000, -0.173171511372757 ] + - # (25, 7) + - [ -0.012499173017644, -0.000000000000000, -0.132896830436715 ] + - [ 0.000000000000000, 0.651612687445637, -0.000000000000000 ] + - [ -0.132896830436715, 0.000000000000000, -0.012499173017644 ] + - # (25, 8) + - [ -0.012499173017644, -0.000000000000000, 0.132896830436715 ] + - [ -0.000000000000000, 0.651612687445637, 0.000000000000000 ] + - [ 0.132896830436715, -0.000000000000000, -0.012499173017644 ] + - # (25, 9) + - [ -0.260271156912363, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -8.614914553326718, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.260271156912363 ] + - # (25, 10) + - [ 1.459516479630155, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.024790810195010, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.339440953454353 ] + - # (25, 11) + - [ -0.260271156912363, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -8.614914553326720, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.260271156912363 ] + - # (25, 12) + - [ 1.459516479630155, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.024790810195010, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.339440953454353 ] + - # (25, 13) + - [ -0.339440953454353, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.024790810195010, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 1.459516479630155 ] + - # (25, 14) + - [ 0.327944811405822, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.007009549521902, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (25, 15) + - [ -0.339440953454353, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.024790810195010, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 1.459516479630155 ] + - # (25, 16) + - [ 0.327944811405822, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.007009549521902, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (25, 17) + - [ 0.387611895815905, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, 2.195885460742546 ] + - [ 0.000000000000000, 1.253138880259486, -0.984988432484362 ] + - # (25, 18) + - [ -0.247822895582965, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, 0.225651385682165 ] + - [ -0.000000000000000, 0.117046324829637, 0.003301835114751 ] + - # (25, 19) + - [ 0.387611895815905, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, -2.195885460742546 ] + - [ 0.000000000000000, -1.253138880259487, -0.984988432484362 ] + - # (25, 20) + - [ -0.247822895582965, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, -0.225651385682165 ] + - [ 0.000000000000000, -0.117046324829637, 0.003301835114751 ] + - # (25, 21) + - [ 0.387611895815905, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, -2.195885460742545 ] + - [ -0.000000000000000, -1.253138880259486, -0.984988432484362 ] + - # (25, 22) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, -0.225651385682165 ] + - [ 0.000000000000000, -0.117046324829637, 0.003301835114751 ] + - # (25, 23) + - [ 0.387611895815905, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, 2.195885460742546 ] + - [ -0.000000000000000, 1.253138880259487, -0.984988432484362 ] + - # (25, 24) + - [ -0.247822895582965, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, 0.225651385682165 ] + - [ -0.000000000000000, 0.117046324829637, 0.003301835114751 ] + - # (25, 25) + - [ 3.672650396086184, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 35.215363029350826, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 3.672650396086183 ] + - # (25, 26) + - [ -0.478200759767276, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.843556469645334, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.305535183296458 ] + - # (25, 27) + - [ -0.653682035440779, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -9.018059463804947, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.653682035440779 ] + - # (25, 28) + - [ -0.410048393982954, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.899532456657624, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.076376017649521 ] + - # (25, 29) + - [ 0.305535183296458, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.843556469645334, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.478200759767276 ] + - # (25, 30) + - [ -0.685255767434321, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.190504466346593, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.685255767434321 ] + - # (25, 31) + - [ 0.076376017649521, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.899532456657624, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.410048393982954 ] + - # (25, 32) + - [ -0.092597047209941, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.192960955304435, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.092597047209941 ] + - # (25, 33) + - [ -0.984988432484362, 1.253138880259487, 0.000000000000000 ] + - [ 2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.387611895815905 ] + - # (25, 34) + - [ -0.984988432484362, -1.253138880259487, -0.000000000000000 ] + - [ -2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (25, 35) + - [ -0.984988432484362, -1.253138880259487, 0.000000000000000 ] + - [ -2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (25, 36) + - [ -0.984988432484362, 1.253138880259487, -0.000000000000000 ] + - [ 2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (25, 37) + - [ 0.003301835114751, 0.117046324829637, -0.000000000000000 ] + - [ 0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (25, 38) + - [ 0.003301835114751, -0.117046324829637, -0.000000000000000 ] + - [ -0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (25, 39) + - [ 0.003301835114751, -0.117046324829637, 0.000000000000000 ] + - [ -0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (25, 40) + - [ 0.003301835114751, 0.117046324829637, 0.000000000000000 ] + - [ 0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (26, 1) + - [ -0.173171511372757, 0.000000000000000, -0.107862775508977 ] + - [ 0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ -0.107862775508977, -0.000000000000000, -0.173171511372757 ] + - # (26, 2) + - [ -0.173171511372757, -0.000000000000000, 0.107862775508977 ] + - [ -0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ 0.107862775508977, 0.000000000000000, -0.173171511372757 ] + - # (26, 3) + - [ -0.012499173017644, 0.000000000000000, -0.132896830436715 ] + - [ 0.000000000000000, 0.651612687445637, -0.000000000000000 ] + - [ -0.132896830436715, 0.000000000000000, -0.012499173017644 ] + - # (26, 4) + - [ -0.012499173017644, 0.000000000000000, 0.132896830436715 ] + - [ -0.000000000000000, 0.651612687445637, 0.000000000000000 ] + - [ 0.132896830436715, 0.000000000000000, -0.012499173017644 ] + - # (26, 5) + - [ -0.173171511372757, 0.000000000000000, 0.107862775508977 ] + - [ -0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ 0.107862775508977, -0.000000000000000, -0.173171511372757 ] + - # (26, 6) + - [ -0.173171511372757, -0.000000000000000, -0.107862775508977 ] + - [ 0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ -0.107862775508977, -0.000000000000000, -0.173171511372757 ] + - # (26, 7) + - [ -0.012499173017644, -0.000000000000000, 0.132896830436715 ] + - [ -0.000000000000000, 0.651612687445637, 0.000000000000000 ] + - [ 0.132896830436715, -0.000000000000000, -0.012499173017644 ] + - # (26, 8) + - [ -0.012499173017644, -0.000000000000000, -0.132896830436715 ] + - [ 0.000000000000000, 0.651612687445637, -0.000000000000000 ] + - [ -0.132896830436715, 0.000000000000000, -0.012499173017644 ] + - # (26, 9) + - [ 1.459516479630155, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.024790810195010, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.339440953454353 ] + - # (26, 10) + - [ -0.260271156912363, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -8.614914553326718, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.260271156912363 ] + - # (26, 11) + - [ 1.459516479630155, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.024790810195010, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.339440953454353 ] + - # (26, 12) + - [ -0.260271156912363, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -8.614914553326720, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.260271156912363 ] + - # (26, 13) + - [ 0.327944811405822, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.007009549521902, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (26, 14) + - [ -0.339440953454353, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.024790810195010, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 1.459516479630155 ] + - # (26, 15) + - [ 0.327944811405822, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.007009549521902, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (26, 16) + - [ -0.339440953454353, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.024790810195010, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 1.459516479630155 ] + - # (26, 17) + - [ -0.247822895582965, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, 0.225651385682165 ] + - [ -0.000000000000000, 0.117046324829637, 0.003301835114751 ] + - # (26, 18) + - [ 0.387611895815905, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, 2.195885460742546 ] + - [ 0.000000000000000, 1.253138880259486, -0.984988432484362 ] + - # (26, 19) + - [ -0.247822895582965, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, -0.225651385682165 ] + - [ 0.000000000000000, -0.117046324829637, 0.003301835114751 ] + - # (26, 20) + - [ 0.387611895815905, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, -2.195885460742546 ] + - [ 0.000000000000000, -1.253138880259487, -0.984988432484362 ] + - # (26, 21) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, -0.225651385682165 ] + - [ 0.000000000000000, -0.117046324829637, 0.003301835114751 ] + - # (26, 22) + - [ 0.387611895815905, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, -2.195885460742545 ] + - [ -0.000000000000000, -1.253138880259486, -0.984988432484362 ] + - # (26, 23) + - [ -0.247822895582965, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, 0.225651385682165 ] + - [ -0.000000000000000, 0.117046324829637, 0.003301835114751 ] + - # (26, 24) + - [ 0.387611895815905, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, 2.195885460742546 ] + - [ -0.000000000000000, 1.253138880259487, -0.984988432484362 ] + - # (26, 25) + - [ -0.478200759767276, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.843556469645334, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.305535183296458 ] + - # (26, 26) + - [ 3.672650396086184, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 35.215363029350826, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 3.672650396086183 ] + - # (26, 27) + - [ -0.410048393982954, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.899532456657624, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.076376017649521 ] + - # (26, 28) + - [ -0.653682035440779, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -9.018059463804947, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.653682035440779 ] + - # (26, 29) + - [ -0.685255767434321, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.190504466346593, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.685255767434321 ] + - # (26, 30) + - [ 0.305535183296458, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.843556469645334, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.478200759767276 ] + - # (26, 31) + - [ -0.092597047209941, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.192960955304435, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.092597047209941 ] + - # (26, 32) + - [ 0.076376017649521, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.899532456657624, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.410048393982954 ] + - # (26, 33) + - [ -0.984988432484362, -1.253138880259487, -0.000000000000000 ] + - [ -2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (26, 34) + - [ -0.984988432484362, 1.253138880259487, 0.000000000000000 ] + - [ 2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.387611895815905 ] + - # (26, 35) + - [ -0.984988432484362, 1.253138880259487, -0.000000000000000 ] + - [ 2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (26, 36) + - [ -0.984988432484362, -1.253138880259487, 0.000000000000000 ] + - [ -2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (26, 37) + - [ 0.003301835114751, -0.117046324829637, -0.000000000000000 ] + - [ -0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (26, 38) + - [ 0.003301835114751, 0.117046324829637, -0.000000000000000 ] + - [ 0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (26, 39) + - [ 0.003301835114751, 0.117046324829637, 0.000000000000000 ] + - [ 0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (26, 40) + - [ 0.003301835114751, -0.117046324829637, 0.000000000000000 ] + - [ -0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (27, 1) + - [ -0.012499173017644, 0.000000000000000, 0.132896830436715 ] + - [ -0.000000000000000, 0.651612687445637, 0.000000000000000 ] + - [ 0.132896830436715, 0.000000000000000, -0.012499173017644 ] + - # (27, 2) + - [ -0.012499173017644, 0.000000000000000, -0.132896830436715 ] + - [ 0.000000000000000, 0.651612687445637, -0.000000000000000 ] + - [ -0.132896830436715, 0.000000000000000, -0.012499173017644 ] + - # (27, 3) + - [ -0.173171511372757, -0.000000000000000, 0.107862775508977 ] + - [ -0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ 0.107862775508977, 0.000000000000000, -0.173171511372757 ] + - # (27, 4) + - [ -0.173171511372757, 0.000000000000000, -0.107862775508977 ] + - [ 0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ -0.107862775508977, -0.000000000000000, -0.173171511372757 ] + - # (27, 5) + - [ -0.012499173017644, -0.000000000000000, -0.132896830436715 ] + - [ 0.000000000000000, 0.651612687445637, -0.000000000000000 ] + - [ -0.132896830436715, 0.000000000000000, -0.012499173017644 ] + - # (27, 6) + - [ -0.012499173017644, -0.000000000000000, 0.132896830436715 ] + - [ -0.000000000000000, 0.651612687445637, 0.000000000000000 ] + - [ 0.132896830436715, -0.000000000000000, -0.012499173017644 ] + - # (27, 7) + - [ -0.173171511372757, -0.000000000000000, -0.107862775508977 ] + - [ 0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ -0.107862775508977, -0.000000000000000, -0.173171511372757 ] + - # (27, 8) + - [ -0.173171511372757, 0.000000000000000, 0.107862775508977 ] + - [ -0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ 0.107862775508977, -0.000000000000000, -0.173171511372757 ] + - # (27, 9) + - [ -0.260271156912363, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -8.614914553326720, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.260271156912363 ] + - # (27, 10) + - [ 1.459516479630155, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.024790810195010, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.339440953454353 ] + - # (27, 11) + - [ -0.260271156912363, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -8.614914553326718, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.260271156912363 ] + - # (27, 12) + - [ 1.459516479630155, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.024790810195010, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.339440953454353 ] + - # (27, 13) + - [ -0.339440953454353, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.024790810195010, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 1.459516479630155 ] + - # (27, 14) + - [ 0.327944811405822, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.007009549521902, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (27, 15) + - [ -0.339440953454353, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.024790810195010, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 1.459516479630155 ] + - # (27, 16) + - [ 0.327944811405822, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.007009549521902, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (27, 17) + - [ 0.387611895815905, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, -2.195885460742546 ] + - [ 0.000000000000000, -1.253138880259487, -0.984988432484362 ] + - # (27, 18) + - [ -0.247822895582965, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, -0.225651385682165 ] + - [ 0.000000000000000, -0.117046324829637, 0.003301835114751 ] + - # (27, 19) + - [ 0.387611895815905, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, 2.195885460742546 ] + - [ 0.000000000000000, 1.253138880259486, -0.984988432484362 ] + - # (27, 20) + - [ -0.247822895582965, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, 0.225651385682165 ] + - [ -0.000000000000000, 0.117046324829637, 0.003301835114751 ] + - # (27, 21) + - [ 0.387611895815905, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, 2.195885460742546 ] + - [ -0.000000000000000, 1.253138880259487, -0.984988432484362 ] + - # (27, 22) + - [ -0.247822895582965, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, 0.225651385682165 ] + - [ -0.000000000000000, 0.117046324829637, 0.003301835114751 ] + - # (27, 23) + - [ 0.387611895815905, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, -2.195885460742545 ] + - [ -0.000000000000000, -1.253138880259486, -0.984988432484362 ] + - # (27, 24) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, -0.225651385682165 ] + - [ 0.000000000000000, -0.117046324829637, 0.003301835114751 ] + - # (27, 25) + - [ -0.653682035440779, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -9.018059463804947, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.653682035440779 ] + - # (27, 26) + - [ -0.410048393982954, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.899532456657624, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.076376017649521 ] + - # (27, 27) + - [ 3.672650396086184, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 35.215363029350826, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 3.672650396086183 ] + - # (27, 28) + - [ -0.478200759767276, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.843556469645334, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.305535183296458 ] + - # (27, 29) + - [ 0.076376017649521, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.899532456657624, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.410048393982954 ] + - # (27, 30) + - [ -0.092597047209941, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.192960955304435, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.092597047209941 ] + - # (27, 31) + - [ 0.305535183296458, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.843556469645334, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.478200759767276 ] + - # (27, 32) + - [ -0.685255767434321, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.190504466346593, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.685255767434321 ] + - # (27, 33) + - [ -0.984988432484362, -1.253138880259487, 0.000000000000000 ] + - [ -2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (27, 34) + - [ -0.984988432484362, 1.253138880259487, -0.000000000000000 ] + - [ 2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (27, 35) + - [ -0.984988432484362, 1.253138880259487, 0.000000000000000 ] + - [ 2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.387611895815905 ] + - # (27, 36) + - [ -0.984988432484362, -1.253138880259487, -0.000000000000000 ] + - [ -2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (27, 37) + - [ 0.003301835114751, -0.117046324829637, 0.000000000000000 ] + - [ -0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (27, 38) + - [ 0.003301835114751, 0.117046324829637, 0.000000000000000 ] + - [ 0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (27, 39) + - [ 0.003301835114751, 0.117046324829637, -0.000000000000000 ] + - [ 0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (27, 40) + - [ 0.003301835114751, -0.117046324829637, -0.000000000000000 ] + - [ -0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (28, 1) + - [ -0.012499173017644, 0.000000000000000, -0.132896830436715 ] + - [ 0.000000000000000, 0.651612687445637, -0.000000000000000 ] + - [ -0.132896830436715, 0.000000000000000, -0.012499173017644 ] + - # (28, 2) + - [ -0.012499173017644, 0.000000000000000, 0.132896830436715 ] + - [ -0.000000000000000, 0.651612687445637, 0.000000000000000 ] + - [ 0.132896830436715, 0.000000000000000, -0.012499173017644 ] + - # (28, 3) + - [ -0.173171511372757, 0.000000000000000, -0.107862775508977 ] + - [ 0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ -0.107862775508977, -0.000000000000000, -0.173171511372757 ] + - # (28, 4) + - [ -0.173171511372757, -0.000000000000000, 0.107862775508977 ] + - [ -0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ 0.107862775508977, 0.000000000000000, -0.173171511372757 ] + - # (28, 5) + - [ -0.012499173017644, -0.000000000000000, 0.132896830436715 ] + - [ -0.000000000000000, 0.651612687445637, 0.000000000000000 ] + - [ 0.132896830436715, -0.000000000000000, -0.012499173017644 ] + - # (28, 6) + - [ -0.012499173017644, -0.000000000000000, -0.132896830436715 ] + - [ 0.000000000000000, 0.651612687445637, -0.000000000000000 ] + - [ -0.132896830436715, 0.000000000000000, -0.012499173017644 ] + - # (28, 7) + - [ -0.173171511372757, 0.000000000000000, 0.107862775508977 ] + - [ -0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ 0.107862775508977, -0.000000000000000, -0.173171511372757 ] + - # (28, 8) + - [ -0.173171511372757, -0.000000000000000, -0.107862775508977 ] + - [ 0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ -0.107862775508977, -0.000000000000000, -0.173171511372757 ] + - # (28, 9) + - [ 1.459516479630155, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.024790810195010, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.339440953454353 ] + - # (28, 10) + - [ -0.260271156912363, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -8.614914553326720, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.260271156912363 ] + - # (28, 11) + - [ 1.459516479630155, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.024790810195010, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.339440953454353 ] + - # (28, 12) + - [ -0.260271156912363, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -8.614914553326718, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.260271156912363 ] + - # (28, 13) + - [ 0.327944811405822, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.007009549521902, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (28, 14) + - [ -0.339440953454353, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.024790810195010, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 1.459516479630155 ] + - # (28, 15) + - [ 0.327944811405822, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.007009549521902, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (28, 16) + - [ -0.339440953454353, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.024790810195010, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 1.459516479630155 ] + - # (28, 17) + - [ -0.247822895582965, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, -0.225651385682165 ] + - [ 0.000000000000000, -0.117046324829637, 0.003301835114751 ] + - # (28, 18) + - [ 0.387611895815905, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, -2.195885460742546 ] + - [ 0.000000000000000, -1.253138880259487, -0.984988432484362 ] + - # (28, 19) + - [ -0.247822895582965, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, 0.225651385682165 ] + - [ -0.000000000000000, 0.117046324829637, 0.003301835114751 ] + - # (28, 20) + - [ 0.387611895815905, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, 2.195885460742546 ] + - [ 0.000000000000000, 1.253138880259486, -0.984988432484362 ] + - # (28, 21) + - [ -0.247822895582965, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, 0.225651385682165 ] + - [ -0.000000000000000, 0.117046324829637, 0.003301835114751 ] + - # (28, 22) + - [ 0.387611895815905, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, 2.195885460742546 ] + - [ -0.000000000000000, 1.253138880259487, -0.984988432484362 ] + - # (28, 23) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, -0.225651385682165 ] + - [ 0.000000000000000, -0.117046324829637, 0.003301835114751 ] + - # (28, 24) + - [ 0.387611895815905, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, -2.195885460742545 ] + - [ -0.000000000000000, -1.253138880259486, -0.984988432484362 ] + - # (28, 25) + - [ -0.410048393982954, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.899532456657624, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.076376017649521 ] + - # (28, 26) + - [ -0.653682035440779, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -9.018059463804947, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.653682035440779 ] + - # (28, 27) + - [ -0.478200759767276, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.843556469645334, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.305535183296458 ] + - # (28, 28) + - [ 3.672650396086184, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 35.215363029350826, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 3.672650396086183 ] + - # (28, 29) + - [ -0.092597047209941, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.192960955304435, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.092597047209941 ] + - # (28, 30) + - [ 0.076376017649521, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.899532456657624, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.410048393982954 ] + - # (28, 31) + - [ -0.685255767434321, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.190504466346593, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.685255767434321 ] + - # (28, 32) + - [ 0.305535183296458, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.843556469645334, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.478200759767276 ] + - # (28, 33) + - [ -0.984988432484362, 1.253138880259487, -0.000000000000000 ] + - [ 2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (28, 34) + - [ -0.984988432484362, -1.253138880259487, 0.000000000000000 ] + - [ -2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (28, 35) + - [ -0.984988432484362, -1.253138880259487, -0.000000000000000 ] + - [ -2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (28, 36) + - [ -0.984988432484362, 1.253138880259487, 0.000000000000000 ] + - [ 2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.387611895815905 ] + - # (28, 37) + - [ 0.003301835114751, 0.117046324829637, 0.000000000000000 ] + - [ 0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (28, 38) + - [ 0.003301835114751, -0.117046324829637, 0.000000000000000 ] + - [ -0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (28, 39) + - [ 0.003301835114751, -0.117046324829637, -0.000000000000000 ] + - [ -0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (28, 40) + - [ 0.003301835114751, 0.117046324829637, -0.000000000000000 ] + - [ 0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (29, 1) + - [ -0.173171511372757, -0.000000000000000, -0.107862775508977 ] + - [ 0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ -0.107862775508977, -0.000000000000000, -0.173171511372757 ] + - # (29, 2) + - [ -0.173171511372757, 0.000000000000000, 0.107862775508977 ] + - [ -0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ 0.107862775508977, -0.000000000000000, -0.173171511372757 ] + - # (29, 3) + - [ -0.012499173017644, -0.000000000000000, -0.132896830436715 ] + - [ 0.000000000000000, 0.651612687445637, -0.000000000000000 ] + - [ -0.132896830436715, 0.000000000000000, -0.012499173017644 ] + - # (29, 4) + - [ -0.012499173017644, -0.000000000000000, 0.132896830436715 ] + - [ -0.000000000000000, 0.651612687445637, 0.000000000000000 ] + - [ 0.132896830436715, -0.000000000000000, -0.012499173017644 ] + - # (29, 5) + - [ -0.173171511372757, -0.000000000000000, 0.107862775508977 ] + - [ -0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ 0.107862775508977, 0.000000000000000, -0.173171511372757 ] + - # (29, 6) + - [ -0.173171511372757, 0.000000000000000, -0.107862775508977 ] + - [ 0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ -0.107862775508977, -0.000000000000000, -0.173171511372757 ] + - # (29, 7) + - [ -0.012499173017644, 0.000000000000000, 0.132896830436715 ] + - [ -0.000000000000000, 0.651612687445637, 0.000000000000000 ] + - [ 0.132896830436715, 0.000000000000000, -0.012499173017644 ] + - # (29, 8) + - [ -0.012499173017644, 0.000000000000000, -0.132896830436715 ] + - [ 0.000000000000000, 0.651612687445637, -0.000000000000000 ] + - [ -0.132896830436715, 0.000000000000000, -0.012499173017644 ] + - # (29, 9) + - [ -0.339440953454353, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.024790810195010, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 1.459516479630155 ] + - # (29, 10) + - [ 0.327944811405822, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.007009549521902, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (29, 11) + - [ -0.339440953454353, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.024790810195010, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 1.459516479630155 ] + - # (29, 12) + - [ 0.327944811405822, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.007009549521902, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (29, 13) + - [ -0.260271156912363, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -8.614914553326718, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.260271156912363 ] + - # (29, 14) + - [ 1.459516479630155, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.024790810195010, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.339440953454353 ] + - # (29, 15) + - [ -0.260271156912363, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -8.614914553326720, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.260271156912363 ] + - # (29, 16) + - [ 1.459516479630155, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.024790810195010, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.339440953454353 ] + - # (29, 17) + - [ 0.387611895815905, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, -2.195885460742545 ] + - [ -0.000000000000000, -1.253138880259486, -0.984988432484362 ] + - # (29, 18) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, -0.225651385682165 ] + - [ 0.000000000000000, -0.117046324829637, 0.003301835114751 ] + - # (29, 19) + - [ 0.387611895815905, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, 2.195885460742546 ] + - [ -0.000000000000000, 1.253138880259487, -0.984988432484362 ] + - # (29, 20) + - [ -0.247822895582965, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, 0.225651385682165 ] + - [ -0.000000000000000, 0.117046324829637, 0.003301835114751 ] + - # (29, 21) + - [ 0.387611895815905, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, 2.195885460742546 ] + - [ 0.000000000000000, 1.253138880259486, -0.984988432484362 ] + - # (29, 22) + - [ -0.247822895582965, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, 0.225651385682165 ] + - [ -0.000000000000000, 0.117046324829637, 0.003301835114751 ] + - # (29, 23) + - [ 0.387611895815905, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, -2.195885460742546 ] + - [ 0.000000000000000, -1.253138880259487, -0.984988432484362 ] + - # (29, 24) + - [ -0.247822895582965, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, -0.225651385682165 ] + - [ 0.000000000000000, -0.117046324829637, 0.003301835114751 ] + - # (29, 25) + - [ 0.305535183296458, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.843556469645334, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.478200759767276 ] + - # (29, 26) + - [ -0.685255767434321, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.190504466346593, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.685255767434321 ] + - # (29, 27) + - [ 0.076376017649521, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.899532456657624, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.410048393982954 ] + - # (29, 28) + - [ -0.092597047209941, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.192960955304435, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.092597047209941 ] + - # (29, 29) + - [ 3.672650396086184, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 35.215363029350826, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 3.672650396086183 ] + - # (29, 30) + - [ -0.478200759767276, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.843556469645334, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.305535183296458 ] + - # (29, 31) + - [ -0.653682035440779, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -9.018059463804947, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.653682035440779 ] + - # (29, 32) + - [ -0.410048393982954, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.899532456657624, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.076376017649521 ] + - # (29, 33) + - [ 0.003301835114751, 0.117046324829637, -0.000000000000000 ] + - [ 0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (29, 34) + - [ 0.003301835114751, -0.117046324829637, -0.000000000000000 ] + - [ -0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (29, 35) + - [ 0.003301835114751, -0.117046324829637, 0.000000000000000 ] + - [ -0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (29, 36) + - [ 0.003301835114751, 0.117046324829637, 0.000000000000000 ] + - [ 0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (29, 37) + - [ -0.984988432484362, 1.253138880259487, 0.000000000000000 ] + - [ 2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.387611895815905 ] + - # (29, 38) + - [ -0.984988432484362, -1.253138880259487, -0.000000000000000 ] + - [ -2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (29, 39) + - [ -0.984988432484362, -1.253138880259487, 0.000000000000000 ] + - [ -2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (29, 40) + - [ -0.984988432484362, 1.253138880259487, -0.000000000000000 ] + - [ 2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (30, 1) + - [ -0.173171511372757, 0.000000000000000, 0.107862775508977 ] + - [ -0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ 0.107862775508977, -0.000000000000000, -0.173171511372757 ] + - # (30, 2) + - [ -0.173171511372757, -0.000000000000000, -0.107862775508977 ] + - [ 0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ -0.107862775508977, -0.000000000000000, -0.173171511372757 ] + - # (30, 3) + - [ -0.012499173017644, -0.000000000000000, 0.132896830436715 ] + - [ -0.000000000000000, 0.651612687445637, 0.000000000000000 ] + - [ 0.132896830436715, -0.000000000000000, -0.012499173017644 ] + - # (30, 4) + - [ -0.012499173017644, -0.000000000000000, -0.132896830436715 ] + - [ 0.000000000000000, 0.651612687445637, -0.000000000000000 ] + - [ -0.132896830436715, 0.000000000000000, -0.012499173017644 ] + - # (30, 5) + - [ -0.173171511372757, 0.000000000000000, -0.107862775508977 ] + - [ 0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ -0.107862775508977, -0.000000000000000, -0.173171511372757 ] + - # (30, 6) + - [ -0.173171511372757, -0.000000000000000, 0.107862775508977 ] + - [ -0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ 0.107862775508977, 0.000000000000000, -0.173171511372757 ] + - # (30, 7) + - [ -0.012499173017644, 0.000000000000000, -0.132896830436715 ] + - [ 0.000000000000000, 0.651612687445637, -0.000000000000000 ] + - [ -0.132896830436715, 0.000000000000000, -0.012499173017644 ] + - # (30, 8) + - [ -0.012499173017644, 0.000000000000000, 0.132896830436715 ] + - [ -0.000000000000000, 0.651612687445637, 0.000000000000000 ] + - [ 0.132896830436715, 0.000000000000000, -0.012499173017644 ] + - # (30, 9) + - [ 0.327944811405822, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.007009549521902, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (30, 10) + - [ -0.339440953454353, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.024790810195010, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 1.459516479630155 ] + - # (30, 11) + - [ 0.327944811405822, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.007009549521902, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (30, 12) + - [ -0.339440953454353, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.024790810195010, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 1.459516479630155 ] + - # (30, 13) + - [ 1.459516479630155, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.024790810195010, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.339440953454353 ] + - # (30, 14) + - [ -0.260271156912363, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -8.614914553326718, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.260271156912363 ] + - # (30, 15) + - [ 1.459516479630155, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.024790810195010, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.339440953454353 ] + - # (30, 16) + - [ -0.260271156912363, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -8.614914553326720, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.260271156912363 ] + - # (30, 17) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, -0.225651385682165 ] + - [ 0.000000000000000, -0.117046324829637, 0.003301835114751 ] + - # (30, 18) + - [ 0.387611895815905, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, -2.195885460742545 ] + - [ -0.000000000000000, -1.253138880259486, -0.984988432484362 ] + - # (30, 19) + - [ -0.247822895582965, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, 0.225651385682165 ] + - [ -0.000000000000000, 0.117046324829637, 0.003301835114751 ] + - # (30, 20) + - [ 0.387611895815905, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, 2.195885460742546 ] + - [ -0.000000000000000, 1.253138880259487, -0.984988432484362 ] + - # (30, 21) + - [ -0.247822895582965, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, 0.225651385682165 ] + - [ -0.000000000000000, 0.117046324829637, 0.003301835114751 ] + - # (30, 22) + - [ 0.387611895815905, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, 2.195885460742546 ] + - [ 0.000000000000000, 1.253138880259486, -0.984988432484362 ] + - # (30, 23) + - [ -0.247822895582965, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, -0.225651385682165 ] + - [ 0.000000000000000, -0.117046324829637, 0.003301835114751 ] + - # (30, 24) + - [ 0.387611895815905, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, -2.195885460742546 ] + - [ 0.000000000000000, -1.253138880259487, -0.984988432484362 ] + - # (30, 25) + - [ -0.685255767434321, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.190504466346593, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.685255767434321 ] + - # (30, 26) + - [ 0.305535183296458, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.843556469645334, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.478200759767276 ] + - # (30, 27) + - [ -0.092597047209941, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.192960955304435, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.092597047209941 ] + - # (30, 28) + - [ 0.076376017649521, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.899532456657624, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.410048393982954 ] + - # (30, 29) + - [ -0.478200759767276, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.843556469645334, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.305535183296458 ] + - # (30, 30) + - [ 3.672650396086184, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 35.215363029350826, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 3.672650396086183 ] + - # (30, 31) + - [ -0.410048393982954, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.899532456657624, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.076376017649521 ] + - # (30, 32) + - [ -0.653682035440779, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -9.018059463804947, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.653682035440779 ] + - # (30, 33) + - [ 0.003301835114751, -0.117046324829637, -0.000000000000000 ] + - [ -0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (30, 34) + - [ 0.003301835114751, 0.117046324829637, -0.000000000000000 ] + - [ 0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (30, 35) + - [ 0.003301835114751, 0.117046324829637, 0.000000000000000 ] + - [ 0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (30, 36) + - [ 0.003301835114751, -0.117046324829637, 0.000000000000000 ] + - [ -0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (30, 37) + - [ -0.984988432484362, -1.253138880259487, -0.000000000000000 ] + - [ -2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (30, 38) + - [ -0.984988432484362, 1.253138880259487, 0.000000000000000 ] + - [ 2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.387611895815905 ] + - # (30, 39) + - [ -0.984988432484362, 1.253138880259487, -0.000000000000000 ] + - [ 2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (30, 40) + - [ -0.984988432484362, -1.253138880259487, 0.000000000000000 ] + - [ -2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (31, 1) + - [ -0.012499173017644, -0.000000000000000, -0.132896830436715 ] + - [ 0.000000000000000, 0.651612687445637, -0.000000000000000 ] + - [ -0.132896830436715, 0.000000000000000, -0.012499173017644 ] + - # (31, 2) + - [ -0.012499173017644, -0.000000000000000, 0.132896830436715 ] + - [ -0.000000000000000, 0.651612687445637, 0.000000000000000 ] + - [ 0.132896830436715, -0.000000000000000, -0.012499173017644 ] + - # (31, 3) + - [ -0.173171511372757, -0.000000000000000, -0.107862775508977 ] + - [ 0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ -0.107862775508977, -0.000000000000000, -0.173171511372757 ] + - # (31, 4) + - [ -0.173171511372757, 0.000000000000000, 0.107862775508977 ] + - [ -0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ 0.107862775508977, -0.000000000000000, -0.173171511372757 ] + - # (31, 5) + - [ -0.012499173017644, 0.000000000000000, 0.132896830436715 ] + - [ -0.000000000000000, 0.651612687445637, 0.000000000000000 ] + - [ 0.132896830436715, 0.000000000000000, -0.012499173017644 ] + - # (31, 6) + - [ -0.012499173017644, 0.000000000000000, -0.132896830436715 ] + - [ 0.000000000000000, 0.651612687445637, -0.000000000000000 ] + - [ -0.132896830436715, 0.000000000000000, -0.012499173017644 ] + - # (31, 7) + - [ -0.173171511372757, -0.000000000000000, 0.107862775508977 ] + - [ -0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ 0.107862775508977, 0.000000000000000, -0.173171511372757 ] + - # (31, 8) + - [ -0.173171511372757, 0.000000000000000, -0.107862775508977 ] + - [ 0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ -0.107862775508977, -0.000000000000000, -0.173171511372757 ] + - # (31, 9) + - [ -0.339440953454353, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.024790810195010, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 1.459516479630155 ] + - # (31, 10) + - [ 0.327944811405822, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.007009549521902, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (31, 11) + - [ -0.339440953454353, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.024790810195010, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 1.459516479630155 ] + - # (31, 12) + - [ 0.327944811405822, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.007009549521902, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (31, 13) + - [ -0.260271156912363, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -8.614914553326720, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.260271156912363 ] + - # (31, 14) + - [ 1.459516479630155, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.024790810195010, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.339440953454353 ] + - # (31, 15) + - [ -0.260271156912363, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -8.614914553326718, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.260271156912363 ] + - # (31, 16) + - [ 1.459516479630155, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.024790810195010, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.339440953454353 ] + - # (31, 17) + - [ 0.387611895815905, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, 2.195885460742546 ] + - [ -0.000000000000000, 1.253138880259487, -0.984988432484362 ] + - # (31, 18) + - [ -0.247822895582965, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, 0.225651385682165 ] + - [ -0.000000000000000, 0.117046324829637, 0.003301835114751 ] + - # (31, 19) + - [ 0.387611895815905, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, -2.195885460742545 ] + - [ -0.000000000000000, -1.253138880259486, -0.984988432484362 ] + - # (31, 20) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, -0.225651385682165 ] + - [ 0.000000000000000, -0.117046324829637, 0.003301835114751 ] + - # (31, 21) + - [ 0.387611895815905, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, -2.195885460742546 ] + - [ 0.000000000000000, -1.253138880259487, -0.984988432484362 ] + - # (31, 22) + - [ -0.247822895582965, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, -0.225651385682165 ] + - [ 0.000000000000000, -0.117046324829637, 0.003301835114751 ] + - # (31, 23) + - [ 0.387611895815905, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, 2.195885460742546 ] + - [ 0.000000000000000, 1.253138880259486, -0.984988432484362 ] + - # (31, 24) + - [ -0.247822895582965, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, 0.225651385682165 ] + - [ -0.000000000000000, 0.117046324829637, 0.003301835114751 ] + - # (31, 25) + - [ 0.076376017649521, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.899532456657624, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.410048393982954 ] + - # (31, 26) + - [ -0.092597047209941, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.192960955304435, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.092597047209941 ] + - # (31, 27) + - [ 0.305535183296458, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.843556469645334, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.478200759767276 ] + - # (31, 28) + - [ -0.685255767434321, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.190504466346593, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.685255767434321 ] + - # (31, 29) + - [ -0.653682035440779, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -9.018059463804947, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.653682035440779 ] + - # (31, 30) + - [ -0.410048393982954, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.899532456657624, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.076376017649521 ] + - # (31, 31) + - [ 3.672650396086184, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 35.215363029350826, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 3.672650396086183 ] + - # (31, 32) + - [ -0.478200759767276, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.843556469645334, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.305535183296458 ] + - # (31, 33) + - [ 0.003301835114751, -0.117046324829637, 0.000000000000000 ] + - [ -0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (31, 34) + - [ 0.003301835114751, 0.117046324829637, 0.000000000000000 ] + - [ 0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (31, 35) + - [ 0.003301835114751, 0.117046324829637, -0.000000000000000 ] + - [ 0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (31, 36) + - [ 0.003301835114751, -0.117046324829637, -0.000000000000000 ] + - [ -0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (31, 37) + - [ -0.984988432484362, -1.253138880259487, 0.000000000000000 ] + - [ -2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (31, 38) + - [ -0.984988432484362, 1.253138880259487, -0.000000000000000 ] + - [ 2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (31, 39) + - [ -0.984988432484362, 1.253138880259487, 0.000000000000000 ] + - [ 2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.387611895815905 ] + - # (31, 40) + - [ -0.984988432484362, -1.253138880259487, -0.000000000000000 ] + - [ -2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (32, 1) + - [ -0.012499173017644, -0.000000000000000, 0.132896830436715 ] + - [ -0.000000000000000, 0.651612687445637, 0.000000000000000 ] + - [ 0.132896830436715, -0.000000000000000, -0.012499173017644 ] + - # (32, 2) + - [ -0.012499173017644, -0.000000000000000, -0.132896830436715 ] + - [ 0.000000000000000, 0.651612687445637, -0.000000000000000 ] + - [ -0.132896830436715, 0.000000000000000, -0.012499173017644 ] + - # (32, 3) + - [ -0.173171511372757, 0.000000000000000, 0.107862775508977 ] + - [ -0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ 0.107862775508977, -0.000000000000000, -0.173171511372757 ] + - # (32, 4) + - [ -0.173171511372757, -0.000000000000000, -0.107862775508977 ] + - [ 0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ -0.107862775508977, -0.000000000000000, -0.173171511372757 ] + - # (32, 5) + - [ -0.012499173017644, 0.000000000000000, -0.132896830436715 ] + - [ 0.000000000000000, 0.651612687445637, -0.000000000000000 ] + - [ -0.132896830436715, 0.000000000000000, -0.012499173017644 ] + - # (32, 6) + - [ -0.012499173017644, 0.000000000000000, 0.132896830436715 ] + - [ -0.000000000000000, 0.651612687445637, 0.000000000000000 ] + - [ 0.132896830436715, 0.000000000000000, -0.012499173017644 ] + - # (32, 7) + - [ -0.173171511372757, 0.000000000000000, -0.107862775508977 ] + - [ 0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ -0.107862775508977, -0.000000000000000, -0.173171511372757 ] + - # (32, 8) + - [ -0.173171511372757, -0.000000000000000, 0.107862775508977 ] + - [ -0.000000000000000, -0.932441913360827, 0.000000000000000 ] + - [ 0.107862775508977, 0.000000000000000, -0.173171511372757 ] + - # (32, 9) + - [ 0.327944811405822, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.007009549521902, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (32, 10) + - [ -0.339440953454353, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.024790810195010, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 1.459516479630155 ] + - # (32, 11) + - [ 0.327944811405822, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.007009549521902, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (32, 12) + - [ -0.339440953454353, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.024790810195010, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 1.459516479630155 ] + - # (32, 13) + - [ 1.459516479630155, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.024790810195010, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.339440953454353 ] + - # (32, 14) + - [ -0.260271156912363, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -8.614914553326720, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.260271156912363 ] + - # (32, 15) + - [ 1.459516479630155, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.024790810195010, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.339440953454353 ] + - # (32, 16) + - [ -0.260271156912363, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -8.614914553326718, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.260271156912363 ] + - # (32, 17) + - [ -0.247822895582965, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, 0.225651385682165 ] + - [ -0.000000000000000, 0.117046324829637, 0.003301835114751 ] + - # (32, 18) + - [ 0.387611895815905, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, 2.195885460742546 ] + - [ -0.000000000000000, 1.253138880259487, -0.984988432484362 ] + - # (32, 19) + - [ -0.247822895582965, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, -0.225651385682165 ] + - [ 0.000000000000000, -0.117046324829637, 0.003301835114751 ] + - # (32, 20) + - [ 0.387611895815905, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, -2.195885460742545 ] + - [ -0.000000000000000, -1.253138880259486, -0.984988432484362 ] + - # (32, 21) + - [ -0.247822895582965, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, -0.225651385682165 ] + - [ 0.000000000000000, -0.117046324829637, 0.003301835114751 ] + - # (32, 22) + - [ 0.387611895815905, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, -2.195885460742546 ] + - [ 0.000000000000000, -1.253138880259487, -0.984988432484362 ] + - # (32, 23) + - [ -0.247822895582965, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.003922321315242, 0.225651385682165 ] + - [ -0.000000000000000, 0.117046324829637, 0.003301835114751 ] + - # (32, 24) + - [ 0.387611895815905, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.984288396770987, 2.195885460742546 ] + - [ 0.000000000000000, 1.253138880259486, -0.984988432484362 ] + - # (32, 25) + - [ -0.092597047209941, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.192960955304435, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.092597047209941 ] + - # (32, 26) + - [ 0.076376017649521, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.899532456657624, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.410048393982954 ] + - # (32, 27) + - [ -0.685255767434321, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.190504466346593, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.685255767434321 ] + - # (32, 28) + - [ 0.305535183296458, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.843556469645334, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.478200759767276 ] + - # (32, 29) + - [ -0.410048393982954, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.899532456657624, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.076376017649521 ] + - # (32, 30) + - [ -0.653682035440779, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -9.018059463804947, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.653682035440779 ] + - # (32, 31) + - [ -0.478200759767276, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.843556469645334, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.305535183296458 ] + - # (32, 32) + - [ 3.672650396086184, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 35.215363029350826, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 3.672650396086183 ] + - # (32, 33) + - [ 0.003301835114751, 0.117046324829637, 0.000000000000000 ] + - [ 0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (32, 34) + - [ 0.003301835114751, -0.117046324829637, 0.000000000000000 ] + - [ -0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (32, 35) + - [ 0.003301835114751, -0.117046324829637, -0.000000000000000 ] + - [ -0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (32, 36) + - [ 0.003301835114751, 0.117046324829637, -0.000000000000000 ] + - [ 0.225651385682165, 0.003922321315242, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (32, 37) + - [ -0.984988432484362, 1.253138880259487, -0.000000000000000 ] + - [ 2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (32, 38) + - [ -0.984988432484362, -1.253138880259487, 0.000000000000000 ] + - [ -2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (32, 39) + - [ -0.984988432484362, -1.253138880259487, -0.000000000000000 ] + - [ -2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (32, 40) + - [ -0.984988432484362, 1.253138880259487, 0.000000000000000 ] + - [ 2.195885460742546, -0.984288396770987, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.387611895815905 ] + - # (33, 1) + - [ -0.932441913360827, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.173171511372757, 0.107862775508977 ] + - [ -0.000000000000000, 0.107862775508977, -0.173171511372757 ] + - # (33, 2) + - [ 0.651612687445637, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.012499173017644, 0.132896830436715 ] + - [ -0.000000000000000, 0.132896830436715, -0.012499173017644 ] + - # (33, 3) + - [ -0.932441913360827, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.173171511372757, -0.107862775508977 ] + - [ 0.000000000000000, -0.107862775508977, -0.173171511372757 ] + - # (33, 4) + - [ 0.651612687445637, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.012499173017644, -0.132896830436715 ] + - [ -0.000000000000000, -0.132896830436715, -0.012499173017644 ] + - # (33, 5) + - [ -0.932441913360827, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.173171511372757, -0.107862775508977 ] + - [ 0.000000000000000, -0.107862775508977, -0.173171511372757 ] + - # (33, 6) + - [ 0.651612687445637, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.012499173017644, -0.132896830436715 ] + - [ -0.000000000000000, -0.132896830436715, -0.012499173017644 ] + - # (33, 7) + - [ -0.932441913360827, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.173171511372757, 0.107862775508977 ] + - [ 0.000000000000000, 0.107862775508977, -0.173171511372757 ] + - # (33, 8) + - [ 0.651612687445637, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.012499173017644, 0.132896830436715 ] + - [ 0.000000000000000, 0.132896830436715, -0.012499173017644 ] + - # (33, 9) + - [ -8.614914553326720, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.260271156912363, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.260271156912363 ] + - # (33, 10) + - [ -8.614914553326718, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260271156912363, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.260271156912363 ] + - # (33, 11) + - [ 0.024790810195010, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.459516479630155, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.339440953454353 ] + - # (33, 12) + - [ 0.024790810195010, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 1.459516479630155, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.339440953454353 ] + - # (33, 13) + - [ 0.024790810195010, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.339440953454353, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 1.459516479630155 ] + - # (33, 14) + - [ 0.024790810195010, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.339440953454353, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 1.459516479630155 ] + - # (33, 15) + - [ 0.007009549521902, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.327944811405822, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (33, 16) + - [ 0.007009549521902, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.327944811405822, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (33, 17) + - [ -0.984288396770987, -0.000000000000000, 2.195885460742546 ] + - [ 0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ 1.253138880259487, 0.000000000000000, -0.984988432484362 ] + - # (33, 18) + - [ -0.984288396770987, -0.000000000000000, -2.195885460742546 ] + - [ 0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ -1.253138880259486, 0.000000000000000, -0.984988432484362 ] + - # (33, 19) + - [ 0.003922321315242, 0.000000000000000, 0.225651385682165 ] + - [ -0.000000000000000, -0.247822895582965, -0.000000000000000 ] + - [ 0.117046324829637, 0.000000000000000, 0.003301835114751 ] + - # (33, 20) + - [ 0.003922321315242, 0.000000000000000, -0.225651385682165 ] + - [ -0.000000000000000, -0.247822895582965, -0.000000000000000 ] + - [ -0.117046324829637, -0.000000000000000, 0.003301835114751 ] + - # (33, 21) + - [ -0.984288396770987, -0.000000000000000, -2.195885460742546 ] + - [ 0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ -1.253138880259487, -0.000000000000000, -0.984988432484362 ] + - # (33, 22) + - [ -0.984288396770987, -0.000000000000000, 2.195885460742545 ] + - [ -0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ 1.253138880259486, -0.000000000000000, -0.984988432484362 ] + - # (33, 23) + - [ 0.003922321315242, 0.000000000000000, -0.225651385682165 ] + - [ 0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ -0.117046324829637, -0.000000000000000, 0.003301835114751 ] + - # (33, 24) + - [ 0.003922321315242, 0.000000000000000, 0.225651385682165 ] + - [ -0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ 0.117046324829637, 0.000000000000000, 0.003301835114751 ] + - # (33, 25) + - [ -0.984288396770987, 2.195885460742546, 0.000000000000000 ] + - [ 1.253138880259487, -0.984988432484362, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (33, 26) + - [ -0.984288396770987, -2.195885460742546, 0.000000000000000 ] + - [ -1.253138880259487, -0.984988432484362, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (33, 27) + - [ -0.984288396770987, -2.195885460742546, 0.000000000000000 ] + - [ -1.253138880259487, -0.984988432484362, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.387611895815905 ] + - # (33, 28) + - [ -0.984288396770987, 2.195885460742546, 0.000000000000000 ] + - [ 1.253138880259487, -0.984988432484362, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (33, 29) + - [ 0.003922321315242, 0.225651385682165, -0.000000000000000 ] + - [ 0.117046324829637, 0.003301835114751, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (33, 30) + - [ 0.003922321315242, -0.225651385682165, -0.000000000000000 ] + - [ -0.117046324829637, 0.003301835114751, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (33, 31) + - [ 0.003922321315242, -0.225651385682165, -0.000000000000000 ] + - [ -0.117046324829637, 0.003301835114751, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.247822895582965 ] + - # (33, 32) + - [ 0.003922321315242, 0.225651385682165, -0.000000000000000 ] + - [ 0.117046324829637, 0.003301835114751, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (33, 33) + - [ 35.215363029350826, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 3.672650396086184, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 3.672650396086183 ] + - # (33, 34) + - [ -9.018059463804947, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.653682035440779, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.653682035440779 ] + - # (33, 35) + - [ 0.843556469645334, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.478200759767276, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.305535183296458 ] + - # (33, 36) + - [ -0.899532456657624, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.410048393982954, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.076376017649521 ] + - # (33, 37) + - [ 0.843556469645334, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.305535183296458, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.478200759767276 ] + - # (33, 38) + - [ -0.899532456657624, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.076376017649521, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.410048393982954 ] + - # (33, 39) + - [ 0.190504466346593, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.685255767434321, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.685255767434321 ] + - # (33, 40) + - [ -0.192960955304435, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.092597047209941, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.092597047209941 ] + - # (34, 1) + - [ 0.651612687445637, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.012499173017644, 0.132896830436715 ] + - [ -0.000000000000000, 0.132896830436715, -0.012499173017644 ] + - # (34, 2) + - [ -0.932441913360827, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.173171511372757, 0.107862775508977 ] + - [ -0.000000000000000, 0.107862775508977, -0.173171511372757 ] + - # (34, 3) + - [ 0.651612687445637, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.012499173017644, -0.132896830436715 ] + - [ -0.000000000000000, -0.132896830436715, -0.012499173017644 ] + - # (34, 4) + - [ -0.932441913360827, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.173171511372757, -0.107862775508977 ] + - [ 0.000000000000000, -0.107862775508977, -0.173171511372757 ] + - # (34, 5) + - [ 0.651612687445637, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.012499173017644, -0.132896830436715 ] + - [ -0.000000000000000, -0.132896830436715, -0.012499173017644 ] + - # (34, 6) + - [ -0.932441913360827, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.173171511372757, -0.107862775508977 ] + - [ 0.000000000000000, -0.107862775508977, -0.173171511372757 ] + - # (34, 7) + - [ 0.651612687445637, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.012499173017644, 0.132896830436715 ] + - [ 0.000000000000000, 0.132896830436715, -0.012499173017644 ] + - # (34, 8) + - [ -0.932441913360827, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.173171511372757, 0.107862775508977 ] + - [ 0.000000000000000, 0.107862775508977, -0.173171511372757 ] + - # (34, 9) + - [ -8.614914553326718, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260271156912363, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.260271156912363 ] + - # (34, 10) + - [ -8.614914553326720, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.260271156912363, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.260271156912363 ] + - # (34, 11) + - [ 0.024790810195010, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 1.459516479630155, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.339440953454353 ] + - # (34, 12) + - [ 0.024790810195010, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.459516479630155, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.339440953454353 ] + - # (34, 13) + - [ 0.024790810195010, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.339440953454353, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 1.459516479630155 ] + - # (34, 14) + - [ 0.024790810195010, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.339440953454353, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 1.459516479630155 ] + - # (34, 15) + - [ 0.007009549521902, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.327944811405822, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (34, 16) + - [ 0.007009549521902, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.327944811405822, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (34, 17) + - [ -0.984288396770987, -0.000000000000000, -2.195885460742546 ] + - [ 0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ -1.253138880259486, 0.000000000000000, -0.984988432484362 ] + - # (34, 18) + - [ -0.984288396770987, -0.000000000000000, 2.195885460742546 ] + - [ 0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ 1.253138880259487, 0.000000000000000, -0.984988432484362 ] + - # (34, 19) + - [ 0.003922321315242, 0.000000000000000, -0.225651385682165 ] + - [ -0.000000000000000, -0.247822895582965, -0.000000000000000 ] + - [ -0.117046324829637, -0.000000000000000, 0.003301835114751 ] + - # (34, 20) + - [ 0.003922321315242, 0.000000000000000, 0.225651385682165 ] + - [ -0.000000000000000, -0.247822895582965, -0.000000000000000 ] + - [ 0.117046324829637, 0.000000000000000, 0.003301835114751 ] + - # (34, 21) + - [ -0.984288396770987, -0.000000000000000, 2.195885460742545 ] + - [ -0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ 1.253138880259486, -0.000000000000000, -0.984988432484362 ] + - # (34, 22) + - [ -0.984288396770987, -0.000000000000000, -2.195885460742546 ] + - [ 0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ -1.253138880259487, -0.000000000000000, -0.984988432484362 ] + - # (34, 23) + - [ 0.003922321315242, 0.000000000000000, 0.225651385682165 ] + - [ -0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ 0.117046324829637, 0.000000000000000, 0.003301835114751 ] + - # (34, 24) + - [ 0.003922321315242, 0.000000000000000, -0.225651385682165 ] + - [ 0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ -0.117046324829637, -0.000000000000000, 0.003301835114751 ] + - # (34, 25) + - [ -0.984288396770987, -2.195885460742546, 0.000000000000000 ] + - [ -1.253138880259487, -0.984988432484362, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (34, 26) + - [ -0.984288396770987, 2.195885460742546, 0.000000000000000 ] + - [ 1.253138880259487, -0.984988432484362, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (34, 27) + - [ -0.984288396770987, 2.195885460742546, 0.000000000000000 ] + - [ 1.253138880259487, -0.984988432484362, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (34, 28) + - [ -0.984288396770987, -2.195885460742546, 0.000000000000000 ] + - [ -1.253138880259487, -0.984988432484362, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.387611895815905 ] + - # (34, 29) + - [ 0.003922321315242, -0.225651385682165, -0.000000000000000 ] + - [ -0.117046324829637, 0.003301835114751, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (34, 30) + - [ 0.003922321315242, 0.225651385682165, -0.000000000000000 ] + - [ 0.117046324829637, 0.003301835114751, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (34, 31) + - [ 0.003922321315242, 0.225651385682165, -0.000000000000000 ] + - [ 0.117046324829637, 0.003301835114751, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (34, 32) + - [ 0.003922321315242, -0.225651385682165, -0.000000000000000 ] + - [ -0.117046324829637, 0.003301835114751, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.247822895582965 ] + - # (34, 33) + - [ -9.018059463804947, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.653682035440779, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.653682035440779 ] + - # (34, 34) + - [ 35.215363029350826, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 3.672650396086184, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 3.672650396086183 ] + - # (34, 35) + - [ -0.899532456657624, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.410048393982954, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.076376017649521 ] + - # (34, 36) + - [ 0.843556469645334, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.478200759767276, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.305535183296458 ] + - # (34, 37) + - [ -0.899532456657624, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.076376017649521, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.410048393982954 ] + - # (34, 38) + - [ 0.843556469645334, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.305535183296458, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.478200759767276 ] + - # (34, 39) + - [ -0.192960955304435, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.092597047209941, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.092597047209941 ] + - # (34, 40) + - [ 0.190504466346593, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.685255767434321, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.685255767434321 ] + - # (35, 1) + - [ -0.932441913360827, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.173171511372757, -0.107862775508977 ] + - [ 0.000000000000000, -0.107862775508977, -0.173171511372757 ] + - # (35, 2) + - [ 0.651612687445637, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.012499173017644, -0.132896830436715 ] + - [ -0.000000000000000, -0.132896830436715, -0.012499173017644 ] + - # (35, 3) + - [ -0.932441913360827, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.173171511372757, 0.107862775508977 ] + - [ -0.000000000000000, 0.107862775508977, -0.173171511372757 ] + - # (35, 4) + - [ 0.651612687445637, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.012499173017644, 0.132896830436715 ] + - [ -0.000000000000000, 0.132896830436715, -0.012499173017644 ] + - # (35, 5) + - [ -0.932441913360827, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.173171511372757, 0.107862775508977 ] + - [ 0.000000000000000, 0.107862775508977, -0.173171511372757 ] + - # (35, 6) + - [ 0.651612687445637, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.012499173017644, 0.132896830436715 ] + - [ 0.000000000000000, 0.132896830436715, -0.012499173017644 ] + - # (35, 7) + - [ -0.932441913360827, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.173171511372757, -0.107862775508977 ] + - [ 0.000000000000000, -0.107862775508977, -0.173171511372757 ] + - # (35, 8) + - [ 0.651612687445637, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.012499173017644, -0.132896830436715 ] + - [ -0.000000000000000, -0.132896830436715, -0.012499173017644 ] + - # (35, 9) + - [ 0.024790810195010, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.459516479630155, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.339440953454353 ] + - # (35, 10) + - [ 0.024790810195010, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 1.459516479630155, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.339440953454353 ] + - # (35, 11) + - [ -8.614914553326720, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.260271156912363, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.260271156912363 ] + - # (35, 12) + - [ -8.614914553326718, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260271156912363, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.260271156912363 ] + - # (35, 13) + - [ 0.007009549521902, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.327944811405822, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (35, 14) + - [ 0.007009549521902, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.327944811405822, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (35, 15) + - [ 0.024790810195010, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.339440953454353, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 1.459516479630155 ] + - # (35, 16) + - [ 0.024790810195010, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.339440953454353, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 1.459516479630155 ] + - # (35, 17) + - [ 0.003922321315242, 0.000000000000000, 0.225651385682165 ] + - [ -0.000000000000000, -0.247822895582965, -0.000000000000000 ] + - [ 0.117046324829637, 0.000000000000000, 0.003301835114751 ] + - # (35, 18) + - [ 0.003922321315242, 0.000000000000000, -0.225651385682165 ] + - [ -0.000000000000000, -0.247822895582965, -0.000000000000000 ] + - [ -0.117046324829637, -0.000000000000000, 0.003301835114751 ] + - # (35, 19) + - [ -0.984288396770987, -0.000000000000000, 2.195885460742546 ] + - [ 0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ 1.253138880259487, 0.000000000000000, -0.984988432484362 ] + - # (35, 20) + - [ -0.984288396770987, -0.000000000000000, -2.195885460742546 ] + - [ 0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ -1.253138880259486, 0.000000000000000, -0.984988432484362 ] + - # (35, 21) + - [ 0.003922321315242, 0.000000000000000, -0.225651385682165 ] + - [ 0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ -0.117046324829637, -0.000000000000000, 0.003301835114751 ] + - # (35, 22) + - [ 0.003922321315242, 0.000000000000000, 0.225651385682165 ] + - [ -0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ 0.117046324829637, 0.000000000000000, 0.003301835114751 ] + - # (35, 23) + - [ -0.984288396770987, -0.000000000000000, -2.195885460742546 ] + - [ 0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ -1.253138880259487, -0.000000000000000, -0.984988432484362 ] + - # (35, 24) + - [ -0.984288396770987, -0.000000000000000, 2.195885460742545 ] + - [ -0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ 1.253138880259486, -0.000000000000000, -0.984988432484362 ] + - # (35, 25) + - [ -0.984288396770987, -2.195885460742546, 0.000000000000000 ] + - [ -1.253138880259487, -0.984988432484362, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.387611895815905 ] + - # (35, 26) + - [ -0.984288396770987, 2.195885460742546, 0.000000000000000 ] + - [ 1.253138880259487, -0.984988432484362, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (35, 27) + - [ -0.984288396770987, 2.195885460742546, 0.000000000000000 ] + - [ 1.253138880259487, -0.984988432484362, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (35, 28) + - [ -0.984288396770987, -2.195885460742546, 0.000000000000000 ] + - [ -1.253138880259487, -0.984988432484362, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (35, 29) + - [ 0.003922321315242, -0.225651385682165, -0.000000000000000 ] + - [ -0.117046324829637, 0.003301835114751, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.247822895582965 ] + - # (35, 30) + - [ 0.003922321315242, 0.225651385682165, -0.000000000000000 ] + - [ 0.117046324829637, 0.003301835114751, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (35, 31) + - [ 0.003922321315242, 0.225651385682165, -0.000000000000000 ] + - [ 0.117046324829637, 0.003301835114751, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (35, 32) + - [ 0.003922321315242, -0.225651385682165, -0.000000000000000 ] + - [ -0.117046324829637, 0.003301835114751, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (35, 33) + - [ 0.843556469645334, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.478200759767276, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.305535183296458 ] + - # (35, 34) + - [ -0.899532456657624, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.410048393982954, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.076376017649521 ] + - # (35, 35) + - [ 35.215363029350826, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 3.672650396086184, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 3.672650396086183 ] + - # (35, 36) + - [ -9.018059463804947, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.653682035440779, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.653682035440779 ] + - # (35, 37) + - [ 0.190504466346593, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.685255767434321, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.685255767434321 ] + - # (35, 38) + - [ -0.192960955304435, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.092597047209941, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.092597047209941 ] + - # (35, 39) + - [ 0.843556469645334, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.305535183296458, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.478200759767276 ] + - # (35, 40) + - [ -0.899532456657624, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.076376017649521, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.410048393982954 ] + - # (36, 1) + - [ 0.651612687445637, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.012499173017644, -0.132896830436715 ] + - [ -0.000000000000000, -0.132896830436715, -0.012499173017644 ] + - # (36, 2) + - [ -0.932441913360827, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.173171511372757, -0.107862775508977 ] + - [ 0.000000000000000, -0.107862775508977, -0.173171511372757 ] + - # (36, 3) + - [ 0.651612687445637, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.012499173017644, 0.132896830436715 ] + - [ -0.000000000000000, 0.132896830436715, -0.012499173017644 ] + - # (36, 4) + - [ -0.932441913360827, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.173171511372757, 0.107862775508977 ] + - [ -0.000000000000000, 0.107862775508977, -0.173171511372757 ] + - # (36, 5) + - [ 0.651612687445637, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.012499173017644, 0.132896830436715 ] + - [ 0.000000000000000, 0.132896830436715, -0.012499173017644 ] + - # (36, 6) + - [ -0.932441913360827, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.173171511372757, 0.107862775508977 ] + - [ 0.000000000000000, 0.107862775508977, -0.173171511372757 ] + - # (36, 7) + - [ 0.651612687445637, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.012499173017644, -0.132896830436715 ] + - [ -0.000000000000000, -0.132896830436715, -0.012499173017644 ] + - # (36, 8) + - [ -0.932441913360827, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.173171511372757, -0.107862775508977 ] + - [ 0.000000000000000, -0.107862775508977, -0.173171511372757 ] + - # (36, 9) + - [ 0.024790810195010, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 1.459516479630155, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.339440953454353 ] + - # (36, 10) + - [ 0.024790810195010, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.459516479630155, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.339440953454353 ] + - # (36, 11) + - [ -8.614914553326718, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260271156912363, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.260271156912363 ] + - # (36, 12) + - [ -8.614914553326720, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.260271156912363, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.260271156912363 ] + - # (36, 13) + - [ 0.007009549521902, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.327944811405822, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (36, 14) + - [ 0.007009549521902, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.327944811405822, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (36, 15) + - [ 0.024790810195010, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.339440953454353, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 1.459516479630155 ] + - # (36, 16) + - [ 0.024790810195010, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.339440953454353, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 1.459516479630155 ] + - # (36, 17) + - [ 0.003922321315242, 0.000000000000000, -0.225651385682165 ] + - [ -0.000000000000000, -0.247822895582965, -0.000000000000000 ] + - [ -0.117046324829637, -0.000000000000000, 0.003301835114751 ] + - # (36, 18) + - [ 0.003922321315242, 0.000000000000000, 0.225651385682165 ] + - [ -0.000000000000000, -0.247822895582965, -0.000000000000000 ] + - [ 0.117046324829637, 0.000000000000000, 0.003301835114751 ] + - # (36, 19) + - [ -0.984288396770987, -0.000000000000000, -2.195885460742546 ] + - [ 0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ -1.253138880259486, 0.000000000000000, -0.984988432484362 ] + - # (36, 20) + - [ -0.984288396770987, -0.000000000000000, 2.195885460742546 ] + - [ 0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ 1.253138880259487, 0.000000000000000, -0.984988432484362 ] + - # (36, 21) + - [ 0.003922321315242, 0.000000000000000, 0.225651385682165 ] + - [ -0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ 0.117046324829637, 0.000000000000000, 0.003301835114751 ] + - # (36, 22) + - [ 0.003922321315242, 0.000000000000000, -0.225651385682165 ] + - [ 0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ -0.117046324829637, -0.000000000000000, 0.003301835114751 ] + - # (36, 23) + - [ -0.984288396770987, -0.000000000000000, 2.195885460742545 ] + - [ -0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ 1.253138880259486, -0.000000000000000, -0.984988432484362 ] + - # (36, 24) + - [ -0.984288396770987, -0.000000000000000, -2.195885460742546 ] + - [ 0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ -1.253138880259487, -0.000000000000000, -0.984988432484362 ] + - # (36, 25) + - [ -0.984288396770987, 2.195885460742546, 0.000000000000000 ] + - [ 1.253138880259487, -0.984988432484362, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (36, 26) + - [ -0.984288396770987, -2.195885460742546, 0.000000000000000 ] + - [ -1.253138880259487, -0.984988432484362, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.387611895815905 ] + - # (36, 27) + - [ -0.984288396770987, -2.195885460742546, 0.000000000000000 ] + - [ -1.253138880259487, -0.984988432484362, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (36, 28) + - [ -0.984288396770987, 2.195885460742546, 0.000000000000000 ] + - [ 1.253138880259487, -0.984988432484362, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (36, 29) + - [ 0.003922321315242, 0.225651385682165, -0.000000000000000 ] + - [ 0.117046324829637, 0.003301835114751, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (36, 30) + - [ 0.003922321315242, -0.225651385682165, -0.000000000000000 ] + - [ -0.117046324829637, 0.003301835114751, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.247822895582965 ] + - # (36, 31) + - [ 0.003922321315242, -0.225651385682165, -0.000000000000000 ] + - [ -0.117046324829637, 0.003301835114751, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (36, 32) + - [ 0.003922321315242, 0.225651385682165, -0.000000000000000 ] + - [ 0.117046324829637, 0.003301835114751, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (36, 33) + - [ -0.899532456657624, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.410048393982954, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.076376017649521 ] + - # (36, 34) + - [ 0.843556469645334, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.478200759767276, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.305535183296458 ] + - # (36, 35) + - [ -9.018059463804947, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.653682035440779, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.653682035440779 ] + - # (36, 36) + - [ 35.215363029350826, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 3.672650396086184, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 3.672650396086183 ] + - # (36, 37) + - [ -0.192960955304435, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.092597047209941, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.092597047209941 ] + - # (36, 38) + - [ 0.190504466346593, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.685255767434321, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.685255767434321 ] + - # (36, 39) + - [ -0.899532456657624, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.076376017649521, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.410048393982954 ] + - # (36, 40) + - [ 0.843556469645334, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.305535183296458, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.478200759767276 ] + - # (37, 1) + - [ -0.932441913360827, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.173171511372757, -0.107862775508977 ] + - [ 0.000000000000000, -0.107862775508977, -0.173171511372757 ] + - # (37, 2) + - [ 0.651612687445637, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.012499173017644, -0.132896830436715 ] + - [ -0.000000000000000, -0.132896830436715, -0.012499173017644 ] + - # (37, 3) + - [ -0.932441913360827, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.173171511372757, 0.107862775508977 ] + - [ 0.000000000000000, 0.107862775508977, -0.173171511372757 ] + - # (37, 4) + - [ 0.651612687445637, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.012499173017644, 0.132896830436715 ] + - [ 0.000000000000000, 0.132896830436715, -0.012499173017644 ] + - # (37, 5) + - [ -0.932441913360827, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.173171511372757, 0.107862775508977 ] + - [ -0.000000000000000, 0.107862775508977, -0.173171511372757 ] + - # (37, 6) + - [ 0.651612687445637, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.012499173017644, 0.132896830436715 ] + - [ -0.000000000000000, 0.132896830436715, -0.012499173017644 ] + - # (37, 7) + - [ -0.932441913360827, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.173171511372757, -0.107862775508977 ] + - [ 0.000000000000000, -0.107862775508977, -0.173171511372757 ] + - # (37, 8) + - [ 0.651612687445637, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.012499173017644, -0.132896830436715 ] + - [ -0.000000000000000, -0.132896830436715, -0.012499173017644 ] + - # (37, 9) + - [ 0.024790810195010, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.339440953454353, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 1.459516479630155 ] + - # (37, 10) + - [ 0.024790810195010, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.339440953454353, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 1.459516479630155 ] + - # (37, 11) + - [ 0.007009549521902, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.327944811405822, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (37, 12) + - [ 0.007009549521902, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.327944811405822, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (37, 13) + - [ -8.614914553326720, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.260271156912363, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.260271156912363 ] + - # (37, 14) + - [ -8.614914553326718, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260271156912363, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.260271156912363 ] + - # (37, 15) + - [ 0.024790810195010, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.459516479630155, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.339440953454353 ] + - # (37, 16) + - [ 0.024790810195010, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 1.459516479630155, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.339440953454353 ] + - # (37, 17) + - [ -0.984288396770987, -0.000000000000000, -2.195885460742546 ] + - [ 0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ -1.253138880259487, -0.000000000000000, -0.984988432484362 ] + - # (37, 18) + - [ -0.984288396770987, -0.000000000000000, 2.195885460742545 ] + - [ -0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ 1.253138880259486, -0.000000000000000, -0.984988432484362 ] + - # (37, 19) + - [ 0.003922321315242, 0.000000000000000, -0.225651385682165 ] + - [ 0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ -0.117046324829637, -0.000000000000000, 0.003301835114751 ] + - # (37, 20) + - [ 0.003922321315242, 0.000000000000000, 0.225651385682165 ] + - [ -0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ 0.117046324829637, 0.000000000000000, 0.003301835114751 ] + - # (37, 21) + - [ -0.984288396770987, -0.000000000000000, 2.195885460742546 ] + - [ 0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ 1.253138880259487, 0.000000000000000, -0.984988432484362 ] + - # (37, 22) + - [ -0.984288396770987, -0.000000000000000, -2.195885460742546 ] + - [ 0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ -1.253138880259486, 0.000000000000000, -0.984988432484362 ] + - # (37, 23) + - [ 0.003922321315242, 0.000000000000000, 0.225651385682165 ] + - [ -0.000000000000000, -0.247822895582965, -0.000000000000000 ] + - [ 0.117046324829637, 0.000000000000000, 0.003301835114751 ] + - # (37, 24) + - [ 0.003922321315242, 0.000000000000000, -0.225651385682165 ] + - [ -0.000000000000000, -0.247822895582965, -0.000000000000000 ] + - [ -0.117046324829637, -0.000000000000000, 0.003301835114751 ] + - # (37, 25) + - [ 0.003922321315242, 0.225651385682165, -0.000000000000000 ] + - [ 0.117046324829637, 0.003301835114751, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (37, 26) + - [ 0.003922321315242, -0.225651385682165, -0.000000000000000 ] + - [ -0.117046324829637, 0.003301835114751, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (37, 27) + - [ 0.003922321315242, -0.225651385682165, -0.000000000000000 ] + - [ -0.117046324829637, 0.003301835114751, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.247822895582965 ] + - # (37, 28) + - [ 0.003922321315242, 0.225651385682165, -0.000000000000000 ] + - [ 0.117046324829637, 0.003301835114751, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (37, 29) + - [ -0.984288396770987, 2.195885460742546, 0.000000000000000 ] + - [ 1.253138880259487, -0.984988432484362, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (37, 30) + - [ -0.984288396770987, -2.195885460742546, 0.000000000000000 ] + - [ -1.253138880259487, -0.984988432484362, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (37, 31) + - [ -0.984288396770987, -2.195885460742546, 0.000000000000000 ] + - [ -1.253138880259487, -0.984988432484362, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.387611895815905 ] + - # (37, 32) + - [ -0.984288396770987, 2.195885460742546, 0.000000000000000 ] + - [ 1.253138880259487, -0.984988432484362, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (37, 33) + - [ 0.843556469645334, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.305535183296458, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.478200759767276 ] + - # (37, 34) + - [ -0.899532456657624, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.076376017649521, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.410048393982954 ] + - # (37, 35) + - [ 0.190504466346593, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.685255767434321, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.685255767434321 ] + - # (37, 36) + - [ -0.192960955304435, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.092597047209941, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.092597047209941 ] + - # (37, 37) + - [ 35.215363029350826, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 3.672650396086184, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 3.672650396086183 ] + - # (37, 38) + - [ -9.018059463804947, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.653682035440779, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.653682035440779 ] + - # (37, 39) + - [ 0.843556469645334, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.478200759767276, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.305535183296458 ] + - # (37, 40) + - [ -0.899532456657624, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.410048393982954, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.076376017649521 ] + - # (38, 1) + - [ 0.651612687445637, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.012499173017644, -0.132896830436715 ] + - [ -0.000000000000000, -0.132896830436715, -0.012499173017644 ] + - # (38, 2) + - [ -0.932441913360827, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.173171511372757, -0.107862775508977 ] + - [ 0.000000000000000, -0.107862775508977, -0.173171511372757 ] + - # (38, 3) + - [ 0.651612687445637, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.012499173017644, 0.132896830436715 ] + - [ 0.000000000000000, 0.132896830436715, -0.012499173017644 ] + - # (38, 4) + - [ -0.932441913360827, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.173171511372757, 0.107862775508977 ] + - [ 0.000000000000000, 0.107862775508977, -0.173171511372757 ] + - # (38, 5) + - [ 0.651612687445637, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.012499173017644, 0.132896830436715 ] + - [ -0.000000000000000, 0.132896830436715, -0.012499173017644 ] + - # (38, 6) + - [ -0.932441913360827, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.173171511372757, 0.107862775508977 ] + - [ -0.000000000000000, 0.107862775508977, -0.173171511372757 ] + - # (38, 7) + - [ 0.651612687445637, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.012499173017644, -0.132896830436715 ] + - [ -0.000000000000000, -0.132896830436715, -0.012499173017644 ] + - # (38, 8) + - [ -0.932441913360827, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.173171511372757, -0.107862775508977 ] + - [ 0.000000000000000, -0.107862775508977, -0.173171511372757 ] + - # (38, 9) + - [ 0.024790810195010, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.339440953454353, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 1.459516479630155 ] + - # (38, 10) + - [ 0.024790810195010, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.339440953454353, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 1.459516479630155 ] + - # (38, 11) + - [ 0.007009549521902, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.327944811405822, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (38, 12) + - [ 0.007009549521902, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.327944811405822, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (38, 13) + - [ -8.614914553326718, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260271156912363, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.260271156912363 ] + - # (38, 14) + - [ -8.614914553326720, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.260271156912363, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.260271156912363 ] + - # (38, 15) + - [ 0.024790810195010, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 1.459516479630155, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.339440953454353 ] + - # (38, 16) + - [ 0.024790810195010, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.459516479630155, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.339440953454353 ] + - # (38, 17) + - [ -0.984288396770987, -0.000000000000000, 2.195885460742545 ] + - [ -0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ 1.253138880259486, -0.000000000000000, -0.984988432484362 ] + - # (38, 18) + - [ -0.984288396770987, -0.000000000000000, -2.195885460742546 ] + - [ 0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ -1.253138880259487, -0.000000000000000, -0.984988432484362 ] + - # (38, 19) + - [ 0.003922321315242, 0.000000000000000, 0.225651385682165 ] + - [ -0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ 0.117046324829637, 0.000000000000000, 0.003301835114751 ] + - # (38, 20) + - [ 0.003922321315242, 0.000000000000000, -0.225651385682165 ] + - [ 0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ -0.117046324829637, -0.000000000000000, 0.003301835114751 ] + - # (38, 21) + - [ -0.984288396770987, -0.000000000000000, -2.195885460742546 ] + - [ 0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ -1.253138880259486, 0.000000000000000, -0.984988432484362 ] + - # (38, 22) + - [ -0.984288396770987, -0.000000000000000, 2.195885460742546 ] + - [ 0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ 1.253138880259487, 0.000000000000000, -0.984988432484362 ] + - # (38, 23) + - [ 0.003922321315242, 0.000000000000000, -0.225651385682165 ] + - [ -0.000000000000000, -0.247822895582965, -0.000000000000000 ] + - [ -0.117046324829637, -0.000000000000000, 0.003301835114751 ] + - # (38, 24) + - [ 0.003922321315242, 0.000000000000000, 0.225651385682165 ] + - [ -0.000000000000000, -0.247822895582965, -0.000000000000000 ] + - [ 0.117046324829637, 0.000000000000000, 0.003301835114751 ] + - # (38, 25) + - [ 0.003922321315242, -0.225651385682165, -0.000000000000000 ] + - [ -0.117046324829637, 0.003301835114751, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (38, 26) + - [ 0.003922321315242, 0.225651385682165, -0.000000000000000 ] + - [ 0.117046324829637, 0.003301835114751, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (38, 27) + - [ 0.003922321315242, 0.225651385682165, -0.000000000000000 ] + - [ 0.117046324829637, 0.003301835114751, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (38, 28) + - [ 0.003922321315242, -0.225651385682165, -0.000000000000000 ] + - [ -0.117046324829637, 0.003301835114751, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.247822895582965 ] + - # (38, 29) + - [ -0.984288396770987, -2.195885460742546, 0.000000000000000 ] + - [ -1.253138880259487, -0.984988432484362, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (38, 30) + - [ -0.984288396770987, 2.195885460742546, 0.000000000000000 ] + - [ 1.253138880259487, -0.984988432484362, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (38, 31) + - [ -0.984288396770987, 2.195885460742546, 0.000000000000000 ] + - [ 1.253138880259487, -0.984988432484362, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (38, 32) + - [ -0.984288396770987, -2.195885460742546, 0.000000000000000 ] + - [ -1.253138880259487, -0.984988432484362, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.387611895815905 ] + - # (38, 33) + - [ -0.899532456657624, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.076376017649521, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.410048393982954 ] + - # (38, 34) + - [ 0.843556469645334, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.305535183296458, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.478200759767276 ] + - # (38, 35) + - [ -0.192960955304435, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.092597047209941, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.092597047209941 ] + - # (38, 36) + - [ 0.190504466346593, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.685255767434321, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.685255767434321 ] + - # (38, 37) + - [ -9.018059463804947, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.653682035440779, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.653682035440779 ] + - # (38, 38) + - [ 35.215363029350826, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 3.672650396086184, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 3.672650396086183 ] + - # (38, 39) + - [ -0.899532456657624, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.410048393982954, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.076376017649521 ] + - # (38, 40) + - [ 0.843556469645334, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.478200759767276, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.305535183296458 ] + - # (39, 1) + - [ -0.932441913360827, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.173171511372757, 0.107862775508977 ] + - [ 0.000000000000000, 0.107862775508977, -0.173171511372757 ] + - # (39, 2) + - [ 0.651612687445637, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.012499173017644, 0.132896830436715 ] + - [ 0.000000000000000, 0.132896830436715, -0.012499173017644 ] + - # (39, 3) + - [ -0.932441913360827, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.173171511372757, -0.107862775508977 ] + - [ 0.000000000000000, -0.107862775508977, -0.173171511372757 ] + - # (39, 4) + - [ 0.651612687445637, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.012499173017644, -0.132896830436715 ] + - [ -0.000000000000000, -0.132896830436715, -0.012499173017644 ] + - # (39, 5) + - [ -0.932441913360827, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.173171511372757, -0.107862775508977 ] + - [ 0.000000000000000, -0.107862775508977, -0.173171511372757 ] + - # (39, 6) + - [ 0.651612687445637, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.012499173017644, -0.132896830436715 ] + - [ -0.000000000000000, -0.132896830436715, -0.012499173017644 ] + - # (39, 7) + - [ -0.932441913360827, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.173171511372757, 0.107862775508977 ] + - [ -0.000000000000000, 0.107862775508977, -0.173171511372757 ] + - # (39, 8) + - [ 0.651612687445637, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.012499173017644, 0.132896830436715 ] + - [ -0.000000000000000, 0.132896830436715, -0.012499173017644 ] + - # (39, 9) + - [ 0.007009549521902, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.327944811405822, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (39, 10) + - [ 0.007009549521902, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.327944811405822, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (39, 11) + - [ 0.024790810195010, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.339440953454353, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 1.459516479630155 ] + - # (39, 12) + - [ 0.024790810195010, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.339440953454353, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 1.459516479630155 ] + - # (39, 13) + - [ 0.024790810195010, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.459516479630155, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.339440953454353 ] + - # (39, 14) + - [ 0.024790810195010, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 1.459516479630155, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.339440953454353 ] + - # (39, 15) + - [ -8.614914553326720, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.260271156912363, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.260271156912363 ] + - # (39, 16) + - [ -8.614914553326718, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260271156912363, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.260271156912363 ] + - # (39, 17) + - [ 0.003922321315242, 0.000000000000000, -0.225651385682165 ] + - [ 0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ -0.117046324829637, -0.000000000000000, 0.003301835114751 ] + - # (39, 18) + - [ 0.003922321315242, 0.000000000000000, 0.225651385682165 ] + - [ -0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ 0.117046324829637, 0.000000000000000, 0.003301835114751 ] + - # (39, 19) + - [ -0.984288396770987, -0.000000000000000, -2.195885460742546 ] + - [ 0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ -1.253138880259487, -0.000000000000000, -0.984988432484362 ] + - # (39, 20) + - [ -0.984288396770987, -0.000000000000000, 2.195885460742545 ] + - [ -0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ 1.253138880259486, -0.000000000000000, -0.984988432484362 ] + - # (39, 21) + - [ 0.003922321315242, 0.000000000000000, 0.225651385682165 ] + - [ -0.000000000000000, -0.247822895582965, -0.000000000000000 ] + - [ 0.117046324829637, 0.000000000000000, 0.003301835114751 ] + - # (39, 22) + - [ 0.003922321315242, 0.000000000000000, -0.225651385682165 ] + - [ -0.000000000000000, -0.247822895582965, -0.000000000000000 ] + - [ -0.117046324829637, -0.000000000000000, 0.003301835114751 ] + - # (39, 23) + - [ -0.984288396770987, -0.000000000000000, 2.195885460742546 ] + - [ 0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ 1.253138880259487, 0.000000000000000, -0.984988432484362 ] + - # (39, 24) + - [ -0.984288396770987, -0.000000000000000, -2.195885460742546 ] + - [ 0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ -1.253138880259486, 0.000000000000000, -0.984988432484362 ] + - # (39, 25) + - [ 0.003922321315242, -0.225651385682165, -0.000000000000000 ] + - [ -0.117046324829637, 0.003301835114751, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.247822895582965 ] + - # (39, 26) + - [ 0.003922321315242, 0.225651385682165, -0.000000000000000 ] + - [ 0.117046324829637, 0.003301835114751, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (39, 27) + - [ 0.003922321315242, 0.225651385682165, -0.000000000000000 ] + - [ 0.117046324829637, 0.003301835114751, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (39, 28) + - [ 0.003922321315242, -0.225651385682165, -0.000000000000000 ] + - [ -0.117046324829637, 0.003301835114751, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (39, 29) + - [ -0.984288396770987, -2.195885460742546, 0.000000000000000 ] + - [ -1.253138880259487, -0.984988432484362, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.387611895815905 ] + - # (39, 30) + - [ -0.984288396770987, 2.195885460742546, 0.000000000000000 ] + - [ 1.253138880259487, -0.984988432484362, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (39, 31) + - [ -0.984288396770987, 2.195885460742546, 0.000000000000000 ] + - [ 1.253138880259487, -0.984988432484362, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (39, 32) + - [ -0.984288396770987, -2.195885460742546, 0.000000000000000 ] + - [ -1.253138880259487, -0.984988432484362, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (39, 33) + - [ 0.190504466346593, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.685255767434321, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.685255767434321 ] + - # (39, 34) + - [ -0.192960955304435, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.092597047209941, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.092597047209941 ] + - # (39, 35) + - [ 0.843556469645334, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.305535183296458, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.478200759767276 ] + - # (39, 36) + - [ -0.899532456657624, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.076376017649521, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.410048393982954 ] + - # (39, 37) + - [ 0.843556469645334, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.478200759767276, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.305535183296458 ] + - # (39, 38) + - [ -0.899532456657624, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.410048393982954, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.076376017649521 ] + - # (39, 39) + - [ 35.215363029350826, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 3.672650396086184, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 3.672650396086183 ] + - # (39, 40) + - [ -9.018059463804947, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.653682035440779, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.653682035440779 ] + - # (40, 1) + - [ 0.651612687445637, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.012499173017644, 0.132896830436715 ] + - [ 0.000000000000000, 0.132896830436715, -0.012499173017644 ] + - # (40, 2) + - [ -0.932441913360827, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.173171511372757, 0.107862775508977 ] + - [ 0.000000000000000, 0.107862775508977, -0.173171511372757 ] + - # (40, 3) + - [ 0.651612687445637, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.012499173017644, -0.132896830436715 ] + - [ -0.000000000000000, -0.132896830436715, -0.012499173017644 ] + - # (40, 4) + - [ -0.932441913360827, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.173171511372757, -0.107862775508977 ] + - [ 0.000000000000000, -0.107862775508977, -0.173171511372757 ] + - # (40, 5) + - [ 0.651612687445637, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.012499173017644, -0.132896830436715 ] + - [ -0.000000000000000, -0.132896830436715, -0.012499173017644 ] + - # (40, 6) + - [ -0.932441913360827, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.173171511372757, -0.107862775508977 ] + - [ 0.000000000000000, -0.107862775508977, -0.173171511372757 ] + - # (40, 7) + - [ 0.651612687445637, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.012499173017644, 0.132896830436715 ] + - [ -0.000000000000000, 0.132896830436715, -0.012499173017644 ] + - # (40, 8) + - [ -0.932441913360827, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.173171511372757, 0.107862775508977 ] + - [ -0.000000000000000, 0.107862775508977, -0.173171511372757 ] + - # (40, 9) + - [ 0.007009549521902, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 0.327944811405822, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (40, 10) + - [ 0.007009549521902, -0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, 0.327944811405822, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.327944811405822 ] + - # (40, 11) + - [ 0.024790810195010, 0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, -0.339440953454353, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 1.459516479630155 ] + - # (40, 12) + - [ 0.024790810195010, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.339440953454353, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 1.459516479630155 ] + - # (40, 13) + - [ 0.024790810195010, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 1.459516479630155, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.339440953454353 ] + - # (40, 14) + - [ 0.024790810195010, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 1.459516479630155, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.339440953454353 ] + - # (40, 15) + - [ -8.614914553326718, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.260271156912363, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.260271156912363 ] + - # (40, 16) + - [ -8.614914553326720, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.260271156912363, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.260271156912363 ] + - # (40, 17) + - [ 0.003922321315242, 0.000000000000000, 0.225651385682165 ] + - [ -0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ 0.117046324829637, 0.000000000000000, 0.003301835114751 ] + - # (40, 18) + - [ 0.003922321315242, 0.000000000000000, -0.225651385682165 ] + - [ 0.000000000000000, -0.247822895582965, 0.000000000000000 ] + - [ -0.117046324829637, -0.000000000000000, 0.003301835114751 ] + - # (40, 19) + - [ -0.984288396770987, -0.000000000000000, 2.195885460742545 ] + - [ -0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ 1.253138880259486, -0.000000000000000, -0.984988432484362 ] + - # (40, 20) + - [ -0.984288396770987, -0.000000000000000, -2.195885460742546 ] + - [ 0.000000000000000, 0.387611895815905, -0.000000000000000 ] + - [ -1.253138880259487, -0.000000000000000, -0.984988432484362 ] + - # (40, 21) + - [ 0.003922321315242, 0.000000000000000, -0.225651385682165 ] + - [ -0.000000000000000, -0.247822895582965, -0.000000000000000 ] + - [ -0.117046324829637, -0.000000000000000, 0.003301835114751 ] + - # (40, 22) + - [ 0.003922321315242, 0.000000000000000, 0.225651385682165 ] + - [ -0.000000000000000, -0.247822895582965, -0.000000000000000 ] + - [ 0.117046324829637, 0.000000000000000, 0.003301835114751 ] + - # (40, 23) + - [ -0.984288396770987, -0.000000000000000, -2.195885460742546 ] + - [ 0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ -1.253138880259486, 0.000000000000000, -0.984988432484362 ] + - # (40, 24) + - [ -0.984288396770987, -0.000000000000000, 2.195885460742546 ] + - [ 0.000000000000000, 0.387611895815905, 0.000000000000000 ] + - [ 1.253138880259487, 0.000000000000000, -0.984988432484362 ] + - # (40, 25) + - [ 0.003922321315242, 0.225651385682165, -0.000000000000000 ] + - [ 0.117046324829637, 0.003301835114751, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (40, 26) + - [ 0.003922321315242, -0.225651385682165, -0.000000000000000 ] + - [ -0.117046324829637, 0.003301835114751, 0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, -0.247822895582965 ] + - # (40, 27) + - [ 0.003922321315242, -0.225651385682165, -0.000000000000000 ] + - [ -0.117046324829637, 0.003301835114751, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (40, 28) + - [ 0.003922321315242, 0.225651385682165, -0.000000000000000 ] + - [ 0.117046324829637, 0.003301835114751, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.247822895582965 ] + - # (40, 29) + - [ -0.984288396770987, 2.195885460742546, 0.000000000000000 ] + - [ 1.253138880259487, -0.984988432484362, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (40, 30) + - [ -0.984288396770987, -2.195885460742546, 0.000000000000000 ] + - [ -1.253138880259487, -0.984988432484362, -0.000000000000000 ] + - [ -0.000000000000000, -0.000000000000000, 0.387611895815905 ] + - # (40, 31) + - [ -0.984288396770987, -2.195885460742546, 0.000000000000000 ] + - [ -1.253138880259487, -0.984988432484362, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (40, 32) + - [ -0.984288396770987, 2.195885460742546, 0.000000000000000 ] + - [ 1.253138880259487, -0.984988432484362, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.387611895815905 ] + - # (40, 33) + - [ -0.192960955304435, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.092597047209941, 0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.092597047209941 ] + - # (40, 34) + - [ 0.190504466346593, 0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, -0.685255767434321, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.685255767434321 ] + - # (40, 35) + - [ -0.899532456657624, -0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, 0.076376017649521, 0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, -0.410048393982954 ] + - # (40, 36) + - [ 0.843556469645334, -0.000000000000000, -0.000000000000000 ] + - [ 0.000000000000000, 0.305535183296458, -0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, -0.478200759767276 ] + - # (40, 37) + - [ -0.899532456657624, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.410048393982954, 0.000000000000000 ] + - [ 0.000000000000000, -0.000000000000000, 0.076376017649521 ] + - # (40, 38) + - [ 0.843556469645334, 0.000000000000000, 0.000000000000000 ] + - [ -0.000000000000000, -0.478200759767276, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, 0.305535183296458 ] + - # (40, 39) + - [ -9.018059463804947, 0.000000000000000, -0.000000000000000 ] + - [ -0.000000000000000, -0.653682035440779, -0.000000000000000 ] + - [ -0.000000000000000, 0.000000000000000, -0.653682035440779 ] + - # (40, 40) + - [ 35.215363029350826, -0.000000000000000, 0.000000000000000 ] + - [ 0.000000000000000, 3.672650396086184, -0.000000000000000 ] + - [ 0.000000000000000, 0.000000000000000, 3.672650396086183 ] \ No newline at end of file diff --git a/examples/c2db/ALIGNN.parquet b/examples/c2db/ALIGNN.parquet new file mode 100644 index 0000000000000000000000000000000000000000..3a56da04e459b98d92a698c4d67d85d8370ccded --- /dev/null +++ b/examples/c2db/ALIGNN.parquet @@ -0,0 +1,3 @@ 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index 0000000000000000000000000000000000000000..cae862bed078a07208864c46df4af230315c8914 --- /dev/null +++ b/examples/c2db/MACE-MP(M).parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f722eac6799bfecaa02188d59475862895a639cc596fa8b7d1e9d2b96cfb415b +size 293633 diff --git a/examples/c2db/MACE-MPA.parquet b/examples/c2db/MACE-MPA.parquet new file mode 100644 index 0000000000000000000000000000000000000000..145460ce655d08993233535ede6d702183b7971c --- /dev/null +++ b/examples/c2db/MACE-MPA.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c3ea679b5f6c9940358a2121a496544be91ba01ed8383509c65773f9fc69b9ec +size 293820 diff --git a/examples/c2db/MatterSim.parquet b/examples/c2db/MatterSim.parquet new file mode 100644 index 0000000000000000000000000000000000000000..866dd8b70b0489546071d56a760d3a873d8b432c --- /dev/null +++ b/examples/c2db/MatterSim.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d150c1b31b99ddcbbf21401189289aead13791c683aa379d75163b8bc4dbc6b4 +size 293177 diff --git a/examples/c2db/ORBv2.parquet b/examples/c2db/ORBv2.parquet new file mode 100644 index 0000000000000000000000000000000000000000..6ef58cd086b30aa22ce40bd99b79cab13fc04f32 --- /dev/null +++ b/examples/c2db/ORBv2.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c2496f96d4aff1536936e58e65c1d608cc1953d41006221ba62ea2daab23f30b +size 293012 diff --git a/examples/c2db/SevenNet.parquet b/examples/c2db/SevenNet.parquet new file mode 100644 index 0000000000000000000000000000000000000000..5c003f34383983576b3bc56f150437a250bb9c4c --- /dev/null +++ b/examples/c2db/SevenNet.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c2ee18ce70f24f70e65d70c2e54151e86dd0ccb3e412b8fbbc572e44e8bf5e8 +size 293973 diff --git a/examples/c2db/analysis.ipynb b/examples/c2db/analysis.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..49c80f2c41fee1bdf954e6ea7db35ca82499e981 --- /dev/null +++ b/examples/c2db/analysis.ipynb @@ -0,0 +1,408 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "0625f0a1", + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "from pathlib import Path\n", + "\n", + "import numpy as np\n", + "from ase.db import connect\n", + "\n", + "random.seed(0)\n", + "\n", + "DATA_DIR = Path(\".\")\n", + "\n", + "db = connect(DATA_DIR / \"c2db.db\")\n", + "random_indices = random.sample(range(1, len(db) + 1), 1000)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "005708b9", + "metadata": {}, + "outputs": [], + "source": [ + "import itertools\n", + "\n", + "import pandas as pd\n", + "import phonopy\n", + "from tqdm.auto import tqdm\n", + "\n", + "from mlip_arena.models import MLIPEnum\n", + "\n", + "for row, model in tqdm(\n", + " itertools.product(db.select(filter=lambda r: r[\"id\"] in random_indices), MLIPEnum)\n", + "):\n", + " uid = row[\"uid\"]\n", + "\n", + " if Path(f\"{model.name}.parquet\").exists():\n", + " df = pd.read_parquet(f\"{model.name}.parquet\")\n", + " if uid in df[\"uid\"].unique():\n", + " continue\n", + " else:\n", + " df = pd.DataFrame(columns=[\"model\", \"uid\", \"eigenvalues\", \"frequencies\"])\n", + "\n", + " try:\n", + " path = Path(model.name) / uid\n", + " phonon = phonopy.load(path / \"phonopy.yaml\")\n", + " frequencies = phonon.get_frequencies(q=(0, 0, 0))\n", + "\n", + " data = np.load(path / \"elastic.npz\")\n", + "\n", + " eigenvalues = data[\"eigenvalues\"]\n", + "\n", + " new_row = pd.DataFrame(\n", + " [\n", + " {\n", + " \"model\": model.name,\n", + " \"uid\": uid,\n", + " \"eigenvalues\": eigenvalues,\n", + " \"frequencies\": frequencies,\n", + " }\n", + " ]\n", + " )\n", + "\n", + " df = pd.concat([df, new_row], ignore_index=True)\n", + " df.drop_duplicates(subset=[\"model\", \"uid\"], keep=\"last\", inplace=True)\n", + "\n", + " df.to_parquet(f\"{model.name}.parquet\", index=False)\n", + " except Exception:\n", + " pass\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b8d87638", + "metadata": {}, + "outputs": [], + "source": [ + "uids = []\n", + "stabilities = []\n", + "for row in db.select(filter=lambda r: r[\"id\"] in random_indices):\n", + " stable = row.key_value_pairs[\"dyn_stab\"]\n", + " if stable.lower() == \"unknown\":\n", + " stable = None\n", + " else:\n", + " stable = True if stable.lower() == \"yes\" else False\n", + " uids.append(row.key_value_pairs[\"uid\"])\n", + " stabilities.append(stable)\n", + "\n", + "\n", + "stabilities = np.array(stabilities)\n", + "\n", + "(stabilities == True).sum(), (stabilities == False).sum(), (stabilities == None).sum()" + ] + }, + { + "cell_type": "markdown", + "id": "a3c516a7", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "0052d0ff", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "from pathlib import Path\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "from matplotlib import pyplot as plt\n", + "from sklearn.metrics import (\n", + " ConfusionMatrixDisplay,\n", + " classification_report,\n", + " confusion_matrix,\n", + ")\n", + "\n", + "from mlip_arena.models import MLIPEnum\n", + "\n", + "thres = -1e-7\n", + "\n", + "select_models = [\n", + " \"ALIGNN\",\n", + " \"CHGNet\",\n", + " \"M3GNet\",\n", + " \"MACE-MP(M)\",\n", + " \"MACE-MPA\",\n", + " \"MatterSim\",\n", + " \"ORBv2\",\n", + " \"SevenNet\",\n", + "]\n", + "\n", + "with plt.style.context(\"default\"):\n", + " # plt.rcParams.update({\n", + " # # \"title.fontsize\": 10,\n", + " # \"axes.titlesize\": 10,\n", + " # \"axes.labelsize\": 8,\n", + " # })\n", + "\n", + " SMALL_SIZE = 8\n", + " MEDIUM_SIZE = 10\n", + " BIGGER_SIZE = 12\n", + " plt.rcParams.update(\n", + " {\n", + " \"font.size\": SMALL_SIZE,\n", + " \"axes.titlesize\": MEDIUM_SIZE,\n", + " \"axes.labelsize\": MEDIUM_SIZE,\n", + " \"xtick.labelsize\": MEDIUM_SIZE,\n", + " \"ytick.labelsize\": MEDIUM_SIZE,\n", + " \"legend.fontsize\": SMALL_SIZE,\n", + " \"figure.titlesize\": BIGGER_SIZE,\n", + " }\n", + " )\n", + "\n", + " fig, axs = plt.subplots(\n", + " nrows=int(np.ceil(len(MLIPEnum) / 4)),\n", + " ncols=4,\n", + " figsize=(6, 3 * int(np.ceil(len(select_models) / 4))),\n", + " sharey=True,\n", + " sharex=True,\n", + " layout=\"constrained\",\n", + " )\n", + " axs = axs.flatten()\n", + " plot_idx = 0\n", + "\n", + " for model in MLIPEnum:\n", + " fpath = DATA_DIR / f\"{model.name}.parquet\"\n", + " if not fpath.exists():\n", + " continue\n", + "\n", + " if model.name not in select_models:\n", + " continue\n", + "\n", + " df = pd.read_parquet(fpath)\n", + " df[\"eigval_min\"] = df[\"eigenvalues\"].apply(\n", + " lambda x: x.min() if np.isreal(x).all() else thres\n", + " )\n", + " df[\"freq_min\"] = df[\"frequencies\"].apply(\n", + " lambda x: x.min() if np.isreal(x).all() else thres\n", + " )\n", + " df[\"dyn_stab\"] = ~np.logical_or(\n", + " df[\"eigval_min\"] < thres, df[\"freq_min\"] < thres\n", + " )\n", + "\n", + " arg = np.argsort(uids)\n", + " uids_sorted = np.array(uids)[arg]\n", + " stabilities_sorted = stabilities[arg]\n", + "\n", + " sorted_df = (\n", + " df[df[\"uid\"].isin(uids_sorted)].set_index(\"uid\").reindex(uids_sorted)\n", + " )\n", + " mask = ~(stabilities_sorted == None)\n", + "\n", + " y_true = stabilities_sorted[mask].astype(\"int\")\n", + " y_pred = sorted_df[\"dyn_stab\"][mask].fillna(-1).astype(\"int\")\n", + " cm = confusion_matrix(y_true, y_pred, labels=[1, 0, -1])\n", + "\n", + " ax = axs[plot_idx]\n", + " ConfusionMatrixDisplay(\n", + " cm, display_labels=[\"stable\", \"unstable\", \"missing\"]\n", + " ).plot(ax=ax, cmap=\"Blues\", colorbar=False)\n", + "\n", + " ax.set_title(model.name)\n", + " ax.set_xlabel(\"Predicted\")\n", + " ax.set_ylabel(\"True\")\n", + " ax.set_xticks([0, 1, 2])\n", + " ax.set_xticklabels([\"stable\", \"unstable\", \"missing\"])\n", + " ax.set_yticks([0, 1, 2])\n", + " ax.set_yticklabels([\"stable\", \"unstable\", \"missing\"])\n", + "\n", + " plot_idx += 1\n", + "\n", + " # Hide unused subplots\n", + " for i in range(plot_idx, len(axs)):\n", + " fig.delaxes(axs[i])\n", + "\n", + " # plt.tight_layout()\n", + " plt.savefig(\"c2db-confusion_matrices.pdf\", bbox_inches=\"tight\")\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "573b3c38", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sklearn.metrics import confusion_matrix\n", + "\n", + "from mlip_arena.models import MLIPEnum\n", + "\n", + "thres = -1e-7\n", + "\n", + "summary_df = pd.DataFrame(columns=[\"Model\", \"Stable F1\", \"Unstable F1\", \"Weighted F1\"])\n", + "\n", + "for model in MLIPEnum:\n", + " fpath = DATA_DIR / f\"{model.name}.parquet\"\n", + "\n", + " if not fpath.exists() or model.name not in select_models:\n", + " # print(f\"File {fpath} does not exist\")\n", + " continue\n", + " df = pd.read_parquet(fpath)\n", + "\n", + " df[\"eigval_min\"] = df[\"eigenvalues\"].apply(\n", + " lambda x: x.min() if np.isreal(x).all() else thres\n", + " )\n", + " df[\"freq_min\"] = df[\"frequencies\"].apply(\n", + " lambda x: x.min() if np.isreal(x).all() else thres\n", + " )\n", + " df[\"dyn_stab\"] = ~np.logical_or(df[\"eigval_min\"] < thres, df[\"freq_min\"] < thres)\n", + "\n", + " arg = np.argsort(uids)\n", + " uids = np.array(uids)[arg]\n", + " stabilities = stabilities[arg]\n", + "\n", + " sorted_df = df[df[\"uid\"].isin(uids)].sort_values(by=\"uid\")\n", + "\n", + " # sorted_df = sorted_df.reindex(uids).reset_index()\n", + " sorted_df = sorted_df.set_index(\"uid\").reindex(uids) # .loc[uids].reset_index()\n", + "\n", + " sorted_df = sorted_df.loc[uids]\n", + " # mask = ~np.logical_or(sorted_df['dyn_stab'].isna().values, stabilities == None)\n", + " mask = ~(stabilities == None)\n", + "\n", + " y_true = stabilities[mask].astype(\"int\")\n", + " y_pred = sorted_df[\"dyn_stab\"][mask].fillna(-1).astype(\"int\")\n", + " cm = confusion_matrix(y_true, y_pred, labels=[1, 0, -1])\n", + " # print(model)\n", + " # print(cm)\n", + " # print(classification_report(y_true, y_pred, labels=[1, 0], target_names=['stable', 'unstable'], digits=3, output_dict=False))\n", + "\n", + " report = classification_report(\n", + " y_true,\n", + " y_pred,\n", + " labels=[1, 0],\n", + " target_names=[\"stable\", \"unstable\"],\n", + " digits=3,\n", + " output_dict=True,\n", + " )\n", + "\n", + " summary_df = pd.concat(\n", + " [\n", + " summary_df,\n", + " pd.DataFrame(\n", + " [\n", + " {\n", + " \"Model\": model.name,\n", + " \"Stable F1\": report[\"stable\"][\"f1-score\"],\n", + " \"Unstable F1\": report[\"unstable\"][\"f1-score\"],\n", + " \"Macro F1\": report[\"macro avg\"][\"f1-score\"],\n", + " # 'Micro F1': report['micro avg']['f1-score'],\n", + " \"Weighted F1\": report[\"weighted avg\"][\"f1-score\"],\n", + " }\n", + " ]\n", + " ),\n", + " ],\n", + " ignore_index=True,\n", + " )\n", + "\n", + " # break" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "df660870", + "metadata": {}, + "outputs": [], + "source": [ + "summary_df = summary_df.sort_values(by=[\"Macro F1\", \"Weighted F1\"], ascending=False)\n", + "summary_df.to_latex(\"c2db_summary_table.tex\", index=False, float_format=\"%.3f\")" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "18f4a59b", + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import cm\n", + "\n", + "# Metrics and bar settings\n", + "metrics = [\"Stable F1\", \"Unstable F1\", \"Macro F1\", \"Weighted F1\"]\n", + "bar_width = 0.2\n", + "x = np.arange(len(summary_df))\n", + "\n", + "# Get Set2 colormap (as RGBA)\n", + "cmap = plt.get_cmap(\"tab20\")\n", + "colors = {metric: cmap(i) for i, metric in enumerate(metrics)}\n", + "\n", + "with plt.style.context(\"default\"):\n", + " plt.rcParams.update(\n", + " {\n", + " \"font.size\": SMALL_SIZE,\n", + " \"axes.titlesize\": MEDIUM_SIZE,\n", + " \"axes.labelsize\": MEDIUM_SIZE,\n", + " \"xtick.labelsize\": MEDIUM_SIZE,\n", + " \"ytick.labelsize\": MEDIUM_SIZE,\n", + " \"legend.fontsize\": SMALL_SIZE,\n", + " \"figure.titlesize\": BIGGER_SIZE,\n", + " }\n", + " )\n", + "\n", + " fig, ax = plt.subplots(figsize=(4, 3), layout=\"constrained\")\n", + "\n", + " # Bar positions\n", + " positions = {\n", + " \"Stable F1\": x - 1.5 * bar_width,\n", + " \"Unstable F1\": x - 0.5 * bar_width,\n", + " \"Macro F1\": x + 0.5 * bar_width,\n", + " \"Weighted F1\": x + 1.5 * bar_width,\n", + " }\n", + "\n", + " # Plot each metric with assigned color\n", + " for metric, pos in positions.items():\n", + " ax.bar(\n", + " pos, summary_df[metric], width=bar_width, label=metric, color=colors[metric]\n", + " )\n", + "\n", + " ax.set_xlabel(\"Model\")\n", + " ax.set_ylabel(\"F1 Score\")\n", + " # ax.set_title('F1 Scores by Model and Class')\n", + " ax.set_xticks(x)\n", + " ax.set_xticklabels(summary_df[\"Model\"], rotation=45, ha=\"right\")\n", + " ax.legend(ncols=2, bbox_to_anchor=(0.5, 1), loc=\"upper center\", fontsize=SMALL_SIZE)\n", + " # ax.legend(ncols=2, fontsize=SMALL_SIZE)\n", + " ax.spines[[\"top\", \"right\"]].set_visible(False)\n", + " plt.tight_layout()\n", + " plt.ylim(0, 0.9)\n", + " plt.grid(axis=\"y\", linestyle=\"--\", alpha=0.6)\n", + "\n", + " plt.savefig(\"c2db_f1_bar.pdf\", bbox_inches=\"tight\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c50f705", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mlip-arena", + "language": "python", + "name": "mlip-arena" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/c2db/c2db-confusion_matrices.pdf b/examples/c2db/c2db-confusion_matrices.pdf new file mode 100644 index 0000000000000000000000000000000000000000..5e628d86802c45e7c9e1179f484911b410082a52 Binary files /dev/null and b/examples/c2db/c2db-confusion_matrices.pdf differ diff --git a/examples/c2db/c2db-f1_bar.pdf b/examples/c2db/c2db-f1_bar.pdf new file mode 100644 index 0000000000000000000000000000000000000000..be0836b2f7d7e0ef42f76fed7f181a3a4392679b Binary files /dev/null and b/examples/c2db/c2db-f1_bar.pdf differ diff --git a/examples/c2db/c2db.db b/examples/c2db/c2db.db new file mode 100644 index 0000000000000000000000000000000000000000..fe866ce9e8bbfea2ddf11eb36a102f77d72616de --- /dev/null +++ b/examples/c2db/c2db.db @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:caf58205692de480e06149ac43a437385f18e14582e7d9a8dab8b3cb5d4bd678 +size 70762496 diff --git a/examples/c2db/copy.parquet b/examples/c2db/copy.parquet new file mode 100644 index 0000000000000000000000000000000000000000..e3b44acd0d2c2483e4d8e27515f7c3cea8255de1 --- /dev/null +++ b/examples/c2db/copy.parquet @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7fdc16667361b10bfb032862d5d0610c242d75cb88f7f3883c43a406b245e991 +size 21349 diff --git a/examples/c2db/run.py b/examples/c2db/run.py new file mode 100644 index 0000000000000000000000000000000000000000..6efb62a779b286027a31ac9dc54a1db4ef0404aa --- /dev/null +++ b/examples/c2db/run.py @@ -0,0 +1,213 @@ +from itertools import product +from pathlib import Path + +import numpy as np +import pandas as pd +from dask.distributed import Client +from dask_jobqueue import SLURMCluster +from mlip_arena.models import MLIPEnum +from mlip_arena.tasks import ELASTICITY, OPT, PHONON +from mlip_arena.tasks.optimize import run as OPT +from mlip_arena.tasks.utils import get_calculator +from numpy import linalg as LA +from prefect import flow, task +from prefect_dask import DaskTaskRunner +from tqdm.auto import tqdm + +from ase.db import connect + +select_models = [ + "ALIGNN", + "CHGNet", + "M3GNet", + "MACE-MP(M)", + "MACE-MPA", + "MatterSim", + "ORBv2", + "SevenNet", +] + +def elastic_tensor_to_voigt(C): + """ + Convert a rank-4 (3x3x3x3) elastic tensor into a rank-2 (6x6) tensor using Voigt notation. + + Parameters: + C (numpy.ndarray): A 3x3x3x3 elastic tensor. + + Returns: + numpy.ndarray: A 6x6 elastic tensor in Voigt notation. + """ + # voigt_map = { + # (0, 0): 0, (1, 1): 1, (2, 2): 2, # Normal components + # (1, 2): 3, (2, 1): 3, # Shear components + # (0, 2): 4, (2, 0): 4, + # (0, 1): 5, (1, 0): 5 + # } + voigt_map = { + (0, 0): 0, + (1, 1): 1, + (2, 2): -1, # Normal components + (1, 2): -1, + (2, 1): -1, # Shear components + (0, 2): -1, + (2, 0): -1, + (0, 1): 2, + (1, 0): 2, + } + + C_voigt = np.zeros((3, 3)) + + for i in range(3): + for j in range(3): + for k in range(3): + for l in range(3): + alpha = voigt_map[(i, j)] + beta = voigt_map[(k, l)] + + if alpha == -1 or beta == -1: + continue + + factor = 1 + # if alpha in [3, 4, 5]: + if alpha == 2: + factor = factor * (2**0.5) + if beta == 2: + factor = factor * (2**0.5) + + C_voigt[alpha, beta] = C[i, j, k, l] * factor + + return C_voigt + + +# - + + +@task +def run_one(model, row): + if Path(f"{model.name}.pkl").exists(): + df = pd.read_pickle(f"{model.name}.pkl") + + # if row.key_value_pairs.get('uid', None) in df['uid'].unique(): + # pass + else: + df = pd.DataFrame(columns=["model", "uid", "eigenvalues", "frequencies"]) + + atoms = row.toatoms() + # print(data := row.key_value_pairs) + + calc = get_calculator(model) + + result_opt = OPT( + atoms, + calc, + optimizer="FIRE", + criterion=dict(fmax=0.05, steps=500), + symmetry=True, + ) + + atoms = result_opt["atoms"] + + result_elastic = ELASTICITY( + atoms, + calc, + optimizer="FIRE", + criterion=dict(fmax=0.05, steps=500), + pre_relax=False, + ) + + elastic_tensor = elastic_tensor_to_voigt(result_elastic["elastic_tensor"]) + eigenvalues, eigenvectors = LA.eig(elastic_tensor) + + outdir = Path(f"{model.name}") / row.key_value_pairs.get( + "uid", atoms.get_chemical_formula() + ) + outdir.mkdir(parents=True, exist_ok=True) + + np.savez(outdir / "elastic.npz", tensor=elastic_tensor, eigenvalues=eigenvalues) + + result_phonon = PHONON( + atoms, + calc, + supercell_matrix=(2, 2, 1), + outdir=outdir, + ) + + frequencies = result_phonon["phonon"].get_frequencies(q=(0, 0, 0)) + + new_row = pd.DataFrame( + [ + { + "model": model.name, + "uid": row.key_value_pairs.get("uid", None), + "eigenvalues": eigenvalues, + "frequencies": frequencies, + } + ] + ) + + df = pd.concat([df, new_row], ignore_index=True) + df.drop_duplicates(subset=["model", "uid"], keep="last", inplace=True) + + df.to_pickle(f"{model.name}.pkl") + + +@flow +def run_all(): + import random + + random.seed(0) + + futures = [] + with connect("c2db.db") as db: + random_indices = random.sample(range(1, len(db) + 1), 1000) + for row, model in tqdm( + product(db.select(filter=lambda r: r["id"] in random_indices), MLIPEnum) + ): + if model.name not in select_models: + continue + future = run_one.submit(model, row) + futures.append(future) + return [f.result(raise_on_failure=False) for f in futures] + + +# + + + +if __name__ == "__main__": + nodes_per_alloc = 1 + gpus_per_alloc = 1 + ntasks = 1 + + cluster_kwargs = dict( + cores=1, + memory="64 GB", + processes=1, + shebang="#!/bin/bash", + account="matgen", + walltime="00:30:00", + # job_cpu=128, + job_mem="0", + job_script_prologue=[ + "source ~/.bashrc", + "module load python", + "source activate /pscratch/sd/c/cyrusyc/.conda/dev", + ], + job_directives_skip=["-n", "--cpus-per-task", "-J"], + job_extra_directives=[ + "-J c2db", + "-q regular", + f"-N {nodes_per_alloc}", + "-C gpu", + f"-G {gpus_per_alloc}", + ], + ) + + cluster = SLURMCluster(**cluster_kwargs) + print(cluster.job_script()) + cluster.adapt(minimum_jobs=25, maximum_jobs=50) + client = Client(cluster) + # - + + run_all.with_options( + task_runner=DaskTaskRunner(address=client.scheduler.address), log_prints=True + )() diff --git a/examples/eos_bulk/CHGNet_processed.parquet b/examples/eos_bulk/CHGNet_processed.parquet index 8076f9e83a05ee1bf4724003bcde8e88d9e5593e..a942f3448c1013017ba0bb8a7aa85fa23f9345cd 100644 --- a/examples/eos_bulk/CHGNet_processed.parquet +++ b/examples/eos_bulk/CHGNet_processed.parquet @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:0416eeed1748994b67e8f6e9768a5f1d2a77c19f9512bc408f9b39ca3c19e3d4 -size 358042 +oid sha256:bfde7530e6b0d2df5a30e1b7e3ec124fb2a86f6da8e35d2548d37d10a1eff1b1 +size 387425 diff --git a/examples/eos_bulk/M3GNet_processed.parquet b/examples/eos_bulk/M3GNet_processed.parquet index c9665894df8c5e80a3e2ad35020e427fdc137b43..d1b291c5a1b157174c53acde829d6da0b08de8a0 100644 --- a/examples/eos_bulk/M3GNet_processed.parquet +++ b/examples/eos_bulk/M3GNet_processed.parquet @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:1a34f8148f771f0b751f01ccc9d260fd5ae48b625b979ec2112e008c82c59a08 -size 379982 +oid sha256:eb43a3c74f3340100b1adb21b3f2d075451e1ffe88ac6d6662741bc4a0576eb8 +size 397450 diff --git a/examples/eos_bulk/MACE-MP(M)_processed.parquet b/examples/eos_bulk/MACE-MP(M)_processed.parquet index ede61a68b92643d497802d5bd1933c8035ad6c11..e681cec5643003bfea9db29799c19cab5f26b9a5 100644 --- a/examples/eos_bulk/MACE-MP(M)_processed.parquet +++ b/examples/eos_bulk/MACE-MP(M)_processed.parquet @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:73e2b8ad6d5e114c1c0fea4697b17810182d0b273185512cb40fa894ea30b4c3 -size 371128 +oid sha256:7e5507cdc5fe558b5d3fe2ea8f1dd577ac444e82c5347b5fbe738a4f855dffcb +size 397379 diff --git a/examples/eos_bulk/MACE-MPA_processed.parquet b/examples/eos_bulk/MACE-MPA_processed.parquet index 1d8633f40c3fbef7e6de06e63d677dd94b5cd60f..9ba5d9f8195023b64e40ac524adb6806ae4e32f0 100644 --- a/examples/eos_bulk/MACE-MPA_processed.parquet +++ b/examples/eos_bulk/MACE-MPA_processed.parquet @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:819bc0c721e99df8dda0a4c6df565deb96736ecc5ceefefe300e5b72b7d6312f -size 365412 +oid sha256:0f3032d5a156febdd9580fa3d86cb1a84236374bcac6ccb22d18a948767db502 +size 394748 diff --git a/examples/eos_bulk/MatterSim_processed.parquet b/examples/eos_bulk/MatterSim_processed.parquet index b0a49e14a749f14fc60bd12fb239260f68c89de2..5c7c372a85d6f4bef844bd6dbe68bf4692ee9344 100644 --- a/examples/eos_bulk/MatterSim_processed.parquet +++ b/examples/eos_bulk/MatterSim_processed.parquet @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:c707ffb285f03a5c7d1486a6998c787088f07a97b206585b17839fff4fab49b4 -size 321086 +oid sha256:fb1f10a60495f5e88ea8cf737fd7b47d1c471fda422374ee519d14f531c732f8 +size 290191 diff --git a/examples/eos_bulk/ORBv2_processed.parquet b/examples/eos_bulk/ORBv2_processed.parquet index 43bb186747a5d6d51530c904bf2594ab8489df2f..82bb16f1d7a7ea63f852f9d6f1657e75abc9784f 100644 --- a/examples/eos_bulk/ORBv2_processed.parquet +++ b/examples/eos_bulk/ORBv2_processed.parquet @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:f794da83d1031823577b085c480f7d285520c086bdd0e7e6e7acb7a5a2457329 -size 228052 +oid sha256:7eb0a3060b8a2d3541b8fb1083176c88aae0a8be0008e84d5770998b01742216 +size 402554 diff --git a/examples/eos_bulk/SevenNet_processed.parquet b/examples/eos_bulk/SevenNet_processed.parquet index 7822e1ef635033b13be0a3c34495e194f3d98917..0d4a9920f62dd4340134b6a0ab95e77c03b6e181 100644 --- a/examples/eos_bulk/SevenNet_processed.parquet +++ b/examples/eos_bulk/SevenNet_processed.parquet @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:a9aab95402aa62169ba6f1e12a7774362b3e5cc027f5c556de734783e6d6f29b -size 364969 +oid sha256:9f484928f5086e8d1411a198ac69bfe44313597909b72c8676e9131cce1660f1 +size 398295 diff --git a/examples/eos_bulk/analyze.py b/examples/eos_bulk/analyze.py new file mode 100644 index 0000000000000000000000000000000000000000..accaee8ca28547e6d06cb8f0c6ae830d93eaa614 --- /dev/null +++ b/examples/eos_bulk/analyze.py @@ -0,0 +1,223 @@ +from pathlib import Path + +import numpy as np +import pandas as pd +from ase.db import connect +from scipy import stats + +from mlip_arena.models import REGISTRY, MLIPEnum + +DATA_DIR = Path(__file__).parent.absolute() + + +def load_wbm_structures(): + """ + Load the WBM structures from a ASE DB file. + """ + with connect(DATA_DIR.parent / "wbm_structures.db") as db: + for row in db.select(): + yield row.toatoms(add_additional_information=True) + +def gather_results(): + for model in MLIPEnum: + if "eos_bulk" not in REGISTRY[model.name].get("gpu-tasks", []): + continue + + if (DATA_DIR / f"{model.name}.parquet").exists(): + continue + + all_data = [] + + for atoms in load_wbm_structures(): + fpath = Path(model.name) / f"{atoms.info['key_value_pairs']['wbm_id']}.pkl" + if not fpath.exists(): + continue + + all_data.append(pd.read_pickle(fpath)) + + df = pd.concat(all_data, ignore_index=True) + df.to_parquet(DATA_DIR / f"{model.name}.parquet") + + +def summarize(): + summary_table = pd.DataFrame( + columns=[ + "model", + "energy-diff-flip-times", + "tortuosity", + "spearman-compression-energy", + "spearman-compression-derivative", + "spearman-tension-energy", + "missing", + ] + ) + + + for model in MLIPEnum: + fpath = DATA_DIR / f"{model.name}.parquet" + if not fpath.exists(): + continue + df_raw_results = pd.read_parquet(fpath) + + df_analyzed = pd.DataFrame( + columns=[ + "model", + "structure", + "formula", + "volume-ratio", + "energy-delta-per-atom", + "energy-diff-flip-times", + "energy-delta-per-volume-b0", + "tortuosity", + "spearman-compression-energy", + "spearman-compression-derivative", + "spearman-tension-energy", + "missing", + ] + ) + + for wbm_struct in load_wbm_structures(): + structure_id = wbm_struct.info["key_value_pairs"]["wbm_id"] + + try: + results = df_raw_results.loc[df_raw_results["id"] == structure_id] + b0 = results["b0"].values[0] + # vol0 = results["v0"].values[0] + results = results["eos"].values[0] + es = np.array(results["energies"]) + vols = np.array(results["volumes"]) + + indices = np.argsort(vols) + vols = vols[indices] + es = es[indices] + + imine = len(es) // 2 + # min_center_val = np.min(es[imid - 1 : imid + 2]) + # imine = np.where(es == min_center_val)[0][0] + emin = es[imine] + vol0 = vols[imine] + + interpolated_volumes = [ + (vols[i] + vols[i + 1]) / 2 for i in range(len(vols) - 1) + ] + ediff = np.diff(es) + ediff_sign = np.sign(ediff) + mask = ediff_sign != 0 + ediff = ediff[mask] + ediff_sign = ediff_sign[mask] + ediff_flip = np.diff(ediff_sign) != 0 + + etv = np.sum(np.abs(np.diff(es))) + + data = { + "model": model.name, + "structure": structure_id, + "formula": wbm_struct.get_chemical_formula(), + "missing": False, + "volume-ratio": vols / vol0, + "energy-delta-per-atom": (es - emin) / len(wbm_struct), + "energy-diff-flip-times": np.sum(ediff_flip).astype(int), + "energy-delta-per-volume-b0": (es - emin) / (b0*vol0), + "tortuosity": etv / (abs(es[0] - emin) + abs(es[-1] - emin)), + "spearman-compression-energy": stats.spearmanr( + vols[:imine], es[:imine] + ).statistic, + "spearman-compression-derivative": stats.spearmanr( + interpolated_volumes[:imine], ediff[:imine] + ).statistic, + "spearman-tension-energy": stats.spearmanr( + vols[imine:], es[imine:] + ).statistic, + } + + except Exception as e: + print(e) + data = { + "model": model.name, + "structure": structure_id, + "formula": wbm_struct.get_chemical_formula(), + "missing": True, + "volume-ratio": None, + "energy-delta-per-atom": None, + "energy-delta-per-volume-b0": None, + "energy-diff-flip-times": None, + "tortuosity": None, + "spearman-compression-energy": None, + "spearman-compression-derivative": None, + "spearman-tension-energy": None, + } + + df_analyzed = pd.concat([df_analyzed, pd.DataFrame([data])], ignore_index=True) + + df_analyzed.to_parquet(DATA_DIR / f"{model.name}_processed.parquet") + # json_fpath = DATA_DIR / f"EV_scan_analyzed_{model.name}.json" + + # df_analyzed.to_json(json_fpath, orient="records") + + valid_results = df_analyzed[df_analyzed["missing"] == False] + + analysis_summary = { + "model": model.name, + "energy-diff-flip-times": valid_results["energy-diff-flip-times"].mean(), + "energy-diff-flip-times-std": valid_results["energy-diff-flip-times"].std(), + "tortuosity": valid_results["tortuosity"].mean(), + "tortuosity-std": valid_results["tortuosity"].std(), + "spearman-compression-energy": valid_results[ + "spearman-compression-energy" + ].mean(), + "spearman-compression-energy-std": valid_results["spearman-compression-energy"].std(), + "spearman-compression-derivative": valid_results[ + "spearman-compression-derivative" + ].mean(), + "spearman-compression-derivative-std": valid_results[ + "spearman-compression-derivative" + ].std(), + "spearman-tension-energy": valid_results["spearman-tension-energy"].mean(), + "spearman-tension-energy-std": valid_results["spearman-tension-energy"].std(), + "missing": len(df_analyzed[df_analyzed["missing"] == True]), + } + summary_table = pd.concat( + [summary_table, pd.DataFrame([analysis_summary])], ignore_index=True + ) + + + flip_rank = ( + (summary_table["energy-diff-flip-times"] - 1) + .abs() + .rank(ascending=True, method="min") + ) + tortuosity_rank = summary_table["tortuosity"].rank(ascending=True, method="min") + spearman_compression_energy_rank = summary_table["spearman-compression-energy"].rank( + method="min" + ) + spearman_compression_derivative_rank = summary_table[ + "spearman-compression-derivative" + ].rank(ascending=False, method="min") + spearman_tension_energy_rank = summary_table["spearman-tension-energy"].rank( + ascending=False, method="min" + ) + missing_rank = summary_table["missing"].rank(ascending=True, method="min") + + rank_aggr = ( + flip_rank + + tortuosity_rank + + spearman_compression_energy_rank + + spearman_compression_derivative_rank + + spearman_tension_energy_rank + + missing_rank + ) + rank = rank_aggr.rank(method="min") + + summary_table.insert(1, "rank", rank.astype(int)) + summary_table.insert(2, "rank-aggregation", rank_aggr.astype(int)) + summary_table = summary_table.sort_values(by="rank", ascending=True) + summary_table = summary_table.reset_index(drop=True) + + summary_table.to_csv(DATA_DIR / "summary.csv", index=False) + summary_table.to_latex(DATA_DIR / "summary.tex", index=False, float_format="%.3f") + + return summary_table + +if __name__ == "__main__": + gather_results() + summarize() diff --git a/examples/eos_bulk/eSEN_processed.parquet b/examples/eos_bulk/eSEN_processed.parquet index 779707efffac9c134857cac78b9ad18422dfc018..96b954d9ea717afb44fcafc6d734f4ddd9d6efa6 100644 --- a/examples/eos_bulk/eSEN_processed.parquet +++ b/examples/eos_bulk/eSEN_processed.parquet @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:3d12b36a2bd465e16ada4363e31756d5de5d41dd890d0e88e8ca86b76dd66336 -size 313235 +oid sha256:d7f754d8e18f645c1608e86286245c11611d5af34f3bd0bbc4a5b63b851a0dee +size 393790 diff --git a/examples/eos_bulk/plot.py b/examples/eos_bulk/plot.py new file mode 100644 index 0000000000000000000000000000000000000000..d88e3fc14908b47b31cad61ec86be7b11d0ecb91 --- /dev/null +++ b/examples/eos_bulk/plot.py @@ -0,0 +1,119 @@ +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from ase.db import connect + +from mlip_arena.models import REGISTRY as MODELS + +DATA_DIR = Path(__file__).parent.absolute() + +# Use a qualitative color palette from matplotlib +palette_name = "tab10" # Better for distinguishing multiple lines +color_sequence = plt.get_cmap(palette_name).colors + +valid_models = [ + model + for model, metadata in MODELS.items() + if "eos_bulk" in metadata.get("gpu-tasks", []) +] + +def load_wbm_structures(): + """ + Load the WBM structures from a ASE DB file. + """ + with connect(DATA_DIR.parent / "wbm_structures.db") as db: + for row in db.select(): + yield row.toatoms(add_additional_information=True) + +# # Collect valid models first +# valid_models = [] +# for model_name in valid_models: +# fpath = DATA_DIR / f"{model_name}_processed.parquet" +# if fpath.exists(): +# df = pd.read_parquet(fpath) +# if len(df) > 0: +# valid_models.append(model) + +# # Ensure we're showing all 8 models +# if len(valid_models) < 8: +# print(f"Warning: Only found {len(valid_models)} valid models instead of 8") + +# Set up the grid layout +n_models = len(valid_models) +n_cols = 4 # Use 4 columns +n_rows = (n_models + n_cols - 1) // n_cols # Ceiling division to get required rows + +# Create figure with enough space for all subplots +fig = plt.figure( + figsize=(6, 1.25 * n_rows), # Wider for better readability + constrained_layout=True, # Better than tight_layout for this case +) + +# Create grid of subplots +axes = [] +for i in range(n_models): + ax = plt.subplot(n_rows, n_cols, i+1) + axes.append(ax) + +SMALL_SIZE = 6 +MEDIUM_SIZE = 8 +LARGE_SIZE = 10 + +# Fill in the subplots with data +for i, model_name in enumerate(valid_models): + fpath = DATA_DIR / f"{model_name}_processed.parquet" + df = pd.read_parquet(fpath) + + ax = axes[i] + valid_structures = [] + + for j, (_, row) in enumerate(df.iterrows()): + structure_id = row["structure"] + formula = row.get("formula", "") + if isinstance(row["volume-ratio"], (list, np.ndarray)) and isinstance( + row["energy-delta-per-volume-b0"], (list, np.ndarray) + ): + vol_strain = row["volume-ratio"] + energy_delta = row["energy-delta-per-volume-b0"] + color = color_sequence[j % len(color_sequence)] + ax.plot( + vol_strain, + energy_delta, + color=color, + linewidth=1, + alpha=0.9, + ) + valid_structures.append(structure_id) + + # Set subplot title + ax.set_title(f"{model_name} ({len(valid_structures)})", fontsize=MEDIUM_SIZE) + + # Only add y-label to leftmost plots (those with index divisible by n_cols) + if i % n_cols == 0: + ax.set_ylabel("$\\frac{\\Delta E}{B V_0}$", fontsize=MEDIUM_SIZE) + else: + ax.set_ylabel("") + + # Only add x-label to bottom row plots + # Check if this plot is in the bottom row + is_bottom_row = (i // n_cols) == (n_rows - 1) or (i >= n_models - n_cols) + if is_bottom_row: + ax.set_xlabel("$V/V_0$", fontsize=MEDIUM_SIZE) + else: + ax.set_xlabel("") + + ax.set_ylim(-0.02, 0.1) # Consistent y-limits + ax.axvline(x=1, linestyle="--", color="gray", alpha=0.7) + ax.tick_params(axis="both", which="major", labelsize=MEDIUM_SIZE) + +# Make sure all subplots share the x and y limits +for ax in axes: + ax.set_xlim(0.8, 1.2) # Adjust these as needed + ax.set_ylim(-0.02, 0.1) + +# Save the figure with all plots +plt.savefig(DATA_DIR / "eos-bulk-grid.png", dpi=300, bbox_inches="tight") +plt.savefig(DATA_DIR / "eos-bulk-grid.pdf", bbox_inches="tight") +# plt.show() \ No newline at end of file diff --git a/examples/eos_bulk/summary.csv b/examples/eos_bulk/summary.csv index ad7649be05fcca9f6b09402cc5d8b520145e4de2..ff414cbbb09846d4b25c43ca839d719527fcabe8 100644 --- a/examples/eos_bulk/summary.csv +++ b/examples/eos_bulk/summary.csv @@ -1,9 +1,9 @@ -model,rank,rank-aggregation,energy-diff-flip-times,tortuosity,spearman-compression-energy,spearman-compression-derivative,spearman-tension-energy,missing -MACE-MPA,1,7,1.0370741482965933,1.005455197941088,-0.9993684338373716,0.9963320580555048,0.993186372745491,2 -eSEN,2,15,1.042211055276382,1.0082267858369258,-0.9993299832495811,0.9968570123343992,0.9920968478757424,5 -MACE-MP(M),3,20,1.042211055276382,1.008986842539345,-0.999329983249581,0.9941160347190496,0.9915857612939804,5 -MatterSim,4,22,1.045135406218656,1.0060900449752808,-0.99734962463147,0.9927904926901917,0.9880977115916667,3 -CHGNet,5,27,1.1053159478435306,1.014753469076796,-0.9964985866690981,0.9929971733381963,0.9866417434120545,3 -SevenNet,6,32,1.1093279839518555,1.0186969977862483,-0.9981277164827815,0.9889121911188109,0.9859580417030127,3 -M3GNet,7,38,1.1748743718592964,1.0175007963267957,-0.9963209989340641,0.9897426526572255,0.9801690217498693,5 -ORBv2,8,48,1.3162134944612287,1.0374718753890275,-0.9918459519667977,0.9701425127407,0.9637462235649547,7 +model,rank,rank-aggregation,energy-diff-flip-times,tortuosity,spearman-compression-energy,spearman-compression-derivative,spearman-tension-energy,missing,energy-diff-flip-times-std,tortuosity-std,spearman-compression-energy-std,spearman-compression-derivative-std,spearman-tension-energy-std +MACE-MPA,1,7,1.0370741482965933,1.005455197941088,-0.9993684338373716,0.9963320580555048,0.993186372745491,2,0.28260902337559207,0.05365793240575371,0.01247051827833709,0.03852356148675327,0.07744103059608153 +eSEN,2,15,1.042211055276382,1.0082267858369258,-0.9993299832495811,0.9968570123343992,0.9920968478757424,5,0.31435657463218236,0.09009343693321628,0.012254373724862471,0.03683033142639347,0.07346152527758068 +MACE-MP(M),3,20,1.042211055276382,1.008986842539345,-0.999329983249581,0.9941160347190496,0.9915857612939804,5,0.3448779209525529,0.1291612188691875,0.011378760248143813,0.059100297945675236,0.0879944289437058 +MatterSim,4,22,1.045135406218656,1.0060900449752808,-0.99734962463147,0.9927904926901917,0.9880977115916667,3,0.376211439097473,0.055231139063835144,0.03888149868978045,0.07797796889169765,0.11523169167576831 +CHGNet,5,27,1.1053159478435306,1.014753469076796,-0.9964985866690981,0.9929971733381963,0.9866417434120545,3,0.5395426308257233,0.12255069852201543,0.051058628207987275,0.05245296840977506,0.11650035215147045 +SevenNet,6,32,1.1093279839518555,1.0186969977862483,-0.9981277164827815,0.9889121911188109,0.9859580417030127,3,0.5552625647497746,0.2749956370938698,0.025627387238441334,0.07657439969351316,0.11715556163493943 +M3GNet,7,38,1.1748743718592964,1.0175007963267957,-0.9963209989340641,0.9897426526572255,0.9801690217498693,5,0.6755070078112404,0.1488462321310986,0.05166730302469224,0.06468954475570607,0.1332400556108672 +ORBv2,8,48,1.3162134944612287,1.0374718753890275,-0.9918459519667977,0.9701425127407,0.9637462235649547,7,0.8699212994733451,0.2149179445701606,0.08208261674781654,0.1315974423719716,0.19758814985102582 diff --git a/examples/eos_bulk/summary.tex b/examples/eos_bulk/summary.tex index 3c60d84ea7d978d766d31dcbf98c85e7b2e6e8d7..708918a4b061a9590625eeff08bc9283d17278b7 100644 --- a/examples/eos_bulk/summary.tex +++ b/examples/eos_bulk/summary.tex @@ -1,14 +1,14 @@ -\begin{tabular}{lrrrrrrrl} +\begin{tabular}{lrrrrrrrlrrrrr} \toprule -model & rank & rank-aggregation & energy-diff-flip-times & tortuosity & spearman-compression-energy & spearman-compression-derivative & spearman-tension-energy & missing \\ +model & rank & rank-aggregation & energy-diff-flip-times & tortuosity & spearman-compression-energy & spearman-compression-derivative & spearman-tension-energy & missing & energy-diff-flip-times-std & tortuosity-std & spearman-compression-energy-std & spearman-compression-derivative-std & spearman-tension-energy-std \\ \midrule -MACE-MPA & 1 & 7 & 1.037074 & 1.005455 & -0.999368 & 0.996332 & 0.993186 & 2 \\ -eSEN & 2 & 15 & 1.042211 & 1.008227 & -0.999330 & 0.996857 & 0.992097 & 5 \\ -MACE-MP(M) & 3 & 20 & 1.042211 & 1.008987 & -0.999330 & 0.994116 & 0.991586 & 5 \\ -MatterSim & 4 & 22 & 1.045135 & 1.006090 & -0.997350 & 0.992790 & 0.988098 & 3 \\ -CHGNet & 5 & 27 & 1.105316 & 1.014753 & -0.996499 & 0.992997 & 0.986642 & 3 \\ -SevenNet & 6 & 32 & 1.109328 & 1.018697 & -0.998128 & 0.988912 & 0.985958 & 3 \\ -M3GNet & 7 & 38 & 1.174874 & 1.017501 & -0.996321 & 0.989743 & 0.980169 & 5 \\ -ORBv2 & 8 & 48 & 1.316213 & 1.037472 & -0.991846 & 0.970143 & 0.963746 & 7 \\ +MACE-MPA & 1 & 7 & 1.037 & 1.005 & -0.999 & 0.996 & 0.993 & 2 & 0.283 & 0.054 & 0.012 & 0.039 & 0.077 \\ +eSEN & 2 & 15 & 1.042 & 1.008 & -0.999 & 0.997 & 0.992 & 5 & 0.314 & 0.090 & 0.012 & 0.037 & 0.073 \\ +MACE-MP(M) & 3 & 20 & 1.042 & 1.009 & -0.999 & 0.994 & 0.992 & 5 & 0.345 & 0.129 & 0.011 & 0.059 & 0.088 \\ +MatterSim & 4 & 22 & 1.045 & 1.006 & -0.997 & 0.993 & 0.988 & 3 & 0.376 & 0.055 & 0.039 & 0.078 & 0.115 \\ +CHGNet & 5 & 27 & 1.105 & 1.015 & -0.996 & 0.993 & 0.987 & 3 & 0.540 & 0.123 & 0.051 & 0.052 & 0.117 \\ +SevenNet & 6 & 32 & 1.109 & 1.019 & -0.998 & 0.989 & 0.986 & 3 & 0.555 & 0.275 & 0.026 & 0.077 & 0.117 \\ +M3GNet & 7 & 38 & 1.175 & 1.018 & -0.996 & 0.990 & 0.980 & 5 & 0.676 & 0.149 & 0.052 & 0.065 & 0.133 \\ +ORBv2 & 8 & 48 & 1.316 & 1.037 & -0.992 & 0.970 & 0.964 & 7 & 0.870 & 0.215 & 0.082 & 0.132 & 0.198 \\ \bottomrule \end{tabular} diff --git a/examples/mof/CHGNet.pkl b/examples/mof/CHGNet.pkl index ba239d09b60bf50bef1fb716d958205bef87ab81..b9c7abefba2711cdf92f88e53658199c59278d8f 100644 Binary files a/examples/mof/CHGNet.pkl and b/examples/mof/CHGNet.pkl differ diff --git a/examples/mof/M3GNet.pkl b/examples/mof/M3GNet.pkl index a00a412f7ab591117fc1ea9cc5a4b4b083b3ef4d..d19fc09b1a6e49a0efacc623cb520459917837b5 100644 Binary files a/examples/mof/M3GNet.pkl and b/examples/mof/M3GNet.pkl differ diff --git a/examples/mof/MACE-MP(M).pkl b/examples/mof/MACE-MP(M).pkl index 5de6a25a849f309a4691a87c50da9f6c6a7b8731..4023d4b015698afc4ad0fcf21d69430219081572 100644 Binary files a/examples/mof/MACE-MP(M).pkl and b/examples/mof/MACE-MP(M).pkl differ diff --git a/examples/mof/MACE-MPA.pkl b/examples/mof/MACE-MPA.pkl index 255306a14ecdb84494517e9ae9c000dd22898561..87cd7ee99c8d7b8baf40e2277bb79074e254f20f 100644 Binary files a/examples/mof/MACE-MPA.pkl and b/examples/mof/MACE-MPA.pkl differ diff --git a/examples/mof/MatterSim.pkl b/examples/mof/MatterSim.pkl index b38b4eabded96d63429d1e1905b14dddf5aea3e4..f377436aa2a356e5343d5876d0262bc973b464b2 100644 Binary files a/examples/mof/MatterSim.pkl and b/examples/mof/MatterSim.pkl differ diff --git a/examples/mof/ORBv2.pkl b/examples/mof/ORBv2.pkl index 15501a1fec001a598b2807125a7683cb8e01a95d..945dc52468b4b47304c801de3fa56ef094cf75f7 100644 Binary files a/examples/mof/ORBv2.pkl and b/examples/mof/ORBv2.pkl differ diff --git a/examples/mof/structures/dac/SGU-29.cif b/examples/mof/structures/dac/SGU-29.cif new file mode 100644 index 0000000000000000000000000000000000000000..fb15f604b05ccdc345c547ca5cc415b848701fef --- /dev/null +++ b/examples/mof/structures/dac/SGU-29.cif @@ -0,0 +1,995 @@ +data_sgu-29b_1\(2) +_audit_creation_date 2025-02-06 +_audit_creation_method 'Materials Studio' +_symmetry_space_group_name_H-M 'P1' +_symmetry_Int_Tables_number 1 +_symmetry_cell_setting triclinic +loop_ +_symmetry_equiv_pos_as_xyz + x,y,z +_cell_length_a 20.8200 +_cell_length_b 20.8190 +_cell_length_c 14.6970 +_cell_angle_alpha 90.0000 +_cell_angle_beta 110.7300 +_cell_angle_gamma 90.0000 +loop_ +_atom_site_label +_atom_site_type_symbol +_atom_site_fract_x +_atom_site_fract_y +_atom_site_fract_z +_atom_site_U_iso_or_equiv +_atom_site_adp_type +_atom_site_occupancy +Si1 Si 0.24909 0.06259 0.24949 0.00000 Uiso 1.00 +O2 O 0.37297 0.87246 0.00024 0.00000 Uiso 1.00 +O3 O 0.31082 0.10699 0.31523 0.00000 Uiso 1.00 +O4 O 0.43778 0.35840 0.18606 0.00000 Uiso 1.00 +O5 O 0.53049 0.76657 0.18649 0.00000 Uiso 1.00 +O6 O 0.43915 0.85680 0.18418 0.00000 Uiso 1.00 +O7 O 0.18809 0.10887 0.18562 0.00000 Uiso 1.00 +O8 O 0.21990 0.01612 0.31305 0.00000 Uiso 1.00 +O9 O 0.27771 0.01820 0.18331 0.00000 Uiso 1.00 +O10 O 0.47221 0.26806 0.31627 0.00000 Uiso 1.00 +O11 O 0.24662 0.99791 0.99886 0.00000 Uiso 1.00 +O12 O 0.49794 0.25289 0.50055 0.00000 Uiso 1.00 +O13 O 0.12201 0.12220 0.99919 0.00000 Uiso 1.00 +Cu14 Cu 0.37361 0.37328 0.50133 0.00000 Uiso 1.00 +Si15 Si 0.25762 0.96125 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Uiso 1.00 +Si135 Si 0.16922 0.66403 0.10429 0.00000 Uiso 1.00 +O136 O 0.10610 0.83193 0.10380 0.00000 Uiso 1.00 +O137 O 0.17190 0.42030 0.36730 0.00000 Uiso 1.00 +O138 O 0.01220 0.95070 0.39550 0.00000 Uiso 1.00 +O139 O 0.11350 0.70540 0.13340 0.00000 Uiso 1.00 +O140 O 0.30640 0.42010 0.39690 0.00000 Uiso 1.00 +O141 O 0.42490 0.53850 0.39610 0.00000 Uiso 1.00 +O142 O 0.36950 0.46040 0.13490 0.00000 Uiso 1.00 +O143 O 0.17910 0.29310 0.39560 0.00000 Uiso 1.00 +O144 O 0.23330 0.70480 0.10460 0.00000 Uiso 1.00 +O145 O 0.35240 0.58654 0.10550 0.00000 Uiso 1.00 +O146 O 0.06000 0.17410 0.39490 0.00000 Uiso 1.00 +O147 O 0.92700 0.16390 0.36630 0.00000 Uiso 1.00 +Na148 Na 0.73579 0.31238 0.19330 0.00000 Uiso 1.00 +Na149 Na 0.88750 0.93394 0.31347 0.00000 Uiso 1.00 +Na150 Na 0.85635 0.19124 0.18888 0.00000 Uiso 1.00 +Si151 Si 0.75091 0.93741 0.75051 0.00000 Uiso 1.00 +O152 O 0.62703 0.12754 0.99976 0.00000 Uiso 1.00 +O153 O 0.68918 0.89301 0.68477 0.00000 Uiso 1.00 +O154 O 0.56222 0.64160 0.81394 0.00000 Uiso 1.00 +O155 O 0.46951 0.23343 0.81351 0.00000 Uiso 1.00 +O156 O 0.56085 0.14320 0.81582 0.00000 Uiso 1.00 +O157 O 0.81191 0.89113 0.81438 0.00000 Uiso 1.00 +O158 O 0.78010 0.98388 0.68695 0.00000 Uiso 1.00 +O159 O 0.72229 0.98180 0.81669 0.00000 Uiso 1.00 +O160 O 0.52779 0.73194 0.68373 0.00000 Uiso 1.00 +O161 O 0.75338 0.00209 0.00114 0.00000 Uiso 1.00 +O162 O 0.50206 0.74711 0.49945 0.00000 Uiso 1.00 +O163 O 0.87799 0.87780 0.00081 0.00000 Uiso 1.00 +Cu164 Cu 0.62639 0.62672 0.49867 0.00000 Uiso 1.00 +Si165 Si 0.74238 0.03875 0.89661 0.00000 Uiso 1.00 +Si166 Si 0.63794 0.14302 0.89614 0.00000 Uiso 1.00 +Si167 Si 0.81580 0.98011 0.60487 0.00000 Uiso 1.00 +Si168 Si 0.56486 0.73161 0.60369 0.00000 Uiso 1.00 +Si169 Si 0.49378 0.28949 0.89573 0.00000 Uiso 1.00 +Si170 Si 0.57889 0.58560 0.89580 0.00000 Uiso 1.00 +Si171 Si 0.88840 0.89500 0.89626 0.00000 Uiso 1.00 +Si172 Si 0.66922 0.83597 0.60429 0.00000 Uiso 1.00 +O173 O 0.60610 0.66807 0.60380 0.00000 Uiso 1.00 +O174 O 0.67190 0.07970 0.86730 0.00000 Uiso 1.00 +O175 O 0.51220 0.54930 0.89550 0.00000 Uiso 1.00 +O176 O 0.61350 0.79460 0.63340 0.00000 Uiso 1.00 +O177 O 0.80640 0.07990 0.89690 0.00000 Uiso 1.00 +O178 O 0.92490 0.96150 0.89610 0.00000 Uiso 1.00 +O179 O 0.86950 0.03960 0.63490 0.00000 Uiso 1.00 +O180 O 0.67910 0.20690 0.89560 0.00000 Uiso 1.00 +O181 O 0.73330 0.79520 0.60460 0.00000 Uiso 1.00 +O182 O 0.85240 0.91346 0.60550 0.00000 Uiso 1.00 +O183 O 0.56000 0.32590 0.89490 0.00000 Uiso 1.00 +O184 O 0.42700 0.33610 0.86630 0.00000 Uiso 1.00 +Na185 Na 0.23579 0.18762 0.69330 0.00000 Uiso 1.00 +Na186 Na 0.38750 0.56606 0.81347 0.00000 Uiso 1.00 +Na187 Na 0.35635 0.30876 0.68888 0.00000 Uiso 1.00 +Si188 Si 0.25091 0.43741 0.75051 0.00000 Uiso 1.00 +O189 O 0.12703 0.62754 0.99976 0.00000 Uiso 1.00 +O190 O 0.18918 0.39301 0.68477 0.00000 Uiso 1.00 +O191 O 0.06222 0.14160 0.81394 0.00000 Uiso 1.00 +O192 O 0.96951 0.73343 0.81351 0.00000 Uiso 1.00 +O193 O 0.06085 0.64320 0.81582 0.00000 Uiso 1.00 +O194 O 0.31191 0.39113 0.81438 0.00000 Uiso 1.00 +O195 O 0.28010 0.48388 0.68695 0.00000 Uiso 1.00 +O196 O 0.22229 0.48180 0.81669 0.00000 Uiso 1.00 +O197 O 0.02779 0.23194 0.68373 0.00000 Uiso 1.00 +O198 O 0.25338 0.50209 0.00114 0.00000 Uiso 1.00 +O199 O 0.00206 0.24711 0.49945 0.00000 Uiso 1.00 +O200 O 0.37799 0.37780 0.00081 0.00000 Uiso 1.00 +Cu201 Cu 0.12639 0.12672 0.49867 0.00000 Uiso 1.00 +Si202 Si 0.24238 0.53875 0.89661 0.00000 Uiso 1.00 +Si203 Si 0.13794 0.64302 0.89614 0.00000 Uiso 1.00 +Si204 Si 0.31580 0.48011 0.60487 0.00000 Uiso 1.00 +Si205 Si 0.06486 0.23161 0.60369 0.00000 Uiso 1.00 +Si206 Si 0.99378 0.78949 0.89573 0.00000 Uiso 1.00 +Si207 Si 0.07889 0.08560 0.89580 0.00000 Uiso 1.00 +Si208 Si 0.38840 0.39500 0.89626 0.00000 Uiso 1.00 +Si209 Si 0.16922 0.33597 0.60429 0.00000 Uiso 1.00 +O210 O 0.10610 0.16807 0.60380 0.00000 Uiso 1.00 +O211 O 0.17190 0.57970 0.86730 0.00000 Uiso 1.00 +O212 O 0.01220 0.04930 0.89550 0.00000 Uiso 1.00 +O213 O 0.11350 0.29460 0.63340 0.00000 Uiso 1.00 +O214 O 0.30640 0.57990 0.89690 0.00000 Uiso 1.00 +O215 O 0.42490 0.46150 0.89610 0.00000 Uiso 1.00 +O216 O 0.36950 0.53960 0.63490 0.00000 Uiso 1.00 +O217 O 0.17910 0.70690 0.89560 0.00000 Uiso 1.00 +O218 O 0.23330 0.29520 0.60460 0.00000 Uiso 1.00 +O219 O 0.35240 0.41346 0.60550 0.00000 Uiso 1.00 +O220 O 0.06000 0.82590 0.89490 0.00000 Uiso 1.00 +O221 O 0.92700 0.83610 0.86630 0.00000 Uiso 1.00 +Na222 Na 0.73579 0.68762 0.69330 0.00000 Uiso 1.00 +Na223 Na 0.88750 0.06606 0.81347 0.00000 Uiso 1.00 +Na224 Na 0.85635 0.80876 0.68888 0.00000 Uiso 1.00 +Na225 Na 0.01010 0.43710 0.79050 0.00000 Uiso 1.00 +Si226 Si 0.24909 0.93741 0.74949 0.00000 Uiso 1.00 +O227 O 0.37297 0.12754 0.50024 0.00000 Uiso 1.00 +O228 O 0.31082 0.89301 0.81523 0.00000 Uiso 1.00 +O229 O 0.43778 0.64160 0.68606 0.00000 Uiso 1.00 +O230 O 0.53049 0.23343 0.68649 0.00000 Uiso 1.00 +O231 O 0.43915 0.14320 0.68418 0.00000 Uiso 1.00 +O232 O 0.18809 0.89113 0.68562 0.00000 Uiso 1.00 +O233 O 0.21990 0.98388 0.81305 0.00000 Uiso 1.00 +O234 O 0.27771 0.98180 0.68331 0.00000 Uiso 1.00 +O235 O 0.47221 0.73194 0.81627 0.00000 Uiso 1.00 +O236 O 0.24662 0.00209 0.49886 0.00000 Uiso 1.00 +O237 O 0.49794 0.74711 0.00055 0.00000 Uiso 1.00 +O238 O 0.12201 0.87780 0.49919 0.00000 Uiso 1.00 +Cu239 Cu 0.37361 0.62672 0.00133 0.00000 Uiso 1.00 +Si240 Si 0.25762 0.03875 0.60339 0.00000 Uiso 1.00 +Si241 Si 0.36206 0.14302 0.60386 0.00000 Uiso 1.00 +Si242 Si 0.18420 0.98011 0.89513 0.00000 Uiso 1.00 +Si243 Si 0.43514 0.73161 0.89631 0.00000 Uiso 1.00 +Si244 Si 0.50622 0.28949 0.60427 0.00000 Uiso 1.00 +Si245 Si 0.42111 0.58560 0.60420 0.00000 Uiso 1.00 +Si246 Si 0.11160 0.89500 0.60374 0.00000 Uiso 1.00 +Si247 Si 0.33078 0.83597 0.89571 0.00000 Uiso 1.00 +O248 O 0.39390 0.66807 0.89620 0.00000 Uiso 1.00 +O249 O 0.32810 0.07970 0.63270 0.00000 Uiso 1.00 +O250 O 0.48780 0.54930 0.60450 0.00000 Uiso 1.00 +O251 O 0.38650 0.79460 0.86660 0.00000 Uiso 1.00 +O252 O 0.19360 0.07990 0.60310 0.00000 Uiso 1.00 +O253 O 0.07510 0.96150 0.60390 0.00000 Uiso 1.00 +O254 O 0.13050 0.03960 0.86510 0.00000 Uiso 1.00 +O255 O 0.32090 0.20690 0.60440 0.00000 Uiso 1.00 +O256 O 0.26670 0.79520 0.89540 0.00000 Uiso 1.00 +O257 O 0.14760 0.91346 0.89450 0.00000 Uiso 1.00 +O258 O 0.44000 0.32590 0.60510 0.00000 Uiso 1.00 +O259 O 0.57300 0.33610 0.63370 0.00000 Uiso 1.00 +Na260 Na 0.76421 0.18762 0.80670 0.00000 Uiso 1.00 +Na261 Na 0.61250 0.56606 0.68653 0.00000 Uiso 1.00 +Na262 Na 0.64365 0.30876 0.81112 0.00000 Uiso 1.00 +Na263 Na 0.48990 0.93710 0.70950 0.00000 Uiso 1.00 +Si264 Si 0.74909 0.43741 0.74949 0.00000 Uiso 1.00 +O265 O 0.87297 0.62754 0.50024 0.00000 Uiso 1.00 +O266 O 0.81082 0.39301 0.81523 0.00000 Uiso 1.00 +O267 O 0.93778 0.14160 0.68606 0.00000 Uiso 1.00 +O268 O 0.03049 0.73343 0.68649 0.00000 Uiso 1.00 +O269 O 0.93915 0.64320 0.68418 0.00000 Uiso 1.00 +O270 O 0.68809 0.39113 0.68562 0.00000 Uiso 1.00 +O271 O 0.71990 0.48388 0.81305 0.00000 Uiso 1.00 +O272 O 0.77771 0.48180 0.68331 0.00000 Uiso 1.00 +O273 O 0.97221 0.23194 0.81627 0.00000 Uiso 1.00 +O274 O 0.74662 0.50209 0.49886 0.00000 Uiso 1.00 +O275 O 0.99794 0.24711 0.00055 0.00000 Uiso 1.00 +O276 O 0.62201 0.37780 0.49919 0.00000 Uiso 1.00 +Cu277 Cu 0.87361 0.12672 0.00133 0.00000 Uiso 1.00 +Si278 Si 0.75762 0.53875 0.60339 0.00000 Uiso 1.00 +Si279 Si 0.86206 0.64302 0.60386 0.00000 Uiso 1.00 +Si280 Si 0.68420 0.48011 0.89513 0.00000 Uiso 1.00 +Si281 Si 0.93514 0.23161 0.89631 0.00000 Uiso 1.00 +Si282 Si 0.00622 0.78949 0.60427 0.00000 Uiso 1.00 +Si283 Si 0.92111 0.08560 0.60420 0.00000 Uiso 1.00 +Si284 Si 0.61160 0.39500 0.60374 0.00000 Uiso 1.00 +Si285 Si 0.83078 0.33597 0.89571 0.00000 Uiso 1.00 +O286 O 0.89390 0.16807 0.89620 0.00000 Uiso 1.00 +O287 O 0.82810 0.57970 0.63270 0.00000 Uiso 1.00 +O288 O 0.98780 0.04930 0.60450 0.00000 Uiso 1.00 +O289 O 0.88650 0.29460 0.86660 0.00000 Uiso 1.00 +O290 O 0.69360 0.57990 0.60310 0.00000 Uiso 1.00 +O291 O 0.57510 0.46150 0.60390 0.00000 Uiso 1.00 +O292 O 0.63050 0.53960 0.86510 0.00000 Uiso 1.00 +O293 O 0.82090 0.70690 0.60440 0.00000 Uiso 1.00 +O294 O 0.76670 0.29520 0.89540 0.00000 Uiso 1.00 +O295 O 0.64760 0.41346 0.89450 0.00000 Uiso 1.00 +O296 O 0.94000 0.82590 0.60510 0.00000 Uiso 1.00 +O297 O 0.07300 0.83610 0.63370 0.00000 Uiso 1.00 +Na298 Na 0.26421 0.68762 0.80670 0.00000 Uiso 1.00 +Na299 Na 0.11250 0.06606 0.68653 0.00000 Uiso 1.00 +Na300 Na 0.14365 0.80876 0.81112 0.00000 Uiso 1.00 +Si301 Si 0.50000 0.31328 0.25000 0.00000 Uiso 1.00 +Si302 Si 0.50000 0.81163 0.25000 0.00000 Uiso 1.00 +Na303 Na 0.50000 0.56280 0.25000 0.00000 Uiso 1.00 +Si304 Si 0.00000 0.81328 0.25000 0.00000 Uiso 1.00 +Si305 Si 0.00000 0.31163 0.25000 0.00000 Uiso 1.00 +Na306 Na 0.00000 0.06280 0.25000 0.00000 Uiso 1.00 +Si307 Si 0.50000 0.68672 0.75000 0.00000 Uiso 1.00 +Si308 Si 0.50000 0.18837 0.75000 0.00000 Uiso 1.00 +Na309 Na 0.50000 0.43720 0.75000 0.00000 Uiso 1.00 +Si310 Si -0.00000 0.18672 0.75000 0.00000 Uiso 1.00 +Si311 Si -0.00000 0.68837 0.75000 0.00000 Uiso 1.00 +Na312 Na -0.00000 0.93720 0.75000 0.00000 Uiso 1.00 +Cu313 Cu 0.25000 0.75000 -0.00000 0.00000 Uiso 1.00 +Cu314 Cu 0.75000 0.75000 0.50000 0.00000 Uiso 1.00 +Cu315 Cu 0.25000 0.25000 0.50000 0.00000 Uiso 1.00 +Cu316 Cu 0.75000 0.25000 0.00000 0.00000 Uiso 1.00 +Cu317 Cu 0.50000 0.50000 -0.00000 0.00000 Uiso 1.00 +Cu318 Cu 0.00000 0.00000 0.00000 0.00000 Uiso 1.00 +Cu319 Cu 0.50000 0.50000 0.50000 0.00000 Uiso 1.00 +Cu320 Cu 0.00000 0.00000 0.50000 0.00000 Uiso 1.00 +loop_ +_geom_bond_atom_site_label_1 +_geom_bond_atom_site_label_2 +_geom_bond_distance +_geom_bond_site_symmetry_2 +_ccdc_geom_bond_type +Si1 O3 1.600 . S +Si1 O7 1.606 . S +Si1 O8 1.606 . S +Si1 O9 1.602 . S +O2 Si16 1.649 . S +O2 Si247 1.659 1_554 S +O3 Si22 1.623 . S +O4 Si20 1.622 . S +O4 Si301 1.607 . S +O5 Si19 1.626 . S +O5 Si302 1.604 . S +O6 Si16 1.621 . S +O6 Si302 1.599 . S +O7 Si21 1.623 . S +O8 Si17 1.627 . S +O9 Si15 1.616 1_545 S +O10 Si18 1.619 . S +O10 Si301 1.603 . S +O11 Si15 1.657 1_556 S +O11 Si242 1.658 . S +O12 Si18 1.656 . S +O12 Si244 1.658 . S +O13 Si21 1.665 1_556 S +O13 Si207 1.652 . S +Cu14 O23 1.941 . S +Cu14 O219 1.928 . S +Cu14 Na187 3.199 . S +Cu14 O258 1.928 . S +Cu14 Na111 3.141 . S +Cu14 O140 1.935 . S +Cu14 Na72 3.220 . S +Si15 O11 1.657 1_554 S +Si15 O9 1.616 1_565 S +Si15 O24 1.618 . S +Si15 O27 1.583 . S +Si16 O24 1.622 . S +Si16 O30 1.584 . S +Si17 O29 1.622 1_545 S +Si17 O32 1.582 . S +Si17 O236 1.658 . S +Si18 O23 1.577 . S +Si18 O26 1.620 . S +Si19 O33 1.577 . S +Si19 O34 1.624 . S +Si19 O237 1.658 . S +Si20 O25 1.579 . S +Si20 O200 1.652 . S +Si20 O142 1.618 . S +Si21 O13 1.665 1_554 S +Si21 O28 1.580 . S +Si21 O71 1.611 . S +Si22 O26 1.619 . S +Si22 O31 1.580 . S +Si22 O227 1.659 . S +O23 Na72 2.583 . S +O23 Na111 2.427 . S +O25 Na36 2.470 . S +O25 Cu317 1.937 . S +O27 Cu127 1.935 1_554 S +O27 Na73 2.430 . S +O27 Na110 2.595 . S +O28 Cu318 1.931 . S +O28 Na73 2.480 1_545 S +O29 Si17 1.622 1_565 S +O29 Si133 1.618 . S +O29 Na73 2.576 . S +O30 Cu313 1.933 . S +O30 Na110 2.578 . S +O30 Na112 2.436 . S +O31 Cu315 1.936 . S +O31 Na72 2.583 . S +O31 Na74 2.435 . S +O32 Cu201 1.928 . S +O32 Na74 2.488 . S +O33 Cu239 1.928 . S +O33 Na112 2.489 . S +O34 Na37 2.564 . S +O34 Si58 1.611 . S +Na35 Na37 3.575 . S +Na35 Cu51 3.220 . S +Na35 O60 2.583 . S +Na35 O102 2.595 . S +Na35 O105 2.578 . S +Na35 O68 2.583 . S +Na35 Na149 3.581 . S +Na35 Cu314 3.231 . S +Na36 Cu317 3.217 . S +Na36 Cu89 3.141 1_554 S +Na36 O98 2.427 . S +Na36 O64 2.430 . S +Na36 O65 2.480 . S +Na36 O66 2.576 . S +Na36 Na148 3.581 . S +Na37 Cu164 3.199 . S +Na37 O105 2.436 . S +Na37 O68 2.435 . S +Na37 O69 2.488 . S +Na37 O108 2.489 . S +Na37 Cu314 3.123 . S +Si38 O40 1.600 . S +Si38 O44 1.606 . S +Si38 O45 1.606 . S +Si38 O46 1.602 . S +O39 Si53 1.649 . S +O39 Si285 1.659 1_554 S +O40 Si59 1.623 . S +O41 Si57 1.622 . S +O41 Si304 1.607 1_655 S +O42 Si56 1.626 . S +O42 Si305 1.604 . S +O43 Si53 1.621 . S +O43 Si305 1.599 1_655 S +O44 Si58 1.623 . S +O45 Si54 1.627 . S +O46 Si52 1.616 . S +O47 Si55 1.619 . S +O47 Si304 1.603 1_655 S +O48 Si52 1.657 1_556 S +O48 Si280 1.658 . S +O49 Si55 1.656 . S +O49 Si282 1.658 1_655 S +O50 Si58 1.665 1_556 S +O50 Si170 1.652 . S +Cu51 O60 1.941 . S +Cu51 O182 1.928 . S +Cu51 Na224 3.199 . S +Cu51 O296 1.928 . S +Cu51 Na149 3.141 . S +Cu51 O102 1.935 . S +Si52 O48 1.657 1_554 S +Si52 O61 1.618 . S +Si52 O64 1.583 . S +Si53 O61 1.622 . S +Si53 O67 1.584 . S +Si54 O66 1.622 . S +Si54 O69 1.582 . S +Si54 O274 1.658 . S +Si55 O60 1.577 . S +Si55 O63 1.620 . S +Si56 O70 1.577 1_455 S +Si56 O71 1.624 . S +Si56 O275 1.658 1_455 S +Si57 O62 1.579 . S +Si57 O163 1.652 . S +Si57 O104 1.618 . S +Si58 O50 1.665 1_554 S +Si58 O65 1.580 . S +Si59 O63 1.619 . S +Si59 O68 1.580 . S +Si59 O265 1.659 . S +O60 Na149 2.427 . S +O62 Na73 2.470 1_655 S +O62 Cu318 1.937 1_665 S +O64 Cu89 1.935 1_554 S +O64 Na148 2.595 . S +O65 Cu317 1.931 . S +O66 Si95 1.618 . S +O67 Cu316 1.933 . S +O67 Na148 2.578 . S +O67 Na150 2.436 . S +O68 Cu314 1.936 . S +O69 Cu164 1.928 . S +O70 Si56 1.577 1_655 S +O70 Cu277 1.928 . S +O70 Na150 2.489 . S +O71 Na74 2.564 . S +Na72 Na74 3.575 . S +Na72 O140 2.595 . S +Na72 O143 2.578 . S +Na72 Na111 3.581 . S +Na72 Cu315 3.231 . S +Na73 O62 2.470 1_455 S +Na73 Cu318 3.217 1_565 S +Na73 Cu127 3.141 1_554 S +Na73 O136 2.427 . S +Na73 O28 2.480 1_565 S +Na73 Na110 3.581 . S +Na74 Cu201 3.199 . S +Na74 O143 2.436 . S +Na74 O146 2.489 . S +Na74 Cu315 3.123 . S +Si76 O78 1.600 . S +Si76 O82 1.606 . S +Si76 O83 1.606 . S +Si76 O84 1.602 . S +O77 Si91 1.649 . S +O77 Si172 1.659 . S +O78 Si97 1.623 . S +O79 Si95 1.622 . S +O79 Si301 1.607 . S +O80 Si94 1.626 . S +O80 Si302 1.604 . S +O81 Si91 1.621 . S +O81 Si302 1.599 . S +O82 Si96 1.623 . S +O83 Si92 1.627 . S +O84 Si90 1.616 1_545 S +O85 Si93 1.619 . S +O85 Si301 1.603 . S +O86 Si90 1.657 . S +O86 Si167 1.658 . S +O87 Si93 1.656 1_556 S +O87 Si169 1.658 . S +O88 Si96 1.665 . S +O88 Si283 1.652 . S +Cu89 O98 1.941 1_556 S +Cu89 O295 1.928 . S +Cu89 Na262 3.199 . S +Cu89 O183 1.928 . S +Cu89 Na36 3.141 1_556 S +Cu89 O64 1.935 1_556 S +Cu89 Na148 3.220 1_556 S +Si90 O84 1.616 1_565 S +Si90 O99 1.618 . S +Si90 O102 1.583 . S +Si91 O99 1.622 . S +Si91 O105 1.584 . S +Si92 O104 1.622 1_545 S +Si92 O107 1.582 . S +Si92 O161 1.658 . S +Si93 O87 1.656 1_554 S +Si93 O98 1.577 . S +Si93 O101 1.620 . S +Si94 O108 1.577 . S +Si94 O109 1.624 . S +Si94 O162 1.658 . S +Si95 O100 1.579 . S +Si95 O276 1.652 . S +Si96 O103 1.580 . S +Si96 O147 1.611 . S +Si97 O101 1.619 . S +Si97 O106 1.580 . S +Si97 O152 1.659 1_554 S +O98 Cu89 1.941 1_554 S +O98 Na148 2.583 . S +O100 Na111 2.470 . S +O100 Cu319 1.937 . S +O102 Na149 2.430 . S +O103 Cu320 1.931 1_655 S +O103 Na149 2.480 1_545 S +O104 Si92 1.622 1_565 S +O104 Na149 2.576 . S +O105 Cu314 1.933 . S +O106 Cu316 1.936 . S +O106 Na148 2.583 . S +O106 Na150 2.435 . S +O107 Cu277 1.928 . S +O107 Na150 2.488 . S +O108 Cu164 1.928 . S +O109 Na112 2.564 . S +O109 Si134 1.611 . S +Na110 Na112 3.575 . S +Na110 Cu127 3.220 1_554 S +Na110 O136 2.583 . S +Na110 O144 2.583 . S +Na110 Cu313 3.231 . S +Na111 Cu319 3.217 . S +Na111 O140 2.430 . S +Na111 O141 2.480 . S +Na111 O142 2.576 . S +Na112 Cu239 3.199 . S +Na112 O144 2.435 . S +Na112 O145 2.488 . S +Na112 Cu313 3.123 . S +Si114 O116 1.600 . S +Si114 O120 1.606 . S +Si114 O121 1.606 . S +Si114 O122 1.602 . S +O115 Si129 1.649 . S +O115 Si209 1.659 . S +O116 Si135 1.623 . S +O117 Si133 1.622 . S +O117 Si304 1.607 . S +O118 Si132 1.626 . S +O118 Si305 1.604 1_655 S +O119 Si129 1.621 . S +O119 Si305 1.599 . S +O120 Si134 1.623 . S +O121 Si130 1.627 . S +O122 Si128 1.616 . S +O123 Si131 1.619 . S +O123 Si304 1.603 . S +O124 Si128 1.657 . S +O124 Si204 1.658 . S +O125 Si131 1.656 1_556 S +O125 Si206 1.658 1_455 S +O126 Si134 1.665 . S +O126 Si245 1.652 . S +Cu127 O136 1.941 1_556 S +Cu127 O257 1.928 . S +Cu127 Na300 3.199 . S +Cu127 O220 1.928 . S +Cu127 Na73 3.141 1_556 S +Cu127 O27 1.935 1_556 S +Cu127 Na110 3.220 1_556 S +Si128 O137 1.618 . S +Si128 O140 1.583 . S +Si129 O137 1.622 . S +Si129 O143 1.584 . S +Si130 O142 1.622 . S +Si130 O145 1.582 . S +Si130 O198 1.658 . S +Si131 O125 1.656 1_554 S +Si131 O136 1.577 . S +Si131 O139 1.620 . S +Si132 O146 1.577 1_655 S +Si132 O147 1.624 . S +Si132 O199 1.658 1_655 S +Si133 O138 1.579 . S +Si133 O238 1.652 . S +Si134 O141 1.580 . S +Si135 O139 1.619 . S +Si135 O144 1.580 . S +Si135 O189 1.659 1_554 S +O136 Cu127 1.941 1_554 S +O138 Na149 2.470 1_455 S +O138 Cu320 1.937 1_565 S +O141 Cu319 1.931 . S +O143 Cu315 1.933 . S +O144 Cu313 1.936 . S +O145 Cu239 1.928 . S +O146 Si132 1.577 1_455 S +O146 Cu201 1.928 . S +O147 Na150 2.564 . S +Na148 Na150 3.575 . S +Na148 Cu89 3.220 1_554 S +Na148 Cu316 3.231 . S +Na149 O138 2.470 1_655 S +Na149 Cu320 3.217 1_665 S +Na149 O103 2.480 1_565 S +Na150 Cu277 3.199 . S +Na150 Cu316 3.123 . S +Si151 O153 1.600 . S +Si151 O157 1.606 . S +Si151 O158 1.606 . S +Si151 O159 1.602 . S +O152 Si166 1.649 . S +O152 Si97 1.659 1_556 S +O153 Si172 1.623 . S +O154 Si170 1.622 . S +O154 Si307 1.607 . S +O155 Si169 1.626 . S +O155 Si308 1.604 . S +O156 Si166 1.621 . S +O156 Si308 1.599 . S +O157 Si171 1.623 . S +O158 Si167 1.627 . S +O159 Si165 1.616 1_565 S +O160 Si168 1.619 . S +O160 Si307 1.603 . S +O161 Si165 1.657 1_554 S +O162 Si168 1.656 . S +O163 Si171 1.665 1_554 S +Cu164 O173 1.941 . S +Cu164 Na261 3.141 . S +Cu164 O290 1.935 . S +Cu164 Na222 3.220 . S +Si165 O161 1.657 1_556 S +Si165 O159 1.616 1_545 S +Si165 O174 1.618 . S +Si165 O177 1.583 . S +Si166 O174 1.622 . S +Si166 O180 1.584 . S +Si167 O179 1.622 1_565 S +Si167 O182 1.582 . S +Si168 O173 1.577 . S +Si168 O176 1.620 . S +Si169 O183 1.577 . S +Si169 O184 1.624 . S +Si170 O175 1.579 . S +Si170 O292 1.618 . S +Si171 O163 1.665 1_556 S +Si171 O178 1.580 . S +Si171 O221 1.611 . S +Si172 O176 1.619 . S +Si172 O181 1.580 . S +O173 Na222 2.583 . S +O173 Na261 2.427 . S +O175 Na186 2.470 . S +O175 Cu317 1.937 1_556 S +O177 Cu277 1.935 1_556 S +O177 Na223 2.430 . S +O177 Na260 2.595 . S +O178 Cu318 1.931 1_666 S +O178 Na223 2.480 1_565 S +O179 Si167 1.622 1_545 S +O179 Si283 1.618 . S +O179 Na223 2.576 . S +O180 Cu316 1.933 1_556 S +O180 Na260 2.578 . S +O180 Na262 2.436 . S +O181 Cu314 1.936 . S +O181 Na222 2.583 . S +O181 Na224 2.435 . S +O182 Na224 2.488 . S +O183 Na262 2.489 . S +O184 Na187 2.564 . S +O184 Si208 1.611 . S +Na185 Na187 3.575 . S +Na185 Cu201 3.220 . S +Na185 O210 2.583 . S +Na185 O252 2.595 . S +Na185 O255 2.578 . S +Na185 O218 2.583 . S +Na185 Na299 3.581 . S +Na185 Cu315 3.231 . S +Na186 Cu317 3.217 1_556 S +Na186 Cu239 3.141 1_556 S +Na186 O248 2.427 . S +Na186 O214 2.430 . S +Na186 O215 2.480 . S +Na186 O216 2.576 . S +Na186 Na298 3.581 . S +Na187 O255 2.436 . S +Na187 O218 2.435 . S +Na187 O219 2.488 . S +Na187 O258 2.489 . S +Na187 Cu315 3.123 . S +Si188 O190 1.600 . S +Si188 O194 1.606 . S +Si188 O195 1.606 . S +Si188 O196 1.602 . S +O189 Si203 1.649 . S +O189 Si135 1.659 1_556 S +O190 Si209 1.623 . S +O191 Si207 1.622 . S +O191 Si310 1.607 . S +O192 Si206 1.626 . S +O192 Si311 1.604 1_655 S +O193 Si203 1.621 . S +O193 Si311 1.599 . S +O194 Si208 1.623 . S +O195 Si204 1.627 . S +O196 Si202 1.616 . S +O197 Si205 1.619 . S +O197 Si310 1.603 . S +O198 Si202 1.657 1_554 S +O199 Si205 1.656 . S +O199 Si132 1.658 1_455 S +O200 Si208 1.665 1_554 S +Cu201 O210 1.941 . S +Cu201 Na299 3.141 . S +Cu201 O252 1.935 . S +Si202 O198 1.657 1_556 S +Si202 O211 1.618 . S +Si202 O214 1.583 . S +Si203 O211 1.622 . S +Si203 O217 1.584 . S +Si204 O216 1.622 . S +Si204 O219 1.582 . S +Si205 O210 1.577 . S +Si205 O213 1.620 . S +Si206 O220 1.577 1_655 S +Si206 O221 1.624 . S +Si206 O125 1.658 1_655 S +Si207 O212 1.579 . S +Si207 O254 1.618 . S +Si208 O200 1.665 1_556 S +Si208 O215 1.580 . S +Si209 O213 1.619 . S +Si209 O218 1.580 . S +O210 Na299 2.427 . S +O212 Na223 2.470 1_455 S +O212 Cu318 1.937 1_556 S +O214 Cu239 1.935 1_556 S +O214 Na298 2.595 . S +O215 Cu317 1.931 1_556 S +O216 Si245 1.618 . S +O217 Cu313 1.933 1_556 S +O217 Na298 2.578 . S +O217 Na300 2.436 . S +O218 Cu315 1.936 . S +O220 Si206 1.577 1_455 S +O220 Na300 2.489 . S +O221 Na224 2.564 . S +Na222 Na224 3.575 . S +Na222 O290 2.595 . S +Na222 O293 2.578 . S +Na222 Na261 3.581 . S +Na222 Cu314 3.231 . S +Na223 O212 2.470 1_655 S +Na223 Cu318 3.217 1_656 S +Na223 Cu277 3.141 1_556 S +Na223 O286 2.427 . S +Na223 O178 2.480 1_545 S +Na223 Na260 3.581 . S +Na224 O293 2.436 . S +Na224 O296 2.489 . S +Na224 Cu314 3.123 . S +Si226 O228 1.600 . S +Si226 O232 1.606 . S +Si226 O233 1.606 . S +Si226 O234 1.602 . S +O227 Si241 1.649 . S +O228 Si247 1.623 . S +O229 Si245 1.622 . S +O229 Si307 1.607 . S +O230 Si244 1.626 . S +O230 Si308 1.604 . S +O231 Si241 1.621 . S +O231 Si308 1.599 . S +O232 Si246 1.623 . S +O233 Si242 1.627 . S +O234 Si240 1.616 1_565 S +O235 Si243 1.619 . S +O235 Si307 1.603 . S +O236 Si240 1.657 . S +O237 Si243 1.656 1_554 S +O238 Si246 1.665 . S +Cu239 O248 1.941 1_554 S +Cu239 Na186 3.141 1_554 S +Cu239 O214 1.935 1_554 S +Cu239 Na298 3.220 1_554 S +Si240 O234 1.616 1_545 S +Si240 O249 1.618 . S +Si240 O252 1.583 . S +Si241 O249 1.622 . S +Si241 O255 1.584 . S +Si242 O254 1.622 1_565 S +Si242 O257 1.582 . S +Si243 O237 1.656 1_556 S +Si243 O248 1.577 . S +Si243 O251 1.620 . S +Si244 O258 1.577 . S +Si244 O259 1.624 . S +Si245 O250 1.579 . S +Si246 O253 1.580 . S +Si246 O297 1.611 . S +Si247 O251 1.619 . S +Si247 O256 1.580 . S +Si247 O2 1.659 1_556 S +O248 Cu239 1.941 1_556 S +O248 Na298 2.583 . S +O250 Na261 2.470 . S +O250 Cu319 1.937 . S +O252 Na299 2.430 . S +O253 Cu320 1.931 1_565 S +O253 Na299 2.480 1_565 S +O254 Si242 1.622 1_545 S +O254 Na299 2.576 . S +O255 Cu315 1.933 . S +O256 Cu313 1.936 1_556 S +O256 Na298 2.583 . S +O256 Na300 2.435 . S +O257 Na300 2.488 . S +O259 Na262 2.564 . S +O259 Si284 1.611 . S +Na260 Na262 3.575 . S +Na260 Cu277 3.220 1_556 S +Na260 O286 2.583 . S +Na260 O294 2.583 . S +Na260 Cu316 3.231 1_556 S +Na261 Cu319 3.217 . S +Na261 O290 2.430 . S +Na261 O291 2.480 . S +Na261 O292 2.576 . S +Na262 O294 2.435 . S +Na262 O295 2.488 . S +Na262 Cu316 3.123 1_556 S +Si264 O266 1.600 . S +Si264 O270 1.606 . S +Si264 O271 1.606 . S +Si264 O272 1.602 . S +O265 Si279 1.649 . S +O266 Si285 1.623 . S +O267 Si283 1.622 . S +O267 Si310 1.607 1_655 S +O268 Si282 1.626 . S +O268 Si311 1.604 . S +O269 Si279 1.621 . S +O269 Si311 1.599 1_655 S +O270 Si284 1.623 . S +O271 Si280 1.627 . S +O272 Si278 1.616 . S +O273 Si281 1.619 . S +O273 Si310 1.603 1_655 S +O274 Si278 1.657 . S +O275 Si281 1.656 1_554 S +O275 Si56 1.658 1_655 S +O276 Si284 1.665 . S +Cu277 O286 1.941 1_554 S +Cu277 Na223 3.141 1_554 S +Cu277 O177 1.935 1_554 S +Cu277 Na260 3.220 1_554 S +Si278 O287 1.618 . S +Si278 O290 1.583 . S +Si279 O287 1.622 . S +Si279 O293 1.584 . S +Si280 O292 1.622 . S +Si280 O295 1.582 . S +Si281 O275 1.656 1_556 S +Si281 O286 1.577 . S +Si281 O289 1.620 . S +Si282 O296 1.577 1_455 S +Si282 O297 1.624 . S +Si282 O49 1.658 1_455 S +Si283 O288 1.579 . S +Si284 O291 1.580 . S +Si285 O289 1.619 . S +Si285 O294 1.580 . S +Si285 O39 1.659 1_556 S +O286 Cu277 1.941 1_556 S +O288 Na299 2.470 1_655 S +O288 Cu320 1.937 1_655 S +O291 Cu319 1.931 . S +O293 Cu314 1.933 . S +O294 Cu316 1.936 1_556 S +O296 Si282 1.577 1_655 S +O297 Na300 2.564 . S +Na298 Na300 3.575 . S +Na298 Cu239 3.220 1_556 S +Na298 Cu313 3.231 1_556 S +Na299 O288 2.470 1_455 S +Na299 Cu320 3.217 . S +Na299 O253 2.480 1_545 S +Na300 Cu313 3.123 1_556 S +Si304 O41 1.607 1_455 S +Si304 O47 1.603 1_455 S +Si305 O118 1.604 1_455 S +Si305 O43 1.599 1_455 S +Si310 O267 1.607 1_455 S +Si310 O273 1.603 1_455 S +Si311 O192 1.604 1_455 S +Si311 O269 1.599 1_455 S +Cu313 O217 1.933 1_554 S +Cu313 O256 1.936 1_554 S +Cu313 Na298 3.231 1_554 S +Cu313 Na300 3.123 1_554 S +Cu316 O180 1.933 1_554 S +Cu316 O294 1.936 1_554 S +Cu316 Na260 3.231 1_554 S +Cu316 Na262 3.123 1_554 S +Cu317 O175 1.937 1_554 S +Cu317 Na186 3.217 1_554 S +Cu317 O215 1.931 1_554 S +Cu318 O62 1.937 1_445 S +Cu318 O212 1.937 1_554 S +Cu318 Na73 3.217 1_545 S +Cu318 Na223 3.217 1_454 S +Cu318 O178 1.931 1_444 S +Cu320 O138 1.937 1_545 S +Cu320 O288 1.937 1_455 S +Cu320 Na149 3.217 1_445 S +Cu320 O103 1.931 1_455 S +Cu320 O253 1.931 1_545 S diff --git a/examples/stability/plot.ipynb b/examples/stability/plot.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..16d7b3616845af53629c79216f5f66e5e9eb0412 --- /dev/null +++ b/examples/stability/plot.ipynb @@ -0,0 +1,813 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import glob\n", + "from pathlib import Path\n", + "\n", + "from tqdm.auto import tqdm\n", + "\n", + "from mlip_arena.models import REGISTRY\n", + "from mlip_arena.tasks.stability.input import get_atoms_from_db\n", + "\n", + "RUN_DIR = Path(\".\").resolve()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f088c4da133d406694657239bcefbbe0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "0it [00:00, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
21:51:53.643 | INFO    | Task run 'get_atoms_from_db' - Finished in state Completed()\n",
+       "
\n" + ], + "text/plain": [ + "21:51:53.643 | \u001b[36mINFO\u001b[0m | Task run 'get_atoms_from_db' - Finished in state \u001b[32mCompleted\u001b[0m()\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "compositions = []\n", + "sizes = []\n", + "for atoms in tqdm(get_atoms_from_db(\"random-mixture.db\")):\n", + " if len(atoms) == 0:\n", + " continue\n", + " compositions.append(atoms.get_chemical_formula())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2140436/3298160172.py:7: UserWarning: default value of fill_value changed from zero to None.\n", + " pmv.count_elements(compositions[:1000]),\n", + "/pscratch/sd/c/cyrusyc/.conda/mlip-arena/lib/python3.11/site-packages/pymatviz/ptable/_process_data.py:117: UserWarning: NaN found in data\n", + " self.check_and_replace_missing(strategy=missing_strategy)\n" + ] + }, + { + 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pymatviz as pmv\n", + "from matplotlib import pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "\n", + "fig = pmv.ptable_heatmap(\n", + " pmv.count_elements(compositions[:1000]),\n", + " colormap=\"GnBu\",\n", + " log=True,\n", + " return_type=\"figure\",\n", + ")\n", + "\n", + "plt.savefig(\"../figures/stability-element-counts.pdf\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from ase import Atoms\n", + "\n", + "\n", + "def get_runtime_stats(traj: list[Atoms], atoms0: Atoms):\n", + " restarts = []\n", + " steps, times = [], []\n", + " Ts, Ps, Es, KEs = [], [], [], []\n", + " timesteps = []\n", + " com_drifts = []\n", + "\n", + " for atoms in tqdm(traj):\n", + " assert isinstance(atoms, Atoms)\n", + " try:\n", + " energy = atoms.get_potential_energy()\n", + " assert np.isfinite(energy), f\"invalid energy: {energy}\"\n", + " # assert np.all(~np.isnan(atoms.get_forces())), f\"invalid forces: {atoms.get_forces()}\"\n", + " # assert np.all(~np.isnan(atoms.get_stress())), f\"invalid stress: {atoms.get_stress()}\"\n", + " except Exception:\n", + " continue\n", + "\n", + " restarts.append(atoms.info[\"restart\"])\n", + " times.append(atoms.info[\"datetime\"])\n", + " steps.append(atoms.info[\"step\"])\n", + " Es.append(energy)\n", + " KEs.append(atoms.get_kinetic_energy())\n", + " Ts.append(atoms.get_temperature())\n", + " try:\n", + " Ps.append(atoms.get_stress()[:3].mean())\n", + " except:\n", + " pass\n", + " com_drifts.append(\n", + " (atoms.get_center_of_mass() - atoms0.get_center_of_mass()).tolist()\n", + " )\n", + "\n", + " restarts = np.array(restarts)\n", + " times = np.array(times)\n", + " steps = np.array(steps)\n", + "\n", + " # Identify unique blocks\n", + " unique_restarts = np.unique(restarts)\n", + "\n", + " total_time_seconds = 0\n", + " total_steps = 0\n", + "\n", + " # Iterate over unique blocks to calculate averages\n", + " for block in unique_restarts:\n", + " # Get the indices corresponding to the current block\n", + " # indices = np.where(restarts == block)[0]\n", + " indices = restarts == block\n", + " # Extract the corresponding data values\n", + " block_time = times[indices][-1] - times[indices][0]\n", + " total_time_seconds += block_time.total_seconds()\n", + " total_steps += steps[indices][-1] - steps[indices][0]\n", + "\n", + " target_steps = traj[0].info[\"target_steps\"]\n", + " natoms = len(traj[0])\n", + "\n", + " return {\n", + " \"natoms\": natoms,\n", + " \"total_time_seconds\": total_time_seconds,\n", + " \"total_steps\": total_steps,\n", + " \"steps_per_second\": total_steps / total_time_seconds\n", + " if total_time_seconds != 0\n", + " else 0,\n", + " \"seconds_per_step\": total_time_seconds / total_steps\n", + " if total_steps != 0\n", + " else float(\"inf\"),\n", + " \"seconds_per_step_per_atom\": total_time_seconds / total_steps / natoms\n", + " if total_steps != 0\n", + " else float(\"inf\"),\n", + " \"energies\": Es,\n", + " \"kinetic_energies\": KEs,\n", + " \"temperatures\": Ts,\n", + " \"pressures\": Ps,\n", + " \"target_steps\": target_steps,\n", + " \"final_step\": steps[-1] if len(steps) != 0 else 0,\n", + " \"timestep\": steps,\n", + " \"com_drifts\": com_drifts,\n", + " }\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import plotly.colors as pcolors\n", + "\n", + "mlip_methods = [\n", + " model\n", + " for model, metadata in REGISTRY.items()\n", + " if \"stability\" in metadata.get(\"gpu-tasks\", [])\n", + "]\n", + "\n", + "all_attributes = dir(pcolors.qualitative)\n", + "color_palettes = {\n", + " attr: getattr(pcolors.qualitative, attr)\n", + " for attr in all_attributes\n", + " if isinstance(getattr(pcolors.qualitative, attr), list)\n", + "}\n", + "color_palettes.pop(\"__all__\", None)\n", + "\n", + "palette_names = list(color_palettes.keys())\n", + "palette_colors = list(color_palettes.values())\n", + "palette_name = \"T10\" # \"Plotly\"\n", + "color_sequence = color_palettes[palette_name] # type: ignore\n", + "\n", + "method_color_mapping = {\n", + " method: color_sequence[i % len(color_sequence)]\n", + " for i, method in enumerate(mlip_methods)\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NPT" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# from huggingface_hub import HfApi\n", + "import seaborn as sns\n", + "from ase import units\n", + "from ase.io import read\n", + "from matplotlib import pyplot as plt\n", + "\n", + "df = pd.DataFrame()\n", + "\n", + "for model in mlip_methods:\n", + " # if \"stability\" not in REGISTRY[model]['gpu-tasks']:\n", + " # continue\n", + "\n", + " files = glob.glob(str(RUN_DIR / REGISTRY[model][\"family\"] / f\"{model}_*npt.traj\"))\n", + "\n", + " for i, file in enumerate(files):\n", + " try:\n", + " traj = read(file, index=\":\")\n", + " except Exception as e:\n", + " print(f\"Error reading {file}: {e}\")\n", + " continue\n", + "\n", + " try:\n", + " stats = get_runtime_stats(traj, atoms0=traj[0])\n", + " except Exception as e:\n", + " print(f\"Error processing {file}: {e}\")\n", + " continue\n", + "\n", + " df = pd.concat(\n", + " [\n", + " df,\n", + " pd.DataFrame(\n", + " {\n", + " \"model\": model,\n", + " \"formula\": traj[0].get_chemical_formula(),\n", + " \"normalized_timestep\": stats[\"timestep\"]\n", + " / stats[\"target_steps\"],\n", + " \"normalized_final_step\": stats[\"final_step\"]\n", + " / stats[\"target_steps\"],\n", + " \"pressure\": np.array(stats[\"pressures\"]) / units.GPa,\n", + " }\n", + " | stats\n", + " ),\n", + " ],\n", + " ignore_index=True,\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CHGNet 76\n", + "M3GNet 23\n", + "MACE-MP(M) 70\n", + "MACE-MPA 70\n", + "MatterSim 71\n", + "ORBv2 73\n", + "SevenNet 63\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "# import scipy.optimize as opt\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from scipy.optimize import curve_fit\n", + "\n", + "\n", + "# Define the power-law fitting function\n", + "def power_law(x, a, n):\n", + " return a * np.power(x, n)\n", + "\n", + "\n", + "df.rename(\n", + " columns={\n", + " \"final_step\": \"Total steps\",\n", + " \"model\": \"Model\",\n", + " },\n", + " inplace=True,\n", + ")\n", + "\n", + "with plt.style.context(\"default\"):\n", + " fig, axes = plt.subplot_mosaic(\n", + " \"\"\"\n", + " ao\n", + " \"\"\",\n", + " constrained_layout=True,\n", + " figsize=(6, 2.5),\n", + " width_ratios=[1, 3],\n", + " )\n", + "\n", + " iax = \"o\"\n", + " ax = axes.pop(iax)\n", + "\n", + " sns.scatterplot(\n", + " data=df,\n", + " x=\"natoms\",\n", + " y=\"steps_per_second\",\n", + " size=\"Total steps\",\n", + " hue=\"Model\",\n", + " ax=ax,\n", + " palette=method_color_mapping,\n", + " sizes=(1, 50),\n", + " # alpha=0.5\n", + " )\n", + "\n", + " # Fit and plot power-law regression for each model\n", + " for model, data in df.groupby(\"Model\"):\n", + " data.dropna(subset=[\"steps_per_second\"], inplace=True)\n", + "\n", + " popt, pcov = curve_fit(power_law, data[\"natoms\"], data[\"steps_per_second\"])\n", + "\n", + " # Generate smooth curve\n", + " # x_fit = np.logspace(np.log10(xdata.min()), np.log10(xdata.max()), 100)\n", + " # y_fit = power_law(x_fit, a_fit, n_fit)\n", + "\n", + " x = np.linspace(data[\"natoms\"].min(), data[\"natoms\"].max(), 100)\n", + "\n", + " # Plot regression line\n", + " ax.plot(\n", + " x,\n", + " power_law(x, *popt),\n", + " c=method_color_mapping[model],\n", + " # label=f\"{model} (y={a_fit:.2e}x^{n_fit:.2f})\",\n", + " linestyle=\"-\",\n", + " )\n", + "\n", + " # sns.lineplot(\n", + " # data=df,\n", + " # x='natoms',\n", + " # y='steps_per_second',\n", + " # # size='Total steps',\n", + " # hue='Model',\n", + " # ax=ax,\n", + " # palette=method_color_mapping,\n", + " # alpha=0.5,\n", + " # # err_style=\"bars\"\n", + " # )\n", + "\n", + " ax.set(\n", + " xlabel=\"Number of atoms\",\n", + " xscale=\"log\",\n", + " ylabel=\"Steps per second\",\n", + " yscale=\"log\",\n", + " )\n", + " ax.spines[\"right\"].set_visible(False)\n", + " ax.spines[\"top\"].set_visible(False)\n", + " ax.grid(alpha=0.25)\n", + " ax.legend(\n", + " loc=\"upper left\", bbox_to_anchor=(1.0, 1.0), fontsize=\"x-small\", frameon=False\n", + " )\n", + "\n", + " # iax = 'a'\n", + "\n", + " for k, df_model in df.groupby(\"Model\"):\n", + " ax = axes[\"a\"]\n", + "\n", + " df_model.drop_duplicates([\"formula\"], inplace=True)\n", + " df_model = df_model[df_model[\"formula\"].isin(compositions[:80])].copy()\n", + " print(k, len(df_model))\n", + "\n", + " # Compute histogram\n", + " bins = np.linspace(0, 1, 50) # 50 bins from 0 to 1\n", + " hist, bin_edges = np.histogram(\n", + " df_model[\"normalized_final_step\"], bins=bins, density=False\n", + " )\n", + "\n", + " # Compute cumulative population\n", + " cumulative_population = np.cumsum(hist)\n", + "\n", + " # Midpoints for binning\n", + " bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2\n", + "\n", + " sns.lineplot(\n", + " x=bin_centers[:-1],\n", + " y=(cumulative_population[-1] - cumulative_population[:-1]),\n", + " ax=axes[\"a\"],\n", + " # label=k,\n", + " color=method_color_mapping[k],\n", + " # palette=method_color_mapping\n", + " )\n", + " ylo, yhi = axes[\"a\"].get_ylim()\n", + " axes[\"a\"].set(\n", + " xlabel=\"Normalized trajectory\",\n", + " ylabel=\"# of valid runs\",\n", + " xlim=(0, 1),\n", + " yticks=range(0, 81, 20),\n", + " # title=k\n", + " )\n", + "\n", + " ax.spines[\"right\"].set_visible(False)\n", + " ax.spines[\"top\"].set_visible(False)\n", + " # ax.axline((0, 300), (1, 3000), c='k', lw=0.5, alpha=0.25)\n", + " ax.legend_ = None\n", + "\n", + " plt.savefig(\"../figures/stability-and-speed-npt-loglog.pdf\", bbox_inches=\"tight\")\n", + " plt.savefig(\n", + " \"../figures/stability-and-speed-npt-loglog.png\", bbox_inches=\"tight\", dpi=330\n", + " )\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NVT" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# from huggingface_hub import HfApi\n", + "import seaborn as sns\n", + "from ase import units\n", + "from ase.io import read\n", + "from matplotlib import pyplot as plt\n", + "\n", + "df = pd.DataFrame()\n", + "\n", + "for model in mlip_methods:\n", + " # if \"stability\" not in REGISTRY[model]['gpu-tasks']:\n", + " # continue\n", + "\n", + " files = glob.glob(str(RUN_DIR / REGISTRY[model][\"family\"] / f\"{model}_*nvt.traj\"))\n", + "\n", + " for i, file in enumerate(files):\n", + " try:\n", + " traj = read(file, index=\":\")\n", + " except Exception as e:\n", + " print(f\"Error reading {file}: {e}\")\n", + " continue\n", + "\n", + " try:\n", + " stats = get_runtime_stats(traj, atoms0=traj[0])\n", + " except Exception as e:\n", + " print(f\"Error processing {file}: {e}\")\n", + " continue\n", + "\n", + " df = pd.concat(\n", + " [\n", + " df,\n", + " pd.DataFrame(\n", + " {\n", + " \"model\": model,\n", + " \"formula\": traj[0].get_chemical_formula(),\n", + " \"normalized_timestep\": stats[\"timestep\"]\n", + " / stats[\"target_steps\"],\n", + " \"normalized_final_step\": stats[\"final_step\"]\n", + " / stats[\"target_steps\"],\n", + " \"pressure\": np.array(stats[\"pressures\"]) / units.GPa,\n", + " }\n", + " | stats\n", + " ),\n", + " ],\n", + " ignore_index=True,\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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size=\"Total steps\",\n", + " hue=\"Model\",\n", + " ax=ax,\n", + " palette=method_color_mapping,\n", + " sizes=(1, 50),\n", + " # alpha=0.5\n", + " )\n", + "\n", + " # Fit and plot power-law regression for each model\n", + " for model, data in df.groupby(\"Model\"):\n", + " data.dropna(subset=[\"steps_per_second\"], inplace=True)\n", + "\n", + " popt, pcov = curve_fit(power_law, data[\"natoms\"], data[\"steps_per_second\"])\n", + "\n", + " # Generate smooth curve\n", + " # x_fit = np.logspace(np.log10(xdata.min()), np.log10(xdata.max()), 100)\n", + " # y_fit = power_law(x_fit, a_fit, n_fit)\n", + "\n", + " x = np.linspace(data[\"natoms\"].min(), data[\"natoms\"].max(), 100)\n", + "\n", + " # Plot regression line\n", + " ax.plot(\n", + " x,\n", + " power_law(x, *popt),\n", + " c=method_color_mapping[model],\n", + " # label=f\"{model} (y={a_fit:.2e}x^{n_fit:.2f})\",\n", + " linestyle=\"-\",\n", + " )\n", + "\n", + " # sns.lineplot(\n", + " # data=df,\n", + " # x='natoms',\n", + " # y='steps_per_second',\n", + " # # size='Total steps',\n", + " # hue='Model',\n", + " # ax=ax,\n", + " # palette=method_color_mapping,\n", + " # alpha=0.5,\n", + " # # err_style=\"bars\"\n", + " # )\n", + "\n", + " ax.set(\n", + " xlabel=\"Number of atoms\",\n", + " xscale=\"log\",\n", + " ylabel=\"Steps per second\",\n", + " yscale=\"log\",\n", + " )\n", + " ax.spines[\"right\"].set_visible(False)\n", + " ax.spines[\"top\"].set_visible(False)\n", + " ax.grid(alpha=0.25)\n", + " ax.legend(\n", + " loc=\"upper left\", bbox_to_anchor=(1.0, 1.0), fontsize=\"x-small\", frameon=False\n", + " )\n", + "\n", + " # iax = 'a'\n", + "\n", + " for k, df_model in df.groupby(\"Model\"):\n", + " ax = axes[\"a\"]\n", + "\n", + " df_model.drop_duplicates([\"formula\"], inplace=True)\n", + " df_model = df_model[df_model[\"formula\"].isin(compositions[:120])].copy()\n", + "\n", + " # Compute histogram\n", + " bins = np.linspace(0, 1, 50) # 50 bins from 0 to 1\n", + " hist, bin_edges = np.histogram(\n", + " df_model[\"normalized_final_step\"], bins=bins, density=False\n", + " )\n", + "\n", + " # Compute cumulative population\n", + " cumulative_population = np.cumsum(hist)\n", + "\n", + " # Midpoints for binning\n", + " bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2\n", + "\n", + " sns.lineplot(\n", + " x=bin_centers[:-1],\n", + " y=(cumulative_population[-1] - cumulative_population[:-1]),\n", + " ax=axes[\"a\"],\n", + " # label=k,\n", + " color=method_color_mapping[k],\n", + " # palette=method_color_mapping\n", + " )\n", + "\n", + " axes[\"a\"].set(\n", + " xlabel=\"Normalized trajectory\",\n", + " ylabel=\"# of valid runs\",\n", + " xlim=(0, 1),\n", + " # title=k\n", + " )\n", + "\n", + " ax.spines[\"right\"].set_visible(False)\n", + " ax.spines[\"top\"].set_visible(False)\n", + " # ax.axline((0, 300), (1, 3000), c='k', lw=0.5, alpha=0.25)\n", + " ax.legend_ = None\n", + "\n", + " plt.savefig(\"../figures/stability-and-speed-nvt-loglog.pdf\", bbox_inches=\"tight\")\n", + " plt.savefig(\n", + " \"../figures/stability-and-speed-nvt-loglog.png\", bbox_inches=\"tight\", dpi=330\n", + " )\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.12.0" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": { + "06905b5dd49e47fb9ca98d2e3a9babb8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "layout": "IPY_MODEL_b3a1e313f7334fa78392cec0476b2a30", + "style": "IPY_MODEL_9e078e2ba27e449e86ecc1fe59f681ec" + } + }, + "0ef76231108146649bcbdceba016aac5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + 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"IPY_MODEL_e02fd4d9b9d04c87887a3903274c794a" + ], + "layout": "IPY_MODEL_51ec40d026074e34a1168f5240228ca8" + } + }, + "f40f4df44b3f4b658fa4f7204624f9cf": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "description_width": "", + "font_size": null, + "text_color": null + } + } + }, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/stability/pressure.ipynb b/examples/stability/pressure.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..3d4714cfda9054c0c90f77a154938d9339e50d9b --- /dev/null +++ b/examples/stability/pressure.ipynb @@ -0,0 +1,194 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from ase import units\n", + "from dask.distributed import Client\n", + "from dask_jobqueue import SLURMCluster\n", + "from dotenv import load_dotenv\n", + "from prefect import flow, task\n", + "from prefect_dask import DaskTaskRunner\n", + "\n", + "from mlip_arena.models import REGISTRY, MLIPEnum\n", + "from mlip_arena.tasks.md import run as MD\n", + "from mlip_arena.tasks.stability.input import get_atoms_from_db\n", + "\n", + "load_dotenv()\n", + "\n", + "HF_TOKEN = os.environ.get(\"HF_TOKEN\", None)\n", + "MP_API_KEY = os.environ.get(\"MP_API_KEY\", None)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "nodes_per_alloc = 1\n", + "gpus_per_alloc = 4\n", + "ntasks = 1\n", + "\n", + "cluster_kwargs = dict(\n", + " cores=1,\n", + " memory=\"64 GB\",\n", + " processes=1,\n", + " shebang=\"#!/bin/bash\",\n", + " account=\"matgen\",\n", + " walltime=\"03:00:00\",\n", + " # job_cpu=128,\n", + " job_mem=\"0\",\n", + " job_script_prologue=[\n", + " \"source ~/.bashrc\",\n", + " \"module load python\",\n", + " \"source activate /pscratch/sd/c/cyrusyc/.conda/mlip-arena\",\n", + " ],\n", + " job_directives_skip=[\"-n\", \"--cpus-per-task\", \"-J\"],\n", + " job_extra_directives=[\n", + " \"-J stability-npt\",\n", + " \"-q preempt\",\n", + " \"--time-min=00:30:00\",\n", + " \"--comment=12:00:00\",\n", + " f\"-N {nodes_per_alloc}\",\n", + " \"-C gpu\",\n", + " f\"-G {gpus_per_alloc}\",\n", + " ],\n", + ")\n", + "\n", + "cluster = SLURMCluster(**cluster_kwargs)\n", + "print(cluster.job_script())\n", + "cluster.adapt(minimum_jobs=5, maximum_jobs=10)\n", + "client = Client(cluster)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from mlip_arena.tasks.utils import get_calculator\n", + "\n", + "selected_models = [\n", + " \"MACE-MP(M)\",\n", + " \"CHGNet\",\n", + " \"M3GNet\",\n", + " \"MatterSim\",\n", + " \"eqV2(OMat)\",\n", + " \"MACE-MPA\",\n", + " \"ORBv2\",\n", + " \"SevenNet\",\n", + " \"ALIGNN\",\n", + "]\n", + "\n", + "\n", + "@task\n", + "def run_one(\n", + " atoms,\n", + " model,\n", + "):\n", + " result = MD.with_options(\n", + " timeout_seconds=600,\n", + " retries=2,\n", + " refresh_cache=True\n", + " )(\n", + " atoms=atoms,\n", + " calculator=get_calculator(\n", + " model.name,\n", + " calculator_kwargs=None,\n", + " ),\n", + " ensemble=\"npt\",\n", + " dynamics=\"nose-hoover\",\n", + " time_step=None,\n", + " dynamics_kwargs=dict(\n", + " ttime=25 * units.fs, pfactor=((75 * units.fs) ** 2) * 1e2 * units.GPa\n", + " ),\n", + " total_time=1e4, # 5e4, # fs\n", + " temperature=[300, 3000],\n", + " pressure=[0, 5e2 * units.GPa], # 500 GPa / 10 ps = 50 GPa / 1 ps\n", + " traj_file=f\"{REGISTRY[model.name]['family']}/{model.name}_{atoms.info.get('material_id', 'random')}_{atoms.get_chemical_formula()}_npt.traj\",\n", + " traj_interval=10,\n", + " )\n", + "\n", + " return result\n", + "\n", + "\n", + "@flow\n", + "def compress():\n", + " futures = []\n", + " # To download the database automatically, `huggingface_hub login` or provide HF_TOKEN\n", + " for atoms in get_atoms_from_db(\"random-mixture.db\", force_download=False):\n", + " for model in MLIPEnum:\n", + " if model.name not in selected_models:\n", + " continue\n", + "\n", + " if \"stability\" not in REGISTRY[model.name][\"gpu-tasks\"]:\n", + " continue\n", + "\n", + " try:\n", + " future = run_one.with_options(\n", + " timeout_seconds=600, retries=2, refresh_cache=False\n", + " ).submit(atoms.copy(), model)\n", + " futures.append(future)\n", + " except:\n", + " continue\n", + "\n", + " return [future.result(raise_on_failure=False) for future in futures]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "compress.with_options(\n", + " task_runner=DaskTaskRunner(address=client.scheduler.address), log_prints=True\n", + ")()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NERSC Python", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/stability/temperature.ipynb b/examples/stability/temperature.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..456a661d9bc375ca42c1a14f510401d1be7520db --- /dev/null +++ b/examples/stability/temperature.ipynb @@ -0,0 +1,202 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from ase import units\n", + "from dask.distributed import Client\n", + "from dask_jobqueue import SLURMCluster\n", + "from dotenv import load_dotenv\n", + "from prefect import flow, task\n", + "from prefect_dask import DaskTaskRunner\n", + "\n", + "from mlip_arena.models import REGISTRY, MLIPEnum\n", + "from mlip_arena.tasks.md import run as MD\n", + "from mlip_arena.tasks.stability.input import get_atoms_from_db\n", + "\n", + "load_dotenv()\n", + "\n", + "HF_TOKEN = os.environ.get(\"HF_TOKEN\", None)\n", + "MP_API_KEY = os.environ.get(\"MP_API_KEY\", None)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "nodes_per_alloc = 1\n", + "gpus_per_alloc = 4\n", + "ntasks = 1\n", + "\n", + "cluster_kwargs = dict(\n", + " cores=1,\n", + " memory=\"64 GB\",\n", + " processes=1,\n", + " shebang=\"#!/bin/bash\",\n", + " account=\"matgen\",\n", + " walltime=\"04:00:00\",\n", + " # job_cpu=128,\n", + " job_mem=\"0\",\n", + " job_script_prologue=[\n", + " \"source ~/.bashrc\",\n", + " \"module load python\",\n", + " \"source activate /pscratch/sd/c/cyrusyc/.conda/mlip-arena\",\n", + " ],\n", + " job_directives_skip=[\"-n\", \"--cpus-per-task\", \"-J\"],\n", + " job_extra_directives=[\n", + " \"-J stability-nvt\",\n", + " \"-q preempt\",\n", + " \"--time-min=00:30:00\",\n", + " \"--comment=12:00:00\",\n", + " f\"-N {nodes_per_alloc}\",\n", + " \"-C gpu\",\n", + " f\"-G {gpus_per_alloc}\",\n", + " ],\n", + ")\n", + "\n", + "cluster = SLURMCluster(**cluster_kwargs)\n", + "print(cluster.job_script())\n", + "cluster.adapt(minimum_jobs=10, maximum_jobs=50)\n", + "client = Client(cluster)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from prefect.cache_policies import INPUTS, TASK_SOURCE\n", + "from prefect.futures import wait\n", + "\n", + "from mlip_arena.tasks.utils import get_calculator\n", + "\n", + "selected_models = [\n", + " \"MACE-MP(M)\",\n", + " \"CHGNet\",\n", + " \"M3GNet\",\n", + " \"MatterSim\",\n", + " \"eqV2(OMat)\",\n", + " \"MACE-MPA\",\n", + " \"ORBv2\",\n", + " \"SevenNet\",\n", + " \"ALIGNN\",\n", + "]\n", + "\n", + "\n", + "@task(cache_policy=TASK_SOURCE + INPUTS)\n", + "def run_one(\n", + " atoms,\n", + " model,\n", + "):\n", + " try:\n", + " result = MD.with_options(\n", + " # timeout_seconds=600,\n", + " # retries=1,\n", + " refresh_cache=True\n", + " )(\n", + " atoms=atoms,\n", + " calculator=get_calculator(\n", + " model.name,\n", + " calculator_kwargs=None,\n", + " ),\n", + " ensemble=\"nvt\",\n", + " dynamics=\"nose-hoover\",\n", + " time_step=None,\n", + " dynamics_kwargs=dict(\n", + " ttime=25 * units.fs,\n", + " # pfactor=((75 * units.fs) ** 2) * 1e2 * units.GPa\n", + " ),\n", + " total_time=1e4, # 5e4, # fs\n", + " temperature=[300, 3000],\n", + " pressure=None,\n", + " traj_file=f\"{REGISTRY[model.name]['family']}/{model.name}_{atoms.info.get('material_id', 'random')}_{atoms.get_chemical_formula()}_nvt.traj\",\n", + " traj_interval=10,\n", + " )\n", + " except Exception as e:\n", + " print(e)\n", + " return e\n", + "\n", + " return result\n", + "\n", + "\n", + "@flow\n", + "def heat():\n", + " futures = []\n", + " # To download the database automatically, `huggingface_hub login` or provide HF_TOKEN\n", + " for atoms in get_atoms_from_db(\"random-mixture.db\", force_download=False):\n", + " for model in MLIPEnum:\n", + " if model.name not in selected_models:\n", + " continue\n", + "\n", + " future = run_one.with_options(\n", + " timeout_seconds=600, retries=2, refresh_cache=False\n", + " ).submit(atoms.copy(), model)\n", + " futures.append(future)\n", + "\n", + " wait(futures)\n", + "\n", + " return [\n", + " f.result(timeout=None, raise_on_failure=False)\n", + " for f in futures\n", + " if f.state.is_completed()\n", + " ]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "heat.with_options(\n", + " task_runner=DaskTaskRunner(address=client.scheduler.address), log_prints=True\n", + ")()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mlip-arena", + "language": "python", + "name": "mlip-arena" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/vacancy_migration/Table-A1-fcc.csv b/examples/vacancy_migration/Table-A1-fcc.csv new file mode 100644 index 0000000000000000000000000000000000000000..a08d6b18ebaaa66c64a012158df33f3498e91133 --- /dev/null +++ b/examples/vacancy_migration/Table-A1-fcc.csv @@ -0,0 +1,58 @@ +symbol,cohesive_energy,K,K',volume_per_atom,e_vacform,e_vacmig +Ac,3.70,21.63,2.03,45.37,1.26,0.45 +Ag,2.49,84.11,5.15,17.87,0.68,0.70 +Al,3.43,76.90,4.23,16.47,0.61,0.58 +Ar,0.02,1.60,3.45,45.00,0.01,0.06 +Au,2.99,136.19,7.45,18.07,0.40,0.53 +Ba,1.87,8.01,2.81,64.13,1.09,0.36 +Be,3.64,116.96,3.90,7.88,-0.06,0.75 +Ca,1.91,16.82,1.70,42.17,1.13,0.47 +Cd,0.74,41.66,5.71,22.60,0.30,0.23 +Ce,4.58,37.65,4.10,26.10,1.30,0.54 +Co,4.92,251.54,5.17,10.30,1.84,1.45 +Co_mag,5.11,209.89,5.07,10.90,1.79,1.01 +Cs,0.70,2.40,2.36,116.46,0.34,0.13 +Cu,3.48,136.99,5.86,11.97,1.07,0.72 +Dy,4.23,,,31.37,1.72, +Er,1.55,16.02,2.66,40.96,1.02,0.47 +Fe,4.69,283.59,4.89,10.22,2.32,1.38 +Ga,2.61,29.64,6.02,18.91,-0.04,0.18 +Ge,3.40,,,19.51,0.10, +He,0.01,1.60,3.36,16.54,0.00,0.02 +Hf,6.41,107.35,3.59,22.26,2.09,0.81 +Ho,4.20,40.86,4.04,30.88,1.74,0.73 +In,2.31,33.65,4.53,27.33,0.28,0.23 +Ir,7.23,345.27,5.44,14.53,1.55,2.54 +K,0.86,3.20,6.19,73.60,0.34,0.16 +Kr,0.02,0.80,7.90,57.44,0.00,0.07 +La,4.22,24.83,3.36,37.10,1.44,0.21 +Li,1.61,13.62,3.75,20.22,0.60,0.13 +Mg,1.49,36.05,4.04,23.03,0.82,0.41 +Mn,3.76,276.38,5.42,10.73,2.38,0.65 +Mn_mag,3.76,275.57,5.58,10.72,2.36,0.69 +Na,1.11,9.61,3.08,35.08,0.38,0.14 +Ni,4.77,197.87,5.29,10.85,1.39,0.94 +Ni_mag,4.83,193.06,5.45,10.90,1.43,1.08 +Os,8.17,398.14,4.99,14.29,2.75,2.73 +Pa,6.86,96.13,4.16,25.21,1.54,0.77 +Pb,2.94,39.25,4.07,31.69,0.45,0.54 +Pd,3.71,165.83,6.62,15.37,1.16,0.95 +Pr,3.58,,,23.79,0.76, +Pt,5.45,246.74,6.10,15.67,0.61,1.24 +Rb,0.76,2.40,7.13,90.81,0.30,0.14 +Re,7.73,366.10,4.73,14.94,2.94,1.81 +Rh,5.63,250.74,5.86,14.13,1.57,1.79 +Ru,6.56,314.03,4.81,13.88,2.51,1.85 +Sc,4.09,54.47,4.07,24.43,2.07,0.60 +Sn,3.11,46.46,6.10,27.82,0.26,0.38 +Sr,1.61,11.22,3.72,54.74,0.95,0.45 +Ta,8.09,198.67,3.86,18.63,2.23, +Tb,4.26,38.45,4.55,31.88,1.75,0.71 +Tc,6.84,298.81,4.91,14.50,2.62,1.17 +Th,6.34,55.28,3.50,32.07,1.92,1.25 +Ti,5.43,102.54,2.89,17.25,1.95,0.45 +Tl,1.99,27.24,5.55,30.50,0.37,0.10 +W,7.98,,,16.25,1.67, +Xe,0.03,0.80,2.22,80.21,0.01,0.09 +Y,4.14,38.45,4.10,32.35,1.72,0.67 +Zr,6.21,90.52,4.42,23.26,2.02,0.50 \ No newline at end of file diff --git a/examples/vacancy_migration/Table-A2-hcp.csv b/examples/vacancy_migration/Table-A2-hcp.csv new file mode 100644 index 0000000000000000000000000000000000000000..408dd62e938b50f68ac7d735f7684d3d73d1cddd --- /dev/null +++ b/examples/vacancy_migration/Table-A2-hcp.csv @@ -0,0 +1,58 @@ +symbol,cohesive_energy,K,K',volume_per_atom,e_vacform,e_vacmig_v,e_vacmig +Ag,2.49,82.51,5.15,17.93,0.76,0.50,0.60 +Al,3.40,74.50,4.70,16.63,0.62,0.41,0.46 +Ar,0.02,0.88,7.43,46.11,0.01,0.06, +Au,2.98,130.58,7.58,18.10,0.40,0.49,0.57 +Ba,1.87,8.01,3.38,63.55,1.11,0.34,0.37 +Be,3.72,122.57,3.57,7.92,1.04,0.74,0.87 +Bi,2.38,,,31.44,0.00,, +Ca,1.91,17.62,3.44,42.39,1.06,0.38,0.42 +Cd,0.74,43.26,6.04,22.60,0.25,0.23,0.16 +Ce,4.49,,,26.43,1.23,, +Co,4.90,248.34,3.29,10.33,1.67,1.02,1.19 +Co_mag,5.12,214.69,4.91,10.85,1.93,0.81,0.78 +Cr,3.63,,,11.77,1.77,, +Cs,0.70,1.60,7.60,116.42,0.31,0.12,0.13 +Cu,3.47,134.58,5.61,12.00,1.04,0.57,0.69 +Fe,4.77,288.39,5.60,10.16,2.43,1.52,1.51 +Ga,2.60,44.06,4.37,18.92,0.21,0.12,0.16 +Ge,3.40,,,19.25,0.26,, +He,0.01,1.60,3.46,16.54,0.00,0.02,0.01 +Hf,6.48,112.95,3.59,22.26,2.26,0.89,1.00 +In,2.31,32.84,4.68,27.40,0.31,0.20,0.21 +Ir,7.16,339.66,3.32,14.60,1.25,1.57,1.95 +K,0.86,3.20,5.95,73.72,0.35,0.12,0.14 +Kr,0.02,0.80,6.73,58.10,0.00,0.07,0.07 +La,4.20,,,37.42,1.45,, +Li,1.61,13.62,3.82,20.24,0.63,0.10,0.12 +Mg,1.50,36.05,4.31,22.85,0.78,0.40,0.42 +Mn,3.82,190.66,11.04,10.63,2.51,0.43,0.11 +Mo,5.91,,,15.95,1.96,, +Nb,6.71,,,18.61,2.08,, +Ne,0.01,6.41,1.65,19.15,-0.01,0.04,0.04 +Ni,4.74,134.58,12.07,10.87,1.35,0.69,0.79 +Ni_mag,4.81,192.26,5.34,10.94,1.37,0.77,0.89 +Os,8.31,406.15,5.04,14.23,3.03,3.03,3.22 +Pa,3.09,90.52,3.96,15.12,0.28,0.38, +Pb,2.92,37.65,4.49,31.51,0.41,0.44,0.41 +Pd,3.67,163.42,6.50,15.45,1.10,0.68,0.73 +Pt,5.39,241.13,6.22,15.77,0.70,0.70, +Rb,0.76,2.40,6.45,91.19,0.32,0.13,0.12 +Re,7.79,370.10,4.67,14.89,3.42,1.79,1.17 +Rh,5.59,250.74,5.76,14.18,1.53,1.25,1.47 +Ru,6.68,311.62,5.16,13.81,2.68,2.17,2.18 +Sc,4.14,52.87,4.82,24.48,1.87,0.74,0.69 +Si,4.04,85.72,4.88,14.35,0.08,0.04,0.26 +Sn,3.11,47.26,5.92,27.55,0.40,0.31,0.33 +Sr,1.61,11.22,3.77,55.16,0.98,0.36,0.40 +Ta,8.05,,,18.55,2.35,, +Tc,6.91,302.01,4.90,14.44,2.85,1.21,0.63 +Te,2.23,48.87,5.03,31.47,0.25,, +Ti,5.49,109.75,5.07,17.29,2.06,0.49,0.59 +Tl,2.01,25.63,5.47,30.97,0.40,0.23,0.16 +V,5.15,,,13.75,1.84,, +W,7.96,,,16.32,2.44,, +Xe,0.03,0.80,2.32,79.60,0.01,0.07,0.08 +Y,4.16,40.05,3.72,32.82,1.87,0.69,0.67 +Zn,1.10,73.70,6.02,15.23,0.47,0.34,0.20 +Zr,6.25,93.73,4.34,23.44,2.03,0.57,0.70 \ No newline at end of file diff --git a/examples/vacancy_migration/analysis.py b/examples/vacancy_migration/analysis.py new file mode 100644 index 0000000000000000000000000000000000000000..b3c250d139d71c9c48d03116e1457de1d7b6256b --- /dev/null +++ b/examples/vacancy_migration/analysis.py @@ -0,0 +1,284 @@ +import glob +import pickle +from pathlib import Path + +import numpy as np +import pandas as pd +from matplotlib import pyplot as plt +from pymatgen.core import Element + +from mlip_arena.models import REGISTRY + +DATA_DIR = Path(__file__).parent + +mlip_models = ["MACE-MP(M)", "MatterSim", "ORBv2", "M3GNet", "CHGNet", "SevenNet"] + +fcc_pbe = pd.read_csv(DATA_DIR / "Table-A1-fcc.csv") +hcp_pbe = pd.read_csv(DATA_DIR / "Table-A2-hcp.csv") + +# fcc + +# Initialize an empty DataFrame +results_df = pd.DataFrame(columns=["symbol", "model", "fit_path", "fit_energies"]) + +for model in mlip_models: + out_dir = Path(REGISTRY[model]["family"]) + + for index, row in fcc_pbe.iterrows(): + symbol = row["symbol"] + + if Element(symbol.split("_")[0]).is_noble_gas: + continue + + files = glob.glob(str(out_dir / f"{model}-fcc-{symbol.split('_')[0]}108.pkl")) + if len(files) == 0: + print("skip", model, symbol) + # Add missing data to the DataFrame + # if symbol not in results_df['symbol'].values: + # Create a new row if the symbol is not yet in the DataFrame + new_row = { + "symbol": symbol, + "model": model, + "pbe_e_vacmig": row["e_vacmig"], + "fit_path": [], + "fit_energies": [], + } + results_df = pd.concat( + [results_df, pd.DataFrame([new_row])], ignore_index=True + ) + continue + file = files[0] + with open(file, "rb") as f: + result = pickle.load(f) + + # Add data to the DataFrame + # if symbol not in results_df['symbol'].values: + # Create a new row if the symbol is not yet in the DataFrame + forcefit = result["neb"]["forcefit"] + new_row = { + "symbol": symbol, + "model": model, + "pbe_e_vacmig": row["e_vacmig"], + "fit_path": forcefit.fit_path, + "fit_energies": forcefit.fit_energies, + } + results_df = pd.concat([results_df, pd.DataFrame([new_row])], ignore_index=True) + + +nrows = 2 +ncols = len(mlip_models) // nrows + +fig, axes = plt.subplots( + nrows=nrows, + ncols=ncols, + figsize=(6, 4), + sharex=True, + sharey=True, + constrained_layout=True, + dpi=300, +) + +for i, (ax, model) in enumerate(zip(axes.ravel(), mlip_models, strict=False)): + filtered_df = results_df[results_df["model"] == model] + + asymmetries = [] + middle_deviations = [] + + for index, row in filtered_df.iterrows(): + if len(row["fit_path"]) == 0 or pd.isna(row["pbe_e_vacmig"]): + continue + + x = row["fit_path"] / max(row["fit_path"]) + y = row["fit_energies"] / row["pbe_e_vacmig"] + + # middle_idx = np.argmin(np.abs(x - 0.5)) + + left_side = y[x <= 0.5] + right_side = y[x >= 0.5][::-1] + min_len = min(len(left_side), len(right_side)) + left_side = left_side[:min_len] + right_side = right_side[:min_len] + + asymmetry = np.abs(left_side - right_side).mean() + # middle = (left_side[-1] + right_side[-1]) / 2 + middle = max(y) + + if np.abs(np.array(y)).max() > 10: + continue + + asymmetries.append(asymmetry) + middle_deviations.append(middle - 1) + + ax.plot( + x, + y, + alpha=0.5, + color=method_color_mapping[model], + label=model, + ) + + asymmetries = np.array(asymmetries) + middle_deviations = np.array(middle_deviations) + + ax.text( + 0.05, + 0.95, + "\n".join( + [ + f"Miss: {len(filtered_df) - len(asymmetries) - filtered_df['pbe_e_vacmig'].isna().sum()}", + f"Asym: {asymmetries.mean():.3f}", + f"MAPE@max: {np.abs(middle_deviations).mean() * 100:.1f}", + ] + ), + transform=ax.transAxes, + ha="left", + va="top", + fontsize="small", + # fontsize=6, + ) + + ax.set( + title=model, + xlabel="Normalized path" if i >= len(models) - ncols else None, + ylabel="Normalized energy" if i % ncols == 0 else None, + ylim=(-0.1, 2), + ) + +with open(DATA_DIR / "fcc.pkl", "wb") as f: + pickle.dump(fig, f) + +# hcp + +# Initialize an empty DataFrame +results_df = pd.DataFrame(columns=["symbol", "model", "fit_path", "fit_energies"]) + +for model in mlip_models: + out_dir = Path(REGISTRY[model]["family"]) + + for index, row in hcp_pbe.iterrows(): + symbol = row["symbol"] + + if Element(symbol.split("_")[0]).is_noble_gas: + continue + + files = glob.glob(str(out_dir / f"{model}-hcp-{symbol.split('_')[0]}36.pkl")) + if len(files) == 0: + print("skip", model, symbol) + # Add missing data to the DataFrame + # if symbol not in results_df['symbol'].values: + # Create a new row if the symbol is not yet in the DataFrame + new_row = { + "symbol": symbol, + "model": model, + "pbe_e_vacmig": row["e_vacmig"], + "fit_path": [], + "fit_energies": [], + } + results_df = pd.concat( + [results_df, pd.DataFrame([new_row])], ignore_index=True + ) + # else: + # # Update the existing row with the model's prediction + # results_df.loc[results_df['symbol'] == symbol, model] = pd.NA + continue + file = files[0] + with open(file, "rb") as f: + result = pickle.load(f) + + # Add data to the DataFrame + # if symbol not in results_df['symbol'].values: + # Create a new row if the symbol is not yet in the DataFrame + forcefit = result["neb"]["forcefit"] + new_row = { + "symbol": symbol, + "model": model, + "pbe_e_vacmig": row["e_vacmig"], + "fit_path": forcefit.fit_path, + "fit_energies": forcefit.fit_energies, + } + results_df = pd.concat([results_df, pd.DataFrame([new_row])], ignore_index=True) + + + +nrows = 2 +ncols = len(mlip_models) // nrows + +threshold = 0.10 + +fig, axes = plt.subplots( + nrows=nrows, + ncols=ncols, + figsize=(6, 4), + sharex=True, + sharey=True, + constrained_layout=True, + dpi=300, +) + +for i, (ax, model) in enumerate(zip(axes.ravel(), mlip_models, strict=False)): + filtered_df = results_df[results_df["model"] == model] + + asymmetries = [] + middle_deviations = [] + + for index, row in filtered_df.iterrows(): + if len(row["fit_path"]) == 0 or pd.isna(row["pbe_e_vacmig"]): + continue + + x = row["fit_path"] / max(row["fit_path"]) + y = row["fit_energies"] / row["pbe_e_vacmig"] + + # middle_idx = np.argmin(np.abs(x - 0.5)) + + left_side = y[x <= 0.5] + right_side = y[x >= 0.5][::-1] + min_len = min(len(left_side), len(right_side)) + left_side = left_side[:min_len] + right_side = right_side[:min_len] + + asymmetry = np.abs(left_side - right_side).mean() + # middle = (left_side[-1] + right_side[-1]) / 2 + middle = max(y) + + if np.abs(np.array(y)).max() > 10: + continue + + asymmetries.append(asymmetry) + middle_deviations.append(middle - 1) + + ax.plot( + x, + y, + alpha=0.5, + color=method_color_mapping[model], + label=model, + ) + + asymmetries = np.array(asymmetries) + middle_deviations = np.array(middle_deviations) + + ax.text( + 0.05, + 0.95, + "\n".join( + [ + f"Miss: {len(filtered_df) - len(asymmetries) - filtered_df['pbe_e_vacmig'].isna().sum()}", + f"Asym: {asymmetries.mean():.3f}", + f"MAPE@max: {np.abs(middle_deviations).mean() * 100:.1f}", + ] + ), + transform=ax.transAxes, + ha="left", + va="top", + fontsize="small", + ) + + ax.set( + title=model, + xlabel="Normalized path" if i >= len(mlip_models) - ncols else None, + ylabel="Normalized energy" if i % ncols == 0 else None, + ylim=(-0.1, 2), + ) + +with open(DATA_DIR / "hcp.pkl", "wb") as f: + pickle.dump(fig, f) diff --git a/examples/wbm_ev/analyze.py b/examples/wbm_ev/analyze.py new file mode 100644 index 0000000000000000000000000000000000000000..ebc5bef0ea0b85eb3a74dbfefe90e7058ef6231d --- /dev/null +++ b/examples/wbm_ev/analyze.py @@ -0,0 +1,210 @@ +from pathlib import Path + +import numpy as np +import pandas as pd +from ase.db import connect +from scipy import stats + +from mlip_arena.models import REGISTRY, MLIPEnum + +DATA_DIR = Path(__file__).parent.absolute() + + +def load_wbm_structures(): + """ + Load the WBM structures from a ASE DB file. + """ + with connect(DATA_DIR.parent / "wbm_structures.db") as db: + for row in db.select(): + yield row.toatoms(add_additional_information=True) + +def gather_results(): + for model in MLIPEnum: + if "wbm_ev" not in REGISTRY[model.name].get("gpu-tasks", []): + continue + + if (DATA_DIR / f"{model.name}.parquet").exists(): + continue + + all_data = [] + + for atoms in load_wbm_structures(): + fpath = Path(model.name) / f"{atoms.info['key_value_pairs']['wbm_id']}.json" + if not fpath.exists(): + continue + + all_data.append(pd.read_json(fpath)) + + df = pd.concat(all_data, ignore_index=True) + df.to_parquet(DATA_DIR / f"{model.name}.parquet") + + +def summarize(): + summary_table = pd.DataFrame( + columns=[ + "model", + "energy-diff-flip-times", + "tortuosity", + "spearman-compression-energy", + "spearman-compression-derivative", + "spearman-tension-energy", + "missing", + ] + ) + + + for model in MLIPEnum: + fpath = DATA_DIR / f"{model.name}.parquet" + if not fpath.exists(): + continue + df_raw_results = pd.read_parquet(fpath) + + df_analyzed = pd.DataFrame( + columns=[ + "model", + "structure", + "formula", + "volume-ratio", + "energy-delta-per-atom", + "energy-diff-flip-times", + "tortuosity", + "spearman-compression-energy", + "spearman-compression-derivative", + "spearman-tension-energy", + "missing", + ] + ) + + for wbm_struct in load_wbm_structures(): + structure_id = wbm_struct.info["key_value_pairs"]["wbm_id"] + + try: + results = df_raw_results.loc[df_raw_results["id"] == structure_id] + results = results["eos"].values[0] + es = np.array(results["energies"]) + vols = np.array(results["volumes"]) + vol0 = wbm_struct.get_volume() + + indices = np.argsort(vols) + vols = vols[indices] + es = es[indices] + + imine = len(es) // 2 + # min_center_val = np.min(es[imid - 1 : imid + 2]) + # imine = np.where(es == min_center_val)[0][0] + emin = es[imine] + + interpolated_volumes = [ + (vols[i] + vols[i + 1]) / 2 for i in range(len(vols) - 1) + ] + ediff = np.diff(es) + ediff_sign = np.sign(ediff) + mask = ediff_sign != 0 + ediff = ediff[mask] + ediff_sign = ediff_sign[mask] + ediff_flip = np.diff(ediff_sign) != 0 + + etv = np.sum(np.abs(np.diff(es))) + + data = { + "model": model.name, + "structure": structure_id, + "formula": wbm_struct.get_chemical_formula(), + "missing": False, + "volume-ratio": vols / vol0, + "energy-delta-per-atom": (es - emin) / len(wbm_struct), + "energy-diff-flip-times": np.sum(ediff_flip).astype(int), + "tortuosity": etv / (abs(es[0] - emin) + abs(es[-1] - emin)), + "spearman-compression-energy": stats.spearmanr( + vols[:imine], es[:imine] + ).statistic, + "spearman-compression-derivative": stats.spearmanr( + interpolated_volumes[:imine], ediff[:imine] + ).statistic, + "spearman-tension-energy": stats.spearmanr( + vols[imine:], es[imine:] + ).statistic, + } + + except Exception: + data = { + "model": model.name, + "structure": structure_id, + "formula": wbm_struct.get_chemical_formula(), + "missing": True, + "volume-ratio": None, + "energy-delta-per-atom": None, + "energy-diff-flip-times": None, + "tortuosity": None, + "spearman-compression-energy": None, + "spearman-compression-derivative": None, + "spearman-tension-energy": None, + } + + df_analyzed = pd.concat([df_analyzed, pd.DataFrame([data])], ignore_index=True) + + df_analyzed.to_parquet(DATA_DIR / f"{model.name}_processed.parquet") + # json_fpath = DATA_DIR / f"EV_scan_analyzed_{model.name}.json" + + # df_analyzed.to_json(json_fpath, orient="records") + + valid_results = df_analyzed[df_analyzed["missing"] == False] + + analysis_summary = { + "model": model.name, + "energy-diff-flip-times": valid_results["energy-diff-flip-times"].mean(), + "tortuosity": valid_results["tortuosity"].mean(), + "spearman-compression-energy": valid_results[ + "spearman-compression-energy" + ].mean(), + "spearman-compression-derivative": valid_results[ + "spearman-compression-derivative" + ].mean(), + "spearman-tension-energy": valid_results["spearman-tension-energy"].mean(), + "missing": len(df_analyzed[df_analyzed["missing"] == True]), + } + summary_table = pd.concat( + [summary_table, pd.DataFrame([analysis_summary])], ignore_index=True + ) + + + flip_rank = ( + (summary_table["energy-diff-flip-times"] - 1) + .abs() + .rank(ascending=True, method="min") + ) + tortuosity_rank = summary_table["tortuosity"].rank(ascending=True, method="min") + spearman_compression_energy_rank = summary_table["spearman-compression-energy"].rank( + method="min" + ) + spearman_compression_derivative_rank = summary_table[ + "spearman-compression-derivative" + ].rank(ascending=False, method="min") + spearman_tension_energy_rank = summary_table["spearman-tension-energy"].rank( + ascending=False, method="min" + ) + missing_rank = summary_table["missing"].rank(ascending=True, method="min") + + rank_aggr = ( + flip_rank + + tortuosity_rank + + spearman_compression_energy_rank + + spearman_compression_derivative_rank + + spearman_tension_energy_rank + + missing_rank + ) + rank = rank_aggr.rank(method="min") + + summary_table.insert(1, "rank", rank.astype(int)) + summary_table.insert(2, "rank-aggregation", rank_aggr.astype(int)) + summary_table = summary_table.sort_values(by="rank", ascending=True) + summary_table = summary_table.reset_index(drop=True) + + summary_table.to_csv(DATA_DIR / "summary.csv", index=False) + summary_table.to_latex(DATA_DIR / "summary.tex", index=False) + + return summary_table + +if __name__ == "__main__": + gather_results() + summarize() diff --git a/mlip_arena/models/externals/fairchem.py b/mlip_arena/models/externals/fairchem.py index 58d31a3731c86f513797f05b120661e9e43f0d9e..403a8b4e5f2f7063c0a4b15fc7ae5b365bc2398b 100644 --- a/mlip_arena/models/externals/fairchem.py +++ b/mlip_arena/models/externals/fairchem.py @@ -87,7 +87,7 @@ class EquiformerV2(OCPCalculator): self, checkpoint=REGISTRY["EquiformerV2(OC22)"]["checkpoint"], # TODO: cannot assign device - local_cache="/tmp/ocp/", + local_cache="~/.cache/ocp/", cpu=False, seed=0, **kwargs, @@ -114,7 +114,7 @@ class EquiformerV2OC20(OCPCalculator): self, checkpoint=REGISTRY["EquiformerV2(OC22)"]["checkpoint"], # TODO: cannot assign device - local_cache="/tmp/ocp/", + local_cache="~/.cache/ocp/", cpu=False, seed=0, **kwargs, @@ -133,7 +133,7 @@ class eSCN(OCPCalculator): self, checkpoint="eSCN-L6-M3-Lay20-S2EF-OC20-All+MD", # TODO: import from registry # TODO: cannot assign device - local_cache="/tmp/ocp/", + local_cache="~/.cache/ocp/", cpu=False, seed=0, **kwargs, diff --git a/mlip_arena/tasks/diatomics/homonuclear-diatomics.tex b/mlip_arena/tasks/diatomics/homonuclear-diatomics.tex new file mode 100644 index 0000000000000000000000000000000000000000..1ab4f94f01f882a9a591574e286473b07c7ac614 --- /dev/null +++ b/mlip_arena/tasks/diatomics/homonuclear-diatomics.tex @@ -0,0 +1,23 @@ +\begin{tabular}{lrrrrrrrrrrr} +\toprule + & Rank & Rank aggr. & Conservation deviation [eV/Å] & Spearman's coeff. (E: repulsion) & Spearman's coeff. (F: descending) & Energy jump [eV] & Force flips & Tortuosity \\ +Model & & & & & & & & \\ +\midrule +MACE-MPA & 1 & 13 & 0.077 & -0.997 & -0.975 & 0.010 & 1.371 & 1.006 \\ +MACE-MP(M) & 2 & 15 & 0.070 & -0.997 & -0.980 & 0.038 & 1.449 & 1.161 \\ +MatterSim & 3 & 20 & 0.013 & -0.980 & -0.972 & 0.008 & 2.766 & 1.021 \\ +M3GNet & 4 & 24 & 0.026 & -0.991 & -0.947 & 0.029 & 3.528 & 1.016 \\ +ORBv2 & 5 & 30 & 9.751 & -0.883 & -0.988 & 0.991 & 0.991 & 1.287 \\ +eSCN(OC20) & 6 & 32 & 2.045 & -0.939 & -0.984 & 0.806 & 0.640 & 5.335 \\ +CHGNet & 7 & 35 & 1.066 & -0.992 & -0.925 & 0.291 & 2.255 & 2.279 \\ +ORB & 8 & 39 & 10.220 & -0.881 & -0.954 & 1.019 & 1.026 & 1.798 \\ +SevenNet & 9 & 41 & 34.005 & -0.986 & -0.928 & 0.392 & 2.112 & 1.292 \\ +eqV2(OMat) & 10 & 51 & 15.477 & -0.880 & -0.976 & 4.118 & 3.126 & 2.515 \\ +eSEN & 11 & 55 & 1.170 & -0.692 & -0.919 & 5.562 & 4.000 & 1.838 \\ +ALIGNN & 12 & 59 & 5.164 & -0.913 & -0.310 & 9.876 & 30.669 & 1.818 \\ +EquiformerV2(OC20) & 13 & 71 & 21.385 & -0.680 & -0.891 & 38.282 & 22.775 & 8.669 \\ +EquiformerV2(OC22) & 14 & 75 & 27.687 & -0.415 & -0.855 & 64.837 & 21.674 & 15.880 \\ +MACE-OFF(M) & 15 & 85 & NaN & NaN & NaN & NaN & NaN & NaN \\ +ANI2x & 16 & 91 & NaN & NaN & NaN & NaN & NaN & NaN \\ +\bottomrule +\end{tabular} diff --git a/mlip_arena/tasks/registry.yaml b/mlip_arena/tasks/registry.yaml index d35035f9abfdac42070cc766c9db73608eb2a343..64f711e9d4cb915e9477248c82e987f7d02e6ca9 100644 --- a/mlip_arena/tasks/registry.yaml +++ b/mlip_arena/tasks/registry.yaml @@ -31,3 +31,8 @@ Lattice thermal conductivity: task-page: thermal-conductivity task-layout: centered rank-page: thermal-conductivity +# 2D materials: +# category: Properties and Physical Behaviors +# task-page: c2db +# task-layout: centered +# rank-page: diff --git a/serve/ranks/homonuclear-diatomics.py b/serve/ranks/homonuclear-diatomics.py index 2236add26400006760015aaf9f40bc36d2859eae..f5d635e2b37b45eef6d5da9a93ded1c01e23129c 100644 --- a/serve/ranks/homonuclear-diatomics.py +++ b/serve/ranks/homonuclear-diatomics.py @@ -20,6 +20,24 @@ dfs = [ ] df = pd.concat(dfs, ignore_index=True) +# df = df[df["method"].isin([ +# "SevenNet", +# "ORBv2", +# "ORB", +# "MatterSim", +# "MACE-MPA", +# "MACE-MP(M)", +# "M3GNet", +# "eSEN", +# "eSCN(OC20)", +# "eqV2(OMat)", +# "EquiformerV2(OC22)", +# "EquiformerV2(OC20)", +# "CHGNet", +# "ALIGNN" +# ] +# )] + table = pd.DataFrame() for model in valid_models: @@ -29,21 +47,13 @@ for model in valid_models: new_row = { "Model": model, "Conservation deviation [eV/Å]": rows["conservation-deviation"].mean(), - "Spearman's coeff. (E: repulsion)": rows[ - "spearman-repulsion-energy" - ].mean(), - "Spearman's coeff. (F: descending)": rows[ - "spearman-descending-force" - ].mean(), + "Spearman's coeff. (E: repulsion)": rows["spearman-repulsion-energy"].mean(), + "Spearman's coeff. (F: descending)": rows["spearman-descending-force"].mean(), "Tortuosity": rows["tortuosity"].mean(), "Energy jump [eV]": rows["energy-jump"].mean(), "Force flips": rows["force-flip-times"].mean(), - "Spearman's coeff. (E: attraction)": rows[ - "spearman-attraction-energy" - ].mean(), - "Spearman's coeff. (F: ascending)": rows[ - "spearman-ascending-force" - ].mean(), + "Spearman's coeff. (E: attraction)": rows["spearman-attraction-energy"].mean(), + "Spearman's coeff. (F: ascending)": rows["spearman-ascending-force"].mean(), "PBE energy MAE [eV]": rows["pbe-energy-mae"].mean(), "PBE force MAE [eV/Å]": rows["pbe-force-mae"].mean(), } @@ -55,14 +65,10 @@ table.set_index("Model", inplace=True) table.sort_values("Conservation deviation [eV/Å]", ascending=True, inplace=True) table["Rank"] = np.argsort(table["Conservation deviation [eV/Å]"].to_numpy()) -table.sort_values( - "Spearman's coeff. (E: repulsion)", ascending=True, inplace=True -) +table.sort_values("Spearman's coeff. (E: repulsion)", ascending=True, inplace=True) table["Rank"] += np.argsort(table["Spearman's coeff. (E: repulsion)"].to_numpy()) -table.sort_values( - "Spearman's coeff. (F: descending)", ascending=True, inplace=True -) +table.sort_values("Spearman's coeff. (F: descending)", ascending=True, inplace=True) table["Rank"] += np.argsort(table["Spearman's coeff. (F: descending)"].to_numpy()) # NOTE: it's not fair to models trained on different level of theory @@ -83,28 +89,45 @@ table["Rank"] += np.argsort(np.abs(table["Force flips"].to_numpy() - 1)) table["Rank"] += 1 -table.sort_values(["Rank", "Conservation deviation [eV/Å]"], ascending=True, inplace=True) +table.sort_values( + ["Rank", "Conservation deviation [eV/Å]"], ascending=True, inplace=True +) table["Rank aggr."] = table["Rank"] -table["Rank"] = table["Rank aggr."].rank(method='min').astype(int) +table["Rank"] = table["Rank aggr."].rank(method="min").astype(int) table = table.reindex( columns=[ "Rank", "Rank aggr.", "Conservation deviation [eV/Å]", - "PBE energy MAE [eV]", - "PBE force MAE [eV/Å]", "Spearman's coeff. (E: repulsion)", "Spearman's coeff. (F: descending)", "Energy jump [eV]", "Force flips", "Tortuosity", + "PBE energy MAE [eV]", + "PBE force MAE [eV/Å]", "Spearman's coeff. (E: attraction)", "Spearman's coeff. (F: ascending)", ] ) +# cloned = table.copy() +# cloned.drop(columns=[ +# "PBE energy MAE [eV]", +# "PBE force MAE [eV/Å]", +# "Spearman's coeff. (E: attraction)", +# "Spearman's coeff. (F: ascending)",], +# inplace=True +# ) +# cloned.to_latex( +# DATA_DIR / "homonuclear-diatomics.tex", +# float_format="%.3f", +# index=True, +# column_format="l" + "r" * (len(table.columns) - 1), +# ) + s = ( table.style.background_gradient( cmap="viridis_r", @@ -135,7 +158,7 @@ s = ( subset=["Rank", "Rank aggr."], ) .format( - "{:.3f}", + "{:.3f}", subset=[ "Conservation deviation [eV/Å]", "Spearman's coeff. (E: repulsion)", @@ -147,7 +170,7 @@ s = ( "Spearman's coeff. (F: ascending)", "PBE energy MAE [eV]", "PBE force MAE [eV/Å]", - ] + ], ) ) @@ -177,4 +200,7 @@ def render(): - **Force flips**: The number of force direction changes. """ ) - st.info('PBE energies and forces are provided __only__ for reference. Due to the known convergence issue of plane-wave DFT with diatomic molecules and different dataset the models might be trained on, comparing models with PBE is not rigorous and thus these metrics are excluded from rank aggregation.', icon=":material/warning:") + st.info( + "PBE energies and forces are provided __only__ for reference. Due to the known convergence issue of plane-wave DFT with diatomic molecules and different dataset the models might be trained on, comparing models with PBE is not rigorous and thus these metrics are excluded from rank aggregation.", + icon=":material/warning:", + ) diff --git a/serve/tasks/eos_bulk.py b/serve/tasks/eos_bulk.py index 39c126f0103214f865d79aae1ee846fcf5f54668..e901a1d13d0cf822eae345a715a228b04f94a801 100644 --- a/serve/tasks/eos_bulk.py +++ b/serve/tasks/eos_bulk.py @@ -34,7 +34,7 @@ selected_models = methods_container.multiselect( ) # Visualization settings -st.markdown("### Visualization Settings") +st.markdown("### Visualization settings") vis = st.container(border=True) # Column settings @@ -83,6 +83,7 @@ def generate_dataframe(model_name): "formula", "volume-ratio", "energy-delta-per-atom", + "energy-delta-per-volume-b0", "energy-diff-flip-times", "tortuosity", "spearman-compression-energy", @@ -97,10 +98,12 @@ def generate_dataframe(model_name): try: results = df_raw_results.loc[df_raw_results["id"] == structure_id] + b0 = results["b0"].values[0] + # vol0 = results["v0"].values[0] results = results["eos"].values[0] es = np.array(results["energies"]) vols = np.array(results["volumes"]) - vol0 = wbm_struct.get_volume() + indices = np.argsort(vols) vols = vols[indices] @@ -110,6 +113,7 @@ def generate_dataframe(model_name): # min_center_val = np.min(es[imid - 1 : imid + 2]) # imine = np.where(es == min_center_val)[0][0] emin = es[imine] + vol0 = vols[imine] interpolated_volumes = [ (vols[i] + vols[i + 1]) / 2 for i in range(len(vols) - 1) @@ -131,6 +135,7 @@ def generate_dataframe(model_name): "volume-ratio": vols / vol0, "energy-delta-per-atom": (es - emin) / len(wbm_struct), "energy-diff-flip-times": np.sum(ediff_flip).astype(int), + "energy-delta-per-volume-b0": (es - emin) / (vol0 * b0), "tortuosity": etv / (abs(es[0] - emin) + abs(es[-1] - emin)), "spearman-compression-energy": stats.spearmanr( vols[:imine], es[:imine] @@ -151,6 +156,7 @@ def generate_dataframe(model_name): "missing": True, "volume-ratio": None, "energy-delta-per-atom": None, + "energy-delta-per-volume-b0": None, "energy-diff-flip-times": None, "tortuosity": None, "spearman-compression-energy": None, @@ -185,10 +191,10 @@ def get_plots(selected_models): structure_id = row["structure"] formula = row.get("formula", "") if isinstance(row["volume-ratio"], list | np.ndarray) and isinstance( - row["energy-delta-per-atom"], list | np.ndarray + row["energy-delta-per-volume-b0"], list | np.ndarray ): vol_strain = row["volume-ratio"] - energy_delta = row["energy-delta-per-atom"] + energy_delta = row["energy-delta-per-volume-b0"] color = color_sequence[i % len(color_sequence)] fig.add_trace( go.Scatter( @@ -203,7 +209,7 @@ def get_plots(selected_models): structure_id + "
" "Formula: " + str(formula) + "
" "Volume ratio V/V₀: %{x:.3f}
" - "ΔEnergy: %{y:.3f} eV/atom
" + "ΔE/(BV₀): %{y:.3f} eV/atom
" "" ), @@ -215,10 +221,10 @@ def get_plots(selected_models): fig.update_layout( title=f"{model_name} ({len(valid_structures)} / {len(df)} structures)", xaxis_title="Volume ratio V/V₀", - yaxis_title="Relative energy ΔE (eV/atom)", + yaxis_title="Relative energy ΔE/(BV₀)", height=500, showlegend=False, # Disable legend for the whole plot - yaxis=dict(range=[-0.1, 1]), # Set y-axis limits + yaxis=dict(range=[-0.02, 0.1]), # Set y-axis limits ) fig.add_vline(x=1, line_dash="dash", line_color="gray", opacity=0.7) figs.append((model_name, fig, valid_structures)) diff --git a/tests/test_external_calculators.py b/tests/test_external_calculators.py index 9894200c73611e0823b7f79856b867130174def5..e9194036ab6f99c68aea8b26dfeea4292ad9687a 100644 --- a/tests/test_external_calculators.py +++ b/tests/test_external_calculators.py @@ -17,12 +17,13 @@ def test_calculate(model: MLIPEnum): if model.name == "ORB": pytest.xfail("Orbital Materials deprecated the model a month after its premature release in favor of ORBv2") + if model.name == "M3GNet": + pytest.xfail("Cache sometimes fails") + try: calc = MLIPEnum[model.name].value() - - except (LocalTokenNotFoundError, HTTPError): - # Gracefully skip the test if HF_TOKEN is not available - pytest.skip("Skipping test because HF_TOKEN is not available for downloading the model.") + except (LocalTokenNotFoundError, HTTPError, FileNotFoundError) as e: + pytest.skip(str(e)) atoms = Atoms( "OO",