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‎MDN_two_planets_prediction.ipynb‎

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},
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"id": "OHSSb5g_iWsK"
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},
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"outputs": [
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'sklearn'",
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"output_type": "error",
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"traceback": [
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"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
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"\u001B[1;31mModuleNotFoundError\u001B[0m Traceback (most recent call last)",
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"\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_19692\\2450612377.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m 22\u001B[0m \u001B[0mcompile\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mFalse\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 23\u001B[0m )\n\u001B[1;32m---> 24\u001B[1;33m \u001B[0minput_scaler\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mjoblib\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mload\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"MDN_MRFe(Mg+Si)k2_Xscaler_20220917.save\"\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 25\u001B[0m \u001B[0moutput_scaler\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mjoblib\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mload\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"MDN_MRFe(Mg+Si)k2_yscaler_20220917.save\"\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
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"\u001B[1;32m~\\anaconda3\\envs\\deepexo\\lib\\site-packages\\joblib\\numpy_pickle.py\u001B[0m in \u001B[0;36mload\u001B[1;34m(filename, mmap_mode)\u001B[0m\n\u001B[0;32m 603\u001B[0m \u001B[1;32mreturn\u001B[0m \u001B[0mload_compatibility\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mfobj\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 604\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 605\u001B[1;33m \u001B[0mobj\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0m_unpickle\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mfobj\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mfilename\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mmmap_mode\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 606\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 607\u001B[0m \u001B[1;32mreturn\u001B[0m \u001B[0mobj\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
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"\u001B[1;32m~\\anaconda3\\envs\\deepexo\\lib\\site-packages\\joblib\\numpy_pickle.py\u001B[0m in \u001B[0;36m_unpickle\u001B[1;34m(fobj, filename, mmap_mode)\u001B[0m\n\u001B[0;32m 527\u001B[0m \u001B[0mobj\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 528\u001B[0m \u001B[1;32mtry\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 529\u001B[1;33m \u001B[0mobj\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0munpickler\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mload\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 530\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0munpickler\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mcompat_mode\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 531\u001B[0m warnings.warn(\"The file '%s' has been generated with a \"\n",
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"\u001B[1;32m~\\anaconda3\\envs\\deepexo\\lib\\pickle.py\u001B[0m in \u001B[0;36mload\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 1086\u001B[0m \u001B[1;32mraise\u001B[0m \u001B[0mEOFError\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 1087\u001B[0m \u001B[1;32massert\u001B[0m \u001B[0misinstance\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mkey\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mbytes_types\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 1088\u001B[1;33m \u001B[0mdispatch\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mkey\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 1089\u001B[0m \u001B[1;32mexcept\u001B[0m \u001B[0m_Stop\u001B[0m \u001B[1;32mas\u001B[0m \u001B[0mstopinst\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 1090\u001B[0m \u001B[1;32mreturn\u001B[0m \u001B[0mstopinst\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mvalue\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
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"\u001B[1;32m~\\anaconda3\\envs\\deepexo\\lib\\pickle.py\u001B[0m in \u001B[0;36mload_global\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m 1374\u001B[0m \u001B[0mmodule\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mreadline\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m-\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdecode\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"utf-8\"\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 1375\u001B[0m \u001B[0mname\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mreadline\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m-\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdecode\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m\"utf-8\"\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 1376\u001B[1;33m \u001B[0mklass\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mfind_class\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mmodule\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mname\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 1377\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mappend\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mklass\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 1378\u001B[0m \u001B[0mdispatch\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mGLOBAL\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m]\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mload_global\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
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"\u001B[1;32m~\\anaconda3\\envs\\deepexo\\lib\\pickle.py\u001B[0m in \u001B[0;36mfind_class\u001B[1;34m(self, module, name)\u001B[0m\n\u001B[0;32m 1424\u001B[0m \u001B[1;32melif\u001B[0m \u001B[0mmodule\u001B[0m \u001B[1;32min\u001B[0m \u001B[0m_compat_pickle\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mIMPORT_MAPPING\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 1425\u001B[0m \u001B[0mmodule\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0m_compat_pickle\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mIMPORT_MAPPING\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mmodule\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m-> 1426\u001B[1;33m \u001B[0m__import__\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mmodule\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mlevel\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 1427\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mproto\u001B[0m \u001B[1;33m>=\u001B[0m \u001B[1;36m4\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 1428\u001B[0m \u001B[1;32mreturn\u001B[0m \u001B[0m_getattribute\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0msys\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmodules\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mmodule\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mname\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
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"\u001B[1;31mModuleNotFoundError\u001B[0m: No module named 'sklearn'"
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]
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}
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],
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"outputs": [],
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"source": [
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"input_parameters = [\n",
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" 'Mass', \n",
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(1, 4)"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"scaled_input.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-10-17T02:52:10.770472Z",
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[1.77 ],\n",
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" [1.228],\n",
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" [0.685],\n",
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" [0.819]])"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"input_array"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"a = [2,3,4,5]\n",
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"b = np.array([a]).T"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[2],\n",
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" [3],\n",
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" [5]])"
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(1, 260)"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"pred.shape\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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{
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},
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},
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"hide_input": false,
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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‎README.md‎

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**Model A** has a better predictive accuracy, but its application is limited by some difficulties in measuring the tidal Love number `k2` of rocky exoplanets. **Model B** significantly breaks the density-composition degeneracy and accurately predicts the interior properties of rocky exoplanets. Along with the development of space-based observation technologies, orbital or shape observations could be possible to determine the Love number `k2` of rocky exoplanets and hence the machine learning models B and C would be applied more broadly.
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## Quick Start
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### Step 1:
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[Fork and clone](https://help.github.com/articles/fork-a-repo) a copy of the `Rocky_Exoplanets_v2` repository to your local machine.

‎deepexo/model/model_b.h5‎

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‎deepexo/model/model_b_scaler.save‎

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‎deepexo/rockyplanet.py‎

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from tensorflow.keras.models import load_model
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import matplotlib.pyplot as plt
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from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)
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import numpy as np
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import math
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import mdn
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import joblib
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import os
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OUTPUT_DIMS = 6 # 6 outputs:
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# 'H2O_radial_frac', 'Mantle_radial_frac', 'Core_radial_frac', 'Core_mass_frac', 'P_CMB', 'T_CMB',
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N_MIXES = 20 # 20 mixtures
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# print(os.getcwd())
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model_path = os.path.join(os.getcwd(), "deepexo/model")
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class RockyPlanet:
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def __init__(self):
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self.model_a = load_model(r"model/model_a.h5", custom_objects={
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# print('init RockyPlanet')
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self.model_a = load_model(os.path.join(model_path, "model_a.h5"), custom_objects={
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'MDN': mdn.MDN(OUTPUT_DIMS, N_MIXES),
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"mdn_loss_func": mdn.get_mixture_loss_func(OUTPUT_DIMS, N_MIXES)}, compile=False)
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self.model_a_scaler = joblib.load(os.path.join(model_path, "model_a_scaler.save"))
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self.model_b = load_model(os.path.join(model_path, "model_b.h5"), custom_objects={
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self.model_b = load_model(r"model/model_b.h5", custom_objects={
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self.model_b_scaler = joblib.load(r"model/model_b_scaler.save")
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self.model_b_scaler = joblib.load(os.path.join(model_path, "model_b_scaler.save"))
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2230
def predict(self, planet_params):
31+
"""Predictes the .
2332
if len(planet_params) == 3:
2433
print("正在调用inputs={}的model A进行预测".format(planet_params))
2534
scaled_params = self.model_a_scaler.transform(np.array(planet_params).reshape(1, -1))
26-
return self.model_a.predict(scaled_params)[0]
35+
return self.model_a.predict(scaled_params)
2736
elif len(planet_params) == 4:
2837
print("正在调用inputs={}的model B进行预测".format(planet_params))
29-
scaled_params = seyhlf.model_b_scaler.transform(np.array(planet_params).reshape(1, -1))
30-
return self.model_b.predict(scaled_params)[0]
38+
scaled_params = self.model_b_scaler.transform(np.array(planet_params).reshape(1, -1))
39+
return self.model_b.predict(scaled_params)
3140
else:
3241
raise ValueError(
3342
"Invalid number of planet parameters. Expected 3 or 4, but got {}".format(len(planet_params)))
43+
44+
def plot(self, pred):
45+
46+
(y_min1, y_max1, y_min2, y_max2, y_min3, y_max3,
47+
y_min4, y_max4, y_min5, y_max5, y_min6, y_max6) = \
48+
0.00015137, 0.145835, 0.127618, 0.973427, 0.00787023, 0.799449, 1.17976e-06, 0.699986, 10.7182, 1999.49, 1689.37, 5673.87
49+
mus = np.apply_along_axis((lambda a: a[:N_MIXES * OUTPUT_DIMS]), 1, pred)
50+
sigs = np.apply_along_axis((lambda a: a[N_MIXES * OUTPUT_DIMS:2 * N_MIXES * OUTPUT_DIMS]), 1, pred)
51+
pis = np.apply_along_axis((lambda a: mdn.softmax(a[-N_MIXES:])), 1, pred)
52+
53+
for m in range(OUTPUT_DIMS):
54+
locals()['mus' + str(m)] = []
55+
locals()['sigs' + str(m)] = []
56+
for n in range(20):
57+
locals()['mus' + str(m)].append(mus[0][n * OUTPUT_DIMS + m])
58+
locals()['sigs' + str(m)].append(sigs[0][n * OUTPUT_DIMS + m])
59+
x_max = [1, 1, 1, 1, 1, 1]
60+
x_maxlabels = [
61+
y_min1 + (y_max1 - y_min1) * x_max[0],
62+
y_min2 + (y_max2 - y_min2) * x_max[1],
63+
y_min3 + (y_max3 - y_min3) * x_max[2],
64+
y_min4 + (y_max4 - y_min4) * x_max[3],
65+
y_min5 + (y_max5 - y_min5) * x_max[4],
66+
y_min6 + (y_max6 - y_min6) * x_max[5],
67+
]
68+
69+
wrf = [] # H2O_radial_frac
70+
mrf = [] # Mantle_radial_frac
71+
crf = [] # Core_radial_frac
72+
cmf = [] # Core mass frac
73+
pcmb = [] # CMB temperature
74+
tcmb = [] # CMB pressure
75+
76+
for x1 in np.arange(0.02, 0.15, 0.04):
77+
wrf.append((x1 - y_min1) / (x_maxlabels[0] - y_min1) * x_max[0])
78+
for x2 in np.arange(0.2, 1, 0.2):
79+
mrf.append((x2 - y_min2) / (x_maxlabels[1] - y_min2) * x_max[1])
80+
for x3 in np.arange(0.1, 0.9, 0.2):
81+
crf.append((x3 - y_min3) / (x_maxlabels[2] - y_min3) * x_max[2])
82+
for x4 in np.arange(0.1, 0.8, 0.2):
83+
cmf.append((x4 - y_min4) / (x_maxlabels[3] - y_min4) * x_max[3])
84+
for x5 in np.arange(200, 2000, 400):
85+
pcmb.append((x5 - y_min5) / (x_maxlabels[4] - y_min5) * x_max[4])
86+
for x6 in np.arange(2000, 6000, 1000):
87+
tcmb.append((x6 - y_min6) / (x_maxlabels[5] - y_min6) * x_max[5])
88+
89+
xticklabels = [[round(x, 2) for x in np.arange(0.02, 0.15, 0.04)],
90+
[round(x, 2) for x in np.arange(0.2, 1, 0.2)],
91+
[round(x, 2) for x in np.arange(0.1, 0.9, 0.2)],
92+
[round(x, 2) for x in np.arange(0.1, 0.8, 0.2)],
93+
[round(x, 2) for x in np.arange(200, 2000, 400)],
94+
[round(x, 2) for x in np.arange(2000, 6000, 1000)],
95+
]
96+
xticks = [wrf, mrf, crf, cmf, pcmb, tcmb]
97+
98+
colors = [
99+
"steelblue",
100+
"#EA7D1A",
101+
"red",
102+
"gold",
103+
"#2ecc71",
104+
"#03a9f4"
105+
]
106+
predict_label = [
107+
"Water radial fraction",
108+
"Mantle radial fraction",
109+
"Core radial fraction",
110+
"Core mass fraction",
111+
r"CMB pressure ($10^2$ GPa)",
112+
r"CMB temperature ($10^3$ K)"
113+
]
114+
115+
y_label = np.arange(0, 1, 0.001).reshape(-1, 1)
116+
fig = plt.figure(figsize=(6, 6))
117+
fig.subplots_adjust(hspace=0.7, wspace=0.2)
118+
for i in range(OUTPUT_DIMS):
119+
ax = fig.add_subplot(3, 2, i + 1)
120+
mus_ = np.array(locals()['mus' + str(i)])
121+
sigs_ = np.array(locals()['sigs' + str(i)])
122+
factors = 1 / math.sqrt(2 * math.pi) / sigs_
123+
exponent = np.exp(-1 / 2 * np.square((y_label - mus_) / sigs_))
124+
GMM_PDF = np.sum(pis[0] * factors * exponent, axis=1) # Summing multiple Gaussian distributions
125+
plt.plot(
126+
y_label,
127+
GMM_PDF,
128+
color=colors[i],
129+
# label=label,
130+
lw=2,
131+
zorder=10,
132+
)
133+
ax.set_xlim(0, x_max[i])
134+
ax.set_ylim(bottom=0)
135+
136+
ax.xaxis.set_minor_locator(AutoMinorLocator())
137+
ax.yaxis.set_minor_locator(AutoMinorLocator())
138+
# print(xticks[i])
139+
# xticklabels[i]
140+
ax.set_xticks(xticks[i])
141+
ax.set_xticklabels(xticklabels[i])
142+
ax.set_xlabel(predict_label[i])
143+
144+
return plt.show()

‎requirements.txt‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -2,7 +2,7 @@ hdf5==1.10.4
22
h5py==2.10.0
33
joblib==0.14.1
44
tensorflow==2.3.0
5-
scikit-learn==0.24.0
5+
scikit-learn==0.22.1
66
tensorflow_probability==0.11.0
77
tensorflow-estimator==2.3.0
88
matplotlib==3.1.3

‎run.py‎

Lines changed: 18 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -1,10 +1,22 @@
11
from deepexo.rockyplanet import RockyPlanet
22

3-
M = 1.77, # mass in Earth masses
4-
R = 1.228, # radius in Earth radii
5-
cFeMg = 0.685, # bulk Fe/(Mg + Si) (molar ratio)
6-
k2 = 0.819, # tide Love number
3+
M = 1.77 # mass in Earth masses
4+
R = 1.228 # radius in Earth radii
5+
cFeMg = 0.685 # bulk Fe/(Mg + Si) (molar ratio)
6+
k2 = 0.819 # tide Love number
77

8-
planet_params = [M, R, cFeMg, k2]
8+
planet_params = [
9+
M,
10+
R,
11+
cFeMg,
12+
k2,
13+
]
914
rp = RockyPlanet()
10-
pred = rp.predict(planet_params)
15+
pred = rp.predict(planet_params)
16+
rp.plot(pred)
17+
# model_a = rp.load_model("model/model_a.h5")
18+
# model_b = rp.load_model("model/model_b.h5")
19+
# model_a_scaler = rp.load_scaler("model/model_a_scaler.save")
20+
# model_b_scaler = rp.load_scaler("model/model_b_scaler.save")
21+
22+
# pred = rp.predict(planet_params, model_a, model_a_scaler)

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