{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Initial imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PLAMS working folder: /home/hellstrom/temp/newirmd/plams_workdir\n" ] } ], "source": [ "import scm.plams as plams\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "plams.init()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Molecule" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAH4AAABcCAYAAACoRwG1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAaOElEQVR4nO2deXgUZbr276eruqu6ujsrHEICgRAiBiICCQRkHAgji4ijGFRcQERFRkHRw1EzniMfgzMuI+jnjJ4RcAkIjGFRGFBUxiUiB0HiGNkDsgQCCUKSTnf1Ul31fH8EOWIS0p3uTvAjv+uqv+pd7s5d776EmBntXHqY2lpAO21Du/GXKO3GX6K0G3+J0m78JUq78ZcoUTWeiNo/rIuUiBtDRJfHxMQslSTJQ0S6w+GokiTpKSKKjXRe7bSciBpPRMMVRdkxe/bs2w4fPiwHAgEUFxd3vP766wvsdnsJEXWIZH7thAEzR+QBYJVlufaf//wn/xzDMPihhx7yxcbGvhep/NqfMP2KWELAXXl5eXXcBNXV1Wy1Wj0Aktr6R7c/HLmqPjY29jcTJkywN/U+Li4OOTk5PgA5RCQQUX8iGklE1xHR0PZmoHURI5UQMxu6rl8wjKZpJgG4RQbeTCCSOhMZEhHOMOOgYUixRBudwAsAtvDZaqSdKBGpqgPALYMGDXJyE1RWVrIsSXyLIKhfKwqzw3Hec8Zu5/mSpKcQuRzAPgC92ro6/P/5oUgVLCKyKIpyrLCwsOOECRPOe2cYBibdcQfEDRtQ2Ew6BjMWaZrxqM/nUoGRzLwtIgJbGSKSAYwH0BNALYA1zHysbVX9LxEzHgCIaICiKJ9MnjxZvv/++6XOnTujpKQEf5o3D7xzJzYyw04UVFr/CAQw0eOpVYGBzFwWMZGtgCiKd1osllcHDhyIoUOH2srLy/1r1qyBKIqrnE7nvczsa2uNEa9CACTLsvyMw+E4YbVaPR0cDv2/JYm9dnuD6r255y+SpMcC37R1tRjKYzKZxicmJrq//fZb/im1tbU8atQoNSYmZnVba4xoVd8YcUQ7C2W5zw1mc4viB5iR5HKpp4GrmPnbCMuLOEREDofj6Nq1a7vk5eU1eO/1epGcnKxWV1fnMvPONpB4jqjNpRPRADNR2nViywcOIhFmWiwWO/BIBKVFk5y4uLi44cOHN/pSlmVMnz7dYrVap7aurIZEzXgHMHOm2SyJQbbpTTHNbBYDwC1EZIuQtGjSqUePHjpd4DdnZGSIsiyntqKmRoma8RbgyqGCIISbTmeTCfFEAQBdIiAr2lTs379fuFDzuWvXLs3j8XzfipoaJWrGG0BsXJil/UdiiAwAcRFJLLp843a7T23cuLHRly6XC4sWLQp4vd7XW1lXA6LXxgNeb4Q6jj5mAuCJSGJRhJnZ6XQ+eNttt6lffvnlee9OnTqFMWPGqMy8ipn3tZHEc0RsyrYRKo8y9x4aZiIBZpxilgCcioSoaMPMHxDRHaNGjXo9PT3dnJmZKe3bt8/YtWuXQEQ7NE1bT0TdARzhaA6pmiFqwzkimvxrQXjlc0VpcuEmGN7TNNzt9X5Xzdw3UtqiDRF1N5vNs5j5fqvVSl27duX4+HgTAK6qqvIdO3ZMNAyjTtO0Fw3DWMTMZ1pdYxSNl63Aqe9sNnu6qeUtytWqWrdZ13/HzMsiKC8qEJEgCMJjgiD815AhQ4Rf/epXlo4dOzYIx8w4cuQIPv/8c8/OnTv9mqbdw8yrW1VrNGsbhejFe83mB16WZUtL4u/VdQxQ1ToP0JEvhmnOC0BEdkmSNnbu3LnfpEmTbImJiUHFO3ToEAoLC92qqi7z+/2/Y2YjylIBRNl4IkpRgJ3LZTku1Nm7amZku93qCeYCD/PLUZIYEYjIIknS51lZWf3uuOMO2RRiDaeqKl555RX11KlThT6f74EoyTyPaM7cEQCbCrxws9frH+fz4Qm/H29rGvbqOi70wVUaBq5WVfUUc+HFbjoAmM3mOV27du3bEtMBQFEUPPjgg4osy3cR0fVRkNiAiJd4IjIDuEuW5SdMJlPn7t2766mpqXZJksjv86HqyBGUHz2KeF3Hw8yYajZDOjver2VGoabxPL/f42Z+yQP8Z1v2fIOBiPpIkrS9oKDAGhcXF1ZaBw4cwGuvvVataVoqM7uIKA7ARAnoJQGJGuD0AEcBFDHz4XDyiuhwjoiulCRpZXJycvLo0aNtl112GRorAcyMsrIy/HXjRjx7/DimMeOgYXj/HgjAAmyqBZ5j5s2R1BYtJEkqGDFihCVc0wGgZ8+e6Nmzp2XPnj2PxxB1k4Gbx4iiMdhkUmKIoALYbxj+ZZo2N55oaw3wPIAPW9IviORGjBssFsvy/Px866BBg+hC89U/wszYsWMHVhYVGQG/f5UOPMzMJyMiqBUgIocoilVz5syRHQ5H2OkZhoEVhYU4UFrKj5rNxn1ms9CpkYKjMuPvgQCe9flcJ5lL6oBxzFwXSl4RaeOJ6DeSJC2fMWOGkpubG5TpZ+MhJycHD8+aZTLL8vUABkRCTysysFOnTr5ImM7MWP3228C+fdivKPSfktSo6QCgEGGq2YzdNps9XxQHOYCviCgmlPzCNp6I4s1mc9HUqVOV1NRUuN1uVFZWwuVyBZ1GcnIypk2bZjWbzct/Ybtts9PS0qyRSOjj99+He/dufCqK6BBkB1EkwhuyLE8QxTQHsJ6Igl4UC7uNlyRpwYABA2wOhwNLly7F3r170aFDB5w+fRrp6enIy8tD9+7dm02nR48eGDJkiLxt27ZXANwarq7WwGKxdEtMTGzRHMVPcTqd+OLTT1EmSbCFuLBFRFgoy/L/qGr/vYYxFsA/gokXVoknokRd1ydeeeWV0sKFC3HnnXfixIkTOHz4MKqqqjBjxgwUFhZi377g1iSuvfZaSdf13xJRUji6fqYxhYh+Q0T5RDSOiAafHXmEDdcvHoXNV1u24BazGU1V7c0hEqHAYrHHAY8FGyfcqv6O3r17G+vXr8fChQsxa9Ys/NjeWa1W3HvvvVi1ahWKiorQ3J77H+P0798fJpPp7nBEEZGJiEbHEW1SgIM5JtPqUYLwxjBBeDud6EMbUCkRzSWi5CDTiyei7kTUhYjO1ZKaph2trq7WwtGq6zq+Ki7GzDCXsG8RRQTqD6ukBxM+LOMVRbk2KSlJkWUZP99S/SMjRoxAeno6du3aFVSaffr0kWVZHtNSTUTUzwYc6Um06nlJGlFpt0vbbbbYDxUl5jNFiT1gt8dsUZT4SWbzY1bgewfRaz8182waJiK6VlGU9ZIk/SCKYqXNZttptVr3mUwm1Waz7Tabzf8HwKHvv/9ebalWAPj++++RAuDKMPesyESYYjabBOD2YMKH1cbrut4fAIYPH44L9eRHjhyJHTt2oG/f5hfYunTpgkAg0KKVOCLKU4B/LJRl5TZRbHJ00VcQsFgQ5BckCTd6PHeW6PplRDSGmX1ENNxisSyNjY2Ny8vLs2dkZCAxMREmk8kM1G+YLC8vz/z6668fLykpocrKSvHMmTNISEhoiWTU1tYiPUIbVi4zmSwK0D2YsGEZHwgEYu12O2pray8Yrrq6GuYg5+pjY2OhaVrI4yMi6qsA6zZYrbbhQW7wjCPCx1arMt7jyf1c1/8uSdIxRVGm3n777UqfPn0a/ZhlWUZGRgYyMjLk3/72t1i5cqW+YMECPPDAA0hODqrlOA+/3w97hOZS7EQQgaDuIQipqicihYjuTkxM/DIxMXEXEVkyMjKwceNGOJ3ORuP4/X4UFRWhT58+QeVhGAaIKKS/BBGRA1j1qiwHbfqPmInwjixbLWbzuISEhGn9+vVTVq1ahccffxwLFixAcXEx/H5/o3FtNhumTJki3HjjjXj11Vdx/PjxkPIG6j8kZ4RKvJMZGnA6mLBBG09EaTabrSwvL+/lhQsXXrV27dreHTp0MAUCAWRlZWHatGkIBALnxTEMA4888ghSUlKQlBRcR/3MmTMwm82hbkwY4iBKniSKLfoLvqbrMNvtosfjsYwYMQLffPMNqqursWTJEtTV1WHRokXwer1Nxs/JycFNN92ExYsXXzBcYyQkJKC0mUWrYNmu6x4V2B9M2KCmbIlIsNvtB+bOndv10UcfPdcLuf3226HrOrKzs7FkyRIwMx5++GFcfvnlOHToEF5++WU4nU5MnToViqIEJ377drz77rsfuN3usWfzJgA9AKQAIABVAPYz87lhQizRmqck6YZ/t1hC7qyWGQZy/H7AasV7772Hnx+EYGZMnjwZR48exU033XTBtJYtWwZBEDBx4sSg82dmLPjDH7DE60WotdVPqWZGisvl9QDdmLmqufDB5nRtampq4k9NB4AxY8Zg/vz5uOqqq3DPPfdg3759WLx4Merq6mCz2dCvXz/06dMHoeyyLikpcauquo6IRlqt1n8XRfHXFouF4+PjNQBwOp2CqqpmRVH+5fF4FgD4RAbG3m02t2iEMscw0K1XL6R269bAdKB+guTPf/4zMjIycO2118JqbXqiLj8/H3PmzEFubi7S0tKCyv/YsWM44/P5X/D7TcPFljv/lqYZZmCjGoTpQJDGOxyOCffdd1+DDtfNN9+MWbNm4dSpU+jYsSMyMzORmZkZquZz1NTUoKysTLBYLI/ExMQkjxgxwnbFFVfQz+fCVVXFvn37cj/99NPFJ0+e9Mf5/YEEIinU/H4wDKwPBHC51Yr8/PwmwyUlJSEzMxPHjh1DRkZGk+FkWcagQYP0V199lYcNG4ahQ4eK8fHxjYY9fvw4iouLvSUlJQFN0x79J/DSLl0X+7RgWOdkxnN+v+fs3QJBEZTxoigqMTEN1wCsVitmzZqFNWvWYMqUKRcc0jUHM6OoqMhvMpnE8ePHZwwePLjJ4ZiiKOjfvz/69+/v2L17N5a//Tb+IxDA84IQkoaPdR2XpaVBFMVm29hg2+Dc3Fxh+/btVcXFxWs/++yzO2JiYgLdu3cX4+PjJWbmyspKz9GjR8nn82m6rv9F1/VXmblSJPKO8Hj+VqIoSkoIM3g+ZozzeFQXcxGALcHGC6qNF0Xx0fz8/HnvvPNOg4ba7/ejX79+yMrKwtChLd9MvW3bNl69ejVmzZpFnTt3Dimu2+3GG3/9K8bW1OD/hmD+I5qGAyNGwOZwoLa2Fh988EGj4SoqKtCrVy88+eSTF6zqgfqZuMcee0zTdT0RgB/AFahfdewAwABwHMAOAPt+2k8BABvRE3ai//rUalV6B1Hyq5kxVlXV3YbxqRO4kZkDzUY6S1Cflq7rhevWraM9e/Y0eGexWDB69Gh97dq1gZKSkhZ1TUtLS7Fy5Uq66667QjYdqB9W3TNzJtbIMlYEgv7t2C0I+LekJAwYMABbt27Fpk2bGoQxDAOzZ89GdnZ2s6YDgCAIiI2N9QDoycw+Zv6amRcy85+Y+VlmXsrMu39uOgC4mZ89zfy7Aarqyfd41M2BQKM1zX7DwENer6+ry+XdaRhvOoEbQjEdCNJ4Zj6tadr0oUOHqkVFRdC0+unpqqoqFBQU6AsXLqzx+/3jVqxYcWbVqlU+ny+4DbF+vx/vvvuuf+nSpcaoUaO4d+/eoWg/D0VRcMuUKZip66gygtuQ4gcgiiIkScKkSZNw880344knnkBZWRlqa2uxadMmXHPNNdixYwfGjh0btBZRFBlAixaCAsxLfEDye4HA78d6PMfT3e66mz0e1/1er/d2j8ed43Y7+7nddYs17S9uILOOeUZjH1FzhLQDh4hGxcXF/cnv92c5HA6ttrZWNJvNq+vq6gqYuZyIEiRJWiSK4pi8vDxp8ODBgt3e8DyF2+3GV199ZXzyySdev9+/t2PHjr1mz55tC6eP8COrly/HsNJSzAtipvBGw0DMTTchOzsbAPDDDz9g8+bNKC0thcfjQVJSErKzs5Gbmxv0zCMAPPXUUy6n0zmImRtWkSFwtpMzFEA66mfk3ABOAtgU7nbzFm29IqJOAGIAnGDmBjsuiGigLMuPapo23mazacnJyYYsyyafz8fHjx8nt9ttMZvN671e7wtWq/VvEydO7HvllVeG8zvOceLECbzx4os4brHA3MyHNM/vR/HgwRg3fnxE8gbq5/J///vfa4ZhKKFWv61Ji8aNzFwJoPIC77cDuI2IBKfT2cvpdPYBoKD+4ONuAHsDgUCAiGL8fn9mVlZWS2Q0SufOnWG12VDq8yG7mQ5SrsmEpXv3RixvoH61TZKkvaqqXrSmA9E9NImzbc/us09j9O/YsaMqCEJELzhOSU3Fjr17mzV+hCDgVHU1KioqWrTA0hjFxcUuj8fz14gkFkXa+lrx7klJSWFfnvBz4lNS8Hdd5zNNNGPMjC26jlu9XlXVNPf7778f2gR7E1RUVODgwYMAsDwS6UWTqJb4IBBMJlNIPbpDhw5hy5YtKCsrAzMjLS0Nubm5580YmkwmbDGMgykuV9d8UTSuEUVrHAAfgGPM/Jrf7zrB7PIAL+jAiv37928vLS1NCWa/QFPouo633nrLHQgEZjXW77nYaGvja+rq6oIeinzxxRfYvHkzCgoKMGHCBAiCgA0bNmDevHk4dOjQuSFXXV1dwGcYbwBY/E4gMPX9QGCgiSgRzF4/cLIOWIH6nrEBAER0y7Jlyz5OSEhQunQJ/cYVwzCwfPlyr9Pp3MrMb4ScQBsQ1UOTzWZOlGa1Wnc+88wzzS7dHT58GCtWrMC2bduQmnr+3UFnzpzBoEGDMGzYMPTt2xcLFiyoPXr06G3M3PhUXCOYTKabLBbL0smTJyvB7h0A6tcNli9f7ikrK9vp8/nymNkddOQ2pK3b+MOBQCBQWdnkAOEcW7duxWOPPdbAdKB+TfuPf/wjtm7dCq/Xi4qKChnA9lCEGIaxxufzjS0sLKxasmSJ99SpC1/AEQgEsGPHDjz99NNqWVnZMp/PN+yXYjrQxiUeACwWywuDBw+ekZ+ff8HVtaeffhrbt29vco++z+eDzWbD+PHjef369R97PJ7RLdFDRHaLxTKXmad16dKFMzMz7V27diWbzQZd11FVVYUjR474SkpKQES1JpNpr67rW71e72L+BV292ubGE1E3i8Wyp6CgwNrUEiYAzJs3DyUlJY2WeADQNA1WqxVWq9XtcrnGMfNnYeqyAhhnNpuvNpvNVzFzHBFpzHxQ1/V0m83WbebMmZa0tDThu+++0xYvXhwwDONtl8s1vbUuNwiHtq7qwcxHDMN4dunSpW7jAnPsPXr0wLp165p8v379enTo0IE1TdsQrulndXmYeaXf73/I7XbnqKra0+12Z5rN5sB1113X7eTJk9a5c+cKU6ZMwfz5883l5eXWyy+//HabzfancPNuDS6GEp8Gk/ygRcSDAwb0k2+99dZGl1UPHjyI1atXo6SkBJ06dTrvXV1dHQYOHIgDBw5U67qezszVUdKa4XA4SisrK+XGVuqOHz+Onj17erxeb6dQT6+2Nm1S4onIQkRDBNHxoUmw7e7Q/Z6ZKQOWy7vLPHjzreVQ1YZnFNLT0zFgwADk5OTgzTffhNPphKqqKCoqQnZ2NsrLyz26rmdHy/SzuvMnTZokNLU8m5KSgsGDB2sAWnwgpLVo1XE8EUlWq3WOxWJ5SDdEJa7LbZSc9QwEsX4Fz95hGE7uKcAfn5mPcWOvQXZ2NiyW/z2TeM011yAlJQUvvfQSpk+fDsMwYLfbDZfLVRoIBEYHs8kwHERRjE9OTr7gMl1ycrKAIPe2tyWtZjwRWRwOx6asrKyBpTv3Syl9FiE26fw1bkG0IfmKl+E6fSs2bX4e7617Gj3TeyGteyckJCSAiOB0OmEymdyCIJAgCPtqamr+AGBta1yZomla2ZdffukG0OiFysyMr776ygBwMNpawqXV2niTyTQ9Jydn/tHyKsWa+jzikm9oNo5fLYfr9Bfw1X4Nw18OZh1qzd6A6irfCtbuY+bILq01AxE5JEmq/Pbbb629evVq8P6jjz5Cfn5+hcvl6nqx9+xbzfi4uLiDQ68e0eObvTHolPVai9MJ+GuwZ1NvVfefGcPMX0RQYlBIkjQtLi7uxZUrVypXX301iAiGYWDdunWYPHmyWldXN56ZP2ptXaHSmiVel62xpvRh30GUGt72GAqnD7+Oil1PfhLwV/8mQvJCQhTFiYqizI+Pj3ekpqYa+/fvF30+3/Ha2trpzPxpW2gKlVYzXhRFrWO3W8Skvm+GnZYecGPXB129hq6mM3NFBOSFzNn/lJ2D+t2zx5i5tC10tJRWG86ZRMdpS/zIiKQliDYo8QP9AAZHJMEWwMwGM29j5vd/aaYDrWi8ruuCNbZfxNKzJQyxg8Rf2i1ZFw1RN57OwkZAEi0tuzygMUSpg8lkCrOzcAkTzbtsR8XHx38uCIJGRAG73aJUH3sHkRrlGIYPzPpF/18rLlaiYryiKE926tTp3QULFvy6pqZG8Hq9preXLhFijRWo3HlfRMz31PzLzYY3uIt12mlANC4xzk1ISPhk586dys+PQ3k8Hgwekodq091I7DalxXkwM3Z/mO7SvBW/ZuZvwpR8SRLxEh8TEzO7oKBAbuwMnNVqxfPPzYXnxN/CysN9Zgv0QF0tgH+FldAlTMSNZ+arr7/++ibTHTlyJGp+2A2jhc0zM+Pknrmqoat/vtivNL+YibjxRMQXuszQMIyzJ0Bbdk6uuvxtVmtKKsD6f7dQYjuITon/aPXq1U06v2HDBtjsMYahh34vYN2pz1D+7cMeI+CawMyNX0XVTlBEo3PXNyYm5n927Nih9OzZ87x3NTU1yM7Odh86VF4syknD0gatUJT4nGbTZDbww6HXjIpdT3pYV8PeT9dOdEp8qdfrfSQnJ0d97rnnjMOHD6OiogKvv/46srKy3FVVVW8xa9dpnmP3lm0eWXe0ZJrXU9v4f9RmI4CairXY/9kQ94ndT+1iXc1pNz0yRPP/zmXHxMT8h67row3DECRJ+rqmpuY5AB/92Ckjon8jkzSDSJhhEmwWa3y2YbGmmJl19jp3+T3OnbLJZNmva7XPAninJRcAtNM4bb7ZEji30pWB+rtiOgLQUb+LpSTa26kuVS4K49tpfdp8X307bUO78Zco7cZforQbf4nSbvwlSrvxlyjtxl+i/D/Xo4uI7cvk5gAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "mol = plams.from_smiles(\"NC(CO)OCC=O\")\n", "plams.plot_molecule(mol)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Engine settings" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Engine GFNFF\n", "EndEngine\n", "\n", "\n" ] } ], "source": [ "s = plams.Settings()\n", "s.input.GFNFF # GFN-FF force field\n", "# s.input.MLPotential.Model = \"AIMNet2-wB97MD3\" # new ml model in AMS2024\n", "\n", "print(plams.AMSJob(settings=s).get_input())\n", "\n", "# run in serial\n", "s.runscript.nproc = 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Some general parameters" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "T = 298 # temperature in K\n", "max_freq = 4000 # plot spectrum up to 4000 cm^-1\n", "max_dt_fs = 2000 # maximum correlation in fs for dipole derivative acf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Equilibration\n", "\n", "``temperature=(500, T, T)`` means that in the first half the simulation the system is cooled from 500 K to the gievn temperature, and then kept constant at that temperature.\n", "\n", "The initial temperature of 500 K does some preliminary conformer search." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[18.02|08:09:28] JOB nvt_eq STARTED\n", "[18.02|08:09:28] JOB nvt_eq RUNNING\n", "[18.02|08:09:31] JOB nvt_eq FINISHED\n", "[18.02|08:09:31] JOB nvt_eq SUCCESSFUL\n" ] } ], "source": [ "eq_job = plams.AMSNVTJob(\n", " settings=s,\n", " name=\"nvt_eq\",\n", " molecule=mol,\n", " timestep=0.5,\n", " nsteps=10000,\n", " temperature=(500, T, T),\n", ")\n", "eq_job.run();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## NVE production simulation\n", "\n", "The ``binlog_dipolemoment`` option stores the dipole moment at every time step." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[18.02|08:09:31] JOB nve_single_prod STARTED\n", "[18.02|08:09:31] JOB nve_single_prod RUNNING\n", "[18.02|08:09:48] JOB nve_single_prod FINISHED\n", "[18.02|08:09:48] JOB nve_single_prod SUCCESSFUL\n" ] } ], "source": [ "job = plams.AMSNVEJob.restart_from(\n", " eq_job,\n", " name=\"nve_single_prod\",\n", " nsteps=50000,\n", " binlog_dipolemoment=True,\n", " binlog_time=True,\n", " samplingfreq=100,\n", " timestep=0.5,\n", ")\n", "job.run();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dipole derivative autocorrelation function" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "times, dipole_deriv_acf = job.results.get_dipole_derivatives_acf(start_fs=0, max_dt_fs=max_dt_fs)\n", "plt.plot(times, dipole_deriv_acf)\n", "plt.xlabel(\"Time (fs)\")\n", "plt.ylabel(\"Dipole deriv. autocorrelation (e bohr / fs)^2\")\n", "plt.title(\"Raw autocorrelation function\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ideally, you should set ``max_dt_fs`` above to a large enough number so that the autocorrelation function decreases to a constant value of 0 (**and** have a long enough MD simulation to get enough statistics!)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## IR spectrum\n", "\n", "The IR spectrum is the Fourier transform of the above autocorrelation function:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x_freq, y_intens_raw = job.results.get_ir_spectrum_md(times, dipole_deriv_acf, max_freq=max_freq)\n", "plt.plot(x_freq, y_intens_raw)\n", "plt.xlabel(\"Frequency (cm^-1)\")\n", "plt.title(\"IR spectrum (from raw autocorrelation function)\")\n", "plt.xlim(500, max_freq)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There seems to be quite some \"noise\" in the IR spectrum. One reason for this is that there is still some signal (or noise?) in the autocorrelation function at dt = 2000 fs.\n", "\n", "However, it's also possible to use a tapering (window) function to make the autocorrelation function smoothly decrease to 0. This will make the resulting IR spectrum look a bit more tidy. See the next section." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tapering function for autocorrelation function" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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mfV3EJY9+ymfLtwUdS0TqkAo9hpkZ1/dvz9t3DSC9QSI3jpvJA5OXcKhMX5iKxCIVej3QvVVj3rlrANf2bccTH6/kujEzWL9zX9CxRKSWqdDriYZJCdx/9ak8MrgPSzeVMPDhqXy0eHPQsUSkFqnQ65lBfbL454/PpF2zhtw2voCHPliqozeKxAgVej3UoXkj3rj9DK7Nacuj/17BLc/P0rHWRWKACr2eSkmM509X9+aPV/Vi5qoiLnvsM+YX7gw6loicABV6PTc4tz2vjc4H4OqnpjNp1tqAE4nI8apWoZvZxWa21MxWmNm9FYxPN7O/m9mXZrbQzG6t/ahSV3q3a8Lff3Qm/Ts14943F3DvG/N1LBiRKHTMQjezeOAJYCDQAxhiZj3KTXYnsMg51xs4F3jQzJJqOavUoWaNknjh1lzuPK8zk2avY+izM9i2W78uFYkm1VlDzwVWOOdWOecOAJOAQeWmcUCamRmQChQBOjh3lImPM/7jou48NuQ0FqwvZtDjn7Now66gY4lINVWn0LOAdSG3C/1hoR4HTgY2AAuAu51z3/nMbmYjzazAzAq2bt16nJGlrv2gdxteG3UGh53jh09N470FG4OOJCLVUJ1CtwqGld9x+SJgHtAG6AM8bmaNv3Mn58Y453KcczmZmZk1jCrh1KttOm/fNYDurdO4fcJcHv5wmY7aKBLhqlPohUC7kNtt8dbEQ90KvOk8K4Cvge61E1GC0iIthYkj8rjq9Cwe/nA5d02cq9PciUSw6hT6bKCrmXX0v+gcDLxTbpq1wPkAZtYSOAlYVZtBJRgpifE8eE1vfn3Jybz/1SYGj5nBlpLSoGOJSAWOWejOuUPAXcBkYDHwqnNuoZmNNrPR/mT/C5xhZguAj4BfOud0rNYYYWaMOLsTY27MYfnm3Vz5xDSdDUkkAplzwWwXzcnJcQUFBYE8txy/BYXFDBs/m9IDZTx1Q1/O7JoRdCSResXM5jjncioap1+KSo30apvO3+4cQJsmDbjl+Vm8Onvdse8kImGhQpcay2rSgNduzye/c3N+8cZ8Hpi8RHvAiEQAFbocl8YpiYy7pR9Dcr2TZtz9yjxKD5YFHUukXksIOoBEr8T4OP5wZS/aN2vE/e8vYfOuUsbelEN6g8Sgo4nUS1pDlxNiZtx+bmceHXIaX6zdwXXPTGfzLu3WKBIEFbrUist7t+H5W3JZV7SXq56cxootu4OOJFLvqNCl1pzZNYNXRuWz/1AZ1zw9jS/W7gg6kki9okKXWtUzK503bj+Dxg0SuX7sTD5esiXoSCL1hgpdal2H5o14ffQZdG7RiOEvFvD6nMKgI4nUCyp0qROZaclMGplPfqfm3PPalzz1yUqC+lWySH2hQpc6k5qcwLhb+nF57zbc//4S7ntviUpdpA5pP3SpU0kJcTx8XR+aNkxkzNRVlJQe5PdX9CI+rqLD7IvIiVChS52LizN+e/kppKUk8vjHKygpPcRD1/YhKUEfEEVqkwpdwsLMuOeik0hLSeC+95awZ/8hnrqhLymJ8UFHE4kZWkWSsBp1Tmfuu6oXnyzbyk3jZlFSejDoSCIxQ4UuYTcktz2PDD6NuWt2cP3YmRTtORB0JJGYoEKXQFzeuw1jburLss0lXPfMdDYV6/gvIidKhS6B+V73lowflsvG4lKueWYaa7bvCTqSSFRToUug8jo15+UR/SkpPcS1z0xn5VYd1EvkeKnQJXCntm3CKyPzKTsM1z0zg6WbdAJqkeOhQpeIcFKrNCaNzCPOYMjYGSzcUBx0JJGoo0KXiNGlRSqvjsonJSGO68fOZH7hzqAjiUQVFbpElOyMRrwyKp/GDRIYOnYmc9bomOoi1aVCl4jTrllDXhmZT0ZaMjc+N5OZq7YHHUkkKqjQJSK1adKAV0bm0aZJA25+fhafLd8WdCSRiKdCl4jVonEKk0bmkd28EcPGz+bjpTr7kUhVVOgS0TJSk5k4Io9uLVMZ9eIcPli4KehIIhFLhS4Rr2mjJCYMz+PkNo25Y8Jc3luwMehIIhFJhS5RIb1BIi/dlkvvdk340cQvVOoiFVChS9RIS0nkhVv7qdRFKlGtQjezi81sqZmtMLN7K5nmXDObZ2YLzWxK7cYU8ajURSp3zEI3s3jgCWAg0AMYYmY9yk3TBHgSuNw5dwpwTe1HFfGo1EUqVp019FxghXNulXPuADAJGFRumuuBN51zawGcc9q/TOqUSl3ku6pT6FnAupDbhf6wUN2Apmb2iZnNMbObKnogMxtpZgVmVrB169bjSyziU6mLHK06hW4VDHPlbicAfYFLgYuA/zKzbt+5k3NjnHM5zrmczMzMGocVKS8tJZHxw7T3iwhUr9ALgXYht9sCGyqY5n3n3B7n3DZgKtC7diKKVC01OUGlLkL1Cn020NXMOppZEjAYeKfcNG8DZ5lZgpk1BPoDi2s3qkjlVOoi1Sh059wh4C5gMl5Jv+qcW2hmo81stD/NYuB9YD4wC3jWOfdV3cUW+S6VutR35lz5zeHhkZOT4woKCgJ5boltu/cf4uZxs5i3biePDzmNgb1aBx1JpNaY2RznXE5F4/RLUYk5R9bU+/hr6u9/pQN6Sf2gQpeYdKTUT22bzl0vz+VfizYHHUmkzqnQJWalJifwwrBcTslK544Jc/h4iX7vJrFNhS4xrXFKIi8Oy6V7q8aMemkOU5bpB20Su1ToEvPSGyTy19ty6ZKZysgXC/h8hU5nJ7FJhS71QpOGSUwY3p+OGY24bfxspq/Uiacl9qjQpd7wznzUn/bNGjLshdnM+roo6EgitUqFLvVK89RkJgzPo02TFG55fhZz1qjUJXao0KXeyUzzTjzdqnEKN4+bzRdrdwQdSaRWqNClXmrROIWXR+TRPDWJm56bxfzCnUFHEjlhKnSpt1qlpzBxRB5NGiVyw7Mz+Wp9cdCRRE6ICl3qtTZNGvDy8DzSUhK54bmZLNqwK+hIIsdNhS71XrtmDZk4Io8GifHc8NxMlm4qCTqSyHFRoYsA7Zt7pZ4Yb1w/dgbLN6vUJfqo0EV82RmNeHlEHnFxxpCxM1m5dXfQkURqRIUuEqJzZioTR/QHHEPGzODrbXuCjiRSbSp0kXK6tEhjwvA8Dh32Sn3NdpW6RAcVukgFTmqVxku39af0UBnXj53JuqK9QUcSOSYVukglerRpzEu39aek9CBDxs5g/c59QUcSqZIKXaQKPbPSeWl4f4r3HWTImBlsLFapS+RSoYscw6ltm/DisFyK9hxgyJgZbN5VGnQkkQqp0EWq4bT2TRk/rB9bS/YzZOwMtpSo1CXyqNBFqqlvh2Y8f2suG3eWcv3YmWwt2R90JJGjqNBFaiC3YzPG3dKPwh17GfrsDLbtVqlL5FChi9RQfufmjLulH2uL9jJ07Ey2q9QlQqjQRY7DGZ0zGHdzP1Zv38P1KnWJECp0keN0RpcMxt3ilfrQZ2dStOdA0JGknlOhi5yAAV0yeO7mfny9bQ/Xj52hUpdAqdBFTtCZXTN49uYcVm3z1tR3qNQlICp0kVpwVtdMnr0ph5Vbd6vUJTAqdJFacna3TMbelMOKrbu54bmZ7NyrUpfwqlahm9nFZrbUzFaY2b1VTNfPzMrM7OraiygSPc7plsmYG/uyfIu3pq5Sl3A6ZqGbWTzwBDAQ6AEMMbMelUx3PzC5tkOKRJNzT2rBMzf2Zflmb029eO/BoCNJPVGdNfRcYIVzbpVz7gAwCRhUwXQ/At4AttRiPpGodJ5f6ss2qdQlfKpT6FnAupDbhf6wb5hZFnAl8HRVD2RmI82swMwKtm7dWtOsIlHlvO4tePrG01m6qYQbx82keJ9KXepWdQrdKhjmyt1+GPilc66sqgdyzo1xzuU453IyMzOrGVEken2ve0ueuuF0Fm/cxU3PqdSlblWn0AuBdiG32wIbyk2TA0wys9XA1cCTZnZFbQQUiXbnn9ySp4b2ZdHGXdw0bha7SlXqUjeqU+izga5m1tHMkoDBwDuhEzjnOjrnsp1z2cDrwB3Oub/VdliRaHVBj5Y8ObQvizYUc+Nzs7RNXerEMQvdOXcIuAtv75XFwKvOuYVmNtrMRtd1QJFY8f2QUh/63Az9+EhqnTlXfnN4eOTk5LiCgoJAnlskSB8v3cKov86hU0YjJgzvT/PU5KAjSRQxsznOuZyKxumXoiJhdt5JLb459O7gMTqdndQeFbpIAM7smsHzt+Syfuc+Bj8zg03FKnU5cSp0kYDkd27O+GG5bCnZz3VjprN+576gI0mUU6GLBKhfdjNevC2Xoj0HuO6Z6awr2ht0JIliKnSRgJ3evikvD8+jpPQQ1z0zndXb9gQdSaKUCl0kAvRqm87EEXmUHjrMdWOms2LL7qAjSRRSoYtEiB5tGjNxRB5lhx2Dx8xg2eaSoCNJlFGhi0SQk1qlMWlkPnEGg8fM4Kv1xUFHkiiiQheJMF1apPLKqHwaJMYzZOwMClYXBR1JooQKXSQCdcxoxKuj88lMTebG52YxdZkONy3HpkIXiVBZTRrwyqh8sjMaMXx8Ae9/tTHoSBLhVOgiESwzLZlJI/LomdWYOybM5fU5hUFHkgimQheJcOkNE/nrbf05o3MG97z2JeOnrQ46kkQoFbpIFGiUnMCzN+dwYY+W/Pc7C3n838sJ6kipErlU6CJRIiUxnieHns5Vp2Xx5w+W8cf3lqjU5SgJQQcQkepLiI/jz9f0plFyAs9MXcWu0kP8/oqexMdVdOpfqW9U6CJRJi7O+N2gU0hLSeDJT1ayc+8B/nJdH1IS44OOJgFToYtEITPjFxd3p3lqMv/7j0UU7ZnF2JtzaJySGHQ0CZC2oYtEsdvO7Mgjg/swd+0Orn16Opt36UQZ9ZkKXSTKDeqTxbhb+rGuaC9XPTmNVVt1pMb6SoUuEgPO6prJpJH5lB4s4+qnpzNv3c6gI0kAVOgiMaJX23TeuP0MUpMTGDJmBp8s3RJ0JAkzFbpIDMnOaMTrt+fTKdM7/subc3WogPpEhS4SY1qkpTBpZB79OzXjZ69+qV+V1iMqdJEYlJaSyLhb+n3zq9JfvD6fg2WHg44ldUz7oYvEqOSEeB68tjftmjXkkY+Ws6F4H08O7Ut6A+2rHqu0hi4Sw8yMn36/G3++pjezvi7i6qemUbhjb9CxpI6o0EXqgav7tmX8sFw27yrliiem8aV2a4xJKnSReuKMzhm8eccZpCTGcd2Y6XywcFPQkaSWqdBF6pEuLdJ4644BnNSqMaNemsOYqSu1B0wMqVahm9nFZrbUzFaY2b0VjB9qZvP9yzQz6137UUWkNhw5rd3Anq34w7tL+PlrX1J6sCzoWFILjlnoZhYPPAEMBHoAQ8ysR7nJvgbOcc6dCvwvMKa2g4pI7WmQFM/jQ07npxd048256xkydgZbdGCvqFedNfRcYIVzbpVz7gAwCRgUOoFzbppzbod/cwbQtnZjikhti4sz7r6gK0/fcDpLNpZw+eOfM79wZ9Cx5ARUp9CzgHUhtwv9YZW5DXivohFmNtLMCsysYOvWrdVPKSJ15uKerXnj9jOIjzOueXo6b89bH3QkOU7VKfSKzm1V4bcoZnYeXqH/sqLxzrkxzrkc51xOZmZm9VOKSJ3q0aYx79w1gN5tm3D3pHn86f0llB3Wl6XRpjqFXgi0C7ndFthQfiIzOxV4FhjknNteO/FEJFyapybz0vD+DMltx5OfrGTYC7PZsedA0LGkBqpT6LOBrmbW0cySgMHAO6ETmFl74E3gRufcstqPKSLhkJQQxx+u7MX/XdmT6Su3c9ljn7GgsDjoWFJNxyx059wh4C5gMrAYeNU5t9DMRpvZaH+y/wc0B540s3lmVlBniUWkTpkZQ/t34LXR+Tjn+OFT05g0a23QsaQaLKgfFeTk5LiCAvW+SCQr2nOAuyd9wafLt3FtTlt+N6gnKYnxQceq18xsjnMup6Jx+qWoiFSqWaMkXrg1lx99rwuvFhTyw6emsXa7Du4VqVToIlKl+Djj5xeexHM357CuaC+XPvYp/5y/MehYUgEVuohUy/knt+SfPz6LTpmp3PnyXP7zrQU6ZECEUaGLSLW1a9aQ10fnM+qcTrw8cy2XP/4ZyzaXBB1LfCp0EamRxPg4fjXwZF4clkvRngNc/vhnTJy1VkdtjAAqdBE5Lmd3y+Tdu8+iX3YzfvXmAu56+Qv9EClgKnQROW4t0lIYf2suv7y4O5MXbuKih6fy8dItQceqt1ToInJC4uKM28/tzN/uHEDThknc+vxs/vOtBezZfyjoaPWOCl1EakXPrHTevmsAo87uxMRZaxn4yKcUrC4KOla9okIXkVqTkhjPry45mVdG5uNwXPPMdO57d7F2bwwTFbqI1Lrcjs147+6zGdyvHc9MXcXFD09l2sptQceKeSp0EakTqckJ3HfVqUwY3h8HXD92Jr98fT7Few8GHS1mqdBFpE4N6JLB+3efzahzOvH63ELOf2gK7y7YqP3W64AKXUTqXIOkeH418GTevnMArdKTuWPCXIa9MJvV2/YEHS2mqNBFJGx6ZqXztzsG8OtLTmbW10Vc+Jep/HnyUvYe0C6OtUGFLiJhlRAfx4izO/Hve87l0lNb8/jHK7jgQW2GqQ0qdBEJRMvGKfzluj68Oiqfxg0SuWPCXG54biaLNuwKOlrUUqGLSKByOzbjHz86k98NOoWv1u/i0sc+5WevzmP9zn1BR4s6OgWdiESM4r0HeXLKCp7/fDUAtw7I5o5zu5DeIDHYYBGkqlPQqdBFJOKs37mPhz5YxptfFNI4JZHbz+3MjXkdaJScEHS0wKnQRSQqLdqwiz9NXsInS7fSrFESw8/qyE352aTW42JXoYtIVPti7Q4e+Wg5nyzdSpOGiYw4qxM35XcgLaX+bYpRoYtITJi3biePfrScfy/ZQlpKAtf3b88tZ2TTOr1B0NHCRoUuIjFlfuFOnpmyive+2kicGT/o3YbhZ3XklDbpQUercyp0EYlJ64r2Mu7zr3ll9jr2Higjr1MzhvbvwEWntCIpITb3ylahi0hMK953kImz1vLSjDUU7thH80ZJXN23LUNy25Od0SjoeLVKhS4i9cLhw45PV2zj5Zlr+HDxFsoOO/I7NeeK09pwcc/WMbE/uwpdROqdLbtKebVgHW/MXc/X2/aQFB/Hed0zGdQni+91b0FKYnzQEY+LCl1E6i3nHAvWF/P2vA38/csNbCnZT8OkeM7qmsEFJ7fke91b0Dw1OeiY1aZCFxEByg47ZqzazuSFm/hw0WY2FJdiBqe3b8o53TLJ79yc3m2bRPQXqip0EZFynHMs2riLDxdt4cPFm/lqQzHOQYPEeHKym5LfuTmnt29Kz6z0iPpl6gkXupldDDwCxAPPOuf+WG68+eMvAfYCtzjn5lb1mCp0EYkkO/ceYMaqImas2s60ldtYtnk3AGbQJTOVU9s2oVdWY7q2TKNLi1RapCXjVV94VVXox/yzY2bxwBPA94FCYLaZveOcWxQy2UCgq3/pDzzl/ysiEhWaNEzi4p6tuLhnKwC2797P/MJivizcyfzCYqYs28Ibcwu/mT4tOYFOLVLJbt6QVukptG6cQqv0BrROT6FpwyTSUhJIS0kgIT58m2+q8zkiF1jhnFsFYGaTgEFAaKEPAl503ur+DDNrYmatnXMbaz2xiEgYNE9N5rzuLTivewvA20SzpWQ/K7bsZsWW3azc6v07d+0ONhWXcrCs4q0djZLiaZScQGJ8HPFxRkK8MaRfe0ac3anWM1en0LOAdSG3C/nu2ndF02QBRxW6mY0ERgK0b9++pllFRAJjZrRsnELLxikM6JJx1LjDhx1Few+wqbiUjcWlFO87yK59BykpPcSu0oPs2X+Ig2WOssOHOXjYkZlWN3vVVKfQK9pIVP5PUXWmwTk3BhgD3jb0ajy3iEjEi4szMlKTyUhNpmdWcMeTqc7GnUKgXcjttsCG45hGRETqUHUKfTbQ1cw6mlkSMBh4p9w07wA3mScPKNb2cxGR8DrmJhfn3CEzuwuYjLfb4jjn3EIzG+2Pfxp4F2+XxRV4uy3eWneRRUSkItXaW9459y5eaYcOezrkugPurN1oIiJSE5H7+1YREakRFbqISIxQoYuIxAgVuohIjAjsaItmthVYc5x3zwC21WKc2hKpuSBysylXzShXzcRirg7OucyKRgRW6CfCzAoqO9pYkCI1F0RuNuWqGeWqmfqWS5tcRERihApdRCRGRGuhjwk6QCUiNRdEbjblqhnlqpl6lSsqt6GLiMh3ResauoiIlKNCFxGJEVFX6GZ2sZktNbMVZnZvmJ+7nZl9bGaLzWyhmd3tD/+tma03s3n+5ZKQ+/zKz7rUzC6qw2yrzWyB//wF/rBmZvYvM1vu/9s0nLnM7KSQeTLPzHaZ2U+CmF9mNs7MtpjZVyHDajx/zKyvP59XmNmjdoJnCa4k1wNmtsTM5pvZW2bWxB+ebWb7Qubb0yH3CUeuGi+3MOV6JSTTajOb5w8P5/yqrBvC+x5zzkXNBe/wvSuBTkAS8CXQI4zP3xo43b+eBiwDegC/Be6pYPoefsZkoKOfPb6Osq0GMsoN+xNwr3/9XuD+cOcqt+w2AR2CmF/A2cDpwFcnMn+AWUA+3lm63gMG1kGuC4EE//r9IbmyQ6cr9zjhyFXj5RaOXOXGPwj8vwDmV2XdENb3WLStoX9zwmrn3AHgyAmrw8I5t9E5N9e/XgIsxjt3amUGAZOcc/udc1/jHS8+t+6THvX84/3r44ErAsx1PrDSOVfVr4PrLJdzbipQVMHzVXv+mFlroLFzbrrz/ue9GHKfWsvlnPvAOXfIvzkD7wxglQpXrioEOr+O8NdkrwUmVvUYdZSrsm4I63ss2gq9spNRh52ZZQOnATP9QXf5H5HHhXysCmdeB3xgZnPMOxk3QEvnnznK/7dFALmOGMzR/9GCnl9Q8/mT5V8PVz6AYXhraUd0NLMvzGyKmZ3lDwtnrpost3DPr7OAzc655SHDwj6/ynVDWN9j0Vbo1ToZdZ2HMEsF3gB+4pzbBTwFdAb6ABvxPvZBePMOcM6dDgwE7jSzs6uYNqzz0bxTF14OvOYPioT5VZXKcoR7vv0aOARM8AdtBNo7504Dfga8bGaNw5irpsst3MtzCEevNIR9flXQDZVOWkmGE8oWbYUe+MmozSwRb4FNcM69CeCc2+ycK3POHQbG8u1mgrDldc5t8P/dArzlZ9jsf4Q78jFzS7hz+QYCc51zm/2Mgc8vX03nTyFHb/6os3xmdjNwGTDU/+iN//F8u399Dt52127hynUcyy2c8ysBuAp4JSRvWOdXRd1AmN9j0Vbo1TlhdZ3xt9E9Byx2zj0UMrx1yGRXAke+gX8HGGxmyWbWEeiK94VHbedqZGZpR67jfan2lf/8N/uT3Qy8Hc5cIY5acwp6foWo0fzxPzKXmFme/164KeQ+tcbMLgZ+CVzunNsbMjzTzOL96538XKvCmKtGyy1cuXwXAEucc99srgjn/KqsGwj3e+xEvtkN4oJ3MupleH9tfx3m5z4T7+PPfGCef7kE+CuwwB/+DtA65D6/9rMu5QS/Sa8iVye8b8y/BBYemS9Ac+AjYLn/b7Nw5vKfpyGwHUgPGRb2+YX3B2UjcBBvLei245k/QA5eka0EHsf/tXUt51qBt331yHvsaX/aH/rL90tgLvCDMOeq8XILRy5/+AvA6HLThnN+VdYNYX2P6af/IiIxIto2uYiISCVU6CIiMUKFLiISI1ToIiIxQoUuIhIjVOgiIjFChS4iEiP+P6tSX1HBXV2yAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def tapered_cosine(x):\n", " return 0.5 * (np.cos(np.pi * x / np.max(x)) + 1)\n", "\n", "\n", "plt.plot(times, tapered_cosine(times))\n", "plt.title(\"Tapering / cutoff / window function\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now apply this function to the autocorrelation function:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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aPPLQx9AqpVQmDR55BHVJdqWUyqDBIw9dnkQppTJp8MhD53kopVQmDR556AxzpZTKpMEjD32GuVJKZdLgkYc+hlYppTJp8MjDrXno8iRKKdVMg0cezUN127kgSil1ANHgkYdoh7lSSmXQ4JGH22wV1+ChlFJJGjzy0CXZlVIqkwaPPPQZ5koplUmDRx66PIlSSmXS4JGHjrZSSqlMGjzy0D4PpZTKpMEjDx1tpZRSmTR45KHLkyilVCYNHnno8iRKKZVJg0ceuiS7Ukpl0uCRh87zUEqpTK0SPERkooisEJEqEZnqs19E5D67/10ROTFfXhHpKiIvishK+7OL3X6eiMwXkSX258da4xyyCdgrpDUPpZRq1uLgISJB4LfAJGAkcIWIjExLNgkYZv9dB9xfQN6pwBxjzDBgjn0PsA24yBhzLDAFeKSl55CLNlsppVSm1qh5jAWqjDGrjDFNwAxgclqaycDDxvEmUCkivfPknQxMt6+nA5cAGGMWGmOq7falQKmIlLTCefjSZ5grpVSm1ggefYF1nvfr7bZC0uTK28sYsxHA/uzpc+xPAQuNMY1+BROR60RknojM27p1a4Gnkyqgy5MopVSG1gge4rMt/U6bLU0hef0PKjIKuBv4crY0xpgHjTFjjDFjevToUcjH+h0H0JqHUkp5tUbwWA/097zvB1QXmCZX3s22aQv7c4ubSET6AU8BVxpjPmyFc8hK53kopVSm1gge7wDDRGSwiESAy4GZaWlmAlfaUVenADW2KSpX3pk4HeLYn88AiEgl8BxwmzHmP61Q/pySCyNq1UMppZJCLf0AY0xMRG4EngeCwEPGmKUicr3d/wAwC7gQqALqgKty5bUffRfwpIhcA6wFLrPbbwSGAt8Tke/ZbecbY5I1k9bkNlvFNXYopVRSi4MHgDFmFk6A8G57wPPaADcUmtdu3w5M8Nl+B3BHC4tcMG22UkqpTDrDPA+d56GUUpk0eOSh8zyUUiqTBo889DG0SimVSYNHHs1PEmzngiil1AFEg0ce7lDduLZbKaVUkgaPPHR5EqWUyqTBIw9dnkQppTJp8ChAQHSeh1JKeWnwKEBARJutlFLKQ4NHAZzg0d6lUEqpA4cGjwIEArowolJKeWnwKIA2WymlVCoNHgXQZiullEqlwaMAIoXP84gnjI7MUkod8jR4FCAgUvDyJGPueJGvPLpg/xZIKaXamQaPAgSKqHnsrIsye+mm/VwipZRqXxo8ChAQ0bWtlFLKQ4NHAQIB7TBXSikvDR4F0OVJlFIqlQaPAug8D6WUSqXBowCFzvPQWehKqcOFBo8CFDrPI5pItEFplFKq/bVK8BCRiSKyQkSqRGSqz34Rkfvs/ndF5MR8eUWkq4i8KCIr7c8udns3EXlFRPaKyG9ao/z5BEQKqlXE4lrzUEodHlocPEQkCPwWmASMBK4QkZFpySYBw+y/64D7C8g7FZhjjBkGzLHvARqA7wHfamnZCxUscLSVBg+l1OGiNWoeY4EqY8wqY0wTMAOYnJZmMvCwcbwJVIpI7zx5JwPT7evpwCUAxphaY8wbOEGkTRTabNUU12YrpdThoTWCR19gnef9erutkDS58vYyxmwEsD97FlswEblOROaJyLytW7cWmz2p0OVJYln6PGYuruaDzXv2+fhKKXWgaY3gIT7b0m+12dIUknefGWMeNMaMMcaM6dGjxz5/TqHLk0Rj/mluenwh5//itX0+vlJKHWhaI3isB/p73vcDqgtMkyvvZtu0hf25pRXKuk8Kneeho62UUoeL1gge7wDDRGSwiESAy4GZaWlmAlfaUVenADW2KSpX3pnAFPt6CvBMK5R1n4gIhXRneDvMC53z0RCNU9cU29eiKaVUuwi19AOMMTERuRF4HggCDxljlorI9Xb/A8As4EKgCqgDrsqV1370XcCTInINsBa4zD2miKwGOgEREbkEON8Y835LzyWbQpcniXoiTCxhiATyL6h4xt0vU9cU5/0fTmxxOZVSqq20OHgAGGNm4QQI77YHPK8NcEOhee327cCELHkGtaC4RXOG6hYXPNygEc1TZdm2t6llhVNKqXagM8wLIJ7lSYwxPLNoA02xzKAQ9TRbuSOv8gUPpZQ6GGnwKIB3tNXLy7fw9RmL+PXLKzPSxXxrHvt/4uCehmjKsdvb7oYoNfXR9i6GUmo/0uBRgKDnYVC7G5yb4toddRnpmtL6PKBtah6TfvU6n33wTcAJWlVb2ndOyZg7XmLcnS+1axmUUvuXBo8ClEWCNETjgDNsF/DtCPeOtnJft0XwWL+znvlrdgJw7wsrOPfnr7F2e2Zwc+1piNIYixf8+dNmLuWXL31QcPqmWIKG6IFTE1JKtT4NHgUojwSpa3Jutm4zVDiYeem8M8yb+zzadr2rf3/gzKTfurcxa5pjp73AdQ/PL/gz/zx3Nb98KbOZTil1+NLgUYCKSIi9jc5cjHpbA4n4BI8mT6AodLSVK5EwvLJiCyO+N7uo/oL0+SRuUNvTkPsz3CCjlFL7QoNHAcpLmmsetTaIhEOZK6t4O63dGoffqCw/0USCX7z4AfXROKu27i24bI1pnx8JOb/S+ib/Zil9nK5SqjVo8ChARSSUDBruT7fvw8tvnkcsxyRB7408njDJ2kwxTV1uTcjlfkZtluDRliv/GmPYWdvEI/9drUFLqUNMq0wSPNSVR0I0xhLE4olk85Vfc1Sx8zzSR2eFgk5AKmbYbXrwcD8j25In6TWV/SmWMNw9ezkz3lnHkJ4dOG1I9zY7tlJq/9KaRwEqSoIAvLRsM3/6z2oAGn1GE6UsT+KOtspxs46mjc5y+ysaiwkeaTUM9zNqG/1rHn7lzqXY0WLeGkY0nmB7rTODfrfO+1DqkKLBowDlEaeC9ttXPkxu87vBp69tBbmbibyBJZZIEAw4tYZibvANaTUP+xFZax7p6Y0xydoUwLoddSxcu9M3fSGLPXoDYlMskWxGayqwKW75pt1c+KvX2Vmry7YodSDT4FEAtykoEGju5/DrCPduc5uecj2atimtj8TtR8kVcP7w+ir++W7zivfp8zXc42WteaSVe86yLRzzv8+zYpMzsfB//vgWn/zdXN/0TfEEiYRhZY4HW3nL3hRPJDvwc9XAvH79chXvb9zNayt1NJhSBzINHgUI2aBRHg4mt/kGjyKH6qYGG5OsNTRG41k7mO94bhk3PrYwmf/phamPTnFv3oXWPP42fz0AyzbuBmC1nVzoNod500fjCZ6Yt47zfvEa76zekfecmmIJwsH8AfHVFVuYtWQjACVFDhqIJwzvrt+lHfJKtTENHgVwm5PcGgg4N8bvPr2EG/6yIGWbK+rTbJXe7JPeYe7WPG7527vJAJHLY2+t4ZE316R+pi1DttFW6TWP0rDzJ7A7bV7IjrqmjPRNsQSv2xpB9a56389PuQaefpzaxuzPLPnin97hq/Y6hpPBo7CayjOLNnDxb/7Dy8vb7VlhSh2WNHgUwL2pu3M9KsvDNMbiPPrmWp6z35ghfahugi27G/j6jEXJbenDdtPTe4f/ej83mw+2pM4HSSRM8jPr7M36r/PW8ZNZy5Jp0pu53GalXXWpwaPGvk+teRiCgYAtr/83/dTgkUgGg2zzTtK5TYO5hjh7LdlQA8BH22oLSu/HGMMCTz+PUio/DR4FcJutahtjDOhazokDuvg2w6z23MCiccOdnps2ZN5wvc88j8YNgTy/jfQhvBWRYMr7aCKRbO6ptc1Wt/ztXf7vtVXJZp305ja3jyQ9eFx43+tsqmnIqHmU2mDjjp6qqY+yzrNIZFM8npLeXY343hc/4F95AqIxzU13DQUGGzf4tWQI8vS5q7n0d3OTtSqlVH4aPArgfhuOxp0RUZFgIOMmPH/NDuZ4mk7iCZMxByOW9ozz9A5z8Zl4uLshyv2vfkg8YTJukCWh1OARi5tkuerSbr5Lq3dnbDfGJN/vqs8c3fSXt9akjPxqiicosc1cNfVOcPr8H95k/D2vJFfybUhL761dfcU2TdXURZn93qaMfoqGaAL3EixctzPZNLZ9byOvrvBvlnJray3p83jf9vf4rZTsJxpPMHNxdVHzcQ6kJfOVag0aPArg1jya4gkC4nzbTR9Z5d6cXdF4grJw6s09nnBmXM9fsyOZxps+M3TAT2ev4O7Zy3lp2eaUJiRjDOmxJuZptkrvY/jEr9/gy4/MS/YtOMdsDnA1dZnzMDqUhFICXDSeSNae9jY66d/b4Jz3uT9/jacXbkgdbRVL+NYIpj27lOsfnc+CtbtS+oHqo/HkdZ21ZBOn3fUy4IzA+uKf3mH9zsybu3sJ5n64nVeWb2HazKW8tWp7RrqMMsxcyqfud0aVuU1xuUbGeT21YAM3Pb6QZxZV508MvF+9m6O/NzvrIAOlDkYaPAqQrHnEjFPzCAVSvmE3xhKE0tqc4gmTbFJxxRKG6x6Zx6fu/y8N0XjGciZ+/Qhu81NNXTTlRtwYS/g0QSU8o60ym32eX7o55X00nkgGj122GSroGY4cDEjKMRo9weDxt9fx7vpdKZ+3YvOejD4P73t35NVTCzcA8OHWvdR5AmJ9NE69zxwXdxHHKtvH420qc6/Y3A+3c9Wf3+HPc1fz2QffzBgAkO7Pc1cnl7F37c3Rqb9uRx3LNzmBcoOtEa3aln0NstnvbeQvb62xZdtGLGGYWWCw2bqnkZ+/sEJrK+qApsGjAEFpbrYKiBM8dtY1N/M0xhIZo4Ni8czgEU8Ylm90mnc21TSk1TxMRifxqO/P5h8LnBttICApNY+GaDzjW32umoefaDyR7Mh2zyfsGVFW3xTPGHrrHnNvY4yLf/OflM9Lb86LpjVbdSwNs7GmeZRW9a76lHIuq97tOznQbZLassdZZv4bTyxi/D2v8N6GmqxNTcdNeyGlL8a1bW8jt/3j3eT7eKK5qc/vmlXvquekH73I+HteYeIvX08pT31T6vV/auF6tu5ppKYuyvWPLuD2p96z6Zuvh9+5vV+9O6XZ7e7Zy7nv5ap9Xvl4w676nIFQqdagwaMAqc1WTp+H98btrUWceVQPALbsaSASTOuT8KxftachRpOnwzyWSGR80/QOt00Yk1LbqWvKDB7eb/p1TdnniriaPDWP7XubMMZk1hzSOsBzrRLcEM0MNt73nUpDvLO6+dv+rrpoyg372ofn8UbVtpTP/PqMhcm5J1tt8HCH5X7i12/w3LvZO+GXVtckXxtj+PWclfzw2fd5/O11ye21TbFkGdwb7rf+upjrHp4HwPw1O5NLrIATbNz3O2qbn5lSvauebzyxmJN//BKjf/hCcns0nkgOe671+Z38Y8EGLrzvdV5f2Xze7hydbMOt0320rZZTfzKH9+zIs9PveplPeSZ6ptu6p5H/+eNbLRqhppQujFiA9A7zkrQaRWMskaw1/PiSYxh/zyv87IUPGNKjIiVdPN7cKb6nMZqxFlau4amN0TgNsdQmHr+RU267fSxh2OnTj+HVFGuuedTUR6mPxvEWoTGt2akxlhmwAC4Y1Yvnl26mPq021BQ3KX0g4WCA1z7YSueyMKXhABtr6rnW3qSz8fYruMGjUG6w/dE/32fRul0ZzVQAb6/awcbdDUBz8HAnTiYSJmOy5e76aLKJ7+lF1VSUhLj94yNYuHaXbxn2NsSStalnF1ezcvMeHvvSKZSGA5RHQrxnA9z/zlzKFWP788LSzcyz5axtjBGNJ/jlSx9w2Un9GdS9+e/pj298xKqte/nxJ4/lleVb2FjTwKX3z+WYPp0ApwkxmzeqtvL6ym2c87NXOWlgF7pWRJh0zBFcemK/HFczk9Pv5tdTpw4HrRI8RGQi8CsgCPzBGHNX2n6x+y8E6oAvGmMW5MorIl2BJ4BBwGrgM8aYnXbfbcA1QBy4yRjzfGucRzbJmkcsQcD2eXg1xuLJ/opSTyf5h1tTv9nFjUl28O5tiKUEj/teXplz8cBfzalim+fpgBPu/TcDupYDcO6IXry0bDOxRCLlZu1tnvETjRsaY3FEnKYVd3TTLRcM59cvryQaM+xpaL55NsUSNEYzvw2fM7wn766vYfveJqY9uzQlfTRmOKpXB0rDQXbWNbFkfQ0nD+rCuh31GX0w+Wy2N/lCPbNoA//9cDtPzFuXNY03eP1z8UYmjjoi+X7DrvqMAPzqB1tY5fm9/uWttcxfs5Plm/xv1rsboinXcPmmPXzivtcpCQc5b2SvZD/OR9tquXPW8pS8t/1jCfe+sIJte5v413ubePmbZ7NuRx3fe+Y9Xl3hNGl98oS+yRpEUyzBgrQgVtsY48l561i5ZS+nHNmNIT0qmLOseeSaG1BffH8zTy3cwLkjelFdU89tk0Yk06zeVsvjb6/lpgnDqCgJcfOMhUTjzsPLHr12HCcO6JLl6jriCUNDNM7jb6/lirEDqCgJsXl3A907lBAMCDtqm3h2cTWfPbl/yv8fr/qmOJFQIKVPLpdddU10Kg2nLCmkWleLg4eIBIHfAucB64F3RGSmMeZ9T7JJwDD7bxxwPzAuT96pwBxjzF0iMtW+v1VERgKXA6OAPsBLInKUMabwh3IXyf0DTBgISuZTBBujzW377oxtP/FE81DU2qbU4JHtm6trm89jZdfuqOPoIzryqRP78tKyzUTjTp/H6UO78Z+q7azf6T8L3OUEgwR9OpexYVd9Mn3H0hDlkRCz39tIdU3zDXvb3kbf+S0dSkOUhYMZExvro3Ea4wmO6FxG38pSXlq2hYZoEycMqMyYV1KI5Zv28On7szfHpHtlRXF9Bk3xBNc90vx43k/dP5fTh6YuI/+NJxb7liubN1dtp7om9ffgXtMHX1uVt0zb9jq1lo27nDwzF1cnAwfApx/4b9a8P3h2KeFgIHmcpxdusP1Q/jXc11duSzaffWZMfwZ3q+DyB9/kbTtKrLYpxqBuFTztqQ1e+ru5nDSwC3//ymk0ROPMW72TsYO78toHWzmuX2d6dirlCs9nuGXZsqeRC489gt99/iR+MmsZf52/nt31Ub42YRjXTp/H6u219O9SxllH9eCNqu3MW7OD04Z046rTB9O9QwlLNtTQu3MpfSrL2LqnkdrGGKGAMKxXR/6xYD0/e2EFZx/Vk/u/cCI766K8vnIrJaEAR3QuoywcJJZI0KtTKZ1Kwyxat4uNNfUM7FbB8f0rWVpdw+srt3Fs386cPKgrjbE4SzbUEAoEOK5fZxqjTlNkRSRIfTROMCDUNsbpXBamvCTI7vootY1xupSH6VAaQhB2N0RpiiWoLA8jIuxpiJIwpIywdO8N4tnavI20F83pJGWbv64VkVavJbZGzWMsUGWMWQUgIjOAyYA3eEwGHjZOg++bIlIpIr1xahXZ8k4Gzrb5pwOvArfa7TOMMY3ARyJSZcuQ/X9RCwU9F93tMPdqiMaJxZ2RWH7PNnc1xZqr+XsbWqdDMxIKNC/lHkuQMDB2UDcao4m8j7N1O8D7dXGCxzobPEpCASLBQErgAPjeM06t4syjetC/Sxl/eWst4Azp9fvG+L2nnQ7jc0f0oiQUpCEapyEap7I8QsfS4v70BnUr56NttW3aTr9lT2NyZNi+uvXvS1qlLPXROJ++f27OZ9Oncx8fMKxnB64dP7iosky499+cO6Jn8qYP8Oiba33Tzl+zkwn3vsqqbbWkd7ON7teZxeub+562eJoeZy3ZxJn3vJIc9HDvix/wqzkrk823VVv2pnwBmLVkE7OWbCr4HGYv3cTYO+dQUx8t+Ime5ZFgykjFrhURorEEew7yAQjLfzQxa61uX7VG8OgLeNsF1uPULvKl6Zsnby9jzEYAY8xGEenp+aw3fT4rg4hcB1wHMGDAgAJPJ5O3quzXbNVg+zyCAUk2cfmJJZrncuxpjGVM8tsX4WAg2Qn/tcft+lAhobI8nFwp99ozBvPoW2tSOtzBaW5riicY2K2ctz7awRp7Yy4NB30fs+vqWBLi6CM6Nr8vDVEWyX4uJaEApeFgsvnG+UYWLvgcrz9rCMFA6pL4+9tXzh7C/a9mP96lJ/RlWK+O3D17edY0rW2eT59NIY7vX5m3acnPS7Z56ytnD+GV5Vty1rDcJtqxg7sSDgr/qXLm2ngDR+/OpWysaWDy8X2YdExvvvb4AtbuqOPSE/py0fF9uOWvi5M1rdsmHc2sJRtZvL6GbhURTh/anflrdrJhVz1l4SA/ufRYOpaG+GDzXnY3RDHG6VPasKueL5wygP933nAef3st//5gKwJ8e+JwIsEga3bUUr2rHmOcpsKl1bs5Z3gP+nUt56X3NxMJBejftZzPjR3A8k17eHrRBgIiXHpiX3bsbeLF9zfTvWOELuURGqJxOpaGKQsHqSwP0xRPUNsYo1NpmIqSELvqnQEhxjj/RyKhADV1UQyGjqVhvLcKN+h6Y2/zNpPyPiWdZ6PxbEqvZOS6L+2r1ggefqVKrxdnS1NI3n05nrPRmAeBBwHGjBmzz1OQU+Y+ZK15JAgHxLdN9sQBlSxYu4tovHkuR11j3PdRtsUKeWo763Y4NYdIMECnsnDy29LxAyqZs3xLxrf2Gx5zgk3/LuUEBFZvd/a7NY9sIqEAHTw1hw4l4ZzNdeGgpOzvUh6hQ0nhf3pXnjowZfZ+W/jEcb1Zsr6GN6q2cfKgLvz8M8cz/p5Xkvs7lYX5xHG9k8GjIhJkUPeKjMmirk+f1I/Jx/fhf/74tu++d9fv4oPN2eeNhINC/67lrNpay00ThvGFcQMYe+ec5P6eHUvYsqeRK08dyIjenZi5qJr/2smSw4/oyGBPZ/tZR/UgEgrw4vv+fU5zvnkW73y0g6n/WEKHkhC3TjyaM4Z259cvr6RzWZihPTtw4znD2La3keeXbuKO55xleG6aMIwvn3kkFSUhtuxp4Pan3kseY9kPJ9IUTzD7vY1cdlJ/AgFhxnWnUBoOMqpPZwDmffc8Fq3bRUUkyLBeHbnmjMHMW7OTcYO7pjS51DXFks/YmTCiV3L7LRcMZ9nG3Yzq0wkR4YZzhnLDOUNTzu3Yfp2zXuPPjOmf8r5/13LOG9krZdunTipuUMGhrDWCx3rAe9X7AemzobKlieTIu1lEettaR2/AvXsUcrxWFUqbOJd+Y22Ixu0w3EBGu+KQHhV86/zhfO4PbxGNJ5KT/nbWNfGbV6paXLZwMJDxrSIcDNDRc3MuCwdT5m+4Nu92mhDKS0L06lSaDC4loSCRHLWiklCAikjz51eUBCnNkT5iax6uyvIwnTzBx+2jcV1+cn/6dy3np8+vAJxvbb07lWb9/NbQr0tZSh9Rjw4l9K0sA6BzWYROaTWlxliCHh1Lku8fuXYcO/Y2ce3D8+jftYxbLjiamx5vXhm5IhJkYNfU0XdXnz6Ytz7azjfOO4raxhjn/+I137KNH9ad3185hlBAeGrhBj5+XO/kzRPgL9eOY8ygLjREE3Quc8p5xdgBjLnjJbbtbeSoXh0JBQMM7l7Bmu21/P7KMTTFE7z4/iaqdzVQHgnStSLChBG9EKCiJMSR3SvYsqeR04d2A+D0od0z+n/6dy3n2vFHMm5wN4IBYaQd6QXQs2Mpv79yDP/9cDsN0ThlkSBlBPnsyc0tACcN7Jpxrsf3r0y+DgUDnHJkt4w03nP3CgaEY/pmDw6qdbXGPI93gGEiMlhEIjid2TPT0swErhTHKUCNbZLKlXcmMMW+ngI849l+uYiUiMhgnE74zK9zrSiQp9nK7TD3u0GHgwHCNv2Njy1MNh253/JbKhwUQmnBLBQUKtKCh7fMXz17SEr6klCA3p1Lk/MpnJpH9lpRes2jY2k4ueZVtvSlnuN3qWiuefTvWsaj16S2cnbrEGHCiJ7J9xWRUMqNKZ+zjuqRs5oeDgpXnz44Zdu5I3oxd+rHku+7VkTo18UJHqXh1PMF55p6A2KPDiVUljs37hP6d+Gi43qnpK+PxlOCzfIfTeT7F43kuZvG07eyjC7lkZR9U04dCMDr3z6HP0wZQ2k4SCgY4LIx/ZM3z4evHsvDV4/l9KHdKQkFk4HD9a3zjwJgdL9KAJ647hRe+MaZzu+vJMQnT+jHDecM5arTBzP5+L50KAkl/25EhJsmDPO9wac7tl/nrL+fU4d045yje/ruUwe3FgcPY0wMuBF4HlgGPGmMWSoi14vI9TbZLGAVUAX8Hvhqrrw2z13AeSKyEmc01l02z1LgSZxO9dnADftzpBWQ0rwUEDLmedRHnaG6fk1W3n4Q74ipbXta5zGroWBmE1M4GEgJHiXhYLJp64QBlUw+PrWLKBIK0LuyLNmkVhIOpASbI9Pmq0SCATqWNN+oOpSEUmoe156RemMOB1NrHu4oFHCaAdNra/EEKTWbQEDoU1nGg/9zErNuGs+9l43OdjkAmH71WH7x2ePp0bGEY/p2QgQGditPNp2dMKAL3/vEiJQ8k4/vQ/cOzTf3UDBAXxs8ahtjKb/bM4Z256rTB6Xk79GxhBMGdOGWC4Yz7eJRGedUHkntF0rvvOxWEUnZ980LhvPPr51B/67lWfvGzjyqR3JSqp/Lxw7gwzsvpLMNaj07lTK0Z8es6ZUqRqvM8zDGzMIJEN5tD3heG+CGQvPa7duBCVny/Bj4cQuKXBTvaKtsfR7RuEmub1VZHk4ORQ0FA74jsNxRM5ee0Jd/eEb0XHjsERzfvzJjzH82EU+HuXebdzRTmSd4RIKBjPKXhAIpzUIloeb0g7tX8NI3zuLI7zT/itJrHsGAUGJvhhWRIN/9xEj+8MZHKem9N85KT5+H3zj8gDijXtKdb+dgjOzTiW/+NXPIrNdFo/tw0eg+1NRH2V0fpW9lGXOWb+FLD88jFk+k3Nw/vPNC38Dfs6NzTfrY5ivX9KvHJtN/9+Mj+HDr3mQw8Laxf/zY3uxuiHJUr47J2t64wV3Z7TPSLv06dCoNt0oTTKHzIpQqls4wL4B3zcNAQOhQkto80BBNEEs0N1u9edsEjpv2Ak3xREqHttcOO+v402P6pQSPyvIIR3bvkHz/xq3ncOvf303pE/AKBSWjuSwcTO2TKIsEk7WTSMgveATp7blBlnqauUpCgYwbm9vs4eV+q/cbdVUSDKS0U3cuCyeDm9+gga4VkZSaUyH6dC7NGFrsHsttznH7WdKnOXhvsJef3J/enZ1rMe7Irtw26Wg+e3L/rOmvHX9k1jL95nMnAKQEqj9ddXLWgRJ/vurkrO35Sh1odG2rAqSPtvK2XQM8+241sbhJ9j2UhoOUlzg30VBAMmoGXukdsaG0PpXencsyVuwdP6y7J30gY3/Yp8/DDTAloWBGM1dJOECfzs01j9Jwc1OYW6Pw9pP4Bw8nnV/wCAcDKTWJcDCQDMDpfRM/uHgUU04blNE0mMtlJ/Vj7m2+ldQU7jU5wdMpm+6uTx3H188dliznl88aQqXtj7h4dJ+Ua5+P+DTJlUf858QAnD28J2MH5+9jUOpAoF9zCpDS5xGAXp2c4HHR6D48u7iaqi17aYjGU26o7g097NMn0b1DSbL/I/0bdiiQWjMIBiRjHPIj14zjsgfm8s7qnURCkuyQd4XTmqa8zVYlvjWPAOXh1Pb4ZM3D5vv2xKN5c9V2FqzdRUkomDE01+3z8KtlpTdbAckAfHzajXzKaYMy8udy5yePZfLxfQpKe0zfzjxyzVjGDXZG8HznwqMpK+Kb/n1XnFBU2ZQ6lGnwKEAgbYZ5eSTEy988i/5dy3l2sTNKeGdtU3K0DTR/ow761Dx6dvQGj9SbajiY2afitzqu24kaCgQIp317Lw0HU27WZZFgMsBEQoGMb/UloWBGH0kyeHiCRNjT9CUi/ODiUcnOdDeY+NWxIqFARh/G8CM68tAXxyRH88yd+rGMwPOzy0anTEb087lxzUM/7/zksRmLUaYbP6y5g/m6M4fkSKmUykWDRwHSH5AEcGQPp19i4qgjmL10E7VN8ZTmIzdghIOZfR49O5Xwvl0GqiLtm6/fPJKET/Bwb9bODPPU9B1LU5tGIqFAsgZR4hM8OpeF6OQZ5lkaDibTeMuSXhvx1hLc47mB9v7Pn8iNjy8knjBOs1U480/tY0c3T8BK75QGZ/JcNrNvHk9tY+ogO28gUUrtX9rnUQDvF/tgWhv21ElHJ197O67dmodTM0i9zO78AcgcVRQKZt7c3WXWh/bswJfGO8NgS5LNRJk1mw4loYw+ifRaw6ybxif3dSoNU1nWPFTUG8C8taCwzzaXG8zc4DHp2N6cafsHIqEA3To4n1/smlbZHH1EJ04aWPySG0qp1qE1jwKkTxL0Su+fcLm1gWBAMtaJcr9lB4SMDtVwlrWzwHlWyDg749ZtTgoFJSU4PfCFkxjUvYI9aY9hddO7QWFkn05Mv3os0+eupnuHkqznVeJzfv7Bwwlm3tNx05eEnHknd3/qWEbn6KxWSh08NHgUIJg2SdCrxOebOXhqHkFJmeT13E1nZDxzIeVYPn0eF4/uw+J1uziis3cuRnOzlbfGM/EYZy5EenNYmb25e/swzjqqB2dlmWTmV8twn3To1ynunqPfss/uPu/SFEqpg5s2WxXAr8/D5b25hlJqHs3NVm6e0f06M6pPZzqkdZL/4OJRydfhQOborKtPH8Tbt09gYLfmzmC3eSgc9H9AjluTcHe5N/xcS8b/9NPH8cvPHp9yXt707jpDA7uVZ+TN1WGuE9WUOvRozaMA3i/T6RO8vLWKoLfD3L52A8ri75+f/NbvTgRzv6VPOW0QC9fu5OlF1YR8ah4ikpzt3Hys5g75bA95+dNVJ9PL5nODSa7Hml/mWVXULYP3o68dfyTnjeyVHCzg5TZbaaBQ6vCgwaMAKcuTZKxgK76vk0N17bbOnmG86U1K0ByUQj7Lh/iWKVmzyH6zPmd484J0brH9hv368VuSPRgQ38DhLYe3Ga95VdrCn92hlDo4aPAoQMrDoNJu1iJCSShAYyyRMmS2udkq8+aePrcDmmsGITvSqWNJiE+M7p2RLlkmWw53GO+YgV0Y2tP/xu6kay5vIQoJYF5ugDjOruAKcMvEozlpUFdOHqSjopQ61GjwKIBI9uABzo22MZZICRRhz2irdO6scr8WHneV2bdvPzfnDdytP7jPMf7bV07LeQ4JGz2KbVaSrE9FTjX8iI48du04xgxqXl6jQ0mIi0cXNvtbKXVw0Q7zArk3Xb/+Zrepxi94+NU83Lkd3qYh0xwNAGdWeK4bvbt8ut+qtH7cmkFZlnWV0jVGs4+syua0od2LrrEopQ5OWvMoUFCEOMb3Zu0GgZDPUN1gIPNm6s7mPmt48zBZ9znFhdYL3OaqHGsupvjMyf3ZVtvI508pbLise55+s9uVUkqDR4ECASCeOcMcSK4blVLz8Nnm6lQa5p9fO4NhvTx9FEX2SVTaAFTpeQJdLp3Lwtw2aUT+hNZnxvRj3uodXH+Wrv+klMqkwaNAbl+Hb59HsubhCR6ehRH9ZHvQT6E1j+vPHkLXiggX7ac+hY6lYe7/wkn75bOVUgc/DR4Fcmscvs1WPrUM7+ipQrhrPqU/Kzub8kiIL6Y9h1sppdqKBo9C2Rjg22zl0+fhjlIKFtgp8f/OH06/LuWcN6JX/sRKKdXONHgUya8i4VfzcGNMoTWPzmVhvnRm9keaKqXUgUTHVRbJr9kq7FnHKpnOran4jLZSSqmDXYvubCLSVUReFJGV9qfvVGIRmSgiK0SkSkSmFpJfRG6z6VeIyAWe7T8WkXUisrclZd9XvosQJpcW8dQ8KK7PQymlDiYt/Vo8FZhjjBkGzLHvU4hIEPgtMAkYCVwhIiNz5bf7LwdGAROB39nPAXgWGNvCcu8zv1iQDB4pHebOT10oUCl1KGpp8JgMTLevpwOX+KQZC1QZY1YZY5qAGTZfrvyTgRnGmEZjzEdAlf0cjDFvGmM2trDc+8xvqK4bH1IfB+tsLHDahlJKHVRaGjx6uTdy+7OnT5q+wDrP+/V2W678ufIUTESuE5F5IjJv69atxWb3lbPZKmUBRednQidoK6UOQXlHW4nIS8ARPrtuL/AYft+9891S9yVPZgZjHgQeBBgzZkyLbuNugfxqHuLX5+G+1OU9lFKHoLzBwxhzbrZ9IrJZRHobYzaKSG9gi0+y9UB/z/t+QLV9nS1/rjztym+0ldta5a152Ce2FrzciFJKHUxa2mw1E5hiX08BnvFJ8w4wTEQGi0gEpyN8Zp78M4HLRaRERAYDw4C3W1jWVpG7w7z5ckZt9PB7qJJSSh3sWnpnuws4T0RWAufZ94hIHxGZBWCMiQE3As8Dy4AnjTFLc+W3+58E3gdmAzcYY+L2s+8RkfVAuYisF5FpLTyHovjNMPcbqhuzwSMc0pqHUurQ06IZ5saY7cAEn+3VwIWe97OAWYXmt/t+DPzYZ/u3gW/ve6lbxvexr3aTt4nq3JG9eHpRNaP6+C+AqJRSBzNdnqRIfn0e7lP6vH0enziuD2cP70mHEr3ESqlDjzbIF8mvCyMa93/EqwYOpdShSoNHgSTH8zzK7GNlw4U+1k8ppQ5y+tW4SH7B44ZzhlDbGGPMoK7tUCKllGp7GjyK5Bc8jj6iEw998eR2KI1SSrUPbbYqkk7bUEopDR5F0xnjSimlwaNofpMElVLqcKPBo0j6fA6llNLgUTCR1J9KKXU40+BRJK15KKWUBo+i+a5tpZRShxkNHkXS4KGUUho8iqatVkoppcGjaNrnoZRSGjyK5rcku1JKHW40eBTIDRna56GUUho8iqYzzJVSSoNH0TR2KKWUBo+CGftTO8yVUkqDR8Hi9jnlkZBeMqWU0jthgWL2OeURfaCHUkq1LHiISFcReVFEVtqfXbKkmygiK0SkSkSmFpJfRG6z6VeIyAV2W7mIPCciy0VkqYjc1ZLyFyOWSABQojUPpZRqcc1jKjDHGDMMmGPfpxCRIPBbYBIwErhCREbmym/3Xw6MAiYCv7OfA/AzY8zRwAnA6SIyqYXnUJCYbbYqCQXzpFRKqUNfS4PHZGC6fT0duMQnzVigyhizyhjTBMyw+XLlnwzMMMY0GmM+AqqAscaYOmPMKwD2sxYA/Vp4DgXpVBoGIBzSDnOllGpp8OhljNkIYH/29EnTF1jneb/ebsuVP1ceAESkErgIp8biS0SuE5F5IjJv69athZ6Tr79/5TRuuWA45ZFQiz5HKaUOBXnvhCLyEnCEz67bCzyG31d147Ot4DwiEgIeB+4zxqzK9iHGmAeBBwHGjBmT75g5De3ZgaE9h7bkI5RS6pCRN3gYY87Ntk9ENotIb2PMRhHpDWzxSbYe6O953w+otq+z5c+VB5yAsNIY88t85VdKKdX6WtpsNROYYl9PAZ7xSfMOMExEBotIBKcjfGae/DOBy0WkREQGA8OAtwFE5A6gM3BzC8uulFJqH7U0eNwFnCciK4Hz7HtEpI+IzAIwxsSAG4HngWXAk8aYpbny2/1PAu8Ds4EbjDFxEemH01w2ElggIotE5NoWnoNSSqkiiTEt6go4aIwZM8bMmzevvYuhlFIHFRGZb4wZk75dZ7wppZQqmgYPpZRSRdPgoZRSqmgaPJRSShXtsOkwF5GtwJp9zN4d2NaKxWktWq7iaLmKo+UqzqFaroHGmB7pGw+b4NESIjLPb7RBe9NyFUfLVRwtV3EOt3Jps5VSSqmiafBQSilVNA0ehXmwvQuQhZarOFqu4mi5inNYlUv7PJRSShVNax5KKaWKpsFDKaVU0TR45CAiE0VkhYhUiUjG89n387H7i8grIrJMRJaKyNft9mkissGuKLxIRC705LnNlnWFiFywH8u2WkSW2OPPs9u6isiLIrLS/uzSluUSkeGea7JIRHaLyM3tdb1E5CER2SIi73m2FX2NROQke62rROQ+EWnRc5CzlOunIrJcRN4VkafsUzoRkUEiUu+5dg+0cbmK/t21Ubme8JRptYgsstvb5HrluDe07d+XMUb/+fwDgsCHwJFABFgMjGzD4/cGTrSvOwIf4CxFPw34lk/6kbaMJcBgW/bgfirbaqB72rZ7gKn29VTg7rYuV9rvbhMwsL2uF3AmcCLwXkuuEc5zbE7Febrmv4BJ+6Fc5wMh+/puT7kGedOlfU5blKvo311blCtt/73A99vyepH93tCmf19a88huLFBljFlljGkCZgCT2+rgxpiNxpgF9vUenGeh9M2RZTIwwxjTaIz5CKjCOYe2MhmYbl9PBy5px3JNAD40xuRaUWC/lssY8xqww+eYBV8jcZ6u2ckY81/j/E9/2JOn1cpljHnBOM/dAXgT58mdWbVVuXJo1+vlst/SP4PzSOysWrtcOe4Nbfr3pcEju77AOs/79eS+ee83IjIIOAF4y2660TYxPOSpmrZleQ3wgojMF5Hr7LZexpiN4PxxAz3boVyuy0n9D93e18tV7DXqa1+3ZRmvxvkG6hosIgtF5N8iMt5ua8tyFfO7a+vrNR7YbIxZ6dnWptcr7d7Qpn9fGjyy82v7a/NxzSLSAfg7cLMxZjdwPzAEOB7YiFNthrYt7+nGmBOBScANInJmjrRteh3FedTxxcBf7aYD4Xrlk60sbX3tbgdiwF/spo3AAGPMCcD/Ax4TkU5tWK5if3dt/Tu9gtQvKW16vXzuDVmTZjl+i8qlwSO79UB/z/t+QHVbFkBEwjh/HH8xxvwDwBiz2RgTN8YkgN/T3NTSZuU1xlTbn1uAp2wZNttqsFtN39LW5bImAQuMMZttGdv9enkUe43Wk9qEtN/KKCJTgE8An7dNGNhmju329XyctvKj2qpc+/C7a8vrFQIuBZ7wlLfNrpffvYE2/vvS4JHdO8AwERlsv81eDsxsq4Pb9tQ/AsuMMT/3bO/tSfZJwB0FMhO4XERKRGQwMAynM6y1y1UhIh3d1zidre/Z40+xyaYAz7RluTxSvg229/VKU9Q1sk0Pe0TkFPv3cKUnT6sRkYnArcDFxpg6z/YeIhK0r4+05VrVhuUq6nfXVuWyzgWWG2OSzT5tdb2y3Rto67+vfe3xPxz+ARfijGT4ELi9jY99Bk4V8l1gkf13IfAIsMRunwn09uS53ZZ1BS0cZZKjXEfijNxYDCx1rwvQDZgDrLQ/u7ZluexxyoHtQGfPtna5XjgBbCMQxfmGd82+XCNgDM5N80PgN9hVIVq5XFU4beLu39kDNu2n7O94MbAAuKiNy1X0764tymW3/xm4Pi1tm1wvst8b2vTvS5cnUUopVTRttlJKKVU0DR5KKaWKpsFDKaVU0TR4KKWUKpoGD6WUUkXT4KFUgUSkm2fF1E3SvOLrXhH53X465s0icqV9fbQ93kIRGZIl/QwRGbY/yqKUlw7VVWofiMg0YK8x5mf78RghnPkCJxpjYuI8FqDMGPO/OfKcBXzBGPOl/VUupUBrHkq1mIicLSL/tK+nich0EXlBnGc9XCoi99hnJsy2y0q4z1H4t11c8vm02dSuj+EstRIT51kWNwPXivMshwoReU5EFovIeyLyWZvndeBcG3iU2m80eCjV+oYAH8dZCvtR4BVjzLFAPfBxG0B+DXzaGHMS8BDwY5/POR2YD2CMmQU8APzCGHMOMBGoNsaMNsYcA8y26RI4M8ZH78fzU0qDh1L7wb+MMVGcpTWC2Bu7fT8IGA4cA7wozlPovov/MzR6A1uzHGMJTg3jbhEZb4yp8ezbAvRp6UkolYtWbZVqfY3g1AJEJGqaOxYTOP/nBFhqjDk1z+fUA6V+O4wxH4jISThrGv1ERF4wxvzQ7i61eZXab7TmoVTbWwH0EJFTwVleW0RG+aRbBgz1+wAR6QPUGWMeBX6G86hU11E4C/Qptd9ozUOpNmaMaRKRTwP3iUhnnP+HvyTzhv8vnJVl/RwL/FREEjgrvn4FQER6AfXGPlFOqf1Fh+oqdQATkaeAb5vUR53mSv8NYLcx5o/7t2TqcKfNVkod2KbidJwXahcwff8URalmWvNQSilVNK15KKWUKpoGD6WUUkXT4KGUUqpoGjyUUkoVTYOHUkqpov1/Iu3h4fyG/LAAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dipole_deriv_acf_tapered_cosine = dipole_deriv_acf * tapered_cosine(times)\n", "plt.plot(times, dipole_deriv_acf_tapered_cosine)\n", "plt.xlabel(\"Time (fs)\")\n", "plt.title(\"Autocorrelation cosine tapering\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And calculate the IR spectrum:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x_freq, y_intens_cosine = job.results.get_ir_spectrum_md(times, dipole_deriv_acf_tapered_cosine, max_freq=max_freq)\n", "plt.plot(x_freq, y_intens_cosine)\n", "plt.xlabel(\"Frequency (cm^-1)\")\n", "plt.title(\"IR spectrum (from cosine-tapered autocorrelation function)\")\n", "plt.xlim(500, max_freq);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Above we see that using the cosine-tapered autocorrelation function gives a smoother IR spectrum without affecting the intensities too much." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compare to IR spectrum calculated from harmonic approximation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's compare to an IR spectrum calculated with a geometry optimization + frequencies job, starting from the final frame of the MD simulation." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[18.02|08:09:49] JOB harmonic STARTED\n", "[18.02|08:09:49] JOB harmonic RUNNING\n", "[18.02|08:09:49] JOB harmonic FINISHED\n", "[18.02|08:09:49] JOB harmonic SUCCESSFUL\n" ] } ], "source": [ "ams_s = plams.Settings()\n", "ams_s.input.ams.Task = \"GeometryOptimization\"\n", "ams_s.input.ams.Properties.NormalModes = \"Yes\"\n", "harmonic_mol = job.results.get_main_molecule()\n", "harmonic_job = plams.AMSJob(settings=ams_s + s, name=\"harmonic\", molecule=harmonic_mol)\n", "harmonic_job.run();" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "harmonic_freq, harmonic_intens = harmonic_job.results.get_ir_spectrum(broadening_type=\"lorentzian\", broadening_width=20)\n", "plt.plot(harmonic_freq, harmonic_intens)\n", "rescale_factor = np.sum(harmonic_intens) / np.sum(y_intens_cosine)\n", "plt.plot(x_freq, y_intens_cosine * rescale_factor) # rescale\n", "plt.legend([\"Harmonic\", \"MD NVE\"])\n", "plt.title(\"IR spectrum\")\n", "plt.xlabel(\"Frequency (cm^-1)\")\n", "plt.xlim(500, max_freq);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this case, the MD simulation samples multiple conformers so there are more peaks than for the harmonic calculation.\n", "\n", "For example, the peak for the MD at 3600 cm^-1 corresponds to the \"free\" OH stretch of the hydroxyl group, but in conformer used for the harmonic approximation the hydroxyl donates a hydrogen bond to the aldehyde oxygen (giving a lower vibrational frequency):" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plams.plot_molecule(harmonic_job.results.get_main_molecule())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## View the trajectory in AMSmovie" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "!amsmovie \"{job.results.rkfpath()}\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## View the normal modes in AMSspectra" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "!amsspectra \"{harmonic_job.results.rkfpath(file='engine')}\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Appendix: Average over multiple short NVE simulations\n", "\n", "Best practice is to run multiple NVE simulations starting from different points of the NVT simulation, assuming that the NVT simulation samples the correct equilibrium distribution of structures/conformers.\n", "\n", "Let's make this more explicit with another NVT simulation, followed by multiple NVE simulations from different points of the NVT simulation. See also the \"Molecular Dynamics with Python\" tutorial." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[18.02|08:10:03] JOB nvt_prod STARTED\n", "[18.02|08:10:03] JOB nvt_prod RUNNING\n", "[18.02|08:10:18] JOB nvt_prod FINISHED\n", "[18.02|08:10:18] JOB nvt_prod SUCCESSFUL\n" ] } ], "source": [ "nvt_prod_job = plams.AMSNVTJob.restart_from(\n", " eq_job,\n", " name=\"nvt_prod\",\n", " nsteps=50000,\n", " samplingfreq=100,\n", " timestep=0.5,\n", " thermostat=\"NHC\",\n", " tau=100,\n", " temperature=T,\n", ")\n", "nvt_prod_job.run();" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[18.02|08:10:18] JOB nvespawner-nvt_prod STARTED\n", "[18.02|08:10:18] JOB nvespawner-nvt_prod RUNNING\n", "[18.02|08:10:18] JOB nvespawner-nvt_prod/nve1 STARTED\n", "[18.02|08:10:18] JOB nvespawner-nvt_prod/nve1 RUNNING\n", "[18.02|08:10:25] JOB nvespawner-nvt_prod/nve1 FINISHED\n", "[18.02|08:10:25] JOB nvespawner-nvt_prod/nve1 SUCCESSFUL\n", "[18.02|08:10:25] JOB nvespawner-nvt_prod/nve2 STARTED\n", "[18.02|08:10:25] JOB nvespawner-nvt_prod/nve2 RUNNING\n", "[18.02|08:10:31] JOB nvespawner-nvt_prod/nve2 FINISHED\n", "[18.02|08:10:32] JOB nvespawner-nvt_prod/nve2 SUCCESSFUL\n", "[18.02|08:10:32] JOB nvespawner-nvt_prod/nve3 STARTED\n", "[18.02|08:10:32] JOB nvespawner-nvt_prod/nve3 RUNNING\n", "[18.02|08:10:38] JOB nvespawner-nvt_prod/nve3 FINISHED\n", "[18.02|08:10:38] JOB nvespawner-nvt_prod/nve3 SUCCESSFUL\n", "[18.02|08:10:38] JOB nvespawner-nvt_prod/nve4 STARTED\n", "[18.02|08:10:38] JOB nvespawner-nvt_prod/nve4 RUNNING\n", "[18.02|08:10:45] JOB nvespawner-nvt_prod/nve4 FINISHED\n", "[18.02|08:10:45] JOB nvespawner-nvt_prod/nve4 SUCCESSFUL\n", "[18.02|08:10:45] JOB nvespawner-nvt_prod/nve5 STARTED\n", "[18.02|08:10:45] JOB nvespawner-nvt_prod/nve5 RUNNING\n", "[18.02|08:10:52] JOB nvespawner-nvt_prod/nve5 FINISHED\n", "[18.02|08:10:52] JOB nvespawner-nvt_prod/nve5 SUCCESSFUL\n", "[18.02|08:10:52] JOB nvespawner-nvt_prod/nve6 STARTED\n", "[18.02|08:10:52] JOB nvespawner-nvt_prod/nve6 RUNNING\n", "[18.02|08:10:58] JOB nvespawner-nvt_prod/nve6 FINISHED\n", "[18.02|08:10:58] JOB nvespawner-nvt_prod/nve6 SUCCESSFUL\n", "[18.02|08:10:58] JOB nvespawner-nvt_prod/nve7 STARTED\n", "[18.02|08:10:58] JOB nvespawner-nvt_prod/nve7 RUNNING\n", "[18.02|08:11:05] JOB nvespawner-nvt_prod/nve7 FINISHED\n", "[18.02|08:11:05] JOB nvespawner-nvt_prod/nve7 SUCCESSFUL\n", "[18.02|08:11:05] JOB nvespawner-nvt_prod/nve8 STARTED\n", "[18.02|08:11:05] JOB nvespawner-nvt_prod/nve8 RUNNING\n", "[18.02|08:11:12] JOB nvespawner-nvt_prod/nve8 FINISHED\n", "[18.02|08:11:12] JOB nvespawner-nvt_prod/nve8 SUCCESSFUL\n", "[18.02|08:11:12] JOB nvespawner-nvt_prod/nve9 STARTED\n", "[18.02|08:11:12] JOB nvespawner-nvt_prod/nve9 RUNNING\n", "[18.02|08:11:19] JOB nvespawner-nvt_prod/nve9 FINISHED\n", "[18.02|08:11:19] JOB nvespawner-nvt_prod/nve9 SUCCESSFUL\n", "[18.02|08:11:19] JOB nvespawner-nvt_prod/nve10 STARTED\n", "[18.02|08:11:19] JOB nvespawner-nvt_prod/nve10 RUNNING\n", "[18.02|08:11:25] JOB nvespawner-nvt_prod/nve10 FINISHED\n", "[18.02|08:11:25] JOB nvespawner-nvt_prod/nve10 SUCCESSFUL\n", "[18.02|08:11:25] JOB nvespawner-nvt_prod FINISHED\n", "[18.02|08:11:26] JOB nvespawner-nvt_prod SUCCESSFUL\n" ] } ], "source": [ "nvespawner_job = plams.AMSNVESpawnerJob(\n", " nvt_prod_job,\n", " name=\"nvespawner-\" + nvt_prod_job.name,\n", " n_nve=10, # the number of NVE simulations to run\n", " timestep=0.5,\n", " binlog_time=True,\n", " binlog_dipolemoment=True,\n", " nsteps=20000,\n", ")\n", "nvespawner_job.run();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's check that the temperature during the NVE is not too far from the requested temperature." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Set temperature during NVT: 298.0 K\n", "Mean temperature during NVE: 277.8\n" ] } ], "source": [ "avg_T = nvespawner_job.results.get_mean_temperature()\n", "\n", "print(f\"Set temperature during NVT: {T:.1f} K\")\n", "print(f\"Mean temperature during NVE: {avg_T:.1f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate the average dipole derivative autocorrelation function.\n", "\n", "To calculate the IR spectrum from our custom set of averaged data, we directly call the ``power_spectrum`` function from PLAMS:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "avg_x, avg_y = nvespawner_job.results.get_dipole_derivatives_acf(start_fs=0, max_dt_fs=max_dt_fs)\n", "avg_y *= tapered_cosine(avg_x)\n", "\n", "x_freq_multiple, y_intens_cosine_multiple = plams.trajectories.analysis.power_spectrum(times, avg_y, max_freq=max_freq)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(x_freq, y_intens_cosine)\n", "plt.plot(x_freq_multiple, y_intens_cosine_multiple)\n", "plt.xlabel(\"Frequency (cm^-1)\")\n", "plt.title(\"IR spectrum from multiple NVE simulations\")\n", "plt.legend([\"single\", \"multiple\"])\n", "plt.xlim(500, 4000);" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 4 }