diff --git a/examples/Multiple turbine and multiple local conditions implementations developement .ipynb b/examples/Multiple turbine and multiple local conditions implementations developement .ipynb new file mode 100644 index 0000000..aa899a2 --- /dev/null +++ b/examples/Multiple turbine and multiple local conditions implementations developement .ipynb @@ -0,0 +1,375 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "import numpy as np\n", + "from IPython.display import HTML, Latex, Markdown, Pretty\n", + "\n", + "from windIO.Plant import WTLayout\n", + "from fusedwake.WindFarm import WindFarm\n", + "\n", + "from fusedwake.gcl import GCL\n", + "import fusedwake.gcl.fortran as fgcl\n", + "\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "#filename = 'middelgrunden.yml'\n", + "#filename = 'lillgrund.yml'\n", + "filename = 'hornsrev.yml'\n", + "#filename = 'test_WF.yml'\n", + "\n", + "#wtl = WTLayout(filename)\n", + "wf = WindFarm(yml=filename)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "gcl = GCL(WF=wf, TI=0.1, z0=1e-10)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Inputs\n", + "WS=10.0*np.ones([wf.nWT])\n", + "WD=270*np.ones([wf.nWT])\n", + "TI=0.1*np.ones([wf.nWT])\n", + "version = 'fort_gclm_s' # \n", + "\n", + "# Run the models\n", + "gcl(WS=WS, WD=WD, TI=TI, version='fort_gclm_s')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "53223432.296349496" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gcl(WS=WS, WD=WD, TI=TI).p_wt.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Inputs\n", + "WS=10.0+np.random.normal(loc=0.0, scale=0.25, size=[wf.nWT])#np.ones([wf.nWT])\n", + "WD=270+np.random.normal(loc=0.0, scale=5, size=[wf.nWT])#np.ones([wf.nWT])\n", + "TI=0.1+np.random.normal(loc=0.0, scale=0.01, size=[wf.nWT])#*np.ones([wf.nWT])\n", + "version = 'fort_gclm_s' # \n", + "\n", + "# Run the models\n", + "fort_gclm_s = gcl(WS=WS, WD=WD, TI=TI, version='fort_gclm_s')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "70132996.399100527" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fort_gclm_s.p_wt.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+wawqK+VC2kFuTR7NlEmTLLIppeSzz1ew9/R5wuOG4eTqBoCuUUvOiQw8GutY\n9NgjODs7W2T37Nmz/P2zD/FK7I9feJ/L4yqvkPydR/nV7DuJi4uzyKZOp+Ptf35ATqWW8Jg47B0c\nAdA01HPuWBp9fV14+FcPYGNj2faPg4cOsfTrb/AfmoCX/+VxVZSTTc3J4yx68EFCQ0MtstmddIeA\nv3f6eYv7PdzvL9dcwC2aOkkpt0spZxn/LpdSTpRS9pdSTr4k3sa6V6SUfaWUUZfE21ieKqUcKKXs\n1554X09IKfnsy9X0GhBHQEiYaaYphMDHP5DwhCT+tfprGhsbAaivr+err77i9ttvJyIigi1btvDN\nN9/w7rvv8vzzz/PWW28BkJaWRoXMYcTkgSbxBnBydmDklIGUNZ0nPT29Xb+Kioo4c+YMLb/ktFot\nn3/xNf1jR+PT63J4x8rKiqDe4fgFD+LfK1Zj6Rfj1q3bqNU6EBkVZxJvABcXNwYOHcO+A8c5f/68\nRTbLysr44cf/EDM02STeANbWNoRFRGHrHMSatT+Y7bt27VrefvttduzYQWVlZau6b374AaugEPoM\niDaJN4C7tw+RScl8u2kLFRUVZu3ef//93HPPPbz55pts2rSJ/Px8pJScOXOGvSdOETVqrEm8AWzt\n7Ok7JJ4yYcu2HTssun4pJZ99sxLfpIEE9A1tPa56BxA2OZHP1q42jauOkpqaytniKgYMHWkSbwAH\nRyeiho0iM6/0Z8eVObRaLZ99/Q2hSWPwDmg9rgLCwnGPHcS/vvrK4nF1vfF/OQZ+w5KTk0NxTT0x\nca2TZXSNWk6k7idtx1bSdmzmH4v/2q4NBwcHZs+ezaJFi4iPb37y9c4DW+k/MtjsrFUIQf+EYHbu\n30piYiIajYbKykqqqqqoqqqisrKSefPmUVNTg42NDYMHD2b8+PHNszVrV1zdzMeQ/fyDOXzwBBcu\nXGgTSmlsbESj0eDm5taqXK/Xs23HXsKjxpi1aW1tg5dvGDt37SU8PLzd9+BKDhw4iItHIPb25uOy\nAUGh/Oc/q+nl7UlJSQk5OTlcuHCBnJwctm/fjq2tLY6OjtTU1ODt7c27777LlClTOJyZxYAp083a\nbNLrKNc08vJf/4q9nR2nTp3i1KlTZGVltRLKL7/8Er1eD8DGjRvJySvAvU94qy+ElgQPiGHzrl1M\nnTwZa2vrDl1/dnY2pfp6ooMDzNa7enuS6+HIiRMnGDJkSJv6NWvWsHz5cmbOnMn48eNNd1Wbt+0i\nMCKq3XHBtaDAAAAgAElEQVQVED6Azdt3MWzYsDb1s2bNIicnh5SUFMaPH8/IkSNxdXUlIyODJjc3\nnN3d2/QB8A3uQ+aJ4+Tm5hIcHGy2TU+gp2ahKAH/GcrLy7FzcW/1gXj9iQc5fzKjVbuEhAR+85vf\nMHv2bDw9PYHm29m1a9eSnJyMt7d3q/bFZQUM9b8cdjm8N5PHbn65zfn/8OgzP+ufwWBg7969HDly\nhIceehh757BWdb//zc1UV5W36vPZx2/Rv39/+vbtazo+/PBD9u3bx/Dhw5k/fz4333wzfn5+1NfX\n06gz4OR0OcZ7YO8WPnj7BRydXJg09XaGJIwhL6/wZ/28kvyCIlzcvEyvNQ31PLJgcpt2K/61rE0Z\nNM9g9Xo9vr6+3H777QwZMoSKigpsnF1aCe2a999h68oVV/WnT58+XLhwAScnJxwcHLjtttuYN28e\nSUlJvPT633GPuByPLsnP5S/z5+LfJ5R5f3iWsOhYGrSNNDQ0dDgWXl5ejq2Xa6tx9c7Cpzi5+1Cr\ndu/w87f1mzdvRqPR4OXlxaJFiygoKiE6aripPutYOm8885s2/Z5d9ES7No8dO8bixYuxsrJi9erV\nODo5Yet+eVJgMBj46113UpqbS8LkKUy69z5s3NwpKyvr0QLeXZEQIcRHwAyaM/cGGcva3bV+RV93\nmvfaxAIG4H4p5f6fPd/1eOtzvcTAjx8/zvurviNq5DhTWUVJMTa2trh6NAt1Ztp+5o5JICkpqcN2\nX3jlaRLnhOLm2fyB1+ub2P1TGo7ODri4OYGUnN5VxkvP/c1s9sCjjz7K9u3bufXWW0lJSSE+Pp5d\nu3bxw8Y0omITTe1OnTzMiWMHKS7Kpbgoj4K8bLRazVX9E0Lw0ksv8cQTT/DEoucYkjjDFDetqizj\n8+VvkHpgW5t+o0eP5vHHH2f27NlUVVWxZMkS5s+fT//+/Vu1+3Llao6dqSS872VhPJuVgRAC717+\nuLh6cPTgTzz39GP4+fm16vv555+Tk5PDLbfcwoABA0wimJ+fz/97559ETUoxlVWXl1F88QK+wSG4\nenqSc/I4w/28mHvrrW1837ZtG05OTiQkJLRalH39rbep8fSnV2BvAAxNTXy+5BUObFxvamNjY8Py\n5cuZN29eh9YCMjIy+GDLt0RNHWUqyz+dzfnDx9E36tA36ijIymaARwC+vr5otdpWx+eff46trS3W\n1ta4uLgwcuRIfvWrX7F19z6CBo/GycUVgCa9nrTdWzEYDAgE2oZ6avLP8Yu5tyKEMB0Ad911F0II\nevXqxfjx45k6dSopKSkcPXqUr9KP0G/Y5XGVumkjX7+xhPqamlbX9Ytf/ILnnnuOqKgoU1lFRYVp\nUvPfojti4OvyvrK43/Sg29qcVwiRBNQCn7UQ8MU0J3y81jLhw4wfy4HtUspPhBA2gJOUsvpnfb8e\nhPJKrhcB12q1PPn8n+mTOB5HM4tUTU16jm9dx8vPLmozy/451v7wHdmNB4lLGmC2Pm1nJn0dhzN9\n2swO2ywtLeX5P/+NuOHTzN7u19fXcvbEdl796wvY29u3qvvlL3/JqlWr6NevHwkJCSQkJDB+/Hgi\nIiJ4/4OPKCy3pk9oP7PnTT2wGSebWr777jtOnz7dpt7BwYFx48bx0ksvMXRo811HVlYWb7//OYMT\nJ5kVvLKSQjSVZ3juj092eHFUSsnzr7yKbd8oPH39zNaf2PITixbMJyIiokM2Afbs2cuKrTsZMHJ0\nm7pGrZav3n2DvevXtip/9dVXeeKJJzh48CAFBQXMmTOn1XVoNBoW/fUFwmaPxsHFqY3dJp2e4ys3\n8dffPYOXl1eb+gsXLrBv3z5uuukmevfubSr/Zs137M7Mo99A84ufWUcOMX5wBDOmT2tTl5eXh7W1\nNf7+/q3KS0pK+OOSvzNg6jSszSx+VpeXs/mdtziRnkZpaWmrupEjR7J3715GjRrFkiVLGD58eJv+\n3UF3CPgbmX+xuN/vBjxv9rxCiBDg+xYCngmMbZFyvU1KOeCKPm5AupSy44MTCxcxbzTs7e2ZNnEc\np1N3o7tiQcnQ1MSpQ3sYFT/YIvEGSLppNEUnGsjLLmpTl5ddREmmhpEjRpnp2T4+Pj6MSBzEiaN7\nMRiaWtXpGrVkZuwhZer4NuINsHTpUqqrq0lNTeWDDz7ggQceMInc1CkTKCnIpOqKUAzAhewsvN1s\neOmll8jKykJKiZSSY8eOERISghACjUbDjz/+SHx8PDff3PzLCX379iW0tzenT6a3WfxqqK8j+3Qa\ns2dOtSizRQjBLSlTyU0/SENdXas6KSVnD6fRz8/Holg9wNChcbgbGsk9fapNnaaulsEx0eTk5NDU\n1MRHH30EwNNPP42dnR2jRo3izjvvZNiwYRw+fNjUz8HBgWlJ4zm9db/ZcZX5n/2MHhhvVryhOeQz\nd+7cVuINMHZ0Ek0V+ZQUtt1eUZyfCzXFjLpppFmbQUFBbcQboFevXtwUG8OZ/XsxNF0xrrRaLqYe\n4k9PPUlJSQlSSjQaDe+//z4BAQHs3du8IXv37t0kJyczduxYDh061OYc1wMGhMWHBfi23LUO+Jpp\nEwaUCiE+EUKkCSE+FEI4mmnXCjUDvwoGg4E1a79n4869OPr1xtHFDU19LfVFuSTGDuCeeb/o1IaW\nnJwcPvr8n9j76PEL9wApKTxXSWOZLQvveqhTOds6nY5//XsFqYdP4eYRhIOjM/V11dRW5jF54ihm\nzZrZqTzoY8eO8dEnK7C288TV3Re9XkdNZT7eHvY8/NAvzab75ebm8vbbb+Pg4ICDgwN2dnaEhoZy\nqzF8UVtby9Jln3A2pxg3r97Y2tlTV12OpraIubfNZMzojoekWrJtxw5WrVuPjY8fTp7eNGo01Bfk\nEhngy68WLLA43Q+aZ6HvfLiMUq0eJ79ArKytqSsrwaqmggfvnkdsbGybPlu2bGHixImm10II7r77\nbpYsWUKvXr0wGAx8+/13bErdg2O4P46ebmhr6qg7U8DwfrHcPffOTo2r7Oxs3lv6CQ1WDrj6BCCR\n1JYW4EQjv3ng/k7lbDc2NvLpihUcyjqNY+/e2Dk501BVRWN+HtOSkpg1Y7rZcbVt2zYmT55s+mKH\n5i+fc+fOWezDz9EdM/C/nXzJ4n6Lov7U0Rl4uZTSq0V9mZTS+4o+8cA+YKSU8pAQ4k2gSkr5ws/6\nfr0IZUuuJwG/RGlpKYdSUykpq8DDzZWhcUMICuraT7nodDoyMjI4fb55dtcvrD+xsbFd3uGYl5dH\nWtphqqqr6eXjRUJCgsV3CVfS0NBAWlo6OTkXsbW1ITY2mv79+3dph6OUkvPnz3P4yFHq6zUEBfqT\nkBCPq6trl3ytrq7mUGoqeYVFODs5EjdoEKGhoV3a4djU1MSpU6fIOHkSnU5PWJ8+xMUNwdGx/UnS\nkiVL2LFjB2VlZVRUVFBTU8PSpUuZMuXyj3CWlpZyKC2VkspyPJxdiY8bSmBgYKf9hGbBzcjI4NTp\nswAMiOzb5XElpSQ/P5/U9HSqamrx9fYiIT6+y+OqO+gOAX/tZNskgis5e+AcZw9cTpnd/O7Wjgr4\nSSC5RQjlP1LKqCv6+AF7pZThxtdJwFNSyp+NoyoBVygUPZruEPBXT7afCtweT0c9256Ah9Is4AON\nrxcD5VfuWjfTbzvwgJQySwjxAs2LmE/9nA8qjVChUNzwdNfGHCHECiAZ8BZCXABeAF4FVgsh7se4\na93YNgBYKqWcYez+GPC5EMIWOAcsuNr5lIArFIobnu7KA5dSzmunauKVBVLKAppzxi+9PgK03WX1\nMygBVygUNzxqJ6ZCoVD0UNRT6RUKhaKHombgCoVC0UNRM3CFQqHooUS79b96o+sQJeAKheKGp6fu\nOlECrlAobngyqtr+EFtPQAm4QqG44TH00Cm4EnCFQnHDI1UWikKhUPRMrpdnXFqKEnCFQnHDo9II\nFQqFoodiuNYOdBIl4AqF4oZHzcAVCoWih6K20isUCkUPRc3AFQqFooei8sAVCoWih6LywG8gDAYD\n+fn56PV6fHx8cHFx6Ra7lZWVVFRU4OjoiJ+fX5cewnsJnU5HQUEBUkr8/PxwcHDoBk+bn9ReW1uL\nq6ur2afSd4aGhgaKi4sRQhAQENDlhztD88N4CwsL0Wg0eHp64uHh0Q2eQk1NDWVlZdja2hIQENCl\nhztfoqmpiYKCgm4fVxUVFVRWVvaIcXWtGOLZ91q70CmUgFuAlJJdu/ewbvMWqnVNWNnYoK+vZeSQ\nQdw8Ywbu7u6dsltYWMg3337P8ZNnsXNwRteowdfHjZtnpTBo0KBO2dTr9WzasoWftu+k0doGKytr\n0NSTPDyRGdNSOv2BO3v2LF+tX8fZ4gJsnJ3Q19XTzy+Q26bPICwsrFM2Gxoa+OGH9ezcfRBrGyek\nNGBjpWfixDFMmjgBa2vrTtk9cuQIa9b8SFl5DTY29jRqa4mJjWTOLTPx8/PrlM3KykrW/riW9Kx0\nHDwd0Wl1ODY5kjJ2KiNHjOyUOEop2blrN+s2bqFWZ8DaxgZ9Qy3D4wZxy6yZuLm5dcrXgoICvln7\nAxlZZ7F1ckGv1eDv6cYtM1IYOHBgp2zq9Xp+2ryZTTt2orOxQ1hZQUM940YMZ8a0FOzt7Ttl91qT\nXn72WrvQKdRT6S3gu+9/YP2+Q4TEDcPNywsAXWMjFzKP41RdzlOPP2bxhy0/P5/X33gPF88wgkP6\nYm1tg5SSstJCsk+ncc8vZjFy5AiLbBoMBpYt/5T03ELCBg/FybXZJ21DPeePHqGPozWPP/wQdnZ2\nFtnNzMzkjX8vx3PoQHqFBGNlZYXBYKAk+wKV6cd5Yv4CIiMjLbKp1Wp56633KCqX9O03CHsHRwDq\n6qo5nZnG4Nje3HfvPRbPcHfv3sOKL74nLHwo3t7Ns86mJj0XL5yltiabP/zhEfz9/S2yWVVVxZJ/\n/h2HSEf6Du6LrX3zHUJFcQUn/nOc5OhkZqRMt8gmwDdrvmPTvnRCBw7DzfPSuNJyIesEjppKnvzd\no7i6ulpkMz8/n9feeg/HwL4EhUWYxlV5cSEXj6ey4PZZjBg+3CKbBoOBpZ8s53B+MaFxQ3FyafZJ\n21BP9tEjhDrY8NhDv7Z4XHWV7ngq/W8O/tPifu8Oe6hL5+0Oun7fd4OQn5/Pj7v2MGB0skm8AWzt\n7IgYFEetswc/btzYbn+DwfxWgZWrvsHdpy+h4QOwtm6+IRJC4NMrgKhBo/li5Rrq6+vb9NPpdOzb\nt4+6uro2dSdOnCD1XA5RI0ebxBvA3tGJ/okjyK7RsGfv3g5fOzTf3n+8eiV+o4bhFxZiElQrKyv8\nwkPpNTKej1d92e51tsfu3XvIL9YSHZtoEm8AZ2c3BsWNJv3IWTIzM832LSwsNHu+uro6Vq78jpjY\nMfj4+JtmxdbWNoSG9cfVPYxVq761yE+AHzf/iF2EPVGJUSbxBvD09WTY7ES2HNpMUVGR2b41NTXc\ncccdPPHEE3z22WekpaXR0NBAXl4eG3fuJ2rkOJN4A9ja2RMRG0etrSsbNm6y2NcvVn+Dc3B/+vTt\n32pcefsFEDlsDJ9/tYaGhoY2/QoKChgzZgx33XUXr7/+OuvWrePcuXMAHD9+nLTsCwwYNdok3tA8\nriITR3C2pp79Bw5Y7Ov1gJSWH9cDKoTSQfbs249TYB9s7czfIvYZEM3OHZu4eebMNreRX375Jffd\ndx8DBw5k0qRJjB07lhEjRqDVajlzLo+4EdPM2nR2ccPW0Zu0tHSGDBlMWVkZZWVllJaW8s4777Bu\n3TqsrKwIDAxkxIgRzJkzhzvuuIOtu3bjEx6JlZnQgxCCoP5RbNqxk+SxY9vUf/DBB7z//vtMmzaN\nCRMmMGLECJycnMjKyqLaBgL8zYcevAL9OXU4gzNnznR4Fi6lZPOWnYSEDTEberCyssY3oB/bd+wm\nOjoaaP4iqampIT09nfHjx+Pg4EBMTAzjxo0jKSmJWbNmkZaWjr2jD05O5mPIwcERpKVuoLS0tMPx\n+4aGBg4cO0jiXeZnrda21lj72PLB0g/w6+XHmTNnWh0ajcbU1s7OjsbGRqysrFj28ce4BYRga2t+\n1tonMpqd+zYze+aMDs9si4qKOHMxn9jkOLP1BinJL63k+eefR6PRcOLECY4fP97my+eLL75ASom1\ntTWNjY3N46pvf7N3Q0IIAvtHs3HHTkYnJbWp379/Pz/88ANTp04lMTGxW9Y3uhP1Wyj/x7lYWIib\nj6/pdU1FOc/eNqNNu3dfe7VdG4cOHeLQoUO88sorjBgxgo8//hgHJ/dWH4jlH77Kzv/80Krf39s3\nCTTfHaxZswYXFxfuuOMOcgsK6RUfbqrPPXuaxQ/e26bfX59/rl2bx44d45VXXsHKyopFixYxffp0\nbDwvLwBKKXln4SOcP3wUgIj4IfQKDmJ/nwj69evXRpAvXryIk5MT3t7epjK9Xk95ZRX9Yi6XHU7b\nxVtLnm7jz8MPPWjWT41GQ3p6OqmpqSxbtoyioiIKCopwdvI0tdHpGnnk4Rk0NjaLqK9vED4+vvj5\nOnPbbbcxYMAAk7/vvfcedXV13H333QQEBJhsVFVVYeNsg73j5S/nNe+v4bOXl7f7HrbE0dGRhoYG\nnJ2dcXZ2Zu7cudxxxx3sO5SOm3fQ5fOUl/HMPTNNr3sFBOHp6UnvXp5MnDiRQYMGYWPT/LFdt24d\nGzduZMGCBQwePNh0DWVlZdi5eLQeV0v+woGtG37WRw8PD1xdXbl48SL29vbExsayYMEC5syZg5WV\nFRcLCvAbcXmxL/dMFq89cF8bOy/98Vmz9oUQvPXWWzQ2NjJixAh+97vfMWvWrKu/ef8DVB74/3Gc\nHR0o0Vy+5XT19GLhi39FU1eHjZ0tVtY25B9L59fz78bLywt7e3vTsXr1al544QVCQ0OZOnUqKSkp\njBkzhrKyMvR6bavzpMy6m6jYBFxd3XFx9aC4MJeJY2KYM+eWVu127NjBwoULmT59OrNmzSIpKck0\nQ3N0cKBRq8HRmMUQGBrOXYuepVGjRSLR63SUnDrOnBnN8VopJQaDASklr732GtXV1QghGDJkCCkp\nKdx6663odDoM2kbT+YUQTH7gXnZ8sZrM3fs5m3qYs6mH2bdmHfPnz2/la1JSErt27cLW1papU6fy\n+OOPk5ycjLW1NTbWVugataY7m6iYBBb+6o/YOzji6OiMTteI1BXz1JO/w83NDWdnZ6ysrMjNzWXQ\noEFEREQwZ84cpk6dyuDBg7GyssLR0QGd7vL7amtrx/0Ln2L//i2czjpGcXEexcV5PP98Os8//3yb\n/2s7Ozuee+454uLiePjhh5kzZw729vboNDqklCahHHf7OAJC/fEPDcA/xJ+C7AI8it1ZcNd9bWwa\nDAZWrVpFdHQ0AwcONNnIyMyiqsW4cvP0Yu6vn+BE6j7OZ2ZQUpBHSUEeTz31lLlhiRCCZcuW4ePj\nw4MPPsi9996LnZ0dTbrW42rGvIUMHz+VgOAw3L19OJORzuQhfZk6ZUqrdlJKfvrpJ+Li4tos9Do6\nONCo0eDg5AxAQFg4c3+3iLqqSjT19dTX1FB2/gwD+valpqam1ZGfn2+yo9frOXPmDKdPXz8PUbhO\nIiIWoxYxO8iRI0f44Nt1RI8ZZ7a+6GIOrhXFPPn4Y23qpJRUVlbi6enZqlyv1/PMH/9C74jhuLq1\nTW+TUpK2bz3PPvkbgoKC2tS3x8ZNm/gh/TiRCeZv98+fyGCojxt33XlHm7ry8nKysrIYOnRoq1v2\n6upqnnz1ZSJmT8HGzK28Tqvl3NqNvP7sc5SUlLBhwwZ+/PFHNmzYgE6na9Pez8+PdevWcfxEJqez\n6wiPiDHr6/Fj+5g2aSjjx7d931uKaUsuXLjAa3/7gPiEKWbrq6sryc89xMsvP4+1tTWFhYXs2bOH\nXbt28cYbb+Dg4ICtrS01NTVAcwhs7ty5LHl3CU6DXQgIC2hjE+DAmn3MGzvPogyP9PR0PvrmR2JG\nmh9XhRezcdOW8fvHHjH6Xs3BgwfZu3cvzz33HLa2tjg6OlJTU4OUkmeffZY///nPPPX8XwgYPApn\n17aL6gaDgWPb1vH8E48QGBjYYV83bNzI+mMn6RefaLY++/gx4nt5MG/u3DZ1Bw4cYMOGDdx0000M\nGzas0xlb5uiORcwH9n5ocb+lIx+85ouYagbeQWJiYvBdv4GczBOEDIhuVVdfU03R8aPcdf98s32F\nEG3EG8DGxobpKRP4as0WYocmY9civi6l5GTGQWKjwiz6kAGMGD6cDdt3UnzxAr7BfVrVVZaUoLl4\nnvG3Pmq2r5eXFyNGtM16cXNzY3xCIlt27qPf2Juwtrk8dJr0es7s3MeUxBG4uLjg4uLCQw89xEMP\nPWRqk5SURFFREYGBgfTp04fw8HD8/f3x9/fn4N/eodLLDw/P1vHogvwcrGQ1iYnDzPraXspecHAw\n/fsHc+pkKv2j4lu102o1ZJ06wJ13TDWlJ/r7+zNnzhzmzJlDUlISxcXFhISEEBoaSkhICE5OTgCk\njEth6Xcf4eblirN76/h6VloWro1uREVFmfWpPWJjY/Fet4ELp0/Sp1/rvvU11RRmHWXeg/eZytzc\n3JgwYQITJkxg0qRJpKamEhYWRnh4OCEhIab00BmTx7NywzaiRyS3WreRUpJ15ACDI8MtHlcjhw9n\n445dFOdexLd3cKu6ipJiNBezGX9r2wkMQGJiIomJ5oX/euD6mi52HDUDt4CKigre+XAp+XUaXAN6\nY2NrR01ZMYbyEu6+ZTYjzQjf1ZBSsm7dj/y4cQcuHoE4u3qi1TRQXX6RyL5B/PL+e3F0dLy6oSvI\nzc3lnWUfUy1scPUPxMrKmuriAmxrq3jovvn072/5U7j1ej2fr17F7hPHcAgNxsHNBU1VDQ05uYyJ\nGcQvbrvdFJ+1hBMnTrD0o39jZeOBu6cfBoOByvJ8nBz0PPLwAxYLDUB9fT0ffbSc02cKcHcPwt7B\nkdqaCmprC5iWMo6UFPOz86uxd99eVv+0Grdwdzz8PWjU6ig9U4KX8OLX9/6qUxuFysvLeeeDZRTV\naHD1ax5XtWXF6KtKuPv2my1O94PmcfXDuvWs27oTJ9/eOLt5oNXUU1N4kZiw3iy8b36nx9VbS5dR\nZ2OPS0AgQlhRW1SATW0VD993r8VppN1Bd8zAF+5ZanG/j2564JrPwJWAW4jBYODUqVMcPpaBRttI\naO8gEhLiLc7TvZKysjIOHDhIYXEpri5OxA+NIzQ0tEu75nQ6HRkZGWRknqLJYGBARDhDhgzp8q65\n/Px8DqalUlZVhbe7O4nxCa0W/DpDQ0MD6emHOXPmPNbWVsTEDCAmJqZL2QpSSs6fP09a2mFqa+vx\n9+9FYuIwvFqkgXaG6upqDqUdIrcgF3t7ewZHDyYyMrJLuzGbmprIysri8NEMNI3N42rYsIQu78Ys\nLS3l4KFDxnHlTMLQOEJCQro8ro4dO8bxU1ndOq46S3cI+P27l1nc7+NRv1QCbo7rWcAVCsX1RXcI\n+IJOCPgnZgRcCPERMAMoklIOMpZ5AiuBECAbmCulrGrHFyvgEJArpbxqio6KgSsUihueBG/Lfwbi\nk/aL3wY+a1H2NLBZSvmaEOIp4BljmTl+C5wAOrSlWwm4QqG44TlYmt0tdqSUu4QQIVcUzwYu7Zr7\nFNiGGQEXQvQGpgEvA0905HxKwBUKxQ3Pfzli6yulLGo+jywUQvi20+4NYBHQ4RxL9VsoCoXihkdK\nYfHRldNdWSCEmE5z3PwwIIzHVVEzcIVCccPTkQl4YXomRenmf1jtKhQJIfyklEVCCH+g2EybUcAs\nIcQ0wBFwFUJ8JqU0v7nEiBJwhUJxw9ORGbXfkCj8hlzebHV0+dr2ml45g14L3AcsBu4Fvmt7fvks\n8CyAEGIs8PuriTeoEIpCoVA0T8EtPcwghFgB7AEihRAXhBALgFeBSUKIU8AE42uEEAFCiB/MW+oY\nagauUChueLrrmZhSynntVE0007aA5pzxK8u3A9s7cr6rzsCFEPZCiP1CiHQhxDEhxAvGck8hxEYh\nxCkhxE9CCPcWfZ4RQpwWQpwUQkxuUT5UCHFUCJElhHizIw4qFArFf5ue+kCHqwq4lFILjJNSxgFD\ngBQhRCKXk9P7A1tpTk5HCBENzAWigBTgPXF53+4/gYVSykiabzFa/5alQqFQKDpMh2LgUspLz/Sy\npznsImlOTv/UWP4pcLPx71nAl1JKvZQyGzgNJBpXX12llAeN7T5r0UehUCiuGf/jNMJuo0MCLoSw\nEkKkA4XAJqMI+7VMTgcuJacHARdbdM8zlgUBuS3Kc41lCoVCcW3ppkXM/zUdWsSUUhqAOCGEG/Ct\nECKGtpfQrZf04osvmv5OTk4mOTm5O80rFIoeyrZt29i2bVu32hzuG2pxny+61YPOYVEWipSyWgix\nDZhK+8npeUDLX3vvbSxrr9wsLQVcoVAoLnHlhO7Pf/5zl23uK8rpso1rQUeyUHwuZZgIIRyBScBJ\nLienQ+vk9LXAnUIIOyFEGNAXOGAMs1QJIRKNi5rzMZPQrlAoFIqO0ZEZeADwqfF3aq2AlVLK9UKI\nfcAqIcT9QA7NmSdIKU8IIVbR/JOIOuDhFj/u/RtgOeAArJdS/vxjshUKheJ/wPWSFmgp6oEOCoWi\nR9MdD3S4Y+O/LO63cvI91/yJPGon5v9n77zDo6ry//+60zPpPaQ3UkiBhCR0CCi9uYjoWlewt7Wi\n/lx3V11Xxbar6FpRVAQFFOkgIi2UVFpCEkIIJAHS2ySZfn9/BAZiBmVCXJev9/U88zwz95zzuefO\nfOZ9z/mcc+6RkJCQuELbi5KAS0hI/O65QvVbEvDLpbOzk4aGBuRyOf7+/pe1se05RFGkpqYGk8mE\nl5cXzs7OfVBTaGtro7m5GbVaja+v72VtbHsOi8VCTU0NVqsVX19f1Gp1H9QUmpqa0Ol0ODs7X/Ym\nxCYSIxwAACAASURBVOcwGo3U1dUhCAJ+fn4oFJfv/qIoUl9fj16vx93dHTe3S9oJ6xfp6OigsbGx\nT/3KarVSW1uLyWTC29sbrVbbBzU971cajQYfH58+8av/Ov8jC3McRRLwXqLT6Vi9bj27c/MRVFos\nZhMeTmqmThjH8GHDeuXEoiiSk5PLms3fU6/rQK5QYdV3MCx1IDOmTsHd/ZI36uhGXV0da1atpSD7\nECq5BpPZiH+wNzOunUZSUlKvbFqtVn7cvo0NO7fTLrMiyGTIOgyMTR/K1ImTer1D+cmTJ1mz+TuO\nny7DyU2Fvs1IqG8E0ybMJCLC8X0LAQwGA99v2cTegh/RuFgRRRFLp5oRQ8YzLvMq5HJ5r+wWFRXx\n7eb1VDbXo3DSYNZ1MCgyjj9MmYq/v3+vbLa1tbF6/Xqy8gsQNFqsZhOeajXTxl/FsKFDe+1X2dnZ\nrF/zPc0NOhRyJSarnoxhKcyYOa3XN526ujq++24t+/cXo1RpMZv0BAR4MXPmZBITE3tl8zfjCm2C\nS4OYvUCn0/HaWwtpkTsTFpuI6qxYtTTWc+JgLpOGpXHNzOkO2924eTPf/LCT0OQ0PHy6Wsgmo4GT\nJUU4tTcy/88POizitbW1vPbSv3A2eREWGIlCoUQUReqaaiivLebGO2YzfPgwh2yKoshny74kq7KM\n0IwUXLw8ANC3d3Ay/xDhViV/vuteh1vjx44d44OlC4kaEUhoTDByuQyr1Url0VOU7KjkzuvvIyYm\nxiGbJpOJDxctxOh0ktRRUbi4OgHQ3Kgjd/sxArSDuPmPf3K4hZubl8cHq78mYGgyPqGBCIKAxWSm\nuuQY5iOVzL/rfvr16+eQTZ1Ox6tvvU2zkwuhCUmozn5/LfV1nCzIY/qwDKZPneqQTYANGzax4dtt\nRIcOxMvDBwCj0UB5ZTFy53Yem/+wwyJeW1vLq6+9jcYpmLCwGBQKRZdf1Z3mxPH93HzzTIYNG+pw\nXXtDXwxiztmwxOFyX0++6TcfxJSeB94LNm/5gSac6D8wzSbeAO5ePsQPH8vGnXuorr7oGiW71NXV\nsWrzj8SPGIenr5+tpaVUqYlKSqFT68XaDY7Pulzx1Te4WHyICo1FoVACXQ7v5xVAUlgaX322Ap1O\n55DN0tJSso4dIfbqUTbxBtA4a+k/MoNySwdZu3c7ZNNqtfLlN58z4OpwIuJDkcu7XFMmkxEWG8yg\nyf1Z8s1iLBaLQ3azs/ehkx9n1OQEm3gDeHi5MHZGEicb8iksLHTIZmdnJ4tXLSdy4nB8w4Jsv5Vc\nqSA0MRZ1UjhLV610yCbAxs3f06jWEp2aZhNvAHcfX+JGj2Xtjl2cOnXKIZu1tbWsX7WFgXEjbOIN\noFKpiYsaiFmnZf06x/1q+fJvcXYOIypqgC0U1RWaCmRA4iiWLvuW9vb2HuVEUaS1tdXh8/3qXKFL\n6SUBdxCTycT2PdmExSXYTVeq1Lj0C2P33r0O2d27LxungOBuN4QLCYkdwO68Ajo7Oy/ZZmNjI0UH\nSgnrF2k33dnJBY3oRl5efo+0hQsX8uKLL5KTk9NDNH/cnYV7bCRyOzFkQRAITI5nU9ZO7PWi6uvr\nGT58OHfccQfLly+ntrZrAW95eTl6RRuB4QF26+oX7IPV2UBpaand9KKiIvz9/cnMzGTBggXs2bMH\ng8FAVvYWEtND7IYe5HIZcYP92Z29za7Ni3HgwAFEf3ecPez3hvrFRFJaU0VNTY3d9KysLNatW9dN\n4IxGI9uzswkdcBG/UqvRBoexe+8+h+q6d88+3LUBqFX2/SoyNI49u3LR6/U90goKCmhpaelxvKGh\ngaIjxwkNi7Zr09nZFbXah4KC/d2OW61WXnjhBdzd3QkJCWHOnDm8//77lJSUOHRNEueRYuAO0tra\nihkZGu35gcW936/n8zf+YfvcLyyCnf0CaKirIy0tjaSkJFs44dZbbyUwMJBbb72VAQMG2MqcqD6F\nm7ev7XN7aytPXjcJAO+AQJKGjsDNSUNtbS1hYWHd6mSxWKitre3RZW9oaMBJ4dwtxrt0/Ses3/5t\nt3wLPvi73WtVKBS88sorWCwWhg8fzoIFC0hJSeHkmVN4jBxky1dz/AQvX9Nz96cFz/7Nrl2APXv2\n8Pnnn2M0GomIiGDx4sW4BXQfVHvi2uc5sLuo27F/8PpFbUJXi3PHjh2Iooi3tzd/umc2/oFptvTc\nrGLuvuY1ho9L5M9/v46AYC8Kd5z8WZs/5VRtDWqf8+JtMVv4f6OuobO1e0/m/edesb1XKBR4enri\n5eVFSUkJWq0Ws9lMcnIys2fPZsqUKVjkiu5+tWEtX77yTwBUGg1BUf05FNQPtVLB0KFD8fU97y/v\nv/8+ra2t3HTTTQQGBtqOV56sxtPtfMu7rb2V+566tsc1/efDfxEUFERQUBDBwcFoNBoWL16MSqVi\n4MCB/PGPf2T69OlER0d3+ZXGDZnsvF99ueQtvt+8opvN1169+HdYVVXFihUrWL58ObNmzWLlSsd7\nLH3J0IBQh8t8/SvUw1EkAXcQlUqFxWTEarXa4qaxg9IYPmk6lUdLqDxWyukTxzl94jjZe/fYtaFU\nKnn77bfx9/dn7ty5PPDAA2idnDC2n28FaV1dmX77PezdvI666kq2rVoOwOql3RccjBo1isbGRgoL\nCxk1ahSPP/44U6ZMQaFQoFQqsYjmbvlHD74ajcoJg1GPwainrqkGFy8NHp4etLe3215FRUUolUqs\n1q5Bv/b2dlsrzUmtxqTXg3tX3NQ3NJjZzzyCXteBKFqxmMzUHyph6lXjEQQBq9Vqe5WXl7Ns2TI0\nGg0uLi6MGjWKyZMn4+TkhKmze13n/r8/UpRbikIpRy6Xc7LkNIlBqcTGxqJQKLq9Dhw4wF/+8hec\nnJzIyMhg+vTpjB07luWrP0XfacRJ23UDjYoLIjYplN1bD7N762HbuWRmVx599NFLmvHjpFZjbjLa\nPssVcm568UmOFxTS0dJGR0srdccrcVWo6ejosM2oqauro66uDuiaZaLVasnNzeXw4cMEBgZiMXb3\nq5jUNDImTqGi6DC1lSc5XniI44WH+GHzZrv1UqlUPPvssyQnJ3Pvvfcye/ZsNE4aWk3n6+qidWXu\nDY9QUVlKY3M9jc111DacRq/vpKKigoqKim42jUYjubm55OTk8Oijj9LQ0IBSqcRsMXbLN3bcNQQE\nhKDRaNFotNTXn2FwajhTp07B1dUVV1dXnJ2dWbRoEQ8++CDDhg1j1qxZTJgwweFxjV+Dvacdu4n/\nryANYvaC1/79Nq1aHwJCwu2mH9mzndumX01aWlfLT6/Xc/DgQXbv3s0jjzyCu7s7JpMJg8FAv379\n2Lp1K3q9nv98vYqEUT12XgKg/swp5KePc/fc29i4cSNr1qxhzZo1Pbq+MpkMd3d3Xn75ZebNm8cz\n8/9KqHM8bi49u/uiKJJzdCePPHNfjxke33zzDc3NzQwbNozY2Nhug3w/bN3KN8X59B+ZYbeulUUl\nDDCrmXdzz1a5KIps3bqV/v37Exp6vtWj0+n4++vPMPa2NFQaVY9yJqOZrZ/s4y9/fh4PD48e6QBl\nZWVERER063F8vWIJHS5FJKf1DCN1dhh45cmlrFm2q9vxxMRE3nzzTa6++mpWrlyJs7MzEydOtIVh\nqqqqeOHjhSTMnmg3NNPR0saZTXtZ8MzfUSqVPdKff/55KisrGTVqFBkZGcTExCCTyVjw77do9fLD\nPzSsRxmA4p3bmDdlIqmpqVitVo4ePcrevXvZu3cv7733Hmq1GpVKRVtbG4IgsGzZMuLi4vj0/ZWk\nxo+ya7Om7hRGZQ1PPfN4t2tpaGhg7Nix+Pv7M3LkSFJTUxk0aBDBwcFYLBaeeeYFgkLScHPr+VuI\nokh+3iYef+yuHr1F6Oox9nbmjz36YhDzurWOD2Iun/bbD2JKLfBeMG3ieN786DPcPL3Rurh2S6sq\nK8FdbiE5Odl2TKPRkJGRQUZGBvHx8chkMqKjowkJCbENAJnNZvy1GzhRXERY3IBuNvUd7VQfyueB\nW67H19eXW265hVtuucWW/txzz/HGG2/g5eVFQEAAgYGBuLq6IpfLmTpzEss/XcOgqAxUyvMDY6Io\nUnz8MJHxIYSHh/e4xlmzZl30+jPS01m/cxu1FSfxC+/e9WxraKS98BgT7rrfbllBELjqqqt6HHdx\ncWH4wFHkbN7H0CkpyBXn/+AWi5XcLQdJTxx+UfEGiI7uGZMdNWIc7yzaS7/gZnwDupetr2khJSmD\nz/6zBg8PD4qKipg/fz7r1q1j/PjxtnwajYakpCTeeecd0tPTCQoKIjkokpI9+UQPS+0mfCajkfId\nOVw35iq74g3w17/+1e7x6RPG8+biL3D19ELr+hO/Ki3BC9E2PU8mkxEbG0tsbCy33XYbV199NTU1\nNURGRhIZGUlYWBhqtRqz2Yyn7zrKT5YQGRrbzWZnZzsVpwu568Gbe9yIvL29OXjwoN16KhQKpky5\nihUrtpA8cDRKVXe/Ki0pIDoqsNsN+kL6Urz7jCt0HrjUAu8le/bu5YsVq1B6BeDq7YfZbKL1dCU+\nGjkP3nMnPj4+v2zkJzQ1NbHwgw85ozPgGhCCUq2mraEOY/0p5kybQuaY0Q7bFEWRdWvXs+m7H3BT\n+ODm7IHBqKexs4bQ2ADuuvdOXFxcHLZbXV3Nvxd9RJtWgWtYIDK5nLbqMwg1Tdw958ZezS83m80s\nW/Elhyry6TfAG1cPZ3StHZwuqie2XxI333DrRUXx5zhy5AhfrvwAnzAZgRHuWK0ilUeb0dUouf2m\n+y/aSvz000+54447uh2fMmUKH3/8MW5ubry/+BNKGk/jEh2MWutER1MzHWWnmJg2nD9Mn9GrOdu7\n9+zh81XfofTth6ufHyajAV11Nb5KGQ/e1Tu/amxs5N23P6ChpgNv10CUSiUtbQ20GWu57oYZjO6l\nX61du56Nm3bi5haEq5sHen0nzU1VhIf7cvddc/tsAdov0Sct8DVfOlxu+fQbf/MWuCTgl0FrayvZ\nOTkcP1mJWqkiZWAS8fHxl7XCz2KxUFJSQu7+AxgMBkKDAhmSkfGzLc9Loa6ujn37sjlTXYOzi5a0\njMFERUVd1go/o9HI4cOHOXCkCLPVQkxYBGmDB1/WH1cURaqrq8nJz6appREPN0/SUzMIDg6+rBV+\nHR0dFBTkU36iBEEQiIlKZODAgT87V10URV544QWOHj2KTqejs7MTvV7Pp59+Snh4OKIoUl5eTnZB\nPm0d7fTz9mVIejp+fn4XtXkptLS0kJ2TQ0VVFSqVitSkJAYMGHBZLVeLxUJxcTEFeQfRG/SEhQWT\nnpHeR36Vw5kztTg7a0lLSyE6Ovq/uhqzT+aBr3ZcwL+eIQm4Xa4UAZeQkPjt6RMB/64XAj7ztxdw\nKQYuISEhcYXGwKWFPBISEhJXKFILXEJCQuIKjdhKAi4hISEhCbiEhITEFcoVGgOXBFxCQuJ3z7Cg\nEIfL2HsWiiAIHwPTgBpRFJPPHvMEvgLCgApgjiiKLT8pFwx8BvgDVuBDURTf+qU6SAIuISHxu2dv\ndWVfmfoEeJsuMT7HU8AWURQXCILwJPD02WMXYgYeFUVxvyAILkCeIAibRVEs/rmTSbNQJCQkJPro\neeCiKO4Cmn5yeCaw+Oz7xcA1dsqdEUVx/9n3OuAIEPRL1ZYEXEJCQuLXxU8UxRroEmrgZ5fqCoIQ\nDgwCfvHh71IIRUJCQuK/Owvlomc7Gz5ZAfz5bEv8Z5EEXEJCQuISBLy2sJDawqJfztiTGkEQ/EVR\nrBEEIQCotZdJEAQFXeL9uSiK312KYUnAJSQkJC4Bv4QE/BLOb3lXuOKiuwgJZ1/nWA38CXgFuA24\nmDgvAopEUfz3pdZJioFLSEj87hFEx1927QjCl8BuIEYQhJOCINwOvAyMFwShBLjq7GcEQegnCMLa\ns+9HADcB4wRBKBAEIV8QhEm/VG+pBS4hISHRRzFwURRvvEhSj622RFE8TdeccURRzAIcfl6wJOAS\nEhISV+hSeimEIiEhIXGFIrXAJSQkfvcI0rNQJCQkJK5MhoQ4/iyUpb9CPRxFEnAJCYnfPftO9tmz\nUP6rSALeR4iiSE1NDZ2dnbi5ueHt7d0ndtvb26mrq0Mul9OvX7/L2jD5HFarldOnT2M0GvH29sbN\nza0Patq1GW9jYyNqtZqAgIDL2jD5HGazmVOnTmG1WvHz80Or1fZBTaG+vp62tja0Wi1+fn59sgmv\nXq+npqYGgH79+qFSqS7b5oV+5e7ujpeX12XbBNDpdNTX1yOXywkMDLysDZPP8Wv51X+FK3QQUxLw\nPqCwsJAV69dzuqUFuZMTZp2O+NBQZk+fTnBwcK9strW18d26NewpLEDp4YzFbMbJJDB55DjGjB7T\nK3EURZHs7L1k7VyNWtGM1klOTb2ZkLBUJk2+ttc3nbq6OlauWs2BI0dRaV0xGw14uaiZOWUSaWmD\ne2XTarWyZetWNmzfjl4pR5DJEdvbGZWayswpU+ntzvcnTpxgxeq1lFZVoXR2wdzRQYiPF7OnTyM2\nNrZXNg0GA+vXb2T7tj3IBE2XGMgMjB03gkmTJqJUKntl1+ZXrc3INef8Kuzy/WrVGvZl70elcMZq\ntaBWw6Qp4xg9enSv/WrP3r2s+f4Hmg1G5Co1Zl0baQnxzJoxvc9uOr8mV2YEXNqV/rLJy8/nvRUr\n8E9NwTswEEEQsFosnDl+nI4jxTxx992EhYU5ZLO9vZ1X3/kXugANEanxKNVdLTldYwtlOwsYEzGQ\nObNmO9xq3LJlI0eLVjJ9UiiB/dwBMBrN5BVUsTtPztw7nnJYxOvq6njlzbdReIcSEhWL/GwPobmh\njopD2cyZehXjxo51yKYoiny+bCm7yo8Rlp6Os3tXS86oN3Dy4CH89QYef+ABnJycHLJbXl7O6x98\nhHtsAv5h4chkMkRRpK66itqDBdx/8x9JTk52yKbJZOKdhe9TdVJH/+iBOGm6egidne2Ulu0nOsaH\nu+6e53ALNzcvj/dXrsB/cGo3vzpdXk5ncQnz776H0NBQh2zqdDpef+0t9J3OREYOQKVUA9Da1szR\nsjxGjkriuuuuddivNmzaxKodWYSkpOPu7QOA2WSiqrQYRf1pnnzowV9VxPtiV/obP//K4XJf3nL9\nb74rvTSN8DIwGAwsXrmSsFEj8QkKsjm+TC4nMDoa16QkPl++HHs3o+bmZlJTU8nMzOSRRx5h0aJF\nZGVlYbFY2PLjVlp8FMQMG2gTbwAXL3eSpoxk+5E8Tpw44VBdGxoayM/+lluuj7WJN4BKpWDYkHCG\npVr4fvMlPX6hGytXrUbhE0Z4bIJNvAE8vH2JGzKWFWs20dLS8jMWelJWVsbOI0eIzRxjE28AlUZN\ndEYap+QC23bscMimKIp8uuwrvJNS6BcRaWtpCoKAX3AIoUNH8MnXyzGZTA7ZzcnJ5UR5I0kJQ23i\nDeDk5ExSwjCKj5ziwIEDDtnU6/UsXrmCsNGjevhVUP/+uCQm8MWKFXb96uf44YetdLSriYtNsYk3\ngJurBwOTRrNzRx4nT550yGZ9fT2rfviRmJFjbeINoFAqCU9IwuQdwOr16x2y+ZvQR4+T/W8jCfhl\ncPjwYUyurrh4ePRIs1qtCHIZO/ftY/78+Vx//fUkJCQgCAKCIODp6UlBQQHbt2/nX//6F/PmzSMz\nM5MzZ86wLXc34YPi7J5TrlTgERfCrn17HKprXt4+UpI0aLX247KDU4KpKM9Bp/vFB6DZaG5u5kBR\nKSFRMXbTNU5a1F4BZOfkOFTX7Xt24xYV2e2GcCHBiQl8vzsLq9V6yTYrKiqo69DjE2j/EctuXt4Y\nVE4cOXKkR5ooiqxZs4YDBw70EM2tP+wkJCTWbqtVJpMR1K8/W7futHvO6upquyJ8+PBhzB7udv0K\nwD88nIrGBqqrq3ukmUwmrr/+eubPn88PP/xAZ2en7fj2bXuJCB9g16ZCocTTI5Rdu3r6VW1tLQsW\nLCA7OxuLxdItbe++bJz6BaPSaOzaDY6JY9+BQ7S3t9tN/5/hChVwKQZ+GdTU1qL0PP8ns1qtzM8c\ni8Vs7pZv80/KJSQkEB4ezrp161CpVAwZMoS5c+cyc+ZMZDIZRsGCk+v5GO/e77bwyROv2D77hgYS\n7N+Pptp6MjIyGDRoEGp1V4uqvr6ep556iilTpjBlyhQ0Z/9YDfVVJMect9nS0klQzLNYLFYWvDCT\nu+eNwMtDRlNTEy4uLpd0/Y2Njaic3ZHLz7vRuqWLWPvlR8gVCjIyJxIZl0TVKZ+fsdKTyjM1eCSd\nf2hQXWUV/7zuegDihg4hY+oUFO3t6PX6Sx7UbGhoQOHm3k1o33vqMYp+ciP8z9+esb13dXXF19cX\nrVbL4cOHcXZ2RqlUMmnSJK699lpmzJjBmdM1DB50Ps5fXLKfl159uMf5Fy58k4iICCIjI4mMjKSj\no4N//vOf+Pj4MGvWLG644QZGjRqFQqHgTG0tKg9PW1mrxcL/mzAJQ0dHN5sfvvjPi16vTCbj3Xff\nRa/Xk5yczLJlyzCbRZyczvvAzl0b+PiTl3uUvfnmP9q16erqitVqJTMzk9tuu43rrruOqpoaXLzO\nh906dG08NWMiAE4uriQNH4mnmyuVlZXExXVvlIiiSGVlpcOhoF8DKQbeh1wpMfBt27ax/OAB+qen\n245VlpRgtVjwDwtD4+xM6Y/bePAPfyA+Pr5H+fz8fCIiIvD0PP9n7ezs5NEX/0LyTROQK7ripq0N\nTWz+aDkVB4upOFiCyWD8xbq5urpisViYPn06d955J81Npwn1O8DglPPzXf/+4npeeGVTt3JJSUm8\n+eabXHXVVb94jurqal56630SR00+f+zEMb5b/B6HcrLslklISODmm28mMzOTOXPmMGnSJB5//HFi\nYs634l9569/oQoLwDgwEugRs/fsfsPvb7+hsa7Nr8/bbb+fmm2+moaGBxx9/nPvuu48pU6bYQiUH\nDx7kgzUbiB0x2lausrSEI9l70bU0o2tuouZEBRpEOjo6qKurw2js+T2r1WoMBgNarZbS0lLefONd\n+keMsAmj0WRgy9Zvqamppq7uFDW1VdQ31PzidwkQGBhIdXU1P2zdyreFh4lOT7OlleUXcOpYGRaT\nGbPRSE3pUVKjo3FxccFgMGA0GjEYDNTX17Nhwwbc3Nzo7OwkNjaW8ePHM3/+fJ5//jXSU6cik3X5\nlU7XQtbuTbR3tNHe3kZ9wxmMxlbc3FxoamqisbGRpqYmW6vb2dnZFmKaNm0aK1eu5LOlSzncaiAk\n5vwA8M7vviF703pOFF/80asDBw5Eo9Gwb98+kpKSeOyxx5gzZ47D4xrQNzHwmz51PAa+5E+/fQxc\nEvDLoLa2lr+8+SZxU6cgszNI1anTcXrbdl77298cmlK28KP3qAuQExQbYTe9cNNubsqYREZGBtAV\nMz1w4ADZ2dksXLiQ0tJS3NzcMBgMGAwGoqKiWLt2LTu3/pu5N8fZ7e6v3XCY+c/+QElpebfjN910\nE//4xz/w9/fnjTfe4LrrrrOJrdVq5dkX/olreDIe3r49bIqiyOFdm5g1YTRZWVksWbKE0tLSbnkU\nCgVKpZK0tDSeeeYZJkyYwK6sLJbu20PMyBF2r7+yuIR+za0IZjOffPIJhYWFPfK4uLjg5ubG008/\nze23345MJuOJvz9PeOZ41HZEwmI2c2TTWl56+skeA26dnZ3cfPPNBAYGMnz4cAYPHkx0dDQymYwV\nK76hILea2P72Bz+PFOczYkws06ZN6XZ8//79ZGZmEhwczODBg8nIyCAlJYXhw4dTU1PDs/96k7hp\nU+37VVsbZ7bt4FU7fiWKIqtXr8bPz4/U1FRbzwzg7bffo7VJQ3BwpN26Hjy0kxtunED6BQ0SgGPH\njrFgwQKGDh1Keno68fHxtkHZI0eO8NaXXxM/drxdv2o4fQpZVTmP3X8fW7duZfXq1axevZra2q5H\nYp8VX2QyGWq1mscff5znn3/ebv0uhiTg/2NcKQIOsOizz8ipqaH/8GHdpmCZjUZKtm1n9ogRTLy6\nx4PIfpby8nJe++w9oicPwcXLvVtaZVEZ4pFann3kyW5/znO0tbXx0UcfERoaSlRUFJGRkbi5uWG1\nWnnvP68wIKqWUcPDu/3ZWlv1fPbVUUaPu59BgwZhtVpZunQp8+fP59SpU93sazQarrvuOl566SWC\ngoLIzs5h0deriRuSicbpfDhDFEXKDucTpBV55KEHevy5t2zZwsSJE9FoNBiNRqxWK8HBwZw4cYKO\njg6ee+1ViIqkX3RU9+traKRqVxbP3HNvj653Z2cnM2fO5Mcff0Sr1dLe3o7FYmHUqFHs2LGDtes3\nsDY7l9gRY1BcMLXParFQsjeL4ZFh3PzHGy7xV+qirq6OF//xBqFBg/DxDuiWVlNbTU19Ec/85bFu\nvaxL4aPFi8mrr6X/MDt+9eM25owcxfhL6CVdyLFjx3jzjQ+JixmOq2v3+PqJk6VYrGf4f888Ydev\nLobVauXlN96kQe1CWEJSt9+5s72dsl0/8sCN19ud3bN06VLuuecevLy88Pf3JzAwkKuuuor777/f\noevqEwH/pBcCfrsk4Ha5kgTcYDDw6ZIl5JeXowkJRu3sjL65BUN1NROHDuWa6dN7Nbc2vyCfT75d\nhjzIA7dAH8wmE20VtXibVTww9258fByLK0OXuH/x+bvIrMdJHqBFq1VRVd3OoWIzI8fcwMiRY+yW\na2lpYcaMGew4O/NDEATkcjkPPvggr7/+Olt//JEVazfj5N0PZ3dvjPpOdLVVRAX5cPcdcy86Z7u4\nuBhnZ2c8PT1xdnbu9uevqanhrY8+pFEAbVAgcrkCXU0N8sYm7rrhjyQlJdm12djYyMaNG3Fzc8PV\n1RU3NzebOFitVpZ/8w1bc/Jw6heCk5sb+nYdHdWVZMTHcOuNN/ZqznZ5eTn/eXcRVosTHm7+SXB3\nVAAAIABJREFUADS3nEGpNnLf/Xf0KsZrMBhY9MXn7K+owCkkBJVWi76lGUNVNZOHD2fmtOm9WnyU\nn1/A4k+/QqX0wt3NF7PFRHPLKby81dx335298qvW1lbe/ehjKhpbcA4MRqXW0N5Yj7nuDDdMn8bo\nUSMdtukIfSHgNy9yXMC/mCsJuF2uJAGHrtZmVVUVOfn5tLa34+vlRcbgwfj69gwrOEJ7ezt5Bfkc\nrzqJSqFk4IBEYmNjL2vVnCiKlJWVUVRYgNHYgY9vCKmpabi7u/9subq6Ol544QXazw4e6vV6/P39\neffdd4GuGSk5ubmcOlOLVqMmNWUQkZGRl7XC0Ww2c+TIEQ4eOYLZYiE6LIzUlJRexUkvpLa2luzc\nXOoam/B0cyN9cCqBZ+da9xaDwcDBgwcpLi5DJkD8gFgSExMvazXmuUG+3IICWtt1+Hl5k5GW1iuR\nvZD29nby8vKpqDiJSqUiOTnhsv3KarVy7Ngx8g4cQG8wEhIQQHp62n9lNWZfCPhbW3c7XO6hccMl\nAbfHlSbgEhISvx2/5xa4NI1QQkJC4gptL0oCLiEh8bvnYntc/q/zi6NrgiAEC4KwVRCEQkEQDgmC\n8NDZ456CIGwWBKFEEIRNgiC4X1DmaUEQjgqCcEQQhAkXHE8VBOGgIAilgiD869e5JAkJCQkHuUJX\nYl7K9Agz8KgoignAMOB+QRDigKeALaIoxgJbgacBBEEYAMwB4oHJwLvC+dGh/wDzRFGMoWvX5ol9\nejUSEhISvyN+UcBFUTwjiuL+s+91wBEgGJgJLD6bbTFwzdn3M4BloiiaRVGsAI4CGYIgBACuoiie\nezDGZxeUkZCQkPjtuEJb4A7FwAVBCAcGAXsBf1EUa6BL5AVB8DubLQi48AET1WePmYGqC45XnT0u\nISEh8ZtypcbAL1nABUFwAVYAfxZFUScIPS65T7+Cv//977b3mZmZZGZm9qV5CQmJK5Rt27axbdu2\nvjX6f1nABUFQ0CXen4uieO6h0TWCIPiLolhzNjxSe/Z4NXDhDqHBZ49d7LhdLhRwCQkJiXP8tEH3\n3HPP/XaV+Y251DXei4AiURT/fcGx1cCfzr6/DfjuguM3CIKgEgQhAogGskVRPAO0CIKQcXZQ89YL\nykhISEj8dvxfjYELgjACuAk4JAhCAV1V/3/AK8DXgiDMBU7QNfMEURSLBEH4GigCTMB9FyyrvB/4\nFNAA60VR3Ni3lyMhISHhOP9nY+CiKGYBF3tIgt3H7Imi+BLwkp3jeYD9pxBJSEhI/Fb8XxVwCQkJ\nif/rZESF/HKmn7DYzjFBED4GpgE1oigmnz3mCXwFhAEVwBxRFHtsFCsIwiTgX3SFtj8WRfGVn+b5\nKZKAS0hI/O7JOVbZV6Y+Ad6ma53LOc4telwgCMKTdC16fOrCQoIgyICFwFXAKSBHEITvRFEs/rmT\nSZsaS0hISPTRIKYoiruApp8cvtiixwvJAI6KonhCFEUTsOxsuZ9FEnAJCQmJX3cWit+Fix4BPzt5\ngoALuwGXtNBREnAJCQmJ/y59NmQqxcAlJCR+91zKNMLTRws5c7SoN+YvtujxQqqBC/fe+9mFjueQ\nBFxCQkLiEujXP4F+/RNsn/dvXHGxrMLZ1znOLXp8he6LHi8kB4gWBCEMOA3cAPzxl+okhVAkJCQk\n+igGLgjCl8Buuh6XfVIQhNuBl4HxgiCU0DXL5OWzefsJgrAWQBRFC/AAsBkopOuJrkd+qdpSC/xX\nwGQyUVNTA4Cfn99lbWx7IfX19XR0dODq6oqnp2ef2NTr9dTV1SGTyQgICLisjW3PIYoitbW1GAwG\nPD09cXV17YOagk6no7GxEZVKhb+//2VtQnwOi8VCTU0NFosFHx+fy94w+RzNzc20trai1Wrx9vbu\nk7r+2n7l5uaGh4dHn9js7Oykvr4euVyOv79/n/jVr0ofRaVFUbzxIkk9Fj2Koniarjnj5z5vBGId\nOZ8k4H2I2Wxm8/db+H7bLswoQBCQW41cPWYEEyeMR6lU9spuWVkZK9avpbzuDApnLea2dhLDIpg1\nZRrBwcEXLbd3716am5tRqVS2V0pKCkqlko6ODtZt2MD2vbkIai1WixlnhYzJV41hzOjRyGS965zl\n5xewZs0m6hvaUCrVGI3tpKYkMHPmVLy9vXtls6mpiVXfrSWvoBClSovZZMTL05kZ0yYyeHBqr2yK\nosiOXbtYt+0H2gQLMoUcdJ2MHJTGjMlTcHZ27pXd6upqvl27jsLSchRaZ8x6PWEBvsyaPoWYmJhe\n2TSZTGz6fgs/7MzCJCgQBAG5xcjVoy7Pr44ePco3a9ZTcaoGhZMT5o52BvSPYNb0aQQF9e5Jzx0d\nHaxZv54dOXkImi6/cpHLmDJuLKNHjey1X/3aXKlL6aVd6fsIi8XCBx99QtHJeiITUtG6dLU6O9t1\nHCsqIKafG/fedQcKhWP3zMOHD/PWV1/gnZaET2gwMpkMq8XC6bLjdB4+yvx5dxMWFtajXE1NDQEB\nAbi7u2Mymejo6EAmk3Hw4EEiIyN54+13qDUrCB+QjFrT1epsa26k4lAeo5PjuWHObIdbjdu2bWf5\nik1ERg3Gy9sPQRAwm01UHC/BbDrNE48/5LCINzY28urrb4PKj/CIOJRKFaIo0tRYS3lpHrOvuYpx\n48Y5ZFMURZZ/u5ItpQcJHTYYV++u3oyxU8+JgsP4tZl5/N4H0Gq1DtmtrKzk1YXv4RzSn8CIKORy\nRVdvpLqS2uID3HvrDSQnJztk02Kx8N5HH1N0uomIxBSbX3Xo2qgoLCDO34N77pjnsF8dOnSIdxZ/\niX/cIPyCQhAEAavFQnXFMdpPlvL4/XcTGhr6y4YuoLOzk9cXvkMNSsISklCf7c20NTVyYn8eY5Pi\nuX624371S/TFrvRz33R8V/pFj/z2u9L/b94Or0AOHjzIofJTDEgfZfuTATg5u5CQNpLiynry8/Md\nsmkymfh4+VcEjRmCX3iorfUik8sJio3GbXACn674CqvVSlZWFrPP/jkEQSAgIADo+lNZrVZuvfVW\nSktLSUhIYPuOHdQYBGJTh9jEG8DVw4sBw8ayPe8A5eXlDtW1paWFFSvXk5icibfP+fCGQqEkun8i\nSlUgq1atsVvWaDRysRv2d6vXgcqP/jHJKJVdIQNBEPDy9icxJZNvVm2iqemn6yZg165dhIaGMnr0\naB544AH+85//sGdP1z4jJ06cYMuhfGInjrGJN4DKSUP/4WnUaAW2XuR507W1tQQEBBAREcGECRN4\n4oknWLRoESaTic+XfY1HVCIh0bHI5QpbXf2DQwkbPIJPvvwao9HYw2ZVVRVpaWnMnDmT5557jq+/\n/ppDhw4BUFBQwOGTZ4jPGNnNr7QursRnjKKwspaCggK7db0YJpOJT5Z+TVjqCPyDQ22/lUwuJyQq\nBo/oRBZ/+dVFf5OL8eP27Zy2CMSkZdjEG8DV04v4UWP5Mf8gFRUVdstOmTKFkJAQxo4dy8MPP8z7\n779PVVWV3by/BoLo+Ot/ASmE0kds2baTgIg4u11EQRAIjIxny7adZGRk9Ehfvnw5JpOJ8ePH4+vr\nazt+5MgROl3UhPr6dMuva2pm78o17FiyHF1TM39//Elbmru7Ow8//DD33HMPjzzyCL6+vjz11FME\nBgYCYLVa2bIji+CBw+1eh1yhwCM4kp1Ze4iKirrk68/JyUHrHICTk/1Wa1h4LPvzN9Da2oqbm1u3\ntIyMDMrKykhJSWHChAmMGDGCoUOHYrVaySsoJGmw/a1T9Z0dnK5t4uGHH0GnayM/P7+HQFRWVrJz\n504A0tPTyc7OZue+PbjGhKGwE3owGQygVPDmu2+z5rvv2L9/P/n5+ZjN5h55Kyoq+P777/H39yc9\nPZ3K+iYSE4fZratcoaDidA3PPvsszc3NHDp0iEOHDqHT6Wx58vLyWL16NQBqtZqOjg5+2JGFf1T8\nRf2qX3Q8W3bsIj093e557VFUVIRRqcXdy35vyD84jMNlRVRWVtpthdfW1uLn130tisVi4fudWYRk\njLRrs7NdR2N7O08+/TSCKJKfn09ZWVmPfFVVVWzbtg2lUomPj8/Phgj7krT+jj8L5eNfoR6OIgl4\nH1FVfYboIee7x8dLCnll/p098j3z5OPdPqtUKoxGI87OzpjNZkJCQrjmmmt46KGHqKurQ+7lbssr\niiLz06/CcoGYhCcO4KkHHuKuu+7q0TVdunRpj/N3dnai0xuIdDtv98Du7XzwwtNonLQMmzidpCEj\nOdnR4ND1nzpVg6ubl+2zwdDJPXdO6JFv4cIFF7Wxa9cudu3aBcAXX3zBqFGjUKq0tpY3wKoVH7Pm\nm08uasPV1ZWUlBQ6OjrIzc3Fw8ODG264gdtuu40hQ4YAcOL0KdyTI2xlGqpP88KkG3rY2nrB+/j4\neFJSUujfvz/PPfcc7u7uXH/99dx2220MHTqUgwcPonL16PYbvPfcfA7t3XXRugIEBwfj7u5OYWEh\narWaIUOGMHfuXGbOnIlMJqPq9GmiowfZ8h8vLuS1R877lUbrjKenJ1XlZSQlJdlebm5u3HvvvRw4\ncIB58+bxhz/8AS+vrt+nprYWpcv5399qtfLCXTdSW32yW92++vAdoqOjCQ8PJzw8nIiICKqrq3nx\nxRcJCQnh+uuvZ/bs2aSnp9PZ2UmHyYTzBTfn7M0b+OLlFy567VqtltTUVFJTU/nwww8xmUyMGDGC\nO++8kxkzZvTZ4PelkFvaZ89C+a8iCXgfodGoMRr0qNRqAIIj+zPrtvtpa22ms72NtpZmmmuq8Pfz\noaWlhebmZlpaWtDr9UDXbBCtVktZWRlLly5lxowZqNVqRMP5LrcgCDz21SLcfb1xcuty7qM/7GT4\n8OGXHFdUKpVgtWKxmG3d/PC4RBIzhnM4ezc/rvqKH1d1xQP/9vR8AG666Sbmzp1LZmYm8+bNIyws\njAceeAAfn/M9AycnDUZjve2zWu3EQw+/RHNzA2q1GpXKiarKQm69ZTZhYWFotVqcnZ1xdnYmMzOT\n48ePM3r0aGbOnMn48eOJjIzkzJkzmE1d4ZVz1zdm3AzCI2IJi4jFw9OHE8dLiIt05cY/zul2naIo\nsn//fpKTk3vMgNBqNDSe/d4BvAIDuOPtf+Lh70tAdASCIFD81Xre+ts/0Gg0Pb7Du+66q8fMCpVK\nhdXUPTwy80/3MnLyNQRFROHh48fR/blcMyy5x/aAoiiyfft2Bg0a1GMWiEatxmTQozw74yQ4MppJ\nN9zGybISqo+X0dJQz+mOdt59990e9TzHwYMHufvuuxkyZAhvvvkmThoNlgvqKpPJuPauhyjM2U39\nmdM01pym7nQVTU1N5OTkkJOT08NmZWUlr732Gq+99hpJSUldeSzd/SppxGjm/f2fBPePwSugHw2n\nT+HacJrHH3qwh72//vWvXaExL68eaf8V/kdCIo4iCXgfMTxjMDsPH6N/YtesCKVSxYRZN9nSy4r2\nMyQmgNmz/tCj7IIFCzAYDAwdOpS0tDTbFMH6+nrMG9ZgMZuRnx2kCogKt5UzdHYiNLYSHR19yfVU\nqVQMHBBH5YnjBEf2B8Ddy5t7n3vNlufIvl3EeDmRm5vDZ599xpIlS1iyZIktXaPR8Morr3Ddddfx\n9NNPEx8fz6BByezM+gJRHGAT25TU893ppqZ6PD1Errnmmh7hgJycHFQqVY/j/v7+eHk609RYi5e3\nPwCeXr54enWFmURRpLmhktRre7aeBUEgJSXF7ncwdGAqX+Rswzc02JY3MXOELf1U6TGSo/rbFW/A\nFo66kMjISOhsQ9/RjkbbNYOlX1gE/cK6WvpWiwV9w2kSEnquzRAE4aJ7vg5PG8yuo8eISuq6FqVK\nzfTb7ralHztcwPCoQK79w/nnI1mtVioqKhg0aBBmsxm9Xm+bytje3k5SUhLG79ZjtViQnb0JJWYM\nJzGjK6ym7+zg+J4tvPr833p8B9u2bWPq1KmEhYWRlpbGkCFDSEtLQ61WkxwbQ2VFBUFRXf7o5OzM\nwNHnr6vhxHEmjBmBPXo7Q+n3jjSI2UeMHDEMU9Mp6mtO90hrrKvB0FDFmFH244Pz58/n2WefZfz4\n8d3md/v4+DA8IYljWTlYrdZuZSwmM+Xb9zJp+CiH5y5PunocTeVH0LU090g7faIcZ0sH8+bNZfHi\nxYiiaHtlZWUB2MT2888/Z8CAARw5coSoqChCgjwoO3qwx+CXwdBJ2dFcpk2dYDeWq9FoLhrjnT51\nAuWl+ej1Hd3SRFHk2NFDBPq7Ojw9LzUlBbc2E6dLj/VIa29uoWl/CZPH2t2r5KJoNBomjR3D0bw9\nmE2mbmlWq5XSgn1kJMZ3G+O4FEaNHI6pvpoGe35VewZTbTWjR3YXRZlMRmRkJIWFheTm5tLe3k5N\nTQ27du1i7Nix+Pj4MCR5ACX5e3v6ldnM0bzdTBwz2u4NLDMzE51OR1FREZ999hn333+/LTQ1+epx\nNJUW2fer48dwNnaSmmr/pvpbc6UOYkrTCPuQ48eP884HizAqnPHw7WrdtdSfQm5s4747bnOopXwO\no9HIoiWfk3+yHKfwYDSuLnS2tKIvr2RMUgo3XDu7V4sk9u/fz0dLvkLm7oObbwAWs5nWM9W4yy08\ndM+dtlksP2XTpk22xUQuLi64uroSFxeHXC6nra2N9977iMrqZjw8g1GrNbS2NqJrPcXMGVczfvzV\nDk8hE0WRH3/8kW9WbULrHoibmzdGo4GmhkqC/F259547cHd3/2VDP6Gmpoa3Fn1Io9KKS3gQcoWC\nttO1iNX1zP3DHNIGD3bYptVqZcU337B1bx5a/xCc3TzQd+jQnakkKSqUebfdivpsiM0RysvLeeej\nTzCqXXDz65qf3VpbjdKg4/55f3JosPkcBoOBTz77gv1HK3DtF4JG60J7WzMdZ6rIzEhhzuxrezVn\nOz+/gI+XfYXMyxdXvwAsFjO6U9V4yKw8dNed+Pv7O2zzl+iLaYR3LHB8GuFH83/7aYSSgPcxBoOB\n/fv3U1RyFFEUiY+JJiUl5aLd8UtBFEVOnjxJdn4eTW2t+Hl6MSQtnX79+l1WXXU6Hbl5eRyrOIlC\nLmdg4gASEhJ6vTAEukTs6NGj5OUfoLOjk6CgADIy0i87ttnY2Eh2dg7Vp87gpNGQmjqQmJiYy1oY\nYjabKSoqoqDwMEazieiQMNLT0nBxcbmsup45c4Z92bnUNjbg4epC+uDBhIWFXdb8Z71ez/79+zly\ntAxRFBkQ059Bgwb1iV/l5OXR1NqGn5c3GemD+8SvcnJzKT9ZiUIuZ1BiAgkJCQ7PVb9U+kTAX+mF\ngD8pCbhdrmQBl5CQ+O/SFwJ+5yvLHC734ZM3/OYCLg1iSkhISFyh7UVJwCUkJH73/K8MSjqKJOAS\nEhISV6iAS9MIJSQkJK5QpBa4hITE757BcY4/C+V/AUnAJSQkfvfkH5GehSIhISFxZXKFxsAlAZeQ\nkJCQBFxCQkLiyuQ3XY1zGUgCLiEhISG1wCUkJCSuTKSFPBISEhJXKpKAS0hISFyhSAIuISEhcWUi\nhVAkJCQkrlSu0MdXS89CkZCQkLhCkVrgEhISv3tSE6RnoUhISEhckRQc7rtnoQiC8GfgjrMfPxRF\n8a2fpLsBXwChgBx4XRTFT3tzLimEIiEhISH24mUHQRASgHlAGjAImCYIQuRPst0PFIqiOAgYC7wu\nCEKvGtNSC/xXpr29nebmZjQaDV5eXpe1se05LBYLdXV1iKKIt7c3KpWqD2oKLS0t6HQ6XFxcerXT\nuz1MJhP19fUIgoCvry9yufyybYqiSGNjI3q9Hnd398vehPgcer2exsZGFAoFvr6+ffJbiaJIXV0d\nZrMZT09PnJyc+qCmXRsHt7S0/G79qq/pw1ko8cA+URQNAIIg7ABmAa9dkEcEXM++dwUaRFE09+Zk\nkoD/StTX17NmzQby8g+hUGoxmwwEB/syY/okBgwY0CubVquV7dt3sGnLdnSdZgRBhkIwM3b0UCZO\nnIBare6V3crKSlavWkfx4TKUCg1Gs564hCimz5xCWFhYr2wajUY2f7+F73dlYZYrEEURZ7mMSZmj\nGZuZ2evd5IuLi/l2/QYqztSiUGuw6jtJSxzAjCmT8fX17ZVNnU7HunUbyNqThyDTYLGY8PZyZurk\n8aSnp9kVx8bGRrZt2wZgSw8LCyM1NRXoEu7c3DzWbvyeuhYdcoUK0dTJyCGDmTp5Eq6urj1sXgr1\n9fWsXrWWgpxDKOVqTGYjIRH9mPGHqcTFxfXKptVqZduO7WzYuZ020QKCgNJoZvzQEUwaP77XQn7y\n5Em+W72e4uJylCotJmMH8fGRzJg+hdDQ0F7Z/PXoMwU/DPxDEARPwABMAXJ+kmchsFoQhFOAC3B9\nb08m7Ur/K1BXV8erry9Epe5HWHgsCoUSURSpra2m8sQBbrtlFhkZ6Q7ZFEWRJV8uY19BGRGxqbi5\newLQ0aGjvOQAof4aHrjv7h5/NlEU+fTTTzEYDPj5+eHr64ufnx/R0dHI5XLKy8t56/X38XWNJCQw\nHJlMjtVqofrMSc60HOXBR+4iOjraobqaTCbe+eBDSpt1hCcNQuvSJVZtzU2cOJjP0Khwbr3pRodF\nPDc3jw+/Xol/wiB8g4IRBAGzyURVWQnWM5U8+eD9+Pv7O2RTp9PxxpsLadc7ERGdiFqtQRRFmhrr\nKD+ax9TJI5kyeVKPcrfccgtff/01arWa9vZ2rFYrc+bM4auvvgJgw8ZNrN6SRWh8Kp4+fgiCgNGg\np6L0MC6ijiceftDhnkNtbS2vvfxvtFYfwoOjbX5VU3+Kipoj3Hb39aSn9/Sr2bNnU1ZWhqurK25u\nbri7u/Pss88SHx+PKIp8tuxLsk4eIzhjEK5eXX7V2abjZN5BYpTOPHjn3SiVyh52y8rKuP/++/Hy\n8iIoKAh/f3+ioqKYNWsWx44d4623P8LbN5ag4IjzflV1nIa6Eh568A6ioqIcuv6L0Re70j/w9JcO\nl1v40o12zysIwu10hUl0QCFgEEXx0QvSrwWGi6L4mCAIUcD3QLIoijpH6yC1wH8Fli//Fo02mIiI\neNsxQRDw9w/G2dmNL5asJDExAa1We8k2S0pK2JN3hOT0q5HLz/9sWq0LCYOGcyh/O7v37CFzzBhb\nWnFxMUuWLOEf//iHreve2dkJwIEDB0hMTGTxoiWE+CTg7xtoKyeTyQkJjECjduLTj7/g+Rf/6pDY\n7tuXTXF9MwkjM7u1Xl09PBkwIpM9274no7jYoZ5IZ2cni5evIHLoGJwv6IYrlErC4xOpVChYtvIb\n/nzfvZdsE2Djps20daoZkJhmOyYIAl7efji7jGXd+u9JGTSQgwcP8uKLL7Jz585u5ZVKJXFxcbz6\n6qtMnjwZgJqaGtZs3saAYRNQqTW2vCq1hpikNEoO5rBh4yaum31tz/ps3Ehubi7JyckkJiYSHh5u\n++6/XrYSVyGAiPD+3eoa4BuEs9aVJZ8uJzExEScnJw4ePMjHH3/Mxx9/THt7e7dzxMfHYzZ39diL\ni4vZVVZM7JRxyBXn/crJ1YWYMcMo3rKD3Xv2MGb0aNvvsHHjRpYvX87SpUt71H/o0KFcc801fPLp\nlwSGpODn9xO/Co1G46Tlk0+/5PnnnunhV8uWLWPr1q2kpKQQHx/PgAED+iyc9XNcSgil6kQR1SeL\nfjGfKIqfAJ8ACILwIvDTEdLbgZfO5j0mCMJxIA7IdajSSALe5zQ2NlJ0pJxBg3u22gBcXNxQqr3I\nzy9g5MgRPdItFovdOPH2HbvxCYjqJt4AZrOJksJ8cvfu5JN3X0ana7N7XplMhiiK3HHHHfz5z38m\nMTGRsrIyWhsN9B8QaLeMr3cAJ4tKOHr0KLGxsd3STCYTCoXC7h9r844dBMUl2E2TyeV4R/Zn685d\nDgn4gQMHEN28uon3hQRF9qdwyzrq6+vx8fHpllZdXc3TTz/NyJEjueqqq4iMjOxqERuN7NyVQ1zS\nuG75LRYzOXu3smbVZ5w+VcG7C1+xpSUlJfHMM88QExPDo48+ypNPPsnEiRO7Xeuevdk4+4Z0E+8L\nCY0ewMYt3+Ht5UllZSXHjh2zvfbt24dMJsPFxYW2tjZUKhUvvPACt99+OyWHj5EeN7abLVEUKT52\niO37NpOV+wP/fvdVu+dUKBSMGzeOv/3tbwwbNsxW3x/3ZOERH9VNvAGMnXqOZO0ld+0mPpr/V36u\nR6zRaJg2bRpPPPEEGRkZlJaW0qazEBVj36+8vf0pPLSLjz76iNbWVoqLi22vhoYGm02DwYAoijz2\n2GO89tprdm31GZcg4MGhAwgOPe+z2Vnf2M0nCIKvKIp1/7+9846Pqkr7+Pckk95DSSAVTEKLARJI\ngpQECF2NoALKu1h3RQXb2ldfWdlXUXfFBRR1rbii64qiIoIoAkpJqIFQkwDpBFJIT6ad948Z0mZY\nmTAhZD3fz2c+uffUZ86c+d3nPufcjBAiFJgOJLYpkgukANuEEAFAFHCiPWYrAbczZ8+exdXNp5XQ\nfv7Zm6xb+3HTuaubO9+sCWbEiET69etHv379iIqKIj09nQULFjBx4kR+97vfMXnyZDw8PADILSgk\nILz59rgw7wQLH/udRf8xMTFMnz6d6dOnExMTgxCCZ599lrCwMG655Zam9pps1Xi3qv/Kiqc5cLg5\nZOfm6sGPv3xFXFxck51RUVHMmTOH4uJiZs6cyezZsxkxYgSOjo4YDAaKz5YSk9Acj87av5elf5yP\nf2AvxqTeyJDRyeQdz7FpXAuLT+Pm6990btDrefrmadRVt75gvf3K4gu28cUXX2AwGPDw8CA1NZXF\nixcj0eDq2nwntP7bVXy26o2m874Rg5g8aTzLl71mcUH66aefrPaTW1CIj3/z+6+urOCxudMsyr33\n1nKr9YUQ6HQ6goODmT17Ntdffz2lpaW4OXvh4NB8cf/HJ6+yNX1jq7pjxiSxaNHzjB6+MEEKAAAg\nAElEQVQ9usnerKwsHBwcrIYsThUX4hfZPK+Kjufw15l3WJSbMmUKM2fOJDU1FT8/U5ilsbGR1atX\nM3HixFYXzdLSUlxcW19oFy38AydPHGmVtvIDq28fjca0ZhIfH8/tt9/OjBkzrBe0J/YN2a4WQvgD\nOuA+KWWVEOIeQEop3wb+AnwghDhgLv+4lLK8PR0pAbczzs7OGPTaVmnDho/lXEUZJafzOX06j9ra\narKyjpOVddxqG1999RXffPMNDg4O3HvvvSxduhRXZxd02samMr2Cw7j/0cX0jYrG28cPvV7HgR3f\nsPzvL1vcli5atMhqP05OThilrlXapKTpuLt6UHK2kNNnC6lvqOXgwYMcPHjQahtvvPEGr7/+Oj4+\nPqxZs4akpCQ0Dg7odVqcnE2Lqr369CVySCxZ+/ey5q3lrDEL1yvPLyQhIYH777+fm2++GVdXVzZs\n2EBVVRUzZsxodSfi4uKMXtc8ro4aDbMffoK8Y0fR63TodVpK808RGRKMs7MzDQ0NNDY20tjYyPbt\n25vubLRaLQEBAQQEBODk5ITBoENK2SR2o5OvZcCgYYSFRwFQWHCCHn46m27h3dxcKKtt/qw8vX25\n+9HncXB0pEdgEN0CepO1axP/9+zjTWJ4nh9++IFdu3Zx4403EhUV1ZSek5ODwdj6s0qdcAsTRl9P\neLBpjeLYiUPET+jPGHO44zyRkZFcCFcnF3SNjbiZN0UEXhXOXUsX03fI1bh5e6HXasn+/DveXPyK\nxRi4uLhw6623WrTp5OSE0dh6U8Utcx6grOw0vXqFERAYQnbWPmbdlGwRsz9w4AB79uwhNTUVf39/\nLht21G8p5RgraW+1OC4GJtmjr18VcCHEu8C1QImUMsac5gf8CwgDTgEzpZSV5ryngDsBPfCglPJ7\nc3os8AHgCqyTUj5kjzdwpRESEoJGo6emuhJPL5MXEt6nH3ff8yfAdMu7b/cG/vjI3YSHh7eq+/LL\nL/PEE0/g6+tLZGQkcXFxTJpk+pxHJAxl/dZMfM2enYODI0OGj26qW5iXzfBhg22KVUdFRVGn/xSd\nTouTk2nxM2bgcGIGmr5Uer2OPUd/ZNHiZ/D19W1VNyIigry8PEJDQ4mJiSE+Pp6IiAiEECTGDiHz\nRDZh/QcB4OnjywN/a/Y2M7dtRRTn8tOmTaSlpZGWlsbcuXOb8l1cXHjwwQd59tlnueOOO3B1dSUm\nOpq1W7cjowc3CcnQMeMYOsYU/qirrqJ41y+8vPB/LRbcampq+OCDDxg6dChxcXG4uro2fRahwQGc\nKSkkIDAYAA8Pbzw8mu9Kys7kMTVlykWPKcDwoUM4+Pl3BIWZPF4hBMNGpzTlny0uILhXD4sxBUhJ\nSSElJcUiPSwsDAdnIzW11Xh6mMS2Z/deTflSSiobSoiJuckmW68ZEss32Yfw7mHyoB0cHRk05pqm\n/KJjOYyIGWLTBSwqKor6us/R63VoNKbPIiIymojIaMA0rxrqSi3CcmC6g4yJibHpPdiDrvrPrC7m\n2/4+lleLJ4EfpJT9gE3AUwBCiIHATEx7IacAb4jmT34FcJeUMgqIEkLY5Qp0paHRaJg2dTzHjqWj\n07X2xKWUZB3fT9++gVa35z366KNUVFRQUVFBeno6K1asYNo00613QkICaMs5c7rAol5VZQXlp7NJ\nGZdsk63e3t6MTk4kM2s3RqOhVZ7RaCDz+G5GjB5mVWgOHjxIfX092dnZfPHFFzz55JMEB5tEMCU5\nmdrcE1SVW94VlhYV4lpfw4svvMDRo0eRUiKlJDc3l6effhow3ZoXFxdz3333ERgYyJYtWwgJCWFA\naBA5GXst4rE6rZacPTuZNn6s1d0Snp6ezJ8/n5EjRzaJN5iEddrUCeSdzKC+rtaiXl7ucbw8IDo6\n+iJGs5no6Gj8XCH/xDGLvIa6WgqO7ee6yRNsEkWNRsPU6ydw5NRedHrLeXX0xEEiBoTavD1vREIC\nTsVllOYXWuRVl1dQf/QEKUnJNrXp4+PDqGviOHxop9V5dfjQTkaNHIa3t/cFWlBcLBe1jVAIEQZ8\n08IDPwokSSlLhBCBwGYpZX8hxJOY4jwvmct9ByzEFLTfJKUcaE6fba5vdctAV99GKKXk66+/4fuN\n2/H2Dcbb25+GhnrOlecTEuzLvHl3t+vhk7y8PJaveAct7vh1D8bBwZFzZcXo60q56/bZ7fJcdDod\nH61cRcauI/h59MbDw5u6uirKaoqIiY3itjt+Z1UUf43MzEze+ucq8O2GT2BvpNHIuaICXBtreeDu\nuy64v3zJkiUcPXoUT09PPD09cXNzY/bs2YSHh1NbW8uKd94l52wFXkGhuLq7U11RRl1RPhNHJDDj\nhtR27VbYsvVn/v35t3h498LHtyd6vZby0nx8vBxYcP8fLBZFL4bS0lKWvfkPyuuM+AaEoHFyprLi\nDI1lRdycOpXkJIu77F9FSsmaL79i0/pf8HPthZenDw2N9ZTVFBMa0ZN77vt9qzWOiyU3N5el779L\nnY8b3qFBCEcHqgqKEafLmTfrVq6++mqb29TpdKxc+TH7MrLw9Qsxz6tqKsrzGDo4krlz57RrXlnD\nHtsI31/5868XbMMdc0dfUr/2oL0CXi6l9G+RXy6l9BdCLAN2SClXmdPfAdZhEvAXpZQTzemjMAXu\nr79Af11awM9TUlJCWlo6RUUleHp6MHx4LJGRke1+iAVM3mlGRgYHDh7GYDQSFdmX4cOGXdLTiFJK\n8vPzSdu5i7LSCvy7+RKfMIywsLBL2r5VU1PD7j17OJKdg8bRgav792fIkCGtvGBbMRqNZGdnk7Zn\nD9W1dfTq0YMRCfEEBga2u02AiooK0tLSyc0txNnFibjYwQwcOBCNpv3LRHq9nsOHD7N73wG0Wi3h\noUEkxMdbxL1t5fTp06TtTKO46AxeXp4Mi7fPvNq/fz8Zx45gMBgZ0PcqhsXFXfK8ysvLIy1tF+Xl\n5/D39yUhYTihoaF23RZoDwF/8I8f/3rBNvz9b3P+awS8TErZTQm4QqG43NhFwB/5p831/v7q/3S6\ngLfXvSgRQgS0CKGcMacXAi3/L2OwOe1C6Rdk4cKFTcfJyckkJye301SFQvHfxObNm5v+jYHd6KL+\n4sV64OGYPPCrzecvAeVSypeEEE8AflLKJ82LmB8DCUAQpkdEI6WUUgixE3gA0/8F+BZYKqVcf4H+\nlAeuUCguCnt44A89ZLsH/tprXcADF0KsApKBbkKIPOA5YDHwbyHEnZjCIzMBpJSHhRCfAYdp3sR+\nXonvp/U2QqvirVAoFJedLuow/qqASyktd+qbsNysair/Iubn/Nuk7wFsX85WKBSKjqZr6rd6ElOh\nUCi6qoIrAVcoFL95uuqTmErAFQqFQgm4QqFQdE3Ef+sipkKhUPzX0zX1Wwm4QqFQDB7avt9+7WyU\ngCsUit88GXtzO9uEdqEEXKFQKFQMXKFQKLooSsAVCoWia6L2gSsUCkVXpYt64O3/D/AKhUKh6FSU\nB65QKBRd1ANXAq5QKBTGzjagfSgBVygUv3lEF30UUwn4ZUJKSUVFBTqdDh8fn0v6Yd+W1NfXU1VV\nhbOz8yX/WO55jEYj5eXlGI1G/Pz87Pbr4TU1NdTU1ODh4YGXl5dd2tTr9ZSXlyOEwN/fH0dHR7u0\nW1lZSX19Pd7e3ri7u9ulTa1WS0VFBRqNBn9/f7v8sG9HzqvKykpcXV3x9fW1S5sdNa/sggqhKC7E\ngQMH+HrDRvLPlqJxckbotIwaHsu0yZPbLWTnzp1j7Xfr2bFnP0LjgkGnJTwokOunTGTAgAHtalNK\nyfadO1i3eRNl2jocHB1w0hmZkDCKCePH4+Li0q52i4uLWfvtejIyjuKocUGva2DQwAiuvXYyoaGh\n7WpTr9fz46ZNbNzyC/UGQEo8XTRMHptEUtKYdv9Ce05ODt+s3cDx7Fw0zi4Y9Y3EDY3m2mmT6dGj\nR7varKurY8P3G9myLR2DcMRg0NPTz5tpk1IYNiyu3UKekZHBt2vWUXyqBEcHR6TGyMixI5gybUq7\n51VFRQXfrt/Ajr2meaXXaekTFEjqlEn079+/XW1KKdm2fTvrNv1ERX0DwsEBZ2kkZeQ1TExJwdnZ\nuV3t2pWuqd8X95uYl5v/pt/E3LxlK6vWbaB39FC69eqNEILG+nryjh7Cu6Gaxx6Yj7e3t0U9KSW1\ntbV4enpa5FVUVPDSkmXovLoTGjkQZxcXjEYjZ4sKKD6WwZ0zbyAhPt4mO6WUfL7mCzYe3U9wwmB8\nA0xiVVdVTe7ug0Q4eLLg9/fY/GXLz8/nb0tW4O3Xl5DQCDQaDQaDnsKCU5SWHOOB+XcSGRlpU5sG\ng4G3332fzIKzhA8cgqePyUOsqijn1KG9xPcL57b/mWOziB88eJC33llFYMggAnuH4eDggE6nJe9U\nFo01+Tz2yHwCAgJsarO+vp4lS1/nTL0D4f2uxs3dw+Q1l5aQe3Qf140fybQpk21qE2DzT5v593tf\n0sc/gh6+PU3zSttATnE2br01PPLkwzaLeHl5OS+9tgy9T09CIge0mlenj+znrpkziI8fblObUko+\nW72aHw8cImTwUHy6m+dVdRV5mQeI8HRj/h/+cEkibo/fxFz5+g8215t7f0qn/yam2kbYgVRUVPCv\nteuIGjWW7r2DmjwtFzc3IocOo9rNh2/WfWe17vLly/Hy8sLT05OIiAgmTJjA0qVLAVi95msMPgFE\nRA/F2ewVOzg4EBAcSlR8Eh999gU1NTUWbdbU1HDbbbfx1FNPsWrVKvbt20ddXR0AJ0+eZOPBPfSf\nktQk3gDu3l70HzuCbH0Vv2zbZtP7l1Ly4Uef0rPXIPr07Y9GY7rhc3TUEBoWQWjfON77YBUGg8Fq\nXWvpAHv27OFAbjEDE8Y0iTeAt58/0SPGkn44m8OHD9tkq1ar5f0PP+WqASPoHdynSfydnJy5KnIQ\nnn5XseqTz63WbWhosDreAD/8uIkzdYIBQxNxc/cATILj3yOQQfHjWPv9FoqKiizq6XQ6Fi5cyOuv\nv86WLVsoLS1tyisvL2f1R18SExxLT7+A5nnl7MrAsGgaigx8+823Nr1/gM/XfIXBrxdXRQ+xmFcR\nicms/PcX1NbW2tRmTk4Om/Zl0H/M2CbxBnD38qZf4kiOn6th2/btNttqbw6kn7D5dSWgQigdyM70\ndFwCeuNq/uK2RKfVYpBGXl/xJqs+WsmOHTuoqKiwKFdbW8upU6coKytj3LhxVFZWsifzCIOSp1nt\n093LG52zBx+uXImriwtHjhxpep08eRIwCYiTkxNarRYhBGVlZWzZuR2f/n3QWIlLCiEIGjKA73/Z\nytjkZItbfoPBwHvvvUffvn0ZOXJkUxw2NzeXkjNVDB2WaNXW7t0DKcw7zLFjxxg4cGCrvEWLFvHC\nCy8QExPD+PHjGTVqFCNGjMDf35+Nm3+md8RAqx62g6MjPfr048etvxAdHW2R//7771NRUcGECROI\njo5uei+ZmZlIRy98fLtZtTU49Cr27VxHSUlJKy9cq9Vy0003sW7dOkJCQrjmmmtITk5m4sSJhISE\nsOnn7YTFjLHaprOLC549Q/nyyzVERkaQlZVFVlYW2dnZpKWlUVpaiqurKw4ODtTV1eHn58exY8fY\nlb4bT3xxc3Gz2m7f3hHs2JxG6vRU3Nysl2nLuXPn2Hv4GIPGWp9XHl7eOPh0Z8/evYwZPfqi2gTY\nvG073uFXXXBeBQ+M5vutv5CclGQxr7766isefvhhxo0bR0pKCqNHjyYoKOii+7YJO97wCyEeBO42\nn/5DSrnUSplkYAngBJyVUo5tT19KwDuQ3IJCPLs1ex11NdU8ccOkC5aPjIxk1KhRjBo1ivLycv70\npz8xbdo05s2bx/jx43F0dCQnJwcndy8cNc0f3dcfvsX6f3140XZ5eHjg6urKrFmzmDNnDj4+Ppws\nLMAvsTl2XppXyKKpcyzqvvTMQou2jEZj04JfQ0MDsbGxzJgxgxEjRuDq5tvqi7lsydPs2/Nzqzb+\n/upzF7R1165d7Nq1C4BZs2bxySefkFdYRMzAhKYy2Zn7efXx+yzqPnT/vVbbdHJy4rnnnkMIQVJS\nEn/5y18oLCrGzaN5EdhoNPD4gpupKD/Tqu4byxZf0Na8vDzy8vL49NNPef7555k/fz5aA7h7Nocy\ntv/4LSuXvXDBNtri7OxMY2MjKSkpzJ07F29vb/JO5uHr3mxrbX0Nj7x2Pw3aBrzcvegXNgAPbw9+\n/vnnpnkDUFhYyIoVK7j55psZPHhwq35KS0tx9vRpNa9++HwVX76znMHXJDH2hpm4+/pTUFR80bYD\nnCosxG/QkKbzM/l5/N/vZluUW/zcsxds49133+X999/HaDQyY8YMVq9ebZMNF4O9ftBBCDEIuAsY\nBuiB74QQa6WUJ1qU8QFeByZKKQuFEN3b258S8A7E1cUFfa226dzNw5M7n/0Lbp5ehA8YiIubO4d/\nWMefH3mAnj17WtR/8MEHLVbqnZycMOh1rdLix03Cx787gSFhBIaGU1ZymoTw7tw0Y3qrckajkY8+\n+ojo6GhiY2NbCaurszO6xmZb/XoHMPne2ygvLqGhpo6GmlrO5RbSzdeP6upqqqqqqKqqanVL3dDQ\ngEajIS0tjbKyMhISEjAa9a1smDj5ZkJCI3BxccXFxY3S0iJGJg4iLi4ODw8PPD098fDw4LXXXuPt\nt99m4MCBpKamMnnyZBISEhBC4OLsjE6nxcXR5F0G940k5cY51NdUoW1spL62htryM4QE9aaurq7V\n68yZMwghTDHjxkYKCgqorKzExdkZvb75/Ts4OHLr7Q9SXJiLm7sn7h6enCk+yawbpzB48GB8fX3x\n9fXFzc2N+fPn8/HHHzNhwgRmzJhBSkoKPXr0oKamBqNej9FobLpbGBSbyI23z6dHr2B69g7GaDDQ\nQ1PHA/fPazVOBoOBl19+mf79+zNp0qRWO2FcXV0o1Vc2nbu7ejAxYSo7MrdxtqKE3UfSAdgy6SeL\nOQWwZMkSAgMDmTdvHnPnziUgIAAnJyeMOm2rcmFRA3Dz9CJj+xYytm9pSr/vnj/w0EMPce+99xIQ\nEIDRaOTLL79k9OjRFvPY1dkZvba53W69enPt7+fRUFfXdGEpyz7O9GlTcXJywtHREY1Gg0aj4aOP\nPiI9PR1nZ2eGDRvGddddx9SpU62+p0vGfmtuA4A0KWUjgBBiKzAD+GuLMrcCq6WUhaauZalFKxeJ\nWsTsQA4ePMibq79mwOhxVvMrzpSgO3GEPz/95EXvRDAYDDz53CJ6RMfj5WN92+DBrRt4+M45Ni0O\n/rhpE2uO7yVytPVFqsKj2YRXwb133GWRV19fz8MPP0zfvn1JTExsEuOamhqefHoRMUMn4uRsuYPF\nYNCzb9d3PL/wcbp1ax26MBqN1NbWWl2I+/iTT8k4XU2f/pYhEoDsg3tJGhDG9ddda5G3atUqysrK\nGDlyJFdffXXTBbKgoIAXX3mD2MSpVj+L+rpasg5t5qUXnrPYjXN+rlqr98qSpdS79CQgyPpum8O7\nf+GWaWNITLQeZrJGRkYGH772MUPChlnNL6sspcqjjP9d9AxlZWVs27aNrVu38uqrrwLg5uaGVqvF\nYDAQGxvLnj170Ov1PLVwET1iElutK5zHaDSy8Z9vU3+2mI0bN1rt19XVlTlz5vDMM88QHh4OwMYf\nfuDr/ZlEDk+wWqcg6zhXORi45847LfIaGxvJzMxk8ODBTesn1rDHIuZjc960ud4rH8+z6FcI0R9Y\nA4wAGoEfgF1SygdblDkfOhkEeAJLpZQftcd2tYjZgQwYMIAeLhryjx+1yNM2NJB/YC/XTZxg0zYy\nR0dHrp04npMZ6ejaeEwAJ44cILSbNxERETbZGj98OE4llZzNK7DIq62soiIji0lJ1sN0bm5uvPnm\nmzz++OOMGTMGDw9TzN/T05Ok0fEcOZyO0dh6QVJKydFDuxkWF20h3mBaPLvQLoqxSWOoLTxBZXmZ\nRV752RJ0pUWMvGaE1bq33norCxYsIDY2ttXdTVBQEAP6hXHs8F7aOg96vZ5jh9KZMH6M1a2U5z16\na0yblEJx9gHq6ywX/4ryTuBOPUOGDLFS88IMGjQInyAvTp0+aZHXqG0gp+w4026YghCC7t27k5qa\nyt/+9jdycnJ4/PHHWbJkCWvXruXQoUNs3boVAI1Gw7QJ4zixLw2d1nJe5R7N5Jq4oWzYsAEpJVJK\nqqqqWLZsWVOZhoaGprWQm266CaPRSEJ8PJpz5ZQWWptXlVRmH2PiWOvzysXFhbi4uP8o3nZDSttf\nVpuRR4GXgI3AOmAf0HY1XgPEAlOAycCzQgjbvrBmlAfewZSVlbH0zbcp1Ul8gkJxcnGhqvQs9cX5\npKYkM3niRJv3AUsp+errtaz/eTuegaF4+XVH21BPZXEegd6uLLjn9/j4+Nhsa35+Pq+99w8a/Nzw\nCQ/CQeNIVeFpdLlnuPOGmxk+zLrH95/Q6/WsXPkx+zKy8O0WiqenD7W11Zwry6NfVBB333V7u/aX\nZ2Zm8tbKVTh6d8c3IAiQlJ8uxLG2gvvvvt3mCxiYFozffOtdcgvK8esRhpu7B9WVFZwry2Vk4mBm\nz7q5XfvLt/78C59+sRa3br3x7R6IXqeloqQAT0cdD9z7ewIDA21us6ysjOWvvk5lYQ3d3Hri7OTM\nudoKqmQ5182axgQbHQMwzasvv/qajdvScO8VipdfN9O8Ksylt7cbC+b9weqW16KiIu644w7A5IW7\nuLjQs2dPli9fDpjWBv7+j3docPfEJygEB0dHKouLMJwt4c6ZNzEsLs7m998Su3jgt7zxq+XySo6T\nf+Z40/n2zHW/2q8Q4v+AfCnlmy3SngBcpZR/Np+/A3wnpbQ5uK8E/DKg0+k4fPgw6fv209DQSGhQ\nb0YkxFuNe9tCcXExO9LSKSg+jYe7OwlxQxkwYMAlPY3Y0NDA/v372Xf0EHq9gaiwcBKGx1/S03hS\nSnJzc9mxM52y0gp8/bxJTBjOVVdddUlPI1ZXV7Nr126OZOUgBFw9oD9xcbGX9OSk0Wjk+PHjpKXv\noaq6hsCe3UlMjCc4OPiSbC0rK2NnWjonc/NxdnYidvDVxMTEXNL+Z51Ox6FDh9idtpfG+gZCwoNJ\nvCbRLvNq+840Ck+X2H1e7c08hMFooF+fPiTEx7fL0WiLXQR89us213vl0/ut9iuE6CGlPCuECAXW\nA4lSyqoW+f2BZZi8bxcgDZglpbRt7ytKwBUKRRfHLgI+a7nN9V751/wLCfhWwB/QAQ9LKTcLIe4B\npJTybXOZR4E7MIVX/iGlXNa2nYtB7UJRKBQKOzqMUkqLjf9SyrfanP+V1jtT2oUScIVCoeiid/xK\nwBUKxW+emMR2bAL5t/3tsBUl4AqF4jfPgR3ZnW1Cu1ACrlAoFCqEolAoFF0VJeAKhULRNVEeuEKh\nUHRRjErAFQqFoouiBFyhUCi6JiqEolAoFF0TqUIoCoVC0UVRHrhCoVB0UZSAKxQKRRdFCbhCoVB0\nUZSAKxQKRdckZlR/2yt9Z387bEUJuEKh+M1z4OcjnW1Cu1ACrlAofvN01V8AUwKuUCgUSsAVCoWi\ni9JFH+RxuNwdCiEmCyGOCiGOCyGeuNz9KxQKhQVS2v66ArisAi6EcACWA5OAQcAtQoh2LP92Dps3\nb+5sE6yi7LKdK9U2ZVfnIKW0+XUlcLk98HggS0qZK6XUAZ8CqZfZhnZzpU5iZZftXKm2Kbs6CeWB\nXxRBQH6L8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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=[6,5])\n", + "ax = fig.add_subplot(111)\n", + "plt.scatter(x=wf.xyz[0,:],\n", + " y=wf.xyz[1,:],\n", + " s=2.*np.array(wf.R),\n", + " c=WS,\n", + " alpha=0.5,\n", + " cmap=plt.cm.viridis,\n", + " )\n", + "plt.colorbar()\n", + "for i in range(wf.nWT):\n", + " #ax.annotate(str(wf.__getattr__('name')[i][-2:]).zfill(2),wf.xyz[[0,1],i])\n", + " ax.arrow(x=wf.xyz[0,i],\n", + " y=wf.xyz[1,i],\n", + " dx=wf.R[1]*WS[i]*np.cos(np.deg2rad(270-WD[i])),\n", + " dy=wf.R[1]*WS[i]*np.sin(np.deg2rad(270-WD[i])),\n", + " head_length=3*wf.R[1],head_width=wf.R[1],\n", + " length_includes_head=True,\n", + " fc='k',ec='k',\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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09XSNRiM7du5i+87vaRSOaDR2FFw6C0Lw2GOPcfLkSeLj41mwYAHOzs5W23rx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\nP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R4NWGvak13ArxYW8FNtut2zz5NftwGbRcRia17YCcT3gl8O+yYiZhF5QkQmi8j1WNtz\nD/dlnrVhHXBDx2d0sNUbeeZ69EbDN3AJP3UOXIG1rXdQhzShgJvtPATrlz3Qdp2MtdahsH7pr5Cf\nOjpaOgcW0HXnQMv5gJ741SH9n2nfmdgrfjmQZxfwUwfQaKz/vg0433mGtUNmD9YOYnfgmzbPP6/v\n0vbcE0Boh/DzmV+/46cOOx9bmigXKWP9gP628znAtj7OszFtwn8NfGA7b+lM9ATCaN+Z2Gt55ipH\nXwj1ESCfDkPKgNuxDuXah/VLfm2b+6cAB233rmgT7gV8YAtPps2XD7jbFn6Y7ocondWvDuk7CnWv\n+OVAnt3QIc+ucpU8w1ory8Q6TGqJC/l1CbCri/vPm19YxfkDW35lulgZCwFysIr410BwH+fZelsZ\n2g98Bgxvk+4PWAW64/C8XsszVzn0hBeNRqNxcfp8hxeNRqPROIYWao1Go3FxtFBrNBqNi6OFWqPR\naFwcLdQajUbj4mih1mg0GhdHC7VGo9G4OFqoNRqNxsX5X4XDuR9q9sbbAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=[6,5])\n", + "ax = fig.add_subplot(111)\n", + "plt.scatter(x=wf.xyz[0,:],\n", + " y=wf.xyz[1,:],\n", + " s=2.*np.array(wf.R),\n", + " c=fort_gclm_s.p_wt/1e6,\n", + " alpha=0.5,\n", + " cmap=plt.cm.viridis,\n", + " )\n", + "plt.colorbar()\n", + "for i in range(wf.nWT):\n", + " ax.annotate(str(wf.__getattr__('name')[i][-2:]).zfill(2),wf.xyz[[0,1],i])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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XQELDBuJgdQqnTx7m+OFv+fg/7/H8c8+zbds27ruv3YkAjZw4cYJvz2cwaNSNjY4dwMnF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\n+4GjwMs26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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=[6,5])\n", + "ax = fig.add_subplot(111)\n", + "plt.scatter(x=wf.xyz[0,:],\n", + " y=wf.xyz[1,:],\n", + " s=2.*np.array(wf.R),\n", + " c=TI,\n", + " alpha=0.5,\n", + " cmap=plt.cm.viridis,\n", + " )\n", + "plt.colorbar()\n", + "for i in range(wf.nWT):\n", + " ax.annotate(str(wf.__getattr__('name')[i][-2:]).zfill(2),wf.xyz[[0,1],i])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "WD = np.arange(-50,50)+270\n", + "WS = 8." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "P_rat_py_v0 = []\n", + "P_rat_py_v1 = []\n", + "P_rat_fgcl = []\n", + "P_rat_fgclm = []\n", + "P_rat_fgclm_rdn = []\n", + "for wd in WD:\n", + " out = gcl(WS=WS*np.ones([wf.nWT]), WD=wd*np.ones([wf.nWT]), TI=0.1*np.ones([wf.nWT]), version='py_gcl_v0')\n", + " P_rat_py_v0 = np.append(P_rat_py_v0,out.p_wt[1]/out.p_wt[0])\n", + " \n", + " out = gcl(WS=WS*np.ones([wf.nWT]), WD=wd*np.ones([wf.nWT]), TI=0.1*np.ones([wf.nWT]), version='py_gcl_v1')\n", + " P_rat_py_v1 = np.append(P_rat_py_v1,out.p_wt[1]/out.p_wt[0])\n", + " \n", + " out = gcl(WS=WS, WD=wd, TI=0.1, version='fort_gcl')\n", + " P_rat_fgcl = np.append(P_rat_fgcl,out.p_wt[1]/out.p_wt[0])\n", + " \n", + " out = gcl(WS=WS*np.ones([wf.nWT]), WD=wd*np.ones([wf.nWT]), TI=0.1**np.ones([wf.nWT]), version='fort_gclm_s')\n", + " P_rat_fgclm = np.append(P_rat_fgclm,out.p_wt[1]/out.p_wt[0])\n", + " \n", + " out = gcl(WS=WS*np.ones([wf.nWT]), WD=wd+np.random.normal(loc=0.0, scale=2, size=[wf.nWT]), \n", + " TI=0.1**np.ones([wf.nWT]), version='fort_gclm_s')\n", + " P_rat_fgclm_rdn = np.append(P_rat_fgclm_rdn,out.p_wt[1]/out.p_wt[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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cNzbW79CejArf2CiJq7RPrI9riTXF+riWWFOsj2uJNcX6uJZYU6yPa8y5Y+Xbi/W5kJ7Q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\nEMD/B6AU5AzigdpkAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=[10,10])\n", + "ax = fig.add_subplot(111)\n", + "\n", + "plt.plot(WD-270,P_rat_py_v0,'-',label ='py_gcl_v0')\n", + "#plt.plot(270-WD,P_rat_py_v0,'-',label ='py_gcl_v0_inv')\n", + "\n", + "plt.plot(WD-270,P_rat_py_v1,'x--',label ='py_gcl_v1')\n", + "#plt.plot(270-WD,P_rat_py_v1,'x--',label ='py_gcl_v1_inv')\n", + "\n", + "plt.plot(WD-270,P_rat_fgcl,'+--',label ='fort_gcl')\n", + "#plt.plot(270-WD,P_rat_fgcl,'+--',label ='fort_gcl')\n", + "\n", + "plt.plot(WD-270,P_rat_fgclm,'.-',label ='fort_gclm_s')\n", + "#plt.plot(270-WD,P_rat_fgclm,'.-',label ='fort_gclm_s')\n", + "\n", + "plt.plot(WD-270,P_rat_fgclm_rdn,'o',label ='fort_gclm_s_rdn')\n", + "\n", + "plt.legend(loc=4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/Multiple turbines development .ipynb b/examples/Multiple turbines development .ipynb new file mode 100644 index 0000000..8c2069c --- /dev/null +++ b/examples/Multiple turbines development .ipynb @@ -0,0 +1,412 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "import numpy as np\n", + "from IPython.display import HTML, Latex, Markdown, Pretty\n", + "\n", + "from windIO.Plant import WTLayout\n", + "from fusedwake.WindFarm import WindFarm\n", + "\n", + "from fusedwake.gcl import GCL\n", + "import fusedwake.gcl.fortran as fgcl\n", + "\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#filename = 'middelgrunden.yml'\n", + "#filename = 'lillgrund.yml'\n", + "#filename = 'hornsrev.yml'\n", + "#filename = 'test_WF.yml'\n", + "filename = 'test_WF_2Turbines.yml'\n", + "\n", + "#wtl = WTLayout(filename)\n", + "wf = WindFarm(yml=filename)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "gcl = GCL(WF=wf, TI=0.1, z0=1e-10, alpha=1.)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Inputs\n", + "WS=10.0*np.ones([wf.nWT])\n", + "WD=270*np.ones([wf.nWT])\n", + "TI=0.1*np.ones([wf.nWT])\n", + "version = 'fort_gclm_s' # \n", + "\n", + "# Run the models\n", + "gcl(WS=WS, WD=WD, TI=TI, version='fort_gclm_s')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2233421.0030666208" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gcl(WS=WS, WD=WD, TI=TI).p_wt.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Inputs\n", + "WS=10.0+np.random.normal(loc=0.0, scale=0.25, size=[wf.nWT])#np.ones([wf.nWT])\n", + "WD=270+np.random.normal(loc=0.0, scale=5, size=[wf.nWT])#np.ones([wf.nWT])\n", + "TI=0.1+np.random.normal(loc=0.0, scale=0.01, size=[wf.nWT])#*np.ones([wf.nWT])\n", + "version = 'fort_gclm_s' # \n", + "\n", + "# Run the models\n", + "fort_gclm_s = gcl(WS=WS, WD=WD, TI=TI, version='fort_gclm_s')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2250556.7678174935" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fort_gclm_s.p_wt.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-500, 500)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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xt42c22PyZlaUNMbkf/SbF0su99XLz27VY/LNmkIpaRrwTeDc+gCfmAs8LOlu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\n5iBvZpZh/x+ovJQU5lnu8gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=[6,5])\n", + "ax = fig.add_subplot(111)\n", + "plt.scatter(x=wf.xyz[0,:],\n", + " y=wf.xyz[1,:],\n", + " s=2.*np.array(wf.R),\n", + " c=WS,\n", + " alpha=0.5,\n", + " cmap=plt.cm.viridis,\n", + " )\n", + "plt.colorbar()\n", + "for i in range(wf.nWT):\n", + " #ax.annotate(str(wf.__getattr__('name')[i][-2:]).zfill(2),wf.xyz[[0,1],i])\n", + " ax.arrow(x=wf.xyz[0,i],\n", + " y=wf.xyz[1,i],\n", + " dx=wf.R[1]*WS[i]*np.cos(np.deg2rad(270-WD[i])),\n", + " dy=wf.R[1]*WS[i]*np.sin(np.deg2rad(270-WD[i])),\n", + " head_length=3*wf.R[1],head_width=wf.R[1],\n", + " length_includes_head=True,\n", + " fc='k',ec='k',\n", + " )\n", + "ax.set_xlim([-40, 960])\n", + "ax.set_ylim([-500, 500])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-500, 500)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=[6,5])\n", + "ax = fig.add_subplot(111)\n", + "plt.scatter(x=wf.xyz[0,:],\n", + " y=wf.xyz[1,:],\n", + " s=2.*np.array(wf.R),\n", + " c=fort_gclm_s.p_wt/1e6,\n", + " alpha=0.5,\n", + " cmap=plt.cm.viridis,\n", + " )\n", + "plt.colorbar()\n", + "for i in range(wf.nWT):\n", + " ax.annotate(str(wf.__getattr__('name')[i][-2:]).zfill(2),wf.xyz[[0,1],i])\n", + "ax.set_xlim([-40, 960])\n", + "ax.set_ylim([-500, 500]) " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-500, 500)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=[6,5])\n", + "ax = fig.add_subplot(111)\n", + "plt.scatter(x=wf.xyz[0,:],\n", + " y=wf.xyz[1,:],\n", + " s=2.*np.array(wf.R),\n", + " c=TI,\n", + " alpha=0.5,\n", + " cmap=plt.cm.viridis,\n", + " )\n", + "plt.colorbar()\n", + "for i in range(wf.nWT):\n", + " ax.annotate(str(wf.__getattr__('name')[i][-2:]).zfill(2),wf.xyz[[0,1],i])\n", + "ax.set_xlim([-40, 960])\n", + "ax.set_ylim([-500, 500])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "WD = np.linspace(-30,30,100)+270.\n", + "WS = 8." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "P_rat_py_v0 = []\n", + "P_rat_py_v1 = []\n", + "P_rat_fgcl = []\n", + "P_rat_fgclm = []\n", + "P_rat_fgclm_rdn = []\n", + "for wd in WD:\n", + " out = gcl(WS=WS*np.ones([wf.nWT]), WD=wd*np.ones([wf.nWT]), TI=0.1*np.ones([wf.nWT]), version='py_gcl_v0')\n", + " P_rat_py_v0 = np.append(P_rat_py_v0,out.p_wt[1]/out.p_wt[0])\n", + " \n", + " out = gcl(WS=WS*np.ones([wf.nWT]), WD=wd*np.ones([wf.nWT]), TI=0.1*np.ones([wf.nWT]), version='py_gcl_v1')\n", + " P_rat_py_v1 = np.append(P_rat_py_v1,out.p_wt[1]/out.p_wt[0])\n", + " \n", + " out = gcl(WS=WS, WD=wd, TI=0.1, version='fort_gcl')\n", + " P_rat_fgcl = np.append(P_rat_fgcl,out.p_wt[1]/out.p_wt[0])\n", + " \n", + " out = gcl(WS=WS*np.ones([wf.nWT]), WD=wd*np.ones([wf.nWT]), TI=0.1**np.ones([wf.nWT]), version='fort_gclm_s')\n", + " P_rat_fgclm = np.append(P_rat_fgclm,out.p_wt[1]/out.p_wt[0])\n", + " \n", + " out = gcl(WS=WS*np.ones([wf.nWT]), WD=wd+np.random.normal(loc=0.0, scale=2, size=[wf.nWT]), \n", + " TI=0.1**np.ones([wf.nWT]), version='fort_gclm_s')\n", + " P_rat_fgclm_rdn = np.append(P_rat_fgclm_rdn,out.p_wt[1]/out.p_wt[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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2wWrhqRoKbJe23aP/9Qoa42egu7uF2y7RCAkppeoMAAAhhAyULET+oLU54Z42\nEynt3WjKS4SxtBzm5GzVsYhuSGtzwpdTCJO7H825Cdx2KSwJISClFKO5DY90ESlirnIhpdMNHSQy\nXB0wVzeqjkQ0IuYqF8y9builj9su0SiwdBEpohVkoSnbBB+A5iQjtPxM1ZGIRqTWOAkSgM9gQFOW\nidsu0QixdBEpoLk12Mo2wlhajsr0NThV9I+wlW2E5tZURyO6Ls2t4efPfg2HUmKg27ULxtJybrtE\nI8TSRaRASUMJNqzeAHNyNjqK7od+/3FsWL0BJQ0lqqMRXVdJQwnmHonA8ZwZwLJlMCdnc9slGiEO\npCdS7NDmPTB+8R4UdJ5VHYVoRN6ZkormNZ/BZ3/9c9VRiJQZy0B6li4ixfrd/egxR6F17zEUzp+h\nOg7RdXkGPOiIj0TTu/sxe8UC1XGIlOG7F4mCUGR0JA6mJqH0D39SHYXohnZv3Y7zkXoWLqIxYOki\nCgD1U2ZDlharjkF0Q73bjsCRulR1DKKgxNJFFAAmrfw4CuqPq45BdEPGjxyIXvBp1TGIghJLF1EA\nWPX4ZzCn7Tw6tU7VUYiuK7O+BGkP8pI/RGPB0kUUANJzM1AfPQVVL5arjkJ0TWdPnkFSfzMK7p+l\nOgpRUGLpIgoQZ3LWosO+X3UMomuq/tNeVCUuhT5SrzoKUVBi6SIKEIZVVkTv5wSTFLh63tuDztnL\nVccgClosXUQBIufR5chr2QPp43x1FJiia/+AvjtyVccgClosXUQBInN5DnzQwbW7TnUUomG6Orow\n61wTZn3mTtVRiIIWSxdRgBA6gbr05WjYxFOMFHg+eO5FVJuikTklS3UUoqDF0kUUQGrnT0LjRz9T\nHYNomNZ3X8fxrHzVMYiCGksXUQCJu6sI010nVccgGibt5H70zl+lOgZRUOMFr4kCSFdHF5Ach44T\nTp7GoYDh8/pw2hQB54sfYMk6Fi8igBe8Jgp6RpMRh1NM2PXMH1VHIRrUuKceOnciFq1ZoToKUVBj\n6SIKMNU5M+Au2a46BtGghk17UJN2G3R6vmQQ3QzuQUQBxO6ww7tkJXJrjg4u09wa7A67wlQUjuwO\nOzS3BgDw7NqDvoXLuS0S3SSWLqIAYrVYcXB2KxY4e+HduRtamxO2YhusFl5gmPzLarHCVmyD1uZE\nruMdGO8o4LZIdJM4kJ4owGhtTkSn5SISAk05ZhhLy2FOzlYdi8KQ1uZE16J5yGw4h8acJBj3cVsk\nuoQD6YnWK58QAAAgAElEQVRCgLnKhQifhM7nRYarA+bqRtWRKEyZq1xId2kQADKauC0S3SyWLqIA\noxVk4Wz8JHgh0JRlgpafqToShSmtIAtnYnTwAtwWicYBSxdRANHcGmxlG9H085fRbMiBsbQctrKN\ngwOaifzl0rZYkWzG2w88wm2RaBxwTBdRALE77LBarDDCCPckE3qqaxCZHoWShhKsL1yvOh6FEbvD\njlszb0V/ajIat+7CwrsuvHuR2yLRBWMZ08XSRRSg9mSa4PyCDZ/64bdVR6EwdeDdPch8YCVSOgY4\nRxfRFTiQniiEVGdPRc+ed1XHoDB2ZMtLOJSawsJFNE64JxEFKN/8VUivrVQdg8JY9KFytOTNUx2D\nKGSwdBEFqFkPfQLzTrfB5/WpjkJhavoJL+as+ZbqGEQhg2O6iAKUz+vDaVMEGrfuxKK7OAs4+ddA\nzwD6YhPgrW+EyWJSHYco4HBMF1EI0el1OJqcB9eru1RHoTBU/doRnI60sHARjSOWLqIAZpj6OMyl\nZ1THoDDUuq0MTZalqmMQhRSWLqIAFn/XUiScKlUdg8KQ2FcKuZili2g8sXQRBbCCRxcj/3w5BnoG\nVEehMJPa+AES1i1QHYMopLB0EQWw+Kx4tETloPq1I6qjUBhprHEhY6AO+ffNUh2FKKSwdBEFuObs\nJWh9g6cYyX9Kfr8ZlZPjMClukuooRCGFpYsowHUtnIru8i2qY1AY6drzLqqzp6qOQRRyWLqIApxY\nnYec1t2qY1AYyaipgGfuCtUxiEIOJ0clCnDuHjc85hh0nHAic0qW6jgU4i5Nyut6+UMsXsPiRXQt\nnByVKARFT4pGRXI8dv9+k+ooFAbq9lZB54nCwjuXq45CFHJYuoiCQE3WVHTveU91DAoDp7dWoM58\nJ3R6vjwQjTfuVURBwDtvJTJqKlTHoDDQt7MUvbOWqI5BFJJYuoiCwLxPfBrzXL2QPo57pImV4ChF\n3J2ciZ5oInAgPVEQkD6Jtoh0DOwuQ+atFtVxKER53B64Y8zw1rlgyjGrjkMU0DiQnihECZ1AbcpS\nNLzESVJp4lS/dgSnI7NZuIgmCEsXUYCzO+zQ3Bp6Zy1B364yAIDm1mB32BUno1BxaRs7vPUPqM69\nMC0JtzGi8cfSRRTgrBYrbMU2RFunIPPYu9DanLAV22C1WFVHoxBxaRszVLyKvjTJbYxogoxoTJcQ\nYi2A/8SFkvaMlPKnV3w9HsCfAFgA6AH8XEr53EhuO+R7cEwX0TVobU50LZqHzIZzaMxNgrGsHObk\nbNWxKIRobU5EpVsQKXVozkmAsZTbGNH1TMiYLiGEDsAvAawBcAuAR4UQ069Y7asAjkop5wG4HcDP\nhRCGEd6WiG7AXOVCZtN5CADpTg3m6kbVkSjE+HYfRrQX0Pt8yHB1cBsjmgAjOb24BMApKWW9lHIA\nwGYA912xjgQQd/HjOABnpZSeEd6WiG5AK8hCY7YJXgCnjQZo+ZmqI1GIKT50CL0GwBdhQFOWidsY\n0QQYSenKBOAc8rnr4rKhfglgphCiCcBhAH83itsS0XVobg22so0wlpbDbl2F3Tk5sJVthObWVEej\nEKG5NZzY9yy2zZwK3c5dMJaWcxsjmgDjNZB+DYByKWUGgPkAnhZCGMfpexOFtZKGEmxYvQHm5Gx4\n7vsUppxuxIbVG1DSUKI6GoWIkoYSLD+Zj6h5jwHLlsGcnM1tjGgCGEawTiMuDJC/JOvisqE+D+An\nACClrBZC1AKYPsLbDnrqqacGPy4qKkJRUdEI4hGFtvWF6wc/XvHowzB+96vw9RkuW050M9YXrket\n8xsYuPfBwWXmaDO3MaIhduzYgR07dtzU97jhuxeFEHoAJwGsBtAMoAzAo1LK40PWeRpAq5TyB0KI\nVAD7AcwF0HGj2w75Hnz3ItEIHJkcjYYfP4N1f/uY6igUIjoaOqDPyUR0rwZD9Ej+FieiCXn3opTS\nC+BrAN4BcBTAZinlcSHEl4UQX7q42o8BLBdCVAB4F8C3pZTnrnXb0QQkosudTvo4onedUx2DQkjN\nloOoiZvHwkU0wUa0h0kp3wYw7YplvxnycTMujOsa0W2JaOwM8++COFCuOgaFkI739kHkL1Idgyjk\ncUZ6oiAzec0ipDTsVx2DQkhU5X4YlrF0EU00li6iIJP/wBxYek+i91yv6igUItI67Ij5WJ7qGEQh\nj6WLKMhEm6NRHzMd1VsrVEehEFBdeQqJ/T2Yc/dS1VGIQh5LF1EQastZjLNvl6mOQSGgbNOLqEg2\nwxDBQfREE42liygINcwVOHv8v1THoBDgLtuBusypqmMQhQWWLqIgNHn1bZjW0qA6BoWAtLqj8M1Z\nrjoGUVhg6SIKQis/eS9yz/fhdEOL6igU5Ga1tmLafferjkEUFli6iIJQrCkWxxJjsfuFLaqjUBBr\nrWiBsduIJWtXqY5CFBZYuoiCVFXmFGi731Mdg4JY3Zb9qDEvgU7PlwIif+CeRhSkemcsweSqI6pj\nUBDr2bkfndMXq45BFDZYuoiClPXB/4M5NdyFaexij+1D9ArORE/kL0JKqToDAEAIIQMlC1Ew8Lg9\n6I1JgK/OCVOOWXUcCjLSJ3HGkIaBvfuRsTRbdRyioCOEgJRSjOY2/DOZKEgZog2oiZ+Hmi0HVUeh\nINRQWgsIIH1xluooRGGDpYsoiGn5i9BRzItf0+h9+MJvcMgiIHSj+kOdiG4CSxdREDMsW4Soin2q\nY1AwOrgbjTnTVacgCissXURBLP2ehchs26s6BgWhbNcJGBZyfi4if2LpIgpSdocd8SsmwyQaUXX4\nJABAc2uwO+yKk1Ggsjvs0NwafF4f5ra2Y/4nHuI2Q+RHLF1EQcpqseL7O7+PI0nxqPn5v0Nrc8JW\nbIPVYlUdjQKU1WKFrdiGXS9uwYBOIDM/kdsMkR9xygiiIKa1OTGQm4fEXi+ac5NgLC2HOZlv/6dr\n09qc6CmchlStF8153GaIxopTRhCFGXOVC4luCb0EMlwdMFc3qo5EAc5c5UKq1gc9uM0Q+RtLF1EQ\n0wqy0JRqhA9AU5YJWn6m6kgU4LSCLPQadPDq9NxmiPyMpYsoSGluDbayjYg9WIFuvR7dz9lhK9sI\nza2pjkYBSnNr+G7pT+DxTML5P74OY2k5txkiP+KYLqIgZXfYYbVYYY42ozzhDni/9SQKvrkUJQ0l\nWF+4XnU8CkB2hx159Zkwf/weZHicAC4UMW4zRKM3ljFdLF1EIWDHom8BiYkoeue7qqNQgNv7jZeg\n2/QnLG1+TXUUoqDGgfREYSry1oWIPnpAdQwKAu1730bH9BmqYxCFJZYuohCQfvdCZLbywtd0YzFN\nr6JtYbTqGERhiacXiUKAz+NDV4QZHkctEqcmqY5DAcrn9aHdaEDz+wcx69Z5quMQBTWeXiQKUzqD\nDo6kPJRt4jgdurYD7+1Bn16wcBEpwtJFFCJOTNGjqeQl1TEogB1/43UcSZmsOgZR2GLpIgoRAzOX\nIrn2iOoYFMDkoRI0ZU9XHYMobLF0EYWIvLXrMaOtRXUMCmBT6jTELlqrOgZR2OJAeqIQ0e/uR58p\nCu0VtbBMy1UdhwKM9EmcMaTCU3oQ6YuzVMchCnocSE8UxiKjI3EkKR4fbX5ZdRQKQM37XAAE0hby\nWotEqhhUByCi8dOaZkV0WZPqGBSAnK8dhG7yQiTrRvWHORGNIx7pIgohSQs/hclHWLpouN7dB9Bd\nuEB1DKKwxtJFFEJS1y1EejMvB0TDxZ44gGjrQtUxiMIaB9IThRCP24O+GBO8zmbEZ8WrjkMBpCUq\nHu7ig8hdUaA6ClFI4EB6ojBniDagxjgHNS+Xq45CAaRy90EY9F2w3DpFdRSisMbSRRRizuUthPY+\nTzHSX1S88ioqkhOh0/Mpn0gl7oFEIUabn4yeE8+rjkEBpL98F5xZhapjEIU9li6iEBNz20xMOcPL\nAdFfZDQcB+bcqjoGUdhj6SIKMSs/eTeyO/vR0tCsOgoFiFta2zDjnvtUxyAKeyxdRCEmxhiDY4mx\n2LPpFdVRKACcrmzGJHc0Fq1ZoToKUdhj6SIKQdXpOWgvKVYdgwJAwyvlqIu7lYPoiQIA90KiENQz\nbRESqg+rjkEBoHvXQZyfyklRiQIBSxdRiLE77Mi78wHMqv3LnH2aW4PdYVeYivzJ7rBDc2sAgOhj\nBxC5fCG3AaIAwNJFFGKsFiu2JL6NrN5G9Nrfh9bmhK3YBqvFqjoa+YnVYoWt2AatzYn803uQsCKd\n2wBRAOBlgIhCkNbmRGR6HqIk0JxjhrG0HObkbNWxyI+0Nie6F8xFhqsdrrwkxHEbIBpXvAwQEQEA\nzFUuRHl90Pu8yHB1wFzdqDoS+Zm5yoW0pg4IAJncBogCAksXUQjSCrJwNn4SvNChKcsELT9TdSTy\nM60gC2cm6eAFuA0QBQiWLqIQo7k12Mo2wvnjTXBG5cNYWg5b2cbBgdUU+i5tAwdSJ+PN9Q9wGyAK\nEBzTRRRi7A47rBYrDF0G6NLj4TmtwTfJh5KGEqwvXK86HvnBpW2gLT0Frl+/jNsfuQeaW+M2QDSO\nxjKmi6WLKIRVJsfA+aPfYt3fPqY6CvmZs6oB5ltyENXRh8joSNVxiEIOB9IT0WVOpWSgZcc7qmOQ\nAqV/3opjSUYWLqIAwtJFFMLa8+di0skDqmOQAgllvehIvld1DCIagqWLKISZl9+BKS11qmOQAlGV\nRxGzYLXqGEQ0BEsXUQhb/siDuOVsN/p6+lRHIT9LazqIlLULVMcgoiFYuohCWHpuBk6LAjjfrVId\nhfyo50wPMvpqkbd+puooRDQESxdRiGtJW4SWtw6qjkF+VPNqBeomzUSkkYPoiQIJSxdRiOu/ZT68\nZSxd4eTsuwdwJnu+6hhEdAWWLqIQF1+0AKYalq5w0nb8l6ib5VUdg4iuwNJFFOJyH5iPKR3l8Hl8\nqqOQnxS01MO80qo6BhFdgaWLKMQlTk1ChyEBp949qjoK+UFXRxcK23thfeR+1VGI6AosXURhoDJn\nAB+9+qzqGOQHu198AzWmKCSlJqmOQkRXYOkiCgMtOTMgKveqjkF+0PT+djhSM1XHIKKrYOkiCgNR\n81ciu/GU6hjkB7EnDqN9yhzVMYjoKli6iMLA3Ifux+y2dvi8HEwf6mZWRWP+Xf9XdQwiugohpVSd\nAQAghJCBkoUoFDUb9Wh6dTcW3nmr6ig0Qbz9XvRGmeBzNiE+K151HKKQJoSAlFKM5jY80kUUJo5N\nzkDdmztVx6AJVPv2SbRFZLBwEQUoli6iMKHP/hySDvSojkET6PRbB9GcxotcEwUqli6iMBG9fAEm\nneDM9KFsoOwg+mexdBEFKpYuojCRdc98ZJ9h6QplcbV7ELuS71wkClQsXURhInN5DqJlL1orWlRH\noQngGfCgoKcUCevyVEchomtg6SIKE0InUGtegPpXy1VHoQlQ9uaH0CL1KJg7TXUUIroGli6iMGB3\n2KG5NZyZWoCWXW8CADS3BrvDrjgZ3axLj63jrW04lpICgI8tUaBi6SIKA1aLFbZiG5qm9SHx+GZo\nbU7Yim2wWqyqo9FNuvTYGg59CK8pg48tUQDj5KhEYUJrc6JzzixktpxHU14SjKXlMCdnq45F40Br\nc0KfmYNJXoHmnAQ+tkR+wMlRieiazFUuZJ7pgQ5AhrMD5upG1ZFonJgcThgHJPQ+HzJcfGyJAhVL\nF1GY0Aqy0JRtghdAkykSWn6m6kg0Tk516eERAr4IA5qyTHxsiQIUSxdRGNDcGmxlG2EsLcd70wrw\n7oLlsJVthObWVEejm6S5NWx65Qc4nHgbdDt3wVhazseWKECxdBGFgZKGEmxYvQHm5Gy0L70fmbU9\n2LB6A0oaSlRHo5tU0lAC68k56JpuBZYtgzk5m48tUYDiQHqiMOPYUgH9Y59Cft9x1VFonJSm3Qvf\nX30Wt/7bQ6qjEIWNsQykZ+kiCjMDPQMYiDXB19wKY5pRdRwaB836LHg+2IXsVZyNnshf+O5FIrqh\niEkRqI2dhZqth1VHoXFQU34K0UJD1opc1VGI6AZGVLqEEGuFECeEEA4hxJNX+fq3hBDlQoiDQohK\nIYRHCGG++LU6IcThi18vG+9fgIhG76xlAdrf5+WAQkHpSy+iIiMCQjeqP7iJSAHDjVYQQugA/BLA\nagBNAPYJIV6TUp64tI6U8mcAfnZx/bsB/L2U8tJbZ3wAiqSU7eMdnojGaMEC6Mo+Up2CxkHvvp1o\nyChQHYOIRmAkR7qWADglpayXUg4A2Azgvuus/yiATUM+FyP8OUTkJ9G35yOx9W3VMWgcpNQfhWfm\nEtUxiGgERlKGMgE4h3zuurhsGCFEDIC1AF4eslgCeFcIsU8I8cWxBiWi8VN430LkdTfj/LnzqqPQ\nTZrRehpT1q5XHYOIRmC8j0DdA2D3kFOLAGCVUi4AsA7AV4UQK8b5ZxLRKJknm1FljsbuP7+uOgrd\nhAZHPVJ6Pbj13jtVRyGiEbjhmC4AjQAsQz7Purjsah7B5acWIaVsvvh/mxBiKy6crtx9tRs/9dRT\ngx8XFRWhqKhoBPGIaCwcqZk4v+Md4CufUR2FxqjutQM4bcrF4uhI1VGIQt6OHTuwY8eOm/oeN5yn\nSwihB3ASFwbSNwMoA/ColPL4FeuZANQAyJJS9l5cNgmATkrZJYSIBfAOgB9IKd+5ys/hPF1EfvTM\nfQ8htuEEHik/qjoKjdGO+/4dorYWt1X8l+ooRGFnQubpklJ6AXwNFwrTUQCbpZTHhRBfFkJ8aciq\n9wPYfqlwXZQKYLcQohzARwDeuFrhIiL/S1i+GvnN9apj0E2IqDgI3aIFqmMQ0QhxRnqiMHW6vgXG\nKbmI6u6CIXokIw0o0FRHzYTnj5sw7ZNzVUchCjuckZ6IRiw1Jw2nDbmofevEjVemgNPd2o20/npM\nuXum6ihENEIsXURhrCVtPk6/dVB1DBqDmlcrUD9pBiImRaiOQkQjxNJFFMb6Zy2Adx9LVzA6uv2P\nqMtLUh2DiEaBpYsojMUXLYC5hqUrGBkcxWienqg6BhGNAksXURjLe3A+cs8fhs/jUx2FRqngtBPJ\nq+5SHYOIRoGliyiMJeQnoj3Wh4+2faA6Co3C+XPnUdjeixWful91FCIaBZYuojB3LC0ejjd5OaBg\nsvvFN1BjikJiKk8vEgUTli6iMHc6ZwZ0Rz5SHYNGoWXHO3CkZqqOQUSjxNJFFOYiF66ExVWlOgaN\nQvZRDwxTH1Ydg4hGiaWLKMzNf+gBzDrTDp+Xg+mDRVpdDXLuvFt1DCIaJZYuojBmd9iRMdeCAZ1A\n+QelAADNrcHusCtORleyO+zQ3Bo8bg+mdFUg7/65fKyIggxLF1EYs1qssBXbUB+7GBHPvQ+tzQlb\nsQ1Wi1V1NLrCpcfq2Es70KFPhC+qg48VUZDhBa+JwpzW5oQ3bxrM3X1ozkuAsbQc5uRs1bHoKrQ2\nJ/oKZyJZ60ZTXiIfKyKFeMFrIho1c5UL5t4+6OFDhqsD5upG1ZHoGsxVLkzWeqCD5GNFFIRYuojC\nnFaQhZb0eEgATVkmaPmciiBQaQVZ6DVIeHU6PlZEQYiliyiMaW4NtrKNmHTgMM4jHu5ntsNWthGa\nW1Mdja6guTV8d++/wCckTv/xzzCWlvOxIgoyHNNFFMbsDjusFivM0Wbsn7wG3ie+imnfXYWShhKs\nL1yvOh4NYXfYoTvQi2lfeRRTtAEAF4oYHysiNcYypouli4gAAM9bi9AfKfD5D3gdxkD13Be+huSd\nr2L9KZfqKERhjwPpiWjMPHMWIr3hhOoYdB36ox/hdO4tqmMQ0RixdBERAGDa3fdgVmur6hh0HbmN\n1YhZvEp1DCIaI55eJCIAgM/rQ0esAS07KzBjySzVcegKPo8P5yMT0FNxFBmzslTHIQp7PL1IRGOm\n0+tQkWxG+cuvqI5CV+H8sAbdOhMLF1EQM6gOQESBoyFrKnBgn+oYdBWN2w5Cl7IQnJmLKHjxSBcR\nDZqy6O8x9ViE6hh0Ff17DsA9Y4HqGER0E1i6iGhQ5r2LkNV6UHUMuoq4qoOIXbVQdQwiugkcSE9E\ng3weHzojEuB11CBxapLqOHSRz+tDR8RkeCqOI3lWquo4RAQOpCeim6Qz6FBrmo/al3m0K5AceG8P\nemLPs3ARBTmWLiK6jFawEJ07DqiOQUMcf+01HEmZrDoGEd0kli4iuoxYPAu64x+qjkFDyIo9aM6e\nrjoGEd0kli4iutztqcjseE91Choiy3UShgUrVccgopvEgfREdJl+dz/6TFE4d6gGOTPyVMcJez6v\nD2fjDGjaXoa5KxepjkNEF3EgPRHdtMjoSFROjkfpi5yZPhA49hyFF3rMXs45uoiCHUsXEQ1Tlz4F\nnR+9rzoGAeh4sxpO413Q6fl0TRTsuBcT0TDumYsxufaI6hgEoLfkILqncVJUolDA0kVEw+R/7F7c\n0tShOgYBiD1xANE8tUgUEjiQnoiG8bg96IsxwetsRnxWvOo4Ye20Ph2e3aXIvNWiOgoRDcGB9EQ0\nLgzRBtQY56Dm5XLVUcJay8EmGOQAMpZmq45CROOApYuIrupc3kJoH/ByQCpVvvQ2qibPgNCN6o9p\nIgpQLF1ENIzdYYdnwXREHP7L5YA0twa7w64wVXiwO+zQ3BoAwPXRH3Fyqof3PVGIYOkiomGsFiu2\nF+xCWlMZsHcvtDYnbMU2WC1W1dFCntViha3YBq3NiRmOCshps3jfE4UIDqQnoqs6U1+LhNwpEAYD\nmrJNMJaWw5zMsUX+oLU50bV0PjJrz8KVHo+4w0d43xMFGA6kJ6JxM7mpBQKAzuNBhqsD5upG1ZHC\nhrnKhfQGDQJA5pke3vdEIYKli4iuSivIQnuMHl4h0JRlgpafqTpS2NAKstBiioQX4H1PFEJYuoho\nGM2twVa2Ea+tfwi7LakwlpbDVrZxcIA3TZxL9/2+GR/HB7OX874nCiEsXUQ0TElDCTas3oDsuz+J\n9PNnYU7OxobVG1DSUKI6Wsi7dN9nnOqDce03ed8ThRAOpCeia3L3uOExx6D9WD2yCzgjur9In0Rb\nRDo8e/ZxYlSiAMWB9EQ0rqInReOEKR/1r3CSVH9qOdAIISXSF2epjkJE44ili4iuqzvrbnh2OFTH\nCCsNr+xH3eRFnImeKMSwdBHRdRmWLUJU5X7VMcJK78596J6xSHUMIhpnLF1EdF3p9y5GZgtLlz8Z\narfCa52iOgYRjTOWLiK6rty7psLkOYuzJ8+ojhIWfF4fZrSfQOo9c1VHIaJxxtJFRNelM+hQY16A\n2i0Hbrwy3bT923ej1yAw69Z5qqMQ0Thj6SKiG6ovTMCpD36rOkZYOP7aVhxJSVEdg4gmAEsXEd1Q\nx7wcmOs4Oac/6Cr2oCnvFtUxiGgCsHQR0Q1Nu/c+zD7dqjpGWMhxORCz5HbVMYhoAnBGeiK6IZ/X\nh3ajAS3vl+OWWznAe6L4PD50Rhlx7lAl8mbnq45DRNfBGemJaELo9DocTklE+ZaXVUcJaXXvnkKH\nLo2FiyhEsXQR0Yg4s6fBs/9D1TFCWvMb+9GYxklRiUIVSxcRjUj60s9i+km96hghzfPRfvTNZuki\nClUsXUQ0IrM++XHktx6F9HHs5UQxV++HaTVLF1Go4kB6IhoR6ZNoi0iHZ88+ZCzNVh0n5HjcHvTG\nJMBX74LJYlIdh4hugAPpiWjCCJ1A3eRFaHh5n+ooIWnXljfREqdn4SIKYSxdRDRiPTMWwb2bF7+e\nCLVvv46jmWbVMYhoArF0EdGITVq1CHEnWbomQszxUpzN5/UWiUIZSxcRjVjSPYXI6y6Gz+tTHSXk\nFDTWImHlXapjENEE4kB6IhoVZ7wBruffwa333KE6Ssjo6uiCSI5DV1UzUi1pquMQ0QhwID0RTRi7\nww7NreFISgpOvvEaAEBza7A77IqTBa9L9+nuF99AXXwUUi1pvE+JQhhLFxGNiNViha3YhtbMKUgt\neQdamxO2YhusFqvqaEHr0n0a8V4N3DG38j4lCnE8vUhEI6a1OdE1YxoyzvaiKS8JxtJymJM5Z9fN\n0NqckJapiO8bQHNuAu9ToiDB04tENKHMVS5kdPRDByDD1QFzdaPqSEHPXOWCyd0HvfTxPiUKcSxd\nRDRiWkEWmrLN8AFoSYyFlp+pOlLQa4pLgATgMxjQlGXifUoUwli6iGhENLcGW9lGGEvLUZmxFidv\n+0fYyjZCc2uqowUtza3hV899B8eNC6DbtQvG0nLep0QhjKWLiEakpKEEG1ZvgDk5G5r1bhgOncKG\n1RtQ0lCiOlrQKmkowcqjc3C2YCWwbBnMydm8T4lCGAfSE9GoHf/TARj+5nOY6q5UHSXovVmQgbO3\nP4q/+t+fq45CRKPAgfRE5BcFD85BRl8NzrvOq44S9OaePo1pD9ynOgYR+QFLFxGNWsSkCFTF34LD\nzxerjhLUDn24DxE+iUVrVqiOQkR+wNJFRGNSOc2LU+/9t+oYQa3izy/iUGoydHo+FROFA+7pRDQm\nnvkrkVFToTpGUBMHd8A1ZbbqGETkJyxdRDQmsx7+BOa1tMLn9amOErQK6hsQu/xjqmMQkZ/w3YtE\nNCY+rw+tpgi4tu7Eort4rcDR8rg9cMeY4a1vhMliUh2HiEaJ714kIr/R6XU4lJqCo1u2qI4SlKq2\nVuJ0pIWFiyiMsHQR0ZidmbIccQcdqmMEpdY3StGUs0x1DCLyI5YuIhqzW9Z+FQUnecmasRD7SiEX\nL1Udg4j8iKWLiMYs/1OLkN95CAM9A6qjBJ2MhlKk3svSRRRORlS6hBBrhRAnhBAOIcSTV/n6t4QQ\n5UKIg0KISiGERwhhHsltiSh4xWfFoykqD1WvcOqI0Wg85ULqQB3y75ulOgoR+dENS5cQQgfglwDW\nALgFwKNCiOlD15FS/kxKOV9KuQDAPwLYIaXURnJbIgpuLTnL0LqtVHWMoLLrD3/CoYwIGKINqqMQ\nkamitCwAACAASURBVB+N5EjXEgCnpJT1UsoBAJsBXO9CYY8C2DTG2xJRkJFLl0K/7yPVMYJK7573\nUJs9TXUMIvKzkZSuTADOIZ+7Li4bRggRA2AtgJdHe1siCk4J6+ci7fQ7qmMElazaSsj5t6mOQUR+\nNt4D6e8BsFtKybczEYWJaffNQ+rAadQdr1EdJSj4vD7Ma2nDnE99UnUUIvKzkQwoaARgGfJ51sVl\nV/MI/nJqcbS3xVNPPTX4cVFREYqKikYQj4hUioyORGmK+f+3d+fxVVX33sc/6xACgUwMCSRkIBCR\nKaC14hAHhIpYJ4oWgSpcxF6vqK2PXr3eJw6o4KXSPrZgW7VEK/Qy3IpcheBAGQpSIFKRWUIChAwo\nYTghwQQyrOePhGNAhiScc/ZJ8n2/Xufl2cNZ+7tXMPzYe521yZ8zj+6vpDkdJ+BlfryauFaGy268\n0ukoItIAq1atYtWqVRfVxgUfA2SMaQXsAoYCB4BMYIy1ducZ+0UAe4A4a21ZQz5bu68eAyTSRM2+\n9mqq2oQwYeVKp6MEvCX/9mvafPouN+/Z6nQUEbkIPnkMkLW2CngU+BTYDsy31u40xjxkjPnXOruO\nAD45VXCd77MNCSgigc9cMZj4fdudjtEkhK3Po3Xv+52OISIO0AOvReSiZGRl0KkwjOQfD6ZjSSWu\nVi7c5W7W7l/Lbb1uczpeQMjIyiA1IZXItpFsC72ayqm/ovtDA9VHIk2YHngtIn6XmpDKnOMLKK8O\no2DmfNxFeaQtTyM1IdXpaAEjNSGVtOVpHNyTQ8/jX9Ll5q7qI5EWSFe6ROSiuYvycMUl076ykgOJ\nHQjdsInIqHinYwUUd1Ee3/ZPoevBYgqTOqmPRJo4XekSEUdEZufTvqKSVtXVxOYXE5lzzi8pt1iR\n2fl0KSrBBeojkRZKRZeIXDR3chzfRIdRDRTGReDuqTmQz+ROjqO0taHK5VIfibRQKrpE5KK4y92k\nZU4jeMMmygjB/nUVaZnTcJdrjuRT3OVu/u+GVzjRqordr71B6IZN6iORFkhjukTkotT9Zt4XHYdS\n9csnueQ/rtU38+rIyMqg9eaT9J74UxKOVQLoG54iTVxjxnSp6BIRr3nvxtFUVB5hzFo9i/FMb//s\nATr9cwV3fbXP6Sgi4gUaSC8ijqq84TIS9m1wOkZACtvyGYf6Xu10DBFxkIouEfGaGyeOY2DRMY4d\nOeZ0lIAzsGAf8Xfe7XQMEXGQii4R8ZqY7rHs7hDCirfnXXjnFuTgtm+IPhbM4NF3OR1FRBykoktE\nvGpnfC+O/O0Dp2MElD1z/sHujjcQ3DbY6Sgi4iAVXSLiVRVXDqHb7i+cjhFQypd/xvHLrnM6hog4\nTEWXiHjVDRMmceW+k1RXVjsdJWB0/uozOtyhokukpdOUESLidbmte1KxcDHJd/Z1Oorjjh88Dl2i\ncR0+REjHEKfjiIiXaMoIEQkIeQnXUfg/nzkdIyBk/SWTPaEDVXCJiIouEfG+6tTrcK1T0QWwZcl/\nsaNfO6djiEgAUNElIl7XbdR1JOxX0QUQn/MFJ6+5yukYIhIANKZLRLzOVluOBEVRuj6TxEE9nI7j\nmPJvyznRMYSiDV+RPPBSp+OIiBdpTJeIBATjMmxMCmZl+gynozhq1X8vorB9sAouEQFUdImIj+T3\nupzgf650OoajCjIWsjWu5V7pE5HTqegSEZ/oPGwEfffvdjqGo7rs+Cell93gdAwRCRAa0yUiPlHi\nLoEu4RzbmUe3HnFOx/E7W235unUcZctX0mNwL6fjiIiXaUyXiASMsMgwNkdFsCZ9jtNRHJH/2T4A\nkm64xNkgIhIwVHSJiE9kZGWQk5SCWbPes85d7iYjK8PBVL6XkZWBu9xN7tzP2NvtOozLtIjzFpEL\nU9ElIj6RmpDKnoGR9NpaBOvW4S7KI215GqkJqU5H86nUhFTSlqdRvfJjQnp2azHnLSIXpjFdIuIz\neZu2E/uD/pigIArjIwjdsInIqHinY/mcuyiPtl27E4yhMDGyxZy3SEuiMV0iElDiy49hAFdlJbH5\nxUTmFDgdyS+OL15Hm+pqXNVVLeq8ReT8VHSJiM+4k+M42q4tVcZFYVwE7p7dnI7kF5+uWMKxNobq\n1kEt6rxF5PxUdImIT7jL3aRlTiPriVnsCelP6IZNpGVOw13udjqaT7nL3bTe+iELf/RjXKvXtJjz\nFpEL05guEfGJjKwMUhNSCS4NprpLR47v3Ueb6Las3b+W23rd5nQ8n1n81WIGDvoJe2d9yI2jfgzU\nFGLN/bxFWprGjOlS0SUiPrcmvgP7xzzOz159wekoPrd20TISxw0n1l2Bq5VuJog0VxpILyIBKbvX\nFdi/L3Y6hl989d/vkJnQXQWXiHyPfiuIiM91vH00A/ZsdzqGX3T/Zzblg37idAwRCUC6vSgiPlf+\nbTnlHUP4+rNt9P5hP6fj+EzVySqK20ZzcuNWuv4g1uk4IuJDur0oIgGpbbu2rO/WlfVvvul0FJ/K\n+p8vOdq6iwouETkrFV0i4helKfcQu3a/0zF86pu5y8m/dKjTMUQkQKnoEhG/uOyBh+mze7PTMXwq\nbMPfaHv7j5yOISIBSmO6RMQvbLXlm9bdOLn8MxIG93A6jteVu8up6BBF9b48IhIjnY4jIj6mMV0i\nErCMy5CdOJR9s/7mdBSfWPb6u2R3jFLBJSLnpKJLRPxnyFCCVi93OoVPHPlkDlv6xDgdQ0QCmIou\nEfGbHj8fSq/8FVRXVjsdxev6Zm8m5OaRTscQkQCmoktE/Cb2qniOhp1k2Z//6nQUr8rL3k/vI6UM\ne2Si01FEJICp6BIRv/pnzwTy35/jdAyv+vvrb7Kxa0ciO2s8l4icm4ouEfGrk9feSvcdG5yO4VWu\nz5awt/cgp2OISIBT0SUifpORlcHlPx/LlQcOUVpcCoC73E1GVobDyRouIysDd7kbgEHbj9Nn1ONN\n9lxExD9UdImI36QmpPJG/p/IiWjL5888j7soj7TlaaQmpDodrcFSE1JJW55G9vKNRJcfpNfwS5vs\nuYiIf2hyVBHxK3dRHhXde9Dx20oOJHUidMMmIqPinY7VKO6iPE706kuU+ziFSR2b9LmISMNoclQR\nCXiR2fl0LK+mFRCbX0xkToHTkRotMjufKPdxXNgmfy4i4nsqukTEr9zJcRyI74AFDnQJw92zm9OR\nGu1QbFeqgeqgIArjIpr0uYiI76noEhG/cZe7ScucRujnm9jSdRhZV/07aZnTPAPSmxJ3uZvXXnuM\nrHYDcK1ZQ+iGTU32XETEP1R0iYjfrN2/lqlDpxIZFc+3Yx4k9O9rmDp0Kmv3r3U6WoOt3b+Wvp/t\nI/OaWLj6aiKj4pvsuYiIf2ggvYg4oqSwBNutG+TlEx4X7nScBquuqiavQzA5f1jIkPvucjqOiPiZ\nBtKLSJMRFhvGzugr+exX7zgdpVE+fXsBJ1u5GDzmDqejiEgToaJLRByz7YauVCx7zekYjfL1f7/J\n+ksH4GqlX6MiUj/6bSEijrnqiae4Pne/Z3b6puTy7Rto/5MJTscQkSZERZeIOKb/NZeRHdmOJb9+\n3ekoDZK9aicxpdXc/sufOx1FRJoQFV0i4qhtKddhl851OkaDFMz4lF3x9xHcNtjpKCLShKjoEhFH\n9XjgUa7bvYPqqmqno9Rb+MoPCP6pvrEoIg2jKSNExFHVVdXsiYym5PX/5fLx1zkd54KO5hzBlZxE\n8OGvCekY4nQcEXGIpowQkSbH1cpFfp8HKJ71idNR6mX79Ax2xgxRwSUiDaaiS0Qc13HCXcRu/MDp\nGPXSaskHVP5YtxZFpOF0e1FEHFd1soojbWMpW7GOhME9nI5zTu5DbkxMFyq25tO5d5TTcUTEQbq9\nKCJN0sf7Pmb7Jbew57ffXe1yl7vJyMpwMFWNjKwMz0OsM6b9lm1dQgjq3jogsolI06KiS0Qcl5qQ\nyqohh+mwagGsW4e7KI+05WmkJqQ6HY3UhFTSlqfhLsqj66J32N3vqoDJJiJNi24vikhAOLAri+je\nl2KCWlEYH0nohk1ERsU7HQsAd1EeJYMuJ27fYfJjwgnbvC1gsomIM3R7UUSarJgjhwFwVVYRm19M\nZE6Bw4m+E5mdT+z+oxig26FvAyqbiDQdKrpEJCC4k+P4umMI1UBhXATunt2cjuThTo7jWGuoMibg\nsolI06GiS0Qc5y53k5Y5jeAvt1HUzsXmx18mLXOaZwC709meXzoZc6IdB+e8R+iGTQGTTUSaFo3p\nEhHHZWRlkJqQSmTbSP584420KT3KrWtXs3b/Wm7rdZvj2YKf2Ua7LzaTuq/mGZHucndAZBMR5zRm\nTJeKLhEJKBv/9g+Sbr+BtnnFtI9q73QcbLUlu90Ajk+byWWPD3Y6jogECA2kF5Em74c/upY94cPY\nlLbQ6SgAbJu1nqDqEwz8xY1ORxGRJk5Fl4gEnKoJPyd8/ltOxwDA/es/kfujBzGuBv2DVkTke3R7\nUUQCTsW3FRwJS6Bk0XKS7+zrWI787DzaX9qXyi05RPWLdiyHiAQe3V4UkWahdbvW7LxqAvkvznI0\nxyfPPM3anhEquETEK1R0iUhASpoykf6b5lDuLncswxWrP6D0p484dnwRaV5UdIlIQEoc0pPd0TG8\n/9Tzjhx/6R//QsSJCu6Z/JQjxxeR5kdFl4gErKy7hhL38Z8cOXbJG1NZdeVggloHOXJ8EWl+VHSJ\nSEDKyMpg6EtPcumRYtYuWgbUTEqakZXh02O6y918s/9rbs7axQ8n/8rnxxSRlkNFl4gEpNSEVP5r\n43/xt74pFD//S9xFeaQtTyM1IdWnx0xbnsaWp2aRG9aX+EujfH5MEWk5NGWEiAQsd1EeJQP6E/f1\nMfITOhC2cTORUfE+PeaRA7mExPck2FoOJHYgdMMmnx9TRJoeTRkhIs1KZHY+cYe+xQCx+91E5hT4\n/Jj7J75Bm6oqWlVXE5tf7JdjikjLoKJLRAKWOzmOgvgIqoOCqAa2Zh/36fGO7D5Mt4//xNddIqhu\nHURhXATunt18ekwRaTlUdIlIQHKXu0nLnEbohk241qxhze2TKXl6HEe/PeqzY26580mWXR5Lu61b\nca1eQ+iGTaRlTsNd7vbZMUWk5dCYLhEJSBlZGaQmpBLZNhKAyvJKdnf6AV+MuYWfzZru9eMtnvEO\nP3z6F1Rs2UZCr0TPene5m7X713Jbr9u8fkwRaboaM6arXkWXMWY48FtqroylW2t/dZZ9BgOvAa2B\nImvtTbXr9wHFQDVQYa0ddI5jqOgSkfPa+tY6oh6+m5C9O4lIiPBauyfLT7I9MZIvh49jwrtveK1d\nEWm+fFJ0GWNcQBYwFCgEPgdGW2u/qrNPBPAPYJi1tsAY09lae6h22x7gCmvtee8JqOgSkfpY02si\nFe2DGbLpj15rM/0n99L780+4JvcIrlYadSEiF+arby8OAnZba3OttRXAfOCuM/YZCyy01hYAnCq4\nTuWq53FERC6ocMaN9N/1Fp+kL/Csa+gEpqcmQQXY+fk27vzkrxx9+XU+yvnI63lFRE6pTzHUDcir\ns5xfu66uXkBHY8xKY8znxpj762yzwLLa9T+/uLgi0tLdMvhOZg7rSYe0iVSuWdOoSVNPTYLqLsrj\nm1HD+bRffz6KXqdJUEXEp+pze/Fu4BZr7b/WLt8HDLLW/qLOPjOBK4AhQHtgHfBja222MSbGWnvA\nGBMFLAMetdZ+dpbj6PaiiNTLofy9tEvqQZsqw4HuHRs1gam7KI+yvv3oeqjEbxOvikjz0Zjbi/V5\nkmsBkFBnOa52XV35wCFrbTlQboxZDQwEsq21BwCstUXGmEXU3K78XtEFMHnyZM/7wYMHM3jw4Pqd\nhYi0KJ3zvqaq2kUrW03MvsPYXbnQwIJp37/8kYGHSjBAtwMluHIKGtyGiLQcq1atYtWqVRfVRn2u\ndLUCdlEzkP4AkAmMsdburLNPb2AmMBxoA2wA7gX2AS5rbakxpj3wKfCitfbTsxxHV7pEpF7cRXmU\nXnU5MXnFlFHJts7X0mfD0np9o/Fk6UnWX/04cVmf0qbzYWIOlVIYF6HH/YhIg/hkIL21tgp4lJqC\naTsw31q70xjzkDHmX2v3+Qr4BNgCrAfestbuALoAnxljNtWuX3y2gktEpL7qTpraas0aynbnsC/m\nGEV9+rLlg8zT9l2wfQELtn034P7glq/ZEXcjtjiT3749mPabt2gSVBHxG02OKiJNypmTpkJNIfbh\nA89w68J32fXwTK67tx/u5Dj+/R8vgoFfX/MCW6f+kR5v/o6Mq/sR/tv/w/BLb/1eG5oEVUTqy2eT\no/qDii4RuVj/nLmYlF+OoLWtphoorx212ray5rJ+YYcQ2u/apduIInLRfDVPl4hIk3DFDzsT1MqF\nAUxQECcWZnDivSWYoCAMEFNaQWTOmd8DEhHxDxVdItJsuJPjKIyPoLp1EIXxEbiuScF17YDv1sVF\n4O555jSDIiL+oaJLRJqFugPsTw2O//d/vMi/r3vxtHUaMC8iTtGYLhFpFs42wH7B9gVg4d7+93rW\nacC8iHiDBtKLiIiI+IEG0ouIiIgEKBVdIiIiIn6goktERETED1R0iYiIiPiBii4RERERP1DRJSIi\nIuIHKrpERERE/EBFl4iIiIgfqOgSERER8QMVXSIiIiJ+oKJLRERExA9UdImIiIj4gYouERERET9Q\n0SUiIiLiByq6RERERPxARZeIiIiIHwQ5HUBERKQ56969O7m5uU7HkEZKTExk3759XmnLWGu90tDF\nMsbYQMkiIiLiLcYY9Pdb03Wun1/tetOQtnR7UURERMQPVHSJiIiI+IGKLhERERE/UNElIiIijnK5\nXOzZs8fpGD6noktEREQcZUyDxqOf1WuvvUZMTAyRkZE8+OCDVFRUeCGZd6noEhEREUdd7Lc7P/nk\nE1599VVWrlxJbm4uOTk5vPDCC15K5z0qukRERFqopKQkpk2bRr9+/ejUqRMTJ07kxIkTpKSkkJGR\n4dmvsrKSqKgoNm/efN72Zs+eTffu3YmKimLKlCkkJSWxYsUKAKqrq3nllVdITk4mIiKCK6+8koKC\ngnrlzMzMJCYm5rTibNGiRVx22WWe406cOJHevXsTERHB888/zzvvvNPQ7vA5FV0iIiIt2Ny5c1m2\nbBk5OTns2rWLKVOmMH78eObMmePZJyMjg9jYWAYOHHjOdnbs2MEjjzzCvHnzOHDgAMXFxRQWFnq2\n/+Y3v2HBggV8/PHHFBcX8/bbb9OuXbt6ZRw0aBChoaGeAg5g3rx5/OxnPwNg+/btp2UbOHAgBw8e\n5OjRo/XuB39Q0SUiItKCPfbYY8TGxhIZGUlaWhrz58/nvvvuY+nSpZSWlgLwl7/8hfvvv/+87Sxc\nuJA777yTa665hqCgIF566aXTtqenpzN16lSSk5MBSElJoUOHDvXOOXr0aObOnQtASUkJS5cuZcyY\nMQCUlpYSERHh2Tc8PBxrLSUlJfVu3x9UdImIiDjIGO+8GisuLs7zPjExkcLCQrp27UpqaioLFy6k\nuLiYjz76yHNV6VwKCwuJj4/3LIeEhNCpUyfPcl5eHj169Gh0zrFjx7Jo0SIqKip4//33ueKKKzzZ\nQ0NDOXbsmGff4uJijDGEhYU1+ni+oKJLRETEQdZ659VYeXl5nve5ubnExsYCeG4x/vWvf+Xaa68l\nJibmvO3ExMSQn5/vWS4rK+Pw4cOe5fj4eHJychqds0+fPiQmJrJ06VLmzZvH2LFjPdv69et32niz\nL7/8ki5dujToSpo/qOgSERFpwX7/+99TUFDAkSNHeOWVVxg9ejQAI0aM4IsvvmDGjBmMGzfugu3c\nc889LF68mPXr11NRUcHkyZNP2/7ggw/y3HPPkZ2dDcDWrVsbPOZq7Nix/O53v2PNmjX89Kc/9awf\nN24c6enp7Ny5k6NHjzJlyhQmTJjQoLb9QUWXiIhICzZ27FiGDRtGcnIyl1xyCWlpaQC0bduWu+++\nm7179zJy5MgLttO3b19mzpzJvffeS2xsLOHh4URHR9OmTRsAnnjiCUaNGsWwYcOIiIjgwQcfpKys\nDKj/PF2jR49m9erVDB06lI4dO3rW33LLLTz99NPcdNNNJCUl0bNnz+8VfYHABMqTz40xNlCyiIiI\neIsx5qLnofKVpKQk0tPTGTJkyFm3v/zyy+zevZvZs2c3uO3jx48TGRlJdnY2iYmJFxvVMef6+dWu\nb9BoOl3pEhERke85cuQI6enpPPTQQ/X+zJIlSygrK+P48eM8+eSTDBgwoEkXXN6moktERKSFOtdt\nvVmzZpGQkMBtt91GamqqZ/3cuXMJCwsjPDzc8woLCyMlJQWADz74gNjYWOLi4sjJyWH+/PkNytO/\nf//vtR0eHs68efMaf5IBRLcXRUREfCiQby/Khen2ooiIiEgTo6JLRERExA9UdImIiIj4gYouERER\nET9Q0SUiIiLiByq6RERExFEul4s9e/Y4HcPnVHSJiIiIo+r7GKBz2b59O8OHDycqKopWrVp5KZX3\nqegSERERR13sPGatW7fm3nvv5e233/ZSIt9Q0SUiIuJnGVkZuMvdp61zl7vJyMrwaxtJSUlMmzaN\nfv360alTJyZOnMiJEydISUkhI+O7diorK4mKimLz5s3nbW/27Nl0796dqKgopkyZQlJSEitWrACg\nurqaV155heTkZCIiIrjyyispKCioV87MzExiYmJOK84WLVrEwIEDAejVqxcTJkygb9++9T53J6jo\nEhER8bPUhFTSlqd5iiZ3uZu05WmkJqRe4JPebQNqHu2zbNkycnJy2LVrF1OmTGH8+PHMmTPHs09G\nRgaxsbGeIudsduzYwSOPPMK8efM4cOAAxcXFFBYWerb/5je/YcGCBXz88ccUFxfz9ttv065du3pl\nHDRoEKGhoZ4CDmDevHncd999DTpXx1lrA+JVE0VERKR5Odffb0fLjtpJSybZvUf32klLJtmjZUcb\n3PbFttG9e3f71ltveZaXLl1qk5OT7YEDB2xYWJgtKSmx1lp7zz332OnTp5+3rZdeesmOHTvWs/zt\nt9/a4OBgu3z5cmuttZdeeqldvHjxWT9rjLE5OTnnbf/ZZ5+1DzzwgLXW2mPHjtn27dvb/fv3n7ZP\ndna2dblc522noc7186td36BaJ8jRik9ERKSFimwbyVOpT5H0uyQA/rDxD6dtf+HGF5g8ePL3Pjd5\n1WRe/PuLp637w8Y/sPeXe4lsG9ngHHFxcZ73iYmJFBYW0rVrV1JTU1m4cCEjRozgo48+YsaMGedt\np7CwkPj4eM9ySEgInTp18izn5eXRo0ePBuc7ZezYsaSmpvLGG2/w/vvvc8UVV5x2vKZARZeIiIgD\n3OVupq+dzt5f7mX62ulMHTq1XkXT5MGTPcXYqVuKT6U+1aA26srLy/O8z83NJTY2FoDx48cza9Ys\nKioquPbaa4mJiTlvOzExMWRlZXmWy8rKOHz4sGc5Pj6enJycRo+76tOnD4mJiSxdupR58+YxduzY\nRrXjqIZeGvPVC91eFBGRZuhsf7+dui146nbgmcv14Y02unfvbgcMGGDz8/Pt4cOH7XXXXWefffZZ\na621ZWVltkOHDjYlJcXOmTPngm1t377dhoeH23Xr1tmTJ0/ap59++rTbi9OnT7cDBw60u3fvttZa\nu2XLFnvkyBFrbf1uL1pr7auvvmpvuukm265dO3v48OHTtpWXl9vt27dbY4wtLy+3J06cqHc/nM+5\n6hMacXtRA+lFRET8bO3+taddlYpsG8nUoVNZu3+tX9uAmtt2w4YNIzk5mUsuuYS0tDQA2rZty913\n383evXsZOXLkBdvp27cvM2fO5N577yU2Npbw8HCio6Np06YNAE888QSjRo1i2LBhRERE8OCDD1JW\nVgbUf56u0aNHs3r1aoYOHUrHjh0963NzcwkJCSElJQVjDCEhIfTu3btB/eAPxl7k3BjeYoyxgZJF\nRETEW4wxFz0Pla8kJSWRnp7OkCFDzrr95ZdfZvfu3cyePbvBbR8/fpzIyEiys7NJTEy82KiOOdfP\nr3Z9g2Z11ZUuERER+Z4jR46Qnp7OQw89VO/PLFmyhLKyMo4fP86TTz7JgAEDmnTB5W0qukRERFqo\nc93WmzVrFgkJCdx2222kpn4379fcuXMJCwsjPDzc8woLCyMlJQWADz74gNjYWOLi4sjJyWH+/PkN\nytO/f//vtR0eHs68efMaf5IBRLcXRUREfCiQby/Khen2ooiIiEgTo6JLRERExA9UdImIiIj4gYou\nERERET9Q0SUiIiLiByq6REREWqisrCwuv/xyIiIieP31152O8z0vvvgi999/v9MxvEZFl4iISAv1\n6quvMmTIEIqLi3n00Ucb9Nl3332X66+/3kfJvlPfRwQ1BSq6REREWqjc3Fz69evX4M9VVVVhrW1W\nBZE/qOgSERFpgYYOHcrKlSt55JFHCA8PZ8uWLYwbN47o6GiSkpKYOnWqZ993332X6667jieeeILO\nnTszevRoHn74YdatW0dYWNhpD58+myNHjnDHHXcQERHBVVddxXPPPXfaVbLt27czbNgwOnXqRExM\nDNOmTfteG7m5ubhcLv785z+TkJBAp06dePPNN9m4cSMDBw6kY8eOPPbYYxc875ycHAYPHkxkZCTR\n0dGMGTOmAb12cYL8diQRERE5XUkJbNsG/ftDWJhf21i+fDk33XQT48aNY8KECYwbN46SkhL27dtH\nUVERw4YNIzY2lgkTJgCwYcMGxo4dy8GDB6moqGD+/Pmkp6ezevXqCx5r0qRJhIWFcfDgQfbs2cMt\nt9xC9+7dASgtLeXmm2/m6aefZsmSJVRUVLBjx45ztpWZmUl2djarV6/mjjvu4NZbb2XFihWcOHGC\nyy+/nFGjRp33tudzzz3HLbfcwqpVqzh58iQbN26sd59dLF3pEhERcUJJCVx/PdxwQ81/S0ocE71B\ntgAACKlJREFUacNaS3V1NQsWLGDatGm0a9eOxMREnnzySebMmePZr1u3bkyaNAmXy0WbNm3q3X51\ndTXvv/8+L730Em3atKFPnz6MHz/es33JkiXExMTw+OOPExwcTPv27bnyyivP2pYxhueff57g4GB+\n9KMf0b59e8aMGUOnTp2IjY3l+uuvZ9OmTefN07p1a3JzcykoKCA4OJhrr7223udysVR0iYiIOGHb\nNti+HSorYfNmCA8HYxr2Cg+v+WxlJezYUdNeIxw6dIjKykoSEhI86xITEykoKPAsx8fHN6rtoqIi\nqqqqiIuLO2tbeXl59OzZs97tRUdHe96HhITQpUuX05ZLS0vP+/np06dTXV3NoEGDSElJ4Z133qn3\nsS+Wii4REREn9O8P/fpB69YwcCAcOwbWNux17FjNZ1u3hr59a9prhM6dOxMUFERubq5nXW5uLt26\ndfMsnzlovr6D6KOioggKCiI/P9+zLi8vz/M+Pj6enJycRuVujOjoaN566y0KCgp44403mDRpEnv2\n7PHLsVV0iYiIOCEsDNasgdWra/7bmDFd3mgDcLlcjBo1irS0NEpLS8nNzeW111477xxZXbp0IT8/\nn4qKigu2PXLkSCZPnkxZWRlfffUVs2fP9my//fbb+frrr5kxYwYnT56ktLSUzMzMs7ZlrW3U+dX1\n3nvvea7gRUZG4nK5cLn8Uw6p6BIREXFKWBhcfXXjB9FfZBt1r1bNnDmTdu3a0aNHD2644Qbuu+8+\nzyD6sxkyZAj9+vWja9eup93yO5uZM2fidruJiYlh/PjxjB071jMuLDQ0lGXLlvHhhx/StWtXevXq\nxapVqy6Ytz7LZ/P5559z1VVXER4ezogRI5gxY4ZnUL+vGW9Ujd5gjLGBkkVERMRbjDFeuULTnDzz\nzDN88803fh1P1Vjn+vnVrm/QRGW60iUiIiI+tWvXLrZu3QrUTPmQnp7OyJEjHU7lfyq6RERE5KL1\n79+f8PBwzyssLIzw8HDmzZtHSUkJI0eOJDQ0lDFjxvDUU09xxx13+CzLww8/7Dl+3SyTJk3y2THr\nQ7cXRUREfEi3F5s23V4UERERaWJUdImIiIj4gYouERERET9Q0SUiIiLiB0FOBxAREWnOEhMT6/3I\nHAk8iYmJXmurXt9eNMYMB35LzZWxdGvtr86yz2DgNaA1UGStvam+n63dT99eFBERkSbBJ99eNMa4\ngNeBW4B+wBhjTO8z9okAfg/cbq3tD/y0vp8V55zrMQviO+pz/1Of+5/63P/U501DfcZ0DQJ2W2tz\nrbUVwHzgrjP2GQsstNYWAFhrDzXgs+IQ/U/qf+pz/1Of+5/63P/U501DfYqubkBeneX82nV19QI6\nGmNWGmM+N8bc34DPioiIiDR73hpIHwT8ABgCtAfWGWPWealtERERkSbvggPpjTFXA5OttcNrl58B\nbN0B8caY/wDaWmtfrF2eBXwEFFzos3Xa0Ch6ERERaTIaOpC+Ple6PgeSjTGJwAFgNDDmjH0+AGYa\nY1oBbYCrgP8H7KrHZxsVXERERKQpuWDRZa2tMsY8CnzKd9M+7DTGPFSz2b5lrf3KGPMJsAWoAt6y\n1u4AONtnfXUyIiIiIoGqXvN0iYiIiMjFcfQxQMaYl4wxm40xm4wxHxtjutbZ9p/GmN3GmJ3GmGFO\n5mxOjDGv1vbpl8aYhcaY8Drb1Oc+YIy5xxizzRhTZYz5wRnb1Oc+YowZboz5yhiTVTvuVLzMGJNu\njPnGGLOlzroOxphPjTG7jDGf1M7jKF5ijIkzxqwwxmw3xmw1xvyidr363UeMMW2MMRtqa5WtxpgX\natc3uM+dfvbiq9bagdbay4EM4NSJ9AVGAX2AW4E/GD1DwVs+BfpZay8DdgP/CepzH9sK/AT4e92V\nxpg+qM99QhMz+8071PRxXc8Af7PWXgqsoPZ3jHhNJfCEtbYfcA3wSO2fbfW7j1hrTwA31dYqlwG3\nGmMG0Yg+d7TostaW1llsD1TXvr8TmG+trbTW7qOmOBjk53jNkrX2b9baU/28Hoirfa8+9xFr7S5r\n7W7gzILqLtTnvqKJmf3AWvsZcPSM1XcB79a+fxcY4ddQzZy19mtr7Ze170uBndT8Hle/+5C19tva\nt22oGQ9vaUSfO32lC2PMFGPMfmpmtX++dvWZk6oWoElVfeEBYGnte/W5/6nPfUcTMzsn2lr7DdQU\nCEC0w3maLWNMd2quvKwHuqjffccY4zLGbAK+BpZZaz+nEX3urclRz8kYswzoUncVNRVimrV2sbX2\nWeDZ2jEXjwGTfZ2pubtQn9fukwZUWGvnORCx2alPn4u0UPq2lg8YY0KB94BfWmtLzzLXpfrdi2rv\nEF1eOw56kTGmH9/v4wv2uc+LLmvtzfXcdS4147omU/Mv/vg62+Jq10k9XKjPjTH/AvyYmicInKI+\nvwgN+HNel/rcdwqAhDrL6lv/+cYY08Va+03tl6MOOh2ouTHGBFFTcM2x1n5Qu1r97gfW2mPGmFXA\ncBrR505/ezG5zuII4Kva9x8Co40xwcaYJCAZyPR3vubIGDMceAq4s3Zw4Cnqc/+oO65Lfe47nkmd\njTHB1EzM/KHDmZorw/f/XP9L7fvx1EyeLd71NrDDWvu7OuvU7z5ijOl86puJxpgQ4GZqxtI1uM8d\nnafLGPMeNQ/LrgZygX+z1h6o3fafwESggprLp586FrQZMcbsBoKBw7Wr1ltrJ9VuU5/7gDFmBDAT\n6Ay4gS+ttbfWblOf+0jtPzB+x3cTM09zOFKzY4yZCwwGOgHfUPMN9P8F/krNVdxcYJS11u1UxubG\nGJMKrKbmW9G29vV/qfkH2/+gfvc6Y0wKNQPlXbWvBdbaqcaYjjSwzzU5qoiIiIgfOP7tRREREZGW\nQEWXiIiIiB+o6BIRERHxAxVdIiIiIn6goktERETED1R0iYiIiPiBii4RERERP1DRJSIiIuIH/x9P\nr18ltSEoAAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=[10,10])\n", + "ax = fig.add_subplot(111)\n", + "\n", + "plt.plot(WD-270,P_rat_py_v0,'-',label ='py_gcl_v0')\n", + "#plt.plot(270-WD,P_rat_py_v0,'-',label ='py_gcl_v0_inv')\n", + "\n", + "plt.plot(WD-270,P_rat_py_v1,'x--',label ='py_gcl_v1')\n", + "#plt.plot(270-WD,P_rat_py_v1,'x--',label ='py_gcl_v1_inv')\n", + "\n", + "#plt.plot(WD-270,P_rat_fgcl,'+--',label ='fort_gcl')\n", + "#plt.plot(270-WD,P_rat_fgcl,'+--',label ='fort_gcl')\n", + "\n", + "plt.plot(WD-270,P_rat_fgclm,'.-',label ='fort_gclm_s')\n", + "#plt.plot(270-WD,P_rat_fgclm,'.-',label ='fort_gclm_s')\n", + "\n", + "#plt.plot(WD-270,P_rat_fgclm_rdn,'o',label ='fort_gclm_s_rdn')\n", + "\n", + "plt.legend(loc=4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/Problem_yml_wf_description.yml b/examples/Problem_yml_wf_description.yml new file mode 100644 index 0000000..424a41c --- /dev/null +++ b/examples/Problem_yml_wf_description.yml @@ -0,0 +1,215 @@ +layout: + - name: WT01 + row: 1 + position: [0, 0] + turbine_type: B76 + - name: WT02 + row: 2 + position: [560, 0] + turbine_type: v80 + +plant_data: + utm: + code: 32 + letter: U + name: Test WF + owner: DTU + +turbine_types: + # Sources: + # [1] https://windbench.net/system/files/hornsrev_v80_0.pdf + # [2] http://www.thewindpower.net/turbine_en_30_vestas_2000.php + # [3] http://en.wind-turbine-models.com/turbines/668-vestas-v-80-offshore + # [4] WAsP wind turbine library (distributed as part of the WAsP software) + - name: V80 + hub_height: 90.0 # [1] + rotor_diameter: 80.0 # [1] + rated_power: 2000.0 # [1] + cut_in_wind_speed: 4.0 # [1] + cut_out_wind_speed: 25.0 # [1] + rated_wind_speed: 16.0 # [1] + wind_class: IEC Ia (DIBt III) # [2] + air_density: 1.225 # guess + gear_box: + speed_number: 3 + ratio: 1:100,5 # [2] + type: spur/planetary # [3] + nacelle: # [2] + weight: 68000.0 # kg [2], 69000.0 kg according to [3] + rotor: # [2, 3] + weight: 37000.0 # kg [2] + tip_speed: 80.0 # m/s [3] + min_speed: 9 # rd/min [2] + max_speed: 19 # rd/min [2] + manufacturer: Vestas + hub: + weight: 18000 # kg [3] + tower: # [2] + weight: 198000.0 #kg [2], max 148000.0 kg according to [3] + material: steel + manufacturer: Welcon + control: # [1] + type: Active Pitch, Variable Speed + generator: # [2] + type: ASYNC + number: 1 + max_output_speed: 1909 #rounds/minute + output_voltage: 690 #V + grid_frequency: 50/60 # Hz + power_curve: # [1] + - [3.0, 0.0] + - [4.0, 66.6] + - [5.0, 154.0] + - [6.0, 282.0] + - [7.0, 460.0] + - [8.0, 696.0] + - [9.0, 996.0] + - [10.0, 1341.0] + - [11.0, 1661.0] + - [12.0, 1866.0] + - [13.0, 1958.0] + - [14.0, 1988.0] + - [15.0, 1997.0] + - [16.0, 1999.0] + - [17.0, 2000.0] + - [18.0, 2000.0] + - [19.0, 2000.0] + - [20.0, 2000.0] + - [21.0, 2000.0] + - [22.0, 2000.0] + - [23.0, 2000.0] + - [24.0, 2000.0] + - [25.0, 2000.0] + c_t_curve: + - [3.0, 0.00] + - [4.0, 0.818] + - [5.0, 0.806] + - [6.0, 0.804] + - [7.0, 0.805] + - [8.0, 0.806] + - [9.0, 0.807] + - [10.0, 0.793] + - [11.0, 0.739] + - [12.0, 0.709] + - [13.0, 0.409] + - [14.0, 0.314] + - [15.0, 0.249] + - [16.0, 0.202] + - [17.0, 0.167] + - [18.0, 0.140] + - [19.0, 0.119] + - [20.0, 0.102] + - [21.0, 0.088] + - [22.0, 0.077] + - [23.0, 0.067] + - [24.0, 0.060] + - [25.0, 0.053] + c_t_idle: 0.053 # [4] + blade: + geometry: # [1] + # [radius [m], c [m], twist [deg], airfoil ] + - [2.563, 2.004, 9.50, 'Cylinder 1'] + - [4.389, 2.523, 9.50, 'Cylinder 1'] + - [6.216, 3.015, 9.50, 'FFA W3-301'] + - [8.042, 3.278, 9.50, 'FFA W3-301'] + - [9.868, 3.309, 9.50, 'FFA W3-301'] + - [11.694, 3.195, 9.50, 'FFA W3-301'] + - [13.520, 3.039, 9.22, 'FFA W3-241'] + - [15.346, 2.863, 7.81, 'FFA W3-211'] + - [17.173, 2.687, 6.40, 'FFA W3-211'] + - [18.999, 2.511, 5.11, 'FFA W3-211'] + - [20.825, 2.334, 3.83, 'FFA W3-211'] + - [22.651, 2.158, 2.61, 'NACA 63-221'] + - [24.477, 1.982, 1.48, 'NACA 63-221'] + - [26.304, 1.806, 0.42, 'NACA 63-221'] + - [28.130, 1.630, 0.49, 'NACA 63-221'] + - [29.956, 1.454, 1.23, 'NACA 63-218'] + - [31.782, 1.278, 1.79, 'NACA 63-218'] + - [33.608, 1.102, 2.24, 'NACA 63-218'] + - [35.435, 0.926, 2.61, 'NACA 63-218'] + - [37.261, 0.749, 2.84, 'NACA 63-218'] + - [39.087, 0.573, 2.97, 'NACA 63-218'] + + # Sources: + # [1] http://www.middelgrunden.dk/middelgrunden/sites/default/files/public/file/Location.pdf + # [2] http://www.thewindpower.net/turbine_en_229_bonus_2000.php + # [3] http://en.wind-turbine-models.com/turbines/121-bonus-b76-2000 + # [4] WAsP wind turbine library (distributed as part of the WAsP software) + - name: B76 + hub_height: 64.0 #[1] + rotor_diameter: 76.0 #[1] + rated_power: 2000.0 #[1] + cut_in_wind_speed: 3.0 #[2] + cut_out_wind_speed: 25.0 #[2] + rated_wind_speed: 15.0 #[2] + air_density: 1.225 # guess + gear_box: + speed_number: 3 + ratio: 1:89 # [2], or [3]: 1:93 + manufacturer: Flender + nacelle: # [2] + weight: 65000.0 #kg [2], or [3]: 82500 kg + rotor: # [2, 3] + weight: 38000.0 #kg [2], or [3]: 52000.0 kg + max_speed: 17 #rd/min [2], or [3]: 16 + tip_speed: 63.7 #m/s [3] + manufacturer: LM Glasfiber, Bonus + tower: # [2] + weight: 162000.0 #kg [2] + control: # [2] + type: Stall + generator: # [2] + type: ASYNC + number: 1 + max_output_speed: 1500 #rounds/minute + output_voltage: 690 #V + manufacturer: ABB + power_curve: # [4] + - [3.0, 0.0] + - [4.0, 43.0] + - [5.0, 133.0] + - [6.0, 237.0] + - [7.0, 401.0] + - [8.0, 623.0] + - [9.0, 886.0] + - [10.0, 1190.0] + - [11.0, 1502.0] + - [12.0, 1740.0] + - [13.0, 1891.0] + - [14.0, 1962.0] + - [15.0, 1988.0] + - [16.0, 1996.0] + - [17.0, 1999.0] + - [18.0, 2000.0] + - [19.0, 2000.0] + - [20.0, 2000.0] + - [21.0, 2000.0] + - [22.0, 2000.0] + - [23.0, 2000.0] + - [24.0, 2000.0] + - [25.0, 2000.0] + c_t_curve: # [4] + - [3.0, 0.00] + - [4.0, 0.857] + - [5.0, 0.858] + - [6.0, 0.810] + - [7.0, 0.853] + - [8.0, 0.870] + - [9.0, 0.811] + - [10.0, 0.756] + - [11.0, 0.679] + - [12.0, 0.584] + - [13.0, 0.511] + - [14.0, 0.439] + - [15.0, 0.383] + - [16.0, 0.338] + - [17.0, 0.301] + - [18.0, 0.271] + - [19.0, 0.246] + - [20.0, 0.225] + - [21.0, 0.207] + - [22.0, 0.192] + - [23.0, 0.179] + - [24.0, 0.168] + - [25.0, 0.158] + c_t_idle: 0.158 # [4] diff --git a/examples/Script.ipynb b/examples/Script.ipynb index 7bd938e..e995fb6 100644 --- a/examples/Script.ipynb +++ b/examples/Script.ipynb @@ -58,14 +58,25 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "wf.WindFarm?" + ] + }, + { + "cell_type": "code", + "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "v80 = wt.WindTurbine('Vestas v80 2MW offshore','V80_2MW_offshore.dat',70,40)\n", - "HR1 = wf.WindFarm('Horns Rev 1','HR_coordinates.dat',v80)\n", + "HR1 = wf.WindFarm(name='Horns Rev 1',yml='hornsrev.yml')#,v80)\n", "WD = range(0,360,1)" ] }, @@ -78,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -89,7 +100,7 @@ "\"\\n##Fused inputs\\ninputs = dict(\\n wind_speed=8.0,\\n roughness=0.0001,\\n TI=0.05,\\n NG=4,\\n sup='lin',\\n wt_layout = fused_gcl.generate_GenericWindFarmTurbineLayout(HR1))\\n\\nfgcl = fused_gcl.FGCLarsen()\\n# Setting the inputs\\nfor k,v in inputs.iteritems():\\n setattr(fgcl, k, v)\\n\\nfP_WF = np.zeros([len(WD)])\\nfor iwd, wd in enumerate(WD):\\n fgcl.wind_direction = wd\\n fgcl.run()\\n fP_WF[iwd] = fgcl.power\\n\"" ] }, - "execution_count": 4, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -127,11 +138,23 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'GCLarsen' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mP_WF_v0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mWD\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0miwd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwd\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mWD\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mP_WT\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mU_WT\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mCT_WT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGCLarsen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mWS\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m8.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mz0\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0001\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mTI\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.05\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mWD\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mWF\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mHR1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mNG\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msup\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'lin'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mP_WF\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0miwd\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mP_WT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mP_WT\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mU_WT\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mCT_WT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGCLarsen_v0\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mWS\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m8.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mz0\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0001\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mTI\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.05\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mWD\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwd\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mWF\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mHR1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mNG\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msup\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'lin'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'GCLarsen' is not defined" + ] + } + ], "source": [ "P_WF = np.zeros([len(WD)])\n", "P_WF_v0 = np.zeros([len(WD)])\n", @@ -524,7 +547,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.10" + "version": "2.7.11" } }, "nbformat": 4, diff --git a/examples/Test_for_agreement_between implementations.ipynb b/examples/Test_for_agreement_between implementations.ipynb new file mode 100644 index 0000000..4d57e93 --- /dev/null +++ b/examples/Test_for_agreement_between implementations.ipynb @@ -0,0 +1,672 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "import numpy as np\n", + "from IPython.display import HTML, Latex, Markdown, Pretty\n", + "\n", + "from windIO.Plant import WTLayout\n", + "from fusedwake.WindFarm import WindFarm\n", + "\n", + "from fusedwake.gcl import GCL\n", + "import fusedwake.gcl.fortran as fgcl\n", + "import fusedwake.gcl.python as pygcl\n", + "\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def circles(x, y, s, c='b', vmin=None, vmax=None,**kwargs):\n", + " \"\"\"\n", + " Make a scatter of circles plot of x vs y, where x and y are sequence \n", + " like objects of the same lengths. The size of circles are in data scale.\n", + "\n", + " Parameters\n", + " ----------\n", + " x,y : scalar or array_like, shape (n, )\n", + " Input data\n", + " s : scalar or array_like, shape (n, ) \n", + " Radius of circle in data unit.\n", + " c : color or sequence of color, optional, default : 'b'\n", + " `c` can be a single color format string, or a sequence of color\n", + " specifications of length `N`, or a sequence of `N` numbers to be\n", + " mapped to colors using the `cmap` and `norm` specified via kwargs.\n", + " Note that `c` should not be a single numeric RGB or RGBA sequence \n", + " because that is indistinguishable from an array of values\n", + " to be colormapped. (If you insist, use `color` instead.) \n", + " `c` can be a 2-D array in which the rows are RGB or RGBA, however. \n", + " vmin, vmax : scalar, optional, default: None\n", + " `vmin` and `vmax` are used in conjunction with `norm` to normalize\n", + " luminance data. If either are `None`, the min and max of the\n", + " color array is used.\n", + " kwargs : `~matplotlib.collections.Collection` properties\n", + " Eg. alpha, edgecolor(ec), facecolor(fc), linewidth(lw), linestyle(ls), \n", + " norm, cmap, transform, etc.\n", + "\n", + " Returns\n", + " -------\n", + " paths : `~matplotlib.collections.PathCollection`\n", + "\n", + " Examples\n", + " --------\n", + " a = np.arange(11)\n", + " circles(a, a, a*0.2, c=a, alpha=0.5, edgecolor='none')\n", + " plt.colorbar()\n", + "\n", + " License\n", + " --------\n", + " This code is under [The BSD 3-Clause License]\n", + " (http://opensource.org/licenses/BSD-3-Clause)\n", + " \n", + " (http://stackoverflow.com/questions/9081553/python-scatter-plot-size-and-style-of-the-marker/24567352#24567352)\n", + " \"\"\"\n", + " import numpy as np\n", + " import matplotlib.pyplot as plt\n", + " from matplotlib.patches import Circle\n", + " from matplotlib.collections import PatchCollection\n", + "\n", + " if np.isscalar(c):\n", + " kwargs.setdefault('color', c)\n", + " c = None\n", + " if 'fc' in kwargs: kwargs.setdefault('facecolor', kwargs.pop('fc'))\n", + " if 'ec' in kwargs: kwargs.setdefault('edgecolor', kwargs.pop('ec'))\n", + " if 'ls' in kwargs: kwargs.setdefault('linestyle', kwargs.pop('ls'))\n", + " if 'lw' in kwargs: kwargs.setdefault('linewidth', kwargs.pop('lw'))\n", + "\n", + " patches = [Circle((x_, y_), s_) for x_, y_, s_ in np.broadcast(x, y, s)]\n", + " collection = PatchCollection(patches, **kwargs)\n", + " if c is not None:\n", + " collection.set_array(np.asarray(c))\n", + " collection.set_clim(vmin, vmax)\n", + "\n", + " ax = plt.gca()\n", + " ax.add_collection(collection)\n", + " ax.autoscale_view()\n", + " if c is not None:\n", + " plt.sci(collection)\n", + " return collection" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#filename = 'middelgrunden.yml'\n", + "#filename = 'lillgrund.yml'\n", + "#filename = 'hornsrev.yml'\n", + "\n", + "#filename = 'test_WF.yml'\n", + "#filename = 'test_WF_4.yml'\n", + "filename = 'test_WF_4Turbines.yml'\n", + "\n", + "#wtl = WTLayout(filename)\n", + "wf = WindFarm(yml=filename)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "[True, True, True]\n", + "True\n", + "[True, True, True]\n", + "True\n", + "True\n", + "True\n", + "True\n" + ] + } + ], + "source": [ + "# Fixed parameters\n", + "a1 = 0.435449861\n", + "a2 = 0.797853685\n", + "a3 = -0.124807893\n", + "a4 = 0.136821858\n", + "b1 = 15.6298\n", + "b2 = 1.0\n", + "\n", + "# Variables\n", + "D = 80.0\n", + "CT = 0.98\n", + "TI = 0.10\n", + "\n", + "\n", + "print np.allclose(fgcl.get_r96(D, CT, TI, a1, a2, a3, a4, b1, b2),\n", + " pygcl.get_r96(D, CT, TI, pars=[a1, a2, a3, a4, b1, b2]))\n", + "\n", + "R = D/2.\n", + "x=4.*D\n", + "r=0.*R\n", + "\n", + "print [np.allclose(fgcl.get_rw(x, D, CT, TI, a1, a2, a3, a4, b1, b2)[i],\n", + " pygcl.get_Rw(x, R, TI, CT, pars=[a1, a2, a3, a4, b1, b2])[i]) for i in range(3)]\n", + "\n", + "print np.allclose(fgcl.get_du(x,r,D,CT, TI, a1, a2, a3, a4, b1, b2),\n", + " pygcl.get_dU(x,r,R, CT, TI, pars=[a1, a2, a3, a4, b1, b2]))\n", + "\n", + "R = D/2.\n", + "x=D*np.linspace(0.,10.,100)\n", + "r=0.*R*np.ones_like(x)\n", + "\n", + "print [np.allclose(fgcl.get_rw(x, D, CT, TI, a1, a2, a3, a4, b1, b2)[i],\n", + " pygcl.get_Rw(x, R, TI, CT, pars=[a1, a2, a3, a4, b1, b2])[i]) for i in range(3)]\n", + "\n", + "print np.allclose(fgcl.get_du(x,r,D,CT, TI, a1, a2, a3, a4, b1, b2),\n", + " pygcl.get_dU(x,r,R, CT, TI, pars=[a1, a2, a3, a4, b1, b2]))\n", + "\n", + "\n", + "R = D/2.\n", + "x=D*np.linspace(0.,10.,100)\n", + "r=D*np.linspace(0.,2.,100) \n", + "\n", + "print np.allclose(fgcl.get_du(x,r,D,CT, TI, a1, a2, a3, a4, b1, b2),\n", + " pygcl.get_dU(x,r,R, CT, TI, pars=[a1, a2, a3, a4, b1, b2]))\n", + "\n", + "\n", + "dx = 7.*D\n", + "dy = 6.*D\n", + "dz = -2.*D\n", + "# Wake operating turbine\n", + "RT = 2.*R\n", + "DT = 2.*RT\n", + "\n", + "print np.allclose(fgcl.get_dueq(dx,dy,dz,DT,D,CT,TI,a1,a2,a3,a4,b1,b2),\n", + " pygcl.get_dUeq(dx,dy,dz,RT,R,CT,TI,pars=[a1, a2, a3, a4, b1, b2]))\n", + "\n", + "\n", + "dx = np.array([6.,10.,15.])*D\n", + "dy = np.array([1.,-5.,0.])*D\n", + "dz = np.array([-2,2.,1.])*D\n", + "\n", + "# Wake operating turbines\n", + "Rop = np.array([1.,2.,.5])*D \n", + "Dop = 2.*Rop\n", + "\n", + "print np.allclose(fgcl.get_dueq(dx,dy,dz,Dop,D,CT,TI,a1,a2,a3,a4,b1,b2),\n", + " pygcl.get_dUeq(dx,dy,dz,Rop,R,CT,TI,pars=[a1, a2, a3, a4, b1, b2]))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "gcl = GCL(WF=wf)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "271.0\n" + ] + } + ], + "source": [ + "# Inputs\n", + "WS=10.0*np.ones([wf.nWT])\n", + "WD=271.*np.ones([wf.nWT])#np.random.normal(270.,30.)*np.ones([wf.nWT])#\n", + "TI=0.1*np.ones([wf.nWT])\n", + "\n", + "print np.mean(WD)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-50, 1000)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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phcuBE8Y48qNn/i7h/T648vK0P8Y2sok8bvvevbz/STGr/r2NveUVtM7NpeDw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\nAuPMLM/MWgFTgR71nPn7wLP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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=[6,5])\n", + "ax = fig.add_subplot(111)\n", + "circles(x=wf.xyz[0,:],\n", + " y=wf.xyz[1,:],\n", + " s=np.array(wf.R),\n", + " c=WS, \n", + " cmap=plt.cm.viridis,lw=0,\n", + " )\n", + "plt.colorbar()\n", + "for i in range(wf.nWT):\n", + " ax.annotate(str(wf.__getattr__('name')[i][-2:]).zfill(2),wf.xyz[[0,1],i]+0.7*np.array([wf.R[i],wf.R[i]]))\n", + " ax.arrow(x=wf.xyz[0,i],\n", + " y=wf.xyz[1,i],\n", + " dx=0.5*wf.R[1]*WS[i]*np.cos(np.deg2rad(270-WD[i])),\n", + " dy=0.5*wf.R[1]*WS[i]*np.sin(np.deg2rad(270-WD[i])),\n", + " head_length=2*wf.R[1],head_width=wf.R[1],\n", + " length_includes_head=True,\n", + " fc='k',ec='k',\n", + " )\n", + "ax.axis('equal')\n", + "ax.set_xlim([-50, 1000])\n", + "#ax.set_ylim([-400, 400])\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "fort_gclm_s : 4768476.6996 \n", + "\n", + "py_gcl_v0 : 4768476.69851 \n", + "\n", + "True\n", + "True \n", + "\n", + "py_gcl_v1 : 4768476.69851 \n", + "\n", + "True\n", + "True \n", + "\n" + ] + } + ], + "source": [ + "gcl = GCL(WF=wf,WS=WS, WD=WD, TI=TI)\n", + "# Run the models\n", + "out_fort_gclm_s=gcl(version='fort_gclm_s')\n", + "#print gcl._get_kwargs(version='fort_gclm_s'),'\\n'\n", + "print gcl.version, ':', out_fort_gclm_s.p_wt.sum(),'\\n'\n", + "\n", + "# Run the models\n", + "out_py_gcl_v0=gcl(version='py_gcl_v0')\n", + "#print gcl._get_kwargs(version='py_gcl_v0'),'\\n'\n", + "print gcl.version, ':', out_py_gcl_v0.p_wt.sum(),'\\n'\n", + "\n", + "print np.allclose(out_fort_gclm_s.p_wt,out_py_gcl_v0.p_wt)\n", + "print np.allclose(out_fort_gclm_s.u_wt,out_py_gcl_v0.u_wt),'\\n'\n", + "\n", + "# Run the models\n", + "out_py_gcl_v1=gcl(version='py_gcl_v1')\n", + "#print gcl._get_kwargs(version='py_gcl_v1'),'\\n'\n", + "print gcl.version, ':', out_py_gcl_v1.p_wt.sum(),'\\n'\n", + "print np.allclose(out_fort_gclm_s.p_wt,out_py_gcl_v1.p_wt)\n", + "print np.allclose(out_fort_gclm_s.u_wt,out_py_gcl_v1.u_wt),'\\n'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-100, 1000)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "varout=out_py_gcl_v0.u_wt#p_wt/1e6\n", + " \n", + "fig = plt.figure(figsize=[6,5])\n", + "ax = fig.add_subplot(111)\n", + "circles(x=wf.xyz[0,:],\n", + " y=wf.xyz[1,:],\n", + " s=np.array(wf.R),\n", + " c=varout,\n", + " cmap=plt.cm.viridis,lw=0,\n", + " )\n", + "plt.colorbar()\n", + "for i in range(wf.nWT):\n", + " ax.annotate(str(wf.__getattr__('name')[i][-2:]).zfill(2),wf.xyz[[0,1],i]+0.7*np.array([wf.R[i],wf.R[i]]))\n", + "ax.axis('equal')\n", + "ax.set_xlim([-100, 1000])\n", + "#ax.set_ylim([-500, 500])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "4984990.3598591276" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Inputs\n", + "WS=10.0*np.ones([wf.nWT])+np.random.normal(loc=0.0, scale=0.25, size=[wf.nWT])\n", + "WD=285*np.ones([wf.nWT])+np.random.normal(loc=0.0, scale=3., size=[wf.nWT])\n", + "TI=0.1*np.ones([wf.nWT])+np.random.normal(loc=0.0, scale=0.02, size=[wf.nWT])\n", + "version = 'py_gcl_v0' #'fort_gclm_s' # \n", + "\n", + "# Run the models\n", + "results=gcl(WS=WS, WD=WD, TI=TI, version=version)\n", + "results.p_wt.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-50, 1000)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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426/c86ra29nmhtvUmFnSubL1Szfz5p9m8dmsL/CUHT/GHp8Yz8U3nseND11D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\npaWleL1eEhMTSUpKolGjRiQ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=[6,5])\n", + "ax = fig.add_subplot(111)\n", + "circles(x=wf.xyz[0,:],\n", + " y=wf.xyz[1,:],\n", + " s=np.array(wf.R),\n", + " c=WS,\n", + " cmap=plt.cm.viridis,lw=0,\n", + " )\n", + "plt.colorbar()\n", + "for i in range(wf.nWT):\n", + " ax.annotate(str(wf.__getattr__('name')[i][-2:]).zfill(2),wf.xyz[[0,1],i]+0.7*np.array([wf.R[i],wf.R[i]]))\n", + " ax.arrow(x=wf.xyz[0,i],\n", + " y=wf.xyz[1,i],\n", + " dx=0.5*wf.R[1]*WS[i]*np.cos(np.deg2rad(270-WD[i])),\n", + " dy=0.5*wf.R[1]*WS[i]*np.sin(np.deg2rad(270-WD[i])),\n", + " head_length=2*wf.R[1],head_width=wf.R[1],\n", + " length_includes_head=True,\n", + " fc='k',ec='k',\n", + " )\n", + "ax.axis('equal')\n", + "ax.set_xlim([-50, 1000])\n", + "#ax.set_ylim([-400, 400])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-100, 1000)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "varout=results.u_wt#p_wt/1e6\n", + "\n", + "fig = plt.figure(figsize=[6,5])\n", + "ax = fig.add_subplot(111)\n", + "circles(x=wf.xyz[0,:],\n", + " y=wf.xyz[1,:],\n", + " s=np.array(wf.R),\n", + " c=varout,\n", + " cmap=plt.cm.viridis,lw=0,\n", + " )\n", + "plt.colorbar()\n", + "for i in range(wf.nWT):\n", + " ax.annotate(str(wf.__getattr__('name')[i][-2:]).zfill(2),wf.xyz[[0,1],i]+0.7*np.array([wf.R[i],wf.R[i]]))\n", + "ax.axis('equal')\n", + "ax.set_xlim([-100, 1000])\n", + "#ax.set_ylim([-500, 500])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "WD = (np.arange(-50,50)+270.)#np.linspace(-50,50,200)+270\n", + "WS = 10." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "P_rat_py_v0 = []\n", + "P_rat_py_v1 = []\n", + "P_rat_fgcl = []\n", + "P_rat_fgclm_s = []\n", + "P_rat_fgclm = []\n", + "P_rat_fgclm_rdn = []\n", + "for wd in WD: \n", + " out = gcl(WF=wf, WS=WS, WD=wd, TI=0.1, version='fort_gcl')\n", + " P_rat_fgcl = np.append(P_rat_fgcl,out.p_wt[1]/out.p_wt[0])\n", + " \n", + " out = gcl(WF=wf, WS=WS*np.ones([wf.nWT]), WD=wd*np.ones([wf.nWT]), TI=0.1*np.ones([wf.nWT]), version='fort_gclm_s')\n", + " P_rat_fgclm_s = np.append(P_rat_fgclm_s,out.p_wt[1]/out.p_wt[0])\n", + " \n", + " #out = gcl(WF=wf, WS=WS*np.ones([wf.nWT]), WD=wd+np.random.normal(loc=0.0, scale=2, size=[wf.nWT]), \n", + " # TI=0.1*np.ones([wf.nWT]), version='fort_gclm_s')\n", + " #P_rat_fgclm_rdn = np.append(P_rat_fgclm_rdn,out.p_wt[1]/out.p_wt[0])\n", + " \n", + " out = gcl(WS=WS*np.ones([wf.nWT]), WD=wd*np.ones([wf.nWT]), TI=0.1*np.ones([wf.nWT]), version='py_gcl_v1')\n", + " P_rat_py_v1 = np.append(P_rat_py_v1,out.p_wt[1]/out.p_wt[0])\n", + "\n", + " out = gcl(WF=wf, WS=WS*np.ones([wf.nWT]), WD=wd*np.ones([wf.nWT]), TI=0.1*np.ones([wf.nWT]), version='py_gcl_v0')\n", + " P_rat_py_v0 = np.append(P_rat_py_v0,out.p_wt[1]/out.p_wt[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "WDm = WD.reshape([-1,1])*np.ones([1,wf.nWT])\n", + "out = gcl(WF=wf, WS=WS*np.ones_like(WDm), WD=WDm, TI=0.1*np.ones_like(WDm), version='fort_gclm')\n", + "P_rat_fgclm = out.p_wt[:,1]/out.p_wt[:,0]\n", + "\n", + "out = gcl(WF=wf, WS=WS*np.ones_like(WDm), WD=WDm, TI=0.1*np.ones_like(WDm), version='fort_gclm_av')\n", + "P_rat_fgclm_av = out.p_wt[:,1]/out.p_wt[:,0]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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w8Elyl0fZLZfgXK0ebof3s6XiPEYPL42NvW2W55ALjRDayu6m8srpS7Q6sIC611OY37kb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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=[15,5])\n", + "ax = fig.add_subplot(111)\n", + "\n", + "plt.plot(WD,P_rat_py_v1,'-sk',ms=9,mfc='w',label ='py_gcl_v1')\n", + "#plt.plot(-WD,P_rat_py_v1,'x--',label ='py_gcl_v1_inv')\n", + "\n", + "plt.plot(WD,P_rat_py_v0,'-define('plotly', function(require, exports, module) {/**\n", - "* plotly.js v1.8.0\n", + "" + "!function(t){if(\"object\"==typeof exports&&\"undefined\"!=typeof module)module.exports=t();else if(\"function\"==typeof define&&define.amd)define([],t);else{var e;e=\"undefined\"!=typeof window?window:\"undefined\"!=typeof global?global:\"undefined\"!=typeof self?self:this,e.Plotly=t()}}(function(){var t;return function e(t,r,n){function i(o,s){if(!r[o]){if(!t[o]){var l=\"function\"==typeof require&&require;if(!s&&l)return l(o,!0);if(a)return a(o,!0);var c=new Error(\"Cannot find module '\"+o+\"'\");throw c.code=\"MODULE_NOT_FOUND\",c}var u=r[o]={exports:{}};t[o][0].call(u.exports,function(e){var r=t[o][1][e];return i(r?r:e)},u,u.exports,e,t,r,n)}return r[o].exports}for(var a=\"function\"==typeof require&&require,o=0;or;++r)e[r]=0;return e}function o(t,e,r){switch(arguments.length){case 0:return new i([0],[0],0);case 1:if(\"number\"==typeof t){var n=a(t);return new i(n,n,0)}return new i(t,a(t.length),0);case 2:if(\"number\"==typeof e){var n=a(t.length);return new i(t,n,+e)}r=0;case 3:if(t.length!==e.length)throw new Error(\"state and velocity lengths must match\");return new i(t,e,r)}}e.exports=o;var s=t(\"cubic-hermite\"),l=t(\"binary-search-bounds\"),c=i.prototype;c.flush=function(t){var e=l.gt(this._time,t)-1;0>=e||(this._time.splice(0,e),this._state.splice(0,e*this.dimension),this._velocity.splice(0,e*this.dimension))},c.curve=function(t){var e=this._time,r=e.length,i=l.le(e,t),a=this._scratch[0],o=this._state,c=this._velocity,u=this.dimension,f=this.bounds;if(0>i)for(var h=u-1,d=0;u>d;++d,--h)a[d]=o[h];else if(i>=r-1)for(var h=o.length-1,p=t-e[r-1],d=0;u>d;++d,--h)a[d]=o[h]+p*c[h];else{for(var h=u*(i+1)-1,g=e[i],v=e[i+1],m=v-g||1,y=this._scratch[1],b=this._scratch[2],x=this._scratch[3],_=this._scratch[4],w=!0,d=0;u>d;++d,--h)y[d]=o[h],x[d]=c[h]*m,b[d]=o[h+u],_[d]=c[h+u]*m,w=w&&y[d]===b[d]&&x[d]===_[d]&&0===x[d];if(w)for(var d=0;u>d;++d)a[d]=y[d];else s(y,x,b,_,(t-g)/m,a)}for(var k=f[0],A=f[1],d=0;u>d;++d)a[d]=n(k[d],A[d],a[d]);return a},c.dcurve=function(t){var e=this._time,r=e.length,n=l.le(e,t),i=this._scratch[0],a=this._state,o=this._velocity,c=this.dimension;if(n>=r-1)for(var u=a.length-1,f=(t-e[r-1],0);c>f;++f,--u)i[f]=o[u];else{for(var u=c*(n+1)-1,h=e[n],d=e[n+1],p=d-h||1,g=this._scratch[1],v=this._scratch[2],m=this._scratch[3],y=this._scratch[4],b=!0,f=0;c>f;++f,--u)g[f]=a[u],m[f]=o[u]*p,v[f]=a[u+c],y[f]=o[u+c]*p,b=b&&g[f]===v[f]&&m[f]===y[f]&&0===m[f];if(b)for(var f=0;c>f;++f)i[f]=0;else{s.derivative(g,m,v,y,(t-h)/p,i);for(var f=0;c>f;++f)i[f]/=p}}return i},c.lastT=function(){var t=this._time;return t[t.length-1]},c.stable=function(){for(var t=this._velocity,e=t.length,r=this.dimension-1;r>=0;--r)if(t[--e])return!1;return!0},c.jump=function(t){var e=this.lastT(),r=this.dimension;if(!(e>t||arguments.length!==r+1)){var i=this._state,a=this._velocity,o=i.length-this.dimension,s=this.bounds,l=s[0],c=s[1];this._time.push(e,t);for(var u=0;2>u;++u)for(var f=0;r>f;++f)i.push(i[o++]),a.push(0);this._time.push(t);for(var f=r;f>0;--f)i.push(n(l[f-1],c[f-1],arguments[f])),a.push(0)}},c.push=function(t){var e=this.lastT(),r=this.dimension;if(!(e>t||arguments.length!==r+1)){var i=this._state,a=this._velocity,o=i.length-this.dimension,s=t-e,l=this.bounds,c=l[0],u=l[1],f=s>1e-6?1/s:0;this._time.push(t);for(var h=r;h>0;--h){var d=n(c[h-1],u[h-1],arguments[h]);i.push(d),a.push((d-i[o++])*f)}}},c.set=function(t){var e=this.dimension;if(!(t0;--l)r.push(n(o[l-1],s[l-1],arguments[l])),i.push(0)}},c.move=function(t){var 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r=this.computedMatrix;if(e){for(var n=0;16>n;++n)e[n]=r[n];return e}return r},d.idle=function(t){this.center.idle(t),this.radius.idle(t),this.rotation.idle(t)},d.flush=function(t){this.center.flush(t),this.radius.flush(t),this.rotation.flush(t)},d.pan=function(t,e,r,i){e=e||0,r=r||0,i=i||0,this.recalcMatrix(t);var a=this.computedMatrix,o=a[1],s=a[5],l=a[9],c=n(o,s,l);o/=c,s/=c,l/=c;var u=a[0],f=a[4],h=a[8],d=u*o+f*s+h*l;u-=o*d,f-=s*d,h-=l*d;var p=n(u,f,h);u/=p,f/=p,h/=p;var g=a[2],v=a[6],m=a[10],y=g*o+v*s+m*l,b=g*u+v*f+m*h;g-=y*o+b*u,v-=y*s+b*f,m-=y*l+b*h;var x=n(g,v,m);g/=x,v/=x,m/=x;var _=u*e+o*r,w=f*e+s*r,k=h*e+l*r;this.center.move(t,_,w,k);var A=Math.exp(this.computedRadius[0]);A=Math.max(1e-4,A+i),this.radius.set(t,Math.log(A))},d.rotate=function(t,e,r,a){this.recalcMatrix(t),e=e||0,r=r||0;var o=this.computedMatrix,s=o[0],l=o[4],c=o[8],u=o[1],f=o[5],h=o[9],d=o[2],p=o[6],g=o[10],v=e*s+r*u,m=e*l+r*f,y=e*c+r*h,b=-(p*y-g*m),x=-(g*v-d*y),_=-(d*m-p*v),w=Math.sqrt(Math.max(0,1-Math.pow(b,2)-Math.pow(x,2)-Math.pow(_,2))),k=i(b,x,_,w);k>1e-6?(b/=k,x/=k,_/=k,w/=k):(b=x=_=0,w=1);var A=this.computedRotation,M=A[0],T=A[1],E=A[2],L=A[3],S=M*w+L*b+T*_-E*x,C=T*w+L*x+E*b-M*_,z=E*w+L*_+M*x-T*b,P=L*w-M*b-T*x-E*_;if(a){b=d,x=p,_=g;var R=Math.sin(a)/n(b,x,_);b*=R,x*=R,_*=R,w=Math.cos(e),S=S*w+P*b+C*_-z*x,C=C*w+P*x+z*b-S*_,z=z*w+P*_+S*x-C*b,P=P*w-S*b-C*x-z*_}var O=i(S,C,z,P);O>1e-6?(S/=O,C/=O,z/=O,P/=O):(S=C=z=0,P=1),this.rotation.set(t,S,C,z,P)},d.lookAt=function(t,e,r,n){this.recalcMatrix(t),r=r||this.computedCenter,e=e||this.computedEye,n=n||this.computedUp;var i=this.computedMatrix;c(i,e,r,n);var o=this.computedRotation;h(o,i[0],i[1],i[2],i[4],i[5],i[6],i[8],i[9],i[10]),a(o,o),this.rotation.set(t,o[0],o[1],o[2],o[3]);for(var s=0,l=0;3>l;++l)s+=Math.pow(r[l]-e[l],2);this.radius.set(t,.5*Math.log(Math.max(s,1e-6))),this.center.set(t,r[0],r[1],r[2])},d.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},d.setMatrix=function(t,e){var r=this.computedRotation;h(r,e[0],e[1],e[2],e[4],e[5],e[6],e[8],e[9],e[10]),a(r,r),this.rotation.set(t,r[0],r[1],r[2],r[3]);var n=this.computedMatrix;f(n,e);var i=n[15];if(Math.abs(i)>1e-6){var o=n[12]/i,s=n[13]/i,l=n[14]/i;this.recalcMatrix(t);var c=Math.exp(this.computedRadius[0]);this.center.set(t,o-n[2]*c,s-n[6]*c,l-n[10]*c),this.radius.idle(t)}else this.center.idle(t),this.radius.idle(t)},d.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},d.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-(1/0),e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},d.getDistanceLimits=function(t){var e=this.radius.bounds;\n", + "return t?(t[0]=Math.exp(e[0][0]),t[1]=Math.exp(e[1][0]),t):[Math.exp(e[0][0]),Math.exp(e[1][0])]},d.toJSON=function(){return this.recalcMatrix(this.lastT()),{center:this.computedCenter.slice(),rotation:this.computedRotation.slice(),distance:Math.log(this.computedRadius[0]),zoomMin:this.radius.bounds[0][0],zoomMax:this.radius.bounds[1][0]}},d.fromJSON=function(t){var e=this.lastT(),r=t.center;r&&this.center.set(e,r[0],r[1],r[2]);var n=t.rotation;n&&this.rotation.set(e,n[0],n[1],n[2],n[3]);var i=t.distance;i&&i>0&&this.radius.set(e,Math.log(i)),this.setDistanceLimits(t.zoomMin,t.zoomMax)}},{\"./lib/quatFromFrame\":36,\"filtered-vector\":20,\"gl-mat4/fromQuat\":134,\"gl-mat4/invert\":137,\"gl-mat4/lookAt\":138}],38:[function(t,e,r){\"use strict\";function n(t,e,r){return Math.sqrt(Math.pow(t,2)+Math.pow(e,2)+Math.pow(r,2))}function i(t){return Math.min(1,Math.max(-1,t))}function a(t){var e=Math.abs(t[0]),r=Math.abs(t[1]),n=Math.abs(t[2]),i=[0,0,0];e>Math.max(r,n)?i[2]=1:r>Math.max(e,n)?i[0]=1:i[1]=1;for(var a=0,o=0,s=0;3>s;++s)a+=t[s]*t[s],o+=i[s]*t[s];for(var s=0;3>s;++s)i[s]-=o/a*t[s];return h(i,i),i}function o(t,e,r,n,i,a,o,s){this.center=l(r),this.up=l(n),this.right=l(i),this.radius=l([a]),this.angle=l([o,s]),this.angle.bounds=[[-(1/0),-Math.PI/2],[1/0,Math.PI/2]],this.setDistanceLimits(t,e),this.computedCenter=this.center.curve(0),this.computedUp=this.up.curve(0),this.computedRight=this.right.curve(0),this.computedRadius=this.radius.curve(0),this.computedAngle=this.angle.curve(0),this.computedToward=[0,0,0],this.computedEye=[0,0,0],this.computedMatrix=new Array(16);for(var c=0;16>c;++c)this.computedMatrix[c]=.5;this.recalcMatrix(0)}function s(t){t=t||{};var e=t.center||[0,0,0],r=t.up||[0,1,0],i=t.right||a(r),s=t.radius||1,l=t.theta||0,c=t.phi||0;if(e=[].slice.call(e,0,3),r=[].slice.call(r,0,3),h(r,r),i=[].slice.call(i,0,3),h(i,i),\"eye\"in t){var u=t.eye,p=[u[0]-e[0],u[1]-e[1],u[2]-e[2]];f(i,p,r),n(i[0],i[1],i[2])<1e-6?i=a(r):h(i,i),s=n(p[0],p[1],p[2]);var g=d(r,p)/s,v=d(i,p)/s;c=Math.acos(g),l=Math.acos(v)}return s=Math.log(s),new o(t.zoomMin,t.zoomMax,e,r,i,s,l,c)}e.exports=s;var l=t(\"filtered-vector\"),c=t(\"gl-mat4/invert\"),u=t(\"gl-mat4/rotate\"),f=t(\"gl-vec3/cross\"),h=t(\"gl-vec3/normalize\"),d=t(\"gl-vec3/dot\"),p=o.prototype;p.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-(1/0),e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},p.getDistanceLimits=function(t){var e=this.radius.bounds[0];return t?(t[0]=Math.exp(e[0][0]),t[1]=Math.exp(e[1][0]),t):[Math.exp(e[0][0]),Math.exp(e[1][0])]},p.recalcMatrix=function(t){this.center.curve(t),this.up.curve(t),this.right.curve(t),this.radius.curve(t),this.angle.curve(t);for(var e=this.computedUp,r=this.computedRight,i=0,a=0,o=0;3>o;++o)a+=e[o]*r[o],i+=e[o]*e[o];for(var s=Math.sqrt(i),l=0,o=0;3>o;++o)r[o]-=e[o]*a/i,l+=r[o]*r[o],e[o]/=s;for(var c=Math.sqrt(l),o=0;3>o;++o)r[o]/=c;var u=this.computedToward;f(u,e,r),h(u,u);for(var d=Math.exp(this.computedRadius[0]),p=this.computedAngle[0],g=this.computedAngle[1],v=Math.cos(p),m=Math.sin(p),y=Math.cos(g),b=Math.sin(g),x=this.computedCenter,_=v*y,w=m*y,k=b,A=-v*b,M=-m*b,T=y,E=this.computedEye,L=this.computedMatrix,o=0;3>o;++o){var S=_*r[o]+w*u[o]+k*e[o];L[4*o+1]=A*r[o]+M*u[o]+T*e[o],L[4*o+2]=S,L[4*o+3]=0}var C=L[1],z=L[5],P=L[9],R=L[2],O=L[6],I=L[10],N=z*I-P*O,j=P*R-C*I,F=C*O-z*R,D=n(N,j,F);N/=D,j/=D,F/=D,L[0]=N,L[4]=j,L[8]=F;for(var o=0;3>o;++o)E[o]=x[o]+L[2+4*o]*d;for(var o=0;3>o;++o){for(var l=0,B=0;3>B;++B)l+=L[o+4*B]*E[B];L[12+o]=-l}L[15]=1},p.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;16>n;++n)e[n]=r[n];return e}return r};var g=[0,0,0];p.rotate=function(t,e,r,n){if(this.angle.move(t,e,r),n){this.recalcMatrix(t);var i=this.computedMatrix;g[0]=i[2],g[1]=i[6],g[2]=i[10];for(var a=this.computedUp,o=this.computedRight,s=this.computedToward,l=0;3>l;++l)i[4*l]=a[l],i[4*l+1]=o[l],i[4*l+2]=s[l];u(i,i,n,g);for(var l=0;3>l;++l)a[l]=i[4*l],o[l]=i[4*l+1];this.up.set(t,a[0],a[1],a[2]),this.right.set(t,o[0],o[1],o[2])}},p.pan=function(t,e,r,i){e=e||0,r=r||0,i=i||0,this.recalcMatrix(t);var a=this.computedMatrix,o=(Math.exp(this.computedRadius[0]),a[1]),s=a[5],l=a[9],c=n(o,s,l);o/=c,s/=c,l/=c;var u=a[0],f=a[4],h=a[8],d=u*o+f*s+h*l;u-=o*d,f-=s*d,h-=l*d;var p=n(u,f,h);u/=p,f/=p,h/=p;var g=u*e+o*r,v=f*e+s*r,m=h*e+l*r;this.center.move(t,g,v,m);var y=Math.exp(this.computedRadius[0]);y=Math.max(1e-4,y+i),this.radius.set(t,Math.log(y))},p.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},p.setMatrix=function(t,e,r,a){var o=1;\"number\"==typeof r&&(o=0|r),(0>o||o>3)&&(o=1);var s=(o+2)%3;e||(this.recalcMatrix(t),e=this.computedMatrix);var l=e[o],u=e[o+4],f=e[o+8];if(a){var h=Math.abs(l),d=Math.abs(u),p=Math.abs(f),g=Math.max(h,d,p);h===g?(l=0>l?-1:1,u=f=0):p===g?(f=0>f?-1:1,l=u=0):(u=0>u?-1:1,l=f=0)}else{var v=n(l,u,f);l/=v,u/=v,f/=v}var m=e[s],y=e[s+4],b=e[s+8],x=m*l+y*u+b*f;m-=l*x,y-=u*x,b-=f*x;var _=n(m,y,b);m/=_,y/=_,b/=_;var w=u*b-f*y,k=f*m-l*b,A=l*y-u*m,M=n(w,k,A);w/=M,k/=M,A/=M,this.center.jump(t,H,G,Y),this.radius.idle(t),this.up.jump(t,l,u,f),this.right.jump(t,m,y,b);var T,E;if(2===o){var L=e[1],S=e[5],C=e[9],z=L*m+S*y+C*b,P=L*w+S*k+C*A;T=0>N?-Math.PI/2:Math.PI/2,E=Math.atan2(P,z)}else{var R=e[2],O=e[6],I=e[10],N=R*l+O*u+I*f,j=R*m+O*y+I*b,F=R*w+O*k+I*A;T=Math.asin(i(N)),E=Math.atan2(F,j)}this.angle.jump(t,E,T),this.recalcMatrix(t);var D=e[2],B=e[6],U=e[10],V=this.computedMatrix;c(V,e);var q=V[15],H=V[12]/q,G=V[13]/q,Y=V[14]/q,X=Math.exp(this.computedRadius[0]);this.center.jump(t,H-D*X,G-B*X,Y-U*X)},p.lastT=function(){return Math.max(this.center.lastT(),this.up.lastT(),this.right.lastT(),this.radius.lastT(),this.angle.lastT())},p.idle=function(t){this.center.idle(t),this.up.idle(t),this.right.idle(t),this.radius.idle(t),this.angle.idle(t)},p.flush=function(t){this.center.flush(t),this.up.flush(t),this.right.flush(t),this.radius.flush(t),this.angle.flush(t)},p.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},p.lookAt=function(t,e,r,a){this.recalcMatrix(t),e=e||this.computedEye,r=r||this.computedCenter,a=a||this.computedUp;var o=a[0],s=a[1],l=a[2],c=n(o,s,l);if(!(1e-6>c)){o/=c,s/=c,l/=c;var u=e[0]-r[0],f=e[1]-r[1],h=e[2]-r[2],d=n(u,f,h);if(!(1e-6>d)){u/=d,f/=d,h/=d;var p=this.computedRight,g=p[0],v=p[1],m=p[2],y=o*g+s*v+l*m;g-=y*o,v-=y*s,m-=y*l;var b=n(g,v,m);if(!(.01>b&&(g=s*h-l*f,v=l*u-o*h,m=o*f-s*u,b=n(g,v,m),1e-6>b))){g/=b,v/=b,m/=b,this.up.set(t,o,s,l),this.right.set(t,g,v,m),this.center.set(t,r[0],r[1],r[2]),this.radius.set(t,Math.log(d));var x=s*m-l*v,_=l*g-o*m,w=o*v-s*g,k=n(x,_,w);x/=k,_/=k,w/=k;var A=o*u+s*f+l*h,M=g*u+v*f+m*h,T=x*u+_*f+w*h,E=Math.asin(i(A)),L=Math.atan2(T,M),S=this.angle._state,C=S[S.length-1],z=S[S.length-2];C%=2*Math.PI;var P=Math.abs(C+2*Math.PI-L),R=Math.abs(C-L),O=Math.abs(C-2*Math.PI-L);R>P&&(C+=2*Math.PI),R>O&&(C-=2*Math.PI),this.angle.jump(this.angle.lastT(),C,z),this.angle.set(t,L,E)}}}}},{\"filtered-vector\":20,\"gl-mat4/invert\":137,\"gl-mat4/rotate\":141,\"gl-vec3/cross\":23,\"gl-vec3/dot\":24,\"gl-vec3/normalize\":27}],39:[function(t,e,r){\"use strict\";function n(t,e){this._controllerNames=Object.keys(t),this._controllerList=this._controllerNames.map(function(e){return t[e]}),this._mode=e,this._active=t[e],this._active||(this._mode=\"turntable\",this._active=t.turntable),this.modes=this._controllerNames,this.computedMatrix=this._active.computedMatrix,this.computedEye=this._active.computedEye,this.computedUp=this._active.computedUp,this.computedCenter=this._active.computedCenter,this.computedRadius=this._active.computedRadius}function i(t){t=t||{};var e=t.eye||[0,0,1],r=t.center||[0,0,0],i=t.up||[0,1,0],l=t.distanceLimits||[0,1/0],c=t.mode||\"turntable\",u=a(),f=o(),h=s();return u.setDistanceLimits(l[0],l[1]),u.lookAt(0,e,r,i),f.setDistanceLimits(l[0],l[1]),f.lookAt(0,e,r,i),h.setDistanceLimits(l[0],l[1]),h.lookAt(0,e,r,i),new n({turntable:u,orbit:f,matrix:h},c)}e.exports=i;var a=t(\"turntable-camera-controller\"),o=t(\"orbit-camera-controller\"),s=t(\"matrix-camera-controller\"),l=n.prototype,c=[[\"flush\",1],[\"idle\",1],[\"lookAt\",4],[\"rotate\",4],[\"pan\",4],[\"translate\",4],[\"setMatrix\",2],[\"setDistanceLimits\",2],[\"setDistance\",2]];c.forEach(function(t){for(var e=t[0],r=[],n=0;ne)){var r=this._active,n=this._controllerList[e],i=Math.max(r.lastT(),n.lastT());r.recalcMatrix(i),n.setMatrix(i,r.computedMatrix),this._active=n,this._mode=t,this.computedMatrix=this._active.computedMatrix,this.computedEye=this._active.computedEye,this.computedUp=this._active.computedUp,this.computedCenter=this._active.computedCenter,this.computedRadius=this._active.computedRadius}}},l.getMode=function(){return this._mode}},{\"matrix-camera-controller\":28,\"orbit-camera-controller\":37,\"turntable-camera-controller\":38}],40:[function(t,e,r){function n(t,e){return a(i(t,e))}e.exports=n;var i=t(\"alpha-complex\"),a=t(\"simplicial-complex-boundary\")},{\"alpha-complex\":41,\"simplicial-complex-boundary\":44}],41:[function(t,e,r){\"use strict\";function n(t,e){return i(e).filter(function(r){for(var n=new Array(r.length),i=0;ii;++i)r+=t[i]*e[i];return r}function i(t){var e=t.length;if(0===e)return[];var r=(t[0].length,o([t.length+1,t.length+1],1)),i=o([t.length+1],1);r[e][e]=0;for(var a=0;e>a;++a){for(var l=0;a>=l;++l)r[l][a]=r[a][l]=2*n(t[a],t[l]);i[a]=n(t[a],t[a])}for(var c=s(r,i),u=0,f=c[e+1],a=0;aa;++a){for(var f=c[a],d=0,l=0;ls;++s)r[s]+=t[a][s]*n[a];return r}var o=t(\"dup\"),s=t(\"robust-linear-solve\");a.barycenetric=i,e.exports=a},{dup:115,\"robust-linear-solve\":256}],44:[function(t,e,r){\"use strict\";function n(t){return a(i(t))}e.exports=n;var i=t(\"boundary-cells\"),a=t(\"reduce-simplicial-complex\")},{\"boundary-cells\":45,\"reduce-simplicial-complex\":48}],45:[function(t,e,r){\"use strict\";function n(t){for(var 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n=\"\",i=0,e=0|e||1,a=0,o=0;o>>24-i&16777215,n=0!==a||o!==this.length-1?p[6-l.length]+l+n:l+n,i+=2,i>=26&&(i-=26,o--)}for(0!==a&&(n=a.toString(16)+n);n.length%e!==0;)n=\"0\"+n;return this.sign&&(n=\"-\"+n),n}if(t===(0|t)&&t>=2&&36>=t){var c=g[t],u=v[t],n=\"\",f=this.clone();for(f.sign=!1;0!==f.cmpn(0);){var h=f.modn(u).toString(t);f=f.idivn(u),n=0!==f.cmpn(0)?p[c-h.length]+h+n:h+n}return 0===this.cmpn(0)&&(n=\"0\"+n),this.sign&&(n=\"-\"+n),n}r(!1,\"Base should be between 2 and 36\")},i.prototype.toJSON=function(){return this.toString(16)},i.prototype.toArray=function(t){this.strip();var e=new Array(this.byteLength());e[0]=0;var r=this.clone();if(\"le\"!==t)for(var n=0;0!==r.cmpn(0);n++){var i=r.andln(255);r.ishrn(8),e[e.length-n-1]=i}else for(var n=0;0!==r.cmpn(0);n++){var i=r.andln(255);r.ishrn(8),e[n]=i}return e},Math.clz32?i.prototype._countBits=function(t){return 32-Math.clz32(t)}:i.prototype._countBits=function(t){var e=t,r=0;return e>=4096&&(r+=13,e>>>=13),e>=64&&(r+=7,e>>>=7),e>=8&&(r+=4,e>>>=4),e>=2&&(r+=2,e>>>=2),r+e},i.prototype._zeroBits=function(t){if(0===t)return 26;var e=t,r=0;return 0===(8191&e)&&(r+=13,e>>>=13),0===(127&e)&&(r+=7,e>>>=7),0===(15&e)&&(r+=4,e>>>=4),0===(3&e)&&(r+=2,e>>>=2),0===(1&e)&&r++,r},i.prototype.bitLength=function(){var t=0,e=this.words[this.length-1],t=this._countBits(e);return 26*(this.length-1)+t},i.prototype.zeroBits=function(){if(0===this.cmpn(0))return 0;for(var t=0,e=0;et.length?this.clone().ior(t):t.clone().ior(this)},i.prototype.iand=function(t){this.sign=this.sign&&t.sign;var e;e=this.length>t.length?t:this;for(var r=0;rt.length?this.clone().iand(t):t.clone().iand(this)},i.prototype.ixor=function(t){this.sign=this.sign||t.sign;var e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;nt.length?this.clone().ixor(t):t.clone().ixor(this)},i.prototype.setn=function(t,e){r(\"number\"==typeof t&&t>=0);for(var n=t/26|0,i=t%26;this.length<=n;)this.words[this.length++]=0;return e?this.words[n]=this.words[n]|1<t.length?(r=this,n=t):(r=t,n=this);for(var i=0,a=0;a>>26}for(;0!==i&&a>>26}if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;at.length?this.clone().iadd(t):t.clone().iadd(this)},i.prototype.isub=function(t){if(t.sign){t.sign=!1;var e=this.iadd(t);return t.sign=!0,e._normSign()}if(this.sign)return this.sign=!1,this.iadd(t),this.sign=!0,this._normSign();var r=this.cmp(t);if(0===r)return this.sign=!1,this.length=1,this.words[0]=0,this;var n,i;r>0?(n=this,i=t):(n=t,i=this);for(var a=0,o=0;o>26,this.words[o]=67108863&e}for(;0!==a&&o>26,this.words[o]=67108863&e}if(0===a&&o>>26,a=67108863&r,o=Math.min(n,t.length-1),s=Math.max(0,n-this.length+1);o>=s;s++){var l=n-s,c=0|this.words[l],u=0|t.words[s],f=c*u,h=67108863&f;i=i+(f/67108864|0)|0,h=h+a|0,a=67108863&h,i=i+(h>>>26)|0}e.words[n]=a,r=i}return 0!==r?e.words[n]=r:e.length--,e.strip()},i.prototype._bigMulTo=function(t,e){e.sign=t.sign!==this.sign,e.length=this.length+t.length;for(var r=0,n=0,i=0;i=l;l++){var c=i-l,u=0|this.words[c],f=0|t.words[l],h=u*f,d=67108863&h;a=a+(h/67108864|0)|0,d=d+o|0,o=67108863&d,a=a+(d>>>26)|0,n+=a>>>26,a&=67108863}e.words[i]=o,r=a,a=n}return 0!==r?e.words[i]=r:e.length--,e.strip()},i.prototype.mulTo=function(t,e){var r;return r=this.length+t.length<63?this._smallMulTo(t,e):this._bigMulTo(t,e)},i.prototype.mul=function(t){var e=new i(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},i.prototype.imul=function(t){if(0===this.cmpn(0)||0===t.cmpn(0))return this.words[0]=0,this.length=1,this;var e=this.length,r=t.length;this.sign=t.sign!==this.sign,this.length=this.length+t.length,this.words[this.length-1]=0;for(var n=this.length-2;n>=0;n--){for(var i=0,a=0,o=Math.min(n,r-1),s=Math.max(0,n-e+1);o>=s;s++){var l=n-s,c=this.words[l],u=t.words[s],f=c*u,h=67108863&f;i+=f/67108864|0,h+=a,a=67108863&h,i+=h>>>26}this.words[n]=a,this.words[n+1]+=i,i=0}for(var i=0,l=1;l>>26}return this.strip()},i.prototype.imuln=function(t){r(\"number\"==typeof t);for(var e=0,n=0;n>=26,e+=i/67108864|0,e+=a>>>26,this.words[n]=67108863&a}return 0!==e&&(this.words[n]=e,this.length++),this},i.prototype.muln=function(t){return this.clone().imuln(t)},i.prototype.sqr=function(){return this.mul(this)},i.prototype.isqr=function(){return this.mul(this)},i.prototype.ishln=function(t){r(\"number\"==typeof t&&t>=0);var e=t%26,n=(t-e)/26,i=67108863>>>26-e<<26-e;if(0!==e){for(var a=0,o=0;o>>26-e}a&&(this.words[o]=a,this.length++)}if(0!==n){for(var o=this.length-1;o>=0;o--)this.words[o+n]=this.words[o];for(var o=0;n>o;o++)this.words[o]=0;this.length+=n}return this.strip()},i.prototype.ishrn=function(t,e,n){r(\"number\"==typeof t&&t>=0);var i;i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<c;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o){this.length-=o;for(var c=0;c=0&&(0!==u||c>=i);c--){var f=this.words[c];this.words[c]=u<<26-a|f>>>a,u=f&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip(),this},i.prototype.shln=function(t){return this.clone().ishln(t)},i.prototype.shrn=function(t){return this.clone().ishrn(t)},i.prototype.testn=function(t){r(\"number\"==typeof t&&t>=0);var e=t%26,n=(t-e)/26,i=1<=0);var e=t%26,n=(t-e)/26;if(r(!this.sign,\"imaskn works only with positive numbers\"),0!==e&&n++,this.length=Math.min(n,this.length),0!==e){var i=67108863^67108863>>>e<t?this.isubn(-t):this.sign?1===this.length&&this.words[0]=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},i.prototype.isubn=function(t){if(r(\"number\"==typeof t),0>t)return this.iaddn(-t);if(this.sign)return this.sign=!1,this.iaddn(t),this.sign=!0,this;this.words[0]-=t;for(var e=0;e>26)-(c/67108864|0),this.words[i+n]=67108863&l}for(;i>26,this.words[i+n]=67108863&l}if(0===s)return this.strip();r(-1===s),s=0;for(var i=0;i>26,this.words[i]=67108863&l}return this.sign=!0,this.strip()},i.prototype._wordDiv=function(t,e){var r=this.length-t.length,n=this.clone(),a=t,o=a.words[a.length-1],s=this._countBits(o);r=26-s,0!==r&&(a=a.shln(r),n.ishln(r),o=a.words[a.length-1]);var l,c=n.length-a.length;if(\"mod\"!==e){l=new i(null),l.length=c+1,l.words=new Array(l.length);for(var u=0;u=0;h--){var d=67108864*n.words[a.length+h]+n.words[a.length+h-1];for(d=Math.min(d/o|0,67108863),n._ishlnsubmul(a,d,h);n.sign;)d--,n.sign=!1,n._ishlnsubmul(a,1,h),0!==n.cmpn(0)&&(n.sign=!n.sign);l&&(l.words[h]=d)}return l&&l.strip(),n.strip(),\"div\"!==e&&0!==r&&n.ishrn(r),{div:l?l:null,mod:n}},i.prototype.divmod=function(t,e){if(r(0!==t.cmpn(0)),this.sign&&!t.sign){var n,a,o=this.neg().divmod(t,e);return\"mod\"!==e&&(n=o.div.neg()),\"div\"!==e&&(a=0===o.mod.cmpn(0)?o.mod:t.sub(o.mod)),{div:n,mod:a}}if(!this.sign&&t.sign){var n,o=this.divmod(t.neg(),e);return\"mod\"!==e&&(n=o.div.neg()),{div:n,mod:o.mod}}return this.sign&&t.sign?this.neg().divmod(t.neg(),e):t.length>this.length||this.cmp(t)<0?{div:new i(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new i(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new i(this.modn(t.words[0]))}:this._wordDiv(t,e)},i.prototype.div=function(t){return this.divmod(t,\"div\").div},i.prototype.mod=function(t){return this.divmod(t,\"mod\").mod},i.prototype.divRound=function(t){var e=this.divmod(t);if(0===e.mod.cmpn(0))return e.div;var r=e.div.sign?e.mod.isub(t):e.mod,n=t.shrn(1),i=t.andln(1),a=r.cmp(n);return 0>a||1===i&&0===a?e.div:e.div.sign?e.div.isubn(1):e.div.iaddn(1)},i.prototype.modn=function(t){r(67108863>=t);for(var e=(1<<26)%t,n=0,i=this.length-1;i>=0;i--)n=(e*n+this.words[i])%t;return n},i.prototype.idivn=function(t){r(67108863>=t);for(var e=0,n=this.length-1;n>=0;n--){var i=this.words[n]+67108864*e;this.words[n]=i/t|0,e=i%t}return this.strip()},i.prototype.divn=function(t){return this.clone().idivn(t)},i.prototype.egcd=function(t){r(!t.sign),r(0!==t.cmpn(0));var e=this,n=t.clone();e=e.sign?e.mod(t):e.clone();for(var a=new i(1),o=new i(0),s=new i(0),l=new i(1),c=0;e.isEven()&&n.isEven();)e.ishrn(1),n.ishrn(1),++c;for(var u=n.clone(),f=e.clone();0!==e.cmpn(0);){for(;e.isEven();)e.ishrn(1),a.isEven()&&o.isEven()?(a.ishrn(1),o.ishrn(1)):(a.iadd(u).ishrn(1),o.isub(f).ishrn(1));for(;n.isEven();)n.ishrn(1),s.isEven()&&l.isEven()?(s.ishrn(1),l.ishrn(1)):(s.iadd(u).ishrn(1),l.isub(f).ishrn(1));e.cmp(n)>=0?(e.isub(n),a.isub(s),o.isub(l)):(n.isub(e),s.isub(a),l.isub(o))}return{a:s,b:l,gcd:n.ishln(c)}},i.prototype._invmp=function(t){r(!t.sign),r(0!==t.cmpn(0));var e=this,n=t.clone();e=e.sign?e.mod(t):e.clone();for(var a=new i(1),o=new i(0),s=n.clone();e.cmpn(1)>0&&n.cmpn(1)>0;){for(;e.isEven();)e.ishrn(1),a.isEven()?a.ishrn(1):a.iadd(s).ishrn(1);for(;n.isEven();)n.ishrn(1),o.isEven()?o.ishrn(1):o.iadd(s).ishrn(1);e.cmp(n)>=0?(e.isub(n),a.isub(o)):(n.isub(e),o.isub(a))}return 0===e.cmpn(1)?a:o},i.prototype.gcd=function(t){if(0===this.cmpn(0))return t.clone();if(0===t.cmpn(0))return this.clone();var e=this.clone(),r=t.clone();e.sign=!1,r.sign=!1;for(var n=0;e.isEven()&&r.isEven();n++)e.ishrn(1),r.ishrn(1);for(;;){for(;e.isEven();)e.ishrn(1);for(;r.isEven();)r.ishrn(1);var i=e.cmp(r);if(0>i){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.ishln(n)},i.prototype.invm=function(t){return this.egcd(t).a.mod(t)},i.prototype.isEven=function(){return 0===(1&this.words[0])},i.prototype.isOdd=function(){return 1===(1&this.words[0])},i.prototype.andln=function(t){return this.words[0]&t},i.prototype.bincn=function(t){r(\"number\"==typeof t);var e=t%26,n=(t-e)/26,i=1<a;a++)this.words[a]=0;return this.words[n]|=i,this.length=n+1,this}for(var o=i,a=n;0!==o&&a>>26,s&=67108863,this.words[a]=s}return 0!==o&&(this.words[a]=o,this.length++),this},i.prototype.cmpn=function(t){var e=0>t;if(e&&(t=-t),this.sign&&!e)return-1;if(!this.sign&&e)return 1;t&=67108863,this.strip();var r;if(this.length>1)r=1;else{var n=this.words[0];r=n===t?0:t>n?-1:1}return this.sign&&(r=-r),r},i.prototype.cmp=function(t){if(this.sign&&!t.sign)return-1;if(!this.sign&&t.sign)return 1;var e=this.ucmp(t);return this.sign?-e:e},i.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length=0;r--){var n=this.words[r],i=t.words[r];if(n!==i){i>n?e=-1:n>i&&(e=1);break}}return e},i.red=function(t){return new h(t)},i.prototype.toRed=function(t){return r(!this.red,\"Already a number in reduction context\"),r(!this.sign,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},i.prototype.fromRed=function(){return r(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},i.prototype._forceRed=function(t){return this.red=t,this},i.prototype.forceRed=function(t){return r(!this.red,\"Already a number in reduction context\"),this._forceRed(t)},i.prototype.redAdd=function(t){return r(this.red,\"redAdd works only with red numbers\"),this.red.add(this,t)},i.prototype.redIAdd=function(t){return r(this.red,\"redIAdd works only with red numbers\"),this.red.iadd(this,t)},i.prototype.redSub=function(t){return r(this.red,\"redSub works only with red numbers\"),this.red.sub(this,t)},i.prototype.redISub=function(t){return r(this.red,\"redISub works only with red numbers\"),this.red.isub(this,t)},i.prototype.redShl=function(t){return r(this.red,\"redShl works only with red numbers\"),this.red.shl(this,t)},i.prototype.redMul=function(t){return r(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.mul(this,t)},i.prototype.redIMul=function(t){return r(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.imul(this,t)},i.prototype.redSqr=function(){return r(this.red,\"redSqr works only with red numbers\"),this.red._verify1(this),this.red.sqr(this)},i.prototype.redISqr=function(){return r(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},i.prototype.redSqrt=function(){return r(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},i.prototype.redInvm=function(){return r(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},i.prototype.redNeg=function(){return r(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},i.prototype.redPow=function(t){return r(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var m={k256:null,p224:null,p192:null,p25519:null};s.prototype._tmp=function(){var t=new i(null);return t.words=new Array(Math.ceil(this.n/13)),t},s.prototype.ireduce=function(t){var e,r=t;do this.split(r,this.tmp),r=this.imulK(r),r=r.iadd(this.tmp),e=r.bitLength();while(e>this.n);var n=e0?r.isub(this.p):r.strip(),r},s.prototype.split=function(t,e){t.ishrn(this.n,0,e)},s.prototype.imulK=function(t){return t.imul(this.k)},n(l,s),l.prototype.split=function(t,e){for(var r=4194303,n=Math.min(t.length,9),i=0;n>i;i++)e.words[i]=t.words[i];if(e.length=n,t.length<=9)return t.words[0]=0,void(t.length=1);var a=t.words[9];e.words[e.length++]=a&r;for(var i=10;i>>22,a=o}t.words[i-10]=a>>>22,t.length-=9},l.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e,r=0,n=0;n>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},i._prime=function y(t){if(m[t])return m[t];var y;if(\"k256\"===t)y=new l;else if(\"p224\"===t)y=new c;else if(\"p192\"===t)y=new u;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);y=new f}return m[t]=y,y},h.prototype._verify1=function(t){r(!t.sign,\"red works only with positives\"),r(t.red,\"red works only with red numbers\")},h.prototype._verify2=function(t,e){r(!t.sign&&!e.sign,\"red works only with positives\"),r(t.red&&t.red===e.red,\"red works only with red numbers\")},h.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.mod(this.m)._forceRed(this)},h.prototype.neg=function(t){var e=t.clone();return e.sign=!e.sign,e.iadd(this.m)._forceRed(this)},h.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},h.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},h.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},h.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},h.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.shln(e))},h.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},h.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},h.prototype.isqr=function(t){return this.imul(t,t)},h.prototype.sqr=function(t){return this.mul(t,t)},h.prototype.sqrt=function(t){if(0===t.cmpn(0))return t.clone();var e=this.m.andln(3);if(r(e%2===1),3===e){var n=this.m.add(new i(1)).ishrn(2),a=this.pow(t,n);return a}for(var o=this.m.subn(1),s=0;0!==o.cmpn(0)&&0===o.andln(1);)s++,o.ishrn(1);r(0!==o.cmpn(0));var l=new i(1).toRed(this),c=l.redNeg(),u=this.m.subn(1).ishrn(1),f=this.m.bitLength();for(f=new i(2*f*f).toRed(this);0!==this.pow(f,u).cmp(c);)f.redIAdd(c);for(var h=this.pow(f,o),a=this.pow(t,o.addn(1).ishrn(1)),d=this.pow(t,o),p=s;0!==d.cmp(l);){for(var g=d,v=0;0!==g.cmp(l);v++)g=g.redSqr();r(p>v);var m=this.pow(h,new i(1).ishln(p-v-1));a=a.redMul(m),h=m.redSqr(),d=d.redMul(h),p=v}return a},h.prototype.invm=function(t){var 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n(t,e,r){this.plot=t,this.shader=e,this.buffer=r,this.bounds=[1/0,1/0,-(1/0),-(1/0)],this.numPoints=0,this.color=[0,0,0,1]}function i(t,e){var r=a(t.gl,l.vertex,l.fragment),i=o(t.gl),s=new n(t,r,i);return s.update(e),t.addObject(s),s}var a=t(\"gl-shader\"),o=t(\"gl-buffer\"),s=t(\"typedarray-pool\"),l=t(\"./lib/shaders\");e.exports=i;var c=[[1,0,0,1,0,0],[1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,1,0,0],[1,0,0,1,0,0],[1,0,-1,0,0,1],[1,0,-1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,1],[1,0,-1,0,0,1],[-1,0,-1,0,0,1],[-1,0,-1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,1],[-1,0,-1,0,0,1],[0,1,1,0,0,0],[0,1,-1,0,0,0],[0,-1,-1,0,0,0],[0,-1,-1,0,0,0],[0,1,1,0,0,0],[0,-1,1,0,0,0],[0,1,0,-1,1,0],[0,1,0,-1,-1,0],[0,1,0,1,-1,0],[0,1,0,1,1,0],[0,1,0,-1,1,0],[0,1,0,1,-1,0],[0,-1,0,-1,1,0],[0,-1,0,-1,-1,0],[0,-1,0,1,-1,0],[0,-1,0,1,1,0],[0,-1,0,-1,1,0],[0,-1,0,1,-1,0]],u=n.prototype;u.draw=function(){var t=[1,0,0,0,1,0,0,0,1],e=[1,1];return function(){var r=this.plot,n=this.shader,i=this.buffer,a=this.bounds,o=this.numPoints;if(o){var s=r.gl,l=r.dataBox,u=r.viewBox,f=r.pixelRatio,h=a[2]-a[0],d=a[3]-a[1],p=l[2]-l[0],g=l[3]-l[1];t[0]=2*h/p,t[4]=2*d/g,t[6]=2*(a[0]-l[0])/p-1,t[7]=2*(a[1]-l[1])/g-1;var v=u[2]-u[0],m=u[3]-u[1];e[0]=2*f/v,e[1]=2*f/m,i.bind(),n.bind(),n.uniforms.viewTransform=t,\n", + "n.uniforms.pixelScale=e,n.uniforms.color=this.color,n.attributes.position.pointer(s.FLOAT,!1,16,0),n.attributes.pixelOffset.pointer(s.FLOAT,!1,16,8),s.drawArrays(s.TRIANGLES,0,o*c.length)}}}(),u.drawPick=function(t){return t},u.pick=function(t,e){return null},u.update=function(t){t=t||{};var e,r,n,i=t.positions||[],a=t.errors||[],o=1;\"lineWidth\"in t&&(o=+t.lineWidth);var l=5;\"capSize\"in t&&(l=+t.capSize),this.color=(t.color||[0,0,0,1]).slice();var u=this.bounds=[1/0,1/0,-(1/0),-(1/0)],f=this.numPoints=i.length>>1;for(e=0;f>e;++e)r=i[2*e],n=i[2*e+1],u[0]=Math.min(r,u[0]),u[1]=Math.min(n,u[1]),u[2]=Math.max(r,u[2]),u[3]=Math.max(n,u[3]);u[2]===u[0]&&(u[2]+=1),u[3]===u[1]&&(u[3]+=1);var h=1/(u[2]-u[0]),d=1/(u[3]-u[1]),p=u[0],g=u[1],v=s.mallocFloat32(f*c.length*4),m=0;for(e=0;f>e;++e){r=i[2*e],n=i[2*e+1];for(var y=a[4*e],b=a[4*e+1],x=a[4*e+2],_=a[4*e+3],w=0;wA?A*=y:A>0&&(A*=b),0>M?M*=x:M>0&&(M*=_),v[m++]=h*(r-p+A),v[m++]=d*(n-g+M),v[m++]=o*k[2]+(l+o)*k[4],v[m++]=o*k[3]+(l+o)*k[5]}}this.buffer.update(v),s.free(v)},u.dispose=function(){this.plot.removeObject(this),this.shader.dispose(),this.buffer.dispose()}},{\"./lib/shaders\":120,\"gl-buffer\":118,\"gl-shader\":197,\"typedarray-pool\":278}],120:[function(t,e,r){e.exports={vertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec2 pixelOffset;\\n\\nuniform mat3 viewTransform;\\nuniform vec2 pixelScale;\\n\\nvoid main() {\\n vec3 scrPosition = viewTransform * vec3(position, 1);\\n gl_Position = vec4(\\n scrPosition.xy + scrPosition.z * pixelScale * pixelOffset,\\n 0,\\n scrPosition.z);\\n}\\n\",fragment:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n gl_FragColor = vec4(color.rgb * color.a, color.a);\\n}\\n\"}},{}],121:[function(t,e,r){\"use strict\";function n(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-(1/0),-(1/0),-(1/0)]],this.clipBounds=[[-(1/0),-(1/0),-(1/0)],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1}function i(t,e){for(var r=0;3>r;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}function a(t,e,r,n){for(var i=h[n],a=0;a=1},f.isTransparent=function(){return this.opacity<1},f.drawTransparent=f.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||u,i=r.projection=t.projection||u;r.model=t.model||u,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],o=n[13],s=n[14],l=n[15],c=this.pixelRatio*(i[3]*a+i[7]*o+i[11]*s+i[15]*l)/e.drawingBufferHeight;this.vao.bind();for(var f=0;3>f;++f)e.lineWidth(this.lineWidth[f]),r.capSize=this.capSize[f]*c,e.drawArrays(e.LINES,this.lineOffset[f],this.lineCount[f]);this.vao.unbind()};var h=function(){for(var t=new Array(3),e=0;3>e;++e){for(var r=[],n=1;2>=n;++n)for(var i=-1;1>=i;i+=2){var a=(n+e)%3,o=[0,0,0];o[a]=i,r.push(o)}t[e]=r}return t}();f.update=function(t){t=t||{},\"lineWidth\"in t&&(this.lineWidth=t.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),\"capSize\"in t&&(this.capSize=t.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),\"opacity\"in t&&(this.opacity=t.opacity);var e=t.color||[[0,0,0],[0,0,0],[0,0,0]],r=t.position,n=t.error;if(Array.isArray(e[0])||(e=[e,e,e]),r&&n){var o=[],s=r.length,l=0;this.bounds=[[1/0,1/0,1/0],[-(1/0),-(1/0),-(1/0)]],this.lineCount=[0,0,0];for(var c=0;3>c;++c){this.lineOffset[c]=l;t:for(var u=0;s>u;++u){for(var f=r[u],h=0;3>h;++h)if(isNaN(f[h])||!isFinite(f[h]))continue t;var d=n[u],p=e[c];if(Array.isArray(p[0])&&(p=e[u]),3===p.length&&(p=[p[0],p[1],p[2],1]),!isNaN(d[0][c])&&!isNaN(d[1][c])){if(d[0][c]<0){var g=f.slice();g[c]+=d[0][c],o.push(f[0],f[1],f[2],p[0],p[1],p[2],p[3],0,0,0,g[0],g[1],g[2],p[0],p[1],p[2],p[3],0,0,0),i(this.bounds,g),l+=2+a(o,g,p,c)}if(d[1][c]>0){var g=f.slice();g[c]+=d[1][c],o.push(f[0],f[1],f[2],p[0],p[1],p[2],p[3],0,0,0,g[0],g[1],g[2],p[0],p[1],p[2],p[3],0,0,0),i(this.bounds,g),l+=2+a(o,g,p,c)}}}this.lineCount[c]=l-this.lineOffset[c]}this.buffer.update(o)}},f.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},{\"./shaders/index\":122,\"gl-buffer\":118,\"gl-vao\":226}],122:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, offset;\\nattribute vec4 color;\\nuniform mat4 model, view, projection;\\nuniform float capSize;\\nvarying vec4 fragColor;\\nvarying vec3 fragPosition;\\n\\nvoid main() {\\n vec4 worldPosition = model * vec4(position, 1.0);\\n worldPosition = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\\n gl_Position = projection * view * worldPosition;\\n fragColor = color;\\n fragPosition = position;\\n}\",a=\"precision mediump float;\\n#define GLSLIFY 1\\nuniform vec3 clipBounds[2];\\nuniform float opacity;\\nvarying vec3 fragPosition;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if(any(lessThan(fragPosition, clipBounds[0])) || any(greaterThan(fragPosition, clipBounds[1]))) {\\n discard;\\n }\\n gl_FragColor = opacity * fragColor;\\n}\";e.exports=function(t){return n(t,i,a,null,[{name:\"position\",type:\"vec3\"},{name:\"offset\",type:\"vec3\"},{name:\"color\",type:\"vec4\"}])}},{\"gl-shader\":197}],123:[function(t,e,r){\"use strict\";function n(t){var e=t.getParameter(t.FRAMEBUFFER_BINDING),r=t.getParameter(t.RENDERBUFFER_BINDING),n=t.getParameter(t.TEXTURE_BINDING_2D);return[e,r,n]}function i(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function a(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);y=new Array(r+1);for(var n=0;r>=n;++n){for(var i=new Array(r),a=0;n>a;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(var a=n;r>a;++a)i[a]=t.NONE;y[n]=i}}function o(t){switch(t){case p:throw new Error(\"gl-fbo: Framebuffer unsupported\");case g:throw new Error(\"gl-fbo: Framebuffer incomplete attachment\");case v:throw new Error(\"gl-fbo: Framebuffer incomplete dimensions\");case m:throw new Error(\"gl-fbo: Framebuffer incomplete missing attachment\");default:throw new Error(\"gl-fbo: Framebuffer failed for unspecified reason\")}}function s(t,e,r,n,i,a){if(!n)return null;var o=d(t,e,r,i,n);return o.magFilter=t.NEAREST,o.minFilter=t.NEAREST,o.mipSamples=1,o.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,a,t.TEXTURE_2D,o.handle,0),o}function l(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function c(t){var e=n(t.gl),r=t.gl,a=t.handle=r.createFramebuffer(),c=t._shape[0],u=t._shape[1],f=t.color.length,h=t._ext,d=t._useStencil,p=t._useDepth,g=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,a);for(var v=0;f>v;++v)t.color[v]=s(r,c,u,g,r.RGBA,r.COLOR_ATTACHMENT0+v);0===f?(t._color_rb=l(r,c,u,r.RGBA4,r.COLOR_ATTACHMENT0),h&&h.drawBuffersWEBGL(y[0])):f>1&&h.drawBuffersWEBGL(y[f]);var m=r.getExtension(\"WEBGL_depth_texture\");m?d?t.depth=s(r,c,u,m.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):p&&(t.depth=s(r,c,u,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):p&&d?t._depth_rb=l(r,c,u,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):p?t._depth_rb=l(r,c,u,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=l(r,c,u,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var b=r.checkFramebufferStatus(r.FRAMEBUFFER);if(b!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(var v=0;vl;++l)this.color[l]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var u=this,f=[0|e,0|r];Object.defineProperties(f,{0:{get:function(){return u._shape[0]},set:function(t){return u.width=t}},1:{get:function(){return u._shape[1]},set:function(t){return u.height=t}}}),this._shapeVector=f,c(this)}function f(t,e,r){if(t._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(t._shape[0]!==e||t._shape[1]!==r){var a=t.gl,s=a.getParameter(a.MAX_RENDERBUFFER_SIZE);if(0>e||e>s||0>r||r>s)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var l=n(a),c=0;ce||e>o||0>r||r>o)throw new Error(\"gl-fbo: Parameters are too large for FBO\");n=n||{};var s=1;if(\"color\"in n){if(s=Math.max(0|n.color,0),0>s)throw new Error(\"gl-fbo: Must specify a nonnegative number of colors\");if(s>1){if(!i)throw new Error(\"gl-fbo: Multiple draw buffer extension not supported\");if(s>t.getParameter(i.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error(\"gl-fbo: Context does not support \"+s+\" draw buffers\")}}var l=t.UNSIGNED_BYTE,c=t.getExtension(\"OES_texture_float\");if(n.float&&s>0){if(!c)throw new Error(\"gl-fbo: Context does not support floating point textures\");l=t.FLOAT}else n.preferFloat&&s>0&&c&&(l=t.FLOAT);var f=!0;\"depth\"in n&&(f=!!n.depth);var h=!1;return\"stencil\"in n&&(h=!!n.stencil),new u(t,e,r,l,s,f,h,i)}var d=t(\"gl-texture2d\");e.exports=h;var p,g,v,m,y=null,b=u.prototype;Object.defineProperties(b,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0|t[0],r=0|t[1];return f(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return t=0|t,f(this,t,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t=0|t,f(this,this._shape[0],t),t},enumerable:!1}}),b.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},b.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e 0.25) {\\n discard;\\n }\\n gl_FragColor = vec4(color.rgb, color.a);\\n}\\n\",r.pickVertex=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 a, d;\\nattribute vec4 pick0, pick1;\\n\\nuniform mat3 matrix;\\nuniform vec2 screenShape;\\nuniform float width;\\n\\nvarying vec4 pickA, pickB;\\n\\nfloat inverse_1_0(float m) {\\n return 1.0 / m;\\n}\\n\\nmat2 inverse_1_0(mat2 m) {\\n return mat2(m[1][1],-m[0][1],\\n -m[1][0], m[0][0]) / (m[0][0]*m[1][1] - m[0][1]*m[1][0]);\\n}\\n\\nmat3 inverse_1_0(mat3 m) {\\n float a00 = m[0][0], a01 = m[0][1], a02 = m[0][2];\\n float a10 = m[1][0], a11 = m[1][1], a12 = m[1][2];\\n float a20 = m[2][0], a21 = m[2][1], a22 = m[2][2];\\n\\n float b01 = a22 * a11 - a12 * a21;\\n float b11 = -a22 * a10 + a12 * a20;\\n float b21 = a21 * a10 - a11 * a20;\\n\\n float det = a00 * b01 + a01 * b11 + a02 * b21;\\n\\n return mat3(b01, (-a22 * a01 + a02 * a21), (a12 * a01 - a02 * a11),\\n b11, (a22 * a00 - a02 * a20), (-a12 * a00 + a02 * a10),\\n b21, (-a21 * a00 + a01 * a20), (a11 * a00 - a01 * a10)) / det;\\n}\\n\\nmat4 inverse_1_0(mat4 m) {\\n float\\n a00 = m[0][0], a01 = m[0][1], a02 = m[0][2], a03 = m[0][3],\\n a10 = m[1][0], a11 = m[1][1], a12 = m[1][2], a13 = m[1][3],\\n a20 = m[2][0], a21 = m[2][1], a22 = m[2][2], a23 = m[2][3],\\n a30 = m[3][0], a31 = m[3][1], a32 = m[3][2], a33 = m[3][3],\\n\\n b00 = a00 * a11 - a01 * a10,\\n b01 = a00 * a12 - a02 * a10,\\n b02 = a00 * a13 - a03 * a10,\\n b03 = a01 * a12 - a02 * a11,\\n b04 = a01 * a13 - a03 * a11,\\n b05 = a02 * a13 - a03 * a12,\\n b06 = a20 * a31 - a21 * a30,\\n b07 = a20 * a32 - a22 * a30,\\n b08 = a20 * a33 - a23 * a30,\\n b09 = a21 * a32 - a22 * a31,\\n b10 = a21 * a33 - a23 * a31,\\n b11 = a22 * a33 - a23 * a32,\\n\\n det = b00 * b11 - b01 * b10 + b02 * b09 + b03 * b08 - b04 * b07 + b05 * b06;\\n\\n return mat4(\\n a11 * b11 - a12 * b10 + a13 * b09,\\n a02 * b10 - a01 * b11 - a03 * b09,\\n a31 * b05 - a32 * b04 + a33 * b03,\\n a22 * b04 - a21 * b05 - a23 * b03,\\n a12 * b08 - a10 * b11 - a13 * b07,\\n a00 * b11 - a02 * b08 + a03 * b07,\\n a32 * b02 - a30 * b05 - a33 * b01,\\n a20 * b05 - a22 * b02 + a23 * b01,\\n a10 * b10 - a11 * b08 + a13 * b06,\\n a01 * b08 - a00 * b10 - a03 * b06,\\n a30 * b04 - a31 * b02 + a33 * b00,\\n a21 * b02 - a20 * b04 - a23 * b00,\\n a11 * b07 - a10 * b09 - a12 * b06,\\n a00 * b09 - a01 * b07 + a02 * b06,\\n a31 * b01 - a30 * b03 - a32 * b00,\\n a20 * b03 - a21 * b01 + a22 * b00) / det;\\n}\\n\\n\\n\\nvoid main() {\\n vec3 base = matrix * vec3(a, 1);\\n vec2 n = width *\\n normalize(screenShape.yx * vec2(d.y, -d.x)) / screenShape.xy;\\n gl_Position = vec4(base.xy/base.z + n, 0, 1);\\n pickA = pick0;\\n pickB = pick1;\\n}\\n\",r.pickFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 pickOffset;\\n\\nvarying vec4 pickA, pickB;\\n\\nvoid main() {\\n vec4 fragId = vec4(pickA.xyz, 0.0);\\n if(pickB.w > pickA.w) {\\n fragId.xyz = pickB.xyz;\\n }\\n\\n fragId += pickOffset;\\n\\n fragId.y += floor(fragId.x / 256.0);\\n fragId.x -= floor(fragId.x / 256.0) * 256.0;\\n\\n fragId.z += floor(fragId.y / 256.0);\\n fragId.y -= floor(fragId.y / 256.0) * 256.0;\\n\\n fragId.w += floor(fragId.z / 256.0);\\n fragId.z -= floor(fragId.z / 256.0) * 256.0;\\n\\n gl_FragColor = fragId / 255.0;\\n}\\n\",r.fillVertex=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 a, d;\\n\\nuniform mat3 matrix;\\nuniform vec2 projectAxis;\\nuniform float projectValue;\\nuniform float depth;\\n\\nvoid main() {\\n vec3 base = matrix * vec3(a, 1);\\n vec2 p = base.xy / base.z;\\n if(d.y < 0.0 || (d.y == 0.0 && d.x < 0.0)) {\\n if(dot(p, projectAxis) < projectValue) {\\n p = p * (1.0 - abs(projectAxis)) + projectAxis * projectValue;\\n }\\n }\\n gl_Position = vec4(p, depth, 1);\\n}\\n\",r.fillFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n gl_FragColor = vec4(color.rgb * color.a, color.a);\\n}\\n\"},{}],125:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s){this.plot=t,this.dashPattern=e,this.lineBuffer=r,this.pickBuffer=n,this.lineShader=i,this.mitreShader=a,this.fillShader=o,this.pickShader=s,this.usingDashes=!1,this.bounds=[1/0,1/0,-(1/0),-(1/0)],this.width=1,this.color=[0,0,1,1],this.fill=[!1,!1,!1,!1],this.fillColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.data=null,this.numPoints=0,this.vertCount=0,this.pickOffset=0,this.lodBuffer=[]}function i(t){return t.map(function(t){return t.slice()})}function a(t,e){var r=t.gl,i=s(r),a=s(r),c=l(r,[1,1]),u=o(r,f.lineVertex,f.lineFragment),h=o(r,f.mitreVertex,f.mitreFragment),d=o(r,f.fillVertex,f.fillFragment),p=o(r,f.pickVertex,f.pickFragment),g=new n(t,c,i,a,u,h,d,p);return t.addObject(g),g.update(e),g}e.exports=a;var o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"gl-texture2d\"),c=t(\"ndarray\"),u=t(\"typedarray-pool\"),f=t(\"./lib/shaders\"),h=n.prototype;h.draw=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0],r=[1,0],n=[-1,0],i=[0,1],a=[0,-1];return function(){var o=this.plot,s=this.color,l=this.width,c=(this.numPoints,this.bounds),u=this.vertCount;if(u){var f=o.gl,h=o.viewBox,d=o.dataBox,p=o.pixelRatio,g=c[2]-c[0],v=c[3]-c[1],m=d[2]-d[0],y=d[3]-d[1],b=h[2]-h[0],x=h[3]-h[1];t[0]=2*g/m,t[4]=2*v/y,t[6]=2*(c[0]-d[0])/m-1,t[7]=2*(c[1]-d[1])/y-1,e[0]=b,e[1]=x;var _=this.lineBuffer;_.bind();var w=this.fill;if(w[0]||w[1]||w[2]||w[3]){var k=this.fillShader;k.bind();var A=k.uniforms;A.matrix=t,A.depth=o.nextDepthValue();var M=k.attributes;M.a.pointer(f.FLOAT,!1,16,0),M.d.pointer(f.FLOAT,!1,16,8),f.depthMask(!0),f.enable(f.DEPTH_TEST);var T=this.fillColor;w[0]&&(A.color=T[0],A.projectAxis=n,A.projectValue=1,f.drawArrays(f.TRIANGLES,0,u)),w[1]&&(A.color=T[1],A.projectAxis=a,A.projectValue=1,f.drawArrays(f.TRIANGLES,0,u)),w[2]&&(A.color=T[2],A.projectAxis=r,A.projectValue=1,f.drawArrays(f.TRIANGLES,0,u)),w[3]&&(A.color=T[3],A.projectAxis=i,A.projectValue=1,f.drawArrays(f.TRIANGLES,0,u)),f.depthMask(!1),f.disable(f.DEPTH_TEST)}var E=this.lineShader;E.bind();var L=E.uniforms;L.matrix=t,L.color=s,L.width=l*p,L.screenShape=e,L.dashPattern=this.dashPattern.bind(),L.dashLength=this.dashLength*p;var S=E.attributes;if(S.a.pointer(f.FLOAT,!1,16,0),S.d.pointer(f.FLOAT,!1,16,8),f.drawArrays(f.TRIANGLES,0,u),l>2&&!this.usingDashes){var C=this.mitreShader;C.bind();var z=C.uniforms;z.matrix=t,z.color=s,z.screenShape=e,z.radius=l*p,C.attributes.p.pointer(f.FLOAT,!1,48,0),f.drawArrays(f.POINTS,0,u/3|0)}}}}(),h.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0],r=[0,0,0,0];return function(n){var i=this.plot,a=this.pickShader,o=this.lineBuffer,s=this.pickBuffer,l=this.width,c=this.numPoints,u=this.bounds,f=this.vertCount,h=i.gl,d=i.viewBox,p=i.dataBox,g=i.pickPixelRatio,v=u[2]-u[0],m=u[3]-u[1],y=p[2]-p[0],b=p[3]-p[1],x=d[2]-d[0],_=d[3]-d[1];if(this.pickOffset=n,!f)return n+c;t[0]=2*v/y,t[4]=2*m/b,t[6]=2*(u[0]-p[0])/y-1,t[7]=2*(u[1]-p[1])/b-1,e[0]=x,e[1]=_,r[0]=255&n,r[1]=n>>>8&255,r[2]=n>>>16&255,r[3]=n>>>24,a.bind();var w=a.uniforms;w.matrix=t,w.width=l*g,w.pickOffset=r,w.screenShape=e;var k=a.attributes;return o.bind(),k.a.pointer(h.FLOAT,!1,16,0),k.d.pointer(h.FLOAT,!1,16,8),s.bind(),k.pick0.pointer(h.UNSIGNED_BYTE,!1,8,0),k.pick1.pointer(h.UNSIGNED_BYTE,!1,8,4),h.drawArrays(h.TRIANGLES,0,f),n+c}}(),h.pick=function(t,e,r){var n=this.pickOffset,i=this.numPoints;if(n>r||r>=n+i)return null;var a=r-n,o=this.data;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}},h.update=function(t){t=t||{};var e=this.plot.gl;!!t.connectGaps;this.color=(t.color||[0,0,1,1]).slice(),this.width=+(t.width||1),this.fill=(t.fill||[!1,!1,!1,!1]).slice(),this.fillColor=i(t.fillColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);for(var r=t.dashes||[1],n=0,a=0;a1,this.dashPattern=l(e,c(o,[n,1,4],[1,0,0])),this.dashPattern.minFilter=e.NEAREST,this.dashPattern.magFilter=e.NEAREST,this.dashLength=n,u.free(o);var d=t.positions;this.data=d;var p=this.bounds;p[0]=p[1]=1/0,p[2]=p[3]=-(1/0);var g=this.numPoints=d.length>>>1;if(0!==g){for(var a=0;g>a;++a){var v=d[2*a],m=d[2*a+1];isNaN(v)||isNaN(m)||(p[0]=Math.min(p[0],v),p[1]=Math.min(p[1],m),p[2]=Math.max(p[2],v),p[3]=Math.max(p[3],m))}p[0]===p[2]&&(p[2]+=1),p[3]===p[1]&&(p[3]+=1);for(var y=u.mallocFloat32(24*(g-1)),b=u.mallocUint32(12*(g-1)),x=y.length,_=b.length,s=g,w=0;s>1;){var k=--s,v=d[2*s],m=d[2*s+1],A=k-1,M=d[2*A],T=d[2*A+1];if(!(isNaN(v)||isNaN(m)||isNaN(M)||isNaN(T))){w+=1,v=(v-p[0])/(p[2]-p[0]),m=(m-p[1])/(p[3]-p[1]),M=(M-p[0])/(p[2]-p[0]),T=(T-p[1])/(p[3]-p[1]);var E=M-v,L=T-m,S=k|1<<24,C=k-1,z=k,P=k-1|1<<24;y[--x]=-L,y[--x]=-E,y[--x]=m,y[--x]=v,b[--_]=S,b[--_]=C,y[--x]=L,y[--x]=E,y[--x]=T,y[--x]=M,b[--_]=z,b[--_]=P,y[--x]=-L,y[--x]=-E,y[--x]=T,y[--x]=M,b[--_]=z,b[--_]=P,y[--x]=L,y[--x]=E,y[--x]=T,y[--x]=M,b[--_]=z,b[--_]=P,y[--x]=-L,y[--x]=-E,y[--x]=m,y[--x]=v,b[--_]=S,b[--_]=C,y[--x]=L,y[--x]=E,y[--x]=m,y[--x]=v,b[--_]=S,b[--_]=C}}this.vertCount=6*w,this.lineBuffer.update(y.subarray(x)),this.pickBuffer.update(b.subarray(_)),u.free(y),u.free(b)}},h.dispose=function(){this.plot.removeObject(this),this.lineBuffer.dispose(),this.pickBuffer.dispose(),this.lineShader.dispose(),this.mitreShader.dispose(),this.fillShader.dispose(),this.pickShader.dispose(),this.dashPattern.dispose()}},{\"./lib/shaders\":124,\"gl-buffer\":118,\"gl-shader\":197,\"gl-texture2d\":222,ndarray:253,\"typedarray-pool\":278}],126:[function(t,e,r){var n=t(\"gl-shader\"),i=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, nextPosition;\\nattribute float arcLength, lineWidth;\\nattribute vec4 color;\\n\\nuniform vec2 screenShape;\\nuniform float pixelRatio;\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 fragColor;\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\n\\nvoid main() {\\n vec4 projected = projection * view * model * vec4(position, 1.0);\\n vec4 tangentClip = projection * view * model * vec4(nextPosition - position, 0.0);\\n vec2 tangent = normalize(screenShape * tangentClip.xy);\\n vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(tangent.y, -tangent.x) / screenShape;\\n\\n gl_Position = vec4(projected.xy + projected.w * offset, projected.zw);\\n\\n worldPosition = position;\\n pixelArcLength = arcLength;\\n fragColor = color;\\n}\\n\",a=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float dashScale;\\nuniform float opacity;\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if(any(lessThan(worldPosition, clipBounds[0])) || any(greaterThan(worldPosition, clipBounds[1]))) {\\n discard;\\n }\\n float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n if(dashWeight < 0.5) {\\n discard;\\n }\\n gl_FragColor = fragColor * opacity;\\n}\\n\",o=\"precision mediump float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX 1.70141184e38\\n#define FLOAT_MIN 1.17549435e-38\\n\\nlowp vec4 encode_float_1_0(highp float v) {\\n highp float av = abs(v);\\n\\n //Handle special cases\\n if(av < FLOAT_MIN) {\\n return vec4(0.0, 0.0, 0.0, 0.0);\\n } else if(v > FLOAT_MAX) {\\n return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n } else if(v < -FLOAT_MAX) {\\n return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n }\\n\\n highp vec4 c = vec4(0,0,0,0);\\n\\n //Compute exponent and mantissa\\n highp float e = floor(log2(av));\\n highp float m = av * pow(2.0, -e) - 1.0;\\n \\n //Unpack mantissa\\n c[1] = floor(128.0 * m);\\n m -= c[1] / 128.0;\\n c[2] = floor(32768.0 * m);\\n m -= c[2] / 32768.0;\\n c[3] = floor(8388608.0 * m);\\n \\n //Unpack exponent\\n highp float ebias = e + 127.0;\\n c[0] = floor(ebias / 2.0);\\n ebias -= c[0] * 2.0;\\n c[1] += floor(ebias) * 128.0; \\n\\n //Unpack sign bit\\n c[0] += 128.0 * step(0.0, -v);\\n\\n //Scale back to range\\n return c / 255.0;\\n}\\n\\n\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if(any(lessThan(worldPosition, clipBounds[0])) || any(greaterThan(worldPosition, clipBounds[1]))) {\\n discard;\\n }\\n gl_FragColor = vec4(pickId/255.0, encode_float_1_0(pixelArcLength).xyz);\\n}\",s=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];r.createShader=function(t){return n(t,i,a,null,s)},r.createPickShader=function(t){return n(t,i,o,null,s)}},{\"gl-shader\":197}],127:[function(t,e,r){\"use strict\";function n(t,e){for(var r=0,n=0;3>n;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function i(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;3>r;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function a(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function o(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-(1/0),-(1/0),-(1/0)],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.dirty=!0,this.pixelRatio=1}function s(t){var e=t.gl||t.scene&&t.scene.gl,r=g(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var n=v(e);n.attributes.position.location=0,n.attributes.nextPosition.location=1,n.attributes.arcLength.location=2,n.attributes.lineWidth.location=3,n.attributes.color.location=4;for(var i=l(e),a=c(e,[{buffer:i,size:3,offset:0,stride:48},{buffer:i,size:3,offset:12,stride:48},{buffer:i,size:1,offset:24,stride:48},{buffer:i,size:1,offset:28,stride:48},{buffer:i,size:4,offset:32,stride:48}]),s=d(new Array(1024),[256,1,4]),f=0;1024>f;++f)s.data[f]=255;var h=u(e,s);h.wrap=e.REPEAT;var p=new o(e,r,n,i,a,h);return p.update(t),p}e.exports=s;var l=t(\"gl-buffer\"),c=t(\"gl-vao\"),u=t(\"gl-texture2d\"),f=t(\"glsl-read-float\"),h=t(\"binary-search-bounds\"),d=t(\"ndarray\"),p=t(\"./lib/shaders\"),g=p.createShader,v=p.createPickShader,m=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],y=o.prototype;y.isTransparent=function(){return this.opacity<1},y.isOpaque=function(){return this.opacity>=1},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||m,view:t.view||m,projection:t.projection||m,clipBounds:i(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount)},y.drawPick=function(t){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||m,view:t.view||m,projection:t.projection||m,pickId:this.pickId,clipBounds:i(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount)},y.update=function(t){var e,r;this.dirty=!0;var i=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),\"opacity\"in t&&(this.opacity=+t.opacity);var a=t.position||t.positions;if(a){var o=t.color||t.colors||[0,0,0,1],s=t.lineWidth||1,l=[],c=[],u=[],f=0,p=0,g=[[1/0,1/0,1/0],[-(1/0),-(1/0),-(1/0)]],v=!1;t:for(e=1;er;++r){if(isNaN(m[r])||isNaN(y[r])||!isFinite(m[r])||!isFinite(y[r])){if(!i&&l.length>0){for(var b=0;24>b;++b)l.push(l[l.length-12]);p+=2,v=!0}continue t}g[0][r]=Math.min(g[0][r],m[r],y[r]),g[1][r]=Math.max(g[1][r],m[r],y[r])}\n", + "var x,_;Array.isArray(o[0])?(x=o[e-1],_=o[e]):x=_=o,3===x.length&&(x=[x[0],x[1],x[2],1]),3===_.length&&(_=[_[0],_[1],_[2],1]);var w;w=Array.isArray(s)?s[e-1]:s;var k=f;if(f+=n(m,y),v){for(r=0;2>r;++r)l.push(m[0],m[1],m[2],y[0],y[1],y[2],k,w,x[0],x[1],x[2],x[3]);p+=2,v=!1}l.push(m[0],m[1],m[2],y[0],y[1],y[2],k,w,x[0],x[1],x[2],x[3],m[0],m[1],m[2],y[0],y[1],y[2],k,-w,x[0],x[1],x[2],x[3],y[0],y[1],y[2],m[0],m[1],m[2],f,-w,_[0],_[1],_[2],_[3],y[0],y[1],y[2],m[0],m[1],m[2],f,w,_[0],_[1],_[2],_[3]),p+=4}if(this.buffer.update(l),c.push(f),u.push(a[a.length-1].slice()),this.bounds=g,this.vertexCount=p,this.points=u,this.arcLength=c,\"dashes\"in t){var A=t.dashes,M=A.slice();for(M.unshift(0),e=1;ee;++e){for(r=0;4>r;++r)T.set(e,0,r,0);1&h.le(M,M[M.length-1]*e/255)?T.set(e,0,0,0):T.set(e,0,0,255)}this.texture.setPixels(T)}}},y.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=f(t.value[0],t.value[1],t.value[2],0),r=h.le(this.arcLength,e);if(0>r)return null;if(r===this.arcLength.length-1)return new a(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],o=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),s=1-o,l=[0,0,0],c=0;3>c;++c)l[c]=s*n[c]+o*i[c];var u=Math.min(.5>o?r:r+1,this.points.length-1);return new a(e,l,u,this.points[u])}},{\"./lib/shaders\":126,\"binary-search-bounds\":128,\"gl-buffer\":118,\"gl-texture2d\":222,\"gl-vao\":226,\"glsl-read-float\":129,ndarray:253}],128:[function(t,e,r){arguments[4][21][0].apply(r,arguments)},{dup:21}],129:[function(t,e,r){function n(t,e,r,n){return i[0]=n,i[1]=r,i[2]=e,i[3]=t,a[0]}e.exports=n;var i=new Uint8Array(4),a=new Float32Array(i.buffer)},{}],130:[function(t,e,r){function n(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=u*o-s*c,h=-u*a+s*l,d=c*a-o*l,p=r*f+n*h+i*d;return p?(p=1/p,t[0]=f*p,t[1]=(-u*n+i*c)*p,t[2]=(s*n-i*o)*p,t[3]=h*p,t[4]=(u*r-i*l)*p,t[5]=(-s*r+i*a)*p,t[6]=d*p,t[7]=(-c*r+n*l)*p,t[8]=(o*r-n*a)*p,t):null}e.exports=n},{}],131:[function(t,e,r){function n(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}e.exports=n},{}],132:[function(t,e,r){function n(){var t=new Float32Array(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}e.exports=n},{}],133:[function(t,e,r){function n(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],c=t[8],u=t[9],f=t[10],h=t[11],d=t[12],p=t[13],g=t[14],v=t[15],m=e*o-r*a,y=e*s-n*a,b=e*l-i*a,x=r*s-n*o,_=r*l-i*o,w=n*l-i*s,k=c*p-u*d,A=c*g-f*d,M=c*v-h*d,T=u*g-f*p,E=u*v-h*p,L=f*v-h*g;return m*L-y*E+b*T+x*M-_*A+w*k}e.exports=n},{}],134:[function(t,e,r){function n(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,c=r*o,u=n*o,f=n*s,h=i*o,d=i*s,p=i*l,g=a*o,v=a*s,m=a*l;return t[0]=1-f-p,t[1]=u+m,t[2]=h-v,t[3]=0,t[4]=u-m,t[5]=1-c-p,t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-c-f,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}e.exports=n},{}],135:[function(t,e,r){function n(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,c=a+a,u=n*s,f=n*l,h=n*c,d=i*l,p=i*c,g=a*c,v=o*s,m=o*l,y=o*c;return t[0]=1-(d+g),t[1]=f+y,t[2]=h-m,t[3]=0,t[4]=f-y,t[5]=1-(u+g),t[6]=p+v,t[7]=0,t[8]=h+m,t[9]=p-v,t[10]=1-(u+d),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}e.exports=n},{}],136:[function(t,e,r){function n(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}e.exports=n},{}],137:[function(t,e,r){function n(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],d=e[11],p=e[12],g=e[13],v=e[14],m=e[15],y=r*s-n*o,b=r*l-i*o,x=r*c-a*o,_=n*l-i*s,w=n*c-a*s,k=i*c-a*l,A=u*g-f*p,M=u*v-h*p,T=u*m-d*p,E=f*v-h*g,L=f*m-d*g,S=h*m-d*v,C=y*S-b*L+x*E+_*T-w*M+k*A;return C?(C=1/C,t[0]=(s*S-l*L+c*E)*C,t[1]=(i*L-n*S-a*E)*C,t[2]=(g*k-v*w+m*_)*C,t[3]=(h*w-f*k-d*_)*C,t[4]=(l*T-o*S-c*M)*C,t[5]=(r*S-i*T+a*M)*C,t[6]=(v*x-p*k-m*b)*C,t[7]=(u*k-h*x+d*b)*C,t[8]=(o*L-s*T+c*A)*C,t[9]=(n*T-r*L-a*A)*C,t[10]=(p*w-g*x+m*y)*C,t[11]=(f*x-u*w-d*y)*C,t[12]=(s*M-o*E-l*A)*C,t[13]=(r*E-n*M+i*A)*C,t[14]=(g*b-p*_-v*y)*C,t[15]=(u*_-f*b+h*y)*C,t):null}e.exports=n},{}],138:[function(t,e,r){function n(t,e,r,n){var a,o,s,l,c,u,f,h,d,p,g=e[0],v=e[1],m=e[2],y=n[0],b=n[1],x=n[2],_=r[0],w=r[1],k=r[2];return Math.abs(g-_)<1e-6&&Math.abs(v-w)<1e-6&&Math.abs(m-k)<1e-6?i(t):(f=g-_,h=v-w,d=m-k,p=1/Math.sqrt(f*f+h*h+d*d),f*=p,h*=p,d*=p,a=b*d-x*h,o=x*f-y*d,s=y*h-b*f,p=Math.sqrt(a*a+o*o+s*s),p?(p=1/p,a*=p,o*=p,s*=p):(a=0,o=0,s=0),l=h*s-d*o,c=d*a-f*s,u=f*o-h*a,p=Math.sqrt(l*l+c*c+u*u),p?(p=1/p,l*=p,c*=p,u*=p):(l=0,c=0,u=0),t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=d,t[11]=0,t[12]=-(a*g+o*v+s*m),t[13]=-(l*g+c*v+u*m),t[14]=-(f*g+h*v+d*m),t[15]=1,t)}var i=t(\"./identity\");e.exports=n},{\"./identity\":136}],139:[function(t,e,r){function n(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],d=e[10],p=e[11],g=e[12],v=e[13],m=e[14],y=e[15],b=r[0],x=r[1],_=r[2],w=r[3];return t[0]=b*n+x*s+_*f+w*g,t[1]=b*i+x*l+_*h+w*v,t[2]=b*a+x*c+_*d+w*m,t[3]=b*o+x*u+_*p+w*y,b=r[4],x=r[5],_=r[6],w=r[7],t[4]=b*n+x*s+_*f+w*g,t[5]=b*i+x*l+_*h+w*v,t[6]=b*a+x*c+_*d+w*m,t[7]=b*o+x*u+_*p+w*y,b=r[8],x=r[9],_=r[10],w=r[11],t[8]=b*n+x*s+_*f+w*g,t[9]=b*i+x*l+_*h+w*v,t[10]=b*a+x*c+_*d+w*m,t[11]=b*o+x*u+_*p+w*y,b=r[12],x=r[13],_=r[14],w=r[15],t[12]=b*n+x*s+_*f+w*g,t[13]=b*i+x*l+_*h+w*v,t[14]=b*a+x*c+_*d+w*m,t[15]=b*o+x*u+_*p+w*y,t}e.exports=n},{}],140:[function(t,e,r){function n(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}e.exports=n},{}],141:[function(t,e,r){function n(t,e,r,n){var i,a,o,s,l,c,u,f,h,d,p,g,v,m,y,b,x,_,w,k,A,M,T,E,L=n[0],S=n[1],C=n[2],z=Math.sqrt(L*L+S*S+C*C);return Math.abs(z)<1e-6?null:(z=1/z,L*=z,S*=z,C*=z,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],d=e[6],p=e[7],g=e[8],v=e[9],m=e[10],y=e[11],b=L*L*o+a,x=S*L*o+C*i,_=C*L*o-S*i,w=L*S*o-C*i,k=S*S*o+a,A=C*S*o+L*i,M=L*C*o+S*i,T=S*C*o-L*i,E=C*C*o+a,t[0]=s*b+f*x+g*_,t[1]=l*b+h*x+v*_,t[2]=c*b+d*x+m*_,t[3]=u*b+p*x+y*_,t[4]=s*w+f*k+g*A,t[5]=l*w+h*k+v*A,t[6]=c*w+d*k+m*A,t[7]=u*w+p*k+y*A,t[8]=s*M+f*T+g*E,t[9]=l*M+h*T+v*E,t[10]=c*M+d*T+m*E,t[11]=u*M+p*T+y*E,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t)}e.exports=n},{}],142:[function(t,e,r){function n(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];return e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}e.exports=n},{}],143:[function(t,e,r){function n(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];return e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}e.exports=n},{}],144:[function(t,e,r){function n(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];return e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}e.exports=n},{}],145:[function(t,e,r){function n(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}e.exports=n},{}],146:[function(t,e,r){function n(t,e,r){var n,i,a,o,s,l,c,u,f,h,d,p,g=r[0],v=r[1],m=r[2];return e===t?(t[12]=e[0]*g+e[4]*v+e[8]*m+e[12],t[13]=e[1]*g+e[5]*v+e[9]*m+e[13],t[14]=e[2]*g+e[6]*v+e[10]*m+e[14],t[15]=e[3]*g+e[7]*v+e[11]*m+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],d=e[10],p=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=d,t[11]=p,t[12]=n*g+s*v+f*m+e[12],t[13]=i*g+l*v+h*m+e[13],t[14]=a*g+c*v+d*m+e[14],t[15]=o*g+u*v+p*m+e[15]),t}e.exports=n},{}],147:[function(t,e,r){function n(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}e.exports=n},{}],148:[function(t,e,r){\"use strict\";function n(t,e){for(var r=[0,0,0,0],n=0;4>n;++n)for(var i=0;4>i;++i)r[i]+=t[4*n+i]*e[n];return r}function i(t,e,r,i,a){for(var o=n(i,n(r,n(e,[t[0],t[1],t[2],1]))),s=0;3>s;++s)o[s]/=o[3];return[.5*a[0]*(1+o[0]),.5*a[1]*(1-o[1])]}function a(t,e){if(2===t.length){for(var r=0,n=0,i=0;2>i;++i)r+=Math.pow(e[i]-t[0][i],2),n+=Math.pow(e[i]-t[1][i],2);return r=Math.sqrt(r),n=Math.sqrt(n),1e-6>r+n?[1,0]:[n/(r+n),r/(n+r)]}if(3===t.length){var a=[0,0];return c(t[0],t[1],t[2],e,a),l(t,a)}return[]}function o(t,e){for(var r=[0,0,0],n=0;no;++o)r[o]+=a*i[o];return r}function s(t,e,r,n,s,l){if(1===t.length)return[0,t[0].slice()];for(var c=new Array(t.length),u=0;up;++p)d+=Math.pow(c[u][p]-e[p],2);h>d&&(h=d,f=u)}for(var g=a(c,e),v=0,u=0;3>u;++u){if(g[u]<-.001||g[u]>1.0001)return null;v+=g[u]}return Math.abs(v-1)>.001?null:[f,o(t,g),g]}var l=t(\"barycentric\"),c=t(\"polytope-closest-point/lib/closest_point_2d.js\");e.exports=s},{barycentric:151,\"polytope-closest-point/lib/closest_point_2d.js\":153}],149:[function(t,e,r){var n=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, normal;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model\\n , view\\n , projection;\\nuniform vec3 eyePosition\\n , lightPosition;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n vec4 m_position = model * vec4(position, 1.0);\\n vec4 t_position = view * m_position;\\n gl_Position = projection * t_position;\\n f_color = color;\\n f_normal = normal;\\n f_data = position;\\n f_eyeDirection = eyePosition - position;\\n f_lightDirection = lightPosition - position;\\n f_uv = uv;\\n}\",i=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution_2_0(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\n\\n\\nfloat cookTorranceSpecular_1_1(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness,\\n float fresnel) {\\n\\n float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n //Half angle vector\\n vec3 H = normalize(lightDirection + viewDirection);\\n\\n //Geometric term\\n float NdotH = max(dot(surfaceNormal, H), 0.0);\\n float VdotH = max(dot(viewDirection, H), 0.000001);\\n float LdotH = max(dot(lightDirection, H), 0.000001);\\n float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n float G = min(1.0, min(G1, G2));\\n \\n //Distribution term\\n float D = beckmannDistribution_2_0(NdotH, roughness);\\n\\n //Fresnel term\\n float F = pow(1.0 - VdotN, fresnel);\\n\\n //Multiply terms and done\\n return G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n , fresnel\\n , kambient\\n , kdiffuse\\n , kspecular\\n , opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if(any(lessThan(f_data, clipBounds[0])) || \\n any(greaterThan(f_data, clipBounds[1]))) {\\n discard;\\n }\\n\\n vec3 N = normalize(f_normal);\\n vec3 L = normalize(f_lightDirection);\\n vec3 V = normalize(f_eyeDirection);\\n \\n if(!gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = cookTorranceSpecular_1_1(L, V, N, roughness, fresnel);\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = litColor * opacity;\\n}\",a=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n f_color = color;\\n f_data = position;\\n f_uv = uv;\\n}\",o=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if(any(lessThan(f_data, clipBounds[0])) || \\n any(greaterThan(f_data, clipBounds[1]))) {\\n discard;\\n }\\n\\n gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\",s=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if(any(lessThan(position, clipBounds[0])) || \\n any(greaterThan(position, clipBounds[1]))) {\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n }\\n gl_PointSize = pointSize;\\n f_color = color;\\n f_uv = uv;\\n}\",l=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n vec2 pointR = gl_PointCoord.xy - vec2(0.5,0.5);\\n if(dot(pointR, pointR) > 0.25) {\\n discard;\\n }\\n gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\",c=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n f_id = id;\\n f_position = position;\\n}\",u=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if(any(lessThan(f_position, clipBounds[0])) || \\n any(greaterThan(f_position, clipBounds[1]))) {\\n discard;\\n }\\n gl_FragColor = vec4(pickId, f_id.xyz);\\n}\",f=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute float pointSize;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if(any(lessThan(position, clipBounds[0])) || \\n any(greaterThan(position, clipBounds[1]))) {\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n gl_PointSize = pointSize;\\n }\\n f_id = id;\\n f_position = position;\\n}\",h=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n}\",d=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n gl_FragColor = vec4(contourColor,1);\\n}\\n\";r.meshShader={vertex:n,fragment:i,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.wireShader={vertex:a,fragment:o,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.pointShader={vertex:s,fragment:l,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},r.pickShader={vertex:c,fragment:u,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},r.pointPickShader={vertex:f,fragment:u,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},r.contourShader={vertex:h,fragment:d,attributes:[{name:\"position\",type:\"vec3\"}]}},{}],150:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s,l,c,u,f,h,d,p,g,v,m,y,b,x,_,w,k,A,M,T){this.gl=t,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=c,this.triangleVAO=d,this.triangleCount=0,this.lineWidth=1,this.edgePositions=p,this.edgeColors=v,this.edgeUVs=m,this.edgeIds=g,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=b,this.pointColors=_,this.pointUVs=w,this.pointSizes=k,this.pointIds=x,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=T,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-(1/0),-(1/0),-(1/0)]],this.clipBounds=[[-(1/0),-(1/0),-(1/0)],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this._model=I,this._view=I,this._projection=I,this._resolution=[1,1]}function i(t){for(var e=A({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;256>n;++n){for(var i=e[n],a=0;3>a;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return k(r,[256,256,4],[4,0,1])}function a(t,e,r){for(var n=new Array(e),i=0;e>i;++i)n[i]=0;for(var a=t.length,i=0;a>i;++i)for(var o=t[i],s=0;sn;++n)r[n]=t[n][2];return r}function s(t){var e=v(t,S);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function l(t){var e=v(t,C);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function c(t){var e=v(t,z);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function u(t){var e=v(t,P);return e.attributes.position.location=0,e.attributes.id.location=1,e}function f(t){var e=v(t,R);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function h(t){var e=v(t,O);return e.attributes.position.location=0,e}function d(t){var e=t.gl,r=s(e),i=l(e),a=c(e),o=u(e),d=f(e),p=h(e),g=b(e,k(new Uint8Array([255,255,255,255]),[1,1,4]));g.generateMipmap(),g.minFilter=e.LINEAR_MIPMAP_LINEAR,g.magFilter=e.LINEAR;var v=m(e),x=m(e),_=m(e),w=m(e),A=m(e),M=y(e,[{buffer:v,type:e.FLOAT,size:3},{buffer:A,type:e.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:x,type:e.FLOAT,size:4},{buffer:_,type:e.FLOAT,size:2},{buffer:w,type:e.FLOAT,size:3}]),T=m(e),E=m(e),L=m(e),S=m(e),C=y(e,[{buffer:T,type:e.FLOAT,size:3},{buffer:S,type:e.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:E,type:e.FLOAT,size:4},{buffer:L,type:e.FLOAT,size:2}]),z=m(e),P=m(e),R=m(e),O=m(e),I=m(e),N=y(e,[{buffer:z,type:e.FLOAT,size:3},{buffer:I,type:e.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:P,type:e.FLOAT,size:4},{buffer:R,type:e.FLOAT,size:2},{buffer:O,type:e.FLOAT,size:1}]),j=m(e),F=y(e,[{buffer:j,type:e.FLOAT,size:3}]),D=new n(e,g,r,i,a,o,d,p,v,A,x,_,w,M,T,S,E,L,C,z,I,P,R,O,N,j,F);return D.update(t),D}var p=1e-6,g=1e-6,v=t(\"gl-shader\"),m=t(\"gl-buffer\"),y=t(\"gl-vao\"),b=t(\"gl-texture2d\"),x=t(\"normals\"),_=t(\"gl-mat4/multiply\"),w=t(\"gl-mat4/invert\"),k=t(\"ndarray\"),A=t(\"colormap\"),M=t(\"simplicial-complex-contour\"),T=t(\"typedarray-pool\"),E=t(\"./lib/shaders\"),L=t(\"./lib/closest-point\"),S=E.meshShader,C=E.wireShader,z=E.pointShader,P=E.pickShader,R=E.pointPickShader,O=E.contourShader,I=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],N=n.prototype;N.isOpaque=function(){return this.opacity>=1},N.isTransparent=function(){return this.opacity<1},N.pickSlots=1,N.setPickBase=function(t){this.pickId=t},N.highlight=function(t){if(!t||!this.contourEnable)return void(this.contourCount=0);for(var e=M(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=T.mallocFloat32(6*a),s=0,l=0;a>l;++l)for(var c=r[l],u=0;2>u;++u){var f=c[0];2===c.length&&(f=c[u]);for(var h=n[f][0],d=n[f][1],p=i[f],g=1-p,v=this.positions[h],m=this.positions[d],y=0;3>y;++y)o[s++]=p*v[y]+g*m[y]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),T.free(o)},N.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=b(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(i(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var s=[],l=[],c=[],u=[],f=[],h=[],d=[],v=[],m=[],y=[],_=[],w=[],k=[],A=[];this.cells=r,this.positions=n;var M=t.vertexNormals,T=t.cellNormals,E=void 0===t.vertexNormalsEpsilon?p:t.vertexNormalsEpsilon,L=void 0===t.faceNormalsEpsilon?g:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=x.faceNormals(r,n,L)),T||M||(M=x.vertexNormals(r,n,E));var S=t.vertexColors,C=t.cellColors,z=t.meshColor||[1,1,1,1],P=t.vertexUVs,R=t.vertexIntensity,O=t.cellUVs,I=t.cellIntensity,N=1/0,j=-(1/0);if(!P&&!O)if(R)for(var F=0;Fq;++q)!isNaN(V[q])&&isFinite(V[q])&&(this.bounds[0][q]=Math.min(this.bounds[0][q],V[q]),this.bounds[1][q]=Math.max(this.bounds[1][q],V[q]));var H=0,G=0,Y=0;t:for(var F=0;Fq;++q)if(isNaN(V[q])||!isFinite(V[q]))continue t;y.push(V[0],V[1],V[2]);var Z;Z=S?S[W]:C?C[F]:z,3===Z.length?_.push(Z[0],Z[1],Z[2],1):_.push(Z[0],Z[1],Z[2],Z[3]);var K;K=P?P[W]:R?[(R[W]-N)/(j-N),0]:O?O[F]:I?[(I[F]-N)/(j-N),0]:[(V[2]-N)/(j-N),0],w.push(K[0],K[1]),B?k.push(B[W]):k.push(U),A.push(F),Y+=1;break;case 2:for(var q=0;2>q;++q)for(var W=X[q],V=n[W],$=0;3>$;++$)if(isNaN(V[$])||!isFinite(V[$]))continue t;for(var q=0;2>q;++q){var W=X[q],V=n[W];h.push(V[0],V[1],V[2]);var Z;Z=S?S[W]:C?C[F]:z,3===Z.length?d.push(Z[0],Z[1],Z[2],1):d.push(Z[0],Z[1],Z[2],Z[3]);var K;K=P?P[W]:R?[(R[W]-N)/(j-N),0]:O?O[F]:I?[(I[F]-N)/(j-N),0]:[(V[2]-N)/(j-N),0],v.push(K[0],K[1]),m.push(F)}G+=1;break;case 3:for(var q=0;3>q;++q)for(var W=X[q],V=n[W],$=0;3>$;++$)if(isNaN(V[$])||!isFinite(V[$]))continue t;for(var q=0;3>q;++q){var W=X[q],V=n[W];s.push(V[0],V[1],V[2]);var Z;Z=S?S[W]:C?C[F]:z,3===Z.length?l.push(Z[0],Z[1],Z[2],1):l.push(Z[0],Z[1],Z[2],Z[3]);var K;K=P?P[W]:R?[(R[W]-N)/(j-N),0]:O?O[F]:I?[(I[F]-N)/(j-N),0]:[(V[2]-N)/(j-N),0],u.push(K[0],K[1]);var Q;Q=M?M[W]:T[F],c.push(Q[0],Q[1],Q[2]),f.push(F)}H+=1}}this.pointCount=Y,this.edgeCount=G,this.triangleCount=H,this.pointPositions.update(y),this.pointColors.update(_),this.pointUVs.update(w),this.pointSizes.update(k),this.pointIds.update(new Uint32Array(A)),this.edgePositions.update(h),this.edgeColors.update(d),this.edgeUVs.update(v),this.edgeIds.update(new Uint32Array(m)),this.trianglePositions.update(s),this.triangleColors.update(l),this.triangleUVs.update(u),this.triangleNormals.update(c),this.triangleIds.update(new Uint32Array(f))}},N.drawTransparent=N.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||I,n=t.view||I,i=t.projection||I,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;3>o;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,contourColor:this.contourColor,texture:0};this.texture.bind(0);var l=new Array(16);_(l,s.view,s.model),_(l,s.projection,l),w(l,l);for(var o=0;3>o;++o)s.eyePosition[o]=l[12+o]/l[15];for(var c=l[15],o=0;3>o;++o)c+=this.lightPosition[o]*l[4*o+3];for(var o=0;3>o;++o){for(var u=l[12+o],f=0;3>f;++f)u+=l[4*f+o]*this.lightPosition[f];s.lightPosition[o]=u/c}if(this.triangleCount>0){var h=this.triShader;h.bind(),h.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}if(this.edgeCount>0&&this.lineWidth>0){var h=this.lineShader;h.bind(),h.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()}if(this.pointCount>0){var h=this.pointShader;h.bind(),h.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind()}if(this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0){var h=this.contourShader;h.bind(),h.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind()}},N.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||I,n=t.view||I,i=t.projection||I,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;3>o;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255},l=this.pickShader;if(l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0){var l=this.pointPickShader;l.bind(),l.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind()}},N.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;ao;++o){for(var s=new Array(r+1),l=0;r>=l;++l)s[l]=t[l][o];i[o]=s}i[r]=new Array(r+1);for(var o=0;r>=o;++o)i[r][o]=1;for(var c=new Array(r+1),o=0;r>o;++o)c[o]=e[o];c[r]=1;var u=a(i,c),f=n(u[r+1]);0===f&&(f=1);for(var h=new Array(r+1),o=0;r>=o;++o)h[o]=n(u[o])/f;return h}e.exports=i;var a=t(\"robust-linear-solve\")},{\"robust-linear-solve\":256}],152:[function(t,e,r){var n=1e-6,i=1e-6;r.vertexNormals=function(t,e,r){for(var i=e.length,a=new Array(i),o=void 0===r?n:r,s=0;i>s;++s)a[s]=[0,0,0];for(var s=0;sx;++x)v[x]=d[x]-p[x],m+=v[x]*v[x],y[x]=g[x]-p[x],b+=y[x]*y[x];if(m*b>o)for(var _=a[u],w=1/Math.sqrt(m*b),x=0;3>x;++x){var k=(x+1)%3,A=(x+2)%3;_[x]+=w*(y[k]*v[A]-y[A]*v[k])}}for(var s=0;i>s;++s){for(var _=a[s],M=0,x=0;3>x;++x)M+=_[x]*_[x];if(M>o)for(var w=1/Math.sqrt(M),x=0;3>x;++x)_[x]*=w;else for(var x=0;3>x;++x)_[x]=0}return a},r.faceNormals=function(t,e,r){for(var n=t.length,a=new Array(n),o=void 0===r?i:r,s=0;n>s;++s){for(var l=t[s],c=new Array(3),u=0;3>u;++u)c[u]=e[l[u]];for(var f=new Array(3),h=new Array(3),u=0;3>u;++u)f[u]=c[1][u]-c[0][u],h[u]=c[2][u]-c[0][u];for(var d=new Array(3),p=0,u=0;3>u;++u){var g=(u+1)%3,v=(u+2)%3;d[u]=f[g]*h[v]-f[v]*h[g],p+=d[u]*d[u]}p=p>o?1/Math.sqrt(p):0;for(var u=0;3>u;++u)d[u]*=p;a[s]=d}return a}},{}],153:[function(t,e,r){\"use 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y=[0,0,0],b=[0,0,0],x=[0,0,0];p.draw=function(t){t=t||m;for(var e=this.gl,r=t.model||d,i=t.view||d,a=t.projection||d,s=this.bounds,l=f(r,i,a,s),c=l.cubeEdges,u=l.axis,h=i[12],p=i[13],_=i[14],w=i[15],k=this.pixelRatio*(a[3]*h+a[7]*p+a[11]*_+a[15]*w)/e.drawingBufferHeight,A=0;3>A;++A)this.lastCubeProps.cubeEdges[A]=c[A],this.lastCubeProps.axis[A]=u[A];for(var M=g,A=0;3>A;++A)o(g[A],A,this.bounds,c,u);for(var e=this.gl,T=v,A=0;3>A;++A)this.backgroundEnable[A]?T[A]=u[A]:T[A]=0;this._background.draw(r,i,a,s,T,this.backgroundColor),this._lines.bind(r,i,a,this);for(var A=0;3>A;++A){var E=[0,0,0];u[A]>0?E[A]=s[1][A]:E[A]=s[0][A];for(var L=0;2>L;++L){var S=(A+1+L)%3,C=(A+1+(1^L))%3;this.gridEnable[S]&&this._lines.drawGrid(S,C,this.bounds,E,this.gridColor[S],this.gridWidth[S]*this.pixelRatio)}for(var L=0;2>L;++L){var S=(A+1+L)%3,C=(A+1+(1^L))%3;this.zeroEnable[C]&&s[0][C]<=0&&s[1][C]>=0&&this._lines.drawZero(S,C,this.bounds,E,this.zeroLineColor[C],this.zeroLineWidth[C]*this.pixelRatio)}}for(var 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a=t(\"gl-buffer\"),o=t(\"gl-vao\"),s=t(\"./shaders/index\");e.exports=i;var l=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],c=n.prototype,u=[0,0,0],f=[0,0,0],h=[0,0];c.isTransparent=function(){return!1},c.drawTransparent=function(t){},c.draw=function(t){var e=this.gl,r=this.vao,n=this.shader;r.bind(),n.bind();var i,a=t.model||l,o=t.view||l,s=t.projection||l;this.axes&&(i=this.axes.lastCubeProps.axis);for(var c=u,d=f,p=0;3>p;++p)i&&i[p]<0?(c[p]=this.bounds[0][p],d[p]=this.bounds[1][p]):(c[p]=this.bounds[1][p],d[p]=this.bounds[0][p]);h[0]=e.drawingBufferWidth,h[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=o,n.uniforms.projection=s,n.uniforms.coordinates=[this.position,c,d],n.uniforms.colors=this.colors,n.uniforms.screenShape=h;for(var p=0;3>p;++p)n.uniforms.lineWidth=this.lineWidth[p]*this.pixelRatio,this.enabled[p]&&(r.draw(e.TRIANGLES,6,6*p),this.drawSides[p]&&r.draw(e.TRIANGLES,12,18+12*p));r.unbind()},c.update=function(t){t&&(\"bounds\"in t&&(this.bounds=t.bounds),\"position\"in 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e=O.indexOf(t);0>e||(O.splice(e,1),I.pop(),F=!0,D=!0,r())}},H.dispose=function(){if(!_&&(_=!0,window.removeEventListener(\"resize\",e),w.removeEventListener(\"webglcontextlost\",s),H.mouseListener.enabled=!1,!H.contextLost)){z.dispose(),R.dispose();for(var t=0;ts;++s){var l=j[s].query(e,G[1]-r-1,H.pickRadius);if(l){if(l.distance>T.distance)continue;for(var c=0;i>c;++c){var u=O[c];if(I[c]===s){var f=u.pick(l);f&&(T.buttons=t,T.screen=l.coord,T.distance=l.distance,T.object=u,T.index=f.distance,T.dataPosition=f.position,T.dataCoordinate=f.dataCoordinate,T.data=f,o=!0)}}}}}a&&a!==T.object&&(a.highlight&&a.highlight(null),F=!0),T.object&&(T.object.highlight&&T.object.highlight(T.data),F=!0),o=o||T.object!==a,o&&H.onselect&&H.onselect(T),1&t&&!(1&X)&&H.onclick&&H.onclick(T),X=t}}),w.addEventListener(\"webglcontextlost\",s);var W=[[1/0,1/0,1/0],[-(1/0),-(1/0),-(1/0)]],Z=[W[0].slice(),W[1].slice()];return x(),H.redraw=function(){_||(F=!0,b())},H}e.exports=s;var l=t(\"3d-view-controls\"),c=t(\"gl-axes3d\"),u=t(\"gl-axes3d/properties\"),f=t(\"gl-spikes3d\"),h=t(\"gl-select-static\"),d=t(\"gl-fbo\"),p=t(\"a-big-triangle\"),g=t(\"mouse-change\"),v=t(\"gl-mat4/perspective\"),m=t(\"./lib/shader\")},{\"./lib/shader\":166,\"3d-view-controls\":167,\"a-big-triangle\":169,\"gl-axes3d\":170,\"gl-axes3d/properties\":180,\"gl-fbo\":123,\"gl-mat4/perspective\":140,\"gl-select-static\":196,\"gl-spikes3d\":182,\"mouse-change\":241}],184:[function(t,e,r){\"use strict\";e.exports={vertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec2 offset;\\nattribute vec4 color;\\n\\nuniform mat3 viewTransform;\\nuniform vec2 pixelScale;\\n\\nvarying vec4 fragColor;\\n\\nvec4 computePosition_1_0(vec2 position, vec2 offset, mat3 view, vec2 scale) {\\n vec3 xposition = view * vec3(position, 1.0);\\n return vec4(\\n xposition.xy + scale * offset * xposition.z,\\n 0,\\n xposition.z);\\n}\\n\\n\\n\\n\\nvoid main() {\\n fragColor = color;\\n\\n gl_Position = computePosition_1_0(\\n position,\\n offset,\\n viewTransform,\\n pixelScale);\\n}\\n\",fragment:\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\\n}\\n\",pickVertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec2 offset;\\nattribute vec4 id;\\n\\nuniform mat3 viewTransform;\\nuniform vec2 pixelScale;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragColor;\\n\\nvec4 computePosition_1_0(vec2 position, vec2 offset, mat3 view, vec2 scale) {\\n vec3 xposition = view * vec3(position, 1.0);\\n return vec4(\\n xposition.xy + scale * offset * xposition.z,\\n 0,\\n xposition.z);\\n}\\n\\n\\n\\n\\nvoid main() {\\n vec4 fragId = id + pickOffset;\\n\\n fragId.y += floor(fragId.x / 256.0);\\n fragId.x -= floor(fragId.x / 256.0) * 256.0;\\n\\n fragId.z += floor(fragId.y / 256.0);\\n fragId.y -= floor(fragId.y / 256.0) * 256.0;\\n\\n fragId.w += floor(fragId.z / 256.0);\\n fragId.z -= floor(fragId.z / 256.0) * 256.0;\\n\\n fragColor = fragId / 255.0;\\n\\n gl_Position = computePosition_1_0(\\n position,\\n offset,\\n viewTransform,\\n pixelScale);\\n}\\n\",pickFragment:\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n gl_FragColor = fragColor;\\n}\\n\"}},{}],185:[function(t,e,r){\"use strict\";function n(t){if(t in h)return h[t];var e=u(t,{polygons:!0,font:\"sans-serif\",textAlign:\"left\",textBaseline:\"alphabetic\"}),r=[],n=[];e.forEach(function(t){t.forEach(function(t){for(var e=0;eo;++o)i[o]=Math.min(i[o],r[a+o]),i[2+o]=Math.max(i[2+o],r[a+o]);return h[t]={coords:r,normals:n,bounds:i}}function i(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.offsetBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.bounds=[1/0,1/0,-(1/0),-(1/0)],this.numPoints=0,this.numVertices=0,this.pickOffset=0,this.points=null}function a(t,e){var r=t.gl,n=o(r,f.vertex,f.fragment),a=o(r,f.pickVertex,f.pickFragment),l=s(r),c=s(r),u=s(r),h=s(r),d=new i(t,n,a,l,c,u,h);return d.update(e),t.addObject(d),d}e.exports=a;var o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"text-cache\"),c=t(\"typedarray-pool\"),u=t(\"vectorize-text\"),f=t(\"./lib/shaders\"),h={},d=i.prototype;!function(){function t(){var t=this.plot,n=this.bounds,i=t.viewBox,a=t.dataBox,o=t.pixelRatio,s=n[2]-n[0],l=n[3]-n[1],c=a[2]-a[0],u=a[3]-a[1];e[0]=2*s/c,e[4]=2*l/u,e[6]=2*(n[0]-a[0])/c-1,e[7]=2*(n[1]-a[1])/u-1;var f=i[2]-i[0],h=i[3]-i[1];r[0]=2*o/f,r[1]=2*o/h}var e=[1,0,0,0,1,0,0,0,1],r=[1,1];d.draw=function(){var n=this.plot,i=this.shader,a=this.numVertices;if(a){var o=n.gl;t.call(this),i.bind(),i.uniforms.pixelScale=r,i.uniforms.viewTransform=e,this.positionBuffer.bind(),i.attributes.position.pointer(),this.offsetBuffer.bind(),i.attributes.offset.pointer(),this.colorBuffer.bind(),i.attributes.color.pointer(o.UNSIGNED_BYTE,!0),o.drawArrays(o.TRIANGLES,0,a)}};var n=[0,0,0,0];d.drawPick=function(i){var a=this.plot,o=this.pickShader,s=this.numVertices,l=a.gl;if(this.pickOffset=i,!s)return i;for(var c=0;4>c;++c)n[c]=i>>8*c&255;return t.call(this),o.bind(),o.uniforms.pixelScale=r,o.uniforms.viewTransform=e,o.uniforms.pickOffset=n,this.positionBuffer.bind(),o.attributes.position.pointer(),this.offsetBuffer.bind(),o.attributes.offset.pointer(),this.idBuffer.bind(),o.attributes.id.pointer(l.UNSIGNED_BYTE,!1),l.drawArrays(l.TRIANGLES,0,s),i+this.numPoints}}(),d.pick=function(t,e,r){var n=this.pickOffset,i=this.numPoints;if(n>r||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}},d.update=function(t){t=t||{};var e=t.positions||[],r=t.colors||[],i=t.glyphs||[],a=t.sizes||[],o=t.borderWidths||[],s=t.borderColors||[];this.points=e;for(var u=this.bounds=[1/0,1/0,-(1/0),-(1/0)],f=0,h=0;h>1;for(var d=0;2>d;++d)u[d]=Math.min(u[d],e[2*h+d]),u[2+d]=Math.max(u[2+d],e[2*h+d])}u[0]===u[2]&&(u[2]+=1),u[3]===u[1]&&(u[3]+=1);for(var p=1/(u[2]-u[0]),g=1/(u[3]-u[1]),v=u[0],m=u[1],y=c.mallocFloat32(2*f),b=c.mallocFloat32(2*f),x=c.mallocUint8(4*f),_=c.mallocUint32(f),w=0,h=0;h 1.0) {\\n discard;\\n }\\n vec4 baseColor = mix(borderColor, color, smoothStep(radius, centerFraction));\\n float alpha = 1.0 - pow(1.0 - baseColor.a, fragWeight);\\n gl_FragColor = vec4(baseColor.rgb * alpha, alpha);\\n}\\n\",r.pickVertex=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n vec3 hgPosition = matrix * vec3(position, 1);\\n gl_Position = vec4(hgPosition.xy, 0, hgPosition.z);\\n gl_PointSize = pointSize;\\n\\n vec4 id = pickId + pickOffset;\\n id.y += floor(id.x / 256.0);\\n id.x -= floor(id.x / 256.0) * 256.0;\\n\\n id.z += floor(id.y / 256.0);\\n id.y -= floor(id.y / 256.0) * 256.0;\\n\\n id.w += floor(id.z / 256.0);\\n id.z -= floor(id.z / 256.0) * 256.0;\\n\\n fragId = id;\\n}\\n\",r.pickFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n float radius = length(2.0*gl_PointCoord.xy-1.0);\\n if(radius > 1.0) {\\n discard;\\n }\\n gl_FragColor = fragId / 255.0;\\n}\\n\"},{}],187:[function(t,e,r){arguments[4][62][0].apply(r,arguments)},{dup:62}],188:[function(t,e,r){\"use strict\";function n(t,e,r,n,a){4*h>=a?i(0,a-1,t,e,r,n):f(0,a-1,t,e,r,n)}function i(t,e,r,n,i,a){for(var o=t+1;e>=o;++o){for(var s=r[o],l=n[2*o],c=n[2*o+1],u=i[o],f=a[o],h=o;h>t;){var d=r[h-1],p=n[2*(h-1)];if((d-s||l-p)>=0)break;r[h]=d,n[2*h]=p,n[2*h+1]=n[2*h-1],i[h]=i[h-1],a[h]=a[h-1],h-=1}r[h]=s,n[2*h]=l,n[2*h+1]=c,i[h]=u,a[h]=f}}function a(t,e,r,n,i,a){var o=r[t],s=n[2*t],l=n[2*t+1],c=i[t],u=a[t];r[t]=r[e],n[2*t]=n[2*e],n[2*t+1]=n[2*e+1],i[t]=i[e],a[t]=a[e],r[e]=o,n[2*e]=s,n[2*e+1]=l,i[e]=c,a[e]=u}function o(t,e,r,n,i,a){r[t]=r[e],n[2*t]=n[2*e],n[2*t+1]=n[2*e+1],i[t]=i[e],a[t]=a[e]}function s(t,e,r,n,i,a,o){var s=n[t],l=i[2*t],c=i[2*t+1],u=a[t],f=o[t];n[t]=n[e],i[2*t]=i[2*e],i[2*t+1]=i[2*e+1],a[t]=a[e],o[t]=o[e],n[e]=n[r],i[2*e]=i[2*r],i[2*e+1]=i[2*r+1],a[e]=a[r],o[e]=o[r],n[r]=s,i[2*r]=l,i[2*r+1]=c,a[r]=u,o[r]=f}function l(t,e,r,n,i,a,o,s,l,c,u){s[t]=s[e],l[2*t]=l[2*e],l[2*t+1]=l[2*e+1],c[t]=c[e],u[t]=u[e],s[e]=r,l[2*e]=n,l[2*e+1]=i,c[e]=a,u[e]=o}function c(t,e,r,n,i){return(r[t]-r[e]||n[2*e]-n[2*t]||i[t]-i[e])<0}function u(t,e,r,n,i,a,o,s){return(e-a[t]||o[2*t]-r||i-s[t])<0}function f(t,e,r,n,d,p){var g=(e-t+1)/6|0,v=t+g,m=e-g,y=t+e>>1,b=y-g,x=y+g,_=v,w=b,k=y,A=x,M=m,T=t+1,E=e-1,L=0;c(_,w,r,n,d,p)&&(L=_,_=w,w=L),c(A,M,r,n,d,p)&&(L=A,A=M,M=L),c(_,k,r,n,d,p)&&(L=_,_=k,k=L),c(w,k,r,n,d,p)&&(L=w,w=k,k=L),c(_,A,r,n,d,p)&&(L=_,_=A,A=L),c(k,A,r,n,d,p)&&(L=k,k=A,A=L),c(w,M,r,n,d,p)&&(L=w,w=M,M=L),c(w,k,r,n,d,p)&&(L=w,w=k,k=L),c(A,M,r,n,d,p)&&(L=A,A=M,M=L);var S=r[w],C=n[2*w],z=n[2*w+1],P=d[w],R=p[w],O=r[A],I=n[2*A],N=n[2*A+1],j=d[A],F=p[A],D=_,B=k,U=M,V=v,q=y,H=m,G=r[D],Y=r[B],X=r[U];r[V]=G,r[q]=Y,r[H]=X;for(var W=0;2>W;++W){var Z=n[2*D+W],K=n[2*B+W],$=n[2*U+W];n[2*V+W]=Z,n[2*q+W]=K,n[2*H+W]=$}var Q=d[D],J=d[B],tt=d[U];d[V]=Q,d[q]=J,d[H]=tt;var et=p[D],rt=p[B],nt=p[U];p[V]=et,p[q]=rt,p[H]=nt,o(b,t,r,n,d,p),o(x,e,r,n,d,p);for(var it=T;E>=it;++it)if(u(it,S,C,z,P,r,n,d))it!==T&&a(it,T,r,n,d,p),++T;else if(!u(it,O,I,N,j,r,n,d))for(;;){if(u(E,O,I,N,j,r,n,d)){u(E,S,C,z,P,r,n,d)?(s(it,T,E,r,n,d,p),++T,--E):(a(it,E,r,n,d,p),--E);break}if(--E=T-2-t?i(t,T-2,r,n,d,p):f(t,T-2,r,n,d,p),h>=e-(E+2)?i(E+2,e,r,n,d,p):f(E+2,e,r,n,d,p),h>=E-T?i(T,E,r,n,d,p):f(T,E,r,n,d,p)}e.exports=n;var h=32},{}],189:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s){for(var l=r,c=r;n>c;++c){var u=t[2*c],f=t[2*c+1],h=e[c];u>=i&&o>=u&&f>=a&&s>=f&&(c===l?l+=1:(t[2*c]=t[2*l],t[2*c+1]=t[2*l+1],e[c]=e[l],t[2*l]=u,t[2*l+1]=f,e[l]=h,l+=1))}return l}function i(t,e,r){this.pixelSize=t,this.offset=e,this.count=r}function a(t,e,r,a){function l(i,a,o,s,c,u){var f=.5*o,h=s+1,d=c-s;r[_]=d,x[_++]=u;for(var p=0;2>p;++p)for(var g=0;2>g;++g){var v=i+p*f,m=a+g*f,y=n(t,e,h,c,v,m,v+f,m+f);if(y!==h){if(y-h>=Math.max(.9*d,32)){var b=c+s>>>1;l(v,m,f,h,b,u+1),h=b}l(v,m,f,h,y,u+1),h=y}}}var c=t.length>>>1;if(1>c)return[];for(var u=1/0,f=1/0,h=-(1/0),d=-(1/0),p=0;c>p;++p){var g=t[2*p],v=t[2*p+1];u=Math.min(u,g),h=Math.max(h,g),f=Math.min(f,v),d=Math.max(d,v),e[p]=p}u===h&&(h+=1+Math.abs(h)),f===d&&(d+=1+Math.abs(h));var m=1/(h-u),y=1/(d-f),b=Math.max(h-u,d-f);a=a||[0,0,0,0],a[0]=u,a[1]=f,a[2]=h,a[3]=d;var x=o.mallocInt32(c),_=0;l(u,f,b,0,c,0),s(x,t,e,r,c);for(var w=[],k=0,A=c,_=c-1;_>=0;--_){t[2*_]=(t[2*_]-u)*m,t[2*_+1]=(t[2*_+1]-f)*y;var M=x[_];M!==k&&(w.push(new i(b*Math.pow(.5,M),_+1,A-(_+1))),A=_+1,k=M)}return w.push(new i(b*Math.pow(.5,M+1),0,A)),o.free(x),w}var o=t(\"typedarray-pool\"),s=t(\"./lib/sort\");e.exports=a},{\"./lib/sort\":188,\"typedarray-pool\":278}],190:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.weightBuffer=n,this.shader=i,this.pickShader=a,this.scales=[],this.size=12,this.borderSize=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.bounds=[1/0,1/0,-(1/0),-(1/0)],this.pickOffset=0,this.points=null,this.xCoords=null}function i(t,e){var r=t.gl,i=o(r),s=o(r),l=o(r),c=a(r,u.pointVertex,u.pointFragment),f=a(r,u.pickVertex,u.pickFragment),h=new n(t,i,s,l,c,f);return h.update(e),t.addObject(h),h}var a=t(\"gl-shader\"),o=t(\"gl-buffer\"),s=t(\"binary-search-bounds\"),l=t(\"snap-points-2d\"),c=t(\"typedarray-pool\"),u=t(\"./lib/shader\");e.exports=i;var f=n.prototype;f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.xCoords&&c.free(this.xCoords),this.plot.removeObject(this)},f.update=function(t){function e(e,r){return e in t?t[e]:r}t=t||{},this.size=e(\"size\",12),this.color=e(\"color\",[1,0,0,1]).slice(),this.borderSize=e(\"borderSize\",1),this.borderColor=e(\"borderColor\",[0,0,0,1]).slice(),this.xCoords&&c.free(this.xCoords);var r=t.positions,n=c.mallocFloat32(r.length),i=c.mallocInt32(r.length>>>1);n.set(r);var a=c.mallocFloat32(r.length);this.points=r,this.scales=l(n,i,a,this.bounds),this.offsetBuffer.update(n),this.pickBuffer.update(i),this.weightBuffer.update(a);for(var o=c.mallocFloat32(r.length>>>1),s=0,u=0;s>>1,this.pickOffset=0},f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.scales,o=this.offsetBuffer,l=this.pickBuffer,c=this.bounds,u=this.size,f=this.borderSize,h=n.gl,d=n.pickPixelRatio,p=n.viewBox,g=n.dataBox;if(0===this.pointCount)return r;var v=c[2]-c[0],m=c[3]-c[1],y=g[2]-g[0],b=g[3]-g[1],x=(p[2]-p[0])*d/n.pixelRatio,_=(p[3]-p[1])*d/n.pixelRatio,w=Math.min(y/x,b/_);t[0]=2*v/y,t[4]=2*m/b,t[6]=2*(c[0]-g[0])/y-1,t[7]=2*(c[1]-g[1])/b-1,this.pickOffset=r,e[0]=255&r,e[1]=r>>8&255,e[2]=r>>16&255,e[3]=r>>24&255,i.bind(),i.uniforms.matrix=t,i.uniforms.color=this.color,i.uniforms.borderColor=this.borderColor,i.uniforms.pointSize=d*(u+f),i.uniforms.pickOffset=e,0===this.borderSize?i.uniforms.centerFraction=2:i.uniforms.centerFraction=u/(u+f+1.25),o.bind(),i.attributes.position.pointer(),l.bind(),i.attributes.pickId.pointer(h.UNSIGNED_BYTE);for(var k=this.xCoords,A=(g[0]-c[0]-w*u*d)/v,M=(g[2]-c[0]+w*u*d)/v,T=a.length-1;T>=0;--T){var E=a[T];if(!(E.pixelSize1)){var L=E.offset,S=E.count+L,C=s.ge(k,A,L,S-1),z=s.lt(k,M,C,S-1)+1;z>C&&h.drawArrays(h.POINTS,C,z-C)}}return r+this.pointCount}}(),f.draw=function(){var t=[1,0,0,0,1,0,0,0,1];return function(){var e=this.plot,r=this.shader,n=this.scales,i=this.offsetBuffer,a=this.bounds,o=this.size,l=this.borderSize,c=e.gl,u=e.pixelRatio,f=e.viewBox,h=e.dataBox;if(0!==this.pointCount){var d=a[2]-a[0],p=a[3]-a[1],g=h[2]-h[0],v=h[3]-h[1],m=f[2]-f[0],y=f[3]-f[1],b=Math.min(g/m,v/y);t[0]=2*d/g,t[4]=2*p/v,t[6]=2*(a[0]-h[0])/g-1,t[7]=2*(a[1]-h[1])/v-1,r.bind(),r.uniforms.matrix=t,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointSize=u*(o+l),r.uniforms.useWeight=1,0===this.borderSize?r.uniforms.centerFraction=2:r.uniforms.centerFraction=o/(o+l+1.25),i.bind(),r.attributes.position.pointer(),this.weightBuffer.bind(),r.attributes.weight.pointer();for(var x=this.xCoords,_=(h[0]-a[0]-b*o*u)/d,w=(h[2]-a[0]+b*o*u)/d,k=!0,A=n.length-1;A>=0;--A){var M=n[A];if(!(M.pixelSize1)){var T=M.offset,E=M.count+T,L=s.ge(x,_,T,E-1),S=s.lt(x,w,L,E-1)+1;S>L&&c.drawArrays(c.POINTS,L,S-L),k&&(k=!1,r.uniforms.useWeight=0)}}}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(n>r||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{\"./lib/shader\":186,\"binary-search-bounds\":187,\"gl-buffer\":118,\"gl-shader\":197,\"snap-points-2d\":189,\"typedarray-pool\":278}],191:[function(t,e,r){\"use strict\";function n(t,e){var r=a[e];if(r||(r=a[e]={}),t in r)return r[t];for(var n=i(t,{textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e}),o=i(t,{triangles:!0,textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e}),s=[[1/0,1/0],[-(1/0),-(1/0)]],l=0;lu;++u)s[0][u]=Math.min(s[0][u],c[u]),s[1][u]=Math.max(s[1][u],c[u]);return r[t]=[o,n,s]}var i=t(\"vectorize-text\");e.exports=n;var a={}},{\"vectorize-text\":280}],192:[function(t,e,r){function n(t,e){var r=i(t,e),n=r.attributes;return n.position.location=0,n.color.location=1,n.glyph.location=2,n.id.location=3,r}var i=t(\"gl-shader\"),a=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if(any(lessThan(position, clipBounds[0])) || \\n any(greaterThan(position, clipBounds[1])) ) {\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float scale = 1.0;\\n if(distance(highlightId, id) < 0.0001) {\\n scale = highlightScale;\\n }\\n\\n vec4 worldPosition = model * vec4(position, 1);\\n vec4 viewPosition = view * worldPosition;\\n viewPosition = viewPosition / viewPosition.w;\\n vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n \\n gl_Position = clipPosition;\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = position;\\n }\\n}\",o=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if(any(lessThan(position, clipBounds[0])) || any(greaterThan(position, clipBounds[1]))) {\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float scale = pixelRatio;\\n if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n scale *= highlightScale;\\n }\\n\\n vec4 worldPosition = model * vec4(position, 1.0);\\n vec4 viewPosition = view * worldPosition;\\n vec4 clipPosition = projection * viewPosition;\\n clipPosition /= clipPosition.w;\\n \\n gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = position;\\n }\\n}\",s=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if(any(lessThan(position, clipBounds[0])) ||\\n any(greaterThan(position, clipBounds[1])) ) {\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float lscale = pixelRatio * scale;\\n if(distance(highlightId, id) < 0.0001) {\\n lscale *= highlightScale;\\n }\\n\\n vec4 clipCenter = projection * view * model * vec4(position, 1);\\n vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n gl_Position = clipPosition;\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = dataPosition;\\n }\\n}\\n\",l=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if(any(lessThan(dataCoordinate, fragClipBounds[0])) ||\\n any(greaterThan(dataCoordinate, fragClipBounds[1])) ) {\\n discard;\\n } else {\\n gl_FragColor = interpColor * opacity;\\n }\\n}\\n\",c=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if(any(lessThan(dataCoordinate, fragClipBounds[0])) || \\n any(greaterThan(dataCoordinate, fragClipBounds[1])) ) {\\n discard;\\n } else {\\n gl_FragColor = vec4(pickGroup, pickId.bgr);\\n }\\n}\",u=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],f={vertex:a,fragment:l,attributes:u},h={vertex:o,fragment:l,attributes:u},d={vertex:s,fragment:l,attributes:u},p={vertex:a,fragment:c,attributes:u},g={vertex:o,fragment:c,attributes:u},v={vertex:s,fragment:c,attributes:u};r.createPerspective=function(t){return n(t,f)},r.createOrtho=function(t){return n(t,h)},r.createProject=function(t){return n(t,d)},r.createPickPerspective=function(t){return n(t,p)},r.createPickOrtho=function(t){return n(t,g)},r.createPickProject=function(t){return n(t,v)}},{\"gl-shader\":197}],193:[function(t,e,r){\"use strict\";function n(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,\n", + "t}function i(t,e,r,i){return n(i,i,r),n(i,i,e),n(i,i,t)}function a(t,e){this.index=t,this.dataCoordinate=this.position=e}function o(t,e,r,n,i,o,s,l,c,u,f,h){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=o,this.glyphBuffer=s,this.idBuffer=l,this.vao=c,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.pickId=0,this.pickPerspectiveShader=u,this.pickOrthoShader=f,this.pickProjectShader=h,this.points=[],this._selectResult=new a(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-(1/0),-(1/0),-(1/0)]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-(1/0),-(1/0),-(1/0)],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-(1/0),-(1/0),-(1/0)],[1/0,1/0,1/0]],this.dirty=!0}function s(t){return t[0]=t[1]=t[2]=0,t}function l(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function c(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function u(t){for(var e=S,r=0;2>r;++r)for(var n=0;3>n;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}function f(t,e,r,n,a){var o,f=e.axesProject,h=e.gl,d=t.uniforms,p=r.model||x,g=r.view||x,v=r.projection||x,y=e.axesBounds,b=u(e.clipBounds);o=e.axes?e.axes.lastCubeProps.axis:[1,1,1],w[0]=2/h.drawingBufferWidth,w[1]=2/h.drawingBufferHeight,t.bind(),d.view=g,d.projection=v,d.screenSize=w,d.highlightId=e.highlightId,d.highlightScale=e.highlightScale,d.clipBounds=b,d.pickGroup=e.pickId/255,d.pixelRatio=e.pixelRatio;for(var _=0;3>_;++_)if(f[_]&&e.projectOpacity[_]<1===n){d.scale=e.projectScale[_],d.opacity=e.projectOpacity[_];for(var S=E,C=0;16>C;++C)S[C]=0;for(var C=0;4>C;++C)S[5*C]=1;S[5*_]=0,o[_]<0?S[12+_]=y[0][_]:S[12+_]=y[1][_],m(S,p,S),d.model=S;var z=(_+1)%3,P=(_+2)%3,R=s(k),O=s(A);R[z]=1,O[P]=1;var I=i(v,g,p,l(M,R)),N=i(v,g,p,l(T,O));if(Math.abs(I[1])>Math.abs(N[1])){var j=I;I=N,N=j,j=R,R=O,O=j;var F=z;z=P,P=F}I[0]<0&&(R[z]=-1),N[1]>0&&(O[P]=-1);for(var D=0,B=0,C=0;4>C;++C)D+=Math.pow(p[4*z+C],2),B+=Math.pow(p[4*P+C],2);R[z]/=Math.sqrt(D),O[P]/=Math.sqrt(B),d.axes[0]=R,d.axes[1]=O,d.fragClipBounds[0]=c(L,b[0],_,-1e8),d.fragClipBounds[1]=c(L,b[1],_,1e8),e.vao.draw(h.TRIANGLES,e.vertexCount),e.lineWidth>0&&(h.lineWidth(e.lineWidth),e.vao.draw(h.LINES,e.lineVertexCount,e.vertexCount))}}function h(t,e,r,n,i,a){var o=r.gl;if(r.vao.bind(),i===r.opacity<1||a){t.bind();var s=t.uniforms;s.model=n.model||x,s.view=n.view||x,s.projection=n.projection||x,w[0]=2/o.drawingBufferWidth,w[1]=2/o.drawingBufferHeight,s.screenSize=w,s.highlightId=r.highlightId,s.highlightScale=r.highlightScale,s.fragClipBounds=P,s.clipBounds=r.axes.bounds,s.opacity=r.opacity,s.pickGroup=r.pickId/255,s.pixelRatio=r.pixelRatio,r.vao.draw(o.TRIANGLES,r.vertexCount),r.lineWidth>0&&(o.lineWidth(r.lineWidth),r.vao.draw(o.LINES,r.lineVertexCount,r.vertexCount))}f(e,r,n,i,a),r.vao.unbind()}function d(t){var e=t.gl,r=y.createPerspective(e),n=y.createOrtho(e),i=y.createProject(e),a=y.createPickPerspective(e),s=y.createPickOrtho(e),l=y.createPickProject(e),c=p(e),u=p(e),f=p(e),h=p(e),d=g(e,[{buffer:c,size:3,type:e.FLOAT},{buffer:u,size:4,type:e.FLOAT},{buffer:f,size:2,type:e.FLOAT},{buffer:h,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new o(e,r,n,i,c,u,f,h,d,a,s,l);return v.update(t),v}var p=t(\"gl-buffer\"),g=t(\"gl-vao\"),v=t(\"typedarray-pool\"),m=t(\"gl-mat4/multiply\"),y=t(\"./lib/shaders\"),b=t(\"./lib/glyphs\"),x=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];e.exports=d;var _=o.prototype;_.pickSlots=1,_.setPickBase=function(t){this.pickId=t},_.isTransparent=function(){if(this.opacity<1)return!0;for(var t=0;3>t;++t)if(this.axesProject[t]&&this.projectOpacity[t]<1)return!0;return!1},_.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;3>t;++t)if(this.axesProject[t]&&this.projectOpacity[t]>=1)return!0;return!1};var w=[0,0],k=[0,0,0],A=[0,0,0],M=[0,0,0,1],T=[0,0,0,1],E=x.slice(),L=[0,0,0],S=[[0,0,0],[0,0,0]],C=[-1e8,-1e8,-1e8],z=[1e8,1e8,1e8],P=[C,z];_.draw=function(t){var e=this.useOrtho?this.orthoShader:this.shader;h(e,this.projectShader,this,t,!1,!1)},_.drawTransparent=function(t){var e=this.useOrtho?this.orthoShader:this.shader;h(e,this.projectShader,this,t,!0,!1)},_.drawPick=function(t){var e=this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader;h(e,this.pickProjectShader,this,t,!1,!0)},_.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||0>e)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;3>i;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},_.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},_.update=function(t){if(t=t||{},\"perspective\"in t&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(\"projectOpacity\"in t)if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{var r=+t.projectOpacity;this.projectOpacity=[r,r,r]}\"opacity\"in t&&(this.opacity=t.opacity),this.dirty=!0;var n=t.position;if(n){var i=t.font||\"normal\",a=t.alignment||[0,0],o=[1/0,1/0,1/0],s=[-(1/0),-(1/0),-(1/0)],l=t.glyph,c=t.color,u=t.size,f=t.angle,h=t.lineColor,d=0,p=0,g=0,m=n.length;t:for(var y=0;m>y;++y){for(var x=n[y],_=0;3>_;++_)if(isNaN(x[_])||!isFinite(x[_]))continue t;var w;w=Array.isArray(l)?b(l[y],i):l?b(l,i):b(\"\\u25cf\",i);var k=w[0],A=w[1],M=w[2];p+=3*k.cells.length,g+=2*A.edges.length}var T=p+g,E=v.mallocFloat(3*T),L=v.mallocFloat(4*T),S=v.mallocFloat(2*T),C=v.mallocUint32(T),z=[0,a[1]],P=0,R=p,O=[0,0,0,1],I=[0,0,0,1],N=Array.isArray(c)&&Array.isArray(c[0]),j=Array.isArray(h)&&Array.isArray(h[0]);t:for(var y=0;m>y;++y){for(var x=n[y],_=0;3>_;++_){if(isNaN(x[_])||!isFinite(x[_])){d+=1;continue t}s[_]=Math.max(s[_],x[_]),o[_]=Math.min(o[_],x[_])}var w;w=Array.isArray(l)?b(l[y],i):l?b(l,i):b(\"\\u25cf\",i);var k=w[0],A=w[1],M=w[2];if(Array.isArray(c)){var F;if(F=N?c[y]:c,3===F.length){for(var _=0;3>_;++_)O[_]=F[_];O[3]=1}else if(4===F.length)for(var _=0;4>_;++_)O[_]=F[_]}else O[0]=O[1]=O[2]=0,O[3]=1;if(Array.isArray(h)){var F;if(F=j?h[y]:h,3===F.length){for(var _=0;3>_;++_)I[_]=F[_];I[_]=1}else if(4===F.length)for(var _=0;4>_;++_)I[_]=F[_]}else I[0]=I[1]=I[2]=0,I[3]=1;var D=.5;Array.isArray(u)?D=+u[y]:u?D=+u:this.useOrtho&&(D=12);var B=0;Array.isArray(f)?B=+f[y]:f&&(B=+f);for(var U=Math.cos(B),V=Math.sin(B),x=n[y],_=0;3>_;++_)s[_]=Math.max(s[_],x[_]),o[_]=Math.min(o[_],x[_]);a[0]<0?z[0]=a[0]*(1+M[1][0]):a[0]>0&&(z[0]=-a[0]*(1+M[0][0]));for(var q=k.cells,H=k.positions,_=0;_Y;++Y){for(var X=0;3>X;++X)E[3*P+X]=x[X];for(var X=0;4>X;++X)L[4*P+X]=O[X];C[P]=d;var W=H[G[Y]];S[2*P]=D*(U*W[0]-V*W[1]+z[0]),S[2*P+1]=D*(V*W[0]+U*W[1]+z[1]),P+=1}for(var q=A.edges,H=A.positions,_=0;_Y;++Y){for(var X=0;3>X;++X)E[3*R+X]=x[X];for(var X=0;4>X;++X)L[4*R+X]=I[X];C[R]=d;var W=H[G[Y]];S[2*R]=D*(U*W[0]-V*W[1]+z[0]),S[2*R+1]=D*(V*W[0]+U*W[1]+z[1]),R+=1}d+=1}this.vertexCount=p,this.lineVertexCount=g,this.pointBuffer.update(E),this.colorBuffer.update(L),this.glyphBuffer.update(S),this.idBuffer.update(new Uint32Array(C)),v.free(E),v.free(L),v.free(S),v.free(C),this.bounds=[o,s],this.points=n,this.pointCount=n.length}},_.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{\"./lib/glyphs\":191,\"./lib/shaders\":192,\"gl-buffer\":118,\"gl-mat4/multiply\":139,\"gl-vao\":226,\"typedarray-pool\":278}],194:[function(t,e,r){\"use strict\";r.boxVertex=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 vertex;\\n\\nuniform vec2 cornerA, cornerB;\\n\\nvoid main() {\\n gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\\n}\\n\",r.boxFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n gl_FragColor = color;\\n}\\n\"},{}],195:[function(t,e,r){\"use strict\";function 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n(t){this.plot=t,this.enable=[!0,!0,!1,!1],this.width=[1,1,1,1],this.color=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.center=[1/0,1/0]}function i(t,e){var r=new n(t);return r.update(e),t.addOverlay(r),r}e.exports=i;var a=n.prototype;a.update=function(t){t=t||{},this.enable=(t.enable||[!0,!0,!1,!1]).slice(),this.width=(t.width||[1,1,1,1]).slice(),this.color=(t.color||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]).map(function(t){return t.slice()}),this.center=(t.center||[1/0,1/0]).slice(),this.plot.setOverlayDirty()},a.draw=function(){var t=this.enable,e=this.width,r=this.color,n=this.center,i=this.plot,a=i.line,o=i.dataBox,s=i.viewBox;if(a.bind(),o[0]<=n[0]&&n[0]<=o[2]&&o[1]<=n[1]&&n[1]<=o[3]){var l=s[0]+(n[0]-o[0])/(o[2]-o[0])*(s[2]-s[0]),c=s[1]+(n[1]-o[1])/(o[3]-o[1])*(s[3]-s[1]);t[0]&&a.drawLine(l,c,s[0],c,e[0],r[0]),t[1]&&a.drawLine(l,c,l,s[1],e[1],r[1]),t[2]&&a.drawLine(l,c,s[2],c,e[2],r[2]),t[3]&&a.drawLine(l,c,l,s[3],e[3],r[3])}},a.dispose=function(){this.plot.removeOverlay(this)}},{}],216:[function(t,e,r){var n=t(\"gl-shader\"),i=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute vec3 f;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 lightPosition, eyePosition;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\n\\nvoid main() {\\n worldCoordinate = vec3(uv.zw, f.x);\\n vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n vec4 clipPosition = projection * view * worldPosition;\\n gl_Position = clipPosition;\\n kill = f.y;\\n value = f.z;\\n planeCoordinate = uv.xy;\\n\\n //Lighting geometry parameters\\n vec4 cameraCoordinate = view * worldPosition;\\n cameraCoordinate.xyz /= cameraCoordinate.w;\\n lightDirection = lightPosition - cameraCoordinate.xyz;\\n eyeDirection = eyePosition - cameraCoordinate.xyz;\\n surfaceNormal = normalize((vec4(normal,0) * inverseModel).xyz);\\n}\\n\",a=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution_2_0(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\n\\n\\nfloat beckmannSpecular_1_1(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness) {\\n return beckmannDistribution_2_0(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\\n}\\n\\n\\n\\nuniform vec3 lowerBound, upperBound;\\nuniform float contourTint;\\nuniform vec4 contourColor;\\nuniform sampler2D colormap;\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\n\\nvoid main() {\\n if(kill > 0.0 ||\\n any(lessThan(worldCoordinate, clipBounds[0])) || any(greaterThan(worldCoordinate, clipBounds[1]))) {\\n discard;\\n }\\n\\n vec3 N = normalize(surfaceNormal);\\n vec3 V = normalize(eyeDirection);\\n vec3 L = normalize(lightDirection);\\n\\n if(gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = beckmannSpecular_1_1(L, V, N, roughness);\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n vec4 surfaceColor = texture2D(colormap, vec2(value, value));\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\\n}\\n\",o=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute float f;\\n\\nuniform mat3 permutation;\\nuniform mat4 model, view, projection;\\nuniform float height, zOffset;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\n\\nvoid main() {\\n vec3 dataCoordinate = permutation * vec3(uv.xy, height);\\n vec4 worldPosition = model * vec4(dataCoordinate, 1.0);\\n\\n vec4 clipPosition = projection * view * worldPosition;\\n clipPosition.z = clipPosition.z + zOffset;\\n\\n gl_Position = clipPosition;\\n value = f;\\n kill = -1.0;\\n worldCoordinate = dataCoordinate;\\n planeCoordinate = uv.zw;\\n\\n //Don't do lighting for contours\\n surfaceNormal = vec3(1,0,0);\\n eyeDirection = vec3(0,1,0);\\n lightDirection = vec3(0,0,1);\\n}\\n\",s=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n float vh = 255.0 * v;\\n float upper = floor(vh);\\n float lower = fract(vh);\\n return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n if(kill > 0.0 ||\\n any(lessThan(worldCoordinate, clipBounds[0])) || any(greaterThan(worldCoordinate, clipBounds[1]))) {\\n discard;\\n }\\n vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\";r.createShader=function(t){var e=n(t,i,a,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,i,s,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,o,a,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,o,s,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{\"gl-shader\":197}],217:[function(t,e,r){arguments[4][21][0].apply(r,arguments)},{dup:21}],218:[function(t,e,r){\"use strict\";function n(t){if(t in l)return l[t];for(var e=[],r=0;t>r;++r)e.push(\"out\",r,\"s=0.5*(inp\",r,\"l-inp\",r,\"r);\");for(var n=[\"array\"],i=[\"junk\"],r=0;t>r;++r){n.push(\"array\"),i.push(\"out\"+r+\"s\");var a=o(t);a[r]=-1,n.push({array:0,offset:a.slice()}),a[r]=1,n.push({array:0,offset:a.slice()}),i.push(\"inp\"+r+\"l\",\"inp\"+r+\"r\")}return l[t]=s({args:n,pre:u,post:u,body:{body:e.join(\"\"),args:i.map(function(t){return{name:t,lvalue:0===t.indexOf(\"out\"),rvalue:0===t.indexOf(\"inp\"),count:\"junk\"!==t|0}}),thisVars:[],localVars:[]},funcName:\"fdTemplate\"+t})}function i(t){function e(e){for(var r=a-e.length,n=[],i=[],s=[],l=0;a>l;++l)e.indexOf(l+1)>=0?s.push(\"0\"):e.indexOf(-(l+1))>=0?s.push(\"s[\"+l+\"]-1\"):(s.push(\"-1\"),n.push(\"1\"),i.push(\"s[\"+l+\"]-2\"));var c=\".lo(\"+n.join()+\").hi(\"+i.join()+\")\";if(0===n.length&&(c=\"\"),r>0){o.push(\"if(1\");for(var l=0;a>l;++l)e.indexOf(l+1)>=0||e.indexOf(-(l+1))>=0||o.push(\"&&s[\",l,\"]>2\");o.push(\"){grad\",r,\"(src.pick(\",s.join(),\")\",c);for(var l=0;a>l;++l)e.indexOf(l+1)>=0||e.indexOf(-(l+1))>=0||o.push(\",dst.pick(\",s.join(),\",\",l,\")\",c);o.push(\");\")}for(var l=0;l1){dst.set(\",s.join(),\",\",u,\",0.5*(src.get(\",h.join(),\")-src.get(\",d.join(),\")))}else{dst.set(\",s.join(),\",\",u,\",0)};\"):o.push(\"if(s[\",u,\"]>1){diff(\",f,\",src.pick(\",h.join(),\")\",c,\",src.pick(\",d.join(),\")\",c,\");}else{zero(\",f,\");};\");break;case\"mirror\":0===r?o.push(\"dst.set(\",s.join(),\",\",u,\",0);\"):o.push(\"zero(\",f,\");\");break;case\"wrap\":var p=s.slice(),g=s.slice();e[l]<0?(p[u]=\"s[\"+u+\"]-2\",g[u]=\"0\"):(p[u]=\"s[\"+u+\"]-1\",g[u]=\"1\"),0===r?o.push(\"if(s[\",u,\"]>2){dst.set(\",s.join(),\",\",u,\",0.5*(src.get(\",p.join(),\")-src.get(\",g.join(),\")))}else{dst.set(\",s.join(),\",\",u,\",0)};\"):o.push(\"if(s[\",u,\"]>2){diff(\",f,\",src.pick(\",p.join(),\")\",c,\",src.pick(\",g.join(),\")\",c,\");}else{zero(\",f,\");};\");break;default:throw new Error(\"ndarray-gradient: Invalid boundary condition\")}}r>0&&o.push(\"};\")}var r=t.join(),i=c[r];if(i)return i;for(var a=t.length,o=[\"function gradient(dst,src){var s=src.shape.slice();\"],s=0;1<s;++s){for(var u=[],d=0;a>d;++d)s&1<=s;++s)v.push(\"grad\"+s),m.push(n(s));v.push(o.join(\"\"));var y=Function.apply(void 0,v),i=y.apply(void 0,m);return l[r]=i,i}function a(t,e,r){if(Array.isArray(r)){if(r.length!==e.dimension)throw new Error(\"ndarray-gradient: invalid boundary conditions\")}else r=\"string\"==typeof r?o(e.dimension,r):o(e.dimension,\"clamp\");if(t.dimension!==e.dimension+1)throw new Error(\"ndarray-gradient: output dimension must be +1 input dimension\");if(t.shape[e.dimension]!==e.dimension)throw new Error(\"ndarray-gradient: output shape must match input shape\");for(var n=0;nr;++r)for(o=o||e.surfaceProject[r],n=0;3>n;++n)s=s||e.contourProject[r][n];for(r=0;3>r;++r){var l=D.projections[r];for(n=0;16>n;++n)l[n]=0;for(n=0;4>n;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],k(l,t.model,l);var c=D.clipBounds[r];for(i=0;2>i;++i)for(n=0;3>n;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return D.showSurface=o,D.showContour=s,D}function s(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=B;n.model=t.model||R,n.view=t.view||R,n.projection=t.projection||R,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.contourColor=this.contourColor[0],n.inverseModel=A(n.inverseModel,n.model);for(var i=0;2>i;++i)for(var a=n.clipBounds[i],s=0;3>s;++s)a[s]=Math.min(Math.max(this.clipBounds[i][s],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=V;var l=U;for(k(l,n.view,n.model),k(l,n.projection,l),A(l,l),i=0;3>i;++i)n.eyePosition[i]=l[12+i]/l[15];var c=l[15];for(i=0;3>i;++i)c+=this.lightPosition[i]*l[4*i+3];for(i=0;3>i;++i){var u=l[12+i];for(s=0;3>s;++s)u+=l[4*s+i]*this.lightPosition[s];n.lightPosition[i]=u/c}var f=o(n,this);if(f.showSurface&&e===this.opacity<1){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;3>i;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=f.projections[i],this._shader.uniforms.clipBounds=f.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(f.showContour&&!e){var h=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,h.bind(),h.uniforms=n;var d=this._contourVAO;for(d.bind(),i=0;3>i;++i)for(h.uniforms.permutation=I[i],r.lineWidth(this.contourWidth[i]),s=0;si;++i)for(h.uniforms.model=f.projections[i],h.uniforms.clipBounds=f.clipBounds[i],s=0;3>s;++s)if(this.contourProject[i][s]){h.uniforms.permutation=I[s],r.lineWidth(this.contourWidth[s]);for(var p=0;pi;++i)if(0!==this._dynamicCounts[i])for(h.uniforms.model=n.model,h.uniforms.clipBounds=n.clipBounds,h.uniforms.permutation=I[i],r.lineWidth(this.dynamicWidth[i]),h.uniforms.contourColor=this.dynamicColor[i],h.uniforms.contourTint=this.dynamicTint[i],h.uniforms.height=this.dynamicLevel[i],d.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),s=0;3>s;++s)this.contourProject[s][i]&&(h.uniforms.model=f.projections[s],h.uniforms.clipBounds=f.clipBounds[s],d.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));d.unbind()}}function l(t,e){var r=e.shape.slice(),n=t.shape.slice();b.assign(t.lo(1,1).hi(r[0],r[1]),e),b.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),b.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),b.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),b.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))}function c(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function u(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function f(t){if(Array.isArray(t)){if(Array.isArray(t))return[u(t[0]),u(t[1]),u(t[2])];var e=u(t);return[e.slice(),e.slice(),e.slice()]}}function h(t){var e=t.gl,r=L(e),n=C(e),i=S(e),o=z(e),s=p(e),l=g(e,[{buffer:s,size:4,stride:P,offset:0},{buffer:s,size:3,stride:P,offset:16},{buffer:s,size:3,stride:P,offset:28}]),c=p(e),u=g(e,[{buffer:c,size:4,stride:20,offset:0},{buffer:c,size:1,stride:20,offset:16}]),f=p(e),h=g(e,[{buffer:f,size:2,type:e.FLOAT}]),d=v(e,1,N,e.RGBA,e.UNSIGNED_BYTE);d.minFilter=e.LINEAR,d.magFilter=e.LINEAR;var m=new a(e,[0,0],[[0,0,0],[0,0,0]],r,n,s,l,d,i,o,c,u,f,h),y={levels:[[],[],[]]};for(var b in t)y[b]=t[b];return y.colormap=y.colormap||\"jet\",m.update(y),m}e.exports=h;var d=t(\"bit-twiddle\"),p=t(\"gl-buffer\"),g=t(\"gl-vao\"),v=t(\"gl-texture2d\"),m=t(\"typedarray-pool\"),y=t(\"colormap\"),b=t(\"ndarray-ops\"),x=t(\"ndarray-pack\"),_=t(\"ndarray\"),w=t(\"surface-nets\"),k=t(\"gl-mat4/multiply\"),A=t(\"gl-mat4/invert\"),M=t(\"binary-search-bounds\"),T=t(\"ndarray-gradient\"),E=t(\"./lib/shaders\"),L=E.createShader,S=E.createContourShader,C=E.createPickShader,z=E.createPickContourShader,P=40,R=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],O=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],I=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];!function(){for(var t=0;3>t;++t){var e=I[t],r=(t+1)%3,n=(t+2)%3;e[r+0]=1,e[n+3]=1,e[t+6]=1}}();var N=265,j=a.prototype;j.isTransparent=function(){return this.opacity<1},j.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;3>t;++t)if(this._contourCounts[t].length>0||this._dynamicCounts[t]>0)return!0;return!1},j.pickSlots=1,j.setPickBase=function(t){this.pickId=t};var F=[0,0,0],D={showSurface:!1,showContour:!1,projections:[R.slice(),R.slice(),R.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]},B={model:R,view:R,projection:R,inverseModel:R.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1},U=R.slice(),V=[1,0,0,0,1,0,0,0,1];j.draw=function(t){return s.call(this,t,!1)},j.drawTransparent=function(t){return s.call(this,t,!0)};var q={model:R,view:R,projection:R,inverseModel:R,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};j.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=q;r.model=t.model||R,r.view=t.view||R,r.projection=t.projection||R,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.permutation=V;for(var n=0;2>n;++n)for(var i=r.clipBounds[n],a=0;3>a;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var s=o(r,this);if(s.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;3>n;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=s.projections[n],this._pickShader.uniforms.clipBounds=s.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(s.showContour){var l=this._contourPickShader;l.bind(),l.uniforms=r;var c=this._contourVAO;for(c.bind(),a=0;3>a;++a)for(e.lineWidth(this.contourWidth[a]),l.uniforms.permutation=I[a],n=0;nn;++n)for(l.uniforms.model=s.projections[n],l.uniforms.clipBounds=s.clipBounds[n],a=0;3>a;++a)if(this.contourProject[n][a]){l.uniforms.permutation=I[a],e.lineWidth(this.contourWidth[a]);for(var u=0;u>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;2>u;++u)for(var f=u?a:1-a,h=0;2>h;++h)for(var d=h?l:1-l,p=i+u,g=s+h,v=f*d,m=0;3>m;++m)c[m]+=this._field[m].get(p,g)*v;for(var y=this._pickResult.level,b=0;3>b;++b)if(y[b]=M.le(this.contourLevels[b],c[b]),y[b]<0)this.contourLevels[b].length>0&&(y[b]=0);else if(y[b]Math.abs(_-c[b])&&(y[b]+=1)}for(r.index[0]=.5>a?i:i+1,r.index[1]=.5>l?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],m=0;3>m;++m)r.dataCoordinate[m]=this._field[m].get(r.index[0],r.index[1]);return r},j.update=function(t){t=t||{},this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=c(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=c(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=c(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=f(t.contourColor)),\"contourProject\"in t&&(this.contourProject=c(t.contourProject,function(t){return c(t,Boolean)})),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=f(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=c(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=c(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds);var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t||\"coords\"in t){var n=(e.shape[0]+2)*(e.shape[1]+2);n>this._field[2].data.length&&(m.freeFloat(this._field[2].data),this._field[2].data=m.mallocFloat(d.nextPow2(n))),this._field[2]=_(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),l(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;2>o;++o)this._field[2].size>this._field[o].data.length&&(m.freeFloat(this._field[o].data),this._field[o].data=m.mallocFloat(this._field[2].size)),this._field[o]=_(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var s=t.coords;if(!Array.isArray(s)||3!==s.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;2>o;++o){var u=s[o];for(y=0;2>y;++y)if(u.shape[y]!==a[y])throw new Error(\"gl-surface: coords have incorrect shape\");l(this._field[o],u)}}else if(t.ticks){var h=t.ticks;if(!Array.isArray(h)||2!==h.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;2>o;++o){var p=h[o];if((Array.isArray(p)||p.length)&&(p=_(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var g=_(p.data,a);g.stride[o]=p.stride[0],g.stride[1^o]=0,l(this._field[o],g)}}else{for(o=0;2>o;++o){var v=[0,0];v[o]=1,this._field[o]=_(this._field[o].data,[a[0]+2,a[1]+2],v,0)}this._field[0].set(0,0,0);for(var y=0;yo;++o)T(x.pick(o),b[o],\"mirror\");var k=_(m.mallocFloat(3*b[2].size),[a[0]+2,a[1]+2,3]);for(o=0;oI?(I=Math.max(Math.abs(z),Math.abs(P),Math.abs(R)),1e-8>I?(R=1,P=z=0,I=1):I=1/I):I=1/Math.sqrt(I),k.set(o,y,0,z*I),k.set(o,y,1,P*I),k.set(o,y,2,R*I)}m.free(x.data);var N=[1/0,1/0,1/0],j=[-(1/0),-(1/0),-(1/0)],F=1/0,D=-(1/0),B=(a[0]-1)*(a[1]-1)*6,U=m.mallocFloat(d.nextPow2(10*B)),V=0,q=0;for(o=0;oH;++H)for(var G=0;2>G;++G)for(var Y=0;3>Y;++Y){var X=this._field[Y].get(1+o+H,1+y+G);if(isNaN(X)||!isFinite(X))continue t}for(Y=0;6>Y;++Y){var W=o+O[Y][0],Z=y+O[Y][1],K=this._field[0].get(W+1,Z+1),$=this._field[1].get(W+1,Z+1);X=this._field[2].get(W+1,Z+1);var Q=X;z=k.get(W+1,Z+1,0),P=k.get(W+1,Z+1,1),R=k.get(W+1,Z+1,2),t.intensity&&(Q=t.intensity.get(W,Z)),U[V++]=W,U[V++]=Z,U[V++]=K,U[V++]=$,U[V++]=X,U[V++]=0,U[V++]=Q,U[V++]=z,U[V++]=P,U[V++]=R,N[0]=Math.min(N[0],K),N[1]=Math.min(N[1],$),N[2]=Math.min(N[2],X),F=Math.min(F,Q),j[0]=Math.max(j[0],K),j[1]=Math.max(j[1],$),j[2]=Math.max(j[2],X),D=Math.max(D,Q),q+=1}}for(t.intensityBounds&&(F=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;V>o;o+=10)U[o]=(U[o]-F)/(D-F);this._vertexCount=q,this._coordinateBuffer.update(U.subarray(0,V)),m.freeFloat(U),m.free(k.data),this.bounds=[N,j],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===F&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[F,D]}if(\"levels\"in t){var J=t.levels;for(J=Array.isArray(J[0])?J.slice():[[],[],J],o=0;3>o;++o)J[o]=J[o].slice(),J.sort(function(t,e){return t-e});t:for(o=0;3>o;++o){if(J[o].length!==this.contourLevels[o].length){r=!0;break}for(y=0;yet;++et){J=this.contourLevels[et];var rt=[],nt=[],it=[0,0,0];for(o=0;oY;++Y){var st=at.positions[ot[Y]],lt=st[0],ct=0|Math.floor(lt),ut=lt-ct,ft=st[1],ht=0|Math.floor(ft),dt=ft-ht,pt=!1;\n", + "e:for(var gt=0;3>gt;++gt){it[gt]=0;var vt=(et+gt+1)%3;for(H=0;2>H;++H){var mt=H?ut:1-ut;for(W=0|Math.min(Math.max(ct+H,0),a[0]),G=0;2>G;++G){var yt=G?dt:1-dt;if(Z=0|Math.min(Math.max(ht+G,0),a[1]),X=2>gt?this._field[vt].get(W,Z):(this.intensity.get(W,Z)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(X)||isNaN(X)){pt=!0;break e}var bt=mt*yt;it[gt]+=bt*X}}}if(pt){if(Y>0){for(var xt=0;5>xt;++xt)tt.pop();q-=1}continue t}tt.push(it[0],it[1],st[0],st[1],it[2]),q+=1}}nt.push(q)}this._contourOffsets[et]=rt,this._contourCounts[et]=nt}var _t=m.mallocFloat(tt.length);for(o=0;ot;++t)m.freeFloat(this._field[t].data)},j.highlight=function(t){if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(var e=0;3>e;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;var r;if(r=this.snapToData?t.dataCoordinate:t.position,this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=m.mallocFloat(12*i[0]*i[1]),o=0;3>o;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var s=(o+1)%3,l=(o+2)%3,c=this._field[o],u=this._field[s],f=this._field[l],h=(this.intensity,w(c,r[o])),d=h.cells,p=h.positions;for(this._dynamicOffsets[o]=n,e=0;ev;++v){var y=p[g[v]],b=+y[0],x=0|b,_=0|Math.min(x+1,i[0]),k=b-x,A=1-k,M=+y[1],T=0|M,E=0|Math.min(T+1,i[1]),L=M-T,S=1-L,C=A*S,z=A*L,P=k*S,R=k*L,O=C*u.get(x,T)+z*u.get(x,E)+P*u.get(_,T)+R*u.get(_,E),I=C*f.get(x,T)+z*f.get(x,E)+P*f.get(_,T)+R*f.get(_,E);if(isNaN(O)||isNaN(I)){v&&(n-=1);break}a[2*n+0]=O,a[2*n+1]=I,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),m.freeFloat(a)}}},{\"./lib/shaders\":216,\"binary-search-bounds\":217,\"bit-twiddle\":50,colormap:100,\"gl-buffer\":118,\"gl-mat4/invert\":137,\"gl-mat4/multiply\":139,\"gl-texture2d\":222,\"gl-vao\":226,ndarray:253,\"ndarray-gradient\":218,\"ndarray-ops\":252,\"ndarray-pack\":219,\"surface-nets\":272,\"typedarray-pool\":278}],222:[function(t,e,r){\"use strict\";function n(t){v=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],m=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],y=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function i(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(0>e||e>i||0>r||r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function a(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}function o(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function s(t,e,r,n,i,a,s,l){var c=l.dtype,u=l.shape.slice();if(u.length<2||u.length>3)throw new Error(\"gl-texture2d: Invalid ndarray, must be 2d or 3d\");var f=0,h=0,v=o(u,l.stride.slice());\"float32\"===c?f=t.FLOAT:\"float64\"===c?(f=t.FLOAT,v=!1,c=\"float32\"):\"uint8\"===c?f=t.UNSIGNED_BYTE:(f=t.UNSIGNED_BYTE,v=!1,c=\"uint8\");var m=1;if(2===u.length)h=t.LUMINANCE,u=[u[0],u[1],1],l=d(l.data,u,[l.stride[0],l.stride[1],1],l.offset);else{if(3!==u.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===u[2])h=t.ALPHA;else if(2===u[2])h=t.LUMINANCE_ALPHA;else if(3===u[2])h=t.RGB;else{if(4!==u[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");h=t.RGBA}m=u[2]}if(h!==t.LUMINANCE&&h!==t.ALPHA||i!==t.LUMINANCE&&i!==t.ALPHA||(h=i),h!==i)throw new Error(\"gl-texture2d: Incompatible texture format for setPixels\");var y=l.size,x=s.indexOf(n)<0;if(x&&s.push(n),f===a&&v)0===l.offset&&l.data.length===y?x?t.texImage2D(t.TEXTURE_2D,n,i,u[0],u[1],0,i,a,l.data):t.texSubImage2D(t.TEXTURE_2D,n,e,r,u[0],u[1],i,a,l.data):x?t.texImage2D(t.TEXTURE_2D,n,i,u[0],u[1],0,i,a,l.data.subarray(l.offset,l.offset+y)):t.texSubImage2D(t.TEXTURE_2D,n,e,r,u[0],u[1],i,a,l.data.subarray(l.offset,l.offset+y));else{var _;_=a===t.FLOAT?g.mallocFloat32(y):g.mallocUint8(y);var w=d(_,u,[u[2],u[2]*u[0],1]);f===t.FLOAT&&a===t.UNSIGNED_BYTE?b(w,l):p.assign(w,l),x?t.texImage2D(t.TEXTURE_2D,n,i,u[0],u[1],0,i,a,_.subarray(0,y)):t.texSubImage2D(t.TEXTURE_2D,n,e,r,u[0],u[1],i,a,_.subarray(0,y)),a===t.FLOAT?g.freeFloat32(_):g.freeUint8(_)}}function l(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function c(t,e,r,n,i){var o=t.getParameter(t.MAX_TEXTURE_SIZE);if(0>e||e>o||0>r||r>o)throw new Error(\"gl-texture2d: Invalid texture shape\");if(i===t.FLOAT&&!t.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var s=l(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new a(t,s,e,r,n,i)}function u(t,e,r,n){var i=l(t);return t.texImage2D(t.TEXTURE_2D,0,r,r,n,e),new a(t,i,0|e.width,0|e.height,r,n)}function f(t,e){var r=e.dtype,n=e.shape.slice(),i=t.getParameter(t.MAX_TEXTURE_SIZE);if(n[0]<0||n[0]>i||n[1]<0||n[1]>i)throw new Error(\"gl-texture2d: Invalid texture size\");var s=o(n,e.stride.slice()),c=0;\"float32\"===r?c=t.FLOAT:\"float64\"===r?(c=t.FLOAT,s=!1,r=\"float32\"):\"uint8\"===r?c=t.UNSIGNED_BYTE:(c=t.UNSIGNED_BYTE,s=!1,r=\"uint8\");var u=0;if(2===n.length)u=t.LUMINANCE,n=[n[0],n[1],1],e=d(e.data,n,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==n.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===n[2])u=t.ALPHA;else if(2===n[2])u=t.LUMINANCE_ALPHA;else if(3===n[2])u=t.RGB;else{if(4!==n[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");u=t.RGBA}}c!==t.FLOAT||t.getExtension(\"OES_texture_float\")||(c=t.UNSIGNED_BYTE,s=!1);var f,h,v=e.size;if(s)f=0===e.offset&&e.data.length===v?e.data:e.data.subarray(e.offset,e.offset+v);else{var m=[n[2],n[2]*n[0],1];h=g.malloc(v,r);var y=d(h,n,m,0);\"float32\"!==r&&\"float64\"!==r||c!==t.UNSIGNED_BYTE?p.assign(y,e):b(y,e),f=h.subarray(0,v)}var x=l(t);return t.texImage2D(t.TEXTURE_2D,0,u,n[0],n[1],0,u,c,f),s||g.free(h),new a(t,x,n[0],n[1],u,c)}function h(t){if(arguments.length<=1)throw new Error(\"gl-texture2d: Missing arguments for texture2d constructor\");if(v||n(t),\"number\"==typeof arguments[1])return c(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return c(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(\"object\"==typeof arguments[1]){var e=arguments[1];if(e instanceof HTMLCanvasElement||e instanceof HTMLImageElement||e instanceof HTMLVideoElement||e instanceof ImageData)return u(t,e,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return f(t,e)}throw new Error(\"gl-texture2d: Invalid arguments for texture2d constructor\")}var d=t(\"ndarray\"),p=t(\"ndarray-ops\"),g=t(\"typedarray-pool\");e.exports=h;var v=null,m=null,y=null,b=function(t,e){p.muls(t,e,255)},x=a.prototype;Object.defineProperties(x,{minFilter:{get:function(){return this._minFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&v.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")||(t=e.NEAREST)),m.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,t),this._minFilter=t}},magFilter:{get:function(){return this._magFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&v.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")||(t=e.NEAREST)),m.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,t),this._magFilter=t}},mipSamples:{get:function(){return this._anisoSamples},set:function(t){var e=this._anisoSamples;if(this._anisoSamples=0|Math.max(t,1),e!==this._anisoSamples){var r=gl.getExtension(\"EXT_texture_filter_anisotropic\");r&&this.gl.texParameterf(this.gl.TEXTURE_2D,r.TEXTURE_MAX_ANISOTROPY_EXT,this._anisoSamples)}return this._anisoSamples}},wrapS:{get:function(){return this._wrapS},set:function(t){if(this.bind(),y.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_S,t),this._wrapS=t}},wrapT:{get:function(){return this._wrapT},set:function(t){if(this.bind(),y.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_T,t),this._wrapT=t}},wrap:{get:function(){return this._wrapVector},set:function(t){if(Array.isArray(t)||(t=[t,t]),2!==t.length)throw new Error(\"gl-texture2d: Must specify wrap mode for rows and columns\");for(var e=0;2>e;++e)if(y.indexOf(t[e])<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);this._wrapS=t[0],this._wrapT=t[1];var r=this.gl;return this.bind(),r.texParameteri(r.TEXTURE_2D,r.TEXTURE_WRAP_S,this._wrapS),r.texParameteri(r.TEXTURE_2D,r.TEXTURE_WRAP_T,this._wrapT),t}},shape:{get:function(){return this._shapeVector},set:function(t){if(Array.isArray(t)){if(2!==t.length)throw new Error(\"gl-texture2d: Invalid texture shape\")}else t=[0|t,0|t];return i(this,0|t[0],0|t[1]),[0|t[0],0|t[1]]}},width:{get:function(){return this._shape[0]},set:function(t){return t=0|t,i(this,t,this._shape[1]),t}},height:{get:function(){return this._shape[1]},set:function(t){return t=0|t,i(this,this._shape[0],t),t}}}),x.bind=function(t){var e=this.gl;return void 0!==t&&e.activeTexture(e.TEXTURE0+(0|t)),e.bindTexture(e.TEXTURE_2D,this.handle),void 0!==t?0|t:e.getParameter(e.ACTIVE_TEXTURE)-e.TEXTURE0},x.dispose=function(){this.gl.deleteTexture(this.handle)},x.generateMipmap=function(){this.bind(),this.gl.generateMipmap(this.gl.TEXTURE_2D);for(var t=Math.min(this._shape[0],this._shape[1]),e=0;t>0;++e,t>>>=1)this._mipLevels.indexOf(e)<0&&this._mipLevels.push(e)},x.setPixels=function(t,e,r,n){var i=this.gl;if(this.bind(),Array.isArray(e)?(n=r,r=0|e[1],e=0|e[0]):(e=e||0,r=r||0),n=n||0,t instanceof 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h=this.lastPickResult=f;this.spikes.update({center:u.dataCoord}),h.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(u.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(u.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio];var d=h.hoverinfo;if(\"all\"!==d){var p=d.split(\"+\");-1===p.indexOf(\"x\")&&(h.traceCoord[0]=void 0),-1===p.indexOf(\"y\")&&(h.traceCoord[1]=void 0),-1===p.indexOf(\"z\")&&(h.traceCoord[2]=void 0),-1===p.indexOf(\"text\")&&(h.textLabel=void 0),-1===p.indexOf(\"name\")&&(h.name=void 0)}l.loneHover({x:h.screenCoord[0],y:h.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",h.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",h.traceCoord[1]),zLabel:h.traceCoord[2],text:h.textLabel,name:h.name,color:h.color},{container:this.svgContainer}),this.lastPickResult={dataCoord:u.dataCoord}}}else!u&&this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,l.loneUnhover(this.svgContainer))}t.draw()}},m.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return s.tickText(r,r.c2l(e),\"hover\").text}}},{\"../../lib/html2unicode\":380,\"../../lib/show_no_webgl_msg\":390,\"../../plots/cartesian/axes\":403,\"../../plots/cartesian/graph_interact\":410,\"./camera\":434,\"./convert\":435,\"gl-plot2d\":165,\"gl-select-box\":195,\"gl-spikes2d\":215}],438:[function(t,e,r){\"use strict\";function n(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];\"distanceLimits\"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]),\"zoomMin\"in e&&(r[0]=e.zoomMin),\"zoomMax\"in e&&(r[1]=e.zoomMax);var n=a({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||\"orbit\",distanceLimits:r}),l=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],c=0,u=t.clientWidth,f=t.clientHeight,h={keyBindingMode:\"rotate\",view:n,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:n.modes,tick:function(){var e=i(),r=this.delay,a=e-2*r;n.idle(e-r),n.recalcMatrix(a),n.flush(e-(100+2*r));for(var o=!0,s=n.computedMatrix,h=0;16>h;++h)o=o&&l[h]===s[h],l[h]=s[h];var d=t.clientWidth===u&&t.clientHeight===f;return u=t.clientWidth,f=t.clientHeight,o?!d:(c=Math.exp(n.computedRadius[0]),!0)},lookAt:function(t,e,r){n.lookAt(n.lastT(),t,e,r)},rotate:function(t,e,r){n.rotate(n.lastT(),t,e,r)},pan:function(t,e,r){n.pan(n.lastT(),t,e,r)},translate:function(t,e,r){n.translate(n.lastT(),t,e,r)}};Object.defineProperties(h,{matrix:{get:function(){return n.computedMatrix},set:function(t){return n.setMatrix(n.lastT(),t),n.computedMatrix},enumerable:!0},mode:{get:function(){return n.getMode()},set:function(t){var e=n.computedUp.slice(),r=n.computedEye.slice(),a=n.computedCenter.slice();if(n.setMode(t),\"turntable\"===t){var o=i();n._active.lookAt(o,r,a,e),n._active.lookAt(o+500,r,a,[0,0,1]),n._active.flush(o)}return n.getMode()},enumerable:!0},center:{get:function(){return n.computedCenter},set:function(t){return n.lookAt(n.lastT(),null,t),n.computedCenter},enumerable:!0},eye:{get:function(){return n.computedEye},set:function(t){return n.lookAt(n.lastT(),t),n.computedEye},enumerable:!0},up:{get:function(){return n.computedUp},set:function(t){return n.lookAt(n.lastT(),null,null,t),n.computedUp},enumerable:!0},distance:{get:function(){return c},set:function(t){return n.setDistance(n.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return n.getDistanceLimits(r)},set:function(t){return 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i=t(\"right-now\"),a=t(\"3d-view\"),o=t(\"mouse-change\"),s=t(\"mouse-wheel\")},{\"3d-view\":39,\"mouse-change\":241,\"mouse-wheel\":245,\"right-now\":255}],439:[function(t,e,r){\"use strict\";function n(t,e){for(var r=0;3>r;++r){var n=s[r];e[n]._gd=t}}var i=t(\"./scene\"),a=t(\"../plots\"),o=t(\"../../constants/xmlns_namespaces\"),s=[\"xaxis\",\"yaxis\",\"zaxis\"];r.name=\"gl3d\",r.attr=\"scene\",r.idRoot=\"scene\",r.idRegex=/^scene([2-9]|[1-9][0-9]+)?$/,r.attrRegex=/^scene([2-9]|[1-9][0-9]+)?$/,r.attributes=t(\"./layout/attributes\"),r.layoutAttributes=t(\"./layout/layout_attributes\"),r.supplyLayoutDefaults=t(\"./layout/defaults\"),r.plot=function(t){var e=t._fullLayout,r=t._fullData,o=a.getSubplotIds(e,\"gl3d\");e._paperdiv.style({width:e.width+\"px\",height:e.height+\"px\"}),t._context.setBackground(t,e.paper_bgcolor);for(var s=0;sr;++r){var n=t[c[r]];e.labels[r]=o(n.title),\"titlefont\"in n&&(n.titlefont.color&&(e.labelColor[r]=s(n.titlefont.color)),n.titlefont.family&&(e.labelFont[r]=n.titlefont.family),n.titlefont.size&&(e.labelSize[r]=n.titlefont.size)),\"showline\"in n&&(e.lineEnable[r]=n.showline),\"linecolor\"in n&&(e.lineColor[r]=s(n.linecolor)),\"linewidth\"in n&&(e.lineWidth[r]=n.linewidth),\"showgrid\"in n&&(e.gridEnable[r]=n.showgrid),\"gridcolor\"in n&&(e.gridColor[r]=s(n.gridcolor)),\"gridwidth\"in n&&(e.gridWidth[r]=n.gridwidth),\"log\"===n.type?e.zeroEnable[r]=!1:\"zeroline\"in n&&(e.zeroEnable[r]=n.zeroline),\"zerolinecolor\"in n&&(e.zeroLineColor[r]=s(n.zerolinecolor)),\"zerolinewidth\"in n&&(e.zeroLineWidth[r]=n.zerolinewidth),\"ticks\"in n&&n.ticks?e.lineTickEnable[r]=!0:e.lineTickEnable[r]=!1,\"ticklen\"in n&&(e.lineTickLength[r]=e._defaultLineTickLength[r]=n.ticklen),\"tickcolor\"in n&&(e.lineTickColor[r]=s(n.tickcolor)),\"tickwidth\"in n&&(e.lineTickWidth[r]=n.tickwidth),\"tickangle\"in n&&(e.tickAngle[r]=\"auto\"===n.tickangle?0:Math.PI*-n.tickangle/180),\"showticklabels\"in n&&(e.tickEnable[r]=n.showticklabels),\"tickfont\"in n&&(n.tickfont.color&&(e.tickColor[r]=s(n.tickfont.color)),n.tickfont.family&&(e.tickFont[r]=n.tickfont.family),n.tickfont.size&&(e.tickSize[r]=n.tickfont.size)),\"mirror\"in n?-1!==[\"ticks\",\"all\",\"allticks\"].indexOf(n.mirror)?(e.lineTickMirror[r]=!0,e.lineMirror[r]=!0):n.mirror===!0?(e.lineTickMirror[r]=!1,e.lineMirror[r]=!0):(e.lineTickMirror[r]=!1,e.lineMirror[r]=!1):e.lineMirror[r]=!1,\"showbackground\"in n&&n.showbackground!==!1?(e.backgroundEnable[r]=!0,\n", + "e.backgroundColor[r]=s(n.backgroundcolor)):e.backgroundEnable[r]=!1}},e.exports=i},{\"../../../lib/html2unicode\":380,\"../../../lib/str2rgbarray\":392,arraytools:49}],444:[function(t,e,r){\"use strict\";function n(t,e,r,n){for(var a=r(\"bgcolor\"),l=i.combine(a,n.paper_bgcolor),c=Object.keys(o.camera),u=0;ue;++e){var r=t[o[e]];this.enabled[e]=r.showspikes,this.colors[e]=a(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness}},e.exports=i},{\"../../../lib/str2rgbarray\":392}],447:[function(t,e,r){\"use strict\";function n(t){for(var e=new Array(3),r=0;3>r;++r){for(var n=t[r],i=new Array(n.length),a=0;au;++u){var f=i[s[u]];if(f._length=(r[u].hi-r[u].lo)*r[u].pixelsPerDataUnit/t.dataScale[u],Math.abs(f._length)===1/0)c[u]=[];else{f.range[0]=r[u].lo/t.dataScale[u],f.range[1]=r[u].hi/t.dataScale[u],f._m=1/(t.dataScale[u]*r[u].pixelsPerDataUnit),f.range[0]===f.range[1]&&(f.range[0]-=1,f.range[1]+=1);var h=f.tickmode;if(\"auto\"===f.tickmode){f.tickmode=\"linear\";var d=f.nticks||a.Lib.constrain(f._length/40,4,9);a.Axes.autoTicks(f,Math.abs(f.range[1]-f.range[0])/d)}for(var p=a.Axes.calcTicks(f),g=0;gu;++u){l[u]=.5*(t.glplot.bounds[0][u]+t.glplot.bounds[1][u]);for(var g=0;2>g;++g)e.bounds[g][u]=t.glplot.bounds[g][u]}t.contourLevels=n(c)}e.exports=i;var a=t(\"../../../plotly\"),o=t(\"../../../lib/html2unicode\"),s=[\"xaxis\",\"yaxis\",\"zaxis\"],l=[0,0,0]},{\"../../../lib/html2unicode\":380,\"../../../plotly\":400}],448:[function(t,e,r){\"use strict\";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;4>r;++r)for(n=0;4>n;++n)i[n]+=t[4*r+n]*e[r];return i}function i(t,e){var r=n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])));return r}e.exports=i},{}],449:[function(t,e,r){\"use strict\";function n(t){function e(e,r){if(\"string\"==typeof r)return r;var n=t.fullSceneLayout[e];return g.tickText(n,n.c2l(r),\"hover\").text}var r,n=t.svgContainer,i=t.container.getBoundingClientRect(),a=i.width,o=i.height;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+a+\" \"+o),n.setAttributeNS(null,\"width\",a),n.setAttributeNS(null,\"height\",o),A(t),t.glplot.axes.update(t.axesOptions);for(var s=Object.keys(t.traces),l=null,c=t.glplot.selection,u=0;ua;++a)l=u[T[a]],_(l);t?Array.isArray(t)||(t=[t]):t=[];var h=[[1/0,1/0,1/0],[-(1/0),-(1/0),-(1/0)]];for(a=0;ao;++o)h[0][o]>h[1][o]?d[o]=1:h[1][o]===h[0][o]?d[o]=1:d[o]=1/(h[1][o]-h[0][o]);for(this.dataScale=d,a=0;aa;++a){if(l=u[T[a]],c=l.type,c in x?(x[c].acc*=d[a],x[c].count+=1):x[c]={acc:d[a],count:1},l.autorange){for(y[0][a]=1/0,y[1][a]=-(1/0),o=0;oy[1][a])y[0][a]=-1,y[1][a]=1;else{var k=y[1][a]-y[0][a];y[0][a]-=k/32,y[1][a]+=k/32}}else{var A=u[T[a]].range;y[0][a]=A[0],y[1][a]=A[1]}y[0][a]===y[1][a]&&(y[0][a]-=1,y[1][a]+=1),b[a]=y[1][a]-y[0][a],this.glplot.bounds[0][a]=y[0][a]*d[a],this.glplot.bounds[1][a]=y[1][a]*d[a]}var M=[1,1,1];for(a=0;3>a;++a){l=u[T[a]],c=l.type;var E=x[c];M[a]=Math.pow(E.acc,1/E.count)/d[a]}var L,S=4;if(\"auto\"===u.aspectmode)L=Math.max.apply(null,M)/Math.min.apply(null,M)<=S?M:[1,1,1];else if(\"cube\"===u.aspectmode)L=[1,1,1];else if(\"data\"===u.aspectmode)L=M;else{if(\"manual\"!==u.aspectmode)throw new Error(\"scene.js aspectRatio was not one of the enumerated types\");var C=u.aspectratio;L=[C.x,C.y,C.z]}u.aspectratio.x=f.aspectratio.x=L[0],u.aspectratio.y=f.aspectratio.y=L[1],u.aspectratio.z=f.aspectratio.z=L[2],this.glplot.aspect=L;var z=u.domain||null,P=e._size||null;if(z&&P){var R=this.container.style;R.position=\"absolute\",R.left=P.l+z.x[0]*P.w+\"px\",R.top=P.t+(1-z.y[1])*P.h+\"px\",R.width=P.w*(z.x[1]-z.x[0])+\"px\",R.height=P.h*(z.y[1]-z.y[0])+\"px\"}this.glplot.redraw()}},M.destroy=function(){this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null},M.setCameraToDefault=function(){this.setCamera({eye:{x:1.25,y:1.25,z:1.25},center:{x:0,y:0,z:0},up:{x:0,y:0,z:1}})},M.getCamera=function(){return this.glplot.camera.view.recalcMatrix(this.camera.view.lastT()),c(this.glplot.camera)},M.setCamera=function(t){var e={};e[this.id]=t,this.glplot.camera.lookAt.apply(this,l(t)),this.graphDiv.emit(\"plotly_relayout\",e)},M.saveCamera=function(t){function e(t,e,r,n){var 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o=i?t._fullData:t.data,s=i?t._fullLayout:t.layout,l={data:(o||[]).map(function(t){var r=a(t);return e&&delete r.fit,r})};return e||(l.layout=a(s)),t.framework&&t.framework.isPolar&&(l=t.framework.getConfig()),\"object\"===n?l:JSON.stringify(l)}},{\"../components/color\":303,\"../lib\":381,\"../plotly\":400,\"./attributes\":401,\"./font_attributes\":421,\"./layout_attributes\":451,d3:113,\"fast-isnumeric\":117}],453:[function(t,e,r){\"use strict\";var n=t(\"../../traces/scatter/attributes\"),i=n.marker;e.exports={r:n.r,t:n.t,marker:{color:i.color,size:i.size,symbol:i.symbol,opacity:i.opacity}}},{\"../../traces/scatter/attributes\":554}],454:[function(t,e,r){\"use strict\";function n(t,e){var 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r={data:[\"a\",\"b\",\"c\"],legendConfig:{elements:[{symbol:\"line\",color:\"red\"},{symbol:\"square\",color:\"yellow\"},{symbol:\"diamond\",color:\"limegreen\"}],height:150,colorBandWidth:30,fontSize:12,container:\"body\",isContinuous:null,textColor:\"grey\",reverseOrder:!1}};return r},a.tooltipPanel=function(){var t,e,r,n={container:null,hasTick:!1,fontSize:12,color:\"white\",padding:5},s=\"tooltip-\"+a.tooltipPanel.uid++,l=10,c=function(){t=n.container.selectAll(\"g.\"+s).data([0]);var i=t.enter().append(\"g\").classed(s,!0).style({\"pointer-events\":\"none\",display:\"none\"});return r=i.append(\"path\").style({fill:\"white\",\"fill-opacity\":.9}).attr({d:\"M0 0\"}),e=i.append(\"text\").attr({dx:n.padding+l,dy:.3*+n.fontSize}),c};return c.text=function(a){var o=i.hsl(n.color).l,s=o>=.5?\"#aaa\":\"white\",u=o>=.5?\"black\":\"white\",f=a||\"\";e.style({fill:u,\"font-size\":n.fontSize+\"px\"}).text(f);var 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n.geometry):(\"scatter\"===n.type?\"lines\"===n.mode?n.geometry=\"LinePlot\":\"markers\"===n.mode?n.geometry=\"DotPlot\":\"lines+markers\"===n.mode&&(n.geometry=\"LinePlot\",n.dotVisible=!0):\"area\"===n.type?n.geometry=\"AreaChart\":\"bar\"===n.type&&(n.geometry=\"BarChart\"),delete n.mode,delete n.type),n}),!e&&t.layout&&\"stack\"===t.layout.barmode)){var n=a.util.duplicates(r.data.map(function(t,e){return t.geometry}));r.data.forEach(function(t,e){var i=n.indexOf(t.geometry);-1!=i&&(r.data[e].groupId=i)})}if(t.layout){var s=o({},t.layout),l=[[s,[\"plot_bgcolor\"],[\"backgroundColor\"]],[s,[\"showlegend\"],[\"showLegend\"]],[s,[\"radialaxis\"],[\"radialAxis\"]],[s,[\"angularaxis\"],[\"angularAxis\"]],[s.angularaxis,[\"showline\"],[\"gridLinesVisible\"]],[s.angularaxis,[\"showticklabels\"],[\"labelsVisible\"]],[s.angularaxis,[\"nticks\"],[\"ticksCount\"]],[s.angularaxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.angularaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.angularaxis,[\"range\"],[\"domain\"]],[s.angularaxis,[\"endpadding\"],[\"endPadding\"]],[s.radialaxis,[\"showline\"],[\"gridLinesVisible\"]],[s.radialaxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.radialaxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.radialaxis,[\"range\"],[\"domain\"]],[s.angularAxis,[\"showline\"],[\"gridLinesVisible\"]],[s.angularAxis,[\"showticklabels\"],[\"labelsVisible\"]],[s.angularAxis,[\"nticks\"],[\"ticksCount\"]],[s.angularAxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.angularAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.angularAxis,[\"range\"],[\"domain\"]],[s.angularAxis,[\"endpadding\"],[\"endPadding\"]],[s.radialAxis,[\"showline\"],[\"gridLinesVisible\"]],[s.radialAxis,[\"tickorientation\"],[\"tickOrientation\"]],[s.radialAxis,[\"ticksuffix\"],[\"ticksSuffix\"]],[s.radialAxis,[\"range\"],[\"domain\"]],[s.font,[\"outlinecolor\"],[\"outlineColor\"]],[s.legend,[\"traceorder\"],[\"reverseOrder\"]],[s,[\"labeloffset\"],[\"labelOffset\"]],[s,[\"defaultcolorrange\"],[\"defaultColorRange\"]]];if(l.forEach(function(t,r){a.util.translator.apply(null,t.concat(e))}),e?(\"undefined\"!=typeof 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s.angularaxis.rewriteTicks,delete s.angularaxis.nticks,delete s.radialaxis.ticksCount,delete s.radialaxis.ticksCount,delete s.radialaxis.ticksStep,delete s.radialaxis.rewriteTicks,delete s.radialaxis.nticks),r.layout=s}return r},t}},{\"../../plotly\":400,\"./micropolar_manager\":456,d3:113}],456:[function(t,e,r){\"use strict\";var n=t(\"../../plotly\"),i=t(\"d3\"),a=t(\"./undo_manager\"),o=e.exports={},s=n.Lib.extendDeepAll;o.framework=function(t){function e(e,a){return a&&(f=a),i.select(i.select(f).node().parentNode).selectAll(\".svg-container>*:not(.chart-root)\").remove(),r=r?s(r,e):e,c||(c=n.micropolar.Axis()),u=n.micropolar.adapter.plotly().convert(r),c.config(u).render(f),t.data=r.data,t.layout=r.layout,o.fillLayout(t),r}var r,l,c,u,f,h=new a;return e.isPolar=!0,e.svg=function(){return c.svg()},e.getConfig=function(){return r},e.getLiveConfig=function(){return 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z=[\"x\",\"y\",\"z\"];for(r.project=[!1,!1,!1],r.projectScale=[1,1,1],r.projectOpacity=[1,1,1],n=0;3>n;++n){var P=e.projection[z[n]];(r.project[n]=P.show)&&(r.projectOpacity[n]=P.opacity,r.projectScale[n]=P.scale)}r.errorBounds=A(e,g);var R=a([e.error_x,e.error_y,e.error_z]);return r.errorColor=R.color,r.errorLineWidth=R.lineWidth,r.errorCapSize=R.capSize,r.delaunayAxis=e.surfaceaxis,r.delaunayColor=b(e.surfacecolor),r}function f(t){if(Array.isArray(t)){var e=t[0];return Array.isArray(e)&&(t=e),\"rgb(\"+t.slice(0,3).map(function(t){return Math.round(255*t)})+\")\"}return null}function h(t,e){var r=new n(t,e.uid);return r.update(e),r}var 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c=t.opacity;if(t.marker&&t.marker.opacity&&(c*=t.marker.opacity),r={gl:o,position:l.position,color:l.scatterColor,size:l.scatterSize,glyph:l.scatterMarker,opacity:c,orthographic:!0,lineWidth:l.scatterLineWidth,lineColor:l.scatterLineColor,project:l.project,projectScale:l.projectScale,projectOpacity:l.projectOpacity},-1!==this.mode.indexOf(\"markers\")?this.scatterPlot?this.scatterPlot.update(r):(this.scatterPlot=p(r),this.scatterPlot.highlightScale=1,this.scene.glplot.add(this.scatterPlot)):this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose(),this.scatterPlot=null),a={gl:o,position:l.position,glyph:l.text,color:l.textColor,size:l.textSize,angle:l.textAngle,alignment:l.textOffset,font:l.textFont,orthographic:!0,lineWidth:0,project:!1,opacity:t.opacity},this.textLabels=l.text,-1!==this.mode.indexOf(\"text\")?this.textMarkers?this.textMarkers.update(a):(this.textMarkers=p(a),this.textMarkers.highlightScale=1,this.scene.glplot.add(this.textMarkers)):this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose(),this.textMarkers=null),n={gl:o,position:l.position,color:l.errorColor,error:l.errorBounds,lineWidth:l.errorLineWidth,capSize:l.errorCapSize,opacity:t.opacity},this.errorBars?l.errorBounds?this.errorBars.update(n):(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose(),this.errorBars=null):l.errorBounds&&(this.errorBars=g(n),this.scene.glplot.add(this.errorBars)),l.delaunayAxis>=0){var 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f=m.calcGeoJSON(e,t.topojson),h=a(t,e),d=o(e),p=e.hoverinfo,v=\"all\"===p||-1!==p.indexOf(\"name\"),y=null;c&&i.selectAll(\"path.point\").data(f).enter().append(\"path\").classed(\"point\",!0).on(\"mouseover\",r).on(\"click\",n).on(\"mouseout\",function(){l.loneUnhover(t.hoverContainer),t.graphDiv.emit(\"plotly_unhover\",y)}).on(\"mousedown\",function(){l.loneUnhover(t.hoverContainer)}).on(\"mouseup\",r),u&&i.selectAll(\"g\").data(f).enter().append(\"g\").append(\"text\")}}),m.style(t)},m.style=function(t){var e=t.framework.selectAll(\"g.trace.scattergeo\");e.style(\"opacity\",function(t){return t.opacity}),e.each(function(t){s.select(this).selectAll(\"path.point\").call(p.pointStyle,t),s.select(this).selectAll(\"text\").call(p.textPointStyle,t)}),e.selectAll(\"path.js-line\").style(\"fill\",\"none\").each(function(t){var 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" ], "text/plain": [ "" @@ -1113,7 +1084,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.10" + "version": "2.7.11" } }, "nbformat": 4, diff --git a/examples/hornsrev.yml b/examples/hornsrev.yml index 4cf616d..005f7fa 100644 --- a/examples/hornsrev.yml +++ b/examples/hornsrev.yml @@ -365,7 +365,7 @@ turbine_types: hub: weight: 18000 # kg [3] tower: # [2] - weight: 198000.0 #kg [2], max 148000.0 kg according to [3] + weight: 198000.0 #kg [2], max 148000.0 kg according to [3] material: steel manufacturer: Welcon control: # [1] @@ -448,4 +448,4 @@ turbine_types: - [33.608, 1.102, 2.24, 'NACA 63-218'] - [35.435, 0.926, 2.61, 'NACA 63-218'] - [37.261, 0.749, 2.84, 'NACA 63-218'] - - [39.087, 0.573, 2.97, 'NACA 63-218'] + - [39.087, 0.573, 2.97, 'NACA 63-218'] \ No newline at end of file diff --git a/examples/test_WF.yml b/examples/test_WF.yml new file mode 100644 index 0000000..6254396 --- /dev/null +++ b/examples/test_WF.yml @@ -0,0 +1,131 @@ +layout: + - name: WT01 + row: 1 + position: [0, 0] + turbine_type: V80 + - name: WT02 + row: 2 + position: [560, 0] + turbine_type: V80 + +plant_data: + utm: + code: 32 + letter: U + name: Test WF + owner: DTU + +turbine_types: + # Sources: + # [1] https://windbench.net/system/files/hornsrev_v80_0.pdf + # [2] http://www.thewindpower.net/turbine_en_30_vestas_2000.php + # [3] http://en.wind-turbine-models.com/turbines/668-vestas-v-80-offshore + # [4] WAsP wind turbine library (distributed as part of the WAsP software) + - name: V80 + hub_height: 70.0 # [1] + rotor_diameter: 80.0 # [1] + rated_power: 2000.0 # [1] + cut_in_wind_speed: 4.0 # [1] + cut_out_wind_speed: 25.0 # [1] + rated_wind_speed: 16.0 # [1] + wind_class: IEC Ia (DIBt III) # [2] + air_density: 1.225 # guess + gear_box: + speed_number: 3 + ratio: 1:100,5 # [2] + type: spur/planetary # [3] + nacelle: # [2] + weight: 68000.0 # kg [2], 69000.0 kg according to [3] + rotor: # [2, 3] + weight: 37000.0 # kg [2] + tip_speed: 80.0 # m/s [3] + min_speed: 9 # rd/min [2] + max_speed: 19 # rd/min [2] + manufacturer: Vestas + hub: + weight: 18000 # kg [3] + tower: # [2] + weight: 198000.0 #kg [2], max 148000.0 kg according to [3] + material: steel + manufacturer: Welcon + control: # [1] + type: Active Pitch, Variable Speed + generator: # [2] + type: ASYNC + number: 1 + max_output_speed: 1909 #rounds/minute + output_voltage: 690 #V + grid_frequency: 50/60 # Hz + power_curve: # [1] + - [3.0, 0.0] + - [4.0, 66.6] + - [5.0, 154.0] + - [6.0, 282.0] + - [7.0, 460.0] + - [8.0, 696.0] + - [9.0, 996.0] + - [10.0, 1341.0] + - [11.0, 1661.0] + - [12.0, 1866.0] + - [13.0, 1958.0] + - [14.0, 1988.0] + - [15.0, 1997.0] + - [16.0, 1999.0] + - [17.0, 2000.0] + - [18.0, 2000.0] + - [19.0, 2000.0] + - [20.0, 2000.0] + - [21.0, 2000.0] + - [22.0, 2000.0] + - [23.0, 2000.0] + - [24.0, 2000.0] + - [25.0, 2000.0] + c_t_curve: + - [3.0, 0.00] + - [4.0, 0.818] + - [5.0, 0.806] + - [6.0, 0.804] + - [7.0, 0.805] + - [8.0, 0.806] + - [9.0, 0.807] + - [10.0, 0.793] + - [11.0, 0.739] + - [12.0, 0.709] + - [13.0, 0.409] + - [14.0, 0.314] + - [15.0, 0.249] + - [16.0, 0.202] + - [17.0, 0.167] + - [18.0, 0.140] + - [19.0, 0.119] + - [20.0, 0.102] + - [21.0, 0.088] + - [22.0, 0.077] + - [23.0, 0.067] + - [24.0, 0.060] + - [25.0, 0.053] + c_t_idle: 0.053 # [4] + blade: + geometry: # [1] + # [radius [m], c [m], twist [deg], airfoil ] + - [2.563, 2.004, 9.50, 'Cylinder 1'] + - [4.389, 2.523, 9.50, 'Cylinder 1'] + - [6.216, 3.015, 9.50, 'FFA W3-301'] + - [8.042, 3.278, 9.50, 'FFA W3-301'] + - [9.868, 3.309, 9.50, 'FFA W3-301'] + - [11.694, 3.195, 9.50, 'FFA W3-301'] + - [13.520, 3.039, 9.22, 'FFA W3-241'] + - [15.346, 2.863, 7.81, 'FFA W3-211'] + - [17.173, 2.687, 6.40, 'FFA W3-211'] + - [18.999, 2.511, 5.11, 'FFA W3-211'] + - [20.825, 2.334, 3.83, 'FFA W3-211'] + - [22.651, 2.158, 2.61, 'NACA 63-221'] + - [24.477, 1.982, 1.48, 'NACA 63-221'] + - [26.304, 1.806, 0.42, 'NACA 63-221'] + - [28.130, 1.630, 0.49, 'NACA 63-221'] + - [29.956, 1.454, 1.23, 'NACA 63-218'] + - [31.782, 1.278, 1.79, 'NACA 63-218'] + - [33.608, 1.102, 2.24, 'NACA 63-218'] + - [35.435, 0.926, 2.61, 'NACA 63-218'] + - [37.261, 0.749, 2.84, 'NACA 63-218'] + - [39.087, 0.573, 2.97, 'NACA 63-218'] diff --git a/examples/test_WF_2Turbines.yml b/examples/test_WF_2Turbines.yml new file mode 100644 index 0000000..114a2db --- /dev/null +++ b/examples/test_WF_2Turbines.yml @@ -0,0 +1,215 @@ +layout: + - name: WT01 + row: 1 + position: [0, 0] + turbine_type: V80 + - name: WT02 + row: 2 + position: [560, 0] + turbine_type: B76 + +plant_data: + utm: + code: 32 + letter: U + name: Test WF + owner: DTU + +turbine_types: + # Sources: + # [1] https://windbench.net/system/files/hornsrev_v80_0.pdf + # [2] http://www.thewindpower.net/turbine_en_30_vestas_2000.php + # [3] http://en.wind-turbine-models.com/turbines/668-vestas-v-80-offshore + # [4] WAsP wind turbine library (distributed as part of the WAsP software) + - name: V80 + hub_height: 90.0 # [1] + rotor_diameter: 80.0 # [1] + rated_power: 2000.0 # [1] + cut_in_wind_speed: 4.0 # [1] + cut_out_wind_speed: 25.0 # [1] + rated_wind_speed: 16.0 # [1] + wind_class: IEC Ia (DIBt III) # [2] + air_density: 1.225 # guess + gear_box: + speed_number: 3 + ratio: 1:100,5 # [2] + type: spur/planetary # [3] + nacelle: # [2] + weight: 68000.0 # kg [2], 69000.0 kg according to [3] + rotor: # [2, 3] + weight: 37000.0 # kg [2] + tip_speed: 80.0 # m/s [3] + min_speed: 9 # rd/min [2] + max_speed: 19 # rd/min [2] + manufacturer: Vestas + hub: + weight: 18000 # kg [3] + tower: # [2] + weight: 198000.0 #kg [2], max 148000.0 kg according to [3] + material: steel + manufacturer: Welcon + control: # [1] + type: Active Pitch, Variable Speed + generator: # [2] + type: ASYNC + number: 1 + max_output_speed: 1909 #rounds/minute + output_voltage: 690 #V + grid_frequency: 50/60 # Hz + power_curve: # [1] + - [3.0, 0.0] + - [4.0, 66.6] + - [5.0, 154.0] + - [6.0, 282.0] + - [7.0, 460.0] + - [8.0, 696.0] + - [9.0, 996.0] + - [10.0, 1341.0] + - [11.0, 1661.0] + - [12.0, 1866.0] + - [13.0, 1958.0] + - [14.0, 1988.0] + - [15.0, 1997.0] + - [16.0, 1999.0] + - [17.0, 2000.0] + - [18.0, 2000.0] + - [19.0, 2000.0] + - [20.0, 2000.0] + - [21.0, 2000.0] + - [22.0, 2000.0] + - [23.0, 2000.0] + - [24.0, 2000.0] + - [25.0, 2000.0] + c_t_curve: + - [3.0, 0.00] + - [4.0, 0.818] + - [5.0, 0.806] + - [6.0, 0.804] + - [7.0, 0.805] + - [8.0, 0.806] + - [9.0, 0.807] + - [10.0, 0.793] + - [11.0, 0.739] + - [12.0, 0.709] + - [13.0, 0.409] + - [14.0, 0.314] + - [15.0, 0.249] + - [16.0, 0.202] + - [17.0, 0.167] + - [18.0, 0.140] + - [19.0, 0.119] + - [20.0, 0.102] + - [21.0, 0.088] + - [22.0, 0.077] + - [23.0, 0.067] + - [24.0, 0.060] + - [25.0, 0.053] + c_t_idle: 0.053 # [4] + blade: + geometry: # [1] + # [radius [m], c [m], twist [deg], airfoil ] + - [2.563, 2.004, 9.50, 'Cylinder 1'] + - [4.389, 2.523, 9.50, 'Cylinder 1'] + - [6.216, 3.015, 9.50, 'FFA W3-301'] + - [8.042, 3.278, 9.50, 'FFA W3-301'] + - [9.868, 3.309, 9.50, 'FFA W3-301'] + - [11.694, 3.195, 9.50, 'FFA W3-301'] + - [13.520, 3.039, 9.22, 'FFA W3-241'] + - [15.346, 2.863, 7.81, 'FFA W3-211'] + - [17.173, 2.687, 6.40, 'FFA W3-211'] + - [18.999, 2.511, 5.11, 'FFA W3-211'] + - [20.825, 2.334, 3.83, 'FFA W3-211'] + - [22.651, 2.158, 2.61, 'NACA 63-221'] + - [24.477, 1.982, 1.48, 'NACA 63-221'] + - [26.304, 1.806, 0.42, 'NACA 63-221'] + - [28.130, 1.630, 0.49, 'NACA 63-221'] + - [29.956, 1.454, 1.23, 'NACA 63-218'] + - [31.782, 1.278, 1.79, 'NACA 63-218'] + - [33.608, 1.102, 2.24, 'NACA 63-218'] + - [35.435, 0.926, 2.61, 'NACA 63-218'] + - [37.261, 0.749, 2.84, 'NACA 63-218'] + - [39.087, 0.573, 2.97, 'NACA 63-218'] + + # Sources: + # [1] http://www.middelgrunden.dk/middelgrunden/sites/default/files/public/file/Location.pdf + # [2] http://www.thewindpower.net/turbine_en_229_bonus_2000.php + # [3] http://en.wind-turbine-models.com/turbines/121-bonus-b76-2000 + # [4] WAsP wind turbine library (distributed as part of the WAsP software) + - name: B76 + hub_height: 64.0 #[1] + rotor_diameter: 76.0 #[1] + rated_power: 2000.0 #[1] + cut_in_wind_speed: 3.0 #[2] + cut_out_wind_speed: 25.0 #[2] + rated_wind_speed: 15.0 #[2] + air_density: 1.225 # guess + gear_box: + speed_number: 3 + ratio: 1:89 # [2], or [3]: 1:93 + manufacturer: Flender + nacelle: # [2] + weight: 65000.0 #kg [2], or [3]: 82500 kg + rotor: # [2, 3] + weight: 38000.0 #kg [2], or [3]: 52000.0 kg + max_speed: 17 #rd/min [2], or [3]: 16 + tip_speed: 63.7 #m/s [3] + manufacturer: LM Glasfiber, Bonus + tower: # [2] + weight: 162000.0 #kg [2] + control: # [2] + type: Stall + generator: # [2] + type: ASYNC + number: 1 + max_output_speed: 1500 #rounds/minute + output_voltage: 690 #V + manufacturer: ABB + power_curve: # [4] + - [3.0, 0.0] + - [4.0, 43.0] + - [5.0, 133.0] + - [6.0, 237.0] + - [7.0, 401.0] + - [8.0, 623.0] + - [9.0, 886.0] + - [10.0, 1190.0] + - [11.0, 1502.0] + - [12.0, 1740.0] + - [13.0, 1891.0] + - [14.0, 1962.0] + - [15.0, 1988.0] + - [16.0, 1996.0] + - [17.0, 1999.0] + - [18.0, 2000.0] + - [19.0, 2000.0] + - [20.0, 2000.0] + - [21.0, 2000.0] + - [22.0, 2000.0] + - [23.0, 2000.0] + - [24.0, 2000.0] + - [25.0, 2000.0] + c_t_curve: # [4] + - [3.0, 0.00] + - [4.0, 0.857] + - [5.0, 0.858] + - [6.0, 0.810] + - [7.0, 0.853] + - [8.0, 0.870] + - [9.0, 0.811] + - [10.0, 0.756] + - [11.0, 0.679] + - [12.0, 0.584] + - [13.0, 0.511] + - [14.0, 0.439] + - [15.0, 0.383] + - [16.0, 0.338] + - [17.0, 0.301] + - [18.0, 0.271] + - [19.0, 0.246] + - [20.0, 0.225] + - [21.0, 0.207] + - [22.0, 0.192] + - [23.0, 0.179] + - [24.0, 0.168] + - [25.0, 0.158] + c_t_idle: 0.158 # [4] diff --git a/examples/test_WF_4.yml b/examples/test_WF_4.yml new file mode 100644 index 0000000..620a955 --- /dev/null +++ b/examples/test_WF_4.yml @@ -0,0 +1,139 @@ +layout: + - name: WT01 + row: 1 + position: [0, 0] + turbine_type: V80 + - name: WT02 + row: 2 + position: [800, 0] + turbine_type: V80 + - name: WT03 + row: 1 + position: [200, 200] + turbine_type: V80 + - name: WT04 + row: 2 + position: [200, -200] + turbine_type: V80 + +plant_data: + utm: + code: 32 + letter: U + name: Test WF + owner: DTU + +turbine_types: + # Sources: + # [1] https://windbench.net/system/files/hornsrev_v80_0.pdf + # [2] http://www.thewindpower.net/turbine_en_30_vestas_2000.php + # [3] http://en.wind-turbine-models.com/turbines/668-vestas-v-80-offshore + # [4] WAsP wind turbine library (distributed as part of the WAsP software) + - name: V80 + hub_height: 70.0 # [1] + rotor_diameter: 80.0 # [1] + rated_power: 2000.0 # [1] + cut_in_wind_speed: 4.0 # [1] + cut_out_wind_speed: 25.0 # [1] + rated_wind_speed: 16.0 # [1] + wind_class: IEC Ia (DIBt III) # [2] + air_density: 1.225 # guess + gear_box: + speed_number: 3 + ratio: 1:100,5 # [2] + type: spur/planetary # [3] + nacelle: # [2] + weight: 68000.0 # kg [2], 69000.0 kg according to [3] + rotor: # [2, 3] + weight: 37000.0 # kg [2] + tip_speed: 80.0 # m/s [3] + min_speed: 9 # rd/min [2] + max_speed: 19 # rd/min [2] + manufacturer: Vestas + hub: + weight: 18000 # kg [3] + tower: # [2] + weight: 198000.0 #kg [2], max 148000.0 kg according to [3] + material: steel + manufacturer: Welcon + control: # [1] + type: Active Pitch, Variable Speed + generator: # [2] + type: ASYNC + number: 1 + max_output_speed: 1909 #rounds/minute + output_voltage: 690 #V + grid_frequency: 50/60 # Hz + power_curve: # [1] + - [3.0, 0.0] + - [4.0, 66.6] + - [5.0, 154.0] + - [6.0, 282.0] + - [7.0, 460.0] + - [8.0, 696.0] + - [9.0, 996.0] + - [10.0, 1341.0] + - [11.0, 1661.0] + - [12.0, 1866.0] + - [13.0, 1958.0] + - [14.0, 1988.0] + - [15.0, 1997.0] + - [16.0, 1999.0] + - [17.0, 2000.0] + - [18.0, 2000.0] + - [19.0, 2000.0] + - [20.0, 2000.0] + - [21.0, 2000.0] + - [22.0, 2000.0] + - [23.0, 2000.0] + - [24.0, 2000.0] + - [25.0, 2000.0] + c_t_curve: + - [3.0, 0.00] + - [4.0, 0.818] + - [5.0, 0.806] + - [6.0, 0.804] + - [7.0, 0.805] + - [8.0, 0.806] + - [9.0, 0.807] + - [10.0, 0.793] + - [11.0, 0.739] + - [12.0, 0.709] + - [13.0, 0.409] + - [14.0, 0.314] + - [15.0, 0.249] + - [16.0, 0.202] + - [17.0, 0.167] + - [18.0, 0.140] + - [19.0, 0.119] + - [20.0, 0.102] + - [21.0, 0.088] + - [22.0, 0.077] + - [23.0, 0.067] + - [24.0, 0.060] + - [25.0, 0.053] + c_t_idle: 0.053 # [4] + blade: + geometry: # [1] + # [radius [m], c [m], twist [deg], airfoil ] + - [2.563, 2.004, 9.50, 'Cylinder 1'] + - [4.389, 2.523, 9.50, 'Cylinder 1'] + - [6.216, 3.015, 9.50, 'FFA W3-301'] + - [8.042, 3.278, 9.50, 'FFA W3-301'] + - [9.868, 3.309, 9.50, 'FFA W3-301'] + - [11.694, 3.195, 9.50, 'FFA W3-301'] + - [13.520, 3.039, 9.22, 'FFA W3-241'] + - [15.346, 2.863, 7.81, 'FFA W3-211'] + - [17.173, 2.687, 6.40, 'FFA W3-211'] + - [18.999, 2.511, 5.11, 'FFA W3-211'] + - [20.825, 2.334, 3.83, 'FFA W3-211'] + - [22.651, 2.158, 2.61, 'NACA 63-221'] + - [24.477, 1.982, 1.48, 'NACA 63-221'] + - [26.304, 1.806, 0.42, 'NACA 63-221'] + - [28.130, 1.630, 0.49, 'NACA 63-221'] + - [29.956, 1.454, 1.23, 'NACA 63-218'] + - [31.782, 1.278, 1.79, 'NACA 63-218'] + - [33.608, 1.102, 2.24, 'NACA 63-218'] + - [35.435, 0.926, 2.61, 'NACA 63-218'] + - [37.261, 0.749, 2.84, 'NACA 63-218'] + - [39.087, 0.573, 2.97, 'NACA 63-218'] diff --git a/examples/test_WF_4Turbines.yml b/examples/test_WF_4Turbines.yml new file mode 100644 index 0000000..2414e4d --- /dev/null +++ b/examples/test_WF_4Turbines.yml @@ -0,0 +1,223 @@ +layout: + - name: WT01 + row: 1 + position: [0, 0] + turbine_type: V80 + - name: WT02 + row: 2 + position: [560, 0] + turbine_type: B76 + - name: WT03 + row: 1 + position: [200, 200] + turbine_type: V80 + - name: WT04 + row: 2 + position: [200, -200] + turbine_type: B76 + +plant_data: + utm: + code: 32 + letter: U + name: Test WF + owner: DTU + +turbine_types: + # Sources: + # [1] https://windbench.net/system/files/hornsrev_v80_0.pdf + # [2] http://www.thewindpower.net/turbine_en_30_vestas_2000.php + # [3] http://en.wind-turbine-models.com/turbines/668-vestas-v-80-offshore + # [4] WAsP wind turbine library (distributed as part of the WAsP software) + - name: V80 + hub_height: 90.0 # [1] + rotor_diameter: 80.0 # [1] + rated_power: 2000.0 # [1] + cut_in_wind_speed: 4.0 # [1] + cut_out_wind_speed: 25.0 # [1] + rated_wind_speed: 16.0 # [1] + wind_class: IEC Ia (DIBt III) # [2] + air_density: 1.225 # guess + gear_box: + speed_number: 3 + ratio: 1:100,5 # [2] + type: spur/planetary # [3] + nacelle: # [2] + weight: 68000.0 # kg [2], 69000.0 kg according to [3] + rotor: # [2, 3] + weight: 37000.0 # kg [2] + tip_speed: 80.0 # m/s [3] + min_speed: 9 # rd/min [2] + max_speed: 19 # rd/min [2] + manufacturer: Vestas + hub: + weight: 18000 # kg [3] + tower: # [2] + weight: 198000.0 #kg [2], max 148000.0 kg according to [3] + material: steel + manufacturer: Welcon + control: # [1] + type: Active Pitch, Variable Speed + generator: # [2] + type: ASYNC + number: 1 + max_output_speed: 1909 #rounds/minute + output_voltage: 690 #V + grid_frequency: 50/60 # Hz + power_curve: # [1] + - [3.0, 0.0] + - [4.0, 66.6] + - [5.0, 154.0] + - [6.0, 282.0] + - [7.0, 460.0] + - [8.0, 696.0] + - [9.0, 996.0] + - [10.0, 1341.0] + - [11.0, 1661.0] + - [12.0, 1866.0] + - [13.0, 1958.0] + - [14.0, 1988.0] + - [15.0, 1997.0] + - [16.0, 1999.0] + - [17.0, 2000.0] + - [18.0, 2000.0] + - [19.0, 2000.0] + - [20.0, 2000.0] + - [21.0, 2000.0] + - [22.0, 2000.0] + - [23.0, 2000.0] + - [24.0, 2000.0] + - [25.0, 2000.0] + c_t_curve: + - [3.0, 0.00] + - [4.0, 0.818] + - [5.0, 0.806] + - [6.0, 0.804] + - [7.0, 0.805] + - [8.0, 0.806] + - [9.0, 0.807] + - [10.0, 0.793] + - [11.0, 0.739] + - [12.0, 0.709] + - [13.0, 0.409] + - [14.0, 0.314] + - [15.0, 0.249] + - [16.0, 0.202] + - [17.0, 0.167] + - [18.0, 0.140] + - [19.0, 0.119] + - [20.0, 0.102] + - [21.0, 0.088] + - [22.0, 0.077] + - [23.0, 0.067] + - [24.0, 0.060] + - [25.0, 0.053] + c_t_idle: 0.053 # [4] + blade: + geometry: # [1] + # [radius [m], c [m], twist [deg], airfoil ] + - [2.563, 2.004, 9.50, 'Cylinder 1'] + - [4.389, 2.523, 9.50, 'Cylinder 1'] + - [6.216, 3.015, 9.50, 'FFA W3-301'] + - [8.042, 3.278, 9.50, 'FFA W3-301'] + - [9.868, 3.309, 9.50, 'FFA W3-301'] + - [11.694, 3.195, 9.50, 'FFA W3-301'] + - [13.520, 3.039, 9.22, 'FFA W3-241'] + - [15.346, 2.863, 7.81, 'FFA W3-211'] + - [17.173, 2.687, 6.40, 'FFA W3-211'] + - [18.999, 2.511, 5.11, 'FFA W3-211'] + - [20.825, 2.334, 3.83, 'FFA W3-211'] + - [22.651, 2.158, 2.61, 'NACA 63-221'] + - [24.477, 1.982, 1.48, 'NACA 63-221'] + - [26.304, 1.806, 0.42, 'NACA 63-221'] + - [28.130, 1.630, 0.49, 'NACA 63-221'] + - [29.956, 1.454, 1.23, 'NACA 63-218'] + - [31.782, 1.278, 1.79, 'NACA 63-218'] + - [33.608, 1.102, 2.24, 'NACA 63-218'] + - [35.435, 0.926, 2.61, 'NACA 63-218'] + - [37.261, 0.749, 2.84, 'NACA 63-218'] + - [39.087, 0.573, 2.97, 'NACA 63-218'] + + # Sources: + # [1] http://www.middelgrunden.dk/middelgrunden/sites/default/files/public/file/Location.pdf + # [2] http://www.thewindpower.net/turbine_en_229_bonus_2000.php + # [3] http://en.wind-turbine-models.com/turbines/121-bonus-b76-2000 + # [4] WAsP wind turbine library (distributed as part of the WAsP software) + - name: B76 + hub_height: 64.0 #[1] + rotor_diameter: 76.0 #[1] + rated_power: 2000.0 #[1] + cut_in_wind_speed: 3.0 #[2] + cut_out_wind_speed: 25.0 #[2] + rated_wind_speed: 15.0 #[2] + air_density: 1.225 # guess + gear_box: + speed_number: 3 + ratio: 1:89 # [2], or [3]: 1:93 + manufacturer: Flender + nacelle: # [2] + weight: 65000.0 #kg [2], or [3]: 82500 kg + rotor: # [2, 3] + weight: 38000.0 #kg [2], or [3]: 52000.0 kg + max_speed: 17 #rd/min [2], or [3]: 16 + tip_speed: 63.7 #m/s [3] + manufacturer: LM Glasfiber, Bonus + tower: # [2] + weight: 162000.0 #kg [2] + control: # [2] + type: Stall + generator: # [2] + type: ASYNC + number: 1 + max_output_speed: 1500 #rounds/minute + output_voltage: 690 #V + manufacturer: ABB + power_curve: # [4] + - [3.0, 0.0] + - [4.0, 43.0] + - [5.0, 133.0] + - [6.0, 237.0] + - [7.0, 401.0] + - [8.0, 623.0] + - [9.0, 886.0] + - [10.0, 1190.0] + - [11.0, 1502.0] + - [12.0, 1740.0] + - [13.0, 1891.0] + - [14.0, 1962.0] + - [15.0, 1988.0] + - [16.0, 1996.0] + - [17.0, 1999.0] + - [18.0, 2000.0] + - [19.0, 2000.0] + - [20.0, 2000.0] + - [21.0, 2000.0] + - [22.0, 2000.0] + - [23.0, 2000.0] + - [24.0, 2000.0] + - [25.0, 2000.0] + c_t_curve: # [4] + - [3.0, 0.00] + - [4.0, 0.857] + - [5.0, 0.858] + - [6.0, 0.810] + - [7.0, 0.853] + - [8.0, 0.870] + - [9.0, 0.811] + - [10.0, 0.756] + - [11.0, 0.679] + - [12.0, 0.584] + - [13.0, 0.511] + - [14.0, 0.439] + - [15.0, 0.383] + - [16.0, 0.338] + - [17.0, 0.301] + - [18.0, 0.271] + - [19.0, 0.246] + - [20.0, 0.225] + - [21.0, 0.207] + - [22.0, 0.192] + - [23.0, 0.179] + - [24.0, 0.168] + - [25.0, 0.158] + c_t_idle: 0.158 # [4] diff --git a/fusedwake/gcl/__init__.py b/fusedwake/gcl/__init__.py index 008f9cb..2a3867a 100644 --- a/fusedwake/gcl/__init__.py +++ b/fusedwake/gcl/__init__.py @@ -4,30 +4,72 @@ class GCL(object): + """GC Larsen wake model applied to flat terrain/offshore wind farms [1]. + + GCL(WF, WS, WD, TI, version, pars) + + This model is able to run with individual turbine rotor averaged WS, WD and TI. + Different turbines types can be used in the same power plant. + + A modification of the rotor averaging quadrature was done. For more + information see the jupyter notebook inside the test folder. + + This class wraps four different implementations (version): + py_gcl_v0: Python version. Double WT loop from the furthest downstream + turbine up to the upstream turbines. Inner loop estimates the + wake due to each upstream turbine. + py_gcl_v1 (Default): Python version. Single WT loop from the further upstream + turbines. The deficit each turbine produces in all the wake affected + turbines is computed in vectorized way. + fort_gcl: Fortran version. Fastest implementation. Is able to receive + multiple flowcases at the same time. + fort_gcl_av: Fortran version with individual turbine availability. + can handle non operating turbines inside the plant. + + Inputs + ---------- + WF fusedwake.WindFarm object + WS Individual rotor averaged undisturbed wind speed + WD Individual rotor averaged undisturbed wind direction + TI Individual rotor averaged ambient turbulent intensity + version py_gcl_v0, py_gcl_v1, fort_gcl, fort_gcl_av + pars GCL wake model parameters [a1, a2, a3, a4, b1, b2] + + @moduleauthor:: Pierre-Elouan Re'thore' + Juan P. Murcia + + References: + [1] Larsen GC. "A simple stationary semi-analytical wake model", 2009 + + """ # The different versions and their respective inputs inputs = { - 'py_gcl_v0': ['WF', 'WS', 'WD', 'TI', 'z0', 'NG', 'sup', 'pars'], - 'py_gcl_v1': ['WF', 'WS', 'WD', 'TI', 'z0', 'alpha', 'inflow', 'NG', 'sup', 'pars'], + 'py_gcl_v0': ['WF', 'WS', 'WD', 'TI', 'pars'], + 'py_gcl_v1': ['WF', 'WS', 'WD', 'TI', 'pars'], 'fort_gcl_av': ['x_g', 'y_g', 'z_g', 'dt', 'p_c', 'ct_c', 'ws', 'wd', 'ti', - 'av', 'a1', 'a2', 'a3', 'a4', 'b1', 'b2', 'ng', 'rho', 'ws_ci', + 'av', 'a1', 'a2', 'a3', 'a4', 'b1', 'b2', 'rho', 'ws_ci', 'ws_co', 'ct_idle'], 'fort_gcl': ['x_g', 'y_g', 'z_g', 'dt', 'p_c', 'ct_c', 'ws', 'wd', 'ti', - 'a1', 'a2', 'a3', 'a4', 'b1', 'b2', 'ng', 'rho', 'ws_ci', + 'a1', 'a2', 'a3', 'a4', 'b1', 'b2', 'rho', 'ws_ci', 'ws_co', 'ct_idle'], - 'fort_gcl_s': ['x_g', 'y_g', 'z_g', 'dt', 'p_c', 'ct_c', 'WS', 'WD', 'TI', - 'a1', 'a2', 'a3', 'a4', 'b1', 'b2', 'NG', 'rho', 'ws_ci', + 'fort_gcl_s': ['x_g', 'y_g', 'z_g', 'dt', 'p_c', 'ct_c', 'ws', 'wd', 'ti', + 'a1', 'a2', 'a3', 'a4', 'b1', 'b2', 'rho', 'ws_ci', + 'ws_co', 'ct_idle'], + 'fort_gclm_s': ['x_g', 'y_g', 'z_g', 'dt', 'p_c', 'ct_c', 'ws', 'wd', 'ti', + 'a1', 'a2', 'a3', 'a4', 'b1', 'b2', 'rho', 'ws_ci', + 'ws_co', 'ct_idle'], + 'fort_gclm': ['x_g', 'y_g', 'z_g', 'dt', 'p_c', 'ct_c', 'ws', 'wd', 'ti', + 'a1', 'a2', 'a3', 'a4', 'b1', 'b2', 'rho', 'ws_ci', + 'ws_co', 'ct_idle'], + 'fort_gclm_av': ['x_g', 'y_g', 'z_g', 'dt', 'p_c', 'ct_c', 'ws', 'wd', 'ti', + 'av', 'a1', 'a2', 'a3', 'a4', 'b1', 'b2', 'rho', 'ws_ci', 'ws_co', 'ct_idle'], } # Default variables for running the wind farm flow model defaults = { 'rho': 1.225, 'version': 'py_gcl_v1', - 'z0': 0.0001, - 'sup': 'lin', # ['lin' | 'quad'] - 'alpha': 0.101, - 'pars': [0.435449861, 0.797853685, -0.124807893, 0.136821858, 15.6298, 1.0], - 'inflow': 'log', - 'NG': 4, + 'pars': [0.435449861, 0.797853685, -0.124807893, 0.136821858, 15.6298, 1.0], 'inflow': 'log', } def __init__(self, **kwargs): self.set(self.defaults) @@ -40,8 +82,8 @@ def versions(self): return versions def set(self, dic): - """ Set the attributes of a dictionary as instance variables. Prepares - for the different versions of the wake model + """ Set the attributes of a dictionary as instance variables. + Prepares for the different versions of the wake model Parameters ---------- @@ -54,12 +96,12 @@ def set(self, dic): # Preparing for the inputs for the fortran version if 'WF' in dic: self.x_g, self.y_g, self.z_g = self.WF.get_T2T_gl_coord2() - self.dt = self.WF.rotor_diameter - self.p_c = self.WF.power_curve - self.ct_c = self.WF.c_t_curve - self.ws_ci = self.WF.cut_in_wind_speed - self.ws_co = self.WF.cut_out_wind_speed - self.ct_idle = self.WF.c_t_idle + self.dt = np.array(self.WF.rotor_diameter) + self.p_c = np.array(self.WF.power_curve) + self.ct_c = np.array(self.WF.c_t_curve) + self.ws_ci = np.array(self.WF.cut_in_wind_speed) + self.ws_co = np.array(self.WF.cut_out_wind_speed) + self.ct_idle = np.array(self.WF.c_t_idle) def _get_kwargs(self, version): """Prepare a dictionary of inputs to be passed to the wind farm flow model @@ -75,12 +117,16 @@ def _get_kwargs(self, version): # fortran only get lowercase inputs return {(k).lower():getattr(self, k) for k in self.inputs[version] if hasattr(self, k)} - def fort_gcl_av(self): + def fortran_gcl_av(self): # Prepare the inputs if isinstance(self.WS, float) or isinstance(self.WS, int): self.ws = np.array([self.WS]) self.wd = np.array([self.WD]) self.ti = np.array([self.TI]) + else: + self.ws = self.WS + self.wd = self.WD + self.ti = self.TI if not hasattr(self, 'wt_available'): self.wt_available = np.ones([len(self.ws), self.WF.nWT]) self.av = self.wt_available @@ -100,7 +146,7 @@ def fort_gcl_av(self): try: self.p_wt, self.t_wt, self.u_wt = fgcl.gcl_av(**self._get_kwargs(self.version)) except Exception as e: - raise Exception('The fortran version {} failed with the followind inputs: {}, and the error message: {}'.format( + raise Exception('The fortran version {} failed with the following inputs: {}, and the error message: {}'.format( self.version, self._get_kwargs(self.version), e)) A = 0.25 * self.WF.WT.rotor_diameter**2.0 self.c_t = self.t_wt / (0.5 * A * self.rho * self.u_wt**2.0) @@ -116,6 +162,10 @@ def fortran_gcl(self): self.ws = np.array([self.WS]) self.wd = np.array([self.WD]) self.ti = np.array([self.TI]) + else: + self.ws = self.WS + self.wd = self.WD + self.ti = self.TI self.a1 = self.pars[0] * np.ones_like(self.ws) self.a2 = self.pars[1] * np.ones_like(self.ws) self.a3 = self.pars[2] * np.ones_like(self.ws) @@ -127,7 +177,7 @@ def fortran_gcl(self): try: self.p_wt, self.t_wt, self.u_wt = fgcl.gcl(**self._get_kwargs(self.version)) except Exception as e: - raise Exception('The fortran version {} failed with the followind inputs: {}, and the error message: {}'.format( + raise Exception('The fortran version {} failed with the following inputs: {}, and the error message: {}'.format( self.version, self._get_kwargs(self.version), e)) A = 0.25 * self.WF.WT.rotor_diameter**2.0 self.c_t = self.t_wt / (0.5 * A * self.rho * self.u_wt**2.0) @@ -139,20 +189,114 @@ def fortran_gcl(self): def fortran_gcl_s(self): + self.ws = np.array([self.WS]) + self.wd = np.array([self.WD]) + self.ti = np.array([self.TI]) self.a1, self.a2, self.a3, self.a4, self.b1, self.b2 = self.pars try: self.p_wt, self.t_wt, self.u_wt = fgcl.gcl_s(**self._get_kwargs(self.version)) except Exception as e: - raise Exception('The fortran version {} failed with the followind inputs: {}, and the error message: {}'.format( + raise Exception('The fortran version {} failed with the following inputs: {}, and the error message: {}'.format( self.version, self._get_kwargs(self.version), e)) A = 0.25 * self.WF.WT.rotor_diameter**2.0 self.c_t = self.t_wt / (0.5 * A * self.rho * self.u_wt**2.0) self.p_wt *= 1.0E3 # Scaling the power back to Watt + def fortran_gclm_s(self): + self.ws = np.array([self.WS]) + self.wd = np.array([self.WD]) + self.ti = np.array([self.TI]) + self.a1, self.a2, self.a3, self.a4, self.b1, self.b2 = self.pars + try: + self.p_wt, self.t_wt, self.u_wt = fgcl.gclm_s(**self._get_kwargs(self.version)) + except Exception as e: + raise Exception('The fortran version {} failed with the following inputs: {}, and the error message: {}'.format( + self.version, self._get_kwargs(self.version), e)) + A = 0.25 * self.WF.WT.rotor_diameter**2.0 + self.c_t = self.t_wt / (0.5 * A * self.rho * self.u_wt**2.0) + self.p_wt *= 1.0E3 # Scaling the power back to Watt + + def fortran_gclm(self): + # Prepare the inputs + if isinstance(self.WS, float) or isinstance(self.WS, int): + self.ws = self.WS*np.ones([1,self.WF.nWT]) + self.wd = self.WD*np.ones([1,self.WF.nWT]) + self.ti = self.TI*np.ones([1,self.WF.nWT]) + elif len(self.WD.shape) == 1: + self.ws = self.WS.reshape([1,self.WF.nWT]) + self.wd = self.WD.reshape([1,self.WF.nWT]) + self.ti = self.TI.reshape([1,self.WF.nWT]) + else: + self.ws = self.WS + self.wd = self.WD + self.ti = self.TI + self.a1 = self.pars[0] * np.ones(self.ws.shape[0]) + self.a2 = self.pars[1] * np.ones(self.ws.shape[0]) + self.a3 = self.pars[2] * np.ones(self.ws.shape[0]) + self.a4 = self.pars[3] * np.ones(self.ws.shape[0]) + self.b1 = self.pars[4] * np.ones(self.ws.shape[0]) + self.b2 = self.pars[5] * np.ones(self.ws.shape[0]) + # Run the fortran code + try: + self.p_wt, self.t_wt, self.u_wt = fgcl.gclm(**self._get_kwargs(self.version)) + except Exception as e: + raise Exception('The fortran version {} failed with the following inputs: {}, and the error message: {}'.format( + self.version, self._get_kwargs(self.version), e)) + A = 0.25 * self.WF.WT.rotor_diameter**2.0 + self.c_t = self.t_wt / (0.5 * A * self.rho * self.u_wt**2.0) + self.p_wt *= 1.0E3 # Scaling the power back to Watt + if len(self.ws) == 1: # We are only returning a 1D array + self.p_wt = self.p_wt[0] + self.u_wt = self.u_wt[0] + self.c_t = self.c_t[0] + + def fortran_gclm_av(self): + # Prepare the inputs + if isinstance(self.WS, float) or isinstance(self.WS, int): + self.ws = self.WS*np.ones([1,self.WF.nWT]) + self.wd = self.WD*np.ones([1,self.WF.nWT]) + self.ti = self.TI*np.ones([1,self.WF.nWT]) + elif len(self.WD.shape) == 1: + self.ws = self.WS.reshape([1,self.WF.nWT]) + self.wd = self.WD.reshape([1,self.WF.nWT]) + self.ti = self.TI.reshape([1,self.WF.nWT]) + else: + self.ws = self.WS + self.wd = self.WD + self.ti = self.TI + if not hasattr(self, 'wt_available'): + self.wt_available = np.ones([self.ws.shape[0], self.WF.nWT]) + self.av = self.wt_available + elif self.wt_available.shape == (self.ws.shape[0], self.WF.nWT): + self.av = self.wt_available + else: + # stacking up the availability vector for each flow case + self.av = np.vstack([self.wt_available for i in range(self.ws.shape[0])]) + self.a1 = self.pars[0] * np.ones(self.ws.shape[0]) + self.a2 = self.pars[1] * np.ones(self.ws.shape[0]) + self.a3 = self.pars[2] * np.ones(self.ws.shape[0]) + self.a4 = self.pars[3] * np.ones(self.ws.shape[0]) + self.b1 = self.pars[4] * np.ones(self.ws.shape[0]) + self.b2 = self.pars[5] * np.ones(self.ws.shape[0]) + + # Run the fortran code + try: + self.p_wt, self.t_wt, self.u_wt = fgcl.gclm_av(**self._get_kwargs(self.version)) + except Exception as e: + raise Exception('The fortran version {} failed with the following inputs: {}, and the error message: {}'.format( + self.version, self._get_kwargs(self.version), e)) + A = 0.25 * self.WF.WT.rotor_diameter**2.0 + self.c_t = self.t_wt / (0.5 * A * self.rho * self.u_wt**2.0) + self.p_wt *= 1.0E3 # Scaling the power back to Watt + if len(self.ws) == 1: # We are only returning a 1D array + self.p_wt = self.p_wt[0] + self.u_wt = self.u_wt[0] + self.c_t = self.c_t[0] def python_v0(self): self.p_wt, self.u_wt, self.c_t = gcl.GCLarsen_v0(**self._get_kwargs(self.version)) + def python_v1(self): self.p_wt, self.u_wt, self.c_t = gcl.GCLarsen(**self._get_kwargs(self.version)) @@ -164,11 +308,17 @@ def __call__(self, **kwargs): elif self.version == 'py_gcl_v1': self.python_v1() elif self.version == 'fort_gcl_av': - self.fort_gcl_av() + self.fortran_gcl_av() elif self.version == 'fort_gcl_s': self.fortran_gcl_s() elif self.version == 'fort_gcl': self.fortran_gcl() + elif self.version == 'fort_gclm_s': + self.fortran_gclm_s() + elif self.version == 'fort_gclm': + self.fortran_gclm() + elif self.version == 'fort_gclm_av': + self.fortran_gclm_av() elif not self.version in self.versions: raise Exception("Version %s is not valid: version=[%s]"%(self.version, '|'.join(self.versions))) else: diff --git a/fusedwake/gcl/fortran/GCL.f b/fusedwake/gcl/fortran/GCL.f index b096685..21118d0 100644 --- a/fusedwake/gcl/fortran/GCL.f +++ b/fusedwake/gcl/fortran/GCL.f @@ -186,17 +186,15 @@ end subroutine get_dU c D (float): Wake generating turbine diameter c TI (float): Ambient turbulence intensity [-] c CT (float): Outputs WindTurbine object's thrust coefficient -c Ng (int): Polynomial order for Gauss-Legendre quadrature integration -c in both radial and angular positions c c Outputs c ---------- c dUeq (float): Wake velocity deficit at a location normalized by c inflow velocity - subroutine get_dUeq(n,x,y,z,DT,D,CT,TI,a1,a2,a3,a4,b1,b2,Ng,dUeq) + subroutine get_dUeq(n,x,y,z,DT,D,CT,TI,a1,a2,a3,a4,b1,b2,dUeq) implicit none - integer :: n,Ng + integer :: n real(kind=8) :: x(n),y(n),z(n),DT(n),D,CT,TI real(kind=8) :: a1,a2,a3,a4,b1,b2,dUeq(n) cf2py integer intent(hide),depend(x) :: n = len(x) @@ -209,115 +207,190 @@ subroutine get_dUeq(n,x,y,z,DT,D,CT,TI,a1,a2,a3,a4,b1,b2,Ng,dUeq) cf2py real(kind=8) optional,intent(in) :: a4=0.136821858 cf2py real(kind=8) optional,intent(in) :: b1=15.6298 cf2py real(kind=8) optional,intent(in) :: b2=1.0 -cf2py integer optional intent(in) :: Ng = 4 cf2py real(kind=8) intent(out),depend(n),dimension(n) :: dUeq ! internal variables real(kind=8), parameter :: pi=3.1415926535897932384626433832795d0 - integer :: i,j,k - real(kind=8) :: tm1,tm2,tm3,tm4 - real(kind=8), dimension(Ng) :: root,weight,r_pr,th_pr - real(kind=8), dimension(1) :: x_e,r_e,dU - real(kind=8), dimension(n) :: RT,r_R,th_R + integer :: i,j + real(kind=8), dimension(40) :: r_node,th_node,weight + real(kind=8), dimension(1) :: x_e,y_e,z_e,r_e,dU + real(kind=8), dimension(n) :: RT RT = DT/2.0d0 - ! Gauss-Legendre quadrature points and weights - select case (ng) - case ( 4 ) - root(1) = -0.3399810435848563d0 - root(2) = 0.3399810435848563d0 - root(3) = -0.8611363115940526d0 - root(4) = 0.8611363115940526d0 - - weight(1) = 0.6521451548625461d0 - weight(2) = 0.6521451548625461d0 - weight(3) = 0.3478548451374538d0 - weight(4) = 0.3478548451374538d0 - case (5) - root(1) = 0.0000000000000000d0 - root(2) = -0.5384693101056831d0 - root(3) = 0.5384693101056831d0 - root(4) = -0.9061798459386640d0 - root(5) = 0.9061798459386640d0 - - weight(1) = 0.5688888888888889d0 - weight(2) = 0.4786286704993665d0 - weight(3) = 0.4786286704993665d0 - weight(4) = 0.2369268850561891d0 - weight(5) = 0.2369268850561891d0 - case (6) - root(1) = 0.6612093864662645d0 - root(2) = -0.6612093864662645d0 - root(3) = -0.2386191860831969d0 - root(4) = 0.2386191860831969d0 - root(5) = -0.9324695142031521d0 - root(6) = 0.9324695142031521d0 - - weight(1) = 0.3607615730481386d0 - weight(2) = 0.3607615730481386d0 - weight(3) = 0.4679139345726910d0 - weight(4) = 0.4679139345726910d0 - weight(5) = 0.1713244923791704d0 - weight(6) = 0.1713244923791704d0 - case (7) - root(1) = 0.0000000000000000d0 - root(2) = 0.4058451513773972d0 - root(3) = -0.4058451513773972d0 - root(4) = -0.7415311855993945d0 - root(5) = 0.7415311855993945d0 - root(6) = -0.9491079123427585d0 - root(7) = 0.9491079123427585d0 - - weight(1) = 0.4179591836734694d0 - weight(2) = 0.3818300505051189d0 - weight(3) = 0.3818300505051189d0 - weight(4) = 0.2797053914892766d0 - weight(5) = 0.2797053914892766d0 - weight(6) = 0.1294849661688697d0 - weight(7) = 0.1294849661688697d0 - case ( 8 ) - root(1) = -0.960289856497536d0 - root(2) = -0.796666477413627d0 - root(3) = -0.525532409916329d0 - root(4) = -0.183434642495650d0 - root(5) = 0.183434642495650d0 - root(6) = 0.525532409916329d0 - root(7) = 0.796666477413627d0 - root(8) = 0.960289856497536d0 - - weight(1) = 0.101228536290374d0 - weight(2) = 0.222381034453375d0 - weight(3) = 0.313706645877888d0 - weight(4) = 0.362683783378363d0 - weight(5) = 0.362683783378363d0 - weight(6) = 0.313706645877888d0 - weight(7) = 0.222381034453375d0 - weight(8) = 0.101228536290374d0 - end select - - ! Location of the turbines in wake coordinates - r_R = (y**(2.0d0) + z**(2.0d0))**(0.5d0) - th_R = modulo(atan2(z,y),2.0d0*pi) + ! 2D Gauss quadrature nodes and weights for polar integration + ! int_(0,R) int_(0,2pi) f(r,theta) r dtheta dr = + ! sum weight f(r_node, theta_node) + r_node(1) = 0.26349922998554242692 + th_node(1) = 4.79436403870179805864 + weight(1) = 0.00579798753740115753 + + r_node(2) = 0.26349922998554242692 + th_node(2) = 5.13630491629471475079 + weight(2) = 0.01299684397858970851 + + r_node(3) = 0.26349922998554242692 + th_node(3) = 5.71955352542765460555 + weight(3) = 0.01905256317618122044 + + r_node(4) = 0.26349922998554242692 + th_node(4) = 0.20924454049880022999 + weight(4) = 0.02341643323656225281 + + r_node(5) = 0.26349922998554242692 + th_node(5) = 1.10309379714216659885 + weight(5) = 0.02569988335562909190 + + r_node(6) = 0.26349922998554242692 + th_node(6) = 2.03849885644762496284 + weight(6) = 0.02569988335562912660 + + r_node(7) = 0.26349922998554242692 + th_node(7) = 2.93234811309099407950 + weight(7) = 0.02341643323656214179 + + r_node(8) = 0.26349922998554242692 + th_node(8) = 3.70522443534172518653 + weight(8) = 0.01905256317618119616 + + r_node(9) = 0.26349922998554242692 + th_node(9) = 4.28847304447466459720 + weight(9) = 0.01299684397858971198 + + r_node(10) = 0.26349922998554242692 + th_node(10) = 4.63041392206758217753 + weight(10) = 0.00579798753740114539 + + r_node(11) = 0.57446451431535072718 + th_node(11) = 4.79436403870179805864 + weight(11) = 0.01086984853977092380 + + r_node(12) = 0.57446451431535072718 + th_node(12) = 5.13630491629471475079 + weight(12) = 0.02436599330905551281 + + r_node(13) = 0.57446451431535072718 + th_node(13) = 5.71955352542765460555 + weight(13) = 0.03571902745281423097 + + r_node(14) = 0.57446451431535072718 + th_node(14) = 0.20924454049880022999 + weight(14) = 0.04390024659093685194 + + r_node(15) = 0.57446451431535072718 + th_node(15) = 1.10309379714216659885 + weight(15) = 0.04818117282305908744 + + r_node(16) = 0.57446451431535072718 + th_node(16) = 2.03849885644762496284 + weight(16) = 0.04818117282305915683 + + r_node(17) = 0.57446451431535072718 + th_node(17) = 2.93234811309099407950 + weight(17) = 0.04390024659093664378 + + r_node(18) = 0.57446451431535072718 + th_node(18) = 3.70522443534172518653 + weight(18) = 0.03571902745281418240 + + r_node(19) = 0.57446451431535072718 + th_node(19) = 4.28847304447466459720 + weight(19) = 0.02436599330905552321 + + r_node(20) = 0.57446451431535072718 + th_node(20) = 4.63041392206758217753 + weight(20) = 0.01086984853977089951 + + r_node(21) = 0.81852948743000586429 + th_node(21) = 4.79436403870179805864 + weight(21) = 0.01086984853977090992 + + r_node(22) = 0.81852948743000586429 + th_node(22) = 5.13630491629471475079 + weight(22) = 0.02436599330905548505 + + r_node(23) = 0.81852948743000586429 + th_node(23) = 5.71955352542765460555 + weight(23) = 0.03571902745281418934 + + r_node(24) = 0.81852948743000586429 + th_node(24) = 0.20924454049880022999 + weight(24) = 0.04390024659093679643 + + r_node(25) = 0.81852948743000586429 + th_node(25) = 1.10309379714216659885 + weight(25) = 0.04818117282305903193 + + r_node(26) = 0.81852948743000586429 + th_node(26) = 2.03849885644762496284 + weight(26) = 0.04818117282305909438 + + r_node(27) = 0.81852948743000586429 + th_node(27) = 2.93234811309099407950 + weight(27) = 0.04390024659093658826 + + r_node(28) = 0.81852948743000586429 + th_node(28) = 3.70522443534172518653 + weight(28) = 0.03571902745281413383 + + r_node(29) = 0.81852948743000586429 + th_node(29) = 4.28847304447466459720 + weight(29) = 0.02436599330905549199 + + r_node(30) = 0.81852948743000586429 + th_node(30) = 4.63041392206758217753 + weight(30) = 0.01086984853977088737 + + r_node(31) = 0.96465960618086743494 + th_node(31) = 4.79436403870179805864 + weight(31) = 0.00579798753740116100 + + r_node(32) = 0.96465960618086743494 + th_node(32) = 5.13630491629471475079 + weight(32) = 0.01299684397858971545 + + r_node(33) = 0.96465960618086743494 + th_node(33) = 5.71955352542765460555 + weight(33) = 0.01905256317618123432 + + r_node(34) = 0.96465960618086743494 + th_node(34) = 0.20924454049880022999 + weight(34) = 0.02341643323656226669 + + r_node(35) = 0.96465960618086743494 + th_node(35) = 1.10309379714216659885 + weight(35) = 0.02569988335562910925 + + r_node(36) = 0.96465960618086743494 + th_node(36) = 2.03849885644762496284 + weight(36) = 0.02569988335562914394 + + r_node(37) = 0.96465960618086743494 + th_node(37) = 2.93234811309099407950 + weight(37) = 0.02341643323656215567 + + r_node(38) = 0.96465960618086743494 + th_node(38) = 3.70522443534172518653 + weight(38) = 0.01905256317618120657 + + r_node(39) = 0.96465960618086743494 + th_node(39) = 4.28847304447466459720 + weight(39) = 0.01299684397858972065 + + r_node(40) = 0.96465960618086743494 + th_node(40) = 4.63041392206758217753 + weight(40) = 0.00579798753740114886 do i = 1, n dUeq(i) = 0.0d0 if (x(i) > 0.0) then - ! Location of evaluation points in the local rotor coordinates - r_pr = RT(i)*(root+1d0)/2.0d0!uniform distribution in [0, RT] - !th_pr = pi*(root+1d0) !uniform distribution in [0,2*pi] - th_pr = pi*(root+1d0)-pi/2.d0 !uniform distribution in [-pi/2,3/2*pi] - ! Location of evaluation points in wake coordinates ! Evaluation of wake and sum of quadrature - do j = 1, Ng - do k = 1, Ng - x_e = x(i) - tm1 = (r_R(i))**(2.0d0) - tm2 = (r_pr(k))**(2.0d0) - tm3 = 2d0*r_R(i)*r_pr(k)*cos(th_R(i) - th_pr(j)) - r_e = sqrt( tm1+tm2+tm3) - call get_dU(1,x_e,r_e,D,CT,TI,a1,a2,a3,a4,b1,b2,dU) - tm4 = weight(j)*weight(k)*dU(1)*(root(k)+1d0)/4d0 - dUeq(i)=dUeq(i)+tm4 - end do + do j = 1, 40 + ! Location of evaluation points in the local rotor coordinates + x_e = x(i) + y_e = y(i) + RT(i)*r_node(j)*cos(th_node(j)) + z_e = z(i) + RT(i)*r_node(j)*sin(th_node(j)) + r_e = (y_e**(2.0d0) + z_e**(2.0d0))**(0.5d0) + call get_dU(1,x_e,r_e,D,CT,TI,a1,a2,a3,a4,b1,b2,dU) + dUeq(i)=dUeq(i)+dU(1)*weight(j) end do end if end do @@ -359,10 +432,10 @@ end subroutine get_dUeq c U (array): Rotor averaged (equivalent) Wind speed at hub height c (nWT,1) [m/s] subroutine gcl_s(n,nP,nCT,x_g,y_g,z_g,DT,P_c,CT_c,WS,WD,TI, - &a1,a2,a3,a4,b1,b2,Ng,rho,WS_CI,WS_CO,CT_idle,P,T,U) + &a1,a2,a3,a4,b1,b2,rho,WS_CI,WS_CO,CT_idle,P,T,U) implicit none - integer :: n,nP,nCT,Ng + integer :: n,nP,nCT real(kind=8) :: x_g(n,n),y_g(n,n),z_g(n,n),DT(n),P_c(n,nP,2) real(kind=8) :: CT_c(n,nCT,2),WS,WD,TI,a1,a2,a3,a4,b1,b2 real(kind=8) :: rho,WS_CI(n),WS_CO(n),CT_idle(n),P(n),T(n),U(n) @@ -380,7 +453,6 @@ subroutine gcl_s(n,nP,nCT,x_g,y_g,z_g,DT,P_c,CT_c,WS,WD,TI, cf2py real(kind=8) optional,intent(in) :: a4=0.136821858 cf2py real(kind=8) optional,intent(in) :: b1=15.6298 cf2py real(kind=8) optional,intent(in) :: b2=1.0 -cf2py integer optional intent(in) :: Ng = 4 cf2py real(kind=8) optional,intent(in) :: rho=1.225 cf2py real(kind=8) optional,intent(in),dimension(n) :: WS_CI=4.0 cf2py real(kind=8) optional,intent(in),dimension(n) :: WS_CO=25.0 @@ -418,7 +490,7 @@ subroutine gcl_s(n,nP,nCT,x_g,y_g,z_g,DT,P_c,CT_c,WS,WD,TI, else CT = CT_idle(i) end if - call get_dUeq(n,x,y,z,DT,D,CT,TI,a1,a2,a3,a4,b1,b2,Ng,dUeq) + call get_dUeq(n,x,y,z,DT,D,CT,TI,a1,a2,a3,a4,b1,b2,dUeq) U = U + U(i)*dUeq end do ! Calculates the power and thrust @@ -469,10 +541,10 @@ end subroutine gcl_s c U (array): Rotor averaged (equivalent) Wind speed at hub height c (nWT,1) [m/s] subroutine gcl(n,nP,nCT,nF,x_g,y_g,z_g,DT,P_c,CT_c,WS,WD,TI,a1,a2, - &a3,a4,b1,b2,Ng,rho,WS_CI,WS_CO,CT_idle,P,T,U) + &a3,a4,b1,b2,rho,WS_CI,WS_CO,CT_idle,P,T,U) implicit none - integer :: n,nP,nCT,nF,Ng + integer :: n,nP,nCT,nF real(kind=8) :: x_g(n,n),y_g(n,n),z_g(n,n),DT(n),P_c(n,nP,2) real(kind=8) :: CT_c(n,nCT,2),rho,WS_CI(n),WS_CO(n),CT_idle(n) real(kind=8),dimension(nF) :: WS,WD,TI,a1,a2,a3,a4,b1,b2 @@ -492,7 +564,6 @@ subroutine gcl(n,nP,nCT,nF,x_g,y_g,z_g,DT,P_c,CT_c,WS,WD,TI,a1,a2, cf2py real(kind=8) optional,intent(in),dimension(nF) :: a4=0.136821858 cf2py real(kind=8) optional,intent(in),dimension(nF) :: b1=15.6298 cf2py real(kind=8) optional,intent(in),dimension(nF) :: b2=1.0 -cf2py integer optional intent(in) :: Ng = 4 cf2py real(kind=8) optional,intent(in) :: rho=1.225 cf2py real(kind=8) optional,intent(in),dimension(n) :: WS_CI=4.0 cf2py real(kind=8) optional,intent(in),dimension(n) :: WS_CO=25.0 @@ -503,7 +574,7 @@ subroutine gcl(n,nP,nCT,nF,x_g,y_g,z_g,DT,P_c,CT_c,WS,WD,TI,a1,a2, do i=1,nF call gcl_s(n,nP,nCT,x_g,y_g,z_g,DT,P_c,CT_c,WS(i),WD(i),TI(i), - & a1(i),a2(i),a3(i),a4(i),b1(i),b2(i),Ng,rho,WS_CI,WS_CO, + & a1(i),a2(i),a3(i),a4(i),b1(i),b2(i),rho,WS_CI,WS_CO, & CT_idle,P(i,:),T(i,:),U(i,:)) end do @@ -544,10 +615,10 @@ end subroutine gcl c U (array): Rotor averaged (equivalent) Wind speed at hub height c (nWT,1) [m/s] subroutine gcl_av(n,nP,nCT,nF,x_g,y_g,z_g,DT,P_c,CT_c,WS,WD,TI,AV, - &a1,a2,a3,a4,b1,b2,Ng,rho,WS_CI,WS_CO,CT_idle,P,T,U) + &a1,a2,a3,a4,b1,b2,rho,WS_CI,WS_CO,CT_idle,P,T,U) implicit none - integer :: n,nP,nCT,nF,Ng,AV(nf,n) + integer :: n,nP,nCT,nF,AV(nf,n) real(kind=8) :: x_g(n,n),y_g(n,n),z_g(n,n),DT(n),P_c(n,nP,2) real(kind=8) :: CT_c(n,nCT,2),rho,WS_CI(n),WS_CO(n),CT_idle(n) real(kind=8),dimension(nF) :: WS,WD,TI,a1,a2,a3,a4,b1,b2 @@ -568,7 +639,6 @@ subroutine gcl_av(n,nP,nCT,nF,x_g,y_g,z_g,DT,P_c,CT_c,WS,WD,TI,AV, cf2py real(kind=8) optional,intent(in),dimension(nF) :: a4=0.136821858 cf2py real(kind=8) optional,intent(in),dimension(nF) :: b1=15.6298 cf2py real(kind=8) optional,intent(in),dimension(nF) :: b2=1.0 -cf2py integer optional intent(in) :: Ng = 4 cf2py real(kind=8) optional,intent(in) :: rho=1.225 cf2py real(kind=8) optional,intent(in),dimension(n) :: WS_CI=4.0 cf2py real(kind=8) optional,intent(in),dimension(n) :: WS_CO=25.0 @@ -589,19 +659,24 @@ subroutine gcl_av(n,nP,nCT,nF,x_g,y_g,z_g,DT,P_c,CT_c,WS,WD,TI,AV, end if end do call gcl_s(n,nP,nCT,x_g,y_g,z_g,DT,P_c_AV,CT_c_AV,WS(i),WD(i), - & TI(i),a1(i),a2(i),a3(i),a4(i),b1(i),b2(i),Ng,rho,WS_CI, + & TI(i),a1(i),a2(i),a3(i),a4(i),b1(i),b2(i),rho,WS_CI, & WS_CO,CT_idle,P(i,:),T(i,:),U(i,:)) end do end subroutine gcl_av c ---------------------------------------------------------------------- -c gcl_GA(x,y,z,DT,P_c,CT_c,WS,WD,TI,STD_WD,Nga) +c New Multiple turbine types (height, P and CT curves) implementation +c ---------------------------------------------------------------------- + + +c ---------------------------------------------------------------------- +c gclm_s(x,y,z,DT,P_c,CT_c,WS,TI) c ---------------------------------------------------------------------- -c GAUSSIAN AVERAGE -c Gauss-Hermite quadrature for normally distributed wind direction -c uncertainty (inside Reynolds averaging time), for a global/unique wind -c direction uncertainty +c SINGLE FLOW CASE +c Computes the WindFarm flow and Power using G. C. Larsen model: +c Larsen, G. C., and P. E. Re'thore'. A simple stationary semi-analytical +c wake model. Technical Report Risoe, 2009. c c Inputs c ---------- @@ -612,12 +687,120 @@ end subroutine gcl_av c P_c (array): Power curves c CT_c (array): Thrust coefficient curves c WS (array): Undisturbed rotor averaged (equivalent) wind speed at hub -c height [m/s] -c WD (array): Undisturbed wind direction at hub height [deg.] +c height [m/s] for each wind turbine +c WD (array): Undisturbed wind direction at hub height [deg.] for each +c turbine. Meteorological coordinates (N=0,E=90,S=180,W=270) +c TI (array): Turbulence intensity [-] for each turbine +c +c rho (float): Air density at which the power curve is valid [kg/m^3] +c WS_CI (array): Cut in wind speed [m/s] for each turbine +c WS_CO (array): Cut out wind speed [m/s] for each turbine +c CT_idle (array): Thrust coefficient at rest [-] for each turbine +c +c Outputs +c ---------- +c P (array): Power production of the wind turbines (nWT,1) [W] +c T (array): Thrust force of the wind turbines (nWT,1) [N] +c U (array): Rotor averaged (equivalent) Wind speed at hub height +c (nWT,1) [m/s] + subroutine gclm_s(n,nP,nCT,x_g,y_g,z_g,DT,P_c,CT_c,WS,WD,TI, + &a1,a2,a3,a4,b1,b2,rho,WS_CI,WS_CO,CT_idle,P,T,U) + + implicit none + integer :: n,nP,nCT + real(kind=8) :: x_g(n,n),y_g(n,n),z_g(n,n),DT(n),P_c(n,nP,2) + real(kind=8) :: CT_c(n,nCT,2),WS(n),WD(n),TI(n),a1,a2,a3,a4,b1,b2 + real(kind=8) :: rho,WS_CI(n),WS_CO(n),CT_idle(n),P(n),T(n),U(n) +cf2py integer intent(hide),depend(DT) :: n = len(DT) +cf2py integer intent(hide),depend(P_c) :: nP = size(P_c,2) +cf2py integer intent(hide),depend(CT_c) :: nCT = size(CT_c,2) +cf2py real(kind=8) intent(in),dimension(n) :: DT +cf2py real(kind=8) intent(in),depend(n),dimension(n,n) :: x_g,y_g,z_g +cf2py real(kind=8) intent(in),dimension(n,nP,2) :: P_c +cf2py real(kind=8) intent(in),dimension(n,nCT,2) :: CT_c +cf2py real(kind=8) intent(in),dimension(n) :: WS,WD,TI +cf2py real(kind=8) optional,intent(in) :: a1=0.435449861 +cf2py real(kind=8) optional,intent(in) :: a2=0.797853685 +cf2py real(kind=8) optional,intent(in) :: a3=-0.124807893 +cf2py real(kind=8) optional,intent(in) :: a4=0.136821858 +cf2py real(kind=8) optional,intent(in) :: b1=15.6298 +cf2py real(kind=8) optional,intent(in) :: b2=1.0 +cf2py real(kind=8) optional,intent(in) :: rho=1.225 +cf2py real(kind=8) optional,intent(in),dimension(n) :: WS_CI=4.0 +cf2py real(kind=8) optional,intent(in),dimension(n) :: WS_CO=25.0 +cf2py real(kind=8) optional,intent(in),dimension(n) :: CT_idle=0.053 +cf2py real(kind=8) intent(out),depend(n),dimension(n) :: P,T,U + ! internal variables + real(kind=8), parameter :: pi=3.1415926535897932384626433832795d0 + integer :: i,j,k,nDownstream(n),idT(n) + real(kind=8) :: x(n),y(n),z(n),D,CT,dUeq(n),angle + real(kind=8) :: x_l(n,n),y_l(n,n) + + ! Rotates the global coordinates to local flow coordinates + do i=1,n + angle = pi*(270.0d0-WD(i))/180.0d0 + do j=1,n + x_l(i,j) = cos(angle)*x_g(i,j)+sin(angle)*y_g(i,j) + y_l(i,j) = -sin(angle)*x_g(i,j)+cos(angle)*y_g(i,j) + end do + ! counts the number of turbines in front of turbine + nDownstream(i) = count(x_l(i,:).lt.0) + end do + ! Indexes of ordered turbines from most upstream turbine + call order_id(n,nDownstream,idT) + ! Initializes the rotor averaged (equivalent) velocity + U = WS + ! Computes the rotor averaged (equivalent) velocity deficit + do j=1,n + i=idT(j) + x = x_l(i,:) + y = y_l(i,:) + z = z_g(i,:) + D = DT(i) + if ((U(i) >= WS_CI(i)).and.(U(i) <= WS_CO(i))) then + call interp_l(CT_c(i,:,1),CT_c(i,:,2),nCT,U(i),CT) + else + CT = CT_idle(i) + end if + call get_dUeq(n,x,y,z,DT,D,CT,TI(i),a1,a2,a3,a4,b1,b2,dUeq) + U = U + U(i)*dUeq + end do + ! Calculates the power and thrust + do k=1,n + if ((U(k) >= WS_CI(k)).and.(U(k) <= WS_CO(k))) then + call interp_l(P_c(k,:,1),P_c(k,:,2),nP,U(k),P(k)) + call interp_l(CT_c(k,:,1),CT_c(k,:,2),nCT,U(k),CT) + else + P(k)=0.0d0 + CT = CT_idle(k) + end if + T(k) = CT*0.5d0*rho*U(k)*U(k)*pi*DT(k)*DT(k)/4.0d0 + end do + + end subroutine gclm_s + +c ---------------------------------------------------------------------- +c gclm(x,y,z,DT,P_c,CT_c,WS,WD,TI) +c ---------------------------------------------------------------------- +c MULTIPLE FLOW CASES +c Computes the WindFarm flow and Power using G. C. Larsen model: +c Larsen, G. C., and P. E. Re'thore'. A simple stationary semi-analytical +c wake model. Technical Report Risoe, 2009. +c +c Inputs +c ---------- +c x_g (array): Distance between turbines in the global coordinates +c y_g (array): Distance between turbines in the global coordinates +c z_g (array): Distance between turbines in the global coordinates +c DT (array): Turbines diameter +c P_c (array): Power curves +c CT_c (array): Thrust coefficient curves +c WS (array): Undisturbed rotor averaged (equivalent) wind speed at hub +c height [m/s] for each wind turbine x flow [nF,n] +c WD (array): Undisturbed wind direction at hub height [deg.] for each +c turbine x flow [nF,n] c Meteorological coordinates (N=0,E=90,S=180,W=270) -c TI (array): Ambient turbulence intensity [-] -c STD_WD(array): Standard deviation of wind direction uncertainty -c Nga (int): Number of quadrature points for Gaussian averaging +c TI (array): Turbulence intensity [-] for each turbine x flow [nF,n] c c rho (float): Air density at which the power curve is valid [kg/m^3] c WS_CI (array): Cut in wind speed [m/s] for each turbine @@ -630,143 +813,53 @@ end subroutine gcl_av c T (array): Thrust force of the wind turbines (nWT,1) [N] c U (array): Rotor averaged (equivalent) Wind speed at hub height c (nWT,1) [m/s] - subroutine gcl_GA(n,nP,nCT,nF,x_g,y_g,z_g,DT,P_c,CT_c,WS,WD, - &TI,STD_WD,Nga,a1,a2,a3,a4,b1,b2,Ng,rho,WS_CI,WS_CO,CT_idle,P,T,U) + subroutine gclm(n,nP,nCT,nF,x_g,y_g,z_g,DT,P_c,CT_c,WS,WD,TI,a1, + &a2,a3,a4,b1,b2,rho,WS_CI,WS_CO,CT_idle,P,T,U) implicit none - integer :: n,nP,nCT,nF,Ng,Nga + integer :: n,nP,nCT,nF real(kind=8) :: x_g(n,n),y_g(n,n),z_g(n,n),DT(n),P_c(n,nP,2) real(kind=8) :: CT_c(n,nCT,2),rho,WS_CI(n),WS_CO(n),CT_idle(n) - real(kind=8),dimension(nF) :: WS,WD,TI,STD_WD,a1,a2,a3,a4,b1,b2 - real(kind=8) :: P(nF,n),T(nF,n),U(nF,n) + real(kind=8),dimension(nF) :: a1,a2,a3,a4,b1,b2 + real(kind=8) :: WS(nF,n),WD(nF,n),TI(nF,n),P(nF,n),T(nF,n),U(nF,n) cf2py integer intent(hide),depend(DT) :: n = len(DT) cf2py integer intent(hide),depend(P_c) :: nP = size(P_c,2) cf2py integer intent(hide),depend(CT_c) :: nCT = size(CT_c,2) -cf2py integer intent(hide),depend(WS) :: nF = len(WS) +cf2py integer intent(hide),depend(WS) :: nF = size(WS,1) cf2py real(kind=8) intent(in),dimension(n) :: DT cf2py real(kind=8) intent(in),depend(n),dimension(n,n) :: x_g,y_g,z_g cf2py real(kind=8) intent(in),dimension(n,nP,2) :: P_c cf2py real(kind=8) intent(in),dimension(n,nCT,2) :: CT_c -cf2py real(kind=8) intent(in),dimension(nF) :: WS,TI,WD,STD_WD -cf2py integer optional,intent(in) :: Nga = 4 +cf2py real(kind=8) intent(in),dimension(nF,n) :: WS,WD,TI cf2py real(kind=8) optional,intent(in),dimension(nF) :: a1=0.435449861 cf2py real(kind=8) optional,intent(in),dimension(nF) :: a2=0.797853685 cf2py real(kind=8) optional,intent(in),dimension(nF) :: a3=-0.124807893 cf2py real(kind=8) optional,intent(in),dimension(nF) :: a4=0.136821858 cf2py real(kind=8) optional,intent(in),dimension(nF) :: b1=15.6298 cf2py real(kind=8) optional,intent(in),dimension(nF) :: b2=1.0 -cf2py integer optional intent(in) :: Ng = 4 cf2py real(kind=8) optional,intent(in) :: rho=1.225 cf2py real(kind=8) optional,intent(in),dimension(n) :: WS_CI=4.0 cf2py real(kind=8) optional,intent(in),dimension(n) :: WS_CO=25.0 cf2py real(kind=8) optional,intent(in),dimension(n) :: CT_idle=0.053 -cf2py real(kind=8) intent(out),depend(n),dimension(nF,n) :: P,T,U +cf2py real(kind=8) intent(out),depend(nF,nF,n),dimension(nF,n) :: P,T,U ! internal variables - real(kind=8), parameter :: pi=3.1415926535897932384626433832795d0 - integer :: i,j - real(kind=8) :: WD_aux - real(kind=8), dimension(n) :: P_aux,T_aux,U_aux - real(kind=8), dimension(Nga) :: root,weight - - ! Gauss-Hermite quadrature points and weights - select case (Nga) - case ( 1 ) - root(1) = 0.0d0 - weight(1) = sqrt(pi) - case ( 4 ) - root(1) = -1.650680123885785d0 - root(2) = -0.524647623275290d0 - root(3) = 0.524647623275290d0 - root(4) = 1.650680123885785d0 - - weight(1) = 0.081312835447245d0 - weight(2) = 0.804914090005513d0 - weight(3) = 0.804914090005513d0 - weight(4) = 0.081312835447245d0 - case ( 5 ) - root(1) = -2.020182870456086d0 - root(2) = -0.958572464613819d0 - root(3) = 0.000000000000000d0 - root(4) = 0.958572464613819d0 - root(5) = 2.020182870456086d0 - - weight(1) = 0.019953242059046d0 - weight(2) = 0.393619323152241d0 - weight(3) = 0.945308720482942d0 - weight(4) = 0.393619323152241d0 - weight(5) = 0.019953242059046d0 - case ( 6 ) - root(1) = -2.350604973674492d0 - root(2) = -1.335849074013697d0 - root(3) = -0.436077411927617d0 - root(4) = 0.436077411927617d0 - root(5) = 1.335849074013697d0 - root(6) = 2.350604973674492d0 - - weight(1) = 0.004530009905509d0 - weight(2) = 0.157067320322856d0 - weight(3) = 0.724629595224392d0 - weight(4) = 0.724629595224392d0 - weight(5) = 0.157067320322856d0 - weight(6) = 0.004530009905509d0 - case ( 7 ) - root(1) = -2.651961356835233d0 - root(2) = -1.673551628767471d0 - root(3) = -0.816287882858965d0 - root(4) = 0.000000000000000d0 - root(5) = 0.816287882858965d0 - root(6) = 1.673551628767471d0 - root(7) = 2.651961356835233d0 - - weight(1) = 0.000971781245100d0 - weight(2) = 0.054515582819127d0 - weight(3) = 0.425607252610128d0 - weight(4) = 0.810264617556807d0 - weight(5) = 0.425607252610128d0 - weight(6) = 0.054515582819127d0 - weight(7) = 0.000971781245100d0 - case ( 8 ) - root(1) = -2.930637420257244d0 - root(2) = -1.981656756695843d0 - root(3) = -1.157193712446780d0 - root(4) = -0.381186990207322d0 - root(5) = 0.381186990207322d0 - root(6) = 1.157193712446780d0 - root(7) = 1.981656756695843d0 - root(8) = 2.930637420257244d0 - - weight(1) = 0.000199604072211d0 - weight(2) = 0.017077983007413d0 - weight(3) = 0.207802325814892d0 - weight(4) = 0.661147012558241d0 - weight(5) = 0.661147012558241d0 - weight(6) = 0.207802325814892d0 - weight(7) = 0.017077983007413d0 - weight(8) = 0.000199604072211d0 - end select + integer :: i do i=1,nF - P(i,:)=0.0d0 - T(i,:)=0.0d0 - U(i,:)=0.0d0 - do j=1,Nga - WD_aux = WD(i)+sqrt(2.0d0)*STD_WD(i)*root(j) - call gcl_s(n,nP,nCT,x_g,y_g,z_g,DT,P_c,CT_c,WS(i),WD_aux, - & TI(i),a1(i),a2(i),a3(i),a4(i),b1(i),b2(i),Ng,rho,WS_CI,WS_CO, - & CT_idle,P_aux,T_aux,U_aux) - P(i,:)=P(i,:)+weight(j)*P_aux*(1.0d0/sqrt(pi)) - T(i,:)=T(i,:)+weight(j)*T_aux*(1.0d0/sqrt(pi)) - U(i,:)=U(i,:)+weight(j)*U_aux*(1.0d0/sqrt(pi)) - end do + call gclm_s(n,nP,nCT,x_g,y_g,z_g,DT,P_c,CT_c,WS(i,:),WD(i,:), + & TI(i,:),a1(i),a2(i),a3(i),a4(i),b1(i),b2(i),rho,WS_CI, + & WS_CO,CT_idle,P(i,:),T(i,:),U(i,:)) end do - end subroutine gcl_GA + end subroutine gclm c ---------------------------------------------------------------------- -c gcl_mult_wd(x,y,z,DT,P_c,CT_c,WS,WD,TI) +c gclm_av(x,y,z,DT,P_c,CT_c,WS,WD,TI,AV) c ---------------------------------------------------------------------- -c MULTIPLE FLOW CASES with individual wind direction for each turbine -c and optional individual Gaussian averaging +c MULTIPLE FLOW CASES with wt available +c Computes the WindFarm flow and Power using G. C. Larsen model: +c Larsen, G. C., and P. E. Re'thore'. A simple stationary semi-analytical +c wake model. Technical Report Risoe, 2009. c c Inputs c ---------- @@ -777,10 +870,12 @@ end subroutine gcl_GA c P_c (array): Power curves c CT_c (array): Thrust coefficient curves c WS (array): Undisturbed rotor averaged (equivalent) wind speed at hub -c height [m/s] -c WD (array): Undisturbed wind direction at hub height [deg.] +c height [m/s] for each wind turbine x flow [nF,n] +c WD (array): Undisturbed wind direction at hub height [deg.] for each +c turbine x flow [nF,n] c Meteorological coordinates (N=0,E=90,S=180,W=270) -c TI (array): Ambient turbulence intensity [-] +c TI (array): Turbulence intensity [-] for each turbine x flow [nF,n] +c AV (array): Wind turbine available per flow [nF,n] c c rho (float): Air density at which the power curve is valid [kg/m^3] c WS_CI (array): Cut in wind speed [m/s] for each turbine @@ -793,59 +888,57 @@ end subroutine gcl_GA c T (array): Thrust force of the wind turbines (nWT,1) [N] c U (array): Rotor averaged (equivalent) Wind speed at hub height c (nWT,1) [m/s] - subroutine gcl_mult_wd(n,nP,nCT,nF,x_g,y_g,z_g,DT,P_c,CT_c,WS,WD, - &TI,STD_WD,Nga,a1,a2,a3,a4,b1,b2,Ng,rho,WS_CI,WS_CO,CT_idle,P,T,U) + subroutine gclm_av(n,nP,nCT,nF,x_g,y_g,z_g,DT,P_c,CT_c,WS,WD,TI, + &AV,a1,a2,a3,a4,b1,b2,rho,WS_CI,WS_CO,CT_idle,P,T,U) implicit none - integer :: n,nP,nCT,nF,Ng,Nga + integer :: n,nP,nCT,nF,AV(nf,n) real(kind=8) :: x_g(n,n),y_g(n,n),z_g(n,n),DT(n),P_c(n,nP,2) real(kind=8) :: CT_c(n,nCT,2),rho,WS_CI(n),WS_CO(n),CT_idle(n) - real(kind=8),dimension(nF) :: WS,TI,a1,a2,a3,a4,b1,b2 - real(kind=8),dimension(nF,n) :: WD,STD_WD - real(kind=8) :: P(nF,n),T(nF,n),U(nF,n) + real(kind=8),dimension(nF) :: a1,a2,a3,a4,b1,b2 + real(kind=8) :: WS(nF,n),WD(nF,n),TI(nF,n),P(nF,n),T(nF,n),U(nF,n) cf2py integer intent(hide),depend(DT) :: n = len(DT) cf2py integer intent(hide),depend(P_c) :: nP = size(P_c,2) cf2py integer intent(hide),depend(CT_c) :: nCT = size(CT_c,2) -cf2py integer intent(hide),depend(WS) :: nF = len(WS) +cf2py integer intent(hide),depend(WS) :: nF = size(WS,1) cf2py real(kind=8) intent(in),dimension(n) :: DT cf2py real(kind=8) intent(in),depend(n),dimension(n,n) :: x_g,y_g,z_g cf2py real(kind=8) intent(in),dimension(n,nP,2) :: P_c cf2py real(kind=8) intent(in),dimension(n,nCT,2) :: CT_c -cf2py real(kind=8) intent(in),dimension(nF) :: WS,TI -cf2py real(kind=8) intent(in),dimension(nF,n) :: WD -cf2py real(kind=8) optional,intent(in),dimension(nF,n) :: STD_WD = 0.0 -cf2py integer optional intent(in) :: Nga = 1 +cf2py real(kind=8) intent(in),dimension(nF,n) :: WS,WD,TI +cf2py integer intent(in),dimension(nF,n) :: AV cf2py real(kind=8) optional,intent(in),dimension(nF) :: a1=0.435449861 cf2py real(kind=8) optional,intent(in),dimension(nF) :: a2=0.797853685 cf2py real(kind=8) optional,intent(in),dimension(nF) :: a3=-0.124807893 cf2py real(kind=8) optional,intent(in),dimension(nF) :: a4=0.136821858 cf2py real(kind=8) optional,intent(in),dimension(nF) :: b1=15.6298 cf2py real(kind=8) optional,intent(in),dimension(nF) :: b2=1.0 -cf2py integer optional intent(in) :: Ng = 4 cf2py real(kind=8) optional,intent(in) :: rho=1.225 cf2py real(kind=8) optional,intent(in),dimension(n) :: WS_CI=4.0 cf2py real(kind=8) optional,intent(in),dimension(n) :: WS_CO=25.0 cf2py real(kind=8) optional,intent(in),dimension(n) :: CT_idle=0.053 -cf2py real(kind=8) intent(out),depend(n),dimension(nF,n) :: P,T,U +cf2py real(kind=8) intent(out),depend(nF,n),dimension(nF,n) :: P,T,U ! internal variables integer :: i,j - real(kind=8), dimension(n) :: P_aux,T_aux,U_aux + real(kind=8) :: CT_c_AV(n,nCT,2), P_c_AV(n,nCT,2) - do j=1,n - do i=1,nF - call gcl_GA(n,nP,nCT,1,x_g,y_g,z_g,DT,P_c,CT_c,WS(i),WD(i,j), - &TI(i),STD_WD(i,j),Nga,a1(i),a2(i),a3(i),a4(i),b1(i),b2(i),Ng,rho, - &WS_CI,WS_CO,CT_idle,P_aux,T_aux,U_aux) -! call gcl_s(n,nP,nCT,x_g,y_g,z_g,DT,P_c,CT_c,WS(i),WD(i,j), -! & TI(i),a1(i),a2(i),a3(i),a4(i),b1(i),b2(i),Ng,rho,WS_CI,WS_CO, -! & CT_idle,P_aux,T_aux,U_aux) - P(i,j)=P_aux(j) - T(i,j)=T_aux(j) - U(i,j)=U_aux(j) + do i=1,nF + CT_c_AV = CT_c + P_c_AV = P_c + ! Re-defines the trust curve for non available turbines + do j=1,n + if (AV(i,j)==0) then + CT_c_AV(j,:,2) = CT_idle(j) + P_c_AV(j,:,2) = 0.0d0 + end if end do + call gclm_s(n,nP,nCT,x_g,y_g,z_g,DT,P_c_AV,CT_c_AV,WS(i,:), + & WD(i,:),TI(i,:),a1(i),a2(i),a3(i),a4(i),b1(i),b2(i),rho, + & WS_CI,WS_CO,CT_idle,P(i,:),T(i,:),U(i,:)) end do - end subroutine gcl_mult_wd + end subroutine gclm_av + c ---------------------------------------------------------------------- c Additional routines diff --git a/fusedwake/gcl/python/gcl.py b/fusedwake/gcl/python/gcl.py index 45f6f22..ec77172 100644 --- a/fusedwake/gcl/python/gcl.py +++ b/fusedwake/gcl/python/gcl.py @@ -9,97 +9,7 @@ import numpy as np import fusedwake.WindTurbine as wt import fusedwake.WindFarm as wf - -def Ua(r,te,zc,us,z0): - """Function of undisturbed inflow wind speed - log law. - - .. math:: - U_a = \\frac{u^*}{\\kappa} \\ln \\left( \\frac{ z_c + r \\sin (te)) }{ z_0 } \\right) - - Parameters - ---------- - r: np.array, float - Radial coord [m] - te: np.array, float - Angle coord where 0 is the horizontal right [rad] - zc: float - Height to hub center [m] - us: float - Friction velocity [m/s] - z0: float - Roughness height [m] - - Returns - ------- - Ua: np.array, float - Axial wind speed [m/s] - """ - kappa = 0.4 # Kappa: von karman constant - return us / kappa * np.log((zc + r * np.sin(te)) / z0) - -def Ua_shear(r,te,zc,uH,alpha): - """Function of undisturbed inflow wind speed - power law. - - .. math:: - U_a = u_H \\left(\\frac{z_c + r \\sin(te) }{ z_c } \\right)^\\alpha - - Parameters - ---------- - r: np.array, float - Radial coord [m] - te: np.array, float - Angle coord where 0 is the horizontal right [rad] - zc: float - Height to hub center [m] - uH: float - Wind Speed at Hub Height [m/s] - alpha: float - Shear Coefficient [-] - - Returns - ------- - Ua: np.array, float - Axial wind speed [m/s] - """ - - return uH * ((zc + r * np.sin(te)) / zc)**alpha - - -def gaussN(R, func, varargin, NG=4): - """Calculate numerically the gauss integration. - [1] eq. 38 - - Parameters - ---------- - R: float - Wind turbine radius [m] - func: function - Wind speed function - varargin: Other arguments for the function besides [r,te] - NG: int - Number of Ga - - Returns - ------- - Ua: float - integrated value - """ - A = np.pi*R**2 - #coefficients - if NG==4: #for speed give the full values - rt = np.array([[ -0.339981043584856, -0.861136311594053, - 0.339981043584856, 0.861136311594053]]) - te = rt.T - w = np.array([[0.652145154862546, 0.347854845137454, - 0.652145154862546, 0.347854845137454]]) - else: - rt,w = np.polynomial.legendre.leggauss(NG) - rt = np.array([rt]) - te = rt.T - w = np.array([w]) - - return (np.pi/4.0)*(R**2./A)*w*w.T*func(R*(rt+1.0)/2.0, - np.pi*(te+1.0),*varargin)*(rt+1.0) +from copy import copy def get_r96(D, CT, TI, pars=[0.435449861, 0.797853685, -0.124807893, 0.136821858, 15.6298, 1.0]): """Computes the wake radius at 9.6D downstream location of a turbine @@ -107,7 +17,7 @@ def get_r96(D, CT, TI, pars=[0.435449861, 0.797853685, -0.124807893, 0.136821858 .. math:: R_{9.6D} = a_1 \\exp (a_2 C_T^2 + a_3 C_T + a_4) (b_1 TI + b_2) D - Parameters + Inputs ---------- D: float Wind turbine diameter @@ -151,7 +61,7 @@ def get_Rw(x, R, TI, CT, pars=[0.435449861, 0.797853685, -0.124807893, 0.1368218 m = \\frac{1}{\\sqrt{1 - C_T}} - Parameters + Inputs ---------- x: float or ndarray Distance between turbines and wake location in the wind direction @@ -186,29 +96,25 @@ def get_Rw(x, R, TI, CT, pars=[0.435449861, 0.797853685, -0.124807893, 0.1368218 if type(x) == float and x+x0 <= 0.: Rw = 0 elif type(x) == np.ndarray: Rw[x + x0 * _ones <= 0.] = 0. - return Rw + return Rw, x0, c1 -def get_dU(x,r,Rw,U,R,TI,CT, +def get_dU(x,r,R,CT,TI, order=1, pars=[0.435449861,0.797853685,-0.124807893,0.136821858,15.6298,1.0]): """Computes the wake velocity deficit at a location - Parameters + Inputs ---------- x: float Distance between turbines and wake location in the wind direction r: float Radial distance between the turbine and the location - Rw: float - Wake radius at location [m] R: float Wake producing turbine's radius [m] - U: float - Undisturbed wind speed [m/s] - TI: float - Ambient turbulence intensity [-] CT: float Outputs WindTurbine object's thrust coefficient + TI: float + Ambient turbulence intensity [-] order: int, optional Returns @@ -220,23 +126,16 @@ def get_dU(x,r,Rw,U,R,TI,CT, D = 2.*R Area=np.pi*D*D/4. - m=1./(np.sqrt(1.-CT)) - k=np.sqrt((m+1.)/2.) - - R96 = get_r96(D,CT,TI,pars) + Rw, x0, c1 = get_Rw(x, R, TI, CT, pars) - x0=(9.6*D)/((2.*R96/(k*D))**3.-1.) - term1=(k*D/2.)**2.5 - term2=(105./(2.*np.pi))**-0.5 - term3=(CT*Area*x0)**(-5./6.) - c1=term1*term2*term3 - - term10=0.1111*U*_ones + term10=(1./9.)*_ones term20=(CT*Area*(x+x0)**(-2.))**(1./3.) term310=(r**(1.5)) term320=(3.*c1*c1*CT*Area*(x+x0))**(-0.5) term30=term310*term320 - term40=((35./(2.*np.pi))**(3./10.))*((3.*c1*c1)**(-0.2)) + term41=(35./(2.*np.pi))**(3./10.) + term42=(3.*c1*c1)**(-0.2) + term40=term41*term42 dU1=-term10*term20*(term30-term40)**2. dU = dU1 @@ -262,7 +161,7 @@ def get_dU(x,r,Rw,U,R,TI,CT, d_3 = d_term*d_3_const d_4 = d_term*d_4_const - dU2_const = U*((CT*Area*((x+x0)**(-2.)))**(2./3.)) + dU2_const = ((CT*Area*((x+x0)**(-2.)))**(2./3.)) dU2_term0 = d_0*(z**0.) dU2_term1 = d_1*(z**1.) dU2_term2 = d_2*(z**2.) @@ -274,46 +173,118 @@ def get_dU(x,r,Rw,U,R,TI,CT, dU=dU1 + dU2 if type(r)==np.ndarray: dU[Rw 0): + LocalDU[uWT] = uWS*get_dUeq(x,y,z,cR,uR,uCT,TI[uWT],pars) # Wake superposition - if sup == 'lin': - DU = LocalDU.sum() - elif sup == 'quad': - DU = -np.sqrt(np.sum(LocalDU**2)) + DU = LocalDU.sum() - U_WT[cWT] = max(0, WS_inf + DU) - if U_WT[cWT] > WF.WT[cWT].u_cutin: + U_WT[cWT] = U_WT[cWT] + DU + if U_WT[cWT] > WF.WT[cWT].u_cutin: Ct[cWT] = WF.WT[cWT].get_CT(U_WT[cWT]) P_WT[cWT] = WF.WT[cWT].get_P(U_WT[cWT]) else: - Ct[cWT] = 0.053 + Ct[cWT] = WF.WT[cWT].CT_idle P_WT[cWT] = 0.0 return (P_WT,U_WT,Ct) def GCLarsen(WF, WS, WD,TI, - z0=0.0001, alpha=0.101, inflow='log', NG=4, sup='lin', pars=[0.435449861,0.797853685,-0.124807893,0.136821858,15.6298,1.0]): """Computes the WindFarm flow and Power using GCLarsen [Larsen, 2009, A simple Stationary...] - Parameters + Inputs ---------- WF: WindFarm Windfarm instance @@ -400,22 +362,6 @@ def GCLarsen(WF, WS, WD,TI, Meteorological axis. North = 0 [deg], clockwise. TI: float Ambient turbulence intensity [-] - z0: float, optional - Roughness height [m] - alpha: float, optional - Shear coefficient [-] - Only used for power-law undisturbed inflow. - inflow: Str, optional - Undisturbed inflow vertical profile: - 'log': Logarithmic law (neutral case); uses z0 - 'pow': Power law profile; uses alpha - NG: int, optional - Number of points in Gaussian Quadrature for equivalent wind - speed integration over rotor distFlowCoord - sup: str, optional - Wake velocity deficit superposition method: - 'lin': Linear superposition - 'quad' Quadratic superposition Returns ------- @@ -426,262 +372,66 @@ def GCLarsen(WF, WS, WD,TI, Ct: float Thrust coefficients for each wind turbine (nWT,1) [-] """ - (distFlowCoord, nDownstream, id0) = WF.turbineDistance(WD) - - # TODO: decide how at what height the us should be defined - if inflow == 'log': - kappa = 0.4 # Kappa: von karman constant - us = WS * kappa / np.log(WF.WT[0].H / z0) # friction velocity - #eq inflow ws - WS_inf = gaussN(WF.WT[0].R, Ua, [WF.WT[0].H,us,z0]).sum() - elif inflow == 'pow': - #eq inflow ws - WS_inf = gaussN(WF.WT[0].R, Ua_shear, [WF.WT[0].H,WS,alpha]).sum() - - # Gauss quadrature points - r_Gc,w_Gc = np.polynomial.legendre.leggauss(NG) - wj,wk=np.meshgrid(w_Gc,w_Gc) - tj,rk=np.meshgrid(r_Gc,r_Gc) - wj = wj.reshape((NG**2)) - tj = tj.reshape((NG**2)) - wk = wk.reshape((NG**2)) - rk = rk.reshape((NG**2)) + distFlowCoord, nDownstream, id0 = WF.turbineDistance(np.mean(WD)) + zg = WF.vectWTtoWT[2,:,:] # Initialize arrays to NaN Ct = np.nan*np.ones([WF.nWT]) P_WT = np.nan*np.ones([WF.nWT]) # Initialize velocity to undisturbed eq ws - U_WT = WS_inf*np.ones([WF.nWT]) - U_WT0 = WS_inf*np.ones([WF.nWT]) - DU_sq = 0.*U_WT - + U_WT = copy(WS) allR = np.array([WF.WT[i].R for i in range(WF.nWT)]) # Extreme wake to define WT's in each wake, including partial wakes - ID_wake = {i:(get_Rw(x=distFlowCoord[0,i,:], # streamwise distance - R=WF.WT[i].R, # Upstream radius - TI=TI, - CT=0.9, #Maximum effect - pars=pars) - > np.abs(distFlowCoord[1,i,:]) + allR).nonzero()[0] + ID_wake = {i:((get_Rw(x=distFlowCoord[0,i,:], # streamwise distance + R=allR[i], # Upstream radius + CT=0.9, # Maximum effect + TI=0.01, + pars=pars)[0])*3. + > np.abs(distFlowCoord[1,i,:]) + allR).nonzero()[0] for i in id0} for i in range(WF.nWT): #Current wind turbine starting from the most upstream cWT = id0[i] # Current radius - cR = WF.WT[i].R - # Current hub wind speed + cR = WF.WT[cWT].R + # Current wind speed cU = U_WT[cWT] - if cU>WF.WT[i].u_cutin: - Ct[cWT] = WF.WT[i].get_CT(U_WT[cWT]) - P_WT[cWT] = WF.WT[i].get_P(U_WT[cWT]) + if cU>WF.WT[cWT].u_cutin: + Ct[cWT] = WF.WT[cWT].get_CT(U_WT[cWT]) + P_WT[cWT] = WF.WT[cWT].get_P(U_WT[cWT]) else: - Ct[cWT] = 0.053 # Drag coefficient of the idled turbine + Ct[cWT] = WF.WT[cWT].CT_idle # Drag coefficient of the idled turbine P_WT[cWT] = 0.0 - # Current turbine CT cCT=Ct[cWT] - #ID_wake = {cWT:(get_Rw(x=distFlowCoord[0,cWT,:],\ - # R=cR*1.5,TI=TI,CT=cCT)\ - # >np.abs(distFlowCoord[1,cWT,:])).nonzero()} #Radial coordinates in cWT for wake affected WT's + distFlowCoord,_,_ = WF.turbineDistance(WD[cWT]) x = distFlowCoord[0, cWT, ID_wake[cWT]] - r_Ri = np.abs(distFlowCoord[1,cWT, ID_wake[cWT]]) - th_Ri = np.pi*(np.sign(distFlowCoord[1, cWT, ID_wake[cWT]]) + 1.0) # <- what is this? [0|2pi] - - # Get all the wake radius at the position of the -in wake- downstream turbines - RW = get_Rw(x=x, R=cR, TI=TI, CT=cCT, pars=pars) - - # Meshgrids (Tensorial) extension of points of evaluation - # to perform Gaussian quadrature - r_Ri_m, rk_m = np.meshgrid(r_Ri, rk) - th_Ri_m, tj_m = np.meshgrid(th_Ri, tj) - x_m, wj_m = np.meshgrid(x, wj) - RW_m, wk_m = np.meshgrid(RW, wk) - - # downstream Radius - downR = np.array([WF.WT[j].R for j in ID_wake[cWT]]) - downR_m, dummyvar = np.meshgrid(downR, np.zeros((NG**2))) - cH = WF.WT[cWT].H - downH = np.array([WF.WT[j].H for j in ID_wake[cWT]]) - cH - downH_m, dummyvar = np.meshgrid(downH, np.zeros((NG**2))) + y = distFlowCoord[1, cWT, ID_wake[cWT]] + z = zg[cWT, ID_wake[cWT]] - # Radial points of evaluation <- probably need to add the turbine height difference here? - r_eval = np.sqrt(r_Ri_m**2.0 + - (downR_m * (rk_m + 1.) / 2.0)**2. + - r_Ri_m * downR_m * (rk_m + 1.) * np.cos(th_Ri_m - np.pi*(tj_m + 1.))) - - # Eval wake velocity deficit - DU_m = get_dU(x=x_m, r=r_eval, Rw=RW_m, - U=cU, R=downR_m, TI=TI, CT=cCT, pars=pars) - - localDU = np.sum((1./4.)*wj_m*wk_m*DU_m*(rk_m+1.0),axis=0) + # Get all the rotor average wake deficits at the position of the -in wake- + # downstream turbines + localDU = cU*get_dUeq(x,y,z,allR[ID_wake[cWT]],cR,cCT,TI[cWT],pars) # Wake superposition - if sup == 'lin': - U_WT[ID_wake[cWT]] = U_WT[ID_wake[cWT]] + localDU - U_WT[U_WT<0.]=0. - elif sup == 'quad': - DU_sq[ID_wake[cWT]] = DU_sq[ID_wake[cWT]] + localDU**2. - U_WT = U_WT0 - np.sqrt(DU_sq) - U_WT[U_WT<0.]=0. - - return (P_WT,U_WT,Ct) - -def GCL_P_GaussQ_Norm_U_WD(WF, WS, meanWD, stdWD, NG_P, TI, - z0=0.0001, alpha=0.101, inflow='log', NG=4, sup='lin', - pars=[0.435449861, 0.797853685, -0.124807893, 0.136821858, 15.6298, 1.0]): - """Computes the Gaussian quadrature average of GCLarsen - power/WS prediction under normally distributed wind direction - uncertainty inside the Reynolds averaging time + U_WT[ID_wake[cWT]] = U_WT[ID_wake[cWT]] + localDU - Parameters - ---------- - WF: WindFarm - Windfarm instance - WS: float - Undisturbed wind speed at hub height [m/s] - meanWD: float - Mean wind direction [deg] - stdWD: float - Std wind direction [deg] - NG_P: int - Number of Gaussian quadrature points for power avg. - WS: float - Undisturbed wind speed at hub height [m/s] - z0: float - Roughness height [m] - TI: float - Ambient turbulence intensity - z0: float, optional - Roughness height [m] - alpha: float, optional - Shear coefficient [-] - Only used for power-law undisturbed inflow. - inflow: Str, optional - Undisturbed inflow vertical profile: - 'log': Logarithmic law (neutral case); uses z0 - 'pow': Power law profile; uses alpha - NG: int, optional - Number of points in Gaussian Quadrature for equivalent wind - speed integration over rotor distFlowCoord - sup: str, optional - Wake velocity deficit superposition method: - 'lin': Linear superposition - 'quad' Quadratic superposition - pars: list, optional - GCL Model parameters - - Returns - ------- - P_WT: ndarray - Mean Power production of the wind turbines (nWT,1) [W] - U_WT: ndarray - Mean Wind speed at hub height (nWT,1) [m/s] - CT_WT: ndarray - Mean thrust coefficient [-] - """ - - xi, wi = np.polynomial.hermite.hermgauss(NG_P) - - meanP_WT = np.zeros([WF.nWT]) - meanU_WT = np.zeros([WF.nWT]) - meanCT_WT = np.zeros([WF.nWT]) - for i in range(NG_P): - P_WT,U_WT,CT_WT = GCLarsen( - WF=WF, - WS=WS, - WD=meanWD+np.sqrt(2.)*stdWD*xi[i], - TI=TI, - z0=z0, - alpha=alpha, - inflow=inflow, - NG=NG, - sup=sup, - pars=pars) - meanP_WT += wi[i]*P_WT*(1./np.sqrt(np.pi)) - meanU_WT += wi[i]*U_WT*(1./np.sqrt(np.pi)) - meanCT_WT += wi[i]*CT_WT*(1./np.sqrt(np.pi)) - - return meanP_WT, meanU_WT, meanCT_WT - -def GCL_P_GaussQ_Uni_U_WD(WF,WS,meanWD,U_WD,NG_P,TI, - z0=0.0001,alpha=0.101,inflow='log',NG=4,sup='lin', - pars=[0.435449861,0.797853685,-0.124807893,0.136821858,15.6298,1.0]): - """Computes the Gaussian quadrature average of GCLarsen - power/WS prediction under normally distributed wind direction - uncertainty - - Parameters - ---------- - WF: WindFarm - Windfarm instance - WS: float - Undisturbed wind speed at hub height [m/s] - meanWD: float - Mean wind direction [deg] - stdWD: float - Std wind direction [deg] - NG_P: int - Number of Gaussian quadrature points for power avg. - WS: float - Undisturbed wind speed at hub height [m/s] - z0: float - Roughness height [m] - TI: float - Ambient turbulence intensity - z0: float, optional - Roughness height [m] - alpha: float, optional - Shear coefficient [-] - Only used for power-law undisturbed inflow. - inflow: Str, optional - Undisturbed inflow vertical profile: - 'log': Logarithmic law (neutral case); uses z0 - 'pow': Power law profile; uses alpha - NG: int, optional - Number of points in Gaussian Quadrature for equivalent wind - speed integration over rotor distFlowCoord - sup: str, optional - Wake velocity deficit superposition method: - 'lin': Linear superposition - 'quad' Quadratic superposition + # Update Power and CT + cU = U_WT[cWT] + if cU>WF.WT[cWT].u_cutin: + Ct[cWT] = WF.WT[cWT].get_CT(U_WT[cWT]) + P_WT[cWT] = WF.WT[cWT].get_P(U_WT[cWT]) + else: + Ct[cWT] = WF.WT[cWT].CT_idle # Drag coefficient of the idled turbine + P_WT[cWT] = 0.0 - Returns - ------- - P_WT: ndarray - Mean Power production of the wind turbines (nWT,1) [W] - U_WT: ndarray - Mean Wind speed at hub height (nWT,1) [m/s] - CT_WT: ndarray - Mean thrust coefficient [-] - """ + return (P_WT,U_WT,Ct) - xi, wi = np.polynomial.legendre.leggauss(NG_P) - - meanP_WT = np.zeros([WF.nWT]) - meanU_WT = np.zeros([WF.nWT]) - meanCT_WT = np.zeros([WF.nWT]) - for i in range(NG_P): - P_WT,U_WT,CT_WT = GCLarsen( - WF=WF, - WS=WS, - WD=meanWD+U_WD*xi[i], - TI=TI, - z0=z0, - alpha=alpha, - inflow=inflow, - NG=NG, - sup=sup, - pars=pars) - meanP_WT += wi[i]*P_WT*1./2. - meanU_WT += wi[i]*U_WT*1./2. - meanCT_WT += wi[i]*CT_WT*1./2. - - return meanP_WT,meanU_WT,meanCT_WT ''' print get_r96(D=80.0,CT=0.5,TI=0.05) print diff --git a/fusedwake/gcl/test/test_WF_4Turbines.yml b/fusedwake/gcl/test/test_WF_4Turbines.yml new file mode 100644 index 0000000..2414e4d --- /dev/null +++ b/fusedwake/gcl/test/test_WF_4Turbines.yml @@ -0,0 +1,223 @@ +layout: + - name: WT01 + row: 1 + position: [0, 0] + turbine_type: V80 + - name: WT02 + row: 2 + position: [560, 0] + turbine_type: B76 + - name: WT03 + row: 1 + position: [200, 200] + turbine_type: V80 + - name: WT04 + row: 2 + position: [200, -200] + turbine_type: B76 + +plant_data: + utm: + code: 32 + letter: U + name: Test WF + owner: DTU + +turbine_types: + # Sources: + # [1] https://windbench.net/system/files/hornsrev_v80_0.pdf + # [2] http://www.thewindpower.net/turbine_en_30_vestas_2000.php + # [3] http://en.wind-turbine-models.com/turbines/668-vestas-v-80-offshore + # [4] WAsP wind turbine library (distributed as part of the WAsP software) + - name: V80 + hub_height: 90.0 # [1] + rotor_diameter: 80.0 # [1] + rated_power: 2000.0 # [1] + cut_in_wind_speed: 4.0 # [1] + cut_out_wind_speed: 25.0 # [1] + rated_wind_speed: 16.0 # [1] + wind_class: IEC Ia (DIBt III) # [2] + air_density: 1.225 # guess + gear_box: + speed_number: 3 + ratio: 1:100,5 # [2] + type: spur/planetary # [3] + nacelle: # [2] + weight: 68000.0 # kg [2], 69000.0 kg according to [3] + rotor: # [2, 3] + weight: 37000.0 # kg [2] + tip_speed: 80.0 # m/s [3] + min_speed: 9 # rd/min [2] + max_speed: 19 # rd/min [2] + manufacturer: Vestas + hub: + weight: 18000 # kg [3] + tower: # [2] + weight: 198000.0 #kg [2], max 148000.0 kg according to [3] + material: steel + manufacturer: Welcon + control: # [1] + type: Active Pitch, Variable Speed + generator: # [2] + type: ASYNC + number: 1 + max_output_speed: 1909 #rounds/minute + output_voltage: 690 #V + grid_frequency: 50/60 # Hz + power_curve: # [1] + - [3.0, 0.0] + - [4.0, 66.6] + - [5.0, 154.0] + - [6.0, 282.0] + - [7.0, 460.0] + - [8.0, 696.0] + - [9.0, 996.0] + - [10.0, 1341.0] + - [11.0, 1661.0] + - [12.0, 1866.0] + - [13.0, 1958.0] + - [14.0, 1988.0] + - [15.0, 1997.0] + - [16.0, 1999.0] + - [17.0, 2000.0] + - [18.0, 2000.0] + - [19.0, 2000.0] + - [20.0, 2000.0] + - [21.0, 2000.0] + - [22.0, 2000.0] + - [23.0, 2000.0] + - [24.0, 2000.0] + - [25.0, 2000.0] + c_t_curve: + - [3.0, 0.00] + - [4.0, 0.818] + - [5.0, 0.806] + - [6.0, 0.804] + - [7.0, 0.805] + - [8.0, 0.806] + - [9.0, 0.807] + - [10.0, 0.793] + - [11.0, 0.739] + - [12.0, 0.709] + - [13.0, 0.409] + - [14.0, 0.314] + - [15.0, 0.249] + - [16.0, 0.202] + - [17.0, 0.167] + - [18.0, 0.140] + - [19.0, 0.119] + - [20.0, 0.102] + - [21.0, 0.088] + - [22.0, 0.077] + - [23.0, 0.067] + - [24.0, 0.060] + - [25.0, 0.053] + c_t_idle: 0.053 # [4] + blade: + geometry: # [1] + # [radius [m], c [m], twist [deg], airfoil ] + - [2.563, 2.004, 9.50, 'Cylinder 1'] + - [4.389, 2.523, 9.50, 'Cylinder 1'] + - [6.216, 3.015, 9.50, 'FFA W3-301'] + - [8.042, 3.278, 9.50, 'FFA W3-301'] + - [9.868, 3.309, 9.50, 'FFA W3-301'] + - [11.694, 3.195, 9.50, 'FFA W3-301'] + - [13.520, 3.039, 9.22, 'FFA W3-241'] + - [15.346, 2.863, 7.81, 'FFA W3-211'] + - [17.173, 2.687, 6.40, 'FFA W3-211'] + - [18.999, 2.511, 5.11, 'FFA W3-211'] + - [20.825, 2.334, 3.83, 'FFA W3-211'] + - [22.651, 2.158, 2.61, 'NACA 63-221'] + - [24.477, 1.982, 1.48, 'NACA 63-221'] + - [26.304, 1.806, 0.42, 'NACA 63-221'] + - [28.130, 1.630, 0.49, 'NACA 63-221'] + - [29.956, 1.454, 1.23, 'NACA 63-218'] + - [31.782, 1.278, 1.79, 'NACA 63-218'] + - [33.608, 1.102, 2.24, 'NACA 63-218'] + - [35.435, 0.926, 2.61, 'NACA 63-218'] + - [37.261, 0.749, 2.84, 'NACA 63-218'] + - [39.087, 0.573, 2.97, 'NACA 63-218'] + + # Sources: + # [1] http://www.middelgrunden.dk/middelgrunden/sites/default/files/public/file/Location.pdf + # [2] http://www.thewindpower.net/turbine_en_229_bonus_2000.php + # [3] http://en.wind-turbine-models.com/turbines/121-bonus-b76-2000 + # [4] WAsP wind turbine library (distributed as part of the WAsP software) + - name: B76 + hub_height: 64.0 #[1] + rotor_diameter: 76.0 #[1] + rated_power: 2000.0 #[1] + cut_in_wind_speed: 3.0 #[2] + cut_out_wind_speed: 25.0 #[2] + rated_wind_speed: 15.0 #[2] + air_density: 1.225 # guess + gear_box: + speed_number: 3 + ratio: 1:89 # [2], or [3]: 1:93 + manufacturer: Flender + nacelle: # [2] + weight: 65000.0 #kg [2], or [3]: 82500 kg + rotor: # [2, 3] + weight: 38000.0 #kg [2], or [3]: 52000.0 kg + max_speed: 17 #rd/min [2], or [3]: 16 + tip_speed: 63.7 #m/s [3] + manufacturer: LM Glasfiber, Bonus + tower: # [2] + weight: 162000.0 #kg [2] + control: # [2] + type: Stall + generator: # [2] + type: ASYNC + number: 1 + max_output_speed: 1500 #rounds/minute + output_voltage: 690 #V + manufacturer: ABB + power_curve: # [4] + - [3.0, 0.0] + - [4.0, 43.0] + - [5.0, 133.0] + - [6.0, 237.0] + - [7.0, 401.0] + - [8.0, 623.0] + - [9.0, 886.0] + - [10.0, 1190.0] + - [11.0, 1502.0] + - [12.0, 1740.0] + - [13.0, 1891.0] + - [14.0, 1962.0] + - [15.0, 1988.0] + - [16.0, 1996.0] + - [17.0, 1999.0] + - [18.0, 2000.0] + - [19.0, 2000.0] + - [20.0, 2000.0] + - [21.0, 2000.0] + - [22.0, 2000.0] + - [23.0, 2000.0] + - [24.0, 2000.0] + - [25.0, 2000.0] + c_t_curve: # [4] + - [3.0, 0.00] + - [4.0, 0.857] + - [5.0, 0.858] + - [6.0, 0.810] + - [7.0, 0.853] + - [8.0, 0.870] + - [9.0, 0.811] + - [10.0, 0.756] + - [11.0, 0.679] + - [12.0, 0.584] + - [13.0, 0.511] + - [14.0, 0.439] + - [15.0, 0.383] + - [16.0, 0.338] + - [17.0, 0.301] + - [18.0, 0.271] + - [19.0, 0.246] + - [20.0, 0.225] + - [21.0, 0.207] + - [22.0, 0.192] + - [23.0, 0.179] + - [24.0, 0.168] + - [25.0, 0.158] + c_t_idle: 0.158 # [4] diff --git a/fusedwake/gcl/test/test_gcls.py b/fusedwake/gcl/test/test_gcls.py index 42d99e0..3bf6b27 100644 --- a/fusedwake/gcl/test/test_gcls.py +++ b/fusedwake/gcl/test/test_gcls.py @@ -1,10 +1,13 @@ import unittest import fusedwake.gcl.fortran as fgcl -import fusedwake.gcl.python as gcl +import fusedwake.gcl.python as pygcl import numpy as np import os +from fusedwake.WindFarm import WindFarm +from fusedwake.gcl import GCL + current_dir = os.path.dirname(os.path.realpath(__file__)) @@ -30,7 +33,135 @@ def test_r96(self): self.assertAlmostEqual(fgcl.get_r96(D, CT, TI, a1, a2, a3, a4, b1, b2), - gcl.get_r96(D, CT, TI, pars=[a1, a2, a3, a4, b1, b2])) + pygcl.get_r96(D, CT, TI, pars=[a1, a2, a3, a4, b1, b2])) + + def test_Rw(self): + """Compare the two implementations of get_Rw + """ + # Fixed parameters + a1 = 0.435449861 + a2 = 0.797853685 + a3 = -0.124807893 + a4 = 0.136821858 + b1 = 15.6298 + b2 = 1.0 + + # Variables + D = 80.0 + CT = 0.98 + TI = 0.10 + R = D/2. + x=D*np.linspace(0.,10.,100) + + self.assertTrue(np.array([np.allclose(fgcl.get_rw(x, D, CT, TI, a1, a2, a3, a4, b1, b2)[i], + pygcl.get_Rw(x, R, TI, CT, pars=[a1, a2, a3, a4, b1, b2])[i]) for i in range(3)]).all()) + + def test_dU(self): + """Compare the two implementations of get_dU + """ + # Fixed parameters + a1 = 0.435449861 + a2 = 0.797853685 + a3 = -0.124807893 + a4 = 0.136821858 + b1 = 15.6298 + b2 = 1.0 + + # Variables + D = 80.0 + CT = 0.98 + TI = 0.10 + R = D/2. + x=D*np.linspace(0.,10.,100) + r=D*np.linspace(0.,2.,100) + + self.assertTrue(np.allclose(fgcl.get_du(x,r,D,CT, TI, a1, a2, a3, a4, b1, b2), + pygcl.get_dU(x,r,R, CT, TI, pars=[a1, a2, a3, a4, b1, b2]))) + + def test_dUeq(self): + """Compare the two implementations of get_dUeq + """ + # Fixed parameters + a1 = 0.435449861 + a2 = 0.797853685 + a3 = -0.124807893 + a4 = 0.136821858 + b1 = 15.6298 + b2 = 1.0 + + # Variables + D = 80.0 + R = D/2. + CT = 0.98 + TI = 0.10 + + dx = np.array([6.,10.,15.])*D + dy = np.array([1.,-5.,0.])*D + dz = np.array([-2,2.,1.])*D + + # Wake operating turbines + Rop = np.array([1.,2.,.5])*D + Dop = 2.*Rop + + self.assertTrue(np.allclose(fgcl.get_dueq(dx,dy,dz,Dop,D,CT,TI,a1,a2,a3,a4,b1,b2), + pygcl.get_dUeq(dx,dy,dz,Rop,R,CT,TI,pars=[a1, a2, a3, a4, b1, b2]))) + + def test_gcl(self): + """Compare the two implementations of gcl + """ + # Fixed parameters + a1 = 0.435449861 + a2 = 0.797853685 + a3 = -0.124807893 + a4 = 0.136821858 + b1 = 15.6298 + b2 = 1.0 + + filename = 'test_WF_4Turbines.yml' + wf = WindFarm(yml=filename) + gcl = GCL(WF=wf) + + # Inputs + WD = np.arange(-50,50)+270. + WS = 10. + TI=0.1 + + P_rat_py_v0 = [] + P_rat_py_v1 = [] + P_rat_fgcl = [] + P_rat_fgclm_s = [] + for wd in WD: + out = gcl(WF=wf, WS=WS, WD=wd, TI=TI, version='fort_gcl') + P_rat_fgcl = np.append(P_rat_fgcl,out.p_wt[1]/out.p_wt[0]) + + out = gcl(WF=wf, WS=WS*np.ones([wf.nWT]), + WD=wd*np.ones([wf.nWT]), + TI=TI*np.ones([wf.nWT]), version='fort_gclm_s') + P_rat_fgclm_s = np.append(P_rat_fgclm_s,out.p_wt[1]/out.p_wt[0]) + + out = gcl(WF=wf, WS=WS*np.ones([wf.nWT]), + WD=wd*np.ones([wf.nWT]), + TI=TI*np.ones([wf.nWT]),version='py_gcl_v1') + P_rat_py_v1 = np.append(P_rat_py_v1,out.p_wt[1]/out.p_wt[0]) + + out = gcl(WF=wf, WS=WS*np.ones([wf.nWT]), + WD=wd*np.ones([wf.nWT]), + TI=TI*np.ones([wf.nWT]), version='py_gcl_v0') + P_rat_py_v0 = np.append(P_rat_py_v0,out.p_wt[1]/out.p_wt[0]) + + WDm = WD.reshape([-1,1])*np.ones([1,wf.nWT]) + out = gcl(WF=wf, WS=WS*np.ones_like(WDm), WD=WDm, + TI=0.1*np.ones_like(WDm), version='fort_gclm') + P_rat_fgclm = out.p_wt[:,1]/out.p_wt[:,0] + + out = gcl(WF=wf, WS=WS*np.ones_like(WDm), WD=WDm, + TI=0.1*np.ones_like(WDm), version='fort_gclm_av') + P_rat_fgclm_av = out.p_wt[:,1]/out.p_wt[:,0] + + self.assertTrue(np.allclose(P_rat_fgclm_s, P_rat_py_v0)&\ + np.allclose(P_rat_fgclm_s, P_rat_py_v1)&\ + np.allclose(P_rat_fgclm_s, P_rat_fgclm)&\ + np.allclose(P_rat_fgclm_s, P_rat_fgclm_av)) if __name__ == "__main__": unittest.main() diff --git a/fusedwake/test/NewQuadratureRule/2D-Gauss-Quadrature-over-a-circle-OLD-no_chaospy.ipynb b/fusedwake/test/NewQuadratureRule/2D-Gauss-Quadrature-over-a-circle-OLD-no_chaospy.ipynb new file mode 100644 index 0000000..98d5c72 --- /dev/null +++ b/fusedwake/test/NewQuadratureRule/2D-Gauss-Quadrature-over-a-circle-OLD-no_chaospy.ipynb @@ -0,0 +1,1840 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import scipy as sp\n", + "import pandas as pd\n", + "from scipy import interpolate\n", + "from scipy import stats\n", + "\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "```python\n", + "R = 40.\n", + "H = 60\n", + "x_w = 0.\n", + "y_w = 60.\n", + "\n", + "f = lambda x,y: - 4.*np.exp( -((x-x_w)**2. + (y-y_w)**2.)/(30.**2.) ) + \\\n", + " - 4.*np.exp( -((x+70.)**2. + (y-60.)**2.)/(60.**2.) ) #+ 15.*(y/H)**0.05 \n", + "#```" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "R = 40.\n", + "H = 60\n", + "x_w = 30.\n", + "y_w = 90.\n", + "Rw = 20\n", + "seed = 1\n", + "\n", + "def f(x,y):\n", + " np.random.seed(seed)\n", + " return - 3.*np.exp( -((x-x_w)**2. + (y-y_w)**2.)/(Rw**2.) ) + \\\n", + " - 3.*np.exp( -((x-30)**2. + (y-40)**2.)/(20**2.) ) + \\\n", + " - 1.5*np.exp( -((x-np.random.uniform(-60,60))**2. + (y-np.random.uniform(40,70))**2.)/(60.**2.) ) #+ 4.*(y/H)**0.0 \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Monte-Carlo integration" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "-1.8588968000880735" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_MC = np.random.uniform(-R,R,9e6)\n", + "y_MC = np.random.uniform(-R,R,9e6) + H\n", + "\n", + "r = np.sqrt(x_MC**2. + (y_MC-H)**2.)\n", + "ind_in = np.where(r <= R)\n", + "\n", + "u_eq_MC = np.mean(f(x_MC[ind_in],y_MC[ind_in]))\n", + "u_eq_MC" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "x_mesh,y_mesh = np.meshgrid(np.arange(-50.,100.,0.1),np.arange(0.,120.,0.1))\n", + "\n", + "F = f(x_mesh,y_mesh)" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAWYAAAEACAYAAACAi9xRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJztnXu8VWWd8L8/ueStRHMAURQ0ULDyRkpFSqUOaaNWU6Pv\n", + "m+Ol13orTe1ioGXUvFNJU2lTTt4HK6XMSzhqQiU2lhdQJBIQ1BwRBERE0VJuv/ePtTZnn33Wfa/L\n", + "s/b+fT+f/Tl7rf2s9fzOPvt8z+/81vM8S1QVwzAMwx22qzoAwzAMozcmZsMwDMcwMRuGYTiGidkw\n", + "DMMxTMyGYRiOYWI2DMNwjEgxi8i1IrJaRBY27fuOiCwWkQUicouI7NL02hQRWSYiS0Tk2CIDNwzD\n", + "KAMRmeQ7bZmIfDmkzQ/81xeIyCHt9hmXMV8HTGrZNws4UFUPApYCU/zAxgL/BIz1j7lcRCwjNwyj\n", + "tohIP+CHeE4bC5wiImNa2hwHvEVVRwGfBP6j3X4jxamq/w282LJvtqpu9TcfBPbyn58I3Kiqm1T1\n", + "aeAJ4PB2AzQMw6iQw4EnVPVpVd0EzMBzXTMnANMBVPVBYJCIDGmn03Yz2jOBO/3nw4Bnm157Ftiz\n", + 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"N_y = 5\n", + "root_x_1D, weight_x = np.polynomial.legendre.leggauss(N_x)\n", + "root_y_1D, weight_y = np.polynomial.legendre.leggauss(N_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "root_x,root_y = np.meshgrid(root_x_1D,root_y_1D)\n", + "root_x,root_y = root_x.flatten(), root_y.flatten()" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-0.90617985 -0.53846931 0. 0.53846931 0.90617985 -0.90617985\n", + " -0.53846931 0. 0.53846931 0.90617985 -0.90617985 -0.53846931\n", + " 0. 0.53846931 0.90617985 -0.90617985 -0.53846931 0.\n", + " 0.53846931 0.90617985 -0.90617985 -0.53846931 0. 0.53846931\n", + " 0.90617985]\n", + "[-0.90617985 -0.90617985 -0.90617985 -0.90617985 -0.90617985 -0.53846931\n", + " -0.53846931 -0.53846931 -0.53846931 -0.53846931 0. 0. 0.\n", + " 0. 0. 0.53846931 0.53846931 0.53846931 0.53846931\n", + " 0.53846931 0.90617985 0.90617985 0.90617985 0.90617985 0.90617985]\n", + "\n", + "[-0.90617985 -0.53846931 0. 0.53846931 0.90617985]\n", + "[-0.90617985 -0.53846931 0. 0.53846931 0.90617985]\n", + "[ 0.23692689 0.47862867 0.56888889 0.47862867 0.23692689]\n", + "[ 0.23692689 0.47862867 0.56888889 0.47862867 0.23692689]\n" + ] + } + ], + "source": [ + "print root_x\n", + "print root_y\n", + "print\n", + "print root_x_1D\n", + "print root_y_1D\n", + "print weight_x\n", + "print weight_y" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "y1 = lambda x: np.sqrt(R**2. - x**2.) + H\n", + "y2 = lambda x: -np.sqrt(R**2. - x**2.) + H" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "xi_1D = R*root_x_1D\n", + "xi = R*root_x\n", + "yij = 0.5*(y1(xi) - y2(xi))*root_y + 0.5*(y1(xi) + y2(xi))\n", + "fij = f(xi,yij)" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.1431909 , -1.08737332, -1.14347377, -2.75447754, -3.35812501],\n", + " [-1.19913278, -1.30050894, -1.57218152, -3.51629652, -2.88520465],\n", + " [-1.23710927, -1.44595598, -1.60785126, -2.32505993, -2.11623796],\n", + " [-1.21881195, -1.3422547 , -1.57543575, -2.88705016, -2.05717667],\n", + " [-1.174949 , -1.14921649, -1.33259117, -3.41319145, -2.4955946 ]])" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fij.reshape(N_x,N_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-3.55581225, -4.16754714, -3.51410316, -3.69668423, -3.81144602])" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.dot(fij.reshape(N_x,N_y),weight_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "G_xi = 0.5*(y1(xi_1D) - y2(xi_1D)) * np.dot(fij.reshape(N_x,N_y),weight_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-1.88090881352\n", + "1.18414391972 %\n" + ] + } + ], + "source": [ + "I = np.dot(R*G_xi,weight_x)/(np.pi*R**2.)\n", + "print I\n", + "print 100*(I-u_eq_MC)/u_eq_MC, '%'" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": [ + 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"cell_type": "code", + "execution_count": 102, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "root_x,root_y = np.meshgrid(root_x_1D,root_y_1D)\n", + "root_x,root_y = root_x.flatten(), root_y.flatten()" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "x1 = lambda y: np.sqrt(R**2. - (y-H)**2.)\n", + "x2 = lambda y: -np.sqrt(R**2. - (y-H)**2.)" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "yi_1D = R*root_y_1D+H\n", + "yi = R*root_y+H\n", + "xij = 0.5*(x1(yi) - x2(yi))*root_x + 0.5*(x1(yi) + x2(yi))\n", + "fij = f(xij,yi)" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.00846822, -1.04107245, -1.14347377, -1.43142994, -1.74884227],\n", + " [-1.14931915, -1.27775483, -1.57218152, -3.13994237, -3.80353492],\n", + " [-1.23710927, -1.44595598, -1.60785126, -2.32505993, -2.11623796],\n", + " [-1.19440144, -1.32625261, -1.57543575, -2.87260182, -3.39835154],\n", + " [-1.08228911, -1.13412714, -1.33259117, -1.88585944, -2.48931432]])" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fij.reshape(N_x,N_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "G_yi = 0.5*(x1(yi_1D) - x2(yi_1D)) * np.dot(weight_x,fij.reshape(N_x,N_y))" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-1.77097460707\n", + "-4.72980495819 %\n" + ] + } + ], + "source": [ + "I = np.dot(R*G_yi,weight_y)/(np.pi*R**2.)\n", + "print I\n", + "print 100*(I-u_eq_MC)/u_eq_MC, '%'" + ] + }, + { + "cell_type": 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"circ = plt.Circle([0.,H],R,ec='k',fill=False)\n", + "ax = plt.gca()\n", + "ax.add_patch(circ)\n", + "\n", + "y_plot = np.arange(-50.,50.2,0.1)+H\n", + "plt.plot(x1(y_plot),y_plot)\n", + "plt.plot(x2(y_plot),y_plot)\n", + "plt.plot(xij,yi,'ok')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Radial Gauss-Leg" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "f_r = lambda r,th: f(-r*np.sin(th),r*np.cos(th)+H)" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def gaussN(R, func, varargin, NGr=4, NGth=4):\n", + " \"\"\"Calculate numerically the gauss integration.\n", + " [1] eq. 38\n", + "\n", + " Inputs\n", + " ----------\n", + " R (float): Wind turbine radius [m]\n", + " func (function): Wind speed function\n", + " varargin: Other arguments for the function besides [r,te]\n", + " NG (int): Number of Ga\n", + "\n", + " Outputs\n", + " ----------\n", + " Ua (float):\n", + " \"\"\"\n", + " A = np.pi*R**2\n", + " #coefficients\n", + " if (NGr==4)&(NGth==4): #for speed give the full values\n", + " rt = np.array([[ -0.339981043584856, -0.861136311594053,\n", + " 0.339981043584856, 0.861136311594053]])\n", + " te = rt.T\n", + " w = np.array([[0.652145154862546, 0.347854845137454,\n", + " 0.652145154862546, 0.347854845137454]])\n", + " wt=w\n", + " else:\n", + " rt,w = np.polynomial.legendre.leggauss(NGr)\n", + " rt = np.array([rt])\n", + " #te = rt.T\n", + " w = np.array([w])\n", + " te,wt = np.polynomial.legendre.leggauss(NGr)\n", + " te = np.array([te]).T\n", + " wt = np.array([wt])\n", + " \n", + " return np.sum((np.pi/4.0)*(R**2./A)*w*wt.T*func(R*(rt+1.0)/2.0,\n", + " np.pi*(te+1.0),*varargin)*(rt+1.0))" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-2.10872137547\n", + "13.4393999373 %\n" + ] + } + ], + "source": [ + "N_r = 4\n", + "N_th = 4\n", + "I = gaussN(R,f_r,[],N_r,N_th)\n", + "print I\n", + "print 100*(I-u_eq_MC)/u_eq_MC, '%'" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAWYAAAEACAYAAACAi9xRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJztnWmYVNW1sN8FDbSIgEiYZBRFARUhoDhEuU5ROwFNYmKM\n", + "cUr0ZhCIyWeccqMm16vRm0QhVxPHaKIxMXEg4oQDmZxAAY2ICIrKrAKKQzc0vb4fp4quPn3mubr3\n", + "+zz1dNepffbeXV311qq1hyOqisFgMBiKQ4e8O2AwGAyGlhgxGwwGQ8EwYjYYDIaCYcRsMBgMBcOI\n", + "2WAwGAqGEbPBYDAUDE8xi8gtIrJORF6qOHa1iLwiIotE5B4R6VHx2IUi8pqILBGRo9PsuMFgMGSB\n", + 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"plt.contourf(x_mesh,y_mesh,F,20)\n", + "plt.colorbar()\n", + "\n", + "circ = plt.Circle([0.,H],R,ec='k',fill=False)\n", + "ax = plt.gca()\n", + "ax.add_patch(circ)\n", + "\n", + "\n", + "rt,w = np.polynomial.legendre.leggauss(N_r)\n", + "rt = np.array([rt])\n", + "te,wt = np.polynomial.legendre.leggauss(N_th)\n", + "te = np.array([te])\n", + "re = R*(rt+1.0)/2.0\n", + "te = np.pi*(te.T+1.0)\n", + "plt.plot(-re*np.sin(te),re*np.cos(te)+H,'ok')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/fusedwake/test/NewQuadratureRule/2D-Gauss-Quadrature-over-a-circle.ipynb b/fusedwake/test/NewQuadratureRule/2D-Gauss-Quadrature-over-a-circle.ipynb new file mode 100644 index 0000000..4e7c8ea --- /dev/null +++ b/fusedwake/test/NewQuadratureRule/2D-Gauss-Quadrature-over-a-circle.ipynb @@ -0,0 +1,3434 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "import scipy as sp\n", + "import pandas as pd\n", + "from scipy import interpolate\n", + "from scipy import stats\n", + "import chaospy as cp\n", + "\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "R = 40.\n", + "H = 60\n", + "x_w = -40#30.\n", + "y_w = 60#90.\n", + "Rw = 40#20\n", + "seed = 3\n", + "\n", + "def f(x,y,seed):\n", + " np.random.seed(seed)\n", + " return - 3.*np.exp( -((x-x_w)**2. + (y-y_w)**2.)/(Rw**2.) ) + \\\n", + " - 3.*np.exp( -((x-30)**2. + (y-40)**2.)/(20**2.) ) + \\\n", + " - 1.5*np.exp( -((x-np.random.uniform(-60,60))**2. + (y-np.random.uniform(40,70))**2.)/(60.**2.) ) #+ 4.*(y/H)**0.0 \n", + " \n", + "def f1(x,y):\n", + " return - 3.*np.exp( -((x-x_w)**2. + (y-y_w)**2.)/(Rw**2.) )\n", + "\n", + "def f3(x,y):\n", + " return - 3.*np.exp( -((x-30)**2. + (y-40)**2.)/(20**2.) )\n", + " \n", + "def f2(x,y,seed):\n", + " np.random.seed(seed)\n", + " return - 1.5*np.exp( -((x-np.random.uniform(-60,60))**2. + (y-np.random.uniform(40,70))**2.)/(60.**2.) ) #+ 4.*(y/H)**0.0 " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "x_mesh,y_mesh = np.meshgrid(np.arange(-50.,100.,0.1),np.arange(0.,120.,0.1))\n", + "\n", + "F = f(x_mesh,y_mesh,seed)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + 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return u_eq_MC, x_MC[ind_in], y_MC[ind_in]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "u_eq_MC, x_MC, y_MC = MC_int(f,seed)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7067113\n" + ] + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAWUAAAEACAYAAABrvGdSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJztnXu0XWV57n+PEWy4k0ITINFQBbmY0hAMVKHslktTUEJP\n", + "q0iHHm61PbWhaClC4JwCo6NW4IioHHsoF4sMAdEiF4GSDe2m0B4IlwBBSEOAKAESQO4YJMB7/lhz\n", + "J2sv1mXOueac3zfXen9jrLHXnOub3/futfZ81ruf7yYzw3Ecx4mD94QOwHEcx9mAi7LjOE5EuCg7\n", + "juNEhIuy4zhORLgoO47jRISLsuM4TkR0FWVJl0haI2lp07lzJD0i6QFJV0vasum1hZIelbRM0sFl\n", + "Bu44jlMFkuYlmvaopJM7lPlm8voDkmb3016vTPk7wLyWc4uA3c1sD2A5sDAJajfgCGC35JpvS/JM\n", + 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{ + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def QMC_int(f,seed):\n", + " dist_y = cp.Uniform(-R,R)\n", + " dist_x = cp.Uniform(-(R**2.-dist_y**2.)**0.5,(R**2.-dist_y**2.)**0.5)#cp.Uniform(-R,R)#\n", + " dist = cp.J(dist_y,dist_x)\n", + "\n", + " sample = dist.sample(size=40,rule='M',antithetic=[1])\n", + " y_MC = sample[0,:]+H\n", + " x_MC = sample[1,:]\n", + "\n", + " r = np.sqrt(x_MC**2. + (y_MC-H)**2.)\n", + " ind_in = np.where(r <= R)\n", + " \n", + " u_eq_QMC = np.mean(f(x_MC[ind_in],y_MC[ind_in],seed))\n", + " \n", + " return u_eq_QMC, x_MC[ind_in], y_MC[ind_in]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-2.60015706384\n", + "-1.49032189306 %\n" + ] + } + ], + "source": [ + "u_eq_QMC, x_MC, y_MC = QMC_int(f,seed)\n", + "\n", + "print u_eq_QMC\n", + "print 100*(u_eq_QMC-u_eq_MC)/u_eq_MC, '%'" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "40\n" + ] + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAWUAAAEACAYAAABrvGdSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJztnXmYFNW5uN9vemaYQVBAWURQJC6IIEHWoIbRqCHimmiM\n", + "Xvyp0RhvxOi9xut+NdEsajQxwQSjxpgQXK8aETdcxmgmIAoiooiKKLKDsqgzzHTP9/ujupmenqru\n", + "qurqruqe8z5PPzNdXXXOme7pt7/+ziaqisFgMBiiQUXYDTAYDAZDG0bKBoPBECGMlA0GgyFCGCkb\n", + "DAZDhDBSNhgMhghhpGwwGAwRIquUReTPIrJORBanHbtZRN4RkUUi8oiI7JL22BUi8p6ILBWRowvZ\n", + "cIPBYCgGIjIp6bT3ROQyh3N+l3x8kYiMzKe+XJHyPcCkjGPPAgeq6ghgGXBFslFDgVOBoclr/iAi\n", + "JhI3GAwli4jEgGlYThsKnCYiB2Sccwywj6ruC5wH/DGfOrNKU1VfBj7LODZHVVuTd+cBA5K/nwDc\n", + "p6otqroCeB8Ym0/jDAaDIWTGAu+r6gpVbQHux3JdOscD9wKo6jygh4j09VthvpHs94Enk7/3Bz5J\n", + 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}, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def QMC_int2(f,seed):\n", + " dist_r = cp.Triangle(0,R,R)\n", + " dist_th = cp.Uniform(0,360)\n", + " dist = cp.J(dist_r,dist_th)\n", + " dist_U = cp.J(cp.Uniform(),cp.Uniform())\n", + " \n", + " sample = dist.sample(size=40,rule='M')\n", + " r = sample[0,:]\n", + " th = sample[1,:]+90\n", + "\n", + " x_MC = r*np.cos(np.deg2rad(th))\n", + " y_MC = r*np.sin(np.deg2rad(th))+H\n", + "\n", + " ind_in = np.where(r <= R)\n", + " \n", + " u_eq_QMC = np.mean(f(x_MC[ind_in],y_MC[ind_in],seed))\n", + " \n", + " return u_eq_QMC, x_MC[ind_in], y_MC[ind_in]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-2.63934519397\n", + "-0.00563847197049 %\n" + ] + } + ], + "source": [ + "u_eq_QMC2, x_MC, y_MC = QMC_int2(f,seed)\n", + "\n", + "print u_eq_QMC2\n", + "print 100*(u_eq_QMC2-u_eq_MC)/u_eq_MC, '%'" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "40\n" + ] + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAWUAAAEACAYAAABrvGdSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXmYFNW5uN9vNmYQkEVW0YwogiIaRJaMeh2NelFc740x\n", + "Eowal/wucUuMiQu5mmSSqESjkSTXqDEaguuN+zoax6uZgCDIJoiCuAADiKADztrz/f6o7pmanqru\n", + "qurqruqZep9nnpmurjp1unv67a+/Ouc7oqpERERERISDgqA7EBERERHRQSTliIiIiBARSTkiIiIi\n", + "RERSjoiIiAgRkZQjIiIiQkQk5YiIiIgQkVLKIvJnEdkiIitM2+aIyGoRWSYifxeRPU33XSsi74nI\n", + "GhE5MZsdj4iIiMgFIjIt7rT3ROQnNvv8Ln7/MhGZkMn50kXK9wHTkra9BIxT1cOAtcC18U4dDJwN\n", + "HBw/5g8iEkXiEREReYuIFAJzMZx2MHCOiByUtM/JwAGqOhq4BPhjJudMKU1VfR3YkbStWlXb4jcX\n", + "AiPjf58OPKiqLaq6AXgfmJxJ5yIiIiICZjLwvqpuUNUW4CEM15k5DbgfQFUXAv1FZKjXE2YayX4X\n", + 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"plt.plot(x_MC,y_MC,'ok',alpha=0.75)\n", + "x_plot = np.arange(-50.,50.1,0.1)\n", + "print len(x_MC)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Integration using Rossenblatt transformation and Gaussian quadrature Cartessian coord" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def PCE_int_cart(f,seed):\n", + " dist_x = cp.Uniform(-40,40)\n", + " dist_y = cp.Uniform(-(R**2.-dist_x**2.)**0.5+H,(R**2.-dist_x**2.)**0.5+H)\n", + " dist = cp.J(dist_x,dist_y)\n", + " dist_U = cp.J(cp.Uniform(),cp.Uniform())\n", + " \n", + " M = 4\n", + " P = cp.orth_ttr(M,dist_U)\n", + " nodes_U, weights = cp.generate_quadrature(M, dist_U, rule='G',sparse=True)\n", + " nodes = dist.inv(dist_U.fwd(nodes_U)) \n", + "\n", + " x_MC = nodes[0,:]\n", + " y_MC = nodes[1,:]\n", + " \n", + " sample_u_train = f(x_MC,y_MC,seed)\n", + " u_hat = cp.fit_quadrature(P, nodes, weights, sample_u_train)\n", + " u_eq_PCE = cp.E(u_hat,dist_U)\n", + " \n", + " return u_eq_PCE, x_MC, y_MC, weights" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-2.72844356419\n", + "3.36994675408 %\n" + ] + } + ], + "source": [ + "u_eq_PCE_cart, x_MC, y_MC, weights = PCE_int_cart(f,seed)\n", + "\n", + "print u_eq_PCE_cart\n", + "print 100*(u_eq_PCE_cart-u_eq_MC)/u_eq_MC, '%'" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "53\n" + ] + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAWUAAAEACAYAAABrvGdSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXucE9X5/99PspvdcL/IVUBEUZRbUQG71bpa4YtSRVsv\n", + 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+ }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "r_lns=np.linspace(0,R,100)\n", + "plt.plot(r_lns,cp.Triangle(0,R,R).pdf(r_lns))\n", + "#```" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def PCE_int(f,seed):\n", + " dist_r = cp.Triangle(0,R,R)\n", + " dist_th = cp.Uniform(0,360)\n", + " dist = cp.J(dist_r,dist_th)\n", + " dist_U = cp.J(cp.Uniform(),cp.Uniform())\n", + " \n", + " M = 9\n", + " P = cp.orth_ttr(M,dist_U)\n", + " nodes_U, weights = cp.generate_quadrature([3,M], dist_U, rule='G')\n", + " nodes = dist.inv(dist_U.fwd(nodes_U)) \n", + "\n", + " r = nodes[0,:]\n", + " th = nodes[1,:]-90\n", + " x_MC = r*np.cos(np.deg2rad(th))\n", + " y_MC = r*np.sin(np.deg2rad(th))+H\n", + "\n", + " sample_u_train = f(x_MC,y_MC,seed)\n", + " u_hat = cp.fit_quadrature(P, nodes, weights, sample_u_train)\n", + " \n", + " u_eq_PCE = cp.E(u_hat,dist_U)\n", + " \n", + " return u_eq_PCE, x_MC, y_MC, nodes, weights " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-2.64018099787\n", + "0.0260268356383 %\n" + ] + } + ], + "source": [ + "u_eq_PCE, x_MC, y_MC, nodes, weights = PCE_int(f,seed)\n", + "\n", + "print u_eq_PCE\n", + "print 100*(u_eq_PCE-u_eq_MC)/u_eq_MC, '%'" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "40\n" + ] + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAWUAAAEACAYAAABrvGdSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXmYFNW5uN+ve1Y2WVQQUXGMigISZL0TTcY1KIlLEmPk\n", + "4haNyTWa5ZrEjXs1Cdn0mpUsXjVGQ3C97lscl/FnMgFBEVFBVERlGVAE2Wbt+X5/VPfQ01PVXVVd\n", + 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}, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.26349922998554242692 , 4.79436403870179805864 , 0.00579798753740115753 ], \n", + "[ 0.26349922998554242692 , 5.13630491629471475079 , 0.01299684397858970851 ], \n", + "[ 0.26349922998554242692 , 5.71955352542765460555 , 0.01905256317618122044 ], \n", + "[ 0.26349922998554242692 , 0.20924454049880022999 , 0.02341643323656225281 ], \n", + "[ 0.26349922998554242692 , 1.10309379714216659885 , 0.02569988335562909190 ], \n", + "[ 0.26349922998554242692 , 2.03849885644762496284 , 0.02569988335562912660 ], \n", + "[ 0.26349922998554242692 , 2.93234811309099407950 , 0.02341643323656214179 ], \n", + "[ 0.26349922998554242692 , 3.70522443534172518653 , 0.01905256317618119616 ], \n", + "[ 0.26349922998554242692 , 4.28847304447466459720 , 0.01299684397858971198 ], \n", + "[ 0.26349922998554242692 , 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5.71955352542765460555 , 0.01905256317618123432 ], \n", + "[ 0.96465960618086743494 , 0.20924454049880022999 , 0.02341643323656226669 ], \n", + "[ 0.96465960618086743494 , 1.10309379714216659885 , 0.02569988335562910925 ], \n", + "[ 0.96465960618086743494 , 2.03849885644762496284 , 0.02569988335562914394 ], \n", + "[ 0.96465960618086743494 , 2.93234811309099407950 , 0.02341643323656215567 ], \n", + "[ 0.96465960618086743494 , 3.70522443534172518653 , 0.01905256317618120657 ], \n", + "[ 0.96465960618086743494 , 4.28847304447466459720 , 0.01299684397858972065 ], \n", + "[ 0.96465960618086743494 , 4.63041392206758217753 , 0.00579798753740114886 ], \n", + "\n", + " r_node(1) = 0.26349922998554242692\n", + " th_node(1) = 4.79436403870179805864\n", + " weight(1) = 0.00579798753740115753\n", + "\n", + " r_node(2) = 0.26349922998554242692\n", + " th_node(2) = 5.13630491629471475079\n", + " weight(2) = 0.01299684397858970851\n", + "\n", + " r_node(3) = 0.26349922998554242692\n", + " th_node(3) = 5.71955352542765460555\n", + " weight(3) = 0.01905256317618122044\n", + "\n", + " r_node(4) = 0.26349922998554242692\n", + " th_node(4) = 0.20924454049880022999\n", + " weight(4) = 0.02341643323656225281\n", + "\n", + " r_node(5) = 0.26349922998554242692\n", + " th_node(5) = 1.10309379714216659885\n", + " weight(5) = 0.02569988335562909190\n", + "\n", + " r_node(6) = 0.26349922998554242692\n", + " th_node(6) = 2.03849885644762496284\n", + " weight(6) = 0.02569988335562912660\n", + "\n", + " r_node(7) = 0.26349922998554242692\n", + " th_node(7) = 2.93234811309099407950\n", + " weight(7) = 0.02341643323656214179\n", + "\n", + " r_node(8) = 0.26349922998554242692\n", + " th_node(8) = 3.70522443534172518653\n", + " weight(8) = 0.01905256317618119616\n", + "\n", + " r_node(9) = 0.26349922998554242692\n", + " th_node(9) = 4.28847304447466459720\n", + " weight(9) = 0.01299684397858971198\n", + "\n", + " r_node(10) = 0.26349922998554242692\n", + " th_node(10) = 4.63041392206758217753\n", + " weight(10) = 0.00579798753740114539\n", + "\n", + " r_node(11) = 0.57446451431535072718\n", + " th_node(11) = 4.79436403870179805864\n", + " weight(11) = 0.01086984853977092380\n", + "\n", + " r_node(12) = 0.57446451431535072718\n", + " th_node(12) = 5.13630491629471475079\n", + " weight(12) = 0.02436599330905551281\n", + "\n", + " r_node(13) = 0.57446451431535072718\n", + " th_node(13) = 5.71955352542765460555\n", + " weight(13) = 0.03571902745281423097\n", + "\n", + " r_node(14) = 0.57446451431535072718\n", + " th_node(14) = 0.20924454049880022999\n", + " weight(14) = 0.04390024659093685194\n", + "\n", + " r_node(15) = 0.57446451431535072718\n", + " th_node(15) = 1.10309379714216659885\n", + " weight(15) = 0.04818117282305908744\n", + "\n", + " r_node(16) = 0.57446451431535072718\n", + " th_node(16) = 2.03849885644762496284\n", + " weight(16) = 0.04818117282305915683\n", + "\n", + " r_node(17) = 0.57446451431535072718\n", + " th_node(17) = 2.93234811309099407950\n", + " weight(17) = 0.04390024659093664378\n", + "\n", + " r_node(18) = 0.57446451431535072718\n", + " th_node(18) = 3.70522443534172518653\n", + " weight(18) = 0.03571902745281418240\n", + "\n", + " r_node(19) = 0.57446451431535072718\n", + " th_node(19) = 4.28847304447466459720\n", + " weight(19) = 0.02436599330905552321\n", + "\n", + " r_node(20) = 0.57446451431535072718\n", + " th_node(20) = 4.63041392206758217753\n", + " weight(20) = 0.01086984853977089951\n", + "\n", + " r_node(21) = 0.81852948743000586429\n", + " th_node(21) = 4.79436403870179805864\n", + " weight(21) = 0.01086984853977090992\n", + "\n", + " r_node(22) = 0.81852948743000586429\n", + " th_node(22) = 5.13630491629471475079\n", + " weight(22) = 0.02436599330905548505\n", + "\n", + " r_node(23) = 0.81852948743000586429\n", + " th_node(23) = 5.71955352542765460555\n", + " weight(23) = 0.03571902745281418934\n", + "\n", + " r_node(24) = 0.81852948743000586429\n", + " th_node(24) = 0.20924454049880022999\n", + " weight(24) = 0.04390024659093679643\n", + "\n", + " r_node(25) = 0.81852948743000586429\n", + " th_node(25) = 1.10309379714216659885\n", + " weight(25) = 0.04818117282305903193\n", + "\n", + " r_node(26) = 0.81852948743000586429\n", + " th_node(26) = 2.03849885644762496284\n", + " weight(26) = 0.04818117282305909438\n", + "\n", + " r_node(27) = 0.81852948743000586429\n", + " th_node(27) = 2.93234811309099407950\n", + " weight(27) = 0.04390024659093658826\n", + "\n", + " r_node(28) = 0.81852948743000586429\n", + " th_node(28) = 3.70522443534172518653\n", + " weight(28) = 0.03571902745281413383\n", + "\n", + " r_node(29) = 0.81852948743000586429\n", + " th_node(29) = 4.28847304447466459720\n", + " weight(29) = 0.02436599330905549199\n", + "\n", + " r_node(30) = 0.81852948743000586429\n", + " th_node(30) = 4.63041392206758217753\n", + " weight(30) = 0.01086984853977088737\n", + "\n", + " r_node(31) = 0.96465960618086743494\n", + " th_node(31) = 4.79436403870179805864\n", + " weight(31) = 0.00579798753740116100\n", + "\n", + " r_node(32) = 0.96465960618086743494\n", + " th_node(32) = 5.13630491629471475079\n", + " weight(32) = 0.01299684397858971545\n", + "\n", + " r_node(33) = 0.96465960618086743494\n", + " th_node(33) = 5.71955352542765460555\n", + " weight(33) = 0.01905256317618123432\n", + "\n", + " r_node(34) = 0.96465960618086743494\n", + " th_node(34) = 0.20924454049880022999\n", + " weight(34) = 0.02341643323656226669\n", + "\n", + " r_node(35) = 0.96465960618086743494\n", + " th_node(35) = 1.10309379714216659885\n", + " weight(35) = 0.02569988335562910925\n", + "\n", + " r_node(36) = 0.96465960618086743494\n", + " th_node(36) = 2.03849885644762496284\n", + " weight(36) = 0.02569988335562914394\n", + "\n", + " r_node(37) = 0.96465960618086743494\n", + " th_node(37) = 2.93234811309099407950\n", + " weight(37) = 0.02341643323656215567\n", + "\n", + " r_node(38) = 0.96465960618086743494\n", + " th_node(38) = 3.70522443534172518653\n", + " weight(38) = 0.01905256317618120657\n", + "\n", + " r_node(39) = 0.96465960618086743494\n", + " th_node(39) = 4.28847304447466459720\n", + " weight(39) = 0.01299684397858972065\n", + "\n", + " r_node(40) = 0.96465960618086743494\n", + " th_node(40) = 4.63041392206758217753\n", + " weight(40) = 0.00579798753740114886\n", + "\n" + ] + } + ], + "source": [ + "for i in range(len(weights)):\n", + " print '[', '{0:0.20f}'.format(nodes[0,i]/R), ', ', '{0:0.20f}'.format(np.mod(np.deg2rad(nodes[1,i]-90.),2.*np.pi)), ', ', '{0:0.20f}'.format(weights[i]), '], '\n", + "print \n", + "\n", + "for i in range(len(weights)):\n", + " print ' r_node('+str(i+1)+') =', '{0:0.20f}'.format(nodes[0,i]/R)\n", + " print ' th_node('+str(i+1)+') =', '{0:0.20f}'.format(np.mod(np.deg2rad(nodes[1,i]-90.),2.*np.pi))\n", + " print ' weight('+str(i+1)+') =', '{0:0.20f}'.format(weights[i])\n", + " print\n", + "\n", + "#print nodes[0,:]/R\n", + "#print nodes[1,:]\n", + "#print weights" + ] + }, + { + "cell_type": "code", + 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+ ], + "source": [ + "r_aux = nodes[0,:]/R\n", + "th_aux = np.mod(np.deg2rad(nodes[1,:]-90.),2.*np.pi)\n", + "\n", + "fig = plt.figure(figsize=[4,4])\n", + "ax = fig.add_subplot(111)\n", + "ax.scatter(r_aux*np.cos(th_aux),r_aux*np.sin(th_aux),s=30,c=weights,cmap=plt.cm.gray_r)\n", + "circ = plt.Circle([0.,0.],1.,ec='k',fill=False)\n", + "ax.add_patch(circ)\n", + "ax.set_xlim([-1.1,1.1])\n", + "ax.set_ylim([-1.1,1.1])\n", + "ax.set_axis_off()\n", + "fig.savefig('PCE_Quad_rotor.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### sum of quadratures is equal to the quadrature of the sum" + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "M = 9\n", + "P = cp.orth_ttr(M,dist_U)\n", + "nodes_U, weights = cp.generate_quadrature([3,M], dist_U, rule='G')\n", + "nodes = dist.inv(dist_U.fwd(nodes_U)) \n", + "\n", + "r = nodes[0,:]\n", + "th = nodes[1,:]+90\n", + "x_MC = r*np.cos(np.deg2rad(th))\n", + "y_MC = r*np.sin(np.deg2rad(th))+H\n", + "\n", + "sample_u_train_1 = f1(x_MC,y_MC)\n", + "u_hat = cp.fit_quadrature(P, nodes, weights, sample_u_train_1)\n", + "u_eq_PCE_1 = cp.E(u_hat,dist_U)\n", + "\n", + "sample_u_train_2 = f2(x_MC,y_MC,seed)\n", + "u_hat = cp.fit_quadrature(P, nodes, weights, sample_u_train_2)\n", + "u_eq_PCE_2 = cp.E(u_hat,dist_U)\n", + "\n", + "sample_u_train_3 = f3(x_MC,y_MC)\n", + "u_hat = cp.fit_quadrature(P, nodes, weights, sample_u_train_3)\n", + "u_eq_PCE_3 = cp.E(u_hat,dist_U)" + ] + }, + { + "cell_type": "code", + "execution_count": 277, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-2.62656050526\n", + "-0.489999815812 %\n" + ] + } + ], + "source": [ + "u_eq_PCE = u_eq_PCE_1 + u_eq_PCE_2 + u_eq_PCE_3\n", + "\n", + "print u_eq_PCE\n", + "print 100*(u_eq_PCE-u_eq_MC)/u_eq_MC, '%'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Larsen's Gaussian quadrature. Current implementation in FusedWake" + ] + }, + { + "cell_type": "code", + "execution_count": 290, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def gaussN(R, func, varargin, NGr=4, NGth=4):\n", + " \"\"\"Calculate numerically the gauss integration.\n", + " [1] eq. 38\n", + "\n", + " Inputs\n", + " ----------\n", + " R (float): Wind turbine radius [m]\n", + " func (function): Wind speed function\n", + " varargin: Other arguments for the function besides [r,te]\n", + " NG (int): Number of Ga\n", + "\n", + " Outputs\n", + " ----------\n", + " Ua (float):\n", + " \"\"\"\n", + " A = np.pi*R**2\n", + " #coefficients\n", + " if (NGr==4)&(NGth==4): #for speed give the full values\n", + " rt = np.array([[ -0.339981043584856, -0.861136311594053,\n", + " 0.339981043584856, 0.861136311594053]])\n", + " te = rt.T\n", + " w = np.array([[0.652145154862546, 0.347854845137454,\n", + " 0.652145154862546, 0.347854845137454]])\n", + " wt=w\n", + " else:\n", + " rt,w = np.polynomial.legendre.leggauss(NGr)\n", + " rt = np.array([rt])\n", + " #te = rt.T\n", + " w = np.array([w])\n", + " te,wt = np.polynomial.legendre.leggauss(NGr)\n", + " te = np.array([te])\n", + " wt = np.array([wt])\n", + " \n", + " return np.sum((np.pi/4.0)*(R**2./A)*w*wt.T*func(R*(rt+1.0)/2.0,\n", + " np.pi*(te+1.0),*varargin)*(rt+1.0))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Convergence" + ] + }, + { + "cell_type": "code", + "execution_count": 289, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def f_test(x,y,seed):\n", + " np.random.seed(seed)\n", + " return - np.random.uniform(0.5,3.)*np.exp( -((x-np.random.uniform(-60,60))**2. + (y-np.random.uniform(40,70))**2.)/(np.random.uniform(10,80)**2.) ) + \\\n", + " - np.random.uniform(0.5,3.)*np.exp( -((x-np.random.uniform(-60,60))**2. + (y-np.random.uniform(40,70))**2.)/(np.random.uniform(10,80)**2.) ) + \\\n", + " - np.random.uniform(0.5,3.)*np.exp( -((x-np.random.uniform(-60,60))**2. + (y-np.random.uniform(40,70))**2.)/(np.random.uniform(10,80)**2.) ) \n", + " \n", + "def plot(f,seed):\n", + " x_mesh,y_mesh = np.meshgrid(np.arange(-50.,100.,0.1),np.arange(0.,120.,0.1))\n", + "\n", + " F2 = f(x_mesh,y_mesh,seed)\n", + "\n", + " plt.contourf(x_mesh,y_mesh,F2,20)\n", + " cb = plt.colorbar()\n", + " circ = plt.Circle([0.,H],R,ec='k',fill=False)\n", + " ax = plt.gca()\n", + " ax.add_patch(circ)\n", + " print \n", + " \n", + "f_r = lambda r,th,seed: f_test(-r*np.sin(th),r*np.cos(th)+H,seed)" + ] + }, + { + "cell_type": "code", + "execution_count": 315, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "rel_error_PCE = []\n", + "rel_error_QMC = []\n", + "rel_error_QMC2 = []\n", + "rel_error_GCL = []\n", + "\n", + "seeds = range(1000)\n", + "\n", + "for seed in seeds: \n", + " u_eq_MC, x_MC, y_MC = MC_int(f_test,seed)\n", + " u_eq_PCE, x_MC, y_MC, nodes, weights = PCE_int(f_test,seed)\n", + " u_eq_QMC, x_MC, y_MC = QMC_int(f_test,seed)\n", + " u_eq_QMC2, x_MC, y_MC = QMC_int2(f_test,seed)\n", + " u_eq_GCL = gaussN(R,f_r,[seed])\n", + "\n", + " rel_error_PCE = np.append(rel_error_PCE,100*(u_eq_PCE-u_eq_MC)/u_eq_MC)\n", + " rel_error_QMC = np.append(rel_error_QMC,100*(u_eq_QMC-u_eq_MC)/u_eq_MC)\n", + " rel_error_QMC2 = np.append(rel_error_QMC2,100*(u_eq_QMC2-u_eq_MC)/u_eq_MC)\n", + " rel_error_GCL = np.append(rel_error_GCL,100*(u_eq_GCL-u_eq_MC)/u_eq_MC)" + ] + }, + { + "cell_type": "code", + "execution_count": 330, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAUwAAAFHCAYAAADKqXWcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XecXVW5//HPNwUSCKELBCIgHQQEpKNGRQVUUASlKXiv\n", + "6JULcsWCWJiMen9wRbEgIEq3AAKKoCBFCSAgndBCr6GE3hJCSPL8/ljrZPacOTNnTzklk+/79ZrX\n", + 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"ax.set_ylabel('PDF [-]')\n", + "plt.legend(loc=1)\n", + "fig.savefig('Rotor_avg_int_approx_comparison.pdf')\n", + "print" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/fusedwake/test/NewQuadratureRule/PCE_Quad_rotor.pdf b/fusedwake/test/NewQuadratureRule/PCE_Quad_rotor.pdf new file mode 100644 index 0000000..a0229fb Binary files /dev/null and b/fusedwake/test/NewQuadratureRule/PCE_Quad_rotor.pdf differ diff --git a/fusedwake/test/NewQuadratureRule/Rotor_avg_int_approx_comparison.pdf b/fusedwake/test/NewQuadratureRule/Rotor_avg_int_approx_comparison.pdf new file mode 100644 index 0000000..7047fb0 Binary files /dev/null and b/fusedwake/test/NewQuadratureRule/Rotor_avg_int_approx_comparison.pdf differ