diff --git a/examples/1.-Estimation-of-the-Banana-distribution.ipynb b/examples/1.-Estimation-of-the-Banana-distribution.ipynb index 640fd6e..47a78c3 100644 --- a/examples/1.-Estimation-of-the-Banana-distribution.ipynb +++ b/examples/1.-Estimation-of-the-Banana-distribution.ipynb @@ -10833,15 +10833,15 @@ ], "metadata": { "kernelspec": { - "display_name": "Julia 1.9.1", + "display_name": "Julia 1.10.0", "language": "julia", - "name": "julia-1.9" + "name": "julia-1.10" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.6.2" + "version": "1.10.0" } }, "nbformat": 4, diff --git a/examples/2.-Conditional-density-estimation-of-the-Banana-distribution.ipynb b/examples/2.-Conditional-density-estimation-of-the-Banana-distribution.ipynb index c3eeb7d..e14b1c5 100644 --- a/examples/2.-Conditional-density-estimation-of-the-Banana-distribution.ipynb +++ b/examples/2.-Conditional-density-estimation-of-the-Banana-distribution.ipynb @@ -64,28 +64,36 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPrecompiling TransportBasedInference [bdf749b0-1400-4207-80d3-e689c0e3f03d]\n", - "\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mType annotations on keyword arguments not currently supported in recipes.\n", - "\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mType information has been discarded\n", - "\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ RecipesBase ~/.julia/packages/RecipesBase/BRe07/src/RecipesBase.jl:117\u001b[39m\n", - "\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mType annotations on keyword arguments not currently supported in recipes.\n", - "\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mType information has been discarded\n", - "\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ RecipesBase 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to install the Plots package.", + "output_type": "error", + "traceback": [ + "ArgumentError: Package Plots not found in current path.\n- Run `import Pkg; Pkg.add(\"Plots\")` to install the Plots package.", + "", + "Stacktrace:", + " [1] macro expansion", + " @ Base ./loading.jl:1766 [inlined]", + " [2] macro expansion", + " @ Base ./lock.jl:267 [inlined]", + " [3] __require(into::Module, mod::Symbol)", + " @ Base ./loading.jl:1747", + " [4] #invoke_in_world#3", + " @ Base ./essentials.jl:921 [inlined]", + " [5] invoke_in_world", + " @ Base ./essentials.jl:918 [inlined]", + " [6] require(into::Module, mod::Symbol)", + " @ Base ./loading.jl:1740" + ] + } + ], "source": [ "using Plots\n", "default(fontfamily = \"Computer Modern\",\n", @@ -140,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 3, "metadata": { "scrolled": true }, @@ -165,116 +196,20 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 4, "metadata": {}, "outputs": [ { - "data": { - "image/png": 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"output_type": "error", + "traceback": [ + "UndefVarError: `cgrad` not defined", + "", + "Stacktrace:", + " [1] top-level scope", + " @ In[4]:13" + ] } ], "source": [ @@ -299,20118 +234,20 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 5, "metadata": {}, "outputs": [ { - "data": { - "image/png": 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QkGnTpqEFAQDACdm2CN98883p06fbdBUAAAAvw+bHCG/dunXixAmtttybxwMAADiQbYvw9OnTDMM0bNhwxIgRycnJ5U178ODBqlWruv9X3759CwoKbBoMAADAyra7Rrdv3y4UCgkh3bp1mzNnzrZt28qcFhQU5O/vP2TIEOuPDMN4eHjYNBgAAICVDYvw5s2bUqnUemP4wMDAc+fOlTdTLpf7+/t369bNdmEAAADKZMNdo/Pnz//555+trzUaja+vr+3WBQAA8GJsuEU4YsSIqKgo6+vjx49PnTrVdusCAAB4MTYswkGDBq1bt46m6YyMjC5duowbN8526wIAAHgxNixCmqbHjx9vu88HAAB4ebjXKAAAuDQUIQAAuDQUIQAAuDQUIQAAuDQUIQAAuDQUIQAAuDQUIQAAuDQUIQAAuDQUIQAAuDQUIQAAuDQUIQAAuDQUIQAAuDQUIQAAuDQUIQAAuDQUIQAAuDQUIQAAuDQUIQAAuDQUIQAAuDQUIQAAuDQUIQAAuDQUIQAAuDQUIQAAuDQUIQAAuDQUIQAAuDQUIQAAuDQUIQAAuDQUIQAAuDSBowMA/CUnJ+fKlSt6vT4gIKBVq1YSicTRiQDAJaAIwfEyMjLGj56c/iAviG7CcFKDIC/dOPGNscPnffpvhmEcnQ4AqjgUIThYUlJSz8792lPTI2XNSxdyQvbCLxsGXBmy78hOmsYOfACwIfyKAQd7Y+i4ztTcMEnzJxfSFNNGOq7krvvqlescFQwAXIQ9ilClUu3Zs8cOK4JK5+7du8Y8caCkXpmjrSVjf1y20s6RAMDV2KMIZ8yYcfnyZTusCCqdixfjA8zNyhsV0wqzjuj1entGAgBXY/MiPHXqFM53gPKo8gpExK2CCTKBe2Fhod3yAIALsm0RqtXq/Pz8mjVr2nQtUHn5B/oZqIp6TmMu8PHxsVseAHBBtj1rdPv27W+99dbChQsrnqZSqZKTk1etWmX9kWGYUaNGicVim2YDZ9C5c9TX9OqWZESZoxpLvrtSLhKJ7JwKAFyKDYvw/Pnz7dq1e579olqt9vHjx9euXfsrk0AwePBgFKErCA8Pr1bf9+G9c7WkHZ4Z5M8Y/zNv6SwHxAIAV2KrItTpdOnp6e3bt3+eyWFhYa1atXr//fdtFAac2c9b1nRp31OnVTWS9qX+u69exxadMXzboX+9/gP6OzYeAFR5tirCu3fvqlQq697Os2fP0jS9fv36t956y0arg8rL29v7wtW4j2bP/3XPaF9RTSGRqblsXqL7aOnsocMGOzodAFR9tirCli1btmzZ0vr69u3bMpkMLQjlkcvly35Y8vV/Fj18+FCj0QQHBwcEBDg6FAC4CpvfYm358uXnz58XCARr1qwZP368rVcHlRfDMBEREY5OAQAux+ZFOHny5MmTJ9t6LQAAAC8G9xoFAACXhiIEAHuLj4/v231Q7dCGtYIa1wpp2LNzv7i4044OBa4Lj2ECALv6bN4XO9Ydayuc1Ez21yHhvNSkGaMWdIk99M33Xzk2G7gmbBECgP3s2L5z37pzAxTfBIj/d2KUr6hGX/lXF/ckrVqx1oHZwGWhCAHAfj7998Ku0g+osn7zREmnL/liGcdx9k8FLg67RsFJZWZmHjly9MG9ZJlc0qpN8+joaNx0tLJLTU0VW7ykYo8yR0W0zFdQ++bNm02bNrVzMHBx2CIEp2M0GieOezc6sv/uT/5M3ep7a7Xwq4nbGtZqcfDAQUdHg5eSkZHhTgdVMEHBBqWnp9stD4AVtgjBufA83z9miOBB3SHyJ59N303PFs9+50OjwTxoSKzDwsHLkUqlLDFWMIFjjFKp1G55AKywRQjOZfOmLfqHns1lw59aLmU8+sgXz5kxT6PROCQYvLw6derkmB5UMCGHvdewYUO75QGwQhGCc/npP2ubC18vc0hMy6tTnQ/sP2DnSPCqKBSK+k1qp+gvlzmaabjjF+aO28yC/aEIwbnk56rcBH7ljfqThpfOXbdnHniFOI4bPnrg8ZIvD+Z8dqVoW74puXQo35R8mv1q5YbvHRgPXBaOEYJzMRqMpPyDRCJaqlaX2DEOvDKXLl0eO/IdpbleR+lUCeNWZM48lvc1z/ON3HurRHe08pS9B7fWqlXL0THBFaEIwYlotdoidSHvxZV5nRkhpNiS1aRWmJ1Twcu7fPnKqNi3YySfe0qCiOSvhc08Yh9qz50x/mf1uh969epFUZRDM4Lrwq5RcCJ79+z1FoU80l4sb8It3YE+/WPsGQleiQmjp8RIFngKn752opa8Q0vJyJPHzqIFwYFQhOBE4o6fb6kYHqf6ycCqnx29rz2Tb05p1qyZ/YPBy7hx44bMEOgpDC5ztJGs355d+3met3MqgFIoQnAiBaoiH3H1zspJmx9PTdZdKl1u4nTnCtZdLPjF29PbgfHgxdy4keBtqlfeKEVoN8YnPz/fnpEAnoRjhOBE/AN9tTcLasnbK0XVzhes+y3vWzEtt/AmilAN3WNGBv+4j5vi6Izwj2m1WgEvrmCCkJJqtVpfX1+7RQJ4EooQnEjPvtHfHt8bTlp4CYP7+M8jhJh5g4ASWc+dSdLFt+vaxtEZ4R8LDQ3RCW9VMEFtzsHlg+BA2DUKTuS113rniW+oTKmlS4SUxNqCFt54hV3zwUczHJcOXlDnzp1TLOd5UvZjJQrM6T6BXhKJpMxRADtAEYITEQgE2/dtOm6Zl6T/fyeOFpjS9mpmzPhoolKpdFQ2eGHu7u79h752Rb/p2SGWN58yLF607DP7pwIoRTnDyVqzZs3y9/d///33HR0EnEJmZuaMKbOvXbrhL65FU4J8U6qBLSaEkgk8BbRIY1E1a9HkiyWfRERE/P1ngXOwWCxD+o/Mu0laica6Cf46FphpuHPe8v27c8ZNfvcdx8YDF4djhOB0goKCtu3ZaDQaHzx4kJ2d/a+JsxoZhzSS9mUooXVC+o0bfboM+2bF53379XFsVHhOAoFgz6Ft27ZuX7b4y/ycAgEtshBjo8YNti1a07hxY0enA1eHLUJwau1ado7IeyNU8vS1gwauZI/23ZOXDoWGhjokGLwMs9ksFAodnQLgLzhGCM7r7NmzJM/72RYkhEhot5bMuC/mf2X/VPDy0ILgVLBrFJzXwb3HwthOhBA9W5ygPpCuTzByWhnjWU3WspFbTE1p291xPzs6IwBUeihCcF7pKY/dBQ3ulfx+rmBdC8/B3X2nS2g3DVvwQHt2Q/q4Xn4fsDzr6IwAUOmhCMF5eXi4JemuPtCeGxO6RkTLrAsljLuPqFojt5itmTPEXihCAHhZOEYIzqttVMtr6l0DAhaUtmAphcDnNb9/mwxmhwQDgKqk7C3CpUuXFhcXR0VFtW3bViZ7+ncQgH1E1I3wEgYpBD5ljgZK6on1spKSEjc3NzsHA4CqpOwtwtq1a9+4cWPQoEFeXl4dO3b8+OOPT548qdfr7RwOXFxWVla4W9MKJgQpIpKTk+2WBwCqpLKLsF+/fvv371epVBcuXIiNjb1x40b//v29vLwGDx585coVO0cEl8VxnEBQ0Xn2FKE5ruw7WIIzMJlMP/24smOrbhHhTeqGN+ncpuf6dT9bLBZH5wL4fyo6RsgwTIsWLd577739+/ffv39/1KhR9evXHzJkyNatW5/z0zmOe/jwIf5mhxdTs2ZNNZNawQSVOaVatWr2igP/zOPHjxvXa7Fk7ppHt7NKcs26PEqf6L3mo2PtWkTh6YPgVMouQpPJdPXqVZ1OV7okMDCwV69e//rXv27durV79+6kpKS//ejs7Ow1a9bk5ubGx8d36tQpLy/vlaUG19C4ceMCkqxji8oczTM98g3y8PT0tHMqeB4Wi6Vdiy7aLLqdx9i3QteND9s0MmR5mKx5cmGCNLNB/5ghznBPKwCrsotw5MiRXbt2DQgIGDly5J49e3Jycnief/DggdFodHNz27Bhw/79+//2o1etWnX06NF27dqNGDHCZDL99NNPrzo8VHEURS1etuA3/QKWNxFCON5yp+T4gZzPfn387q7M2XsLZ3/21UeOzghlmzZlukjjW1/R/VrRjo0ZEzdnTL5cuLWatNWYkFWp2uu6dPGBAwcdnRHgL2UXYUxMTGFh4ZEjR5RK5ZQpUwICAgQCQXJyclBQ0JYtW8xms1wu/9uPHjly5MSJE62vi4qK/P39X2VwcA39B/SbOm/Uds3ES0Wb1qaPyTM+aukxeEDAZ1E+k1rKR4wbMXXjhs2OzghP4zju1192qC152cY/iy3ZFKFFtPye5rdVqSPOqNb0CZiXr87a9stuR8cE+EvZN90+cOBASUnJ4MGDRSIRISQzM5Pn+eDgYIvFEhQUFBkZOWXKlJiYmOdcx+7du1euXLl//36xWFzmhJkzZ3p6ek6dOtX6I03THh4eL/R1oGq6cuVK325DB3l/I6blLG9WCJQMJSKEmDn9Id2/P/3+vdiB/R2dEf5n8+bNM8Z97CUMVQrDOirHi2mFdbmWLTiSs4iiaC2r8q0lvZJwzrE5AazKffrEw4cPjx07NmXKlKeWZ2Vl6fX6GjVqPM+nGwyGbdu2nTp1qmvXrqNHjy5vWs+ePc+ePVv6iGqpVHrjxg1fX9/n+wpQ9Q2LHXXvpCrX+Mhd4C+gxMWWbAWj7KR8O1BST8+q95nfvZd0g2EYR8eEvzSo1dytoJ6M8ergPfbZ0X3ZH6vNud41mWu3Lj47CmB/dnoMU9u2bfv16zd37twyR/EYJqhAenp6w1otOntPaez+GvXfnfm5xkdHchc29xjUyD3mlPGrhZumdujQwbE5wSorK6t9wxidVj8+bBNNlfHXiYnTrUgdGtMvesvOjfaPB/AsG95i7cCBA/fv37e+bteu3YoVK2y3LqjCBvUZEeu/qIl7X+qJ/1z9xDVfD/nhatH2fFOKh7Hmn38mOjAhlFqzan3rZh0Yk1uotGmZLUgIEdEyd4H/oOHYmw3OwoZFOGnSpLVr11pf63Q6pVJpu3VBVXXx4kVS4B0mLeP+MkJKEu37bnzhRpqiLWbcdNTx3p8+d81n+zsLPyA8cRf4VTDTUxgYEhJit2AAFbPh0yfmzp0bGRlJCMnLyzt27Bgun4AXcOTgb+Fcx/JGQ6VNj+cu9fD0qFU7yp6p4Flnz577fcelfoolWrZQa8k3cpoKJosUBCfEgfOwYRFOmTIlLi5u+fLlLMseOXIkIiLCduuCqupxWpa7oE15oxShCUWlmS937LjSnqngWV9/8V1r4duEUHLGW0BL0vR/lD+XLyJptWrVsl84gArZ9nmEnTt37ty5s01XAVWbp7dHNlvRtoWR13zy4bzyrswBu0m8l9hEUsf6urvv9D1ZHybrLleXRT4786b2YM/XugmFFd1FFsCe8DxCcGrto1rnMAnljRaaH/v4e70zeYI9I0GZ+Cdufh4sadRROWFf9iep+mtPTftTdyLZ7eDCrz+zaziACuEJ9eDUeveOmTP946bsMBnj9exovGHVkp8W2D8VPItiCE+40jN7m7j3DRTX35f9iYASRig6uwl8C82PM6j4Fh0anl7/m7u7u2PTAjyJmT9/vqMzkN9++02hULRr187RQcDpCASC+o0ilm3/IIRuKWH+9wBejmd/y/8mWX/p2qUbqSlpjZs2fJ7b/oHtxJ+//Phh4SPt+Rvq/Q+0Z4rMmQGSiHbeo6vJWhk4dULxATEt14jSD5/Yi9NkwNlg1yg4u+hu0T/vXh4nnP+b8dNr6h0J6oPH85euTntdwfhODNobw35zf5ukXbOuJ0+ecnRS12U0GnUG3eWiX92EfpGeI1p4DhHQki2Ppx3JXewlDAoQ1zXzhh5+79Vj+mzZ/LwPcQOwGzvdWaZiuLMM/C2e5//444+5H3yUfl3XVDYoTNpMRMtKR7VswT7djBMXD4aHhzswpMsa0HuI7npoG49RTy7kCXc096tcwwMTrx8YuFApCs81PlQ13r9j/yZH5QQoE7YIoXKgKCo0NDTlXvYg369ryX95zqkAACAASURBVNs/2YKEEDnj3VYw+cNZ8x2UzqUdO3Y89Yr2qRYkhFCEjvGbbSGmph79laJwQoiM8crPUzkiI0BFUIRQaRw9eqwG1Zkq5z/a6tLI+AuX7RwJCCFfLfi2pfzpFvwvqr332GtFO6w/aFmVr5+P3YIBPCcUIVQaD/9MducruC8XJaYUOp3OfoGAkCtXrl67cj1AXKe8CYGSeizPWm80k8rG9+zbxY7pAJ4LihAqDbFYyPIV3VOU5S0CAa4Isqu3x0yVUJ4UocqbIKBEDCVQW3I1lvwk6vfhI4bZMx7A88BvDag0GjdveEK4j5DuTy4sNmfeLDmkNueIGTeTRGN9lDTYx61bt0Q6X3eBqNiS4yEIKHNOgSmNoQWFpozf+HXrt67AVS7ghLBFCJVGdHR0GnvRxP2181PHFq1PG/NzxoQCU7qbwJ/lzRqVJcyvzq1btxyb03XcvHlTaa5bR9HptvpIuXPUhzSW/Lzqxw6e3N6xE54ZCc4IW4RQaUil0s8Wz1v0wbwY2QKWN61JHdXQPSZK+TZD/e+ulUm6+I6tup26eLRZs2YOjOoitFodw0sau7+2IX18TXm7APHTN9Z/qD2fabgTO2jAL1vXOiQhwPNAEUJlMmLkMIvF8um/J2oKTHUUnbr6THlqQg1Zm4F+i/p2H5yR/8ghCV1KaGiITniZoUSDAhfuyprbwK1HE/d+UsadEKKx5F8p2p6gPuAbqPzmh8WOTgpQERQhVDJvjBnZo1e3OqGNu/hMLnNCiLSJQOV59erVli1b2jmbq+nYseO75lmtxWMfG277imrcVh+5VrSLphgBJZYxHjXkbSRScdz5Yz4+uGQCnBqOEULlk5KSImM8xbSivAkR8i6TJk61ZyTXpFAoonq0/yF5wFnVmmzjnzzhGVooZ7zNvKGF55B0Kn7nwc2hoaGOjgnwN7BFCJVPZmamlKnoxs1ygXfCvfw1K9eNf2es3VK5oMTExJ1b9tZ369be+y05421dmKq/diLvuxP5373x9uBOnTo6NiHA88AWIVQ+tWrV0rAV3amrxJJbWxI1/8MvnOFWulVYtw6vtfcc28N3ZmkLEkLCpS3eCFkhpT22/7rTgdkAnh+KECqfhg0bGlmNli0ob8Ldkt+buveXG4M3b/7VnsFcSlJSklFNWnoOeXZISEtj/D4waaiEhHIfqgzgPFCEUPlQFCWQUMdyl5Q5+kBzjhDeSxTiK6q18LOy58DLm/fRx03c+5Q3GiipzxPuwIED9owE8GJQhFApCShxtvHPvVkfGbiS0oU84RLU+w/mfqYUVcsx3jdwak2RsaSkpILPgRej1+sP7T3mJaroRBgZ7Xn37l27RQJ4YThZBiolk9E0IWzLypQRK1OGeQqDvUWhOrYwx3jfX1xncrVdOcYHx/O+KTZlhvjWzMjIqFevnqPzVjW7du7ypEPNnKGCOUZO5+3tXcEEACeBIoTKh2VZqVghod3FtLyZx4A7mmPVpC29RCFB4gY0xRBCwqTNR4Us3/54ZkbhA6FQ+LcfCP/U8YNxdWXRSbr4Ru4xZU4w8wY9VzxkSBlHEAGcDXaNQuVD07RUJiGE0BRzqWjzwICFjdx7h0gaW1vQiiL0gMDPOZa81i32+LHjjgtbNeXl5teSd0jVXS3vlKX4gs08xUdFRdk5GMALQBFC5UNRlEjKmDk9IZSnINhTGFTmNDEtry6LbMVOnv7WJ7t27rFzyKpN6eNt5DTRvv/alDFJY3n6UpY7JceuFm+bMm2CQ7IB/FPYNQqVUlh4yPU7e4S02LvC8zW8hWFGtqSv/KvZ0yd1697Vw6Oiy/Dh+UXHdNoWfznSbYyZN6xJG1lH3rGmvL2IlhWaMhLU+9WWHG8fz0VLvnR0TIDngi1CqJQ4ltxSH/YShpg4bUXTeAtDCcW0vDbVY9fO3XaLV+UNGTr4Af+7li1o6t7v3eoHFALfS4W/nsj77nbJsbpuXbzc/Y6ePEBR5T6tF8CpoAihUtJqNLFBX2rYvEzDnxVMyzLe8xHXIIQE0U3On7psr3RVH8/zGp16Q/q4PNMjhhJ2Ur49OnTV+PBNg4IWPtCeGziyF87UhUoEu0ahUvLz8+cL2VHBK37JeDtNfz1M2vzZOQXmdD1b7C0MJYSIaHlekdruMasmi8XSqnH72lR3pVfYzsw5FCFKUbiXKDTP+CjP9Kij8u3jRw7yPI8tQqgssEUIlVLvAd2SzOcIIT1939+b/bHKlPrUBI1FtTdrXg/f96w/qs05IeFln1MD/9Siz5eIC8OTdZfTDQltvEZ2UI7zF9dJ1l7yEAREeo4oMmW4m6tdvoztb6g0sEUIldKo0SO/Xtg6wtKdpgRKYdimjEkRiqh6im7uQn+NRZWsu5SoievhNzNQUt86/6b64OLX3nVs5qqB47ifflhFG92HBC1xF/iXLu+knHCh4Jd0fUKR+XFz0cAbf9xo3bq1A3MCPD8UIVRKMpls8871r8e+1YgbzhM+WNLATeB3MGeBn7i2gBYbWQ1F0b/lLSM87y+uEyZrkaq/PnfmJ82bNw8ICHB09srt1q1beo3prZClTz5xghBCCNXOe8yx3CVmTq8xq0rUeBgvVBq2LcKNGzeWlJQkJiYGBAS8//77uMcHvEKtWrU8ceHQ++/OVR1O6eA91sKbevi9/3vetyJa2lk5uboskqYYnnCPDbeP5X7tL67dTPdmv16D46+foWkcEXhxR48erS3r+EwL/qW999jNjyeXkMfVanS3czCAF2bD3wjbt29v3br15MmTv/3227i4uI8//th26wLXFBYWtn3fZolEIqAlN9WHzJxBIfB5M3RtTXlb611mKEKHSBqPDdsgYzwLTZnSguoHDhx0dOrKLflBejVpZHmjCoGS5c15VGLXrl3tmQrgZdiwCHfu3PnVV18RQiiKGjRo0JYtW2y3LnBlbu5uV4q2N/cY+Hv+sn7+nzCU6KkJFKFj/OZcKtxck+q2c/M+h4SsOjhKTCsqGOcJ16d/L9xuGyoRG+4anTlzptlstr7WarVyudx26wJX5uXt2UM0/VjuUhnj6SEMLHOOiJYFiOuaOF1q8tPnl8LzMxgMO3bs6KjwLX8KzxLTdz99Y79MAC/NhkVYes6YxWLZuHHjzJkzy5v5559/bt26devWrdYfJRLJ3r17fXxwsB2ey+jxI7YvOBztO+1K4dYKpnkJg+MLNxp0uXYLVvVMm/xeNab9LfWRxuU8kjdDf7N2RE2cDQCViz3OGl24cGHPnj3Hjh1b3oRq1arVrFnzjTfesP7IMIxSqbRDMKgaJk5++5N/h8lpb45wFc9kKKGmRHv58uXIyHKPckF5CgoKNm/cJiAiQviNGe/IaE8vUWgzjwFewhDrBDNvOJy7qJof/oSFSsbmRXj48GGaphcuXFjBHIlE4u/v36JFC1uHgSpJKBTGxPQ8sG+jiJZWMC3LeK+b7/RdmXOmTZoRf+283eJVGRMnTKYIFSJp2MJzsJco1MIZ0/UJWx5PYyhRD98ZEsbjYM4Cf3Gdwtw0RycF+Gdsex75lStXiouLP/zwQ0JI6Z5PgFduzNuv+7uFsbw5RXelzAkFpjQ9q1YKw5t6DLh98158/CU7J6zsjEbjkQO/Dwj4bFDQ4mqyVh6CAKUovKlHv4nhOwLFdY/lLd2bNc/CGf1ENWiG+fuPA3AmNizCxMTE2bNnJyQkzJkzZ/r06XFxcbZbF7i4Hj16eIeLZIzX/uz5+abkp0ZLLHl7s/+63Zq/qJaQkvbq0o/neUckrZRYlp35rw9aug+tIWvz1BBNMX385wkpsYfA38hpWJ5t0KiuQ0ICvDAb7ho9depU7dq1CwsLrT926tTJdusC+HXnL52bDujhPXNjxsT6bt3ryDu5Cfx0bEGS9tID7dmefrMCJfUIIYQiIlqmoJUd23U+d/G0o1NXArt27p4785OCnOKxoRuLzI8TNXEqUxqhiI+oej1FVzeBH00xjdx6W3hTgTk9w3hz1qS5jo4M8M/YsAgnTpxouw8HeErNmjU9A8WUlhZRsnBp84fa81q2QEq7+4hr1lF0Oq1aYeR0YlompuV6Vt0/YP7WazMyMjJCQkIcHdypLf7y6/VL9wzw+n4dP/qManW24c/GHn2aefTnCZ9tvL8zc3Z1eWSU8h1/cZ0H2rM0xWQZ7oWFhTk6NcA/g3tNQdUxb8HsG8YdHsIAraWgu++MAQELQqVN/yjerRRV6+ozNdJzeISis4+wmpiWXyjYKKJkW3/d7ujIzquwsLB/zKCln/00SPlNiv6qgdXmGR+5C/0f629nGRO9RWHNPWLfDFvL8ubf8/5DUwKWWKSMR5iscUZGhqOzA/wzKEKoOoYOG9K5X3OOsOcL1mcbE1P11y4WbgyWNDijWn04d+FD7XmecBzhBJQoy3BPSEmvXUpwdGQnlZ+f36FV16QL2i7KyQau5Eju4hBpk0ivER28xzbz6G/m9BvT37mpPkQROtpnWp4pKUkX7yUMMbIaudhTKq3o3F0AJ4SnT0CVsnLdj9WP1pUWeh3MWaC1FPiJa4VJmzf1iGU5Y4bh1s3iQ608h3X1eTdOtSKheH9K6iNH53VSk8ZNa2J6M97ya5C04Ya0cf0DPqsh+98zlYIkDZp7xO7O/pCmmIZuvSI9h/+Wt2xw4OLrRbvyLA/q16/vwOQALwBFCFVNYJB/Yn6ylHHv5jutgVuv0uUh0iYtPYfszf6YJ3xXnynF5sz79687MKfTUqlUt689GCJ/P45beblwawvPwU+2oJWQlg4M+GJD+rgIeVSgpB5NCRJKDtaWdyjxu4t7QkGlg12jUKUYDIb87GIDr6kmi3yyBa0ElDg2YMG1oh16Vh3tO81iwBUUZbhy5UqwoFmW4Z6OLUrUnGrhOajMaUJaWlvR8ZEuXkCJKEJStFfu6U4s/f5LO6cFeHkoQqhSMjIyFJRSSEnaeI0scwJDiRq497qvPeMu8KcILv0uQ2FhIWUW782e18itFyG8kJKUNzNQXC/flFRgStexxTpWVateePfueAwhVD4oQqhSOI4rNhTQhJExXuXNCRTXS9Fd5ngLTQQsy9ozXqVgNpuv5e9p7hEb6fl6xZvMDCW08KZrxbs43sIyxj0HcRYuVEooQqhSQkJCtCYVIRX9AhfQIpU5bU3aG2bOMOntKXbLVilcunT509mLZbR3fbceQlpi5vR8+bcyLzClsbw5Q58gYdyjunUIDg62Z1SAVwVFCFWKTCbz8PKw8GaWN5c3p8CUXl/RbWzYz7UV7X/9eUd2drY9Ezozg8Ewetj4vrKvjZzGTeDze/63Utr9kfZiOdP5G+r9Cer90b7/ogj15ptv2jMqwCuEIoSq5q2Jo4S0JFFzqrwJt0uO1pJ3EFCiPv4fh0gbxfYbbM94zmz3rt1hbAd3gT9F0fc1ZxM1cXXcOp/KX65lC56dfFq1SsOqOniNzzcl67jC2NhY+wcGeCVQhFDVjBs3lggtcaqVJZYynsF7o3ifG+PjI6pm/bGH7/t3/riPG3BbHT8UF0a3IYSIafnRvMXNPGK7KCf39Hv/14ypN9WHzJzeOi3X+Gh75sxc40MxJavn1vV68e4+/V5j8NAJqLRQhFDV+Pr6dujcOkjSYF3amzfVhyy80bq82Jx1JHdxoiYuxn9O6WRPYRBF6J7RrxUUlLHR42ryclRSxiO+cFOeMYkiTEfvcYSQMGmzkSE/FpozNj2esiZt1JrUkWcLVhWaH7f0HOopDN6cMYVl9Gt/XuHo7AAvDhfUQxX0y+b11QLq+oiqJ2pOncr/UcK4M4SRC5RN3fvV84smhHpysphW/HH+z0Z1Wuw/tqtFi+aOyuwMvJUeh+O/DJE2jlBEadgChhJZl8sYryjlO1HKd0pnnlat/FNzMtf00FdUveugHgqFwkGRAV4BbBFCFeTh4RHRsIa/pHaRObOpe78O3m+ND988Ivi7em7dnmpBQghDCQYGfslqhEP6jSp9aphr0ujVwZJGnZWTco2P5IxnBTNljOefmhNDg74Jljfs3ruzvQIC2ASKEKqmiLp1dJYipShcZU69pzlZ3rQc4wMvUai/uE5Lz6HSkpAlC5fZM6RT0ev1p38/30k5nhCi54pLLHkVTC4yZ1KEMXDFadS5vv362isjgE2gCKFqatSkYY7xvkLgU2BK07PFjw23np3D8ezh3C/9xbVY3tzILabYnHVw3xH7R3UGq1euCw2q4UGH0JTgSO5Cljflm1LU5ux7mhPxhZuuFm3PMNx88oLCJF28gBIeyvt8/ZaVMpnMgckBXh6KEKqmUaNGqi25ReasQEm9/gGfHslZdKfk2JMX2qstORszJurZ4jTdjbVpY4osmRzh8rLzXfAM0i8+Xbx47k9CvZePqPqGtHFZhsS+/vODxPU3PZ78QHO22JJdaM64VrRzbdqYNP0fhJCb6kOhkqYBkrqDR/Xt2rWLo+MDvCycLANVU0BAgIe7R2bJ7YZuMe4C/1EhP215/K841QpvYaiIlpVY8ihCdVROCJY0+FMTF1+4aUfm+xbezOktTetF/rprQ4MGDRz9Dezk/v37P6/YptMbXwv49+7MuaHSpv0DPtmROdvE60WUTMp4yBkvPafON6X4iqr9nv9tsKRhtiFxRPB3ZwvWSKXl3oYUoBJBEUKVNXzkkA0rNxdbsgkhEsa9oVuvi0W/SGhFG+83PAVBUsbDOq2Je5/a8g6bMiZaeHNLj6F3U357LXrgsdP7IyIiHBrfTlYtX+9mqCYS6ndlzqYI3dd/3k31kRzjgw7ebzX16PfkuUV3So7nFW68rzkzIXyLiJYZWDXPuTkwOcCrgl2jUGWNmzjGVxmUpr9uvd2aiJHRRNAv4NNAcb3SFuQJpzKlqi050T7v8jzXwXvsgIAF+iJu3KiJDs1uP/Hnr6RrE/RsUS15hwZuPWhKcKFgQ1vvN5p69H/qDNsGbj3aer2hYHxSdVcJIVnGPyUiuYNSA7xKKEKosho0aNArthNFSFz+T4SQx4ZbEYrODCW0jho5zbHcJatTR55RrbpatP1C4Uae8HdLfvMT12rmEZv2KDstLc2h8e0kLz+HJeahQV8/1J7zF9fheIuJ1zX3KPt+aQ3ceph5Q4b+ZoY+gSZMnbo17JwWwBawaxSqsmU/LNn0y5bbmmMW3qixFNRRdLIuL7Hkbcuc0cZrVA+/mdR//xwsseTuzZ53Kv/HEGkTrVGbkJAQFhbmuOx2UlRQ3N77rceG2x7CII6w+aZUf3EEVf6fyGHSZjmmB0m58V7eXl1wpgxUCdgihKrMZDIJGGF9Rfcs459Juks8/9fTBw/kfNrDd2ZDt17W3/gmTsfxrJvAb1TIT16i0GBJAyGR7ti6x6HZ7cRs4kKkTQrM6b6iGpmG2wZOLavwUnop45FrfNTCc0h4Xb/atWvbLSeA7WCLEKqy48d/a+DRrVhdLKbkvqLqqfo/mnnEZhsTxbQiTNosx3j/fMH6fFOKlHE3cXqK0E3c+3T3nX4qf/n4sE3bDr67b+/+/gP6OfpL2NDK5as5CyuipYTwhebHKlNKY/e+ZT5rolSRObO6rOVFzdrE7TftlhPAplCEUJXduHrTh61nYu4WW7KVomrp+j8MrDpNd72WvMNN9cE/ivf28J0ZKKlnnWzkNBcLNh7P/UbLqmiK6ef7+b8mTqnCRXjr1q2lC1ZwhC02Z4toWZbhTnOPgafyfyg0Z7K8qfRGo0/iCZeiu8JQgq69uvj4+Ng/M4AtYNcoVGU6rUFAicOkzVjepBAo/cV1dmTNKmHzTJzuj+J9I0OWl7YgIURMKzr7TGro3svMGVjeJGe8iUbxxx9/ODC/TX364SKhRmnm9PuzP7levKe2vIOWLSgwZRg5zYm878p8y4WCX1hirufd5bUB3e2cFsB2sEUIVVn12qG3+eSm8tjjed+4W/zV5uxmHgPPF673EAT09Jsl+P8bPQauRM+q68g7XSr8VcsWugv8fYU1t/y6tVmzZo7Kbzs8zx8/eoznqUBJ/djAL+SMd+nQA+3ZIzmLzMTYw+c9IS21LrTwpvMF69P1N4SUKE98Y9jwlQ4KDvDqoQihKovp3es/nw9tTg0ZGPjF1sfTe/nNPqNa2Vk58YxqdYD4f9fLJ6gPXircxFBCKeNp5DRmTr8zc/bw4G+NnPbPO/cdmN92GtRqFiBuYOGMI0N+eOoc0dryjm7Bftsz31unv+UlDJYz3nquqMCU0ci99+vBP6xIHfzTuv/g/qJQlaAIoSoLCwtrHdU44dzuJpKB3X3fO61aES5pcTL/hycuqGd/SX/HXeD/RsjK0oVGTnsq/8fVqa8ztDj1hO7QwcOv9entuC/x6v34w3KS50sRcw/f98q8UiJAHBEmbZFluNtJ+TbPcxLGzUsYYh2SMd55ebn2zQtgWzhGCFXcinXfG2vePKVbGiZtGu0z7a7mOE0Y62//HOODH5NjfcQ1YgO/KG1BQoiYlvfy+6CpRz+ON9eV9Zgyes6pU3EO+wKvGs/zn8z9UkK75Rjv+4lrlTctQt4pUFL/tGpFoKReaQsSQoycJvlhql2SAtgJihCqOLFYfOTEvsmL+17yXHw4d6GFNwtoMUWoHOP9/dmfCGlpD58ZZb6xo/cEmjB/an7v4/nZ22OmVvanUlgslu++/bFxRKuagQ2NBqO/uJaM8apgvkLgI2M8Ckzp90pOlC4sMmcKKYnJaLZ9XgD7sUcR7t27NzcX+1LAYSiKGvH68AvXTjVsUq+xR58Gbj3MnGF/zqdRyre9hWFCWppnenQo54s1aaPWpI1akzpyX/bHmYY7NMUESxpKGI+Ted/TRV4JCQmO/h4vTqPRRLXpdmDp7RhuqVwfXkfesZXncMJzFbxFxxZJaY86iqgT+d+ZeQMhhCfcsdwlfpKatXBnNahabFuEW7duXbJkyfjx43Nycmy6IoDn8e/5M1N1V90Efm28R3G8RS5Qugl8rxfvPpTzZQO3nuPCfhkftml8+KZWnsPiVD+dVa1RisJ0loLHhjslZlVcXJyj47+4MSPG+2RHabSaDenjU3XXopQTCSEKgY/KVO5OziTdpRBpYxnjGSCJeKA5p7bk7MicFSxtqJYk9ezZw47ZAWzOtkU4fPjwWbNmubnhWS3gFHq/1tsnTJahv+kjqh4qbSqi5SpTSqIm7o2QFdVkLf972ggVJGnwevD3Beb0bENi/8AFrbyGlVjyvlywyMHpX9SDBw/uXE26nL8tSFK/jryjmJYrBD6EEDEt/y1v2ZMPKy6lMqVmGe5Wk7XUWPKDJY1Oq1ZsSB/b0nMIJbIMer0fLqWHKgbHCMG1xJ3//ZHuooFV04RRCsPyTSl9/D8qfSTFE6gYv9mZxnuB4oh2XmNaeQw1qLnqgXXfHPl2pXsqxcH9h1UFeQMCF9RTdHuoPf/fSwP5fFOKUhS+O+tDE6d7cv5jw63dWXP7+M+jCJWquxYkqe8tDA2XtrzL7Q/uaFm45DOHfAsA23GKyydyc3Pv3r3Lsn/dEFkgEEyePFkqlTo2FVRJvr6+tJCcK1gnouV6Tu0u9HcT+JU5U0TLQqSNiizZUsaznfeYa8U7hylWpZ+52bXta6s2fte18jx44cRvpyIUXXxFNXONj/zEtfNMj3jC6Vm1XKDs7jtjX/bHq1JHhEmbeolCzZzhseGWiJYPCVriKQy+VrQzTNZMzxZnG/9khPSOXzZHR3d19LcBePWcoggtFotery8sLLT+KBAISksR4NWyWCw0JyiwpAsoUZ4pyV9U0fMTAsQRxeasQHFdmhL4iGrc1Zxs6t4vgK379hvvnr9+wt/f326xX0ZmWm4LxRBCiIErljGeQZIGSdp4X3FNISUmhPQLmL8n698URXsLwyS0orXX63LGm+MtFwp+uV1y+K2wDbsz55o5U5tWHdGCUFU5RREGBQU1a9bs/fffd3QQqPrS0tJowhBCMZToQsF6N6bszUErljeX3oZNwrgVmzMJIXLGuxk1+qsvvln63WJ7JH5p+Xl57h7+hBAp46llC6rJWh7L+3pM6Bq1JZcQQhF6YODCq0U7LhRs8BKFKrTn9WxRnumRnPHp5P32Y/2tLOOfQkY84s3Bjv4eALbiFEUIYDc6nY7nOTnjRQhv5LRF5oouisgy3Gvq3t/6WmNRlR5Lqy3rdPDouzbP+iqcPHlSXaw1ummltPt97ZlH2ovJuksCSrIqdbiAEqtMqUpROCFUS8+hLT2HFpjTdZYCCePhLQpZmzqmyPL4Yt4mhhIyQvLG6JGO/ioAtmKPIuR5nuMqumIJwG4CAwNZwgkI5SkM0bFFCkaZortSTdbq2Zl5piQNq0rRX/UwB/hL6mhZVa6xwMCqJYy7gBLpNHr7h/+nWJYdOmCkgvFJLDl1Xb2H8HwdRZSM8VCZUrKNiQGi2odyPh8Z8mPpE5e8haHewlBCyKn8H7VswfXivTzPUpTwm+ULGYZx6FcBsCHbnjV64sSJTz/9VCQSffvtt6tWrbLpugCeh1KpVLhJeMI39xyotajyzamHcxflmR49Na3Ykr0j8/0wSTMzp7+vPbMudbSbwJemBNuzZqXrE9L1N3Lzck6fPu2Qr/CcOI6L7TPUjQ1t5Tn0YuGmdl5jJlff3dd/XrTPtKFB30yptsdfUtfCmzdmTEzX3yh9V7Ele0/2h7fUhwnhTZxOyEhCawaNGTPGgV8EwNYoZ7hx1KxZs/z9/XGMEOxj185do4ePbe897rp6j4Uz1pK1u689W10W2dCtp0Lgo2OLEjVxafrrr/l/FCxpaH0Lx1tO5H93W33MWxRaYsnzFdcoseRp2PyPFsz6YPYsx36dZ/E8f+rUqX/PnM+k1Yj2mbYtc0YDRY+G7jFPzrHwprslx68W7WR5EyHEwGkkjMLAlkhot0BJvWTdZUJ4M28QUpJvVy9EEULVhmOE4HIGDR40v/aCq492yBkvEy3s7jujj9UxeAAAIABJREFUt//cH1MGyhkvHVuYor/W0Xt8D9+ZNPW/nYE0Jeju+16xJTtM2rKBW7c9WR8a2BJ/UcRXH/9AEXrW7JkO/DpPuX///tD+o/IzSowm46Rqy3KND1Wm1CdbUMcW3is5daVoSwP3Xr395yoYpZYtTNJdvKU+0tC9V5L2Un237o+0FwnhxZSbWKDQ6yrBTmCAl4EiBFe0bdevkY07csQSKml6tWhnW+9REtqtm+/0JN0lMa1o5jGgzHf18J25Lu3NQlPq4MCvtmROq+/W7Y766Bfzlo4YOSwkJKTMt9hZRkZG7+hYTZ5Fzng19OyUrk/YlTnbTei3OvV1itAsMZs5A0UxNKFGhfxUegGlQuDjL67dxL3f1sf/0lhUKbqr1WQtacI81F3wFgcFhwQ79ksB2BruLAOuKCUlRS7ycBcEpOqvxRdtStZdsS5P1V0Nl7Us713uAn+aog2c5tfHU6OUE8+q1g4I/NxPVDOmez97Bf8b0yfNMuTTrTyHlVjyPQQBu7M/kgm8uvlOnxC+eXz4pnfCt40OXcUQJjbwy2dvIyBjPGMDv6AIua85XWTObOk5lCaMSVDQsWNHh3wXALtBEYIrKikpaejZq9icxROeJ+yJ/O/0bBHHs0m6eAld0a1xrZfhyxivW+pDAkp4JHdRH/+PUx6mZ2Rk2C18eXQ63fmzl/RscWP33kau5GjuEgXj/VbYhnBpC0Io6xwRJZUyngHiiDI/wUsY4ib0lzKegZJ6/uI6HGEj27fw9PS045cAcAAUIbiigIAATqx9K3Q9IYQiTA1ZG19xrQT1ASOr07B5FbxRSElpQueaHmYa7tGU0MhpLbxBwSjbt+riwGuEbt269e7E91o1aa/RqBUC3x+SYwkhPGH7+n8spCRPziw0Z/iKKnqIUqi0iY4t7OYzXcsWUIT6atkXto0O4ARQhOCKIiMj04zX3IS+74RvJYS/rT4S4zf7ZP73XqLgOyW/lfeubGOiUhQe4z/XQxBo5EpKLDk8zx3JXUQRuiC/aPTrY+35Faw4jnt34ozXYyZl7PPOSS0U0KIAcZ2xYeun1TjEUMJnH0DPEfbJk4CeRRGqjddImmIeas9L5KLq1avbMj6AU0ARgiuSSqWDh/e/pvtVxnjVkUexxHxatdJPVIsmQo0lP13/x7Nv4XjLb3nftPV6I1Bc18RpvUVhEsYjVNo0SjmxhedgKe1+cNfRb5Z+Y+cvMmv63OPbruTn598qOWzmDd19Z/b0m+Um8NOyBXLG+9n5nsLgCp5BSAjJMd4PkTYxcCVnClZ/v2KpzYIDOBEUIbiozxfP5+s8Oqv/QUCLZYyXypRabMkycup2Xm/sy55/U32QJ//b1Vlkfrw1c3o9RXSgpD4hhKYEkZ7DLJwxQX1Axng18xgwsdr2Bu695s9dNLDvMLvtI3306NH61RuDmMZvhq1r7/VWsLRRXUUXQvirRTvWp42zPlb+KXLG28wbisyZZX6gji0sNmdThF6f9uag4X2GDR9m428A4BRQhOCihELh4d/3DvmgdYbgooU3xfjNoSlRkSXbzBsbuPW8rz27OvX1nVmz92d/8nP6hIM5n7f3fqul51Dre3nCKUXVA8R1RLT8QI71+XxUtM80pbDasSPHfT0CNm3cZIevMOHNKZFuIzsqxwspyfXi3S08BhLCH8hZ8FB7XsF48TzheMtTb9GxRTq2eF/2x8/WJMubd2TOMnKazZmTftq8ZN0vq+3wFQCcAYoQXBdFUZOnvhN/7QzLmyW0gqEEbgLlrZIj4bIWbozPhPBfo32mtfEaPTx42aiQn8KlLazvKjI/tj6oKN+UIqTEJZbsFN1l61An5QR/cYScDZr7zuKuHXpqNBrbhddqtXdvJLbyGkYIMbIlafo/vISht0uOWTijypwspKV6ruhq0Y6n3nUi/7ton6ktPYf+nD7hXsnvZk5PCLHwxvvaM2vTxrC8WUwrukR3GjwYz5oAF4IL6sHVhYaGunlJrhbvaOzem6GEFwp+JoRLN9wsNmd7Ccu4lvxQzhe5pkc7M2fTlCDaZ1q6PmFf9qfvVNsqod0CxfU4YvET1a4pb6t/WBTVttuVhAs0bZM/Ny9dulRNGpljfBBfuCnp/9q7z/goqrUB4GfK9pbsbjbZZFMogdAJIRGBKxAQpHcITSAIgoLK9aoIKtj1IiA1gDSRJr1JbyEYSui9hIT0Tbb3NjvzfliMMQl5vWpYwj7/T7M7Z2aePb+Zfaadc+znCEReMe28ZTnqYZzNRN3bSAZJyLCfCqdIWVENhR18i7hpu8b1sG/oJwihSF6ri8Zt542bKMZNIJaK13Jo+H+3F7+fFDRCKPF/UxAAniZIhACg3Qe2dWrXvYvsjWxb5rioNWvyx7YWD9he/H6/sDkVX7y0ew2biqYhhF4MHiMmw1y05axhvYO2vCgdvadk9vCI+STG9tKedsGjjmkXDgufV1aUPfujTz//6tPaiPnOnTsFlhsl1uy2QUNzbOfErNBgViSBsTrKUgsd13erP0YIMQx9XLfwrHF9a3G/IFZEgfOagvN4IGIxGZosn1ppnUpuEx4hdtthrBgQWCARAoASEhI+/mzG5x9/Q2JsxDBsnH/XesLpNW8pelvMCqvHT+TgIq07J9v268sh7zQTvVK+YLxkoMb9cE/JJyycV+q6T2AsCStMyo7SuHNMnpIO0tQlC8a0atMiLCwsISGBx+P9/VBLSko+ev/T9JMZLiNOYlwXbT2lS3tR+qrLaz2jXy1lR183/dJZPrk3bxaGcAbRhY7rJ7RL71pPGT3FHFwQzm1Ww8q5uIhi3IzL9ffjBKAOgdEnAHhMJg612Zx8QixlR/UIeW+3+qOeig+OaRdaKI2cVU9AykLY9V8IHlF1QY374fbiDwiczSeC2RiXZrylrntcQoIQ4/SaY0RtgiSyQufVnv26zf3+67+TDq9cuTKs75hoT3Kx47bBXYhhuJu2YxhB01Q0PyHHdhbHSC4hYhivhBWu4rbkERI5Oyac22yX+iM5K8boKWbh3N6hs560/l0ls6L5CY2HeeYt+vYvBwlAnQNXhAA8xuPxIrGk+9aMEuet29YjEdzmHsb5qmrlxsKpLyve2Vr8n16KD6ouRTNeN23HEG50F1Gkm0eIeitm+W6oumjrBeOWW+YjOeYsAmOtX7N5w/pNk9+c+NGcD4VC4f8ant1uTxkwVm6Pz3dfSZZPK79nW+S4sVM9k0dIJkVv5hLi25Zjv+pX84lgNs6nGeqG5eChsv8mBo04o/9BzmlQ5symmerb1FOMW+PO4QcRM1Nm/q+xAVCnQSIE4LGkxKRrJwtelI6+aNp2ybi9m/zty8ad0bwEJ23m4EIOxsexysfLDfOBc4YN4dymrSX93LSjwHHVRdvcjB0hlGe/dEQzL14ycIRqkYQMs3uNufbzp3TL9648t3dXx8On9kVGRtYcz+nTp1csXnv92g2v1xsdFS0PD+JbomxefUrE9+V9hyKEzhs3JcunNhP1QAhdNG7NsZ9/NfIHDv57onXR1j3q2TxcrHbeaSXum2n4saO0mk5w0nUrVPzmQQ3c7du3/8t1CEBdBIkQgMc+/frjfyV2T+a/edG4vafi/QNl38pYUZdNO3CMdHrNJM6tVD5Dv0rryh0buYqN88u/NHlKdqpntRT3uWraPVK1pLx7Fz4R1EzUI07YZWPR1BjziwN6Dcu69qvT6dy8acvhfSf1Or1MLnulX3LKiOE8Hs/j8YwZnpqdpWuJDe3PfQMnCX1ewa7zM72059XIHypmQY07x+Y1+LKg0VN0w3JorGplpYTNwYWDld+szh/TWNjZRKk9tPOw5rsusjfKw3bSlnTt8iLPteg4xbY9e2ujbgF4lkEiBOCxFi1ajB4/dMOq9/ik7Lb1OJcQSlhhZ/RrSYyjdT+yUbqKhUtcd/Ptl0eqlmB/bIwrYSlHhH+/Im9EP+Wcqp2cERh7qPK7H/JHRnqafzhj5tYNu2PRy/XYvSOIIFuhYculzK/nzFu9IW3hd0utF8N6iaaXLyhlRTIMLSJDKg2OcdN8qKW4j2/6mnlfu6CRVS9bEUIExuoofe2hLdPsKR2o/HJV/pjblqMSMkxABFsorZXWKsNDP//kg1fHjiaImnoiBeC5BA3qAfjdkrRFstAghqEeWDNC2A007tzBym9wjLxm3kcxLjNVWl7ysnHHS7JJWHVHEJcQtw0aurP4w32lnxc6r1eayyPESk6cxaNLW7CmvufldqJxoZxGIlIRxmn8An98b9Z3A7uP+vXo5STRGF95m1d/XLtoVf5oC6UVs5SV1lbkvBHMCvdNFztvR/HbPOmnRfPbmCl1A0H7bNsZDCHEMCaq+JHjYj3BC98t/vLeoxvjU8dCFgSBCRIhAH+w/ufVIrGAhfMsntLuIdO3F3/QRjKAxDhehjpY9m15B6Rq1z0Vr+WTVlJf0I5DCO9ZT+0onrGhcIqXcVecG8Ft3kz0ypv1dlu92hWPUk5oF+fZL/lmiciQztI3bJR+Vd6o73N6fJ/zyg95o8LYjVIjfxwaPpdArEobcnltHvpxZ2ku2srFn/gODhcXuRmnkJTftBzCMVZyyDQS40Tx4jWs67169/wLFQXAcwNujQLwBx06tF+w6vMRg8excT6JcdpIBgoIaf+wOT8WTOTiwi1F73QPeVfGjmYQU+3loA8PF3MwQUf5eBp5H9kvLn008JWQ9xsJO/nm0og+pV12xbTLy7gdtPm25dhd60mKdkfwmxU6bjAMjSHcQZswRDLI20E6Lk7UJcd+rsR1J99xqdKGMAwvcF5vIGiPEBIQMjOlqbY3HISQhSoTEjI7padodwSnqYwd7WUoJbdp095clUr1D1UeAHUSJEIAKuvTr3fjZg0UJZ0PlH4VJ+pmowwExk6JWLgqf1RH6YQjmnk2r8HhNXgZN4Gxq12DldKoeC1bS/ojhNpIBjm85u0l75mpUl+33SXO2zTjddE2AiMTJIOFZIjda7pm3lvkuMXBhS9JJzUWdiIwFkLITJWe1q1ckjtAxqlncBdiCMt3XInixSOEPLTjof0cgZHXzPskpDJO2KUeP/GB9XRSdS0dEUL3rOn1+Il3rMf7h316XLuYhXHZOF/Yumzx8u21VY8A1BFwaxSAaowaO/y+LaNP2Cc3LYfuWI8ihM4Zfmoq6n7TfEhMhnWRT4nkJWTbMp+0+H1bRsxvnXQjhHiEeETEonOGjZuKpqXrlhc6b/DJ4GCWioXx71pPMIipx0/g4iIeLh6jWt5U1M2XBRFCYjK0bdCwYHak1pVDYCSJsfeoZ5e6Hlw17VlXMEHtutsueHRPxQcWqnRj4Rt2r+GaeV/FB5nlzFRplvFnKTuKhfGk7Ci9pyCYpSLZ+L7DO1msyrdbAQg0cEUIQDVSU1M/n/Wd1p07KWrTotw+D2ynH9kvpUatRQjl2rMe2s66vZYT2sX1+EkV20746Nx5OfbznWWTK35JYpyO0tRLph1lroeTojeXv1DqpC0nNIvTdWk4RvDwoF0lH/EIcTSvTQtxbw4uOG/YdN+WniyfquK28LWauGs9ualoajin6bioNSzscYuORoKXfJeqPEKyqWhaj5B36/FfKN90rv38Ec0CHCOPaxYNj1hQ7LwlIcNwjAySi+HtGAAQJEIAqiWRSEaOGbpj/Y+FjhtR3FZ71Z+9GDzal4rq8ZPq8ZMQQnesxzcVTesdOjOE3aB8wYe2zBPaJQOUn1dtxhDFb5Nl/Hlo+NyKX3JxUa/QmUJdSLbtjMNr8uLCDtLx+Y4rPxa81kSUXOK8O1qVVvFhpJLbJIgMHxr+XaXeYXCMeEXx/sbCN2WsqDO6NUc1C4JZkQghg6fQTdvGRK78qWDSyMgfaIb6pfTLQcqvr5p3DR0/4J+uNgDqJEiEAFRvwZK5j3LzbmfmOL1mCRkqZUdVKtBE2DWIFXFMs9BJW/hEEMMwGvfDGH5iSsRCERlSXkzvKbhk3F7guOZl3E7afFy7uK1kiOS3hhAMou9aTujcj8xUGZ8IMnqKtha9+6J0NIPoi8btr0VtcNOOEucdB20SkSFKTtxN86GEoCHV9pGGEOokm3TVvHdo+Hc047V4NYhhMg3rwziNxaSCgwuzDFvv29J7hX5o9+pz+Ie2ffTEW7sABBRIhABUj8Vi7Tu8s3+fQSeP5GAI8zKeqmWUnLiByi9/KpzcWNBZxWt5sOy/3eRv8whJeYHLpp3XTPs6yiZ0kb9JYmwP43xoy9xQ9GYwGd5a0i+MG7e/9Aslp0lHWeoA9ucMw6hdd8/o12Tq17NxAY4RPxa8hmF4DC9BwlLm2i8ccHxNI2qIcm7VSHzCuc2PaOYjhHCM4OCCk9qlXsYTL+lf4Lhm9xrdjK2rfNoFwxZ2lPH44QMCgaA26g2AOgdGnwCgJhaLJVIR66ZcLUW9uoe8W7XAMc33oZxGLcS9EEK/6tcKCKnvZVGE0ANbxmXjziHh/y1/+cWHYlybCqdxcEGx69bLIf9uXmFcJ58rpt23LUcctKWjNDVO2KX8e5rxntGvKXBcHRGxsNoeZBBCi3P71ue3M1OlNq8hKWh4S3EfBtFr8sezca7Ta7V4y2hE29x6DMOqXRyAAARvjQJQE5FI9NW82TTteWDNcNHWSnMZROfYzzcXP85kbSSDs4w/27x638fTuh/6hn1SKQsihEiM01/5mZO2DFXOPW/YRDGVx/+LlwywUNrOsskVsyBCCMeIl2QTI3mtLhi3VBut3WsQkSFtJIP6hH78WtRPLcV9KMa1s+TDBoJ2Y1QrEoOGkRiHz+dCFgSgIkiEAPw/3nhjikgkRAjtKPnQRdsqzrJ7jXwiuPxlFh4hbhc8Zm3++DzHJZ07L4il5BPB1a5TQoaxcJ6EFd5S3PuCYXOluUWOGwTGaijoUO2yHaTjbpgPIFTNvZyblkMWSnvRtPW+7fRV894jmvmr8kYXOK4lBY1ACDlpG8MwjeNi/8cKAOA5B4kQgP/f6NFjQjmNRIRsXcH407ofCh3XNO6H922nd6lnMYy3vJiZKj1n+OkVxQdXTXs2F70lJsNqWKeMHW30FLcS971nTa/4/Rn96n2ln8UKOz5pQQJjC0iZhdJU+t7oKc7Ur58UvSleMlDvLrhk3B4r6NhD8Z6MHZ1rv4AQyrWf9zKu+FYJ1a0VgMBVuy/L0DR98eJFDofTqlWrWt0QALWK9jJtJIOumfcpOU0e2jIvm3ayMDaDkJgVavH+npBOapd1C3mnHj+poaD9Q9vZR46smtbJeHGMYON8L6LKv7xg2GzyqF+UjnHT9hqWJTH2ZdOOjtLXfPddfa+eZuhX83AxBxequC1V3JZrC1LDuU0vGrfLWDEWqkzvKVC77jYTd6/fKPpv1wcAz5VavCI0mUxjx44Vi8Uej+eNN95wuSo/CAGgrqgXG2Wh1QOVX8YJk9k4l0YUxXhGRiz29Xmtc+chhCjGrXXn+poYIoRCOPXLXNk1rFPjeihl/aFJhou2Xjfv76n4gE9IbZShhmW9jMdDu9cWjF+TP25dQeoPeaMKnTeSgoY3rHAdqeTEqV33L5t2yNj1PLRzU+HUrvJpHqGuR8+X/3pFAPA8qsVEOH/+/K5du8bFxbVt27ZBgwarVq2qvW0BUKt69+35EDuBEGok7DRKlRZEKgmMJWIpxkWtxjBsX+lnFOM2eopl7N8vtsRkKMW49O78aleodt1j43weIaYYF/7bYfjQdjZOlIxjZCS3ZY79/JOCcdN2G6XrFvL2a1EbxkWuGq1aPil6c3vp2Czj1nbBo8qLsXDuSe0yAmMXOa+dM2warPxKwAoKbchv1qzZP1MpADwvajER7tmzJzb28WP5mJiYvXth5GtQV8XGxjZLirlmebwPt5eO4+DCR/aLLIxLIk4Iu/7a/HGFjmvkH/vg7iZ/e7f6k/KXSMuZqdL9pV90C3kbIXTDfNA3dgRCSO8pkLPrI4S4hDiU0/Cm5VC1wRzVLghiq3z3TnGMJDDygS1jU+HU7op3Kw4FXOK8o+K1kLJUj+wXuyveeeS4dFu0ceP2tf9MjQDwHKnFZ4QPHjwQCh+PjiYUCrOzn3ibyOPx6HS6nJwc30eCIKKj4TEGeLas3biyc/vuJfl3X5JOjBMmFziun9AubsBvp+A0sHsNL8lev2s9Xuq6X3ERJbdpsnzqxsI3mwiTGwk78Ylgq1f3wJpxz3qyZ+gMObtemethhu4HFa91vuNyCLuBh3Zg6HHDhpdD/r256C2X1xovGVjej4ybtp/SLntgzZCQ4Wvyx1KMS0DIPIwzihc/PGKBmAwt37STtji8JrvXUObOJnHOCd3SlJFD9i1P53K5T63GAKgraqtBPUVRLBbrzp07cXFxCKGMjIz+/fvr9ZVPjX169uyZlZUlkTzuj4PL5Z46dSokJKTawgD4C03TH8+anbZwNe5l44iwevUKdoPO8sl71Z+Oi1wtJOXrCyb1D/tU8sdx5D2M85bl8EXjNpfXKufUjxN2aS7qYaY0tyyH7lnTB4R9bqZKT2mXOWgLQqiZ6OVOsskIIQbR+Y6rmfp1GldOEDs8hF3fSumKnDdbiHveMh9uFzyaxLk3zQeD2RF9Q2dXiZTZo57tpT35zqsCQurEDDt+2Zyc3KVKMQAAQrXaswyPx8vMzIyPj0cIHTp0aNKkSfn51T8vgZ5lQB3idrt37ti5a+v+A4d+IbxcmqHZOH+I8lsFp2Ge/dIZ/eqUiO+rjFPI7Cv9XEjIHbQpz37RSVsFhDSC2zya10bryXtgPd1c3LNd8Ci9J39DwZRmoh4IYbn2C0puXAS3BYmxde68e7ZTwaQqmK0KYikRwqyU5pbl6MToTb/q15Y47/RQvCdnx/i2pHHnHCmbp3PneRFVX/CCnnm4af+Kf/3rX0+9ngCoM2rx1qhSqTSbzb5pq9WqVCprLg9AncBms1NGpKSMSDlw4MDogZPbBg14ZLuYbctUcBpG8xOMVPH6wsmdZa/H8BMxhCPEFDtvHyqbK2WpOodOxhC+W/0JFxc2EnY6oV2CY2Q9ftKLwWOcXsvqvFcdtKm+oJ3Vq9O4clIivq/Yc3cn+etHNQutlDbfcWV4+AIhKROQ8jP61V3lb+0o+fBg6VcO2oIQ4/CaGUTjGJvE2ARiFbgur9mUBlkQgJrVYiJ85ZVXysrKfNNqtbpr1661ty0Anr6mTZuSJNFY0PmeNf2aeW9rSV8+EdxK3FfFbXneuOm4djFCiGFoF21rKxla6Ly+OLcvGxe4vbYh4f+VsaMJjNVT8QFCSO8uWF84sZN8Sry4n4d2riuYMEa1nEuIK24LQ3j3kOk7SmYoOA2FpAwhlBg0bHXeGI/UgRDdO/QjKTvqvu30wdJv+ERwS3Hv29Zjdq/+46/eGzCwn18qB4A6pBbfGv33v/+9fft2j8djt9uPHDny1ltv1d62AHj6YmJihFLOTcuhEeELhaTsx4LXNO4chJCMHd1L8eFrURuGh8/HMRLD8CzTzwZPoZLTxPdWZxArXOfOC+M08q1nW/F/usinxov7I4Tdt2U0FnaplAXLdZG/af+tfSGG8Bh+Uq49S+8ukLKjPIzzhGYxg2gckVnGn2mGUoaoBg8e9FRqAoC6rRavCBs2bDhv3rxt27ZhGLZq1SqFQlF72wLALzZvX9+jU7/6/BfHqFbctZ7YVTKTxLhKbhyBsYqdtyjGzcJ4A8Lei+A2N3qKnbRVTCp2lHzoYVwexkliHISQ3pPvYVytxL19Kyxx3mn4W2uKqqSsyIod2YhJxXnjxsTgFIOncEfJDAJjYQi3erVsnNdWPkD6L01UVOUxFAEAVdVuF2sqlWrkyJG1ugkA/OiFF5LWbExLHTW5meCVBMmQSdFbrJR2p/pDDLH07oKh4XM9jPOKaXcEt0UQK8K3SCSvVbHzZhgnzugpRgjdMh+J4SWg31pNuGk7B695mMDfB46wUBoTpb5g2JyhWyUhlVZaG8mL17iz4xTtbdGXt67ZXUu/GoDnDHS6DcDfMmjwwELtwxZDBFt1077PeWVV/mizR6N358nYMXvUc8K5zexeY8Wm8S1EvS4YtgSxwk1UicNrtnl1AlJWPldIysxU2ZO2xSC64qATeY7LiGFMVAmBsWjk4eIiN2MTh7HHf9r12OmD0GQQgD8JEiEAf5dQKFy+Ypna9GjxyvlcUhjGbUQzlN6TLyRlP+SNbCrqdttyZI96dqHzOsW4JKywEE79vepPXwwefbDsGxGpqHi3sx7/hXvWk0/aUK79Qjj3cQdpj+xZOEaIyJBEyXABIW0k6GTxloY0xu/n3hw3/lUYcRCAP692b40CEFDGjh8tDhJMHDWVT0j4pNTsUdfjJ921nixx3qWR15e6uLg4nNsEIeakNk3BbvDAlmGmNAyifYMaRvJapeuW5zuuRPHiK63cTdtPapcNUn6NEHpkzzqimc8wDIMoNs4P5TQyU2qSh/Yc2g4pEID/FVwRAvBPGjhw4P4TOynMNSpiqZil4BFiGSumoaC9ituKR4i9jCcpOKVX6Mz+YZ/F8BNZBI9i3F7GlWXYWr6GAWFfHNUsuGzaSTO/D89U7Ly1riA1nNv0tuXIhsI3zhk2sjBuDD+BxDklrjtd5VOzbb8O6DdQLpf740cDULfBFSEA/7B27V5o2DjGbjUMC19wsOxrC6VpJe4rY0e7aUeuPeukdtkZ3epgdiTD0Fp3Tgi7gYdwZRrWUYyrXfAoHCOFpGyMavmv+rXLHg3m4EI2zjNTpV7GwyVERk+xldIhxDhpSyQ//rblSNeQt5oJux/RzI/hJ4arahoHGADwJJAIAfjnvffRO99MW9xL8umAsC807pwbpl8uGH+2Uholp0m/sDlHNQs6yyYLCKmQlOc7Lp/QLokVd8ixn80ybgnjxElYYUZPsdp1P4rXWu26Z3aXcHA+QsjtddAkjSHEJcR6dz7DeKdEb9N58rcd9jwDAAANO0lEQVQUvS1lR4skgpeSO/j7dwNQJ9ViX6N/HvQ1Cp4/r706+fphTbJ4Ohvn+77xMp5fyr7ItWWRGIdG1Msh0+OEXRDCGETvUc9mEP0v6UStO9dMlUjZ0RjCD5fNjZcMeDH41Ye2zGvm/ZH8VlmGn+vz29m9xkLnNcQgGnmlrOgu8ikiMuQ4Pvva3QskCae2APzP4LABoFasWr98+bIfvvhkLN8VLmfXt1P6YtcdPi5hEBPFj9e6Ht00H0rXreDiIi/jsXn1XpoqdLzFwQU4RjAMHcFtMSJiUTBLpXE/3F/6BYaIUE7s+Mh1PEKMEFqS249D8Acovwph1y913T/gnPnzvh8hCwLw18CRA0BtmfzGxNenvHby5Mm0JSuvXMrGzC6zqwRDWN/QT0pdD25aDnoYJ8W4ZeyYpqJu9fntHtoyj2oWNBZ2iRV04BFBZo/6nGHjA+tpBSd2QNjnvN/6XStzPcRwPF42IMebftaxVBiK7f9pm2+8MwDAXwCJEIBahGFYcnJycnJy+TdxMa0oxhXKiQ3lxFYq3EDQ3kXbDpX996ppF4MQB+fH8BJHRabJWL/3lOZhnIdMn81dNofFYgmFwvj4d2NiYp7ObwHgeQWJEICnqu/A3ve3nWoq6FHt3JuWgxK2EjGMndEzpNvDNpePWc8g+pEj65xnxVeLZo0ck/IUQwbgOQeJEICn6v2Z01/c1iWSalNxuEGfB9YMg6dYIYxyi0oXfbekT58+x44dn//1kvxH+YjBMRK179ju4GfbGzZs6JfIAXheQSIE4KkKCQn5ccvKcSmvt3GPjeV1wjECIeSirWeN6287Dvbo+/KYCSN69Oju6yCmd+9evXv38nfIADznIBEC8LR16Ng+4+LRL2Z/u/3wa143w2BIJOaPnzVmyptpLBbL39EBEHAgEQLgB2FhYUtWLPB3FAAAhKCvUQAAAAEOEiEAAICABokQAABAQINEWNnRo0dv3Ljh7yjqqjVr1hiNRn9HUScxDLNgATw1/IvUavWmTZv8HUVdlZWVlZGR4e8o/AkSYWW7d+8O8H3i71iyZElubq6/o6iTXC7XzJkz/R1FXXXnzp1Vq1b5O4q66tixYwcPHvR3FP4EiRAAAEBAg0QIAAAgoEEiBAAAENCeiYF5+/Tpc+7cOYlE4u9AEEJIp9Ox2WyRSOTvQOqkoqKikJAQNpvt70DqHoZh8vLyYCiJv8bpdBoMBqVS6e9A6iSTyUTTdHBwsL8DqRWjRo367LPPai7zTCRCvV6v0Wiekc6lHA4Hi8WCMU7/GovFAucQfxnU3t8BtfeXud1uhmE4HI6/A6kVcrlcLBbXXOaZSIQAAACAv8AzQgAAAAENEiEAAICABokQAABAQINECAAAIKBBIgQAABDQoJHA706ePLl06dLWrVtTFKVWq5ctW4bjfzhRoGl64cKFSqXSaDQ2bNiwW7du/gr1GZSfn79z506Px3Pz5s133nknPj6+UoFdu3Zt27atWbNmTqfTZrPNnz/fL3E+I9LS0sRiscPhUCgU/fr1q1pg7dq1OI4zDMPj8YYPH/70I3xmlZaWbt68maKoGzduvP766+3bt69U4NixYytWrGjVqhVFUaWlpUuXLq10IAey9957z+l0qlSq4uLixMTE0aNHVypQUFCwfv36uLi4nJycsWPHKhQKv8T5lEEi/J3BYMAw7OzZs23btv3mm2+qHjzLli3jcrkpKSkIoWHDhoWHhzdt2tQfkT5zaJreuHHjjBkzMAy7dOlScnLytWvXoqKiKpbRaDS+6m3fvv2MGTP8FeqzYPPmzVqtdsqUKQihiRMnyuXySv/mhw4dunHjhu9c4f333xeJRL169fJPrM+e1atXz5gxA8fx+/fvJyYmnjt3rkmTJhULlB/IiYmJX3/9NWTBimw2W1lZmVqtHjZs2NChQyvNZRhmxIgRe/fulUqlZWVlKSkpJ06c8EucTxsDfrNjx468vLwaCsTHx9+9e9c3PW/evFmzZj2VuOqAnJwcHo93/fp138f69esvW7asUpkVK1bYbLanHtqzqFu3bmfOnPFNr1u3bvLkyZUKDB06dN++fb7pvXv3Dhs27KnG9wwzGAxsNjsjI8P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defined", + "output_type": "error", + "traceback": [ + "UndefVarError: `cgrad` not defined", + "", + "Stacktrace:", + " [1] top-level scope", + " @ In[5]:1" + ] } ], "source": [ @@ -20432,7 +269,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ diff --git a/examples/6.-Radial-basis-nonlinear-ensemble-filtering-Lorenz-63.ipynb b/examples/6.-Radial-basis-nonlinear-ensemble-filtering-Lorenz-63.ipynb index c7c8f36..45a1e76 100644 --- a/examples/6.-Radial-basis-nonlinear-ensemble-filtering-Lorenz-63.ipynb +++ b/examples/6.-Radial-basis-nonlinear-ensemble-filtering-Lorenz-63.ipynb @@ -63,9 +63,32 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "LoadError", + "evalue": "ArgumentError: Package Plots not found in current path.\n- Run `import Pkg; Pkg.add(\"Plots\")` to install the Plots package.", + "output_type": "error", + "traceback": [ + "ArgumentError: Package Plots not found in current path.\n- Run `import Pkg; Pkg.add(\"Plots\")` to install the Plots package.", + "", + "Stacktrace:", + " [1] macro expansion", + " @ Base ./loading.jl:1766 [inlined]", + " [2] macro expansion", + " @ Base ./lock.jl:267 [inlined]", + " [3] __require(into::Module, mod::Symbol)", + " @ Base ./loading.jl:1747", + " [4] #invoke_in_world#3", + " @ Base ./essentials.jl:921 [inlined]", + " [5] invoke_in_world", + " @ Base ./essentials.jl:918 [inlined]", + " [6] require(into::Module, mod::Symbol)", + " @ Base ./loading.jl:1740" + ] + } + ], "source": [ "using Plots\n", "default(tickfont = font(\"CMU Serif\", 18), \n", @@ -246,7 +269,7 @@ { "data": { "text/plain": [ - "StateSpace(TransportBasedInference.lorenz63!, h)" + "StateSpace(TransportBasedInference.lorenz63!, TransportBasedInference.var\"#27#28\"(), h)" ] }, "execution_count": 7, @@ -367,19 +390,19 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3×160 view(::Matrix{Float64}, 4:6, :) with eltype Float64:\n", - " 0.0895131 2.78637 0.557767 0.114936 … 1.17065 -0.53973 -0.102151\n", - " 1.06871 -0.441405 -0.400372 1.70552 1.27571 -1.78328 1.18575\n", - " 0.870316 -0.789789 0.103306 0.593899 1.14601 0.107047 1.20067" + " -0.35624 -2.16495 -0.568341 … -0.132918 -1.87719 1.37072\n", + " 1.31935 0.657937 1.71286 1.9795 0.490409 0.627855\n", + " 1.02976 1.2832 0.422462 -0.0538186 1.76381 1.38149" ] }, - "execution_count": 13, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -403,7 +426,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -412,7 +435,7 @@ "Stochastic EnKF with filtered = false\n" ] }, - "execution_count": 14, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -423,26 +446,26 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 28, "metadata": { "scrolled": true }, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "\u001b[32mProgress: 100%|█████████████████████████████████████████| Time: 0:00:30\u001b[39m\n" + " 5.252679 seconds (63.70 M allocations: 4.741 GiB, 5.65% gc time)\n" ] } ], "source": [ - "Xenkf = seqassim(F, data, Tf, model.ϵx, enkf, deepcopy(X0), model.Ny, model.Nx, t0);" + "@time Xenkf = seqassim(F, data, Tf, model.ϵx, enkf, deepcopy(X0), model.Ny, model.Nx, t0);" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -451,7 +474,7 @@ "2500" ] }, - "execution_count": 16, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -470,16 +493,16 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.7658506810276331" + "0.7239704598696597" ] }, - "execution_count": 17, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -497,22 +520,22 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6×160 Matrix{Float64}:\n", - " 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0\n", - " 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", - " 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", - " -13.52 -14.5601 -14.9094 -13.7911 -13.532 -14.0471 -14.1223\n", - " -12.8032 -13.7816 -15.0133 -13.7177 -11.7354 -11.6489 -12.7902\n", - " 34.3182 35.8206 35.5703 34.0879 … 35.4119 36.7478 35.8159" + " 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0\n", + " 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", + " 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", + " 6.20461 5.92675 6.02035 5.40616 6.2207 6.2342 6.41881\n", + " 0.785221 1.54278 0.660531 0.940627 1.0156 1.03415 1.00516\n", + " 30.7855 29.5893 30.6134 29.2763 … 30.6236 30.64 30.9926" ] }, - "execution_count": 18, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -523,7 +546,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -532,7 +555,7 @@ "2500" ] }, - "execution_count": 19, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -551,18 +574,18 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "SparseRadialSMF(var\"#5#6\"(), h, MultiplicativeInflation(1.0), AdditiveInflation(3, [0.0, 0.0, 0.0], [4.0 0.0 0.0; 0.0 4.0 0.0; 0.0 0.0 4.0], [2.0 0.0 0.0; 0.0 2.0 0.0; 0.0 0.0 2.0]), Sparse Radial Map of dimension Nx = 4 and order p = [[-1], [2, 2], [-1, 2, 0], [-1, 2, 2, 0]]\n", + "SparseRadialSMF(var\"#13#14\"(), h, MultiplicativeInflation(1.0), AdditiveInflation(3, [0.0, 0.0, 0.0], [4.0 0.0 0.0; 0.0 4.0 0.0; 0.0 0.0 4.0], [2.0 0.0 0.0; 0.0 2.0 0.0; 0.0 0.0 2.0]), Sparse Radial Map of dimension Nx = 4 and order p = [[-1], [2, 2], [-1, 2, 0], [-1, 2, 2, 0]]\n", " with parameters (γ, λ, δ, κ) = (2.0, 0.0, 1.0e-8, 10.0)\n", ", 3, 3, 0.05, 0.2, [0.0 1.0 1.0; 1.0 0.0 1.0; 1.0 1.0 0.0], [1 2 3; 1 2 3], [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 0.0 … 0.0 0.0], false, true)" ] }, - "execution_count": 20, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -590,35 +613,35 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 34, "metadata": { "scrolled": true }, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "\u001b[32mProgress: 100%|█████████████████████████████████████████| Time: 0:00:25\u001b[39m\n" + " 10.743401 seconds (167.80 M allocations: 13.074 GiB, 7.14% gc time)\n" ] } ], "source": [ - "Xsmf = seqassim(F, data, Tsmf, model.ϵx, smf, deepcopy(Xspin), model.Ny, model.Nx, tspin);" + "@time Xsmf = seqassim(F, data, Tsmf, model.ϵx, smf, deepcopy(Xspin), model.Ny, model.Nx, tspin);" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.5993417830366374" + "0.570433487414718" ] }, - "execution_count": 22, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -629,16 +652,16 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.21741692227468" + "0.21207629449768403" ] }, - "execution_count": 23, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -653,12 +676,13 @@ "metadata": {}, "outputs": [ { - "data": { - "image/png": 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" - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" + "ename": "LoadError", + "evalue": "LoadError: UndefVarError: `@L_str` not defined\nin expression starting at In[24]:5", + "output_type": "error", + "traceback": [ + "LoadError: UndefVarError: `@L_str` not defined\nin expression starting at In[24]:5", + "" + ] } ], "source": [ @@ -681,19 +705,26 @@ "\n", "plt" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Julia 1.6.2", + "display_name": "Julia 1.10.0", "language": "julia", - "name": "julia-1.6" + "name": "julia-1.10" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.6.2" + "version": "1.10.0" } }, "nbformat": 4, diff --git a/notebooks/11 Nonlinear ensemble filtering Lorenz 63 adaptive radial map.ipynb b/notebooks/11 Nonlinear ensemble filtering Lorenz 63 adaptive radial map.ipynb index 2fa73a5..3a64893 100644 --- a/notebooks/11 Nonlinear ensemble filtering Lorenz 63 adaptive radial map.ipynb +++ b/notebooks/11 Nonlinear ensemble filtering Lorenz 63 adaptive radial map.ipynb @@ -45,36 +45,49 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "┌ Info: Precompiling AdaptiveTransportMap [bdf749b0-1400-4207-80d3-e689c0e3f03d]\n", - "└ @ Base loading.jl:1278\n", - "┌ Warning: Type annotations on keyword arguments not currently supported in recipes. Type information has been discarded\n", - "└ @ RecipesBase ~/.julia/packages/RecipesBase/92zOw/src/RecipesBase.jl:116\n", - "┌ Warning: Type annotations on keyword arguments not currently supported in recipes. Type information has been discarded\n", - "└ @ RecipesBase ~/.julia/packages/RecipesBase/92zOw/src/RecipesBase.jl:116\n", - "┌ Warning: Type annotations on keyword arguments not currently supported in recipes. Type information has been discarded\n", - "└ @ RecipesBase ~/.julia/packages/RecipesBase/92zOw/src/RecipesBase.jl:116\n", - "┌ Warning: Type annotations on keyword arguments not currently supported in recipes. Type information has been discarded\n", - "└ @ RecipesBase ~/.julia/packages/RecipesBase/92zOw/src/RecipesBase.jl:116\n", - "┌ Warning: Type annotations on keyword arguments not currently supported in recipes. Type information has been discarded\n", - "└ @ RecipesBase ~/.julia/packages/RecipesBase/92zOw/src/RecipesBase.jl:116\n", - "┌ Warning: Type annotations on keyword arguments not currently supported in recipes. Type information has been discarded\n", - "└ @ RecipesBase ~/.julia/packages/RecipesBase/92zOw/src/RecipesBase.jl:116\n", - "┌ Warning: Type annotations on keyword arguments not currently supported in recipes. Type information has been discarded\n", - "└ @ RecipesBase ~/.julia/packages/RecipesBase/92zOw/src/RecipesBase.jl:116\n" + "\u001b[32m\u001b[1mPrecompiling\u001b[22m\u001b[39m TransportBasedInference\n", + "\u001b[36m\u001b[1m Info\u001b[22m\u001b[39m Given TransportBasedInference was explicitly requested, output will be shown live \u001b[0K\n", + "\u001b[0K\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mType annotations on keyword arguments not currently supported in recipes.\n", + "\u001b[0K\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mType information has been discarded\n", + "\u001b[0K\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ RecipesBase ~/.julia/packages/RecipesBase/BRe07/src/RecipesBase.jl:117\u001b[39m\n", + "\u001b[0K\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mType annotations on keyword arguments not currently supported in recipes.\n", + "\u001b[0K\u001b[33m\u001b[1m│ 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~/.julia/packages/RecipesBase/BRe07/src/RecipesBase.jl:117\u001b[39m\n", + "\u001b[0K\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mType annotations on keyword arguments not currently supported in recipes.\n", + "\u001b[0K\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mType information has been discarded\n", + "\u001b[0K\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ RecipesBase ~/.julia/packages/RecipesBase/BRe07/src/RecipesBase.jl:117\u001b[39m\n", + "\u001b[0K\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mType annotations on keyword arguments not currently supported in recipes.\n", + "\u001b[0K\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mType information has been discarded\n", + "\u001b[0K\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ RecipesBase ~/.julia/packages/RecipesBase/BRe07/src/RecipesBase.jl:117\u001b[39m\n", + "\u001b[0K\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mType annotations on keyword arguments not currently supported in recipes.\n", + "\u001b[0K\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mType information has been discarded\n", + "\u001b[0K\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ RecipesBase ~/.julia/packages/RecipesBase/BRe07/src/RecipesBase.jl:117\u001b[39m\n", + "\u001b[32m ✓ \u001b[39mTransportBasedInference\n", + " 1 dependency successfully precompiled in 12 seconds. 261 already precompiled.\n", + " \u001b[33m1\u001b[39m dependency had output during precompilation:\u001b[33m\n", + "┌ \u001b[39mTransportBasedInference\u001b[33m\n", + "│ \u001b[39m[Output was shown above]\u001b[33m\n", + "└ \u001b[39m\n" ] } ], "source": [ "using Revise\n", "using LinearAlgebra\n", - "using AdaptiveTransportMap\n", + "using TransportBasedInference\n", "using Statistics\n", "using Distributions\n", "using OrdinaryDiffEq" @@ -82,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -98,15 +111,29 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "┌ Info: Precompiling Plots [91a5bcdd-55d7-5caf-9e0b-520d859cae80]\n", - "└ @ Base loading.jl:1278\n" + "ename": "LoadError", + "evalue": "ArgumentError: Package Plots not found in current path.\n- Run `import Pkg; Pkg.add(\"Plots\")` to install the Plots package.", + "output_type": "error", + "traceback": [ + "ArgumentError: Package Plots not found in current path.\n- Run `import Pkg; Pkg.add(\"Plots\")` to install the Plots package.", + "", + "Stacktrace:", + " [1] macro expansion", + " @ Base ./loading.jl:1766 [inlined]", + " [2] macro expansion", + " @ Base ./lock.jl:267 [inlined]", + " [3] __require(into::Module, mod::Symbol)", + " @ Base ./loading.jl:1747", + " [4] #invoke_in_world#3", + " @ Base ./essentials.jl:921 [inlined]", + " [5] invoke_in_world", + " @ Base ./essentials.jl:918 [inlined]", + " [6] require(into::Module, mod::Symbol)", + " @ Base ./loading.jl:1740" ] } ], @@ -166,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -175,7 +202,7 @@ "3" ] }, - "execution_count": 4, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -194,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -203,7 +230,7 @@ "0.2" ] }, - "execution_count": 74, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -222,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -231,7 +258,7 @@ "20000" ] }, - "execution_count": 75, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -251,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -264,7 +291,7 @@ ")\n" ] }, - "execution_count": 76, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -287,16 +314,16 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "StateSpace(AdaptiveTransportMap.lorenz63!, h)" + "StateSpace(TransportBasedInference.lorenz63!, TransportBasedInference.var\"#27#28\"(), h)" ] }, - "execution_count": 77, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -315,7 +342,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -324,7 +351,7 @@ "AdditiveInflation(3, [0.0, 0.0, 0.0], [4.0 0.0 0.0; 0.0 4.0 0.0; 0.0 0.0 4.0], [2.0 0.0 0.0; 0.0 2.0 0.0; 0.0 0.0 2.0])" ] }, - "execution_count": 78, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -340,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -361,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -370,7 +397,7 @@ "(0.0, 4000.0)" ] }, - "execution_count": 80, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -389,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -398,7 +425,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -414,19 +441,19 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "3×160 view(::Array{Float64,2}, 4:6, :) with eltype Float64:\n", - " 0.104693 -0.0561962 0.355552 … 0.233773 -0.0310585 -0.592427\n", - " -0.981902 0.208767 1.14444 1.38253 -0.680648 0.47345\n", - " -0.332451 -1.18409 -0.296373 -0.450434 -0.405656 -0.83195" + "3×160 view(::Matrix{Float64}, 4:6, :) with eltype Float64:\n", + " 1.34249 0.712405 -0.387338 … 1.85892 -0.951166 -0.305626\n", + " 1.0863 -0.0685739 -1.93264 0.833722 0.572468 -1.55161\n", + " -1.47658 -0.866608 -0.0346448 0.340361 -0.143879 -0.895267" ] }, - "execution_count": 83, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -450,7 +477,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -459,7 +486,7 @@ "Stochastic EnKF with filtered = false\n" ] }, - "execution_count": 84, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -470,26 +497,18 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 19, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32mProgress: 100%|█████████████████████████████████████████| Time: 0:00:40\u001b[39m\n" - ] - } - ], + "outputs": [], "source": [ "Xenkf = seqassim(F, data, Tf, model.ϵx, enkf, deepcopy(X0), model.Ny, model.Nx, t0);" ] }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -498,7 +517,7 @@ "5000" ] }, - "execution_count": 86, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -519,16 +538,16 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.7441405231468667" + "0.7566798645736859" ] }, - "execution_count": 87, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -546,22 +565,22 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "6×160 Array{Float64,2}:\n", - " -2.69926 -4.58184 0.136877 -6.29289 … -2.78391 -0.207968 -4.82961\n", - " -6.87121 -7.44057 -1.99345 -6.4043 -8.57351 -6.64563 -3.87018\n", - " 19.3558 15.6092 18.6457 17.8213 19.2315 18.8244 16.9808\n", - " -2.53311 -3.75875 -2.23689 -3.58389 -3.71358 -3.1691 -3.1581\n", - " -4.14343 -5.94082 -3.64389 -5.65671 -5.90239 -5.06649 -5.05436\n", - " 17.4215 18.1072 17.9454 18.5577 … 18.2949 18.013 18.1908" + "6×160 Matrix{Float64}:\n", + " 1.82367 1.61935 5.53647 1.5747 … 2.92798 -0.345511 -1.97071\n", + " 2.54436 3.91656 6.43681 5.06238 5.10956 -1.56394 1.66558\n", + " 18.1111 17.7289 15.4647 19.6057 17.9869 14.5757 15.5768\n", + " 1.77441 1.33894 2.81993 1.8605 2.8846 0.139909 1.40936\n", + " 3.05908 2.44577 4.54671 3.19636 4.41958 0.794242 2.60389\n", + " 17.4065 16.8136 17.4408 16.4258 … 18.7153 16.4441 17.3536" ] }, - "execution_count": 88, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -582,16 +601,39 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "LoadError", + "evalue": "ArgumentError: Package StatsBase not found in current path.\n- Run `import Pkg; Pkg.add(\"StatsBase\")` to install the StatsBase package.", + "output_type": "error", + "traceback": [ + "ArgumentError: Package StatsBase not found in current path.\n- Run `import Pkg; Pkg.add(\"StatsBase\")` to install the StatsBase package.", + "", + "Stacktrace:", + " [1] macro expansion", + " @ Base ./loading.jl:1766 [inlined]", + " [2] macro expansion", + " @ Base ./lock.jl:267 [inlined]", + " [3] __require(into::Module, mod::Symbol)", + " @ Base ./loading.jl:1747", + " [4] #invoke_in_world#3", + " @ Base ./essentials.jl:921 [inlined]", + " [5] invoke_in_world", + " @ Base ./essentials.jl:918 [inlined]", + " [6] require(into::Module, mod::Symbol)", + " @ Base ./loading.jl:1740" + ] + } + ], "source": [ "using StatsBase" ] }, { "cell_type": "code", - "execution_count": 180, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -600,7 +642,7 @@ "10" ] }, - "execution_count": 180, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -615,16 +657,16 @@ }, { "cell_type": "code", - "execution_count": 181, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "SyntheticData([0.2, 0.4, 0.6000000000000001, 0.8, 1.0, 1.2000000000000002, 1.4000000000000001, 1.6, 1.8, 2.0 … 1998.2, 1998.4, 1998.6000000000001, 1998.8000000000002, 1999.0, 1999.2, 1999.4, 1999.6000000000001, 1999.8000000000002, 2000.0], [-0.8189772311243128, -0.3546071405640554, 0.02030609997799162], [-4.27201160044782 -19.36884128072291 … 0.2629388288103181 -1.1302181777199283; -9.196465700147412 -13.844583458976638 … -0.9265283264794729 -2.0027269988613146; 1.526352356521326 47.15601187799539 … 20.60157586539535 12.20265765814061], [-0.9418697041087536 -18.703136818794576 … -0.5804830286600129 -4.651136996001149; -13.29394774361921 -12.614005112988782 … -2.3423472638133065 0.3238724376346971; 3.0200973355919594 46.9544889934303 … 18.37967359583174 11.510116966812074])" + "SyntheticData([0.2, 0.4, 0.6000000000000001, 0.8, 1.0, 1.2000000000000002, 1.4000000000000001, 1.6, 1.8, 2.0 … 1998.2, 1998.4, 1998.6000000000001, 1998.8000000000002, 1999.0, 1999.2, 1999.4, 1999.6000000000001, 1999.8000000000002, 2000.0], 0.05, [0.5899567289536106, -0.011848654881705655, 0.19366356451076036], [2.262976673010678 17.70375241685305 … -11.304987260876493 -8.158273866273959; 4.9012952589793155 27.399075216062503 … -20.175074668066788 3.572875490006399; 0.5372361947762082 29.541992556400704 … 16.314778658729196 37.351916235551336], [1.5742665457300662 16.584124029942902 … -11.209827813935732 -8.950017887813287; 5.572860603931895 28.36855776926141 … -21.15413218412112 1.2655850360013625; -0.3453140683555008 28.006912014585065 … 16.92917071224066 39.95755482840039])" ] }, - "execution_count": 181, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -635,16 +677,20 @@ }, { "cell_type": "code", - "execution_count": 182, + "execution_count": 26, "metadata": {}, "outputs": [ { - "data": { - "image/png": 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" - }, - "execution_count": 182, - "metadata": {}, - "output_type": "execute_result" + "ename": "LoadError", + "evalue": "UndefVarError: `autocor` not defined", + "output_type": "error", + "traceback": [ + "UndefVarError: `autocor` not defined", + "", + "Stacktrace:", + " [1] top-level scope", + " @ In[26]:1" + ] } ], "source": [ @@ -662,18 +708,18 @@ }, { "cell_type": "code", - "execution_count": 183, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "2×3 Array{Int64,2}:\n", + "2×3 Matrix{Int64}:\n", " 1 2 3\n", " 1 2 3" ] }, - "execution_count": 183, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -694,7 +740,7 @@ }, { "cell_type": "code", - "execution_count": 184, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -702,10 +748,10 @@ "output_type": "stream", "text": [ "size(data_freerun.yt[1, Tdiscard:Δdiscard:end]) = (901,)\n", - " 0.005018 seconds (69.45 k allocations: 8.405 MiB)\n", - " 0.006469 seconds (67.58 k allocations: 8.151 MiB)\n", - " 0.005179 seconds (71.40 k allocations: 8.801 MiB)\n", - " 0.017321 seconds (209.40 k allocations: 25.573 MiB)\n" + " 2.894997 seconds (9.12 M allocations: 596.832 MiB, 2.54% gc time, 99.87% compilation time)\n", + " 0.002201 seconds (67.53 k allocations: 7.097 MiB)\n", + " 0.002310 seconds (69.40 k allocations: 7.297 MiB)\n", + " 3.326160 seconds (10.36 M allocations: 685.613 MiB, 2.21% gc time, 99.72% compilation time)\n" ] } ], @@ -725,16 +771,20 @@ }, { "cell_type": "code", - "execution_count": 185, + "execution_count": 29, "metadata": {}, "outputs": [ { - "data": { - "image/png": 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" - }, - "execution_count": 185, - "metadata": {}, - "output_type": "execute_result" + "ename": "LoadError", + "evalue": "UndefVarError: `grid` not defined", + "output_type": "error", + "traceback": [ + "UndefVarError: `grid` not defined", + "", + "Stacktrace:", + " [1] top-level scope", + " @ In[29]:1" + ] } ], "source": [ @@ -747,7 +797,7 @@ }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -758,7 +808,7 @@ }, { "cell_type": "code", - "execution_count": 187, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -767,7 +817,7 @@ "15000" ] }, - "execution_count": 187, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -786,18 +836,18 @@ }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "SparseRadialSMF(var\"#45#46\"(), h, MultiplicativeInflation(1.0), AdditiveInflation(3, [0.0, 0.0, 0.0], [4.0 0.0 0.0; 0.0 4.0 0.0; 0.0 0.0 4.0], [2.0 0.0 0.0; 0.0 2.0 0.0; 0.0 0.0 2.0]), Sparse Radial Map of dimension Nx = 4 and order p = [[-1], [2, 2], [-1, 2, 0], [-1, 2, 2, 0]]\n", + "SparseRadialSMF(var\"#5#6\"(), h, MultiplicativeInflation(1.0), AdditiveInflation(3, [0.0, 0.0, 0.0], [4.0 0.0 0.0; 0.0 4.0 0.0; 0.0 0.0 4.0], [2.0 0.0 0.0; 0.0 2.0 0.0; 0.0 0.0 2.0]), Sparse Radial Map of dimension Nx = 4 and order p = [[-1], [2, 2], [-1, 2, 0], [-1, 2, 2, 0]]\n", " with parameters (γ, λ, δ, κ) = (2.0, 0.0, 1.0e-8, 10.0)\n", ", 3, 3, 0.05, 0.2, [0.0 1.0 1.0; 1.0 0.0 1.0; 1.0 1.0 0.0], [1 2 3; 1 2 3], [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 0.0 … 0.0 0.0], false, true)" ] }, - "execution_count": 188, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -814,13 +864,13 @@ }, { "cell_type": "code", - "execution_count": 189, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "AdaptiveSparseRadialSMF(var\"#47#48\"(), h, 1.0, AdditiveInflation(3, [0.0, 0.0, 0.0], [4.0 0.0 0.0; 0.0 4.0 0.0; 0.0 0.0 4.0], [2.0 0.0 0.0; 0.0 2.0 0.0; 0.0 0.0 2.0]), SparseRadialMap[Sparse Radial Map of dimension Nx = 4 and order p = [[-1], [2, 1], [2, 2, 0], [-1, 2, 2, 0]]\n", + "AdaptiveSparseRadialSMF(var\"#7#8\"(), h, 1.0, AdditiveInflation(3, [0.0, 0.0, 0.0], [4.0 0.0 0.0; 0.0 4.0 0.0; 0.0 0.0 4.0], [2.0 0.0 0.0; 0.0 2.0 0.0; 0.0 0.0 2.0]), SparseRadialMap[Sparse Radial Map of dimension Nx = 4 and order p = [[-1], [2, 1], [2, 2, 0], [2, -1, 2, 0]]\n", " with parameters (γ, λ, δ, κ) = (2.0, 0.0, 1.0e-8, 10.0)\n", ", Sparse Radial Map of dimension Nx = 4 and order p = [[-1], [2, 1], [2, 2, 0], [2, -1, 2, 0]]\n", " with parameters (γ, λ, δ, κ) = (2.0, 0.0, 1.0e-8, 10.0)\n", @@ -829,7 +879,7 @@ "], 3, 3, 0.05, 0.2, Inf, [0.0 1.0 1.0; 1.0 0.0 1.0; 1.0 1.0 0.0], [1 2 3; 1 2 3], [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 0.0 … 0.0 0.0], false, true)" ] }, - "execution_count": 189, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -860,16 +910,20 @@ }, { "cell_type": "code", - "execution_count": 190, + "execution_count": 34, "metadata": {}, "outputs": [ { - "data": { - "image/png": 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" - }, - "execution_count": 190, - "metadata": {}, - "output_type": "execute_result" + "ename": "LoadError", + "evalue": "UndefVarError: `plot` not defined", + "output_type": "error", + "traceback": [ + "UndefVarError: `plot` not defined", + "", + "Stacktrace:", + " [1] top-level scope", + " @ In[34]:1" + ] } ], "source": [ @@ -878,52 +932,36 @@ }, { "cell_type": "code", - "execution_count": 191, + "execution_count": 35, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32mProgress: 100%|█████████████████████████████████████████| Time: 0:02:02\u001b[39m\n" - ] - } - ], + "outputs": [], "source": [ "Xsmf = seqassim(F, data, Tsmf, model.ϵx, smf, deepcopy(Xspin), model.Ny, model.Nx, tspin);" ] }, { "cell_type": "code", - "execution_count": 192, + "execution_count": 36, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32mProgress: 100%|█████████████████████████████████████████| Time: 0:02:00\u001b[39m\n" - ] - } - ], + "outputs": [], "source": [ "Xsmf_greedy = seqassim(F, data, Tsmf, model.ϵx, smf_greedy, deepcopy(Xspin), model.Ny, model.Nx, tspin);" ] }, { "cell_type": "code", - "execution_count": 193, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.5641982576359317" + "0.5802233825498218" ] }, - "execution_count": 193, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -934,16 +972,16 @@ }, { "cell_type": "code", - "execution_count": 194, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.24181221142209036" + "0.23319833166603401" ] }, - "execution_count": 194, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -954,16 +992,16 @@ }, { "cell_type": "code", - "execution_count": 195, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.6058075008651735" + "0.6445074605036237" ] }, - "execution_count": 195, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -974,16 +1012,16 @@ }, { "cell_type": "code", - "execution_count": 196, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.18589637034776993" + "0.14824288225676543" ] }, - "execution_count": 196, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -992,6 +1030,72 @@ "(rmse_enkf-rmse_greedysmf)/rmse_enkf" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "S = totalordermap(X0, 2; b = \"CstLinProHermite\")" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "Xtest = vcat(zeros(Ny, Ne), Xsmf[end])\n", + "\n", + "observe(F.h, Xtest, 1.0, Ny, Nx)\n", + "\n", + "ϵy(Xtest, 1, Ny)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "m = 20\n", + "S = HermiteMap(m, Xtest; diag = true, factor = 0.5, α = 1e-6, b = \"ProHermiteBasis\");" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0.193805 seconds (8.14 M allocations: 329.884 MiB, 12.03% gc time)\n" + ] + }, + { + "data": { + "text/plain": [ + "Hermite map of dimension 6:\n", + "Hermite map component of dimension 1 with Nψ = 1 active features\n", + "Hermite map component of dimension 2 with Nψ = 1 active features\n", + "Hermite map component of dimension 3 with Nψ = 1 active features\n", + "Hermite map component of dimension 4 with Nψ = 4 active features\n", + "Hermite map component of dimension 5 with Nψ = 4 active features\n", + "Hermite map component of dimension 6 with Nψ = 5 active features\n" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "@time S = optimize(S, Xtest, \"split\"; maxterms = 20, withconstant = true, withqr = true, verbose = false, \n", + " maxpatience = 10, start = Ny+1, hessprecond = true)" + ] + }, { "cell_type": "code", "execution_count": 72, @@ -2593,15 +2697,15 @@ ], "metadata": { "kernelspec": { - "display_name": "Julia 1.5.3", + "display_name": "Julia 1.10.0", "language": "julia", - "name": "julia-1.5" + "name": "julia-1.10" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.5.3" + "version": "1.10.0" } }, "nbformat": 4, diff --git a/notebooks/7 Nonlinear ensemble filtering Lorenz 63 with fixed order Hermite map.ipynb b/notebooks/7 Nonlinear ensemble filtering Lorenz 63 with fixed order Hermite map.ipynb index 2f53bd2..fbf99d9 100644 --- a/notebooks/7 Nonlinear ensemble filtering Lorenz 63 with fixed order Hermite map.ipynb +++ b/notebooks/7 Nonlinear ensemble filtering Lorenz 63 with fixed order Hermite map.ipynb @@ -47,26 +47,43 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "┌ Info: Precompiling AdaptiveTransportMap [bdf749b0-1400-4207-80d3-e689c0e3f03d]\n", - "└ @ Base loading.jl:1278\n", - "┌ Warning: Type annotations on keyword arguments not currently supported in recipes. Type information has been discarded\n", - "└ @ RecipesBase ~/.julia/packages/RecipesBase/92zOw/src/RecipesBase.jl:116\n", - "┌ Warning: Type annotations on keyword arguments not currently supported in recipes. Type information has been discarded\n", - "└ @ RecipesBase ~/.julia/packages/RecipesBase/92zOw/src/RecipesBase.jl:116\n" + "\u001b[32m\u001b[1mPrecompiling\u001b[22m\u001b[39m TransportBasedInference\n", + "\u001b[36m\u001b[1m Info\u001b[22m\u001b[39m Given TransportBasedInference was explicitly requested, output will be shown live \u001b[0K\n", + "\u001b[0K\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mType annotations on keyword arguments not currently supported in recipes.\n", + "\u001b[0K\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mType information has been discarded\n", + "\u001b[0K\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ RecipesBase ~/.julia/packages/RecipesBase/BRe07/src/RecipesBase.jl:117\u001b[39m\n", + "\u001b[0K\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mType annotations on keyword arguments not currently supported in recipes.\n", + "\u001b[0K\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mType information has been discarded\n", + "\u001b[0K\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ RecipesBase ~/.julia/packages/RecipesBase/BRe07/src/RecipesBase.jl:117\u001b[39m\n", + "\u001b[0K\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mType annotations on keyword arguments not currently supported in recipes.\n", + "\u001b[0K\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mType information has been discarded\n", + "\u001b[0K\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ RecipesBase ~/.julia/packages/RecipesBase/BRe07/src/RecipesBase.jl:117\u001b[39m\n", + "\u001b[0K\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mType annotations on keyword arguments not currently supported in recipes.\n", + "\u001b[0K\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mType information has been discarded\n", + "\u001b[0K\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ RecipesBase ~/.julia/packages/RecipesBase/BRe07/src/RecipesBase.jl:117\u001b[39m\n", + "\u001b[0K\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mType annotations on keyword arguments not currently supported in recipes.\n", + "\u001b[0K\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mType information has been discarded\n", + "\u001b[0K\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ RecipesBase ~/.julia/packages/RecipesBase/BRe07/src/RecipesBase.jl:117\u001b[39m\n", + "\u001b[0K\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mType annotations on keyword arguments not currently supported in recipes.\n", + "\u001b[0K\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mType information has been discarded\n", + "\u001b[0K\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ RecipesBase ~/.julia/packages/RecipesBase/BRe07/src/RecipesBase.jl:117\u001b[39m\n", + "\u001b[0K\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mType annotations on keyword arguments not currently supported in recipes.\n", + "\u001b[0K\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mType information has been discarded\n", + "\u001b[0K\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ RecipesBase ~/.julia/packages/RecipesBase/BRe07/src/RecipesBase.jl:117\u001b[39m\n" ] } ], "source": [ "using Revise\n", "using LinearAlgebra\n", - "using AdaptiveTransportMap\n", + "using TransportBasedInference\n", "using Statistics\n", "using Distributions\n", "using OrdinaryDiffEq" @@ -74,7 +91,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -90,22 +107,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "using Plots\n", - "default(tickfont = font(\"CMU Serif\", 9), \n", - " titlefont = font(\"CMU Serif\", 14), \n", - " guidefont = font(\"CMU Serif\", 12),\n", - " legendfont = font(\"CMU Serif\", 10),\n", - " grid = false)\n", - "# pyplot()\n", + "using CairoMakie\n", "\n", "using LaTeXStrings\n", - "# PyPlot.rc(\"text\", usetex = \"true\")\n", - "# PyPlot.rc(\"font\", family = \"CMU Serif\")\n", - "# gr()\n", + "\n", "using ColorSchemes" ] }, @@ -5682,15 +5691,15 @@ ], "metadata": { "kernelspec": { - "display_name": "Julia 1.8.0", + "display_name": "Julia 1.10.0", "language": "julia", - "name": "julia-1.8" + "name": "julia-1.10" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.8.0" + "version": "1.10.0" } }, "nbformat": 4, diff --git a/notebooks/8 Nonlinear ensemble filtering Lorenz 63 with adaptive Hermite map.ipynb b/notebooks/8 Nonlinear ensemble filtering Lorenz 63 with adaptive Hermite map.ipynb index 26f40ce..5cb188e 100644 --- a/notebooks/8 Nonlinear ensemble filtering Lorenz 63 with adaptive Hermite map.ipynb +++ b/notebooks/8 Nonlinear ensemble filtering Lorenz 63 with adaptive Hermite map.ipynb @@ -3481,15 +3481,15 @@ ], "metadata": { "kernelspec": { - "display_name": "Julia 1.5.3", + "display_name": "Julia 1.10.0", "language": "julia", - "name": "julia-1.5" + "name": "julia-1.10" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.5.3" + "version": "1.10.0" } }, "nbformat": 4, diff --git a/notebooks/Test nlfilter radial Lorenz 63.ipynb b/notebooks/Test nlfilter radial Lorenz 63.ipynb index 4b6692b..4dc5a28 100644 --- a/notebooks/Test nlfilter radial Lorenz 63.ipynb +++ b/notebooks/Test nlfilter radial Lorenz 63.ipynb @@ -45,26 +45,49 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "┌ Info: Precompiling AdaptiveTransportMap [bdf749b0-1400-4207-80d3-e689c0e3f03d]\n", - "└ @ Base loading.jl:1278\n", - "┌ Warning: Type annotations on keyword arguments not currently supported in recipes. Type information has been discarded\n", - "└ @ RecipesBase ~/.julia/packages/RecipesBase/92zOw/src/RecipesBase.jl:116\n", - "┌ Warning: Type annotations on keyword arguments not currently supported in recipes. Type information has been discarded\n", - "└ @ RecipesBase ~/.julia/packages/RecipesBase/92zOw/src/RecipesBase.jl:116\n" + "\u001b[32m\u001b[1mPrecompiling\u001b[22m\u001b[39m TransportBasedInference\n", + "\u001b[36m\u001b[1m Info\u001b[22m\u001b[39m Given TransportBasedInference was explicitly requested, output will be shown live \u001b[0K\n", + "\u001b[0K\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mType annotations on keyword arguments not currently supported in recipes.\n", + "\u001b[0K\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mType information has been discarded\n", + "\u001b[0K\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ RecipesBase ~/.julia/packages/RecipesBase/BRe07/src/RecipesBase.jl:117\u001b[39m\n", + "\u001b[0K\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mType annotations on keyword arguments not currently supported in recipes.\n", + "\u001b[0K\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mType information has been discarded\n", + "\u001b[0K\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ RecipesBase ~/.julia/packages/RecipesBase/BRe07/src/RecipesBase.jl:117\u001b[39m\n", + "\u001b[0K\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mType annotations on keyword arguments not currently supported in recipes.\n", + "\u001b[0K\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mType information has been discarded\n", + "\u001b[0K\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ RecipesBase ~/.julia/packages/RecipesBase/BRe07/src/RecipesBase.jl:117\u001b[39m\n", + "\u001b[0K\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mType annotations on keyword arguments not currently supported in recipes.\n", + "\u001b[0K\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mType information has been discarded\n", + "\u001b[0K\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ RecipesBase ~/.julia/packages/RecipesBase/BRe07/src/RecipesBase.jl:117\u001b[39m\n", + "\u001b[0K\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mType annotations on keyword arguments not currently supported in recipes.\n", + "\u001b[0K\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mType information has been discarded\n", + "\u001b[0K\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ RecipesBase ~/.julia/packages/RecipesBase/BRe07/src/RecipesBase.jl:117\u001b[39m\n", + "\u001b[0K\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mType annotations on keyword arguments not currently supported in recipes.\n", + "\u001b[0K\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mType information has been discarded\n", + "\u001b[0K\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ RecipesBase ~/.julia/packages/RecipesBase/BRe07/src/RecipesBase.jl:117\u001b[39m\n", + "\u001b[0K\u001b[33m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[33m\u001b[1mWarning: \u001b[22m\u001b[39mType annotations on keyword arguments not currently supported in recipes.\n", + "\u001b[0K\u001b[33m\u001b[1m│ \u001b[22m\u001b[39mType information has been discarded\n", + "\u001b[0K\u001b[33m\u001b[1m└ \u001b[22m\u001b[39m\u001b[90m@ RecipesBase ~/.julia/packages/RecipesBase/BRe07/src/RecipesBase.jl:117\u001b[39m\n", + "\u001b[32m ✓ \u001b[39mTransportBasedInference\n", + " 1 dependency successfully precompiled in 12 seconds. 261 already precompiled.\n", + " \u001b[33m1\u001b[39m dependency had output during precompilation:\u001b[33m\n", + "┌ \u001b[39mTransportBasedInference\u001b[33m\n", + "│ \u001b[39m[Output was shown above]\u001b[33m\n", + "└ \u001b[39m\n" ] } ], "source": [ "using Revise\n", "using LinearAlgebra\n", - "using AdaptiveTransportMap\n", + "using TransportBasedInference\n", "using Statistics\n", "using Distributions\n", "using OrdinaryDiffEq" @@ -72,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -88,9 +111,32 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "LoadError", + "evalue": "ArgumentError: Package Plots not found in current path.\n- Run `import Pkg; Pkg.add(\"Plots\")` to install the Plots package.", + "output_type": "error", + "traceback": [ + "ArgumentError: Package Plots not found in current path.\n- Run `import Pkg; Pkg.add(\"Plots\")` to install the Plots package.", + "", + "Stacktrace:", + " [1] macro expansion", + " @ Base ./loading.jl:1766 [inlined]", + " [2] macro expansion", + " @ Base ./lock.jl:267 [inlined]", + " [3] __require(into::Module, mod::Symbol)", + " @ Base ./loading.jl:1747", + " [4] #invoke_in_world#3", + " @ Base ./essentials.jl:921 [inlined]", + " [5] invoke_in_world", + " @ Base ./essentials.jl:918 [inlined]", + " [6] require(into::Module, mod::Symbol)", + " @ Base ./loading.jl:1740" + ] + } + ], "source": [ "using Plots\n", "default(tickfont = font(\"CMU Serif\", 9), \n", @@ -140,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -149,7 +195,7 @@ "3" ] }, - "execution_count": 4, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -168,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -177,7 +223,7 @@ "0.2" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -196,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -205,7 +251,7 @@ "500" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -225,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -238,7 +284,7 @@ ")\n" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -261,16 +307,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "StateSpace(AdaptiveTransportMap.lorenz63!, h)" + "StateSpace(TransportBasedInference.lorenz63!, TransportBasedInference.var\"#27#28\"(), h)" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -289,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -298,7 +344,7 @@ "AdditiveInflation(3, [0.0, 0.0, 0.0], [4.0 0.0 0.0; 0.0 4.0 0.0; 0.0 0.0 4.0], [2.0 0.0 0.0; 0.0 2.0 0.0; 0.0 0.0 2.0])" ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -314,19 +360,19 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "3×3 Diagonal{Float64,Array{Float64,1}}:\n", - " 1.0e-20 ⋅ ⋅ \n", - " ⋅ 1.0e-20 ⋅ \n", - " ⋅ ⋅ 1.0e-20" + "3×3 Diagonal{Float64, Vector{Float64}}:\n", + " 0.01 ⋅ ⋅ \n", + " ⋅ 0.01 ⋅ \n", + " ⋅ ⋅ 0.01" ] }, - "execution_count": 10, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -337,7 +383,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -358,21 +404,21 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "\u001b[36mODEProblem\u001b[0m with uType \u001b[36mArray{Float64,1}\u001b[0m and tType \u001b[36mFloat64\u001b[0m. In-place: \u001b[36mtrue\u001b[0m\n", + "\u001b[38;2;86;182;194mODEProblem\u001b[0m with uType \u001b[38;2;86;182;194mVector{Float64}\u001b[0m and tType \u001b[38;2;86;182;194mFloat64\u001b[0m. In-place: \u001b[38;2;86;182;194mtrue\u001b[0m\n", "timespan: (0.0, 100.0)\n", - "u0: 3-element Array{Float64,1}:\n", + "u0: 3-element Vector{Float64}:\n", " 0.0\n", " 0.0\n", " 0.0" ] }, - "execution_count": 12, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -392,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -401,7 +447,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -417,19 +463,19 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "3×300 view(::Array{Float64,2}, 4:6, :) with eltype Float64:\n", - " 1.23081 0.604498 -0.153261 … 0.578052 0.698916 0.30136\n", - " 0.202271 -0.0696071 0.411907 1.13923 -1.88204 0.204233\n", - " 0.177207 -0.323985 0.360247 -1.52934 -0.400928 -1.5781" + "3×300 view(::Matrix{Float64}, 4:6, :) with eltype Float64:\n", + " 1.10411 -1.15526 0.161436 … 0.31848 0.883481 1.23124\n", + " -0.192084 -1.17398 -0.780307 -0.988889 -0.700049 -0.585068\n", + " 1.34883 0.833289 -1.46338 0.179696 0.928431 0.0801317" ] }, - "execution_count": 15, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -453,7 +499,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -462,7 +508,7 @@ "Stochastic EnKF with filtered = false\n" ] }, - "execution_count": 16, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -473,26 +519,18 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32mProgress: 100%|█████████████████████████████████████████| Time: 0:00:08\u001b[39m\n" - ] - } - ], + "outputs": [], "source": [ "Xenkf = seqassim(F, data, Tf, model.ϵx, enkf, deepcopy(X0), model.Ny, model.Nx, t0);" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -501,7 +539,7 @@ "250" ] }, - "execution_count": 18, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -520,16 +558,16 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.6826776463649661" + "0.7919033201181368" ] }, - "execution_count": 19, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -547,22 +585,22 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "6×300 Array{Float64,2}:\n", - " 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0\n", - " 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", - " 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", - " 4.50224 2.8974 2.47031 3.56557 3.08375 3.33651 2.98631\n", - " 5.40887 3.04887 2.612 4.06651 3.48064 3.6251 3.43293\n", - " 20.5057 20.0112 19.4166 20.1042 … 19.6684 20.2709 19.3825" + "6×300 Matrix{Float64}:\n", + " 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0\n", + " 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", + " 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", + " -10.327 -10.7509 -10.8743 -10.7626 -10.492 -10.7172 -10.9578\n", + " -14.6299 -15.4301 -15.2748 -15.2683 -15.0873 -15.1435 -15.5442\n", + " 22.6578 23.7576 23.9253 23.9832 … 23.736 23.8891 23.6032" ] }, - "execution_count": 20, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -573,19 +611,19 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "metadata": {}, "outputs": [ { "ename": "LoadError", - "evalue": "\u001b[91mUndefVarError: smf not defined\u001b[39m", + "evalue": "UndefVarError: `smf` not defined", "output_type": "error", "traceback": [ - "\u001b[91mUndefVarError: smf not defined\u001b[39m", + "UndefVarError: `smf` not defined", "", "Stacktrace:", - " [1] top-level scope at In[21]:14", - " [2] include_string(::Function, ::Module, ::String, ::String) at ./loading.jl:1091" + " [1] top-level scope", + " @ In[23]:14" ] } ], @@ -612,19 +650,19 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "metadata": {}, "outputs": [ { "ename": "LoadError", - "evalue": "\u001b[91mUndefVarError: smf not defined\u001b[39m", + "evalue": "UndefVarError: `smf` not defined", "output_type": "error", "traceback": [ - "\u001b[91mUndefVarError: smf not defined\u001b[39m", + "UndefVarError: `smf` not defined", "", "Stacktrace:", - " [1] top-level scope at In[22]:2", - " [2] include_string(::Function, ::Module, ::String, ::String) at ./loading.jl:1091" + " [1] top-level scope", + " @ In[24]:2" ] } ], @@ -642,7 +680,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -651,7 +689,7 @@ "250" ] }, - "execution_count": 23, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -663,18 +701,18 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "SparseRadialSMF(var\"#19#20\"(), h, 1.0, AdditiveInflation(3, [0.0, 0.0, 0.0], [4.0 0.0 0.0; 0.0 4.0 0.0; 0.0 0.0 4.0], [2.0 0.0 0.0; 0.0 2.0 0.0; 0.0 0.0 2.0]), Sparse Radial Map of dimension Nx = 4 and order p = [[-1], [2, 2], [-1, 2, 0], [-1, 2, 2, 0]]\n", + "SparseRadialSMF(var\"#5#6\"(), h, MultiplicativeInflation(1.0), AdditiveInflation(3, [0.0, 0.0, 0.0], [4.0 0.0 0.0; 0.0 4.0 0.0; 0.0 0.0 4.0], [2.0 0.0 0.0; 0.0 2.0 0.0; 0.0 0.0 2.0]), Sparse Radial Map of dimension Nx = 4 and order p = [[-1], [2, 2], [-1, 2, 0], [-1, 2, 2, 0]]\n", " with parameters (γ, λ, δ, κ) = (2.0, 0.0, 1.0e-8, 10.0)\n", ", 3, 3, 0.05, 0.2, [0.0 1.0 1.0; 1.0 0.0 1.0; 1.0 1.0 0.0], [1 2 3; 1 2 3], [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 0.0 … 0.0 0.0], false, true)" ] }, - "execution_count": 46, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -704,19 +742,19 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "3×1000 view(::Array{Float64,2}, 4:6, :) with eltype Float64:\n", - " -1.02861 -0.484879 -0.25477 … -0.211258 -0.323386 -0.853469\n", - " -0.540356 -0.931507 0.198575 -1.71245 0.562809 -0.802693\n", - " 0.72809 0.0244415 -0.650659 -1.02144 0.820004 0.444832" + "3×1000 view(::Matrix{Float64}, 4:6, :) with eltype Float64:\n", + " 0.325792 -2.07147 0.778709 … -0.732486 0.0351791 1.38539\n", + " 0.0152669 -0.14387 0.339421 0.339787 -1.55939 0.784483\n", + " 0.296231 0.483767 1.01657 0.753127 -2.10141 -0.447518" ] }, - "execution_count": 48, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -733,29 +771,78 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "ename": "LoadError", + "evalue": "DimensionMismatch: array could not be broadcast to match destination", + "output_type": "error", + "traceback": [ + "DimensionMismatch: array could not be broadcast to match destination", + "", + "Stacktrace:", + " [1] check_broadcast_shape", + " @ ./broadcast.jl:579 [inlined]", + " [2] check_broadcast_shape", + " @ ./broadcast.jl:580 [inlined]", + " [3] check_broadcast_axes", + " @ ./broadcast.jl:582 [inlined]", + " [4] instantiate", + " @ ./broadcast.jl:309 [inlined]", + " [5] materialize!", + " @ ./broadcast.jl:914 [inlined]", + " [6] materialize!", + " @ ./broadcast.jl:911 [inlined]", + " [7] (::SparseRadialSMF)(X::Matrix{Float64}, ystar::Float64, t::Float64, idx::Vector{Int64}; P::Serial)", + " @ TransportBasedInference ~/Documents/TransportBasedInference.jl/src/radialmap/stochmapfilter.jl:93", + " [8] SparseRadialSMF", + " @ ~/Documents/TransportBasedInference.jl/src/radialmap/stochmapfilter.jl:70 [inlined]", + " [9] (::SparseRadialSMF)(X::Matrix{Float64}, ystar::Vector{Float64}, t::Float64; P::Serial, localized::Bool)", + " @ TransportBasedInference ~/Documents/TransportBasedInference.jl/src/radialmap/stochmapfilter.jl:131", + " [10] (::SparseRadialSMF)(X::Matrix{Float64}, ystar::Vector{Float64}, t::Float64)", + " @ TransportBasedInference ~/Documents/TransportBasedInference.jl/src/radialmap/stochmapfilter.jl:125", + " [11] seqassim(F::StateSpace, data::SyntheticData, J::Int64, ϵx::AdditiveInflation, algo::SparseRadialSMF, X::Matrix{Float64}, Ny::Int64, Nx::Int64, t0::Float64; isSDE::Bool)", + " @ TransportBasedInference ~/Documents/TransportBasedInference.jl/src/DA/seqassim.jl:87", + " [12] seqassim(F::StateSpace, data::SyntheticData, J::Int64, ϵx::AdditiveInflation, algo::SparseRadialSMF, X::Matrix{Float64}, Ny::Int64, Nx::Int64, t0::Float64)", + " @ TransportBasedInference ~/Documents/TransportBasedInference.jl/src/DA/seqassim.jl:8", + " [13] top-level scope", + " @ In[28]:1" + ] + } + ], "source": [ "Xsmf = seqassim(F, data, Tf, model.ϵx, smf, X0, model.Ny, model.Nx, 0.0);" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 29, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "0.5118256249426444" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" + "ename": "LoadError", + "evalue": "UndefVarError: `Xsmf` not defined", + "output_type": "error", + "traceback": [ + "UndefVarError: `Xsmf` not defined", + "", + "Stacktrace:", + " [1] (::var\"#7#8\")(i::Int64)", + " @ Main ./In[29]:1", + " [2] iterate(g::Base.Generator, s::Vararg{Any})", + " @ Base ./generator.jl:47 [inlined]", + " [3] _collect(c::UnitRange{Int64}, itr::Base.Generator{UnitRange{Int64}, var\"#7#8\"}, ::Base.EltypeUnknown, isz::Base.HasShape{1})", + " @ Base ./array.jl:854", + " [4] collect_similar(cont::UnitRange{Int64}, itr::Base.Generator{UnitRange{Int64}, var\"#7#8\"})", + " @ Base ./array.jl:763", + " [5] map(f::Function, A::UnitRange{Int64})", + " @ Base ./abstractarray.jl:3282", + " [6] top-level scope", + " @ In[29]:1" + ] } ], "source": [ @@ -764,18 +851,20 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 30, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "0.2502674905675505" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" + "ename": "LoadError", + "evalue": "UndefVarError: `rmse_smf` not defined", + "output_type": "error", + "traceback": [ + "UndefVarError: `rmse_smf` not defined", + "", + "Stacktrace:", + " [1] top-level scope", + " @ In[30]:1" + ] } ], "source": [ @@ -784,747 +873,17 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 31, "metadata": {}, "outputs": [ { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "20\n", - "\n", - "\n", - "40\n", - "\n", - "\n", - "60\n", - "\n", - "\n", - "80\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - 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"display_name": "Julia 1.5.3", + "display_name": "Julia 1.10.0", "language": "julia", - "name": "julia-1.5" + "name": "julia-1.10" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.5.3" + "version": "1.10.0" } }, "nbformat": 4, diff --git a/notebooks/Test radial maps II.ipynb b/notebooks/Test radial maps II.ipynb index fb57160..fd8b1ea 100644 --- a/notebooks/Test radial maps II.ipynb +++ b/notebooks/Test radial maps II.ipynb @@ -234,15 +234,15 @@ ], "metadata": { "kernelspec": { - "display_name": "Julia 1.5.3", + "display_name": "Julia 1.10.0", "language": "julia", - "name": "julia-1.5" + "name": "julia-1.10" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.5.3" + "version": "1.10.0" } }, "nbformat": 4, diff --git a/notebooks/Test.ipynb b/notebooks/Test.ipynb index b682555..796e0ac 100644 --- a/notebooks/Test.ipynb +++ b/notebooks/Test.ipynb @@ -866,15 +866,15 @@ ], "metadata": { "kernelspec": { - "display_name": "Julia 1.6.2", + "display_name": "Julia 1.10.0", "language": "julia", - "name": "julia-1.6" + "name": "julia-1.10" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", - "version": "1.6.2" + "version": "1.10.0" } }, "nbformat": 4, diff --git a/src/DA/seqassim.jl b/src/DA/seqassim.jl index a788edd..0650423 100644 --- a/src/DA/seqassim.jl +++ b/src/DA/seqassim.jl @@ -25,7 +25,7 @@ else end # Run filtering algorithm -for i=1:length(Acycle) +@showprogress for i=1:length(Acycle) # Forecast tspan = (t0+(i-1)*algo.Δtobs, t0+i*algo.Δtobs) # prob = remake(prob; tspan=tspan) diff --git a/src/enkf/senkf.jl b/src/enkf/senkf.jl index b516bb3..ef2f1c7 100644 --- a/src/enkf/senkf.jl +++ b/src/enkf/senkf.jl @@ -188,6 +188,59 @@ end # Baptista, Spantini, Marzouk 2019 +# Version without localization +function (enkf::SeqStochEnKF)(X, ystar, t) + h = enkf.h + + Nx = enkf.Nx + Ny = enkf.Ny + Na = Nx+1 + NxX, Ne = size(X) + + cache = enkf.cache + fill!(cache, 0.0) + + # Sequential assimilation of the observations + @inbounds for i=1:Ny + idx1, idx2 = enkf.idx[:,i] + ylocal = ystar[idx1] + + # Inflate state + cache[2:end,:] .= X[Ny+1:Ny+Nx,:] + Aβ = enkf.β + Aβ(cache) + + # Generate samples from local likelihood with inflated ensemble + @inbounds for i=1:Ne + col = view(X, Ny+1:Ny+Nx, i) + cache[1,i] = h(col, idx2, t) + enkf.ϵy.m[idx1] + dot(enkf.ϵy.σ[idx1,:], randn(Ny)) + end + + XYf = copy(cache) .- mean(cache; dims = 2)[:,1] + rmul!(XYf, 1/sqrt(Ne-1)) + + Yf = view(XYf,1,:) + Xf = view(XYf,2:Nx+1, :) + + #Construct the observation with the un-inflated measurement + @inbounds for i=1:Ne + col = view(X, Ny+1:Ny+Nx, i) + cache[1,i] = h(col, idx2, t) + enkf.ϵy.m[idx1] + dot(enkf.ϵy.σ[idx1,:], randn(Ny)) + end + + # Since Yf is a vector (Yf Yf') is reduced to a scalar + "Analysis step with representers, Evensen, Leeuwen et al. 1998" + K = Xf*Yf + # @show K./dot(Yf,Yf) + # K ./= dot(Yf, Yf) + # Bᵀb = K*(ylocal .- view(enkf.ensa.S,1,:)) + #In-place analysis step with gemm! + BLAS.gemm!('N', 'T', 1/dot(Yf,Yf), K, ylocal .- view(cache,1,:), 1.0, view(X, Ny+1:Ny+Nx, :)) + end + return X +end + + # Version with localization function (enkf::SeqStochEnKF)(X, ystar, t, Loc::Localization) h = enkf.h diff --git a/src/hermitemap/hermitemapcomponent.jl b/src/hermitemap/hermitemapcomponent.jl index 90f9deb..724ef06 100644 --- a/src/hermitemap/hermitemapcomponent.jl +++ b/src/hermitemap/hermitemapcomponent.jl @@ -491,7 +491,7 @@ function negative_log_likelihood!(J, dJ, coeff, S::Storage, C::HermiteMapCompone if J != nothing J = 0.0 - @avx for i=1:Ne + for i=1:Ne J += log_pdf(S.cache_integral[i]) + log(C.I.g(S.cache_g[i])) end J *=(-1/Ne) diff --git a/src/hermitemap/hybridinverse.jl b/src/hermitemap/hybridinverse.jl index ca963fa..5cc5863 100644 --- a/src/hermitemap/hybridinverse.jl +++ b/src/hermitemap/hybridinverse.jl @@ -373,7 +373,7 @@ end # # end # end # # Convergence criterion -# # @show min(norm(xa - xk, Inf), norm(xb - xk, Inf)), norm(dx, Inf), norm(fout, Inf) +# # min(norm(xa - xk, Inf), norm(xb - xk, Inf)), norm(dx, Inf), norm(fout, Inf) # if min(norm(xa - xk, Inf), norm(xb - xk, Inf))< ϵx || norm(dx, Inf) < ϵx || norm(fout, Inf) < ϵf # convergence = true # break diff --git a/src/hermitemap/old_rectifier.jl b/src/hermitemap/old_rectifier.jl deleted file mode 100644 index 6d2ecc0..0000000 --- a/src/hermitemap/old_rectifier.jl +++ /dev/null @@ -1,356 +0,0 @@ -export Rectifier, - square, dsquare, d2square, - softplus, dsoftplus, d2softplus, invsoftplus, - sigmoid, dsigmoid, d2sigmoid, invsigmoid, - sigmoid_, dsigmoid_, d2sigmoid_, invsigmoid_, - explinearunit, dexplinearunit, d2explinearunit, invexplinearunit, - inverse!, inverse, vinverse, - grad_x!, grad_x, vgrad_x, - grad_x_logeval, grad_x_logeval!, vgrad_x_logeval, - hess_x_logeval, hess_x_logeval!, vhess_x_logeval, - hess_x!, hess_x, vhess_x, - evaluate!, vevaluate - - -""" -$(TYPEDEF) - -This structure defines a continuous rectifier g, -i.e. a positive and monotonically increasing function -(e.g. square function, exponential , softplus, explinearunit). - -## Fields - -$(TYPEDFIELDS) - -""" -struct Rectifier - T::String - Kmin::Union{Nothing, Float64} - Kmax::Union{Nothing, Float64} - function Rectifier(T::String; Kmin = nothing, Kmax = nothing) - if T == "sigmoid_" - @assert Kmin > 0 "Kmin should be > 0 and cannot be nothing" - @assert Kmax > 0 "Kmax should be > 0 and cannot be nothing" - @assert Kmax > Kmin - end - return new(T, Kmin, Kmax) - end -end - - - -const KMIN = 1e-3 -const KMAX = 100 -square(x) = x^2 -dsquare(x) = 2.0*x -d2square(x) = 2.0 - -# Softplus tools -softplus(x) = (log(1.0 + exp(-abs(log(2.0)*x))) + max(log(2.0)*x, 0.0))/log(2.0) -dsoftplus(x) = 1/(1 + exp(-log(2.0)*x)) -d2softplus(x) = log(2.0)/(2.0*(1.0 + cosh(log(2.0)*x))) -invsoftplus(x) = min(log(exp(log(2.0)*x) - 1.0)/log(2.0), x) - -# Logistic tools -# Sigmoid implementation from NNlib.jl to avoid underflow errors. - -function sigmoid(x) - t = exp(-abs(x)) - ifelse(x ≥ 0, inv(1 + t), t / (1 + t)) -end - -function dsigmoid(x) - σ = sigmoid(x) - return σ*(1-σ) -end -function d2sigmoid(x) - σ = sigmoid(x) - # from dσ*(1-σ) - σ*dσ - return σ*(1-σ)*(1-2*σ) -end -invsigmoid(x) = ifelse(x > 0, log(x) - log(1-x), "Not defined for x ≤ 0 ") - -function sigmoid_(x, K_min, K_max) - return K_min + (K_max-K_min) * sigmoid(x) -end - -function dsigmoid_(x, K_min, K_max) - σ = sigmoid(x) - return (K_max-K_min)*σ*(1-σ) -end - -function d2sigmoid_(x, K_min, K_max) - σ = sigmoid(x) - return (K_max-K_min) * σ*(1-σ)*(1-2*σ) -end - -function invsigmoid_(x, K_min, K_max) - if x > K_min && x < K_max - return log(x-K_min) - log(K_max-x) - else - return "Not defined for x outside [K_min, K_max]" - end -end - -explinearunit(x) = x < 0.0 ? exp(x) : x + 1.0 -dexplinearunit(x) = x < 0.0 ? exp(x) : 1.0 -d2explinearunit(x) = x < 0.0 ? exp(x) : 0.0 -invexplinearunit(x) = x < 1.0 ? log(x) : x - 1.0 - -# Type of the rectifier should be in the following list: -# "squared", "exponential", "softplus", "explinearunit" - - -Rectifier() = Rectifier("softplus") - -function (g::Rectifier)(x) - if g.T=="squared" - return square(x) - elseif g.T=="exponential" - return exp(x) - elseif g.T=="sigmoid" - return sigmoid(x) - elseif g.T=="sigmoid_" - return sigmoid_(x, g.Kmin, g.Kmax) - elseif g.T=="softplus" - return softplus(x) - elseif g.T=="explinearunit" - return explinearunit(x) - end -end - -function evaluate!(result, g::Rectifier, x) - @assert size(result,1) == size(x,1) "Dimension of result and x don't match" - if g.T=="squared" - vmap!(square, result, x) - return result - elseif g.T=="exponential" - vmap!(exp, result, x) - return result - elseif g.T=="sigmoid" - vmap!(sigmoid, result, x) - return result - elseif g.T=="sigmoid_" - vmap!(y -> sigmoid_(y, g.Kmin, g.Kmax), result, x) - return result - elseif g.T=="softplus" - vmap!(softplus, result, x) - return result - elseif g.T=="explinearunit" - vmap!(explinearunit, result, x) - return result - end -end - -evaluate(g::Rectifier, x) = evaluate!(zero(x), g, x) - -function inverse(g::Rectifier, x) - @assert x>=0 "Input to rectifier is negative" - if g.T=="squared" - error("squared rectifier is not invertible") - elseif g.T=="exponential" - return log(x) - elseif g.T=="sigmoid" - return invsigmoid(x) - elseif g.T=="sigmoid_" - return invsigmoid_(x, g.Kmin, g.Kmax) - elseif g.T=="softplus" - return invsoftplus(x) - elseif g.T=="explinearunit" - return invexplinearunit(x) - end -end - -function inverse!(result, g::Rectifier, x) - @assert all(x .> 0) "Input to rectifier is negative" - @assert size(result,1) == size(x,1) "Dimension of result and x don't match" - if g.T=="squared" - error("squared rectifier is not invertible") - elseif g.T=="exponential" - vmap!(log, result, x) - return result - elseif g.T=="sigmoid" - vmap!(invsigmoid, result, x) - return result - elseif g.T=="sigmoid_" - vmap!(y->invsigmoid(y, g.Kmin, g.Kmax), result, x) - elseif g.T=="softplus" - vmap!(invsoftplus, result, x) - return result - elseif g.T=="explinearunit" - vmap!(invexplinearunit, result, x) - return result - end -end - -vinverse(g::Rectifier, x) = inverse!(zero(x), g, x) - - -function grad_x(g::Rectifier, x) - if g.T=="squared" - return dsquare(x) - elseif g.T=="exponential" - return exp(x) - elseif g.T=="sigmoid" - return dsigmoid(x) - elseif g.T=="sigmoid_" - return dsigmoid_(x, g.Kmin, g.Kmax) - elseif g.T=="softplus" - return dsoftplus(x) - elseif g.T=="explinearunit" - return dexplinearunit(x) - end -end - - -function grad_x!(result, g::Rectifier, x) - @assert size(result,1) == size(x, 1) "Dimension of result and x don't match" - if g.T=="squared" - vmap!(dsquare, result, x) - return result - elseif g.T=="exponential" - vmap!(exp, result, x) - return result - elseif g.T=="sigmoid" - vmap!(dsigmoid, result, x) - return result - elseif g.T=="sigmoid_" - vmap!(y->dsigmoid_(y, g.Kmin, g.Kmax), result, x) - return result - elseif g.T=="softplus" - vmap!(dsoftplus, result, x) - return result - elseif g.T=="explinearunit" - vmap!(dexplinearunit, result, x) - return result - end -end - -vgrad_x(g::Rectifier, x) = grad_x!(zero(x), g, x) - -""" Compute g′(x)/g(x) i.e d/dx log(g(x))""" -function grad_x_logeval(g::Rectifier, x::T) where {T <: Real} - if g.T=="squared" - return dsquare(x)/square(x) - elseif g.T=="exponential" - return 1.0 - elseif g.T=="sigmoid" - return dsigmoid(x)/sigmoid(x) - elseif g.T=="sigmoid_" - return dsigmoid_(x, g.Kmin, g.Kmax) / sigmoid_(x, g.Kmin, g.Kmax) - elseif g.T=="softplus" - return dsoftplus(x)/softplus(x) - elseif g.T=="explinearunit" - return dexplinearunit(x)/explinearunit(x) - end -end - -function grad_x_logeval!(result, g::Rectifier, x) - @assert size(result,1) == size(x,1) "Dimension of result and x don't match" - if g.T=="squared" - vmap!(xi->dsquare(xi)/square(xi), result, x) - return result - elseif g.T=="exponential" - vmap!(1.0, result, x) - return result - elseif g.T=="sigmoid" - vmap!(xi->dsigmoid(xi)/sigmoid(xi), result, x) - return result - elseif g.T=="sigmoid_" - vmap!(xi->dsigmoid_(xi, g.Kmin, g.Kmax)/sigmoid_(xi, g.Kmin, g.Kmax), result, x) - return result - elseif g.T=="softplus" - vmap!(xi->dsoftplus(xi)/softplus(xi), result, x) - return result - elseif g.T=="explinearunit" - vmap!(xi->dexplinearunit(xi)/explinearunit(xi), result, x) - return result - end -end - -vgrad_x_logeval(g::Rectifier, x) = grad_x_logeval!(zero(x), g, x) - - -# Compute (g″(x)g(x)-g′(x)^2)/g(x) i.e d^2/dx^2 log(g(x)) -function hess_x_logeval(g::Rectifier, x::T) where {T <: Real} - if g.T=="squared" - return (d2square(x)*square(x) - dsquare(x)^2)/square(x)^2 - elseif g.T=="exponential" - return 0.0 - elseif g.T=="sigmoid" - return (d2sigmoid(x)*sigmoid(x) - dsigmoid(x)^2)/sigmoid(x)^2 - elseif g.T=="sigmoid_" - return (d2sigmoid_(x, g.Kmin, g.Kmax)*sigmoid_(x, g.Kmin, g.Kmax) - dsigmoid_(x, g.Kmin, g.Kmax)^2) / sigmoid_(x, g.Kmin, g.Kmax)^2 - elseif g.T=="softplus" - return (d2softplus(x)*softplus(x) - dsoftplus(x)^2)/softplus(x)^2 - elseif g.T=="explinearunit" - return (d2explinearunit(x)*explinearunit(x) - dexplinearunit(x)^2)/explinearunit(x)^2 - end -end - -function hess_x_logeval!(result, g::Rectifier, x) - @assert size(result,1) == size(x,1) "Dimension of result and x don't match" - if g.T=="squared" - vmap!(xi->(d2square(xi)*square(xi) - dsquare(xi)^2)/square(xi)^2, result, x) - return result - elseif g.T=="exponential" - vmap!(0.0, result, x) - return result - elseif g.T=="sigmoid" - vmap!(xi->(d2sigmoid(xi)*sigmoid(xi) - dsigmoid(xi)^2)/sigmoid(xi)^2, result, x) - return result - elseif g.T=="sigmoid_" - vmap!(xi->(d2sigmoid_(xi, g.Kmin, g.Kmax)*sigmoid_(xi, g.Kmin, g.Kmax) - dsigmoid_(xi, g.Kmin, g.Kmax)^2)/sigmoid_(xi, g.Kmin, g.Kmax)^2, result, x) - return result - elseif g.T=="softplus" - vmap!(xi->(d2softplus(xi)*softplus(xi) - dsoftplus(xi)^2)/softplus(xi)^2, result, x) - return result - elseif g.T=="explinearunit" - vmap!(xi->(d2explinearunit(xi)*explinearunit(xi) - dexplinearunit(xi)^2)/explinearunit(xi)^2, result, x) - return result - end -end - -vhess_x_logeval(g::Rectifier, x) = hess_x_logeval!(zero(x), g, x) - -function hess_x(g::Rectifier, x::T) where {T <: Real} - if g.T=="squared" - return d2square(x) - elseif g.T=="exponential" - return exp(x) - elseif g.T=="sigmoid" - return d2sigmoid(x) - elseif g.T=="sigmoid_" - return d2sigmoid_(x, g.Kmin, g.Kmax) - elseif g.T=="softplus" - return d2softplus(x) - elseif g.T=="explinearunit" - return d2explinearunit(x) - end -end - -function hess_x!(result, g::Rectifier, x) - @assert size(result,1) == size(x,1) "Dimension of result and x don't match" - if g.T=="squared" - vmap!(d2square, result, x) - return result - elseif g.T=="exponential" - vmap!(exp, result, x) - return result - elseif g.T=="sigmoid" - vmap!(d2softplus, result, x) - return result - elseif g.T=="sigmoid_" - vmap!(y->d2sigmoid_(y, g.Kmin, g.Kmax), result, x) - return result - elseif g.T=="softplus" - vmap!(d2softplus, result, x) - return result - elseif g.T=="explinearunit" - vmap!(d2explinearunit, result, x) - return result - end -end - -vhess_x(g::Rectifier, x) = hess_x!(zero(x), g, x) diff --git a/src/hermitemap/optimize.jl b/src/hermitemap/optimize.jl index be6b796..cccebd2 100644 --- a/src/hermitemap/optimize.jl +++ b/src/hermitemap/optimize.jl @@ -94,10 +94,9 @@ function optimize(C::HermiteMapComponent, X, optimkind::Union{Nothing, Int64, St Optim.LBFGS(; m = 10)) end - if Optim.converged(res) - mul!(view(C.I.f.coeff,:), F.Uinv, Optim.minimizer(res)) - else - error("Optimization hasn't converged") + mul!(view(C.I.f.coeff,:), F.Uinv, Optim.minimizer(res)) + if !Optim.converged(res) + println("Optimization hasn't converged") end diff --git a/src/hermitemap/precond.jl b/src/hermitemap/precond.jl index cc5fd72..272e41a 100644 --- a/src/hermitemap/precond.jl +++ b/src/hermitemap/precond.jl @@ -202,6 +202,7 @@ function diagprecond!(P, coeff, S::Storage, C::HermiteMapComponent, X::Array{Flo S.cache_integral[i] += f0i end + # Store g(∂_{xk}f(x_{1:k})) in S.cache_g @avx for i=1:Ne prelogJi = zero(Float64) @@ -214,7 +215,6 @@ function diagprecond!(P, coeff, S::Storage, C::HermiteMapComponent, X::Array{Flo reshape_cacheintegral = reshape(S.cache_integral[Ne+1:Ne+Ne*Nψ], (Ne, Nψ)) # reshape2_cacheintegral = reshape(S.cache_integral[Ne + Ne*Nψ + 1: Ne + Ne*Nψ + Ne*Nψ*Nψ], (Ne, Nψ, Nψ)) - # @show reshape2_cacheintegral fill!(P, 0.0) @inbounds for l=1:Ne # Exploit symmetry of the Hessian @@ -231,7 +231,7 @@ function diagprecond!(P, coeff, S::Storage, C::HermiteMapComponent, X::Array{Flo @inbounds for i=1:Nψ P[i] += 2*C.α end - return P + return Diagonal(P) end """ diff --git a/src/hermitemap/qraccelerated.jl b/src/hermitemap/qraccelerated.jl index fab3539..50b5f41 100644 --- a/src/hermitemap/qraccelerated.jl +++ b/src/hermitemap/qraccelerated.jl @@ -154,7 +154,7 @@ function qrnegative_log_likelihood!(J̃, dJ̃, c̃oeff, F::QRscaling, S::Storage if J̃ != nothing J̃ = 0.0 - @avx for i=1:Ne + for i=1:Ne J̃ += log_pdf(S.cache_integral[i]) + log(C.I.g(S.cache_g[i])) end J̃ *=(-1/Ne) diff --git a/src/hermitemap/stochmapfilter.jl b/src/hermitemap/stochmapfilter.jl index b924a1a..b19c63f 100644 --- a/src/hermitemap/stochmapfilter.jl +++ b/src/hermitemap/stochmapfilter.jl @@ -46,32 +46,32 @@ function (smf::HermiteSMF)(X, ystar::Array{Float64,1}, t::Float64) # Perturbation of the measurements smf.ϵy(X, 1, Ny) + μYX = mean(X; dims = 2)[:,1] + σYX = std(X; dims = 2)[:,1] + + X̃ = Diagonal(σYX) \ (X .- μYX) # if abs(round(Int64, t / smf.Δtfresh) - t / smf.Δtfresh)<1e-6 - M = HermiteMap(30, X; diag = true, b = "CstLinProHermite") - # Perform a kfold optimization of the map - optimize(M, X, "kfolds"; withconstant = false, withqr = true, - verbose = false, start = Ny+1, P = serial, hessprecond = true) - # else - # L = LinearTransform(X; diag = true) - # M = HermiteMap(40, Ny+Nx, L, smf.M.C) - # # M = HermiteMap(40, X; diag = true) - # # Only optimize the existing coefficients of the basis - # optimize(M, X, nothing; withconstant = false, withqr = true, - # verbose = false, start = Ny+1, P = serial, hessprecond = true) - # end + # M = HermiteMap(30, X̃; diag = true, b = "CstLinProHermiteBasis") + # Perform a kfold optimization of the map + # optimize(M, X̃, "split"; maxterms = 20, withconstant = false, withqr = true, + # verbose = false, start = Ny+1, P = serial, hessprecond = true) + + M = totalordermap(X̃, 2; b = "CstLinProHermiteBasis") + optimize(M, X̃, nothing; withconstant = false, withqr = true, + verbose = false, start = Ny+1, P = serial, hessprecond = false) # Evaluate the transport map - F = evaluate(M, X; apply_rescaling = true, start = Ny+1, P = serial) + F = evaluate(M, X̃; apply_rescaling = false, start = Ny+1, P = serial) - # # Rescale ystar - # ystar .-= view(M.L.μ,1:Ny) - # ystar ./= M.L.L.diag[1:Ny] + # Rescale ystar + ỹstar = Diagonal(σYX[1:Ny])\(copy(ystar) - μYX[1:Ny]) # Generate the posterior samples by partial inversion of the map - hybridinverse!(X, F, M, ystar; start = Ny+1, P = serial) + hybridinverse!(X̃, F, M, ỹstar; start = Ny+1, P = serial) # @show getcoeff(M[Nypx]) # @show "after inversion" # @show norm(X) + X[Ny+1:Ny+Nx,:] .= μYX[Ny+1:Ny+Nx] .+ Diagonal(σYX[Ny+1:Ny+Nx])*X̃[Ny+1:Ny+Nx,:] return X end @@ -124,7 +124,7 @@ function (smf::FixedHermiteSMF)(X, ystar::Array{Float64,1}, t::Float64) M = HermiteMap(smf.M.m, smf.M.Nx, L, smf.M.C) clearcoeff!(M) - M = totalordermap(X, 2; b = "CstLinProHermite") + M = totalordermap(X, 2; b = "CstLinProHermiteBasis") optimize(M, X, nothing; withconstant = false, withqr = true, verbose = false, start = Ny+1, P = serial, hessprecond = true) diff --git a/src/radialmap/stochmapfilter.jl b/src/radialmap/stochmapfilter.jl index 0fb5845..4371d64 100644 --- a/src/radialmap/stochmapfilter.jl +++ b/src/radialmap/stochmapfilter.jl @@ -134,14 +134,14 @@ function (smf::SparseRadialSMF)(X, ystar, t; P::Parallel = serial, localized::Bo # Perturbation of the measurements smf.ϵy(X, 1, Ny) - optimize(smf.S, X, nothing; apply_rescaling=true, start = Ny+1) + optimize(smf.S, X, nothing; apply_rescaling = true, start = Ny+1) # Evaluate the transport map - F = evaluate(smf.S, X; apply_rescaling=true, start = Ny+1) + F = evaluate(smf.S, X; apply_rescaling = true, start = Ny+1) # Generate the posterior samples by partial inversion of the map - inverse!(X, F, smf.S, ystar; appply_rescaling, start = Ny+1) + inverse!(X, F, smf.S, ystar; apply_rescaling = true, start = Ny+1) end return X end @@ -165,7 +165,7 @@ struct AdaptiveSparseRadialSMF<:SeqFilter ϵy::AdditiveInflation "Array of Sparse radial maps" - S::Array{SparseRadialMap,1} + S::Union{Array{SparseRadialMap,1}, SparseRadialMap} "Observation dimension" Ny::Int64 @@ -306,14 +306,20 @@ function (smf::AdaptiveSparseRadialSMF)(X, ystar, t; P::Parallel = serial, local # Perturbation of the measurements smf.ϵy(X, 1, Ny) - optimize(smf.S, X, nothing; apply_rescaling=true, start = Ny+1) + updateLinearTransform!(smf.S.L, X) + # @show smf.S + + optimize(smf.S, X, 2, 1, "split"; apply_rescaling = true, start = Ny+1) + + # @show smf.S + # Evaluate the transport map - F = evaluate(smf.S, X; apply_rescaling=true, start = Ny+1) + F = evaluate(smf.S, X; apply_rescaling = true, start = Ny+1) # Generate the posterior samples by partial inversion of the map - inverse!(X, F, smf.S, ystar; appply_rescaling, start = Ny+1) + inverse!(X, F, smf.S, ystar; apply_rescaling = true, start = Ny+1) end return X end diff --git a/src/tools/banana.jl b/src/tools/banana.jl index 06f074d..729efdd 100644 --- a/src/tools/banana.jl +++ b/src/tools/banana.jl @@ -6,7 +6,7 @@ export sample_banana, log_pdf_banana Generate `N` samples [x₁; x₂] of the Banana distribution defined as: x₁ ∼ N(μ, σ²), ϵ ∼ N(0, 1), - x₂ ∼ bananicity × (x₁ - σ²) + ϵ. + x₂ ∼ bananicity × (x₁^2 - σ²) + ϵ. """ function sample_banana(N; μ = 0.0, σ = 2.0, bananicity = 0.2) X = zeros(2,N) diff --git a/test/hermitemap/rectifier.jl b/test/hermitemap/rectifier.jl index f1d94d2..850b621 100644 --- a/test/hermitemap/rectifier.jl +++ b/test/hermitemap/rectifier.jl @@ -35,22 +35,3 @@ end # Test hessian of log evaluation @test abs(ForwardDiff.hessian(y->log(r(y[1])), [x])[1,1] - hess_x_logeval(r, x) ) < 1e-10 end - -@testset "Test FlexSigmoidRectifier" begin - atol = 1e-9 - Kmin = rand() - Kmax = Kmin + 10*rand() - x = rand() - r = FlexSigmoidRectifier(Kmin, Kmax) - # Test gradient - @test abs(ForwardDiff.derivative(y->r(y), x) - grad_x(r, x) ) < 1e-10 - - # Test hessian - @test abs(ForwardDiff.derivative(z->ForwardDiff.derivative(y->r(y), z),x) - hess_x(r, x) ) < 1e-10 - - # Test gradient of log evaluation - @test abs(ForwardDiff.derivative(y->log(r(y)), x) - grad_x_logeval(r, x) ) < 1e-10 - - # Test hessian of log evaluation - @test abs(ForwardDiff.hessian(y->log(r(y[1])), [x])[1,1] - hess_x_logeval(r, x) ) < 1e-10 -end \ No newline at end of file