From 18d8b109070ec5f9c4516f21b047b55c028473be Mon Sep 17 00:00:00 2001 From: Cas van Bemmelen <38755055+casvbem@users.noreply.github.com> Date: Thu, 7 Mar 2024 16:29:38 +0100 Subject: [PATCH] Revert "fixed posdwn = -1 in plotting of 1D and 2D model setups" --- examples/xbeach-setup-1D.ipynb | 130 +++++++++++++++------------- xbTools/grid/extension.py | 4 +- xbTools/xbeachtools.py | 151 ++++++++++++++++++++++++++------- 3 files changed, 192 insertions(+), 93 deletions(-) diff --git a/examples/xbeach-setup-1D.ipynb b/examples/xbeach-setup-1D.ipynb index 4c0f1cd..8114811 100644 --- a/examples/xbeach-setup-1D.ipynb +++ b/examples/xbeach-setup-1D.ipynb @@ -11,18 +11,9 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "83abea17", + "execution_count": 24, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "**no xbTools installation found in environment, adding parent path of notebook to see if it works\n" - ] - } - ], + "outputs": [], "source": [ "# import default modules\n", "import numpy as np\n", @@ -41,7 +32,6 @@ }, { "cell_type": "markdown", - "id": "4bbeb898", "metadata": {}, "source": [ "Import the toolbox." @@ -49,8 +39,7 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "37d3f161", + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -65,7 +54,6 @@ }, { "cell_type": "markdown", - "id": "3758efb7", "metadata": {}, "source": [ "### Data\n", @@ -74,8 +62,7 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "3e9a75ce", + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -84,18 +71,20 @@ "Text(0.5, 1.0, 'bathy')" ] }, - "execution_count": 3, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -123,7 +112,6 @@ }, { "cell_type": "markdown", - "id": "06c2dbd8", "metadata": {}, "source": [ "### Create x-grid\n", @@ -149,18 +137,19 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "2572b832", + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { - "image/png": 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m69at9O3bt15f37x53UeGREQkCmV1xwABxQuWE7K6RahBTU8jNTWxrIopoPr8atUTRj1S8U1ERaAxP/1rxfX6vE4d62l8ysrKuPzyyxkzZgx/+tOfuPrqq9m1axcAvXr14j//+U/A/VUfi4hIHMhsz/+SKtVLWk4Y9XDCTD2BRmoaX/9fQ/ezsQu3UJLUmvT24V9JdMcdd1BUVMSjjz5KixYtWLhwIVdddRVvvvkmN9xwAxMmTGDgwIGceuqpvPDCC3z++ed065Y4yV1EJFFYnh8AyBtwJ13PuCyhAg0o1IRHZntIz8EUF4f9rd577z0efvhhli1bRkZGBgD//ve/OfHEE5k5cybXXXcd33zzDbfeeiuHDh3ikksuYdy4caxevTrsbRMRkaZz6IcD5Ng7wYL0k8ckXKABhZqYd9ZZZ1FeXh5wrUuXLhQVFfkf33XXXdx115GK+OHDh9OjRw//4zlz5oS9nSIiEl75eV/S1TKU0IzsNh0i3ZyIUKiJcwcPHuSJJ57g3HPPxel08vzzz/Puu+9qPxoRkTiz/+sVAOx2ukl3JGbJbGJ+6gRiWRYLFy7kjDPOYMCAASxYsIBXXnmFYcOGRbppIiLSWNb+iz6fTgWgqzcP1v4rsu2JEI3UxLlmzZrx7rvvRroZIiISLkXbYcGN/j1qLEi4Tfd8NFIjIiISy/ZuCdgfDUi4Tfd8FGqqMMbUfpMcNV//WvXci0dERIJbtjsdb+CWe3hxsGx3iwi1KHIUag5LSkoCKgprJXzKysoAcDqdEW6JiEjsy92Qz1Wv7uCx8gv91zzGwe3l47nq1R3kbsiPYOuanmpqDnM6nbRs2dK/E29aWlqDRhNs26asrIxDhw7hSNAq9Kps22b37t2kpaUp1IiINJDXNkxb8CUG2E4rAL7wdmZ8+a0UkI0FTFvwJcN7u3E6EmN0XKGmErfbDeAPNg1hjOGHH36gWbNmmmqpxOFw0KlTJ/WJiEgDrc7bS37RIQB6W9sAWGl6U0A2AAbILzrE6ry9DOmeHalmNimFmkosyyInJ4c2bdpU29CuvsrLy3n//fc544wz/FNbAsnJyTgcjgb3r4hIottVcsj/+36OrwH4n90q5H3xTqEmCKfT2eDpEafTicfjITU1VaFGREQaXZv0VADGOJfSz6pY6XRX0rMc9KTyondotfsSgYo9REREYtCgrlmckLGf6a6n8M3oOy3DdNdTuCnEAnIyUxnUNSui7WxKCjUiIiIxyOmwuPOUZJxW4FYkLsumi2MnAFNG9U6YImFQqBEREYlZgwYMouruah7j4GCLTsy8vD8j+uREpF2RopoaERGRWJXZnr1kkk0RALblYNsp9/LaOZck1AiNj0KNiIhIjNqzaTWtKMI28MMvnqV5p/50T7DznirT9JOIiEgsWvsvsueeA4BlQfPy7xPuAMuqFGpERERiTU0ncxdtj2SrIk6hRkREJNboZO6gFGpERERijPeYbpiq/4RbTsjqFpkGRYmYCTUzZszg5JNPJj09nTZt2jB69Gg2btwY6WaJiIg0qdwN+Zw+cyOzPef4r3lxsL7/NNXURLoBdbV8+XKuv/56Pv74YxYvXkx5eTnnnHMOBw4ciHTTREREmkTuhnyue3Yt+UWHcOAFYKX3OE4/9AgXrOhG7ob8CLcwsmJmSXdubm7A4zlz5tCmTRvWrFnDGWecEaFWiYiINA2vbZi24EsMcIlzGVc4FwMw2PFffuz8nJe8Q5m24EuG93Yn5B41EEOhpqqiooqNhrKyaj7TorS0lNLSUv/j4uJioOIE7XCfEu17fZ1GHZz6p3bqo9qpj0JT/9QulvpoVd5e8osO4aaQGa5Z/vOeHIfPe3rfewL5Rdms3LyLwY103lO09E9d398yxlTdYTnq2bbNBRdcwL59+/jwww9rvG/q1KlMmzat2vW5c+eSlpYWziaKiIg0qjV7LP71tZMhji94Pvneas//suxOPrZ78+ueXga0irl/2kM6ePAgl112GUVFRWRkZNR4X0yGmuuuu45Fixbx4Ycf0qFDhxrvCzZS07FjR/bs2ROyUxpDeXk5ixcvZvjw4SQlJYX1vWKR+qd26qPaqY9CU//ULpb6aFXeXi5/+hPcFPJRyg1UnmHyGAenlz5CAdk8e9XARh2piYb+KS4uplWrVrWGmpibfpo4cSJvvvkm77//fshAA5CSkkJKSkq160lJSU32h9OU7xWL1D+1Ux/VTn0UmvqndrHQR0N6tCEnM5WComy+Nh3oZf0PqAg0t3vGs5NscjJTGdKjTaPX1ES6f+r63jGz+skYw8SJE3nttddYunQpXbt2jXSTREREmozTYTFlVG/asZuO1i4Abiu7mtNLH+El71AApozqnbBFwhBDIzXXX389c+fO5fXXXyc9PZ2CggIAMjMzadasWYRbJyIiEn4jyhZzburvsDAYU3HmU8HhEZopo3ozok9OpJsYUTETambOnAnAWWedFXB99uzZjBs3rukbJCIi0pSqnvdkwfTkp7n4wivp1+f4hB6h8YmZUBOD9cwiIiKNJ8h5Tw5jMyD9e1CgAWKopkZERCShZXXHWDrvKRSFGhERkViQ2Z7Cgbf6HxrLCaMeTvjzniqLmeknERGRRPddcTmtgP85O9Lht28r0FShkRoREZEo57UNW3L/Tr+NfwWgvfd/sGVJhFsVfRRqREREoljuhnwuvO9Fuqy8HV85sIXBLLixYkWU+CnUiIiIRKncDflc9+xamu/fhtMKXAVsGZvVa/4ToZZFJ4UaERGRKOS1DdMWfIkB8mw3tglctu0xDu75uBSvrS1PfBRqREREotDqvL3kFx0CKnYN3mC6+J/znff0eXELVuftjVALo49WP4mIiEShXSWH/L93YNPZqjge6IGyX/CKfSYFZFe7L9FppEZERCQKtUlP9f/+JudLZFo/AHBz0iuc4fw86H2JTqFGREQkCg3qmkVOZio5FHK963X/dadlmO56ihwKyclMZVDXrAi2Mroo1IiIiEQhp8NiyqjedHEUVDvayWXZdHbsZMqo3jrIshLV1IiIiESpEX1ySBnUGtYFXvfi4JrRwxjaJyci7YpWCjUiIiLRau2/OGvdTQAYwKLizCfrp39l6ICTItq0aKRQIyIiEo2KtsOCG7Go2IemYpLJgTV+MVaHAZFsWdRSTY2IiEgU8u7ZDMauctWG8oMRaU8sUKgRERGJMrkb8rnwhYJquwgbywFZ3SLUquinUCMiIhJFfOc9fV7cgjzj9l/3GAeTysaT+53+6a6JekZERCRKVD7v6dfOt+lm5VdcN3C/ZwwveocybcGXOu+pBgo1IiIiUcJ33pObQqa6/oV1ePbJacFtrhdoSyH5RYd03lMNFGpERESihO8cp/6OTTiswNEYl2XTxbEz4D4JpFAjIiISJdqkp3KJcxmPJT1W7TmPcbDVbuu/T6rTPjUiIiJRYlD2DwxKmoWzynWvsbjdM56dZOu8pxAUakRERKKE8/tvgOpFwDeU38Ai+xQAnfcUgqafREREooDXNqwpycIQGFg8xsFauyfuzFRmXt6fETrvqUYaqREREYmw3A35TFvwJT/ev4j+LuM7EwGDxfqTpvHXvuczqGuWRmhqoZEaERGRCPJttmeKtjPDNcu/jBsq9qfZ1+7HDOmerUBTBwo1IiIiEVJ5s72ujgKc1ZZxG15a/L4226sjhRoREZEI8W22B7DfTsFUyS4e42BtyTHabK+OFGpEREQixLeJ3iXOZcxPmYJl4Q82HuPgds94CsjWZnt1pEJhERGRCGmTnoqbQma4ZvmnniyrYl+aC0unsp4e/vukdhqpERERiZBBXbMYkL63Wi2N0zI0d5RhgTbbqweFGhERkQhxOix+MfxMbFN9b5pth49E0GZ7dafpJxERkQga6lqPqTRSYx8+EoHM9swc1Vub7dWDQo2IiEgEeG3Dug1f0P+NGwP3ELZgzJhxzOhzvEZo6kmhRkREpIn5dhDuUrKG55PtgOccGAakfw8KNPWmUCMiItKE/DsIA62o2Jum8i7CxnJgZXWLWPtimQqFRUREmkjlHYRr2ptmhvNavOntItrOWKVQIyIi0kR8OwiH2pvmn/tP1w7CR0mhRkREpIn4dgYOds6Tb2+ayvdJ/SjUiIiINBHfzsB5tjvo3jRbD+9Nox2Ej45CjYiISBMZ1DWLnMxUznR+DlTfm2Yn2dpBuAEUakRERJqI02Ex/ewsprtmBazYtoEPvCcA2kG4IbSkW0REpAkNbV0CVeppXJbhpPTvueBnw7WDcAMo1IiIiDQRr21YV5JFfwjYRdhYTh67/iKcLRVoGkKhRkREpAn4dhE+a/9b9HfhTzUGC2vUwzhbdoho++KBampERETCzL+LcNF27nU9HbCDsNfAMk/fyDUujijUiIiIhFHlXYT7OzbhCFJP89Li9/HaJvgLSJ0p1IiIiISRbxfhS5zL+FvSY9We9xgHa0uO0S7CjUChRkREJEy8tmHF5j3+YxGqrtT2Ht6fpoBs7SLcCFQoLCIiEga+wuD8okNMduVWOxYB4IbyG1honwJoF+HGoFAjIiLSyPyFwUBfNjPB+Va1e7zGYq3dEwtwaxfhRqHpJxERkUZUuTD4Eucy5qfcXW3aCWCWdyQ7yQa0i3Bj0UiNiIhII/IVBvvqaJxBsorHWMz2jMCdmcqUUb21i3AjUagRERFpRL6C366OgqB1NBXFwVfzi6GDuGl4L43QNCKFGhERkUbUqkUKAP2srzGGKhvtWYwuncZ6evB8j9YKNI1MNTUiIiKNJHdDPre8uI6+bOYPrhcDAo0xcJ/nUjbQgxwVBoeFQo2IiEgj8K14OuNALvNT7g4INFAxYrPedANUGBwumn4SERFpIN+Kp7Yhi4MdHGzRiZkX9FdhcJjE3EjN448/TpcuXUhNTWXw4MGsXr060k0SEZEE5rUNc1bkYYq2c0fSsyGKg8dz28VnK9CEUUyFmhdeeIGbb76ZKVOmsHbtWk488UTOPfdcdu3aFemmiYhIAsrdkM/p9y9lY+7fWZFyA6Ocq6rd4zUwunQaL3qHsudAaQRamThiavrpoYceYsKECVx55ZUAPPHEE7z11ls8/fTTTJo0qdr9paWllJYe+QYqLi4GoLy8nPLy8rC21ff64X6fWKX+qZ36qHbqo9DUP7VrSB+9/cVObpj3WcWUU0rwKSeAWd7zWU8PALLTXDH15xEt30N1fX/LGBMTZ52XlZWRlpbGyy+/zOjRo/3Xr7jiCvbt28frr79e7WumTp3KtGnTql2fO3cuaWlp4WyuiIjEKdvA10UWc752kOEp5I6k54KO0EDFJnunlz5KAVm0TIYp/b1BdxeW0A4ePMhll11GUVERGRkZNd4XMyM1e/bswev10rZt24Drbdu25b///W/Qr5k8eTI333yz/3FxcTEdO3bknHPOCdkpjaG8vJzFixczfPhwkpKSwvpesUj9Uzv1Ue3UR6Gpf2pX3z56+4udzFj4XwqKS5ngfJPJKXNrDCm+TfZ2ko0F3PPzEzn3+LbBb45S0fI95JtpqU3MhJqjkZKSQkpKSrXrSUlJTfaH05TvFYvUP7VTH9VOfRSa+qd2demj3A353DDvMwwwwbmA213PV1u27fOG9xSml4+lgGxy4uAohEh/D9X1vWMm1LRq1Qqn08nOnTsDru/cuRO32x2hVomISCIo89jc/toGDOCmkEkhAo3HWP5Ac9f5xzHutK7ak6aJxMzqp+TkZAYMGMCSJUv812zbZsmSJQwZMiSCLRMRkXiWuyGfU2a8y94DZbgpZErSMzUWBVeecsrJTFWgaWIxM1IDcPPNN3PFFVcwcOBABg0axMMPP8yBAwf8q6FEREQak2+X4IoppzeZ7ApeQ2MMPOs9m8c9o9lJNqBdgyMhpkLNmDFj2L17N3fffTcFBQX069eP3NzcasXDIiIiDeG1DR9vKWTSK+tpSyETXa8x1rm0ximn57w/4S7PeIC4qKGJVTEVagAmTpzIxIkTI90MERGJU7kb8pm24Evyiw7VusIJKmpo/ua5kJbNknh8bH9O6ZatEZoIiblQIyIiEi6B002hVzhBYA3NzIv6clqPVk3WVqlOoUZERIQjh1IaoC+bmRwi0FSuoSlvnsPMC/touikKKNSIiIgAq/P2kl90iEucy5jhejLEpnpwn+dSnvSOIqt5Eh9PPptkV8wsJo5rCjUiIiLArpJDuClkhiv4OU5VVzhZwPQL+yrQRBGFGhERSXhe27CnpJSujgKcVvUjESuPzoBWOEUrhRoREUlonxVazPjL+4fPc/oGYwiopfEai9Gl01hPD61winIKNSIikrDe/mInT29yAKWHi4PnBQQac3iEZgM9sID7tMIpqmkiUEREEpLXNtyz8L8AXOJcxvyUu3FUmXqyLFhvuuHOTGXm5f013RTlNFIjIiIJaXXeXgqKS3Gzt8biYI9x8LOhp3PJ2adouikGaKRGREQS0q6SQwAhioMtbveMJ611JwWaGKGRGhERSUht0lMB6GOFLg6+8PB9Ev0UakREJCEN6HwMxzYrZpJdc3FwTmYqg7pmRa6RUi+afhIRkYSTuyGfMx9YRrfSL6pNPfmKgwGmjOqtqacYopEaERFJKL5DKy92LmNG0pPVnvcYBwdbdGLmBVrtFGsUakREJGH4Dq1sW8NxCF5jca/jN7z8h4t1/EEM0p+YiIgkDN+hlTWteLqh/AZm/3AGa7Z9H4HWSUMp1IiISMLwLeP2rXiqzGMcrLV7BtwnsUWhRkREEoLv0Eo3hUGPQ7jf80sKyAaOLPeW2KKaGhERiXu5G/KZtuBL8osOcadrYY3HIViAW8u4Y5ZCjYiIxC2vbfjb0s389d1NAPRlM+Odi6rfZyy22W0BLeOOZQo1IiISl3I35DP1jS+geAdDHAUMsTYw0fV6wLSTzyzvSMhsz8xRvbWMO4Yp1IiISNzw2obVeXtZ/GUBT6/YygTnm0xOeR6HZaodheDjMRYtfvx/fDj8NI3QxDiFGhERiQuV62bcFHKP6zXGOpf6g0ywQFNxaOXVnNJKh1bGgzqFmuLi4nq/cEZGRr2/RkREpD4qj8wsXLGGro4CLnVWTDPVllG8Bv+hlRekpzRNgyWs6hRqWrZsiRUs4tbAsiw2bdpEt27djrphIiIiwVQOMvPX7WDvgTImON/ko1qmmSqrOLTyMjbQg5bJhoGdj2maxktY1Xn66eWXXyYrq/YlbsYYRo4c2aBGiYiIBFN5isnnZueL3OCaH3KaCfCHHa+xuM/zS570/hQL+HkXW1NPcaJOoaZz586cccYZZGdn1+lFu3XrRlJSUoMaJiIiAtWLf33cFDKxSt1Mja9h4HHPz/jI9GWr3ZYCssnJTOWO83rh3bYmvB9AmkydQk1eXl69XnTDhg1H1RgREZHKqo7MuClkgGMTQxxfcJlzaa11M8bAs96zedwz2r9bMMBNw3oy8Sc9sb0eFm4L5yeQpqTVTyIiElWCjcy4KeRKVy4TnG/VKcgETjON8j+Xk5nKlEp70djecH0KiYSjCjX/+c9/WLZsGbt27cK27YDnHnrooUZpmIiIJJ5gNTMTnG8y2TW31jADFYFmtudc3jEn+6eZAMaf1oVhvd0M6pql+pk4Vu9QM336dO6880569epF27ZtA1ZF1WeFlIiIiE/V4wygfjUzUFE3c5/n0pAjMxLf6h1qHnnkEZ5++mnGjRsXhuaIiEii8R1nUFBcChyZarra+RbOOoaZJ73nM8czQiMzCa7eocbhcHDaaaeFoy0iIpIgalrRVJ+pJtvAP6uEGY3MJLZ6h5qbbrqJxx9/nIcffjgMzRERkXgXrG7GTSG/db3Cpc73Qk41GQOvek9jiT2AtXZPCsgmq3kS4/u118iM1D/U3HrrrZx//vl0796d3r17V9uP5tVXX220xomISHwItdfMeNdCxjsX1elYg8o1M5pikqrqHWp++9vfsmzZMoYOHUp2draKg0VEJKSaRmauci3iaufCOoWZyjUzmmKSmtQ71DzzzDO88sornH/++eFoj4iIxImaVjRd6VrEhDqEmaob52lkRmpT71CTlZVF9+7dw9EWERGJE8FXNNUtzEDgVFNOZipPaGRG6qDeoWbq1KlMmTKF2bNnk5aWFo42iYhIDGroTsBQfarJd5yBRmakLuodah599FG2bNlC27Zt6dKlS7VC4bVr1zZa40REJDYEO6OpIWFGozNyNOodakaPHh2GZoiISCyqWjfT0DCjuhlpiHqHmilTpoSjHSIiEgN8U0y7Sg6xdc9B5q7axs6SUo3MSFTQKd0iIlInNS3NnqyRGYkSdQo1WVlZbNq0iVatWtXpRTt16sQHH3xA586dG9Q4ERGJrFCb5jXkfCaNzEg41CnU7Nu3j0WLFpGZmVmnFy0sLMTr9TaoYSIiElnBin+7OgroY+UxyTX3qA+b1IomCZc6Tz9dccUV4WyHiIhEiWCb5l3iXMYM1yyclsEYQp7PBMHDjHYClnCrU6ixbTvc7RARkShQddM8gL5s5j7XLByWAUIHmmBhRnUz0lRUKCwikuBC1c0c7flMoJEZaXoKNSIiCazmuplvmOx6vsYw45uC8hiLWd6RAWEGVDcjkaFQIyKSYEIdZ3C1c2GtdTNeY3Gf55esN93ZarcNCDManZFIUqgREUkgwfaaqSgCfjJgNVOoQDO6dBrr6QGAOyOFmwZ1okur5rRJT1XdjESUQo2ISAIItqLJTSEDHJsOFwHX4TWMxWTP1aynh4p/JSrVO9T85Cc/4cwzz6x2XML333/PRRddxNKlSxutcSIi0nBvf7GTexb+17+i6ciRBgv9K5qCCVY3Y2W216Z5ErXqHWree+891q9fz6effspzzz1H8+bNASgrK2P58uWN3kAREak/r21YlbeXV7daLF/5GW4KGVKPjfOC1c2o+Fei3VFNP7377rtcc801nHLKKSxYsIAuXbo0crNERORoBdbNOOu1cV6w1Uw60kBixVGFmpycHJYvX86VV17JySefzEsvvcRxxx3X2G0TEZF6qFo346uZ8QUaqDnQeIzFb8tvYK3dU5vmScyqd6ixDv+NSElJYe7cudxzzz2MGDGC2267rdEbJyIidVN5J+Cqy7Nr4zUWt3uuZqF9CqBl2RK76h1qjAn8C3LnnXdy3HHH6WwoEZEmFmy/mWDLs6uqaeM8jcxIrKt3qMnLy6N169YB1y666CKOPfZYPvnkk0ZrWGVbt27lT3/6E0uXLqWgoIB27dpx+eWXc8cdd5CcnByW9xQRiWbBdgIe5PiK+1xPhlye7TEO7veMCSgAVs2MxIt6h5rOnTsHvX788cdz/PHHN7hBwfz3v//Ftm3+8Y9/0KNHDzZs2MCECRM4cOAADz74YFjeU0Qk2lQdmfGtaOprbWGS64WQy7OD1cyAjjOQ+BITm++NGDGCESNG+B9369aNjRs3MnPmzJChprS0lNLSIyfNFhcXA1BeXk55eXn4Gnz4PSr/VwKpf2qnPqpdIvSR1zZ8su173v1qF298ls/egxWftT4rmqrWzADkZKZwx3nHcu7xbbG9HmxvuD9JdEqE76GGiJb+qev7W6ZqkUyMuPPOO8nNzQ055TV16lSmTZtW7frcuXNJS0sLZ/NERBrss0KLV7c62Fd2JLFUrGjayGNJj4ccmYGqNTNZAJzpNvTNMnTPMHXaRVgkGhw8eJDLLruMoqIiMjIyarwvJkPN5s2bGTBgAA8++CATJkyo8b5gIzUdO3Zkz549ITulMZSXl7N48WKGDx9OUlJSWN8rFql/aqc+ql089lHlkZk5K78FKp+cnXf45Ozaw0zVqSZ3Rgp3jqwYmZEj4vF7qDFFS/8UFxfTqlWrWkNNRKefJk2axP333x/ynq+++opjjz3W/3j79u2MGDGCiy++OGSggYpl5ykpKdWuJyUlNdkfTlO+VyxS/9ROfVS7eOmjmg+brH2a6ciKJge3e8ZXmmoy/HZoD24c3kt1MyHEy/dQuES6f+r63hENNbfccgvjxo0LeU+3bt38v9+xYwdDhw7l1FNP5Z///GeYWyciEn7BlmVD1cMma9s4r/qKJqiomzmv7UFu+El3BRpJCBENNa1bt662PLwm27dvZ+jQoQwYMIDZs2fjcDjC3DoRkfAKtiy7odNMcGQn4JM6pPN27qKwfgaRaBITq5+2b9/OWWedRefOnXnwwQfZvXu3/zm32x3BlomI1F/V4wygMaaZqu8EHOkVKyJNLSZCzeLFi9m8eTObN2+mQ4cOAc/FYJ2ziCSwyscZQONMM2knYJEKMRFqxo0bV2vtjYhItKqpbuYS57KAMFOTmqaZdEaTSKCYCDUiIrGqprqZjqaA+11P1XuaKat5Ehf2a6+RGZEgFGpERMKgct2M7ziDuhcAV59m0hSTSO0UakREGoFvimlXySG27jnI3FXb2FlSGrIAuOrjYNNMOmxSpO4UakREGijYpnluCjm/lgJgy6o4l8lpmWrTTBqZEak/hRoRkaNQU/Ev1KcA2MGFpVNp7ijzTzOp+Ffk6CnUiIjUU00jMxUFwDu53zWrzgXA6+kBdsVzNw3rycSf9NTIjMhRUqgREamjqpvm1X8H4JqOM9DojEhjUKgREamDqpvmNbQAGFQ3I9LYFGpERGpQtW7GtzR7v51y1AXAoJEZkXBRqBERCaJq3Uxdz2aC4AXAPqqbEQkfhRoRkcPqMzJT05RTsAJg0OiMSFNQqBGRhFY5yMxft4O9B8qA2kdmqk4xVS0AdmekcOmgTnRp1Zw26amqmxFpAgo1IpKwgp3LVNeRmZqmmFT8KxI5CjUiklBCnZhdn5GZqlNMml4SiTyFGhFJGDVtmjcgyHEGdR2ZARX/ikQLhRoRiWtHe5xBbSMzoNEZkWijUCMicashxxmEGplR3YxIdFKoEZG4U/U4A5/6HDSpkRmR2KNQIyJxpepxBqFGZnScgUh8UagRkZjntQ2fbCkMuqIp1MiMjjMQiS8KNSISk7y2YVXeXl7dajHt/vfYe7AcODIy08Hs4n7Xk7WMzFSvm8lqnsSF/dprZEYkBinUiEjMCSwAdgIVgeZoRmZ8dTOaYhKJfQo1IhITgi3N9o3K5Nlu+lh5RzUyoykmkfihUCMiUS/Y0uy6nJqtkRmRxKJQIyJRKdTITE1nM/l+76ORGZHEolAjIlHnaEZmLAv+4Tmf8c5FuCw76F4zOs5AJL4p1IhI1Ki6aV5tIzNVR2Vme0Yw2zOCLo6dAbsAa3RGJDEo1IhIVKi6ad7RnJrtCzEFdsV/VTcjklgUakQkYmo6bNJNYYNOzdbIjEhiUqgRkYioWjfjm2pKM4eY6Jpfba+Z2k7N1qZ5IqJQIyJNpqaRmWCb5tV1ZOZMt82EkYMY0qONgoxIglOoEZGwqhxk5q/bwd4DZcCRkZl2Zne1TfNsA094RvEb11s1rmTKyUzljvN64d22hsEamRERFGpEJIyCLc2G2o8zcFjwvjmRf5WeU20lU+XiX9vrYeG2sH8MEYkRCjUi0qhqO86gt7W1TscZ+IKMbyVTsOJf29sUn0hEYoVCjYg0msY8zqDyaiZtmicidaFQIyINVnXTPJ+almb7fu9TUxGwlmaLSH0o1IhIg1TdNM831dQsxNLs2o4z0KZ5InI0FGpEpN4aujRbxxmISDgo1IhIvdS0aV6O2VPnpdk6zkBEwkGhRkRqVZ+RmcpCLc0GjcyISONSqBGRoIJtmuemkCGHl2YfZ207qqXZOs5ARMJFoUZEqgnH0mxNMYlIuCnUiAhQ8xQTNGxptqaYRKSpKNSISNCRGV8BcIop4wbXa0e1NFub5olIU1KoEUlQoUZmtDRbRGKRQo1IAqrpoEmAs6xPgy7N/ofnp0xwLdTSbBGJWgo1Igkk2HEGlaeZLne9yzDnp9W+zmHBctOPZ0rP1dJsEYlaCjUiCaLqcQYQfJrJa8BB7UuzQSMzIhJdFGpE4ljVupnK+8z0sr4NOs30y7K76OooYLrrqaBTTaCRGRGJTgo1InEm2KZ5ULd9ZhwWOC3Di96hvO89IWCqSZvmiUi0U6gRiSM1FQDXZ5+ZrXZbAP9Uk6aYRCRWKNSIxLialmb7CoCTTRk3uObXeZ8ZLcsWkVilUCMSw2oamWnIPjMamRGRWKVQIxJjQm2aB3DmUe4zk5OZyhMamRGRGKZQIxJDQh1nkGTK+ZVrCcOda6p9XW37zOg4AxGJBwo1IjEg2KZ50PB9ZlQ3IyLxRKFGJMq9/cVO7ln4XwqKS/2jMnm2mx9Z3wWdZrq07E66OHaG3GdGdTMiEo8UakSikNc2rMrby6tbLZav/Ayo+z4zDoug+8yARmZEJL7FXKgpLS1l8ODBfPbZZ3z66af069cv0k0SaVSBdTNOoGH7zGQ1T2K8Ns0TkQQQc6HmD3/4A+3ateOzzz6LdFNEGk2o4wyyrRKmuubUe58ZTTGJSKKJqVCzaNEi3nnnHV555RUWLVoU6eaINEh9jzOo6z4zmmISkUQVM6Fm586dTJgwgfnz55OWllanryktLaW09MiJxMXFxQCUl5dTXl4elnb6+F4/3O8TqxK9fyoX/1ZW0zRTrj2QL+yu/M71So37zIwb0olhx7VhYOdjcDqshOjbRP8+qo36p3bqo9CipX/q+v6WMcbUfltkGWMYOXIkp512GnfeeSdbt26la9eutdbUTJ06lWnTplW7Pnfu3DoHI5HG9lmhxdObHAC42etfzeSbZjrZuana1/yy7E4+tnvjprBa8W/LZMPPu9icmB31f5VFRI7KwYMHueyyyygqKiIjI6PG+yI6UjNp0iTuv//+kPd89dVXvPPOO5SUlDB58uR6vf7kyZO5+eab/Y+Li4vp2LEj55xzTshOaQzl5eUsXryY4cOHk5SUFNb3ikWJ2D++6aZXXvgcKK/XNFPV4l+f3w7tzv+d1S1ha2YS8fuoPtQ/tVMfhRYt/eObaalNREPNLbfcwrhx40Le061bN5YuXcrKlStJSUkJeG7gwIGMHTuWZ555JujXpqSkVPsagKSkpCb7w2nK94pFidI/VXcCdlPoDzRwZJrpbXsAG+xuNU4z+ahuJlCifB8dLfVP7dRHoUW6f+r63hENNa1bt6Z169a13vfoo49yzz33+B/v2LGDc889lxdeeIHBgweHs4kiDZa7IZ/rnl1L5cmhcx2r/YHGx7Jgjvc8PrZ787L3jKDHGWhFk4hIzWKiULhTp04Bj1u0aAFA9+7d6dChQySaJFIrr234eEshk15Z7w80DmwmOudzo+vlaveHmmbSyIyISO1iItSIxJpg000DHJu42rWQkxxbAFhj96Cf9Q3OENNMZ7ptJowcxJAebTQyIyJSi5gMNV26dCEGFm1Jgqo63VT10MlDxsXk8gm8Zv846GomqBiZueO8Xni3rWGwpppEROokJkONSLQq89jc/toGf6Bxs4f7XE9SOZMk4WWl3RsInGbKap7EhZWOM7C9HhZua+IPICISwxRqRBpJ7oZ8bn9tPXsPVGwSlUIZDyT9g6qDLE7L0MWx0x9mWjZL4vGx/TmlW3bAiIztbbKmi4jEBYUakUZQdcqpFUX8M/kv9HdsrnHfGd+l+y7qy2k9WjV1k0VE4o4j0g0QiWVe27Di6z3+FU5uChnjWMqClMn0d2ymyKTxpHckHlPxV61yQbA7M5WZl/fXiiYRkUaikRqRo1R1hVPVguDddgaXlE8hz+TwtOc8f0HwoWZungsy3SQiIg2jUCNyFKpONwUrCM6y9vODSQaOFARbwExNN4mIhIWmn0TqqeoKp2TKuT9pVpCCYJsujp3+x9nNkzXdJCISRhqpEamHqiucsijmH8kPcbJjU8iDKLOaJ7Fy8tkku/T/ESIi4aKfsCJ15Jty2nugHDeFXOx4jzeTb+dkxyaKTRpPec+rVhC8k4opp+kX9lWgEREJM43UiNSi6hlOVQuC99gZjCm/iy2mPbM8IwN2CNaZTSIiTUehRiSE6mc4VS8IPsbazwGTChwpCG7ZLEkrnEREmphCjUgNqq5wSsLD9KSnaiwI9u0QbKEN9UREIkGhRqSKqtNNAMdQzBPJDzPY8d+QBcHZzZO598I+mm4SEYkAhRqRSipPN7kpZIijAGMM9yfNorNjFyWmGS95z+TXzndwWXbADsFa4SQiElkKNSKHVZ5uusS5jBmuWTgt4x+Z+dZuzfjy3/O16cA/Pef7C4K1wklEJDoo1IgQuKGem0J/oIGKQGMMTCi7ma/pABwpCAa0wklEJEoo1EjCq7qhXg/Hdn+g8bEsOMZxAOwj11o2S+JxrXASEYkaCjWS0KqucMpkPzc6X612X+ViYF980QonEZHoolAjCSnYCqdu1g5mJT1IN0cBh4yLJLw4LRNQDAzg1nSTiEhUUqiRhBNshVNr9vGnpNlkWgf5n2nF+LJbKTLNA3YH1nSTiEh0U6iRhFLbCqdP7B9xTdlNFJIJoA31RERiiEKNJISq003BVjjZBm4qu84faHy0oZ6ISGxQqJG4V/X8JoCRzlXVVjg5LGjvKOS7wwXBgDbUExGJIQo1Ere8tuFvSzfz13c3+a+lUMZtrnlc5cqtdn+wFU7aUE9EJHYo1Ejc8NqG1Xl72VVyiK17DjJ31TZ2lpTippCujgLSzCFuT5pLd0c+AB97j+VkxyacVY47AK1wEhGJRQo1EheCTTFB8GLgAnMMt5X/huX2ibgp1AonEZE4oVAjMS3YFJNPJwq4zzULR5Vi4F+VTuJrOgJHjjuw0AonEZFYp1AjMSt3Qz5T3/iCguIjU0x5tpv9NGOscwnXut7wBxofhwXZjhK+tgNfS9NNIiKxT6FGYoqvbmbxlwU8vWIrEDjFZBs4RDJpVhmAf8rJp3IxMOj8JhGReKJQI1GtavHv86u/paD4SN1M1f1mHBakUcZWuw1/815IEh7+5JqNK0gxsKabRETii0KNRK2ain99ulr53O36V7X9ZgAme65mpd0HgGXefgHFwAA5mm4SEYk7CjUSdWoq/vXVzSQZD5e43mOkY3W1mhmomGLKs4+EFV8xsM9Nw3oy8Sc9Nd0kIhJnFGokqlQu/q3sEueygJVMPou9/dlkd+Aa15tBp5gq0+iMiEh8U6iRiAtW/HuE4ULHB9zvejKg4Nc28KuyyawwfQH4t3d4tSkmd0YKlw7qRJdWzWmTnsqgrlkanRERiWMKNRJRb3+xk3sXbSS/6BBuChlyeFn2Lo7hfMfHXOdaQG/Htmpf57DAaznh8MCNpphEREShRpqc1zasytvLq1stlq/8DKi6LNui0KTT2lEMwEGTTCplOEIszfbRFJOISOJSqJEmUXmKaf66Hew9UAY4gWDLsg2trWL2mTSe9ozkGe85nOv8D9NdT9VYNzP+tC4M6+3WFJOISAJTqJGwC7U0O5P93OJ6Meiy7BvLJ7Lc7gfAi96hvO89QUuzRUSkRgo1EhbBin8rH2XgxcHVroWMdS6hhVU97HiMg412x4BrBWRDi3Yq/hURkaAUaqTRBRuZqXpatgcHSVbFAUxf2p351O7OGOd7IZdlq/hXRERCUaiRRhNq07zKNTOWBUnYrLO78rDnF7xn9wMsHvNcWG16CTTFJCIidaNQI42ipk3z+lubuMP1bNCamfs8Y/nY7u1/XHVZtop/RUSkPhRq5KjVXDeTT2v2cZlrGac4vgJqPy27Mo3MiIjI0VCokTqr7cTsMc6lzHA9FXCUQZlx8qr3x3xn2nCT6+WQNTMamRERkYZQqJE6CbUs24WHK5xvc6fruWpHGVxUOoX19ADgFe+PVTMjIiJho1AjIdVU/AuQSiljnO/xG9ebtLcKqz3vsKC5owwqFjkF1MxkNU+ib3opE0YOYkiPNhqZERGRBlOokRoFK/51U8jxjq0MsDZyiet9WlkVRxkUmnSOoaTWowx8U0wndUjn7dxFDNZUk4iINBKFGgkQ6sTs8c6F3OF6NiC4fGe35gnvKF72nsHPnCtqPMqg6hRTeXl5U30kERFJEAo1Ca624l+ADtZubnS+zC+cH1SpmbH4ZdmdbKc1UPNRBto0T0REmoJCTQKrWvzrO8YA3BSQTQ/rf1znWsDPHCtwHd79tzKHZejo2M12u7X/WuW6GRUAi4hIU1KoSUDBin8rH2NgG4svTGf6Orb6n1/l7cXJjk0By7Vr2mtGS7NFRCQSFGoSTE3Fv5WPMXBYhr7W1or7vSfzd88FfG66c4lzWY01M6CRGRERiSyFmgQQqvjXwuYXzuVBjzG4pewaXrHP9D+uWjNDRjtu0onZIiISJRRq4lzluhk3hQxxFJBnu9lNS0Y5VnKd6w16Of5X7es8xsEKu0+1676aGRX/iohItFGoiVNV62aq1sx8b5qT7dgPQLFpxif2jzjTsR5niGMMQFNMIiISvRRq4kTVpdlzV21jZ0lF3Uywmplsaz97TQtmec7n397hlJCGm8KgxxiAin9FRCT6KdTEgVDnMh1DMX9Imhe0ZuZ35dfzvn2i/3Hl5dg+GpkREZFYoVATo4IV//r2mcmz3VjABNdb/NK5jDSrtNrXe4yDTXaHatfdGSlcquJfERGJQTEVat566y3++Mc/8vnnn5OamsqZZ57J/PnzI92sJhdsZKZyzYwx4MXCdXh05nO7K+vs7lzmXFrjcmzQzr8iIhLbYibUvPLKK0yYMIHp06fzk5/8BI/Hw4YNGyLdrCZV04nZbgq5zzXLvzGeZYELwxpvT/7q/QUf2n0Ai797fha0ZkZTTCIiEg9iItR4PB5uvPFGHnjgAcaPH++/3rt37wi2KvxCFf/6nGz9lztczwXs9OvzgHcMH9tH+qhqzYyKf0VEJJ7ERKhZu3Yt27dvx+FwcNJJJ1FQUEC/fv144IEH6NOn+l4qPqWlpZSWHgkBxcXFQMUJ0eE+Jdr3+kf7Pm9/sZN7Fv632s6/FfvMtOVYx3dc73qdkx0VozbGEHDYZE1HGEBF3cydI4/l3OMrnre9HmzvUTXzqDW0fxKB+qh26qPQ1D+1Ux+FFi39U9f3t4wx1f8XP8rMmzePSy+9lE6dOvHQQw/RpUsX/vKXv/DOO++wadMmsrKygn7d1KlTmTZtWrXrc+fOJS0tLdzNrjfbwJZii/Xfw/J8x+GrFUmlas2ML8CUGhcve8/kf6YVt7heCqiZedE7tNKrV/wxn9fB5pwOBg3MiIhIrDh48CCXXXYZRUVFZGRk1HhfREPNpEmTuP/++0Pe89VXX7F27VrGjh3LP/7xD37zm98AFaMwHTp04J577uGaa64J+rXBRmo6duzInj17QnZKYygvL2fx4sUMHz6cpKSkWu8PNjLj05GdLE+5OWCKyRh41ns2j3p+zm6OAQi5z0xOZgp3nHdkdCbS6ts/iUh9VDv1UWjqn9qpj0KLlv4pLi6mVatWtYaaiE4/3XLLLYwbNy7kPd26dSM/Px8IrKFJSUmhW7dufPvttzV+bUpKCikpKdWuJyUlhfUPx2sb1ubtZc0ei2O+LcbpcrFnfylt0lMZ0PkY1mz7nl0lh/yPZ763pVrxL0Aah7jUuYTrXa9Xq5mxLHjLHuIPNBBYMxMrS7PD/WcRD9RHtVMfhab+qZ36KLRI909d3zuioaZ169a0bt261vsGDBhASkoKGzdu5PTTTwcq0uPWrVvp3LlzuJtZL4HLrZ386+s1Ac87rIppJh+LiomhynvM/EAK45xvM871NsdYFUcZ1LVmRsW/IiKSqGKiUDgjI4Nrr72WKVOm0LFjRzp37swDDzwAwMUXXxzh1gFF22HvFpbtTue6V3cQbD6vcmipPDVkqHouE5ThItXyAJBnt2Wm9wJcePmja06N+8xoWbaIiCS6mAg1AA888AAul4tf/epX/PDDDwwePJilS5dyzDHH1P7F4bT2X7DgRjA2Z2LxG+cY3vQOCbjlp86V/MH1gv8wyRe8Z7LZdCDbKqY9u7jA+bF/FMZhQSoeNtntecRzEYvsQdhUFA0v9Z4UtGZGm+aJiIjEUKhJSkriwQcf5MEHH4x0U44o2u4PNAAODJOT5jE5aV6NX+KwDJe63qv1pe/2jONj+/iAa1X3mdHojIiIyBExE2qi0t4t/kBTWZlx+kdXHNgkW9U3gVnh7c0m05FDJolrXG8FFAJX1Mu4q31NrBT/ioiIRIJCTUNkdQfLERBsPMbBGaUP+6eH3BSyIuW3Aadke4yDW8qv89+TRw7TXU/VWC+j4l8REZHaKdQ0RGZ7GPUILPgdGC9eHNxRJZAUkM1kz9UhQ8uL3qG87z2hWr2MppdERETqTqGmofr/GrqfDXu/4f3dLXjx1R3+Zdo+NYWWyqrWy6j4V0REpH4UahpDZnvIbM/QrjAzzV1pn5ojfKHFUSXxVN23RqMzIiIiR0ehppGN6JPD8N5uVm7exTsfrGLYaYNq3VG48mPVzYiIiBwdhZowcDosBnfNovArw5Du2dW2dx7SPTvkYxEREak/R+23iIiIiEQ/hRoRERGJCwo1IiIiEhcUakRERCQuKNSIiIhIXFCoERERkbigUCMiIiJxQaFGRERE4oJCjYiIiMSFhNpR2JiKQ5aKi4vD/l7l5eUcPHiQ4uLiajsKi/qnLtRHtVMfhab+qZ36KLRo6R/fv9u+f8drklChpqSkBICOHTtGuCUiIiJSXyUlJWRmZtb4vGVqiz1xxLZtduzYQXp6OpYV3kMji4uL6dixI9999x0ZGRlhfa9YpP6pnfqoduqj0NQ/tVMfhRYt/WOMoaSkhHbt2uFw1Fw5k1AjNQ6Hgw4dOjTpe2ZkZOgvSgjqn9qpj2qnPgpN/VM79VFo0dA/oUZofFQoLCIiInFBoUZERETigkJNmKSkpDBlyhRSUlIi3ZSopP6pnfqoduqj0NQ/tVMfhRZr/ZNQhcIiIiISvzRSIyIiInFBoUZERETigkKNiIiIxAWFGhEREYkLCjVh8Pjjj9OlSxdSU1MZPHgwq1evjnSTmsz777/PqFGjaNeuHZZlMX/+/IDnjTHcfffd5OTk0KxZM4YNG8bXX38dcM/evXsZO3YsGRkZtGzZkvHjx7N///4m/BThM2PGDE4++WTS09Np06YNo0ePZuPGjQH3HDp0iOuvv57s7GxatGjBRRddxM6dOwPu+fbbbzn//PNJS0ujTZs2/P73v8fj8TTlRwmbmTNncsIJJ/g3+xoyZAiLFi3yP5/o/VPVfffdh2VZ/O53v/NfS/Q+mjp1KpZlBfw69thj/c8nev8AbN++ncsvv5zs7GyaNWtG3759+eSTT/zPx+zPaiONat68eSY5Odk8/fTT5osvvjATJkwwLVu2NDt37ox005rEwoULzR133GFeffVVA5jXXnst4Pn77rvPZGZmmvnz55vPPvvMXHDBBaZr167mhx9+8N8zYsQIc+KJJ5qPP/7YfPDBB6ZHjx7m0ksvbeJPEh7nnnuumT17ttmwYYNZt26dGTlypOnUqZPZv3+//55rr73WdOzY0SxZssR88skn5pRTTjGnnnqq/3mPx2P69Oljhg0bZj799FOzcOFC06pVKzN58uRIfKRG98Ybb5i33nrLbNq0yWzcuNHcfvvtJikpyWzYsMEYo/6pbPXq1aZLly7mhBNOMDfeeKP/eqL30ZQpU8zxxx9v8vPz/b92797tfz7R+2fv3r2mc+fOZty4cWbVqlXmm2++MW+//bbZvHmz/55Y/VmtUNPIBg0aZK6//nr/Y6/Xa9q1a2dmzJgRwVZFRtVQY9u2cbvd5oEHHvBf27dvn0lJSTHPP/+8McaYL7/80gDmP//5j/+eRYsWGcuyzPbt25us7U1l165dBjDLly83xlT0R1JSknnppZf893z11VcGMCtXrjTGVARHh8NhCgoK/PfMnDnTZGRkmNLS0qb9AE3kmGOOMbNmzVL/VFJSUmJ69uxpFi9ebM4880x/qFEfVYSaE088Mehz6h9jbrvtNnP66afX+Hws/6zW9FMjKisrY82aNQwbNsx/zeFwMGzYMFauXBnBlkWHvLw8CgoKAvonMzOTwYMH+/tn5cqVtGzZkoEDB/rvGTZsGA6Hg1WrVjV5m8OtqKgIgKysLADWrFlDeXl5QB8de+yxdOrUKaCP+vbtS9u2bf33nHvuuRQXF/PFF180YevDz+v1Mm/ePA4cOMCQIUPUP5Vcf/31nH/++QF9Afoe8vn6669p164d3bp1Y+zYsXz77beA+gfgjTfeYODAgVx88cW0adOGk046iSeffNL/fCz/rFaoaUR79uzB6/UG/EUAaNu2LQUFBRFqVfTw9UGo/ikoKKBNmzYBz7tcLrKysuKuD23b5ne/+x2nnXYaffr0ASo+f3JyMi1btgy4t2ofBetD33PxYP369bRo0YKUlBSuvfZaXnvtNXr37q3+OWzevHmsXbuWGTNmVHtOfQSDBw9mzpw55ObmMnPmTPLy8vjxj39MSUmJ+gf45ptvmDlzJj179uTtt9/muuuu47e//S3PPPMMENs/qxPqlG6RaHL99dezYcMGPvzww0g3Jer06tWLdevWUVRUxMsvv8wVV1zB8uXLI92sqPDdd99x4403snjxYlJTUyPdnKh03nnn+X9/wgknMHjwYDp37syLL75Is2bNItiy6GDbNgMHDmT69OkAnHTSSWzYsIEnnniCK664IsKtaxiN1DSiVq1a4XQ6q1XR79y5E7fbHaFWRQ9fH4TqH7fbza5duwKe93g87N27N676cOLEibz55pssW7aMDh06+K+73W7KysrYt29fwP1V+yhYH/qeiwfJycn06NGDAQMGMGPGDE488UQeeeQR9Q8V0ye7du2if//+uFwuXC4Xy5cv59FHH8XlctG2bduE76OqWrZsyY9+9CM2b96s7yEgJyeH3r17B1w77rjj/FN0sfyzWqGmESUnJzNgwACWLFniv2bbNkuWLGHIkCERbFl06Nq1K263O6B/iouLWbVqlb9/hgwZwr59+1izZo3/nqVLl2LbNoMHD27yNjc2YwwTJ07ktddeY+nSpXTt2jXg+QEDBpCUlBTQRxs3buTbb78N6KP169cH/EBZvHgxGRkZ1X5QxQvbtiktLVX/AGeffTbr169n3bp1/l8DBw5k7Nix/t8neh9VtX//frZs2UJOTo6+h4DTTjut2lYSmzZtonPnzkCM/6yOWIlynJo3b55JSUkxc+bMMV9++aX5zW9+Y1q2bBlQRR/PSkpKzKeffmo+/fRTA5iHHnrIfPrpp2bbtm3GmIplgi1btjSvv/66+fzzz83PfvazoMsETzrpJLNq1Srz4Ycfmp49e0Z8mWBjue6660xmZqZ57733ApabHjx40H/Ptddeazp16mSWLl1qPvnkEzNkyBAzZMgQ//O+5abnnHOOWbduncnNzTWtW7eOm+WmkyZNMsuXLzd5eXnm888/N5MmTTKWZZl33nnHGKP+Caby6idj1Ee33HKLee+990xeXp5ZsWKFGTZsmGnVqpXZtWuXMUb9s3r1auNyucy9995rvv76a/Pcc8+ZtLQ08+yzz/rvidWf1Qo1YfDYY4+ZTp06meTkZDNo0CDz8ccfR7pJTWbZsmUGqPbriiuuMMZULBW86667TNu2bU1KSoo5++yzzcaNGwNeo7Cw0Fx66aWmRYsWJiMjw1x55ZWmpKQkAp+m8QXrG8DMnj3bf88PP/xg/u///s8cc8wxJi0tzVx44YUmPz8/4HW2bt1qzjvvPNOsWTPTqlUrc8stt5jy8vIm/jThcdVVV5nOnTub5ORk07p1a3P22Wf7A40x6p9gqoaaRO+jMWPGmJycHJOcnGzat29vxowZE7AHS6L3jzHGLFiwwPTp08ekpKSYY4891vzzn/8MeD5Wf1ZbxhgTmTEiERERkcajmhoRERGJCwo1IiIiEhcUakRERCQuKNSIiIhIXFCoERERkbigUCMiIiJxQaFGRERE4oJCjYiIiMQFhRoRiTlbt27Fsiwsy6Jfv34Nfj3fa7Vs2bLBryUikaNQIyIx69133w04dO9o5efn8/DDDze8QSISUQo1IhKzsrOzyc7ObvDruN1uMjMzG6FFIhJJCjUiElG7d+/G7XYzffp0/7WPPvqI5OTkeo/CjBs3jtGjRzN9+nTatm1Ly5Yt+eMf/4jH4+H3v/89WVlZdOjQgdmzZzf2xxCRKOCKdANEJLG1bt2ap59+mtGjR3POOefQq1cvfvWrXzFx4kTOPvvser/e0qVL6dChA++//z4rVqxg/PjxfPTRR5xxxhmsWrWKF154gWuuuYbhw4fToUOHMHwiEYkUjdSISMSNHDmSCRMmMHbsWK699lqaN2/OjBkzjuq1srKyePTRR+nVqxdXXXUVvXr14uDBg9x+++307NmTyZMnk5yczIcfftjIn0JEIk0jNSISFR588EH69OnDSy+9xJo1a0hJSTmq1zn++ONxOI78/1rbtm3p06eP/7HT6SQ7O5tdu3Y1uM0iEl00UiMiUWHLli3s2LED27bZunXrUb9OUlJSwGPLsoJes237qN9DRKKTRmpEJOLKysq4/PLLGTNmDL169eLqq69m/fr1tGnTJtJNE5EYopEaEYm4O+64g6KiIh599FFuu+02fvSjH3HVVVdFulkiEmMUakQkot577z0efvhh/v3vf5ORkYHD4eDf//43H3zwATNnzox080Qkhmj6SUQi6qyzzqK8vDzgWpcuXSgqKqr3a82ZM6fatffee6/atYbU7IhI9FKoEZGYdeqpp9KvXz8++uijBr1OixYt8Hg8pKamNlLLRCQSFGpEJOZ06NCBr7/+GuCol35Xtm7dOqBiubeIxC7LGGMi3QgRERGRhlKhsIiIiMQFhRoRERGJCwo1IiIiEhcUakRERCQuKNSIiIhIXFCoERERkbigUCMiIiJxQaFGRERE4sL/A842SYggICyzAAAAAElFTkSuQmCC", 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", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -179,7 +168,6 @@ }, { "cell_type": "markdown", - "id": "04bbd824", "metadata": {}, "source": [ "### Interpolate\n", @@ -188,18 +176,19 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "d3376bda", + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -217,7 +206,6 @@ }, { "cell_type": "markdown", - "id": "73795206", "metadata": {}, "source": [ "### Seaward extend\n", @@ -228,21 +216,30 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "6ff4b12e", + "execution_count": 29, "metadata": {}, "outputs": [ { - "ename": "ValueError", - "evalue": "could not broadcast input array from shape (24,) into shape (23,)", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[6], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m#d_start, slope, Hm0_shoal = offshore_depth(Hm0=9, Tp=15, depth_offshore_profile=15, depth_boundary_conditions=20)\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m xgr, ygr, zgr \u001b[38;5;241m=\u001b[39m \u001b[43mseaward_extend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mxgr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43mzgr\u001b[49m\u001b[43m,\u001b[49m\u001b[43mslope\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;241;43m20\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mdepth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m20\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 7\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure()\n\u001b[0;32m 8\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(xgr\u001b[38;5;241m.\u001b[39mT,zgr[:,:]\u001b[38;5;241m.\u001b[39mT)\n", - "File \u001b[1;32md:\\gitlab_folder\\xbeach-toolbox\\xbTools\\grid\\extension.py:138\u001b[0m, in \u001b[0;36mseaward_extend\u001b[1;34m(x, y, z, slope, depth)\u001b[0m\n\u001b[0;32m 134\u001b[0m znew \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39minterp(xnew, xp, zp)\n\u001b[0;32m 136\u001b[0m N \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(znew)\n\u001b[1;32m--> 138\u001b[0m \u001b[43mz_extend\u001b[49m\u001b[43m[\u001b[49m\u001b[43mii\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mN\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m znew\n\u001b[0;32m 139\u001b[0m y_extend[ii,:] \u001b[38;5;241m=\u001b[39m y[ii,\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m 141\u001b[0m xgr \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mconcatenate((x_extend,x),\u001b[38;5;241m1\u001b[39m)\n", - "\u001b[1;31mValueError\u001b[0m: could not broadcast input array from shape (24,) into shape (23,)" - ] + "data": { + "text/plain": [ + "Text(0, 0.5, 'z [m]')" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ @@ -260,7 +257,6 @@ }, { "cell_type": "markdown", - "id": "6aab9494", "metadata": {}, "source": [ "### Create model setup\n", @@ -269,10 +265,17 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "32eb75b6", + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test som 2\n" + ] + } + ], "source": [ "xb_setup = XBeachModelSetup('Test som 2')\n", "\n", @@ -281,7 +284,6 @@ }, { "cell_type": "markdown", - "id": "609a65bc", "metadata": {}, "source": [ "Add the grid, wave boundary conditions and parameter to the model.\n", @@ -295,12 +297,11 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "35335d12", + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ - "xb_setup.set_grid(xgr,None,zgr,posdwn=-1)\n", + "xb_setup.set_grid(xgr,None,zgr)\n", "\n", "xb_setup.set_waves('parametric',{'Hm0':2, 'Tp':5, 'mainang':270, 'gammajsp':3.3, 's' : 10000, 'fnyq':1})\n", "#xb_setup.set_waves('jonstable',{'Hm0':[1.5, 2, 1.5],'Tp':[4, 5, 4],'gammajsp':[3.3, 3.3, 3.3], 's' : [20,20,20], 'mainang':[270,280, 290],'duration':[3600, 3600, 3600],'dtbc':[1,1,1]})\n", @@ -318,7 +319,6 @@ }, { "cell_type": "markdown", - "id": "ce4a77d4", "metadata": {}, "source": [ "Write the model setup" @@ -326,10 +326,22 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "617e1e4e", + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "sim_path = os.path.join('xb-1D')\n", "if not os.path.exists(sim_path):\n", @@ -354,7 +366,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.8.12" } }, "nbformat": 4, diff --git a/xbTools/grid/extension.py b/xbTools/grid/extension.py index d3da07d..70ca9a8 100644 --- a/xbTools/grid/extension.py +++ b/xbTools/grid/extension.py @@ -110,8 +110,8 @@ def seaward_extend(x,y,z,slope=1/20,depth=-20): ## dummy array x_dummy = np.arange(x[0, 0]-distance, x[0, 0], dx_grid) # prevent very small grid sizes when floating point zero goes wrong for computing var distance! - #if (x_dummy[-1]-x[0, 0])< 0.01: - # x_dummy = x_dummy[:-1] + if (x_dummy[-1]-x[0, 0])< 0.01: + x_dummy = x_dummy[:-1] x_extend = np.ones((x.shape[0], len(x_dummy) )) x_extend = x_extend * x_dummy diff --git a/xbTools/xbeachtools.py b/xbTools/xbeachtools.py index 542ff02..a136265 100644 --- a/xbTools/xbeachtools.py +++ b/xbTools/xbeachtools.py @@ -28,9 +28,10 @@ def __init__(self,fname): ## by default set wbctype and wavemodel to None self.wbctype = None self.wavemodel = None + self.zs0type = None + ## set default values self.model_path = None - self.friction_layer = None self.wavefriction_layer = None self.struct = None @@ -50,11 +51,11 @@ def set_params(self,input_par_dict): input_par_dict (_type_): _description_ """ ## set wavemodel. Default is Surfbeat - if 'Wavemodel' not in input_par_dict: + if 'wavemodel' not in input_par_dict: print('No wavemodel defined. Wavemodel is set to Surfbeat') self.wavemodel = 'surfbeat' else: - self.wavemodel = input_par_dict['Wavemodel'] + self.wavemodel = input_par_dict['wavemodel'] ## set wbctype if 'wbctype' in input_par_dict: @@ -87,9 +88,9 @@ def set_grid(self,xgr,ygr,zgr, posdwn=1, xori=0, yori=0,alfa=0, thetamin=-90, th """_summary_ Args: - xgr (_type_): _description_ - ygr (_type_): _description_ - zgr (_type_): _description_ + xgr (array): x-grid + ygr (array): y-grid + zgr (array): z-grid posdwn (int, optional): _description_. Defaults to 1. xori (int, optional): _description_. Defaults to 0. yori (int, optional): _description_. Defaults to 0. @@ -113,7 +114,6 @@ def set_grid(self,xgr,ygr,zgr, posdwn=1, xori=0, yori=0,alfa=0, thetamin=-90, th else: self.xgr = xgr self.zgr = zgr - ## 2D model else: self.ygr = ygr @@ -123,7 +123,6 @@ def set_grid(self,xgr,ygr,zgr, posdwn=1, xori=0, yori=0,alfa=0, thetamin=-90, th ## self.nx = self.xgr.shape[1] - 1 self.ny = self.xgr.shape[0] - 1 - ## ## 1D if ygr is None or ygr.shape[0]==1: @@ -131,7 +130,7 @@ def set_grid(self,xgr,ygr,zgr, posdwn=1, xori=0, yori=0,alfa=0, thetamin=-90, th self.ny = 0 else: self.fast1D = False - ## + ## set values self.posdwn = posdwn self.xori = xori self.yori = yori @@ -167,8 +166,8 @@ def set_friction(self, friction, friction_layer = 1): Parameters ---------- - nebed : input of sandy layer thickness - struct : optional. The default is 1. + friction : input of sandy layer thickness + friction_layer : optional yes/no. The default is 1. Returns ------- @@ -178,14 +177,16 @@ def set_friction(self, friction, friction_layer = 1): self.friction = friction self.friction_layer = friction_layer + # TODO option to specify what kind of bed friction there is: Manning/Chezy or else + def set_wavefriction(self, wavefriction, wavefriction_layer = 1): ''' - function to set friction layer for the xbeach model + function to set wave friction layer for the xbeach model Parameters ---------- - nebed : input of sandy layer thickness - struct : optional. The default is 1. + wavefriction : input of sandy layer thickness + wavefriction_layer : optional yes/no. The default is 1. Returns ------- @@ -221,10 +222,64 @@ def set_vegetation(self): """ pass - def set_tide(self): + def set_wind(self): """_summary_ """ - pass + pass + + def set_tide(self, zs0, optional_par_input = {}): + """ + function to set offshore water level boundary conditions for the xbeach model + + Parameters + ---------- + zs0 (array): table of water levels in format [t, zs1, (zs2), (zs3, zs4)] + optional_par_input: dict with optional parameter settings: ['tideloc', 'tidetype', 'paulrevere']. If unspecified, tideloc is inferred from zs0. + + Returns + ------- + None. + """ + + if np.atleast_1d(zs0).size > 1: + self.zs0type = 'list' + else: + self.zs0type = 'par' + ## + + self.tide_boundary = {} + + if self.zs0type == 'list': + self.tide_boundary['zs0file'] = 'tide.txt' + optional_par = ['paulrevere', 'tideloc', 'tidetype'] + + for item in optional_par_input: + if item in optional_par: + self.tide_boundary[item] = optional_par_input[item] + else: + assert False, 'invalid tide parameter specified' + + self.tide_boundary['_tidelen'] = zs0.shape[0] + + # if not overruled by a specific par input, infer the tideloc from shape of zs0 + if not 'tideloc' in optional_par_input: + tideloc_inferred = zs0.shape[1]-1 + if tideloc_inferred in [1, 2, 4]: + self.tide_boundary['tideloc'] = tideloc_inferred + else: + assert False, 'format of zs0 invalid: use either 1, 2 or 4 corners for zs0 specification' + + elif self.zs0type == 'par': + optional_par = [] + self.tide_boundary['zs0'] = zs0 + + else: + assert False, 'Wrong zs0 format' + + self.zs0 = zs0 + + # TODO check that zs0file is at least as long as end time of simulation + def load_model_setup(self,path): """_summary_ @@ -300,7 +355,7 @@ def write_model(self, path, figure=True): f.write('%% Grid \n') f.write('\n') f.write('vardx\t= {}\n'.format(self.vardx).expandtabs(tabnumber)) - f.write('posdwn\t={}\n'.format(self.posdwn).expandtabs(tabnumber)) + f.write('posdwn\t= {}\n'.format(self.posdwn).expandtabs(tabnumber)) f.write('nx\t= {}\n'.format(self.nx).expandtabs(tabnumber)) f.write('ny\t= {}\n'.format(self.ny).expandtabs(tabnumber)) f.write('xori\t= {}\n'.format(self.xori).expandtabs(tabnumber)) @@ -312,10 +367,24 @@ def write_model(self, path, figure=True): f.write('depfile\t= bed.dep\n'.expandtabs(tabnumber)) f.write('thetamin\t= {}\n'.format(self.thetamin).expandtabs(tabnumber)) f.write('thetamax\t= {}\n'.format(self.thetamax).expandtabs(tabnumber)) - f.write('thetanaut\t= {}\n'.format(self.thetanaut)) + f.write('thetanaut\t= {}\n'.format(self.thetanaut).expandtabs(tabnumber)) f.write('dtheta\t= {}\n'.format(self.dtheta).expandtabs(tabnumber)) f.write('dtheta_s\t= {}\n'.format(self.dtheta).expandtabs(tabnumber)) f.write('\n') + + ## tide + if self.zs0type != None: + f.write('%% Tide boundary conditions \n') + f.write('\n') + if self.zs0type == 'par': + f.write('zs0\t= {}\n'.format()) + elif self.zs0type == 'list': + for item in self.tide_boundary: + if item[0] != '_': + f.write('{}\t= {}\n'.format(item, self.tide_boundary[item]).expandtabs(tabnumber)) + f.write('\n') + + ## write input vars for par_category in self.input_par: @@ -329,6 +398,8 @@ def write_model(self, path, figure=True): for par in self.input_par[par_category]: f.write('{}\t= {}\n'.format(par,self.input_par[par_category][par]).expandtabs(tabnumber)) f.write('\n') + + ## write output variables if '_Output' in self.input_par: f.write('%% Output variables \n') @@ -343,9 +414,10 @@ def write_model(self, path, figure=True): ## write grid x with open(os.path.join(path,'x.grd'),'w') as f: + xgr = np.atleast_2d(self.xgr) for ii in range(self.ny+1): for jj in range(self.nx+1): - f.write('{} '.format(self.xgr[ii,jj])) + f.write('{:.3f} '.format(xgr[ii,jj])) f.write('\n') if not self.ygr is None: @@ -353,36 +425,51 @@ def write_model(self, path, figure=True): with open(os.path.join(path,'y.grd'),'w') as f: for ii in range(self.ny+1): for jj in range(self.nx+1): - f.write('{} '.format(self.ygr[ii,jj])) + f.write('{:.3f} '.format(self.ygr[ii,jj])) f.write('\n') ## write dep with open(os.path.join(path,'bed.dep'),'w') as f: + zgr = np.atleast_2d(self.zgr) for ii in range(self.ny+1): for jj in range(self.nx+1): - f.write('{} '.format(self.zgr[ii,jj])) + f.write('{:.3f} '.format(zgr[ii,jj])) f.write('\n') ## write ne-layer if self.struct != None: + nebed = np.atleast_2d(self.nebed) with open(os.path.join(path,'ne_bed.dep'),'w') as f: for ii in range(self.ny+1): for jj in range(self.nx+1): - f.write('{} '.format(self.nebed[ii,jj])) + f.write('{} '.format(nebed[ii,jj])) f.write('\n') - + + ## write bottom friction layer if self.friction_layer != None: + friction = np.atleast_2d(self.friction) with open(os.path.join(path,'friction.dep'),'w') as f: for ii in range(self.ny+1): for jj in range(self.nx+1): - f.write('{} '.format(self.friction[ii,jj])) + f.write('{:.3f} '.format(friction[ii,jj])) f.write('\n') - + + ## write wave bottom friction layer if self.wavefriction_layer != None: + wavefriction = np.atleast_2d(self.wavefriction) with open(os.path.join(path,'wavefriction.dep'),'w') as f: for ii in range(self.ny+1): for jj in range(self.nx+1): - f.write('{} '.format(self.wavefriction[ii,jj])) + f.write('{} '.format(wavefriction[ii,jj])) + f.write('\n') + + ## write tide boundary condition + + if self.zs0type == 'list': + with open(os.path.join(path,'tide.txt'), 'w') as f: + for ir in range(self.tide_boundary['_tidelen']): + for ic in range(self.tide_boundary['tideloc'] + 1): + f.write('{} '.format(self.zs0[ir, ic])) f.write('\n') ## write figures @@ -390,7 +477,7 @@ def write_model(self, path, figure=True): ## plot and write domain self._plotdomain(path) ## plot and write wave boundary - if self.wbctype=='jonstable' or self.wbctype=='jons': + if self.wbctype=='jonstable' or self.wbctype=='parametric': self._plot_boundary(path) def _plot_boundary(self,save_path=None): @@ -421,8 +508,8 @@ def _plot_boundary(self,save_path=None): plt.xlabel('Time') if save_path!=None: plt.savefig(os.path.join(save_path,'jonstable.png')) - elif self.wbctype=='jons': - print('wbctype=jons cannot be plotted') + elif self.wbctype=='parametric': + print('wbctype=parametric cannot be plotted') else: print('Not possible to plot wave boundary') @@ -469,7 +556,7 @@ def _plotdomain(self,save_path=None): thetamin_uv, thetamax_uv = self._make_theta_vectors() if self.fast1D==True: plt.subplot(2,1,1) - plt.plot(np.squeeze(self.xgr),np.squeeze(self.zgr)*-self.posdwn) + plt.plot(np.squeeze(self.xgr),np.squeeze(self.zgr)*self.posdwn) plt.ylabel('z') plt.subplot(2,1,2) plt.plot(np.squeeze(self.xgr)[1:],np.diff(np.squeeze(self.xgr))) @@ -477,7 +564,7 @@ def _plotdomain(self,save_path=None): plt.ylabel('dx') else: plt.subplot(2,2,1) - plt.pcolor(self.xgr,self.ygr,self.zgr*-self.posdwn) + plt.pcolor(self.xgr,self.ygr,self.zgr*self.posdwn) plt.ylabel('y') plt.colorbar() plt.axis('equal') @@ -498,7 +585,7 @@ def _plotdomain(self,save_path=None): plt.subplot(2,2,3) [X_world,Y_world] = rotate_grid(self.xgr,self.ygr,np.deg2rad(self.alfa)) - plt.pcolor(X_world+self.xori,Y_world+self.yori,self.zgr*-self.posdwn) + plt.pcolor(X_world+self.xori,Y_world+self.yori,self.zgr*self.posdwn) plt.xlabel('x') plt.ylabel('y') plt.axis('equal')