diff --git a/demos/01-2d-channel.ipynb b/demos/01-2d-channel.ipynb index f244e8f00..6314869da 100644 --- a/demos/01-2d-channel.ipynb +++ b/demos/01-2d-channel.ipynb @@ -9,7 +9,7 @@ "\n", "The flow is forced by an initial perturbation in the water elevation field centred in the middle of the channel. The model solves the Shallow Water Equations which are not expanded herein for brevity.\n", " \n", - "To run this example, it is necessary to install [firedrake](https://www.firedrakeproject.org/download.html) and [Thetis](https://thetisproject.org/download.html)\n", + "To run this example, it is necessary to install [Firedrake](https://www.firedrakeproject.org/download.html) and [Thetis](https://thetisproject.org/download.html)\n", "\n", "[Link to Tutorials on Thetis Website](https://thetisproject.org/documentation.html)" ] @@ -18,7 +18,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We begin with a number of imports. Note that we use `from thetis import *` which means that we do not need to prepend `thetis.` all the time. This also automatically includes a `from firedrake import *` so that we have access to the all the firedrake objects we saw yesterday such as `RectangleMesh`, `Function`, `FunctionSpace` etc. - without having to prepend `fd` as we did then." + "We begin with a number of imports. Note that we use `from thetis import *` which means that we do not need to prepend `thetis.` all the time. This also automatically includes a `from firedrake import *` so that we have access to Firedrake objects such as `RectangleMesh`, `Function`, `FunctionSpace` etc. - without having to prepend `fd` as we did then." ] }, { @@ -31,7 +31,8 @@ "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import os\n", - "from thetis import *" + "from thetis import *\n", + "from firedrake.pyplot import triplot, tricontourf, quiver" ] }, { @@ -59,7 +60,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can use the built in [plot](https://firedrakeproject.org/firedrake.html#firedrake.plot.plot) function of firedrake to visualise the mesh." + "We can use the built in [plot](https://firedrakeproject.org/firedrake.html#firedrake.plot.plot) function of Firedrake to visualise the mesh." ] }, { @@ -69,14 +70,12 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -159,7 +158,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The standard output of Thetis is in the form of a `.pvd` file with an associated series of `.vtu` files (and `.pvtu` when running in parallel). For visualisation we recommend the program [paraview](https://paraview.org). This program is however not available inside this jupyter environment. Therefore we will here use hdf5 output files instead, which can be used to read back in the solution in Firedrake and Thetis (unlike the `.pvd`+`.vtu` output which cannot be read back in by Firedrake). This output can also be used as checkpoints, from which Thetis can be restarted. Here we will use the hdf5 output to read it back into Firedrake and plot it.\n", + "The standard output of Thetis is in the form of a `.pvd` file with an associated series of `.vtu` files (and `.pvtu` when running in parallel). For visualisation we recommend the program [ParaView](https://paraview.org). This program is however not available inside this jupyter environment. Therefore we will here use hdf5 output files instead, which can be used to read back in the solution in Firedrake and Thetis (unlike the `.pvd`+`.vtu` output which cannot be read back in by Firedrake). This output can also be used as checkpoints, from which Thetis can be restarted. Here we will use the hdf5 output to read it back into Firedrake and plot it.\n", "\n", "We select which fields to output (`'elev_2d'` for elevations and `'uv_2d'` for the 2d velocity vector):" ] @@ -254,7 +253,7 @@ "source": [ "Above we set the water elevation (using key `'elev'`) on the left bounday. Alternatively, we could also prescribe the normal velocity (with key `'un'`), the full 2D velocity vector (`'uv'`) or the total flux through the boundary (`'flux'`). For all supported boundary conditions, see module [shallowwater_eq](https://thetisproject.org/thetis.html#module-thetis.shallowwater_eq).\n", "\n", - "In order to set time-dependent boundary conditions we first define a python function that evaluates the time dependent variable:" + "In order to set time-dependent boundary conditions we first define a Python function that evaluates the time dependent variable:" ] }, { @@ -264,14 +263,12 @@ "outputs": [ { "data": { - "image/png": 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\n", 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Cg7pf223Auuong+MRHhiAcxeasPV4pVs+k6gzVrbOoJxyQyJ0AZ659ISrMRkjtxCToXuHd0eozjWFyu2ZcoP9Fkmf7z0Hi5WLwJL8bDYBK3afAQA8mOm+m3jrAlT4WeuF0Cc7z7jtc4k6UlbbhK3H7BcH97fe0s4fMRkjl6ttbMHXBQYAwH0Z7j3YbkuJQ1SwBpV1Jnx/lL0BJL+txytxtroJYTo1ftK6Bpi7/Lw1+fvuSAVKaprc+tlE7fk87xxsAjAyORLJ0dd+D0pvx2SMXO7LA6UwW2wYEBeKQd3D3PrZGrVSqkHgivzkCcQbFd87vIfbVwPvExOCUb0jYRMgrfdHJBebTZBGTab4ca8YwGSM3EA8+dw3oocsKyqLXd/fHalARV2z2z+fSFTX3IKNh8sBAJOHu6d28lL3tn7uf/eVcA0+ktXOovMorm5EiFaNO9Pj5Q5HVkzGyKVOVNQh/2wNVEoF7h4qzyyZfnGhGNYzAlabgC/2lcoSAxEAfF1QDpPFht4xwW7vJRZNGKSHVq3EiYp6FPB+lSQj8UL9p0MSPPaeke7CZIxc6j959qGQWwfEICbUtWspdUTsDfjyAJMxks8X+fbj4Z6h3WW7716YLgDZqXEAOFRJ8mluseKbAnsvsb8uZ3ExJmPkMoIgYF1r8nPPMHmGZER3DNJDqQAOnKvFmfO8Px+5X4WxGdtPVAGAbL3EoknD7J+/dn8p1xwjWWw5Vol6kwXx4Tpk9OwmdziyYzJGLnOwpBbnLjQhMECF21Jcu+L+1USHaHFj32gAwLoDvHk4ud/a/aWwCUBGr27oGRUkayw3949BRFAAKupMvF0YyUL8Hf7J4Hi/XHH/UkzGyGW+aj3YbkuJ9Yh6gJ8MtheIfrmfQ5XkfuJ/d5OGunc5i/Zo1EpMbC2YXrufQ5XkXo1mC75tncji7uVdPBWTMXIJQRDw1UF7MjZxsGfMkskZqIdaqcARQx1OVPD2SOQ+JTVN2H+uFgoFMGGQZxwP4nG58XA5F0Qmt/ruSAWaWqzoGRmEwT3ccwcKT8dkjFziwLm2IcpbB8g7RCmKCNLgptbbI7F3jNzpm9ZFj2/oFSnrRJaLjUyKRLegAFxobMHu09Vyh0N+ZN3+tiFKuSayeBomY+QS61t7xW5L9YwhSpE4VLnhkEHmSMifiP+95QzSyxxJG7VKidvT7LMqv+bxQG7SZLZi89EKAJ4zauIJmIyR0wmCgPWHWocoPWwhv9tSYqFSKnC0vA7F5xvlDof8QFW9CT+29jzlDIyTORpHOQPtyeHXBeWwcVYlucEPxythstjQo1sg0uLlWWvPEzEZI6c7Vl6Ps9VN0KiVuGVAjNzhOIgI0mBkUiQAYGNhuczRkD/49nA5bAKQ3j0cPbrJO4vyUjf2jUawRgWDsRkHSmrlDof8gHgHitvT4jhEeREmY+R037YmOTf2iUKQRi1zNJfLbh2a2XiYQzPkehta68UmeNAQpUgXoMKtrcvOcOieXM1qE/DdEfsQ5e2pntVLLDcmY+R0YjI2zkMPtvGtydiPpy+gptEsczTky+pNFmmhV08bohSJSaI4yYDIVfYVX8D5BjPCdGrckBwpdzgehckYOVVVvQn5Z2sAAONSPWMW5aUSI4OQog91uEojcoVtxyvRYhWQHB2MvrGhcofTrpv7x0CtVOBUVQNOV/HuFOQ64hDlbSmxCFAx/bgY9wY51XdHKiAIwKDuYYgPD5Q7nCu6XRqqZN0Yuc7mI5UA4HG1kxcL1QXghtY6SnGWG5ErtNWLed6QvdyYjJFTiasqj0vxzCEZkXij5C3HKmG2cMFLcj5BEKTkRu7bgV2NGB97islVTlXW41RVAwJUCtzUP1rucDyO1yVjS5YsQVJSEnQ6HTIzM7F79+4O269atQopKSnQ6XRIT0/H+vXrHV4XBAELFixAfHw8AgMDkZ2djePHj0uvf//991AoFO0+fvzxRwDA6dOn2319586dzt8BHqy5xYptrfUx2R5aLyZK7x6O6BAtGs1W7DnDBS/J+QpKjaioMyFIo8JID6+PuTXF3nO361Q1Gs0WmaMhX7TlmL2XODM5CqG6AJmj8TxelYx9+umnmDt3LhYuXIi9e/diyJAhyMnJQUVF+1dzO3bswNSpUzFz5kzs27cPkyZNwqRJk3Do0CGpzSuvvIK33noLS5cuxa5duxAcHIycnBw0NzcDAEaPHo2ysjKHxy9/+UskJydjxIgRDp/37bffOrTLyMhw3c7wQLuLqtFotiIuTItB3T17/Rilsu3qTPyRIHKmza29TDf2jYZW7TkLH7enT0wIEiMDYbbasP0EbxxOzif+zt7c33OH7OXkVcnYG2+8gVmzZmHGjBlIS0vD0qVLERQUhGXLlrXbfvHixZgwYQLmzZuH1NRUvPjiixg+fDjeeecdAPZesTfffBPPPfcc7r77bgwePBgff/wxSktLsWbNGgCARqOBXq+XHlFRUfjiiy8wY8aMy9ZIiYqKcmgbEOBf2f8Px+0H2039Yrxi/RjxR2HLUSZj5HziEKWn3A6sIwqFQoqTdWPkbM0tVuSetCf5N3tw/aScvCYZM5vNyMvLQ3Z2tvScUqlEdnY2cnNz231Pbm6uQ3sAyMnJkdoXFRXBYDA4tAkPD0dmZuYVt7l27VqcP38eM2bMuOy1u+66C7GxsRgzZgzWrl3b4fcxmUwwGo0OD2+39Zh9iHKsl1z5jO0XA4UCOGKoQ7mxWe5wyIdUN5ixr3VWsTgE6OnE9cY2H6mAIHA1fnKeXUXVMFlsiA/XoV9siNzheCSvScaqqqpgtVoRF+dYixQXFweDof31cQwGQ4ftxf/tyjY/+OAD5OTkoEePHtJzISEheP3117Fq1Sp89dVXGDNmDCZNmtRhQrZo0SKEh4dLj8TExCu29QaG2mYcLa+DQgGM7esdxZmRwRoM7hEBgEOV5Fw/HK+EIAAp+lCPnlV8sazeUdCqlSirbcax8nq5wyEfIo4+3NzfO0ZN5OA1yZgnOHfuHL7++mvMnDnT4fno6GjMnTsXmZmZuOGGG/DSSy/hwQcfxKuvvnrFbc2fPx+1tbXS4+zZs64O36W2tg5RDu4ejm7BGpmj6TxpqJLJGDnRtuP2XuKbvKSXGLCvxi9ONBAn4hA5w5Zj9qFv1otdmdckY9HR0VCpVCgvd1wXqry8HHp9+2uW6PX6DtuL/9vZbX744YeIiorCXXfdddV4MzMzceLEiSu+rtVqERYW5vDwZj944ckHaPtx2Ha8ChYrl7ig6ycIgrTq/hgv6SUWje1nj3fbcV6ckHOcrW7EycoGqJQKjPay48GdvCYZ02g0yMjIwKZNm6TnbDYbNm3ahKysrHbfk5WV5dAeADZu3Ci1T05Ohl6vd2hjNBqxa9euy7YpCAI+/PBDTJ8+vVOF+fn5+YiPj+/09/NmVpsg/Xh7WzI2pEc4wgMDUNvUgv3neKNkun6nqhpQWtsMjUopLabqLW5sPVnuKqrm+nvkFOKF+rDECIQH+tektq7wvLs4d2Du3Ll4+OGHMWLECIwcORJvvvkmGhoapGL66dOno3v37li0aBEA4Mknn8TNN9+M119/HRMnTsTKlSuxZ88evPfeewDsM4ieeuop/OUvf0G/fv2QnJyMP/3pT0hISMCkSZMcPvu7775DUVERfvnLX14W1/Lly6HRaDBs2DAAwOrVq7Fs2TK8//77LtwbnuNQSS0uNLYgVKvG0MQIucPpErVKidF9ovC/QwbsOFGFjF7d5A6JvJw4RDkiqRsCNZ69pMWlUvVhiArW4HyDGfuKLyCzd5TcIZGXk3qJ+7FXrCNelYxNmTIFlZWVWLBgAQwGA4YOHYoNGzZIBfjFxcVQKts6+0aPHo0VK1bgueeewx/+8Af069cPa9aswaBBg6Q2zz77LBoaGvDoo4+ipqYGY8aMwYYNG6DT6Rw++4MPPsDo0aORkpLSbmwvvvgizpw5A7VajZSUFHz66af42c9+5oK94HnE+pKsPlFeeb8xKRk7eR6/GddP7nDIy23z4pOPUqnAjX2jsXZ/KbadqGIyRtfFZhOQe8q+pMWNHKLskELgHGaPYDQaER4ejtraWq+rH3vw/V3YdqIKL9w9ENOzkuQOp8tOVtZj3OtboFErcWDheOgCvKs3gzyHxWrDsBc2os5kwdo5N0qzdb3JZz+exbOfH8DQxAismX2j3OGQFztcasSdb/2AII0K+QvGQ6P2vov1znDG+ds39wy5jclixY+n7bcTGt3HO6+ie0cHQx+mg9liQ96ZC3KHQ15s/7ka1JksiAgKwMCEcLnDuSZij96BczWobWyRORryZjtO2nuJRyZH+mwi5izcO3Rd9hXXwGSxISZUiz4x3rmYn0KhkBLJ7ZzST9dh2/HWIZk+0VApvXM9pYSIQPSOCYZNgDTERHQtdrSuuu+tF+ruxGSMrot4sGX1jvLqxfzEKdfbT/LkQ9duZ2vykuXlJ58b+9iPh51MxugatVht2HVKTMZYL3Y1TMbouuz0kSsfMf6D52pgbObQDHVdc4sVe4vtw9zenoyNai3cZzJG1+rAuVo0mK2ICApAWrx31UHLgckYXbNGswX7ztpPPt5+5ZMQEYjkaPvQzK5T1XKHQ15o/9m2Ifve0cFyh3NdMnvb10c7YqjDhQazzNGQN9ohzrLvHQWllw7ZuxOTMbpme05fQItVQPeIQCRGesf99zoi9o6JRadEXbGzNYkf5eVD9gAQHaKVbui8q4gXJ9R1uad8Y9TEXZiM0TWT6sX6eP/JB4C0ptJunnzoGohDeqN6e9eq+1fCoUq6VmaLTRqyH8W16jqFyRhds11FbcX7viCz9SbJh8uMrBujLrm4XsxXTj5MxuhaHSypQXOLDZHBGvSN9c5Z9u7GZIyuSaPZgoOt93IcmewbPQFxYTokRQVBEIC801xvjDrPl+rFRKwbo2slDm2PTIr0iVETd2AyRtckv7gGFpuAhHAdenTz/noxUWZya29AEXsDqPN8qV5MxLoxulbiJKhMHxmydwcmY3RNpCufZN+68hF7+Vg3Rl3ha/ViIg5VUldZrG13MvGVURN3YDJG10RMVm7wsYNN/PE4eK4WjWaLzNGQNzBbbNISL5k+ejyItzwjuprCsjrUmywI1amRouf6Yp3FZIy6zJdPPomRQegeEQiLTcDeMzVyh0NeoKC0Fs0tNnQLCvDaW4JdyQ1J9uO7sMyIOk5qoU4QJ3aNTIr02luCyYHJGHXZwZJaaaaMr518gIuHKjk0Q1cn9hqN8MFiZX24DomRgbAJwN7iGrnDIS9wcQkLdR6TMeoyaYgyqZvPnXyAth+Rnawbo074sXXm7Q1J3WSOxDVu6GU/HvZwqJKuwmYTpIsTJmNdw2SMuqztYPON9ZQuJQ7N7D9bA7PFJnM05MkEQZCSlBFJvnnyEb8X68boak5VNaCmsQW6ACUGdQ+XOxyvwmSMusThysdHTz59YoIRERQAk8WGgtJaucMhD3aysgEXxJNPgm+efMQev3xenNBViAsfD+4egQAV04uu4N6iLjlRWY+6ZguCNCqkxofKHY5LKBQKZPS0n4DEKdpE7RF7xYYmRkCj9s2f076xIegWFIDmFl6cUMf2tv5eDu/lm0P2ruSbvx7kMvvEK58e4VD78JVPRhKTMbq63afF+knf7CUGWi9OenGokq5O7Bkb3jNC3kC8kO+eTcklxOUehvf07SufEWLR8pkLEARB5mjIU+1pLd731XoxkThU+SNvE0ZXUNvUgmPl9QDYM3YtmIxRl7Rd+fj2wTa4RzgCVApU1plwtrpJ7nDIA1XUNaO4uhEKBTDMx3sCxGQzjxcndAX5Z2sAAL2ighAdopU3GC/EZIw6rbapBccr7Fc+Q3385KMLUEmzgfac4dAMXW5f67pbA+JCEaYLkDcYFxvUPQwalRLVDWYUVzfKHQ55ILFeLMPHL9RdhckYdZq/XfmMaO1q38O6MWqHmIz5eq8YAGjVKqQl2G9ts4+Lv1I7xFGTYRyivCZMxqjTpJkyfnLlIxYt57FOhtohTmYZlugfx4OYdIrfm0hktQnIb03SWbx/bZiMUaf520yZEa1Fy8cq6mDkffnoIharDQfO2Zd58IeeMQAY1noRtq+1h5xIdLyiDnUmC4I1KgyI880lj1yNyRh1is0mSMOUw/ykZyw6RIvEyEAIAnDgLNdXojbHyuvR1GJFqFbtk/dnbc+wxAgAwOFSI5pbrPIGQx5FnGU/JDHCp5c8ciXuNeqUixd7TdH7z5XP0ERx9XEOzVCbfa3/PQxJjIBS6Xv3Z21Pj26BiA7RwmITuPgrORDXY8xgvdg1YzJGnSLWi/n6Yq+XGtraG5DPoRm6iD8V74sUCsVFdWM1ssZCnmWfnyx55Er+c1al6yLNlPGzg+3iZIzrK5FIKt73o2QMaDsemIyRqLrBjFNVDQD873hwJiZj1Cn7pJky/pWMDUwIQ4BKgap6M0pquPgrAbWNLThZaT/5DPWTmZQizqikS4n/LfSJCUZEkEbmaLwXkzG6qosXe/W3Kx9dgAqp8fb1lThUSQCQf64GAJAUFYTIYP86+QzuEQGlAiitbUa5sVnucMgD+MtdWVzN65KxJUuWICkpCTqdDpmZmdi9e3eH7VetWoWUlBTodDqkp6dj/fr1Dq8LgoAFCxYgPj4egYGByM7OxvHjxx3aJCUlQaFQODxeeuklhzYHDhzA2LFjodPpkJiYiFdeecU5X9gD+Ntir5ca0iMCAKR1dMi/if8diEN2/iREq0b/1qULOFRJAIv3ncWrkrFPP/0Uc+fOxcKFC7F3714MGTIEOTk5qKioaLf9jh07MHXqVMycORP79u3DpEmTMGnSJBw6dEhq88orr+Ctt97C0qVLsWvXLgQHByMnJwfNzY5XfS+88ALKysqkx29+8xvpNaPRiPHjx6NXr17Iy8vDq6++iueffx7vvfeea3aEm/nbYq+XYhE/XUycSelv9ZOitvXGOFTp7yxWG/a3LvvDm4NfH69Kxt544w3MmjULM2bMQFpaGpYuXYqgoCAsW7as3faLFy/GhAkTMG/ePKSmpuLFF1/E8OHD8c477wCw94q9+eabeO6553D33Xdj8ODB+Pjjj1FaWoo1a9Y4bCs0NBR6vV56BAcHS6998sknMJvNWLZsGQYOHIgHHngATzzxBN544w2X7Qt38rfFXi8l3ofzYEktWqw2eYMhWQmC4JczKS/GGZUkOmKos6+3p1Ojr5+st+cqXpOMmc1m5OXlITs7W3pOqVQiOzsbubm57b4nNzfXoT0A5OTkSO2LiopgMBgc2oSHhyMzM/Oybb700kuIiorCsGHD8Oqrr8JisTh8zk033QSNpq1+JCcnB0ePHsWFC+1fPZpMJhiNRoeHJ/LHxV4vlRwVjDCdGiaLDUcNdXKHQzIqqmpAbVMLtGolUvRhcocjC/Gi7MC5Glh4ceLXLp5l7y/r7bmK1yRjVVVVsFqtiIuLc3g+Li4OBoOh3fcYDIYO24v/e7VtPvHEE1i5ciU2b96Mxx57DH/961/x7LPPXvVzLv6MSy1atAjh4eHSIzEx8YrfXU7iYq+BAf612OvFlEoFhohT+jlU6dfE3qD07uHQqL3m59OpekeHIFSnRnOLDUd4ceLXpAt1P6yfdDb//DXporlz5+KWW27B4MGD8atf/Qqvv/463n77bZhMpmve5vz581FbWys9zp4968SIncdfF3u9lPhjwyJ+/9ZWLxYhbyAyUioVbeuN8eLEr4n3Zx2SGC5zJN7Pa86u0dHRUKlUKC8vd3i+vLwcer2+3ffo9foO24v/25VtAkBmZiYsFgtOnz7d4edc/BmX0mq1CAsLc3h4ov3SzZD9c4hSJNaN7W9d1oD8k9gT4G/ri12KFydUb7LgZKV9yaP07hHyBuMDvCYZ02g0yMjIwKZNm6TnbDYbNm3ahKysrHbfk5WV5dAeADZu3Ci1T05Ohl6vd2hjNBqxa9euK24TAPLz86FUKhEbGyt9ztatW9HS0uLwOQMGDEC3bt79o32gNfkY0sO/r3zE5S1OVtbD2NzScWPySU1mKwrL7MNy/twzBnBGJQGHSmohCEBCuA4xof635JGzeU0yBtiHC//5z39i+fLlKCwsxOOPP46GhgbMmDEDADB9+nTMnz9fav/kk09iw4YNeP3113HkyBE8//zz2LNnD+bMmQPAfq+1p556Cn/5y1+wdu1aHDx4ENOnT0dCQgImTZoEwF6c/+abb2L//v04deoUPvnkEzz99NN48MEHpUTr5z//OTQaDWbOnImCggJ8+umnWLx4MebOneveHeRkzS1WqWA93c+TsagQLRIjAyEIwIGzvEmyPzpYUgurTUBcmBbx4Tq5w5GVOEx5qrIBNY1meYMhWYgX6oNbL1Tp+qjlDqArpkyZgsrKSixYsAAGgwFDhw7Fhg0bpGL54uJiKJVt+eXo0aOxYsUKPPfcc/jDH/6Afv36Yc2aNRg0aJDU5tlnn0VDQwMeffRR1NTUYMyYMdiwYQN0OvuPrVarxcqVK/H888/DZDIhOTkZTz/9tEOiFR4ejm+++QazZ89GRkYGoqOjsWDBAjz66KNu2jOuUVhmhMUmICpYg+4RgXKHI7uhid1wtroJ+WcvYEy/aLnDITeT7keZ2A0KhX/PHOsWrEFydDCKqhqQf7YGtwyIlTskcjOxhGUw68WcwquSMQCYM2eO1LN1qe+///6y5+677z7cd999V9yeQqHACy+8gBdeeKHd14cPH46dO3deNa7Bgwfjhx9+uGo7b3KwxH6wpfcI9/uTD2DvDfhyfykXf/VTYr3gUD8fohQNTYxgMubH2kpYImSNw1d41TAluZc4U2Zwd175AI4r8QuCIG8w5HbS8eDnQ/YicT8cKuGwvb+50GDG2eomAMAgnh+cgskYXRFrAhwNTAhDgEqBqnozzl1okjsccqPqhra/OU8+dumt+0FMUsl/HGhNwJOjgxEeGCBzNL6ByRi1q8FkwYkK+7Rl9gTY6QJUSI23L0HCoUr/cvCik0+YjicfAEhLCINSAVTUmVBubL76G8hnHGj9/eO5wXmYjFG7CkqNsAmAPkyH2DD/njl2MbE+gsmYfxGH4tLZKyYJ0qjRL9Z+V46D7B3zK1LxPkdNnIbJGLVLHKL09yUtLiVeCR5knYxfaRuy5/FwMfH34QCPB79ysKQGANefdCYmY9QuMdlg8b4j8eRzuNQIm41F/P5C7Plhz5gjcX8c5J0p/Ea5sRnlRhOUCvtQNTkHkzFqlzRzjDeAddA3JgS6ACXqTRYUnW+QOxxyg6p6E0prm6FQAAOZjDlIl3qKjZxh7Cf2t5Zo9I8LRZDG61bH8lhMxugytU0tKKqyJxrsCXCkVimR1lrEzyn9/kHsJe4dHYwQLU8+F0uLD4NKqUBVvQkGFvH7BS7x4hpMxugyYpKRGBmIyGCNzNF4nrahGSZj/uAgi5WvSBegQr/YEABc4sJf7OeSRy7BZIwu07bYa4S8gXgocZ0pFvH7hwOsF+sQF3/1H4IgtNUTs2fMqZiM0WXEmTI82Non1skUsIjfL/B46Fh6aw8Je8Z839nqJtQ0tkCjUiJFz+J9Z2IyRpfZf7btnpR0ORbx+48Kzhy7qsEX9RSziN+3iUOUqfGh0KiZPjgT9yY5OF9vQkkNb/vSEbVKKa3Ez6EZ3yYOyfSNDeHMsSsYoA+FWqlAdYNZ+u0g38Rb5LkOkzFyIC7e2DuGt33pCIv4/UNbvViEvIF4MF2ACgP09pX4eXHi2/ZzJqXLMBkjB9LMMfaKdYhF/P6BxcqdI+4f1o35LptNQEEJZxa7CpMxcnCA0/g7RewZYxG/77p45hiH7Dsm9hzy4sR3nT7fgAazFboAJfq2LmdCzsNkjBzwHnyd0y82BFq1vYj/NIv4fVK50YTKOhNUSoW00C+1L51F/D6voNQIAEjR2xf6JediMkaSCmMzKuo4c6wz1CqltI/YG+CbxAuTfrEhCNSo5A3Gw/XXh0CjUqKmsQXnLrCI3xcdLrMnYzw3uAaTMZKIVz59YjhzrDNYxO/bWC/WeVq1Cinx9iJ+1o35psOt54eBTMZcgskYScSZUDzYOodF/L5N/Lums36yU8Tj4UDrIrnkW8SLdQ7ZuwaTMZIcKmWxclewiN93CYIg9XjyNkidI87A5vIWvqeirhlV9fYSFq687xpMxkhSIHVD8+TTGSzi912ltc0432CGWqlASusaWtSx9IuWt2ARv28Rhyh7x7B+0lWYjBEAoKbRLBXeskCzcy5eiZ9Dlb5FPPn0iwuFLoAnn87oHxcKjUqJumYLzlaziN+XcIjS9ZiMEYC2k0/PyCCEB3Ll/c5K59CMTzrMk0+XBaiU6K+3rz9VUMrjwZdwJqXrMRkjAG31Yize75p0FvH7JDGZ4MmnawbGt9VRku8o5ExKl2MyRgCAQyX2g43F+10zSOoZYxG/L5F6Atgz1iWDutv3F3vGfEeDyYKi1prYVB4PLsNkjAC0/Xjyyqdr+sWFQNNaxH+mulHucMgJaptaWD95jdJaJ/8cYs+YzzhiMEIQgLgwLaJDtHKH47OYjBEaTBacqrJf+XAmZdcEqJTSbLvDPAH5hMLWXrEe3QJZP9lFqfGhUCiAyjoTKuqa5Q6HnOAwZ9m7BZMxQmFZ25VPTCivfLpK7E3k0IxvYPH+tQvSqNE7OhgA68Z8BWdSugeTMZIOtkG88rkm4o+UWGdE3o0zx66P2IPCnmLfwOPBPZiMEW+DdJ3EOhn2BPgG9gRcHxbx+w6L1YYjhjoAPD+4mtclY0uWLEFSUhJ0Oh0yMzOxe/fuDtuvWrUKKSkp0Ol0SE9Px/r16x1eFwQBCxYsQHx8PAIDA5GdnY3jx49Lr58+fRozZ85EcnIyAgMD0adPHyxcuBBms9mhjUKhuOyxc+dO5355F5FW3udMymuSomedjK8wW2w4UWE/+bAn4NqIPWPiDG3yXicrG2C22BCiVSOxW5Dc4fg0r0rGPv30U8ydOxcLFy7E3r17MWTIEOTk5KCioqLd9jt27MDUqVMxc+ZM7Nu3D5MmTcKkSZNw6NAhqc0rr7yCt956C0uXLsWuXbsQHByMnJwcNDfbT6pHjhyBzWbDP/7xDxQUFOBvf/sbli5dij/84Q+Xfd63336LsrIy6ZGRkeGaHeFEJosVx8p55XM9grVqJLfWyXBoxrsdr6hDi1VAeGAAukcEyh2OVxJ/R4qrG2FsbpE5Groeh8vsvZup8aFQKhUyR+PbvCoZe+ONNzBr1izMmDEDaWlpWLp0KYKCgrBs2bJ22y9evBgTJkzAvHnzkJqaihdffBHDhw/HO++8A8DeK/bmm2/iueeew913343Bgwfj448/RmlpKdasWQMAmDBhAj788EOMHz8evXv3xl133YXf/va3WL169WWfFxUVBb1eLz0CAjx/JtYxQz0sNgERQTz5XA/WjfmGi4v3FQqefK5FRJBG+i3hxYl340xK9/GaZMxsNiMvLw/Z2dnSc0qlEtnZ2cjNzW33Pbm5uQ7tASAnJ0dqX1RUBIPB4NAmPDwcmZmZV9wmANTW1iIyMvKy5++66y7ExsZizJgxWLt2bYffx2QywWg0OjzkINZ1DEoI58nnOgxk3ZhPYLGyc7TNMObx4M1YP+k+XpOMVVVVwWq1Ii4uzuH5uLg4GAyGdt9jMBg6bC/+b1e2eeLECbz99tt47LHHpOdCQkLw+uuvY9WqVfjqq68wZswYTJo0qcOEbNGiRQgPD5ceiYmJV2zrSrwNknOI+6+QJx+vxmUtnKPt4oRF/N5KEARenLiRWu4AvElJSQkmTJiA++67D7NmzZKej46Oxty5c6V/33DDDSgtLcWrr76Ku+66q91tzZ8/3+E9RqNRloRMLLJl8f71EX+sis43oMFkQbCWh5a34cnHeaSeMRbxe62y2mbUNLZArVSgX1yI3OH4PK/pGYuOjoZKpUJ5ebnD8+Xl5dDr9e2+R6/Xd9he/N/ObLO0tBS33norRo8ejffee++q8WZmZuLEiRNXfF2r1SIsLMzh4W72acu8AawzRIdoERemhSBA2qfkXc5daEJdswUalRJ9YnjyuR4DW5e3OFFZj+YWq8zR0LUQhyj7xoZAq1bJHI3v85pkTKPRICMjA5s2bZKes9ls2LRpE7Kystp9T1ZWlkN7ANi4caPUPjk5GXq93qGN0WjErl27HLZZUlKCW265BRkZGfjwww+hVF59t+Xn5yM+Pr5L39HdTlU1oLnFhmCNCslRwXKH4/XEoS3WyXgn8e8m3m+Urp0+TIfIYA2sNgFHW9epIu8iDdnzQt0tvGosZe7cuXj44YcxYsQIjBw5Em+++SYaGhowY8YMAMD06dPRvXt3LFq0CADw5JNP4uabb8brr7+OiRMnYuXKldizZ4/Us6VQKPDUU0/hL3/5C/r164fk5GT86U9/QkJCAiZNmgSgLRHr1asXXnvtNVRWVkrxiL1ny5cvh0ajwbBhwwAAq1evxrJly/D++++7a9dcE7GeIy0hjNOWnWBgQjg2H63kDDIvJQ1Rsl7suikUCgxMCMMPx6tQUGrEkMQIuUOiLhKXteBMSvfwqmRsypQpqKysxIIFC2AwGDB06FBs2LBBKsAvLi526LUaPXo0VqxYgeeeew5/+MMf0K9fP6xZswaDBg2S2jz77LNoaGjAo48+ipqaGowZMwYbNmyATqcDYO9JO3HiBE6cOIEePXo4xCMIgvT/X3zxRZw5cwZqtRopKSn49NNP8bOf/cyVu+O6SfViPNicIo0zyLxa2zR+JmPOMDAhHD8cr5ImCZF34UxK91IIF2cUJBuj0Yjw8HDU1ta6rX5s6ns7kXvqPF752WDcP0Ke2Zy+5Mz5Btz86vfQqJUo+HMOAlQc6vImN770HUpqmvDZY1kYmXz50jXUNV/uL8Vv/r0PQxIj8MXsG+UOh7qgtqkFQ/78DQBg/4LxCA/y/DUz5eSM8zfPFn5KEAQUGnjl40yJ3YIQolXDbLHhZGW93OFQF9Q0mlFS0wQASIkPlTka3yD2MB4pM8JitckcDXVFYeuQfY9ugUzE3ITJmJ/itGXnUyoVbSvxc6jSq4j1Yj0jgxCm48nHGZKighGsUcFkseFUVYPc4VAXcIjS/ZiM+anDnLbsEqwb805c7NX5lErFRccD68a8CWdSuh+TMT/FmWOuIf54sWfMu/Dk4xri5KBDXPzVqxSUcialuzEZ81M8+bhG21pjteDcGO/BixPXEPenWINEns9kseJEhb3mlecH92Ey5qd48nGN/nGhCFApYGy2SAXh5NmaW9pOPuLK8eQcUk9xmZEXJ17ieHk9LDYB4YEBSAjXyR2O32Ay5ofqmltQXN0IAEhlMuZUGrUSfWPts/FYN+YdTlTYTz7dggKgD+PJx5n6xYVArVSgprEFZbXNcodDnXDxensKBRcDdxcmY37oSOvtSRLCdegWrJE5Gt8zkHVjXuXiIXuefJxLq1ahb6x9tjaPB+/AURN5MBnzQ+KPInvFXIP3qPQu0m3BeDy4xMVDleT5WE8sjy7dDqmwsBArV67EDz/8gDNnzqCxsRExMTEYNmwYcnJyMHnyZGi1WlfFSk7Cg821xJ4xFi17B6kngMeDS6TFh2E1Stgz5gVsNkE6HjiT0r061TO2d+9eZGdnY9iwYdi2bRsyMzPx1FNP4cUXX8SDDz4IQRDwxz/+EQkJCXj55ZdhMplcHTddB3ZDu1Zq60m9pKYJFxrMMkdDHbHZBBSW2Yft0+J58nEFaa2xMq415unOXmhEvckCjVqJ3jHBcofjVzrVMzZ58mTMmzcP//nPfxAREXHFdrm5uVi8eDFef/11/OEPf3BWjORELVYbjpa3nnzYE+ASYboA9IwMQnF1IwrLjBjdN1rukOgKePJxPfGi72x1E2qbWhAeyDsceCqx93JAXCjvretmnUrGjh07hoCAqx9AWVlZyMrKQktLy3UHRq5xqrIBZosNIVo1ErsFyR2Oz0qLD0NxdSMKSpmMeTLx5JOi58nHVSKCNOgeEYiSmiYcKTMis3eU3CHRFRRcNJOS3KtTvz6dScSupz25j1jHlBofCqWSM8dcZSCLlr0Ch+zdQ5wsxOPBs7F+Uj5dKuAX/fjjj9i8eTMqKipgs9kcXnvjjTecEhi5Bk8+7sHbInkHTmZxj7SEMHxbWM7jwcPxHq3y6XIy9te//hXPPfccBgwYgLi4OId1ebhGj+fjshbuIc5EOlFZj+YWK3QBvBm7Jyrgycct0tgz5vHO15tgMDZDoQBSeDy4XZeTscWLF2PZsmV45JFHXBAOuZIgCOyGdpO4MC0igzWobjDjWHkdBveIkDskuoR48gF48nE1cdj+eHk9zBYbNGrW53ka8dyQFBWMEO01DZrRdejyEaFUKnHjjTe6IhZysXKjCdUNZqiUCvSPC5U7HJ+mUCikExAXf/VM4pIWSVFBPPm4WI9ugQjVqWG22nCysl7ucKgdHKKUV5eTsaeffhpLlixxRSzkYodb1/npExPMYTM3kIZmmIx5JPF4YC+x6ykUCh4PHq6A9ZOy6vLl4G9/+1tMnDgRffr0QVpa2mUzJ1evXu204Mi5eOXjXtJil6Vc7NITtd0QmYu9ukNaQhh2FVXjcJkRk+UOhi7DEhZ5dTkZe+KJJ7B582bceuutiIqKYtG+F5FWGufB5hZtt0Wqg9UmQMWlRDwKZxa7F3vGPFeT2YpTrcPHA3k8yKLLydjy5cvx+eefY+LEia6Ih1yo7eTDngB3SI4OgS5AiaYWK86cb0DvmBC5Q6JWzS1WnKxsAMCLE3e5uKdYEAReyHuQIwYjbAIQHaJFbJhO7nD8UpdrxiIjI9GnTx9XxEIuVG+y4PR5+8knNZ7F++6gUiowQM8p/Z7oqMHeWxkVrEFsqFbucPxCv9hQBKgUMDZbUFLTJHc4dBEOUcqvy8nY888/j4ULF6KxsdEV8ZCLHDUYIQj2JReiQnjycRcOzXimi08+7KFxD41aib6x9gtBHg+ehfXE8uvyMOVbb72FkydPIi4uDklJSZcV8O/du9dpwZHz8GCTRxpvi+SReDzIIy0+DIVlRhwuM2L8QL3c4VAr3pNSfl1OxiZNmuSCMMjV2A0tD/aMeSYeD/JISwjD53t5PHgSq03AEQOPB7l1ORlbuHChK+IgFzsszqRk8b5bpehDoVAAFXUmVNaZEMP6JNnZbAIKy9gTIAfeFsnzFFU1oLnFhsAAFZKiguUOx291qmZMEARXx0EuZLHacIQ9AbII1qqR3PoDV8gTkEc4U92IRrMVugAlkqM5w9WdxN+fcxeaUNvUInM0BLStg5gaH8rld2TUqWRs4MCBWLlyJcxmc4ftjh8/jscffxwvvfSSU4Ij5zh9vgEmiw1BGhV6RQbJHY7fSWXdmEcRTz4D9GE8+bhZeGAAenQLBMCLE0/BIXvP0Klhyrfffhu/+93v8Otf/xq33347RowYgYSEBOh0Oly4cAGHDx/Gtm3bUFBQgDlz5uDxxx93ddzUBWJxZoo+FEqefNwuLT4MXx0oY52Mh2DxvrzS4sNw7kITDpcaMap3lNzh+L2244ElLHLqVDI2btw47NmzB9u2bcOnn36KTz75BGfOnEFTUxOio6MxbNgwTJ8+HdOmTUO3bt1cHTN1Ea985MUZlZ6Fx4O80hLC8M3hch4PHkAQhItuC8bjQU5dWmdszJgxePvtt5Gfn48LFy6gubkZ586dw5dffok5c+a4JRFbsmQJkpKSoNPpkJmZid27d3fYftWqVUhJSYFOp0N6ejrWr1/v8LogCFiwYAHi4+MRGBiI7OxsHD9+3KFNdXU1pk2bhrCwMERERGDmzJmor693aHPgwAGMHTsWOp0OiYmJeOWVV5zzhZ2AVz7yEm8vcqqyHk1mq8zREHvG5CXu9wL2FMuuos6E8w1mKBXAAD0XA5dTlxd9ldOnn36KuXPnYuHChdi7dy+GDBmCnJwcVFRUtNt+x44dmDp1KmbOnIl9+/Zh0qRJmDRpEg4dOiS1eeWVV/DWW29h6dKl2LVrF4KDg5GTk4Pm5mapzbRp01BQUICNGzdi3bp12Lp1Kx599FHpdaPRiPHjx6NXr17Iy8vDq6++iueffx7vvfee63ZGFxSyJ0BWMaFaRIdoYBOAo+V1cofj1yrrTKioM0GhsA/bk/uJv0MnKupgtthkjsa/iRcmfWJCoAtQyRyNf/OqZOyNN97ArFmzMGPGDKSlpWHp0qUICgrCsmXL2m2/ePFiTJgwAfPmzUNqaipefPFFDB8+HO+88w4Ae6/Ym2++ieeeew533303Bg8ejI8//hilpaVYs2YNAKCwsBAbNmzA+++/j8zMTKl3cOXKlSgtLQUAfPLJJzCbzVi2bBkGDhyIBx54AE888QTeeOMNt+yXjlTUNaOqvvXKJ44nHzkoFAqkcr0xjyBemCRHByNY2+WVfcgJukcEIkynRotVwPEKXpzISZzMwiFK+XlNMmY2m5GXl4fs7GzpOaVSiezsbOTm5rb7ntzcXIf2AJCTkyO1LyoqgsFgcGgTHh6OzMxMqU1ubi4iIiIwYsQIqU12djaUSiV27doltbnpppug0WgcPufo0aO4cOFCu7GZTCYYjUaHhyuIJ//eMSEI1PDKRy5tdWO1Mkfi36R6MQ5RykahULQdD7w4kRXrJz2H1yRjVVVVsFqtiIuLc3g+Li4OBoOh3fcYDIYO24v/e7U2sbGxDq+r1WpERkY6tGlvGxd/xqUWLVqE8PBw6ZGYmNj+F79O5+vNCNGqefKRmbj/C8vYEyAnsU6JJx95ifWrLOKXF+uJPQf76WUyf/58zJ07V/q30Wh0SUI2OaMH7hnWHQ1mi9O3TZ0nDgMUlhlhswlcYkQmh1uHZXhxIi/2jMmvrrkFp883AuDFiSfocs9YdnY2PvroI5cNq11JdHQ0VCoVysvLHZ4vLy+HXt/+DWf1en2H7cX/vVqbSycIWCwWVFdXO7RpbxsXf8altFotwsLCHB6uolQqEKoLuHpDcpnk6BDoApRoNFtxprpR7nD8UqPZglNVDQB48pHbwIuWe+EdXuRxxGDvpY8P1yEyWHOV1uRqXU7GBg4ciPnz50Ov1+O+++7DF198gZYW19/WQqPRICMjA5s2bZKes9ls2LRpE7Kystp9T1ZWlkN7ANi4caPUPjk5GXq93qGN0WjErl27pDZZWVmoqalBXl6e1Oa7776DzWZDZmam1Gbr1q0O+2Hjxo0YMGAA110jAIBKqcAAPXsD5HTUUAdBAKJDtIgN1ckdjl/rExMCjUqJumYLzl1okjscv8QlXjxLl5OxxYsXo6SkBGvWrEFwcDCmT5+OuLg4PProo9iyZYsrYpTMnTsX//znP7F8+XIUFhbi8ccfR0NDA2bMmAEAmD59OubPny+1f/LJJ7Fhwwa8/vrrOHLkCJ5//nns2bMHc+bMAWAvJH3qqafwl7/8BWvXrsXBgwcxffp0JCQkYNKkSQCA1NRUTJgwAbNmzcLu3buxfft2zJkzBw888AASEhIAAD//+c+h0Wgwc+ZMFBQU4NNPP8XixYsdhiGJ2m6SzCJ+ObBY2XNo1Er0i7PfF5R1Y/LgTErPck0F/EqlEuPHj8dHH32E8vJy/OMf/8Du3btx2223OTs+B1OmTMFrr72GBQsWYOjQocjPz8eGDRukYvni4mKUlZVJ7UePHo0VK1bgvffew5AhQ/Cf//wHa9aswaBBg6Q2zz77LH7zm9/g0UcfxQ033ID6+nps2LABOl3blfMnn3yClJQUjBs3DnfeeSfGjBnjsIZYeHg4vvnmGxQVFSEjIwPPPPMMFixY4LAWGRHrZOTFlcY9SxqXe5EVL048i0K4jgF7g8GAlStX4l//+hf27t2LkSNHYufOnc6Mz28YjUaEh4ejtrbWpfVjJJ+8Mxcw+d0diAvTYtcfsq/+BnKqSUu2I/9sDd6eOgw/HZIgdzh+78PtRfjzl4dxe1oc/jl9xNXfQE7TYrVh4IKvYbbasHXeregZFSR3SF7NGefvLveMGY1GfPjhh7j99tuRmJiId999F3fddReOHz/ORIyoAyn6UCgUQLnRhKp6k9zh+BWrTcARA3sCPAl7xuRzoqIeZqsNoVo1EiMD5Q6HcA1LW8TFxaFbt26YMmUKFi1a5LAYKhFdWbBWjaSoYBRVNaCwzIix/WLkDslvFFU1oLnFhsAAFZKiguUOhwCktibFJTVNqGk0IyKIM/rcRUyAUxPCoFBwmR1P0OVkbO3atRg3bhyUSq9ZL5bIY6TFh6GoqgGHS5mMuZNYH5MSHwoV13jzCGG6ACRGBuJsdRMOlxkxuk+03CH5Dd6JwvN0OaO6/fbbYbPZ8O233+If//gH6ursa5WUlpaivr7e6QES+ZK0i9ZXIvfhNH7PxKFKeXAmpefpcs/YmTNnMGHCBBQXF8NkMuH2229HaGgoXn75ZZhMJixdutQVcRL5BJ585MGZY54pLT4cXxeU8+LEjQRBaLs44fHgMbrcM/bkk09ixIgRuHDhAgID2wr/7rnnnssWWCUiR+KP38nKejS3WGWOxn+0LWvBe/B5koFc7sXtSmqaYGy2IEClQL/YULnDoVZd7hn74YcfsGPHDmg0jsWWSUlJKCkpcVpgRL4oNlSLqGANzjeYcdRQhyGJEXKH5PMqjM2oqjdBqQAGxPHk40nEi5MTFfUwWazQqlUyR+T7CloT336xodCoWfvtKbr8l7DZbLBaL7+iP3fuHEJD+UNH1BGFQsG6MTcraN3PvWNCEKjhyd6TxIfrEBEUAItNwPFy1hy7A4coPVOXk7Hx48fjzTfflP6tUChQX1+PhQsX4s4773RmbEQ+iXVj7sXifc+lUCguuk0Yjwd34ExKz9TlZOz111/H9u3bkZaWhubmZvz85z+XhihffvllV8RI5FPYM+ZeLN73bLw4cS/eFswzdblmrEePHti/fz9WrlyJAwcOoL6+HjNnzsS0adMcCvqJqH3iyaewzAibTYCS6165VCF7xjwaL07cp6bRjJKaJgBti+6SZ+hyMgYAarUaDz74oLNjIfILydHB0KqVaDRbcaa6EcnRXBHeVRpMFhSdbwDAnjFPJf5dCkt5ceJqYsKbGBmIMF2AzNHQxTqVjK1du7bTG7zrrruuORgif6BWKZGiD8X+c7U4XGpkMuZCRwx1EAQgLkyL6BCt3OFQO/rEhECjUqLOZMG5C028abULSUOU8VzixdN0KhmbNGlSpzamUCjanWlJRI7SEsLsyVhZLSYOjpc7HJ91uHWlcQ5Req4AlRL99SE4VGLE4bJaJmMuxJmUnqtTBfw2m61TDyZiRJ3DomX3YPG+d+Dx4B6cSem5uOIbkQxYtOwebctacFjGk4l3RuDx4DrNLVYcr7Cv5TawO5MxT9PpZOzOO+9EbW2t9O+XXnoJNTU10r/Pnz+PtLQ0pwZH5KsG6MOgUADlRhOq6k1yh+OTLFYbjhjqALBnzNOl8bZILne8vB5Wm4BuQQHQh+nkDocu0elk7Ouvv4bJ1HbS+Otf/4rq6mrp3xaLBUePHnVudEQ+KkSrRlKUvXC/kL0BLlFU1QCTxYYgjQq9IlmH5MlS9Pa7t5TWNuNCg1nmaHzT4bLW+smEMCgUnLHqaTqdjAmC0OG/iahrWCfjWuKQV2p8GJdL8HChugD0ai3c58WJaxRIi71yyN4TsWaMSCasG3OtAq407lV4WyTX4m3BPFunkzGFQnFZ1ya7OomuHXvGXIsnH+/C48F1bDZB6nFk/aRn6vQK/IIg4JFHHoFWa184sbm5Gb/61a8QHGyve7m4noyIrk78UTxZWY/mFit0ASqZI/IdgiBwWQsvI/6dCpiMOd2Z6kY0mK3QqpXozUWmPVKnk7GHH37Y4d/t3Q5p+vTp1x8RkZ+IDdUiKliD8w1mHDXUYUhihNwh+QyDsRnVDWaolAr0jwuVOxzqBDEZO8GLE6cTextT9KFQq1id5Ik6nYx9+OGHroyDyO8oFAqkJYThh+NVKCwzMhlzooIS+8mnb0wIT+peQh+mQ7egAFxobMHx8nqk92ChubNcPJOSPBNTZCIZsWjZNVi8733EixOgLXkg5yiQboPEBNdTMRkjkhEXu3SNglL2BHgjaSV+Hg9Oxcksno/JGJGMUlt/HAvLjLDZuHafs3BNJe/EnmLnq6wzoaLOBIWibXFd8jxMxohk1Ds6GBq1Eg1mK4qrG+UOxyfUNJpRUtMEgD1j3kb8exWW1fHixEnEXuLk6GAEaztdJk5uxmSMSEZqlVK6WmVvgHOIQzKJkYEIDwyQORrqCvHipN5kwdkLvDhxBvYSewcmY0Qy42KXziWdfOJ58vE2DhcnPB6cQuwZG8ReYo/GZIxIZrwtknOJJx/OpPROrBtzLvaMeQevScaqq6sxbdo0hIWFISIiAjNnzkR9fX2H72lubsbs2bMRFRWFkJAQTJ48GeXl5Q5tiouLMXHiRAQFBSE2Nhbz5s2DxWKRXl+9ejVuv/12xMTEICwsDFlZWfj6668dtvH8889Lt4sSHykpKc778uTT2DPmXNLJpzuTMW/EGcbOY2xuwZnz9uFeXpx4Nq9JxqZNm4aCggJs3LgR69atw9atW/Hoo492+J6nn34aX375JVatWoUtW7agtLQU9957r/S61WrFxIkTYTabsWPHDixfvhwfffQRFixYILXZunUrbr/9dqxfvx55eXm49dZb8dOf/hT79u1z+KyBAweirKxMemzbts25O4B8VkprMmYwNuN8PW8rdj2azFacrLRfpLEnwDuJFye8LdL1ExPa7hGB6BaskTka6ohXTK0oLCzEhg0b8OOPP2LEiBEAgLfffht33nknXnvtNSQkJFz2ntraWnzwwQdYsWIFbrvtNgD2uwikpqZi586dGDVqFL755hscPnwY3377LeLi4jB06FC8+OKL+N3vfofnn38eGo0Gb775psN2//rXv+KLL77Al19+iWHDhknPq9Vq6PV61+0E8lkhWjWSooJw+nwjCsvqMKafVu6QvNYRgxE2AYgO0SA2lPvRG116cRIVwr/jtWpb7JW9Yp7OK3rGcnNzERERISViAJCdnQ2lUoldu3a1+568vDy0tLQgOztbei4lJQU9e/ZEbm6utN309HTExcVJbXJycmA0GlFQUNDudm02G+rq6hAZGenw/PHjx5GQkIDevXtj2rRpKC4u7vA7mUwmGI1Ghwf5L6487hwXrzSuUChkjoauhXhxAtiXuKBrV1AiFu+zl9jTeUUyZjAYEBsb6/CcWq1GZGQkDAbDFd+j0WgQERHh8HxcXJz0HoPB4JCIia+Lr7XntddeQ319Pe6//37puczMTHz00UfYsGED3n33XRQVFWHs2LGoq7vyD8miRYsQHh4uPRITE6/Ylnwf68acg7dB8g3SSvy8OLkuPB68h6zJ2O9///vLCt8vfRw5ckTOEB2sWLECf/7zn/HZZ585JId33HEH7rvvPgwePBg5OTlYv349ampq8Nlnn11xW/Pnz0dtba30OHv2rDu+Ankozqh0jsOcSekTWMR//ZpbrDjRWj85qDt7xjydrDVjzzzzDB555JEO2/Tu3Rt6vR4VFRUOz1ssFlRXV1+xTkuv18NsNqOmpsahd6y8vFx6j16vx+7dux3eJ862vHS7K1euxC9/+UusWrXKYeizPREREejfvz9OnDhxxTZarRZaLWshyC6tdU2sk5UNaG6xQhegkjki72Ox2nDEYO+NZvG+d+PyFtfviKEOVpuAqGAN4sJ4rvF0svaMxcTEICUlpcOHRqNBVlYWampqkJeXJ733u+++g81mQ2ZmZrvbzsjIQEBAADZt2iQ9d/ToURQXFyMrKwsAkJWVhYMHDzokehs3bkRYWBjS0tKk5/79739jxowZ+Pe//42JEyde9XvV19fj5MmTiI+P7/I+If8UF6ZFZLAGVpuAowbWyVyLk5UNMFlsCNGq0SsySO5w6DqIPWPixQl1nbjeXlpCGOsnvYBX1IylpqZiwoQJmDVrFnbv3o3t27djzpw5eOCBB6SZlCUlJUhJSZF6usLDwzFz5kzMnTsXmzdvRl5eHmbMmIGsrCyMGjUKADB+/HikpaXhoYcewv79+/H111/jueeew+zZs6VeqxUrVmD69Ol4/fXXkZmZCYPBAIPBgNratlqG3/72t9iyZQtOnz6NHTt24J577oFKpcLUqVPdvKfIWykUCmlo7VAp62SuhXjySY0PhVLJk483iw3VIqr14uRYOS9OrsWhEnuvIocovYNXJGMA8MknnyAlJQXjxo3DnXfeiTFjxuC9996TXm9pacHRo0fR2Nh2P7O//e1v+MlPfoLJkyfjpptugl6vx+rVq6XXVSoV1q1bB5VKhaysLDz44IOYPn06XnjhBanNe++9B4vFgtmzZyM+Pl56PPnkk1Kbc+fOYerUqRgwYADuv/9+REVFYefOnYiJiXHxXiFfIv5oij+i1DVcadx3KBQK1o1dJ9ZPehevWGcMACIjI7FixYorvp6UlARBEBye0+l0WLJkCZYsWXLF9/Xq1Qvr16+/4uvff//9VWNbuXLlVdsQXY04/fxQCXvGrsXFwzLk/dLiw/DD8SrWjV2DFqsNha3lDlzWwjt4Tc8Yka9Lb+0ZO2qog9likzka7yIIgtSDwp4A3yAm1VyJv+tOVtbD3Fo/2ZP1k16ByRiRh0iMDESYTg2z1cY6mS46d6EJxmYLAlQK9IsNlTsccgJxRmVhmRE2m3CV1nSxgtZSh7T4MNZPegkmY0QeQqFQSHVjBSzi7xJxf/WPC4VGzZ81X5AcHQytWolGsxVnqhuv/gaSiJOABnZnL7G34K8WkQcRhyoPsm6sS7jSuO9Rq5TSfSp5cdI1nMzifZiMEXmQgZxReU3E5JXT+H2LtNwLj4dOs9kEFJaKy1rw4sRbMBkj8iBiz1hhmREWK4v4O0MQBBw8Z0/G0pmM+ZS2nuIaeQPxIsXVjagzWaBRK9EnJkTucKiTmIwReZBekUEI0aphstik+8pRx8pqm3G+wQyVUoHUePYE+BIpGTtXe9nSRdQ+cYgyVR+KABVP8d6CfykiD6JUtq3EL/b2UMfEIcp+sSG8p6eP6R8XCo1KCWOzBcUs4u+UQ9J6e+wl9iZMxog8TNtK/EzGOkNMWgf34MnH12jUSqTG25cq4aSWzhF/NziZxbswGSPyMOLQzCEudtkp4kma9WK+aRBnGHeaIAjSfuLFiXdhMkbkYcSTz+FSI6xc7LJDF5980ntEyBsMucTFdWPUsXMXmlDT2AKNSokBei5+7E2YjBF5mOToYARpVGhqseIUi/g7VFrbjOoGM9RKBVJ48vFJ6T3aesZYxN+xA60Ja0p8KLRq1k96EyZjRB5GdXERP4dmOiT2lvSPC2Xxvo8S76pQ12zBmfMs4u/IgdYlQDhk732YjBF5IHHlbC522bGDPPn4vACVUlqyhBcnHeNkFu/FZIzIA6VzRmWnHGxNVtN58vFp6d2ZjF2NzXZR/WT3CHmDoS5jMkbkgcTkoqC0FjYW8bfLvvJ+DQD2jPk6FvFf3ZnqRtQ1W6BVK9EvjivvexsmY0QeqHd0MHQBSjSYrSg63yB3OB6ppKYJFxpboFYqOHPMx4k9PYdKeHFyJQdaL0zSEsK48r4X4l+MyAOpVUqkxYs3SWZvQHvEXpIBehbv+7p+cSH2In6TBWe4En+7xJmUg9lL7JWYjBF5KK7E3zEu9uo/Ai66OBF7gMiReHHC9fa8E5MxIg/Flcc71rbYK5Mxf8BJLVdmtQnSPSk5k9I7MRkj8lBDWq9wD5VwJf5LOay8z54xvyD+nQ+wiP8ypyrr0Wi2IkijQp8YFu97IyZjRB6qb2wIgjQq1JssXIn/EuJtXwJULN73F20zjI0s4r+EmKAOSgiHSqmQORq6FkzGiDyUSqmQegPyz9bIG4yHEXvFBuh52xd/0S82BFq1EvUmC05zhrEDDtl7PyZjRB5sSGIEAGA/i5YdcIjS/6hVSqTxNmHtEic1sF7MezEZI/JgYt3Y/rM8+VzsEFca90tc/PVyFqsNBaWtd6LgxYnXYjJG5MGGJNp/XAvLjGhuscocjWcQBEGqkeHJx7+IM4wPsGdMcryiHiaLDaFaNZKiguUOh64RkzEiD9Y9IhDRIRpYbAIOl/Gm4QBQXN2I2qYWaFRK9Ndz5pg/EYfhDpXUcoZxK3GIclD3cChZvO+1mIwReTCFQnHRUGWNrLF4CnEyQ1pCGIv3/Uy/2FAEa1RoNFtxvKJO7nA8grTyPuvFvBqTMSIPJxXxMxkDAOwrrgEADG3dL+Q/VEqFNGMwv/W/A3/HmZS+gckYkYdrm1HJOhkA2NealA7rGSFrHCSPYT27AWhLyv2ZyWJFYWv5wmBOZvFqTMaIPNyQ1iveoqoG1DSaZY5GXiaLFYWtM8eGJXaTORqSg9gjyrX3gKOGOrRYBUQEBSAxMlDucOg6eE0yVl1djWnTpiEsLAwRERGYOXMm6us7XpW8ubkZs2fPRlRUFEJCQjB58mSUl5c7tCkuLsbEiRMRFBSE2NhYzJs3DxaLRXr9+++/h0KhuOxhMBgctrNkyRIkJSVBp9MhMzMTu3fvdt6XJ78WEaRBUlQQAN4K5nCpEWarDZHBGp58/NSw1mTsWEUd6k2Wjhv7OLF3cEiPCCgULN73Zl6TjE2bNg0FBQXYuHEj1q1bh61bt+LRRx/t8D1PP/00vvzyS6xatQpbtmxBaWkp7r33Xul1q9WKiRMnwmw2Y8eOHVi+fDk++ugjLFiw4LJtHT16FGVlZdIjNjZWeu3TTz/F3LlzsXDhQuzduxdDhgxBTk4OKioqnLcDyK+xbsxOPPkMS+TJx1/FhunQPSIQggAc8Pvj4QIADtn7Aq9IxgoLC7Fhwwa8//77yMzMxJgxY/D2229j5cqVKC0tbfc9tbW1+OCDD/DGG2/gtttuQ0ZGBj788EPs2LEDO3fuBAB88803OHz4MP71r39h6NChuOOOO/Diiy9iyZIlMJsdh4NiY2Oh1+ulh1LZtuveeOMNzJo1CzNmzEBaWhqWLl2KoKAgLFu2zHU7hfyKNKPSz1fiF4emWLzv38S//z5/T8Zav//wnhyy93ZekYzl5uYiIiICI0aMkJ7Lzs6GUqnErl272n1PXl4eWlpakJ2dLT2XkpKCnj17Ijc3V9pueno64uLipDY5OTkwGo0oKChw2N7QoUMRHx+P22+/Hdu3b5eeN5vNyMvLc/gcpVKJ7Oxs6XPaYzKZYDQaHR5EVzJEqpOphSD47/pKUjLGngC/xroxoKrehDPnGwG0/T6Q9/KKZMxgMDgMCwKAWq1GZGTkZbVbF79Ho9EgIiLC4fm4uDjpPQaDwSERE18XXwOA+Ph4LF26FJ9//jk+//xzJCYm4pZbbsHevXsBAFVVVbBare1u50qxAcCiRYsQHh4uPRITE6+yF8ifDUwIg1qpQFW9CaW1zXKHI4vz9SYUVzdCoeDJx9+Jw3L7imv89uJEXNqjb2wIwgMD5A2Grpusydjvf//7dovjL34cOXJEzhAxYMAAPPbYY8jIyMDo0aOxbNkyjB49Gn/729+ua7vz589HbW2t9Dh79qyTIiZfpAtQISU+FID/rq8k9oL0iQlBmI4nH382qHu4dHFSUtMkdziy2HfWXi82nL3EPkEt54c/88wzeOSRRzps07t3b+j1+suK4S0WC6qrq6HX69t9n16vh9lsRk1NjUPvWHl5ufQevV5/2axHcbbllbYLACNHjsS2bdsAANHR0VCpVJfN0rz4c9qj1Wqh1Wqv+DrRpYb0iMChEiP2n6vBxMHxcofjdqwXI5F4cXKoxIj8szXo0S1I7pDcbu+ZGgBt666Rd5O1ZywmJgYpKSkdPjQaDbKyslBTU4O8vDzpvd999x1sNhsyMzPb3XZGRgYCAgKwadMm6bmjR4+iuLgYWVlZAICsrCwcPHjQIdHbuHEjwsLCkJaWdsW48/PzER9vPxlqNBpkZGQ4fI7NZsOmTZukzyFyhiF+XicjzaRkTwChbZ05f1z81WoTpMk8PB58g6w9Y52VmpqKCRMmYNasWVi6dClaWlowZ84cPPDAA0hISAAAlJSUYNy4cfj4448xcuRIhIeHY+bMmZg7dy4iIyMRFhaG3/zmN8jKysKoUaMAAOPHj0daWhoeeughvPLKKzAYDHjuuecwe/ZsqdfqzTffRHJyMgYOHIjm5ma8//77+O677/DNN99I8c2dOxcPP/wwRowYgZEjR+LNN99EQ0MDZsyY4f6dRT5L7BE6eK4WFqsNapVXlHw6hc0mSMt6sGeMAPt/B/9v5xm/vDg5Vl6HRrMVIVo1+sWGyh0OOYFXJGMA8Mknn2DOnDkYN24clEolJk+ejLfeekt6vaWlBUePHkVjY6P03N/+9jeprclkQk5ODv7+979Lr6tUKqxbtw6PP/44srKyEBwcjIcffhgvvPCC1MZsNuOZZ55BSUkJgoKCMHjwYHz77be49dZbpTZTpkxBZWUlFixYAIPBgKFDh2LDhg2XFfUTXY8+MSEI1qjQYLbieEU9UuPD5A7JbU5V1aPOZEFggAoD4njyobYZtYdKamG22KBR+8/Fyd7W9cWGJIZDpeR6e75AIfjrVBQPYzQaER4ejtraWoSF+c9JlrrmgfdysfNUNRbdm46pI3vKHY7bfLbnLJ79zwGMTI7EZ49x+J/svaXDXtyI2qYWrJ1zIwa3rsXnD367aj/+k3cOc27ti9/mDJA7HL/njPO3/1xKEPkAcXHHvWcuyByJe4lDUcM4REmtlEqF39ZRiivvD+8VIW8g5DRMxoi8SEYvezKWV+xnyVhrkTbrxehi0uKvflTEX9NoxsnKBgDA0ETOpPQVTMaIvIg4jf1UZQMuNJiv0to3NJotOGKw36GC0/jpYtLir37UMyb2AiZFBSEyWCNvMOQ0TMaIvEhksAa9Y4IBtC366OsOnquFTQD0YTrow3Vyh0MeZGhrnVhRVQNqGv3j4kRcyoP3o/QtTMaIvIz4I5znJ3VjUr0Y11OiS3QL1iA52n5x4i91Y+JMSh4PvoXJGJGXkerG/CQZE08+rBej9oiTOvxhUovNJlx0ccKeMV/CZIzIy4jJ2P6z9sVffZkgCNhz2n6SHZHEkw9dbnjr8bDHD5KxU1X1qGu2QBegRIqe6+35EiZjRF6mb0wIQnVqNLVYccRQJ3c4LlVU1YDzDWZo1EoM6h4udzjkgW5IigRgH6Zs8fGLE/F+lIN7RPjVHTj8Af+aRF5GqVRIQxR7fXyJC7G3Y0iPcGjVKpmjIU/ULzYEYTo1Gs1WFJYZ5Q7HpcRJO6wX8z1Mxoi8UEZrMvbjaR9Pxk5XAwBGtPZ+EF1KqVRIQ/d7fP54aF3slfViPofJGJEXuqG1furHomr48h3NxJ6xEb148qErE5P1PWeqZY7EdS40mHG8oh4AjwdfxGSMyAsN69kNaqUCBmMzzl1okjsclzhfb8Kp1pXGM3jyoQ6MuKhnzFcvTsQLkz4xwYgK0cocDTkbkzEiLxSoUUkF7T+e9s3eAPHk0y82BBFBXGmcrmxIYgQCVApU1Jlwtto3L07E43xkMofsfRGTMSIvJf4o+2oyJq6jxnoxuhpdQNvFia8OVe4usn+vG3g8+CQmY0ReSvxRFn+kfY2YZN7A9cWoE8TjwRcntTSaLThUUguAyZivYjJG5KXEOpmTlQ04X2+SORrnajRbcPAcTz7UeeLxsLvovMyROF9+cQ0sNgHx4Tr06BYodzjkAkzGiLxUt2AN+seFAPC93oC9Z+wnnwSefKiTRiZHQqGwX5xU1vnWxcnu021DlAqFQuZoyBWYjBF5sbahGd8aqtzV2ruR2TuKJx/qlIggDQbE2W8R5GtD99KQPYv3fRaTMSIv5qvJ2M5TrckYTz7UBaN6RwFo++/HF7RYbdhXXAOA9ZO+jMkYkRfL7G1PVg6V1MLY3CJzNM7R3GLF/rP2erHM1pMrUWeIyfsuH6obO3CuFo1mKyKCAtA/ljcH91VMxoi8WHx4IJKigmAT7Kvx+4K9xRdgttoQG6pFUlSQ3OGQFxGXezlWXo/qBrPM0TiH2Ms3KjkKSiWH7H0VkzEiL5fVx957lHvSN3oDdp2yJ5WjWC9GXRQVopUmtfjKrErxuBaPc/JNTMaIvJxYJ5PrI3UybcX7rBejrstMFuvGvL+n2GSxSovYMhnzbUzGiLxcVmsydrjMiJpG7x6aMVmsUrGyeFIl6goxid/lA8P2+8/WornFhqhgDfrFhsgdDrkQkzEiLxcbpkPvmGAIgvefgPaeqYHJYkN0iBZ9YoLlDoe8kJjEF5YZvb5uTByi5JC972MyRuQDsnxkSv+Ok1UAgBv78uRD1yYmVCutN+btdZRS8T6HKH0ekzEiH+ArRfzbTojJWLTMkZA3E//7Ef978kbNLVbkFdvvrJHFJV58HpMxIh8gFvEfMdR57X0qjc0t2H+2BgCTMbo+Y/rZj4ftXpyM7S2+ALPFhphQDtn7AyZjRD4gOkSLFL19aGa7l/aO7TpVDZsAJEcHo3sE70dJ125kchTUSgWKqxtxtrpR7nCuybbjrb3EfThk7w+YjBH5iLH97L1JPxyrlDmSayP2YoxmfQxdpxCtGkMTIwB4b++YOMQ6tl+MzJGQOzAZI/IR4o/2thNVEARB5mi6Tjz5jOEQJTmBN9eNVTeYcbDEfksw8SKLfJvXJGPV1dWYNm0awsLCEBERgZkzZ6K+vr7D9zQ3N2P27NmIiopCSEgIJk+ejPLycoc2xcXFmDhxIoKCghAbG4t58+bBYrFIrz/yyCNQKBSXPQYOHCi1ef755y97PSUlxbk7gOgqRiZHQqNWoqy2GScrOz42PE25sRknKuqhUHBxS3IOMRnLPXkeNpt3XZxsP1EFQQAGxIUiNkwndzjkBl6TjE2bNg0FBQXYuHEj1q1bh61bt+LRRx/t8D1PP/00vvzyS6xatQpbtmxBaWkp7r33Xul1q9WKiRMnwmw2Y8eOHVi+fDk++ugjLFiwQGqzePFilJWVSY+zZ88iMjIS9913n8NnDRw40KHdtm3bnLsDiK5CF6CSbpS89Zh39QaI9TGDEsIREaSRORryBUMTIxCkUeF8gxmFBqPc4XSJeDywV8x/eEUyVlhYiA0bNuD9999HZmYmxowZg7fffhsrV65EaWlpu++pra3FBx98gDfeeAO33XYbMjIy8OGHH2LHjh3YuXMnAOCbb77B4cOH8a9//QtDhw7FHXfcgRdffBFLliyB2WxfLDA8PBx6vV567NmzBxcuXMCMGTMcPk+tVju0i47mQUTuJw7x/XDcu+rGtrTWufHkQ86iUSulJSG2eFEdpSAI0vE7tj/rxfyFVyRjubm5iIiIwIgRI6TnsrOzoVQqsWvXrnbfk5eXh5aWFmRnZ0vPpaSkoGfPnsjNzZW2m56ejri4OKlNTk4OjEYjCgoK2t3uBx98gOzsbPTq1cvh+ePHjyMhIQG9e/fGtGnTUFxc3OF3MplMMBqNDg+i6yXWje08VQ2TxSpzNJ1jtQnY2nryuWVArMzRkC+5ZYD9ePj+iPckYycrG1Ba2wyNSomRSbw/q7/wimTMYDAgNtbxR1qtViMyMhIGg+GK79FoNIiIiHB4Pi4uTnqPwWBwSMTE18XXLlVaWor//e9/+OUvf+nwfGZmJj766CNs2LAB7777LoqKijB27FjU1dVd8TstWrQI4eHh0iMxMfGKbYk6K0UfiugQLZparMg7c0HucDol/2wNahpbEKpTY3jPCLnDIR8iJvd5xRdQ29QiczSds631wuSG5G4I1KhkjobcRdZk7Pe//327xfEXP44cOSJniA6WL1+OiIgITJo0yeH5O+64A/fddx8GDx6MnJwcrF+/HjU1Nfjss8+uuK358+ejtrZWepw9e9bF0ZM/UCoVuKl1qG/LUe/oDdhytAIAcFO/GKhVXnF9SF4iMTIIvWOCYbUJXrPExebW4/YmLmnhV9RyfvgzzzyDRx55pMM2vXv3hl6vR0VFhcPzFosF1dXV0Ov17b5Pr9fDbDajpqbGoXesvLxceo9er8fu3bsd3ifOtrx0u4IgYNmyZXjooYeg0XRcYBwREYH+/fvjxIkTV2yj1Wqh1Wo73A7RtbgtNRar95Vg05EKzL8zVe5wrur71nqemwfw5EPOd+uAWJyqLML3RytwZ3q83OF0qMFkkW5pNi6VQ/b+RNbL0JiYGKSkpHT40Gg0yMrKQk1NDfLy8qT3fvfdd7DZbMjMzGx32xkZGQgICMCmTZuk544ePYri4mJkZWUBALKysnDw4EGHRG/jxo0ICwtDWlqaw/a2bNmCEydOYObMmVf9XvX19Th58iTi4z37wCffNLZfDNRKBU5U1OPM+Qa5w+lQVb0JB87Z11O6hcXK5AJi3diWY5Uev/7e9hNVMFtt6BkZhD4xIXKHQ27kFWMCqampmDBhAmbNmoXdu3dj+/btmDNnDh544AEkJCQAAEpKSpCSkiL1dIWHh2PmzJmYO3cuNm/ejLy8PMyYMQNZWVkYNWoUAGD8+PFIS0vDQw89hP379+Prr7/Gc889h9mzZ1/Wa/XBBx8gMzMTgwYNuiy+3/72t9iyZQtOnz6NHTt24J577oFKpcLUqVNdvGeILhceGIAbWgt/vztScZXW8tra2is2MCGM6ymRS4xMjkRggArlRhMKy65cx+sJxOP1tpRY3gLJz3hFMgYAn3zyCVJSUjBu3DjceeedGDNmDN577z3p9ZaWFhw9ehSNjW33Ifvb3/6Gn/zkJ5g8eTJuuukm6PV6rF69WnpdpVJh3bp1UKlUyMrKwoMPPojp06fjhRdecPjs2tpafP7551fsFTt37hymTp2KAQMG4P7770dUVBR27tyJmBhe6ZM8xCEOT0/GxPhu4RAluYhWrZJusbX5qOceDzabIB0PHKL0PwrB0/tt/YTRaER4eDhqa2sRFhYmdzjk5U5W1mPc61ugUSmxd8HtCNHKWh7aLpPFiowXv0W9yYLPHx+NjF7d5A6JfNSKXcX4w38PYkhiBL6YfaPc4bTr4Lla/PSdbQjSqLBvwe3QqjmT0ls44/ztNT1jRNR5vaODkRQVBLPVJq3m7WlyT55HvcmCmFAthrXe1JnIFbLTYqFQAPvP1sBQ2yx3OO3adMQ+eWxsv2gmYn6IyRiRD1IoFLgtxb5m3reF5VdpLY9vDtvjuj0tDkol62PIdWJDdRje097zuvFw+2tTyk08TselxF2lJfkiJmNEPmr8QPuP+sbD5Wix2mSOxpHNJmBjazKWM7D95WmInGl8mv14EC8CPMnZ6kYcKjFCqbAvTUP+h8kYkY+6ISkS0SEa1Da1YOep83KH42Df2RpU1pkQqlVL9w8kcqXxrUl/7snzqG30rNX4Nxyy99ZlJkchOoTrT/ojJmNEPkqlVEgnoPUHPWto5pvWoaJbUmKhUfNniFwvOToY/eNCYLEJHjercv2hMgDAHensJfZX/BUk8mF3DLL/uH9TYIDV5hkTpwVBwDcF4hAl62PIfcan2Y8HsSfKE5TVNmFfcQ0UCg7Z+zMmY0Q+bFTvKEQEBeB8gxm7i6rlDgcAUFBqRFFVA7RqpXQjZyJ3mNB6cbL5aAXqmj1jqFJMDEf06oY4Lnzst5iMEfmwAJVSKlz+X+tQiNzW7i8FYF/Y0hPXPyPfNTAhDL1jgmGy2KQJJHL7X2sJwYRBvH2eP2MyRuTj7mj9kf/fIQMsMs+qtNkEfNmajN01pLussZD/USgUuLv1v7sv8ktljgYw1DbjxzP2Hmux1478E5MxIh93Y99odAsKQGWdCdtPyjur8sfT1SirbUaoVs1bIJEs7hpqv5/xthNVOF9vkjWWL/JLIAjADUnd0D0iUNZYSF5Mxoh8nEatxF1D7Ceg1XvPyRrLF629YhMG6aEL4Crj5H7J0cFI7x4Oq03A+oPyDd0LgoDPW4/He4f3kC0O8gxMxoj8gPhj/3WBQbbCZbPFJp387h7KIUqSz92tvWNi/aIcCkqNOFZeD41aiTvTWS/m75iMEfmBwT3C0ScmGM0tNvxPpmn9m49WoKaxBdEhWmT14UKvJJ+fDE6AQgH8ePoCTlc1yBLDf/eVALDfDiw8MECWGMhzMBkj8gMKhULqHZNrqHLl7mIAwOSM7lDxXpQkI324Djf1s9csfrrnrNs/32K14Yt8ezJ27zD2EhOTMSK/Man1R3/nqWqcrW5062eX1DTh+2OVAIAHbujp1s8mas/UkYkAgFV7zrn93q1bjlWiqt6MqGANburPiSzEZIzIb3SPCMTYftEAgH/tOuPWz/7sx7MQBGBU70gkRwe79bOJ2jMuNQ7RIVpU1ZuwqdC9a479v5324+/e4d0RoOJpmJiMEfmVh0b1AgB8+uNZNLdY3fKZVpuAz1qHgqaOZK8YeYYAlRL3jbAP3f97t/uGKk9XNeD7o5VQKIAHW49HIiZjRH5kXGocukcEoqaxRVp81dW2HKtAWW0zIoICeO898igP3GAfqtx6vNJtQ/f/au0Vu7l/DHpFsZeY7JiMEfkRlVKBaaPsvVMf556BILj+5uEfbCsCAPxseA+uLUYepVdUMMb2i4YgAB/tOO3yz2syW6Ve4oezklz+eeQ9mIwR+ZkpIxKhUStxsKQW+87WuPSzDp6rxfYT56FSKjBjTLJLP4voWvxybG8AwL93F6O20bVr8K3JL4Gx2YKekUG4mYX7dBEmY0R+JipEK63Iv/T7ky79rH9stW//riEJvN0LeaSb+kUjRR+KRrPVpRNbLFYb3tt6CoC9dlPJ5V3oIkzGiPzQr27uA4UC+OZwOQ6XGl3yGcXnG6UV9x+9qbdLPoPoeikUCvzq5j4AgA+3F7lsYsu6A2UoqmpAt6AA/DyTE1nIEZMxIj/UNzYEPxls7x17+7vjLvmM9344CZtgL1ROjQ9zyWcQOcPEwfHoHhGIqnozVuU5f1Fkq02QjrNfju2NYK3a6Z9B3o3JGJGf+s1tfQEA/ztkwBGDc3vHiqoasLJ1uYDHb+nj1G0TOVuASolZY+01jW9tOo4Gk8Wp2//qYBlOVjYgPDAA07O4nAVdjskYkZ/qHxeKO9PtS028/s0xp2775f8dgcUm4NYBMRjVm/ehJM/388xe6BkZhMo6E/75wymnbddsseHNb+3H18wxyQjV8T6UdDkmY0R+7Kns/lApFdh4uBybj1Y4ZZt7TldjQ4EBSgUw/85Up2yTyNU0aiV+NyEFAPCPLadQYWx2ynaXbS/CqcoGRAVr8MiNSU7ZJvkeJmNEfqx/XChmjE4CADy/tuC6i5etNgF/+aoQADDlhp7oHxd6vSESuc2d6XoM6xmBphYrXv366HVvr7SmCW9tsteKzb8zFWHsFaMrYDJG5Oeeur0/4sK0OHO+Ef/Ycn3DM+9tPYX8szUI1qjw9O39nBQhkXsoFAo8N9Hem7sq7xw2H7m+3uK/fHUYjWYrbkjqhsnDuzsjRPJRTMaI/FyIVo0/TkwDALyz+Tj2FV+4pu0UlNbijY323oSFPx2I2FCd02IkcpeMXpGY0TqcOO8/B3C+3nRN2/k87xzWHzRApVTghbsHQaHgumJ0ZUzGiAg/HRyPCQP1aLEK+PUne7t8AmoyW/H0p/losQoYnxYn3YCZyBv9bkIK+sWGoKrehN+vPgibrWu3DSsorcUf/nsQADDn1r5c2oWuiskYEUGhUODV+wajd3Qwymqb8Zt/74PZYuvUe00WKx79f3twrLwe0SFaLLo3nb0A5NV0ASq8+cBQBKjsk1teWHe40/dxvdBgxuP/2guTxYZbBsTgyXEcrqer85pkrLq6GtOmTUNYWBgiIiIwc+ZM1NfXd/ie5uZmzJ49G1FRUQgJCcHkyZNRXl7u0OaJJ55ARkYGtFothg4d2u52Dhw4gLFjx0Kn0yExMRGvvPLKZW1WrVqFlJQU6HQ6pKenY/369df8XYnkEKoLwNKHMhCkUWHHyfP4xUc/ov4q6y21WG34zYp9+OF4FYI0KvzjoeGICtG6KWIi1xmYEI6XJw8GYL+J+GvfXL2g/9yFRvxs6Q4UVzeiR7dAvDllKG97RJ3iNcnYtGnTUFBQgI0bN2LdunXYunUrHn300Q7f8/TTT+PLL7/EqlWrsGXLFpSWluLee++9rN0vfvELTJkypd1tGI1GjB8/Hr169UJeXh5effVVPP/883jvvfekNjt27MDUqVMxc+ZM7Nu3D5MmTcKkSZNw6NCh6/vSRG7WPy4USx+0J2TbTlThgfdycaqy/YueExX1uP8fufjmcDk0aiXenz4CGb0i3RwxkevcO7wHXpw0CACwZPNJ/PqTPFRdYQj/x9PVmPzuDpysbEB8uA4fPnIDIoI07gyXvJhC6Gzfq4wKCwuRlpaGH3/8ESNGjAAAbNiwAXfeeSfOnTuHhISEy95TW1uLmJgYrFixAj/72c8AAEeOHEFqaipyc3MxatQoh/bPP/881qxZg/z8fIfn3333Xfzxj3+EwWCARmM/sH7/+99jzZo1OHLkCABgypQpaGhowLp166T3jRo1CkOHDsXSpUs79R2NRiPCw8NRW1uLsDDWF5C8DpyrwYwPf8T5BjOUCvtJacJAPeIjdCitacaWYxX4bM85mC02hGrVeGvqMNyaEit32EQu8dH2Ivzlq0JYbAK6BQVgyg09cVO/aARr1Th3oQmf7TmLLccqAQD9YkOw/BcjkRARKHPU5C7OOH97xQ2ycnNzERERISViAJCdnQ2lUoldu3bhnnvuuew9eXl5aGlpQXZ2tvRcSkoKevbs2W4y1tFn33TTTVIiBgA5OTl4+eWXceHCBXTr1g25ubmYO3euw/tycnKwZs2aK27XZDLBZGq7wjIaXXOzZqJrMbhHBP776xvxwroCfFtYgf/kncN/2rln3839Y7Do3nSeeMinPXJjMkYkRWLefw6gsMyIpVtOYumWkw5tVEoF7svogfl3pCI8iOuJUdd4RTJmMBgQG+t41a1WqxEZGQmDwXDF92g0GkRERDg8HxcXd8X3XGk7ycnJl21DfK1bt24wGAzSc539nEWLFuHPf/5zp+MgcreeUUF4/+EbkH+2Bh+2riJeWtOEsMAAjOkbjdtSYnHLgBgW65NfGNQ9HF/MvhH/O1SGLccqsePEeQgQkBARiLT4MDx6U2/0igqWO0zyUrImY7///e/x8ssvd9imsLDQTdG41/z58x1604xGIxITE2WMiKh9QxMjsPiBYXKHQSQ7jVqJu4d2x91DuYArOZesydgzzzyDRx55pMM2vXv3hl6vR0WF40rIFosF1dXV0Ov17b5Pr9fDbDajpqbGoXesvLz8iu+50nYunYEp/lvczpXadPQ5Wq0WWi1nnREREfk7WWdTxsTEICUlpcOHRqNBVlYWampqkJeXJ733u+++g81mQ2ZmZrvbzsjIQEBAADZt2iQ9d/ToURQXFyMrK6vTMWZlZWHr1q1oaWmRntu4cSMGDBiAbt26SW0u/hyxTVc+h4iIiPyTVyxtkZqaigkTJmDWrFnYvXs3tm/fjjlz5uCBBx6QZlKWlJQgJSUFu3fvBgCEh4dj5syZmDt3LjZv3oy8vDzMmDEDWVlZDsX7J06cQH5+PgwGA5qampCfn4/8/HyYzWYAwM9//nNoNBrMnDkTBQUF+PTTT7F48WKHIcYnn3wSGzZswOuvv44jR47g+eefx549ezBnzhw37iUiIiLySoKXOH/+vDB16lQhJCRECAsLE2bMmCHU1dVJrxcVFQkAhM2bN0vPNTU1Cb/+9a+Fbt26CUFBQcI999wjlJWVOWz35ptvFgBc9igqKpLa7N+/XxgzZoyg1WqF7t27Cy+99NJl8X322WdC//79BY1GIwwcOFD46quvuvT9amtrBQBCbW1tl95HRERE8nHG+dsr1hnzB1xnjIiIyPs44/ztFcOURERERL6KyRgRERGRjJiMEREREcmIyRgRERGRjJiMEREREcmIyRgRERGRjJiMEREREcmIyRgRERGRjJiMEREREclILXcAZCfeCMFoNMocCREREXWWeN6+nhsaMRnzEHV1dQCAxMREmSMhIiKirqqrq0N4ePg1vZf3pvQQNpsNpaWlCA0NhUKhcOq2jUYjEhMTcfbsWd730oW4n92H+9p9uK/dg/vZfZy9rwVBQF1dHRISEqBUXlv1F3vGPIRSqUSPHj1c+hlhYWE8yN2A+9l9uK/dh/vaPbif3ceZ+/pae8RELOAnIiIikhGTMSIiIiIZMRnzA1qtFgsXLoRWq5U7FJ/G/ew+3Nfuw33tHtzP7uOJ+5oF/EREREQyYs8YERERkYyYjBERERHJiMkYERERkYyYjBERERHJiMmYj1uyZAmSkpKg0+mQmZmJ3bt3yx2Sx1i0aBFuuOEGhIaGIjY2FpMmTcLRo0cd2jQ3N2P27NmIiopCSEgIJk+ejPLycoc2xcXFmDhxIoKCghAbG4t58+bBYrE4tPn+++8xfPhwaLVa9O3bFx999NFl8fjT3+qll16CQqHAU089JT3Hfe08JSUlePDBBxEVFYXAwECkp6djz5490uuCIGDBggWIj49HYGAgsrOzcfz4cYdtVFdXY9q0aQgLC0NERARmzpyJ+vp6hzYHDhzA2LFjodPpkJiYiFdeeeWyWFatWoWUlBTodDqkp6dj/fr1rvnSbma1WvGnP/0JycnJCAwMRJ8+ffDiiy863J+Q+/nabN26FT/96U+RkJAAhUKBNWvWOLzuSfu1M7F0ikA+a+XKlYJGoxGWLVsmFBQUCLNmzRIiIiKE8vJyuUPzCDk5OcKHH34oHDp0SMjPzxfuvPNOoWfPnkJ9fb3U5le/+pWQmJgobNq0SdizZ48watQoYfTo0dLrFotFGDRokJCdnS3s27dPWL9+vRAdHS3Mnz9fanPq1CkhKChImDt3rnD48GHh7bffFlQqlbBhwwapjT/9rXbv3i0kJSUJgwcPFp588knpee5r56iurhZ69eolPPLII8KuXbuEU6dOCV9//bVw4sQJqc1LL70khIeHC2vWrBH2798v3HXXXUJycrLQ1NQktZkwYYIwZMgQYefOncIPP/wg9O3bV5g6dar0em1trRAXFydMmzZNOHTokPDvf/9bCAwMFP7xj39IbbZv3y6oVCrhlVdeEQ4fPiw899xzQkBAgHDw4EH37AwX+r//+z8hKipKWLdunVBUVCSsWrVKCAkJERYvXiy14X6+NuvXrxf++Mc/CqtXrxYACP/9738dXvek/dqZWDqDyZgPGzlypDB79mzp31arVUhISBAWLVokY1Seq6KiQgAgbNmyRRAEQaipqRECAgKEVatWSW0KCwsFAEJubq4gCPYfDaVSKRgMBqnNu+++K4SFhQkmk0kQBEF49tlnhYEDBzp81pQpU4ScnBzp3/7yt6qrqxP69esnbNy4Ubj55pulZIz72nl+97vfCWPGjLni6zabTdDr9cKrr74qPVdTUyNotVrh3//+tyAIgnD48GEBgPDjjz9Kbf73v/8JCoVCKCkpEQRBEP7+978L3bp1k/a9+NkDBgyQ/n3//fcLEydOdPj8zMxM4bHHHru+L+kBJk6cKPziF79weO7ee+8Vpk2bJggC97OzXJqMedJ+7UwsncVhSh9lNpuRl5eH7Oxs6TmlUons7Gzk5ubKGJnnqq2tBQBERkYCAPLy8tDS0uKwD1NSUtCzZ09pH+bm5iI9PR1xcXFSm5ycHBiNRhQUFEhtLt6G2Ebchj/9rWbPno2JEydetj+4r51n7dq1GDFiBO677z7ExsZi2LBh+Oc//ym9XlRUBIPB4LAPwsPDkZmZ6bCvIyIiMGLECKlNdnY2lEoldu3aJbW56aaboNFopDY5OTk4evQoLly4ILXp6O/hzUaPHo1Nmzbh2LFjAID9+/dj27ZtuOOOOwBwP7uKJ+3XzsTSWUzGfFRVVRWsVqvDiQsA4uLiYDAYZIrKc9lsNjz11FO48cYbMWjQIACAwWCARqNBRESEQ9uL96HBYGh3H4uvddTGaDSiqanJb/5WK1euxN69e7Fo0aLLXuO+dp5Tp07h3XffRb9+/fD111/j8ccfxxNPPIHly5cDaNtXHe0Dg8GA2NhYh9fVajUiIyOd8vfwhX39+9//Hg888ABSUlIQEBCAYcOG4amnnsK0adMAcD+7iift187E0lnqLrUm8lGzZ8/GoUOHsG3bNrlD8Ulnz57Fk08+iY0bN0Kn08kdjk+z2WwYMWIE/vrXvwIAhg0bhkOHDmHp0qV4+OGHZY7Od3z22Wf45JNPsGLFCgwcOBD5+fl46qmnkJCQwP1MXcaeMR8VHR0NlUp12Wy08vJy6PV6maLyTHPmzMG6deuwefNm9OjRQ3per9fDbDajpqbGof3F+1Cv17e7j8XXOmoTFhaGwMBAv/hb5eXloaKiAsOHD4darYZarcaWLVvw1ltvQa1WIy4ujvvaSeLj45GWlubwXGpqKoqLiwG07auO9oFer0dFRYXD6xaLBdXV1U75e/jCvp43b57UO5aeno6HHnoITz/9tNTzy/3sGp60XzsTS2cxGfNRGo0GGRkZ2LRpk/SczWbDpk2bkJWVJWNknkMQBMyZMwf//e9/8d133yE5Odnh9YyMDAQEBDjsw6NHj6K4uFjah1lZWTh48KDDgb9x40aEhYVJJ8SsrCyHbYhtxG34w99q3LhxOHjwIPLz86XHiBEjMG3aNOn/c187x4033njZEi3Hjh1Dr169AADJycnQ6/UO+8BoNGLXrl0O+7qmpgZ5eXlSm++++w42mw2ZmZlSm61bt6KlpUVqs3HjRgwYMADdunWT2nT09/BmjY2NUCodT6EqlQo2mw0A97OreNJ+7Uwsndalcn/yKitXrhS0Wq3w0UcfCYcPHxYeffRRISIiwmE2mj97/PHHhfDwcOH7778XysrKpEdjY6PU5le/+pXQs2dP4bvvvhP27NkjZGVlCVlZWdLr4nIL48ePF/Lz84UNGzYIMTEx7S63MG/ePKGwsFBYsmRJu8st+Nvf6uLZlILAfe0su3fvFtRqtfB///d/wvHjx4VPPvlECAoKEv71r39JbV566SUhIiJC+OKLL4QDBw4Id999d7tLAwwbNkzYtWuXsG3bNqFfv34OSwPU1NQIcXFxwkMPPSQcOnRIWLlypRAUFHTZ0gBqtVp47bXXhMLCQmHhwoVeveTCxR5++GGhe/fu0tIWq1evFqKjo4Vnn31WasP9fG3q6uqEffv2Cfv27RMACG+88Yawb98+4cyZM4IgeNZ+7UwsncFkzMe9/fbbQs+ePQWNRiOMHDlS2Llzp9wheQwA7T4+/PBDqU1TU5Pw61//WujWrZsQFBQk3HPPPUJZWZnDdk6fPi3ccccdQmBgoBAdHS0888wzQktLi0ObzZs3C0OHDhU0Go3Qu3dvh88Q+dvf6tJkjPvaeb788kth0KBBglarFVJSUoT33nvP4XWbzSb86U9/EuLi4gStViuMGzdOOHr0qEOb8+fPC1OnThVCQkKEsLAwYcaMGUJdXZ1Dm/379wtjxowRtFqt0L17d+Gll166LJbPPvtM6N+/v6DRaISBAwcKX331lfO/sAyMRqPw5JNPCj179hR0Op3Qu3dv4Y9//KPDUgncz9dm8+bN7f42P/zww4IgeNZ+7UwsnaEQhIuWCyYiIiIit2LNGBEREZGMmIwRERERyYjJGBEREZGMmIwRERERyYjJGBEREZGMmIwRERERyYjJGBEREZGMmIwRERERyYjJGBHRdTh69Cj0ej3q6upc/llVVVWIjY3FuXPnXP5ZROQ+TMaIiC5xyy234KmnnupU2/nz5+M3v/kNQkNDXRsUgOjoaEyfPh0LFy50+WcRkfswGSMiukbFxcVYt24dHnnkEbd95owZM/DJJ5+gurrabZ9JRK7FZIyI6CKPPPIItmzZgsWLF0OhUEChUOD06dPttv3ss88wZMgQdO/eXXruzJkz+OlPf4pu3bohODgYAwcOxPr166XXDx06hDvuuAMhISGIi4vDQw89hKqqKul1m82GV155BX379oVWq0XPnj3xf//3f9LrAwcOREJCAv773/86/8sTkSyYjBERXWTx4sXIysrCrFmzUFZWhrKyMiQmJrbb9ocffsCIESMcnps9ezZMJhO2bt2KgwcP4uWXX0ZISAgAoKamBrfddhuGDRuGPXv2YMOGDSgvL8f9998vvX/+/Pl46aWX8Kc//QmHDx/GihUrEBcX5/AZI0eOxA8//ODkb05EclHLHQARkScJDw+HRqNBUFAQ9Hp9h23PnDlzWTJWXFyMyZMnIz09HQDQu3dv6bV33nkHw4YNw1//+lfpuWXLliExMRHHjh1DfHw8Fi9ejHfeeQcPP/wwAKBPnz4YM2aMw2ckJCRg37591/U9ichzMBkjIrpGTU1N0Ol0Ds898cQTePzxx/HNN98gOzsbkydPxuDBgwEA+/fvx+bNm6WesoudPHkSNTU1MJlMGDduXIefGxgYiMbGRud9ESKSFYcpiYiuUXR0NC5cuODw3C9/+UucOnUKDz30EA4ePIgRI0bg7bffBgDU19fjpz/9KfLz8x0ex48fx0033YTAwMBOfW51dTViYmKc/n2ISB5MxoiILqHRaGC1Wq/abtiwYTh8+PBlzycmJuJXv/oVVq9ejWeeeQb//Oc/AQDDhw9HQUEBkpKS0LdvX4dHcHAw+vXrh8DAQGzatKnDzz106BCGDRt2bV+OiDwOkzEiokskJSVh165dOH36NKqqqmCz2dptl5OTg9zcXIfE7amnnsLXX3+NoqIi7N27F5s3b0ZqaioAe3F/dXU1pk6dih9//BEnT57E119/jRkzZsBqtUKn0+F3v/sdnn32WXz88cc4efIkdu7ciQ8++EDafmNjI/Ly8jB+/HjX7gQichsmY0REl/jtb38LlUqFtLQ0xMTEoLi4uN12d9xxB9RqNb799lvpOavVitmzZyM1NRUTJkxA//798fe//x2AvfB++/btsFqtGD9+PNLT0/HUU08hIiICSqX95/hPf/oTnnnmGSxYsACpqamYMmUKKioqpO1/8cUX6NmzJ8aOHevCPUBE7qQQBEGQOwgiIm+1ZMkSrF27Fl9//bVbPm/UqFF44okn8POf/9wtn0dErsfZlERE1+Gxxx5DTU0N6urqXH5LpKqqKtx7772YOnWqSz+HiNyLPWNEREREMmLNGBEREZGMmIwRERERyYjJGBEREZGMmIwRERERyYjJGBEREZGMmIwRERERyYjJGBEREZGMmIwRERERyYjJGBEREZGM/j9nHj8zko7BXAAAAABJRU5ErkJggg==", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -330,7 +327,7 @@ "source": [ "Note that if boundary conditions are not assigned for some boundaries (the lateral boundaries 3 and 4 in this case), Thetis assumes impermeable land conditions.\n", "\n", - "The only missing piece is to add a mechanism that re-evaluates the boundary condition as the simulation progresses. For this purpose we use the optional update_forcings argument of the [iterate()](https://thetisproject.org/thetis.html#thetis.solver2d.FlowSolver2d.iterate) method. update_forcings is a python function that updates all time dependent [Constants](https://firedrakeproject.org/firedrake.html#firedrake.constant.Constant) or [Functions](https://firedrakeproject.org/firedrake.html#firedrake.function.Function) used to force the model. In this case we only need to update `tide_elev_const`:" + "The only missing piece is to add a mechanism that re-evaluates the boundary condition as the simulation progresses. For this purpose we use the optional update_forcings argument of the [iterate()](https://thetisproject.org/thetis.html#thetis.solver2d.FlowSolver2d.iterate) method. update_forcings is a Python function that updates all time dependent [Constants](https://firedrakeproject.org/firedrake.html#firedrake.constant.Constant) or [Functions](https://firedrakeproject.org/firedrake.html#firedrake.function.Function) used to force the model. In this case we only need to update `tide_elev_const`:" ] }, { @@ -341,7 +338,7 @@ "source": [ "def update_forcings(t_new):\n", " \"\"\"Callback function that updates all time dependent forcing fields\"\"\"\n", - " uv, elev = solver_obj.fields.solution_2d.split()\n", + " uv, elev = solver_obj.fields.solution_2d.subfunctions\n", " tide_elev_const.assign(tidal_elevation(t_new))" ] }, @@ -361,46 +358,54 @@ "name": "stderr", "output_type": "stream", "text": [ - "Using default SIPG parameters\n", + "Element family: dg-dg, degree: 1\n", + "2D cell type: triangle\n", + "2D mesh: 78 vertices, 100 elements\n", + "Horizontal element size: 894.43 ... 894.43 m\n", + "Number of 2D elevation DOFs: 300\n", + "Number of 2D velocity DOFs: 600\n", + "Number of cores: 1\n", + "Elevation DOFs per core: ~300.0\n", "dt = 200.0\n", "Using time integrator: CrankNicolson\n", - " 0 0 T= 0.00 eta norm: 0.0000 u norm: 0.0000 0.00\n", - " 1 6 T= 1200.00 eta norm: 6.5453 u norm: 4.5832 1.69\n", - " 2 12 T= 2400.00 eta norm: 17.3241 u norm: 12.1339 0.10\n", - " 3 18 T= 3600.00 eta norm: 29.9394 u norm: 22.0483 0.11\n", - " 4 24 T= 4800.00 eta norm: 38.0665 u norm: 36.7198 0.11\n", - " 5 30 T= 6000.00 eta norm: 38.4731 u norm: 55.5626 0.14\n", - " 6 36 T= 7200.00 eta norm: 41.8948 u norm: 77.3546 0.09\n", - " 7 42 T= 8400.00 eta norm: 49.0878 u norm: 101.4219 0.09\n", - " 8 48 T= 9600.00 eta norm: 55.3362 u norm: 126.8069 0.09\n", - " 9 54 T= 10800.00 eta norm: 55.2865 u norm: 153.0439 0.10\n", - " 10 60 T= 12000.00 eta norm: 48.0631 u norm: 179.1980 0.10\n", - " 11 66 T= 13200.00 eta norm: 45.6190 u norm: 204.4493 0.10\n", - " 12 72 T= 14400.00 eta norm: 45.5563 u norm: 228.1257 0.09\n", - " 13 78 T= 15600.00 eta norm: 43.6845 u norm: 249.4389 0.10\n", - " 14 84 T= 16800.00 eta norm: 34.9302 u norm: 267.8179 0.11\n", - " 15 90 T= 18000.00 eta norm: 21.3827 u norm: 282.5354 0.10\n", - " 16 96 T= 19200.00 eta norm: 14.9964 u norm: 293.2370 0.10\n", - " 17 102 T= 20400.00 eta norm: 10.1270 u norm: 299.6710 0.10\n", - " 18 108 T= 21600.00 eta norm: 4.8039 u norm: 301.6038 0.10\n", - " 19 114 T= 22800.00 eta norm: 9.3595 u norm: 298.9759 0.09\n", - " 20 120 T= 24000.00 eta norm: 23.5248 u norm: 291.7197 0.10\n", - " 21 126 T= 25200.00 eta norm: 28.5038 u norm: 280.2203 0.09\n", - " 22 132 T= 26400.00 eta norm: 31.8381 u norm: 264.8296 0.10\n", - " 23 138 T= 27600.00 eta norm: 36.8431 u norm: 245.9992 0.10\n", - " 24 144 T= 28800.00 eta norm: 46.4826 u norm: 224.2696 0.10\n", - " 25 150 T= 30000.00 eta norm: 54.2397 u norm: 200.2053 0.10\n", - " 26 156 T= 31200.00 eta norm: 52.0340 u norm: 174.7416 0.13\n", - " 27 162 T= 32400.00 eta norm: 49.2293 u norm: 148.5085 0.12\n", - " 28 168 T= 33600.00 eta norm: 48.1833 u norm: 122.3602 0.12\n", - " 29 174 T= 34800.00 eta norm: 51.0326 u norm: 97.0663 0.11\n", - " 30 180 T= 36000.00 eta norm: 49.3360 u norm: 73.4577 0.11\n", - " 31 186 T= 37200.00 eta norm: 38.8214 u norm: 52.4550 0.12\n", - " 32 192 T= 38400.00 eta norm: 29.6042 u norm: 33.9889 0.12\n", - " 33 198 T= 39600.00 eta norm: 23.1776 u norm: 19.0297 0.11\n", - " 34 204 T= 40800.00 eta norm: 20.5860 u norm: 8.2978 0.13\n", - " 35 210 T= 42000.00 eta norm: 12.2982 u norm: 6.4432 0.12\n", - " 36 216 T= 43200.00 eta norm: 2.5717 u norm: 7.4722 0.10\n" + " exp iter time eta norm u norm Tcpu\n", + " 0 0 0.00 0.0000 0.0000 0.00\n", + " 1 6 1200.00 6.5453 4.5832 9.16\n", + " 2 12 2400.00 17.3241 12.1339 1.12\n", + " 3 18 3600.00 29.9394 22.0483 1.23\n", + " 4 24 4800.00 38.0665 36.7198 1.29\n", + " 5 30 6000.00 38.4731 55.5626 1.31\n", + " 6 36 7200.00 41.8948 77.3546 1.03\n", + " 7 42 8400.00 49.0878 101.4219 0.97\n", + " 8 48 9600.00 55.3362 126.8069 0.96\n", + " 9 54 10800.00 55.2865 153.0439 0.94\n", + " 10 60 12000.00 48.0631 179.1980 0.92\n", + " 11 66 13200.00 45.6190 204.4493 0.98\n", + " 12 72 14400.00 45.5563 228.1257 0.78\n", + " 13 78 15600.00 43.6845 249.4389 0.72\n", + " 14 84 16800.00 34.9302 267.8179 0.73\n", + " 15 90 18000.00 21.3827 282.5354 0.74\n", + " 16 96 19200.00 14.9964 293.2370 0.70\n", + " 17 102 20400.00 10.1270 299.6710 0.67\n", + " 18 108 21600.00 4.8039 301.6038 0.68\n", + " 19 114 22800.00 9.3595 298.9759 0.68\n", + " 20 120 24000.00 23.5248 291.7197 0.67\n", + " 21 126 25200.00 28.5038 280.2203 0.68\n", + " 22 132 26400.00 31.8381 264.8296 0.68\n", + " 23 138 27600.00 36.8431 245.9992 0.67\n", + " 24 144 28800.00 46.4826 224.2696 0.66\n", + " 25 150 30000.00 54.2397 200.2053 0.65\n", + " 26 156 31200.00 52.0340 174.7416 0.75\n", + " 27 162 32400.00 49.2293 148.5085 0.80\n", + " 28 168 33600.00 48.1833 122.3602 0.78\n", + " 29 174 34800.00 51.0326 97.0663 0.77\n", + " 30 180 36000.00 49.3360 73.4577 0.80\n", + " 31 186 37200.00 38.8214 52.4550 0.86\n", + " 32 192 38400.00 29.6042 33.9889 0.86\n", + " 33 198 39600.00 23.1776 19.0297 0.85\n", + " 34 204 40800.00 20.5860 8.2978 0.77\n", + " 35 210 42000.00 12.2982 6.4432 0.75\n", + " 36 216 43200.00 2.5717 7.4722 0.75\n" ] } ], @@ -436,7 +441,7 @@ "### Visualising the output\n", "Information on how to visualise results can be found here [outputs and visualization page](https://thetisproject.org/outputs_and_visu.html).\n", "\n", - "Here we will use the hdf5 output (also used for checkpointing) to read back in the solution at different timesteps and plot it using Firedrake. First we need an elevation function, and a velocity function from the same function space that we used in the simulation:" + "Here we will use the hdf5 output (also used for checkpointing) to read back in the solution at different timesteps and plot it using Firedrake. The following code opens the 9th output. We use `os.path.join` to combine the different parts (base output directory, hdf5 subdirectory, and actual filename) into a filename and then load the velocity results on the function space defined earlier using `CheckpointFile`." ] }, { @@ -444,58 +449,40 @@ "execution_count": 17, "metadata": {}, "outputs": [], - "source": [ - "elev = Function(solver_obj.function_spaces.H_2d, name='elev_2d')\n", - "uv = Function(solver_obj.function_spaces.U_2d, name='uv_2d')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The following code opens the 9th output. We use `os.path.join` to combine the different parts (base outputdirectory, hdf5 sub directory, and actual filename) into a filename." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], "source": [ "# NBVAL_SKIP\n", "idx = 9 # which hdf5 do we want to open\n", "filename = os.path.join(options.output_directory, 'hdf5','Elevation2d_%05d' % idx)\n", - "dc = DumbCheckpoint(filename, mode=FILE_READ)" + "with CheckpointFile(filename + '.h5', 'r') as CF:\n", + " mesh2d = CF.load_mesh()\n", + " elev = CF.load_function(mesh2d, name=\"elev_2d\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now we can read the solution into the `elev` and plot it:" + "Now we can plot the solution:" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ "# NBVAL_SKIP\n", - "dc.load(elev)\n", "fig, ax = plt.subplots(figsize=(12,2))\n", "tricontourf(elev, axes=ax, cmap=matplotlib.cm.coolwarm, levels=50)\n", "plt.axis('equal');" @@ -510,19 +497,17 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -530,8 +515,9 @@ "# NBVAL_SKIP\n", "idx = 9 # which hdf5 do we want to open\n", "filename = os.path.join(options.output_directory, 'hdf5','Velocity2d_%05d' % idx)\n", - "dc = DumbCheckpoint(filename, mode=FILE_READ)\n", - "dc.load(uv)\n", + "with CheckpointFile(filename + '.h5', 'r') as CF:\n", + " mesh2d = CF.load_mesh()\n", + " uv = CF.load_function(mesh2d, name=\"uv_2d\")\n", "fig, ax = plt.subplots(figsize=(12,2))\n", "quiver(uv, axes=ax, cmap=matplotlib.cm.coolwarm)\n", "plt.axis('equal');" @@ -546,453 +532,377 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "scrolled": false - }, + "execution_count": 20, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1002,9 +912,10 @@ "matplotlib.rcParams['figure.max_open_warning'] = last_idx+1 # avoid warning\n", "for idx in range(last_idx+1):\n", " filename = os.path.join(options.output_directory, 'hdf5','Elevation2d_%05d' % idx)\n", - " dc = DumbCheckpoint(filename, mode=FILE_READ)\n", - " dc.load(elev)\n", - " dc.close()\n", + " with CheckpointFile(filename + '.h5', 'r') as CF:\n", + " mesh2d = CF.load_mesh()\n", + " elev = CF.load_function(mesh2d, name=\"elev_2d\")\n", + " CF.close()\n", " fig, ax = plt.subplots(figsize=(16,1))\n", " tricontourf(elev, axes=ax, cmap=matplotlib.cm.coolwarm, levels=50)\n", " # Firedrake sets an automatic colorbar range which therefore changes per timestep\n", @@ -1076,7 +987,7 @@ "\n", "def update_forcings(t_new):\n", " \"\"\"Callback function that updates all time dependent forcing fields\"\"\"\n", - " uv, elev = solver_obj.fields.solution_2d.split()\n", + " uv, elev = solver_obj.fields.solution_2d.subfunctions\n", " tide_elev_const.assign(tidal_elevation(t_new))\n", "\n", "solver_obj.iterate(update_forcings=update_forcings)" @@ -1165,9 +1076,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.8.10" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/demos/02-2d-tsunami.ipynb b/demos/02-2d-tsunami.ipynb index 36db35bb3..2c69cfa2f 100644 --- a/demos/02-2d-tsunami.ipynb +++ b/demos/02-2d-tsunami.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The 1945 Makran Tsunami was a large tsunami which originated due to the 1945 Balochistan earthquake. The resulting tsunami is beielved to have killed around 4000 people along the coast of modern day Pakistan, India, Iran and Oman. Tidal records indicate that the tsunami was recorded as far as the islands of Seychells and Minicoy. Moden simulations of the tsunami indicate tsunami elevations would have been observed across the islands of the Maldives as well. \n", + "The 1945 Makran Tsunami was a large tsunami which originated due to the 1945 Balochistan earthquake. The resulting tsunami is believed to have killed around 4000 people along the coast of modern day Pakistan, India, Iran and Oman. Tidal records indicate that the tsunami was recorded as far as the islands of Seychelles and Minicoy. Modern simulations of the tsunami indicate tsunami elevations would have been observed across the islands of the Maldives as well. \n", "\n", "Here we will model the tsunami using elevations from actual fault estimations." ] @@ -27,7 +27,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As usual we begin by importing the required python libraries and modules. " + "As usual we begin by importing the required Python libraries and modules. " ] }, { @@ -43,7 +43,8 @@ "import scipy.interpolate # used for interpolation\n", "import pyproj # used for coordinate transformations\n", "import math\n", - "from thetis import *" + "from thetis import *\n", + "from firedrake.pyplot import triplot, tricontourf, quiver" ] }, { @@ -51,7 +52,7 @@ "metadata": {}, "source": [ "### Importing the mesh\n", - "Next we will import the mesh (this mesh was actually created using [qmesh](http://qmesh.org) mentioned in yesterday's lecture). Additionally we will visualise the mesh as well using the firedrake plot utility function. The plot function provides an easy way to plot of firedrake functions. " + "Next we will import the mesh and visualise it using the Firedrake plot utility function. The plot function provides an easy way to plot of Firedrake functions. " ] }, { @@ -61,14 +62,12 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -107,7 +106,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To start of with however, we will simply use a constant depth of 2000m. The bathymetry function will be defined within the function space of piecewise linear functions (\"CG\" for Continous Galerkin and a polynomial degree of 1). We will again visualise the depth to confirm if the value is assigned across the domain." + "To start off with however, we will simply use a constant depth of 2000m. The bathymetry function will be defined within the function space of piecewise linear functions (\"CG\" for Continous Galerkin and a polynomial degree of 1). We will again visualise the depth to confirm if the value is assigned across the domain." ] }, { @@ -117,14 +116,12 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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\n", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -351,7 +346,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Additionaly we set the initial conditions for our simulation. Here we use the `init_elev` we set up above. Initial velocities are zero (by default):" + "Additionally we set the initial conditions for our simulation. Here we use the `init_elev` we set up above. Initial velocities are zero (by default):" ] }, { @@ -365,7 +360,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "Using default SIPG parameters\n", + "Element family: dg-dg, degree: 1\n", + "2D cell type: triangle\n", + "2D mesh: 13192 vertices, 23067 elements\n", + "Horizontal element size: 317.21 ... 63853.19 m\n", + "Number of 2D elevation DOFs: 69201\n", + "Number of 2D velocity DOFs: 138402\n", + "Number of cores: 1\n", + "Elevation DOFs per core: ~69201.0\n", "dt = 5.0\n", "Using time integrator: CrankNicolson\n" ] @@ -394,15 +396,16 @@ "name": "stderr", "output_type": "stream", "text": [ - " 0 0 T= 0.00 eta norm: 477519.7838 u norm: 0.0000 0.00\n", - " 1 180 T= 900.00 eta norm: 261265.7773 u norm: 25396.9495 312.31\n", - " 2 360 T= 1800.00 eta norm: 288047.1541 u norm: 21171.9421 310.63\n", - " 3 540 T= 2700.00 eta norm: 294684.3989 u norm: 18837.3311 311.97\n", - " 4 720 T= 3600.00 eta norm: 256980.6728 u norm: 20564.1581 316.67\n", - " 5 900 T= 4500.00 eta norm: 281777.9836 u norm: 18151.1857 315.31\n", - " 6 1080 T= 5400.00 eta norm: 268474.9461 u norm: 17317.1590 315.30\n", - " 7 1260 T= 6300.00 eta norm: 224371.0474 u norm: 19226.2677 315.96\n", - " 8 1440 T= 7200.00 eta norm: 256650.3876 u norm: 16484.0618 312.93\n" + " exp iter time eta norm u norm Tcpu\n", + " 0 0 0.00 477519.7838 0.0000 0.00\n", + " 1 180 900.00 261265.7773 25396.9495 557.36\n", + " 2 360 1800.00 288047.1541 21171.9421 534.53\n", + " 3 540 2700.00 294684.3989 18837.3311 497.67\n", + " 4 720 3600.00 256980.6728 20564.1581 501.23\n", + " 5 900 4500.00 281777.9836 18151.1857 487.68\n", + " 6 1080 5400.00 268474.9461 17317.1590 491.95\n", + " 7 1260 6300.00 224371.0474 19226.2677 478.61\n", + " 8 1440 7200.00 256650.3876 16484.0618 505.04\n" ] } ], @@ -418,14 +421,12 @@ "outputs": [ { "data": { - "image/png": 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -440,9 +441,10 @@ "\n", "for idx, ax in enumerate(axes.flatten()):\n", " filename = os.path.join(options.output_directory, 'hdf5','Elevation2d_%05d' % idx)\n", - " dc = DumbCheckpoint(filename, mode=FILE_READ)\n", - " dc.load(elev)\n", - " dc.close()\n", + " with CheckpointFile(filename + '.h5', 'r') as CF:\n", + " mesh2d = CF.load_mesh()\n", + " elev = CF.load_function(mesh2d, name=\"elev_2d\")\n", + " CF.close()\n", " # by specifying cmap=None above, we avoid displaying a colorbar\n", " tricontourf(elev, axes=ax, cmap=matplotlib.cm.coolwarm, levels=50)\n", " # Firedrake sets an automatic colorbar range which therefore changes per timestep\n", @@ -466,7 +468,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "a. Instead of the constant bathymetry, load the bathymetry file provided in the runfiles folder, visuallize the bathymetry and use this bathymetry to run the model. Notice any difference?\n", + "a. Instead of the constant bathymetry, load the bathymetry file provided in the runfiles folder, visualize the bathymetry and use this bathymetry to run the model. Notice any difference?\n", "\n", "Hint, to load the bathymetry, use:" ] @@ -478,8 +480,9 @@ "outputs": [], "source": [ "# NBVAL_SKIP\n", - "dc = DumbCheckpoint('runfiles/bathy', mode=FILE_READ)\n", - "dc.load(bathymetry_2d, name='bathy');" + "with CheckpointFile(\"runfiles/bathy.h5\", 'r') as CF:\n", + " mesh = CF.load_mesh()\n", + " bathymetry_2d = CF.load_function(mesh, name=\"bathymetry\")" ] }, { @@ -506,9 +509,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.8.10" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/demos/demo_2d_north_sea.py b/demos/demo_2d_north_sea.py index 43f8bcbe1..29c860aa2 100644 --- a/demos/demo_2d_north_sea.py +++ b/demos/demo_2d_north_sea.py @@ -39,34 +39,30 @@ # correspond to open ocean (tag 100) or coasts (tag 200). This is because we impose # different boundary conditions in each case. # -# We set up the (UTM) mesh and calculate its longitude-latitude coordinates as follows: - -mesh2d = Mesh("north_sea.msh") -lonlat = coord_system.get_mesh_lonlat_function(mesh2d) -lon, lat = lonlat - -# With the mesh, we can now move on to set up fields defined upon it. For the -# bathymetry data, we use the -# `ETOPO1 data set `__ -# :cite:`ETOPO1:2009`, :cite:`ETOPO1tech:2009`. -# A NetCDF file containing such data for the North Sea can be downloaded from the -# webpage, stored as `etopo1.nc` and then interpolated onto the unstructured mesh -# using SciPy. An `interpolate_bathymetry` script is provided in the -# `example `__ -# in the Thetis source code, which follows the -# `recommendations in the Firedrake documentation for interpolating data `__. +# Normally, we would read in this mesh using ``mesh2d = Mesh('north_sea.msh)``; +# Here however, we use a pre-prepared bathymetry that is stored in a checkpoint +# file and therefore also read in the mesh from the checkpoint file. +# +# For the bathymetry data, we used the `ETOPO1 data set +# `__ :cite:`ETOPO1:2009`, +# :cite:`ETOPO1tech:2009`. A NetCDF file containing such data for the North +# Sea can be downloaded from the webpage, stored as `etopo1.nc` and then +# interpolated onto the unstructured mesh using SciPy. An +# `interpolate_bathymetry` function is provided in model_config module in the +# `example +# `__ +# in the Thetis source code, which follows the `recommendations in the +# Firedrake documentation for interpolating data +# `__. # However, the NetCDF file cannot be included here for # copyright reasons, so we insteady provide a HDF5 file containing the data already # interpolated onto the mesh. Note that HDF5 files currently have to be saved and # loaded using the same number of processors. The bathymetry field was generated by # a serial run, so the following will not work in parallel. -P1_2d = get_functionspace(mesh2d, "CG", 1) -bathymetry_2d = Function(P1_2d, name="Bathymetry") with CheckpointFile("north_sea_bathymetry.h5", "r") as f: - m = f.load_mesh("firedrake_default") - g = f.load_function(m, "Bathymetry") - bathymetry_2d.assign(g) + mesh2d = f.load_mesh("firedrake_default") + bathymetry_2d = f.load_function(mesh2d, "Bathymetry") # The resulting bathymetry field is plotted below. # @@ -94,11 +90,13 @@ def read_station_data(): # We also require fields for the Manning friction coefficient and the Coriolis forcing. # These can be set up as follows: +P1_2d = get_functionspace(mesh2d, "CG", 1) manning_2d = Function(P1_2d, name="Manning coefficient") manning_2d.assign(3.0e-02) omega = 7.292e-05 coriolis_2d = Function(P1_2d, name="Coriolis forcing") +lon, lat = coord_system.get_mesh_lonlat_function(mesh2d) coriolis_2d.interpolate(2 * omega * sin(lat * pi / 180.0)) # We also need to choose a time window of interest and discretise it appropriately. @@ -206,7 +204,7 @@ def read_station_data(): # # For the purposes of this demo, we have included HDF5 files containing spun-up # elevation and velocity fields in the `outputs_spinup` directory. These can be -# used to initialise the model as follows. Again, the spun-up HDF5 data were +# used to initialise the model as follows. The spun-up HDF5 data were # generated by a serial run, so this demo will not work in parallel. solver_obj.load_state(14, outputdir="outputs_spinup", t=0, iteration=0) diff --git a/demos/outputs_spinup/hdf5/Elevation2d_00014.h5 b/demos/outputs_spinup/hdf5/Elevation2d_00014.h5 index 905102467..26b4031da 100644 Binary files a/demos/outputs_spinup/hdf5/Elevation2d_00014.h5 and b/demos/outputs_spinup/hdf5/Elevation2d_00014.h5 differ diff --git a/demos/outputs_spinup/hdf5/Velocity2d_00014.h5 b/demos/outputs_spinup/hdf5/Velocity2d_00014.h5 index fbe64a24e..944db0e87 100644 Binary files a/demos/outputs_spinup/hdf5/Velocity2d_00014.h5 and b/demos/outputs_spinup/hdf5/Velocity2d_00014.h5 differ diff --git a/demos/runfiles/bathy.h5 b/demos/runfiles/bathy.h5 index 832de8b1d..17c9656cc 100644 Binary files a/demos/runfiles/bathy.h5 and b/demos/runfiles/bathy.h5 differ diff --git a/docs/Makefile b/docs/Makefile index 0aa5e8770..f1069d041 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -26,7 +26,7 @@ copy_demos: cp ../demos/*.py source/demos cp ../demos/figures/*.png source/demos cp ../demos/demo_references.bib source/demos - for file in source/demos/*.py; do ${VIRTUAL_ENV}/src/firedrake/pylit/pylit.py -c $$file; mv $$file.txt $$file.rst; done + for file in source/demos/*.py; do pylit -c $$file; mv $$file.txt $$file.rst; done install -d $(BUILDDIR)/html/demos cp source/demos/*.py $(BUILDDIR)/html/demos diff --git a/docs/source/docutils.conf b/docs/source/docutils.conf new file mode 100644 index 000000000..d22dcc571 --- /dev/null +++ b/docs/source/docutils.conf @@ -0,0 +1,2 @@ +[html writers] +table-style: colwidths-grid diff --git a/docs/source/outputs_and_visu.rst b/docs/source/outputs_and_visu.rst index 45aea1c14..92dd648c5 100644 --- a/docs/source/outputs_and_visu.rst +++ b/docs/source/outputs_and_visu.rst @@ -98,15 +98,17 @@ Restarting a simulation ~~~~~~~~~~~~~~~~~~~~~~~ If you have stored the required HDF5 files, you can continue a simulation -using :py:meth:`~.FlowSolver.load_state` method, provided that you use the same -mesh and the same number of MPI processes. This call replaces the +using :py:meth:`~.FlowSolver.load_state` method, provided that you use the +mesh from that checkpoint file. This call replaces the :py:meth:`~.FlowSolver.assign_initial_conditions` call. If initial conditions are not set, add ``load_state`` call above the :py:meth:`~.FlowSolver.iterate` call. -In the simplest form, one only defines the export index that is used as initial -condition:: +In the simplest form, the mesh needs to be loaded from a HDF5 checkpointfile +and then the index given to the :py:meth:`~.FlowSolver.load_state` method:: + mesh2d = read_mesh_from_checkpoint(outputdir) + ...set-up the thetis solver object with mesh2d... solver_obj.load_state(155) This also loads simulation time from the stored state. diff --git a/docs/source/publications.rst b/docs/source/publications.rst index 932246ef5..bd7836397 100644 --- a/docs/source/publications.rst +++ b/docs/source/publications.rst @@ -8,9 +8,38 @@ Citing Thetis If you publish results obtained with Thetis, please cite the `2018 GMD paper `_. Also see `instructions for citing Firedrake `_. +2023 +---- + +Wallwork, J. G., Angeloudis, A., Barral, N., Mackie, L., Kramer, S. C., Piggott, M. D.: +Tidal turbine array modelling using goal-oriented mesh adaptation. +*Journal of Ocean Engineering and Marine Energy*, +doi: `10.1007/s40722-023-00307-9 `_, 2023. + +Kärnä, T., Wallwork, J. G., Kramer, S. C.: +Adjoint-based optimization of a regional water elevation model. +*Journal of Advances in Modeling Earth Systems*, +doi: `10.1029/2022MS003169 `_, 2023. + +Woodroffe, S., Hill, J., Bustamante-Fernandez, E., Lloyd, JM., Luff, J., Richards, S., Shennan, I: +On the varied impact of the Storegga tsunami in northwest Scotland. +*Journal of Quaternary Science*, +doi: `10.1002/jqs.3539 `_, 2023. + +Hill, J., Rush, G., Peakall, J., Johnson, M., Hodson, L., Barlow, N.L.M., +Bowman, E.T., Gehrels, W.R., Hodgson, D.M., Kesserwani, G: +Resolving tsunami wave dynamics: integrating sedimentology and numerical modelling. +*The Depositional Record* +doi: `10.1002/dep2.247 `_, 2023. + 2022 ---- +Lee, K.C., Webster, J.M., Salles, T., Mawson, E.E., Hill, J.: +Tidal dynamics drive ooid formation in the Capricorn Channel since the Last Glacial Maximum. +*Marine Geology*, 454:106944, +doi:`10.1016/j.margeo.2022.106944 `_, 2022. + Mawson, E.E., Lee, K.C., Hill, J.: Sea level rise and the Great Barrier Reef: the future implications on reef tidal dynamics. *Journal of Geophysical Research: Oceans*, 127:e2021JC017823, @@ -24,6 +53,26 @@ Clare, M. C. A., Wallwork, J. G., Kramer, S. C., Weller, H., Cotter, C. J., Pigg Multi-scale hydro-morphodynamic modelling using mesh movement methods. *GEM - International Journal on Geomathematics*, 13:1, doi:`10.1007/s13137-021-00191-1 `_, 2022. +Clare, M. C. A., Kramer, S. C., Cotter, C. J., Piggott, M. D.: +Calibration, inversion and sensitivity analysis for hydro-morphodynamic models through the application of adjoint methods. +*Computers & Geosciences* 163, p. 105104, +doi: `10.1016/j.cageo.2022.105104 `_, 2022. + +Pan, W., Kramer, S. C., Piggott, M. D., Xiping, Y.: +Modeling landslide generated waves using the discontinuous finite element method. +*International Journal for Numerical Methods in Fluids*, 94 (8), pp. 1298-1330, +doi: `10.1002/fld.5090 `_, 2022. + +Zhang, C., Kramer, S. C., Angeloudis, A., Zhang, J., Lin, X., Piggott, M. D.: +Improving tidal turbine array performance through the optimisation of layout and yaw angles. +*International Marine Energy Journal* 5.3, pp. 273–280, +doi: `10.36688/imej.5.273-280 `_, 2022 + +Zhang, J., Zhang, C., Angeloudis, A., Kramer, S. C., He, R., Piggott, M. D.: +Interactions between tidal stream turbine arrays and their hydrodynamic impact around Zhoushan Island, China +*Ocean Engineering* 246, p. 110431, +doi: `10.1016/j.oceaneng.2021.110431 `_, 2022 + 2021 ---- @@ -31,6 +80,11 @@ Fofonova, V., Kärnä, T., Klingbeil, K., Androsov, A., Kuznetsov, I., Sidorenko Bateman, M. D., Kinnaird, T. C., Hill, J., Ashurst, R. A., Mohan, J., Bateman, R. B. I., Robinson, R.: Detailing the impact of the Storegga Tsunami at Montrose, Scotland. *Boreas*, bor.12532, doi:`10.1111/bor.12532 `_, 2021. +Mackie, L., Kramer, S. C., Piggott, M. D., Angeloudis, A.: +Assessing impacts of tidal power lagoons of a consistent design. +*Ocean Engineering* 240, p. 109879. +doi: `10.1016/j.oceaneng.2021.109879 ` + Pan, W., Kramer, S. C., and Piggott, M. D.: A sigma-coordinate non-hydrostatic discontinuous finite element coastal ocean model. *Ocean Modelling*, 157:101732, doi:`10.1016/j.ocemod.2020.101732 `_, 2021. Rasheed, S., Warder, S. C., Plancherel, Y., and Piggott, M. D.: @@ -46,6 +100,11 @@ doi:`10.1016/j.cageo.2020.104658 `_ Warder, S. C., Horsburgh, K. J. and Piggott, M. D.: Adjoint-based sensitivity analysis for a numerical storm surge model. *Ocean Modelling*, 160:101766, doi:`10.1016/j.ocemod.2021.101766 `_, 2021. +Goss, Z. L., Coles, D. S., Kramer, S. C., Piggott, M. D.: +Efficient economic optimisation of large-scale tidal stream arrays. +*Applied Energy* 295, p. 116975. +doi: `10.1016/j.apenergy.2021.116975 `_, 2021. + 2020 ---- diff --git a/examples/bottomFriction/ekman_bottom.py b/examples/bottomFriction/ekman_bottom.py index 61db42155..41cb22f7b 100644 --- a/examples/bottomFriction/ekman_bottom.py +++ b/examples/bottomFriction/ekman_bottom.py @@ -21,8 +21,11 @@ def bottom_ekman_test(layers=50, verify=True, iterate=True, lx = nx*dx ny = 3 ly = ny*dx - mesh2d = PeriodicRectangleMesh(nx, ny, lx, ly, direction='both', - reorder=True) + if load_export_ix: + mesh2d = read_mesh_from_checkpoint(outputdir) + else: + mesh2d = PeriodicRectangleMesh(nx, ny, lx, ly, direction='both', + reorder=True) dt = 90.0 t_end = 5 * 3600.0 # sufficient to reach ~steady state diff --git a/examples/bottomFriction/ekman_surface.py b/examples/bottomFriction/ekman_surface.py index 9e701feaf..131c59ab0 100644 --- a/examples/bottomFriction/ekman_surface.py +++ b/examples/bottomFriction/ekman_surface.py @@ -20,8 +20,11 @@ def surface_ekman_test(layers=50, verify=True, iterate=True, lx = nx*dx ny = 3 ly = ny*dx - mesh2d = PeriodicRectangleMesh(nx, ny, lx, ly, direction='both', - reorder=True) + if load_export_ix: + mesh2d = read_mesh_from_checkpoint(outputdir) + else: + mesh2d = PeriodicRectangleMesh(nx, ny, lx, ly, direction='both', + reorder=True) dt = 90.0 t_end = 6 * 3600.0 diff --git a/examples/bottomFriction/steadyChannel.py b/examples/bottomFriction/steadyChannel.py index cf99d25a6..d1dcda8c0 100644 --- a/examples/bottomFriction/steadyChannel.py +++ b/examples/bottomFriction/steadyChannel.py @@ -38,7 +38,10 @@ def bottom_friction_test(layers=25, gls_closure='k-omega', lx = nx*dx ny = 3 # nb elements in cross direction ly = ny*dx - mesh2d = PeriodicRectangleMesh(nx, ny, lx, ly, direction='x', reorder=True) + if load_export_ix: + mesh2d = read_mesh_from_checkpoint(outputdir) + else: + mesh2d = PeriodicRectangleMesh(nx, ny, lx, ly, direction='x', reorder=True) dt = 25.0 t_end = 12 * 3600.0 # sufficient to reach ~steady state diff --git a/examples/channel_inversion/inverse_problem.py b/examples/channel_inversion/inverse_problem.py index 8d9f3f059..9339bca7b 100644 --- a/examples/channel_inversion/inverse_problem.py +++ b/examples/channel_inversion/inverse_problem.py @@ -1,5 +1,5 @@ from thetis import * -from firedrake_adjoint import * +from firedrake.adjoint import * import numpy import thetis.inversion_tools as inversion_tools from model_config import construct_solver @@ -31,6 +31,9 @@ pwd = os.path.abspath(os.path.dirname(__file__)) output_dir = f'{pwd}/outputs_new_' + '-'.join(controls) + '-opt' +# annotate all Firedrake operations of the forward run +continue_annotation() + # Create the solver object # NOTE We disable all exports here as they would only be active during # the initial forward run @@ -90,7 +93,7 @@ sta_manager.set_model_field(solver_obj.fields.elev_2d) # Define the scaling for the cost function so that J ~ O(1) -J_scalar = Constant(solver_obj.dt / options.simulation_end_time) +J_scalar = Constant(solver_obj.dt / options.simulation_end_time, domain=mesh2d) # Create inversion manager and add controls inv_manager = inversion_tools.InversionManager( diff --git a/examples/columbia_plume/bathymetry.py b/examples/columbia_plume/bathymetry.py index 309b5e48b..6202e68df 100644 --- a/examples/columbia_plume/bathymetry.py +++ b/examples/columbia_plume/bathymetry.py @@ -150,6 +150,6 @@ def get_boundary_relaxation_field(mask_func, bnd_id, dist_scale, # remove negative values mask_func.dat.data[mask_func.dat.data < 0.0] = 0.0 if scalar is not None: - mask_func.assign(mask_func*scalar) + mask_func.interpolate(mask_func*scalar) return mask_func diff --git a/examples/columbia_plume/cre-plume.py b/examples/columbia_plume/cre-plume.py index 339a0a121..4456cc715 100644 --- a/examples/columbia_plume/cre-plume.py +++ b/examples/columbia_plume/cre-plume.py @@ -221,8 +221,8 @@ def interp_ocean_bnd(time): oce_bnd_interp.set_fields(time) - uvel_bnd_3d.assign(uvel_bnd_3d*ncom_vel_mask_3d) - vvel_bnd_3d.assign(vvel_bnd_3d*ncom_vel_mask_3d) + uvel_bnd_3d.interpolate(uvel_bnd_3d*ncom_vel_mask_3d) + vvel_bnd_3d.interpolate(vvel_bnd_3d*ncom_vel_mask_3d) # tides diff --git a/examples/discrete_turbines/channel-optimisation.py b/examples/discrete_turbines/channel-optimisation.py new file mode 100644 index 000000000..8b16efc5e --- /dev/null +++ b/examples/discrete_turbines/channel-optimisation.py @@ -0,0 +1,238 @@ +""" +Basic example of discrete turbine optimisation in a channel +""" +from thetis import * +from firedrake.adjoint import * +from pyadjoint import minimize +import numpy as np +import random +op2.init(log_level=INFO) + +output_dir = 'outputs' + +if os.getenv('THETIS_REGRESSION_TEST') is not None: + test_gradient = True # test gradient using Taylor test (see below) + optimise = False # skip actual gradient based optimisation +else: + test_gradient = False + optimise = True + +# annotate all Firedrake operations of the model run +continue_annotation() + +# ---- set up the Thetis solver obj as usual ---- # +if not os.path.exists('mesh.msh'): + os.system('gmsh -2 mesh.geo -o mesh.msh') +mesh2d = Mesh('mesh.msh') + +# choose a depth +H = 50 +h_viscosity = Constant(2.) + +# create solver and set options +solver_obj = solver2d.FlowSolver2d(mesh2d, Constant(H)) +options = solver_obj.options +# for a steady state solve the timestep is not relevant +# we just have to ensure that we perform exactly one timestep +options.timestep = 1. +options.simulation_export_time = 1. +options.simulation_end_time = 0.5 +options.output_directory = output_dir +options.check_volume_conservation_2d = True +options.element_family = 'dg-cg' +options.swe_timestepper_type = 'SteadyState' +# for steady state we use a direct solve (preonly+lu) in combination with a Newton snes solver +options.swe_timestepper_options.solver_parameters = {'snes_monitor': None, + 'snes_rtol': 1e-12 + } +options.horizontal_viscosity = h_viscosity + +options.quadratic_drag_coefficient = Constant(0.0025) + +# assign boundary conditions +left_tag = 1 +right_tag = 2 +coasts_tag = 3 + +u_inflow = 3.0 +inflow_bc = {'uv': Constant((u_inflow, 0.0))} +outflow_bc = {'elev': 0.} +freeslip_bc = {'un': 0.} + +solver_obj.bnd_functions['shallow_water'] = { + left_tag: inflow_bc, + right_tag: outflow_bc, + coasts_tag: freeslip_bc +} + +# Define the thrust curve of the turbine using a tabulated approach: +# thrusts_AR2000 contains the values for the thrust coefficient of an AR2000 tidal turbine at corresponding speeds in +# speeds_AR2000 which have been determined using a curve fitting technique based on: +# cut-in speed = 1m/s +# rated speed = 3.05m/s +# cut-out speed = 5m/s +# There is a ramp up and down to cut-in and at cut-out speeds for model stability. +speeds_AR2000 = [0., 0.75, 0.85, 0.95, 1., 3.05, 3.3, 3.55, 3.8, 4.05, 4.3, 4.55, 4.8, 5., 5.001, 5.05, 5.25, 5.5, 5.75, + 6.0, 6.25, 6.5, 6.75, 7.0] +thrusts_AR2000 = [0.010531, 0.032281, 0.038951, 0.119951, 0.516484, 0.516484, 0.387856, 0.302601, 0.242037, 0.197252, + 0.16319, 0.136716, 0.115775, 0.102048, 0.060513, 0.005112, 0.00151, 0.00089, 0.000653, 0.000524, + 0.000442, 0.000384, 0.000341, 0.000308] + +# initialise discrete turbine farm characteristics +farm_options = DiscreteTidalTurbineFarmOptions() +farm_options.turbine_type = 'table' +farm_options.turbine_options.thrust_speeds = speeds_AR2000 +farm_options.turbine_options.thrust_coefficients = thrusts_AR2000 +farm_options.turbine_options.diameter = 20 +farm_options.upwind_correction = False # higher SNES tolerance required when using upwind correction + +site_x = 320. +site_y = 160. +site_x_start = 160. +site_y_start = 80. +r = farm_options.turbine_options.diameter/2. + +# a list contains the coordinates of all turbines: regular, non-staggered 4 x 2 layout +farm_options.turbine_coordinates = [[Constant(x+cos(y), domain=mesh2d), Constant(y+numpy.sin(x), domain=mesh2d)] + for x in np.linspace(site_x_start + 4*r, site_x_start + site_x - 4*r, 4) + for y in np.linspace(site_y_start + 0.5*site_y-2*r, + site_y_start + 0.5*site_y+2*r, 2)] + +# apply these turbine farm settings to subdomain 2 +# this means that the mesh needs to have a Physical Surface id of 2 (see mesh.geo file) +# attached to the part of the domain where the turbines are operational +# Any turbines placed outside this subdomain will not produce any power (the corresponding +# integral is only performed over the subdomain). If you want the turbines to be operational +# everywhere, you can use the key "everywhere" instead of 2 below. +# Regardless of this, the area where the turbines are places need to have sufficiently high +# resolution to resolve the Gaussian bump functions that represent the turbines +options.discrete_tidal_turbine_farms[2] = [farm_options] + +# set initial condition (initial velocity should not be exactly 0 to avoid failures in the Newton solve) +solver_obj.assign_initial_conditions(uv=(as_vector((1e-3, 0.0)))) + +# Operation of tidal turbine farm through a callback +# This can be used for diagnostic output (to the log and hdf5 file) +# but here is also used to define the functional that we optimise for +# through cb.integrated_power +cb = turbines.TurbineFunctionalCallback(solver_obj) +solver_obj.add_callback(cb, 'timestep') + +# run forward model +solver_obj.iterate() + + +# set up interest functional and control +power_output = sum(cb.integrated_power) +interest_functional = power_output + +print_output("Functional in forward model {}".format(interest_functional)) + +# specifies the control we want to vary in the optimisation +# this needs to be a flat list of controls corresponding to the x and y coordinates +# of the turbines +c = [Control(x) for xy in farm_options.turbine_coordinates for x in xy] + +# interpolate the turbine density (a sum of Gaussian bump functions representing the turbines) +# to a P1 CG function. This is not used here, but will be used in the UserExportOptimisationCallback +# below to output the farm layouts as a series of vtus +turbine_density = Function(solver_obj.function_spaces.P1_2d, name='turbine_density') +turbine_density.interpolate(solver_obj.tidal_farms[0].turbine_density) + +# a number of callbacks to provide output during the optimisation iterations: +# - ConstantControlOptimisationCallback - outputs the control values to +# the log and hdf5 files (can be controled by append_to_log and export_to_hdf5 keyword arguments) +# - DerivativesExportOptimisationCallback - outputs the derivatives of the functional +# with respect to the control values (here the x,y coordinates of the turbines) as calculated by the adjoint +# - UserExportOptimisationCallback can be used to output any further functions used in the +# forward model. Note that only function states that contribute to the functional are +# guaranteed to be updated when the model is replayed for different control values. +# - FunctionalOptimisationCallback simply writes out the functional values to log and hdf5 +# +# finally, the OptimisationCallbackList combines multiple optimisation callbacks in one +callback_list = optimisation.OptimisationCallbackList([ + optimisation.ConstantControlOptimisationCallback(solver_obj, array_dim=len(c)), + optimisation.DerivativeConstantControlOptimisationCallback(solver_obj, array_dim=len(c)), + optimisation.UserExportOptimisationCallback(solver_obj, [turbine_density, solver_obj.fields.uv_2d]), + optimisation.FunctionalOptimisationCallback(solver_obj), + turbines.TurbineOptimisationCallback(solver_obj, cb), +]) + + +# here we define some additional callbacks just to clearly indicate in the log what the model is doing: +# callbacks to indicate start of forward and adjoint runs in log +def eval_cb_pre(controls): + print_output("FORWARD RUN:") + print_output("positions: {}".format([float(c_) for c_ in controls])) + + +def derivative_cb_pre(controls): + print_output("ADJOINT RUN:") + print_output("positions: {}".format([float(c_.data()) for c_ in controls])) + return controls + + +# this reduces the functional J(u, m) to a function purely of the control m: +# rf(m) = J(u(m), m) where the velocities u(m) of the entire simulation +# are computed by replaying the forward model for any provided turbine coordinates m +# with rf.derivative() we can also compute the derivative with respect to the controls +# which is done through the automated adjoint +# Through the eval_cb_pre/post and derivative_cb_post arguments we can specify callbacks +# that are called at the beginning (_pre) and after (_post) the evaluation of the forward model (eval) +# and the derivative/adjoint model respectively +# NOTE, that we use -interest_functional so that we can *minimize* this reduced functional +# to maximize the power output +rf = ReducedFunctional(-interest_functional, c, derivative_cb_post=callback_list, + eval_cb_pre=eval_cb_pre, derivative_cb_pre=derivative_cb_pre) + +if test_gradient: + # whenever the forward model is changed - for example different terms in the equation, + # different types of boundary conditions, etc. - it is a good idea to test whether the + # gradient computed by the adjoint is still correct, as some steps in the model may + # not have been annotated correctly. This can be done via the Taylor test. + # Using the standard Taylor series, we should have (for a sufficiently smooth problem): + # rf(td0+h*dtd) - rf(td0) - < drf/dtd(rf0), h dtd> = O(h^2) + + # we choose the same starting layout but with a small perturbation + def perturbation(r): + dx = random.uniform(-r, r) + # ensure we use the same perturbation on all processes when parallel: + return mesh2d.comm.bcast(dx, 0) + + m0 = [Constant(float(x) + perturbation(r), domain=mesh2d) for xy in farm_options.turbine_coordinates for x in xy] + + # the perturbation over which we test the Taylor approximation + # (the taylor test below starts with a 1/100th of that, followed by a series of halvings + h0 = [Constant(perturbation(1), domain=mesh2d) for xy in farm_options.turbine_coordinates for x in xy] + + # this tests whether the above Taylor series residual indeed converges to zero at 2nd order in h as h->0 + minconv = taylor_test(rf, m0, h0) + print_output("Order of convergence with taylor test (should be 2) = {}".format(minconv)) + + assert minconv > 1.95 + +if optimise: + # Optimise the control (turbine positions) for minimal functional (i.e. maximum profit) + # with a gradient based optimisation algorithm using the reduced functional + # to replay the model, and computing its derivative via the adjoint + + # There are two types of constraints we need here: + # - first we don't want the turbines to leave the specified (high resolution) + # farm area. Since this area is rectangular we can specify this through + # simple box constraints: lb < c < ub + lb = np.array([[site_x_start+r, site_y_start+r] for _ in farm_options.turbine_coordinates]).flatten() + ub = np.array([[site_x_start+site_x-r, site_y_start+site_y-r] for _ in farm_options.turbine_coordinates]).flatten() + + # - secondly, we don't want the turbines to be placed arbitrary close. This is enforced + # through more general constraints of the form h(m)>0 where here + # we use h(m) = [dist((x1,y1) to (x2,y2))**2 - min_dist**2 for any combination of turbines] + # the MinimumDistanceConstraints implements this constraint (and its derivative), here + # with a minimum distantce min_dist= 1.5*D = 3*r + mdc = turbines.MinimumDistanceConstraints(farm_options.turbine_coordinates, 3*r) + + # finally, the optimisation call. Here, we can't use the default (L-BFGS-B) which only handles + # box constraints, but use SLSQP which also handles more general constraints + # see https://docs.scipy.org/doc/scipy/reference/optimize.minimize-slsqp.html + # for further options + td_opt = minimize(rf, method='SLSQP', constraints=mdc, bounds=[lb, ub], + options={'maxiter': 75, 'ftol': 1e-06}) diff --git a/examples/discrete_turbines/headland.geo b/examples/discrete_turbines/headland.geo new file mode 100644 index 000000000..fc90ac32c --- /dev/null +++ b/examples/discrete_turbines/headland.geo @@ -0,0 +1,53 @@ +basin_x = 2000; +basin_y = 600; + +headland_x_scale = 0.2; +headland_y = 200; + +site_x = 160; +site_y = 100; +site_x_start = basin_x/2-site_x/2; +site_x_end = basin_x/2+site_x/2; + +site_y_start = basin_y/2 - 50; +site_y_end = site_y_start+site_y; + +element_size = 1; +element_size_tip = 10; +element_size_coarse = 50; + +Point(1) = {0, 0, 0, element_size_coarse}; +Point(2) = {basin_x, 0, 0, element_size_coarse}; + + +// Generate nodes for the headland +res = 100; +For k In {0:res:1} + x = basin_x/res*k; + b = 10; + y = basin_y - headland_y*Exp(-0.5*((headland_x_scale*(x-basin_x/2))/b)^2); + locres = ((basin_y-y)*element_size_tip + (y-(basin_y-headland_y))*element_size_coarse)/headland_y; + Point(10+k) = {x, y, 0, locres}; +EndFor + +// Generate lines for the headland + +BSpline(9999) = { 10 : res/2+10 }; +BSpline(10000) = { res/2+10 : res+10 }; + +Line(10001) = {10, 1}; +Line(10002) = {1, 2}; +Line(10003) = {2, res+10}; +Line Loop(10004) = {9999, 10000, -10003, -10002, -10001}; + +// Generate site nodes +Point(100000) = {site_x_start, site_y_start, 0, element_size}; +Extrude{site_x, 0, 0} { Point{100000}; Layers{site_x/element_size}; } +Extrude{0, site_y, 0} { Line{10005}; Layers{site_y/element_size}; } +Line Loop(10010) = {10006, -10008, -10005, 10007}; +Plane Surface(10011) = {10004, 10010}; +Physical Line(1) = {10001}; +Physical Line(2) = {10003}; +Physical Line(3) = {9999, 10000, 10002}; 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+site_y = 160; +element_size = 2.5; +element_size_coarse = 10; + +Point(1) = {0, 0, 0, element_size_coarse}; +Point(2) = {basin_x, 0, 0, element_size_coarse}; +Point(3) = {0, basin_y, 0, element_size_coarse}; +Point(4) = {basin_x, basin_y, 0, element_size_coarse}; + +Point(5) = {(basin_x - site_x)/2, (basin_y - site_y)/2, 0, element_size}; +Extrude{site_x, 0, 0} { Point{5}; Layers{site_x/element_size}; } +Extrude{0, site_y, 0} { Line{1}; Layers{site_y/element_size}; } + +Line(6) = {1, 2}; +Line(7) = {2, 4}; +Line(8) = {4, 3}; +Line(9) = {3, 1}; +Line Loop(10) = {9, 6, 7, 8}; +Line Loop(11) = {3, 2, -4, -1}; +Plane Surface(12) = {10, 11}; +Physical Surface(0) = {12}; +Physical Surface(2) = {5}; +Physical Line(2) = {7}; +Physical Line(1) = {9}; +Physical Line(3) = {8, 6}; diff --git a/examples/discrete_turbines/mesh.msh b/examples/discrete_turbines/mesh.msh new file mode 100644 index 000000000..445f33252 --- /dev/null +++ b/examples/discrete_turbines/mesh.msh @@ -0,0 +1,56388 @@ 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a/examples/discrete_turbines/tidal_array.py b/examples/discrete_turbines/tidal_array.py new file mode 100644 index 000000000..3e0ab5031 --- /dev/null +++ b/examples/discrete_turbines/tidal_array.py @@ -0,0 +1,125 @@ +""" +Basic example of a discrete turbine array placed in the high energy region +of flow past a headland. Turbines are based on the SIMEC Atlantis AR2000 +with cut-in, rated and cut-out speeds of 1m/s, 3.05m/s and 5m/s respectively. +Flow becomes steady after an initial ramp up. +""" + +from thetis import * + +# Set output directory, load mesh, set simulation export and end times +outputdir = 'outputs' +if not os.path.exists('headland.msh'): + os.system('gmsh -2 headland.geo -o headland.msh') +mesh2d = Mesh('headland.msh') +site_ID = 2 # mesh PhysID for subdomain where turbines are to be sited +print_output('Loaded mesh ' + mesh2d.name) +print_output('Exporting to ' + outputdir) + +t_end = 2 * 3600 +t_export = 200.0 + +if os.getenv('THETIS_REGRESSION_TEST') is not None: + t_end = 5*t_export + +# Bathymetry and viscosity sponges +P1_2d = FunctionSpace(mesh2d, 'CG', 1) +bathymetry_2d = Function(P1_2d, name='Bathymetry') +bathymetry_2d.assign(Constant(50.0)) +x = SpatialCoordinate(mesh2d) +h_viscosity = Function(P1_2d).interpolate(conditional(le(x[0], 50), 51-x[0], 1.0)) +File(outputdir + '/viscosity/viscosity.pvd').write(h_viscosity) + + +# Turbine options +turbine_thrust_def = 'table' # 'table' or 'constant' +include_support_structure = True # choose whether we want to add additional drag due to the support structure + +# Define the thrust curve of the turbine using a tabulated approach: +# thrusts_AR2000 contains the values for the thrust coefficient of an AR2000 tidal turbine at corresponding speeds in +# speeds_AR2000 which have been determined using a curve fitting technique based on: +# cut-in speed = 1m/s +# rated speed = 3.05m/s +# cut-out speed = 5m/s +# There is a ramp up and down to cut-in and at cut-out speeds for model stability. +speeds_AR2000 = [0., 0.75, 0.85, 0.95, 1., 3.05, 3.3, 3.55, 3.8, 4.05, 4.3, 4.55, 4.8, 5., 5.001, 5.05, 5.25, 5.5, 5.75, + 6.0, 6.25, 6.5, 6.75, 7.0] +thrusts_AR2000 = [0.010531, 0.032281, 0.038951, 0.119951, 0.516484, 0.516484, 0.387856, 0.302601, 0.242037, 0.197252, + 0.16319, 0.136716, 0.115775, 0.102048, 0.060513, 0.005112, 0.00151, 0.00089, 0.000653, 0.000524, + 0.000442, 0.000384, 0.000341, 0.000308] + +# initialise discrete turbine farm characteristics +farm_options = DiscreteTidalTurbineFarmOptions() +farm_options.turbine_type = turbine_thrust_def +if turbine_thrust_def == 'table': + farm_options.turbine_options.thrust_speeds = speeds_AR2000 + farm_options.turbine_options.thrust_coefficients = thrusts_AR2000 +else: + farm_options.turbine_options.thrust_coefficient = 0.6 +if include_support_structure: + farm_options.turbine_options.C_support = 0.7 # support structure thrust coefficient + farm_options.turbine_options.A_support = 2.6*14.0 # cross-sectional area of support structure +farm_options.turbine_options.diameter = 20 +farm_options.upwind_correction = True # See https://arxiv.org/abs/1506.03611 for more details +turbine_density = Function(FunctionSpace(mesh2d, "CG", 1), name='turbine_density').assign(0.0) +farm_options.turbine_density = turbine_density +farm_options.turbine_coordinates = [[Constant(x), Constant(y)] + for x in numpy.arange(940, 1061, 60) + for y in numpy.arange(260, 341, 40)] + +# --- create solver --- +solver_obj = solver2d.FlowSolver2d(mesh2d, bathymetry_2d) +options = solver_obj.options +options.simulation_export_time = t_export +options.simulation_end_time = t_end +options.output_directory = outputdir +options.check_volume_conservation_2d = True +options.fields_to_export = ['uv_2d', 'elev_2d'] +options.quadratic_drag_coefficient = Constant(0.0025) +options.swe_timestepper_type = 'CrankNicolson' +options.swe_timestepper_options.implicitness_theta = 0.5 +options.horizontal_viscosity = h_viscosity +options.use_wetting_and_drying = True +options.wetting_and_drying_alpha = Constant(0.5) +if not hasattr(options.swe_timestepper_options, 'use_automatic_timestep'): + options.timestep = 50.0 +options.discrete_tidal_turbine_farms[site_ID] = [farm_options] + +# Use direct solver instead of default iterative settings +# (see SemiImplicitSWETimeStepperOptions2d in thetis/options.py) +# to make it more robust for larger timesteps and low viscosity +options.swe_timestepper_options.solver_parameters = {'ksp_type': 'preonly', + 'pc_type': 'lu', + 'pc_factor_mat_solver_type': 'mumps'} + +# Boundary conditions - steady state case +left_tag = 1 +right_tag = 2 +tidal_elev = Function(P1_2d).assign(0.0) +tidal_vel = Function(P1_2d).assign(0.0) +solver_obj.bnd_functions['shallow_water'] = {right_tag: {'un': tidal_vel}, + left_tag: {'elev': tidal_elev}} + +# initial conditions, piecewise linear function +elev_init = Function(P1_2d) +elev_init.assign(0.0) +solver_obj.assign_initial_conditions(elev=elev_init, uv=(as_vector((1e-3, 0.0)))) + +print_output(str(options.swe_timestepper_type) + ' solver options:') +print_output(options.swe_timestepper_options.solver_parameters) + +# Operation of tidal turbine farm through a callback +cb_turbines = turbines.TurbineFunctionalCallback(solver_obj) +solver_obj.add_callback(cb_turbines, 'timestep') +powers = [] # create empty list to append instantaneous powers to + + +def update_forcings(t_new): + ramp = tanh(t_new / 2000.) + tidal_vel.project(Constant(ramp * 3.)) + powers.append(cb_turbines.instantaneous_power[0]) + + +# See channel-optimisation example for a completely steady state simulation (no ramp) +solver_obj.iterate(update_forcings=update_forcings) +powers.append(cb_turbines.instantaneous_power[0]) # add final power, should be the same as callback hdf5 file! diff --git a/examples/katophillips/katophillips.py b/examples/katophillips/katophillips.py index f7e20bd38..8178d8bec 100644 --- a/examples/katophillips/katophillips.py +++ b/examples/katophillips/katophillips.py @@ -50,7 +50,10 @@ def katophillips_test(layers=25, gls_closure='k-omega', lx = nx*dx ny = 3 # nb elements in cross direction ly = ny*dx - mesh2d = PeriodicRectangleMesh(nx, ny, lx, ly, direction='both', reorder=True) + if load_export_ix: + mesh2d = read_mesh_from_checkpoint(outputdir) + else: + mesh2d = PeriodicRectangleMesh(nx, ny, lx, ly, direction='both', reorder=True) # move mesh, center to (0,0) mesh2d.coordinates.dat.data[:, 0] -= lx/2 mesh2d.coordinates.dat.data[:, 1] -= ly/2 diff --git a/examples/lockExchange/lockExchange.py b/examples/lockExchange/lockExchange.py index f1f34fd32..adcaa1908 100644 --- a/examples/lockExchange/lockExchange.py +++ b/examples/lockExchange/lockExchange.py @@ -114,6 +114,8 @@ def run_lockexchange(reso_str='coarse', poly_order=1, element_family='dg-dg', 'lf-trc{:.1f}'.format(laxfriedrichs_trc), ]) + lim_str outputdir = 'outputs_' + options_str + if load_export_ix: + mesh2d = read_mesh_from_checkpoint(outputdir) # bathymetry p1_2d = get_functionspace(mesh2d, 'CG', 1) diff --git a/examples/north_sea/model_config.py b/examples/north_sea/model_config.py index be5332a09..106eaa1ee 100644 --- a/examples/north_sea/model_config.py +++ b/examples/north_sea/model_config.py @@ -6,6 +6,7 @@ import os import scipy.interpolate as si import numpy +import types # Setup zones sim_tz = timezone.pytz.utc @@ -22,7 +23,8 @@ def read_station_data(): corresponding region code used in the CMEMS database """ - with open("stations_elev.csv", "r") as csvfile: + pwd = os.path.abspath(os.path.dirname(__file__)) + with open(os.path.join(pwd, "stations_elev.csv"), "r") as csvfile: stations = { d["name"]: { "latlon": (float(d["latitude"]), float(d["longitude"])), @@ -53,20 +55,21 @@ def interpolate_bathymetry(bathymetry_2d, dataset="etopo1", cap=10.0): interp = si.RectBivariateSpline( nc.variables["lat"][:], # latitude nc.variables["lon"][:], # longitude - nc.variables["Band1"][:, :], # elevation + nc.variables["z"][:, :], # elevation ) # Interpolate at mesh vertices lonlat_func = coord_system.get_mesh_lonlat_function(mesh) lon, lat = lonlat_func.dat.data_ro.T - bathymetry_2d.dat.data[:] = numpy.maximum(-interp(lat, lon), cap) + bathymetry_2d.dat.data[:] = numpy.maximum(-interp(lat, lon, grid=False), cap) -def construct_solver(spinup=False, store_station_time_series=True, **model_options): +def construct_solver(mesh2d, spinup=False, store_station_time_series=True, **model_options): """ Construct a :class:`FlowSolver2d` instance for inverse modelling in the North Sea. + :kwarg mesh2d: 2D mesh to use :kwarg spinup: is this a spin-up run, or a subsequent simulation? :kwarg store_station_time_series: should gauge measurements be stored to disk? @@ -74,20 +77,17 @@ def construct_solver(spinup=False, store_station_time_series=True, **model_optio simulation and a function for updating forcings """ + pwd = os.path.abspath(os.path.dirname(__file__)) + h5_file_name = os.path.join(pwd, "north_sea_bathymetry.h5") + with CheckpointFile(h5_file_name, "r") as f: + bathymetry_2d = f.load_function(mesh2d, "Bathymetry") + # Setup mesh and lonlat coords - mesh2d = Mesh("north_sea.msh") lonlat = coord_system.get_mesh_lonlat_function(mesh2d) lon, lat = lonlat - # Setup bathymetry - P1_2d = get_functionspace(mesh2d, "CG", 1) - bathymetry_2d = Function(P1_2d, name="Bathymetry") - with CheckpointFile("north_sea_bathymetry.h5", "r") as f: - m = f.load_mesh("firedrake_default") - g = f.load_function(m, "Bathymetry") - bathymetry_2d.assign(g) - # Setup Manning friction + P1_2d = get_functionspace(mesh2d, "CG", 1) manning_2d = Function(P1_2d, name="Manning coefficient") manning_2d.assign(3.0e-02) @@ -104,8 +104,6 @@ def construct_solver(spinup=False, store_station_time_series=True, **model_optio dt = 3600.0 t_export = 3600.0 t_end = (end_date - start_date).total_seconds() - if os.getenv("THETIS_REGRESSION_TEST") is not None: - t_end = 5 * t_export # Create solver solver_obj = solver2d.FlowSolver2d(mesh2d, bathymetry_2d) @@ -152,23 +150,33 @@ def construct_solver(spinup=False, store_station_time_series=True, **model_optio ) solver_obj.add_callback(cb) - # Setup forcings - data_dir = os.path.join(os.environ.get("DATA", "./data"), "tpxo") - if not os.path.exists(data_dir): - raise IOError(f"Data directory {data_dir} does not exist") - forcing_constituents = ["Q1", "O1", "P1", "K1", "N2", "M2", "S2", "K2"] elev_tide_2d = Function(solver_obj.function_spaces.P1_2d, name="Tidal elevation") - tbnd = forcing.TPXOTidalBoundaryForcing( - elev_tide_2d, - start_date, - coord_system, - data_dir=data_dir, - constituents=forcing_constituents, - boundary_ids=[100], - ) - - # Set time to zero for the tidal forcings - tbnd.set_tidal_field(0.0) + if os.getenv("THETIS_REGRESSION_TEST") is None: + # Setup forcings + data_dir = os.path.join(os.environ.get("DATA", "./data"), "tpxo") + if not os.path.exists(data_dir): + raise IOError(f"Data directory {data_dir} does not exist") + forcing_constituents = ["Q1", "O1", "P1", "K1", "N2", "M2", "S2", "K2"] + tbnd = forcing.TPXOTidalBoundaryForcing( + elev_tide_2d, + start_date, + coord_system, + data_dir=data_dir, + constituents=forcing_constituents, + boundary_ids=[100], + ) + + # Set time to zero for the tidal forcings + tbnd.set_tidal_field(0.0) + else: + # for CI testing setup dummy tbnd - as we don't want to commit TPXO data files + elev_tide_2d.assign(1.) + + def set_tidal_field(t): + pass + + tbnd = types.SimpleNamespace() + tbnd.set_tidal_field = set_tidal_field # Account for spinup bnd_time = Constant(0.0) diff --git a/examples/north_sea/north_sea_bathymetry.h5 b/examples/north_sea/north_sea_bathymetry.h5 index 5259a5cea..907864236 100644 Binary files a/examples/north_sea/north_sea_bathymetry.h5 and b/examples/north_sea/north_sea_bathymetry.h5 differ diff --git a/examples/north_sea/run.py b/examples/north_sea/run.py index 54204c56b..0fec10d33 100644 --- a/examples/north_sea/run.py +++ b/examples/north_sea/run.py @@ -3,7 +3,9 @@ from model_config import * +mesh2d = read_mesh_from_checkpoint('north_sea_bathymetry.h5') solver_obj, start_time, update_forcings = construct_solver( + mesh2d, start_date=datetime.datetime(2022, 1, 15, tzinfo=sim_tz), end_date=datetime.datetime(2022, 1, 18, tzinfo=sim_tz), ) diff --git a/examples/north_sea/spinup.py b/examples/north_sea/spinup.py index a0891a29e..73f0fc2cb 100644 --- a/examples/north_sea/spinup.py +++ b/examples/north_sea/spinup.py @@ -4,7 +4,9 @@ # Setup solver +mesh2d = read_mesh_from_checkpoint('north_sea_bathymetry.h5') solver_obj, start_time, update_forcings = construct_solver( + mesh2d, output_directory="outputs_spinup", spinup=True, start_date=datetime.datetime(2022, 1, 1, tzinfo=sim_tz), @@ -13,8 +15,6 @@ fields_to_export_hdf5=["elev_2d", "uv_2d"], simulation_export_time=24 * 3600.0, ) -output_dir = solver_obj.options.output_directory -mesh2d = solver_obj.mesh2d solver_obj.assign_initial_conditions() update_forcings(0.0) diff --git a/examples/north_sea/test_north_sea.py b/examples/north_sea/test_north_sea.py new file mode 100644 index 000000000..a5afa70b5 --- /dev/null +++ b/examples/north_sea/test_north_sea.py @@ -0,0 +1,53 @@ +# this script is only run as part of the CI suite +from thetis import * +from model_config import * + +# Run 6 hour spin up run with hourly checkpoints +pwd = os.path.abspath(os.path.dirname(__file__)) +mesh2d = read_mesh_from_checkpoint(f'{pwd}/north_sea_bathymetry.h5') +solver_obj, start_time, update_forcings = construct_solver( + mesh2d, + output_directory="outputs_spinup", + spinup=False, # we don't want to ramp up for this test + start_date=datetime.datetime(2022, 1, 1, tzinfo=sim_tz), + end_date=datetime.datetime(2022, 1, 1, 6, tzinfo=sim_tz), + fields_to_export=[], + fields_to_export_hdf5=["elev_2d", "uv_2d"], + simulation_export_time=3600.0, +) +solver_obj.assign_initial_conditions() +update_forcings(0.0) +solver_obj.iterate(update_forcings=update_forcings) + +# Pick up from third hour +solver_obj2, start_time, update_forcings = construct_solver( + mesh2d, + start_date=datetime.datetime(2022, 1, 1, 3, tzinfo=sim_tz), + end_date=datetime.datetime(2022, 1, 1, 6, tzinfo=sim_tz), +) +solver_obj2.load_state( + 3, outputdir="outputs_spinup", t=0, iteration=0, i_export=0 +) +update_forcings(0.0) +solver_obj2.iterate(update_forcings=update_forcings) + +# should end up with same end state at hour 6 +assert errornorm(solver_obj.fields.uv_2d, solver_obj2.fields.uv_2d) < 1e-6 * norm(solver_obj.fields.uv_2d) +assert errornorm(solver_obj.fields.elev_2d, solver_obj2.fields.elev_2d) < 1e-6 * norm(solver_obj.fields.elev_2d) + +# finally, check that the committed checkpoint of the demo is still functional +solver_obj, start_time, update_forcings = construct_solver( + mesh2d, + start_date=datetime.datetime(2022, 1, 15, tzinfo=sim_tz), + end_date=datetime.datetime(2022, 1, 15, 3, tzinfo=sim_tz), +) +solver_obj.load_state(14, outputdir=f"{pwd}/../../demos/outputs_spinup", t=0, iteration=0) +update_forcings(0.0) +solver_obj.iterate(update_forcings=update_forcings) + +# just check things haven't gone completely haywire +# (they will be a bit random as we don't test with the correct forcing) +assert solver_obj.fields.elev_2d.dat.data[:].min() > -4 +assert solver_obj.fields.elev_2d.dat.data[:].max() < 4 +assert solver_obj.fields.uv_2d.dat.data[:].min() > -4 +assert solver_obj.fields.uv_2d.dat.data[:].max() < 4 diff --git a/examples/sediment_meander_2d/meander_example.py b/examples/sediment_meander_2d/meander_example.py index 415978c49..2131e865b 100644 --- a/examples/sediment_meander_2d/meander_example.py +++ b/examples/sediment_meander_2d/meander_example.py @@ -12,10 +12,14 @@ """ from thetis import * -# import bathymetry and mesh for meander -from meander_setup import * +from meander_setup import meander_bathymetry # Note it is necessary to run meander_hydro first to get the hydrodynamics simulation +# We pick up from a checkpoint file produced by trench_hydro. +# Therefore, we have to read the mesh from it as well +mesh2d = read_mesh_from_checkpoint('outputs_hydro') +bathymetry_2d = meander_bathymetry(mesh2d) + def update_forcings_bnd(t_new): diff --git a/examples/sediment_meander_2d/meander_hydro.py b/examples/sediment_meander_2d/meander_hydro.py index 98495c31d..8b9f0503d 100644 --- a/examples/sediment_meander_2d/meander_hydro.py +++ b/examples/sediment_meander_2d/meander_hydro.py @@ -12,8 +12,11 @@ """ from thetis import * -# import bathymetry and mesh for meander -from meander_setup import * +from meander_setup import meander_bathymetry + +# create bathymetry and mesh for meander +mesh2d = Mesh('meander.msh') +bathymetry_2d = meander_bathymetry(mesh2d) # define function spaces P1_2d = FunctionSpace(mesh2d, 'DG', 1) diff --git a/examples/sediment_meander_2d/meander_setup.py b/examples/sediment_meander_2d/meander_setup.py index b8494e234..a467ae6cb 100644 --- a/examples/sediment_meander_2d/meander_setup.py +++ b/examples/sediment_meander_2d/meander_setup.py @@ -13,23 +13,23 @@ from thetis import * -# define mesh -mesh2d = Mesh("meander.msh") -x, y = SpatialCoordinate(mesh2d) -# define function spaces -V = FunctionSpace(mesh2d, 'CG', 1) -# define underlying bathymetry -bathymetry_2d = Function(V, name='Bathymetry') -gradient = Constant(0.0035) -L_function = Function(V).interpolate(conditional(x > 5, pi*4*((pi/2)-acos((x-5)/(sqrt((x-5)**2+(y-2.5)**2))))/pi, - pi*4*((pi/2)-acos((-x+5)/(sqrt((x-5)**2+(y-2.5)**2))))/pi)) -bathymetry_curve = Function(V).interpolate(conditional(y > 2.5, - conditional(x < 5, (L_function*gradient) + 9.97072, - -(L_function*gradient) + 9.97072), 9.97072)) -init = max(bathymetry_curve.dat.data[:]) -final = min(bathymetry_curve.dat.data[:]) -bathymetry_straight = Function(V).interpolate(conditional(x <= 5, - conditional(y <= 2.5, -9.97072 + gradient*abs(y - 2.5) + init, 0), - conditional(y <= 2.5, -9.97072 - gradient*abs(y - 2.5) + final, 0))) -bathymetry_2d = Function(V).interpolate(-bathymetry_curve - bathymetry_straight) +def meander_bathymetry(mesh2d): + x, y = SpatialCoordinate(mesh2d) + # define function spaces + V = FunctionSpace(mesh2d, 'CG', 1) + + # define underlying bathymetry + gradient = Constant(0.0035) + L_function = Function(V).interpolate(conditional(x > 5, pi*4*((pi/2)-acos((x-5)/(sqrt((x-5)**2+(y-2.5)**2))))/pi, + pi*4*((pi/2)-acos((-x+5)/(sqrt((x-5)**2+(y-2.5)**2))))/pi)) + bathymetry_curve = Function(V).interpolate(conditional(y > 2.5, + conditional(x < 5, (L_function*gradient) + 9.97072, + -(L_function*gradient) + 9.97072), 9.97072)) + init = max(bathymetry_curve.dat.data[:]) + final = min(bathymetry_curve.dat.data[:]) + bathymetry_straight = Function(V).interpolate(conditional(x <= 5, + conditional(y <= 2.5, -9.97072 + gradient*abs(y - 2.5) + init, 0), + conditional(y <= 2.5, -9.97072 - gradient*abs(y - 2.5) + final, 0))) + bathymetry_2d = Function(V).interpolate(-bathymetry_curve - bathymetry_straight) + return bathymetry_2d diff --git a/examples/sediment_trench_2d/trench_example.py b/examples/sediment_trench_2d/trench_example.py index f9e8404ea..825d077ea 100644 --- a/examples/sediment_trench_2d/trench_example.py +++ b/examples/sediment_trench_2d/trench_example.py @@ -19,12 +19,9 @@ # Note it is necessary to run trench_hydro first to get the hydrodynamics simulation -# define mesh -lx = 16 -ly = 1.1 -nx = lx*5 -ny = 5 -mesh2d = RectangleMesh(nx, ny, lx, ly) +# We pick up from a checkpoint file produced by trench_hydro. +# Therefore, we have to read the mesh from it as well +mesh2d = read_mesh_from_checkpoint('outputs_hydro') x, y = SpatialCoordinate(mesh2d) diff --git a/examples/tidal_barrage/lagoon2d.py b/examples/tidal_barrage/lagoon2d.py index 575e96036..c97e8c873 100644 --- a/examples/tidal_barrage/lagoon2d.py +++ b/examples/tidal_barrage/lagoon2d.py @@ -70,7 +70,7 @@ elev_init = Function(P1_2d) elev_init.assign(0.0) -tidal_elev = Function(bathymetry_2d.function_space()) +tidal_elev = Constant(0.0) # Initialise hydraulic structure boundaries and max them as fluxes lagoon_hydraulic_structures = {"sl_i": Constant(0.), "sl_o": Constant(0.), "tb_i": Constant(0.), "tb_o": Constant(0.)} @@ -91,7 +91,7 @@ def update_forcings(t_new,): - tidal_elev.assign(Constant(tanh((t_new)/(4*3600.)) * amplitude * sin(omega * t_new))) + tidal_elev.assign(tanh((t_new)/(4*3600.)) * sin(omega * t_new) * amplitude) # Solve the system diff --git a/examples/tidalfarm/tidalfarm.py b/examples/tidalfarm/tidalfarm.py index b81289108..fceba5bd9 100644 --- a/examples/tidalfarm/tidalfarm.py +++ b/examples/tidalfarm/tidalfarm.py @@ -20,10 +20,12 @@ """ from thetis import * -# this import automatically starts the annotation: -from firedrake_adjoint import * +from firedrake.adjoint import * op2.init(log_level=INFO) +# start annotating all Firedrake opterations in the forward model +continue_annotation() + # setup the Thetis solver obj as usual: mesh2d = Mesh('headland.msh') @@ -55,7 +57,7 @@ options.element_family = 'dg-cg' options.swe_timestepper_type = 'CrankNicolson' options.swe_timestepper_options.implicitness_theta = 0.6 -# using direct solver as PressurePicard does not work with dolfin-adjoint (due to .split() not being annotated correctly) +# using direct solver as PressurePicard doesn't work with dolfin-adjoint (due to .split() not being annotated correctly) options.swe_timestepper_options.solver_parameters = {'snes_monitor': None, 'snes_rtol': 1e-9, 'ksp_type': 'preonly', @@ -72,7 +74,7 @@ coasts_tag = 3 tidal_elev = Function(get_functionspace(mesh2d, "CG", 1), name='tidal_elev') tidal_elev_bc = {'elev': tidal_elev} -# noslip currently doesn't work (vector Constants are broken in firedrake_adjoint) +# noslip currently doesn't work (vector Constants are broken in firedrake adjoint) freeslip_bc = {'un': Constant(0.0)} solver_obj.bnd_functions['shallow_water'] = { left_tag: tidal_elev_bc, @@ -105,7 +107,7 @@ def update_forcings(t): # in such a way that the cost is expressed in kW which can be subtracted from the profit # which is calculated as the power extracted by the turbines farm_options.break_even_wattage = 200 -options.tidal_turbine_farms[2] = farm_options +options.tidal_turbine_farms[2] = [farm_options] # we first run the "forward" model with no turbines turbine_density.assign(0.0) @@ -127,7 +129,7 @@ def update_forcings(t): solver_obj.add_callback(cb, 'timestep') -# run as normal (this run will be annotated by firedrake_adjoint) +# run as normal (this run will be annotated by firedrake adjoint) solver_obj.assign_initial_conditions(uv=as_vector((1e-7, 0.0)), elev=tidal_elev) solver_obj.iterate(update_forcings=update_forcings) @@ -143,7 +145,7 @@ def update_forcings(t): # also we multiply by -1 so that if we minimize the functional, we maximize profit # (maximize is also availble from pyadjoint but currently broken) scaling = -1./assemble(max(farm_options.break_even_wattage, 100) * max_density * dx(2, domain=mesh2d)) -scaled_functional = scaling * cb.average_profit +scaled_functional = scaling * cb.average_profit[0] # specifies the control we want to vary in the optimisation c = Control(turbine_density) @@ -206,6 +208,6 @@ def update_forcings(t): # By default scipy's implementation of L-BFGS-B is used, see # https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fmin_l_bfgs_b.html # options, such as maxiter and pgtol can be passed on. - td_opt = minimise(rf, bounds=[0, max_density], + td_opt = minimize(rf, bounds=[0, max_density], options={'maxiter': 100, 'pgtol': 1e-3}) File('optimal_density.pvd').write(td_opt) diff --git a/examples/tohoku_inversion/forward_run.py b/examples/tohoku_inversion/forward_run.py index 401f30fa9..41322c33b 100644 --- a/examples/tohoku_inversion/forward_run.py +++ b/examples/tohoku_inversion/forward_run.py @@ -26,7 +26,10 @@ # Setup initial condition pwd = os.path.abspath(os.path.dirname(__file__)) -mesh2d = Mesh(f"{pwd}/japan_sea.msh") +with CheckpointFile(f"{pwd}/japan_sea_bathymetry.h5", "r") as f: + mesh2d = f.load_mesh("firedrake_default") + bathymetry = f.load_function(mesh2d, "Bathymetry") + initial_guess = None if args.load: print_output(f"Loading controls from {input_dir}") @@ -38,7 +41,7 @@ # Solve forward solver_obj = construct_solver( - source.elev_init, + bathymetry, source.elev_init, output_directory=output_dir, store_station_time_series=not no_exports, no_exports=no_exports, diff --git a/examples/tohoku_inversion/inverse_problem.py b/examples/tohoku_inversion/inverse_problem.py index b7a297934..d6cae62f8 100644 --- a/examples/tohoku_inversion/inverse_problem.py +++ b/examples/tohoku_inversion/inverse_problem.py @@ -1,5 +1,5 @@ from thetis import * -from firedrake_adjoint import * +from firedrake.adjoint import * import thetis.inversion_tools as inversion_tools from model_config import * import argparse @@ -37,9 +37,14 @@ do_taylor_test = not args.no_taylor_test suffix = args.suffix +# annotate all Firedrake operations of the forward run +continue_annotation() + # Setup initial condition pwd = os.path.abspath(os.path.dirname(__file__)) -mesh2d = Mesh(f"{pwd}/japan_sea.msh") +with CheckpointFile(f"{pwd}/japan_sea_bathymetry.h5", "r") as f: + mesh2d = f.load_mesh("firedrake_default") + bathymetry = f.load_function(mesh2d, "Bathymetry") source = get_source(mesh2d, source_model) if source_model == "okada": source.subfault_variables = args.okada_parameters @@ -49,7 +54,7 @@ if suffix is not None: output_dir = "_".join([output_dir, suffix]) solver_obj = construct_solver( - source.elev_init, + bathymetry, source.elev_init, output_directory=output_dir, store_station_time_series=False, no_exports=True, diff --git a/examples/tohoku_inversion/japan_sea_bathymetry.h5 b/examples/tohoku_inversion/japan_sea_bathymetry.h5 index a4e6bba8b..93039aec1 100644 Binary files a/examples/tohoku_inversion/japan_sea_bathymetry.h5 and b/examples/tohoku_inversion/japan_sea_bathymetry.h5 differ diff --git a/examples/tohoku_inversion/model_config.py b/examples/tohoku_inversion/model_config.py index a4c1fce77..b4f8147db 100644 --- a/examples/tohoku_inversion/model_config.py +++ b/examples/tohoku_inversion/model_config.py @@ -76,7 +76,7 @@ def get_source(mesh2d, source_model, initial_guess=None): raise ValueError(f"Element family {family} not supported for source inversion") degree = int(source_model[2:]) element = FiniteElement(family, mesh2d.ufl_cell(), degree) - return FiniteElementTsunamiSource(mesh2d, element, initial_guess=initial_guess) + return FiniteElementTsunamiSource(mesh2d, coord_system, element, initial_guess=initial_guess) def interpolate_bathymetry(bathymetry_2d, dataset="etopo1", cap=30.0): @@ -106,10 +106,10 @@ def interpolate_bathymetry(bathymetry_2d, dataset="etopo1", cap=30.0): # Interpolate at mesh vertices lonlat_func = coord_system.get_mesh_lonlat_function(mesh) lon, lat = lonlat_func.dat.data_ro.T - bathymetry_2d.dat.data[:] = numpy.maximum(-interp(lat, lon), cap) + bathymetry_2d.dat.data[:] = numpy.maximum(-interp(lat, lon, grid=False), cap) -def construct_solver(elev_init, store_station_time_series=True, **model_options): +def construct_solver(bathymetry_2d, elev_init, store_station_time_series=True, **model_options): """ Construct a *linear* shallow water equation solver for tsunami propagation modelling. @@ -124,13 +124,6 @@ def construct_solver(elev_init, store_station_time_series=True, **model_options) t_end = 5 * t_export # Bathymetry - P1_2d = get_functionspace(mesh2d, "CG", 1) - bathymetry_2d = Function(P1_2d, name="Bathymetry") - pwd = os.path.abspath(os.path.dirname(__file__)) - with CheckpointFile(f"{pwd}/japan_sea_bathymetry.h5", "r") as f: - m = f.load_mesh("firedrake_default") - g = f.load_function(m, "Bathymetry") - bathymetry_2d.assign(g) bathymetry_2d.interpolate(bathymetry_2d - elev_init) # Create solver diff --git a/requirements.txt b/requirements.txt index 026d5e746..12c795878 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,3 +4,4 @@ pyproj uptide pytz traitlets +pylit diff --git a/test/conftest.py b/test/conftest.py index e03e25d49..b807d1d13 100644 --- a/test/conftest.py +++ b/test/conftest.py @@ -1,6 +1,3 @@ -from subprocess import check_call - - def pytest_runtest_teardown(item, nextitem): """Clear Thetis caches after running a test""" from firedrake.tsfc_interface import TSFCKernel @@ -11,84 +8,3 @@ def pytest_runtest_teardown(item, nextitem): # get very big with bendy on. TSFCKernel._cache.clear() GlobalKernel._cache.clear() - - -def parallel(item): - """ - Run a test in parallel. - - NOTE: Copied from firedrake/src/firedrake/tests/conftest.py - - :arg item: The test item to run. - """ - from mpi4py import MPI - if MPI.COMM_WORLD.size > 1: - raise RuntimeError("Parallel test can't be run within parallel environment") - marker = item.get_closest_marker("parallel") - if marker is None: - raise RuntimeError("Parallel test doesn't have parallel marker") - nprocs = marker.kwargs.get("nprocs", 3) - if nprocs < 2: - raise RuntimeError("Need at least two processes to run parallel test") - - # Only spew tracebacks on rank 0. - # Run xfailing tests to ensure that errors are reported to calling process - call = [ - "mpiexec", "-n", "1", "python", "-m", "pytest", "--runxfail", "-s", "-q", - "%s::%s" % (item.fspath, item.name) - ] - call.extend([ - ":", "-n", "%d" % (nprocs - 1), "python", "-m", "pytest", "--runxfail", "--tb=no", "-q", - "%s::%s" % (item.fspath, item.name) - ]) - check_call(call) - - -def pytest_configure(config): - """ - Register an additional marker. - - NOTE: Copied from firedrake/src/firedrake/tests/conftest.py - """ - config.addinivalue_line( - "markers", - "parallel(nprocs): mark test to run in parallel on nprocs processors") - - -def pytest_runtest_setup(item): - """ - Special setup for parallel tests. - - NOTE: Copied from firedrake/src/firedrake/tests/conftest.py - """ - if item.get_closest_marker("parallel"): - from mpi4py import MPI - if MPI.COMM_WORLD.size > 1: - # Turn on source hash checking - from firedrake import parameters - from functools import partial - - def _reset(check): - parameters["pyop2_options"]["check_src_hashes"] = check - - # Reset to current value when test is cleaned up - item.addfinalizer(partial(_reset, - parameters["pyop2_options"]["check_src_hashes"])) - - parameters["pyop2_options"]["check_src_hashes"] = True - else: - # Blow away function arg in "master" process, to ensure - # this test isn't run on only one process. - item.obj = lambda *args, **kwargs: True - - -def pytest_runtest_call(item): - """ - Special call for parallel tests. - - NOTE: Copied from firedrake/src/firedrake/tests/conftest.py - """ - from mpi4py import MPI - if item.get_closest_marker("parallel") and MPI.COMM_WORLD.size == 1: - # Spawn parallel processes to run test - parallel(item) diff --git a/test/operations/test_operations_2d-3d.py b/test/operations/test_operations_2d-3d.py index c9d66def2..bb1bb494e 100644 --- a/test/operations/test_operations_2d-3d.py +++ b/test/operations/test_operations_2d-3d.py @@ -189,6 +189,18 @@ def test_minimum_angle(mesh2d): assert numpy.allclose(min_angle, pi/4) +def test_cell_widths(mesh2d): + cell_width = utility.get_cell_widths_2d(mesh2d).dat.data + assert numpy.allclose(cell_width, 1./5) + + +def test_anisotropic_cell_size(mesh2d): + cell_size = utility.anisotropic_cell_size(mesh2d).dat.data + # seems to produce two different cellsizes depending on orientation: + assert numpy.allclose(cell_size.max(), 1./5) + assert numpy.allclose(cell_size.min(), (numpy.sqrt(5)-1)/10) + + if __name__ == '__main__': """Run all tests""" import os diff --git a/test/sphere/test_williamson.py b/test/sphere/test_williamson.py index e6cafc669..0ba03e4a0 100644 --- a/test/sphere/test_williamson.py +++ b/test/sphere/test_williamson.py @@ -24,9 +24,9 @@ def coords_xyz_to_lonlat(mesh): """ x, y, z = SpatialCoordinate(mesh) z_norm = z / sqrt(x**2 + y**2 + z**2) - z_norm = Min(Max(z_norm, -1.0), 1.0) # avoid silly roundoff errors + z_norm = min_value(max_value(z_norm, -1.0), 1.0) # avoid silly roundoff errors lat = asin(z_norm) - lon = atan_2(y, x) + lon = atan2(y, x) return lon, lat @@ -90,7 +90,7 @@ def setup_williamson5(mesh, time): R0 = pi / 9. lon_c = -pi / 2. lat_c = pi / 6. - r = sqrt(Min(R0**2, (lon - lon_c)**2 + (lat - lat_c)**2)) + r = sqrt(min_value(R0**2, (lon - lon_c)**2 + (lat - lat_c)**2)) bath_expr = depth - 2000 * (1 - r / R0) # NOTE scale uv to fit the modified bathymetry to reduce initial shock diff --git a/test/swe2d/test_anisotropic.py b/test/swe2d/test_anisotropic.py index ac1b6e549..9a83c8be3 100644 --- a/test/swe2d/test_anisotropic.py +++ b/test/swe2d/test_anisotropic.py @@ -190,8 +190,8 @@ def estimate_error(mesh, **model_options): # Plot if not model_options.get("no_exports", False): - File("outputs/forward.pvd").write(*f0.split()) - a0u, a0eta = a0.split() + File("outputs/forward.pvd").write(*f0.subfunctions) + a0u, a0eta = a0.subfunctions a0u.rename("uv_2d (adjoint)") a0eta.rename("elev_2d (adjoint)") File("outputs/adjoint.pvd").write(a0u, a0eta) diff --git a/test/swe2d/test_atmospheric_pressure.py b/test/swe2d/test_atmospheric_pressure.py index a0cef3037..65e5eb00b 100644 --- a/test/swe2d/test_atmospheric_pressure.py +++ b/test/swe2d/test_atmospheric_pressure.py @@ -84,7 +84,7 @@ def test_pressure_forcing(element_family, timestepper): solverObj.assign_initial_conditions(uv=Constant((1e-7, 0.))) solverObj.iterate() - uv, eta = solverObj.fields.solution_2d.split() + uv, eta = solverObj.fields.solution_2d.subfunctions eta_ana = project(eta_expr, solverObj.function_spaces.H_2d) eta_errs.append(errornorm(eta_ana, eta)/numpy.sqrt(area)) diff --git a/test/swe2d/test_rossby_wave.py b/test/swe2d/test_rossby_wave.py index 3305e5168..33a47675f 100644 --- a/test/swe2d/test_rossby_wave.py +++ b/test/swe2d/test_rossby_wave.py @@ -138,8 +138,9 @@ def asymptotic_expansion_elev(H_2d, order=1, time=0.0, soliton_amplitude=0.395): def run(refinement_level, **model_options): order = model_options.pop('expansion_order') - family = model_options.get('element_family') - stepper = model_options.get('swe_timestepper_type') + # these are normal Thetis options, but we need to set them early on + family = model_options.pop('element_family') + stepper = model_options.pop('swe_timestepper_type') print_output("--- running refinement level {:d} in {:s} space".format(refinement_level, family)) # Set up domain @@ -158,7 +159,10 @@ def run(refinement_level, **model_options): # Create solver object solver_obj = solver2d.FlowSolver2d(mesh2d, bathymetry2d) options = solver_obj.options + # set timestepper early so we can set the relevant suboptions options.swe_timestepper_type = stepper + # set element_type early so we can create function spaces + options.element_family = family if hasattr(options.swe_timestepper_options, 'use_automatic_timestep'): options.swe_timestepper_options.use_automatic_timestep = False options.timestep = 0.96/refinement_level if stepper == 'SSPRK33' else 9.6/refinement_level diff --git a/test/swe2d/test_standing_wave.py b/test/swe2d/test_standing_wave.py index 725c3e703..a707cf558 100644 --- a/test/swe2d/test_standing_wave.py +++ b/test/swe2d/test_standing_wave.py @@ -89,7 +89,7 @@ def test_standing_wave_channel(timesteps, max_rel_err, timestepper, tmpdir, do_e solver_obj.iterate() - uv, eta = solver_obj.fields.solution_2d.split() + uv, eta = solver_obj.fields.solution_2d.subfunctions area = lx*ly rel_err = errornorm(elev_init, eta)/math.sqrt(area) diff --git a/test/swe2d/test_steady_state_basin_mms.py b/test/swe2d/test_steady_state_basin_mms.py index ec466751f..7840b03bd 100644 --- a/test/swe2d/test_steady_state_basin_mms.py +++ b/test/swe2d/test_steady_state_basin_mms.py @@ -249,8 +249,8 @@ def run(setup, refinement, order, do_export=True, options=None, solver_obj.iterate() - elev_l2_err = errornorm(elev_ana_ho, solver_obj.fields.solution_2d.split()[1])/numpy.sqrt(area) - uv_l2_err = errornorm(uv_ana_ho, solver_obj.fields.solution_2d.split()[0])/numpy.sqrt(area) + elev_l2_err = errornorm(elev_ana_ho, solver_obj.fields.solution_2d.subfunctions[1])/numpy.sqrt(area) + uv_l2_err = errornorm(uv_ana_ho, solver_obj.fields.solution_2d.subfunctions[0])/numpy.sqrt(area) print_output('elev L2 error {:.12f}'.format(elev_l2_err)) print_output('uv L2 error {:.12f}'.format(uv_l2_err)) return elev_l2_err, uv_l2_err diff --git a/test/swe2d/test_steady_state_channel.py b/test/swe2d/test_steady_state_channel.py index 776c1967c..29daca10f 100644 --- a/test/swe2d/test_steady_state_channel.py +++ b/test/swe2d/test_steady_state_channel.py @@ -55,7 +55,7 @@ def test_steady_state_channel(do_export=False): solver_obj.iterate() - uv, eta = solver_obj.fields.solution_2d.split() + uv, eta = solver_obj.fields.solution_2d.subfunctions x_2d, y_2d = SpatialCoordinate(mesh2d) eta_ana = interpolate(1 - x_2d/lx, p1_2d) diff --git a/test/swe2d/test_steady_state_channel_mms.py b/test/swe2d/test_steady_state_channel_mms.py index 5a32067d1..f2aa3da4b 100644 --- a/test/swe2d/test_steady_state_channel_mms.py +++ b/test/swe2d/test_steady_state_channel_mms.py @@ -90,7 +90,7 @@ def test_steady_state_channel_mms(element_family, do_exports=False): File('source_{}.pvd'.format(i)).write(source_func) solver_obj.iterate() - uv, eta = solver_obj.fields.solution_2d.split() + uv, eta = solver_obj.fields.solution_2d.subfunctions eta_ana = project(eta_expr, solver_obj.function_spaces.H_2d) if do_exports: diff --git a/test/swe2d/test_thacker.py b/test/swe2d/test_thacker.py index d5d079828..58746154b 100644 --- a/test/swe2d/test_thacker.py +++ b/test/swe2d/test_thacker.py @@ -74,7 +74,7 @@ def test_thacker(stepper, n, dt, max_err): # run for one cycle solverObj.iterate() - uv, eta = solverObj.fields.solution_2d.split() + uv, eta = solverObj.fields.solution_2d.subfunctions # mask out dry areas with a smooth function r = sqrt((x-X0)**2 + (y-Y0)**2) diff --git a/test/time_integration/test_convergence_ode.py b/test/time_integration/test_convergence_ode.py index 1f2cf8f8b..44fa243b3 100644 --- a/test/time_integration/test_convergence_ode.py +++ b/test/time_integration/test_convergence_ode.py @@ -63,7 +63,7 @@ class SimpleODEEquation(Equation): """ def __init__(self, function_space, alpha, mode='explicit'): super(SimpleODEEquation, self).__init__(function_space) - self.a_space, self.b_space = function_space.split() + self.a_space, self.b_space = function_space.subfunctions self.a_test, self.b_test = TestFunctions(function_space) self.alpha = alpha if mode == 'imex': @@ -134,7 +134,7 @@ def run(timeintegrator_class, timeintegrator_options, refinement=1): ti = timeintegrator_class(equation, solution, fields, dt, timeintegrator_options(), {}) ti.initialize(solution) simulation_time = 0 - sol_a, sol_b = solution.split() + sol_a, sol_b = solution.subfunctions values[0, :] = sol_a.dat.data[0], sol_b.dat.data[0] for i in range(ntimesteps): simulation_time = (i+1)*dt diff --git a/test_adjoint/test_swe_adjoint.py b/test_adjoint/test_swe_adjoint.py index c5db33667..6b0806fbc 100644 --- a/test_adjoint/test_swe_adjoint.py +++ b/test_adjoint/test_swe_adjoint.py @@ -1,16 +1,18 @@ """ Tests whether we can compute a consistent gradient of some functional based on the forward model with respect to the bottom friction -via firedrake_adjoint. +via firedrake adjoint. Stephan Kramer 25-05-16 """ import pytest from thetis import * -from firedrake_adjoint import * +from firedrake.adjoint import * op2.init(log_level=INFO) +continue_annotation() + velocity_u = 2.0 diff --git a/thetis/__init__.py b/thetis/__init__.py index 4e08cf61d..0f9501399 100644 --- a/thetis/__init__.py +++ b/thetis/__init__.py @@ -16,7 +16,7 @@ import thetis.diagnostics # NOQA from thetis._version import get_versions from thetis.assembledschur import AssembledSchurPC # NOQA -from thetis.options import TidalTurbineFarmOptions # NOQA +from thetis.options import TidalTurbineFarmOptions, DiscreteTidalTurbineFarmOptions # NOQA import os # NOQA import datetime # NOQA import numpy # NOQA diff --git a/thetis/assembledschur.py b/thetis/assembledschur.py index a5d9ed52d..dabdb3676 100644 --- a/thetis/assembledschur.py +++ b/thetis/assembledschur.py @@ -29,7 +29,7 @@ def initialize(self, pc): test, trial = a.arguments() W = test.function_space() - V, Q = W.split() + V, Q = W.subfunctions v = TestFunction(V) u = TrialFunction(V) mass = dot(v, u)*dx diff --git a/thetis/configuration.py b/thetis/configuration.py index d16644904..f78b86e84 100644 --- a/thetis/configuration.py +++ b/thetis/configuration.py @@ -2,9 +2,9 @@ Utility function and extensions to traitlets used for specifying Thetis options """ import textwrap +import traitlets from textwrap import dedent from traitlets.config.configurable import Configurable -from traitlets import * from firedrake import Constant, Function import datetime import ufl @@ -72,7 +72,7 @@ def rst_all_options(cls, nspace=0, prefix=None): return "\n".join(lines) -class PositiveInteger(Integer): +class PositiveInteger(traitlets.Integer): def info(self): return u'a positive integer' @@ -82,7 +82,7 @@ def validate(self, obj, proposal): return proposal -class PositiveFloat(Float): +class PositiveFloat(traitlets.Float): def info(self): return u'a positive float' @@ -92,7 +92,7 @@ def validate(self, obj, proposal): return proposal -class NonNegativeInteger(Integer): +class NonNegativeInteger(traitlets.Integer): def info(self): return u'a non-negative integer' @@ -102,7 +102,7 @@ def validate(self, obj, proposal): return proposal -class NonNegativeFloat(Float): +class NonNegativeFloat(traitlets.Float): def info(self): return u'a non-negative float' @@ -112,8 +112,8 @@ def validate(self, obj, proposal): return proposal -class BoundedInteger(Integer): - def __init__(self, default_value=Undefined, bounds=None, **kwargs): +class BoundedInteger(traitlets.Integer): + def __init__(self, default_value=traitlets.Undefined, bounds=None, **kwargs): super(BoundedInteger, self).__init__(default_value, **kwargs) self.minval = bounds[0] self.maxval = bounds[1] @@ -128,8 +128,8 @@ def validate(self, obj, proposal): return proposal -class BoundedFloat(Float): - def __init__(self, default_value=Undefined, bounds=None, **kwargs): +class BoundedFloat(traitlets.Float): + def __init__(self, default_value=traitlets.Undefined, bounds=None, **kwargs): self.minval = bounds[0] self.maxval = bounds[1] super(BoundedFloat, self).__init__(default_value, **kwargs) @@ -144,7 +144,7 @@ def validate(self, obj, proposal): return proposal -class FiredrakeConstantTraitlet(TraitType): +class FiredrakeConstantTraitlet(traitlets.TraitType): default_value = None info_text = 'a Firedrake Constant' @@ -157,7 +157,7 @@ def default_value_repr(self): return 'Constant({:})'.format(float(self.default_value)) -class FiredrakeCoefficient(TraitType): +class FiredrakeCoefficient(traitlets.TraitType): default_value = None info_text = 'a Firedrake Constant or Function' @@ -172,7 +172,7 @@ def default_value_repr(self): return 'Function' -class FiredrakeScalarExpression(TraitType): +class FiredrakeScalarExpression(traitlets.TraitType): default_value = None info_text = 'a scalar UFL expression' @@ -190,7 +190,7 @@ def default_value_repr(self): return 'UFL scalar expression' -class FiredrakeVectorExpression(TraitType): +class FiredrakeVectorExpression(traitlets.TraitType): default_value = None info_text = 'a vector UFL expression' @@ -208,7 +208,7 @@ def default_value_repr(self): return 'UFL vector expression' -class DatetimeTraitlet(TraitType): +class DatetimeTraitlet(traitlets.TraitType): default_value = None info_text = 'a timezone-aware datetime object' @@ -218,7 +218,7 @@ def validate(self, obj, value): self.error(obj, value) -class PETScSolverParameters(Dict): +class PETScSolverParameters(traitlets.Dict): """PETSc solver options dictionary""" info_text = 'a PETSc solver options dictionary' @@ -228,7 +228,7 @@ def validate(self, obj, value): self.error(obj, value) -class PairedEnum(Enum): +class PairedEnum(traitlets.Enum): """A enum whose value must be in a given sequence. This enum controls a slaved option, with default values provided here. @@ -239,7 +239,7 @@ class PairedEnum(Enum): :arg paired_name: trait name this enum is paired with. :arg default_value: default value. """ - def __init__(self, values, paired_name, default_value=Undefined, **kwargs): + def __init__(self, values, paired_name, default_value=traitlets.Undefined, **kwargs): self.paired_defaults = dict(values) self.paired_name = paired_name values, _ = zip(*values) @@ -269,7 +269,7 @@ def update(self, options): if isinstance(options, dict): params_dict = options else: - assert isinstance(options, HasTraits), 'options must be a dict or HasTraits object' + assert isinstance(options, traitlets.HasTraits), 'options must be a dict or HasTraits object' params_dict = options._trait_values for key in params_dict: self.__setattr__(key, params_dict[key]) @@ -286,12 +286,12 @@ def __str__(self): # HasTraits and Configurable (and all their subclasses) have MetaHasTraits as their metaclass # to subclass from HasTraits and another class with ABCMeta as its metaclass, we need a combined # meta class that sub(meta)classes from ABCMeta and MetaHasTraits -class ABCMetaHasTraits(ABCMeta, MetaHasTraits): +class ABCMetaHasTraits(ABCMeta, traitlets.MetaHasTraits): """Combined metaclass of ABCMeta and MetaHasTraits""" pass -class FrozenHasTraits(OptionsBase, HasTraits): +class FrozenHasTraits(OptionsBase, traitlets.HasTraits): __metaclass__ = ABCMetaHasTraits """ A HasTraits class that only allows adding new attributes in the class @@ -347,8 +347,8 @@ def _default(self): if hasattr(name_trait, 'default_value') and name_trait.default_value is not None: return name_trait.paired_defaults[name_trait.default_value]() - obs_handler = observe(name, type="change") - def_handler = default(name_trait.paired_name) + obs_handler = traitlets.observe(name, type="change") + def_handler = traitlets.default(name_trait.paired_name) def update_class(cls): "Programmatically update the class" @@ -362,6 +362,7 @@ def update_class(cls): value_trait.class_init(cls, name_trait.paired_name) obs_handler.class_init(cls, "_%s_observer" % name) def_handler.class_init(cls, "_%s_default" % name_trait.paired_name) + cls.setup_class(cls.__dict__) return cls return update_class diff --git a/thetis/coordsys.py b/thetis/coordsys.py index 3ebdf99a4..9df02b8bd 100644 --- a/thetis/coordsys.py +++ b/thetis/coordsys.py @@ -32,19 +32,13 @@ def get_vector_rotator(self, x, y): pass -def proj_transform(x, y, trans=None, source=None, destination=None): +def proj_transform(x, y, trans): """ Transform coordinates from source to target system. :arg x,y: coordinates, float or numpy.array_like - :kwarg trans: pyproj Transformer object (optional) - :kwarg source: source coordinate system, Proj object - :kwarg destination: destination coordinate system, Proj object + :kwarg trans: pyproj Transformer object """ - if trans is None: - assert source is not None and destination is not None, \ - 'Either trans or source and destination must be defined' - trans = None x_is_array = isinstance(x, numpy.ndarray) y_is_array = isinstance(y, numpy.ndarray) numpy_inputs = x_is_array or y_is_array @@ -65,9 +59,9 @@ class UTMCoordinateSystem(CoordinateSystem): """ Represents Universal Transverse Mercator coordinate systems """ - def __init__(self, utm_zone): + def __init__(self, utm_zone, south=False): self.proj_obj = pyproj.Proj(proj='utm', zone=utm_zone, datum='WGS84', - units='m', errcheck=True) + units='m', errcheck=True, south=south) self.transformer_lonlat = pyproj.Transformer.from_crs( self.proj_obj.srs, LL_WGS84.srs) self.transformer_xy = pyproj.Transformer.from_crs( @@ -129,38 +123,13 @@ def get_vector_rotator(self, lon, lat): The rotator converts vector-valued data from longitude, latitude coordinates to mesh coordinate system. """ - return VectorCoordSysRotation(LL_WGS84, self.proj_obj, lon, lat) + return VectorCoordSysRotation(self.transformer_xy, lon, lat) -def convert_coords(source_sys, target_sys, x, y): - """ - Converts coordinates from source_sys to target_sys - - This function extends pyproj.transform method by handling NaNs correctly. - - :arg source_sys: pyproj coordinate system where (x, y) are defined in - :arg target_sys: target pyproj coordinate system - :arg x: x coordinate - :arg y: y coordinate - :type x: float or numpy.array_like - :type y: float or numpy.array_like - """ - if isinstance(x, numpy.ndarray): - # proj may give wrong results if nans in the arrays - lon = numpy.full_like(x, numpy.nan) - lat = numpy.full_like(y, numpy.nan) - goodIx = numpy.logical_and(numpy.isfinite(x), numpy.isfinite(y)) - lon[goodIx], lat[goodIx] = pyproj.transform( - source_sys, target_sys, x[goodIx], y[goodIx]) - else: - lon, lat = pyproj.transform(source_sys, target_sys, x, y) - return lon, lat - - -def get_vector_rotation_matrix(source_sys, target_sys, x, y, delta=None): +def get_vector_rotation_matrix(trans, x, y, delta=None): """ Estimate rotation matrix that converts vectors defined in source_sys to - target_sys. + target_sys. Conversion is carried out by the given `trans` object. Assume that we have a vector field defined in source_sys: vectors located at (x, y) define the x and y components. We can then rotate the vectors to @@ -169,7 +138,8 @@ def get_vector_rotation_matrix(source_sys, target_sys, x, y, delta=None): .. code-block:: python - R, theta = get_vector_rotation_matrix(source_sys, target_sys, x, lat) + trans = pyproj.Transformer.from_crs(source_sys, target_sys) + R, theta = get_vector_rotation_matrix(trans, x, y) v_xy = numpy.array([[v_x], [v_y]]) v_new = numpy.matmul(R, v_xy) v_x2, v_y2 = v_new @@ -177,9 +147,8 @@ def get_vector_rotation_matrix(source_sys, target_sys, x, y, delta=None): """ if delta is None: delta = 1e-6 # ~1 m in LL_WGS84 - x1, y1 = pyproj.transform(source_sys, target_sys, x, y) - - x2, y2 = pyproj.transform(source_sys, target_sys, x, y + delta) + x1, y1 = trans.transform(x, y) + x2, y2 = trans.transform(x, y + delta) dxdl = (x2 - x1) / delta dydl = (y2 - y1) / delta theta = numpy.arctan2(-dxdl, dydl) @@ -197,14 +166,12 @@ class VectorCoordSysRotation(object): system. """ - def __init__(self, source_sys, target_sys, x, y): + def __init__(self, trans, x, y): """ - :arg source_sys: pyproj coordinate system where (x, y) are defined in - :arg target_sys: target pyproj coordinate system - :arg x: x coordinate - :arg y: y coordinate + :arg trans: pyproj.Transformer object, maps from source to destination system + :arg x, y: coordinates in the source coordinate system """ - R, theta = get_vector_rotation_matrix(source_sys, target_sys, x, y) + R, theta = get_vector_rotation_matrix(trans, x, y) self.rotation_sin = numpy.sin(theta) self.rotation_cos = numpy.cos(theta) diff --git a/thetis/diagnostics.py b/thetis/diagnostics.py index 5ec227243..034014329 100644 --- a/thetis/diagnostics.py +++ b/thetis/diagnostics.py @@ -130,7 +130,7 @@ def __init__(self, field_2d, hessian_2d, gradient_2d=None, **kwargs): g, H = TrialFunctions(W) phi, tau = TestFunctions(W) sol = Function(W) - self._gradient, self._hessian = sol.split() + self._gradient, self._hessian = sol.subfunctions # The formulation is chosen such that f does not need to have any # finite element derivatives diff --git a/thetis/exner_eq.py b/thetis/exner_eq.py index 6b386d3d4..9e72951b6 100644 --- a/thetis/exner_eq.py +++ b/thetis/exner_eq.py @@ -15,8 +15,8 @@ :math:`\nabla_h` denotes horizontal gradient, :math:`m` is the morphological scale factor, :math:`p` is the porosity and :math:`\textbf{Q_b}` is the bedload transport vector """ -from .equation import Term, Equation from .utility import * +from .equation import Term, Equation __all__ = [ 'ExnerEquation', diff --git a/thetis/exporter.py b/thetis/exporter.py index 2b4e9c712..19fb4c136 100644 --- a/thetis/exporter.py +++ b/thetis/exporter.py @@ -22,11 +22,11 @@ def get_visu_space(fs): """ mesh = fs.mesh() family = 'Lagrange' if is_cg(fs) else 'Discontinuous Lagrange' - if len(fs.ufl_element().value_shape()) == 1: - dim = fs.ufl_element().value_shape()[0] + if len(fs.ufl_element().value_shape) == 1: + dim = fs.ufl_element().value_shape[0] visu_fs = get_functionspace(mesh, family, 1, family, 1, vector=True, dim=dim) - elif len(fs.ufl_element().value_shape()) == 2: + elif len(fs.ufl_element().value_shape) == 2: visu_fs = get_functionspace(mesh, family, 1, family, 1, tensor=True) else: @@ -170,7 +170,7 @@ def export_as_index(self, iexport, function): :arg int iexport: export index >= 0 :arg function: :class:`Function` to export """ - assert function.function_space() == self.function_space,\ + assert function.function_space() == self.function_space, \ 'Function space does not match' filename = self.gen_filename(iexport) if self.verbose: @@ -204,7 +204,7 @@ def load(self, iexport, function): :arg int iexport: export index >= 0 :arg function: target :class:`Function` """ - assert function.function_space() == self.function_space,\ + assert function.function_space() == self.function_space, \ 'Function space does not match' filename = self.gen_filename(iexport) if self.verbose: @@ -214,12 +214,9 @@ def load(self, iexport, function): f.load(function) else: with CheckpointFile(filename, 'r') as f: - if not f._get_mesh_name_topology_name_map(): - raise IOError(f'File "{filename}" does not contain mesh topology, try loading it with the legacy DumbCheckpoint option?') - mesh_name = function.function_space().mesh().name - if mesh_name is None: - mesh_name = 'firedrake_default' - mesh = f.load_mesh(mesh_name) + mesh = function.function_space().mesh() + if not hasattr(mesh, 'sfXC'): + raise IOError('When loading fields from hdf5 checkpoint files, you should also read the mesh from checkpoint. See the documentation for `read_mesh_from_checkpoint()`') g = f.load_function(mesh, function.name()) function.assign(g) diff --git a/thetis/field_defs.py b/thetis/field_defs.py index 913980f5c..fa0733acc 100644 --- a/thetis/field_defs.py +++ b/thetis/field_defs.py @@ -250,3 +250,9 @@ 'unit': 'm s-1', 'filename': 'VertVelo2d', } +field_metadata['turbine_density_2d'] = { + 'name': 'Turbine density', + 'shortname': 'Turbine density', + 'unit': 'm-2', + 'filename': 'turbine_density_2d' +} diff --git a/thetis/interpolation.py b/thetis/interpolation.py index 5f9f182a0..de3fa33a4 100644 --- a/thetis/interpolation.py +++ b/thetis/interpolation.py @@ -360,7 +360,7 @@ def __init__(self, function_space, coord_system, fill_mode=None, used. Otherwise a constant fill value will be used (default). :kwarg float fill_value: Set the fill value (default: NaN) """ - assert function_space.ufl_element().value_shape() == () + assert function_space.ufl_element().value_shape == () # construct local coordinates on_sphere = function_space.mesh().geometric_dimension() == 3 @@ -377,7 +377,7 @@ def __init__(self, function_space, coord_system, fill_mode=None, fsy = Function(function_space).interpolate(y).dat.data_with_halos coords = (fsx, fsy) - lon, lat = coord_system.to_lonlat(coord_system, *coords) + lon, lat = coord_system.to_lonlat(*coords) self.mesh_lonlat = numpy.array([lon, lat]).T self.fill_mode = fill_mode diff --git a/thetis/inversion_tools.py b/thetis/inversion_tools.py index 829cdc5d2..3012cab32 100644 --- a/thetis/inversion_tools.py +++ b/thetis/inversion_tools.py @@ -1,5 +1,5 @@ import firedrake as fd -from firedrake_adjoint import * +from firedrake.adjoint import * import ufl from .configuration import FrozenHasTraits from .solver2d import FlowSolver2d @@ -47,7 +47,7 @@ def __init__(self, sta_manager, output_dir='outputs', no_exports=False, real=Fal self.real = real self.penalty_parameters = penalty_parameters self.cost_function_scaling = cost_function_scaling or fd.Constant(1.0) - self.sta_manager.cost_function_scaling.assign(cost_function_scaling) + self.sta_manager.cost_function_scaling = self.cost_function_scaling self.test_consistency = test_consistency self.test_gradient = test_gradient self.outfiles_m = [] @@ -207,6 +207,7 @@ def rf_kwargs(self): def gradient_eval_cb(j, djdm, m): self.set_control_state(j, djdm, m) self.nb_grad_evals += 1 + return djdm params = { 'derivative_cb_post': gradient_eval_cb, @@ -249,7 +250,7 @@ def get_cost_function(self, solver_obj, weight_by_variance=False): var = fd.Function(self.sta_manager.fs_points_0d) for i, j in enumerate(self.sta_manager.local_station_index): var.dat.data[i] = numpy.var(self.sta_manager.observation_values[j]) - self.sta_manager.station_weight_0d.assign(1/var) + self.sta_manager.station_weight_0d.interpolate(1/var) def cost_fn(t): misfit = self.sta_manager.eval_cost_function(t) @@ -475,6 +476,8 @@ def construct_evaluator(self): self.mod_values_0d = fd.Function(self.fs_points_0d, name='model values') self.indicator_0d = fd.Function(self.fs_points_0d, name='station use indicator') self.indicator_0d.assign(1.0) + self.cost_function_scaling_0d = fd.Constant(0.0, domain=mesh0d) + self.cost_function_scaling_0d.assign(self.cost_function_scaling) self.station_weight_0d = fd.Function(self.fs_points_0d, name='station-wise weighting') self.station_weight_0d.assign(1.0) interp_kw = {} @@ -544,7 +547,7 @@ def eval_cost_function(self, t): # compute square error self.obs_values_0d.assign(obs_func) self.mod_values_0d.interpolate(self.model_observation_field, ad_block_tag='observation') - s = self.cost_function_scaling * self.indicator_0d * self.station_weight_0d + s = self.cost_function_scaling_0d * self.indicator_0d * self.station_weight_0d self.J_misfit = fd.assemble(s * self.misfit_expr ** 2 * fd.dx, ad_block_tag='misfit_eval') return self.J_misfit diff --git a/thetis/limiter.py b/thetis/limiter.py index 77243109d..007e49289 100644 --- a/thetis/limiter.py +++ b/thetis/limiter.py @@ -3,7 +3,6 @@ """ from .utility import * from firedrake import VertexBasedLimiter -import ufl from pyop2.profiling import timed_stage import numpy @@ -24,25 +23,25 @@ def assert_function_space(fs, family, degree): if not isinstance(family, list): fam_list = [family] ufl_elem = fs.ufl_element() - if isinstance(ufl_elem, ufl.VectorElement): - ufl_elem = ufl_elem.sub_elements()[0] + if isinstance(ufl_elem, firedrake.VectorElement): + ufl_elem = ufl_elem.sub_elements[0] if ufl_elem.family() == 'TensorProductElement': # extruded mesh - A, B = ufl_elem.sub_elements() - assert A.family() in fam_list,\ + A, B = ufl_elem.sub_elements + assert A.family() in fam_list, \ 'horizontal space must be one of {0:s}'.format(fam_list) - assert B.family() in fam_list,\ + assert B.family() in fam_list, \ 'vertical space must be {0:s}'.format(fam_list) - assert A.degree() == degree,\ + assert A.degree() == degree, \ 'degree of horizontal space must be {0:d}'.format(degree) - assert B.degree() == degree,\ + assert B.degree() == degree, \ 'degree of vertical space must be {0:d}'.format(degree) else: # assume 2D mesh - assert ufl_elem.family() in fam_list,\ + assert ufl_elem.family() in fam_list, \ 'function space must be one of {0:s}'.format(fam_list) - assert ufl_elem.degree() == degree,\ + assert ufl_elem.degree() == degree, \ 'degree of function space must be {0:d}'.format(degree) diff --git a/thetis/optimisation.py b/thetis/optimisation.py index ace6261a8..bd1748bce 100644 --- a/thetis/optimisation.py +++ b/thetis/optimisation.py @@ -15,10 +15,36 @@ from .callback import DiagnosticCallback from .exporter import ExportManager import thetis.field_defs as field_defs -from abc import abstractmethod +from abc import abstractmethod, ABC import numpy +class OptimisationCallback(ABC): + """Base class for callback that can be used as callbacks of a :class:`ReducedFunctional` + + Called at various stages during the optimisation process: + - eval_cb_pre(controls) and eval_cb_post(functional, controls) called before and after (re)evaluation of the forward model + - derivative_cb_pre(controls) and eval_cb_post(functional, derivative, controls) called before and after the gradient computation using the adjoint of the model + - hessian_cb_pre(controls) and eval_cb_post(functional, derivative, controls) called before and after the hessian computation + OptimisationCallbacks that (can) use controls, functional and derivative information, work out + what is provided by the number of arguments: current control values are always in the last argument; + if more than 2 arguments are provided, the first is the latest evaluated functional value. + """ + @abstractmethod + def callback(self, *args): + pass + + def __call__(self, *args): + self.callback(*args) + + # if used as derivative callback, we need to return the derivatives (args[1]) or the controls (args[0]) for resp. the post or pre callback + # for other callbacks it doesn't matter what we return + if len(args) > 1: + return args[1] + else: + return args[0] + + class UserExportManager(ExportManager): """ ExportManager for user provided functions (not necessarily known to Thetis) @@ -57,14 +83,14 @@ def __init__(self, solver_obj_or_outputdir, functions_to_export, for field_name, function in zip(field_name_list, functions_to_export): field_dict[field_name] = function if shortnames is None and field_name in field_defs.field_metadata: - field_metadata[field_name] = {'shortname': field_defs.field_metadata[field_name]} + field_metadata[field_name] = {'shortname': field_defs.field_metadata[field_name]['shortname']} else: field_metadata[field_name] = {'shortname': field_name} if filenames is None: for field_name in field_name_list: if field_name in field_defs.field_metadata: - field_metadata[field_name]['filename'] = field_defs.field_metadata['filename'] + field_metadata[field_name]['filename'] = filename_prefix + field_defs.field_metadata[field_name]['filename'] else: field_metadata[field_name]['filename'] = filename_prefix + field_name else: @@ -116,7 +142,7 @@ def export(self, functions, suggested_names=None): self.export_manager.export() -class UserExportOptimisationCallback(UserExportManager): +class UserExportOptimisationCallback(UserExportManager, OptimisationCallback): """A :class:`.UserExportManager` that can be used as a :class:`ReducedFunctional` callback Any callback arguments (functional value, derivatives, controls) are ignored""" @@ -133,7 +159,7 @@ def __init__(self, solver_obj_or_outputdir, functions_to_export, **kwargs): # that determine what will be written self.orig_functions = self.functions.copy() - def __call__(self, *args): + def callback(self, *args): """ Ensure the :class:`.UserExportManager` uses the checkpointed values and call its export(). @@ -143,7 +169,7 @@ def __call__(self, *args): self.export() -class ControlsExportOptimisationCallback(DeferredExportManager): +class ControlsExportOptimisationCallback(DeferredExportManager, OptimisationCallback): """A callback that exports the current control values (assumed to all be :class:`Function` s) The control values are assumed to be the last argument in the callback (as for all :class:`ReducedFunctional` callbacks).""" @@ -155,11 +181,11 @@ def __init__(self, solver_obj_or_outputdir, **kwargs): kwargs.setdefault('filename_prefix', 'control_') super().__init__(solver_obj_or_outputdir, **kwargs) - def __call__(self, *args): + def callback(self, *args): self.export(args[-1]) -class DerivativesExportOptimisationCallback(DeferredExportManager): +class DerivativesExportOptimisationCallback(DeferredExportManager, OptimisationCallback): """A callback that exports the derivatives calculated by the adjoint. The derivatives are assumed to be the second argument in the callback. This can therefore @@ -172,9 +198,9 @@ def __init__(self, solver_obj_or_outputdir, **kwargs): kwargs.setdefault('filename_prefix', 'derivative_') super().__init__(solver_obj_or_outputdir, **kwargs) - def __call__(self, *args): + def callback(self, *args): if len(args) != 3: - raise TypeError("DerivativesExportOptimsationCallback called with wrong number of arguments: should be used for derivative_cb_post callback only.") + raise TypeError("DerivativesExportOptimisationCallback called with wrong number of arguments: should be used for derivative_cb_post callback only.") try: # get name from controls args[-1] names = [function.name() for function in args[-1]] @@ -184,19 +210,19 @@ def __call__(self, *args): self.export(args[1], suggested_names=names) -class OptimisationCallbackList(list): +class OptimisationCallbackList(list, OptimisationCallback): """ A list of callbacks that can be used as a single callback itself. Calls all callbacks in order.""" - def __call__(self, *args): - for callback in self: - callback(*args) + def callback(self, *args): + for cb in self: + cb(*args) -class DiagnosticOptimisationCallback(DiagnosticCallback): +class DiagnosticOptimisationCallback(OptimisationCallback, DiagnosticCallback): """ - An OptimsationCallback similar to :class:`.DiagnosticCallback` that can be used as callback in a :class:`ReducedFunctional`. + An OptimisationCallback similar to :class:`.DiagnosticCallback` that can be used as callback in a :class:`ReducedFunctional`. Note that in this case the computing of the values needs to be defined in the compute_values method, not in the __call__ method (as this one is directly called from the :class:`ReducedFunctional`). In addition, @@ -237,7 +263,7 @@ def evaluate(self, *args, index=None): if self.append_to_hdf5: self.push_to_hdf5(functional, values) - def __call__(self, *args): + def callback(self, *args): self.evaluate(*args) @@ -254,3 +280,37 @@ def compute_values(self, *args): def message_str(self, functional): return 'Functional value: {}'.format(functional) + + +class ConstantControlOptimisationCallback(DiagnosticOptimisationCallback): + """ + OptimisationCallback that records the control values (which are assumed to be a list of Constants) in the log and/or hdf5 file.""" + variable_names = ['controls'] + name = 'controls' + + def compute_values(self, *args): + controls = args[-1] + if self.array_dim != len(controls): + raise ValueError("Need array_dim argument in ConstantControlOptimisationCallback set to the number of controls") + return [[float(c) for c in controls]] + + def message_str(self, *controls): + return 'Controls value: {}'.format(controls) + + +class DerivativeConstantControlOptimisationCallback(DiagnosticOptimisationCallback): + """ + OptimisationCallback that records the derivatives with respect to the controls, assumed to be a list of Constants, in the log and/or hdf5 file.""" + variable_names = ['derivatives'] + name = 'derivatives' + + def compute_values(self, *args): + if len(args) != 3: + raise TypeError("DerivativesExportOptimisationCallback called with wrong number of arguments: should be used for derivative_cb_post callback only.") + derivatives = args[1] + if self.array_dim != len(derivatives): + raise ValueError("Need array_dim argument in ConstantControlOptimisationCallback set to the number of controls") + return [[float(d) for d in derivatives]] + + def message_str(self, *derivatives): + return 'Derivatives: {}'.format(derivatives) diff --git a/thetis/options.py b/thetis/options.py index c98785e12..fd0578de0 100644 --- a/thetis/options.py +++ b/thetis/options.py @@ -5,6 +5,7 @@ objects. """ from .configuration import * +from traitlets import Integer, Float, TraitType, Dict, Enum, Unicode, Bool, Instance, List, Type from firedrake import Constant from .sediment_model import SedimentModel from collections import OrderedDict @@ -63,6 +64,7 @@ class SteadyStateTimeStepperOptions2d(TimeStepperOptions): solver_parameters = PETScSolverParameters({ 'ksp_type': 'preonly', 'pc_type': 'lu', + 'pc_factor_mat_solver_type': 'mumps', 'mat_type': 'aij' }).tag(config=True) @@ -457,22 +459,59 @@ class LinearEquationOfStateOptions(EquationOfStateOptions): class TidalTurbineOptions(FrozenHasTraits): """Tidal turbine parameters""" - thrust_coefficient = PositiveFloat( - 0.8, help='Thrust coefficient C_T').tag(config=True) + name = 'Tidal turbine options' diameter = PositiveFloat( 18., help='Turbine diameter').tag(config=True) + C_support = NonNegativeFloat( + 0., help='Thrust coefficient for support structure').tag(config=True) + A_support = NonNegativeFloat( + 0., help='Cross section of support structure').tag(config=True) + + +class ConstantTidalTurbineOptions(TidalTurbineOptions): + """Options for tidal turbine with constant thrust""" + name = 'Constant tidal turbine options' + thrust_coefficient = PositiveFloat( + 0.8, help='Thrust coefficient C_T').tag(config=True) +class TabulatedTidalTurbineOptions(TidalTurbineOptions): + """Options for tidal turbine with tabulated thrust coefficient""" + name = 'Tabulated tidal turbine options' + thrust_coefficients = List([3.0], help='Table of thrust coefficients') + thrust_speeds = List( + [0.8, 0.8], + help="""List of speeds at which thrust_coefficients are applied. + First and last entry function as cut-in and cut-out speeds respectively""") + + +@attach_paired_options("turbine_type", + PairedEnum([('constant', ConstantTidalTurbineOptions), + ('table', TabulatedTidalTurbineOptions), + ], + "turbine_options", + default_value='constant', + help='Type of turbine thrust specification').tag(config=True), + Instance(TidalTurbineOptions, args=()).tag(config=True)) class TidalTurbineFarmOptions(FrozenHasTraits, TraitType): """Tidal turbine farm options""" name = 'Farm options' - turbine_options = TidalTurbineOptions() turbine_density = FiredrakeScalarExpression( Constant(0.0), help='Density of turbines within the farm') break_even_wattage = NonNegativeFloat( 0.0, help='Average power production per turbine required to break even') +class DiscreteTidalTurbineFarmOptions(TidalTurbineFarmOptions): + """Discrete Tidal turbine farm options - defaults to 0 turbines in the field""" + name = 'Discrete Farm options' + turbine_coordinates = List(default_value=[], help="Coordinates for turbines").tag(config=True) + upwind_correction = Bool(True, + help='bool: Apply flow correction to correct for upwind velocity').tag(config=True) + quadrature_degree = PositiveInteger(10, + help='Quadrature degree for thrust force and power output integral').tag(config=True) + + class TracerFieldOptions(FrozenHasTraits): """Tracer field options""" name = 'Tracer options' @@ -842,8 +881,14 @@ class ModelOptions2d(CommonModelOptions): Note this is only relevant if `use_automatic_wetting_and_drying_alpha` is set to ``True``. """).tag(config=True) - tidal_turbine_farms = Dict(trait=TidalTurbineFarmOptions(), - default_value={}, help='Dictionary mapping subdomain ids to the options of the corresponding farm') + tidal_turbine_farms = Dict(trait=List(TidalTurbineFarmOptions()), default_value={}, + help='Dictionary mapping subdomain ids to a list of TidalTurbineFarmOptions instances ' + 'corresponding to one or more farms.') + + discrete_tidal_turbine_farms = Dict(trait=List(DiscreteTidalTurbineFarmOptions()), default_value={}, + help='Dictionary mapping subdomain ids to a list of DiscreteTidalTurbineFarmOptions ' + 'instances corresponding to one or more farms.') + check_tracer_conservation = Bool( False, help=""" Compute total tracer mass at every export @@ -948,7 +993,7 @@ def add_tracer_system_2d(self, labels, names, filenames, shortnames=None, units= kwargs[label]['mixed'] = True self.tracer_fields[','.join(labels)] = function if function is not None: - for label, child in zip(labels, function.split()): + for label, child in zip(labels, function.subfunctions): kwargs[label]['function'] = child for label, name, filename, shortname, unit in zip(labels, names, filenames, shortnames, units): self.add_tracer_2d(label, name, filename, shortname=shortname, unit=unit, **kwargs[label]) diff --git a/thetis/sediment_model.py b/thetis/sediment_model.py index 182bb3b85..02328cb1e 100644 --- a/thetis/sediment_model.py +++ b/thetis/sediment_model.py @@ -139,10 +139,10 @@ def __init__(self, options, mesh2d, uv, elev, depth): self.dstar = Function(self.P1_2d).interpolate(self.average_size*((self.g*self.R)/(self.viscosity**2))**(1/3)) if float(max(self.dstar.dat.data[:])) < 1: raise ValueError('dstar value less than 1') - self.thetacr = Function(self.P1_2d).assign(conditional(self.dstar < 4, 0.24*(self.dstar**(-1)), - conditional(self.dstar < 10, 0.14*(self.dstar**(-0.64)), - conditional(self.dstar < 20, 0.04*(self.dstar**(-0.1)), - conditional(self.dstar < 150, 0.013*(self.dstar**(0.29)), 0.055))))) + self.thetacr = Function(self.P1_2d).interpolate(conditional(self.dstar < 4, 0.24*(self.dstar**(-1)), + conditional(self.dstar < 10, 0.14*(self.dstar**(-0.64)), + conditional(self.dstar < 20, 0.04*(self.dstar**(-0.1)), + conditional(self.dstar < 150, 0.013*(self.dstar**(0.29)), 0.055))))) # critical bed shear stress self.taucr = Function(self.P1_2d).interpolate((self.rhos-self.rhow)*self.g*self.average_size*self.thetacr) diff --git a/thetis/shallowwater_eq.py b/thetis/shallowwater_eq.py index c4e81dd08..0925ec083 100644 --- a/thetis/shallowwater_eq.py +++ b/thetis/shallowwater_eq.py @@ -775,16 +775,19 @@ class TurbineDragTerm(ShallowWaterMomentumTerm): c_t = (C_T A_T d)/2 """ + def __init__(self, u_test, u_space, eta_space, + depth, options=None, tidal_farms=None): + super().__init__(u_test, u_space, eta_space, depth, options=options) + self.tidal_farms = tidal_farms + def residual(self, uv, eta, uv_old, eta_old, fields, fields_old, bnd_conditions): total_h = self.depth.get_total_depth(eta_old) f = 0 - for subdomain_id, farm_options in self.options.tidal_turbine_farms.items(): - density = farm_options.turbine_density - C_T = farm_options.turbine_options.thrust_coefficient - A_T = pi * (farm_options.turbine_options.diameter/2.)**2 - C_D = (C_T * A_T * density)/2. + for farm in self.tidal_farms: + density = farm.turbine_density + c_t = farm.friction_coefficient(uv_old, total_h) unorm = sqrt(dot(uv_old, uv_old)) - f += C_D * unorm * inner(self.u_test, uv) / total_h * self.dx(subdomain_id) + f += c_t * density * unorm * inner(self.u_test, uv) / total_h * farm.dx return -f @@ -861,7 +864,7 @@ def __init__(self, function_space, self.depth = depth self.options = options - def add_momentum_terms(self, *args): + def add_momentum_terms(self, *args, tidal_farms=None): self.add_term(ExternalPressureGradientTerm(*args), 'implicit') self.add_term(HorizontalAdvectionTerm(*args), 'implicit') self.add_term(HorizontalViscosityTerm(*args), 'explicit') @@ -872,8 +875,9 @@ def add_momentum_terms(self, *args): self.add_term(LinearDragTerm(*args), 'implicit') self.add_term(BoundaryDragTerm(*args), 'implicit') self.add_term(BottomDrag3DTerm(*args), 'source') - self.add_term(TurbineDragTerm(*args), 'implicit') self.add_term(MomentumSourceTerm(*args), 'source') + if tidal_farms: + self.add_term(TurbineDragTerm(*args, tidal_farms), 'implicit') def add_continuity_terms(self, *args): self.add_term(HUDivTerm(*args), 'implicit') @@ -893,7 +897,7 @@ class ShallowWaterEquations(BaseShallowWaterEquation): This defines the full 2D SWE equations :eq:`swe_freesurf` - :eq:`swe_momentum`. """ - def __init__(self, function_space, depth, options): + def __init__(self, function_space, depth, options, tidal_farms=None): """ :arg function_space: Mixed function space where the solution belongs :arg depth: :class: `DepthExpression` containing depth info @@ -902,9 +906,9 @@ def __init__(self, function_space, depth, options): super(ShallowWaterEquations, self).__init__(function_space, depth, options) u_test, eta_test = TestFunctions(function_space) - u_space, eta_space = function_space.split() + u_space, eta_space = function_space.subfunctions - self.add_momentum_terms(u_test, u_space, eta_space, depth, options) + self.add_momentum_terms(u_test, u_space, eta_space, depth, options, tidal_farms=tidal_farms) self.add_continuity_terms(eta_test, eta_space, u_space, depth, options) self.bathymetry_displacement_mass_term = BathymetryDisplacementMassTerm( @@ -941,7 +945,7 @@ def __init__(self, function_space, depth, options): super(ModeSplit2DEquations, self).__init__(function_space, depth, options) u_test, eta_test = TestFunctions(function_space) - u_space, eta_space = function_space.split() + u_space, eta_space = function_space.subfunctions self.add_momentum_terms(u_test, u_space, eta_space, depth, options) @@ -998,7 +1002,7 @@ class ShallowWaterMomentumEquation(BaseShallowWaterEquation): 2D depth averaged momentum equation :eq:`swe_momentum` in non-conservative form. """ - def __init__(self, u_test, u_space, eta_space, depth, options): + def __init__(self, u_test, u_space, eta_space, depth, options, tidal_farms=None): """ :arg u_test: test function of the velocity function space :arg u_space: velocity function space @@ -1007,7 +1011,7 @@ def __init__(self, u_test, u_space, eta_space, depth, options): :arg options: :class:`.AttrDict` object containing all circulation model options """ super(ShallowWaterMomentumEquation, self).__init__(u_space, depth, options) - self.add_momentum_terms(u_test, u_space, eta_space, depth, options) + self.add_momentum_terms(u_test, u_space, eta_space, depth, options, tidal_farms=tidal_farms) def residual(self, label, solution, solution_old, fields, fields_old, bnd_conditions): uv = solution diff --git a/thetis/solver.py b/thetis/solver.py index e42e99e2a..23152b64e 100644 --- a/thetis/solver.py +++ b/thetis/solver.py @@ -492,7 +492,7 @@ def create_fields(self): # ----- fields self.fields.solution_2d = Function(self.function_spaces.V_2d) # correct treatment of the split 2d functions - uv_2d, eta2d = self.fields.solution_2d.split() + uv_2d, eta2d = self.fields.solution_2d.subfunctions self.fields.uv_2d = uv_2d self.fields.elev_2d = eta2d # elevation field seen by the 3D mode, different from elev_2d @@ -861,7 +861,7 @@ def create_timestepper(self): # compute maximal diffusivity for explicit schemes degree_h, degree_v = self.function_spaces.H.ufl_element().degree() max_diff_alpha = 1.0/60.0/max((degree_h*(degree_h + 1)), 1.0) # FIXME depends on element type and order - self.fields.max_h_diff.assign(max_diff_alpha/self.dt * self.fields.h_elem_size_3d**2) + self.fields.max_h_diff.interpolate(max_diff_alpha/self.dt * self.fields.h_elem_size_3d**2) d = self.fields.max_h_diff.dat.data print_output('max h diff {:} - {:}'.format(d.min(), d.max())) diff --git a/thetis/solver2d.py b/thetis/solver2d.py index eefb030fd..916fd647d 100644 --- a/thetis/solver2d.py +++ b/thetis/solver2d.py @@ -14,6 +14,7 @@ import time as time_mod from mpi4py import MPI from . import exporter +from .turbines import TidalTurbineFarm, DiscreteTidalTurbineFarm from .field_defs import field_metadata from .options import ModelOptions2d from . import callback @@ -384,7 +385,7 @@ def add_new_field(self, function, label, name, filename, shortname=None, unit='- self._field_preproc_funcs[label] = preproc_func @unfrozen - @PETSc.Log.EventDecorator("thetis.FlowSolver2d.create_equations") + @PETSc.Log.EventDecorator("thetis.FlowSolver2d.create_fields") def create_fields(self): """ Creates field Functions @@ -407,7 +408,7 @@ def create_fields(self): # Add fields for shallow water modelling self.fields.solution_2d = Function(self.function_spaces.V_2d, name='solution_2d') - uv_2d, elev_2d = self.fields.solution_2d.split() # correct treatment of the split 2d functions + uv_2d, elev_2d = self.fields.solution_2d.subfunctions # correct treatment of the split 2d functions self.fields.uv_2d = uv_2d self.fields.elev_2d = elev_2d @@ -423,7 +424,7 @@ def create_fields(self): if num_labels > 1: self.fields[system] = parent self.options.tracer_fields[system] = parent - children = [parent] if num_labels == 1 else parent.split() + children = [parent] if num_labels == 1 else parent.subfunctions for label, function in zip(labels, children): tracer = self.options.tracer[label] function.rename(label) @@ -457,14 +458,40 @@ def create_equations(self): self.create_fields() self.equations = AttrDict() + # tidal farms, if any + if len(self.options.tidal_turbine_farms) + len(self.options.discrete_tidal_turbine_farms) > 0: + self.tidal_farms = [] + p = self.function_spaces.U_2d.ufl_element().degree() + quad_degree = 2*p + 1 + for subdomain, farm_options_list in self.options.tidal_turbine_farms.items(): + if not isinstance(farm_options_list, list): + error_msg = "Farm options must be entered as a list e.g. " \ + "solver2d.FlowSolver2d(mesh2d, bathymetry_2d).options.discrete_tidal_turbine_farms[site_ID] = " \ + "[farm_options]" + raise TypeError(error_msg) + for farm_options in farm_options_list: + fdx = dx(subdomain, degree=quad_degree) + self.tidal_farms.append(TidalTurbineFarm(farm_options.turbine_density, fdx, farm_options)) + for subdomain, farm_options_list in self.options.discrete_tidal_turbine_farms.items(): + if not isinstance(farm_options_list, list): + error_msg = "Farm options must be entered as a list e.g. " \ + "solver2d.FlowSolver2d(mesh2d, bathymetry_2d).options.discrete_tidal_turbine_farms[site_ID] = " \ + "[farm_options]" + raise TypeError(error_msg) + for farm_options in farm_options_list: + fdx = dx(subdomain, degree=farm_options.quadrature_degree) + self.tidal_farms.append(DiscreteTidalTurbineFarm(self.mesh2d, fdx, farm_options)) + else: + self.tidal_farms = None # Shallow water equations for hydrodynamic modelling self.equations.sw = shallowwater_eq.ShallowWaterEquations( self.fields.solution_2d.function_space(), self.depth, self.options, + tidal_farms=self.tidal_farms ) self.equations.sw.bnd_functions = self.bnd_functions['shallow_water'] - uv_2d, elev_2d = self.fields.solution_2d.split() + uv_2d, elev_2d = self.fields.solution_2d.subfunctions # Passive tracer equations for system, parent in self.options.tracer_fields.items(): @@ -535,7 +562,8 @@ def get_swe_timestepper(self, integrator): self.equations.mom = shallowwater_eq.ShallowWaterMomentumEquation( u_test, self.function_spaces.U_2d, self.function_spaces.H_2d, self.depth, - options=self.options + options=self.options, + tidal_farms=self.tidal_farms ) self.equations.mom.bnd_functions = bnd_conditions return integrator(self.equations.sw, self.equations.mom, self.fields.solution_2d, @@ -549,7 +577,7 @@ def get_tracer_timestepper(self, integrator, system): """ Gets tracer timestepper object with appropriate parameters """ - uv, elev = self.fields.solution_2d.split() + uv, elev = self.fields.solution_2d.subfunctions fields = { 'elev_2d': elev, 'uv_2d': uv, @@ -574,7 +602,7 @@ def get_sediment_timestepper(self, integrator): """ Gets sediment timestepper object with appropriate parameters """ - uv, elev = self.fields.solution_2d.split() + uv, elev = self.fields.solution_2d.subfunctions fields = { 'elev_2d': elev, 'uv_2d': uv, @@ -591,7 +619,7 @@ def get_exner_timestepper(self, integrator): """ Gets exner timestepper object with appropriate parameters """ - uv, elev = self.fields.solution_2d.split() + uv, elev = self.fields.solution_2d.subfunctions if not self.options.sediment_model_options.solve_suspended_sediment: self.fields.sediment_2d = Function(elev.function_space()).interpolate(Constant(0.0)) fields = { @@ -729,7 +757,7 @@ def assign_initial_conditions(self, elev=None, uv=None, sediment=None, **tracers """ if not self._initialized: self.initialize() - uv_2d, elev_2d = self.fields.solution_2d.split() + uv_2d, elev_2d = self.fields.solution_2d.subfunctions if elev is not None: elev_2d.project(elev) if uv is not None: @@ -895,8 +923,8 @@ def print_state(self, cputime, print_header=False): e = (label, norm_q, '10.4f') entries.append(e) else: - norm_h = norm(self.fields.solution_2d.split()[1]) - norm_u = norm(self.fields.solution_2d.split()[0]) + norm_h = norm(self.fields.solution_2d.subfunctions[1]) + norm_u = norm(self.fields.solution_2d.subfunctions[0]) entries += [ ('eta norm', norm_h, '14.4f'), ('u norm', norm_u, '14.4f'), diff --git a/thetis/timeintegrator.py b/thetis/timeintegrator.py index 6f3a499ae..9348ac66c 100644 --- a/thetis/timeintegrator.py +++ b/thetis/timeintegrator.py @@ -85,6 +85,32 @@ def advance_picard(self, t, update_forcings=None, update_lagged=True, update_fie """ raise NotImplementedError(f"Picard iterations are not supported for {self} time integrators.") + def create_fields_old(self): + """ + Create a copy of fields dictionary to store beginning of timestep values + """ + # create functions to hold the values of previous time step + self.fields_old = {} + for k in sorted(self.fields): + if self.fields[k] is not None: + if isinstance(self.fields[k], Function): + self.fields_old[k] = Function( + self.fields[k].function_space()) + elif isinstance(self.fields[k], Constant): + # Although Constants may be changed by the user, we just + # use the same value here, as assigning to domain-less Constants + # causes issues in the adjoint. Constants with a domain, created by Constaint(.., domain=...), + # are now just Functions on the Real space, so are dealt with under isinstance(..., Function) + self.fields_old[k] = self.fields[k] + + def update_fields_old(self): + """ + Update the values of fields_old with those of fields + """ + for k in sorted(self.fields): + if isinstance(self.fields[k], Function): + self.fields_old[k].assign(self.fields[k]) + class ForwardEuler(TimeIntegrator): """Standard forward Euler time integration scheme.""" @@ -105,15 +131,7 @@ def __init__(self, equation, solution, fields, dt, options, bnd_conditions): super(ForwardEuler, self).__init__(equation, solution, fields, dt, options) self.solution_old = Function(self.equation.function_space) - # create functions to hold the values of previous time step - self.fields_old = {} - for k in sorted(self.fields): - if self.fields[k] is not None: - if isinstance(self.fields[k], Function): - self.fields_old[k] = Function( - self.fields[k].function_space()) - elif isinstance(self.fields[k], Constant): - self.fields_old[k] = Constant(self.fields[k]) + self.create_fields_old() u_old = self.solution_old u_tri = self.equation.trial @@ -135,9 +153,7 @@ def update_solver(self): def initialize(self, solution): """Assigns initial conditions to all required fields.""" self.solution_old.assign(solution) - # assign values to old functions - for k in sorted(self.fields_old): - self.fields_old[k].assign(self.fields[k]) + self.update_fields_old() @PETSc.Log.EventDecorator("thetis.ForwardEuler.advance") def advance(self, t, update_forcings=None): @@ -146,9 +162,7 @@ def advance(self, t, update_forcings=None): update_forcings(t + self.dt) self.solution_old.assign(self.solution) self.solver.solve() - # shift time - for k in sorted(self.fields_old): - self.fields_old[k].assign(self.fields[k]) + self.update_fields_old() class CrankNicolson(TimeIntegrator): @@ -175,16 +189,7 @@ def __init__(self, equation, solution, fields, dt, options, bnd_conditions): else: self.solver_parameters.setdefault('snes_type', 'newtonls') self.solution_old = Function(self.equation.function_space, name='solution_old') - # create functions to hold the values of previous time step - # TODO is this necessary? is self.fields sufficient? - self.fields_old = {} - for k in sorted(self.fields): - if self.fields[k] is not None: - if isinstance(self.fields[k], Function): - self.fields_old[k] = Function( - self.fields[k].function_space(), name=self.fields[k].name()+'_old') - elif isinstance(self.fields[k], Constant): - self.fields_old[k] = Constant(self.fields[k]) + self.create_fields_old() u = self.solution u_old = self.solution_old @@ -224,9 +229,7 @@ def update_solver(self): def initialize(self, solution): """Assigns initial conditions to all required fields.""" self.solution_old.assign(solution) - # assign values to old functions - for k in sorted(self.fields_old): - self.fields_old[k].assign(self.fields[k]) + self.update_fields_old() @PETSc.Log.EventDecorator("thetis.CrankNicolson.advance") def advance(self, t, update_forcings=None): @@ -235,9 +238,7 @@ def advance(self, t, update_forcings=None): update_forcings(t + self.dt) self.solution_old.assign(self.solution) self.solver.solve() - # shift time - for k in sorted(self.fields_old): - self.fields_old[k].assign(self.fields[k]) + self.update_fields_old() @PETSc.Log.EventDecorator("thetis.CrankNicolson.advance_picard") def advance_picard(self, t, update_forcings=None, update_lagged=True, update_fields=True): @@ -248,9 +249,7 @@ def advance_picard(self, t, update_forcings=None, update_lagged=True, update_fie self.solution_old.assign(self.solution) self.solver.solve() if update_fields: - # shift time - for k in sorted(self.fields_old): - self.fields_old[k].assign(self.fields[k]) + self.update_fields_old() class SteadyState(TimeIntegrator): @@ -353,8 +352,8 @@ def __init__(self, equation, equation_mom, solution, fields, dt, options, bnd_co self.solution_lagged = Function(self.equation.function_space) else: self.solution_lagged = self.solution_old - uv_lagged, eta_lagged = self.solution_lagged.split() - uv_old, eta_old = self.solution_old.split() + uv_lagged, eta_lagged = self.solution_lagged.subfunctions + uv_old, eta_old = self.solution_old.subfunctions if (solver_parameters['ksp_type'] == 'preonly' and 'fieldsplit_H_2d' in solver_parameters @@ -370,15 +369,8 @@ def __init__(self, equation, equation_mom, solution, fields, dt, options, bnd_co raise Exception("The timestepper PressureProjectionPicard is only recommended in combination with the " "dg-cg element_family. If you want to use it in combination with dg-dg or rt-dg you need to adjust the solver_parameters_pressure option.") - # create functions to hold the values of previous time step - self.fields_old = {} - for k in sorted(self.fields): - if self.fields[k] is not None: - if isinstance(self.fields[k], Function): - self.fields_old[k] = Function( - self.fields[k].function_space()) - elif isinstance(self.fields[k], Constant): - self.fields_old[k] = Constant(self.fields[k]) + self.create_fields_old() + # for the mom. eqn. the 'eta' field is just one of the 'other' fields fields_mom = self.fields.copy() fields_mom_old = self.fields_old.copy() @@ -452,9 +444,7 @@ def initialize(self, solution): """Assigns initial conditions to all required fields.""" self.solution_old.assign(solution) self.solution_lagged.assign(solution) - # assign values to old functions - for k in sorted(self.fields_old): - self.fields_old[k].assign(self.fields[k]) + self.update_fields_old() @PETSc.Log.EventDecorator("thetis.PressureProjectionPicard.advance") def advance(self, t, update_forcings=None): @@ -471,9 +461,7 @@ def advance(self, t, update_forcings=None): with timed_stage("Pressure solve"): self.solver.solve() - # shift time - for k in sorted(self.fields_old): - self.fields_old[k].assign(self.fields[k]) + self.update_fields_old() class LeapFrogAM3(TimeIntegrator): diff --git a/thetis/tsunami.py b/thetis/tsunami.py index ba4c1825b..5709bdbf8 100644 --- a/thetis/tsunami.py +++ b/thetis/tsunami.py @@ -1,5 +1,6 @@ import firedrake as fd -from thetis.configuration import * +import traitlets +from thetis.configuration import BoundedFloat, PositiveInteger, FrozenConfigurable, PositiveFloat import thetis.utility as utility import ufl from ufl import cos, sin, sqrt, ln, atan, pi @@ -43,7 +44,7 @@ def __init__(self, mesh2d, coord_system, element=None): self.mesh2d = mesh2d self.coord_system = coord_system if element is None: - element = ufl.FiniteElement("Lagrange", mesh2d.ufl_cell(), 1) + element = fd.FiniteElement("Lagrange", mesh2d.ufl_cell(), 1) self.function_space = utility.get_functionspace(mesh2d, element.family(), element.degree()) self._elev_init = fd.Function(self.function_space, name="Elevation") self.xy = ufl.SpatialCoordinate(mesh2d) @@ -108,29 +109,32 @@ class FiniteElementTsunamiSource(TsunamiSource): Generate a tsunami source condition in a finite element space. """ - mask_shape = Enum( + mask_shape = traitlets.Enum( ["rectangle", "circle"], default_value="rectangle", allow_none=True, help="Shape of mask function").tag(config=True) @utility.unfrozen - def __init__(self, mesh2d, element=None, initial_guess=None): + def __init__(self, mesh2d, coord_system, element=None, initial_guess=None): """ :arg mesh2d: mesh upon which to define the source + :arg coord_system: :class:`CoordinateSystem` instance :kwarg element: :class:`FiniteElement` with which to define the source :kwarg initial_guess: :class:`Function` or filename to use for the initial guess """ - super().__init__(mesh2d, element=element) + super().__init__(mesh2d, coord_system, element=element) self.initial_guess = initial_guess + @utility.unfrozen def generate(self): mask = self.mask self._controls = [fd.Function(self.function_space, name="Elevation")] if self.initial_guess is None: + eps = 1.0e-03 self._controls[0].interpolate(eps * mask) elif isinstance(self.initial_guess, str): - with CheckpointFile(self.initial_guess, "r") as chk: + with fd.CheckpointFile(self.initial_guess, "r") as chk: mesh_name = self.mesh2d.name if mesh_name is None: mesh_name = "firedrake_default" @@ -165,7 +169,7 @@ def mask(self): r = self.fault_width return ufl.conditional(x ** 2 + y ** 2 < r ** 2, 1, 0) else: - raise ValueError(f"Mask shape '{shape}' not supported") + raise ValueError(f"Mask shape '{self.mask_shape}' not supported") @property def control_bounds(self): diff --git a/thetis/turbines.py b/thetis/turbines.py index 8f05397f0..dc9e917f5 100644 --- a/thetis/turbines.py +++ b/thetis/turbines.py @@ -6,64 +6,154 @@ from .log import * from .callback import DiagnosticCallback from .optimisation import DiagnosticOptimisationCallback +import pyadjoint import numpy -class TurbineFarm(object): +class TidalTurbine: + def __init__(self, options, upwind_correction=False): + self.diameter = options.diameter + self.C_support = options.C_support + self.A_support = options.A_support + self.upwind_correction = upwind_correction + + def _thrust_area(self, uv): + C_T = self.thrust_coefficient(uv) + A_T = pi * self.diameter**2 / 4 + fric = C_T * A_T + if self.C_support: + fric += self.C_support * self.A_support + return fric + + def velocity_correction(self, uv, depth): + fric = self._thrust_area(uv) + if self.upwind_correction: + return 0.5*(1+sqrt(1-fric/(self.diameter*depth))) + else: + return 1 + + def friction_coefficient(self, uv, depth): + thrust_area = self._thrust_area(uv) + alpha = self.velocity_correction(uv, depth) + return thrust_area/2./alpha**2 + + def power(self, uv, depth): + # ratio of discrete to upstream velocity (NOTE: should include support drag!) + alpha = self.velocity_correction(uv, depth) + A_T = pi * self.diameter**2 / 4 + uv3 = dot(uv, uv)**1.5 / alpha**3 # upwind cubed velocity + C_T = self.thrust_coefficient(uv3**(1/3)) + # this assumes the velocity through the turbine does not change due to the support (is this correct?) + return 0.25*C_T*A_T*(1+sqrt(1-C_T))*uv3 + + +class ConstantThrustTurbine(TidalTurbine): + def __init__(self, options, upwind_correction=False): + super().__init__(options, upwind_correction=upwind_correction) + self.C_T = options.thrust_coefficient + + def thrust_coefficient(self, uv): + return self.C_T + + +def linearly_interpolate_table(x_list, y_list, y_final, x): + """Return UFL expression that linearly interpolates between y-values in x-points + + :param x_list: (1D) x-points + :param y_list: y-values in those points + :param y_final: value for x>x_list[-1] + :param x: point to interpolate (assumed x>x_list[0]) """ - Evaluates power output, costs and profit of tidal turbine farm + # below x1, interpolate between x0 and x1: + below_x1 = ((x_list[1]-x)*y_list[0] + (x-x_list[0])*y_list[1])/(x_list[1]-x_list[0]) + # above x1, interpolate from rest of the table, or take final value: + if len(x_list) > 2: + above_x1 = linearly_interpolate_table(x_list[1:], y_list[1:], y_final, x) + else: + above_x1 = y_final + + return conditional(x < x_list[1], below_x1, above_x1) + + +class TabulatedThrustTurbine(TidalTurbine): + def __init__(self, options, upwind_correction=False): + super().__init__(options, upwind_correction=upwind_correction) + self.C_T = options.thrust_coefficients + self.speeds = options.thrust_speeds + if not len(self.C_T) == len(self.speeds): + raise ValueError("In tabulated thrust curve the number of thrust coefficients and speed values should be the same.") + + def thrust_coefficient(self, uv): + umag = dot(uv, uv)**0.5 + return conditional(umag < self.speeds[0], 0, linearly_interpolate_table(self.speeds, self.C_T, 0, umag)) + - Cost is simply the number of turbines evaluated as the integral of the - turbine density. +class TidalTurbineFarm: + def __init__(self, turbine_density, dx, options): + """ + :arg turbine_density: turbine distribution density field or expression + :arg dx: measure to integrate power output, n/o turbines + :arg options: a :class:`TidalTurbineFarmOptions` options dictionary + """ + upwind_correction = getattr(options, 'upwind_correction', False) + if options.turbine_type == 'constant': + self.turbine = ConstantThrustTurbine(options.turbine_options, upwind_correction=upwind_correction) + elif options.turbine_type == 'table': + self.turbine = TabulatedThrustTurbine(options.turbine_options, upwind_correction=upwind_correction) + self.dx = dx + self.turbine_density = turbine_density + self.break_even_wattage = options.break_even_wattage - Power output is calculated as the amount of energy - taken out of the flow by the turbine drag term. This is in general - an over-estimate of the 'usefully extractable' energy. Furthermore no - density is included, so that assuming a density of 1000 kg/m^3 and - all further quantities in SI, the power is measured in kW. + def number_of_turbines(self): + return assemble(self.turbine_density * self.dx) - Profit is calculated as: + def power_output(self, uv, depth): + return assemble(self.turbine.power(uv, depth) * self.turbine_density * self.dx) - Profit = Power - break_even_wattage * Cost + def friction_coefficient(self, uv, depth): + return self.turbine.friction_coefficient(uv, depth) - With the above assumptions, break_even_wattage should be specified in - kW and can be interpreted as the average power per turbine required - to 'break even', i.e. Profit=0. - Power output and profit are time-integrated (simple first order) and - time-averages are available as average_power and average_profit. +class DiscreteTidalTurbineFarm(TidalTurbineFarm): """ - def __init__(self, farm_options, subdomain_id, u, v, dt): + Class that can be used for the addition of turbines in the turbine density field + """ + + def __init__(self, mesh, dx, options): + """ + :arg mesh: mesh domain + :arg dx: measure to integrate power output, n/o turbines + :arg options: a :class:`TidalTurbineFarmOptions` options dictionary """ - :arg farm_options: a :class:`TidalTurbineFarmOptions` object that define the farm and the turbines used - :arg int subdomain_id: the farm is restricted to this subdomain - :arg u,v: the depth-averaged velocity field - :arg float dt: used for time-integration.""" - turbine_density = farm_options.turbine_density - C_T = farm_options.turbine_options.thrust_coefficient - A_T = pi*(farm_options.turbine_options.diameter/2.)**2 - C_D = C_T*A_T/2.*turbine_density - self.power_integral = C_D * (u*u + v*v)**1.5 * dx(subdomain_id) - # cost integral is n/o turbines = \int turbine_density - self.cost = assemble(turbine_density * dx(subdomain_id)) - self.break_even_wattage = farm_options.break_even_wattage - self.dt = dt - # time-integrated quantities: - self.integrated_power = 0. - self.average_power = 0. - self.average_profit = 0. - self.time_period = 0. + # Preliminaries + self.mesh = mesh + # this sets self.turbine_expr=0 + super().__init__(0, dx, options) - @PETSc.Log.EventDecorator("thetis.TurbineFarm.evaluate_timestep") - def evaluate_timestep(self): - """Perform time integration and return current power and time-averaged power and profit.""" - self.time_period = self.time_period + self.dt - current_power = assemble(self.power_integral) - self.integrated_power = self.integrated_power + current_power * self.dt - self.average_power = self.integrated_power / self.time_period - self.average_profit = self.average_power - self.break_even_wattage * self.cost - return current_power, self.average_power, self.average_profit + # Adding turbine distribution in the domain + self.add_turbines(options.turbine_coordinates) + + def add_turbines(self, coordinates): + """ + :param coords: Array with turbine coordinates to be positioned + :param function: turbine density function to be adapted + :param mesh: computational mesh domain + :param radius: radius where the bump will be applied + :return: updated turbine density field + """ + x = SpatialCoordinate(self.mesh) + + radius = self.turbine.diameter * 0.5 + for coord in coordinates: + dx0 = (x[0] - coord[0])/radius + dx1 = (x[1] - coord[1])/radius + psi_x = conditional(lt(abs(dx0), 1), exp(1-1/(1-dx0**2)), 0) + psi_y = conditional(lt(abs(dx1), 1), exp(1-1/(1-dx1**2)), 0) + bump = psi_x * psi_y + + unit_bump_integral = 1.45661 # integral of bump function for radius=1 (copied from OpenTidalFarm who used Wolfram) + self.turbine_density = self.turbine_density + bump/(radius**2 * unit_bump_integral) class TurbineFunctionalCallback(DiagnosticCallback): @@ -78,41 +168,47 @@ def __init__(self, solver_obj, **kwargs): """ :arg solver_obj: a :class:`.FlowSolver2d` object containing the tidal_turbine_farms :arg kwargs: see :class:`DiagnosticCallback`""" - nfarms = len(solver_obj.options.tidal_turbine_farms) + if not hasattr(solver_obj, 'tidal_farms'): + solver_obj.create_equations() + self.farms = solver_obj.tidal_farms + nfarms = len(self.farms) super().__init__(solver_obj, array_dim=nfarms, **kwargs) - solver_obj.create_equations() - # TODO: was u, eta = split(solution) - u, v, eta = solver_obj.fields.solution_2d - dt = solver_obj.options.timestep + self.uv, eta = split(solver_obj.fields.solution_2d) + self.dt = solver_obj.options.timestep + self.depth = solver_obj.fields.bathymetry_2d - self.farms = [TurbineFarm(farm_options, subdomain_id, u, v, dt) for subdomain_id, farm_options in solver_obj.options.tidal_turbine_farms.items()] - """The sum of the number of turbines in all farms""" - self.cost = sum(farm.cost for farm in self.farms) + self.cost = [farm.number_of_turbines() for farm in self.farms] if self.append_to_log: - print_output('Number of turbines = {}'.format(self.cost)) + print_output('Number of turbines = {}'.format(sum(self.cost))) + self.break_even_wattage = [farm.break_even_wattage for farm in self.farms] + + self.instantaneous_power = [0] * nfarms + # time-integrated quantities: + self.integrated_power = [0] * nfarms + self.average_power = [0] * nfarms + self.average_profit = [0] * nfarms + self.time_period = 0. + + def _evaluate_timestep(self): + """Perform time integration and return current power and time-averaged power and profit.""" + self.time_period = self.time_period + self.dt + current_power = [] + for i, farm in enumerate(self.farms): + power = farm.power_output(self.uv, self.depth) + current_power.append(power) + self.instantaneous_power[i] = power + self.integrated_power[i] += power * self.dt + self.average_power[i] = self.integrated_power[i] / self.time_period + self.average_profit[i] = self.average_power[i] - self.break_even_wattage[i] * self.cost[i] + return current_power, self.average_power, self.average_profit def __call__(self): - return numpy.transpose([farm.evaluate_timestep() for farm in self.farms]) + return self._evaluate_timestep() def message_str(self, current_power, average_power, average_profit): return 'Current power, average power and profit for each farm: {}, {}, {}'.format(current_power, average_power, average_profit) - @property - def average_profit(self): - """The sum of the time-averaged profit output of all farms""" - return sum(farm.average_profit for farm in self.farms) - - @property - def average_power(self): - """The sum of the time-averaged power output of all farms""" - return sum(farm.average_power for farm in self.farms) - - @property - def integrated_power(self): - """The sum of the time-integrated power output of all farms""" - return sum(farm.integrated_power for farm in self.farms) - class TurbineOptimisationCallback(DiagnosticOptimisationCallback): """ @@ -131,10 +227,86 @@ def __init__(self, solver_obj, turbine_functional_callback, **kwargs): super().__init__(solver_obj, **kwargs) def compute_values(self, *args): - costs = [farm.cost.block_variable.saved_output for farm in self.tfc.farms] - powers = [farm.average_power.block_variable.saved_output for farm in self.tfc.farms] - profits = [farm.average_profit.block_variable.saved_output for farm in self.tfc.farms] + costs = [x.block_variable.saved_output for x in self.tfc.cost] + powers = [x.block_variable.saved_output for x in self.tfc.average_power] + profits = [x.block_variable.saved_output for x in self.tfc.average_profit] return costs, powers, profits def message_str(self, cost, average_power, average_profit): return 'Costs, average power and profit for each farm: {}, {}, {}'.format(cost, average_power, average_profit) + + +class MinimumDistanceConstraints(pyadjoint.InequalityConstraint): + """This class implements minimum distance constraints between turbines. + + .. note:: This class subclasses ``pyadjoint.InequalityConstraint``. The + following methods must be implemented: + + * ``length(self)`` + * ``function(self, m)`` + * ``jacobian(self, m)`` + """ + def __init__(self, turbine_positions, minimum_distance): + """Create MinimumDistanceConstraints + + :param turbine_positions: list of [x,y] where x and y are either float or Constant + :param minimum_distance: The minimum distance allowed between turbines. + """ + self._turbines = [float(xi) for xy in turbine_positions for xi in xy] + self._minimum_distance = minimum_distance + self._nturbines = len(turbine_positions) + + def length(self): + """Returns the number of constraints ``len(function(m))``.""" + return int(self._nturbines*(self._nturbines-1)/2) + + def function(self, m): + """Return an object which must be positive for the point to be feasible. + + :param m: The serialized paramaterisation of the turbines. + :type m: numpy.ndarray. + :returns: numpy.ndarray -- each entry must be positive for the positions to be + feasible. + """ + print_output("Calculating minimum distance constraints.") + inequality_constraints = [] + for i in range(self._nturbines): + for j in range(self._nturbines): + if i <= j: + continue + inequality_constraints.append((m[2*i]-m[2*j])**2 + (m[2*i+1]-m[2*j+1])**2 - self._minimum_distance**2) + + inequality_constraints = numpy.array(inequality_constraints) + if any(inequality_constraints <= 0): + print_output( + "Minimum distance inequality constraints (should all " + "be > 0): %s" % inequality_constraints) + return inequality_constraints + + def jacobian(self, m): + """Returns the gradient of the constraint function. + + Return a list of vector-like objects representing the gradient of the + constraint function with respect to the parameter m. + + :param m: The serialized paramaterisation of the turbines. + :type m: numpy.ndarray. + :returns: numpy.ndarray -- the gradient of the constraint function with + respect to each input parameter m. + """ + print_output("Calculating gradient of equality constraint") + + grad_h = numpy.zeros((self.length(), self._nturbines*2)) + row = 0 + for i in range(self._nturbines): + for j in range(self._nturbines): + if i <= j: + continue + + grad_h[row, 2*i] = 2*(m[2*i] - m[2*j]) + grad_h[row, 2*j] = -2*(m[2*i] - m[2*j]) + grad_h[row, 2*i+1] = 2*(m[2*i+1] - m[2*j+1]) + grad_h[row, 2*j+1] = -2*(m[2*i+1] - m[2*j+1]) + row += 1 + + return grad_h diff --git a/thetis/turbulence.py b/thetis/turbulence.py index 2aa8b270c..300a5215f 100644 --- a/thetis/turbulence.py +++ b/thetis/turbulence.py @@ -261,7 +261,7 @@ def solve(self, init_solve=False): gamma = self.relaxation if not init_solve else 1.0 mu_comp[i_comp].assign(gamma*self.mu_tmp + (1.0 - gamma)*mu_comp[i_comp]) - self.m2 += mu_comp[i_comp]*mu_comp[i_comp] + self.m2.interpolate(self.m2 + mu_comp[i_comp]*mu_comp[i_comp]) # crop small/negative values set_func_min_val(self.m2, self.minval) @@ -541,7 +541,7 @@ def postprocess(self): set_func_min_val(self.psi, o.psi_min) # udpate epsilon - self.epsilon.assign(cmu0**(3.0 + p/n)*self.k**(3.0/2.0 + m/n)*self.psi**(-1.0/n)) + self.epsilon.interpolate(cmu0**(3.0 + p/n)*self.k**(3.0/2.0 + m/n)*self.psi**(-1.0/n)) if o.limit_eps: # impose Galperin limit on eps eps_min = cmu0**3.0/(numpy.sqrt(2)*galp_clim)*numpy.sqrt(n2_pos)*k_arr @@ -550,7 +550,7 @@ def postprocess(self): set_func_min_val(self.epsilon, o.eps_min) # update L - self.l.assign(cmu0**3.0 * self.k**(3.0/2.0) / self.epsilon) + self.l.interpolate(cmu0**3.0 * self.k**(3.0/2.0) / self.epsilon) if o.limit_len_min: set_func_min_val(self.l, o.len_min) if o.limit_len: diff --git a/thetis/utility.py b/thetis/utility.py index 7c7853f1d..f787f8f5d 100755 --- a/thetis/utility.py +++ b/thetis/utility.py @@ -2,6 +2,7 @@ Utility functions and classes for 2D and 3D ocean models """ import os +import glob import sys from collections import OrderedDict, namedtuple # NOQA @@ -179,13 +180,13 @@ def get_extruded_base_element(ufl_element): In case of a non-extruded mesh, returns the element itself. """ - if isinstance(ufl_element, ufl.HDivElement): + if isinstance(ufl_element, firedrake.HDivElement): ufl_element = ufl_element._element - if isinstance(ufl_element, ufl.MixedElement): - ufl_element = ufl_element.sub_elements()[0] - if isinstance(ufl_element, ufl.VectorElement): - ufl_element = ufl_element.sub_elements()[0] # take the first component - if isinstance(ufl_element, ufl.EnrichedElement): + if isinstance(ufl_element, firedrake.MixedElement): + ufl_element = ufl_element.sub_elements[0] + if isinstance(ufl_element, firedrake.VectorElement): + ufl_element = ufl_element.sub_elements[0] # take the first component + if isinstance(ufl_element, firedrake.EnrichedElement): ufl_element = ufl_element._elements[0] return ufl_element @@ -219,11 +220,11 @@ def element_continuity(ufl_element): } base_element = get_extruded_base_element(ufl_element) - if isinstance(elem, ufl.HDivElement): + if isinstance(elem, firedrake.HDivElement): horiz_type = 'hdiv' vert_type = 'hdiv' - elif isinstance(base_element, ufl.TensorProductElement): - a, b = base_element.sub_elements() + elif isinstance(base_element, firedrake.TensorProductElement): + a, b = base_element.sub_elements horiz_type = elem_types[a.family()] vert_type = elem_types[b.family()] else: @@ -248,6 +249,48 @@ def create_directory(path, comm=COMM_WORLD): return path +def read_mesh_from_checkpoint(filename_or_outputdir, mesh_name=None): + """ + Read mesh from a hdf5 checkpoint file. + :arg filename_or_outputdir: file name of the hdf5 checkpoint file, + or the output directory used in the previous run that create the + checkpoint. In the latter case the mesh will be read from the + first file that's found in outputdir/hdf5/*.h5 + + When loading fields from a checkpoint file in a run, the mesh used in that + run needs to be read from that same checkpoint file. + + When multiple checkpoint files are used as model inputs which have + been created in separate runs/scripts, make sure that only one of those + scripts created the original mesh (by reading a .msh file or using a mesh + creation utility like RectangleMesh) and all other script read their mesh + from the checkpoint created by that first script. For example, a + preprocessing script might read in a .msh file and interpolate and smoothen + the bathymetry on that mesh and write out the result in a checkpoint. A + first Thetis run should read its mesh from that checkpoint, and may then + save a series of hdf5 files. A second Thetis run can then read in the mesh + and bathymetry from the checkpoint of the preprocessing script, and call + load_state() to pick up from the previous run. + """ + if os.path.isdir(filename_or_outputdir): + # grab the first outputdir/hdf5/*.h5 file we can find + path = os.path.join(filename_or_outputdir, 'hdf5', '*.h5') + try: + filename = next(glob.iglob(path)) + except StopIteration: + raise IOError(f'No checkpoint files found in {path}') + else: + # assume we're being pointed to the hdf5 file directly + filename = filename_or_outputdir + + with CheckpointFile(filename, 'r') as f: + if mesh_name is None: + mesh_name = 'firedrake_default' + mesh = f.load_mesh(mesh_name) + + return mesh + + @PETSc.Log.EventDecorator("thetis.get_facet_mask") def get_facet_mask(function_space, facet='bottom'): """ @@ -268,7 +311,7 @@ def get_facet_mask(function_space, facet='bottom'): assert isinstance(elem, TensorProductElement), \ f'function space must be defined on an extruded 3D mesh: {elem}' # figure out number of nodes in sub elements - h_elt, v_elt = elem.sub_elements() + h_elt, v_elt = elem.sub_elements nb_nodes_h = create_finat_element(h_elt).space_dimension() nb_nodes_v = create_finat_element(v_elt).space_dimension() # compute top/bottom facet indices @@ -441,7 +484,7 @@ def extend_function_to_3d(func, mesh_extruded): family = ufl_elem.family() degree = ufl_elem.degree() name = func.name() - if isinstance(ufl_elem, ufl.VectorElement): + if isinstance(ufl_elem, firedrake.VectorElement): # vector function space fs_extended = get_functionspace(mesh_extruded, family, degree, 'R', 0, dim=2, vector=True) @@ -610,8 +653,12 @@ def get_minimum_angles_2d(mesh2d): raise NotImplementedError("Minimum angle only currently implemented for triangles.") edge_lengths = get_facet_areas(mesh2d) min_angles = Function(FunctionSpace(mesh2d, "DG", 0)) - par_loop("""for (int i=0; i + JTJ = dot(Jacobian(mesh).T, Jacobian(mesh)) -using namespace Eigen; + # based on https://github.com/michalhabera/dolfiny/blob/master/dolfiny/invariants.py + I1 = JTJ[0, 0] + JTJ[1, 1] + D = (JTJ[0, 0] - JTJ[1, 1])**2 + 4 * JTJ[0, 1] * JTJ[1, 0] + D += numpy.finfo(float).eps -void eigmin(double minEval[1], const double * J_) { - - // Map input onto an Eigen object - Map > J((double *)J_); - - // Compute J^T * J - Matrix A = J.transpose()*J; - - // Solve eigenvalue problem - SelfAdjointEigenSolver> eigensolver(A); - Vector2d D = eigensolver.eigenvalues(); - - // Take the square root - double lambda1 = sqrt(fabs(D(0))); - double lambda2 = sqrt(fabs(D(1))); - - // Select minimum eigenvalue in modulus - minEval[0] = fmin(lambda1, lambda2); -} -""" - kernel = op2.Kernel(kernel_str, 'eigmin', cpp=True, include_dirs=include_dir) - op2.par_loop(kernel, P0_ten.node_set, min_evalue.dat(op2.RW), J.dat(op2.READ)) - return min_evalue + # Compute smallest singular value of J + P0 = FunctionSpace(mesh, "DG", 0) + cell_size = Function(P0, name="Smallest singular value") + cell_size.interpolate(sqrt((I1-sqrt(D))/2.)) + return cell_size def beta_plane_coriolis_params(latitude): @@ -1057,51 +1087,47 @@ def form2indicator(F): r""" Deduce the cell-wise contributions to a functional. - Given a UFL form `F` that does not contain test or trial functions, - multiply each of its terms by a :math:`\mathbb P0` test function and - assemble, so that we deduce its contributions from each cell. + Given a 0-form, multiply the integrand of each of its integrals by a + :math:`\mathbb P0` test function and reassemble to give an element-wise error + indicator. - Modified code based on - https://github.com/pyroteus/pyroteus/blob/main/pyroteus/error_estimation.py + Note that a 0-form does not contain any :class:`firedrake.ufl_expr.TestFunction`\s + or :class:`firedrake.ufl_expr.TrialFunction`\s. - :arg F: the UFL form + :arg F: the 0-form + :return: the corresponding error indicator field + + Modified code based on + https://github.com/pyroteus/goalie/blob/main/goalie/error_estimation.py """ if len(F.arguments()) > 0: - raise ValueError("Input form cannot contain test or trial functions") + raise ValueError("Input form should be 0-form") mesh = F.ufl_domain() P0 = FunctionSpace(mesh, "DG", 0) p0test = TestFunction(P0) + h = ufl.CellVolume(mesh) + + rhs = 0 + for integral in F.integrals_by_type("exterior_facet"): + dsi = ds(integral.subdomain_id()) + rhs += h * p0test * integral.integrand() * dsi + for integral in F.integrals_by_type("interior_facet"): + dSi = dS(integral.subdomain_id()) + rhs += h("+") * p0test("+") * integral.integrand() * dSi + rhs += h("-") * p0test("-") * integral.integrand() * dSi + for integral in F.integrals_by_type("cell"): + dxi = dx(integral.subdomain_id()) + rhs += h * p0test * integral.integrand() * dxi + + assert rhs != 0 indicator = Function(P0) - - # Contributions from surface integrals - flux_terms = 0 - integrals = F.integrals_by_type("exterior_facet") - if len(integrals) > 0: - for integral in integrals: - tag = integral.subdomain_id() - flux_terms += p0test * integral.integrand() * ds(tag) - integrals = F.integrals_by_type("interior_facet") - if len(integrals) > 0: - for integral in integrals: - tag = integral.subdomain_id() - flux_terms += p0test("+") * integral.integrand() * dS(tag) - flux_terms += p0test("-") * integral.integrand() * dS(tag) - if flux_terms != 0: - mass_term = TrialFunction(P0) * p0test * dx - sp = { + solve( + TrialFunction(P0) * p0test * dx == rhs, + indicator, + solver_parameters={ "snes_type": "ksponly", "ksp_type": "preonly", "pc_type": "jacobi", - } - solve(mass_term == flux_terms, indicator, solver_parameters=sp) - - # Contributions from volume integrals - cell_terms = 0 - integrals = F.integrals_by_type("cell") - if len(integrals) > 0: - for integral in integrals: - tag = integral.subdomain_id() - cell_terms += p0test * integral.integrand() * dx(tag) - indicator += assemble(cell_terms) - + }, + ) return indicator diff --git a/thetis/utility3d.py b/thetis/utility3d.py index 7101131ca..32c8f5f07 100644 --- a/thetis/utility3d.py +++ b/thetis/utility3d.py @@ -502,8 +502,8 @@ def __init__(self, input_2d, output_3d, elem_height=None): family_2d = self.fs_2d.ufl_element().family() base_element_3d = get_extruded_base_element(self.fs_3d.ufl_element()) - assert isinstance(base_element_3d, ufl.TensorProductElement) - family_3dh = base_element_3d.sub_elements()[0].family() + assert isinstance(base_element_3d, firedrake.TensorProductElement) + family_3dh = base_element_3d.sub_elements[0].family() if family_2d != family_3dh: raise Exception('2D and 3D spaces do not match: {0:s} {1:s}'.format(family_2d, family_3dh)) self.do_hdiv_scaling = family_2d in ['Raviart-Thomas', 'RTCF', 'Brezzi-Douglas-Marini', 'BDMCF'] @@ -625,8 +625,8 @@ def __init__(self, input_3d, output_2d, family_2d = self.fs_2d.ufl_element().family() base_element_3d = get_extruded_base_element(self.fs_3d.ufl_element()) - assert isinstance(base_element_3d, ufl.TensorProductElement) - family_3dh = base_element_3d.sub_elements()[0].family() + assert isinstance(base_element_3d, firedrake.TensorProductElement) + family_3dh = base_element_3d.sub_elements[0].family() if family_2d != family_3dh: raise Exception('2D and 3D spaces do not match: {0:s} {1:s}'.format(family_2d, family_3dh)) self.do_hdiv_scaling = family_2d in ['Raviart-Thomas', 'RTCF', 'Brezzi-Douglas-Marini', 'BDMCF'] @@ -747,8 +747,8 @@ def __init__(self, solver): family_2d = self.fs_2d.ufl_element().family() base_element_3d = get_extruded_base_element(self.fs_3d.ufl_element()) - assert isinstance(base_element_3d, ufl.TensorProductElement) - family_3dh = base_element_3d.sub_elements()[0].family() + assert isinstance(base_element_3d, firedrake.TensorProductElement) + family_3dh = base_element_3d.sub_elements[0].family() if family_2d != family_3dh: raise Exception('2D and 3D spaces do not match: "{0:s}" != "{1:s}"'.format(family_2d, family_3dh))