part3_Chile2010/part3_Chile2010.ipynb

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    "# Workshop on Modelling Tsunamis and Flash Flood Inundation with GeoClaw \n",
    "\n",
    "## Part 3: \"Propagation of the Chile, 2010 tsunami\"\n",
    "\n",
    "### José Galaz\n",
    "[https://jgalazm.github.io](https://jgalazm.github.io)"
   ]
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    "## Introduction\n",
    "\n",
    "The objective of this workshop is to work on simple cases that may allow the student to experience some example applications of the *GeoClaw* software, completely functional in a virtual machine, and that may serve as starting point to model more complex scenarios.\n",
    "\n",
    "GeoClaw is a part of [ClawPack](https://www.clawpack.org), a free open source software developed by the University of Washington. It consists of several packages that by means of numerical methods integrate a family of partial differential equations often called **conservation laws**, and hence the name: *ClawPack = Conservation Laws Package*. Geoclaw specializes at solving geophysical problems, as tsunamis, storm-surges amongst others.\n",
    "\n",
    "In our case, it allows us to solve the initial boundary value problem (IBVP) of the **Non Linear Shallow Water Equations** including **strong variations in topography**, **bottom friction** and handling **shock waves** (discontinuities) by solving a ** Riemann problem** between each cell's interface. Also, to improve the performance of the computations it employes **adaptive grids**, that allow to vary the grid size (say $\\Delta x$) both in space and time.\n",
    "\n",
    "In case you do not understand some of the words in **bold**, I suggest you to investigate. There exists excelent material for studying on the internet, for example that of the [PASI-TSUNAMI course that took place the year 2013 at the Federico Santa María university in Valparaíso, Chile](http://www.bu.edu/pasi-tsunami/materials).\n",
    "\n",
    "The (excelent) documentation, license information, contribution guidelines (in case you are interested in cooperating as developer), and other topics can be found in the [official site](https://www.clawpack.org).\n"
   ]
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   "source": [
    "### How to work with the Jupyter Notebook\n",
    "\n",
    "[Jupyter notebooks](https://jupyter.org) are documents that can be edited using a web-based user interface. As they describe it\n",
    "\n",
    ">The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text\n",
    "\n",
    "They split their content in cells, which can contain code or formatted text, and you can also write/append its content to a file. At the begining you will probably only work with the toolbar and menus at the top of the page, but what I recommend the most is to memorize some keyboard shortcuts. At least the ones I use are:\n",
    "\n",
    "* select a cell and press enter: edit the content of the current cell\n",
    "* esc: exit editing mode\n",
    "* ctrl + enter: Execute current cell\n",
    "* shift+ enter: Execute current cell and move to the next one.\n",
    "\n",
    "When out of edit mode, select a cell and use the following\n",
    "\n",
    "* m: change cell to normal text mode\n",
    "* y: change cell to code text mode\n",
    "* a: insert cell above\n",
    "* b: insert cell below\n",
    "* d+d: delete current cell\n",
    "\n",
    "You can find more in the dropdown menu **Help > Keyboard Shortcuts**"
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   "source": [
    "### Overview of this notebook\n",
    "\n",
    "We will simulate the generation and propagation of a tsunami wave over the pacific ocean. First we shall see how to read the **topography** from an external file. Then we will calculate the deformation of the free-surface due to the movement in a **tectonic fault** produced by an earthquake in the ocean. Before running any calculation, we will set GeoClaw to save **maximum wave amplitude** and **arrival times** at run-time. After that we will **configure** and **run** the simulation. Finally we will see how we can obtain **propagation maps**, **snapshots** of the simulation as also punctual results that may be compared with buoy data for example."
   ]
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   "source": [
    "### Disclaimer\n",
    "\n",
    "The content in this notebook is a modified version of the example available in the [ClawPack web site](http://www.clawpack.org/gallery/gallery_geoclaw.html). Also the style sheets were borrowed from [here](http://openedx.seas.gwu.edu/courses/GW/MAE6286/2014_fall/info)."
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   "source": [
    "## Preparing the data for the simulation"
   ]
  },
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   "source": [
    "### Topography"
   ]
  },
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   "source": [
    "GeoClaw allows three different formats for the topography which are well documented [here](http://www.clawpack.org/topo.html#topo). For this example, we will use a file already prepared in format number 3. We should remember this number, since we will have to write it in the *setrun.py* file later."
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   "source": [
    "First of all, we import some fundamental python packages"
   ]
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  {
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
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   "source": [
    "And download the data from the web-site."
   ]
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     "text": [
      "Skipping http://www.geoclaw.org/topo/etopo/etopo10min120W60W60S0S.asc \n",
      "  because file already exists: ./etopo10min120W60W60S0S.asc\n"
     ]
    }
   ],
   "source": [
    "from clawpack.geoclaw import topotools\n",
    "import clawpack.clawutil.data as clawutildata\n",
    "topo_fname = 'etopo10min120W60W60S0S.asc'\n",
    "url = 'http://www.geoclaw.org/topo/etopo/' + topo_fname\n",
    "clawutildata.get_remote_file(url, output_dir='.', file_name=topo_fname,\n",
    "        verbose=True)"
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    "And we can check the file was properly read by GeoClaw by using the function\n",
    "    \n",
    "    topotools.Topography(topo_fname, topotype)\n",
    "\n",
    "that returns a \"Topography\" object with the information from the file. We can actually plot it to see how it looks like."
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KTYnAairuO0LbB3d3LUf1titNAsdXigNzbXyvKkAYJpVXB1iaWVEYFSm38aI8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\nKnjpc9xt3lIEQh1xTjpv5Wa94g9yN7AW/Q72GloapctmUzVltW3LgG2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fP6un3X13zUUGU0q+bShRyKdrb0r1HlKl+tzJVK0Dyvx85tM85dhPhYonp0XFVBrXKc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Jk2h1uIkSAVQgrVYeoUIO9NyUfOa0ASq0+aye8+p0VmGCDxYrKPF+sAZrYdqVaL+u214nVRjP\nCd4oK40jSznWJ5WvoLMe6/mtwrsSxMe0yFv1wcMFoXmt0clSDDspvuPL7sSvPXMFT12ctLihkpW2\nHM7LWfTGxgx3n1rGHzxxCd0uFaauXp2iaQySRPrsMkkkThwZotYGV7dmuLppcMeRAWWPqfS0pmnZ\noO/EszlL7Pcy5K6N9KVSo9tN0DRcFDRIEtUKorEWbaok6qbxPl1e0rHS6HYSf64aPxPIWo69VROg\nIqa6cdME7yMAKKeSlUrZOu9AG2uXUuDs1RnObc5xx3KOB4708PqVIRpjsdLPnMALkEiBM/9S+wB4\nz7skzn+bAb6N/jZdEnpJLlHw3fz7wOEfBmRNme/d75VoThKcYHthOdMFTv60hNqA584+965ABTv7\nw8ZDQ5M5wSNb09pfQ6mS2CkrHBt2SXVM0HmiIuTNCbzXmvLXl55EffnJV17/ZQT5hRDfDeCX3XKP\nCyGMEGLNWrsBuvVhrf2UEOJZUJb7qoT6b+U4EO2vP/v4i3uoMbFFsjYW2wVpoSopsNZLcH67akEE\nAMEAHGSzhIpVccZaaoMHjvTwmQvTPXbNngcbtaPG7ayxfmv8uVkivAMCBzKueLPikokudO57Z3k9\npk3FClxx9xVPt3lKPuykuGs1x6w2OLc5x7io92ivstzgYuPE9rjE2Wev4o1vPA4pBHbGc5SlbhWg\nOKvs9SjTVEpga2uOo0cHKIoadx1fagnHVI3Bxu7cB+VUSU/vms9r9HoZ3eDjOTqdFHmqUDi4wVjr\nLbzTROLochcvXp20GhmYUZA5DJjberWxGPUyZAmd01GP/K+2p1WL7xt/l3F7LWfLcXszQwSTUntX\n2rhN2V8rQuCewz08cKSHo70OuomCEALdTOGOlY7Pmq21uP9/VJ62de+jEjvfAix9gGy38zPEGlC7\npLwl6iBJGMMKT/5Eg61pjU5KDQ7aWHQzhYtbBNxOKpJ+3JpX0Nbi0rTEk87anRtD4nsptsx5/3e8\n+YbbX5f/65+7rmW3f/7bXvVnCSG+C8Cd1tp/KoS4HrBDwAAAIABJREFUH8BvWWvvcoWtTWutEUKc\nBvARUNFrWwjxCQD/A4DHAfwagH9lrf1QtM2fBvBBa+1/eDX78mrHgchcg3pUGKNO4nVQtbG4ayXH\nC1sllrsSZ64WriAVdFl5uWGu0FiLojIonBnhMKfMBRo4uzH3eCpZvhhfHPPD4axFo327K9O/AKou\n843KsoGdVKF0RZmVfoqNcaT8bwOViPVLY2V9vrn5b4Cm2EoIL+zCav3dTKKXknUNZ2K8LnMbY2Fq\nDqzsLnvqrlWUlcbAFX3iAlOMi3LWNp3WHjaYzepWVgwAE+dMq1xWzhSx7XGJ4SD3+9TppD4bnU4r\n7OzMMRzm6HaogaGT5ZgUNbUGN9UeLjFvnwczFQbdFKmSWOmneP7q1J9DKUjO0OPSkgRG+MHns2X3\n3fFnVYZciKumLe0oEXiy9xzuobEWz2/Pcaibo2g0VnsZJvMGf3JhjDtWuljupUgVZbM8bEqBVc4o\nsFannf7ALmWv8WAXhKcfM44ForA+rmCtxbw2+PSFLbz+0BI+eXETF3cJWtqYuuKoNj7Djx0vgouE\nQJrKVsHwRsYtxlx/GsC/EUJ8FkAJ4Nvd+18N4IeEEBVoHvX3rLXb7n/fC+BnAHQA/CcOrEKItwL4\nAIBlAF8vhPhn1tqHbtWOH4jM9X2/+xwyRUWrHTe1YeyTClICjaWp/3KXboTLjvDNGSkQFacWsoyQ\nEcJTrZi7CgRPLL4I2QeLCzcxT5VHbJ+9U4TGhlpTh8/WtPY6ALRvgcy/2HWljUUnUy35vY5rmY3x\n5NiCu6x0S0cACIWLOLOMX1/dmmE8rpBlCsNhjsuXJ6G9tRMKPMZY5LnCeFyh00l89njsyADjaYmi\naDCZVDh+fEhMAkft6nYTZFniA+IR5/bKEEd8MzeNaXWGrUXaB5zl8/9yh+XG+9c0Bv0uFc+G3RSj\nXgZjLK7uzlusBYDYBD3XZcXnP9aC5SAU84bTRBIkVLWpb/xd0vVE6w27Kb7uvlXkicJqJ8PYaWIA\nwJtOjpC4c3w9/NV73kX0rrhja1ZpXNqe4/df2kA/TbA+rbA+a/wDnyEsvp74/MXaCovCQGVND9if\n+taHbjhzHX7rz17XsuP3/+3bwi1/GmN7rqGNu5kUd1JRsYHVq4AQVOPBuOrGrHZ9/sIXwUptvegK\ndLDDzpVoZYvxcokEwLxU1bZZ4YwzU5IaGmSgQ/EyW9O61fM+r9vme7wsE/FjJX5uWeVAW0aMgLoJ\npPrFIhc/DDhAMA+UpQi3JiWklFhZ6VIb7qzCYJDt4aIqRVCAMRb9foqy1D67HU9L7OyUmM8bnDw5\n8hBIv5+hqhovK9ftJJjOKpy/MsbqqAspaN1OJ8XOzhyHVntQbnsAsLFReM+u8aTE6qiLNJF+nViy\n0RiLnZ05RqMOpkWNLFPo5QnWd+eYO1UwAB5yiHnFjMEaYzGuAs6dRQ0fHIjrxmDmMOvceYNV2nhp\nSNab1ZYC+s/94QUAwKm1Ht5xehVIqMvwifO7uPfoAHlKeKcULx9kuXjlv1dt8OL6DDtljaIyeHZ9\nCm3IkWO1n2C7oOWZjsbFPCBQGRcDK5/L/dqpX8u43aG1/zgQwZW7RbJEYjLXGLvef8JY9y4fB5fK\nQQE8itq0sFTGSvk6qhqn8iQtyjrgqTwYVwVIAatqiEXAYhelk/CrdeiX58q9Nta3jBpjURrdwlL5\nZo+7kWJWAC+byaBhytPXNJGtwldZE5Gf2QKMtxprsTOrMHJk+9iJNk0kBp0UZ89t485jQ1xen7bJ\n+8p1Z9UGaRoyuzQNUAEAV2Bq0DQSxhjUdcDwptMK47FFt5ugrg2ee3Hb46VlqdHvZxhPS9Q1KVll\nWYKjRwf03c0pQI/6GdZ350gS5YtXHFiTRGI4zN1rA0BhZ0owAgdFHhxodWJ9AwEPZjbE+CzgsmQd\nsuSRc7blbro0EoQ1xmLYca3LLktcn1T45Sev4PAgxV+99zDmWuPslSlSJbA2zLE2yCDdtfRyQdY6\nqIwnYaQc565BbfFX7l3BXaM+fuGPL3jMP02kpywuPoD5ePj1frOx1zpupPvqz/M4EMH1Q5+7il6e\n4NShHrqpxFo/dQHXthwC4hGzA7aLxkv/LXcVTfkdrUpza6zDYWNOKo8a9HQvK6eHarlqDJ81Mame\naTzcq88BzNuuiOD1FGcLvG4c7IAgmQcAy/3Mk/l5xDfIYgfYzqxq6aXGcABjrrzN8bjE8rCDrUmJ\nN9y9is1Jicmk8s0EHABjUWNuKEgShaah4s5wSOpUZanRNDWSSGw7PmYulPV6qQ/ORdHg8uUJskw5\nTqvAeDzzQXxlpYvlUQc704qKNiyYksEL3wBAp5tiXmu3XwaTxmCpn3mNBh7sisu8YQB+dlA7/y/W\nGaijjJXbf+vGoHR0t4Gz3QFAamO1xtGlDFcntQ/iaSKRK4GtWY0XNgr8XxsvegrY175hFSuTDEf7\nHXQzhROrXaSKAmwrADpK1bzW+JOLY0yqBh97cctrFJPKm8DZ7QKfOLeLv3T3Cn7xM5dazSqL3OhM\nSWyXVQsK4WvrZgzT7J1N3h4HJLgeXe7CGOq3Z9O7w0sdnD7U8XgqV/4ZA1us6itBUMB21PmiTRC7\nbjjYOom//WTX8ky1cKo4GNZRthK/H2ehaRLM8pQQMKApZ+5uUD42pgUxTsjBnm2lUyVRO/fTVBFp\nnj+PM9oYxxykqYcBGHPjdccFubN2OhQQRv0MZ17YwmjUQVVpZJnyVfmY5C+l9NM92kcJIGSoHDCZ\nvzp1+rRxsOVt1a5YmCQSS0u5z+wPrfawuV3QOU0l+l06jklR0+dkQFnTsoMuMQ1YPnE+r7E87EBK\ngUlBdjjzSntxcMZWY/1bIEjvaWMhHfxU+YcDPSCnBdHL5ia4I8zKBqmSfmairSX3Ave9rQ0zbyHT\nSRUODTJU2mC3oIfvb5/ZwtEl4grfsZTixHoHb3/dIeRuX4Wgrip2QJ7XButFiac3Z342xvUHKl6R\njsVvn93EidUOqsZia1a3NBZ432K9CtanYGbETRnmxniuf17HgQiu3P2z2kvxB0+vQ0qBi+sTKHnY\n42CMefm2S3cRjfoZGtANSDhre+od06aYBsUi0iyGwvsAoHVxMqWmdvxXfl9H6/PQxkI7Vf1at3VH\nGfeMiyW9PEE/d7xNIVqfzX+XtfZBNt7/fp5QBqYCv/Py+hRJotDNE1/YurpdoNtJ/OelSmJjd477\n71rBk89u4E1fcgQAsLEzR9NoUtiSjLmGABmCrkJVaTQOWhmNOpjPawASeQTNMKTAQynht82BVGuL\n8bTE8qhDcMqsws54jpELmMdXezh3ZeyPaVLU2B4TVKBdsI+hk2E3xfruvGXznSYSo36Gy9sFlKRl\nLm8XPguNZw3xua81dWilicShpU4oHLriFQDkDs+elQ2GXXLozVKyiSnqIDZzaoXs1beLBjsFFffO\nbxX4hNnBL33mMlIlsTbM8OUnh3jdEsEjtTH4/OYuxqXB2E35J2XAevk7rxuDSkk0xvjuM04O+Pk2\nSjKfTacJ3e6j7v6OE6953A6u+44DEVznFV2k67tz3Hmoj7vWunjq4gRnL49h3HS9qhpkWYLjqz0M\nuilGnQSTUmNc1D5Ybs8q1I1BUTZhGjvIkafKiy1zwaeXJ3u87TmQc3Ben84xjDqL4mr3rGm3py5y\nS5k6RWIw1llLB46mSazPVAOtizDAB08s4amLEwCEw5a1xqGlDhHkeXsm8BVrbdDvZT4r5s/vOgZA\n3HvfzRNsTyu89UuO+uAAAMNBjl6eYNTLUNYal90UgAn4PPUdFzXGU9eQUWl0OqljEwBSSt8swPKF\nfF47nQAZFEXti2abjqs5HNL3NC1qrI0ooB1d7beccbOMmAupklAd4QtaDI1wIORstdYGu9MKa0sd\nlM5zbGWQe9YAV9VjVgdAYjipInPEC5szz9aIYRlmkYz6GZQg+/S6MRgeJsmsrVmYKmeK1Nu6mYRq\n6FzGl17VWHzoTzah5JY/X9/11pPo5wq//swVHOrRw/QzL00xnofstHaUqzxToT3c/d4tKv+dTUqN\nlV7q28Iv7xLssqjB8JrH7eC67zgQwdX38QM4spRDu1bKmECvDemLXtyceVI5Z3+X3dRyZZgjzRN/\n0XDw2NwpcPHKxGdlHHBX3Y3GxRBtLVItMeyk2JlVWB2EJljG7HhauR/Rf1HXNBY2UTLYUcdNA/FN\nzcWsoqLAHHcUcbMAZ9N8bLNZhdGw01KJ4gDeKvC4m3la1JSlaoOq0ijLBkcP9f12nzi7gTSVGA5y\nDzHcdXgJq/0Ea/0Un3px1+8Tt+zOmxBkAz81PIg48HIhbLFYprXF1laBqtIYDDLsTCvKQN35is9h\nnRjfpJBllEkjg39IeK1SPv4IGgAAIy0V7JSEdB1Kw27qO7uMtRi7LkDpvjN+6LE/GR97nEXOCsJs\nn7k6w7Cb4tAgw6w2jiPtWrARGhVYHL6bSix3JGa5QpYIvLBeQEqB//33nsdbTg3RSyX+4PldHF3K\n8eaTfZzbrrA5JclIYyx6XUd9SwPdsKgNDg2ywIKBa4yoSRrzjuU8cL9vxrgdXPcdByK4xgIkz1+d\neroIF49SJaFBmUPPBcTtaYVJUXvqDA8lCa9l/h/ddF3sTCtfXR8XNbbHpZ/m9fIEL27NCLeb11gd\ndVFrgxNOZcpnh3lCxRBtMCsDzYenoHGvfOxBlTsmgSf7u5vW6ID/GUvHN+imKDVV/D2U4JJAxhMn\ncyLzr7ps0xjaFn+mFAJShW6kujFYHeQYFzXuPrWCF65OfIbI2y1rjbLSaBqDpjG+m6qsNc5c2EHj\nikb/2X2rnvqkjcXf/cqT+H8+d9VrxHIABZzurWMSdLuJC7LCBVqgLBt0uymahizF+/3UF9GmRY2i\nqHH8yICyzlmFJFFYcRnu9rhE1xXi+MEVy0HGWrNAaLEFSFqSAzfNHlK/vnSFSg6iZQT9MOyw3HcU\nNsePzhLhrzXm5O40BoNcodRk271TaOxMK4z6GYoqXDtFbbxte6WB+4+SKMxyN8ERN9v6ytNLuDJu\n8OHPbzqdhxRf+4ZVnFkvMCk1NsYV1ss5Vgc5Si0cLkv3wj2HOnh+s8TrVnNcGdfYdo0524XGod5N\nuv1vB9d9x4EIrjHJWwrqpmHgnYsz5ZSCFE9xO5nyQtNATDMxeOLFLbzu6JCmca7LqefwzTSRNPVN\nKZsZRwG6VlQVZpjhwuYMhStgLA8pi2XfJqbypI4YyxqsHEAZz+PA5rmwjsMaZ6A8tLHYHJetjGLR\nIHBWNpStAV4smqftzIFlWCNNZAsqGPUzbE5KvOnUMrJE4JnLU0ydJOGon2HQST38sjLIMZnXHj7h\n7XzuwiTglU2D33hqExc3Z76IlCoq4K1vzpDnymOxSaJaXFUpBXL3YGBhFy50VU5LdjTq4NyFXY/v\nHl3uQkqBnWmFpX7WMoGM6VTx+ePvpW7Cd6aNhXIzIxbW4WvIFygjrQg+Nn4IbU5KHFrq+MaTqnEq\naLnyVL1USaxPSCP2aq1xeJCSoHoZagLKXQMq0p8ttUFPkhvGpy9NsNxNkCuBh48NsTEjruu81vjo\n2R2cXuvg4WMDnNkgeUSeXfWWcoxL49yOmRcNnFjOsFbTLX9ht8KfXJq8xjt2YdwOrvuOAxFc71jp\nEaVJG3Ryylw2x2WLWsRTt7gAkSoKPlxdTlz2mGcKZy/STbm1VaDTSbA86mDQST3RXknhp9p1HYIS\nBz3N09Yk80W03WmFoqjR72cYRFgscyhlNMX3nMicpvT8HhebGLeLAwH3si/e5NIEyw4lBbodKlrF\nDALpsjQfcEBZ7LCbYqWfoqgMLu8USJXEHUspskRS9gKSKwQoC+v1MsznNXZmlS8MmcLxgd15YyFt\nPofxcRlrUcwbFywNsizxWWTT0MOLz2OaSkgpvRoXsRaosNY0NIUeDnMYY7DiMu9am1YWCwDjrRJz\nAFmW+HMQWleDV9nOtMLAZaXcpMHfdTyrWOwUi79Tvh4nRe0LlcvueogbDnamFYbd1LsbcMWfbYQq\nbXB8KcN20fiiazeTjr1A8pfaWDy/WeItJ/pYn5e4/3AXs9pgMtc4MkyxXTT4wwtjVA1dM/ce7qGb\nSXz2pQm+7AQVx7qZxD2HOjhzZY6tGc0sTq91sNxN8KYTQ7z/Vd+t+4y6uhlb+XM3DkRwvbo7p6mW\nE5teVPtnOtHEgfk566Q68z/iYoYbH4n0nlD9PuGv40mJK1enHq/tdlOP23pupsMxUyV9lsNMBsYw\n2VtqVjaevrTh7FHY8G+x1XIugmXJyE0p46BU1oE+tNh5BcBjqLHYtEbgvvLYmVWe66mkQA3jrV1G\n/QyjXoYvf90Sib6szz3DYlY22JlW/gF1x6GBDzZX3VQ4niXE9i0Mm/DIU+Kw9vIEmzsF5vMaJkv8\ng2hS1OhHDyambvFDhYJtG2KRUuHqduGDdd9Nl/na6PcyLwDDIy40MgRgjPXGjAG+gVuOfsfNGMH9\nIRQoO1k0e3IFrgubM6wO88A8cUFWytApyPhspamFe9NYTzMclwaJpKl6L5UoYTBMFI4OUgxzg/Pb\nFc5cnuItp4aYzDWOj1Jc3KnRWNuymB+XGuNS447lHOuzBkVl8KV39LE5q/Hg8R60sTja6+DybI6i\nNvt2O76mYZpXXuaLcNxwcBVC/AMA3wOgAfBr1tofdO+/C8Dfce8/Yq39zZfbDgdVnt7HXvVEbwqt\nn1ypZaEPniqXJRVLCpfJciDiv/v91GVFBuMxZWvVMPcVZyAUnuaVDpJtrgOIp5EckCcOow3FGg2T\nOPsY9ruyrCmgPE56x1KK3316EwA87AFEwTQKVnz8SopWKxlP+3m9xcIYZ+IMh6iixrio8fFnDY6P\nOpiU2j+g3nLXshcW3ykIhhgXlL12UoVtp2g16mXYmVV7HAUGbkrP3lX88Os5Hm9VNa6hIPMZZdcV\nw6oqwB5E8WozOEK7sHHc24Ct7kwrTKcVVpe7ntUQwy8A/INyXNStQM7XxKLtjMfPIyw8ldI/FLkr\nK4YNGDKYVxqrTm92wN/9QhdWpiQSQQLcLBbETS65EtgpGoy6CbYLjVltPC764J0DPL9Vtr7j7VmD\nQU5NM8td5Tu1mhpeovOFrRKlNii1xT0rXazPSzy/VSIRApd2b1LGeRsW2HfcUHC9lr+NEOIBAN8C\n4AGQnuKHhRD32WuoxNTaYLxV+qmekQLjKZHEOajyhc1Tf6YhzWvtuJbEp+z3MkxnlS+kxK2c3H0U\nKz8VrnASj34/w/i5T+P4l7zNt7fGWTS3uuau04h7+RlWGHZTvLQ+xYa7Yfn9UT/D/9/emQbJVVVx\n/Hdev9fL7BAwgEESDC4YUUEWZdFCqtxFlirLUsB9wy9aUn7QAixKRUXU0hSuVS4FWoq7sikoKgoE\nBBFHDVtICIFJZjJJZqaX6e7jh3vv6zudyYKZ191M7r9qarrv6+Xfbzn3vHPP+Z/HJ2Z4dLO5qNwC\nVXMeo+q3zAZzEY//924OOKrVMiit9rIelr+aXcznTE1/eZaRvry5eJMcEzuqqbr/jO1F9Z/HJlNv\nbrg/T18hZrCYULUx7oMP6JuT+uV+U7lSZ2z0LgaWH+N5orm2rAFN461bt5Yp2LCP82Z9Ix1FuXn3\nhdk2tx1MrVYniiJKpYTp8izVqhGTWfGsETaNruGAo45NS5DdomISR0zWqnOqlPyut/64M85u0as6\n20izR1wObDExdz7FxLRMd69L4ogDBwoMlVrVfmP/XsOyVScCpFWHfUnk9XOLKOSiNDRQsOGpyXKD\n9VsrnPrsYQYLOYYKMdurdcZ2zKaFBbGVSfSlApwGcXm2xqpD+3hoSyVVzdo0WaGQz3H8swb4DguA\nYFznxb7WaOyqv82ZwI9Uta6q64AHgBN29SHFJMdgf4FSIU5LH0cGi0zuqKQr2FEk6e2kKyl0BiGO\nc+kqc7lSnxPHS5KIarVBPh+Ty7XESdw2E/drXeRJElGr1RkbXcNUeZYpa3jdLXu76r9bSHExUNeh\ntFSM0xYpW8ZnmNhWZsPYVHq76Rsf9/nOA200dU4M02HbQ/fM4ern3/qTkCuF9Bd8ms2WKleSi4xY\nifVsXZlpYkMcG7dMM7p+K9PVOgNFE1YZLCUsHSmlnnujqZSKMVPr7qVYTMjnY+I4ZxWyWjqopUKc\ninBHkaQTXLPZ0o5N4pZId0szYGf4C3vtHu7IcJFi0eRB19bfZ4sdzF1KIclRzLcyDEYGC+lv7u/L\nmwkyjuZ8voMrcx7uM6Lc0zbE4gzqbMPGWfM5Dh4qcqCVWXxyssz4jhqlJKKURDx87x0mnuopuNW1\n1eb9OQeVWNIfzzm38rEwUsqxdCjP7et2MDPb5JGJSpoCOFIyv8FpZxRyJqbrvx9g7eZyen5NVe3i\nZSHH2I4Fup1vNvbubz/DvoYF2vvbfExV78b0rPmb97qN7KaPjUv5AdILd2JbOb2tTG/ZPM/Jl6Nz\nRmLz1pl0UQTMAketVqdUiq2nM3/HURfr8+EnxLsVdH/V3KWIubCEW7F38dbJ6RrT1jD395sYYbPZ\nTPMTXViiv99c3O1hkHb4vaZ8ry5x9fe2K6xbANtcrRgDSzP1pvyGflMVTWvN/dzdvuFiGn5xpZ4D\ntrKpaD1ZV68PpEbZhSGWDJmKpImp6pxbfoBlhwzSaCqbxqbSSdGFZKZnKqmH6xcg+At87nf7E1Ma\nL68ZUZhNEzOUaw2GbccDIO25Fdu0NF++0E040KrS8hfEkpy5w2jErUnQLYa575+wxzLvKu8wsdko\nEh7ZbFqBN9UsLpVrTbaV6xx5UJHTDl/C2EyF29Zv499jM/PG3HfUlSX9CUv6E/oSI2z04HiVkVKO\nupXk7EtMY02Aiem6ERxqGv0N0zRzrlhLKTFqXptCWCBT7NG4PsX+Nj/B9Ld5ShjfXmHJkMm5bKqC\nzbNsT2tyJ3P7ReX+uzACYFWbGp7hnOsNtQzrzlVazqi6+np3G1qrtTzNRkMpFGK2Txt9VF8Vy68q\niiJJV5Knre5pHEf09+fTmJ+L27q4Yhzv7Nn6GQT+4+psIzW81Ugo2Vxel67lsh8GrbaAX9jglOoB\notxcvdXBUmImgkjTiS+JIwYis2DmJodypc7ktgq5nBF4KVfqqQiMH9MEeHJ8mnw+Zni4SLOpaVeC\nKe94+4LYc4/Tzv220rsNG6Jwv6neaM4x0G4/Rt5E4OAmSfDamMdxGjefbbR0Xl0Mtr8Qc8rKYWp1\n5baHJsnb/VmZNSpog6Uk1XN1VWPby7Pc/9h2lg6XGCjk2FFtcO3oE6l3mYsk9WLjnDOyToPYKL/N\nzDapq7JyiVmwcnAdjxtNOHQob7Re7fWSpujZdYsoMp0zkrjVmmifEYzrvNgnsWwRuQ74nKreap8/\nAJwEvBdAVS+34zcAl6jqHfN8RtArCwjoIvZRLHsdcMRevvxRVV3+/37X0w37Ghb4BXA6cKvtb5NX\n1XER+RVwtYhciQkHrATunO8D9idl8oCAxYb9yVg+VeyrcZ23v42qjorIj4FRjFzqh3aVKRAQEBCw\nGNH1HloBAQEBixEL1+shICAgICBFzxtXETlNRJ7bbR4+Aqfd8jhURErd5uGjFzlBb/LqRU5PV/Ss\ncRWRl9iVyEuBK0Xk5d1lFDjtgcdKm7b3V+AqEXlRN3j0OifoTV69yOnpjp4xriKyVETy3tCZwMWq\nejrwG+AdInKCfW1HMgxEZKBtqBc4tXsWXeMkIn4y6ZuAf6rqCuAh4OMisnT+d2bKach7emYvcLK8\nXi0ih9unZ/UCLxE5QUQOtE97Zl8tFnTVuIpIJCLni8iNwO+A1SJynN18DHCQffxT4FHgHPfWjHmd\nIyJ/B74vIs/wNh3dRU7tnoUTGXgBcHCnOInIESJylYjcCXzEpuABlIBt9vFqoAGc3IlJR0SWi8jX\nRORfwBUicm63OVlex4nIV+y59GOMDgdA0i1eIpIXkYtE5BHgI7QqJ7vGabGi257rs4GXAKtV9Rhg\nO/Apu+23wIn28RaMUXm+iPSp6gKVluwSK4C/AOuAU7zxbnJq9ywusl7azzrM6RygArwdeAL4gh3P\nAWP2eyeAfwGrgCUZcGjH2Rhj8HrgJuCj3eZkKxY/A6wH3gn8CPi73ZzvFi9gOeacPl5V36qq/7Tj\nxS5yWpTomHEVkeeJyE9E5AoRcc2pxoErVPVX9vnXMQcfTNHBoIgcYY3EGLAJOCojTn5IYjUmhrkR\nON4bv7eLnNo9izpwBvAfoE9ElmfFyeMWA+8Gvqiqa4FrgReJyKnAPfY7l9mX3wk8D8hUSdmGJs4C\nfmpFghJMGTbABkw5dkc5AajqGlV9tap+UVX/AZxMy1A9jjnPO84LU9DzoKpuEZFlIrLCjj+McXa6\nwWlRoiPGVUQOwHg8gxhjdRiAqk6o6kbvpa8ErrOPH8TM+u+xz8vAIZgLJgtOqbCMqpbt7L0WOMi7\n9X2gW5zY2bMYxUg6bgYeyYqTxy1v1c/WA++3w0dgJsj3A7cA/cBLAVT1FuBUILNEasupAdwAnC+m\nmGU18GIReQvG+A91kpPlFYlI5Dja4T9jDBvArcBAJ3nZiRHgWGBWRC7GePmXicilwM8tp+M7xWmx\noyPGVVW3Aler6mswF+NL/e3egT8F44mhqmXgW8AbROQC4BPApDUsmXLy4kxrMaGKV9j3zADf7iQn\nb9FoAzt7O6swt+bfB16/0JzEIGe5OQ/mMuCZInIT8A3gSoycZA1zgX5IRM4QkQsxE+WCqnrMx0lV\nPw3cCPxaVQ+0nK6ynK4FLsyS0zy8mi4ko6o1ERnBxH9H7dh/geuBD3ZwXzmllz9gQkx9qno04ETt\nhzDHL1NO+xMyb/MiIpE90dbboT8BrxKR61V1CsyBF5EjgVWqeoF9X15V7xaRdwIfwNxKfalTnCw2\nYoz9Mvu+gqreJSLvwGjZZs4JYzS2YzyfYzGiTVe3AAACp0lEQVSTwFpVvVlEvou5SNYs9H4SEbEl\nyw37/CTgPOBmTOeJl2Hiu4OW51JVvUFMJsP7gGHgk3ZCWhDshtNNwNuw8XpV/YeI3A+cpao/tLHp\nTDjtgdctwC9VdVJEVmH2lXv9b0QkyYrXbjjdgJm4x+35vEGM4NIbVPUbYjJkMttX+xVUdcH/MKvU\nufYx+38FZvZ8IRB52y/HxDnPA24H3tVNTt7/N2PiiU8CH3fv6SCnYzwub8QY2TOACzGeY38Wx9Dj\ncIk9Hn/EyEwOt73mw8Bn28aiDnK61I5/D/iefXwqZgHpyE5w2pt9hZnwLmrn0qV99V6Md38aJrT0\nQ+DFndpX+8tfFgdU2p6fhImDnQPEduwa4G3+wQTuAiYwi1qv6zYnO3Y2RnjmGszM3guczsWk9dyI\nWfHN5sQwBvx+4POYNj7+tjzGU7zPXrindORk3T2no+y5cw8mpnlh+z7uBi+7fcga+zd2gs9e7Kt+\njJrddcBtmJSsYFAX+C8T4Ra7Ank+8FpM2s7vga+q6ja7/c2YdJknMLe4n7QrzndoK7bXLU6PA4+p\n6sdEpA9oqmqlBzhtUNWL7LgLIWQGEYm1FadzYzlAVbVpU40q2krlyRy74dRUVbW32Yer6sOd4rQ3\nvOzzw1T18R7jdLCqbu4Up/0NC25cReQM4MuYWfEH/sVnV1BPxNyGzGBm82+q6hMLSmKBOHlxq57h\nlAWXPfCcc0H2AnqRE/Qmr11xyvLcDjDIwrjuybtYiVmEuW9BvzhwCggI6CFkpuf6dJrFu4le5BQQ\nELDvCGLZAQEBARmg29oCAQEBAYsSwbgGBAQEZIBgXAMCAgIyQDCuAQEBARkgGNeAgICADBCMa0BA\nQEAGCMY1ICAgIAME4xoQEBCQAYJxDQgICMgA/wO800UYy+pxxQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd0275d7890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topo = topotools.Topography(topo_fname, topo_type=2)\n",
    "topo.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From tectonic fault to free surface deformation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GeoClaw has implemented the dislocation model proposed by Okada (\"Surface deformation due to Shear and Tensile Faults in a Half Space\", Bulletin of the Seismological Society of America, Vol. 75, No. 4, pp. 1135-1154, August 1985 ) to convert the rupture characterized by a set of geophysical parameters into free surface initial amplitude."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We initialize our fault object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from clawpack.geoclaw import dtopotools\n",
    "fault = dtopotools.Fault()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due to the linear nature of the problem solved in the Okada model, the total deformation results from the superposition of a finite number of disjoint \"subfaults\". In our case, we will simulate using the homogeneous fault proposed by the USGS for the 2010 event of Chile. This means we will use only one subfault."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a subfault"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Specify subfault parameters for this simple fault model consisting\n",
    "# of a single subfault:\n",
    "usgs_subfault = dtopotools.SubFault()\n",
    "usgs_subfault.strike = 16.\n",
    "usgs_subfault.length = 450.e3\n",
    "usgs_subfault.width = 100.e3\n",
    "usgs_subfault.depth = 35.e3\n",
    "usgs_subfault.slip = 15.\n",
    "usgs_subfault.rake = 104.\n",
    "usgs_subfault.dip = 14.\n",
    "usgs_subfault.longitude = -72.668\n",
    "usgs_subfault.latitude = -35.826\n",
    "usgs_subfault.coordinate_specification = \"top center\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And add it to the collection of subfaults of our fault object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fault.subfaults = [usgs_subfault]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be interesting to ask how is the energy level of this scenario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mw =  8.92090917611\n"
     ]
    }
   ],
   "source": [
    "print \"Mw = \",fault.Mw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can calculate the initial deformation of the water free surface, and write it to a file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Not regenerating dtopo file (already exists): ./dtopo_usgs100227.tt3\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "dtopo_fname = os.path.join('.', \"dtopo_usgs100227.tt3\")\n",
    "if os.path.exists(dtopo_fname):\n",
    "    print \"*** Not regenerating dtopo file (already exists): %s\" \\\n",
    "                % dtopo_fname\n",
    "else:\n",
    "    print \"Using Okada model to create dtopo file\"\n",
    "\n",
    "    x = np.linspace(-77, -67, 100)\n",
    "    y = np.linspace(-40, -30, 100)\n",
    "    times = [1.]\n",
    "\n",
    "    fault.create_dtopography(x,y,times)\n",
    "    dtopo = fault.dtopo\n",
    "    dtopo.write(dtopo_fname, dtopo_type=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can compare graphically the *slip* distribution of the fault model, with the generated vertical displacement of the free surface. In the next cell, the most important line is that which contains the *dtopo.plot_dZ_colors.*, since the others are only *matplotlib* commands to make the figure prettier or for keeping the sanity of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading in dtopo file...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd024b921d0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RHlG5OPv0ibgdWb06IaZoNNwQvYbRQIhhHLQXgJEB5/6kqier6iCcnUTGhyjrA0ao6qBE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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd024f5da10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if fault.dtopo is None:\n",
    "    # read in the pre-existing file:\n",
    "    print \"Reading in dtopo file...\"\n",
    "    dtopo = dtopotools.DTopography()\n",
    "    dtopo.read(dtopo_fname, dtopo_type=3)\n",
    "    x = dtopo.x\n",
    "    y = dtopo.y\n",
    "plt.figure(figsize=(10,5))\n",
    "ax1 = plt.subplot(121)\n",
    "ax2 = plt.subplot(122)\n",
    "fault.plot_subfaults(axes=ax1,slip_color=True)\n",
    "ax1.set_xlim(x.min(),x.max())\n",
    "ax1.set_ylim(y.min(),y.max())\n",
    "\n",
    "\n",
    "#the important one\n",
    "dtopo.plot_dZ_colors(1.,axes=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuring the simulation parameters and options"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save maximum heights and arrival times *on the run*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To save time, GeoClaw allows to trace the maximum heights and their times of occurence at run-time, which can save a significant amount of time when processing the output.  For this, it is neccessary to create an *fgmax* file as follows.\n",
    "\n",
    "It is indeed not a trivial task to do, since we have different grid sizes in space that also vary in time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------- \n",
      "Created file  fgmax_grid.txt\n",
      "   specifying fixed grid with shape 301 by 301, with  90601 points\n",
      "   lower left  = (-120.0000000000, -60.0000000000)\n",
      "   upper right = ( -60.0000000000,   0.0000000000)\n",
      "   dx = 2.0000000000e-01,  dy = 2.0000000000e-01\n",
      "Box:   -120.000000  -60.000000  -60.000000    0.000000\n",
      "Created  fgmax_grid.kml\n"
     ]
    }
   ],
   "source": [
    "from clawpack.geoclaw import fgmax_tools\n",
    "fg = fgmax_tools.FGmaxGrid()\n",
    "fg.point_style = 2       # will specify a 2d grid of points\n",
    "fg.x1 = -120.\n",
    "fg.x2 = -60.\n",
    "fg.y1 = -60.\n",
    "fg.y2 = 0.\n",
    "fg.dx = 0.2 \n",
    "fg.tstart_max =  10.     # when to start monitoring max values\n",
    "fg.tend_max = 1.e10       # when to stop monitoring max values\n",
    "fg.dt_check = 60.         # target time (sec) increment between updating \n",
    "                           # max values\n",
    "fg.min_level_check = 2    # which levels to monitor max on\n",
    "fg.arrival_tol = 1.e-2    # tolerance for flagging arrival\n",
    "\n",
    "fg.input_file_name = 'fgmax_grid.txt'\n",
    "fg.write_input_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting a simulation run: the setrun.py file\n",
    "\n",
    "To run a simulation we need to define a python script called *setrun.py* file. Also it is mandatory to have in the same folder a file called **Makefile** which you won't need to edit, but only to make sure it is there: this is the one responsible for gathering and translating all the code into a machine executable file. On the other side, through the *setrun.py* we will define all the parameters such as simulation final time, and link the topography and initial condition data files we created before.\n",
    "\n",
    "You will see the *setrun.py* is quite long, but there is only a few sections we are commonly interested on editing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A comment on adaptive refinement with GeoClaw\n",
    "\n",
    "GeoClaw asks you to define refinement regions that define the extent in space and time, over which you want to allow it to use finer grids. What you have to define beforehand is:\n",
    "\n",
    "1. A list of **refinement ratios** (integer numbers), say $r_x =[r_2,...,r_l]$, with $l$ the number of **refinement levels** in the simulation, such that $$\\dfrac{\\Delta x_{1}}{\\Delta x_{2}} = r_1, \\dfrac{\\Delta x_{2}}{\\Delta x_{3}} = r_2$$, and so on. Notice that $\\Delta x_1$ is the cell size of the coarsest level, which covers the full domain.\n",
    "\n",
    "2. (optional) A list of **refinement regions** with min and max level of  refinement level allowed in a rectangle inside the domain, for a given time interval of the simulation.\n",
    "\n",
    "Given the restrictions in 2., GeoClaw will try to refine up to the highest level defined in 1, when a specific criteria is satisfied. If you don't define 2., GeoClaw will assume you allow the maximum level of refinement everywhere. The criteria of refinement is when the amplitude of the wave, with respect to the default sea-level is greater than a defined tolerance (of course, the tolerance is also defined in the setrun.py)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I suggest you to read the setrun.py carefully once, but then just \"ctrl+f\" the section enclosed in \"#-----\" that is of your interest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The setrun.py file\n",
    "\n",
    "This is the most important file, and all of its fields are well explained in detail [here](http://www.clawpack.org/setrun.html). Those which are of most interest to us can be found in the following sections:\n",
    "\n",
    "\n",
    "* Extent of the computational domain, and resolution of the coarser level.\n",
    "\n",
    "        # ---------------\n",
    "        # Spatial domain:\n",
    "        # ---------------\n",
    "\n",
    "        ...\n",
    "\n",
    "* Simulation instants when you want to save the whole simulation state to a file. Be careful with this, since it can grow large very easily.\n",
    "\n",
    "        # -------------\n",
    "        # Output times:\n",
    "        #--------------\n",
    "\n",
    "        ...\n",
    "\n",
    "* Time step configuration of the simulation. Normally you will only edit the \"CFL_...\" parameters, when the computation misteriously \"blows up\".\n",
    "\n",
    "        # --------------\n",
    "        # Time stepping:\n",
    "        # --------------\n",
    "\n",
    "        ...\n",
    "\n",
    "* Boundary conditions: There is only \"closed\" (reflective) and \"open\" (extrapolation) boundaries. More complex boundary conditions must be programed in fortran by the user.\n",
    "\n",
    "        # --------------------\n",
    "        # Boundary conditions:\n",
    "        # --------------------\n",
    "\n",
    "        ...\n",
    "\n",
    "\n",
    "* Adaptive Refinement parameters and **output virtual gauges location**.\n",
    "\n",
    "        # ---------------\n",
    "        # AMR parameters:\n",
    "        # ---------------   \n",
    "\n",
    "        ...\n",
    "    \n",
    "* Other geophysical related parameters such as the **wave_tolerance** for adaptive refinement, bottom friction, coordinate sistem (lat-lon or cartesian), amongst others.\n",
    "\n",
    "        #-------------------\n",
    "        def setgeo(rundata):\n",
    "        #-------------------    \n",
    "        ...         "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I suggest you to read the setrun.py carefully once, but then just \"ctrl+f\" the section enclosed in \"#-----\" that is of your interest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Editing the setrun.py file\n",
    "\n",
    "We will use the ipython %%writefile magic function, this will allow us to write all the content in a cell (except for the first line) in the specified file.\n",
    "\n",
    "If you are wondering, I just copy-pasted an existing setrun.py file from the geoclaw examples directory, and then modified its content to fulfill this requirements of this example.\n",
    "\n",
    "You can execute\n",
    "\n",
    "    %%writefile?\n",
    "\n",
    "to see its docstring.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting setrun.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile setrun.py\n",
    "# %load setrun.py\n",
    "\"\"\"\n",
    "Module to set up run time parameters for Clawpack.\n",
    "\n",
    "The values set in the function setrun are then written out to data files\n",
    "that will be read in by the Fortran code.\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "try:\n",
    "    CLAW = os.environ['CLAW']\n",
    "except:\n",
    "    raise Exception(\"*** Must first set CLAW enviornment variable\")\n",
    "\n",
    "\n",
    "#------------------------------\n",
    "def setrun(claw_pkg='geoclaw'):\n",
    "#------------------------------\n",
    "\n",
    "    \"\"\"\n",
    "    Define the parameters used for running Clawpack.\n",
    "\n",
    "    INPUT:\n",
    "        claw_pkg expected to be \"geoclaw\" for this setrun.\n",
    "\n",
    "    OUTPUT:\n",
    "        rundata - object of class ClawRunData\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    from clawpack.clawutil import data\n",
    "\n",
    "    assert claw_pkg.lower() == 'geoclaw',  \"Expected claw_pkg = 'geoclaw'\"\n",
    "\n",
    "    num_dim = 2\n",
    "    rundata = data.ClawRunData(claw_pkg, num_dim)\n",
    "\n",
    "\n",
    "    #------------------------------------------------------------------\n",
    "    # Problem-specific parameters to be written to setprob.data:\n",
    "    #------------------------------------------------------------------\n",
    "    \n",
    "    #probdata = rundata.new_UserData(name='probdata',fname='setprob.data')\n",
    "\n",
    "\n",
    "    #------------------------------------------------------------------\n",
    "    # GeoClaw specific parameters:\n",
    "    #------------------------------------------------------------------\n",
    "    rundata = setgeo(rundata)\n",
    "\n",
    "    #------------------------------------------------------------------\n",
    "    # Standard Clawpack parameters to be written to claw.data:\n",
    "    #   (or to amr2ez.data for AMR)\n",
    "    #------------------------------------------------------------------\n",
    "    clawdata = rundata.clawdata  # initialized when rundata instantiated\n",
    "\n",
    "\n",
    "    # Set single grid parameters first.\n",
    "    # See below for AMR parameters.\n",
    "\n",
    "\n",
    "    # ---------------\n",
    "    # Spatial domain:\n",
    "    # ---------------\n",
    "\n",
    "    # Number of space dimensions:\n",
    "    clawdata.num_dim = num_dim\n",
    "\n",
    "    # Lower and upper edge of computational domain:\n",
    "    clawdata.lower[0] = -120.0      # west longitude\n",
    "    clawdata.upper[0] = -60.0       # east longitude\n",
    "\n",
    "    clawdata.lower[1] = -60.0       # south latitude\n",
    "    clawdata.upper[1] = 0.0         # north latitude\n",
    "\n",
    "\n",
    "\n",
    "    # Number of grid cells: Coarsest grid\n",
    "    clawdata.num_cells[0] = 30\n",
    "    clawdata.num_cells[1] = 30\n",
    "\n",
    "    # ---------------\n",
    "    # Size of system:\n",
    "    # ---------------\n",
    "\n",
    "    # Number of equations in the system:\n",
    "    clawdata.num_eqn = 3\n",
    "\n",
    "    # Number of auxiliary variables in the aux array (initialized in setaux)\n",
    "    clawdata.num_aux = 4\n",
    "\n",
    "    # Index of aux array corresponding to capacity function, if there is one:\n",
    "    clawdata.capa_index = 2\n",
    "\n",
    "    \n",
    "    \n",
    "    # -------------\n",
    "    # Initial time:\n",
    "    # -------------\n",
    "\n",
    "    clawdata.t0 = 0.0\n",
    "\n",
    "\n",
    "    # Restart from checkpoint file of a previous run?\n",
    "    # Note: If restarting, you must also change the Makefile to set:\n",
    "    #    RESTART = True\n",
    "    # If restarting, t0 above should be from original run, and the\n",
    "    # restart_file 'fort.chkNNNNN' specified below should be in \n",
    "    # the OUTDIR indicated in Makefile.\n",
    "\n",
    "    clawdata.restart = False               # True to restart from prior results\n",
    "    clawdata.restart_file = 'fort.chk00036'  # File to use for restart data\n",
    "\n",
    "    # -------------\n",
    "    # Output times:\n",
    "    #--------------\n",
    "\n",
    "    # Specify at what times the results should be written to fort.q files.\n",
    "    # Note that the time integration stops after the final output time.\n",
    "    # The solution at initial time t0 is always written in addition.\n",
    "\n",
    "    clawdata.output_style = 1\n",
    "\n",
    "    if clawdata.output_style==1:\n",
    "        # Output nout frames at equally spaced times up to tfinal:\n",
    "        clawdata.num_output_times = 5\n",
    "        clawdata.tfinal = 10*3600.0\n",
    "        clawdata.output_t0 = False  # output at initial (or restart) time?\n",
    "\n",
    "    elif clawdata.output_style == 2:\n",
    "        # Specify a list of output times.\n",
    "        clawdata.output_times = [30., 60., 300., 600.]\n",
    "\n",
    "    elif clawdata.output_style == 3:\n",
    "        # Output every iout timesteps with a total of ntot time steps:\n",
    "        clawdata.output_step_interval = 1\n",
    "        clawdata.total_steps = 3\n",
    "        clawdata.output_t0 = True\n",
    "        \n",
    "\n",
    "    clawdata.output_format = 'ascii'      # 'ascii' or 'netcdf' \n",
    "\n",
    "    clawdata.output_q_components = 'all'   # need all\n",
    "    clawdata.output_aux_components = 'none'  # eta=h+B is in q\n",
    "    clawdata.output_aux_onlyonce = False    # output aux arrays each frame\n",
    "\n",
    "\n",
    "\n",
    "    # ---------------------------------------------------\n",
    "    # Verbosity of messages to screen during integration:\n",
    "    # ---------------------------------------------------\n",
    "\n",
    "    # The current t, dt, and cfl will be printed every time step\n",
    "    # at AMR levels <= verbosity.  Set verbosity = 0 for no printing.\n",
    "    #   (E.g. verbosity == 2 means print only on levels 1 and 2.)\n",
    "    clawdata.verbosity = 1\n",
    "\n",
    "\n",
    "\n",
    "    # --------------\n",
    "    # Time stepping:\n",
    "    # --------------\n",
    "\n",
    "    # if dt_variable==1: variable time steps used based on cfl_desired,\n",
    "    # if dt_variable==0: fixed time steps dt = dt_initial will always be used.\n",
    "    clawdata.dt_variable = True\n",
    "\n",
    "    # Initial time step for variable dt.\n",
    "    # If dt_variable==0 then dt=dt_initial for all steps:\n",
    "    clawdata.dt_initial = 0.2\n",
    "\n",
    "    # Max time step to be allowed if variable dt used:\n",
    "    clawdata.dt_max = 1e+99\n",
    "\n",
    "    # Desired Courant number if variable dt used, and max to allow without\n",
    "    # retaking step with a smaller dt:\n",
    "    clawdata.cfl_desired = 0.75\n",
    "    clawdata.cfl_max = 1.0\n",
    "\n",
    "    # Maximum number of time steps to allow between output times:\n",
    "    clawdata.steps_max = 5000\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    # ------------------\n",
    "    # Method to be used:\n",
    "    # ------------------\n",
    "\n",
    "    # Order of accuracy:  1 => Godunov,  2 => Lax-Wendroff plus limiters\n",
    "    clawdata.order = 2\n",
    "    \n",
    "    # Use dimensional splitting? (not yet available for AMR)\n",
    "    clawdata.dimensional_split = 'unsplit'\n",
    "    \n",
    "    # For unsplit method, transverse_waves can be \n",
    "    #  0 or 'none'      ==> donor cell (only normal solver used)\n",
    "    #  1 or 'increment' ==> corner transport of waves\n",
    "    #  2 or 'all'       ==> corner transport of 2nd order corrections too\n",
    "    clawdata.transverse_waves = 2\n",
    "\n",
    "    # Number of waves in the Riemann solution:\n",
    "    clawdata.num_waves = 3\n",
    "    \n",
    "    # List of limiters to use for each wave family:  \n",
    "    # Required:  len(limiter) == num_waves\n",
    "    # Some options:\n",
    "    #   0 or 'none'     ==> no limiter (Lax-Wendroff)\n",
    "    #   1 or 'minmod'   ==> minmod\n",
    "    #   2 or 'superbee' ==> superbee\n",
    "    #   3 or 'mc'       ==> MC limiter\n",
    "    #   4 or 'vanleer'  ==> van Leer\n",
    "    clawdata.limiter = ['mc', 'mc', 'mc']\n",
    "\n",
    "    clawdata.use_fwaves = True    # True ==> use f-wave version of algorithms\n",
    "    \n",
    "    # Source terms splitting:\n",
    "    #   src_split == 0 or 'none'    ==> no source term (src routine never called)\n",
    "    #   src_split == 1 or 'godunov' ==> Godunov (1st order) splitting used, \n",
    "    #   src_split == 2 or 'strang'  ==> Strang (2nd order) splitting used,  not recommended.\n",
    "    clawdata.source_split = 'godunov'\n",
    "\n",
    "\n",
    "    # --------------------\n",
    "    # Boundary conditions:\n",
    "    # --------------------\n",
    "\n",
    "    # Number of ghost cells (usually 2)\n",
    "    clawdata.num_ghost = 2\n",
    "\n",
    "    # Choice of BCs at xlower and xupper:\n",
    "    #   0 => user specified (must modify bcN.f to use this option)\n",
    "    #   1 => extrapolation (non-reflecting outflow)\n",
    "    #   2 => periodic (must specify this at both boundaries)\n",
    "    #   3 => solid wall for systems where q(2) is normal velocity\n",
    "\n",
    "    clawdata.bc_lower[0] = 'extrap'\n",
    "    clawdata.bc_upper[0] = 'extrap'\n",
    "\n",
    "    clawdata.bc_lower[1] = 'extrap'\n",
    "    clawdata.bc_upper[1] = 'extrap'\n",
    "\n",
    "\n",
    "\n",
    "    # --------------\n",
    "    # Checkpointing:\n",
    "    # --------------\n",
    "\n",
    "    # Specify when checkpoint files should be created that can be\n",
    "    # used to restart a computation.\n",
    "\n",
    "    clawdata.checkpt_style = 0\n",
    "\n",
    "    if clawdata.checkpt_style == 0:\n",
    "        # Do not checkpoint at all\n",
    "        pass\n",
    "\n",
    "    elif clawdata.checkpt_style == 1:\n",
    "        # Checkpoint only at tfinal.\n",
    "        pass\n",
    "\n",
    "    elif clawdata.checkpt_style == 2:\n",
    "        # Specify a list of checkpoint times.  \n",
    "        clawdata.checkpt_times = [0.1,0.15]\n",
    "\n",
    "    elif clawdata.checkpt_style == 3:\n",
    "        # Checkpoint every checkpt_interval timesteps (on Level 1)\n",
    "        # and at the final time.\n",
    "        clawdata.checkpt_interval = 5\n",
    "\n",
    "\n",
    "    # ---------------\n",
    "    # AMR parameters:\n",
    "    # ---------------\n",
    "    amrdata = rundata.amrdata\n",
    "\n",
    "    # max number of refinement levels:\n",
    "    amrdata.amr_levels_max = 2\n",
    "\n",
    "    # List of refinement ratios at each level (length at least mxnest-1)\n",
    "    amrdata.refinement_ratios_x = [4,3]\n",
    "    amrdata.refinement_ratios_y = [4,3]\n",
    "    amrdata.refinement_ratios_t = [4,3]\n",
    "\n",
    "\n",
    "    # Specify type of each aux variable in amrdata.auxtype.\n",
    "    # This must be a list of length maux, each element of which is one of:\n",
    "    #   'center',  'capacity', 'xleft', or 'yleft'  (see documentation).\n",
    "\n",
    "    amrdata.aux_type = ['center','capacity','yleft','center']\n",
    "\n",
    "\n",
    "    # Flag using refinement routine flag2refine rather than richardson error\n",
    "    amrdata.flag_richardson = False    # use Richardson?\n",
    "    amrdata.flag2refine = True\n",
    "\n",
    "    # steps to take on each level L between regriddings of level L+1:\n",
    "    amrdata.regrid_interval = 3\n",
    "\n",
    "    # width of buffer zone around flagged points:\n",
    "    # (typically the same as regrid_interval so waves don't escape):\n",
    "    amrdata.regrid_buffer_width  = 2\n",
    "\n",
    "    # clustering alg. cutoff for (# flagged pts) / (total # of cells refined)\n",
    "    # (closer to 1.0 => more small grids may be needed to cover flagged cells)\n",
    "    amrdata.clustering_cutoff = 0.700000\n",
    "\n",
    "    # print info about each regridding up to this level:\n",
    "    amrdata.verbosity_regrid = 0  \n",
    "\n",
    "    #  ----- For developers ----- \n",
    "    # Toggle debugging print statements:\n",
    "    amrdata.dprint = False      # print domain flags\n",
    "    amrdata.eprint = False      # print err est flags\n",
    "    amrdata.edebug = False      # even more err est flags\n",
    "    amrdata.gprint = False      # grid bisection/clustering\n",
    "    amrdata.nprint = False      # proper nesting output\n",
    "    amrdata.pprint = False      # proj. of tagged points\n",
    "    amrdata.rprint = False      # print regridding summary\n",
    "    amrdata.sprint = False      # space/memory output\n",
    "    amrdata.tprint = True       # time step reporting each level\n",
    "    amrdata.uprint = False      # update/upbnd reporting\n",
    "    \n",
    "    # More AMR parameters can be set -- see the defaults in pyclaw/data.py\n",
    "\n",
    "    # ---------------\n",
    "    # Regions:\n",
    "    # ---------------\n",
    "    rundata.regiondata.regions = []\n",
    "    # to specify regions of refinement append lines of the form\n",
    "    #  [minlevel,maxlevel,t1,t2,x1,x2,y1,y2]\n",
    "    #rundata.regiondata.regions.append([1, 2, 0., 1e9, -360,360,-90,90])\n",
    "    rundata.regiondata.regions.append([3, 3, 0., 600., -76,-72,-38,-33])\n",
    "\n",
    "    # ---------------\n",
    "    # Gauges:\n",
    "    # ---------------\n",
    "    rundata.gaugedata.gauges = []\n",
    "    # for gauges append lines of the form  [gaugeno, x, y, t1, t2]\n",
    "    rundata.gaugedata.gauges.append([32412, -86.392, -17.975, 0., 1.e10])\n",
    "    \n",
    "\n",
    "    return rundata\n",
    "    # end of function setrun\n",
    "    # ----------------------\n",
    "\n",
    "\n",
    "#-------------------\n",
    "def setgeo(rundata):\n",
    "#-------------------\n",
    "    \"\"\"\n",
    "    Set GeoClaw specific runtime parameters.\n",
    "    For documentation see ....\n",
    "    \"\"\"\n",
    "\n",
    "    try:\n",
    "        geo_data = rundata.geo_data\n",
    "    except:\n",
    "        print \"*** Error, this rundata has no geo_data attribute\"\n",
    "        raise AttributeError(\"Missing geo_data attribute\")\n",
    "       \n",
    "    # == Physics ==\n",
    "    geo_data.gravity = 9.81\n",
    "    geo_data.coordinate_system = 2\n",
    "    geo_data.earth_radius = 6367.5e3\n",
    "\n",
    "    # == Forcing Options\n",
    "    geo_data.coriolis_forcing = False\n",
    "\n",
    "    # == Algorithm and Initial Conditions ==\n",
    "    geo_data.sea_level = 0.0\n",
    "    geo_data.dry_tolerance = 1.e-3\n",
    "    geo_data.friction_forcing = True\n",
    "    geo_data.manning_coefficient =.025\n",
    "    geo_data.friction_depth = 1e6\n",
    "\n",
    "    # Refinement settings\n",
    "    refinement_data = rundata.refinement_data\n",
    "    refinement_data.variable_dt_refinement_ratios = True\n",
    "    refinement_data.wave_tolerance = 1.e-2\n",
    "    refinement_data.deep_depth = 1e2\n",
    "    refinement_data.max_level_deep = 3\n",
    "\n",
    "    # == settopo.data values ==\n",
    "    topo_data = rundata.topo_data\n",
    "    # for topography, append lines of the form\n",
    "    #    [topotype, minlevel, maxlevel, t1, t2, fname]\n",
    "    topo_path = './etopo10min120W60W60S0S.asc'\n",
    "    topo_data.topofiles.append([2, 1, 3, 0., 1.e10, topo_path])\n",
    "\n",
    "    # == setdtopo.data values ==\n",
    "    dtopo_data = rundata.dtopo_data\n",
    "    # for moving topography, append lines of the form :   (<= 1 allowed for now!)\n",
    "    #   [topotype, minlevel,maxlevel,fname]\n",
    "    dtopo_path = './dtopo_usgs100227.tt3'\n",
    "    dtopo_data.dtopofiles.append([3,3,3,dtopo_path])\n",
    "    dtopo_data.dt_max_dtopo = 0.2\n",
    "\n",
    "\n",
    "    # == setqinit.data values ==\n",
    "    rundata.qinit_data.qinit_type = 0\n",
    "    rundata.qinit_data.qinitfiles = []\n",
    "    # for qinit perturbations, append lines of the form: (<= 1 allowed for now!)\n",
    "    #   [minlev, maxlev, fname]\n",
    "\n",
    "    # == setfixedgrids.data values ==\n",
    "    fixed_grids = rundata.fixed_grid_data\n",
    "    # for fixed grids append lines of the form\n",
    "    # [t1,t2,noutput,x1,x2,y1,y2,xpoints,ypoints,\\\n",
    "    #  ioutarrivaltimes,ioutsurfacemax]\n",
    "\n",
    "    # == fgmax.data values ==\n",
    "    fgmax_files = rundata.fgmax_data.fgmax_files\n",
    "    # for fixed grids append to this list names of any fgmax input files\n",
    "    fgmax_files.append('fgmax_grid.txt')\n",
    "    rundata.fgmax_data.num_fgmax_val = 1  # Save depth only\n",
    "\n",
    "\n",
    "\n",
    "    return rundata\n",
    "    # end of function setgeo\n",
    "    # ----------------------\n",
    "\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    # Set up run-time parameters and write all data files.\n",
    "    import sys\n",
    "    rundata = setrun(*sys.argv[1:])\n",
    "    rundata.write()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running the simulation\n",
    "\n",
    "Once the setrun.py is ready we will use some commands defined in the **Makefile** that should be in the same folder as this notebook. The Makefile will compile the Fortran source code and create an executable that will also be called from the Makefile command.\n",
    "\n",
    "You can see which commands are defined in the Makefile by executing from a terminal\n",
    "\n",
    "    make help\n",
    "\n",
    "or simply put a \"!\" in an ipython cell, to run as if it was from the terminal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Makefile:187: warning: overriding commands for target `all'\r\n",
      "/home/jose/Downloads/clawpack-5.3.1//clawutil/src/Makefile.common:259: warning: ignoring old commands for target `all'\r\n",
      "   \"make .objs\"    to compile object files\r\n",
      "   \"make .exe\"     to create executable\r\n",
      "   \"make .data\"    to create data files using setrun.py\r\n",
      "   \"make .output\"  to run code\r\n",
      "   \"make output\"   to run code with no dependency checking\r\n",
      "   \"make .plots\"   to produce plots\r\n",
      "   \"make plots\"    to produce plots with no dependency checking\r\n",
      "   \"make .htmls\"   to produce html versions of files\r\n",
      "   \"make .program\" to produce single program file\r\n",
      "   \"make new\"      to remove all objs and then make .exe\r\n",
      "   \"make clean\"    to clean up compilation and html files\r\n",
      "   \"make clobber\"  to also clean up output and plot files\r\n",
      "   \"make help\"     to print this message\r\n"
     ]
    }
   ],
   "source": [
    "!make help"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell will delete any precompiled files with the\n",
    "\n",
    "    make clean\n",
    "\n",
    "command, and compile the code again and execute it with\n",
    "\n",
    "    make .output\n",
    "\n",
    "You can write the magic\n",
    "    \n",
    "    %%capture out\n",
    "    \n",
    "in the first line, if you find the screen output too annoying, and save it in the \"out\" variable.\n",
    "\n",
    "Now sit back, and wait for those results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%capture out\n",
    "!make clean\n",
    "!make .output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using only two refinement levels, it took me about 40 seconds to run."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing the results: GeoClaw predefined plotting functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two kind of figures we can generate with GeoClaw for tsunamis: the standard snapshots of the simulation and output gauges, and propagation maps showing the maximum height of the wave and its arrival times."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating animations and punctual time series with results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To easily generate some nice figures we have to configure the *setplot.py* file. All figures will be generated in the folder\n",
    "\n",
    "    ./_plots\n",
    "\n",
    "We shall proceed just as with the setrun.py file.\n",
    "\n",
    "Notice that snapshots values are taken from the stored values at times defined in the section\n",
    "\n",
    "        # -------------\n",
    "        # Output times:\n",
    "        #--------------\n",
    "\n",
    "        ...\n",
    "        \n",
    "in the *setrun.py* file.\n",
    "\n",
    "Also, if you read the *setplot.py* file you will notice that snapshot figures are defined in the section\n",
    "\n",
    "    #-----------------------------------------\n",
    "    # Figure for surface\n",
    "    #-----------------------------------------\n",
    "    ...\n",
    "and output gauges in\n",
    "\n",
    "\n",
    "    #-----------------------------------------\n",
    "    # Figures for gauges\n",
    "    #-----------------------------------------\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting setplot.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile setplot.py\n",
    "# %load setplot.py\n",
    "\n",
    "\"\"\" \n",
    "Set up the plot figures, axes, and items to be done for each frame.\n",
    "\n",
    "This module is imported by the plotting routines and then the\n",
    "function setplot is called to set the plot parameters.\n",
    "    \n",
    "\"\"\" \n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from clawpack.geoclaw import topotools\n",
    "\n",
    "try:\n",
    "    TG32412 = np.loadtxt('32412_notide.txt')\n",
    "except:\n",
    "    print \"*** Could not load DART data file\"\n",
    "\n",
    "#--------------------------\n",
    "def setplot(plotdata):\n",
    "#--------------------------\n",
    "    \n",
    "    \"\"\" \n",
    "    Specify what is to be plotted at each frame.\n",
    "    Input:  plotdata, an instance of pyclaw.plotters.data.ClawPlotData.\n",
    "    Output: a modified version of plotdata.\n",
    "    \n",
    "    \"\"\" \n",
    "\n",
    "\n",
    "    from clawpack.visclaw import colormaps, geoplot\n",
    "    from numpy import linspace\n",
    "\n",
    "    plotdata.clearfigures()  # clear any old figures,axes,items data\n",
    "\n",
    "\n",
    "    # To plot gauge locations on pcolor or contour plot, use this as\n",
    "    # an afteraxis function:\n",
    "\n",
    "    def addgauges(current_data):\n",
    "        from clawpack.visclaw import gaugetools\n",
    "        gaugetools.plot_gauge_locations(current_data.plotdata, \\\n",
    "             gaugenos='all', format_string='ko', add_labels=True)\n",
    "    \n",
    "\n",
    "    #-----------------------------------------\n",
    "    # Figure for surface\n",
    "    #-----------------------------------------\n",
    "    plotfigure = plotdata.new_plotfigure(name='Surface', figno=0)\n",
    "\n",
    "    # Set up for axes in this figure:\n",
    "    plotaxes = plotfigure.new_plotaxes('pcolor')\n",
    "    plotaxes.title = 'Surface'\n",
    "    plotaxes.scaled = True\n",
    "\n",
    "    def fixup(current_data):\n",
    "        import pylab\n",
    "        addgauges(current_data)\n",
    "        t = current_data.t\n",
    "        t = t / 3600.  # hours\n",
    "        pylab.title('Surface at %4.2f hours' % t, fontsize=20)\n",
    "        pylab.xticks(fontsize=15)\n",
    "        pylab.yticks(fontsize=15)\n",
    "    plotaxes.afteraxes = fixup\n",
    "\n",
    "    # Water\n",
    "    plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')\n",
    "    #plotitem.plot_var = geoplot.surface\n",
    "    plotitem.plot_var = geoplot.surface_or_depth\n",
    "    plotitem.pcolor_cmap = geoplot.tsunami_colormap\n",
    "    plotitem.pcolor_cmin = -0.2\n",
    "    plotitem.pcolor_cmax = 0.2\n",
    "    plotitem.add_colorbar = True\n",
    "    plotitem.amr_celledges_show = [0,0,0]\n",
    "    plotitem.patchedges_show = 1\n",
    "\n",
    "    # Land\n",
    "    plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')\n",
    "    plotitem.plot_var = geoplot.land\n",
    "    plotitem.pcolor_cmap = geoplot.land_colors\n",
    "    plotitem.pcolor_cmin = 0.0\n",
    "    plotitem.pcolor_cmax = 100.0\n",
    "    plotitem.add_colorbar = False\n",
    "    plotitem.amr_celledges_show = [1,0,0]\n",
    "    plotitem.patchedges_show = 1\n",
    "    plotaxes.xlimits = [-120,-60]\n",
    "    plotaxes.ylimits = [-60,0]\n",
    "\n",
    "    # add contour lines of bathy if desired:\n",
    "    plotitem = plotaxes.new_plotitem(plot_type='2d_contour')\n",
    "    plotitem.show = False\n",
    "    plotitem.plot_var = geoplot.topo\n",
    "    plotitem.contour_levels = linspace(-3000,-3000,1)\n",
    "    plotitem.amr_contour_colors = ['y']  # color on each level\n",
    "    plotitem.kwargs = {'linestyles':'solid','linewidths':2}\n",
    "    plotitem.amr_contour_show = [1,0,0]  \n",
    "    plotitem.celledges_show = 0\n",
    "    plotitem.patchedges_show = 0\n",
    "\n",
    "\n",
    "    #-----------------------------------------\n",
    "    # Figures for gauges\n",
    "    #-----------------------------------------\n",
    "    plotfigure = plotdata.new_plotfigure(name='Surface at gauges', figno=300, \\\n",
    "                    type='each_gauge')\n",
    "    plotfigure.clf_each_gauge = True\n",
    "\n",
    "    # Set up for axes in this figure:\n",
    "    plotaxes = plotfigure.new_plotaxes()\n",
    "    plotaxes.xlimits = 'auto'\n",
    "    plotaxes.ylimits = 'auto'\n",
    "    plotaxes.title = 'Surface'\n",
    "\n",
    "    # Plot surface as blue curve:\n",
    "    plotitem = plotaxes.new_plotitem(plot_type='1d_plot')\n",
    "    plotitem.plot_var = 3\n",
    "    plotitem.plotstyle = 'b-'\n",
    "\n",
    "    # Plot topo as green curve:\n",
    "    plotitem = plotaxes.new_plotitem(plot_type='1d_plot')\n",
    "    plotitem.show = False\n",
    "\n",
    "    def gaugetopo(current_data):\n",
    "        q = current_data.q\n",
    "        h = q[0,:]\n",
    "        eta = q[3,:]\n",
    "        topo = eta - h\n",
    "        return topo\n",
    "        \n",
    "    plotitem.plot_var = gaugetopo\n",
    "    plotitem.plotstyle = 'g-'\n",
    "\n",
    "    def add_zeroline(current_data):\n",
    "        from pylab import plot, legend, xticks, floor, axis, xlabel\n",
    "        t = current_data.t \n",
    "        gaugeno = current_data.gaugeno\n",
    "\n",
    "        if gaugeno == 32412:\n",
    "            try:\n",
    "                plot(TG32412[:,0], TG32412[:,1], 'r')\n",
    "                legend(['GeoClaw','Obs'],'lower right')\n",
    "            except: pass\n",
    "            axis((0,t.max(),-0.3,0.3))\n",
    "\n",
    "        plot(t, 0*t, 'k')\n",
    "        n = int(floor(t.max()/3600.) + 2)\n",
    "        xticks([3600*i for i in range(n)], ['%i' % i for i in range(n)])\n",
    "        xlabel('time (hours)')\n",
    "\n",
    "    plotaxes.afteraxes = add_zeroline\n",
    "\n",
    "\n",
    "    #-----------------------------------------\n",
    "    # Figures for fgmax - max values on fixed grids\n",
    "    #-----------------------------------------\n",
    "    otherfigure = plotdata.new_otherfigure(name='max amplitude and arrival times', \n",
    "                    fname='amplitude_times.png')\n",
    "\n",
    "\n",
    "\n",
    "    #-----------------------------------------\n",
    "    \n",
    "    # Parameters used only when creating html and/or latex hardcopy\n",
    "    # e.g., via pyclaw.plotters.frametools.printframes:\n",
    "\n",
    "    plotdata.printfigs = True                # print figures\n",
    "    plotdata.print_format = 'png'            # file format\n",
    "    plotdata.print_framenos = 'all'          # list of frames to print\n",
    "    plotdata.print_gaugenos = 'all'          # list of gauges to print\n",
    "    plotdata.print_fignos = 'all'            # list of figures to print\n",
    "    plotdata.html = True                     # create html files of plots?\n",
    "    plotdata.html_homelink = '../README.html'   # pointer for top of index\n",
    "    plotdata.latex = True                    # create latex file of plots?\n",
    "    plotdata.latex_figsperline = 2           # layout of plots\n",
    "    plotdata.latex_framesperline = 1         # layout of plots\n",
    "    plotdata.latex_makepdf = False           # also run pdflatex?\n",
    "\n",
    "    return plotdata\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And again, we will use the **make plots** command from the **Makefile** to generate the figures defined in the **setplot.py** file (don't forget the \"!\" at the beginning)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Makefile:187: warning: overriding commands for target `all'\n",
      "/home/jose/Downloads/clawpack-5.3.1//clawutil/src/Makefile.common:259: warning: ignoring old commands for target `all'\n",
      "rm -f .plots\n",
      "python /home/jose/Downloads/clawpack-5.3.1//visclaw/src/python/visclaw/plotclaw.py _output                _plots                setplot.py      \n",
      "Importing setplot.setplot from /home/jose/github/tsunami_workshop/en/tsunami.\n",
      "*** Could not load DART data file\n",
      "*** Could not load DART data file\n",
      "Executed setplot successfully\n",
      "Will plot 5 frames numbered: [1, 2, 3, 4, 5]\n",
      "Will make 1 figure(s) for each frame, numbered:  [0]\n",
      "\n",
      "-----------------------------------\n",
      "\n",
      "\n",
      "Creating html pages for figures...\n",
      "\n",
      "Directory '/home/jose/github/tsunami_workshop/en/tsunami/_plots' \n",
      "    already exists, files may be overwritten \n",
      "Now making png files for all figures...\n",
      "    Reading  Frame 1 at t = 7200  from outdir = /home/jose/github/tsunami_workshop/en/tsunami/_output\n",
      "Frame 1 at time t = 7200.0\n",
      "    Reading  Frame 2 at t = 14400  from outdir = /home/jose/github/tsunami_workshop/en/tsunami/_output\n",
      "Frame 2 at time t = 14400.0\n",
      "    Reading  Frame 3 at t = 21600  from outdir = /home/jose/github/tsunami_workshop/en/tsunami/_output\n",
      "Frame 3 at time t = 21600.0\n",
      "    Reading  Frame 4 at t = 28800  from outdir = /home/jose/github/tsunami_workshop/en/tsunami/_output\n",
      "Frame 4 at time t = 28800.0\n",
      "    Reading  Frame 5 at t = 36000  from outdir = /home/jose/github/tsunami_workshop/en/tsunami/_output\n",
      "Frame 5 at time t = 36000.0\n",
      "Reading gauge data from /home/jose/github/tsunami_workshop/en/tsunami/_output/fort.gauge\n",
      "In fort.gauge file, found gauge numbers [32412]\n",
      "Read in gauges [32412]\n",
      "Found data for Gauge 32412 \n",
      "\n",
      "-----------------------------------\n",
      "\n",
      "Creating latex file...\n",
      "Directory '/home/jose/github/tsunami_workshop/en/tsunami/_plots' \n",
      "    already exists, files may be overwritten \n",
      "\n",
      "Latex file created:  \n",
      "  /home/jose/github/tsunami_workshop/en/tsunami/_plots/plots.tex\n",
      "\n",
      "Use pdflatex to create pdf file\n",
      "Created JSAnimation for figure 0\n",
      "\n",
      "--------------------------------------------------------\n",
      "\n",
      "Point your browser to:\n",
      "    file:///home/jose/github/tsunami_workshop/en/tsunami/_plots/_PlotIndex.html\n"
     ]
    }
   ],
   "source": [
    "!make plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The animation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The animation that resulted from this *setplot.py* can be loaded here as an *iframe* element of IPython."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"600\"\n",
       "            height=\"600\"\n",
       "            src=\"_plots/movieframe_allframesfig0.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7fd024f8d0d0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import IFrame\n",
    "IFrame('_plots/movieframe_allframesfig0.html',width=600, height=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The gauge\n",
    "\n",
    "The virtual gauge can be shown here using the Image command from IPython."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rc3nz/b+QnEDqafWoRx99VG+99ZZuuukmffjhhwX2kDRv3lwPPPCAx8fmz5+vU6dOafjw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\nnTq5bh8AAFiJAgIARVSvXu4/9+513T6WLJGqVJG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tJzU1VadPn9Yvv/yiF198URcvXlTr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\nAAAAAJjm/wM96osADbf1VgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "image/png": {
       "width": 600
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('_plots/gauge32412fig300.png', width=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Propagation map: Maximum heights and arrival times"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, to generate the propagation map, we have to load the *fgmax* data we created before running the simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from clawpack.geoclaw import fgmax_tools, geoplot\n",
    "fg = fgmax_tools.FGmaxGrid()\n",
    "fg.read_input_data('fgmax_grid.txt')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and with this we can read the generated output just by doing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading _output/fort.FG1.valuemax ...\n",
      "Reading _output/fort.FG1.aux1 ...\n"
     ]
    }
   ],
   "source": [
    "fg.read_output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that these results were stored in the *fort.FG1.valuemax* and *fort.FG1.aux1* files in the *_output* folder.\n",
    "\n",
    "Now we have load the data, we can produce a nice figure with the matplotlib.pyplot functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fd01dbe44d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fkSqYhQ7xfA8ccM+yZaUPxeGc6YA1OKjSqVMndu7cedj6kZFxOGlzBqcGjYST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\nCtwHDAXU/0lKuXnwvf8D3IPeX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WfOsH3NGv6ffJS1y7YR19FBc6JJf58gRghxHfAT9HX7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\n5ePD6ehoRh84QMzJk1wKDubI8OEkHT1KUXAwhTb/i47GmKPVe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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd024145f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clines_zeta = [0.01] + list(np.linspace(0.05,0.3,6)) + [0.5,1.0,10.0]\n",
    "colors = geoplot.discrete_cmap_1(clines_zeta)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.clf()\n",
    "zeta = np.where(fg.B>0, fg.h, fg.h+fg.B)   # surface elevation in ocean\n",
    "plt.contourf(fg.X,fg.Y,zeta,clines_zeta,colors=colors)\n",
    "plt.colorbar()\n",
    "plt.contour(fg.X,fg.Y,fg.B,[0.],colors='k')  # coastline\n",
    "\n",
    "# plot arrival time contours and label:\n",
    "arrival_t = fg.arrival_time/3600.  # arrival time in hours\n",
    "clines_t = np.linspace(0,8,17)  # hours\n",
    "clines_t_label = clines_t[::2]  # which ones to label \n",
    "clines_t_colors = ([.5,.5,.5],)\n",
    "con_t = plt.contour(fg.X,fg.Y,arrival_t, clines_t,colors=clines_t_colors) \n",
    "plt.clabel(con_t, clines_t_label)\n",
    "\n",
    "# fix axes:\n",
    "plt.ticklabel_format(format='plain',useOffset=False)\n",
    "plt.xticks(rotation=20)\n",
    "plt.gca().set_aspect(1./np.cos(fg.Y.mean()*np.pi/180.))\n",
    "plt.title(\"Maximum amplitude [m] / arrival times [h]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### THE END"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}