{ "metadata": { "name": "", "signature": "sha256:7cde6e10152b81a7fee58f762d5fca3f9ac5ab65869822922daea679f548597f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Assimilation and Visualization Short Course\n", "# Visualization practical\n", "\n", "## Wednesday 17th September 2014\n", "## Jon Blower\n", "\n", "# Lesson 3: Plotting data from NetCDF files\n", "\n", "The `netCDF4` library provides access to data stored in [NetCDF](http://www.unidata.ucar.edu/software/netcdf/) format, which is a very common modern format for storing scientific data. NetCDF files hold multidimensional arrays of data, plus attributes that describe the data. Different communities use different sets of attributes, but the most common set of attributes are defined by the [Climate and Forecast conventions](http://cfconventions.org), otherwise known as the \"CF Conventions\".\n", "\n", "In this practical we won't have time to look into the details of NetCDF or the CF conventions, but we'll just look at some simple code to extract and plot data.\n", "\n", "(Many of the techniques in this lesson can apply to other file formats that store multidimensional arrays too, such as [HDF](http://en.wikipedia.org/wiki/Hierarchical_Data_Format).)\n", "\n", "## Objectives\n", "1. Learn a little about the structure of NetCDF files\n", "2. Learn how to extract multidimensional data from NetCDF files\n", "3. Learn how to create map plots and other kinds of plots\n", "\n", "## Creating map plots\n", "\n", "First, we'll import the NetCDF library:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import netCDF4" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We've put some data files in the `data` directory. Let's open one of them. This is some ocean potential temperature data from [NCEP's Global Ocean Data Assimilation System](http://www.esrl.noaa.gov/psd/data/gridded/data.godas.html)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "nc = netCDF4.Dataset('../data/pottmp.2014.1time.nc')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see what *variables* we have in the dataset with `nc.variables`. This returns a [*dictionary*](http://www.pythonforbeginners.com/dictionary/how-to-use-dictionaries-in-python) of Variable objects, the keys of which give the identifiers of the variables:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "nc.variables.keys()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "[u'date', u'lat', u'level', u'lon', u'pottmp', u'time', u'timePlot']" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "(The leading 'u' means that these are [Unicode](http://en.wikipedia.org/wiki/Unicode) strings. Don't worry about this, it just means that the strings could contain international characters like \u001d", "\n", "\u00fc or \u00e9 if we wanted.)\n", "\n", "\n", "\n", "We want to plot the potential temperature variable. Let's get a handle to this variable:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pottmp = nc.variables['pottmp']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can find its units (\"units\" is an *attribute* of the vorticity variable):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pottmp.units" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "u'K'" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "(Which means \"Kelvin\" of course.) We can find its *long name*, which is a human-readable string that describes what the variable represents:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pottmp.long_name" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "u'Potential temperature'" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some datasets provide *standard names*, which are more precise description of the variable. This (unfortunately) isn't provided in this dataset, but if it were, you could have found it using `sst.standard_name`. \n", "\n", "You can look up the definition of the standard name in the CF conventions' [table of standard names](http://cfconventions.org/standard-names.html) if you want. (Note that in this particular case the standard name may not be quite correct.)\n", "\n", "(Note that not all NetCDF files provide long names or standard names for their variables, but it's good practice to do so.)\n", "\n", "Now let's extract some data from the variable. When we read data from a NetCDF variable, we get a Numpy array that we can manipulate in exactly the same way as any other Numpy array.\n", "\n", "It's usually a good idea to check how big the data array will be, since they can be very large and can contain any number of dimensions. The `shape` attribute let's us find this, without actually reading any data at this point:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pottmp.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "(1, 40, 418, 360)" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "So the temperature variable is a four-dimensional array, but what are the dimensions? We can find this out too:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pottmp.dimensions" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "(u'time', u'level', u'lat', u'lon')" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "So there is 1 value of time, 40 vertical levels, 418 latitude values and 360 of longitude. We're just going to plot a simple map, so we're going to *slice* the data at the first time value and level:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = pottmp[0,0]\n", "data.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "(418, 360)" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now `data` is a 2D array of all the data at the first timestep and vertical level. We can plot the data using matplotlib's `contourf` function, which creates a filled contour plot. In this case we're using 20 levels of colour." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import matplotlib.pyplot as plt\n", "\n", "# These two lines are needed to make matplotlib plot figures inline and at a decent size.\n", "# They are not needed in scripts.\n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = (12.0, 8.0)\n", "\n", "# These lines do the actual plotting\n", "plt.contourf(data, 20)\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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SqxA75OfnvumblWAR+TEAXwLw1wA8F8AfGGNuE5HnA/gkgBcCeBTAG4wx39t+\n5jYAbwLwIwBvNcZ8Md7uE0JWM9ZBhRioYdMZUWRJLBRPn1YkMQS4+/tNHvtyfe/npff6ol2gS5xd\nQhmL6RAi8hPGmB+KyLMBPADgNwC8BsCTxpjbReSdAJ5njLlVRK4E8AkA1wI4DOA+AC8xxjwz+E6m\nQxCSgXtw4/7qcGNR4D5sWMkatJQrxavLjTG5GlsqxtIgYglwx4cc0iFSo12Ex9DSds+cu1S5wB3e\n6RDGmB9uf3wugGcB+C42EvyK7esfBXACwK0AXgvgLmPMUwAeFZEzAK4D4z2EqGBnrtKWpiKyOdbQ\nnUfIx8DD783Z0a0pN6kf50acOi10ioTXghShCdVb19K2lDgVWAkpE0pYlGARuQjAfwDwtwF82Bjz\nJyJyiTHmie1bngBwyfbny3CwCp3DJiJMCCF2hHzE59oRp+y4124rVaQ15jlJ1VlHnkN4rQyrkF8y\nT4kyDORLmSjkPNlEgp8BcJWI/A0AXxCRXxj83YjI3DMb5j2Uyi0jj6PGHluRYrjw6Ono9t8pJY8c\nO4YD9DpcGvBaIlC+jB2/TweY+jzG7qwTLKYxJrNzYrwov11qQj8t4s5enZ2bYiwnrmWn62tK6F9K\nXVQn99OjAbkHxHVYzw5hjPkrEfkjAC8F8ISIXGqMeVxEDgH41vZt5wFc0fvY5dvXdjh+/PiFn/f2\n9rC3t+e256XT5Tt9pYBKT0hsbAbPzDXgrYvvErY3FBrO43AfQnbcGaJ5zUV5NZShnGiOGCsZSHf2\n6CEcibyNEydO4MSJE4vvmx0YJyIXA3jaGPM9EflxAF8A8D4AfxfAXxpjPigitwL4mcHAuOuwPzDu\n6HAUHAfGoQwJZiS4fsaucUeqhtKl0xzuU+sdbmuELJMaJWWKlJHgOxeeDk3dpNjWxblIar8/1DIw\nbg1ay1iKtn1hUFzqSLDXYhki8nPYDHy7aPvv48aY/2U7RdrdAH4Wu1OkvQubKdKeBvA2Y8wXRr6X\nElwSc6JkA8VZL2sleM1gMwos8SF0B65VVPoMJXhYd0K2sXMSvKbOtjo8Xmv5ii3CU8edY8YTeM4O\nYYz5OoCfH3n9OwBeOfGZDwD4gOd+EkJKwbZDpOySkIR+pJsgV9ibThyPZt2LDb71uFX51U7MHGGt\n9WkErhhHCHGHYrthqoMvqBMolpCduMY8zqmylaPusb77o/kmi1CCiQXdo7a1aRGkDlrqEEMu6cyO\nMDyho1mLC9wDAAAgAElEQVRaZFhT9LSl+h4LrSKceMaIFAPiXKEE56aEAXJrKWn6mxoJOZCm9g4x\npnwwahyHGB15LmmZKiNdHR6rfyehtx/RJPNknND1Z6LeaF1qnBJMSMu4dPYlCHCJna7vPlOe45I6\nKrxUDqYEOCYl1PlS0BoNbhxKcG603bmT9phqnPsRAq2dYYnSG4rhsbfcwcZ8rBtbhlsuw0QHyhbS\nSMlFuXeAbLlW9v/VCnOK03KnHHyM2hfZWwZlbW4QTioBPjnyb+n9ZJ/Wz0fschr6/NqUcVdC9iFa\nb3xLpvY6unCjqGWVuD6MBBNSM1MdWYzG2Gc5URfRvX7kNXIQLQO7aiXEI23t5Tey/J5/aPe1w9fE\n3SYhU1CCCZmKUGsfyOe6shOwOdYcAmz7npifb4mxm4cWSPFY1/ZGI2R5zZEP7MtYu7mNTo8JsBXb\ntMHzst/mFSvOWnODG02JoAQTUmP0LGWnqbUzJhtilG/Ng/lCL6YxxZjMsC54cf4hd6n1+YwatIpw\ng1CCCemopWHyEWDf42anXw5zcxenuo4p61gKGVZW/rtI6+G8u5GMC8dbogzXFnwp9DgowSQtmuYM\nHhtAUooIT6VCrFnatITjJmHJIXGpy1rpj3ktb2r7qQbnRXDYeLaxvosjLbTpPqkQtlJffFS4D9vh\npHB2CJIeZdGTHbTv3xSpR3OXep5IflKXndJmOjgFp5lZvHNtFTE8BtdjquEcAIgza4gta+pJof0B\nI8GKuFCJ19zFxyDE1GbDCjI1jU+KeZNtphDSugrTWAQ4RwdfaIPny7CDLTbq1DIaI8KlybkrjtO1\nTYlsf0BcM1T2dO7Imcdw9uhl6qZJowST+DQmTMVi2+hWfD1to0lL76MkK0WDCK8V34rr3xpmA0el\nzr9fmgiXtr+gBJPYsMGOByPAah+BFj1gJxW5OswcIlx7xJeQjsJEmBKsha8YHMb+Y5/u/6xpETWu\n8OYTEdCYFuFCKHFVIsBaxXcMyrBSUohwZvFd3Ye4DJCLMCiOZCJE3ShIhCnBJA5KhOkApT4SG6Ox\nQXCld6JFj16vlRhTqGVYurn0upEL1snIFCLClGASBo3SWysuHa3rdek3XAmvaQsdOTvdETR0lGtl\nuPVUBw3TXTpwYCq5Ep7U5KgjGnLnE0EJVsZhY8oaCbtSlHZG3a/7unFqiAB3s0KsFeBhSsfUXMkr\naUFqfaAIj6BBhIHdujUlAbmk17NeBkuLqBjWy0gM6vaRM48BR7PtzSiUYOIOo75kBIqvHexwR9C4\nepamCG/hbe7ha/S3D6yXbUIJJvssDYAI3BBHbxQnIsBFNnauHbLltQp1LrR3cKQQ+uVWkxDnJFC7\nO3zCqGou+gDMPUEtrr0fwpSIaHDFOGJH4ZEIoJD8rwysEdjzD1GAfeA5s+Bk719tLB2b5bGXVI7Y\n7q6kxnqgAEowWaaiyldSp6Ednst18Pw5UEsbNCa2J0f+VQpFeCUlLjeuvDwzHUI5SeYLzjAfcK5U\niOIaYZ9r49HouKRFUN5IFrQMoPNBoQgkSY/oBuL22uNU+cFT7VkJ+cmzpM6frzwtgpFgMo/CxtuX\n4gRYIUV3Hgrh+XSkxPYo0j6z7Ezj09YX1z+krAunev8qg5FghXR35EmmSrtWpu8oS+xwWocdbnEU\nOVAzJyVHhBVzXiTeE8fB1IyHr5VobYpNXepHg4uuezlmVRkT4blIsfK6KibDCFERMTm2S3r00wXG\nCmlkAZ5qAL0b4YhzAV9oLHOUWZd0CJtrNuiMdh6JDjoEym86iu6Mc6C8cwVQ5E3pbDmMsHR8qGBP\n8/Und32YEuH+fh3N530iAmPMTmFjJLh1uDrYKCXsYwy6qGSrx58TRoQdaTQinE2AFVPqfgcld32Y\nyh3OvV8LUIJbYSpSqizlIclAwLntz3QwYxGLA41vhCiJNQGvIwU4HxThiojQtmYX4K4fidDWddvf\nWUWU9cGe3IvOFDiIjhJcCFHztQiAguVP2Y0MWQdF2AHlUaZQxG6bNJU3TftSLI3UixBwdohCWJUr\nGzFftgbWLPiQXZwpwFWSvVyVROV1gGWBeJGrXowNnFNcRxkJJknR1KBr2hcvLBqWnUeLcfaERKCK\n0eup0Bb5CrXUccb5dAkJykkAR3PvxC6MBNcKI8Cz1C7AU9HtJNPukaBwaWpLFEebqoP9SxloigYr\nhZFgQlaSNIfTQn5JnXDAkAUaIsIFyfiaMnT+ofqfLPXrHOubI4UMkmMkuDYU36FrETQt++FMBAE+\nbMyBf6QcGCGeoCAJzUkIqTsvcuHf6u9SXpZZ3zwoICLMSHAtKBVfX3JPlVYabJzbhbnDI9hEhIey\nvDaCXJB8hygrw/nEdxbe8Wi7vZ+q9a9dgoWeWNcc6ERYaVSYEkySQEmLB88tASjDO/RlyEaS1syx\nWpAAhyTGCpOTq4lOlevh9cqwAJRqNKQIARsZvin3TuxCCa6ByqLAxJ7QAtxFbziArlwowyO4CJGt\nDBcqWTHLxdSCFyFwLtcBZHhq9cziosFaRFghYjI8bhYRk2O7tXAhVaCQSrimQbR+pOZ4IxBcHq9B\nnBXjbtke10RD7psHvAQluB5KaSfILlHaqdRMtItr2pid44h4wzJ3DYqtW9fD/pyElucP5XE/EYEx\nZqfQMRJcIMVWPKIC5lu3RXFRK3KBpevmIsk1lYHRMt2Xq1tk93XPJ6ZT0eDJ/SiBGE9GCoUSXDNj\nd+CJUyc05qtq3CdCYsKpnuqkfy2rjFjaMNbPjUUbl6KfnoPrihVhVyqVYaZDlMxQaG0exyeU4JCy\nuRi1tDiumPIbNR0i8tzAY+eW6RBkiSY6fhKOwGkRF8qfbbt7i8d2em1vszcZLrgIcuK0iKl0CEpw\naySS4FgDtibJLMFApPQCCwkGwuVdU36JKxQAYkVoCQ7V3o6lTgzZ9i8UYUvGZHjYj8UIGs3AnGCS\nBK2pBlr3i5DSYaoFmePCrA55dyMIc/nBHZydBUXNnEIJJkHQLJma920Sx0d3No0zIbFxnuOVNEPN\nA3JZvj3ont4mjggPWZRgEbkCwMcA/E0ABsC/MMb8UxE5DuAfAvj29q3vMsZ8fvuZ2wC8CcCPALzV\nGPPFCPtOFKBGvL4yPgI41f5FawQd7qgpwkQrS+WSErELI4qJcMxNZTtbFzaR4KcA/CNjzMMi8pMA\nvioi92IjxHcYY+7ov1lErgTwRgBXYvME5D4ReYkx5pnA+04yw4aALHGvw3tfFW0viHbG2hJb2ShN\nEofHNLb/B5YjLliGra7fyAI9c+MUNESS58omJbksFiXYGPM4gMe3P/9ARP4U++k9Y89sXwvgLmPM\nUwAeFZEzAK5DUVkiZIkSKnkJ+7hDF8l2mcy8h5YG2EV+xz5DISa25ThETvKUhPt8dupza+tlyTJc\nI3PXgdeoHJxygkXkRQCuxqZ7vgHALSLyKwAeAvAOY8z3AFyGg933OdSRE182gWaFyCVY50VURACm\nOP8QC3lIKMTEB1shnXr/2N/mvmNqSV0X+tux+Wwp89JOLjBRcW4wKQ9rCd6mQvwbAG/bRoQ/DOD9\n2z//JoDfAfDmiY/vlPbjx49f+Hlvbw97e3u2u0IyoSHC2AwR1ru3+uygY/KZvsgnCmz7fRRi4kKI\nNmtKOkO3hy7fV2NUeEqKx9IlSqTGa+bDTjmPdFN04sQJnDhxYvF9VvMEi8hzAPxbAJ83xvyTkb+/\nCMAfGmN+TkRuBQBjzG9v//a/A3ivMeZU7/2cJzg1KyPBGgTYqpJYzOcYiyCVOEDE3vXY5/bbteMJ\nLcBTUIZJSobioqE97KNNrGzPT3HRYM/2eU26TU3MzrMcuSx4zxMsIgLg9wA80hdgETlkjHls++vr\nAHx9+/NnAXxCRO7A5gnxiwF8eeX+k4xoa/ABHGyMMk+xogXf6xTq8WQqAV7aFgWZxERje6gpyugU\n0Z5pe6YGymVlYhYiH0pJawmFxnoD2KVD3ADgJgBfE5HT29feBeCXReQqbFIdzgL4dQAwxjwiIncD\neATA0wDewrAvCcGBRrGhxmMJLY3Lq5BWhKdYuw8+Eu26TYp6WXTCoqWuTZFr4RLt5yUnS3nnNfRl\nJV9/LptcOxker6dibu34HPljOdIhQl6bpWiMLRpEuBQowyQ2oSSrL2yx+oSpqdHURIKHWLbXNcyT\nnWzO/UjXmssmE1IZWm9OtESES+BeUIRJXGxTJWxnpojJ5E23ktXFYrE0+DKXJOfoY1LPHpJdgou4\n2yMqqeVRUo1QhAnRQwgBJnEZ9mfDBVPW9nU2g/NaLAdZJXh418f5A4krnJ/3IKFSGghpnSkhKZFU\n07w1x3Cg3PXb11cuDZZj+r1Wy0I2CWbHXAYlVAyWpTCEPo+MBi/DVAhd1PxkqZTBfVUwkGGXhVDm\n/uZaPpe2qbkspAqKZk+HGHIvgJtz70QNBFohjuhEc+NFSGksyUUt8ljiMRRxYzKWq3yLbGR4ZVR4\niRKvqSbUSTAAfGR7B3Az0yKIQqZGMOdk7m65lhWXaoNRYB3YSlZN6REkAR8KN6fwFC2Uw/MiVtFg\n3/FlKiWYkFLIkb9+YC5Qx8YhJUyFmIYCnJe10cWSI8OxpzkLSRFR4CW6KHGAdjjFNHUaGUuNmOvX\nXPo8VRLMTlMXLVWyJfpl8+ZcO1EArMMkNVWIUgYY2SZEkQSz8yRaYdlchueIpIby68bc+So5st0a\nLV+nGE811UjwGFMHzCnUZqh4hbgcJJe7hbXpNV4bCjBJhSbxzSmOMc5Di4/ZCVEhwexE9cAGcJ+p\ncqll4Gbum0HW27rRJEWa5LdP6P2yWdAgNlxAIQ4coKyT7BI815FOLSlqO1qQuMHGThEjEWFen3rQ\nvlxyX4Rqi3hqRuPxcpENUjPZJZgQjdhEOVNGhLV1OowC14uriMWIGGuUQbJPqmhx970sDyQWYjJE\nVEXEnOv9PtehTkVLGAkesDIXWJtk5cZF8nKnRSwR+vEbBTgcmqLBIUXDtT2h5NRDjL6kpv6e6RB5\nuByAMWbn5KuOBGvqIFTD1eEIISsILaGU2nbh1GukJFRIcCe79468RuLDhorYwihwXVBWlXO9w3sj\nL8/rg035mup/Dl+D8eWICQmICgnuoPg6wunQovEqUPiG8HzUBQVYKS7iO/Y5hTI8B8shyYkqCSZp\noQATGyi/9UHxUISv9M59X2Ei3BKcKk0XlOBGoQCHQ8u8wTGgAMcj15MvCnBiQkuuyzYpw4TMQgku\nEQ6EIwmgAMeBaV8VMBTbkxOv54YyTMgslODGYAS4PYYySwnLB899wcwJrjb5HbK0f9okmQPiSCKK\nlOCa5gxMCQU4Lh8RUZkS0d8n5qHlRftKcWQE7YIbgpAR48JntCBu5FxRMgRFSjAhhJRKThFmPrAl\nLYjvGKmPu+FBfKUNkJtrO0qeG5oSXBLMBSakCroUldQyfP4hivAsrcpvThoW4RoJ3b7ElmpKMCET\nDOcK5tzBJDRcIEgRFOB8NCzCpUSEc91Au2zTR5jFZMhhFBFzbsXnm80JXhEJLu0RhXaWZFhjbjBg\n39Dayv7YcX5EeWNeAjGFmJHgGSjCefmQznYzFdpFGNDffky5zuUAjDE7J5iR4BJgGgQhTREzXYIp\nETNMRSNtB45RognJiutAvYvi7Qoh9VLqo+vDxhz4N8balI+bjVEbCS8Npt8o4STsHtfbvo+QEeba\nZS2U8FTZ5SafkeAGKKHQlghzhOeZE2GmTNgTYzYJRoMjMybCjBITS0rJE9ZM174t+Q8luHIowHko\ndSllSr1OYqRHdG2DTWdBYQ6A1lXlCKmYC23XRPtW5MA4oJHBcRwIVwRz4qhdgvuRhjUC7HqcjAT7\noyEVh1K8EorwOI0PjBuiMRJcat2Xh5QNjCt9lZEU+D6y5Hkl2pmSZsrxMhpWnQvVxpTaoRKSgmGw\nL5UUTy1+UWN9zZoOQRFexqUA8lymh+kDG0pN/ygVDSIcgmGbVWMnO8pJMBpMnBl7Ah5bjGuvk9ln\nhzh8jWe0U0Tlo4KYnH9oXHSnXidxoQCHhxLdNmzLCHGjidTQiKgZGGc7ko/wHPnSj54NBdY1qlaL\nAGschTwUYaZIjFNLNJgQso7DxgRtw1uaPUaNBHdQhkkM7h38P/V3YFwsfKW3tTSB1o6XxKGJTpgp\nEYRkR50Ed9jmC3d3P3wkQPqsidTWEuXNCWWYrKUJESaEZEWtBANtD5zT9Hi6JCiwfmg+b51IMy2i\nPWofmU5IKDSmtpWAagkG2hRhFmI3NAscCQdleJcYi2hoZbi4ByFklxAy3FIdyz47hA0tXRDiRikC\n/BERyhuJRin1IATVzCDBfGBCsqM+EkzIkJI7fObKkli0FBUGKogMc2DcQY7l3gFSbF1aQTESPJcW\n0eKFa5WSBTg2c4+/hgNHS45Ku9xAlHycvlCGC4IiTCJhs9pckXUmMMVI8BS8iPVD8V3HvQDQgAy2\nKLxzLE37VxvFDqKjCDMKnImibyADISbDY1kRMWbFSR+9cF+p5/EyB8ZtaEV+16ZG2JSXXOcyRdoH\n5dedFqQYKLBzb1mGjwG4qZ5+XBtL/URxdcUReQgwxuychMVIsIhcAeBjAP4mAAPgXxhj/qmIPB/A\nJwG8EMCjAN5gjPne9jO3AXgTgB8BeKsx5ouhDgSo/2K5sHblM420Ir81w5xn3YzVsRrajiHFRbpO\nbv9vVYbv3DoKZTg5rc7LvRgJFpFLAVxqjHlYRH4SwFcB/H0A/wDAk8aY20XknQCeZ4y5VUSuBPAJ\nANcCOAzgPgAvMcY80/vOVZHgWSqICNtGgqdkscTOjOLrL44aI8GMAJdNiW3IEkV28K3JcJcWQQkO\njssT5iLrygJTkeDFKdKMMY8bYx7e/vwDAH+Kjdy+BsBHt2/7KDZiDACvBXCXMeYpY8yjAM4AuG71\nEZADlCSN92J+ueKSjqVEeH6JK/eivrrZTa1W1PRqJ5ffQkhoiqojK3EaGCciLwJwNYBTAC4xxjyx\n/dMTAC7Z/nwZDlbdc9hIM2mMYQdaU4daCjznZC01zjZR1CC6lgbOnQIHySmhlfQIawnepkJ8CsDb\njDHfl15o3RhjRGTu+QWfbTQCpcufqcf7vqkFvBYkJDXKMDAe9VLX+bckwgBzg5XQgghbSbCIPAcb\nAf64MeYz25efEJFLjTGPi8ghAN/avn4ewBW9j1++fe0Ax3uv7P0UsPfT7jvfKinkxnYbXYdI4YrH\nUI6HUlz7mvH94+8fO5dRzkONg3GHFBUprg1Gg0kATvwn4MT3l99nMzBOsMn5/UtjzD/qvX779rUP\nisitAH5mMDDuOuwPjDtqehviwLh5lmRmSTjXdEqtyuzcOdN6TtYMPostjiEHxk1J8NjfSX5qlGI1\nItxSNJhTpgVlTZBETflfgfcUaQBuAHATgK+JyOnta7cB+G0Ad4vIm7GdIg0AjDGPiMjdAB4B8DSA\nt5gckxETZ7TKXmyWOm2t0W7KH9FIjYt0qHks3FpaBCGRKXKxjFkYCfbqeLQJXgp4nuIRKxI89/28\nKdBLLTKsQoSBNkSYkeCgMBLsOUUaIbXxKvh3yrV05jXCBTr0UuOUa4S0QA0CPAcluDIY3ZxmjfwO\nv4cQ4kcr7U1UOH8wseS8SLWDpkNACVbIYWMujPgfgxLmTuhzxmtAiD+MChcAZ2hontqjwIDjYhlF\ncO32jqeC3GBXGAVOy/B881ymY2naOFIG96KsG0o1A+Ricmzk51MZ9uMUAHC+4FwUWc7HcuUXnprU\nJ8ENMCZbFOBpUnWylOJ8MspBceVSkggXKQYhOIY8Iky8qSoFYii3J2f+NvXZiaWgKcGVUFJHkpKc\n56S/7dqFmJFYsga2X47Emh1iLgUiZ1SYJCXrzZ5N2Q5Y/inBFcGO5CCazsXUvpQqx5ReQjKRQ4DH\n3kcZrpIsApxxyr98A+OuRxtzHSamVKlqFU2iTkhOtLddKlIhNPWZx3r/CPFBgQfmjwR3J4BTvgSD\nEeGyeBX0CwAhJDOaBHhIX4QZIc5KEbnAisqyninSFJ2UGrCRKooyIYQskz0KHLt/DBnNDfldnVDf\nKZt/JCpJyrky18sfCe5zPRgRDkh/ruEi7g4bppRoMHOBSWuoHySkEeYNkzEUlmc9keCOtSfpK6b6\nOYJDRnAZDdaD9mtBASYkIQqFwRnmDSeDK8P5oU+CgToqP8mKdqGcotT9JmQtLPuVcwyUYqIOXekQ\nfThgzhvXiF0pj+KXqKUTdb0ec8e95roy8ktaJ1sqRMpAUC4h9UmZuFO4elwkopd1pcFNvRLcQRlO\nQskiXIv89umOae6a2By3zyp2lF+SEs31t4llknPD/GGSEf0S3EEZjo6NeGlDcwcagtDHV+I1JvVS\ne/0lDkwtzXwKTJ8g0ShHgjs4g0R0ShAldp7rKOEaj1HqfpdGqPo1vE6st2QWRoWz0GoqBFCiBAO7\nUWGKcRQ0pkiwEw1LCVLZpWf0Rz677vdYudF8zLmI9eShZJgSkYFhVJjRYBKJMiW4o393Ufm0aDbE\nyOXMKcI1dKAl0F3fEnOB15SR/mcpxKxvcyQX4ZNIEz3TLJYU4TpQHAUGSpfgRkndYccWYXa+JDc+\nAwhrgnWQkDaJenOnXIABSnA1xI7ixXhszo43LyVHgGNTQppIKFgP7ag2GlwSpwBgmxbFqdJIAHQu\nlkEm8e2UQ3Xmr+r9W/sdNXL4GuYP1kTNZRWo+9hIBYylP5wCB86VQCE3cIwEF07OKB470H2G4tv9\nfv6h9Ptiw70oLwJ8eGSAXCpKiwyHmmeaHITRYEXcsm0HPlRWO9YEBZXZOiS4smR5m07eR2DuBTu+\n0Cx1iP2/axViYo/GGVP6DOs363t4OFtEYqbmD+6gDJMVlC/BlQnwHGs7X3aIYXHtCCnEdVBaVJgQ\nQsg45UpwpfIb+lFv930U4DCEigDlTJc4bAxuTr/Z6tA2xRrreKUwJcIORoSJB+UNjDuGagV4Dg2d\nbMvEGvCW+rEqy1Ecah9ARw7CJzmEzFDQ4mVlRYIblF+A4pKDlHJ6+Bp2qrXAlelIFGJFg0tZgGIp\nL5h4wdz2kiLBJVTUFZwXyTLqneySo2FgY1QvKaLEjEITsuUW2U+NIPkoJBpcjgQTkoCcMkoRrpsQ\nc2wTQggJRxkSXHkUeA4+Sk2HBgmNvQ+lzQ08xmFjLswZXCohZZhSTZqgYQ8olgKiwTpyghsu3EyB\n0IEGAe5gjnA7+M4wQfFtEM4S4UZLs0Vcq9gjlJfbfBLcsPgSsoT2FedIeObmH6b0EkJG0SzABaAj\nEhyCO7cF4aYy7voYAdaDpijwEN+ocOnpAjYMj7GWOkXhJWQCzhJRJoqjwfVIMKmOKTkNGR3VLMAd\nTI8ghESjlGnSSNkoFeEyBsbZovAEE3eWFqYIsXBFrMUvYlHSvhJSO9nqYwEDjaJDYQ9CU33KTJmp\nS4JJ0biKqW8lLrXyz94YbGdMqGHmhNyUWj4IaQaKcJnkuIlbKCtMhyDZWSMdrgPIsgjO8AnFioaA\nqRHx6JeN/s8834QQEohUaRGWN0qUYOW8Cn5zBWsdJBRLQoff2xeX5OK7VMG7v3vKcF/8GfXd57Ax\n3uV+Kf0GcJNh3qwQsoLrsdw+cpBcuSjKD6YEkySkFlEVEV+b96+MCpN1+KbfDAV37HvGXqMYlw/r\nXUSuH/xMEbaGN949HNJlKMEkGs10FmvuaFdGhYkbocrk2nx0dlaEkNDMPRFVh5JocH0D487I5l9F\nlDZvaGkzL6wiVCVW0BjUwNzAQE1lUtO+EKIWm3ax5UFyCwtlqG9nFAR/6pNgkhX1lS4U1yO8uFKE\nm6Kpm0VClphq/9guriLFfPslw3SIQvAdIJeKpjrzmI3yyjxhUh6t5PL5tBHazku17Zz2BTOW2sXW\ncoMdl0pW3cbESItwKM+U4ILo0iI0yXC1ncIUKaISFOHmqDlXOMQUiENqPE+ENEvG/ODFdAgR+X0R\neUJEvt577biInBOR09t/r+797TYR+Y8i8g0R+cVYO75DQ49MNOQI81FuZBoqzykopazWVK9iHkv3\n3f1/sVFxXVpuF5aOXXMkWwE25TfrzWWmwI9NJPh/A/AhAB/rvWYA3GGMuaP/RhG5EsAbAVwJ4DCA\n+0TkJcaYZwLtL8mEig4gN6k7IEaEvTlsDHCtbOZSLrDs+u6zhghprvOt+pFvCWhPiQDc28QPcR71\nOcbqS9Y59kNGhC3L82Ik2BhzP4DvjvxpLCnltQDuMsY8ZYx5FMAZANct70YEzgjO4rIsm87JeZGg\nC2XUFJlaRa4IzNwAvK+wgZ/lK6a5spuzvmpoK2LtQ+7jIj3m2uK+9FQgwF1/Pvzni2s5buGmck1O\n8C0i8isAHgLwDmPM9wBchoP3aeewiQiTgmCDr5CWH4MSZ3yXfZ7KTS6tTQiZY63m2NkG7NP4U7I1\nT7hcn5h0701WDxJHg30l+MMA3r/9+TcB/A6AN0+8d/R27Pg9+z/v/Zebf96wcQiCmsZeGyxfpGB8\n6nUtbcFaGVZzHlK1QblSIkIen/aUjkIpLbXsxDngxHkA5+ff5yXBxphvdT+LyO8C+MPtr+cBXNF7\n6+VTu3D89T5btufs0UNxNxCY3PlsJRXuZFB+y6dLGXGcUojUhasMq2oPU7dDJeQGA1VGg0OmMs7h\n6xvJosIBosF7l2/+dWX5fZ8ef5+XBIvIIWPMY9tfXwegmzniswA+ISJ3YJMG8WIAX/bZRiv0C1MO\nEVbV2OeG0ktI1RTX3rFNIpFY4xtFRYUXbuoWJVhE7gLwCgAXi8hfAHgvgD0RuQqbVIezAH4dAIwx\nj4jI3QAeAfA0gLcYM7GGKRktRCnmCy2m8A6Z6hBsowHsUAghpZCzvSolGkyyEV2EE80dvCjBxphf\nHnn592fe/wEAH1izU1UyeBy7VHiW7tJ8HpsUKb82lYByS1JQ4eNXQiZJJcJr2u+K6mSqVIg+udMw\nNcNSd2UAACAASURBVFDtinFHzjxWXF7wEJsCavue4qDYktyMlcGlcllJh0wyw/aPzBCyT1+bFhF6\nf6Iws6R2tRJcCzZTFs0VYvWFc4zaO4BhdKWlNe81ErK89b+LQkxKh2kR6og1D/aaiHBROcIDKMGF\nYJM+ARR0ZzZG7fILjHco3WuU4boYK88UY1Ia2kW4opSIOWL36WtEuEjf2FKHBCdKoC4BFYVxqfNv\n9VotdSTHQBGunWHZb6DzDlbfWzhXhAxI2ae7DMxPsl8J3K4OCdZMK/OTLhXUVsW3wzaS4iLCt0gV\nS4M2TW05xjHr+drZYUpBY1sZKxqs8VgVkXMJ9FaoWoKPnHkMOJp7LyqDjZY7rp0HI8LhKXXRjK6+\naRe9nO3C3La1n7eS0JwWUVlKREsSmpt6JJgpEXHgOV2Hb6dBESZ9NKZRlNA2cKBiWCjCJCUa5gkm\nDVFCp1YSazuLpQFzWjsjEp/UEeIa2gaNNxJDSjjP2kW4UBj9HZDoWtYlwWPR4DPbx59HG86dLLhh\nKI4YnYPWDqcGSq8bMWagKP2c2KItStzKee/DJ7jlpWilIGGZqEuCyYbWG5XYDCO0lFSiCdZ/d3Lm\nXvN6EbIhQ12oT4Jbu7Ns6VhDsbRYhctMDoSQekgVHWa7Tcg+sevDMQD/bPxP9UnwFGckbUpErEcc\nLTWeLpK5NIhs7rtKltk7t+XspobTfWzgI0fiimuqyZRAt9RmE+JK5vpRpwTXGA2u7XimWDObAiGk\nPGzrrobZUmzb4Vrb6xLa2ZbH/4zhUhZTpgMpqSN1SnAtKCkkO7g2hHOdVwmNKiG2LJVnDSKnAZ96\nP/UZntM4sG0uGx9/SLXUe0q3WSjH9Upwzmiwy6NXraLbEaIhZGNKNBKi7vkshNLRkrzFbAN44xEe\nttnxcU3Rsp3iL7RThMiTz+U5FuW4XgkGdKdFaN0vNn6EzBOqjizNA106WtqSVm88fMhxzRz66bNH\nDwHYrgbbClPnJodDaPWWMSzLct0SrBVNBUlLR0VITHxXk0pRP2paHVB7e7I0M0zLKL92nQB3P1cv\nwpo8oSQcy3H9Ety/y9SwcIaGgq28sSMkCp0I29TB1HWk9GhlqW0K84w3lHr9SsQmDUKDJ5SGZxmu\nX4IBPWkRMffBphNlQ+dGyOulYUUqolOAp7avVcRyn58U2Byj1utjS+HXcSwafPboIRzJtD/B0OAq\nJbGyHLchwUB+EQ697Vrnvc1NrDKibYlWop+5ehxLwNh22KMhiux6w1TZ9a0uLSKHo+RoZ9YQuAy3\nI8FAPBG+RewnUV9LZY1YMObO8UmL96SkJCE+47nIhJa5Om9x3P9S6lfo9IlSjrsEQkWRXa5Jw9ev\nE+F+zrBKNC3Y47sqao4bvMiUJcG+g1tqoOFG7gCh5j7Uwty0Ny2X9xyUWsd80idKPdZa4PkPinoB\ntiFGPxW6nLnefBdQznVL8Fih6F4rRQ5yzEVaC5rlNRbDYy4palwysepYqsnngbbaCds5U4l+Wmzn\nh5ToCZW0N7ok2KUglCbDvmgsaGy08sDIcBxi1LG5OsLrOM6admXqszzPVVBFpHcK33Kv0Q0KRI8E\n+xYE1w7lJICjntsaspRvuFYWUxRyCi3p45sDrA3bXODQdcy2PrVyEz9FqnbHZqwAITHp2qKhq/jU\nAYpvcPJLcK0Stua4KL9kCkYRw5FLgIefael6amp3fPalpWtFwsOorzrySbCmxjAEoY4nZmGv7ZxH\nIuSjtyjT97QmTjEIWc9Yr5ap5Rwx7YKkhgIclfyR4BzcuX08cdPMNE7de1JNCbK2oNfSyQQmdy5Z\ntLXufUU4VbqDhtUZpwjRqYSsbzXf1LTSLtkcZ63XmMQhpfyGfioSY0B3pLakTQnWxprC3konY0Fu\n4Z0jigy3nlc6x3DasFAdSqj6Nrw5OIr9G+9aYNt0EM5osQvLyC6lPA12Hf+gFErwHCmiwBRgazRL\nri1RZZjsok1+W4Dnyo6U0+dpxLKc1NDuW8EUrSy0LcG5Iy++hb6xAl5jIxhchrXO6nBG0qZEhKzT\nqetZ6WkRjbVLUXA9h6WWl1rLiusKlR3aUrQaog4JjtF5xI4CN5bsXqPIhqB/XqIMoiPusDMZh+dF\nHyUuphOpHB3BN+N8cUwov9mpQ4JLopEBcJRed8bOWSliPNz3Uvb7ABrqVu48bw3ngPix5tq5ljfb\nbXXf20q5Gvbvc8E0CrAK6pHgkNHgWFHgyqK/pYruaVwV7buvxsNBvy+nXK65vgc/e9nse1VEcFJ3\nJkuzZ6SebaCRztS1TBd5M+dDrOufqFydxlU4kmZTOqi0vq7pc3zrqhiTfgojETF/ZsYPdnWjE6Jj\niCHBFQ3QKUl+YwqvDaGluCNG55zrugaVYJ+c4Jx1akyCffK7fdo9BW1JCFKV22aEWBlL11fFTXTH\nWPsz5hOFD4g/e/RQ0PoQqw53+3j26CH8LXkMxpidC6ROgsdwPtlrRTikBGtYlSoQ2uU3t/AuoVWI\nc1/XLBKsoCMBEE6ChwzbQC3HG4Dc5TUUlOpllq71aVyF1+NzifbGgqn2p+8UBQ6Ir6HOTUlwEekQ\nzncdufPqmPYQBe2Su0S3/xpSJrRc0+Qk7kgWr02sRUUoveopeQwAceQYNiLs6gYZ6nGt9W2KIiQY\n8Ay/+46c7Qrs0nsaQEOFKF1+h8SS4Q4N10wlgTqUELnSs23ZUIi1Tn8XgdbL7pIYtzSbTHVlYckZ\nIglvdecxMMVIMLByblXX6LBGyU10Vxi70tQmtb70z0MsISZYXW9i1IfQOXUlwU7ZjanzVcWMLBMU\nW0Z8BuhTfheZc4a1fWdREtyxqgPJnSqhjFQVheI7z9j5oRh7MKzfKzqY2HWjJRGuqUPWiu051l7m\nii8rNo5B8bXCxhvWBpOKGBg3hYqZJFIRodLErDCU3ji0IsZOg+UCpwvk6Ehmj7fQdIjaOuRWyCHJ\nvmUl98C4s9vpH3PfWNRW10L4w7CvVDcwzuUgpzr+1UvPlhAVLkh+axLfh3G10/uvwulIe3KQ4Tlu\nRYpToE5+C6W2Drk15q5f6TPRxCL1U50Sz6PtWJhQHrH7PePXp4h0iKVwd7EynHmkeihKkF9XqQ3x\n/SnEmHnF6zugEjsUjdR0HlmvxqnpGocmpghrPO++/f6UDOfyiGzpEJ8yr/b+vE2jtKowZsjliU2M\nSqRJfmNLbghSRYuBejpum0ipzyNJLZ1KDWkQWs7lGrS0ZbXU21RET4cYqYOLi3cM2iHfIJ2GeqWl\nXoTgl+TzuhbLWCPBHT4NRu7cnRTUOrtDCaJri1Yh9rm2oTruseibiwT3meqItKFdgqeiW1rPpw01\ndewtS3N3HVNKcMnl3oZS6kbnAi796JQEL6ZDiMjvA/hvAHzLGPNz29eeD+CTAF4I4FEAbzDGfG/7\nt9sAvAnAjwC81RjzReu9dMRnvtUap5WpfYaHmuS3o39MsYU49nWL8f3ddx7x/HztnVUobM5TDeey\nlM7dlanjalmOUxF7vvc11Frehy4QIg1xMRIsIi8H8AMAH+tJ8O0AnjTG3C4i7wTwPGPMrSJyJYBP\nALgWwGEA9wF4iTHmmcF3BokED1lTGEuR4drSGmoU3BCkjBSXwFy0ZywSXApzkeCzuGyxXXJ5NFuD\nzC5Ra+cfA43y5kLOSLCWKS1LKu/Dvt61j/N1hW473pFgY8z9IvKiwcuvAfCK7c8fBXACwK0AXgvg\nLmPMUwAeFZEzAK7DSJbtVDjb5kCnTt6aO7OllXhq7EAY2dVLroF2ZJ/QqSEhZ4KwbY9Kb7dK6uRL\nQ0MU2XcRhBzlYqkuncZVUc9daXVhqZ/3SWlYtx+fH/277+wQlxhjntj+/ASAS7Y/X4aDwnsOm4iw\nxQ7as3Ty1t6lld5xTJG7ElF+17H2Tposs7aOhH5EOhxUU2vb1JG7jSLho5xrZxHQQL/eze1X6BsL\nTedgirX9uo0Mx3SH1VOkGWOMiMzlVIz+7UvH//2Fn1+490K8aO+Fztt2yavUcNcbC80VheIbD0rx\nemLVHZeokE06R23yq7nNIru0eL3O4jIgUL1zuTnWeq5TTTPqkx0wxqMn/hx/fuLPF9/nK8FPiMil\nxpjHReQQgG9tXz8P4Ire+y7fvrbDK47/Hc9Nj+MbWndZfEBr4VxLrlwdXx7CS3ENvpp0myWQcrBd\nyaSqx7Efj5ZGre0naYdQT4qAg64Rsm5oDjw9hJde+HmqD3fZ//73Db/3RYPg6r9/3wOj3+ErwZ8F\n8KsAPrj9/zO91z8hIndgkwbxYgBf9tzGAbqDXZKftXkmLTXUU4VNYyUaFvbh75Tig7SUU1x6na1V\nlku/LkMextXB6hCf4pTP2vKwtn5o7KenGJNVW6ez/T6bv41hMzvEXdgMgrsYm/zf9wD4AwB3A/hZ\n7E6R9i5spkh7GsDbjDFfGPlO82rzqQu/90+CywH4io+WBmeqEvmIfEkVYohrofVhqays3QdK+C4h\n69ncCPB7cGOw7cRgac7jbv9rEOEaxFdjW6qlz9JIrNkhhmlK/bKd6yZGY9mcIpbL+fbVn5df0rVY\nRl+CQxBCQpYKsk90rZRCu+aubM32aoRCbIdLx9GCBA/fXwI1SC9QTjsdg9IFO7QEj+Xozwlwnxjn\nsqSy6duv2/aZa7xhSoJXD4zTQgiJWzNTRYnMPaIAwslczcI7RuobilJJ3Zmkxrdz1jwJP6BbfEtu\nj3Mxdc7STV2VdrsuDMu6zbRfIfdfe3kO1bfb9JmxPKIaCe4oJVe0BDG0EeISjiMXc0n7ZJ5h4//6\nDNvsk6Nj1pIrrEl6tUtBTeQ616nmj13CVYBDEmpb2vrnpcHbU4PebY/D57xlS4c4ZP7MK+c1dMXw\nyUUJnchNiA0UaOD9+K0LP/umQYToYGzaoaVIsO3+j4nwGjEN/X0xoOySPnP1LVQ6RJcGMVYXXMvj\nGk9ZU/Y1esbS8ax1Otvz9Zj8LV05wYfMnwX5rtx3i1pZU5FSnlPbR2ItzXYQglqF2VaCU0nUWBm0\n7ZS15zKnhuJLXOi3BSGYkmCfcunbN/lsS6P4dtgej8/5cj1XUxJcfDpE6VPNaGz418w3G+p4bL6H\n8+JO00IqRv/pUM5Ht2s6vNbLrcb2j7SJbQQ41pgP17pQg/wO32/THoZuM4qPBNuiqbMpteGfm85N\nK5quu3Zqk+SU9MvZXCT4PXi31XfUjPY2g5RB6Ejw2JOZfll1DSzY1ueaor+h6vbUuVvz/dWmQ+RG\n8+pqj506YvW+Q8fORt6T/MSYyq4VaaEc22HTKc9JcJ/aylYr4rtGTljP3IgtwcMyO3Vt14hwzuiv\nzbb7+19SHR51n+tFlwTjpKlSvmLcwbhgK75T1HhNhnTXKNY1qU1gQtBCBx9SgoHyy1FJneYUuSNu\nvvVmab9rqI8xJdhWgDt8RDi1AOeuj3NuEsI7Ft1HowT70oKo2bJWeudwPc+h9qWm61u6yMSkho64\nT2gJ7lNSOcrd2fqSW3htyDGPaipc24O1Ejw1MNVVgDtitmdrrm3u+ujiBVGdY0KCixwYt3TgpUlU\nTJFdQ3+/ps5pjH23+c5SrrGWOS81YtOw1ybKvmieHSV3J1u6/LkwnLu9pmOPsVBTDbQiwMP3p3KO\nIiPBocgtUlrltzRyX0cftAhM6Sx1lilX74sZCbZFw/SGsalJ/Mg8c/U2RiTYNwrcR8NKq6XJbxJq\nSofQRq60ATJNiWI8hKKsD5sObqxzji2/c9SyjCvlt22GdS+2BE+VN5une2tF2Lesp66fRbkMJTgN\nS/KVvdA8OPLaDcn3Igk1iPAYlGMdTHV0cxKsIb+wFCi9ZIzPBV5EvZPhpSiwT0pSytVlfQU4u5Ok\nQl1O8IOoUr66AjUUsOwFbUx+h39LcT3m9iPw9h87daRKEY49FyOxwyXVwrVjG76/JSmm/JKUzC2I\nYfvZqbZ0aW7hEGXdeeU0jS4y1/e7+knfLed8Y0vegXEWO3iBwoQ5e0ED3M7v8P0hz7ftfqSU8cDY\nJPRrY67xpCDbk0LaWpBiyi/RwFg5XBJNlwHQocp5FfJr8zeX93i8N186xO9kTIfQLlnDC7i0v66y\n68rY9mNv02YfLHGR0mFDMfbZFhchoRTroxYRpvwSF0KnQwzz9X0keIwYbabPfmSXXyC9L4zxDm05\nwTkleA0xBdqmoPS3r6Fg5SDANUiZrlKTDA+hHOenZBluWYCHQlNiXQo9EMvmHMSU4FACPMT32q5a\nJpjyexBKcETWSpmmglIS2iP6jtQoyyV27KVSkgy3Kr+uS9VqIvXMA1PnIZYExxLgKcaOL9T2sgiw\ndo+hBCfERs60F5iSqEyGbalRmufQKgfa0C7DrQjwWqHJVd5zzzE7ZHgeQkvwjbhn9HVt58EGJ/lt\nzUEmJLjIFePU01rhcsC8ff9nuSPQl1Y608gSNvnLNdHvlEoV4hQrCKZcIMSF0uQ3twTFLiu5j8+W\nElbdnJPPFO2ytfzSTXagBBMdPDDy2sscPl/wzBKhcH0EVrI0lzizRX+fbQVkzbFokOGSxFerFD6M\nq1eVA63H5cra8+C6LVuW2t2paVN98EpzKFl8x7ygw8UPZmA6BEnKaCR4rqB3uBb4hmXYl5KlOAQu\nHaxLbqemeZ1DCrFWwa1F+oYsXf9aj3vINxE233WYDmF7HlUMPJujVvldYsoVmBNMNHBAgl1Tu3zv\n/CjEzrQuxJoJHQ2zFWON0tuK+PUZXv/WzkFMCQ4ZAc5KqQK8Rn6n6LxBdU7w0oHbyE+IkxcovE4i\n8QD8rpHrvMukyMU/WiH0Y2GNcjtHa9I3pPXjj0FxC0/MUaIAx5Bfy+/OFwl+XUWR4Fjy7Ct92llb\n4EOdE80yrHQ1RQqxDrTmPceC4kemWBsZvgxubZpKAXbpL2z73xTuEVN+h3xaWzpETRLcZ03ByVU4\nU8p2yEJfkwyHuHvPdByHjp116hgo0mFoQYQpv2SJlBL82KkjMMf2f5dTqza9y9xTy7V9RKi+d22/\nm1J8+1CCM7BUWHIVBhemjsF1NofYx7q2YsYSyFyPpjSIvQW2y1JTnMepUYQpvm0Qqp6nkuBuf50l\nOHd6QmrP6PpibX5DCSZNsEaGQ4hj7gZviULkeAyK8C61SDDFtw5Cpgq41ndfGV6S4OExOUlwzv5A\nm4TmhhJMmiPlbBLa5XeKAqV4qnOcG8wXcq5ObZQowpTeekiRI2tTb0NL8NRxWUswBVgXlGDSLLFS\nJUoV3ykKFOI11CTEmkSYgtsGqQeILdXXUBK8dFzqJJjCa8eEBOuYIo2QmKwd+Feb7E7RP84GhLjm\n6HBMKLltk2t2hBT1NdixxewzKL1BYSSYtEON083FpgEZBvKJcMj5mENEg1sQ3KHohLz2NQ/q1DQ1\n2Ng5XRMJDn5soSWY4rsepkOQ5qEE+5NKhqc6jwTbTykrU51uqH2YE2LNohtrqr0QkmM7k4nvd2lG\nkwBP0U9TcCH6NGeuUHjjoFqC7wfw8uS7Mc/9g9+17R/xgyK8DhcZjfVIcClHe4Uwp5CTOaEoTY7W\nUIJYxUbz9S7t+lQhwakEeOg3HSV5jqujqZPgi2e2G+pCTF3otWgoKP1j07A/JUERDkMJAwY9hDiW\nmNhKhWYx8qU0oUqNlmte8nVSIcHaBdjWibQ6xRqne7IkCS6BXIVkqRBoLby+xIrIU4Tbw1GI58TE\nJa/UVSy0CNEaSpapXIS47i2fd18J7lglwyWkQLgKpCaXCBHQpAQHJnUBcSkEmgrvGmLftVKE28Mz\nMqxtOihttCxfIXG97jzv+2ST4BBPvmJL8BqJTOUTsZ7cd1CCI5CicJRQeEPje8w+x0sR1slUpxDi\nehUw40UJEpx9RH0B19GXkE8WWsdWjp0lOGTaVwwJ1p4OGlt6h1CCIxFLNEMVkNJEeO1xaxbhmGJX\nC2s7A9tzWYBAaRLhoOKVesAkaZqgEhyj7IYS4NRSWRqU4IiEFE3td28xCXHsmlIjQjRuLQlyyGiI\nzXkrQJpSiXD06KKmwZIdBVx/sp4gEhyr/FKA00EJToCPgOUovL6iGDNHN+R5WCP8a6Uzdm5XrVIc\n67z5nC9lcpR7tgpvNIrvWpSVDRIZ7Su/UX7toQQnZE7CtBXasX1NmYoR43zkmEEi9QTnNcmw1hsH\nRcJTzMwBNYrvGIrKBomA5rSHDm0uoR1KMMlGTNG23V6N1CDCqW4eYp+rRFKUepU0a3LMj6qh/FOG\n6yOUAMds2yjA7lCCSRO0IsB9NMiADzVHzyPLUcglfL3xlYWY1z1XXaAM10Ht0521TAwJFpFHAfwn\nAD8C8JQx5joReT6ATwJ4IYBHAbzBGPO9wecowSQOLUpwRykynFp+++Q4R7UIkvbVsIZQiIkLJZRv\nCrA/kST4LICXGmO+03vtdgBPGmNuF5F3AnieMebWwecowSQeLYuwD7mniHNhrBMINSPIcP9CnZcS\npaiER8K2UIbLYazcxT6PrmU9dZmm+IYhogRfY4z5y95r3wDwCmPMEyJyKYATxpj/YvA5SjCJByW4\nXnIvG17BILtJahJfGypKjymWmAMpp865xjSeKSjA4YgkwX8G4K+wSYf458aYfyki3zXGPG/7dwHw\nne733ucowSQelOA60bZ0uItEaZSg0AJSivwuEVOONZaDGNQ2Swijv+UzIcHPXvm1NxhjHhORFwC4\ndxsFvoAxxojIuO3+5+P7Pz9nD3ju3spdIYSQLfdDz82QJvHROvWTphuc/vGEFuLu/GsqE67UJrhz\nUH7L5f87ATx1YvFtwWaHEJH3AvgBgF8DsGeMeVxEDgH4Y6ZDkORoESAShjWdQ6yyUMoSzTWI7xQp\n63msCLFP+WhJRHNA+d3w5Dm39198eZz9CEHoSLCI/ASAZxljvi8ifx3ALwJ4H4DPAvhVAB/c/v8Z\n320QQshqug6mlRsjzatche7sU17b7thjRYeBcSGm8KYjpfxqFF9X6XX5vFJB9o4Ei8gRAJ/e/vps\nAP/KGPOPt1Ok3Q3gZ8Ep0khOWpGeFtCyrHYfLTnBKSRJm/xOkarOlzIdIbEjpPxqlNs51oqvK7lk\nmItlkCahCJdP6k7Ftsy4ilAoEU4ZGSxFfsfQNjiS6KNE+XWR1inhTC2+Q3KIMCWYNAtFuGxyiJRN\nmUkxXVquR+Ely+8UuXPDiR7WlO9cZTu3uMYgpQxTgkmzUILLJVeHEysaPERbDmiN8jtF6HaBMqyb\nEsUXqFN+bQkpyZRg0jyU4XLQIFOpRDg3oR4Ja7hmPoRsF0ovC7VRqvgCbcvvGGuFmBJMCMoV4ZDL\nBWsld6czxPX8liJAoUfAa7tua6AQl0/pTzMov27YyjElmJAtWuUx1bypOYQ6d8fii+950SJAMad8\nKvWa2hKjTmgpFzEYlrWUx6pl3uo5KLfxsBFhSjAhPUJ0cLVLANlnTXlJLT4p5jptqeyXNMtEzNXu\nprbjSoj9KmlWB8pvGpZEmBJMyAiuHVxLnT/ZZa0QxRKTVJP8t1r+tT49Iv4ULb/3jbz2ygjfGeq7\nEzEnwpRgQmaY6+Ra7fjJNCGkaK0Qc2nX9FCGyydFOU4uwC68cuV3KBZiSjAhhCSiBSGi+O7SwnWv\nEY2LWXixVoJDUKAIU4IJISQCNUkRpdeOmq557aQs000IcEdhIkwJnmGq4OZa45oQEo6ljilGPS9J\nkii+fpR0jVskdbluSoAB1RIM7LbrlOARbAutTydJsa6fEGu4kzis6ZBCXysNskTRjYeG60v2yVHW\nF9ubocC6CqQ2AQYowSvILsFapizRJEbDc7JTgGbOmabjmKN/DGv2OXT5KeX8lUDIaxP7unBu5vqg\nEOcjV3l3FmAb+oKpUYAB9RIMHGzDKcHQI79DUktQ7POgRepsjtNlX1OVHy3nrxRqKc+cu7otYgjz\n1PWvUc5zl/Uo8lsKBQgwULkEu0b1tApwn5CdrbbjjSESoY5xcZJtZefShxDnP8Z5CJlqlIJcQpy7\nwydp8JVV3/Lhs72lbYUU7szl3nzb7n0iwzaJAqyGrs0uXoJrEBFbShMDV3xFIsUxHrhzLOicknQw\nUk9y0JfLkm6KXKRY2XH5SXCtAlyY/Pa5+PKCJZgiUje1RPFJe9QiwxzgSVrgyXPO5ddegj/ivj/F\nULD8HuCKgiSY0kOKYfyu35ibFz+5+wiNNEdKqSytXaVwk7U4ztJkK71D6pXgWgQYmJLgZ+fYFQDl\nNciEEBKaUDOW2Hx/aXT7ThkmPsyVfZYtC2oS4GnySTAhxVNr7lfNrJ2vMyJrhbhk4Z3D9bgoNm3j\nUl4cZbjeiO8QRe1iZPKlQ+Avkm+3VYzx6xT2H9fbyJ6+SuN73Brop0qsv352zG1n+F2xzq1/ikjI\nGxJ9ZZl4kmvmF4p4OlZcQ9d2jBJcMtrSIUhldBKSo/Ioju6RyMSIxt8HlqFKyBUdr2FxIS3U+oRD\nNTn787RQgskCrpKRSiDm9qv7280J9oPkIXYqCkWYRGJM6ijGu1B+FVB/gIkSTGbwFY2Yd5HMwyWp\nygBFmCQi9gDJ0lAiwO2kP9jSb3vraBspwZUQI0fTZpovF9ZPCWYnP6H3OwchrmfIMpEqv9pmO7KT\n1UXCQvnPisd8tlkInfKhRHyJLXUIMSWYZIIRXVIC7eTGbbhv8D/QzrErQuMUXj6zLpBGKLe9qEyC\nS4xgzMlgacdiCwWYlMZSma2hrk4dYwvHrhQNUeHGhJYpEGux7d91tBsVTJFWukTOF5gaHu0v4dro\ntHBOSBzSdXAltD1D1tyclni8BZFDhCuW3/npID+SbkcI0rUd1U2RVkM0sYZjSA07W1ICJT6VWkNr\nx5sY3/SIikWWkBAUKsG28siGuS54LQnRC9vb6DQttUzLqZO87UZh6RA+kdM8J7fk1coIISEei5bW\nKYd6MlXacRPd6JsViCkTMYjdbhSZDhGiUWZ0ghBCCIlPyP6W6YJtMVV2XMqBe9lTKsEs/ISQoNTe\n0QAABtdJREFU0uENOKmdsb567XRZ7P/bZe21d/98tnSIHNslhBBX1j36LEmCQ8qH9uMufVah3LiW\nFZtzuq785Zw1iOkRuhgrCyJSYjoEIYSUDKPBurARLQryPD6yOrXoDKO+JC+UYEIIiUprq85pJJRs\nlbsyVhjSP662pYvGch554gLTIQghJDL7j0s1i1NoQdFwrDkijRqOOxb6I7daJJgpEvHwucZT6RAX\nhdghQgghNuiXiHDkPtZc28993IQQWyjBhBBCItGqiObePiHEBuYEE0JIUlobLJfyeFuRz6njbKlc\nEbIeSjAhhESmy2FjnmBMtAlwaPl3mdli3XanVzy92el7WN6JL6lyu5kOQQghydEmbEC5UcT7oPN8\nhsT1+Go/H4SEgZFgQgghBdKC6LVwjITkgxJMCCFkyysRR7xCpQZQCu3Jn3uuZboyogON5YESTAgh\nxBEfWbaVMoouISQNUXKCReTvicg3ROQ/isg7Y2yDuHHixIncu9AkPO/p0XjORT5S0CChKVF9Ze8f\nBj9/w/K777P4R+ywPeckJGPtS1e/+/9IOGK26cEjwSLyLAD/DJvW8TyAr4jIZ40xfxp6W8SeEydO\nYG9vL/duNAfPe3p4zkPg8hj9lQC+FmtHVBP/8e709x8/fhzHj+/+/aCA2adETM8IQfqwfVkmdL2I\nec5jRIKvA3DGGPOoMeYpAP8awGsjbIcQQgql1JkY5uhHhgkhRD8xJPgwgL/o/X5u+xohhJDqoQiX\nB9NQSJuIMSbsF4r8EoC/Z4z5te3vNwE4Zoy5pfeesBslhBBCCCFkAmOMDF+LMTvEeQBX9H6/Apto\n8OyOEEIIIYQQkooY6RAPAXixiLxIRJ4L4I0APhthO4QQQgghhHgRPBJsjHlaRP4nAF8A8CwAv8eZ\nIQghhBBCiCaC5wQTQgghhBCinSiLZczBhTTSICKPisjXROS0iHx5+9rzReReEfl/ROSLIvIzufez\nZETk90XkCRH5eu+1yXMsIrdty/03ROQX8+x12Uyc8+Micm5b1k+LyKt7f+M5X4mIXCEifywifyIi\n/5eIvHX7Ost6RGbOO8t7JETkx0TklIg8LCKPiMg/3r7Osh6JmXOepJwnjQRvF9L4v9FbSAPALzNd\nIjwichbAS40x3+m9djuAJ40xt29vQJ5njLk1204Wjoi8HMAPAHzMGPN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"text": [ "" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This plot isn't very satisfactory. Apart from the fact that there are no labels or colour bar on the plot to indicate what we are looking at, the axis values are wrong. We can correct this by finding the variables that hold these values.\n", "\n", "We know from above that there is a dimension called 'lat' and a dimension called 'lon'. The variables that hold the values of latitude and longitude have the same names, so we can get handles to the variable objects like this:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "lon = nc.variables['lon']\n", "lat = nc.variables['lat']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can read the actual values of longitude and latitude. Note that \"`[:]`\" means \"read all the values from the variable\"." ] }, { "cell_type": "code", "collapsed": false, "input": [ "lonvals = lon[:]\n", "latvals = lat[:]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can make a better plot. Note how we use the `long_name` and `units` of the variables for the title and axis labels. Also, we are using a more appropriate [colour scale](http://matplotlib.org/examples/color/colormaps_reference.html) than the default rainbow scale (question: why is this scale more appropriate?)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.title (pottmp.long_name + ' (' + pottmp.units + ')')\n", "plt.xlabel(lon.long_name + ' (' + lon.units + ')')\n", "plt.ylabel(lat.long_name + ' (' + lat.units + ')')\n", "plt.contourf(lonvals, latvals, data, 20, cmap=plt.get_cmap('YlGnBu_r'))\n", "plt.colorbar()\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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y4CLyeAD/BeCNqvqHqmP2UmgCmyt7V3k1CekzLq2YddzmfReYXRGzGCAzKVqS0rfYNIIT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"text": [ "" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "(By the way, you can resize the plot by dragging its bottom right-hand corner. Try it!)\n", "\n", "Note that the axis labels and title are automatically read from the data file, which is neat.\n", "\n", "## Plot a vertical profile\n", "We can create visualizations of many different views through the data. Recall that `pottmp` is a four-dimensional variable with dimensions `(time, level, lat, lon)`. Let's say we want to take a vertical profile at a certain point in the data (i.e. we want to find temperature as a function of depth). How can we do this?\n", "\n", "First, of course, we have to extract the data from the variable. We're going to pick a point roughly in the middle of the above plot. Recall that we have 360 values of longitude and 418 of latitude. So let's pick the longitude at index 180 and the latitude at index 214 (i.e. roughly in the middle). We're going to pick the first time value (there is only one anyway). We can extract all the values of temperature with the following command:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "profile = pottmp[0,:,214,180]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "You should read this as \"give me temperature values for all values of depth at the 214th latitude index and the 180th longitude index\". (Recall that Python array indices start at zero, not one.)\n", "\n", "If we're going to plot a graph, we also need the values of depth:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "depthvar = nc.variables['level']\n", "depthvals = depthvar[:]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can create a plot of temperature versus depth:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.plot(depthvals, profile)\n", "plt.xlabel(depthvar.long_name + ' (' + depthvar.units + ')')\n", "plt.ylabel(pottmp.long_name + ' (' + pottmp.units + ')')\n", "plt.title('Vertical profile: first attempt')\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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gjo6y+eZw7rl1VyFJkqROMGjgjoitgKOB6Q3vz8w8uMK62potJZIkSWpWMy0l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\nRNxWLiB0Y7lvSkRcFhH3RMSlETGp4f0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"text": [ "" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Well, this is OK, but to be more intuitive, we would probably like the depth axis to be the vertical axis, so we should plot depth versus temperature, not the other way around. However, this would result in depth *increasing* vertically, which is not what we want. So we have to insert some code to reverse the depth axis. Here's how:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "plt.plot(profile, depthvals)\n", "plt.xlabel(pottmp.long_name + ' (' + pottmp.units + ')')\n", "plt.ylabel(depthvar.long_name + ' (' + depthvar.units + ')')\n", "\n", "plt.gca().invert_yaxis() # Gets the axes of the plot and reverses the y axis\n", "\n", "plt.title('Vertical profile: this is better')\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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AS2oZS5YUs5h8/euw5prQ3Q3veQ+MGlV1ZZIkDR8DuKTKLVoEP/tZ0eO9ySbwve/BO9/p\nTZWSpJHJAC6pMi+8AD/5CXznO7DDDsXy3ntXXZUkSY1lAJfUdM8+C2edBaeeCm94Q9F2sueeVVcl\nSVJzGMAlNc1TT8EPfgCnnw5dXTB1KuyyS9VVSZLUXAZwSQ03f34Rus88E979brj2Wthpp6qrkiSp\nGs4tIKlhZs+GL3wBtt++eGrltGlw/vmGb0lSZ6ssgEfErIi4PSJuiYhp5bb1I+KqiLgnIq6MiHE1\nx58QEfdGxN0RMamquiUN7Lbb4LDD4O//HpYuLdbPOgte+9qqK5MkqXpVjoAn0JWZu2bmHuW244Gr\nMnN74OpynYiYCBwITAQmA2dEhKP3UgvJhCuvhEmTijaTv/s7uO++YkrBLbaoujpJklpH1T3gy8/y\n+z5gn3L5PKCHIoTvB1yYmYuBWRExE9gD+FOT6pTUj0WL4L//G7773WK0+wtfgEMOgdGjq65MkqTW\nVGUAT+B3EbEUOCszzwY2zsx55f55wMbl8qa8MmzPATZrWqWSXuWpp+DHPy5urtxxR/j2t+Fd7/Lh\nOZIkDaTKAL5XZj4SERsCV0XE3bU7MzMjIlfw/j73dXd3v7Tc1dVFV1fXMJQqqdfs2UXoPuccmDwZ\nLr4Ydtut6qokSWqcnp4eenp6hu18kbmijNscEXES8CzwMYq+8EcjYjxwTWbuGBHHA2Tmt8rjrwBO\nyswblztPtsLnkUaiW28tHpxz2WXw0Y/C5z4HW25ZdVWSJDVfRJCZQ/6dbyU3MkbEmhExtlxeC5gE\n3AFcDBxRHnYEcFG5fDFwUESMjohtgAnAtOZWLXWe3hsr3/lO+Kd/gp13hvvvh9NOM3xLkjRUVbWg\nbAz8Jopm0VWBn2fmlRFxEzAlIo4EZgEHAGTm9IiYAkwHlgBHO9QtNc6iRfDLXxY3VkJxY+VBB3lj\npSRJw6ElWlCGiy0o0sqpvbFyp52K4D1pkjdWSpJUa2VbUKqehlBSC5g9G77/fTj3XNh3X7jkEth1\n16qrkiRpZPJhNlIHu+UW+MhHYJddilHuW26Bn/3M8C1JUiMZwKUOkwm//S284x3w3vcWj4u///6i\n39sbKyVJajxbUKQO8pe/wOc/D/Pnw5e+5I2VkiRVwRFwqQPMmQOHH16MeB92GNx+e7Fu+JYkqfkM\n4NII9uyzcOKJRZvJllvCjBlw1FGwyipVVyZJUucygEsj0NKl8NOfwg47FP3dt9wC//EfMHZs1ZVJ\nkiR7wKUR5uqriz7vddaBiy6CN76x6ookSVItA7g0Qtx9N3zxizB9OnznO/CBD/gAHUmSWpEtKFKb\nmz8fPvMZeOtboaurCOAf/KDhW5KkVmUAl9rUiy8Wc3fvtFMRtu+6C447DlZfverKJEnSitiCIrWZ\nTPj1r4t5vF/3Orj+ethxx6qrkiRJ9TKAS21k2rTiBstnn4Uf/xje/vaqK5IkSYNlC4rUBh56CA49\nFN7/fjjyyOKJloZvSZLakwFcamHPPANf+Qrsuitst13xIJ1//mcfpCNJUjszgEstaMmSosVk++2L\nx8jfdhucfDKsvXbVlUmSpJVlD7jUYq68spjNZP314dJLYffdq65IkiQNJwO41CKmT4cvfAHuvRdO\nOQX228+5vCVJGolsQZEq9thj8MlPwj77wKRJcOedsP/+hm9JkkYqA7hUoUsvhZ13Lh6ec/fd8LnP\nwejRVVclSZIayRYUqQJLl0J3N5x7LvzmN/DmN1ddkSRJahYDuNRk8+cXc3ovWgQ33QQbb1x1RZIk\nqZlsQZGa6M9/LmY12WUXuOoqw7ckSZ3IEXCpCTKLeb2/+lU466ziiZaSJKkzGcClBlu4EI4+unh8\n/B/+UDxcR5IkdS5bUKQGuu++4gbLxYvhT38yfEuSJAO41DCXXlqE76OOgp/9DNZaq+qKJElSK7AF\nRRpmS5fCSSfBeefBRRfBm95UdUWSJKmVGMClYTR/PhxyCCxZUvR8b7RR1RVJkqRWYwuKNEymTSum\nGNxtN7jySsO3JEnqmyPg0krKLKYWPPHEYqrB/fevuiJJktTKDODSSli4ED75Sbj5ZrjhBpgwoeqK\nJElSq7MFRRqimTOLGyyXLSumGDR8S5KkehjApSG4+OJiisF//Vc4/3ynGJQkSfWzBUUahKVLi17v\nCy4oQvg//EPVFUmSpHZjAJfq9PjjxRSDy5YVUwxuuGHVFUmSpHZkC4pUhxtvLKYYfMMb4Le/NXxL\nkqShcwRcWoFMOPNM6O6Gs8+G/faruiJJktTuDOBSPxYuhI9/HG67Df74R9huu6orkiRJI4EtKFIf\nnnsO/vEfi+U//cnwLUmShk9DA3hE/DQi5kXEHTXb1o+IqyLinoi4MiLG1ew7ISLujYi7I2JSzfbd\nI+KOct/pjaxZWrIEDjgAdtqpmGJwzTWrrkiSJI0kjR4BPweYvNy244GrMnN74OpynYiYCBwITCzf\nc0ZERPmeM4EjM3MCMCEilj+nNCwyi7aTzOKx8i/9DZQkSRomDQ3gmXk9sGC5ze8DziuXzwP2L5f3\nAy7MzMWZOQuYCewZEeOBsZk5rTzu/Jr3SMOquxtuvx2mTIHVVqu6GkmSNBJVcRPmxpk5r1yeB2xc\nLm8K/KnmuDnAZsDicrnX3HK7NKzOOgt+/vPihsu11666GkmSNFJVOgtKZmZEZJU1SFA81bK7G66/\nHjbaqOpqJEnSSFZFAJ8XEZtk5qNle8lj5fa5wBY1x21OMfI9t1yu3T63v5N3d3e/tNzV1UVXV9fw\nVK0R63//F446Ci67zNlOJEnSq/X09NDT0zNs54vMxg5AR8TWwCWZuXO5/h3gicz8dkQcD4zLzOPL\nmzB/AexB0WLyO2C7cpT8RuCzwDTgMuA/M/OKPq6Vjf48GllmzIB99oFzzoF99626GkmS1A4igswc\n8lQNDR0Bj4gLgX2ADSJiNnAi8C1gSkQcCcwCDgDIzOkRMQWYDiwBjq5J00cD5wJjgKl9hW9psB55\nBCZPhm99y/AtSZKap+Ej4M3kCLjq9fTTxcj3hz4EX/lK1dVIkqR2srIj4AZwdZxFi+Dd74YJE+CM\nM5zrW5IkDY4BvIYBXANZtgwOPxyefRZ+/WtYZZWqK5IkSe2mpXvApVZz/PHwwAPwu98ZviVJUjUM\n4OoYp58Ol1wCf/gDjBlTdTWSJKlTGcDVEaZMgVNOgRtugNe8pupqJElSJzOAa8S79lr49Kfhyith\nq62qrkaSJHW6UVUXIDXSX/8KH/4wXHgh7LJL1dVIkiQZwDWCzZ5dTDd4+unw9rdXXY0kSVLBAK4R\nacGC4umWxxwDBx9cdTWSJEkvcx5wjTgvvADvehfsthucdpoP2pEkScPLB/HUMIBr6VI46CAYNaro\n+x7l73gkSdIw80E8UikTjj0W5s+HK64wfEuSpNZkANeIccop0NMD110Hq69edTWSJEl9M4BrRLjg\nAvjhD+GPf4Rx46quRpIkqX/2gKvt/eEP8MEPwu9/D697XdXVSJKkkW5le8DtklVbe/55+Jd/gR/9\nyPAtSZLagyPgamvHHw/33Qe/+lXVlUiSpE7hLCjqWDffDD/9Kdx+e9WVSJIk1c8WFLWlxYvhyCOL\nmU822aTqaiRJkupnAFdb+u53YaON4PDDq65EkiRpcOwBV9uZMQP22gtuugm23rrqaiRJUqdxFhR1\nlGXL4Kij4MQTDd+SJKk9GcDVVs46C5YsgU99qupKJEmShsYWFLWNhx6C3XYrHjU/cWLV1UiSpE5l\nC4o6QiZ88pNwzDGGb0mS1N6cB1xt4cILYfZs+M1vqq5EkiRp5diCopb3+OOw885wySXwxjdWXY0k\nSep0K9uCYgBXyzvkENh002Lub0mSpKr5KHqNaJdeCtOm+bh5SZI0chjA1bKefrq48fK882DNNauu\nRpIkaXjYgqKW9clPFnN+n3121ZVIkiS9zBYUjUjXXVfcdPnXv1ZdiSRJ0vByHnC1nOefLx43/4Mf\nwLhxVVcjSZI0vGxBUcs5/ni4/36YMqXqSiRJkl7NFhSNKDffDOec46wnkiRp5LIFRS1j8WI48kj4\nzndg442rrkaSJKkxDOBqGd/9Lmy0ERx+eNWVSJIkNY494GoJM2bAXnvBTTfB1ltXXY0kSVL/VrYH\n3BFwVW7ZsmLWkxNPNHxLkqSRzwCuyv3oR7B0KXzqU1VXIkmS1Hi2oKhSDz0Eu+8O114LEydWXY0k\nSdLAWroFJSJ+GhHzIuKOmm3dETEnIm4pX/vW7DshIu6NiLsjYlLN9t0j4o5y3+mNrFnNkwmf+AR8\n9rOGb0mS1Dka3YJyDjB5uW0JnJaZu5avywEiYiJwIDCxfM8ZEdH7L4szgSMzcwIwISKWP6fa0IUX\nwpw58KUvVV2JJElS8zQ0gGfm9cCCPnb1NWS/H3BhZi7OzFnATGDPiBgPjM3MaeVx5wP7N6JeNc+S\nJfCVr8AZZ8Do0VVXI0mS1DxV3YT5mYi4LSJ+EhHjym2bAnNqjpkDbNbH9rnldrWxX/8aNt8c3vKW\nqiuRJElqrioeRX8m8LVy+d+BU4Ejh+vk3d3dLy13dXXR1dU1XKfWMMmEU04pph2UJElqdT09PfT0\n9Azb+Ro+C0pEbA1ckpk7r2hfRBwPkJnfKvddAZwEPAhck5k7ldsPBvbJzE/0cT5nQWkDv/99MeXg\nnXfCKCfClCRJbaalZ0HpS9nT3ev9QO8MKRcDB0XE6IjYBpgATMvMR4GnI2LP8qbMw4CLmlq0htUp\np8Bxxxm+JUlSZ2poC0pEXAjsA2wQEbMpRrS7ImIXitlQHgA+DpCZ0yNiCjAdWAIcXTOcfTRwLjAG\nmJqZVzSybjXOHXfArbfCb35TdSWSJEnV8EE8aqojjoAdd4QTTqi6EkmSpKFZ2RYUA7iaZs4ceP3r\n4b77YL31qq5GkiRpaNquB1yd6/TTixFww7ckSepkjoCrKZ56CrbZBm65BbbaqupqJEmShs4RcLWF\ns86Cffc1fEuSJDkCroZbtKgY/b7sMthll6qrkSRJWjmOgKvl/eIXMHGi4VuSJAmqeRS9OkgmfPe7\n8L3vVV2JJElSa1hhAI+IjYAPA3sDW1M8POdB4DrgV5n5WKMLVHu7/HJYbTV4xzuqrkSSJKk19NsD\nHhE/AbYFLgemAY8AAYwH9gAmAzMz86jmlDowe8BbT1cXfOxjcOihVVciSZI0PBr2IJ6IeH1m3j7A\nxQc8ppkM4K3lz3+GD30IZs4sRsElSZJGAp+EWcMA3loOOADe9CY49tiqK5EkSRo+DQ/gEfFe4GsU\nPeC9PeOZmesM9aKNYgBvHfffD3vsAQ88AGPHVl2NJEnS8GlGAL8PeD/w18xcNtQLNYMBvHV8+tOw\nzjrwjW9UXYkkSdLwWtkAXs80hHOAO1s9fKt1zJ9fzP19551VVyJJktR66gngXwIuj4hrgEXltszM\n0xpXltrZD38IH/gAjB9fdSWSJEmtp54A/u/AM8AawOjGlqN2t3AhnHEG9PRUXYkkSVJrqieAj8/M\ndza8Eo0I550He+4JO+1UdSWSJEmtaVQdx0yNiHc1vBK1vaVL4dRT4YtfrLoSSZKk1lVPAD+aogf8\nhYh4pnw93ejC1H4uugg23BDe8paqK5EkSWpdA7agZObazShE7S0TTjkF/s//gRjypDySJEkjX78j\n4BGx7UBvrucYdYY//AGeeAL226/qSiRJklrbikbAvxERawEXAzcBjwABjAfeALyPYnaUgxpdpFrf\nd74Dxx0Hq6xSdSWSJEmtbYVPwoyI7SgC9l7AVuXmB4E/ABdm5v0Nr3AQfBJmNaZPh7e9DWbNgjFj\nqq5GkiSpsRr+KPp2YgCvxpFHwlZbwYknVl2JJElS4xnAaxjAm++RR2DiRLj3Xthgg6qrkSRJaryV\nDeD1TEMo9etHP4JDDjF8S5Ik1csArpVy8cVFAJckSVJ9+m1BiYjdgX77OTLz5kYVNVS2oDTX3Lnw\n+tfDvHmw6oAzykuSJI0MK9uCsqLYdCorCODA24Z6UY0MV1wBkyYZviVJkgaj3+iUmV1NrENtaOpU\n2H//qquQJElqLwPOglI+jOfzwJaZ+bGImADskJmXNqPAwbAFpXkWLYKNNoJ77in+lCRJ6hTNmAXl\nHGAR8OYXMdbeAAAgAElEQVRy/WHg60O9oEaGG26AHXYwfEuSJA1WPQF828z8NkUIJzOfa2xJagdT\np8K73111FZIkSe2nngD+YkS89IDxiNgWeLFxJakdGMAlSZKGpp75K7qBK4DNI+IXwF7ARxtYk1rc\nrFnw+OOw++5VVyJJktR+BgzgmXllRNwM/EO56ZjMfLyxZamVXX45TJ4Mo3yMkyRJ0qANGMAj4hLg\nQuD/2f8tKAK4T7+UJEkamnqmIewCDgTeDfwZ+CVwaWa+0PDqBslpCBvvhReKmU9mzYL116+6GkmS\npOZr5JMwAcjMHqAnIlalePrlx4CfAusM9aJqX9ddVzx+3vAtSZI0NHU9RLycBeV9wAHAbsB5jSxK\nrcvZTyRJklZOPT3gU4A9KWZC+QFwbWYua3Rhak1Tp8KUKVVXIUmS1L7qmcfip8BrM/PjmXnNYMJ3\nRGwREddExJ0R8deI+Gy5ff2IuCoi7omIKyNiXM17ToiIeyPi7oiYVLN994i4o9x3+mA+pIbHvffC\ns8/C3/991ZVIkiS1r3oC+PXAlyPibICImBAR76nz/IuBYzPzdRTTGH4qInYCjgeuysztgavLdSJi\nIsUNnxOBycAZEdHb4H4mcGRmTgAmRMTkOmvQMLn88qL9JIZ8y4EkSZLqCeDnUDyG/s3l+sPA1+s5\neWY+mpm3lsvPAncBm1H0k/f2kZ8H7F8u7wdcmJmLM3MWMBPYMyLGA2Mzc1p53Pk171GT2P8tSZK0\n8uoJ4Ntm5rcpQjhDnQs8IrYGdgVuBDbOzHnlrnnAxuXypsCcmrfNoQjsy2+fW25Xkzz3HNxwA7zj\nHVVXIkmS1N7qmQXlxXIWFAAiYlvgxcFcJCLWBn5N8RTNZ6KmhyEzMyKGbfLu7u7ul5a7urro6uoa\nrlN3tGuugTe8AdZx8klJktRhenp66OnpGbbz1fMgnknAVyj6sq8C9gI+mpnX1HWBiNWAS4HLM/P7\n5ba7ga7MfLRsL7kmM3eMiOMBMvNb5XFXACcBD5bH7FRuPxjYJzM/sdy1fBBPgxx9NGyzDXzxi1VX\nIkmSVK2VfRDPgC0omXkl8EHgn4FfALsPInwH8BNgem/4Ll0MHFEuHwFcVLP9oIgYHRHbABOAaZn5\nKPB0ROxZnvOwmveowTLt/5YkSRou/Y6AR8TuQO3O3pSfAJl584Anj3gLcB1we825TgCmAVOALYFZ\nwAGZ+WT5ni8D/wIsoWhZ+W1NPecCY4CpmfnZPq7nCHgD3HUXTJ5cPH7eGVAkSVKnW9kR8BUF8B5e\nGcBfITPfNtSLNooBvDFOPRVmzoQzz6y6EkmSpOqtbADv9ybMzOwa6kk1skydCp/7XNVVSJIkjQwD\n3oTZThwBH35PPw2bbQaPPgprrVV1NZIkSdVr+E2Y6mxXXw1vfrPhW5IkabgYwLVCzn4iSZI0vOpq\nQYmI9SmmBFy9d1tmXtfAuobEFpThlQmbbw49PTBhQtXVSJIktYaG3YRZc4GPAZ8FNgduBf4B+F/g\nH4d6UbWH22+HNdc0fEuSJA2nelpQjgH2AB4spx7cFXiqoVWpJdh+IkmSNPzqCeAvZObzABGxRmbe\nDezQ2LLUCqZOhX33rboKSZKkkWXAFhRgdkSsR/Ho96siYgHF0ys1gi1YALfdBvvsU3UlkiRJI8uA\nATwz318udpdPx1wHuKKRRal6V14Je+8NY8ZUXYkkSdLIUs8IOBHxVmC7zDwnIjYENgMeaGhlqtTl\nl9v/LUmS1AgDTkMYEd3A7sAOmbl9RGwGTMnMvZpQ36A4DeHwWLYMxo+HP/0Jttmm6mokSZJaSzOe\nhPl+YD/gOYDMnAuMHeoF1foefBBGjzZ8S5IkNUI9AfzFzFzWuxIRPpR8hJsxA3bcseoqJEmSRqZ6\nAvivIuIsYFxE/CtwNfBfjS1LVZoxA3ZwoklJkqSGqGcWlFMiYhLwDLA98NXMvKrhlakyM2bATjtV\nXYUkSdLINOAIeNlycnVmfgE4GxgTEas1vDJVZsYM2H77qquQJEkameppQbkeWL2c/eS3wGHAuY0s\nStW65x5bUCRJkhqlngAembkQ+ABwRmZ+GPi7xpalqjz3HDzxBGy5ZdWVSJIkjUz1BHAi4k3AocBl\ng3mf2s8998B228Eof8KSJEkNUU/M+hxwAvCbzLwzIrYFrmlsWaqKM6BIkiQ1Vj2zoFwLXFuzfh/w\n2UYWpeoYwCVJkhrLRgO9ggFckiSpsQzgegWnIJQkSWqsyMwVHxCxRma+0KR6VkpE5ECfR/3LhHXW\ngdmzYdy4qquRJElqTRFBZsZQ3z9gDzhwZ0TMo5gP/DrgD5n51FAvqNb1yCOw5pqGb0mSpEYasAUl\nM7cFDgZuB94D3B4Rtza6MDWf/d+SJEmNN+AIeERsDuwFvBXYBbiTYjRcI4wBXJIkqfHqaUF5CPgz\n8E3gkzZZj1wGcEmSpMarZxaUXYELKNpQ/hgR50fEUY0tS1UwgEuSJDXegLOgAETEWIo2lL2BjwBk\n5paNLW3wnAVl5Wy7LUydagiXJElakZWdBaWeaQhvAtYA/kgxC8r1mfngUC/YSAbwoXvxRVh3XXjm\nGVhttaqrkSRJal3NmIbw3Zn52FAvoPYwcyZstZXhW5IkqdHq6QFfFBHfi4i/lK9TI2Ldhlemprrn\nHltPJEmSmqGeAP5T4Gngw8ABwDPAOY0sSs3nDZiSJEnNUU8LyraZ+YGa9e6IuK1RBakaM2bAXntV\nXYUkSdLIV88I+PMR8dbelYh4C7CwcSWpCo6AS5IkNUc9I+CfAM6v6fteABzRuJJUhRkzYPvtq65C\nkiRp5KtrHnCAiFgHIDOfbmhFK8FpCIdm/nzYbjtYsABiyBPqSJIkdYaGTUMYEcfVrGbN9gAyM08b\n6kXVWnrbTwzfkiRJjbeiHvCxwNrla2zNq3d9QBGxRURcExF3RsRfI+Kz5fbuiJgTEbeUr31r3nNC\nRNwbEXdHxKSa7btHxB3lvtMH/1HVH/u/JUmSmqffEfDM7B6G8y8Gjs3MWyNibeAvEXEVxYj6acuP\nokfEROBAYCKwGfC7iJhQ9pWcCRyZmdMiYmpETM7MK4ahxo5nAJckSWqeAWdBiYgdIuLqiLizXH99\nRPxbPSfPzEcz89Zy+VngLopgDdBXw8N+wIWZuTgzZwEzgT0jYjwwNjOnlcedD+xfTw0amA/hkSRJ\nap56piE8G/gysKhcvwM4eLAXioitgV2BP5WbPhMRt0XETyJiXLltU2BOzdvmUAT25bfP5eUgr5Xk\nCLgkSVLz1DMN4ZqZeWOUd+hlZkbE4sFcpGw/+R/gmMx8NiLOBL5W7v534FTgyMGcsz/d3d0vLXd1\nddHV1TUcpx2xliyB++8vZkGRJEnSq/X09NDT0zNs56sngD8eES/Fs4j4EPBIvReIiNWAXwM/y8yL\nADLzsZr9/wVcUq7OBbaoefvmFCPfc8vl2u1z+7pebQDXwGbNgk02gTFjqq5EkiSpNS0/qHvyySev\n1PnqaUH5NHAWsGNEPAwcC3yynpOXUxb+BJiemd+v2T6+5rD3U7S1AFwMHBQRoyNiG2ACMC0zHwWe\njog9y3MeBlxUTw1aMdtPJEm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"text": [ "" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is what we expect to see in a typical deep-ocean location: a shallow, warm, well-mixed layer at the surface followed by a steep gradient (the *thermocline*) to cold temperatures at depth." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises\n", "\n", "A script to generate the map plot above can be found in `temperature.py`. You can use this as the basis for your experiments in these exercises.\n", "\n", "1. Adjust the code to create a latitude-longitude plot at a different vertical level within the dataset.\n", "1. How can you improve the title of the plot so that it reads \"Potential temperature at *x* m depth (K)\" where *x* is the real depth in metres of the slice you are showing?\n", "1. Create a *transect* plot, which is a plot of potential temperature versus distance along a horizontal line. You could pick a line running north-south through the Pacific Ocean, for instance.\n", "1. Similarly, create a *vertical section* plot, which is a 2D plot of depth versus horizontal distance, using colour to indicate temperature values. You can use the same horizontal line as in the previous exercise.\n", "1. The `data` folder contains output from [Phil Browne](http://www.met.reading.ac.uk/~vb904302/)'s barotropic vorticity model in a file called `barotropic.nc`. Create some plots (e.g. maps and transects) using this data file. The file has no vertical dimension so you can't create vertical profiles or sections.\n", " * The above data are from a model that is not really referenced to longitude and latitude (in fact the domain is toroidal, meaning that the boundary conditions are periodic on all edges of the domain). So the values of latitude and longitude don't actually mean anything physical in this dataset.\n", "\n", "## Further reading\n", "NetCDF tutorial from Unidata: http://www.unidata.ucar.edu/software/netcdf/docs/netcdf-tutorial.html\n", "\n", "The NetCDF operators (http://nco.sourceforge.net/) are an extremely useful set of command-line tools for manipulating data in NetCDF files. (Some of the data used in this practical were prepared using these tools. See the file `ReadMe.txt` in the `data` directory.) " ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 } ], "metadata": {} } ] }