Spacegrids.ipynb

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      "Spacegrids Documentation\n",
      "========================\n",
      "\n",
      "Spacegrids is an open source library providing a Numpy array with grids, labelled axes and associated grid-related methods such as regridding and integration. Spacegrids provides an object data model of Netcdf data that ensures consistency between a Numpy data array and its grid under common operations (and so avoiding common pitfalls related to axis interpretation), and much more. It is a write less do more library for everyday use. These <a href=\"http://willospace.pythonanywhere.com/\">Interactive Netcdf data plots using d3.js</a> are based on Spacegrids.\n",
      "\n",
      "\n",
      "The Spacegrids module is a bit like \"Numpy on grids\". It allows code like:\n",
      "\n",
      "    TEMP = P['some_dataset']['temperature']\n",
      "    V = P['some_dataset']['y_velocity'] \n",
      "    TV = TEMP*V\n",
      "    zonal_mean_TV = TV.mean(X)\n",
      "    TEMP2 = P['some_other_dataset']['temperature'] # defined on a different grid\n",
      "    deltaTEMP = TEMP - TEMP2.regrid(TEMP.grid)\n",
      "    \n",
      "It allows easy manipulation of spatial data, for instance a spatial grid of discrete latitude-longitude-depth coordinates covering the Earth's surface. This type of data is often stored in Netcdf file format, and indexed according to axes that are fully described in the Netcdf metadata. This allows interpretation of the data, consisting of the construction of axis and grid objects to be linked with a ```Field```. This contains the data array (e.g. temperature) when loading data into memory. Axis alignment upon multiplication and other operations with fields is then automatic, and no knowledge is required of the data index order. The module maintains a focus on notational simplicity, ease of use and maintenance of consistency between data and grid information under data manipulation. New coordinate objects and grids can be naturally constructed from experiment parameters or aggregated data values.\n",
      "\n",
      "Features\n",
      "--------\n",
      "\n",
      "- A numpy array with grid allowing automatic alignment and dimension broadcasting\n",
      "- Easy to use and intuitive regridding functionality\n",
      "- A data object model corresponding closely to Netcdf\n",
      "- Easier IO via abstraction of IO with multiple Netcdf files\n",
      "- Makes working with output of many experiments easy via aggregation methods\n",
      "- The ```Field``` class eliminates errors arising from picking the wrong array index\n",
      "- Quicker plotting due to automatic labels, axes etc.\n",
      "- Distance-related methods such as spatial differentiation and integration on sphere\n",
      "- Extensive unit tests and documentation\n",
      "\n",
      "There is lots of documentation, both in the source code and elsewhere. Other documentation can be found at: \n",
      "<ul>\n",
      "    <li><a href='http://nbviewer.ipython.org/github/willo12/spacegrids/blob/master/advanced.ipynb'> Avanced tutorial </a></li>\n",
      "    <li><a href=\"http://web.maths.unsw.edu.au/~wsijp/html/index.html\">Documentation of all classes, methods and functions</a></li>\n",
      "</ul>\n",
      "    \n",
      "Installation\n",
      "------------\n",
      "\n",
      "Install spacegrids simply by running (on command line):\n",
      "\n",
      "    pip install spacegrids\n",
      "\n",
      "On Mac, pip can be installed via \"sudo easy_install pip\". On Ubuntu/ Debian, install dependencies via package manager if pip install fails:\n",
      "\n",
      "    apt-get install python-{tk,numpy,matplotlib,scipy}\n",
      "\n",
      "\n",
      "License\n",
      "-------\n",
      "\n",
      "The project is licensed under the BSD license.\n",
      "\n",
      "\n",
      "Tutorial\n",
      "--------\n",
      "\n",
      "\n",
      "In this tutorial, we will analyse some real output from the <a href='http://climate.uvic.ca/model/'> UVic climate model</a>. We will compare ocean temperatures and circulation between the imaginary case where the Drake Passage (between Antarctica and South America) is closed, and one where it is open (as it is today). I am interested to hear feedback, and you are welcome to e-mail me. "
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Data files and initializing a project"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Data on disk"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Before using the spacegrids module for data analysis, some minimal organizing of your Netcdf data files inside directories is helpful. The module looks for projects inside the ```~/PROJECTS``` directory tree. Create ```~/PROJECTS``` and mark a subdirectory of ```~/PROJECTS```, say ```test_project``` as a project directory simply by including a text file named projname containing the name of the project (e.g. ```echo foo > projname ``` for a project named foo). Passing the ```nonick = True``` argument to ```sg.info``` and ```sg.Project``` removes the need for the projname file (see below). This is recommended for initial use. Subdirectories, and Netcdf files placed alongside them, in such a project directory (```test_project```) are interpreted as experiment output. So both a directory and a Netcdf file can be associated with an experiment. The case where a subdirectory represents an experiment allows the added advantage of being able to store the configuration files that were used by the numerical (Fortran or C) experiments alongside their Netcdf output files. There is a natural framework in spacegrids for parsing these configuration files and adding the parameters to ```exper``` objects, allowing the subsequent creation of new coord and axis objects across experiments from these parameters (e.g. CO2 concentrations, specified in the configuration file, might vary across experiments: maybe you would like to see how the average air temp and other quantities depend on it, casting pCO2 as a ```Coord```). However, basic productive use of spacegrids is much simpler than this.\n",
      "\n",
      "We will analyze some climate model data using a ```~/PROJECTS``` tree containing two numerical model output files (experiments) and one file containing observational data (also captured in an experiment object), in this case each placed within their own experiment directory. This example project tree can be obtained by: wget  http://web.science.unsw.edu.au/~wsijp/code/examples.tar.gz. (Unpacking is done inside the ```$HOME``` directory using tar xvzf examples.tar.gz, unless a different root path is set.) This provides a project called ```test_project``` (note the text file called projname). The UVic climate model output consists of equilibrium fields where the model has been run with two geographic configurations. One where the Drake Passage has been closed by introducing an artificial land bridge (the ```DPC``` subdirectory), and one where the passage remains open (the ```DPO``` subdirectory).  In addition, there is some observational data, the file ```Lev.cdf``` placed alongside the two experiment directories, to compare the model results to. "
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Getting started"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "With the directory tree in place, we can start analyzing the data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import spacegrids as sg"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "figsize(8, 4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Overview of the available data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The info function returns a dictionary of all the available project names and their paths. Here, we have one project (named ```my_project```). Make sure there is at least one project listed when following these examples. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "D = sg.info_dict()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "D"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "{'my_project': '/home/wim/PROJECTS/test_project/'}"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Using ```ls``` to examine the project directory content for ```test_project```, we see two experiment directories and one Netcdf file. The sg module will interpret all three as experiment objects. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls /home/wim/PROJECTS/test_project/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[0m\u001b[01;34mDPC\u001b[0m/  \u001b[01;34mDPO\u001b[0m/  \u001b[01;32mLev.cdf\u001b[0m*  projname\r\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To obtain a summary of the available projects while obtaining the paths, use ```D = sg.info()```. To start a project instance, named ```P```, it is easy to use:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "P = sg.Project(D['my_project'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Creating exp object DPO from /home/wim/PROJECTS/test_project/\n",
        "Creating exp object DPC from /home/wim/PROJECTS/test_project/"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Creating exp object Lev.cdf from /home/wim/PROJECTS/test_project/"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "OK. Inferring direction from dimension name X itself. Renaming Coord to X_crd. \n",
        "OK. Inferring direction from dimension name Z itself. Renaming Coord to Z_crd. \n",
        "OK. Inferring direction from dimension name Y itself. Renaming Coord to Y_crd. \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        " spacegrids/fieldcls.py:4507: UserWarning:(mild): no units assigned to pft in cdfsniff \n",
        " spacegrids/fieldcls.py:4507: UserWarning:(mild): no units assigned to type in cdfsniff \n",
        " spacegrids/fieldcls.py:4507: UserWarning:(mild): no units assigned to pft_edges in cdfsniff \n",
        " spacegrids/fieldcls.py:4507: UserWarning:(mild): no units assigned to type_edges in cdfsniff \n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Ignore the harmless warnings. They relate to the interpretation of the axes and dimension names found in the Netcdf files. Note that the ```Lev.cdf``` experiment object is named after the file, whereas the other two are named after the directories. Passing the nonick = True option would have been: ```D = sg.info(nonick=True)``` and ```P = sg.Project(D['my_project'],nonick = True)```. In this case, no projname file would be required, and the project would receive the same name as the directory, unless the ```name``` is specified.  Upon project initialization, meta data associated with the grids, dimensions and axis directions is collected from the data files and interpreted. The project is now initialized. Subdirectories of the ```test_project``` directory correspond to (numerical) experiments and also ```Exper``` objects. If Netcdf files had been present in the ```test_project``` directory, they would have been interpreted as experiments. In this case, an ```Exper``` object (experiments) corresponds not to subdirectory, but a single Netcdf file. This latter arrangement corresponds to the structure of a project saved with ```P.write()```, where all experiments are written to experiment files. If a path to a file (not project directory) is provided to ```sg.Project```, a project is initialised assuming all project is contained in that single file."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "The Field class"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Fields and their grids"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To look at an example of fields (e.g. temperature or humidity), it is best to load one into the project."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "P.load('O_temp')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "OK. Fetched Field O_temp for DPO. 1 file found.\n",
        "OK. Fetched Field O_temp for DPC. 1 file found.\n",
        "O_temp for Lev.cdf could not be read."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "\n",
      "The ocean temperature ```Field``` is now loaded into memory and associated with the project P. How do you know what variable name to pick? Inspection on the Bash command line of the Netcdf file with ```ncdump -h``` usually provides this information. Alternatively, experiments have an ls function: ```P['DPO'].ls()``` would yield a long list of available variables to be loaded as fields."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Notice that ```O_temp``` has not been loaded for Lev. This is because the Netcdf file inside the ```Lev``` directory follows different naming conventions. Inspection of the Netcdf file in the ```Lev``` directory reveals that temperature is referred to as ```otemp```. To load it, we use:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "P.load('theta')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        " spacegrids/fieldcls.py:4689: RuntimeWarning:invalid value encountered in equal \n",
        " spacegrids/fieldcls.py:4689: RuntimeWarning:invalid value encountered in isnan \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "theta for DPO could not be read.\n",
        "theta for DPC could not be read.\n",
        "OK. Fetched Field theta for Lev.cdf. 1 file found."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        " spacegrids/fieldcls.py:4708: RuntimeWarning:invalid value encountered in equal \n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now all three temperature fields are loaded. We access the temperature fields of the ```DPO``` experiment and the observations ```Lev``` and test resulting field instances:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TEMP = P['DPO']['O_temp'] \n",
      "TEMP2 = P['Lev.cdf']['theta']\n",
      "\n",
      "TEMP # a field"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "O_temp"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The shape of a ```Field``` corresponds to the shape of the Numpy array it contains and is consistent with the shape of its grid:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TEMP.shape # show the shape of the numpy array containing the data. This is a 3D field"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "(19, 100, 100)"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TEMP.grid # show the grid on which the temp data is defined"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "(depth, latitude, longitude)"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The names of the grid components depth, latitude and longitude correspond to the names used in the Netcdf file. For instance, there are different naming conventions for the Lev data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TEMP2.grid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "(Z_crd, Y_crd, X_crd)"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In fact, the ```Coord``` (dim) names in the Lev.cdf file were X, Y, Z, but since these names coincide with the axes names, they have been automatically renamed with the _crd suffix to distinguish them from the axes:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TEMP2.grid[0].axis"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "Z"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Fields can be saved to Netcdf again, along with all their metadata and coordinates via their write method, e.g. ```TEMP2.write()```. To specify the path for instance: ```TEMP2.write(path = '/home/me/')```. Experiment and coord objects also have save methods. In the case of an experiment, all loaded fields are saved to one file. An entire project can be saved with ```P.save()```, yielding a project file structure (directory containing a projname text file and Netcdf files corresponding to experiments). Generally, it is good to provide a ```name``` argument different from the project name, and safeguards exist to avoid overwriting the original project on disk, but no safeguards exist yet to check for multiple instances of the same project name (defined in the projname file): for now, this is up to the user to check. Finally, an arbitrary list of fields can be saved with ```sg.nugget```."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "An overview of the experiments and loaded fields corresponding to the project are shown by:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "P.show() # the registered experiments can be viewed here"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "---- my_project ----\n",
        "DPC         \tDPO         \tLev.cdf     \n",
        "\n",
        "\n",
        "Project using 9.62 Mb.\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And for an experiment:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "P['DPO'].show() # the loaded fields can be viewed here"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DPO\n",
        "----------\n",
        "O_temp                        \n",
        "\n",
        "Exper using 0.73 Mb. 1 field loaded.  \n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "*** Warning: whenever the sg module is reloaded (e.g. with reload(sg)), all the above steps and the below steps need to be done again in sequence. Stale objects will lead to strange errors! ***"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Slicing, plotting and interpolation"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Objects representing general coordinate directions (X,Y axes etc) have been constructed during initialization of project P, and can be accessed as follows:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "P['DPO'].axes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "[T, X, Y, Z]"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Bring these axes into the namespace under their own names:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for c in P['DPO'].axes:\n",
      "  exec c.name + ' = c'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So that we can reference them:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "X"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Field objects allow slicing by reference to axis name and using ndarray index values. Here, we use the axis object, followed by a slice object (e.g. ```TEMP[X,5]``` or ```TEMP[Z,:]``` or ```TEMP[Y,55:]``` or ```TEMP[X,slice(55,None,None))]```. We will refer to the use of ```Ax``` and ```Coord``` objects in ```__getitem__``` as \"hybrid slice notation\". To plot the surface temperature using sg version of contourf:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sg.contourf(sg.squeeze(TEMP[Z,0]))\n",
      "clb = plt.colorbar()\n",
      "clb.set_label(TEMP.units)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dTNI9RDPfnQCsBcyPR1C8PtTcAEl6ebifl7Q+8FOiGtH7iYqUtyR6hQcAjwLv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\nBeCC6ziO4zgF4ILrOI7jOAXw/wGyjYYdi2cQiwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xafbf98ac>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The sg module automatically selects sensible contour levels and x,y ticks. Versions older than 1.1.4 lack this functionality. The number of levels can be set using argument ```num_cont```. Alternatively, the Matplotlib levels arguments can be passed. Labels and orientation are extracted from the grid metadata. The field grid is used to extract plotting information, unless another grid is specified in the grid argument."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sg.contourf(sg.squeeze(TEMP[X,50])) # a section at index 50.\n",
      "clb = plt.colorbar()\n",
      "clb.set_label(TEMP.units)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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F7NzrM+oXeD1wlKS3AVsD20u6JCLe3+0S2/EQt5mZWRIRp0bEtIjYCzgW+I9+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\ngv4/1gFM6+EPFLYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xafbf9b8c>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Slicing can also be done via indices using the normal ndarray notation:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(TEMP[0,:,:]).shape == (TEMP[Z,0]).shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Another note of warning on stale objects. Slicing using ```Ax``` objects is a common case where stale objects after a module reload lead to strange errors. If we were to reload the module in the above case, and bring the ```X```,```Y```,```Z```,```T``` ```Ax``` objects into the namespace again, but use the now stale old ```TEMP``` field, the above slicing with ```Z``` will lead to a perplexing error. All objects will need to be refreshed in this case, which means redoing all the previous steps. In most common cases a module reload is not required, and this type of confusion arises mostly in module code development situations.  "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The ```load``` method (of the Project and Exper classes) also allows memory loading of subsets of the data only by passing the ```slices``` argument: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "P.load('O_sal',slices=(Z,0,X,50))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "OK. Fetched Field O_sal for DPO. 1 file found.\n",
        "OK. Fetched Field O_sal for DPC. 1 file found.\n",
        "O_sal for Lev.cdf could not be read.\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "P['DPO'].show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DPO\n",
        "----------\n",
        "O_temp                         O_sal_sliced                  \n",
        "\n",
        "Exper using 0.73 Mb. 2 fields loaded.  \n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "P['DPO']['O_sal_sliced'].grid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "(latitude)"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Means are calculated easily by division."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Determine zonal mean mT of TEMP.\n",
      "\n",
      "mTEMP = TEMP/X\n",
      "mTEMP2 = TEMP2/X"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        " spacegrids/fieldcls.py:3342: RuntimeWarning:invalid value encountered in multiply \n",
        " spacegrids/fieldcls.py:3505: RuntimeWarning:invalid value encountered in isnan \n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Basin-wide means are calculated using the ```maskout``` method to mask out all values outside the domain enclosing the node (a x,y location) argument. ```maskout``` uses a floodfill algorithm to mask the basin up to its boundaries. Note the hybrid notation for the node coordinates."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# mask out the world ocean outside the Atlantic with NaN and take zonal mean.\n",
      "\n",
      "mTEMP_masked=(TEMP[Y,33:].maskout(node = (X,85,Y,30) ) )/X # node is a point in the Atlantic"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plotting these two means for the upper 1600m or so:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "# --- start figure ---\n",
      "\n",
      "lbl = ord('a')\n",
      "\n",
      "pan = 0\n",
      "\n",
      "height = 2\n",
      "width = 1\n",
      "\n",
      "rows = 2\n",
      "cols = 1\n",
      "\n",
      "\n",
      "ax = plt.subplot2grid((height, width), (int(np.floor(pan/cols)), pan%cols) )\n",
      "\n",
      "# use the sg wrapper for the contour function.\n",
      "cs= sg.contourf(mTEMP[Z,:10],linestyles='solid',showland=True); # slice only upper ocean\n",
      "plt.tick_params(axis='x',labelbottom='off')\n",
      "plt.xlabel('')\n",
      "plt.ylabel('Depth (m)')\n",
      "tle = plt.title('('+ chr(lbl) +') Global upper ocean temp ')\n",
      "\n",
      "lbl += 1\n",
      "pan += 1\n",
      "\n",
      "ax = plt.subplot2grid((height, width), (int(np.floor(pan/cols)), pan%cols) )\n",
      "\n",
      "cs= sg.contourf(mTEMP_masked[Z,:10],linestyles='solid',showland=True);  # slice only upper ocean\n",
      "\n",
      "# Overiding the xlabel assigned by sg:\n",
      "plt.xlabel('Latitude')\n",
      "tle = plt.title('('+ chr(lbl) +') Atlantic upper ocean temp ')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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OYt9ryDrLCE+cfn/Xon4jD/X3QY36/Usn/mbWtnluZqcCpyak3wzsnqddA+PCvzYpQv9p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\njuM4NcPF33Ecx3Fqxv8Hmh4AQ7O74wAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xaf6e56ac>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mTEMP.grid  # the dimensionality of the zonal mean is less"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "(depth, latitude)"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A calculated field such as ```mTEMP``` can be inserted into the project using the ```insert``` method. This is generally not needed, but can be useful when the project or experiment is later written to disk. The insert method takes (name,value) pairs. If the value is a field, it is inserted into the ```vars``` attribute under ```name```, otherwise into ```params```."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "P['DPO'].insert( ('mtemp', mTEMP ) )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, ```mtemp``` is part of the DPO experiment:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "P['DPO'].show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DPO\n",
        "----------\n",
        "O_temp                         O_sal_sliced                  \n",
        "mtemp                         \n",
        "\n",
        "Exper using 0.73 Mb. 3 fields loaded.  \n"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To see the effect of opening Drake Passage in the climate model: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sg.contourf(sg.squeeze(P['DPO']['O_temp'][Z,0]-P['DPC']['O_temp'][Z,0]),cmap = mpl.cm.RdYlBu_r,levels = range(-8,9,1))\n",
      "clb = plt.colorbar(ticks = range(-8,9,2))\n",
      "clb.set_label(TEMP.units)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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TkxzvT7i42PT9h+25Y2XlmNaoJuVXRsRngJ0lvTl7E8lMUx+qtHQF9K+R2H+9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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xaf981fac>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Data is generally defined on different grids, so interpolation is needed. In our example, the observations are defined on a finer grid than the model data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TEMP2.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "(33, 180, 360)"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To overlay a plot of the model geography on atmospheric fields, we can load the ocean bathymetry field G_kmt, interpolate it on a finer grid with ```sg.finer_field``` (to avoid angled contours and show true resolution) and use contour plot over the 0.5 contour to capture the transition from land (value 0) to ocean(values 1 to 19). Here, we overlay this geographic outline over sea level air temperature:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "P.load(['G_kmt','A_slat'])\n",
      "KMT = P['DPO']['G_kmt']\n",
      "SAT = P['DPO']['A_slat']\n",
      "nc = 10\n",
      "sg.contourf(SAT, num_cont = nc,cmap = mpl.cm.RdYlBu_r)\n",
      "cs=sg.contour(SAT,linestyles='solid', num_cont = nc, colors = 'k')\n",
      "plt.clabel(cs,fmt='%1.0f')\n",
      "sg.contour(sg.finer_field(KMT), colors='k',levels=[0.5,]  )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "OK. Fetched Field G_kmt for DPO. 1 file found.\n",
        "OK. Fetched Field G_kmt for DPC. 1 file found.\n",
        "G_kmt for Lev.cdf could not be read.\n",
        "OK. Fetched Field A_slat for DPO. 1 file found."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "OK. Fetched Field A_slat for DPC. 1 file found.\n",
        "A_slat for Lev.cdf could not be read.\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        " spacegrids/fieldcls.py:4711: UserWarning:missing value not set to NaN. \n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "<matplotlib.contour.QuadContourSet instance at 0xaed210ac>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        " /usr/local/lib/python2.7/dist-packages/matplotlib/text.py:52: UnicodeWarning:Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal \n",
        " /usr/local/lib/python2.7/dist-packages/matplotlib/text.py:54: UnicodeWarning:Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal \n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NMuQL4vOFGBoK4feHCQQipFLpcVLEAMQ6m81x1JHzOPzwOWg0KtRqlTSIVKkU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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xaf6e554c>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To interpolate a field ```F``` in Spacegrids, simply call the ```regrid``` method of ```F``` on the desired grid ```gr``` as ```F(gr)```. For instance, to compute the difference between the zonal mean of the model and the observations:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dmTEMP = mTEMP.regrid(mTEMP2.grid) - mTEMP2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As a shorthand, call the regrid method simply by calling the field itself on the target grid:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dmTEMP = mTEMP(mTEMP2.grid) - mTEMP2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cont_int = np.arange(-6.,6.,1.)\n",
      "cmap = mpl.cm.RdYlBu_r\t# Colormap red yellow blue reversed\n",
      "pl1 = sg.contourf(dmTEMP,  levels = cont_int, cmap = cmap);\n",
      "\n",
      "clb = plt.colorbar(ticks = cont_int)\n",
      "clb.set_label(TEMP.units)\n",
      "plt.xlim([-80.,70.])\n",
      "plt.xticks([-60,-30,0,30,60])\n",
      "plt.ylabel('Depth (m)')\n",
      "tle = plt.title(' Zonal avg. T difference model - observations')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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q158+5Yr6RZYBk3OvJ5PVortupGdxf5Xs+vPXgBcAU4H9yIL6V4HdIuLr3cik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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xaf70066c>"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The model is pretty good! It is generally a bit too warm (perhaps due to the use of post-industrialized CO2). The resulting grid is defined on that of ```Lev```:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dmTEMP.grid # the resulting grid is that of the Lev data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "(Z_crd, Y_crd)"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We have seen that it is easy to regrid data. This is also the case for data defined on grids of different dimensions. The ocean exhibits siginificant variations from its mean with longitude, leading to differences in local climate. How would we compare the sea surface temperature to its zonal mean? The following is bound to give an error, as the zonal mean is defined on a different (smaller dimensional) grid than temperature:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sTEMP = TEMP[Z,0]# slice to obtain surface temperature \n",
      "dTEMP = sTEMP - mTEMP[Z,0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "Exception",
       "evalue": "Field shape error in O_temp_sliced-O_temp_sliced with Field O_temp_sliced: shapes must match. Try F - G(F.grid) or F(G.grid) - G.",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-38-1428ceee0c29>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0msTEMP\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTEMP\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mZ\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;31m# slice to obtain surface temperature\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdTEMP\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msTEMP\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mmTEMP\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mZ\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;32m/home/wim/github/spacegrids/spacegrids/fieldcls.pyc\u001b[0m in \u001b[0;36m__sub__\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m   3132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3133\u001b[0m       \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3134\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Field shape error in %s-%s with Field %s: shapes must match. Try F - G(F.grid) or F(G.grid) - G.'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mother\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3135\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3136\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mException\u001b[0m: Field shape error in O_temp_sliced-O_temp_sliced with Field O_temp_sliced: shapes must match. Try F - G(F.grid) or F(G.grid) - G."
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The error message provides a hint: regrid one term. regridding ```mTEMP[Z,0]``` onto ```sTEMP.gr``` expands the right term (by creating equal copies in the X direction, this is called broadcasting) to yield the right shape:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "dTEMP = sTEMP - (sg.squeeze(mTEMP[Z,0])).regrid(sTEMP.grid)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pl1 = sg.contourf(sg.squeeze(dTEMP), cmap = mpl.cm.RdYlBu_r)\n",
      "clb = plt.colorbar()\n",
      "clb.set_label('C')\n",
      "tle = plt.title('Sea Surface Temperature difference from zonal mean')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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xdil7XJy73MYc9hS6P/z9iIj8CRgB/mSsc62xyXXAro0W0iCrS9RXAETkdFX9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\nYZSDIAiCoCSEUQ6CIAiCkvAmNAFSI4dsxg4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xaecf874c>"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The North Atlantic is warmer than at similar Pacific latitudes, leading to a milder western European climate. A pronounced zonal temperature difference is also apparent in the tropical Pacific (related to the \"warm pool\")."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To compute the meridional streamfunction, we load the velocity in the y direction and apply the calculations in simple notation:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Give the experiment objects convenient names E, E2:\n",
      "E = P['DPO'];E2 = P['DPC']\n",
      "varname = 'O_velY'\n",
      "\n",
      "# load velocity by netcdf name.\n",
      "P.load(varname)\n",
      "\n",
      "# obtain velocity as sg field object V from project.\n",
      "V = E[varname]\n",
      "V2 = E2[varname]\n",
      "\n",
      "# take the meridional stream function by taking zonal grid-integral X*V \n",
      "# and the vertical primitive of the result along Z using the pipe.\n",
      "PSI = Z|(X*V)*1e-6\n",
      "PSI2 = Z|(X*V2)*1e-6"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "OK. Fetched Field O_velY for DPO. 1 file found.\n",
        "OK. Fetched Field O_velY for DPC. 1 file found."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "O_velY for Lev.cdf could not be read.\n"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now that we have the required fields, we can plot them. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lvls = np.arange(-100,100,4)\n",
      "xlims = [-80,70]\n",
      "ylims = [-4500., -0.]\n",
      "\n",
      "# --- start figure ---\n",
      "\n",
      "lbl = ord('a')\n",
      "\n",
      "pan = 0\n",
      "\n",
      "height = 2\n",
      "width = 1\n",
      "\n",
      "rows = 2\n",
      "cols = 1\n",
      "\n",
      "F = PSI\n",
      "ax = plt.subplot2grid((height, width), (int(np.floor(pan/cols)), pan%cols) )\n",
      "\n",
      "# use the sg wrapper for the contour function.\n",
      "cs= sg.contour(F,linestyles='solid',showland=True,levels = lvls,colors='k');\n",
      "\n",
      "plt.clabel(cs,fmt='%1.1f');\n",
      "\n",
      "plt.xlim(xlims)\n",
      "plt.ylim(ylims)\n",
      "plt.tick_params(axis='x',labelbottom='off')\n",
      "plt.xlabel('')\n",
      "plt.ylabel('Depth (m)')\n",
      "tle = plt.title('('+ chr(lbl) +') MOC Drake Passage open ')\n",
      "\n",
      "lbl += 1\n",
      "pan += 1\n",
      "\n",
      "F = PSI2\n",
      "ax = plt.subplot2grid((height, width), (int(np.floor(pan/cols)), pan%cols) )\n",
      "\n",
      "cs= sg.contour(F,linestyles='solid',showland=True,levels = lvls,colors='k');\n",
      "plt.clabel(cs,fmt='%1.1f');\n",
      "plt.xlim(xlims)\n",
      "plt.ylim(ylims)\n",
      "\n",
      "# Overiding the xlabel assigned by sg:\n",
      "plt.xlabel('Latitude')\n",
      "tle = plt.title('('+ chr(lbl) +') MOC Drake Passage closed ')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Q09m+zc3NKSoq4v79+9y/f5/c3Fzu37/PvXv3FJ8LU1NTevTogbW1NUlJSURG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\nIpFIKhhS/CUSiUQiqWD8PxV3WzMafi7jAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xaeb0f76c>"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There is strong southern sinking in the DP closed case, and a switch to northern sinking in the DP open case. That's why there was warming in the NH."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Again, the Atlantic MOC can be computed by using the ```maskout``` of Fields, using ```V_masked = V[Y,33:].maskout(node = (X,85,Y,30) )``` instead of ```V``` (see above description for zonal temperature mean). See our special recipy for this."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Field concatenation is done with the concatenate function, similar to Numpy. If we split surface air temperature ```SAT``` latitudinally into three pieces:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SAT1=SAT[Y,:40];SAT2=SAT[Y,40:50];SAT3=SAT[Y,50:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And re-assemble along the default axis ```ax = None```, sg will guess to use the incomplete (differing) axes:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SAT_cat = sg.concatenate([SAT1,SAT2,SAT3])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 44
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "(This yields the same result as ```sg.concatenate([SAT1,SAT2,SAT3],ax=Y)```.) Drawing the zonal mean shows that ```SAT``` has been re-assembled correctly:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(SAT_cat/X).draw()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "([<matplotlib.lines.Line2D at 0xaea1806c>], None)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9+7e/xY5ERCR3tt02LKN75JHw7bexo8lfK0z47v60u3cFdgReBs4F1jOzO2o6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\ndp7QMXY4IwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xb0838ecc>"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If multiple files are found in an experiment directory, and they contain the same field, sg will assume they are pieces of the same field and concatenate them upon loading. An example would be files containing different time slices of the same field (e.g. temperature)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Tip: field objects have a squeeze and unsqueeze method: use help to learn more about them. Fields are squeezed upon loading and unsqueezed when saved."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Grids and coord objects"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Grid (```Gr```) objects model the grids on which the field data is defined. They consist of tuples containing ```Coord``` objects. ```Coord``` objects consist of points along an axis and are read and named from the Netcdf files. To bring these objects into the namespace under their own name: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for c in E.cstack:\n",
      "  exec c.name +' = c'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This allows us to reference the ```Coord``` objects built from the Netcdf file by the names extracted from the metadata:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "depth[:] # depth coordinate points"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "array([   17.5,    82.5,   177.5,   302.5,   457.5,   642.5,   857.5,\n",
        "        1102.5,  1377.5,  1682.5,  2017.5,  2382.5,  2777.5,  3202.5,\n",
        "        3657.5,  4142.5,  4657.5,  5202.5,  5777.5])"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "latitude[:10] # latitudinal coordinate points (truncated)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "array([-89.09999847, -87.30000305, -85.5       , -83.69999695,\n",
        "       -81.90000153, -80.09999847, -78.30000305, -76.5       ,\n",
        "       -74.69999695, -72.90000153])"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The result of the ```__getitem__``` method here is the ndarray containing the data of the coordinate points. It is important to realize that these ```coord``` names (depth, latitude etc) have been read from the Netcdf file and have not been hard coded into Spacegrids. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A shorthand for grid construction from ```Coord``` objects is via multiplication:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "latitude*longitude"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "(latitude, longitude)"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "latitude*longitude*depth"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "(latitude, longitude, depth)"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The latter 3 dimensional grid is associated with ndarray data where the first index represents latitude, the second longitude and the third depth. Field objects contain a grid object that is always congruent with the ndarray holding the field data. The grid objects contained in fields are used to always ensure data alignment, for instance under multiplication."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Coord objects and grid objects are not the same. To construct a one dimensional ```Gr``` (grid) object: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "latitude**2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "(latitude)"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Axis alignment"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We had:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TEMP.grid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "(depth, latitude, longitude)"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "With data in the ndarray:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(TEMP[:]).shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "(19, 100, 100)"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "where we recognise the length of the depth coord as 19: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(depth[:])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "19"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Rearrangement of the coordinates:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TEMP_shuffled = TEMP(latitude*longitude*depth)\n",
      "TEMP_shuffled.grid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "(latitude, longitude, depth)"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(TEMP_shuffled[:]).shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "(100, 100, 19)"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Data axis alignment is automatic. For instance, multiplying temperature with velocity:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "F = TEMP*V"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "F.grid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "(depth, latitude, longitude)"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Multiplication alignment works regardless of the order of the coordinates:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "F2 = TEMP_shuffled*V\n",
      "F2.grid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "(latitude, longitude, depth)"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here, ```V``` is actually defined on a different grid: the velocity grid:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "V.grid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "(depth, latitude_V, longitude_V)"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Why then is the result of ```TEMP*V``` defined on the temperature grid? This is because multiplication automatically triggers interpolation of the right multiplicant to the grid of the left multiplicant. Vice versa:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(V*TEMP).grid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 61,
       "text": [
        "(depth, latitude_V, longitude_V)"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Axis objects"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The objects ```P['DPO']```, ```P['DPC']``` and ```P['Lev']``` are experiment objects, and correspond to the directories containing the Netcdf files. Different data (\"experiment\") files may contain data defined on different grid and use different coordinate naming conventions. A list of the coord objects associated with an experiment object is accessed from the .cstack attribute:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "P['Lev.cdf'].cstack"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "[X_crd, Z_crd, Y_crd]"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "P['DPO'].cstack[:4] # truncated list of coord objects"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 63,
       "text": [
        "[time, longitude, longitude_edges, latitude]"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The depth coordinates differ for the model and the observational data."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "However, both coord objects point in the same direction: ```Z```. The name (\"Z\") of this direction has also been read from the Netcdf files. This is encapsulated in the ```ax``` class (axis)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "depth.axis"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "Z"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Multiplication of a ```Gr``` (grid) with an ```Ax``` object (representing an axis) picks the ```Coord``` component along that axis."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X*(latitude*longitude)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "longitude"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We also saw the ```Ax``` object being used to calculate zonal means. In the following example, we load the surface heat flux, demonstrate some operations using ```Ax``` objects and compute the poleward heat transport."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# load heat flux by netcdf name.\n",
      "P.load('F_heat')\n",
      "\n",
      "# obtain oceanic heat flux as sg field object HF from project.\n",
      "HF = P['DPO']['F_heat']\n",
      "HF2 = P['DPC']['F_heat']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "OK. Fetched Field F_heat for DPO. 1 file found.\n",
        "OK. Fetched Field F_heat for DPC. 1 file found.\n",
        "F_heat for Lev.cdf could not be read."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Again, ignore the harmless warning: the Lev data contains no heat flux data. The heat flux data is 2 dimensional, as can be seen from the grid:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HF.grid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 67,
       "text": [
        "(latitude, longitude)"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And looks like this:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pl = sg.contourf(HF,levels = range(-200,200,20),cmap = mpl.cm.RdYlBu)\n",
      "clb = plt.colorbar()\n",
      "clb.set_label(HF.units)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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fM5vyhXrJgrlD20Vi3bZImzj3kyK5uHcFfqOq54jII0Vkmaquqatgk0RW146k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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xaf7ee96c>"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Shift fields by reference to the ```Ax``` objects:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HF_shifted = sg.roll(HF,coord = X,shift = 50)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pl = sg.contourf(HF_shifted,levels = range(-200,200,20),cmap = mpl.cm.RdYlBu)\n",
      "clb = plt.colorbar()\n",
      "clb.set_label(HF.units)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Ktsg5ih5vz/nzUjOYZVGXxRw/vjET1zuz5/x5mffcV5wjhq+73/PQpcq7zLMc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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xaf90ad6c>"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HF_shifted.grid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 71,
       "text": [
        "(latitude, longitude_rolled)"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note that the grid is updated along with the data (the zero meridian still passes through Greenwich), resulting in a different grid (```longitude_rolled``` appears in place of ```longitude```). This behaviour can be disabled."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Compute the zonal integral (using multiplication notation ```HF*X```) followed by the primitive in the ```Y``` direction (using the pipe | notation):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Determine the total oceanic poleward heat transport via the y-primitive and X-intergral, and scale to Petawatt.\n",
      "\n",
      "PHT = Y|(HF*X)*1e-15\n",
      "PHT2 = Y|(HF2*X)*1e-15"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pl1, = sg.plot(PHT,color = 'b');\n",
      "pl2, = sg.plot(PHT2,color='r');\n",
      "\n",
      "plt.grid()\n",
      "plt.xlim([-80.,80.])\n",
      "plt.xticks([-60,-30,0,30,60])\n",
      "plt.ylabel('PW')\n",
      "tle = plt.title(' Ocean PHT (PW) ')\n",
      "lgnd = plt.legend([pl1,pl2],['DP open','DP closed'],loc=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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OndJWuXKYTuSjOnXgxRcJvPoq/PGHdjEMHAjbtx/7vYaKFfX6aM4c+Oc/tXEn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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xac94cd2c>"
       ]
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Interesting, the Drake Passage closed configuration has more southward poleward heat transport. We could have used the ```coord``` names to compute the PHT:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "PHT = latitude|(HF*longitude)*1e-15"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 74
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The axis names X,Y,... are a shorthand for these coord names, and are easier to use. "
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "More grid operations"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Operations such as derivatives and integration are methods of the ```coord``` class:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "latitude.der # the derivative on the sphere in the latitudinal direction"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 75,
       "text": [
        "<bound method YCoord.der of latitude>"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X.der"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 76,
       "text": [
        "<bound method Ax.der of X>"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HF_shifted2 = longitude.coord_shift(HF,50)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HF_shifted2.grid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 78,
       "text": [
        "(latitude, longitude_rolled)"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Do we get the same result from both shifts? This is a shorthand to access the X ```coord``` of ```HF_shifted2```:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# obtain coord in X direction\n",
      "X*(HF_shifted2.grid) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 79,
       "text": [
        "longitude_rolled"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The two shifted ```coord``` objects are different objects"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# compare the ndarray content of the X components of the two shifted grids\n",
      "X*(HF_shifted2.grid) == X*(HF_shifted.grid)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 80,
       "text": [
        "False"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "But contain the same values:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "((X*(HF_shifted2.grid))[:] == (X*(HF_shifted.grid))[:]).all()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 81,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Other derived ```Coord``` objects are obtained from slicing:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HF_sliced = HF_shifted[Y,:36]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pl = sg.contourf(HF_sliced,levels = range(-200,200,20),cmap = mpl.cm.RdYlBu)\n",
      "clb = plt.colorbar()\n",
      "clb.set_label(HF.units)\n",
      "tle = plt.title('Heat flux in Southern Ocean')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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cevj4n1M/eUPcpwIHmdn+ZrY/cBDw2frMKkBaLbuMOHvi+ZFjFES6zDW0uYLS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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xaf7818ac>"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Similar to the roll function, slicing gives a new grid:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HF3 = HF[X,:50,Y,:36]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HF3.grid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 85,
       "text": [
        "(latitude_sliced, longitude_sliced)"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(Y*HF3.grid)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 86,
       "text": [
        "36"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The sum method of a ```Coord``` object only sums the ```ndarray``` content of a ```Field```:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sTEMP = longitude.sum(TEMP)\n",
      "sTEMP.grid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 87,
       "text": [
        "(depth, latitude)"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Similar to Pandas, the NaN (```np.nan```) values are not counted in these operations. Similarly, the vsum method is the sum weighted with the volumes (lengths, areas) of the grid cells:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "help(longitude.vsum)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Help on method vsum in module spacegrids.fieldcls:\n",
        "\n",
        "vsum(self, F) method of spacegrids.fieldcls.XCoord instance\n",
        "    Sums Field along self Coord, weighted with grid cell width (using self.d(), called by self.vol(F.grid)). Note: due to possible dependence of one Coord on the other, only use mean method of grid. There is no mean method for Coord objects.\n",
        "    \n",
        "    Method of Coord  that sums Field F along self Coord direction, weighted with the grid cell width. Calls sum method. See sum method.\n",
        "    \n",
        "    Calculation is self.sum(F*(self.vol(F.grid))). For grids with grid cell width depending on coordinates, use corresponding Gr methods.     \n",
        "    \n",
        "    Args:\n",
        "      F: (Field) field of certain dimension n to sum\n",
        "      \n",
        "    Returns:\n",
        "      Field of dimension n-1 or float if n=1. \n",
        "    \n",
        "    Raises:\n",
        "      ValueError: when Coord (self) is not in F.grid (grid of Field).\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Similar for cumsum and vcumsum."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Grid objects also have volume weighted sum methods. These methods are preferred over their ```Coord``` counterparts as coordinate cell dimensions may depend on other coordinates (e.g. the distance between longitudinal arcs diminishes towards the poles), and grid objects take care of this. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "my_grid = latitude*longitude\n",
      "my_grid.vsum(TEMP)[:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 89,
       "text": [
        "array([  6.43187995e+15,   5.82743640e+15,   4.73322880e+15,\n",
        "         3.79210234e+15,   3.00009346e+15,   2.34159369e+15,\n",
        "         1.79678076e+15,   1.34723259e+15,   1.01153068e+15,\n",
        "         8.09927754e+14,   6.79646188e+14,   5.63901912e+14,\n",
        "         4.67317201e+14,   3.97696671e+14,   3.17287739e+14,\n",
        "         2.27232412e+14,   1.43312843e+14,   8.57740304e+13,\n",
        "         3.19512842e+13])"
       ]
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "my_grid.vsum(TEMP).grid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 90,
       "text": [
        "(depth)"
       ]
      }
     ],
     "prompt_number": 90
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The volume of the domain of non NaN values inside TEMP is found from: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TEMP.grid.vsum(TEMP.ones())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 91,
       "text": [
        "1.3576934808828324e+18"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This can also be achieved with the multiplication notation:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TEMP.ones()*(X*Y*Z)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 92,
       "text": [
        "1.3576934808828324e+18"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The ```ones``` method of ```Field``` yields a new ```Field```, where all non- NaN values (of ```TEMP``` in this case) are set to 1 and the NaN values remain NaN. Here, the one values constitute the domain of ocean water, as NaN values indicate land (rock)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This allows a calculation of the mean ocean temperature in the model:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TEMP.grid.vsum(TEMP)/TEMP.grid.vsum(TEMP.ones())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 93,
       "text": [
        "3.9464440090035096"
       ]
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# more succinctly:\n",
      "TEMP.grid.mean(TEMP)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 94,
       "text": [
        "3.9464440090035096"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This can also be achieved with the division notation:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TEMP/(X*Y*Z)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 95,
       "text": [
        "3.9464440090035096"
       ]
      }
     ],
     "prompt_number": 95
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The ```Ax``` class has a derivative method:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Calculate derivative in X direction:\n",
      "dTEMPdX = X.der(TEMP)\n",
      "dTEMPdX.grid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 96,
       "text": [
        "(depth, latitude, longitude)"
       ]
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# compute the vertical temperature profile and plot it.\n",
      "vpTEMP = TEMP/(Y*X)\n",
      "pl, = sg.plot(vpTEMP)\n",
      "lb = plt.xlabel('degrees C');tle = title('Vertical profile of average ocean temp')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zfwIzM6tV5fQp+HfgUGApQEQ8C2xXzaB6MicFZmZWq8pJCt6IiNZbJrfe+6D4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\nwEmBmZmZJU4KzMzMDID/D88lL5E/1djmAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xac8c7dac>"
       ]
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# take the vertical derivative of the vertical temperature profile and plot it.\n",
      "pl, = sg.plot(Z.der(vpTEMP))\n",
      "lb = plt.xlabel('degs C per m');tle = plt.title('Vertical temperature gradient')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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V4aTBzMwa3rBh7p6oBicNZmbW8AalUXp+gqJvnDSYmVlTcBdF3zlpMDOzpuCk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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xac3f840c>"
       ]
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Another notation, involving ```Ax``` objects, uses the ^ (carat) symbol:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# test whether nparray content is the same. Excluding the first nan value.\n",
      "((Z^vpTEMP).value[1:] == (Z.der(vpTEMP)).value[1:]).all()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 99,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The volume weighted cumulative sum method ```vcumsum``` of the ```Coord``` class, the primitive integral along that axis, is easily accessed with the pipe | notation and the ```ax``` class (here the instance ```Z```). In the latter case the name of the particular coord need not be known."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# test whether the depth coord method vcumsum yields the same result as Z|\n",
      "(depth.vcumsum(vpTEMP)[:] == (Z|vpTEMP)[:]).all() "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 100,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 100
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Operators, divergences and vector fields."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Spacegrids allows representations of vector fields (e.g. velocity fields). We're loading the zonal velocity to examine a 2 dimensional vector fields constructed from ```U``` and ```V```."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# load the ocean velocity in the X direction.\n",
      "P.load('O_velX')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "OK. Fetched Field O_velX for DPO. 1 file found.\n",
        "OK. Fetched Field O_velX for DPC. 1 file found."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "O_velX for Lev.cdf could not be read.\n"
       ]
      }
     ],
     "prompt_number": 101
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Earlier, multiplication of two fields yielded a new field. In contrast, multiplying the two fields ```U``` and ```V``` does not result in a new field here. Instead, we get a container class holding the two fields. This container is the ```vfield``` class (vector field)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# obtain horizontal velocity fields\n",
      "U = P['DPC']['O_velX']\n",
      "V = P['DPO']['O_velY']\n",
      "# create a 2 dimensional vector field defined on 3 dimensional space\n",
      "my_vector_field = U*V\n",
      "my_vector_field"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 102,
       "text": [
        "(O_velX, O_velY)"
       ]
      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Why do these fields behave differently under multiplication? Fields have a direction attribute:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "U.direction"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 103,
       "text": [
        "X"
       ]
      }
     ],
     "prompt_number": 103
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "V.direction"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 104,
       "text": [
        "Y"
       ]
      }
     ],
     "prompt_number": 104
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "TEMP.direction"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 105,
       "text": [
        "scalar"
       ]
      }
     ],
     "prompt_number": 105
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The velocity fields ```U``` and ```V``` have directions ```X``` and ```Y```, obviously indicating the direction they are pointing in: they are \"directional\" fields. In contrast, temperature ```TEMP``` is marked as a scaler, this is a \"scalar\" field. In general, fields of like direction multiply to yield a new field containing the multiplied nparray data. For instance:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# This gives a field\n",
      "U*U"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 106,
       "text": [
        "O_velX_times_O_velX"
       ]
      }
     ],
     "prompt_number": 106
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This gives a vector field of the velocity components squared:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(U*V)*(U*V)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 107,
       "text": [
        "(O_velX_times_O_velX, O_velY_times_O_velY)"
       ]
      }
     ],
     "prompt_number": 107
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Vector fields have a direction method that yields a container of the directions of its component fields."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "my_vector_field.direction()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 108,
       "text": [
        "(X,Y,)"
       ]
      }
     ],
     "prompt_number": 108
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The sg module has its own wrapper to the quiver plot method of Matplotlib that takes vector fields."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The (field) ```Operator``` class and its derived classes (e.g. sg.Der, sg.Integ, sg.FieldMul etc.) allows the construction of operators that act on fields and vector fields. E.g. div and curl operators, etc. Ideas for useful operators are shown below. Operators, say ```K1``` and ```K2```, can be multiplied before they are applied. E.g. if ```K = K1*K2```, no evaluation has occurred yet, and evaluation yields: ```K*F = K1(K2(F))```. So ```K2``` acts on ```F``` first, then ```K1``` acts on the resulting field. the curl of a 2D vectorfield U*V would then be ```curl*(U*V)```. If ```U*V``` is defined on 3D space, the ```curl``` operator acts on each z-level. For instance, the X-derivative object can be instantiated as follows:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Instatiate an X-derivative operator object\n",
      "ddX = sg.Der(X)\n",
      "ddX"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 109,
       "text": [
        "<spacegrids.ops.Der at 0xac4b276c>"
       ]
      }
     ],
     "prompt_number": 109
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ddX*TEMP"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 110,
       "text": [
        "O_temp"
       ]
      }
     ],
     "prompt_number": 110
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is equivalent to calling the ```ddX``` field operator object on ```TEMP```:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ddX(TEMP)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 111,
       "text": [
        "O_temp"
       ]
      }
     ],
     "prompt_number": 111
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Applying field operators in succession:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ddX*ddX"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 112,
       "text": [
        "<spacegrids.ops.Mul at 0xac47e3ac>"
       ]
      }
     ],
     "prompt_number": 112
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Where:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create second order X-derivative\n",
      "d2dX2 = ddX*ddX\n",
      "# Apply it to the temperature field\n",
      "d2dX2*TEMP"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 113,
       "text": [
        "O_temp"
       ]
      }
     ],
     "prompt_number": 113
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is the same as successive calls of ```ddX``` on the ```Field```:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Succesive calls of the ddX object\n",
      "( (d2dX2*TEMP).value == ( ddX(ddX(TEMP)) ).value ).any()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 114,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 114
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We could have obtained the Z derivative of the vertical temperature profile above with an operator class:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create Z-derivative object\n",
      "ddZ = sg.Der(Z)\n",
      "# Apply the derivative operator and plot the result\n",
      "pl, = sg.plot(ddZ*vpTEMP)\n",
      "lb = plt.xlabel('degs C per meter')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rgWPT9kzgirR9PXBEHeofdJr9P3z3r3E1c9/A/Wt0zd6/assrNLQBh6XphA5J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\nmZmVxKHBzMzMSvJfBAAN1XozJ2UAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xac4673ec>"
       ]
      }
     ],
     "prompt_number": 115
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Another useful field operator is ```Pick```. It picks a component of a vector field:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sg.Pick(X)*my_vector_field"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 116,
       "text": [
        "O_velX"
       ]
      }
     ],
     "prompt_number": 116
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here are definitions of some useful field operators:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ddX = sg.Der(X) # derivative in X\n",
      "ddY = sg.Der(Y) # etc\n",
      "ddZ = sg.Der(Z)\n",
      "\n",
      "pX = sg.Pick(X) # pick the component with direction X from vfield object (projection)\n",
      "pY = sg.Pick(Y) # etc.\n",
      "pZ = sg.Pick(Z)\n",
      "\n",
      "mX = sg.Mean(X) # take zonal mean of any field argument or right-multiplicant.\n",
      "mY = sg.Mean(Y) # etc\n",
      "mZ = sg.Mean(Z)\n",
      "\n",
      "sX = sg.Integ(X) # take integral\n",
      "sY = sg.Integ(Y)\n",
      "sZ = sg.Integ(Z)\n",
      "\n",
      "intX = sg.Prim(X) # take primitive function of any field argument\n",
      "intY = sg.Prim(Y)\n",
      "intZ = sg.Prim(Z)\n",
      "\n",
      "set2X = sg.SetDirection(X) # change the direction attribute of field argument to X\n",
      "set2Y = sg.SetDirection(Y)\n",
      "set2Z = sg.SetDirection(Z)\n",
      "set2scalar = sg.SetDirection(sg.ID())\n",
      "\n",
      "Avg = mZ*mX*mY  # a 3D average\n",
      "\n",
      "curl = ddX*set2scalar*pY - ddY*set2scalar*pX      #  curl operator expressed in basic operators\n",
      "\n",
      "div = ddX*set2scalar*pX + ddY*set2scalar*pY       # divergence \n",
      "\n",
      "div3 = ddX*set2scalar*pX + ddY*set2scalar*pY + ddZ*set2scalar*pZ\n",
      "\n",
      "grad = set2X*ddX + set2Y*ddY    # gradient. To be used on single field objects.\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 117
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Take surface slices\n",
      "sU = U[Z,0]\n",
      "sV = V[Z,0]\n",
      "surface_vectors = sU*sV\n",
      "(div*surface_vectors).direction"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Duplicate grids. FYI: replacing right gr.\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 118,
       "text": [
        "scalar"
       ]
      }
     ],
     "prompt_number": 118
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pl = sg.contourf(sg.squeeze((div*surface_vectors)[Y,10:-10]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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es3Gg0MwxXVjBRQWhjnmRZT0rq5p/I7N1RJ/Me9Fvb6oR6bKgS1ojoM2FYLpo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\nkLWJzOnLU8/yiJbYS6PtaT9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       "text": [
        "<matplotlib.figure.Figure at 0xac3fbdac>"
       ]
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<a href='http://nbviewer.ipython.org/github/willo12/spacegrids/blob/master/advanced.ipynb'> An advanced tutorial is here. </a>"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}