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 "metadata": {
  "name": "",
  "signature": "sha256:a19bcc3b7970ea198aeb7b4572a348b602c96533339019c3c74c5cb606aac0f7"
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "name = '2015-09-28-xray_get_attr'\n",
      "title = \"Using get_variables_by_attributes with xray\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "\n",
      "import os\n",
      "from datetime import datetime\n",
      "from IPython.core.display import HTML\n",
      "\n",
      "with open('creative_commons.txt', 'r') as f:\n",
      "    html = f.read()\n",
      "\n",
      "hour = datetime.utcnow().strftime('%H:%M')\n",
      "comments=\"true\"\n",
      "\n",
      "date = '-'.join(name.split('-')[:3])\n",
      "slug = '-'.join(name.split('-')[3:])\n",
      "\n",
      "metadata = dict(title=title,\n",
      "                date=date,\n",
      "                hour=hour,\n",
      "                comments=comments,\n",
      "                slug=slug,\n",
      "                name=name)\n",
      "\n",
      "markdown = \"\"\"Title: {title}\n",
      "date:  {date} {hour}\n",
      "comments: {comments}\n",
      "slug: {slug}\n",
      "\n",
      "{{% notebook {name}.ipynb cells[2:] %}}\n",
      "\"\"\".format(**metadata)\n",
      "\n",
      "content = os.path.abspath(os.path.join(os.getcwd(),\n",
      "                                       os.pardir,\n",
      "                                       os.pardir, \n",
      "                                       '{}.md'.format(name)))\n",
      "with open('{}'.format(content), 'w') as f:\n",
      "    f.writelines(markdown)\n",
      "    \n",
      "html = '''\n",
      "<small>\n",
      "<p> This post was written as an IPython notebook.\n",
      " It is available for <a href='https://ocefpaf.github.com/python4oceanographers/downloads/notebooks/%s.ipynb'>download</a>\n",
      " or as a static <a href='https://nbviewer.ipython.org/url/ocefpaf.github.com/python4oceanographers/downloads/notebooks/%s.ipynb'>html</a>.</p>\n",
      "<p></p>\n",
      "%s''' % (name, name, html)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In my last post I showed the new `get_variables_by_attributes` method that will be available in netCDF4-python `1.2.0`.  But what if you want to use that now?  You can just copy the [method]((https://github.com/ocefpaf/netcdf4-python/blob/ef69cd01915aacfec7949a2e61884f2ccad0c2d5/netCDF4/_netCDF4.pyx#L2275-L2315) as a local function and change the `self` argument to take the the `netCDF4.Dataset` (`nc`) object.\n",
      "\n",
      "In fact we can even expand that to `xray` or `iris`.\n",
      "Well... If your dataset is CF-compliant then it makes no sense to use this in `iris`.\n",
      "Just use iris methods instead ;-)\n",
      "\n",
      "Below is an example with `xray`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_variables_by_attributes(ds, **kwargs):\n",
      "    vs = []\n",
      "    \n",
      "    has_value_flag  = False\n",
      "    for vname, var in ds.items():\n",
      "        for k, v in kwargs.items():\n",
      "            if callable(v):\n",
      "                has_value_flag = v(getattr(var, k, None))\n",
      "                if has_value_flag is False:\n",
      "                    break\n",
      "            elif hasattr(var, k) and getattr(var, k) == v:\n",
      "                has_value_flag = True\n",
      "            else:\n",
      "                has_value_flag = False\n",
      "                break\n",
      "        if has_value_flag is True:\n",
      "            vs.append(ds[vname])\n",
      "\n",
      "    return vs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And now let's do the same thing we did in the last post: construct the dimensioned vertical coordinate."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import xray\n",
      "\n",
      "\n",
      "url = (\"http://omgsrv1.meas.ncsu.edu:8080/thredds/dodsC/fmrc/sabgom/\"\n",
      "       \"SABGOM_Forecast_Model_Run_Collection_best.ncd\")\n",
      "\n",
      "\n",
      "ds = xray.open_dataset(url)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "formula_terms = lambda v: v is not None\n",
      "\n",
      "var = get_variables_by_attributes(ds, formula_terms=formula_terms)[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "var.attrs.get('formula_terms')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "u's: s_rho C: Cs_r eta: zeta depth: h depth_c: hc'"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "var.attrs.get('standard_name')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "u'ocean_s_coordinate_g1'"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now that we know the variable holding the `formula_terms` and all the variables in the `formula_terms` itself we can get them from the dataset.\n",
      "\n",
      "Note that I am getting only the last time-step for `eta`.  That is because\n",
      "I was unable to find a way to lazily re-shape arrays in `xray`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = ds['s_rho']\n",
      "C = ds['Cs_r']\n",
      "eta = ds['zeta'][-1, ...]  # Last time step.\n",
      "depth = ds['h']\n",
      "depth_c = ds['hc']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Remember that we need to match all the shapes before computing the vertical coordinate.\n",
      "Here I will take a different approach from the last post,\n",
      "that soon will be the default behavior in [`odvc`](https://github.com/pyoceans/odvc),\n",
      "I will align the shapes based on a final desired shape.\n",
      "\n",
      "Right now this approach is incomplete because it does not guess the final shape,\n",
      "but in `odvc` this will be [possible](https://github.com/pyoceans/odvc/blob/master/odvc/utils.py#L62-L76).  The current shapes are,"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.shape, C.shape, eta.shape, depth.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "((36,), (36,), (320, 440), (320, 440))"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We want the vertical coordinate to be the leftmost dimension.\n",
      "That means the final shape should be `(36, 320, 440)`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "final = (36, 320, 440)\n",
      "\n",
      "def align(shape_in, shape_out):\n",
      "    dims = [k for k in shape_in]\n",
      "    def select(dim):\n",
      "        return dim if dim in dims else 1\n",
      "    return tuple(select(k) for k in shape_out)\n",
      "\n",
      "\n",
      "def to_shape(arr, shape):\n",
      "    reshape = align(shape_in=arr.shape, shape_out=final)\n",
      "    return arr.data.reshape(reshape)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = to_shape(s, shape=final)\n",
      "C = to_shape(C, shape=final)\n",
      "eta = to_shape(eta, shape=final)\n",
      "depth = to_shape(depth, shape=final)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s.shape, C.shape, eta.shape, depth.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "((36, 1, 1), (36, 1, 1), (1, 320, 440), (1, 320, 440))"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Nice!  It works!!  Now let's end the post with a fancy figure."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from odvc import ocean_s_coordinate_g1\n",
      "\n",
      "z = ocean_s_coordinate_g1(s, C, eta, depth, depth_c.data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "salt = get_variables_by_attributes(ds, standard_name='sea_water_salinity')[0]\n",
      "\n",
      "temp = get_variables_by_attributes(ds, standard_name='sea_water_potential_temperature')[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "import mpl_toolkits.axisartist as AA\n",
      "from mpl_toolkits.axes_grid1 import host_subplot\n",
      "\n",
      "import seaborn\n",
      "seaborn.set(style='ticks')\n",
      "\n",
      "\n",
      "def adjust_xlim(ax, offset):\n",
      "    x1, x2 = ax.get_xlim()\n",
      "    ax.set_xlim(x1 - offset, x2 + offset)\n",
      "\n",
      "\n",
      "fig = plt.figure(figsize=(6, 9))\n",
      "fig.subplots_adjust(bottom=0.1, top=0.85)\n",
      "ax0 = host_subplot(111, axes_class=AA.Axes)\n",
      "\n",
      "ax0.invert_yaxis()\n",
      "ax1 = ax0.twiny()\n",
      "\n",
      "new_axis0 = ax0.get_grid_helper().new_fixed_axis\n",
      "new_axis1 = ax1.get_grid_helper().new_fixed_axis\n",
      "\n",
      "ax0.axis[\"top\"] = new_axis0(loc=\"top\", axes=ax0, offset=(0, 0))\n",
      "ax1.axis[\"top\"] = new_axis1(loc=\"top\", axes=ax1, offset=(0, 40))\n",
      "\n",
      "ax0.axis[\"bottom\"].toggle(all=False)\n",
      "ax1.axis[\"bottom\"].toggle(all=False)\n",
      "\n",
      "ax0.set_ylabel(\"Depth (m)\")\n",
      "ax0.set_xlabel(u\"Temperature (\\xb0C)\")\n",
      "ax1.set_xlabel(r\"Salinity (g kg$^{-1}$)\")\n",
      "\n",
      "kw = dict(linewidth=2.5)\n",
      "l0, = ax0.plot(temp[-1, :, 5, 364], -z[:, 5, 364], **kw)\n",
      "l1, = ax1.plot(salt[-1, :, 5, 364], -z[:, 5, 364], **kw)\n",
      "\n",
      "adjust_xlim(ax0, offset=0.05)\n",
      "adjust_xlim(ax1, offset=0.05)\n",
      "\n",
      "ax0.axis[\"top\"].label.set_color(l0.get_color())\n",
      "ax1.axis[\"top\"].label.set_color(l1.get_color())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SvNqiLvdFBUWwOHkuC5PnEBF4/quNDnYSHEIMYoqi8NWhUt789CT5pXVd7hsW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\n73//u4n1Wcm24bg735DoOAD4r2qVuXjxogYGBhQ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       "text": [
        "<matplotlib.figure.Figure at 0x7fabfce7e550>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "HTML(html)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "<small>\n",
        "<p> This post was written as an IPython notebook.\n",
        " It is available for <a href='https://ocefpaf.github.com/python4oceanographers/downloads/notebooks/2015-09-28-xray_get_attr.ipynb'>download</a>\n",
        " or as a static <a href='https://nbviewer.ipython.org/url/ocefpaf.github.com/python4oceanographers/downloads/notebooks/2015-09-28-xray_get_attr.ipynb'>html</a>.</p>\n",
        "<p></p>\n",
        "<a rel=\"license\" href=\"https://creativecommons.org/licenses/by-sa/4.0/\"><img\n",
        "alt=\"Creative Commons License\" style=\"border-width:0\"\n",
        "src=\"https://i.creativecommons.org/l/by-sa/4.0/88x31.png\" /></a><br /><span\n",
        "xmlns:dct=\"https://purl.org/dc/terms/\"\n",
        "property=\"dct:title\">python4oceanographers</span> by <a\n",
        "xmlns:cc=\"https://creativecommons.org/ns#\" href=\"https://ocefpaf.github.io/\"\n",
        "property=\"cc:attributionName\" rel=\"cc:attributionURL\">Filipe Fernandes</a> is\n",
        "licensed under a <a rel=\"license\"\n",
        "href=\"https://creativecommons.org/licenses/by-sa/4.0/\">Creative Commons\n",
        "Attribution-ShareAlike 4.0 International License</a>.<br />Based on a work at <a\n",
        "xmlns:dct=\"https://purl.org/dc/terms/\" href=\"https://ocefpaf.github.io/\"\n",
        "rel=\"dct:source\">https://ocefpaf.github.io/</a>.\n"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "<IPython.core.display.HTML at 0x7f6c3c166450>"
       ]
      }
     ],
     "prompt_number": 16
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   ],
   "metadata": {}
  }
 ]
}