sjpfenninger/merra-example.ipynb

## merra-example.ipynb
{
 "worksheets": [
  {
   "cells": [
    {
     "metadata": {},
     "cell_type": "code",
     "input": "%pylab inline",
     "prompt_number": 1,
     "outputs": [
      {
       "output_type": "stream",
       "text": "Populating the interactive namespace from numpy and matplotlib\n",
       "stream": "stdout"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "import netCDF4",
     "prompt_number": 2,
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "root_grp = netCDF4.Dataset('./data/MERRA300.prod.assim.tavg1_2d_slv_Nx.20110101.SUB.nc')",
     "prompt_number": 3,
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "root_grp.variables.keys()",
     "prompt_number": 4,
     "outputs": [
      {
       "text": "[u'time',\n u'longitude',\n u'latitude',\n u'slp',\n u'ps',\n u'u850',\n u'u500',\n u'u250',\n u'v850',\n u'v500',\n u'v250',\n u't850',\n u't500',\n u't250',\n u'q850',\n u'q500',\n u'q250',\n u'h1000',\n u'h850',\n u'h500',\n u'h250',\n u'omega500',\n u'u10m',\n u'u2m',\n u'u50m',\n u'v10m',\n u'v2m',\n u'v50m',\n u't10m',\n u't2m',\n u'qv10m',\n u'qv2m',\n u'ts',\n u'disph',\n u'troppv',\n u'troppt',\n u'troppb',\n u'tropt',\n u'tropq',\n u'cldprs',\n u'cldtmp']",
       "output_type": "pyout",
       "metadata": {},
       "prompt_number": 4
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "root_grp.variables['v10m'].dimensions",
     "prompt_number": 5,
     "outputs": [
      {
       "text": "(u'time', u'latitude', u'longitude')",
       "output_type": "pyout",
       "metadata": {},
       "prompt_number": 5
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "# Pick desired lat/lon coordinates\n\nroot_grp.variables['latitude'][:]",
     "prompt_number": 6,
     "outputs": [
      {
       "text": "array([ 49.5,  50. ,  50.5,  51. ,  51.5,  52. ,  52.5,  53. ,  53.5,\n        54. ,  54.5,  55. ,  55.5,  56. ,  56.5,  57. ,  57.5,  58. ,\n        58.5,  59. ,  59.5,  60. ])",
       "output_type": "pyout",
       "metadata": {},
       "prompt_number": 6
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "root_grp.variables['longitude'][:]",
     "prompt_number": 7,
     "outputs": [
      {
       "text": "array([-10.66667175, -10.        ,  -9.33332825,  -8.66667175,\n        -8.        ,  -7.33332825,  -6.66667175,  -6.        ,\n        -5.33332825,  -4.66667175,  -4.        ,  -3.33332825,\n        -2.66667175,  -2.        ,  -1.33332825,  -0.66667175,\n         0.        ,   0.66667175,   1.33332825,   2.        ])",
       "output_type": "pyout",
       "metadata": {},
       "prompt_number": 7
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "lat = 4  # --> 51.5\nlon = 12  # --> -2.66",
     "prompt_number": 8,
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": true
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "# Extract time series for the given lat/lon\n\n# a 3-dimensional array: root_grp.variables['v10m']\n# slice the array: [::, lat, lon]\nroot_grp.variables['v10m'][::, lat, lon]",
     "prompt_number": 9,
     "outputs": [
      {
       "text": "array([-1.68991947, -1.87889957, -2.10991478, -2.33902216, -2.70778918,\n       -3.08883452, -3.38284755, -3.55104518, -3.59184098, -3.38293219,\n       -3.06175923, -2.75952411, -2.64530683, -2.73178911, -2.92505336,\n       -3.18042636, -3.19108367, -2.95623493, -2.64634514, -2.32550693,\n       -2.15126038, -2.19025636, -2.27992964, -2.35522366], dtype=float32)",
       "output_type": "pyout",
       "metadata": {},
       "prompt_number": 9
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "v10m = root_grp.variables['v10m'][::, lat, lon]\nu10m = root_grp.variables['u10m'][::, lat, lon]",
     "prompt_number": 10,
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "# Convert u/v vector components to wind speed and direction\n\nimport numpy as np\n\ndef convert_wind(u, v):\n    d = (180. + (180. / np.pi) * np.arctan2(u, v))\n    f = np.sqrt(u * u + v * v)\n    return d, f",
     "prompt_number": 11,
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "direction, speed = convert_wind(u10m, v10m)",
     "prompt_number": 12,
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "direction",
     "prompt_number": 13,
     "outputs": [
      {
       "text": "array([ 323.71469116,  317.29345703,  314.73510742,  315.07971191,\n        318.89245605,  324.15539551,  329.04504395,  332.76464844,\n        334.25531006,  333.30737305,  331.86959839,  330.42755127,\n        329.58172607,  330.70150757,  335.63095093,  345.20425415,\n        359.4609375 ,   17.87113953,   37.27511597,   51.41349792,\n         57.94335938,   57.97425842,   55.08329773,   52.00296021], dtype=float32)",
       "output_type": "pyout",
       "metadata": {},
       "prompt_number": 13
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "# Turn the resulting data into a dataframe and plot it\n\nimport pandas as pd\nimport seaborn as sns\n\ndf = pd.DataFrame({'direction': direction, 'speed': speed})",
     "prompt_number": 14,
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "df",
     "prompt_number": 15,
     "outputs": [
      {
       "text": "     direction     speed\n0   323.714691  2.096466\n1   317.293457  2.556892\n2   314.735107  2.997761\n3   315.079712  3.303283\n4   318.892456  3.593726\n5   324.155396  3.810516\n6   329.045044  3.944681\n7   332.764648  3.993824\n8   334.255310  3.987663\n9   333.307373  3.786459\n10  331.869598  3.471867\n11  330.427551  3.172843\n12  329.581726  3.067548\n13  330.701508  3.132491\n14  335.630951  3.211145\n15  345.204254  3.289498\n16  359.460938  3.191225\n17   17.871140  3.106107\n18   37.275116  3.325654\n19   51.413498  3.728593\n20   57.943359  4.053190\n21   57.974258  4.130220\n22   55.083298  3.983211\n23   52.002960  3.825771\n\n[24 rows x 2 columns]",
       "html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>direction</th>\n      <th>speed</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0 </th>\n      <td> 323.714691</td>\n      <td> 2.096466</td>\n    </tr>\n    <tr>\n      <th>1 </th>\n      <td> 317.293457</td>\n      <td> 2.556892</td>\n    </tr>\n    <tr>\n      <th>2 </th>\n      <td> 314.735107</td>\n      <td> 2.997761</td>\n    </tr>\n    <tr>\n      <th>3 </th>\n      <td> 315.079712</td>\n      <td> 3.303283</td>\n    </tr>\n    <tr>\n      <th>4 </th>\n      <td> 318.892456</td>\n      <td> 3.593726</td>\n    </tr>\n    <tr>\n      <th>5 </th>\n      <td> 324.155396</td>\n      <td> 3.810516</td>\n    </tr>\n    <tr>\n      <th>6 </th>\n      <td> 329.045044</td>\n      <td> 3.944681</td>\n    </tr>\n    <tr>\n      <th>7 </th>\n      <td> 332.764648</td>\n      <td> 3.993824</td>\n    </tr>\n    <tr>\n      <th>8 </th>\n      <td> 334.255310</td>\n      <td> 3.987663</td>\n    </tr>\n    <tr>\n      <th>9 </th>\n      <td> 333.307373</td>\n      <td> 3.786459</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td> 331.869598</td>\n      <td> 3.471867</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td> 330.427551</td>\n      <td> 3.172843</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td> 329.581726</td>\n      <td> 3.067548</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td> 330.701508</td>\n      <td> 3.132491</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td> 335.630951</td>\n      <td> 3.211145</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td> 345.204254</td>\n      <td> 3.289498</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td> 359.460938</td>\n      <td> 3.191225</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>  17.871140</td>\n      <td> 3.106107</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>  37.275116</td>\n      <td> 3.325654</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>  51.413498</td>\n      <td> 3.728593</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>  57.943359</td>\n      <td> 4.053190</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>  57.974258</td>\n      <td> 4.130220</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>  55.083298</td>\n      <td> 3.983211</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>  52.002960</td>\n      <td> 3.825771</td>\n    </tr>\n  </tbody>\n</table>\n<p>24 rows × 2 columns</p>\n</div>",
       "output_type": "pyout",
       "metadata": {},
       "prompt_number": 15
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "df.plot(figsize=(20, 5), secondary_y=['speed'])",
     "prompt_number": 16,
     "outputs": [
      {
       "text": "<matplotlib.axes.AxesSubplot at 0x107c1e5d0>",
       "output_type": "pyout",
       "metadata": {},
       "prompt_number": 16
      },
      {
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JEfnvYlQRNgqpCCeFeEJJ/JINAHxV7l7ldJmVAk0nj71h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ABAV9bobNI7e9/TttLtXvN4a2/dl3EXP2MAAIAe6cmX/6PS79e/nZMWpUdvHmpwIuDsqmys0gvb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\nVIrFY/L3NGpn+1B4aV9XvaKHfHjhcJz2V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       "text": "<matplotlib.figure.Figure at 0x107c1e090>",
       "output_type": "display_data",
       "metadata": {
        "png": {
         "width": 1173,
         "height": 314
        }
       }
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "",
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    }
   ],
   "metadata": {}
  }
 ],
 "metadata": {
  "name": "",
  "signature": "sha256:c9f508d0543a198a1518b1f2cc68372ffa90f1497421c9732c63e409f66b4fc1"
 },
 "nbformat": 3
}