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Visualizing and animating sea-surface height in the double-gyre example.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will use `matplotlib.pyplot` for plotting and scipy's netcdf package for reading the model output. The `%pylab inline` causes figures to appear in the page and conveniently alias pyplot to `plt` (which is becoming a widely used alias).\n",
"\n",
"This analysis assumes you changed `DAYMAX` to some multiple of 5 so that there are multiple time records in the model output.\n",
"\n",
"To see this notebook with figures, see https://gist.github.com/adcroft/2a2b91d66625fd534372."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline\n",
"import scipy.io.netcdf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We first create a netcdf object, or \"handle\", to the netcdf file. We'll also list all the objects in the netcdf object."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'Time': <scipy.io.netcdf.netcdf_variable at 0xb60364ac>,\n",
" 'e': <scipy.io.netcdf.netcdf_variable at 0xb603686c>,\n",
" 'h': <scipy.io.netcdf.netcdf_variable at 0xb603676c>,\n",
" 'u': <scipy.io.netcdf.netcdf_variable at 0xb603668c>,\n",
" 'v': <scipy.io.netcdf.netcdf_variable at 0xb603670c>,\n",
" 'xh': <scipy.io.netcdf.netcdf_variable at 0xb603648c>,\n",
" 'xq': <scipy.io.netcdf.netcdf_variable at 0xb603634c>,\n",
" 'yh': <scipy.io.netcdf.netcdf_variable at 0xb603632c>,\n",
" 'yq': <scipy.io.netcdf.netcdf_variable at 0xb603652c>,\n",
" 'zi': <scipy.io.netcdf.netcdf_variable at 0xb60365ec>,\n",
" 'zl': <scipy.io.netcdf.netcdf_variable at 0xb60363cc>}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prog_file = scipy.io.netcdf_file('prog__0001_006.nc')\n",
"prog_file.variables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we will create a variable object for the \"e\" variable in the file. Again, I'm labelling it as a handle to distinguish it from a numpy array or raw data.\n",
"\n",
"We'll also look at an \"attribute\" and print the shape of the data."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Description = b'Interface Height Relative to Mean Sea Level'\n",
"Shape = (20, 3, 40, 44)\n"
]
}
],
"source": [
"e_handle = prog_file.variables['e']\n",
"print('Description =', e_handle.long_name)\n",
"print('Shape =',e_handle.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\"e\" is 4-dimensional. netcdf files and objects are index [n,k,j,i] for the time-, vertical-, meridional-, zonal-axes.\n",
"\n",
"Let's take a quick look at the first record [n=0] of the top interface [k=0]. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0xb601178c>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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b5Wl3APytu//jVLwSQggxNhMHdHf/HwCvnaIvQgghMlDaohBCNIT5lv47gIQa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\nhGgICuhCCNEQ/g8dugtsgRcR4gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xb603e3ac>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.pcolormesh( e_handle[0,0] )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data looks OKish. No scale! And see that \"`<matplotlib...>`\" line? That's a handle returned by the matplotlib function. Hide it with a semicolon. Let's add a scale and change the colormap."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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N7Fv7E1XlfrIqx1OT9mSEbAB+sXJf2bvJ6tXl9rOSrKjK9v1JMK7NgGVthWVb\n43/XHuv+/eX2SVdYVnTeKyRt63u+sUm2eJGkuymnzP3pS15xjiSOxKeBj9M74I8DnwB+f9AGHtvE\nzKbcUJ33/oi4cNAKEXFZtkzS45JWRcTeAUkce4Bz+p6vbtqIiMf79vUZ4I65DnjUgKWZWQfMT7YJ\nwyVxbAXWSDpP0knANc12M1l7M94BDMgb7vGVt5lNsSC/WdiqG4Hbm4SOHwPvBJD0GuCWiLgyIo5K\nuh64k94dxlsj4v5m+/8saV1zwLuA98z1gu68zWyKzU/AMiKepJDEERE/Aa7se74FeFkaYES8q/Y1\n3Xmb2ZSbl2yTebdIsk2yHIXaH3ptsXU2/nc5P+H008v7ybJNsvYsc2RgrXASrq/9hzD7SWQl5FlW\nSRZKzz5Q2U86yyo5I2mHfLT1rH3pqlXlBbVvXJKd8iyvKLZnGRZZtkltFsqgZW1lm9RmldSWxx88\nOOlbGvOWKjjvfOVtZlNuOsc2cedtZlPMV95mZh3UVmHg4uPO28ymmK+8Jyz74Wbt2V/SbP0sTJcF\nOMvhu2w459pqanYk0ZsswgXEofKI21lAMStrzwYEaCtknP2ks9fN2gcFLF+dtC8/NSmQzwZWz8Yv\nqAxYZnHm2oDlfExAnLXXBiZrA5ZZez6QQ5t8z9vMrGN85W1m1lHuvM3MOsYBSzOzDgp8z9vMrJN8\n26QlpbyGnyfrZnOgZ+21pbZ1UxAsX15eO8tCOWN58hc/C70PSCuQynklvxDlQvVswoIsGyT7yWVl\n8LWTN5QLyPMJFJIfNQDLTk8K4S+4oK49y0JJsk2eToZNyEq/2yqDb3MyhoXKKjl0KLv6nfQtDQcs\niyTtAg7S++kcnWswczOz+eXOe5A3R0SeoGxmtqB8z9vMrGOmN9tk3GnQArhb0nclbWjjgMzM2jNv\n06DNu3GvvC+JiD2SzgTukvRQRHyzf4WmU2869pXJbrK7Ltn41rV3aWqLv8thvSxgmbWn0Zts8OQT\nBrwdyYssf+qpYnsWOKwNTGbl99ls8NnrZuOFL82Cj9nM7pCXr2cByNqAZVI2vyuZVTArg8/a2xqD\ne5RtJh3IzIOr2dXvfFwVd7NznstYV94RMTPz8T7gy8BFhXU2RsSFvWBmlgNhZjYJ03vlPXLnLemV\nkk6d+R54G0PMeGxmNn9minTmenTPOFfeK4F7JX0f+A7wPyPiq+0clplZWyZ/5S3pDEl3SXq4+Vq8\nHyjpVkn8+3FLAAAGTElEQVT7JO0YZft+I3feEfFIRLy+efxaRPzFqPsyM5uMmWyTuR5juwG4JyLW\nAPc0z0s+B6wfY/sXjZttYma2iM3bPe+rgE3N95uAq4tH00vo+Omo2/dbgNnjS/kO/5ys/0jSnpXT\nZ0P7J/XrabF4eU7zU5Kpzk/OUiky2QZZnT2kGRDLktrsZYNSFGqOqbY9+yHVji2QzmQxYFmWhZJN\nupBkmzyyq3xNs1CTLmTJSYO2Waj2I0ey+8eTniV+kKHuaa+QtK3v+caI2FjxIisjYm/z/WPkqXWt\nbe8iHTObYkOXx++fa3gPSXcDpSuHP33JK0aEpCz7dk7Dbu/O28ymWHtjm0TEZdkySY9LWhUReyWt\nAvZV7r56e9/zNrMpNm8By83Adc331wFfmfT27rzNbMrNS573jcDlkh4GLmueI+k1krbMrCTpC8C3\ngF+RtFvSuwdtP8g83zY5BpRmQS+XeOczS2fl7uXxluFVleuXg3FZ9XoWvDm2vDwH+pIsgDZo4OYs\nSDdom5K2ApO1Acts/eoxB8h/Fkn7syeX34fdu8q72ZW0Z+N2T7oMflDsOQtmttVeH0TNblFk7ZMu\nkJmfIWEj4kngrYX2nwBX9j2/tmb7QXzP28ymXDfL3+fiztvMppgnYzAz66hujl0yF3feZjbFjjGt\nkzEsks57VdKeVUxmJY1ZhWK2/yxgWf43a9euckXmffeV95IFOC+44N8W23/5kkuS4yGPitVWUmYH\nNen2JGD5/NFywtOg00onxa0MKNa2z0fFZK22ApNtve7i5NsmZmYd43veZmYd5c7bzKxjZiZjmD7u\nvM1syvnK28ysY5xt0hJRLm3/pWT97C9mVh6fZaEsS9qzccF/VGzdurU8M9HWrWcW22+5pXw8eXV3\nPtTMihXl18iqyNuqXq9MHqlOQmlTWyXebc28PunS9flQ+34eWchhu1O+8jYz65jpvec91qiCktZL\n+pGknZLmnHPNzGz+zcs0aPNu5M5b0lLgJuAKYC1wraS1bR2Ymdn45m0Oy3k3zm2Ti4CdEfEIgKTb\n6E2i+UAbB2ZmNr6ZyRimzzid99nAo33PdwNvnL2SpA3Ahubpc/D2HWO8Zqc81RumfAWwv9D+Mg8+\nOOkjmjcvO+fjwPF2zvNxvlkmQ4Wf3Ql3DJjZ+0Wde+8mHrBsZmDeCCBp21yTfE4bn/Px4Xg7566c\nb0SsX+hjmJRxApZ7gHP6nq9u2szMbMLG6by3AmsknSfpJOAaepNompnZhI182yQijkq6HriTXtXM\nrRFx/xybbRz19TrM53x8ON7O+Xg730VHEbHQx2BmZpXGKtIxM7OF4c7bzKyD5qXzPl7K6CXdKmmf\npB19bWdIukvSw83X8uhWHSTpHElfl/SApPslfaBpn+ZzPlnSdyR9vznnjzXtU3vOMyQtlfQ9SXc0\nz6f+nBeziXfex1kZ/eeA2XmlNwD3RMQa4J7m+bQ4CnwoItYCFwPva97baT7n54C3RMTrgXXAekkX\nM93nPOMDQH8p2fFwzovWfFx5v1hGHxHPAzNl9FMnIr4J/HRW81XApub7TcDV83pQExQReyPin5rv\nD9L7xT6b6T7niIiZQWBPbB7BFJ8zgKTVwG8Ct/Q1T/U5L3bz0XmXyujPnofXXSxWRsTe5vvHgJUL\neTCTIulc4A3At5nyc25uH2wH9gF3RcTUnzPwV8Af89LxVaf9nBc1ByznUfTyMqcuN1PSKcAXgQ9G\nxNP9y6bxnCPihYhYR6+q+CJJF8xaPlXnLOntwL6I+G62zrSdcxfMR+d9vJfRPy5pFUDzdd8CH0+r\nJJ1Ir+P+fER8qWme6nOeEREHgK/Ti3NM8zm/CfgtSbvo3fZ8i6S/Y7rPedGbj877eC+j3wxc13x/\nHfCVBTyWVkkS8FngwYj4ZN+iaT7nV0ta3ny/DLgceIgpPueI+JOIWB0R59L7/f1aRPwOU3zOXTAv\nFZaSrqR3z2ymjP4vJv6iC0DSF4BL6Q2X+TjwZ8A/ArcDrwV+DLwzImYHNTtJ0iXA/wZ+yL/cC/0I\nvfve03rOv04vOLeU3sXP7RHx55JexZSecz9JlwJ/FBFvP17OebFyebyZWQc5YGlm1kHuvM3MOsid\nt5lZB7nzNjPrIHfeZmYd5M7bzKyD3HmbmXXQ/wfE7jTG9A7mSAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xabcea8ac>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.pcolormesh( e_handle[0,0], cmap=cm.seismic ); plt.colorbar();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have 4D data but can only visualize by projecting on a 2D medium (the page). Let's solve that by going interactive!"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import ipywidgets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll need to know the range to fix the color scale..."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[-0.28871199, 0.61446452]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[e_handle[:,0].min(), e_handle[:,0].max()]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We define a simple function that takes the record number as an argument and then plots the top interface (k=0) for that record. We then use the `interact()` function to do some magic!"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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LhcLvYLZw/jPJ6Iunks0IDiQjbLJRGXuS8tIRGb3euy/7LctGa2TlpVOxS8/f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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xabbf964c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def plot_ssh(record):\n",
" plt.pcolormesh( e_handle[record,0], cmap=cm.spectral )\n",
" plt.clim(-.5,.8) # Fixed scale here\n",
" plt.colorbar()\n",
"\n",
"ipywidgets.interact(plot_ssh, record=(0,e_handle.shape[0]-1,1));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unable to scroll the slider steadily enough? We'll use a loop to redraw for us..."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from IPython import display"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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q3bPJ4gQryhYduyUpP5gMDd+UZDocl9Rz5cbO5bP3d6nUlqS8dHXt7BPPMkuS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\nM2soN9BmZg3lBtrMrKHcQJuZNdT/B9QEZeGjc4jaAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xabb35a4c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for n in range( e_handle.shape[0]):\n",
" display.display(plt.gcf())\n",
" plt.clf()\n",
" plot_ssh(n)\n",
" display.clear_output(wait=True)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
},
"widgets": {
"state": {
"cd4b41895495420c9a3daa7e56e4e60f": {
"views": [
{
"cell_index": 15
}
]
}
},
"version": "1.2.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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