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Xarray time-series resampling
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{
"cells": [
{
"metadata": {
"deletable": true,
"editable": true
},
"cell_type": "markdown",
"source": "# Xarray time-series resampling of raw ADCP data"
},
{
"metadata": {
"deletable": true,
"editable": true
},
"cell_type": "markdown",
"source": "An example of how to reduce a large single ping ADCP .pd0 format file that has been converted by TRDIpd0tonetcdf\nThis example uses a 3 GB containing >3 Mil ensembles"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2017-02-24T11:48:50.906716Z",
"start_time": "2017-02-24T06:48:50.193181-05:00"
},
"collapsed": true,
"deletable": true,
"editable": true,
"trusted": true
},
"cell_type": "code",
"source": "%matplotlib inline\nimport xarray as xr\nimport matplotlib.pyplot as plt\nimport datetime as dt\nimport datetime",
"execution_count": 1,
"outputs": []
},
{
"metadata": {
"deletable": true,
"editable": true
},
"cell_type": "markdown",
"source": "This is raw ADCP data, continuous at 2 Hz, 1 m bins deployed at MVCO in 22.5 m The big file is a netCDF4 file with over 3 million ensembles The 'D' file is a netCDF4 file with several hundred thousand ensembles"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2017-02-24T11:48:50.912700Z",
"start_time": "2017-02-24T06:48:50.908690-05:00"
},
"collapsed": true,
"deletable": true,
"editable": true,
"trusted": true
},
"cell_type": "code",
"source": "#cdffile = 'c:\\\\data\\\\wh767mvco\\\\67DEP000short.cdf'\ncdffile = '67DEP000.000.cdf'",
"execution_count": 2,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2017-02-24T11:48:51.470231Z",
"start_time": "2017-02-24T06:48:50.917715-05:00"
},
"collapsed": false,
"deletable": true,
"editable": true,
"trusted": true
},
"cell_type": "code",
"source": "# note that we allow timestamps to be interpreted here\n# time is expressed as decimal seconds since the first ping in the data set. \n# Acoustic current profiler time stamps may have resolution of as little as 16 Hz, depending on the instrument\nds = xr.open_dataset(cdffile)\n# opening 3 Mil points takes just a few seconds, you will notice the delay and it's not bad",
"execution_count": 3,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2017-02-24T11:48:51.853511Z",
"start_time": "2017-02-24T06:48:51.846495-05:00"
},
"collapsed": false,
"deletable": true,
"editable": true,
"trusted": true
},
"cell_type": "code",
"source": "ds.dims",
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "Frozen(SortedKeysDict({'time': 3334756, 'depth': 23}))"
},
"metadata": {},
"execution_count": 10
}
]
},
{
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"trusted": true
},
"cell_type": "code",
"source": "for item in iter(ds['time'][0:10].values):\n print(item)",
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"text": "2014-07-01T00:00:00.000000000\n2014-07-01T00:00:02.000000000\n2014-07-01T00:00:04.000000000\n2014-07-01T00:00:06.000000000\n2014-07-01T00:00:08.000000000\n2014-07-01T00:00:10.000000000\n2014-07-01T00:00:12.000000000\n2014-07-01T00:00:14.000000000\n2014-07-01T00:00:16.000000000\n2014-07-01T00:00:18.000000000\n",
"name": "stdout"
}
]
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2017-02-24T11:48:51.843485Z",
"start_time": "2017-02-24T06:48:51.472239-05:00"
},
"collapsed": false,
"deletable": true,
"editable": true,
"trusted": true
},
"cell_type": "code",
"source": "# plot part of the data, this is 2 sec interval data, so express the amount as days\n# skip the first day which has bad data\nds['Pressure'][(24*1800):(120*24*1800)].plot()\n# plotting nearly the entire deployment comes up in 1-2 seconds",
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "[<matplotlib.lines.Line2D at 0xa92e248048>]"
},
"metadata": {},
"execution_count": 8
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0xa92dd05f98>",
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vhRtAbw+8Dfwuaq4Rs7YyDMMohtLCk1wB/AX3QX8K2AD3Yb+r3GJWClMehmEY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\nbha+KO66CyZOLE1OI14uuCB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},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2017-02-24T11:49:25.363964Z",
"start_time": "2017-02-24T06:48:51.857522-05:00"
},
"collapsed": false,
"deletable": true,
"editable": true,
"trusted": true
},
"cell_type": "code",
"source": "starttime = dt.datetime.now()\nprint(\"start resample at %s\" % starttime)\nds_lp = ds.resample('H','time','mean')\nendtime = dt.datetime.now()\nprint(\"finished resample at %s\" % endtime)\nprint(\"processing time was %s\" % (endtime-starttime))",
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"text": "start resample at 2017-03-01 08:07:23.892506\nfinished resample at 2017-03-01 08:20:57.718312\nprocessing time was 0:13:33.825806\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "raw",
"source": "The above instruction had the following result:\nstart resample at 2017-03-01 08:07:23.892506\nfinished resample at 2017-03-01 08:20:57.718312\nprocessing time was 0:13:33.825806\nWhile processing it used 27% of memory and about 35% of CPU\nThis is on a HP ZBook with a 16 GB RAM, win 8.1, intel i7-5500U CPU at 2.4 GHz"
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2017-02-24T11:49:25.373954Z",
"start_time": "2017-02-24T06:49:25.366936-05:00"
},
"collapsed": false,
"deletable": true,
"editable": true,
"trusted": true
},
"cell_type": "code",
"source": "ds_lp.dims",
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "Frozen(SortedKeysDict(OrderedDict([('time', 1853), ('depth', 23)])))"
},
"metadata": {},
"execution_count": 12
}
]
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2017-02-24T11:49:25.555436Z",
"start_time": "2017-02-24T06:49:25.376963-05:00"
},
"collapsed": false,
"deletable": true,
"editable": true,
"trusted": true
},
"cell_type": "code",
"source": "ds_lp['Pressure'][24:].plot() # skip the first 24 hours out of water",
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "[<matplotlib.lines.Line2D at 0xa92fd2b9e8>]"
},
"metadata": {},
"execution_count": 15
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0xa92fca4ac8>",
"image/png": 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fqedAFJMkUuWt9FVL2eWWkbETHfMYqfHwKCkWq828+0idW7Z1yhnwkEeOgbIC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\n9GaG834PebB4mTuNjJ9YzmEKLcq2S70kra+vRB48q946vtOywS1PMl6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awWZmtq354nLN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\nY5kAAAAASUVORK5CYII=\n"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"trusted": true
},
"cell_type": "code",
"source": "for item in iter(ds_lp['time'][0:10].values):\n print(item)",
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"text": "2014-07-01T00:00:00.000000000\n2014-07-01T01:00:00.000000000\n2014-07-01T02:00:00.000000000\n2014-07-01T03:00:00.000000000\n2014-07-01T04:00:00.000000000\n2014-07-01T05:00:00.000000000\n2014-07-01T06:00:00.000000000\n2014-07-01T07:00:00.000000000\n2014-07-01T08:00:00.000000000\n2014-07-01T09:00:00.000000000\n",
"name": "stdout"
}
]
},
{
"metadata": {
"editable": true,
"deletable": true
},
"cell_type": "markdown",
"source": "Wee - no need to iterate through the netcdf variables!"
},
{
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true,
"trusted": true
},
"cell_type": "code",
"source": "ds_lp['vel1'][24:][:].plot()",
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "<matplotlib.collections.QuadMesh at 0xa92e433828>"
},
"metadata": {},
"execution_count": 17
},
{
"output_type": "display_data",
"data": {
"text/plain": "<matplotlib.figure.Figure at 0xa93018cda0>",
"image/png": 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wE+GY2FnLinrBbWAm/c1d93HrXfftcHczG0kmCH/i7lcDuPuLNU1+SFaEATKN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\nRU5ErkJggg==\n"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "ds_lp.to_netcdf('ADCP-a1h.cdf')",
"execution_count": 20,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "# for me to explore what methods are in xarray\nfor item in sorted(dir(ds_lp)):\n print(item)",
"execution_count": null,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
}
],
"metadata": {
"_draft": {
"nbviewer_url": "https://gist.github.com/42ce96249221914e13da7b61ece735e6"
},
"gist": {
"id": "42ce96249221914e13da7b61ece735e6",
"data": {
"description": "Xarray time-series resampling",
"public": true
}
},
"kernelspec": {
"name": "python3",
"display_name": "Python [default]",
"language": "python"
},
"language_info": {
"name": "python",
"version": "3.6.0",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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