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Xarray tutorial for Rossbypalooza
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xarray-tutorial-with-answers.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# xarray tutorial (with answers!)\n",
"\n",
"[Stephan Hoyer](http://stephanhoyer.com), Rossbypalooza, 2016\n",
"\n",
"-------------\n",
"\n",
"This notebook introduces xarray for new users in the geophysical sciences.\n",
"\n",
"## For more information about xarray\n",
"\n",
"- Read the [online documentation](http://xarray.pydata.org/)\n",
"- Ask questions on [StackOverflow](http://stackoverflow.com/questions/tagged/python-xarray)\n",
"- View the source code and file bug reports on [GitHub](http://github.com/pydata/xarray/)\n",
"\n",
"## For more doing data analysis with Python:\n",
"\n",
"- Thomas Wiecki, [A modern guide to getting started with Data Science and Python](http://twiecki.github.io/blog/2014/11/18/python-for-data-science/)\n",
"- Wes McKinney, [Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do) (book)\n",
"\n",
"## Packages building on xarray for the geophysical sciences\n",
"\n",
"For analyzing GCM output:\n",
"\n",
"- [xgcm](https://github.com/xgcm/xgcm) by Ryan Abernathey\n",
"- [oogcm](https://github.com/lesommer/oocgcm) by Julien Le Sommer\n",
"- [MPAS xarray](https://github.com/pwolfram/mpas_xarray) by Phil Wolfram\n",
"- [marc_analysis](https://github.com/darothen/marc_analysis) by Daniel Rothenberg\n",
"\n",
"Other tools:\n",
"\n",
"- [windspharm](https://github.com/ajdawson/windspharm): wind spherical harmonics by Andrew Dawson\n",
"- [eofs](https://github.com/ajdawson/eofs): empirical orthogonal functions by Andrew Dawson\n",
"- [infinite-diff](https://github.com/spencerahill/infinite-diff) by Spencer Hill \n",
"- [aospy](https://github.com/spencerahill/aospy) by Spencer Hill and Spencer Clark\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"-------------------\n",
"\n",
"## xarray basics"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.core.options.set_options at 0x7f36440f90d0>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# standard imports\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import xarray as xr\n",
"\n",
"%matplotlib inline\n",
"\n",
"np.set_printoptions(precision=3, linewidth=80, edgeitems=1) # make numpy less verbose\n",
"xr.set_options(line_width=70)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### `xarray.Dataset` is like a Python dictionary (of `xarray.DataArray` objects)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll use the \"air_temperature\" tutorial dataset:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ds = xr.tutorial.load_dataset('air_temperature')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (lat: 25, lon: 53, time: 2920)\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 62.5 60.0 57.5 55.0 52.5 ...\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 210.0 212.5 215.0 217.5 ...\n",
" * time (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00 ...\n",
"Data variables:\n",
" air (time, lat, lon) float64 241.2 242.5 243.5 244.0 244.1 243.9 ...\n",
"Attributes:\n",
" Conventions: COARDS\n",
" title: 4x daily NMC reanalysis (1948)\n",
" description: Data is from NMC initialized reanalysis\n",
"(4x/day). These are the 0.9950 sigma level values.\n",
" platform: Model\n",
" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n",
"array([[[ 241.2 , 242.5 , ..., 235.5 , 238.6 ],\n",
" [ 243.8 , 244.5 , ..., 235.3 , 239.3 ],\n",
" ..., \n",
" [ 295.9 , 296.2 , ..., 295.9 , 295.2 ],\n",
" [ 296.29, 296.79, ..., 296.79, 296.6 ]],\n",
"\n",
" [[ 242.1 , 242.7 , ..., 233.6 , 235.8 ],\n",
" [ 243.6 , 244.1 , ..., 232.5 , 235.7 ],\n",
" ..., \n",
" [ 296.2 , 296.7 , ..., 295.5 , 295.1 ],\n",
" [ 296.29, 297.2 , ..., 296.4 , 296.6 ]],\n",
"\n",
" ..., \n",
" [[ 245.79, 244.79, ..., 243.99, 244.79],\n",
" [ 249.89, 249.29, ..., 242.49, 244.29],\n",
" ..., \n",
" [ 296.29, 297.19, ..., 295.09, 294.39],\n",
" [ 297.79, 298.39, ..., 295.49, 295.19]],\n",
"\n",
" [[ 245.09, 244.29, ..., 241.49, 241.79],\n",
" [ 249.89, 249.29, ..., 240.29, 241.69],\n",
" ..., \n",
" [ 296.09, 296.89, ..., 295.69, 295.19],\n",
" [ 297.69, 298.09, ..., 296.19, 295.69]]])\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 62.5 60.0 57.5 55.0 52.5 ...\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 210.0 212.5 215.0 217.5 ...\n",
" * time (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00 ...\n",
"Attributes:\n",
" long_name: 4xDaily Air temperature at sigma level 995\n",
" units: degK\n",
" precision: 2\n",
" GRIB_id: 11\n",
" GRIB_name: TMP\n",
" var_desc: Air temperature\n",
" dataset: NMC Reanalysis\n",
" level_desc: Surface\n",
" statistic: Individual Obs\n",
" parent_stat: Other\n",
" actual_range: [ 185.16 322.1 ]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.air"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[u'lat', u'air', u'lon', u'time']"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(ds.keys())"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Frozen(SortedKeysDict({u'lat': 25, u'lon': 53, u'time': 2920}))"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.dims"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"OrderedDict([(u'Conventions', u'COARDS'),\n",
" (u'title', u'4x daily NMC reanalysis (1948)'),\n",
" (u'description',\n",
" u'Data is from NMC initialized reanalysis\\n(4x/day). These are the 0.9950 sigma level values.'),\n",
" (u'platform', u'Model'),\n",
" (u'references',\n",
" u'http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html')])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.attrs"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds['air'].identical(ds.air)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[[ 241.2 , 242.5 , ..., 235.5 , 238.6 ],\n",
" [ 243.8 , 244.5 , ..., 235.3 , 239.3 ],\n",
" ..., \n",
" [ 295.9 , 296.2 , ..., 295.9 , 295.2 ],\n",
" [ 296.29, 296.79, ..., 296.79, 296.6 ]],\n",
"\n",
" [[ 242.1 , 242.7 , ..., 233.6 , 235.8 ],\n",
" [ 243.6 , 244.1 , ..., 232.5 , 235.7 ],\n",
" ..., \n",
" [ 296.2 , 296.7 , ..., 295.5 , 295.1 ],\n",
" [ 296.29, 297.2 , ..., 296.4 , 296.6 ]],\n",
"\n",
" ..., \n",
" [[ 245.79, 244.79, ..., 243.99, 244.79],\n",
" [ 249.89, 249.29, ..., 242.49, 244.29],\n",
" ..., \n",
" [ 296.29, 297.19, ..., 295.09, 294.39],\n",
" [ 297.79, 298.39, ..., 295.49, 295.19]],\n",
"\n",
" [[ 245.09, 244.29, ..., 241.49, 241.79],\n",
" [ 249.89, 249.29, ..., 240.29, 241.69],\n",
" ..., \n",
" [ 296.09, 296.89, ..., 295.69, 295.19],\n",
" [ 297.69, 298.09, ..., 296.19, 295.69]]])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.air.values"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(u'time', u'lat', u'lon')"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.air.dims"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"OrderedDict([(u'long_name', u'4xDaily Air temperature at sigma level 995'),\n",
" (u'units', u'degK'),\n",
" (u'precision', 2),\n",
" (u'GRIB_id', 11),\n",
" (u'GRIB_name', u'TMP'),\n",
" (u'var_desc', u'Air temperature'),\n",
" (u'dataset', u'NMC Reanalysis'),\n",
" (u'level_desc', u'Surface'),\n",
" (u'statistic', u'Individual Obs'),\n",
" (u'parent_stat', u'Other'),\n",
" (u'actual_range', array([ 185.16, 322.1 ], dtype=float32))])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.air.attrs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Reading and writing netCDF"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Under the covers, this uses scipy or the [netCDF4-Python](https://github.com/unidata/netcdf4-python) library:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/software/python-2.7-2015q2-el6-x86_64/lib/python2.7/site-packages/xarray/conventions.py:1022: RuntimeWarning: saving variable air with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
" for k, v in iteritems(variables))\n"
]
}
],
"source": [
"ds.to_netcdf('another-copy-2.nc')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (lat: 25, lon: 53, time: 2920)\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 62.5 60.0 57.5 55.0 52.5 ...\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 210.0 212.5 215.0 217.5 ...\n",
" * time (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00 ...\n",
"Data variables:\n",
" air (time, lat, lon) float64 241.2 242.5 243.5 244.0 244.1 243.9 ...\n",
"Attributes:\n",
" Conventions: COARDS\n",
" title: 4x daily NMC reanalysis (1948)\n",
" description: Data is from NMC initialized reanalysis\n",
"(4x/day). These are the 0.9950 sigma level values.\n",
" platform: Model\n",
" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xr.open_dataset('another-copy-2.nc')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"netcdf another-copy {\r\n",
"dimensions:\r\n",
"\tlat = 25 ;\r\n",
"\ttime = 2920 ;\r\n",
"\tlon = 53 ;\r\n",
"variables:\r\n",
"\tfloat lat(lat) ;\r\n",
"\t\tlat:standard_name = \"latitude\" ;\r\n",
"\t\tlat:long_name = \"Latitude\" ;\r\n",
"\t\tlat:units = \"degrees_north\" ;\r\n",
"\t\tlat:axis = \"Y\" ;\r\n",
"\tshort air(time, lat, lon) ;\r\n",
"\t\tair:long_name = \"4xDaily Air temperature at sigma level 995\" ;\r\n",
"\t\tair:units = \"degK\" ;\r\n",
"\t\tair:precision = 2s ;\r\n",
"\t\tair:GRIB_id = 11s ;\r\n",
"\t\tair:GRIB_name = \"TMP\" ;\r\n",
"\t\tair:var_desc = \"Air temperature\" ;\r\n",
"\t\tair:dataset = \"NMC Reanalysis\" ;\r\n",
"\t\tair:level_desc = \"Surface\" ;\r\n",
"\t\tair:statistic = \"Individual Obs\" ;\r\n",
"\t\tair:parent_stat = \"Other\" ;\r\n",
"\t\tair:actual_range = 185.16f, 322.1f ;\r\n",
"\t\tair:scale_factor = 0.01 ;\r\n",
"\tfloat lon(lon) ;\r\n",
"\t\tlon:standard_name = \"longitude\" ;\r\n",
"\t\tlon:long_name = \"Longitude\" ;\r\n",
"\t\tlon:units = \"degrees_east\" ;\r\n",
"\t\tlon:axis = \"X\" ;\r\n",
"\tfloat time(time) ;\r\n",
"\t\ttime:standard_name = \"time\" ;\r\n",
"\t\ttime:long_name = \"Time\" ;\r\n",
"\t\ttime:units = \"hours since 1800-01-01\" ;\r\n",
"\t\ttime:calendar = \"standard\" ;\r\n",
"\r\n",
"// global attributes:\r\n",
"\t\t:Conventions = \"COARDS\" ;\r\n",
"\t\t:title = \"4x daily NMC reanalysis (1948)\" ;\r\n",
"\t\t:description = \"Data is from NMC initialized reanalysis\\n(4x/day). These are the 0.9950 sigma level values.\" ;\r\n",
"\t\t:platform = \"Model\" ;\r\n",
"\t\t:references = \"http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html\" ;\r\n",
"}\r\n"
]
}
],
"source": [
"! ncdump -h another-copy.nc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Indexing with named dimensions"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray 'air' (time: 4, lat: 25, lon: 53)>\n",
"array([[[ 241.2 , 242.5 , ..., 235.5 , 238.6 ],\n",
" [ 243.8 , 244.5 , ..., 235.3 , 239.3 ],\n",
" ..., \n",
" [ 295.9 , 296.2 , ..., 295.9 , 295.2 ],\n",
" [ 296.29, 296.79, ..., 296.79, 296.6 ]],\n",
"\n",
" [[ 242.1 , 242.7 , ..., 233.6 , 235.8 ],\n",
" [ 243.6 , 244.1 , ..., 232.5 , 235.7 ],\n",
" ..., \n",
" [ 296.2 , 296.7 , ..., 295.5 , 295.1 ],\n",
" [ 296.29, 297.2 , ..., 296.4 , 296.6 ]],\n",
"\n",
" [[ 242.3 , 242.2 , ..., 236.1 , 238.7 ],\n",
" [ 244.6 , 244.39, ..., 232. , 235.7 ],\n",
" ..., \n",
" [ 296.2 , 296.5 , ..., 296. , 295.6 ],\n",
" [ 296.4 , 296.29, ..., 297. , 296.79]],\n",
"\n",
" [[ 241.89, 241.8 , ..., 235.5 , 237.6 ],\n",
" [ 246.3 , 245.3 , ..., 231.5 , 234.5 ],\n",
" ..., \n",
" [ 297. , 297.5 , ..., 296.6 , 296.29],\n",
" [ 297.5 , 297.7 , ..., 298. , 297.9 ]]])\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 62.5 60.0 57.5 55.0 52.5 ...\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 210.0 212.5 215.0 217.5 ...\n",
" * time (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00 ...\n",
"Attributes:\n",
" long_name: 4xDaily Air temperature at sigma level 995\n",
" units: degK\n",
" precision: 2\n",
" GRIB_id: 11\n",
" GRIB_name: TMP\n",
" var_desc: Air temperature\n",
" dataset: NMC Reanalysis\n",
" level_desc: Surface\n",
" statistic: Individual Obs\n",
" parent_stat: Other\n",
" actual_range: [ 185.16 322.1 ]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.air.sel(time='2013-01-01')"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray 'air' (time: 2920, lat: 5, lon: 29)>\n",
"array([[[ 273.7 , 273.6 , ..., 246.2 , 246.8 ],\n",
" [ 274.79, 275.2 , ..., 250.7 , 249.5 ],\n",
" ..., \n",
" [ 276.7 , 277.4 , ..., 249.6 , 249.39],\n",
" [ 277.29, 277.4 , ..., 249.89, 252.3 ]],\n",
"\n",
" [[ 272.1 , 272.7 , ..., 245.2 , 246.8 ],\n",
" [ 274. , 274.4 , ..., 248.89, 248.89],\n",
" ..., \n",
" [ 275.79, 276. , ..., 252. , 251.8 ],\n",
" [ 276.29, 276.4 , ..., 249.3 , 252.1 ]],\n",
"\n",
" ..., \n",
" [[ 274.29, 273.89, ..., 258.69, 256.19],\n",
" [ 275.59, 276.29, ..., 258.69, 257.19],\n",
" ..., \n",
" [ 276.79, 277.29, ..., 255.39, 254.19],\n",
" [ 277.59, 278.29, ..., 254.19, 254.59]],\n",
"\n",
" [[ 272.59, 271.99, ..., 256.49, 255.19],\n",
" [ 274.29, 274.49, ..., 260.29, 259.49],\n",
" ..., \n",
" [ 276.89, 277.29, ..., 258.79, 257.39],\n",
" [ 277.59, 277.39, ..., 258.59, 258.69]]])\n",
"Coordinates:\n",
" * lat (lat) float32 60.0 57.5 55.0 52.5 50.0\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 210.0 212.5 215.0 217.5 ...\n",
" * time (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00 ...\n",
"Attributes:\n",
" long_name: 4xDaily Air temperature at sigma level 995\n",
" units: degK\n",
" precision: 2\n",
" GRIB_id: 11\n",
" GRIB_name: TMP\n",
" var_desc: Air temperature\n",
" dataset: NMC Reanalysis\n",
" level_desc: Surface\n",
" statistic: Individual Obs\n",
" parent_stat: Other\n",
" actual_range: [ 185.16 322.1 ]"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.air.sel(lat=slice(60, 50), lon=slice(200, 270))"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray 'air' (time: 2920)>\n",
"array([ 268.9 , 264.2 , ..., 254.69, 257.89])\n",
"Coordinates:\n",
" lat float32 42.5\n",
" lon float32 272.5\n",
" * time (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00 ...\n",
"Attributes:\n",
" long_name: 4xDaily Air temperature at sigma level 995\n",
" units: degK\n",
" precision: 2\n",
" GRIB_id: 11\n",
" GRIB_name: TMP\n",
" var_desc: Air temperature\n",
" dataset: NMC Reanalysis\n",
" level_desc: Surface\n",
" statistic: Individual Obs\n",
" parent_stat: Other\n",
" actual_range: [ 185.16 322.1 ]"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.air.sel(lat=41.8781, lon=360-87.6298, method='nearest', tolerance=5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Computation\n",
"\n",
"You can do arithmetic directly on `Dataset` and `DataArray` objects. Labels are preserved, although attributes removed."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (lat: 25, lon: 53, time: 2920)\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 62.5 60.0 57.5 55.0 52.5 ...\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 210.0 212.5 215.0 217.5 ...\n",
" * time (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00 ...\n",
"Data variables:\n",
" air (time, lat, lon) float64 482.4 485.0 487.0 488.0 488.2 487.8 ...\n",
"Attributes:\n",
" Conventions: COARDS\n",
" title: 4x daily NMC reanalysis (1948)\n",
" description: Data is from NMC initialized reanalysis\n",
"(4x/day). These are the 0.9950 sigma level values.\n",
" platform: Model\n",
" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"2 * ds"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also apply NumPy \"universal functions\" like `np.sqrt` to `DataArray` objects:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n",
"array([[[ 15.531, 15.572, ..., 15.346, 15.447],\n",
" [ 15.614, 15.636, ..., 15.339, 15.469],\n",
" ..., \n",
" [ 17.202, 17.21 , ..., 17.202, 17.181],\n",
" [ 17.213, 17.228, ..., 17.228, 17.222]],\n",
"\n",
" [[ 15.56 , 15.579, ..., 15.284, 15.356],\n",
" [ 15.608, 15.624, ..., 15.248, 15.353],\n",
" ..., \n",
" [ 17.21 , 17.225, ..., 17.19 , 17.178],\n",
" [ 17.213, 17.239, ..., 17.216, 17.222]],\n",
"\n",
" ..., \n",
" [[ 15.678, 15.646, ..., 15.62 , 15.646],\n",
" [ 15.808, 15.789, ..., 15.572, 15.63 ],\n",
" ..., \n",
" [ 17.213, 17.239, ..., 17.178, 17.158],\n",
" [ 17.257, 17.274, ..., 17.19 , 17.181]],\n",
"\n",
" [[ 15.655, 15.63 , ..., 15.54 , 15.55 ],\n",
" [ 15.808, 15.789, ..., 15.501, 15.546],\n",
" ..., \n",
" [ 17.207, 17.23 , ..., 17.196, 17.181],\n",
" [ 17.254, 17.265, ..., 17.21 , 17.196]]])\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 62.5 60.0 57.5 55.0 52.5 ...\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 210.0 212.5 215.0 217.5 ...\n",
" * time (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00 ..."
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sqrt(ds.air)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"xarray also implements standard aggregation functions:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: ()\n",
"Coordinates:\n",
" *empty*\n",
"Data variables:\n",
" air float64 317.4"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.max()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (lat: 25, lon: 53)\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 62.5 60.0 57.5 55.0 52.5 ...\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 210.0 212.5 215.0 217.5 ...\n",
"Data variables:\n",
" air (lat, lon) float64 260.4 260.2 259.9 259.5 259.0 258.6 258.2 ..."
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.mean(dim='time')"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (time: 2920)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00 ...\n",
"Data variables:\n",
" air (time) float64 277.5 276.7 276.2 276.8 277.0 275.3 275.6 275.4 ..."
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.median(dim=['lat', 'lon'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises\n",
"\n",
"Calculate the maximum air surface temperature over time for San Francisco, CA (latitude=37.7749, longitude=122.4194)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: ()\n",
"Coordinates:\n",
" *empty*\n",
"Data variables:\n",
" air float64 301.4"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.sel(lat=37.77, lon=360-122.419, method='nearest', tolerance=2).max()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Convert the dataset from Kelvin to degrees Celsius and save to a new netCDF file.\n",
"\n",
"Don't forget to fix the temperature units! Recall `degC = degK - 273`."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ds_celsius = ds - 273\n",
"ds_celsius.air.attrs['units'] = 'kelvin'\n",
"ds_celsius.to_netcdf('temperature-in-kelvin.nc')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----------------------"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## xarray comes with built-in plotting, based on matplotlib"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fec57478d90>]"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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hvGvFupUiJjtwlpEu/Oa7kWGHHeKWoDnxu5UKBYsMCzMOIWE+9OljtdUyIQua\nJU9BJXqaL7yLEQ2tWrlkX5FeM9rLNQ/LlsUtgVEpIpl4S83y//31r3FLYJTD0qX5KyyffBLeNc04\nhIT1zaaTxYvdd7MMzn71VdwSGJWQHUr9wgvDu5YZhwRSKLeDET5rr+2+zz8/XjkMIx89esDRR2fW\nS2WPrAUzDnXk44/hlFPKL5/teeCz3nr1kceonF693Pd998UrR1TMn19euVNPDVcOo3yC3mVhRhE2\n41BH7r0XRo8ub2r7448XThZuk5GSwRdfFM+h3AiUGym4UG4BI3oOOiia65hxqJF33oF//avltrZt\nSx+3886u5fDII+HIZdTOyy8XNuDNxN57w2GHxS2F4bP99uWVE4EPPqj+OjYJrkbWXRc+/NDVMv0J\nVOUQFPXAA1vOiG6QvyS1ZE8K8/+Piy923Sul4g+liVIT4JYta+4otUnlrLOcPkLh94WIm1X9/PPF\nz2WT4EJm4MDqj7311owP+QMP1Eceo348/rj7PussN67ULCy3nBmGpPLnP5dX7oUX4JVXqruGGYca\n8R+eWbPKP+bEE1uut21rs0+TzPjxzjBAup0Fvv8e1l/f6ezs2aXHxl5/PRq5jPozenRmedCg6s5h\nxqFG/D493z++EHffnVleYYX8Zfbcs/o/0giPFVbINOEhfd1+663nDEL79pnkUxddBBMmFD7m5JNh\nww2jkc+oP08/Xfs5zDhERPCFsuKK+cvcf78L4WDEy+abt1z/y19arqdt9nS+QcnLLivecrjiivCj\nfhq1ke0sMWUKfPSRW87unvZ19oknyk8UZH9/RAQNgt9FYSST4cOL72+UuFmFXCK7dIlWDqM6+vZ1\n336Sqv79YZ118pf97jv3vcsu8PDDcNVVpc9vxiEi/BbBT39auFvJSAbBtIz58LNy1cp228GLL9bn\nXPWk3LkPRrz4LvNvvVW67MEHZ5YPOQSOP750C9iMQwRMm1abN5MRLaUmGe2+e32u8+yzGU+osCh3\nBnSQZolIm3Z845A9BjZqVG7Z6dMz3UnffOO+Fy0qfv7IjYOI9BKRSSIyVURmiMiZ3vZxIvKG9/lI\nRN7wtvcWkQWBfVdHLXOtbLxxxqvJBvmST6m+9nnzar+GP/kx7BhO1cTe2W+/+sth1B/fOGTr6wUX\n5JZ9//3cybmljEMc03kWAyNVdZqIdABeF5HHVPUAv4CIXAoE1fp9VR0QtaDFWLQIbr+9dLlf/jKz\n/P33znfzhpi1AAAdmUlEQVTcSA9nnAGXXNJyWyGHgkp4993SZSqdWJmPauYpBHMWG8nF/58Khe3u\n0QM++6zw8aU8LCNvOajql6o6zVueD0wBuvv7RUSA/YE7opatElZYobw0oLfdlllu184evLRwyikw\nYAAcfnjuvhkzaj9/0GNk9mxnCLLp3h1OP7226xx3XGXlzzijtusZ0eEb/mOPhdVXz93fti1MmlT4\neH8guxCxjjmISG9gC+C5wOZtgS9VNeiA11tEJovICyIS+3SxZ54pr1yjB21rZC6/3E0Cyw6V8ZOf\nuO9KJj3mI2gcdtghEw32/fdhwYLM/ssuq+06999fftn27VvO5zDSQz4nicGDYdttCx9TKkZTbMbB\n61K6GzhJVb8N7DoIGBtY/xzooaqbAscBt4pI5+gkzWXIkPLKpXk2reEIvsS33Tbj4VFpjTyboDvs\nlCluffp0+NGP3DhEOcEb681110V/TSM8br7ZfRca51y4sPjxsYQQE5G2wHhgrKpOCGxvA+wDbOZv\nU9XFuHEKVPUNEZkGbADkOAFeEBiJGTJkCEPKfYtn8c03rsm20kqZbSef7Fz8Hnyw/PP4fshGevGT\n/wAcdZSLU/Poo7UH38s3EcnXl+wxjkLMneuSztcjEGDaZn0bpfG7sC+9FPbYI7hnovfJP3jtE3lU\nVm9M4WZgtqqekrVvN+AsVR0a2NYVmKuqy7xuqOeB/qo6K+vYukRlveQSOPNMtxw83eabw2uvVXYu\ni2jZGLzxhhsv2mAD1520yiqun/eaa6o/ZyV6USzqZrH9Tz0FO+1U2zWMZJOtR2utlQkOGfxP8+lb\n//4weXKyorIOBoYDQwPuqbt5+w4gdyB6KDBFRKYADwInZhuGbESq8++GlpY0OJpfqWHw5TDSz4AB\nzjAAdPY6NNMQnbVcw2A0Dtdc4yZXbrdd8XK//S28+SY89ljhMpF3K6nqcxQwSqqak1JEVcfjuqDK\nPL/7fuaZ7KZUeXz/fWa5UcIkGPXDb6onPUlTPu+nQtx1V3hyGOEyYIBr2fp07w7//Gfh8v/4h3sv\nfvqpWy/m8NBwM6SXLHHfP/1p5cdmG4NyA1QFGTGi8mOM9FBra/DII92nEkqFORBxg9kff9zSvbFc\nttmmMnmM5HDLLS3X+/d3FZhsl3lVV/HdfXfnEefPtyrmWt9wxqGWuDfZMWV8Q1MJe+9d/fWNdNCh\nQ/XHjhnjPpXw6qu52xYsaLn+ySfw3/9m1iuJHGvjDellgw1cEL3rry9dtl07V3lYd93MtmLvuIYy\nDo8/Xr6baT6yZxNee215x51wQmZ51VXd99ZbVy+HkWz8Fubbb0dzvQMPzN32xBMt10UyMaEWL4aH\nHgpfLiN+2rSBkSNda7QSI+8bhWLurA1lHHbd1eVzLoRq8RuY3cQ699zyrhs8Z79+5R1jpBe/SV5p\nCyCbcmc/+zH6g2S3DP71r0zehkoD59UaosNIH75xKBZCo6GMQyn69IGf/7zw/nqEtmjdGi68EH7z\nm9rPZSST57z5/IVi2lxySXnB+Q49tPj+YcMK75s5s+X6735X+nqFMK+65mPTTd33V18VLhP5PIew\nEBGFlr/lD39w7nyDBrnafatWbmKbH7I2m3PPdekTg6i6geliM1Y//DCTZKNBbqdRhMWLM7VzVdfN\nNHmy61Ls1cu9bMePh333zT02+CKeMcNVWAoxZkwmfte777rZ0/nOUy5LluTXY9PZ5mSbbeD55wGS\nM88hMn7960wgMb/5NG9ervvW/PnuYcs2DD6//33x6wRn0RqNT/YLdptt3CTJfMagGKUi9B4WcOw+\n++zKzp3NBx+4/ulCmcKM5qNUa7OhjQO4qISvvNLSuyM7sfqbbxY/R6HuA3AeAOAGhYzmIFhrX7iw\nZTY3v99/3LjS51lpJbj33vz71l/fXcd/mdcaIsM/zwcf1B400GgM1lqreGWhIY1DdiTChx9uOe/h\n229b7i/WRJ83r3jyF7/VcOWV1c/KNtJL0Ai8+mom2OJdd5V2L+zaNVP+668z2z/5JDOuMcDLYiKS\nmXcze3ZtMnfrVtvxRmPQpk1xB56GNA7BgHngXu6ub82R3VLINg7B7FmdOpXnldKmjQt5bDQXxWrh\nRx9duD/fH2zu18+V6dIFOnZ023r1cvGbIOOVNG5cpjvrlVcqk/G88wrvG1927AGj0SjVGo0lKmvY\nZL/si3UbiWS6hnw6dSr/WjaY15xsvrlrKZRyR126NPMQ+rpy0035PZX22it3ln6+yWyV5rDOp6Nv\nveUSxHTpUtm5jMahlHFoyJZD9sOQr3b07LOZ5ezZpoZRinInmfmDyscfnxmPKDQQfeutMHZsy22/\n+lVV4rWge/fcbRtuaIah2WlK47BDGbnittvOubpWw/vvZ2p0cSRlMeKn3P/90Ufd91VXZVxRt9ii\n/Otkh2MplRQ+H8ccU/kxRuNTqhu8oeY5/OIXytixruVQy8QeVTjpJPjLXwrvBxfsrFMn6Nmz+msZ\n6WTRIpdHvBSrrebyKgQTP1X6yHXqVN6kukI0yCNuhESS8jmExi9/mVnea6/qzjF4sPvO90D985+u\n1eCz8cZmGJqVckNUzJlTe0ZAm8FsxEFDGYedd4Z77nHL48YV99IohN8NkG8gcOjQlhENDSObGTPg\nxz/OrBeLXVMuxVyp//jHljOnDaNeNJRxaNsW9tvPLS+/fO4MwHIG9/xwzNnGodgDajQnF17ovh97\nzLmtvvSSm7z2wAP1vU4h3ZswAc45xxmIfOyzj5vjYxjV0PCvPN93HFxX0aRJ5R2XHfOmHkncjcbC\nT8W4yy4uvPugQW591VWdq2u9KJTuM1/XZzBxz/jxlbu9GoZPwxuHl1/OLLdqlTt7OkhwtuCJJ7bc\nZ8bByKbYWMAmm9TvOqUmYQaNRDD3g41VGLXQ8MYhOMHt4ouLlw0G0Mt+sOrhb240D/UI/+5TyG3W\nNwrmjWSEQcMbB7+/duBAWHnlwuWC2dzyUcit1WheikXjveyy/Jnidtut8uuUMg4+f/kLHHBA5ec3\njHw0vHHwH6Bgt9BDD2WSXfjky+AW7IKqZ03QaAx69ixca+/Y0Q1OBznqKBgxovLrFOoe8j3nfBlO\nOKF4BcgwKqHhe9L9AGa+iyvAHnu4j//Q9eiROxPVMGol6GU0d25lMbuy6dChZdTfiy6C/v3d8jbb\nwJ57ZvYdf3x+V2zDqISGNw7t2hWu3e2xhwvlfcwx+Wtnv/udm9vgh1U2jGq49dbaDAO4MPNBHQ0G\n6OvRA+6/P7P+f/9X27UMAxosfEYYv2XxYlcLKydUgmGESdA4vPACbLVVfLIYjUOh8BlmHAwjJQSN\ng6m6US+aIraSYTQyG28ctwRGM2EtB8NIEX7rwVTdqBfWcjCMBqFQsiDDqCdmHAwjZZjbtREFZhwM\nI2VYzCQjCsw4GEbKMONgREHkk+BEpBdwO9AFWA4Yo6oXi8g4wA840BmYq6oDvGPOAQ4GlgKnqerj\nUcttGEngrrtgs83ilsJoBuJoOSwGRqpqP2AgcKSI9FfVA1R1gGcQxnsfRGQgsC/QD9gNuFZEUjkk\nN3HixLhFKAuTs77UU85hw8LLRpiG+5kGGX3SJGs+IjcOqvqlqk7zlucDU4Du/n4REWB/4A5v0x7A\nnaq6VFU/A6YDg6KVuj6kRVlMzvpictaPNMjokyZZ8xHrmIOI9Aa2AJ4LbN4W+FJVP/DWewCfBvZ/\nCvSMQj7DMIxmJTbjICIdgLuBk1T128Cug4Cx8UhlGIZhQEwzpEWkLfAQ8KiqXhHY3gbXMthMVT/3\ntp0HLFDVS731h4CLVPX5rHPanFHDMIwqyDdDOg5vJQHGAG8FDYPHTsDbvmHweBj4m4iMBlYH+gIv\nZx2X98cZhmEY1RFHPofBwHBgioi84W07R1UfBQ4gMxANgKq+JiL34QaulwHHqOqSKAU2DMNoNhom\n8J5hGEZcSANG/kzNDGkR2V5E1vTGKxKLiGwjIpYaqA6IyMYicpSIdC9dOj5EZC8R6RO3HOUgIvt7\n34nthvVlE5FEZ24XkXVF5AqARjMMkALj4L0gHgCuBP4I/ClmkfIiIvuKyEvA73FjJDvELVM+RKSD\niPxJRE73JhgmDhFp5z10dwE7Ahcl+H6ujpPzGBHpHLc8hRDHEcCdIjJUVTVpBkJEWonIxcA1cctS\nJkcCJ4nIgeDkj1meupLoHyMiawOnA0+o6qbA1cBqIrJ8vJK1RESGAYcBZwE7A18SmNiXFETkV8Dz\nuPAkqwLHe+FMksYhwDJV3VhVDwR+AL6OWaZCrADMADYENk9qbder2S4G3gbOEZHWCaztdgS2BI4S\nkb6qujSJ9zNgBF4H/g+4UkTaqOqyGMWqO4k2DsDHwKmq6qdMPwRYhPNYipWsWteTwL6qOhE3yD8Y\nWCIia8YhWxG6Aoeq6rE4j7GFqvqfmGUCQES6BFZvUNXTvO27AjsA24tIImbGZ72wOgKXAY8DR8Qj\nUS6Brpk2gZfZasB+QGtgZLBc3HgyLgbuBP4M3AKgqkvjlMvH10/PqPpGYBhwEW4S7x/iki0sEmUc\nROSnIjJZRPzoMaKqc7xuhrOBtYF3gFtE5GjvmMh/g4icDtwa2DRXVZd4co8D/ourAd0jIj2ils9H\nRHp43R4AqOrlqjrZM1p/BXYUkXNFZKhXPo57ua43d+W2wFjNUm9fP+A43IO3MnCBiAyIWkZPlv/p\nplej9T39ugC7e27ZKwJXiMhxccb/Cuqnqv4A+AZgFaAPcAxwnDeHKJbko0HdDLxwOwH7qeq5QEev\nYuCXj+Vdla2f3n/v/7fv4P7zg3Ct8Oc9nW0IEmMcRGRn4ERgIfB3b/MyAFVdANykqruq6iW4rqaz\nvX2RNeW8PtGLgJ8DG/kGClcTA/gIOFpV91XVU3DNzhOiki8gp4jIZcCbuDGQbLYAJuDiVi0CzhCR\njjE1i88BvgP+A4wI7lDVqaq6p6r+HbgEeA/YKmoBC+imX6P9FnjKM2w9cC/ehaq6OAY5C+lnK6+F\nMAuYjGvtdANOBWZG2XrIp5veC7cV7nl/xSt6PnC/iEyTeJ1QcvRTVRd78q7ufXzd6K6qUxtl7CHu\n2EoSqC1OBc5Q1S2BPiKylzdo1hZAVWcGDv0KeCRQewtbzuVEpJX38rwP1zQ/GRghIiuq6g9+7SdL\nzlk4AxE1nXFdCCOBdiKyB/xvZjqqOl5Vr1LV93C/51sgsrEHEVk98N+NAk4C7gV2EpG1VXVZdl+z\nqs7DjeNMjkjGkrrp7esMXIsLCDkOFxKmq0TosVZCP9ur6hJP3jVxtd2/k3npdYl47CGvbnqyrwhs\nKiIjgXNxY3dPevOaIht7KKWfAXnnAc/gWl/rAz1FZJeGGXtQ1Vg+uBr1JFzfd9+sfcOBjwLrgjNk\ny+HyOkwDTo9AxtbA33AT8/6QtU+Am4FLvPU2gX2dcEo1FRgU0f0cBKwHdPTWVwXaA8fivGla+3Jn\nHbcX8ACwfAQybga8imu1jAPaBfatguu/vTQoJ7A8sCfwmHfcaknSTW/bsb5c3m8807/fSdFPb9sA\n4KjA+vm4bpyk6GYH4Amc08QGuK7EZcAqYctYhX62wqUc2CBQZm9goyhkjeR+xHJR2Bx4BFeT+TVu\n8GnPrDKvAOcF1pcHRgMPAgMjkLEV8BtcLWsNXA3ht0CvQJm+ngHYOLBtPeBfOHe8rhHI2Q7n5vue\nJ+tjWfvXBW4CjvPW/ZfuIFyAwzeAvYL7QpLTf1kd5a3fgfM+6xS431t6/++PA8eshwuXckBSdTOw\nvW0UMlahn339Y6KSr0LdPN5bXx7onVVmUNi6WYV+bhn8v3FOKKFXBqL+RNatlNUPtwGupv0Jzhq/\nCmwrIkEvpMNwgzwbicipuCbnn1T1Z+pCarQKs29PXdNwA+BZVf0C15e8NjDU7zJQl5diLHC6iPQX\nkX1V9X3ci2yEqn6d3T0SAj2BDVX1R6p6JNBGRE4TF/UWXF/pA8Dunsx+F0J34D11CZbuz9oXBn6f\n8hfe+gjc/dxVRNp693s6LsnT8SJyITBMVd9X1UGqOi4swWrUzZO9AX40ENYl7H7nCvXzNBHpD+yT\n3RXrdZ+FNeZQrm7u5sm7CE8//EFfVX3Z+w6766sS/TzO08+9xLmw/qAJ8aqqJ5EYB88r4v9EZF9v\n0+vAJyLSz7vpTwBKYLDRU2zF1Ww7qOoc9frzA/37devb87wnrhSREQGPmGlAe29c4R3gWVzNcq3A\noY8Ah+K6Pb73ZP/Ee+ZahaE0IrJBYHU5YJZkPLzOBobiMuehbmD0fuANEZnkffqq6gRVPd87X93H\nbkTkEBF5UtyEu229+7AAWMHz+piL61IYhhfjS13o9l64ZE+bARPrLVceOWvVzY6eIWlBPXXTk7Me\n+jlfnfdSUE6t54u3Rt2ciPOm8veFRp30c1L2/WwkQjUOIvJjEZmMaz5OwdW2jsBZ57l4D5yqvg18\nBqzjHbeyiFwPvIBrZv4ueN56v3C9AbCJOD/r9YALRWQ13DyLPt42gHtwtYk1vOO2wjWLL1LV1dUF\nD/Rl1BBeEJuLyD+Bm0RktIgMBmYDbYHOIiKq+gpuotP+gUMH4JR8eWCU93Lzzyn1VHARWUlEbgV+\nhat5K3C4iHTDdcfsg+tLRlVvxA3k7eAduzPwE2BnVd1DVf9bL7nyyFkv3bwwLBkDstZLPx8LUcZ6\n6OaFqjolLBk9OVOhn4kgzD4r3I3eP7B+EPBXb/kI3OShnb31TYAX8QZ2cW5h/nFtCKnPEVe7GUWm\nX3Z14CpcRrrOwPW4gbPu3v5L8Ab/cIrfLShniPdyB+A1XOTabrj+ZX8w/HLcxKFu3nov3AttFW/9\nNLwxB2897P7bE8n01fbBedD49+9e73729NYvBA4OU5606mZa9DNNupkW/UzCJ6yb7w96tsd57vjr\np5MZ7V8D16/3GrC1p+R/ApbLOldoAz14A3TeA9cmIOc/gW285d1wA+FjvJfERGD7rN/ZmpAG+wLX\n6ICbbOVv3x+4x1teG9d3e3DgBXYLeTx7wnpBZN3PdsFr4bo7BnrL2+Am4N2CCzfyPq5fOhqFT4lu\npkE/06SbadHPJH3q1tfsNRsVMoNHqvqdt68VrvkGrqmJukG0a0Rkmac4i3EeIC1yNWj9u5Ba++dU\nr9tHM2MZ4g2WfQPM8fY9KiJTgVNwk3buU9Vnsn5nGOMK7VR1gaoLkKaq80Xk8UCRzwH1yn0kItcC\nPwX2FOeLPQXvXvu/zZO1rl1I6uYf+P9/cNIi6uZ/rI+bIDTV2/aciMzAdSWsC/xEVd+tl0wF5EyF\nbnryJF4/06Cb3nlToZ+JpVbrgjcHIWtb9rpfw3gc2Npb3iywv22hY+v1gRz//v60nJvgy7g+8Fpg\n+/q+jEHZss9XZ1nPwTXNc+YekKn9nAmMztq3AvALvJpjmB9c98EfveX1gD4F/vNdcbPbATbCq/FG\n8UmLbubTp6TqZxp007te4vUz6Z+aBqS9Wo6qm9G6kYiMFDcjc5m3368RqIh0xXkDLBCRu4A/iEg3\nz6Iv8WpF/izPuhGUwVvfUkT+jmv6/u/3+/txD9+L3oDls8C+Xu1yqfc7WwdronWW1W/JPQdsh3NV\nzMa/7hrAveICq50qIgNVdaGqjlWv5ighuNH651TV2UBvEXkX5963UYFD1gRai8ivcfF+OhQoV3c5\nk66b2XJ464nUzzToZvC8SdfPVFCrdcHVCA4DXsIpzlVkamASKNcb50c8jcAAVJgfsvqEcZOClgFn\nFTnmDK/MU8BucVltXB/3aLxZpYHt4n0m4CJYvuaVXT5YJgR5JOv/XB8X4mA2sF2R4x7EvXj/gHNJ\njvIeJlY306yfSdPNtOpn0j+V/gGts/6A1riZj2966+2A3+G8K1bytvlNzU29P2DF4PEhKUqwed0e\nF4jM9464G3jAW87XND4DOKnQ+UL7I1wtcTVcSIMtce50E3HN3uwuhzW8F8RYArOzw5QtsLwzzo3z\nTE/mM4AHvX3BLhg/JMI+BLppQpQxFbqZRv1Msm6mRT/T+Kn2z9gAWNlb3gUXgKqHt74bcAVezJZs\n5fG2her+F7jOfjjf5aeBh3A+yl1xk9XWy1KSnAcs5BfE5cBvvOVVve/lcWE3zvHWj8VN418tWyYC\nMZu8h6CuLwicy+HuOHdJ/yW6NS40yK5ZZafg8lkAdPb/47D/3zTrZpL1M+m6mWb9TNOnXEU5z1vu\ng6vZvICbcbmVt/1a4FpvuT3OWl/rP5RZ5wtDUXYE1g6st8P5qn+CV3vx1m/FTek/F3jK257vBSH5\nttdZ5u1w2c38e+r71A/FuSXuRqaJfjgZt7vsmlpdldx7mP+Am6w0zntpXeHt2wMYGyi7gvd9IK7r\n5m+4oGmdIlHeFOhmGvUzqbqZNv1M+6ecP2NbT1E6eg/Vkd72ibh+3Ha42aOvBh7IQcDQSH6Aq2l9\nhsvGdrS3TTwZZgL7eNvWBC4GDvHWlwE7xHLTM54Sd+Im4BwI3BLYfwEu6NdywM9wEUJXjUi2Y3A1\nQj+o2JrAv3FdHyNxaRE7Bcr7D+A+uEBwkciZBt1Mo34mWTfTpp9p/5SrKPcCf/OWN8dFyRztPXRn\nBJRmUuQ/wDUrH8T5oz+PG4D0m7dn0LImcR1wgrfcP7abnrmvXXE+68M8pfZfDFsBn5KJENk7IrmW\nA/4BDPHWO3jfw3FxZgbiYg2d6L2QN8X16w+I8R4mVjfTqJ9J1c206WcjfMpVlG64vtt1cNmjfudt\nPwaXMGQtXNTUDYLHRag0t+AmAW3uPWC/xvl99/AeyKtx/bnvkOl7bBX8jvzGZ14Q5+O8OnbEects\nigs3cHPwBRHVPcV1IxybfW9wQeZ+hssidw1ulu404Bcx3b9U6GYa9TOpupkm/WyET9F5Dqqqnr/4\nbFztYTzwA7CciKzjPXgvAu1V9XtVfcfzB9di5w2B+3CeHa/iZjqeiXOhm+PJPRiXiGN/Vb3X+23L\ngt9Ro5lZsKNw3h9dcbHv/wYsU9VDVfXNQPnQ76nnc/8csK6IrKzOb76jt/th3EDjK6o6AjhVVfuq\n6tiw5cpHinQTUqafSdRNSJd+NgIlJ8EFFOXXeGn8cGklXwZmqeqOqvpWoHwcL9sOwGYiMg7nRXEy\nblbk/wHzcbFdPlbVKd7EnETkeA3IcRYuWNr1uBmk53j7I0uNCP97yJ/EDdwO87Z96+3uiauN+WUj\nSddZjJToJqRQP5Omm5A+/Uw95TQvyDRx9wU+8Ja7BvbHmgUJF0Dta7yomt629YEhOH/33XA1izXi\nlLOA7H73yETgIP9+ElN3l3f9vXAuged49+4BnFdI97hkKiJronXTkyGV+plE3fRkSI1+pvnj//kl\n8afki0vIca2q3uFNqV+q5Z4kRETkCuARVX08GLzM29cRWtQyEoUXTO1uXDz7F+KWB0BEhuAiVG4O\nPKSqf49XosIkXTchvfqZRN2EdOlnWik7Kqv38HXATTX/2NuWpCxI6+CyOOVkX0viQ5fF5rgMZC/F\nLYiPqk4EJoYVR6qepEA3Ib36mTjdhHTpZ1opu+UA/7PWOwO/zVbwuBGRLqo6J245jHhIsm6C6aeR\nPioyDmkgrOiZhlEPTD+NtNBwxsEwDMOondhd5gzDMIzkYcbBMAzDyMGMg2EYhpGDGQfDMAwjBzMO\nhlEFItJJREZ4y2uIyN1xy2QY9cS8lQyjCkSkNy79ZL+YRTGMUCh7hrRhGC34Ey466BvAe8CGqtpP\nRH6Fi7C6PC5l6WXACrikOa2AXVR1loj0wUU57QosBA5X1enR/wzDyI91KxlGdZyFC/Q3AJe0J8jG\nuMxjW+BSWs5R1c1xAex+5ZW5AZcwpz8uOc21EchsGGVjLQfDqA4psAzwtKouBBaKyFxcxFVwuRw2\nFZFuwGbA3S5FAeBSmhpGYjDjYBj1Z1FgeVlgfRmutS7AV16rwzASiXUrGUZ1LMAlGKoEAVDVWcBX\nIvJTcCHHRWTjOstnGDVhLQfDqAJV/VJEJovIW7hcxb7bnwaWybPsrx8AXC8if8Ql0LkLsAFpIzGY\nK6thGIaRg3UrGYZhGDmYcTAMwzByMONgGIZh5GDGwTAMw8jBjINhGIaRgxkHwzAMIwczDoZhGEYO\nZhwMwzCMHP4f1TflmsjMK0wAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fec574bfe10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ds.mean(['lat', 'lon']).air.plot()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x7fa9f06ad910>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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3a8rRmLwQQkDdXvrujU+yZ+OTh62araZ7G3DjyNpc7r615vO/Ab6Xvd0ELK+p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\n/x9M5jEwZNfyhAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa9f089d710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ds.min('time').air.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Time series"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Xarray implements the \"split-apply-combine\" paradigm with `groupby`. This works really well for calculating climatologies:"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (season: 4)\n",
"Coordinates:\n",
" * season (season) object 'DJF' 'JJA' 'MAM' 'SON'\n",
"Data variables:\n",
" air (season) float64 273.6 289.2 279.0 283.0"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.groupby('time.season').mean()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"clim = ds.groupby('time.month').mean('time')"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (lat: 25, lon: 53, month: 12)\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 62.5 60.0 57.5 55.0 52.5 ...\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 210.0 212.5 215.0 217.5 ...\n",
" * month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
"Data variables:\n",
" air (month, lat, lon) float64 246.3 246.4 246.2 245.8 245.2 244.6 ..."
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clim"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also do arithmetic with groupby objects, which repeats the arithmetic over each group:"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"anomalies = ds.groupby('time.month') - clim"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (lat: 25, lon: 53, time: 2920)\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 62.5 60.0 57.5 55.0 52.5 ...\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 210.0 212.5 215.0 217.5 ...\n",
" month (time) int32 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ...\n",
" * time (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00 ...\n",
"Data variables:\n",
" air (time, lat, lon) float64 -5.15 -3.886 -2.715 -1.812 -1.125 ...\n",
"Attributes:\n",
" Conventions: COARDS\n",
" title: 4x daily NMC reanalysis (1948)\n",
" description: Data is from NMC initialized reanalysis\n",
"(4x/day). These are the 0.9950 sigma level values.\n",
" platform: Model\n",
" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"anomalies"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Resample adjusts a time series to a new resolution:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tmin = ds.air.resample('1D', dim='time', how='min')\n",
"tmax = ds.air.resample('1D', dim='time', how='max')"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray 'air' (time: 730, lat: 25, lon: 53)>\n",
"array([[[ 241.2 , 241.8 , ..., 233.6 , 235.8 ],\n",
" [ 243.6 , 244.1 , ..., 231.5 , 234.5 ],\n",
" ..., \n",
" [ 295.9 , 296.2 , ..., 295.5 , 295.1 ],\n",
" [ 296.29, 296.29, ..., 296.4 , 296.6 ]],\n",
"\n",
" [[ 243.2 , 243.1 , ..., 238.8 , 240.89],\n",
" [ 246.39, 245.3 , ..., 234.89, 237.2 ],\n",
" ..., \n",
" [ 296.7 , 296.29, ..., 296.4 , 296.1 ],\n",
" [ 297.5 , 297.1 , ..., 296.5 , 296.9 ]],\n",
"\n",
" ..., \n",
" [[ 243.09, 243.39, ..., 245.59, 244.49],\n",
" [ 247.69, 248.19, ..., 242.39, 244.19],\n",
" ..., \n",
" [ 296.69, 297.39, ..., 295.99, 295.2 ],\n",
" [ 297.79, 298.49, ..., 295.89, 295.5 ]],\n",
"\n",
" [[ 242.49, 242.39, ..., 241.49, 241.79],\n",
" [ 248.39, 248.79, ..., 240.29, 241.69],\n",
" ..., \n",
" [ 296.09, 296.89, ..., 295.09, 294.39],\n",
" [ 297.69, 298.09, ..., 295.49, 295.19]]])\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 62.5 60.0 57.5 55.0 52.5 ...\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 210.0 212.5 215.0 217.5 ...\n",
" * time (time) datetime64[ns] 2013-01-01 2013-01-02 2013-01-03 ...\n",
"Attributes:\n",
" long_name: 4xDaily Air temperature at sigma level 995\n",
" units: degK\n",
" precision: 2\n",
" GRIB_id: 11\n",
" GRIB_name: TMP\n",
" var_desc: Air temperature\n",
" dataset: NMC Reanalysis\n",
" level_desc: Surface\n",
" statistic: Individual Obs\n",
" parent_stat: Other\n",
" actual_range: [ 185.16 322.1 ]"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tmin"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ds_extremes = xr.Dataset({'tmin': tmin, 'tmax': tmax})"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (lat: 25, lon: 53, time: 730)\n",
"Coordinates:\n",
" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 62.5 60.0 57.5 55.0 52.5 ...\n",
" * lon (lon) float32 200.0 202.5 205.0 207.5 210.0 212.5 215.0 217.5 ...\n",
" * time (time) datetime64[ns] 2013-01-01 2013-01-02 2013-01-03 ...\n",
"Data variables:\n",
" tmax (time, lat, lon) float64 242.3 242.7 243.5 244.0 244.1 243.9 ...\n",
" tmin (time, lat, lon) float64 241.2 241.8 241.8 242.1 242.6 243.3 ..."
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds_extremes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pandas\n",
"\n",
"[Pandas](http://pandas.pydata.org) is the best way to work with tabular data (e.g., CSV files) in Python. It's also a highly flexible data analysis tool, with way more functionality than xarray."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"df = ds.to_dataframe()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>air</th>\n",
" </tr>\n",
" <tr>\n",
" <th>lat</th>\n",
" <th>lon</th>\n",
" <th>time</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">75</th>\n",
" <th rowspan=\"5\" valign=\"top\">200</th>\n",
" <th>2013-01-01 00:00:00</th>\n",
" <td>241.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01 06:00:00</th>\n",
" <td>242.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01 12:00:00</th>\n",
" <td>242.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-01 18:00:00</th>\n",
" <td>241.89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02 00:00:00</th>\n",
" <td>243.20</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" air\n",
"lat lon time \n",
"75 200 2013-01-01 00:00:00 241.20\n",
" 2013-01-01 06:00:00 242.10\n",
" 2013-01-01 12:00:00 242.30\n",
" 2013-01-01 18:00:00 241.89\n",
" 2013-01-02 00:00:00 243.20"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pandas provides very robust tools for reading and writing CSV:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"lat,lon,time,air\n",
"75.0,200.0,2013-01-01 00:00:00,241.2\n",
"75.0,200.0,2013-01-01 06:00:00,242.1\n",
"75.0,200.0,2013-01-01 12:00:00,242.3\n",
"75.0,200.0,2013-01-01 18:00:00,241.89\n",
"75.0,200.0,2013-01-02 00:00:00,243.2\n",
"75.0,200.0,2013-01-02 06:00:00,244.1\n",
"75.0,200.0,2013-01-02 12:00:00,243.3\n",
"75.0,200.0,2013-01-02 18:00:00,243.8\n",
"75.0,200.0,2013-01-03 00:00:00,244.8\n",
"75.0,200.0,2013-01-03 06:00:00,243.89\n",
"\n"
]
}
],
"source": [
"print(df.head(10).to_csv())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Of course, it's just as easy to convert back from pandas:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (lat: 25, lon: 53, time: 2920)\n",
"Coordinates:\n",
" * lat (lat) float64 15.0 17.5 20.0 22.5 25.0 27.5 30.0 32.5 35.0 37.5 ...\n",
" * lon (lon) float64 200.0 202.5 205.0 207.5 210.0 212.5 215.0 217.5 ...\n",
" * time (time) datetime64[ns] 2013-01-01 2013-01-01T06:00:00 ...\n",
"Data variables:\n",
" air (lat, lon, time) float64 296.3 296.3 296.4 297.5 297.8 297.5 ..."
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xr.Dataset.from_dataframe(df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you're using pandas 0.18 or newer, you can write `df.to_xarray()`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Things you can do with pandas"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>air</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>3869000.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>281.255037</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>16.320412</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>221.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>272.200000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>285.200000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>294.600000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>317.400000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" air\n",
"count 3869000.000000\n",
"mean 281.255037\n",
"std 16.320412\n",
"min 221.000000\n",
"25% 272.200000\n",
"50% 285.200000\n",
"75% 294.600000\n",
"max 317.400000"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>air</th>\n",
" </tr>\n",
" <tr>\n",
" <th>lat</th>\n",
" <th>lon</th>\n",
" <th>time</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>17.5</th>\n",
" <th>247.5</th>\n",
" <th>2014-06-16 06:00:00</th>\n",
" <td>298.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20.0</th>\n",
" <th>317.5</th>\n",
" <th>2013-11-04 18:00:00</th>\n",
" <td>298.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">17.5</th>\n",
" <th>327.5</th>\n",
" <th>2013-06-07 18:00:00</th>\n",
" <td>296.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>290.0</th>\n",
" <th>2014-02-05 06:00:00</th>\n",
" <td>298.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27.5</th>\n",
" <th>287.5</th>\n",
" <th>2013-02-03 12:00:00</th>\n",
" <td>293.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67.5</th>\n",
" <th>312.5</th>\n",
" <th>2013-08-08 06:00:00</th>\n",
" <td>261.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">17.5</th>\n",
" <th>222.5</th>\n",
" <th>2014-09-29 06:00:00</th>\n",
" <td>298.29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>225.0</th>\n",
" <th>2013-05-10 18:00:00</th>\n",
" <td>295.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57.5</th>\n",
" <th>232.5</th>\n",
" <th>2013-10-19 12:00:00</th>\n",
" <td>273.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35.0</th>\n",
" <th>212.5</th>\n",
" <th>2014-04-23 06:00:00</th>\n",
" <td>288.79</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" air\n",
"lat lon time \n",
"17.5 247.5 2014-06-16 06:00:00 298.70\n",
"20.0 317.5 2013-11-04 18:00:00 298.40\n",
"17.5 327.5 2013-06-07 18:00:00 296.70\n",
" 290.0 2014-02-05 06:00:00 298.20\n",
"27.5 287.5 2013-02-03 12:00:00 293.40\n",
"67.5 312.5 2013-08-08 06:00:00 261.40\n",
"17.5 222.5 2014-09-29 06:00:00 298.29\n",
" 225.0 2013-05-10 18:00:00 295.40\n",
"57.5 232.5 2013-10-19 12:00:00 273.70\n",
"35.0 212.5 2014-04-23 06:00:00 288.79"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.sample(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Statistical visualization with [Seaborn](https://stanford.edu/~mwaskom/software/seaborn/):"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f28b5027490>"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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s6YNDKrYWBAFTFzs1e8tZv35N1LrO9uzZzRtvvExlZQWGhCwsHUaGXJR9MlK8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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f28b517fe10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"\n",
"data = (ds_extremes\n",
" .sel_points(lat=[41.8781, 37.7749], lon=[360-87.6298, 360-122.4194],\n",
" method='nearest', tolerance=3,\n",
" dim=xr.DataArray(['Chicago', 'San Francisco'],\n",
" name='location', dims='location'))\n",
" .to_dataframe()\n",
" .reset_index()\n",
" .assign(month=lambda x: x.time.dt.month))\n",
"\n",
"plt.figure(figsize=(10, 5))\n",
"sns.violinplot('month', 'tmax', 'location', data=data, split=True, inner=None)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"-------------------"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculate anomalies for `tmin`. Plot a 2D map of these anomalies for `2014-12-31`."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x7fa9f05bd350>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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6u+8Edo52H4d8zWxmrwc+S5bbdSNwFPBz4AWjPagQQtQbXmaum+UrV7J85cr9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\nzv8PZjq4PjPAB78AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa9f05e8c90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tmin_clim = tmin.groupby('time.month').mean('time')\n",
"tmin_anom = tmin.groupby('time.month') - tmin_clim\n",
"tmin_anom.sel(time='2014-12-31').plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"-------------------"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## xarray also works for data that doesn't fit in memory"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's a quick demo of [how xarray can leverage dask](http://xarray.pydata.org/en/stable/dask.html) to work with data that doesn't fit in memory. This lets xarray substitute for tools like `cdo` and `nco`."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"agcm aquaplanet Q925\treadme\tT925 U925 V925\r\n"
]
}
],
"source": [
"! ls /project/rossby/datasets/Tiffany"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"T925_1979.nc T925_1987.nc T925_1995.nc T925_2003.nc\tT925_2011.nc\r\n",
"T925_1980.nc T925_1988.nc T925_1996.nc T925_2004.nc\tT925_2012.nc\r\n",
"T925_1981.nc T925_1989.nc T925_1997.nc T925_2005.nc\tT925_2013.nc\r\n",
"T925_1982.nc T925_1990.nc T925_1998.nc T925_2006.nc\tT925_2014.nc\r\n",
"T925_1983.nc T925_1991.nc T925_1999.nc T925_2007.nc\tT925_2015.nc\r\n",
"T925_1984.nc T925_1992.nc T925_2000.nc T925_2008.nc\r\n",
"T925_1985.nc T925_1993.nc T925_2001.nc T925_2009.nc\r\n",
"T925_1986.nc T925_1994.nc T925_2002.nc T925_2010.nc\r\n"
]
}
],
"source": [
"! ls /project/rossby/datasets/Tiffany/T925"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"! rsync -a /project/rossby/datasets/Tiffany/T925 /scratch/local/era-interim"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Tell dask we want to use 4 threads (one for each core we have):"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<dask.context.set_options at 0x7f36c85534d0>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import dask\n",
"from multiprocessing.pool import ThreadPool\n",
"\n",
"dask.set_options(pool=ThreadPool(4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Open a bunch of netCDF files from disk using `xarray.open_mfdataset`:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ds = xr.open_mfdataset('/scratch/local/era-interim/T925/*.nc', engine='scipy',\n",
" chunks={'time': 100, 'latitude': 121, 'longitude': 121})"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (latitude: 241, longitude: 480, time: 54056)\n",
"Coordinates:\n",
" * latitude (latitude) float32 90.0 89.25 88.5 87.75 87.0 86.25 85.5 ...\n",
" * longitude (longitude) float32 0.0 0.75 1.5 2.25 3.0 3.75 4.5 5.25 6.0 ...\n",
" * time (time) datetime64[ns] 1979-01-01 1979-01-01T06:00:00 ...\n",
"Data variables:\n",
" t (time, latitude, longitude) float64 243.0 243.0 243.0 243.0 ...\n",
"Attributes:\n",
" Conventions: CF-1.6\n",
" history: 2016-07-05 17:04:17 GMT by grib_to_netcdf-1.14.6: grib_to_netcdf /data/data01/scratch/_mars-atls19-95e2cf679cd58ee9b4db4dd119a05a8d-onEJKq.grib -o /data/data01/scratch/_grib2netcdf-atls01-95e2cf679cd58ee9b4db4dd119a05a8d-nbnLIR.nc -utime"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"46.59036171808839"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.nbytes * (2 ** -30)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 676 ms, sys: 16 ms, total: 692 ms\n",
"Wall time: 673 ms\n"
]
}
],
"source": [
"%time ds_seasonal = ds.groupby('time.season').mean('time')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 3min 59s, sys: 2min 21s, total: 6min 21s\n",
"Wall time: 1min 45s\n"
]
},
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (latitude: 241, longitude: 480, season: 4)\n",
"Coordinates:\n",
" * latitude (latitude) float32 90.0 89.25 88.5 87.75 87.0 86.25 85.5 ...\n",
" * longitude (longitude) float32 0.0 0.75 1.5 2.25 3.0 3.75 4.5 5.25 6.0 ...\n",
" * season (season) object 'DJF' 'JJA' 'MAM' 'SON'\n",
"Data variables:\n",
" t (season, latitude, longitude) float64 251.3 251.3 251.3 251.3 ..."
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time ds_seasonal.load()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.plot.facetgrid.FacetGrid at 0x7f33a4f0cf10>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/software/python-2.7-2015q2-el6-x86_64/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" if self._edgecolors == str('face'):\n"
]
},
{
"data": {
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UMiCLFdjjuPbRx31MtE+2TLjRVmg9YVn76JqhgKgmzhijg8TMaqA9sD9IAjYn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\nkiRJrfcXwVoNA2alPwcAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f33a4f0c0d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(ds_seasonal['t']\n",
" .sel(season=['DJF', 'MAM', 'JJA', 'SON'])\n",
" .plot(col='season', size=3, cmap='Spectral_r'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For more details, read this blog post: http://continuum.io/blog/xray-dask"
]
}
],
"metadata": {
"celltoolbar": "Raw Cell Format",
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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