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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# xarray tutorial\n",
"\n",
"[Stephan Hoyer](http://stephanhoyer.com), Rossbypalooza, 2016\n",
"\n",
"-------------\n",
"\n",
"This notebook introduces xarray for new users.\n",
"\n",
"For more information about xarray:\n",
"\n",
"- Read the [online documentation](http://xarray.readthedocs.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/)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"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=100, edgeitems=2) # make numpy less verbose"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"-------------------\n",
"\n",
"## xarray basics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### `xray.Dataset` is like a Python dictionary (of `xray.DataArray` objects)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll use the \"air_temperature\" tutorial dataset:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ds = xr.tutorial.load_dataset('air_temperature')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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": 9,
"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": [
"xray 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": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"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": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----------------------"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pandas\n",
"\n",
"[Pandas](http://pandas.pydata.org) is the best way to work with tabular data in Python. It's also a highly flexible data analysis tool, with way more functionality than xarray."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false,
"scrolled": 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": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.to_dataframe().head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pandas provides very robust tools for reading and writing CSV:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"lat,lon,time,air\n",
"75.0,200.0,2013-01-01 00:00:00,241.200012207\n",
"75.0,200.0,2013-01-01 06:00:00,242.100006104\n",
"75.0,200.0,2013-01-01 12:00:00,242.300018311\n",
"75.0,200.0,2013-01-01 18:00:00,241.899993896\n",
"75.0,200.0,2013-01-02 00:00:00,243.200012207\n",
"75.0,200.0,2013-01-02 06:00:00,244.100006104\n",
"75.0,200.0,2013-01-02 12:00:00,243.300018311\n",
"75.0,200.0,2013-01-02 18:00:00,243.800018311\n",
"75.0,200.0,2013-01-03 00:00:00,244.800018311\n",
"75.0,200.0,2013-01-03 06:00:00,243.899993896\n",
"\n"
]
}
],
"source": [
"print ds.to_dataframe().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": 36,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = pd.DataFrame({'x': [1, 2, 3], 'y': ['a', 'b', 'c']}).set_index('x')"
]
},
{
"cell_type": "code",
"execution_count": 37,
"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>y</th>\n",
" </tr>\n",
" <tr>\n",
" <th>x</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>a</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>b</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>c</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" y\n",
"x \n",
"1 a\n",
"2 b\n",
"3 c"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (x: 3)\n",
"Coordinates:\n",
" * x (x) int64 1 2 3\n",
"Data variables:\n",
" y (x) object 'a' 'b' 'c'"
]
},
"execution_count": 44,
"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": 45,
"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": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.to_dataframe().describe()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"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>30.0</th>\n",
" <th>320.0</th>\n",
" <th>2013-04-09 12:00:00</th>\n",
" <td>293.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17.5</th>\n",
" <th>285.0</th>\n",
" <th>2014-01-07 18:00:00</th>\n",
" <td>300.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">62.5</th>\n",
" <th>260.0</th>\n",
" <th>2013-12-17 06:00:00</th>\n",
" <td>243.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>300.0</th>\n",
" <th>2014-08-03 12:00:00</th>\n",
" <td>281.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17.5</th>\n",
" <th>320.0</th>\n",
" <th>2013-03-05 00:00:00</th>\n",
" <td>296.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65.0</th>\n",
" <th>290.0</th>\n",
" <th>2014-08-19 00:00:00</th>\n",
" <td>280.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37.5</th>\n",
" <th>245.0</th>\n",
" <th>2013-03-31 00:00:00</th>\n",
" <td>291.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75.0</th>\n",
" <th>212.5</th>\n",
" <th>2014-09-15 06:00:00</th>\n",
" <td>269.79</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27.5</th>\n",
" <th>295.0</th>\n",
" <th>2013-05-08 18:00:00</th>\n",
" <td>296.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62.5</th>\n",
" <th>265.0</th>\n",
" <th>2013-02-10 12:00:00</th>\n",
" <td>242.20</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" air\n",
"lat lon time \n",
"30.0 320.0 2013-04-09 12:00:00 293.20\n",
"17.5 285.0 2014-01-07 18:00:00 300.40\n",
"62.5 260.0 2013-12-17 06:00:00 243.30\n",
" 300.0 2014-08-03 12:00:00 281.20\n",
"17.5 320.0 2013-03-05 00:00:00 296.70\n",
"65.0 290.0 2014-08-19 00:00:00 280.90\n",
"37.5 245.0 2013-03-31 00:00:00 291.40\n",
"75.0 212.5 2014-09-15 06:00:00 269.79\n",
"27.5 295.0 2013-05-08 18:00:00 296.40\n",
"62.5 265.0 2013-02-10 12:00:00 242.20"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.to_dataframe().sample(10)"
]
},
{
"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": {
"image/png": 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hvGvFupUiJjtwlpEu/Oa7kWGHHeKWoDnxu5UKBYsMCzMOIWE+9OljtdUyIQua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\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": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.contour.QuadContourSet at 0x7f83a0c9bb70>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/software/python-3.4-2015q1-el6-x86_64/lib/python3.4/site-packages/matplotlib/collections.py:571: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" if self._edgecolors == str('face'):\n"
]
},
{
"data": {
"image/png": 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jlHnxbcVdt6ydehkRwjRO0ahM8K0ickTTfRSGa0TkT4BTgSeLyKrUR3sRzWbS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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f835015a828>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ds.min('time').air.plot.contourf(levels=20, cmap='Spectral_r')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Time series"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Xray 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": 54,
"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": 59,
"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": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tmin"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ds_extremes = xr.Dataset({'tmin': tmin, 'tmax': tmax})"
]
},
{
"cell_type": "code",
"execution_count": 60,
"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": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds_extremes"
]
},
{
"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": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"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": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Q925 T925 U925 V925\r\n"
]
}
],
"source": [
"! ls /project/rossby/datasets/Tiffany"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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": 2,
"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 8 threads:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<dask.context.set_options at 0x7f773415add0>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import dask\n",
"from multiprocessing.pool import ThreadPool\n",
"\n",
"dask.set_options(pool=ThreadPool(8))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Open a bunch of netCDF files from disk using `xarray.open_mfdataset`:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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": 18,
"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": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"46.59036171808839"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.nbytes * (2 ** -30)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 2 s, sys: 251 ms, total: 2.25 s\n",
"Wall time: 1.93 s\n"
]
}
],
"source": [
"%time ds_seasonal = ds.groupby('time.season').mean('time')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 4min 49s, sys: 2min 11s, total: 7min\n",
"Wall time: 1min\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": 7,
"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 0x7f73bccfde10>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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VwLZBbf2Sb5poFo7IuU9RAjDZXl3cxYvGVNu417YjcmEr0OXJup/PwJFDSO09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lQY7Z7i7Wply9Cj9xBZAP13810GUD8e3qbQQiVncPmdehNuMVLA1+69eEObe1eSDN/xRut+JICg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\nQhp/aexr47185jykuy/C47U8XulhKfzPlVLfC+A3w/uBv8Y59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rGVsa7ez4mfR/MegoXb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rpbP1HgENF/OWLBSsycFbeU8WCtYmr5ZxOpGk8PtSLoYWliCGnl8ZtiblcUnjwIHkmqx9U6Xy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T+yzmtmukZrB3+oxPxkAPjxpQL4yhyrOTFtc8HYgNEesopGTJmF8rJTAHXrVt5mpuarUd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\nMDAwMHBJ+P/k3DRU0VkF2gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f73bccfded0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ds_seasonal['t'].plot(col='season', col_wrap=2, size=4, aspect=1.5)"
]
},
{
"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"
},
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"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
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