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shoyer/xray-pydata-pv2014.ipynb

Last active August 29, 2015 14:00
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xray-pydata-pv2014.ipynb
{
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"signature": "sha256:8170a590ec873beee1a518b732269eb6afc522bfd7b834ab58617d2c12557d7f"
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"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"xray: extended arrays for scientific datasets"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Stephan Hoyer [(shoyer)](https://twitter.com/shoyer)\n",
"\n",
"#### [PyData Silicon Valley, May 3, 2014](http://pydata.org/sv2014/)\n",
"\n",
"GitHub: [github.com/xray/xray](http://github.com/xray/xray) <br />\n",
"Docs: [xray.readthedocs.org](http://xray.readthedocs.org) <br />\n",
"\n",
"---------------------"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Setup/imports"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import pandas as pd\n",
"import xray"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xray.__version__"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"'0.1.0'"
]
}
],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Creating a Dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To make an `xray.Dataset` from scratch, pass in a dictionary with values in the form `(dimensions, data[, attributes])`.\n",
"\n",
"You can also pass in `xray.Variable` or `xray.DataArray` objects directly."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo_values = np.random.RandomState(0).rand(3, 4)\n",
"times = pd.date_range('2000-01-01', periods=3)\n",
"\n",
"ds = xray.Dataset({'time': ('time', times),\n",
" 'foo': (['time', 'space'], foo_values)})\n",
"ds"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"<xray.Dataset>\n",
"Dimensions: (space: 4, time: 3)\n",
"Coordinates:\n",
" space X \n",
" time X \n",
"Noncoordinates:\n",
" foo 1 0 \n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds['numbers'] = ('space', [10, 10, 20, 20])\n",
"ds['abc'] = ('time', ['A', 'B', 'C'])\n",
"ds"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"<xray.Dataset>\n",
"Dimensions: (space: 4, time: 3)\n",
"Coordinates:\n",
" space X \n",
" time X \n",
"Noncoordinates:\n",
" foo 1 0 \n",
" numbers 0 \n",
" abc 0 \n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"'foo' in ds"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"True"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds.keys()"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"['foo', 'time', 'space', 'numbers', 'abc']"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds['time']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"<xray.DataArray 'time' (time: 3)>\n",
"array(['1999-12-31T16:00:00.000000000-0800',\n",
" '2000-01-01T16:00:00.000000000-0800',\n",
" '2000-01-02T16:00:00.000000000-0800'], dtype='datetime64[ns]')\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds['space']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"<xray.DataArray 'space' (space: 4)>\n",
"array([0, 1, 2, 3])\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds['foo']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
"<xray.DataArray 'foo' (time: 3, space: 4)>\n",
"array([[ 0.5488135 , 0.71518937, 0.60276338, 0.54488318],\n",
" [ 0.4236548 , 0.64589411, 0.43758721, 0.891773 ],\n",
" [ 0.96366276, 0.38344152, 0.79172504, 0.52889492]])\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`xray.Dataset` is dict-like, so it also has `values`, `items`, `__del__`, `get`, `update` etc."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***attributes*: a dictionary for metadata**"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds.attrs"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"OrderedDict()"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds.attrs['title'] = 'example attribute'\n",
"ds"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"<xray.Dataset>\n",
"Dimensions: (space: 4, time: 3)\n",
"Coordinates:\n",
" space X \n",
" time X \n",
"Noncoordinates:\n",
" foo 1 0 \n",
" numbers 0 \n",
" abc 0 \n",
"Attributes:\n",
" title: example attribute"
]
}
],
"prompt_number": 12
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"DataArray object"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The contents of `Dataset`s are `DataArray`s:\n",
"\n",
"- labeled dimensions (axes)\n",
"- labeled coordinates (indices)\n",
"- data with homogeneous dtype\n",
"- attributes (metadata)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo = ds['foo']\n",
"foo"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"<xray.DataArray 'foo' (time: 3, space: 4)>\n",
"array([[ 0.5488135 , 0.71518937, 0.60276338, 0.54488318],\n",
" [ 0.4236548 , 0.64589411, 0.43758721, 0.891773 ],\n",
" [ 0.96366276, 0.38344152, 0.79172504, 0.52889492]])\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Like pandas objects, they can be thought of as fancy wrapper around a numpy array:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo.values"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"array([[ 0.5488135 , 0.71518937, 0.60276338, 0.54488318],\n",
" [ 0.4236548 , 0.64589411, 0.43758721, 0.891773 ],\n",
" [ 0.96366276, 0.38344152, 0.79172504, 0.52889492]])"
]
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"They also have dimension labels:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo.dimensions"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"('time', 'space')"
]
}
],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And their own attributes:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo.attrs"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"OrderedDict()"
]
}
],
"prompt_number": 16
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Indexing"
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Normal indexing works (mostly) like numpy:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo[0]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
"<xray.DataArray 'foo' (space: 4)>\n",
"array([ 0.5488135 , 0.71518937, 0.60276338, 0.54488318])\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo[:2]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"<xray.DataArray 'foo' (time: 2, space: 4)>\n",
"array([[ 0.5488135 , 0.71518937, 0.60276338, 0.54488318],\n",
" [ 0.4236548 , 0.64589411, 0.43758721, 0.891773 ]])\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo[:, [2, 1]]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": [
"<xray.DataArray 'foo' (time: 3, space: 2)>\n",
"array([[ 0.60276338, 0.71518937],\n",
" [ 0.43758721, 0.64589411],\n",
" [ 0.79172504, 0.38344152]])\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 19
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Location based indexing like pandas:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo.loc['2000-01-01':'2000-01-02', 0]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 20,
"text": [
"<xray.DataArray 'foo' (time: 2)>\n",
"array([ 0.5488135, 0.4236548])\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 20
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Use dimension names explicitly with `indexed` or `labeled`:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# index by array indices\n",
"foo.indexed(space=0, time=slice(0, 2))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 21,
"text": [
"<xray.DataArray 'foo' (time: 2)>\n",
"array([ 0.5488135, 0.4236548])\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# index by coordinate labels\n",
"foo.labeled(space=0, time=slice('2000-01-01', '2000-01-02'))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": [
"<xray.DataArray 'foo' (time: 2)>\n",
"array([ 0.5488135, 0.4236548])\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 22
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Indexing Datasets"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds.indexed(space=[0], time=[0])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
"<xray.Dataset>\n",
"Dimensions: (space: 1, time: 1)\n",
"Coordinates:\n",
" space X \n",
" time X \n",
"Noncoordinates:\n",
" foo 1 0 \n",
" numbers 0 \n",
" abc 0 \n",
"Attributes:\n",
" title: example attribute"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds.labeled(time='2000-01-01')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 24,
"text": [
"<xray.Dataset>\n",
"Dimensions: (space: 4)\n",
"Coordinates:\n",
" space X \n",
"Noncoordinates:\n",
" foo 0 \n",
" time \n",
" numbers 0 \n",
" abc \n",
"Attributes:\n",
" title: example attribute"
]
}
],
"prompt_number": 24
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Combining data"
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Concatenating"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xray.DataArray.concat([foo[0], foo[1]], 'new_dim')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 25,
"text": [
"<xray.DataArray 'foo' (new_dim: 2, space: 4)>\n",
"array([[ 0.5488135 , 0.71518937, 0.60276338, 0.54488318],\n",
" [ 0.4236548 , 0.64589411, 0.43758721, 0.891773 ]])\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xray.Dataset.concat([ds.labeled(time='2000-01-01'),\n",
" ds.labeled(time='2000-01-03')],\n",
" 'new_dim')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 26,
"text": [
"<xray.Dataset>\n",
"Dimensions: (new_dim: 2, space: 4)\n",
"Coordinates:\n",
" new_dim X \n",
" space X \n",
"Noncoordinates:\n",
" numbers 0 \n",
" foo 0 1 \n",
" abc 0 \n",
" time 0 \n",
"Attributes:\n",
" title: example attribute"
]
}
],
"prompt_number": 26
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Merging"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds.merge({'hello': ('space', np.arange(4) + 10)})"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 27,
"text": [
"<xray.Dataset>\n",
"Dimensions: (space: 4, time: 3)\n",
"Coordinates:\n",
" space X \n",
" time X \n",
"Noncoordinates:\n",
" foo 1 0 \n",
" numbers 0 \n",
" abc 0 \n",
" hello 0 \n",
"Attributes:\n",
" title: example attribute"
]
}
],
"prompt_number": 27
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"`DataArray` math"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Basic math like numpy or pandas:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo - 3"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 28,
"text": [
"<xray.DataArray 'foo___sub___other' (time: 3, space: 4)>\n",
"array([[-2.4511865 , -2.28481063, -2.39723662, -2.45511682],\n",
" [-2.5763452 , -2.35410589, -2.56241279, -2.108227 ],\n",
" [-2.03633724, -2.61655848, -2.20827496, -2.47110508]])\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.sin(foo)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 29,
"text": [
"<xray.DataArray 'foo_' (time: 3, space: 4)>\n",
"array([[ 0.52167534, 0.65576039, 0.56692103, 0.51831819],\n",
" [ 0.41109488, 0.60191268, 0.42375525, 0.77818647],\n",
" [ 0.82128673, 0.37411428, 0.71156637, 0.50457956]])\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo.T"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 30,
"text": [
"<xray.DataArray 'foo' (space: 4, time: 3)>\n",
"array([[ 0.5488135 , 0.4236548 , 0.96366276],\n",
" [ 0.71518937, 0.64589411, 0.38344152],\n",
" [ 0.60276338, 0.43758721, 0.79172504],\n",
" [ 0.54488318, 0.891773 , 0.52889492]])\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 30
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Aggregate by dimension name, not axis number:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo.sum('time')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 31,
"text": [
"<xray.DataArray 'foo' (space: 4)>\n",
"array([ 1.93613106, 1.744525 , 1.83207563, 1.9655511 ])\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 31
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo.std(['time', 'space'])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 32,
"text": [
"<xray.DataArray 'foo' ()>\n",
"array(0.17751915478163727)\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 32
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Math alignment (broadcasting) based on dimension names"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Operations vectorize across missing dimensions\n",
"- Dimensions are reordered if necessary"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo[:, 0]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 33,
"text": [
"<xray.DataArray 'foo' (time: 3)>\n",
"array([ 0.5488135 , 0.4236548 , 0.96366276])\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo[0, :]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 34,
"text": [
"<xray.DataArray 'foo' (space: 4)>\n",
"array([ 0.5488135 , 0.71518937, 0.60276338, 0.54488318])\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 34
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo[:, 0] * foo[0, :]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 35,
"text": [
"<xray.DataArray 'foo___mul___foo' (time: 3, space: 4)>\n",
"array([[ 0.30119626, 0.39250558, 0.33080468, 0.29903925],\n",
" [ 0.23250747, 0.30299341, 0.2553636 , 0.23084238],\n",
" [ 0.52887114, 0.68920136, 0.58086062, 0.52508363]])\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo - foo.T"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 36,
"text": [
"<xray.DataArray 'foo___sub___foo' (time: 3, space: 4)>\n",
"array([[ 0., 0., 0., 0.],\n",
" [ 0., 0., 0., 0.],\n",
" [ 0., 0., 0., 0.]])\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 36
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Alignment on coordinate values (like pandas)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bar = (10 * foo[:2, :2]).rename('bar')\n",
"bar"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 37,
"text": [
"<xray.DataArray 'bar' (time: 2, space: 2)>\n",
"array([[ 5.48813504, 7.15189366],\n",
" [ 4.23654799, 6.45894113]])\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo.reindex_like(bar)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 38,
"text": [
"<xray.DataArray 'foo' (time: 2, space: 2)>\n",
"array([[ 0.5488135 , 0.71518937],\n",
" [ 0.4236548 , 0.64589411]])\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 38
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bar.reindex_like(foo)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 39,
"text": [
"<xray.DataArray 'bar' (time: 3, space: 4)>\n",
"array([[ 5.48813504, 7.15189366, nan, nan],\n",
" [ 4.23654799, 6.45894113, nan, nan],\n",
" [ nan, nan, nan, nan]])\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xray.align(foo, bar, join='inner')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 40,
"text": [
"(<xray.DataArray 'foo' (time: 2, space: 2)>\n",
" array([[ 0.5488135 , 0.71518937],\n",
" [ 0.4236548 , 0.64589411]])\n",
" Attributes:\n",
" Empty, <xray.DataArray 'bar' (time: 2, space: 2)>\n",
" array([[ 5.48813504, 7.15189366],\n",
" [ 4.23654799, 6.45894113]])\n",
" Attributes:\n",
" Empty)"
]
}
],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds.reindex_like(bar)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 41,
"text": [
"<xray.Dataset>\n",
"Dimensions: (space: 2, time: 2)\n",
"Coordinates:\n",
" space X \n",
" time X \n",
"Noncoordinates:\n",
" foo 1 0 \n",
" numbers 0 \n",
" abc 0 \n",
"Attributes:\n",
" title: example attribute"
]
}
],
"prompt_number": 41
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Transform to and from pandas objects"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"* `xray.Dataset` &#8596; `pandas.DataFrame` with a `MultiIndex`\n",
"* `xray.DataArray` &#8596; `pandas.Series` with a `MultiIndex`\n",
"\n",
"Think Hadley Wickham's [\"tidy data\"](http://vita.had.co.nz/papers/tidy-data.pdf)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 42,
"text": [
"<xray.Dataset>\n",
"Dimensions: (space: 4, time: 3)\n",
"Coordinates:\n",
" space X \n",
" time X \n",
"Noncoordinates:\n",
" foo 1 0 \n",
" numbers 0 \n",
" abc 0 \n",
"Attributes:\n",
" title: example attribute"
]
}
],
"prompt_number": 42
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Convert to/from a dataframe:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = ds.to_dataframe()\n",
"df"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>foo</th>\n",
" <th>numbers</th>\n",
" <th>abc</th>\n",
" </tr>\n",
" <tr>\n",
" <th>space</th>\n",
" <th>time</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">0</th>\n",
" <th>2000-01-01</th>\n",
" <td> 0.548814</td>\n",
" <td> 10</td>\n",
" <td> A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-02</th>\n",
" <td> 0.423655</td>\n",
" <td> 10</td>\n",
" <td> B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-03</th>\n",
" <td> 0.963663</td>\n",
" <td> 10</td>\n",
" <td> C</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">1</th>\n",
" <th>2000-01-01</th>\n",
" <td> 0.715189</td>\n",
" <td> 10</td>\n",
" <td> A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-02</th>\n",
" <td> 0.645894</td>\n",
" <td> 10</td>\n",
" <td> B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-03</th>\n",
" <td> 0.383442</td>\n",
" <td> 10</td>\n",
" <td> C</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">2</th>\n",
" <th>2000-01-01</th>\n",
" <td> 0.602763</td>\n",
" <td> 20</td>\n",
" <td> A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-02</th>\n",
" <td> 0.437587</td>\n",
" <td> 20</td>\n",
" <td> B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-03</th>\n",
" <td> 0.791725</td>\n",
" <td> 20</td>\n",
" <td> C</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">3</th>\n",
" <th>2000-01-01</th>\n",
" <td> 0.544883</td>\n",
" <td> 20</td>\n",
" <td> A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-02</th>\n",
" <td> 0.891773</td>\n",
" <td> 20</td>\n",
" <td> B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-03</th>\n",
" <td> 0.528895</td>\n",
" <td> 20</td>\n",
" <td> C</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>12 rows \u00d7 3 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 43,
"text": [
" foo numbers abc\n",
"space time \n",
"0 2000-01-01 0.548814 10 A\n",
" 2000-01-02 0.423655 10 B\n",
" 2000-01-03 0.963663 10 C\n",
"1 2000-01-01 0.715189 10 A\n",
" 2000-01-02 0.645894 10 B\n",
" 2000-01-03 0.383442 10 C\n",
"2 2000-01-01 0.602763 20 A\n",
" 2000-01-02 0.437587 20 B\n",
" 2000-01-03 0.791725 20 C\n",
"3 2000-01-01 0.544883 20 A\n",
" 2000-01-02 0.891773 20 B\n",
" 2000-01-03 0.528895 20 C\n",
"\n",
"[12 rows x 3 columns]"
]
}
],
"prompt_number": 43
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xray.Dataset.from_dataframe(df)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 44,
"text": [
"<xray.Dataset>\n",
"Dimensions: (space: 4, time: 3)\n",
"Coordinates:\n",
" space X \n",
" time X \n",
"Noncoordinates:\n",
" foo 0 1 \n",
" numbers 0 1 \n",
" abc 0 1 \n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 44
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Convert to/from a pandas.Series:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"s = foo.to_series()\n",
"s"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 45,
"text": [
"time space\n",
"2000-01-01 0 0.548814\n",
" 1 0.715189\n",
" 2 0.602763\n",
" 3 0.544883\n",
"2000-01-02 0 0.423655\n",
" 1 0.645894\n",
" 2 0.437587\n",
" 3 0.891773\n",
"2000-01-03 0 0.963663\n",
" 1 0.383442\n",
" 2 0.791725\n",
" 3 0.528895\n",
"Name: foo, dtype: float64"
]
}
],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xray.DataArray.from_series(s)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 46,
"text": [
"<xray.DataArray 'foo' (time: 3, space: 4)>\n",
"array([[ 0.5488135 , 0.71518937, 0.60276338, 0.54488318],\n",
" [ 0.4236548 , 0.64589411, 0.43758721, 0.891773 ],\n",
" [ 0.96366276, 0.38344152, 0.79172504, 0.52889492]])\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 46
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## GroupBy operations like pandas"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds['numbers']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 47,
"text": [
"<xray.DataArray 'numbers' (space: 4)>\n",
"array([10, 10, 20, 20])\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 47
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds.groupby('numbers')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 48,
"text": [
"<xray.groupby.DatasetGroupBy at 0x106e21a50>"
]
}
],
"prompt_number": 48
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"list(ds.groupby('numbers'))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 49,
"text": [
"[(10, <xray.Dataset>\n",
" Dimensions: (space: 2, time: 3)\n",
" Coordinates:\n",
" space X \n",
" time X \n",
" Noncoordinates:\n",
" foo 1 0 \n",
" numbers 0 \n",
" abc 0 \n",
" Attributes:\n",
" title: example attribute), (20, <xray.Dataset>\n",
" Dimensions: (space: 2, time: 3)\n",
" Coordinates:\n",
" space X \n",
" time X \n",
" Noncoordinates:\n",
" foo 1 0 \n",
" numbers 0 \n",
" abc 0 \n",
" Attributes:\n",
" title: example attribute)]"
]
}
],
"prompt_number": 49
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"list(foo.groupby('numbers'))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 50,
"text": [
"[(10, <xray.DataArray 'foo' (time: 3, space: 2)>\n",
" array([[ 0.5488135 , 0.71518937],\n",
" [ 0.4236548 , 0.64589411],\n",
" [ 0.96366276, 0.38344152]])\n",
" Attributes:\n",
" Empty), (20, <xray.DataArray 'foo' (time: 3, space: 2)>\n",
" array([[ 0.60276338, 0.54488318],\n",
" [ 0.43758721, 0.891773 ],\n",
" [ 0.79172504, 0.52889492]])\n",
" Attributes:\n",
" Empty)]"
]
}
],
"prompt_number": 50
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo.groupby('numbers').mean()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 51,
"text": [
"<xray.DataArray 'foo' (numbers: 2)>\n",
"array([ 0.61344268, 0.63293779])\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 51
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def standardize(x):\n",
" return (x - np.mean(x)) / np.std(x)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo.groupby('numbers').apply(standardize)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 53,
"text": [
"<xray.DataArray 'foo___sub___foo___div___foo' (time: 3, space: 4)>\n",
"array([[-0.33215565, 0.52291769, -0.1909289 , -0.55716642],\n",
" [-0.97539723, 0.16678115, -1.23608278, 1.63778246],\n",
" [ 1.7999237 , -1.18206966, 1.00472795, -0.65833232]])\n",
"Attributes:\n",
" Empty"
]
}
],
"prompt_number": 53
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Limitations: it only works for grouping by a single 1D variable (for now)"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Reading and writing to disk (NetCDF)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### What is NetCDF?\n",
"\n",
"* Fully self-described data, just like `xray.Dataset`\n",
"* Widely used in the geosciences\n",
"* A subset of HDF5 \n",
"* Portable - supported on many platforms\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds.to_netcdf('saved_on_disk.nc')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 54
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds_disk = xray.open_dataset('saved_on_disk.nc')\n",
"ds_disk"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 55,
"text": [
"<xray.Dataset>\n",
"Dimensions: (space: 4, time: 3)\n",
"Coordinates:\n",
" space X \n",
" time X \n",
"Noncoordinates:\n",
" foo 1 0 \n",
" numbers 0 \n",
" abc 0 \n",
"Attributes:\n",
" title: example attribute"
]
}
],
"prompt_number": 55
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Working with remote datasets (OpenDAP)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"xray includes support for OpenDAP (via the NetCDF library), which lets us access large datasets over HTTP"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"remote_data = xray.open_dataset(\n",
" 'http://iridl.ldeo.columbia.edu/SOURCES/.OSU/.PRISM/.monthly/dods')\n",
"remote_data"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 56,
"text": [
"<xray.Dataset>\n",
"Dimensions: (T: 1432, X: 1405, Y: 621)\n",
"Coordinates:\n",
" T X \n",
" X X \n",
" Y X \n",
"Noncoordinates:\n",
" ppt 0 2 1 \n",
" tdmean 0 2 1 \n",
" tmax 0 2 1 \n",
" tmin 0 2 1 \n",
"Attributes:\n",
" Conventions: IRIDL\n",
" expires: 1401580800"
]
}
],
"prompt_number": 56
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"remote_data['tmax']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 57,
"text": [
"<xray.DataArray 'tmax' (T: 1432, Y: 621, X: 1405)>\n",
"[1249427160 values with dtype=float64]\n",
"Attributes:\n",
" pointwidth: 120\n",
" units: Celsius_scale\n",
" missing_value: -9999\n",
" standard_name: air_temperature\n",
" expires: 1401580800"
]
}
],
"prompt_number": 57
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tmax = remote_data['tmax'][:500, ::3, ::3]\n",
"tmax"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 58,
"text": [
"<xray.DataArray 'tmax' (T: 500, Y: 207, X: 469)>\n",
"[48541500 values with dtype=float64]\n",
"Attributes:\n",
" pointwidth: 120\n",
" units: Celsius_scale\n",
" missing_value: -9999\n",
" standard_name: air_temperature\n",
" expires: 1401580800"
]
}
],
"prompt_number": 58
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tmax_ss = tmax[0]\n",
"tmax_ss.values[tmax_ss.values < -99] = np.nan"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 59
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import matplotlib.pyplot as plt\n",
"from matplotlib.cm import get_cmap\n",
"\n",
"plt.figure(figsize=(9, 5))\n",
"plt.gca().patch.set_color('0') \n",
"plt.contourf(tmax_ss['X'], tmax_ss['Y'], tmax_ss.values, 20,\n",
" cmap=get_cmap('RdBu_r'))\n",
"plt.colorbar();"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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P3FebVOKd5PO0qTSXRoBDus2B8AKexPLWtY3f5rvtYrU81Tabc2RF2ijyPNzl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\nvVQqUYU0IhhpxJsX7UYhpKUdU0EbgmhkrNPBTjv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WqPekXC6X//Iv/7K8cuXKmu2NfE+y\ngkqeEgRBEESBqFtXOUEQBEHUIyTcBEEQBFEgSLgJgiAIokCQcBMEQRBEgSDhJgiCIIgCQcJNEARB\nEAWChJsgCIIgCgQJN0EQBEEUiP8Pui9nt03eM7IAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x109543710>"
]
}
],
"prompt_number": 60
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Features in the works"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Automatic alignment for math operations\n",
" * \"outer\" join like pandas?\n",
" * \"inner\" join to avoid memory blow-ups?\n",
"* A constructor to make a `DataArray` directly\n",
"* More features from pandas (dropna, idxmax, count, switch to nanmean/nanstd/nanvar, etc.)\n",
"* Incremental writes of data to disk"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Thoughts for PyData"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Heterogeneous arrays aren\u2019t really necessary (if you have good enough containers)\n",
"* The pandas.Index is really powerful\n",
"* Out of core computation is essential"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Thanks to"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Co-workers at [The Climate Corporation](http://climate.com) who have contributed to or tested pre-release versions of xray:\n",
"* Alex Kleeman\n",
"* Holly Dail\n",
"* Todd Small\n",
"* Eugene Brevdo\n",
"\n",
"You!"
]
}
],
"metadata": {}
}
]
}
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