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shoyer/xray-repr-examples.ipynb

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xray-repr-examples.ipynb
{
"metadata": {
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"signature": "sha256:246e288aef59cc17b63f35b68b7ed204b0b9bd5a86620aa6b857a56e8d6ef2d1"
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"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Changes to `DataArray.__repr__`"
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Setup the example from the tutorial:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import pandas as pd\n",
"import xray"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"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['numbers'] = ('space', [10, 10, 20, 20])\n",
"ds['abc'] = ('time', ['A', 'B', 'C'])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 48
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Some DataArray representations:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds['time']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 49,
"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",
"Coordinates:\n",
" time: <class 'pandas.tseries.index.DatetimeIndex'>\n",
" [2000-01-01, ..., 2000-01-03]\n",
" Length: 3, Freq: D, Timezone: None\n",
"Linked dataset variables:\n",
" foo, space, numbers, abc"
]
}
],
"prompt_number": 49
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds['space']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 50,
"text": [
"<xray.DataArray 'space' (space: 4)>\n",
"array([0, 1, 2, 3])\n",
"Coordinates:\n",
" space: Int64Index([0, 1, 2, 3], dtype='int64')\n",
"Linked dataset variables:\n",
" foo, time, numbers, abc"
]
}
],
"prompt_number": 50
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds['foo']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 51,
"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",
"Coordinates:\n",
" time: <class 'pandas.tseries.index.DatetimeIndex'>\n",
" [2000-01-01, ..., 2000-01-03]\n",
" Length: 3, Freq: D, Timezone: None\n",
" space: Int64Index([0, 1, 2, 3], dtype='int64')\n",
"Linked dataset variables:\n",
" numbers, abc"
]
}
],
"prompt_number": 51
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds['foo'].attrs['units'] = 'kelvin'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds['foo']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 53,
"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",
"Coordinates:\n",
" time: <class 'pandas.tseries.index.DatetimeIndex'>\n",
" [2000-01-01, ..., 2000-01-03]\n",
" Length: 3, Freq: D, Timezone: None\n",
" space: Int64Index([0, 1, 2, 3], dtype='int64')\n",
"Linked dataset variables:\n",
" numbers, abc\n",
"Attributes:\n",
" units: kelvin"
]
}
],
"prompt_number": 53
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds['foo'][0, 0]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 54,
"text": [
"<xray.DataArray 'foo' ()>\n",
"array(0.5488135039273248)\n",
"Linked dataset variables:\n",
" time, space, numbers, abc\n",
"Attributes:\n",
" units: kelvin"
]
}
],
"prompt_number": 54
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Because \"numbers\" is a linked variable, this works:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds['foo']['numbers']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 56,
"text": [
"<xray.DataArray 'numbers' (space: 4)>\n",
"array([10, 10, 20, 20])\n",
"Coordinates:\n",
" space: Int64Index([0, 1, 2, 3], dtype='int64')\n",
"Linked dataset variables:\n",
" foo, time, abc"
]
}
],
"prompt_number": 56
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Ultimately, this is because data arrays always keeps track of an associated dataset:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this case it's the original dataset `ds`."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ds['foo'].dataset"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 57,
"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 "
]
}
],
"prompt_number": 57
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Calling `.select()` removes these linked variables:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo_selected = ds['foo'].select()\n",
"foo_selected"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 63,
"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",
"Coordinates:\n",
" time: <class 'pandas.tseries.index.DatetimeIndex'>\n",
" [2000-01-01, ..., 2000-01-03]\n",
" Length: 3, Freq: D, Timezone: None\n",
" space: Int64Index([0, 1, 2, 3], dtype='int64')"
]
}
],
"prompt_number": 63
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"A few more examples:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo_selected.attrs = {}\n",
"foo_selected"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 64,
"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",
"Coordinates:\n",
" time: <class 'pandas.tseries.index.DatetimeIndex'>\n",
" [2000-01-01, ..., 2000-01-03]\n",
" Length: 3, Freq: D, Timezone: None\n",
" space: Int64Index([0, 1, 2, 3], dtype='int64')"
]
}
],
"prompt_number": 64
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo_selected[0, 0]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 65,
"text": [
"<xray.DataArray 'foo' ()>\n",
"array(0.5488135039273248)\n",
"Linked dataset variables:\n",
" time, space"
]
}
],
"prompt_number": 65
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo_selected[0, 0].select()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 66,
"text": [
"<xray.DataArray 'foo' ()>\n",
"array(0.5488135039273248)"
]
}
],
"prompt_number": 66
}
],
"metadata": {}
}
]
}
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