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Presentation for PyData London 2014 on Biggus.
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"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
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"slide_type": "slide"
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},
"source": [
"<img style=\"float: right\" src=\"http://www.romsaf.org/images/MO_Master_B.jpg\" width=300 />\n",
"\n",
"## Introducing Biggus\n",
"## Philip Elson - github.com/pelson\n",
"### 2014-02-23 - PyData London 2014 \n",
"\n",
"<br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br /><br />\n",
"http://nbviewer.ipython.org/gist/pelson/9171602"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
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},
"source": [
"In the Met Office, we are working with gridded data all the time. The data comes from a variety of sources and with a variety of different and challenging metadata, so we are developing an open source package to unify our gridded data source into a single data model."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import iris\n",
"\n",
"print iris.load('sample.nc')"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0: temperature / (K) (ensemble: 6; time: 114; height: 70; lat: 100; lon: 100)\n",
"1: air_pressure / (K) (ensemble: 6; time: 114; height: 70; lat: 100; lon: 100)\n",
"2: eastward_wind / (K) (ensemble: 6; time: 114; height: 70; lat: 100; lon: 100)\n",
"3: northward_wind / (K) (ensemble: 6; time: 114; height: 70; lat: 100; lon: 100)\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"As you can see, we have to deal with a lot of high dimensional arrays, and we really can't afford to have all of this data in memory, so we needed:\n",
"\n",
" * An array like object which needn't allocate all of the memory of the array that it represents.\n",
" * Which can defer loading from disk until they are actually requested.\n",
" * Are capable of indexing to be able to load only a subset of the data.\n",
" * Can be combined together efficiently in stack and mosaic forms.\n",
" \n",
"Additionally, we would like to be able to apply aggregations in a chunking fashion such that arrays bigger than memory can be processed.\n",
"\n",
"Importantly, what we didn't need was:\n",
"\n",
" * Ragged arrays - we have arrays which tend to fill a numpy \"shape\".\n",
" * heterogeneous **dtype** - i.e. we just need one data type for our big array.\n",
" * better than numpy performance - we just want to be **able** to do the analysis\n",
"\n",
"There are some tools which either already exist, or are currently under development which will potentially help - but they are not there yet and are all trying to solve a bigger and more complex problem."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"So we wrote our own."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import biggus"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print biggus.__version__"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0.5.x\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"We implemented an Array baseclass which expresses the biggus array interface. On top of this we have an adapter which can contain actual array like objects:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"\n",
"np_array = np.empty((700, 200), dtype=np.int32)\n",
"arr = biggus.NumpyArrayAdapter(np_array) \n",
"print arr"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<NumpyArrayAdapter shape=(700, 200) dtype=dtype('int32')>\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"The containment concept is pivotal to biggus, and allows us to have some really interesting properties."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Just like numpy, we can do things like concatenation - in biggus we use the **LinearMosaic** array subclass:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 'Original:', np_array.shape\n",
"\n",
"axis = 0\n",
"print np.concatenate([np_array, np_array], axis=axis).shape\n",
"\n",
"bigger_arr = biggus.LinearMosaic([arr, arr], axis=axis)\n",
"print bigger_arr"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Original: (700, 200)\n",
"(700, 400)\n",
"<LinearMosaic shape=(700, 400) dtype=dtype('int32')>\n"
]
}
],
"prompt_number": 29
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"So you can imagine building up long arrays such as time series from a collection of smaller (though still potentially huge) arrays."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print biggus.LinearMosaic([arr, arr] * 20, axis=axis)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<LinearMosaic shape=(700, 8000) dtype=dtype('int32')>\n"
]
}
],
"prompt_number": 30
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"We can also stack to produce a new dimension:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"biggus.ArrayStack(np.array([arr, arr]))"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"<ArrayStack shape=(2, 700, 200) dtype=dtype('int32')>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"These operations are not copying any data - the two arrays, which themselves are array containers, are simply held within this new object, which can index efficiently with knowledge of the contained array's shapes."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"The array containement isn't limited to numpy arrays - we can provide anything which has a dtype, shape, and ``__getitem__`` (i.e. square bracket indexing). As such, we can pass a HDF5 dataset to the array adapter:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import h5py\n",
"\n",
"hdf_dataset = h5py.File('data.hdf5')['arange']\n",
"arr_hdf = biggus.NumpyArrayAdapter(hdf_dataset)\n",
"\n",
"print arr_hdf"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<NumpyArrayAdapter shape=(100, 200) dtype=dtype('int32')>\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Additionally, we can combine arrays from different sources:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bigger_arr = biggus.LinearMosaic([bigger_arr, arr_hdf], axis=0)\n",
"print bigger_arr"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<LinearMosaic shape=(1500, 200) dtype=dtype('int32')>\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Data isn't realised until it is requested with the ndarray method. Of course, this requires a contiguous block of memory to construct the numpy array:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"type(bigger_arr.ndarray()), bigger_arr.ndarray().shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
"(numpy.ndarray, (1500, 200))"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"We can also index these arrays, as you'd expect:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bigger_arr[-150:-50, ::2]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"<LinearMosaic shape=(100, 100) dtype=dtype('int32')>"
]
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Because we still need to be able to do analyses on the arrays which cannot be fit into memory, we've implemented a simple processing engine which operates in sequential chunks of the data, effectively reducing the overall memory usage."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mean = biggus.mean(bigger_arr, axis=0)\n",
"std = biggus.std(bigger_arr, axis=0)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 31
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"So the first thing to know is that the operations have not actually taken place on the arrays yet - we've just got biggus arrays **``mean``** and **``std``** which have a reference to the original array and enough information to construct the aggregated array upon request."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print mean\n",
"print np.all(mean.ndarray() == bigger_arr.ndarray().mean(axis=0))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<_Aggregation shape=(200,) dtype=dtype('float64')>\n",
"True"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"As before, we can use the ndarray to realise the individual arrays, at which point the data will be calculated for us.\n",
"\n",
"However, there is an efficiency to be gained if we are iterating over a really large array and doing multiple operations on the same data. With numpy one has to do each process sequentially, such that the data is iterated over twice. With biggus the chunking operation gives us an opportunity to compute muliple aggregations at the same time:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mean_np, std_np = biggus.ndarrays([mean, std])\n",
"\n",
"print type(mean_np)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<type 'numpy.ndarray'>\n"
]
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Currently the aggregations are limited to axis 0 - but we are expecting a PR to add further axis support in the next few weeks.\n",
"\n",
"Already this chunk processing can make use of multiple threads to parallelise the processing (though often these problems are not CPU bound, but rather I/O bound in some way)."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Sometimes we need to not only work on arrays which are greater than the available memory, **and** who's aggregated result is also bigger than memory. At this point we must accept that we cannot realise the array into a numpy array (unless we do further aggregation/indexing) and so we need to stream the data into a file:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"with h5py.File('result.hdf5', mode='w') as target_file:\n",
" target_dataset = target_file.create_dataset('my_result', mean.shape, mean.dtype)\n",
" \n",
" biggus.save([mean], [target_dataset])"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is done using the same chunking machinery and will therefore allow you to operate on arrays of **any size** and compute aggregations of **any size**."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"A toy example:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import io\n",
"import urllib\n",
"\n",
"import PIL.Image\n",
"\n",
"\n",
"class TileLoader(object):\n",
" \"\"\"\n",
" Represents a single (256, 256, 3) image from the OpenStreetMap image\n",
" tiles and will retrieve the data from the MapQuest server upon indexing.\n",
"\n",
" \"\"\"\n",
"\n",
" URL_TEMPLATE = 'http://otile1.mqcdn.com/tiles/1.0.0/sat/{z}/{x}/{y}.jpg'\n",
" \n",
" def __init__(self, x, y, z):\n",
" self.xyz = x, y, z\n",
" \n",
" #: A cached version of the array that has been downloaded.\n",
" self._cached_array = None\n",
" \n",
" # The required attributes of a \"concrete\" array, according to biggus,\n",
" # are 'ndim', 'dtype', and 'shape'.\n",
" self.ndim = 3\n",
" self.dtype = np.uint8\n",
" self.shape = (256, 256, 3)\n",
" \n",
" def __getitem__(self, slices): \n",
" # Implement the slicing interface to retrive the data.\n",
" if self._cached_array is None:\n",
" print 'Downloading tile x={} y={} z={}'.format(*self.xyz)\n",
" url = self.URL_TEMPLATE.format(x=self.xyz[0], y=self.xyz[1], z=self.xyz[2])\n",
" tile_fh = urllib.urlopen(url)\n",
" image_file = io.BytesIO(tile_fh.read())\n",
" self._cached_array = np.array(PIL.Image.open(image_file))\n",
" return self._cached_array[slices]"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"defered_img = TileLoader(x=15, y=10, z=5)\n",
"\n",
"img = biggus.NumpyArrayAdapter(defered_img)\n",
"\n",
"print img"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<NumpyArrayAdapter shape=(256, 256, 3) dtype=<type 'numpy.uint8'>>\n"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"plt.imshow(img.ndarray())\n",
"plt.show()"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Downloading tile x=15 y=10 z=5\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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K9OfUHCkfU+e/5yAfskShqSAmqBv15KhmyI7HioVSyrJRrAXxzHQoQGNd6u3Y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\nVcSRYddqAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1084a7f50>"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def tile_array(z):\n",
" columns = []\n",
" for x in xrange(2 ** z):\n",
" column = [biggus.NumpyArrayAdapter(TileLoader(x=x, y=y, z=z)) for y in xrange(2 ** z)]\n",
" columns.append(biggus.LinearMosaic(column, axis=0))\n",
" return biggus.LinearMosaic(columns, axis=1)\n",
"\n",
"z9 = tile_array(z=9)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print z9"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<LinearMosaic shape=(131072, 131072, 3) dtype=<type 'numpy.uint8'>>\n"
]
}
],
"prompt_number": 22
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"I make that array to be 48GB uncompressed."
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"smaller_img = z9[54600:55000, 59300:59850]\n",
"plt.imshow(smaller_img.ndarray())\n",
"plt.show()"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Downloading tile x=231 y=213 z=9\n",
"Downloading tile x=231 y=214 z=9"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Downloading tile x=232 y=213 z=9"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Downloading tile x=232 y=214 z=9\n",
"Downloading tile x=233 y=213 z=9"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Downloading tile x=233 y=214 z=9\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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DMFhfX8c0TW7cuMH29jadTocwDJFSsry8XK6cKpUKm5ubDIfDogCL8Ol3eyRR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\nvhkrlLkGlNvpdK5cVQK1/C0UFW2mNKwCJUAJg/VDNWHgcfDALJcX5lF5y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"text": [
"<matplotlib.figure.Figure at 0x10bab9d50>"
]
}
],
"prompt_number": 28
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"I have an interactive version of this which modifies the matplotlib AxesImage class to support the biggus interface. As a result, we can simply add the whole 48GB image to the matplotlib figure and it will pan-and-zoom, automatically downloading the images as required. See http://pelson.github.io for more."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Summary"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"In summary, biggus is a simple Python package (currently <1200 LOC) which:\n",
"\n",
" * Defines a new array-like class.\n",
" * Provides array containers which can be indexed, aggregated and concatenated.\n",
" * Supports streaming of processing directly to disk."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"As a result we can:\n",
"\n",
" * Define arrays that are conceptually of arbitrary size (unbound by any physical constraints).\n",
" * Seamlessly operate on the arrays in a chunk-wise manner to allow us to process arrays bigger than can be fit in memory."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"What's left to do:\n",
"\n",
" * Flesh out some of the ideas that come from allowing array containment.\n",
" * Implement an efficient chunking engine which can handle complex chains of evaluations."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"We're pretty excited by it."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Questions?"
]
}
],
"metadata": {}
}
]
}
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