rabernat/mfdataset_laziness_bug.ipynb

## mfdataset_laziness_bug.ipynb
{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# XArray open_mfdataset performance bug\n\nThis notebook is intended to be a minimum reproducible example of what\nI consider to be a serious performance problem in xarray / dask.\n\nThe scenario is the following: I have a collection of large netCDF files\non a disk. Each file can be though of as representing geospatial data:\nthe first dimension is time, and the higher dimensions are spatial dimensions (e.g. x and y).\nI want to open the data as an xarray dataset, concatenating along the time\ndimension, select a single point in space, and _lazily_ load the data for this\npoint into memory, creating a timeseries. This is an extremely common workflow\nin climate science and related fields.\n\nThe function `open_mfdataset` is responsible for creating the concatenated\ndataset and underlying dask array. The basic problem is that it appears that\nxarray is fully loading each file into memory before selecting the point.\nThis violates the lazy access paradigm that I previously assumed was being followed.\nAs a result, the time it takes to extract the timeseries from the dataset\ncreated with `open_mfdataset` is close to 1000x slower than a simple loop over\nthe files. It also uses _much_ more memory, since apparently the whole dataset\nis loaded into memory.\n\nWhile the ~10s wait times encountered here might seem acceptable, this issue gets\nmuch worse when dealing with much bigger datasets. The example here is designed\nto highlight the issue without overwhelming system resources."
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": true
   },
   "cell_type": "code",
   "source": "import numpy as np\nimport xarray as xr\nimport dask\nfrom dask.dot import dot_graph\nfrom dask.diagnostics import ProgressBar\nfrom glob import glob",
   "execution_count": 1,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "I'm using very recent versions of xarray and dask."
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "print(xr.__version__)\nprint(dask.__version__)",
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "stream",
     "text": "0.9.5-9-gd5c7e06\n0.14.3+19.gbcd0426\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Create some test data\n\nThis test data is typical of many climate datasets.\nEach file has 12 entires in the time dimension, representing e.g. each month of the year.\nThere are 10 separate files, representing e.g. consecutive years.\nThe cell below writes about 9GB of data to disk."
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "data_dir = '/data/scratch/rpa/xarray_test_data/'\nnfiles = 10\nnt = 12\n\nfor n in range(nfiles):\n    data = np.random.rand(nt,1000,10000)\n    time = (n*nt) + np.arange(nt)\n    da = xr.DataArray(data, dims=['time', 'y', 'x'],\n                      coords={'time': time})\n    da.to_dataset(name='data').to_netcdf(data_dir + 'test_data.%03d.nc' % n)",
   "execution_count": 3,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Open the test data\n\nNow we load the test data using xarray's `open_mfdataset` function.\nThere is no cf metadata to decode, and we specify no special options.\n`open_mfsdataset` correcly decides to concatenate along the time dimension."
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "all_files = glob(data_dir + '*.nc')\nds = xr.open_mfdataset(all_files)\nds",
   "execution_count": 4,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<xarray.Dataset>\nDimensions:  (time: 120, x: 10000, y: 1000)\nCoordinates:\n  * time     (time) int64 84 85 86 87 88 89 90 91 92 93 94 95 72 73 74 75 76 ...\nDimensions without coordinates: x, y\nData variables:\n    data     (time, y, x) float64 0.7713 0.04064 0.2432 0.8389 0.2052 0.4792 ..."
     },
     "metadata": {},
     "execution_count": 4
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "The default chunking is to allocate one chunk to each file."
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "ds.chunks",
   "execution_count": 5,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Frozen(SortedKeysDict({u'y': (1000,), u'x': (10000,), u'time': (12, 12, 12, 12, 12, 12, 12, 12, 12, 12)}))"
     },
     "metadata": {},
     "execution_count": 5
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Time loading the whole dataset into memory\n\nAs a point of reference, let's see how long it takes to read all the data\nfrom disk and load it into memory. (We make a deep copy of the dataset first\nin order to preserve the original chunked dataset for later use.)"
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "# time loading the whole damn dataset into memory\nds_copy = ds.copy(deep=True)\nwith ProgressBar():\n    %time _ = ds_copy.load()",
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "stream",
     "text": "[########################################] | 100% Completed |  7.1s\nCPU times: user 4.35 s, sys: 12.9 s, total: 17.3 s\nWall time: 17.2 s\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "On this high-performance server with a relatively fast RAID filesystem, it takes 17s.\n\n## Time loading a single point\n\n*This is the crux issue.* We now want to just extract a timeseries of single point\nin space. If the loading is lazy, it should go much faster and use much less memory\ncompared to the example above."
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "# time extracting a timeseries of a single point\ny, x = 200, 300\nwith ProgressBar():\n    %time ts = ds.data[:, y, x].load()",
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "stream",
     "text": "[########################################] | 100% Completed |  8.7s\nCPU times: user 186 ms, sys: 8.55 s, total: 8.74 s\nWall time: 8.72 s\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "We can see that this about half the time of loading the whole dataset!\nFurthermore, by monitoring memory usage via `top`, I observe that resident memory\nincreases strongly as the command executes.\n\n## Loop-based version\n\nFor comparison, now we code the extraction of a single point using loops.\nWe implement this using xarray and also the lower-level netCDF4 interface."
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": true
   },
   "cell_type": "code",
   "source": "def extract_point_xarray(y, x):\n    ts = []\n    for f in all_files:\n        ds = xr.open_dataset(f)\n        data_point = ds['data'][:, y, x]\n        ts.append(data_point)\n    return xr.concat(ts, dim='time')\n\nfrom netCDF4 import Dataset\n\ndef extract_point_netcdf(y, x):\n    ts = []\n    for f in all_files:\n        nc = Dataset(f)\n        data_point = nc['data'][:, y, x]\n        ts.append(data_point)\n        nc.close()\n    return np.hstack(ts)",
   "execution_count": 8,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "%time ts_nc = extract_point_netcdf(y, x)",
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "stream",
     "text": "CPU times: user 8.82 ms, sys: 6.74 ms, total: 15.6 ms\nWall time: 14.3 ms\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "%time ts_xr = extract_point_xarray(y, x)",
   "execution_count": 10,
   "outputs": [
    {
     "output_type": "stream",
     "text": "CPU times: user 34.3 ms, sys: 6 ms, total: 40.3 ms\nWall time: 38.8 ms\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "assert ts.equals(ts_xr)",
   "execution_count": 11,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "These loops run extremely fast compared to the earlier example.\nWe incur a small penalty for opening the files with `xarray.open_dataset`\nrather than `netCDF4.Dataset`, but this is nothing compared to the overhead\nwe experience with `open_mfsdataset`. \n\n## Summary\n\nMy impression is that the lazy indexing of the underlying netCDF arrays is\nbreaking down somewhere within the dask graph. I don't know if this bug has\nalways been with us, or whether it was introduced in some recent version.\nThe answer might lie in the [netcdf4 backend](https://github.com/pydata/xarray/blob/master/xarray/backends/netCDF4_.py#L220).\nWhatever the case, I see it as a pretty serious problem which is becoming\na major obstacle to my science. I often want to work with 10TB+ datasets.\nRight now, it seems more efficient to bypass xarray completely to do this\nsort of timeseries extraction.\n\nI am happy to work with anyone who is interested in trying to sort out this\nbug. Contact me at [rpa@ldeo.columbia.edu](mailto:rpa@ldeo.columbia.edu).\n\n## Debugging\n\nHere is some detail on the dask graphs. I don't really know what to make of\nthis stuff, but maybe it means something to someone."
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": false
   },
   "cell_type": "code",
   "source": "dot_graph(ds.data[:, y, x].data.dask)",
   "execution_count": 12,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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jqywPvA+fhB3ZFBRFwEGEoFWrQhFXIIJVBEVqXaigdf+0\ndhSXajvjW1xbtMVatVOndSlJXRDUIjtIQEBxXxAhICgWN4wLm+yc94/MOLRAOISc576Tc11/CcEn\nP/jM8PX0zrmTTj/33HOhhwDkED3alh4BJE+PtqVHAMnTo23pEUDy9GhbegSQPD3alh4BxKB79+4N\nGjSYOHFi6CGx+NOf/nT11VcvXrx4//33D70FIHfdd999119//ZIlS/bbb7/QW6Lw6aefHnjggcOH\nD7/iiitCbwHIIXr0L/QIIAbOm7blvInqy8UNVANXXXXVU089tXjx4rp164beEouSkpLCwsJZs2Yd\nd9xxobcA5Ao92pYeASRPj7alRwDJ06Nt6RFA8vRoW3oEkDw92pYeAQT34osvHnvssc8///wJJ5wQ\nekss1q9f3759+3POOed3v/td6C0AOWrjxo0dOnQ4/fTT//CHP4TeEpGf/vSnY8eOXbRokReVAMnQ\no+3SI4DgnDdty3kT1ZeLG4jdmjVr9t1331tvvfXqq68OvSUuxx577AEHHPDYY4+FHgKQE/RoR/QI\nIEl6tCN6BJAkPdoRPQJIkh7tiB4BJEmPdkSPAMI6++yzP/7441mzZoUeEpfhw4cPHTr0008/bdiw\nYegtALnoySefHDhw4OLFi9u2bRt6S0SWLl3avn37J5544gc/+EHoLQA5QY+2S48AwnLetCPOm6im\n8kMPgJ0YPXr0hg0bzj///NBDovPjH//4mWee+frrr0MPAcgJerQjegSQJD3aET0CSJIe7YgeASRJ\nj3ZEjwCSpEc7okcAAa1YsWLs2LEXXXRR6CHRGTRo0Lp165555pnQQwBy1IgRI3r37u1dsv/igAMO\nOOmkk4qKikIPAcgVerRdegQQlvOmHXHeRDXl4gZiV1RUdNppp7Vo0SL0kOicc845eXl5o0ePDj0E\nICfo0Y7oEUCS9GhH9AggSXq0I3oEkCQ92hE9AkiSHu2IHgEE9MQTT6RSKd8ldVt77733Kaec4p1I\nAEF8/vnnkyZNGjRoUOghMRo0aND48ePLyspCDwGo+fSoAnoEEJDzph1x3kQ15eIGovbxxx+XlJR4\nXbRdTZo06du3r5MkgAToUQX0CCAxelQBPQJIjB5VQI8AEqNHFdAjgMToUQX0CCCgoqKi/v37N23a\nNPSQGA0aNGjy5MkfffRR6CEAOefRRx9t0KDBGWecEXpIjM4888x69eqVX70EQFbpUQX0CCAU500V\ncN5ENeXiBqL2t7/9rWnTpn369Ak9JFKDBw9+/vnnly5dGnoIQA2nRxXTI4Bk6FHF9AggGXpUMT0C\nSIYeVUyPAJKhRxXTI4AglixZMnv2bF/nvSP9+vVr0qTJ448/HnoIQM4pLi7+wQ9+0LBhw9BDYrTH\nHnsMGDDAO5EAEqBHFdAjgFCcN1XMeRPVkYsbiNr48eP79etXr1690EMi9f3vf79Ro0YTJkwIPQSg\nhtOjiukRQDL0qGJ6BJAMPaqYHgEkQ48qpkcAydCjiukRQBATJkxo3Lhx7969Qw+JVP369fv27StP\nAAn77LPPXnvttbPOOiv0kHidddZZL7/8cllZWeghADWZHu2UHgEE4bypYs6bqI5c3EC81q1bN2fO\nnMLCwtBD4lWnTp3jjz9++vTpoYcA1GR6tFN6BJAAPdopPQJIgB7tlB4BJECPdkqPABKgRzulRwBB\nlJSUdO/evXbt2qGHxKtnz56zZ89et25d6CEAOaSkpKRWrVrHH3986CHxOvHEE/Pz872AAsgqPdop\nPQJInvOmnXLeRHXk4gbiVX5A0qNHj9BDotazZ8+SkpJ0Oh16CECNpUeZ0COAbNOjTOgRQLbpUSb0\nCCDb9CgTegSQbXqUCT0CSFg6nZ4xY0bPnj1DD4lar169yr8gPvQQgBxSUlLStWvXJk2ahB4Sr6ZN\nm3bu3LmkpCT0EICaTI92So8Akue8KRPOm6h2XNxAvEpKSjp06NC2bdvQQ6JWWFi4fPnyd955J/QQ\ngBpLjzKhRwDZpkeZ0COAbNOjTOgRQLbpUSb0CCDb9CgTegSQsLfeequsrMw36KtY27ZtDzzwQO9E\nAkhSSUmJe4V2qrCwcNq0aaFXANRkepQJPQJImPOmTDhvotpxcQPxmj59utdFO3XkkUfutdde06dP\nDz0EoMbSo0zoEUC26VEm9Agg2/QoE3oEkG16lAk9Asg2PcqEHgEkbMaMGc2bNz/ssMNCD4ldYWGh\nixsAEvPRRx8tWrTIC6idKiwsXLBgwSeffBJ6CEDNpEcZ0iOAhDlvyoTzJqodFzcQr7lz53bp0iX0\nitjl5+cfccQRc+fODT0EoMbSo0zoEUC26VEm9Agg2/QoE3oEkG16lAk9Asg2PcqEHgEk7O233z7q\nqKPy831R6E507txZngASU/5XbufOnUMPiV35H5FvIQuQJXqUIT0CeIm+lAAAIABJREFUSJjzpkw4\nb6La8b/RE6lPPvlk5cqVBQUFoYdUAwUFBaWlpaFXANRMepQ5PQLIHj3KnB4BZI8eZU6PALJHjzKn\nRwDZo0eZ0yOAJJWWlspTJjp27Pj1118vX7489BCAnFBaWtqyZctmzZqFHhK7vfbaq0WLFl5AAWSJ\nHmVIjwCS5Lwpc86bqF5c3ECkyv8mFZ5MdOzYUXgAskSPMqdHANmjR5nTI4Ds0aPM6RFA9uhR5vQI\nIHv0KHN6BJAkFzdkqPxPacGCBaGHAOQEecqcF1AA2aNHmdMjgMQ4b8qcPFG9uLiBSC1YsKBp06Yt\nW7YMPSSVSqWOPvro66+/PvSKHSooKPjss8+++uqr0EMAaiA9ypweAWSPHmVOjwCyR48yp0cA2aNH\nmdMjgOzRo8zpEUBivvzyy7Kyso4dO4Ye8j9iLlSrVq0aN27sS70BkhHVG2VjzlMqlSooKHCvEECW\n6FHm9AggMc6bMue8ierFxQ1EatGiRQGPkf7xj39s/cOWLVvutddeO/pocOUvIBcuXBh6CEANpEeZ\n0yOA7NGjzOkRQPboUeb0CCB79ChzegSQPXqUOT0CSEz5NQQKlYm8vDzfow8gMQsXLpSnDHXq1Mmr\nJ4As0aPM6RFAYpw3Zc55E9WLixuI1Oeff77vvvsG+dTvv//+wIEDt/6ZMWPG/OIXv9jRR4Mr/4Mq\nKysLPQSgBtKjzOkRQPboUeb0CCB79ChzegSQPXqUOT0CyB49ypweASSm/C/b/fbbL8hnr3aFatmy\npTwBJMMLqMy1bNny888/D70CoGbSo8zpEUBi5ClzzpuoXmqHHgDbt2rVqsaNGyf/eZctW9a3b9/N\nmzdX4qOhNGjQoFatWqtWrQo9BKAG0qPM6RFA9uhR5vQIIHv0KHN6BJA9epQ5PQLIHj3KnB4BJGbV\nqlW1a9euV69e8p+6OhaqcePG8gSQgI0bN65fv75Ro0bJf+pqmqe1a9du2rSpdm1v8QCoSnq0S/QI\nIDHOmzLnvInqJT/0ANi+VatWVcnronQ6PXz48PPOO++KK66oV69e3v9KpVKrV6++7bbbfvzjH3fp\n0qVXr15z585NpVKPPPLIu+++++mnn15xxRWpVGrz5s0jR4684IILTjzxxG0/uqOHrFmzpri4+Lzz\nzjv22GNHjRrVqlWrrl27Lliw4M033zz55JObNm36ve99791339393125vLy8Ro0aCQ9ANuhR5vQI\nIHv0KHN6BJA9epQ5PQLIHj3KnB4BZI8eZU6PABJThV/nnQuFcnEDQDLK/7KtkkLlSJ7Kl1TVAwEo\np0e7RI8AEuO8KXPOm6hm0hClrl27Xnvttbv/nN///vf5+fllZWXpdPr+++9PpVJDhgxJp9NbtmwZ\nNGjQ/Pnzy39Z796999lnnxUrVqTT6VQqVVBQ8O0Tvvjii61/Zut/3tFDNm/evHDhwlQq1axZs0mT\nJn344YepVKp9+/a/+c1vvv766zfffDOVSp188sm7/7v7VuvWrX/3u99V4QMBKKdHu0SPALJEj3aJ\nHgFkiR7tEj0CyBI92iV6BJAlerRL9AggGXfddVfbtm2r5FG5UKghQ4Z069atqp4GwI588MEHqVTq\npZde2v1H5UKeXnzxxVQq9eGHH1bVAwEop0e7RI8AEuO8aZc4b6Iaya/cdQ+QbatXr66SG4MmTJiQ\nTqfLr3w766yzUqlU+auI2bNnFxUVHXzwweUXCE2ZMmX58uXPP//8tk/Yc889d/TwHT0kPz//oIMO\nSqVSLVu2PPnkk9u0adO6devFixffcMMNTZs2/e53v9uyZctXXnll939332rSpMnKlSur8IEAlNOj\nXaJHAFmiR7tEjwCyRI92iR4BZIke7RI9AsgSPdolegSQjNWrV1fJ949N5UahmjRp4hv0ASSg/C9b\nL6AyVP678wIKoMrp0S7RI4DEOG/aJc6bqEZc3EANd+yxx6bT6XHjxqVSqa+++iqVSvXu3TuVSr3y\nyiuHHHLIv1xk0rdv322fkJeXt6OHV/CQf/m39thjj61/2KxZs/IxAOQIPQIgBnoEQAz0CIAY6BEA\nMdAjAOKkUABESJ4AiIEeARAheYLY1A49ALavUaNGq1ev3v3n3HTTTa1atbr44otfeOGF995779e/\n/vX111+fSqVWr179/vvvr1mzZuskbN68uVatWpk/vEoeUiVWrlzZpEmThD8pQC7Qo12iRwBZoke7\nRI8AskSPdokeAWSJHu0SPQLIEj3aJXoEkIxGjRqVfxfZ3ZcLhVq5cmX5NyEEIKvK/7L1AipD5Sn3\nAgqgyunRLtEjgMQ4b9olzpuoRvJDD4Dta9y4cZWcJG3evPmdd96ZM2fOXXfd9fTTT994443lVTjk\nkEPWrl07bNiwb3/lu+++e99995X/86ZNmyp45rcfrfghSVq1apWTJIBs0KNdokcAWaJHu0SPALJE\nj3aJHgFkiR7tEj0CyBI92iV6BJCMqspTKjcKJU8AySj/y9YLqAyV/0EpFECV06NdokcAiXHetEv8\nD3pUI7VDD4Dtq6rw3HHHHc8+++xhhx22ZMmSJk2aNG/evF27dnXr1u3Xr1+HDh1uvfXWjz76qLCw\ncP78+S+//PKoUaNSqdR+++338ccfv/XWW9/97ndT//uq49vri7b+aAUPWb9+fSqVSqfT5f/Wxo0b\nyx/SqFGjbz9aVXcLpdPp1atXCw9ANuhR5vQIIHv0KHN6BJA9epQ5PQLIHj3KnB4BZI8eZU6PABJT\nhRc35EKhfJ03QDKq8I2yOZKnVCpV/mQAqpAe7RI9AkiM86bMOW+imklDlAYNGtSvX7/df87kyZP3\n2Wefrf9vfs899ywuLk6n0x9++OEZZ5yx5557tmzZ8tJLL12+fHn5v/Lwww/vueee//Ef/5FOp1ev\nXn3jjTeW/4t33333ihUrtv7ojh7y6aefXnvttalUqm7dulOmTJk4cWJ5YP793/+9rKzs3nvvLX/g\nsGHDPv/8893/Pa5ZsyaVSo0dO3b3HwXAv9CjzOkRQPboUeb0CCB79ChzegSQPXqUOT0CyB49ypwe\nASRmzJgxqVRq7dq1u/+oXChUnz59Lrzwwt1/DgA7Va9evREjRuz+c3IhT4888kjDhg13/zkAbEuP\nMqdHAIlx3pQ5501UL3np/73RBKJy7bXXzpw58+WXX97N5xQXF5eVlV199dWpVGrLli0ff/xxSUnJ\nkCFDysrKqmJmFJYsWdK+ffs5c+YcffTRobcA1DR6lDk9AsgePcqcHgFkjx5lTo8AskePMqdHANmj\nR5nTI4DEzJkz55hjjnn//ff/7d/+bTcflQuF6tKlS8+ePe+6667QQwBqvjZt2lx11VXXXXfdbj4n\nF/J055133n///UuXLg09BKAG0qPM6RFAYpw3Zc55E9VLfugBsH0dO3YsLS3dzYfccsstgwYNuuCC\nC8p/mJ+f37p162OPPbZ9+/a7PTAi5X9QBQUFoYcA1EB6lDk9AsgePcqcHgFkjx5lTo8AskePMqdH\nANmjR5nTI4DEdOzYMZVKLVy4cDefkwuFSqfTCxculCeAZHgBlbkFCxbIE0CW6FHm9AggMfKUOedN\nVC8ubiBSBQUFK1eu/PTTT3fnIbNmzUqlUnffffe6detSqVQ6nX7llVd+8YtfFBUVVc3KOJSWlrZs\n2bJZs2ahhwDUQHqUOT0CyB49ypweAWSPHmVOjwCyR48yp0cA2aNHmdMjgMTstddeLVq02P0v9c6F\nQn388cerVq3ydd4AySgoKJCnDJWWlsoTQJboUeb0CCAxzpsy57yJ6sXFDUSq/D/0d/OlUVFR0U9+\n8pPi4uJWrVqdcMIJZ5111uuvv15cXFx+v3iN4XURQPboUeb0CCB79ChzegSQPXqUOT0CyB49ypwe\nAWSPHmVOjwCSVCXvRMqFQvkGfQBJkqfMeQEFkD16lDk9AkiM86bMyRPVS+3QA2D79ttvvyZNmpSW\nlnbv3r3SD2nZsuX9999fhaviJDwA2aNHmdMjgOzRo8zpEUD26FHm9Agge/Qoc3oEkD16lDk9AkhS\nlbwTKRcKVVpa2qxZs3322Sf0EICcUFBQsHz58q+++mrPPfes9ENyIU9ffvnlF1984QUUQJboUYb0\nCCBJzpsy57yJ6iU/9ADYocMOO+y1114LvSJ2W7ZsefPNNw877LDQQwBqLD3KhB4BZJseZUKPALJN\njzKhRwDZpkeZ0COAbNOjTOgRQMIOP/zwN954Y8uWLaGHxO7111+XJ4DEHH744alUyguonXr11VdT\nqdShhx4aeghAzaRHGdIjgIQ5b8qE8yaqHRc3EK8ePXqUlJSEXhG7N95446uvvurZs2foIQA1lh5l\nQo8Ask2PMqFHANmmR5nQI4Bs06NM6BFAtulRJvQIIGE9evT44osv3n777dBDYjdt2rTCwsLQKwBy\nRatWrTp06DB9+vTQQ2JXUlJy8MEH77fffqGHANRMepQhPQJImPOmTDhvotpxcQPx6tmz56JFi5Yu\nXRp6SNSmTZu2zz77fOc73wk9BKDG0qNM6BFAtulRJvQIINv0KBN6BJBtepQJPQLINj3KhB4BJOzw\nww9v0aLFtGnTQg+J2ocffrhkyRJf5w2QpMLCQnnaqWnTpskTQFbpUSb0CCBhzpsy4byJasfFDcTr\nuOOOq1+/vjvtKlZSUtKzZ8+8vLzQQwBqLD3KhB4BZJseZUKPALJNjzKhRwDZpkeZ0COAbNOjTOgR\nQMLy8vJ8j76dmjp1av369Y8++ujQQwBySM+ePV955ZWVK1eGHhKvFStWvPbaa94oC5BVerRTegSQ\nPOdNmXDeRLXj4gbiVb9+/WOOOcaddhXYuHHjzJkzvS4CyCo92ik9AkiAHu2UHgEkQI92So8AEqBH\nO6VHAAnQo53SI4Agevbs+fzzz2/atCn0kHhNmzat/AviQw8ByCE9e/bcvHnzzJkzQw+J14wZM9Lp\ndI8ePUIPAajJ9Gin9Aggec6bdsp5E9WRixuIWp8+fZ599tn169eHHhKpiRMnfvPNN6eeemroIQA1\nnB5VTI8AkqFHFdMjgGToUcX0CCAZelQxPQJIhh5VTI8Agjj11FNXrVo1efLk0EMitW7dunHjxvXp\n0yf0EIDcss8++3Tp0mXUqFGhh8Rr1KhRXbt2bdGiReghADWZHu2UHgEE4bypYs6bqI5c3EDUBg4c\nuHLlyrFjx4YeEqkRI0aceOKJbdu2DT0EoIbTo4rpEUAy9KhiegSQDD2qmB4BJEOPKqZHAMnQo4rp\nEUAQ7dq1O+6444qKikIPidSYMWNWrVo1cODA0EMAcs75558/atSo1atXhx4SozVr1jz99NODBg0K\nPQSg5tOjCugRQCjOmyrmvInqyMUNRK1Vq1aFhYVOkrZrxYoV48aN87oIIAF6VAE9AkiMHlVAjwAS\no0cV0COAxOhRBfQIIDF6VAE9Agho0KBBzzzzzNdffx16SIyKiop69+697777hh4CkHPOO++89evX\njxkzJvSQGI0ePXr9+vVnn3126CEANZ8eVUCPAEJx3lQB501UUy5uIHaDBg0aP358WVlZ6CHReeKJ\nJ9Lp9IABA0IPAcgJerQjegSQJD3aET0CSJIe7YgeASRJj3ZEjwCSpEc7okcAAZ1zzjl5eXmjR48O\nPSQ6n3/++aRJk3ydN0AQe++99ymnnOKdSNtVVFR02mmntWjRIvQQgJpPjyqgRwABOW/aEedNVFMu\nbiB2AwYMqFevnpdG23rooYf69+/frFmz0EMAcoIe7YgeASRJj3ZEjwCSpEc7okcASdKjHdEjgCTp\n0Y7oEUBATZs27dev34MPPhh6SHRGjBhRv379/v37hx4CkKMGDx48ZcqUpUuXhh4Slw8++KCkpMS9\nQgCJ0aPt0iOAsJw37YjzJqopFzcQuz322OPiiy8ePnz4+vXrQ2+JyHPPPffSSy9deeWVoYcA5Ao9\n2i49AkiYHm2XHgEkTI+2S48AEqZH26VHAAnTo+3SI4DghgwZ8uKLL86YMSP0kIisX7/+7rvvvuyy\nyxo2bBh6C0COOuOMM9q0afPb3/429JC4DBs2bP/99+/Xr1/oIQC5Qo+2S48AwnLetF3Om6i+8tLp\ndOgNsBPLli076KCD7r333ksvvTT0llgUFhbWqlVrypQpoYcA5BA92pYeASRPj7alRwDJ06Nt6RFA\n8vRoW3oEkDw92pYeAcSgR48edevWnTx5cughsfjjH/94zTXXLFmypFWrVqG3AOSu+++//7rrrlu8\neLG/jct98sknBx544O9///vLL7889BaAHKJH/0KPAGLgvGlbzpuovlzcQPVw+eWXT506tbS0tHbt\n2qG3hDdnzpxjjjmmpKSkR48eobcA5BY92poeAYSiR1vTI4BQ9GhregQQih5tTY8AQtGjrekRQCSm\nTJly8sknv/DCC8ccc0zoLeFt3LixY8eOp5122n333Rd6C0BOW7duXfv27X/0ox/deeedobdEYciQ\nIU888cSSJUvq168fegtADtGjf6FHAJFw3rQ1501Uay5uoHpYsmRJQUHBQw89NGjQoNBbwuvTp8/K\nlStnzZoVeghAztGjrekRQCh6tDU9AghFj7amRwCh6NHW9AggFD3amh4BxKNbt2777LPPmDFjQg8J\n76GHHrriiisWLVp0wAEHhN4CkOt++9vf3nzzze+//36LFi1Cbwls+fLl7dq1u+OOO6666qrQWwBy\njh59S48A4uG8aWvOm6jWXNxAtXHJJZdMmjRp/vz5jRo1Cr0lpAkTJvTp02fq1KknnXRS6C0AuUiP\nyukRQFh6VE6PAMLSo3J6BBCWHpXTI4Cw9KicHgFEZeLEiaeeeurkyZN79+4dektIK1euPPjgg08/\n/fT/+q//Cr0FgNTq1as7duzYr1+/Bx54IPSWwC6++OLJkyeXlpY2bNgw9BaAnKNH39IjgKg4byrn\nvInqzsUNVBtffPFFQUHBRRdddOedd4beEsz69esPP/zwI4888vHHHw+9BSBH6VFKjwAioEcpPQKI\ngB6l9AggAnqU0iOACOhRSo8AonTmmWfOmzdv7ty59erVC70lmCFDhhQVFS1YsMC30gWIxKOPPjpo\n0KDZs2d369Yt9JZgXnjhheOPP37kyJFnnXVW6C0AOUqPUnoEEB/nTSnnTdQItYYOHRp6A2SkYcOG\njRs3/tWvftW/f/+WLVuGnhPG7bffPnny5DFjxjRp0iT0FoAcpUcpPQKIgB6l9AggAnqU0iOACOhR\nSo8AIqBHKT0CiNJxxx03bNiw/Pz8E088MfSWMN55551LLrn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gGES4+So0cAaTVjxowOHTq0bNky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     },
     "metadata": {},
     "execution_count": 12
    }
   ]
  },
  {
   "metadata": {
    "trusted": true,
    "collapsed": false,
    "scrolled": false
   },
   "cell_type": "code",
   "source": "dict(ds.data[:,y,x].data.dask)",
   "execution_count": 13,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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 ('concatenate-0a4ab563e8d1f9c6fecaa3a4b2fc20a0', 6, 0, 0),\n  (slice(None, None, None), 200, 300)),\n ('getitem-dec61fc940534f73100b2340598a8de6', 7): (<function operator.getitem>,\n  ('concatenate-0a4ab563e8d1f9c6fecaa3a4b2fc20a0', 7, 0, 0),\n  (slice(None, None, None), 200, 300)),\n ('getitem-dec61fc940534f73100b2340598a8de6', 8): (<function operator.getitem>,\n  ('concatenate-0a4ab563e8d1f9c6fecaa3a4b2fc20a0', 8, 0, 0),\n  (slice(None, None, None), 200, 300)),\n ('getitem-dec61fc940534f73100b2340598a8de6', 9): (<function operator.getitem>,\n  ('concatenate-0a4ab563e8d1f9c6fecaa3a4b2fc20a0', 9, 0, 0),\n  (slice(None, None, None), 200, 300)),\n u'/data/scratch/rpa/xarray_test_data/test_data.000.nc:/data-dae450ada5b402ecc93cc00c290384e6': CopyOnWriteArray(array=LazilyIndexedArray(array=LazilyIndexedArray(array=<xarray.backends.netCDF4_.NetCDF4ArrayWrapper object at 0x7f7116780290>, key=(slice(None, None, None), slice(None, None, None), slice(None, None, None))), key=(slice(None, None, None), slice(None, None, None), slice(None, None, None)))),\n u'/data/scratch/rpa/xarray_test_data/test_data.001.nc:/data-5e45e866de64676ddf60edd4fb9a9a16': CopyOnWriteArray(array=LazilyIndexedArray(array=LazilyIndexedArray(array=<xarray.backends.netCDF4_.NetCDF4ArrayWrapper object at 0x7f7116779cd0>, key=(slice(None, None, None), slice(None, None, None), slice(None, None, None))), key=(slice(None, None, None), slice(None, None, None), slice(None, None, None)))),\n u'/data/scratch/rpa/xarray_test_data/test_data.002.nc:/data-8cb3bd1165645d2440d679ab56f0528d': CopyOnWriteArray(array=LazilyIndexedArray(array=LazilyIndexedArray(array=<xarray.backends.netCDF4_.NetCDF4ArrayWrapper object at 0x7f7116780810>, key=(slice(None, None, None), slice(None, None, None), slice(None, None, None))), key=(slice(None, None, None), slice(None, None, None), slice(None, None, None)))),\n u'/data/scratch/rpa/xarray_test_data/test_data.003.nc:/data-aa827ccff2da2ee7523ef5b21b006176': CopyOnWriteArray(array=LazilyIndexedArray(array=LazilyIndexedArray(array=<xarray.backends.netCDF4_.NetCDF4ArrayWrapper object at 0x7f7116780d90>, key=(slice(None, None, None), slice(None, None, None), slice(None, None, None))), key=(slice(None, None, None), slice(None, None, None), slice(None, None, None)))),\n u'/data/scratch/rpa/xarray_test_data/test_data.004.nc:/data-793955c2fa63d36ae925d78aa31092de': CopyOnWriteArray(array=LazilyIndexedArray(array=LazilyIndexedArray(array=<xarray.backends.netCDF4_.NetCDF4ArrayWrapper object at 0x7f71167791d0>, key=(slice(None, None, None), slice(None, None, None), slice(None, None, None))), key=(slice(None, None, None), slice(None, None, None), slice(None, None, None)))),\n u'/data/scratch/rpa/xarray_test_data/test_data.005.nc:/data-36df4d4a303598f98bb6551d8e99f52f': CopyOnWriteArray(array=LazilyIndexedArray(array=LazilyIndexedArray(array=<xarray.backends.netCDF4_.NetCDF4ArrayWrapper object at 0x7f7116779750>, key=(slice(None, None, None), slice(None, None, None), slice(None, None, None))), key=(slice(None, None, None), slice(None, None, None), slice(None, None, None)))),\n u'/data/scratch/rpa/xarray_test_data/test_data.006.nc:/data-a5744e113f3d65bc033b1b83524e386e': CopyOnWriteArray(array=LazilyIndexedArray(array=LazilyIndexedArray(array=<xarray.backends.netCDF4_.NetCDF4ArrayWrapper object at 0x7f71786e34d0>, key=(slice(None, None, None), slice(None, None, None), slice(None, None, None))), key=(slice(None, None, None), slice(None, None, None), slice(None, None, None)))),\n u'/data/scratch/rpa/xarray_test_data/test_data.007.nc:/data-9b5300246bb923527547075534ad2193': CopyOnWriteArray(array=LazilyIndexedArray(array=LazilyIndexedArray(array=<xarray.backends.netCDF4_.NetCDF4ArrayWrapper object at 0x7f7154488a90>, key=(slice(None, None, None), slice(None, None, None), slice(None, None, None))), key=(slice(None, None, None), slice(None, None, None), slice(None, None, None)))),\n u'/data/scratch/rpa/xarray_test_data/test_data.008.nc:/data-5306849c9ee81abff2151725aff487e4': CopyOnWriteArray(array=LazilyIndexedArray(array=LazilyIndexedArray(array=<xarray.backends.netCDF4_.NetCDF4ArrayWrapper object at 0x7f711678f8d0>, key=(slice(None, None, None), slice(None, None, None), slice(None, None, None))), key=(slice(None, None, None), slice(None, None, None), slice(None, None, None)))),\n u'/data/scratch/rpa/xarray_test_data/test_data.009.nc:/data-d806755a0093024ba339082ef05ad6eb': CopyOnWriteArray(array=LazilyIndexedArray(array=LazilyIndexedArray(array=<xarray.backends.netCDF4_.NetCDF4ArrayWrapper object at 0x7f711678f350>, key=(slice(None, None, None), slice(None, None, None), slice(None, None, None))), key=(slice(None, None, None), slice(None, None, None), slice(None, None, None))))}"
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