rsignell-usgs/zarr-AWS_to_zarr-GCE.ipynb

## zarr-AWS_to_zarr-GCE.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# National Water Model (NWM) model data:  AWS Zarr to GCE Zarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import s3fs\n",
    "except:\n",
    "    !conda install s3fs -y\n",
    "    import s3fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask.distributed import Client, progress, LocalCluster\n",
    "from dask_kubernetes import KubeCluster\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import gcsfs\n",
    "import zarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "if False:\n",
    "    cluster = KubeCluster.from_yaml('/home/jovyan/s3worker.yml')\n",
    "    cluster.scale(10)\n",
    "else:\n",
    "    cluster = LocalCluster()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5ec0725d0210482fbbd5fa5ec478adb5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>VBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "VBox(children=(HTML(value='<h2>LocalCluster</h2>'), HBox(children=(HTML(value='\\n<div>\\n  <style scoped>\\n    .dataframe tbody tr th:only-of-type {\\n        vertical-align: middle;\\n    }\\n\\n    .dataframe tbody tr th {\\n        vertical-align: top;\\n    }\\n\\n    .dataframe thead th {\\n        text-align: right;\\n    }\\n  </style>\\n  <table style=\"text-align: right;\">\\n    <tr><th>Workers</th> <td>4</td></tr>\\n    <tr><th>Cores</th> <td>4</td></tr>\\n    <tr><th>Memory</th> <td>15.78 GB</td></tr>\\n  </table>\\n</div>\\n', layout=Layout(min_width='150px')), Accordion(children=(HBox(children=(IntText(value=0, description='Workers', layout=Layout(width='150px')), Button(description='Scale', layout=Layout(width='150px'), style=ButtonStyle()))), HBox(children=(IntText(value=0, description='Minimum', layout=Layout(width='150px')), IntText(value=0, description='Maximum', layout=Layout(width='150px')), Button(description='Adapt', layout=Layout(width='150px'), style=ButtonStyle())))), layout=Layout(min_width='500px'), selected_index=None, _titles={'0': 'Manual Scaling', '1': 'Adaptive Scaling'}))), HTML(value='<p><b>Dashboard: </b><a href=\"/user/rsignell-usgs/proxy/8787/status\" target=\"_blank\">/user/rsignell-usgs/proxy/8787/status</a></p>\\n')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3>Client</h3>\n",
       "<ul>\n",
       "  <li><b>Scheduler: </b>tcp://127.0.0.1:39478\n",
       "  <li><b>Dashboard: </b><a href='/user/rsignell-usgs/proxy/8787/status' target='_blank'>/user/rsignell-usgs/proxy/8787/status</a>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3>Cluster</h3>\n",
       "<ul>\n",
       "  <li><b>Workers: </b>4</li>\n",
       "  <li><b>Cores: </b>4</li>\n",
       "  <li><b>Memory: </b>15.78 GB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: scheduler='tcp://127.0.0.1:39478' processes=4 cores=4>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client = Client(cluster)\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "s3_bucket  = 'rsignell/nwm/test01'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.61 s, sys: 146 ms, total: 1.76 s\n",
      "Wall time: 13.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "fs = s3fs.S3FileSystem(anon=False)\n",
    "s3map = s3fs.S3Map(s3_bucket, s3=fs)\n",
    "ds = xr.open_zarr(s3map, auto_chunk=False)   # auto_chunk=True fails"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5 ms, sys: 0 ns, total: 5 ms\n",
      "Wall time: 4.89 ms\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "ds2 = ds.chunk({'time':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                     (nv: 2, reference_time: 11, time: 11, x: 4608, y: 3840)\n",
       "Coordinates:\n",
       "  * reference_time              (reference_time) datetime64[ns] 2018-04-01T18:00:00 ...\n",
       "  * time                        (time) datetime64[ns] 2018-04-01T19:00:00 ...\n",
       "  * x                           (x) float64 -2.304e+06 -2.303e+06 -2.302e+06 ...\n",
       "  * y                           (y) float64 -1.92e+06 -1.919e+06 -1.918e+06 ...\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    LWDOWN                      (time, y, x) float64 dask.array<shape=(11, 3840, 4608), chunksize=(1, 3840, 4608)>\n",
       "    PSFC                        (time, y, x) float64 dask.array<shape=(11, 3840, 4608), chunksize=(1, 3840, 4608)>\n",
       "    ProjectionCoordinateSystem  (time) |S64 dask.array<shape=(11,), chunksize=(1,)>\n",
       "    Q2D                         (time, y, x) float64 dask.array<shape=(11, 3840, 4608), chunksize=(1, 3840, 4608)>\n",
       "    RAINRATE                    (time, y, x) float32 dask.array<shape=(11, 3840, 4608), chunksize=(1, 3840, 4608)>\n",
       "    SWDOWN                      (time, y, x) float64 dask.array<shape=(11, 3840, 4608), chunksize=(1, 3840, 4608)>\n",
       "    T2D                         (time, y, x) float64 dask.array<shape=(11, 3840, 4608), chunksize=(1, 3840, 4608)>\n",
       "    U2D                         (time, y, x) float64 dask.array<shape=(11, 3840, 4608), chunksize=(1, 3840, 4608)>\n",
       "    V2D                         (time, y, x) float64 dask.array<shape=(11, 3840, 4608), chunksize=(1, 3840, 4608)>\n",
       "    time_bounds                 (time, nv) datetime64[ns] dask.array<shape=(11, 2), chunksize=(1, 2)>\n",
       "Attributes:\n",
       "    DODS.strlen:                     0\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time\n",
       "    model_initialization_time:       2018-04-01_18:00:00\n",
       "    model_output_valid_time:         2018-04-01_19:00:00"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1ef6003518>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(ds2['T2D'][0,::4,::4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Please visit this URL to authorize this application: https://accounts.google.com/o/oauth2/auth?response_type=code&client_id=586241054156-0asut23a7m10790r2ik24309flribp7j.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdevstorage.full_control&state=jJGf9eUxVnolqMXCA76NgrWJKhkEQ7&prompt=consent&access_type=offline\n",
      "Enter the authorization code: 4/AACL3wNVP5WEJiqhH1ikswPwLn8nUDtoDXtOvJPZGC0MN6-EyeOlplQ\n"
     ]
    }
   ],
   "source": [
    "fs = gcsfs.GCSFileSystem(project='pangeo-181919', token='browser')\n",
    "#fs = gcsfs.GCSFileSystem(project='pangeo-181919')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "bucket = 'pangeo-data/rsignell/nwm/test01'\n",
    "gcsmap = gcsfs.mapping.GCSMap(bucket, gcs=fs, check=False, create=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#client.get_versions(check=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "distributed.scheduler - ERROR - error from worker tcp://127.0.0.1:39575: EOF when reading a line\n",
      "distributed.utils - ERROR - EOF when reading a line\n",
      "Traceback (most recent call last):\n",
      "  File \"/opt/conda/lib/python3.6/site-packages/distributed/utils.py\", line 238, in f\n",
      "    result[0] = yield make_coro()\n",
      "  File \"/opt/conda/lib/python3.6/site-packages/tornado/gen.py\", line 1099, in run\n",
      "    value = future.result()\n",
      "  File \"/opt/conda/lib/python3.6/site-packages/tornado/gen.py\", line 1107, in run\n",
      "    yielded = self.gen.throw(*exc_info)\n",
      "  File \"/opt/conda/lib/python3.6/site-packages/distributed/client.py\", line 1378, in _gather\n",
      "    traceback)\n",
      "  File \"/opt/conda/lib/python3.6/site-packages/six.py\", line 692, in reraise\n",
      "    raise value.with_traceback(tb)\n",
      "  File \"/opt/conda/lib/python3.6/site-packages/distributed/protocol/pickle.py\", line 59, in loads\n",
      "    return pickle.loads(x)\n",
      "  File \"/opt/conda/lib/python3.6/site-packages/gcsfs/core.py\", line 1011, in __setstate__\n",
      "    self.connect(self.token)\n",
      "  File \"/opt/conda/lib/python3.6/site-packages/gcsfs/core.py\", line 407, in connect\n",
      "    self.__getattribute__('_connect_' + method)()\n",
      "  File \"/opt/conda/lib/python3.6/site-packages/gcsfs/core.py\", line 379, in _connect_browser\n",
      "    credentials = flow.run_console()\n",
      "  File \"/opt/conda/lib/python3.6/site-packages/google_auth_oauthlib/flow.py\", line 362, in run_console\n",
      "    code = input(authorization_code_message)\n",
      "EOFError: EOF when reading a line\n"
     ]
    },
    {
     "ename": "EOFError",
     "evalue": "EOF when reading a line",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mEOFError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36mto_zarr\u001b[0;34m(self, store, mode, synchronizer, group, encoding)\u001b[0m\n\u001b[1;32m   1163\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackends\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mto_zarr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1164\u001b[0m         return to_zarr(self, store=store, mode=mode, synchronizer=synchronizer,\n\u001b[0;32m-> 1165\u001b[0;31m                        group=group, encoding=encoding)\n\u001b[0m\u001b[1;32m   1166\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1167\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__unicode__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/xarray/backends/api.py\u001b[0m in \u001b[0;36mto_zarr\u001b[0;34m(dataset, store, mode, synchronizer, group, encoding)\u001b[0m\n\u001b[1;32m    777\u001b[0m     \u001b[0;31m# I think zarr stores should always be sync'd immediately\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    778\u001b[0m     \u001b[0;31m# TODO: figure out how to properly handle unlimited_dims\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 779\u001b[0;31m     \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdump_to_store\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstore\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msync\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    780\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mstore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36mdump_to_store\u001b[0;34m(self, store, encoder, sync, encoding, unlimited_dims)\u001b[0m\n\u001b[1;32m   1068\u001b[0m                     unlimited_dims=unlimited_dims)\n\u001b[1;32m   1069\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1070\u001b[0;31m             \u001b[0mstore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msync\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1071\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1072\u001b[0m     def to_netcdf(self, path=None, mode='w', format=None, group=None,\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/xarray/backends/zarr.py\u001b[0m in \u001b[0;36msync\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    365\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    366\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msync\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 367\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msync\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    368\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    369\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/xarray/backends/common.py\u001b[0m in \u001b[0;36msync\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    268\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msources\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    269\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mdask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mda\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 270\u001b[0;31m             \u001b[0mda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msources\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    271\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msources\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    272\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/dask/array/core.py\u001b[0m in \u001b[0;36mstore\u001b[0;34m(sources, targets, lock, regions, compute, return_stored, **kwargs)\u001b[0m\n\u001b[1;32m    953\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    954\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcompute\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 955\u001b[0;31m             \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    956\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    957\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/dask/base.py\u001b[0m in \u001b[0;36mcompute\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    153\u001b[0m         \u001b[0mdask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m         \"\"\"\n\u001b[0;32m--> 155\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraverse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    156\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/dask/base.py\u001b[0m in \u001b[0;36mcompute\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    402\u001b[0m     postcomputes = [a.__dask_postcompute__() if is_dask_collection(a)\n\u001b[1;32m    403\u001b[0m                     else (None, a) for a in args]\n\u001b[0;32m--> 404\u001b[0;31m     \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdsk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    405\u001b[0m     \u001b[0mresults_iter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    406\u001b[0m     return tuple(a if f is None else f(next(results_iter), *a)\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/distributed/client.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, dsk, keys, restrictions, loose_restrictions, resources, sync, asynchronous, direct, **kwargs)\u001b[0m\n\u001b[1;32m   2073\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2074\u001b[0m                 results = self.gather(packed, asynchronous=asynchronous,\n\u001b[0;32m-> 2075\u001b[0;31m                                       direct=direct)\n\u001b[0m\u001b[1;32m   2076\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2077\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfutures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/distributed/client.py\u001b[0m in \u001b[0;36mgather\u001b[0;34m(self, futures, errors, maxsize, direct, asynchronous)\u001b[0m\n\u001b[1;32m   1498\u001b[0m             return self.sync(self._gather, futures, errors=errors,\n\u001b[1;32m   1499\u001b[0m                              \u001b[0mdirect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdirect\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_worker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlocal_worker\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1500\u001b[0;31m                              asynchronous=asynchronous)\n\u001b[0m\u001b[1;32m   1501\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1502\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoroutine\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/distributed/client.py\u001b[0m in \u001b[0;36msync\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    613\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    614\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 615\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0msync\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    616\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    617\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/distributed/utils.py\u001b[0m in \u001b[0;36msync\u001b[0;34m(loop, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    252\u001b[0m             \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 254\u001b[0;31m         \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    255\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/six.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb)\u001b[0m\n\u001b[1;32m    691\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    692\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 693\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    694\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    695\u001b[0m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/distributed/utils.py\u001b[0m in \u001b[0;36mf\u001b[0;34m()\u001b[0m\n\u001b[1;32m    236\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmoment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m             \u001b[0mthread_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masynchronous\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m             \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mmake_coro\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    239\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m             \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1097\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1098\u001b[0m                     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1099\u001b[0;31m                         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1100\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1101\u001b[0m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhad_exception\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1105\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mexc_info\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1106\u001b[0m                         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1107\u001b[0;31m                             \u001b[0myielded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1108\u001b[0m                         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1109\u001b[0m                             \u001b[0;31m# Break up a reference to itself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/distributed/client.py\u001b[0m in \u001b[0;36m_gather\u001b[0;34m(self, futures, errors, direct, local_worker)\u001b[0m\n\u001b[1;32m   1376\u001b[0m                             six.reraise(type(exception),\n\u001b[1;32m   1377\u001b[0m                                         \u001b[0mexception\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1378\u001b[0;31m                                         traceback)\n\u001b[0m\u001b[1;32m   1379\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'skip'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1380\u001b[0m                         \u001b[0mbad_keys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/six.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb)\u001b[0m\n\u001b[1;32m    690\u001b[0m                 \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    691\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 692\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    693\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    694\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/distributed/protocol/pickle.py\u001b[0m in \u001b[0;36mloads\u001b[0;34m()\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     60\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m         \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Failed to deserialize %s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m10000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/gcsfs/core.py\u001b[0m in \u001b[0;36m__setstate__\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1009\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1010\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1011\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1012\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1013\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/gcsfs/core.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m()\u001b[0m\n\u001b[1;32m    405\u001b[0m                     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    406\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 407\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'_connect_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    408\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    409\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/gcsfs/core.py\u001b[0m in \u001b[0;36m_connect_browser\u001b[0;34m()\u001b[0m\n\u001b[1;32m    377\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_connect_browser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\u001b[0m         \u001b[0mflow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mInstalledAppFlow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_client_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclient_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscope\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 379\u001b[0;31m         \u001b[0mcredentials\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_console\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    380\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtokens\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproject\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccess\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcredentials\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    381\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_save_tokens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/google_auth_oauthlib/flow.py\u001b[0m in \u001b[0;36mrun_console\u001b[0;34m()\u001b[0m\n\u001b[1;32m    360\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mauthorization_prompt_message\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mauth_url\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 362\u001b[0;31m         \u001b[0mcode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mauthorization_code_message\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    363\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    364\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch_token\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mEOFError\u001b[0m: EOF when reading a line"
     ]
    }
   ],
   "source": [
    "%time ds2.to_zarr(store=gcsmap, mode='w')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test to see if we can read what we wrote"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# works if auto_chunk=False\n",
    "ds3 = xr.open_zarr(gcsmap, auto_chunk=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds4 = xr.open_zarr(gcsmap, auto_chunk=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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