rsignell-usgs/xarray_data_fuse.ipynb

## xarray_data_fuse.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis of Gridded Ensemble Precipitation and Temperature Estimates over the Contiguous United States\n",
    "====\n",
    "\n",
    "For this example, we'll open a 9 members of a 100 member ensemble of precipitation and temperature data. The analysis we do below is quite simple but the problem is a good illustration of an IO bound task. \n",
    "\n",
    "Link to dataset: https://www.earthsystemgrid.org/dataset/gridded_precip_and_temp.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connect to Dask Distributed Cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "40b89a9f2516492d8697db1ea2034bb3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<h2>KubeCluster</h2>'), HBox(children=(HTML(value='\\n<div>\\n  <style scoped>\\n    .…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from dask.distributed import Client, progress\n",
    "# HPC\n",
    "# client = Client(scheduler_file='/glade/scratch/jhamman/scheduler.json')\n",
    "# client\n",
    "\n",
    "from dask_kubernetes import KubeCluster\n",
    "cluster = KubeCluster(n_workers=10)\n",
    "cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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://10.21.217.7:43318\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>10</li>\n",
       "  <li><b>Cores: </b>20</li>\n",
       "  <li><b>Memory: </b>60.00 GB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: scheduler='tcp://10.21.217.7:43318' processes=10 cores=20>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client = Client(cluster)\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Open Dataset\n",
    "\n",
    "We try storing a traditional atmospheric dataset on the cloud with two approaches\n",
    "\n",
    "1.  Hosting raw netCDF files from FUSE mounted file system based on the [gcsfs](gcsfs.readthedocs.io/en/latest/) Python project\n",
    "2.  Storing the data in an easier-to-read but new format, [Zarr](http://zarr.readthedocs.io/en/stable/)\n",
    "3.  Loading an [Intake](https://intake.readthedocs.io/en/latest/) catalog file from GCS, specifying the location of the zarr data.\n",
    "\n",
    "The dataset has dimensions of time, latitude, longitude, and ensmemble member. Each format is self-describing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Load with FUSE File system\n",
    "ds = xr.open_mfdataset('/gcs/newmann-met-ensemble-netcdf/conus_ens_00*.nc',\n",
    "                      engine='netcdf4', concat_dim='ensemble', chunks={'time': 50})\n",
    "\n",
    "### Load with Cloud object storage\n",
    "import gcsfs\n",
    "\n",
    "fs = gcsfs.GCSFileSystem(project='pangeo-181919', token='anon', access='read_only')\n",
    "gcsmap = gcsfs.mapping.GCSMap('pangeo-data/newman-met-ensemble',\n",
    "                              gcs=fs, check=False, create=False)\n",
    "#ds = xr.open_zarr(gcsmap)\n",
    "\n",
    "### Load with intake catalog service\n",
    "# import intake\n",
    "# cat = intake.Catalog('https://raw.githubusercontent.com/pangeo-data/pangeo/master/gce/catalog.yaml')\n",
    "# ds = cat.newmann_zarr.read_chunked()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (ensemble: 9, lat: 224, lon: 464, time: 12054)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 1980-01-01 1980-01-02 1980-01-03 ...\n",
       "  * lon        (lon) float64 -124.9 -124.8 -124.7 -124.6 -124.4 -124.3 ...\n",
       "  * lat        (lat) float64 25.06 25.19 25.31 25.44 25.56 25.69 25.81 25.94 ...\n",
       "Dimensions without coordinates: ensemble\n",
       "Data variables:\n",
       "    elevation  (ensemble, lat, lon) float64 dask.array<shape=(9, 224, 464), chunksize=(1, 224, 464)>\n",
       "    pcp        (ensemble, time, lat, lon) float64 dask.array<shape=(9, 12054, 224, 464), chunksize=(1, 50, 224, 464)>\n",
       "    t_mean     (ensemble, time, lat, lon) float64 dask.array<shape=(9, 12054, 224, 464), chunksize=(1, 50, 224, 464)>\n",
       "    t_range    (ensemble, time, lat, lon) float64 dask.array<shape=(9, 12054, 224, 464), chunksize=(1, 50, 224, 464)>\n",
       "    mask       (ensemble, lat, lon) int32 dask.array<shape=(9, 224, 464), chunksize=(1, 224, 464)>\n",
       "    t_max      (ensemble, time, lat, lon) float64 dask.array<shape=(9, 12054, 224, 464), chunksize=(1, 50, 224, 464)>\n",
       "    t_min      (ensemble, time, lat, lon) float64 dask.array<shape=(9, 12054, 224, 464), chunksize=(1, 50, 224, 464)>\n",
       "Attributes:\n",
       "    history:                   Version 1.0 of ensemble dataset, created Decem...\n",
       "    nco_openmp_thread_number:  1\n",
       "    institution:               National Center for Atmospheric Research (NCAR...\n",
       "    title:                     CONUS daily 12-km gridded ensemble precipitati...\n",
       "    source:                    Generated using version 1.0 of CONUS ensemble ...\n",
       "    references:                Newman et al. 2015: Gridded Ensemble Precipita..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print dataset\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure: Elevation and domain mask\n",
    "\n",
    "A quick plot of the mask to give us an idea of our spatial domain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Domain Elevation')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "elevation = ds['elevation'].isel(ensemble=0).persist()\n",
    "elevation = elevation.where(elevation > 0)\n",
    "elevation.plot(figsize=(10, 6))\n",
    "plt.title('Domain Elevation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intra-ensemble range\n",
    "\n",
    "We calculate the intra-ensemble range for all the mean daily temperature in this dataset.  This gives us a sense of uncertainty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 't_mean' (lat: 224, lon: 464)>\n",
       "dask.array<shape=(224, 464), dtype=float64, chunksize=(224, 464)>\n",
       "Coordinates:\n",
       "  * lon      (lon) float64 -124.9 -124.8 -124.7 -124.6 -124.4 -124.3 -124.2 ...\n",
       "  * lat      (lat) float64 25.06 25.19 25.31 25.44 25.56 25.69 25.81 25.94 ..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_mean = ds['t_mean'].mean(dim='time')\n",
    "spread = (temp_mean.max(dim='ensemble')\n",
    "        - temp_mean.min(dim='ensemble'))\n",
    "spread"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calling compute\n",
    "The expressions above didn't actually compute anything. They just build the task graph. To do the computations, we call the `compute` or `persist` methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ef20361ab76f48a4b07fad943d93f1f2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spread = spread.persist()\n",
    "progress(spread)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Figure: Intra-ensemble range\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-2ee9827831a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mspread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrobust\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\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      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Intra-ensemble range in mean annual temperature'\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/plot/plot.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    372\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    373\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 374\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_da\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    375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    376\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mfunctools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhist\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/plot/plot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(darray, row, col, col_wrap, ax, rtol, subplot_kws, **kwargs)\u001b[0m\n\u001b[1;32m    155\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ax'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    156\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 157\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mplotfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdarray\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    158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/xarray/plot/plot.py\u001b[0m in \u001b[0;36mnewplotfunc\u001b[0;34m(darray, x, y, figsize, size, aspect, ax, row, col, col_wrap, xincrease, yincrease, add_colorbar, add_labels, vmin, vmax, cmap, center, robust, extend, levels, infer_intervals, colors, subplot_kws, cbar_ax, cbar_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m    607\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    608\u001b[0m         \u001b[0;31m# Pass the data as a masked ndarray too\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 609\u001b[0;31m         \u001b[0mzval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdarray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_masked_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    610\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    611\u001b[0m         \u001b[0m_ensure_plottable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myval\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/core/dataarray.py\u001b[0m in \u001b[0;36mto_masked_array\u001b[0;34m(self, copy)\u001b[0m\n\u001b[1;32m   1453\u001b[0m             \u001b[0mMasked\u001b[0m \u001b[0mwhere\u001b[0m \u001b[0minvalid\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnan\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0minf\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0moccur\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1454\u001b[0m         \"\"\"\n\u001b[0;32m-> 1455\u001b[0;31m         \u001b[0misnull\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1456\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1457\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36mvalues\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    402\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvalues\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    403\u001b[0m         \u001b[0;34m\"\"\"The array's data as a numpy.ndarray\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 404\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvariable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    406\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/xarray/core/variable.py\u001b[0m in \u001b[0;36mvalues\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    385\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvalues\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    386\u001b[0m         \u001b[0;34m\"\"\"The variable's data as a numpy.ndarray\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 387\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_as_array_or_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    389\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/xarray/core/variable.py\u001b[0m in \u001b[0;36m_as_array_or_item\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[0mTODO\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mremove\u001b[0m \u001b[0mthis\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mreplace\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0monce\u001b[0m \u001b[0mthese\u001b[0m \u001b[0missues\u001b[0m \u001b[0mare\u001b[0m \u001b[0mfixed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m     \"\"\"\n\u001b[0;32m--> 211\u001b[0;31m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\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[1;32m    213\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'M'\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/numpy/core/numeric.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    490\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m     \"\"\"\n\u001b[0;32m--> 492\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\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;36m__array__\u001b[0;34m(self, dtype, **kwargs)\u001b[0m\n\u001b[1;32m   1190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1191\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1192\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute\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   1193\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1194\u001b[0m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\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    152\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    153\u001b[0m         \"\"\"\n\u001b[0;32m--> 154\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    155\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    156\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    405\u001b[0m     \u001b[0mkeys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dask_keys__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    406\u001b[0m     \u001b[0mpostcomputes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dask_postcompute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 407\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    408\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mrepack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpostcomputes\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    409\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;36mget\u001b[0;34m(self, dsk, keys, restrictions, loose_restrictions, resources, sync, asynchronous, direct, retries, priority, fifo_timeout, **kwargs)\u001b[0m\n\u001b[1;32m   2096\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2097\u001b[0m                 results = self.gather(packed, asynchronous=asynchronous,\n\u001b[0;32m-> 2098\u001b[0;31m                                       direct=direct)\n\u001b[0m\u001b[1;32m   2099\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2100\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   1506\u001b[0m             return self.sync(self._gather, futures, errors=errors,\n\u001b[1;32m   1507\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-> 1508\u001b[0;31m                              asynchronous=asynchronous)\n\u001b[0m\u001b[1;32m   1509\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1510\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    249\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_set\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--> 251\u001b[0;31m             \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[0m\u001b[1;32m    252\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[1;32m    253\u001b[0m         \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[0;32m/opt/conda/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    549\u001b[0m             \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    550\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 551\u001b[0;31m                 \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    552\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    297\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    298\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mtimeout\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--> 299\u001b[0;31m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    300\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "spread.plot(robust=True, figsize=(10, 6))\n",
    "plt.title('Intra-ensemble range in mean annual temperature')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average seasonal snowfall\n",
    "\n",
    "We can compute a crude estimate of average seasonal snowfall using the temperature and precipitation variables in our dataset. Here, we'll look at the first 4 ensemble members and make some maps of the seasonal total snowfall in each ensemble member."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "da_snow = ds['pcp'].where(ds['t_mean'] < 0.).resample(time='QS-Mar').sum('time')\n",
    "seasonal_snow = da_snow.isel(ensemble=slice(0, 4)).groupby('time.season').mean('time').persist()\n",
    "progress(seasonal_snow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# properly sort the seasons\n",
    "seasonal_snow = seasonal_snow.sel(season=['DJF', 'MAM','JJA', 'SON'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Figure: Average seasonal snowfall totoals "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# units here are in mm/season\n",
    "seasonal_snow.plot.pcolormesh(col='season', row='ensemble', cmap='Blues', robust=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract a time series of annual maximum precipitation events over a region\n",
    "\n",
    "In the previous two examples, we've mostly reduced the time and/or ensemble dimension. Here, we'll do a reduction operation on the spatial dimension to look at a time series of extreme precipitation events near Austin, TX (30.2672° N, 97.7431° W)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "buf = 0.25  # look at Austin +/- 0.25 deg\n",
    "\n",
    "ds_tx = ds.sel(lon=slice(-97.7431-buf, -97.7431+buf), lat=slice(30.2672-buf, 30.2672+buf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pcp_ann_max = ds_tx['pcp'].resample(time='AS').max('time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pcp_ann_max_ts = pcp_ann_max.max(('lat', 'lon')).persist()\n",
    "progress(pcp_ann_max_ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Figure: Timeseries of maximum precipitation near Austin, Tx."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = pcp_ann_max_ts.transpose().to_pandas().plot(title='Maximum precipitation near Austin, Tx')"
   ]
  }
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