From f2045f2a4f0e1343658248e90bf24a1f7702351e Mon Sep 17 00:00:00 2001
From: wassname <wassname@users.noreply.github.com>
Date: Mon, 12 Oct 2020 16:14:41 +0800
Subject: [PATCH] xarray

---
 .../c05_Big_Data/Working_with_Big_Data.ipynb  | 2044 ++++++++++++++++-
 .../c05_Big_Data/Working_with_Big_Data.py     |   31 +
 2 files changed, 2066 insertions(+), 9 deletions(-)

diff --git a/notebooks/c05_Big_Data/Working_with_Big_Data.ipynb b/notebooks/c05_Big_Data/Working_with_Big_Data.ipynb
index 904bc2a..b0c8b0a 100644
--- a/notebooks/c05_Big_Data/Working_with_Big_Data.ipynb
+++ b/notebooks/c05_Big_Data/Working_with_Big_Data.ipynb
@@ -82,9 +82,21 @@
     "    pid = os.getpid()\n",
     "    mem_bytes = psutil.Process(pid).memory_info().rss\n",
     "    print('[Process {} uses {:.1f}MB]'.format(pid, mem_bytes / 1024 / 1024))\n",
-    "    return mem_bytes / 1024 / 1024\n"
+    "    return mem_bytes / 1024 / 1024"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-04T05:48:45.857630Z",
+     "start_time": "2020-10-04T05:48:45.846756Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
@@ -501,8 +513,8 @@
    "execution_count": null,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2020-10-04T05:48:45.857630Z",
-     "start_time": "2020-10-04T05:48:45.846756Z"
+     "end_time": "2020-10-04T06:40:44.790440Z",
+     "start_time": "2020-10-04T06:40:43.466984Z"
     }
    },
    "outputs": [],
@@ -784,12 +796,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-10-04T06:40:44.790440Z",
-     "start_time": "2020-10-04T06:40:43.466984Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   },
@@ -2257,6 +2264,2025 @@
     "</details>"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Xarray\n",
+    "\n",
+    "Xarray is pandas for N-dimensional data. It also has a [dask backend](http://xarray.pydata.org/en/stable/dask.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-12T08:10:18.705068Z",
+     "start_time": "2020-10-12T08:10:18.539317Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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+       ".xr-section-summary-in:checked + label > span {\n",
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+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
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+       ".xr-array-wrap {\n",
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+       "  grid-template-columns: 20px auto;\n",
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+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
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+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
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+       ".xr-array-in:checked ~ .xr-array-data,\n",
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+       "  display: inline-block;\n",
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+       "  display: inline-block !important;\n",
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+       "}\n",
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+       "  margin: 0;\n",
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+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
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+       "}\n",
+       "\n",
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+       ".xr-icon-file-text2 {\n",
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:  (time: 36, x: 275, y: 205)\n",
+       "Coordinates:\n",
+       "  * time     (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n",
+       "    xc       (y, x) float64 dask.array&lt;chunksize=(205, 275), meta=np.ndarray&gt;\n",
+       "    yc       (y, x) float64 dask.array&lt;chunksize=(205, 275), meta=np.ndarray&gt;\n",
+       "Dimensions without coordinates: x, y\n",
+       "Data variables:\n",
+       "    Tair     (time, y, x) float64 dask.array&lt;chunksize=(10, 205, 275), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    title:                     /workspace/jhamman/processed/R1002RBRxaaa01a/l...\n",
+       "    institution:               U.W.\n",
+       "    source:                    RACM R1002RBRxaaa01a\n",
+       "    output_frequency:          daily\n",
+       "    output_mode:               averaged\n",
+       "    convention:                CF-1.4\n",
+       "    references:                Based on the initial model of Liang et al., 19...\n",
+       "    comment:                   Output from the Variable Infiltration Capacity...\n",
+       "    nco_openmp_thread_number:  1\n",
+       "    NCO:                       netCDF Operators version 4.7.9 (Homepage = htt...\n",
+       "    history:                   Fri Aug  7 17:57:38 2020: ncatted -a bounds,,d...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-120f486b-5721-4a12-87f8-c67c02513717' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-120f486b-5721-4a12-87f8-c67c02513717' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 36</li><li><span>x</span>: 275</li><li><span>y</span>: 205</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c4cf9255-4510-429d-8dd6-de1cd6ad3851' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c4cf9255-4510-429d-8dd6-de1cd6ad3851' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1980-09-16 12:00:00 ... 1983-08-...</div><input id='attrs-967165a5-4c45-4382-8c03-13266036ec17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-967165a5-4c45-4382-8c03-13266036ec17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0dc01eb0-9163-46cc-903d-843954f4534c' class='xr-var-data-in' type='checkbox'><label for='data-0dc01eb0-9163-46cc-903d-843954f4534c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>type_preferred :</span></dt><dd>int</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1980, 9, 16, 12, 0, 0, 0, 5, 259),\n",
+       "       cftime.DatetimeNoLeap(1980, 10, 17, 0, 0, 0, 0, 1, 290),\n",
+       "       cftime.DatetimeNoLeap(1980, 11, 16, 12, 0, 0, 0, 3, 320),\n",
+       "       cftime.DatetimeNoLeap(1980, 12, 17, 0, 0, 0, 0, 6, 351),\n",
+       "       cftime.DatetimeNoLeap(1981, 1, 17, 0, 0, 0, 0, 2, 17),\n",
+       "       cftime.DatetimeNoLeap(1981, 2, 15, 12, 0, 0, 0, 3, 46),\n",
+       "       cftime.DatetimeNoLeap(1981, 3, 17, 0, 0, 0, 0, 5, 76),\n",
+       "       cftime.DatetimeNoLeap(1981, 4, 16, 12, 0, 0, 0, 0, 106),\n",
+       "       cftime.DatetimeNoLeap(1981, 5, 17, 0, 0, 0, 0, 3, 137),\n",
+       "       cftime.DatetimeNoLeap(1981, 6, 16, 12, 0, 0, 0, 5, 167),\n",
+       "       cftime.DatetimeNoLeap(1981, 7, 17, 0, 0, 0, 0, 1, 198),\n",
+       "       cftime.DatetimeNoLeap(1981, 8, 17, 0, 0, 0, 0, 4, 229),\n",
+       "       cftime.DatetimeNoLeap(1981, 9, 16, 12, 0, 0, 0, 6, 259),\n",
+       "       cftime.DatetimeNoLeap(1981, 10, 17, 0, 0, 0, 0, 2, 290),\n",
+       "       cftime.DatetimeNoLeap(1981, 11, 16, 12, 0, 0, 0, 4, 320),\n",
+       "       cftime.DatetimeNoLeap(1981, 12, 17, 0, 0, 0, 0, 0, 351),\n",
+       "       cftime.DatetimeNoLeap(1982, 1, 17, 0, 0, 0, 0, 3, 17),\n",
+       "       cftime.DatetimeNoLeap(1982, 2, 15, 12, 0, 0, 0, 4, 46),\n",
+       "       cftime.DatetimeNoLeap(1982, 3, 17, 0, 0, 0, 0, 6, 76),\n",
+       "       cftime.DatetimeNoLeap(1982, 4, 16, 12, 0, 0, 0, 1, 106),\n",
+       "       cftime.DatetimeNoLeap(1982, 5, 17, 0, 0, 0, 0, 4, 137),\n",
+       "       cftime.DatetimeNoLeap(1982, 6, 16, 12, 0, 0, 0, 6, 167),\n",
+       "       cftime.DatetimeNoLeap(1982, 7, 17, 0, 0, 0, 0, 2, 198),\n",
+       "       cftime.DatetimeNoLeap(1982, 8, 17, 0, 0, 0, 0, 5, 229),\n",
+       "       cftime.DatetimeNoLeap(1982, 9, 16, 12, 0, 0, 0, 0, 259),\n",
+       "       cftime.DatetimeNoLeap(1982, 10, 17, 0, 0, 0, 0, 3, 290),\n",
+       "       cftime.DatetimeNoLeap(1982, 11, 16, 12, 0, 0, 0, 5, 320),\n",
+       "       cftime.DatetimeNoLeap(1982, 12, 17, 0, 0, 0, 0, 1, 351),\n",
+       "       cftime.DatetimeNoLeap(1983, 1, 17, 0, 0, 0, 0, 4, 17),\n",
+       "       cftime.DatetimeNoLeap(1983, 2, 15, 12, 0, 0, 0, 5, 46),\n",
+       "       cftime.DatetimeNoLeap(1983, 3, 17, 0, 0, 0, 0, 0, 76),\n",
+       "       cftime.DatetimeNoLeap(1983, 4, 16, 12, 0, 0, 0, 2, 106),\n",
+       "       cftime.DatetimeNoLeap(1983, 5, 17, 0, 0, 0, 0, 5, 137),\n",
+       "       cftime.DatetimeNoLeap(1983, 6, 16, 12, 0, 0, 0, 0, 167),\n",
+       "       cftime.DatetimeNoLeap(1983, 7, 17, 0, 0, 0, 0, 3, 198),\n",
+       "       cftime.DatetimeNoLeap(1983, 8, 17, 0, 0, 0, 0, 6, 229)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xc</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(205, 275), meta=np.ndarray&gt;</div><input id='attrs-779c662c-855a-4ef6-8ef4-1341a7ead05d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-779c662c-855a-4ef6-8ef4-1341a7ead05d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3995eec8-002a-47d0-858f-78d17312aadb' class='xr-var-data-in' type='checkbox'><label for='data-3995eec8-002a-47d0-858f-78d17312aadb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
+       "<tr>\n",
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+       "</tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>yc</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(205, 275), meta=np.ndarray&gt;</div><input id='attrs-633c2140-2500-4219-b219-4bc583a0267a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-633c2140-2500-4219-b219-4bc583a0267a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6efb530b-a902-46d9-8394-8481a16e9198' class='xr-var-data-in' type='checkbox'><label for='data-6efb530b-a902-46d9-8394-8481a16e9198' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-0707da92-158d-4fe1-8008-082f3a5dd12a' class='xr-section-summary-in' type='checkbox'  ><label for='section-0707da92-158d-4fe1-8008-082f3a5dd12a' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>/workspace/jhamman/processed/R1002RBRxaaa01a/lnd/temp/R1002RBRxaaa01a.vic.ha.1979-09-01.nc</dd><dt><span>institution :</span></dt><dd>U.W.</dd><dt><span>source :</span></dt><dd>RACM R1002RBRxaaa01a</dd><dt><span>output_frequency :</span></dt><dd>daily</dd><dt><span>output_mode :</span></dt><dd>averaged</dd><dt><span>convention :</span></dt><dd>CF-1.4</dd><dt><span>references :</span></dt><dd>Based on the initial model of Liang et al., 1994, JGR, 99, 14,415- 14,429.</dd><dt><span>comment :</span></dt><dd>Output from the Variable Infiltration Capacity (VIC) model.</dd><dt><span>nco_openmp_thread_number :</span></dt><dd>1</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 4.7.9 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</dd><dt><span>history :</span></dt><dd>Fri Aug  7 17:57:38 2020: ncatted -a bounds,,d,, rasm.nc\n",
+       "Tue Dec 27 14:15:22 2016: ncatted -a dimensions,,d,, rasm.nc rasm.nc\n",
+       "Tue Dec 27 13:38:40 2016: ncks -3 rasm.nc rasm.nc\n",
+       "history deleted for brevity</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:  (time: 36, x: 275, y: 205)\n",
+       "Coordinates:\n",
+       "  * time     (time) object 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\n",
+       "    xc       (y, x) float64 dask.array<chunksize=(205, 275), meta=np.ndarray>\n",
+       "    yc       (y, x) float64 dask.array<chunksize=(205, 275), meta=np.ndarray>\n",
+       "Dimensions without coordinates: x, y\n",
+       "Data variables:\n",
+       "    Tair     (time, y, x) float64 dask.array<chunksize=(10, 205, 275), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    title:                     /workspace/jhamman/processed/R1002RBRxaaa01a/l...\n",
+       "    institution:               U.W.\n",
+       "    source:                    RACM R1002RBRxaaa01a\n",
+       "    output_frequency:          daily\n",
+       "    output_mode:               averaged\n",
+       "    convention:                CF-1.4\n",
+       "    references:                Based on the initial model of Liang et al., 19...\n",
+       "    comment:                   Output from the Variable Infiltration Capacity...\n",
+       "    nco_openmp_thread_number:  1\n",
+       "    NCO:                       netCDF Operators version 4.7.9 (Homepage = htt...\n",
+       "    history:                   Fri Aug  7 17:57:38 2020: ncatted -a bounds,,d..."
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "ds = xr.tutorial.open_dataset('rasm').load().chunk(dict(time=10))\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-12T08:14:21.253683Z",
+     "start_time": "2020-10-12T08:14:20.618495Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:  (x: 275, y: 205)\n",
+       "Coordinates:\n",
+       "    time     object 1981-07-17 00:00:00\n",
+       "    xc       (y, x) float64 dask.array&lt;chunksize=(205, 275), meta=np.ndarray&gt;\n",
+       "    yc       (y, x) float64 dask.array&lt;chunksize=(205, 275), meta=np.ndarray&gt;\n",
+       "Dimensions without coordinates: x, y\n",
+       "Data variables:\n",
+       "    Tair     (y, x) float64 dask.array&lt;chunksize=(205, 275), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    title:                     /workspace/jhamman/processed/R1002RBRxaaa01a/l...\n",
+       "    institution:               U.W.\n",
+       "    source:                    RACM R1002RBRxaaa01a\n",
+       "    output_frequency:          daily\n",
+       "    output_mode:               averaged\n",
+       "    convention:                CF-1.4\n",
+       "    references:                Based on the initial model of Liang et al., 19...\n",
+       "    comment:                   Output from the Variable Infiltration Capacity...\n",
+       "    nco_openmp_thread_number:  1\n",
+       "    NCO:                       netCDF Operators version 4.7.9 (Homepage = htt...\n",
+       "    history:                   Fri Aug  7 17:57:38 2020: ncatted -a bounds,,d...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-33c97cf6-92fe-4df6-b917-36ff815ee83c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-33c97cf6-92fe-4df6-b917-36ff815ee83c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>x</span>: 275</li><li><span>y</span>: 205</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-60b1e987-3b09-455e-969f-21e34c4617ce' class='xr-section-summary-in' type='checkbox'  checked><label for='section-60b1e987-3b09-455e-969f-21e34c4617ce' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1981-07-17 00:00:00</div><input id='attrs-5b69636b-05a5-45d1-86e3-e73c9b1d0557' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b69636b-05a5-45d1-86e3-e73c9b1d0557' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a70bc568-0f32-4b6d-8799-8b037c06b47c' class='xr-var-data-in' type='checkbox'><label for='data-a70bc568-0f32-4b6d-8799-8b037c06b47c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>type_preferred :</span></dt><dd>int</dd></dl></div><div class='xr-var-data'><pre>array(cftime.DatetimeNoLeap(1981, 7, 17, 0, 0, 0, 0, 1, 198), dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xc</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(205, 275), meta=np.ndarray&gt;</div><input id='attrs-c4a91d6b-afec-4766-828f-cfebc2b34fd1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4a91d6b-afec-4766-828f-cfebc2b34fd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3391e69c-d594-4b35-ba22-adcb4526162d' class='xr-var-data-in' type='checkbox'><label for='data-3391e69c-d594-4b35-ba22-adcb4526162d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
+       "<tr>\n",
+       "<td>\n",
+       "<table>\n",
+       "  <thead>\n",
+       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr><th> Bytes </th><td> 451.00 kB </td> <td> 451.00 kB </td></tr>\n",
+       "    <tr><th> Shape </th><td> (205, 275) </td> <td> (205, 275) </td></tr>\n",
+       "    <tr><th> Count </th><td> 1 Tasks </td><td> 1 Chunks </td></tr>\n",
+       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</td>\n",
+       "<td>\n",
+       "<svg width=\"170\" height=\"139\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"89\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"89\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"109.454545\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >275</text>\n",
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+       "</svg>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>yc</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(205, 275), meta=np.ndarray&gt;</div><input id='attrs-397cba3b-4460-4341-8523-d8ed6d8298c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-397cba3b-4460-4341-8523-d8ed6d8298c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2bf4974-805e-49dd-ab86-fc3efcc9b821' class='xr-var-data-in' type='checkbox'><label for='data-d2bf4974-805e-49dd-ab86-fc3efcc9b821' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
+       "<tr>\n",
+       "<td>\n",
+       "<table>\n",
+       "  <thead>\n",
+       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr><th> Bytes </th><td> 451.00 kB </td> <td> 451.00 kB </td></tr>\n",
+       "    <tr><th> Shape </th><td> (205, 275) </td> <td> (205, 275) </td></tr>\n",
+       "    <tr><th> Count </th><td> 1 Tasks </td><td> 1 Chunks </td></tr>\n",
+       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</td>\n",
+       "<td>\n",
+       "<svg width=\"170\" height=\"139\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"89\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"89\" style=\"stroke-width:2\" />\n",
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+       "  <polygon points=\"0.000000,0.000000 120.000000,0.000000 120.000000,89.454545 0.000000,89.454545\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"109.454545\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >275</text>\n",
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+       "</svg>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-57270d3e-bf67-43e7-b9fc-dffa171276ff' class='xr-section-summary-in' type='checkbox'  checked><label for='section-57270d3e-bf67-43e7-b9fc-dffa171276ff' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Tair</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(205, 275), meta=np.ndarray&gt;</div><input id='attrs-d01a4a2e-d2ca-4e8d-8f16-7cb16d1c3b53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d01a4a2e-d2ca-4e8d-8f16-7cb16d1c3b53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39acb83b-e1e3-44c6-bb07-de057fd761ab' class='xr-var-data-in' type='checkbox'><label for='data-39acb83b-e1e3-44c6-bb07-de057fd761ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>C</dd><dt><span>long_name :</span></dt><dd>Surface air temperature</dd><dt><span>type_preferred :</span></dt><dd>double</dd><dt><span>time_rep :</span></dt><dd>instantaneous</dd></dl></div><div class='xr-var-data'><table>\n",
+       "<tr>\n",
+       "<td>\n",
+       "<table>\n",
+       "  <thead>\n",
+       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr><th> Bytes </th><td> 451.00 kB </td> <td> 451.00 kB </td></tr>\n",
+       "    <tr><th> Shape </th><td> (205, 275) </td> <td> (205, 275) </td></tr>\n",
+       "    <tr><th> Count </th><td> 6 Tasks </td><td> 1 Chunks </td></tr>\n",
+       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</td>\n",
+       "<td>\n",
+       "<svg width=\"170\" height=\"139\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
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+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"109.454545\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >275</text>\n",
+       "  <text x=\"140.000000\" y=\"44.727273\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,44.727273)\">205</text>\n",
+       "</svg>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-33a269fd-9c19-47f6-932b-74cb39731485' class='xr-section-summary-in' type='checkbox'  ><label for='section-33a269fd-9c19-47f6-932b-74cb39731485' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>/workspace/jhamman/processed/R1002RBRxaaa01a/lnd/temp/R1002RBRxaaa01a.vic.ha.1979-09-01.nc</dd><dt><span>institution :</span></dt><dd>U.W.</dd><dt><span>source :</span></dt><dd>RACM R1002RBRxaaa01a</dd><dt><span>output_frequency :</span></dt><dd>daily</dd><dt><span>output_mode :</span></dt><dd>averaged</dd><dt><span>convention :</span></dt><dd>CF-1.4</dd><dt><span>references :</span></dt><dd>Based on the initial model of Liang et al., 1994, JGR, 99, 14,415- 14,429.</dd><dt><span>comment :</span></dt><dd>Output from the Variable Infiltration Capacity (VIC) model.</dd><dt><span>nco_openmp_thread_number :</span></dt><dd>1</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 4.7.9 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</dd><dt><span>history :</span></dt><dd>Fri Aug  7 17:57:38 2020: ncatted -a bounds,,d,, rasm.nc\n",
+       "Tue Dec 27 14:15:22 2016: ncatted -a dimensions,,d,, rasm.nc rasm.nc\n",
+       "Tue Dec 27 13:38:40 2016: ncks -3 rasm.nc rasm.nc\n",
+       "history deleted for brevity</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:  (x: 275, y: 205)\n",
+       "Coordinates:\n",
+       "    time     object 1981-07-17 00:00:00\n",
+       "    xc       (y, x) float64 dask.array<chunksize=(205, 275), meta=np.ndarray>\n",
+       "    yc       (y, x) float64 dask.array<chunksize=(205, 275), meta=np.ndarray>\n",
+       "Dimensions without coordinates: x, y\n",
+       "Data variables:\n",
+       "    Tair     (y, x) float64 dask.array<chunksize=(205, 275), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    title:                     /workspace/jhamman/processed/R1002RBRxaaa01a/l...\n",
+       "    institution:               U.W.\n",
+       "    source:                    RACM R1002RBRxaaa01a\n",
+       "    output_frequency:          daily\n",
+       "    output_mode:               averaged\n",
+       "    convention:                CF-1.4\n",
+       "    references:                Based on the initial model of Liang et al., 19...\n",
+       "    comment:                   Output from the Variable Infiltration Capacity...\n",
+       "    nco_openmp_thread_number:  1\n",
+       "    NCO:                       netCDF Operators version 4.7.9 (Homepage = htt...\n",
+       "    history:                   Fri Aug  7 17:57:38 2020: ncatted -a bounds,,d..."
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# You can use isel instead of iloc. You always need to specify the dimension\n",
+    "ds.isel(time=10)['Tair'].plot.pcolormesh(\n",
+    "        vmin=-30, vmax=30, cmap='Spectral_r',\n",
+    "        add_colorbar=True, extend='both')\n",
+    "\n",
+    "ds.isel(time=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-12T08:14:04.619997Z",
+     "start_time": "2020-10-12T08:14:04.542987Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
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+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;Tair&#x27; (time: 4)&gt;\n",
+       "dask.array&lt;getitem, shape=(4,), dtype=float64, chunksize=(1,), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "  * time     (time) object 1980-12-31 00:00:00 ... 1983-12-31 00:00:00\n",
+       "    xc       float64 dask.array&lt;chunksize=(), meta=np.ndarray&gt;\n",
+       "    yc       float64 dask.array&lt;chunksize=(), meta=np.ndarray&gt;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'Tair'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2f664edb-595d-4a72-a101-dc6a4e6bb055' class='xr-array-in' type='checkbox' checked><label for='section-2f664edb-595d-4a72-a101-dc6a4e6bb055' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "<tr>\n",
+       "<td>\n",
+       "<table>\n",
+       "  <thead>\n",
+       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr><th> Bytes </th><td> 32 B </td> <td> 8 B </td></tr>\n",
+       "    <tr><th> Shape </th><td> (4,) </td> <td> (1,) </td></tr>\n",
+       "    <tr><th> Count </th><td> 35 Tasks </td><td> 4 Chunks </td></tr>\n",
+       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</td>\n",
+       "<td>\n",
+       "<svg width=\"170\" height=\"92\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"42\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"42\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"42\" />\n",
+       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"42\" />\n",
+       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"42\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"42\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.000000,0.000000 120.000000,0.000000 120.000000,42.898796 0.000000,42.898796\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"62.898796\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n",
+       "  <text x=\"140.000000\" y=\"21.449398\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,21.449398)\">1</text>\n",
+       "</svg>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-e41882bb-7452-4f62-998c-6a47fc840417' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e41882bb-7452-4f62-998c-6a47fc840417' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1980-12-31 00:00:00 ... 1983-12-...</div><input id='attrs-ed8200bd-cb5c-4f6a-8a32-f05f303928b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ed8200bd-cb5c-4f6a-8a32-f05f303928b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a38d6d21-a0a7-4676-a904-9da3b1b5c499' class='xr-var-data-in' type='checkbox'><label for='data-a38d6d21-a0a7-4676-a904-9da3b1b5c499' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1980, 12, 31, 0, 0, 0, 0, 6, 365),\n",
+       "       cftime.DatetimeNoLeap(1981, 12, 31, 0, 0, 0, 0, 0, 365),\n",
+       "       cftime.DatetimeNoLeap(1982, 12, 31, 0, 0, 0, 0, 1, 365),\n",
+       "       cftime.DatetimeNoLeap(1983, 12, 31, 0, 0, 0, 0, 2, 365)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xc</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(), meta=np.ndarray&gt;</div><input id='attrs-1b9b85f8-132d-4ca3-b52d-fb73f944109e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b9b85f8-132d-4ca3-b52d-fb73f944109e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e013c43f-d1b5-4584-94c6-4c19d2216dc0' class='xr-var-data-in' type='checkbox'><label for='data-e013c43f-d1b5-4584-94c6-4c19d2216dc0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
+       "<tr>\n",
+       "<td>\n",
+       "<table>\n",
+       "  <thead>\n",
+       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr><th> Bytes </th><td> 8 B </td> <td> 8 B </td></tr>\n",
+       "    <tr><th> Shape </th><td> () </td> <td> () </td></tr>\n",
+       "    <tr><th> Count </th><td> 3 Tasks </td><td> 1 Chunks </td></tr>\n",
+       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>yc</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(), meta=np.ndarray&gt;</div><input id='attrs-d686a3a4-6ed6-4ec6-bea1-28ecc7a53149' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d686a3a4-6ed6-4ec6-bea1-28ecc7a53149' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e6cf069-f0ae-41ba-a851-9cdb5ce732fb' class='xr-var-data-in' type='checkbox'><label for='data-3e6cf069-f0ae-41ba-a851-9cdb5ce732fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
+       "<tr>\n",
+       "<td>\n",
+       "<table>\n",
+       "  <thead>\n",
+       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr><th> Bytes </th><td> 8 B </td> <td> 8 B </td></tr>\n",
+       "    <tr><th> Shape </th><td> () </td> <td> () </td></tr>\n",
+       "    <tr><th> Count </th><td> 3 Tasks </td><td> 1 Chunks </td></tr>\n",
+       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</td>\n",
+       "<td>\n",
+       "\n",
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+      ],
+      "text/plain": [
+       "<xarray.DataArray 'Tair' (time: 4)>\n",
+       "dask.array<getitem, shape=(4,), dtype=float64, chunksize=(1,), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * time     (time) object 1980-12-31 00:00:00 ... 1983-12-31 00:00:00\n",
+       "    xc       float64 dask.array<chunksize=(), meta=np.ndarray>\n",
+       "    yc       float64 dask.array<chunksize=(), meta=np.ndarray>"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# You can also resample by date\n",
+    "res = ds.resample(time='A').mean().isel(x=200, y=200)['Tair']\n",
+    "# The result is a dask array\n",
+    "res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-10-12T08:14:13.932955Z",
+     "start_time": "2020-10-12T08:14:13.880801Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/wassname/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/dask/array/numpy_compat.py:40: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  x = np.divide(x1, x2, out)\n"
+     ]
+    },
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;Tair&#x27; (time: 4)&gt;\n",
+       "array([ 6.75662201,  8.97479849, 10.49235584,  9.59892096])\n",
+       "Coordinates:\n",
+       "  * time     (time) object 1980-12-31 00:00:00 ... 1983-12-31 00:00:00\n",
+       "    xc       float64 42.47\n",
+       "    yc       float64 44.82</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'Tair'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-676ec267-4883-4df3-8d0d-4594236d69e6' class='xr-array-in' type='checkbox' checked><label for='section-676ec267-4883-4df3-8d0d-4594236d69e6' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>6.757 8.975 10.49 9.599</span></div><div class='xr-array-data'><pre>array([ 6.75662201,  8.97479849, 10.49235584,  9.59892096])</pre></div></div></li><li class='xr-section-item'><input id='section-73481cc5-450a-4d43-a2f0-2dc0c20fbc45' class='xr-section-summary-in' type='checkbox'  checked><label for='section-73481cc5-450a-4d43-a2f0-2dc0c20fbc45' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1980-12-31 00:00:00 ... 1983-12-...</div><input id='attrs-33d70350-46d8-4a9c-b8ce-4e7ea56bf916' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-33d70350-46d8-4a9c-b8ce-4e7ea56bf916' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2a76b5f-76af-4f96-86e0-8d8bc57aa8d6' class='xr-var-data-in' type='checkbox'><label for='data-d2a76b5f-76af-4f96-86e0-8d8bc57aa8d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1980, 12, 31, 0, 0, 0, 0, 6, 365),\n",
+       "       cftime.DatetimeNoLeap(1981, 12, 31, 0, 0, 0, 0, 0, 365),\n",
+       "       cftime.DatetimeNoLeap(1982, 12, 31, 0, 0, 0, 0, 1, 365),\n",
+       "       cftime.DatetimeNoLeap(1983, 12, 31, 0, 0, 0, 0, 2, 365)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xc</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>42.47</div><input id='attrs-f7cb2f9c-95fa-47c9-8896-6e9265f84af6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7cb2f9c-95fa-47c9-8896-6e9265f84af6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-732d7dfb-8648-4fc3-b0a9-21c53c393782' class='xr-var-data-in' type='checkbox'><label for='data-732d7dfb-8648-4fc3-b0a9-21c53c393782' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array(42.4748837)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>yc</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>44.82</div><input id='attrs-54d167b4-bcf9-4dd7-9311-3f954316d5c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54d167b4-bcf9-4dd7-9311-3f954316d5c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f435bee4-2f08-435c-81ee-8676ac644a41' class='xr-var-data-in' type='checkbox'><label for='data-f435bee4-2f08-435c-81ee-8676ac644a41' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of grid cell center</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array(44.82000698)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-95b4647a-6596-49bd-a901-37b5d5b17b43' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-95b4647a-6596-49bd-a901-37b5d5b17b43' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'Tair' (time: 4)>\n",
+       "array([ 6.75662201,  8.97479849, 10.49235584,  9.59892096])\n",
+       "Coordinates:\n",
+       "  * time     (time) object 1980-12-31 00:00:00 ... 1983-12-31 00:00:00\n",
+       "    xc       float64 42.47\n",
+       "    yc       float64 44.82"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# But you can use .compute\n",
+    "res.compute()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/notebooks/c05_Big_Data/Working_with_Big_Data.py b/notebooks/c05_Big_Data/Working_with_Big_Data.py
index e663302..6331c08 100644
--- a/notebooks/c05_Big_Data/Working_with_Big_Data.py
+++ b/notebooks/c05_Big_Data/Working_with_Big_Data.py
@@ -523,6 +523,37 @@ def can_compile(x):
 #     
 # </details>
 
+# # Xarray
+#
+# Xarray is pandas for N-dimensional data. It also has a [dask backend](http://xarray.pydata.org/en/stable/dask.html)
+
+# +
+# %matplotlib inline
+import numpy as np
+import pandas as pd
+import xarray as xr
+import matplotlib.pyplot as plt
+
+ds = xr.tutorial.open_dataset('rasm').load().chunk(dict(time=10))
+ds
+
+# +
+# You can use isel instead of iloc. You always need to specify the dimension
+ds.isel(time=10)['Tair'].plot.pcolormesh(
+        vmin=-30, vmax=30, cmap='Spectral_r',
+        add_colorbar=True, extend='both')
+
+ds.isel(time=10)
+# -
+
+# You can also resample by date
+res = ds.resample(time='A').mean().isel(x=200, y=200)['Tair']
+# The result is a dask array
+res
+
+# But you can use .compute
+res.compute()
+
 # # References
 # The following sources where used for creation of this notebook:
 # - https://github.com/NCAR/ncar-python-tutorial