From d1cd62279ef5257d9f5b66bbf6140054ffb0183f Mon Sep 17 00:00:00 2001
From: Joseph Hamman <jhamman@ucar.edu>
Date: Sat, 3 Nov 2018 14:08:28 +0000
Subject: [PATCH 01/11] first draft of xarray tutorial for dask examples

---
 images/dataset-diagram-logo.png | Bin 0 -> 108946 bytes
 xarray.ipynb                    | 361 ++++++++++++++++++++++++++++++++
 2 files changed, 361 insertions(+)
 create mode 100644 images/dataset-diagram-logo.png
 create mode 100644 xarray.ipynb

diff --git a/images/dataset-diagram-logo.png b/images/dataset-diagram-logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..dab01949fa2b39deb431c02333957e8912000695
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diff --git a/xarray.ipynb b/xarray.ipynb
new file mode 100644
index 00000000..88caa2cf
--- /dev/null
+++ b/xarray.ipynb
@@ -0,0 +1,361 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Xarray with Dask Arrays\n",
+    "\n",
+    "<img src=\"images/dataset-diagram-logo.png\" \n",
+    "     align=\"right\"\n",
+    "     width=\"66%\"\n",
+    "     alt=\"Xarray Dataset\">\n",
+    "     \n",
+    "**Xarray** is an open source project and Python package that extends the labeled data functionality of pandas N-dimensional array-like datasets. It shares a similar API to Numpy and Pandas and supports Dask arrays under the hood."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Start Dask Client for Dashboard\n",
+    "\n",
+    "Starting the Dask Client is optional.  It will provide a dashboard which \n",
+    "is useful to gain insight on the computation.  \n",
+    "\n",
+    "The link to the dashboard will become visible when you create the client below.  We recommend having it open on one side of your screen while using your notebook on the other side.  This can take some effort to arrange your windows, but seeing them both at the same is very useful when learning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dask.distributed import Client, progress\n",
+    "client = Client(n_workers=2, threads_per_worker=2, memory_limit='1GB')\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Open a sample dataset\n",
+    "\n",
+    "We will use some of xarray's tutorial data for this example. All we need to do is specify the chunk shape and xarray will automatically create Dask arrays for the variables in the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "\n",
+    "ds = xr.tutorial.load_dataset('air_temperature',\n",
+    "                              chunks={'lat': 25, 'lon': 25, 'time': 1000})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By specifying the chunk sizes by dimension name, we have configured the data variables in our `Dataset` to be returned backed by Dask arrays. In xarray, `Datasets` are dict-like container of labeled arrays akin to the `pandas.DataFrame`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Quickly inspecting the Dataset above, we'll note that this Dataset has three dimensions akin to axis in NumPy (lat, lon, and time), three coordinate variables akin to Pandas.Index objects (also named lat, lon, and time), and one data variable (air). Xarray also holds Dataset specific metadata in as Attributes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "da = ds['air']\n",
+    "da"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each data variable in xarray is called a `DataArray`. These are the fundemental labeled array object in xarray. Much like the Dataset, DataArray's still have dimensions and coordinate that support many of its array opperations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "da.data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Accessing the underlying array of data is done via the `data` property. Here we can see that we have a Dask array."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use Standard Xarray Operations\n",
+    "\n",
+    "Most common xarray operations operate identically on xarray objects backed by dask. The "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "da2 = da.groupby('time.month').mean('time')\n",
+    "da3 = da - da2\n",
+    "da3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Call `.compute()` when you want your result as a xarray.DataArray with data stored as NumPy arrays.\n",
+    "\n",
+    "If you started `Client()` above then you may want to watch the status page during computation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "computed_da = da3.compute()\n",
+    "type(computed_da.data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "computed_da"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Persist data in memory\n",
+    "\n",
+    "If you have the available RAM for your dataset then you can persist data in memory.  \n",
+    "\n",
+    "This allows future computations to be much faster."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "da = da.persist()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Time Series Operations\n",
+    "\n",
+    "Because we have a datetime index time-series operations work efficiently"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "da.resample(time='1w').mean('time').std('time')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "da.resample(time='1w').mean('time').std('time').compute().plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "da_smooth = da.rolling(time=30).mean()\n",
+    "da_smooth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since xarray stores each of its coordinate variables in memory, slicing by label is trivial and entirely lazy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%time da.sel(time='2013-01-01T18:00:00')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%time da.sel(time='2013-01-01T18:00:00').compute()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Custom workflows and automatic parallelization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Almost all of xarray’s built-in operations work on dask arrays. If you want to use a function that isn’t wrapped by xarray, one option is to extract dask arrays from xarray objects (.data) and use dask directly.\n",
+    "\n",
+    "Another option is to use xarray’s apply_ufunc(), which can automate embarrassingly parallel “map” type operations where a functions written for processing NumPy arrays should be repeatedly applied to xarray objects containing dask arrays. It works similarly to dask.array.map_blocks() and dask.array.atop(), but without requiring an intermediate layer of abstraction.\n",
+    "\n",
+    "Here we show an example using NumPy operations and a fast function from bottleneck, which we use to calculate Spearman’s rank-correlation coefficient:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "import bottleneck\n",
+    "\n",
+    "def covariance_gufunc(x, y):\n",
+    "    return ((x - x.mean(axis=-1, keepdims=True))\n",
+    "            * (y - y.mean(axis=-1, keepdims=True))).mean(axis=-1)\n",
+    "\n",
+    "def pearson_correlation_gufunc(x, y):\n",
+    "    return covariance_gufunc(x, y) / (x.std(axis=-1) * y.std(axis=-1))\n",
+    "\n",
+    "def spearman_correlation_gufunc(x, y):\n",
+    "    x_ranks = bottleneck.rankdata(x, axis=-1)\n",
+    "    y_ranks = bottleneck.rankdata(y, axis=-1)\n",
+    "    return pearson_correlation_gufunc(x_ranks, y_ranks)\n",
+    "\n",
+    "def spearman_correlation(x, y, dim):\n",
+    "    return xr.apply_ufunc(\n",
+    "        spearman_correlation_gufunc, x, y,\n",
+    "        input_core_dims=[[dim], [dim]],\n",
+    "        dask='parallelized',\n",
+    "        output_dtypes=[float])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the examples above, we were working with an some air temperature data. For this example, we'll calculate the spearman correlation using the raw air temperature data with the smoothed version that we also created (`da_smooth`). For this, we'll also have to rechunk the data ahead of time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corr = spearman_correlation(da.chunk({'time': -1}),\n",
+    "                            da_smooth.chunk({'time': -1}),\n",
+    "                            'time')\n",
+    "corr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corr.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 8d2a392930518360b33b4fb9b9710a20efc9bf0b Mon Sep 17 00:00:00 2001
From: Joseph Hamman <jhamman@ucar.edu>
Date: Sat, 3 Nov 2018 16:31:25 +0000
Subject: [PATCH 02/11] add xarray to env

---
 binder/environment.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/binder/environment.yml b/binder/environment.yml
index b78047d9..af9d6b44 100644
--- a/binder/environment.yml
+++ b/binder/environment.yml
@@ -17,6 +17,7 @@ dependencies:
   - nomkl
   - h5py
   - py-xgboost
+  - xarray
   - pip:
     - graphviz
     - dask_xgboost

From 698ad30b67fd6e095ad1f34644cb5e111fd0a6a0 Mon Sep 17 00:00:00 2001
From: Joseph Hamman <jhamman@ucar.edu>
Date: Sun, 4 Nov 2018 05:06:44 -0800
Subject: [PATCH 03/11] updates to xarray example

---
 binder/environment.yml |  3 ++-
 xarray.ipynb           | 16 ++++------------
 2 files changed, 6 insertions(+), 13 deletions(-)

diff --git a/binder/environment.yml b/binder/environment.yml
index af9d6b44..f4662d39 100644
--- a/binder/environment.yml
+++ b/binder/environment.yml
@@ -16,10 +16,11 @@ dependencies:
   - nbserverproxy
   - nomkl
   - h5py
+  - h5netcdf
   - py-xgboost
-  - xarray
   - pip:
     - graphviz
     - dask_xgboost
     - seaborn
     - mimesis
+    - xarray
diff --git a/xarray.ipynb b/xarray.ipynb
index 88caa2cf..6cfd157d 100644
--- a/xarray.ipynb
+++ b/xarray.ipynb
@@ -11,7 +11,7 @@
     "     width=\"66%\"\n",
     "     alt=\"Xarray Dataset\">\n",
     "     \n",
-    "**Xarray** is an open source project and Python package that extends the labeled data functionality of pandas N-dimensional array-like datasets. It shares a similar API to Numpy and Pandas and supports Dask arrays under the hood."
+    "**Xarray** is an open source project and Python package that extends the labeled data functionality of pandas to N-dimensional array-like datasets. It shares a similar API to Numpy and Pandas and supports both Dask and NumPy arrays under the hood."
    ]
   },
   {
@@ -54,8 +54,7 @@
    "source": [
     "import xarray as xr\n",
     "\n",
-    "ds = xr.tutorial.load_dataset('air_temperature',\n",
-    "                              chunks={'lat': 25, 'lon': 25, 'time': 1000})"
+    "ds = xr.tutorial.load_dataset('air_temperature').chunk({'lat': 25, 'lon': 25, 'time': 1000})"
    ]
   },
   {
@@ -215,7 +214,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "da.resample(time='1w').mean('time').std('time').compute().plot()"
+    "da.resample(time='1w').mean('time').std('time').compute().plot(figsize=(12, 8))"
    ]
   },
   {
@@ -326,15 +325,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "corr.plot()"
+    "corr.plot(figsize=(12, 8))"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From 08bcce1dc2fcc70fd1eed3b0c02b02afc37664ff Mon Sep 17 00:00:00 2001
From: Joseph Hamman <jhamman@ucar.edu>
Date: Sun, 4 Nov 2018 09:28:25 -0800
Subject: [PATCH 04/11] bump versions and put xarray back in conda env

---
 binder/environment.yml | 9 ++++-----
 xarray.ipynb           | 5 +++--
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/binder/environment.yml b/binder/environment.yml
index f4662d39..ae61d889 100644
--- a/binder/environment.yml
+++ b/binder/environment.yml
@@ -3,9 +3,9 @@ channels:
 dependencies:
   - python=3
   - bokeh=0.13
-  - dask=0.19.4
+  - dask=0.20
   - dask-ml=0.10.0
-  - distributed=1.23.3
+  - distributed=1.24
   - jupyterlab=0.35.1
   - nodejs=8.9
   - numpy
@@ -16,11 +16,10 @@ dependencies:
   - nbserverproxy
   - nomkl
   - h5py
-  - h5netcdf
+  - xarray
   - py-xgboost
   - pip:
     - graphviz
     - dask_xgboost
     - seaborn
-    - mimesis
-    - xarray
+    - mimesis
\ No newline at end of file
diff --git a/xarray.ipynb b/xarray.ipynb
index 6cfd157d..c38a5a30 100644
--- a/xarray.ipynb
+++ b/xarray.ipynb
@@ -54,7 +54,8 @@
    "source": [
     "import xarray as xr\n",
     "\n",
-    "ds = xr.tutorial.load_dataset('air_temperature').chunk({'lat': 25, 'lon': 25, 'time': 1000})"
+    "ds = xr.tutorial.load_dataset('air_temperature').chunk(\n",
+    "    {'lat': 25, 'lon': 25, 'time': 1000})"
    ]
   },
   {
@@ -94,7 +95,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Each data variable in xarray is called a `DataArray`. These are the fundemental labeled array object in xarray. Much like the Dataset, DataArray's still have dimensions and coordinate that support many of its array opperations."
+    "Each data variable in xarray is called a `DataArray`. These are the fundemental labeled array object in xarray. Much like the Dataset, DataArray's still have dimensions and coordinates that support many of its array opperations."
    ]
   },
   {

From 842cfc09ac139b3f642a124d0f101434600b9b11 Mon Sep 17 00:00:00 2001
From: Joseph Hamman <jhamman@ucar.edu>
Date: Sun, 4 Nov 2018 10:02:56 -0800
Subject: [PATCH 05/11] copy editing

---
 xarray.ipynb | 25 +++++++++----------------
 1 file changed, 9 insertions(+), 16 deletions(-)

diff --git a/xarray.ipynb b/xarray.ipynb
index c38a5a30..3f16e16b 100644
--- a/xarray.ipynb
+++ b/xarray.ipynb
@@ -43,7 +43,7 @@
    "source": [
     "## Open a sample dataset\n",
     "\n",
-    "We will use some of xarray's tutorial data for this example. All we need to do is specify the chunk shape and xarray will automatically create Dask arrays for the variables in the dataset."
+    "We will use some of xarray's tutorial data for this example. By specifying the chunk shape, xarray will automatically create Dask arrays for each data variable in the `Dataset`. In xarray, `Datasets` are dict-like container of labeled arrays, analogous to the `pandas.DataFrame`. Note that we're taking advantage of xarray's dimension labels when specifying chunk shapes."
    ]
   },
   {
@@ -58,13 +58,6 @@
     "    {'lat': 25, 'lon': 25, 'time': 1000})"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "By specifying the chunk sizes by dimension name, we have configured the data variables in our `Dataset` to be returned backed by Dask arrays. In xarray, `Datasets` are dict-like container of labeled arrays akin to the `pandas.DataFrame`."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -78,7 +71,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Quickly inspecting the Dataset above, we'll note that this Dataset has three dimensions akin to axis in NumPy (lat, lon, and time), three coordinate variables akin to Pandas.Index objects (also named lat, lon, and time), and one data variable (air). Xarray also holds Dataset specific metadata in as Attributes."
+    "Quickly inspecting the `Dataset` above, we'll note that this `Dataset` has three _dimensions_ akin to axes in NumPy (`lat`, `lon`, and `time`), three _coordinate variables_ akin to `pandas.Index` objects (also named `lat`, `lon`, and `time`), and one data variable (`air`). Xarray also holds Dataset specific metadata in as _attributes_."
    ]
   },
   {
@@ -95,7 +88,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Each data variable in xarray is called a `DataArray`. These are the fundemental labeled array object in xarray. Much like the Dataset, DataArray's still have dimensions and coordinates that support many of its array opperations."
+    "Each data variable in xarray is called a `DataArray`. These are the fundemental labeled array object in xarray. Much like the `Dataset`, `DataArrays` also have _dimensions_ and _coordinates_ that support many of its label-based opperations."
    ]
   },
   {
@@ -111,7 +104,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Accessing the underlying array of data is done via the `data` property. Here we can see that we have a Dask array."
+    "Accessing the underlying array of data is done via the `data` property. Here we can see that we have a Dask array. If this array were to be backed by a NumPy array, this property would point to the actual values in the array."
    ]
   },
   {
@@ -120,7 +113,7 @@
    "source": [
     "## Use Standard Xarray Operations\n",
     "\n",
-    "Most common xarray operations operate identically on xarray objects backed by dask. The "
+    "In almost all cases, operations using xarray objects are identical, regardless if the underlying data is stored as a Dask array or a NumPy array."
    ]
   },
   {
@@ -138,7 +131,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Call `.compute()` when you want your result as a xarray.DataArray with data stored as NumPy arrays.\n",
+    "Call `.compute()` when you want your result as a `xarray.DataArray` with data stored as NumPy arrays.\n",
     "\n",
     "If you started `Client()` above then you may want to watch the status page during computation."
    ]
@@ -264,11 +257,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Almost all of xarray’s built-in operations work on dask arrays. If you want to use a function that isn’t wrapped by xarray, one option is to extract dask arrays from xarray objects (.data) and use dask directly.\n",
+    "Almost all of xarray’s built-in operations work on Dask arrays. If you want to use a function that isn’t wrapped by xarray, one option is to extract Dask arrays from xarray objects (.data) and use Dask directly.\n",
     "\n",
-    "Another option is to use xarray’s apply_ufunc(), which can automate embarrassingly parallel “map” type operations where a functions written for processing NumPy arrays should be repeatedly applied to xarray objects containing dask arrays. It works similarly to dask.array.map_blocks() and dask.array.atop(), but without requiring an intermediate layer of abstraction.\n",
+    "Another option is to use xarray’s `apply_ufunc()` function, which can automate embarrassingly parallel “map” type operations where a functions written for processing NumPy arrays should be repeatedly applied to xarray objects containing Dask arrays. It works similarly to `dask.array.map_blocks()` and `dask.array.atop()`, but without requiring an intermediate layer of abstraction.\n",
     "\n",
-    "Here we show an example using NumPy operations and a fast function from bottleneck, which we use to calculate Spearman’s rank-correlation coefficient:"
+    "Here we show an example using NumPy operations and a fast function from `bottleneck`, which we use to calculate Spearman’s rank-correlation coefficient:"
    ]
   },
   {

From fc7f10f92b1e1269e36c9f2e99d5e20be676148a Mon Sep 17 00:00:00 2001
From: Joseph Hamman <jhamman@ucar.edu>
Date: Wed, 7 Nov 2018 19:47:43 -0800
Subject: [PATCH 06/11] updates -- in sync with xarray 0.11 release

---
 xarray.ipynb | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/xarray.ipynb b/xarray.ipynb
index 3f16e16b..39440f80 100644
--- a/xarray.ipynb
+++ b/xarray.ipynb
@@ -11,7 +11,7 @@
     "     width=\"66%\"\n",
     "     alt=\"Xarray Dataset\">\n",
     "     \n",
-    "**Xarray** is an open source project and Python package that extends the labeled data functionality of pandas to N-dimensional array-like datasets. It shares a similar API to Numpy and Pandas and supports both Dask and NumPy arrays under the hood."
+    "**[Xarray](http://xarray.pydata.org/en/stable/)** is an open source project and Python package that extends the labeled data functionality of [Pandas](https://pandas.pydata.org/) to N-dimensional array-like datasets. It shares a similar API to [NumPy](http://www.numpy.org/) and [Pandas](https://pandas.pydata.org/) and supports both [Dask](https://dask.org/) and [NumPy](http://www.numpy.org/) arrays under the hood."
    ]
   },
   {
@@ -54,8 +54,8 @@
    "source": [
     "import xarray as xr\n",
     "\n",
-    "ds = xr.tutorial.load_dataset('air_temperature').chunk(\n",
-    "    {'lat': 25, 'lon': 25, 'time': 1000})"
+    "ds = xr.tutorial.open_dataset('air_temperature',\n",
+    "                              chunks={'lat': 25, 'lon': 25, 'time': 1000})"
    ]
   },
   {
@@ -339,7 +339,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.6"
   }
  },
  "nbformat": 4,

From cb86a9cc82e324c71dad078dae1813f90b4d6855 Mon Sep 17 00:00:00 2001
From: Joseph Hamman <jhamman@ucar.edu>
Date: Thu, 8 Nov 2018 09:10:56 -0800
Subject: [PATCH 07/11] add bottleneck to env

---
 binder/environment.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/binder/environment.yml b/binder/environment.yml
index ae61d889..dc850b37 100644
--- a/binder/environment.yml
+++ b/binder/environment.yml
@@ -17,6 +17,7 @@ dependencies:
   - nomkl
   - h5py
   - xarray
+  - bottleneck
   - py-xgboost
   - pip:
     - graphviz

From 7274bc2632cf451d43c0aa5d4ee2e0d89d7a59ef Mon Sep 17 00:00:00 2001
From: Joseph Hamman <jhamman@ucar.edu>
Date: Fri, 9 Nov 2018 06:59:12 -0800
Subject: [PATCH 08/11] add xarray to toc

---
 index.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/index.rst b/index.rst
index 9d4cef2f..77ee031a 100644
--- a/index.rst
+++ b/index.rst
@@ -23,6 +23,7 @@ You can run these examples in a live session here: |Binder|
    delayed
    futures
    machine-learning
+   xarray
 
 .. toctree::
    :maxdepth: 1

From e373d6802cf3f86318f07618255c97e3f7f61a79 Mon Sep 17 00:00:00 2001
From: Joseph Hamman <jhamman@ucar.edu>
Date: Fri, 9 Nov 2018 14:16:04 -0800
Subject: [PATCH 09/11] small cleanups of xarray example

---
 xarray.ipynb | 54 +++++++++++++++++++++++++++++-----------------------
 1 file changed, 30 insertions(+), 24 deletions(-)

diff --git a/xarray.ipynb b/xarray.ipynb
index 39440f80..8de3f3c4 100644
--- a/xarray.ipynb
+++ b/xarray.ipynb
@@ -14,6 +14,18 @@
     "**[Xarray](http://xarray.pydata.org/en/stable/)** is an open source project and Python package that extends the labeled data functionality of [Pandas](https://pandas.pydata.org/) to N-dimensional array-like datasets. It shares a similar API to [NumPy](http://www.numpy.org/) and [Pandas](https://pandas.pydata.org/) and supports both [Dask](https://dask.org/) and [NumPy](http://www.numpy.org/) arrays under the hood."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "from dask.distributed import Client\n",
+    "import xarray as xr"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -32,7 +44,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from dask.distributed import Client, progress\n",
     "client = Client(n_workers=2, threads_per_worker=2, memory_limit='1GB')\n",
     "client"
    ]
@@ -52,18 +63,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import xarray as xr\n",
-    "\n",
     "ds = xr.tutorial.open_dataset('air_temperature',\n",
-    "                              chunks={'lat': 25, 'lon': 25, 'time': 1000})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "                              chunks={'lat': 25, 'lon': 25, 'time': 1000})\n",
     "ds"
    ]
   },
@@ -131,7 +132,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Call `.compute()` when you want your result as a `xarray.DataArray` with data stored as NumPy arrays.\n",
+    "Call `.compute()` or `.load()` when you want your result as a `xarray.DataArray` with data stored as NumPy arrays.\n",
     "\n",
     "If you started `Client()` above then you may want to watch the status page during computation."
    ]
@@ -142,7 +143,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "computed_da = da3.compute()\n",
+    "computed_da = da3.load()\n",
     "type(computed_da.data)"
    ]
   },
@@ -181,7 +182,7 @@
    "source": [
     "## Time Series Operations\n",
     "\n",
-    "Because we have a datetime index time-series operations work efficiently"
+    "Because we have a datetime index time-series operations work efficiently. Here we demo the use of xarray's resample method:"
    ]
   },
   {
@@ -190,7 +191,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline"
+    "da.resample(time='1w').mean('time').std('time')"
    ]
   },
   {
@@ -199,16 +200,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "da.resample(time='1w').mean('time').std('time')"
+    "da.resample(time='1w').mean('time').std('time').load().plot(figsize=(12, 8))"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "da.resample(time='1w').mean('time').std('time').compute().plot(figsize=(12, 8))"
+    "and rolling window operations:"
    ]
   },
   {
@@ -217,7 +216,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "da_smooth = da.rolling(time=30).mean()\n",
+    "da_smooth = da.rolling(time=30).mean().persist()\n",
     "da_smooth"
    ]
   },
@@ -243,7 +242,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%time da.sel(time='2013-01-01T18:00:00').compute()"
+    "%time da.sel(time='2013-01-01T18:00:00').load()"
    ]
   },
   {
@@ -321,6 +320,13 @@
    "source": [
     "corr.plot(figsize=(12, 8))"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -339,7 +345,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,

From 9847b48f2da84d08f7564a4abcd663323816a1dc Mon Sep 17 00:00:00 2001
From: Joseph Hamman <jhamman@ucar.edu>
Date: Fri, 9 Nov 2018 15:42:40 -0800
Subject: [PATCH 10/11] bigger xarray chunks

---
 xarray.ipynb | 425 +++++++++++++++++++++++++++++++++++++++++++++------
 1 file changed, 377 insertions(+), 48 deletions(-)

diff --git a/xarray.ipynb b/xarray.ipynb
index 8de3f3c4..c5ed4fd1 100644
--- a/xarray.ipynb
+++ b/xarray.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,9 +40,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table style=\"border: 2px solid white;\">\n",
+       "<tr>\n",
+       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
+       "<h3>Client</h3>\n",
+       "<ul>\n",
+       "  <li><b>Scheduler: </b>tcp://127.0.0.1:60186\n",
+       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/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>2</li>\n",
+       "  <li><b>Cores: </b>4</li>\n",
+       "  <li><b>Memory: </b>2.00 GB</li>\n",
+       "</ul>\n",
+       "</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Client: scheduler='tcp://127.0.0.1:60186' processes=2 cores=4>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "client = Client(n_workers=2, threads_per_worker=2, memory_limit='1GB')\n",
     "client"
@@ -59,12 +91,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:  (lat: 25, lon: 53, time: 2920)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time, lat, lon) float32 dask.array<shape=(2920, 25, 53), chunksize=(2920, 25, 25)>\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..."
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ds = xr.tutorial.open_dataset('air_temperature',\n",
-    "                              chunks={'lat': 25, 'lon': 25, 'time': 1000})\n",
+    "                              chunks={'lat': 25, 'lon': 25, 'time': -1})\n",
     "ds"
    ]
   },
@@ -77,9 +133,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n",
+       "dask.array<shape=(2920, 25, 53), dtype=float32, chunksize=(2920, 25, 25)>\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "da = ds['air']\n",
     "da"
@@ -94,9 +178,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dask.array<open_dataset-335e670447fe234d3db7ca2d44dec20bair, shape=(2920, 25, 53), dtype=float32, chunksize=(2920, 25, 25)>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "da.data"
    ]
@@ -119,9 +214,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53, month: 12)>\n",
+       "dask.array<shape=(2920, 25, 53, 12), dtype=float32, chunksize=(2920, 25, 25, 1)>\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "da2 = da.groupby('time.month').mean('time')\n",
     "da3 = da - da2\n",
@@ -139,9 +251,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "computed_da = da3.load()\n",
     "type(computed_da.data)"
@@ -149,9 +272,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53, month: 12)>\n",
+       "array([[[[-5.149872, ..., -6.770996],\n",
+       "         ...,\n",
+       "         [-7.046707, ..., -4.028244]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[-0.864166, ..., -1.590881],\n",
+       "         ...,\n",
+       "         [ 0.963531, ..., -0.937622]]],\n",
+       "\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "\n",
+       "       [[[-1.259872, ..., -2.880997],\n",
+       "         ...,\n",
+       "         [-3.856705, ..., -0.838242]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[ 0.535828, ..., -0.190887],\n",
+       "         ...,\n",
+       "         [ 0.053528, ..., -1.847626]]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "computed_da"
    ]
@@ -169,7 +331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -187,18 +349,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.DataArray 'air' (lat: 25, lon: 53)>\n",
+       "dask.array<shape=(25, 53), dtype=float32, chunksize=(25, 25)>\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "da.resample(time='1w').mean('time').std('time')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x30e319fd0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "da.resample(time='1w').mean('time').std('time').load().plot(figsize=(12, 8))"
    ]
@@ -212,9 +410,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.DataArray '_trim-066a93220806e34be40039f99192cf3b' (time: 2920, lat: 25, lon: 53)>\n",
+       "dask.array<shape=(2920, 25, 53), dtype=float32, chunksize=(2920, 25, 25)>\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "da_smooth = da.rolling(time=30).mean().persist()\n",
     "da_smooth"
@@ -229,18 +443,97 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 7.7 ms, sys: 2.41 ms, total: 10.1 ms\n",
+      "Wall time: 11.2 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.DataArray 'air' (lat: 25, lon: 53)>\n",
+       "dask.array<shape=(25, 53), dtype=float32, chunksize=(25, 25)>\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "    time     datetime64[ns] 2013-01-01T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "%time da.sel(time='2013-01-01T18:00:00')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 29.7 ms, sys: 5.39 ms, total: 35.1 ms\n",
+      "Wall time: 39.7 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.DataArray 'air' (lat: 25, lon: 53)>\n",
+       "array([[241.89   , 241.79999, 241.79999, ..., 234.39   , 235.5    , 237.59999],\n",
+       "       [246.29999, 245.29999, 244.2    , ..., 230.89   , 231.5    , 234.5    ],\n",
+       "       [256.6    , 254.7    , 252.09999, ..., 230.7    , 231.79999, 236.09999],\n",
+       "       ...,\n",
+       "       [296.6    , 296.4    , 296.     , ..., 296.5    , 295.79   , 295.29   ],\n",
+       "       [297.     , 297.5    , 297.1    , ..., 296.79   , 296.6    , 296.29   ],\n",
+       "       [297.5    , 297.69998, 297.5    , ..., 297.79   , 298.     , 297.9    ]],\n",
+       "      dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "    time     datetime64[ns] 2013-01-01T18:00:00\n",
+       "Attributes:\n",
+       "    long_name:     4xDaily Air temperature at sigma level 995\n",
+       "    units:         degK\n",
+       "    precision:     2\n",
+       "    GRIB_id:       11\n",
+       "    GRIB_name:     TMP\n",
+       "    var_desc:      Air temperature\n",
+       "    dataset:       NMC Reanalysis\n",
+       "    level_desc:    Surface\n",
+       "    statistic:     Individual Obs\n",
+       "    parent_stat:   Other\n",
+       "    actual_range:  [185.16 322.1 ]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "%time da.sel(time='2013-01-01T18:00:00').load()"
    ]
@@ -265,7 +558,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -302,9 +595,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.DataArray (lat: 25, lon: 53)>\n",
+       "dask.array<shape=(25, 53), dtype=float64, chunksize=(25, 25)>\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "corr = spearman_correlation(da.chunk({'time': -1}),\n",
     "                            da_smooth.chunk({'time': -1}),\n",
@@ -314,9 +622,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x313118ac8>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "corr.plot(figsize=(12, 8))"
    ]
@@ -331,9 +660,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python (pangeo)",
    "language": "python",
-   "name": "python3"
+   "name": "pangeo"
   },
   "language_info": {
    "codemirror_mode": {

From 3c6aed213cc46841371ba5c61b9d32ca25e9de1a Mon Sep 17 00:00:00 2001
From: Joseph Hamman <jhamman@ucar.edu>
Date: Tue, 13 Nov 2018 08:29:30 -0800
Subject: [PATCH 11/11] strip out kernel

---
 xarray.ipynb | 425 ++++++---------------------------------------------
 1 file changed, 48 insertions(+), 377 deletions(-)

diff --git a/xarray.ipynb b/xarray.ipynb
index c5ed4fd1..099fae44 100644
--- a/xarray.ipynb
+++ b/xarray.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,41 +40,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table style=\"border: 2px solid white;\">\n",
-       "<tr>\n",
-       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
-       "<h3>Client</h3>\n",
-       "<ul>\n",
-       "  <li><b>Scheduler: </b>tcp://127.0.0.1:60186\n",
-       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/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>2</li>\n",
-       "  <li><b>Cores: </b>4</li>\n",
-       "  <li><b>Memory: </b>2.00 GB</li>\n",
-       "</ul>\n",
-       "</td>\n",
-       "</tr>\n",
-       "</table>"
-      ],
-      "text/plain": [
-       "<Client: scheduler='tcp://127.0.0.1:60186' processes=2 cores=4>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "client = Client(n_workers=2, threads_per_worker=2, memory_limit='1GB')\n",
     "client"
@@ -91,33 +59,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:  (lat: 25, lon: 53, time: 2920)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Data variables:\n",
-       "    air      (time, lat, lon) float32 dask.array<shape=(2920, 25, 53), chunksize=(2920, 25, 25)>\n",
-       "Attributes:\n",
-       "    Conventions:  COARDS\n",
-       "    title:        4x daily NMC reanalysis (1948)\n",
-       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
-       "    platform:     Model\n",
-       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..."
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "ds = xr.tutorial.open_dataset('air_temperature',\n",
     "                              chunks={'lat': 25, 'lon': 25, 'time': -1})\n",
@@ -133,37 +77,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>\n",
-       "dask.array<shape=(2920, 25, 53), dtype=float32, chunksize=(2920, 25, 25)>\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "da = ds['air']\n",
     "da"
@@ -178,20 +94,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dask.array<open_dataset-335e670447fe234d3db7ca2d44dec20bair, shape=(2920, 25, 53), dtype=float32, chunksize=(2920, 25, 25)>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "da.data"
    ]
@@ -214,26 +119,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53, month: 12)>\n",
-       "dask.array<shape=(2920, 25, 53, 12), dtype=float32, chunksize=(2920, 25, 25, 1)>\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "da2 = da.groupby('time.month').mean('time')\n",
     "da3 = da - da2\n",
@@ -251,20 +139,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "numpy.ndarray"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "computed_da = da3.load()\n",
     "type(computed_da.data)"
@@ -272,48 +149,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53, month: 12)>\n",
-       "array([[[[-5.149872, ..., -6.770996],\n",
-       "         ...,\n",
-       "         [-7.046707, ..., -4.028244]],\n",
-       "\n",
-       "        ...,\n",
-       "\n",
-       "        [[-0.864166, ..., -1.590881],\n",
-       "         ...,\n",
-       "         [ 0.963531, ..., -0.937622]]],\n",
-       "\n",
-       "\n",
-       "       ...,\n",
-       "\n",
-       "\n",
-       "       [[[-1.259872, ..., -2.880997],\n",
-       "         ...,\n",
-       "         [-3.856705, ..., -0.838242]],\n",
-       "\n",
-       "        ...,\n",
-       "\n",
-       "        [[ 0.535828, ..., -0.190887],\n",
-       "         ...,\n",
-       "         [ 0.053528, ..., -1.847626]]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
-       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "computed_da"
    ]
@@ -331,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -349,54 +187,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.DataArray 'air' (lat: 25, lon: 53)>\n",
-       "dask.array<shape=(25, 53), dtype=float32, chunksize=(25, 25)>\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "da.resample(time='1w').mean('time').std('time')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x30e319fd0>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "da.resample(time='1w').mean('time').std('time').load().plot(figsize=(12, 8))"
    ]
@@ -410,25 +212,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.DataArray '_trim-066a93220806e34be40039f99192cf3b' (time: 2920, lat: 25, lon: 53)>\n",
-       "dask.array<shape=(2920, 25, 53), dtype=float32, chunksize=(2920, 25, 25)>\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "da_smooth = da.rolling(time=30).mean().persist()\n",
     "da_smooth"
@@ -443,97 +229,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 7.7 ms, sys: 2.41 ms, total: 10.1 ms\n",
-      "Wall time: 11.2 ms\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.DataArray 'air' (lat: 25, lon: 53)>\n",
-       "dask.array<shape=(25, 53), dtype=float32, chunksize=(25, 25)>\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "    time     datetime64[ns] 2013-01-01T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%time da.sel(time='2013-01-01T18:00:00')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 29.7 ms, sys: 5.39 ms, total: 35.1 ms\n",
-      "Wall time: 39.7 ms\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.DataArray 'air' (lat: 25, lon: 53)>\n",
-       "array([[241.89   , 241.79999, 241.79999, ..., 234.39   , 235.5    , 237.59999],\n",
-       "       [246.29999, 245.29999, 244.2    , ..., 230.89   , 231.5    , 234.5    ],\n",
-       "       [256.6    , 254.7    , 252.09999, ..., 230.7    , 231.79999, 236.09999],\n",
-       "       ...,\n",
-       "       [296.6    , 296.4    , 296.     , ..., 296.5    , 295.79   , 295.29   ],\n",
-       "       [297.     , 297.5    , 297.1    , ..., 296.79   , 296.6    , 296.29   ],\n",
-       "       [297.5    , 297.69998, 297.5    , ..., 297.79   , 298.     , 297.9    ]],\n",
-       "      dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
-       "    time     datetime64[ns] 2013-01-01T18:00:00\n",
-       "Attributes:\n",
-       "    long_name:     4xDaily Air temperature at sigma level 995\n",
-       "    units:         degK\n",
-       "    precision:     2\n",
-       "    GRIB_id:       11\n",
-       "    GRIB_name:     TMP\n",
-       "    var_desc:      Air temperature\n",
-       "    dataset:       NMC Reanalysis\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Individual Obs\n",
-       "    parent_stat:   Other\n",
-       "    actual_range:  [185.16 322.1 ]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "%time da.sel(time='2013-01-01T18:00:00').load()"
    ]
@@ -558,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -595,24 +302,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.DataArray (lat: 25, lon: 53)>\n",
-       "dask.array<shape=(25, 53), dtype=float64, chunksize=(25, 25)>\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
-       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "corr = spearman_correlation(da.chunk({'time': -1}),\n",
     "                            da_smooth.chunk({'time': -1}),\n",
@@ -622,30 +314,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x313118ac8>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "corr.plot(figsize=(12, 8))"
    ]
@@ -660,9 +331,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python (pangeo)",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "pangeo"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -674,7 +345,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.6"
   }
  },
  "nbformat": 4,