Merge branch 'master' into bokeh_improvements

.travis.yml

@@ -47,11 +47,8 @@ after_script:
   - zip -r test_data.zip doc/test_data/
   - aws s3 cp --region eu-west-1 ./test_data.zip "s3://preview.holoviews.org/$TRAVIS_BUILD_NUMBER/test_data_py${TRAVIS_PYTHON_VERSION:0:1}.zip"
   - aws s3 cp --recursive --region eu-west-1 ./doc/test_html "s3://travis.holoviews.org/build_$TRAVIS_BUILD_NUMBER"
-  - curl -s -X GET "https://kq5y73i0xe.execute-api.eu-west-1.amazonaws.com/dev/travis-index"
   - aws s3 rm --recursive --region eu-west-1 s3://preview.holoviews.org/$(($TRAVIS_BUILD_NUMBER - 4))
+  - curl -s -X GET "https://kq5y73i0xe.execute-api.eu-west-1.amazonaws.com/dev/travis-holoviews-index"
+  - curl -s -X GET "https://kq5y73i0xe.execute-api.eu-west-1.amazonaws.com/dev/github-status"
 
 after_success: coveralls
-
-after_deploy:
-  - curl -s -X GET "https://kq5y73i0xe.execute-api.eu-west-1.amazonaws.com/dev/travis-index"
-  - aws s3 rm --recursive --region eu-west-1 s3://preview.holoviews.org/$(($TRAVIS_BUILD_NUMBER - 4))

doc/Tutorials/Bokeh_Backend.ipynb

@@ -530,7 +530,7 @@
    },
    "outputs": [],
    "source": [
-    "macro_df = pd.read_csv('http://ioam.github.com/holoviews/Tutorials/macro.csv', '\\t')"
+    "macro_df = pd.read_csv('http://assets.holoviews.org/macro.csv', '\\t')"
    ]
   },
   {

doc/Tutorials/Columnar_Data.ipynb

@@ -636,7 +636,7 @@
    },
    "outputs": [],
    "source": [
-    "macro_df = pd.read_csv('http://ioam.github.com/holoviews/Tutorials/macro.csv', '\\t')\n",
+    "macro_df = pd.read_csv('http://assets.holoviews.org/macro.csv', '\\t')\n",
     "\n",
     "dimensions = {'unem':    'Unemployment',\n",
     "              'capmob':  'Capital Mobility',\n",

doc/Tutorials/Composing_Data.ipynb

@@ -117,7 +117,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<center><img src=\"https://ioam.github.io/holoviews/Tutorials/nesting-diagram.png\"></center>"
+    "<center><img src=\"https://assets.holoviews.org/nesting-diagram.png\"></center>"
    ]
   },
   {

doc/Tutorials/Exploring_Data.ipynb

@@ -104,7 +104,7 @@
    },
    "outputs": [],
    "source": [
-    "iobuffer = BytesIO(urlopen('http://ioam.github.com/holoviews/Tutorials/hurricane.npz').read())\n",
+    "iobuffer = BytesIO(urlopen('http://assets.holoviews.org/hurricane.npz').read())\n",
     "data = np.load(BytesIO(iobuffer.getvalue()))\n",
     "dates = data['dates']\n",
     "surface_data, nearsrfc_data = data['surface'], data['near_surface']"
@@ -656,7 +656,7 @@
     "main_cols = ['temp', 'humidity', 'pressure']\n",
     "\n",
     "tables = hv.HoloMap(kdims=['City', date_dim])\n",
-    "iobuffer = BytesIO(urlopen('http://ioam.github.com/holoviews/Tutorials/weather.json').read())\n",
+    "iobuffer = BytesIO(urlopen('http://assets.holoviews.org/weather.json').read())\n",
     "weather_json = json.loads(iobuffer.read().decode())\n",
     "for entry in weather_json:\n",
     "    city, date = entry['key']\n",

doc/Tutorials/Pandas_Conversion.ipynb

@@ -8,7 +8,7 @@
     "\n",
     "Combining the high-performance data analysis tools and I/O capabilities that Pandas provides with the interactivity and ease of generating complex visualization in HoloViews makes the two libraries a perfect match.\n",
     "\n",
-    "In this tutorial we will explore how you can easily convert between Pandas dataframes and HoloViews components. The tutorial assumes you are already familiar with some of the core concepts of both libraries, so if you need more background on HoloViews have a look at the [Introduction](http://ioam.github.io/holoviews/Tutorials/Introduction) and [Exploring Data](http://ioam.github.io/holoviews/Tutorials/Exploring_Data) and [Columnar Data](http://ioam.github.io/holoviews/Tutorials/Columnar_Data) tutorials."
+    "In this tutorial we will explore how you can easily convert between Pandas dataframes and HoloViews components. The tutorial assumes you are already familiar with some of the core concepts of both libraries, so if you need more background on HoloViews have a look at the [Introduction](holoviews.org/Tutorials/Introduction) and [Exploring Data](http://holoviews.org/Tutorials/Exploring_Data) and [Columnar Data](http://holoviews.org/Tutorials/Columnar_Data) tutorials."
    ]
   },
   {
@@ -48,7 +48,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The first thing to understand when working with pandas dataframes in HoloViews is how data is indexed. Pandas dataframes are structured as tables with any number of columns and indexes. HoloViews, on the other hand, deals with Dimensions. HoloViews container objects such as [HoloMap](https://ioam.github.io/holoviews/Tutorials/Containers.html#HoloMap), [NdLayout](https://ioam.github.io/holoviews/Tutorials/Containers.html#NdLayout), [GridSpace](https://ioam.github.io/holoviews/Tutorials/Containers.html#GridSpace) and [NdOverlay](https://ioam.github.io/holoviews/Tutorials/Containers.html#NdOverlay) have kdims, which provide metadata about the data along that dimension and how they can be sliced. [Element](https://ioam.github.io/holoviews/Tutorials/Elements.html) objects, on the other hand, have both key dimensions (``kdims``) and value dimensions (``vdims``). The kdims of a HoloViews datastructure represent the position, bin or category along a particular dimension, while the value dimensions usually represent some continuous variable.\n",
+    "The first thing to understand when working with pandas dataframes in HoloViews is how data is indexed. Pandas dataframes are structured as tables with any number of columns and indexes. HoloViews, on the other hand, deals with Dimensions. HoloViews container objects such as [HoloMap](https://ioam.github.io/holoviews/Tutorials/Containers.html#HoloMap), [NdLayout](https://holoviews.org/Tutorials/Containers.html#NdLayout), [GridSpace](https://holoviews.org/Tutorials/Containers.html#GridSpace) and [NdOverlay](https://holoviews.org/Tutorials/Containers.html#NdOverlay) have kdims, which provide metadata about the data along that dimension and how they can be sliced. [Element](https://holoviews.org/Tutorials/Elements.html) objects, on the other hand, have both key dimensions (``kdims``) and value dimensions (``vdims``). The kdims of a HoloViews datastructure represent the position, bin or category along a particular dimension, while the value dimensions usually represent some continuous variable.\n",
     "\n",
     "Let's start by constructing a Pandas dataframe of a few columns and display it as its HTML format (throughout this notebook we will visualize the dataframes using the IPython HTML display function, to allow this notebook to be tested automatically, but in ordinary work you can visualize dataframes directly without this mechanism)."
    ]

doc/Tutorials/index.rst

@@ -5,8 +5,7 @@ HoloViews Tutorials
 The HoloViews tutorials are the best way to learn what HoloViews can
 do and how to use it.  The web site has static copies of each
 tutorial, but it is much more effective to install HoloViews and try
-it out live for yourself, using the notebook files in this `zip
-archive`_.
+it out live for yourself, using the notebook files in this `tutorial notebooks`_.
 
 
 Introductory Tutorials

doc/conf.py

@@ -26,11 +26,11 @@
 # The full version, including alpha/beta/rc tags.
 release = setup_args['version']
 
+ASSETS_URL = 'http://assets.holoviews.org'
 
 rst_epilog = """
-.. _zip archive: notebooks-{version}.zip
-.. _tutorial notebooks: Tutorials/notebooks-{version}.zip
-""".format(version=version)
+.. _tutorial notebooks: {url}/Tutorials/notebooks-{version}.zip
+""".format(url=ASSETS_URL, version=version)
 
 # Override IOAM theme
 import sphinx_rtd_theme