Update and secure references

content/articles/2015/10/distributed-ad-hoc.md

@@ -15,8 +15,8 @@ Distributed
 The `distributed` project prototype provides distributed computing on a cluster
 in pure Python.
 
-*  [docs](http://distributed.readthedocs.org/en/latest/),
-   [source](http://github.com/mrocklin/distributed/),
+*  [docs](https://distributed.dask.org/en/latest/),
+   [source](https://github.com/mrocklin/distributed/),
    [chat](https://gitter.im/mrocklin/distributed)
 
 concurrent.futures interface
@@ -109,8 +109,8 @@ As an example we perform a binary tree reduction on a sequence of random
 arrays.
 
 This is the kind of algorithm you would find hard-coded into a library like
-[Spark](http://spark.apache.org/) or
-[dask.array](http://dask.pydata.org/en/latest/array.html)/[dask.dataframe](http://dask.pydata.org/en/latest/dataframe.html)
+[Spark](https://spark.apache.org/) or
+[dask.array](https://docs.dask.org/en/latest/array.html)/[dask.dataframe](https://docs.dask.org/en/latest/dataframe.html)
 but that we can accomplish by hand with some for loops while still using
 parallel distributed computing.  The difference here is that we're not limited
 to the algorithms chosen for us and can screw around more freely.
@@ -156,15 +156,15 @@ Notes
 -----
 
 Various other Python frameworks provide distributed function evaluation.  A few
-are listed [here](http://distributed.readthedocs.org/en/latest/related-work.html)
+are listed [here](https://distributed.dask.org/en/latest/related-work.html)
 .  Notably we're stepping on the toes of
-[SCOOP](http://scoop.readthedocs.org/en/0.7/), an excellent library that also
+[SCOOP](https://scoop.readthedocs.org/en/0.7/), an excellent library that also
 provides a distributed `concurrent.futures` interface.
 
 The `distributed` project could use a more distinct name.  Any suggestions?
 
 For more information see the following links:
 
-*   [Documentation](http://distributed.readthedocs.org/en/latest/)
-*   [Source on Github](http://github.com/mrocklin/distributed/)
+*   [Documentation](https://distributed.dask.org/en/latest/)
+*   [Source on Github](https://github.com/mrocklin/distributed/)
 *   [Gitter chat](https://gitter.im/mrocklin/distributed)

content/articles/2015/10/distributed-hdfs.md

@@ -63,7 +63,7 @@ We put a dataset on HDFS instance through the command line interface:
 Then we query the namenode to discover how it sharded this file.
 
 To avoid JVM dependence we use Spotify's
-[snakebite](http://snakebite.readthedocs.org/en/latest/) library which
+[snakebite](https://snakebite.readthedocs.org/en/latest/) library which
 includes the protobuf headers necessary to interact with the namenode directly,
 without using the Java HDFS client library.
 
@@ -159,8 +159,8 @@ think about remote hosts that have files on their local file systems.  HDFS has
 played its part and can exit the stage.
 
 *Note: since writing this we've found a
-[wonderful article](http://jvns.ca/blog/2014/05/15/diving-into-hdfs/) by
-[Julia Evans](http://jvns.ca/), that describes a similar process.*
+[wonderful article](https://jvns.ca/blog/2014/05/15/diving-into-hdfs/) by
+[Julia Evans](https://jvns.ca/), that describes a similar process.*
 
 
 Data-local tasks with distributed
@@ -198,7 +198,7 @@ Or alternatively we've wrapped up both steps into a little convenience function:
 ```
 
 As a reminder from
-[last time](http://blaze.pydata.org/blog/2015/10/27/distributed-ad-hoc/) these
+[last time](https://blaze.pydata.org/blog/2015/10/27/distributed-ad-hoc/) these
 operations produce `Future` objects that point to remote results on the worker
 computers.  This does not pull results back to local memory.  We can use these
 futures in future computations with the executor.

content/articles/2016/02/dask-distributed-1.md

@@ -36,7 +36,7 @@ cluster.
 
 We provision nine `m3.2xlarge` nodes on EC2.  These have eight cores and 30GB
 of RAM each.  On this cluster we provision one scheduler and nine workers (see
-[setup docs](http://distributed.readthedocs.org/en/latest/setup.html)).  (More
+[setup docs](https://distributed.dask.org/en/latest/setup.html)).  (More
 on launching in later posts.)  We have five months of data, from 2015-01-01 to
 2015-05-31 on the `githubarchive-data` bucket in S3.  This data is publicly
 avaialble if you want to play with it on EC2.  You can download the full
@@ -105,7 +105,7 @@ records = e.persist(records)
 
 The data lives in S3 in hourly files as gzipped encoded, line delimited JSON.
 The `s3.read_text` and `text.map` functions produce
-[dask.bag](http://dask.pydata.org/en/latest/bag.html) objects which track our
+[dask.bag](https://docs.dask.org/en/latest/bag.html) objects which track our
 operations in a lazily built task graph.  When we ask the executor to `persist`
 this collection we ship those tasks off to the scheduler to run on all of the
 workers in parallel.  The `persist` function gives us back another `dask.bag`
@@ -192,7 +192,7 @@ overhead.
 Investigate Jupyter
 -------------------
 
-We investigate the activities of [Project Jupyter](http://jupyter.org/).  We
+We investigate the activities of [Project Jupyter](https://jupyter.org/).  We
 chose this project because it's sizable and because we understand the players
 involved and so can check our accuracy.  This will require us to filter our
 data to a much smaller subset, then find popular repositories and members.
@@ -489,10 +489,10 @@ done differently with more time.
 Links
 -----
 
-*   [dask](https://dask.pydata.org/en/latest/), the original project
-*   [distributed](https://distributed.readthedocs.org/en/latest/), the
+*   [dask](https://docs.dask.org/en/latest/), the original project
+*   [distributed](https://distributed.dask.org/en/latest/), the
     distributed memory scheduler powering the cluster computing
-*   [dask.bag](http://dask.pydata.org/en/latest/bag.html), the user API we've
+*   [dask.bag](https://docs.dask.org/en/latest/bag.html), the user API we've
     used in this post.
 *   This post largely repeats work by [Blake Griffith](https://github.com/cowlicks) in a
     [similar post](https://www.continuum.io/content/dask-distributed-and-anaconda-cluster)

content/pages/overview.md

@@ -30,17 +30,17 @@ The following characteristics can define a particular Data Processing System:
 The goal of the Blaze ecosystem is to simplify data processing for users by providing:
 
 - A common language to describe data that it's independent of the Data Processing System, called
-[**datashape**](http://blaze.github.io/pages/projects/datashape).
+[**datashape**](https://blaze.pydata.org/pages/projects/datashape).
 - A common interface to query data that it's independent of the Data Processing System, called
-[**blaze**](http://blaze.github.io/pages/projects/blaze).
+[**blaze**](https://blaze.pydata.org/pages/projects/blaze).
 - A common utility library to move data from one format or system to another, called
-[**odo**](http://blaze.github.io/pages/projects/odo).
-- Compressed column stores, called [**bcolz**](http://blaze.github.io/pages/projects/bcolz) and
-[**castra**](http://blaze.github.io/pages/projects/castra).
-- A parallel computational engine, called [**dask**](http://blaze.github.io/pages/projects/dask).
+[**odo**](https://blaze.pydata.org/pages/projects/odo).
+- Compressed column stores, called [**bcolz**](https://blaze.pydata.org/pages/projects/bcolz) and
+[**castra**](https://blaze.pydata.org/pages/projects/castra).
+- A parallel computational engine, called [**dask**](https://blaze.pydata.org/pages/projects/dask).
 
 
 ## Learn more
 
 The project repositories can be found under the [Github Blaze Organization](https://github.com/blaze). Feel free to
-reach out to the Blaze Developers through our mailing list, [email protected].
+reach out to the Blaze Developers through our mailing list, [email protected].

content/pages/projects/blaze.md

@@ -2,4 +2,4 @@ Title: Blaze
 Project: core
 Category: Projects
 Subtitle: An interface to query data on different storage systems
-Docs: http://blaze.readthedocs.org/en/latest/index.html
+Docs: https://blaze.readthedocs.org/en/latest/index.html

content/pages/projects/dask.md

@@ -2,4 +2,4 @@ Title: Dask
 Project: core
 Category: Projects
 Subtitle: Parallel computing through task scheduling and blocked algorithms
-Docs: http://dask.readthedocs.org/en/latest/
+Docs: https://docs.dask.org/en/latest/

content/pages/projects/datashape.md

@@ -2,4 +2,4 @@ Title: Datashape
 Project: core
 Category: Projects
 Subtitle: A data description language
-Docs: http://datashape.readthedocs.org/en/latest/
+Docs: https://datashape.readthedocs.io/en/latest/

content/pages/projects/odo.md

@@ -2,4 +2,4 @@ Title: Odo
 Project: core
 Category: Projects
 Subtitle: Data migration between different storage systems
-Docs: http://odo.readthedocs.org/en/latest/
+Docs: https://odo.readthedocs.io/en/latest/

content/pages/talks/ep2015-blaze.md

@@ -5,5 +5,5 @@ Category: Talks
 Tags: blaze,dask,odo,datashape
 Video: https://www.youtube.com/embed/QKBcnEhkCtk
 Site: https://ep2015.europython.eu/conference/talks/scale-your-data-not-your-process-welcome-to-the-blaze-ecosystem
-Slides: http://chdoig.github.io/ep2015-blaze/
+Slides: https://chdoig.github.io/ep2015-blaze/
 

pelicanconf.py

@@ -4,7 +4,7 @@
 
 AUTHOR = u'Blaze Developers'
 SITENAME = u'The Blaze Ecosystem'
-SITEURL = 'http://blaze.github.io/'
+SITEURL = 'https://blaze.github.io/'
 TITLE = 'The Blaze Ecosystem'
 SUBTITLE = 'Connecting people to data'
 

theme/templates/base.html

@@ -15,8 +15,8 @@
 
   <!-- FONT
   –––––––––––––––––––––––––––––––––––––––––––––––––– -->
-  <link href='//fonts.googleapis.com/css?family=Raleway:100,200,300,400,600' rel='stylesheet' type='text/css'>
-  <link href='http://fonts.googleapis.com/css?family=Droid+Serif:400,700' rel='stylesheet' type='text/css'>
+  <link href='https://fonts.googleapis.com/css?family=Raleway:100,200,300,400,600' rel='stylesheet' type='text/css'>
+  <link href='https://fonts.googleapis.com/css?family=Droid+Serif:400,700' rel='stylesheet' type='text/css'>
 
   <!-- CSS
   –––––––––––––––––––––––––––––––––––––––––––––––––– -->
@@ -29,7 +29,7 @@
   <!-- Scripts
   –––––––––––––––––––––––––––––––––––––––––––––––––– -->
 
-  <script src="//ajax.googleapis.com/ajax/libs/jquery/2.1.1/jquery.min.js"></script>
+  <script src="https://ajax.googleapis.com/ajax/libs/jquery/2.1.1/jquery.min.js"></script>
 
   <!-- Favicon
   –––––––––––––––––––––––––––––––––––––––––––––––––– -->
@@ -72,4 +72,4 @@
   –––––––––––––––––––––––––––––––––––––––––––––––––– -->
 </body>
 
-</html>
+</html>

theme/templates/includes/comments.html

@@ -15,9 +15,9 @@
                 (document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq);
             })();
         </script>
-        <noscript>Please enable JavaScript to view the <a href="http://disqus.com/?ref_noscript">comments powered by
+        <noscript>Please enable JavaScript to view the <a href="https://disqus.com/?ref_noscript">comments powered by
             Disqus.</a></noscript>
-        <a href="http://disqus.com" class="dsq-brlink">comments powered by <span class="logo-disqus">Disqus</span></a>
+        <a href="https://disqus.com" class="dsq-brlink">comments powered by <span class="logo-disqus">Disqus</span></a>
         <div style="margin-bottom:20px"></div>
     </section>
-{% endif %}
+{% endif %}

theme/templates/includes/twitter.html

@@ -1,7 +1,7 @@
 {% if TWITTER_USERNAME %}
 <div style="text-align:right">
-<a href="http://twitter.com/share" class="twitter-share-button" data-count="horizontal" data-via="{{TWITTER_USERNAME}}">Tweet</a>
+<a href="https://twitter.com/share" class="twitter-share-button" data-count="horizontal" data-via="{{TWITTER_USERNAME}}">Tweet</a>
 </div>
-<script type="text/javascript" src="http://platform.twitter.com/widgets.js"></script>
+<script type="text/javascript" src="https://platform.twitter.com/widgets.js"></script>
 
-{% endif %}
+{% endif %}