Fixes #gh_82 updates for figure shortcodes and alignment

Author: shaloo

content/en/case-studies/blackhole-image.md

@@ -1,19 +1,14 @@
 ---
-title: "Case Study: The First Image of a Black Hole, ever!"
+title: "Case Study: The First Image of a Black Hole"
 sidebar: false
 ---
 
-<img
-  src="/images/content_images/cs/blackhole.jpg"
-  alt="bh"
-  title="Black Hole M87">
+{{< figure src="/images/content_images/cs/blackhole.jpg" caption="**Black Hole M87**" alt="bhimg" attr="(**Image Credits:** Event Horizon Telescope Collaboration)" attrlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" >}}
 
-```
-Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see!
-
-
-                                                                    - Katie Bouman
-```
+<blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs">
+    <p>Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see.</p>
+    <footer align="right">—Katie Bouman, <cite>Assistant Professor, Computing & Mathematical Sciences, Caltech</cite></footer>
+</blockquote>
 
 ## About Event Horizon Telescope
 
@@ -41,41 +36,27 @@ The [Event Horizon telescope (EHT)](https://eventhorizontelescope.org), is an ar
 
     The sequence of correlation and engineering releases represents a year-long effort of identifying and mitigating data issues, and developing new software and procedures that could reliably choose the most likely image based on actual measurements.
 
-<img
-  src="/images/content_images/cs/dataprocessbh.png"
-  alt="dpipe"
-  title="Data Processing Pipeline">
-
-Figure: Data processing pathway of an EHT observation from recording to image (or other physical parameters) estimation (Source: [IOPScience Paper](https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57))
+{{< figure src="/images/content_images/cs/dataprocessbh.png" class="fig-center" caption="**EHT Data Processing Pipeline**" alt="dpipe" align="middle" attr="(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)" attrlink="https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57" >}}
 
 ## NumPy’s Role in Black Hole Imaging
 
 There are several aspects to black hole imaging besides data collection, noise elimination, data cleanup, reduction and correlation. Imaging is crucial as it can help  to predict not only the black hole mass but also rule out whether a black hole could be a wormhole, a theoretical bridge between distant points in spacetime. But it is also incredibly hard to measure given the astronomical distances involved. As Katie Bouman mentions in her [TED talk](https://www.youtube.com/watch?v=BIvezCVcsYs), ‘It is like taking a picture of an orange on the surface of the moon.’
 
-<img
-  src="/images/content_images/cs/bh_numpy_role.png"
-  alt="nrole"
-  title="NumPy role in bh imaging">
+{{< figure src="/images/content_images/cs/bh_numpy_role.png" class="fig-center" alt="numpyr" caption="**The role of NumPy in Black Hole imaging**" >}}
 
 Once the key challenges posed by EHT, data collection and reduction are taken care of, the next big challenge in data processing is related to imaging. The imaging algorithms form the core of this task as through imaging, scientists could calculate the shadow of the black hole which forms the crux of several other calculations related to event horizon and nearby objects. One of the key algorithms used in imaging was developed by Katie Bouman – Continuous High-resolution Image Reconstruction using Patch priors, or ‘CHIRP’. It can parse the cumulative telescope data gathered by the Event Horizon Telescope project.For imaging tasks, researchers banked on Python to run the datasets on these algorithms, arraying and plotting data for meaningful insights.  
 
 Besides NumPy, there were other packages such as [SciPy](https://www.scipy.org), [Pandas](https://pandas.io) and [Matplotlib](https://matplotlib.org) that helped in imaging and [data processing for imaging the black hole](https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57). The standard astronomical file formats and time/coordinate transformations were handled by [Astropy](https://www.astropy.org) while Matplotlib was used in visualizing data throughout the analysis pipeline, including the generation of the final image of the black hole.
 
-One of the researchers of this project, Andrew Chael, came up with [eht-imaging](https://github.com/achael/eht-imaging) Python modules for simulating and manipulating VLBI data and producing images with regularized maximum likelihood methods. NumPy is at the core of array data processing used in this software package named ehtim as indicated by the partial software dependency chart below.
+Andrew Chael and the ehtim project team came up with [eht-imaging](https://github.com/achael/eht-imaging) Python modules for simulating and manipulating VLBI data and producing images with regularized maximum likelihood methods. NumPy is at the core of array data processing used in this software package named ehtim as indicated by the partial software dependency chart below.
 
-<img
-  src="/images/content_images/cs/ehtim_numpy.png"
-  alt="ehtim"
-  title="NumPy is at the core of ehtim-imaging package">
+{{< figure src="/images/content_images/cs/ehtim_numpy.png" class="fig-center" alt="ehtimnumpy" caption="**Software dependency chart of ehtim package highlighting NumPy**" >}}
 
-The challenge posed during reconstruction of an image using VLBI measurements is that there can be an infinite number of possible images that explain the data.  Andrew’s ehtim software addresses this challenge by implementing algorithms that help find a set of most likely reasonable images that respects prior scientific assumptions while still satisfying the observed data.
+The challenge posed during reconstruction of an image using VLBI measurements is that there can be an infinite number of possible images that explain the data.  The ehtim software addresses this challenge by implementing algorithms that help find a set of most likely reasonable images that respects prior scientific assumptions while still satisfying the observed data.
 
 
 ## Summary
 
 NumPy enabled researchers to manipulate large numerical datasets through its efficient data structures of vectors and matrices leading to imaging and plotting of the first ever image of a black hole. Imaging of M87 black hole is a major scientific feat that was almost presumed impossible a century ago.  In a way, black hole image has helped in a stunning confirmation of Einstein’s general theory of relativity. This is not only a breakthrough in technology, but an example of international scale collaboration that uses connections between the world's best radio observatories, over 200 scientists worked with observations collected over 10 days and analyzed for over a year. They used innovative algorithms and data processing techniques improving upon existing astronomical models to help unfold some of the mysteries of the universe.
 
-<img
-  src="/images/content_images/cs/numpy_benefits.png"
-  alt="nben"
-  title="NumPy Benefits">
+{{< figure src="/images/content_images/cs/numpy_benefits.png" class="fig-center" alt="benefits" caption="**Key NumPy Capabilities utilized**" >}}

static/css/styles.css

@@ -250,6 +250,35 @@ p {
     font-size: 12px;
 }
 
+figcaption {
+  text-align: center;
+}
+
+.fig-center {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+  width: 80%;
+}
+
+blockquote {
+    margin: 10px;
+}
+
+blockquote p {
+    padding: 15px;
+    background: #eee;
+    border-radius: 5px;
+}
+
+blockquote p::before {
+    content: '\201C';
+}
+
+blockquote p::after {
+    content: '\201D';
+}
+
 @media only screen and (max-width: 1150px) {
     .sign-up-input {
         font-size: 12px;