Restructured images for 2D discrete pipelines (#114)

README.md

@@ -77,13 +77,10 @@ There is also a number of Jupyter notebooks implementing various experiemtal pip
 ## Functionality
 The use-cases currently supported by *PolyPhy* are divided according to the data workflow they are built around. Each use case has a corresponding (extensible) pipeline specified as a command line parameter under its name. Experimental pipelines not yet implemented in the main build are located in **./experiments/Jupyter**. This section reviews them case by case, and the following section provides an extensive tutorial recorded at the recent OSPO Symposium 2022.
 
-- **2D self-patterning** is the most basic use-case implemented within the **2d_discrete** pipeline. The ability of MCPM to generate a diversity of patterns with network characteristics is achieved by *disabling the data marker deposition*, leaving only the MCPM agents to generate the marker responsible for maintaining structure.<p>
-  ![2D_self-patterning](https://user-images.githubusercontent.com/26778894/215976261-d9509124-e3bf-4b82-9cc8-b96a40ab3db2.jpg)
-</p>
-
-- **2D procedural pipeline** provide an easy environment to experiment with the behavior of *PolyPhy* in the presence of discrete data with different spatial frequencies. Editing (adding new data points) is also supported. This is invoked by specifying **2d_discrete** pipeline without providing any input data file, thus prompting *PolyPhy* to generate the data procedurally.<p>
-  ![2D_discrete_procedural](https://user-images.githubusercontent.com/26778894/215980005-f927d227-0090-46dd-8ec6-fde9b800dfa0.jpg)
-</p>
+| **2D Self-Patterning** | **2D Procedural Pipeline** |
+|------------------------|-----------------------------|
+| The most basic use-case implemented within the **2d_discrete** pipeline. The ability of MCPM to generate a diversity of patterns with network characteristics is achieved by *disabling the data marker deposition*, leaving only the MCPM agents to generate the marker responsible for maintaining structure. | Provides an easy environment to experiment with the behavior of *PolyPhy* in the presence of discrete data with different spatial frequencies. Editing (adding new data points) is also supported. This is invoked by specifying **2d_discrete** pipeline without providing any input data file, thus prompting *PolyPhy* to generate the data procedurally. |
+| ![2D_self-patterning](https://user-images.githubusercontent.com/26778894/215976261-d9509124-e3bf-4b82-9cc8-b96a40ab3db2.jpg) | ![2D_discrete_procedural](https://user-images.githubusercontent.com/26778894/215980005-f927d227-0090-46dd-8ec6-fde9b800dfa0.jpg) |
 
 - **2D discrete pipeline** implements the canonical way of working with custom data defined by a CSV file. The example below demonstrates fitting to a 2D projection of the SDSS galaxy dataset. It is invoked by specifying **2d_discrete** pipeline and a custom input data file.<p>
   ![2D_discrete_explicit](https://user-images.githubusercontent.com/26778894/215980486-f77da2ec-8780-4a23-bacc-a03c164ebe2a.jpg)
@@ -121,7 +118,11 @@ The use-cases currently supported by *PolyPhy* are divided according to the data
 Below is a recording of the [PolyPhy Workshop](https://elek.pub/workshop_cross2022.html) given as part of the [OSPO Symposium 2022](https://ospo.ucsc.edu/event/20220927/).<br/>
 This 90-minute workshop covers *PolyPhy*'s research background, all of the 5 above usecases, and extended technical discussion.
 
-[![](http://i3.ytimg.com/vi/3-hm7iTqz0U/hqdefault.jpg)](https://www.youtube.com/watch?v=3-hm7iTqz0U "PolyPhy Workshop")
+<p align="center">
+  <a href="https://www.youtube.com/watch?v=3-hm7iTqz0U" title="PolyPhy Workshop">
+    <img src="http://i3.ytimg.com/vi/3-hm7iTqz0U/hqdefault.jpg" alt="PolyPhy Workshop"/>
+  </a>
+</p>
 
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