Added images to docs.

Author: NJ-Thomson

docs/create_docs.sh

@@ -169,6 +169,10 @@ relatively rigid with sites that are spatially static, for example internal
 water cavities in membrane proteins. Here we demonstrate the preprocessing for 
 water density, however the same procedure would be used for ions.   
 
+.. image:: images/Density.png
+   :height: 300px
+   :align: center
+   :alt: Density of protein
 
 Files and Directories
 ---------------------
@@ -236,7 +240,7 @@ This helps us avoid memory errors with large python arrays.
         out_name_water_a+".gro", out_name_water_a+"_aligned.xtc",
         out_name_water_b+".gro", out_name_water_b+".xtc",
         atomgroup="OH2", write_grid_as="TIP3P",
-        out_name="traj/water_grid_ab_",
+        out_name="ab_grid_",
         use_memmap=True, memmap='traj/combined.mymemmap'
     )
                           

docs/images/Density.png

@@ -9,6 +9,12 @@ Note that all reader functions load the names of the features and
 their values separately.
 
 
+.. image:: images/Torsions.jpg
+   :height: 300px
+   :align: center
+   :alt: Torsion angles.
+
+
 Basic Example
 *************
 
@@ -133,14 +139,20 @@ molecules occupy that are of interest. Water pocket featurization extracts
 a distribution that represents whether or not a specific protein cavity is occupied 
 by a water molecule, and what that water molecule's orientation (polarisation) is. 
 
+
+.. image:: images/WaterFeatures.jpg
+   :width: 300px
+   :align: center
+   :alt: Water features derived from density. 
+
 .. code:: python
 
     from pensa.features import read_water_features
 
 For the pdb visualisation, the trajectory needs to be fit to the first frame of the simulation
 so that the density and protein align with each other.
 
-Here we featurize the top 3 most probable water sites (top_waters = 3).
+Here we featurize the top 2 most probable water sites (top_waters = 2).
 Orientation of the waters (water_data - spherical coordinates [radians]) is a 
 timeseries distribution. When water is not present at the site, the orientation 
 is recorded as 10000.0 to represent an empty state. If write=True, we can 
@@ -161,7 +173,7 @@ water
     water_feat, water_data = read_water_features(
         structure_input = struc, 
         xtc_input = xtc,
-        top_waters = 1,
+        top_waters = 2,
         atomgroup = "OH2",
         write = True,
         write_grid_as="TIP3P",
@@ -176,11 +188,11 @@ This way, sites are the same across both ensembles and can be compared.
 
     struc = "traj/condition-a_water.gro"
     xtc = "traj/condition-a_water_aligned.xtc"
-    grid = "traj/water_grid_ab_OH2_density.dx"
+    grid = "ab_grid_OH2_density.dx"
     water_feat, water_data = read_water_features(
         structure_input = struc,
         xtc_input = xtc,
-        top_waters = 5,
+        top_waters = 2,
         atomgroup = "OH2",
         grid_input = grid
     )
@@ -206,10 +218,10 @@ written (write=True) using the default density conversion "Angstrom^{-3}" in MDA
     atom_feat, atom_data = read_atom_features(
         structure_input = struc,
         xtc_input = xtc,
-        top_atoms = 1,
+        top_atoms = 2,
         atomgroup = "SOD",
         element = "Na",
         write = True,
         out_name = "features/11426_dyn_151_sodium"
     )
-                                            
+                                            

docs/images/JSD_pdb.png

@@ -87,6 +87,19 @@ distance per residue in the "B factor" field of a PDB file.
         y_label='max. JS dist. of BB torsions'
     )
 
+
+.. image:: images/JSD_pdb.png
+   :height: 300px
+   :align: center
+   :alt: JSD pbd b-factor visualisaton file.
+
+
+.. image:: images/sc-jsd.png
+   :height: 300px
+   :align: center
+   :alt: Jensen-Shannon Distance PDF output.
+
+
 Let's now save the resulting data in CSV files.
 
 .. code:: python
@@ -159,6 +172,18 @@ We can plot the results in the same way as we did for the backbone analysis.
     )   
 
 
+.. image:: images/SSI_pdb.png
+   :height: 300px
+   :align: center
+   :alt: SSI pbd b-factor visualisaton file.
+
+.. image:: images/sc-ssi.png
+   :height: 300px
+   :align: center
+   :alt: State-Specific Information PDF output.
+
+
+
 Comparing Distances
 -------------------
 
@@ -194,3 +219,11 @@ divergence, Kolmogorov-Smirnov statistic etc. instead).
         names_bbdist, jsd_bbdist, "plots/receptor_jsd-bbdist.pdf",
         vmin = 0.0, vmax = 1.0, cbar_label='JSD'
     )
+
+
+
+.. image:: images/bb-dists.png
+   :height: 300px
+   :align: center
+   :alt: JSD distances pbf plot.
+

docs/images/SSI_pdb.png

@@ -199,6 +199,14 @@ We now perform the actual clustering on the combined data.
     )
     cidx, cond, oidx, wss, centroids = cc
 
+
+
+
+.. image:: images/bb-clusts.png
+   :height: 300px
+   :align: center
+   :alt: BB torsion cluster pbf plot.
+
 ... and save the results to a CSV file.
 
 .. code:: python