RascalSoftware · StephenNneji · Jan 29, 2025 · Jan 16, 2025 · Jan 16, 2025 · Jan 21, 2025
diff --git a/source/examples/DSPC_custom_XY.rst b/source/examples/DSPC_custom_XY.rst
@@ -16,7 +16,7 @@ In this model, we make distributions to represent the volume fractions of each o
 We also make our volume fractions as optional outputted parameters from our file. The optional nature of this output means we can suppress it to run the model, then
 activate it to make final output plots of our analysis.
 
-.. image:: ../images/examples/volumeFractions.jpg
+.. image:: ../images/examples/volumeFractions.png
     :align: center
     :alt: Volume fractions
 
@@ -64,7 +64,7 @@ This example can be run as a script or interactively using the instructions belo
         .. code-block:: Python 
 
             import RATapi as RAT
-            problem, results = RATapi.examples.non_polarised.DSPC_custom_XY()
+            problem, results = RAT.examples.non_polarised.DSPC_custom_XY()
 
         **Run Interactively**:  
 

diff --git a/source/examples/DSPC_custom_layers.rst b/source/examples/DSPC_custom_layers.rst
@@ -58,7 +58,7 @@ This example can be run as a script or interactively using the instructions belo
         .. code-block:: Python 
 
             import RATapi as RAT
-            problem, results = RATapi.examples.non_polarised.DSPC_custom_layers()
+            problem, results = RAT.examples.non_polarised.DSPC_custom_layers()
 
         **Run Interactively**:  
 

diff --git a/source/examples/DSPC_standard_layers.rst b/source/examples/DSPC_standard_layers.rst
@@ -1,3 +1,5 @@
+.. _DSPC_Standard_Layers:
+
 ====================
 DSPC Standard Layers
 ====================
@@ -44,7 +46,7 @@ This example can be run as a script or interactively using the instructions belo
         .. code-block:: Python 
 
             import RATapi as RAT
-            problem, results = RATapi.examples.non_polarised.DSPC_standard_layers()
+            problem, results = RAT.examples.non_polarised.DSPC_standard_layers()
 
         **Run Interactively**:  
 

diff --git a/source/examples/convert_r1_project.rst b/source/examples/convert_r1_project.rst
@@ -43,7 +43,7 @@ This example can be run using the instructions below.
         .. code-block:: Python 
 
             import RATapi as RAT
-            problem, results = RATapi.examples.convert_rascal_project.convert_rascal()
+            problem, results = RAT.examples.convert_rascal_project.convert_rascal()
 
         **Run Interactively**:  
 

diff --git a/source/examples/domains_custom_XY.rst b/source/examples/domains_custom_XY.rst
@@ -51,7 +51,7 @@ This example can be run as a script or interactively using the instructions belo
         .. code-block:: Python 
 
             import RATapi as RAT
-            problem, results = RATapi.examples.domains.domains_custom_XY()
+            problem, results = RAT.examples.domains.domains_custom_XY()
 
         **Run Interactively**:  
 

diff --git a/source/examples/domains_custom_layers.rst b/source/examples/domains_custom_layers.rst
@@ -51,7 +51,7 @@ This example can be run as a script or interactively using the instructions belo
         .. code-block:: Python 
 
             import RATapi as RAT
-            problem, results = RATapi.examples.domains.domains_custom_layers()
+            problem, results = RAT.examples.domains.domains_custom_layers()
 
         **Run Interactively**:  
 

diff --git a/source/examples/domains_standard_layers.rst b/source/examples/domains_standard_layers.rst
@@ -55,7 +55,7 @@ This example can be run as a script or interactively using the instructions belo
         .. code-block:: Python 
 
             import RATapi as RAT
-            problem, results = RATapi.examples.domains.domains_standard_layers()
+            problem, results = RAT.examples.domains.domains_standard_layers()
 
         **Run Interactively**:  
 

diff --git a/source/examples/imaginary.rst b/source/examples/imaginary.rst
@@ -47,7 +47,7 @@ This example can be run using the instructions below.
         .. code-block:: Python 
 
             import RATapi as RAT
-            problem, results = RATapi.examples.absorption.absorption()
+            problem, results = RAT.examples.absorption.absorption()
 
         **Run Interactively**:  
 

diff --git a/source/images/advanced/layersPic.png b/source/images/advanced/layersPic.png
diff --git a/source/images/advanced/resamplePic.png b/source/images/advanced/resamplePic.png
diff --git a/source/images/examples/critEdge.png b/source/images/examples/critEdge.png
diff --git a/source/images/examples/volumeFractions.jpg b/source/images/examples/volumeFractions.jpg
diff --git a/source/images/examples/volumeFractions.png b/source/images/examples/volumeFractions.png
diff --git a/source/images/misc/bayes95.png b/source/images/misc/bayes95.png
diff --git a/source/images/misc/bayesRef1.png b/source/images/misc/bayesRef1.png
diff --git a/source/images/misc/bayesq4.png b/source/images/misc/bayesq4.png
diff --git a/source/images/misc/chainPlot.png b/source/images/misc/chainPlot.png
diff --git a/source/images/misc/cornerPlot.png b/source/images/misc/cornerPlot.png
diff --git a/source/images/misc/histNoSmooth.png b/source/images/misc/histNoSmooth.png
diff --git a/source/images/misc/histSmooth.png b/source/images/misc/histSmooth.png
diff --git a/source/images/misc/r1Converted.png b/source/images/misc/r1Converted.png
diff --git a/source/images/misc/resamPlot.png b/source/images/misc/resamPlot.png
diff --git a/source/images/misc/runDE.png b/source/images/misc/runDE.png
diff --git a/source/images/misc/simPlot1.png b/source/images/misc/simPlot1.png
diff --git a/source/images/misc/simPlot2.png b/source/images/misc/simPlot2.png
diff --git a/...ce/images/userManual/chapter3/bilayer.png → source/images/tutorial/bilayer.png b/...ce/images/userManual/chapter3/bilayer.png → source/images/tutorial/bilayer.png
diff --git a/...userManual/chapter3/customTwoLayerFig.png → source/images/tutorial/customTwoLayerFig.png b/...userManual/chapter3/customTwoLayerFig.png → source/images/tutorial/customTwoLayerFig.png
diff --git a/...es/userManual/chapter1/lipidMonolayer.png → source/images/tutorial/lipidMonolayer.png b/...es/userManual/chapter1/lipidMonolayer.png → source/images/tutorial/lipidMonolayer.png
diff --git a/source/images/tutorial/plotAfterOptimization.png b/source/images/tutorial/plotAfterOptimization.png
diff --git a/source/images/tutorial/plotBeforeOptimization.png b/source/images/tutorial/plotBeforeOptimization.png
diff --git a/...mages/userManual/chapter3/simpleLayer.png → source/images/tutorial/simpleLayer.png b/...mages/userManual/chapter3/simpleLayer.png → source/images/tutorial/simpleLayer.png
diff --git a/...mages/userManual/chapter3/twoLayerRAT.png → source/images/tutorial/twoLayerRAT.png b/...mages/userManual/chapter3/twoLayerRAT.png → source/images/tutorial/twoLayerRAT.png
diff --git a/source/images/userManual/chapter1/plots.png b/source/images/userManual/chapter1/plots.png
diff --git a/source/images/userManual/chapter2/plot1.png b/source/images/userManual/chapter2/plot1.png
diff --git a/source/images/userManual/chapter2/plot2.png b/source/images/userManual/chapter2/plot2.png
diff --git a/source/images/userManual/chapter2/projectClassMethods.png b/source/images/userManual/chapter2/projectClassMethods.png
diff --git a/source/images/userManual/chapter3/copyProject1.png b/source/images/userManual/chapter3/copyProject1.png
diff --git a/source/images/userManual/chapter3/copyProject2.png b/source/images/userManual/chapter3/copyProject2.png
diff --git a/source/images/userManual/chapter3/loadingBack.png b/source/images/userManual/chapter3/loadingBack.png
diff --git a/source/images/userManual/chapter3/ratSimpleLayer.png b/source/images/userManual/chapter3/ratSimpleLayer.png
diff --git a/source/images/userManual/chapter3/referenceChange.png b/source/images/userManual/chapter3/referenceChange.png
diff --git a/source/images/userManual/chapter3/resultsStruct.png b/source/images/userManual/chapter3/resultsStruct.png
diff --git a/source/tutorial/chapter1.rst b/source/tutorial/chapter1.rst
@@ -86,7 +86,7 @@ heads which are adjacent (or embedded) in the bulk aqueous phase.
 
 In our example, the layers can be either deuterated or hydrogenated, and the bulk water can either be D2O or ACMW.
 
-.. image:: ../images/userManual/chapter1/lipidMonolayer.png
+.. image:: ../images/tutorial/lipidMonolayer.png
     :width: 300
     :alt: The lipid monolayer example
 
@@ -364,14 +364,14 @@ We can now plot the output, either manually (by taking the relevant parts from t
 .. tab-set-code::
     .. code-block:: Matlab
 
-        fig(1); clf;
+        figure(1); clf;
         plotRefSLD(problem, results)
 
     .. code-block:: Python
 
         RAT.plotting.plot_ref_sld(problem, results)
 
-.. image:: ../images/userManual/chapter1/plots.png
+.. image:: ../images/tutorial/plotBeforeOptimization.png
     :alt: reflectivity and SLD plots
 
 We can see that our model is looking fairly sensible, but that our guess values for the parameters are pretty wide off the mark. Further analysis

diff --git a/source/tutorial/chapter2.rst b/source/tutorial/chapter2.rst
@@ -1303,7 +1303,7 @@ We then send all of this to RAT, and plot the output:
 
 
 
-.. image:: ../images/userManual/chapter2/plot1.png
+.. image:: ../images/tutorial/plotBeforeOptimization.png
     :alt: Displays reflectivity and SLD plot
 
 To do a fit, we change the *procedure* attribute of the controls class to **simplex**. We will also change the 'parallel' option to 'contrasts', so that each contrast gets its own calculation thread, 
@@ -1315,7 +1315,7 @@ and modify the output to only display the final result (rather than each iterati
         controls.procedure = 'simplex';
         controls.parallel = 'contrasts';
         controls.display = 'final';
-        [problem, results] = RAT(problem, controls)
+        [problem, results] = RAT(problem, controls);
 
     .. code-block:: Python
 
@@ -1383,5 +1383,5 @@ We can now plot the results of our fit:
 
         RAT.plotting.plot_ref_sld(problem, results)
 
-.. image:: ../images/userManual/chapter2/plot2.png
+.. image:: ../images/tutorial/plotAfterOptimization.png
     :alt: Displays reflectivity and SLD plot
diff --git a/source/tutorial/customModels.rst b/source/tutorial/customModels.rst
@@ -55,7 +55,7 @@ In terms of the SLD's, because the volume and composition of each layer is known
 
 The figure shows the system we are measuring. We have a single, hydrogenated DSPC bilayer supported on a silicon surface. The silicon is, as always, coated with an oxide layer. The bilayer may or may not be complete (i.e. partially hydrated) so we will need hydration parameters, and there is the possibility of a thin water layer between the bilayer and the substrate. We also need some roughness parameters, both for the substrate and the bilayer itself. We will build a custom model for this and use it to analyse the bilayer data at three contrasts.
 
-.. image:: ../images/userManual/chapter3/bilayer.png
+.. image:: ../images/tutorial/bilayer.png
     :alt: Bilayer on silicon diagram
 
 Looking at our system, we can see that we are going to need 8 parameters in total:
@@ -765,7 +765,7 @@ The following code snippet we'll make an example of a simple layer....
         plt.axis((0 100 0 1.5))
         plt.show()
 
-.. image:: ../images/userManual/chapter3/simpleLayer.png
+.. image:: ../images/tutorial/simpleLayer.png
     :width: 800
     :alt: simple layer
 
@@ -947,12 +947,12 @@ run it and plot the results
         problem,resuts = RAT.run(problem, controls)
         RAT.plotting.plot_ref_sld(problem, results)
 
-.. image:: ../images/userManual/chapter3/customTwoLayerFig.png
+.. image:: ../images/tutorial/customTwoLayerFig.png
     :width: 500
     :alt: Dtwo layers XY
 
 When sent to RAT, customXY SLD profiles are automatically resampled into layers with adaptive resampling:
 
-.. image:: ../images/userManual/chapter3/twoLayerRAT.png
+.. image:: ../images/tutorial/twoLayerRAT.png
     :width: 800
     :alt: Displays the final customXY result
diff --git a/source/utilities/plotFuns.rst b/source/utilities/plotFuns.rst
@@ -5,7 +5,8 @@ Basic Plotting
 ==============
 
 The simplest plot available is a simple display of the contents of the *problem* and *results* blocks.
-This takes the following form:
+The initial problem and result used in this section were made by running :ref:`DSPC Standard Layers<DSPC_Standard_Layers>`, after 
+running the example a basic plot takes the following form:
 
 .. tab-set-code::
     .. code-block:: Matlab
@@ -18,7 +19,7 @@ This takes the following form:
         RAT.plotting.plot_ref_sld(problem, results)
 
 .. image:: ../images/misc/simPlot1.png
-    :alt: disp(results)
+    :alt: simple plot 
 
 
 This produces a basic plot of the reflectivity and SLD.
@@ -37,7 +38,7 @@ If this plot is not cleared before subsequent plots, then *plotRefSLD* will over
 
     .. code-block:: Python
 
-        controls = RAT.Controls(procedure='DE', display='final', parallel='contrast')
+        controls = RAT.Controls(procedure='DE', display='final', parallel='contrasts')
         problem, results = RAT.run(problem, controls)
         RAT.plotting.plot_ref_sld(problem, controls)
 

diff --git a/source/utilities/plotFunsBayes.rst b/source/utilities/plotFunsBayes.rst
@@ -4,17 +4,38 @@
 Plotting Bayesian Analysis
 ==========================
 
-A number of function exist for plotting the results of Bayesian analysis.
+A number of functions exist for plotting the results of Bayesian analysis. The problem and result used in this section were made using the  
+:ref:`DSPC Standard Layers<DSPC_Standard_Layers>` example and a control object with the procedure set to ``DREAM``.
 
+.. tab-set-code::
+    .. code-block:: Matlab
+
+        % Run the DSPC standard layers example
+        controls = controlsClass();
+        controls.procedure = 'dream';
+        [problem, results] = RAT(problem, controls);
+
+    .. code-block:: Python
+
+        # Run the DSPC standard layer example
+        controls = RAT.Controls()
+        controls.procedure = 'dream'
+        problem, results = RAT.run(problem, controls)
+
+********************
 Reflectivity and SLD
-....................
+********************
 
 A simple reflectivity shaded plot can be displayed as follows:
 
-.. code-block:: MATLAB
+.. tab-set-code::
+    .. code-block:: Matlab
 
-    figure(1); clf;
-    bayesShadedPlot(problem,results)
+        bayesShadedPlot(problem, results)
+
+    .. code-block:: Python
+
+        RAT.plotting.plot_ref_sld(problem, results, bayes=65)
 
 
 .. image:: ../images/misc/bayesRef1.png
@@ -28,9 +49,14 @@ There are a number of options to customise the plot:
 
 **Interval** - You can sepcify either the 65% or 95% confidence interval to display:
 
-.. code-block:: MATLAB
+.. tab-set-code::
+    .. code-block:: Matlab
+
+        bayesShadedPlot(problem, results, 'interval', 95)
+
+    .. code-block:: Python
 
-    bayesShadedPlot(problem,results,'interval',95)
+        RAT.plotting.plot_ref_sld(problem, results, bayes=95)
 
 .. image:: ../images/misc/bayes95.png
     :width: 800
@@ -39,6 +65,15 @@ There are a number of options to customise the plot:
 
 **Type** - You can also specify a q4 plot for the reflectivity:
 
+.. tab-set-code::
+    .. code-block:: Matlab
+
+        bayesShadedPlot(problem, results, 'q4', true)
+
+    .. code-block:: Python
+
+        RAT.plotting.plot_ref_sld(problem, results, bayes=65, q4=True)
+
 .. image:: ../images/misc/bayesq4.png
     :width: 800
     :alt: bayes q4 plot
@@ -50,19 +85,29 @@ Posterior Histograms
 
 You can easily view the marginalised Bayesian posteriors from your analysis:
 
-.. code-block:: MATLAB
+.. tab-set-code::
+    .. code-block:: Matlab
 
-    plotHists(results)
+        plotHists(results)
+
+    .. code-block:: Python
+
+        RAT.plotting.plot_hists(results)
 
 .. image:: ../images/misc/histSmooth.png
     :width: 800
     :alt: smooth hists
 
 By default, *plotHists* carries out a KDE smooth of the histograms. You can optionally choose no smoothing:
 
-.. code-block:: MATLAB
+.. tab-set-code::
+    .. code-block:: Matlab
+
+        plotHists(results,'smooth',false)
+
+    .. code-block:: Python
 
-    plotHists(results,'smooth',false)
+        RAT.plotting.plot_hists(results, smooth=False)
 
 .. image:: ../images/misc/histNoSmooth.png
     :width: 800
@@ -75,9 +120,16 @@ Corner Plots
 
 To produce a cornerplot, simply use the *cornerPlot* function:
 
-.. code-block:: MATLAB
+.. tab-set-code::
+    .. code-block:: Matlab
+
+        cornerPlot(results)
+
+    .. code-block:: Python
+
+        RAT.plotting.plot_corner(results)
 
-    cornerPlot(results)
+
 
 .. image:: ../images/misc/cornerPlot.png
     :width: 800
@@ -89,11 +141,16 @@ Chain View
 
 Finally, you can check the integrity of your markov chain as follows:
 
-.. code-block:: MATLAB
+.. tab-set-code::
+    .. code-block:: Matlab
+
+        plotChain(results);
+
+    .. code-block:: Python
+
+        RAT.plotting.plot_chain(results)
 
-    mcmcplot(results.chain,[],results.fitNames,'chainpanel');
 
 .. image:: ../images/misc/chainPlot.png
     :width: 800
     :alt: chainPlot
-