Updates version to 1.0.0

CITATION.cff

@@ -0,0 +1,40 @@
+abstract: <p><span>The Reflectivity Algorithms Toolbox (RAT) for Rascal is a MATLAB
+  toolbox for analysing non-polarized neutron reflectivity data. The toolbox is designed
+  to fit multiple contrast neutron reflectivity data, using the Abeles formalism with
+  either traditional layer models, or using user-defined custom models.</span></p>
+authors:
+- affiliation: Science and Technology Facilities Council
+  family-names: Hughes
+  given-names: Arwel Vaughan
+  orcid: 0009-0007-1918-8401
+- family-names: Pastula
+  given-names: Sethu Pavan Venkata Reddy
+  orcid: 0000-0001-7962-1827
+- affiliation: Science and Technology Facilities Council
+  family-names: Room
+  given-names: Alex H.
+  orcid: 0000-0002-5314-2331
+- affiliation: Science and Technology Facilities Council
+  family-names: Farooq
+  given-names: Rabiya
+  orcid: 0009-0008-1436-3933
+- affiliation: Science and Technology Facilities Council
+  family-names: Sharp
+  given-names: Paul
+  orcid: 0000-0003-3072-6155
+- affiliation: Science and Technology Facilities Council
+  family-names: Nneji
+  given-names: Stephen
+  orcid: 0000-0001-9369-1346
+cff-version: 1.2.0
+date-released: '2025-04-11'
+doi: 10.5281/zenodo.15193992
+keywords:
+- Reflectometry
+- Neutron
+license:
+- gpl-3.0-or-later
+message: If you use this software, please cite it using the metadata from this file.
+title: Reflectivity Algorithms Toolbox for RasCAL
+type: software
+version: 1.0.0

CONTRIBUTING.md

@@ -10,7 +10,6 @@ The MATLAB IDE is recommended for development, the following toolboxes are requi
 - MATLAB Coder
 - MATLAB Compiler
 - Parallel Computing Toolbox
-- Statistics and Machine Learning Toolbox (for DREAM Minimizer)
 
 After installing the IDE, proceed by creating a fork of the RAT repo, then clone the fork
 

README.md

@@ -1,9 +1,13 @@
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15193992.svg)](https://doi.org/10.5281/zenodo.15193992)
 [![license](https://img.shields.io/github/license/RascalSoftware/RAT.svg)](https://github.com/RascalSoftware/RAT/blob/master/LICENSE)
 [![release](https://img.shields.io/github/release/RascalSoftware/RAT.svg)](https://github.com/RascalSoftware/RAT/releases)
 [![Unit Tests](https://github.com/RascalSoftware/RAT/actions/workflows/runTests.yml/badge.svg)](https://github.com/RascalSoftware/RAT/actions/workflows/runTests.yml)
 
+
 RAT
 ===
-RAT is an acronym for Reflectivity Algorithms Toolbox for Rascal. This is a MATLAB toolbox for analysing non-polarized neutron reflectivity data at multiple contrasts. This is designed to fit multiple contrast neutron reflectivity data, primarily using the Abeles layer model but user-defined model function are also supported.
+The Reflectivity Algorithms Toolbox (RAT) for Rascal is a MATLAB toolbox for analysing non-polarized neutron reflectivity data. The toolbox is designed to fit multiple contrast neutron reflectivity data, using the Abeles formalism with either traditional layer models, or using user-defined custom models.
 
-This is currently still in development. We are working towards a first release of the toolbox. In the meantime, please note that usage may change and that releases are not yet stable.
+How to Use
+----------
+MATLAB is required to RAT, the minimum supported version is **R2023a**. To use RAT, download and extract the appropriate version for you OS from the [release](https://github.com/RascalSoftware/RAT/releases) page. In the extracted folder, run the `addPaths` script in the MATLAB console and you can now run your analysis.

utilities/plotting/plotHists.m

@@ -1,4 +1,4 @@
-function h = plotHists(result, options)   
+function plotHists(result, options)   
     % Plots the Bayes histogram plot from the chain, with or without smoothing.
     % If selected, smoothing is via a moving average algorithm.
     %
@@ -28,7 +28,7 @@
     smooth = options.smooth;
 
     if ~isempty(options.figure)
-      h = figure(options.figure);
+        h = figure(options.figure);
     else
         h = figure();
     end
@@ -53,9 +53,9 @@
             N = smoothdata(N, 'movmean');
         end
 
-        bar(edges2,N,1,'w')
+        bar(edges2,N,1,'w');
 
         set(h,'ytick',[]);
-        title(sprintf('%s',fitNames{i}))
+        title(sprintf('%s',fitNames{i}));
     end
 end

version.txt

@@ -1 +1 @@
-1.0.0-beta
+1.0.0