Minor comments and refactoring

Author: Abhinab Bhattacharjee

.gitignore

@@ -4,3 +4,4 @@
 *.fig
 *.pdf
 *.mat
+*.html

docs/CONTRIBUTING.md

@@ -0,0 +1,17 @@
+# Contributing to DATools
+
+General Guidelines for adding new filters/algorithms to DATools
+
+## Submitting new pull requests
+
+To request a pull request, please follow the steps below
+
+* Create your own branch if you are starting new or modify the existing one
+* Commit your changes to local branch and push to Github
+* Create a draft pull request
+* Do not merge or modify the master  branch
+
+## Style Conventions
+
+In order to prevent confusions and maintain a consistent coding style, the following styles should be strictly followed. The conventions match most of the MATLAB's own conventions. Failing to follow the conventions can delay in processing and approving new pull requests.
+

src/+datools/+error/Gaussian.m

@@ -22,7 +22,7 @@
         end
 
         function xp = adderr(obj, ~, x)
-            A = obj.CovarianceSqrt * randn(size(x, 1), size(x, 2));
+            A = obj.CovarianceSqrt * randn(size(x), 'like', x);
             xp = x + A + obj.Bias;
         end
 

src/+datools/+error/Laplace.m

@@ -20,7 +20,7 @@
         end
 
         function xp = adderr(obj, ~, x)
-            X = obj.CovarianceSqrt * randn(size(x, 1), size(x, 2));
+            X = obj.CovarianceSqrt * randn(size(x), 'like', x);
             Z = exprnd(1, 1, size(x, 2));
             Y = sqrt(Z) .* X;
             xp = x + Y + obj.Bias;

src/+datools/+observation/+operator/Indexed.m renamed to src/+datools/+observation/+operator/IndexedObservation.m

@@ -1,4 +1,4 @@
-classdef Indexed < datools.observation.operator.Observation
+classdef IndexedObservation < datools.observation.operator.Observation
 %INDEXED Defines the indexed observation operator
 
     properties
@@ -7,7 +7,7 @@
 
     methods
 
-        function obj = Indexed(nvars, varargin)
+        function obj = IndexedObservation(nvars, varargin)
             p = inputParser;
             p.KeepUnmatched = true;
             addParameter(p, 'Indices', 1);

src/+datools/+observation/+operator/Linear.m renamed to src/+datools/+observation/+operator/LinearObservation.m

@@ -1,4 +1,4 @@
-classdef Linear < datools.observation.operator.Observation
+classdef LinearObservation < datools.observation.operator.Observation
 %LINEAR Defines the linear observation operator
 %   H is already linearized
 
@@ -8,7 +8,7 @@
 
     methods
 
-        function obj = Linear(nvars, varargin)
+        function obj = LinearObservation(nvars, varargin)
             p = inputParser;
             p.KeepUnmatched = true;
             addParameter(p, 'H', speye(nvars));

src/+datools/+observation/+operator/Nonlinear.m renamed to src/+datools/+observation/+operator/NonlinearObservation.m

@@ -1,4 +1,4 @@
-classdef Nonlinear < datools.observation.operator.Observation
+classdef NonlinearObservation < datools.observation.operator.Observation
 %NONLINEAR Defines the non linear observation operator
 
     properties
@@ -9,7 +9,7 @@
 
     methods
 
-        function obj = Nonlinear(nvars, varargin)
+        function obj = NonlinearObservation(nvars, varargin)
             p = inputParser;
             p.KeepUnmatched = true;
             addParameter(p, 'F', @(~, x) x);

src/+datools/+observation/StateObservation.m

@@ -3,10 +3,10 @@
     %   Observed state are noisy snapshots of reality (truth)
 
     properties
-        Y         % Current Observation of States (maybe sparse)
-        R         % Observation Error Covariance
-        Noise     % Noise
-        NumObs    % Number of Observations(Make sure > 0)
+        Y             % Current Observation of States (maybe sparse)
+        Covariance    % Observation Error Covariance
+        Noise         % Noise
+        NumObs        % Number of Observations(Make sure > 0)
     end
 
     methods
@@ -30,11 +30,11 @@
             unMatched = p.Unmatched;
 
             p = inputParser;
-            addParameter(p, 'R', speye(obj.NumObs));
+            addParameter(p, 'Covariance', speye(obj.NumObs));
 
             parse(p, unMatched);
             s = p.Results;
-            obj.R = s.R;
+            obj.Covariance = s.Covariance;
         end
 
         function updateY(obj, y)

src/+datools/+statistical/+ensemble/DEnKF.m

@@ -2,14 +2,16 @@
 
     methods
 
-        function analysis(obj, R, y)
+        function analysis(obj)
 
             inflation = obj.Inflation;
 
             tc = obj.Model.TimeSpan(1);
 
             xf = obj.Ensemble;
             ensN = obj.NumEnsemble;
+            
+            R = obj.Observation.Covariance;
 
             xfm = mean(xf, 2);
 
@@ -43,7 +45,7 @@ function analysis(obj, R, y)
 
             Aa = Af - 0.5 * (PfHt * (dS \ HAf));
 
-            d = y - Hxfm;
+            d = obj.Observation.Y - Hxfm;
             xam = xfm + (PfHt * (dS \ d));
 
             obj.Ensemble = repmat(xam, 1, ensN) + Aa;

src/+datools/+statistical/+ensemble/ETKF.m

@@ -3,15 +3,20 @@
     methods
 
         function analysis(obj)
-
+            %ANALYSIS   Method to overload the analysis function
+            %
+            %   ANALYSIS(OBJ) assimilates the current observation with the
+            %   background/prior information to get a better estimate
+            %   (analysis/posterior)
+            
             inflation = obj.Inflation;
 
             tc = obj.Model.TimeSpan(1);
 
             xf = obj.Ensemble;
             ensN = obj.NumEnsemble;
 
-            R = obj.Observation.R;
+            R = obj.Observation.Covariance;
 
             xfm = mean(xf, 2);
             Af = xf - repmat(xfm, 1, ensN);