From af56b8ad70df4071318f2cf2259a299df25efd5a Mon Sep 17 00:00:00 2001
From: Andrew Simms <andrew.simms@nrel.gov>
Date: Tue, 31 Dec 2024 16:01:58 -0700
Subject: [PATCH] Style: Remove trailing whitespace

---
 mhkit/wave/resource/environmental_contours.m | 61 ++++++++++----------
 1 file changed, 30 insertions(+), 31 deletions(-)

diff --git a/mhkit/wave/resource/environmental_contours.m b/mhkit/wave/resource/environmental_contours.m
index 209fc1d7..779c3118 100644
--- a/mhkit/wave/resource/environmental_contours.m
+++ b/mhkit/wave/resource/environmental_contours.m
@@ -1,49 +1,49 @@
 function environmental_contour=environmental_contours(x1, x2, dt, period, method, options)
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% Calculates environmental contours of extreme sea 
-% states using the improved joint probability distributions 
-% with the inverse first-order reliability method (IFORM) 
-% probability for the desired return period (period). Given the 
+% Calculates environmental contours of extreme sea
+% states using the improved joint probability distributions
+% with the inverse first-order reliability method (IFORM)
+% probability for the desired return period (period). Given the
 % period of interest a circle of iso-probability is created in the
-% in the PCA joint probability (x1, x2) reference frame.  
-% Using the joint probability value the CDF of the marginal 
-% distribution is used to find the quantile of each component. 
+% in the PCA joint probability (x1, x2) reference frame.
+% Using the joint probability value the CDF of the marginal
+% distribution is used to find the quantile of each component.
 % Finally, using the improved PCA methodology
-% the component 2 contour lines are calculated from component 1 using 
-% the relationships defined in Exkert-Gallup et. al. 2016.	
-% 
-% Eckert-Gallup, A. C., Sallaberry, C. J., Dallman, A. R., & 
-% Neary, V. S. (2016). Application of principal component 
-% analysis (PCA) and improved joint probability distributions to 
-% the inverse first-order reliability method (I-FORM) for predicting 
-% extreme sea states. Ocean Engineering, 112, 307-319. 
-%     
+% the component 2 contour lines are calculated from component 1 using
+% the relationships defined in Exkert-Gallup et. al. 2016.
+%
+% Eckert-Gallup, A. C., Sallaberry, C. J., Dallman, A. R., &
+% Neary, V. S. (2016). Application of principal component
+% analysis (PCA) and improved joint probability distributions to
+% the inverse first-order reliability method (I-FORM) for predicting
+% extreme sea states. Ocean Engineering, 112, 307-319.
+%
 % Parameters
 % ------------
 %     x1 : vector
 %         component 1 data
 %
 %     x2 : vector
-%         component 2 data  
+%         component 2 data
 %
 %     dt : double
 %         x1 and x2 sample rate (seconds)
-%     
+%
 %     period : scalar or vector
 %         Desired return period (years) for calculation of environmental
 %         contour, can be a scalar or a vector.
-%     
+%
 %     PCA: Structure (optional)
-% 	      principal component analysis dictionary from previous function 
-%         call. When supplied the function will skip the PCA calculation 
-%         for the passe x1, and x2. 
+% 	      principal component analysis dictionary from previous function
+%         call. When supplied the function will skip the PCA calculation
+%         for the passe x1, and x2.
 %         to call: environmental_contour(x1,x2,dt,period,"PCA",PCA)
-%     
+%
 %     bin_size : double (optional)
-%         Data points in each bin 
+%         Data points in each bin
 %         to call: environmental_contour(x1,x2,dt,period,"bin_size",bin_size)
-%       
+%
 %     nb_steps : int (optional)
 %         Discretization of the circle in the normal space used for
 %         IFORM calculation.
@@ -52,17 +52,17 @@
 %     return_PCA: boolean
 % 	      Default False, if True will retun the PCA dictionary
 %         to call: environmental_contour(x1,x2,dt,period,"return_PCA",return_PCA)
-%     
-% 
+%
+%
 % Returns
 % ---------
-%     environmental_contour: Structure 
+%     environmental_contour: Structure
 %         Structure with fields contour1, contour2, and optionally PCA
 %
 %
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
-arguments 
+arguments
     x1
     x2
     dt
@@ -83,7 +83,7 @@
 if isscalar(period)
     period_py = period;
 elseif isvector(period)
-    period_py = py.numpy.array(period); 
+    period_py = py.numpy.array(period);
 else
     ME = MException('MATLAB:environmental_contour','period must be a vector or scalar');
     throw(ME);
@@ -106,4 +106,3 @@
     environmental_contour.fit = struct(data_struct.(varfit));
 end
 
-