From 2f9db684af12abb78512d6a746396492115f6197 Mon Sep 17 00:00:00 2001
From: hippyhippohops <akravi923@gmail.com>
Date: Sun, 5 May 2024 23:39:26 -0500
Subject: [PATCH] Formatted Code

---
 src/dae_solve.jl    | 14 +++++++-------
 test/NNDAE_tests.jl |  6 ++----
 2 files changed, 9 insertions(+), 11 deletions(-)

diff --git a/src/dae_solve.jl b/src/dae_solve.jl
index df18ad08d9..5c2936c4d3 100644
--- a/src/dae_solve.jl
+++ b/src/dae_solve.jl
@@ -79,19 +79,20 @@ function generate_loss(strategy::GridTraining, phi, f, autodiff::Bool, tspan, p,
     return loss
 end
 
-function generate_loss(strategy::WeightedIntervalTraining, phi, f, autodiff::Bool, tspan, p,
-    differential_vars::AbstractVector)
+function generate_loss(
+        strategy::WeightedIntervalTraining, phi, f, autodiff::Bool, tspan, p,
+        differential_vars::AbstractVector)
     autodiff && throw(ArgumentError("autodiff not supported for GridTraining."))
     minT = tspan[1]
     maxT = tspan[2]
-    
+
     weights = strategy.weights ./ sum(strategy.weights)
 
     N = length(weights)
     points = strategy.points
 
-    difference = (maxT-minT)/N
-    
+    difference = (maxT - minT) / N
+
     data = Float64[]
     for (index, item) in enumerate(weights)
         temp_data = rand(1, trunc(Int, points * item)) .* difference .+ minT .+
@@ -102,12 +103,11 @@ function generate_loss(strategy::WeightedIntervalTraining, phi, f, autodiff::Boo
     ts = data
 
     function loss(θ, _)
-        sum(inner_loss(phi, f, autodiff, ts, θ, p,  differential_vars))
+        sum(inner_loss(phi, f, autodiff, ts, θ, p, differential_vars))
     end
     return loss
 end
 
-
 function SciMLBase.__solve(prob::SciMLBase.AbstractDAEProblem,
         alg::NNDAE,
         args...;
diff --git a/test/NNDAE_tests.jl b/test/NNDAE_tests.jl
index e5930ec063..1f9a31438d 100644
--- a/test/NNDAE_tests.jl
+++ b/test/NNDAE_tests.jl
@@ -85,7 +85,8 @@ end
     opt = OptimizationOptimisers.Adam(0.1)
     weights = [0.7, 0.2, 0.1]
     points = 200
-    alg = NeuralPDE.NNDAE(chain, OptimizationOptimisers.Adam(0.1), strategy = NeuralPDE.WeightedIntervalTraining(weights, points); autodiff = false)
+    alg = NeuralPDE.NNDAE(chain, OptimizationOptimisers.Adam(0.1),
+        strategy = NeuralPDE.WeightedIntervalTraining(weights, points); autodiff = false)
 
     sol = solve(prob,
         alg, verbose = false, dt = 1 / 100.0f0,
@@ -93,6 +94,3 @@ end
 
     @test ground_sol(0:(1 / 100):(pi / 2))≈sol atol=0.4
 end
-
-
-