style: julia formatter and cleanup whitespaces

src/datasets/datasets.jl

@@ -1,8 +1,8 @@
 """
     abstract type AbstractDataset
-    
-Abstract type for use in fitting routines. High level representation of some underlying 
-data structures. 
+
+Abstract type for use in fitting routines. High level representation of some underlying
+data structures.
 
 Fitting data is considered to have an *objective* and a *domain*. As
 the domain may be, for example, energy bins (high and low), or
@@ -87,7 +87,7 @@ make_model_domain(layout::AbstractLayout, dataset::AbstractDataset) =
     error("Layout $(layout) is not implemented for $(typeof(dataset))")
 
 """
-    make_domain_variance(layout::AbstractLayout, dataset::AbstractDataset) 
+    make_domain_variance(layout::AbstractLayout, dataset::AbstractDataset)
 
 Make the variance vector associated with the domain.
 """
@@ -99,7 +99,7 @@ make_domain_variance(layout::AbstractLayout, dataset::AbstractDataset) =
 
 Returns the array used as the output domain. That is, in cases where the model
 input and output map to different domains, the input domain is said to be the
-model domain, the input domain is said to be the model domain. 
+model domain, the input domain is said to be the model domain.
 
 The distinction is mainly used for the purposes of simulating data and for
 visualising data.

src/datasets/response.jl

@@ -1,4 +1,4 @@
-# TODO: could be Response or Redistribution : how do we track this? 
+# TODO: could be Response or Redistribution : how do we track this?
 mutable struct ResponseMatrix{T}
     matrix::SparseMatrixCSC{T,Int}
     channels::Vector{Int}

src/fitting/problem.jl

@@ -88,7 +88,7 @@ end
 
 `items` is a tuple or vector of lengths `n1, n2, ...`
 
-Returns a tuple or array with same length as items, which gives the index boundaries of 
+Returns a tuple or array with same length as items, which gives the index boundaries of
 an array with size `n1 + n2 + ...`.
 """
 function _accumulated_indices(items)

src/fitting/result.jl

@@ -13,7 +13,7 @@ function _pretty_print_result(model, u, σ, chi2)
       Model: $(model)
       . u     : [$(ppu)]
       . σᵤ    : [$(ppσ)]
-      . χ²    : $(ppx2) 
+      . χ²    : $(ppx2)
     """
 end
 

src/utils.jl

@@ -102,4 +102,4 @@ function count_error(k, σ)
     p = Distributions.cdf(Distributions.Normal(), σ)
     kₑ = gamma_inc_inv(k + 1, p, 1 - p)
     abs(k - kₑ)
-end
+end