remove _first_or_nothing and just check if init_params is of the righ…

…t length

src/sample.jl

@@ -312,8 +312,8 @@ function mcmcsample(
     # Create a seed for each chain using the provided random number generator.
     seeds = rand(rng, UInt, nchains)
 
-    # Ensure that initial parameters are `nothing` or indexable
-    _init_params = _first_or_nothing(init_params, nchains)
+    # Ensure that initial parameters are `nothing` or of the correct length
+    check_initial_params(init_params, nchains)
 
     # Set up a chains vector.
     chains = Vector{Any}(undef, nchains)
@@ -364,10 +364,10 @@ function mcmcsample(
                                 _sampler,
                                 N;
                                 progress=false,
-                                init_params=if _init_params === nothing
+                                init_params=if init_params === nothing
                                     nothing
                                 else
-                                    _init_params[chainidx]
+                                    init_params[chainidx]
                                 end,
                                 kwargs...,
                             )
@@ -410,8 +410,8 @@ function mcmcsample(
         @warn "Number of chains ($nchains) is greater than number of samples per chain ($N)"
     end
 
-    # Ensure that initial parameters are `nothing` or indexable
-    _init_params = _first_or_nothing(init_params, nchains)
+    # Ensure that initial parameters are `nothing` or of the correct length
+    check_initial_params(init_params, nchains)
 
     # Create a seed for each chain using the provided random number generator.
     seeds = rand(rng, UInt, nchains)
@@ -469,10 +469,10 @@ function mcmcsample(
                         # Return the new chain.
                         return chain
                     end
-                    chains = if _init_params === nothing
+                    chains = if init_params === nothing
                         Distributed.pmap(sample_chain, pool, seeds)
                     else
-                        Distributed.pmap(sample_chain, pool, seeds, _init_params)
+                        Distributed.pmap(sample_chain, pool, seeds, init_params)
                     end
                 finally
                     # Stop updating the progress bar.
@@ -502,8 +502,8 @@ function mcmcsample(
         @warn "Number of chains ($nchains) is greater than number of samples per chain ($N)"
     end
 
-    # Ensure that initial parameters are `nothing` or indexable
-    _init_params = _first_or_nothing(init_params, nchains)
+    # Ensure that initial parameters are `nothing` or of the correct length
+    check_initial_params(init_params, nchains)
 
     # Create a seed for each chain using the provided random number generator.
     seeds = rand(rng, UInt, nchains)
@@ -525,10 +525,10 @@ function mcmcsample(
         )
     end
 
-    chains = if _init_params === nothing
+    chains = if init_params === nothing
         map(sample_chain, 1:nchains, seeds)
     else
-        map(sample_chain, 1:nchains, seeds, _init_params)
+        map(sample_chain, 1:nchains, seeds, init_params)
     end
 
     # Concatenate the chains together.
@@ -538,31 +538,9 @@ end
 tighten_eltype(x) = x
 tighten_eltype(x::Vector{Any}) = map(identity, x)
 
-"""
-    _first_or_nothing(x, n::Int)
-
-Return the first `n` elements of collection `x`, or `nothing` if `x === nothing`.
-
-If `x !== nothing`, then `x` has to contain at least `n` elements.
-"""
-function _first_or_nothing(x, n::Int)
-    y = _first(x, n)
-    length(y) == n || throw(
+check_initial_params(x::Nothing, n::Int) = nothing
+function check_initial_params(x, n::Int)
+    length(x) == n || throw(
         ArgumentError("not enough initial parameters (expected $n, received $(length(y))"),
     )
-    return y
-end
-_first_or_nothing(::Nothing, ::Int) = nothing
-
-# `first(x, n::Int)` requires Julia 1.6
-function _first(x, n::Int)
-    @static if VERSION >= v"1.6.0-DEV.431"
-        first(x, n)
-    else
-        if x isa AbstractVector
-            @inbounds x[firstindex(x):min(firstindex(x) + n - 1, lastindex(x))]
-        else
-            collect(Iterators.take(x, n))
-        end
-    end
 end