remove use of eachindex

exercises/solved_notebooks/P4_probmod/probmod_2-basics.jl

@@ -167,7 +167,7 @@ md"### 2: Dirtprism"
 # ╔═╡ 6b009ba3-83a8-4176-86d4-dd9f70ed29ec
 @model function dirtprism()
     rolls = zeros(20) # also possible to write out all 20 rolls by hand
-	for i in eachindex(rolls)
+	for i in 1:length(rolls)
 		rolls[i] ~ DiscreteUniform(1, 4)
 	end
     dicesum = sum(rolls)
@@ -292,7 +292,7 @@ count_occurences([5, 107, 364, 5, 5, 364]) # three 5's, one 107 and two 364's
 	# number of students used as an input to the function so you can solve the question for any number of students - this was not asked but it's nice
 
 	bdays = zeros(n_students)
-	for bday_idx in eachindex(bdays)
+	for bday_idx in 1:length(bdays)
 		bdays[bday_idx] ~ DiscreteUniform(1, 365)
 	end
     occurences = count_occurences(bdays)

exercises/solved_notebooks/P5_mcmc/MCMC_1-intro.jl

@@ -328,7 +328,7 @@ As prior knowledge we can use the fact that it must have evolved _after_ the ray
 	α ~ prior_alpha # prior distribution of parameter
 
 	N = zeros(length(ts)) # output variable: in this model we have multiple values, so we need to preallocate a vector
-	for i in eachindex(ts)
+	for i in 1:length(ts)
 		N_average = α * ts[i]
 		N[i] ~ Poisson(N_average)
 	end
@@ -369,7 +369,7 @@ let
 		α ~ Exponential(2) # prior distribution of parameter
 
 		N = zeros(length(ts)) # output variable: in this model we have multiple values, so we need to preallocate a vector
-		for i in eachindex(ts)
+		for i in 1:length(ts)
 			N_average = α * ts[i]
 			N[i] ~ Poisson(N_average)
 		end

exercises/solved_notebooks/P5_mcmc/MCMC_4-review.jl

@@ -63,7 +63,7 @@ md"""
     y_nest ~ Uniform(0, 1000)
     v_wasp ~ Gamma(8) # or something similar
 
-    for i in eachindex(ts)
+    for i in 1:length(ts)
 		dist = sqrt((xs[i] - x_nest)^2 + (ys[i] - y_nest)^2)
         ts[i] ~ Normal(2*dist / v_wasp, 10)
     end

exercises/solved_notebooks/P8_modselect/model_selection_friction_sol.jl

@@ -364,7 +364,7 @@ function posterior(AICs) 	# AICs vector of AIC values
 	AICmin = minimum(AICs)
 	posterior = zeros(length(AICs))
 
-	for i in eachindex(1:length(AICs))
+	for i in 1:length(AICs)
 		posterior[i] = exp((AICmin-AICs[i])/2)
 	end
 

exercises/solved_notebooks/P8_modselect/model_selection_intro.jl

@@ -446,7 +446,7 @@ function posterior(AICs) 	# AICs vector of AIC values
 	AICmin = minimum(AICs)
 	posterior = zeros(length(AICs))
 
-	for i in eachindex(1:length(AICs))
+	for i in 1:length(AICs)
 		posterior[i] = exp((AICmin-AICs[i])/2)
 	end
 

exercises/student_notebooks/P5_mcmc/MCMC_1-intro.jl

@@ -328,7 +328,7 @@ As prior knowledge we can use the fact that it must have evolved _after_ the ray
 	α ~ prior_alpha # prior distribution of parameter
 
 	N = zeros(length(ts)) # output variable: in this model we have multiple values, so we need to preallocate a vector
-	for i in eachindex(ts)
+	for i in 1:length(ts)
 		N_average = α * ts[i]
 		N[i] ~ Poisson(N_average)
 	end
@@ -369,7 +369,7 @@ let
 		α ~ Exponential(2) # prior distribution of parameter
 
 		N = zeros(length(ts)) # output variable: in this model we have multiple values, so we need to preallocate a vector
-		for i in eachindex(ts)
+		for i in 1:length(ts)
 			N_average = α * ts[i]
 			N[i] ~ Poisson(N_average)
 		end

exercises/student_notebooks/P8_modselect/model_selection_friction_sol.jl

@@ -358,7 +358,7 @@ function posterior(AICs) 	# AICs vector of AIC values
 	AICmin = minimum(AICs)
 	posterior = zeros(length(AICs))
 
-	for i in eachindex(1:length(AICs))
+	for i in 1:length(AICs)
 		posterior[i] = exp((AICmin-AICs[i])/2)
 	end
 

exercises/student_notebooks/P8_modselect/model_selection_intro.jl

@@ -440,7 +440,7 @@ function posterior(AICs) 	# AICs vector of AIC values
 	AICmin = minimum(AICs)
 	posterior = zeros(length(AICs))
 
-	for i in eachindex(1:length(AICs))
+	for i in 1:length(AICs)
 		posterior[i] = exp((AICmin-AICs[i])/2)
 	end