remove statsplots

Author: TorkelE

docs/Project.toml

@@ -40,7 +40,6 @@ SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 SciMLSensitivity = "1ed8b502-d754-442c-8d5d-10ac956f44a1"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
-StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
 SteadyStateDiffEq = "9672c7b4-1e72-59bd-8a11-6ac3964bc41f"
 StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"
 StructuralIdentifiability = "220ca800-aa68-49bb-acd8-6037fa93a544"
@@ -87,7 +86,6 @@ SciMLBase = "2.46"
 SciMLSensitivity = "7.60"
 SpecialFunctions = "2.4"
 StaticArrays = "1.9"
-StatsPlots = "0.15.7"
 SteadyStateDiffEq = "2.2"
 StochasticDiffEq = "6.65"
 StructuralIdentifiability = "0.5.11"

docs/src/model_simulation/examples/activation_time_distribution_measurement.md

@@ -45,10 +45,9 @@ eprob = EnsembleProblem(sprob; output_func, safetycopy = true)
 esol = solve(eprob, ImplicitEM(); trajectories = 250, callback)
 nothing # hide
 ```
-Finally, we can plot the distribution of activation times. For this, we will use the [StatsPlots.jl](https://docs.juliaplots.org/latest/generated/statsplots/) package's `density` function (essentially a smoothed histogram). The input to `density` is the activation times (which our output function has saved to `esol.u`).
+Finally, we can plot the distribution of activation times. For this, we will use the [`histogram`](@ref https://docs.juliaplots.org/latest/series_types/histogram/) function (with the `normalize = true` argument to create a probability density function). An alternative we also recommend is [StatsPlots.jl](https://docs.juliaplots.org/latest/generated/statsplots/)'s `density` function (which creates a smoothed histogram that is also easier to combine with other plots). The input to `density` is the activation times (which our output function has saved to `esol.u`).
 ```@example activation_time_distribution_measurement
-using StatsPlots
-density(esol.u; label = "Activation time distribution", xlabel = "Activation time")
+histogram(esol.u; normalize = true, label = "Activation time distribution", xlabel = "Activation time")
 ```
 Here we that the activation times take some form of long tail distribution (for non-trivial models like this one, it is generally not possible to identify the activation times as any known statistical distribution).