Update examples.jl

docs/src/examples.jl

@@ -99,3 +99,67 @@ ax.set_xlabel("Time")
 ax.set_ylabel("Stress")
 ax.grid("on")
 #!nb fig #hide
+
+# ## Example 4
+
+# This example demonstrates generating a timeline and stress data, fitting multiple models to the data,
+# calling the `extractfitdata` function, listing the errors, and determining which model fits the best.
+
+using RHEOS
+
+# Generate Timeline
+datat = timeline(t_start = 0, t_end = 20.0, step = 0.02)  # Create a timeline from 0 to 20 seconds with a step size of 0.02 seconds
+
+# Generate Stress Data (Ramp & hold)
+dramp_stress = stressfunction(datat, ramp(offset = 4.0, gradient = 0.8))  # Generate a ramp stress function with offset 4.0 and gradient 0.8
+dhold_stress = dramp_stress - stressfunction(datat, ramp(offset = 5.0, gradient = 0.8))  # Generate a hold stress function by subtracting a shifted ramp
+
+# Define the rheological model and predict
+model = RheoModel(SLS_Zener, (η = 1, kᵦ = 1, kᵧ = 1))
+SLS_predict = modelpredict(dhold_stress, model)
+data = SLS_predict
+
+# Fit three models to the data
+SLS_Zener_model = modelfit(data, SLS_Zener, strain_imposed)
+Maxwell_model = modelfit(data, Maxwell, strain_imposed)
+BurgersLiquid_model = modelfit(data, BurgersLiquid, strain_imposed)
+
+# Call the extractfitdata function to extract fitting data
+extracted_data = extractfitdata(data)
+
+# Determine which model fits best by comparing errors
+best_model = ""
+min_error = Inf
+
+for (model_name, entries) in extracted_data
+    total_error = sum(entry.info.error for entry in entries)
+
+    println("Model: $model_name, Total Error: $total_error")
+
+    if total_error < min_error
+        min_error = total_error
+        best_model = model_name
+    end
+end
+
+println("Best fitting model: $best_model with total error: $min_error")
+
+# Create strain-only data for model predictions
+strain_only_data = onlystrain(data)
+
+# Get model predictions for plotting
+SLS_Zener_predict = modelpredict(strain_only_data, SLS_Zener_model)
+Maxwell_predict = modelpredict(strain_only_data, Maxwell_model)
+BurgersLiquid_predict = modelpredict(strain_only_data, BurgersLiquid_model)
+
+# Plot data and fitted models
+fig, ax = subplots(1, 1, figsize = (7, 5))
+ax.plot(data.t, data.σ, ".", color = "green", label = "Original Data")
+ax.plot(SLS_Zener_predict.t, SLS_Zener_predict.σ, "-", color = "red", label = "SLS_Zener Model")
+ax.plot(Maxwell_predict.t, Maxwell_predict.σ, "--", color = "blue", label = "Maxwell Model")
+ax.plot(BurgersLiquid_predict.t, BurgersLiquid_predict.σ, ":", color = "purple", label = "BurgersLiquid Model")
+ax.set_xlabel("Time")
+ax.set_ylabel("Stress")
+ax.legend()
+ax.grid("on")
+#!nb fig #hide