update docs

docs/src/advanced_tutorial.md

@@ -6,6 +6,28 @@ Pages = ["advanced_tutorial.md"]
 Depth = 3
 ```
 
+## Model uncertainties
+The predictions for the observables can be affected by uncertainties that
+dependend on the current value of the model parameters. Such uncertainties can be included
+in the analysis by letting the observable functions return a tuple of numbers, where
+the first number is the prediction and the second number is the absolute uncertainty on that prediction.
+
+````julia
+function xsec1(params)
+    coeffs = [20.12, 5.56, 325.556]
+    prediction = myfunc(params, coeffs)
+    uncertainty = 0.1 * prediction # assume a 10% uncertainty
+    return (prediction, uncertainty)
+end
+````
+
+Note: It is possible to specify a model uncertainty only for some of the observables.
+When the observable functions returns a single number, EFTfitter.jl will assume that the
+model uncertainty is zero for this observable. However, please note that as soon as one observable function
+returns a tuple, the runtime of the model will probably increase, as the model uncertainties then need to be evaluate in each step.
+Therefore, model uncertainties should only be used when the uncertainty value is depending on the
+model parameters. Constant uncertainties should just be specified in the `uncertainties` field of the measurements.
+
 ## Vector of functions for a BinnedMeasurement
 When using binned measurements, a vector of functions giving the predictions
 for the observable needs to be passed. It contains a function for each of bin and

docs/src/literate/advanced_tutorial_lit/lit_advanced_tutorial_inputs.jl

@@ -37,6 +37,28 @@
 #!md     return c[1] * params.C1 + c[2] * params.C1 * params.C2+ c[3] * params.C2
 #!md end
 
+
+#md # ## Model uncertainties
+#~The predictions for the observables can be affected by uncertainties that 
+#~dependend on the current value of the model parameters. Such uncertainties can be included 
+#~in the analysis by letting the observable functions return a tuple of numbers, where
+#~the first number is the prediction and the second number is the absolute uncertainty on that prediction.
+function xsec1(params)
+    coeffs = [20.12, 5.56, 325.556]
+    prediction = myfunc(params, coeffs)
+    uncertainty = 0.1 * prediction # assume a 10% uncertainty
+    return (prediction, uncertainty)
+end
+
+#~Note: It is possible to specify a model uncertainty only for some of the observables.
+#~When the observable functions returns a single number, EFTfitter.jl will assume that the
+#~model uncertainty is zero for this observable. However, please note that as soon as one observable function 
+#~returns a tuple, the runtime of the model will probably increase, as the model uncertainties then need to be evaluate in each step.
+#~Therefore, model uncertainties should only be used when the uncertainty value is depending on the
+#~model parameters. Constant uncertainties should just be specified in the `uncertainties` field of the measurements. 
+
+
+
 #-
 #md # ## Vector of functions for a BinnedMeasurement
 #~When using binned measurements, a vector of functions giving the predictions 

examples/advanced_tutorial/advanced_inputs.jl

@@ -30,6 +30,25 @@ function myfunc(params, c)
     return c[1] * params.C1 + c[2] * params.C1 * params.C2+ c[3] * params.C2
 end
 
+
+# The predictions for the observables can be affected by uncertainties that
+# dependend on the current value of the model parameters. Such uncertainties can be included
+# in the analysis by letting the observable functions return a tuple of numbers, where
+# the first number is the prediction and the second number is the absolute uncertainty on that prediction.
+function xsec1(params)
+    coeffs = [20.12, 5.56, 325.556]
+    prediction = myfunc(params, coeffs)
+    uncertainty = 0.1 * prediction # assume a 10% uncertainty
+    return (prediction, uncertainty)
+end
+
+# Note: It is possible to specify a model uncertainty only for some of the observables.
+# When the observable functions returns a single number, EFTfitter.jl will assume that the
+# model uncertainty is zero for this observable. However, please note that as soon as one observable function
+# returns a tuple, the runtime of the model will probably increase, as the model uncertainties then need to be evaluate in each step.
+# Therefore, model uncertainties should only be used when the uncertainty value is depending on the
+# model parameters. Constant uncertainties should just be specified in the `uncertainties` field of the measurements.
+
 # When using binned measurements, a vector of functions giving the predictions
 # for the observable needs to be passed. It contains a function for each of bin and
 # has only the model parameters as its argument. Defining a separate function for each

src/plotting/plot_intervals.jl

@@ -5,7 +5,7 @@ function get_parameter_names(samples::DensitySampleVector)
     return parameter_names
 end
 
-
+#TODO: move to BAT.jl (type piracy)
 @recipe function f(
     maybe_shaped_samples::DensitySampleVector,
     p::Float64;
@@ -37,7 +37,7 @@ end
             seriestype --> :path
             markerstrokealpha := 0
             linewidth --> 1e-20
-            markerstrokewidth --> 1
+            markerstrokewidth --> 3
             markersize --> 5
             markerstrokecolor --> :steelblue
             linecolor := internal_color