update tutorial

docs/src/tutorial.md

@@ -26,12 +26,12 @@ For our example, we consider two parameters with the names `C1` and `C2`.
 For `C1` we choose a uniform (flat) prior in the range (-3, 3).
 For `C2` we choose a gaussian prior with μ=0 and σ=0.5.
 
-```julia
+````julia
 parameters = BAT.NamedTupleDist(
     C1 = -3..3, # short for: Uniform(-3, 3)
     C2 = Normal(0, 0.5) # Normal distribution
 )
-```
+````
 
 A parameter can be fixed (and therefore excluded from the fit) by setting its
 prior to a certain value, e.g.: `C2 = 0`.
@@ -51,7 +51,7 @@ the parameters and that internally calls a more complex function and passes the
 corresponding arguments. In this example, the function `xsec2` calls the
 function `myfunc` and passes further arguments (`coeffs`).
 
-```julia
+````julia
 function xsec1(params)
     c = [20.12, 5.56, 325.556]
     return c[1] * params.C1 + c[2] * params.C1 * params.C2+ c[3] * params.C2
@@ -65,13 +65,13 @@ end
 function myfunc(params, c)
     return c[1] * params.C1 + c[2] * params.C1 * params.C2+ c[3] * params.C2
 end
-```
+````
 
 If your observable is a distribution, you can define a vector of functions
 with one function for each bin of the distribution.
 (You could also treat each bin as a separate observable as shown above.)
 
-```julia
+````julia
 function diff_xsec_bin1(params)
     coeffs = [2.2, 5.5, 6.6]
     return myfunc(params, coeffs)
@@ -88,7 +88,7 @@ function diff_xsec_bin3(params)
 end
 
 diff_xsec = [diff_xsec_bin1, diff_xsec_bin2, diff_xsec_bin3]
-```
+````
 
 Note: Another way to define a vector of functions for the bins of distributions
 is shown [here](https://tudo-physik-e4.github.io/EFTfitter.jl/dev/advanced_tutorial/#Creating-a-vector-of-functions-for-distributions-1)
@@ -117,7 +117,7 @@ A `Measurement` can be excluded from the model by setting the switch `active=fal
 For a `MeasurementDistribution`, the keyword `active` accepts `true` or `false`
 to (de)activate the whole distribution or a vector of booleans for (de)activating only certain bins.
 
-```julia
+````julia
 measurements = (
     Meas1 = Measurement(xsec1, 21.6, uncertainties = (stat=0.8, syst=1.8, another_unc=2.3),
             active=true), # `active = false`: exclude measurement from fit (default: active = true)
@@ -129,7 +129,7 @@ measurements = (
                uncertainties = (stat = [0.7, 1.1, 1.2], syst= [0.7, 0.8, 1.3], another_unc = [1.0, 1.2, 1.9]),
                active=[true, false, true]), # `active = false`: exclude all bins from fit, `active = [true, true, false]`: exclude only third bin from fit
 )
-```
+````
 
 Further information on the constructors see the API documentation of [`Measurement`](https://tudo-physik-e4.github.io/EFTfitter.jl/dev/api/#EFTfitter.Measurement)
 and [`MeasurementDistribution`](https://tudo-physik-e4.github.io/EFTfitter.jl/dev/api/#EFTfitter.MeasurementDistribution).
@@ -162,7 +162,7 @@ the Xth bin of the distribution can be accessed.
 Note: This function is evaluated from top to bottom, so if you overwrite a
 specific correlation value, the last value entered will be used.
 
-```julia
+````julia
 dist_corr = [1.0 0.5 0.0;
              0.5 1.0 0.0;
              0.0 0.0 1.0]
@@ -173,9 +173,9 @@ another_corr_matrix = to_correlation_matrix(measurements,
     (:MeasDist, :MeasDist, dist_corr), # correlate the bins of :MeasDist according to the matrix dist_corr
     (:MeasDist_bin2, :MeasDist_bin3, 0.3), # correlate bin2 of :MeasDist with bin3 with 0.3 (overwrites the corresponding element set in the previous line, but ignored in fit since MeasDist_bin2 is inactive)
 )
-```
+````
 
-```julia
+````julia
 correlations = (
     stat = NoCorrelation(active=true), # will use the identity matrix of the correct size
 
@@ -187,37 +187,37 @@ correlations = (
 
     another_unc = Correlation(another_corr_matrix, active=true)
 )
-```
+````
 
 ## File "runTutorial.jl"
 Here, we create the `EFTfitterModel` from our inputs and run the actual analysis.
 
 First, we need to setup EFTfitter, BAT and some other Julia packages:
 
-```julia
+````julia
 using EFTfitter
 using BAT            # for sampling
 using IntervalSets   # for specifying the prior
 using Distributions  # for specifying the prior
 using Plots # for plotting
-```
+````
 
 We can then build the `EFTfitterModel` which combines all our inputs into
 one object that is then used to perform the analysis on.
 
-```julia
+````julia
 model = EFTfitterModel(parameters, measurements, correlations)
-```
+````
 
 To sample the posterior distribution, we specify that our `EFTfitterModel`
 should be used and then setup BAT.jl to sample the EFTfitter likelihood.
 
-```julia
+````julia
 posterior = PosteriorMeasure(model)
 
 algorithm = MCMCSampling(mcalg = MetropolisHastings(), nsteps = 10^5, nchains = 4)
 samples = bat_sample(posterior, algorithm).result;
-```
+````
 
 For further information on settings & algorithms when sampling with BAT.jl
 see the BAT.jl [tutorial](https://bat.github.io/BAT.jl/dev/tutorial/#Parameter-Space-Exploration-via-MCMC)
@@ -226,7 +226,7 @@ and [documentation](https://bat.github.io/BAT.jl/dev/stable_api/#BAT.bat_sample)
 We can then inspect the results of the sampling using BAT.jl's `SampledDensity`,
 giving a summary of the sampling and the results of the model parameters.
 
-```julia
+````julia
 sd = SampledDensity(posterior, samples)
 display(sd)
 ```
@@ -258,16 +258,16 @@ cov ╲  │          C1           C2
 ───────┼─────────────────────────
 C1     │    0.122271   -0.0070394
 C2     │  -0.0070394  0.000442548
-```
+````
 
 Information about the smallest 1d intervals containing p% proability can be
 obtained using the `get_smallest_interval_edges` function:
 
-```julia
+````julia
 intervals_1d_C1 = get_smallest_interval_edges(samples, :C1, 0.9, bins=200, atol=0.1)
 println("lower interval edges: $(intervals_1d_C1.lower)")
 println("upper interval edges: $(intervals_1d_C1.upper)")
-```
+````
 
 The keyword `atol` controls the absolute tolerance for which intervals are joined
 together when they are seperated less than this value. This is particularly useful
@@ -276,21 +276,21 @@ when a large number of bins is used.
 Of course, plotting the resulting posterior distributions is also simple
 using Plots.jl and the BAT.jl plotting recipes:
 
-```julia
+````julia
 p = plot(samples)
 savefig(p, "plot.pdf")
-```
+````
 
 ![example plot](plots/plot.png)
 
 For information on how to customize plots of the samples, please see the BAT.jl
 [plotting documentation](https://bat.github.io/BAT.jl/dev/plotting/) and
 [examples](https://github.com/bat/BAT.jl/blob/master/examples/dev-internal/plotting_examples.jl).
 
-```julia
+````julia
 p = plot(samples, 0.9)
 savefig(p, "plot_1d.pdf")
-```
+````
 
 ![example plot](plots/interval_plot_1.png)