[i229] Doc: hugo now using Examples from top-level source directory (Closes #229)

Merging branch 'i229'  into 'main'.

See merge request !726

hugo/config.toml

@@ -21,6 +21,8 @@ PygmentsStyle = "vs"
   url_userapi = "https://bornagainproject.org/ext/api/git-main/user-API"
   version_mm = "git-main"
   version_mmp = "1.19.0"
+  version_repo = "main"
+  url_blob = "https://jugit.fz-juelich.de/mlz/bornagain/-/blob"
   manual_version = "1.7.2"
   recommended_python_major = "3"
   recommended_python_minor = "9"

hugo/content/py/fitting/advanced/find-background/index.md

@@ -16,4 +16,4 @@ Here we are simulating cylinders on top of a substrate without interference. The
 The radius and height of the cylinders are passed to the function constructing the multi layer
 while the scale and background values are used to initialize the instrument.
 
-{{< highlightfile file="/static/files/python/fitting/ex02_AdvancedExamples/find_background.py" language="python" >}}
+{{< highlightfile file="Examples/fit52_Advanced/find_background.py" >}}

hugo/content/py/fitting/advanced/fit-along-slices/index.md

@@ -19,4 +19,4 @@ Technically, the idea is to mask the whole detector except thin lines, one verti
 {{< figscg src="fit_along_slices.png" width="600px" caption="Fit window">}}
 {{< /galleryscg >}}
 
-{{< highlightfile file="/static/files/python/fitting/ex02_AdvancedExamples/fit_along_slices.py" language="python" >}}
+{{< highlightfile file="Examples/fit52_Advanced/fit_along_slices.py" >}}

hugo/content/py/fitting/advanced/fit-with-masks/index.md

@@ -22,4 +22,4 @@ where `Rectangle` is related to the shape of the mask in detector coordinates, `
 {{< figscg src="fit_with_masks.png" width="600px" caption="Fit window">}}
 {{< /galleryscg >}}
 
-{{< highlightfile file="/static/files/python/fitting/ex02_AdvancedExamples/fit_with_masks.py" language="python" >}}
+{{< highlightfile file="Examples/fit52_Advanced/fit_with_masks.py" >}}

hugo/content/py/fitting/advanced/multiple-datasets/index.md

@@ -21,4 +21,4 @@ To do this, we define one dataset (a pair of real data and corresponding simulat
 {{< figscg src="multiple_datasets.png" width="600px" caption="Fit window">}}
 {{< /galleryscg >}}
 
-{{< highlightfile file="/static/files/python/fitting/ex02_AdvancedExamples/multiple_datasets.py" language="python" >}}
+{{< highlightfile file="Examples/fit52_Advanced/multiple_datasets.py" >}}

hugo/content/py/fitting/basic/consecutive-fitting/index.md

@@ -11,4 +11,4 @@ This example demonstrates how to run two fits one after the other using differen
 * During the first (started at line 101) fit we are setting the initial values of the fit parameters to be quite far from the expected values and use a genetic minimizer to explore a large parameter space.
 * The second fit at line 112 starts from the best parameter values found in the previous step and uses one of the gradient descent algorithms to find the precise location of the minimum.
 
-{{< highlightfile file="/static/files/python/fitting/ex01_BasicExamples/consecutive_fitting.py" language="python" >}}
+{{< highlightfile file="Examples/fit51_Basic/consecutive_fitting.py" >}}

hugo/content/py/fitting/basic/minimizer-settings/index.md

@@ -24,4 +24,4 @@ print(ba.MinimizerFactory().catalogueDetailsToString())
 For more information, see the
 [minimizer settings tutorial]({{% ref-py "fitting/minimizers/index.md" %}}).
 
-{{< highlightfile file="/static/files/python/fitting/ex01_BasicExamples/minimizer_settings.py" language="python" >}}
+{{< highlightfile file="Examples/fit51_Basic/minimizer_settings.py" >}}

hugo/content/py/fitting/extended/custom-objective-function/index.md

@@ -20,4 +20,4 @@ This is done by defining our own `MyObjective` class at line 14. It is derived f
 
 Later in the code, the `MyObjective.evaluate_residual` function is used to setup a custom objective function for the minimizer (line 116).
 
-{{< highlightfile file="/static/files/python/fitting/ex03_ExtendedExamples/custom_objective_function/custom_objective_function.py" language="python" >}}
+{{< highlightfile file="Examples/fit53_CustomObjective/custom_objective_function.py" >}}

hugo/content/py/fitting/extended/experiment-at-galaxi/index.md

@@ -5,7 +5,7 @@ weight = 30
 
 ## Experiment at GALAXI
 
-This is an example of a real data fit. We use our own measurements performed at the laboratory diffractometer [GALAXI](http://www.fz-juelich.de/jcns/jcns-2//DE/Leistungen/GALAXI/_node.html) in Forschungszentrum Jülich. 
+This is an example of a real data fit. We use our own measurements performed at the laboratory diffractometer [GALAXI](http://www.fz-juelich.de/jcns/jcns-2//DE/Leistungen/GALAXI/_node.html) in Forschungszentrum Jülich.
 
 {{< galleryscg >}}
 {{< figscg src="FitGALAXIData_setup.jpg" width="350px" caption="Real-space model">}}
@@ -17,7 +17,7 @@ This is an example of a real data fit. We use our own measurements performed at
 * The sample is generated with the help of a `SampleBuilder`, which is able to create samples depending on parameters defined in the constructor and passed through to the `create_sample` method.
 * The nanoparticles have a broad log-normal size distribution.
 
-{{< highlightfile file="/static/files/python/fitting/ex03_ExtendedExamples/experiment_at_galaxi/sample_builder.py" language="python" >}}
+{{< highlightfile file="Examples/fit61_Galaxi/sample_builder.py" >}}
 
 <hr>
 
@@ -27,4 +27,4 @@ This is an example of a real data fit. We use our own measurements performed at
 * The real data is loaded from a tiff file into a histogram representing the detector's channels.
 * The `run_fitting()` function contains the initialization of the fitting kernel: loading experimental data, assignment of fit pair, fit parameters selection (line 62).
 
-{{< highlightfile file="/static/files/python/fitting/ex03_ExtendedExamples/experiment_at_galaxi/fit_galaxi_data.py" language="python" >}}
+{{< highlightfile file="Examples/fit61_Galaxi/fit_galaxi_data.py" >}}

hugo/content/py/fitting/extended/external-minimizer-with-plotting/index.md

@@ -37,4 +37,4 @@ The complete script to plot the fitting progress and the image produced by it ar
 
 
 
-{{< highlightfile file="/static/files/python/fitting/ex03_ExtendedExamples/external_minimizer/lmfit_with_plotting.py" language="python" >}}
+{{< highlightfile file="Examples/fit54_ExternalMinimizer/lmfit_with_plotting.py" >}}

hugo/content/py/fitting/extended/external-minimizer/index.md

@@ -42,4 +42,4 @@ print(result.params.pretty_print())
 
 The complete script for the `lmfit` based fitting is shown below.
 
-{{< highlightfile file="/static/files/python/fitting/ex03_ExtendedExamples/external_minimizer/lmfit_basics.py" language="python" >}}
+{{< highlightfile file="Examples/fit54_ExternalMinimizer/lmfit_basics.py" >}}

hugo/content/py/fitting/extended/fit-specular-data/index.md

@@ -44,6 +44,6 @@ in the BornAgain directory.
 
 ### Complete script and data
 
-{{< highlightfile file="/static/files/python/fitting/ex03_ExtendedExamples/specular/FitSpecularBasics.py" language="python" >}}
+{{< highlightfile file="Examples/fit55_SpecularBasic/FitSpecularBasics.py" >}}
 
 {{% filelink file="/static/files/python/fitting/ex03_ExtendedExamples/specular/genx_interchanging_layers.dat.gz" name="Reference data" %}}

hugo/content/py/fitting/extended/fit-with-uncertainties/index.md

@@ -20,6 +20,6 @@ This example follows closely the tutorial on [Fitting reflectometry data](/py/fi
 
 {{% figure src="FitWithUncertainties.png" command="Resize" options="450x" caption="Fitting with uncertainties plot. Notice the $RQ^4$ scale of the Intensity axis" class="center" %}}
 
-{{< highlightfile file="/static/files/python/fitting/ex03_ExtendedExamples/specular/FitWithUncertainties.py" language="python" >}}
+{{< highlightfile file="Examples/fit55_SpecularBasic/FitWithUncertainties.py" >}}
 
 {{% filelink file="/static/files/python/fitting/ex03_ExtendedExamples/specular/genx_interchanging_layers.dat.gz" name="Reference data" %}}

hugo/content/py/fitting/extended/polarized-spinasymmetry-fit/index.md

@@ -97,4 +97,4 @@ python3 PolarizedSpinAsymmetryFit.py
 
 Here is the complete example:
 
-{{< highlightfile file="src/Examples/fit55_SpecularBasic/PolarizedSpinAsymmetryFit.py"  language="python" >}}
+{{< highlightfile file="Examples/fit55_SpecularBasic/PolarizedSpinAsymmetryFit.py" >}}

hugo/content/py/fitting/extended/real-life-reflectometry/index.md

@@ -26,7 +26,6 @@ Be patient, since it takes some time to run.
 {{% figure src="Figure.png" command="Resize" options="450x" caption="Figure obtained after running the script below" class="center" %}}
 
 
-{{< highlightfile file="/static/files/python/fitting/ex03_ExtendedExamples/specular/RealLifeReflectometryFitting.py" language="python" >}}
+{{< highlightfile file="Examples/fit55_SpecularBasic/RealLifeReflectometryFitting.py" >}}
 
 {{% filelink file="/static/files/python/fitting/ex03_ExtendedExamples/specular/mg6a_Merged.txt.gz" name="Reference data" %}}
-

hugo/content/py/fitting/extended/reflectometry-honeycomb/index.md

@@ -8,7 +8,7 @@ weight = 60
 
 In this example, we want to demonstrate how to fit a more complex sample.
 For this purpose, we utilize the reflectometry data of an
-artificial magnetic honeycomb lattice published by A. Glavic et al., in 
+artificial magnetic honeycomb lattice published by A. Glavic et al., in
 [this paper](https://doi.org/10.1002/advs.201700856)
 
 The experiment was performed with polarized neutrons, but without polarization analysis.
@@ -24,21 +24,21 @@ The experimental data consists of four datasets that should be fitted simultaneo
 These datasets arise from the two polarization channels for up and down polarization of the incoming beam
 and both of these channels are measured at two temperatures (300K and 150K).
 
-All of this is measured on the same sample, so all parameters are assumed to be the same, 
+All of this is measured on the same sample, so all parameters are assumed to be the same,
 except the magnetization being temperature dependent.
-Therefore, we introduce a scaling parameter for the magnetization as the ratio of the 
+Therefore, we introduce a scaling parameter for the magnetization as the ratio of the
 magnetizations at 150K and 300K: $M_{s150} = M_{150K} / M_{300K}$.
 
 
 #### Magnetization model
 
-To model a magnetic material, one can assign a magnetization vector to any material, as is demonstrated 
+To model a magnetic material, one can assign a magnetization vector to any material, as is demonstrated
 in the [basic polarized reflectometry tutorial]({{% ref-py "instr/polarized/basic-polarized-reflectometry" %}}).
 When a non-vanishing magnetization vector is specified for at least one layer in a sample,
 BornAgain will automatically utilize the polarized computational engine.
 This leads to lower performance as the computations are more invovled.
 
-In this example, the magnetization is (anti)parallel to the neutron spin and hence we instead parametrize 
+In this example, the magnetization is (anti)parallel to the neutron spin and hence we instead parametrize
 the magnetic layers with an effective SLD that is the sum/difference of the nuclear and their magnetic SLD:
 
 $$\rho_\pm = \rho_{\text{N}} \pm \rho_{\text{M}}$$
@@ -55,21 +55,21 @@ The SLDs of these three layers are taken from the literature and kept constant.
 
 Then we model the lattice structure with a three-layer model: two layers to account for density fluctuations
 in $z$-direction and another oxide layer on top.
-This lattice structure is assumed to be magnetic and we fit all of their SLDs, magnetic SLDs, 
+This lattice structure is assumed to be magnetic and we fit all of their SLDs, magnetic SLDs,
 thicknesses and roughnesses.
 The magnetic SLD depends on the temperature of the dataset, according to the scaling described above, where
 the $M_{s150}$ parameter is fitted.
 
 
 All layers are modeled without absorption, i.e. no imaginary part of the SLD.
-Ferthermore, we apply a resolution correction as described in [this tutorial]({{% ref-py "simulation/reflectometry/tofr-with-resolution" %}}) 
+Ferthermore, we apply a resolution correction as described in [this tutorial]({{% ref-py "simulation/reflectometry/tofr-with-resolution" %}})
 with a fixed value for $\Delta Q / Q = 0.018$.
 The experimental data is normalized to unity, but we still fit the intensity, as is necessary due to the resolution correction.
 
 
 #### Running a computation
 
-In order to run a computation, we define some functions to utilize a common simulation function 
+In order to run a computation, we define some functions to utilize a common simulation function
 for the two spin channels and both temperatures:
 
 {{< highlight python>}}
@@ -88,7 +88,7 @@ def run_Simulation_150_m( qzs, params ):
 
 Here, the given arguments specify whether we want to simulate a spin-up beam (`sign = 1`)
 and whether the scaling of the magnetization should be applied (`ms150=True`).
-For the latter, `true` means that a dataset at 150K is simulated while `false` 
+For the latter, `true` means that a dataset at 150K is simulated while `false`
 corresponds to 300K and the scaling parameter is set to unity.
 
 All four reflectivity curves are then computed using:
@@ -101,7 +101,7 @@ q_150_p, r_150_p = qr( run_Simulation_150_p( qzs, paramsInitial ) )
 q_150_m, r_150_m = qr( run_Simulation_150_m( qzs, paramsInitial ) )
 {{< /highlight >}}
 
-We choose some sensible initial parameters and these yield the following 
+We choose some sensible initial parameters and these yield the following
 simulation result
 
 {{< galleryscg >}}
@@ -120,9 +120,9 @@ As a measure for the goodness of the fit, we use the relative difference:
 
 $$\Delta = \sum_{j = 1}^4 \frac{1}{N_j} \sum_{i = 1}^N \left( \frac{d_{ji} - s_{ji}}{d_{ji} + s_{ji}} \right)^2$$
 
-Here the sum over $i$ sums up the fitting error at every data point as usual and 
+Here the sum over $i$ sums up the fitting error at every data point as usual and
 the sum over $j$ adds the contributions from all four datasets.
-This is implemented in the `FitObjective::__call__` function and 
+This is implemented in the `FitObjective::__call__` function and
 the `FitObjective` object holds all four datasets, and also performs the corresponding simulations.
 In the function `run_fit_differential_evolution` this is dispatched to the differential evolution algorithm.
 
@@ -146,7 +146,6 @@ On a four-core workstation, the fitting procedure takes roughly 45 minutes to co
 
 As can be seen from the plot of the SLDs, the magnetization is indeed larger for the measurement at lower temperature, exactly as expected.
 
-{{< highlightfile file="/static/files/python/fitting/ex03_ExtendedExamples/honeycomb/Honeycomb_fit.py" language="python" >}}
+{{< highlightfile file="Examples/fit56_SpecularAdvanced/Honeycomb_fit.py" >}}
 
 {{% filelink file="/static/files/python/fitting/ex03_ExtendedExamples/honeycomb/honeycomb_data.zip" name="Reference data" %}}
-

hugo/content/py/fitting/extended/reflectometry-pt-layer/index.md

@@ -98,6 +98,6 @@ python3 Pt_layer_fit.py
 a simulation is performed with our fit results and one should obtain the result shown above.
 
 
-{{< highlightfile file="/static/files/python/fitting/ex03_ExtendedExamples/pt-layer/Pt_layer_fit.py" language="python" >}}
+{{< highlightfile file="Examples/fit56_SpecularAdvanced/Pt_layer_fit.py" >}}
 
 {{% filelink file="/static/files/python/fitting/ex03_ExtendedExamples/pt-layer/RvsQ_36563_36662.txt.gz" name="Reference data" %}}

hugo/content/py/fitting/gisas-fit2d/index.md

@@ -40,7 +40,7 @@ and terminates with the following result:
 
 ### Parametric sample and simulation model
 
-{{< highlightfile file="/static/files/python/fitting/gisas2d/gisas_model1.py" language="python" >}}
+{{< highlightfile file="Examples/fit/gisas2d/gisas_model1.py" >}}
 
 
 ### Explanation
@@ -62,7 +62,7 @@ in order to facilitate the benchmarking of different fith methods.
 
 ### Main fit script
 
-{{< highlightfile file="/static/files/python/fitting/gisas2d/fit_gisas.py" language="python" >}}
+{{< highlightfile file="Examples/fit/gisas2d/fit_gisas.py" >}}
 <p>
 
 ### Explanation

hugo/content/py/fitting/minimizers/index.md

@@ -16,7 +16,7 @@ so the BornAgain setup looks very similar.
 In the code snippet below we give an example of finding the minimum of the [Rosenbrock function](https://en.wikipedia.org/wiki/Rosenbrock_function)
 using the BornAgain minimizer with default settings.
 
-{{< highlightfile file="/static/files/python/fitting/algo/fit_rosenbrock.py" language="python" >}}
+{{< highlightfile file="Examples/fit/algo/fit_rosenbrock.py" >}}
 <p>
 
 The rest of the page gives additional details on

hugo/content/py/instr/beam/angular-divergence/index.md

@@ -18,6 +18,6 @@ The observed reflectometry signal can be affected either by a spread in the beam
 
 In this example, a Gaussian distribution is used to spread the incident angle, with a standard deviation of $\sigma_{\alpha} = 0.01^{\circ}$.
 
-{{< highlightfile file="src/Examples/specular/BeamAngularDivergence.py"  language="python" >}}
+{{< highlightfile file="Examples/specular/BeamAngularDivergence.py" >}}
 
-{{% filelink file="src/Examples/scatter2d/genx_angular_divergence.dat.gz" name="Reference data" %}}
+{{% filelink file="Examples/scatter2d/genx_angular_divergence.dat.gz" name="Reference data" %}}