[from21] From21 ()

Merging branch 'from21'  into 'main'.

See merge request !1891

hugo/content/ex/fit/bayesian.md

@@ -7,7 +7,7 @@ weight = 40
 
 Bayesian sampling of reflectometry models is a common tool in the analysis of specular reflectometry data.
 The Python programming language has a powerful infrastructure for this modelling, including packages such as [PyMC3](https://docs.pymc.io) and [PyStan](https://pystan.readthedocs.io/en/latest/).
-Here, we will show how the [emcee](https://emcee.readthedocs.io/en/stable/) Python may be used to enable Bayesian sampling in BornAgain and the differential evoulation optimisation algorithm from the [scipy](https://www.scipy.org/scipylib/index.html).
+Here, we will show how the [emcee](https://emcee.readthedocs.io/en/stable/) Python may be used to enable Bayesian sampling in BornAgain and the differential evoulation optimisation algorithm from the [scipy](https://docs.scipy.org/doc/scipy/).
 
 ### Example script
 
@@ -16,7 +16,9 @@ To generate these images of the probability distributions of the parameters and
 {{< figscg src="/files/fitted/CornerPlotBayes.png" width="500" class="center">}}
 {{< figscg src="/files/fitted/ReflectivityBayes.png" width="500" class="center">}}
 
-run [this script]({{% ref-ex "bayesian/likelihood_sampling.py" %}}).
+run this script
+
+{{< show-ex file="bayesian/likelihood_sampling.py" >}}
 
 
 ### Explanation
@@ -27,7 +29,7 @@ We know the scattering length densities for each, and that in total there are 10
 This sample is created in the `get_sample` function.
 
 Having built the sample, it is necessary to obtain the real experimental data.
-For the above code to work locally, the following [data file]({{% ref-ex "data/genx_interchanging_layers.dat.gz?inline=false" %}}) is required and the Python script (in particular the `get_real_data` function) should be adapted appropriately.
+For the above code to work locally, the data file {{% ref-data genx_alternating_layers.dat.gz %}} is required and the Python script (in particular the `get_real_data` function) should be adapted appropriately.
 This function defined an uncertainty in the reflectivity of 10 %.
 
 The simulation is then defined in the `get_simulation` function, which is passed a series of angles, however, this may be modified to perform a Q-scan as necessary.

hugo/content/ex/fit/external-minimizer-with-plotting.md

@@ -5,7 +5,7 @@ weight = 40
 
 ## External Minimizers: Plotting Fit Progress
 
-In this example we are demonstrating how to run a typical fitting task in BornAgain using a third party minimizer while plotting the results. As in our [previous example](/ex/fit/extended/external-minimizer), we use lmfit for sake of illustration.
+In this example we are demonstrating how to run a typical fitting task in BornAgain using a third party minimizer while plotting the results. As in our [previous example](/ex/fit/external-minimizer), we use lmfit for sake of illustration.
 
 To plot the fit progress, it is needed to use the lmfit iteration callback function. It will come handy to define the plotting callback function as a specialized class:
 

hugo/content/ex/fit/fit-with-uncertainties.md

@@ -10,7 +10,7 @@ In this example we are demonstrating how to allow for uncertainties during a Ref
 
 The reference data was generated with GENX, setting the thickness of the Ti layers equal to 3 nm.
 
-This example follows closely the tutorial on [Fitting reflectometry data](/ex/fit/extended/fit-specular-data/). The main points to focus on here are the following:
+The main points to focus on here are the following:
 
  - Added artificial uncertainties to the data being fitted
  - Use of the the $RQ^4$ view for plotting

hugo/content/ex/fit/galaxi.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](https://www.fz-juelich.de/en/jcns/jcns-2/expertise/in-house-x-ray/galaxi) in Forschungszentrum Jülich.
 
 {{< galleryscg >}}
 {{< figscg src="/img/draw/FitGALAXIData_setup.jpg" width="350px" caption="Real-space model">}}
@@ -20,8 +20,6 @@ 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).
 
-{{< show-ex file="fit/scatter2d/expfit_galaxi.py" >}}
-
 ## Experiment description
 
 To successfully simulate and fit results of some real experiment it is important to have
@@ -32,10 +30,7 @@ To successfully simulate and fit results of some real experiment it is important
 
 ### Experiment
 
-As an example we will 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.
-
-A complete example, containing less explanations but more code, can be found in
-[Real life fit example: experiment at GALAXI](/ex/fit/extended/experiment-at-galaxi).
+As an example we will use our own measurements performed  at the laboratory diffractometer [GALAXI](https://www.fz-juelich.de/en/jcns/jcns-2/expertise/in-house-x-ray/galaxi) in Forschungszentrum Jülich.
 
 Our sample represents a 3-layer system (substrate, teflon and air)
 with Ag nanoparticles sitting on top of the teflon layer.
@@ -125,3 +120,7 @@ data = img.data.astype("float64")
 ```
 
 The main requirement is that the shape of the numpy array coincides with the number of detector channels (i.e. `npx, npy = 981, 1043` for given example).
+
+{{< show-ex file="fit/scatter2d/expfit_galaxi.py" >}}
+
+Data to be fitted: {{% ref-data galaxi_data.tif.gz %}}

hugo/content/ex/fit/gisas-fit2d.md

@@ -26,8 +26,7 @@ to make the subsequent fitting more difficult and more realistic:
   * Beam wavelength and alpha_i slightly changed;
   * Detector x-axis skewed.
 
-The faked data can be found at
-{{% ref-ex "data/faked-gisas1.txt.gz" %}}.
+The faked data: {{% ref-data faked-gisas1.txt.gz %}}
 
 ## Fit script
 

hugo/content/ex/fit/minimizer-settings.md

@@ -22,6 +22,6 @@ print(ba.MinimizerFactory().catalogueDetailsToString())
 
 
 For more information, see the
-[minimizer settings tutorial](/ex/fit/minimizers/index.md).
+[minimizer settings tutorial](/ref/fit/minimizers).
 
 {{< show-ex file="fit/scatter2d/minimizer_settings.py" >}}

hugo/content/ex/fit/polarized-spinasymmetry-fit.md

@@ -41,7 +41,7 @@ If no uncertainties are available, using the relative difference `fit_objective.
 If the relative difference is selected and uncertainties are provided, BornAgain automatically falls back to the above $\chi^2$ metric.
 
 The fitting of polarized reflectometry data proceeds similar to the lines presented in
-[the tutorial on multiple datasets](/ex/fit/advanced/multiple-datasets).
+[the tutorial on multiple datasets](/ex/fit/multiple-datasets).
 We need to add the reflectivity curves for the up-up and down-down channel
 to the fit objective:
 
@@ -98,3 +98,5 @@ python3 PolarizedSpinAsymmetryFit.py
 Here is the complete example:
 
 {{< show-ex file="fit/specular/PolarizedSpinAsymmetryFit.py" >}}
+
+Data to be fitted: {{% ref-data MAFO_Saturated_mm.tab %}}, {{% ref-data MAFO_Saturated_pp.tab %}}

hugo/content/ex/fit/reflectometry-honeycomb.md

@@ -33,7 +33,7 @@ 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
-in the [basic polarized reflectometry tutorial](/ref/instr/pol/basic-polarized-reflectometry).
+in the [magnetic material tutorial](/ref/sample/material/magnetization).
 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.
@@ -148,4 +148,4 @@ As can be seen from the plot of the SLDs, the magnetization is indeed larger for
 
 {{< show-ex file="fit/specular/Honeycomb_fit.py" >}}
 
-Reference data: {{% ref-ex "data/honeycomb" %}}
+Data to be fitted: {{% ref-data honeycomb150m.dat %}}, {{% ref-data honeycomb150p.dat %}}, {{% ref-data honeycomb300m.dat %}}, {{% ref-data honeycomb300p.dat %}}

hugo/content/ex/fit/reflectometry-pt-layer.md

@@ -97,7 +97,6 @@ python3 Pt_layer_fit.py
 ```
 a simulation is performed with our fit results and one should obtain the result shown above.
 
-
 {{< show-ex file="fit/specular/Pt_layer_fit.py" >}}
 
-Data to be fitted: {{% ref-ex "data/RvsQ_36563_36662.txt.gz" %}}
+Data to be fitted: {{% ref-data RvsQ_36563_36662.txt.gz %}}

hugo/content/ex/result/export/_index.md

@@ -82,6 +82,3 @@ hist.save("result.int.gz")
 Additional information can be found in the following pages:
 
 * [Plotting with axes in different units](/ex/result/export/axes-in-different-units)
-* [SimulationResult C++ class reference](http://apps.jcns.fz-juelich.de/doxy/BornAgain/classSimulationResult.html)
-* [Histogram1D C++ class reference](http://apps.jcns.fz-juelich.de/doxy/BornAgain/classHistogram1D.html)
-* [Histogram2D C++ class reference](http://apps.jcns.fz-juelich.de/doxy/BornAgain/classHistogram2D.html)

hugo/content/ex/result/export/axes-in-different-units/index.md

@@ -6,7 +6,7 @@ weight = 20
 ### Plotting with axes in different units
 
 In this example we demonstrate how to plot intensity data with detector axes expressed in different units. It serves as a supporting example to the
-[Accessing simulation results](/py/export/_index.md) tutorial.
+[Accessing simulation results](/ex/result/export/more) tutorial.
 
 * The standard [Cylinders in DWBA](/ex/sim/gisas)
 sample is used to setup the simulation.

hugo/content/ex/sample/roughness/specular_2models.md

@@ -8,8 +8,8 @@ weight = 15
 This example demonstrates how to apply different roughness models
 in a specular reflectivity calculation. The considered sample is
 exactly the same as the one described in the
-[reflectometry tutorial](/ref/sim/class/specular/_index.md),
-and the [basic roughness tutorial](/ref/sample/roughness/specular).
+[reflectometry tutorial](/ref/sim/class/specular),
+and the [basic roughness tutorial](/ref/sample/roughness).
 Hewever, now the computation is performed twice with the standard $tanh$ interface profile
 and the Névot-Croce roughness model that arises from a Gaussian distribution of the
 deviation from the mean-surface position.

hugo/content/ex/sample/roughness/specular_default.md

@@ -8,7 +8,7 @@ weight = 10
 This example demonstrates how to compute reflected signal from
 a multilayered sample with surface roughness. All the experiment
 layout is exactly the same as the one described in
-[reflectometry tutorial](/ref/sim/class/specular/_index.md),
+[reflectometry tutorial](/ref/sim/class/specular),
 but now all the layers (except the ambient media) have roughness on the top surface. The
 roughness is characterized by root-mean-square deviation from the mean surface position
 $\sigma = 1$ nm.
@@ -16,7 +16,7 @@ $\sigma = 1$ nm.
 {{<figscg src="/img/auto/specular/SpecularSimulationWithRoughness.png" width="350px">}}
 
 When comparing the result of the simulation to the result obtained in the
-[reflectometry tutorial](/ref/sim/class/specular/_index.md),
+[reflectometry tutorial](/ref/sim/class/specular),
 one can notice up to two orders of magnitude attenuation of the reflected signal due to
 the roughness of the sample.
 

hugo/content/ex/sim/specular.md

@@ -10,8 +10,8 @@ by a sample that consists of 10 Ti/Ni double layers on a Si substrate.
 
 ##### Full script
 
-The same script has been used in the tutorial to explain [usage](/py/run.md)
-and [syntax](/py/syntax.md) of BornAgain Python code.
+The same script has been used in the tutorial to explain [usage](/py/run)
+and [syntax](/py/syntax) of BornAgain Python code.
 
 Root class reference: [SpecularSimulation](/ref/sim/class/specular).
 

hugo/content/installation/py/linux.md

@@ -14,9 +14,13 @@ some point (e.g. Debian 12) disabled Pip, which terminates with
 `error: externally-managed-environment`. While this behavior can be
 overridden by a special flag, we advise against.
 
+#### Python in pyenv
+
 Rather, we recommend escaping from Python version hell by using the
 Python version manager [pyenv](https://github.com/pyenv/pyenv).
 
+Install the venerable GUI toolkit Tk (e.g. Debian package tk-dev).
+
 Prepare the shell by adding
 ```
 export PYENV_ROOT="$HOME/.pyenv"
@@ -40,4 +44,4 @@ which python # shows path in virtual environment
 pip install numpy
 ```
 etc.
-For the full list of modules required by BornAgain, see the [modules](modules) page.
+For the full list of modules required by BornAgain, see the [modules](/installation/py/modules/) page.

hugo/content/installation/py/modules.md

@@ -19,6 +19,7 @@ Furthermore, a small number of fit script examples require the Python modules
   * lmfit
   * scipy
   * tqdm
+
 These are not required for _installing_ and _running_ most of BornAgain.
 However, they should be present when _building_ BornAgain
 lest some tests will fail.

hugo/content/ref/fit/minimizers.md

@@ -190,4 +190,4 @@ BornAgain fitting can also be done using other minimization packages. A short li
 + [lmfit](https://lmfit.github.io/lmfit-py/)
 + [bumps](https://bumps.readthedocs.io/en/latest/)
 
-In [this example](/ex/fit/extended/external-minimizer) we demonstrate how to use the `lmfit` minimizer for a typical fit of GISAS data.
+In [this example](/ex/fit/external-minimizer) we demonstrate how to use the `lmfit` minimizer for a typical fit of GISAS data.

hugo/content/ref/instr/beam/_index.md

@@ -24,7 +24,7 @@ After creation of a beam, special properties can be set with
 beam.setFootprintFactor(footprint)
 beam.setPolarization(polarization)
 ```
-For the arguments, see [footprint](footprint) and [polarization](../polarized).
+For the arguments, see [footprint](footprint) and [polarization](../pol).
 
 
 {{% children  %}}

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

@@ -8,7 +8,7 @@ weight = 60
 This example demonstrates beam angular spread effects in reflectivity computations.
 It also offers a comparison with data generated using another well known code: GenX.
 Further information about reflectometry simulations can be found in the
-[Reflectometry Simulation Tutorial](/ref/sim/class/specular/_index.md).
+[Reflectometry Simulation Tutorial](/ref/sim/class/specular).
 
 The observed reflectometry signal can be affected either by a spread in the beam wavelength or in the incident angle.
 

hugo/content/ref/instr/beam/footprint/index.md

@@ -47,7 +47,7 @@ The incident angle range was made rather small in this example
 (from $0.0$ to $0.6$ degrees) in order to emphasize
 the footprint impact at small incident angles.
 In other respects this example exactly matches the
-[reflectometry simulation tutorial](/ref/sim/class/specular/_index.md).
+[reflectometry simulation tutorial](/ref/sim/class/specular).
 
 {{< galleryscg >}}
 {{< figscg src="/img/auto/specular/FootprintCorrection.png" width="450px">}}