## 1 Introduction  
The production of materials containing ordered lattices of nanoparticles (NPs) or other nano-objects (NOs) is of great importance today, due to their very interesting properties and many relevant applications in modern nanotechnology [1, 2, 3]. For example, semiconductor quantum dots (QDs) are the focus of modern science because of their unique optical and electrical properties, tunable by their size and internal structure due to the confinement effects [1, 2, 3, 4, 5]. Especially interesting are QDs with a core-shell structure, in which confinement effects are very strong in both the core and the shell [6, 7, 8, 9]. They strongly influence the current carrier and optical properties, ensuring that such materials are highly applicative in solar cells, detectors sensors, and many other devices. Quantum wires (QWs) are another very interesting material due to their specific shape, high surface to volume ratio, and multiple exciton generation effects, which are also highly explored in many photo-sensitive devices [10, 11]. As all these properties are highly size- and shape-sensitive, it is very important to develop effective tools for their accurate determination. In addition to the nanoparticle shape and size, another important factor for the application of such materials is their ordering on the surface or within the matrix. The regular ordering is usually desirable because it allows for controlling the separation of NPs, which greatly influences the material electrical properties. The regularity in ordering can also induce some novel effects in the material, such as mini-band formation, or specific conduction in the desired spatial direction, as well as many others [12, 13]. Therefore, the tools for the detailed characterization of size, shape, and arrangement of NPs in a material are of great importance. Grazing-incidence small-angle X-ray scattering (GISAXS) is the leading technique for this purpose because it provides data in reciprocal space with excellent statistics [14; 15; 16; 17; 18; 19]. The materials with ordered lattices of NPs show characteristic peaks in GISAXS, whose positions and widths are determined by the ordering type of NPs and the degree of regularity in their ordering. Due to that fact, even a visual inspection of the GISAXS map enables a rough estimation of the materials structure, including NP arrangement, separation, and shape. However, for obtaining detailed information about the systems structure, a numerical analysis of a GISAXS map should be performed. The main problem with the analysis of GISAXS intensity distributions is the proper choice of a model that describes NP ordering, especially the deviations from the ideal NP positions given by the lattice type. The deviations from the ideal NP positions are very important because they significantly affect the position and the width of the peaks in the GISAXS map. Therefore, only the proper choice of the model will lead to the yielding of accurate data from GISAXS analysis. We have analyzed different types of NP ordering in 3D lattices and proposed theoretical models for the description of GISAXS in such systems. These models provide accurate structural data [20]. In another work [21], we have analyzed GISAXS from ion-beam treated materials in which the ion beam forms NPs along its tracks. Finally, some examples are given in [22]. Although these models are published and have proven to be efficient for the analysis of the GISAXS maps, they are usable only by very few experts in GISAXS analysis and modeling. Many users from various disciplines (physics, chemistry, biology, medicine, industry) are in need of the GISAXS technique, but most of them do not know how to analyze the data. Therefore, the software platform which incorporates these models and which is usable by a wide audience is still missing. Several excellent platforms for description and analysis of GISAXS exist [23; 24; 25; 26; 27; 28; 29], while a full list of the available software for GISAXS analysis can be found in [30].