## 3 Results
### Visual Estimate Option  
In general, a program fits the given set of parameters. These parameters have reasonable defaults set by the GisaxStudio, which can be manually tweaked by the user. Therefore, before the fitting process, it is very useful to use the option _Vis guess_, which calculates the GISAXS map from the current parameters in the list. The same parameters will be remembered and used as initial parameters for the subsequent fit. This is an optional step, but it enables an experienced user to help the fitting process by moving the initial default parameters closer to the optimal values. The number on the left of the tab _'Vis guess'_ sets the resolution of the simulated map, as it significantly affects the time needed to calculate the map--lower resolutions will, of course, yield faster results. The window that appears after pressing the _Vis guess_ button is shown in Figure 5. This window allows for setting the desired model and its main parameters. These are specified in six tabs: _Fit, Model, Deviation, Nanoparticle shape, Fixed Params,_ and Experiment Params. Figure 4: Window for setup the analysis options for 3 dLattice experiment type. It contains the GISAXS map which should be analysed, as well as the options for image processing and simulation in the middle columns, followed by the list of loaded files in the current project on the right side. The _Fit params_ tab (Figure 5a) allows us to choose the model most suitable for our system. This tab, in addition to the model, allows us to choose the parameters, including the overall constant, which depends on the intensity collection time, background, and surface roughness of the sample. The choice of the model is crucial for the accuracy of the analysis. Therefore, three main types of the NP ordering, demonstrated in Figure 5b, are available. The program is suitable for the analysis of NP lattices with three basic types of disorder: long range ordering (LRO), short-range ordering (SRO), and random ordering (RO). The disorder describes deviations from the ideal positions of the NPs in a specific 3D lattice. In the LRO model, the positions of the NPs fluctuate around predefined ideal lattice positions, while the separation between the NPs fluctuate in the SRO model. Therefore, the fluctuation probability is increasing from the first to the last NP along the given basis vector. Random ordering shows NP correlations in the NP positions (these are random). The disorder type can differ in various spatial directions. Because of this, in the description of the 3D lattice, the final model is a combination of three ordering types, which can be different. Each ordering type is associated with one of the three basis vectors that defines the ordering of NP in a 3D lattice, or with some of its components. The model can be set by the variable _Model type_ in the tab _'Fit parameters'_, as shown in Figure 5a. For example, the system that is grown by the self-assembly process, without any restrictions, usually shows SRO in all spatial directions. Therefore, in order to properly describe it, Model 1 (SRO, SRO, SRO) should be chosen. However, for the deposition of NPs in a multilayer, the vertical positions of the layers where the NP form are predefined by the deposition process. Here, the vertical (\(z\) component) of all basis vectors should be LRO, while all \(x\), \(y\) components that define deviations in the plane within the layers are SRO. Therefore, the appropriate choice for a multilayer system is Model 3 (all \(x\), \(y\) SRO, all \(z\) LRO). In some cases, the positions of the NPs are predefined along one basis vector (assumed \(a_{3}\)). For these types of systems, Model 2 is the appropriate choice. Finally, in some cases, the positions of the NPs are random. In this case, we mainly see, as shown on the GISAXS map, the contribution of the NP shape, and the appropriate choice is Model 4 (all random). Figure 5: (**a**) Window for setting the parameter values that appears for the _‘Vis Guess’_ option in the main window.