Lectures:
- Visualization history vs. modern tools and libraries, basic methods of data representation (presentation and exploratory forms of graphics), characteristics of datasets (continuous, sampled and discrete data, topological and geometric dimensions of data, dimensions of attributes).
- Methods of reconstruction of sampled data, cell types, regular grid, scattered data (point clouds), possibilities of their interpolation, approximation and extrapolation.
- Mapping data to a color scale, design of effective color and grayscale transitions, their interpretation, an overview of proven and safe color scales.
- Types of graphs and their significance for visualization of quantity, density, distribution, relations, correlation, structures, time and other statistical quantities; aesthetics and layout of the graph, the so-called lie factor, data-ink ratio, the trifecta framework, the chartjunk concept and other cognitive aspects of graphs.
- Visualization of scalar quantities in the plane and space, capturing the properties of attributes and their relationships; bar, line, pie, area, point, polar and radar graphs, micrographs, temperature maps, elevation maps and isolines.
- Visualization of static and dynamic vector fields, calculation of integral curves, their convolution, flowfield characteristics (streamlines, streaklines, pathlines, and timelines).
- Tensor data, their meaning, possibilities of interpretation and visualization (fiber tracking, hyperstreamlines).
- Visualization of volumetric data, methods for direct and indirect rendering, transfer functions, classification.
- Visualization of structured data and graphs, graph layout algorithms, interactivity (e.g. details on demand).
- Methods of multidimensional data visualization, dimension reduction (selection, extraction, principal component analysis, projection, multidimensional scaling), parallel coordinate plots, hierarchical (dendrograms) and non-hierarchical approaches (k-means, etc.) of cluster analysis.
- Illustrative visualization, web presentations, possibilities of tabular data formatting, aspects of selection of a specific visualization tool.
- Tools from the field of image processing and their use in visualization (point operations, histograms, segmentation, classification, mathematical morphology).
- Visualization using virtual and augmented reality tools, graphics engines and APIs.
- Technical aspects of the final presentation and publication of visualization outputs, arrangement and layout of individual parts, interchangeable formats.
Practical exercise on computer labs:
- Analysis of the structure and properties of sample datasets, determination of dimensions and data processing using scripts (e.g. visualization of temperature time series in gnuplot and matplotlib using Python language).
- Examples of approximation of sampled data affected by measurement error by selected approximation function, interpolation of scalar values over different types of grid cells.
- Mapping data to a color ramp, design of effective color and grayscale gradients, their interpretation, an overview of proven and safe color ramps.
- Work with different types of graphs from the lecture, adjustment of their layout and evaluation of their contribution from the perspective of interpretation of the content of displayed data.
- Practical examples for visualization of various properties of one or more attributes.
- Visualization of vector fields applied to outputs from fluid flow simulations, calculation of integral curves and plotting of stream lines.
- Examples of tensor data and possibilities of their visualization (e.g. surface curvature, process of diffusion of particles of one substance into another).
- Visualization of medical data (CT and MRI images) by direct methods (DVR) and visualization of fluid dynamics simulation using indirect methods (Marching Cubes).
- Practical implementation of an algorithm for graph distribution and its modification according to the needs of visualization, use of the tool such as d3.js to achieve interactivity of tree structures and general graphs.
- Processing of multidimensional datasets, dimension reduction, projection, visualization (e.g. FastMap, PCA, PCP).
- Tools for creating interactive graphs in the web environment (e.g. d3.js, Vega-Lite, Tableau, Plotly, Google Charts, RAWGraphs).
- Processing image data (e.g. medical images) by means of digital image processing and analysis for the purposes of their pre-processing and final editing (e.g. noise reduction, highlighting of key parts, classification).
- Use of standard graphics APIs (e.g. OpenGL) for rendering 3D visualizations (e.g. terrain map) in VR/AR environment.
The exercises solve specific tasks from the discussed area. The implementation languages are C ++, Python and JavaScript.
- Visualization history vs. modern tools and libraries, basic methods of data representation (presentation and exploratory forms of graphics), characteristics of datasets (continuous, sampled and discrete data, topological and geometric dimensions of data, dimensions of attributes).
- Methods of reconstruction of sampled data, cell types, regular grid, scattered data (point clouds), possibilities of their interpolation, approximation and extrapolation.
- Mapping data to a color scale, design of effective color and grayscale transitions, their interpretation, an overview of proven and safe color scales.
- Types of graphs and their significance for visualization of quantity, density, distribution, relations, correlation, structures, time and other statistical quantities; aesthetics and layout of the graph, the so-called lie factor, data-ink ratio, the trifecta framework, the chartjunk concept and other cognitive aspects of graphs.
- Visualization of scalar quantities in the plane and space, capturing the properties of attributes and their relationships; bar, line, pie, area, point, polar and radar graphs, micrographs, temperature maps, elevation maps and isolines.
- Visualization of static and dynamic vector fields, calculation of integral curves, their convolution, flowfield characteristics (streamlines, streaklines, pathlines, and timelines).
- Tensor data, their meaning, possibilities of interpretation and visualization (fiber tracking, hyperstreamlines).
- Visualization of volumetric data, methods for direct and indirect rendering, transfer functions, classification.
- Visualization of structured data and graphs, graph layout algorithms, interactivity (e.g. details on demand).
- Methods of multidimensional data visualization, dimension reduction (selection, extraction, principal component analysis, projection, multidimensional scaling), parallel coordinate plots, hierarchical (dendrograms) and non-hierarchical approaches (k-means, etc.) of cluster analysis.
- Illustrative visualization, web presentations, possibilities of tabular data formatting, aspects of selection of a specific visualization tool.
- Tools from the field of image processing and their use in visualization (point operations, histograms, segmentation, classification, mathematical morphology).
- Visualization using virtual and augmented reality tools, graphics engines and APIs.
- Technical aspects of the final presentation and publication of visualization outputs, arrangement and layout of individual parts, interchangeable formats.
Practical exercise on computer labs:
- Analysis of the structure and properties of sample datasets, determination of dimensions and data processing using scripts (e.g. visualization of temperature time series in gnuplot and matplotlib using Python language).
- Examples of approximation of sampled data affected by measurement error by selected approximation function, interpolation of scalar values over different types of grid cells.
- Mapping data to a color ramp, design of effective color and grayscale gradients, their interpretation, an overview of proven and safe color ramps.
- Work with different types of graphs from the lecture, adjustment of their layout and evaluation of their contribution from the perspective of interpretation of the content of displayed data.
- Practical examples for visualization of various properties of one or more attributes.
- Visualization of vector fields applied to outputs from fluid flow simulations, calculation of integral curves and plotting of stream lines.
- Examples of tensor data and possibilities of their visualization (e.g. surface curvature, process of diffusion of particles of one substance into another).
- Visualization of medical data (CT and MRI images) by direct methods (DVR) and visualization of fluid dynamics simulation using indirect methods (Marching Cubes).
- Practical implementation of an algorithm for graph distribution and its modification according to the needs of visualization, use of the tool such as d3.js to achieve interactivity of tree structures and general graphs.
- Processing of multidimensional datasets, dimension reduction, projection, visualization (e.g. FastMap, PCA, PCP).
- Tools for creating interactive graphs in the web environment (e.g. d3.js, Vega-Lite, Tableau, Plotly, Google Charts, RAWGraphs).
- Processing image data (e.g. medical images) by means of digital image processing and analysis for the purposes of their pre-processing and final editing (e.g. noise reduction, highlighting of key parts, classification).
- Use of standard graphics APIs (e.g. OpenGL) for rendering 3D visualizations (e.g. terrain map) in VR/AR environment.
The exercises solve specific tasks from the discussed area. The implementation languages are C ++, Python and JavaScript.