#!/usr/bin/env python # coding: utf-8 # UXarray logo

# # # Rendering Techniques # # ### In this section, you'll learn: # # * Rendering methods that can be used for unstructured grids visualization. # # # ### Related Documentation # # * No UXarray documentation is referenced here since this section covers the content from a general point of view. # # # ### Prerequisites # # | Concepts | Importance | Notes | # | --- | --- | --- | # | Geometry | Necessary | | # # **Time to learn**: 10 minutes # # ----- # # # Since Unstructured Grids require significantly more overhead to represent compared to Structured grids, the choice of rendering technique plays an important role in obtaining high-resolution, accurate, and scalable visualizations. # # # This notebook introduces relevant concepts and techniques that will be mentioned and used throughout this Cookbook. # ## Vector (Shape) Geometries # # The nodes (vertices), edges, and faces (cells) that make up an Unstructured Grid can each be converted into a geometric shape for visualization. These geometric shapes can often be referred to as vector graphics, since each geometry is mathematically represented when rendering. # # For example, in the "Plotting with UXarray" chapter, we will showcase how we can convert the faces in our Unstructured Grid into Polygons. # # When constructing unstructured grids visualization, we can render each face directly onto the screen. # # Rendering each face as a polygon will lead to visuals that look like this, which are high-quality since they represent the exact geometry of each face. # # # # Continents

# # # Another example of Vector Geometries is encountered when adding features to a visualization, such as Contents or Borders. The geometries of these features are drawn onto our screen. # # # # Continents

# # # ### Shapely Example # One Python package which is used for representing and manipulating geometries is [Shapely](https://shapely.readthedocs.io/en/stable/manual.html). # # Shapely can be paired with SpatialPandas and other packages to represent unstructured grid elements (nodes, edges, faces) as geometries for visualization. # # The following code snippets are basic examples of how these elements can be represented as geometries. # # In[1]: import shapely as sp # A node is represented as a pair of longitude and latitude coordinates # In[2]: sp.Point([0.0, 0.0]) # An edge is represented as a pair of nodes. # In[3]: sp.LineString([[0.0, 0.0], [180, -90]]) # A face is represented as a counter-clockwise set of nodes, with the first and final nodes in the set being equivalent (form a closed face) # In[4]: sp.Polygon([[100, 40], [100, 50], [90, 50], [90, 40], [100, 40]]) # ## Rasterization # # While there is definitely merit in rendering each geometric shape directly, this operation is computationally expensive for large datasets. # # Rasterization is a technique in computer graphics that converts vector (a.k.a geometric shapes) graphics into a raster image, which can be thought of as a regularly-sampled array of pixel values used for rendering. # # The figure below shows a simplified example of how rasterization "approximates" the geometry of different elements. # # # # Rasterization Example

# # # For unstructured grids, rasterization looks something like the following. # # # # raster and vector

# # # The black edges outline the expected geometry of each face (a.k.a polygon). # # We can observe the jaggedness in the shading, which is the product of rasterization approximating each face. #

Note:

# The selection between vector graphics and rasterization needs to be made taking into account several factors such as how large is the dataset (i.e. how fine-resolution the data is), what data fidelity with the visualization is desired, what performance is expected, etc. #