# Introduction

*Denali* is a tool for visualizing complex and possibly high-dimensional data intuitively and efficiently. The central concept used by *denali* is that of the *landscape metaphor*: a 2-d "mountainous terrain" embedded in 3-d which preserves important features of the data and presents them to the user in an intuitive fashion. This software package includes tools for computing, visualizing, and manipulating landscape metaphors.

## Can I use *denali* to visualize my data?

Broadly-speaking, there are two pre-requisites for visualizing your data with *denali*:

- Each data point must have an associated scalar value, such as a probability, score, or cost.
- You must have some method of extracting tree-like structure from your data.

Included with *denali* are tools for satisfying the second prerequisite for many types of data that may not appear, at first glance, to exhibit tree-like structure.

For example, suppose we have drawn many samples from a probability distribution. Can we attempt to understand the structure of the distribution by visualizing the samples with *denali*? For each sample we have an associated probability, so the first prerequisite is clearly met. However our data is in the form of a point cloud, not a tree, and so it appears that the second requirement is not satisfied. In fact, we *can* extract tree-like structure from this point cloud in a very natural way using the concept of the *contour tree*. The contour tree captures the important topological characteristics of a scalar function, and, as its name implies, it exhibits tree-like structure. Hence our second requirement is met, and we *can* visualize the probability distribution using *denali*.

*Denali* includes tools for quickly and easily computing contour trees, making it simply and straightforward to visualize many different types of data as landscape metaphors.

## What is included with *denali*?

Several tools are provided alongside *denali*. *ctree* is tool for computing contour trees from point clouds. *pydenali* is a package of Python utilities for interacting with *denali* and *ctree*.

## Using this documentation

The suggested method of using this documentation is as follows:

First, if you have yet to install

*denali*, see the installation instructions.After installation, jump right into the tutorial. The tutorial is an in-depth practical discussion of how to use

*denali*, including how to interpret the visualization, provide input, and use some of*denali*'s advanced features.For tips on how to use

*denali*to visualize several different types of data, see the cookbook.