This is the documentation for the Projection Space Explorer.
- On the bottom right there is a tool list which let you select one of the predefined tools (select, move, etc). Hovering over them will show you a description of what they do.
- Use the mouse wheel to zoom in and out
- Hold the right mouse button pressed while moving the mouse (dragging) on the context view to move the projection around
- Right click the context to open a context-agnostic menu (has different actions when pressing on a cluster for example)
This section describes the individual UI components of the main view.
Shows the current projection and allows the user to do selections, and show contextual menus and information by clicking the items shown.
Shows different groups of actions that can be set.
Shows the Toolbar selection and the logo of the application.
This section describes how the .csv data has to be formatted in order to be readable by this application.
The file name needs to end with .csv and the data needs to be formatted as a correct csv file including a header row. The seperator used is the normal comma , (instead of the ; which is used sometimes). We use the .csv reader provided by d3 in this application. (You can read exact specifications like how spaces in data fields are handled in their API).
The minimal columns a file needs are
- x: the x value of the coordinates (floating point format)
- y: the y value of the coordinates (floating point format)
An example file could look like this
| x | y |
|---|---|
| 1 | 1 |
| -1 | -1 |
| 1 | -1 |
| -1 | 1 |
which will be displayed like this
The columns x and y are necessary for the file to load, but there other columns which are optional and only help you to explore the data. This includes:
- line an attribute that specifies a sequence along the data, this attribute can be numerical or categorical, but it has to be distinct for every line. The line index of the current line is inferred automatically by the order of the data points in the csv file.
- algo the group this data point belongs to. This attribute can also be categorical or numerical.
- If the attributes line and algo are omitted, the data is projected as points without lines.
Additional columns might exist in the source file. If this is the case the tool will display meaningful options to change visualization properties in respect to these attributes.
Possible visualization properties that can be changed are:
- color (by categorical and sequential/diverging attributes)
- brightness (by sequential/diverging attributes)
- transparency (by sequential/diverging attributes)
- size (by sequential/diverging attributes)
Currently there is a distinction between categorical, sequential and diverging attributes. The information is infered from the csv header and the value range.
An attribute is categorical if:
- It has at most 8 different distinct values in its value range
- It has at least 1 non-floating point number in its value range
An attribute is sequential if:
- It has only floating point numbers in its value range
- It has a range annotation in its header (see below)
An attribute is diverging if:
- It has only floating point numbers in its value range or
- It has a range annotation in its header (see below)
Range annotations are a way to set the value range of attributes directly in the csv header. For example lets say we have a header that looks like this:
x,y,line,algo,metadata[0;10]
Now metadata is an additional attribute with a range annotation that sets the value range to 0-10 and makes this attribute sequential. The value range is important for the correct mapping to the color scale (since the minimum and maximum values might not be the true value range).
Some columns can be set to provide additional metadata that can be displayed in the explorer.
The column 'changes' can be supplied with text values that will be displayed with the line selection tool. It should annotate the change from the last state. For example a good value for 'changes' for the Rubik data would be the move that lead to this state (R, U...).
The column 'multiplicity' can be supplied to denote how often this state occurs in the dataset (with the exact same position). This is sometimes necessary if you want to project the data without any duplicates, but need it in the explorer to display the lines correctly (since only single edges are allowed). For example if the same state occurs in 3 lines, it would have a multiplicity of 3 in each row occurence.
There are some toy datasets available in this repository under datasets/toy which are small examples of how the files should look like. They also provide a playground for testing different projection methods.
Use a git tool to clone this repository to your computer.
git clone https://github.com/JKU-ICG/projection-space-explorerThen navigate to this folder in a terminal using
cd projection-space-explorerand run the command to install the required packages
npm installThere is always a valid build in the repository, but in case you want to make changes, you can use the local build server. Start it with the command
npm run webpack:dev
Whenever a file is changed while this server is running, it will automatically build a new version and deploy it in the /dist folder.
To start the application you just need to start the index.html locally. The easiest way to this is by using the live server provided by either Atom or Visual Studio Code.
For the projection algorithms, we use the following libraries:
https://github.com/karpathy/tsnejs