Table

Table, sometimes called a data frame is a collection of columns and rows. The structure and limitations are as follows:

• The column names have to be unique (case sensitve), but can have spaces and unicode characters.
• The number of rows in each column are the same, but entries can be missing.
• The type can vary and there is a number of different types, not just numerical and text.
• The structure of the table, that is the name and type of each column is defined even if there is no data. This is similar to other data types like images and means that column names and types will show up in menus in the user interface even when nothing has been computed. This also means that if you have a time sequence of tables every time value has the same structure.

Column types

• Number – numerical value.
• Date – encodes a calendar date. Can view this as a numerical value, and in that case is the number of seconds since the reference date Jan 1st 1970.
• Point in 2D – In expressions you refer to the entries as .x and .y
• Point in 3D – In expressions you refer to .x,.y,.z
• Path in 2D – a polygon for every row. Same restriction as the 2D Path variable.
• Surface in 3D – a triangulated surface in every row.
• Mask in 2D
• Mask in 3D

Creating a table

Use the table button in the gear menu to create a table from scratch. The main methods are

• From a file: Read in a text file, such as a comma or tab separated file. You create an importer and define the structure. This is a very flexible method to import data and allows you to apply filters during the import.
• Using expressions: Use this to create a table from scratch. For example if you want to evaluate a function at uniformly spaced points, create a table with random numbers etc.

Modifying a table

You can not modify individual entries in a table easily. Use a program like DataGraph to do that. ImageTank is geared towards a computational pipeline where data comes from files or computation. You can modify entries programmatically however, and use the gear menu for that.

• Subset, could be called a filter, extracts a subset of rows based on a criteria. This creates a table with the same structure. Use this for example if you want to focus on rows/records where a given column is positive or inside/outside some interval.
• Combine time values: Variables in ImageTank can be collections, called time series. For example if you have 100 files and create a table from each file using the table importer, if you want to concatenate all of these tables you are combining the time values.
• Map Columns: Allows you to map individual columns. For example if you want to sum up columns, or break a point column into a x column and a y column or combine two numerical columns into a single point column etc.
• Append Tables: Combine tables from different variables with the same structure. The combine action combines across time for a single variable, this combines tables from multiple variables.

Example : Use expressions

The most common column type is the numerical column. This is what you will use for a xy plot for example. Take a simple example, and generate a x-y table from an expression. There are number of ticks and concepts used in the following figure, and we will go through them below the figure

• The variables N,a,b,w,x are local variables that are created using the small plus button that it is to the left of the “Add column” menu.
• The top entry N is used to specify the number of rows. Since it is a local variable it can be used in any expression field inside the variable as well as subsequent local variables.
• a,b define the starting an ending of the interval and the w variable then refers to them. For local variables you can use other local variables as long as they are defined above. The w variable is used to show that and to simplify the x variable a little bit.
• The x is a local variable, but local variables can vary based on the row number if they depend on the row number. The # variable is defined implicitly as a local varialbe and is essentially a column with length N. That makes x a variable a column as well.
• Below the local variables are the column names and here we define two columns x and y. This is not a local variable. The reason you can use a function of x for the y column is that x is already a local variable. If you called the first column xcol you could not use xcol to define the second column.
• The variable monitor is switched to a x-y plot and the columns can be varied to quickly do a x-y graph.

Example : Draw a x-y graph

You can draw the content of a table in various graphing windows, xy,2D,3D etc. The standard is the x-y plot. Increase the document window to reveal the drawing canvas and drag the table onto the right side. Since you have a numerical column it suggests two drawing commands, scatter and line. Drag onto the line command

This creates a new xy graph and adds a single command. Currently this is relatively limited compared to DataGraph, but that will be fleshed out based on requests. In fact in order for this window to work you need DataGraph to be licensed on the machine. You can recognize the style, canvas and axis commands and they are sub-sets of what DataGraph has.

One big contrast with DataGraph is that

Example : Draw in 2D

XY graphs are two dimensional graphics, but the x and y axis are independent so it is not very good to use them for data where the x and y values are really spatial coordinates and you want the scale to be the same in x and y. This is what the 2D space drawing handles. It was not an option in the previous example because there wasn’t a 2D point column. So let us add that to the above table. Add a 2D Point using the “Add column” menu for the table. Use an expression with two components to define the point value for each row. When you drag the table onto an empty drawing region you get the xy option as above but also a 2D option with a scatter option. The expression is “r*(cos(2π*x),sin(2π*y))” where r is a local variable defined as x, but you could use x instead and avoid the local variable.

Example : Mapping a table

If you have a table that has a 2D Point column but you want to draw the components you need to extract the x and y components into columns in order to draw them in a x-y graph. This is done by going to the gear menu for the variable and select the “Map Columns” option.

• The x column is copied, and if you have column types that are not a number or point this is how you carry them over to the new table.
• The x and y columns come from the spiral column in the source image. At the top you see a “Defined:” list that shows the numerical values that are defined as local variables. No additional local variables are defined here, but they could. The x is then simply spiral.x to be the x component of the spiral entry. This is evaluated for every row.

Sending data to DataGraph

DataGraph will not be replaced by ImageTank. See this writeup that addresses the difference between the programs. It is easy to send data from ImageTank to DataGraph. Since DataGraph looks at all data as a table, you do that by sending a table over. This uses the same mechanism as the CRAN package to save a data frame into a file that DataGraph can easily import.

The first step is to export the ImageTank file into a dtable file

What you get is a single object to save into a file. Just type in the name of this file, ending in .dtable

Once you have this file, just drag and drop it into the column list in DataGraph

If you keep the Save checkbox checked, every time the data changes the file is resaved. Once it is resaved DataGraph will detect that it was updated and reload it. That will trigger any derivated content such as graphics to be updated.

The file will stay even if the ImageTank file closes, but if you use the /tmp folder it will be removed automatically by the system when it reboots or decides to clean up old files. That is the temporary folder after all. For that you should save the file somewere more permanent. Note that it might not be good to save it in a location that DropBox or other syncing utilities are trying to keep sync’d because the file might be written very frequently when you change values.