Updated Paint Data widget documentation

doc/visual-programming/source/widgets/data/paintdata.md

@@ -11,16 +11,15 @@ The widget supports the creation of a new dataset by visually placing data point
 
 ![](images/PaintData-stamped.png)
 
-1. Name the axes and select a class to paint data instances. You can add or remove classes. Use only one class to create classless, unsupervised datasets.
-2. Drawing tools. Paint data points with *Brush* (multiple data instances) or *Put* (individual data instance). Select data points with *Select* and remove them with the Delete/Backspace key. Reposition data points with [Jitter](https://en.wikipedia.org/wiki/Jitter) (spread) and *Magnet* (focus). Use *Zoom* and scroll to zoom in or out. Below, set the radius and intensity for Brush, Put, Jitter and Magnet tools.
+1. Name the axes and select a class to paint data instances. The checkbox at Variable Y allows to output 2D or 1D data. Below, you can add or remove classes. Use only one class to create classless, unsupervised datasets.
+2. Drawing tools. Paint data points with *Brush* (multiple data instances) or *Put* (individual data instance). Select data points with *Select* and remove them with the Delete/Backspace key. Reposition data points with [Jitter](https://en.wikipedia.org/wiki/Jitter) (spread) and *Magnet* (focus). Use *Clear* to erase all points. Below, set the radius and intensity for the Brush, Put, Jitter and Magnet tools or change the symbol size.
 3. Reset to Input Data.
-4. *Save Image* saves the image to your computer in a .svg or .png format.
+4. Tick the box on the left to automatically commit changes. Alternatively, press *Send* to apply them.
 5. Produce a report.
-6. Tick the box on the left to automatically commit changes to other widgets. Alternatively, press *Send* to apply them.
 
 Example
 -------
 
 In the example below, we have painted a dataset with 4 classes. Such dataset is great for demonstrating k-means and hierarchical clustering methods. In the screenshot, we see that [k-Means](../unsupervised/kmeans.md), overall, recognizes clusters better than [Hierarchical Clustering](../unsupervised/hierarchicalclustering.md). It returns a score rank, where the best score (the one with the highest value) means the most likely number of clusters. Hierarchical clustering, however, doesn’t group the right classes together. This is a great tool for learning and exploring statistical concepts.
 
-![](images/PaintData-Example.png)
+![](images/PaintData-example.png)