Sometimes, you have a large file or input stream that is mostly sorted by some fields with just a few records out of order now and then. You may not care about those few outliers, all you want is most of your data sorted.
Now, you can discard the records out of order, using csv-sort, e.g:
As usual, you can sort by multiple key fields (e.g. csv-sort --discard-out-of-order --fields=a,b,c), sort block by block (e.g. csv-sort --discard-out-of-order --fields=t,block), etc.
linear-combination operations of
cv-cat have been extended to support assignment to multiple channels. Previously, these operations would take up to 4 input channels (symbolically always named r, g, b, and a, regardless of the actual contents of the data) and produce a single-channel, grey-scale output. Now you can assign up to four channels:
The right-hand side of the ratio / linear combination operations contains comma-separated expressions defining each of the output channels through the input channels. The number of output channels is the number of comma-separated fields, it may differ from the number of input channels. As a shortcut, an empty field, such as in
is interpreted as channel pass-through. In the example above the output has three channels, with channels 0 and 2 assigned verbatim to the input channels 0 and 2 (r and b, symbolically), and the channel 1 (symbolic g) assigned to the sum of all three channels.
As yet another shortcut,
cv-cat provides a shuffle operation that re-arranges the input channels without changing their values:
In this case, the order of the first 3 channels is reversed, while the former channel r is also duplicated into channel 3 (alpha). Internally, shuffling is implemented as a restricted case of linear combination, and therefore, other usual rules apply: the number of output channels is up to 4, it does not depend on the number of input channels, and an empty field in the right-hand side is interpreted as channel pass-through.
When using view-points, there often is a need to quickly visualise or hide several point clouds or other graphic primitives.
Now, you can group data in view-points, using groups key word. A source can be assigned to one or more groups by using the groups arguments. Basic usage is:
For example if we have two graphs as follows:
We can separate the graphs as well as group together nodes and edges of different graphs as follows:
Try to switch on/off checkboxes for various groups (e.g. "graph01", "nodes", etc) and observe the effect.