biopython v1.71.0 Bio.Graphics.BasicChromosome
Draw representations of organism chromosomes with added information.
These classes are meant to model the drawing of pictures of chromosomes. This can be useful for lots of things, including displaying markers on a chromosome (ie. for genetic mapping) and showing syteny between two chromosomes.
The structure of these classes is intended to be a Composite, so that it will be easy to plug in and switch different parts without breaking the general drawing capabilities of the system. The relationship between classes is that everything derives from _ChromosomeComponent, which specifies the overall interface. The parts then are related so that an Organism contains Chromosomes, and these Chromosomes contain ChromosomeSegments. This representation differents from the canonical composite structure in that we don’t really have ‘leaf’ nodes here — all components can potentially hold sub-components.
Most of the time the ChromosomeSegment class is what you’ll want to customize for specific drawing tasks.
For providing drawing capabilities, these classes use reportlab:
http://www.reportlab.com
This provides nice output in PDF, SVG and postscript. If you have reportlab’s renderPM module installed you can also use PNG etc.
Link to this section Summary
Functions
Try to layout label co-ordinates (or other floats, PRIVATE)
Link to this section Functions
Try to layout label co-ordinates (or other floats, PRIVATE).
Originally written for the y-axis vertical positioning of labels on a chromosome diagram (where the minimum gap between y-axis co-ordinates is the label height), it could also potentially be used for x-axis placement, or indeed radial placement for circular chromosomes within GenomeDiagram.
In essence this is an optimisation problem, balancing the desire to have each label as close as possible to its data point, but also to spread out the labels to avoid overlaps. This could be described with a cost function (modelling the label distance from the desired placement, and the inter- label separations as springs) and solved as a multi-variable minimization problem - perhaps with NumPy or SciPy.
For now however, the implementation is a somewhat crude ad hoc algorithm.
NOTE - This expects the input data to have been sorted!