Backwards #275
Backwards #275
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| at 300 generations ago, there were three extant genomes | ||
| from which the samples inherited, and the inherited segments are | ||
| as listed here. | ||
| Note that this does not mean that "node 2 was laive 300 generations ago" |
| (clearly, as node 2 represetns an extant, sampled genome), | ||
| but rather that there are no other ancestral genomes recorded explicitly | ||
| in the tree sequence that lie on the path along | ||
| which node 2 has inherited it's genome. |
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Nice. I like this. Note that there are a few examples of iterating up and down the graph at https://tskit.dev/tutorials/args.html#graph-traversal, but I don't actually do anything with the traversals, so your examples are better. Also note that some of stuff might also link in to tskit-dev/tskit#2869, and there are some suggestions of things you might want to calculate there. One thing that is much easier to do compared to the tree-by-tree approach is to find all the descendant samples of a particular ancestral node (or alternatively, all the internal nodes that are ancestors of a particular sample). |
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| ```{code-cell} ipython3 | ||
| for e in ts.edges(): | ||
| t = ts.node(e.parent).time |
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Maybe use ts.nodes_time[e.parent] so that that this is more easily translatable to numba?
| from which the samples inherited, and the inherited segments are | ||
| as listed here. | ||
| Note that this does not mean that "node 2 was laive 300 generations ago" | ||
| (clearly, as node 2 represetns an extant, sampled genome), |
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| (clearly, as node 2 represetns an extant, sampled genome), | |
| (clearly, as node 2 represents an extant, sampled genome), |
| Here is a data structure for a list of segments with labels: | ||
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| ```{code-cell} ipython3 | ||
| class LabelSegmentList: |
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It might be worth considering inheriting from collections.abc.MutableSequence here, as this would give you all the dunder methods that you're implementing. I think these might be a bit scary to non-python people, and are a bit of a distraction from the main point.
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I guess you'd need to inherit from list then to get the actual storage. Maybe that's OK?
| Now, edges in the EdgeTable are sorted by parent time, | ||
| so if we iterate through the edges in order, we move back in time. | ||
| So, we can use this to see the state of the process at, say, | ||
| 500 generations in the past: |
In working out an algorithm that wants to move back through time, it seemed helpful to do a simple explainer on iterating back in time - i.e., taking the haplotype view instead of the tree-by-tree view.
This is a draft of that. Suggestions for nicer python or fun examples welcome! So far it's not demonstrating anything that you couldn't do tree-by-tree.