Merge pull request #236 from hyanwong/update-old-issues

Remove links to solved issues

Author: jeromekelleher

getting_started.md

@@ -159,17 +159,11 @@ plt.show()
 It's obvious that there's something unusual about the trees in the middle of this
 chromosome, where the selective sweep occurred. 
 
-:::{margin} Comments needed
-If you have a need for efficient random access to trees like this, please comment on
-[this GitHub issue](https://github.com/tskit-dev/tskit/issues/684) to help us implement
-a solution.
-:::
-
-Currently, it's not particularly efficient to pull out individual trees from the middle
-of a tree sequence, but it *can* be done, via the
-{meth}`TreeSequence.at` method. Here's the tree at location
-$5\ 000\ 000$ --- the position of the sweep --- drawn using the 
-{meth}`Tree.draw_svg` method.
+Although tskit is designed so that is it rapid to pass through trees sequentially,
+it is also possible to pull out individual trees from the middle of a tree sequence
+via the {meth}`TreeSequence.at` method. Here's how you can use that to extract
+the tree at location $5\ 000\ 000$ --- the position of the sweep --- and draw it
+using the {meth}`Tree.draw_svg` method.
 
 ```{code-cell} ipython3
 swept_tree = ts.at(5_000_000)  # or you can get e.g. the nth tree using ts.at_index(n)

tables_and_editing.md

@@ -738,10 +738,10 @@ print("Original ts has", ts.num_mutations, "mutations on", ts.num_trees, "trees"
 
 tables = ts.dump_tables()  # Copy the table collection associated with this tree sequence
 tables.mutations.clear()  # Clear all mutations in the table collection copy
-v_iter = ts.variants()  # Workaround until https://github.com/tskit-dev/tskit/issues/605 is solved
+variant = tskit.Variant(ts)  # Reuse the same Variant object
 for tree in ts.trees():
     for site in tree.sites():
-        variant = next(v_iter)
+        variant.decode(site.id)  # Efficient if ids are sequential
         anc_state, mutations = tree.map_mutations(variant.genotypes, variant.alleles)
         if len(mutations) <  len(site.mutations):
             # Genotypes can be explained with a more parsimonious distribution of mutations