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Intro page
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_config.yml

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config:
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todo_include_todos: true
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intersphinx_mapping:
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py: ["https://docs.python.org/3", null]
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tskit: ["https://tskit.dev/tskit/docs/stable", null]
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msprime: ["https://tskit.dev/msprime/docs/stable", null]
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pyslim: ["https://pyslim.readthedocs.io/en/stable", null]

analysing_tree_sequences.md

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# Analysing tree sequences
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# _Analysing tree sequences_
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:::{todo}
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Add all stats material from https://tskit.dev/tskit/docs/stable/tutorial.html

args.md

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# ARGs as tree sequences
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# _ARGs as tree sequences_
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:::{todo}
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We can simulate some ARGs in msprime, explain how we can losslessly represent ARGs and get all the information you could want.

basics.md

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strictly greater than the time of the child node, and the difference in these times is
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sometimes referred to as the "length" of the edge. Since trees in a tree sequence are
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usually taken to represent marginal trees along a genome, as well as the time dimension
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each edge also has a genomic _span_, defined by the edge's *left* and a *right* position
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each edge also has a genomic _span_, defined by a *left* and a *right* position
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along the genome. There are 15 edges in the tree sequence above. Here's an example of
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one of them:
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a {attr}`~Mutation.derived_state` of "G", we know that the samples descending from node
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8 in the first tree (sample genomes 2, 4, and 5) have a "G" at {attr}`~Site.position` 257,
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while the others have the {attr}`~Site.ancestral_state` of "T". This means that Ada is
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homozygous for "T", Bob is homozygous for "G", and Cat is heterozygous "T|G".
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homozygous for "T", Bob is homozygous for "G", and Cat is heterozygous "T/G".
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In other words the ancestral state and the details of any mutations at that site,
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when coupled with the tree topology at the site {attr}`~Site.position` is sufficient to
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when coupled with the tree topology at the site {attr}`~Site.position`, is sufficient to
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define the allelic state possessed by each sample.
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Note that even though the genome is 1000 base pairs long, the tree sequence only contains

completing-forward-sims.md

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# Completing forwards simulations
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:::{note}
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Link to other tutorial sections on forward sims
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:::
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The ``msprime`` simulator generates tree sequences using the backwards in
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time coalescent model. But it is also possible to output tree sequences
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from [forwards-time](https://doi.org/10.1371/journal.pcbi.1006581)
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simulators such as [SLiM](https://messerlab.org/slim)
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and [fwdpy11](https://fwdpy11.readthedocs.io/).
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and [fwdpy11](https://fwdpy11.readthedocs.io/) (see the
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{ref}`sec_tskit_forward_simulations` tutorial).
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There are many advantages to using forward-time simulators, but they
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are usually quite slow compared to similar coalescent simulations. In this
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section we show how to combine the best of both approaches by simulating

counting_topologies.md

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# Counting topologies
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# _Counting topologies_
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Add examples of using {meth}`TreeSequence.count_topologies` and introduce {ref}`sec_combinatorics`

development.md

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# Development
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---
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jupytext:
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language: python
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---
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# _Development_
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:::{note}
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Add content for helping developers to add more tutorials.

forward_sims.md

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# Forward simulations
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---
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---
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# _Forward simulations_
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:::{note}
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Add forward simulations.
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:::

getting_started.md

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print(f"Tree {ts.last().index} covers {ts.last().interval}")
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```
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::::{margin}
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:::{warning}
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:::{caution}
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For efficiency, {meth}`~TreeSequence.trees` repeatedly returns the
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same tree object, updating it internally to reflect the (usually small) changes
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between adjacent trees. So take care to treat each tree within the ``for``
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Above, we stopped iterating after Tree 4 to limit the printed output, but iterating
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forwards through trees in a tree sequence (or indeed backwards using the standard Python
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``reversed`` function) is efficient. That means it's quick, for example to check if all
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{func}`~py:reversed` function) is efficient. That means it's quick, for example to check if all
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the trees in a tree sequence have fully coalesced (which is to be expected in
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reverse-time, coalescent simulations, but not always for tree sequences produced by
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{ref}`forward simulation <sec_tskit_forward_simulations>`).

intro.md

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# Tskit tutorials
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jupytext:
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---
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This is the home of tskit tutorials.
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# Tree sequence tutorials
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**Welcome!**
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:::{warning}
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This site is under heavy development.
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This site contains a number of tutorials to develop your understanding of
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[tree sequences](https://tskit.dev/learn.html#what) and software programs,
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such as [msprime](https://tskit.dev/msprime), that use them.
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If you are new to the world of tree sequences, we suggest you start with the
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first tutorial: {ref}`sec_what_is`
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We aim to be a friendly, welcoming open source community.
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Questions and discussion about using {program}`tskit`, the tree sequence toolkit
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should be directed to the
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[GitHub discussion forum](https://github.com/tskit-dev/tskit/discussions), and there are
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similar forums for other software in the tree sequence [development community](https://github.com/tskit-dev),
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such as for [msprime](https://github.com/tskit-dev/msprime/discussions) and
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[tsinfer](https://github.com/tskit-dev/tsinfer/discussions).
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:::{note}
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Tutorials are under constant development. Those that are still a work in progress and
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not yet ready for use are shown in _italics_ in the list of tutorials.
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We very much welcome help developing existing tutorials or writing new ones. Please open
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or contribute to a [GitHub issue](https://github.com/tskit-dev/tutorials/issues) if you
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would like to help out.
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no_mutations.md

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# Do you really need mutations?
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# _Do you really need mutations?_
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Maybe not! This tutorial takes you through a number of common tasks
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and shows how to do things by working directly with the underlying

simplification.md

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# create_notebook_data() # uncomment to recreate the tree seqs used in this notebook
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# Simplification
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# _Simplification_
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Create content. See https://github.com/tskit-dev/tutorials/issues/52

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