Merge branch 'main' into gg_tutorial_edits

Author: gregorgorjanc

.github/workflows/build.yml

@@ -10,23 +10,23 @@ on:
 # This job installs dependencies, build the book, and pushes it to `gh-pages`
 jobs:
   build:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-24.04
     steps:
     - name: Cancel Previous Runs
-      uses: styfle/[email protected].0
+      uses: styfle/[email protected].1
       with:
         access_token: ${{ github.token }}
 
     - name: Checkout
-      uses: actions/checkout@v4
+      uses: actions/checkout@v4.2.2
 
     # Install dependencies
     - name: Install Graphviz
       run: |
         sudo apt-get install graphviz {lib,}graphviz-dev
 
     - name: Set up Python 3.11
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5.4.0
       with:
         python-version: "3.11"
         cache: "pip"
@@ -58,7 +58,7 @@ jobs:
     # Push the book's HTML to github-pages
     - name: GitHub Pages action
       if: github.ref == 'refs/heads/main'
-      uses: peaceiris/actions-gh-pages@v3.9.3
+      uses: peaceiris/actions-gh-pages@v4.0.0
       with:
         github_token: ${{ secrets.GITHUB_TOKEN }}
         publish_dir: ./_build/html

args.md

@@ -26,7 +26,7 @@ which is additionally annotated with genomic positions such that at each
 position, a path through the edges exists which defines a tree. This graph
 interpretation of a tree sequence maps very closely to the concept of
 an "ancestral recombination graph" (or ARG). See
-[this preprint](https://www.biorxiv.org/content/10.1101/2023.11.03.565466v2) for further details.
+[this paper](http://dx.doi.org/10.1093/genetics/iyae100) for further details.
 
 ## Full ARGs
 

getting_started.md

@@ -31,9 +31,10 @@ Throughout, we'll also provide pointers to where you can learn more.
 
 :::{note}
 The examples in this
-tutorial are all written using the {ref}`tskit:sec_python_api`, but it's also possible to
-{ref}`use R<sec_tskit_r>`, or access the API in other languages, notably
-{ref}`C<sec_c_api>` and [Rust](https://github.com/tskit-dev/tskit-rust).
+tutorial are all written using the {ref}`tskit:sec_python_api`,
+but it's also possible to {ref}`use R<sec_tskit_r>`,
+or access the API in other languages,
+notably {ref}`C<sec_c_api>` and [Rust](https://github.com/tskit-dev/tskit-rust).
 :::
 
 A number of different software programs can generate tree sequences. For the purposes

popgen.md

@@ -94,7 +94,7 @@ large genomic datasets.
 
 Several simulation tools output tree sequences. Below we use the
 standard library for population genetic simulation models
-([stdpopsim](https://popsim-consortium.github.io/)) to generate a model of
+([stdpopsim](https://popsim-consortium.github.io/stdpopsim-docs/)) to generate a model of
 *Homo sapiens*, in which African, Eurasian,
 and Asian populations combine to generate a mixed American population. We can use the
 [demesdraw](https://pypi.org/project/demesdraw/) package to plot a schematic of the
@@ -107,7 +107,7 @@ import demesdraw
 from matplotlib import pyplot as plt
 
 species = stdpopsim.get_species("HomSap")
-model = species.get_demographic_model("AmericanAdmixture_4B11")
+model = species.get_demographic_model("AmericanAdmixture_4B18")
 
 # Plot a schematic of the model
 demesdraw.tubes(model.model.to_demes(), ax=plt.gca(), seed=1, log_time=True)
@@ -125,7 +125,7 @@ succinct tree sequence named `ts`:
 contig = species.get_contig("chr1", mutation_rate=model.mutation_rate, right=20_000)
 samples = {"AFR": 4, "EUR": 4, "ASIA": 4, "ADMIX": 4} # 16 diploid samples
 engine = stdpopsim.get_engine("msprime")
-ts = engine.simulate(model, contig, samples, seed=9)
+ts = engine.simulate(model, contig, samples, seed=9).trim()  # trim to first 20kb simulated
 print(f"Simulated a tree sequence of {ts.num_samples} haploid genomes:")
 print(f"{ts.num_sites} variable sites over {ts.sequence_length} base pairs")
 ```

requirements-CI.txt

@@ -9,7 +9,7 @@ numpy==1.26.4
 pandas==2.2.2
 pygraphviz==1.13
 scikit-allel==1.3.8
-stdpopsim==0.2.0
+stdpopsim==0.3.0
 tqdm==4.66.3
 tskit==0.5.8
 tskit_arg_visualizer==0.0.1

requirements.txt

@@ -8,7 +8,7 @@ networkx
 pandas
 pygraphviz
 scikit-allel
-stdpopsim
+stdpopsim>=0.3
 tqdm
 tskit>=0.5.4
 tskit_arg_visualizer

viz.md

@@ -448,6 +448,23 @@ the `tree` group contains the ID of the tree (e.g. `t0`), the `site` group on th
 contains the site ID (e.g. `s15`) the `mut` class contains the mutation ID (e.g. `m16`),
 and so on.
 
+#### CSS selector quick reference
+
+If you don't do this all the time it's not easy to remember what the various separators
+mean, so here's a quick reference (for more, see
+[these docs](https://developer.mozilla.org/en-US/docs/Web/CSS/CSS_selectors)):
+
+- `abc` (Type `abc`, like `g` for a `<g>...</g>` tag)
+- `.xyz` (Class `xyz`)
+- `#uvw` (ID `uvw`)
+- `,` (Selector list, means "or")
+- `>` (Child combinator)
+- "` `" (Descendant combinator, a space)
+- `+` (Next-sibling combinator)
+- `~` (Subsequent sibling combinator)
+- `|` (Namespace separator)
+
+
 #### Styling graphical elements 
 
 The classes above make it easy to target specific nodes or edges in one or multiple
@@ -1134,7 +1151,6 @@ def make_introgression_ts(sequence_length, random_seed=None):
             msprime.SampleSet(1, ploidy=1, time=30 * time_units, population='Neanderthal'),
         ],
         demography = demography,
-        record_migrations=True,  # Needed for tracking segments.
         random_seed=random_seed,
     )