From e7baf246df0f5462706bb38e055ed1cfb56c65cd Mon Sep 17 00:00:00 2001 From: PopSim-bot Date: Fri, 20 Dec 2024 16:21:01 +0000 Subject: [PATCH] Automated doc build for refs/heads/main --- ...og_anapla_models_mallardblackduck_2l19.png | Bin 37903 -> 36541 bytes ...g_homsap_models_americanadmixture_4b18.png | Bin 72753 -> 73394 bytes ...catalog_homsap_models_outofafrica_2t12.png | Bin 54207 -> 54765 bytes ...catalog_homsap_models_outofafrica_3g09.png | Bin 71203 -> 69016 bytes ...catalog_homsap_models_outofafrica_4j17.png | Bin 72647 -> 72981 bytes ...odels_outofafricaarchaicadmixture_5r19.png | Bin 74914 -> 74445 bytes ...aextendedneandertaladmixturepulse_3i21.png | Bin 31303 -> 31302 bytes ...homsap_models_papuansoutofafrica_10j19.png | Bin 63864 -> 63909 bytes ...orysat_models_bottleneckmigration_3c07.png | Bin 60570 -> 58673 bytes ...catalog_pantro_models_bonoboghost_4k19.png | Bin 40762 -> 40920 bytes ..._catalog_ponabe_models_twospecies_2l11.png | Bin 46067 -> 47055 bytes 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a/main/_sources/tutorial.rst.txt b/main/_sources/tutorial.rst.txt index 5c21185..5f15164 100644 --- a/main/_sources/tutorial.rst.txt +++ b/main/_sources/tutorial.rst.txt @@ -683,7 +683,16 @@ defined above. The simulation results are recorded in a tree sequence object .. code-block:: python - ts = engine.simulate(model, contig, samples) + ts = engine.simulate( + model, contig, samples, msprime_model="dtwf", msprime_change_model=[(20, "hudson")] + ) + +The `msprime_model` argument is optional; it specifies that the most recent 20 +generations be simulated with the discrete time Wright-Fisher ("dtwf") model +(followed by a switch to the more efficient "hudson" coalescent model). This is +good practice for large samples, for which the Hudson model can be an +`inaccurate approximation `__ +for the most recent few generations. Sanity check the tree sequence output ------------------------------------- diff --git a/main/searchindex.js b/main/searchindex.js index c4ff5e9..699a687 100644 --- a/main/searchindex.js +++ b/main/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["api", "catalog", "changelogs", "cli_arguments", "development", "index", "installation", "introduction", "tutorial"], "filenames": ["api.rst", "catalog.rst", "changelogs.rst", "cli_arguments.rst", "development.rst", "index.rst", "installation.rst", "introduction.rst", "tutorial.rst"], "titles": ["API", "Catalog", "Changelogs", "Command-line options", "Development", "Welcome to stdpopsim\u2019s documentation!", "Installation", "Introduction", "Tutorials"], "terms": {"function": [0, 1, 4, 6, 8], "get": [0, 1, 2, 5, 6, 8], "thing": [0, 4, 8], "class": [0, 2], "object": [0, 1, 4, 8], "The": [0, 1, 2, 3, 4, 6, 7], "catalog": [0, 2, 3, 4, 5, 7, 8], "contain": [0, 1, 4, 6, 8], "larg": [0, 4, 8], "number": [0, 1, 3, 4, 7, 8], "which": [0, 1, 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-828,9 +828,17 @@

Choose a sampling scheme, and simulateFinally, we simulate the model with the contig length and number of samples we defined above. The simulation results are recorded in a tree sequence object (tskit.TreeSequence).

-
ts = engine.simulate(model, contig, samples)
+
ts = engine.simulate(
+    model, contig, samples, msprime_model="dtwf", msprime_change_model=[(20, "hudson")]
+)
 

 

+
The msprime_model argument is optional; it specifies that the most recent 20
+generations be simulated with the discrete time Wright-Fisher (“dtwf”) model
+(followed by a switch to the more efficient “hudson” coalescent model). This is
+good practice for large samples, for which the Hudson model can be an
+inaccurate approximation
+for the most recent few generations.

 
 

 
Sanity check the tree sequence output

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