Flesh out the basic workbooks with coalescence rate calcs and trees

Author: hyanwong

content/data/demo.arg

@@ -0,0 +1,48 @@
+# demo.tree file created using the following script
+
+import tskit
+import msprime
+import collections
+
+from matplotlib import pyplot as plt
+
+seq_length = 1e6
+recent_generations = 200
+rho = 1e-7
+tree = "((A:250,B:250):150,(C:210,D:210):190);"
+initial_size = collections.defaultdict(lambda: 500)
+initial_size.update({"A": 500, "B": 500, "C": 300, "D": 1000})
+samples = {"A": 10, "B": 10, "C": 10, "D": 10}
+
+ts_store = []
+for name, Ne in initial_size.items():
+    demography = msprime.Demography()
+    demography.add_population(name=name, initial_size=Ne)
+    # Place a selective sweep in population A
+    if name == "A":
+        p = 1 / (2 * Ne)
+        sweep_model = msprime.SweepGenicSelection(
+            position=seq_length//3,
+            s=0.1,
+            start_frequency=p,
+            end_frequency=1 - p,
+            dt=1 / (40 * Ne),
+        )
+    ts_store.append(msprime.sim_ancestry(
+        samples[name],
+        model=(sweep_model, msprime.StandardCoalescent()) if name == "A" else msprime.StandardCoalescent(),
+        demography=demography,
+        recombination_rate=rho,
+        sequence_length=seq_length,
+        end_time=recent_generations,
+        random_seed=123,
+    ))
+tables = ts_store[0].dump_tables()
+for ts in ts_store[1:]:
+    tables.union(ts.dump_tables(), node_mapping=[tskit.NULL] * ts.num_nodes)
+tables.provenances.clear()
+
+demography = msprime.Demography.from_species_tree(tree, initial_size)
+ts = msprime.sim_ancestry(initial_state=tables.tree_sequence(), recombination_rate=rho, sequence_length=seq_length, demography=demography, random_seed=123).simplify()
+ts = msprime.sim_mutations(ts, rate=5e-8, random_seed=123)
+ts.dump("data/demo.trees")