Add a Tree.generate_star method

python/CHANGELOG.rst

@@ -4,6 +4,9 @@
 
 **Features**
 
+- Added ``Tree.generate_star`` static method to create star-topologies (:user:`hyanwong`,
+  :pr:`934`).
+
 - Added ``equals`` method to TableCollection and each of the tables which
   provides more flexible equality comparisons, for example, allowing
   users to ignore metadata or provenance in the comparison.

python/tests/test_topology.py

@@ -7917,3 +7917,37 @@ def test_is_isolated_bad(self):
                 tree.is_isolated("abc")
             with pytest.raises(TypeError):
                 tree.is_isolated(1.1)
+
+
+class TestExampleTrees:
+    """
+    Test hard-coded example tree (sequence) generation
+    """
+
+    def test_star_equivalent(self):
+        for extra_params in [{}, {"span": 2.5}]:
+            for n in range(2, 6):
+                ts = tskit.Tree.generate_star(n, **extra_params).tree_sequence
+                equiv_ts = tskit.Tree.unrank((0, 0), n, **extra_params).tree_sequence
+                assert ts.tables.equals(equiv_ts.tables, ignore_provenance=True)
+
+    def test_star_bad_params(self):
+        for n in [-1, 0, 1, np.array([1, 2])]:
+            with pytest.raises(ValueError):
+                tskit.Tree.generate_star(n)
+        for n in [None, "", []]:
+            with pytest.raises(TypeError):
+                tskit.Tree.generate_star(n)
+        with pytest.raises(tskit.LibraryError):
+            tskit.Tree.generate_star(2, span=0)
+        with pytest.raises(tskit.LibraryError):
+            tskit.Tree.generate_star(2, branch_length=0)
+
+    def test_star_branch_length(self):
+        branch_length = 10
+        n = 7
+        ts = tskit.Tree.generate_star(n, branch_length=branch_length).tree_sequence
+        topological_equiv_ts = tskit.Tree.unrank((0, 0), n).tree_sequence
+
+        assert ts.node(ts.first().root).time == branch_length
+        assert ts.kc_distance(topological_equiv_ts) == 0

python/tskit/trees.py

@@ -30,6 +30,7 @@
 import copy
 import functools
 import itertools
+import json
 import math
 import os
 import textwrap
@@ -45,6 +46,7 @@
 import tskit.exceptions as exceptions
 import tskit.formats as formats
 import tskit.metadata as metadata_module
+import tskit.provenance as provenance
 import tskit.tables as tables
 import tskit.util as util
 import tskit.vcf as vcf
@@ -2342,6 +2344,52 @@ def kc_distance(self, other, lambda_=0.0):
         """
         return self._ll_tree.get_kc_distance(other._ll_tree, lambda_)
 
+    @staticmethod
+    def generate_star(num_leaves, *, span=1, branch_length=1, record_provenance=True):
+        """
+        Generate a single :class:<Tree> whose leaf nodes all have the same parent (i.e.
+        a "star" tree). The leaf nodes are all at time 0 and are marked as sample nodes.
+
+        .. note::
+            This is similar to ``tskit.Tree.unrank((0,0), n, span=span)`` but is more
+            efficient for large n. However, the ``unrank`` method provides
+            a concise way of generating alternative (non-star) topologies.
+
+        :param int num_leaves: The number of leaf nodes in the returned tree (must be
+            be 2 or greater).
+        :param float span: The span of the tree, and therefore the
+            :attr:`~TreeSequence.sequence_length` of the :attr:`.tree_sequence`
+            property of the returned :class:<Tree>.
+        :param float branch_length: The length of every branch in the tree (equivalent
+            to the time of the root node).
+        :return: A star-shaped tree. Its corresponding :class:`TreeSequence` is available
+            via the :attr:`.tree_sequence` attribute.
+        :rtype: Tree
+        """
+        if num_leaves < 2:
+            raise ValueError("The number of leaves must be 2 or greater")
+        tc = tables.TableCollection(sequence_length=span)
+        tc.nodes.set_columns(
+            flags=np.full(num_leaves, NODE_IS_SAMPLE, dtype=np.uint32),
+            time=np.zeros(num_leaves),
+        )
+        root = tc.nodes.add_row(time=branch_length)
+        tc.edges.set_columns(
+            left=np.full(num_leaves, 0),
+            right=np.full(num_leaves, span),
+            parent=np.full(num_leaves, root, dtype=np.int32),
+            child=np.arange(num_leaves, dtype=np.int32),
+        )
+        if record_provenance:
+            # TODO replace with a version of https://github.com/tskit-dev/tskit/pull/243
+            # TODO also make sure we convert all the arguments so that they are
+            # definitely JSON encodable.
+            parameters = {"command": "generate_star", "TODO": "add parameters"}
+            tc.provenances.add_row(
+                record=json.dumps(provenance.get_provenance_dict(parameters))
+            )
+        return tc.tree_sequence().first()
+
 
 def load(file):
     """