Merge pull request #356 from hyanwong/API-extras

Use direct memory access for checks

tsdate/core.py

@@ -181,12 +181,12 @@ def precalculate_mutation_likelihoods(self, num_threads=None, unique_method=0):
                 if e.child not in self.fixednodes
             }
         else:
-            edges = self.ts.tables.edges
             fixed_nodes = np.array(list(self.fixednodes))
             keys = np.unique(
                 np.core.records.fromarrays(
-                    (self.mut_edges, edges.right - edges.left), names="muts,span"
-                )[np.logical_not(np.isin(edges.child, fixed_nodes))]
+                    (self.mut_edges, self.ts.edges_right - self.ts.edges_left),
+                    names="muts,span",
+                )[np.logical_not(np.isin(self.ts.edges_child, fixed_nodes))]
             )
             if unique_method == 1:
                 self.unfixed_likelihood_cache = dict.fromkeys({tuple(t) for t in keys})
@@ -603,8 +603,8 @@ def __init__(self, priors, lik, *, progress=False):
         self.priors.force_probability_space(lik.probability_space)
 
         self.spans = np.bincount(
-            self.ts.tables.edges.child,
-            weights=self.ts.tables.edges.right - self.ts.tables.edges.left,
+            self.ts.edges_child,
+            weights=self.ts.edges_right - self.ts.edges_left,
         )
         self.spans = np.pad(self.spans, (0, self.ts.num_nodes - len(self.spans)))
 
@@ -654,15 +654,15 @@ def edges_by_child_then_parent_desc(self, grouped=True):
         """
         wtype = np.dtype(
             [
-                ("child_age", self.ts.tables.nodes.time.dtype),
-                ("child_node", self.ts.tables.edges.child.dtype),
-                ("parent_age", self.ts.tables.nodes.time.dtype),
+                ("child_age", self.ts.nodes_time.dtype),
+                ("child_node", self.ts.edges_child.dtype),
+                ("parent_age", self.ts.nodes_time.dtype),
             ]
         )
         w = np.empty(self.ts.num_edges, dtype=wtype)
-        w["child_age"] = self.ts.tables.nodes.time[self.ts.tables.edges.child]
-        w["child_node"] = self.ts.tables.edges.child
-        w["parent_age"] = -self.ts.tables.nodes.time[self.ts.tables.edges.parent]
+        w["child_age"] = self.ts.nodes_time[self.ts.edges_child]
+        w["child_node"] = self.ts.edges_child
+        w["parent_age"] = -self.ts.nodes_time[self.ts.edges_parent]
         sorted_child_parent = (
             self.ts.edge(i)
             for i in reversed(
@@ -741,9 +741,7 @@ def inside_pass(self, *, standardize=True, cache_inside=False, progress=None):
             if standardize:
                 marginal_lik = self.lik.combine(marginal_lik, denominator[parent])
         if cache_inside:
-            self.g_i = self.lik.ratio(
-                g_i, denominator[self.ts.tables.edges.child, None]
-            )
+            self.g_i = self.lik.ratio(g_i, denominator[self.ts.edges_child, None])
         # Keep the results in this object
         self.inside = inside
         self.denominator = denominator
@@ -792,7 +790,7 @@ def outside_pass(
         for child, edges in tqdm(
             self.edges_by_child_desc(),
             desc="Outside",
-            total=len(np.unique(self.ts.tables.edges.child)),
+            total=len(np.unique(self.ts.edges_child)),
             disable=not progress,
         ):
             if child in self.fixednodes:
@@ -860,9 +858,7 @@ def outside_maximization(self, *, eps, progress=None):
 
         mut_edges = self.lik.mut_edges
         mrcas = np.where(
-            np.isin(
-                np.arange(self.ts.num_nodes), self.ts.tables.edges.child, invert=True
-            )
+            np.isin(np.arange(self.ts.num_nodes), self.ts.edges_child, invert=True)
         )[0]
         for i in mrcas:
             if i not in self.fixednodes:
@@ -871,7 +867,7 @@ def outside_maximization(self, *, eps, progress=None):
         for child, edges in tqdm(
             self.edges_by_child_then_parent_desc(),
             desc="Maximization",
-            total=len(np.unique(self.ts.tables.edges.child)),
+            total=len(np.unique(self.ts.edges_child)),
             disable=not progress,
         ):
             if child in self.fixednodes:

tsdate/prior.py

@@ -469,7 +469,7 @@ def __init__(self, tree_sequence, *, progress=False, allow_unary=False):
 
         self.ts = tree_sequence
         self.sample_node_set = set(self.ts.samples())
-        if np.any(self.ts.tables.nodes.time[self.ts.samples()] != 0):
+        if np.any(self.ts.nodes_time[self.ts.samples()] != 0):
             raise ValueError(
                 "The SpansBySamples class needs a tree seq with all samples at time 0"
             )
@@ -1034,7 +1034,7 @@ def fill_priors(
     # convert timepoints to generational timescale
     prior_times = base.NodeGridValues(
         ts.num_nodes,
-        datable_nodes[np.argsort(ts.tables.nodes.time[datable_nodes])].astype(np.int32),
+        datable_nodes[np.argsort(ts.nodes_time[datable_nodes])].astype(np.int32),
         population_size.to_natural_timescale(timepoints),
     )
 
@@ -1169,9 +1169,7 @@ def make_parameter_grid(self, population_size, progress=False):
 
         prior_pars = base.NodeGridValues(
             self.tree_sequence.num_nodes,
-            datable_nodes[np.argsort(ts.tables.nodes.time[datable_nodes])].astype(
-                np.int32
-            ),
+            datable_nodes[np.argsort(ts.nodes_time[datable_nodes])].astype(np.int32),
             np.array([0, np.inf]),
         )
         prior_pars.probability_space = base.GAMMA_PAR

tsdate/util.py

@@ -39,7 +39,7 @@ def reduce_to_contemporaneous(ts):
     Simplify the ts to only the contemporaneous samples, and return the new ts + node map
     """
     samples = ts.samples()
-    contmpr_samples = samples[ts.tables.nodes.time[samples] == 0]
+    contmpr_samples = samples[ts.nodes_time[samples] == 0]
     return ts.simplify(
         contmpr_samples,
         map_nodes=True,
@@ -187,7 +187,7 @@ def nodes_time_unconstrained(tree_sequence):
     stored in the node metadata). Will produce an error if the tree sequence does
     not contain this information.
     """
-    nodes_time = tree_sequence.tables.nodes.time.copy()
+    nodes_time = tree_sequence.nodes_time.copy()
     metadata = tree_sequence.tables.nodes.metadata
     metadata_offset = tree_sequence.tables.nodes.metadata_offset
     for index, met in enumerate(tskit.unpack_bytes(metadata, metadata_offset)):
@@ -270,7 +270,7 @@ def sites_time_from_ts(
             e.args += "Try calling sites_time_from_ts() with unconstrained=False."
             raise
     else:
-        nodes_time = tree_sequence.tables.nodes.time
+        nodes_time = tree_sequence.nodes_time
     sites_time = np.full(tree_sequence.num_sites, np.nan)
 
     for tree in tree_sequence.trees():