Fix up tests

python/requirements/CI-complete/requirements.txt

@@ -1,16 +1,15 @@
-biopython==1.81
-coverage==7.2.7
-dendropy==4.6.1
+biopython==1.83
+coverage==7.5.4
+dendropy==5.0.1
 h5py==3.9.0
-kastore==0.3.2
+kastore==0.3.3
 lshmm==0.0.8
-msgpack==1.0.5
-msprime==1.2.0
-networkx==3.1
-portion==2.4.1
-pytest==7.4.0
-pytest-cov==4.1.0
-pytest-xdist==3.3.1
-tszip==0.2.2
+msgpack==1.0.8
+msprime==1.3.1
+networkx==3.3
+portion==2.4.2
+pytest==8.2.2
+pytest-cov==5.0.0
+pytest-xdist==3.6.1
+tszip==0.2.3
 xmlunittest==0.5.0
-llvmlite==0.39.1

python/requirements/CI-tests-conda/requirements.txt

@@ -1,15 +1,3 @@
-biopython==1.79
+msprime==1.3.1
+tszip==0.2.3
 h5py==3.11.0
-kastore==0.3.3
-lxml==4.9.2
-msgpack-python==1.0.4
-msprime==1.2.0
-networkx==3.1
-numba<=0.59.1 #Pinned directly as 0.60.0 fails
-numpy==1.26.4
-portion==2.3.0
-pytest-cov==4.0.0
-pytest-xdist==2.5.0
-pytest # Relaxing pin because pytest is picky about versions
-svgwrite==1.4.3
-tszip==0.2.2

python/requirements/CI-tests-pip/requirements.txt

@@ -1,4 +1,14 @@
-dendropy==4.5.2
 lshmm==0.0.8
-newick==1.3.2
+pytest==8.2.2
+pytest-cov==5.0.0
+pytest-xdist==3.6.1
+svgwrite==1.4.3
+portion==2.4.2
 xmlunittest==0.5.0
+biopython==1.83
+dendropy==5.0.1
+networkx==3.3
+msgpack==1.0.8
+newick==1.9.0
+kastore==0.3.3
+jsonschema==4.22.0

python/tests/simplify.py

@@ -157,7 +157,9 @@ def __init__(
             if self.update_sample_flags:
                 flags = self.tables.nodes.flags
                 # Zero out other sample flags
-                flags = np.bitwise_and(flags, ~tskit.NODE_IS_SAMPLE)
+                flags = np.bitwise_and(
+                    flags, np.uint32(~tskit.NODE_IS_SAMPLE & 0xFFFFFFFF)
+                )
                 flags[sample] |= tskit.NODE_IS_SAMPLE
                 self.tables.nodes.flags = flags.astype(np.uint32)
 

python/tests/test_coalrate.py

@@ -464,7 +464,7 @@ def test_missing_leaves(self):
         """
         t = self.example_ts().dump_tables()
         ss0 = np.flatnonzero(t.nodes.population == 0)
-        remove = np.in1d(t.edges.child, ss0)
+        remove = np.isin(t.edges.child, ss0)
         assert np.any(remove)
         t.edges.set_columns(
             left=t.edges.left[~remove],

python/tests/test_ld_matrix.py

@@ -1034,7 +1034,7 @@ def pi2_unbiased(
         w_Ab = state[1, k]
         w_aB = state[2, k]
         w_ab = n - (w_AB + w_Ab + w_aB)
-        with suppress_division_by_zero_warning():
+        with np.errstate(over="ignore", divide="ignore", invalid="ignore"):
             result[k] = (1 / (n * (n - 1) * (n - 2) * (n - 3))) * (
                 ((w_AB + w_Ab) * (w_aB + w_ab) * (w_AB + w_aB) * (w_Ab + w_ab))
                 - ((w_AB * w_ab) * (w_AB + w_ab + (3 * w_Ab) + (3 * w_aB) - 1))
@@ -1052,7 +1052,7 @@ def dz_unbiased(
         w_Ab = state[1, k]
         w_aB = state[2, k]
         w_ab = n - (w_AB + w_Ab + w_aB)
-        with suppress_division_by_zero_warning():
+        with np.errstate(over="ignore", divide="ignore", invalid="ignore"):
             result[k] = (1 / (n * (n - 1) * (n - 2) * (n - 3))) * (
                 (
                     ((w_AB * w_ab) - (w_Ab * w_aB))
@@ -1074,7 +1074,7 @@ def d2_unbiased(
         w_Ab = state[1, k]
         w_aB = state[2, k]
         w_ab = n - (w_AB + w_Ab + w_aB)
-        with suppress_division_by_zero_warning():
+        with np.errstate(over="ignore", divide="ignore", invalid="ignore"):
             result[k] = (1 / (n * (n - 1) * (n - 2) * (n - 3))) * (
                 ((w_aB**2) * (w_Ab - 1) * w_Ab)
                 + ((w_ab - 1) * w_ab * (w_AB - 1) * w_AB)

python/tests/test_lowlevel.py

@@ -1671,7 +1671,7 @@ def test_divergence_matrix(self):
                 ts.divergence_matrix(windows, [1, 1], [0, bad_node])
         with pytest.raises(ValueError, match="Sum of sample_set_sizes"):
             ts.divergence_matrix(windows, [1, 2], [0, 1])
-        with pytest.raises(ValueError, match="Overflow"):
+        with pytest.raises((ValueError, OverflowError), match="Overflow|out of bounds"):
             ts.divergence_matrix(windows, [-1, 2], [0])
 
         with pytest.raises(TypeError, match="str"):
@@ -3321,23 +3321,19 @@ def test_bad_tracked_samples(self):
                 )
         for bad_sample in [10**6, -1e6]:
             with pytest.raises(ValueError):
-                # Implicit conversion to integers using __int__ is deprecated
-                with pytest.deprecated_call():
-                    _tskit.Tree(
-                        ts,
-                        options=options,
-                        tracked_samples=[bad_sample],
-                    )
+                _tskit.Tree(
+                    ts,
+                    options=options,
+                    tracked_samples=[bad_sample],
+                )
             with pytest.raises(ValueError):
-                with pytest.deprecated_call():
-                    _tskit.Tree(
-                        ts,
-                        options=options,
-                        tracked_samples=[1, bad_sample],
-                    )
+                _tskit.Tree(
+                    ts,
+                    options=options,
+                    tracked_samples=[1, bad_sample],
+                )
             with pytest.raises(ValueError):
-                with pytest.deprecated_call():
-                    _tskit.Tree(ts, tracked_samples=[1, bad_sample, 1])
+                _tskit.Tree(ts, tracked_samples=[1, bad_sample, 1])
 
     def test_while_loop_semantics(self):
         for ts in self.get_example_tree_sequences():

python/tests/test_tables.py

@@ -1254,7 +1254,7 @@ def verify_metadata_vector(self, table, key, dtype, default_value=9999):
                 else:
                     md = default_value
                     break
-            assert np.all(np.cast[dtype](md) == x)
+            assert np.all(np.asarray(md, dtype=dtype) == x)
 
     def test_metadata_vector_errors(self):
         table = self.table_class()

python/tests/test_tree_stats.py

@@ -1280,7 +1280,7 @@ def site_segregating_sites(ts, sample_sets, windows=None, span_normalise=True):
         haps = ts.genotype_matrix(isolated_as_missing=False)
         site_positions = [x.position for x in ts.sites()]
         for i, X in enumerate(sample_sets):
-            X_index = np.where(np.in1d(samples, X))[0]
+            X_index = np.where(np.isin(samples, X))[0]
             for k in range(ts.num_sites):
                 if (site_positions[k] >= begin) and (site_positions[k] < end):
                     num_alleles = len(set(haps[k, X_index]))
@@ -1428,7 +1428,7 @@ def site_tajimas_d(ts, sample_sets, windows=None):
             nn = n[i]
             S = 0
             T = 0
-            X_index = np.where(np.in1d(samples, X))[0]
+            X_index = np.where(np.isin(samples, X))[0]
             for k in range(ts.num_sites):
                 if (site_positions[k] >= begin) and (site_positions[k] < end):
                     hX = haps[k, X_index]
@@ -4571,7 +4571,7 @@ def branch_trait_covariance(ts, W, windows=None, span_normalise=True):
                     has_trees = True
                 SS = 0
                 for u in range(ts.num_nodes):
-                    below = np.in1d(samples, list(tr.samples(u)))
+                    below = np.isin(samples, list(tr.samples(u)))
                     branch_length = tr.branch_length(u)
                     SS += covsq(w, below) * branch_length
                 S += SS * (min(end, tr.interval.right) - max(begin, tr.interval.left))
@@ -4606,7 +4606,7 @@ def node_trait_covariance(ts, W, windows=None, span_normalise=True):
                     break
                 SS = np.zeros(ts.num_nodes)
                 for u in range(ts.num_nodes):
-                    below = np.in1d(samples, list(tr.samples(u)))
+                    below = np.isin(samples, list(tr.samples(u)))
                     SS[u] += covsq(w, below)
                 S += SS * (min(end, tr.interval.right) - max(begin, tr.interval.left))
             out[j, :, i] = S
@@ -4782,7 +4782,7 @@ def branch_trait_correlation(ts, W, windows=None, span_normalise=True):
                     has_trees = True
                 SS = 0
                 for u in range(ts.num_nodes):
-                    below = np.in1d(samples, list(tr.samples(u)))
+                    below = np.isin(samples, list(tr.samples(u)))
                     p = np.mean(below)
                     if p > 0 and p < 1:
                         branch_length = tr.branch_length(u)
@@ -4823,7 +4823,7 @@ def node_trait_correlation(ts, W, windows=None, span_normalise=True):
                     break
                 SS = np.zeros(ts.num_nodes)
                 for u in range(ts.num_nodes):
-                    below = np.in1d(samples, list(tr.samples(u)))
+                    below = np.isin(samples, list(tr.samples(u)))
                     p = np.mean(below)
                     if p > 0 and p < 1:
                         # SS[u] += sum(w[below])**2 / 2
@@ -5046,7 +5046,7 @@ def branch_trait_linear_model(ts, W, Z, windows=None, span_normalise=True):
                     has_trees = True
                 SS = 0
                 for u in range(ts.num_nodes):
-                    below = np.in1d(samples, list(tr.samples(u)))
+                    below = np.isin(samples, list(tr.samples(u)))
                     branch_length = tr.branch_length(u)
                     SS += linear_model(w, below, Z) * branch_length
                 S += SS * (min(end, tr.interval.right) - max(begin, tr.interval.left))
@@ -5081,7 +5081,7 @@ def node_trait_linear_model(ts, W, Z, windows=None, span_normalise=True):
                     break
                 SS = np.zeros(ts.num_nodes)
                 for u in range(ts.num_nodes):
-                    below = np.in1d(samples, list(tr.samples(u)))
+                    below = np.isin(samples, list(tr.samples(u)))
                     SS[u] += linear_model(w, below, Z)
                 S += SS * (min(end, tr.interval.right) - max(begin, tr.interval.left))
             out[j, :, i] = S

python/tests/test_util.py

@@ -1,6 +1,6 @@
 # MIT License
 #
-# Copyright (c) 2018-2023 Tskit Developers
+# Copyright (c) 2018-2024 Tskit Developers
 #
 # Permission is hereby granted, free of charge, to any person obtaining a copy
 # of this software and associated documentation files (the "Software"), to deal
@@ -196,8 +196,7 @@ def test_nonrectangular_input(self):
                 # On some platforms and Python / numpy versions, a ValueError
                 # occurs instead
                 with pytest.raises((TypeError, ValueError)):
-                    with pytest.deprecated_call():
-                        util.safe_np_int_cast(bad_input, dtype)
+                    util.safe_np_int_cast(bad_input, dtype)
 
 
 class TestIntervalOps:

python/tskit/formats.py

@@ -1,6 +1,6 @@
 # MIT License
 #
-# Copyright (c) 2018-2023 Tskit Developers
+# Copyright (c) 2018-2024 Tskit Developers
 # Copyright (c) 2016-2017 University of Oxford
 #
 # Permission is hereby granted, free of charge, to any person obtaining a copy
@@ -150,9 +150,9 @@ def _load_legacy_hdf5_v2(root, remove_duplicate_positions):
     population[cr_node] = cr_population
     if "samples" in root:
         samples_group = root["samples"]
-        population[:sample_size] = samples_group["population"]
+        population[:sample_size] = np.array(samples_group["population"], copy=True)
         if "time" in samples_group:
-            time[:sample_size] = samples_group["time"]
+            time[:sample_size] = np.array(samples_group["time"], copy=True)
     tables.nodes.set_columns(flags=flags, population=population, time=time)
     _set_populations(tables)