Use sgkit.distarray for Hardy-Weinberg Equilibrium

.github/workflows/cubed.yml

@@ -30,4 +30,4 @@ jobs:
 
     - name: Test with pytest
       run: |
-        pytest -v sgkit/tests/test_aggregation.py -k 'test_count_call_alleles or test_sample_stats or (test_count_variant_alleles and not test_count_variant_alleles__chunked[call_genotype]) or (test_variant_stats and not test_variant_stats__chunks[chunks2-False])' --use-cubed
+        pytest -v sgkit/tests/test_{aggregation,hwe}.py -k 'test_count_call_alleles or test_hwep or test_sample_stats or (test_count_variant_alleles and not test_count_variant_alleles__chunked[call_genotype]) or (test_variant_stats and not test_variant_stats__chunks[chunks2-False])' --use-cubed

sgkit/stats/aggregation.py

@@ -457,9 +457,8 @@ def genotype_coords(
     G = da.map_blocks(_index_as_genotype, X, K, new_axis=1, chunks=chunks)
     # allow enough room for all alleles and separators
     dtype = "|S{}".format(max_chars * ploidy + ploidy - 1)
-    S = da.map_blocks(
-        genotype_as_bytes, G, False, max_chars, drop_axis=1, dtype=dtype
-    ).astype("U")
+    S = da.map_blocks(genotype_as_bytes, G, False, max_chars, drop_axis=1, dtype=dtype)
+    S = da.astype(S, "U{}".format(max_chars * ploidy + ploidy - 1))
     new_ds = create_dataset({variables.genotype_id: ("genotypes", S)})
     ds = conditional_merge_datasets(ds, new_ds, merge)
     if assign_coords:

sgkit/stats/hwe.py

@@ -1,9 +1,9 @@
 from typing import Hashable, Optional
 
-import dask.array as da
 import numpy as np
 from xarray import Dataset
 
+import sgkit.distarray as da
 from sgkit import variables
 from sgkit.accelerate import numba_jit
 from sgkit.stats.aggregation import count_variant_genotypes