Fix CI for Zarr Python v3

.github/workflows/build.yml

@@ -43,7 +43,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        zarr: ["==3.0.0b1"]
+        zarr: [">=3"]
     steps:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v5

docs/examples/1kg.schema.json

@@ -1368,6 +1368,29 @@
             },
             "filters": []
         },
+        {
+            "name": "variant_length",
+            "dtype": "i1",
+            "shape": [
+                10879
+            ],
+            "chunks": [
+                10000
+            ],
+            "dimensions": [
+                "variants"
+            ],
+            "description": "The length of the variant measured in bases",
+            "vcf_field": "rlen",
+            "compressor": {
+                "id": "blosc",
+                "cname": "zstd",
+                "clevel": 7,
+                "shuffle": 0,
+                "blocksize": 0
+            },
+            "filters": []
+        },
         {
             "name": "call_AD",
             "dtype": "i1",

requirements-dev.txt

@@ -13,7 +13,7 @@ partd
 bed-reader
 rechunker
 cbgen < 1.0.5
-bio2zarr; platform_system != "Windows"
+bio2zarr @ git+https://github.com/sgkit-dev/bio2zarr.git; platform_system != "Windows"
 yarl
 matplotlib
 asv

sgkit/tests/io/test_dataset.py

@@ -58,7 +58,7 @@ def test_save_unequal_chunks_error():
     # Normal zarr errors shouldn't be caught
     with pytest.raises(
         (FileExistsError, ValueError),
-        match="(path '' contains an array|Store already exists)",
+        match="(path '' contains an array|is not empty)",
     ):
         save_dataset(ds, {".zarray": ""})
 

sgkit/tests/test_testing.py

@@ -7,6 +7,9 @@
 from sgkit.testing import simulate_genotype_call_dataset
 
 
+@pytest.mark.filterwarnings(
+    "ignore::UserWarning"
+)  # codec `vlen-utf8` not in Zarr v3 spec`
 def test_simulate_genotype_call_dataset__zarr(tmp_path):
     path = str(tmp_path / "ds.zarr")
     ds = simulate_genotype_call_dataset(n_variant=10, n_sample=10)