(feat): begin unit tests

anndata/tests/test_read_remote.py

@@ -0,0 +1,79 @@
+from pathlib import Path
+import re
+
+import joblib
+import pytest
+import numpy as np
+from scipy import sparse
+
+import anndata as ad
+from anndata.tests.helpers import (
+    as_dense_dask_array,
+    GEN_ADATA_DASK_ARGS,
+    gen_adata,
+    assert_equal,
+    subset_func,
+)
+from anndata.experimental.read_remote import read_remote
+from anndata.utils import asarray
+
+subset_func2 = subset_func
+# -------------------------------------------------------------------------------
+# Some test data
+# -------------------------------------------------------------------------------
+
+
+@pytest.fixture
+def adata():
+    X_list = [
+        [1, 2, 3],
+        [4, 5, 6],
+        [7, 8, 9],
+    ]  # data matrix of shape n_obs x n_vars
+    X = np.array(X_list)
+    obs_dict = dict(  # annotation of observations / rows
+        row_names=["name1", "name2", "name3"],  # row annotation
+        oanno1=["cat1", "cat2", "cat2"],  # categorical annotation
+        oanno2=["o1", "o2", "o3"],  # string annotation
+        oanno3=[2.1, 2.2, 2.3],  # float annotation
+    )
+    var_dict = dict(vanno1=[3.1, 3.2, 3.3])  # annotation of variables / columns
+    uns_dict = dict(  # unstructured annotation
+        oanno1_colors=["#000000", "#FFFFFF"], uns2=["some annotation"]
+    )
+    return ad.AnnData(
+        X,
+        obs=obs_dict,
+        var=var_dict,
+        uns=uns_dict,
+        obsm=dict(o1=np.zeros((X.shape[0], 10))),
+        varm=dict(v1=np.ones((X.shape[1], 20))),
+        layers=dict(float=X.astype(float), sparse=sparse.csr_matrix(X)),
+    )
+
+
+@pytest.fixture(
+    params=[sparse.csr_matrix, sparse.csc_matrix, np.array, as_dense_dask_array],
+    ids=["scipy-csr", "scipy-csc", "np-array", "dask_array"],
+)
+def mtx_format(request):
+    return request.param
+
+
+@pytest.fixture(params=[sparse.csr_matrix, sparse.csc_matrix])
+def sparse_format(request):
+    return request.param
+
+
+def test_read_write_X(tmp_path, mtx_format):
+    base_pth = Path(tmp_path)
+    orig_pth = base_pth / "orig.zarr"
+    # remote_pth = base_pth / "backed.zarr"
+
+    orig = ad.AnnData(mtx_format(asarray(sparse.random(10, 10, format="csr"))))
+    orig.write_zarr(orig_pth)
+
+    remote = read_remote(orig_pth)
+    # remote.write_zarr(remote_pth) # need to implement writing!
+
+    assert np.all(asarray(orig.X) == asarray(remote.X))