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apis/python/test_03_versions.py

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+# These are test that need to run first to check basic functionality, before we go on to test other,
+# more complex things.
+from __future__ import annotations
+
+import os
+
+import pandas as pd
+import pytest
+
+import tiledbsoma
+import tiledbsoma.io
+import tiledbsoma.logging
+
+if os.getenv("TILEDB_REST_TOKEN") is None:
+    pytest.skip(reason="$TILEDB_REST_TOKEN is not set", allow_module_level=True)
+
+
+@pytest.mark.parametrize(
+    "uri",
+    [
+        "tiledb://unittest/pbmc3k_unprocessed_1_7_3",
+        "tiledb://unittest/pbmc3k_unprocessed_1_12_3",
+        "tiledb://unittest/pbmc3k_unprocessed_1_14_5",
+        "tiledb://unittest/pbmc3k_unprocessed_1_15_0",
+        "tiledb://unittest/pbmc3k_unprocessed_1.15.7",
+    ],
+)
+def test_basic_readback(uri):
+    with tiledbsoma.Experiment.open(uri) as exp:
+
+        md = dict(exp.metadata)
+        assert md["dataset_type"] == "soma"
+        assert md["soma_object_type"] == "SOMAExperiment"
+
+        md = dict(exp.obs.metadata)
+        assert md["soma_object_type"] == "SOMADataFrame"
+
+        md = dict(exp.ms["RNA"].var.metadata)
+        assert md["soma_object_type"] == "SOMADataFrame"
+
+        md = dict(exp.ms["RNA"].X["data"].metadata)
+        assert md["soma_object_type"] == "SOMASparseNDArray"
+
+        obs_df = exp.obs.read().concat()
+        # XXX asserts
+        var_df = exp.ms["RNA"].var.read().concat()
+        # XXX asserts
+        X = exp.ms["RNA"].X["data"].read().tables().concat()
+        # XXX asserts
+
+        adata = tiledbsoma.io.to_anndata(exp, "RNA")
+        assert adata.obs.shape == (2700, 4)
+        assert adata.var.shape == (13714, 0)
+        assert adata.X.shape == (2700, 13714)
+
+# ----------------------------------------------------------------
+## Slicing and filtering objects
+#exp.obs.read(
+#    coords=[slice(0, 99)],
+#    value_filter="total_counts > 4000",
+#    column_names=["soma_joinid", "obs_id", "total_counts"],
+#).concat().to_pandas()
+#exp.obs.read(
+#    coords=[slice(0, 99)],
+#    value_filter='parity == "even" and total_counts > 4000',
+#    column_names=["soma_joinid", "obs_id", "total_counts", "parity"],
+#).concat().to_pandas()
+#exp.ms["RNA"].var.read(
+#    value_filter="var_id in ['ANXA1', 'IFI44', 'IFI44L', 'OAS1']",
+#    column_names=["var_id", "gene_ids", "mean_counts"],
+#).concat().to_pandas()
+
+# ----------------------------------------------------------------
+## Slicing and filtering experiments
+#exp.uri
+#query = tiledbsoma.ExperimentAxisQuery(
+#    experiment=exp,
+#    measurement_name="RNA",
+#    obs_query=tiledbsoma.AxisQuery(
+#        value_filter="n_genes_by_counts > 1000",
+#    ),
+#    var_query=tiledbsoma.AxisQuery(
+#        value_filter="n_cells_by_counts > 100",
+#    ),
+#)
+#
+#(query.n_obs, query.n_vars)
+
+# ----------------------------------------------------------------
+#query.obs().concat().to_pandas()
+#query.X(layer_name="data").tables().concat().to_pandas()
+#query.to_anndata(X_name="data")
+#query.close()
+#with tiledbsoma.ExperimentAxisQuery(
+#    experiment=exp,
+#    measurement_name="RNA",
+#    obs_query=tiledbsoma.AxisQuery(
+#        value_filter="n_genes_by_counts > 1000",
+#    ),
+#    var_query=tiledbsoma.AxisQuery(
+#        value_filter="n_cells_by_counts > 100",
+#    ),
+#) as query:
+#    print(query.to_anndata(X_name="data"))
+
+#with tiledbsoma.ExperimentAxisQuery(
+#    experiment=exp,
+#    measurement_name="RNA",
+#    obs_query=tiledbsoma.AxisQuery(
+#        value_filter='n_genes_by_counts > 1000 and parity == "even"',
+#    ),
+#    var_query=tiledbsoma.AxisQuery(
+#        value_filter="n_cells_by_counts > 100",
+#    ),
+#) as query:
+#    print(query.to_anndata(X_name="data"))