add args to validator.reset() to set schema constants to match fixture

cellxgene_schema_cli/cellxgene_schema/validate.py

@@ -67,7 +67,7 @@ def __init__(self, ignore_labels=False):
         # keys will be one of gencode.SupportedOrganisms
         self.gene_checkers = dict()
 
-    def reset(self):
+    def reset(self, hi_res_size: Optional[int] = None, true_mat_size: Optional[int] = None):
         self.errors = []
         self.warnings = []
         self.is_valid = False
@@ -76,6 +76,8 @@ def reset(self):
         self.is_spatial = None
         self.is_visium = None
         self.is_visium_and_is_single_true = None
+        self._hires_max_dimension_size = hi_res_size
+        self._visium_and_is_single_true_matrix_size = true_mat_size
 
         # Matrix (e.g., X, raw.X, ...) number non-zero cache
         self.number_non_zero = dict()
@@ -99,6 +101,7 @@ def visium_and_is_single_true_matrix_size(self) -> Optional[int]:
             if bool(
                 self.adata.obs["assay_ontology_term_id"]
                 .apply(lambda t: is_ontological_descendant_of(ONTOLOGY_PARSER, t, ASSAY_VISIUM_11M, True))
+                .astype(bool)
                 .any()
             ):
                 self._visium_error_suffix = f"{ERROR_SUFFIX_VISIUM_11M} and {ERROR_SUFFIX_IS_SINGLE}"
@@ -118,6 +121,7 @@ def hires_max_dimension_size(self) -> Optional[int]:
             if bool(
                 self.adata.obs["assay_ontology_term_id"]
                 .apply(lambda t: is_ontological_descendant_of(ONTOLOGY_PARSER, t, ASSAY_VISIUM_11M, True))
+                .astype(bool)
                 .any()
             ):
                 self._visium_error_suffix = ERROR_SUFFIX_VISIUM_11M
@@ -1981,6 +1985,7 @@ def _is_visium_including_descendants(self) -> bool:
                 self.adata.obs[_assay_key]
                 .astype("string")
                 .apply(lambda assay: is_ontological_descendant_of(ONTOLOGY_PARSER, assay, ASSAY_VISIUM, True))
+                .astype(bool)
                 .any()
             )
 
@@ -2099,15 +2104,15 @@ def validate_adata(self, h5ad_path: Union[str, bytes, os.PathLike] = None) -> bo
         :rtype bool
         """
         logger.info("Starting validation...")
-        # Re-start errors in case a new h5ad is being validated
-        self.reset()
 
         if h5ad_path:
             logger.debug("Reading the h5ad file...")
             self.adata = read_h5ad(h5ad_path)
             self.h5ad_path = h5ad_path
             self._validate_encoding_version()
             logger.debug("Successfully read the h5ad file")
+            # Re-start errors in case a new h5ad is being validated
+            self.reset()
 
         # Fetches schema def for latest major schema version
         self._set_schema_def()

cellxgene_schema_cli/tests/test_schema_compliance.py

@@ -86,8 +86,8 @@ def validator_with_spatial_and_is_single_false(validator) -> Validator:
 @pytest.fixture
 def validator_with_visium_assay(validator) -> Validator:
     validator.adata = examples.adata_visium.copy()
-    validator._visium_and_is_single_true_matrix_size = 2
-    validator._hires_max_dimension_size = None
+    validator.reset(None, None)
+
     return validator
 
 
@@ -208,6 +208,7 @@ def test_raw_values__invalid_spatial(self, validator_with_visium_assay, invalid_
 
         validator = validator_with_visium_assay
         validator.adata.raw.X[0, 1] = invalid_value
+        validator.reset(None, 2)
         validator.validate_adata()
         assert validator.errors == [
             "ERROR: All non-zero values in raw matrix must be positive integers of type numpy.float32.",
@@ -248,7 +249,8 @@ def test_raw_values__contains_zero_row_in_tissue_1(self, validator_with_visium_a
         Raw Matrix contains a row with all zeros and in_tissue is 1, but no values are in_tissue 0.
         """
 
-        validator = validator_with_visium_assay
+        validator: Validator = validator_with_visium_assay
+        validator.reset(None, 2)
         validator.adata.obs["in_tissue"] = 1
         validator.adata.X[0] = numpy.zeros(validator.adata.var.shape[0], dtype=numpy.float32)
         validator.adata.raw.X[0] = numpy.zeros(validator.adata.var.shape[0], dtype=numpy.float32)
@@ -266,6 +268,7 @@ def test_raw_values__contains_zero_row_in_tissue_1_mixed_in_tissue_values(self,
         validator: Validator = validator_with_visium_assay
         validator.adata.X[1] = numpy.zeros(validator.adata.var.shape[0], dtype=numpy.float32)
         validator.adata.raw.X[1] = numpy.zeros(validator.adata.var.shape[0], dtype=numpy.float32)
+        validator.reset(None, 2)
         validator.validate_adata()
         assert validator.errors == [
             "ERROR: Each observation with obs['in_tissue'] == 1 must have at least one "
@@ -287,6 +290,7 @@ def test_raw_values__contains_all_zero_rows_in_tissue_0(self, validator_with_vis
         )
         validator.adata.raw = validator.adata.copy()
         validator.adata.raw.var.drop("feature_is_filtered", axis=1, inplace=True)
+        validator.reset(None, 2)
         validator.validate_adata()
         assert validator.errors == [
             "ERROR: If obs['in_tissue'] contains at least one value 0, then there must be at least "
@@ -305,6 +309,7 @@ def test_raw_values__contains_some_zero_rows_in_tissue_0(self, validator_with_vi
         validator.adata.obs["cell_type_ontology_term_id"] = "unknown"
         validator.adata.X[0] = numpy.zeros(validator.adata.var.shape[0], dtype=numpy.float32)
         validator.adata.raw.X[0] = numpy.zeros(validator.adata.var.shape[0], dtype=numpy.float32)
+        validator.reset(None, 2)
         validator.validate_adata()
         assert validator.errors == []
 
@@ -329,8 +334,6 @@ def test_raw_values__invalid_visium_and_is_single_true_row_length(
         validator.adata.obs["assay_ontology_term_id"] = assay_ontology_term_id
 
         # hires image size must be present in order to validate the raw.
-        validator._visium_and_is_single_true_matrix_size = None
-        validator._hires_max_dimension_size = image_size
         validator.adata.uns["spatial"][visium_library_id]["images"]["hires"] = numpy.zeros(
             (1, image_size, 3), dtype=numpy.uint8
         )
@@ -641,13 +644,16 @@ def test_assay_ontology_term_id__as_categorical(self, validator_with_visium_assa
         validator: Validator = validator_with_visium_assay
 
         # check encoding as string
-        validator._check_spatial_obs()
+        validator.reset(None, 2)
+        validator._check_spatial()
+        validator._validate_raw()
         assert validator.errors == []
-        validator.reset()
 
         # force encoding as 'categorical'
+        validator.reset(None, 2)
         validator.adata.obs["assay_ontology_term_id"] = validator.adata.obs["assay_ontology_term_id"].astype("category")
-        validator._check_spatial_obs()
+        validator._check_spatial()
+        validator._validate_raw()
         assert validator.errors == []
 
     @pytest.mark.parametrize(
@@ -1721,6 +1727,7 @@ def test_genetic_ancestry_same_donor_id(self, validator_with_adata):
 
         # Second row should have identical donor id + genetic ancestry values, so this should pass validation
         validator.adata.obs.iloc[1] = validator.adata.obs.iloc[0].values
+
         validator.validate_adata()
         assert validator.errors == []
 
@@ -1731,11 +1738,13 @@ def test_genetic_ancestry_same_donor_id(self, validator_with_adata):
         validator.adata.obs["genetic_ancestry_Indigenous_American"] = [0.0, 0.0]
         validator.adata.obs["genetic_ancestry_Oceanian"] = [0.0, 0.0]
         validator.adata.obs["genetic_ancestry_South_Asian"] = [0.0, 0.0]
+        validator.reset(None, 2)
         validator.validate_adata()
         assert len(validator.errors) > 0
 
         # Change the donor id back to two different donor id's. Now, this should pass validation
         validator.adata.obs["donor_id"] = original_donor_id_column
+        validator.reset(None, 2)
         validator.validate_adata()
         assert validator.errors == []
 
@@ -1818,6 +1827,7 @@ def test_feature_is_filtered(self, validator_with_adata):
             X[i, 0] = 0
         X[0, 0] = 1
 
+        validator.reset(None, 2)
         validator.validate_adata()
         assert validator.errors == [
             "ERROR: Some features are 'True' in 'feature_is_filtered' of dataframe 'var', "
@@ -1827,6 +1837,7 @@ def test_feature_is_filtered(self, validator_with_adata):
 
         # Test that feature_is_filtered is a bool and not a string
         var["feature_is_filtered"] = "string"
+        validator.reset(None, 2)
         validator.validate_adata()
         assert validator.errors == [
             "ERROR: Column 'feature_is_filtered' in dataframe 'var' must be boolean, not 'object'."
@@ -2406,6 +2417,7 @@ def test_obsm_values_nan(self, validator_with_visium_assay, key):
 
         # Check embedding has any NaN
         obsm[key][0:100, 1] = numpy.nan
+        validator.reset(None, 2)
         validator.validate_adata()
 
         if key != "spatial":
@@ -2416,6 +2428,7 @@ def test_obsm_values_nan(self, validator_with_visium_assay, key):
         # Check embedding has all NaNs
         all_nan = numpy.full(obsm[key].shape, numpy.nan)
         obsm[key] = all_nan
+        validator.reset(None, 2)
         validator.validate_adata()
         if key != "spatial":
             assert validator.errors == [f"ERROR: adata.obsm['{key}'] contains all NaN values."]
@@ -2442,6 +2455,7 @@ def test_obsm_values_no_X_embedding__visium_dataset(self, validator_with_visium_
         validator = validator_with_visium_assay
         validator.adata.uns["default_embedding"] = "spatial"
         del validator.adata.obsm["X_umap"]
+        validator.reset(None, 2)
         validator.validate_adata()
         assert validator.errors == []
         assert validator.is_spatial is True
@@ -2545,6 +2559,7 @@ def test_obsm_key_name_whitespace(self, validator_with_adata):
 
         del obsm["X_ umap"]
         obsm["u m a p"] = obsm["X_umap"]
+        validator.reset(None, 2)
         validator.validate_adata()
         assert validator.errors == [
             "ERROR: Embedding key in 'adata.obsm' u m a p does not match the regex pattern ^[a-zA-Z][a-zA-Z0-9_.-]*$."