rename X_approx_distribution to X_approximate_distribution (#2337)

backend/server/cli/launch.py

@@ -151,8 +151,8 @@ def dataset_args(func):
         help="URL providing more information about the dataset (hint: must be a fully specified absolute URL).",
     )
     @click.option(
-        "--X-approx-distribution",
-        default=DEFAULT_CONFIG.dataset_config.X_approx_distribution,
+        "--X-approximate-distribution",
+        default=DEFAULT_CONFIG.dataset_config.X_approximate_distribution,
         show_default=True,
         type=click.Choice(["auto", "normal", "count"], case_sensitive=False),
         help="Specify the approximate distribution of X matrix values. 'auto' will use a heuristic "
@@ -326,7 +326,7 @@ def launch(
     disable_diffexp,
     config_file,
     dump_default_config,
-    x_approx_distribution,
+    x_approximate_distribution,
 ):
     """Launch the cellxgene data viewer.
     This web app lets you explore single-cell expression data.
@@ -385,7 +385,7 @@ def launch(
             embeddings__names=embedding,
             diffexp__enable=not disable_diffexp,
             diffexp__lfc_cutoff=diffexp_lfc_cutoff,
-            X_approx_distribution=x_approx_distribution,
+            X_approximate_distribution=x_approximate_distribution,
         )
 
         diff = cli_config.server_config.changes_from_default()

backend/server/common/config/dataset_config.py

@@ -38,7 +38,7 @@ class DatasetConfig(BaseConfig):
             self.diffexp__lfc_cutoff = default_config["diffexp"]["lfc_cutoff"]
             self.diffexp__top_n = default_config["diffexp"]["top_n"]
 
-            self.X_approx_distribution = default_config["X_approx_distribution"]
+            self.X_approximate_distribution = default_config["X_approximate_distribution"]
 
         except KeyError as e:
             raise ConfigurationError(f"Unexpected config: {str(e)}")
@@ -52,7 +52,7 @@ class DatasetConfig(BaseConfig):
         self.handle_user_annotations(context)
         self.handle_embeddings()
         self.handle_diffexp(context)
-        self.handle_X_approx_distribution()
+        self.handle_X_approximate_distribution()
 
     def get_data_adaptor(self):
         server_config = self.app_config.server_config
@@ -186,9 +186,9 @@ class DatasetConfig(BaseConfig):
                 "CAUTION: due to the size of your dataset, " "running differential expression may take longer or fail."
             )
 
-    def handle_X_approx_distribution(self):
-        self.validate_correct_type_of_configuration_attribute("X_approx_distribution", str)
-        if self.X_approx_distribution not in ["auto", "normal", "count"]:
+    def handle_X_approximate_distribution(self):
+        self.validate_correct_type_of_configuration_attribute("X_approximate_distribution", str)
+        if self.X_approximate_distribution not in ["auto", "normal", "count"]:
             raise ConfigurationError(
-                "X_approx_distribution has unknown value -- must be 'auto', 'normal' or 'count'."
+                "X_approximate_distribution has unknown value -- must be 'auto', 'normal' or 'count'."
             )

backend/server/data_anndata/anndata_adaptor.py

@@ -9,7 +9,7 @@ from scipy import sparse
 import backend.common.compute.diffexp_generic as diffexp_generic
 import backend.common.compute.estimate_distribution as estimate_distribution
 from backend.common.colors import convert_anndata_category_colors_to_cxg_category_colors
-from backend.common.constants import Axis, MAX_LAYOUTS, XApproxDistribution
+from backend.common.constants import Axis, MAX_LAYOUTS, XApproximateDistribution
 from backend.server.common.corpora import corpora_get_props_from_anndata
 from backend.common.errors import PrepareError, DatasetAccessError
 from backend.common.utils.type_conversion_utils import get_schema_type_hint_of_array
@@ -29,7 +29,7 @@ class AnndataAdaptor(DataAdaptor):
     def __init__(self, data_locator, app_config=None, dataset_config=None):
         super().__init__(data_locator, app_config, dataset_config)
         self.data = None
-        self.X_approx_distribution = None
+        self.X_approximate_distribution = None
         self._load_data(data_locator)
         self._validate_and_initialize()
 
@@ -192,12 +192,12 @@ class AnndataAdaptor(DataAdaptor):
         self.gene_count = self.data.shape[1]
         self._create_schema()
 
-        if self.dataset_config.X_approx_distribution == "auto":
+        if self.dataset_config.X_approximate_distribution == "auto":
             """Lazy evaluate the heuristic if we are backed."""
             if not self.data.isbacked:
-                self.X_approx_distribution = estimate_distribution.estimate_approximate_distribution(self.data.X)
+                self.X_approximate_distribution = estimate_distribution.estimate_approximate_distribution(self.data.X)
         else:
-            self.X_approx_distribution = self.dataset_config.X_approx_distribution
+            self.X_approximate_distribution = self.dataset_config.X_approximate_distribution
 
         # heuristic
         n_values = self.data.shape[0] * self.data.shape[1]
@@ -331,15 +331,15 @@ class AnndataAdaptor(DataAdaptor):
         X = self.data.X[obs_mask, var_mask]
         return X
 
-    def get_X_approx_distribution(self) -> XApproxDistribution:
+    def get_X_approximate_distribution(self) -> XApproximateDistribution:
         """return the approximate distribution of the X matrix."""
-        if self.X_approx_distribution is None:
+        if self.X_approximate_distribution is None:
             """Not yet evaluated."""
-            assert(self.dataset_config.X_approx_distribution == "auto")
+            assert(self.dataset_config.X_approximate_distribution == "auto")
             self.data = self.data.to_memory()  # loads data
-            self.X_approx_distribution = estimate_distribution.estimate_approximate_distribution(self.data.X)
+            self.X_approximate_distribution = estimate_distribution.estimate_approximate_distribution(self.data.X)
 
-        return self.X_approx_distribution
+        return self.X_approximate_distribution
 
     def get_shape(self):
         return self.data.shape

backend/server/data_common/data_adaptor.py

@@ -6,7 +6,7 @@ from scipy import sparse
 from server_timing import Timing as ServerTiming
 
 from backend.server.common.config.app_config import AppConfig
-from backend.common.constants import Axis, XApproxDistribution
+from backend.common.constants import Axis, XApproximateDistribution
 from backend.common.errors import FilterError, JSONEncodingValueError, ExceedsLimitError, UnsupportedSummaryMethod
 from backend.common.utils.utils import jsonify_numpy
 from backend.common.fbs.matrix import encode_matrix_fbs
@@ -72,9 +72,9 @@ class DataAdaptor(metaclass=ABCMeta):
         the return type is either ndarray or scipy.sparse.spmatrix."""
         pass
 
-    def get_X_approx_distribution(self) -> XApproxDistribution:
+    def get_X_approximate_distribution(self) -> XApproximateDistribution:
         """return the approximate distribution of the X matrix."""
-        return XApproxDistribution.NORMAL
+        return XApproximateDistribution.NORMAL
 
     @abstractmethod
     def get_shape(self):

backend/server/default_config.py

@@ -77,7 +77,7 @@ dataset:
     lfc_cutoff: 0.01
     top_n: 10
 
-  X_approx_distribution: auto
+  X_approximate_distribution: auto
 
 external:
   # You can retrieve configuration parameters from this config file, the environment,