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rename X_approx_distribution to X_approximate_distribution (#2337)

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Bruce Martin
2021-07-27 13:43:04 -07:00
committed by GitHub
parent 1998c0ad63
commit 0b1ab02a60
20 changed files with 79 additions and 77 deletions
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backend/server/data_anndata/anndata_adaptor.py
+10 -10
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@@ -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