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Splitting the format validation and mandatory annotations

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This commit is contained in:
Charlotte Weaver
2018-08-10 16:38:07 -07:00
parent 58000c1815
commit 55fa9b892a
2 changed files with 11 additions and 58 deletions
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4
server/app/driver/driver.py
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@@ -10,10 +10,6 @@ class CXGDriver(metaclass=ABCMeta):
def _load_data(data):
pass
@abstractmethod
def _load_or_infer_schema(data):
pass
@abstractmethod
def cells(self):
pass

65
server/app/scanpy_engine/scanpy_engine.py
View File

@@ -1,21 +1,20 @@
import os
import warnings
import numpy as np
from pandas import Series
import scanpy.api as sc
from scipy import stats
from server.app.app import cache
from server.app.driver.driver import CXGDriver
from server.app.util.schema_parse import parse_schema
class ScanpyEngine(CXGDriver):
def __init__(self, data, schema=None, graph_method="umap", diffexp_method="ttest"):
def __init__(self, data, graph_method="umap", diffexp_method="ttest"):
self.data = self._load_data(data)
self._format_data()
self.schema = self._load_or_infer_schema(data, schema)
self._set_cell_names()
self._validatate_data_types()
self._add_mandatory_annotations()
self.cell_count = self.data.shape[0]
self.gene_count = self.data.shape[1]
self.graph_method = graph_method
@@ -40,59 +39,17 @@ class ScanpyEngine(CXGDriver):
def _load_data(data):
return sc.read(os.path.join(data, "data.h5ad"))
# TODO delete after v0.1 v2.0 transition
def _load_or_infer_schema(self, data, schema):
if not os.path.isfile(os.path.join(data, schema)):
# Initialize with cell name which is built off the index
data_schema = {
"CellName": {
"type": "string",
"variabletype": "categorical",
"displayname": "Name",
"include": True
}
}
metadata_fields = list(self.data.obs)
for m in metadata_fields:
# Since there are many type of float/int in numpy datatypes the kind attribute of a datatype object
# offers a decent insight into whether it can be lumped in with floats or ints, which is what we
# care about here.
data_kind = self.data.obs[m].dtype.kind
variable_type = "categorical"
data_type = "string"
if data_kind == 'f':
variable_type = "continuous"
data_type = "float"
elif data_kind in ['i', 'u']:
data_type = "int"
if self.data.obs[m].nunique() > 50:
variable_type = "continuous"
data_schema[m] = {
"type": data_type,
"variabletype": variable_type,
"displayname": m,
"include": True
}
else:
data_schema = parse_schema(os.path.join(data, schema))
return data_schema
def _format_data(self):
def _add_mandatory_annotations(self):
# ensure gene
self.data.var["name"] = list(self.data.var.index)
self.data.var.index = list(range(self.data.var.shape[0]))
self.data.var.index = Series(list(range(self.data.var.shape[0])), dtype='int32')
# ensure cell name
self.data.obs["name"] = list(self.data.obs.index)
self.data.obs.index = list(range(self.data.obs.shape[0]))
# ensure formats correct/ reform old format
for annotation in self.data.obs:
data_type = self.data.obs[annotation].dtype
if data_type.kind == 'f' and data_type != "float32":
self.data.obs[annotation] = self.data.obs[annotation].astype("float32")
elif data_type.kind in ['i', 'u'] and data_type != "int32":
self.data.obs[annotation] = self.data.obs[annotation].astype("int32")
self.data.obs.index = Series(list(range(self.data.obs.shape[0])), dtype='int32')
def _validatate_data_types(self):
if self.data.X.dtype != 'float32':
self.data.X = self.data.X.astype("float32")
warnings.warn(f"Scanpy data matrix is in {self.data.X.dtype} format not float32. Precision may be truncated.")
def cells(self):
return list(self.data.obs.index)