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Move cxgtool into CLI and modularize conversion functions (#1701)

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maniarathi
2020-08-17 17:28:29 -07:00
committed by GitHub
parent 1acb8e4a6f
commit 994c20c094
17 changed files with 1032 additions and 758 deletions
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server/test/unit/compute/test_diffexp_cxg.py
+18 -15
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@@ -1,14 +1,16 @@
import os
import tempfile
import unittest
from server.data_common.matrix_loader import MatrixDataLoader
from server.test import PROJECT_ROOT, app_config, FIXTURES_ROOT
import numpy as np
import server.compute.diffexp_cxg as diffexp_cxg
import server.compute.diffexp_generic as diffexp_generic
from server.converters.cxgtool import write_cxg, create_cxg_group_metadata
from server.test.performance.create_test_matrix import create_test_h5ad
from server.converters.h5ad_data_file import H5ADDataFile
from server.data_common.fbs.matrix import encode_matrix_fbs, decode_matrix_fbs
import numpy as np
import tempfile
import os
from server.data_common.matrix_loader import MatrixDataLoader
from server.test import PROJECT_ROOT, app_config, FIXTURES_ROOT
from server.test.performance.create_test_matrix import create_test_h5ad
class DiffExpTest(unittest.TestCase):
@@ -98,21 +100,22 @@ class DiffExpTest(unittest.TestCase):
def sparse_diffexp(self, apply_col_shift):
with tempfile.TemporaryDirectory() as dirname:
# create a sparse matrix
h5adfile = os.path.join(dirname, "sparse.h5ad")
create_test_h5ad(h5adfile, 2000, 2000, 10, apply_col_shift)
adaptor_anndata = self.load_dataset(h5adfile, extra_dataset_config=dict(embeddings__names=[]))
adata = adaptor_anndata.data
h5adfile_path = os.path.join(dirname, "sparse.h5ad")
create_test_h5ad(h5adfile_path, 2000, 2000, 10, apply_col_shift)
h5ad_file_to_convert = H5ADDataFile(h5adfile_path, use_corpora_schema=False)
sparsename = os.path.join(dirname, "sparse.cxg")
cxg_group_metadata = create_cxg_group_metadata(adata=adata, basefname="sparse.h5ad", title="sparse",)
write_cxg(adata=adata, container=sparsename, cxg_group_metadata=cxg_group_metadata, sparse_threshold=11)
h5ad_file_to_convert.to_cxg(sparsename, 11, True)
adaptor_anndata = self.load_dataset(h5adfile_path, extra_dataset_config=dict(embeddings__names=[]))
adaptor_sparse = self.load_dataset(sparsename)
assert adaptor_sparse.open_array("X").schema.sparse
assert adaptor_sparse.has_array("X_col_shift") == apply_col_shift
densename = os.path.join(dirname, "dense.cxg")
cxg_group_metadata = create_cxg_group_metadata(adata=adata, basefname="dense.h5ad", title="dense",)
write_cxg(adata=adata, container=densename, cxg_group_metadata=cxg_group_metadata, sparse_threshold=0)
h5ad_file_to_convert.to_cxg(densename, True, 0)
adaptor_dense = self.load_dataset(densename)
assert not adaptor_dense.open_array("X").schema.sparse
assert not adaptor_dense.has_array("X_col_shift")