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test_scanpy_zappy.py
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import anndata as ad
import concurrent.futures
import dask.array as da
import logging
import numpy.testing as npt
import pytest
import zappy.base as np # zappy includes everything in numpy, with some overrides and new functions
import zappy.direct
import zappy.executor
import zappy.spark
import zarr
from pyspark.sql import SparkSession
from scanpy.api.pp import *
from scanpy.preprocessing._distributed import materialize_as_ndarray
def data_file(path):
return "data/%s" % path
input_file = data_file("10x-10k-subset.zarr")
class TestScanpy:
@pytest.fixture(scope="module")
def sc(self):
# based on https://blog.cambridgespark.com/unit-testing-with-pyspark-fb31671b1ad8
logger = logging.getLogger("py4j")
logger.setLevel(logging.WARN)
spark = (
SparkSession.builder.master("local[2]")
.appName("my-local-testing-pyspark-context")
.getOrCreate()
)
yield spark.sparkContext
spark.stop()
@pytest.fixture()
def adata(self):
a = ad.read_zarr(input_file) # regular anndata
a.X = a.X[:] # convert to numpy array
return a
# "pywren" tests do not yet all pass
# "spark" tests are currently failing due to an environment issue
@pytest.fixture(params=["direct", "executor", "dask"])
def adata_dist(self, sc, request):
# regular anndata except for X, which we replace on the next line
a = ad.read_zarr(input_file)
input_file_X = input_file + "/X"
if request.param == "direct":
a.X = zappy.direct.from_zarr(input_file_X)
yield a
elif request.param == "executor":
with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
a.X = zappy.executor.from_zarr(executor, input_file_X)
yield a
elif request.param == "spark":
a.X = zappy.spark.from_zarr(sc, input_file_X)
yield a
elif request.param == "dask":
a.X = da.from_zarr(input_file_X)
yield a
elif request.param == "pywren":
import s3fs.mapping
s3 = s3fs.S3FileSystem()
input_file_X = s3fs.mapping.S3Map(
"sc-tom-test-data/10x-10k-subset.zarr/X", s3=s3
)
executor = zappy.executor.PywrenExecutor()
a.X = zappy.executor.from_zarr(executor, input_file_X)
yield a
def test_log1p(self, adata, adata_dist):
log1p(adata_dist)
result = materialize_as_ndarray(adata_dist.X)
log1p(adata)
assert result.shape == adata.shape
assert result.shape == (adata.n_obs, adata.n_vars)
npt.assert_allclose(result, adata.X)
def test_normalize_per_cell(self, adata, adata_dist):
normalize_per_cell(adata_dist)
result = materialize_as_ndarray(adata_dist.X)
normalize_per_cell(adata)
assert result.shape == adata.shape
assert result.shape == (adata.n_obs, adata.n_vars)
npt.assert_allclose(result, adata.X)
def test_filter_cells(self, adata, adata_dist):
filter_cells(adata_dist, min_genes=3)
result = materialize_as_ndarray(adata_dist.X)
filter_cells(adata, min_genes=3)
assert result.shape == adata.shape
assert result.shape == (adata.n_obs, adata.n_vars)
npt.assert_allclose(result, adata.X)
def test_filter_genes(self, adata, adata_dist):
filter_genes(adata_dist, min_cells=2)
result = materialize_as_ndarray(adata_dist.X)
filter_genes(adata, min_cells=2)
assert result.shape == adata.shape
assert result.shape == (adata.n_obs, adata.n_vars)
npt.assert_allclose(result, adata.X)
# This produces a warning due to zero variances leading to nans.
# It can be avoided by adding a small value (e.g. 1e-8) to the scale value
# in scanpy simple.py _scale().
def test_scale(self, adata, adata_dist):
if isinstance(adata_dist.X, da.Array):
return # fails for dask
scale(adata_dist)
result = materialize_as_ndarray(adata_dist.X)
scale(adata)
assert result.shape == adata.shape
assert result.shape == (adata.n_obs, adata.n_vars)
npt.assert_allclose(result, adata.X)
def test_recipe_zheng17(self, adata, adata_dist):
recipe_zheng17(adata_dist, n_top_genes=100)
result = materialize_as_ndarray(adata_dist.X)
recipe_zheng17(adata, n_top_genes=100)
assert result.shape == adata.shape
assert result.shape == (adata.n_obs, adata.n_vars)
# Note the low tolerance required to get this to pass.
# Not sure why results diverge so much. (Seems to be scaling again.)
# Find the element that differs the most with
# import numpy
# am = (numpy.absolute(result - adata.X)/ numpy.absolute(adata.X)).argmax()
# ind = numpy.unravel_index(am, result.shape)
# print(result[ind], adata.X[ind])
npt.assert_allclose(result, adata.X, 1e-1)
def test_write_zarr(self, adata, adata_dist):
log1p(adata_dist)
temp_store = zarr.TempStore()
chunks = adata_dist.X.chunks
# write metadata using regular anndata
adata.write_zarr(temp_store, chunks)
if isinstance(adata_dist.X, da.Array):
adata_dist.X.to_zarr(temp_store.dir_path("X"))
else:
adata_dist.X.to_zarr(temp_store.dir_path("X"), chunks)
# read back as zarr (without using RDDs) and check it is the same as adata.X
adata_log1p = ad.read_zarr(temp_store)
log1p(adata)
npt.assert_allclose(adata_log1p.X, adata.X)