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Copy pathtest_DelayedArray.py
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375 lines (297 loc) · 14.7 KB
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import numpy
import delayedarray
import pytest
from utils import simulate_ndarray, simulate_SparseNdarray, assert_close_ndarrays, assert_identical_ndarrays
def test_DelayedArray_dense():
raw = (numpy.random.rand(40, 30) * 5 - 10).astype(numpy.int32)
x = delayedarray.DelayedArray(raw)
assert x.shape == raw.shape
assert x.dtype == raw.dtype
assert not delayedarray.is_sparse(x)
assert delayedarray.chunk_grid(x).shape == x.shape
out = str(x)
assert out.find("<40 x 30> DelayedArray object of type 'int32'") != -1
dump = numpy.array(x)
assert isinstance(dump, numpy.ndarray)
assert dump.dtype == x.dtype
assert (dump == raw).all()
dump = numpy.array(x, dtype=numpy.float64)
assert isinstance(dump, numpy.ndarray)
assert dump.dtype == numpy.float64
assert (dump == raw).all()
def test_DelayedArray_dask():
raw = (numpy.random.rand(40, 30) * 5 - 10).astype(numpy.int32)
x = delayedarray.DelayedArray(raw)
dump = numpy.array(x)
import dask.array
da = delayedarray.create_dask_array(x)
assert isinstance(da, dask.array.core.Array)
assert (dump == da.compute()).all()
def test_DelayedArray_colmajor():
raw = numpy.random.rand(30, 40).T
x = delayedarray.DelayedArray(raw)
assert x.shape == raw.shape
assert x.dtype == raw.dtype
assert delayedarray.chunk_grid(x).shape == x.shape
out = str(x)
assert out.find("<40 x 30> DelayedArray object of type 'float64'") != -1
def test_DelayedArray_wrap():
raw = numpy.random.rand(30, 40)
x = delayedarray.wrap(raw)
assert isinstance(x, delayedarray.DelayedArray)
assert x.shape == raw.shape
x = delayedarray.wrap(x)
assert isinstance(x, delayedarray.DelayedArray)
def test_DelayedArray_sparse():
import scipy.sparse
y = scipy.sparse.csc_matrix([[1, 2, 0], [0, 0, 3], [4, 0, 5]])
x = delayedarray.wrap(y)
out = delayedarray.to_sparse_array(x)
assert isinstance(out, delayedarray.SparseNdarray)
assert delayedarray.chunk_grid(x).shape == x.shape
assert delayedarray.is_sparse(x)
def test_DelayedArray_masked():
raw = numpy.random.rand(30, 40)
y = numpy.ma.MaskedArray(raw, raw > 0.5)
x = delayedarray.wrap(y)
assert delayedarray.is_masked(x)
dump = numpy.array(x)
assert isinstance(dump, numpy.ndarray)
assert dump.dtype == x.dtype
assert (dump == numpy.array(y)).all()
dump = numpy.array(x, dtype=numpy.float32)
assert isinstance(dump, numpy.ndarray)
assert dump.dtype == numpy.float32
assert (dump == numpy.array(y, dtype=numpy.float32)).all()
#######################################################
#######################################################
@pytest.mark.parametrize("mask_rate", [0, 0.5])
@pytest.mark.parametrize("buffer_size", [100, 500, 2000])
def test_SparseNdarray_sum_dense(mask_rate, buffer_size):
raw = simulate_ndarray((30, 40, 15), mask_rate = mask_rate)
assert_identical_ndarrays(raw.sum(), delayedarray.wrap(raw).sum())
assert_identical_ndarrays(raw.sum(axis=0), delayedarray.wrap(raw).sum(axis=0))
y = delayedarray.wrap(raw) * 5
ref = raw * 5
assert numpy.isclose(ref.sum(), y.sum(buffer_size=buffer_size))
assert_close_ndarrays(ref.sum(axis=1), y.sum(axis=1, buffer_size=buffer_size))
assert_close_ndarrays(ref.sum(axis=-1), y.sum(axis=-1, buffer_size=buffer_size))
assert_close_ndarrays(ref.sum(axis=(0, 2)), y.sum(axis=(0, 2), buffer_size=buffer_size))
# Trying with a single dimension.
test_shape = (100,)
raw = simulate_ndarray((100,), mask_rate=mask_rate)
y = delayedarray.wrap(raw) * 5
ref = raw * 5
assert numpy.isclose(ref.sum(), y.sum(buffer_size=buffer_size))
# Full masking is respected.
y = delayedarray.wrap(numpy.ma.MaskedArray([1], mask=True)) * 5
assert y.sum() is numpy.ma.masked
@pytest.mark.parametrize("mask_rate", [0, 0.5])
@pytest.mark.parametrize("buffer_size", [100, 500, 2000])
def test_SparseNdarray_sum_sparse(mask_rate, buffer_size):
raw = simulate_SparseNdarray((20, 30, 25), mask_rate = mask_rate)
y = delayedarray.wrap(raw) * 10
ref = raw * 10
assert numpy.isclose(ref.sum(), y.sum(buffer_size=buffer_size))
assert_close_ndarrays(ref.sum(axis=1), y.sum(axis=1, buffer_size=buffer_size))
assert_close_ndarrays(ref.sum(axis=-1), y.sum(axis=-1, buffer_size=buffer_size))
assert_close_ndarrays(ref.sum(axis=(0, 2)), y.sum(axis=(0, 2), buffer_size=buffer_size))
# Trying with a single dimension.
test_shape = (100,)
raw = simulate_SparseNdarray((100,), mask_rate=mask_rate)
y = delayedarray.wrap(raw) * 10
ref = raw * 10
assert numpy.isclose(ref.sum(), y.sum(buffer_size=buffer_size))
# Full masking is respected.
ref = delayedarray.SparseNdarray((1,), (numpy.zeros(1, dtype=numpy.int_), numpy.ma.MaskedArray([1], mask=True)))
y = delayedarray.wrap(ref) * 10
assert y.sum() is numpy.ma.masked
@pytest.mark.parametrize("mask_rate", [0, 0.5])
@pytest.mark.parametrize("buffer_size", [100, 500, 2000])
def test_SparseNdarray_mean_dense(mask_rate, buffer_size):
raw = simulate_ndarray((30, 40, 15), mask_rate = mask_rate)
assert_identical_ndarrays(raw.mean(), delayedarray.wrap(raw).mean())
assert_identical_ndarrays(raw.mean(axis=0), delayedarray.wrap(raw).mean(axis=0))
y = delayedarray.wrap(raw) - 12
ref = raw - 12
assert numpy.isclose(ref.mean(), y.mean(buffer_size=buffer_size))
assert_close_ndarrays(ref.mean(axis=1), y.mean(axis=1, buffer_size=buffer_size))
assert_close_ndarrays(ref.mean(axis=-1), y.mean(axis=-1, buffer_size=buffer_size))
assert_close_ndarrays(ref.mean(axis=(0, 2)), y.mean(axis=(0, 2), buffer_size=buffer_size))
# Trying with a single dimension.
test_shape = (100,)
raw = simulate_ndarray((100,), mask_rate=mask_rate)
y = delayedarray.wrap(raw) + 29
ref = raw + 29
assert numpy.isclose(ref.mean(), y.mean(buffer_size=buffer_size))
# Full masking is respected.
y = delayedarray.wrap(numpy.ma.MaskedArray([1], mask=True)) + 20
assert y.mean() is numpy.ma.masked
# Zero-length array is respected.
with pytest.warns(RuntimeWarning):
y = delayedarray.wrap(numpy.ndarray((10, 0))) * 50
assert numpy.isnan(y.mean())
@pytest.mark.parametrize("mask_rate", [0, 0.5])
@pytest.mark.parametrize("buffer_size", [100, 500, 2000])
def test_SparseNdarray_mean_sparse(mask_rate, buffer_size):
raw = simulate_SparseNdarray((20, 30, 25), mask_rate = mask_rate)
ref = raw * 19
y = delayedarray.wrap(raw) * 19
assert numpy.isclose(ref.mean(), y.mean(buffer_size=buffer_size))
assert_close_ndarrays(ref.mean(axis=1), y.mean(axis=1, buffer_size=buffer_size))
assert_close_ndarrays(ref.mean(axis=-1), y.mean(axis=-1, buffer_size=buffer_size))
assert_close_ndarrays(ref.mean(axis=(0, 2)), y.mean(axis=(0, 2), buffer_size=buffer_size))
# Trying with a single dimension.
test_shape = (100,)
raw = simulate_SparseNdarray((100,), mask_rate=mask_rate)
y = delayedarray.wrap(raw) * 12
ref = raw * 12
assert numpy.isclose(ref.mean(), y.mean(buffer_size=buffer_size))
# Full masking is respected.
ref = delayedarray.SparseNdarray((1,), (numpy.zeros(1, dtype=numpy.int_), numpy.ma.MaskedArray([1], mask=True)))
y = delayedarray.wrap(ref) / 5
assert y.mean() is numpy.ma.masked
# Zero-length array is respected.
with pytest.warns(RuntimeWarning):
y = delayedarray.wrap(delayedarray.SparseNdarray((0,), None, dtype=numpy.int32, index_dtype=numpy.int32)) * 50
assert numpy.isnan(y.mean())
@pytest.mark.parametrize("mask_rate", [0, 0.5])
@pytest.mark.parametrize("buffer_size", [100, 500, 2000])
def test_SparseNdarray_var_dense(mask_rate, buffer_size):
raw = simulate_ndarray((30, 40, 15), mask_rate = mask_rate)
assert_identical_ndarrays(raw.var(), delayedarray.wrap(raw).var())
assert_identical_ndarrays(raw.var(axis=0), delayedarray.wrap(raw).var(axis=0))
y = delayedarray.wrap(raw) - 12
ref = raw - 12
assert numpy.isclose(ref.var(), y.var(buffer_size=buffer_size))
assert_close_ndarrays(ref.var(axis=1), y.var(axis=1, buffer_size=buffer_size))
assert_close_ndarrays(ref.var(axis=-1), y.var(axis=-1, buffer_size=buffer_size))
assert_close_ndarrays(ref.var(axis=(0, 2)), y.var(axis=(0, 2), buffer_size=buffer_size))
# Trying with a single dimension.
test_shape = (100,)
raw = simulate_ndarray((100,), mask_rate=mask_rate)
y = delayedarray.wrap(raw) + 29
ref = raw + 29
assert numpy.isclose(ref.var(), y.var(buffer_size=buffer_size))
# Full masking is respected.
y = delayedarray.wrap(numpy.ma.MaskedArray([1], mask=True)) + 20
with pytest.warns(RuntimeWarning):
assert y.var() is numpy.ma.masked
# Zero-length array is respected.
with pytest.warns(RuntimeWarning):
y = delayedarray.wrap(numpy.ndarray((10, 0))) * 50
assert numpy.isnan(y.var())
@pytest.mark.parametrize("mask_rate", [0, 0.5])
@pytest.mark.parametrize("buffer_size", [100, 500, 2000])
def test_SparseNdarray_var_sparse(mask_rate, buffer_size):
raw = simulate_SparseNdarray((20, 30, 25), mask_rate = mask_rate)
ref = raw * 19
y = delayedarray.wrap(raw) * 19
assert numpy.isclose(ref.var(), y.var(buffer_size=buffer_size))
assert_close_ndarrays(ref.var(axis=1), y.var(axis=1, buffer_size=buffer_size))
assert_close_ndarrays(ref.var(axis=-1), y.var(axis=-1, buffer_size=buffer_size))
assert_close_ndarrays(ref.var(axis=(0, 2)), y.var(axis=(0, 2), buffer_size=buffer_size))
# Trying with a single dimension.
test_shape = (100,)
raw = simulate_SparseNdarray((100,), mask_rate=mask_rate)
y = delayedarray.wrap(raw) * 12
ref = raw * 12
assert numpy.isclose(ref.var(), y.var(buffer_size=buffer_size))
# Full masking is respected.
ref = delayedarray.SparseNdarray((1,), (numpy.zeros(1, dtype=numpy.int_), numpy.ma.MaskedArray([1], mask=True)))
y = delayedarray.wrap(ref) / 5
with pytest.warns(RuntimeWarning):
assert y.var() is numpy.ma.masked
# Zero-length array is respected.
with pytest.warns(RuntimeWarning):
y = delayedarray.wrap(delayedarray.SparseNdarray((0,), None, dtype=numpy.int32, index_dtype=numpy.int32)) * 50
assert numpy.isnan(y.var())
@pytest.mark.parametrize("mask_rate", [0, 0.5])
@pytest.mark.parametrize("buffer_size", [100, 500, 2000])
def test_SparseNdarray_any_dense(mask_rate, buffer_size):
raw = simulate_ndarray((30, 40), mask_rate = mask_rate)
assert raw.any() == delayedarray.wrap(raw).any()
assert (raw.any(axis=0) == delayedarray.wrap(raw).any(axis=0)).all()
# convert to boolean and set one of the columns to True
ref = raw == numpy.nan
ref[10, :] = True
y = delayedarray.wrap(ref)
assert ref.any() == y.any(buffer_size=buffer_size)
assert (ref.any(axis=1) == y.any(axis=1, buffer_size=buffer_size)).all()
assert (ref.any(axis=0) == y.any(axis=0, buffer_size=buffer_size)).all()
# Trying with a single dimension.
raw = simulate_ndarray((100,), mask_rate=mask_rate)
y = delayedarray.wrap(raw)
assert raw.any() == y.any(buffer_size=buffer_size)
# Full masking is respected.
y = delayedarray.wrap(numpy.ma.MaskedArray([1], mask=True)) + 20
assert y.any() is numpy.ma.masked
# Zero-length array is respected.
y = delayedarray.wrap(numpy.ndarray((10, 0))) * 50
assert y.any() == False
@pytest.mark.parametrize("mask_rate", [0, 0.5])
@pytest.mark.parametrize("buffer_size", [100, 500, 2000])
def test_SparseNdarray_any_sparse(mask_rate, buffer_size):
ref = simulate_SparseNdarray((20, 30, 25), mask_rate = mask_rate)
y = delayedarray.wrap(ref)
assert ref.any() == y.any(buffer_size=buffer_size)
assert (ref.any(axis=1) == y.any(axis=1, buffer_size=buffer_size)).all()
assert (ref.any(axis=-1) == y.any(axis=-1, buffer_size=buffer_size)).all()
assert (ref.any(axis=(0, 2)) == y.any(axis=(0, 2), buffer_size=buffer_size)).all()
# Trying with a single dimension.
ref = simulate_SparseNdarray((100,), mask_rate=mask_rate)
y = delayedarray.wrap(ref)
assert numpy.isclose(ref.any(), y.any(buffer_size=buffer_size))
# Full masking is respected.
ref = delayedarray.SparseNdarray((1,), (numpy.zeros(1, dtype=numpy.int_), numpy.ma.MaskedArray([1], mask=True)))
y = delayedarray.wrap(ref) / 5
assert y.any() is numpy.ma.masked
# Zero-length array is respected.
y = delayedarray.wrap(delayedarray.SparseNdarray((0,), None, dtype=numpy.int32, index_dtype=numpy.int32)) * 50
assert y.any() == False
@pytest.mark.parametrize("mask_rate", [0, 0.5])
@pytest.mark.parametrize("buffer_size", [100, 500, 2000])
def test_SparseNdarray_all_dense(mask_rate, buffer_size):
raw = simulate_ndarray((30, 40), mask_rate = mask_rate)
assert raw.all() == delayedarray.wrap(raw).all()
assert (raw.all(axis=0) == delayedarray.wrap(raw).all(axis=0)).all()
# convert to boolean and set one of the columns to True
ref = raw == numpy.nan
ref[:, 10] = True
y = delayedarray.wrap(ref)
assert ref.any() == y.any(buffer_size=buffer_size)
assert (ref.any(axis=1) == y.any(axis=1, buffer_size=buffer_size)).all()
assert (ref.any(axis=0) == y.any(axis=0, buffer_size=buffer_size)).all()
# Trying with a single dimension.
raw = simulate_ndarray((100,), mask_rate=mask_rate)
y = delayedarray.wrap(raw) + 29
ref = raw + 29
assert ref.all() == y.all(buffer_size=buffer_size)
# Full masking is respected.
y = delayedarray.wrap(numpy.ma.MaskedArray([1], mask=True)) + 20
assert y.all() is numpy.ma.masked
# Zero-length array is respected.
y = delayedarray.wrap(numpy.ndarray((10, 0))) * 50
assert y.all()
@pytest.mark.parametrize("mask_rate", [0, 0.5])
@pytest.mark.parametrize("buffer_size", [100, 500, 2000])
def test_SparseNdarray_all_sparse(mask_rate, buffer_size):
raw = simulate_SparseNdarray((20, 30, 25), mask_rate = mask_rate)
ref = raw * 19
y = delayedarray.wrap(raw) * 19
assert ref.all() == y.all(buffer_size=buffer_size)
assert (ref.all(axis=1) == y.all(axis=1, buffer_size=buffer_size)).all()
assert (ref.all(axis=-1) == y.all(axis=-1, buffer_size=buffer_size)).all()
assert (ref.all(axis=(0, 2)) == y.all(axis=(0, 2), buffer_size=buffer_size)).all()
# Trying with a single dimension.
raw = simulate_SparseNdarray((100,), mask_rate=mask_rate)
y = delayedarray.wrap(raw) * 12
ref = raw * 12
assert ref.any() == y.all(buffer_size=buffer_size)
# Full masking is respected.
ref = delayedarray.SparseNdarray((1,), (numpy.zeros(1, dtype=numpy.int_), numpy.ma.MaskedArray([1], mask=True)))
y = delayedarray.wrap(ref) / 5
assert y.all() is numpy.ma.masked
# Zero-length array is respected.
y = delayedarray.wrap(delayedarray.SparseNdarray((0,), None, dtype=numpy.int32, index_dtype=numpy.int32)) * 50
assert y.all()