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2c3c408 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 | import sys
import warnings
from datetime import datetime
import numpy as np
import pytest
import tiledb
from .common import DiskTestCase
# Skip this test if dask is unavailable
da_array = pytest.importorskip("dask.array")
da_distributed = pytest.importorskip("dask.distributed")
class TestDaskSupport(DiskTestCase):
def test_dask_from_numpy_1d(self):
uri = self.path("np_1attr")
A = np.random.randn(50, 50)
T = tiledb.from_numpy(uri, A, tile=50)
T.close()
with tiledb.open(uri) as T:
D = da_array.from_tiledb(T)
np.testing.assert_array_equal(D, A)
D2 = da_array.from_tiledb(uri)
np.testing.assert_array_equal(D2, A)
self.assertAlmostEqual(
np.mean(A), D2.mean().compute(scheduler="single-threaded")
)
def _make_multiattr_2d(self, uri, shape=(0, 100), tile=10):
dom = tiledb.Domain(
tiledb.Dim("x", (0, 10), dtype=np.uint64, tile=tile),
tiledb.Dim("y", (0, 50), dtype=np.uint64, tile=tile),
)
schema = tiledb.ArraySchema(
attrs=(tiledb.Attr("attr1"), tiledb.Attr("attr2")), domain=dom
)
tiledb.DenseArray.create(uri, schema)
@pytest.mark.filterwarnings("ignore:There is no current event loop")
def test_dask_multiattr_2d(self):
uri = self.path("multiattr")
self._make_multiattr_2d(uri)
with tiledb.DenseArray(uri, "w") as T:
ar1 = np.random.randn(*T.schema.shape)
ar2 = np.random.randn(*T.schema.shape)
T[:] = {"attr1": ar1, "attr2": ar2}
with tiledb.DenseArray(uri, mode="r", attr="attr2") as T:
# basic round-trip from dask.array
D = da_array.from_tiledb(T, attribute="attr2")
np.testing.assert_array_equal(ar2, np.array(D))
# smoke-test computation
# note: re-init from_tiledb each time, or else dask just uses the cached materialization
D = da_array.from_tiledb(uri, attribute="attr2")
self.assertAlmostEqual(np.mean(ar2), D.mean().compute(scheduler="threads"))
D = da_array.from_tiledb(uri, attribute="attr2")
self.assertAlmostEqual(
np.mean(ar2), D.mean().compute(scheduler="single-threaded")
)
D = da_array.from_tiledb(uri, attribute="attr2")
self.assertAlmostEqual(np.mean(ar2), D.mean().compute(scheduler="processes"))
# test dask.distributed
D = da_array.from_tiledb(uri, attribute="attr2")
with da_distributed.Client():
np.testing.assert_approx_equal(D.mean().compute(), np.mean(ar2))
def test_dask_write(self):
uri = self.path("dask_w")
D = da_array.random.random(10, 10)
D.to_tiledb(uri)
DT = da_array.from_tiledb(uri)
np.testing.assert_array_equal(D, DT)
def test_dask_overlap_blocks(self):
uri = self.path("np_overlap_blocks")
A = np.ones((2, 50, 50))
T = tiledb.from_numpy(uri, A, tile=(1, 5, 5))
T.close()
with tiledb.open(uri) as T:
D = da_array.from_tiledb(T)
np.testing.assert_array_equal(D, A)
D2 = da_array.from_tiledb(uri)
np.testing.assert_array_equal(D2, A)
D3 = D2.map_overlap(
lambda x: x + 1, depth={0: 0, 1: 1, 2: 1}, dtype=A.dtype, boundary="none"
).compute()
np.testing.assert_array_equal(D2 * 2, D3)
def test_labeled_dask_overlap_blocks(self):
uri = self.path("np_labeled_overlap_blocks")
A = np.ones((2, 50, 50))
dom = tiledb.Domain(
tiledb.Dim(name="BANDS", domain=(0, 1), tile=1),
tiledb.Dim(name="Y", domain=(0, 49), tile=5, dtype=np.uint64),
tiledb.Dim(name="X", domain=(0, 49), tile=5, dtype=np.uint64),
)
schema = tiledb.ArraySchema(
domain=dom,
sparse=False,
attrs=[tiledb.Attr(name="TDB_VALUES", dtype=A.dtype)],
)
tiledb.DenseArray.create(uri, schema)
with tiledb.open(uri, "w", attr="TDB_VALUES") as T:
T[:] = A
D2 = da_array.from_tiledb(uri, attribute="TDB_VALUES")
D3 = D2.map_overlap(
lambda x: x + 1, depth={0: 0, 1: 1, 2: 1}, dtype=D2.dtype, boundary="none"
).compute()
np.testing.assert_array_equal(D2 + 1, D3)
def test_labeled_dask_blocks(self):
uri = self.path("np_labeled_map_blocks")
A = np.ones((2, 50, 50))
dom = tiledb.Domain(
tiledb.Dim(name="BANDS", domain=(0, 1), tile=1),
tiledb.Dim(name="Y", domain=(0, 49), tile=5, dtype=np.uint64),
tiledb.Dim(name="X", domain=(0, 49), tile=5, dtype=np.uint64),
)
schema = tiledb.ArraySchema(
domain=dom,
sparse=False,
attrs=[tiledb.Attr(name="TDB_VALUES", dtype=A.dtype)],
)
tiledb.DenseArray.create(uri, schema)
with tiledb.open(uri, "w", attr="TDB_VALUES") as D1:
D1[:] = A
D2 = da_array.from_tiledb(uri, attribute="TDB_VALUES")
D3 = D2.map_blocks(lambda x: x + 1, dtype=D2.dtype).compute(
scheduler="processes"
)
np.testing.assert_array_equal(D2 + 1, D3)
def test_sc33742_dask_array_object_dtype_conversion():
# This test verifies that an array can be converted to buffer after serialization
# through several dask.distributed compute steps. The original source of the issue
# was that a `dtype == dtype("O")` check was returning false, presumably because the
# dtype object was not === after serialization.
import random
import dask
import numpy as np
@dask.delayed
def get_data():
dd = dask.delayed(
lambda x=0: {
"Z": np.array(
[
np.zeros((random.randint(60, 100),), np.dtype("float64")),
np.zeros((random.randint(1, 50),), np.dtype("float64")),
],
dtype=np.dtype("O"),
)
}
)()
return dask.delayed([dd])
@dask.delayed
def use_data(data):
f = dask.compute(data, traverse=True)[0][0]
from tiledb import main
main.array_to_buffer(f["Z"], True, False)
# Various warnings are raised by dask.distributed in different Python versions and
# package combinations (eg Python 3.7 and older tornado), but they are not relevant to
# this test.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
global client
client = da_distributed.Client(
da_distributed.LocalCluster(scheduler_port=9786, dashboard_address=9787)
)
w = []
data = dask.delayed(get_data)()
w.append(use_data(data))
futures = client.compute(w)
client.gather(futures)
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