from __future__ import annotations import contextlib import copy import pathlib import re import xml.etree.ElementTree import pytest from dask._task_spec import DataNode, List, Task, TaskRef np = pytest.importorskip("numpy") import itertools import math import operator import os import time import warnings from operator import add, sub from threading import Lock from packaging.version import Version from tlz import concat, merge from tlz.curried import identity import dask import dask.array as da from dask.array.chunk import getitem from dask.array.core import ( Array, BlockView, blockdims_from_blockshape, broadcast_chunks, broadcast_shapes, broadcast_to, common_blockdim, concatenate, concatenate3, concatenate_axes, dotmany, from_array, from_delayed, from_func, getter, graph_from_arraylike, load_store_chunk, normalize_chunks, normalize_chunks_cached, optimize, stack, store, ) from dask.array.numpy_compat import NUMPY_GE_200, NUMPY_GE_210 from dask.array.reshape import _not_implemented_message from dask.array.utils import assert_eq, same_keys from dask.base import collections_to_expr, compute_as_if_collection, tokenize from dask.blockwise import ( _make_blockwise_graph, broadcast_dimensions, optimize_blockwise, ) from dask.delayed import delayed from dask.highlevelgraph import HighLevelGraph, MaterializedLayer from dask.layers import Blockwise from dask.utils import SerializableLock, key_split, tmpdir, tmpfile from dask.utils_test import dec, hlg_layer_topological, inc try: from distributed import futures_of except ModuleNotFoundError: futures_of = lambda *args, **kwargs: [] if da._array_expr_enabled(): pytest.skip("parametrize using unsupported functions", allow_module_level=True) def skip_if_no_sparray(): try: import scipy except ImportError: skip = True else: skip = Version(scipy.__version__) < Version("1.11") return pytest.mark.skipif(skip, reason="scipy<1.11 has no sparray") def assert_has_persisted_data(arr): has_persisted_data = False if futures_of(arr): has_persisted_data = True else: for layer in arr.dask.layers.values(): if isinstance(layer, MaterializedLayer) and any( map(lambda obj: isinstance(obj, np.ndarray), layer.mapping.values()) ): has_persisted_data = True break assert has_persisted_data @pytest.mark.parametrize("inline_array", [True, False]) def test_graph_from_arraylike(inline_array): d = 2 chunk = (2, 3) shape = tuple(d * n for n in chunk) arr = np.ones(shape) dsk = graph_from_arraylike( arr, chunk, shape=shape, name="X", inline_array=inline_array ) assert isinstance(dsk, HighLevelGraph) if inline_array: assert len(dsk.layers) == 1 assert isinstance(hlg_layer_topological(dsk, 0), Blockwise) else: assert len(dsk.layers) == 2 assert isinstance(hlg_layer_topological(dsk, 0), MaterializedLayer) assert isinstance(hlg_layer_topological(dsk, 1), Blockwise) dsk = dict(dsk) # Somewhat odd membership check to avoid numpy elemwise __in__ overload assert any(arr is v for v in dsk.values()) is not inline_array def test_top(): t = Task("inc", inc, TaskRef("A")) assert _make_blockwise_graph( t, "z", "ij", "x", "ij", numblocks={"x": (2, 2)}, keys=("A",) ) == { ("z", 0, 0): Task(("z", 0, 0), inc, TaskRef(("x", 0, 0))), ("z", 0, 1): Task(("z", 0, 1), inc, TaskRef(("x", 0, 1))), ("z", 1, 0): Task(("z", 1, 0), inc, TaskRef(("x", 1, 0))), ("z", 1, 1): Task(("z", 1, 1), inc, TaskRef(("x", 1, 1))), } keys = list(map(str, range(2))) t = Task("add", add, *(TaskRef(k) for k in keys)) assert _make_blockwise_graph( t, "z", "ij", "x", "ij", "y", "ij", numblocks={"x": (2, 2), "y": (2, 2)}, keys=keys, ) == { ("z", 0, 0): Task(("z", 0, 0), add, TaskRef(("x", 0, 0)), TaskRef(("y", 0, 0))), ("z", 0, 1): Task(("z", 0, 1), add, TaskRef(("x", 0, 1)), TaskRef(("y", 0, 1))), ("z", 1, 0): Task(("z", 1, 0), add, TaskRef(("x", 1, 0)), TaskRef(("y", 1, 0))), ("z", 1, 1): Task(("z", 1, 1), add, TaskRef(("x", 1, 1)), TaskRef(("y", 1, 1))), } keys = list(map(str, range(2))) t = Task( "dotmany", dotmany, TaskRef(keys[0]), TaskRef(keys[1]), ) out = _make_blockwise_graph( t, "z", "ik", "x", "ij", "y", "jk", numblocks={"x": (2, 2), "y": (2, 2)}, keys=keys, ) expected = { ("z", 0, 0): Task( ("z", 0, 0), dotmany, List(TaskRef(("x", 0, 0)), TaskRef(("x", 0, 1))), List(TaskRef(("y", 0, 0)), TaskRef(("y", 1, 0))), ), ("z", 0, 1): Task( ("z", 0, 1), dotmany, List(TaskRef(("x", 0, 0)), TaskRef(("x", 0, 1))), List(TaskRef(("y", 0, 1)), TaskRef(("y", 1, 1))), ), ("z", 1, 0): Task( ("z", 1, 0), dotmany, List(TaskRef(("x", 1, 0)), TaskRef(("x", 1, 1))), List(TaskRef(("y", 0, 0)), TaskRef(("y", 1, 0))), ), ("z", 1, 1): Task( ("z", 1, 1), dotmany, List(TaskRef(("x", 1, 0)), TaskRef(("x", 1, 1))), List(TaskRef(("y", 0, 1)), TaskRef(("y", 1, 1))), ), } assert out[("z", 0, 0)] == expected[("z", 0, 0)] assert out == expected t = Task("identity", identity, TaskRef("0")) assert _make_blockwise_graph( t, "z", "", "x", "ij", numblocks={"x": (2, 2)}, keys=("0",) ) == { ("z",): Task( ("z",), identity, List( List(TaskRef(("x", 0, 0)), TaskRef(("x", 0, 1))), List(TaskRef(("x", 1, 0)), TaskRef(("x", 1, 1))), ), ) } def test_top_supports_broadcasting_rules(): t = Task("add", add, TaskRef("A"), TaskRef("B")) assert _make_blockwise_graph( t, "z", "ij", "x", "ij", "y", "ij", numblocks={"x": (1, 2), "y": (2, 1)}, keys=("A", "B"), ) == { ("z", 0, 0): Task(("z", 0, 0), add, TaskRef(("x", 0, 0)), TaskRef(("y", 0, 0))), ("z", 0, 1): Task(("z", 0, 1), add, TaskRef(("x", 0, 1)), TaskRef(("y", 0, 0))), ("z", 1, 0): Task(("z", 1, 0), add, TaskRef(("x", 0, 0)), TaskRef(("y", 1, 0))), ("z", 1, 1): Task(("z", 1, 1), add, TaskRef(("x", 0, 1)), TaskRef(("y", 1, 0))), } def test_top_literals(): t = Task("add", add, TaskRef("A"), TaskRef("B")) assert _make_blockwise_graph( t, "z", "ij", "x", "ij", 123, None, numblocks={"x": (2, 2)}, keys=("A", "B") ) == { ("z", 0, 0): Task(("z", 0, 0), add, TaskRef(("x", 0, 0)), DataNode(None, 123)), ("z", 0, 1): Task(("z", 0, 1), add, TaskRef(("x", 0, 1)), DataNode(None, 123)), ("z", 1, 0): Task(("z", 1, 0), add, TaskRef(("x", 1, 0)), DataNode(None, 123)), ("z", 1, 1): Task(("z", 1, 1), add, TaskRef(("x", 1, 1)), DataNode(None, 123)), } def test_blockwise_literals(): x = da.ones((10, 10), chunks=(5, 5)) z = da.blockwise(add, "ij", x, "ij", 100, None, dtype=x.dtype) assert_eq(z, x + 100) z = da.blockwise( lambda x, y, z: x * y + z, "ij", 2, None, x, "ij", 100, None, dtype=x.dtype ) assert_eq(z, 2 * x + 100) z = da.blockwise(getitem, "ij", x, "ij", slice(None), None, dtype=x.dtype) assert_eq(z, x) def test_blockwise_1_in_shape_I(): def test_f(a, b): assert 1 in b.shape p, k, N = 7, 2, 5 arr = da.blockwise( test_f, "x", da.zeros((2 * p, 9, k * N), chunks=(p, 3, k)), "xzt", da.zeros((2 * p, 9, 1), chunks=(p, 3, -1)), "xzt", concatenate=True, dtype=float, ) arr.compute() def test_blockwise_1_in_shape_II(): def test_f(a, b): assert 1 in b.shape p, k, N = 7, 2, 5 da.blockwise( test_f, "x", da.zeros((2 * p, 9, k * N, 8), chunks=(p, 9, k, 4)), "xztu", da.zeros((2 * p, 9, 1, 8), chunks=(p, 9, -1, 4)), "xztu", concatenate=True, dtype=float, ).compute() def test_blockwise_1_in_shape_III(): def test_f(a, b): assert 1 in b.shape k, N = 2, 5 da.blockwise( test_f, "x", da.zeros((k * N, 9, 8), chunks=(k, 3, 4)), "xtu", da.zeros((1, 9, 8), chunks=(-1, 3, 4)), "xtu", concatenate=True, dtype=float, ).compute() def test_concatenate3_on_scalars(): assert_eq(concatenate3([1, 2]), np.array([1, 2])) def test_chunked_dot_product(): x = np.arange(400).reshape((20, 20)) o = np.ones((20, 20)) getx = graph_from_arraylike(x, (5, 5), shape=(20, 20), name="x") geto = graph_from_arraylike(o, (5, 5), shape=(20, 20), name="o") keys = list(map(str, range(2))) t = Task( "dotmany", dotmany, TaskRef(keys[0]), TaskRef(keys[1]), ) result = _make_blockwise_graph( t, "out", "ik", "x", "ij", "o", "jk", numblocks={"x": (4, 4), "o": (4, 4)}, keys=keys, ) dsk = merge(getx, geto, result) out = dask.get(dsk, [[("out", i, j) for j in range(4)] for i in range(4)]) assert_eq(np.dot(x, o), concatenate3(out)) def test_chunked_transpose_plus_one(): x = np.arange(400).reshape((20, 20)) getx = graph_from_arraylike(x, (5, 5), shape=(20, 20), name="x") f = Task("f", lambda x: x.T + 1, TaskRef("x")) comp = _make_blockwise_graph( f, "out", "ij", "x", "ji", numblocks={"x": (4, 4)}, keys=("x",) ) dsk = merge(getx, comp) out = dask.get(dsk, [[("out", i, j) for j in range(4)] for i in range(4)]) assert_eq(concatenate3(out), x.T + 1) def test_broadcast_dimensions_works_with_singleton_dimensions(): argpairs = [("x", "i")] numblocks = {"x": ((1,),)} assert broadcast_dimensions(argpairs, numblocks) == {"i": (1,)} def test_broadcast_dimensions(): argpairs = [("x", "ij"), ("y", "ij")] d = {"x": ("Hello", 1), "y": (1, (2, 3))} assert broadcast_dimensions(argpairs, d) == {"i": "Hello", "j": (2, 3)} def test_Array(): arr = object() # arraylike is unimportant since we never compute shape = (1000, 1000) chunks = (100, 100) name = "x" dsk = graph_from_arraylike(arr, chunks, shape, name) a = Array(dsk, name, chunks, shape=shape, dtype="f8") assert a.numblocks == (10, 10) assert a.__dask_keys__() == [[("x", i, j) for j in range(10)] for i in range(10)] assert a.chunks == ((100,) * 10, (100,) * 10) assert a.shape == shape assert len(a) == shape[0] with pytest.raises(ValueError): Array(dsk, name, chunks, shape=shape) with pytest.raises(TypeError): Array(dsk, name, chunks, shape=shape, dtype="f8", meta=np.empty(0, 0)) def test_uneven_chunks(): a = Array({}, "x", chunks=(3, 3), shape=(10, 10), dtype="f8") assert a.chunks == ((3, 3, 3, 1), (3, 3, 3, 1)) def test_numblocks_suppoorts_singleton_block_dims(): arr = object() # arraylike is unimportant since we never compute shape = (100, 10) chunks = (10, 10) name = "x" dsk = graph_from_arraylike(arr, chunks, shape, name) a = Array(dsk, name, chunks, shape=shape, dtype="f8") assert set(concat(a.__dask_keys__())) == {("x", i, 0) for i in range(10)} def test_keys(): dsk = {("x", i, j): () for i in range(5) for j in range(6)} dx = Array(dsk, "x", chunks=(10, 10), shape=(50, 60), dtype="f8") assert dx.__dask_keys__() == [[(dx.name, i, j) for j in range(6)] for i in range(5)] # Cache works assert dx.__dask_keys__() is dx.__dask_keys__() # Test mutating names clears key cache dx.dask = {("y", i, j): () for i in range(5) for j in range(6)} dx._name = "y" new_keys = [[(dx.name, i, j) for j in range(6)] for i in range(5)] assert dx.__dask_keys__() == new_keys assert np.array_equal(dx._key_array, np.array(new_keys, dtype="object")) d = Array({}, "x", (), shape=(), dtype="f8") assert d.__dask_keys__() == [("x",)] def test_Array_computation(): a = Array({("x", 0, 0): np.eye(3)}, "x", shape=(3, 3), chunks=(3, 3), dtype="f8") assert_eq(np.array(a), np.eye(3)) assert isinstance(a.compute(), np.ndarray) assert float(a[0, 0]) == 1 @pytest.mark.parametrize("asarray", [np.asarray, np.asanyarray, np.array]) def test_numpy_asarray_dtype(asarray): """Test dtype= parameter of np.*array()""" x = da.arange(10, dtype="i2") nx = asarray(x, dtype="i4") assert nx.dtype == np.int32 @pytest.mark.parametrize( "asarray", [ np.array, pytest.param( np.asarray, marks=pytest.mark.skipif(not NUMPY_GE_200, reason="no copy kwarg"), ), pytest.param( np.asanyarray, marks=pytest.mark.skipif(not NUMPY_GE_210, reason="no copy kwarg"), ), ], ) @pytest.mark.parametrize("chunks", [5, 10]) def test_numpy_asarray_copy_true(asarray, chunks): """Test np.*array(x, copy=True)""" x = da.asarray(np.arange(10), chunks=chunks) nx = asarray(x, copy=True) nx[0] = 42 # Did not write back to the buffer held in the Dask graph assert x[0].compute() == 0 @pytest.mark.parametrize( "asarray", [ pytest.param( np.array, marks=pytest.mark.skipif( not NUMPY_GE_200, reason="copy kwarg not forwarded to dask" ), ), pytest.param( np.asarray, marks=pytest.mark.skipif(not NUMPY_GE_200, reason="no copy kwarg"), ), pytest.param( np.asanyarray, marks=pytest.mark.skipif(not NUMPY_GE_210, reason="no copy kwarg"), ), ], ) @pytest.mark.parametrize("chunks", [5, 10]) def test_numpy_asarray_copy_false(asarray, chunks): """Test that np.*array(x, copy=False) is forbidden""" x = da.asarray(np.arange(10), chunks=chunks) # Loudly crash if numpy demands for a view of dask's memory with pytest.warns(FutureWarning, match="Can't acquire a memory view"): y = asarray(x, copy=False) assert_eq(y, np.arange(10)) @pytest.mark.parametrize( "asarray", [ pytest.param( np.array, marks=pytest.mark.skipif(not NUMPY_GE_200, reason="copy=None invalid"), ), pytest.param( np.asarray, marks=pytest.mark.skipif(not NUMPY_GE_200, reason="no copy kwarg"), ), pytest.param( np.asanyarray, marks=pytest.mark.skipif(not NUMPY_GE_210, reason="no copy kwarg"), ), ], ) @pytest.mark.parametrize("chunks", [5, 10]) def test_numpy_asarray_copy_none(asarray, chunks): """Test np.*array(x, copy=None)""" x = da.asarray(np.arange(10), chunks=chunks) nx = asarray(x, copy=None) nx[0] = 42 # Did not write back to the buffer held in the Dask graph assert x[0].compute() == 0 @pytest.mark.parametrize("asarray", [np.asarray, np.asanyarray, np.array]) @pytest.mark.parametrize("chunks", [5, 10]) def test_numpy_asarray_copy_default(asarray, chunks): """Test that np.*array() never returns an object that shares a buffer with the dask graph or a process-local Worker """ x = da.asarray(np.arange(10), chunks=chunks) # array() defaults to copy=True. # asarray() and asanyarray() default to copy=None. # For Dask, it doesn't make a difference. nx = asarray(x) nx[0] = 42 # Did not write back to the buffer held in the Dask graph assert x[0].compute() == 0 def test_array_interface_deprecated_kwargs(): x = da.ones(10) with pytest.warns(FutureWarning, match="ignored"): x.__array__(something="foo") @pytest.mark.parametrize("chunks", [5, 10]) def test_compute_copy(chunks): """Test that compute() never returns an object that shares a buffer with the dask graph or a process-local Worker """ x = da.asarray(np.arange(10), chunks=chunks) nx = x.compute() nx[0] = 42 # Did not write back to the buffer held in the Dask graph assert x[0].compute() == 0 (nx,) = dask.compute(x) nx[0] = 42 assert x[0].compute() == 0 def test_Array_numpy_gufunc_call__array_ufunc__01(): x = da.random.default_rng().normal(size=(3, 10, 10), chunks=(2, 10, 10)) nx = x.compute() ny = np.linalg._umath_linalg.inv(nx) y = np.linalg._umath_linalg.inv(x) assert_eq(ny, y) def test_Array_numpy_gufunc_call__array_ufunc__02(): x = da.random.default_rng().normal(size=(3, 10, 10), chunks=(2, 10, 10)) nx = x.compute() nw, nv = np.linalg._umath_linalg.eig(nx) w, v = np.linalg._umath_linalg.eig(x) assert_eq(nw, w) assert_eq(nv, v) def test_stack(): a, b, c = ( Array( graph_from_arraylike(object(), chunks=(2, 3), shape=(4, 6), name=name), name, chunks=(2, 3), dtype="f8", shape=(4, 6), ) for name in "ABC" ) s = stack([a, b, c], axis=0) colon = slice(None, None, None) assert s.shape == (3, 4, 6) assert s.chunks == ((1, 1, 1), (2, 2), (3, 3)) assert s.chunksize == (1, 2, 3) assert s.dask[(s.name, 0, 1, 0)] == (getitem, ("A", 1, 0), (None, colon, colon)) assert s.dask[(s.name, 2, 1, 0)] == (getitem, ("C", 1, 0), (None, colon, colon)) assert same_keys(s, stack([a, b, c], axis=0)) s2 = stack([a, b, c], axis=1) assert s2.shape == (4, 3, 6) assert s2.chunks == ((2, 2), (1, 1, 1), (3, 3)) assert s2.chunksize == (2, 1, 3) assert s2.dask[(s2.name, 0, 1, 0)] == (getitem, ("B", 0, 0), (colon, None, colon)) assert s2.dask[(s2.name, 1, 1, 0)] == (getitem, ("B", 1, 0), (colon, None, colon)) assert same_keys(s2, stack([a, b, c], axis=1)) s2 = stack([a, b, c], axis=2) assert s2.shape == (4, 6, 3) assert s2.chunks == ((2, 2), (3, 3), (1, 1, 1)) assert s2.chunksize == (2, 3, 1) assert s2.dask[(s2.name, 0, 1, 0)] == (getitem, ("A", 0, 1), (colon, colon, None)) assert s2.dask[(s2.name, 1, 1, 2)] == (getitem, ("C", 1, 1), (colon, colon, None)) assert same_keys(s2, stack([a, b, c], axis=2)) pytest.raises(ValueError, lambda: stack([])) pytest.raises(ValueError, lambda: stack([a, b, c], axis=3)) assert set(b.dask.keys()).issubset(s2.dask.keys()) assert stack([a, b, c], axis=-1).chunks == stack([a, b, c], axis=2).chunks def test_stack_zero_size(): x = np.empty((2, 0, 3)) y = da.from_array(x, chunks=1) result_np = np.concatenate([x, x]) result_da = da.concatenate([y, y]) assert_eq(result_np, result_da) def test_short_stack(): x = np.array([1]) d = da.from_array(x, chunks=(1,)) s = da.stack([d]) assert s.shape == (1, 1) chunks = compute_as_if_collection(Array, s.dask, s.__dask_keys__()) assert chunks[0][0].shape == (1, 1) def test_stack_scalars(): d = da.arange(4, chunks=2) s = da.stack([d.mean(), d.sum()]) assert s.compute().tolist() == [np.arange(4).mean(), np.arange(4).sum()] def test_stack_promote_type(): i = np.arange(10, dtype="i4") f = np.arange(10, dtype="f4") di = da.from_array(i, chunks=5) df = da.from_array(f, chunks=5) res = da.stack([di, df]) assert_eq(res, np.stack([i, f])) def test_stack_rechunk(): rng = da.random.default_rng() x = rng.random(10, chunks=5) y = rng.random(10, chunks=4) z = da.stack([x, y], axis=0) assert z.shape == (2, 10) assert z.chunks == ((1, 1), (4, 1, 3, 2)) assert_eq(z, np.stack([x.compute(), y.compute()], axis=0)) def test_stack_unknown_chunksizes(): pd = pytest.importorskip("pandas") dd = pytest.importorskip("dask.dataframe") a_df = pd.DataFrame({"x": np.arange(12)}) b_df = pd.DataFrame({"y": np.arange(12) * 10}) a_ddf = dd.from_pandas(a_df, sort=False, npartitions=3) b_ddf = dd.from_pandas(b_df, sort=False, npartitions=3) a_x = a_ddf.values b_x = b_ddf.values assert np.isnan(a_x.shape[0]) assert np.isnan(b_x.shape[0]) with pytest.raises(ValueError) as exc_info: da.stack([a_x, b_x], axis=0) assert "shape" in str(exc_info.value) assert "nan" in str(exc_info.value) c_x = da.stack([a_x, b_x], axis=0, allow_unknown_chunksizes=True) assert_eq(c_x, np.stack([a_df.values, b_df.values], axis=0)) with pytest.raises(ValueError) as exc_info: da.stack([a_x, b_x], axis=1) assert "shape" in str(exc_info.value) assert "nan" in str(exc_info.value) c_x = da.stack([a_x, b_x], axis=1, allow_unknown_chunksizes=True) assert_eq(c_x, np.stack([a_df.values, b_df.values], axis=1)) m_df = pd.DataFrame({"m": np.arange(12) * 100}) n_df = pd.DataFrame({"n": np.arange(12) * 1000}) m_ddf = dd.from_pandas(m_df, sort=False, npartitions=3) n_ddf = dd.from_pandas(n_df, sort=False, npartitions=3) m_x = m_ddf.values n_x = n_ddf.values assert np.isnan(m_x.shape[0]) assert np.isnan(n_x.shape[0]) with pytest.raises(ValueError) as exc_info: da.stack([[a_x, b_x], [m_x, n_x]]) assert "shape" in str(exc_info.value) assert "nan" in str(exc_info.value) c_x = da.stack([[a_x, b_x], [m_x, n_x]], allow_unknown_chunksizes=True) assert_eq(c_x, np.stack([[a_df.values, b_df.values], [m_df.values, n_df.values]])) def test_concatenate(): a, b, c = ( Array( graph_from_arraylike(object(), chunks=(2, 3), shape=(4, 6), name=name), name, chunks=(2, 3), dtype="f8", shape=(4, 6), ) for name in "ABC" ) x = concatenate([a, b, c], axis=0) assert x.shape == (12, 6) assert x.chunks == ((2, 2, 2, 2, 2, 2), (3, 3)) assert x.dask[(x.name, 0, 1)] == ("A", 0, 1) assert x.dask[(x.name, 5, 0)] == ("C", 1, 0) assert same_keys(x, concatenate([a, b, c], axis=0)) y = concatenate([a, b, c], axis=1) assert y.shape == (4, 18) assert y.chunks == ((2, 2), (3, 3, 3, 3, 3, 3)) assert y.dask[(y.name, 1, 0)] == ("A", 1, 0) assert y.dask[(y.name, 1, 5)] == ("C", 1, 1) assert same_keys(y, concatenate([a, b, c], axis=1)) assert set(b.dask.keys()).issubset(y.dask.keys()) z = concatenate([a], axis=0) assert z.shape == a.shape assert z.chunks == a.chunks assert z.dask == a.dask assert z is a assert ( concatenate([a, b, c], axis=-1).chunks == concatenate([a, b, c], axis=1).chunks ) pytest.raises(ValueError, lambda: concatenate([])) pytest.raises(ValueError, lambda: concatenate([a, b, c], axis=2)) @pytest.mark.parametrize( "dtypes", [((">f8", ">f8"), "float64"), (("=2.0 def test_elemwise_dtype(): values = [ da.from_array(np.ones(5, np.float32), chunks=3), da.from_array(np.ones(5, np.int16), chunks=3), da.from_array(np.ones(5, np.int64), chunks=3), da.from_array(np.ones((), np.float64), chunks=()) * 1e200, np.ones(5, np.float32), 1, 1.0, 1e200, np.int64(1), np.ones((), np.int64), ] for x in values: for y in values: assert da.maximum(x, y).dtype == da.result_type(x, y) def test_operators(): x = np.arange(10) y = np.arange(10).reshape((10, 1)) a = from_array(x, chunks=(5,)) b = from_array(y, chunks=(5, 1)) c = a + 1 assert_eq(c, x + 1) c = a + b assert_eq(c, x + x.reshape((10, 1))) expr = (3 / a * b) ** 2 > 5 with warnings.catch_warnings(): warnings.simplefilter("ignore", RuntimeWarning) # divide by zero assert_eq(expr, (3 / x * y) ** 2 > 5) c = da.exp(a) assert_eq(c, np.exp(x)) assert_eq(abs(-a), a) assert_eq(a, +x) def test_binary_operator_delegation(mocker): # Binary operator delegation in Dask should follow Numpy's: # https://numpy.org/neps/nep-0013-ufunc-overrides.html#behavior-in-combination-with-python-s-binary-operations x = from_array([1, 2, 3]) # Mock various types of `other` objects ufunc_none = mocker.Mock() ufunc_none.__array_ufunc__ = None ufunc_none.__radd__ = mocker.Mock() ufunc_high_priority = mocker.Mock() ufunc_high_priority.__array_priority__ = x.__array_priority__ + 1 ufunc_high_priority.__radd__ = mocker.Mock() ufunc_low_priority = mocker.Mock() ufunc_low_priority.__array_priority__ = x.__array_priority__ - 1 ufunc_low_priority.__radd__ = mocker.Mock() ufunc_no_priority = mocker.Mock() ufunc_no_priority.__radd__ = mocker.Mock() # If `other.__array_ufunc__ is None`, delegates back to Python # and therefore call reflected operator on `other` x + ufunc_none ufunc_none.__radd__.assert_called_once() # If the `__array_ufunc__` attribute is absent on other and # `other.__array_priority__ > self.__array_priority__`, also delegates back # to Python and therefore call reflected operator on `other` x + ufunc_high_priority ufunc_high_priority.__radd__.assert_called_once() # If `other.__array_priority__ <= self.__array_priority__`, does not # delegate (here it raises an error) with pytest.raises(TypeError): x + ufunc_low_priority ufunc_low_priority.__radd__.assert_not_called() # If `other.__array_priority__` is absent, does not delegate (raises) with pytest.raises(TypeError): x + ufunc_no_priority ufunc_no_priority.__radd__.assert_not_called() @pytest.mark.filterwarnings("ignore:overflow encountered in cast") # numpy >=2.0 def test_operator_dtype_promotion(): x = np.arange(10, dtype=np.float32) y = np.array([1]) a = from_array(x, chunks=(5,)) assert_eq(x + 1, a + 1) # still float32 assert_eq(x + 1e50, a + 1e50) # now float64 assert_eq(x + y, a + y) # also float64 def test_field_access(): x = np.array([(1, 1.0), (2, 2.0)], dtype=[("a", "i4"), ("b", "f4")]) y = from_array(x, chunks=(1,)) assert_eq(y["a"], x["a"]) assert_eq(y[["b", "a"]], x[["b", "a"]]) assert same_keys(y[["b", "a"]], y[["b", "a"]]) def test_field_access_with_shape(): dtype = [("col1", ("f4", (3, 2))), ("col2", ("f4", 3))] data = np.ones((100, 50), dtype=dtype) x = da.from_array(data, 10) assert_eq(x["col1"], data["col1"]) assert_eq(x[["col1"]], data[["col1"]]) assert_eq(x["col2"], data["col2"]) assert_eq(x[["col1", "col2"]], data[["col1", "col2"]]) def test_matmul(): rng = np.random.default_rng() x = rng.random((5, 5)) y = rng.random((5, 2)) a = from_array(x, chunks=(1, 5)) b = from_array(y, chunks=(5, 1)) assert_eq(operator.matmul(a, b), a.dot(b)) assert_eq(operator.matmul(a, b), operator.matmul(x, y)) assert_eq(operator.matmul(a, y), operator.matmul(x, b)) list_vec = list(range(1, 6)) assert_eq(operator.matmul(list_vec, b), operator.matmul(list_vec, y)) assert_eq(operator.matmul(x, list_vec), operator.matmul(a, list_vec)) z = rng.random((5, 5, 5)) c = from_array(z, chunks=(1, 5, 1)) assert_eq(operator.matmul(a, z), operator.matmul(x, c)) assert_eq(operator.matmul(z, a), operator.matmul(c, x)) def test_matmul_array_ufunc(): # regression test for https://github.com/dask/dask/issues/4353 rng = np.random.default_rng() x = rng.random((5, 5)) y = rng.random((5, 2)) a = from_array(x, chunks=(1, 5)) b = from_array(y, chunks=(5, 1)) result = b.__array_ufunc__(np.matmul, "__call__", a, b) assert_eq(result, x.dot(y)) def test_T(): x = np.arange(400).reshape((20, 20)) a = from_array(x, chunks=(5, 5)) assert_eq(x.T, a.T) def test_broadcast_to(): x = np.random.default_rng().integers(10, size=(5, 1, 6)) a = from_array(x, chunks=(3, 1, 3)) for shape in [a.shape, (5, 0, 6), (5, 4, 6), (2, 5, 1, 6), (3, 4, 5, 4, 6)]: xb = np.broadcast_to(x, shape) ab = broadcast_to(a, shape) assert_eq(xb, ab) if a.shape == ab.shape: assert a is ab pytest.raises(ValueError, lambda: broadcast_to(a, (2, 1, 6))) pytest.raises(ValueError, lambda: broadcast_to(a, (3,))) def test_broadcast_to_array(): x = np.random.default_rng().integers(10, size=(5, 1, 6)) for shape in [(5, 0, 6), (5, 4, 6), (2, 5, 1, 6), (3, 4, 5, 4, 6)]: a = np.broadcast_to(x, shape) d = broadcast_to(x, shape) assert_eq(a, d) def test_broadcast_to_scalar(): x = 5 for shape in [tuple(), (0,), (2, 3), (5, 4, 6), (2, 5, 1, 6), (3, 4, 5, 4, 6)]: a = np.broadcast_to(x, shape) d = broadcast_to(x, shape) assert_eq(a, d) def test_broadcast_to_chunks(): x = np.random.default_rng().integers(10, size=(5, 1, 6)) a = from_array(x, chunks=(3, 1, 3)) for shape, chunks, expected_chunks in [ ((5, 3, 6), (3, -1, 3), ((3, 2), (3,), (3, 3))), ((5, 3, 6), (3, 1, 3), ((3, 2), (1, 1, 1), (3, 3))), ((2, 5, 3, 6), (1, 3, 1, 3), ((1, 1), (3, 2), (1, 1, 1), (3, 3))), ]: xb = np.broadcast_to(x, shape) ab = broadcast_to(a, shape, chunks=chunks) assert_eq(xb, ab) assert ab.chunks == expected_chunks with pytest.raises(ValueError): broadcast_to(a, a.shape, chunks=((2, 3), (1,), (3, 3))) with pytest.raises(ValueError): broadcast_to(a, a.shape, chunks=((3, 2), (3,), (3, 3))) with pytest.raises(ValueError): broadcast_to(a, (5, 2, 6), chunks=((3, 2), (3,), (3, 3))) def test_broadcast_arrays(): assert np.broadcast_arrays() == da.broadcast_arrays() a = np.arange(4) d_a = da.from_array(a, chunks=tuple(s // 2 for s in a.shape)) a_0 = np.arange(4)[None, :] a_1 = np.arange(4)[:, None] d_a_0 = d_a[None, :] d_a_1 = d_a[:, None] a_r = np.broadcast_arrays(a_0, a_1) d_r = da.broadcast_arrays(d_a_0, d_a_1) assert isinstance(d_r, (list, tuple)) assert len(a_r) == len(d_r) for e_a_r, e_d_r in zip(a_r, d_r): assert_eq(e_a_r, e_d_r) def test_broadcast_arrays_uneven_chunks(): x = da.ones(30, chunks=(3,)) y = da.ones(30, chunks=(5,)) z = np.broadcast_arrays(x, y) assert_eq(z, z) x = da.ones((1, 30), chunks=(1, 3)) y = da.ones(30, chunks=(5,)) z = np.broadcast_arrays(x, y) assert_eq(z, z) @pytest.mark.parametrize( "u_shape, v_shape", [ [tuple(), (2, 3)], [(1,), (2, 3)], [(1, 1), (2, 3)], [(0, 3), (1, 3)], [(2, 0), (2, 1)], [(1, 0), (2, 1)], [(0, 1), (1, 3)], ], ) def test_broadcast_operator(u_shape, v_shape): rng = np.random.default_rng() u = rng.random(u_shape) v = rng.random(v_shape) d_u = from_array(u, chunks=1) d_v = from_array(v, chunks=1) w = u * v d_w = d_u * d_v assert_eq(w, d_w) @pytest.mark.parametrize( "original_shape,new_shape,chunks", [ ((10,), (10,), (3, 3, 4)), ((10,), (10, 1, 1), 5), ((10,), (1, 10), 5), ((24,), (2, 3, 4), 12), ((1, 24), (2, 3, 4), 12), ((2, 3, 4), (24,), (1, 3, 4)), ((2, 3, 4), (24,), 4), ((2, 3, 4), (24, 1), 4), ((2, 3, 4), (1, 24), 4), ((4, 4, 1), (4, 4), 2), ((4, 4), (4, 4, 1), 2), ((1, 4, 4), (4, 4), 2), ((1, 4, 4), (4, 4, 1), 2), ((1, 4, 4), (1, 1, 4, 4), 2), ((4, 4), (1, 4, 4, 1), 2), ((4, 4), (1, 4, 4), 2), ((2, 3), (2, 3), (1, 2)), ((2, 3), (3, 2), 3), ((4, 2, 3), (4, 6), 4), ((3, 4, 5, 6), (3, 4, 5, 6), (2, 3, 4, 5)), ((), (1,), 1), ((1,), (), 1), ((24,), (3, 8), 24), ((24,), (4, 6), 6), ((24,), (4, 3, 2), 6), ((24,), (4, 6, 1), 6), ((24,), (4, 6), (6, 12, 6)), ((64, 4), (8, 8, 4), (16, 2)), ((4, 64), (4, 8, 4, 2), (2, 16)), ((4, 8, 4, 2), (2, 1, 2, 32, 2), (2, 4, 2, 2)), ((4, 1, 4), (4, 4), (2, 1, 2)), ((0, 10), (0, 5, 2), (5, 5)), ((5, 0, 2), (0, 10), (5, 2, 2)), ((0,), (2, 0, 2), (4,)), ((2, 0, 2), (0,), (4, 4, 4)), ], ) def test_reshape(original_shape, new_shape, chunks): x = np.random.default_rng().integers(10, size=original_shape) a = from_array(x, chunks=chunks) xr = x.reshape(new_shape) ar = a.reshape(new_shape) if a.shape == new_shape: assert a is ar assert_eq(xr, ar) def test_reshape_exceptions(): x = np.random.default_rng().integers(10, size=(5,)) a = from_array(x, chunks=(2,)) with pytest.raises(ValueError): da.reshape(a, (100,)) def test_reshape_splat(): x = da.ones((5, 5), chunks=(2, 2)) assert_eq(x.reshape((25,)), x.reshape(25)) def test_reshape_not_implemented_error(): a = da.ones((4, 5, 6), chunks=(2, 2, 3)) for new_shape in [(2, 10, 6), (5, 4, 6), (6, 5, 4)]: with pytest.raises( NotImplementedError, match=re.escape(_not_implemented_message) ): a.reshape(new_shape) def test_reshape_unknown_dimensions(): for original_shape in [(24,), (2, 12), (2, 3, 4)]: for new_shape in [(-1,), (2, -1), (-1, 3, 4)]: x = np.random.default_rng().integers(10, size=original_shape) a = from_array(x, 24) assert_eq(x.reshape(new_shape), a.reshape(new_shape)) pytest.raises(ValueError, lambda: da.reshape(a, (-1, -1))) def test_full(): d = da.full((3, 4), 2, chunks=((2, 1), (2, 2))) assert d.chunks == ((2, 1), (2, 2)) assert_eq(d, np.full((3, 4), 2)) def test_map_blocks(): x = np.arange(400).reshape((20, 20)) d = from_array(x, chunks=(7, 7)) e = d.map_blocks(inc, dtype=d.dtype) assert d.chunks == e.chunks assert_eq(e, x + 1) e = d.map_blocks(inc, token="increment") assert e.name.startswith("increment-") assert d.map_blocks(inc, token="foo").name != d.map_blocks(dec, token="foo").name d = from_array(x, chunks=(10, 10)) e = d.map_blocks(lambda x: x[::2, ::2], chunks=(5, 5), dtype=d.dtype) assert e.chunks == ((5, 5), (5, 5)) assert_eq(e, x[::2, ::2]) d = from_array(x, chunks=(8, 8)) e = d.map_blocks( lambda x: x[::2, ::2], chunks=((4, 4, 2), (4, 4, 2)), dtype=d.dtype ) assert_eq(e, x[::2, ::2]) def test_map_blocks2(): x = np.arange(10, dtype="i8") d = from_array(x, chunks=(2,)) def func(block, block_id=None, c=0): return np.ones_like(block) * sum(block_id) + c out = d.map_blocks(func, dtype="i8") expected = np.array([0, 0, 1, 1, 2, 2, 3, 3, 4, 4], dtype="i8") assert_eq(out, expected) assert same_keys(d.map_blocks(func, dtype="i8"), out) out = d.map_blocks(func, dtype="i8", c=1) expected = expected + 1 assert_eq(out, expected) assert same_keys(d.map_blocks(func, dtype="i8", c=1), out) def test_map_blocks_block_info(): x = da.arange(50, chunks=10) def func(a, b, c, block_info=None): for idx in [0, 2, None]: # positions in args assert block_info[idx]["shape"] == (50,) assert block_info[idx]["num-chunks"] == (5,) start, stop = block_info[idx]["array-location"][0] assert stop - start == 10 assert 0 <= start <= 40 assert 10 <= stop <= 50 assert 0 <= block_info[idx]["chunk-location"][0] <= 4 assert block_info[None]["chunk-shape"] == (10,) assert block_info[None]["dtype"] == x.dtype return a + b + c z = da.map_blocks(func, x, 100, x + 1, dtype=x.dtype) assert_eq(z, x + x + 1 + 100) def test_map_blocks_block_info_with_new_axis(): # https://github.com/dask/dask/issues/4298 values = da.from_array(np.array(["a", "a", "b", "c"]), 2) def func(x, block_info=None): assert block_info.keys() == {0, None} assert block_info[0]["shape"] == (4,) assert block_info[0]["num-chunks"] == (2,) assert block_info[None]["shape"] == (4, 3) assert block_info[None]["num-chunks"] == (2, 1) assert block_info[None]["chunk-shape"] == (2, 3) assert block_info[None]["dtype"] == np.dtype("f8") assert block_info[0]["chunk-location"] in {(0,), (1,)} if block_info[0]["chunk-location"] == (0,): assert block_info[0]["array-location"] == [(0, 2)] assert block_info[None]["chunk-location"] == (0, 0) assert block_info[None]["array-location"] == [(0, 2), (0, 3)] elif block_info[0]["chunk-location"] == (1,): assert block_info[0]["array-location"] == [(2, 4)] assert block_info[None]["chunk-location"] == (1, 0) assert block_info[None]["array-location"] == [(2, 4), (0, 3)] return np.ones((len(x), 3)) z = values.map_blocks(func, chunks=((2, 2), 3), new_axis=1, dtype="f8") assert_eq(z, np.ones((4, 3), dtype="f8")) def test_map_blocks_block_info_with_drop_axis(): # https://github.com/dask/dask/issues/4584 values = da.from_array( np.array( [[1, 2, 4], [8, 16, 32], [64, 128, 256], [1024, 2048, 4096]], dtype="u4" ), (2, 1), ) def func(x, block_info=None): assert block_info.keys() == {0, None} assert block_info[0]["shape"] == (4, 3) # drop_axis concatenates along the dropped dimension, hence not (2, 3) assert block_info[0]["num-chunks"] == (2, 1) assert block_info[None]["shape"] == (4,) assert block_info[None]["num-chunks"] == (2,) assert block_info[None]["chunk-shape"] == (2,) assert block_info[None]["dtype"] == np.dtype("u4") assert block_info[0]["chunk-location"] in {(0, 0), (1, 0)} if block_info[0]["chunk-location"] == (0, 0): assert block_info[0]["array-location"] == [(0, 2), (0, 3)] assert block_info[None]["chunk-location"] == (0,) assert block_info[None]["array-location"] == [(0, 2)] elif block_info[0]["chunk-location"] == (1, 0): assert block_info[0]["array-location"] == [(2, 4), (0, 3)] assert block_info[None]["chunk-location"] == (1,) assert block_info[None]["array-location"] == [(2, 4)] return np.sum(x, axis=1, dtype="u4") z = values.map_blocks(func, drop_axis=1, dtype="u4") assert_eq(z, np.array([7, 56, 448, 7168], dtype="u4")) def test_map_blocks_block_info_with_broadcast(): expected0 = [ { "shape": (3, 4), "num-chunks": (1, 2), "array-location": [(0, 3), (0, 2)], "chunk-location": (0, 0), }, { "shape": (3, 4), "num-chunks": (1, 2), "array-location": [(0, 3), (2, 4)], "chunk-location": (0, 1), }, ] expected1 = [ { "shape": (6, 2), "num-chunks": (2, 1), "array-location": [(0, 3), (0, 2)], "chunk-location": (0, 0), }, { "shape": (6, 2), "num-chunks": (2, 1), "array-location": [(3, 6), (0, 2)], "chunk-location": (1, 0), }, ] expected2 = [ { "shape": (4,), "num-chunks": (2,), "array-location": [(0, 2)], "chunk-location": (0,), }, { "shape": (4,), "num-chunks": (2,), "array-location": [(2, 4)], "chunk-location": (1,), }, ] expected = [ { 0: expected0[0], 1: expected1[0], 2: expected2[0], None: { "shape": (6, 4), "num-chunks": (2, 2), "dtype": np.float64, "chunk-shape": (3, 2), "array-location": [(0, 3), (0, 2)], "chunk-location": (0, 0), }, }, { 0: expected0[1], 1: expected1[0], 2: expected2[1], None: { "shape": (6, 4), "num-chunks": (2, 2), "dtype": np.float64, "chunk-shape": (3, 2), "array-location": [(0, 3), (2, 4)], "chunk-location": (0, 1), }, }, { 0: expected0[0], 1: expected1[1], 2: expected2[0], None: { "shape": (6, 4), "num-chunks": (2, 2), "dtype": np.float64, "chunk-shape": (3, 2), "array-location": [(3, 6), (0, 2)], "chunk-location": (1, 0), }, }, { 0: expected0[1], 1: expected1[1], 2: expected2[1], None: { "shape": (6, 4), "num-chunks": (2, 2), "dtype": np.float64, "chunk-shape": (3, 2), "array-location": [(3, 6), (2, 4)], "chunk-location": (1, 1), }, }, ] def func(x, y, z, block_info=None): for info in expected: if block_info[None]["chunk-location"] == info[None]["chunk-location"]: assert block_info == info break else: assert False return x + y + z a = da.ones((3, 4), chunks=(3, 2)) b = da.ones((6, 2), chunks=(3, 2)) c = da.ones((4,), chunks=(2,)) d = da.map_blocks(func, a, b, c, chunks=((3, 3), (2, 2)), dtype=a.dtype) assert d.chunks == ((3, 3), (2, 2)) assert_eq(d, 3 * np.ones((6, 4))) def test_map_blocks_with_constants(): d = da.arange(10, chunks=3) e = d.map_blocks(add, 100, dtype=d.dtype) assert_eq(e, np.arange(10) + 100) assert_eq(da.map_blocks(sub, d, 10, dtype=d.dtype), np.arange(10) - 10) assert_eq(da.map_blocks(sub, 10, d, dtype=d.dtype), 10 - np.arange(10)) def test_map_blocks_with_kwargs(): d = da.arange(10, chunks=5) result = d.map_blocks(np.max, axis=0, keepdims=True, dtype=d.dtype, chunks=(1,)) assert_eq(result, np.array([4, 9])) def test_map_blocks_infer_chunks_broadcast(): dx = da.from_array([[1, 2, 3, 4]], chunks=((1,), (2, 2))) dy = da.from_array([[10, 20], [30, 40]], chunks=((1, 1), (2,))) result = da.map_blocks(lambda x, y: x + y, dx, dy) assert result.chunks == ((1, 1), (2, 2)) assert_eq(result, np.array([[11, 22, 13, 24], [31, 42, 33, 44]])) def test_map_blocks_with_chunks(): dx = da.ones((5, 3), chunks=(2, 2)) dy = da.ones((5, 3), chunks=(2, 2)) dz = da.map_blocks(np.add, dx, dy, chunks=dx.chunks) assert_eq(dz, np.ones((5, 3)) * 2) def test_map_blocks_dtype_inference(): x = np.arange(50).reshape((5, 10)) y = np.arange(10) dx = da.from_array(x, chunks=5) dy = da.from_array(y, chunks=5) def foo(x, *args, **kwargs): cast = kwargs.pop("cast", "i8") return (x + sum(args)).astype(cast) assert_eq(dx.map_blocks(foo, dy, 1), foo(dx, dy, 1)) assert_eq(dx.map_blocks(foo, dy, 1, cast="f8"), foo(dx, dy, 1, cast="f8")) assert_eq( dx.map_blocks(foo, dy, 1, cast="f8", dtype="f8"), foo(dx, dy, 1, cast="f8", dtype="f8"), ) def foo(x): raise RuntimeError("Woops") with pytest.raises(ValueError) as e: dx.map_blocks(foo) msg = str(e.value) assert "dtype" in msg def test_map_blocks_infer_newaxis(): x = da.ones((5, 3), chunks=(2, 2)) y = da.map_blocks(lambda x: x[None], x, chunks=((1,), (2, 2, 1), (2, 1))) assert_eq(y, da.ones((1, 5, 3))) def test_map_blocks_no_array_args(): def func(dtype, block_info=None): loc = block_info[None]["array-location"] return np.arange(loc[0][0], loc[0][1], dtype=dtype) x = da.map_blocks(func, np.float32, chunks=((5, 3),), dtype=np.float32) assert x.chunks == ((5, 3),) assert_eq(x, np.arange(8, dtype=np.float32)) def test_map_blocks_unique_name_enforce_dim(): def func(some_3d, block_info=None): return some_3d input_arr = da.zeros((3, 4, 5), chunks=((3,), (4,), (5,)), dtype=np.float32) x = da.map_blocks(func, input_arr, enforce_ndim=True, dtype=np.float32) y = da.map_blocks(func, input_arr, enforce_ndim=False, dtype=np.float32) assert x._name != y._name def test_map_blocks_unique_name_chunks_dtype(): def func(block_info=None): loc = block_info[None]["array-location"] dtype = block_info[None]["dtype"] return np.arange(loc[0][0], loc[0][1], dtype=dtype) x = da.map_blocks(func, chunks=((5, 3),), dtype=np.float32) assert x.chunks == ((5, 3),) assert_eq(x, np.arange(8, dtype=np.float32)) y = da.map_blocks(func, chunks=((2, 2, 1, 3),), dtype=np.float32) assert y.chunks == ((2, 2, 1, 3),) assert_eq(y, np.arange(8, dtype=np.float32)) assert x.name != y.name z = da.map_blocks(func, chunks=((5, 3),), dtype=np.float64) assert z.chunks == ((5, 3),) assert_eq(z, np.arange(8, dtype=np.float64)) assert x.name != z.name assert y.name != z.name def test_map_blocks_unique_name_drop_axis(): def func(some_3d, block_info=None): if not block_info: return some_3d dtype = block_info[None]["dtype"] return np.zeros(block_info[None]["shape"], dtype=dtype) input_arr = da.zeros((3, 4, 5), chunks=((3,), (4,), (5,)), dtype=np.float32) x = da.map_blocks(func, input_arr, drop_axis=[0], dtype=np.float32) assert x.chunks == ((4,), (5,)) assert_eq(x, np.zeros((4, 5), dtype=np.float32)) y = da.map_blocks(func, input_arr, drop_axis=[2], dtype=np.float32) assert y.chunks == ((3,), (4,)) assert_eq(y, np.zeros((3, 4), dtype=np.float32)) assert x.name != y.name def test_map_blocks_unique_name_new_axis(): def func(some_2d, block_info=None): if not block_info: return some_2d dtype = block_info[None]["dtype"] return np.zeros(block_info[None]["shape"], dtype=dtype) input_arr = da.zeros((3, 4), chunks=((3,), (4,)), dtype=np.float32) x = da.map_blocks(func, input_arr, new_axis=[0], dtype=np.float32) assert x.chunks == ((1,), (3,), (4,)) assert_eq(x, np.zeros((1, 3, 4), dtype=np.float32)) y = da.map_blocks(func, input_arr, new_axis=[2], dtype=np.float32) assert y.chunks == ((3,), (4,), (1,)) assert_eq(y, np.zeros((3, 4, 1), dtype=np.float32)) assert x.name != y.name @pytest.mark.parametrize("func", [lambda x, y: x + y, lambda x, y, block_info: x + y]) def test_map_blocks_optimize_blockwise(func): # Check that map_blocks layers can merge with elementwise layers base = [da.full((1,), i, chunks=1) for i in range(4)] a = base[0] + base[1] b = da.map_blocks(func, a, base[2], dtype=np.int8) c = b + base[3] dsk = c.__dask_graph__() optimized = optimize_blockwise(dsk) # Everything should be fused into a single layer. # If the lambda includes block_info, there will be two layers. assert len(optimized.layers) == max(len(dsk.layers) - 7, 1) def test_repr(): d = da.ones((4, 4), chunks=(2, 2)) assert key_split(d.name) in repr(d) assert str(d.shape) in repr(d) assert str(d.dtype) in repr(d) d = da.ones((4000, 4), chunks=(4, 2)) assert len(str(d)) < 1000 def test_repr_meta(): d = da.ones((4, 4), chunks=(2, 2)) assert "chunktype=numpy.ndarray" in repr(d) # Test non-numpy meta sparse = pytest.importorskip("sparse") s = d.map_blocks(sparse.COO) assert "chunktype=sparse.COO" in repr(s) def test_repr_html_array_highlevelgraph(): pytest.importorskip("jinja2") x = da.ones((9, 9), chunks=(3, 3)).T[0:4, 0:4] hg = x.dask assert xml.etree.ElementTree.fromstring(hg._repr_html_()) is not None for layer in hg.layers.values(): assert xml.etree.ElementTree.fromstring(layer._repr_html_()) is not None def test_slicing_with_ellipsis(): x = np.arange(256).reshape((4, 4, 4, 4)) d = da.from_array(x, chunks=((2, 2, 2, 2))) assert_eq(d[..., 1], x[..., 1]) assert_eq(d[0, ..., 1], x[0, ..., 1]) def test_slicing_with_ndarray(): x = np.arange(64).reshape((8, 8)) d = da.from_array(x, chunks=((4, 4))) assert_eq(d[np.arange(8)], x) assert_eq(d[np.ones(8, dtype=bool)], x) assert_eq(d[np.array([1])], x[[1]]) assert_eq(d[np.array([True, False, True] + [False] * 5)], x[[0, 2]]) def test_slicing_flexible_type(): a = np.array([["a", "b"], ["c", "d"]]) b = da.from_array(a, 2) assert_eq(a[:, 0], b[:, 0]) def test_slicing_with_object_dtype(): # https://github.com/dask/dask/issues/6892 d = da.from_array(np.array(["a", "b"], dtype=object), chunks=(1,)) assert d.dtype == d[(0,)].dtype def test_dtype(): d = da.ones((4, 4), chunks=(2, 2)) assert d.dtype == d.compute().dtype assert (d * 1.0).dtype == (d + 1.0).compute().dtype assert d.sum().dtype == d.sum().compute().dtype # no shape def test_blockdims_from_blockshape(): assert blockdims_from_blockshape((10, 10), (4, 3)) == ((4, 4, 2), (3, 3, 3, 1)) pytest.raises(TypeError, lambda: blockdims_from_blockshape((10,), None)) assert blockdims_from_blockshape((1e2, 3), [1e1, 3]) == ((10,) * 10, (3,)) assert blockdims_from_blockshape((np.int8(10),), (5,)) == ((5, 5),) def test_coerce(): d0 = da.from_array(np.array(1), chunks=(1,)) d1 = da.from_array(np.array([1]), chunks=(1,)) with dask.config.set(scheduler="sync"): for d in d0, d1: assert bool(d) is True assert int(d) == 1 assert float(d) == 1.0 assert complex(d) == complex(1) a2 = np.arange(2) d2 = da.from_array(a2, chunks=(2,)) for func in (int, float, complex): pytest.raises(TypeError, lambda func=func: func(d2)) def test_bool(): arr = np.arange(100).reshape((10, 10)) darr = da.from_array(arr, chunks=(10, 10)) with pytest.raises(ValueError): bool(darr) bool(darr == darr) def test_store_kwargs(): d = da.ones((10, 10), chunks=(2, 2)) a = d + 1 called = [False] def get_func(*args, **kwargs): assert kwargs.pop("foo") == "test kwarg" r = dask.get(*args, **kwargs) called[0] = True return r called[0] = False at = np.zeros(shape=(10, 10)) store([a], [at], scheduler=get_func, foo="test kwarg") assert called[0] called[0] = False at = np.zeros(shape=(10, 10)) a.store(at, scheduler=get_func, foo="test kwarg") assert called[0] called[0] = False at = np.zeros(shape=(10, 10)) store([a], [at], scheduler=get_func, return_stored=True, foo="test kwarg") assert called[0] def test_store_delayed_target(): from dask.delayed import delayed d = da.ones((4, 4), chunks=(2, 2)) a, b = d + 1, d + 2 # empty buffers to be used as targets targs = {} def make_target(key): a = np.empty((4, 4)) targs[key] = a return a # delayed calls to these targets atd = delayed(make_target)("at") btd = delayed(make_target)("bt") # test not keeping result st = store([a, b], [atd, btd]) at = targs["at"] bt = targs["bt"] assert st is None assert_eq(at, a) assert_eq(bt, b) # test keeping result for st_compute in [False, True]: targs.clear() st = store([a, b], [atd, btd], return_stored=True, compute=st_compute) if st_compute: for arr in st: assert_has_persisted_data(arr) st = dask.compute(*st) at = targs["at"] bt = targs["bt"] assert st is not None assert isinstance(st, tuple) assert all([isinstance(v, np.ndarray) for v in st]) assert_eq(at, a) assert_eq(bt, b) assert_eq(st[0], a) assert_eq(st[1], b) pytest.raises(ValueError, lambda at=at, bt=bt: store([a], [at, bt])) pytest.raises(ValueError, lambda at=at: store(at, at)) pytest.raises(ValueError, lambda at=at, bt=bt: store([at, bt], [at, bt])) def test_store(): d = da.ones((4, 4), chunks=(2, 2)) a, b = d + 1, d + 2 at = np.empty(shape=(4, 4)) bt = np.empty(shape=(4, 4)) st = store([a, b], [at, bt]) assert st is None assert (at == 2).all() assert (bt == 3).all() pytest.raises(ValueError, lambda: store([a], [at, bt])) pytest.raises(ValueError, lambda: store(at, at)) pytest.raises(ValueError, lambda: store([at, bt], [at, bt])) def test_store_regions(): d = da.ones((4, 4, 4), dtype=int, chunks=(2, 2, 2)) a, b = d + 1, d + 2 a = a[:, 1:, :].astype(float) region = (slice(None, None, 2), slice(None), [1, 2, 4, 5]) # Single region: at = np.zeros(shape=(8, 3, 6)) bt = np.zeros(shape=(8, 4, 6)) v = store([a, b], [at, bt], regions=region, compute=False) assert all([isinstance(a, Array) for a in v]) assert (at == 0).all() and (bt[region] == 0).all() results = dask.compute(*v) assert all([ev.size == 0 for ev in results]) assert (at[region] == 2).all() and (bt[region] == 3).all() assert not (bt == 3).all() and not (bt == 0).all() assert not (at == 2).all() and not (at == 0).all() # Multiple regions: at = np.zeros(shape=(8, 3, 6)) bt = np.zeros(shape=(8, 4, 6)) v = store([a, b], [at, bt], regions=[region, region], compute=False) assert (at == 0).all() and (bt[region] == 0).all() results = dask.compute(*v) assert all([ev.size == 0 for ev in results]) assert (at[region] == 2).all() and (bt[region] == 3).all() assert not (bt == 3).all() and not (bt == 0).all() assert not (at == 2).all() and not (at == 0).all() # Single region (keep result): for st_compute in [False, True]: at = np.zeros(shape=(8, 3, 6)) bt = np.zeros(shape=(8, 4, 6)) v = store( [a, b], [at, bt], regions=region, compute=st_compute, return_stored=True ) assert isinstance(v, tuple) assert all([isinstance(e, da.Array) for e in v]) if st_compute: for arr in v: assert_has_persisted_data(arr) else: assert (at == 0).all() and (bt[region] == 0).all() ar, br = v assert ar.dtype == a.dtype assert br.dtype == b.dtype assert ar.shape == a.shape assert br.shape == b.shape assert ar.chunks == a.chunks assert br.chunks == b.chunks ar, br = da.compute(ar, br) assert (at[region] == 2).all() and (bt[region] == 3).all() assert not (bt == 3).all() and not (bt == 0).all() assert not (at == 2).all() and not (at == 0).all() assert (br == 3).all() assert (ar == 2).all() # Multiple regions (keep result): for st_compute in [False, True]: at = np.zeros(shape=(8, 3, 6)) bt = np.zeros(shape=(8, 4, 6)) v = store( [a, b], [at, bt], regions=[region, region], compute=st_compute, return_stored=True, ) assert isinstance(v, tuple) assert all([isinstance(e, da.Array) for e in v]) if st_compute: for arr in v: assert_has_persisted_data(arr) else: assert (at == 0).all() and (bt[region] == 0).all() ar, br = v assert ar.dtype == a.dtype assert br.dtype == b.dtype assert ar.shape == a.shape assert br.shape == b.shape assert ar.chunks == a.chunks assert br.chunks == b.chunks ar, br = da.compute(ar, br) assert (at[region] == 2).all() and (bt[region] == 3).all() assert not (bt == 3).all() and not (bt == 0).all() assert not (at == 2).all() and not (at == 0).all() assert (br == 3).all() assert (ar == 2).all() def test_store_compute_false(): d = da.ones((4, 4), chunks=(2, 2)) a, b = d + 1, d + 2 at = np.zeros(shape=(4, 4)) bt = np.zeros(shape=(4, 4)) v = store([a, b], [at, bt], compute=False) assert (at == 0).all() and (bt == 0).all() results = dask.compute(*v) assert all([ev.size == 0 for ev in results]) assert (at == 2).all() and (bt == 3).all() at = np.zeros(shape=(4, 4)) bt = np.zeros(shape=(4, 4)) dat, dbt = store([a, b], [at, bt], compute=False, return_stored=True) assert isinstance(dat, Array) and isinstance(dbt, Array) assert (at == 0).all() and (bt == 0).all() assert (dat.compute() == at).all() and (dbt.compute() == bt).all() assert (at == 2).all() and (bt == 3).all() at = np.zeros(shape=(4, 4)) bt = np.zeros(shape=(4, 4)) dat, dbt = store( [a, b], [at, bt], compute=False, return_stored=True, load_stored=False ) assert isinstance(dat, Array) and isinstance(dbt, Array) assert (at == 0).all() and (bt == 0).all() dask.compute(dat, dbt) assert (at == 2).all() and (bt == 3).all() def test_store_nocompute_regions(): x = da.ones(10, chunks=1) y = np.zeros((2, 10)) d1 = da.store(x, y, regions=(0,), compute=False) d2 = da.store(x, y, regions=(1,), compute=False) for l, r in zip(d1, d2): assert l.name != r.name class ThreadSafetyError(Exception): pass class NonthreadSafeStore: def __init__(self): self.in_use = False def __setitem__(self, key, value): if self.in_use: raise ThreadSafetyError() self.in_use = True time.sleep(0.001) self.in_use = False class ThreadSafeStore: def __init__(self): self.concurrent_uses = 0 self.max_concurrent_uses = 0 def __setitem__(self, key, value): self.concurrent_uses += 1 self.max_concurrent_uses = max(self.concurrent_uses, self.max_concurrent_uses) time.sleep(0.01) self.concurrent_uses -= 1 class BrokenStore: def __setitem__(self, key, value): raise RuntimeError("Broken store") class CounterLock: def __init__(self, *args, **kwargs): self.lock = Lock(*args, **kwargs) self.acquire_count = 0 self.release_count = 0 def acquire(self, *args, **kwargs): self.acquire_count += 1 return self.lock.acquire(*args, **kwargs) def release(self, *args, **kwargs): self.release_count += 1 return self.lock.release(*args, **kwargs) def test_store_locks_failure_lock_released(): d = da.ones((10, 10), chunks=(2, 2)) lock = CounterLock() # Ensure same lock applies over multiple stores with pytest.raises(RuntimeError): store(d, BrokenStore(), lock=lock, scheduler="threads") assert lock.acquire_count == lock.release_count > 0 def test_store_locks(): d = da.ones((10, 10), chunks=(2, 2)) a, b = d + 1, d + 2 at = np.zeros(shape=(10, 10)) bt = np.zeros(shape=(10, 10)) lock = CounterLock() # Ensure same lock applies over multiple stores at = NonthreadSafeStore() v = store([a, b], [at, at], lock=lock, scheduler="threads", num_workers=10) assert lock.acquire_count == lock.release_count == a.npartitions + b.npartitions assert v is None # Don't assume thread safety by default at = NonthreadSafeStore() assert store(a, at, scheduler="threads", num_workers=10) is None assert a.store(at, scheduler="threads", num_workers=10) is None # Ensure locks can be removed at = ThreadSafeStore() for i in range(10): st = a.store(at, lock=False, scheduler="threads", num_workers=10) assert st is None if at.max_concurrent_uses > 1: break if i == 9: assert False # Verify number of lock calls nchunks = sum(math.prod(map(len, e.chunks)) for e in (a, b)) for c in (False, True): at = np.zeros(shape=(10, 10)) bt = np.zeros(shape=(10, 10)) lock = CounterLock() v = store([a, b], [at, bt], lock=lock, compute=c, return_stored=True) assert all(isinstance(e, Array) for e in v) da.compute(v) # When `return_stored=True` and `compute=False`, # the lock should be acquired only once for store and load steps # as they are fused together into one step. assert lock.acquire_count == lock.release_count if c: assert lock.acquire_count == 2 * nchunks else: assert lock.acquire_count == nchunks def test_store_method_return(): d = da.ones((10, 10), chunks=(2, 2)) a = d + 1 for compute in [False, True]: for return_stored in [False, True]: at = np.zeros(shape=(10, 10)) r = a.store( at, scheduler="threads", compute=compute, return_stored=return_stored ) if compute and not return_stored: assert r is None else: assert isinstance(r, Array) def test_store_multiprocessing_lock(): d = da.ones((10, 10), chunks=(2, 2)) a = d + 1 at = np.zeros(shape=(10, 10)) st = a.store(at, scheduler="processes", num_workers=10) assert st is None @pytest.mark.parametrize("return_stored", [False, True]) @pytest.mark.parametrize("delayed_target", [False, True]) def test_store_deterministic_keys(return_stored, delayed_target): a = da.ones((10, 10), chunks=(2, 2)) at = np.zeros(shape=(10, 10)) if delayed_target: at = delayed(at) # Note: Disable the lock since every call to store would instantiate a new # SerializebleLock which would mess with the tokens st1 = a.store(at, return_stored=return_stored, compute=False, lock=False) st2 = a.store(at, return_stored=return_stored, compute=False, lock=False) assert set(st1.dask.keys()) == set(st2.dask.keys()) def test_to_hdf5(): h5py = pytest.importorskip("h5py") x = da.ones((4, 4), chunks=(2, 2)) y = da.ones(4, chunks=2, dtype="i4") with tmpfile(".hdf5") as fn: x.to_hdf5(fn, "/x") with h5py.File(fn, mode="r+") as f: d = f["/x"] assert_eq(d[:], x) assert d.chunks == (2, 2) with tmpfile(".hdf5") as fn: x.to_hdf5(fn, "/x", chunks=None) with h5py.File(fn, mode="r+") as f: d = f["/x"] assert_eq(d[:], x) assert d.chunks is None with tmpfile(".hdf5") as fn: x.to_hdf5(fn, "/x", chunks=(1, 1)) with h5py.File(fn, mode="r+") as f: d = f["/x"] assert_eq(d[:], x) assert d.chunks == (1, 1) with tmpfile(".hdf5") as fn: da.to_hdf5(fn, {"/x": x, "/y": y}) with h5py.File(fn, mode="r+") as f: assert_eq(f["/x"][:], x) assert f["/x"].chunks == (2, 2) assert_eq(f["/y"][:], y) assert f["/y"].chunks == (2,) def test_to_dask_dataframe(): pytest.importorskip("pandas") dd = pytest.importorskip("dask.dataframe") a = da.ones((4,), chunks=(2,)) d = a.to_dask_dataframe() assert isinstance(d, dd.Series) a = da.ones((4, 4), chunks=(2, 2)) d = a.to_dask_dataframe() assert isinstance(d, dd.DataFrame) def test_np_array_with_zero_dimensions(): d = da.ones((4, 4), chunks=(2, 2)) assert_eq(np.array(d.sum()), np.array(d.compute().sum())) def test_dtype_complex(): x = np.arange(24).reshape((4, 6)).astype("f4") y = np.arange(24).reshape((4, 6)).astype("i8") z = np.arange(24).reshape((4, 6)).astype("i2") a = da.from_array(x, chunks=(2, 3)) b = da.from_array(y, chunks=(2, 3)) c = da.from_array(z, chunks=(2, 3)) def assert_eq(a, b): return isinstance(a, np.dtype) and isinstance(b, np.dtype) and str(a) == str(b) assert_eq(a.dtype, x.dtype) assert_eq(b.dtype, y.dtype) assert_eq((a + 1).dtype, (x + 1).dtype) assert_eq((a + b).dtype, (x + y).dtype) assert_eq(a.T.dtype, x.T.dtype) assert_eq(a[:3].dtype, x[:3].dtype) assert_eq((a.dot(b.T)).dtype, (x.dot(y.T)).dtype) assert_eq(stack([a, b]).dtype, np.vstack([x, y]).dtype) assert_eq(concatenate([a, b]).dtype, np.concatenate([x, y]).dtype) assert_eq(b.std().dtype, y.std().dtype) assert_eq(c.sum().dtype, z.sum().dtype) assert_eq(a.min().dtype, a.min().dtype) assert_eq(b.std().dtype, b.std().dtype) assert_eq(a.argmin(axis=0).dtype, a.argmin(axis=0).dtype) assert_eq(da.sin(c).dtype, np.sin(z).dtype) assert_eq(da.exp(b).dtype, np.exp(y).dtype) assert_eq(da.floor(a).dtype, np.floor(x).dtype) assert_eq(da.isnan(b).dtype, np.isnan(y).dtype) with contextlib.suppress(ImportError): assert da.isnull(b).dtype == "bool" assert da.notnull(b).dtype == "bool" x = np.array([("a", 1)], dtype=[("text", "S1"), ("numbers", "i4")]) d = da.from_array(x, chunks=(1,)) assert_eq(d["text"].dtype, x["text"].dtype) assert_eq(d[["numbers", "text"]].dtype, x[["numbers", "text"]].dtype) def test_astype(): x = np.ones((5, 5), dtype="f8") d = da.from_array(x, chunks=(2, 2)) assert d.astype("i8").dtype == "i8" assert_eq(d.astype("i8"), x.astype("i8")) assert same_keys(d.astype("i8"), d.astype("i8")) with pytest.raises(TypeError): d.astype("i8", casting="safe") with pytest.raises(TypeError): d.astype("i8", not_a_real_kwarg="foo") # smoketest with kwargs assert_eq(d.astype("i8", copy=False), x.astype("i8", copy=False)) # Check it's a noop assert d.astype("f8") is d def test_astype_gh1151(): a = np.arange(5).astype(np.int32) b = da.from_array(a, (1,)) assert_eq(a.astype(np.int16), b.astype(np.int16)) def test_astype_gh9318(): # `order`` kwarg in `astype` should not cause an error a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]], order="C") b = da.from_array(a, chunks=(2, 2)) result_a = a.astype(float, order="F") result_b = b.astype(float, order="F") # if no error at this line, pytest passes assert_eq(result_a, result_b) # won't check the order matches, but checks results @pytest.mark.xfail(reason="Github issue https://github.com/dask/dask/issues/9316") def test_astype_gh9316(): # Issue https://github.com/dask/dask/issues/9316 # Can be combined with test_astype_gh9318 above when XFAIL marker is removed a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]], order="C") b = da.from_array(a, chunks=(2, 2)) result_a = a.astype(float, order="F") result_b = b.astype(float, order="F") result_b = result_b.compute() assert result_a.flags.c_contiguous == result_b.flags.c_contiguous assert result_a.flags.f_contiguous == result_b.flags.f_contiguous def test_arithmetic(): x = np.arange(5).astype("f4") + 2 y = np.arange(5).astype("i8") + 2 z = np.arange(5).astype("i4") + 2 a = da.from_array(x, chunks=(2,)) b = da.from_array(y, chunks=(2,)) c = da.from_array(z, chunks=(2,)) assert_eq(a + b, x + y) assert_eq(a * b, x * y) assert_eq(a - b, x - y) assert_eq(a / b, x / y) assert_eq(b & b, y & y) assert_eq(b | b, y | y) assert_eq(b ^ b, y ^ y) assert_eq(a // b, x // y) assert_eq(a**b, x**y) assert_eq(a % b, x % y) assert_eq(a > b, x > y) assert_eq(a < b, x < y) assert_eq(a >= b, x >= y) assert_eq(a <= b, x <= y) assert_eq(a == b, x == y) assert_eq(a != b, x != y) assert_eq(a + 2, x + 2) assert_eq(a * 2, x * 2) assert_eq(a - 2, x - 2) assert_eq(a / 2, x / 2) assert_eq(b & True, y & True) assert_eq(b | True, y | True) assert_eq(b ^ True, y ^ True) assert_eq(a // 2, x // 2) assert_eq(a**2, x**2) assert_eq(a % 2, x % 2) assert_eq(a > 2, x > 2) assert_eq(a < 2, x < 2) assert_eq(a >= 2, x >= 2) assert_eq(a <= 2, x <= 2) assert_eq(a == 2, x == 2) assert_eq(a != 2, x != 2) assert_eq(2 + b, 2 + y) assert_eq(2 * b, 2 * y) assert_eq(2 - b, 2 - y) assert_eq(2 / b, 2 / y) assert_eq(True & b, True & y) assert_eq(True | b, True | y) assert_eq(True ^ b, True ^ y) assert_eq(2 // b, 2 // y) assert_eq(2**b, 2**y) assert_eq(2 % b, 2 % y) assert_eq(2 > b, 2 > y) assert_eq(2 < b, 2 < y) assert_eq(2 >= b, 2 >= y) assert_eq(2 <= b, 2 <= y) assert_eq(2 == b, 2 == y) assert_eq(2 != b, 2 != y) assert_eq(-a, -x) assert_eq(abs(a), abs(x)) assert_eq(~(a == b), ~(x == y)) assert_eq(~(a == b), ~(x == y)) assert_eq(da.logaddexp(a, b), np.logaddexp(x, y)) assert_eq(da.logaddexp2(a, b), np.logaddexp2(x, y)) assert_eq(da.exp(b), np.exp(y)) assert_eq(da.log(a), np.log(x)) assert_eq(da.log10(a), np.log10(x)) assert_eq(da.log1p(a), np.log1p(x)) assert_eq(da.expm1(b), np.expm1(y)) assert_eq(da.sqrt(a), np.sqrt(x)) assert_eq(da.square(a), np.square(x)) assert_eq(da.sin(a), np.sin(x)) assert_eq(da.cos(b), np.cos(y)) assert_eq(da.tan(a), np.tan(x)) assert_eq(da.arcsin(b / 10), np.arcsin(y / 10)) assert_eq(da.arccos(b / 10), np.arccos(y / 10)) assert_eq(da.arctan(b / 10), np.arctan(y / 10)) assert_eq(da.arctan2(b * 10, a), np.arctan2(y * 10, x)) assert_eq(da.hypot(b, a), np.hypot(y, x)) assert_eq(da.sinh(a), np.sinh(x)) assert_eq(da.cosh(b), np.cosh(y)) assert_eq(da.tanh(a), np.tanh(x)) assert_eq(da.arcsinh(b * 10), np.arcsinh(y * 10)) assert_eq(da.arccosh(b * 10), np.arccosh(y * 10)) assert_eq(da.arctanh(b / 10), np.arctanh(y / 10)) assert_eq(da.deg2rad(a), np.deg2rad(x)) assert_eq(da.rad2deg(a), np.rad2deg(x)) assert_eq(da.logical_and(a < 1, b < 4), np.logical_and(x < 1, y < 4)) assert_eq(da.logical_or(a < 1, b < 4), np.logical_or(x < 1, y < 4)) assert_eq(da.logical_xor(a < 1, b < 4), np.logical_xor(x < 1, y < 4)) assert_eq(da.logical_not(a < 1), np.logical_not(x < 1)) assert_eq(da.maximum(a, 5 - a), np.maximum(a, 5 - a)) assert_eq(da.minimum(a, 5 - a), np.minimum(a, 5 - a)) assert_eq(da.fmax(a, 5 - a), np.fmax(a, 5 - a)) assert_eq(da.fmin(a, 5 - a), np.fmin(a, 5 - a)) assert_eq(da.isreal(a + 1j * b), np.isreal(x + 1j * y)) assert_eq(da.iscomplex(a + 1j * b), np.iscomplex(x + 1j * y)) assert_eq(da.isfinite(a), np.isfinite(x)) assert_eq(da.isinf(a), np.isinf(x)) assert_eq(da.isnan(a), np.isnan(x)) assert_eq(da.signbit(a - 3), np.signbit(x - 3)) assert_eq(da.copysign(a - 3, b), np.copysign(x - 3, y)) assert_eq(da.nextafter(a - 3, b), np.nextafter(x - 3, y)) assert_eq(da.ldexp(c, c), np.ldexp(z, z)) assert_eq(da.fmod(a * 12, b), np.fmod(x * 12, y)) assert_eq(da.floor(a * 0.5), np.floor(x * 0.5)) assert_eq(da.ceil(a), np.ceil(x)) assert_eq(da.trunc(a / 2), np.trunc(x / 2)) assert_eq(da.degrees(b), np.degrees(y)) assert_eq(da.radians(a), np.radians(x)) assert_eq(da.rint(a + 0.3), np.rint(x + 0.3)) assert_eq(da.fix(a - 2.5), np.fix(x - 2.5)) assert_eq(da.angle(a + 1j), np.angle(x + 1j)) assert_eq(da.real(a + 1j), np.real(x + 1j)) assert_eq((a + 1j).real, np.real(x + 1j)) assert_eq(da.imag(a + 1j), np.imag(x + 1j)) assert_eq((a + 1j).imag, np.imag(x + 1j)) assert_eq(da.conj(a + 1j * b), np.conj(x + 1j * y)) assert_eq((a + 1j * b).conj(), (x + 1j * y).conj()) assert_eq(da.clip(b, 1, 4), np.clip(y, 1, 4)) assert_eq(b.clip(1, 4), y.clip(1, 4)) assert_eq(da.fabs(b), np.fabs(y)) assert_eq(da.sign(b - 2), np.sign(y - 2)) assert_eq(da.absolute(b - 2), np.absolute(y - 2)) assert_eq(da.absolute(b - 2 + 1j), np.absolute(y - 2 + 1j)) l1, l2 = da.frexp(a) r1, r2 = np.frexp(x) assert_eq(l1, r1) assert_eq(l2, r2) l1, l2 = da.modf(a) r1, r2 = np.modf(x) assert_eq(l1, r1) assert_eq(l2, r2) assert_eq(da.around(a, -1), np.around(x, -1)) def test_elemwise_consistent_names(): a = da.from_array(np.arange(5, dtype="f4"), chunks=(2,)) b = da.from_array(np.arange(5, dtype="f4"), chunks=(2,)) assert same_keys(a + b, a + b) assert same_keys(a + 2, a + 2) assert same_keys(da.exp(a), da.exp(a)) assert same_keys(da.exp(a, dtype="f8"), da.exp(a, dtype="f8")) assert same_keys(da.maximum(a, b), da.maximum(a, b)) def test_optimize(): x = np.arange(5).astype("f4") a = da.from_array(x, chunks=(2,)) expr = a[1:4] + 1 result = optimize(expr.dask, expr.__dask_keys__()) assert isinstance(result, dict) assert all(key in result for key in expr.__dask_keys__()) def test_slicing_with_non_ndarrays(): class ARangeSlice: dtype = np.dtype("i8") ndim = 1 def __init__(self, start, stop): self.start = start self.stop = stop def __array__(self): return np.arange(self.start, self.stop) class ARangeSlicable: dtype = np.dtype("i8") ndim = 1 def __init__(self, n): self.n = n @property def shape(self): return (self.n,) def __getitem__(self, key): return ARangeSlice(key[0].start, key[0].stop) x = da.from_array(ARangeSlicable(10), chunks=(4,)) assert_eq((x + 1).sum(), (np.arange(10, dtype=x.dtype) + 1).sum()) @pytest.mark.filterwarnings("ignore:the matrix subclass") def test_getter(): assert type(getter(np.matrix([[1]]), 0)) is np.ndarray assert type(getter(np.matrix([[1]]), 0, asarray=False)) is np.matrix assert_eq(getter([1, 2, 3, 4, 5], slice(1, 4)), np.array([2, 3, 4])) assert_eq(getter(np.arange(5), (None, slice(None, None))), np.arange(5)[None, :]) def test_size(): x = da.ones((10, 2), chunks=(3, 1)) assert x.size == np.array(x).size assert isinstance(x.size, int) def test_nbytes(): x = da.ones((10, 2), chunks=(3, 1)) assert x.nbytes == np.array(x).nbytes def test_itemsize(): x = da.ones((10, 2), chunks=(3, 1)) assert x.itemsize == 8 def test_Array_normalizes_dtype(): x = da.ones((3,), chunks=(1,), dtype=int) assert isinstance(x.dtype, np.dtype) @pytest.mark.parametrize("inline_array", [True, False]) def test_from_array_with_lock(inline_array): x = np.arange(10) class FussyLock(SerializableLock): def acquire(self, blocking=True, timeout=-1): if self.locked(): raise RuntimeError("I am locked") return super().acquire(blocking, timeout) lock = FussyLock() d = da.from_array(x, chunks=5, lock=lock, inline_array=inline_array) lock.acquire() with pytest.raises(RuntimeError): d.compute() lock.release() assert_eq(d, x) lock = CounterLock() e = da.from_array(x, chunks=5, lock=lock, inline_array=inline_array) assert_eq(e, x) # Note: the specific counts for composite arithmetic operations can vary # significantly based on the complexity of the computation, whether we are inlining, # and optimization fusion settings. But for this simple comparison it seems pretty # stable. assert lock.release_count == 2 assert lock.acquire_count == 2 class MyArray: def __init__(self, x): self.x = x self.dtype = x.dtype self.shape = x.shape self.ndim = len(x.shape) def __getitem__(self, i): return self.x[i] @pytest.mark.parametrize( "x,chunks", [ (np.arange(25).reshape((5, 5)), (5, 5)), (np.arange(25).reshape((5, 5)), -1), (np.array([[1]]), 1), (np.array(1), 1), ], ) @pytest.mark.parametrize("inline_array", [True, False]) def test_from_array_tasks_always_call_getter(x, chunks, inline_array): dx = da.from_array( MyArray(x), chunks=chunks, asarray=False, inline_array=inline_array ) assert_eq(x, dx) @pytest.mark.parametrize( "x", [ np.array([[1, 2], [3, 4]]), np.ma.array([[1, 2], [3, 4]], mask=[[True, False], [False, False]]), np.ma.array([1], mask=[True]), np.ma.array([1.5], mask=[True]), np.ma.array(1, mask=True), np.ma.array(1.5, mask=True), ], ) def test_from_array_ndarray_onechunk(x): """ndarray with a single chunk produces a minimal single key dict""" dx = da.from_array(x, chunks=-1) assert_eq(x, dx) assert len(dx.dask) == 1 assert dx.dask[(dx.name,) + (0,) * dx.ndim] is not x assert_eq(dx.dask[(dx.name,) + (0,) * dx.ndim], x) def test_from_array_ndarray_getitem(): """For ndarray, don't use getter / getter_nofancy; use the cleaner operator.getitem""" x = np.array([[1, 2], [3, 4]]) dx = da.from_array(x, chunks=(1, 2)) assert_eq(x, dx) assert (dx.dask[dx.name, 0, 0] == np.array([[1, 2]])).all() @pytest.mark.parametrize("x", [[1, 2], (1, 2), memoryview(b"abc")]) def test_from_array_list(x): """Lists, tuples, and memoryviews are automatically converted to ndarray""" dx = da.from_array(x, chunks=-1) assert_eq(np.array(x), dx) assert isinstance(dx.dask[dx.name, 0], np.ndarray) dx = da.from_array(x, chunks=1) assert_eq(np.array(x), dx) assert dx.dask[dx.name, 0][0] == x[0] # On MacOS Python 3.9, the order of the np.ScalarType tuple randomly changes across # interpreter restarts, thus causing pytest-xdist failures; setting PYTHONHASHSEED does # not help @pytest.mark.parametrize( "type_", sorted((t for t in np.ScalarType if t is not memoryview), key=str) ) def test_from_array_scalar(type_): """Python and numpy scalars are automatically converted to ndarray""" if type_ == np.datetime64: x = np.datetime64("2000-01-01") else: x = type_(1) dx = da.from_array(x, chunks=-1) assert_eq(np.array(x), dx) assert isinstance( dx.dask[dx.name,], np.ndarray, ) @pytest.mark.parametrize("asarray,cls", [(True, np.ndarray), (False, np.matrix)]) @pytest.mark.parametrize("inline_array", [True, False]) @pytest.mark.filterwarnings("ignore:the matrix subclass") def test_from_array_no_asarray(asarray, cls, inline_array): def assert_chunks_are_of_type(x): chunks = compute_as_if_collection(Array, x.dask, x.__dask_keys__()) # If it's a tuple of tuples we want to concat, but if it's a tuple # of 1d arrays, we just want to iterate directly for c in concat(chunks) if isinstance(chunks[0], tuple) else chunks: assert type(c) is cls x = np.matrix(np.arange(100).reshape((10, 10))) dx = da.from_array(x, chunks=(5, 5), asarray=asarray, inline_array=inline_array) assert_chunks_are_of_type(dx) assert_chunks_are_of_type(dx[0:5]) assert_chunks_are_of_type(dx[0:5][:, 0]) @pytest.mark.parametrize("wrap", [True, False]) @pytest.mark.parametrize("inline_array", [True, False]) def test_from_array_getitem(wrap, inline_array): x = np.arange(10) called = False def my_getitem(a, ind): nonlocal called called = True return a[ind] xx = MyArray(x) if wrap else x y = da.from_array(xx, chunks=(5,), getitem=my_getitem, inline_array=inline_array) assert_eq(x, y) # If we have a raw numpy array we eagerly slice, so custom getters # are not called. assert called is wrap def test_from_array_minus_one(): x = np.arange(10) y = da.from_array(x, -1) assert y.chunks == ((10,),) assert_eq(x, y) @pytest.mark.parametrize("chunks", [-1, 2]) def test_array_copy_noop(chunks): # Regression test for https://github.com/dask/dask/issues/9533 # Which is a revert of the solution for https://github.com/dask/dask/issues/3751 x = np.arange(10) y = da.from_array(x, chunks=chunks) y_c = y.copy() assert y.name == y_c.name def test_from_array_dask_array(): x = np.array([[1, 2], [3, 4]]) dx = da.from_array(x, chunks=(1, 2)) with pytest.raises(ValueError): da.from_array(dx) def test_from_array_dask_collection_warns(): class CustomCollection(np.ndarray): def __dask_graph__(self): return {"bar": 1} x = CustomCollection([1, 2, 3]) with pytest.warns(UserWarning): da.from_array(x) # Ensure da.array warns too with pytest.warns(UserWarning): da.array(x) def test_from_array_inline(): class MyArray(np.ndarray): pass a = np.array([1, 2, 3]).view(MyArray) dsk = dict(da.from_array(a, name="my-array", inline_array=False).dask) assert dsk["original-my-array"] is not a assert_eq(dsk["original-my-array"], a) dsk = dict(da.from_array(a, name="my-array", inline_array=True).dask) assert "original-my-array" not in dsk @pytest.mark.parametrize("asarray", [da.asarray, da.asanyarray]) def test_asarray(asarray): assert_eq(asarray([1, 2, 3]), np.asarray([1, 2, 3])) x = asarray([1, 2, 3]) assert asarray(x) is x y = [x[0], 2, x[2]] assert_eq(asarray(y), x) @pytest.mark.parametrize("asarray", [da.asarray, da.asanyarray]) def test_asarray_array_dtype(asarray): # test array input x = asarray([1, 2]) assert_eq(asarray(x, dtype=da.float32), np.asarray(x, dtype=np.float32)) # dask->dask x = asarray(x, dtype=da.float64) assert x.dtype == da.float64 x = asarray(x, dtype=da.int32) assert x.dtype == da.int32 x = asarray(x) assert x.dtype == da.int32 # Test explicit null dtype. astype(None) converts to float! x = asarray(x, dtype=None) assert x.dtype == da.int32 # non-dask->dask x = asarray(np.asarray([1, 2], dtype=np.int8)) assert x.dtype == da.int8 x = asarray(np.asarray([1, 2], dtype=np.int8), dtype=None) assert x.dtype == da.int8 x = asarray(np.asarray([1, 2], dtype=np.int8), dtype=da.float64) assert x.dtype == da.float64 @pytest.mark.parametrize("asarray", [da.asarray, da.asanyarray]) def test_asarray_dask_dataframe(asarray): # https://github.com/dask/dask/issues/3885 pd = pytest.importorskip("pandas") dd = pytest.importorskip("dask.dataframe") s = dd.from_pandas(pd.Series([1, 2, 3, 4]), 2) result = asarray(s) expected = s.values assert_eq(result, expected) df = s.to_frame(name="s") result = asarray(df) expected = df.values assert_eq(result, expected) @pytest.mark.parametrize("asarray", [da.asarray, da.asanyarray]) @pytest.mark.parametrize("inline_array", [True, False]) def test_asarray_h5py(asarray, inline_array): h5py = pytest.importorskip("h5py") with tmpfile(".hdf5") as fn: with h5py.File(fn, mode="a") as f: d = f.create_dataset("/x", shape=(2, 2), dtype=float) x = asarray(d, inline_array=inline_array) # Check for the array in the dsk dsk = dict(x.dask) assert (d in dsk.values()) is not inline_array assert not any(isinstance(v, np.ndarray) for v in dsk.values()) def test_asarray_chunks(): with dask.config.set({"array.chunk-size": "100 B"}): x = np.ones(1000) d = da.asarray(x) assert d.npartitions > 1 @pytest.mark.filterwarnings("ignore:the matrix subclass") def test_asanyarray(): x = np.matrix([1, 2, 3]) dx = da.asanyarray(x) assert dx.numblocks == (1, 1) chunks = compute_as_if_collection(Array, dx.dask, dx.__dask_keys__()) assert isinstance(chunks[0][0], np.matrix) assert da.asanyarray(dx) is dx def test_asanyarray_dataframe(): pd = pytest.importorskip("pandas") dd = pytest.importorskip("dask.dataframe") df = pd.DataFrame({"x": [1, 2, 3]}) ddf = dd.from_pandas(df, npartitions=2) x = np.asanyarray(df) dx = da.asanyarray(ddf) assert isinstance(dx, da.Array) assert_eq(x, dx) x = np.asanyarray(df.x) dx = da.asanyarray(ddf.x) assert isinstance(dx, da.Array) assert_eq(x, dx) def test_asanyarray_datetime64(): x = np.array(["2000-01-01"], dtype="datetime64") dx = da.asanyarray(x) assert isinstance(dx, da.Array) assert_eq(x, dx) def test_from_func(): x = np.arange(10) f = lambda n: n * x d = from_func(f, (10,), x.dtype, kwargs={"n": 2}) assert d.shape == x.shape assert d.dtype == x.dtype assert_eq(d, 2 * x) assert same_keys(d, from_func(f, (10,), x.dtype, kwargs={"n": 2})) def test_concatenate3_2(): x = np.array([1, 2]) assert_eq(concatenate3([x, x, x]), np.array([1, 2, 1, 2, 1, 2])) x = np.array([[1, 2]]) assert ( concatenate3([[x, x, x], [x, x, x]]) == np.array([[1, 2, 1, 2, 1, 2], [1, 2, 1, 2, 1, 2]]) ).all() assert ( concatenate3([[x, x], [x, x], [x, x]]) == np.array([[1, 2, 1, 2], [1, 2, 1, 2], [1, 2, 1, 2]]) ).all() x = np.arange(12).reshape((2, 2, 3)) assert_eq( concatenate3([[[x, x, x], [x, x, x]], [[x, x, x], [x, x, x]]]), np.array( [ [ [0, 1, 2, 0, 1, 2, 0, 1, 2], [3, 4, 5, 3, 4, 5, 3, 4, 5], [0, 1, 2, 0, 1, 2, 0, 1, 2], [3, 4, 5, 3, 4, 5, 3, 4, 5], ], [ [6, 7, 8, 6, 7, 8, 6, 7, 8], [9, 10, 11, 9, 10, 11, 9, 10, 11], [6, 7, 8, 6, 7, 8, 6, 7, 8], [9, 10, 11, 9, 10, 11, 9, 10, 11], ], [ [0, 1, 2, 0, 1, 2, 0, 1, 2], [3, 4, 5, 3, 4, 5, 3, 4, 5], [0, 1, 2, 0, 1, 2, 0, 1, 2], [3, 4, 5, 3, 4, 5, 3, 4, 5], ], [ [6, 7, 8, 6, 7, 8, 6, 7, 8], [9, 10, 11, 9, 10, 11, 9, 10, 11], [6, 7, 8, 6, 7, 8, 6, 7, 8], [9, 10, 11, 9, 10, 11, 9, 10, 11], ], ] ), ) def test_map_blocks3(): x = np.arange(10) y = np.arange(10) * 2 d = da.from_array(x, chunks=5) e = da.from_array(y, chunks=5) assert_eq( da.core.map_blocks(lambda a, b: a + 2 * b, d, e, dtype=d.dtype), x + 2 * y ) z = np.arange(100).reshape((10, 10)) f = da.from_array(z, chunks=5) func = lambda a, b: a + 2 * b res = da.core.map_blocks(func, d, f, dtype=d.dtype) assert_eq(res, x + 2 * z) assert same_keys(da.core.map_blocks(func, d, f, dtype=d.dtype), res) assert_eq(da.map_blocks(func, f, d, dtype=d.dtype), z + 2 * x) def test_from_array_with_missing_chunks(): x = np.random.default_rng().standard_normal((2, 4, 3)) d = da.from_array(x, chunks=(None, 2, None)) assert d.chunks == da.from_array(x, chunks=(2, 2, 3)).chunks @pytest.mark.parametrize("func", [normalize_chunks, normalize_chunks_cached]) def test_normalize_chunks(func): assert func(3, (4, 6)) == ((3, 1), (3, 3)) assert func(((3, 3), (8,)), (6, 8)) == ((3, 3), (8,)) assert func((4, 5), (9,)) == ((4, 5),) assert func((4, 5), (9, 9)) == ((4, 4, 1), (5, 4)) assert func(-1, (5, 5)) == ((5,), (5,)) assert func((3, -1), (5, 5)) == ((3, 2), (5,)) assert func((3, None), (5, 5)) == ((3, 2), (5,)) if func is normalize_chunks: assert func({0: 3}, (5, 5)) == ((3, 2), (5,)) assert func([[2, 2], [3, 3]]) == ((2, 2), (3, 3)) assert func(10, (30, 5)) == ((10, 10, 10), (5,)) assert func((), (0, 0)) == ((0,), (0,)) assert func(-1, (0, 3)) == ((0,), (3,)) assert func(((float("nan"),),)) == ((np.nan,),) assert func("auto", shape=(20,), limit=5, dtype="uint8") == ((5, 5, 5, 5),) assert func(("auto", None), (5, 5), dtype=int) == ((5,), (5,)) with pytest.raises(ValueError): func(((10,),), (11,)) with pytest.raises(ValueError): func(((5,), (5,)), (5,)) def test_single_element_tuple(): assert normalize_chunks( (100, "auto"), (500, 500_000), dtype=np.int64, previous_chunks=((1,), (500,)) ) == ( (100,) * 5, ( 167_500, 167_500, 165_000, ), ) def test_align_chunks_to_previous_chunks(): chunks = normalize_chunks( "auto", shape=(2000,), previous_chunks=(512,), limit="600 B", dtype=np.uint8 ) assert chunks == ((512, 512, 512, 2000 - 512 * 3),) chunks = normalize_chunks( "auto", shape=(2000,), previous_chunks=(128,), limit="600 B", dtype=np.uint8 ) assert chunks == ((512, 512, 512, 2000 - 512 * 3),) chunks = normalize_chunks( "auto", shape=(2000,), previous_chunks=(512,), limit="1200 B", dtype=np.uint8 ) assert chunks == ((1024, 2000 - 1024),) chunks = normalize_chunks( "auto", shape=(3, 10211, 10376), previous_chunks=(1, 512, 512), limit="1MiB", dtype=np.float32, ) assert chunks[0] == (1, 1, 1) assert all(c % 512 == 0 for c in chunks[1][:-1]) assert all(c % 512 == 0 for c in chunks[2][:-1]) chunks = normalize_chunks( "auto", shape=(48, 720, 1440), previous_chunks=((36, 12), (720,), (1440,)), limit=134217728, dtype=np.float32, ) assert chunks == ((36, 12), (720,), (1440,)) def test_raise_on_no_chunks(): x = da.ones(6, chunks=3) try: Array(x.dask, x.name, chunks=None, dtype=x.dtype, shape=None) assert False except ValueError as e: assert "dask" in str(e) assert ".org" in str(e) def test_chunks_is_immutable(): x = da.ones(6, chunks=3) try: x.chunks = 2 assert False except TypeError as e: assert "rechunk(2)" in str(e) def test_raise_on_bad_kwargs(): x = da.ones(5, chunks=3) try: da.minimum(x, foo=None) except TypeError as e: assert "minimum" in str(e) assert "foo" in str(e) def test_long_slice(): x = np.arange(10000) d = da.from_array(x, chunks=1) assert_eq(d[8000:8200], x[8000:8200]) def test_h5py_newaxis(): h5py = pytest.importorskip("h5py") with tmpfile("h5") as fn: with h5py.File(fn, mode="a") as f: x = f.create_dataset("/x", shape=(10, 10), dtype="f8") d = da.from_array(x, chunks=(5, 5)) assert d[None, :, :].compute(scheduler="sync").shape == (1, 10, 10) assert d[:, None, :].compute(scheduler="sync").shape == (10, 1, 10) assert d[:, :, None].compute(scheduler="sync").shape == (10, 10, 1) assert same_keys(d[:, :, None], d[:, :, None]) def test_ellipsis_slicing(): assert_eq(da.ones(4, chunks=2)[...], np.ones(4)) def test_point_slicing(): x = np.arange(56).reshape((7, 8)) d = da.from_array(x, chunks=(3, 4)) result = d.vindex[[1, 2, 5, 5], [3, 1, 6, 1]] assert_eq(result, x[[1, 2, 5, 5], [3, 1, 6, 1]]) result = d.vindex[[0, 1, 6, 0], [0, 1, 0, 7]] assert_eq(result, x[[0, 1, 6, 0], [0, 1, 0, 7]]) assert same_keys(result, d.vindex[[0, 1, 6, 0], [0, 1, 0, 7]]) def test_point_slicing_with_full_slice(): from dask.array.core import _get_axis x = np.arange(4 * 5 * 6 * 7).reshape((4, 5, 6, 7)) d = da.from_array(x, chunks=(2, 3, 3, 4)) inds = [ [[1, 2, 3], None, [3, 2, 1], [5, 3, 4]], [[1, 2, 3], None, [4, 3, 2], None], [[1, 2, 3], [3, 2, 1]], [[1, 2, 3], [3, 2, 1], [3, 2, 1], [5, 3, 4]], [[], [], [], None], [np.array([1, 2, 3]), None, np.array([4, 3, 2]), None], [None, None, [1, 2, 3], [4, 3, 2]], [None, [0, 2, 3], None, [0, 3, 2]], ] for ind in inds: slc = [ i if isinstance(i, (np.ndarray, list)) else slice(None, None) for i in ind ] result = d.vindex[tuple(slc)] # Rotate the expected result accordingly axis = _get_axis(ind) expected = x[tuple(slc)] expected = expected.transpose( [axis] + list(range(axis)) + list(range(axis + 1, expected.ndim)) ) assert_eq(result, expected) # Always have the first axis be the length of the points k = len(next(i for i in ind if isinstance(i, (np.ndarray, list)))) assert result.shape[0] == k def test_slice_with_floats(): d = da.ones((5,), chunks=(3,)) with pytest.raises(IndexError): d[1.5] with pytest.raises(IndexError): d[0:1.5] with pytest.raises(IndexError): d[[1, 1.5]] @pytest.mark.parametrize("dtype", [np.int32, np.int64, np.uint32, np.uint64]) def test_slice_with_integer_types(dtype): x = np.arange(10) dx = da.from_array(x, chunks=5) inds = np.array([0, 3, 6], dtype=dtype) assert_eq(dx[inds], x[inds]) @pytest.mark.parametrize("cls", [int, np.int32, np.int64, np.uint32, np.uint64]) def test_index_with_integer_types(cls): x = np.arange(10) dx = da.from_array(x, chunks=5) inds = cls(3) assert_eq(dx[inds], x[inds]) def test_vindex_basic(): x = np.arange(56).reshape((7, 8)) d = da.from_array(x, chunks=(3, 4)) # cases where basic and advanced indexing coincide result = d.vindex[0] assert_eq(result, x[0]) result = d.vindex[0, 1] assert_eq(result, x[0, 1]) result = d.vindex[[0, 1], ::-1] # slices last assert_eq(result, x[:2, ::-1]) def test_vindex_nd(): x = np.arange(56).reshape((7, 8)) d = da.from_array(x, chunks=(3, 4)) result = d.vindex[[[0, 1], [6, 0]], [[0, 1], [0, 7]]] assert_eq(result, x[[[0, 1], [6, 0]], [[0, 1], [0, 7]]]) result = d.vindex[np.arange(7)[:, None], np.arange(8)[None, :]] assert_eq(result, x) result = d.vindex[np.arange(7)[None, :], np.arange(8)[:, None]] assert_eq(result, x.T) @pytest.mark.parametrize("size", [0, 1]) def test_vindex_preserve_chunksize(size): np_arr = np.random.rand(10_000 * 40).reshape(100, 100, 40) arr = da.from_array(np_arr, chunks=(50, 50, 20)) indices_2d = np.random.choice(np.arange(100), size=(10000 + size, 2)) idx1 = indices_2d[:, 0] idx2 = indices_2d[:, 0] result = arr.vindex[idx1, idx2, slice(None)] assert result.chunks == ( (2500, 2500, 2500, 2500) + ((1,) if size else ()), (20, 20), ) assert_eq(result, np_arr[idx1, idx2, :]) def test_vindex_negative(): x = np.arange(10) d = da.from_array(x, chunks=(5, 5)) result = d.vindex[np.array([0, -1])] assert_eq(result, x[np.array([0, -1])]) def test_vindex_errors(): d = da.ones((5, 5, 5), chunks=(3, 3, 3)) pytest.raises(IndexError, lambda: d.vindex[np.newaxis]) pytest.raises(IndexError, lambda: d.vindex[[1, 2], [1, 2, 3]]) pytest.raises(IndexError, lambda: d.vindex[[True] * 5]) pytest.raises(IndexError, lambda: d.vindex[[0], [5]]) pytest.raises(IndexError, lambda: d.vindex[[0], [-6]]) with pytest.raises(IndexError, match="does not support indexing with dask objects"): d.vindex[[0], [0], da.array([0])] def test_vindex_merge(): from dask.array.core import _vindex_merge locations = [1], [2, 0] values = [np.array([[1, 2, 3]]), np.array([[10, 20, 30], [40, 50, 60]])] assert ( _vindex_merge(locations, values) == np.array([[40, 50, 60], [1, 2, 3], [10, 20, 30]]) ).all() def test_vindex_identity(): rng = da.random.default_rng(42) a, b = 10, 20 x = rng.random(a, chunks=a // 2) assert x is x.vindex[:] assert x is x.vindex[:a] pytest.raises(IndexError, lambda: x.vindex[: a - 1]) pytest.raises(IndexError, lambda: x.vindex[1:]) pytest.raises(IndexError, lambda: x.vindex[0:a:2]) x = rng.random((a, b), chunks=(a // 2, b // 2)) assert x is x.vindex[:, :] assert x is x.vindex[:a, :b] pytest.raises(IndexError, lambda: x.vindex[:, : b - 1]) pytest.raises(IndexError, lambda: x.vindex[:, 1:]) pytest.raises(IndexError, lambda: x.vindex[:, 0:b:2]) def test_empty_array(): assert_eq(np.arange(0), da.arange(0, chunks=5)) def test_memmap(): with tmpfile("npy") as fn_1: with tmpfile("npy") as fn_2: try: x = da.arange(100, chunks=15) target = np.memmap(fn_1, shape=x.shape, mode="w+", dtype=x.dtype) x.store(target) assert_eq(target, x, check_type=False) np.save(fn_2, target) assert_eq(np.load(fn_2, mmap_mode="r"), x, check_type=False) finally: target._mmap.close() def test_to_npy_stack(): x = np.arange(5 * 10 * 10).reshape((5, 10, 10)) d = da.from_array(x, chunks=(2, 4, 4)) with tmpdir() as dirname: stackdir = os.path.join(dirname, "test") da.to_npy_stack(stackdir, d, axis=0) assert os.path.exists(os.path.join(stackdir, "0.npy")) assert (np.load(os.path.join(stackdir, "1.npy")) == x[2:4]).all() e = da.from_npy_stack(stackdir) assert_eq(d, e) def test_view(): x = np.arange(56).reshape((7, 8)) d = da.from_array(x, chunks=(2, 3)) assert_eq(x.view(), d.view()) assert_eq(x.view("i4"), d.view("i4")) assert_eq(x.view("i2"), d.view("i2")) assert all(isinstance(s, int) for s in d.shape) x = np.arange(8, dtype="i1") d = da.from_array(x, chunks=(4,)) assert_eq(x.view("i4"), d.view("i4")) with pytest.raises(ValueError): x = np.arange(8, dtype="i1") d = da.from_array(x, chunks=(3,)) d.view("i4") with pytest.raises(ValueError): d.view("i4", order="asdf") def test_view_fortran(): x = np.asfortranarray(np.arange(64).reshape((8, 8))) d = da.from_array(x, chunks=(2, 3)) assert_eq(x.T.view("i4").T, d.view("i4", order="F")) assert_eq(x.T.view("i2").T, d.view("i2", order="F")) def test_h5py_tokenize(): h5py = pytest.importorskip("h5py") with tmpfile("hdf5") as fn1: with tmpfile("hdf5") as fn2: f = h5py.File(fn1, mode="a") g = h5py.File(fn2, mode="a") f["x"] = np.arange(10).astype(float) g["x"] = np.ones(10).astype(float) x1 = f["x"] x2 = g["x"] assert tokenize(x1) != tokenize(x2) def test_map_blocks_with_changed_dimension(): x = np.arange(56).reshape((7, 8)) d = da.from_array(x, chunks=(7, 4)) e = d.map_blocks(lambda b: b.sum(axis=0), chunks=(4,), drop_axis=0, dtype=d.dtype) assert e.chunks == ((4, 4),) assert_eq(e, x.sum(axis=0)) # Provided chunks have wrong shape with pytest.raises(ValueError): d.map_blocks(lambda b: b.sum(axis=0), chunks=(), drop_axis=0) with pytest.raises(ValueError): d.map_blocks(lambda b: b.sum(axis=0), chunks=((4, 4, 4),), drop_axis=0) with pytest.raises(ValueError): d.map_blocks(lambda b: b.sum(axis=1), chunks=((3, 4),), drop_axis=1) d = da.from_array(x, chunks=(4, 8)) e = d.map_blocks(lambda b: b.sum(axis=1), drop_axis=1, dtype=d.dtype) assert e.chunks == ((4, 3),) assert_eq(e, x.sum(axis=1)) x = np.arange(64).reshape((8, 8)) d = da.from_array(x, chunks=(4, 4)) e = d.map_blocks( lambda b: b[None, :, :, None], chunks=(1, 4, 4, 1), new_axis=[0, 3], dtype=d.dtype, ) assert e.chunks == ((1,), (4, 4), (4, 4), (1,)) assert_eq(e, x[None, :, :, None]) e = d.map_blocks(lambda b: b[None, :, :, None], new_axis=[0, 3], dtype=d.dtype) assert e.chunks == ((1,), (4, 4), (4, 4), (1,)) assert_eq(e, x[None, :, :, None]) # Adding axis with a gap with pytest.raises(ValueError): d.map_blocks(lambda b: b, new_axis=(3, 4)) # Both new_axis and drop_axis d = da.from_array(x, chunks=(8, 4)) e = d.map_blocks( lambda b: b.sum(axis=0)[:, None, None], drop_axis=0, new_axis=(1, 2), dtype=d.dtype, ) assert e.chunks == ((4, 4), (1,), (1,)) assert_eq(e, x.sum(axis=0)[:, None, None]) d = da.from_array(x, chunks=(4, 8)) e = d.map_blocks( lambda b: b.sum(axis=1)[:, None, None], drop_axis=1, new_axis=(1, 2), dtype=d.dtype, ) assert e.chunks == ((4, 4), (1,), (1,)) assert_eq(e, x.sum(axis=1)[:, None, None]) def test_map_blocks_with_negative_drop_axis(): x = np.arange(56).reshape((7, 8)) d = da.from_array(x, chunks=(7, 4)) for drop_axis in [0, -2]: # test with equivalent positive and negative drop_axis e = d.map_blocks( lambda b: b.sum(axis=0), chunks=(4,), drop_axis=drop_axis, dtype=d.dtype ) assert e.chunks == ((4, 4),) assert_eq(e, x.sum(axis=0)) def test_map_blocks_with_invalid_drop_axis(): x = np.arange(56).reshape((7, 8)) d = da.from_array(x, chunks=(7, 4)) for drop_axis in [x.ndim, -x.ndim - 1]: with pytest.raises(ValueError): d.map_blocks( lambda b: b.sum(axis=0), chunks=(4,), drop_axis=drop_axis, dtype=d.dtype ) def test_map_blocks_custom_name(): res = da.map_blocks(lambda _: np.arange(4), chunks=(4,), name="foo", dtype=np.int64) assert res.name == "foo", res.name def test_map_blocks_with_changed_dimension_and_broadcast_chunks(): # https://github.com/dask/dask/issues/4299 a = da.from_array([1, 2, 3], 3) b = da.from_array(np.array([0, 1, 2, 0, 1, 2]), chunks=3) result = da.map_blocks(operator.add, a, b, chunks=b.chunks) expected = da.from_array(np.array([1, 3, 5, 1, 3, 5]), chunks=3) assert_eq(result, expected) def test_broadcast_chunks(): assert broadcast_chunks() == () assert broadcast_chunks(((2, 3),)) == ((2, 3),) assert broadcast_chunks(((5, 5),), ((5, 5),)) == ((5, 5),) a = ((10, 10, 10), (5, 5)) b = ((5, 5),) assert broadcast_chunks(a, b) == ((10, 10, 10), (5, 5)) assert broadcast_chunks(b, a) == ((10, 10, 10), (5, 5)) a = ((10, 10, 10), (5, 5)) b = ((1,), (5, 5)) assert broadcast_chunks(a, b) == ((10, 10, 10), (5, 5)) a = ((10, 10, 10), (5, 5)) b = ((3, 3), (5, 5)) with pytest.raises(ValueError): broadcast_chunks(a, b) a = ((1,), (5, 5)) b = ((1,), (5, 5)) assert broadcast_chunks(a, b) == a a = ((1,), (np.nan, np.nan, np.nan)) b = ((3, 3), (1,)) r = broadcast_chunks(a, b) assert r[0] == b[0] and np.allclose(r[1], a[1], equal_nan=True) a = ((3, 3), (1,)) b = ((1,), (np.nan, np.nan, np.nan)) r = broadcast_chunks(a, b) assert r[0] == a[0] and np.allclose(r[1], b[1], equal_nan=True) a = ((3, 3), (5, 5)) b = ((1,), (np.nan, np.nan, np.nan)) with pytest.raises(ValueError): broadcast_chunks(a, b) def test_chunks_error(): x = np.ones((10, 10)) with pytest.raises(ValueError): da.from_array(x, chunks=(5,)) def test_array_compute_forward_kwargs(): x = da.arange(10, chunks=2).sum() x.compute(bogus_keyword=10) def test_dont_fuse_outputs(): dsk = {("x", 0): np.array([1, 2]), ("x", 1): (inc, ("x", 0))} a = da.Array(dsk, "x", chunks=(2,), shape=(4,), dtype=np.array([1]).dtype) assert_eq(a, np.array([1, 2, 2, 3], dtype=a.dtype)) def test_dont_dealias_outputs(): dsk = { ("x", 0, 0): np.ones((2, 2)), ("x", 0, 1): np.ones((2, 2)), ("x", 1, 0): np.ones((2, 2)), ("x", 1, 1): ("x", 0, 0), } a = da.Array(dsk, "x", chunks=(2, 2), shape=(4, 4), dtype=np.ones(1).dtype) assert_eq(a, np.ones((4, 4))) def test_timedelta_op(): x = np.array([np.timedelta64(10, "h")]) y = np.timedelta64(1, "h") a = da.from_array(x, chunks=(1,)) / y assert a.compute() == x / y def test_to_delayed(): x = da.random.default_rng().random((4, 4), chunks=(2, 2)) y = x + 10 [[a, b], [c, d]] = y.to_delayed() assert_eq(a.compute(), y[:2, :2]) s = 2 x = da.from_array(np.array(s), chunks=0) a = x.to_delayed()[tuple()] assert a.compute() == s def test_to_delayed_optimize_graph(): x = da.ones((4, 4), chunks=(2, 2)) y = x[1:][1:][1:][:, 1:][:, 1:][:, 1:] # optimizations d = y.to_delayed().flatten().tolist()[0] assert len([k for k in d.dask if k[0].startswith("getitem")]) == 1 assert d.key == (y.name, 0, 0) assert d.dask.layers.keys() == {"delayed-" + y.name} assert d.dask.dependencies == {"delayed-" + y.name: set()} assert d.__dask_layers__() == ("delayed-" + y.name,) # no optimizations d2 = y.to_delayed(optimize_graph=False).flatten().tolist()[0] assert d2.dask is y.dask assert d2.key == (y.name, 0, 0) assert d2.__dask_layers__() == y.__dask_layers__() assert (d.compute() == d2.compute()).all() def test_cumulative(): rng = np.random.default_rng(0) x = da.arange(20, chunks=5) assert_eq(x.cumsum(axis=0), np.arange(20).cumsum()) assert_eq(x.cumprod(axis=0), np.arange(20).cumprod()) assert_eq(da.nancumsum(x, axis=0), np.nancumsum(np.arange(20))) assert_eq(da.nancumprod(x, axis=0), np.nancumprod(np.arange(20))) a = rng.random(20) a[rng.random(a.shape) < 0.5] = np.nan x = da.from_array(a, chunks=5) assert_eq(da.nancumsum(x, axis=0), np.nancumsum(a)) assert_eq(da.nancumprod(x, axis=0), np.nancumprod(a)) a = rng.random((20, 24)) x = da.from_array(a, chunks=(6, 5)) assert_eq(x.cumsum(axis=0), a.cumsum(axis=0)) assert_eq(x.cumsum(axis=1), a.cumsum(axis=1)) assert_eq(x.cumprod(axis=0), a.cumprod(axis=0)) assert_eq(x.cumprod(axis=1), a.cumprod(axis=1)) assert_eq(da.nancumsum(x, axis=0), np.nancumsum(a, axis=0)) assert_eq(da.nancumsum(x, axis=1), np.nancumsum(a, axis=1)) assert_eq(da.nancumprod(x, axis=0), np.nancumprod(a, axis=0)) assert_eq(da.nancumprod(x, axis=1), np.nancumprod(a, axis=1)) a = rng.random((20, 24)) a[rng.random(a.shape) < 0.5] = np.nan x = da.from_array(a, chunks=(6, 5)) assert_eq(da.nancumsum(x, axis=0), np.nancumsum(a, axis=0)) assert_eq(da.nancumsum(x, axis=1), np.nancumsum(a, axis=1)) assert_eq(da.nancumprod(x, axis=0), np.nancumprod(a, axis=0)) assert_eq(da.nancumprod(x, axis=1), np.nancumprod(a, axis=1)) a = rng.random((20, 24, 13)) x = da.from_array(a, chunks=(6, 5, 4)) for axis in [0, 1, 2, -1, -2, -3]: assert_eq(x.cumsum(axis=axis), a.cumsum(axis=axis)) assert_eq(x.cumprod(axis=axis), a.cumprod(axis=axis)) assert_eq(da.nancumsum(x, axis=axis), np.nancumsum(a, axis=axis)) assert_eq(da.nancumprod(x, axis=axis), np.nancumprod(a, axis=axis)) a = rng.random((20, 24, 13)) a[rng.random(a.shape) < 0.5] = np.nan x = da.from_array(a, chunks=(6, 5, 4)) for axis in [0, 1, 2, -1, -2, -3]: assert_eq(da.nancumsum(x, axis=axis), np.nancumsum(a, axis=axis)) assert_eq(da.nancumprod(x, axis=axis), np.nancumprod(a, axis=axis)) with pytest.raises(ValueError): x.cumsum(axis=3) with pytest.raises(ValueError): x.cumsum(axis=-4) def test_from_delayed(): v = delayed(np.ones)((5, 3)) x = from_delayed(v, shape=(5, 3), dtype=np.ones(0).dtype) assert isinstance(x, Array) assert_eq(x, np.ones((5, 3))) def test_from_delayed_meta(): v = delayed(np.ones)((5, 3)) x = from_delayed(v, shape=(5, 3), meta=np.ones(0)) assert isinstance(x, Array) assert isinstance(x._meta, np.ndarray) def test_from_delayed_future(): # https://github.com/dask/distributed/issues/9050 distributed = pytest.importorskip("distributed") arr = np.zeros((10, 10)) with distributed.Client(n_workers=1) as client: client.wait_for_workers(1) fut = client.scatter(arr) result = da.from_delayed(fut, shape=arr.shape, meta=arr[:0, :0]) assert_eq(result, arr, scheduler=client) del fut assert_eq(result, arr, scheduler=client) def test_A_property(): x = da.ones(5, chunks=(2,)) assert x.A is x def test_copy_mutate(): x = da.arange(5, chunks=(2,)) y = x.copy() memo = {} y2 = copy.deepcopy(x, memo=memo) x[x % 2 == 0] = -1 xx = np.arange(5) xx[xx % 2 == 0] = -1 assert_eq(x, xx) assert_eq(y, np.arange(5)) assert_eq(y2, np.arange(5)) assert memo[id(x)] is y2 def test_npartitions(): assert da.ones(5, chunks=(2,)).npartitions == 3 assert da.ones((5, 5), chunks=(2, 3)).npartitions == 6 def test_elemwise_name(): assert (da.ones(5, chunks=2) + 1).name.startswith("add-") def test_map_blocks_name(): assert da.ones(5, chunks=2).map_blocks(inc).name.startswith("inc-") def test_from_array_names(): x = np.ones(10) a = da.from_array(x, chunks=2) assert a.dask.keys() == {(a.name, i) for i in range(5)} @pytest.mark.parametrize( "array", [da.arange(100, chunks=25), da.ones((10, 10), chunks=25)] ) def test_array_picklable(array): from pickle import dumps, loads a2 = loads(dumps(array)) assert_eq(array, a2) a3 = da.ma.masked_equal(array, 0) assert isinstance(a3._meta, np.ma.MaskedArray) a4 = loads(dumps(a3)) assert_eq(a3, a4) assert isinstance(a4._meta, np.ma.MaskedArray) def test_from_array_raises_on_bad_chunks(): x = np.ones(10) with pytest.raises(ValueError): da.from_array(x, chunks=(5, 5, 5)) # with pytest.raises(ValueError): # da.from_array(x, chunks=100) with pytest.raises(ValueError): da.from_array(x, chunks=((5, 5, 5),)) def test_concatenate_axes(): x = np.ones((2, 2, 2)) assert_eq(concatenate_axes([x, x], axes=[0]), np.ones((4, 2, 2))) assert_eq(concatenate_axes([x, x, x], axes=[0]), np.ones((6, 2, 2))) assert_eq(concatenate_axes([x, x], axes=[1]), np.ones((2, 4, 2))) assert_eq(concatenate_axes([[x, x], [x, x]], axes=[0, 1]), np.ones((4, 4, 2))) assert_eq(concatenate_axes([[x, x], [x, x]], axes=[0, 2]), np.ones((4, 2, 4))) assert_eq(concatenate_axes([[x, x, x], [x, x, x]], axes=[1, 2]), np.ones((2, 4, 6))) with pytest.raises(ValueError): concatenate_axes( [[x, x], [x, x]], axes=[0] ) # not all nested lists accounted for with pytest.raises(ValueError): concatenate_axes([x, x], axes=[0, 1, 2, 3]) # too many axes def test_blockwise_concatenate(): x = da.ones((4, 4, 4), chunks=(2, 2, 2)) y = da.ones((4, 4), chunks=(2, 2)) def f(a, b): assert isinstance(a, np.ndarray) assert isinstance(b, np.ndarray) assert a.shape == (2, 4, 4) assert b.shape == (4, 4) return (a + b).sum(axis=(1, 2)) z = da.blockwise(f, "i", x, "ijk", y, "jk", concatenate=True, dtype=x.dtype) assert_eq(z, np.ones(4) * 32) z = da.blockwise(add, "ij", y, "ij", y, "ij", concatenate=True, dtype=x.dtype) assert_eq(z, np.ones((4, 4)) * 2) def f(a, b, c): assert isinstance(a, np.ndarray) assert isinstance(b, np.ndarray) assert isinstance(c, np.ndarray) assert a.shape == (4, 2, 4) assert b.shape == (4, 4) assert c.shape == (4, 2) return np.ones(2) z = da.blockwise( f, "j", x, "ijk", y, "ki", y, "ij", concatenate=True, dtype=x.dtype ) assert_eq(z, np.ones(4), check_shape=False) def test_common_blockdim(): assert common_blockdim([(5,), (5,)]) == (5,) assert common_blockdim([(5,), (2, 3)]) == (2, 3) assert common_blockdim([(5, 5), (2, 3, 5)]) == (2, 3, 5) assert common_blockdim([(5, 5), (2, 3, 5)]) == (2, 3, 5) assert common_blockdim([(5, 2, 3), (2, 3, 5)]) == (2, 3, 2, 3) assert common_blockdim([(1, 2), (2, 1)]) == (1, 1, 1) assert common_blockdim([(1, 2, 2), (2, 1, 2), (2, 2, 1)]) == (1, 1, 1, 1, 1) def test_uneven_chunks_that_fit_neatly(): x = da.arange(10, chunks=((5, 5),)) y = da.ones(10, chunks=((5, 2, 3),)) assert_eq(x + y, np.arange(10) + np.ones(10)) z = x + y assert z.chunks == ((5, 2, 3),) def test_elemwise_uneven_chunks(): rng = da.random.default_rng() x = da.arange(10, chunks=((4, 6),)) y = da.ones(10, chunks=((6, 4),)) assert_eq(x + y, np.arange(10) + np.ones(10)) z = x + y assert z.chunks == ((4, 2, 4),) x = rng.random((10, 10), chunks=((4, 6), (5, 2, 3))) y = rng.random((4, 10, 10), chunks=((2, 2), (6, 4), (2, 3, 5))) z = x + y assert_eq(x + y, x.compute() + y.compute()) assert z.chunks == ((2, 2), (4, 2, 4), (2, 3, 2, 3)) def test_uneven_chunks_blockwise(): rng = da.random.default_rng() x = rng.random((10, 10), chunks=((2, 3, 2, 3), (5, 5))) y = rng.random((10, 10), chunks=((4, 4, 2), (4, 2, 4))) z = da.blockwise(np.dot, "ik", x, "ij", y, "jk", dtype=x.dtype, concatenate=True) assert z.chunks == (x.chunks[0], y.chunks[1]) assert_eq(z, x.compute().dot(y)) def test_warn_bad_rechunking(): x = da.ones((20, 20), chunks=(20, 1)) y = da.ones((20, 20), chunks=(1, 20)) with pytest.warns(da.core.PerformanceWarning, match="factor of 20"): x + y def test_concatenate_stack_dont_warn(): with warnings.catch_warnings(record=True) as record: da.concatenate([da.ones(2, chunks=1)] * 62) assert not record with warnings.catch_warnings(record=True) as record: da.stack([da.ones(2, chunks=1)] * 62) assert not record def test_map_blocks_delayed(): x = da.ones((10, 10), chunks=(5, 5)) y = np.ones((5, 5)) z = x.map_blocks(add, y, dtype=x.dtype) z.dask.validate() dask.optimize(z)[0].dask.validate() yy = delayed(y) zz = x.map_blocks(add, yy, dtype=x.dtype) zz.dask.validate() dask.optimize(zz)[0].dask.validate() assert_eq(z, zz) assert yy.key in zz.dask def test_no_chunks(): X = np.arange(11) dsk = {("x", 0): np.arange(5), ("x", 1): np.arange(5, 11)} x = Array(dsk, "x", ((np.nan, np.nan),), np.arange(1).dtype) assert_eq(x + 1, X + 1) assert_eq(x.sum(), X.sum()) assert_eq((x + 1).std(), (X + 1).std()) assert_eq((x + x).std(), (X + X).std()) assert_eq((x + x).std(keepdims=True), (X + X).std(keepdims=True)) def test_no_chunks_2d(): X = np.arange(24).reshape((4, 6)) x = da.from_array(X, chunks=(2, 2)) x._chunks = ((np.nan, np.nan), (np.nan, np.nan, np.nan)) with warnings.catch_warnings(): warnings.simplefilter("ignore", RuntimeWarning) # divide by zero assert_eq(da.log(x), np.log(X)) assert_eq(x.T, X.T) assert_eq(x.sum(axis=0, keepdims=True), X.sum(axis=0, keepdims=True)) assert_eq(x.sum(axis=1, keepdims=True), X.sum(axis=1, keepdims=True)) assert_eq(x.dot(x.T + 1), X.dot(X.T + 1)) def test_no_chunks_yes_chunks(): X = np.arange(24).reshape((4, 6)) x = da.from_array(X, chunks=(2, 2)) x._chunks = ((2, 2), (np.nan, np.nan, np.nan)) assert (x + 1).chunks == ((2, 2), (np.nan, np.nan, np.nan)) assert (x.T).chunks == ((np.nan, np.nan, np.nan), (2, 2)) assert (x.dot(x.T)).chunks == ((2, 2), (2, 2)) def test_raise_informative_errors_no_chunks(): X = np.arange(10) a = da.from_array(X, chunks=(5, 5)) a._chunks = ((np.nan, np.nan),) b = da.from_array(X, chunks=(4, 4, 2)) b._chunks = ((np.nan, np.nan, np.nan),) for op in [ lambda: a + b, lambda: a[1], lambda: a[::2], lambda: a[-5], lambda: a.rechunk(3), lambda: a.reshape(2, 5), ]: with pytest.raises(ValueError) as e: op() if "chunk" not in str(e.value) or "unknown" not in str(e.value): op() def test_no_chunks_slicing_2d(): X = np.arange(24).reshape((4, 6)) x = da.from_array(X, chunks=(2, 2)) x._chunks = ((2, 2), (np.nan, np.nan, np.nan)) assert_eq(x[0], X[0]) for op in [lambda: x[:, 4], lambda: x[:, ::2], lambda: x[0, 2:4]]: with pytest.raises(ValueError, match="chunk sizes are unknown"): op() def test_index_array_with_array_1d(): x = np.arange(10) dx = da.from_array(x, chunks=(5,)) dx._chunks = ((np.nan, np.nan),) assert_eq(x[x > 6], dx[dx > 6]) assert_eq(x[x % 2 == 0], dx[dx % 2 == 0]) dy = da.ones(11, chunks=(3,)) with pytest.raises(ValueError): dx[dy > 5] def test_index_array_with_array_2d(): x = np.arange(24).reshape((4, 6)) dx = da.from_array(x, chunks=(2, 2)) assert_eq(x[x > 6], dx[dx > 6]) assert_eq(x[x % 2 == 0], dx[dx % 2 == 0]) # Test with unknown chunks dx._chunks = ((2, 2), (np.nan, np.nan, np.nan)) with pytest.warns(UserWarning, match="different ordering") as record: assert sorted(x[x % 2 == 0].tolist()) == sorted( dx[dx % 2 == 0].compute().tolist() ) assert sorted(x[x > 6].tolist()) == sorted(dx[dx > 6].compute().tolist()) assert len(record) == 2 @pytest.mark.xfail(reason="Chunking does not align well") def test_index_array_with_array_3d_2d(): x = np.arange(4**3).reshape((4, 4, 4)) dx = da.from_array(x, chunks=(2, 2, 2)) ind = np.random.default_rng().random((4, 4)) > 0.5 ind = np.arange(4**2).reshape((4, 4)) % 2 == 0 dind = da.from_array(ind, (2, 2)) assert_eq(x[ind], dx[dind]) assert_eq(x[:, ind], dx[:, dind]) def test_setitem_1d(): x = np.arange(10) dx = da.from_array(x.copy(), chunks=(5,)) x[x > 6] = -1 x[x % 2 == 0] = -2 x[[2, 3]] = -3 dx[dx > 6] = -1 dx[dx % 2 == 0] = -2 dx[da.asarray([2, 3])] = -3 assert_eq(x, dx) def test_setitem_masked(): # Test np.ma.masked assignment to object-type arrays x = np.ma.array(["a", 1, 3.14], dtype=object) dx = da.from_array(x.copy(), chunks=2) x[...] = np.ma.masked dx[...] = np.ma.masked assert_eq(x.mask, da.ma.getmaskarray(dx)) def test_setitem_hardmask(): x = np.ma.array([1, 2, 3, 4], dtype=int) x.harden_mask() y = x.copy() assert y.hardmask x[0] = np.ma.masked x[0:2] = np.ma.masked dx = da.from_array(y) dx[0] = np.ma.masked dx[0:2] = np.ma.masked assert_eq(x, dx) def test_setitem_slice_twice(): x = np.array([1, 2, 3, 4, 5, 6], dtype=int) val = np.array([0, 0], dtype=int) y = x.copy() x[0:2] = val x[4:6] = val dx = da.from_array(y) dx[0:2] = val dx[4:6] = val assert_eq(x, dx) def test_setitem_2d(): x = np.arange(24).reshape((4, 6)) dx = da.from_array(x.copy(), chunks=(2, 2)) x[x > 6] = -1 x[x % 2 == 0] = -2 dx[dx > 6] = -1 dx[dx % 2 == 0] = -2 assert_eq(x, dx) def test_setitem_extended_API_0d(): # 0-d array x = np.array(9) dx = da.from_array(9) x[()] = -1 dx[()] = -1 assert_eq(x, dx.compute()) x[...] = -11 dx[...] = -11 assert_eq(x, dx.compute()) @pytest.mark.parametrize( "index, value", [ [Ellipsis, -1], [slice(2, 8, 2), -2], [slice(8, None, 2), -3], [slice(8, None, 2), [-30]], [slice(1, None, -2), -4], [slice(1, None, -2), [-40]], [slice(3, None, 2), -5], [slice(-3, None, -2), -6], [slice(1, None, -2), -4], [slice(3, None, 2), -5], [slice(3, None, 2), [10, 11, 12, 13]], [slice(-4, None, -2), [14, 15, 16, 17]], ], ) def test_setitem_extended_API_1d(index, value): # 1-d array x = np.arange(10) dx = da.from_array(x, chunks=(4, 6)) dx[index] = value x[index] = value assert_eq(x, dx.compute()) @pytest.mark.parametrize( "index, value", [ [Ellipsis, -1], [(slice(None, None, 2), slice(None, None, -1)), -1], [slice(1, None, 2), -1], [[4, 3, 1], -1], [(Ellipsis, 4), -1], [5, -1], [(slice(None), 2), range(6)], [3, range(10)], [(slice(None), [3, 5, 6]), [-30, -31, -32]], [([-1, 0, 1], 2), [-30, -31, -32]], [(slice(None, 2), slice(None, 3)), [-50, -51, -52]], [(slice(None), [6, 1, 3]), [-60, -61, -62]], [(slice(1, 3), slice(1, 4)), [[-70, -71, -72]]], [(slice(None), [9, 8, 8]), [-80, -81, 91]], [([True, False, False, False, True, False], 2), -1], [(3, [True, True, False, True, True, False, True, False, True, True]), -1], [(np.array([False, False, True, True, False, False]), slice(5, 7)), -1], [ ( 4, da.from_array( [False, False, True, True, False, False, True, False, False, True] ), ), -1, ], [ ( slice(2, 4), da.from_array( [False, False, True, True, False, False, True, False, False, True] ), ), [[-100, -101, -102, -103], [-200, -201, -202, -203]], ], [slice(5, None, 2), -99], [slice(5, None, 2), range(1, 11)], [slice(1, None, -2), -98], [slice(1, None, -2), range(11, 21)], ], ) def test_setitem_extended_API_2d(index, value): # 2-d array x = np.ma.arange(60).reshape((6, 10)) dx = da.from_array(x, chunks=(2, 3)) dx[index] = value x[index] = value assert_eq(x, dx.compute()) def test_setitem_extended_API_2d_rhs_func_of_lhs(): # Cases: # * RHS and/or indices are a function of the LHS # * Indices have unknown chunk sizes # * RHS has extra leading size 1 dimensions compared to LHS x = np.arange(60).reshape((6, 10)) chunks = (2, 3) dx = da.from_array(x, chunks=chunks) dx[2:4, dx[0] > 3] = -5 x[2:4, x[0] > 3] = -5 assert_eq(x, dx.compute()) dx = da.from_array(x, chunks=chunks) dx[2, dx[0] < -2] = -7 x[2, x[0] < -2] = -7 assert_eq(x, dx.compute()) dx = da.from_array(x, chunks=chunks) dx[dx % 2 == 0] = -8 x[x % 2 == 0] = -8 assert_eq(x, dx.compute()) dx = da.from_array(x, chunks=chunks) dx[dx % 2 == 0] = -8 x[x % 2 == 0] = -8 assert_eq(x, dx.compute()) dx = da.from_array(x, chunks=chunks) dx[3:5, 5:1:-2] = -dx[:2, 4:1:-2] x[3:5, 5:1:-2] = -x[:2, 4:1:-2] assert_eq(x, dx.compute()) dx = da.from_array(x, chunks=chunks) dx[0, 1:3] = -dx[0, 4:2:-1] x[0, 1:3] = -x[0, 4:2:-1] assert_eq(x, dx.compute()) dx = da.from_array(x, chunks=chunks) dx[...] = dx x[...] = x assert_eq(x, dx.compute()) dx = da.from_array(x, chunks=chunks) dx[...] = dx[...] x[...] = x[...] assert_eq(x, dx.compute()) dx = da.from_array(x, chunks=chunks) dx[0] = dx[-1] x[0] = x[-1] assert_eq(x, dx.compute()) dx = da.from_array(x, chunks=chunks) dx[0, :] = dx[-2, :] x[0, :] = x[-2, :] assert_eq(x, dx.compute()) dx = da.from_array(x, chunks=chunks) dx[:, 1] = dx[:, -3] x[:, 1] = x[:, -3] assert_eq(x, dx.compute()) index = da.from_array([0, 2], chunks=(2,)) dx = da.from_array(x, chunks=chunks) dx[index, 8] = [99, 88] x[[0, 2], 8] = [99, 88] assert_eq(x, dx.compute()) dx = da.from_array(x, chunks=chunks) dx[:, index] = dx[:, :2] x[:, [0, 2]] = x[:, :2] assert_eq(x, dx.compute()) index = da.where(da.arange(3, chunks=(1,)) < 2)[0] dx = da.from_array(x, chunks=chunks) dx[index, 7] = [-23, -33] x[index.compute(), 7] = [-23, -33] assert_eq(x, dx.compute()) index = da.where(da.arange(3, chunks=(1,)) < 2)[0] dx = da.from_array(x, chunks=chunks) dx[(index,)] = -34 x[(index.compute(),)] = -34 assert_eq(x, dx.compute()) index = index - 4 dx = da.from_array(x, chunks=chunks) dx[index, 7] = [-43, -53] x[index.compute(), 7] = [-43, -53] assert_eq(x, dx.compute()) index = da.from_array([0, -1], chunks=(1,)) x[[0, -1]] = 9999 dx[(index,)] = 9999 assert_eq(x, dx.compute()) dx = da.from_array(x, chunks=(-1, -1)) dx[...] = da.from_array(x, chunks=chunks) assert_eq(x, dx.compute()) # RHS has extra leading size 1 dimensions compared to LHS dx = da.from_array(x.copy(), chunks=(2, 3)) v = x.reshape((1, 1) + x.shape) x[...] = v dx[...] = v assert_eq(x, dx.compute()) index = da.where(da.arange(3, chunks=(1,)) < 2)[0] v = -np.arange(12).reshape(1, 1, 6, 2) x[:, [0, 1]] = v dx[:, index] = v assert_eq(x, dx.compute()) @pytest.mark.parametrize( "index, value", [ [(1, slice(1, 7, 2)), np.ma.masked], [(slice(1, 5, 2), [7, 5]), np.ma.masked_all((2, 2))], ], ) def test_setitem_extended_API_2d_mask(index, value): x = np.ma.arange(60).reshape((6, 10)) dx = da.from_array(x.data, chunks=(2, 3)) # See https://github.com/numpy/numpy/issues/23000 for the `RuntimeWarning` with warnings.catch_warnings(): warnings.filterwarnings( "ignore", category=RuntimeWarning, message="invalid value encountered in cast", ) x[index] = value dx[index] = value dx = dx.persist() assert_eq(x, dx.compute()) assert_eq(x.mask, da.ma.getmaskarray(dx).compute()) def test_setitem_on_read_only_blocks(): # Outputs of broadcast_trick-style functions contain read-only # arrays dx = da.empty((4, 6), dtype=float, chunks=(2, 2)) dx[0] = 99 assert_eq(dx[0, 0], 99.0) dx[0:2] = 88 assert_eq(dx[0, 0], 88.0) def test_setitem_errs(): x = da.ones((4, 4), chunks=(2, 2)) with pytest.raises(ValueError): x[x > 1] = x # Shape mismatch with pytest.raises(ValueError): x[[True, True, False, False], 0] = [2, 3, 4] with pytest.raises(ValueError): x[[True, True, True, False], 0] = [2, 3] with pytest.raises(ValueError): x[0, [True, True, True, False]] = [2, 3] with pytest.raises(ValueError): x[0, [True, True, True, False]] = [1, 2, 3, 4, 5] with pytest.raises(ValueError): x[da.from_array([True, True, True, False]), 0] = [1, 2, 3, 4, 5] with pytest.raises(ValueError): x[0, da.from_array([True, False, False, True])] = [1, 2, 3, 4, 5] with pytest.raises(ValueError): x[:, 0] = [2, 3, 4] with pytest.raises(ValueError): x[0, :] = [1, 2, 3, 4, 5] x = da.ones((4, 4), chunks=(2, 2)) # Too many indices with pytest.raises(IndexError): x[:, :, :] = 2 # 2-d boolean indexing a single dimension with pytest.raises(IndexError): x[[[True, True, False, False]], 0] = 5 # 2-d indexing a single dimension with pytest.raises(IndexError): x[[[1, 2, 3]], 0] = 5 # Multiple 1-d boolean/integer arrays with pytest.raises(NotImplementedError): x[[1, 2], [2, 3]] = 6 with pytest.raises(NotImplementedError): x[[True, True, False, False], [2, 3]] = 5 with pytest.raises(NotImplementedError): x[[True, True, False, False], [False, True, False, False]] = 7 # scalar boolean indexing with pytest.raises(NotImplementedError): x[True] = 5 with pytest.raises(NotImplementedError): x[np.array(True)] = 5 with pytest.raises(NotImplementedError): x[0, da.from_array(True)] = 5 # Scalar arrays y = da.from_array(np.array(1)) with pytest.raises(IndexError): y[:] = 2 # RHS has non-brodacastable extra leading dimensions x = np.arange(12).reshape((3, 4)) dx = da.from_array(x, chunks=(2, 2)) with pytest.raises(ValueError): dx[...] = np.arange(24).reshape((2, 1, 3, 4)) # RHS doesn't have chunks set dx = da.unique(da.random.default_rng().random([10])) with pytest.raises(ValueError, match="Arrays chunk sizes are unknown"): dx[0] = 0 # np.nan assigned to integer array x = da.ones((3, 3), dtype=int) with pytest.raises(ValueError, match="cannot convert float NaN to integer"): x[:, 1] = np.nan with pytest.raises(ValueError, match="cannot convert float infinity to integer"): x[:, 1] = np.inf with pytest.raises(ValueError, match="cannot convert float infinity to integer"): x[:, 1] = -np.inf @pytest.mark.parametrize("idx_namespace", [np, da]) def test_setitem_bool_index_errs(idx_namespace): x = da.ones((3, 4), chunks=(2, 2)) y = da.ones(4, chunks=2) array = idx_namespace.array # Shape mismatch with pytest.raises(ValueError): y[array([True, True, True, False])] = [2, 3] with pytest.raises(ValueError): # A naive where(idx, val, x) would produce a result y[array([True, True, True, False])] = [1, 2, 3, 4] with pytest.raises(ValueError): y[array([True, True, True, False])] = [1, 2, 3, 4, 5] with pytest.raises(ValueError): y[array([True, False, False, True])] = [1, 2, 3, 4, 5] # Too many/not enough booleans with pytest.raises(IndexError): y[array([True, False, True])] = 5 with pytest.raises(IndexError): y[array([False, True, True, True, False])] = 5 # Situations where a naive da.where(idx, val, x) would produce a result with pytest.raises(IndexError): x[array([True, False, False, True])] = 1 with pytest.raises(IndexError): y[array([[True], [False]])] = 1 # da.where would broadcast to ndim=2 def test_zero_slice_dtypes(): x = da.arange(5, chunks=1) y = x[[]] assert y.dtype == x.dtype assert y.shape == (0,) assert_eq(x[[]], np.arange(5)[[]]) def test_zero_sized_array_rechunk(): x = da.arange(5, chunks=1)[:0] y = da.blockwise(identity, "i", x, "i", dtype=x.dtype) assert_eq(x, y) def test_blockwise_zero_shape(): da.blockwise( lambda x: x, "i", da.arange(10, chunks=10), "i", da.from_array(np.ones((0, 2)), ((0,), 2)), "ab", da.from_array(np.ones((0,)), ((0,),)), "a", dtype="float64", ) def test_blockwise_zero_shape_new_axes(): da.blockwise( lambda x: np.ones(42), "i", da.from_array(np.ones((0, 2)), ((0,), 2)), "ab", da.from_array(np.ones((0,)), ((0,),)), "a", dtype="float64", new_axes={"i": 42}, ) def test_broadcast_against_zero_shape(): assert_eq(da.arange(1, chunks=1)[:0] + 0, np.arange(1)[:0] + 0) assert_eq(da.arange(1, chunks=1)[:0] + 0.1, np.arange(1)[:0] + 0.1) assert_eq(da.ones((5, 5), chunks=(2, 3))[:0] + 0, np.ones((5, 5))[:0] + 0) assert_eq(da.ones((5, 5), chunks=(2, 3))[:0] + 0.1, np.ones((5, 5))[:0] + 0.1) assert_eq(da.ones((5, 5), chunks=(2, 3))[:, :0] + 0, np.ones((5, 5))[:, :0] + 0) assert_eq(da.ones((5, 5), chunks=(2, 3))[:, :0] + 0.1, np.ones((5, 5))[:, :0] + 0.1) def test_from_array_name(): x = np.array([1, 2, 3, 4, 5]) chunks = x.shape # Default is tokenize the array dx = da.from_array(x, chunks=chunks) hashed_name = dx.name assert da.from_array(x, chunks=chunks).name == hashed_name # Specify name directly assert da.from_array(x, chunks=chunks, name="x").name == "x" # False gives a random name dx2 = da.from_array(x, chunks=chunks, name=False) dx3 = da.from_array(x, chunks=chunks, name=False) assert dx2.name != hashed_name assert dx3.name != hashed_name assert dx2.name != dx3.name def test_concatenate_errs(): with pytest.raises(ValueError, match=r"Shapes.*\(2, 1\)"): da.concatenate( [da.zeros((2, 1), chunks=(2, 1)), da.zeros((2, 3), chunks=(2, 3))] ) with pytest.raises(ValueError): da.concatenate( [da.zeros((1, 2), chunks=(1, 2)), da.zeros((3, 2), chunks=(3, 2))], axis=1 ) def test_stack_errs(): with pytest.raises(ValueError) as e: da.stack([da.zeros((2,), chunks=2)] * 10 + [da.zeros((3,), chunks=3)] * 10) assert ( str(e.value) == "Stacked arrays must have the same shape. The first array had shape (2,), while array 11 has shape (3,)." ) assert len(str(e.value)) < 105 def test_blockwise_with_numpy_arrays(): x = np.ones(10) y = da.ones(10, chunks=(5,)) assert_eq(x + y, x + x) s = da.sum(x) assert any(isinstance(v, np.ndarray) for v in s.dask.values()) @pytest.mark.parametrize("chunks", (100, 6)) @pytest.mark.parametrize("other", [[0, 0, 1], [2, 1, 3], (0, 0, 1)]) def test_elemwise_with_lists(chunks, other): x = np.arange(12).reshape((4, 3)) d = da.arange(12, chunks=chunks).reshape((4, 3)) x2 = np.vstack([x[:, 0], x[:, 1], x[:, 2]]).T d2 = da.vstack([d[:, 0], d[:, 1], d[:, 2]]).T assert_eq(x2, d2) x3 = x2 * other d3 = d2 * other assert_eq(x3, d3) def test_constructor_plugin(): L = [] L2 = [] with dask.config.set(array_plugins=[L.append, L2.append]): x = da.ones(10, chunks=5) y = x + 1 assert L == L2 == [x, y] with dask.config.set(array_plugins=[lambda x: x.compute()]): x = da.ones(10, chunks=5) y = x + 1 assert isinstance(y, np.ndarray) assert len(L) == 2 def test_no_warnings_on_metadata(): x = da.ones(5, chunks=3) with warnings.catch_warnings(record=True) as record: da.arccos(x) assert not record def test_delayed_array_key_hygeine(): a = da.zeros((1,), chunks=(1,)) d = delayed(identity)(a) b = da.from_delayed(d, shape=a.shape, dtype=a.dtype) assert_eq(a, b) def test_empty_chunks_in_array_len(): x = da.ones((), chunks=()) with pytest.raises(TypeError) as exc_info: len(x) err_msg = "len() of unsized object" assert err_msg in str(exc_info.value) @pytest.mark.parametrize("dtype", [None, [("a", "f4"), ("b", object)]]) def test_meta(dtype): a = da.zeros((1,), chunks=(1,)) assert a._meta.dtype == a.dtype assert isinstance(a._meta, np.ndarray) assert a.nbytes < 1000 @pytest.mark.parametrize( "shape,limit,expected", [ (100, 10, (10,) * 10), (20, 10, (10, 10)), (20, 5, (5, 5, 5, 5)), (24, 5, (5, 5, 5, 5, 4)), (23, 5, (5, 5, 5, 5, 3)), # relatively prime, don't use 1s (1000, 167, (167, 167, 167, 167, 167, 165)), ], ) def test_normalize_chunks_auto_1d(shape, limit, expected): result = normalize_chunks("auto", (shape,), limit=limit, dtype=np.uint8) assert result == (expected,) @pytest.mark.parametrize( "shape,chunks,limit,expected", [ ((20, 20), ("auto", 2), 20, ((10, 10), (2,) * 10)), ( (20, 20), ("auto", (2, 2, 2, 2, 2, 5, 5)), 20, ((4, 4, 4, 4, 4), (2, 2, 2, 2, 2, 5, 5)), ), ((1, 20), "auto", 10, ((1,), (10, 10))), ], ) def test_normalize_chunks_auto_2d(shape, chunks, limit, expected): result = normalize_chunks(chunks, shape, limit=limit, dtype="uint8") assert result == expected def test_normalize_chunks_auto_3d(): result = normalize_chunks( ("auto", "auto", 2), (20, 20, 20), limit=200, dtype="uint8" ) expected = ((10, 10), (10, 10), (2,) * 10) assert result == expected result = normalize_chunks("auto", (20, 20, 20), limit=8, dtype="uint8") expected = ((2,) * 10,) * 3 assert result == expected def test_constructors_chunks_dict(): x = da.ones((20, 20), chunks={0: 10, 1: 5}) assert x.chunks == ((10, 10), (5, 5, 5, 5)) x = da.ones((20, 20), chunks={0: 10, 1: "auto"}) assert x.chunks == ((10, 10), (20,)) def test_from_array_chunks_dict(): with dask.config.set({"array.chunk-size": "128kiB"}): x = np.empty((100, 100, 100)) y = da.from_array(x, chunks={0: 10, 1: -1, 2: "auto"}) z = da.from_array(x, chunks=(10, 100, (16,) * 6 + (4,))) assert y.chunks == z.chunks @pytest.mark.parametrize("dtype", [object, [("a", object), ("b", int)]]) def test_normalize_chunks_object_dtype(dtype): x = np.array(["a", "abc"], dtype=object) with pytest.raises(NotImplementedError): da.from_array(x, chunks="auto") def test_normalize_chunks_tuples_of_tuples(): result = normalize_chunks(((2, 3, 5), "auto"), (10, 10), limit=10, dtype=np.uint8) expected = ((2, 3, 5), (2, 2, 2, 2, 2)) assert result == expected def test_normalize_chunks_nan(): with pytest.raises(ValueError) as info: normalize_chunks("auto", (np.nan,), limit=10, dtype=np.uint8) assert "auto" in str(info.value) with pytest.raises(ValueError) as info: normalize_chunks(((np.nan, np.nan), "auto"), (10, 10), limit=10, dtype=np.uint8) assert "auto" in str(info.value) def test_pandas_from_dask_array(): pd = pytest.importorskip("pandas") a = da.ones((12,), chunks=4) s = pd.Series(a, index=range(12)) assert s.dtype == a.dtype assert_eq(s.values, a) def test_from_zarr_unique_name(): zarr = pytest.importorskip("zarr") a = zarr.array([1, 2, 3]) b = zarr.array([4, 5, 6]) assert da.from_zarr(a).name != da.from_zarr(b).name def test_from_zarr_name(): zarr = pytest.importorskip("zarr") a = zarr.array([1, 2, 3]) assert da.from_zarr(a, name="foo").name == "foo" def test_zarr_roundtrip(): pytest.importorskip("zarr") with tmpdir() as d: a = da.zeros((3, 3), chunks=(1, 1)) a.to_zarr(d) a2 = da.from_zarr(d) assert_eq(a, a2) assert a2.chunks == a.chunks def test_zarr_roundtrip_with_path_like(): pytest.importorskip("zarr") with tmpdir() as d: path = pathlib.Path(d) a = da.zeros((3, 3), chunks=(1, 1)) a.to_zarr(path) a2 = da.from_zarr(path) assert_eq(a, a2) assert a2.chunks == a.chunks def test_to_zarr_accepts_empty_array_without_exception_raised(): pytest.importorskip("zarr") with tmpdir() as d: a = da.from_array(np.arange(0)) a.to_zarr(d) @pytest.mark.parametrize("compute", [False, True]) def test_zarr_return_stored(compute): pytest.importorskip("zarr") with tmpdir() as d: a = da.zeros((3, 3), chunks=(1, 1)) a2 = a.to_zarr(d, compute=compute, return_stored=True) assert isinstance(a2, Array) assert_eq(a, a2, check_graph=False) assert a2.chunks == a.chunks @pytest.mark.parametrize("inline_array", [True, False]) def test_zarr_inline_array(inline_array): zarr = pytest.importorskip("zarr") a = zarr.array([1, 2, 3]) dsk = dict(da.from_zarr(a, inline_array=inline_array).dask) assert len(dsk) == (0 if inline_array else 1) + 1 assert (a in dsk.values()) is not inline_array def test_zarr_existing_array(): zarr = pytest.importorskip("zarr") c = (1, 1) a = da.ones((3, 3), chunks=c) z = zarr.zeros_like(a, chunks=c) a.to_zarr(z) a2 = da.from_zarr(z) assert_eq(a, a2) assert a2.chunks == a.chunks def test_to_zarr_unknown_chunks_raises(): pytest.importorskip("zarr") a = da.random.default_rng().random((10,), chunks=(3,)) a = a[a > 0.5] with pytest.raises(ValueError, match="unknown chunk sizes"): a.to_zarr({}) def test_read_zarr_chunks(): pytest.importorskip("zarr") a = da.zeros((9,), chunks=(3,)) with tmpdir() as d: a.to_zarr(d) arr = da.from_zarr(d, chunks=(5,)) assert arr.chunks == ((5, 4),) def test_zarr_pass_store(): zarr = pytest.importorskip("zarr") with tmpdir() as d: if Version(zarr.__version__) < Version("3.0.0.a0"): store = zarr.storage.DirectoryStore(d) else: store = zarr.storage.LocalStore(d, read_only=False) a = da.zeros((3, 3), chunks=(1, 1)) a.to_zarr(store) a2 = da.from_zarr(store) assert_eq(a, a2) assert a2.chunks == a.chunks def test_zarr_group(): zarr = pytest.importorskip("zarr") with tmpdir() as d: a = da.zeros((3, 3), chunks=(1, 1)) a.to_zarr(d, component="test") with pytest.raises((OSError, ValueError)): a.to_zarr(d, component="test", overwrite=False) a.to_zarr(d, component="test", overwrite=True) # second time is fine, group exists a.to_zarr(d, component="test2", overwrite=False) a.to_zarr(d, component="nested/test", overwrite=False) group = zarr.open_group(store=d, mode="r") assert set(group) == {"nested", "test", "test2"} assert "test" in group["nested"] a2 = da.from_zarr(d, component="test") assert_eq(a, a2) assert a2.chunks == a.chunks @pytest.mark.parametrize( "shape, chunks, expect_rechunk", [ ((6, 2), ((2, 1, 1, 2), 1), True), ((6, 2), ((2, 1, 2, 1), 1), True), ((7, 2), ((2, 2, 2, 1), 1), False), ((2, 7), (1, (2, 2, 2, 1)), False), ((2, 6), (1, (2, 1, 2, 1)), True), ], ) def test_zarr_irregular_chunks(shape, chunks, expect_rechunk): pytest.importorskip("zarr") with tmpdir() as d: a = da.zeros(shape, chunks=chunks) # ((2, 1, 1, 2), 1)) store_delayed = a.to_zarr(d, component="test", compute=False) assert ( any("rechunk" in key_split(k) for k in dict(store_delayed.dask)) is expect_rechunk ) store_delayed.compute() @pytest.mark.parametrize( "data", [ [(), True], [((1,),), True], [((1, 1, 1),), True], [((1,), (1,)), True], [((2, 2, 1),), True], [((2, 2, 3),), False], [((1, 1, 1), (2, 2, 3)), False], [((1, 2, 1),), False], ], ) def test_regular_chunks(data): from dask.array.core import _check_regular_chunks chunkset, expected = data assert _check_regular_chunks(chunkset) == expected def test_zarr_nocompute(): pytest.importorskip("zarr") with tmpdir() as d: a = da.zeros((3, 3), chunks=(1, 1)) out = a.to_zarr(d, compute=False) assert isinstance(out, Array) dask.compute(out) a2 = da.from_zarr(d) assert_eq(a, a2) assert a2.chunks == a.chunks def test_zarr_regions(): zarr = pytest.importorskip("zarr") a = da.arange(16).reshape((4, 4)).rechunk(2) z = zarr.zeros_like(a, chunks=2) a[:2, :2].to_zarr(z, region=(slice(2), slice(2))) a2 = da.from_zarr(z) expected = [[0, 1, 0, 0], [4, 5, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]] assert_eq(a2, expected) assert a2.chunks == a.chunks a[:3, 3:4].to_zarr(z, region=(slice(1, 4), slice(2, 3))) a2 = da.from_zarr(z) expected = [[0, 1, 0, 0], [4, 5, 3, 0], [0, 0, 7, 0], [0, 0, 11, 0]] assert_eq(a2, expected) assert a2.chunks == a.chunks a[3:, 3:].to_zarr(z, region=(slice(2, 3), slice(1, 2))) a2 = da.from_zarr(z) expected = [[0, 1, 0, 0], [4, 5, 3, 0], [0, 15, 7, 0], [0, 0, 11, 0]] assert_eq(a2, expected) assert a2.chunks == a.chunks with pytest.raises(ValueError): with tmpdir() as d: a.to_zarr(d, region=(slice(2), slice(2))) def test_tiledb_roundtrip(): tiledb = pytest.importorskip("tiledb") # 1) load with default chunking # 2) load from existing tiledb.DenseArray # 3) write to existing tiledb.DenseArray rng = da.random.default_rng() a = rng.random((3, 3)) with tmpdir() as uri: da.to_tiledb(a, uri) tdb = da.from_tiledb(uri) assert_eq(a, tdb) assert a.chunks == tdb.chunks # from tiledb.array with tiledb.open(uri) as t: tdb2 = da.from_tiledb(t) assert_eq(a, tdb2) with tmpdir() as uri2: with tiledb.empty_like(uri2, a) as t: a.to_tiledb(t) assert_eq(da.from_tiledb(uri2), a) # specific chunking with tmpdir() as uri: a = rng.random((3, 3), chunks=(1, 1)) a.to_tiledb(uri) tdb = da.from_tiledb(uri) assert_eq(a, tdb) assert a.chunks == tdb.chunks def test_tiledb_multiattr(): tiledb = pytest.importorskip("tiledb") dom = tiledb.Domain( tiledb.Dim("x", (0, 1000), tile=100), tiledb.Dim("y", (0, 1000), tile=100) ) schema = tiledb.ArraySchema( attrs=(tiledb.Attr("attr1"), tiledb.Attr("attr2")), domain=dom ) with tmpdir() as uri: tiledb.DenseArray.create(uri, schema) tdb = tiledb.DenseArray(uri, "w") rng = np.random.default_rng() ar1 = rng.standard_normal(tdb.schema.shape) ar2 = rng.standard_normal(tdb.schema.shape) tdb[:] = {"attr1": ar1, "attr2": ar2} tdb = tiledb.DenseArray(uri, "r") # basic round-trip from dask.array d = da.from_tiledb(uri, attribute="attr2") assert_eq(d, ar2) # smoke-test computation directly on the TileDB view d = da.from_tiledb(uri, attribute="attr2") assert_eq(np.mean(ar2), d.mean().compute(scheduler="threads")) def test_blockview(): x = da.arange(10, chunks=2) blockview = BlockView(x) assert x.blocks == blockview assert isinstance(blockview[0], da.Array) assert_eq(blockview[0], x[:2]) assert_eq(blockview[-1], x[-2:]) assert_eq(blockview[:3], x[:6]) assert_eq(blockview[[0, 1, 2]], x[:6]) assert_eq(blockview[[3, 0, 2]], np.array([6, 7, 0, 1, 4, 5])) assert_eq(blockview.shape, tuple(map(len, x.chunks))) assert_eq(blockview.size, math.prod(blockview.shape)) assert_eq( blockview.ravel(), [blockview[idx] for idx in np.ndindex(blockview.shape)] ) x = da.random.default_rng().random((20, 20), chunks=(4, 5)) blockview = BlockView(x) assert_eq(blockview[0], x[:4]) assert_eq(blockview[0, :3], x[:4, :15]) assert_eq(blockview[:, :3], x[:, :15]) assert_eq(blockview.shape, tuple(map(len, x.chunks))) assert_eq(blockview.size, math.prod(blockview.shape)) assert_eq( blockview.ravel(), [blockview[idx] for idx in np.ndindex(blockview.shape)] ) x = da.ones((40, 40, 40), chunks=(10, 10, 10)) blockview = BlockView(x) assert_eq(blockview[0, :, 0], np.ones((10, 40, 10))) assert_eq(blockview.shape, tuple(map(len, x.chunks))) assert_eq(blockview.size, math.prod(blockview.shape)) assert_eq( blockview.ravel(), [blockview[idx] for idx in np.ndindex(blockview.shape)] ) x = da.ones((2, 2), chunks=1) with pytest.raises(ValueError): blockview[[0, 1], [0, 1]] with pytest.raises(ValueError): blockview[np.array([0, 1]), [0, 1]] with pytest.raises(ValueError) as info: blockview[np.array([0, 1]), np.array([0, 1])] assert "list" in str(info.value) with pytest.raises(ValueError) as info: blockview[None, :, :] assert "newaxis" in str(info.value) and "not supported" in str(info.value) with pytest.raises(IndexError) as info: blockview[100, 100] def test_blocks_indexer(): x = da.arange(10, chunks=2) assert isinstance(x.blocks[0], da.Array) assert_eq(x.blocks[0], x[:2]) assert_eq(x.blocks[-1], x[-2:]) assert_eq(x.blocks[:3], x[:6]) assert_eq(x.blocks[[0, 1, 2]], x[:6]) assert_eq(x.blocks[[3, 0, 2]], np.array([6, 7, 0, 1, 4, 5])) x = da.random.default_rng().random((20, 20), chunks=(4, 5)) assert_eq(x.blocks[0], x[:4]) assert_eq(x.blocks[0, :3], x[:4, :15]) assert_eq(x.blocks[:, :3], x[:, :15]) x = da.ones((40, 40, 40), chunks=(10, 10, 10)) assert_eq(x.blocks[0, :, 0], np.ones((10, 40, 10))) x = da.ones((2, 2), chunks=1) with pytest.raises(ValueError): x.blocks[[0, 1], [0, 1]] with pytest.raises(ValueError): x.blocks[np.array([0, 1]), [0, 1]] with pytest.raises(ValueError) as info: x.blocks[np.array([0, 1]), np.array([0, 1])] assert "list" in str(info.value) with pytest.raises(ValueError) as info: x.blocks[None, :, :] assert "newaxis" in str(info.value) and "not supported" in str(info.value) with pytest.raises(IndexError) as info: x.blocks[100, 100] def test_partitions_indexer(): # .partitions is an alias of .blocks for dask arrays x = da.arange(10, chunks=2) assert isinstance(x.partitions[0], da.Array) assert_eq(x.partitions[0], x[:2]) assert_eq(x.partitions[-1], x[-2:]) assert_eq(x.partitions[:3], x[:6]) assert_eq(x.partitions[[0, 1, 2]], x[:6]) assert_eq(x.partitions[[3, 0, 2]], np.array([6, 7, 0, 1, 4, 5])) x = da.random.default_rng().random((20, 20), chunks=(4, 5)) assert_eq(x.partitions[0], x[:4]) assert_eq(x.partitions[0, :3], x[:4, :15]) assert_eq(x.partitions[:, :3], x[:, :15]) x = da.ones((40, 40, 40), chunks=(10, 10, 10)) assert_eq(x.partitions[0, :, 0], np.ones((10, 40, 10))) x = da.ones((2, 2), chunks=1) with pytest.raises(ValueError): x.partitions[[0, 1], [0, 1]] with pytest.raises(ValueError): x.partitions[np.array([0, 1]), [0, 1]] with pytest.raises(ValueError) as info: x.partitions[np.array([0, 1]), np.array([0, 1])] assert "list" in str(info.value) with pytest.raises(ValueError) as info: x.partitions[None, :, :] assert "newaxis" in str(info.value) and "not supported" in str(info.value) with pytest.raises(IndexError) as info: x.partitions[100, 100] @pytest.mark.filterwarnings("ignore:the matrix subclass:PendingDeprecationWarning") @pytest.mark.parametrize( "container", [pytest.param("array", marks=skip_if_no_sparray()), "matrix"] ) def test_dask_array_holds_scipy_sparse_containers(container): pytest.importorskip("scipy.sparse") import scipy.sparse cls = scipy.sparse.csr_matrix if container == "matrix" else scipy.sparse.csr_array kind = scipy.sparse.spmatrix if container == "matrix" else scipy.sparse.sparray x = da.random.default_rng().random((1000, 10), chunks=(100, 10)) x[x < 0.9] = 0 xx = x.compute() y = x.map_blocks(cls) vs = y.to_delayed().flatten().tolist() values = dask.compute(*vs, scheduler="single-threaded") assert all(isinstance(v, cls) for v in values) yy = y.compute(scheduler="single-threaded") assert isinstance(yy, kind) assert (yy == xx).all() z = x.T.map_blocks(cls) zz = z.compute(scheduler="single-threaded") assert isinstance(zz, kind) assert (zz == xx.T).all() @pytest.mark.filterwarnings("ignore:the matrix subclass:PendingDeprecationWarning") @pytest.mark.parametrize( "container", [pytest.param("array", marks=skip_if_no_sparray()), "matrix"] ) def test_dask_array_setitem_singleton_sparse(container): pytest.importorskip("scipy.sparse") import scipy.sparse cls = scipy.sparse.csr_matrix if container == "matrix" else scipy.sparse.csr_array x = cls(scipy.sparse.eye(100)) x_dask = da.from_array(x) x[slice(10), slice(10)] = 0 x_dask[slice(10), slice(10)] = 0 np.testing.assert_almost_equal( x_dask.compute(scheduler="single-threaded").toarray(), x.toarray() ) @pytest.mark.parametrize( "index", [ [5, 8], 0, slice(5, 8), np.array([5, 8]), np.array([True, False] * 500), [True, False] * 500, ], ) @pytest.mark.parametrize( ("sparse_module_path", "container"), [ ("scipy.sparse", "csr_matrix"), pytest.param("scipy.sparse", "csr_array", marks=skip_if_no_sparray()), ("cupyx.scipy.sparse", "csr_matrix"), ], ) def test_scipy_sparse_indexing(index, sparse_module_path, container): sp = pytest.importorskip(sparse_module_path) if sparse_module_path == "cupyx.scipy.sparse": backend = "cupy" else: backend = "numpy" with dask.config.set({"array.backend": backend}): x = da.random.default_rng().random((1000, 10), chunks=(100, 10)) x[x < 0.9] = 0 y = x.map_blocks(getattr(sp, container)) assert not ( x[index, :].compute(scheduler="single-threaded") != y[index, :].compute(scheduler="single-threaded") ).sum() @pytest.mark.parametrize("axis", [0, 1]) @pytest.mark.parametrize( "container", [pytest.param("array", marks=skip_if_no_sparray()), "matrix"] ) def test_scipy_sparse_concatenate(axis, container): pytest.importorskip("scipy.sparse") import scipy.sparse cls = scipy.sparse.csr_matrix if container == "matrix" else scipy.sparse.csr_array rng = da.random.default_rng() xs = [] ys = [] for _ in range(2): x = rng.random((1000, 10), chunks=(100, 10)) x[x < 0.9] = 0 xs.append(x) ys.append(x.map_blocks(cls)) z = da.concatenate(ys, axis=axis) z = z.compute() if axis == 0: sp_concatenate = scipy.sparse.vstack elif axis == 1: sp_concatenate = scipy.sparse.hstack z_expected = sp_concatenate([cls(e.compute()) for e in xs]) assert (z != z_expected).nnz == 0 @pytest.mark.parametrize("func", [da.asarray, da.asanyarray]) @pytest.mark.parametrize("src", [[[1, 2]], np.asarray([[1, 2]]), da.asarray([[1, 2]])]) def test_scipy_sparse_asarray_like(src, func): """scipy.sparse.csr_matrix objects are not a valid argument for np.asarray(..., like=...) and require special-casing. """ pytest.importorskip("scipy.sparse") import scipy.sparse mtx = scipy.sparse.csr_matrix([[3, 4, 5], [6, 7, 8]]) like = da.from_array(mtx) a = func(src, like=like) assert isinstance(a._meta, type(mtx)) assert isinstance(a.compute(), type(mtx)) # Respect dtype; quietly disregard order a = func(src, dtype=np.float32, order="C", like=like) assert a.dtype == np.float32 assert a.compute().dtype == np.float32 assert isinstance(a._meta, type(mtx)) assert isinstance(a.compute(), type(mtx)) def test_3851(): with warnings.catch_warnings(record=True) as record: Y = da.random.default_rng().random((10, 10), chunks="auto") da.argmax(Y, axis=0).compute() assert not record def test_3925(): x = da.from_array(np.array(["a", "b", "c"], dtype=object), chunks=-1) assert (x[0] == x[0]).compute(scheduler="sync") def test_map_blocks_large_inputs_delayed(): a = da.ones(10, chunks=(5,)) b = np.ones(1000000) c = a.map_blocks(add, b) assert any(b is v() for v in c.dask.values() if isinstance(v, DataNode)) assert repr(dict(c.dask)).count(repr(b)[:10]) == 1 # only one occurrence d = a.map_blocks(lambda x, y: x + y.sum(), y=b) assert_eq(d, d) assert any(b is v() for v in d.dask.values() if isinstance(v, DataNode)) assert repr(dict(c.dask)).count(repr(b)[:10]) == 1 # only one occurrence def test_blockwise_large_inputs_delayed(): def func(a, b): return a a = da.ones(10, chunks=(5,)) b = np.ones(1000000) c = da.blockwise(func, "i", a, "i", b, None, dtype=a.dtype) assert any(b is v() for v in c.dask.values() if isinstance(v, DataNode)) assert repr(dict(c.dask)).count(repr(b)[:10]) == 1 # only one occurrence assert_eq(c, c) d = da.blockwise(lambda x, y: x, "i", a, "i", y=b, dtype=a.dtype) assert any(b is v() for v in d.dask.values() if isinstance(v, DataNode)) assert repr(dict(c.dask)).count(repr(b)[:10]) == 1 # only one occurrence assert_eq(d, d) def test_slice_reversed(): x = da.ones(10, chunks=-1) y = x[6:3] assert_eq(y, np.ones(0)) def test_map_blocks_chunks(): x = da.arange(400, chunks=(100,)) y = da.arange(40, chunks=(10,)) def func(a, b): return np.array([a.max(), b.max()]) assert_eq( da.map_blocks(func, x, y, chunks=(2,), dtype=x.dtype), np.array([99, 9, 199, 19, 299, 29, 399, 39]), ) def test_nbytes_auto(): chunks = normalize_chunks("800B", shape=(500,), dtype="float64") assert chunks == ((100, 100, 100, 100, 100),) chunks = normalize_chunks("200B", shape=(10, 10), dtype="float64") assert chunks == ((5, 5), (5, 5)) chunks = normalize_chunks((5, "200B"), shape=(10, 10), dtype="float64") assert chunks == ((5, 5), (5, 5)) chunks = normalize_chunks("33B", shape=(10, 10), dtype="float64") assert chunks == ((2, 2, 2, 2, 2), (2, 2, 2, 2, 2)) chunks = normalize_chunks("1800B", shape=(10, 20, 30), dtype="float64") assert chunks == ((6, 4), (6, 6, 6, 2), (6, 6, 6, 6, 6)) with pytest.raises(ValueError): normalize_chunks("10B", shape=(10,), limit=20, dtype="float64") with pytest.raises(ValueError): normalize_chunks("100B", shape=(10, 10), limit=20, dtype="float64") with pytest.raises(ValueError): normalize_chunks(("100B", "10B"), shape=(10, 10), dtype="float64") with pytest.raises(ValueError): normalize_chunks(("10B", "10B"), shape=(10, 10), limit=20, dtype="float64") def test_auto_chunks(): chunks = ((1264, 1264, 1264, 1264, 1264, 1264, 1045), (1264, 491)) shape = sum(chunks[0]), sum(chunks[1]) result = normalize_chunks( ("auto", "auto"), shape=shape, dtype="int32", previous_chunks=chunks ) assert result == ((8629,), (1755,)) def test_auto_chunks_h5py(): h5py = pytest.importorskip("h5py") with tmpfile(".hdf5") as fn: with h5py.File(fn, mode="a") as f: d = f.create_dataset( "/x", shape=(1000, 1000), chunks=(32, 64), dtype="float64" ) d[:] = 1 with h5py.File(fn, mode="a") as f: d = f["x"] with dask.config.set({"array.chunk-size": "1 MiB"}): x = da.from_array(d) assert isinstance(x._meta, np.ndarray) assert x.chunks == ((256, 256, 256, 232), (512, 488)) def test_no_warnings_from_blockwise(): with warnings.catch_warnings(record=True) as record: x = da.ones((3, 10, 10), chunks=(3, 2, 2)) da.map_blocks(lambda y: np.mean(y, axis=0), x, dtype=x.dtype, drop_axis=0) assert not record with warnings.catch_warnings(record=True) as record: x = da.ones((15, 15), chunks=(5, 5)) (x.dot(x.T + 1) - x.mean(axis=0)).std() assert not record with warnings.catch_warnings(record=True) as record: x = da.ones((1,), chunks=(1,)) 1 / x[0] assert not record def test_from_array_meta(): sparse = pytest.importorskip("sparse") x = np.ones(10) meta = sparse.COO.from_numpy(x) y = da.from_array(x, meta=meta) assert isinstance(y._meta, sparse.COO) def test_compute_chunk_sizes(): x = da.from_array(np.linspace(-1, 1, num=50), chunks=10) y = x[x < 0] assert np.isnan(y.shape[0]) assert y.chunks == ((np.nan,) * 5,) z = y.compute_chunk_sizes() assert y is z assert z.chunks == ((10, 10, 5, 0, 0),) assert len(z) == 25 # check that dtype of chunk dimensions is `int` assert isinstance(z.chunks[0][0], int) def test_compute_chunk_sizes_2d_array(): X = np.linspace(-1, 1, num=9 * 4).reshape(9, 4) X = da.from_array(X, chunks=(3, 4)) idx = X.sum(axis=1) > 0 Y = X[idx] # This is very similar to the DataFrame->Array conversion assert np.isnan(Y.shape[0]) and Y.shape[1] == 4 assert Y.chunks == ((np.nan, np.nan, np.nan), (4,)) Z = Y.compute_chunk_sizes() assert Y is Z assert Z.chunks == ((0, 1, 3), (4,)) assert Z.shape == (4, 4) def test_compute_chunk_sizes_3d_array(N=8): X = np.linspace(-1, 2, num=8 * 8 * 8).reshape(8, 8, 8) X = da.from_array(X, chunks=(4, 4, 4)) idx = X.sum(axis=0).sum(axis=0) > 0 Y = X[idx] idx = X.sum(axis=1).sum(axis=1) < 0 Y = Y[:, idx] idx = X.sum(axis=2).sum(axis=1) > 0.1 Y = Y[:, :, idx] # Checking to make sure shapes are different on outputs assert Y.compute().shape == (8, 3, 5) assert X.compute().shape == (8, 8, 8) assert Y.chunks == ((np.nan, np.nan),) * 3 assert all(np.isnan(s) for s in Y.shape) Z = Y.compute_chunk_sizes() assert Z is Y assert Z.shape == (8, 3, 5) assert Z.chunks == ((4, 4), (3, 0), (1, 4)) def _known(num=50): return da.from_array(np.linspace(-1, 1, num=num), chunks=10) @pytest.fixture() def unknown(): x = _known() y = x[x < 0] assert y.chunks == ((np.nan,) * 5,) return y def test_compute_chunk_sizes_warning_fixes_rechunk(unknown): y = unknown with pytest.raises(ValueError, match="compute_chunk_sizes"): y.rechunk("auto") y.compute_chunk_sizes() y.rechunk("auto") def test_compute_chunk_sizes_warning_fixes_to_zarr(unknown): pytest.importorskip("zarr") y = unknown with tmpdir() as d: with pytest.raises(ValueError, match="compute_chunk_sizes"): y.to_zarr(d) y.compute_chunk_sizes() y.to_zarr(d) def test_compute_chunk_sizes_warning_fixes_to_svg(unknown): y = unknown with pytest.raises(NotImplementedError, match="compute_chunk_sizes"): y.to_svg() y.compute_chunk_sizes() y.to_svg() def test_compute_chunk_sizes_warning_fixes_concatenate(): x = _known(num=100).reshape(10, 10) idx = x.sum(axis=0) > 0 y1 = x[idx] y2 = x[idx] with pytest.raises(ValueError, match="compute_chunk_sizes"): da.concatenate((y1, y2), axis=1) y1.compute_chunk_sizes() y2.compute_chunk_sizes() da.concatenate((y1, y2), axis=1) def test_compute_chunk_sizes_warning_fixes_reduction(unknown): y = unknown with pytest.raises(ValueError, match="compute_chunk_sizes"): da.argmin(y) y.compute_chunk_sizes() da.argmin(y) def test_compute_chunk_sizes_warning_fixes_reshape(unknown): y = unknown with pytest.raises(ValueError, match="compute_chunk_sizes"): da.reshape(y, (5, 5)) y.compute_chunk_sizes() da.reshape(y, (5, 5)) def test_compute_chunk_sizes_warning_fixes_slicing(): x = _known(num=100).reshape(10, 10) y = x[x.sum(axis=0) < 0] with pytest.raises(ValueError, match="compute_chunk_sizes"): y[:3, :] y.compute_chunk_sizes() y[:3, :] def test_rechunk_auto(): x = da.ones(10, chunks=(1,)) y = x.rechunk() assert y.npartitions == 1 def test_chunk_assignment_invalidates_cached_properties(): x = da.ones((4,), chunks=(1,)) y = x.copy() # change chunks directly, which should change all of the tested properties y._chunks = ((2, 2), (0, 0, 0, 0)) assert x.ndim != y.ndim assert x.shape != y.shape assert x.size != y.size assert x.numblocks != y.numblocks assert x.npartitions != y.npartitions assert x.__dask_keys__() != y.__dask_keys__() assert not np.array_equal(x._key_array, y._key_array) def test_map_blocks_series(): pd = pytest.importorskip("pandas") import dask.dataframe as dd pytest.skip("array roundtrips don't work yet") from dask.dataframe.utils import assert_eq as dd_assert_eq x = da.ones(10, chunks=(5,)) s = x.map_blocks(pd.Series) assert isinstance(s, dd.Series) assert s.npartitions == x.npartitions dd_assert_eq(s, s) @pytest.mark.xfail(reason="need to remove singleton index dimension") def test_map_blocks_dataframe(): pd = pytest.importorskip("pandas") import dask.dataframe as dd from dask.dataframe.utils import assert_eq as dd_assert_eq x = da.ones((10, 2), chunks=(5, 2)) s = x.map_blocks(pd.DataFrame) assert isinstance(s, dd.DataFrame) assert s.npartitions == x.npartitions dd_assert_eq(s, s) def test_dask_layers(): a = da.ones(1) assert a.dask.layers.keys() == {a.name} assert a.dask.dependencies == {a.name: set()} assert a.__dask_layers__() == (a.name,) b = a + 1 assert b.dask.layers.keys() == {a.name, b.name} assert b.dask.dependencies == {a.name: set(), b.name: {a.name}} assert b.__dask_layers__() == (b.name,) def test_len_object_with_unknown_size(): a = da.random.default_rng().random(size=(20, 2)) b = a[a < 0.5] with pytest.raises(ValueError, match="on object with unknown chunk size"): assert len(b) @pytest.mark.parametrize("ndim", [0, 1, 3, 8]) def test_chunk_shape_broadcast(ndim): from functools import partial def f(x, ndim=0): # Ignore `x` and return arbitrary one-element array of dimensionality `ndim` # For example, # f(x, 0) = array(5) # f(x, 1) = array([5]) # f(x, 2) = array([[5]]) # f(x, 3) = array([[[5]]]) return np.array(5)[(np.newaxis,) * ndim] array = da.from_array([1] + [2, 2] + [3, 3, 3], chunks=((1, 2, 3),)) out_chunks = ((1, 1, 1),) # check ``enforce_ndim`` keyword parameter of ``map_blocks()`` out = array.map_blocks(partial(f, ndim=ndim), chunks=out_chunks, enforce_ndim=True) if ndim != 1: with pytest.raises(ValueError, match="Dimension mismatch:"): out.compute() else: out.compute() # should not raise an exception # check ``check_ndim`` keyword parameter of ``assert_eq()`` out = array.map_blocks(partial(f, ndim=ndim), chunks=out_chunks) expected = np.array([5, 5, 5]) try: assert_eq(out, expected) except AssertionError: assert_eq(out, expected, check_ndim=False) else: if ndim != 1: raise AssertionError("Expected a ValueError: Dimension mismatch") def test_chunk_non_array_like(): array = da.from_array([1] + [2, 2] + [3, 3, 3], chunks=((1, 2, 3),)) out_chunks = ((1, 1, 1),) # check ``enforce_ndim`` keyword parameter of ``map_blocks()`` out = array.map_blocks(lambda x: 5, chunks=out_chunks, enforce_ndim=True) with pytest.raises(ValueError, match="Dimension mismatch:"): out.compute() expected = np.array([5, 5, 5]) # check ``check_ndim`` keyword parameter of ``assert_eq()`` out = array.map_blocks(lambda x: 5, chunks=out_chunks) try: assert_eq(out, expected) except AssertionError: assert_eq(out, expected, check_chunks=False) else: raise AssertionError("Expected a ValueError: Dimension mismatch") def test_to_backend(): # Test that `Array.to_backend` works as expected with dask.config.set({"array.backend": "numpy"}): # Start with numpy-backed array x = da.ones(10) assert isinstance(x._meta, np.ndarray) # Default `to_backend` shouldn't change data assert_eq(x, x.to_backend()) # Moving to a "missing" backend should raise an error with pytest.raises(ValueError, match="No backend dispatch registered"): x.to_backend("missing") def test_from_array_copies(): x = np.arange(60).reshape((6, 10)) original_array = x.copy() chunks = (2, 3) dx = da.from_array(x, chunks=chunks) x[2:4, x[0] > 3] = -5 assert_eq(original_array, dx) def test_from_array_xarray_dataarray(): xr = pytest.importorskip("xarray") arr = xr.DataArray(da.random.random((1000, 1000), chunks=(50, 50))) dask_array = da.from_array(arr) dsk = collections_to_expr([dask_array]).__dask_graph__() assert len(dsk) == 400 assert all(k[0].startswith("random_sample") for k in dsk) assert_eq(dask_array, arr.data) arr = xr.DataArray(np.random.random((100, 100))) dask_array = da.from_array(arr) assert_eq(dask_array.compute().data, arr.data) def test_load_store_chunk(): actual = np.array([0, 0, 0, 0, 0, 0]) load_store_chunk( x=np.array([1, 2, 3]), out=actual, region=None, index=slice(2, 5), lock=False, return_stored=False, load_stored=False, ) expected = np.array([0, 0, 1, 2, 3, 0]) assert all(actual == expected) # index should not be used on empty array actual = load_store_chunk( x=np.array([]), region=None, out=np.array([]), index=2, lock=False, return_stored=True, load_stored=False, ) expected = np.array([]) assert all(actual == expected) def test_scalar_setitem(): """After a da.Array.__getitem__ call that returns a scalar, the chunk contains a read-only np.generic instead of a writeable np.ndarray. This is a specific quirk of numpy; cupy and other backends always return a 0-dimensional array. Make sure that __setitem__ still works. """ x = da.zeros(1) y = x[0] assert isinstance(y.compute(), np.generic) y[()] = 2 assert_eq(y, 2.0) assert isinstance(y.compute(), np.ndarray) @pytest.mark.parametrize( "idx", [[0], [True, False], da.array([0]), da.array([True, False])] ) @pytest.mark.parametrize( "val", [3.3, np.float64(3.3), np.int64(3), da.array(3.3), da.array(3, dtype=np.int64)], ) def test_setitem_no_dtype_broadcast(idx, val): x = da.array([1, 2], dtype=np.int32) x[idx] = val assert_eq(x, da.array([3, 2], dtype=np.int32)) def test_store_sources_unoptimized_nocompute(): """Test that two sources can be optimized and share tasks after storing.""" total_calls = 0 def _shared_task(arr1): nonlocal total_calls total_calls += 1 return np.stack([arr1 + 1, arr1 + 2]) start = da.zeros((2, 2), chunks=1) src = da.map_blocks( _shared_task, start, dtype=start.dtype, meta=np.array((), dtype=start.dtype), new_axis=[0], chunks=(2,) + start.chunks, ) target1 = np.zeros((2, 2)) target2 = np.zeros((2, 2)) with dask.config.set(scheduler="single-threaded"): store_res1 = da.store(src[0], target1, compute=False) store_res2 = da.store(src[1], target2, compute=False) da.compute(store_res1, store_res2) assert total_calls == start.blocks.size def test_blockwise_fusion(): def custom_scheduler_get(dsk, keys, **kwargs): """Custom scheduler that returns the result of the first key.""" dsk = dsk.__dask_graph__() # two sum # one sum agg # one finalize Alias assert len(dsk) == 4, "False number of tasks" return [42 for _ in keys] # First test that this mocking stuff works as expecged with pytest.raises(AssertionError, match="False number of tasks"): dask.compute(da.ones(10), scheduler=custom_scheduler_get) a = ((da.ones(10, chunks=5) + 1) + 2).sum() dask.compute(a, scheduler=custom_scheduler_get)