from __future__ import absolute_import, division, print_function from bisect import bisect import copy from functools import partial, wraps from itertools import product import math from numbers import Number, Integral import operator from operator import add, getitem, mul import os import re import sys import traceback import pickle from threading import Lock import uuid import warnings try: from cytoolz import (partition, concat, first, groupby, accumulate) from cytoolz.curried import pluck except ImportError: from toolz import (partition, concat, first, groupby, accumulate) from toolz.curried import pluck from toolz import map, reduce, frequencies import numpy as np from . import chunk from .. import config from ..base import (DaskMethodsMixin, tokenize, dont_optimize, compute_as_if_collection, persist, is_dask_collection) from ..blockwise import broadcast_dimensions, subs from ..context import globalmethod from ..utils import (ndeepmap, ignoring, concrete, is_integer, IndexCallable, funcname, derived_from, SerializableLock, Dispatch, factors, parse_bytes, has_keyword, M, ndimlist) from ..compatibility import (unicode, zip_longest, Iterable, Iterator, Mapping) from ..core import quote from ..delayed import delayed, Delayed from .. import threaded, core from ..sizeof import sizeof from ..highlevelgraph import HighLevelGraph from ..bytes.core import get_mapper, get_fs_token_paths from .numpy_compat import _Recurser, _make_sliced_dtype from .slicing import slice_array, replace_ellipsis from .blockwise import blockwise config.update_defaults({'array': { 'chunk-size': '128MiB', 'rechunk-threshold': 4 }}) concatenate_lookup = Dispatch('concatenate') tensordot_lookup = Dispatch('tensordot') einsum_lookup = Dispatch('einsum') concatenate_lookup.register((object, np.ndarray), np.concatenate) tensordot_lookup.register((object, np.ndarray), np.tensordot) einsum_lookup.register((object, np.ndarray), np.einsum) class PerformanceWarning(Warning): """ A warning given when bad chunking may cause poor performance """ def getter(a, b, asarray=True, lock=None): if isinstance(b, tuple) and any(x is None for x in b): b2 = tuple(x for x in b if x is not None) b3 = tuple(None if x is None else slice(None, None) for x in b if not isinstance(x, Integral)) return getter(a, b2, asarray=asarray, lock=lock)[b3] if lock: lock.acquire() try: c = a[b] if asarray: c = np.asarray(c) finally: if lock: lock.release() return c def getter_nofancy(a, b, asarray=True, lock=None): """ A simple wrapper around ``getter``. Used to indicate to the optimization passes that the backend doesn't support fancy indexing. """ return getter(a, b, asarray=asarray, lock=lock) def getter_inline(a, b, asarray=True, lock=None): """ A getter function that optimizations feel comfortable inlining Slicing operations with this function may be inlined into a graph, such as in the following rewrite **Before** >>> a = x[:10] # doctest: +SKIP >>> b = a + 1 # doctest: +SKIP >>> c = a * 2 # doctest: +SKIP **After** >>> b = x[:10] + 1 # doctest: +SKIP >>> c = x[:10] * 2 # doctest: +SKIP This inlining can be relevant to operations when running off of disk. """ return getter(a, b, asarray=asarray, lock=lock) from .optimization import optimize, fuse_slice def slices_from_chunks(chunks): """ Translate chunks tuple to a set of slices in product order >>> slices_from_chunks(((2, 2), (3, 3, 3))) # doctest: +NORMALIZE_WHITESPACE [(slice(0, 2, None), slice(0, 3, None)), (slice(0, 2, None), slice(3, 6, None)), (slice(0, 2, None), slice(6, 9, None)), (slice(2, 4, None), slice(0, 3, None)), (slice(2, 4, None), slice(3, 6, None)), (slice(2, 4, None), slice(6, 9, None))] """ cumdims = [list(accumulate(add, (0,) + bds[:-1])) for bds in chunks] shapes = product(*chunks) starts = product(*cumdims) return [tuple(slice(s, s + dim) for s, dim in zip(start, shape)) for start, shape in zip(starts, shapes)] def getem(arr, chunks, getitem=getter, shape=None, out_name=None, lock=False, asarray=True, dtype=None): """ Dask getting various chunks from an array-like >>> getem('X', chunks=(2, 3), shape=(4, 6)) # doctest: +SKIP {('X', 0, 0): (getter, 'X', (slice(0, 2), slice(0, 3))), ('X', 1, 0): (getter, 'X', (slice(2, 4), slice(0, 3))), ('X', 1, 1): (getter, 'X', (slice(2, 4), slice(3, 6))), ('X', 0, 1): (getter, 'X', (slice(0, 2), slice(3, 6)))} >>> getem('X', chunks=((2, 2), (3, 3))) # doctest: +SKIP {('X', 0, 0): (getter, 'X', (slice(0, 2), slice(0, 3))), ('X', 1, 0): (getter, 'X', (slice(2, 4), slice(0, 3))), ('X', 1, 1): (getter, 'X', (slice(2, 4), slice(3, 6))), ('X', 0, 1): (getter, 'X', (slice(0, 2), slice(3, 6)))} """ out_name = out_name or arr chunks = normalize_chunks(chunks, shape, dtype=dtype) keys = list(product([out_name], *[range(len(bds)) for bds in chunks])) slices = slices_from_chunks(chunks) if getitem is not operator.getitem and (not asarray or lock): values = [(getitem, arr, x, asarray, lock) for x in slices] else: # Common case, drop extra parameters values = [(getitem, arr, x) for x in slices] return dict(zip(keys, values)) def dotmany(A, B, leftfunc=None, rightfunc=None, **kwargs): """ Dot product of many aligned chunks >>> x = np.array([[1, 2], [1, 2]]) >>> y = np.array([[10, 20], [10, 20]]) >>> dotmany([x, x, x], [y, y, y]) array([[ 90, 180], [ 90, 180]]) Optionally pass in functions to apply to the left and right chunks >>> dotmany([x, x, x], [y, y, y], rightfunc=np.transpose) array([[150, 150], [150, 150]]) """ if leftfunc: A = map(leftfunc, A) if rightfunc: B = map(rightfunc, B) return sum(map(partial(np.dot, **kwargs), A, B)) def _concatenate2(arrays, axes=[]): """ Recursively Concatenate nested lists of arrays along axes Each entry in axes corresponds to each level of the nested list. The length of axes should correspond to the level of nesting of arrays. >>> x = np.array([[1, 2], [3, 4]]) >>> _concatenate2([x, x], axes=[0]) array([[1, 2], [3, 4], [1, 2], [3, 4]]) >>> _concatenate2([x, x], axes=[1]) array([[1, 2, 1, 2], [3, 4, 3, 4]]) >>> _concatenate2([[x, x], [x, x]], axes=[0, 1]) array([[1, 2, 1, 2], [3, 4, 3, 4], [1, 2, 1, 2], [3, 4, 3, 4]]) Supports Iterators >>> _concatenate2(iter([x, x]), axes=[1]) array([[1, 2, 1, 2], [3, 4, 3, 4]]) """ if isinstance(arrays, Iterator): arrays = list(arrays) if not isinstance(arrays, (list, tuple)): return arrays if len(axes) > 1: arrays = [_concatenate2(a, axes=axes[1:]) for a in arrays] concatenate = concatenate_lookup.dispatch(type(max(arrays, key=lambda x: x.__array_priority__))) return concatenate(arrays, axis=axes[0]) def apply_infer_dtype(func, args, kwargs, funcname, suggest_dtype='dtype', nout=None): """ Tries to infer output dtype of ``func`` for a small set of input arguments. Parameters ---------- func: Callable Function for which output dtype is to be determined args: List of array like Arguments to the function, which would usually be used. Only attributes ``ndim`` and ``dtype`` are used. kwargs: dict Additional ``kwargs`` to the ``func`` funcname: String Name of calling function to improve potential error messages suggest_dtype: None/False or String If not ``None`` adds suggestion to potential error message to specify a dtype via the specified kwarg. Defaults to ``'dtype'``. nout: None or Int ``None`` if function returns single output, integer if many. Deafults to ``None``. Returns ------- : dtype or List of dtype One or many dtypes (depending on ``nout``) """ args = [np.ones((1,) * x.ndim, dtype=x.dtype) if isinstance(x, Array) else x for x in args] try: with np.errstate(all='ignore'): o = func(*args, **kwargs) except Exception as e: exc_type, exc_value, exc_traceback = sys.exc_info() tb = ''.join(traceback.format_tb(exc_traceback)) suggest = ("Please specify the dtype explicitly using the " "`{dtype}` kwarg.\n\n".format(dtype=suggest_dtype)) if suggest_dtype else "" msg = ("`dtype` inference failed in `{0}`.\n\n" "{1}" "Original error is below:\n" "------------------------\n" "{2}\n\n" "Traceback:\n" "---------\n" "{3}").format(funcname, suggest, repr(e), tb) else: msg = None if msg is not None: raise ValueError(msg) return o.dtype if nout is None else tuple(e.dtype for e in o) def normalize_arg(x): """ Normalize user provided arguments to blockwise or map_blocks We do a few things: 1. If they are string literals that might collide with blockwise_token then we quote them 2. IF they are large (as defined by sizeof) then we put them into the graph on their own by using dask.delayed """ if is_dask_collection(x): return x elif isinstance(x, str) and re.match(r'_\d+', x): return delayed(x) elif isinstance(x, list) and len(x) >= 10: return delayed(x) elif sizeof(x) > 1e6: return delayed(x) else: return x def map_blocks(func, *args, **kwargs): """ Map a function across all blocks of a dask array. Parameters ---------- func : callable Function to apply to every block in the array. args : dask arrays or other objects dtype : np.dtype, optional The ``dtype`` of the output array. It is recommended to provide this. If not provided, will be inferred by applying the function to a small set of fake data. chunks : tuple, optional Chunk shape of resulting blocks if the function does not preserve shape. If not provided, the resulting array is assumed to have the same block structure as the first input array. drop_axis : number or iterable, optional Dimensions lost by the function. new_axis : number or iterable, optional New dimensions created by the function. Note that these are applied after ``drop_axis`` (if present). token : string, optional The key prefix to use for the output array. If not provided, will be determined from the function name. name : string, optional The key name to use for the output array. Note that this fully specifies the output key name, and must be unique. If not provided, will be determined by a hash of the arguments. **kwargs : Other keyword arguments to pass to function. Values must be constants (not dask.arrays) Examples -------- >>> import dask.array as da >>> x = da.arange(6, chunks=3) >>> x.map_blocks(lambda x: x * 2).compute() array([ 0, 2, 4, 6, 8, 10]) The ``da.map_blocks`` function can also accept multiple arrays. >>> d = da.arange(5, chunks=2) >>> e = da.arange(5, chunks=2) >>> f = map_blocks(lambda a, b: a + b**2, d, e) >>> f.compute() array([ 0, 2, 6, 12, 20]) If the function changes shape of the blocks then you must provide chunks explicitly. >>> y = x.map_blocks(lambda x: x[::2], chunks=((2, 2),)) You have a bit of freedom in specifying chunks. If all of the output chunk sizes are the same, you can provide just that chunk size as a single tuple. >>> a = da.arange(18, chunks=(6,)) >>> b = a.map_blocks(lambda x: x[:3], chunks=(3,)) If the function changes the dimension of the blocks you must specify the created or destroyed dimensions. >>> b = a.map_blocks(lambda x: x[None, :, None], chunks=(1, 6, 1), ... new_axis=[0, 2]) If ``chunks`` is specified but ``new_axis`` is not, then it is inferred to add the necessary number of axes on the left. Map_blocks aligns blocks by block positions without regard to shape. In the following example we have two arrays with the same number of blocks but with different shape and chunk sizes. >>> x = da.arange(1000, chunks=(100,)) >>> y = da.arange(100, chunks=(10,)) The relevant attribute to match is numblocks. >>> x.numblocks (10,) >>> y.numblocks (10,) If these match (up to broadcasting rules) then we can map arbitrary functions across blocks >>> def func(a, b): ... return np.array([a.max(), b.max()]) >>> da.map_blocks(func, x, y, chunks=(2,), dtype='i8') dask.array >>> _.compute() array([ 99, 9, 199, 19, 299, 29, 399, 39, 499, 49, 599, 59, 699, 69, 799, 79, 899, 89, 999, 99]) Your block function get information about where it is in the array by accepting a special ``block_info`` keyword argument. >>> def func(block, block_info=None): ... pass This will receive the following information: >>> block_info # doctest: +SKIP {0: {'shape': (1000,), 'num-chunks': (10,), 'chunk-location': (4,), 'array-location': [(400, 500)]}, None: {'shape': (1000,), 'num-chunks': (10,), 'chunk-location': (4,), 'array-location': [(400, 500)], 'chunk-shape': (100,), 'dtype': dtype('float64')}} For each argument and keyword arguments that are dask arrays (the positions of which are the first index), you will receive the shape of the full array, the number of chunks of the full array in each dimension, the chunk location (for example the fourth chunk over in the first dimension), and the array location (for example the slice corresponding to ``40:50``). The same information is provided for the output, with the key ``None``, plus the shape and dtype that should be returned. These features can be combined to synthesize an array from scratch, for example: >>> def func(block_info=None): ... loc = block_info[None]['array-location'][0] ... return np.arange(loc[0], loc[1]) >>> da.map_blocks(func, chunks=((4, 4),), dtype=np.float_) dask.array >>> _.compute() array([0, 1, 2, 3, 4, 5, 6, 7]) You may specify the key name prefix of the resulting task in the graph with the optional ``token`` keyword argument. >>> x.map_blocks(lambda x: x + 1, name='increment') # doctest: +SKIP dask.array """ if not callable(func): msg = ("First argument must be callable function, not %s\n" "Usage: da.map_blocks(function, x)\n" " or: da.map_blocks(function, x, y, z)") raise TypeError(msg % type(func).__name__) name = kwargs.pop('name', None) token = kwargs.pop('token', None) if token: warnings.warn("The token= keyword to map_blocks has been moved to name=") name = token name = '%s-%s' % (name or funcname(func), tokenize(func, *args, **kwargs)) dtype = kwargs.pop('dtype', None) chunks = kwargs.pop('chunks', None) drop_axis = kwargs.pop('drop_axis', []) new_axis = kwargs.pop('new_axis', None) new_axes = {} if isinstance(drop_axis, Number): drop_axis = [drop_axis] if isinstance(new_axis, Number): new_axis = [new_axis] # TODO: handle new_axis arrs = [a for a in args if isinstance(a, Array)] argpairs = [(a, tuple(range(a.ndim))[::-1]) if isinstance(a, Array) else (a, None) for a in args] if arrs: out_ind = tuple(range(max(a.ndim for a in arrs)))[::-1] else: out_ind = () if has_keyword(func, 'block_id'): kwargs['block_id'] = '__block_id_dummy__' if has_keyword(func, 'block_info'): kwargs['block_info'] = '__block_info_dummy__' original_kwargs = kwargs if dtype is None: dtype = apply_infer_dtype(func, args, original_kwargs, 'map_blocks') if drop_axis: out_ind = tuple(x for i, x in enumerate(out_ind) if i not in drop_axis) if new_axis is None and chunks is not None and len(out_ind) < len(chunks): new_axis = range(len(chunks) - len(out_ind)) if new_axis: # new_axis = [x + len(drop_axis) for x in new_axis] out_ind = list(out_ind) for ax in sorted(new_axis): n = len(out_ind) + len(drop_axis) out_ind.insert(ax, n) if chunks is not None: new_axes[n] = chunks[ax] else: new_axes[n] = 1 out_ind = tuple(out_ind) if max(new_axis) > max(out_ind): raise ValueError("New_axis values do not fill in all dimensions") out = blockwise(func, out_ind, *concat(argpairs), name=name, new_axes=new_axes, dtype=dtype, concatenate=True, align_arrays=False, **kwargs) if (has_keyword(func, 'block_id') or has_keyword(func, 'block_info') or drop_axis): dsk = out.dask.layers[out.name] dsk = dict(dsk) out.dask.layers[out.name] = dsk if has_keyword(func, 'block_id'): for k, vv in dsk.items(): v = copy.copy(vv[0]) # Need to copy and unpack subgraph callable v.dsk = copy.copy(v.dsk) [(key, task)] = v.dsk.items() task = subs(task, {'__block_id_dummy__': k[1:]}) v.dsk[key] = task dsk[k] = (v,) + vv[1:] if chunks is not None: if len(chunks) != len(out.numblocks): raise ValueError("Provided chunks have {0} dims, expected {1} " "dims.".format(len(chunks), len(out.numblocks))) chunks2 = [] for i, (c, nb) in enumerate(zip(chunks, out.numblocks)): if isinstance(c, tuple): # We only check cases where numblocks > 1. Becuase of # broadcasting, we can't (easily) validate the chunks # when the number of blocks is 1. # See https://github.com/dask/dask/issues/4299 for more. if nb > 1 and len(c) != nb: raise ValueError("Dimension {0} has {1} blocks, " "chunks specified with " "{2} blocks".format(i, nb, len(c))) chunks2.append(c) else: chunks2.append(nb * (c,)) out._chunks = normalize_chunks(chunks2) # If func has block_info as an argument, add it to the kwargs for each call if has_keyword(func, 'block_info'): starts = {} num_chunks = {} shapes = {} for i, arg in enumerate(args): if isinstance(arg, Array): starts[i] = [np.cumsum((0,) + c) for c in arg.chunks] shapes[i] = arg.shape num_chunks[i] = arg.numblocks for k, v in kwargs.items(): if isinstance(v, Array): starts[k] = [np.cumsum((0,) + c) for c in v.chunks] shapes[k] = arg.shape num_chunks[i] = arg.numblocks out_starts = [np.cumsum((0,) + c) for c in out.chunks] for k, v in dsk.items(): vv = v v = v[0] [(key, task)] = v.dsk.items() # unpack subgraph callable if new_axis: # anything using the keys in dsk is incorrect, as the # original array doesn't have values for `new_axis`. old_k = tuple(x for i, x in enumerate(k[1:]) if i not in new_axis) else: old_k = k[1:] info = {} for i, shape in shapes.items(): # Compute chunk key in the array, taking broadcasting into # account. We don't directly know which dimensions are # broadcast, but any dimension with only one chunk can be # treated as broadcast. arr_k = old_k[-len(shape):] arr_k = tuple(j if num_chunks[i][ij] > 1 else 0 for ij, j in enumerate(arr_k)) info[i] = {'shape': shape, 'num-chunks': num_chunks[i], 'array-location': [(starts[i][ij][j], starts[i][ij][j + 1]) for ij, j in enumerate(arr_k)], 'chunk-location': arr_k} info[None] = {'shape': out.shape, 'num-chunks': out.numblocks, 'array-location': [(out_starts[ij][j], out_starts[ij][j + 1]) for ij, j in enumerate(k[1:])], 'chunk-location': k[1:], 'chunk-shape': tuple(out.chunks[ij][j] for ij, j in enumerate(k[1:])), 'dtype': dtype} v = copy.copy(v) # Need to copy and unpack subgraph callable v.dsk = copy.copy(v.dsk) [(key, task)] = v.dsk.items() task = subs(task, {'__block_info_dummy__': info}) v.dsk[key] = task dsk[k] = (v,) + vv[1:] return out def broadcast_chunks(*chunkss): """ Construct a chunks tuple that broadcasts many chunks tuples >>> a = ((5, 5),) >>> b = ((5, 5),) >>> broadcast_chunks(a, b) ((5, 5),) >>> a = ((10, 10, 10), (5, 5),) >>> b = ((5, 5),) >>> broadcast_chunks(a, b) ((10, 10, 10), (5, 5)) >>> a = ((10, 10, 10), (5, 5),) >>> b = ((1,), (5, 5),) >>> broadcast_chunks(a, b) ((10, 10, 10), (5, 5)) >>> a = ((10, 10, 10), (5, 5),) >>> b = ((3, 3,), (5, 5),) >>> broadcast_chunks(a, b) Traceback (most recent call last): ... ValueError: Chunks do not align: [(10, 10, 10), (3, 3)] """ if not chunkss: return () elif len(chunkss) == 1: return chunkss[0] n = max(map(len, chunkss)) chunkss2 = [((1,),) * (n - len(c)) + c for c in chunkss] result = [] for i in range(n): step1 = [c[i] for c in chunkss2] if all(c == (1,) for c in step1): step2 = step1 else: step2 = [c for c in step1 if c != (1,)] if len(set(step2)) != 1: raise ValueError("Chunks do not align: %s" % str(step2)) result.append(step2[0]) return tuple(result) def store(sources, targets, lock=True, regions=None, compute=True, return_stored=False, **kwargs): """ Store dask arrays in array-like objects, overwrite data in target This stores dask arrays into object that supports numpy-style setitem indexing. It stores values chunk by chunk so that it does not have to fill up memory. For best performance you can align the block size of the storage target with the block size of your array. If your data fits in memory then you may prefer calling ``np.array(myarray)`` instead. Parameters ---------- sources: Array or iterable of Arrays targets: array-like or Delayed or iterable of array-likes and/or Delayeds These should support setitem syntax ``target[10:20] = ...`` lock: boolean or threading.Lock, optional Whether or not to lock the data stores while storing. Pass True (lock each file individually), False (don't lock) or a particular ``threading.Lock`` object to be shared among all writes. regions: tuple of slices or list of tuples of slices Each ``region`` tuple in ``regions`` should be such that ``target[region].shape = source.shape`` for the corresponding source and target in sources and targets, respectively. If this is a tuple, the contents will be assumed to be slices, so do not provide a tuple of tuples. compute: boolean, optional If true compute immediately, return ``dask.delayed.Delayed`` otherwise return_stored: boolean, optional Optionally return the stored result (default False). Examples -------- >>> x = ... # doctest: +SKIP >>> import h5py # doctest: +SKIP >>> f = h5py.File('myfile.hdf5') # doctest: +SKIP >>> dset = f.create_dataset('/data', shape=x.shape, ... chunks=x.chunks, ... dtype='f8') # doctest: +SKIP >>> store(x, dset) # doctest: +SKIP Alternatively store many arrays at the same time >>> store([x, y, z], [dset1, dset2, dset3]) # doctest: +SKIP """ if isinstance(sources, Array): sources = [sources] targets = [targets] if any(not isinstance(s, Array) for s in sources): raise ValueError("All sources must be dask array objects") if len(sources) != len(targets): raise ValueError("Different number of sources [%d] and targets [%d]" % (len(sources), len(targets))) if isinstance(regions, tuple) or regions is None: regions = [regions] if len(sources) > 1 and len(regions) == 1: regions *= len(sources) if len(sources) != len(regions): raise ValueError("Different number of sources [%d] and targets [%d] than regions [%d]" % (len(sources), len(targets), len(regions))) # Optimize all sources together sources_dsk = HighLevelGraph.merge(*[e.__dask_graph__() for e in sources]) sources_dsk = Array.__dask_optimize__( sources_dsk, list(core.flatten([e.__dask_keys__() for e in sources])) ) sources2 = [Array(sources_dsk, e.name, e.chunks, e.dtype) for e in sources] # Optimize all targets together targets2 = [] targets_keys = [] targets_dsk = [] for e in targets: if isinstance(e, Delayed): targets2.append(e.key) targets_keys.extend(e.__dask_keys__()) targets_dsk.append(e.__dask_graph__()) elif is_dask_collection(e): raise TypeError( "Targets must be either Delayed objects or array-likes" ) else: targets2.append(e) targets_dsk = HighLevelGraph.merge(*targets_dsk) targets_dsk = Delayed.__dask_optimize__(targets_dsk, targets_keys) load_stored = (return_stored and not compute) toks = [str(uuid.uuid1()) for _ in range(len(sources))] store_dsk = HighLevelGraph.merge(*[ insert_to_ooc(s, t, lock, r, return_stored, load_stored, tok) for s, t, r, tok in zip(sources2, targets2, regions, toks) ]) store_keys = list(store_dsk.keys()) store_dsk = HighLevelGraph.merge(store_dsk, targets_dsk, sources_dsk) if return_stored: load_store_dsk = store_dsk if compute: store_dlyds = [Delayed(k, store_dsk) for k in store_keys] store_dlyds = persist(*store_dlyds, **kwargs) store_dsk_2 = HighLevelGraph.merge(*[e.dask for e in store_dlyds]) load_store_dsk = retrieve_from_ooc( store_keys, store_dsk, store_dsk_2 ) result = tuple( Array(load_store_dsk, 'load-store-%s' % t, s.chunks, s.dtype) for s, t in zip(sources, toks) ) return result else: name = 'store-' + str(uuid.uuid1()) dsk = HighLevelGraph.merge({name: store_keys}, store_dsk) result = Delayed(name, dsk) if compute: result.compute(**kwargs) return None else: return result def blockdims_from_blockshape(shape, chunks): """ >>> blockdims_from_blockshape((10, 10), (4, 3)) ((4, 4, 2), (3, 3, 3, 1)) >>> blockdims_from_blockshape((10, 0), (4, 0)) ((4, 4, 2), (0,)) """ if chunks is None: raise TypeError("Must supply chunks= keyword argument") if shape is None: raise TypeError("Must supply shape= keyword argument") if np.isnan(sum(shape)) or np.isnan(sum(chunks)): raise ValueError("Array chunk sizes are unknown. shape: %s, chunks: %s" % (shape, chunks)) if not all(map(is_integer, chunks)): raise ValueError("chunks can only contain integers.") if not all(map(is_integer, shape)): raise ValueError("shape can only contain integers.") shape = tuple(map(int, shape)) chunks = tuple(map(int, chunks)) return tuple(((bd,) * (d // bd) + ((d % bd,) if d % bd else ()) if d else (0,)) for d, bd in zip(shape, chunks)) def finalize(results): if not results: return concatenate3(results) results2 = results while isinstance(results2, (tuple, list)): if len(results2) > 1: return concatenate3(results) else: results2 = results2[0] return unpack_singleton(results) CHUNKS_NONE_ERROR_MESSAGE = """ You must specify a chunks= keyword argument. This specifies the chunksize of your array blocks. See the following documentation page for details: https://docs.dask.org/en/latest/array-creation.html#chunks """.strip() class Array(DaskMethodsMixin): """ Parallel Dask Array A parallel nd-array comprised of many numpy arrays arranged in a grid. This constructor is for advanced uses only. For normal use see the ``da.from_array`` function. Parameters ---------- dask : dict Task dependency graph name : string Name of array in dask shape : tuple of ints Shape of the entire array chunks: iterable of tuples block sizes along each dimension See Also -------- dask.array.from_array """ __slots__ = 'dask', '_name', '_cached_keys', '_chunks', 'dtype' def __new__(cls, dask, name, chunks, dtype, shape=None): self = super(Array, cls).__new__(cls) assert isinstance(dask, Mapping) if not isinstance(dask, HighLevelGraph): dask = HighLevelGraph.from_collections(name, dask, dependencies=()) self.dask = dask self.name = name if dtype is None: raise ValueError("You must specify the dtype of the array") self.dtype = np.dtype(dtype) self._chunks = normalize_chunks(chunks, shape, dtype=self.dtype) if self._chunks is None: raise ValueError(CHUNKS_NONE_ERROR_MESSAGE) for plugin in config.get('array_plugins', ()): result = plugin(self) if result is not None: self = result return self def __reduce__(self): return (Array, (self.dask, self.name, self.chunks, self.dtype)) def __dask_graph__(self): return self.dask def __dask_layers__(self): return (self.name,) def __dask_keys__(self): if self._cached_keys is not None: return self._cached_keys name, chunks, numblocks = self.name, self.chunks, self.numblocks def keys(*args): if not chunks: return [(name,)] ind = len(args) if ind + 1 == len(numblocks): result = [(name,) + args + (i,) for i in range(numblocks[ind])] else: result = [keys(*(args + (i,))) for i in range(numblocks[ind])] return result self._cached_keys = result = keys() return result def __dask_tokenize__(self): return self.name __dask_optimize__ = globalmethod(optimize, key='array_optimize', falsey=dont_optimize) __dask_scheduler__ = staticmethod(threaded.get) def __dask_postcompute__(self): return finalize, () def __dask_postpersist__(self): return Array, (self.name, self.chunks, self.dtype) @property def numblocks(self): return tuple(map(len, self.chunks)) @property def npartitions(self): return reduce(mul, self.numblocks, 1) @property def shape(self): return tuple(map(sum, self.chunks)) @property def chunksize(self): return tuple(max(c) for c in self.chunks) @property def _meta(self): return np.empty(shape=(), dtype=self.dtype) def _get_chunks(self): return self._chunks def _set_chunks(self, chunks): raise TypeError("Can not set chunks directly\n\n" "Please use the rechunk method instead:\n" " x.rechunk(%s)" % str(chunks)) chunks = property(_get_chunks, _set_chunks, "chunks property") def __len__(self): if not self.chunks: raise TypeError("len() of unsized object") return sum(self.chunks[0]) def __array_ufunc__(self, numpy_ufunc, method, *inputs, **kwargs): out = kwargs.get('out', ()) for x in inputs + out: if not isinstance(x, (np.ndarray, Number, Array)): return NotImplemented if method == '__call__': if numpy_ufunc is np.matmul: from .routines import matmul # special case until apply_gufunc handles optional dimensions return matmul(*inputs, **kwargs) if numpy_ufunc.signature is not None: from .gufunc import apply_gufunc return apply_gufunc(numpy_ufunc, numpy_ufunc.signature, *inputs, **kwargs) if numpy_ufunc.nout > 1: from . import ufunc try: da_ufunc = getattr(ufunc, numpy_ufunc.__name__) except AttributeError: return NotImplemented return da_ufunc(*inputs, **kwargs) else: return elemwise(numpy_ufunc, *inputs, **kwargs) elif method == 'outer': from . import ufunc try: da_ufunc = getattr(ufunc, numpy_ufunc.__name__) except AttributeError: return NotImplemented return da_ufunc.outer(*inputs, **kwargs) else: return NotImplemented def __repr__(self): """ >>> import dask.array as da >>> da.ones((10, 10), chunks=(5, 5), dtype='i4') dask.array<..., shape=(10, 10), dtype=int32, chunksize=(5, 5)> """ chunksize = str(self.chunksize) name = self.name.rsplit('-', 1)[0] return ("dask.array<%s, shape=%s, dtype=%s, chunksize=%s>" % (name, self.shape, self.dtype, chunksize)) @property def ndim(self): return len(self.shape) @property def size(self): """ Number of elements in array """ return reduce(mul, self.shape, 1) @property def nbytes(self): """ Number of bytes in array """ return self.size * self.dtype.itemsize @property def itemsize(self): """ Length of one array element in bytes """ return self.dtype.itemsize @property def name(self): return self._name @name.setter def name(self, val): self._name = val # Clear the key cache when the name is reset self._cached_keys = None __array_priority__ = 11 # higher than numpy.ndarray and numpy.matrix def __array__(self, dtype=None, **kwargs): x = self.compute() if dtype and x.dtype != dtype: x = x.astype(dtype) if not isinstance(x, np.ndarray): x = np.array(x) return x def __array_function__(self, func, types, args, kwargs): import dask.array as module for submodule in func.__module__.split('.')[1:]: try: module = getattr(module, submodule) except AttributeError: return NotImplemented if not hasattr(module, func.__name__): return NotImplemented da_func = getattr(module, func.__name__) if da_func is func: return NotImplemented return da_func(*args, **kwargs) @property def _elemwise(self): return elemwise @wraps(store) def store(self, target, **kwargs): r = store([self], [target], **kwargs) if kwargs.get("return_stored", False): r = r[0] return r def to_hdf5(self, filename, datapath, **kwargs): """ Store array in HDF5 file >>> x.to_hdf5('myfile.hdf5', '/x') # doctest: +SKIP Optionally provide arguments as though to ``h5py.File.create_dataset`` >>> x.to_hdf5('myfile.hdf5', '/x', compression='lzf', shuffle=True) # doctest: +SKIP See Also -------- da.store h5py.File.create_dataset """ return to_hdf5(filename, datapath, self, **kwargs) def to_dask_dataframe(self, columns=None, index=None): """ Convert dask Array to dask Dataframe Parameters ---------- columns: list or string list of column names if DataFrame, single string if Series index : dask.dataframe.Index, optional An optional *dask* Index to use for the output Series or DataFrame. The default output index depends on whether the array has any unknown chunks. If there are any unknown chunks, the output has ``None`` for all the divisions (one per chunk). If all the chunks are known, a default index with known divsions is created. Specifying ``index`` can be useful if you're conforming a Dask Array to an existing dask Series or DataFrame, and you would like the indices to match. See Also -------- dask.dataframe.from_dask_array """ from ..dataframe import from_dask_array return from_dask_array(self, columns=columns, index=index) def __bool__(self): if self.size > 1: raise ValueError("The truth value of a {0} is ambiguous. " "Use a.any() or a.all()." .format(self.__class__.__name__)) else: return bool(self.compute()) __nonzero__ = __bool__ # python 2 def _scalarfunc(self, cast_type): if self.size > 1: raise TypeError("Only length-1 arrays can be converted " "to Python scalars") else: return cast_type(self.compute()) def __int__(self): return self._scalarfunc(int) __long__ = __int__ # python 2 def __float__(self): return self._scalarfunc(float) def __complex__(self): return self._scalarfunc(complex) def __setitem__(self, key, value): from .routines import where if isinstance(key, Array): if isinstance(value, Array) and value.ndim > 1: raise ValueError('boolean index array should have 1 dimension') y = where(key, value, self) self.dtype = y.dtype self.dask = y.dask self.name = y.name self._chunks = y.chunks return self else: raise NotImplementedError("Item assignment with %s not supported" % type(key)) def __getitem__(self, index): # Field access, e.g. x['a'] or x[['a', 'b']] if (isinstance(index, (str, unicode)) or (isinstance(index, list) and index and all(isinstance(i, (str, unicode)) for i in index))): if isinstance(index, (str, unicode)): dt = self.dtype[index] else: dt = _make_sliced_dtype(self.dtype, index) if dt.shape: new_axis = list(range(self.ndim, self.ndim + len(dt.shape))) chunks = self.chunks + tuple((i,) for i in dt.shape) return self.map_blocks(getitem, index, dtype=dt.base, chunks=chunks, new_axis=new_axis) else: return self.map_blocks(getitem, index, dtype=dt) if not isinstance(index, tuple): index = (index,) from .slicing import normalize_index, slice_with_int_dask_array, slice_with_bool_dask_array index2 = normalize_index(index, self.shape) dependencies = {self.name} for i in index2: if isinstance(i, Array): dependencies.add(i.name) if any(isinstance(i, Array) and i.dtype.kind in 'iu' for i in index2): self, index2 = slice_with_int_dask_array(self, index2) if any(isinstance(i, Array) and i.dtype == bool for i in index2): self, index2 = slice_with_bool_dask_array(self, index2) if all(isinstance(i, slice) and i == slice(None) for i in index2): return self out = 'getitem-' + tokenize(self, index2) dsk, chunks = slice_array(out, self.name, self.chunks, index2) graph = HighLevelGraph.from_collections(out, dsk, dependencies=[self]) return Array(graph, out, chunks, dtype=self.dtype) def _vindex(self, key): if not isinstance(key, tuple): key = (key,) if any(k is None for k in key): raise IndexError( "vindex does not support indexing with None (np.newaxis), " "got {}".format(key)) if all(isinstance(k, slice) for k in key): if all(k.indices(d) == slice(0, d).indices(d) for k, d in zip(key, self.shape)): return self raise IndexError( "vindex requires at least one non-slice to vectorize over " "when the slices are not over the entire array (i.e, x[:]). " "Use normal slicing instead when only using slices. Got: {}" .format(key)) return _vindex(self, *key) @property def vindex(self): """Vectorized indexing with broadcasting. This is equivalent to numpy's advanced indexing, using arrays that are broadcast against each other. This allows for pointwise indexing: >>> x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) >>> x = from_array(x, chunks=2) >>> x.vindex[[0, 1, 2], [0, 1, 2]].compute() array([1, 5, 9]) Mixed basic/advanced indexing with slices/arrays is also supported. The order of dimensions in the result follows those proposed for ndarray.vindex [1]_: the subspace spanned by arrays is followed by all slices. Note: ``vindex`` provides more general functionality than standard indexing, but it also has fewer optimizations and can be significantly slower. _[1]: https://github.com/numpy/numpy/pull/6256 """ return IndexCallable(self._vindex) def _blocks(self, index): from .slicing import normalize_index if not isinstance(index, tuple): index = (index,) if sum(isinstance(ind, (np.ndarray, list)) for ind in index) > 1: raise ValueError("Can only slice with a single list") if any(ind is None for ind in index): raise ValueError("Slicing with np.newaxis or None is not supported") index = normalize_index(index, self.numblocks) index = tuple(slice(k, k + 1) if isinstance(k, Number) else k for k in index) name = 'blocks-' + tokenize(self, index) new_keys = np.array(self.__dask_keys__(), dtype=object)[index] chunks = tuple(tuple(np.array(c)[i].tolist()) for c, i in zip(self.chunks, index)) keys = list(product(*[range(len(c)) for c in chunks])) layer = {(name,) + key: tuple(new_keys[key].tolist()) for key in keys} graph = HighLevelGraph.from_collections(name, layer, dependencies=[self]) return Array(graph, name, chunks, self.dtype) @property def blocks(self): """ Slice an array by blocks This allows blockwise slicing of a Dask array. You can perform normal Numpy-style slicing but now rather than slice elements of the array you slice along blocks so, for example, ``x.blocks[0, ::2]`` produces a new dask array with every other block in the first row of blocks. You can index blocks in any way that could index a numpy array of shape equal to the number of blocks in each dimension, (available as array.numblocks). The dimension of the output array will be the same as the dimension of this array, even if integer indices are passed. This does not support slicing with ``np.newaxis`` or multiple lists. Examples -------- >>> import dask.array as da >>> x = da.arange(10, chunks=2) >>> x.blocks[0].compute() array([0, 1]) >>> x.blocks[:3].compute() array([0, 1, 2, 3, 4, 5]) >>> x.blocks[::2].compute() array([0, 1, 4, 5, 8, 9]) >>> x.blocks[[-1, 0]].compute() array([8, 9, 0, 1]) Returns ------- A Dask array """ return IndexCallable(self._blocks) @derived_from(np.ndarray) def dot(self, other): from .routines import tensordot return tensordot(self, other, axes=((self.ndim - 1,), (other.ndim - 2,))) @property def A(self): return self @property def T(self): return self.transpose() @derived_from(np.ndarray) def transpose(self, *axes): from .routines import transpose if not axes: axes = None elif len(axes) == 1 and isinstance(axes[0], Iterable): axes = axes[0] return transpose(self, axes=axes) @derived_from(np.ndarray) def ravel(self): from .routines import ravel return ravel(self) flatten = ravel @derived_from(np.ndarray) def choose(self, choices): from .routines import choose return choose(self, choices) @derived_from(np.ndarray) def reshape(self, *shape): from .reshape import reshape if len(shape) == 1 and not isinstance(shape[0], Number): shape = shape[0] return reshape(self, shape) def topk(self, k, axis=-1, split_every=None): """The top k elements of an array. See ``da.topk`` for docstring""" from .reductions import topk return topk(self, k, axis=axis, split_every=split_every) def argtopk(self, k, axis=-1, split_every=None): """The indices of the top k elements of an array. See ``da.argtopk`` for docstring""" from .reductions import argtopk return argtopk(self, k, axis=axis, split_every=split_every) def astype(self, dtype, **kwargs): """Copy of the array, cast to a specified type. Parameters ---------- dtype : str or dtype Typecode or data-type to which the array is cast. casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional Controls what kind of data casting may occur. Defaults to 'unsafe' for backwards compatibility. * 'no' means the data types should not be cast at all. * 'equiv' means only byte-order changes are allowed. * 'safe' means only casts which can preserve values are allowed. * 'same_kind' means only safe casts or casts within a kind, like float64 to float32, are allowed. * 'unsafe' means any data conversions may be done. copy : bool, optional By default, astype always returns a newly allocated array. If this is set to False and the `dtype` requirement is satisfied, the input array is returned instead of a copy. """ # Scalars don't take `casting` or `copy` kwargs - as such we only pass # them to `map_blocks` if specified by user (different than defaults). extra = set(kwargs) - {'casting', 'copy'} if extra: raise TypeError("astype does not take the following keyword " "arguments: {0!s}".format(list(extra))) casting = kwargs.get('casting', 'unsafe') dtype = np.dtype(dtype) if self.dtype == dtype: return self elif not np.can_cast(self.dtype, dtype, casting=casting): raise TypeError("Cannot cast array from {0!r} to {1!r}" " according to the rule " "{2!r}".format(self.dtype, dtype, casting)) return self.map_blocks(chunk.astype, dtype=dtype, astype_dtype=dtype, **kwargs) def __abs__(self): return elemwise(operator.abs, self) def __add__(self, other): return elemwise(operator.add, self, other) def __radd__(self, other): return elemwise(operator.add, other, self) def __and__(self, other): return elemwise(operator.and_, self, other) def __rand__(self, other): return elemwise(operator.and_, other, self) def __div__(self, other): return elemwise(operator.div, self, other) def __rdiv__(self, other): return elemwise(operator.div, other, self) def __eq__(self, other): return elemwise(operator.eq, self, other) def __gt__(self, other): return elemwise(operator.gt, self, other) def __ge__(self, other): return elemwise(operator.ge, self, other) def __invert__(self): return elemwise(operator.invert, self) def __lshift__(self, other): return elemwise(operator.lshift, self, other) def __rlshift__(self, other): return elemwise(operator.lshift, other, self) def __lt__(self, other): return elemwise(operator.lt, self, other) def __le__(self, other): return elemwise(operator.le, self, other) def __mod__(self, other): return elemwise(operator.mod, self, other) def __rmod__(self, other): return elemwise(operator.mod, other, self) def __mul__(self, other): return elemwise(operator.mul, self, other) def __rmul__(self, other): return elemwise(operator.mul, other, self) def __ne__(self, other): return elemwise(operator.ne, self, other) def __neg__(self): return elemwise(operator.neg, self) def __or__(self, other): return elemwise(operator.or_, self, other) def __pos__(self): return self def __ror__(self, other): return elemwise(operator.or_, other, self) def __pow__(self, other): return elemwise(operator.pow, self, other) def __rpow__(self, other): return elemwise(operator.pow, other, self) def __rshift__(self, other): return elemwise(operator.rshift, self, other) def __rrshift__(self, other): return elemwise(operator.rshift, other, self) def __sub__(self, other): return elemwise(operator.sub, self, other) def __rsub__(self, other): return elemwise(operator.sub, other, self) def __truediv__(self, other): return elemwise(operator.truediv, self, other) def __rtruediv__(self, other): return elemwise(operator.truediv, other, self) def __floordiv__(self, other): return elemwise(operator.floordiv, self, other) def __rfloordiv__(self, other): return elemwise(operator.floordiv, other, self) def __xor__(self, other): return elemwise(operator.xor, self, other) def __rxor__(self, other): return elemwise(operator.xor, other, self) def __matmul__(self, other): from .routines import matmul return matmul(self, other) def __rmatmul__(self, other): from .routines import matmul return matmul(other, self) @derived_from(np.ndarray) def any(self, axis=None, keepdims=False, split_every=None, out=None): from .reductions import any return any(self, axis=axis, keepdims=keepdims, split_every=split_every, out=out) @derived_from(np.ndarray) def all(self, axis=None, keepdims=False, split_every=None, out=None): from .reductions import all return all(self, axis=axis, keepdims=keepdims, split_every=split_every, out=out) @derived_from(np.ndarray) def min(self, axis=None, keepdims=False, split_every=None, out=None): from .reductions import min return min(self, axis=axis, keepdims=keepdims, split_every=split_every, out=out) @derived_from(np.ndarray) def max(self, axis=None, keepdims=False, split_every=None, out=None): from .reductions import max return max(self, axis=axis, keepdims=keepdims, split_every=split_every, out=out) @derived_from(np.ndarray) def argmin(self, axis=None, split_every=None, out=None): from .reductions import argmin return argmin(self, axis=axis, split_every=split_every, out=out) @derived_from(np.ndarray) def argmax(self, axis=None, split_every=None, out=None): from .reductions import argmax return argmax(self, axis=axis, split_every=split_every, out=out) @derived_from(np.ndarray) def sum(self, axis=None, dtype=None, keepdims=False, split_every=None, out=None): from .reductions import sum return sum(self, axis=axis, dtype=dtype, keepdims=keepdims, split_every=split_every, out=out) @derived_from(np.ndarray) def trace(self, offset=0, axis1=0, axis2=1, dtype=None): from .reductions import trace return trace(self, offset=offset, axis1=axis1, axis2=axis2, dtype=dtype) @derived_from(np.ndarray) def prod(self, axis=None, dtype=None, keepdims=False, split_every=None, out=None): from .reductions import prod return prod(self, axis=axis, dtype=dtype, keepdims=keepdims, split_every=split_every, out=out) @derived_from(np.ndarray) def mean(self, axis=None, dtype=None, keepdims=False, split_every=None, out=None): from .reductions import mean return mean(self, axis=axis, dtype=dtype, keepdims=keepdims, split_every=split_every, out=out) @derived_from(np.ndarray) def std(self, axis=None, dtype=None, keepdims=False, ddof=0, split_every=None, out=None): from .reductions import std return std(self, axis=axis, dtype=dtype, keepdims=keepdims, ddof=ddof, split_every=split_every, out=out) @derived_from(np.ndarray) def var(self, axis=None, dtype=None, keepdims=False, ddof=0, split_every=None, out=None): from .reductions import var return var(self, axis=axis, dtype=dtype, keepdims=keepdims, ddof=ddof, split_every=split_every, out=out) def moment(self, order, axis=None, dtype=None, keepdims=False, ddof=0, split_every=None, out=None): """Calculate the nth centralized moment. Parameters ---------- order : int Order of the moment that is returned, must be >= 2. axis : int, optional Axis along which the central moment is computed. The default is to compute the moment of the flattened array. dtype : data-type, optional Type to use in computing the moment. For arrays of integer type the default is float64; for arrays of float types it is the same as the array type. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original array. ddof : int, optional "Delta Degrees of Freedom": the divisor used in the calculation is N - ddof, where N represents the number of elements. By default ddof is zero. Returns ------- moment : ndarray References ---------- .. [1] Pebay, Philippe (2008), "Formulas for Robust, One-Pass Parallel Computation of Covariances and Arbitrary-Order Statistical Moments" (PDF), Technical Report SAND2008-6212, Sandia National Laboratories """ from .reductions import moment return moment(self, order, axis=axis, dtype=dtype, keepdims=keepdims, ddof=ddof, split_every=split_every, out=out) @wraps(map_blocks) def map_blocks(self, func, *args, **kwargs): return map_blocks(func, self, *args, **kwargs) def map_overlap(self, func, depth, boundary=None, trim=True, **kwargs): """ Map a function over blocks of the array with some overlap We share neighboring zones between blocks of the array, then map a function, then trim away the neighboring strips. Parameters ---------- func: function The function to apply to each extended block depth: int, tuple, or dict The number of elements that each block should share with its neighbors If a tuple or dict then this can be different per axis boundary: str, tuple, dict How to handle the boundaries. Values include 'reflect', 'periodic', 'nearest', 'none', or any constant value like 0 or np.nan trim: bool Whether or not to trim ``depth`` elements from each block after calling the map function. Set this to False if your mapping function already does this for you **kwargs: Other keyword arguments valid in ``map_blocks`` Examples -------- >>> x = np.array([1, 1, 2, 3, 3, 3, 2, 1, 1]) >>> x = from_array(x, chunks=5) >>> def derivative(x): ... return x - np.roll(x, 1) >>> y = x.map_overlap(derivative, depth=1, boundary=0) >>> y.compute() array([ 1, 0, 1, 1, 0, 0, -1, -1, 0]) >>> import dask.array as da >>> x = np.arange(16).reshape((4, 4)) >>> d = da.from_array(x, chunks=(2, 2)) >>> d.map_overlap(lambda x: x + x.size, depth=1).compute() array([[16, 17, 18, 19], [20, 21, 22, 23], [24, 25, 26, 27], [28, 29, 30, 31]]) >>> func = lambda x: x + x.size >>> depth = {0: 1, 1: 1} >>> boundary = {0: 'reflect', 1: 'none'} >>> d.map_overlap(func, depth, boundary).compute() # doctest: +NORMALIZE_WHITESPACE array([[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23], [24, 25, 26, 27]]) """ from .overlap import map_overlap return map_overlap(self, func, depth, boundary, trim, **kwargs) def cumsum(self, axis, dtype=None, out=None): """ See da.cumsum for docstring """ from .reductions import cumsum return cumsum(self, axis, dtype, out=out) def cumprod(self, axis, dtype=None, out=None): """ See da.cumprod for docstring """ from .reductions import cumprod return cumprod(self, axis, dtype, out=out) @derived_from(np.ndarray) def squeeze(self, axis=None): from .routines import squeeze return squeeze(self, axis) def rechunk(self, chunks, threshold=None, block_size_limit=None): """ See da.rechunk for docstring """ from . import rechunk # avoid circular import return rechunk(self, chunks, threshold, block_size_limit) @property def real(self): from .ufunc import real return real(self) @property def imag(self): from .ufunc import imag return imag(self) def conj(self): from .ufunc import conj return conj(self) @derived_from(np.ndarray) def clip(self, min=None, max=None): from .ufunc import clip return clip(self, min, max) def view(self, dtype, order='C'): """ Get a view of the array as a new data type Parameters ---------- dtype: The dtype by which to view the array order: string 'C' or 'F' (Fortran) ordering This reinterprets the bytes of the array under a new dtype. If that dtype does not have the same size as the original array then the shape will change. Beware that both numpy and dask.array can behave oddly when taking shape-changing views of arrays under Fortran ordering. Under some versions of NumPy this function will fail when taking shape-changing views of Fortran ordered arrays if the first dimension has chunks of size one. """ dtype = np.dtype(dtype) mult = self.dtype.itemsize / dtype.itemsize if order == 'C': chunks = self.chunks[:-1] + (tuple(ensure_int(c * mult) for c in self.chunks[-1]),) elif order == 'F': chunks = ((tuple(ensure_int(c * mult) for c in self.chunks[0]), ) + self.chunks[1:]) else: raise ValueError("Order must be one of 'C' or 'F'") return self.map_blocks(chunk.view, dtype, order=order, dtype=dtype, chunks=chunks) @derived_from(np.ndarray) def swapaxes(self, axis1, axis2): from .routines import swapaxes return swapaxes(self, axis1, axis2) @derived_from(np.ndarray) def round(self, decimals=0): from .routines import round return round(self, decimals=decimals) def copy(self): """ Copy array. This is a no-op for dask.arrays, which are immutable """ if self.npartitions == 1: return self.map_blocks(M.copy) else: return Array(self.dask, self.name, self.chunks, self.dtype) def __deepcopy__(self, memo): c = self.copy() memo[id(self)] = c return c def to_delayed(self, optimize_graph=True): """Convert into an array of ``dask.delayed`` objects, one per chunk. Parameters ---------- optimize_graph : bool, optional If True [default], the graph is optimized before converting into ``dask.delayed`` objects. See Also -------- dask.array.from_delayed """ keys = self.__dask_keys__() graph = self.__dask_graph__() if optimize_graph: graph = self.__dask_optimize__(graph, keys) # TODO, don't collape graph name = 'delayed-' + self.name graph = HighLevelGraph.from_collections(name, graph, dependencies=()) L = ndeepmap(self.ndim, lambda k: Delayed(k, graph), keys) return np.array(L, dtype=object) @derived_from(np.ndarray) def repeat(self, repeats, axis=None): from .creation import repeat return repeat(self, repeats, axis=axis) @derived_from(np.ndarray) def nonzero(self): from .routines import nonzero return nonzero(self) def to_zarr(self, *args, **kwargs): """Save array to the zarr storage format See https://zarr.readthedocs.io for details about the format. See function ``to_zarr()`` for parameters. """ return to_zarr(self, *args, **kwargs) def to_tiledb(self, uri, *args, **kwargs): """Save array to the TileDB storage manager See function ``to_tiledb()`` for argument documentation. See https://docs.tiledb.io for details about the format and engine. """ from .tiledb_io import to_tiledb return to_tiledb(self, uri, *args, **kwargs) def ensure_int(f): i = int(f) if i != f: raise ValueError("Could not coerce %f to integer" % f) return i def normalize_chunks(chunks, shape=None, limit=None, dtype=None, previous_chunks=None): """ Normalize chunks to tuple of tuples This takes in a variety of input types and information and produces a full tuple-of-tuples result for chunks, suitable to be passed to Array or rechunk or any other operation that creates a Dask array. Parameters ---------- chunks: tuple, int, dict, or string The chunks to be normalized. See examples below for more details shape: Tuple[int] The shape of the array limit: int (optional) The maximum block size to target in bytes, if freedom is given to choose dtype: np.dtype previous_chunks: Tuple[Tuple[int]] optional Chunks from a previous array that we should use for inspiration when rechunking auto dimensions. If not provided but auto-chunking exists then auto-dimensions will prefer square-like chunk shapes. Examples -------- Specify uniform chunk sizes >>> normalize_chunks((2, 2), shape=(5, 6)) ((2, 2, 1), (2, 2, 2)) Also passes through fully explicit tuple-of-tuples >>> normalize_chunks(((2, 2, 1), (2, 2, 2)), shape=(5, 6)) ((2, 2, 1), (2, 2, 2)) Cleans up lists to tuples >>> normalize_chunks([[2, 2], [3, 3]]) ((2, 2), (3, 3)) Expands integer inputs 10 -> (10, 10) >>> normalize_chunks(10, shape=(30, 5)) ((10, 10, 10), (5,)) Expands dict inputs >>> normalize_chunks({0: 2, 1: 3}, shape=(6, 6)) ((2, 2, 2), (3, 3)) The values -1 and None get mapped to full size >>> normalize_chunks((5, -1), shape=(10, 10)) ((5, 5), (10,)) Use the value "auto" to automatically determine chunk sizes along certain dimensions. This uses the ``limit=`` and ``dtype=`` keywords to determine how large to make the chunks. The term "auto" can be used anywhere an integer can be used. See array chunking documentation for more information. >>> normalize_chunks(("auto",), shape=(20,), limit=5, dtype='uint8') ((5, 5, 5, 5),) You can also use byte sizes (see ``dask.utils.parse_bytes``) in place of "auto" to ask for a particular size >>> normalize_chunks("1kiB", shape=(2000,), dtype='float32') ((250, 250, 250, 250, 250, 250, 250, 250),) Respects null dimensions >>> normalize_chunks((), shape=(0, 0)) ((0,), (0,)) """ if dtype and not isinstance(dtype, np.dtype): dtype = np.dtype(dtype) if chunks is None: raise ValueError(CHUNKS_NONE_ERROR_MESSAGE) if isinstance(chunks, list): chunks = tuple(chunks) if isinstance(chunks, (Number, str)): chunks = (chunks,) * len(shape) if isinstance(chunks, dict): chunks = tuple(chunks.get(i, None) for i in range(len(shape))) if isinstance(chunks, np.ndarray): chunks = chunks.tolist() if not chunks and shape and all(s == 0 for s in shape): chunks = ((0,),) * len(shape) if (shape and len(shape) == 1 and len(chunks) > 1 and all(isinstance(c, (Number, str)) for c in chunks)): chunks = chunks, if shape and len(chunks) != len(shape): raise ValueError( "Chunks and shape must be of the same length/dimension. " "Got chunks=%s, shape=%s" % (chunks, shape)) if -1 in chunks or None in chunks: chunks = tuple(s if c == -1 or c is None else c for c, s in zip(chunks, shape)) # If specifying chunk size in bytes, use that value to set the limit. # Verify there is only one consistent value of limit or chunk-bytes used. for c in chunks: if isinstance(c, str) and c != 'auto': parsed = parse_bytes(c) if limit is None: limit = parsed elif parsed != limit: raise ValueError( "Only one consistent value of limit or chunk is allowed." "Used %s != %s" % (parsed, limit)) # Substitute byte limits with 'auto' now that limit is set. chunks = tuple('auto' if isinstance(c, str) and c != 'auto' else c for c in chunks) if any(c == 'auto' for c in chunks): chunks = auto_chunks(chunks, shape, limit, dtype, previous_chunks) if shape is not None: chunks = tuple(c if c not in {None, -1} else s for c, s in zip(chunks, shape)) if chunks and shape is not None: chunks = sum((blockdims_from_blockshape((s,), (c,)) if not isinstance(c, (tuple, list)) else (c,) for s, c in zip(shape, chunks)), ()) for c in chunks: if not c: raise ValueError("Empty tuples are not allowed in chunks. Express " "zero length dimensions with 0(s) in chunks") if shape is not None: if len(chunks) != len(shape): raise ValueError("Input array has %d dimensions but the supplied " "chunks has only %d dimensions" % (len(shape), len(chunks))) if not all(c == s or (math.isnan(c) or math.isnan(s)) for c, s in zip(map(sum, chunks), shape)): raise ValueError("Chunks do not add up to shape. " "Got chunks=%s, shape=%s" % (chunks, shape)) return tuple(tuple(int(x) if not math.isnan(x) else x for x in c) for c in chunks) def auto_chunks(chunks, shape, limit, dtype, previous_chunks=None): """ Determine automatic chunks This takes in a chunks value that contains ``"auto"`` values in certain dimensions and replaces those values with concrete dimension sizes that try to get chunks to be of a certain size in bytes, provided by the ``limit=`` keyword. If multiple dimensions are marked as ``"auto"`` then they will all respond to meet the desired byte limit, trying to respect the aspect ratio of their dimensions in ``previous_chunks=``, if given. Parameters ---------- chunks: Tuple A tuple of either dimensions or tuples of explicit chunk dimensions Some entries should be "auto" shape: Tuple[int] limit: int, str The maximum allowable size of a chunk in bytes previous_chunks: Tuple[Tuple[int]] See also -------- normalize_chunks: for full docstring and parameters """ if previous_chunks is not None: previous_chunks = tuple(c if isinstance(c, tuple) else (c,) for c in previous_chunks) chunks = list(chunks) autos = {i for i, c in enumerate(chunks) if c == 'auto'} if not autos: return tuple(chunks) if limit is None: limit = config.get('array.chunk-size') if isinstance(limit, str): limit = parse_bytes(limit) if dtype is None: raise TypeError("DType must be known for auto-chunking") if dtype.hasobject: raise NotImplementedError( "Can not use auto rechunking with object dtype. " "We are unable to estimate the size in bytes of object data") for x in tuple(chunks) + tuple(shape): if (isinstance(x, Number) and np.isnan(x) or isinstance(x, tuple) and np.isnan(x).any()): raise ValueError("Can not perform automatic rechunking with unknown " "(nan) chunk sizes") limit = max(1, limit) largest_block = np.prod([cs if isinstance(cs, Number) else max(cs) for cs in chunks if cs != 'auto']) if previous_chunks: # Base ideal ratio on the median chunk size of the previous chunks result = {a: np.median(previous_chunks[a]) for a in autos} ideal_shape = [] for i, s in enumerate(shape): chunk_frequencies = frequencies(previous_chunks[i]) mode, count = max(chunk_frequencies.items(), key=lambda kv: kv[1]) if mode > 1 and count >= len(previous_chunks[i]) / 2: ideal_shape.append(mode) else: ideal_shape.append(s) # How much larger or smaller the ideal chunk size is relative to what we have now multiplier = limit / dtype.itemsize / largest_block / np.prod(list(result.values())) last_multiplier = 0 last_autos = set() while (multiplier != last_multiplier or autos != last_autos): # while things change last_multiplier = multiplier # record previous values last_autos = set(autos) # record previous values # Expand or contract each of the dimensions appropriately for a in sorted(autos): proposed = result[a] * multiplier ** (1 / len(autos)) if proposed > shape[a]: # we've hit the shape boundary autos.remove(a) largest_block *= shape[a] chunks[a] = shape[a] del result[a] else: result[a] = round_to(proposed, ideal_shape[a]) # recompute how much multiplier we have left, repeat multiplier = limit / dtype.itemsize / largest_block / np.prod(list(result.values())) for k, v in result.items(): chunks[k] = v return tuple(chunks) else: size = (limit / dtype.itemsize / largest_block) ** (1 / len(autos)) small = [i for i in autos if shape[i] < size] if small: for i in small: chunks[i] = (shape[i],) return auto_chunks(chunks, shape, limit, dtype) for i in autos: chunks[i] = round_to(size, shape[i]) return tuple(chunks) def round_to(c, s): """ Return a chunk dimension that is close to an even multiple or factor We want values for c that are nicely aligned with s. If c is smaller than s then we want the largest factor of s that is less than the desired chunk size, but not less than half, which is too much. If no such factor exists then we just go with the original chunk size and accept an uneven chunk at the end. If c is larger than s then we want the largest multiple of s that is still smaller than c. """ if c <= s: try: return max(f for f in factors(s) if c / 2 <= f <= c) except ValueError: # no matching factors within factor of two return max(1, int(c)) else: return c // s * s def from_array(x, chunks='auto', name=None, lock=False, asarray=True, fancy=True, getitem=None): """ Create dask array from something that looks like an array Input must have a ``.shape`` and support numpy-style slicing. Parameters ---------- x : array_like chunks : int, tuple How to chunk the array. Must be one of the following forms: - A blocksize like 1000. - A blockshape like (1000, 1000). - Explicit sizes of all blocks along all dimensions like ((1000, 1000, 500), (400, 400)). - A size in bytes, like "100 MiB" which will choose a uniform block-like shape - The word "auto" which acts like the above, but uses a configuration value ``array.chunk-size`` for the chunk size -1 or None as a blocksize indicate the size of the corresponding dimension. name : str, optional The key name to use for the array. Defaults to a hash of ``x``. By default, hash uses python's standard sha1. This behaviour can be changed by installing cityhash, xxhash or murmurhash. If installed, a large-factor speedup can be obtained in the tokenisation step. Use ``name=False`` to generate a random name instead of hashing (fast) lock : bool or Lock, optional If ``x`` doesn't support concurrent reads then provide a lock here, or pass in True to have dask.array create one for you. asarray : bool, optional If True (default), then chunks will be converted to instances of ``ndarray``. Set to False to pass passed chunks through unchanged. fancy : bool, optional If ``x`` doesn't support fancy indexing (e.g. indexing with lists or arrays) then set to False. Default is True. Examples -------- >>> x = h5py.File('...')['/data/path'] # doctest: +SKIP >>> a = da.from_array(x, chunks=(1000, 1000)) # doctest: +SKIP If your underlying datastore does not support concurrent reads then include the ``lock=True`` keyword argument or ``lock=mylock`` if you want multiple arrays to coordinate around the same lock. >>> a = da.from_array(x, chunks=(1000, 1000), lock=True) # doctest: +SKIP If your underlying datastore has a ``.chunks`` attribute (as h5py and zarr datasets do) then a multiple of that chunk shape will be used if you do not provide a chunk shape. >>> a = da.from_array(x, chunks='auto') # doctest: +SKIP >>> a = da.from_array(x, chunks='100 MiB') # doctest: +SKIP >>> a = da.from_array(x) # doctest: +SKIP """ if isinstance(x, (list, tuple, memoryview) + np.ScalarType): x = np.array(x) previous_chunks = getattr(x, 'chunks', None) chunks = normalize_chunks( chunks, x.shape, dtype=x.dtype, previous_chunks=previous_chunks ) if name in (None, True): token = tokenize(x, chunks) original_name = 'array-original-' + token name = name or 'array-' + token elif name is False: original_name = name = 'array-' + str(uuid.uuid1()) else: original_name = name if lock is True: lock = SerializableLock() # Always use the getter for h5py etc. Not using isinstance(x, np.ndarray) # because np.matrix is a subclass of np.ndarray. if type(x) is np.ndarray and all(len(c) == 1 for c in chunks): # No slicing needed dsk = {(name, ) + (0, ) * x.ndim: x} else: if getitem is None: if type(x) is np.ndarray and not lock: # simpler and cleaner, but missing all the nuances of getter getitem = operator.getitem elif fancy: getitem = getter else: getitem = getter_nofancy dsk = getem(original_name, chunks, getitem=getitem, shape=x.shape, out_name=name, lock=lock, asarray=asarray, dtype=x.dtype) dsk[original_name] = x return Array(dsk, name, chunks, dtype=x.dtype) def from_zarr(url, component=None, storage_options=None, chunks=None,name=None, **kwargs): """Load array from the zarr storage format See https://zarr.readthedocs.io for details about the format. Parameters ---------- url: Zarr Array or str or MutableMapping Location of the data. A URL can include a protocol specifier like s3:// for remote data. Can also be any MutableMapping instance, which should be serializable if used in multiple processes. component: str or None If the location is a zarr group rather than an array, this is the subcomponent that should be loaded, something like ``'foo/bar'``. storage_options: dict Any additional parameters for the storage backend (ignored for local paths) chunks: tuple of ints or tuples of ints Passed to ``da.from_array``, allows setting the chunks on initialisation, if the chunking scheme in the on-disc dataset is not optimal for the calculations to follow. name : str, optional An optional keyname for the array. Defaults to hashing the input kwargs: passed to ``zarr.Array``. """ import zarr storage_options = storage_options or {} if isinstance(url, zarr.Array): z = url elif isinstance(url, str): fs, fs_token, path = get_fs_token_paths( url, 'rb', storage_options=storage_options) assert len(path) == 1 mapper = get_mapper(fs, path[0]) z = zarr.Array(mapper, read_only=True, path=component, **kwargs) else: mapper = url z = zarr.Array(mapper, read_only=True, path=component, **kwargs) chunks = chunks if chunks is not None else z.chunks if name is None: name = 'from-zarr-' + tokenize(z, component, storage_options, chunks, **kwargs) return from_array(z, chunks, name=name ) def to_zarr(arr, url, component=None, storage_options=None, overwrite=False, compute=True, return_stored=False, **kwargs): """Save array to the zarr storage format See https://zarr.readthedocs.io for details about the format. Parameters ---------- arr: dask.array Data to store url: Zarr Array or str or MutableMapping Location of the data. A URL can include a protocol specifier like s3:// for remote data. Can also be any MutableMapping instance, which should be serializable if used in multiple processes. component: str or None If the location is a zarr group rather than an array, this is the subcomponent that should be created/over-written. storage_options: dict Any additional parameters for the storage backend (ignored for local paths) overwrite: bool If given array already exists, overwrite=False will cause an error, where overwrite=True will replace the existing data. compute, return_stored: see ``store()`` kwargs: passed to the ``zarr.create()`` function, e.g., compression options """ import zarr if isinstance(url, zarr.Array): z = url if (isinstance(z.store, (dict, zarr.DictStore)) and 'distributed' in config.get('scheduler', '')): raise RuntimeError('Cannot store into in memory Zarr Array using ' 'the Distributed Scheduler.') arr = arr.rechunk(z.chunks) return arr.store(z, lock=False, compute=compute, return_stored=return_stored) if not _check_regular_chunks(arr.chunks): raise ValueError('Attempt to save array to zarr with irregular ' 'chunking, please call `arr.rechunk(...)` first.') storage_options = storage_options or {} if isinstance(url, str): fs, fs_token, path = get_fs_token_paths( url, 'rb', storage_options=storage_options) assert len(path) == 1 mapper = get_mapper(fs, path[0]) else: # assume the object passed is already a mapper mapper = url chunks = [c[0] for c in arr.chunks] z = zarr.create(shape=arr.shape, chunks=chunks, dtype=arr.dtype, store=mapper, path=component, overwrite=overwrite, **kwargs) return arr.store(z, lock=False, compute=compute, return_stored=return_stored) def _check_regular_chunks(chunkset): """Check if the chunks are regular "Regular" in this context means that along every axis, the chunks all have the same size, except the last one, which may be smaller Parameters ---------- chunkset: tuple of tuples of ints From the ``.chunks`` attribute of an ``Array`` Returns ------- True if chunkset passes, else False Examples -------- >>> import dask.array as da >>> arr = da.zeros(10, chunks=(5, )) >>> _check_regular_chunks(arr.chunks) True >>> arr = da.zeros(10, chunks=((3, 3, 3, 1), )) >>> _check_regular_chunks(arr.chunks) True >>> arr = da.zeros(10, chunks=((3, 1, 3, 3), )) >>> _check_regular_chunks(arr.chunks) False """ for chunks in chunkset: if len(chunks) == 1: continue if len(set(chunks[:-1])) > 1: return False if chunks[-1] > chunks[0]: return False return True def from_delayed(value, shape, dtype, name=None): """ Create a dask array from a dask delayed value This routine is useful for constructing dask arrays in an ad-hoc fashion using dask delayed, particularly when combined with stack and concatenate. The dask array will consist of a single chunk. Examples -------- >>> from dask import delayed >>> value = delayed(np.ones)(5) >>> array = from_delayed(value, (5,), float) >>> array dask.array >>> array.compute() array([1., 1., 1., 1., 1.]) """ from dask.delayed import delayed, Delayed if not isinstance(value, Delayed) and hasattr(value, 'key'): value = delayed(value) name = name or 'from-value-' + tokenize(value, shape, dtype) dsk = {(name,) + (0,) * len(shape): value.key} chunks = tuple((d,) for d in shape) # TODO: value._key may not be the name of the layer in value.dask # This should be fixed after we build full expression graphs graph = HighLevelGraph.from_collections(name, dsk, dependencies=[value]) return Array(graph, name, chunks, dtype) def from_func(func, shape, dtype=None, name=None, args=(), kwargs={}): """ Create dask array in a single block by calling a function Calling the provided function with func(*args, **kwargs) should return a NumPy array of the indicated shape and dtype. Examples -------- >>> a = from_func(np.arange, (3,), dtype='i8', args=(3,)) >>> a.compute() array([0, 1, 2]) This works particularly well when coupled with dask.array functions like concatenate and stack: >>> arrays = [from_func(np.array, (), dtype='i8', args=(n,)) for n in range(5)] >>> stack(arrays).compute() array([0, 1, 2, 3, 4]) """ name = name or 'from_func-' + tokenize(func, shape, dtype, args, kwargs) if args or kwargs: func = partial(func, *args, **kwargs) dsk = {(name,) + (0,) * len(shape): (func,)} chunks = tuple((i,) for i in shape) return Array(dsk, name, chunks, dtype) def common_blockdim(blockdims): """ Find the common block dimensions from the list of block dimensions Currently only implements the simplest possible heuristic: the common block-dimension is the only one that does not span fully span a dimension. This is a conservative choice that allows us to avoid potentially very expensive rechunking. Assumes that each element of the input block dimensions has all the same sum (i.e., that they correspond to dimensions of the same size). Examples -------- >>> common_blockdim([(3,), (2, 1)]) (2, 1) >>> common_blockdim([(1, 2), (2, 1)]) (1, 1, 1) >>> common_blockdim([(2, 2), (3, 1)]) # doctest: +SKIP Traceback (most recent call last): ... ValueError: Chunks do not align """ if not any(blockdims): return () non_trivial_dims = set([d for d in blockdims if len(d) > 1]) if len(non_trivial_dims) == 1: return first(non_trivial_dims) if len(non_trivial_dims) == 0: return max(blockdims, key=first) if np.isnan(sum(map(sum, blockdims))): raise ValueError("Arrays chunk sizes are unknown: %s", blockdims) if len(set(map(sum, non_trivial_dims))) > 1: raise ValueError("Chunks do not add up to same value", blockdims) # We have multiple non-trivial chunks on this axis # e.g. (5, 2) and (4, 3) # We create a single chunk tuple with the same total length # that evenly divides both, e.g. (4, 1, 2) # To accomplish this we walk down all chunk tuples together, finding the # smallest element, adding it to the output, and subtracting it from all # other elements and remove the element itself. We stop once we have # burned through all of the chunk tuples. # For efficiency's sake we reverse the lists so that we can pop off the end rchunks = [list(ntd)[::-1] for ntd in non_trivial_dims] total = sum(first(non_trivial_dims)) i = 0 out = [] while i < total: m = min(c[-1] for c in rchunks) out.append(m) for c in rchunks: c[-1] -= m if c[-1] == 0: c.pop() i += m return tuple(out) def unify_chunks(*args, **kwargs): """ Unify chunks across a sequence of arrays Parameters ---------- *args: sequence of Array, index pairs Sequence like (x, 'ij', y, 'jk', z, 'i') Examples -------- >>> import dask.array as da >>> x = da.ones(10, chunks=((5, 2, 3),)) >>> y = da.ones(10, chunks=((2, 3, 5),)) >>> chunkss, arrays = unify_chunks(x, 'i', y, 'i') >>> chunkss {'i': (2, 3, 2, 3)} >>> x = da.ones((100, 10), chunks=(20, 5)) >>> y = da.ones((10, 100), chunks=(4, 50)) >>> chunkss, arrays = unify_chunks(x, 'ij', y, 'jk', 'constant', None) >>> chunkss # doctest: +SKIP {'k': (50, 50), 'i': (20, 20, 20, 20, 20), 'j': (4, 1, 3, 2)} >>> unify_chunks(0, None) ({}, [0]) Returns ------- chunkss : dict Map like {index: chunks}. arrays : list List of rechunked arrays. See Also -------- common_blockdim """ if not args: return {}, [] arginds = [(asanyarray(a) if ind is not None else a, ind) for a, ind in partition(2, args)] # [x, ij, y, jk] args = list(concat(arginds)) # [(x, ij), (y, jk)] warn = kwargs.get('warn', True) arrays, inds = zip(*arginds) if all(ind is None for ind in inds): return {}, list(arrays) if all(ind == inds[0] for ind in inds) and all(a.chunks == arrays[0].chunks for a in arrays): return dict(zip(inds[0], arrays[0].chunks)), arrays nameinds = [(a.name if i is not None else a, i) for a, i in arginds] blockdim_dict = {a.name: a.chunks for a, ind in arginds if ind is not None} chunkss = broadcast_dimensions(nameinds, blockdim_dict, consolidate=common_blockdim) max_parts = max(arg.npartitions for arg, ind in arginds if ind is not None) nparts = np.prod(list(map(len, chunkss.values()))) if warn and nparts and nparts >= max_parts * 10: warnings.warn("Increasing number of chunks by factor of %d" % (nparts / max_parts), PerformanceWarning, stacklevel=3) arrays = [] for a, i in arginds: if i is None: arrays.append(a) else: chunks = tuple(chunkss[j] if a.shape[n] > 1 else a.shape[n] if not np.isnan(sum(chunkss[j])) else None for n, j in enumerate(i)) if chunks != a.chunks and all(a.chunks): arrays.append(a.rechunk(chunks)) else: arrays.append(a) return chunkss, arrays def unpack_singleton(x): """ >>> unpack_singleton([[[[1]]]]) 1 >>> unpack_singleton(np.array(np.datetime64('2000-01-01'))) array('2000-01-01', dtype='datetime64[D]') """ while isinstance(x, (list, tuple)): try: x = x[0] except (IndexError, TypeError, KeyError): break return x def block(arrays, allow_unknown_chunksizes=False): """ Assemble an nd-array from nested lists of blocks. Blocks in the innermost lists are concatenated along the last dimension (-1), then these are concatenated along the second-last dimension (-2), and so on until the outermost list is reached Blocks can be of any dimension, but will not be broadcasted using the normal rules. Instead, leading axes of size 1 are inserted, to make ``block.ndim`` the same for all blocks. This is primarily useful for working with scalars, and means that code like ``block([v, 1])`` is valid, where ``v.ndim == 1``. When the nested list is two levels deep, this allows block matrices to be constructed from their components. Parameters ---------- arrays : nested list of array_like or scalars (but not tuples) If passed a single ndarray or scalar (a nested list of depth 0), this is returned unmodified (and not copied). Elements shapes must match along the appropriate axes (without broadcasting), but leading 1s will be prepended to the shape as necessary to make the dimensions match. allow_unknown_chunksizes: bool Allow unknown chunksizes, such as come from converting from dask dataframes. Dask.array is unable to verify that chunks line up. If data comes from differently aligned sources then this can cause unexpected results. Returns ------- block_array : ndarray The array assembled from the given blocks. The dimensionality of the output is equal to the greatest of: * the dimensionality of all the inputs * the depth to which the input list is nested Raises ------ ValueError * If list depths are mismatched - for instance, ``[[a, b], c]`` is illegal, and should be spelt ``[[a, b], [c]]`` * If lists are empty - for instance, ``[[a, b], []]`` See Also -------- concatenate : Join a sequence of arrays together. stack : Stack arrays in sequence along a new dimension. hstack : Stack arrays in sequence horizontally (column wise). vstack : Stack arrays in sequence vertically (row wise). dstack : Stack arrays in sequence depth wise (along third dimension). vsplit : Split array into a list of multiple sub-arrays vertically. Notes ----- When called with only scalars, ``block`` is equivalent to an ndarray call. So ``block([[1, 2], [3, 4]])`` is equivalent to ``array([[1, 2], [3, 4]])``. This function does not enforce that the blocks lie on a fixed grid. ``block([[a, b], [c, d]])`` is not restricted to arrays of the form:: AAAbb AAAbb cccDD But is also allowed to produce, for some ``a, b, c, d``:: AAAbb AAAbb cDDDD Since concatenation happens along the last axis first, `block` is _not_ capable of producing the following directly:: AAAbb cccbb cccDD Matlab's "square bracket stacking", ``[A, B, ...; p, q, ...]``, is equivalent to ``block([[A, B, ...], [p, q, ...]])``. """ # This was copied almost verbatim from numpy.core.shape_base.block # See numpy license at https://github.com/numpy/numpy/blob/master/LICENSE.txt # or NUMPY_LICENSE.txt within this directory def atleast_nd(x, ndim): x = asanyarray(x) diff = max(ndim - x.ndim, 0) return x[(None,) * diff + (Ellipsis,)] def format_index(index): return 'arrays' + ''.join('[{}]'.format(i) for i in index) rec = _Recurser(recurse_if=lambda x: type(x) is list) # ensure that the lists are all matched in depth list_ndim = None any_empty = False for index, value, entering in rec.walk(arrays): if type(value) is tuple: # not strictly necessary, but saves us from: # - more than one way to do things - no point treating tuples like # lists # - horribly confusing behaviour that results when tuples are # treated like ndarray raise TypeError( '{} is a tuple. ' 'Only lists can be used to arrange blocks, and np.block does ' 'not allow implicit conversion from tuple to ndarray.'.format( format_index(index) ) ) if not entering: curr_depth = len(index) elif len(value) == 0: curr_depth = len(index) + 1 any_empty = True else: continue if list_ndim is not None and list_ndim != curr_depth: raise ValueError( "List depths are mismatched. First element was at depth {}, " "but there is an element at depth {} ({})".format( list_ndim, curr_depth, format_index(index) ) ) list_ndim = curr_depth # do this here so we catch depth mismatches first if any_empty: raise ValueError('Lists cannot be empty') # convert all the arrays to ndarrays arrays = rec.map_reduce( arrays, f_map=asanyarray, f_reduce=list ) # determine the maximum dimension of the elements elem_ndim = rec.map_reduce( arrays, f_map=lambda xi: xi.ndim, f_reduce=max ) ndim = max(list_ndim, elem_ndim) # first axis to concatenate along first_axis = ndim - list_ndim # Make all the elements the same dimension arrays = rec.map_reduce( arrays, f_map=lambda xi: atleast_nd(xi, ndim), f_reduce=list ) # concatenate innermost lists on the right, outermost on the left return rec.map_reduce( arrays, f_reduce=lambda xs, axis: concatenate( list(xs), axis=axis, allow_unknown_chunksizes=allow_unknown_chunksizes ), f_kwargs=lambda axis: dict(axis=(axis + 1)), axis=first_axis ) def concatenate(seq, axis=0, allow_unknown_chunksizes=False): """ Concatenate arrays along an existing axis Given a sequence of dask Arrays form a new dask Array by stacking them along an existing dimension (axis=0 by default) Parameters ---------- seq: list of dask.arrays axis: int Dimension along which to align all of the arrays allow_unknown_chunksizes: bool Allow unknown chunksizes, such as come from converting from dask dataframes. Dask.array is unable to verify that chunks line up. If data comes from differently aligned sources then this can cause unexpected results. Examples -------- Create slices >>> import dask.array as da >>> import numpy as np >>> data = [from_array(np.ones((4, 4)), chunks=(2, 2)) ... for i in range(3)] >>> x = da.concatenate(data, axis=0) >>> x.shape (12, 4) >>> da.concatenate(data, axis=1).shape (4, 12) Result is a new dask Array See Also -------- stack """ n = len(seq) ndim = len(seq[0].shape) if axis < 0: axis = ndim + axis if axis >= ndim: msg = ("Axis must be less than than number of dimensions" "\nData has %d dimensions, but got axis=%d") raise ValueError(msg % (ndim, axis)) if n == 1: return seq[0] if (not allow_unknown_chunksizes and not all(i == axis or all(x.shape[i] == seq[0].shape[i] for x in seq) for i in range(ndim))): if any(map(np.isnan, seq[0].shape)): raise ValueError("Tried to concatenate arrays with unknown" " shape %s. To force concatenation pass" " allow_unknown_chunksizes=True." % str(seq[0].shape)) raise ValueError("Shapes do not align: %s", [x.shape for x in seq]) inds = [list(range(ndim)) for i in range(n)] for i, ind in enumerate(inds): ind[axis] = -(i + 1) uc_args = list(concat(zip(seq, inds))) _, seq = unify_chunks(*uc_args, warn=False) bds = [a.chunks for a in seq] chunks = (seq[0].chunks[:axis] + (sum([bd[axis] for bd in bds], ()), ) + seq[0].chunks[axis + 1:]) cum_dims = [0] + list(accumulate(add, [len(a.chunks[axis]) for a in seq])) seq_dtypes = [a.dtype for a in seq] if len(set(seq_dtypes)) > 1: dt = reduce(np.promote_types, seq_dtypes) seq = [x.astype(dt) for x in seq] else: dt = seq_dtypes[0] names = [a.name for a in seq] name = 'concatenate-' + tokenize(names, axis) keys = list(product([name], *[range(len(bd)) for bd in chunks])) values = [(names[bisect(cum_dims, key[axis + 1]) - 1],) + key[1:axis + 1] + (key[axis + 1] - cum_dims[bisect(cum_dims, key[axis + 1]) - 1], ) + key[axis + 2:] for key in keys] dsk = dict(zip(keys, values)) graph = HighLevelGraph.from_collections(name, dsk, dependencies=seq) return Array(graph, name, chunks, dtype=dt) def load_store_chunk(x, out, index, lock, return_stored, load_stored): """ A function inserted in a Dask graph for storing a chunk. Parameters ---------- x: array-like An array (potentially a NumPy one) out: array-like Where to store results too. index: slice-like Where to store result from ``x`` in ``out``. lock: Lock-like or False Lock to use before writing to ``out``. return_stored: bool Whether to return ``out``. load_stored: bool Whether to return the array stored in ``out``. Ignored if ``return_stored`` is not ``True``. Examples -------- >>> a = np.ones((5, 6)) >>> b = np.empty(a.shape) >>> load_store_chunk(a, b, (slice(None), slice(None)), False, False, False) """ result = None if return_stored and not load_stored: result = out if lock: lock.acquire() try: if x is not None: out[index] = np.asanyarray(x) if return_stored and load_stored: result = out[index] finally: if lock: lock.release() return result def store_chunk(x, out, index, lock, return_stored): return load_store_chunk(x, out, index, lock, return_stored, False) def load_chunk(out, index, lock): return load_store_chunk(None, out, index, lock, True, True) def insert_to_ooc(arr, out, lock=True, region=None, return_stored=False, load_stored=False, tok=None): """ Creates a Dask graph for storing chunks from ``arr`` in ``out``. Parameters ---------- arr: da.Array A dask array out: array-like Where to store results too. lock: Lock-like or bool, optional Whether to lock or with what (default is ``True``, which means a ``threading.Lock`` instance). region: slice-like, optional Where in ``out`` to store ``arr``'s results (default is ``None``, meaning all of ``out``). return_stored: bool, optional Whether to return ``out`` (default is ``False``, meaning ``None`` is returned). load_stored: bool, optional Whether to handling loading from ``out`` at the same time. Ignored if ``return_stored`` is not ``True``. (default is ``False``, meaning defer to ``return_stored``). tok: str, optional Token to use when naming keys Examples -------- >>> import dask.array as da >>> d = da.ones((5, 6), chunks=(2, 3)) >>> a = np.empty(d.shape) >>> insert_to_ooc(d, a) # doctest: +SKIP """ if lock is True: lock = Lock() slices = slices_from_chunks(arr.chunks) if region: slices = [fuse_slice(region, slc) for slc in slices] name = 'store-%s' % (tok or str(uuid.uuid1())) func = store_chunk args = () if return_stored and load_stored: name = 'load-%s' % name func = load_store_chunk args = args + (load_stored,) dsk = { (name,) + t[1:]: (func, t, out, slc, lock, return_stored) + args for t, slc in zip(core.flatten(arr.__dask_keys__()), slices) } return dsk def retrieve_from_ooc(keys, dsk_pre, dsk_post=None): """ Creates a Dask graph for loading stored ``keys`` from ``dsk``. Parameters ---------- keys: Sequence A sequence containing Dask graph keys to load dsk_pre: Mapping A Dask graph corresponding to a Dask Array before computation dsk_post: Mapping, optional A Dask graph corresponding to a Dask Array after computation Examples -------- >>> import dask.array as da >>> d = da.ones((5, 6), chunks=(2, 3)) >>> a = np.empty(d.shape) >>> g = insert_to_ooc(d, a) >>> retrieve_from_ooc(g.keys(), g) # doctest: +SKIP """ if not dsk_post: dsk_post = {k: k for k in keys} load_dsk = { ('load-' + k[0],) + k[1:]: (load_chunk, dsk_post[k]) + dsk_pre[k][3:-1] for k in keys } return load_dsk def asarray(a, **kwargs): """Convert the input to a dask array. Parameters ---------- a : array-like Input data, in any form that can be converted to a dask array. Returns ------- out : dask array Dask array interpretation of a. Examples -------- >>> import dask.array as da >>> import numpy as np >>> x = np.arange(3) >>> da.asarray(x) dask.array >>> y = [[1, 2, 3], [4, 5, 6]] >>> da.asarray(y) dask.array """ if isinstance(a, Array): return a elif hasattr(a, 'to_dask_array'): return a.to_dask_array() elif isinstance(a, (list, tuple)) and any(isinstance(i, Array) for i in a): a = stack(a) elif not isinstance(getattr(a, 'shape', None), Iterable): a = np.asarray(a) return from_array(a, chunks=a.shape, getitem=getter_inline, **kwargs) def asanyarray(a): """Convert the input to a dask array. Subclasses of ``np.ndarray`` will be passed through as chunks unchanged. Parameters ---------- a : array-like Input data, in any form that can be converted to a dask array. Returns ------- out : dask array Dask array interpretation of a. Examples -------- >>> import dask.array as da >>> import numpy as np >>> x = np.arange(3) >>> da.asanyarray(x) dask.array >>> y = [[1, 2, 3], [4, 5, 6]] >>> da.asanyarray(y) dask.array """ if isinstance(a, Array): return a elif hasattr(a, 'to_dask_array'): return a.to_dask_array() elif hasattr(a, 'data') and type(a).__module__.startswith('xarray.'): return asanyarray(a.data) elif isinstance(a, (list, tuple)) and any(isinstance(i, Array) for i in a): a = stack(a) elif not isinstance(getattr(a, 'shape', None), Iterable): a = np.asanyarray(a) return from_array(a, chunks=a.shape, getitem=getter_inline, asarray=False) def is_scalar_for_elemwise(arg): """ >>> is_scalar_for_elemwise(42) True >>> is_scalar_for_elemwise('foo') True >>> is_scalar_for_elemwise(True) True >>> is_scalar_for_elemwise(np.array(42)) True >>> is_scalar_for_elemwise([1, 2, 3]) True >>> is_scalar_for_elemwise(np.array([1, 2, 3])) False >>> is_scalar_for_elemwise(from_array(np.array(0), chunks=())) False >>> is_scalar_for_elemwise(np.dtype('i4')) True """ # the second half of shape_condition is essentially just to ensure that # dask series / frame are treated as scalars in elemwise. maybe_shape = getattr(arg, 'shape', None) shape_condition = (not isinstance(maybe_shape, Iterable) or any(is_dask_collection(x) for x in maybe_shape)) return (np.isscalar(arg) or shape_condition or isinstance(arg, np.dtype) or (isinstance(arg, np.ndarray) and arg.ndim == 0)) def broadcast_shapes(*shapes): """ Determines output shape from broadcasting arrays. Parameters ---------- shapes : tuples The shapes of the arguments. Returns ------- output_shape : tuple Raises ------ ValueError If the input shapes cannot be successfully broadcast together. """ if len(shapes) == 1: return shapes[0] out = [] for sizes in zip_longest(*map(reversed, shapes), fillvalue=-1): if np.isnan(sizes).any(): dim = np.nan else: dim = 0 if 0 in sizes else np.max(sizes) if any(i not in [-1, 0, 1, dim] and not np.isnan(i) for i in sizes): raise ValueError("operands could not be broadcast together with " "shapes {0}".format(' '.join(map(str, shapes)))) out.append(dim) return tuple(reversed(out)) def elemwise(op, *args, **kwargs): """ Apply elementwise function across arguments Respects broadcasting rules Examples -------- >>> elemwise(add, x, y) # doctest: +SKIP >>> elemwise(sin, x) # doctest: +SKIP See Also -------- blockwise """ out = kwargs.pop('out', None) if not set(['name', 'dtype']).issuperset(kwargs): msg = "%s does not take the following keyword arguments %s" raise TypeError(msg % (op.__name__, str(sorted(set(kwargs) - set(['name', 'dtype']))))) args = [np.asarray(a) if isinstance(a, (list, tuple)) else a for a in args] shapes = [] for arg in args: shape = getattr(arg, "shape", ()) if any(is_dask_collection(x) for x in shape): # Want to excluded Delayed shapes and dd.Scalar shape = () shapes.append(shape) shapes = [s if isinstance(s, Iterable) else () for s in shapes] out_ndim = len(broadcast_shapes(*shapes)) # Raises ValueError if dimensions mismatch expr_inds = tuple(range(out_ndim))[::-1] need_enforce_dtype = False if 'dtype' in kwargs: dt = kwargs['dtype'] else: # We follow NumPy's rules for dtype promotion, which special cases # scalars and 0d ndarrays (which it considers equivalent) by using # their values to compute the result dtype: # https://github.com/numpy/numpy/issues/6240 # We don't inspect the values of 0d dask arrays, because these could # hold potentially very expensive calculations. Instead, we treat # them just like other arrays, and if necessary cast the result of op # to match. vals = [np.empty((1,) * max(1, a.ndim), dtype=a.dtype) if not is_scalar_for_elemwise(a) else a for a in args] try: dt = apply_infer_dtype(op, vals, {}, 'elemwise', suggest_dtype=False) except Exception: return NotImplemented need_enforce_dtype = any(not is_scalar_for_elemwise(a) and a.ndim == 0 for a in args) name = kwargs.get('name', None) or '%s-%s' % (funcname(op), tokenize(op, dt, *args)) blockwise_kwargs = dict(dtype=dt, name=name, token=funcname(op).strip('_')) if need_enforce_dtype: blockwise_kwargs['enforce_dtype'] = dt blockwise_kwargs['enforce_dtype_function'] = op op = _enforce_dtype result = blockwise(op, expr_inds, *concat((a, tuple(range(a.ndim)[::-1]) if not is_scalar_for_elemwise(a) else None) for a in args), **blockwise_kwargs) return handle_out(out, result) def handle_out(out, result): """ Handle out parameters If out is a dask.array then this overwrites the contents of that array with the result """ if isinstance(out, tuple): if len(out) == 1: out = out[0] elif len(out) > 1: raise NotImplementedError("The out parameter is not fully supported") else: out = None if isinstance(out, Array): if out.shape != result.shape: raise ValueError( "Mismatched shapes between result and out parameter. " "out=%s, result=%s" % (str(out.shape), str(result.shape))) out._chunks = result.chunks out.dask = result.dask out.dtype = result.dtype out.name = result.name elif out is not None: msg = ("The out parameter is not fully supported." " Received type %s, expected Dask Array" % type(out).__name__) raise NotImplementedError(msg) else: return result def _enforce_dtype(*args, **kwargs): """Calls a function and converts its result to the given dtype. The parameters have deliberately been given unwieldy names to avoid clashes with keyword arguments consumed by blockwise A dtype of `object` is treated as a special case and not enforced, because it is used as a dummy value in some places when the result will not be a block in an Array. Parameters ---------- enforce_dtype : dtype Result dtype enforce_dtype_function : callable The wrapped function, which will be passed the remaining arguments """ dtype = kwargs.pop('enforce_dtype') function = kwargs.pop('enforce_dtype_function') result = function(*args, **kwargs) if hasattr(result, 'dtype') and dtype != result.dtype and dtype != object: if not np.can_cast(result, dtype, casting='same_kind'): raise ValueError("Inferred dtype from function %r was %r " "but got %r, which can't be cast using " "casting='same_kind'" % (funcname(function), str(dtype), str(result.dtype))) if np.isscalar(result): # scalar astype method doesn't take the keyword arguments, so # have to convert via 0-dimensional array and back. result = result.astype(dtype) else: try: result = result.astype(dtype, copy=False) except TypeError: # Missing copy kwarg result = result.astype(dtype) return result def broadcast_to(x, shape, chunks=None): """Broadcast an array to a new shape. Parameters ---------- x : array_like The array to broadcast. shape : tuple The shape of the desired array. chunks : tuple, optional If provided, then the result will use these chunks instead of the same chunks as the source array. Setting chunks explicitly as part of broadcast_to is more efficient than rechunking afterwards. Chunks are only allowed to differ from the original shape along dimensions that are new on the result or have size 1 the input array. Returns ------- broadcast : dask array See Also -------- :func:`numpy.broadcast_to` """ x = asarray(x) shape = tuple(shape) if x.shape == shape and (chunks is None or chunks == x.chunks): return x ndim_new = len(shape) - x.ndim if ndim_new < 0 or any(new != old for new, old in zip(shape[ndim_new:], x.shape) if old != 1): raise ValueError('cannot broadcast shape %s to shape %s' % (x.shape, shape)) if chunks is None: chunks = (tuple((s,) for s in shape[:ndim_new]) + tuple(bd if old > 1 else (new,) for bd, old, new in zip(x.chunks, x.shape, shape[ndim_new:]))) else: chunks = normalize_chunks(chunks, shape, dtype=x.dtype, previous_chunks=x.chunks) for old_bd, new_bd in zip(x.chunks, chunks[ndim_new:]): if old_bd != new_bd and old_bd != (1,): raise ValueError('cannot broadcast chunks %s to chunks %s: ' 'new chunks must either be along a new ' 'dimension or a dimension of size 1' % (x.chunks, chunks)) name = 'broadcast_to-' + tokenize(x, shape, chunks) dsk = {} enumerated_chunks = product(*(enumerate(bds) for bds in chunks)) for new_index, chunk_shape in (zip(*ec) for ec in enumerated_chunks): old_index = tuple(0 if bd == (1,) else i for bd, i in zip(x.chunks, new_index[ndim_new:])) old_key = (x.name,) + old_index new_key = (name,) + new_index dsk[new_key] = (np.broadcast_to, old_key, quote(chunk_shape)) graph = HighLevelGraph.from_collections(name, dsk, dependencies=[x]) return Array(graph, name, chunks, dtype=x.dtype) @wraps(np.broadcast_arrays) def broadcast_arrays(*args, **kwargs): subok = bool(kwargs.pop("subok", False)) to_array = asanyarray if subok else asarray args = tuple(to_array(e) for e in args) if kwargs: raise TypeError("unsupported keyword argument(s) provided") shape = broadcast_shapes(*(e.shape for e in args)) chunks = broadcast_chunks(*(e.chunks for e in args)) result = [broadcast_to(e, shape=shape, chunks=chunks) for e in args] return result def offset_func(func, offset, *args): """ Offsets inputs by offset >>> double = lambda x: x * 2 >>> f = offset_func(double, (10,)) >>> f(1) 22 >>> f(300) 620 """ def _offset(*args): args2 = list(map(add, args, offset)) return func(*args2) with ignoring(Exception): _offset.__name__ = 'offset_' + func.__name__ return _offset def chunks_from_arrays(arrays): """ Chunks tuple from nested list of arrays >>> x = np.array([1, 2]) >>> chunks_from_arrays([x, x]) ((2, 2),) >>> x = np.array([[1, 2]]) >>> chunks_from_arrays([[x], [x]]) ((1, 1), (2,)) >>> x = np.array([[1, 2]]) >>> chunks_from_arrays([[x, x]]) ((1,), (2, 2)) >>> chunks_from_arrays([1, 1]) ((1, 1),) """ if not arrays: return () result = [] dim = 0 def shape(x): try: return x.shape except AttributeError: return (1,) while isinstance(arrays, (list, tuple)): result.append(tuple([shape(deepfirst(a))[dim] for a in arrays])) arrays = arrays[0] dim += 1 return tuple(result) def deepfirst(seq): """ First element in a nested list >>> deepfirst([[[1, 2], [3, 4]], [5, 6], [7, 8]]) 1 """ if not isinstance(seq, (list, tuple)): return seq else: return deepfirst(seq[0]) def shapelist(a): """ Get the shape of nested list """ if type(a) is list: return tuple([len(a)] + list(shapelist(a[0]))) else: return () def reshapelist(shape, seq): """ Reshape iterator to nested shape >>> reshapelist((2, 3), range(6)) [[0, 1, 2], [3, 4, 5]] """ if len(shape) == 1: return list(seq) else: n = int(len(seq) / shape[0]) return [reshapelist(shape[1:], part) for part in partition(n, seq)] def transposelist(arrays, axes, extradims=0): """ Permute axes of nested list >>> transposelist([[1,1,1],[1,1,1]], [2,1]) [[[1, 1], [1, 1], [1, 1]]] >>> transposelist([[1,1,1],[1,1,1]], [2,1], extradims=1) [[[[1], [1]], [[1], [1]], [[1], [1]]]] """ if len(axes) != ndimlist(arrays): raise ValueError("Length of axes should equal depth of nested arrays") if extradims < 0: raise ValueError("`newdims` should be positive") if len(axes) > len(set(axes)): raise ValueError("`axes` should be unique") ndim = max(axes) + 1 shape = shapelist(arrays) newshape = [shape[axes.index(i)] if i in axes else 1 for i in range(ndim + extradims)] result = list(core.flatten(arrays)) return reshapelist(newshape, result) def stack(seq, axis=0): """ Stack arrays along a new axis Given a sequence of dask arrays, form a new dask array by stacking them along a new dimension (axis=0 by default) Examples -------- Create slices >>> import dask.array as da >>> import numpy as np >>> data = [from_array(np.ones((4, 4)), chunks=(2, 2)) ... for i in range(3)] >>> x = da.stack(data, axis=0) >>> x.shape (3, 4, 4) >>> da.stack(data, axis=1).shape (4, 3, 4) >>> da.stack(data, axis=-1).shape (4, 4, 3) Result is a new dask Array See Also -------- concatenate """ n = len(seq) ndim = len(seq[0].shape) if axis < 0: axis = ndim + axis + 1 if axis > ndim: raise ValueError("Axis must not be greater than number of dimensions" "\nData has %d dimensions, but got axis=%d" % (ndim, axis)) if not all(x.shape == seq[0].shape for x in seq): idx = np.where(np.asanyarray([x.shape for x in seq]) != seq[0].shape)[0] raise ValueError("Stacked arrays must have the same shape. " "The first {0} had shape {1}, while array " "{2} has shape {3}".format(idx[0], seq[0].shape, idx[0] + 1, seq[idx[0]].shape)) ind = list(range(ndim)) uc_args = list(concat((x, ind) for x in seq)) _, seq = unify_chunks(*uc_args) dt = reduce(np.promote_types, [a.dtype for a in seq]) seq = [x.astype(dt) for x in seq] assert len(set(a.chunks for a in seq)) == 1 # same chunks chunks = (seq[0].chunks[:axis] + ((1,) * n,) + seq[0].chunks[axis:]) names = [a.name for a in seq] name = 'stack-' + tokenize(names, axis) keys = list(product([name], *[range(len(bd)) for bd in chunks])) inputs = [(names[key[axis + 1]], ) + key[1:axis + 1] + key[axis + 2:] for key in keys] values = [(getitem, inp, (slice(None, None, None),) * axis + (None, ) + (slice(None, None, None), ) * (ndim - axis)) for inp in inputs] layer = dict(zip(keys, values)) graph = HighLevelGraph.from_collections(name, layer, dependencies=seq) return Array(graph, name, chunks, dtype=dt) def concatenate3(arrays): """ Recursive np.concatenate Input should be a nested list of numpy arrays arranged in the order they should appear in the array itself. Each array should have the same number of dimensions as the desired output and the nesting of the lists. >>> x = np.array([[1, 2]]) >>> concatenate3([[x, x, x], [x, x, x]]) array([[1, 2, 1, 2, 1, 2], [1, 2, 1, 2, 1, 2]]) >>> concatenate3([[x, x], [x, x], [x, x]]) array([[1, 2, 1, 2], [1, 2, 1, 2], [1, 2, 1, 2]]) """ arrays = concrete(arrays) if not arrays: return np.empty(0) advanced = max(core.flatten(arrays, container=(list, tuple)), key=lambda x: getattr(x, '__array_priority__', 0)) if concatenate_lookup.dispatch(type(advanced)) is not np.concatenate: x = unpack_singleton(arrays) return _concatenate2(arrays, axes=list(range(x.ndim))) ndim = ndimlist(arrays) if not ndim: return arrays chunks = chunks_from_arrays(arrays) shape = tuple(map(sum, chunks)) def dtype(x): try: return x.dtype except AttributeError: return type(x) result = np.empty(shape=shape, dtype=dtype(deepfirst(arrays))) for (idx, arr) in zip(slices_from_chunks(chunks), core.flatten(arrays)): if hasattr(arr, 'ndim'): while arr.ndim < ndim: arr = arr[None, ...] result[idx] = arr return result def concatenate_axes(arrays, axes): """ Recursively call np.concatenate along axes """ if len(axes) != ndimlist(arrays): raise ValueError("Length of axes should equal depth of nested arrays") extradims = max(0, deepfirst(arrays).ndim - (max(axes) + 1)) return concatenate3(transposelist(arrays, axes, extradims=extradims)) def to_hdf5(filename, *args, **kwargs): """ Store arrays in HDF5 file This saves several dask arrays into several datapaths in an HDF5 file. It creates the necessary datasets and handles clean file opening/closing. >>> da.to_hdf5('myfile.hdf5', '/x', x) # doctest: +SKIP or >>> da.to_hdf5('myfile.hdf5', {'/x': x, '/y': y}) # doctest: +SKIP Optionally provide arguments as though to ``h5py.File.create_dataset`` >>> da.to_hdf5('myfile.hdf5', '/x', x, compression='lzf', shuffle=True) # doctest: +SKIP This can also be used as a method on a single Array >>> x.to_hdf5('myfile.hdf5', '/x') # doctest: +SKIP See Also -------- da.store h5py.File.create_dataset """ if len(args) == 1 and isinstance(args[0], dict): data = args[0] elif (len(args) == 2 and isinstance(args[0], str) and isinstance(args[1], Array)): data = {args[0]: args[1]} else: raise ValueError("Please provide {'/data/path': array} dictionary") chunks = kwargs.pop('chunks', True) import h5py with h5py.File(filename) as f: dsets = [f.require_dataset(dp, shape=x.shape, dtype=x.dtype, chunks=tuple([c[0] for c in x.chunks]) if chunks is True else chunks, **kwargs) for dp, x in data.items()] store(list(data.values()), dsets) def interleave_none(a, b): """ >>> interleave_none([0, None, 2, None], [1, 3]) (0, 1, 2, 3) """ result = [] i = j = 0 n = len(a) + len(b) while i + j < n: if a[i] is not None: result.append(a[i]) i += 1 else: result.append(b[j]) i += 1 j += 1 return tuple(result) def keyname(name, i, okey): """ >>> keyname('x', 3, [None, None, 0, 2]) ('x', 3, 0, 2) """ return (name, i) + tuple(k for k in okey if k is not None) def _vindex(x, *indexes): """Point wise indexing with broadcasting. >>> x = np.arange(56).reshape((7, 8)) >>> x array([[ 0, 1, 2, 3, 4, 5, 6, 7], [ 8, 9, 10, 11, 12, 13, 14, 15], [16, 17, 18, 19, 20, 21, 22, 23], [24, 25, 26, 27, 28, 29, 30, 31], [32, 33, 34, 35, 36, 37, 38, 39], [40, 41, 42, 43, 44, 45, 46, 47], [48, 49, 50, 51, 52, 53, 54, 55]]) >>> d = from_array(x, chunks=(3, 4)) >>> result = _vindex(d, [0, 1, 6, 0], [0, 1, 0, 7]) >>> result.compute() array([ 0, 9, 48, 7]) """ indexes = replace_ellipsis(x.ndim, indexes) nonfancy_indexes = [] reduced_indexes = [] for i, ind in enumerate(indexes): if isinstance(ind, Number): nonfancy_indexes.append(ind) elif isinstance(ind, slice): nonfancy_indexes.append(ind) reduced_indexes.append(slice(None)) else: nonfancy_indexes.append(slice(None)) reduced_indexes.append(ind) nonfancy_indexes = tuple(nonfancy_indexes) reduced_indexes = tuple(reduced_indexes) x = x[nonfancy_indexes] array_indexes = {} for i, (ind, size) in enumerate(zip(reduced_indexes, x.shape)): if not isinstance(ind, slice): ind = np.array(ind, copy=True) if ind.dtype.kind == 'b': raise IndexError('vindex does not support indexing with ' 'boolean arrays') if ((ind >= size) | (ind < -size)).any(): raise IndexError('vindex key has entries out of bounds for ' 'indexing along axis %s of size %s: %r' % (i, size, ind)) ind %= size array_indexes[i] = ind if array_indexes: x = _vindex_array(x, array_indexes) return x def _vindex_array(x, dict_indexes): """Point wise indexing with only NumPy Arrays.""" try: broadcast_indexes = np.broadcast_arrays(*dict_indexes.values()) except ValueError: # note: error message exactly matches numpy shapes_str = ' '.join(str(a.shape) for a in dict_indexes.values()) raise IndexError('shape mismatch: indexing arrays could not be ' 'broadcast together with shapes ' + shapes_str) broadcast_shape = broadcast_indexes[0].shape lookup = dict(zip(dict_indexes, broadcast_indexes)) flat_indexes = [lookup[i].ravel().tolist() if i in lookup else None for i in range(x.ndim)] flat_indexes.extend([None] * (x.ndim - len(flat_indexes))) flat_indexes = [ list(index) if index is not None else index for index in flat_indexes ] bounds = [list(accumulate(add, (0,) + c)) for c in x.chunks] bounds2 = [ b for i, b in zip(flat_indexes, bounds) if i is not None ] axis = _get_axis(flat_indexes) token = tokenize(x, flat_indexes) out_name = 'vindex-merge-' + token points = list() for i, idx in enumerate(zip(*[i for i in flat_indexes if i is not None])): block_idx = [np.searchsorted(b, ind, 'right') - 1 for b, ind in zip(bounds2, idx)] inblock_idx = [ind - bounds2[k][j] for k, (ind, j) in enumerate(zip(idx, block_idx))] points.append((i, tuple(block_idx), tuple(inblock_idx))) chunks = [c for i, c in zip(flat_indexes, x.chunks) if i is None] chunks.insert(0, (len(points),) if points else (0,)) chunks = tuple(chunks) if points: per_block = groupby(1, points) per_block = dict((k, v) for k, v in per_block.items() if v) other_blocks = list(product(*[list(range(len(c))) if i is None else [None] for i, c in zip(flat_indexes, x.chunks)])) full_slices = [ slice(None, None) if i is None else None for i in flat_indexes ] name = 'vindex-slice-' + token dsk = dict((keyname(name, i, okey), (_vindex_transpose, (_vindex_slice, (x.name,) + interleave_none(okey, key), interleave_none(full_slices, list(zip(*pluck(2, per_block[key]))))), axis)) for i, key in enumerate(per_block) for okey in other_blocks) dsk.update((keyname('vindex-merge-' + token, 0, okey), (_vindex_merge, [list(pluck(0, per_block[key])) for key in per_block], [keyname(name, i, okey) for i in range(len(per_block))])) for okey in other_blocks) result_1d = Array( HighLevelGraph.from_collections(out_name, dsk, dependencies=[x]), out_name, chunks, x.dtype ) return result_1d.reshape(broadcast_shape + result_1d.shape[1:]) # output has a zero dimension, just create a new zero-shape array with the # same dtype from .wrap import empty result_1d = empty( tuple(map(sum, chunks)), chunks=chunks, dtype=x.dtype, name=out_name ) return result_1d.reshape(broadcast_shape + result_1d.shape[1:]) def _get_axis(indexes): """ Get axis along which point-wise slicing results lie This is mostly a hack because I can't figure out NumPy's rule on this and can't be bothered to go reading. >>> _get_axis([[1, 2], None, [1, 2], None]) 0 >>> _get_axis([None, [1, 2], [1, 2], None]) 1 >>> _get_axis([None, None, [1, 2], [1, 2]]) 2 """ ndim = len(indexes) indexes = [slice(None, None) if i is None else [0] for i in indexes] x = np.empty((2,) * ndim) x2 = x[tuple(indexes)] return x2.shape.index(1) def _vindex_slice(block, points): """ Pull out point-wise slices from block """ points = [p if isinstance(p, slice) else list(p) for p in points] return block[tuple(points)] def _vindex_transpose(block, axis): """ Rotate block so that points are on the first dimension """ axes = [axis] + list(range(axis)) + list(range(axis + 1, block.ndim)) return block.transpose(axes) def _vindex_merge(locations, values): """ >>> locations = [0], [2, 1] >>> values = [np.array([[1, 2, 3]]), ... np.array([[10, 20, 30], [40, 50, 60]])] >>> _vindex_merge(locations, values) array([[ 1, 2, 3], [40, 50, 60], [10, 20, 30]]) """ locations = list(map(list, locations)) values = list(values) n = sum(map(len, locations)) shape = list(values[0].shape) shape[0] = n shape = tuple(shape) dtype = values[0].dtype x = np.empty(shape, dtype=dtype) ind = [slice(None, None) for i in range(x.ndim)] for loc, val in zip(locations, values): ind[0] = loc x[tuple(ind)] = val return x def to_npy_stack(dirname, x, axis=0): """ Write dask array to a stack of .npy files This partitions the dask.array along one axis and stores each block along that axis as a single .npy file in the specified directory Examples -------- >>> x = da.ones((5, 10, 10), chunks=(2, 4, 4)) # doctest: +SKIP >>> da.to_npy_stack('data/', x, axis=0) # doctest: +SKIP $ tree data/ data/ |-- 0.npy |-- 1.npy |-- 2.npy |-- info The ``.npy`` files store numpy arrays for ``x[0:2], x[2:4], and x[4:5]`` respectively, as is specified by the chunk size along the zeroth axis. The info file stores the dtype, chunks, and axis information of the array. You can load these stacks with the ``da.from_npy_stack`` function. >>> y = da.from_npy_stack('data/') # doctest: +SKIP See Also -------- from_npy_stack """ chunks = tuple((c if i == axis else (sum(c),)) for i, c in enumerate(x.chunks)) xx = x.rechunk(chunks) if not os.path.exists(dirname): os.mkdir(dirname) meta = {'chunks': chunks, 'dtype': x.dtype, 'axis': axis} with open(os.path.join(dirname, 'info'), 'wb') as f: pickle.dump(meta, f) name = 'to-npy-stack-' + str(uuid.uuid1()) dsk = {(name, i): (np.save, os.path.join(dirname, '%d.npy' % i), key) for i, key in enumerate(core.flatten(xx.__dask_keys__()))} graph = HighLevelGraph.from_collections(name, dsk, dependencies=[xx]) compute_as_if_collection(Array, graph, list(dsk)) def from_npy_stack(dirname, mmap_mode='r'): """ Load dask array from stack of npy files See ``da.to_npy_stack`` for docstring Parameters ---------- dirname: string Directory of .npy files mmap_mode: (None or 'r') Read data in memory map mode """ with open(os.path.join(dirname, 'info'), 'rb') as f: info = pickle.load(f) dtype = info['dtype'] chunks = info['chunks'] axis = info['axis'] name = 'from-npy-stack-%s' % dirname keys = list(product([name], *[range(len(c)) for c in chunks])) values = [(np.load, os.path.join(dirname, '%d.npy' % i), mmap_mode) for i in range(len(chunks[axis]))] dsk = dict(zip(keys, values)) return Array(dsk, name, chunks, dtype)