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numpy.py
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numpy.py
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import math
import numpy as np
from .utils import frame_split_size, merge_frames
from .serialize import dask_serialize, dask_deserialize
from . import pickle
from ..utils import log_errors
def itemsize(dt):
""" Itemsize of dtype
Try to return the itemsize of the base element, return 8 as a fallback
"""
result = dt.base.itemsize
if result > 255:
result = 8
return result
@dask_serialize.register(np.ndarray)
def serialize_numpy_ndarray(x):
if x.dtype.hasobject:
header = {"pickle": True}
frames = [pickle.dumps(x)]
return header, frames
# We cannot blindly pickle the dtype as some may fail pickling,
# so we have a mixture of strategies.
if x.dtype.kind == "V":
# Preserving all the information works best when pickling
try:
# Only use stdlib pickle as cloudpickle is slow when failing
# (microseconds instead of nanoseconds)
dt = (1, pickle.pickle.dumps(x.dtype))
pickle.loads(dt[1]) # does it unpickle fine?
except Exception:
# dtype fails pickling => fall back on the descr if reasonable.
if x.dtype.type is not np.void or x.dtype.alignment != 1:
raise
else:
dt = (0, x.dtype.descr)
else:
dt = (0, x.dtype.str)
# Only serialize non-broadcasted data for arrays with zero strided axes
if 0 in x.strides:
broadcast_to = x.shape
x = x[tuple(slice(None) if s != 0 else slice(1) for s in x.strides)]
else:
broadcast_to = None
if not x.shape:
# 0d array
strides = x.strides
data = x.ravel()
elif x.flags.c_contiguous or x.flags.f_contiguous:
# Avoid a copy and respect order when unserializing
strides = x.strides
data = x.ravel(order="K")
else:
x = np.ascontiguousarray(x)
strides = x.strides
data = x.ravel()
if data.dtype.fields or data.dtype.itemsize > 8:
data = data.view("u%d" % math.gcd(x.dtype.itemsize, 8))
try:
data = data.data
except ValueError:
# "ValueError: cannot include dtype 'M' in a buffer"
data = data.view("u%d" % math.gcd(x.dtype.itemsize, 8)).data
header = {"dtype": dt, "shape": x.shape, "strides": strides}
if broadcast_to is not None:
header["broadcast_to"] = broadcast_to
if x.nbytes > 1e5:
frames = frame_split_size([data])
else:
frames = [data]
header["lengths"] = [x.nbytes]
return header, frames
@dask_deserialize.register(np.ndarray)
def deserialize_numpy_ndarray(header, frames):
with log_errors():
if len(frames) > 1:
frames = merge_frames(header, frames)
if header.get("pickle"):
return pickle.loads(frames[0])
is_custom, dt = header["dtype"]
if is_custom:
dt = pickle.loads(dt)
else:
dt = np.dtype(dt)
if header.get("broadcast_to"):
shape = header["broadcast_to"]
else:
shape = header["shape"]
x = np.ndarray(shape, dtype=dt, buffer=frames[0], strides=header["strides"])
return x
@dask_serialize.register(np.ma.core.MaskedConstant)
def serialize_numpy_ma_masked(x):
return {}, []
@dask_deserialize.register(np.ma.core.MaskedConstant)
def deserialize_numpy_ma_masked(header, frames):
return np.ma.masked
@dask_serialize.register(np.ma.core.MaskedArray)
def serialize_numpy_maskedarray(x):
data_header, frames = serialize_numpy_ndarray(x.data)
header = {"data-header": data_header, "nframes": len(frames)}
# Serialize mask if present
if x.mask is not np.ma.nomask:
mask_header, mask_frames = serialize_numpy_ndarray(x.mask)
header["mask-header"] = mask_header
frames += mask_frames
# Only a few dtypes have python equivalents msgpack can serialize
if isinstance(x.fill_value, (np.integer, np.floating, np.bool_)):
serialized_fill_value = (False, x.fill_value.item())
else:
serialized_fill_value = (True, pickle.dumps(x.fill_value))
header["fill-value"] = serialized_fill_value
return header, frames
@dask_deserialize.register(np.ma.core.MaskedArray)
def deserialize_numpy_maskedarray(header, frames):
data_header = header["data-header"]
data_frames = frames[: header["nframes"]]
data = deserialize_numpy_ndarray(data_header, data_frames)
if "mask-header" in header:
mask_header = header["mask-header"]
mask_frames = frames[header["nframes"] :]
mask = deserialize_numpy_ndarray(mask_header, mask_frames)
else:
mask = np.ma.nomask
pickled_fv, fill_value = header["fill-value"]
if pickled_fv:
fill_value = pickle.loads(fill_value)
return np.ma.masked_array(data, mask=mask, fill_value=fill_value)