from __future__ import annotations
from functools import wraps
import numpy as np
from dask.array import chunk
from dask.array.core import asanyarray, blockwise, elemwise, map_blocks
from dask.array.reductions import reduction
from dask.array.routines import _average
from dask.array.routines import nonzero as _nonzero
from dask.base import normalize_token
from dask.utils import derived_from
@normalize_token.register(np.ma.masked_array)
def normalize_masked_array(x):
data = normalize_token(x.data)
mask = normalize_token(x.mask)
fill_value = normalize_token(x.fill_value)
return (data, mask, fill_value)
[docs]@derived_from(np.ma)
def filled(a, fill_value=None):
a = asanyarray(a)
return a.map_blocks(np.ma.filled, fill_value=fill_value)
def _wrap_masked(f):
@wraps(f)
def _(a, value):
a = asanyarray(a)
value = asanyarray(value)
ainds = tuple(range(a.ndim))[::-1]
vinds = tuple(range(value.ndim))[::-1]
oinds = max(ainds, vinds, key=len)
return blockwise(f, oinds, a, ainds, value, vinds, dtype=a.dtype)
return _
masked_greater = _wrap_masked(np.ma.masked_greater)
masked_greater_equal = _wrap_masked(np.ma.masked_greater_equal)
masked_less = _wrap_masked(np.ma.masked_less)
masked_less_equal = _wrap_masked(np.ma.masked_less_equal)
masked_not_equal = _wrap_masked(np.ma.masked_not_equal)
[docs]@derived_from(np.ma)
def masked_equal(a, value):
a = asanyarray(a)
if getattr(value, "shape", ()):
raise ValueError("da.ma.masked_equal doesn't support array `value`s")
inds = tuple(range(a.ndim))
return blockwise(np.ma.masked_equal, inds, a, inds, value, (), dtype=a.dtype)
[docs]@derived_from(np.ma)
def masked_invalid(a):
return asanyarray(a).map_blocks(np.ma.masked_invalid)
[docs]@derived_from(np.ma)
def masked_inside(x, v1, v2):
x = asanyarray(x)
return x.map_blocks(np.ma.masked_inside, v1, v2)
[docs]@derived_from(np.ma)
def masked_outside(x, v1, v2):
x = asanyarray(x)
return x.map_blocks(np.ma.masked_outside, v1, v2)
[docs]@derived_from(np.ma)
def masked_where(condition, a):
cshape = getattr(condition, "shape", ())
if cshape and cshape != a.shape:
raise IndexError(
"Inconsistent shape between the condition and the "
"input (got %s and %s)" % (cshape, a.shape)
)
condition = asanyarray(condition)
a = asanyarray(a)
ainds = tuple(range(a.ndim))
cinds = tuple(range(condition.ndim))
return blockwise(
np.ma.masked_where, ainds, condition, cinds, a, ainds, dtype=a.dtype
)
[docs]@derived_from(np.ma)
def masked_values(x, value, rtol=1e-05, atol=1e-08, shrink=True):
x = asanyarray(x)
if getattr(value, "shape", ()):
raise ValueError("da.ma.masked_values doesn't support array `value`s")
return map_blocks(
np.ma.masked_values, x, value, rtol=rtol, atol=atol, shrink=shrink
)
[docs]@derived_from(np.ma)
def fix_invalid(a, fill_value=None):
a = asanyarray(a)
return a.map_blocks(np.ma.fix_invalid, fill_value=fill_value)
[docs]@derived_from(np.ma)
def getdata(a):
a = asanyarray(a)
return a.map_blocks(np.ma.getdata)
[docs]@derived_from(np.ma)
def getmaskarray(a):
a = asanyarray(a)
return a.map_blocks(np.ma.getmaskarray)
def _masked_array(data, mask=np.ma.nomask, masked_dtype=None, **kwargs):
if "chunks" in kwargs:
del kwargs["chunks"] # A Dask kwarg, not NumPy.
return np.ma.masked_array(data, mask=mask, dtype=masked_dtype, **kwargs)
[docs]@derived_from(np.ma)
def masked_array(data, mask=np.ma.nomask, fill_value=None, **kwargs):
data = asanyarray(data)
inds = tuple(range(data.ndim))
arginds = [inds, data, inds]
if getattr(fill_value, "shape", ()):
raise ValueError("non-scalar fill_value not supported")
kwargs["fill_value"] = fill_value
if mask is not np.ma.nomask:
mask = asanyarray(mask)
if mask.size == 1:
mask = mask.reshape((1,) * data.ndim)
elif data.shape != mask.shape:
raise np.ma.MaskError(
"Mask and data not compatible: data shape "
"is %s, and mask shape is "
"%s." % (repr(data.shape), repr(mask.shape))
)
arginds.extend([mask, inds])
if "dtype" in kwargs:
kwargs["masked_dtype"] = kwargs["dtype"]
else:
kwargs["dtype"] = data.dtype
return blockwise(_masked_array, *arginds, **kwargs)
def _set_fill_value(x, fill_value):
if isinstance(x, np.ma.masked_array):
x = x.copy()
np.ma.set_fill_value(x, fill_value=fill_value)
return x
[docs]@derived_from(np.ma)
def set_fill_value(a, fill_value):
a = asanyarray(a)
if getattr(fill_value, "shape", ()):
raise ValueError("da.ma.set_fill_value doesn't support array `value`s")
fill_value = np.ma.core._check_fill_value(fill_value, a.dtype)
res = a.map_blocks(_set_fill_value, fill_value)
a.dask = res.dask
a._name = res.name
[docs]@derived_from(np.ma)
def average(a, axis=None, weights=None, returned=False, keepdims=False):
return _average(a, axis, weights, returned, is_masked=True, keepdims=keepdims)
def _chunk_count(x, axis=None, keepdims=None):
return np.ma.count(x, axis=axis, keepdims=keepdims)
@derived_from(np.ma)
def count(a, axis=None, keepdims=False, split_every=None):
return reduction(
a,
_chunk_count,
chunk.sum,
axis=axis,
keepdims=keepdims,
dtype=np.intp,
split_every=split_every,
out=None,
)
[docs]@derived_from(np.ma.core)
def ones_like(a, **kwargs):
a = asanyarray(a)
return a.map_blocks(np.ma.core.ones_like, **kwargs)
[docs]@derived_from(np.ma.core)
def zeros_like(a, **kwargs):
a = asanyarray(a)
return a.map_blocks(np.ma.core.zeros_like, **kwargs)
[docs]@derived_from(np.ma.core)
def empty_like(a, **kwargs):
a = asanyarray(a)
return a.map_blocks(np.ma.core.empty_like, **kwargs)
[docs]@derived_from(np.ma.core)
def nonzero(a):
return _nonzero(getdata(a) * ~getmaskarray(a))
[docs]@derived_from(np.ma.core)
def where(condition, x=None, y=None):
if (x is None) != (y is None):
raise ValueError("either both or neither of x and y should be given")
if (x is None) and (y is None):
return nonzero(condition)
else:
return elemwise(np.ma.where, condition, x, y)