from functools import wraps
import numpy as np
from ..base import normalize_token
from .core import (
concatenate_lookup,
tensordot_lookup,
map_blocks,
asanyarray,
blockwise,
)
from .routines import _average
from ..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)
@concatenate_lookup.register(np.ma.masked_array)
def _concatenate(arrays, axis=0):
out = np.ma.concatenate(arrays, axis=axis)
fill_values = [i.fill_value for i in arrays if hasattr(i, "fill_value")]
if any(isinstance(f, np.ndarray) for f in fill_values):
raise ValueError(
"Dask doesn't support masked array's with non-scalar `fill_value`s"
)
if fill_values:
# If all the fill_values are the same copy over the fill value
fill_values = np.unique(fill_values)
if len(fill_values) == 1:
out.fill_value = fill_values[0]
return out
@tensordot_lookup.register(np.ma.masked_array)
def _tensordot(a, b, axes=2):
# Much of this is stolen from numpy/core/numeric.py::tensordot
# Please see license at https://github.com/numpy/numpy/blob/master/LICENSE.txt
try:
iter(axes)
except TypeError:
axes_a = list(range(-axes, 0))
axes_b = list(range(0, axes))
else:
axes_a, axes_b = axes
try:
na = len(axes_a)
axes_a = list(axes_a)
except TypeError:
axes_a = [axes_a]
na = 1
try:
nb = len(axes_b)
axes_b = list(axes_b)
except TypeError:
axes_b = [axes_b]
nb = 1
# a, b = asarray(a), asarray(b) # <--- modified
as_ = a.shape
nda = a.ndim
bs = b.shape
ndb = b.ndim
equal = True
if na != nb:
equal = False
else:
for k in range(na):
if as_[axes_a[k]] != bs[axes_b[k]]:
equal = False
break
if axes_a[k] < 0:
axes_a[k] += nda
if axes_b[k] < 0:
axes_b[k] += ndb
if not equal:
raise ValueError("shape-mismatch for sum")
# Move the axes to sum over to the end of "a"
# and to the front of "b"
notin = [k for k in range(nda) if k not in axes_a]
newaxes_a = notin + axes_a
N2 = 1
for axis in axes_a:
N2 *= as_[axis]
newshape_a = (-1, N2)
olda = [as_[axis] for axis in notin]
notin = [k for k in range(ndb) if k not in axes_b]
newaxes_b = axes_b + notin
N2 = 1
for axis in axes_b:
N2 *= bs[axis]
newshape_b = (N2, -1)
oldb = [bs[axis] for axis in notin]
at = a.transpose(newaxes_a).reshape(newshape_a)
bt = b.transpose(newaxes_b).reshape(newshape_b)
res = np.ma.dot(at, bt)
return res.reshape(olda + oldb)
[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(
"Inconsistant 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, **kwargs):
dtype = kwargs.pop("masked_dtype", None)
return np.ma.masked_array(data, mask=mask, dtype=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):
return _average(a, axis, weights, returned, is_masked=True)