def do_axis_reduce(self, obj, dtype, dim, keepdims):
from pypy.module.micronumpy.interp_numarray import AxisReduce,\
W_NDimArray
if keepdims:
shape = obj.shape[:dim] + [1] + obj.shape[dim + 1:]
else:
shape = obj.shape[:dim] + obj.shape[dim + 1:]
result = W_NDimArray(support.product(shape), shape, dtype)
arr = AxisReduce(self.func, self.name, self.identity, obj.shape, dtype,
result, obj, dim)
loop.compute(arr)
return arr.left
def reduce(self, space, w_obj, multidim, promote_to_largest, dim,
keepdims=False):
from pypy.module.micronumpy.interp_numarray import convert_to_array, \
Scalar, ReduceArray
if self.argcount != 2:
raise OperationError(space.w_ValueError, space.wrap("reduce only "
"supported for binary functions"))
assert isinstance(self, W_Ufunc2)
obj = convert_to_array(space, w_obj)
if dim >= len(obj.shape):
raise OperationError(space.w_ValueError, space.wrap("axis(=%d) out of bounds" % dim))
if isinstance(obj, Scalar):
raise OperationError(space.w_TypeError, space.wrap("cannot reduce "
"on a scalar"))
size = obj.size
if self.comparison_func:
dtype = interp_dtype.get_dtype_cache(space).w_booldtype
else:
dtype = find_unaryop_result_dtype(
space, obj.find_dtype(),
promote_to_float=self.promote_to_float,
promote_to_largest=promote_to_largest,
promote_bools=True
)
shapelen = len(obj.shape)
if self.identity is None and size == 0:
raise operationerrfmt(space.w_ValueError, "zero-size array to "
"%s.reduce without identity", self.name)
if shapelen > 1 and dim >= 0:
return self.do_axis_reduce(obj, dtype, dim, keepdims)
arr = ReduceArray(self.func, self.name, self.identity, obj, dtype)
return loop.compute(arr)
def do_axis_reduce(self, obj, dtype, axis, result):
from pypy.module.micronumpy.interp_numarray import AxisReduce
arr = AxisReduce(self.func, self.name, self.identity, obj.shape, dtype,
result, obj, axis)
loop.compute(arr)
return arr.left
def reduce(self, space, w_obj, multidim, promote_to_largest, axis,
keepdims=False, out=None):
from pypy.module.micronumpy.interp_numarray import convert_to_array, \
Scalar, ReduceArray, W_NDimArray
if self.argcount != 2:
raise OperationError(space.w_ValueError, space.wrap("reduce only "
"supported for binary functions"))
assert isinstance(self, W_Ufunc2)
obj = convert_to_array(space, w_obj)
if axis >= len(obj.shape):
raise OperationError(space.w_ValueError, space.wrap("axis(=%d) out of bounds" % axis))
if isinstance(obj, Scalar):
raise OperationError(space.w_TypeError, space.wrap("cannot reduce "
"on a scalar"))
size = obj.size
if self.comparison_func:
dtype = interp_dtype.get_dtype_cache(space).w_booldtype
else:
dtype = find_unaryop_result_dtype(
space, obj.find_dtype(),
promote_to_float=self.promote_to_float,
promote_to_largest=promote_to_largest,
promote_bools=True
)
shapelen = len(obj.shape)
if self.identity is None and size == 0:
raise operationerrfmt(space.w_ValueError, "zero-size array to "
"%s.reduce without identity", self.name)
if shapelen > 1 and axis >= 0:
if keepdims:
shape = obj.shape[:axis] + [1] + obj.shape[axis + 1:]
else:
shape = obj.shape[:axis] + obj.shape[axis + 1:]
if out:
#Test for shape agreement
if len(out.shape) > len(shape):
raise operationerrfmt(space.w_ValueError,
'output parameter for reduction operation %s' +
' has too many dimensions', self.name)
elif len(out.shape) < len(shape):
raise operationerrfmt(space.w_ValueError,
'output parameter for reduction operation %s' +
' does not have enough dimensions', self.name)
elif out.shape != shape:
raise operationerrfmt(space.w_ValueError,
'output parameter shape mismatch, expecting [%s]' +
' , got [%s]',
",".join([str(x) for x in shape]),
",".join([str(x) for x in out.shape]),
)
#Test for dtype agreement, perhaps create an itermediate
#if out.dtype != dtype:
# raise OperationError(space.w_TypeError, space.wrap(
# "mismatched dtypes"))
return self.do_axis_reduce(obj, out.find_dtype(), axis, out)
else:
result = W_NDimArray(shape, dtype)
return self.do_axis_reduce(obj, dtype, axis, result)
if out:
if len(out.shape)>0:
raise operationerrfmt(space.w_ValueError, "output parameter "
"for reduction operation %s has too many"
" dimensions",self.name)
arr = ReduceArray(self.func, self.name, self.identity, obj,
out.find_dtype())
val = loop.compute(arr)
assert isinstance(out, Scalar)
out.value = val
else:
arr = ReduceArray(self.func, self.name, self.identity, obj, dtype)
val = loop.compute(arr)
return val
def do_axis_reduce(self, obj, dtype, axis, result):
from pypy.module.micronumpy.interp_numarray import AxisReduce
arr = AxisReduce(self.func, self.name, self.identity, obj.shape, dtype,
result, obj, axis)
loop.compute(arr)
return arr.left
def reduce(self, space, w_obj, multidim, promote_to_largest, axis,
keepdims=False, out=None):
from pypy.module.micronumpy.interp_numarray import convert_to_array, \
Scalar, ReduceArray, W_NDimArray
if self.argcount != 2:
raise OperationError(space.w_ValueError, space.wrap("reduce only "
"supported for binary functions"))
assert isinstance(self, W_Ufunc2)
obj = convert_to_array(space, w_obj)
if axis >= len(obj.shape):
raise OperationError(space.w_ValueError, space.wrap("axis(=%d) out of bounds" % axis))
if isinstance(obj, Scalar):
raise OperationError(space.w_TypeError, space.wrap("cannot reduce "
"on a scalar"))
size = obj.size
if self.comparison_func:
dtype = interp_dtype.get_dtype_cache(space).w_booldtype
else:
dtype = find_unaryop_result_dtype(
space, obj.find_dtype(),
promote_to_float=self.promote_to_float,
promote_to_largest=promote_to_largest,
promote_bools=True
)
shapelen = len(obj.shape)
if self.identity is None and size == 0:
raise operationerrfmt(space.w_ValueError, "zero-size array to "
"%s.reduce without identity", self.name)
if shapelen > 1 and axis >= 0:
if keepdims:
shape = obj.shape[:axis] + [1] + obj.shape[axis + 1:]
else:
shape = obj.shape[:axis] + obj.shape[axis + 1:]
if out:
#Test for shape agreement
if len(out.shape) > len(shape):
raise operationerrfmt(space.w_ValueError,
'output parameter for reduction operation %s' +
' has too many dimensions', self.name)
elif len(out.shape) < len(shape):
raise operationerrfmt(space.w_ValueError,
'output parameter for reduction operation %s' +
' does not have enough dimensions', self.name)
elif out.shape != shape:
raise operationerrfmt(space.w_ValueError,
'output parameter shape mismatch, expecting [%s]' +
' , got [%s]',
",".join([str(x) for x in shape]),
",".join([str(x) for x in out.shape]),
)
#Test for dtype agreement, perhaps create an itermediate
#if out.dtype != dtype:
# raise OperationError(space.w_TypeError, space.wrap(
# "mismatched dtypes"))
return self.do_axis_reduce(obj, out.find_dtype(), axis, out)
else:
result = W_NDimArray(shape, dtype)
return self.do_axis_reduce(obj, dtype, axis, result)
if out:
if len(out.shape)>0:
raise operationerrfmt(space.w_ValueError, "output parameter "
"for reduction operation %s has too many"
" dimensions",self.name)
arr = ReduceArray(self.func, self.name, self.identity, obj,
out.find_dtype())
val = loop.compute(arr)
assert isinstance(out, Scalar)
out.value = val
else:
arr = ReduceArray(self.func, self.name, self.identity, obj, dtype)
val = loop.compute(arr)
return val
