You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
309 lines
9.1 KiB
309 lines
9.1 KiB
"""
|
|
Template for each `dtype` helper function for sparse ops
|
|
|
|
WARNING: DO NOT edit .pxi FILE directly, .pxi is generated from .pxi.in
|
|
"""
|
|
|
|
# ----------------------------------------------------------------------
|
|
# Sparse op
|
|
# ----------------------------------------------------------------------
|
|
|
|
ctypedef fused sparse_t:
|
|
float64_t
|
|
int64_t
|
|
|
|
|
|
cdef inline float64_t __div__(sparse_t a, sparse_t b):
|
|
if b == 0:
|
|
if a > 0:
|
|
return INF
|
|
elif a < 0:
|
|
return -INF
|
|
else:
|
|
return NaN
|
|
else:
|
|
return float(a) / b
|
|
|
|
|
|
cdef inline float64_t __truediv__(sparse_t a, sparse_t b):
|
|
return __div__(a, b)
|
|
|
|
|
|
cdef inline sparse_t __mod__(sparse_t a, sparse_t b):
|
|
if b == 0:
|
|
if sparse_t is float64_t:
|
|
return NaN
|
|
else:
|
|
return 0
|
|
else:
|
|
return a % b
|
|
|
|
|
|
cdef inline sparse_t __floordiv__(sparse_t a, sparse_t b):
|
|
if b == 0:
|
|
if sparse_t is float64_t:
|
|
# Match non-sparse Series behavior implemented in mask_zero_div_zero
|
|
if a > 0:
|
|
return INF
|
|
elif a < 0:
|
|
return -INF
|
|
return NaN
|
|
else:
|
|
return 0
|
|
else:
|
|
return a // b
|
|
|
|
|
|
# ----------------------------------------------------------------------
|
|
# sparse array op
|
|
# ----------------------------------------------------------------------
|
|
|
|
{{py:
|
|
|
|
# dtype, arith_comp_group, logical_group
|
|
dtypes = [('float64', True, False),
|
|
('int64', True, True),
|
|
('uint8', False, True)]
|
|
# do not generate arithmetic / comparison template for uint8,
|
|
# it should be done in fused types
|
|
|
|
def get_op(tup):
|
|
assert isinstance(tup, tuple)
|
|
assert len(tup) == 4
|
|
|
|
opname, lval, rval, dtype = tup
|
|
|
|
ops_dict = {'add': '{0} + {1}',
|
|
'sub': '{0} - {1}',
|
|
'mul': '{0} * {1}',
|
|
'div': '__div__({0}, {1})',
|
|
'mod': '__mod__({0}, {1})',
|
|
'truediv': '__truediv__({0}, {1})',
|
|
'floordiv': '__floordiv__({0}, {1})',
|
|
'pow': '{0} ** {1}',
|
|
'eq': '{0} == {1}',
|
|
'ne': '{0} != {1}',
|
|
'lt': '{0} < {1}',
|
|
'gt': '{0} > {1}',
|
|
'le': '{0} <= {1}',
|
|
'ge': '{0} >= {1}',
|
|
|
|
'and': '{0} & {1}', # logical op
|
|
'or': '{0} | {1}',
|
|
'xor': '{0} ^ {1}'}
|
|
|
|
return ops_dict[opname].format(lval, rval)
|
|
|
|
|
|
def get_dispatch(dtypes):
|
|
|
|
ops_list = ['add', 'sub', 'mul', 'div', 'mod', 'truediv',
|
|
'floordiv', 'pow',
|
|
'eq', 'ne', 'lt', 'gt', 'le', 'ge',
|
|
'and', 'or', 'xor']
|
|
|
|
for opname in ops_list:
|
|
for dtype, arith_comp_group, logical_group in dtypes:
|
|
|
|
if opname in ('div', 'truediv'):
|
|
rdtype = 'float64'
|
|
elif opname in ('eq', 'ne', 'lt', 'gt', 'le', 'ge'):
|
|
# comparison op
|
|
rdtype = 'uint8'
|
|
elif opname in ('and', 'or', 'xor'):
|
|
# logical op
|
|
rdtype = 'uint8'
|
|
else:
|
|
rdtype = dtype
|
|
|
|
if opname in ('and', 'or', 'xor'):
|
|
if logical_group:
|
|
yield opname, dtype, rdtype
|
|
else:
|
|
if arith_comp_group:
|
|
yield opname, dtype, rdtype
|
|
|
|
}}
|
|
|
|
|
|
{{for opname, dtype, rdtype in get_dispatch(dtypes)}}
|
|
|
|
|
|
@cython.wraparound(False)
|
|
@cython.boundscheck(False)
|
|
cdef inline tuple block_op_{{opname}}_{{dtype}}({{dtype}}_t[:] x_,
|
|
BlockIndex xindex,
|
|
{{dtype}}_t xfill,
|
|
{{dtype}}_t[:] y_,
|
|
BlockIndex yindex,
|
|
{{dtype}}_t yfill):
|
|
'''
|
|
Binary operator on BlockIndex objects with fill values
|
|
'''
|
|
|
|
cdef:
|
|
BlockIndex out_index
|
|
Py_ssize_t xi = 0, yi = 0, out_i = 0 # fp buf indices
|
|
int32_t xbp = 0, ybp = 0 # block positions
|
|
int32_t xloc, yloc
|
|
Py_ssize_t xblock = 0, yblock = 0 # block numbers
|
|
|
|
{{dtype}}_t[:] x, y
|
|
ndarray[{{rdtype}}_t, ndim=1] out
|
|
|
|
# to suppress Cython warning
|
|
x = x_
|
|
y = y_
|
|
|
|
out_index = xindex.make_union(yindex)
|
|
out = np.empty(out_index.npoints, dtype=np.{{rdtype}})
|
|
|
|
# Wow, what a hack job. Need to do something about this
|
|
|
|
# walk the two SparseVectors, adding matched locations...
|
|
for out_i in range(out_index.npoints):
|
|
if yblock == yindex.nblocks:
|
|
# use y fill value
|
|
out[out_i] = {{(opname, 'x[xi]', 'yfill', dtype) | get_op}}
|
|
xi += 1
|
|
|
|
# advance x location
|
|
xbp += 1
|
|
if xbp == xindex.lenbuf[xblock]:
|
|
xblock += 1
|
|
xbp = 0
|
|
continue
|
|
|
|
if xblock == xindex.nblocks:
|
|
# use x fill value
|
|
out[out_i] = {{(opname, 'xfill', 'y[yi]', dtype) | get_op}}
|
|
yi += 1
|
|
|
|
# advance y location
|
|
ybp += 1
|
|
if ybp == yindex.lenbuf[yblock]:
|
|
yblock += 1
|
|
ybp = 0
|
|
continue
|
|
|
|
yloc = yindex.locbuf[yblock] + ybp
|
|
xloc = xindex.locbuf[xblock] + xbp
|
|
|
|
# each index in the out_index had to come from either x, y, or both
|
|
if xloc == yloc:
|
|
out[out_i] = {{(opname, 'x[xi]', 'y[yi]', dtype) | get_op}}
|
|
xi += 1
|
|
yi += 1
|
|
|
|
# advance both locations
|
|
xbp += 1
|
|
if xbp == xindex.lenbuf[xblock]:
|
|
xblock += 1
|
|
xbp = 0
|
|
|
|
ybp += 1
|
|
if ybp == yindex.lenbuf[yblock]:
|
|
yblock += 1
|
|
ybp = 0
|
|
|
|
elif xloc < yloc:
|
|
# use y fill value
|
|
out[out_i] = {{(opname, 'x[xi]', 'yfill', dtype) | get_op}}
|
|
xi += 1
|
|
|
|
# advance x location
|
|
xbp += 1
|
|
if xbp == xindex.lenbuf[xblock]:
|
|
xblock += 1
|
|
xbp = 0
|
|
else:
|
|
# use x fill value
|
|
out[out_i] = {{(opname, 'xfill', 'y[yi]', dtype) | get_op}}
|
|
yi += 1
|
|
|
|
# advance y location
|
|
ybp += 1
|
|
if ybp == yindex.lenbuf[yblock]:
|
|
yblock += 1
|
|
ybp = 0
|
|
|
|
return out, out_index, {{(opname, 'xfill', 'yfill', dtype) | get_op}}
|
|
|
|
|
|
@cython.wraparound(False)
|
|
@cython.boundscheck(False)
|
|
cdef inline tuple int_op_{{opname}}_{{dtype}}({{dtype}}_t[:] x_,
|
|
IntIndex xindex,
|
|
{{dtype}}_t xfill,
|
|
{{dtype}}_t[:] y_,
|
|
IntIndex yindex,
|
|
{{dtype}}_t yfill):
|
|
cdef:
|
|
IntIndex out_index
|
|
Py_ssize_t xi = 0, yi = 0, out_i = 0 # fp buf indices
|
|
int32_t xloc, yloc
|
|
int32_t[:] xindices, yindices, out_indices
|
|
{{dtype}}_t[:] x, y
|
|
ndarray[{{rdtype}}_t, ndim=1] out
|
|
|
|
# suppress Cython compiler warnings due to inlining
|
|
x = x_
|
|
y = y_
|
|
|
|
# need to do this first to know size of result array
|
|
out_index = xindex.make_union(yindex)
|
|
out = np.empty(out_index.npoints, dtype=np.{{rdtype}})
|
|
|
|
xindices = xindex.indices
|
|
yindices = yindex.indices
|
|
out_indices = out_index.indices
|
|
|
|
# walk the two SparseVectors, adding matched locations...
|
|
for out_i in range(out_index.npoints):
|
|
if xi == xindex.npoints:
|
|
# use x fill value
|
|
out[out_i] = {{(opname, 'xfill', 'y[yi]', dtype) | get_op}}
|
|
yi += 1
|
|
continue
|
|
|
|
if yi == yindex.npoints:
|
|
# use y fill value
|
|
out[out_i] = {{(opname, 'x[xi]', 'yfill', dtype) | get_op}}
|
|
xi += 1
|
|
continue
|
|
|
|
xloc = xindices[xi]
|
|
yloc = yindices[yi]
|
|
|
|
# each index in the out_index had to come from either x, y, or both
|
|
if xloc == yloc:
|
|
out[out_i] = {{(opname, 'x[xi]', 'y[yi]', dtype) | get_op}}
|
|
xi += 1
|
|
yi += 1
|
|
elif xloc < yloc:
|
|
# use y fill value
|
|
out[out_i] = {{(opname, 'x[xi]', 'yfill', dtype) | get_op}}
|
|
xi += 1
|
|
else:
|
|
# use x fill value
|
|
out[out_i] = {{(opname, 'xfill', 'y[yi]', dtype) | get_op}}
|
|
yi += 1
|
|
|
|
return out, out_index, {{(opname, 'xfill', 'yfill', dtype) | get_op}}
|
|
|
|
|
|
cpdef sparse_{{opname}}_{{dtype}}({{dtype}}_t[:] x,
|
|
SparseIndex xindex, {{dtype}}_t xfill,
|
|
{{dtype}}_t[:] y,
|
|
SparseIndex yindex, {{dtype}}_t yfill):
|
|
|
|
if isinstance(xindex, BlockIndex):
|
|
return block_op_{{opname}}_{{dtype}}(x, xindex.to_block_index(), xfill,
|
|
y, yindex.to_block_index(), yfill)
|
|
elif isinstance(xindex, IntIndex):
|
|
return int_op_{{opname}}_{{dtype}}(x, xindex.to_int_index(), xfill,
|
|
y, yindex.to_int_index(), yfill)
|
|
else:
|
|
raise NotImplementedError
|
|
|
|
{{endfor}}
|
|
|