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