739 行
21 KiB
Cython
739 行
21 KiB
Cython
cimport cython
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import numpy as np
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cimport numpy as cnp
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from numpy cimport (
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float32_t,
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float64_t,
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int8_t,
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int16_t,
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int32_t,
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int64_t,
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ndarray,
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uint8_t,
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)
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cnp.import_array()
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# -----------------------------------------------------------------------------
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# Preamble stuff
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cdef float64_t NaN = <float64_t>np.NaN
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cdef float64_t INF = <float64_t>np.inf
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# -----------------------------------------------------------------------------
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cdef class SparseIndex:
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"""
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Abstract superclass for sparse index types.
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"""
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def __init__(self):
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raise NotImplementedError
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cdef class IntIndex(SparseIndex):
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"""
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Object for holding exact integer sparse indexing information
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Parameters
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----------
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length : integer
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indices : array-like
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Contains integers corresponding to the indices.
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check_integrity : bool, default=True
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Check integrity of the input.
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"""
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cdef readonly:
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Py_ssize_t length, npoints
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ndarray indices
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def __init__(self, Py_ssize_t length, indices, bint check_integrity=True):
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self.length = length
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self.indices = np.ascontiguousarray(indices, dtype=np.int32)
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self.npoints = len(self.indices)
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if check_integrity:
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self.check_integrity()
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def __reduce__(self):
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args = (self.length, self.indices)
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return IntIndex, args
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def __repr__(self) -> str:
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output = 'IntIndex\n'
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output += f'Indices: {repr(self.indices)}\n'
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return output
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@property
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def nbytes(self) -> int:
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return self.indices.nbytes
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cdef check_integrity(self):
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"""
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Checks the following:
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- Indices are strictly ascending
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- Number of indices is at most self.length
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- Indices are at least 0 and at most the total length less one
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A ValueError is raised if any of these conditions is violated.
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"""
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if self.npoints > self.length:
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raise ValueError(
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f"Too many indices. Expected {self.length} but found {self.npoints}"
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)
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# Indices are vacuously ordered and non-negative
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# if the sequence of indices is empty.
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if self.npoints == 0:
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return
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if self.indices.min() < 0:
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raise ValueError("No index can be less than zero")
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if self.indices.max() >= self.length:
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raise ValueError("All indices must be less than the length")
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monotonic = np.all(self.indices[:-1] < self.indices[1:])
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if not monotonic:
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raise ValueError("Indices must be strictly increasing")
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def equals(self, other: object) -> bool:
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if not isinstance(other, IntIndex):
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return False
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if self is other:
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return True
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same_length = self.length == other.length
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same_indices = np.array_equal(self.indices, other.indices)
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return same_length and same_indices
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@property
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def ngaps(self) -> int:
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return self.length - self.npoints
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cpdef to_int_index(self):
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return self
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def to_block_index(self):
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locs, lens = get_blocks(self.indices)
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return BlockIndex(self.length, locs, lens)
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cpdef IntIndex intersect(self, SparseIndex y_):
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cdef:
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Py_ssize_t out_length, xi, yi = 0, result_indexer = 0
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int32_t xind
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ndarray[int32_t, ndim=1] xindices, yindices, new_indices
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IntIndex y
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# if is one already, returns self
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y = y_.to_int_index()
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if self.length != y.length:
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raise Exception('Indices must reference same underlying length')
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xindices = self.indices
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yindices = y.indices
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new_indices = np.empty(min(
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len(xindices), len(yindices)), dtype=np.int32)
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for xi in range(self.npoints):
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xind = xindices[xi]
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while yi < y.npoints and yindices[yi] < xind:
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yi += 1
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if yi >= y.npoints:
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break
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# TODO: would a two-pass algorithm be faster?
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if yindices[yi] == xind:
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new_indices[result_indexer] = xind
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result_indexer += 1
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new_indices = new_indices[:result_indexer]
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return IntIndex(self.length, new_indices)
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cpdef IntIndex make_union(self, SparseIndex y_):
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cdef:
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ndarray[int32_t, ndim=1] new_indices
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IntIndex y
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# if is one already, returns self
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y = y_.to_int_index()
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if self.length != y.length:
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raise ValueError('Indices must reference same underlying length')
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new_indices = np.union1d(self.indices, y.indices)
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return IntIndex(self.length, new_indices)
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@cython.wraparound(False)
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cpdef int32_t lookup(self, Py_ssize_t index):
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"""
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Return the internal location if value exists on given index.
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Return -1 otherwise.
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"""
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cdef:
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int32_t res
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ndarray[int32_t, ndim=1] inds
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inds = self.indices
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if self.npoints == 0:
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return -1
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elif index < 0 or self.length <= index:
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return -1
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res = inds.searchsorted(index)
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if res == self.npoints:
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return -1
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elif inds[res] == index:
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return res
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else:
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return -1
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@cython.wraparound(False)
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cpdef ndarray[int32_t] lookup_array(self, ndarray[int32_t, ndim=1] indexer):
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"""
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Vectorized lookup, returns ndarray[int32_t]
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"""
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cdef:
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Py_ssize_t n, i, ind_val
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ndarray[int32_t, ndim=1] inds
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ndarray[uint8_t, ndim=1, cast=True] mask
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ndarray[int32_t, ndim=1] masked
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ndarray[int32_t, ndim=1] res
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ndarray[int32_t, ndim=1] results
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n = len(indexer)
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results = np.empty(n, dtype=np.int32)
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results[:] = -1
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if self.npoints == 0:
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return results
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inds = self.indices
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mask = (inds[0] <= indexer) & (indexer <= inds[len(inds) - 1])
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masked = indexer[mask]
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res = inds.searchsorted(masked).astype(np.int32)
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res[inds[res] != masked] = -1
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results[mask] = res
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return results
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cpdef get_blocks(ndarray[int32_t, ndim=1] indices):
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cdef:
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Py_ssize_t init_len, i, npoints, result_indexer = 0
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int32_t block, length = 1, cur, prev
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ndarray[int32_t, ndim=1] locs, lens
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npoints = len(indices)
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# just handle the special empty case separately
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if npoints == 0:
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return np.array([], dtype=np.int32), np.array([], dtype=np.int32)
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# block size can't be longer than npoints
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locs = np.empty(npoints, dtype=np.int32)
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lens = np.empty(npoints, dtype=np.int32)
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# TODO: two-pass algorithm faster?
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prev = block = indices[0]
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for i in range(1, npoints):
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cur = indices[i]
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if cur - prev > 1:
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# new block
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locs[result_indexer] = block
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lens[result_indexer] = length
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block = cur
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length = 1
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result_indexer += 1
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else:
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# same block, increment length
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length += 1
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prev = cur
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locs[result_indexer] = block
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lens[result_indexer] = length
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result_indexer += 1
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locs = locs[:result_indexer]
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lens = lens[:result_indexer]
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return locs, lens
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# -----------------------------------------------------------------------------
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# BlockIndex
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cdef class BlockIndex(SparseIndex):
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"""
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Object for holding block-based sparse indexing information
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Parameters
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----------
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"""
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cdef readonly:
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int32_t nblocks, npoints, length
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ndarray blocs, blengths
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cdef:
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object __weakref__ # need to be picklable
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int32_t *locbuf
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int32_t *lenbuf
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def __init__(self, length, blocs, blengths):
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self.blocs = np.ascontiguousarray(blocs, dtype=np.int32)
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self.blengths = np.ascontiguousarray(blengths, dtype=np.int32)
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# in case we need
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self.locbuf = <int32_t*>self.blocs.data
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self.lenbuf = <int32_t*>self.blengths.data
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self.length = length
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self.nblocks = np.int32(len(self.blocs))
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self.npoints = self.blengths.sum()
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# self.block_start = blocs
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# self.block_end = blocs + blengths
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self.check_integrity()
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def __reduce__(self):
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args = (self.length, self.blocs, self.blengths)
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return BlockIndex, args
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def __repr__(self) -> str:
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output = 'BlockIndex\n'
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output += f'Block locations: {repr(self.blocs)}\n'
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output += f'Block lengths: {repr(self.blengths)}'
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return output
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@property
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def nbytes(self) -> int:
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return self.blocs.nbytes + self.blengths.nbytes
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@property
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def ngaps(self) -> int:
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return self.length - self.npoints
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cdef check_integrity(self):
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"""
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Check:
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- Locations are in ascending order
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- No overlapping blocks
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- Blocks to not start after end of index, nor extend beyond end
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"""
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cdef:
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Py_ssize_t i
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ndarray[int32_t, ndim=1] blocs, blengths
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blocs = self.blocs
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blengths = self.blengths
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if len(blocs) != len(blengths):
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raise ValueError('block bound arrays must be same length')
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for i in range(self.nblocks):
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if i > 0:
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if blocs[i] <= blocs[i - 1]:
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raise ValueError('Locations not in ascending order')
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if i < self.nblocks - 1:
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if blocs[i] + blengths[i] > blocs[i + 1]:
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raise ValueError(f'Block {i} overlaps')
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else:
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if blocs[i] + blengths[i] > self.length:
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raise ValueError(f'Block {i} extends beyond end')
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# no zero-length blocks
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if blengths[i] == 0:
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raise ValueError(f'Zero-length block {i}')
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def equals(self, other: object) -> bool:
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if not isinstance(other, BlockIndex):
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return False
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if self is other:
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return True
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same_length = self.length == other.length
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same_blocks = (np.array_equal(self.blocs, other.blocs) and
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np.array_equal(self.blengths, other.blengths))
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return same_length and same_blocks
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def to_block_index(self):
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return self
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cpdef to_int_index(self):
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cdef:
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int32_t i = 0, j, b
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int32_t offset
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ndarray[int32_t, ndim=1] indices
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indices = np.empty(self.npoints, dtype=np.int32)
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for b in range(self.nblocks):
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offset = self.locbuf[b]
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for j in range(self.lenbuf[b]):
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indices[i] = offset + j
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i += 1
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return IntIndex(self.length, indices)
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@property
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def indices(self):
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return self.to_int_index().indices
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cpdef BlockIndex intersect(self, SparseIndex other):
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"""
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Intersect two BlockIndex objects
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Returns
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-------
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BlockIndex
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"""
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cdef:
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BlockIndex y
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ndarray[int32_t, ndim=1] xloc, xlen, yloc, ylen, out_bloc, out_blen
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Py_ssize_t xi = 0, yi = 0, max_len, result_indexer = 0
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int32_t cur_loc, cur_length, diff
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y = other.to_block_index()
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if self.length != y.length:
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raise Exception('Indices must reference same underlying length')
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xloc = self.blocs
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xlen = self.blengths
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yloc = y.blocs
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ylen = y.blengths
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# block may be split, but can't exceed original len / 2 + 1
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max_len = min(self.length, y.length) // 2 + 1
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out_bloc = np.empty(max_len, dtype=np.int32)
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out_blen = np.empty(max_len, dtype=np.int32)
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while True:
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# we are done (or possibly never began)
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if xi >= self.nblocks or yi >= y.nblocks:
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break
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# completely symmetric...would like to avoid code dup but oh well
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if xloc[xi] >= yloc[yi]:
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cur_loc = xloc[xi]
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diff = xloc[xi] - yloc[yi]
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if ylen[yi] <= diff:
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# have to skip this block
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yi += 1
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continue
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if ylen[yi] - diff < xlen[xi]:
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# take end of y block, move onward
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cur_length = ylen[yi] - diff
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yi += 1
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else:
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# take end of x block
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cur_length = xlen[xi]
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xi += 1
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else: # xloc[xi] < yloc[yi]
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cur_loc = yloc[yi]
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diff = yloc[yi] - xloc[xi]
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if xlen[xi] <= diff:
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# have to skip this block
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xi += 1
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continue
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if xlen[xi] - diff < ylen[yi]:
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# take end of x block, move onward
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cur_length = xlen[xi] - diff
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xi += 1
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else:
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# take end of y block
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cur_length = ylen[yi]
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yi += 1
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out_bloc[result_indexer] = cur_loc
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out_blen[result_indexer] = cur_length
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result_indexer += 1
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out_bloc = out_bloc[:result_indexer]
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out_blen = out_blen[:result_indexer]
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return BlockIndex(self.length, out_bloc, out_blen)
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cpdef BlockIndex make_union(self, SparseIndex y):
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"""
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Combine together two BlockIndex objects, accepting indices if contained
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in one or the other
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Parameters
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----------
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other : SparseIndex
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Notes
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-----
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union is a protected keyword in Cython, hence make_union
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Returns
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-------
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BlockIndex
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"""
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return BlockUnion(self, y.to_block_index()).result
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cpdef Py_ssize_t lookup(self, Py_ssize_t index):
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"""
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Return the internal location if value exists on given index.
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Return -1 otherwise.
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"""
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cdef:
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Py_ssize_t i, cum_len
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ndarray[int32_t, ndim=1] locs, lens
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locs = self.blocs
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lens = self.blengths
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if self.nblocks == 0:
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return -1
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elif index < locs[0]:
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return -1
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cum_len = 0
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for i in range(self.nblocks):
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if index >= locs[i] and index < locs[i] + lens[i]:
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return cum_len + index - locs[i]
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cum_len += lens[i]
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return -1
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@cython.wraparound(False)
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cpdef ndarray[int32_t] lookup_array(self, ndarray[int32_t, ndim=1] indexer):
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"""
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Vectorized lookup, returns ndarray[int32_t]
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"""
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cdef:
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Py_ssize_t n, i, j, ind_val
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ndarray[int32_t, ndim=1] locs, lens
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ndarray[int32_t, ndim=1] results
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locs = self.blocs
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lens = self.blengths
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n = len(indexer)
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results = np.empty(n, dtype=np.int32)
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results[:] = -1
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if self.npoints == 0:
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return results
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for i in range(n):
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ind_val = indexer[i]
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if not (ind_val < 0 or self.length <= ind_val):
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cum_len = 0
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for j in range(self.nblocks):
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if ind_val >= locs[j] and ind_val < locs[j] + lens[j]:
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results[i] = cum_len + ind_val - locs[j]
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cum_len += lens[j]
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return results
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@cython.internal
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cdef class BlockMerge:
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"""
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Object-oriented approach makes sharing state between recursive functions a
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lot easier and reduces code duplication
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"""
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cdef:
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BlockIndex x, y, result
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ndarray xstart, xlen, xend, ystart, ylen, yend
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int32_t xi, yi # block indices
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def __init__(self, BlockIndex x, BlockIndex y):
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self.x = x
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self.y = y
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if x.length != y.length:
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raise Exception('Indices must reference same underlying length')
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|
self.xstart = self.x.blocs
|
|
self.ystart = self.y.blocs
|
|
|
|
self.xend = self.x.blocs + self.x.blengths
|
|
self.yend = self.y.blocs + self.y.blengths
|
|
|
|
# self.xlen = self.x.blengths
|
|
# self.ylen = self.y.blengths
|
|
|
|
self.xi = 0
|
|
self.yi = 0
|
|
|
|
self.result = self._make_merged_blocks()
|
|
|
|
cdef _make_merged_blocks(self):
|
|
raise NotImplementedError
|
|
|
|
cdef _set_current_indices(self, int32_t xi, int32_t yi, bint mode):
|
|
if mode == 0:
|
|
self.xi = xi
|
|
self.yi = yi
|
|
else:
|
|
self.xi = yi
|
|
self.yi = xi
|
|
|
|
|
|
@cython.internal
|
|
cdef class BlockUnion(BlockMerge):
|
|
"""
|
|
Object-oriented approach makes sharing state between recursive functions a
|
|
lot easier and reduces code duplication
|
|
"""
|
|
|
|
cdef _make_merged_blocks(self):
|
|
cdef:
|
|
ndarray[int32_t, ndim=1] xstart, xend, ystart
|
|
ndarray[int32_t, ndim=1] yend, out_bloc, out_blen
|
|
int32_t nstart, nend, diff
|
|
Py_ssize_t max_len, result_indexer = 0
|
|
|
|
xstart = self.xstart
|
|
xend = self.xend
|
|
ystart = self.ystart
|
|
yend = self.yend
|
|
|
|
max_len = min(self.x.length, self.y.length) // 2 + 1
|
|
out_bloc = np.empty(max_len, dtype=np.int32)
|
|
out_blen = np.empty(max_len, dtype=np.int32)
|
|
|
|
while True:
|
|
# we are done (or possibly never began)
|
|
if self.xi >= self.x.nblocks and self.yi >= self.y.nblocks:
|
|
break
|
|
elif self.yi >= self.y.nblocks:
|
|
# through with y, just pass through x blocks
|
|
nstart = xstart[self.xi]
|
|
nend = xend[self.xi]
|
|
self.xi += 1
|
|
elif self.xi >= self.x.nblocks:
|
|
# through with x, just pass through y blocks
|
|
nstart = ystart[self.yi]
|
|
nend = yend[self.yi]
|
|
self.yi += 1
|
|
else:
|
|
# find end of new block
|
|
if xstart[self.xi] < ystart[self.yi]:
|
|
nstart = xstart[self.xi]
|
|
nend = self._find_next_block_end(0)
|
|
else:
|
|
nstart = ystart[self.yi]
|
|
nend = self._find_next_block_end(1)
|
|
|
|
out_bloc[result_indexer] = nstart
|
|
out_blen[result_indexer] = nend - nstart
|
|
result_indexer += 1
|
|
|
|
out_bloc = out_bloc[:result_indexer]
|
|
out_blen = out_blen[:result_indexer]
|
|
|
|
return BlockIndex(self.x.length, out_bloc, out_blen)
|
|
|
|
cdef int32_t _find_next_block_end(self, bint mode) except -1:
|
|
"""
|
|
Wow, this got complicated in a hurry
|
|
|
|
mode 0: block started in index x
|
|
mode 1: block started in index y
|
|
"""
|
|
cdef:
|
|
ndarray[int32_t, ndim=1] xstart, xend, ystart, yend
|
|
int32_t xi, yi, xnblocks, ynblocks, nend
|
|
|
|
if mode != 0 and mode != 1:
|
|
raise Exception('Mode must be 0 or 1')
|
|
|
|
# so symmetric code will work
|
|
if mode == 0:
|
|
xstart = self.xstart
|
|
xend = self.xend
|
|
xi = self.xi
|
|
|
|
ystart = self.ystart
|
|
yend = self.yend
|
|
yi = self.yi
|
|
ynblocks = self.y.nblocks
|
|
else:
|
|
xstart = self.ystart
|
|
xend = self.yend
|
|
xi = self.yi
|
|
|
|
ystart = self.xstart
|
|
yend = self.xend
|
|
yi = self.xi
|
|
ynblocks = self.x.nblocks
|
|
|
|
nend = xend[xi]
|
|
|
|
# done with y?
|
|
if yi == ynblocks:
|
|
self._set_current_indices(xi + 1, yi, mode)
|
|
return nend
|
|
elif nend < ystart[yi]:
|
|
# block ends before y block
|
|
self._set_current_indices(xi + 1, yi, mode)
|
|
return nend
|
|
else:
|
|
while yi < ynblocks and nend > yend[yi]:
|
|
yi += 1
|
|
|
|
self._set_current_indices(xi + 1, yi, mode)
|
|
|
|
if yi == ynblocks:
|
|
return nend
|
|
|
|
if nend < ystart[yi]:
|
|
# we're done, return the block end
|
|
return nend
|
|
else:
|
|
# merge blocks, continue searching
|
|
# this also catches the case where blocks
|
|
return self._find_next_block_end(1 - mode)
|
|
|
|
|
|
# -----------------------------------------------------------------------------
|
|
# Sparse arithmetic
|
|
|
|
include "sparse_op_helper.pxi"
|
|
|
|
|
|
# -----------------------------------------------------------------------------
|
|
# SparseArray mask create operations
|
|
|
|
def make_mask_object_ndarray(ndarray[object, ndim=1] arr, object fill_value):
|
|
cdef:
|
|
object value
|
|
Py_ssize_t i
|
|
Py_ssize_t new_length = len(arr)
|
|
ndarray[int8_t, ndim=1] mask
|
|
|
|
mask = np.ones(new_length, dtype=np.int8)
|
|
|
|
for i in range(new_length):
|
|
value = arr[i]
|
|
if value == fill_value and type(value) == type(fill_value):
|
|
mask[i] = 0
|
|
|
|
return mask.view(dtype=bool)
|