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1006 lines
31 KiB
1006 lines
31 KiB
import cython
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from cython import Py_ssize_t
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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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int64_t,
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intp_t,
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ndarray,
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uint64_t,
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)
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cnp.import_array()
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from pandas._libs.algos import groupsort_indexer
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from pandas._libs.dtypes cimport (
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numeric_object_t,
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numeric_t,
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)
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@cython.wraparound(False)
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@cython.boundscheck(False)
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def inner_join(const intp_t[:] left, const intp_t[:] right,
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Py_ssize_t max_groups):
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cdef:
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Py_ssize_t i, j, k, count = 0
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intp_t[::1] left_sorter, right_sorter
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intp_t[::1] left_count, right_count
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intp_t[::1] left_indexer, right_indexer
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intp_t lc, rc
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Py_ssize_t left_pos = 0, right_pos = 0, position = 0
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Py_ssize_t offset
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left_sorter, left_count = groupsort_indexer(left, max_groups)
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right_sorter, right_count = groupsort_indexer(right, max_groups)
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with nogil:
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# First pass, determine size of result set, do not use the NA group
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for i in range(1, max_groups + 1):
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lc = left_count[i]
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rc = right_count[i]
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if rc > 0 and lc > 0:
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count += lc * rc
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left_indexer = np.empty(count, dtype=np.intp)
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right_indexer = np.empty(count, dtype=np.intp)
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with nogil:
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# exclude the NA group
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left_pos = left_count[0]
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right_pos = right_count[0]
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for i in range(1, max_groups + 1):
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lc = left_count[i]
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rc = right_count[i]
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if rc > 0 and lc > 0:
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for j in range(lc):
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offset = position + j * rc
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for k in range(rc):
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left_indexer[offset + k] = left_pos + j
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right_indexer[offset + k] = right_pos + k
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position += lc * rc
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left_pos += lc
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right_pos += rc
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# Will overwrite left/right indexer with the result
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_get_result_indexer(left_sorter, left_indexer)
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_get_result_indexer(right_sorter, right_indexer)
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return np.asarray(left_indexer), np.asarray(right_indexer)
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@cython.wraparound(False)
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@cython.boundscheck(False)
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def left_outer_join(const intp_t[:] left, const intp_t[:] right,
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Py_ssize_t max_groups, bint sort=True):
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cdef:
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Py_ssize_t i, j, k, count = 0
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ndarray[intp_t] rev
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intp_t[::1] left_count, right_count
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intp_t[::1] left_sorter, right_sorter
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intp_t[::1] left_indexer, right_indexer
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intp_t lc, rc
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Py_ssize_t left_pos = 0, right_pos = 0, position = 0
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Py_ssize_t offset
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left_sorter, left_count = groupsort_indexer(left, max_groups)
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right_sorter, right_count = groupsort_indexer(right, max_groups)
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with nogil:
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# First pass, determine size of result set, do not use the NA group
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for i in range(1, max_groups + 1):
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if right_count[i] > 0:
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count += left_count[i] * right_count[i]
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else:
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count += left_count[i]
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left_indexer = np.empty(count, dtype=np.intp)
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right_indexer = np.empty(count, dtype=np.intp)
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with nogil:
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# exclude the NA group
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left_pos = left_count[0]
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right_pos = right_count[0]
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for i in range(1, max_groups + 1):
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lc = left_count[i]
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rc = right_count[i]
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if rc == 0:
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for j in range(lc):
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left_indexer[position + j] = left_pos + j
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right_indexer[position + j] = -1
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position += lc
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else:
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for j in range(lc):
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offset = position + j * rc
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for k in range(rc):
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left_indexer[offset + k] = left_pos + j
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right_indexer[offset + k] = right_pos + k
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position += lc * rc
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left_pos += lc
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right_pos += rc
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# Will overwrite left/right indexer with the result
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_get_result_indexer(left_sorter, left_indexer)
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_get_result_indexer(right_sorter, right_indexer)
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if not sort: # if not asked to sort, revert to original order
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if len(left) == len(left_indexer):
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# no multiple matches for any row on the left
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# this is a short-cut to avoid groupsort_indexer
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# otherwise, the `else` path also works in this case
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rev = np.empty(len(left), dtype=np.intp)
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rev.put(np.asarray(left_sorter), np.arange(len(left)))
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else:
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rev, _ = groupsort_indexer(left_indexer, len(left))
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return np.asarray(left_indexer).take(rev), np.asarray(right_indexer).take(rev)
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else:
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return np.asarray(left_indexer), np.asarray(right_indexer)
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@cython.wraparound(False)
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@cython.boundscheck(False)
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def full_outer_join(const intp_t[:] left, const intp_t[:] right,
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Py_ssize_t max_groups):
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cdef:
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Py_ssize_t i, j, k, count = 0
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intp_t[::1] left_sorter, right_sorter
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intp_t[::1] left_count, right_count
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intp_t[::1] left_indexer, right_indexer
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intp_t lc, rc
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intp_t left_pos = 0, right_pos = 0
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Py_ssize_t offset, position = 0
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left_sorter, left_count = groupsort_indexer(left, max_groups)
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right_sorter, right_count = groupsort_indexer(right, max_groups)
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with nogil:
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# First pass, determine size of result set, do not use the NA group
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for i in range(1, max_groups + 1):
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lc = left_count[i]
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rc = right_count[i]
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if rc > 0 and lc > 0:
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count += lc * rc
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else:
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count += lc + rc
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left_indexer = np.empty(count, dtype=np.intp)
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right_indexer = np.empty(count, dtype=np.intp)
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with nogil:
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# exclude the NA group
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left_pos = left_count[0]
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right_pos = right_count[0]
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for i in range(1, max_groups + 1):
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lc = left_count[i]
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rc = right_count[i]
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if rc == 0:
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for j in range(lc):
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left_indexer[position + j] = left_pos + j
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right_indexer[position + j] = -1
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position += lc
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elif lc == 0:
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for j in range(rc):
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left_indexer[position + j] = -1
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right_indexer[position + j] = right_pos + j
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position += rc
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else:
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for j in range(lc):
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offset = position + j * rc
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for k in range(rc):
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left_indexer[offset + k] = left_pos + j
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right_indexer[offset + k] = right_pos + k
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position += lc * rc
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left_pos += lc
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right_pos += rc
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# Will overwrite left/right indexer with the result
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_get_result_indexer(left_sorter, left_indexer)
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_get_result_indexer(right_sorter, right_indexer)
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return np.asarray(left_indexer), np.asarray(right_indexer)
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@cython.wraparound(False)
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@cython.boundscheck(False)
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cdef void _get_result_indexer(intp_t[::1] sorter, intp_t[::1] indexer) nogil:
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"""NOTE: overwrites indexer with the result to avoid allocating another array"""
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cdef:
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Py_ssize_t i, n, idx
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if len(sorter) > 0:
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# cython-only equivalent to
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# `res = algos.take_nd(sorter, indexer, fill_value=-1)`
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n = indexer.shape[0]
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for i in range(n):
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idx = indexer[i]
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if idx == -1:
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indexer[i] = -1
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else:
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indexer[i] = sorter[idx]
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else:
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# length-0 case
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indexer[:] = -1
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def ffill_indexer(const intp_t[:] indexer) -> np.ndarray:
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cdef:
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Py_ssize_t i, n = len(indexer)
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ndarray[intp_t] result
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intp_t val, last_obs
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result = np.empty(n, dtype=np.intp)
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last_obs = -1
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for i in range(n):
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val = indexer[i]
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if val == -1:
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result[i] = last_obs
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else:
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result[i] = val
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last_obs = val
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return result
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# ----------------------------------------------------------------------
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# left_join_indexer, inner_join_indexer, outer_join_indexer
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# ----------------------------------------------------------------------
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# Joins on ordered, unique indices
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# right might contain non-unique values
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@cython.wraparound(False)
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@cython.boundscheck(False)
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def left_join_indexer_unique(
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ndarray[numeric_object_t] left,
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ndarray[numeric_object_t] right
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):
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"""
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Both left and right are strictly monotonic increasing.
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"""
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cdef:
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Py_ssize_t i, j, nleft, nright
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ndarray[intp_t] indexer
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numeric_object_t lval, rval
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i = 0
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j = 0
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nleft = len(left)
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nright = len(right)
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indexer = np.empty(nleft, dtype=np.intp)
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while True:
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if i == nleft:
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break
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if j == nright:
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indexer[i] = -1
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i += 1
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continue
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rval = right[j]
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while i < nleft - 1 and left[i] == rval:
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indexer[i] = j
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i += 1
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if left[i] == right[j]:
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indexer[i] = j
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i += 1
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while i < nleft - 1 and left[i] == rval:
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indexer[i] = j
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i += 1
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j += 1
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elif left[i] > rval:
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indexer[i] = -1
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j += 1
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else:
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indexer[i] = -1
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i += 1
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return indexer
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@cython.wraparound(False)
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@cython.boundscheck(False)
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def left_join_indexer(ndarray[numeric_object_t] left, ndarray[numeric_object_t] right):
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"""
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Two-pass algorithm for monotonic indexes. Handles many-to-one merges.
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Both left and right are monotonic increasing, but at least one of them
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is non-unique (if both were unique we'd use left_join_indexer_unique).
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"""
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cdef:
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Py_ssize_t i, j, k, nright, nleft, count
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numeric_object_t lval, rval
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ndarray[intp_t] lindexer, rindexer
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ndarray[numeric_object_t] result
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nleft = len(left)
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nright = len(right)
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# First pass is to find the size 'count' of our output indexers.
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i = 0
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j = 0
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count = 0
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if nleft > 0:
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while i < nleft:
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if j == nright:
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count += nleft - i
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break
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lval = left[i]
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rval = right[j]
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if lval == rval:
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# This block is identical across
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# left_join_indexer, inner_join_indexer, outer_join_indexer
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count += 1
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if i < nleft - 1:
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if j < nright - 1 and right[j + 1] == rval:
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j += 1
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else:
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i += 1
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if left[i] != rval:
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j += 1
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elif j < nright - 1:
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j += 1
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if lval != right[j]:
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i += 1
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else:
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# end of the road
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break
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elif lval < rval:
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count += 1
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i += 1
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else:
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j += 1
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# do it again now that result size is known
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lindexer = np.empty(count, dtype=np.intp)
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rindexer = np.empty(count, dtype=np.intp)
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result = np.empty(count, dtype=left.dtype)
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i = 0
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j = 0
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count = 0
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if nleft > 0:
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while i < nleft:
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if j == nright:
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while i < nleft:
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lindexer[count] = i
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rindexer[count] = -1
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result[count] = left[i]
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i += 1
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count += 1
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break
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lval = left[i]
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rval = right[j]
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if lval == rval:
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lindexer[count] = i
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rindexer[count] = j
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result[count] = lval
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count += 1
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if i < nleft - 1:
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if j < nright - 1 and right[j + 1] == rval:
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j += 1
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else:
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i += 1
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if left[i] != rval:
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j += 1
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elif j < nright - 1:
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j += 1
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if lval != right[j]:
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i += 1
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else:
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# end of the road
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break
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elif lval < rval:
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# i.e. lval not in right; we keep for left_join_indexer
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lindexer[count] = i
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rindexer[count] = -1
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result[count] = lval
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count += 1
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i += 1
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else:
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# i.e. rval not in left; we discard for left_join_indexer
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j += 1
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return result, lindexer, rindexer
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@cython.wraparound(False)
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@cython.boundscheck(False)
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def inner_join_indexer(ndarray[numeric_object_t] left, ndarray[numeric_object_t] right):
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"""
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Two-pass algorithm for monotonic indexes. Handles many-to-one merges.
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Both left and right are monotonic increasing but not necessarily unique.
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"""
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cdef:
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Py_ssize_t i, j, k, nright, nleft, count
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numeric_object_t lval, rval
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ndarray[intp_t] lindexer, rindexer
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ndarray[numeric_object_t] result
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nleft = len(left)
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nright = len(right)
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# First pass is to find the size 'count' of our output indexers.
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i = 0
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j = 0
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count = 0
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if nleft > 0 and nright > 0:
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while True:
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if i == nleft:
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break
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if j == nright:
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break
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lval = left[i]
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rval = right[j]
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if lval == rval:
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count += 1
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if i < nleft - 1:
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if j < nright - 1 and right[j + 1] == rval:
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j += 1
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else:
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i += 1
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if left[i] != rval:
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j += 1
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elif j < nright - 1:
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j += 1
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if lval != right[j]:
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i += 1
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else:
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# end of the road
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break
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elif lval < rval:
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# i.e. lval not in right; we discard for inner_indexer
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i += 1
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else:
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# i.e. rval not in left; we discard for inner_indexer
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j += 1
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# do it again now that result size is known
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lindexer = np.empty(count, dtype=np.intp)
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rindexer = np.empty(count, dtype=np.intp)
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result = np.empty(count, dtype=left.dtype)
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i = 0
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j = 0
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count = 0
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if nleft > 0 and nright > 0:
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while True:
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if i == nleft:
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break
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if j == nright:
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break
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lval = left[i]
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rval = right[j]
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if lval == rval:
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lindexer[count] = i
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rindexer[count] = j
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result[count] = lval
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count += 1
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if i < nleft - 1:
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if j < nright - 1 and right[j + 1] == rval:
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j += 1
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else:
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i += 1
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if left[i] != rval:
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j += 1
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elif j < nright - 1:
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j += 1
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if lval != right[j]:
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i += 1
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else:
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# end of the road
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break
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elif lval < rval:
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# i.e. lval not in right; we discard for inner_indexer
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i += 1
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else:
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# i.e. rval not in left; we discard for inner_indexer
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j += 1
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return result, lindexer, rindexer
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@cython.wraparound(False)
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@cython.boundscheck(False)
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def outer_join_indexer(ndarray[numeric_object_t] left, ndarray[numeric_object_t] right):
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"""
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Both left and right are monotonic increasing but not necessarily unique.
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"""
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cdef:
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Py_ssize_t i, j, nright, nleft, count
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numeric_object_t lval, rval
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ndarray[intp_t] lindexer, rindexer
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ndarray[numeric_object_t] result
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|
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nleft = len(left)
|
|
nright = len(right)
|
|
|
|
# First pass is to find the size 'count' of our output indexers.
|
|
# count will be length of left plus the number of elements of right not in
|
|
# left (counting duplicates)
|
|
i = 0
|
|
j = 0
|
|
count = 0
|
|
if nleft == 0:
|
|
count = nright
|
|
elif nright == 0:
|
|
count = nleft
|
|
else:
|
|
while True:
|
|
if i == nleft:
|
|
count += nright - j
|
|
break
|
|
if j == nright:
|
|
count += nleft - i
|
|
break
|
|
|
|
lval = left[i]
|
|
rval = right[j]
|
|
if lval == rval:
|
|
count += 1
|
|
if i < nleft - 1:
|
|
if j < nright - 1 and right[j + 1] == rval:
|
|
j += 1
|
|
else:
|
|
i += 1
|
|
if left[i] != rval:
|
|
j += 1
|
|
elif j < nright - 1:
|
|
j += 1
|
|
if lval != right[j]:
|
|
i += 1
|
|
else:
|
|
# end of the road
|
|
break
|
|
elif lval < rval:
|
|
count += 1
|
|
i += 1
|
|
else:
|
|
count += 1
|
|
j += 1
|
|
|
|
lindexer = np.empty(count, dtype=np.intp)
|
|
rindexer = np.empty(count, dtype=np.intp)
|
|
result = np.empty(count, dtype=left.dtype)
|
|
|
|
# do it again, but populate the indexers / result
|
|
|
|
i = 0
|
|
j = 0
|
|
count = 0
|
|
if nleft == 0:
|
|
for j in range(nright):
|
|
lindexer[j] = -1
|
|
rindexer[j] = j
|
|
result[j] = right[j]
|
|
elif nright == 0:
|
|
for i in range(nleft):
|
|
lindexer[i] = i
|
|
rindexer[i] = -1
|
|
result[i] = left[i]
|
|
else:
|
|
while True:
|
|
if i == nleft:
|
|
while j < nright:
|
|
lindexer[count] = -1
|
|
rindexer[count] = j
|
|
result[count] = right[j]
|
|
count += 1
|
|
j += 1
|
|
break
|
|
if j == nright:
|
|
while i < nleft:
|
|
lindexer[count] = i
|
|
rindexer[count] = -1
|
|
result[count] = left[i]
|
|
count += 1
|
|
i += 1
|
|
break
|
|
|
|
lval = left[i]
|
|
rval = right[j]
|
|
|
|
if lval == rval:
|
|
lindexer[count] = i
|
|
rindexer[count] = j
|
|
result[count] = lval
|
|
count += 1
|
|
if i < nleft - 1:
|
|
if j < nright - 1 and right[j + 1] == rval:
|
|
j += 1
|
|
else:
|
|
i += 1
|
|
if left[i] != rval:
|
|
j += 1
|
|
elif j < nright - 1:
|
|
j += 1
|
|
if lval != right[j]:
|
|
i += 1
|
|
else:
|
|
# end of the road
|
|
break
|
|
elif lval < rval:
|
|
# i.e. lval not in right; we keep for outer_join_indexer
|
|
lindexer[count] = i
|
|
rindexer[count] = -1
|
|
result[count] = lval
|
|
count += 1
|
|
i += 1
|
|
else:
|
|
# i.e. rval not in left; we keep for outer_join_indexer
|
|
lindexer[count] = -1
|
|
rindexer[count] = j
|
|
result[count] = rval
|
|
count += 1
|
|
j += 1
|
|
|
|
return result, lindexer, rindexer
|
|
|
|
|
|
# ----------------------------------------------------------------------
|
|
# asof_join_by
|
|
# ----------------------------------------------------------------------
|
|
|
|
from pandas._libs.hashtable cimport (
|
|
HashTable,
|
|
Int64HashTable,
|
|
PyObjectHashTable,
|
|
UInt64HashTable,
|
|
)
|
|
|
|
ctypedef fused by_t:
|
|
object
|
|
int64_t
|
|
uint64_t
|
|
|
|
|
|
def asof_join_backward_on_X_by_Y(numeric_t[:] left_values,
|
|
numeric_t[:] right_values,
|
|
by_t[:] left_by_values,
|
|
by_t[:] right_by_values,
|
|
bint allow_exact_matches=True,
|
|
tolerance=None):
|
|
|
|
cdef:
|
|
Py_ssize_t left_pos, right_pos, left_size, right_size, found_right_pos
|
|
ndarray[intp_t] left_indexer, right_indexer
|
|
bint has_tolerance = False
|
|
numeric_t tolerance_ = 0
|
|
numeric_t diff = 0
|
|
HashTable hash_table
|
|
by_t by_value
|
|
|
|
# if we are using tolerance, set our objects
|
|
if tolerance is not None:
|
|
has_tolerance = True
|
|
tolerance_ = tolerance
|
|
|
|
left_size = len(left_values)
|
|
right_size = len(right_values)
|
|
|
|
left_indexer = np.empty(left_size, dtype=np.intp)
|
|
right_indexer = np.empty(left_size, dtype=np.intp)
|
|
|
|
if by_t is object:
|
|
hash_table = PyObjectHashTable(right_size)
|
|
elif by_t is int64_t:
|
|
hash_table = Int64HashTable(right_size)
|
|
elif by_t is uint64_t:
|
|
hash_table = UInt64HashTable(right_size)
|
|
|
|
right_pos = 0
|
|
for left_pos in range(left_size):
|
|
# restart right_pos if it went negative in a previous iteration
|
|
if right_pos < 0:
|
|
right_pos = 0
|
|
|
|
# find last position in right whose value is less than left's
|
|
if allow_exact_matches:
|
|
while (right_pos < right_size and
|
|
right_values[right_pos] <= left_values[left_pos]):
|
|
hash_table.set_item(right_by_values[right_pos], right_pos)
|
|
right_pos += 1
|
|
else:
|
|
while (right_pos < right_size and
|
|
right_values[right_pos] < left_values[left_pos]):
|
|
hash_table.set_item(right_by_values[right_pos], right_pos)
|
|
right_pos += 1
|
|
right_pos -= 1
|
|
|
|
# save positions as the desired index
|
|
by_value = left_by_values[left_pos]
|
|
found_right_pos = (hash_table.get_item(by_value)
|
|
if by_value in hash_table else -1)
|
|
left_indexer[left_pos] = left_pos
|
|
right_indexer[left_pos] = found_right_pos
|
|
|
|
# if needed, verify that tolerance is met
|
|
if has_tolerance and found_right_pos != -1:
|
|
diff = left_values[left_pos] - right_values[found_right_pos]
|
|
if diff > tolerance_:
|
|
right_indexer[left_pos] = -1
|
|
|
|
return left_indexer, right_indexer
|
|
|
|
|
|
def asof_join_forward_on_X_by_Y(numeric_t[:] left_values,
|
|
numeric_t[:] right_values,
|
|
by_t[:] left_by_values,
|
|
by_t[:] right_by_values,
|
|
bint allow_exact_matches=1,
|
|
tolerance=None):
|
|
|
|
cdef:
|
|
Py_ssize_t left_pos, right_pos, left_size, right_size, found_right_pos
|
|
ndarray[intp_t] left_indexer, right_indexer
|
|
bint has_tolerance = False
|
|
numeric_t tolerance_ = 0
|
|
numeric_t diff = 0
|
|
HashTable hash_table
|
|
by_t by_value
|
|
|
|
# if we are using tolerance, set our objects
|
|
if tolerance is not None:
|
|
has_tolerance = True
|
|
tolerance_ = tolerance
|
|
|
|
left_size = len(left_values)
|
|
right_size = len(right_values)
|
|
|
|
left_indexer = np.empty(left_size, dtype=np.intp)
|
|
right_indexer = np.empty(left_size, dtype=np.intp)
|
|
|
|
if by_t is object:
|
|
hash_table = PyObjectHashTable(right_size)
|
|
elif by_t is int64_t:
|
|
hash_table = Int64HashTable(right_size)
|
|
elif by_t is uint64_t:
|
|
hash_table = UInt64HashTable(right_size)
|
|
|
|
right_pos = right_size - 1
|
|
for left_pos in range(left_size - 1, -1, -1):
|
|
# restart right_pos if it went over in a previous iteration
|
|
if right_pos == right_size:
|
|
right_pos = right_size - 1
|
|
|
|
# find first position in right whose value is greater than left's
|
|
if allow_exact_matches:
|
|
while (right_pos >= 0 and
|
|
right_values[right_pos] >= left_values[left_pos]):
|
|
hash_table.set_item(right_by_values[right_pos], right_pos)
|
|
right_pos -= 1
|
|
else:
|
|
while (right_pos >= 0 and
|
|
right_values[right_pos] > left_values[left_pos]):
|
|
hash_table.set_item(right_by_values[right_pos], right_pos)
|
|
right_pos -= 1
|
|
right_pos += 1
|
|
|
|
# save positions as the desired index
|
|
by_value = left_by_values[left_pos]
|
|
found_right_pos = (hash_table.get_item(by_value)
|
|
if by_value in hash_table else -1)
|
|
left_indexer[left_pos] = left_pos
|
|
right_indexer[left_pos] = found_right_pos
|
|
|
|
# if needed, verify that tolerance is met
|
|
if has_tolerance and found_right_pos != -1:
|
|
diff = right_values[found_right_pos] - left_values[left_pos]
|
|
if diff > tolerance_:
|
|
right_indexer[left_pos] = -1
|
|
|
|
return left_indexer, right_indexer
|
|
|
|
|
|
def asof_join_nearest_on_X_by_Y(numeric_t[:] left_values,
|
|
numeric_t[:] right_values,
|
|
by_t[:] left_by_values,
|
|
by_t[:] right_by_values,
|
|
bint allow_exact_matches=True,
|
|
tolerance=None):
|
|
|
|
cdef:
|
|
Py_ssize_t left_size, right_size, i
|
|
ndarray[intp_t] left_indexer, right_indexer, bli, bri, fli, fri
|
|
numeric_t bdiff, fdiff
|
|
|
|
left_size = len(left_values)
|
|
right_size = len(right_values)
|
|
|
|
left_indexer = np.empty(left_size, dtype=np.intp)
|
|
right_indexer = np.empty(left_size, dtype=np.intp)
|
|
|
|
# search both forward and backward
|
|
bli, bri = asof_join_backward_on_X_by_Y(
|
|
left_values,
|
|
right_values,
|
|
left_by_values,
|
|
right_by_values,
|
|
allow_exact_matches,
|
|
tolerance,
|
|
)
|
|
fli, fri = asof_join_forward_on_X_by_Y(
|
|
left_values,
|
|
right_values,
|
|
left_by_values,
|
|
right_by_values,
|
|
allow_exact_matches,
|
|
tolerance,
|
|
)
|
|
|
|
for i in range(len(bri)):
|
|
# choose timestamp from right with smaller difference
|
|
if bri[i] != -1 and fri[i] != -1:
|
|
bdiff = left_values[bli[i]] - right_values[bri[i]]
|
|
fdiff = right_values[fri[i]] - left_values[fli[i]]
|
|
right_indexer[i] = bri[i] if bdiff <= fdiff else fri[i]
|
|
else:
|
|
right_indexer[i] = bri[i] if bri[i] != -1 else fri[i]
|
|
left_indexer[i] = bli[i]
|
|
|
|
return left_indexer, right_indexer
|
|
|
|
|
|
# ----------------------------------------------------------------------
|
|
# asof_join
|
|
# ----------------------------------------------------------------------
|
|
|
|
def asof_join_backward(numeric_t[:] left_values,
|
|
numeric_t[:] right_values,
|
|
bint allow_exact_matches=True,
|
|
tolerance=None):
|
|
|
|
cdef:
|
|
Py_ssize_t left_pos, right_pos, left_size, right_size
|
|
ndarray[intp_t] left_indexer, right_indexer
|
|
bint has_tolerance = False
|
|
numeric_t tolerance_ = 0
|
|
numeric_t diff = 0
|
|
|
|
# if we are using tolerance, set our objects
|
|
if tolerance is not None:
|
|
has_tolerance = True
|
|
tolerance_ = tolerance
|
|
|
|
left_size = len(left_values)
|
|
right_size = len(right_values)
|
|
|
|
left_indexer = np.empty(left_size, dtype=np.intp)
|
|
right_indexer = np.empty(left_size, dtype=np.intp)
|
|
|
|
right_pos = 0
|
|
for left_pos in range(left_size):
|
|
# restart right_pos if it went negative in a previous iteration
|
|
if right_pos < 0:
|
|
right_pos = 0
|
|
|
|
# find last position in right whose value is less than left's
|
|
if allow_exact_matches:
|
|
while (right_pos < right_size and
|
|
right_values[right_pos] <= left_values[left_pos]):
|
|
right_pos += 1
|
|
else:
|
|
while (right_pos < right_size and
|
|
right_values[right_pos] < left_values[left_pos]):
|
|
right_pos += 1
|
|
right_pos -= 1
|
|
|
|
# save positions as the desired index
|
|
left_indexer[left_pos] = left_pos
|
|
right_indexer[left_pos] = right_pos
|
|
|
|
# if needed, verify that tolerance is met
|
|
if has_tolerance and right_pos != -1:
|
|
diff = left_values[left_pos] - right_values[right_pos]
|
|
if diff > tolerance_:
|
|
right_indexer[left_pos] = -1
|
|
|
|
return left_indexer, right_indexer
|
|
|
|
|
|
def asof_join_forward(numeric_t[:] left_values,
|
|
numeric_t[:] right_values,
|
|
bint allow_exact_matches=True,
|
|
tolerance=None):
|
|
|
|
cdef:
|
|
Py_ssize_t left_pos, right_pos, left_size, right_size
|
|
ndarray[intp_t] left_indexer, right_indexer
|
|
bint has_tolerance = False
|
|
numeric_t tolerance_ = 0
|
|
numeric_t diff = 0
|
|
|
|
# if we are using tolerance, set our objects
|
|
if tolerance is not None:
|
|
has_tolerance = True
|
|
tolerance_ = tolerance
|
|
|
|
left_size = len(left_values)
|
|
right_size = len(right_values)
|
|
|
|
left_indexer = np.empty(left_size, dtype=np.intp)
|
|
right_indexer = np.empty(left_size, dtype=np.intp)
|
|
|
|
right_pos = right_size - 1
|
|
for left_pos in range(left_size - 1, -1, -1):
|
|
# restart right_pos if it went over in a previous iteration
|
|
if right_pos == right_size:
|
|
right_pos = right_size - 1
|
|
|
|
# find first position in right whose value is greater than left's
|
|
if allow_exact_matches:
|
|
while (right_pos >= 0 and
|
|
right_values[right_pos] >= left_values[left_pos]):
|
|
right_pos -= 1
|
|
else:
|
|
while (right_pos >= 0 and
|
|
right_values[right_pos] > left_values[left_pos]):
|
|
right_pos -= 1
|
|
right_pos += 1
|
|
|
|
# save positions as the desired index
|
|
left_indexer[left_pos] = left_pos
|
|
right_indexer[left_pos] = (right_pos
|
|
if right_pos != right_size else -1)
|
|
|
|
# if needed, verify that tolerance is met
|
|
if has_tolerance and right_pos != right_size:
|
|
diff = right_values[right_pos] - left_values[left_pos]
|
|
if diff > tolerance_:
|
|
right_indexer[left_pos] = -1
|
|
|
|
return left_indexer, right_indexer
|
|
|
|
|
|
def asof_join_nearest(numeric_t[:] left_values,
|
|
numeric_t[:] right_values,
|
|
bint allow_exact_matches=True,
|
|
tolerance=None):
|
|
|
|
cdef:
|
|
Py_ssize_t left_size, i
|
|
ndarray[intp_t] left_indexer, right_indexer, bli, bri, fli, fri
|
|
numeric_t bdiff, fdiff
|
|
|
|
left_size = len(left_values)
|
|
|
|
left_indexer = np.empty(left_size, dtype=np.intp)
|
|
right_indexer = np.empty(left_size, dtype=np.intp)
|
|
|
|
# search both forward and backward
|
|
bli, bri = asof_join_backward(left_values, right_values,
|
|
allow_exact_matches, tolerance)
|
|
fli, fri = asof_join_forward(left_values, right_values,
|
|
allow_exact_matches, tolerance)
|
|
|
|
for i in range(len(bri)):
|
|
# choose timestamp from right with smaller difference
|
|
if bri[i] != -1 and fri[i] != -1:
|
|
bdiff = left_values[bli[i]] - right_values[bri[i]]
|
|
fdiff = right_values[fri[i]] - left_values[fli[i]]
|
|
right_indexer[i] = bri[i] if bdiff <= fdiff else fri[i]
|
|
else:
|
|
right_indexer[i] = bri[i] if bri[i] != -1 else fri[i]
|
|
left_indexer[i] = bli[i]
|
|
|
|
return left_indexer, right_indexer
|
|
|